dataesr/scientific-paragraphs-categorization huggingface dataset:
iop. org/ERL/13/ 044024/mmedia). Information on the location, installed power capacity and hub height of 2013 wind turbines is taken from thewindpower. net database (www. thewindpower. net). We use the current wind farm distribution to assess future relative changes in wind power potential, even though a large increase in wind capacity is expected. This assumption was shown to lead to impacts with similar orders of magnitude as the future wind installations scenario  (Tobin et al 2015 (Tobin et al , 2016)).
The impacts of changing streamflow and water temperature on thermoelectric power generation were quantified using the model of  Koch and Vögele (2009) , which was modified as described in  van Vliet et al (2012a). This model simulates in a first step the required water demand for cooling and in a second step the usable power plant capacity. A distinction was made between thermoelectric power plants using once-through cooling and recirculation cooling (see supplementary information). The thermoelectric power production model was applied to 407 power plants in Europe which include nuclear, fossil-and biomass-fuelled power plants. For the description of power plant characteristics, we used the data of the World Electric Power Plant Database version 2013 (UDI 2013). We selected power plants according to the following main criteria: use of river water as source for cooling, availability of information on installed capacity and cooling system type (van Vliet et al 2016a).
Thermoelectric power plants using sea water or groundwater as main source for cooling or using dry (air cooling) systems were excluded from the analyses. Considering the long design life of thermoelectric power (30-60 years) and a lack of detailed information on future power plant changes, we assumed that the power plant settings will remain constant in time. The impact of climate change on the river flow and water temperature was first calculated for each thermal power plant by extracting the relevant data for the grid cell that contains it. The individual power plant results were then aggregated at the national scale.
These limitations lead to uncertainties, so results should be taken in a qualitative way. Also, climate data are not bias-corrected for wind power and PV while they are for hydropower and thermoelectric power. However, previous studies have shown that relative effects are only marginally sensitive to bias-correction, at least for wind power; see Tobin et al  (2015). Thus we argue that despite the limitations of this study, the orders of magnitude are relevant and reliable. We have assessed mean changes in electricity production from technologies. Climate change is also known for affecting changes in the variability of some climate variables, such as temperature. However, previous studies have not detected major changes in the variability of wind and solar PV energy production  (Tobin et al 2016 , Jerez et al 2015a).
( 9 ) For the link between the user and the antenna of index n r at the BS, H n r 0 represents the component of the channel that allows for receiving the useful data c 0. On the contrary, H n r p∈ * p are the components of the channel that lead to ICI on the data c 0 carried out by the adjacent sub-carriers c p∈ * p. Thus, if the propagation channel is static, H n r p∈ * p = 0. With N r receive antennas, the following channel vectors are defined: H (N r ) p = H 0 p H 1 p • • • H N r -1 p T , ( 10 ) with p ∈ p , the noise vector is: b (N r ) = b 0 b 1 • • • b N r -1 T , (11) and the received data vector before equalization is given by: y (N r ) = y 0 y 1 • • • y N r -1 T. ( 12 ) The propagation channel is estimated via pilots on the BS side. A perfect channel estimation is assumed for the sake of simplicity and the receiver performs an equalization using the low-complexity MRC technique.
A. STATISTICAL ANALYSIS OF H p The channel components H p with p ∈ p are studied here. The propagation channel is modeled by the sum-of-sinusoid method defined in  [32]. Indeed, the authors show that this method is useful to design and simulate Rayleigh fading channels and MIMO channels. In practice, this sum-of-sinusoid method is used in the communication system toolbox TM of the simulation environment MATLAB R  [33]. Thereby: h l (t) = P l N l N l n=1 cos 2π f (1) l,n t + θ (1) l,n + j P l N l N l n=1 cos 2π f (2) l,n t + θ (2) l,n , (24) with P l being the average power of the l th path, N l the considered number of sinusoids for the l th path, θ l,n and θ l,n two independent random variables having a uniform distribution over the interval ]0, 2π ] and f (i) l,n defined as follows: f (i) l,n = f max D cos π 2N l n - 1 2 + (-1) i-1 π 4N l l L h + 2 , ( 25 ) f max D being the maximum Doppler frequency.
