dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We thank  Littoral-Environnement-Télédétection-Géomatique (LETG)-Rennes laboratory , University of Rennes-2, and  Indian Institute of Science (IISc) -Bengaluru  for facilitating the study. The authors also would like to thank the " Assimilation of Multi satellite data at Berambadi watershed for Hydrology And land Surface experiment " (AMBHAS) team and the  Berambadi  watershed farmers for their kind cooperation. The authors also thank the  United States Geological Survey (USGS) ,  Indian Space Research Organization (ISRO) , and  Le site du Centre national d'études spatiales (CNES)  for providing satellite images.
This study was funded by  Indo-French collaboration research projects such as IFC-PAR/CEFIPRA AICHA  project  4700-W1  ( 2013-2016 ), ATCHA  ANR-16-CE03-0006  ( 2017-2020 ), a UBL Ph. D. student grant for mobility (2017), and  CNES/TOSCA  ( Irriga-Detection  project ( 2017-2019 )). Institutional Review Board Statement: Not applicable.
The present study focuses on DRX. Hence, we selected for post- mortem analysis of the microstructure the most deformed samples, with bulk strains (l f -l 0 )/l 0 ranging between 29. 6 and 50. 1%. These samples exhibit non-uniform deformation with a necking zone where strain and stress concentration led to DRX. We performed a detailed EBSD mapping of one to three strongly recrystallized domains in the necking zone of each sample. The low deformation zone close to the piston of the finegrained mylonite, which preserved the original microstructure of the sample, was also mapped and used as a reference for DRX under natural conditions. The mechanical behaviour and the microstructure evolution of the full experimental dataset are presented in a companion article  (Ben Ismail et al. , 2021).
Electron backscatter diffraction (EBSD) data acquisition EBSD maps were performed with a CamScan X500-FE CrystalProbe SEM-EBSD equipped with the Oxford NordlysNano EBSD detector at Geosciences Montpellier (France). Operating conditions were an acceleration voltage of 15 kV and a working distance of 24-25 mm under low vacuum conditions (~5 Pa). EBSD patterns were indexed using the AZtecHKL software of Oxford Instruments. For EBSD analysis, samples were pre-polished with colloidal silica suspension to remove any damage produced during the mechanical polishing with diamond paste and not carbon-coated. We analysed local maps with step sizes varying between 1 and 2. 5 μm (mainly 2 μm) covering areas from 716 × 638 up to 5365 × 3456 μm 2. Indexing rates in raw maps range between 72 and 87% of the surface analysed.
Electron backscatter diffraction (EBSD) data treatment Post-acquisition data treatment was performed using the MTEX toolbox v5. 2 or higher  (Bachmann et al. , 2010; Mainprice et al. , 2014)  and in-house MATLAB codes provided in Supplementary Material. Wild spikes and orientation data with a mean angular deviation (MAD) above 1. 3 • were removed from raw data. Grains were then segmented using a Voronoi decomposition algorithm with the threshold misorientation set to 15 •. For the characterization of the misorientation (angle and axis of rotation), we selected the one that produced the smallest rotation angle, that is, the disorientation  (Lloyd et al. , 1997; Mainprice et al. , 1993; Randle and Ralph, 1986; Wheeler et al. , 2001). Thus, all misorientation angle/axis pairs in this study are strictly disorientations although the general term misorientation is used to refer to them.
The direction of extension is oriented E-W in all maps. generated minimum stress and preferred slip system inverse pole figure (IPF) maps using in-house Matlab codes provided in the Supplementary Material. The procedure to estimate the minimum stress (normalized by the CRSS of the weakest slip system) required for plastic deformation of a crystal with a given orientation relative to the imposed stress field and the dominant and subdominant slip systems within the IPF space were as follows: 1) A large number (10 5 ) of olivine random orientations are generated to uniformly cover all possible orientations in the IPF space. 2) The slip systems expected to be active in olivine (Table  1 ) including all symmetrically equivalent slip planes, their CRSS values for hightemperature deformation conditions, and the extension direction are defined.
