UNIONRADIO-SCIENTIFIQUE
INTERNATIONALE

ISSN 1024-4530

Radio Science Bulletin

INTERNATIONALUNION OFRADIO SCIENCE

Vol. 2021, No. 378
September 2021

URSI, c/o Ghent University (INTEC)Technologiepark - Zwijnaarde 126, B-9052 Gent (Belgium)

Contents
Radio Science Bulletin Staﬀ ....................................................................................... 3
URSI Oﬃcers and Secretariat.................................................................................... 6
Announcement of the Final Issue of the RSB by the URSI
Secretary General ................................................................................................. 10
In Memoriam: W. Ross Stone ................................................................................. 11
Maxwell Foundation Newsletter Available ............................................................. 12
Correction .................................................................................................................. 12
Introduction to the Special Issue of the Student Paper Contest of the
15th Congress of the URSI Portuguese National Meeting 2021 ....................... 13
Complex Permittivity Study of a Pinus Pinaster Needle Bed on Fire .................. 14
An IoT Radio Coverage Survey in Subway Tunnels ............................................. 22
Antenna Element Operating at 100 GHz ................................................................ 29
A Preventive Maintenance Approach to Optimize Fault Management
using Machine Learning ....................................................................................... 36
Quasi-Yagi Microstrip Dipole Antenna with Circular Arc Parasitic
Elements for Wireless Sensing Networks ............................................................ 44
A Simple Method for Calibrating GPS-Disciplined Clocks via Direct
Comparison to a UTC(k) Time Scale .................................................................. 51
On the Radar Cross Section of Dragons ................................................................. 59
SCOSTEP’s New Research Program: “Predictability of Variable
Solar-Terrestrial Coupling (PRESTO)”.............................................................. 62
Special Issue of the URSI-JRSM 2022 Student Paper Competition .................... 68
Thunderstorms and Total Lightning Characteristics Causing Heavy
Precipitation in Japan : A Case Study................................................................. 70
A Bayesian k-vector Estimation Method for Electromagnetic Waves in
Magnetized Cold Plasma ...................................................................................... 77
Et Cetera .................................................................................................................... 83
In Memoriam: Dr. Ljiljana R. Cander................................................................... 84
In Memoriam: Patricia Doherty ............................................................................. 85
In Memoriam: Robert Crane .................................................................................. 86
Telecommunications Health and Safety .................................................................. 88
Early Career Representative Column ..................................................................... 91
Women in Radio Science .......................................................................................... 93
Ionospheric eﬀects of the Hunga Tonga Volcanic Eruption of 22
January 2022 ........................................................................................................ 95
URSI Conference Calendar...................................................................................... 99
Information for Authors ......................................................................................... 101
Become An Individual Member of URSI.....................................................................102
Cover: A Picture of W. Ross Stone, Assistant Secretary General for Publications and GASS and the Editor of the Radio
Science Bulletin from 2001 until his death in March 2023.
Copyright © 2023 International Union of Radio Science (URSI) and the authors of the respective articles. All rights reserved.
Radio science researchers and instructors are permitted to copy, for non-commercial use without fee and with credit to the source, material covered bysuch (URSI) copyright. Permission to use author-copyrighted material must be obtained from the authors concerned. The articles reﬂect the authors’opinions and are published as presented. Their inclusion in this publication does not necessarily constitute endorsement by the publisher. Neither
URSI nor its contributors accept liability for errors or consequential damages.

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Radio Science Bulletin No 378 (September 2021)

Radio Science Bulletin Staﬀ
EditorW. R. Stone
Stoneware Limited
840 Armada Terrace
San Diego, CA 92106, USA
Tel: +1-619 222 1915, Fax: +1-619 222 1606
E-mail: r.stone@ieee.org

Editor-in-Chief

Production Editor

P. Van Daele
URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent, BELGIUM
Tel: +32 9-264 33 20, Fax: +32 9-264 42 88
E-mail: Pet.VanDaele@UGent.be

I. Lievens
URSI Secretariat / Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent, BELGIUM
Tel: +32 9-264.33.20, Fax: +32 9-264.42.88
E-mail: ingeursi@ugent.be, info@ursi.org

Senior Associate Editor
A. Pellinen-Wannberg
Department of Physics
Umea University
BOX 812SE-90187 Umea, SWEDEN
Tel: +46 90 786 74 92, Fax: +46 90 786 66 76
E-mail: asta.pellinen-wannberg@umu.se

Associate Editors, Commissions
Commission A

Commission B

Amitava Sengupta
Department of Applied Sciences
The Northcap University, Sector 23 A, Gurgaon, Haryana
122017,India
E-mail: sengupta53@yahoo.com

Henrik Wallén
Dept. Radio Science and Engineering
Aalto University
PO Box 13000, Otakaari 5 A, FI-00076 Aalto FinlandE-mail: henrik.wallen@aalto.ﬁ

Nosherwan Shoaib
Department of Electrical Engineering
National University of Sciences and Technology RIMMS,
H-12, Islamabad, Pakistan
E-mail: nosherwan.shoaib@seecs.edu.pk

Dimitrios Tzarouchis
University of Pennsylvania
School of Engineering and Applied Science
220 South 33rd Street, 107 Towne Building Philadelphia,
PA 19104 USA
E-mail: dtz@seas.upenn.edu

Giovanna Signorile
INRiM (National Institute of Metrological Research)
Via Torrazza Piemonte 41, Turin, Italy
E-mail: g.signorile@inrim.it

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Commission C
Pape Abdoulaye Fam
Dakar University, Senegal
E-mail: pafam@esp.sn
Ruisi He
State Key Lab of Rail Traﬃc Control and Safety
Beijing Jiaotong University
No. 3 Shangyuancun, Beijing 100044, China
E-mail: ruisi.he@bjtu.edu.cn
Alberto Tarable
CNR, Institute of Electronics, Information Engineeringand Telecommunications
Corso Duca degli Abruzzi 2410129 Torino, Italy
E-mail: alberto.tarable@ieiit.cnr.it

Commission D
Atsushi Kanno
Optical Access Technology Laboratory
Photonic ICT Research Center
Network Research InstituteNational Institute of Information and Communications
Technology
4-2-1 Nukui-kitamachi, Koganei
Tokyo 184-8795, Japan
E-mail: kanno@nict.go.jp

Commission E
Carlo Carobbi
Dipartimento di Ingegneria dell’Informazione
Università degli Studi di Firenze - UniFI
P.zza S.Marco, 4 - 50121 Firenze, Italy
E-mail: carlo.carobbi@uniﬁ.it
Chaouki Kasmi
Directed Energy Research Center
Technology Innovation Institute
PO Box 9639, Masdar City
Abu Dhabi, United Arab Emirates
E-mail: Chaouki.Kasmi@tii.ae

4Commission F
Swaroop Sahoo
Department of Electrical Engineering
Indian Institute of Technology Palakkad
Ahalia Integrated Campus
Kozhippara PO Palakkad, Kerala, India
E-mail: swaroop@iitpkd.ac.in

Commission G
Iwona Stanislawska
Polish Academy of Sciences
Space Research Centre
ul. Bartycka 18 A,00-716 Warsaw, Poland
E-mail: stanis@cbk.waw.pl

Commission H
Craig Rodger
Department of Physics
University of Otago
PO Box 56, Dunedin 9016, New ZealandE-mail: craig.rodger@otago.ac.nz

Commission J
Richard Bradley
Technology Center
National Radio Astronomy Observatory
1180 Boxwood Estate Road
Charlottesville VA 22903, USA
E-mail: rbradley@nrao.edu

Commission K
Emily Porter
Electrical and Computer Engineering
University of Texas Austin
2501 Speedway, C0803
Austin, TX 78712 USA
E-mail: emily.e.porter@ieee.org

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Radio Science Bulletin No 378 (September 2021)

Associate Editors, Columns
Book Reviews
G. Trichopoulos
Electrical, Computer & Energy Engineering ISTB4 555D
Arizona State University
781 E Terrace Road, Tempe, AZ, 85287 USA
Tel: +1 (614) 364-2090
E-mail: gtrichop@asu.edu

Historical Papers
J. D. Mathews
Communications and Space Sciences Lab (CSSL)The Pennsylvania State University
323A, EE East
University Park, PA 16802-2707, USA
Tel: +1(814) 777-5875, Fax: +1 814 863 8457
E-mail: JDMathews@psu.edu

Et Cetera
T. Akgül
Dept. of Electronics and Communications Engineering
Telecommunications Division
Istanbul Technical University
80626 Maslak Istanbul, TURKEY
Tel: +90 212 285 3605, Fax: +90 212 285 3565
E-mail: tayfunakgul@itu.edu.tr.

Women in Radio Science

Solution Box
Ö. Ergül
Department of Electrical and Electronics Engineering
Middle East Technical University
TR-06800, Ankara, Turkey
E-mail: ozgur.ergul@eee.metu.edu.tr

Telecommunications Health & Safety
J. C. Lin
University of Illinois at Chicago
851 South Morgan Street, M/C 154
Chicago, IL 60607-7053 USA
Tel: +1 312 413 1052, Fax: +1 312 996 6465
E-mail: lin@uic.edu

Historical Column
G. Pelosi
Department of Information Engineering
University of FlorenceVia di S. Marta, 3, 50139 Florence, Italy
E-mail: giuseppe.pelosi@uniﬁ.it

Early Career Representative Column

A. Pellinen-Wannberg
Department of Physics and Swedish Institute of Space
PhysicsUmeå University
S-90187 Umeå, Sweden
Tel: +46 90 786 7492
E-mail: asta.pellinen-wannberg@umu.se

Nosherwan Shoaib
Research Institute for Microwave and Millimeter-Wave
Studies (RIMMS)
School of Electrical Engineering and Computer Science(SEECS)
National University of Sciences and Technology (NUST)
H-12, Islamabad, Pakistan
E-mail: nosherwan.shoaib@seecs.edu.pk

Ethically Speaking

Education Column

R. L. Haupt
Colorado School of Mines
Brown Building 249
1510 Illinois Street, Golden, CO 80401 USA
Tel: +1 (303) 273 3721
E-mail: haupt@ieee.org

Madhu Chandra
Microwave Engineering and Electromagnetic Theory
Technische Universität Chemnitz
Reichenhainerstrasse 70
09126 Germany
E-mail: madhu.chandra@etit.tu-chemnitz.de

A. J. Shockley
E-mail: aj4317@gmail.com

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URSI Oﬃcers and Secretariat
Oﬃcers triennium 2020-2023
Vice President

President

K. Kobayashi

P. L. E. Uslenghi
Dept. of ECE (MC 154)
University of Illinois at Chicago 851
S. Morgan Street
Chicago, IL 60607-7053
USA
Tel: +1 312 996-6059
Fax: +1 312 996 8664
E-mail: uslenghi@uic.edu

Dept of Electrical, and Comm. Eng.
Chuo University
1-13-27 Kasuga, Bunkyo-ku
TOKYO, 112-8551
JAPAN
Tel. +81 3 3817 1846/69
Fax +81 3 3817 1847
E-mail kazuya@tamacc.chuo-u.ac.jp

Past President

Vice President

M. AndoG. Manara
Dip. di Ingegn.
dell’Informazione
Universita di Pisa
Via G. Caruso 1656122 Pisa
ITALY
Tel. +39-050-2217552
Fax +39-050-2217522
E-mail g.manara@iet.unipi.it

Senior Executive Director
National Institute of Technology
701-2, Higashi Asakawa, Hachioji,
Tokyo 193-0834, Japan
Tel:+81-42-662-3123,
Fax: +81-42-662-3131
E-mail: ando@kosen-k.go.jp,
mando@antenna.ee.titech.ac.jp

Secretary General

Vice President

P. Van Daele

A. Sihvola

URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264 33 20
Fax: +32 9-264 42 88
E-mail: Pet.VanDaele@UGent.beElectronic Science Department Aalto
University
School of Electrical Engineering
PO Box 13000
FI-00076 AALTOFINLAND
Tel: +358 50 5871286
E-mail: Ari.Sihvola@aalto.fi

Vice President
P. Doherty
Boston College
Institute for Scientiﬁc Research,
885 Centre Street
Newton, MA 2459, USA
Tel. +1 617 552 8767
Fax +1 617 552 2818
E-mail Patricia.Doherty@bc.edu
Deceased 13 July 2022

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Radio Science Bulletin No 378 (September 2021)

Oﬃcers NEW triennium 2023-2026
At the GASS 2023, held in Sapporo, Japan (19-26 August 2023), the Board Members
were chosen for the new triennium 2023-2026.
President

Vice President

A. Sihvola

K. Kobayashi

Electronic Science Department Aalto
University
School of Electrical Engineering
PO Box 13000
FI-00076 AALTOFINLAND
Tel: +358 50 5871286
E-mail: Ari.Sihvola@aalto.fi

Dept of Electrical, and Comm. Eng.
Chuo University
1-13-27 Kasuga, Bunkyo-ku
TOKYO, 112-8551
JAPAN
Tel. +81 3 3817 1846/69
Fax +81 3 3817 1847
E-mail kazuya@tamacc.chuo-u.ac.jp

Past President

Vice President

P. L. E. Uslenghi
Dept. of ECE (MC 154)
University of Illinois at Chicago 851
S. Morgan Street
Chicago, IL 60607-7053
USA
Tel: +1 312 996-6059
Fax: +1 312 996 8664
E-mail: uslenghi@uic.edu

G. Manara
Dip. di Ingegn.
dell’Informazione
Universita di Pisa
Via G. Caruso 1656122 Pisa
ITALY
Tel. +39-050-2217552
Fax +39-050-2217522
E-mail g.manara@iet.unipi.it

Vice President

Vice President

S. Salous
Department of Engineering
Durham University
Lower Mountjoy
South Road
Durham, DH13LE
UNITED KINGDOM
Tel: +44 1913342532
E-mail sana.salous@durham.ac.uk

Department of Space Physics
CAS
Bocni II 1401
141 31 Prague
CZECH REPUBLIC
Tel: +420 272 764 336
E-mail: os@ufa.cas.cz

O. Santolik

Secretary General
P. Van Daele
URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264 33 20
Fax: +32 9-264 42 88
E-mail: Pet.VanDaele@UGent.be

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Radio Science Bulletin No 378 (September 2021)

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URSI Secretariat

Assistant
Secretary General AP-RASC

Secretary General
P. Van Daele

K. Kobayashi

URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264 33 20
Fax: +32 9-264 42 88
E-mail: Pet.VanDaele@UGent.be

Dept. of Electr and Commun. Eng,
Chuo University
1-13-27 Kasuga, Bunkyo-ku
Tokyo, 112-8551, JAPAN
Tel: +81 3 3817 1846/69
Fax: +81 3 3817 1847
E-mail: kazuya@tamacc.chuo-u.ac.jp

Assistant
Secretary General

Assistant
Secretary General AT-RASC

Stefan J. Wijnholds
Netherlands Institute forRadio Astronomy
Oude Hoogeveensedijk 47991 PD Dwingeloo
The Netherlands
E-mail: wijnholds@astron.nl

W. BaanAstronAsserweg 45
9411 LP Beilen
THE NETHERLANDS
Tel: +31 521-595 773/100
Fax: +31 521-595 101
E-mail: baan@astron.nl

Assistant
Secretary General
Publications & GASS

Executive Secretary
I. Heleu
URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel. +32 9-264.33.20
Fax +32 9-264.42.88
E-mail info@ursi.org

W. R. Stone
840 Armada Terrace
San Diego, CA 92106
USA
Tel: +1-619 222 1915
Fax: +1-619 222 1606
E-mail: r.stone@ieee.org

Deceased March 2023

Congress Manager
I. Lievens
URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264.33.20
Fax: +32 9-264.42.88
E-mail: ingeursi@ugent.be

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The

Radio Science Bulletin No 378 (September 2021)

URSI Secretariat NEW triennium 2023-2026
At the GASS 2023, held in Sapporo, Japan (19-26 August 2023), the Members of the Secretariat
were chosen for the new triennium 2023-2026.

Secretary General
P. Van Daele
URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264 33 20
Fax: +32 9-264 42 88
E-mail: Pet.VanDaele@UGent.be

K. Kobayashi
Dept. of Electr and Commun. Eng,
Chuo University
1-13-27 Kasuga, Bunkyo-ku
Tokyo, 112-8551, JAPAN
Tel: +81 3 3817 1846/69
Fax: +81 3 3817 1847
E-mail: kazuya@tamacc.chuo-u.ac.jp

Assistant Secretary General& AT-RASC

Assistant Secretary General
WIRS & ITU

Stefan J. Wijnholds
Netherlands Institute forRadio Astronomy
Oude Hoogeveensedijk 47991 PD Dwingeloo
The Netherlands
E-mail: wijnholds@astron.nl

S. Salous
Department of Engineering
Durham University
Lower Mountjoy
South Road
Durham, DH13LE
UNITED KINGDOM
Tel: +44 1913342532
E-mail sana.salous@durham.ac.uk

Assistant Secretary General
Publications

Associate
Secretary General GASS

P. L. E. Uslenghi

The

Assistant
Secretary General GASS &AP-RASC

J. Volakis

Dept. of ECE (MC 154)
University of Illinois at Chicago 851
S. Morgan Street
Chicago, IL 60607-7053
USA
Tel: +1 312 996-6059
Fax: +1 312 996 8664
E-mail: uslenghi@uic.edu

College of Engineering and Computing
Florida International University
10555 W. Flagler Street
Miami, FL 33174
USA
Tel: +1 305.348.5338
E-mail: jvolakis@fiu.edu

Executive Secretary

Congress Manager

I. Heleu

I. Lievens

URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel. +32 9-264.33.20
Fax +32 9-264.42.88
E-mail info@ursi.org

URSI Secretariat
Ghent University - INTEC
Technologiepark - Zwijnaarde 126
B-9052 Gent
BELGIUM
Tel: +32 9-264.33.20
Fax: +32 9-264.42.88
E-mail: ingeursi@ugent.beRadio Science Bulletin No 378 (September 2021)

9

Announcement of the Final Issue of the
RSB by the URSI Secretary General
Dear Colleagues, Dear Radioscientists,
All of us in the URSI community and beyond wereshocked by the passing away of Dr. Ross Stone, the Editor of
the Radio Science Bulletin, also serving as URSI Assistant
Secretary General for GASS and Publications. Ross has been
an URSI scientist for many decades and was committed tocontribute and serve our community.
Because of Ross Stone’s long illness, the Radio
Science Bulletin was two years behind schedule, thelast issue having been published in June 2021. Thus, theRadio Science Bulletin became in serious violation of theguidelines for indexing, which require timely publishing.
After Ross Stone’s death on 29 March 2023, the Board
examined the possibility of bringing the Radio Science
Bulletin up to date in a short time, but concluded that thissolution was not feasible, both operationally and ﬁnancially.
Consequently, the Board decided to discontinue publicationof the Radio Science Bulletin and accepted the oﬀer by
Kazuya Kobayashi to act as Interim Editor for this lastissue of the Radio Science Bulletin.
The origins of the Radio Science Bulletin lay in themerger of 2 URSI publications, namely, the “Bulletin” and“The Radio Scientist” under a new name “INFORMATIONBULLETIN D’INFORMATION”. This decision was taken
at the General Assembly in Kyoto in 1993. The name quickly
changed into “Radio Science Bulletin” in 1994 with Prof.
Paul Delogne as editor.

electronically and distributed via e-mail and made available
for download from the URSI Website. The Secretariat iscurrently working on making all previous issues of the Radio
Science Bulletin, which were at that time printed on paperand distributed through mail, also available for download.
The Board have now decided to replace the Radio
Science Bulletin with a much simpler URSI Newsletter,that will not be indexed and will be edited by the Secretary
General with the assistance of his staﬀ. The Newsletter will
be distributed electronically to all member committees andindividual members. It will contain informational items,
such as minutes of the Board, reports of working groups,
calendar of conferences, etc.
The 1993 Kyoto General Assembly lead to the startof the “Radio Science Bulletin” and now 30 years later,shortly after our 2023 General Assembly in Sapporo, theﬁnal issue is being published. The Radio Science Bulletinhas been one of the major communication channels within
our community and has oﬀered an extra and unique channel
for many of our URSI scientists to publish results. We hope
you will enjoy this ﬁnal issue, but as in many situations,
we have to be ﬂexible and adapt to new circumstances andnew challenges.
Together with the Board, we hope we will be able
to fulﬁll the communication needs with the new URSI
Newsletter,Peter Van Daele
URSI Secretary General

Dr. Ross Stone became editor for the Radio
Science Bulletin in September 2001. Shortly after that, in
September 2002, the Radio Science Bulletin was published

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Radio Science Bulletin No 378 (September 2021)

In Memoriam: W. Ross Stone
26 August 1947 - 29 March 2023
It is with great sadness that we inform allradio scientists of the passing of Ross Stone,
Editor of the URSI Radio Science Bulletin.
Ross was born in San Diego, California, USA
on August 26, 1947. He grew up and went
to school in San Diego, where he received
his BA in geophysics in 1967 at the ageof 19, and his M.Sc. and Ph.D. degrees inapplied physics in 1973 and 1978, all fromthe University of California, San Diego. He
died in San Diego on March 29, 2023, after
a long illness.
Ross was the president of Stoneware
Limited, a consulting engineering company which hefounded in 1976. He also served as chief scientist and on
the governing boards of several engineering companies andwas an Adjunct Professor at the Beijing Jiaotong University..
He devoted an enormous amount of time and energy to
the development of professional societies in all areas ofelectrical and computer engineering. In addition to URSI, his
contributions to IEEE, EuCAP, ISAP and MAPE have left apermanent, beneﬁcial imprint on those organizations. Ross
was the inventor of the holographic radio camera and theeditor of several books. Among the many honors he received
are Fellow of URSI, Fellow of the IEEE, Fellow of theElectromagnetics Academy, Fellow of the Chinese Instituteof Electronics, recipient of the IEEE Third Millennium
Medal and of the IEEE AP-S Distinguished Achievement
Award, and Honorary Life Member of the Administrative
Committee of the IEEE Antennas and Propagation Society.
He was the EIC of the IEEE AP-S Newsletter from 1984
and transformed it into the IEEE AP-S Magazine in 1990,
therefrom editing it until 2014.

The

Radio Science Bulletin No 378 (September 2021)

Ross was a member of USNC-URSI
Commissions A, B, E, F and G and served
on many committees within the USNC.
Among the many positions which he held
in international URSI, he was Assistant
Secretary General for Publications and
GASS and the Editor of the Radio Science
Bulletin from 2001until his death. Throughyears of pain and hospital stays, Ross
fought hard to regain his health, never lost
hope, never waivered in his commitments
and never relinquished any of the many
chores which he had taken on to beneﬁt
professional societies.
Ross’ passing has left a large void among his family,
friends and colleagues. Those who knew him will cherish
the memory of, and miss, his kindness, congeniality, senseof humor, willingness to help, and encyclopedic knowledge.
He will be remembered fondly for a very long time. The
deepest condolences of the URSI Board, of the URSI
Secretariat, of the staﬀ of the Radio Science Bulletin, andof the entire radio science community go to his wife Sue,
his daughter Michele, and their family.

Piergiorgio L. E. Uslenghi
URSI PresidentPeter Van Daele
URSI Secretary General11Maxwell Foundation Newsletter
AvailableT

he latest editions of the James Clerk Maxwell Foundation’s Newsletter are now available. Issue No. 17
contains an article by Dusa McDuﬀ and David Forfar, “Some Advances in Pure Mathematics Made in the
19th Century.” Issue No. 18 contains an article by Naomi C. Robertson, “An Introduction to Black Holes.” Theissues are available for free at:https://clerkmaxwellfoundation.org/Newsletter_2022_Summer_V17.pdf
https://clerkmaxwellfoundation.org/Newsletter_2022_Summer_V18.pdf

CorrectionT

wo ﬁgure captions in the article “In Memoriam: Oleg Alexandrovich Tretyakov” (URSI Radio Science
Bulletin, 377, June 2021, pp. 60-61) contained mis-prints. The correct captions are as follows:

Figure 1. Oleg A. Tretyakov on December 19th, 2016.
Figure 3. Oleg A. Tretyakov and his wife, Tetyana Tretyakova, lived in Istanbul from 1998 to 2022, until
he passed away.

12

The

Radio Science Bulletin No 378 (September 2021)

Introduction to the Special Issue ofthe Student Paper Contest of the
15th Congress of the URSI Portuguese
National Meeting 2021
With the aim of encouraging young Portuguese
authors, ANACOM sponsored the Best Student Paper
Award, given to the three best papers presented by students(ﬁrst authors) at the 15th Congress of the URSI Portuguese
Committee, which was held on November 24, 2021, usinga hybrid model.
Subject of the conferrence was “Environmental
sustainability in the use of radio spectrum”. Sustainable
development is a concept that combines economic andsocial development with preservation of the environment,
providing a response to current needs without undermining
the capacity of future generations to satisfy their own needs.
Following the presentations made during the morning
session, the jury, chaired by António Topa (Instituto de
Telecomunicações - IT) and including Pedro Cruz (Bosch)and José Pedro Borrego (ANACOM), agreed unanimously
to award the prize to:- 1st place - Mário António Patrício Carreira Vala from
Instituto Superior Técnico and IT-Leiria, with the paper“Complex Permittivity Study of a Pinus Pinaster Needle
Bed on Fire”.

--

2nd place - Tiago Emanuel da Silva Oliveira from
Instituto Politécnico de Leiria, with the paper “A Waning
Crescent Quasi-Yagi Microstrip Antenna for WirelessSensing Networks”.
3rd place - José Miguel Neves from Universidade
do Porto, with the paper “Design and Experimental
Evaluation of Square Loop CPW Feed Patch Antenna
at 100 GHz”.

These awards were presented at the closing sessionof the Congress by ANACOM’s Board Member, Sandro
Mendonça, and the Chair of the URSI Portuguese
Committee, Luísa Mendes.
This special issue includes the papers of the three
awarded authors and two other papers also presented in
the Best Student Paper Award session:- A preventive Maintenance Approach to optimize Fault Management using Machine Learning
Marcio Filipe Godinho Pereira, Instituto Superior deEngenharia de Lisboa- Study of radio coverage for IoT networks in the subway
tunnels
Catarina Sousa, Instituto Superior de Engenharia deLisboaFigure 1. Winners of the Best Student Paper Award; Luísa Mendes, Chair of the URSI .
Portuguese Committee; Sandro Mendonça, Member of ANACOM’s Board of Directors

The

Radio Science Bulletin No 378 (September 2021)

13

Complex Permittivity Study of a
Pinus Pinaster Needle Bed on Fire
Mário Vala1,3, Stefânia Faria1,2,3, Nuno Leonor1,2, João M. Felício1,3,4,
Carlos Fernandes1,3, Carlos Salema1,3, and Rafael Caldeirinha1,2
1Instituto de Telecomunicações
Leiria, Portugal
E-mail: mario.vala@co.it.pt
2

3

4

Polytechnic of Leiria
Leiria, Portugal

Instituto Superior Técnico
University of Lisbon
Lisbon, Portugal

Centro de Investigação Naval
Escola Naval
Almada, Portugal

Abstract
In this paper, a range of complex permittivity values,
estimated from a Pinus Pinaster fuel bed on ﬁre is presented.
The Fire Dynamics Simulator (FDS) was used to model
the ﬁre environment and the Cold Plasma Model (CPM)
was applied to evaluate the complex permittivities of suchturbulent media. The retrieved values served as input
parameters describing the propagation medium, enablinga full-wave analysis based on the Finite-Diﬀerence TimeDomain (FDTD) method to evaluate and ascertain the impactof a weakly ionised plasma generated during vegetation
combustion on radiowave propagation. Comparisonswere obtained against two other methods, namely theTransmission-Line Method (TLM) and the Full-Stack(FS) technique, as a way to reduce the computational time.
These analyses yielded attenuation values up to 6 dB for
the simulated scenario, in which good agreement among
techniques was obtained. Preliminary results clearly
indicated that ﬁre may degrade communications in realﬁre situations.

1. IntroductionW

ildﬁres have been a constant problem all aroundthe world. Depending on the fuel area dimensions
and weather conditions, they can rapidly spread from theinitial source and cause enormous damage to communities
and natural landscapes and heritages [1]. During wildﬁresuppression operations, radiowave communication systems
are massively employed by ﬁreﬁghters and civil protection

14

personnel. In this case, the technology usually operates infrequency bands lower than mobile network frequencies
and allows fast and independent communications between
radio terminals [2]. Such communications are termed
mission-critical push-to-talk (MC-PTP) communications.
However, extensive ﬁre environments may have theeﬀect of suppressing radiowaves in the Very- and Ultra-Highfrequency ranges. Such conditions may impact the capability
for coordinating and controlling wildﬁre outbreaks by
ﬁreﬁghters, as reported, e.g., during the Dwellingup (1961)
and Lara (1969) bushﬁre disasters in Australia [3].
Initial research has been performed on the estimationof the impact of ﬁres on signal propagation. This was the casein [4] and [5], where authors demonstrated that a controlled
pine-litter ﬁre could cause signal losses between 1.6 dB and
5.8 dB, especially in the VHF and UHF frequency bands. In[6], the dependency of the attenuation caused by ﬁre withfrequency was studied, where values of 5.43 dB and 0.26 dB
were measured at 360 MHz and 400 MHz, respectively.
One of the causes of signal attenuation in wildﬁres
is the formation of an ionized plume in the ﬁre region.
This plume, also deﬁned as a plasma, is composed offree electrons released from alkali and alkaline-earth
metals (A-AEM) during vegetation combustion [7]. The
generated plume can thus be electrically characterized
and modeled by the Cold Plasma Model (CPM) [8]. The
Cold Plasma Model requires previous knowledge of thecollision-frequency and electron-density proﬁles from theenvironment in order to calculate the medium’s permittivity
and, later, attenuation values.

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Radio Science Bulletin No 378 (September 2021)

In this paper, a ﬁre spreading in a ﬂat terrain is modeledconsidering two ignition lines with diﬀerent incident
angles over a uniformly Pinus Pinaster needle bed using
the Fire Dynamics Simulator (FDS). From the results, theimpact of the estimated permittivity values was analyzed
on radiowave propagation using the Finite-Diﬀerence
Time-Domain model (FDTD). A delay and an advance inthe wave’s electric ﬁeld was detected, in addition to theexcess attenuation introduced by the ﬁre. Fire parameterssuch as the heat-release rate (HRR) and mass-loss rate(MLR) are presented.
This paper is organized as follows. In Section 2,
the Cold Plasma Model is presented. Fire modeling using
the Fire Dynamics Simulator is deﬁned in Section 3, andthe electromagnetic analysis of the estimated complex
permittivity applying the Finite-Diﬀerence Time-Domain
model, the Transmission-Line Method, and the FullStack technique are demonstrated in Section 4. Finalconsiderations are presented in Section 5.

2. The Cold Plasma Model andRadiowave Absorption
As previously mentioned, the input parameters to theCold Plasma Model are the electron density of a plasma
and the eﬀective collision frequency of the air molecules
present in the medium [9]. The electron density can becalculated using Equation (1) [10]:g  2 me kT 
K1 = 2 i
g0
h3

32

eV
i
e kT ,
(2)

where gi and g 0 are the internal partition functions ofions and neutrons, respectively; me is the electron mass
(9.109  1031 kg) ; k is Boltzmann’s constant(1.381 1023 J/K) ; h i s P l a n c k ’s c o n s t a n t(6.626  1034 Js) ; e i s t h e e l e c t r o n c h a r g e(1.602  1019 C) ; T is the absolute temperature; and Vi
is the ionization energy.
Still referring to Equation (1), N a is the total density

 m3  of alkali and alkaline-earth particles, ions, and atoms,

given by [10]N a = n0  ne = 7.335  1027


T

,

(3)

where n0 and ne are the densities per cubic meter ofneutral particles and electrons, respectively, and  is thepressure expressed in atmospheres (atm) applied by thealkali and alkaline-earth at a given temperature, T .
When considering the electron-neutral collisions as
the dominant interactions, the eﬀective collision frequency
between these particles can be computed by Equation (4) [4]:

 eff = 7.33  103 N m a 2 T s 1  ,
1/2
1/2

 K  K 
1/2 
N e =  K1 N a  1  1    1    m 3 
 4 N a 
 4 N a  (1)with K1 being the ionization equilibrium constant, given
by Saha’s equation [10]:





(4)

where N m is the density of air molecules, a is the radius of
air element molecules, and T is the absolute temperature.
From the electron density and the eﬀective collision
frequency, it is possible to estimate the medium’s complex
permittivity as in [11]:Figure 1. The designed scenario in the Fire Dynamics Simulator for (a) t  0 s; b) t  49.68 s.

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Radio Science Bulletin No 378 (September 2021)

15

Still referring to Equation 6, e is the electron
charge (1.602  1019 C) , me is the electron mass
(9.109  1031 kg) , and  0 is the vacuum permittivity(8.854 1012 F/m) .

3. Fire Modeling
The ﬁre environment was modeled using the Fire
Dynamics Simulator (FDS) [13]. This is a computationalfluid-dynamics software program from which bothtemperature and density proﬁles of the medium wereextracted in order to characterize the generated plasma.
Fire parameters such as heat-release rate, mass-loss rate,
and fuel consumption were also assessed for a typical ﬂat
terrain consumed by two distinct ﬁre fronts.

3.1 Flat Terrain Design

Figure 2. The design of the structure.



 2p

,
 =  0 r =  0 1   i eff   







(5)

where  is the electric ﬁeld angular frequency and  2p is
the plasma frequency given by Equation (6) [12]:N e2
 2p = e .
me 0

(6)

A triangular Pinus Pinaster needle bed was designed,
as depicted in Figure 1. The fuel-bed mass was 5.72 kg
and the alkali and alkaline-earth pine needle content wasestablished as K = 0.45% , Ca = 0.25% and Mg = 0.13%
The full computational volume of 4 m  8 m  5 m wasdiscretized in cubic unit cells of 2.5 cm  2.5 cm  2.5 cm
generating a matrix of 160  320  200 cells. Each cellcorresponded to an independent permittivity value that wascalculated punctually from the Fire Dynamics Simulator
parameters. The simulation ran 109 s.
The structure designed is illustrated in more detailin Figure 2. A linear burner was allocated along each of
the red lines and the fuel was uniformly distributed inside
the triangle, taking into consideration the informed angle
disposition.
This scenario was implemented in order to analyze
the impact of the junction of two independent ﬁre fronts onradiowave propagation and, subsequently, to estimate thecomplex permittivities of such media from their electron
densities and eﬀective collision frequencies.

Figure 3. The ﬂat-terrain ﬁre parameters: (a) The heat- release rate (HRR); (b) the mass-loss rate (MLR);
and (c) the fuel consumption.

16

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Radio Science Bulletin No 378 (September 2021)

3.2 Fire Parameters
From the results obtained, a temperature increase
was observed when the junction of the two ﬁre fronts
happened. The heat-release rate, mass-loss rate, and fuelheap consumption parameters are presented in Figure 3.
At the end of the simulation (109 s), there was still 1.85 kg
of biomass to be consumed.

