dataesr/scientific-paragraphs-categorization huggingface dataset:
and is widely used in discrete element simulations in the field of geotechnical engineering. Most in-house software is developed independently in university laboratories. Note that we did not investigate the details of them used in the round robin test. Yade is an open-source framework for DEM simulations. Although the core computation parts are written in C++, the user interface is prepared with the Python language for easy handling. LIGGGHTS is an open-source discrete element simulator and is an extension of the molecular dynamics software, LAMMPS (described below). In comparison with LAMMPS, LIGGGHTS has the following additional features: CAD geometry handling, heat conduction, contact force formulation, and particle arrangement using 3-D meshes.
LAMMPS is a classical molecular dynamics simulation code (open-source). While LAMMPS is designed for molecular dynamics simulations, it comes with an original granular mechanics package which is to be distinguished from LIGGGHTS. HiDEM is a Fortran 90/95 based commercial software developed by Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Furthermore, DEPTH is a commercial software developed from HiDEM that implements an iterative dynamic load balancer algorithms  (Furuichi et al. , 2017)  enabling it to run the world's largest-scale DEM simulation on the massive parallel computer systems.
This implies that the variation in the particle configuration had a greater e↵ect on the angle of repose than the variation in the physical properties. This round robin test allowed us to consider the trend in DEM software. We found that the use of PFC3D or in-house software is relatively frequent. Moreover, we found that powerful open source DEM software was also used (Yade, LIGGGHTS, LAMMPS, Kratos). When introducing DEM software, ease of installation, documentation, richness of functions, and ease of use are important considerations, and it was determined that the open source software listed here meets these criteria. In addition to the popularization of powerful DEM software, developing software specializing in particle shape modeling (e. g. ,  Angelidakis et al. (2021) ) further promotes the use of DEM in various engineering fields.
Conclusions According to the tabulation of the DEM simulation results for two types of experimental settings, most simulation cases submitted by participants agreed with the experimental results with a certain level of accuracy in both average and variance values for the angle of repose, irrespective of the types of experiment. For a few cases where the discrepancy with the experimental results was large, it was concluded that this discrepancy was attributed to the selected values of modeling parameters, and to the employed modeling approach (i. e. clumps versus rounded convex particles). In other words, most of the software used in the round robin test works correctly providing the proper parameter settings are used. The collected data also revealed trends in the selection of the interparticle constitutive model (Voigt and Hertz-Mindlin models) and the DEM software (PFC3D, in-house, and Yade).
Acknowledgement We would like to thank  Professor Catherine O'Sullivan  of the  Imperial College London  for providing us with materials on an example for DEM verification. We also would like to express our appreciation to the participants for their cooperation: Assoc. Prof. Mitsuteru Asai  of  Kyushu University ;  Dr. Stéphane Bonelli  of  INRAE Aix Marseille University ;  Mr. Yu Hirano  of the  University of Tokyo ;  Mr. Ryoh Kuramoto  of  Kozo Keikaku Engineering Inc. ;  Prof. Takashi Matsushima  of  University of Tsukuba ;  Dr. Shintaro Ohno  of Kajima corporation;  Dr. Pierre Philippe  of  INRAE Aix Marseille University ;  Prof. Stefano Utili  of  Newcastle University ;  Dr. Mori Utsuno  of Kajima corporation;  Dr. Yishu Wang  of  Hohai University. In addition, we appreciate the TC105 international committee for supporting the international activity, especially for advisement of the core members on the advertisement and invitation of the participants.
Hidden layer 1 Hidden layer 2 Entry layer Output layer The Python program used for training the neural network was created with the dedicated Python library, Tensorflow  [28]. The training phase employed the Adaptive Moment Estimation (ADAM) optimizer  [29]. The training was conducted using 50,000 epochs of the experimental dataset. It took approximately 15 min to train the ANN model and obtain the converged parameters on a Dell XPS-13 7390 laptop running Ubuntu 22. 04 LTS 64-bit with 16 GB of RAM and an Intel 4-core i7-10510U processor.