Acknowledgements We dedicate this article to  A. Nicolas , an exceptional researcher who set the basis of the petrophysical analysis of mantle deformation. The experiments were carried out with support from an  EU-Marie Skłodowska-Curie postdoctoral  fellowship to WBI. The EBSD data analysis was supported by a postdoctoral fellowship co-funded by the  European Union  and the  Government of the Principality of Asturias (Spain)  [grant number within the  Marie Skłodowska-Curie COFUND Actions FP7  to MALS and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation programme  (grant agreement No  882450 -ERC RhEoVOLUTION ). Fig. 15. Synoptic scheme showing the evolution and main features of the boundaries leading to dynamic recrystallization by subgrain rotation in olivine.
The data are gathered in Table  4. The mean diameter of the SG aggregates measured by SANS and AFM modulus images are in very good agreement. For the lowest SG uptake, the number of primary particles within an aggregate ((D agg /D PP )  3  ) is mainly proportional to the SG uptake while it increases less rapidly for higher SG content. This observation suggests that the size of the SG aggregates may be limited by the accommodation of the sPEEK matrix (mechanical resistance and deformation of the polymeric aggregates, free volume). The size of the primary particles remains independent (D PP_SANS $ 11 nm) of the SG uptake, which is an expected result. The primary particles are also too large to reside within the ionic domains. In a nutshell, AFM, 3D FIB-SEM, SANS and WAXS experiments show that the sPEEK and SG phase coexist as two independent phases without signicant interaction.
Acknowledgement The authors declare no competing interests in the publishing of this work. They acknowledge the support of the  Universities of Strasbourg and Oslo , of the  CNRS INSU ALEAS program  and of the  IRP France-Norway D-FFRACT. We thank the  Research Council of Norway through its Centres of Excellence funding scheme , project number  262644. We are also grateful for the support of the  Lavrentyev Institute of Hydrodynamics , through Grant No  14. W03. 31. 0002  of the Russian Government.
, 2020). As an alternative,  Tournassat and Steefel (2015)  used a model based on continuous equations and properties of negative surfaces to successfully interpret the experimental dataset previously reported in  Tertre et al. (2015). However, the interlayer diffusion coefficients of cations reported by  Tournassat and Steefel (2015)  were found to vary with the salinity of the aqueous reservoir. These molecular diffusion coefficients are thus apparent and cannot be considered as self-diffusion coefficients (i. e. , depending only on the environment of the confined probe in the interlayer space at thermodynamic equilibrium) commonly derived from molecular simulations. Accordingly, the question remains open regarding the ability of continuum approaches to connect the molecular self-diffusion coefficients derived from molecular simulations at short timescales with laboratory experiments timescales for cationexchange reactions in a single clay disk immersed in different aqueous reservoirs.
In this context, the aim of this present work is firstly to assess, within the framework of continuous approaches, the possibility to account for molecular self-diffusion coefficients to predict the dynamics of the cation-exchange reaction in the case of a single disk immersed in aqueous reservoirs. For this purpose a simple finite-volume model constrained by (i) interlayer self-diffusion coefficients stemming from MD simulations and (ii) interlayer selectivity coefficients, describing interlayer exchange, is used to simulate cation-exchange rates as a function of the diffusion time for a millimetre-sized particle immersed in an aqueous reservoir characterized by different concentrations. Secondly, the roles played by both self-diffusion and selectivity coefficients on the dynamics of the cation-exchange are assessed by the confrontation of predicted data with the experimental dataset.
, 2016). Experimental results are expressed in terms of the % Ca (relative to the CEC) released in solution as a function of the diffusion time. Aqueous concentrations of Ca cations were measured using an atomic absorption spectrophotometer (Varian AA240FS) with all samples analyzed in 2% HNO 3 solutions. To take into account possible interferences during analyses due to the extreme saline conditions used, LaCl 3 and KCl solutions were added in the samples. As recommended by  Lajunen and Peramaki (2004) , amounts of these added solutions were chosen in order to have final concentrations in the samples equal to 1 and 2 g/kg water for LaCl 3 and KCl, respectively. In this present study, Sr 2+ -for-Ca 2+ data were obtained for three contrasted initial aqueous SrCl 2 concentrations (i. e. , 10 -2 , 2х10 -4 , and 3х10 -5 mol/kg water ).
