dataesr/scientific-paragraphs-categorization huggingface dataset:
Silica and aluminium were measured by Inductively Coupled Plasma NexION 300D device (PerkinElmer, USA) with standard deviations less than 3%. Thermodynamic calculations, ions activity, solution saturation state, and pCO2 partial pressure were performed using the Geochemist's Workbench  (Bethke et al. , 2018)  and PhreeqC (the LLNL database, version 3. 06 Parkhurst and and Appelo 2010) softwares.
Fracture Observations Transverse and longitudinal views of the fracture surfaces were observed using the SEM Philips FEI: Quanta 200FEG/ESEM, 20kV. Thereafter, a nickel layer was electrodeposited in order to protect the fracture surface during the following mechanical polishing (up to 0. 02 µm colloidal silica suspension). EBSD orientation maps of the cross section of a quasi-cleavage fracture surface were performed similar to the procedure conducted by Shibata et al. [55]. TSL OIM software was used for the EBSD data collection and posterior analysis.
-Numerical model -Plasticity, Diffusion and Trapping Equations Finite element calculations (FEM) were performed for each specimen design to provide mechanical and hydrogen distribution within the bulk up to fracture, assuming J2 flow theory with isotropic and kinematic hardening  [56]  and diffusion-trapping model  [57]. Numerical calculations are computed with the finite element software Comsol Multiphysics. During loading, the mesh is reorganized to take into account the displacement. An elastoplastic law is identified from the plastic behavior of the alloy before necking in the framework of the classical elastoplastic theory  [56, 58, 59 ]. The validity of the FEM calculations was checked by comparing the calculated variations of engineering stress vs average longitudinal engineering strain and specimens' radius vs displacement until fracture with the experimental values.
25 Même si les objectifs ne sont pas les mêmes, le résultat des deux traitements est assez proches. Le travail du SERTIT étant de proposer un produit opérationnel dans des délais restreints, nous avons réalisé des analyses complémentaires en suivant la décrue et en estimant le nombre de bâtiment touchés par les inondations. Apport d'OpenStreetMap (OSM) et de la très haute résolution spatiale pour la détection des infrastructures impactées 36 OSM est un projet collaboratif qui a pour but de constituer une base de données géographiques sous licence libre (ODbL). Toute donnée ajoutée à la base est librement réutilisable. Depuis le début des années 2010 la communauté des contributeurs d'OSM est régulièrement sollicitée pour des actions de cartographie post catastrophes au travers de l'interface HOT (Humanitarian OpenStreetMap Team) dans les pays ne disposant pas de cartographie de référence à grande échelle.
Illustration 10 -Méthodologie de traitement des images SPOT 5 pour l'extraction des pixels en eau Le jeu d'images SPOT 5 qui a été mobilisé pour ce suivi a été téléchargé dans la base de données de la station SEAS-OI, qui disposait d'images de qualité aux périodes et sur les zones concernées. Il s'agit d'une image de référence en saison sèche (acquise le 17/08/2012), d'une image prise quelques jours après le passage du cyclone (26/02/2012).
Apports de l'imagerie satellite pour le suivi de l'impact des événements cycl. EchoGéo, 51 | 2020 confirmé par le calcul du nombre de bâtiment impacté. Grâce à la BDD OSM nous avons calculé que seuls 10 bâtiments avaient encore les pieds dans l'eau quatre jours après le passage du cyclone (environ 1/1000). Illustration 21 -Localisation des bâtiments issue de la base de données OSM sur le sud de la ville de Sambava Localisation utilisée pour la cartographie des bâtiments impactés suite au passage d'Enawo. Auteur : C. Révillion. 37 L'illustration 22 présente quant à elle une cartographie de l'impact des inondations sur les routes et les bâtiments, réalisée à partir d'une image GeoEye DigitalGlobe du 11/03/2017 dans le cadre de l'Emergency Management Service (EMS) de Copernicus, avec deux niveaux d'impact sur les routes et le bâti : faiblement endommagés en jaune et fortement endommagés en orange.