4. Electromagnetic Simulation and
Results Analysis
4.1 Finite-Diﬀerence Time-Domain
MethodIn the previous sections, how the ﬁre propagatedthrough a ﬂat terrain and how to obtain the complex
permittivities of ﬁre media were explained. However, it isimportant to ascertain how this variation in permittivitiesof the ﬁre environment may aﬀect the propagation ofradio signals. Since the majority of the scenario at eachgiven time instant is comprised of cells with the dielectric
properties of air, there is no necessity to individually
analyze each cell. For this particular study, only the  r of
the cells with the maximum and minimum magnitudes ofcomplex permittivities were considered, as the remaining
cells would have an impact in between these two values. A
Fire Dynamics Simulator simulation time instant of 49.68 s
was considered, where values of complex  r ranging from
0.98  0.09 j to 0.85  1.25 j were observed. For ease ofunderstanding, these values will henceforth be identiﬁed
as dielectric1 and dielectric2, respectively.

To achieve and quantize the impact of this variationof permittivities in a propagating wave, a set of simulations
using the Finite-Diﬀerence Time-Domain (FDTD) method
was carried out [12]. The FDTD method provides a full-wave
electromagnetic analysis by solving Maxwell’s equations,allowing the calculation of the electric and magnetic ﬁelds
in the time domain.
The simulation environment considered for this setof simulations is presented in Figure 4. From this ﬁgure,it can be seen that the scenario was composed of only one
dielectric slab with dimensions of 7.5 cm  7.5 cm  2.5 cm
and two electric-ﬁeld probes. The ﬁeld probes wereplaced below and above the dielectric slab to register theelectric ﬁelds reﬂected and transmitted through the slab,
respectively. Moreover, a plane wave with a Gaussian
waveform traveling in the positive z direction was used as
excitation for the simulation.
Three diﬀerent simulations were performed, in which
only the dielectric properties of the slab were altered.
In the ﬁrst simulation, the slab was deﬁned as having
the same characteristics as air (  r  1 and conductivity,  0 ) to serve as a reference. The remaining two
simulations were performed with the characteristics ofdielectric1 and dielectric2 previously described. However,in order to characterize a dielectric material in the
FDTD simulation, its permittivity,  r , and conductivity,
 , are needed. Both these values could be obtained fromthe complex permittivity:  r is the real part, while  is
obtained using Equation (7): =  r  0 ,(7)

where  r  is the imaginary part of the complex
permittivity,  is the angular frequency, and  0 is thevacuum permittivity. Using this formula, and considering afrequency of 700 MHz (the frequency used to calculate thepermittivities from the Fire Dynamics Simulator data), theconductivities of dielectric1 and dielectric2 were 0.004 S/m
and 0.049 S/m, respectively.

Figure 4. The FDTD simulation environment: (a) top
view, and (b) side view.

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Radio Science Bulletin No 378 (September 2021)

After the simulations were performed, the results
were divided into two distinct analyses: one in the time
domain and the other in the frequency domain. The resultsof the time domain are presented in Figure 5 for both probes.
Figure 5a is the result corresponding to the electric-ﬁeld
probe placed below the slab that shows the electric ﬁeld
reﬂected, and Figure 5b corresponds to the probe above
the slab that measured the electric ﬁeld measured after thewave was transmitted through the dielectric. As can be seenfrom the reﬂected electric ﬁelds, in the simulation with
dielectric2 the values were higher than the remaining values,
due to the impedance mismatch in the air-slab interface.
There was electric ﬁeld that was reﬂected on dielectric1,
but since the impedance mismatch of the interface airsubstrate was not as pronounced as in the previous case,17

Figure 5. The electric ﬁelds in the FDTD simulation: (a) the reﬂected electric ﬁelds below the dielectric slab, and(b) the electric ﬁelds measured above the dielectric slab.
this value was smaller. Lastly, and as expected, there wasno signal reﬂected by the slab in the case in which air wasconsidered, since there was no impedance mismatch of themedia.
In terms of the results present in Figure 5b, it could beseen that the curves corresponding to the air and dielectric1
were very similar in terms of amplitude, since the  r weresimilar and the conductivities were close to zero. However,in the simulation with dielectric2, diﬀerences in amplitude
were veriﬁed due to the higher conductivity of this material.
The results of the frequency analysis are presented
in Figure 6. As could be seen, when air was used forthe substrate the transmission coeﬃcient was 0 dB as
expected, since there was no impedance mismatch. When

Figure 6. The reﬂection and transmission coeﬃcients: (a - b) air dielectric, (c - d)dielectric1, and (e - f) dielectric2.

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Radio Science Bulletin No 378 (September 2021)

For this analysis, consider Figure 7, which represents
the simulated geometry divided into cubic unit cells. The
volume designed was split into m  n longitudinal dielectric
tubes in the y-axis direction, assuming wave propagation
also along the y axis. Each voxel considered is illustrated
as a black dot in the corner of each cell and presented
uniform electrical properties in electrical permittivity andconductivity that varied according to ﬁre and fuel conditions.
Figure 8 illustrates the voxels with permittivities that
contributed most to the excess-loss estimation. These points
were mainly located in the ﬁre and burning gas regions.
Top and side views are presented.

Figure 7. An illustration of the per-tube analysis.
the permittivity and the conductivity of the material started
to increase, as in dielectric1, the transmission coeﬃcient
started to decrease, reaching 0.15 dB at 700 MHz. Thisvalue was even higher when dielectric2 was considered,achieving an attenuation of 1.8 dB at 700 MHz. Inversely,
the higher the conductivity of the medium, the higher thereﬂection coeﬃcient, leading to maximum values of 14.8
dB at 700 MHz for the simulation where dielectric2 wasconsidered.

4.2 Transmission-Line Method
and Full-Stack Technique
In order to evaluate the overall attenuation introducedby ﬁre for the time step considered in the previous section,
the Transmission-Line Method (TLM) was applied. By
implementing this method, it was possible to study thetransmission and reﬂection coeﬃcients in the propagation
path on a per-tube basis. This method was chosen due to
its low computational cost and time consumption.

In this work, the TLM was applied to recursively
calculate the S21 parameter of a transmission-line cascade.
By ensuring impedance matching at the multiple dielectricslabs, propagation and marching matrices were calculated.
Here, the ABCD matrix method for a lossy medium wasconsidered, which is given for each slab [15] by Am,nC m, nBm,n 
Dm,n 

 cosh   m,n lm,n = jYm,n sinh   m,n lm,n 

jZ m,n sinh   m,n lm,n   , (8)cosh   m,n lm,n  

where Y and Z are the admittance and impedance of thevoxel, l is the length of the slab, and  is the propagation
constant, given by m,n = j 0 0 r

m, n

,

(9)

where  is the angular frequency,  ris the relativem, npermittivity of each voxel, and 0 and  0 are the vacuum
permeability and permittivity, respectively.
When multiplying the matrices of each voxel, a ﬁnal
ABCD matrix is obtained for the entire tube:

n A B
A B


=
 C D  ,C D 
 m i =1  m, n(10)

and the transmission coeﬃcient, S21 , is ﬁnally obtained[15] asS21 =Figure 8. Permittivity cells from the simulated environment: (a) top view and (b) side view.

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Radio Science Bulletin No 378 (September 2021)

2,
A  B Z 0  CZ 0  D(11)where Z 0 is the free-space impedance.

19

For this scenario, each tube was made up of 320
voxels and the time instant of t  49.68 s was once moreconsidered. The projection plane, which is the plane view atthe end of the simulated volume, was used as the reference
in order to compute the excess loss introduced by ﬁre. The
estimated result is illustrated in Figure 9. Results are shownfor heights up to 1 m in order to highlight attenuation
values. From the results, it was observed that even though
this was a small-scale scenario where the total fuel massavailable was approximately 5.72 kg, attenuation values
around 6 dB were quantized. The highest values could beseen at approximately 0.3 m above the fuel bed.
The assessed results were also compared with anempirical homemade model based on the superposition
theorem, called the Full-Stack (FS) technique [6]. In thisprocedure, no coupling eﬀects between adjacent voxels wereconsidered and the cumulative attenuation was obtainedby summing the attenuation of each voxel that comprised
the tube, the ﬁnal result being denormalized by its physicaldimension along the y axis. In this case, the attenuation
constant was given in dB/m, as dB = 20log10  e    m,n  .

(12)Figure 10 presents the Full Stack results and showed thatboth methods were in good agreement, indicating that wildﬁres may signiﬁcantly impact radiowave communications.

5. Conclusions
In this work the junction of two distinct ﬁre fonts andits impact on radiowave propagation were studied. The
ﬁre environment was modeled using the Fire Dynamics
Simulator and a range of complex permittivities of themedium were estimated considering the Cold Plasma
Model for a 700 MHz signal. The inﬂuence of maximum

Figure 9. The total attenuation from a per-tube
analysis using the Transmission-Line Method att  49.68 s.
and minimum values of complex permittivities was assessedusing the FDTD method, with an aim to understand theimpact of a weakly ionized plasma’s electric characteristics
on a propagating electromagnetic wave.
A set of simulations using an FDTD simulation tool
was performed in which the eﬀects of diﬀerent permittivities
found in a ﬁre environment were assessed, both in the timeand frequency domains. The time-domain analysis showed
an attenuation of the electric ﬁelds that was higher for higher
values of conductivity. These results were better analyzed
in the frequency analysis, where a maximum of 1.8 dB ofattenuation was found.
Besides the FDTD study, an overall excess attenuationin the projection plane was estimated using two diﬀerent
methods: the Transmission-Line Method and the Full-Stacktechnique. From both methods, an attenuation of up to
1 dB/m was quantiﬁed.
These results showed that the permittivities found inﬁre environments lead to the attenuation of the propagating
radiowave. If there is a big cluster of such materials
concentrated around a certain area, it can even aﬀect thecommunications between the professionals present in theterrain.
Future work will address the simulation and theassessment of complex permittivity values for a numberof diﬀerent scenarios and conﬁgurations, such as smallscale, bent terrain, etc. Measurements of attenuation in acontrolled ﬁre environment, using diﬀerent vegetation as
fuel, are also planned to experimentally verify the resultspresented herein.

6. Acknowledgements
Figure 10. The total attenuation from a per-tube
analysis using the Full-Stack technique at t  49.68 s.

20This work was part of the projects RESCuE-TOOL(PCIF/SSI/0194/2017) and UID/EEA/50008/2021, both
funded by the Portuguese Government, Portuguese Foun-

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Radio Science Bulletin No 378 (September 2021)

dation for Science and Technology (FCT). We gratefully
thank Prof. Xavier Viegas and the Forest Fire ResearchLaboratory team for all support in the forest ﬁre area ofexpertise.

7. References
1. M. D. Chinthaka, N. H. Malka, K. Ramamohanarao, B.
Moran, and P. Farrell, “The Signal Propagation Eﬀects
on IEEE 802.15.4 Radio Link in Fire Environment,”2010 Fifth International Conference on Information
and Automation for Sustainability, 2010, pp. 411-414.
2. A. Aguiar, A. Navarro, A. Rodrigues, C. Fernandes, C.
Salema, J. Sanguino, N. B. d. Carvalho, and R. Caldeirinha, “Estudo do funcionamento do SIRESP - Parte
I,” Instituto de Telecomunicações de Portugal, Tech.
Rep., 2017.
3. K. M. Mphale, “Radiowave Propagation Measurements
and Prediction in Bushﬁres,” PhD dissertation, Schoolof Mathematical and Physical Sciences - James Cook
University, 2008.
4. K. Mphale and M. Heron, “Microwave Measurement ofElectron Density and Collision Frequency of a Pine Fire,”
J. Phys. D: Appl. Phys, 40, May 2007, pp. 2818-2825.
5. K. Mphale, M. Jacob, and M. Heron, “Prediction andMeasurement of Electron Density and Collision Frequency in a Weakly Ionised Pine Fire,” International
Journal of Infrared and Millimeter Waves, 28, 3, 2007,
pp. 251-262.
6. Y. Wu Li, H. Yong Yuan, Y. Lu, R. Feng Xu, M. Fu, M.
Yuan, and L. Han, “Experimental Studies of Electromagnetic Wave Attenuation by Flame and Smoke in Structure
Fire,” Fire Technology, 53, 1, July 2016, pp. 5-27.

8. J. Boan, “Radio Propagation in Fire Environments,” PhD
dissertation, University of Adelaide, School of Electricaland Electronic Engineering, 2009.
9. S. Faria, M. Vala, P. Coimbra, J. Felício, N. Leonor, C.
Fernandes, C. Salema, and R. Caldeirinha, “Comparative Study of Computational Electromagnetics Applied
to Radiowave Propagation in Wildﬁres,” 2020 XXXIIIrd General Assembly and Scientific Symposium of theInternational Union of Radio Science, 2020, pp. 1-4.
10. L. S. Frost, “Conductivity of Seeded AtmosphericPressure Plasmas,” Journal of Applied Physics, 32,
10, 1961, pp. 2029-2036. Available: https://doi.
org/10.1063/1.1728283
11. K. G. Budden, The Propagation of Radio Waves: The
Theory of Radio Waves of Low Power in the Ionosphereand Magnetosphere, Cambridge, Cambridge University
Press, 1985.
12. K. Mphale and M. Heron, “Plant Alkali Content andRadio Wave Communication Eﬃciency in High Intensity
Savanna Wildﬁres,” Journal of Atmospheric and SolarTerrestrial Physics, 69, 04, 2007, p. 471-484.
13. K. McGrattan, R. McDermott, S. Hostikka, J. Floyd,
and M. Vanella, Fire Dynamics Simulator, Sixth Edition, 2018.
14. A. Z. Elsherbeni and V. Demir, The Finite-Diﬀerence
Time-Domain in Electromagnetics, London, Institutionof Engineering and Technology, 2015.
15. D. Pozar, Microwave Engineering, Fourth Edition,
New York, Wiley, 2011.

7. S. Faria, M. Vala, P. Coimbra, N. Leonor, J. Felício,C. A. Fernandes, C. Salema, and R. F. S. Caldeirinha,“Radiowave Propagation Modelling of Dual WildﬁreFront Spreading Over Hilly Terrain at 700 MHz,” 2021
Telecoms Conference (ConfTELE), 2021, pp. 1-6.

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An IoT Radio Coverage Survey in
Subway Tunnels
Catarina Sousa1, Cláudia Alves1, João Casaleiro1,2, António Serrador1
1

Departamento de Engenharia Eletrónica Telecomunicações e Computadores
Instituto Superior de Eng. de Lisboa (ISEL), Portugal
2

Centre of Technology and Systems (CTS-UNINOVA), Portugal

Abstract
It is becoming increasingly relevant to use wirelesscommunications in subway tunnels to gather sensors data foradvanced maintenance and security purposes. In this work,
we present a propagation survey over IoT radio bands using
LoRa modulation in Lisbon’s Subway. Several scenarios
such as straight and curved tunnels, bifurcations, andpartial tunnel obstructions were considered. Measurements
were taken with LoRa modulation with a transmitter andreceiver set to 868 MHz with spreading factors from 7 to
12. The transmit power was 14 dBm, and half-wave whip
antennas with vertical polarization were used. Based on
the measurement results and the COST-231 two-slope
propagation model, a loss factor of 58.5 dB/km and 80.5
dB/km, were obtained respectively, for unobstructed curved
and bifurcation tunnels. An extra attenuation of 10 dB
was observed for partial tunnel obstruction by one loaded
convoy. The results of this study indicate that using a legaltransmission power and for straight and curved tunnels,
communication can be maintained for distances exceeding
one kilometre. Keywords—Subway tunnels, propagation
model, LoRa, IoT.

1. Introduction

S

ubway operators are switching from traditional
maintenance methods, such as preventative, runto-failure, or a combination of the two, to predictive
maintenance techniques. Predictive maintenance is aninvaluable subject for subway operators because it allows
for the early detection of faulty components, minimizes
failures and unplanned downtime. An infrastructure andtrain network of low-power wireless sensors is usually
installed to collect the relevant data. A wireless network is
required to forward data to a central point where it can beprocessed. The Internet of Things (IoT) technologies arewell suited to transmitting data in this manner.
In the literature several propagation models for tunnels
are available. There are studies focusing on a speciﬁc

22

technology, or service, and others on propagation models.
Several works focus on the propagation in tunnels formobile communications [1-5]. Other studies are focussed
on the propagation and deployment of wireless networks
to provide internet access to passengers [9-11, 14-17].
Recently, train-to-train communications have been reportedwith interesting results [18].
There are already mobile communication systems
installed in subways worldwide, and Wi-Fi networks arealso available, but the access points are typically located in
the stations or on board the trains to enable passengers toaccess the internet. The main disadvantage of using these
networks to gather sensory data is that radio transceivers
consume a lot of power, in comparison with the LoRatransceivers.
The aim of this work is to determine the coverage rangeof LoRa gateways considering the tunnel’s propagation
characteristics and the antennas installed in the tunnel’s
walls. From the available propagation models for tunnels[12] we selected the two-slope model which ﬁtted wellwith the measurements.
The paper is organized as follows: in Section II, abrief review of the two-slope empirical model and therequired parameters are presented. The measurement
setup, the developed ﬁrmware and measured scenarios arepresented in Section III. Finally, concluding remarks are
given in Section IV.

2. Two-slope Model
The two-slope empirical model was proposed forradio propagation in tunnels in [1], a detailed description
is available in [1, 12, 19, 20]. The model determines thepath loss based on the Received Signal Strength Indicator(RSSI), Pr, and the reference power level, P0, obtained near
the transmitter. The path loss is divided in the near- and farregions. A linear regression is applied to the measurementsof the received power in the far region to obtain the model
parameters from which one can estimate the path loss by

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Radio Science Bulletin No 378 (September 2021)

Figure 1 . Block diagram of the transmitter (left), and receiver (right).

(1)
where and are the received power level at a distanceand , respectively, away from de transmitter antenna,and is the loss factor per unit length, i.e. dB/km. The
loss factor is higher for the near-region due to the several
propagation modes and is lower in the far-region due to theless propagation modes [12]. Loss factor is a parameter thatis dependent on several factors, such as signal frequency,
tunnel cross section dimensions, forms, and obstruction
percentage.
The transition from near to far region is not smooth andis referred to has the break point, or critical distance, andis obtained by(2)
where is the tunnel cross section largest dimension andis the wavelength of the transmitted RF signal [2].
The two-slope model is suitable for line of sight andwhen the transmitter and receiver antennas are installed

several wavelengths away from the tunnel ﬂoor and walls[12]. However, for practical reasons the transmitter andreceiver antennas must be mounted near the walls. Sensors
will be installed along the tunnel walls and ﬂoor, and near
the tracks, which makes the use of directive antennas
unfeasible.

3. Measurements
Measurements are performed using two low-cost
devices, having each a LoRa transceiver (based on the
Semtech SX1272), a whip antenna for the 868 MHz band,a microcontroller (Espressif ESP32) with 4 MB of RAM
and 8 MB of Flash and an memory card (SDCard) to store
the measurements, as shown in Figure 1.

3.1 Measurement setup
The transmitter is ﬁxed and installed at approximately
1.8 m height from the tunnel ﬂoor and at 30 cm from thetunnel’s wall, as shown in Figure 2(a) and Figure 3. The
receiver is portable, and for each measurement the receiver
was positioned approximately 1 m and 1.5 m respectively

Figure 2. Transmitter (a), highlighted by red circle, installed, and transmitting anda photo of the receiver (b).

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Radio Science Bulletin No 378 (September 2021)

23

The receiver starts in the IDLE state where all the
peripherals are asleep to save energy. After pressing theonboard button, S1, the device wakes up and switches to
RX_SF12 state. This is where it conﬁgures the LoRa radio
to accept packets with a SF of 12 and wait to receive 10
packets in sequence. After receiving 10 LoRa packets thedevice switches to RX_SF7 state where it stays until itcollects the last 10 packets.
Finally, after receiving a total of 20 packets, the datais stored on the memory card and the device returns to the
IDLE state. To consider the scenario where it is not possible
to receive packets, in either of the two states, a timeout
timer of 20 seconds is triggered. This timer is implemented
to abort the cycle, dump the data, and return to the IDLE
state. Furthermore, an RGB LED set to Red, Blue, andGreen respectively for the IDLE, RX_SF12, and RX_SF7
states is used. This is used to allow the person carrying thereceiver to evaluate whether the measurement is complete.

Figure 3. Photo of the transmitter.
from the tunnel ﬂoor and walls. A photo of the receiveris shown in Figure 2(b). Both devices were powered by
a commercial power bank through the USB connection.
The transmitter firmware configures the LoRatransceiver for a transmitter power of 14 dBm, bandwidthof 125 kHz and to broadcast indeﬁnitely a cycle of 10
LoRa packets with a spreading factor (SF) of 12 followedby 10 packets with a spreading factor of 7. To receive
packets in the correct order and correct spreading factor, thereceiver must synchronize itself with the transmitter. Thissynchronization is done automatically by the developed
ﬁrmware and in accordance with the state machine shownin Figure 4.

The transmitter broadcasts LoRa packets with arelative counter between 1 and 10 and a general counter
which is always increasing. After reaching the value 10, therelative counter is reset, and the spread factor is changed.
Therefore, 10 packets are sent cyclically in each spreading
factor until the module is shut down or restarted. The receiverstores the counters, RSSI and SNR values of each packet
received and the timeout events on the memory card.

3.2 Measured Scenarios
Measurements were taken in the following scenarios:•••

Tunnel with bifurcation empty;
Curved tunnel empty;
Straight tunnel empty and partial obstructed.

Figure 4. Diagram of the receiver state machine.

24

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Radio Science Bulletin No 378 (September 2021)

Figure 5. Photo of the bifurcation at "Carnide" station (a), and oval tunnel (b).

Table 1 presents the receiver and transmitter conﬁguration
for all previous scenarios.

3.2.1 Empty tunnel with abifurcation
Measurements was done between Lisbon Metro
stations: “Carnide” and “Colégio Militar”. The transmitter
was installed near the “Carnide” station. The tunnel from
“Carnide” has a rectangular geometry of 13.5 x 6.0 m (widthx height). Approximately 80 meters into the tunnel, twooval tunnels with a smooth curve and a 4.5 x 6 m geometry
appear, as shown in Figure 5. We measured along a distanceof 650 meters into the left tunnel, with 50 meter intervals
between measurements. The measurement results are shownin Figure 6. A total of 600 measurements were taken, 10
measurements at each distance and for each spreading
factor. To determine the exact breakpoint, measurements
were taken 10 m apart near the transmitter. According to thedata, the break point is about 20 meters from the transmitter.
The parameters of the model are derived from the linear
regression of the data and displayed graphically, as shownin Figure 6. The loss factor is  = 55,8 dB/km and the P0(d0 = 20 m) = -55.1 dBm.

3.2.2 Empty curved tunnel
Measurement was done between “Carnide” and“Pontinha” stations. The transmitter was installed near
the “Carnide” station. The tunnel leading from “Carnide”
to “Pontinha” is 800 m long and with a curvature radiusof 87.5 m. Along the length of the tunnel, the geometry
changes from oval to rectangular to oval again, at 300 and
600 m respectively. In the oval sections the dimensions are
8.3 x 8.3 m and in the rectangular sections, 7.9 x 4.0 m.

at each distance and for each spreading factor. The model’s
parameters are derived from the linear regression of the data.
The loss factor is  = 80.5 dB/km and the P0 (d0= 100 m)= -48.2 dBm. As expected, the results for both spreading
factors are identical.

3.2.3 Straight tunnel empty andpartial obstructed
The measurement was made between the stations“Anjos” and “Intendente”. The tunnel is 250 m long,straight, and both transmitter and receiver were stationary
on the nearest stations’ ledges.
A total of 50 measurements were taken on two
scenarios: an empty tunnel and in an obstructed tunnel
by one loaded convoy. Based on the measurements, the
RSSI median are presented in Table 2, from which one canconclude that a single loaded convoy increases attenuation
approximately by 10 dB.
Table 1. Receiver and Transmitter Equipment andConﬁguration
TX
Frequency

868 MHz

Modulation

LoRa (SF=7, SF=12)

Bandwidth

125 kHz

Transmit Power
Receiver Sensitivy

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Radio Science Bulletin No 378 (September 2021)

14 dBm-120 dBm(SF=7)-132 dBm(SF=12)

Antenna

Type
Polarization

The measurement results are shown in Figure 7. A
total of 340 measurements were taken, 10 measurements

RX

Frequency band

λ/2 Whip Antenna
Linear Vertical
868 MHz25

Figure 6. Measured RSSI values along the tunnel for SF=7 (red) and SF=12 (blue).

4. Conclusions
This work conﬁrms the feasibility of using low-cost
devices to obtain and measure the path loss, in locations
and environments with diﬃcult access and complexity for
installing conventional RF measurement equipment. The
two-slope model is also validated for measurements near
the tunnel walls and ﬂoor.
Table 2 Median RSSI values between “Anjos” and“Intendente”
TunnelEmpty
Partially Obstructed

RSSI [dBm]
SF=12
-85
-96

RSSI [dBm]
SF=7
-87
-95

For straight or slightly curved tunnels, the loss factor
is -55.8 dB/km, which allows communication ranges over
1.2 km assuming a SF of 12 and a receiver sensitivity of-132 dBm.
Since the loss factor for curved tunnels is 80.5 dB/km, a maximum range of 1 km is expected, assuming themost optimistic case of an empty tunnel, a SF = 12 and thereceiver sensitivity of -132 dBm.
A loaded convoy obstructing the tunnel has an
impact on the overall attenuation of approximately 10
dB. In addition to continuing the investigation, it will beinteresting to measure the impact of obstructing the tunnel
completely with two convoys.

Figure 7. RSSI values along the curved tunnel for SF=7 and SF=12.

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Radio Science Bulletin No 378 (September 2021)

6. Acknowledgements
The authors acknowledge the Metropolitano deLisboa for providing both technical and human resources
for the development of this work. Special thanks to Tomé
Canas and Rui Rodrigues, for their technical discussions,availability, sympathy, and patience while in the tunnels.

7. References
1.

2.

3.

H. Xia, H. L. Bertoni, L. R. Maciel, A. LindsayStewart and R. Rowe, “Radio propagation
characteristics for line-of-sight microcellular andpersonal communications,” in IEEE Transactions on
Antennas and Propagation, vol. 41, no. 10, pp. 14391447, Oct. 1993, doi: 10.1109/8.247785.
T. Klemenschits and E. Bonek, “Radio coverageof road tunnels at 900 and 1800 MHz by discreteantennas,” 5th IEEE International Symposium on
Personal, Indoor and Mobile Radio Communications,
Wireless Networks - Catching the Mobile
Future., 1994, pp. 411-415 vol.2, doi: 10.1109/
WNCMF.1994.529121.
S. Betrencourt, M. Lienard, P. Degauque and
D. Degardin, “Mobile communication in roadtunnels: inﬂuence of the traﬃc conditions on thechannel characteristics,” 2000 10th MediterraneanElectrotechnical Conf. Information Tech. andElectrotechnology for the Mediterranean Countries.
Proceedings. MeleCon 2000, pp. 432-435 vol.1, doi:
10.1109/MELCON.2000.880458.

Antennas & Propagation, IET. 4. 571 - 582.
doi: 10.1049/iet-map.2009.0159.
8. Z. Chang-sen and G. Li-fang, “Research on propagation characteristics of electromagnetic wave in tunnels
with arbitrary cross sections,” 2010 2nd International
Conf. on Future Computer and Communication, 2010,
pp. V1-22-V1-25, doi: 10.1109/ICFCC.2010.5497844.
9. R. He, Z. Zhong and C. Briso, “Broadband ChannelLong Delay Cluster Measurements and Analysis at
2.4GHz in Subway Tunnels,” 2011 IEEE 73rd VehicularTechnology Conference (VTC Spring), 2011, pp. 1-5,
doi: 10.1109/VETECS.2011.5956539.
10. J. Ferrer-Coll, P. Ängskog, H. Shabai, J. Chilo and
P. Stenumgaard, “Analysis of wireless communications in underground tunnels for industrial use,”
IECON 2012 - 38th Annual Conference on IEEE
Industrial Electronics Society, 2012, pp. 3216-3220,
doi: 10.1109/IECON.2012.6389383.
11. K. Guan, Z. Zhong, J. I. Alonso and C. BrisoRodriguez, “Measurement of Distributed Antenna
Systems at 2.4 GHz in a Realistic Subway Tunnel
Environment,” in IEEE Transactions on VehicularTechnology, vol. 61, no. 2, pp. 834-837, Feb. 2012,
doi: 10.1109/TVT.2011.2178623.
12.A. Hrovat, G. Kandus and T. Javornik, “A Survey of Radio Propagation Modeling for Tunnels,”in IEEE Communications Surveys & Tutorials,
vol. 16, no. 2, pp. 658-669, Second Quarter 2014,
doi: 10.1109/SURV.2013.091213.00175.

4.

Y. P. Zhang, “Novel model for propagation loss
prediction in tunnels,” in IEEE Transactions on
Vehicular Technology, vol. 52, no. 5, pp. 1308-1314,
Sept. 2003, doi: 10.1109/TVT.2003.816647.

13.J. Moreno, J. M. Riera, L. d. Haro and C. Rodriguez, “A
survey on future railway radio communications services:challenges and opportunities,” in IEEE Communications
Magazine, vol. 53, no. 10, pp. 62-68, October 2015, doi:
10.1109/MCOM.2015.7295465.

5.

Z. changsen and M. Yan, “Eﬀects of cross sectionof mine tunnel on the propagation characteristics of
UHF radio wave,” 2006 7th International Symposium
on Antennas, Propagation & EM Theory, 2006, pp.
1-5, doi: 10.1109/ISAPE.2006.353521.

14.K. Guan et al., “Measurements and Analysis of
Large-Scale Fading Characteristics in Curved Subway
Tunnels at 920 MHz, 2400 MHz, and 5705 MHz,”in IEEE Transactions on Intelligent Transportation
Systems, vol. 16, no. 5, pp. 2393-2405, Oct. 2015,
doi: 10.1109/TITS.2015.2404851.

6.

K. Guan, Z. Zhong, B. Ai and C. Briso-Rodríguez,“Statistic modeling for propagation in tunnels
based on distributed antenna systems,” 2013 IEEE
Antennas and Propagation Society International
Symposium (APSURSI), 2013, pp. 1920-1921, doi:
10.1109/APS.2013.6711618.

7.

Hrovat, Andrej & Kandus, Gorazd & Javornik,
Tomaz. (2010). Four-slope channel model for pathloss prediction in tunnels at 400 MHz. Microwaves,

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15.J. Li, Y. Zhao, J. Zhang, R. Jiang, C. Tao and Z. Tan, “Radio channel measurements and analysis at 2.4/5GHz in
subway tunnels,” in China Communications, vol. 12, no.
1, pp. 36-45, Jan. 2015, doi: 10.1109/CC.2015.7084382.
16.G. S. Ching, S. Nishida, A. Okuno and Y. Kishiki, “Radio propagation simulation and measurement inside a
curved and sloped subway tunnel,” 2016 International
Symposium on Antennas and Propagation (ISAP), 2016,
pp. 210-211.

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17.X. Zhang, N. Sood and C. D. Sarris, “Fast Radio-Wave
Propagation Modeling in Tunnels With a Hybrid Vector
Parabolic Equation/Waveguide Mode Theory Method,”in IEEE Transactions on Antennas and Propagation, vol.
66, no. 12, pp. 6540-6551, Dec. 2018, doi: 10.1109/
TAP.2018.2864344.
18.C. Briso-Rodríguez, P. Fratilescu and Y. Xu, “Path Loss
Modeling for Train-to-Train Communications in Subway
Tunnels at 900/2400 MHz,” in IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 6, pp. 1164-1168,
June 2019, doi: 10.1109/LAWP.2019.2911406.

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19. Hrovat, Andrej & Kandus, Gorazd & Javornik, Tomaz.
(2012). Path loss analyses in tunnels and underground
corridors. International journal of communications. 6.
136-144.
20. E. Bonek: “Tunnels, Corridors, and Other Special
Environments“; in: “COST Action 231: Digital mobile
radio towards future generation systems“, E. Damosso,
L. Correira (eds.); European Union Publications, Brüssel, 1999, 190 – 207. ISBN: 9282854167.

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Radio Science Bulletin No 378 (September 2021)

Antenna Element Operating at 100 GHz
J.M. Neves1, L.M. Pessoa1, H.M. Salgado1
1

INESC TEC and Faculty of Engineering, University of Porto, Porto, Portugal
henrique.salgado@inesctec.pt

Abstract
In this work, a novel patch antenna fed by a CPW square
loop feed, was design in HFSS to work at a centre frequencyof 100 GHz, having a minimum requirement of at least 5
percent of fractional bandwidth. An end launch connector, up
to 110 GHz, was used for measurement purposes, imposing
the design of a GCPW-to-CPW transition, as well as a
TRL calibration kit. The antenna and calibration kit were
fabricated in RT/duroid 5880 substrate with 0.127 mm ofthickness. The experimentally obtained -10 dB impedance
bandwidth was 6.7 GHz (versus 7.66 GHz simulated) andthe measured peak gain at 100 GHz was 6.3 dBi (versus
6.1 dBi simulated).
Keywords—Patch antenna, 100 GHz, square loop feed,
GCPW (Grounded Coplanar Waveguide), CPW (Coplanar
Waveguide), TRL, calibration, RT/duroid 5880.

1. IntroductionIn recent years, due to an exponential growth in wirelesstraﬃc data yet again, the need for higher bit rates has
resurfaced. In the 4G to 5G transition, there was a movetowards millimetre wave spectrum motivated by the needfor higher bandwidth. Now the trend towards 6G is to
move to even higher zones of the frequency spectrum, into
the sub- terahertz (sub-THz) region, in order to mitigate
the spectrum scarcity and capacity limitations of current
wireless communication systems [1].

