SPEDAS training session- advanced course -T. Hori, M. Teramoto, T.-F. Chang, Y. Tsugawa, M. Shoji, S.
Kurita, Y. Miyoshi, N. Umemura, T. Segawa(ERG Science Center, ISEE, Nagoya Univ.)

1

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Goal of this training} To get familiar with how to load, plot, andmanipulate the particle data of the ERG satellite.
load and plot the particle flux data of LEP-e, LEP-i, MEPe, MEP-i, HEP, and XEP.
} Use the "part_products" libraries to make a plot of:}} Energy-time spectrogram} Phi-/Theta-angle spectrogram} Pitch angle spectrogram} Gyro-phase spectrogram} velocity moments

2

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Keep in mind upon the training} This is a "hands-on" training for SPEDAS, not a time for

e-mail check!

} Communicate with lecturers, tutors, and neighboringskilled users.

} Today's session might not be able to cover all topics in

the handouts due to time limitation. It is
recommended to go through the entire contents laterby yourself.

} We will proceed rather slowly with intermediate-level

users, but you can practice at your own pace.

3

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Keep in mind upon the training (cont'd)} We are going to download fairly large data files

today. For a smooth and successful training,please be sure to follow the rules listed below,
when you are hooked up to the special WiFi
network for the training:

Stop using/synchronizing online storages (Dropbox,
Google drive, OneDrive, iCloud, etc.) to save the band
width of the network.
} Turn off Windows Update temporarily (Windows users).
} Do not access online movie sites (Youtube, Niko-NikoDouga(ニコニコ動画), etc.).
}}
4

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Brief introduction of Arase's particle
data

5

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

About particle data obtained by Arase
LEP-e (PI: S.-Y. Wang, ASIAA)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623017-0748-6
LEP-i (PI: K. Asamura, JAXA/ISAS)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623018-0846-0
MEP-e (PI: S. Kasahara, Univ. of Tokyo)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623018-0847-z
MEP-i (PI: S. Yokota, Osaka Univ.)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623017-0754-8
HEP (PI: T. Mitani, JAXA/ISAS)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623018-0853-1
XEP (PI: N. Higashio, JAXA/RDD)} https://earth-planetsspace.springeropen.com/articles/10.1186/s40623018-0901-x

}

}

}

}

}

}

6

SPEDAS training, ERG particle data WS @Tohoku Univ.

[Miyoshi+2018]

Sep. 20, 2018

Omni-flux data

7

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Loading and plotting omni-flux data (1);; Set the time spantimespan, '2017-04-19';; Load data
erg_load_xep, datatype='omniflux'
erg_load_hep, datatype='omniflux'
erg_load_mepe, datatype='omniflux'
erg_load_lepe, datatype='omniflux'
erg_load_mepi_nml, datatype='omniflux'
erg_load_lepi_nml, datatype='omniflux'
tplot_names

8

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Loading and plotting omni-flux data (2)tplot,[ 'erg_xep_l2_FEDO_SSD', $'erg_hep_l2_FEDO_H','erg_hep_l2_FEDO_L', $'erg_mepe_l2_omniflux_FEDO', $'erg_lepe_l2_omniflux_FEDO', $'erg_mepi_l2_omniflux_FPDO', $'erg_lepi_l2_omniflux_FPDO'

9]SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Loading and plotting omni-flux data (3)vn = 'erg_xep_l2_FEDO_SSD'
options, vn, spec=0 & ylim, vn, 0, 0, 1
tplot ;; Replot the previously plotted variables
erg_load_hep, datatype='omniflux', /lineplot
tplot, ['erg_xep_l2_FEDO_SSD', 'erg_hep_l2*line']

10

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

3-D flux data

11

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Loading and plotting 3-D flux data (1);; ERG working group ID/password
uname = '???????????'&

pass = '????????'

timespan, '2017-04-19';; erg_load_hep, datatype='3dflux', uname=uname, pass=pass
erg_load_mepe, datatype='3dflux';; erg_load_lepe, datatype='3dflux', uname=uname, pass=pass;; erg_load_mepi_nml, datatype='3dflux';; erg_load_lepi_nml, datatype='3dflux', uname=uname, pass=passSetting the keyword,
datatype='3dflux', makes
erg_load_??? load 3-D flux data,
except for XEP whose full
resolution data are not available
as of this training.
12!! CAUTION !!
}Loading any two 3-D flux data of MEP-e,
MEP-i, LEP-e, and LEP-i at once might use up
the memory of your PC and make the PC
unstable. If your PC has 8 GB memory or
less, it is recommended to remove data preexisting on memory by typing del_data, '*'before loading a new one.

