dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reduction was performed using the Athena and Artemis packages  (Ravel and Newville, 2005)  based on the IFEFFIT program  (Newville, 2001). Averaged experimental spectra were normalized to the absorption edge height and background removed using the automatic background subtraction routine AUTOBK included in the Athena software. To minimize the contribution of features at distances below the atom-atom contact distance, the R bkg parameter, which represents the minimum distance for which information is provided by the signal, was set to 1. 3 Å. For all spectra, the absorption energy E 0 was set to 13. 474 keV, which corresponds to the maximum of the first derivative of the absorption edge. Based on previous studies of Br and Cl speciation in aqueous solutions and silicate glasses  (Ayala et al.
0 ab initio code (Mustre de  Leon et al. , 1991)  using an aqueous Br ion [Br(H 2 O) 6 ] -with mean Br-O distance of 3. 37 Å and the NaBr salt crystallographic structure with Br-Na distance of 2. 98 Å  (Deshpande, 1961; Makino, 1995). Multiple scattering within a linear Br … H-O cluster was also included to model the hydration shell around Br, with the H-O distance fixed to 1. 0 Å  (Silvestrelli and Parrinello, 1999; Soper and Benmore, 2008). The χ(k) EXAFS function were Fourier filtered over the 1. 5 to 6 Å -1 k-range for most spectra. For all samples, modelling of the EXAFS oscillations was performed using 4 variables: average coordination number (N), distance to nearest neighbor (R), Debye-Waller factor σ 2 , and the energy shift ΔE. The amplitude reduction factor was set to 1 based on previous fits of aqueous NaBr, KBr and GaBr 3 solutions  (Da Silva et al.
Aqueous solutions and silicate glasses at room conditions The XANES and EXAFS spectra collected at ambient conditions from the 3 wt%NaBr aqueous solution and Br-bearing silicate glasses are reported respectively in Fig. 4  and Fig. 5 , together with data for a KBr aqueous solution from  Ferlat et al. (2002). These spectra were employed to validate the theoretical backscattering amplitude and phase shift functions for Br-O and Br-Na scattering paths used in EXAFS modeling. The XANES spectrum of the 3 wt% NaBr aqueous solution is characterized by an absorption edge at 13. 474 keV and a white line that peaks at 13. 478 keV (Fig. 4 ). It displays close similarities to that of the KBr aqueous solution from  Ferlat et al. (2002)  and overall resembles other alkali bromide aqueous solutions found in the literature  (Wallen et al.
Acknowledgements: We would like to thank  M. Doebeli  and  J. H. Seo  for conducting the RBS and LA-ICPMS analysis, respectively. The  Paul Scherrer Institute (PSI)  and the  Swiss Light Source (SLS)  are acknowledged for providing beamtime for the experiments. This work was supported by the  Swiss National Science Foundation  (grants  200021-120575  and  200020-132208  https://doi. org/10. 5194/se-2019-205Preprint. Discussion started: 10 January 2020 c Author(s)  2020. CC BY 4. 0 License.
We created a set of SPDF-based scenario earthquakes for an expected future M w 8. 2 Tonankai earthquake in the eastern half of the Nankai trough, off southwest Japan, and computed the ensuing tsunamis. We found that the estimated peak coastal amplitudes among the ensemble of tsunamis along the near-field coast differed by factors of 3 to 9, and the earliest and latest arrivals at each observation site differed by 400 to 700 s. The variations in both peak tsunami amplitude and arrival time at each site were well approximated by a Gaussian distribution. For cases in which the slip distribution is unknown, the average and standard deviation of these scenario datasets can provide first approximations of forecast tsunami height and arrival time and their uncertainties, respectively. At most coastal observation sites, tsunamis modeled similarly but using a uniform slip distribution underpredicted tsunami amplitudes but gave earlier arrival times than those modeled with a heterogeneous slip distribution.
2016a; Takahashi et al. 2017 Takahashi et al. , 2018)). These approaches require a pre-computed database of tsunami parameters for a set of scenario earthquakes in the target area of various magnitudes, source locations, and source geometries. After creation of such a tsunami database, the computational cost of estimating coastal heights and arrival times is lower. Because of the low cost and simplicity of implementation of this type of system, without the need for near-real time source estimations, they are used by some local governments and suppliers of lifeline services (e. g. ,  Takahashi et al. 2017 Takahashi et al. , 2018;; Ishibashi et al. 2018). The accuracy of tsunami heights estimated using scenario earthquakes depends on the ensemble of scenarios providing adequate coverage of the range of possible future earthquakes.
