dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description The land cover analysis of Poitiers in 1993 used aerial images at a Very High Resolution (VHR) and Landsat-5 TM images at High Resolution (HR). In addition, training and validating polygons were created to conduct learning-based classifications (i. e. , Random Forest classification) et evaluate their performance ( Figs. 1 2 3 4 5 6 ). Digital aerial images were provided by the IGN, contained a NIR, red and green channel and were acquired in summer 1993 over the area of interest. They were acquired during two different missions: 20 tiles for the north of Poitiers were taken on 16 August 1993 and 30 tiles for the south of Poitiers were taken on 28 July 1993 ( Table  1  ). It is important to note that different date acquisition can influence spectral information due to phenological variation of the vegetation.
Training and validating polygons The selection of training and validating polygons is crucial for the classification accuracy. The best classification accuracy is obtained when statistics of categories show low intra-class variation and high inter-class variation. Thus, they must represent statistical variation within each class. Training and validating polygons were chose across all the studied area, to avoid spatial autocorrelation, which can have an influence on the evaluation of classification accuracy ( Fig. 1  ;  [12]  ). From the segmentation, we selected segments by photointerpretation (based on the orthophotos and Landsat images) to construct the training dataset. Seven target classes were identified for the classification: Impervious surface contained buildings, roads, bare soils; cropland contained annual crops; grassland contained meadows and herbaceous vegetation; coniferous forest; deciduous forest; water contained the main water bodies and courses; and shadow ( Table  2  ).
Random forest classification We used the Random Forest algorithm for the two classifications. For the parameter values of the TrainVectorClassifier algorithm, we chose a value of 250 for the number of trees and a value of 4 for the depth of tree (i. e. , the square root of the number of the input variables)  [18]. A stratified subsampling followed by a cross-validation process was also used by dividing the training dataset into five roughly equal subsets to conduct 5 different classifications using the VectorClassifier algorithm  [6 , 14]. Then, these classifications were merged with a majority voting (i. e. , FusionOfClassifications algorithm). The overall accuracy and the Kappa index were computed to estimate the global performance of the two classifications. The precision, recall and F-Score were calculated for each land cover category.
Compiling data We compiled the created data and available institutional databases to build a map at very high spatial resolution ( Table  6  ; Fig. 8  ). Buildings, roads, railways and water courses and bodies were manually modified from existing data (BD Topo of IGN) by photointerpretation. Deciduous and coniferous forests, croplands, grasslands and artificial areas were extracted from the multi-temporal classification. Water from the HR classification was not used due to its coarse resolution and because this category was mainly integrated into the classification to not overestimate the impervious surfaces which could have close radiometric information. Urban vegetation strata, detected within the artificial areas of the multi-temporal classification, were extracted from the mono-temporal classifications.
Acknowledgments This work was funded by the  Agence Nationale de la Recherche  (Grant No. ANR-21-CE32-0 0 02-01 [RECODE ] to N. B. ),  PEPS-CNRS 2019   EcoMob Grant  ( CORECO ) (for N. B. ), the  French Office for Biodiversity  (  OFB InOV 2022  grant),  Région Nouvelle Aquitaine (France) ,  Grand Poitiers Communauté urbaine (France) ,  2015-2020 State-Region Planning Contracts  ( CPER ), the  European Regional Development Fund (FEDER) , partnership arrangements in ecology and the environment (DIPEE), and intramural funds from the  Centre National de la Recherche Scientifique  and the  University of Poitiers.
Data accessibility All data are freely available in Zenodo: https://zenodo. org/record/8220468 doi: 10. 5281/zenodo. 8220468 Related research article Morin, E. , Herrault, P. A. , Guinard, Y. , Grandjean, F. , & Bech, N. (2022). The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning. Ecological indicators, 139, 108930. https://www. sciencedirect. com/science/article/pii/S1470160x22004010 ( https://hexagon. com/products/erdas-imagine ). Landsat images were downloaded on United States Geological Survey (USGS) website ( https://earthexplorer. usgs. gov/ ). The reference data and manual corrections were made with the QGIS software ( www. qgis. org ). Object-based classifications were performed using Orfeo Toolbox ( www. orfeo-toolbox.
