dataesr/scientific-paragraphs-categorization huggingface dataset:
All data of the TU1208 database are being shared with the scientific community via the Action website and/or dedicated open access publications (such as this paper), along with detailed descriptions of the investigated structures and full information about the employed equipment. To the best of our knowledge, TU1208 Open Database of Radargrams is the first open collection of GPR responses: Similar initiatives were never undertaken in the past, in the GPR field. The paper is structured as follows. In Section 2, COST Action TU1208 is shortly introduced; the objectives of the Open Database of Radargrams are outlined and the context in which the initiative was born is portrayed. In Section 3, the geophysical test site is described in detail: schematic maps and sections of the site, as well as photos shot during the site construction, are presented; moreover, information about the filling materials and buried targets is given.
We would like to underline that, although the site was built in 1996, such a comprehensive explanation of its realization method, geometry, and nature of the involved materials, was not published before on the international scientific literature. In Section 4, full information about the GPR equipment used to perform the measurements and relevant data-collection settings is provided. In Section 5, all profiles are plotted in grey-scale maps and several comments are given, which we deem useful for facilitating the comprehension and usage of the data. Conclusions are drawn in Section 6, where plans for future work are traced and ideas for possible investigations based on the presented dataset are suggested.
COST Action TU1208 and the Open Database of Radargrams Initiative COST Action TU1208 was running from April 2013 to October 2017  [4, 5]. It involved more than Denmark, Estonia, Finland, France, former Yugoslav Republic of Macedonia, Germany, Greece, Ireland, Italy, Latvia, Malta, the Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, and the United Kingdom), a COST cooperating state (Israel), 6 COST near neighboring countries (Albania, Armenia, Egypt, Jordan, Russia, Ukraine) and 6 COST international partner countries (Australia, Colombia, Hong Kong, The Philippines, Rwanda, the United States). University researchers, software developers, civil and electronic engineers, archaeologists, geophysics experts, non-destructive testing equipment designers and manufacturers, end-users from private companies and public agencies participated in the Action.
The primary objective of COST Action TU108 was to exchange and increase scientific-technical knowledge and experience of GPR technique, whilst promoting a wider and more effective use of this safe and non-destructive method. The scientific structure of the Action included four Working Groups (WGs), which research activities covered all areas of GPR technology, methodology, and applications. The Open Database of Radargrams is a joint outcome of WG 2 and WG 3. WG 1 focused on the development of novel GPR instrumentation. Within this WG, novel equipment was designed, implemented and tested  [6] [7] [8] [9] [10] [11]. Moreover, new tests were proposed for checking the performance and stability of GPR systems. WG 2 focused on the use of GPR in civil engineering. This WG developed guidelines for GPR inspection of flexible pavements, utility detection in urban areas, and evaluation of concrete structures (the afore mentioned system performance compliance tests are included in these guidelines, which are currently being refined before being published).
As a follow-up of COST Action TU1208, a non-profit association was founded in September 2017 (TU1208 GPR Association), to keep the scientific network alive and further support cooperation between Universities, research centers, private companies and public agencies active in the GPR field. The Open Database of Radargrams project consists in gathering and organizing a collection of interesting GPR profiles, which are made accessible to scientists willing to test and validate, against reliable data, their electromagnetic modeling, inversion, imaging and signal-processing methods. Descriptions of the inspected structures and employed equipment are provided along with the profiles. It is often difficult, for scientists involved in theoretical and numerical studies, to get access to high-quality usable experimental data, and this is one of the reasons why we believe that the Open Database of Radargrams is a promising initiative.
By using analytical and numerical techniques for the solution of forward scattering problems, scientists can try and reproduce the data included in the database, by modeling the scenario at hand. Inversion and imaging methods can be applied to the various radargrams, to try and reconstruct the geometrical and physical properties of the inspected subsurface or structures. The effectiveness of signal processing algorithms and procedures can be verified and tested. An impressive result, which we hope can be achieved by the GPR scientific community in the forthcoming years, will be that of describing the state of the art of the research in the field by applying different techniques to the radargrams included in the database. Let us point out that this open-science initiative does not aim at defining the "best" methods but more properly at indicating the range of reliability and efficiency of each approach in terms of accuracy, potential, advantages, and drawbacks.
The idea to start the database came out during a TU1208 Short-Term Scientific Mission  [53]. It was lately discussed during a WG 3 meeting of the Action  [54]  and presented at the 2015 edition of the European Geosciences Union General Assembly  [55]. It takes inspiration by successful past ventures carried out in different areas. For example, the Ipswich database  [56] [57] [58]  in the field of free-space electromagnetic scattering is a collection of experimental X-band data measured on metallic and dielectric scatterers, in anechoic chamber, at the USAF (United States Air Force) Ipswich Measurement Facility. The subsequent Fresnel database  [59] [60] [61] [62]  aims at extending the scope of applications and includes data measured over a wider frequency range, on a series of homogeneous scatterers, in the anechoic chamber of the Centre Commun de Ressourses Microondes at Marseille, France.
