dataesr/scientific-paragraphs-categorization huggingface dataset:
Still, local observations provide a correct estimation of precipitation at the foothill of the Himalaya, where there is a relatively high density of rain gauges. Comparing our simulation with these observations in this area, we found our RCM to underestimate precipitation. We further expect a significant underestimation of precipitation in the observational datasets at high altitude. Therefore, it is more complicated to validate model outputs in these areas. As snow cover extent is well estimated with satellite products, checking the ability of models to simulate snow cover is a way to partially analyse its performance in terms of snowfall description. In particular, our MAR simulation clearly underestimates snowfall in the eastern part of the Himalaya, whereas it appears more correct in central Himalaya and in western Nepal.
reanalysis, and modelled snow cover extent, Dee et al. (2011) TRMM 3B42 50 km (0. 5 • ) 1998-2010 Precipitation estimated from satellite data, Kummerow et al. (1998, 2000) APHRODITE 25 km 1951-2007 Long-term daily gridded precipitation dataset for Asia based on a dense network APHRO_ V1003R1 (0. 25 • ) of rain gauges, Yatagai et al. (2012) Monsoon Asia CMC 24 km 1998-2012 Daily snow water equivalent analysis, based on a surface model forced with atmospheric analysis, coupled with statistical interpolation of snow depth, Brasnett (1999) IMS 24 km (also 1966-2008 Daily Northern Hemisphere snow and ice cover extent analysis from satellite available observation, available at http://nsidc. org/data/g02156. html (NOAA, at 4 km) NSIDC, 2004) MODIS ∼ 5 km (0. 05 • ) Since 2000 Snow cover extent estimated from satellite product: MODIS/Terra Snow Cover Monthly L3 Global 0.
Acknowledgements. This work was supported by  Agence Nationale de la Recherche  under contract  ANR-09-CEP-005-02/PAPRIKA. We would like to thank the " Institut du Développement et des ressources en Informatique " (IDRIS, CNRS), and the  "CIMENT" resources (UJF)  for providing computer time for the simulations presented in this paper. The figures have been prepared with Ferret free software. We thank the  National Snow and Ice Data Centre (NSIDC, Boulder, CO)  for providing MODIS Snow cover data. Edited by:  A. Langousis The publication of this article is financed by  CNRS-INSU.
Acknowledgements. We are grateful to the captain and crew of the  Canadian Icebreaker CCGS Amundsen. This study was conducted as part of the  Malina Scientific Program  funded by  ANR (Agence nationale de la recherche) ,  INSU-CNRS (Institut national des sciences de l'univers -Centre national de la recherche scientifique) ,  CNES  ( Centre national d'études spatiales)  and  ESA  ( European Space Agency). We thank  Joséphine Ras  for the HPLC pigment analyses and  David Doxaran  for providing the SPM data. Edited by:  K. Suzuki The publication of this article is financed by  CNRS-INSU.
This is explained theoretically by the fact that intermolecular collisions induce transfer of absorption intensity among the internal levels defining the optical transitions, thus resulting in intensity exchanges between the various spectral components  (Hartmann et al. , 2008). The CO 2 line mixing effects are now taken into account in Atmosphit up to 30 km for a wide range of atmospheres (i. e. for a wide range of pressure-temperature profiles up to 30 km in order to cover all kind of atmospheres that can be encountered on earth) by calculating absorption cross sections of CO 2 following the method given in  Gamache et al. (2012)  and  Lamouroux et al. (2012a, b). From 30 km to the top of the atmosphere, where collisions are less frequent, the individual line parameters from the HITRAN spectroscopic database  (Rothman et al.
Detailed description of ground-based FTIR data set, retrieval method and error budget can be found in  Vigouroux et al. (2012)  for Reunion Island and in  Mahieu et al. (2008)  and  Li et al. (2009)  for Jungfraujoch. Abundances retrieved from IASI spectra are calculated as columns above the altitude of the stations: 50 m and 3580 m a. m. s. l. (above mean sea level) for Reunion Island and Jungfraujoch, respectively. IASI cloudy spectra were removed from the data set using a 10 % contamination threshold on the cloud fraction in the pixel, and a posterior filter was also applied to remove poor fits, corresponding to a residual root mean square (RMS) greater than 4 × 10 -6 Wm -2 msr -1. In all, 31 % of the total clear sky spectra fits were removed because of a high RMS fit error. The resulting mean total retrieval errors on the total columns for HCN and C 2 H 2 are 53 and 47 % at Reunion Island, and 92 and 77 % at Jungfraujoch.
