dataesr/scientific-paragraphs-categorization huggingface dataset:
As example, Fig. 2  shows the Symbolic Nuclear Analyses Package (SNAP) (Applied Programming Technology, 2012) GUI to enter the properties to be set for the UQ. Fig. 3  shows how all the UQ steps have been implemented in the UQ plug-in of the MAAP GUI. The major issue with the GUI is the limited flexibility compared to scripting. In fact, for the statistical analysis, the user can only use the options already available; if the users want to adopt other statistical parameters, this cannot be performed unless the GUI developers implement the needed features, or a mixed approach (e. g. GUI + scripting) is adopted. A similar situation happens if there are some bugs in the GUI that requires the developer intervention. In particular, the issue of managing the failed calculation has been identified in one of the GUI used and the solution required interactions with the GUI developer.
If a GUI is not available, the use of scripting to couple SA code and UT (e. g. Python (Python, 2023), MATLAB (MathWorks, 2023), Visual Basic (Microsoft, 2023) etc. ) is needed. This is a high time demanding process and in general it requires a teamwork. Some partners observed that, scripting is powerful, but not user-friendly as a GUI. Moreover, it is UT and SA code dependent, and it could be characterized by a limited portability from one input-deck to another. In addition, compatibility issues between UT and SA code have been underlined by several partners. In general, these issues have been solved with further scripting, which is high time demanding. Along the WP4, for example, some partners preferred to develop their own UT to have major flexibility; another partner initially preferred to develop the pre-processing phase manually.
Finally, several tools and programming languages may be adopted in the same SA code and UT coupling to perform the various steps, e. g. as shown in Fig. 4. Regarding the data post processing, different approaches have been adopted to extract the required data from each specific SA code. In general, each partner has a different way to extract the data from a specific SA code and currently there are no common code user guidelines on it. Along the exercise it has been also observed some compatibility issues of UT (or base programming language) and code plot variable (syntax problem) and the fact that UT could have problem to access the SA code data file. This latter point is a major effort for the code users. Concerning the possible implementation of the SA code/UT coupling on a cluster to speed up the UQ, some issues have been observed depending on the dimension and number of users of the cluster.
Acknowledgements This work was supported by the technology facility network  RENA-TECH , the  French National Research Agency (ANR)  through the projects  ASTRID GoSiMP  ( ANR-16-ASTR-0006-01 ) and the " Investissement d'Avenir" program   GaNeX  ( ANR-11-LABX-0014 ). Author works (MB, DA) have also been supported by  GaN4AP  project from the  Electronic Component Systems for European Leadership Joint Undertaking (ECSEL JU) , under grant agreement No. 101007310. This has received financial support from  GaN4AP.
The intention is here to characterize the lateral torsional buckling response of such girders, and to propose a practical design approach that better accounts for all aspects previously cited. The following classical research strategy was adopted: ■ Perform a reduced number of experimental tests on cellular and Angelina 2  girders in order to provide reference experimental results; ■ Develop F. E. models and validate their adequacy with respect to the experimental results; ■ Use the numerical tools to build a database of reference results through extensive parametric studies; ■ Investigate various mechanical models able to provide more accurate and economic design formulae, the latter being validated through the F. E. reference results. This first paper focuses on experimental activities and on the development of suitable F.
4) Next, another random interstitial site is picked from this new lattice of "available sites" and the whole process is repeated until no more available sites exist. The code for these numerical simulations is available in github. com/oapedroso/JMAK model. Although the calculated values of f b using the JMAK model were not identical to the simulated ones, the JMAK model could reasonably predict the fraction of blocked sites when blocking overlapping occurs. This results in a better description of the configurational entropy of the interstitial solid solutions with SBE, as can be seen in Figures  2 (b), (d),  and (f). Whereas the Garcés model underestimates the maximum solubility of the solid solutions (because of the overestimation of blocked sites) and leads to maximum configurational entropies lower than the simulated ones, the JMAK model could better describe both the maximum solubility and the maximum configurational entropy for the different levels of blocking for the BCC solid solutions.