While rigorous scientiﬁc research and developmentin sub-THz devices and systems is still in progress, it isexpected that it will have a signiﬁcant societal impact, withapplications ranging from wireless communications tosensing domains, including biomedical, radar and security
systems.
In [2], a silicon-based micro-machined
Benzocyclobutene-cavity patch antenna and two arrays
were developed at 100 GHz, where the antenna element
presented a 2.8 GHz -10 dB bandwidth and a 6.7 dBi peak
gain. Furthermore in [3], a 94 GHz double-folded slot
antenna was manufactured on a high-resistivity silicon
substrate, fed by a CPW transmission line and backed by adielectric lens, measured from 90 to 100 GHz, providing aradiation pattern with side lobes below -15dB. An on-chip

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Radio Science Bulletin No 378 (September 2021)

Quasi-Yagi multi-layered antenna in [4] was integrated ona low resistivity silicon substrate and manufactured by apost-back-end-of-line process, having obtained a 5.7 dBi
gain at 100 GHz and a -6 dB impedance bandwidth within
89-104 GHz frequency range. As such, antenna designs
targeting the sub-THz region generally involve complex
manufacturing processes with multi-layer conﬁgurations,in this paper we propose to achieve a more cost eﬀective
and less complex manufacture process. This led us tosolutions that avoided the use of a multi-layered substrate
conﬁguration.
In this work, given the advantages that coplanar
waveguide (CPW) have over microstrip conﬁgurations
at high frequencies, an antenna element fed by a CPW
structure is proposed in order to evaluate its performanceat an operation frequency of 100 GHz, which was selected
since this frequency is located within a limited band thatexhibits a low atmospheric attenuation of EM waves [5]and has been considered as part of the research in 6G
systems. This approach was considered, given that, at
higher frequencies, where radiation loss dominates, justiﬁes
the need for more expensive alternatives such as multilayered substrate structures, other more complex feeding
methods such as stripline or SIW, or even the use of noncommercially available substrate materials. This antenna
element aims to avoid such expensive alternatives and it
explores the use of a single substrate layer through theapplication of uniplanar transmission lines such as CPW,
to evaluate its performance, eﬃciency and viability in the
sub-Terahertz domain.
This paper is structured in the following way: Afterthe introduction, in Section Ⅱ, the design of the componentsthat form the antenna element are shown and explained,
followed by the design process behind the TRL calibration
kit. Lastly, in Section Ⅲ, a direct comparison between
simulated and measured S11 and radiation pattern of theantenna element is performed.

2. Design of the Antenna Element
2.1 Patch Antenna Fed By a
Square Loop Feed
Using the theoretical values obtained from formulas
for the patch antenna and the quarter-wavelength29

Figure 1. Schematic of the patch antenna (top view)fed by: a) inductive CPW feed, b) capacitive CPW
feed, c) square loop CPW feed and d) side view of
all feed types.
transformer in [6] [7], and for both the CPW and GCPW
transmission lines in [8], a starting point for the dimensionsof all the components was achieved, followed by a process of
optimization through simulations, consisting of parametricsweeps of each variable. Given the implementation of asingle substrate layer, and in order to obtain a high radiationeﬃciency and wide bandwidth [9], a thin Rogers RT/duroid
5880 substrate was selected, with a thickness of 0.127 mm
and dielectric constant of 2.2.
For this design, three forms of antenna feeding wereconsidered, taking advantage of the CPW feed line type,those being: inductive feed (CPW short-end structure –Figure1 (a)), capacitive feed (CPW open-end structure- Figure 1 (b)) [10] and square loop feed (Figure 1 (c))[11]. All three of these feeding types were modeled andsimulated using a commercial ﬁnite element method solver
for electromagnetic structures named HFSS, from ANSYS.
As seen in Figure 2 (a), only the square loop feed (bluesolid line) presents a desirable minimum bandwidth of atleast 5% fractional bandwidth (97.5 – 102.5 GHz). When
comparing all three mentioned feed structures, the square
loop feed structure showed a more symmetrical radiation

30

Figure 2. Simulated reﬂection coeﬃcient(in dB) (a) and simulated radiation pattern(in dBi) (b) of the patch antenna types from
Fig. 1 (inductive feed – red long dashedline, capacitive feed – green dotted line andsquare loop feed – blue solid line).
pattern, the trade-oﬀ being a lower front-to-back ratio and aslightly lower gain value, as seen in Figure2 (b) (inductive
feed: 7.2 dBi, capacitive feed: 7.83 dBi and square loop0
feed: 7.6 dBi – simulated gain values at (  = 0 ). The
simulation results of the full antenna element (seen in Figure5) provide a S11 with a -10 dB impedance bandwidth of
7.66 GHz, and with a resonant dip of -34.97 dB at 100
GHz (see Figure 11.).
It is worth mentioning that it is important that the
CPW feed is completely covered by the patch antenna, tomaximize the electromagnetic coupling eﬃciency [12]. A
substrate height of 0.127 mm was chosen as a starting value,
to minimize the radiation losses from the feed line [13].
However, if the chosen value of the substrate height was notenough to provide the minimum required bandwidth, thenthe following available value of height of the Rogers RT/
duroid 5880 series would need to be used (h=0.254 mm).
The dimensions of the ﬁnal design are given in Table 1.

2.2 GCPW-CPW Feed LineIn this sub-section, a description of the feed line, fromthe GCPW line where the connector is attached, to the CPW
line that connects to the square loop feed (from Figure 1

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Table 1. Variable Values For The Patch
Antenna And The Square Loop Feed
Variable

Values (mm)W

0.08138

gap

0.105
0.76
0.742
0.411
0.235
1.5
0.127

Figure 3. Zoomed-in schematic of GCPW-CPW feedline leading into the antenna feed structure (view ofthe line above and view of the ground plane below).

0.018(c)) is given. A schematic of the line, top and bottom viewsis shown in Figure 3, along with the reference number ofthe diﬀerent line sections.
The connector to be used for the measurement of
the full antenna element is a 1.0 mm jack (female) end
launch connector (DC up to 110 GHz), model 2492-04A6, from Southwest Microwave, to be connected to a 50 Ω
GCPW line (section 1 in Figure 3). The lines represented
in section 3 (GCPW) and 4 (CPW) are chosen to have an
impedance of 90 Ω, due to their similarity in line width andgap values, in order to avoid abrupt value variations when
the changeover between the GCPW and CPW transmission
lines occur. In section 2, a quarter-wavelength transformer
was used to adapt de 50 Ω GCPW line to the 90 Ω GCPW
line. The GCPW-CPW transition (section 3 to section 4) was
implemented by a gradual tampering of the bottom groundplane of the GCPW side until it reached a width limit value,considering the manufacturing design constraints/limits.
In section 5 of the feed line, another quarter-wavelengthtransformer, in the CPW side, was used to adapt the 90 Ω
CPW line to the CPW line leading into the square loop feed
(112.6 Ω CPW line). The values of the impedance as wellas, line width and gap values of each section are presented
in Table Ⅱ. The impedance values were initially obtainedusing the PathWave Design Software LineCalc tool, from
Keysight Technologies, which afterwards were optimized
using HFSS software.

The simulated S-parameters of the entire GCPWCPW feed structure, described previously, are shown in
Figure 4, for the frequency band of interest (from 90 GHz
to 110 GHz).

2.3 Overall Antenna Element
Analysis
Figure 5 shows a schematic of the full antenna
designed in HFSS, which includes an approximate modelfor the 1 mm end launch connector. Upon simulation it wasobserved, as seen in Figure 6 (radiation pattern in dashedred), that initially, without any line length extension, theradiation pattern showed considerable side lobes at theright side of the polar plot. In order to reduce said side
lobes, the distance between the connector structure andthe patch antenna was increased, by extending the 90 Ω
CPW line length, by a length of 15 mm, obtaining theradiation pattern in blue. Not only the radiation pattern
becomes more distorted, but also the antenna radiationeﬃciency decreases slightly (it decreased from 91% to80%), as a result from increasing the line length, leading
to a decision of not increasing above 15 mm, considered
as a trade-oﬀ point.

Table 2. Variable Values For The Various Line Sections Of The GCPW-CPW Feed Line Structure

The

Line
Section

Impedance
Value
(Ω)

Line
Width
(mm)

Line Gap(mm)

1 (GCPW)

50

0.325

0.1

2 (GCPW)

67.08

0.185

0.1

3 (GCPW)

90

0.091

0.1

4 (CPW)

90

0.135

0.08

5 (CPW)

100.66

0.084

0.08

6 (CPW)

112.6

0.081

0.105

Radio Science Bulletin No 378 (September 2021)

Figure 4. S-parameters of GCPW-CPW feed line leading into the antenna feed structure.

31

Fig. 6. Antenna radiation pattern for the θ planeat ϕ=90º for the variation of the CPW 90 Ω line
length – 0 mm (red dashed line) and 15 mm (bluesolid line), at 100 GHz.

Figure 5. Top (a), bottom (b) and side (c) viewof the connector with the full antenna element
HFSS model.

in Figure 8, the simulated results of the S-parameters forthe three standards of the TRL kit are displayed.

3. Simulation And Experimental
Results Comparison

2.4 TRL Calibration Kit Design
In order to allow for the conduction of calibrated
measurements, a calibration kit suitable for the antenna
element was designed. Measurement errors in network
analysis can be divided into systematic and random errors.
Systematic errors are calibrated out of the measurement
process with the use of a calibration kit to allow for an
accurate comparison between simulated and experimental
results, regarding the reference plane. As opposed to the
SOLT calibration, which is not viable at high frequencies,
due to parasitic capacitances on load standard [14], a TRL(Through(Thru)-Reﬂect-Line) kit was designed, where forthis type of calibration, only the line standard needs to beprecisely known. The thru standard is a zero-length distance
between the desired reference planes of each port. For thereﬂect standard, the use of a short standard was preferred,
due to the fact that open standards are prone to radiate. Theline standard is essentially the thru standard, but with an extra
line length between both reference planes. In comparison
with the thru standard, the transmission coeﬃcient phase of
the line standard should only vary between 20º and 160 º,hence as a rule of thumb, 90º is usually preferred, which also
results in a bandwidth limitation [15]. From the simulations
results, the S21 phase diﬀerence between the through and line
standard:   90.530 , as well as, the oﬀset delay of the 90º
electrical length line portion: t  6.82 ps , were obtained,
the latter needed for the VNA conﬁguration of the TRL kit.
In Figure 7, the HFSS model of the TRL kit is shown, and32

In this section, a comparative analysis of S11(reﬂection coeﬃcient) and radiation pattern (θ plane, ϕ=0º)between simulated and measured results is presented. The
measurement setup used to obtain the experimental results
is displayed as a schematic in Figure 9. In this experiment,
the distance between the reference horn antenna and the
AUT (antenna under test) is 0.9 m, placing the measurement
within the far-ﬁeld condition.
A picture of the fabricated antenna element and TRL
calibration kit is displayed in Figure 10. In terms of the S11
results, observed in Figure 11, simulation and experiment are
in close agreement, both regarding the reﬂection coeﬃcientmagnitude at the reference plane, shown previously in Figure
3. The observed discrepancies between both results are
due to manufacturing tolerance and misalignment, as wellas the PCB board ﬂexibility throughout the measurement

Figure 7. HFSS model of the TRL calibration kit.

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Radio Science Bulletin No 378 (September 2021)

Figure 10. Fabricated antenna element (a)and TRL calibration kit (b).

Figure 8. Simulated S-parameters of the Through (a),Reﬂect (Short – (b)) and Line (c) standards.
process, given the small substrate height used. As such,the resonant dip in the measured S11 is shifted slightly
below the intended resonant frequency of 100 GHz, anda bandwidth of approximately 6.7 GHz was measured, incomparison to the 7.66 GHz obtained for the simulation.
Regarding the measured radiation pattern (see Figure 12),the measurement in the anechoic chamber was performed
in steps of 5º and it was only possible to measure from-90º to 90º due to mechanical restrictions, while for thesimulated results, it is shown in steps of 1º for all angles.
Comparing both radiation patterns, although the measured
pattern presents a lower gain for most of the angles, it showsa reasonable agreement regarding the peak gain at ϕ = 0º(simulated peak gain value: 6.09 dBi and measured peak
gain value: 6.33 dBi).

Figure 9. Measurement setup schematic.

The

Radio Science Bulletin No 378 (September 2021)

33

Figure 11. Simulated and Measured Reﬂection Coefﬁcient (S11).

Fig. 12. Simulated (red dashed line) andMeasured (blue solid line) Radiation Patterns for the ϕ plane, at θ = 0º (magnitude
values in dBi).

5. Conclusions
In this work, an antenna element fed by a GCPWCPW square loop feed on a substrate with 0.127 mm
thickness and  r  2.2 , including a TRL calibration kit,
was designed, fabricated and characterized experimentally,
having obtained at least 5 percent of fractional bandwidthand a peak gain of 6.33 dBi, fulﬁlling the minimum
requirements. Future work includes further optimizationand the application of this type of feeding method to anantenna array that also operates at a frequency of 100 GHz.

References
1.

2.

3.

4.

5.

34

Zhang, Zhengquan et al. “6G Wireless Networks:
Vision, Requirements, Architecture, and Key
Technologies.” IEEE Vehicular Technology
Magazine 14 (2019): 28-41.
J. Liu, S. Chen, K. Xiao, and X. Chen, “100 GHz
Silicon‐based micro‐machined patch antenna andarrays,” J. Eng. (Stevenage), vol. 2019, no. 19, pp.
5622–5625, 2019.
G. P. Gauthier, S. Raman and G. M. Rebeiz, “A
90-100 GHz double-folded slot antenna,” in IEEE
Transactions on Antennas and Propagation,
vol.47, no. 6, pp. 1120-1122, June 1999, doi:
10.1109/8.777141.
M. Sun and Y. P. Zhang, “100-GHz Quasi-Yagi
Antenna in Silicon Technology,” in IEEE Electron
Device Letters, vol. 28, no. 5, pp. 455-457, May
2007, doi: 10.1109/LED.2007.895447.
T. S. Rappaport, Y. Xing, G. R. MacCartney, A.
F. Molisch, E. Mellios and J. Zhang, “Overview
of Millimeter Wave Communications for FifthGeneration (5G) Wireless Networks—With a Focus
on Propagation Models,” in IEEE Transactions on

6.

Antennas and Propagation, vol. 65, no. 12, pp. 62136230, Dec. 2017, doi: 10.1109/TAP.2017.2734243.
C. Balanis, Antenna Theory: Analysis and Design.
Wiley, 2016.

7.

Pozar, D., Microwave engineering. Hoboken: John
Wiley & Sons, 2012.

8.

R. Simons, Coplanar Waveguide Circuits,
Components, and Systems. Wiley Series in
Microwave and Optical Engineering, Wiley, 2004.

9.

W. Menzel and W. Grabherr, “A microstrip patch
antenna with coplanar feed line”, in IEEE Microwave
and Guided Wave Letters, vol. 1, no. 11, pp. 340-342,
Nov. 1991, doi: 10.1109/75.93905.

10. K. Elissa, “Title of W. Chai, X. Zhang, and J. Liu,
“Characteristics Study of Four Coplanar WaveguideFeeding Devices”, PIERS Online, vol. 3, no. 1, pp.
48–51, 2007.
11. B. Ghosh, Y. M. M. Antar, A. Petosa and A.
Ittipiboon, “Feed conﬁgurations of CPW fed DRA,”IEEE Antennas and Propagation Society Symposium,
2004., Monterey, CA, USA, 2004, pp. 1347-1350
Vol.2, doi: 10.1109/APS.2004.1330435.
12. Hettak, K., Delisle, G. Y., & Talbi, L., “Design of
60-GHz CPW fed patch antenna for Switched-beamantenna array”, 2013 IEEE Antennas and Propagation
Society International Symposium (APSURSI), 2013.
13. M. Milijić, A. Nešić and B. Milovanović, “Study ofdielectric substrate eﬀect on modelling CPW-fed slot
antenna arrays,” 2017 13th International Conference

The

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on Advanced Technologies, Systems and Services in
Telecommunications (TELSIKS), Nis, 2017, pp. 105108, doi: 10.1109/TELSKS.2017.8246239.
14. V. H. B. Preuss and F. R. de Sousa, “A Study on
Low-Cost Calibration Kits for U.FL Connector
Systems,” 2019 4th International Symposium on
Instrumentation Systems, Circuits and Transducers

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Radio Science Bulletin No 378 (September 2021)(INSCIT), Sao Paulo, Brazil, 2019, pp. 1-6, doi:
10.1109/INSCIT.2019.88.
15. Keysight Technologies, “Specifying Calibration
Standards and Kits for Keysight Vector Network
Analyzers.”,
Available:
https://www.keysight.
com/zz/en/assets/7018-01375/applicationnotes/5989-4840.pdf.

35

A Preventive Maintenance Approach
to Optimize Fault Management
using Machine Learning
M. Pereira1, D. Duarte2, and P. Vieira1,3
1

2Instituto Superior de Engenharia de Lisboa (ISEL), Lisboa, Portugal

CELFINET, Consultoria em Telecomunicações, Lda., Lisboa, Portugal
3

Instituto de Telecomunicações (IT), Lisboa, Portugal

Abstract
Mobile networks’ fault management can take advantage
of Machine Learning (ML) algorithms making its
maintenance more proactive and preventive. Currently,
Network Operations Centers (NOCs) still operate inreactive mode, where the troubleshoot is only done after
the problem identiﬁcation. The network evolution to apreventive maintenance enables the problem prevention or
quick resolution, leading to a greater network and services
availability, to a better operational eﬃciency and, above
all, ensures customer satisfaction.
In this paper, diﬀerent algorithms for Sequential Pattern
Mining (SPM) and Association Rule Learning (ARL) are
explored, using real Fault Management (FM) data from alive Long Term Evolution (LTE) network. A comparative
analysis of the performance and eﬃciency between all thealgorithms was carried out, having observed a decreaseof 3.31% in the total number of alarms and 70.45% in thenumber of alarms corresponding to the same type. There
were also considerable reductions in the number of alarms
per network node or zone, identifying 39 nodes that nolonger had any unresolved alarm.
These results demonstrate that the recognition of sequential
alarm patterns allows the preventive maintenance of amobile communications network.
Keywords – fault management, machine learning, preventive
maintenance, sequential pattern mining.

1. Introduction

F

ault Management (FM) optimization in a mobile
network involves taking advantage of Machine
Learning (ML) techniques to make the mobile network’s
maintenance proactive and preventive.

36

Mobile network operations still work in reactivemode, i.e., the diagnosis and problem solving starts only
after a network malfunction occurs, a service is impacted,
or a customer complains. The engineers have access to a
lot of information such as alarms, performance measuresand more, but they lack an eﬀective way to resolve issues
quickly. Thus, Mean Time To Repair (MTTR) is aﬀected,
impacting network and service availability, operationaleﬃciency, and customer satisfaction.
With a solution for preventive maintenance, operators
can leverage machine learning to reduce operational costs,
improve network and service availability, improve customer
satisfaction, and reduce missed Service Level Agreements(SLAs).
In this context, the main goal of this paper is to createa solution for preventive maintenance of mobile networks’alarms, using real data, that:
1) Mines alarms clusters and relationships between them,forming association rules;
2) Continuously learns from new data, gets better overtime, and builds expertise in the maintenance domain;3) Deﬁnes antecedent and consequent alarms in a sequential
pattern, where they are sorted chronologically;
4) Recognizes the most frequent patterns in order to tellwhat the most concerning faults are and what advantages
come from their prevention.
Some of the works related to preventive fault
management are: [1], [2], [3]. In [1], the alarms’ proactive
management is addressed as a way to take advantage of
ML algorithms to follow the evolution of mobile networks
and services operations. In [2] and [3], research is carriedout on the application of ML techniques and data mining
in the fault management of telecommunications networks,
from an operational point of view.
This paper is organized as follows: Chapter 2 presents,
brieﬂy, the study carried out on Association Rule Learning

The

Radio Science Bulletin No 378 (September 2021)

(ARL) and Sequential Pattern Mining (SPM) algorithms
and their respective evaluation metrics. Chapter 3 presents
the experimental results of all the discussed algorithms’
implementation, with a comparison of their eﬃciencies.
Chapter 4 concludes the article with a summary of the mostimportant points to take.

2. Methodology
2.1 Fault Management data
The dataset used in this work has several alarm logs
from a live LTE network. These logs contain the timestamp,
severity, name, type, technology, detailed information andnetwork node associated with each alarm.
As described in [4], the alarms can be classiﬁed, as
to their severity, as follows:
1) Critical: A critical alarm aﬀects system services. As
soon as it is triggered, immediate actions must be taken,
even when the fault occurs outside working hours;
2) Major: A major alarm aﬀects the quality of service
and requires immediate action, only if occurs during
working hours;
3) Minor: A minor alarm usually does not aﬀect the qualityof service. It should be treated as soon as possible ormonitored to avoid potential severe failures;
4) Warning: A warning alarm indicates a possible errorthat could aﬀect the quality of service. Requires diﬀerent
actions depending on speciﬁc errors.
These alarms can also be categorized by their type,
depending on their operational area: Power; Environment;
Signaling; Trunk; Hardware; Software; System;
Communication; Service quality; Unexpected operation;
Operations and Maintenance Center (OMC); Integrity;
Operation; Physical resource; Security; Time domain;
Running; Processing.

for these evaluation metrics, which help to ﬁlter out lessfrequent rules.
Support: Relative frequency or occurrence probabilityof a transaction. It can take values between 0 and 1.
(1)
Conﬁdence: Probability of a transaction containing theconsequent, knowing that it also contains the antecedent.
Conﬁdence is maximum if the consequent and theantecedent always occur together. It is not symmetric,
that is, the conﬁdence ofis diﬀerent from theconﬁdence of.

(2)

In addition to these two measures, others can be used tobetter classify the association rules, considering otherproperties that both support and conﬁdence cannot quantify.
In the scope of this work, three more evaluation metrics
were used: lift, leverage, and conviction [5], presented inequations (3), (4) and (5), respectively.
Lift: Quantifies how frequent the simultaneous
occurrence of A and C is compared to what would be
expected if they were statistically independent. If A and
C are independent, the lift will be equal to 1.

(3)
Leverage: Diﬀerence between the frequency of A andC occurring together and the frequency that would be
expected if A and C were independent. A value of 0
indicates independence between the two itemsets.

2.2 Association Rule Learning
Association Rule Learning (ARL) allows extracting
correlations or strong associations, hidden between sets ofitems present in transactions that are found in a dataset.
The association rules problem deﬁnes a transaction as aset of items where each item can have diﬀerent attributes.
The dataset in question contains several transactions. An
association rule is an implication, written as
where
A and C are sets of items called antecedent and consequent,respectively.
Generally, there are two metrics for evaluating an
association rule: support and conﬁdence, presented inequations (1) and (2), respectively. As datasets usually store
large amounts of information but only the most frequenttransactions are interesting, minimum values are deﬁned

The

Radio Science Bulletin No 378 (September 2021)(4)
Conviction: A high value means that C is strongly
dependent on A. For example, if the conﬁdence is equal
to 1, the denominator will be 0, so the conviction will be. Like lift, if items are independent, the conviction
is equal to 1
(5)
In the development of the work, three ARL algorithms were
implemented: Apriori [6], Eclat [7] and FPGrowth [8].

37

2.2.1 Apriori
This algorithm has to access the dataset multiple
times to read all the frequent itemsets. In its ﬁrst access,the support is counted for each individual item (level 1
itemsets). Then, with a minimum support value, S, rare items
are excluded, that is, those whose support is lower than S.
In later accesses, higher-level itemsets containing
rare items are removed, because if an item’s support, Si,is lower than S, then all itemsets that contain it will havea support equal or lower than Si and, thus, lower than S(Apriori property).

After building the FP-trees for all the dataset
projections, some of the nodes associated with rare items
are pruned, in order to simplify the tree and speed up theprocess.
Thanks to an eﬃcient implementation of FP-trees,
the FPGrowth algorithm largely outperforms both Apriori
and Eclat algorithms in terms of execution time andmemory usage, since the storage of the dataset using atree representation is more compact than the complete listof transactions.

2.3 Sequential Pattern Mining

This process is done iteratively for every frequent
itemset in the dataset. The ﬁnal list of frequent items is thejunction of each level’s list, including the support values
calculated for each frequent itemset.

Sequential Pattern Mining (SPM), unlike ARL,
takes the items’ order in each sequence into consideration,
allowing to discover frequent patterns in a dataset, which
may prove useful or simply interesting to be explored.

2.2.2 Eclat

The objective of SPM algorithms is to ﬁnd all patterns(sub-sequences) that have a support greater than or equal tothe minimum support value deﬁned by the user. Therefore,
there is one and only one answer to a problem of this type.
What diﬀerentiates the various algorithms is just theireﬃciency in ﬁnding that answer.

Eclat is an improved version of Apriori that uses avertical dataset representation, where it lists the transactions
in which item occurs, instead of indicating the itemsetsthat belong to each transaction (horizontal representation).
Transaction lists for higher-level itemsets are createdrecursively, calculating the intersection of the previous leveltransactions lists. If this intersection is null, the itemset isremoved. This process runs until all intersections becomenull.

In the development of the work, three SPM algorithms
were implemented: PreﬁxSpan [9], SPADE [10] and SPAM
[11].

2.3.1 PreﬁxSpan

If the minimum support is set to the same value, theﬁnal list of frequent itemsets will be identical to that of the
Apriori algorithm. However, Eclat takes up less memory
throughout the process, manages to be faster by using a
vertical approach (in which calculations are done in parallel)
and accesses the dataset less often, because it is possible
to calculate the support values for any level.

Based on FPGrowth, PreﬁxSpan is a pattern-growthalgorithm and the only one of the three studied SPM
algorithms that does not consider all possible combinations
for the patterns that can be found. It recursively accesses
the dataset to concatenate new items until the full pattern isformed, therefore, it only considers the patterns that reallyexist in the dataset.

2.2.3 FPGrowth

These successive accesses can, however, take a very
long time, so the “projected dataset” concept is also applied
here to reduce its size, optimizing the accesses that need
to be made. In terms of memory usage, creating multiple
projections can take up a lot of data storage space.

The FPGrowth algorithm uses the concept of aFrequent Pattern (FP)-tree, a tree that contains the preﬁxesof the transactions. Each tree branch represents a set oftransactions that share the same preﬁx, where each nodecorresponds to a single item. All nodes referring to thesame item are linked together, so that all transactions that
contain an item can be easily found when traversing the tree.
The main process that this algorithm performs is tobuild the FP-tree of a projected dataset, that is, a dataset withthe transactions that contain a certain item, with that item
removed. The projected dataset is processed recursively,
not forgetting that every frequent itemset share the same
preﬁx – the item that was removed.

38

2.3.2 SPADE
Inspired by Eclat, SPADE uses a vertical dataset
representation, created during the ﬁrst access, that indicatesin which itemsets and in which sequence each item occurs.
The vertical representation has two interesting
properties for recognizing sequential patterns. The ﬁrst,
is that the list created for any sequence allows directly
calculating its support. The second property is that theTheRadio Science Bulletin No 378 (September 2021)

Figure 1. Execution times (a) and memory usages (b) of each algorithm, for various time windows.
list of any sequence can be obtained, without the need toaccess the original dataset, by joining the lists of every
sub-sequence that is part of it.

This algorithm has been shown to be faster than both
SPADE and PreﬁxSpan, especially for relatively large
datasets. In terms of memory, SPADE still manages to be
the more eﬃcient of the two.

By taking advantage of these properties, SPADE
performs its discovery for sequential patterns without
repeatedly accessing the dataset and, therefore, without
keeping many patterns in memory.

2.3.3 SPAM
Similar to SPADE, the SPAM algorithm manages tobe even more eﬃcient by optimizing the structure of thepattern list. It encodes these lists as binary vectors, whichcuts the memory needed to store the same information.
Using compression techniques, that reduce the numberof used bits, SPAM improves even further in the speed ofmathematical operations that need to be performed.

3. Experimental Results
In order to discover the association rules and sequential
patterns that arise in the network, and to obtain some
practical conclusions that verify the expected results intheory, real Fault Management (FM) data were used to test,
analyze, and compare the studied algorithms.
To evaluate any and all association rules, the minimum
support value was set at “2 occurrences / Total number oftransactions”, that is, it is enough to have a repetition ofa certain set of items for it to be considered frequent. A
minimum conﬁdence of 50% was also imposed, which

Table 1. The 6 association rules, and respective evaluation metrics calculated by the three algorithms,
with greater Lift.
Minute(s)
15
21
15
21
1
2

The

Network node
BX94BL
BX94BL

Antecedent(s)Consequent(s)SupportLift

Leverage

RF Unit ALD
Current Out of
Range
ALD
Maintenance
Link Failure

ALD
Maintenance
Link Failure
RF Unit ALD
Current Out of
Range
Inter-System
CabinetConﬁguration
Conﬂict

0.001340.001570.001340.00157

741.5
634.0
741.5
634.0

0.001340.001570.001340.001570.001510.00158

662.0
632.0

0.001500.00157

VA08OLX2 Interface
Fault

1
2

VA08OLInter-System
CabinetConﬁguration
Conﬂict

X2 Interface
Fault

0.001510.00158

662.0
632.0

0.001500.00157

2

BX16QL

Certiﬁcate
Invalid

External Clock
Reference
Problem

0.00202494.0

0.002022

BX16QL

External Clock
Reference
Problem

Certiﬁcate
Invalid0.00202494.0

0.00202

Radio Science Bulletin No 378 (September 2021)

39

Table 2. Execution times and memory usages of each algorithm, for the
dynamic time window.
Algorithm
PreﬁxSpan
SPADE
SPAM

Execution time
14 minutes 43 seconds
15 minutes 9 seconds
13 minutes 50 seconds

indicates that, at least, for a given rule, half of the transactions
that contain the antecedent also contain the consequent.

Maximum memory usage179.07 MB
178.06 MB
180.18 MB

Although, with this increase, there are transactions withmore sets of items, the number of transactions is decreasing
and, ultimately, there are fewer evaluation metrics to
calculate, which justiﬁes the time reduction. Comparing theexecutions times of each algorithm, it is possible to verify
that the fastest is FPGrowth, followed by Eclat and, ﬁnally,
the slowest is Apriori. As for Figure 1b), despite constantﬂuctuations, it is possible to verify that memory usage
increases as the time window increases, which is justiﬁedby the increase in the number of items in each transaction.
In general, it is possible to conclude that the most eﬃcientin terms of memory occupation is also FPGrowth. Eclat
manages to be very optimized for small transactions, but
after a certain point it uses even more memory than Apriori.

Before analyzing the results and to assess the eﬃciencyof each algorithm, the execution times and memory usedwere measured. These results were obtained from tests
performed on a machine with the following speciﬁcations:Intel® Core™ i5-7300HQ CPU @ 2.50GHz (4 CPUs),
8GB RAM, Windows 10 Education 64bits.

3.1 Association Rule Learning
In the implementation of the algorithms Apriori andFPGrowth, the MLxtend library [12] was used. For Eclat,
PyFIM [13] was used. The tests were carried out for temporalwindows from 1 to 30 minutes, every minute. Comparativegraphs for the diﬀerent time windows are shown in Figure 1.

The list of rules, identical to all algorithms, was sortedby the Lift metric in descending order, since those with thishigher value are those with a greater dependence between
the rule’s antecedent and consequent. The ﬁrst 6 rules areshown in Table 1. The conﬁdence and conviction metrics
were omitted as they were the same for all evaluated rules

As can be seen in Figure 1a), the execution timesof all algorithms decrease as the time window increases.

Table 3. The 5 sequential patterns, and respective evaluation metrics calculated by the three algorithms, with greater Lift.

40

Network node

Pattern

SupportConﬁdence

Lift

Leverage

VA08OL{Inter-System
CabinetConﬁguration
Conﬂict, X2
Interface
Fault}

0.00259

1.0

386.5

0.00258

VA83WL{RF Unit ALD
Current Out ofRange, ALD
Maintenance
Link Failure}

0.002741.0

364.330.00274

BX16QL{Certiﬁcate
Invalid,
External Clock
Reference
Problem}

0.00197

0.66

226.0

0.00196

AB42AL{Inter-System
CabinetConﬁguration
Conﬂict, X2
Interface
Fault}

0.00476

1.0

210.0

0.00474

TX49BL{ALD
Maintenance
Link Failure,
External Clock
Reference
Problem}

0.00477

1.0

209.660.00475

The

Conviction

2.9912

Radio Science Bulletin No 378 (September 2021)

and time windows, having the values 1 and , respectively.
In the ﬁrst column of this table there are the diﬀerent
lengths of time windows that got the best results. Someof the associations, namely the ﬁrst 4, were aggregatedbecause they were present for consecutive time windows,
hence, the range of values, both in this column and in thecorresponding evaluation metrics.
In spite of the support values being low, the algorithms
demonstrate conviction that these associations are quite
strong, that is, when a certain antecedent alarm occurs, theconsequent alarm will occur afterwards.
It is possible to verify that, for a given time window
and network node, there is always a pair of symmetric rules,
i.e., there is always a second rule where the antecedent is
the consequent, and the consequent is the antecedent of
the ﬁrst rule. Therefore, it is not possible to be sure of theorder of occurrence of the alarms, and it is necessary toresort to sequential pattern mining.

among all the alarms that make up the pattern. The ﬁrst 5
patterns are shown in Table 3.
As can be seen, the same alarms that had alreadyappeared in the association rules of the previous section
continue to appear here. However, this time there are nolonger symmetrical rules, only the patterns themselves,
which, by themselves, already indicate the order in which
the alarms occur.
All patterns listed have been validated using each
alarm’s documentation. Furthermore, assuming that when
the antecedent is resolved, the consequent no longer happens
and that the time interval between them is suﬃcient toreport the failure, trigger a ﬁctious work order and solve
the problem, the consequent will be removed from theoriginal dataset, and the maximum reduction in the numberof alarms will be calculated.
The next sections will present some use cases
considering the patterns found in Table 3.

3.2.1 RF Unit ALD Current Out ofRange → ALD Maintenance Link
Failure3.2 Sequential Pattern Mining
In the implementation of the algorithms, the extensive
framework for data mining SPMF [14] was used.
In the development of the work reported in theprevious section, it was noticed that there was no need totest several time windows (1 to 30 minutes, every minute)in the deﬁnition of transactions, as the results are similar
for all of them. Therefore, for these algorithms, transactions
were deﬁned based on a single dynamic window. Thereis a timeout interval in which if no alarm happens duringthat interval, the next alarm that occurs will belong to thenext transaction.
The timeout value was deﬁned considering thepredominant association rules in the previous section,
namely those that contained alarms associated with the
Antenna Line Device (ALD). As the objective is that
this pair of alarms is always in the same transaction, thetimeout was ﬁxed at 11 minutes, as this is the maximum
time interval registered between the two alarms.
The execution times and memory usage of eachalgorithm were measured, for the dynamic window, andare presented in Table 2.
Observing the average value of three tests executed ina row, it appears that the fastest algorithm is SPAM, followed
by PreﬁxSpan, and ﬁnally SPADE. In terms of occupied
memory, the order is reversed, with SPADE being the mosteﬃcient, followed by PreﬁxSpan and, ﬁnally, SPAM.
The list of patterns, identical to all algorithms, wassorted by the Lift metric in descending order, as those withthis highest value are those with a greater dependency

The

Radio Science Bulletin No 378 (September 2021)

In the context of preventive maintenance, it isnecessary to understand what is gained by being able torecognize the patterns that happened in the past, in order totry to predict and prevent what will happen in the future. In
this case, by preventing the antecedent alarm, 715 “ALD
Maintenance Link Failure” alarms are removed. This mainlytranslates into the reduction of:-“Communication”-type alarms: 1860 → 1145(-38.44%);“VX73KL” node alarms: 40 → 22 (-45%);“CS12KL” node alarms: 87 → 51 (-41.38%);“VX37KL” node alarms: 179 → 107 (-40.22%);“CR29VL” node alarms: 64 → 44 (-31.25%).

