dataesr/scientific-paragraphs-categorization huggingface dataset:
Scintillation model A lot of effort has been put the past decade to character ize the ionospheric scintillation, mainly to obtain effective synthetic models to assess GNSS receivers' performance via simulation. This is particularly useful to obtain statistically significant performance results, which cannot be obtained using real scintillation data. There exist three different ways to obtain such synthetic data  [21] : physics-based models, phase screen models and statistical models. From the differ ent alternatives available in the literature, the most relevant are the Cornell Scintillation Model (CSM)  [21, 22]  and the Global Ionospheric Scintillation Model (GISM)  [23]. The latter is based on a phase screen technique driven by the NeQuick electron density climatological model  [24] , while the former is based on a statistical model and the proper shaping of the spectrum of the entire complex scintillation signal.
Supported in part by the  Northrop Grumman Cybersecurity Research Consortium (CRC)  and by a Microsoft PhD Fellowship. Research supported in part by  DARPA  Grant number  FA8750-11-2-0225 ,  NSF  Awards  CNS-1350619  and  CNS-1413920 , an Alfred P. Sloan Research Fellowship, the  Northrop Grumman Cybersecurity Research Consortium (CRC) ,  Microsoft Faculty Fellowship , and a  Steven and Renee Finn Career Development Chair  from  MIT. CNRS ,  INRIA  and  Columbia University. Supported in part by  ANR-14-CE28-0003  (EnBiD),  NSF  Award  CNS-1445424 ,  ERC  Project  CryptoCloud  ( FP7/2007-2013  Grant Agreement no. 339563 ), the  Alexander von Humboldt Foundation  and a  Google Faculty Research Award.
The practical use of this proxy for improving our ability to assess DOC in the context of large scale ocean color remote sensing application is finally specifically assessed from the data gathered in the latter three coastal sites as well as from independent CDOM-DOC data set gathering a variety of coastal waters differently impacted by DOM terrestrial discharges. A simple approach to derive the CDOM spectral slope coefficient in the UV domain is proposed in this context. Three contrasted coastal sites were investigated in both estuarine and marine waters (N = 158) for the development of the model (Table  1 , Fig. 1 ). These three coastal sites allow a wide representation of the natural variation in DOC and CDOM dynamics in both temperate and tropical waters with watershed differently influenced by anthropogenic forcing.
Validation data set Besides the samples collected in the three coastal sites investigated, a validation data set was built gathering external sea surface (depth < 4m) CDOM and DOC measurements. This data set is firstly composed by measurements hosted by the SeaBass database (NASA Ocean Biology Processing Group, seabass. gsfc. nasa. gov). The latter data have been collected during multiple cruises conducted within the coastal waters of the southern Middle Atlantic Bight between 2005 and 2007 (N = 39, BIOD01, BIOD02, BIOD03), Chesapeake Bay (N = 89, BIOME1, BIOME2, BIOME3) and Gulf of Maine (N = 46, BIODIVERSITY)  [13, 28]. Further, data from the Rhône river delta (France, N = 19  [29] ,), and from a tropical lagoon (southwest lagoon of New Caledonia, N = 32  [30] ,) were also considered. This validation data set therefore covers a wide range of biogeochemical conditions.
(  6 ) ( ± 4%). This possibility to derive basin scale maps of S 275-295 from spatial remote sensing information has been further validated (for S 275-295 ranging from 0. 02 to 0. 05 m -1 ) from a similar study dedicated to the coastal waters of the Gulf of Mexico  [44]  based on a different combination of MODIS R rs bands (i. e. at 443, 488, 555 667 and 678 nm). The possible generalization of the approach has been tested against Vietnamese and French Guiana data for which concurrent CDOM and marine reflectance measurements have been performed. The application of the algorithms by Fichot et al  [20, 44]  on our data set shows varying performance (Fig. 10 ) in its potential to retrieve S 275-295 emphasizing that such algorithms might be un-adapted for certain coastal areas or should be regionally tuned for being fully exploitable.