Tsunami computations We computed the tsunamis generated by each scenario earthquake. Vertical displacements due to dip-slip motion of the triangular fault elements were computed using the dislocation method of  Meade (2007) , and the total seafloor displacement for each scenario was computed by summing the contributions from all the elements within the rupture area. Tsunamis due to these static seafloor displacements were computed using the JAGURS code  (Baba et al. 2014b ). We solved the linear shallow-water equations in a finite difference scheme with a nesting algorithm comprising three layers of which the finest grid spacing was 2 arc-seconds. Although the JAGURS code can also solve the non-linear equations, its computation cost is much higher than that solving the linear one, and we need computations for hundreds of scenario earthquakes.
We have confirmed that the discussion does not significantly change if we use the non-linear equation after some preliminary runs. We used bathymetry from the M7000 Digital Bathymetric Chart provided by the Japan Hydrographic Association. The JAGURS code considers tsunami inundation based on the method of  Jakeman et al. (2010). We assumed a 40 s rise-time for crustal deformation for M w 8. 2 events to initiate the tsunamis. Tsunami observation sites were placed at 2. 5 km intervals on the 3-m isobath along the coastline facing the source area (Fig. 4 ). We included vertical crustal deformations in the tsunami heights because we focused on the near-field effect of the tsunamis. We simulated the tsunami propagations for 2 h with a time step of 0. 1 s to satisfy the stability conditions solving for the finite-difference methods.
Acknowledgements We thank  Dr. T. Iinuma  for sharing the FORTRAN version of the algorithm of Meade (2007). All figures were drawn using Generic Mapping Tools (Wessel and Smith 1998). We thank  Dr. T. Baba  for careful review and constructive comments that allowed us to improve the manuscript. We thank  Dr. D. Melger , an anonymous reviewer, and the editor  Dr. T. Maeda  for careful review and constructive comments, which have improved the manuscript.
Availability of data and materials The code to compute slip distribution on a non-planar fault surface with seafloor rupture is available at https://github. com/s-murfy/k223d. The JAGURS code to compute tsunami wave propagations is available at https://github. com/jagurs-admin/jagurs. We used a M7000 Digital Bathymetric Chart provided by the Japan Hydrographic Association (https://www. jha. or. jp/en/jha/) for our modeling of tsunami wave propagation. We used coastline data from the National Land Numerical Information provided by the Japanese Ministry of Land, Infrastructure, Transport and Tourism (https://nlftp. mlit. go. jp/ksj/ index. html).
Changes in the Arctic environment affect strategic areas including politics, economics (e. g. exploitation of natural resources including minerals, oil and gas, and fish), and security. Besides economic impacts of Antarctic and Arctic changes  (Whiteman et al. , 2013 ), Europe's interest in both polar regions is due to their influence on patterns and variability in global climate change, thermohaline circulation, and the planetary energy balance. Last but not least, changes in the Arctic system have potential impacts on weather, with consequences for extreme events  (Francis et al. , 2017). The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission described in this paper addresses the data and information requirements of these user communities with a particular focus on addressing Copernicus service requirements.
Winter months are months from October to April in the Northern Hemisphere and from May to October in the Southern Hemisphere. The system shall be capable of delivering sea ice thickness measurements with a vertical uncertainty of less than 0. 15 m along orbit segments ≤ 25 km in winter months and of providing meaningful sea ice thickness estimates during summer months. The alongtrack resolution of sea ice thickness measurements shall be at least 80 m. The uncertainty requirement for sea ice thickness comes with a caveat as the thickness uncertainty depends on the uncertainty of auxiliary products. In the case of CRISTAL, snow thickness will be measured by the sys-tem, but snow and ice densities will still have to be estimated by other means. In light of the current 0. 2 m sea ice thickness uncertainty from CryoSat-2 data assessed by  Tilling et al.