Proper selection of an appropriate rockburst assessment method is a fundamental aspect in underground operations. However, there is a limited number of studies that summarize and compare different prediction and prevention methods of rockburst. This paper aims to address this gap by analyzing global trends using CiteSpace software since 1990. It discusses rockburst classification and characteristics, comprehensively reviews research findings related to rockburst prediction, including empirical, simulation, mathematical modeling, and microseismic monitoring methods. Additionally, the paper presents a compilation of current rockburst prevention measures. Notably, the paper emphasizes the significance of control strategies, which provide key insights into the effective utilization of stored energy within rock.
These challenges are inherent to the deep engineering environment. Therefore, it is crucial to carefully consider the selection of prediction and prevention technologies based on the engineering geological conditions of the rock mass. The remaining sections of this paper are structured as follows. In Section 2, the global collaborations, hotspots, and trends in the field of rockburst are analyzed using CiteSpace software  (Chen, 2006). Section 3 provides a summary of typical cases of rockburst in underground projects and elucidates the corresponding rockburst characteristics. Traditional methods of rockburst prediction and microseismic monitoring techniques are reviewed in Section 4. Section 5 presents the underlying control strategies and support methods for mitigating rockburst, emphasizing the framework for safe and efficient excavation.
Various software options are available today for visual analysis, each with its own advantages and limitations. Among these options, CiteSpace stands out due to its comprehensive features, its focus on analyzing the evolutionary path of the research field, and its ability to highlight emerging and cuttingedge topics. These features align well with our goal of conducting a thorough analysis. For this study, CiteSpace software is employed to perform a scientometric analysis of 2,645 English literature articles from the Web of Science Core Collection (WOSCC) database. The literature search in WOSCC includes the following criteria: subject = (rock burst or rock burst), language restricted to English, and the time span is set from 1990 to 2022.
In recent years, there has been an increase in the construction and excavation of deep and long underground space projects. Extensive literature highlights the challenges in accurately predicting and preventing hazards such as high-energy rockburst and mine seismicity in these deep environments. Rockburst is one of the most prominent and complex issues encountered in such projects. Therefore, it is crucial to compile and analyze rockburst data from various sources to provide reference experiences for evaluation and prevention, as well as theoretical support for in-depth dynamic disaster research. To assess the occurrence and severity of rockburst, Zhou (2015a) compiled 102 cases of rockburst in hard rock mines in China (Fig. 8(a) ). They developed a classification model utilizing variables such as maximum tangential stress around the excavation (MTS or r h ), uniaxial compressive strength of rock (UCS or r c ), uniaxial tensile strength of rock (UTS or r t ), stress concentration factor (SCF or r h /r c ), rock brittleness index (B = r c /r t ), and elastic strain energy storage index (W et ).
The stochastic gradient boosting method was used to analyze the data and determine the relative importance of each variable. The results (Fig. 8(c )) indicate that the Wet index is the most sensitive in predicting rockburst intensity, followed closely by MTS. SCF and B are more sensitive than UTS and UCS. Furthermore, Heal (2010) collected 254 databases of rockburst events in Canada and Australia (Fig. 8(b) ), considering variables such as SCF, ground support capacity (GSSC), geological structures (GS), excavation structures (ES), and peak particle velocity (PPV) at the site. The same analysis method was employed to identify the main controlling factors of rockburst occurrence, as depicted in Fig. 8(d). PPV is the most sensitive index in assessing the tendency for rockburst damage, followed by ES.
This has made it a popular choice for researchers in solving nonlinear problems. Various machine learning algorithms such as support vector machines (SVM)  (Zhou et al. , 2012) , logistic regression classifiers  (Li & Jimenez, 2018) , decision trees (DT)  (Shirani & Taheri, 2019) , Bayesian networks  (Li et al. , 2017a; Li et al. , 2021) , random forest (RF) and gradientboosting machine (GBM)  (Zhou et al. , 2016a) , and Knearest neighbor (KNN)  (Afraei et al. , 2019)  have been employed for classification and prediction of rockburst. Ensemble learning is a method of enhancing learning ability by combining multiple weak classifiers  (Wang et al. , 2021). Recently,  Li et al. (2022a)  proposed a deep forest rockburst prediction model based on a database of 329 actual rockburst cases. The model achieved a final test set accuracy of 90.