All thin sections were scanned and analyzed with JMicroVisionÓ (version 1. 2. 7, Roduit, 2007)  to quantify porosity, amount of grains and different kinds of cement for every thin section by point counting. Matlab Ò scripts, developed by  Claes et al. (2016) , were used to determine porosity, overall cement and grain percentages for each thin section, based on segmentation and digital point counting (500 points, 1000 simulations for each thin section). Both scripts were run with Matlab version 7. 9. 0. 529 (R2009b) Copyright Ó 1984-2013 and make use of the Matlab Image Processing Toolbox TM. Micro-Computed Tomography (lCT) allowed to carry out a 3D quantitative and qualitative investigation of the pore space at a high resolution. In this study, this technique was used to determine the interconnectivity of pores.
Image analysis workflow Image analysis forms a crucial step in analysing the diagenetic phases in 2D as well as 3D. The workflow consists of: (i) visualizing, segmenting (dual thresholding) and quantifying the porosity and other diagenetic phases that are distinguishable in the used images; (ii) calculating the Representative Elementary Volume (REV); and (iii) labelling and visualizing the segmented images or volumes in 2D and 3D, respectively. Image treatment is carried out using the Avizo Fire software package (Version 9. 1, VSG) and in-house developed scripts (cf. De Boever et al. , 2012). In order to differentiate gas pore from solid matrix voxels, image segmentation needs to be performed. Quantitative analysis of the porosity requires a voxel by voxel determination of pore or solid phases.
For segmentation an in-house dual-thresholding algorithm is used. This method is an adaptation of the single-threshold approach, which typically selects pixels/voxels on the basis of their unique histogram range. However, manually determining the boundary values is not straightforward in the case of insufficiently ideal histograms, with peaks that are not obviously separated. The applied dual or hysteresis thresholding uses two intervals of the histogram in order to determine the segmentation (Fig. 5 ). Voxels corresponding to the first ''strong'' threshold are classified as foreground voxels, while voxels selected by the second threshold are only considered foreground if they are connected to voxels already selected by the ''strong'' threshold. The advantages of this algorithm are the reduced sensitivity to residual noise in the dataset and the selection of less insulated foreground voxels  (Claes, 2015).
However computational limitations still restrict the number of heterogeneities taken into account in these models, especially on a sub-meter scale. Hence, by the use of the effective medium approximation, a hypothetical homogeneous property replaces a heterogeneous parameter (e. g. Claes, 2015). This indicates the importance of a correct estimation of the REV. Previous studies revealed the difficulty of establishing a general rule concerning the REV  (Claes et al. , 2016 and references therein). In this study the focus lays on geological samples, more specifically on the porosity parameter. The datasets used in this study provide an excellent starting point to analyze the heterogeneity of the pore network. We use the assumption that the conditions essential for geostatistical interpretations can be linked to the definition of the REV.
Var Z x; V i ð Þ ½ ¼constant The semi-variogram is characterized by a sill and range (Fig. 8 ). The sill value represents the plateau value and is equal to the variance of the data. The range or length of correlation is the distance at which the sill value is reached. In this study, semi-variograms of the porosity parameter are calculated based on the segmented datasets (Fig. 8 ). Voxels representing the pore space have a-value of one, while the voxels of the rock phase are given a-value of zero. The obtained semi-variograms are achieved according to the spherical model, which has a linear behaviour near the origin and flattens out at larger distances  (Isaaks and Srivastava, 1989). The porosity REV for each sample group is shown in Figure  8. The 3D volumes of the used plugs exceed the size of the REV for the poorly cemented and heavily cemented group.
Conclusion The overall goal of this research is to come up with a more accurate description and characterization of the pore structure of geological samples. Computer Tomography (CT) coupled with a flow apparatus and numerical solutions proved to be a successful approach for achieving this objective. Because of its inherent 3D data acquisition, the complete internal structure of the scanned sample can be investigated at different resolutions. The visualization of the connectivity of the pore network and the detection of additional phases result in a detailed characterization of the geological samples. The generation of 3D datasets also permits a more quantitative description and calculation of different important parameters such as porosity, permeability and cement content. This new approach can quantify the influence of diagenetic phases (e.
Image Acquisition and Data Processing System A LED lamp of 1. 8 W illuminates the droplet and creates a contrast that gives a clear picture of the droplet. A CCD (Charge Coupled Device) camera connected to a computer through an IEEE 1314 (FWB-PCIE1X20, 2 ports) was used to record droplets size during vaporization process. The CCD camera used is a monochromatic digital camera SONY type SX90 XCD. Its resolution is 1280 pixels horizontally and 960 pixels vertically. It allows acquisition of 30 fps. It is equipped with an optical system composed of three parts: a 0. 25 lens, a 0. 30 -0. 37 m lens and a 2 multiplier. When the suspended droplet was inserted in the chamber, the time dependence of its size recording was performed by the CCD using a specific software, developed by Vision Alliance for the device.