Acknowledgments This work was financed in part by the  Serrapilheira Institute  (grant number  Serra-1709-17362 ), in part by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq  (Grant numbers:  309467/2021-7  and  161341/2021-7 ), and in part by the  CAPES-COFECUB cooperation program  process number  88887. 662596/2022-00 , project number  88887. 191910/2018-00. This study was also financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code   001 ,  Federal University of São Carlos, Graduate Program in Materials Science and Engineering.
Cover crops are intermediary crops planted in between two main cash crops. They play a role in limiting nitrate leaching into ground water. Currently, there is no database pertaining to cover crops despite their importance. The development period of cover crop is characterized by a dense cloud cover in Europe, which obstructs land surface monitoring using optical remote sensing. This study proposes a cover crops mapping method based on SAR (Synthetic Aperture Radar) remote sensing data from the Sentinel-1 (S1) constellation which is unaffected by weather conditions. Our method is based on the dynamics of the S1 backscattering coefficient at the plot level. Using a decision tree, we mapped cover crops. In the decision tree algorithm, filters were added to eliminate other crops that temporally intersect with the cover crop, namely wheat and rapeseed.
For example, "This work was supported in part by the U. S. Department of Commerce under Grant 123456. " The name of the corresponding author appears after the financial information, e. g. (Corresponding author: Second B. Author). Here you may also indicate if authors contributed equally or if there are co-first authors. The next few paragraphs should contain the authors' current affiliations, including current address and e-mail. For example, First A. Author is with the National Institute of Standards and Technology, Boulder, CO 80305 USA (email: author@ boulder. nist. gov). called nitrate leaching, leading to negative health impacts on communities, caused by the contamination of drinking water sources  [5] , and to environmental harm to aquatic ecosystems, caused by the high concentration of nitrates  [6] ,  [7].
Prabhakara et. al,  [38]  studied the relationship between 10 different visible range and near infrared (NIR) indices and the percent ground cover and biomass of winter cover crops was compared, showing that the NDVI was strongly correlated with percent groundcover (r2 = 0. 93) and that the triangular vegetation index (TVI) was best used high ranges of biomass (r2 = 0. 86). The European Sentinel-2 constellation, in particular, produces free and open access data with a high spatial resolution ranging from 10 m to 60 m (10 m x 10 m for the bands used for NDVI) as well a high temporal resolution (5 days revisit time). These characteristics allow for its use in crop detection as well as the mapping of changes in land use throughout the growing season with a high overall accuracy higher than 90%  [39] ,  [40].
They obtain the best performances on the high-density class and the lowest performances on the low-density class. Seeing the potential of SAR in vegetation monitoring and since optical remote sensing data is not reliably available in the winter months, the aim of this study is to map cover crop fields using Sentinel-1 SAR data. S1 time series were used in order to extract the radar signal from each agricultural field in the VV and VH polarizations. In addition to these raw channels, additional channels based on the mean S1 signal at grid scale (5 km x 5 km) were used in order to reduce the soil contribution from the total S1 signal. The classification method we developed at plot scale is a multi-level decision tree that classifies the fields based on criteria from multiple S1 channels. The classification method eliminates fields that have other types of vegetation during the same period as the cover crops (wheat and rapeseed).
B. Sentinel-1 Remote sensing data For this study, we used a time-series of Sentinel 1A (S1A) and Sentinel 1B (S1B) satellite-captured C-band SAR images (5. 405GHz). All the available acquisitions were utilized consisting of the "ascending" (evening at 18:00 UT) and "descending" (morning at 06:00 UT) acquisitions. Fig. 2  shows the extents of the orbits used in this study. We used both the Vertical-Vertical (VV) and the Vertical-Horizontal (VH) polarizations from every acquisition. S1 images have a spatial resolution of 10 m x 10 m. The data is available freely on the European Space Agency's (ESA) website (https://scihub. copernicus. eu/dhus/#/home). We downloaded and processed S1 data from 2017 to 2021. The number of orbits used, as well as the number of S1 images are detailed in Table  I.
The data was calibrated using the ESA S1 toolbox. The radiometric calibration is the first step in the calibration process; it is the processing of transforming the data from a digital number into a backscattering coefficient 𝜎° in linear units. The second type of correction is ortho-rectification; it is the geometric correcting of the SAR images using a digital elevation model at 30 m from the Shuttle Radar Topography Mission (SRTM) as topographical reference.
