dataesr/scientific-paragraphs-categorization huggingface dataset:
Illustrative examples To showcase the possibilities offered by NEOLAB, we can simulate the electrode performance over a broad range of discharging rates (C/20, C/5, C/2, 1C, 2C, 4C, 8C, and 12C), which means from the very low rate of a 20-h discharge all the way up to the very high rate of a 5-min discharge. The initial conditions represent a fully charged lead electrode. The first simulation step consists of completely discharging the electrode and the second to leave it relax during 30 min. The physical quantities provided in the code are related to a 50 mm wide, 55 mm high, and 0. 28 mm thick lead electrode, that is to say a lab-scale thin electrode able to deliver the power usually required to start an internal combustion engine. The electrode voltage allows the user to know what to expect from the physics, according to the model parameter values.
Battery modeling is a very lucrative job for those who know how to do it the proper way and this is particularly true when it involves physics and thus the knowledge of the battery internal state. Most of the people who want to achieve such an understanding of how physics explains the battery behavior are required to pay expensive licenses for software that are very useful when the geometry is complex or when one has no time to invest in learning how it works, but simply wants the things done. For sure, NEOLAB deals with a single spatial dimension, which makes the meshing simple whatever the number of domains to consider. However, this is not too much of a constraint, since the direction normal to the electrode plane is the direction of the moving species and charges and as such the most influential on the battery behavior.
Acknowledgments This work has been realized with the participation from members of  INES. 2S  and received funding from the  French State  under its investment for the Future Programme with the reference  ANR-10-IEED-0014-01. FG acknowledges the  French National Research and Technology Association  for supporting his PhD thesis under the contract CIFRE No. 2021/1034. The work of DD has been supported by the  French National Research Agency , through the  Investments for Future Program  (ref. ANR-18-EURE-0016 -Solar Academy ), and by the  Khalifa University of Science and Technology  under Award No. FSU-2023-014.
Indicator functions have also been used instead of SDF for shape reconstruction  [7]  or for shape generation  [11]. Indicator functions are interesting for they allow to focus on the surface itself without learning accurate distance values anywhere in the ambient space. Following the neural novel view rendering trend  [12]  which encodes a scene radiance field in the parameters of a multilayer perceptron optimized in a self-supervised way, INRs have been used for encoding a signed or unsigned distance field or an occupancy indicator, always with a view of reconstructing the surface. These networks are trained per shape and do not rely on a database. Atzmon and Lipman  [13, 14]  and Chibane et al. [15]  learn an SDF from raw data, point clouds or triangle soups, without needing an oriented normal, hence alleviating the need for a groundtruth sign or watertight surfaces.
Surrogates for medial axis transform have also been studied such as Deep Point Consolidation which optimize a set Fig. 2. Overview of our method. An implicit neural representation is trained on the input point set, uniform surface points and skeletal points are extracted, and the skeleton mesh is recovered using a cover-set formulation solved as a Mixed-Integer Linear Program (MILP). of inner points on a meso-skeleton  [41] , however this method requires a heavy machinery. Yan et al. [42]  describes a medial axis extraction method based on shape voxelization and Voronoi diagram extraction of the boundary voxels. This method has the advantage to come with theoretical guarantees. Our method avoids the voxelization step and relies instead on the regularized signed distance field to extract the medial axis.
Deep learning and skeleton extraction Animation skeleton computation has been tackled from a deep learning perspective  [44] , but few methods have tackled medial axis learning from a dataset. Point2Skeleton  [45]  uses a PointNet encoder  [9]  to synthesize skeletal point, and predict their links using a graph auto-encoder. However the skeletal points are not mathematically defined and the losses only encourage a medial-axis like position. Furthermore it requires to train on a shape database and is therefore limited to shapes that fit in the learned latent space. P2MAT-NET  [46]  estimates the set of medial points and medial spheres using a ground truth skeleton-shape dataset and links the medial points using a Delaunay triangulation which is later pruned. Both these methods are very different from our approach which works on a single shape and does not require a training dataset.