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Loading and plotting 3-D flux data (2);; In case you have loaded MEP-e and LEP-e:
tplot, [ 'erg_mepe_l2_3dflux_FEDU','erg_lepe_l2_3dflux_FEDU' ];; In case you have loaded MEP-e and HEP:;; Please plot FEDO_? instead of FEDU_? for HEP, otherwise tplot stops;; with an error.
tplot, [ 'erg_hep_l2_3dflux_FEDO*', 'erg_mepe_l2_3dflux_FEDU' ]13

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

(Memo)14

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Play with part_products

15

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

What's "part_products"?
} A set of generic routines bundled to SPEDAS to

make tplot variables for various types of spectrum
plot.

3-D flux
data
Typically a 4-D array(time x energy x azim x elev)

16

part_products
library

2-D
spectrumdata• time x energy• time x pitch angle• …
• …

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

What's "part_products"?
} An experimental version of this library is available

from ERG-SC for LEP-e, LEP-i, MEP-e, MEP-i, and HEP so
far.
}
}
}
}

erg_mep_part_products: MEP-e and MEP-i
erg_hep_part_products: HEP
erg_lepe_part_products: LEP-eerg_lepi_part_products: LEP-i

3-D flux
data
Typically a 4-D array(time x energy x azim x elev)

17

part_products
library

2-D
spectrumdata• time x energy• time x phi/theta angle• time x pitch angle• …

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Energy-time spectrogram
del_data, '*'timespan, '2017-04-11/07:00',10, /hour
erg_load_hep, datatype='3dflux', uname=uname, pass=pass
erg_load_mepe, datatype='3dflux'
erg_hep_part_products, 'erg_hep_l2_FEDU_L'
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU'
tplot, ['erg_hep_l2_FEDU_L_energy','erg_mepe_l2_3dflux_FEDU_energy']

Running part_produtcts without any
option gives omni-directional fluxes
by averaging over all directions.
The default unit of energy anddifferential flux becomes eV and#/s/cm2/str/eV when flux data areprocessed with part_products.
18

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Energy-time spectrogram in different units
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', units='flux' ;; Default
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', units='eflux', suffix='_eflux'
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', units='df', suffix='_psd';; With keyword "relativistic" on, phase space density values are calculated;; in (c/MeV/cm)3 considering the relativistic effect. But this conversion;; is still experimental and needs to be further tested.
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', units='df', suffix='_psdgem', /relatplot, ['erg_mepe_l2_3dflux_FEDU_energy*']

Differential number flux
Differential energy flux
Phase space density

19

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Energy-time spectrogram for limited directional
channels
get_timespan, tr
erg_hep_part_products, 'erg_hep_l2_FEDU_L', theta=[-30.,30], trange=tr
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', phi=[0.,90], trange=tr
tplot, ['erg_hep_l2_FEDU_L_energy','erg_mepe_l2_3dflux_FEDU_energy']

Phi and theta should be given
as a range of angle in the DSI
coordinates.
Keywords theta and phi canbe set together to specify alimited area of solid angle in
DSI.
20

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Phi-/Theta-angle spectrogram
del_data, '*'timespan, '2017-04-11/17:00', 6, /hour
erg_load_lepe, datatype='3dflux', /no_sort_enebin, uname=uname, pass=pass, varformat='FEDU'
erg_load_lepi_nml, datatype='3dflux', uname=uname, pass=pass, varformat='FPDU'
get_timespan, tr
erg_lepe_part_products, 'erg_lepe_l2_3dflux_FEDU', outputs='phi', trange=tr, energy=[3000.,10000.]
erg_lepi_part_products, 'erg_lepi_l2_3dflux_FPDU', outputs='theta', trange=tr, energy=[8000.,20000.]
zlim, 'erg_lepi_l2_3dflux_FPDU_theta', 1e-1, 1e+3, 1
tplot, ['erg_lepe_l2_3dflux_FEDU*', 'erg_lepi_l2_3dflux_FPDU*']

Keyword 'energy' specifies anenergy range in eV for which
particle flux data are averaged
to deduce a phi-/thetaspectrogram.