Both curves increase as the simulation becomes yet more clustered than the experiment. these points. The results of this procedure on the confocal images are shown in Figures  3c  and 3d. The simulation method that we use to compare to this experimental data is similar to that of Osborne et al. [14]  using the Chaste software package  [32]  and Farhadifar's potential  [11]. In brief, the tissue is described by a potential, U = i∈V K 2 (A i -A 0 ) 2 + i,j∈V 2 λ ij E ij + i∈V 2 P 2 i ( 9 ) where A i and P i are the area and perimeter of the ith cell, respectively, A 0 is the preferred area of a cell (assumed to be uniform in the population of interest in the present scenario), and E ij is the length of an edge between cells i and j (defined to be zero if the cells are not adjacent). The first term represents compression or dilation of the cell away from its preferred area and the last, the contractility of the perimeter.
ACKNOWLEDGEMENTS This work has made use of the resources provided by the  Edinburgh Compute and Data Facility (ECDF)  and by  Microsoft's donation of Azure  time to the  Alan Turing Institute. The authors wish to thank  N. Behr ,  M. Black ,  P. Buneman ,  A. Clisham ,  R. Farhadifar ,  A. Fletcher  and  G. Plotkin  and the anonymous reviewers for helpful discussion and criticism which greatly improved the quality of this paper. Data processing was driven by the excellent GNU Parallel [42].
The work of W. Waites was supported in part by the  Engineering and Physical Sciences Research Council (EPSRC)  under Grant  EP/J02175X/1 , in part by the  U. K. Research Councils' Synthetic Biology for Growth Programme , in part by the  Biotechnology and Biological Sciences Research Council (BBSRC) , in part by  EPSRC , in part by  MRC , and in part by the  National Academies Keck Futures Initiative of the National Academy of Sciences  Award under Grant  NAKFI CB12. The work of M. Cavaliere was supported in part by  EPSRC  under Grant  EP/J02175X/1 , in part by the  U. K. Research Councils' Synthetic Biology for Growth Programme , in part by  BBSRC , in part by  EPSRC , in part by  MRC. The work of  E. Cachat  and  J. A. Davies  were supported in part by the  BBSRC  under Grant  BB/M018040/1  and in part by the  Leverhulme Trust  under Grant  RPG-2012-558.
cm -2 thickness and a C target of 370(7) mg. cm -2 thickness. The target thicknesses were determined both by weighting and by measuring the magnetic-rigidity deviation of the beam in the ZeroDegree spectrometer after the secondary target. The cross section on hydrogen was extracted from the CH 2 data after subtraction of the normalized carbon component. Inclusive cross sections for two-nucleon removal from C and H were found to be rather similar  [24] , as shown in Tab. I. The setup consisted of the DALI2 array composed of 186 NaI scintillators  [26]  for gamma-ray detection and the ZeroDegree spectrometer for downstream particle identification  [24]. A mass resolution of σ ∼ 0. 001 was achieved, allowing for an unambiguous isotopic identification. The scintillators of the DALI2 array were calibrated in energy with 137 Cs, 88 Y and 60 Co sources, with gamma emission ranging between 661 and 1836 keV.
ACKNOWLEDGMENTS This work has been supported by the  European Research Council  through the  ERC Starting Grant  No. MINOS-258567  and by the  ENSAR European FP7  Project No. 262010. Y. Tanimura  acknowledges the financial support from the  Graduate Program on Physics for the Universe of Tohoku University. A. Jungclaus acknowledges support from the  Spanish Ministerio de Economía y Competitividad  under contract  FPA2014-57196-C5-4-P. D. Sohler  acknowledges support from the  European Regional Development Fund  (Contract No. GINOP-2. 3. 3-15-2016-00034 ). A. Corsi  acknowledges  S. Péru  and  M. Martini  for interesting discussions, and  N. Paul  for careful proofreading.