Comme le laisse suggérer la déclinaison de son « M », le BIM recouvre trois pratiques organisationnelles (Gallot et Le  Moënne, 2015, 5). La première vise à modéliser une maquette numérique de l'ouvrage à exécuter associée à une base de données comprenant l'ensemble des informations techniques liées à chaque composant de l'ouvrage (Model) ; la deuxième pratique consiste à organiser, à écrire et à animer le processus de production collaborative de cette maquette (Modeling) ; et la troisième pratique concerne le management de gestion de la plateforme collaborative (Management). Si les promesses proposées par les éditeurs de logiciels ou les consultants sont superbes lorsqu'il s'agit de démontrer les vertus des technologies et ses capacités à transformer les organisations  (Besson et al. , 2017) , le BIM devra aussi être considéré comme un mot-valise qui porte en lui l'utopie de la communication  (Breton, 2004).
Procedure The procedure for both tests is similar. A cup is filled either with a solid desiccant material or with distilled water leaving a small air space between them and the membrane. The sample of the porous material that needs to be tested is properly sealed to the edge of the cup to prevent side diffusion  [28]. The initial weight of the cup is taken and then the cup is placed in an environmental chamber where the air temperature and relative humidity are continuously measured. The test cup is then weighed periodically to provide enough number of data points throughout the test. For the dry test measures weight is gained inside the cup due to the transfer of the water vapor from the chamber (high humidity) to the desiccant (low humidity). As for the wet test, it marks weight loss where the water vapor is transmitted from the cup (high humidity) to the atmosphere inside the chamber (low humidity).
A fan is turned on to force the air movement above the pans in order to decrease the air side mass transfer resistance. A temperature and water vapor concentration gradient difference exists between the water inside the pans and the air passing above them which creates a difference in the vapor pressure. Thus a vapor flow occurs from the water side through the permeable membranes towards the air side. The inlet temperature and the relative humidity of the air are continuously measured using a digital thermocouple and a hygrometer and the data is recorded. In addition, the temperature of the water inside the cups is repeatedly measured and periodic weighing of the test pans is made to determine the rate of water vapor transmission.
Detailed Application of the Input Sources A detailed application is presented for NWF-1 in experiment 8 where 17 measurements are taken with an average interval of 10 to 15 minutes between each. The Gaussian PDFs of the different inputs are derived by which every input source is represented as an array of 17 elements. Then a Monte Carlo approach is adopted where the total number of trials N is taken as 4000 which corresponds to the number of simulations done. Several input sources of uncertainty can be considered and they are expressed in the following: Air Temperature (°C) The data logger used to measure the input air temperature has a certified value for its temperature ranges, and its calibration certificate states an uncertainty of ±0. 3°C at temperature 25°C. For every value of the temperature, a PDF is used where the mean is taken as the value of the measured temperature and the standard deviation is 0.
Mass (kg) The mass of the water inside the pans is obtained from weighing it periodically by a certified balance so several measured values are taken. The uncertainty associated with the mass using the data from the calibration certificate and the manufacturer's recommendations on uncertainty estimation is ±0. 01 g. So the uncertainty of each measured mass is represented by a separate PDF. The mean value of each PDF is taken as the measured mass value with a standard deviation of 10 -5 kg.
Results By following the same procedure, the uncertainty study is done for every material considering only one experiment for each. The input uncertainty sources are deduced in each experiment and then the mass conductivity of each membrane is calculated by performing simulations. We control Dymola through Python and run the simulation for 4000 times while substituting the actual values of the air temperature and relative humidity, the water temperature, the measured mass, the thickness of each membrane and the exchange area by their previously determined Gaussian PDFs. The simulation for one experiment gives 4000 values of the mass conductivity of the membrane. We determine. , 5 ] the mean for these values with its standard deviation ^ then the percentage uncertainty is calculated by the equation  (12).
Analysis Efficiency: Impact forces (IF) were extracted for all trials, and motions of the weapons were computed from the marker positions. The weapon angles of incidence (AI) at the impact were computed from the markers positions using local frames reconstructions, as shown in Figure  2. The impact time was detected by finding the minimal distance between the tip of the weapon and the target plan. Kinematics: Subject data was analysed thanks to the CusToM toolbox  (Muller et al. 2019). Joint angles for all trials were computed with a Levenberg-Marquardt inverse kinematics method applied to a scaled biomechanical model of the subject (42 dofs model). Ranges of motion (ROMs) for 5 of the armed limb joints were computed per trial. IF, AI, and ROMs were statistically investigated with a repeated measures ANOVA, including a Greenhouse-Geisser sphericity correction.