Molecular dynamic (MD) simulations Self-diffusion coefficients of calcium, strontium and sodium were determined by MD simulations, which started from a prior equilibrium configuration obtained by GCMC simulations. Calculations were performed by using the LAMMPS software  (Plimpton, 1995). It should be noted that when two types of cations are considered in the interlayer (i. e. , Ca 2+ and Sr 2+ or Ca 2+ and Na + ), the water content used in the structural model has been adjusted using the LAMMPS integrated GCMC algorithm. The microcanonical (NVE, in which N, V and E represent the number of particles, volume and energy, respectively) ensemble was applied for the simulation. A Verlet algorithm  (Allen and Tildesley, 1994)  was used to obtain the trajectories of the confined cations. The elementary time step employed for the calculations was fixed to 0.
2) and for different Ca 2+ /Na + and Ca 2+ /Sr 2+ interlayer occupancies (i. e. , XCa value). Furthermore, amounts of initial reactive Ca were calculated from total amounts of Ca exchanged (i. e. , released) at infinite time predicted by means of interlayer selectivity coefficients previously published in  Tertre et al. (2013)  and  Dzene et al. (2016). These total amounts were calculated with the Phreeqc® software  (Parkhurst and Appelo, 1999)  associated to the thermodynamic database proposed by  Johnson et al. (1992)  for aqueous species, assuming thermodynamic equilibrium between the solid and aqueous phases with an initial chemical composition representative of the experiments (solid/solution ratio and initial aqueous concentrations for Na + , Ca 2+ , Sr 2+ and Cl -ions). These calculations were performed by considering (i) an interlayer CEC equal to 175 meq/100 g hydrated-solid as proposed by  Dzene et al.
0х10 -3 39 0. 34 a pH of all initial solutions used are equal to 5. 9 ± 0. 15. b Calculations are performed assuming a thermodynamic equilibrium between initial solution and a mass of hydrated solid equal to 0. 12 g for 1 kg water. This solid/ solution value is representative of experiments using millimetre Ca-disks, and is equivalent to consider a CEC for disk equal to 175 meq/100g hydrated-solid in agreement with published value for this parameter  (Dzene et al. , 2016). Calculations are performed using the Phreeqc® software  (Parkhurst and Appelo, 1999)  and previous models proposed by  Dzene et al. (2016)  and  Tertre et al. (2013)  predicting Sr 2+ -for-Ca 2+ and Ca 2+ -for-Na + interlayer exchange respectively in vermiculite. In these models, decimal logarithms of selectivity coefficients for Sr 2+ -for-Ca 2+ and Ca 2+ -for-Na + (Gaines Thomas convention;  Gaines and Thomas, 1953)  are equal to 0 and 0.
Acknowledgements Hugo Petit  ( IC2MP/HydrASA, Poitiers, France ) is thanked for his experimental contribution to the Ca 2+ -for-Na + isotherm measurement performed with the 50-150 μm size fraction. The authors are grateful to the  EC2CO CNRS/INSU   BIOHEFECT  project (Project  DIFFMATARG ) and the  European Union (ERDF)  and " Région Nouvelle Aquitaine"  for providing the financial support for this study.
Whatever the value chosen for the variable on the abscissa, CO concentration is constant. Figure  3  is a correlogram between intake pressure, CO concentration, fuel/air ratio φ, engine speed N and Spark Advance SA. It allows analyzing the relationship between each pair of numeric variables in a dataset. We notice that neither spark advance, nor engine speed or intake pressure show high correlation factor with CO concentration. Andrianov  [16]  proposes to use a polynomial of degree 5 to model the dependence of the CO concentration on fuel/air ratio. Abida and Claude  [13]  use the root of a polynomial of degree 2, which gives a suitable shape by reducing the number of coefficients to be adjusted. We have therefore chosen to use the form proposed by Abida and Claude (Equation (  1 )) and to adjust the coefficients with the least squares method.
Acknowledgments We thank  Dr. Francius Gr  egory and  Dr. Xie Wang  for providing bacteria strains and initial training in growing the bacteria culture. We acknowledge  Dr. Renard Aur  elien ( LCPME, France ) for XPS analyses. The research activity of NY was financially supported by funds from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement 711859and by financial resources for science in the years 2017e2021 awarded by the  Polish Ministry of Science and Higher Education  for the implementation of an international cofinanced project.