3.2.2 Inter-System Cabinet
Conﬁguration Conﬂict → X2
Interface Fault
By solving the errors and conﬁguration conﬂicts
in the cabinet, 6327 “X2 Interface Fault” alarms can beprevented from happening. This mainly translates into
the reduction of:-

Total number of alarms: 191053 → 184726(-3.31%);“Signaling”-type alarms: 8981 → 2654(-70.45%);“LO01AL” node alarms: 37 → 18 (-51.35%);“VX83EL” node alarms: 74 → 37 (-50%);“VX49KL” node alarms: 53 → 27 (-49.06%).

41

3.2.3 Certiﬁcate Invalid → External
Clock Reference Problem

mobile networks, using two types of machine learningtechniques for handling FM data: ARL and SPM.

By properly validating the certiﬁcate, it can preventa lot of “External Clock Reference Problem” alarms fromhappening. As this pattern shares the same consequent as
the next, the calculation and analysis of this impact is madefor the next pattern.

For ARL algorithms, FPGrowth presented the best
performance in terms of execution time and resources
used. Eclat was the most eﬃcient for short transactions,being surpassed by Apriori for larger transactions. In all ofthem there was a symmetry in the association rules, having
therefore evolved to the SPM algorithms, where the orderof alarms’ occurrence is important.

These two patterns are between “Major” alarms,
while the ﬁrst two were between a “Minor” antecedentand a “Major” consequent. This allows us to deduce, after
analyzing the 4 cases, that the sequence of alarms always
follows an increasing order in terms of severity. Of course,
there may be alarms caused by alarms with the same severity(as in this case), but a less severe alarm than the previous
one is unlikely to occur (for example, Major → Minor).

3.2.4 ALD Maintenance Link
Failure → External Clock
Reference Problem
This pattern is the second (of those described) in which
the “External Clock Reference Problem” alarm is foundas a consequence. This suggests that the problems in theexternal clock reference can be indirectly caused by various
sources on the network, which lead to a bad conﬁgurationof the clock or the malfunction of some hardware element
essential for proper clock synchronization.
Resuming the correct synchronization of the externalreference clock, it prevents the occurrence of 1414 “External
Clock Reference Problem” alarms. This mainly translates
into the reduction of:-“Hardware”-type alarms: 7969 → 6555(-17.74%);
There are 39 nodes that no longer have any unresolved
alarm (-100%);“VX30EG” node alarms: 18 → 1 (-94.44%);“CR06VU” node alarms: 22 → 4 (-81.82%);“VX88EU” node alarms: 50 → 12 (-76%).

It is concluded that, with sequential pattern mining,
it is possible to predict which will be the most likely
alarm to be the consequence of a given antecedent alarm.
Within the scope of preventive maintenance, if the problem
resolution is quick and eﬀective, it is possible to completely
prevent the consequent alarm from occurring, resulting ina decrease in the number of triggered alarms and failurescaused by them.

4. Conclusions
This work aimed to develop a solution for preventive
and proactive maintenance in the fault management of LTE

42

In this case, SPAM turned out to be the most eﬃcientin terms of execution time but the worst in terms of resource
utilization. SPADE was the most eﬃcient in terms ofresource utilization but the slowest of all. On the other hand,PreﬁxSpan oﬀered a good compromise among the three.
In all the performed tests, it was possible to conclude thatthe prevention of consequent alarms results in a decreasein their absolute number in the network’s nodes where
they occurred.
In the best case, there was a decrease of 3.31% in
all analyzed nodes, and 70.45% in terms of alarms of
the same type. It was also found that 39 network nodes
no longer had any unresolved alarm. These results
demonstrate that sequential pattern mining drives thepreventive maintenance of alarms in a LTE mobile network,
reinforcing the preventive maintenance’ importance fortelecommunications operators.

5. Acknowledgements
This work was carried out in the scope of theinternational project Artiﬁcial Intelligence for Green
Networks (AI4GREEN) C2018/1-5, under the CELTICNEXT Core Group and the EUREKA Clusters program.
The authors would like to thank the COMPETE/FEDER
program for funding the national component of the project(16/SI/2019), as well as the Instituto de Telecomunicações(IT) and CELFINET for its support.

6. References
1. C. G. Araújo, G. Cardoso, J. Sousa, M. Coutinho, P.
Neves, “Predictive fault management,” in InnovAction#4, Altice Labs, 2019.
2. D. Mulvey, C. H. Foh, M. A. Imran and R. Tafazolli,“Cell Fault Management Using Machine Learning
Techniques,” IEEE Access, 7, pp. 124514-124539, 2019.
3. M. Nouioua, P. Fournier-Viger, G. He, F. Nouioua, and
M. Zhou, “A survey of machine learning for network
fault management,” in Machine Learning and Data
Mining for Emerging Trend in Cyber Dynamics, 2021.
4. Huawei, 3900 Series Base Station Product Documentation, V100R010C10, Huawei Technologies Co., 2015.

The

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5. “MLxtend evaluation metrics.” http://rasbt.github.io/
mlxtend/user_guide/frequent_patterns/association_
rules/#metrics. Accessed: 2021-09-21.
6. R. Agrawal and R. Srikant, “Fast algorithms for mining
association rules in large databases,” in Proceedingsof the 20th International Conference on Very Large
Data Bases, VLDB ’94, (San Francisco, CA, USA),p. 487–499, Morgan Kaufmann Publishers Inc., 1994.
7. M. Zaki, “Scalable algorithms for association mining,”
IEEE Transactions on Knowledge and Data Engineering, 12, 3, pp. 372–390, 2000.
8. J. Han, J. Pei, Y. Yin, and R. Mao, “Mining frequent patterns without candidate generation: A frequent-pattern
tree approach,” Data Mining and Knowledge Discovery,
8, pp. 53–87, 01 2004.
9. J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q.
Chen, U. Dayal, and M. Hsu, “Mining sequential patterns by pattern-growth: The preﬁxspan approach,” IEEE
Transactions on Knowledge and Data Engineering, 16,
pp. 1424–1440, Nov. 2004.

11. J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, “Sequential pattern mining using a bitmap representation,” inProceedings of the Eighth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining,
KDD ’02, (New York, NY, USA), p. 429–435, Association for Computing Machinery, 2002.
12. S. Raschka, “MLxtend: Providing machine learningand data science utilities and extensions to python’s
scientiﬁc computing stack,” The Journal of Open Source
Software, 3, Apr. 2018.
13. C. Borgelt, “Frequent item set mining,” WIREs Data
Mining and Knowledge Discovery, 2, 6, pp. 437–456,
2012.
14. P. Fournier-Viger et al., “The SPMF Open-Source Data
Mining Library Version 2,” in Machine Learning andKnowledge Discovery in Databases (B. Berendt, B.
Bringmann, É. Fromont, G. Garriga, P. Miettinen, N.
Tatti, and V. Tresp, eds.), (Cham), pp. 36–40, Springer
International Publishing, 2016.

10. M. Zaki, “SPADE: An eﬃcient algorithm for mining
frequent sequences,” Machine Learning, 42, pp. 31–60,
01 2001.

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Radio Science Bulletin No 378 (September 2021)

43

Quasi-Yagi Microstrip Dipole Antenna
with Circular Arc Parasitic Elements
for Wireless Sensing Networks
Tiago E.S. Oliveira1, João R. Reis2, and Rafael F.S. Caldeirinha212

Polythecnic of Leiria, Leiria, Portugal

Instituto de Telecomunicaçoes, Leiria, Portugal
Email: rafael.caldeirinha@ipleiria.pt

Abstract
In this paper, a Quasi-Yagi microstrip dipole antenna
with circular arc parasitic elements for wireless sensing
networks is presented. With the aim of being employed
in a multi-sector base-station (BS) of a Wireless Sensing
Network (WSN), the proposed antenna is designed andoptimised to operate in the 2.4 GHz ISM band. Speciﬁc
project requirements, such as: operating frequency, gain,
half power beam-width (HPBW) and, consequently,
Field of View (FOV) are taken into consideration whendimensioning the antenna. After proper design optimization,
carried out with the aid of a full wave electromagnetic
solver (CST Microwave Studio), an antenna prototype has
been fabricated and experimentally characterized inside ananechoic chamber. From the measurement results obtainedwith the prototype, the antenna yields to a realised gain of
8.6 dBi, an HPBW of 64°and 42 °in the azimuth and elevation
planes, respectively, and a back-to-front ratio of 16.4 dB,
at 2.44 GHz. The measurement results are proved to be ingood agreement with the simulation ones.
Index Terms: Antenna, Quasi-Yagi, WSN, Base-Station,
Director, Circular Arc.

1. IntroductionW

ireless Sensor Networks (WSNs) are nowadays
widely implemented [1], often being oﬀered asa solution for monitoring networks, in particular for:environment monitoring [2], [3], security [4], [5], healthmonitoring [6], [7], agricultural [8], [9], hazard detection[10], among many others. A WSN is typically arranged in apoint to multi-point conﬁguration, composed of a collectionof a large number of Sensor Nodes (SN) that are scatteredin a certain monitoring area [11] and, a Base Station (BS)which collects the nearby environment data and forward
it to higher layer network or upload to a Cloud service forfurther analysis.

44

In the majority of WSN applications, it is desirable tohave a BS covering all its surroundings, typically requiringa Field of View (FOV) of 360 degrees. Antennas thatexhibit an omnidirectional radiation pattern, e.g. dipole ormonopole antennas, can ﬁt the purpose, oﬀering however,relatively low gains, which consequently impact the dynamic
range of the system. To overcome this issue there are manytechniques known in the literature, as e.g. beamforming,
beamsteering and antenna sectorisation.
In particular, antenna sectorisation comprehends thedivision of the area (cell) covered by the antenna radiation
pattern, in sub-sectors, so that higher directivity antennas
with lower FOV can be employed [12]. Ultimately, thenumber of sectors depends on the half power beam-width(HPBW) of a single element antenna [13].
To this extend, it is proposed in this paper a novel
microstrip quasi-Yagi antenna, based on the Yagi-Uda
antenna principle. These antennas typically present
relatively high gain, depending on the number of elements
used, and moderate front-to-back ratio [14], ideal for the
proposed application. In fact, Yagi-Uda antennas are very
well known and their design procedure is well establishedfrom many years ago [15]. These end-ﬁre antennas are
typically composed of parallel metallic rods acting as
reﬂector and directors of a driven element and, widelyused for terrestrial television broadcasting services, in the
HF, VHF and UHF bands [14]. Due to its popular design,
they have rapidly been adapted to microstrip technology,
by etching the directors and reﬂector directly in dielectric
substrates [16], [17]. In recent years, several authors havebeen evolving this particular antenna design or constructed
new antennas based on its physical principle. For example
in [18], the authors have suggested a cloaked Yagi-Uda
design, by introducing novel radiator designs, capable ofbeing electromagnetically undetectable in another desired
frequency range. In [19], the authors recurred to SquareShaped Split Ring Resonators (SRRs) to reduce the widthof planar Yagi-Uda antennas. In [20], the authors developed
a wideband beam-switchable quasi-Yagi antenna array

The

Radio Science Bulletin No 378 (September 2021)

Figure 2. Simulated S11-parameter for several valuesof dl, within the selected parametric range.
Figure 1. Proposed circular arc Quasi-Yagi antennalayout: (a) top view and (b) bottom view.
operating at 28 GHz, for mobile handsets. The proposedarchitecture consists of ﬁve quasi-Yagi antennas printed
on Rogers substrate. More recently in [21], the authors
have presented a three-port vertically polarized Quasi-Yagi
Multiple-Input-Multiple-Output (MIMO) with pattern
diversity for 5G N78 band applications. In this paper,
it is proposed a novel quasi-Yagi antenna design basedon circular arc shaped directors and reﬂector. Design
guideline and simulation workout is presented for theantenna operating in the 2.4 GHz band, aiming at least 9
dBi of gain, a HPBW of 60 degrees, and a back-to-front
ratio greater than 15 dB.
This paper is organised as follows: In Section II theantenna layout is thoroughly described. Section III presents
in detail the parametric simulation and subsequent antenna
optimisation, using a full wave electromagnetic solver(CST Microwave Studio). In section IV, the experimental
setup considered for antenna characterisation is described,
followed by subsequently analysis and discussion of theexperimental results obtained from the prototype. Finally,in Section V the main conclusions are drawn.

2. Antenna Layout
The proposed antenna is depicted in Figure 1. It is based ona Yagi-Uda antenna design applied to microstrip technology,
in which metallic parasitic elements are used as directors
and reﬂector, in order to focus the radiated energy in
one direction. The antenna is composed of a microstrip
dipole, a circular arc shaped reﬂector over a ground plane(deﬁned by gw ) and, six circular arc shaped directors.
The circular arc shaped reﬂector and directors should beseen as a quarter of a circle centred at xi with radius ri
and thickness ti , where i=0,1,2,... represents the numberof the of the reﬂector/director, as depicted in Figure 1.
The overall antenna dimensions are dictated by theparameters WS x LS . The feeding line width ( f w ) and length( fi ) dictates the input impedance of the antenna, while theTheRadio Science Bulletin No 378 (September 2021)

frequency of operation is deﬁned by the dimensions of thedipole arms ( dl ). The dipole arms are perpendicular to thefeeding line thus, creating a 90° bend which can cause an
unnecessary signal reﬂection to the source. To overcome
this issue the bend was mitred, to reduce the capacitance ofthe line, maintaining the desired impedance. The antenna
was designed in a double-sided FR4 substrate with  r =4.4,
a loss tangent of 0.014 and a thickness of h =1.6 mm. The
optimisation of the antenna dimensions and the number of
directors are further analysed and discussed next section.

3. Design and Optimisation
The proposed antenna design conﬁguration wasdimensioned with the assist of a full wave electromagnetic
solver (CST MWS), in an iterative design approach. In
a ﬁrst iteration, the feeding element, i.e. the microstrip
dipole was evaluated isolated (without directors andreﬂector), in order study the impact of the length of thedipole arm in S11 performance and, ﬁnd the optimum value
for antenna resonating at 2.44 GHz (target frequency).
Therefore, a parametric study was carried out on dl , with
the initial value being set at λg/8 and by ﬁxing gw at 32mm, LS = 61 mm and fl = 52 mm. While LS was deﬁned
to accommodate at least the arms length (i.e. 2× λg/8), fl
and gw were deliberately deﬁned to further accommodate
the reﬂector shape. fw was set at 3.1 mm corresponding
to a 50Ω impedance line for the proposed substrate. The
simulation results of the parametric study are depicted in
Figure 2. The parametric simulation run within the rangeof 20.5 to 28.5 mm, with 2 mm step. From the results, it
is possible to observe that dl = 20.5 mm is the optimisedvalue for the desired resonance frequency (i.e. 2.44 GHz).
With the dipole arms length already deﬁned, the next stepin the antenna design is to evaluate the impact of adding
the directors in antenna performance. Therefore, a single
director was added to the feeding antenna, at the position
x1 = 32 mm. At this point, no reﬂector has been considered.
The initial values of t1 =8 mm and r1 = 22 mm wereconsidered as starting point for the optimisation. The
addition of the director provoked two eﬀects in the antenna

45

Figure 4. Impact of the addition of a single circular
arc shaped director in S11-parameter.

Figure 3. Impact of the addition of a single director in the antenna radiation pattern (in azimuth
plane), at 2.44 GHz.
performance: an increase in the overall gain (Figure 3) and an
undesirable deviation of the antenna’s resonating frequency,
as well as an increase of the S11 amplitude (Figure 4). Whilethe ﬁrst eﬀect was expected (in line with the concept of
Quasi-Yagi antenna), the latter is thought to be associatedwith mutual coupling between the driven element (dipole)and the added director. To correct this frequency shift, thelength of the dipole arms dl was optimised to 21.5 mm.
After ensuring the antenna was resonating at thedesired frequency, a parametric study was carried out
in all parameters of the circular arc director (r1, x1 andt1). The study aims to optimise the circular arc director
dimensions to achieve the greatest antenna gain possible,

while ensuring the antenna is still operating at the target
frequency. From an extensive parametric study, it was foundthat the results which best favour the antenna performancein terms of gain are: x1 = 36 mm, t1 =5 mm and r1 = 21 mm.
From Figure 5, it is possible to observe that such values
yield a realised gain of 6.02 dBi, which mean an increaseof 1.65 dB, when compared to the antenna layout without
a director. Furthermore, diﬀerent director shapes, such as
the typical rectangular and, inverted circular arc shape,
were also studied and optimised with the aid of several
parametric simulations (not included in this paper due tospace constraints). The simulated radiation patterns in theazimuth plane for the mentioned shapes (also optimized),
are presented in Figure 6. When comparing the results,
is clear that the circular arc shapes slightly improve theoverall gain of the antenna, the addiction of a director withthe rectangular shape generated a gain of 5.6 dBi, whereas
for the inverted circular arc shape a gain of 5.86 dBi ispresented and, as previously mentioned, the circular arc
shape yields a realised gain of 6.02 dBi. The latter, not only

Figure 5. Radiation pattern in azimuth andelevation planes, at 2.44 GHz, for the optimised
antenna with a single director.

46

Figure 6. Radiation pattern in azimuth
plane, at 2.44 GHz, for diﬀerent optimised
director shapes.

The

Radio Science Bulletin No 378 (September 2021)

Figure 7. Variation of the realised gain against the
number of directors.
presents the highest gain but also the best back-to-front
ratio of 17 dB, therefore the circular arc shape was chosen.
Aiming to design an antenna with a higher gain,
the process of adding directors has been studied. Thus,a parametric workout was carried out to access the gain
improvement against the number of directors (with the same
dimensions). The directors were added progressively to thelayout, at the position xi = xi−1 + 18 mm. The maximum
gain at the antenna boresight has been obtained, and it isplotted against the number of directors (up to 8) in Figure
7. From the analysis of the results, it can be seen thatafter the addition of 6 directors, the ratio between gain
and number of directors decreases. The diﬀerence from 6
to 8 directors is 0.81 dB, while the diﬀerence from 4 to 6
is 1.24 dB. Thus, for the best trade-oﬀ between antenna
dimensions and gain, it was opted for 6 director design
which achieves the target gain of 9.6 dBi. This represents
an additional gain of 3.6 dB, when comparing with the
conﬁguration of a single director.
To further improve the front-to-back ratio of theantenna (already considering 6 directors), an additional

circular arc shape was added to the design. The element
was added prior to the feeding line (at the position x0 =8mm), over the ground plane, acting as a reﬂector. The
dimensions of the reﬂector were also optimised using
CST MWS with the goal of improving the front-to-backratio. From the parametric workout, it was found that thebest front-to-back ratio is of 18.63 dB, achieved with r0 =34.5 mm, x0 = 8 mm, t0 = 5 mm and g = 2 mm, as depicted
Figure 8, which improves 4.28 dB, from the case without
reﬂector (Figure 8a). However, this change came at theexpense of signiﬁcantly increasing the side-lobe-levels,
as it can be observed from the radiation patterns of Figure
8b. This indicates that most of the energy being reﬂectedby the reﬂector is being re-radiated towards the side lobe
direction, instead of the boresight direction, thus justifying
the small increase of gain (only 0.1dB) of the main lobe.
Please note that if the intention was exclusively increasing
the gain of the main lobe, this could have been done byslightly shaping the reﬂector, e.g. making it more parabolic.
Finally, after all the design optimisation performedin this section, an antenna with an overall size of 175 × 61
mm2, and dimensions (in mm): LS = 61, WS = 175, fl = 52,
x0 = 8, g = 2, r0 = 34.5, t0 = 5, fw = 3.1, ti = 5, ri = 21, x1 =36, x2 = 54, x3 = 72, x4 = 90, x5 = 108, x6 = 126, gw = 32
and dl = 21.5, presents a realised gain of 9.7 dBi,a totaleﬃciency of -0.55 dB (89%), a front-to-back ratio of 18.63◦
dB and, a HPBW (FOV) of 60 . The polarization is verticalwith the E-ﬁeld parallel to the dipole arms

4. Experimental Characterisation
4.1. Setup
In order to experimentally characterise the proposed
antenna, the prototype depicted in Figure 9 was fabricated.
Firstly, antenna matching was evaluated by measuring theS11-parameter, using a Vector Network Analyzer (VNA)(R&S ZVM). Subsquently, several antenna radiation

Figure 8. Radiation pattern in azimuth and elevation planes, at 2.44 GHz, (a) without and(b) with the reﬂector.

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Radio Science Bulletin No 378 (September 2021)

47

Figure 9. Photography of the Quasi-Yagi microstrip dipole antenna with circular arc parasitic
elements prototype.
patterns, in the two main antenna planes, i.e. azimuth andelevation, have been obtained. To this end, the setup of
Figure 10a was assembled inside an anechoic chamber
enabling experiments to be performed in a controlled,
electromagnetically quiet and reflection free radio
environment.
In particular, a well characterised Aaronia Hyperlog
30100 antenna was used as the Tx antenna (Figure 10b). It isconnected to a Signal Generator (R&S SMR27) producing
a continuous wave (CW)), with a transmission power of
0 dBm. At the receiver end, a well characterised Aaronia
Hyperlog 60100 antenna was connected to a spectrum
analyser (Agilent E4407B) to be used as reference, later
replaced by the antenna under test (AUT). The antennas
were placed 3,5 meters apart, ensuring that the measurement
took place in the far-ﬁeld region of the antennas. The Tx
antenna was kept ﬁxed throughout the measurements,
while the AUT was rotated around its axis with the assistof motorised pan/tilt head unit. The received power was
acquired for each angular step with 1◦ of resolution, within
the range deﬁned between -180◦ and 180◦ (in the azimuth◦
plane). Both Tx and Rx antennas were further rotated 90 tomeasure the radiation pattern in the elevation plane, using
the same physical setup. The received power acquisition
and movement control were executed in real-time andpost processed in MATLAB, using a software developed
for the eﬀect.

Figure 10. Radiation pattern experimental
characterisation setup (a) diagram and (b)photography
PCB production technique (produced in-house), and theuse of the SMA connector, not considered in simulations.
In terms of the radiation pattern obtained at 2.44
GHz, depicted in Figure 11b and Figure 11c for the azimuthand elevation planes, respectively, it can be observed agood agreement in the shape, between the simulation andmeasured results. According to experiments, the antennapresents a total gain of of 8.6 dBi, an HPBW of 64◦ in azimuth
and 42◦ elevation planes, respectively, and a back-to-front
ratio of 16.4 dB. This compares to simulations by having adecrease of 1.1 dB in total gain and 2.2 dB in back-to-front
ratio. HPBW in simulations also decreases by 5◦ in both
azimuth and elevation planes.

4.2 Experimental Results

5. Conclusions

From the experimental S11, depicted in Figure 11,
it is possible to observe that the measured results are ingood agreement with the simulated ones. Even though
the S11 presents slightly diﬀerence around the resonancepeak at 2.4 GHz, antenna matching is still below -10 dB,
presenting a bandwidth of 390 MHz, deﬁned between
2.12 to 2.51 GHz, in experiments, against a slightly larger
bandwidth of 420 MHz in simulations. Such discrepancy
can be associated to mismatched values of the permittivityof the substrate between the real data and the one provided
by the manufacturer, accuracy issues associated with the

A novel Quasi-Yagi Microstrip Dipole antenna withcircular arc parasitic elements is proposed in this paper.
The antenna is comprised of a microstrip dipole used as
the driven element, a circular arc shaped reﬂector and six
equal circular arc shaped directors. A parametric study on
several antenna parameters, including the shape and thenumber of directors, is performed in order to meet speciﬁc
project requirements (operating frequency, gain, HPBW
and back-to-front ratio). After proper antenna optimisation
using CST MWS, an antenna prototype designed to operate
at 2.44 GHz has been fabricated in FR4 substrate. The

48

The

Radio Science Bulletin No 378 (September 2021)

work will aim at antenna implementation in a multi-sector
WSN base-station, composed of 6 Quasi-Yagi elements,
which will be further tested in a real-world scenario, i.e.
implemented in a wireless sensing network.

Acknowledgement
This research was partially under the project WSN-EM:
PTDC/EEI-EEE/30539/2017 by FCT/MCTES throughnational funds, and when applicable, by EU funds under
the project UID/EEA/50008/2021.

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Radio Science Bulletin No 378 (September 2021)

A Simple Method for Calibrating
GPS-Disciplined Clocks via Direct
Comparison to a UTC(k) Time Scale
Michael A. Lombardi1, Bijunath R. Patla1, Andrew N. Novick1,
Demetrios Matsakis2, and Stephen J. Mitchell3
National Institute of Standards and Technology Boulder, CO, USA
E-mail: lombardi@nist.gov, bijunath.patla@nist.gov, novick@nist.gov
1

Masterclock, Inc.
St. Charles, MO, USA
E-mail: dmatsakis@masterclock.com
2

Applied Physics Laboratory (APL)
Johns Hopkins University
Laurel, MD, USA
E-mail: stephen.mitchell@jhuapl.edu
3

Abstract
Global-Positioning-System- (GPS) disciplined clocks
are referenced to the Coordinated Universal Time (UTC)
scale at the United States Naval Observatory (USNO),
known as UTC(USNO). The UTC(USNO) time scale is avery close approximation of UTC, the oﬃcial world timescale. GPS-disciplined clocks therefore provide a very close
approximation of UTC when they are properly calibrated
to compensate for equipment and cable delays. This paper
provides a simple method for calibrating GPS-disciplined
clocks with respect to UTC so they can replicate UTC
with uncertainties of < 10 ns. The method involves directlycomparing the device under test to any UTC(k) time scale
listed on the weekly Rapid UTC (UTCr) reports publishedby the Bureau International des Poids et Mesures (BIPM).

1. Introduction

G

PS-disciplined clocks (GPSDCs) produce a onepulse-per-second (pps) output that is referenced
to UTC(USNO), the Coordinated Universal Time (UTC)
scale operated by the United States Naval Observatory(USNO). In nearly all cases – unless the antenna cable isunusually long or if the antenna coordinates have a verylarge error – an uncalibrated GPS-disciplined clock should
agree with UTC(USNO) to within less than 1 μs, with~100 ns accuracy being a typical speciﬁcation. However,even when short antenna cables are used and even when theantenna coordinates have been determined to within lessthan 1 m, getting the best available accuracy from a GPS-TheRadio Science Bulletin No 378 (September 2021)

disciplined clock still requires measuring and calibrating
all hardware delays. The antenna cable typically introduces
the largest delay, but the GPS receiver and its associatedelectronics and ﬁrmware, as well as the antenna itself, will
also introduce delays. Once these delays are determined, adelay constant can be keyed into the GPS-disciplined clock
to correct the 1 pps output. Within the semantic frameworkof the International Vocabulary of Metrology [1], thedetermination of the delays and their respective uncertainties
is accomplished by calibration, and the correction of thedelay in the GPS-disciplined clock is accomplished byadjustment. After a GPS-disciplined clock has been both
calibrated and adjusted, it can be utilized as a true UTC
synchronization source.
Several methods of GPS delay calibrations areroutinely practiced. Some methods involve measuring thecable, receiver, and antenna delays separately, and thenappropriately accounting for all delays, but some partsof this process can be diﬃcult. For example, methods formeasuring cable delays are well established [2, 3], but
measuring receiver delays might require the use of a GPS
simulator [4, 5]. Antenna-delay measurements might be evenmore involved, requiring the use of a network analyzer andan anechoic chamber. For these reasons, it is common andusually preferable to calibrate a GPS-disciplined clock asa system that includes the receiver, antenna, and antenna
cable, and obtaining a single delay value that accounts forthe entire system. This is usually done by simultaneously
comparing both the GPS-disciplined clock under test anda previously calibrated reference GPS-disciplined-clock
system, each connected to antennas separated by a shortdistance, to the same clock [6]. The comparisons usually
last for about three to 10 days, with one week being typical.
The reference GPS-disciplined-clock system is assumed to51

be correct, and thus the average time diﬀerence between
the two units is attributed to the GPS-disciplined clock
under test.
This paper introduces a simple method that retains
the advantage of calibrating GPS-disciplined clocks asa system, but that eliminates the need for a reference
GPS-disciplined clock. This is advantageous because thereference GPS-disciplined clock contributes uncertainty to
the measurement, and because in many cases the reference
GPS-disciplined clock is a unit maintained elsewherethat must travel to the site of the calibration. The method
described here consists of directly comparing the GPSdisciplined clock under test to any UTC(k) time scale listedon the Rapid UTC (UTCr) reports that are published weekly
by the Bureau International des Poids et Mesures (BIPM),
and then applying a UTCr correction to the measurement.
The new method works because of the very close agreement
between UTC, UTCr, UTC(USNO), and GPS time. It isviable to implement because the UTC(USNO) time scale
provides the time reference for GPS, and because daily
UTCr – UTC(USNO) results are freely available via the
BIPM. For the purposes of this paper, the method is called
the GUC method, an acronym for GPS/UTC calibration.
Section 2 provides a short discussion of the close
relationship between UTC, UTCr, UTC(USNO), and thetime broadcast by the GPS satellites. Section 3 explains
the GUC method, including the application of UTCr data.
Section 4 provides and discusses measurement results fromseveral GPS-disciplined clocks calibrated with this method
at the National Institute of Standards and Technology(NIST). Section 5 discusses the measurement uncertaintyof the delay calibrations, and Section 6 provides a summary.

2. The Relationship Between UTC,
UTCr, UTC(USNO), and the Time
Broadcast by GPS
Time calibrations, as is the case with nearly all metrological calibrations, should be referenced and traceable
to the International System of Units (SI). The base unit fortime is the second (s), one of the seven base units of theSI. The ultimate reference for establishing traceability fortime calibrations is Coordinated Universal Time (UTC),an atomic time scale that provides the world’s best approximation of the SI second [7]. UTC is computed by the
BIPM from a weighted average of data collected from local
time scales, known as UTC(k), that are located at national
metrology institutes or other facilities that have legal or
technical timekeeping responsibilities.
The UTC(k) time scales produce signals that canserve as a reference for physical measurements, whereas
UTC does not. Instead, UTC is deﬁned by regularly
publishing the time diﬀerence, UTC – UTC(k), for each
institution that contributes to UTC. The oﬃcial time
diﬀerences are published monthly in the BIPM Circular T,

52

Figure 1. Time diﬀerences for UTC – UTC(USNO)
and UTCr – UTC(USNO) for calendar year 2021.
which provides UTC – UTC(k) values at ﬁve-day intervals[8]. Since July 2013, the BIPM has also published weekly
UTCr – UTC(k) time diﬀerences, with values provided at
one-day intervals [9].
The UTC(USNO) time scale is the largest contributor
to UTC, and as previously noted, provides a very close
approximation of UTC. This is illustrated in Figure 1,
which shows both the UTC – UTC(USNO) and UTCr –UTC(USNO) time diﬀerences for the calendar year 2021.
The graph indicates UTC(USNO) did not depart from UTC
by more than ±4 ns at any point during 2021, with theaverage UTC – UTC(USNO) and UTCr – UTC(USNO) time
diﬀerences being 0.58 ns and 0.62 ns , respectively. The
largest diﬀerence between the UTC and UTCr comparisons,for the days spaced at ﬁve-day intervals when both values
were available, was 1.4 ns, with the average diﬀerence being
0.03 ns . This suggests that the results of UTCr, while
non-oﬃcial, agree closely with the results published later in
the Circular T and are suitable for use for GPS-disciplined
clock-delay calibrations, as described in Section 3.
The close approximation of UTC(USNO) to UTC
is advantageous to GPS, because GPS broadcasts a
prediction of UTC(USNO). Even though GPS has its own
time scale, known as GPS time, the satellites broadcast
UTC correction parameters in subframe 4, page 18, of thenavigation message. Nearly all GPS-disciplined clocks
apply these parameters and convert GPS time to a predictionof UTC(USNO). The UTC oﬀset correction, tUTC , iscomputed as [10]

tUTC  t LS  A0  A1 t E  t0t  604800 WN  WN r  
(1)
where
t LS is the number of leap seconds introduced into UTC
since the beginning of the GPS epoch (January 6, 1980),

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Figure 2. GPS delivered predictions of UTC(USNO) compared to actual
UTC(USNO) for 2021.
A0 is the constant UTC oﬀset parameter expressed inseconds,
A1 is a dimensionless frequency-oﬀset value that allows
the correction of the time error accumulated since the UTC
reference time, t0t , which is when A0 was last determined,t E is GPS system time (the time to be converted toUTC(USNO)),
604800 is a constant that equals the number of seconds
in one week,
t0t is the reference time for UTC data,
WN is the GPS week number, and
WNt is the UTC reference week number.

The t LS term is the large, integer-second part of thecorrection, equal to the number of leap seconds that haveoccurred since the beginning of the GPS epoch (January
5, 1980). The A0 term is the small, nanosecond part ofthe correction, equal to the diﬀerence between the GPS
and UTC(USNO) second markers. It is broadcast inunits of seconds, but is typically  1 108 s , or  10
ns in magnitude. The ﬁne tuning of the UTC output of
a GPS-disciplined clock is accomplished by applying adimensionless frequency oﬀset, provided by A1 , as a drift
correction for the interval between the time speciﬁed by
t0t and WNt and the current time. This is normally a subnanosecond correction, because A0 is normally updated
in the GPS broadcast more than once per day and the drift
correction supplied by A1 is typically near 1 ns per day.
The UTC(USNO) prediction broadcast by GPS isbased upon an extrapolation of the observed diﬀerence

Figure 3. The GPSDC – UTC(k) time-diﬀerence measurement.

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53

Table 1. The sample calibration of a GPS-disciplined clock at NIST using UTCr report 2152 (values
are in nanoseconds).
UTCr –
UTCr –
UTC(USNO)
UTC(NIST)
59575
1.3
1.3
59576
1.5
1.4
59577
1.5
1.4
59578
1.3
1.4
59579
1.4
1.6
59580
1.4
1.4
59581
1.5
1.6
Average delay (applied to GPSDC after calibration)

GPSDC –
UTC(k)
85.5
86.8
87.1
84.1
84.5
86.8
85.3

MJD

between GPS and UTC(USNO) from the start to the end of
the previous day. Figure 2 shows the diﬀerences between
the GPS-delivered predictions of UTC(USNO) and actual
UTC(USNO) daily averages for calendar year 2021. The
GPS – UTC(USNO) time diﬀerences fall within a ±2 ns
range and have a mean value of 0.1 ns . The very close
agreement between UTC(USNO) and the time broadcast
by the GPS satellites provides the foundation for the simple
GUC method described in Section 3.