In a quite different direction, it has been argued that taking into account the medial axis for shape recognition leads to better recognition results  [47]. In practice it involves computing a set of medial spheres, using a computational geometry technique, and feeding it to a neural network. Skeleton extraction from a neural implicit perspective, without relying on a database, has not yet been tackled yet. Deep Medial Fields  [48]  was the first paper to link neural fields and medial axis transform, by estimating a medial field. For each point in the ambient space, it evaluates the shape width (i. e. the distance between the surface and the medial axis) in the ''slice'' containing the point. However this medial field is discontinuous near the surface, and two neural networks are used for handling this discontinuity.
Experiments and comparisons Our code is available at https://github. com/MClemot/Skeleto nLearning. We run all our experiments with a Nvidia RTX 3050 laptop GPU. We use the PyTorch library for our network implementation, the Gudhi library  [53]  for the regular triangulation and SciPy for the MILP solver  [54]. We test our algorithm on shapes from Thingi10K  [55]  and AIM@shape. We are interested in particular in shapes that exhibit a high genus and a lot of topological details such as the ones of Figs. 1  and 5. When the input is a mesh, we sample 100k points uniformly on the mesh triangles, following the method of Osada et al. [56]. We compare our skeleton with L 1 -medial skeleton  [34] , and VoxelCores  [42]  using the authors' code, Mean Curvature Skeleton  [32]  using the CGAL implementation, and reimplemented Coverage Axis  [43].
A. equolifaciens Encompasses Two Distinct Genomospecies with Strong Associations with Geography Exploring several public databases, we retrieved 154 high-quality Metagenome-Assembled Genomes (MAGs) of A. equolifaciens and 11 genomes derived from isolates. All genomes came from fecal material, essentially from humans but also from mice and rats (154, 10, and 1, respectively). We computed Average Nucleotide Identity (ANI) between all pairs of genomes, and we performed average linkage hierarchical clustering (UPGMA). Finally, we extracted clusters with a 95% ANI cut-off widely used to delineate microbial species  [32]. Finally, we looked at fecal microbiome data from 166 Spanish patients with liver cirrhosis  [31]  and compared them to 38 Spanish healthy controls of the MetaHIT project. Again, A.
A. equolifaciens Encompasses Two Distinct Genomospecies with Strong Associations with Geography Exploring several public databases, we retrieved 154 high-quality Metagenome-Assembled Genomes (MAGs) of A. equolifaciens and 11 genomes derived from isolates. All genomes came from fecal material, essentially from humans but also from mice and rats (154, 10, and 1, respectively). We computed Average Nucleotide Identity (ANI) between all pairs of genomes, and we performed average linkage hierarchical clustering (UPGMA). Finally, we extracted clusters with a 95% ANI cut-off widely used to delineate microbial species  [32]. Interestingly, genomes were clustered into two phylogroups, defining two distinct genomospecies (Figure  5 ). The first phylogroup consisted of, among others, the A. equolifaciens-type strain (DSM 19450) as well as the type strain of the subspecies celata (DSM 18785).
A. equolifaciens Displays Anti-Inflammatory Effect In Vivo and Impacts Mice Metabolism The effect of DSM 19450 A. equolifaciens strain given by oral gavage and used as a biotherapeutic agent was evaluated in a mouse model of hepatic steatosis, i. e. , C57Bl/6J SPF male mice treated with antibiotics and then inoculated with the gut microbiota of patients with NASH and further exposed to a high-fat, high-fructose diet. Mice were forcefed daily with A. equolifaciens resuspended in PBS or only PBS as control. Food consumption during the 10 weeks of follow up was similar in the two groups (data not shown). Interestingly, mice gavaged with A. equolifaciens gained less weight than the controls (Figure  8a, b ). This persisted during the 10 weeks of the experiment. We also observed that mice that received A.