The end results of this process were three time-series at the field level (marked with a "p") VVp, VHp, (VHp-VVp). In addition to Sentinel-1 images, all cloud-free Sentinel-2 (S2) optical images over each study site/year were downloaded from the Theia website (https://www. theia-land. fr/). The S2 images provided by Theia are corrected for atmospheric interference and so are called Level-2A products. S2 images were mainly used to calculate the NDVI time series. Our approach developed in this study does not use optical data. However, NDVI data (Normalized Difference Vegetation Index) calculated from Sentinel-2 images were used to better understand the phenological cycle of the different reference fields visited during the field surveys.
C. In-situ data The in situ database consists of field observations on the type of crop on numerous reference fields. The field surveys were performed in the years 2017, 2018, 2019 and 2020 between October and December of each year. Field observations mainly provided from the ReadyNov PollDiff Captages collaborative project led by Envilys Dev and financed by European EAFRD funds and the Occitanie Region (2018-2021). For the La Rochelle site 1066 observations were available (one observation = one field) compared to 919 observations for the Arnoult site. Around 40% of the dataset is marked as having a cover crop between two main crops. The French registry for agricultural fields (RPG, Registre Parcellaire Graphique) was used to correctly delineate the boundary of all agricultural plots in our study area.
After normalization, a Gaussian curves fitting was performed on the N_(VHp-VHg) channel in order to find and register the peaks (position of Gaussians) as well as the minimums. Finally, we created a database for all the S1 channels processed containing the position and the amplitude of each maximum and minimum. Each crop type has S1 time series with a specific behavior, depending on its phenological cycle and on vegetation characteristics (e. g. vegetation water content, biomass, structure). This temporal variability in backscattering coefficients between crops opens a perspective to identify and classify the crops, based on their temporal S1 footprint. Thus, the dynamic signature of the backscattering coefficients of different crops constitutes the basis of our classification approach, which will use the decision tree technique.
6% and 88. 7% for La Rochelle study site (Table  VI ). For the Arnoult site, the Recall values were between 25. 3% to 83. 8% (Table  VI ). As shown in Table  VI , the best results for the RF classifier arose from using the same site in the test and in the training datasets. In these cases Recall remained generally high (although in one case it dipped strongly to 25. 3% for Arnoult and 43. 3% for La Rochelle). When we use different study sites for training and testing (ex: training La Rochelle 2017-2018 and testing Arnoult 2018-2019), Recall values were between 5. 4% and 76. 6%. In fact, for these combinations of datasets used in the training and testing, the Recall scores were generally low, with a high variability in the Recall scores between the different combinations. These results indicate an inconsistency in the classification using the RF classifier.
F. Preceding and following crops In this section, we investigated what crops (top 5) are most likely to precede cover crops and which crops are most likely to follow cover crops using the crop cover classification performed data for all the studied years. Table  IX  shows the crops that usually precede (preceding crop) and that usually follow (following crop) a cover crop. It shows that wheat is the most recurrent crop before cover crops with 40. 5% of the total classified fields while maize was the most recurrent crop after cover crops with 34. 1%. Next, and in order to better understand the crop patterns for the classified fields with cover crops, we determined which two crops are commonly paired together in our study period (before and after cover crops). Fig. 13a  shows the percentage of every Y2 crop in function of the total of every Y1 crop for fields having cover crops.
5% to 95. 0%. Additionally, a high Precision was achieved with values ranging between 81. 5% and 89. 2% as well as high Kappa values ranging between 0. 72 and 0. 89. These results show that we are capable of mapping cover crops with high accuracy using solely remote sensing data from SAR Sentinel-1, whereas most other classification methods are based either on optical remote sensing data alone or on a combination of optical and SAR remote sensing data. Some limitations detecting cover crops using S1 data were identified. Firstly, although our decision tree method is designed to accommodate a variety of planting times, strategies and types of cover crops (by using wide windows), this type of classifier might need to be adapted based on the agricultural context in each region and planting strategies (only for regions that are much different from our own).