Icebergs Iceberg detection, volume change, and drift have been listed as a priority user requirement  (Duchossois et al. , 2018a, b). Icebergs present a significant hazard to marine operations. Detection of icebergs in open water and in sea ice generally places a priority on wider satellite swaths to obtain greater geographic coverage. There is a need for automatic detection of icebergs for the safety of navigation and chart production. Iceberg concentration is given in CMEMS' catalogue at 10 km resolution covering Greenland waters. SAR imagery is the core input for iceberg detection. However, iceberg detection (in particular small icebergs) is also possible using high-resolution altimeter waveforms. Tournadre et al. (2018)  demonstrated detection of icebergs from CryoSat-2 altimeter data using several modes and mention promising results with the Sentinel-3 data, which would be fed into a comprehensive dataset already built as part of the ALTIBERG project  (Tournadre et al.
g. Raney, 1998)  is used over this surface type, in which the reflections arriving back at the radar are received after each transmitted burst has finished. -Open and coastal ocean mode: in Fig. 3 , the proposed coverage shown in magenta provides Arctic and southern polar ocean retrieval of SLAs and precision SAR altimetry to complement other ocean topography missions including Sentinel-3, Sentinel-6, and nextgeneration topographic missions. In the case of open ocean, closed-burst SAR operation at Ku-band and Kaband is used, and the RMC on-board processing is applied. This was first implemented in the frame of Sentinel-6, which provides a considerable gain in instrument data rate reduction. In addition, data will be collected over inland water regions using one of the above modes. The latency of CRISTAL data products follows the requirements expressed in the PEG 1 and PEG 2 reports and provides measurements of different latencies according to the application need.
Specific heat has been fixed to 1250 J kg À1 K À1 and the thermal expansion coefficient has been fixed to 3 Â 10 À5 K À1. Equations (  1 )-(  3 ) are numerically integrated via the 2D finite element (FE) thermo-mechanical code SubMar  (Marotta et al. , 2006) , which uses the penalty function formulation to integrate the conservation of momentum equation and the Petrov-Galerkin method to integrate the conservation of energy equation. The numerical integration has been performed in a rectangular domain, 1400 km wide and 700 km deep (Fig. 4 ), discretized by a non-deforming irregular grid composed of 4438 quadratic triangular elements and 9037 nodes, with a denser nodal distribution near the contact region between the plates, where the most significant gradients in temperature and velocity fields are expected.
2-387. 8 Ma and 373. 4-380 Ma (Lotout et al. , 2018) ; (2) the LGU of the Artense area is composed of metapelites and metamorphosed orthogneisses (data Ar2 in Table  3 ). The members of this unit do not show relicts of HP metamorphism and they underwent a retrograde metamorphic evolution after the coupling with the UGU. The older metamorphic imprint of the paragneiss is recorded under amphibolite facies conditions and is coeval with the main foliation. This unit suffered a successive anatectic event during the decompression  (Mercier et al. , 1992) ; (3) in the southernmost portion of the Massif Central, the Montagne Noire exposes the Palaeozoic Fold-Thrust Belt and the southern foreland basin. It is classically divided into three units, characterised by late Visean-early Namurian south-verging recumbent folds: the southern and northern parts, composed of Palaeozoic sedimentary series, and the axial zone, composed of cores of gneisses and migmatites surrounded by micaschists.
Acknowledgements This work was partially supported by the  MIUR-PRIN 2011  project ( 2010AZR98L ) 'Birth and death of oceanic basins: geodynamic processes from rifting to continental collision in Mediterranean and  Circum-Mediterranean  orogens' and by the  SISMA-Pilot Project  ' SISMA-Information System for Monitoring and Alert ' (ASI contract No. I/093/06/0 ). All of the figures were created using the Generic Mapping Tool (GMT) plotting software (Wessel and Smith, 1998). We thank the Editor  Masaki Yoshida ,  Reviewer Michel Faure  and another anonymous reviewer for providing useful suggestions.
DATA The data used in this study are described by  Belart et al. (2019) , and consists of a large collection of stereoscopic imagery available in Iceland from 1945 to 2017, from airborne and spaceborne, frame camera and pushbroom sensors, together with airborne lidar data (Figure  2 ). A total of 836 aerial photographs acquired during 1945-1995 were collected from the National Land Survey of Iceland. The interval between surveys for each glacier was ∼10-20 years (e. g. ,  Magnússon et al. , 2016a; Pedersen et al. , 2018; Belart et al. , 2019). An additional series of photographs of  Mýrdalsjökull and Torfajökull (acquired in 1999)  were obtained from the private company Loftmyndir. This study also used a DEM and orthoimage based on six images from Hexagon KH9 satellite acquired in August 1980, originally processed in  Belart et al.
