dataesr/scientific-paragraphs-categorization huggingface dataset:
Coulais et al. , 2010 Coulais et al. , , 2012 Coulais et al. , , 2014;; Alain Coulais et al. , 2019; Duvert et al. , 2020). As a result, we have recently announced GDL 1. 0. In terms of compatibility, GDL aims at full compatibility with the modern IDL language specification, including partial support of the IDL 8 specification and above. In addition, GDL is partially compatible with PV-WAVE, another data analysis framework forked from IDL and sharing parts of IDL syntax and library interface. GDL aims to closely mimic the behavior of the IDL compiler and libraries distributed by Harris Geospatial Solutions, so that the existing IDL scripts are compatible with GDL without any modifications. The development has been done by relying on IDL and PV-WAVE documentation that has been publicly available on the IDL and PV-WAVE proprietors' websites, IDL documentation that had been publicly available on NASA websites (currently available at the Internet Archive, see https: //web.
archive. org/web/20090423093625/http://idlastro. gsfc. nasa. gov/idl_html_help/), and several published books that describe IDL syntax and library routines  (Bowman, 2005; Fanning, 2003; Galloy, 2015; Gumley, 2010). For developer convenience, GDL comes with its own integrated development environment (IDE), GDL Workbench, based on Eclipse Rich Client Platform (RCP), to allow users to simply edit IDL scripts and run them with GDL. In addition, there is an ongoing effort to maintain a GDL Jupyter kernel gdl_kernel, providing GDL data analysis and plotting functionalities via a familiar interactive notebook interface. GDL is interoperable with Python, by featuring a bi-directional Python bridge offering access to IDL/GDL code from Python and vice versa. GDL, GDL Workbench, and gdl_kernel are free/libre and open-source software, publicly available under the terms of the GNU General Public License v2 or above.
Highlights in GDL 1. 0 For GDL 1. 0, the plotting part has been completely revamped based on wxWidgets by default for all platforms, which provides widget support and also guarantees the same behavior across different operating systems. GDL 1. 0 also provides improved file format support, including reading/writing geoTIFF and writing HDF files. From release 1. 0, the GDL project is automatically compiled and released via Github Actions, providing source code distribution and a convenient installer for Windows systems. In addition to new features, many performance and threading optimizations were also done. Map projections support in GDL is also much more mature compared to earlier major releases of GDL. Finally, the transition from SourceForge to GitHub that preceded the release of GDL 1. 0 helped to consolidate the community efforts, streamline development, and support workflows.
Availability GDL can be installed via the package managing system on Linux (Arch, Debian, Fedora, Gentoo, Mageia, Ubuntu), FreeBSD, and macOS (Homebrew and Macports). On Windows, we provide a precompiled Windows binary, wrapped as a convenient installer based on the Nullsoft Scriptable Install System (NSIS). The source code of GDL, GDL Workbench, and gdl_kernel is available at our GitHub repository (https://github. com/gnudatalanguage). The weekly unstable builds can be obtained from https://github. com/gnudatalanguage/gdl/releases.
Ministale non expected results Initially Ministale has been built to test electronic hardware and software before their deployment on the Vestale site. To insure the reliability of our developement, we acquired data in active mode, during long periods, particularly during holidays (very quite period for anthropic activities near the loop), in our laboratory at Avignon University, a usual standard room with air conditionning insuring a mean temperature around 25 o C. Moreover, the RLCmeter is a precise and sensitive industrial device, designed to maintain its measurement accuracy in a temperature range of 0 degrees to 50 degrees. We observed then some well defined oscillations of particular point of the complex impedance, including a very regular daily fluctuation. To insure that the bench is not responsible of those fluctuations, and in order to characterize its sensibility, we placed Ministale in the Anti Blast Gallery (GAS for french acronym) of the LSBB.