ACKNOWLEDGMENT The authors wish to thank the  French Space Study Center (CNES, TOSCA 2023) , the  National Research Institute for Agriculture, Food and the Environment (INRAE) , and  Envilys  for supporting this work. The authors would like to thank Envilys and the staff of the  Agglomeration of La Rochelle community  and the Eau 17 water syndicate for data collecting and access. The authors would like to also thank the  European Space Agency (ESA)  for providing the S1 images and the  French Land Data Center (Theia)  for providing the S2 images.
e. crushed vs. milled material) and extract solutions. Moreover, δ 34 S and δ 18 O of dissolved sulphate in extract solutions and borehole water were measured. Finally, geochemical modelling based on cation exchange data, which constitutes an independent dataset, was performed in order to assess the plausibility of the two variants for SO 4 concentrations in the porewater of the Opalinus Clay obtained from the different characterization methods (i. e. high-SO 4 variant by aqueous extraction versus low-SO 4 variant by advective displacement, high-pressure squeezing and borehole waters).
Sample description A total of seven drillcore samples from the Opalinus Clay were investigated in this study. The mineralogical composition of the Opalinus Clay is well known. According to the rock parameter database compiled by  Mazurek (2017)  the main mineral fractions of the Opalinus Clay are (in wt. % ± 1σ): clay minerals (52. 9 ± 14. 9), quartz (21. 4 ± 9. 6) calcite (18. 2 ± 11. 7) and feldspars (3. 3 ± 2. 2). The clay minerals in the fraction <2 μm include mainly illite, illite/smectite mixed layers and kaolinite, whereas chlorite contents are subordinate. Pyrite and organic matter are ubiquitous (<0. 1-2. 0 wt% and 0. 5-1. 5 wt%, respectively). Moreover, accessory minerals are present, which are commonly not identified by X-ray diffraction. For example, diagenetic celestite and Ba-Sr sulphate have been detected at instances by microscopic techniques, usually as large grains (>100 μm;  Wersin et al.
Geochemical modelling The scope of this modelling exercise was to evaluate SO 4 data obtained from aqueous extraction in terms of their compatibility with exchangeable cation data and, in more general terms, with regard to their representativeness for in-situ conditions based on the wellestablished database of the Mont Terri Rock Laboratory  (Wersin et al. , 2022). For this purpose, a hypothetical exchanger composition was calculated from aqueous extraction data (S/L ratios of 1 g/mL) based on simple geochemical modelling and then compared with the exchanger composition obtained from Ni-en extracts on the same samples. The calculations were performed for the Opalinus Clay samples from the Mont Terri Rock Laboratory. The modelling approach relies on some simplifying assumptions, including. • The ionic charge is assumed to be carried by the main compounds Na, Ca, Mg, Cl and SO 4.
Other compounds, such as TIC, K and Sr were neglected. • The Ca concentrations of in-situ porewater were derived from assuming gypsum equilibrium. Note that this reflects the maximum possible Ca concentration. Mg concentrations were set to the same concentrations as Ca (based on the similarity of their concentrations in OPA porewater at the Mont Terri Rock Laboratory;  Wersin et al. , 2022). • Na concentrations of in-situ porewater are derived via charge balance. The calculations were performed using PHREEQC  (Parkhurst and Appelo, 2013)  and the PSI/Nagra 2012 thermodynamic database  (Thoenen et al. , 2014)  assuming a temperature of 25 • C. In a first step, Cl and SO 4 concentrations from the aqueous extracts were recalculated to in-situ conditions (section 3. 7), then Na-Ca-Mg-Cl-SO 4 solutions were speciated, assuming gypsum equilibrium and charge balance as mentioned above.
e. for original extract concentrations; Fig. 4a ). For the calculation of saturation indices the software PHREEQC by  Parkhurst and Appelo (2013)  and the PSI/Nagra 2012 thermodynamic data base  (Thoenen et al. , 2014)  were used. For S/L ratios >0. 2 g/mL calcite dissolution during aqueous extraction imposed equilibrium or-near-equilibrium conditions, whereas undersaturation with respect to calcite is observed for aqueous extracts with S/L ratios ≤0. 2 g/mL, probably owing to a limitation in the availability of calcite at these low S/L ratios. Saturation indices for strontianite show a similar trend as for calcite, however, the extracts being slightly undersaturated even at S/L ratios >0. 2 g/mL (Fig. 4b ). The calculated partial pressure of CO 2 generally increases with increasing S/L ratio from around 10 -5 -10 -4 bar to 10 -3.
