dataesr/scientific-paragraphs-categorization huggingface dataset:
Transmission electron microscope (TEM) TEM and EDX analyses were performed on finely divided cement materials of the white crust to examine the texture of the M-S-H phase and to estimate its chemical composition. Observations were carried out with a Philips CM20 microscope coupled with an EDAX EDX operated at 200 kV. The cement phase of the white crust was separated and slightly crushed between two glass plates to eliminate residual carbonate and quartz aggregates. Observations were made on two types of preparation deposited on carbon coated TEM copper grids: 1) a few micrograms of material dispersed in ultra-pure ethanol using an ultrasonic bath, and 2) several microtome thin sections of material embedded in LR white ® resin. The data correspond to raw counts.
Electron microprobe on the white crust Element maps (Si, Al, Fe, K, Na, Ca, Mg, Ti, Mn, S) on the white crust in contact with OPA were processed using the mMAPphase software  (Prêt et al. , 2010)  to display the spatial distribution of the phases over a surface area of 1 Â 1 mm 2 with a resolution of 2 mm. All the pixels of the mapped area were first projected in a Si e Ca e Al 3 FeMg ternary diagram in which the theoretical compositions of the main clinker phases, pozzolans, hydrates and aggregates were superimposed on the diagram to ease reading and interpretation of these chemical scatterplots (Fig. 10 ). The full methodology of mineral segmentation combined with macropore thresholding on the BSE images yielded the detailed phase map where each pixel is associated with mineral species or macropores  (Gaboreau et al.
8, (Ca, Mg) smectite, calcite and amorphous silica was calculated at 10. 3 using the PHREEQC (version 2. 18) software and the THER-MODDEM thermodynamic database (version 2011). This pH is in the range of pH expected between clayrock and cementitious material. The (Ca, Mg) smectite formation might also be interpreted as the result of attenuated interaction between both materials, and rather indicates a relative continuity of the sorption capacities of the materials at the concrete/clayrock interface.
Acknowledgements This research has been financially supported by the partners of the Mont Terri Consortium participating in the  CI Experiment (ANDRA, CRIEPI, FANC, Nagra, Obayashi , and SCK CEN) and the  BRGM Research. We thank  C edric Duee ,  Nicolas Maubec ,  St ephane Touzelet ,  Guillaume Wille  from  BRGM  and  Val erie Montouillout  from  CNRS-CEMHTI (Orl eans ) for their analytical support. We are grateful to  Dr Karen M. Tkaczyk  ( McMillan translation ) for proofreading and editing the English text. The associate editor,  Dr Eric Wieland  and the two anonymous referees are thanked for their constructive comments and the improvement of the manuscript.
This paper review the present technologies for the fabrication of solar cells and modules based on the most common semiconductors namely silicon, CuInGaSe(S) and CdTe materials as well as on III-V concentrated photovoltaic cells and modules. For silion technology, we give insights on the growth of monocrystalline and multicrystalline silicon wafers and then we describe the most common solar cells designs and how to fabricate them. We also provide information about the fabrication of silicon modules and their performances. As for the thin-films solar cells, we present the structurale and optical properties of the CIGS and CdTe materials as well as the solar cell structures. The multi-junction concept cell that involves several III-V materials of different bandgaps is also described, and data on their fabrication, performances and mounting as modules are presented.
, 1998 (Nenes et al. , , 1999) )  model for inorganic thermodynamics and gas-aerosol partitioning. We calculated the organic aerosol concentrations using the SOAP model  (Strader et al. , 1999). The calculation of dry deposition was based on the algorithms of  Zhang et al. (2003). The initial and boundary conditions for the chemical species were obtained from the Model of Ozone and Related Chemical Tracers (MOZART) global model data for 2010 with a time resolution of 6 h  (Horowitz et al. , 2003). These data were then interpolated to the size and resolution of our grid using the CAMx preprocessor MOZART2CAMx (RAMBOLL-ENVIRON, 2016). The photochemistry in CAMx is performed in two steps. First, clear-sky photolysis rates are calculated externally by the Tropospheric Ultraviolet and Visible (TUV) radiation model  (NCAR, 2011)  and then used as input into CAMx, where they are internally adjusted every hour for clouds, aerosols, pressure and temperature  (Emery et al.