ACKNOWLEDGEMENTS The authors acknowledge support from the  FORCeS, 4C  and CONSTRAIN projects funded by the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement Nos. 821205 ,  821003 ,  820829 , respectively; the  Wellcome Trust ,  Our Planet Our Health (Livestock, Environment and People) , award number  205212/Z/16/Z ; the  ESA Earth Observation Programme ; and the  University of Oxford's Strategic Research Fund.
The relationships of carbohydrates and aromatics with latitude and elevation will primarily be due to strong changes in MAT along latitude and elevation gradients. These patterns are predicted to hold even after controlling for botanical origin of the peat because latitude and elevation are closely related to MAT, and they are primarily driven by decomposition  (Hodgkins et al. , 2018). This large database of samples not only expands upon the previously observed global latitudinal trends in peat chemistry  (Hodgkins et al. , 2018)  with a much larger number of samples, it introduces elevation as another significant influence on peat organic matter carbon quality and provides a global-scale explanation of the observed patterns.
Peak Finding A custom R script was used to determine the exact location of peaks in pre-defined regions of the FTIR spectra (https://github. com/shodgkins/FTIRbaselines; R Core Team, 2018). Peak regions were located individually for every sample (Figure  2 ) because variations in peat chemistry slightly shifted the baseline endpoints of the peaks. The minimum absorbance values within the expected region of the peak were used to define the baseline. Several important organic matter functional groups are known to fall between wavenumbers 4,000-650 cm -1 and can be categorized by an absorbance peak maximum. Carbohydrates appear as a peak with absorbance maximum at approximately 1,030 cm -1 , aromatics at 1,510 cm -1 , and 1,630 cm -1 (added together for this study), acids at 1,720 cm -1 , and aliphatics at 2,850 cm -1 and 2,920 cm -1.
After finding the endpoints of the peak, absorbance values were then baseline-corrected by subtracting everything below the baseline, and the exact peak locations and peak heights were then determined by locating the maximum baseline-corrected absorbance value between the baseline endpoints. The R script calculates both peak height and area, and additionally normalizes the absorbance values by dividing them by the total integrated area of the spectrum. To avoid interference from silicates, which produce a large peak that interferes with the carbohydrate peak (∼1,030 cm -1 ), samples that contained clay (determined based on the presence of a peak at 780 cm -1 combined with a large peak at ∼1,030 cm -1 ) were excluded from analysis (Table  S1  in Supporting Information S1). For the 780 cm -1 peak, area-normalized absorbance greater than 0.
Additional Variables Four additional types of variables were derived to utilize in subsequent analyses. First, the package leaderCluster  (Arnold, 2014)  implemented in R 3. 6. 3 (R core team, 2020) was used to group peat samples into 1 and 50 km radius spatial clusters. Second, distance from the equator was calculated from latitude values using the geosphere package  (Hijmans, 2019). Third, MAT was obtained from the 2. 5 min WorldClim (version 2. 1) data set  (Fick & Hijmans, 2017)  using latitude and longitude coordinates for each sample, using the packages sp. (Pebesma & Bivand, 2005)  and raster  (Hijmans, 2020). Because WorldClim does not include Antarctic climate records, MAT for the Antarctic sites were obtained from long-term records of the closest weather stations (Palmer Station: https:// doi.
org/10. 1016/j. quascirev. 2017. 10. 033; Vernadsky Station: http://www. nerc-bas. ac. uk/icd/gjma/faraday. temps. html). Fourth, we created binary vegetation variables to represent three key components of the peat forming vegetation associated with coring locations, which were coded as 0 when below a given % cover threshold, or 1 when equal or above the threshold. These included total ground cover of Sphagnum (threshold = 50% cover), total overstory tree canopy cover (threshold = 50% cover), and total Cyperaceae cover (threshold = 35% cover). In most cases, these variables were obtained from percent cover data collected from 1 m 2 quadrats centered over each core location, although in some cases they were estimated from photos of the core locations. We reasoned that at levels above these thresholds each of the vegetation types would be more likely to be important contributors to the botanical composition of the peat, at least in the upper peat profile, however preliminary analyses with the cover thresholds adjusted up or downwards yielded equivalent interpretations of statistical results.