Conclusion and outlooks We have demonstrated the possibility of retrieving HCN and C 2 H 2 columns from IASI radiance spectra in the 720 cm -1 region, by properly incorporating CO 2 line mixing in the radiative transfer model. This study shows that the sensitivity of IASI to the two species is mostly in the mid-upper troposphere above 6 km. Total columns have been retrieved for a two year period above Reunion Island and Jungfraujoch, where routine FTIR measurements are available. The comparison between IASI and FTIR retrieved total columns demonstrates the capabilities of IASI to capture the seasonality in HCN and C 2 H 2 in most cases (HCN and to lesser extent C 2 H 2 at Reunion Island; and C 2 H 2 at Jungfraujoch). We also note a greater tendency of IASI data to high C 2 H 2 values. The absence of seasonality of HCN at Jungfraujoch could be rationalized by the low sensitivity of IASI to HCN abundance at mid-latitudes.
Acknowledgements. IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the  Met-Op  satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. Part of the research is supported by  EUMETSAT  through the O 3  SAF  project. P. F. C. and L. C. are, respectively, Scientific Research Worker and Research Associate at the F. R. S. -FNRS. The research in Belgium was funded by the  F. R. S. -FNRS , the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA Prodex  arrangements  C4000103226  and the  AGACC-II  project). Financial support by the  "Actions de Recherche Concertées" (Communauté Francaise de Belgique)  is also acknowledged.
Investigation of concrete at various scales The French project SENSO  [1] , which was run from 2005 until 2009, has overcome certain scientific problems, in particular : the creation of a database, command of measurement techniques, selection of sustainability indicators of the concrete , choice of ND paramete rs adapted to diagnosis and data inversion. This was achieved through the development of a set of not only conventional, but also original techniqu es, capable of affecting the material and its structure at all scales, which were implemented as part of an experime ntal design. This project, based on the examination of specimens conditioned under controlled conditions of porosity and saturatio n, allowed material properties to be derived from four techniques (radar, ultrasoun d, electrical resistivity, and capacitance measurements ).
Experimental plan The aim of the ANR-SENSO project  [16, 4]  was to evaluate the indicators by nondestructive techniques on slabs. It was possible to model the relationships of the ND parameters with the indicators: degree of saturation, Young's modulus, porosity, and compressive strength. The indicators were considered to be homogeneous, throughout the slab. For our study, the analysed data was limited to that generated by six batches of concrete, made with the same aggregates (round, siliceous, D max = 14 mm), with water to cement (W/C) ratios varying from 0. 3 to 0. 8. This allowed different concretes to be obtained, with varying porosities (from 12. 5% to 18. 1%), moduli of elasticity (from 21,300 MPa to 35,500 MPa), and concrete strengths (from 20 to 72. 9 MPa) (Table  1 ). The sensitivity of ultrasound propagation and other non-destructive techniques to the concrete's characteristics were studied.
Velocity dependen ce on the degree of saturation Whereas previous analyses have focused on only two cases of saturation, in order to study the sensitivity and accuracy of each ND parameter as a function of the indicators, the experimental protocol was designed to include tests at intermediate degrees of controlled saturation. It was thus possible to test the velocity variations as a function of the degree of free water saturation in the porous network. In this aspect of the study, data for the ND parameters: PW 250 -PW IE -SW me -SW 3 cm was recorded. The variations of two velocities (PW 250 -PW IE) as a function of the degree of saturation are shown in Fig. 14. Each concrete is characteri sed by its batch number and porosity ratio (G1 12. 5% -G2 14. 3% -G3 15. 5% -G3a 16% -G7 15. 9% -G8 18.