Sequencing Data Preprocessing Sequencing data quality control was performed with fastp  [48]  (parameters: --cut_front --cut_tail --n_base_limit 0 --length_required 60) to remove low-quality reads and sequencing adapters. Then, reads mapped to the human genome (T2T CHM13v2. 0 GCA_009914755. 4) with bowtie2  [49]  were removed. Finally, read subsampling was performed with fastqsample (github. com/fplaza/fastq-sample (accessed on 27 May 2023)) to take into account variable sequencing depth across samples. The number of reads subsampled in each sample is indicated in Supplementary Table  S1.
Gene Coverage Table Generation The gene abundance table was generated with the METEOR software suite (forgemia. inra. fr/metagenopolis/meteor (accessed on 27 May 2023)). First, selected high-quality reads from each sample were mapped with bowtie2  [49]  against the updated Integrated Gene Catalogue of the human gut microbiome  [50]  (IGC2, 10. 4 million genes). Alignments with nucleotide identity ≥ 95% were kept to account for intra-species nucleotidic variability and the non-redundant nature of the catalog. Then, raw gene counts were computed with a two-step procedure previously described that handles multi-mapped reads  [51]. Finally, gene coverage was computed by dividing raw gene counts by gene length multiplied by average read length in sample.
Population Genomics 4. 2. 1. Data Availability A total of 154 high-quality Metagenome-Assembled Genomes (MAGs) (completeness ≥ 80% and contamination ≤ 5% assessed with CheckM2  [54] ) of A. equolifaciens were downloaded from various sources including the following: (1) the Unified Human Gastrointestinal Genome collection (UHGG)  [55] , (2) the Human Reference Gut Microbiome (HRGM)  [56] , and (3) the Murine Intestinal Microbiota Integrated Catalog v2 (MIMIC2)  [57]. In addition, 11 genome sequences derived from cultured isolates were downloaded from the NCBI GenBank (September 2022). Full list of genomes used in this study is available in Supplementary Table  S2.
Bioinformatics and Biostatistics Average Nucleotide Identity (ANI) between all pairs of genomes was computed with fastANI  [32]. A 165 × 165 distance matrix was built where distance was defined as "1-ANI". Then, this matrix was submitted to a Non-Metric Multidimensional Scaling (NMDS) process implemented in vegan R package  [58]. The NMDS output was used to map the genomes in a two-dimensional plot. Finally, genomes were clustered into phylogroups using average linkage hierarchical clustering (UPGMA) using a 95% ANI cut-off. Sequences of proteins involved in the equol production pathway were downloaded from the NCBI: daidzein reductase (dzr) = WP_022741749. 1, dihydrodaidzein reductase (ddr) = WP_022741751. 1, tetrahydrodaidzein reductase (tdr) WP_022741752. 1, and WP_022741755. 1 (racemase).
Purified amplicons were sequenced using the MiSeq sequencing technology (Illumina, San Diego, CA, USA) at the GeT-PLaGe platform (Genotoul, Toulouse, France). Paired-end reads obtained from MiSeq sequencing were analyzed using the Galaxy-supported pipeline named find, rapidly, operational taxonomic units (OTUs) with Galaxy Solution (FROGS)  [66]. For the preprocessing, reads with length between 380 bp and 500 bp were retained. The clustering and chimera removal tools, followed the guidelines of FROGS. Assignment was performed using the Silva 132 database updated in December 2017 with top quality pintail 100 (https://www. arbsilva. de/, accessed on December 2017). OTU with abundances lower than 0. 005% were removed from the analysis  [67]. 16S sequencing data were analyzed using the Phyloseq, DESeq2 and ggplot2 R packages in addition to custom scripts as described previously  [68].
Short-Chain Fatty Acids Quantification of Cecal Contents Measurement of the short-chain fatty acids (SCFAs) and branched-chain fatty acids from mice cecal contents was performed using gas chromatography, as previously described  [69]. The analyses were performed on an Agilent (Les Ulis, France) gas chromatograph equipped with a split-splitless injector 7850 and ionization flame detector. Carrier gas (H2) flow rate was 10 mL/min, and inlet, column, and detector temperatures were 200, 100, and 240 • C, respectively. Data were collected and peaks were integrated using OpenLabChem station software (Les Ulis, France).