21

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Pitch-angle spectrogram (1)del_data, '*'timespan, '2017-04-15/00:00', 2, /hour & get_timespan, tr
erg_load_mepi_nml, datatype='3dflux', varformat='FPDU'
erg_load_mgf
erg_load_orb
erg_mep_part_products, 'erg_mepi_l2_3dflux_FPDU', output='pa', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', energy=[108000., 112000.], suffix='_110kev', trange=tr
erg_mep_part_products, 'erg_mepi_l2_3dflux_FPDU', output='pa', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', energy=[54000., 58000.], suffix='_56kev', trange=tr
tplot, 'erg_mepi_l2_3dflux_FPDU*'

The flux modulations discussed by Yamamoto+, GRL, 201822

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Pitch-angle spectrogram (2)del_data, '*'timespan, '2017-04-08/19:00', 30, /min & get_timespan, tr
erg_load_mepe, datatype='3dflux', varformat='FEDU
erg_load_mgf
erg_load_orb
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', output='pa', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', energy=[16000., 18000.], suffix='_17kev', trange=tr
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', output='pa', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', energy=[23000., 25000.], suffix='_24kev', trange=tr
zlim, 'erg_mepe_l2_3dflux_FEDU_pa*', 1e+2, 1e+4, 1
tplot, 'erg_mepe_l2_3dflux_'+['FEDO', 'FEDU_pa*']

The pitch-angle spectra discussed by Kurita+, GRL, 201823

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Energy-time spectrogram for a limited pitchangle range
del_data, '*'timespan, '2017-03-27/10:30', 50, /min
erg_load_mepe, datatype='3dflux'
erg_load_mgf & erg_load_orb
get_timespan, tr
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', output='energy', trange=tr, pitch=[0.,3.], suffix='_pa00-03',/no_regrid
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', output='energy', trange=tr, pitch=[10.,15.], suffix='_pa10-15',/no_regrid
tplot, 'erg_mepe_l2_3dflux_'+['FEDU_energy*']

The electron flux variations discussed by Kasahara+, Nature, 201824

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Gyro-phase spectrogram
del_data, '*'timespan, '2017-04-15/00:00', 2, /hour
erg_load_mepi_nml, datatype='3dflux', varformat='FPDU'
erg_load_mgf
erg_load_orb
get_timespan, tr
erg_mep_part_products, 'erg_mepi_l2_3dflux_FPDU', output='gyro',
pos='erg_orb_l2_pos_gse', mag='erg_mgf_l2_mag_8sec_dsi', energy=[108000., 112000.],
pitch=[85.,95.], trange=tr, suffix='_110kev'
tplot, 'erg_mepi_l2_3dflux_'+['FPDO', 'FPDU_gyro*']The flux modulations discussed by Yamamoto+, GRL, 201825

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Velocity moments of 3-D distributionfunctions

26

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Moment calculation by part_products} A part_products calls moments_3d(), an internalroutine, to calculate various velocity momentsfrom a 3-D distribution function.

} So far part_products for only MEP-e and MEP-i

support the moment calculation.

27

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Moment calculation (1)del_data, '*'timespan, '2017-03-27/10:00', 1, /hour& get_timespan, tr

erg_load_mepe, datatype='3dflux', varformat='FEDU'
erg_load_mepi_nml, datatype='3dflux', varformat='FPDU'
erg_load_mgf & erg_load_orb

erg_mep_part_products, 'erg_mepi_l2_3dflux_FPDU', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', output='moments', trange=tr
erg_mep_part_products, 'erg_mepe_l2_3dflux_FEDU', pos='erg_orb_l2_pos_gse',
mag='erg_mgf_l2_mag_8sec_dsi', output='moments', trange=tr
Primary parameters calculated with the part_products:}

density: number density}

avgtemp: scalar temperature (!)}

velocity: bulk velocity}

vthermal: thermal velocity}

mtens: momentum flux density tensor}

ptens: pressure tensor}

t3: temperature tensor (!)}

magt3: perpendicular/parallel temperature (!)}

flux: number flux}

eflux: energy flux

All vector and tensor quantities in DSI coordinates.
(!) Note that these are NOT a temperature defined
as a width of Maxwellian distribution.