This model is used in the present work. The H 2 flame investigated in this work is studied experimentally by Barlow  [3]. These measured data are utilized in this study for comparison. Barlow et al. [4]  simulated numerically NO x formation in this flame under helium dilution (0, 20 and 40 %) in order to draw comparisons between calculations made with the CMC and PDF combustion models with the (k-ε) model. Fairweather and Woolley  [5]  examined the same flame using a CMC approach with three kinetics schemes (5, 24 and 62 reaction steps). The predictions were based on both k-ε and Reynolds stress/scalar flux turbulence closures. They showed that simulations based on Reynolds stress closures are superior to those obtained using the simpler eddy viscosity-based approach. The predictions are optimized against non-diluted measures results of  Barlow and Carter [6]  in the radial profiles rather than in the axial profile.
It was shown that both combustion models are equally capable of predicting the measured temperatures with reasonable accuracy. The flamelet results of the temperature at position x/l = 1/8 are very close to experiment because preferential diffusion is including the flamelet model whereas the PEUL model does not. More recently, a hybrid method (RANS-TPDF) has been used by  Masri [11]  for the study of the H 2 /N 2 lifted flame structure. He showed that the flame is controlled by chemical kinetics. We can also quote the experimental study of Theron  [12]  on the effects of heat release on mixing processes and flow structure in a high-speed subsonic turbulent H 2 jet. The simulations are performed with the sophisticated commercially available code Fluent which solves the Navier-Stokes equations with the finite volume method.
The scalar-dissipation rate model is based on the assumption that production equals dissipation in the scalar variance equation. For reactive scalars, this relation could be modified to include the co-variance of scalar and scalar production rate appearing in the reactive scalar variance equation. This term is closed in the context of a transported PDF model. In the numerical implementation, the sub-mesh variance is computed directly from the particle composition vector. Hence, the dynamic model for the variance is not required in the PDF context. In the present work, it was found, that C φ varies slightly around the value 3. The computer program MM-INTAS  [23]  was provided as a tool to implement the scalar-dissipation rate model. Combined with the FLUENT/CFD code, this coupled algorithm was sufficient to carry out the computations discussed in this work.
At each site, the expected number of species for a theoretical sample of 100 specimens, ES (100) , was chosen and calculated cumulatively as the total number of expected species retrieved from the three replicates. The species diversity (H , using log-base 2, expressed as H ) was also measured by the Shannon-Wiener index. These indices were calculated from the sum of the individuals of the three cores from each of the sampling sites, using PRIMER v6. 0+ (Plymouth Marine Laboratory, UK;  Clarke and Gorley, 2006). We also measured the turnover diversities (β-diversity) between sampling times, separately for the different depth ranges, using the similarity percentage analysis (SIMPER) routine that is included in the PRIMER v6. 0+ software. Turnover diversity among samling times is reported as the percentage dissimilarity calculated from resemblance matrixes based on Bray-Curtis dissimilarity after transformation of the raw data into presence/absence matrixes.
Univariate tests were carried out using unrestricted permutation of the raw data, whereas multivariate analyses were carried out with permutation of residuals under a reduced model. SIMPER analyses were also carried out to estimate the turnover of nematode diversity between sampling times and depths. SIMPER, uni-and multivariate PERMANOVA tests and CAP analysis were carried out using the PERMANOVA and SIMPER routines included in the PRIMER6+ software.
Acknowledgements. This research was supported by the  Collaborative  Project  HERMIONE ,  EC  contract no. 226354 , under the  European Commission 's  7th Framework Programme , the  National Strategic Project OBAMA (PRIN MIUR, Italy) , the  RTD projects DOS MARES  ( CTM2010-21810-C03-01 ),  REDECO  ( CTM2008-04973-E/MAR ),  GRACCIE-CONSOLIDER  ( CSD2007-00067 ) and  SProCS-ROV  ( CTM2011-14081-E ) funded by the  Spanish RTD program , and  OASIS DEL MAR  funded by  "La Caixa". University of Barcelona  researchers benefited from grant  2009 SGR 1305  by  Generalitat de Catalunya  to excellent research groups. The authors are indebted to  D. Amblas  ( Universitat de Barcelona ) for the precious work in drawing Fig. 1. Edited by:  R. Serrão Santos.