Statistical Analysis A suite of linear mixed models were run using R 3. 6. 3 to identify key predictors of carbohydrates, aromatics, and the von Post index. All mixed models were run with the package lmerTest  (Kuznetsova et al. , 2017) , with F-tests obtained using the Kenward-Roger approximation, and the package partR2  (Stoffel et al. , 2020)  was used to obtain R 2 values. The first set of models used the full data set to examine the relationship of carbohydrates, aromatics and, von Post index with distance from the equator. Each model included distance from the equator as a continuous independent variable, and the categorical fixed effect of depth in the peat profile  (10-20, 30-40, and 60-70 cm). Terms for individual peat core, 1 km radius spatial clusters and 50 km radius spatial clusters were also included in these models as a series of explicitly nested random intercept effects to account for potential non-independence of samples collected from the same core or nearby locations.
Data points from cores that did not have at least one of the three vegetation covariates above the given threshold were excluded from these models because they could have been dominated by a variety of unique non-target peat forming vegetation types (Table  S3  in Supporting Information S1). Lastly, we investigated the relationship of carbohydrates and aromatics with the von Post index. These linear mixed models contained the von Post index as a continuous independent variable, as well as the random effects of peat core, 1 km radius spatial clusters and 50 km radius spatial clusters.
The RFVI+sup also accounted for missing observations of the thermal index (e. g. , ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0. 75, the unbiased root mean squared difference (ubRMSD) values decreased to 0. 032 m 3 /m 3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multi-resource datasets from in situ measurements, remotelysensed and topographical indices.
However, these SM products using active microwave observations are limited over the mountain regions, which can be attributed to two aspects:  (1)  Sentinel-1 has a low temporal resolution (6 or 12 days), which results in the low temporal resolution (equal or larger than 12 days over China)  [15]. Especially over Northwest regions of China, very few high-quality SM information from L2_SM_AP is available; (2) the effect of mountain on the backscatter of SAR or Sentinel-1 is too complex to be modified, which leads to the high uncertainties in the backscatter, further decrease the accuracy of high-resolution SM  [16]. Currently, downscaling microwave-derived coarseresolution SM products by the synergistic coupling of optical/thermal-infrared datasets is the widely used approaches to estimate high-resolution (~1 km) SM information  [17].
The study period was 15th July to 15th October of 2013, 2014 and 2015 to avoid snow periods covering from November to May  [33]. In situ measurements at a depth of 4cm within a time window of 1 h were matched with the instantaneous overpass times of MODIS LST product. In situ measurements from 2013-2014 were used for training the SM model, and in situ measurements in 2015 was used to validate the SM estimates. Fig. 1. The study region. The spatial distributions of in situ measurements stations of the study region was shown in (a), the corresponding land cover, terrain aspect and topographic wetness index (TWI) were indicated in (b), (c), (d), respectively. In (a), elevation information is also showed and the wireless sensor network (WSN) sites are marked as black points. In (b) Land cover types data at a spatial resolution of 30 m provided by the HiWATER project  [52].
Furthermore, the calculation of TWI was based on slope, namely that TWI has included the slope information. TWI is not directly related to the variability of SM, but can identify hydrological flow paths which generally are related to the spatial distribution of SM. Thus, TWI was selected in this study instead of slope. Aspect, elevation and TWI (Table. S2) were calculated using 30-m Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) Global Digital Elevation Model (GDEM). These topographic indices were aggregated into the 1-km resolution data to match the spatial resolution of LST product using a simple average method.
A. Random Forest (RF) algorithm The ML RF method has been applied to retrieve various climate variables (e. g. , SM, evapotranspiration, precipitation and fire)  [46, 50, [64] [65] [66]. The complex and highly nonlinear relationship between the predictors and the response variable can be described by RF method due to its advantage on involving the adaptive and decorrelated decision rules  [50]. In the RF algorithm, the relationship between the predictors and the response was described using lots of decision trees. RF algorithm produces these decision trees in the training phase, by dividing the input dataset into a number of regression trees, known as forest, where each tree is generated using a bootstrap sample method. Then the mean prediction of the individual trees is generated as the output.