Acknowledgments The authors would like to thank all participants and voluntary contributors for collecting data that have been assembled in the SeaBass data set. R. Sempéré,  B. Charrière  and  E. Rochelle-Newall  are kindly acknowledged for sharing their DOC and CDOM data. This study has been performed in the frame of the GlobCoast (www. foresea. fr/globcoast)which is funded by the  Agence Nationale de la Recherche  ( ANR-11-BLAN-BS56-018-01 ). The  GlobCoast  project is affiliated to the LOICZ and  AQUIMER  project. This work has also been supported by the  French Spatial Agency (CNES)  through the  TOSCA program  in the frame of the MODOC and VITEL projects. The authors wish to acknowledge the two anonymous reviewers for their detailed and helpful comments to the manuscript.
RNA isolation and real-time PCR. Total RNA was isolated using the Absolutely RNA Microprep Kit (Stratagene) according to manufacturer's recommendations. Complementary DNA was synthesized using the SuperScript II Reverse Transcriptase kit for RT-PCR (Invitrogen). Real-time PCR was performed using the SYBR-Greener qPCR Supermix (Invitrogen) in the Cycler Real-Time PCR System (BioRad). GAPDH expression level was used to normalize values of gene expression. Data are shown as fold change relative to the sample control and at least two independent experiments in triplicate were performed. Sequences of the primers used in this work are shown in Supplementary Table  4. Western blot analysis. Cell pellets were lysed in 50 mM Tris pH 7. 9/8 M urea/1%Chaps and incubated with shaking at 4 °C for at least 30 min.
Array comparative genomic hybridization. For the detection of CNV, genomic DNA isolated from iPSC and the original human fibroblasts was hybridized in SurePrint G3 Human High Resolution 1X1M Microarrays (CNV) (Agilent Technologies) following manufacturer's instructions. CNV regions in each sample were identified using a Reversible Jump Hidden Markov Model implemented in the software RJaCGH 37 requiring an average posterior probability of the probes 40. 75. Next, calls that did not fulfil the following criterion were filtered out: an average log2value of the region equal to the median±1. 5 Â s. d. of all log2values of the chip. CNVs were also identified using the aberration detection method (ADM-2: 6,5 value) algorithm suggested by Agilent Technologies. We established a log2 ratio (Cy5/Cy3) threshold of ±0,25 and the concurrence of at least four consecutive probes with differential signal intensity for consideration as a CNV.
Acknowledgements We thank  Joaquı ´n Sebastian  for his comments on the MS,  Orlando Dominguez  for his help with arrayCGH analyses and  Sagrario Ortega  for help with chimeric mice. S. R. was funded by a  Ramon y Cajal contract  ( RYC-2011-09242 ) and a grant ( SAF2013-49147-P ) from the  MINECO. Work in NB laboratory was supported by a grant from the  Ontario Institute for Cancer Research. T. M. -B. is supported by grants from the  European Research Council  ( ERC StG 260372 ) and the  Spanish Ministry of Economy and Competitiveness  ( BFU2011-28549 ). Work in O. F. -C. laboratory was supported by  Fundacion Botı ´n , by  Banco Santander  through its  Santander Universities Global Division  and by grants from  MINECO  ( SAF2011-23753 ),  Worldwide Cancer Research  ( 12-0229 ), FundacioLa  Marato de TV3 ,  Howard Hughes Medical Institute  and the  European Research Council  ( ERC-617840 ).
Results The technical procedure of end-to-end sutureless anastomosis with glued prosthesis in rabbit's infra-abdominal aorta was successfully realized, without leakage or visible occlusion. No hematoma was detected in either group (Figure  3 ). In order to test the efficiency of glued prosthesis anastomosis compared to conventional anastomosis, data on four variables have been collected. The anastomosis by glued prosthesis was completed with a shorter completion time. The mean anastomosis time was 7. 20±0. 29 minutes for the study group and 17. 44±0. 30 minutes for the control group. The high magnitude of the t-statistic value shows that the difference between the study group mean and the control group mean is significantly different. Thus, the anastomosis time using glued prosthesis is significantly lower (Figure  4 ).