[26] , we have developed a twohidden-layer artificial neural network-based flow law in Tize Mha et al. [25]. Figure  9  shows the general structure of this ANN. The input layer of the ANN consists of three neurons that correspond to the model inputs, which are ε,. ε, and T. The output layer of the ANN contains one neuron representing σ, while there are two hidden layers. All details regarding the artificial neural network, experimental data setup, and training method are detailed in the paper proposed by Pantalé et al. [26]. We again used the Tensorflow library to develop the training program and employed the ADAM optimizer for the training phase. The training database, as described in Section 2. 1, contains 21,030 quadruples (we have 701 × 30 quadruples because ε ∈ [0, 0. 7] with an increment of 0.
001, so we have 701 values and 30 data curves) of values for strain (ε), strain rate (. ε), temperature (T), and stress (σ). We selected six different networks, named 3-n-m-1, where n represents the number of neurons in the first hidden layer and m indicates the number of neurons in the second hidden layer, to demonstrate the significance of choosing the appropriate number of neurons in these two layers. The training was conducted based on 5000 epochs of the experimental dataset. It took 40 min to train the ANN model and obtain the converged parameters on a Dell XPS-13 7390 laptop running Ubuntu 22. 04 LTS 64-bit operating system with 16 GB of RAM and an Intel 4-core i7-10510U processor. Figure  10  shows the evolution of the training error, defined by the log 10 of the internal E RMS , during the training phase.
This incorporation establishes a connection between theoretical models and practical phenomena, ultimately leading to a more deep comprehension of the complex processes occurring during deformation. Taking our investigation further, we integrated the JMAK model into the widely used ABAQUS software to simulate the evolution of DRX during hot compression tests. This allowed us to capture the process's response to varying conditions without bias. By comparing our simulation results with experimental data and using microstructure analysis, we have validated the accuracy of our simulations and confirmed the effectiveness of our predictive models. It is noteworthy that although our research represents a significant milestone, it also presents opportunities for future exploration. Further enhancing the accuracy and applicability of our predictions can be achieved by fine-tuning our model, expanding our dataset, and refining the integration process.
Acknowledgments: The authors acknowledge  Jean-Benoit Morin , Director of Metallurgy and Quality from  Finkl Steel-Sorel ,  Abdelhalim Loucif  from the  R&D department of Finkl Steel-Sorel, Ecole de Technologie Superieure, and Ecole Nationale d'Ingenieurs de Tarbes, France , for providing technical data, materials, and testing facilities.
Similarly, Erkaya  [12]  used the genetic algorithm to find the Jansen mechanism linkage lengths that will result in the minimum foot trajectory variations. While developing a legged closed-chain passive-locomotion platform, Wei et al. [13]  optimized the foot trajectory. Two objectives were introduced, vertical variation and the longitudinal force between foot and ground. The multi-objective optimization was carried out via a weighted objective function. A new mechanism optimization process simplifies the mechanism design and improves its outcomes. The non-dominated sorting genetic algorithm (NSGA-II) was used for optimization, while objective functions were evaluated through Simscape Multibody (Matlab subroutine). The mechanism's multi-objective optimization was carried out for three objective function pairs.
Maximum Foot Acceleration While reducing the speed fluctuation will result in a more uniform acceleration during the stride, it will not directly affect its maximum value. However, reducing the maximum foot acceleration will result in a lower maximum inertial force, often used as an input while sizing the drive mechanism. Hence, foot acceleration was used as the fourth objective function. The maximum acceleration was obtained from the Simscape data as a result of x-axis and y-axis accelerations and was minimized: f a 4 x max. (6).