By characterizing such phenomena requires the development and application of advanced optical diagnostic techniques, capable of performing in-situ measurements of physical parameters during the injection and combustion events. Such experimental data is required to support the development of more predictive models for engine CFD. To tackle this challenge a group of international laboratories, the ECN (Engine Combustion Network: http:// www. sandia. gov/ecn/), initiated in 2008 a collaborative and voluntary research effort to develop an international database aimed at providing the necessary information for detailed CFD model validation and to coordinate experimental and modelling activities around this database. Since its inception, this collaboration has experienced strong growth, as underlined in Picket et al.
[2]. The latter underlines the growing interest of the engine combustion community, explained by the scientific relevance of the research (relying on international collaboration to address the scientific challenges facing the community), as well as the international visibility as a result of participation in the network. The collaborative project "ECN France", funded by the French National Research Agency (ANR-14-CE22-0015-708 01), was carried out in this context from 2014 to 2019. It brought together four French research laboratories that have gained expertise in the development and application of advanced optical diagnostics for engine combustion research. Its objective was to promote the active participation of the French engine combustion community within the ECN, and to develop innovative diagnostics for Diesel injection and combustion.
The research performed was based on the ECN spray A condition, representative of harsh pressure and temperature conditions, as defined by ECN (in particular injection of n-dodecane fuel at 363 K into gas at 900 K and 22. 8 kg/m 3 ). This special issue presents results from this collaborative project with five new papers  [3] [4] [5] [6] [7]  covering all aspects of the work performed, from standardization issues to advanced optical diagnostic developments. Experience from previous ECN research has taught us that repeating similar experiments in different facilities (i. e. standardization) is both extremely challenging and essential, since it is the prerequisite to enable the construction of a coherent cooperative database, and it tends to reveal hidden biases and uncertainties due to the measurement techniques or the set-up itself.
As an example, the first two ECN workshops in 2011 and 2012 were mainly dedicated to these standardization issues, and more recent workshops continue to address it. The first two papers  [3, 4]  fall within this context to present how the experimental conditions achieved in two new French facilities, both based on measurements performed within a Rapid Compression Machine, can reach the standards proposed by ECN. The accuracies of initial conditions themselves are presented in Ben Houidi et al. [3]  and a focus on data obtained for spray and combustion developments is addressed in Hespel et al. [4]. All data are compared to the IFPEN database, which was already shared within the ECN, and obtained in a precombustion chamber. It is interesting to note that this is the first time that configurations other than constant volume or constant flow cells are used to produce spray A ECN data, bringing complementary information to the ECN state of the art.
Remerciements A Sébastien Douche  ( Metz Métropole ),  Luc Manceau  (LOTERR),  Litifu Paruke ,  Mario Rega  ( CRC Dijon ),  Météo France ,  Atmo Grand-Est , ainsi qu'aux maires (en particulier  Monsieur le Maire de la ville de Metz) et techniciens des communes de Metz Métropole.
4 ). Overpressure development simulations have been performed using the finite element software BASIN  (Bitzer 1996 (Bitzer , 1999)). Modelling the stratigraphic and hydrodynamic evolution of a 2D section, BASIN allows the calculation of non-equilibrium compaction and overpressure generation on sediments by coupling compaction and 2D fluid flow. The consolidation model incorporates porositydependent sediment compressibility through the equation  (Bitzer 1996) : ( 𝜕 𝜕𝑥 𝑘 𝑥(Ø) 𝜕𝑝 𝜕𝑥 ) + ( 𝜕 𝜕𝑥 𝑘 𝑧(Ø) 𝜕𝑝 𝜕𝑧 ) = (1 -Ø) 𝜌𝑔𝛼 (Ø) 𝜕𝑝.