The presence of clouds during the study period is one of the main factors that will increase the uncertainties in the estimation of remote-sensed predictors. Moreover, the available observations of ATI are strongly limited by the availability of both daytime and nighttime LST observations (see the calculation of ATI in Table  S2 ). Although spectral indices were used in the RF models (RFVI+sup) to provide more SM information over the pixels with ATI data, the cloudy conditions still decrease the available number of spectral indices, leading to missing RFVI+sup estimates. Especially over the mountainous regions with high elevation, the missing optical indices due to the cloudy conditions resulted in a low number of RFVI+sup SM estimates. Because the limited predictors (e. g. , VIs or LST) could decrease the number of trained datasets, further decrease the accuracy of RFVI+sup.
Moreover, the SM underestimation was found in this study, mainly caused by the limited sensitivity of opticalthermal indices to SM. As noted by Fan et el. [37] , the limited ability of optical-thermal indices to the extreme dry / wet SM conditions could potentially decrease the performance on SM estimations. It is worth to be noted that passive microwave has a high sensitivity to estimate SM, albeit with low-resolution. However, with the development of downscaling passive microwave datasets, the combination of high-resolution passive microwave observations and optical indices in the proposed framework could improve the SM estimation over the mountainous regions  [17]. Note that it is inevitable that the overlapped information among vegetation indices. For example, NDVI and SAVI both were calculated using the reflectance from MODIS band 1 and 2, potential meaning that the temporal variations in NDVI could be similar with SAVI, although the importance analysis (Fig.
VI. CONCLUSION High-resolution SM methods have generally limited performance over mountainous regions because of the high spatio-temporal variations of SM in mountainous environment. To overcome these limitations, we proposed a regional learning strategy to improve the SM retrievals, by merging optical/thermal remote sensing data and topography data. Remote sensing albedo, ATI, NDVI, NDII5, SAVI and topographical indices (aspect and elevation) were identified as the optimal predictors for capturing temporal SM changes at pixel-scale. The proposed RF model developed in this study showed a strong capability to capture the spatio-temporal patterns of SM, relative to methods only using optical data or topographical indices. The RF-derived SM estimates we computed at 1 km exhibited a high estimation accuracy with overall ubRMSD, R, and slope values of 0.
Our online platform (https://strike. scec. org/cvws/seas/) facilitates the initial comparison of benchmark results, where modelers can upload and immediately visualize time series data and rupture front contours to assess model agreements. More modeling groups participated in BP5-QD than BP4-QD, due to considerations of timing and/or computational costs (Table  2 ). Given the similar problem setup of the two benchmarks, we present main results for BP4-QD and more complete comparisons for BP5-QD, using a selected suite of simulations listed in Tables  3  and 4. Several modelers have performed independent simulations using the same code (BICyclE and GARNET). These efforts ensure correct model setup and code execution and, in the case of BICyclE, expand the set of simulations and reveal the important effect of time stepping parameters (see Section 3.
Grid Discretization The two benchmarks, especially BP5-QD, have a relatively large grid spacing by design, which is a nontrivial factor when we compare different simulations. For example, different codes represent local fault properties within piece-wise constant (BEM) or piece-wise linear (FEM) elements, or on Fourier sample points (SBEM). Most BEM codes use rectangular elements, whereas TriBIE uses triangular elements with their centroids on irregular grids. Additionally, FDM code GARNET uses a fully staggered grid, which means that velocities are not located on the same grid points with some other properties. Consequently, the computational grid points in these simulations are often offset from the observational points specified in the benchmark description. Even though these numerical codes are designed to solve the same continuum problem, different discrete representations of local physical properties, when combined with a relatively large grid spacing, result in nontrivial truncation errors that are different among these codes.