, 2010). In addition, for more accurate radiative transfer calculations, the eight-stream discrete ordinates scheme was used  (Stamnes et al. , 1988). Total Ozone Mapping Spectrometer (TOMS) data obtained by the National Aeronautics and Space Administration (ftp://toms. gsfc. nasa. gov/pub/ omi/data/) served as total ozone column input for both TUV and CAMx. The meteorological input and the emissions are discussed in detail in the next sections.
Meteorology The meteorological parameters required as input for the air quality simulations were generated by the Weather Research and Forecasting model  (WRF, version 3. 7. 1; Skamarock et al. , 2008). The model domain and horizontal resolution were identical to those used for CAMx model (see Sect. 2. 1) while there were 31 vertical layers up to 100 hPa, of which 14 were selected for the CAMx runs for computational efficiency. The terrain and land use data were taken from 10 arcmin data available from the United States Geological Survey (USGS). The selected key physical options for WRF parameterization are summarized in Table  S1. Initial and boundary conditions for WRF were generated using 6 h European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis global data of 0. 72 • × 0.
Emissions We used the TNO-MACC-III European anthropogenic emission inventory for 2010 provided by the Netherlands Organization for Applied Scientific Research (TNO). The TNO-MACC-III is an extension of the TNO-MACC-II emission inventory  (Kuenen et al. , 2014)  with some updates which are described in  Kuik et al. (2016). It contains annual emission data for 10 Selected Nomenclature for Air Pollution (SNAP) categories per grid cell (Table  S2 ). The TNO emission domain covers the same geographical space as our domain (Sect. 2. 1) but with a higher horizontal resolution (0. 125 • × 0. 0625 • ). By applying the monthly, weekly and diurnal profiles provided by TNO, we calculated the hourly gridded anthropogenic emissions of species required for CAMx. The total NO x and NMVOC (non-methane volatile organic compound) emissions per SNAP category in summer 2010 are shown in Fig.
Observations Meteorological observations from European stations with 3 h time intervals were obtained from the British Atmospheric Data Centre (BADC) using the UK Met Office Integrated Data Archive System (MIDAS) Land Surface Stations database  (Meteorological Office, 2013). Even though the UK stations have hourly observations, for the sake of a more homogeneous and consistent model performance evaluation for the whole European domain, the 3 h interval was used for the UK stations as well. The extracted meteorological parameters were dew point and air temperature at 2 m (T ), wind speed and direction at 10 m (WS and WD, respectively) and surface air pressure. The water vapor mixing ratio (q v ) was calculated using the dew point temperature and surface air pressure as described in the literature  (Bolton, 1980; Wagner and Pruß, 2002).
Only stations that belong to the synoptic network (SYNOP) were used for the WRF performance evaluation, as only those stations meet the requirements for forecasting as given in the MIDAS user guide (http://badc. nerc. ac. uk/data/ukmo-midas/ukmo_guide. html) and therefore contain data appropriate for comparison with the instant WRF output values. All data are reported in UTC. There are no direct measurements of the PBLH, but it can be estimated with different methods by using sounding data. Such data were extracted from the University of Wyoming database (http://weather. uwyo. edu/upperair/sounding. html). All 79 sites have one sounding at 12:00 UTC and most of them have also a second one at 00:00 UTC. Since not all sites have soundings at 00:00 UTC and the concept of the PBLH applies only for convective periods, only the soundings at 12:00 UTC were selected for evaluation.
We used the bulk Richardson number (Ri bc ) method to estimate the PBLH aboveground, which is considered as the altitude where the Ri bc exceeds a critical value Ri cr  (Seibert et al. , 2000). Although there is a range of values for Ri cr proposed in the literature  (Richardson et al. , 2013; Zhang et al. , 2014) , we selected the Ri cr to be 0. 25 for both stable and unstable conditions which is also used in the PBLH calculations with the YSU scheme in WRF  (Hong, 2010). The same method and the critical value were also used in other air quality modeling studies for PBLH evaluation  (Brunner et al. , 2015; Bessagnet et al. , 2016). The observational data for the surface air pollutant concentrations (http://acm. eionet. europa. eu/databases/) were taken from the European Air Quality Database v7 (AirBase; Mol ) MB = 1 N N i=1 (M i -O i ) Mean gross error (MGE) MGE = 1 N N i=1 |M i -O i | Root mean square error (RMSE) RMSE = 1 N N i=1 (M i -O i ) 2 Index of agreement (IOA) IOA = 1 - N •RMSE 2 N i=1 M i -O + O i -O 2 Pearson correlation coefficient (r) r = N i=1 (M i -M) • (O i -O) N i=1 (M i -M) 2 • N i=1 (O i -O) 2 Mean fractional bias (MFB) MFB = 1 N N i=1 2 • (M i -O i ) M i +O i Mean fractional error (MFE) MFE = 1 N N i=1 2 • |M i -O i | M i +O i and De Leeuw 2005).