Application to automotive components In this section, two releases of an automotive mechanical component are studied to assess the advantages of the proposed approach. The first release, for which experimental data are available, is taken as the reference design D re f to construct the associated time transformation function of the automotive component based on both, statistical analysis of the experimental data and numerical assessment of the structural reliability. The second one is a modified release of the reference design to better respond to car constructors' requirements. It is considered as the upgraded design D up , where the corresponding true reliability function (i. e. experimental reliability) will be derived by applying the time transformation function to the numerical reliability function obtained through Monte-Carlo simulations on the DDM-based surrogate model.
Therefore, T ≤ χ 2 (1 -α, k -1) which validated the assumption. The median fatigue lifetime Nf ,0 of the component at operating loading conditions, is also estimated and shows that the failure of the component occurs during Low-Cycles Fatigue (LCF) stage, since the fatigue lifetime do not exceeds 10 5 cycles. In addition, the experimental reliability function of the reference design and the corresponding 95% confidence bounds are plotted in Fig. 7 (a). In order to construct the time transformation function associated to the studied mechanical component, numerical analysis is also carried out based on the proposed approach presented in section 3. First of all, a 3D finite element model of the reference design has been developed using the software Abaqus  [43]. The mechanical behavior of the constitutive material follows the well-known Ramberg-Osgood law  [44]  and the Coffin-Manson-Morrow's fatigue model  [45]  was used to compute the fatigue lifetime.
In other words, the time transformation function is rather related to the product and invariant against the design. Thus, the true reliability (i. e. experimental reliability) can be easily obtained by applying the time transformation function to the results of numerical reliability analysis carried out on the upgraded design, following the method developed in section 3. First of all, a 3D finite elements model is developed on the software Abaqus to compute the fatigue lifetime of the upgraded design, using the same assumptions on the boundary conditions as for the reference design. It is important to note that the upgraded design is built of the same material as the reference design and Ramberg-Osgood constitutive law and Coffin-Manson-Morrow fatigue model are also used. However, the loading conditions have been a little bit modified (see Fig.
Analysis of the Second Scenario For this first scenario, the Pareto front is presented in Figure  7. The minimum cost was obtained with the weights  = 0. 45 and  = 0. 45. The first objective resulted in a cost of $12,215. 5, while for the second objective it was $0 because no time windows were violated in any of the nodes. The opening coordinates of each transshipment centers are  (28, -70) ,  (25, 40) ,  (24, 40) ,  (14, -70) , (-51, -40), and (-52, -40), respectively (see Figure  8 ). The arrival times in minutes at each node are: j1 = 150, j2 = 140, j3 = 127, j4 = 128, j5 = 147, and j6=117. Just as in the first scenario, the resulting speeds for the process of distribution in the arc (j,c) are extended to the maximum of the time window to minimize the travel speed (see Table  5 ). To apply the model for real case study, we used a database provided by a company located in Barranquilla, Colombia, dedicated to the supply of products to different super-.
Analysis of the Case Study To apply the model for real case study, we used a database provided by a company located in Barranquilla, Colombia, dedicated to the supply of products to different supermarkets. The data set consists of 2 plants, 3 transshipment ULS, 3 zones, 15 points of sale, 2 speeds range, 3 products, and 29 trips. The vehicles are split up into four categories (9 vehicles of 35 ton, 3 vehicles of 32 ton, 6 vehicles of 20 ton, 6 vehicles of 8 ton, and 5 vehicles of 4 ton). The demand data are set at 8500 packaging units. There are three types of packaging: 60 × 40 × 25 for large products with a total weight of up to 7 kg. For medium-sized products, the measures are 40 × 30 × 25 with a total weight of up to 12 kg. Finally, for small products, the measures are 60 × 40 × 12. 5 with a total weight of up to 10 kg.
Data regarding the actual demand of customer is kept confidential; hence a random allocation was performed for the purpose of this experiment. This randomization was performed for the quantity of packaging for each customer, in addition to the quantity of each type of packaging. The load configuration for each type of vehicle was calculated with the help of Quick Pallet Maker software under the criteria of 99% weight or volume efficiency. The preparation costs for each trip correspond to the monthly costs of 4 forklifts, 50 logistics assistants, and forklift battery costs. Costs were calculated based on the total number of vehicles and by vehicle type. Likewise, penalty costs for violation of time windows were calculated considering the general trip preparation costs (i. e. , penalty costs for each minute corresponds to the cost of one minute of trip preparation).