48% and a performance gain of 12. 7%. In another study,  Li et al. (2022b)  utilized a Bayesian optimization and synthetic minority oversampling technique + Tomek Link approach to build a feedforward neural network model based on 314 rockburst examples. Similarly,  Li et al. (2022c)  developed rockburst prediction methods using combined trees, such as random forest, extremely randomized tree, adaptive boosting machine, gradient boosting machine, extreme gradient boosting machine, light gradient boosting machine, and category gradient boosting machine, using the same rockburst dataset. They integrated three voting, bagging, and stacking procedures with a Bayesian optimization algorithm to enhance model performance. Zhang et al. (2020b).
However, a notable limitation is the requirement for a substantial amount of rockburst data to accurately determine important model parameters. In contrast, microseismic monitoring technologies have the advantage of immediately and continuously collecting essential data, providing accurate insights into the formation mechanism and evolution patterns of rockburst. This enables timely assessment and prediction of rockburst events. Nevertheless, deriving meaningful patterns from large volumes of microseismic data remains a pressing challenge in this area. Overall, comparing the superiority of different methods is challenging without conducting comprehensive evaluations under the same conditions. Each approach is still under development and has its own advantages and disadvantages. Consequently, there is no standardized method or approach that can universally evaluate the effectiveness of all projects in underground space excavation.
Conclusion A growing body of research has emerged in recent years to address the global challenge of rockburst risk reduction. To comprehensively understand the current state and progress of this field, it is imperative to conduct thorough and quantitative literature studies. One commonly used tool for analyzing global trends since 1990 is the software CiteSpace. Analysis of publications reveals that China, the US, Australia, Canada, and Russia are the top five countries contributing significantly to the field. This indicates their notable contributions to rockburst research. Furthermore, an examination of co-institutions demonstrates that while global communication has strengthened with the rapid expansion of the Internet, institutional cooperation primarily occurs among organizations and disciplines with similar interests, with limited interdisciplinary and crossdisciplinary collaboration.
Acknowledgments This research was supported by grant  PID2019-106692RB-I00  funded by  MCIN/AEI/10. 13039/501100011033. A. J. Reynoso  would like to thank  Dominican Republic MESCYT  for the financial support. Likewise, the authors thank for technical support provided by  SGIker of UPV/EHU and European funding (ERDF and ESF).
1 LESIA / Observatoire de Paris, LESIA, Meudon, France (stephane. erard@obspm. fr) The H2020 Europlanet-2020 programme, which ended on Aug 31 st , 2019, included an activity called VESPA (Virtual European Solar and Planetary Access), which focused on adapting Virtual Observatory (VO) techniques to handle Planetary Science data  [1]   [2]. The outcome of this activity is a contributive data distribution system where data services are located and maintained in research institutes, declared in a registry, and accessed by several clients based on a specific access protocol. During Europlanet-2020, 52 data services were installed, including the complete ESA Planetary Science Archive, and the outcome of several EU funded projects. Data are described using the EPN-TAP protocol, which parameters describe acquisition and observing conditions as well as data characteristics (physical quantity, data type, etc).
experimental data produced in other Work Packages of Europlanet-2024; connections between PDS4 and EPN-TAP dictionaries will make PDS metadata searchable from the VESPA portal and vice versa; Solar System data present in astronomical VO catalogues will be made accessible, e. g. from the VizieR database. The search system will be connected with more powerful display and analysing tools: a run-on-demand platform will be installed, as well as  Machine Learning  capacities to process the available content. Finally, long-term sustainability will be improved by setting VESPA hubs to assist data providers in maintaining their services, and by using the new EU-funded  European Open Science Cloud (EOSC). In addition to favoring data exploitation, VESPA will provide a handy and economical solution to Open Science challenges in the field.