This approach could be successfully tested on different HPC systems, including one of Germany's three top-ranked supercomputers, up to a total of 140,000 processes solving a problem with close to one trillion (10 12 ) unknowns. Moreover, as the computed data have to be processed and analyzed in order to draw the necessary conclusions from the given problem statement, methods have to be devised to cope with the huge amount of data generated from a large run. Hence, the data structure is designed to handle interactive data exploration during runtime or to write a smaller, coarser data file for a subsequent offline post-processing. This issue is of major concern when it comes to analyzing HPC simulation data, as the problem sizes typically restrict data exploration and analysis. The work at hand provides a viable solution strategy.
There exist several state-of-the-art frameworks for problem solving environments (PSE), such as Chombo  [1]  or SCIRun  [2] , as well as open source CFD programs, such as OpenFOAM  [3] , which could have been used as foundations for algorithm development regarding an interactive data exploration. Furthermore, work has been done in the broad research area of in situ visualization (e. g. , the state-of-the-art analysis by Rivi et al. [4] ). The applications range from visualizing combustion problems (Yu et al. [5] ) to general procedures and software solutions (Fabian et al. [6] ) and extreme-scale scientific analysis (Bennett et al. [7] ). From reviewing these platforms and concepts, the authors decided to develop a new data structure, allowing one to solve some of the mentioned shortcomings and to provide a flexible environment for further model implementations.
mac. tum. de), a heterogeneous research cluster installed by the Munich Centre of Advanced Computing at Technische Universität München in Germany, and SuperMUC (http://www. lrz. de/services/compute/supermuc/systemdescription), a 155,656 processor core large IBM System iDataPlex supercomputer installed at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. All curves show a tendency of decreased exchange time with increased amount of processes. The curves for depth eight describe a uniformly-refined domain using approximately 78. 5 billion cells, and more than 707 billion variables are exchanged across more than 140,000 processes on SuperMUC. Hence, the data structure in its current form is extremely flexible and is well suited for massive parallel machines.
The Laplacian problem: ∆p(x, y, z) = ∂ 2 p(x, y, z) ∂x 2 + ∂ 2 p(x, y, z) ∂y 2 + ∂ 2 p(x, y, z) ∂z 2 = 0 (8) is defined on a 1 × 1 × 1 m cubic domain, where fixed Dirichlet boundary conditions were applied on the east and west side according to: p(0, y, z) = p(1, y, z) = 1 ∀ y, z (9) whereas all other sides were set to p = 0. This equation describes a pure diffusion process and could be used as well for modeling a stationary temperature diffusion problem without any convective influences or internal loads. Before doing a large-scale analysis of the code's performance for the Laplacian problem on a supercomputer, such as SuperMUC, small-scale analyses were performed in order to determine an optimal data grid size for this type of problem and the given hardware. As mentioned in the section describing the data structure, the overall domain discretization is dependent on the chosen grid refinement r t i and r s i , but also on the chosen data grid size s i.
In order to assess the influence of the data grid size on the performance, the Laplacian problem was solved for different data grid sizes on a Blue Gene/P system of Universitatea de Vest din Timişoara (UVT) in Romania and on the TUM MAC Cluster using 256 processes. Figure  4  plots the number of solved cells per second (in millions) over the data grid size. Two facts can be observed from the resulting plot: there exists somewhere an optimal setting for the data grid size, lying around 16 3 cells per data grid. Moreover, it can be seen that the Blue Gene/P is around an order of magnitude slower than the MAC Cluster. This can be well explained by the hardware differences of the systems. While the Blue Gene/P uses PowerPC processors with a clock speed of 850 MHz, the MAC Cluster and SuperMUC use Sandy-Bridge processors with 2.
Visualization Strategies In order to cope with the tremendous data advent generated by CFD simulations, special consideration has to be given to data visualization and interpretation. Standard methods dump the generated data to hard disks to perform an offline post-processing. In the case of the above-mentioned large example for depth eight computed on SuperMUC having a memory footprint of over 28 terabytes, such a 'standard' solution is unfeasible. Hence, new solutions have to be used in order to tackle this 'Big Data' problem. One goal during the design phase of the given data structure was to provide interactive data exploration capabilities. The general and technical context is explained in detail in Mundani et al. [27]  and will be briefly introduced here. The main idea of the interactive data exploration is based on a constant bandwidth approach, where the data density is kept constant.