The aircraft had a coverage area of approximately 200 ha with a ground sample distance (GSD) of 8 cm in a single flight. An open source piece of software, MICMAC, was used to process the image block in order to produce a high resolution photo-DSM. This photo-DSM was subsequently co-registered with a LiDAR-DTM. Finally, the photo-CHM was compared to a reference LiDAR-CHM. This study mainly aimed to develop a photogrammetric workflow suited to forest UAS imagery. In addition, recurring issues and major opportunities concerning the use of UAS photogrammetry in forested areas are identified and discussed. The assessment of forest density indicators from the UAS canopy height model, such as the volume per hectare, was not investigated further in this research. However, tests were carried out in order to determine how satisfactorily dominant height and individual height might be derived from photo-CHM.
Small UAS Platform and the Sensor Used in the Present Study The Gatewing X100 small UAS  [39]  (wingspan: 100 cm; weight: 2 kg; cruise speed: 80 km/h; flight height: from 100 m to 750 m; maximum flight duration: 40 min) was chosen for its ability to cover a relatively large area in a single flight. This UAS is equipped with an inertial measurement unit (IMU) and a GPS. These sensors determine the position, as well as the altitude of the X100 during the flight. It should, however be pointed out that the GPS accuracy is approximately a few meters, and the orientation angle (yaw, pitch, roll) accuracy is about 2 degrees. The UAS flight plan is prepared using a specific software designed for the X100 (Quickfield R ). A ground control station (GCS) records the flight characteristics (working area size and location, image overlap, flight altitude, location of take-off and landing points, wind and landing directions) using a Yuma Trimble R rugged tablet computer.
The ground control software (Micropilot Horizon R ) is used to control the artificial altitude and heading reference system (AHRS) integrated into the electronic box (ebox) of the X100. The small UAS Gatewing X100 is catapulted with an elastic launcher system (Figure  1 ). The flight is fully automatic from takeoff to landing and complete stop, although the remote pilot has the possibility to intervene on the flight path whenever there is a serious risk of accident. Figure  1. The small unmanned aerial system (UAS) Gatewing X100 on its launcher, ready for take-off. The small UAS is equipped with a Ricoh GR3 still camera (10 megapixel charged coupled device, 6 mm focal length or 28 mm in a 35 mm equivalent focal length) adapted for near-infrared acquisition. Shutter speed and camera sensor sensitivity (ISO) are manually selected according to luminosity.
UAS Survey In the context of a related research, multitemporal UAS image datasets were acquired for the study area. Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small UASs  [3, 40, 41]. UAS flights over Felenne were authorized by the Belgian Civil Aviation Authority. A total of 9 successful flights were carried out in 2012 at different periods, corresponding to various stages of tree growth. Time series are beyond the scope of this paper, which explains why only one single flight, used for the generation of the photo-CHM, is described. This flight was selected on the basis of the quality of the collected images (visual estimation), vegetation stage (all leaves present) and the limited presence of shadows. A total of 441 near-infrared images were acquired during a summertime flight on 1 August 2012, between 16:00 and 16:40 h.
LiDAR Data An aerial small footprint LiDAR dataset, covering the study area, was captured during leaf-on conditions in July 2011. A "Riegl LMSQ680" sensor was used, with an average point density of 13 points/m 2. The first and last returns were recorded and classified by the service provider as " ground", "vegetation", "building", "water" or "unclassified". The dataset (LASer file format, i. e. , LAS) was stored in tiled files (500 m × 500 m). The chosen digital terrain model (DTM) had a 50-cm resolution and was made from the original point cloud by the service provider. The digital surface model (DSM) was generated from the most elevated returns, with a resolution of 50 cm. The LiDAR canopy height model (CHM) raster was realized with a pixel size of 50 cm by subtracting the DTM from the DSM.
The LiDAR DTM accuracy was assessed with altimetric measurements in open-terrain (RTK GPS measurements and reference points from the Belgian Geographic National Institute and from the Walloon public data repository). The altimetric root-mean-square error (RMSE) of the LiDAR DTM is 0. 14 m, and the mean DTM error is 0. 11 ± 0. 08 m (mean error ± standard deviation). The overall planimetric accuracy of the LiDAR dataset, according to the provider, is 25 cm. These values sound coherent compared to previous studies  [44] [45] [46]. Regarding the altimetric accuracy of the LiDAR DTM that reaches the traditional nominal accuracy of high density aerial LiDAR measurements  [47] , this LiDAR dataset (LiDAR DTM, DSM and CHM) is considered as having a very good quality.