Time Stepping Schemes The scheme of non-uniform, adaptive time stepping is essential in SEAS simulations that resolve various phases of earthquake source processes. We do not cover this computational aspect in the benchmark description and presume that modelers will adopt the optimal time stepping schemes for their numerical codes. Most codes use adaptive Runge-Kutta methods for time stepping. FDM code GARNET uses a linear multistep method (BDF2, second order backward differentiation formula) for their time stepping  (Pranger, 2020). SBEM code BICyclE determines the adaptive time steps based on maximum slip rates and stability conditions derived from constitutive laws  (Lapusta et al. , 2000) , which is also adopted in other codes, such as EQsimu and GARNET. In practice, suboptimal time stepping can complicate model comparisons.
When we compare simulations with a halved grid spacing of 500 m, the variability of nucleation location in TriBIE and EQsimu simulations disappears. The distinct behavior of these simulations based on BEM and FEM methods suggests that the earthquake nucleation in this benchmark is still susceptible to the specific setup of a computational model. To further assess model convergence, we compare the sixth event in simulations with smaller grid spacings, including most simulations at 500 m, and BICyclE-2 and ESAM simulations at both 500 and 250 m resolutions (Figure  14 ). Simulations with a grid spacing of 500 m show nearly identical source time function with small time offsets, an overall excellent agreement. However, some codes again display nucleation at the other end of the fault. Similar to the aforementioned results about TriBIE and EQsimu, we find that earthquake nucleation in finer-resolution simulations manuscript submitted to JGR: Solid Earth (250 m) with BICyclE return to the same location that matches other simulations.
We can estimate the minimum stressing rate (in insets of Figure  16a, c ) when the postseismic period transitions to the interseismic period. This minimum stressing rate is well-defined and less susceptible to the complex fault slip history, hence reflecting differences in large-scale, long-term loading in each simulation. In both simulation groups using grid spacings of 1000 and 500 m, we find that the minimum interseismic stressing rate is approximately inversely correlated with the nearly constant recurrence manuscript submitted to JGR: Solid Earth intervals of large events (Figure  16b, d ). This minimum stressing rates in volume-discretized codes EQsimu and GARNET tend to deviate from the cluster of SBEM/BEM results, although the general relationship between interseismic stressing rates and recurrence intervals still holds.
Acknowledgments We thank  Michael Barall  for maintaining the SEAS online platform and  Ruth Harris  for providing experience from the  SCEC/USGS  code verification exercises for dynamic rupture simulations. We thank  Steve Day ,  Ruth Harris , Associate Editor, and Editor for reviewing this manuscript. J. J. and B. A. E. designed the benchmark problems and organized the workshops for code verification exercises. J. J. analyzed all simulation results and led the writing of the manuscript. All remaining authors provided feedback on benchmark design, participated in the benchmark exercises (listed in Table 2), and/or helped with revising the manuscript. V. L. additionally helped with the early tests.
of benchmark problems; the other authors are listed alphabetically. The work of J. J. and B. A. E. on this project and SEAS-themed workshops have been supported by the  Southern California Earthquake Center (SCEC). This research is SCEC Contribution No. 11680. SCEC is funded by  National Science Foundation (NSF) Cooperative Agreement   EAR-1600087  and  United States Geological Survey (USGS)  Cooperative Agreement G17AC00047. The simulations with BICyclE (J. J. ) were conducted with the award  EAR170014  from the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  NSF  Grant No. ACI-1548562. The simulations with BICyclE (V. L. and N. L. ) were carried out on the  High Performance Computing Center cluster of the California Institute of Technology  with the grant support from  SCEC.
Open Research Descriptions of benchmarks BP4 and BP5 are available at https://strike. scec. org/ cvws/seas/download/ (SEAS_BP4_QD. pdf and SEAS_BP5. pdf) and included as supplementary materials. Our online platform (https://strike. scec. org/cvws/seas/) hosts the simulation data for local and global fault properties and rupture times. See publications in Table  2  for the availability and repositories of numerical codes. GARNET is available at https://bitbucket. org/cpranger/garnet/.