Conclusions This paper presented a data structure capable of running efficiently on modern supercomputers, such as SuperMUC in Germany or Shaheen in the Kingdom of Saudi Arabia, and which is able to compute complex indoor airflow scenarios. The mathematical formulations based on the Navier-Stokes equations for the simulation of indoor airflows were introduced, and the basic ideas of the data structure, as well as the solving mechanisms were sketched. Scaling plots showed the efficient usage of the introduced strategies on SuperMUC and Shaheen. Furthermore, validation examples and complex indoor airflow scenarios were presented in order to highlight the possibilities that the code currently provides. Moreover, it is planned to enhance the existing code structure with further features, such as turbulence modeling and immersed boundary methods for a more accurate geometric description.
Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and/or around the study site: 1) estimation of a soil property using a spectral index of the literature and 2) standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (  2007 ) using the spectral bands at 2209, 2133 and 2225 nm.
Two legacy soil databases were tested as inputs of the procedure: the Focused-Legacy database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. Our results showed that: 1) the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation r (0. 71), but also high bias (-414 g/kg) and SEP (439 g/kg), 2) the standardisation using both legacy soil databases allowed an increase of accuracy (r = 0. 76) and a reduction of bias and SEP and 3) a better standardisation was obtained by using the Focused-Legacy database rather than the No-Focused-Legacy database.
g. ,  Gomez et al. , 2008; Lagacherie et al. , 2008)  and partial leastsquares regression (PLSR) (e. g. ,  Selige et al. , 2006; Stevens et al. , 2010). Spectral indexes are based on physical analyses of spectral reflectance, such as slope or absorption band depth value which allow estimating mineral, rock, and soil properties. For example, the absorption band depth values centered at 2206 and 2341 nm can be used to estimate clay  (Chabrillat et al. , 2002)  and CaCO3  (Gaffey, 1986 ) content, respectively. In the PLSR approach, the full spectrum is used to establish a linear regression model where the significant information contained in the VNIR/SWIR spectra is concentrated in a few latent variables that are optimized to produce the best correlation with the desired soil property. The application of these techniques to airborne hyperspectral data included a calibration step that used a set of sites with VNIR/SWIR spectra associated to the studied soil property measured by physico-chemical lab analysis.
Study Area and AISA-DUAL Airborne Data The study area is located in the Cap Bon region in northern Tunisia (36°24'N to 36°53'N; 10°20'E to 10°58'E), 60 km east of Tunis (Figure  1a ). This 300 km² area includes the Lebna catchment, which is primarily rural (>90 %) and devoted to cereals, legumes, olive trees, natural vegetation for breeding and vineyards. VNIR/SWIR AISA-DUAL hyperspectral data were acquired in 2010, over the study area (12 × 25 km) with a spatial resolution of 5 m (Figure  1b ). The AISA-DUAL airborne imaging spectrometer measures the reflected radiance in 359 noncontiguous bands covering the 400-2450 nm spectral domain. Noisy and atmospheric absorption bands were removed and 249 spectral bands 0. 489 µm and 2. 451 µm were retained. To isolate the bare soil areas, pixels with Normalized Difference Vegetation Index (NDVI) values over an expert-calibrated threshold were masked.
Reference Soil Database One hundred and twenty soil samples were collected over the study area covered by the AISA-DUAL data. Among this sample set, 50 samples were collected in June 2008, 30 in October 2009, and 40 in November 2010 (Figure  1b ). All of these soil samples were collected in fields that were bare during the hyperspectral airborne data acquisition in November 2010. They were composed of five sub-samples collected to a depth of 5 cm at random locations within a 10×10 m 2 square centered on the geographical position of the sampling plot, as recorded by a Garmin GPS instrument. The clay content (granulometric fraction < 2 µm) was determined using a pipette method (method NF X 31-107, particle size distribution by sedimentation)  (Baize and Jabiol, 1995). The clay content of the 120 soil samples varied between 108 and 772 g/kg and followed a normal distribution (Table  1 ).