Land use data come from the French database of agricultural land use (RPG -Registre Parcellaire Graphique) from years 2015 to 2021, completed by the OSO map  (Inglada et al. , 2017)  for non-agricultural areas (Fig. 1-d ). Stream flow coming out of the two dams was provided by the CACG. In addition to the "classical" data to set up the SWAT+ model, high resolution remote sensing data were used. Since 2017, the two Sentinel-2 satellites provide images every five days, with resolution ranging from 10 to 60 m depending on the wavelength. The level 2A images from two Sentinel-2 tiles, 31TCJ and 30TYP (Fig. 1-a ) are used in this study. They are post-processed through the MAJA chain  (Hagolle et al. , 2015)  that corrects atmospheric effects and produces a cloud mask. These clouds masks are used in the interpolation process, to obtain gap-filled time series.
River discharge at the outlet was also provided by the CACG, and has been used for calibration and validation. In order to get an overview of the origin of withdrawals, the PAR (Plan Annuel de Répartition -Annual plan for water allocation) database was used. It gathers individual withdrawal permissions issued every year by the French administration, and contains the authorized volumes in each type of water resource. The PKGC (Pratiques Culturales en Grandes Cultures -Agricultural practices in field crops) database, carried out every five years, consists on a large survey that covers all French departments with information about 28000 agricultural plots. Dataset from 2017 has been used in this study, in particular the sowing dates and amounts of annual irrigation of 160 corn and soybean fields in southwestern France.
Model setup The 60. 5. 3 version of SWAT+ has been used in this study as the baseline model, from which features and options were added (see Code availability Section). The watershed has been divided into 149 sub-basins (Fig. 1 ). In this study, we forced HRUs to match individual fields in all agricultural land, so that using remotely sensed indices at plot scale would be consistent. This delineation resulted in about 26000 HRUs. The model was fed with the true crop rotation from 2015 to 2021 according to the French database RPG.
Model for irrigation Based on local water managers knowledge, all fields with a slope lower than 10% and covered by corn, soybean, orchard, or vegetables were considered as irrigated. The actual source of irrigation withdrawals (river or reservoirs) is not known. The PAR database however provides information about the distribution of authorized withdrawals between sources. We assumed real withdrawals distribution to be the same. It results in 58% of the water pumped from the rivers, whereas 42% comes from small reservoirs (36% from connected reservoirs and 6% from disconnected ones). As small reservoirs (<10 ha) are not included in our setup, irrigation water was considered to come from the closest river from each HRU. This should be kept in mind for the interpretation of results. Rules are required to trigger the simulated irrigation.
In order to implement them, we used several surveys conducted in southwestern France to identify farmers' behaviors  (Leenhardt et al. , 2004; Maton et al. , 2005 Maton et al. , , 2007 ; Senthilkumar et al. , 2015). Table  2  presents the implemented rules which include soil water content, the rainfall of the day and the next day as well as crop phenological stages. The p trig parameter (Table  2 ) can vary depending on the crop, and was calibrated for corn and soybean. The calibration method is similar to the one described by  Olivera-Guerra et al. (2023). Calibration and validation data are the annual irrigation depth for 160 irrigated fields in the PKGC database. This led to a value of 0. 57 for corn and soybean, and 0. 55 for silage corn.
Plants phenology The emergence of crops follows the rules described in Section 2. 4. As a result, the emergence dates have very different distributions depending on the method. In the SWAT-O setup, most of the crop growths are synchronized. For instance, in 2017, the model grows all corn during the last week of May. The reason is that the growth in SWAT-O is mostly determined by air temperature, which is nearly homogeneous over the watershed. On the other hand, the use of NDVI to detect the beginning of growth spreads the emergence dates over almost two months. Figure  4  shows this effect on the 700 corn fields in the Gimone watershed. To decide which distribution is the more realistic, those dates have been compared to 150 corn fields from the PKGC database located within 80 km of the study area.