Acknowledgments: We acknowledge the staff of the animal facility (IERP-UE907, INRAE,  Jouyen-Josas, France ) in which animal experiments were performed. The IERP Facility belongs to the  National Distributed Research Infrastructure for the Control of Animal and Zoonotic Emerging Infectious Diseases through In Vivo Investigation (EMERG 'IN; doi:10. 15454/1. 5572352821559333E12). The authors are thankful to  Fabienne Béguet-Crespel  for expert anaerobic bacterial culture.
With the growing demand for lightweight gear transmission systems, composite materials have emerged as a promising solution due to their high specific properties. However, the complexity of designing gear pairs with composite materials necessitates the development of reliable numerical procedures. This study presents a robust numerical approach using a flexible multibody method through the MSC MARC solver to accurately estimate static transmission error (STE) in lightweight gears, considering the nonlinear behavior caused by gear contact. The Finite Element (FE) model uses the Multi-Point Constraint equations (MPCs) to ensure the non-penetration condition considering a node-to-surface contact detection. The proposed method is compared against commercial software for standard gear pair cases, demonstrating its effectiveness in addressing complex structures based on composite materials.
• To compute the static transmission error, root stress, and the stresses arising from the chosen lightweight technique simultaneously. • To establish an adaptable procedure for lightweight gear design, which could be based on either geometry or material modifications (e. g. , gears with slots, holes, or the use of composite materials). • To develop a modeling strategy that effectively considers fiber orientations for composite materials and their impact on the static transmission error response. The structure of this paper is as follows: Section 2 outlines the FE-based contact formulation employed through the MSC MARC solver, detailing the node-to-segment contact procedure in conjunction with the multibody modeling. Section 3 compares the outcomes generated by the proposed numerical procedure and those derived from commercial software for standard gear pair cases.
FE-based contact through MSC MARC solver Several procedures have been developed to detect the motion of bodies and apply constraints to prevent penetration. These procedures employ Perturbed or Augmented Lagrangian methods, penalty methods, and direct constraints. Contact simulation has often necessitated the use of special contact or gap elements handling the interaction between two surfaces coming into contact. However, integrating these elements and ensuring their accurate behavior can be challenging and computationally intensive. Instead, MSC MARC addresses contact interactions at the level of individual nodes and segments within the existing finite element mesh (MSC. Software, 2021). During the iteration process, A bounding box algorithm is employed to swiftly ascertain whether a node is near a segment.
Flexible multibody simulation The FE mesh is generated according to the existing gear pairs geometry using solid elements 3D CHEXA Nastran elements Deformable to deformable contact in MSC MARC  (Kuijpers, 1994). Frontiers in Mechanical Engineering frontiersin. org  (MSC Software Corporation, 2022). A fine mesh is defined in the areas of the tooth where contact is expected to occur as the nonlinear contact can be captured. A much larger mesh was used for the teeth far from the contact zone. The supporting shafts are modeled as infinitely rigid via a central master node rigidly connected to the inner nodes of the gears. The gear backlash is covered by rotating the driving gear to achieve contact at an initial state. Finally, the obtained FE model is imported into MSC MARC Solver for the STE Computation.
Numerical validation In this section, a mesh convergence study is first conducted. Subsequently, the STE results from the method described earlier are compared with those obtained from commercial software, namely, ROMAX © (Romax Technology, 2021) and VIBRAGEAR ©  (Garambois et al. , 2017) , in its module TERRA (Transmission ERRor Analysis) is proposed. Gears are designed without any microgeometry modification to compare the loaded STE caused by the teeth deflection from the meshing process. Two important aspects are being tracked in the following comparison. First, the PPTE, with paramount importance, constitutes the amplitude of the excitation being transmitted to the gearbox system. Second, the STE curve shape as it translates the harmonic distribution of the excitation. Under operating conditions, a harmonic order could make an intersection with a specific mode, potentially affecting the analyzed system's NVH signature.