In order to reduce the uncertainty due to grid resolution, we used only background rural stations with hourly (UTC) measurements for comparison with the model output. The chemical species used in the evaluation are O 3 , NO 2 , SO 2 , CO and PM 2. 5. In addition, we used ozonesonde data from the World Ozone and Ultraviolet Radiation Data Centre (Toronto, Canada; http://woudc. org/data. php) for six sites to evaluate the vertical profiles of ozone, temperature and wind speed (discussed in Sect. 3. 3). A short description of the ozonesonde stations is given in Table  S3. Finally, data quality filters were applied to exclude surface stations with less than 90 % data availability and with elevation higher than 700 m. For the radiosonde sites, a less strict filter of two-thirds data availability was applied due to the low measurement frequency.
Acknowledgements. We would like to thank the following agencies for preparing the datasets used in this study:  TNO  for the anthropogenic emission inventory; the  European Environmental Agency (EEA)  for the air quality data; the  European Centre for Medium-Range Weather Forecasts  (ECMWF) and  British Atmospheric Data Centre (BADC)  for the meteorological data; the  United States Geological Survey (USGS)  for the land use data; the  World Ozone and Ultraviolet Data Centre (WOUDC)  and its data-contributing agencies for the ozonesonde profiles; the  University of Wyoming  for the radiosonde profiles; the  National Aeronautics and Space Administration (NASA)  and its data-contributing agencies (NCAR,  UCAR ) for the TOMS and MODIS data, the global air quality model data and the TUV model. Calculations of meteorological data were performed at the  Swiss National Supercomputing Centre (CSCS).
Here, we will resort to a trip-based formulation of the MFD reservoir dynamics firstly proposed in  (Arnott and Rowse, 1999; Arnott, 2013; Lamotte and Geroliminis, 2016)  and whose properties are analytically investigated in  (Mariotte et al. , 2017). This formulation permits to keep track of all individual vehicles in a given area (reservoir) while the mean spatial speed is given by the MFD with respect to the current accumulation. This formulation makes it possible to individualize the trip-length of all vehicles and then to precisely consider the parking search phase depending on the parking occupancy, i. e. the ratio between the number of occupied and free parking spots. With regards to this latter process, experimental data from the city of Lyon (France) are analyzed to refine parking search laws previously proposed in the literature.
Distance to park with constant parking occupancy From the literature review, it appears that Eq. (  4 ) is the most relevant. We now try to calibrate this relation further referred as SP1 on real data kindly provided by the CEREMA in France. Such data has been collected in ten different districts of the city of Lyon/Villeurbanne (France) in 2008. For each district, several random destination addresses are assigned to some drivers. Each driver has first to reach the address and then starts looking for a parking spot in a pre-defined area that permits to walk no more than 250 m to finally reach the destination. Time and distance to park are recorded. Simultaneously, other investigators walk through the different regions to estimation the value of t. The global experiment lasted four days and t was estimated on average for three different time periods every day: morning (9h30-11h45); afternoon (14h15-16h30) and evening (17h-20h).
Method XANES spectra were analysed using the Athena-Artemis Package  [26]  based on the IFEFFIT  [27]  and AUTOBK  [28]  programs. The spectra were normalised using Athena software (pre-edge line, post-edge line, E 0 position). The background was removed using the AUTOBK algorithm, the k-weight parameter was between one and three, and the k-range was defined from 1. 5 to 12 Å -1. The structures of the standards were used in the XANES calculations to validate the position of the main peak. The XANES calculation is not yet accurate enough to reproduce the first oscillation, but it reproduces the position of the main peak, thus enabling a direct observation of the absorption edge position that could be related to a specific set of Ge oxidation states and ligand types (Figure  1 ). The XANES calculations are reported in Figure  3.