3. The GUC Calibration Method
The GUC delay calibration method consists ofcomparing the 1 pps output of the GPS-disciplined clock
under test to a 1 pps output of any UTC(k) time scale
included on the weekly UTCr reports. Because the GPSdisciplined clock is being calibrated as a system (receiver,
antenna, antenna cable), it should be measured with its delay
compensation equal to 0, in other words, no previously
measured or estimated delay values should be keyed into
the unit. The comparisons are typically done with a timeinterval counter (TIC), as shown in Figure 3.
If the two cables that connect the GPS-disciplined
clock to the time-interval counter and the UTC(k) reference
plane to the time-interval counter do not have equivalentdelays, then the raw time-interval counter readings must
be corrected. Assuming that the GPS-disciplined clock isconnected to the start channel on the time-interval counterand UTC(k) is connected to the stop channel, as shown in
Figure 3, each time-interval counter reading is corrected as

TICcorr  TICraw  UTCdelay  GPSdelay ,(2)where TICraw is the uncorrected reading from the timeinterval counter, UTCdelay is the cable delay between theUTC(k) reference plane and the time-interval counter, andGPSdelay is the cable delay between the 1 pps output of
the GPS-disciplined clock and the time-interval counter.
The corrected time-interval counter readings ( TICcorr )54

GPSDC delay bias, GPSdb
85.5
86.9
87.2
84.0
84.3
86.8
85.2
85.7

are a measurement of GPS-disciplined clock – UTC(k).
These readings can be recorded every second, but the data
points used for the calibration should be one-day averages.
Daily averages are required because UTCr only provides
one value per day, and also have the beneﬁt of attenuating
diurnal variations caused by ionospheric or environmental
factors. The daily averages should ideally be recorded for aperiod of at least seven days that corresponds to the periodof a UTCr report. The last day of a UTCr report is always
a Sunday and the reports are published on Wednesdays.
This means that the GPS-disciplined clock delay calibration
cannot be completed until a few days after the most recent
measurement when the UTCr report becomes available.
The equation to compute the delay bias in a GPSdisciplined clock, GPSdb , for a given day is
GPSdb  UTCr  UTCUSNO   UTCr  UTCk  
  GPSDC  UTCk  ,(3)where UTCr  UTCUSNO  and UTCr  UTCk  are
obtained from the UTCr report, and  GPSDC  UTCk 
is obtained from the time-interval counter measurements.
Table 1 provides an example where a GPS-disciplined
clock was measured at NIST in Boulder, Colorado. In thisexample, the UTC(NIST) time scale served as UTC(k),and correction data were obtained from UTCr report 2152
(published on January 5, 2022). A delay constant of 0 wasused in the GPS-disciplined clock under test, meaning that
all hardware delays, including antenna cable delays, were
unaccounted for and ignored. The purpose of the calibration
was to obtain a single delay value that could be keyed into
the GPS-disciplined clock to compensate for its hardware
delays, and therefore make the GPS-disciplined clock
produce signals that agreed with UTC as closely as possible.
Note that in this example, the close agreement between
the UTC(USNO) and UTC(NIST) time scales meant
the corrections obtained from UTCr and applied to the

TheRadio Science Bulletin No 378 (September 2021)

Figure 4. Time diﬀerences for the reference USN6 GPS receiver –UTC(USNO) for calendar year 2021.
calibration were sub-nanosecond. A direct comparison of
the GPS-disciplined clock to UTC(NIST) would therefore
have produced nearly the same results. For GUCs performed
at other UTC(k) laboratories, the UTCr corrections would
likely be larger. For example, if UTC(USNO) – UTC(k)as obtained from the UTCr report had an average time
diﬀerence of 20 ns, then a GPS-disciplined clock compared
to UTC(k) should also have an average time diﬀerenceof 20 ns after it had been calibrated. The GUC method
calibrates devices to agree with UTC and not with UTC(k),and the method is viable because the diﬀerences between
UTC and UTC(k) are published.

4. Calibration Results
One way to test the validity of the GUC method is
to use it to check for biases in reference GPS time-transfer
receivers that have been previously calibrated with othermore-established methods. The ﬁrst, most-basic check,was done by comparing USN6, a reference GPS receiverat the USNO, to the UTC(USNO) time scale for the entire
year of 2021 (Figure 4). The average time diﬀerence of
this comparison should be near 0 (as previously indicatedby the data shown in Figure 2) if the GPS device has been
properly designed and calibrated. As expected, despite daily
peak-to-peak variations that sometimes reached ±4 ns, theFigure 5. Post-calibration delay variations of the NIST receiver, estimatedwith the GUC method, for calendar year 2021.

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55

Figure 6. Post-calibration delay variations of four single-frequency(L1 band) GPS-disciplined clocks during a one-year interval.
average time diﬀerence for 2021 was just 1.0 ns, indicating
only a small calibration bias.
A second test of the GUC method involved acomparison for the year 2021 involving a reference receiverat NIST, which was previously (and periodically) calibrated
via the BIPM calibration program. Here, the GUC method
was applied to estimate post-calibration delay biases asshown in Figure 5. Daily variations occasionally exceeded±5 ns, but the average time diﬀerence for 2021 was 0.7 ns.
During the ﬁrst two months of 2021, the GUC method
was used to calibrate four low-cost single-frequency (L1band) GPS-disciplined clocks at NIST. These calibrations
were not all performed simultaneously – no more than two
were in progress at one time – and each lasted for about
two weeks (at least two full UTCr reports were used foreach calibration). After these calibrations were completed,the four GPS-disciplined clocks were allowed to run
undisturbed for an entire year, and the GUC method wasthen again applied to the collected data. Figure 6 shows
the post-calibration delay variations of the four GPSdisciplined clocks for the approximate one-year period
from March 1, 2021,through March 6, 2022 (via UTCrreports 2109 through 2209; 53 full one-week reports wereused). Although the daily delay variations were diﬀerent,
the average delay bias for GPS-disciplined clock-C (which
utilized diﬀerent receiver hardware than the other threeunits), was 0.8 ns for the approximate one-year interval,
diﬀering by just 0.3 ns from the NIST reference receiver
over the same interval. The other three units (A, B, and D)
used identical hardware. As such, their daily values closely
tracked each other, and their average values for the oneyear interval were nearly the same: 3.1 ns, 3.3 ns, and3.4 ns, respectively. Units A, B, and D each diﬀered bynearly 3 ns from the NIST reference receiver.

56

Figure 6 indicates that after the original
approximate two-week GUC calibration, all four of the
GPS-disciplined clocks under test were still “biased low”
with respect to UTC, with GPSDC-C having the smallest
bias and the other three units grouped closely together.
No calibration can eliminate the inevitable daily delay
ﬂuctuations, but long-term calibrations can do a betterjob of centering the ﬂuctuations around zero by removing
the bias of seasonal eﬀects. The delay values of each of
the four GPS-disciplined clocks in Figure 6 were thus
subsequently adjusted to remove the revealed calibrationbiases, allowing the one-year observation period to serve
as a lengthy recalibration with a smaller uncertainty. Thisis shown in Figure 7, which includes data for the next six
UTCr reports (2210 to 2215) after the calibration biases
were removed. The average values for this six-week period
then all showed sub-nanosecond agreement with UTC, withvalues of 0.7 ns, 0.5 ns, 0.1 ns, and 0.5 ns for units A,
B, C, and D, respectively.

5. Factors that Limit the Measurement Uncertainty of GUC
Calibrations
The time diﬀerences of the GPS time signal withrespect to UTC(USNO) are tightly controlled, as shown in
Figure 2, but GPS-disciplined clocks will produce larger
time deviations, as indicated by the data shown in Figures 4,5, and 6. These deviations can be attributed either to factorsthat aﬀect the GPS signal as it propagates from the satellite
to the receiving system’s antenna, or to delay changes
within the receiving system itself. For the purposes of thisbrief discussion, uncertainties related to GPS signal
propagation can be labeled as U P . These include
uncertainties in the broadcast ionospheric delay corrections

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Figure 7. Delay variations of four single-frequency (L1 band) GPS-disciplined
clocks during a six-week interval after calibration bias was removed following a one-year observation period.
that are applied by GPS-disciplined clocks, as well as
multipath signal reﬂections, solar activity, troposphericactivity (weather), and other factors that can cause
propagation delays to change. Environmental conditions,such as changes in both indoor and outdoor temperaturethat can cause hardware delay changes, are labeled as U E. For example, outdoor temperature and conditions
such as snow on the antenna can change the delay ofreceiving antennas or cables. Indoor temperature often has
a larger eﬀect, especially in the case of GPS-disciplined
clocks with low-cost local oscillators, such as those graphedin Figure 6. In fact, both indoor and outdoor temperatures
likely contributed to the approximate 3 ns bias in units A,
B, and D, as they were calibrated during the months ofJanuary and February, winter months in Boulder, Colorado.
Some amount of delay variation both during and after thecalibration is inevitable in all GPS-disciplined clocks,
regardless of whether the GUC method or another calibration
method is utilized, and it seems reasonable and pragmatic
to evaluate both U P and U E as Type B uncertainties, andto conservatively assign a value of 3 ns to each
The other factors that must be considered in theuncertainty analysis of the GUC method are the uncertainties
of the links that UTC(USNO) and UTC(k) utilize to send
their data to the BIPM, which can be labeled as UUSN andUUTK , respectively. These uncertainties are provided by
the BIPM in Section 1 of the Circular T. For purposes ofexample, we used the January 2022 Circular T where UUSN
was reported as 1.6 ns. If we selected UTC(NIST) as UTC(k),then UUTK was reported as 2.3 ns. We could also assign
an uncertainty of 1 ns to UUG , or the diﬀerence between
UTC(USNO) and the prediction of UTC(USNO) broadcast
by GPS (obtained by rounding up from the average time
diﬀerence shown in Figure 2). Using standard methods for

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Radio Science Bulletin No 378 (September 2021)

uncertainty analysis [11] and the aforementioned values, weestimated the combined uncertainty, U c , of the calibration
method via UTC(NIST) as2
2
2
U c  k U P2  U E2  UUSN UUTK
 UUG

 2 32  32  1.62  2.32  12  10.4 ns.

(4)

The coverage factor of k  2 indicates that the coverage
probability was 95.45%. This represents the probability thata daily measurement value was within the coverage interval.
All of the post-calibration delay variations shown in Figures
4, 5, and 6 were well within a ±10 ns interval, suggestingthat an approximate 10 ns uncertainty for a one-day averagevalue was probably overestimated. The statistical (TypeA) measurement uncertainty from a GUC calibration
averaged across multiple days should improve by a factorof N , where N is the number of days of the calibration.
However, the systematic (Type B) uncertainties, such as theuncertainties in the BIPM links or GPS-disciplined clock
antenna coordinate errors, will not be reduced by a longer
calibration. Even so, for a seven-day calibration based ona single UTCr report, U c should be ~5 ns ( k  2 ) in thisexample, and if three UTCr reports are used for a 21-day
calibration, Uc should be reduced to  4 ns ( k  2 ).The
combined uncertainty, U c , is with respect to UTC. As
previously noted, the actual number of days required tocomplete the calibration will be N plus the days elapsed
before UTCr is published (the last value shown on a UTCrreport is for Sunday and publication is on Wednesday).

57

6. Summary
This paper has presented a simple method for thedelay calibration of GPS-disciplined clocks, introduced
here as the GUC method (GPS/UTC calibration). The
GUC method can be utilized by any laboratory with aUTC(k) time scale. It has the advantage of not involving areference GPS-disciplined clock, which potentially reduces
the uncertainty of the measurements. The foundation forthe method is the very close agreement between UTC,
UTCr, UTC(USNO), and the time broadcast by the GPS
satellites. The GUC method allows UTC(k) providers to
calibrate GPS-disciplined clocks with respect to UTC, and toestablish calibration services for industrial partners. It may
also be useful for periodic recalibration of any secondary
time-transfer receivers located at UTC(k) laboratories that
do not serve as their primary link to UTC, and for continuous
post-calibration veriﬁcation of primary links.
This paper is a contribution of the US government
and is not subject to copyright.

7. References
1. Joint Committee for Guides in Metrology (JCGM),
International Vocabulary of Metrology – Basic andGeneral Concepts and Associated Terms, VIM, Third
Edition, JCGM 200, 2012.
2. G. de Jong, “Measuring the Propagation Time of Coaxial
Cables Used with GPS Receivers,” Proc. of the 17th
Annual Precise Time and Time Interval (PTTI) Systems
and Application Meeting, December 1985, pp. 223-232.

3. D. Rovera, M. Abgrall, P. Uhrich, and M. Siccardi, “Techniques of Antenna Cable Delay Measurement for GPS
Time Transfer,” Proc. of the 2015 Joint Conference of
the IEEE International Frequency Control Symposium
and the European Frequency and Time Forum, Denver,
Colorado, April 2015, pp. 239-244.
4. G. Petit, Z. Jiang, J. White, R. Beard, and E. Powers, “Absolute Calibration of an Ashtech Z12-T GPS
Receiver,” GPS Solutions, 4, 4, April 2001, pp. 41-46.
5. J. Plumb, K. Larson, J. White, and E. Powers, “Absolute
Calibration of a Geodetic Time Transfer System,” IEEE T.
Ultrason. Ferr., 52, 11, November 2005, pp. 1904-1911.
6.W. Lewandowski, J. Azoubib, and W. Klepczynski, “GPS:Primary Tool for Time Transfer,” Proceedings of theIEEE, 87, 1, January 1999, pp. 163-172.
7. D. Matsakis, J. Levine, and M. Lombardi, “Metrologicaland Legal Traceability of Time Signals,” Proc. of the2018 Precise Time and Time Interval Meeting (ION
PTTI 2018), January 2018, pp. 59-71.
8. BIPM Time Department, “Circular T, available monthly(from 1996 to present) at: https://www.bipm.org/en/
time-ftp/circular-t .
9. BIPM Time Department, “UTCr,” available weekly(from 2013 to present) at: https://www.bipm.org/en/
time-ftp/utcr .
10. Global Positioning Systems Directorate, “Navstar GPS
Space Segment/Navigation User Interfaces,” Interface
Speciﬁcation IS-GPS-200H, 2013.
11. Joint Committee for Guides in Metrology (JCGM),“Evaluation of Measurement Data – Guide to the Expression of Uncertainty in Measurement,” JCGM 100, 2008.

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On the Radar Cross Section of Dragons
Stefano Selleri
Department of Information Engineering
University of FlorenceVia di S. Marta, 3 – I50139 Florence, Italy
E-mail: stefano.selleri@uniﬁ.it

Abstract

around A.D.1300, and this could be due to several factors [7]:An assessment is given of the possibility of detecting
and recognizing dragons based on their radar cross section.

1. A general cooling of the world’s climate, which was
in a transition from the Medieval Warm Period to the
Little Ice Age (Figure 1).

I

t is somewhat assessed that no dragon has been reported
in nature since a few centuries. However, scientiﬁc papers
on dragons with careful investigations are not limited to theMiddle Ages [1, 2] or the Renaissance [3, 4]. Recent accurate
papers on the ecology of dragons have appeared in the XX
and XXI centuries in international referred journals [5-7].

2. The shortage of food. The end of Chivalry and theadvent of gunpowder lead to a scarcity of armoredknights [9], which, together with princesses, were adragon’s preferred meal (Figure 2)1.

While [5] and [6] do treat the general ecology andevolution of dragons, dividing them into two broad families,
those with four limbs (either four legs or two wings andtwo legs) which should, evolutionarily speaking, belong
to our own Tetrapoda superclass; and those with six limbs(two wings and four legs), which albeit being of course
to Chordata must have separated from our clade muchearlier. Of course, this is not to speak of multi-headed or
multi-tailed dragons.

3. The passage from an economy based solely on gold andsilver, hoarded by dragons, to more evolved ﬁnancial
instruments, like the promissory note, introduced earlyin the XIV century, which diminished the possibility
for dragons to haul treasures.

The reason why there are no dragons recorded in thewild might be attributed to their extinction somewhere

To these reasons we might add a third:

However, as stated in [6], a dragon’s life span is in
the range of 103 to 104 years and in the r- or K- strategy,
they surely belong to the extreme K-selection strategy, with
Indeed, sheep-eating and peasant-eating dragons havealso been reported [5].
1Figure 1. Global temperature variations with respect to the average value according to 2° institute [8]. What is conventionally called the “Medieval Warm period”[950-1250] and the “Little Ice Age” [1430-1850] are highlighted.

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Radio Science Bulletin No 378 (September 2021)

59Figure 2. “Saint George Slays a Dragon” (Paolo
Uccello, ca. 1460, oil on canvas, National Gallery,
London, public domain). This is a tetrapod dragon
not having a meal, but being captured by the princess, who binds the dragon with her belt after SaintGeorge has wounded him.
very little oﬀspring and long parental care. Furthermore,
dragons are closer to reptiles, and supposedly ectothermic
beasts [7], which means that, in very cold climates theycan enter a period of brumation (which is the equivalent of
the hibernation of dormice, bears, and other endothermic
beasts).
Existing reptiles may eat more than usual before
the brumation time, but eat less or refuse food as thetemperature drops, and they can go for months without
food. The brumation period is anywhere from one to eightmonths, depending on the air temperature and the reptile
species [10].
If reptiles with lifespan less than 102 can remain
dormant for two-thirds of a year, then it can be inferred thatdragons can withstand centuries of brumation, occasionally
emerging to life [7] but, due to the ongoing Little Ice Age,staying mostly dormant.
Now, global warming has indeed already raised
temperatures well above the maximum of the Medieval
Warm Period, so we might expect dragons to return active,
searching for food and treasures. We therefore should beready to spot them on our early surveillance radars, andwe should focus on the radar signature of a dragon, todistinguish it from man-made ﬂying objects.
We shall suppose that the dragon has slept for centuries
over his treasure, mostly gold and silver. It is well known[11, 12] that dragons prefer to cover their body with these
metals, especially their soft belly, to enhance their resistanceto spears, arrows, and swords. We can hence safely assume
that dragons are good conductors and compute theirradar cross section (RCS) as if they were perfect electric
conductors, with a minimum approximation.

60

Figure 3. A three-dimensional dragon model (available
from cgtrader, authored by Dommk and released underroyalty-free license [18]).
Furthermore, dragons are big, and like most big ﬂying
animals, should rely on gliding rather than on ﬂappingwing ﬂight. This is true for the albatross, which, being thelargest ﬂying bird, has a maximum wingspan of 12 ft [13].
The same was most probably true for Pteranodons, whose
wingspan exceeded 18 ft[14].
Dragons, which are reported to have been larger thanany of the above-mentioned ﬂying beings2, must glide mostof the time and also exploit ascensional currents. We hence
can also neglect wing movements and focus on the radarcross section of a non-ﬂapping gliding dragon.
Figure 3 represents a three-dimensional model of a
six-limbed dragon among those various freely available. It
is a good example and full of details, horns, spikes, claws,
torn leathery wings. The dragon wingspan was about 35 ft,
comparable to that of a Piper PA-18. We checked the RCSof the beast against that of the plane.
Figure 4 ﬁnally presents results, computed via
physical optics [19], of the RCS of the two targets. It isapparent how the large ﬂat surfaces characterizing theplane’s fuselage and wings caused high RCS values (up to
30 dBm 2 ) in very speciﬁc directions, whereas the curved,
organic shape of the dragon led to a lower peak and lessdirection-dependent RCS.
Distinguishing a gliding dragon from an airplane on
the basis of its RCS hence seems possible, as well as theirdisposal via AAM or SAM, even with infrared homing, sinceﬁre breathing dragons are expected to internally match thetemperature of a jet engine, with the notable diﬀerence thatthe mouth is in the front of the creature while the engines
are on the back of a plane.
In [1] it was described how dragons wrestle elephants,
while in [2] the ﬁne art of saddling and bridling wild
dragons was elucidated. In [15, 16] dragon-riding was
uncommon but not unheard of, while in [12, 17] dragons
were never ridden and Fell Beasts were the steeds ofNazgûls.
2

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Radio Science Bulletin No 378 (September 2021)

Figure 4. The RCS for the Piper (top row) and dragon (bottom row)at 900 MHz (ﬁrst column) and 3 GHz (second column). The z axis
corresponds to the axis of the plane and of the dragon.
We can hence sleep easy: our current defense systems
should also be adequate against dragons.

10. https://www.britannica.com/science/dormancy/
Dormancy-in-cold-blooded-vertebrates

References

11. J. R. R. Tolkien, The Hobbit, London, George Allen& Unwin., 1937.

1. Bartholomaeus Anglicus, De proprietatibus rerum,
manuscript, c. 1240.

12. J. R. R. Tolkien, The Silmarillion, [C. Tolkien ed.]
Boston, Houghton Miﬄin, 1977.

2. R. Bacon, Opus Majus, manuscript, c. 1267.
3. G. K. Kirchmaier, Disputationum zoologicarum: publiceante hac habitarum, de basilisco, unicornu, phoenice,behemoth & leviathan, dracone, ac araneâ, hexas,
Wittebergae: Sumptibus haered. Johannisbergeri, typis
haered Melchioris Oelschlegelii, 1661.

13. D. Stone, “Albatross’s Eﬀortless Flight Decoded –May
Influence Future Planes,” National Geographic, September 9, 2012.
14. K. Padian, “A Functional Analysis of Flying and Walking
in Pterosaurs,” Paleobiology, 9, 3, 1983, pp. 218-239.

4. U. Aldovrandi, Serpentum, et draconum historiae libriduo, Bologna, Clementem Ferronium, 1640.

15. G. R. R. Martin, A Song of Ice and Fire, New York,
Bantam, 5 volumes, 1996, 1999, 2000, 2005, 2011.

5. P.J. Hogarth, “Ecological Aspects of Dragons,” Bulletin
British Ecological Society, 7, 2, 1976, pp. 2-5.

16. C. Paolini, The Inheritance Cycle, New York, Alfred
A. Knopf, 4 volumes, 2003, 2005, 2008, 2011.

6. R. M. May, “The Ecology of Dragons,” Nature, 264,
November 4, 1976, pp. 16-17.

17. J. R. R. Tolkien, The Lord of the Rings (originallypublished in three volumes), London, George Allen &Unwin., 1954-1955.

7. A. J. Hamilton, R. M. May, E. K. Waters, “Here be
Dragons,” Nature, 520, April 2, 2015, pp. 42-43.
8. https://www.2degreesinstitute.org/ - https://www.temperaturerecord.org/
9. S. Selleri, “On the Electromagnetic Shielding Eﬀectiveness of Chain-Mail Armor,” URSI Radio Science Bulletin,
No. 367, 2018, pp. 9-11.
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Radio Science Bulletin No 378 (September 2021)

18. https://www.cgtrader.com/free-3d-models/animals/
other/dragon-3d-model
19. G. T. Ruck, D. E. Barrick, W. D. Stuart, C. K. Krichbaum, Radar Cross-Section Handbook, Volumes 1 and
2, New York, Plenum Press, 1970.

61

SCOSTEP’s New Research Program:“Predictability of Variable Solar-Terrestrial
Coupling (PRESTO)”
J. L. Chau1 and C. J. Rodger2
1

Leibniz Institute of Atmospheric Physics at the University of Rostock
Kühlungsborn, Germany
E-mail: chau@iap-kborn.de
2

Department of Physics, University of Otago
Dunedin, New ZealandE-mail: craig.rodger@otago.ac.nz

Abstract
The Scientiﬁc Committee on Solar Terrestrial Physics(SCOSTEP) has recently launched its latest ﬁve-year
scientiﬁc program, “ Predictability of Variable SolarTerrestrial Coupling (PRESTO).” This program is intended
to last from 2020 through 2024. URSI is one of SCOSTEP’s
formal participating bodies, and provides a representative
to the Bureau that governs SCOSTEP. Through this route,
URSI was involved in the selection of the projects thatformed the PRESTO program. The scientiﬁc goals of
PRESTO are relevant to a wide range of URSI members,
and in particular to those from Commissions E, F, G, H, and
J. In this brief article, we wish to alert the URSI community
to PRESTO and its pillars.

1. Background InformationT

he Scientiﬁc Committee on Solar-Terrestrial Physics(SCOSTEP) is a thematic body of the International
Science Council (ISC [1]). SCOSTEP promotes ISC’s
mission to strengthen international science for the beneﬁt
of society. SCOSTEP runs international interdisciplinaryscientiﬁc programs and promotes solar-terrestrial physics
research by providing the necessary scientiﬁc framework
for international collaboration and dissemination of thederived scientiﬁc knowledge in collaboration with other
ISC bodies.
SCOSTEP is a permanent observer at the United
Nations Committee on the Peaceful Uses of Outer Space(COPUOS [2]). SCOSTEP was originally established as an
Inter-Union Commission, following a resolution adopted at
the 11th General Assembly of ICSU (International Council
for Science) in Bombay in January 1966. It was modiﬁedby the 14th General Assembly of ICSU in Helsinki in 1972
to become a Special Committee, and by the 17th General
Assembly in 1978 to become a Scientiﬁc Committee.

62

The governing body of SCOSTEP is the SCOSTEP
Bureau. The Bureau is comprised of SCOSTEP’s President,
Vice President, and Past President; the representativesof the ISC Participating Bodies; and the Scientific
Secretary (ex-oﬃcio). The Bureau directs scientiﬁc,
administrative, and ﬁnancial activities. URSI [3] is oneof the participating bodies inside SCOSTEP, and thus
contributes a representative to the Bureau. The current URSI
representative to SCOSTEP is Jorge L. Chau (Germany),
who took up the role before the URSI General Assemblyin Montreal (2018). Previous to this, the SCOSTEP URSI
representative was C. J. Rodger (New Zealand). The otherParticipating Bodies are COSPAR [4], IAGA [5], IAMAS[6], IAU [7], IUPAP [8], SCAR [9], and ISC-WDS [10].
SCOSTEP organizes and conducts international
solar-terrestrial physics (STP) programs of ﬁnite durationin cooperation with other ICS bodies. They share the resultsof these programs by joining in conducting meetings,
conferences, and workshops, and by publishing newsletters,
handbooks, and special journal issues about these solarterrestrial physics programs. SCOSTEP is an outgrowth ofearlier bodies in ICS involved in planning and implementing
the International Geophysical Year (lGY: 1957-58) and theInternational Quiet Sun Year (lQSY: 1964-65). SCOSTEP
programs completed to date include:
1976-1979 IMS: International Magnetospheric Study
1979-1981 SMY: Solar Maximum Year
1982-1985 MAP: Middle Atmosphere Program
1990-1997 STEP: Solar-Terrestrial Energy Program
1998-2002 SRAMP: STEP-Results, Applications, andModeling Phase
1998-2002 PSMOS: Planetary Scale Mesopause Observing
System1998-2002 EPIC: Equatorial Processes Including Coupling
1998-2002 ISCS: International Solar Cycle Study
2004-2008 CAWSES: Climate and Weather of the SunEarth System

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Radio Science Bulletin No 378 (September 2021)

The sun is a variable star and its variability inﬂuences
the Earth’s space environment. Furthermore, changing
solar magnetic ﬁelds, and radiative and energetic particle
ﬂuxes, together force the Earth’s atmosphere and climate.
Transient energetic events such as ﬂares, coronal-massejections (CMEs), interplanetary shocks, stream-interaction
regions (SIRs), co-rotating interaction regions (CIRs), andenergetic particles adversely impact critical technologies
based in space and on Earth upon which our society is
increasingly dependent.

Figure 1. The PRESTO logo (source: https://
scostep.org/presto/).
2009-2013 CAWSES-II: Climate and Weather of the SunEarth System-II
2014-2018 VarSITI: Variability of the Sun and its Terrestrial
Impact (e.g., [11, 12]).

2. Introducing the PRESTO
Program
In 2017, a committee was established by SCOSTEP’s
Bureau to deﬁne the next SCOSTEP program. Since
their early deliberations, the committee members andscientists participating in the planning stages identiﬁedthat predictability of the sun-Earth system from a few
hours to centuries was a timely scientiﬁc topic. After manyin-person exchanges (meetings in Beijing and Bern at theInternational Space Science Institute) and teleconferences,
PRESTO (PREdictability of the variable Solar-Terrestrial
cOupling) was conceived. PRESTO is the latest SCOSTEP
program, and is expected to cover the 2020-2024 period.

At the same time, the middle and upper atmosphere/
ionosphere are impacted by processes originating at lower
altitudes, e.g., by atmospheric gravity waves, tides andplanetary waves, and changes in radiatively active gases.
The importance of solar inﬂuences on climate is gaining
increasing attention, since variations in solar activity not
only impact middle-atmosphere chemistry and physics, buthave been shown to impact decadal variability at the Earth’s
surface. This is particularly interesting and important for
decadal climate predictions.
With the enhanced understanding of causal
connections in the sun-Earth system maturing over thelast several decades, fuelled by both observations andtheoretical modeling, we are in the position to transform
this understanding to produce improved predictions of thesun-Earth coupled system: this is the focus of the current
PRESTO program, due to the strong relevance to society.
PRESTO addresses the predictability of: (a) space
weather on time scales from seconds to days and months,
including processes at the sun, in the heliosphere, and in
the Earth’s magnetosphere, ionosphere, and atmosphere;(b) sub-seasonal to decadal and centennial variability of thesun-Earth system, with a special focus on climate impacts
and a link to the World Climate Research Programme Grand
Challenge on Near-Term Climate Predictions as well as theIntergovernmental Panel on Climate Change.

Figure 2. Numerical simulations of three interacting CMEs on 7 September 2017 at 18:00 UT. The lime contour
outlines the two ﬁrst CMEs that merged close to the sun, while the blue contour marks the third CME thatreached the previous CMEs further out in interplanetary space (adapted from [13]).

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63

To carry out its objectives, PRESTO activities are
divided into three main pillars, i.e.,
Pillar I: Sun, interplanetary space, and geospace
Pillar II: Space weather and Earth’s atmosphere
Pillar III: Solar activity and its inﬂuence on climate
The PRESTO program is currently led by Ramon E. Lopez(chair, USA); Jie Zhang (co-chair, USA); and Eugene
Rozanow (co-chair, Switzerland). Figure 1 shows the
PRESTO logo.

3. The PRESTO Pillars
The PRESTO pillars mentioned above span a wide
range of ﬁelds that overlap with research eﬀorts of many
URSI scientists across multiple URSI Commissions. We
brieﬂy describe these pillars below and indicate their mainscience questions.

3.1 Pillar I: Sun, Interplanetary
Space, and Geospace
Heliospheric transients originating from the sun,stream-interaction regions (SIRs), fast streams, coronalmass ejections, as well as solar energetic particles (SEPs) are
key agents driving disturbances in geospace. Their formationand evolution, solar wind-magnetospheric coupling, andensuing magnetospheric dynamics form a complex chain.
Accurately predicting and reliably various geospace

disturbances, including changes in near-Earth plasma waves
and radiation environment, requires understanding the key
aspects of related phenomena, physical processes, andtheir interplay from sun to Earth operating over timescales
ranging from milliseconds to days. Despite signiﬁcantadvancements in the ﬁeld, long-lead-time predictions stillrequire major improvements. For example, there are often
signiﬁcant uncertainties related to corona-mass ejection(CME) parameters and boundary conditions that are fed
into the advanced numerical simulations, empirical andsemi-empirical forecasting models (see, e.g., Figure 2).
Improved physical understanding of their nature andgeometry and of the complex interaction properties will
also improve predictions.
The main scientiﬁc questions in Pillar 1 are: (1)Under what conditions are solar eruptions, CMEs, and
SEPs produced, and which indicators of pre-CME andpre-ﬂare activity are reliable? (2) What are the required/critical model input parameters for most successfully
forecasting the arrival of SEPs and the geo-eﬀectivenessof CMEs, SIRs, or co-rotating interaction regions (CIRs),
and the consequences of the interactions between SIRs/
CIRs and CMEs? (3) How are diﬀerent magnetosphericdisturbances and waves (that are critical for the ring current
and radiation-belt dynamics) driven by variable solar-windstructures, and/or internal magnetospheric processes?
Pillar I is led by Allison Jaynes (USA), Emilia Kilpua(Finland), and Spiros Patsourakos (Greece).

Figure 3. A schematic diagram of whole-atmosphere eﬀects during sudden stratosphericwarmings (from [14]).

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Radio Science Bulletin No 378 (September 2021)

Figure 4. The scheme for the physics-based solar cycle prediction/predictability (adapted from [15]).

3.2 Pillar II: Space Weather andEarth’s Atmosphere
The thermosphere and the ionosphere areconventionally known to be directly aﬀected by the varying
magnitudes of solar activity. When the incidence of solar
ﬂux or energetic charged particles suddenly increases dueto the occurrence of solar ﬂares or CMEs combined withmagnetic reconnection in the Earth’s magnetosphere, theadverse eﬀects witnessed in the ionosphere-thermosphere(IT) system as a whole are referred to as space weather
eﬀects. Consequently, these space weather eﬀects leadto adverse eﬀects in several space and ground-based
applications. These eﬀects and consequences are to beunderstood at both fundamental and applications levels.
More recently, it has been recognized that waves
originating in the lower atmosphere are an additional
important source of ionosphere-thermosphere variability.
These waves encompass a vast range of temporal (minutes
to weeks) and spatial (few kilometers to global) scales, andintroduce a similarly large range of spatial and temporalvariability in the ionosphere-thermosphere. Changes in theionosphere-thermosphere due to wave-driven processes
can reach magnitudes similar to moderate geomagnetic
storms, illustrating their importance in modifying theionosphere-thermosphere system. Furthermore, wavedriven processes introduce variability in the entirety of the
Earth’s atmosphere, as shown in Figure 3, which illustrates
the numerous aspects of atmospheric variability that occur
in large-scale phenomenon such as in sudden stratosphericwarming events.
In order to enhance the predictive capability of theionosphere-thermosphere spatial and temporal variability, it

TheRadio Science Bulletin No 378 (September 2021)

is necessary to understand how the ionosphere-thermospheresystem is aﬀected by both coupling from above and below.
Several features that are formed and/or aﬀected due tothese eﬀects are plasma irregularities, plasma bubbles,
ion-drifts, ionospheric currents, thermospheric densities andcomposition, neutral winds and waves, etc. These features
adversely aﬀect trans-ionospheric radio communication,
high-frequency terrestrial radio propagation, and thermaland drag environments for spacecraft in the increasinglycongested low-Earth-orbit (LEO, <2000 km) region.
The main scientiﬁc questions of Pillar II are: (1)How do the thermosphere and ionosphere respond tovarious forcing factors from above and from below?; (2)How do atmospheric waves and composition changes
impact the middle and upper atmosphere?; (3) What are
the magnitude and spectral characteristics of solar andmagnetospheric forcing needed for accurate predictionsof the atmospheric response? (4) What is the chemical anddynamical response of the middle atmosphere to solar andmagnetospheric forcing?
Pillar II is led by Loren C. Chang (Taiwan), Duggirala
Pallamraju (India), and Nicholas M. Pedatella (USA).

3.3 Pillar III: Solar Activity and its
Inﬂuence on Climate
The next ﬁve years, spanning the start of Solar
Cycle 25 through its (near) peak, provide an excellent
opportunity for evaluating our understanding and ability to
predict solar activity on decadal time scales. Predictability
requires improved understanding of the physical pathways
wherein solar variability impacts the atmosphere, from themagnetosphere through the troposphere. The nonlinear

65and stochastic mechanisms that modulate the solar cycle
and aﬀect the scope of the prediction and predictabilityof the solar cycle are still open questions. In some areas,
decadal-scale solar forcing remains to be quantiﬁed
to climate-relevant accuracies, which challenge ourability to determine causal connections in the pathways
explaining solar forcing impacts on climate (see, e.g.,
Figure 4). Furthermore, due to wave-driven coupling in
the atmosphere, improving predictability of solar forcing
also requires improved characterization of the atmosphereocean response to the forcing.
The main questions in Pillar III are: (1) How will
future solar activity vary over diﬀerent timescales andwhat are the physical reasons for the variations?; (2) How
will the solar forcing on the Earth’s system evolve in the
future?; and (3) What is the role of the coupling between
atmospheric regions in the realization of the long- and shortterm solar inﬂuence on the Earth’s system, and how arethose responses aﬀected by increasing green-house gases?
Pillar III is led by Odele Coddington (USA), Jie Jiang(China), and Stergios Misios (Germany).