The Marmousi database  [63]  in seismic science is a two-dimensional synthetic dataset generated at the Institute Francais du Pétrole at Rueil-Malmaison Cedex, France. The geometry of the model is inspired by a real profile through the North Quenguela in the Cuanza basin, in Angola; the model was used to create complex data, which require advanced processing techniques to obtain a correct image of the earth. The Marmousi database was used for a workshop on practical aspects of seismic data inversion, during the 52nd EAEG (European Association of Exploration Geophysicists) meeting held in Copenhagen in 1990, and was subsequently studied by hundreds of researchers through the years. The Musumeci database  [64] , of interest for scientists who analyze seismic events, is a dataset of 151 events leading the 17 July-9 August 2001 lateral eruption at Mt.
Etna volcano, in Italy. As said in the Introduction, the dataset presented in this paper is the core of the Open Database of Radargrams, both because it is the most complete (it includes a high number of profiles, measured with pulsed radar systems produced by three different manufactures and equipped with several antennas working in various frequency ranges) and it was obtained in a controlled environment (complete information concerning the geometry of the test site is available). Another interesting dataset included in the database comes from GPR measurements performed over the historical masonry bridge of Traba, in Spain  [65]. Data were recorded by using two commercial pulsed GPR systems, both equipped with 250 MHz and 500 MHz antennas; two-dimensional gprMax models of the bridge and relevant simulation results are available, as well.
The database comprises also GPR responses recorded on a masonry column of the Hospital de Sant Pau i la Santa Creu in Barcelona, Spain  [40]. The column has a complex internal structure and data were recorded with a commercial pulsed system equipped with an antenna having a central frequency of 1600 MHz. In  [53] , a series of synthetic profiles was produced by implementing and executing two dimensional gprMax models of concrete cells hosting various metallic reinforcing elements, as well as dielectric and metallic pipes. This dataset was subsequently enriched with several new synthetic profiles, obtained by adopting a more accurate representation of concrete (which takes into account its frequency-dispersion properties); the distance between the targets embedded in concrete and their size were also varied.
Equipment and Data Acquisition Three different GPR systems were used to collect data over the test site, manufactured by GSSI (Geophysical Survey Systems, Inc; Nashua, NH, USA), MALÅ Geoscience (Malå, Sweden), and IDS (Ingegneria dei Sistemi; Pisa, Italy). They were equipped with various antennas, with central frequencies from 200 MHz to 900 MHz. From now on, we will denominate 'GPR 1' the GSSI system, 'GPR 2' the MALÅ system, and 'GPR 3' the IDS system. GPR 1 was used in bistatic configuration. For measurements performed with 'old' 200-MHz, 400-MHz, 500-MHz, and 900-MHz shielded antennas, the trace spacing was different from one series to another; in some cases, information about the profile length is not available and has to be deducted by analyzing the data. These measurements were performed with antennas manufactured before the Federal Communications Commission (FCC) restrictions on ultra-wideband GPR were emitted.
, h1); -Optional2: "rev" when the profile is done in reverse; -ext: file extension, manufacturer dependent. For data recorded with GPR 1 and the last-generation 200-MHz, 400-MHz, and 900-MHz antennas, a b is added before the extension, to distinguish them from data collected with older antennas working at the same frequencies. In the silt region (Table  3 ), 15 profiles were recorded by using all GPR systems, with antennas operating at seven different central frequencies. Note that measurements performed over lines 1 and 2 by using GPR 1 with the 200-MHz antenna are (exceptionally) saved in the same file (200MHz_Silt_h2h1. dzt) instead of being saved in two separate files. In particular, data were collected on the second half of line 2, over the pipes; then, the acquisition continued on the first half of line 1, over the polystyrene blocks; a discontinuity in the data allows understanding where line 2 data end and line 1 data start.
In the multilayer (Table  4 ), 4 profiles were recorded, by using GPR 1 and GPR 2 and four different central frequencies. In the limestone region (Table  5 ), 15 profiles were recorded, by using all GPR systems and nine different central frequencies. In the Gneiss 14/20 gravel region (Table  6 ), 15 profiles were recorded, by using all GPR systems and eight different central frequencies. Finally, in the Gneiss 0/20 gravel region (Table  7 ), 18 profiles were recorded by using all GPR systems and eight different central frequencies. Information about the acquisition settings is given in Tables  3 4 5 6 7 , for all regions and data; moreover, information on the settings can of course be found in the data file headers. The profiles from 1999-2000 acquired with GPR 1 system were stored after being pre-processed, by using the settings listed in the tables (the used frequency-band filters were Infinite Impulse Response (IIR)); all other data are available in raw format, without application of any filter nor temporal gain.