Method XANES spectra were analysed using the Athena-Artemis Package  [26]  based on the IFEFFIT  [27]  and AUTOBK  [28]  programs. The spectra were normalised using Athena software (pre-edge line, post-edge line, E0 position). The background was removed using the AUTOBK algorithm, the kweight parameter was between one and three, and the k-range was defined from 1. 5 to 12 Å -1. The structures of the standards were used in the XANES calculations to validate the position of the main peak. The XANES calculation is not yet accurate enough to reproduce the first oscillation, but it reproduces the position of the main peak, thus enabling a direct observation of the absorption edge position that could be related to a specific set of Ge oxidation states and ligand types (Figure  1 ). The XANES calculations are reported in Figure  3.
The first spectrum (Sph1) has its main 11,107. 5 eV peak at the same position as the first peak of the GeI2 standard. This first configuration can be interpreted as germanium under the form 2+ and, as iodine and sulphur have similar electronegativity, Sph1 can be interpreted as germanium 2+ in a sulphur environment. The second spectrum (Sph2) has its main peak at 11,109 eV, which is identical to that of the GeI4 peak of the "GeI2 standard", but is also at the same position as the renierite peak. This configuration can be interpreted as germanium 4+ in a sulphur environment. The last spectrum (Sph3) has its main peak at 11,112 eV, corresponding to the peak acquired on the GeO2 standard. This configuration can be interpreted as germanium 4+ in an oxygen environment. The Athena software was used to decompose each of the spectra acquired on the natural sphalerite samples as a linear combination of Sph1, Sph2, and Sph3 spectra.
This configuration can be interpreted as germanium 4+ in a sulphur environment. The last spectrum (Sph3) has its main peak at 11,112 eV, corresponding to the peak acquired on the GeO 2 standard. This configuration can be interpreted as germanium 4+ in an oxygen environment. The Athena software was used to decompose each of the spectra acquired on the natural sphalerite samples as a linear combination of Sph1, Sph2, and Sph3 spectra. The results for the samples from Central and East Tennessee are presented in ternary diagrams in Figures  6  and 7 , respectively. In these diagrams, the circle radius is proportional to the germanium concentration. Germanium concentrations were estimated qualitatively from the height of the absorption edge. The poles in the ternary diagram are labelled Ge 2+ S (Sph1), Ge 4+ S (Sph2), and Ge 4+ O (Sph3), as defined above (Figure  5  and preceding paragraph).
This distribution is comparable to, and even more marked than, the DCT data. The data from the yellow sphalerite from East Tennessee (YET) are scattered between the Sph2 and Sph3 poles, but are closer to pole Sph3. Prior to synchrotron experiments, the sphalerite samples were imaged and analysed using EPMA, SEM-FEG, and XRF in order to check for solid inclusions in the sulphides. No inclusion was found at theses scales, which are around 100 nm for Back Scattered Electron images and ~20 µm for XRF. These results are shown below (Figure  8 ) and as Figure  S7  for the CY3068-C3 in which spectrum Sph3 was obtained, specific of the Ge oxide found exclusively in the YET samples. Figure  8  shows that synchrotron acquisitions were performed in homogeneous, inclusion-free, non-fractured areas. Fractures are not filled with clays or other phases that may have crystallized after ore deposition.
Acknowledgments: This research project was funded by an inter-CARNOT grant (CARNOT ICEEL and BRGM). We acknowledge the  European Synchrotron Radiation Facility  for the beamtime and access to their installation. We thank the  FAME team  for their help in preparing the experiment, their support during beamtime, and training in data processing, and we are grateful to ID26 for the loan of analyser crystals. The  Royal Museum for Central Africa in Tervuren, Belgium , is thanked for providing the Renierite sample. We also acknowledge  Isabelle Bihannic  of the  LIEC laboratory  ( UMR 7360 ,  Université de Lorraine)  for XRD support, and  Peiffert Chantal of the GeoRessources laboratory  ( UMR 7359 ,  Université de Lorraine)  for the LA-ICP-MS analyses. We express gratitude to the reviewers and editors for their time spent helping us improve this study.