4. Relevance to URSI
The breadth of the scientiﬁc questions posed in the
PRESTO program are relevant to many URSI members.
Traditionally, direct SCOSTEP activities have been mainly
carried out within URSI Commissions G and H. However,
SCOSTEP and PRESTO activities are also indirectly
relevant to other Commissions, e.g., A (improvements inelectromagnetic remote sensing), B (inverse scattering andimaging), C (radar and navigation systems), D (combined
and hybrid photonic and electronic systems for improved
solar-terrestrial observations), E (terrestrial and planetarynoise of natural origin), F (wave propagation throughplanetary, neutral atmospheres), J (radio receivers for sun
or ionospheric scintillation research) and K (assessmentof human exposure to solar electromagnetic ﬁelds).
As SCOSTEP’s role is to provide the framework forinternational collaboration and dissemination, projects
within PRESTO should stimulate more opportunities
for URSI researchers to work together. Conferences,
workshops, and sessions will be organized and supported
around the PRESTO program and the four associatedprojects. In particular, it is worth noting that PRESTO
has funding to provide support for organizing scientiﬁcmeetings, for international and interdisciplinary campaign
activities, and for database construction (see e.g., https://
scostep.org/grant-proposals/). As URSI refocuses itself
onto its three “core” meetings (URSI GASS, URSI ATRASC, URSI AP-RASC), the PRESTO funding may assist
research communities to support focused workshops during
the PRESTO years.

This has included funding to support training and capacitybuilding schools. In addition, there is the SCOSTEP Visiting
Scholar (SVS) program. The objective of this programis to provide training to young scientists and graduate
students from developing countries in well-established solar
terrestrial physics laboratories for periods of between one
and three months. The aim is to fund between three to six
scholars each year. The training will help the young scientists
to advance their careers in solar terrestrial physics, using
the techniques and skills they learn during the training. We
suggest that interested parties should examine the SCOSTEP
and PRESTO Web sites for more information.
More detailed information about PRESTO can befound in [16].

5. PRESTO Implementation in a
Time of COVID-19
While the VarSITI program was originally scheduled
to run from 2014-2018, it eﬀectively continued through to
mid-late 2019, at which point closing activities took place,including the VarSITI Closing Symposium (10-14 June
2019, Soﬁa, Bulgaria). In late 2019, the Chair, co-Chair,and Pillar co-leaders of PRESTO were announced, shortly
followed by the ﬁrst PRESTO newsletter in January 2020.
As will be clear to the readers, the PRESTO program started
just as COVID-19 became a global pandemic. Much of
the operation to date has therefore occurred under COVID
restrictions, forcing the SCOSTEP community to adapt.
As readers will surely know, a very signiﬁcant
impact from the pandemic was the imposition of closed
international borders and the removal of most work-related
travel. This impacted scientiﬁc research, stopping ﬁeld work,
collaborative visits, networking, and of course scientiﬁc
conferences. As one attempt to respond to those diﬃculties,
SCOSTEP/PRESTO hosted a series of online seminars todeliver the latest scientiﬁc topics and review presentations
on solar-terrestrial physics through the PRESTO Online
Seminar Series. This series kicked oﬀ in May 2020, and is
still continuing at the time of writing with the 11th seminar
held in February 2022. The 15th SCOSTEP quadrennial
meeting, which was to be hosted in Alibag, India, in late
February 2022, was also shifted online. A few exceptions
have been PRESTO hybrid sessions with a mix of in-person
and online participation at the URSI General Assembly and
Scientiﬁc Symposium in Rome and the AGU Fall meeting,both of which occurred in 2021.
Of course, not all activities can be run online. The
SCOSTEP Visiting Scholar program was delayed. Evenin mid-2021, most of the 2020 SVS awardees had not yet
started their training programs, with an extension granted
up to the end of 2021.

SCOSTEP’s role is to also support capacity
building for international solar-terrestrial physics research.

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6. References
1. https://council.science
2. https://www.unoosa.org/oosa/en/ourwork/copuos
3. https://www.ursi.org
4. https://cosparhq.cnes.fr
5. http://www.iugg.org/associations/iaga.php
6. http://www.iamas.org
7. https://www.iau.org
8. https://iupap.org
9. http://www.scar.org
10. https://www.icsu-wds.org
11. C. J. Rodger, “SCOSTEP’s New Research Program:‘Variability of the Sun and Its Terrestrial Impact (VarSITI),’” Radio Science Bulletin, 352, pp. 5-8, 2015.
12. K. Shiokawa, and K. Georgieva, “A Review of the
SCOSTEP’s 5-Year Scientiﬁc Program VarSITI –Vari-TheRadio Science Bulletin No 378 (September 2021)

ability of the Sun and its Terrestrial Impact, Prog. Earth
Planet. Sci., 8, 21, https://doi.org/10.1186/s40645-02100410-1, 2021.
13. C. Scolini, E. Chane, M. Temmer, E. K. J. Kilpua, K.
Dissauer, A. M. Veronig, E. Palmerio, J. Pomoell, M.
Dumbovic, J. Guo, L. Rodriguez, and S. Poedts, “CMECME Interactions as Sources of CME Geoeﬀectiveness:
The Formation of the Complex Ejecta and Intense Geomagnetic Storm in 2017 Early September,” Astrophys.
J. Supp. Series, 247, 21, https://doi.org/10.3847/15384365/ab6216, 2020.
14. N. M. Pedatella, J. L. Chau, H. Schmidt, L. P. Goncharenko, C. Stolle, K. Hocke, V. L. Harvey, B. Funke,
and T. A. Siddiqui, “How Sudden Stratospheric Warming
Aﬀects the Whole Atmosphere,” Eos, 99, https://doi.
org/10.1029/2018EO092441, 2018.
15. O. J. Coddington, J. Jiang, and E. Rozanov, “Pillar 3:
Solar Activity and its Inﬂuence on Climate,” SCOSTEP/PRESTO Newsletter, 22, 6-7, 2020.
16. I. A Daglis, L. C. Chang, S. Dasso, N. Gopalswamy, O.
V. Khabarova, E. Kilpua, R. Lopez, D. Marsh, K. Matthes,
D. Nandy, A. Seppälä, K. Shiokawa, R. Thiéblemont, andQ. Zong, “Predictability of Variable Solar – Terrestrial
Coupling, Ann. Geophys., 39, pp. 1013-1035, 2021,
https://doi.org/10.5194/angeo-39-1013-2021.

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Special Issue of the URSI-JRSM
2022 Student Paper Competition
This special issue is a collection of papers by university
students who successfully applied for the Student Paper
Competition organized at the 2022 URSI-Japan Radio
Science Meeting (URSI-JRSM 2022). The conference washeld at Chuo University, Tokyo, Japan, on September 1-2,
2022 (http://www.ursi.jp/conference/jrsm2022/).
The URSI-Japan Radio Science Meeting (URSIJRSM) is a regional URSI conference organized by theJapan National Committee of URSI (JNC-URSI). The
URSI-JRSM provides a regional scientiﬁc forum for radio
scientists and engineers in Japan and the Asian region.
The objective of the Meeting is to review current research
trends, present new discoveries, and make plans for future
research and special projects in all areas of radio science,
and a particular emphasis is placed on enhancing thevisibility of URSI in the Asian countries and encouraging
young scientists to contribute to various URSI activities.
The ﬁrst URSI-JRSM 2014 was held in Tokyo, Japanon September 8, 2014. A one-day program was organized asplenary sessions with three Keynote Lectures and 10 Invited
Talks from the URSI Commissions A-K by outstanding
researchers from Japan and Asia. The second URSI-JRSM
2015 was also held in Tokyo, Japan on September 3-4,
2015, where scientiﬁc sessions were composed of plenary
sessions with two Keynote Lectures, two Special Lecturesand 10 Invited Talks from the URSI Commissions A-K,
and a Poster Session by contributed papers. The third
URSI-JRSM 2019 was again held in Tokyo, Japan on
September 5-6, 2019. Scientiﬁc programs consisted ofplenary sessions with one Keynote Lecture and 10 Invited
Lectures from the URSI Commissions A-K, and oral andposter sessions were arranged by each Commission for
invited and contributed papers. These conferences were
held successfully, each attended with about 200 domestic
and international scientists and engineers.
The “2022 URSI-Japan Radio Science Meeting”(URSI-JRSM 2022) was the fourth URSI-JRSM and had
the plenary sessions consisting of two Keynote Lecturesand 10 Invited Lectures by prominent researchers in theradio science community. Oral Sessions (68 papers) anda poster session (50 papers) were organized by the URSI
Commissions A-K for invited and contributed papers. A
total of 201 scientists and engineers attended from four
countries. This conference was sponsored by The Instituteof Electronics, Information and Communication Engineers(IEICE), and technically supported by URSI. It was heldin cooperation with Science Council of Japan, Japan

68

Geoscience Union, Society of Atmospheric Electricity ofJapan, The Astronomical Society of Japan, The Institute ofElectrical Engineers of Japan, The Laser Society of Japan,
and The Remote Sensing Society of Japan.
As with the previous URSI-JRSM 2015 and URSIJRSM 2019, the Student Paper Competition (SPC) was
organized in URSI-JRSM 2022 for full-time university
students in a degree program, which was ﬁnancially
supported by URSI. A total of nine students applied for
the SPC program. On the ﬁrst day of the conference,
September 1, 2022, the URSI-JRSM 2022 SPC Committee
selected three SPC ﬁnalists based on the applicants’ fulllength papers and poster presentations. All of the three
SPC ﬁnalists subsequently presented their papers orally
at the SPC Special Session held on the second day of theconference, September 2, 2022. After careful consideration,
the URSI-JRSM 2022 SPC Committee decided on thefollowing ranking:-First Prize: Debrupa Mondal, The University of ElectroCommunications, Japan
Second Prize: Yuji Tanaka, Kanazawa University, Japan
Third Prize: Yoshihiko Inoue, The University of Tokyo,
Japan

The above three winners received prizes (certiﬁcates,
prize money and supplementary prizes) at the SPC Award
Ceremony held during the URSI-JRSM 2022 Closing
Ceremony on the second day of the conference. All thethree SPC ﬁnalists are shown in Figure 1.
Among the three SPC prize winners, we have the following
two papers in this special issue of the Radio Science Bulletin:
1. “Thunderstorms and Total Lightning Characteristics
Causing Heavy Precipitation in Japan: A Case Study”
Debrupa Mondal1, Yasuhide Hobara1,2, Hiroshi Kikuchi2,
Jeﬀ Lapierre3
1
Graduate School of Informatics and Engineering,
University of Electro-Communications, Japan, 2 Center
for Space Science and Radio Engineering, University
of Electro-Communications, Japan, 3 Earth Networks,
USA
2. “A Bayesian k-vector Estimation Method forElectromagnetic Waves in Magnetized Cold Plasma”
Yuji Tanaka1, Mamoru Ota2, Shoya Matsuda3, Yoshiya
Kasahara3
1
Kyoto University, Japan,2 National Institute ofTechnology, Toyama College, Japan,3 Kanazawa
University, JapanThe

Radio Science Bulletin No 378 (September 2021)

We are pleased that the SPC program at URSI-JRSM 2022
was successful, mainly due to the eﬀorts of the URSI-JRSM
2022 SPC Committee. We feel grateful to the membersof the committee for their hard work during the selection
process. The ﬁnancial support from URSI for runningthe program is greatly acknowledged. Finally, we would

like to express our appreciation to the SPC ﬁnalists who
contributed to this special issue.
Guest Editors:
Satoshi Yagitani, URSI-JRSM 2022 General Chair
Yasuhide Hobara, URSI-JRSM 2022 Technical
Program Committee Chair

Figure 1. The URSI-JRSM 2022 SPC ﬁnalists. (from left to right: Prof. Satoshi Yagitani, URSI-JRSM
2022 General Chair; Debrupa Mondal, SPC First Prize Winner; Yuji Tanaka, SPC Second Prize Winner; Yoshihiko Inoue, SPC Third Prize Winner; Prof. Kazuya Kobayashi, URSI-JRSM 2022 General
Co-Chair; Prof. Yasuhide Hobara, URSI-JRSM 2022 Technical Program Committee Chair).

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69

Thunderstorms and Total Lightning
Characteristics Causing Heavy
Precipitation in Japan : A Case Study
Debrupa Mondal1, Yasuhide Hobara1,2, Hiroshi Kikuchi2, Jeﬀ Lapierre3
Graduate School of Informatics and Engineering, University of ElectroCommunications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
E-mail: debrupamondaljam@gmail.com
1

2

Center for Space Science and Radio Engineering, University of ElectroCommunications
3

Earth Networks, Germantown, MD 20874, USA

Abstract
We have analyzed an isolated, multicellular thunderstorm
event with heavy precipitation (~100 mm/h) that occurredin Saitama, Japan. The X-band multi-parameter radarderived 3D volume scan and ground-based total lightning
data provide detailed spatio-temporal dependences ofprecipitation cores in diﬀerent altitudes and associateddiﬀerent types of lightning. We found the development ofhigh altitude (~7-10 km) precipitation core shows a highcorrelation with the in-cloud lightning rate and the peak of
IC lightning occurred 5-10 min prior to subsequent heavy
precipitation on the ground. Again, ground precipitationshows a higher correlation with -CG than +CG without
signiﬁcant time lag. Therefore, monitoring total lightning
activity during thunderstorms can be useful for nowcastingand short-time prediction of ground precipitation volume.

1. IntroductionT

he frequency of natural disasters from extreme
weather events (heavy precipitation (50mm/h) causing
ﬂash ﬂoods, storm surges, hurricanes, etc.) is increasing
worldwide, including in Japan, due to the faster surface
temperature rise (~0.5°C) than the global average (~0.3°C),
and scientists predict this long-term trend will continue
steadily or may increase [1]. To mitigate the huge destruction
associated with such kinds of events, it is important to predict
them suﬃciently before their occurrence. Many studies
showed the Total Lightning (TL) behavior (both In-Cloud(IC) and Cloud-to-Ground (CG)) and its correlation withsevere weather such as hail and wind gusts associated withThunderStorms (TS), tornadoes etc [2,3]. Also, diﬀerent

70

research groups have investigated spatial distribution trends[4,5], and temporal dependencies [6,7] between lightningand heavy precipitation and found that lightning can act
as a precursory parameter of heavy precipitation. Sun etal [4], Wang and Liao [5] showed that positive CG ﬂashes
occur at the center of the precipitation core and are morein phase with high-level radar reﬂectivity. Gungle andKridar [7] showed a linear relationship between the ground
Precipitation Volume (PV) and the number of CG ﬂashes
and a variable time lag of 5-20 minutes (CG increases
before precipitation increases) observed for these warm
season storms in Florida.
In the developing stage of a TS, as a result of thestrong updraft, IC pulses are produced in the initial stageof cloud electriﬁcation and CG ﬂashes are more common
in the mature stage of the storm. So, compared to less CG(~30%), a very large number of IC (~70%) as well as TL
improves the accuracy of forecasting and warning time [8, 9].
Few research works have been made for the TS
events in Japan with heavy precipitation and TL. Ogawa
et al. (2018) showed a strong positive correlation between
the time series of lightning stroke rate from Japanese Total
Lightning Network data and ground PV using XRAIN data
along with other meteorological parameters but no major
time lag between these two was observed [10].
In this paper we have analyzed the overall pictureof the development of TS clouds and precipitation cores
associated with TSs that produce heavy precipitation(max >100 mm/h) by using TL data and full-volume scan
high-speed weather radar data. We have also examined atime lag between the occurrence of lightning activity andTheRadio Science Bulletin No 378 (September 2021)

Table 1. XRAIN data speciﬁcations

Collected data
Time resolution

Precipitation (mm/h)
1 min

Spatial resolution

250 m

Precipitation
resolution
Minimum value

0.1 mm/h

Maximum value

409.0 mm/h

0.1 mm/h

maximum precipitation which could be used for forecast/
nowcast purposes for short-duration extreme rainfall events.

2. Data Collection
2.1 eXtended RAdar Information
Network (XRAIN)
In this study we have used precipitation data from
the Ministry of Land, Infrastructure, Transport, andTourism’s eXtended RAdar Information Network. The
network is composed of 26 C-band radars and 39 X-band

multiparameter (X-MP) radars which cover most parts ofJapan around 14 major cities [11]. The full volume scandata produced by these X-MP radar scanning, are collected
to visualize the precipitation core development at a higher
altitude in 5-min time resolution. Twelve elevation angles
are scanned to produce the 3D data ranging from 0° to 20°.
X-band and C-band radar-derived high-resolution (temporal
resolution of 1 min, spatial resolution of 250 m) composite
ground rainfall intensity data [mm/h] is also collectedfrom XRAIN. The ground rainfall data speciﬁcations are
summarized in Table 1.

2.2 Japanese Total Lightning
Network (JTLN)
TL data associated with the event is collected fromJTLN which consists of 11 Earth Networks Total Lightning
Sensors (Figure 1) over Japan (data set in 2017, currently
16 stations nationwide) deployed by the University ofElectro-Communications and jointly operated with Earth
Networks. These sensors can detect lightning pulses in awide frequency range, ranging from 1 Hz to 12 MHz i.e.,extreme low frequency to high frequency range with aspatial resolution of 500 m. TL parameters such as types oflightning (IC and CG), time of occurrence (UT), location(latitude-longitude), and lightning polarity are collected
for each lightning discharge.

Figure 1. JTLN sensor distribution over Japan.

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71

3. Results
In this paper, the preliminary results from the data
analysis of high-resolution 3D volume scan precipitation
data and TL activity have been presented. An isolated TS
event with heavy precipitation, that occurred in Saitama
prefecture and caused ﬂooding of Yanase river on August30, 2017, is analyzed in detail. During this TS event, themaximum precipitation rate of >100 mm/h (instantaneous
value which represents 100 mm of rain would fall in 1 h if

the rain rate remained constant) was observed by XRAIN.
Depending on the diﬀerent developmental stages at aparticular time, diﬀerent cells of a smaller spatial scale areconsidered (multicell TSs).

3.1 Spatio-temporal distribution
From every 5-min full volume scan data of Shinyoko
radar (35.5125° E, 139.5994° N), Constant Altitude Plan
Position Indicator (CAPPI) plots were produced andFigure 2. Maximum radar reﬂectivity projections on horizontal (left panels) and vertical planes (right panels) along with the lightning distributions for the ﬁrst thirty minutes of the storm starting from 1400 JST.

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Radio Science Bulletin No 378 (September 2021)

a smaller area of analysis was selected depending on
precipitation cell location. For a detailed analysis with TL
distribution, we took maximum radar reﬂectivity projection
both on horizontal (SN-WE) and vertical planes (WEAltitude) for the selected range. The left panels of eachtime step in Figure 2 show the maximum radar reﬂectivity
projection on the horizontal plane (for altitude range 0-12km) with the spatial distribution of IC (blue circle), +CG(red asterisk), -CG (black asterisk) for the ﬁrst thirty
minutes of the TS. The horizontal axis represents the WE
distance [km] from the radar location and the vertical axis
represents the SN distance [km] from the radar (the radaris located at (0,0)). The right panels of each time step in
Figure 2 show the maximum radar reﬂectivity projection
on the vertical (WE-Altitude) plane (for SN range 0-60km). The lightning data i.e. IC (black solid line), +CG(black dotted line), -CG (black dashed line) ﬂash rate per
5 min for every 2 km of WE range and the SN range of
0-60 km. Observing the distribution of TL, we consider
two precipitation cells formation and dissipation during
the life cycle of the TS.
The ﬁrst cell (C1 in Figure 2) started evolving around
14:00 JST (Japan Standard Time) within the 5 km east

Figure 3. Maximum radar reﬂectivity projections on horizontal (left panels) and vertical planes (rightpanels) along with the lightning distribution for the remaining stages of the thunderstorm’s life cycle.
The

Radio Science Bulletin No 378 (September 2021)

73

of radar location. IC pulses gradually increased from 20
pulses per 5 min at 14:00 JST to 135 pulses per 5 min at
14:20 JST. TS cloud also started developing above 7 km
altitude and with an IC lightning jump around 14:10 JST (arapid increase from 19 pulses to 92 pulses/5 min), a strong
core formation (blue circle in Figure 2) was observed at
higher altitude (>7 km). This strong core evolved to max
height (~12 km) around 14:20 JST when the IC count(135 pulses/5 min) was maximum. Around 14:20 JST few
dispersed -CG strokes were observed a bit westward of IC
concentration. IC pulses started decreasing gradually after
this and started shifting eastward. The precipitation core athigh altitudes also started diminishing. Around 14:20 JST,IC pulses also started increasing nearly 10 km west of theradar location and the peak gradually shifted eastward,
which is considered as the second cell (C2 in Figure 2 andFigure 3). Simultaneously, TS cells also started evolving
above 7 km altitude.
Figure 3 shows the maximum radar reﬂectivity
projections on horizontal (left panels) and vertical planes(right panels) same as in Figure 2 but for the remaining stagesof the TS’s life cycle. Around 14:40 JST IC distribution isseen to be concentrated in two adjacent locations (2 peaks
with consecutive 48 and 42 pulses/5 min in a 2 km range)
and two relevant clear precipitation cores (blue dashed circle
in Figure 2) were also identiﬁable in higher altitudes which
belongs to second cell i.e. C2. Since C1 already started
diminishing around 14:30, only C2 is shown in Figure 3.

previous IC peak location and the solid vertical line shows
the current IC peak location which also shifts a bit eastward
with time. The developed cores faded with the decrease
in IC pulses. CG pulses are found to be concentrated in
the precipitation core region just before maximum ground
precipitation.
Next, we have analyzed the temporal evolution of highaltitude and ground PV as well as the TL, IC, and both +CG
and -CG rates. Depending on the lightning distribution (or,lightning core area), we selected the area (range of the cellin SN and WE direction) of both the cells with time andcalculated the above-mentioned parameters. Figure 4(a)shows the time series of high altitude (7-10 km) PV (solid
blue line) and IC (black dashed line) and CG (red dashedline) pulse rates per 5 min for the 2nd cell (C2). The peakof IC (around 350 pulses/5 min) and PV around 7-10 km
occurred simultaneously at 14:50 JST, whereas the groundPV peak (blue solid) occurred around 10 min later than the
IC peak (black dashed) as shown in Figure 4(b). The peakCG (53 pulses/5 min, red dotted line) was observed 5 min
before the peak of the ground precipitation. A similar timedelay between rapid IC increase and ground precipitationis also observed for cell 1.

3.2 Correlation between
lightning and precipitation

For both the cells of this heavy precipitation event,precipitation cores at higher altitudes (above 7 km) started
evolving around 2-4 km west from the peak IC lightning
location and gradually migrated towards the peak location.
In Figure 2 and Figure 3, the dashed vertical line shows theWe have carried out a cross-correlation analysis
between diﬀerent lightning rates and PV (normalized crosscorrelation between IC rate and high-altitude PV, IC rate andground PV, CG rate and ground PV for the same time series)
during the entire storm period. After cross-correlation,and considering the resultant time lag, the maximum
correlation coeﬃcients have been calculated. Figure 5(a)shows the cross-correlation function of IC vs high-altitude
PV for the second cell and Figure 5(b) shows the scatterplot and maximum correlation coeﬃcient of the same.
Higher altitude PV and IC rate showed a high correlation(correlation coeﬃcient, R=0.95) and no time lag. Similarly,
for cell 1, max cross correlation between IC and (7-10km)a)b)

After attaining the peak IC at 14:50, these two cores
merged and sustained till 15:00 and then gradually started
diminishing. IC ﬂash rate also decreased and the peak of
IC shifted eastward. The maximum CG (Positive CG +Negative CG) rate was observed around 14:55 (not shownin the ﬁgure).

Figure 4. Temporal evolutions of (a) high-altitude PV and TL, IC, CG pulse rates, and (b) ground PV and IC,
CG pulse rates.

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Radio Science Bulletin No 378 (September 2021)

a)

b)

Figure 5. (a) Cross-correlation coeﬃcient between IC and high-altitude PV for cell 2, and (b) relationshipbetween IC [pulses/5min] and high-altitude PV.

Yuan and Qie [12] showed a relatively stable
relationship and high correlation (>0.7) between ice-phase
precipitation (7-11 km) and lightning frequency. Though

for the complete storm system, the ground precipitation
correlates more with TL [10], the present study showed forindividual cells of the same storm system, higher altitude
precipitation (7-10 km) correlates more with IC. Insteadof considering the entire storm, consideration of a smaller
spatial scale (cell scale) and higher altitude precipitation
results in signiﬁcant improvement in the cross-correlation(from 0.84 to 0.95). Rapid intensiﬁcation of the core athigher altitudes appears with more IC occurrence whichindicates greater charge separation, thus strong updraft
causing more ice particle collisions during the developing
stage of a cell. Liu et al [8] showed continuous tracking
of TL data, especially IC lightning enables tracking andprediction of diﬀerent properties of high-impact weather
events. For this particular event, consideration of the total
storm scale resulted in only 2 min lead time [10], whilst
we showed that individually for both the cells, the peak of
IC (or, IC lightning jump) occurs nearly 5~10 min earlier
than peak ground PV which may be used as a precursory
parameter for short-term prediction of maximum groundrainfall. Besides the multicellular nature, we need to consider
the correlation of merging-separation between the cells withlightning distribution in the TS life cycle and thus further
analysis with more events is required.

a)b)

PV is observed to be 0.96 (not in ﬁgure). These R-values,
calculated for individual cells (selected according to the
TL distribution) of a smaller spatial scale are signiﬁcantly
higher than the correlation coeﬃcient (0.84) calculated for
IC (as well as TL) and ground PV, considering the entire
storm in the previous study [10]. Moreover, a positive lag(lag =1 i.e., time lag = 5min) between ground PV and IC
rate is also observed which indicates IC pulses are 5 ~ 10
min ahead of ground PV for the entire TS activity. Figure6(a) represents the cross-correlation function of negative
CG vs ground PV and Figure 6(b) shows the scatter plot andmaximum correlation coeﬃcient for the same. The ground
PV is observed to be more correlated with -CG (R=0.88)
than +CG (R=0.75, and the time lag between them is shorter
than 5 min. Again, this correlation coeﬃcient value is alsomuch higher than the previous study (for CG and groundrainfall, R = 0.40).

4. Discussion

Figure 6. (a) Cross-correlation coeﬃcient between -CG and ground PV for cell 2, and (b) relationship between
CG [pulses/5min] and ground PV.

TheRadio Science Bulletin No 378 (September 2021)

75

5. Acknowledgments

ometer over the Central China Region,” Atmosphere,10(6), 2019, p. 298

We acknowledge the support from the Ministryof Education, Culture, Sports, Science, and Technology(AiQuSci Scholarship). The authors are grateful to the
Ministry of Land, Infrastructure, Transport and Tourism forproviding XRAIN data and also to the KDDI foundation
for supporting partially this research project. The authors
also thank P. Le Floch and E. DiGangi of EN, Tada Farm,
Noshiro City Children’s Center, Niigata Science Museum,
NAOJ Mizusawa, JAIST, Gunma Univ., Rissho Univ., ISEE
(Nagoya Univ.), NIT Tsuyama College, Kochi Univ., Ama
town, Yamaguchi Univ., Tokai Univ. for their help JTLN
observations in Japan.

5.Y. Wang, S.A. Liao, “Lightning, Radar Reﬂectivity,
Infrared Brightness Temperature, and Surface Rainfall
During The 2–4 July 2004 Severe Convective System
Over Taiwan Area,” Journal of Geophysical Research,
111, 2006, D05206.

6. References
1. Nayak, K. Dairaku, I. Takayabu, A Suzuki-Parker, N. N.
Ishizaki, “Extreme Precipitation Linked to TemperatureOver Japan: Current Evaluation and Projected Changes
With Multi-model Ensemble Downscaling,” Climate
Dynamics, 51, 2018, pp. 4385–4401.
2. Pineda, J. Bech, T. Rigo, J. Montanyà, “A Mediterranean
Nocturnal Heavy Rainfall and Tornadic Event. Part II:
Total Lightning Analysis,” Atmospheric Research, 100,
2011, pp. 638–648.
3. Farnell, T. Rigo, N. Pineda, “Lightning Jump as A Nowcast
Predictor: Application to Severe Weather Events In Catalonia,” Atmospheric Research, 183, 2017, pp. 130–141.
4. Sun, J. Chai, L. Leng, G. Xu, “Analysis of Lightning
and Precipitation Activities in Three Severe Convective
Events Based on Doppler Radar and Microwave Radi-76

6. Farnell, T. Rigo, “The Lightning Jump Algorithm for
Nowcasting Convective Rainfall in Catalonia,” Atmosphere, 11(4), 2020, p. 397
7. Gungle, E. P. Krider, “Cloud-to-ground Lightning and
Surface Rainfall in Warm-season Florida Thunderstorms,” Journal of Geophysical Research: Atmospheres,
111, 2006, D19203
8. Liu, C. Sloop, S. Heckman, “Application of Lightning
in Predicting High Impact Weather,” OBS/IMO/TECO2014
9. A. Rakov and M. A. Uman, “Lightning: Physics andEﬀects”, Cambridge: Cambridge University Press, 2003
10. Ogawa, Y. Hobara, H. Iwasaki, S. Heckman, M. Stock,“Statistical Study of The Relationship Between Total
Lightning and Heavy Rainfall,” 16th International Conference on Atmospheric Electricity, Nara, Japan, 2018
11. T. Maesaka, “Operational Rainfall Estimation by X-band
MP Radar Network in MLIT, Japan,” Environmental
Science, September, 2011
12. T Yuan, X. S. Qie, “Study on Lightning Activity andPrecipitation Characteristics Before and After the Onsetof The South China Sea Summer Monsoon,” Journal ofGeophysical Research, 113, 2008, D14101.

The

Radio Science Bulletin No 378 (September 2021)

A Bayesian k -vector Estimation Method
for Electromagnetic Waves in
Magnetized Cold Plasma
Yuji Tanaka1, Mamoru Ota2, Shoya Matsuda3, and Yoshiya Kasahara3
1

Division of Electrical Engineering, Graduate School of Engineering,
Kyoto University, Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto, Kyoto,
Japan; e-mail: tanaka.yuji.2m@kyoto-u.ac.jp
2

Department of Electrical and Control Systems Engineering,
National Institute of Technology, Toyama College, 13 Hongo-machi,
Toyama, Toyama, Japan; e-mail: m-ota@nc-toyama.ac.jp
3

Division of Electrical Engineering and Computer Science,
Graduate School of Natural Science and Technology, Kanazawa University,
Kakuma, Kanazawa, Ishikawa, Japan;
e-mail: matsuda@staﬀ.kanazawa-u.ac.jp; kasahara@staﬀ.kanazawa-u.ac.jp

Abstract
Using the plasma wave characteristics and remotesensing technology, the k-vector direction of plasma waves
can provide important information for understanding the
global features of space plasma. In this study, we proposeda Bayesian k-vector estimation method in magnetized cold
plasma based on the wave distribution function method. The
proposed method can be applied to various types of sensors
with easy visualization and calculation of the estimation
accuracy. We veriﬁed the eﬀectiveness of the proposed
method through simulations.

1. Introduction
The analysis of plasma waves obtained from in-situ
observations through scientiﬁc satellites is eﬀective forunderstanding the physics of near-Earth space and spaceplasmas in general. To measure plasma waves in bands
from a few Hz to several tens of kHz, a scientiﬁc satellite
is generally equipped with orthogonally aligned dipole
antennas and a tri-axis search coil (e.g. [1, 2]). The k-vector
direction of plasma waves, through the use of plasma wave
characteristics and remote sensing technology, provides
key information for understanding the global features ofspace plasma (e.g. [3]).
The k-vector direction is generally estimated using
a spectral matrix, which is the correlation matrix of theelectromagnetic ﬁeld. Ideally, determining the k-vector
direction including the absolute direction requires theobservation results of at least 5 electromagnetic ﬁeld

The

Radio Science Bulletin No 378 (September 2021)

components. However, estimation of the k-vector direction
is often needed when some sensors are ineﬀective due tosensor damage or some constraints of the scientiﬁc satellites.
Moreover, the k-vector direction should be estimated with
diﬀerent sensor noise levels. In addition, the extent of
estimation accuracy depends on not only the type of sensors
but also the averaging number of the spectral matrix. The
extent of estimation accuracy should be calculated to realize
the detailed propagation analysis.
In this study, we proposed a Bayesian k-vector
estimation method in magnetized cold plasma based on
the wave distribution function (WDF) method [4, 5]. By
introducing the WDF method and the noise integration
kernel (NIK) at diﬀerent sensor noise levels [6], the proposed
method can be applied to various types of sensors. In thisstudy, the proposed method adopts the Bayesian inference
under the assumption that the number of arriving quasiplane waves is 1, and it can visualize, and easily calculate
the corresponding estimation accuracy.

2. Bayesian k-vector Estimation
Method for Electromagnetic
Waves in Magnetized Cold Plasma
Our proposed method can be used to visualize theextent of estimation accuracy by calculating the posterior
distribution of each k-vector direction under the assumptionthat the number of arriving quasi-plane waves is 1.
The observation model of the spectral matrix

S  , ,  can be expressed as:77

S  ,  ,      fa  ,  ,   a H  ,  ,   ,(1)where   0 is the noise level, the superscript H represents
the complex conjugate transpose symbol,  is the NIK
based on the ratio of sensor noise levels [6], and f  0 is theenergy of the arriving waves (WDF), and a  , ,    D( D is the number of sensors) is the amplitude and phaserelation (known as the steering vector [7]) at the directionof arrival  ,  with a central angular frequency  .
The eﬀect of cold plasma is included in a  , ,  whichis uniquely determined from a priori information such asplasma density and background magnetic ﬁeld intensity.

Our proposed method assumes that p  ∣ ,  p  f∣ , 
p   and p   are noninformative priors, and theposterior distribution p  ,∣Rˆ can be expressed
as:









1

  ,   pbeta  rN  ,   N , LN  1(5)

where    is the Gamma function and

(2)  ,    a H  , ,    1a  , ,   ,p Rˆ∣ , f , , p  ∣ ,  p  f∣ ,  p   p   ,





0 0

N 2 pbeta  rN  ,     1, LN  1
1  LN  LN    LN    1  ,
 0




p Rˆ ,  , f , , p  ,  Rˆ    p Rˆ  , f ,  ,  d f dThe proposed method considers the probabilistic
model for parameters  ,  , f , and  and the estimatedvalue of the spectral matrix R̂ using the observation
signals.The joint probability distribution p Rˆ ,  , f , ,
can be expressed as:











where p Rˆ∣ , f , , p  ∣ ,  , p  f∣ ,  p  ,,and p   show the likelihood distribution of R̂ and theprior distributions of  , f ,  and  respectively.

pbeta  rN  ,    ,   

From N independent observations x(vT )   D
 1, ..., N  T the sampling period), R̂ can be expressedusing sample matrix inversion [7] as:rN  ,   (6)      1
 1
r 1  r  , (7)      

a H  ,  ,    1 Rˆ  1a  ,  ,  ,tr  1 Rˆ   ,  





(8)

N

1
Rˆ   x  vT  x H  vT  .
N v 1(3)

We set the likelihood distribution of R̂ to follow a
D -dimensional complex Wishart distribution, withN   D  degrees of freedom as given by:

LN  N  D  1 1 .