Initial thickness of the units, sedimentation rates and boundary conditions, as well as physical properties of the turbidite unit, are kept constant through the different scenarios. Since no samples associated with turbidites were available in our database, we defined its physical properties with values taken from the literature  (Reed et al. 2002). The only variables that change in between the different models are initial porosity, initial hydraulic conductivity and initial specific storage of the hemipelagic layer. In the first set of analysis (Scenario A), we defined the properties of the hemipelagic unit by averaging arithmetically the physical properties values for each study area. In the second series of tests (Scenario B), we assigned physical properties to each hemipelagic layer based on the average specific storage, porosity and hydraulic conductivity of the samples from each grain size class.
Acknowledgements This work was supported by  French Agence Nationale de la Recherche (ANR)  through the projects  LabEx EMC3  ( ANR-10-LABX-09-01 ),  SPLENDID2  ( ANR-19-CE08-0028 ), and the  European project  " NOVAMAT " co-funded by the  European Community funds FEDER  and the  Normandie region.
Summary Spam, the Software for the Practical Analysis of Materials is a Python library that has evolved to cover needs of data analysis from 3D x-ray tomography work and correlated random fields with mechanical applications. Spam is first and foremost a measurement package that extends the extremely convenient framework of NumPy  (Van der Walt, Colbert, & Varoquaux, 2011)  and SciPy  (Virtanen et al. , 2020)  by providing or accelerating tools for the materialscience/mechanics oriented analysis of 2D images or 3D volumes representing field measurements. Typical uses are either the measurement of displacements fields between images of a deforming sample from which strains can be computed, or the characterisation of a particular microstructure (correlation length or particle orientation). The package is organised into a library of Python tools which are expected to be used in user-written scripts and a number of more sophisticated standalone scripts.
ndimage. The filters toolkit is used in the projection tutorial, and illustrated examples gallery • helpers: toolkit of internal helper functions such as the parsers for the scripts, as well as tools for reading and writing TSV and VTK files. The latter partially uses meshio  (Schlömer et al. , 2020)  • kalisphera: wrapper for C++ version of kalisphera  (Tengattini & Andò, 2015)  which generates analytically-correct partial-volume spheres which are useful for testing discrete analysis code (see label below). The kalisphera toolkit is presented in the introduction, is used in the contacts tutorial, and is illustrated in the examples gallery • label: toolkit to measure and manipulate "labelled" images, where discrete particles are labelled with integer voxel patches. The computation of standard quantities such as the volume, centre of mass, and moment of inertia tensor of each particle can be done very conveniently and somewhat faster and with a smaller memory footprint than what is available in scipy.
Meshers based on Gmsh  (Geuzaine & Remacle, 2009)  are used through pygmsh and weighted Delaunay triangulation (Laguerre triangulation) is provided through an interface with CGAL (The CGAL Project, 2020) -alpha-shapes are also implemented to help clean up badly-shaped tetrahedra. In addition, a set of projection functions creates meshes able to represent heterogeneities (phases and interfaces of a given meso/micro structure) based on binary or trinary images or continuous fields (level set) with outputs easily convertible to any FE software  (Roubin et al. , 2015b; Stamati, Roubin, Andò, & Malecot, 2019). The mesh toolkit is presented in the introduction, at the heart of the projection tutorial, and illustrated in the examples gallery • plotting: toolkit of plotting tools based on matplotlib  (Hunter, 2007)  for creating complex plots such as a 3D orientation plot.
The plotting toolkit is presented in the introduction and used throughout tutorials and examples • visual: toolkit of graphical helper functions for scripts A number of scripts are available to be called from the command line. Currently, the most-used scripts are related to image correlation: • spam-ldic and spam-regularStrain: A "local" image correlation script, working for series of greyscale 2D or 3D images where kinematics are measured on independent points spread on a regular grid, accompanied by a strain computation script. spam-ldic is presented in the scripts page and is the subject of the DIC practice. spam-regular Strain is presented in the scripts page and is used in the strain tutorial • spam-ddic and spam-discreteStrain: A "discrete" image correlation script  (Andò, Hall, Viggiani, Desrues, & Bésuelle, 2012; Hall et al.