, 2007) , were used for  Barkárdals-and Tungnahryggsjökull, Torfajökull, Þrándarjökull, and Hofsjökull Eystri in 2004, and  for Hrútfell in 2013. The lidar datasets were collected between 2008 and 2012, starting with Snaefellsjökull and Eiríksjökull and finishing with Snaefell and Þrándarjökull (Figure  2 ;  Jóhannesson et al. , 2013). Pléiades satellite stereo images  (Berthier et al. , 2014)  were acquired over the summers 2016 (Barkárdals-and Tungnahryggsjökull) and 2017 (Öraefajökull). Both of them were surveyed in two satellite acquisitions over the course of two weeks. Data from the ArcticDEM  (Porter et al. , 2018)  from 2013 to 2018 were also used. The large majority of the surveys (∼80%) were carried out in August, September, or October. ∼15% were carried out in July, one in June and one in November.
The gridded precipitation and temperature were measured in millimeters and Celsius degrees, respectively. Remarks on the data quality and associated uncertainties of the climatic records can be found in the mentioned studies and in  Belart et al. (2019). The glacier margins of Icelandic glaciers in ∼2000 were extracted from the GLIMS inventory  (Raup et al. , 2007) , as a first approximation of the glacier outlines of the 14 glaciers.
For a decadal, region-wide comparison, we selected the years  1945, 1960, 1980, 1994, 2004, 2010, and 2017  as the most common survey years (Figure  2 ). The geodetic mass balance of each glacier was temporally homogenized (e. g. ,  Lambrecht and Kuhn, 2007; Fischer et al. , 2015)  to these years when needed, by using the modeled annual mass balance from Equation (  1 ). An uncertainty of ±0. 5 m w. e. a -1 was assumed for each year of modeled annual mass balance when calculating the temporallyhomogenized mass balance. The annual correction was not applied to datasets acquired in 1946 due to lack of climate data. We also estimated the geodetic mass balance for two additional, longer, time periods: 1960-1994 (relatively cold period and close to zero balance) and 1994-2010 (warmer period and the largest ice wastage).
2] Total 1,004. 8 N/A 0. 07 ± 0. 03 -1. 20 ± 0. 09 [N/A] [2. 9 ± 1. 1] [-21. 4 ± 1. 6] The table shows area (km 2 ,  Hannesdóttir et al. , submitted) , specific (m w. e. a -1 ) and total (Gt, in brackets) mass change rates for the three largest glaciers and for the sum of the other glaciers (glaciers <100 km 2 ) as investigated in this study. The values correspond to the fixed-date geodetic mass balance, after temporal homogenization when needed. Mass balance and mass loss involving multiple glaciers is calculated using area-weights. *Values used for Mýrdalsjökull, Eiríksjökull, and Tungnafellsjökull reach only back to 1960. **Þrándarjökull and Snaefell are excluded in this analysis, due to limited data coverage. and are more sensitive to temperature changes than other inland glaciers, hence they were included in the coastal glaciers group.
ACKNOWLEDGMENTS Karsten Kristinsson  is acknowledged for scanning the aerial photographs stored at the  National Land Survey of Iceland. Loftmyndir efh. is acknowledged for contributing aerial photographs from 1999. Pléiades images were acquired at research price thanks to the  CNES ISIS  programme (http:// www. isis-cnes. fr). The  ArcticDEM  project is acknowledged for numerous DEMs used from 2013 to 2018. This study uses the lidar mapping of the glaciers in Iceland, funded by the  Icelandic Research Fund , the  Landsvirkjun research fund , the  Icelandic Road Administration , the  Reykjavík Energy Environmental and Energy Research Fund , the  Klima-og Luftgruppen research fund of the Nordic Council of Ministers , the  Vatnajökull National Park , the organization Friends of Vatnajökull, LMÍ,  IMO , and the UI research fund.
This study uses the GLIMS database of the outlines of Icelandic glaciers. Bolli Pálmason  is acknowledged for providing downscaled precipitation data from regular forecast runs of the IMO for 2016-2019. Ken Moxham  is acknowledged for the English-language editing of the manuscript. Michelle Koutnik  is thanked for fruitful discussions about potential applications from the datasets. MZ, TS, and MH are acknowledged for their valuable comments during the revision of the manuscript. This study is based on the last chapter of the PhD dissertation of Belart (2018).