Despite this observation, a previous study, using  39 ,704 records from 1054 river basins to assess whether introductions of nonindigenous species and extinctions increased the taxonomic similarity of freshwater fish faunas worldwide (i. e. , taxonomic homogenization), had concluded that the current level of homogenization was still low  (25). However, over recent years, documentation and compilation of data on freshwater fish introductions have greatly increased, notably with the release of the most comprehensive global database on freshwater fish occurrences, which tripled the number of records (110,565) and the number of river basins (3119) included  (26). This database compiled both introductions and extirpations of freshwater fishes, making it possible to explore whether biogeographic regions of freshwater fishes have been altered by humans.
From the global database of freshwater fish occurrence, we extracted four pools of species for our analyses: natural (i. e. , native occurrences without introductions or extirpations), Anthropocene (i. e. , present-day distribution of species, native occurrences plus anthropogenic introductions, and extirpations), natural plus introduction only, and natural plus extirpation only. First, we explore how introductions and extirpations together have altered the natural endemism rates in drainage basins within fish biogeographic regions  (12) , endemism defined here as the natural occurrence in a single region. Drainage basins are the spatial units upon which biogeographic regions are built; therefore, we expect any substantial changes in the endemism rates of drainage basins to impact the delineation of biogeographic regions.
reticulata), grass carp (C. idella), European perch (Perca fluviatilis), pumpkinseed (Lepomis gibbosus), Nile tilapia (Oreochromis niloticus), and silver carp (Hypophthalmichthys molitrix). species extirpations (70 documented extirpations) and introductions (6227 documented introductions) in our dataset. We hypothesize that the role of extirpations is unresolved, because our data on extirpations may be incomplete or biased due to the difficulty to ascertain and report extirpations in comparison to introductions  (65). This limit could be addressed in the future by exploring scenarios of extinctions  (19) ; however, such an exploration may be difficult to implement, because the red list for freshwater fishes is not as comprehensive as those for mammals, birds, and amphibians.
Distribution data We used the global database on freshwater fish species occurrence in drainage basins  (26) , version updated in August 2019 (see Data and materials availability statement to access it). This version comprises 110,565 occurrence records for 14,953 species in 3119 drainage basins of the world, covering more than 80% of the terrestrial surface. A drainage basin is defined as the area of land where precipitation collects and drains off into a common outlet that could be either a lake (endorheic basin) or the ocean (exorheic basin). Occurrence records correspond to samplings and collection data from sources published between 1960 and 2019. Species names in the database were validated according to FishBase  (75)  and the Catalogue of Fishes  (76) , and occurrence records were screened by the team developing the database  (26).
We applied the same filters as in  (12) , to define strictly freshwater fishes. To summarize, we used drainage basins as geographical units and species level information. We excluded species that could disperse through marine waters, and retained only families having fewer than 10% of their species occurring in marine waters. The resulting dataset included 66,715 records on 11,333 species in 2771 basins. All records have descriptors detailing the native, introduced, and extirpated status of each species within each drainage basin of the world  (26). Tedesco et al. (26)  defined an introduction as an established nonnative occurrence and an extirpation as the documented disappearance of a species from a drainage basin. Occurrences were confidently classified as "introduced" if the species introduction happened after around 1850.
Before that, there is insufficient literature on introductions to distinguish native from introduced occurrences. As a consequence, older species introductions may have been misclassified as native occurrences in our dataset. Likewise, extirpations before 1850 may have been missed because of the sparse literature available before this date. With the "introduced" and "extirpated" information, we extracted four pools of species from the database (Table  1 ): (i) the natural pool, which includes only the native distribution ranges of species and corresponds to the pool of species used to establish global regions of freshwater fishes  (12)  with marginal differences due to database updates; (ii) the Anthropocene pool, which describes the present-day state of freshwater fish biogeography by considering both introductions and extirpations; (iii) the extirpation-only pool, which contains the natural pool altered only by extirpations; (iv) the introduction-only pool, which contains the natural pool altered only by introductions (Table  1 ).