, 2010) , working on greyscale 3D images plus a "labelled" image of the reference configuration. This script also has its own strain calculation based on a triangulation of grain centres. spam-ddic is presented in the scripts page and is the subject of the discrete DIC tutorial • spam-gdic (in beta test): A "global" image correlation script, where the displacement field between two 3D images is computed as a global problem expressed on a tetrahedral mesh • spam-mmr and spam-mmr-graphical: A pair of "multi-modal registration" scripts (command-line and graphical) allowing 3D images of different modalities (e. g. , a neutron tomography and an x-ray tomography of the same sample) to be registered (i. e. , aligned).
Technical details Spam is based on simple Python data types, avoiding complex data structures, and all functions have a reasonable and safe set of default parameters, with required parameters kept to a minimum. Spam has a number of different use cases: • Interactive use, such as in iPython or Jupyter. Many outputs from 3D analysis in materials science are highly sensitive to the parameters used, encouraging a "live" exploration of optimal settings • Embedded use, such as importing in user-written Python scripts • Standalone use, particularly of the more complex spam-scripts. These chain together a number of functions and are intended to be called from a command line, and produce output as live plots, or files saved to disk Given the large data volumes often encountered in 3D analysis, critical parts of the code are written in C/C++ wrapped with appropriate Python calling functions which are responsible for checking input sanity.
The current wrapping method is with pybind11  (Jakob, Rhinelander, & Moldovan, 2017). Building on the large number of functions already available in NumPy (  Van der Walt et al. , 2011) , SciPy  (Virtanen et al. , 2020 ), scikit-image (Van der Walt et al. , 2014)  and making use of tifffile  (Gohlke, 2020)  and meshio  (Schlömer et al. , 2020) , the spam project adds a large amount of functionality, which means that a number of advanced forms of data analysis can be chained together in ways that are otherwise complex, requiring the combination of many different tools. Spam uses unittest to check each commit with a coverage of more than 90% of lines of code covered as of June 2020. Details of the coverage are available on the GitLab repository.
Online documentation The documentation for this project is available online at this address: https://ttk. gricad-pages. univ-grenoble-alpes. fr/spam/index. html There are three main components to the documentation: • The module index is built automatically by sphinx, and function headers are written in such a way (following the NumPy norm) that a brief description appears in the module index • A "Gallery of Examples" using sphinx-gallery where downloadable Python scripts or Jupyter notebooks are executed during the compilation of the documentation and whose results are visible • A series of "Tutorials" with longer and more detailed explanations are available, which cover the mathematical/mechanics background of the functions provided, and some longer examples of using the provided tools.
State of the field Other software packages exist for material science analysis, such as the popular commercial software Avizo, which is closed-source, cannot be inspected, and therefore has limited trust. Other open-source packages include ITK  (Schroeder et al. , 2003) , which is a quite complex ecosystem, and ImageJ/Fiji  (Rueden et al. , 2017) , which is not fully 3D and not well-suited to scripting or running remotely. All of the above have some version of the label toolkit which allows discrete objects to be characterised as well as some parts of the measurements toolkit. On the specific topic of Digital Image/Volume Correlation, many pieces of software are available for 2D, surface and 3D image correlation. The spam. DIC. register correlation engine uses a well-known mathematical framework dating back to  (Lucas & Kanade, 1981) , but distinguishes itself by being non-rigid, 2/3D compatible, clearly documented and relatively fast.
Acknowledgements We would like to acknowledge: • Cino Viggiani ,  Jacques Desrues , and  Bruno Chareyre in Laboratoire   3SR ,  Grenoble • Stéphane Roux  and  François Hild in LMT Cachan • Emmanuelle Gouillard in Saint Gobain/CNRS  and  Plotly • Jose Luis Cercos-Pita  at the  ESRF  and  Uppsala University  for discussions that have contributed to getting spam into its current form.
, 2017). This soft- ware has a graphical interface for facilitating correlation but is technically limited to displacements/rotations, and has a slow line-search in rotation space • muDIC: Pure Python toolkit, limited to 2D images (Olufsen, Andersen, & Fagerholt, 2020) but has a fully function global approach • UFreckles: Graphical Matlab code (Rethoré, 2018) working on 2D, surface and 3D images with a global approach • StrainMaster: Closed-source software for surface and 3D image correlation from LaVi- sion • VIC3D: High-speed closed-source software for surface imaging from Correlated Solutions Getting spam spam is available for Linux users with Python 3 as a PyPI package installed via pip.