Software assumptions In ROMAX © (Romax Technology, 2021), the microgeometry analysis is based on a thin strip model where the contact teeth are divided into many uncoupled springs across the face width. Figure  4  gives a simplified overview of how this works. The first tooth is modeled as a series of uncoupled springs. These springs capture the profile of the micro geometry modifications. In the example below (for simplicity), only one gear is modeled with springs. However, in the ROMAX © software, both teeth are modeled with springs to capture the difference in pinion and wheel gear tooth stiffness. Considering the backlash, misalignment and torque to calculate the deformed profile, the two gears are bought into contact. This is repeated over the roll angle steps. The microgeometry analysis assumes that the misalignment is constant with the roll step.
The image also shows the absolute value of the TE for one roll step (the Sum of the effects of backlash and deformation). This is calculated at each role step, and the peak-to-peak and harmonics are calculated. On the other hand, VIBRAGEAR © presents two different ways to calculate the flexibility matrix:  Helical gear pairs 0. 5 0. 4 0. 5 • An analytical model based on the Ritz method, used to calculate the flexibility matrices associated with the contact lines from an analytical model of a thick plate of variable thickness using a Reissner-Mindlin theory. • From the generation of a finite element mesh allowing the calculation of flexibility matrices using a FE-software. Then, the interpolation of the flexibility matrices associated with the discretized contact lines is created and ready to be scaled with the input data.
Method validation A spur and helical gear pair configurations were tested to develop different test conditions in order to better understand the general trend between the results. The macro-geometry parameters and the operating torque are reported respectively in Tables 1, 2 for spur and helical pairs. Figures  5A, B  demonstrate that all software yield comparable peak-to-peak transmission error (PPTE) values for the helical and spur gear cases, respectively, with results reported in Table  3. Another comparative criterion is the shape of the curve. ROMAX © and VIBRAGEAR © exhibit similar behavior, with a sudden transition observed from two teeth in contact to one. In contrast, the Multibody FE approach presents a more gradual transition. This difference is attributed to commercial software not directly calculating displacement at a contact point but interpolating it from surrounding nodes, as shown in Figure  6B.
Conclusion This work has successfully demonstrated the development and application of a robust numerical procedure using a flexible multibody method through the MSC MARC solver to analyze lightweight gear transmission systems, especially those incorporating composites. By addressing the inherent complexities associated with designing gear pairs that utilize such materials, the proposed method contributes significantly to the ongoing advancement of their applications in gear transmission systems. The present method demonstrated its effectiveness through validation studies with existing commercial software, ROMAX © , and VIBRAGEAR © , showing a good correlation for spur and helical gears. Moreover, the method revealed certain phenomena that were not captured by other techniques. Further application of the numerical procedure to analyze hybrid metal-composite gear pairs and comparison with holed gears provided valuable insights into the time evolution and harmonic components of STE.
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity, which requires moving away from broadly-defined functional types. Different approaches have been adopted in the last years to incorporate a trait-based perspective into modeling exercises. A common parametrization strategy involves using trait data to represent functional variation between individuals while discard taxonomic identity, but this strategy ignores the phylogenetic signal of trait variation and cannot be employed when predictions for specific taxa are needed, as in applications to inform forest management planning. An alternative strategy involves adapting the taxonomic resolution of model entities to that of the data source employed for large-scale initialization and estimating functional parameters from available plant trait databases while adopting alternative solutions for missing data and non-observable parameters.
Here we report the advantages and limitations of this second strategy according to our experience in the development of MEDFATE (v. 2. 8. 1), a novel cohort-based and trait-enabled model of forest dynamics, for its application over a region in the Western Mediterranean Basin. First, 217 taxonomic entities were defined according to woody species codes of the Spanish National Forest Inventory. While forest inventory data were used to obtain some empirical parameter estimates, a large proportion of physiological, morphological, and anatomical parameters were mapped to measured plant traits, with estimates extracted from multiple databases and averaged at the required taxonomic level. Estimates for non-observable key parameters were obtained using meta-modeling and calibration exercises. Missing values were filled using imputation procedures based on trait coordination, taxonomic averages or both.