We can see the extent of estimation accuracy visually bychecking the posterior distribution p  ,∣Rˆ . We caneasily calculate the estimated values of  and  denotedas ˆ and ˆ respectively), which can be expressed as:det    p Rˆ  , f ,  ,   N Ddet Rˆ 











N

ˆ   m P  m ,m∣Rˆ ,





(10)





(11)

M

 

m 1



1
exp   N  tr     fa  ,  ,   a H  ,  ,    Rˆ 
 ,

N
f




 ND det  I D   1a  , ,   a H  , ,    


 

M

ˆ  m P  m ,m∣Rˆ ,

(4)

m 1





P  m , m Rˆ :=where det   is the determinant and tr   is the sum of
diagonal components (trace) [8, 9].

78

(9)

The



p  m , m Rˆ m '1 P m ' , m ' Rˆ
M



,

(12)

Radio Science Bulletin No 378 (September 2021)

Figure 1: Estimation results of the proposed method with D  6 (the electromagnetic ﬁeld components) and N 16
where  m ,m  (m = 1,…, M) is the discretized directionof arrival, and M is the number of the grids. We can alsoeasily calculate the variances of  and  denoted as  2
and  2 respectively) by:
M



 
2



(13)



 



(14)

 2    m  ˆ P  m ,m∣Rˆ ,
m 1

M

2

 2   m  ˆ P  m ,m∣Rˆ .
m 1

3. Evaluation through Simulations
We conducted relevant simulations using the spectral
matrices estimated from the pseudo-observed signals tosimulate diﬀerent numbers of sensors D and averaging
numbers N of the spectral matrix.

3.1 Evaluation Speciﬁcations
The pseudo-observed signal was generated as anarrow-band signal with thermal noise. Considering thecase where the electromagnetic ﬁeld sensor is placed in
the positive direction of the x -, y - and z - axes, withthe z -axis being parallel to the external magnetic ﬁeld.
The pseudo-observed signal x  t ,    D can be
expressed as:x  t ,   s  t  a  , s ,s   n  t  .

(15)Here, s  t  is the complex amplitude in the direction ofarrival  s ,s  following a zero-mean complex Gaussian

Figure 2: Estimation results of the proposed method with D  6 the electromagnetic ﬁeld components) and N  32

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Radio Science Bulletin No 378 (September 2021)

79Figure 3: Estimation results of the proposed method with D  5 (without the Ez component)
process with a variance of  s2 and n  t    D is the thermal
noise applied to each channel of the electromagnetic
ﬁeld sensor, following a zero-mean complex Gaussian
process with a covariance matrix of  I D where I D is theidentity matrix of an order of D . We set the direction ofarrival  s and s as 30 and 45 respectively, and set thesignal-to-noise ratio (SNR) parameter  s2 /  as 20 dB. We
also assumed that s  t  and n  t  are uncorrelated. The
estimated value of the spectral matrix R̂ was calculated
from Equation (3) using the generated pseudo-observed
signal x T ,  1, ..., N  obtained from Equation(15). To obtain the steering vector a  , ,  , we set
the propagation mode to the R-X mode, the frequency
 / 2  to 1.000 kHz, the plasma frequency to 6.655 kHz,and the cyclotron frequency of electrons to 3.540 kHz. In
the simulation setup, the refractive index was 4.8 and the
SNR of the electric ﬁeld and the magnetic ﬁeld were 7.6
and 19.7 dB, respectively. Then, we used Equation (5) tovisualize the extent of estimation accuracy and calculated
Equations (10) to (14) by using the calculated R̂ . We
discretized the direction of arrival  m ,m  (m = 1,..,M)
using Appendix A in Tanaka et. al [6] with the number ofthe grids M  5, 704 .
We conducted simulations with diﬀerent numbers of sensors
including D  6 (the electromagnetic ﬁeld components)
and D  5 (without Ez or Z 0 H z ) where H z is multipliedby the characteristic impedance of vacuum, denoted as Z 0
in order to convert the dimension of the magnetic ﬁeld tomatch that of the electric ﬁeld. Even when D  5 , theabsolute k-vector direction can still be estimated by
Faraday’s law, but note that the SNR of Z 0 H z was higher
than that of Ez in our simulation setup. We set N as 16
and 32 (two patterns) to check if the extent of estimation
accuracy would increase by increasing N .

3.2 Simulation Results
The estimation results of the proposed method canbe found in Figures 1 to 4. More speciﬁcally, the estimation
results of the electromagnetic ﬁeld components with theaveraging number N 16 and 32 are shown in Figures 1
and 2, respectively. The electromagnetic fieldcomponents without Ez and Z 0 H z are shown in Figures
3 and 4, respectively. The radius of the estimation results
represents the zenith angle  and the circumference angleof the estimation results represents the azimuth angle  .
The posterior probability of the proposed method
P  m ,m∣Rˆ (m = 1,…, M) is represented by diﬀerent
colors from blue to red. The parameters from Equations(10) to (14) in Figures 1 to 4 are summarized in Table 1.

As shown in Figure 1, it can be found that the peak ofP( m , m Rˆ ) was around the true value  s , s    30 , 45  ,Table 1: Parameters calculated from Equations (10) to (14)Speciﬁcationˆ     ˆ     Case in Figure 1
Case in Figure 2
Case in Figure 3
Case in Figure 4
True value

80

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Radio Science Bulletin No 378 (September 2021)

Figure 4: Estimation results of the proposed method with D  5 (without the Z 0 H z component) and N 16

indicating that the proposed method can correctly visualize
the extent of estimation accuracy.

5. Acknowledgments

In Figure 2, it was shown that the peak of P( m , m Rˆ )was sharper than that in Figure 1. From Table 1, it wasalso found that the estimated value ˆ and ˆ in Figure
2 improved by 0.2 and 1.0 deg, respectively. Moreover,the standard deviations   and   in Figure 2 decreased
by 0.4 and 1.0 deg, respectively. These ﬁndings indicatedthat when the averaging number N increased, the extent of
estimation accuracy also increased accordingly.

This research was partially supported by the Grantin-Aid for Scientiﬁc Research from the Japan Society for
the Promotion of Science (Grant Number: 21H01146).
This research was also ﬁnancially supported by the Support
for Pioneering Research Initiated by the Next Generation
program by the Japan Science and Technology Agency(Grant Number: JPMJSP2135). The authors would liketo thank Enago (www.enago.jp) for the English language
review.

As shown in Figure 3, it was found that theestimation results were almost the same as those in Figure
1. Comparatively, the peak of P( m , m Rˆ ) in Figure 4
demonstrated a broader peak, and the errors of the estimation
values and the variances of  and  were larger, as shownin Table 1. This indicated that the proposed method candemonstrate the importance of Z 0 H z in the simulation
setup, as it showed a higher SNR than Ez both visually,and numerically.

6. References

4. Summary and Future WorkIn this study, we proposed the Bayesian k-vector
estimation method in magnetized cold plasma based on the
WDF method. Our proposed method was found robust underdiﬀerent sensor noise levels since the NIK can estimate
k-vector direction even when the number of sensors isvery few, due to the adoption of the Bayesian inference.
We veriﬁed that the proposed method can visualize andcalculate the extent of estimation accuracy and identify
which sensor is important for estimation.

1. C. A. Kletzing, W. S. Kurth, M. Acuna, R. J. MacDowall,
R. B. Torbert, T. Averkamp, D. Bodet, S. R. Bounds,
M. Chutter, J. Connerney, D. Crawford, J. S. Dolan, R.
Dvorsky, G. B. Hospodarsky, J. Howard, V. Jordanova,
R. A. Johnson, D. L. Kirchner, B. Mokrzycki, G. Needell,J. Odom, D. Mark, R. Pfaﬀ, J. R. Phillips, C. W. Piker,
S. L. Remington, D. Rowland, O. Santolik, R. Schnurr,
D. Sheppard, C. W. Smith, R. M. Thorne and J. Tyler,``The Electric and Magnetic Field Instrument Suite andIntegrated Science (EMFISIS) on RBSP,’’ Space Science
Reviews, 179, 1, November 2013, pp. 127-181.
2. Y. Kasahara, Y. Kasaba, H. Kojima, S. Yagitani, K. Ishisaka, A. Kumamoto, F. Tsuchiya, M. Ozaki, S. Matsuda,
T. Imachi, Y. Miyoshi, M. Hikishima, Y. Katoh, M. Ota,
M. Shoji, A. Matsuoka and I. Shinohara, ``The Plasma
Wave Experiment (PWE) on Board the Arase (ERG)
Satellite,’’ Earth, Planets and Space, 70, 1, November
2013, pp. 86.

Our future work is to evaluate the proposed method
when the noise levels are diﬀerent among the sensors, orthe number of the sensors is less than 5.

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Radio Science Bulletin No 378 (September 2021)

81

3. O. V. Agapitov, D. Mourenas, A. V. Artemyev, F. S.
Mozer, G. Hospodarsky, J. Bonnell and V. Krasnoselskikh, ``Synthetic Empirical Chorus Wave Model from
Combined Van Allen Probes and Cluster Statistics,’’
Journal of Geophysical Research: Space Physics, 123,
1, January 2018, pp. 297-314.
4. L. R. O. Storey and F. Lefeuvre, ``The Analysis of
6-Component Measurements of a Random Electromagnetic Wave Field in a Magnetoplasma - I. The Direct
Problem,’’ Geophysical Journal International, 56, 2,
February 1979, pp. 255-269.
5. L. R. O. Storey and F. Lefeuvre, ``The Analysis of 6-Component Measurements of a Random Electromagnetic
Wave Field in a Magnetoplasma - II. The Integration
Kernels,’’ Geophysical Journal International, 62, 1, July
1980, pp. 173-194.

82

6. Y. Tanaka, M. Ota and Y. Kasahara, ``Noise Integration Kernel Design for the Wave Distribution Function
Method: Robust Direction Finding With Diﬀerent Sensor
Noise Levels,’’ Radio Science, 56, 9, August 2021, pp.
e2021RS007291.
7. H. L. Van Trees, Optimum Array Processing, New York,
United States, Wiley-Interscience, 2002.
8. P. Shaman, ``The Inverted Complex Wishart Distribution
and Its Application to Spectral Estimation,’’ Journal ofMultivariate Analysis, 10, 1, March 1980, pp. 51-59.
9. Y. Tanaka, M. Ota and Y. Kasahara, ``Identiﬁcation Approach of Arriving Wave Model Based on Likelihood
Ratio Test With Diﬀerent Sensor Noise Levels,’’ Radio
Science, 57, 8, July 2022, pp. e2022RS007427.

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Radio Science Bulletin No 378 (September 2021)

Et Cetera
Tayfun Akgül
Istanbul Technical University
Dept. of Electronics and Communications Engineering
Telecommunications Division
80626 Maslak Istanbul, Turkey
Tel: +90 212 285 3605; Fax: +90 212 285 3565
E-mail: tayfunakgul@itu.edu.tr.

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Radio Science Bulletin No 378 (September 2021)

83

In Memoriam: Dr. Ljiljana R. CanderD

r. Ljiljana R. Cander suddenly passed
away March 16, 2022, after a life
devoted and dedicated to science. She wasborn on June 17, 1946, in Nishka Banja,
former Yugoslavia. After moving with herfamily to Grocka, she was awarded a BSc
in Electrical Engineering in 1970, an MSc
in Plasma Physics in 1974, and a PhD in
Ionospheric Research at Belgrade University
in 1984.

She started her scientiﬁc career at the
Geomagnetic Institute of Grocka, ﬁnishingup at the Rutherford Appleton Laboratory(RAL) in the UK, where she spent a greatpart of her working life. Starting in 1993, she worked incollaboration with the late Peter Bradley in RAL’s Radio
Communications Research Unit. She became an RAL staﬀ
member in 1997, also obtaining British citizenship. She
settled to live in Wantage, a small Oxfordshire town where
many other RAL staﬀ also lived.
Her principal subjects of interest and research dealt
with observation and investigation of the upper atmosphere,
particularly connected with the terrestrial ionosphere, its
temporal and spatial variability, its medium-term prediction,and forecasting models.
She had one sabbatical year from 1985 to 1986, atIstituto Nazionale di Geoﬁsica in Rome working in travelingionospheric waves. After that she was extremely active inseveral European projects, especially a series of ionosphericprojects supported by the European Cooperation in Scienceand Technology (COST) program: the COST 238 PRIME
Project (Prediction Retrospective Ionospheric Mapping over
Europe); the COST 251 IITS (Improved Quality in Ionospheric
Telecommunication); the COST 271 action entitled Eﬀects ofUpper Atmosphere on Earth and Satellite Communications asVice Chair; and ﬁnally the COST 296 action entitled MIERS(Mitigation of Ionospheric Eﬀects on Radio Systems) as
Chair. She was also active in several space-weather activities
sponsored by ESA, in particular the ESA Space Weather
Working Team, where she served as spokesperson for the
Topical Group on Ionospheric Eﬀects from 2004 to 2007.
She brought to these projects an appreciation of the diﬀerent
temperaments and outlooks within a wide-ranging group ofEuropean scientists, and helped to foster collaboration between
ionospheric radio scientists from so many diﬀerent countries.
During her time working in RAL’s Radio Communications
Research Unit, she led a wide range of studies covering
ionospheric measurements and forecasting, and the impactof space weather on communications systems. Perhaps
most importantly, she was a member of the Ionospheric
Expert Team that supported the work of ESA and European

84

industry to understand the critical role of theionosphere in determining the performanceof EGNOS, the satellite-based augmentation
system that underpins the accuracy andreliability of GNSS services in Europe.
She participated in many international
conferences, especially the European
Geophysical Union and the International
Reference Ionosphere, contributing with
relevant original works. She was one of theorganizers of the course of the International
School of Geophysics on IonosphericPhysics and Propagation, held at the “Ettore
Majorana” Foundation and Centre for
Scientiﬁc Culture in Erice, Italy (September
24-29, 2004). She was also a lecturer of the course on “Groundand Space-Based Instruments for Future Research in SolarTerrestrial Physics Ground and Space-Based Instruments for
Future Research in Solar-Terrestrial Physics,” organized by
the International School of Space Science at L’Aquila, Italy,
on June 6-10, 2016.
Following her nominal retirement in 2006, sheretained her aﬃliation with RAL through a Visiting Scientist
appointment, and continued to live and enjoy life in Wantage.
She would often visit the RAL site to meet with colleagues,
though this became less frequent in later years as her healthmade it more diﬃcult to visit. Nonetheless, she made great useof the Internet and continued to collaborate with colleagues
both at RAL and around the world, right up to her passing
in 2022. In fact, her transition to retirement was the mostscientiﬁcally productive phase of her life, as she published awealth of papers in refereed journals, and her citation rate roseaccordingly. Her professional interests were still on ionosphericstudies and applications, including propagation prediction
and space weather. Dr.-Ing. Lj. R. Cander was Space Science
Editor for Springer’s journal ActaGeofisica, and a Fellow of
the Electromagnetics Academy, USA.
She was co-author of more 200 papers published in theinternational scientiﬁc literature. The last years of her life
were mainly dedicated to writing two books. The ﬁrst one
was entitled Ionospheric Prediction and Forecasting, and thesecond one was Ionospheric for Space Weather. These havebeen recently been published by Springer-Verlag.
Finally, she was a cultured woman, loving literature,music, and ﬁne arts, a curious woman loving to travel inMediterranean countries such as Italy, and in the last times,
in Cyprus.
Bruno Zolesi
E-mail: bruno.zolesi@ingv.it
Mike Hapgood
E-mail: mike.hapgood@stfc.ac.uk

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Radio Science Bulletin No 378 (September 2021)

In Memoriam: Patricia DohertyP

atricia Doherty, Director and SeniorResearch Scientist at Boston College’s
Institute for Scientiﬁc Research (ISR), andan internationally recognized leader inspace weather and its impact on global
navigation systems, died on July 14, 2022.
She was 72.
Ms. Doherty joined Boston College
and the ISR in 1989 as a Research Scientist.
She was named ISR co-Director in 2005.
She became the Director in 2008, leading aninternationally recognized research center
conducting theoretical and experimental
studies of space physics, space chemistry,solar-terrestrial research, space weather,and astrophysical studies. She also was a Fellow of theInstitute of Navigation (ION) and of the African GeophysicalSociety.
Ms. Doherty, whose research interests focusedon the ionosphere, space weather, and the Global
Positioning Satellite System (GPSS), contributed tomore than 80 peer-reviewed scholarly articles. Speciﬁc
research initiatives during her career included radio wave
propagation, focusing on ionospheric eﬀects in satellitebased navigation, including satellite-based augmentation
systems and the Federal Aviation Administration’s WideArea Augmentation System.

honors included ION’s Burka Award in 1995,
the organization’s Distinguished Service
Award in 2015, and ION’s 2014 Captain
P.V.H. Weems Award for her service to theorganization and her eﬀorts to promoteadvanced navigation research throughout
the world.
Patricia actively promoted researchand education in the science of navigation indeveloping countries, organizing workshops
and conferences around the world. For morethan a decade, she lent her expertise to
the International Space Weather Initiative(ISWI) community, giving presentations,organizing meetings, and promoting
outreach and capacity-building in non-industrialized
nations. Since 2019, she served on the ISWI Secretariat as
the Meeting Coordinator, and organized and participatedin many successful meetings in Asia, Europe, Africa, andboth North and South America. It was through ISWI thatshe discovered a true passion for improving diversity andinclusion in the community, especially advocating for andencouraging women in science and engineering. Combinedwith her collaboration with ICTP, this activity led to asustained interest in the education and advancement ofyoung professionals in Africa that earned her the endearing
and enduring nickname “Mama Africa” in the African
geophysics community.

She was also a leader among the global communityof space science researchers. At the time of her death, she
was Vice President of the International Union of Radio
Science (URSI), and former Chair of URSI’s Commission
G, Ionospheric Radio and Propagation. She was a past
President of the Institute of Navigation and current Chairof the organization’s Satellite Division, the world’s
premier professional society dedicated to the science of
positioning, navigation, and timing. She also served as
the Scientiﬁc Secretary to the Scientiﬁc Committee on
Solar-Terrestrial Physics (SCOSTEP), an interdisciplinary
body of the International Council for Science, was Boston
College’s representative to the Universities Space Research
Association, and was a member of the USRA Board ofTrustees. She developed a formal collaboration with
the Abdus Salam International Theoretical Center for
Physics (ICTP) in Trieste, Italy, to hold annual educational
workshops on the applications of GNSS technology forspace science, providing opportunities focused on students
from developing nations.

Those who were fortunate enough to work with Pat
know that her scientiﬁc, organizational, and administrativeskills were exceeded only by her qualities as a human being.
She was generous, patient, and caring in her interactions
with people and led by character and example. No matterthe circumstance, Pat had a smile and kind, encouraging
word for everyone. She will be greatly missed by those sheleft behind, but never forgotten.

Ms. Doherty was recognized for her scientiﬁc researchand her outreach activities throughout her career, including
the 2018 Carrington Education and Outreach Award of the
130,000-member American Geophysical Union. Other

Keith Groves
Boston College
E-mail: keith.groves@bc.edu

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Radio Science Bulletin No 378 (September 2021)

Ms. Doherty is survived by her husband, Charles,
her children, Karen M. Stickney and Brian J. Doherty, andmany other relatives.
To learn more about Pat’s amazing journey in her
own words as a pathﬁnder for women in science, please
read her short autobiographical sketch, P. H. Doherty, “Oh
the Places You Will Go…,” URSI Radio Science Bulletin,
No. 367, December 2018, pp. 22-24, doi: 10.23919/
URSIRSB.2018.8627411.

85

In Memoriam: Robert Crane
Robert K. (Bob) Crane passed in
Newport, NH, on November 30, 2022, atthe age of 86. Nature lover, outdoorsman,history buff, and radio propagation
researcher extraordinaire, Bob contributed
to the scientific community with aremarkable breadth of experimental andtheoretical studies in ionospheric andtropospheric radio propagation, within avariety of professional venues. Among his
aﬃliations were the Mitre Corporation,
MIT Lincoln Laboratory, Environmental
Research & Technology, DartmouthCollege’s Thayer School of Engineering,and the University of Oklahoma (where
he attained Professor Emeritus status in both electricalengineering and meteorology). Among many professional
distinctions, he was an IEEE Life Fellow and a certiﬁed
consulting meteorologist of the American MeteorologicalSociety.
Bob served for six years as international Chairman
of URSI and was an oﬃcer of the URSI/US National
Committee for ﬁfteen years. He represented the UnitedStates numerous times in the Radiocommunication Sectorof the International Telecommunication Union (ITU), andwas responsible for writing many sections of then in-force
ITU Radio Regulations. Bob worked in India under United
Nations sponsorship with members of the Indian space
program, in Australia on a month-long IEEE lecture tour,
and also worked in Peru. Bob wrote two textbooks still usedby college students and researchers today: “Electromagnetic
Wave Propagation Through Rain” in 1996 and “Propagation
Handbook for Wireless Communication System Design”
in 2003.
Bob had a unique capability for adapting large existingmeasurement facilities to pioneering radio propagation
research. He monitored low-orbit US Navy navigation
satellites with the 46m MIT Millstone Hill radar to estimate
the variance and spectra of ionospheric angle-of-arrivaland Doppler ﬂuctuations. High-powered L-band and UHF
radars at Kwajalein Atoll (Marshall Islands) were appliedfor high-resolution spatial and velocity observations ofwinds in the equatorial troposphere. He used other radarobservations to analyze transhorizon propagation, rainscatter interference, and the spatial structure of rainstorms.
When adequate facilities did not exist, Bob built hisown, such as his famous Rain-Range Experiment. The
experimental studies were accompanied with thoroughtheoretical assessments and modeling to enable practicalapplication of his research results, often in association
with detailed subject-area reviews. His contributions tothe understanding of propagation through rain are legend.

86

Because of the scope, high caliber,and longevity of Bob’s research, andhis participation in such a broad range
of venues, Bob inﬂuenced and inspired
numerous other practitioners in the ﬁeld.
Reminiscences by a few of his manycolleagues are shared here.
Dr. David V. Rogers
Communications Research Centre
Canada, Canada (retired)
Dr. Wolfhard J. Vogel
University of Texas, Austin, USA(retired)Reminiscences by Colleagues
Back in 1984 I was working for INTELSAT in
Washington, DC. Dave Rogers from COMSAT Labs called
me one day and said: There is a meeting in Columbia, MD,
to discuss propagation experiments with the NASA ACTS
satellite – why don’t you join in?
This meeting brought together a good number ofwell-known people doing propagation research in the US at
the time. I went there and was introduced to the chairman,
Prof. Bob Crane, whose two-component rain model I had
already used. I was pleasantly surprised to meet a softspoken but well-humored gentleman. His attitude was toencourage discussion and to get all participants to bringin their experience.
Later I attended numerous NAPEX meetings andalways enjoyed his presentations and even more his
contributions to the discussion. He had moved to the
University of Oklahoma and kept working on improved
slant-path propagation prediction models – at the sametime investigating the requirements for equipment up-time
to arrive at valid statistics for rainfall rate and beacon
attenuation. I got involved in the work of the propagation
Study Group of ITU-R and noticed that Bob Crane’s
prediction models were considered most reliable and,combined with the rain climatic zones, most universally
applicable.
The propagation community is indebted to Bob Cranefor his great contributions to understanding the relationshipbetween rain and atmospheric microwave attenuation.,I
will always keep Bob in good memories.
Bertram Arbesser-Rastburg
SpaceTec Partners; ESA/ESTEC Electromagnetics &Space Environment Div. (retired)
Former Chair, ITU-R Study Group 3
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I became acquainted with Bob many years ago at a NASA
Propagation Experimenters (NAPEX) meeting. This turned
into a long-lasting friendship as I started managing the
program. From the onset, it was clear that Bob was a worldclass expert in the ﬁeld. Bob’s contributions to slant path
propagation modelling and analysis were numerous, which
received ample acknowledgment from his peers. The U.S.
satellite industry organizations were heavy users of theNASA Propagation Handbooks during the 80’s and 90’s
and beyond. Since Bob’s contributions to these handbooks
and the related literature were substantial and high-quality,
his name was well known among slant-path propagation
enthusiasts.
On the personal note, I have many good memories
with Bob, particularly on our ITU trips to Geneva. Bob
was very knowledgeable on Switzerland, its geography
and points of interest. He took me to most interesting partsof the country and the Swiss Alps; which, without him, I
would not have been able to see on my own. I also enjoyedvery much working with Bob; in addition to being a great
expert in the ﬁeld, he had good taste and good humor that
he liked to share.
Faramaz Davarian
Jet Propulsion Laboratory
NASA Propagation Program
Lunar Communication Terminal
I worked with Bob Crane since my earliest days at NASAand later through the years at various projects. He was a rolemodel for me, one of the few engineers in the country at thattime dedicated to radiowave propagation and its eﬀect oncommunications. I went on to make that the focus for mycareer also. I remember when Bob was at Environmental
Research & Technology and I was a young engineer at NASA
Goddard, we funded a program with Bob for propagation
research, and Bob developed something later called the
Crane Global Model, which became the ﬁrst propagation
model, based on the use of geophysical data, to provide
a self-contained rain attenuation prediction procedure for
global application. The procedure later evolved into the
Crane Two-component Rain Model, which further enhanced
rain attenuation prediction. These models served as thebasis for many other propagation models over the years
and to this day, which further increased our knowledge ofradio science and space communications. Bob’s legacy issolid, and those of us who had the pleasure of knowing
and working with him are better for it.
Louis J. Ippolito
USNC-URSI Commission F
ITU-R Study Group 3
My ﬁrst interaction with Bob Crane was at the 1975 USNC/URSI meeting in Boulder, CO, as a Communications

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Radio Science Bulletin No 378 (September 2021)

Research Centre Canada (CRC) postdoc. My paper withRod Olsen, my CRC mentor into the radio propagation
arena, estimated propagation delays in rain to assess any
inﬂuence on (≈ 14ns) guard-time objectives for TDMA
satellite systems. We adopted delay/attenuation ratios tocouple rain delay to (better-founded) rain attenuation, which
also damped raindrop-size distribution eﬀects, quite cleverly
I thought. Bob was not that impressed, noting (correctly)that rain delay is small compared to clear-air delay. Still,
dialogue with such an eminent ﬁgure was stimulating, andour analysis (with Dan Hodge, Ohio State) of the A = aRbattenuation-rainrate formalism, introduced at the 1976
Amherst, MA, meeting, had more cachet. Naturally, weleveraged Bob’s pioneering work on radio propagation
through rain. As our paths continued to intersect, Bob
and I became friends. Bob was in Geneva at my ﬁrst ITU
meeting in 1981 to educate me in those initially-perplexing
proceedings. For NASA’s Advanced Communications
Technology Satellite (ACTS) propagation program, Bob
and I served as the Working Group chairs, motivating
further collaboration and an extraordinary beneﬁt as Bob’s
coauthor on several papers. It was a privilege to work withsuch a master.
David V. Rogers
Former Vice-Chair, ITU-R Study Group 3
I met Bob Crane in the early 1970s, as I was starting out tomeasure the propagation eﬀects of rain over terrestrial andsatellite paths at centimeter wavelengths at the Universityof Texas at Austin. Attending appropriate sessions at URSI
and IEEE conferences to learn what others were doing andalso to present my own results, Bob often had calm wordsof encouragement and guidance about the requirements
for statistically meaningful data. I always looked forward
to having him participating in a session.

Later on, when NASA established the NAPEX (NASA
Propagation Experimenters) Group managed through JPL,
I got to know Bob on a more personal level. At one of ourgroup meetings, he presented a report on his work and travelin India and it became clear that he very much included his
family in his adventures, setting an example for all of us.
After visits to his homes in New Hampshire andOklahoma, experiencing their organization and ambianceand chatting about his various collections, I realized thatBob had the uncanny ability to see the forest and not justthe trees. His love for nature and its variability must haveenabled him to create the important holistic propagation
models that he is rightfully known for.
Wolfhard Vogel
Former Chair, USNC-URSI Commission F

87

Telecommunications Health and Safety
James C. Lin
University of Illinois at Chicago
851 South Morgan Street, M/C 154
Chicago, IL 60607-7053 USA
E-mail: lin@uic.edu

Research in High-Power Microwave
Weapons
James C. Lin
University of Illinois at Chicago
851 S. Morgan St. (M/C 154)
Chicago, IL 60607 USA
E-mail: lin@uic.edu

The incidents of sonic attacks on diplomatic personnel
in Havana, Cuba, may likely remain a mystery until
some culprit(s) are caught in the act [1]. The one-time
diplomatic aﬀair, Havana Syndrome, has apparently
transformed into a weapons research program in the UnitedStates [2]. Indeed, it appears that research in high-power
microwaves has persisted worldwide, including some of
the major military powers [3, 4].

hypothesis ﬁrst proposed in late 2017 and discussed at thetwo-day NASEM meeting in early 2020.

An issue that has surfaced concerning the Havana
Syndrome involves whether the microwave auditory eﬀect
can be exploited as a directed-energy weapon. Indeed, if
the microwave auditory eﬀect is weaponized at suﬃciently
high powers, aside from microwave-pulse-induced sonicattacks, the high-power microwave pulses could produce
putative lethal and non-lethal damage to brain tissues by
the reverberating sonic shock waves.

The resilience of the Havana Syndrome and its
impact within governmental circles has been amazing.
Speculation and hypothesized causes are being vigorously
discussed ﬁve years since it was ﬁrst reported. Over that
period, about 200 US personnel have reported similar
attacks while working in places like Havana, Guangzhou,
London, Moscow, Vienna, and Washington, DC, to namea few. There have increasing discussions on the capabilityof high-power-pulsed microwave radiation as a directedenergy weapon in military operations [5].

A US National Academies of Sciences, Engineering,and Medicine (NASEM) study committee [5] concluded thatamong the mechanisms the study committee considered, themost plausible mechanism to explain the accounts – especially in individuals with distinct early symptom – appears
to be directed, pulsed microwave energy. It reaﬃrmed the88

Many scientists and researchers, in and outside the US
government, are of the thinking that the microwave auditory
eﬀect – induced by a targeted beam of high-peak-power
pulsed microwave radiation – is the most likely scientiﬁc
explanation for the Havana Syndrome.

Apparently, the US military has maintained someinterest in the topic of the microwave auditory eﬀect.
Some years ago, the US Navy awarded a research contract
entitled “Remote Personnel Incapacitation System” through

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its small business innovative research program [6, 7]. The
goal of the project was to design and build a prototype
non-lethal weapon based on the microwave auditory
eﬀect. The transient personnel incapacitation system wasdubbed MEDUSA (Mob Excess Deterrent Using Silent
Audio). The weapon relied on a combination of pulse
parameters and pulse power to raise the auditory sensation
to the “discomfort” level to deter personnel from enteringa protected perimeter. While the status or outcome of thisproject may be privileged, there are indications that hardware
was built, and power measurements were taken to conﬁrm
the required pulse parameters enabling observation of thedesired microwave auditory eﬀect, as expected.
It is noteworthy that in December 2020, the US
government announced a research program to develop
low-cost, low-weight, small-size wearable microwaveweapon-exposure detectors [8]. All indications suggest the
research program is currently in progress. Furthermore,
the announcement acknowledged that directed-energy
weapons, including microwave weapons, are a growing
threat to soldiers on the battleﬁeld. It also suggested thatthe determinants of a microwave weapon’s antipersonnel
eﬀects are multi-factorial, and associated injuries may besituational dependent. Evidently, there is the convictionthat some major military powers possess the capabilities
of ﬁelding high-power microwave sources such as thosethat appear to have been used in Cuba and elsewhere. It
appears that the research program has been put into place
as advertised. Indeed, there are reports suggesting that
research in high-power microwaves continues worldwide,
including some of the major military powers.
The recent report from some joint research conductedby the US Air Force and US Army suggests that relevant
investigations may be ongoing within their institutions,as well. A computer simulation study showed that for
certain high-power microwave-pulse exposures, substantial
acoustic pressure may occur within the brain that may
have implications for neuropathological consequences [9].
Although the required power densities were high, they wereachievable with current high-power commercial and military
microwave systems operating under pulsed conditions.
Signiﬁcantly, they were within the permissible limits ofcurrently promulgated safety standards, which conﬁrmed
the conclusion of an earlier study [10]. Furthermore, thestudy showed that to generate tissue-injuring levels ofhigh-power-microwave-induced acoustic pressures inside
the human brain, the theoretical microwave-pulse-induced
temperature elevation would be no more than 1°C, whichis “safe” according to currently promulgated RF andmicrowave safety protection guidelines [11, 12].
The required microwave technology is mature andin general, commercially available in many developed
countries or within major military powers. Longer
distances and higher-power scenarios would require morebulky equipment and sophisticated aiming devices, but“packable” equipment is possible for closer-range non-

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Radio Science Bulletin No 378 (September 2021)

lethal applications [3]. This would not preclude the useof a much-higher-power microwave weapon located ata farther distance from the intended targets to raise theauditory sensation to the “discomfort” levels. Existing
hardware could also be optimized to meet some speciﬁc
requirements in covert or ﬁnely targeted operations.
A recent article [2] noted that the US Air Force
Research Laboratory (USAFRL) conﬁrmed plans toestablish a new center for research into directed energy,
such as high-power electromagnetic (including microwave)
energy applied to destroy speciﬁc targets, in collaboration
with the University of New Mexico (UNM). Apparently,
UNM has been engaged with the USAFRL on directedenergy microwaves systems for more than 30 years. The
new center is in the early planning stages. It is seeded by a
$2.4 million, four-year cooperative partnership agreement
between UNM and USAFRL. It is expected that the center
will go into full operation by 2025. It will enable researchers
to study and develop next-generation capabilities in theincreasingly competitive directed-energy technology arena.
The program appears to be an integral part of thestrategy contained in the US Department of Defense’s
Directed Energy Futures 2060 report, released in August2021 [13]. The report deﬁnes visions for directed energy
and some aspects of electronic warfare with respect totechnology development, military utility, and weapons
proliferation. The report further mentions that the US is
moving aggressively to retain leadership in directed energy,
which it describes as focused beams of electromagnetic
energy that can be used to disrupt or harm humans, devices,missiles, vehicles, and other targets. It suggests that thetechnology is less costly than traditional kinetic systems
and can result in less collateral damage. It declares thatthe technologies are approaching or have passed a tipping
point for the criticality of directed-energy capabilities asapplied to the successful execution of military operations
for the United States.