Results-Maps of All Radargrams Included in the Dataset In this Section, we present grey-scale maps of the radargrams included in the dataset. All files generated by GPR 1 and GPR 2 were opened and displayed by using the free Matlab-based data processing software MatGPR  [70]. However, this software does not open the files generated by GPR 3, which were therefore opened with commercial processing software, exported in ascii format, and then plotted with Matlab. The ascii data files are enclosed to the paper as supplementary material, so that interested researchers can easily open them without needing any commercial tool. We have chosen to map the radargram data, directly, without applying any editing nor processing procedure. Actually, the purpose of this paper is to present the dataset and provide appropriate information about all profiles included in it, so that the scientific community can use them; our main aim is not to carry out an advanced analysis, comparison and interpretation of the data.
Some profiles recorded with GPR 1 were pre-processed by the system before being saved, as pointed out in Section 4; by comparing pre-processed data with raw data acquired at the same frequency and over the same acquisition line, the effects of frequency-band filters application and gain recovery can be clearly appreciated. In particular, due to the geometrical spreading and attenuation of the electromagnetic waves emitted by the radar and transmitted in the subsurface, later trace arrivals in raw data show noticeably lower amplitudes than earlier arrivals; the application of a time-variant gain function allows recovering relative amplitude information (within some inevitable limits of accuracy) and allows seeing on the same grey-scale map the signatures generated by shallower and deeper objects.
By looking at the positions of the target signatures, it can be noticed that the data in Figures  16 17 18 19 20 were collected on a direction opposite to that indicated by the arrows in Figure  2  (as pointed out in Table  3 ). In Figure  16 , the effects of the pipes buried beyond line 2 are visible in the second half of the maps, although the spatial extension of the targets does not reach line 1; such effect is obviously less notable at higher frequencies-a similar phenomenon can be noticed in several figures presented in the following Sections. In Figure  20 , the initial traces of the radargram shown in (a) have to be neglected when analyzing the data. The same is true for various radargrams of the dataset-in general, a proper number of initial traces need (almost) always to be neglected, as they correspond to data recorded above the lateral embankments.
By analyzing the data and implementing preliminary FDTD models of this region, it is reasonable to assume that the relative permittivity of limestone is around ε r,sand = 6, the attenuation of the electromagnetic signal is between 6 and 20 dB/m, the penetration is at least 4. 5 m at the lowest frequency (the bottom of the pit is visible) and 2 m at the highest. Further studies are necessary for a more accurate estimation of these values. 6  and in the figure captions. Some radargrams were collected on a direction opposite to that indicated by the arrows in Figure  2 , as pointed out in Table  6. For this section of the test site, the richness of the dataset allows performing various comparisons between radargrams recorded with different equipment working at the same central frequency. This region was especially designed to propose an artificial lossless medium for GPR-operator beginners in training.
Actually, some objects buried in the most conductive materials have not yet been detected from the surface. In this paper, the geophysical test site and its construction process were described for the first time in detail. Then, a large dataset of GPR profiles, recorded by using three pulsed systems equipped with several antennas working in different frequency ranges, was presented. The profiles highlight the performances and limits of the GPR technique in terms of resolution and depth penetration versus soil and/or target. All results were accompanied by comments throughout the paper, and the effects of the application of pre-processing steps before saving the data were pointed out. All data files are found in an archive, enclosed to this paper as 'supplementary material'. The presented dataset is the core part of the Open Database of Radargrams project of COST (European Cooperation in Science and Technology) Action TU1208 "Civil engineering applications of Ground Penetrating Radar".
The idea beyond such initiative is to share with the scientific community a selection of GPR responses to enable an effective benchmark for direct and inverse electromagnetic scattering methods, imaging techniques and signal processing algorithms. We hope that this dataset will be enriched by the contributions of further users, who are most welcome to visit the geophysical test site and collect new data with their GPR systems. We also hope that the dataset will be made alive by researchers who will process, analyze, invert and interpret the data, or implement electromagnetic models of the test site. It would be very interesting and useful to describe the state of the art of the research in the field by applying different techniques to the radargrams included in this dataset. At last, we hope that the challenge related to the multi-layer section will be achieved.
Acknowledgments: The authors are grateful to  Mercedes Solla  for inviting them to submit this paper to the  Remote Sensing Special Issue "Recent Advances in GPR Imaging ". They thank particularly  J. L. Chazelas  ( Ifsttar ) for the design and monitoring of the implementation of the geophysical test site,  G. Gugole  (Ifsttar) and  F. Xie  ( The Hong Kong Polytechnic University ) for performing some GPR measurements, and  T. Dubreucq  ( Ifsttar ) for the characterization of the materials. This work is a contribution to  COST (European Cooperation in Science and Technology) Action TU1208 "Civil engineering applications of Ground Penetrating Radar " (www. cost. eu,www. GPRadar. eu). The authors thank  COST  for funding and supporting  COST Action   TU1208.