The assumption is made that the dynamics of these plots and of Gimone plots are similar, due to the similarity of altitudes, latitudes and climate. The PKGC database contains the sowing date, whereas the use of NDVI allows the detection the phenological stage of 6-8 leaves (here called "emergence" for more clarity). To be consistent between NDVI and PKGC estimations, a fixed amount of 300 heat units has been added to the database sowing dates to converted it into plausible emergence dates, 300 °C d -1 being the average need to reach the 6-8 leaves stage from sowing. Comparison between the PKGC database and the models clearly shows that spread emergence dates (SWAT-NDVI) fits better to real dates. Surveys conducted in an other sub-watershed in South-Western France in 2005 also showed a spread of sowing dates over five weeks  (Maton et al.
Code availability. The custom SWAT+ sources are available at https://github. com/ElisabethJustin/SWATplus-NDVI Author contributions. E. B. , V. D. and S. S. designed the experiments; E. B. conducted the experiments; E. B. , V. D. , Y. G. and S. S. analysed the results; L. L. provided the data and gave expert assessment on the results. All authors revised the paper.
Haber and Oldenburg [1997]  developed the joint inversion as a generic approach to invert two data sets when the underlying models are linked by the same structural (geological) heterogeneity. The main advantage of this approach is that it does not need any assumption about the relationship between the two data sets. Cardiff and Kitanidis [2009]  showed the interest of the joint inversion via an adaptive zonation approach based on the level-set method. Finsterle and Kowalsky [2008]  performed the joint inversion of ground penetrating radar (GPR) travel times and hydrological data collected during a simulated ponded infiltration experiment. Joint inversion is nowadays widely studied in hydrogeophysics [see  Linde and Doetsch, 2016 , for a review]. In the field of hydraulic parameter estimation partly relying on geophysical data, the work of de Marsily and co-workers can be considered as seminal in initiating a promising research field leading to a new discipline, hydrogeophysics.
Data This work is based on the large database (field data, conceptual, sedimentological, diagenesis studies and models) resulting from the Joint Industrial Project COMPAS led by IFP Energies Nouvelles from 2012 to 2015. Both sedimentary and microbialite facies classifications were adapted for this study since some of the ten facies associations defined by  Deschamps et al. [2020]  are not present in the area and in Sequence 4. Others were merged into a single facies as they are similar in terms of reservoir properties (Table  1 ). For modelling purposes, six facies associations were defined (Figure  3 ; Table  1 ). They are presented with their equivalent to the facies association (FA) of  Deschamps et al. [2020]  in Table  1. These facies associations have been distributed on two separate depositional profiles representative of the Sequence 4 in the Alemania-Metán basin (Figure  3 ): the first one, a siliciclastic-dominated lacustrine margin is typical of the western part of the basin; the second one, a carbonate-dominated lacustrine margin is characteristic of the eastern part.
The relative proportions of microbialites per facies association (Table  2 ) have been computed from the 17 available sections. Although microbialites grow Porosity quantifications were performed by threshold color extraction and point counting methods on scanned thin sections with the free software "JMicroVision"  [Roduit, 2007]. Thin sections from 2 wells studied during the basin-scale study  [Deschamps et al. , 2017 , 2020 , Hamon et al. , 2012 , 2017 , Rohais et al. , 2011 , 2012]  and close to the modelled area were considered: Juramento which is at a distance of 6 km east of the modelled area and "CoreLP05" located 25 km away in the NE direction. Overall, 130 estimated values are available, 62 and 68 respectively coming from Juramento and from "CoreLP05" sections. Sedimentary facies and microbial forms were observed via respectively 96 and 34 thin sections.
The second categorical variable (for example diagenesis types, intensity or microbialite forms) simulation is defined by a second complete PGS model which parameters might be computed from a completely different dataset (hence the possible heterotopy). The two PGS simulations are linked through the given proportions of the second variable in each of the first variable classes. Finally, the different realizations of the two variables are combined to produce and visualize the joint simulation of both variables through a "combined facies". In our modelling workflow, for each value of the categorical variables, Gaussian distribution of their associated porosity were defined based on the thinsection measurements. The porosity values are drawn at random in these distributions according to the simulated facies.