Semi-automatic generation of 3D geometry from an unordered collection of UAS images requires newly-developed computer vision algorithms, referred to as Structure from Motion (SfM)  [7]. The software used in this study was the open source and free-of-charge photogrammetric toolbox, MICMAC, developed by the french mapping agency (the National Institute of Geographic and Forestry Information)  [50]. MICMAC includes a set of photogrammetric tools and offers the possibility of fine-tuning each process  [6]. The three main steps of the workflow are described succinctly below: 1. Generation of tie points based on the scale-invariant feature transform (SIFT) feature extractor  [51]. 2. Camera calibration and image triangulation: the Apero tool  [52]  uses both a computer vision approach for the estimation of the initial solution and photogrammetry for a rigorous bundle block adjustment  [53]  (automatic aerial triangulation).
Internal orientation (camera calibration) may be modeled by various polynomials. 3. Dense matching: MICMAC  [54]  is a multi-image, multi-resolution, matching approach: it is an implementation of a Cox and Roy optimal flow  [55]  image matching algorithm working on different pyramid scale levels in order to accelerate the computational processing. These tools are command-line tools. In the MICMAC toolbox, both simple and complex tools can be distinguished. For instance, Tapas is the simplified tool for camera calibration and image orientation, whereas Apero is the complex tool used for the same purpose. In order to illustrate and to make the processing described here reproducible, a small dataset of forest UAS imagery was integrated into the example dataset of MICMAC. A processing chain based exclusively on simplified tools is provided.
Tie Point, Camera Calibration and Relative Orientation Within this workflow, potential image pairs were determined based on the GPS data by Delaunay triangulation. Telemetry data are useful only at this stage, because their accuracy was judged insufficient for direct georeferencing. Keypoint extraction was then performed on images resampled at the resolution of an image of a width of 2,000 pixels. Camera calibration was carried out in the lab by Gatewing R with a calibration board and the CalCam software (MosaicMill Ltd. , Vantaa, Finland). This internal calibration was primarily converted to suit the MICMAC format. Radial distortions were corrected by a polynomial of 3 coefficients, referred to as Fraser in MICMAC. Orientation was firstly computed by Apero in a relative reference system, with a fixed interior orientation.
Metrics were correlated only for forested windows: crop fields and meadow areas were therefore discarded. Forested areas were identified by means of the forest stand localization map and a mean height threshold of 3 m. • Stand level: stand models were constructed based on inventory data (plot inventory) for predicting dominant height. The model residual (residual mean square error) and the model fit coefficient (r 2 ) served as indicators of CHMs quality. For each CHM, two regression models were adjusted, the first with one single explanatory variable and the second with two explanatory variables. The selection of these variables was performed with the best subset regression analysis. • Tree level: the model for individual tree heights was established. Tree crowns were delineated by hand by a photo interpreter, and metrics were computed on this crown area.
4% (1. 65 m, r 2 of 0. 82) and individual height with an RMSE of 4. 7% (1. 04 m, r 2 of 0. 91) based on photo-CHM. LiDAR-CHM models performed slightly better than photo-CHM models, although differences in terms of model fit and root mean square residuals were no more than a few percent. These regression models only give an overview of the possibility of extracting dendrometric data from high resolution photo-CHM. Indeed, a thorough validation of the photogrammetric forest canopy model would require further specific research. As previously stated, a key problem for the validation of photogrammetric results lies in the difficulty of obtaining sufficiently accurate and contemporary reference data. The results gained in this study through empirical tests of the quality of photo-CHM need to be interpreted with caution, as both reference datasets (LiDAR-CHM and field inventory data) showed their limitations.
Acknowledgments The authors would like to acknowledge the  Belgian Civil Aviation Authority , as well as the municipality of Beauraing for providing authorization for the use of a small UAS in the present study. Thanks also go to  Cédric Geerts ,  Coralie Mengal  and  Frédéric Henrotay  for carrying out field operations and to  Géraldine Le Mire  and  Phillis Smith  for their corrections and advice on the written English.