Legacy Soil Databases Eighty-six soil samples were collected in 2000 over the CapBon region (Figure  1b ). Among these eighty-six soil samples, only sixty-seven samples located over the study area covered by the AISA-DUAL data were used and denoted as the Focused-Legacy database. All of these soil samples correspond to the first soil horizon of profiles described by the IAO (Instituto Agronomico per l'Oltremare) 20th course professional master "remote sensing and natural resources evaluation" field survey staff from 2 to 28 April 2000 according to the IAO framework (IAO, 2002) (Figure  1b ). The soil profiles were located initially on topographic maps georeferenced in CGE (Geodetic system Carthage Tunisia) with Lambert coordinates and the Greenwich Meridian as the origin. Seventy-two soil samples were collected between 1973 and 1979 over the CapBon region (Figure  1b ).
Among these seventy-two soil samples, only sixty-four samples located outside of the study area covered by the AISA-DUAL data were used and denoted as the No-Focused-Legacy database. All of these soil samples correspond to the first soil horizon of profiles described following a set of specifications that were defined by the Tunisian ministry of Agriculture by adapting existing specifications of the French overseas research institute  (ORSTOM -Maignien, 1980). The soil profiles were located initially on topographic maps georeferenced in CGE (Geodetic system Carthage Tunisia) with latitude and longitude coordinates in grade and the Paris Meridian as the origin. Finally, the Focused-Legacy and No-Focused-Legacy databases were grouped to form the Entire-Legacy database containing 158 soil samples.
The clay content of the Focused-Legacy and No-Focused-Legacy databases was determined by hydrometer method  (Baize and Jabiol, 1995). The clay content of Focused-Legacy database varied between 39 and 762 g/kg (Table  1 ). The clay content of No-Focused-Legacy database varied between 10 and 425 g/kg (Table  1 ). The clay content of Entire-Legacy database varied between 10 and 762 g/kg (Table  1 ). All legacy dataset had positive skew distribution.
Spectral Index The clay contents were predicted from the Clay Index (called index) proposed by  Levin et al (2007)  using the spectral bands , and , and following: (1) The HYperspectral SOil Mapper (HYSOMA) software interface was used to derive semi-quantitative soil properties maps from the hyperspectral data. HYSOMA was developed at the GZF German Research Center for Geosciences in the Remote Sensing section and is a software package written in IDL language (www. gfz-potsdam. de/hysoma, Chabrillat et al, 2011).
Prediction Standardisation The clay content predictions obtained from the index were then standardized using a process in three steps. At the first step, a boxcox transformation was applied to transform the distribution of the clay content predictions into normal distribution  (Legendre and Legendre, 1984)  defined as: (2) where is the clay content predictions obtained from the index, is the transformed prediction and α is the transformation parameter. The assumption is that among all transformations with α values between -5 and +5, transformed data has the highest likelihoodbut not a guaranteeto be normally distributed when standard deviation is the smallest. This step is independent to a calibration database (legacy or reference). At the second step the normalized predicted values were scaled from a calibration database as follow: (3) Where And finally at the third and last step the normalized predicted values were centred from a calibration database as follow: (4) and where is the mean of the clay contents from the calibration database and is the mean of the.
Performances Predictions Indicators The prediction performances of the spectral index were evaluated using the coefficient of correlation r of the predicted values ( or ) against the measured values in the REF database. The coefficient of correlation r is defined as the covariance of the two variables ( or ) and , divided by the product of their standard deviations as follow: (5) As recommended by  Davies and Fearn (2006) , prediction performances analyses have to be also guided by the Standard Error of Prediction (SEP) or Root Mean Square Error of Prediction (RMSEP). The SEP is the parameter commonly used in the NIR spectroscopy literature to describe the prediction ability of a model. SEP appears as an averaged error recorded on the validation-sample set, and was calculated as follow: (6) Where is the clay content of the sample i from the REF database, is the estimated clay content of the sample i from the REF database by the spectral index, and N is the number of soil samples in the REF database.
Raw Predictions Clay contents were obtained from the spectral index (equation 1) with a coefficient of correlation r of 0. 71 and a high bias and SEP (Table  2 ). These clay predicted values were ranged from 10 to 305 g/kg (Table  3 ) which were small compared to the one of the REF database (Table  1 ). Moreover, the distribution of these clay predicted values was not normal (high skewness) (Table  3 ).