Because of these features, Map Equation has been recommended for bioregionalization analyses  (10)  and has been shown to outperform other clustering algorithms to detect regions of freshwater fishes  (12). We used the Map Equation community detection algorithm version 2. 6. 0 (77) with 1000 trials to find an optimal solution. The step-by-step code to reproduce the analysis is available in the biogeonetworks R package  (78). We constructed four biogeographic networks with our four pools (Table  1 ), and applied the Map Equation algorithm to each network. The outputs of Map Equation can be conceptualized as a tree with multiple levels, where the largest clusters are found at level 1. These first-level clusters have the highest degree of endemism. Each cluster at level 1 can be subdivided into smaller clusters at level 2, with a lower degree of endemism, and so on until no more biogeographic structure is detected.
At the maximum level of each branch, the leaves of the tree are always nodes (i. e. , species or basins). Here, we mapped specifically the first three clustering levels of our different bioregionalizations. These levels 1, 2, and 3, respectively, corresponded to supercontinental regions, major regions, and subregions of freshwater fish species in Leroy et al. (12). To compare changes among pools, we used the biogeographic structures identified by Map Equation to produce radial trees of each bioregionalization with the R package ggtree  (79) , in order. In addition, we provide visualizations of the four networks constructed with Gephi v0. 10. 0 (80); we spatialized all networks with the Force Atlas 2 algorithm under default settings. Because the algorithm identifies clusters mainly on the basis of endemism, the first levels of the bioregionalization often detect several marginal clusters of minimal size, such as areas with extremely low species richness of endemic species, typically islands.
Since the focus here is on the major biogeographic regions, we defined a threshold area of 1,000,000km 2 to distinguish major Table  1. Description of the four pools of species extracted from the global database on freshwater fish occurrences used for our analyses. Each pool was constructed by considering or not the events of introductions and/or extirpation in each drainage basin from the global dataset. Two species do not have native occurrences because they have recorded introductions outside of their native range, but are native from drainage basins not included in the freshwater fish database  (26). biogeographic regions from minor clusters. Such a threshold is large enough to ensure that minor clusters are not identified as major regions at coarse levels where the difference in area between clusters is generally extreme.
Biogeographic patterns of species introductions To understand the differences in bioregionalization between species pools with respect to introductions, we mapped the flows of introductions within and between natural biogeographic regions with an alluvial diagram, using the ggalluvial R package  (81). We mapped onto this diagram the introductions of fish species from their region of origin to the region(s) where they were introduced. Then, for each introduced species, we calculated three metrics to describe their introduction patterns: (i) the total number of basins where the species was introduced; (ii) from this total number of introduction basins, the percentage of basins outside the native region of the species; and (iii) their dilution value, which describes the degree to which a species expands its range beyond its native region, thereby contributing to weakening the definition of its cluster  (19).
The Relational to RDF Mapping Language (R2RML) became a W3C Recommendation a decade ago. Despite its wide adoption, its potential applicability beyond relational databases was swiftly explored. As a result, several extensions and new mapping languages were proposed to tackle the limitations that surfaced as R2RML was applied in real-world use cases. Over the years, one of these languages, the RDF Mapping Language (RML), has gathered a large community of contributors, users, and compliant tools. So far, there has been no well-defined set of features for the mapping language, nor was there a consensus-marking ontology. Consequently, it has become challenging for non-experts to fully comprehend and utilize the full range of the language's capabilities. After three years of work, the W3C Community Group on Knowledge Graph Construction proposes a new specification for RML.
Introduction In 2012, the Relational to RDF Mapping Language (R2RML)  [37]  was released as a W3C Recommendation. The R2RML ontology  [8]  provides a vocabulary to describe how an RDF graph should be generated from data in a relational database (RDB). Although R2RML gained wide adoption, its potential applicability beyond RDBs quickly appeared as a salient need  [49, 63, 76, 87]. Targeting the generation of RDF from heterogeneous data sources other than RDBs, several extensions  [49, 76, 87 ] preserving R2RML's core structure were proposed. As R2RML and the growing number of extensions were applied in a wider range of use cases, more limitations became evident  [76]. Consequently, these languages were further extended with different features, e. g. , the description of input data sources or output RDF (sub)graphs  [76, 96] , data transformations  [40, 47, 66, 69] , support for RDF-star  [48, 91] , etc.