28

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Moment calculation (2)ylim, '*FPDU*ptens', 0, 1e+5, 0;; set y-ranges with linear scaleylim, '*FEDU*ptens', 0, 1e+4, 0
tplot, ['erg_mep?_l2_3dflux_'+['F?DO','F?DU_ptens']]

Particle flux and pressure variations discussed by Hori+, GRL, 201829

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

(Memo)30

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Appendix:
Internal routines of part_products
An alternative way to deduce pitch-angle spectra rather
manually

31

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

3-D data structure common to particle data that
SPEDAS can handle
ERG> help, dist[100]PROJECT_NAMESPACECRAFTSTRINGLONG'ERG'

DATA_NAMESTRING'LEP-i Proton 3dflux'

UNITS_NAMESTRING'flux'

1

UNITS_PROCEDURE STRING'erg_convert_flux_units'

SPECIESVALID

STRING
BYTE'proton'1

CHARGE

FLOAT

1.00000

MASS

FLOAT

0.0104535

TIME

DOUBLE

1.4959304e+09

END_TIMEDATADOUBLE
FLOAT

1.4959304e+09
Array[30, 16, 8]

BINS

FLOAT

Array[30, 16, 8]

ENERGYFLOATArray[30, 16, 8]

DENERGYFLOAT

Array[30, 16, 8]

NENERGY
NBINS

LONG
LONG

PHI

FLOAT

Array[30, 16, 8]

DPHI

FLOAT

Array[30, 16, 8]

THETA

FLOAT

Array[30, 16, 8]

DTHETAERG>

FLOATArray[30, 16, 8]

32

30
128

An example for LEP-i 3-D flux data:
dist is an array of structures eachof which contains a set of data foreach spin.

"DATA" holds the flux data as a 3-D array of 30 ene.
ch x 16 spin sector x 8 sensors.
ENERGY and DENERGY are the central energies
and energy ranges of the energy channels.
PHI, DPHI, THETA, and DTHETA have phi/theta
angles of particle-going directions and angularwidths measured by directional channels of aparticle instrument in the DSI coordinate system.

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

erg_???_get_dist():Put 3-D flux data in a 3-D data structure
timespan, '2017-05-28'
erg_load_lepi_nml, datatype='3dflux', varformat='FPDU'
dist = erg_lepi_get_dist( 'erg_lepi_l2_3dflux_FPDU', /structure )help, disthelp, dist[0]Each get_dist() should be used for the 3-D flux data of each
instrument, by providing a tplot variable of 3-D flux data as theargument.

33}

For LEP-i: erg_lepi_get_dist()}

For LEP-e: erg_lepe_get_dist()}

For MEP-e: erg_mepe_gest_dist()}

For MEP-i: erg_mepi_get_dist()}

For HEP: erg_hep_get_dist()

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

erg_pgs_make_fac, spd_pgs_do_fac:
Transformation to the field-aligned coordinates (FAC);; Prepare the MGF and orbit data
erg_load_mgf & set_erg_var_label;; Make transformation matrices for the FAC
erg_pgs_make_fac, dist.time, 'erg_mgf_l2_mag_8sec_dsi', 'erg_orb_l2_pos_gse', $fac_output=fac_mat, fac_type='mphism'
dist_fac = dist ;; Make a copy of the dist structure;; Transform phi/theta values in the dist structure to those in FAC for each time frame
for i = 0L, n_elements(dist.time)-1 do begin
spd_pgs_do_fac, dist[i], reform( fac_mat[i, *, *], [3,3] ), $output=dist_tmp,
dist_fac[i] = dist_tmp

error=error

endfor

Note that:
}

}

}

34

Both the magnetic field data in DSI and the orbit data in
GSE should be given to erg_pgs_make_fac. They areautomatically interpolated in time to match time frames ofparticle data given as a 1-D array in SPEDAS time unit
(dist.time in the above case).
The transformation matrix is made for the particle timeframes, as a 3-D array of time x 3 x 3 (fac_mat in the abovecase).
spd_pgs_do_fac changes only phi and theta arrays in aparticle data structure.