Increasing the number of input parameters also allows increasing the number of equations used for the regression. However, this will result in an effective improvement of the robustness only if equations are independent from each other. Indeed, correlated input parameters will lead to a system in which equations can be linearly predicted from each-others, with a substantial degree of accuracy. This is referred as "co-linearity" in statistics, and was partly tackled in C2015 by using a PCR (Principal Component Regression) approach  (Seber et al. , 2003). In C2015 the PCR was computed in two steps. First, a Principal Component Analysis (PCA) was applied to the input data (i. e. to the DE p kj of the multi-ring aggregated emissions) to calculate a new set of input data, called Principal Components, expressed as a linear combination of the original ones.
This contribution combines stress-strain data and measurement of displacement fields by DIC with microstructural investigations in areas selected based on the DIC results. For this, we prepared millimetre-sized high quality cross sections by (BIB) milling followed by SEM to infer microphysical processes of deformation with submicron resolution (Fig. 1 ). The two samples used are from the COX (a cemented claystone): one deformed in plane strain compression at 2 MPa confining pressure (COX-2MPa;  Bésuelle and Hall, 2011)  and another in triaxial compression at 10 MPa confining pressure (COX-10MPa;  Lenoir et al. , 2007). Specimens were taken from the Bure site in Meuse-Haute Marne in France and belong to the clay-rich facies of the COX.
Conclusions The integration of bulk stress-strain data and the analysis of displacement fields by 3-D and 2-D digital image correlation (DIC) with broad ion beam cutting and scanning electron microscopy (BIB-SEM) is a powerful multi-scale method to study the deformation behaviour of mudstones. We studied samples of Callovo-Oxfordian Clay (COX) subjected to triaxial compression at 2 and 10 MPa confining pressure. DIC was used to locate regions deformed to different states of strain and BIB-SEM allows microstructural investigations of mineral and porosity fabrics down to the nanometre scale. Microstructures show evidence for dominantly cataclastic mechanisms (intergranular, transgranular, intragranular cracking, grain rotation, clay particle bending) down to the nanometre scale. At low strain, the dilatant fabric contains individually recognisable open fractures, while at high strain in shear fractures the reworked clay gouge evolves towards smaller pores than the undeformed material and corresponding resealing of initial fracture porosity.
Faults are complex geological features acting either as permeability barrier, baffle or drain to fluid flow in sedimentary basins. Their role can be crucial for over-pressure building and hydrocarbon migration, therefore they have to be properly integrated in basin modelling. The ArcTem basin simulator included in the TemisFlow software has been specifically designed to improve the modelling of faulted geological settings and to get a numerical representation of fault zones closer to the geological description. Here we present new developments in the simulator to compute fault properties through time as a function of available geological parameters, for single-phase 2D simulations. We have used this new prototype to model pressure evolution on a siliciclastic 2D section located onshore in the Niger Delta.
FAULT MODEL IN BASIN MODELLING In this study, we used a new tool for basin modelling developed at IFPEN (TemisFlow software using the ArcTem calculator). Contrary to classical basin simulators, it does not require a structured mesh based on vertical pillars, and handles complex evolutive sections for sedimentary basins having experienced faulting episodes. The mesh is based on hexahedrons with degenerated grid cells on fault planes, which allows us to simulate realistic fault kinematic with layer displacements along a fault plane. The full description of the simulator principles for monophasic flow can be found in previous publications  (Tunc et al. , 2012; Faille et al. , 2014) , here we merely recall the way faults are handled. The numerical representation of fault zones is getting closer to the geological description.
These growth faults subdivide the basin into several sedimentary units, each characterized by its own sand-shale distribution pattern. Pressure data from the wells penetrating the first km's of the section (maximum: 4. 6 km) show that the interbedded deltaic sands and shales are generally at hydrostatic pressure to a depth of 3500-4000 m. Strong over-pressures are recorded in deeper layers and faults appear to be lateral limits of these different over-pressured zones. The Niger Delta is a rich oil and gas province, with light waxy type oils and methane-rich gas  (Caillet and Batiot, 2003). The source rocks are controversial, but the main sources are probably the Akata shales, which reach a high maturity level. Most of the petroleum already found in this area is located in shallow structural traps, either related to faults or to anticlines.