DATA AVAILABILITY STATEMENT The mass-balance records calculated in this study are presented in Supplementary Material, and have been submitted to WGMS (www. wgms. ch). The glacier outlines will be uploaded to GLIMS [www. glims. org], Hannesdóttir et al. , submitted. The DEMs and orthoimages created from aerial photographs, Hexagon KH9 photographs, SPOT 5 and ASTER, as well as the lidar DEMs, are available upon request. Maps of elevation difference using Pléiades data are available upon request.
The western part of the Gulf of Lion (GoL) passive margin, at the foot of the Pyrenees mountain chain hosts the Catalan magnetic anomaly (CMA), a significant NW-SE-trending linear magnetic anomaly 50 km long with no associated gravimetric signal. The CMA is located on the continental slope near the NW-SE-trending Catalan transfer zone (CTZ), and it extends from the shelfbreak to the abyssal plain across thinned to hyper-thinned continental domain. This study interprets an updated geophysical dataset to better understand the Pyrenees-GoL transition and proposes a new interpretation of the CMA's source. Gravimetric and magnetic modelling of the lithosphere along two perpendicular cross-sections indicates that the CMA is generated by a magnetic body (80 × 20 × 12 km) , called here the Catalan body within the thinned continental domain.
Gravimetric data and the Bouguer anomaly map The gravimetric data compilation includes offshore and onshore data. For the offshore domain, Compagnie Générale de Géophysique (CGG) seafloor gravity data (1966), data from several marine campaigns archived at IFREMER-SISMER, and an Elf-Explo campaign of 1996 allowed us to cover the entire GoL margin. The onshore domain was covered by ground surveys archived in the Banque Gravimétrique de la France  (Martelet et al. , 2009)  for French territory and in the International Gravimetric Bureau database (http://bgi. obs-mip. fr/) for Spain. The combined gravity dataset was carefully reprocessed to align all data with the IGSN71-Hayford 1930 gravity reference system. State-of-the-art gravity data reduction was performed, including free-air and Bouguer slab reduction in which a Bouguer reference density of 2.
3 g/cm 3 was used, considering that most data points are located above sedimentary basins. Terrain corrections were computed using a procedure after  Martelet et al. (2002). Topographic and bathymetric datasets used for free air and Bouguer corrections were made using the IGN digital terrain model topographic dataset (250 m resolution) onshore and the EMODnet bathymetric grid (200 m resolution) offshore (http://www. emodnet-bathymetry. eu). Terrain corrections were computed from 200 m to 10 km around the gravity stations, using topographic grids resampled every 200 m, and from 10 km to 167 km using topographic grids resampled every 1 km. Offshore gravity data were reduced, first by trimming the data from different marine campaigns in order to reduce navigation noise and homogenize data sampling.
Instrumental drift was reduced using traditional adjustments at profile intersections, first for each survey and then between different surveys, considering the Elf Explo 1996 cruise as the reference. The CGG 1966 seafloor gravity data was upward-continued to the sea surface before being merged with other datasets. The resulting dataset was finally converted to a 1-km regular grid using a minimum curvature algorithm. The complete Bouguer anomaly map (Fig. 4 ) incorporates all the density contrasts below the topography. Anomalies range from -264 to +194 mGal and display a regional gravity gradient in which anomalies are mostly negative onshore and positive offshore, highlighting the land-sea transition. At first glance, some gravity anomalies appear to correspond to major geological structures, such as the Nîmes fault.
Magnetic data and the magnetic anomaly map The airborne magnetic dataset was acquired in 1965 by CGG using a CSF magnetometer and a Toran positioning system flown at a constant altitude of 900 m above sea level. Magnetic data were collected along flight lines spaced 3 km apart in the NW-SE direction and 12 km apart in the perpendicular direction. After reduction of diurnal variations of the magnetic field, data were interpolated on a 1000-m regular grid using bidirectional gridding. The operator originally applied a regional correction of +2. 8 nT/km along the SE-NW flight lines and -0. 3 nT/km along the SW-NE lines. We back-corrected this regional trend and adopted a standard regional magnetic reduction based on the International Geomagnetic Reference Field (IGRF) for the date of the survey.