We finally deliberately place a very small (0. 5×0. 5 m) conductive anomaly (10 -6 Ω. m) centered at 1. 35 m depth (represented in green Figure  1. a), that could be considered as a manmade metallic pipe. We use Res2Dmod free software  (Loke, 2002)  to simulate non-noised data acquisition from a chosen resistivity model (Figure  1. a) and then use the Res2Dinv software  (Loke and Barker, 1996)  to get the inverted ER section as one would obtain from the processing of survey data (Figure  1. b). The discrimination between sands and clays is manifest while the distinction of the anomaly is not obvious. Indeed, the conductive punctual anomaly generates resistive artefacts close to it and a very large and conductive anomaly deeper in the subsoil, hiding the presence of the silty lens. The image given by the inverted ER (Figure  1.
Construction from geotechnical datacone bearing values We use artificial cone bearing values (expressed in 𝑀𝑃𝑎) as geotechnical data. These physical values could have been obtained from a cone penetrometer test investigation campaign. We simulate a data acquisition from four boreholes with an interspacing of 20 m, drilled to 17 m depth with an acquisition every 50 cm (dashed lines in Figure  1. a). Two of the boreholes are positioned so that they go through the silty lens. We assign intervals of cone bearing values to Θ so that:  10 2 , 8 ⋅ 10 2 ]  and 𝜃 4 = [ 0. 1, 2 [ ∪ ] 8, 20 [ ∪ ] 80, 2 ⋅ 10 2 [ ∪ ] 8 ⋅ 10 2 , 10 3  ]. These intervals can be associated to specific soils  (Robertson, 1990 ) such as clays for low values, silty soils for intermediate values and sands for higher ones. We assume a mass of belief equal to 1 in the borehole and impose an exponential lateral decrease of the trust in the data (following the mean horizontal scale of fluctuation of about 50 m proposed by  Phoon and Kulhawy (1999) ).
Cells were then washed and discriminated by flow cytometry on the basis of cell size, granularity, and presence of CD34, absence of Lin and CD45 markers. Then, depending of the VSELs population studied, presence of NANOG, CD133 and/or CXCR4 markers were gated. Cells viability was monitored by the absence of dye 7-AAD (BD Biosciences) uptake which was added 10 min before acquisition. All flow cytometry sorting or analysis was performed using a BD ARIA III instrument (BD Biosciences). Data acquisition and analysis was conducted using BD FACSDiva software (BD Biosciences).
RNA-Seq Data Processing EasySep™ Lin-CD34 + CD45-purified cells were cultured in conditioned media in presence of SmartFlare™, fluorescent probe (Millipore) for 2 days. Then, 300 sorted VSELs expressing NANOG and control cells negative for NANOG expression were used for cDNA synthesis with the help of Smarter ultra-low input RNA kit for sequencing (Clontech). Sample quality was assessed using Bioanalyzer RNA Nano chips (Agilent). Paired-end, barcoded RNA-Seq sequencing libraries were then generated using the Nextera XT DNA library preparation Kit (Illumina) following the manufacturer's protocols. The quality of library generation was then assessed using a Bioanalyzer platform (Agilent), and Illumina MiSeq-QC run was performed or quantified by qPCR. Sequencing was performed using an Illumina HiSeq2500 using TruSeq SBS v3 chemistry at iGE3 Genomics Platform (University of Geneva).
The normalization and differential expression analysis was performed with the R/Bioconductor package edgeR v. 3. 10. 5, for the genes annotated in the reference genome. The raw count data are filtered. We filter out very lowly expressed genes, keeping genes that are expressed at a reasonable level. We keep genes that achieve 10 counts in at least 2 samples. The filtered data are normalized by the library size and differentially expressed genes are estimated using the GLM approach (Generalized Linear Model). The fold change (FC) of base 2 logarithm of the trimmed mean of M-values normalization method (TMM normalized data) log 2 FC was used to rank the data from top upregulated to top downregulated genes and FDR (0. 05) was used to define significantly differentially expressed genes. RNA-Seq results are accessible in supplementary data 3.
Acknowledgements The authors thank  Dr. Bernard Drénou  ( IRHT and Laboratoire d'Hématologie, CH Mulhouse ) and  Dr. Jalila Chargaoui  ( IRIC, Montreal ) for helpful discussions. Mylène Docquier ,  iGE3 Genomics Platform of the University of Geneva  manager for performing the RNA-Seq experiments and for technical assistance. This study was supported by  Arpège Prévoyance.