Sequential aqueous extracts Fig. 5  shows the concentrations of chloride and sulphate, as well as their ratios for sequential aqueous extracts of Mont Terri samples. All of the investigated samples show the same general trends with strongly decreasing concentrations upon progressively diluting the porewater until complete removal after the third extraction sequence (Fig. 5a  and b ). Contrastingly, the SO 4 /Cl ratio exponentially increases (Fig. 5c ) Fig. 3. Results of aqueous extracts performed on Mont Terri samples at variable S/L ratios. Extraction time was 24 h. Experiments with black circles were performed at the University of Bern. Calcium concentrations plotting at zero are below the detection limit of 0. 5 mg/L extract. Data for squeezed and borehole waters are from  Mazurek et al.
Plausibility checks by geochemical modelling The plausibility of the SO 4 data from aqueous extracts in terms of Fig. 10. a) Exchangeable Na and b) sum of exchangeable Ca + Mg (equivalent fractions) vs. distance (m) from top Opalinus Clay at the Mont Terri Rock Laboratory. Orange squares: calculated from SO 4 and Cl concentrations in aqueous extracts (see text). Green symbols: measured cation data from Ni-en extracts (corrected for dissolved cations in the porewater). Blue circles: modelled cation data based on borehole waters documented in  Wersin et al. (2022). their representativeness for in-situ conditions was tested by simple geochemical modelling as described in section 3. 9. On the basis of the Cl and SO 4 concentrations recalculated to in-situ conditions, a simplified exchanger composition including the main cations Na, Ca and Mg was derived for four of the Mont Terri samples.
The calculations yield high exchangeable Na occupancies of ~0. 8 (equivalent fraction; Fig. 10a ) and low exchangeable Ca + Mg occupancies of ~0. 2 (equivalent fraction; Fig. 10b ). These results are at odds with the exchanger composition obtained from Ni-en extracts of the same samples, which yield distinctly lower Na but higher Ca + Mg occupancies. The latter data are, however, in good agreement with Ni-en extraction data reported by  Wersin et al. (2022)  as well as with data calculated from borehole waters. This comparison indicates that SO 4 recalculated from aqueous extracts implies dissolved and exchangeable cation concentrations which are not compatible with the measured exchangeable cation data and, in more general terms, not compatible with the well-established porewater chemistry database for Mont Terri (see compilation by  Wersin et al.
Acknowledgements The authors thank the  Swiss National Cooperative for the Disposal of Radioactive Waste (Nagra)  and the  Swiss Federal Office of Topography  swisstopo for generously providing drillcore samples. Moreover, we warmly acknowledge the experimental and analytical work performed by  P. Bähler ,  C. Pichler ,  J. Zucha ,  L. Lehmann ,  T. Conte ,  C. Cosson ,  L. Kastler ,  N. Lafaurie ,  C. Quarton  and  Hydroisotop GmbH. We appreciate discussions with  M. Mazurek ,  H. N. Waber ,  U. Mäder ,  E. Gaucher  and  C. Tournassat. Ultimately , we thank  Nagra  for financial support.
Machine Learning: using Random Forests as a regression tool Random Forests  (Breiman, 2001 ) is a machine learning algorithm based on the computation of a large number of decision trees. Decision trees are top-down structures consisting of nodes and branches. At each node a statistical test is performed on the value of one feature of the input data. The outcome of this test tells which branch to use to get the next node. The final nodes of the tree give the decision of the tree. The randomness comes from the use of a random subset of events from the dataset and of features used to characterize the events to build each tree. Each decision tree in the "forest" is therefore different and the model combines hundreds (if not thousands) of decision trees. Random Forests is now successfully used in seismology for automated source classification  (Provost et al.