This join may have a condition to be performed, which is indicated using rr:joinCondition, rr:child, and rr:parent. Subject Map, Predicate Map, Object Map, and Graph Map are subclasses of rr:TermMap, which define how to generate RDF terms. Term Maps can be (i) constant-valued, i. e. , always generating the same RDF term (line 7); (ii) column-valued, i. e. , the RDF terms are directly obtained from cells of a column in the RDB (line 9); or (iii) template-valued, i. e. , the RDF terms are composed from the data in columns and constant strings (line 5). Listing 2: R2RML mapping rules. According to the R2RML specification, an R2RML processor is a system that, given a set of R2RML mapping rules and an input RDB, can construct RDF graphs. Therefore, an R2RML processor should have an SQL connection to the input RDB where the tables reside and a base IRI used to resolve the relative IRIs produced by the R2RML mapping rules.
Motivation and Challenges In this section, we discuss the limitations of generalizing R2RML to construct RDF graphs from heterogeneous data sources and their impact on the ontology. We also consider the required extensions for the ontology to construct RDF graphs that were not possible before, e. g. , RDF collections and containers or RDF-star  [56]. Based on these limitations, we group the challenges in the following high-level categories: data input and RDF output, schema and data transformations, collections and containers, and RDF-star. Data Input and RDF Output. In R2RML, the desired RDF graph is constructed from tables residing in only one RDB. R2RML recommends to hardcode the connection to the RDB in the R2RML processor, hence, rules in a mapping document cannot refer to multiple input RDBs.
To date, a wide range of data formats and structures is considered beyond RDBs, such as CSV, XML, or JSON. These sources may be available locally or via web APIs, statically, or streaming. Thus, a flexible approach for constructing RDF graphs from a combination of these diverse inputs is desired  [95]. The R2RML ontology needs to be extended to also describe what the data source is for each set of mapping rules, e. g. , a NoSQL DB or a Web API, and what the data format is, e. g. , CSV, JSON or XML. In addition, a per row iteration pattern is assumed for RDBs, but this may vary for other data formats. RML  [49]  proposed how to describe heterogeneous data assuming originally that these data appear in local files and a literal value specifies the path to the local file. In parallel, xR2RML  [76]  proposed how to extend R2RML for the document-oriented MongoDB.
A more concrete description of the data sources and their access, e. g. , RDBs, files, Web APIs, etc. was later proposed  [50] , relying on well-known vocabularies to describe the data sources, e. g. , DCAT  [74] , VOID  [23] , or SPARQL-SD [101] and further extended  [96]  to also describe the output RDF (sub)graphs. The description of NULL values  [94] , predetermined in RDBs but not in other data sources, has not been addressed yet. Schema and Data Transformations. Integrating heterogeneous data goes beyond schema-level transformations, as it usually involves additional data-level transformations  [67]. The R2RML ontology describes the schema transformations, i. e. , the correspondences between the ontology and the data schema. It delegates data transformations and joins to the storage layer, by using operators in SQL queries.
However, not all data formats can leverage similar operators, e. g. , JSON does not have a formal specification to describe its data transformation, nor do all formats' operators cover the same data transformations, e. g. , XPath offers a different set of operators compared to SQL. Moreover, there are cases in which such pre-processing is not possible, e. g. , for streaming data. Thus, the R2RML ontology needs to be extended to describe such data transformations. RML+FnO  [39] , R2RML-F  [47] , its successor FunUL  [66]  or D-REPR [100] are examples of the proposals providing support to data operations. How-ever, only RML+FnO describes the transformation functions declaratively. RML+FnO has been well adopted in the community by being included in a number of RML-compliant engines  [25, 58, 59, 86]  and RML+FnO-specific translation engines  [65, 85].