1  and S2 ), both prepared under the same conditions except for relative humidity % in room atmosphere (RH) above DMS oil and initial µD radius (R) (see supplementary material). Both µD generation and subsequent contraction are observed under a transmission optical microscope. We stop imaging when contraction of each µD has reached completion: all the water in the µD has diffused through oil and there is a NaCl crystal at each µD position (Fig. S3  of the supplementary material). The first image just after µD generation (Fig. 1 ) is processed using FIJI software (Image J, NIH, USA). After thresholding and binarisation procedures, we use the "Analyze Particles" plugin to individually detect each µD position and size. A rectangular ROI is then assigned to each µD (Figs. 1(b) and S4). From this ROI, comprising a single µD and its immediate vicinity, we extract the histogram of pixel grey-levels (Fig.
1(c) ). We focus on its standard deviation σ, a measure of the homogeneity of pixel grey-levels. 14 As each individual µD and its immediate vicinity, on each image, is reduced to a single number (σ), µD time evolution can be monitored throughout associated ROI standard deviation time evolution (ROI, σ, t), as reported for a single µD in Fig. 2(a). Each (ROI, σ, t)-plot is then processed with home-made PYTHON codes (www. python. org) to extract characteristic points (the code is available on request). The µD contraction process leading to crystal nucleation was previously described in Ref. 15, and is recalled here (Fig. S3  and video S1 of the supplementary material). Throughout the contraction process, water is leaving the µD, thus increasing NaCl concentration. This increases the µD refractive index, which leads to two characteristic stages.
Devices of a User According to several studies, like Pew Research Center 3  or Newzoo's  4  it has been determined that by 2016, at least 70% of people in countries like South Korea have a mobile device (Phones, tablets, etc). Then more than 80% of people in the world use mobile devices with android operating system  5. Another study published by theverge  6  shows that there are one billon of personal computers running with windows, 100 million of active Mac users between desktops and smartphones and 1. 6% of the desktops are performed by GNU Linux  7. Thus, in this article, it is assumed that each user usually has devices such as a smartphone, a tablet, a desktop PC, a laptop, etc. Kaligreen uses Kalimucho  [6] , therefore manages components programmed in java, which are compatible with PC and Android.
3  to Fig. 5 ), a specific method was developed. In these cases, the top of the chip consisted of the flow-focusing device and chambers and the anchors were located at the bottom of these chips. The anchors mold was designed with RhinoCAM software (MecSoft Corporation) and was fabricated by micro-milling a brass plate (CNCMini-Mill/GX, Minitech Machinery). That was also the case for the droplet library producing chips with an aqueous injector and a slope (see Fig. S3 ). The topography of the molds and masters were measured using an optical profilometer (VeecoWyco NT1100, Veeco). For the fabrication of the top of the chip, poly(dimethylsiloxane) (PDMS, SYLGARD 184, Dow Corning, 1 g of curing agent for 10 g of bulk material) was poured over the master and cured for 2 h at 70 °C. For the 3D anchors, the metallic mold was first covered with PDMS.
Then, a custom Matlab code (R2016a, Mathworks) allowed to detect each anchor and to compute the RGB values in the center of each droplet, before and after merging. For the toxicity experiment, single images of the anchors were acquired automatically with the motorized stage of the microscope. The analysis was conducted on a montage of the detected anchors using a protocol previously described  (21). Briefly, cells were detected using bright field and fluorescent intensities, and spheroids were selected based on morphological parameters. For each spheroid, the local background was used to determine a specific threshold for the fluorescent dead cells. The viability at the spheroid level was then defined as: viability (%) = 1 - N dead pixels A [5] N dead pixels and A being respectively the number of dead pixels and area of the spheroid.
The catalysts after catalytic testing were further characterised by Raman spectroscopy and XRD in order to decode the complex phase composition. As shown in Figure  1a , FSP-1R and FSP-2R both showed narrower Raman bands and more distinct XRD reflections compared to the fresh catalyst, representing an increase in crystallinity with larger and more defined phases during reaction. The Raman spectrum of FSP-1R showed bands corresponding to MoO3 (666, 819, and 995 cm -1 ) and β-CoMoO4 (937 and 945 cm -1 ). However, in contrast to FSP-1F, no broad bands at 937 and 700 cm -1 were present, indicating the absence of α-CoMoO4  [40]. Following catalytic testing, the catalysts clearly retained a very complex mixture of phases. As the presented Raman spectra were actually averaged from area scans, it was possible to process the data in a spatially resolved manner.