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Binning and averaging flux data in FAC to deduce
pitch-angle spectra
dist_fac.theta = 90. - dist_fac.theta;; colat. in FAC = pitch angle;; Prepare data arrays for a selected energy channel
enech = 2 ;; ch02 --> 19.2 keV
dat_arr = reform( dist_fac.data[ enech, *, * ] )pa_arr = reform( dist_fac.theta[ enech, *, * ] )ntimes = n_elements(dist_fac.time)dim = dimen( dist_fac[0].data )t_arr =

rebin( reform( dist_fac.time, [1,1,ntimes] ), [dim[1:*],ntimes] );; Use a generic routine "bin2d" to calculate average fluxes for the time x pitch-angle bins
id = where( finite(dat_arr) and finite(pa_arr) ) ;;To exclude NaN and Inf from the averaging with bin2d
bin2d, t_arr[id], pa_arr[id], dat_arr[id], xrange=minmax(t_arr), yrange=[0.,180.], binum=[ntimes,18], $xc=time_c, yc=pa_c, ave=aveflux, binhist=binnm, /double}

Bin2d calculates an average flux for each time x pitch-angle bin. No smoothing
or interpolation is applied, unlike how data are averaged by part_products.

}

The original version of bin2d.pro does not accept /double keyword. Please
download and use bin2d.pro and bin1d.pro of the SPEDAS-j tools, which
are available from the SPEDAS-j website at:https://github.com/spedas-j/member_contrib/tree/master/misc/bin12d

35

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Binning and averaging flux data in FAC to deduce
pitch-angle spectra (cont'd);; Put the resultant arrays in a tplot varirable
vname = 'erg_lepi_paspec_ene02'
store_data, vname, data={ x:time_c, y:aveflux, v:pa_c };; Set some plot properties
options, vname, spec=1, constant=[45,90,135], ytickinterval=45., yminor=3
ylim, vname, 0, 180, 0zlim, vname, 0, 0, 1 ;; auto-scale in log;; Plot!
tplot, vname

One of the pitch-angle spectra shown in Asamura+, EPS, 2018 is reproduced!

36

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Definition of the FA coordinate system"s" usedby part_products}}

In the field-aligned (FA) coordinate systems, the Z-axis is always in thelocal magnetic field direction. An X-axis or Y-axis should be defined
separately to form a right-handed system. The following options for the
Y-axis are available in the part_products library, which is usually given by
keyword "fac_type" to the erg_pgs_make_fac routine.
'xgse'}}}'(m)phigeo'}}}

Y-axis: the vector product of Z-axis and the Xgse direction (ey = ez x ex_gse)
X-axis: ey x ez

Y-axis: the vector product of Z-axis and the phi direction (roughly eastward) in
the geographical (GEO) coordinate system at a satellite location. mphigeo uses
the negative phi direction (roughly westward) instead.
X-axis: ey x ez (roughly radially outward for phigeo and radially inward for
mphigeo)'(m)phism'
}

}

37

Y-axis: the vector product of Z-axis and the phi direction (roughly eastward) inthe solar-magnetic (SM) coordinate system at a satellite location. mphism uses
the negative phi direction (roughly westward) instead.
X-axis: ey x ez (roughly radially outward for phism and radially inward for
mphism)SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018

Other sources about part_products}(SPEDAS top)/idl/general/science/spd_part_products/}

The directory in which common routines of thepart_products library are placed.

} Brief description about the 3-D data structureformat on SPEDAS wiki}

http://spedas.org/wiki/index.php?title=3D_data_structures} SPEDAS-j wiki?
}

https://github.com/spedas-j/member_contrib/wiki/top

Nothing now, but some may appear in future…

} What else?

38

SPEDAS training, ERG particle data WS @Tohoku Univ.

Sep. 20, 2018