References
1. J. C. Lin, “Mystery of Sonic Health Attacks on HavanaBased Diplomats,” URSI Radio Science Bulletin, No.
362, September 2017, pp. 102-103.
2. B. Vincent, “Air Force to Build New Center to Study andEnhance Directed Energy Capabilities,” NextGov, November 10, 2021 https://www.nextgov.com/emergingtech/2021/11/air-force-build-new-center-develop-andstudy-directed-energy-capabilities/186737/, accessedon November 11, 2021.
3. J. Borger, “Microwave Weapons that Could Cause
Havana Syndrome Exist, Experts Say,” The Guardian,
https://www.theguardian.com/science/2021/jun/02/
microwave-weapons-havana-syndrome-experts.

89

4. J. C. Lin, Auditory Eﬀects of Microwave Radiation,
Springer, Switzerland, September 2021.
5. National Academies of Sciences, Engineering, andMedicine, An Assessment of Illness in U.S. Government
Employees and Their Families at Overseas Embassies,
Washington, DC, The National Academies Press. https://doi.org/10.17226/25889, December 2020.
6. Navy SBIR, “Remote Personnel Incapacitation System,” http://www.navysbirprogram.com/NavySearch/
Summary/summary.aspx?pk=F5B07D68-1B19-4235B140-950CE2E19D08, last accessed in August 2020.
7. J. C. Lin, “The Havana Syndrome and Microwave Weapons,” IEEE Microwave Magazine, 22, 11, November
2021, pp. 13-14.
8. DHA211-005, “Wearable RF Weapon Exposure Detectors,” DoD 2021.1 SBIR Solicitation, https://www.sbir.
gov/node/1841633, last accessed on January 15, 2021.

90

9. A. M. Dagro, J. W. Wilkerson, T. P. Thomas, B. T.
Kalinosky, and J. A. Payne “Computational Modeling
Investigation of Pulsed High Peak Power Microwaves
and the Potential for Traumatic Brain Injury,” Science
Advances, 7, 44, October 2021, pp. 1-10.
10. J. C. Lin, “Sonic Health Attacks by Pulsed Microwaves
in Havana Revisited,” IEEE Microwave Magazine, 22,
3, March 2021, pp. 71-73.
11. IEEE, “Standard for Safety Levels with Respect to
Human Exposure to Electric, Magnetic, and Electromagnetic Fields, 0 Hz to 300 GHz,” (Revision of IEEE
Std C95.1-2005/ Incorporates IEEE Std C95.1-2019/
Cor 1-2019), 4 October 2019, pp. 1-312.
12. ICNIRP, “Guidelines for Limiting Exposure to Electromagnetic Fields (100 kHz to 300 GHz),” Health Phys.,118, 5, 2020, pp. 483-524.
13. DOD, “Directed Energy Futures 2060: Visions for the
Next 40 Years of U.S. Department of Defense Directed
Energy Technologies,” Washington DC, August 2021.

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Radio Science Bulletin No 378 (September 2021)

Early Career Representative Column
Nosherwan Shoaib
Research Institute for Microwave and Millimeter-Wave
Studies (RIMMS)
School of Electrical Engineering and Computer Science(SEECS)
National University of Sciences and Technology (NUST)
H-12, Islamabad, Pakistan
E-mail: nosherwan.shoaib@seecs.edu.pk

Recap of Commissions HGE Meetthe Experts Session
Conveners: Miroslav Hanzelka (H), Claudia Martinez (H),
Bruce Fritz (G), and Chaouki Kasmi (E)Commission H led a joint session with Commissions
E and G at the 2022 AT-AP-RASC meeting entitled“Meet the Experts.” The session was designed especiallyto give early career scientists an opportunity to hear the
career perspectives from three established researchers
in their respective ﬁelds. Each expert gave a 20 minutepresentation about their experiences and thoughts on thepresent state of the ﬁeld, as well as where the future of theresearch ﬁeld is headed. The formal presentations werethen followed by an hour of questions from the audience.

The ﬁrst speaker, Prof. Cathryn Mitchell from the
University of Bath, UK, gave a talk titled “From Aberystwyth
to Hunga Tonga – Lessons in Ionospheric Imaging.” Oneof her overarching themes was that science often happensin “strides and shuﬄes.” She used three key examples
from her career to illustrate the key moments that reallycatapulted both her and the science forward. From one ofher ﬁrst major publications on ionospheric tomographyusing GPS receivers, to revelations made as a result of the
Halloween 2003 storm, and ﬁnally on to the current lessons
being learned from the January 2022 Hunga Tonga volcanic
eruption, Prof. Mitchell has been able to make the most ofthese formative moments. She advised the audience to keep
learning, even re-reading papers to ﬁnd new insights, allin an eﬀort to be ready for the next big wave of exciting
developments that are bound to come our way.

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Radio Science Bulletin No 378 (September 2021)

The second speaker, Prof. Craig Rodger from the
University of Otago, NZ, gave a talk titled “Reﬂections
on Current Questions in the Van Allen Radiation Belts,
Electron Precipitation, and Space Weather.” He used his
experience with research in magnetospheric physics totalk about the impacts he sees on everyday society and thecritical importance of what we do in our research. Prof.
Rodger also advocated for the next generation of sciencethat will be enabled by the proliferation of new technology,
perhaps enabled by multi-point measurements from swarmsof CubeSats, that could drive upcoming discoveries andinnovation in geospace research. Finally, he wrapped up
with the observation that good science can be done byanyone from anywhere, and that the best implementation ofresearch often comes from a diverse group of collaborators
who can bring a variety of backgrounds.
The third speaker, Dr. Felix Vega from the Technology
Innovation Institute in Abu Dhabi, UAE, gave a talktitled “From Plasma Science to High-Power Microwave
Engineering.” He provided a unique insight into thetechnology being developed in the ﬁeld of high-power
microwave sources, and how that will help to drive theadvances we seek in our research community. Dr. Vega
illustrated the wide range of power systems that havebeen developed over the past 100 years, and the wide
range of expertise from diﬀerent disciplines needed to put
together a functioning system. Finally, he foresaw a future
where new technology opens up entirely new channels for91

geospace research by enabling tools that were not available
to generations before.
The Q&A session featured a wide range of insightfulquestions from the audience to the speakers, including
questions that spanned from speciﬁc research issues tocareer planning and the nature of a research-inspired
profession. Some highlights of the experts’ responses arefeatured here. First, space weather impacts are a real risk
to modern society that are starting to gain more and more
attention outside our scientiﬁc community. It will be partly
our responsibility to both educate the public and then develop
the technology to predict/prevent the negative eﬀects onhuman society. Space weather will in fact be a good way tocontinue advocating for the large (expensive) missions werely on for science, but that shouldn’t preclude the idea that
exploration of our natural geospace environment and solar

92

system is still a fundamentally exciting prospect. Finally,
the internal excitement we feel as scientists can be thepassion that drives us forward in our careers. It can be hardto plan out a future when opportunities for both funding
and scientiﬁcally interesting events come and go, but thenatural curiosity about whatever interests you have can bea guiding force to help you along the way to take advantageof the opportunities when they present themselves, whereverthat may take you. There may be many paths to choose
from throughout your career, from academia to industry,
and your inherent interest in what you do can help prepare
you to maximize whatever opportunities come your way.
All in all, the session was a huge success and we arevery grateful to our experts for providing their time andinput. We look forward to similar opportunities in the future,
and are excited for the 2023 GASS in Sapporo, Japan!

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Radio Science Bulletin No 378 (September 2021)

Women in Radio Science
Asta Pellinen-WannbergUmeå University, Department of Physics andSwedish Institute of Space Physics
S-90187 Umeå, Sweden
Tel: +46 90 786 7492
E-mail: asta.pellinen-wannberg@umu.se

Progress within Women in Radio
Science - WIRSThis will be my very last WIRS Column in the Radio Science
Bulletin. I feel a little bit sad to leave this platform, but I
am also happy to see that the work continues in a muchbroader front such as WIRS Chapters in diﬀerent countries
and WIRS events and sessions on several URSI meetings.
Still, we have a lot of work do to.

in 2018. The key to make such an event interesting for abroader public than just a few female researchers was tohave an attractive presentation. Dr Anthea Coster’s talkabout how Lise Meitner was helped to escape from Nazi
Germany supported by Anthea’s grandfather was a successfollowed by intensive discussions to a glass of wine.

This time I will give a short summary of what has
happened within the WIRS forum at URSI. The WIRSColumn started after a dialogue between the Dr Ross Stone,
the Editor of the Radio Science Bulletin and me as a quite
new Senior Associate Editor on an editorial meeting at
the First URSI Atlantic Radio Science Meeting AT-RASC
on Gran Canaria in 2015. This situation when Ross told
me to do something is very nicely described in the section“Anecdotes and Fun Facts” in Porter et al., [2022].

Professor Patricia Doherty was the second female Vice
President to be elected in URSI history. We were all very
sad to receive the message that she suddenly passed away in
July 2022. She had recently been engaged to work towards
enhancing the role of women in URSI by encouraging thedevelopment of Women in Radio Science Chapters in themember countries. I greatly appreciate Pat for her initiative
announced on the International Day of Women and Girls in
Science to encourage the formation of WIRS Chapters in
URSI member countries. The goal is to promote the workand leadership and provide mentorship to women in allphases of their career in Radio Science. URSI authorizeda contribution of 500 € in each triennium for each WIRS
Chapter to support their events.

I thank Ross for all the support and help to the WIRSColumn during the years and feel very sorry that Ross is
not anymore among us. Together we have proﬁled about
twenty female researchers both active in mid-career andstill hard-working retired ones as well as young scientists
with babies. Their stories show that the circumstances fordoing science vary greatly in diﬀerent parts of the world.
Some female scientists do not even dare to write about
their work due to various reasons. Those stories have notbeen narrated.
I also thank Professor Piergiorgio Uslenghi, the current
President of URSI and a wise man, who asked me to organize
the ﬁrst WIRS Workshop at the Second AT-RASC meeting

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Radio Science Bulletin No 378 (September 2021)

The United States National Committee for theInternational Union of Radio Science (USNC-URSI)
formed the very ﬁrst WIRS Chapter in 2019. The founding
members, activities and social program of the Chapter ispresented by its Chair Professor Reyhan Baktur [Baktur,
2020]. WIRS Chapters have now been formed in at least
Italy, Poland, Czechia, France, Japan, Egypt, United
Kingdom and Sweden.

93

By today there have been several WIRS events at
URSI meetings, even though the Covid pandemic probably
disturbed the progress. At URSI-GASS 2021 in Rome
there were several sessions, talks and wrap-up, Women’scontributions in inverse em problems and a Celebrationof Women in Radio Science over the 100 Year History ofCommission B. At the AT-AP-RASC 2022 WIRS event on
Gran Canaria Jocelyn Bell Burnell gave a talk virtually.
During the URSI-GASS 2023 in Sapporo there will be a
Tribute to Pat Doherty, WIRS Chapters Workshops, several
invited keynote talks and other scientiﬁc talks in session
marked with W.
I gave an invited General Lecture “The Radio Physics
of Meteors: High Resolution Radar Methods Oﬀering New
Insights” at the URSI-GASS 2011 in Istanbul. It was a greathonour, but I did not understand at that time how unique
it was. Afterwards I realized that there has not been many
- if any other - ordinary General Lectures given by female
researchers at the URSI Flagship Meetings. It cannot be sothat I would be the only female scientist worth such a merit
to my CV, not even from the statistical point of view. There
are many more important topics and several successful
female scientists to be invited to give such lectures in faircompetition with all others. The current problem might
be that there are not enough wise scientists to remember
to suggest them.

94

To reach genuine equal opportunities it is importantthat younger people see all kind of individuals that look
like themselves in diﬀerent posts. Then every young person
can dream of reaching such roles or careers and we do notneed to keep such a noise about leaving women outside.
I hope that the URSI WIRS Chapters can contribute toimproved diversity in Radio Science at all levels by oﬀering
mentorship to their members at all stages of their careers. I
am also looking forward to seeing more competent womenamong the URSI Oﬃcials.

1. Baktur, R. (2020). Women in Radio Science (WIRS):The Newest Chapter of USNC-URSI, Radio Science
Bulletin, 375, 69-74.
2. Porter, E., Quimby, J., Popovic, Z., and Baktur, R.,(2022). Finding Kinship in the Women in Radio Science Chapter, IEEE Antennas & Propagation Magazine,
10.1109/MAP.2022.3178670

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Radio Science Bulletin No 378 (September 2021)

Ionospheric eﬀects of the Hunga
Tonga Volcanic Eruption of
15 January 2022
Tobias G. W. Verhulst1, Giorgiana De Franceschi2, andClaudio Cesaroni2
1 Royal Meteorological Institute of Belgium Geophysical Center

Rue du Centre de Physique 1, 5670 Viroinval, Belgium
E-mail: tobias.verhulst@oma.be
2 Istituto Nazionale di Geoﬁsica e Vulcanologia, Italy

Via di Vigna Murata 605, 00143 Rome, Italy

During the AT-AP-RASC 2022, a special session wasorganized dedicated to the ionospheric eﬀects of the
exceptional January 2022 eruption of the Hunga volcano
in Tonga. This spectacular event is likely of interest to awider audience within URSI, outside of only the ionosphericresearch community. We therefore give here an overview ofthe various contributions presented during this session. Of
course, this is only a brief summary, and interested readers
are invited to explore the diﬀerent publications listed among
the references for more detailed insights.

1. Introduction
On January 15, 2022, a major eruption took place atthe volcano located between the Hunga Tonga and Hunga
Ha’apai islands in Tonga, which we will here simply callthe “Hunga volcano” [1]. This eruption came after abouta month of ongoing activity, and destroyed the land bridgethat earlier formed between the two islands [2]. The VEI[volcanic explosivity index] of the eruption was rated as5, but the explosive energy released was very large foran eruption of this size [3]. This exceptionally powerful
explosion was explained by the interaction with sea water,as this was an eruption of a largely submarine volcano.
According to diﬀerent criteria, this was the largest volcanic
eruption in terms of explosive power since the 1883 Krakatoa
event (see, among others, [4, 5]).
It is well known that large a volcanic eruption
can produce wave-like disturbances in the atmosphere
propagating up to the height of the ionosphere, above 100 km
altitude [6, 7]. Such traveling ionospheric disturbances(TIDs) can be generated through several diﬀerent physicalmechanisms, including shock waves, acoustic waves, Lambwaves, gravity waves, and infrasound, and several of thesetypes of waves can be simultaneously present. Furthermore, amajor eruption can also cause other geophysical phenomena
such as tropospheric disturbances or, in some cases,
tsunamis, which can in turn also aﬀect the ionosphere [7].
The 2022 Hunga volcanic eruption did indeed produce both
tsunamis [8] as well as atmospheric pressure waves thattraveled around the globe multiple times [9].
This eruption was the most explosive eruption in
the era for which ionospheric observations are available,
and therefore provides a unique opportunity to study theionospheric eﬀects of volcanic eruptions in detail.
The URSI AT-AP-RASC conference, held in Gran
Canaria from 29 May to 3 June, was to our knowledge
the ﬁrst major meeting were ionospheric observations
pertaining to this event could be presented. For thispurpose, a special session was organized, chaired by G.
De Franceschi and C. Cesaroni, and featuring eight invitedcontributions. The list of papers presented in this session is
given below. Far from being exhaustive, here we provide
an overview and summary of the various results discussed
in these presentations.
The contributions to the special session were:•Elvira Astafyeva, “How the 15 January 2022 Hunga
Tonga–Hunga Ha’pai Volcano Eruption Shook the
Ionosphere” (associated publication [10]).
•Tobias Verhulst, “Multi-Instrument Detection in Europeof Ionospheric Disturbances Caused by the 15 January2022 Eruption of the Hunga Volcano” [11].
•David Themens, “Global Propagation of Ionospheric
Traveling Disturbances Associated with the 2022 Tonga
Volcanic Eruption” [12].
•Manuel Hernández-Pajares, “Study of Potential
Ionospheric Signatures Associated to 2022 Tonga

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95

Volcano Eruption and Tsunami from LEO- and GroundBased GNSS Data.”•Angelo de Santis, “A Multi-Parametric and MultiPlatform Approach to Study the Eﬀects in Atmosphereand Ionosphere of the 2022 Hunga Tonga-Hunga
Ha’apai Larger Eruptions” [13].
•Boris Maletckii, “Ionospheric Near Real Time Lookon Hunga Tonga-Hunga Ha’apai Eruptions via GNSS
[14].
•Juha Vierinen, “Ionospheric Perturbations Caused by
the Tonga January 2022 Volcanic Eruption” [15].
•Léo Martire, “Real-Time Ionospheric Monitoring of
the 2022 Tonga Eruption.

2. Results Presented at theConference
As the eruption occurred on a remote and uninhabitedisland, one of the major issues faced in any study relating to it
was the paucity of available observations. Even determining
the exact onset time of the event was therefore diﬃcult. E.
Astafyeva et al. [10] used total-electron-content (TEC) data,obtained from GNSS receivers located on various Paciﬁc
islands surrounding the volcano, to reconstruct some ofthe details of the eruption. They calculated the velocity
with which TIDs traveled outward from the volcano, andfrom this the timing of the eruption was reconstructed. It
was demonstrated that there was ﬁrst a smaller explosion
at 04:03 UT, followed at 04:16 UT by two major events,
succeeded again by two smaller ones. This was in agreementwith the estimation from satellite imagery of the time of
the main event, around 04:15 UT.
In related work presented by B. Maletckii, a method
was demonstrated that allowed performing such analysis innear-real-time [14]. Because of the unique characteristics
of disturbances produced near the site of the volcano, thistechnique could be employed to continuously monitor the
TEC for eruptions. Since TEC observations are available
covering most of the globe, this method could be used
for early warning and ﬁrst characterization of eruptions
occurring at remote locations where no local monitoring
was available. A similar eﬀort was presented by L. Martire,
who demonstrated the use of the GUARDIAN early warning
system for natural hazards, currently under development by
NASA, but soon to be publicly released. This system uses
data from a diﬀerent set of GNSS receivers, but relies onsimilar principles to detect in near-real time the occurrenceof volcanic eruptions, as well as earthquakes and tsunamis,
by monitoring disturbances in TEC.
Another interesting observation from Martire’s
presentation was the detection of a general depletion of
the ionospheric plasma in the immediate vicinity of the96

eruption, in addition to the various waves generated. This
was likely associated with the heating and uplift of theentire atmosphere-thermosphere system in this region, but
the precise process remains to be investigated.
In both the works of D. Themens et al. [12] andZhang et al. [15] (the latter presented by J. Vierinen), theTEC was analyzed on a global scale. These papers clearly
demonstrated that the eruption generated a spectrum of TIDs
with very diﬀerent characteristics. Close to the site of theeruption, large-scale, likely acoustic waves were seen withspeeds over 700 m/s, even up to 1000 m/s. However, these
disturbances dissipated within a few thousand kilometers.
Lamb wave fronts propagated outwards with speeds between
300 m/s and 350 m/s. These were the waves that circled theglobe multiple times, as shown in [15]. Finally, the Lambwavefront was followed by a train of medium-scale TIDs
associated with gravity waves.
Themens’ presentation focused on selected regions
where dense coverage of GNSS receivers provided highresolution data. The propagating waves could be tracked
moving from one region to the other, with consistentpropagation velocities. The work showed by Vierinen
took the approach of constructing keograms, showing the
disturbance as a function of time since and distance fromthe eruption, both for the region around the volcano andfor the entire globe. The results obtained through bothmethods were in good agreement. It was conﬁrmed thatthe ionospheric disturbances encircled the planet at least
three times, similar to what was seen in the troposphere.
One of the issues pointed out in Themens’ contribution
was the anisotropy of the ionospheric disturbances,
particularly evident in the large-scale TIDs close to thevolcano. M. Hernández-Pajares discussed this aspectfurther, using TEC measurements from ground stations
combined with those from LEO satellites, employing theanalysis techniques recently published in [16]. Traditional
keograms display disturbances only as a function of radial
distance from the source, and therefore are not suitable toanalyze anisotropies. In his presentation, Hernández-Pajares
therefore showed “North-East-West-South” keograms,
depicting the propagation of the disturbances separately byquadrant. This provided additional details on the anisotropic
behavior of the TIDs.
A. De Santis presented observations from a wide
range of platforms – seismic and magnetic data from groundobservatories, ionosonde observation from Australia, andmeteorological parameters and TEC from various satellites– and covering a wider area, including some measurementsfrom Europe [13]. This contribution included observations
from the period leading up to the major eruption on
January 15. For some time before this ﬁnal cataclysmic
eruption, there had been an increase in seismic activity
around the volcano as well as intermittent minor eruptions.
By investigating the disturbances in satellite measurements
during this period, potential signs of an approaching major
eruption could be identiﬁed.
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Radio Science Bulletin No 378 (September 2021)

Finally, the work by T. Verhulst et al. [11] focused on
observations of the ionospheric reaction to this event from
Europe, the very “far-ﬁeld” region for this event. Here,far more observations were available than in the region of
the volcano itself: there was a dense coverage of GNSS
receivers providing TEC data and the area was coveredby several ionosonde observatories. This allowed for thedetection and tracking of the TIDs as they proceeded tothe antipode of the eruption, located in Algeria, followed
by a wavefront appearing to emanate from the antipode.
Some of the types of TIDs observed closer to theeruption site dissipated well before reaching Europe.
Nevertheless, the combination of ionograms, plasma-drift
measurements, TEC data, and in-situ electron-density
measurements revealed that several diﬀerent disturbanceswere aﬀecting the ionosphere concurrently, even at
this distance. Various features were observed in MUF
data, electron-density contours, TEC, in-situ electrondensity measurements, and plasma drifts, with diﬀerent
data sets not always showing similar features. This wasinterpreted as diﬀerent types of waves aﬀecting diﬀerent
observations, showing the importance of combining datafrom all available platforms to obtain a full picture of theionosphere’s behavior.

3. Conclusions
It is evident from the above that very signiﬁcantionospheric disturbances were produced by the eruptionof the Hunga volcano. A wide spectrum of traveling
disturbances were seen propagating all around the Earth, aswell as peculiar features detected in the ionosphere above
the volcano, itself.
The contributions to this special session were mostly
focused on showing ionospheric observations from various
platforms, and how to interpret these as eﬀects of theeruption. In many cases, these observations so far raised
more questions than they answered. This is to be expected:
eruptions of the scale of the 2020 Hunga volcano eruption
are rare, and this event was the ﬁrst time the ionospheric
responses could be observed, certainly in such detail andwith decent global coverage.
Some of the open questions were related to associating
the signatures in various data sets to particular types of waves,and to speciﬁc propagation mechanisms. For instance, theanisotropic propagation of the large-scale disturbancesmight be due to the inﬂuence of the geomagnetic ﬁeld or
the zonal winds, or to a combination of diﬀerent factors.
The disagreement in some cases between measurementsfrom diﬀerent platforms – e.g., the ionosonde-derived MUF
compared to the TEC obtained from GNSS receivers – could
be explained by the diﬀerent measurements being sensitive
to, respectively, the secondary waves propagating in thevertical direction or the passing Lamb wavefront moving

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Radio Science Bulletin No 378 (September 2021)

in the horizontal direction. However, these hypotheses will
have to be further investigated before conﬁdent conclusions
can be drawn. Moreover, far-ﬁeld observations revealed
the small diﬀerences in speed and wavelength between the
waves propagating along the longer great-circle segment
and those traveling along the shorter great-circle segment.
The eﬀects on the ionosphere directly above thevolcano also warrant some further attention. Due to theremote location, data from the immediate area were sparse.
Nevertheless, a signiﬁcant depletion was seen of the plasma
above the eruption. The precise process responsible forthis eﬀect will probably have to be identiﬁed by extensivemodeling work of the entire atmosphere-thermosphereionosphere system in response to a sudden, large injectionof energy. Another relevant factor could be the hugeamount of water vapor injected into the atmosphere by thesubmarine eruption aﬀecting the propagation through thelower atmosphere of the GNSS signals used to estimate
TEC. In this case, the tropospheric contribution to thegeometry-free linear combination of the dual-frequency
GNSS signals cannot be considered non-dispersive, as is
usually assumed, causing a non-negligible distortion of theestimation of TEC in the region of the volcano.
One aspect of the ionospheric eﬀects of volcanic
eruptions that is of particular practical interest is thepossibility of using ionospheric monitoring to provide an
early warning when such an event occurs. GNSS receivers
are nowadays providing TEC observations around the globe,
providing coverage also in the region around volcanoes thatare not continuously being monitored by other instruments.
A mature technology allowing identiﬁcation in real-timeof the signs of major eruptions, and other natural hazards,
in TEC data would therefore be very valuable. Morediﬃcult, but potentially of major practical use, is thepossible detection during the period leading up to the major
eruption of peculiar features that could warn of its coming.
The real-time detection and possible forecasting of events
like this one will be an important area of progress for the
new inter-URSI-Commission working group “Risk andDisaster Management.”
It is clear that further study of the collected data will
be needed to really understand all aspects of this unique
event, and serious eﬀorts in modeling and theoretical work
will be required before all observations are explained
and understood. The scientiﬁc community will likely berevisiting this topic for some time to come.

4. References
1. Global Volcanism Program, “Report on Hunga TongaHunga Ha’apai (Tonga),” (A. E. Craﬀord and E. Venzke,eds.), Bulletin of the Global Volcanism Network, 47, 2,
2022, doi:10.5479/si.GVP.BGVN202202-243040.

97

2. T. R. Walter and S. Cesca, and the GFZ-DLR-Geomar Task
Force Team, “Caldera Subsidence During the HungaTonga Explosive Eruption?,” EGU General Assembly
2022, Vienna, Austria, 23-27 May 2022, EGU22-13590,
doi:10.5194/egusphere-egu22-13590, 2022.
3. Global Volcanism Program, “Hunga Tonga-Hunga
Ha’apai (243040)” in Volcanoes of the World, v. 4.11.0(08 Jun 2022), E. Venzke (ed.), Smithsonian Institution,
2013, doi:10.5479/si.GVP.VOTW4-2013.
4. J. P. Terry, J. Goﬀ, N. Winspear, et al., “Tonga Volcanic
Eruption and Tsunami, January 2022: Globally the
Most Signiﬁcant Opportunity to Observe an Explosive
and Tsunamigenic Submarine Eruption Since AD 1883
Krakatau,” Geosci. Lett., 9, 24, June 2022, doi:10.1186/
s40562-022-00232-z.
5. J. Vergoz, P. Hupe, C. Listowski et al., “IMS Observationsof Infrasound and Acoustic-Gravity Waves Produced by
the January 2022 Volcanic Eruption of Hunga, Tonga: A
Global Analysis,” Earth Planet. Sci. Lett., 591, August
2022, doi:10.1016/j.epsl.2022.117639.
6. C. Y. Huang, J. F. Helmboldt, J. Park, T. R. Pedersen,
and R. Willemann, “Ionospheric Detection of Explosive Events,” Rev. of Geophys., 57, 1, January 2019,
doi:10.1029/2017RG000594.
7. E. Astafyeva, “Ionospheric Detection of Natural
Hazards,” Rev. of Geoph., 57, 4, December 2019,
doi:10.1029/2019RG000668.
8. A. R. Gusman and J. Roger, “Hunga Tonga – Hunga
Ha’apai Volcano-Induced Sea Level Oscillations andTsunami Simulations,” GNS Science Web page, accessedat doi:10.21420/DYKJ-RK41, July 2022.
9. Giles Harrison, “Pressure Anomalies from the January2022 Hunga Tonga-Hunga Ha’apai Eruption,” Weather,77, 3, March 2022, doi:10.1002/wea.4170.

98

10. E. Astafyeva, B. Maletckii, T. D. Mikesell et al., “The15 January 2022 Hunga Tonga Eruption History as Inferred From Ionospheric Observations,” Geophys. Res.
Lett., 49, 10, May 2022, doi:10.1029/2022GL098827.
11. T. G. W. Verhulst, D. Altadill, V. Barta et al., “MultiInstrument Detection in Europe of Ionospheric Disturbances Caused by the 15 January 2022 Eruption of the
Hunga Volcano,” J. Space Weath. Space Clim. (underreview), preprint: doi:10.1002/essoar.10510837.3.
12. D. R. Themens, C. Watson, N. Žagar et al., “Global
Propagation of Ionospheric Disturbances AssociatedWith the 2022 Tonga Volcanic Eruption,” Geophys. Res.
Lett., 49, 7, April 2022, doi:10.1029/2022GL098158.
13. S. D’Arcangelo, A. Bonforte, A. De Santis et al., “A
Multi-Parametric and Multi-Layer Study to Investigate
the Largest 2022 Hunga Tonga-Hunga Ha’apai Eruptions,” Remote Sensing, 14, 15, July 2022, doi:10.3390/
rs14153649.
14. B. Maletckii and E. Astafyeva, “Near-Real-Time
Analysis of the Ionospheric Response to the 15 January2022 Hunga Tonga-Hunga Ha’apai Volcanic Eruption,”
J. Geophys. Res. – Space Phys. (submitted), preprint:
doi:10.1002/essoar.10511373.2.
15. S.-R. Zhang, J. Vierinen, E. Aa et al., “2022 Tonga
Volcanic Eruption Induced Global Propagation of
Ionospheric Disturbances via Lamb Waves,” Front.
Astron. Space Sci., 9, March 2022, doi:10.3389/fspas.2022.871275.
16. H. Yang, M. Hernández-Pajares, W. Jarmołowski, etal., ”Systematic Detection of Anomalous Ionospheric
Perturbations Above LEOs From GNSS POD Data
Including Possible Tsunami Signatures,” IEEE Trans.
Geosci. Remote Sens., 60, June 2022, doi:10.1109/
TGRS.2022.3182885.

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Radio Science Bulletin No 378 (September 2021)

URSI Conference Calendar
Please note that this calendar is updated to the latest version published on the URSI website
at http://www.ursi.org/events.php (3 October 2023)

August 2023

November 2023

URSI GASS 2023
XXXVth URSI General Assembly and ScientificSymposium 2023
Sapporo, Hokkaido, Japan, 19 - 26 August 2023
Contact: URSI Secretariat, c/o INTEC, Tech Lane Ghent
Science Park - Campus A, Technologiepark-Zwijnaarde
126, B-9052 Gent, Belgium, E-mail info@ursi.org

2023 IEEE CAMA - Conference on Antenna
Measurements and Applications
Genoa, Italy, 15-17 November 2023
Contact: Christian PICHOT, Université Côte d’Azur, CNRS,
LEAT, SophiaTech Campus, 930 Route des Colles – BP145, 06903 Sophia Antipolis Cedex, France, Tél: +33 4
89 15 44 41, Fax: +33 4 89 15 44 79, christian.pichot@
univ-cotedazur.fr ; https://2023ieeecama.org/

11th URSI Turkish 2023 Scientiﬁc Congress and National
General Assembly Meeting
Istanbul, Turkey, 31 August - 2 September 2023
Contact: Prof. Özlem Ozgun, ursitr23@gmail.com
https://ursitr23.bogazici.edu.tr/

September 2023
XXXVIII Spanish URSI Committee Symposium
Caceres, Spain, 13-15 September 2023
Contact: Secretaría técnica: sevillacongresos@viajeseci.es; Secretaría cientíﬁca: ursi2023@unex.es
https://www.ursicaceres2023.com
Kleinheubacher Tagung 2023
Miltenberg, Germany, 26-28 September 2023
Contact: Prof. Frank Gronwald, Department Elektrotechnik
und Informatik, Universität Siegen, Hölderlinstr. 3, 57076
Siegen, E-Mail: kh2023@uni-siegen.dehttps://kh2023.edas.info/

October 2023
2023 ICEAA-IEEE APWC dual conference
Venice, Italy, 9-13 October 2023
Contact: Prof. Roberto D. Graglia, Chair of Organizing
Committee, Dipartimento di Elettronica e TLC, Politecnico
di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino,
ITALY, roberto.graglia@polito.it ; and Prof. P. L. E.
Uslenghi, Chair of Scientiﬁc Committee, uslenghi@uic.edu
http://www.iceaa.net/
ISAP 2023 - 2023 IEEE International Symposium on
Antennas and Propagation
Kuala Lumpur, Malaysia, 30 October - 2 November 2023
Contact: isap2023@apmttemc.org
https://isap2023.apmttemc.org/

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Radio Science Bulletin No 378 (September 2021)

December 2023
APMC 2023 - Asia-Paciﬁc Microwave Conference 2023
Taipei, China SRS, 5-8 December 2023
Contact: apmc2023secretariat@tl.ntu.edu.tw , https://www.
apmc2023.org

March 2024
EuCAP 2024 - 18th European Conference on Antennas
and Propagation
Glasgow, Scotland, United Kingdom, 17 - 22 March 2024
Contact: George Goussetis, EuCAP 2024 Conference Chair,email-service@euraap.org ; http://www.eucap.org

April 2024
EUSAR 2024 - European Conference on Synthetic Aperture
Radar
Munich, Germany, 23-26 April 2024
Contact: Prof. Madhu Chandra, Technical University
Chemnitz, E-mail madhu.chandra@etit.tu-chemnitz.de
and nicolas.parisel@vde.com
https://www.eusar.de/en

May 2024
AT-RASC 2024 - 4th URSI Atlantic / Asia-Paciﬁc Radio
Science Meeting 2024
Gran Canaria, Spain, 19-24 May 2024
Contact: URSI Secretariat, c/o INTEC, Tech Lane Ghent
Science Park - Campus A, Technologiepark-Zwijnaarde
126, B-9052 Gent, Belgium, E-mail at-rasc@ursi.org
https://www.atrasc.com

99August 2025

August 2029

AP-RASC 2025
Asia-Pacific Radio Science Conference 2025
Sydney, Australia, 17-22 August 2025
Contact: Prof. Paul Smith, Macquarie University, Australia,
E- mail paul.smith@mq.edu.au

URSI GASS 2029
XXXVIIth URSI General Assembly and Scientiﬁc
Symposium 2029
Singapore, 11 - 18 August 2029
Contact: URSI Secretariat, c/o INTEC, Tech Lane Ghent
Science Park - Campus A, Technologiepark-Zwijnaarde
126, B-9052 Gent, Belgium, E-mail: gass@ursi.org

August 2026
URSI GASS 2026
XXXVIth URSI General Assembly and Scientiﬁc
Symposium 2026
Krakow, Poland, 15 - 22 August 2026
Contact: URSI Secretariat, c/o INTEC, Tech Lane Ghent
Science Park - Campus A, Technologiepark-Zwijnaarde
126, B-9052 Gent, Belgium, E-mail: gass@ursi.org URSIGASS 2026 Secretariat, Symposium Cracoviense, PL31511 Kraków, Rakowicka 1/14, Tel. +48 12 4227600; E-mail:ursi2026@symposium.pl, https://www.ursi-gass2026.pl

100

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Radio Science Bulletin No 378 (September 2021)

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101Become An Individual Member ofURSI
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Radio Science Bulletin No 378 (September 2021)