5. 4. 4 show the radargrams gathered over the Gneiss 14/20 gravel region, as outlined in Table6and in the figure captions. Some radargrams were collected on a direction opposite to that indicated by the arrows in Figure2, as pointed out in Table6. For this section of the test site, the richness of the dataset allows performing various comparisons between radargrams recorded with different equipment working at the same central frequency.
In addition, we have utilized parts of the methodologies developed by EDF  [6, 7]  for propagation of uncertainties in chaotic simulation and in analyzing random uncertainties. The Dakota code  [5]   3  produced by the Sandia National Laboratory in the U. S. provides a comprehensive approach to uncertainty quantification in CFD and other simulations. The Dakota code provides capabilities in uncertainty propagation including Monte Carlo sampling, reliability methods, and polynomial chaos expansion methods. In addition to uncertainty propagation, it includes tools to perform sensitivity analysis, optimization and calibration. More recently, the UQLab framework developed by B. Sudret and S. Marelli at ETH Zurich provides a comprehensive overview of uncertainty quantification methods. 4 The MATLAB-based tool posted on the website provides tools for generating random input generation methods such as Monte Carlo, Latin hypercube samples, and polynomial chaos expansion method, tools to connect input descriptions to the simulation model, sensitivity analysis tools, and implementation of Gaussian process regression.
(  2 ) and  (7)  as M c À M $ Nðl T R s þ l val ; r 2 Mc ¼ r 2 M þ r 2 T R s þ r 2 Ms Þ À Nðl M ; r 2 M Þ (8) or 8 The validation domain is only valid if the validation experiment and the measurement are conducted in the same facility and under the same physical conditions. 9 M c À M $ Nðl T R s 2l M 1l val ; r 2 McÀM 5 r 2 Ms 1r 2 T R s 1r 2 val Þ (9) We propose the following test for the null hypothesis H 0 : jl T R s 2l M 1l val j ! zða=2Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi r 2 Ms þ r 2 T R s þ r 2 val q (10) with an acceptance significance level a. The significance level is a preference of the user, and common values are a ¼ 1% or a ¼ 5%. Here, we make use of the standard normal cumulative probability distribution function zðaÞ, which is readily available in tabulated forms as well as implemented in software packages such as MATLAB and R.
It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.
Results We developed DBGWAS, available at https://gitlab. com/leoisl/dbgwas, and validated it on panels for several bacterial species for which genome sequences and antibiotic resistance phenotypes were available. DBGWAS comprises three main steps: it first builds a variant matrix, where each variant is a pattern of presence/absence of unitigs in each genome. Each variant is then tested for association with the phenotype using a linear mixed model, adjusting for the population structure. Finally, it uses the cDBG neighbourhood of significantly associated unitigs as a proxy for their genomic environment. DBGWAS outputs a set of such subgraphs ordered by min q , which is the smallest q-value observed over unitigs in each subgraph. The top subgraphs therefore represent the genomic environment of the unitigs most significantly associated with the tested phenotype.
The type of event represented by the subgraph is colour-coded as: yellow for MGE, light blue for local polymorphism in gene (LPG), and dark blue for local polymorphism in noncoding region (LPN). Known resistance markers are indicated in dark green (Pos), determinants whose presence was described to be caused by co-resistance in orange (CR), unknown variants arriving at the first rank in grey (Ukn). For other subgraphs, an r 2 value relative to the first subgraph is provided as an estimation of linkage disequilibrium with the first subgraph. It was computed between the most significant patterns of the first and the considered subgraphs. https://doi. org/10. 1371/journal. pgen. 1007758. t001 The subgraphs themselves are available at http://pbil. univ-lyon1. fr/datasets/DBGWAS_ support/experiments/#DBGWAS_all_results.
Most unitigs in this gene had a low association with resistance, except for the ones describing this particular SNP. Mapping the sequence of these unitigs on the UniProt database  [24]  revealed an amino-acid change at L83S, right in the enzyme binding site. This SNP was previously shown to be responsible for substrate specificity alteration in a strain of Pseudomonas fluorescens  [25]. It appears to increase the amikacin acetylation ability of aac(6'), making its association to amikacin resistance more significant than the gene presence itself. Finally, for M. tuberculosis ethionamide resistance, the top subgraph (min q = 7. 86 × 10 -11 , Fig  3C ) represented a polymorphic region in a core gene promoter. The subgraph was mostly grey and linear with a localised blue and red fork. The most reliable annotation for this subgraph was fabG1 (also known as mabA), a core gene previously shown to be involved in ethionamide and isoniazid resistance  [26, 27].