Here, we first establish the fundamental link between dwell time, wave sensitivity and resolution in a semi-analytical study of the prototypical example of object localization inside a linear chaotic cavity using spectral DoF. Then, we demonstrate our finding experimentally in the microwave domain, using configurational DoF provided by a simple programmable metasurface. To start, we consider for concreteness a 2D model problem in semi-analytical simulations based on a coupleddipole formalism (see Refs. [15, 46]  and SM). As depicted in Fig. 2(a-c ), we consider three scenarios expected to correspond to different durations of the scattering process: free space, a cavity with quality factor Q = 263 and a cavity with Q = 556. In each case, the transmission S 12 between a transmit and a receive port is measured for various frequencies in order to localize (using ANNbased data analysis) a non-resonant dipole that could be located anywhere along a circular perimeter.
They contain conglomerate and sandstone more abundant than siltstone, clay, limestone, marl, gypsum, salt, felsic, mafic, shale and volcanic rocks. X-ray diffraction analysis was performed on a BRUKER Advance D8 diffractometer in a - configuration using CuK radiation ( = 1. 54 Å) for the three soils. The diffractograms were recorded between 2° and 55° with the Lynxeye detector and are showed figure  3. The duration of a recording is 3 hours. The qualitative analysis was carried out with the software X'Pert HighScore Plus 2. 2. The diffractograms mainly present calcite and quartz. A separation of the fraction less than 2 μm to highlight the clay part was carried out. The presence of chlorite and muscovite was observed. Moreover, the presence of chlorite in the sample was confirmed by heating treatment.
In the supplementary materials, we argue that the latter uncertainty dominates, so that the likelihood is determined by the prior over the illumination slant. The Bayesian model thus depends on the priors over both relief and illumination slant. For relief, we consider a flat prior and an empirical prior favoring small reliefs derived from the Southampton-York Natural Scenes (SYNS) dataset  (Adams, Elder, Graf, Leyland, Lugtigheid, & Muryy, 2016; Ehinger, Adams, Graf, & Elder, 2017). For illumination slant, we consider a flat prior and a two-parameter beta distribution model that we fit to the human depth judgments. The Bayesian model estimates depth from shadows based on the whole range of possible light sources without estimating a specific light source direction. And why would it? Unless we are shielding our eyes from something bright, our actions seldom require a knowledge of the light source location.
Acknowledgments The authors thank  Krista Ehinger  for her assistance with extracting the statistics of surface relief from the  SYNS dataset. Supported by  VISTA grant  " Depth from Shadows " to  James Elder  and  Patrick Cavanagh , an  NSERC   Discovery grant  to  Patrick Cavanagh  and by an  ANR-17-EURE-0017 FrontCog grant  to  Roberto Casati  with technical assistance from  Laura Galin-Corini ,  Josée Rivest , and  Yuqian Hou. Commercial relationships: none. Corresponding author:  Patrick Cavanagh. Email: patcav1@yorku. ca. Address:  Department of Psychology, Glendon College, Toronto ,  ON M4N 3M6 ,  Canada.
As an industrial application, we consider the modeling of the automotive cabins thermal behavior. The data used for the training step are obtained via solutions of differential algebraic equations by means of a design of experiment. Simple cooling simulations are run during the training step. We show a simulation speed-up when the reduced bond graph is used to simulate the driving cycle of the WLTP vehicles homologation procedure, while preserving accuracy on output variables. The variables of the original bond graph are split into a set of primary variables, a set of secondary variables and a set of tertiary variables. The reduced bond graph contains all the primary variables, but none of the tertiary variables. While secondary variables are coupled to primary ones via an artificial neural network.
Introduction Nowadays, engineering systems are getting more and more large, nonlinear and integrated. The Bond Graph (BG) approach  [1]  allows the modeling of such systems thanks to its ability to account for multiphysics interacting systems in a unified manner. These bond graph models rely on a system of Differential Algebraic Equations (DAEs), responding to some input excitations and resulting in few outputs useful in engineering. As part of this work, we will use simulation data generated by bond graph models as inputs to data dimension reduction algorithms in order to setup a reduced DAE system (RDAE) related to a Reduced Bond Graph (RBG). This work belongs to hybrid strategies that marry Ordinary Differential Equations (ODEs), or DAEs, with Artificial Neural Networks (ANN)  [2, 3]. We are using the inherent generative capability of ODEs or DAEs, not for data augmentation as in  [4] , but for Bond Graph reduction.