dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper presents a method to produce long term climatic forcing fields to force Soil-Vegetation-Atmosphere transfer (SVAT) models in off-line mode. The objective is to increase the temporal frequency of existent climate projections databases from daily frequency to hourly time step to be used in impact climate studies. A statistical clustering k-means method is used. A tens of clusters seems to be enough to describe the climate variability in term of wind regimes, precipitation and thermal and humidity amplitude. These clusters are identified in the future projections of climate and reconstructed sequences at hourly frequency are obtained for all the forcing variables needed by a SVAT model, typically: air temperature, specific humidity, wind speed and direction, precipitation, direct short-wave radiation, downward long-wave radiation, and scattered short-wave radiation.
INTRODUCTION Investigations on climate change impact often requires projections of climate at the local scale to be used to force a Soil-Vegetation-Atmosphere transfer (SVAT) model 1 in offline mode. The easier way to obtain long term databases for the atmospheric parameters used to force SVAT models, would be to directly 2 use climate simulations from General Circulation Models or Regional Climate Models when those datasets are available with a high temporal frequency (1-3 h). Due to computational storage limitations, often this product is only available over a restricted area, during short periods of time and emission or land use scenarios available are limited. One example is the study of  Tanaka et al. (2006)  that estimates the impact on hydrological variables of the 1 SVAT models simulate micro-scale energy and matter exchange processes between land surface and the atmospheric layer near the ground level.
They are used by meteorologists, climatologists, ecologists, and biogeochemists to study the radiative transfer, the energy balance, the turbulent and diffusive transfer, the stomatal function, the photosynthesis and respiration, or the liquid phase water flow. 2 A previous bias correction should be performed using observed climate data in order to remove systematic errors from Global and Regional Climate models. Seyhan River basin, Turkey, through off-line simulations for the periods  1994-2003 and 2070-2080. Future climate projections databases currently available for the whole Europe area that includes different emission scenarios and an ensemble of models have daily frequency, as PRUDENCE (http://prudence. dmi. dk/), ENSEMBLES (http:// www. ensembles-eu. org/), or those from the Climatic Research Unit of the New Anglia University (http://www.
cru. uea. ac. uk/ cru/data/hrg/). This means that, for a grid point, only the daily mean or at the best the maximum and minimum values are available. In this case, an increase in the temporal frequency of the variables from daily to hourly time step is needed. One way to do that is to obtain relationships between large scale circulation patterns and local variables through a classification based on atmospheric circulations or weather types. This classification is determined in a high resolution hourly reanalysis database and projected in to a daily database of climate scenarios. These climate scenarios are then reconstructed at high temporal frequency using mean (or a representative) diurnal cycles from the same weather types in the reanalysis database (see  Huth et al. , 2008  for a deep review of this method).
The methodology presented here allows to generate climatic input databases, based on past near surface observations, for SVAT models that need high-resolution atmospheric forcing inputs. In fact, observational data at hourly frequency is available from the operational network of surface stations with large coverage around the world (http://worldweather. wmo. int/). The idea here is to use single-site observations from a sufficiently long period (so all the diversity of weather types affecting the site will be represented) and apply a cluster analysis using only the mean, maximum, and minimum daily data, that are the only one available for modeled future scenarios, to classify the data in function of the type of the diurnal cycle for each variable. These mean meteorological characteristics are identified in the future projections of climate from Regional Climate Models.
An important question that needs to be tackled before applying the k-means clustering algorithm is how many clusters (k) are in the dataset. This is not known a priori and often the strategy is to perform an iterative exercise increasing the number of clusters. There are different means to evaluate the resulting clustering quality in a quantitative manner  (He et al. , 2002). For us the objective is to obtain the best reconstructed series for all variables once the attribution method is applied. Two parameters are used to evaluate the resulting series. The first represents how well the resulting mean diurnal cycles from clusters serves to identify and attribute a cluster number for a particular day. In others words, for the construction period, the frequency distribution of clusters issue from the attribution process is evaluated against those obtained directly from the k-means clustering method.
FIRST VALIDATION: CLUSTER ATTRIBUTION AND HOURLY RECONSTRUCTION FOR THE CONSTRUCTION PERIOD (1998-2008) The cluster methodology aims to represent the climatic variability of a site using a finite set of daily mean cycles. For that, a cluster number is associated by the k-means method to each day of the period and a series that maintains the climatic features and variability of the site can be directly reconstructed using the seasonal diurnal mean cycles associated to each cluster (hereafter direct method). When working with an alternative period in the past (observations or model outputs) or in the future (model outputs) where the clustering process is not possible, it is necessary to attribute a type of day (cluster number) to each day of the alternative period. The attribution method is here based on variables that are available at daily frequency in climate scenarios databases: the mean, max, and min values for temperature and relative humidity; the daily mean values of u-v wind components and precipitation.
These values are obtained for the construction period. The deviation between those values for a day of the alternative series and from each cluster is computed and summed for each cluster (S K ). All the data is normalized with its standard deviation. The cluster number with the minimum value of S K is attributed to the day. A hourly reconstructed series is obtained for all the observables using the observed mean daily value and the shape of the cluster diurnal cycle for the season of this particular day. For temperature and relative humidity, maximum and minimum values are available so the shape amplitude of the cluster diurnal cycle is modulated as follows: T(h) i = T i + ( T(h) season_mean ) k * Cte Cte = (T max -T min ) obs (T max -T min ) k Where i is the day number and k the cluster number.
Precipitation was not available in this database so this variable was not used as a prescriptor in the attribution process. The application of the attribution method to climate projections is possible assuming that under future climate conditions the occurrence frequencies of clusters might be modified but their dynamical characteristics remain unchanged. It is the same hypothesis as for methodologies based on weather classes and this approach is supported by studies as those from  Corti et al. (1999)  and  Stone et al. (2001)  which suggested that anthropogenic climate change may manifest itself as a projection onto the pre-existing natural modes of variability of the climate system  (Najac et al. , 2011). Figure  9  shows the performance of the methodology to reconstruct 10 consecutive days in February and August 2061 for the A1B scenario for Paris.
DISCUSSION When dynamical downscaling is needed in impact or adaptation studies high temporal frequency atmospheric fields are required to force SVAT models. Future climate projections databases currently available for the whole Europe area have often daily frequency and for a grid point they provide only the daily mean or at the best the maximum and minimum values of atmospheric fields. This study presents a method to increase the temporal frequency of these variables from daily to hourly time step based on past near surface observations from a typical operational surface station. A statistical k-means method is applied to a construction period to classify observations in function of the type of the diurnal cycle for each variable. These clusters are identified in the future projections of climate and reconstructed sequences at hourly frequency are obtained.
Six model outputs from the EU-FP6 ENSEMBLES were also reconstructed during the project at hourly frequency from the daily database. The ENSEMBLES database is a good example of  After a bias correction, a cluster number was attributed to every day of climate scenarios produced by these seven models using the method explained in Section First validation: cluster attribution and hourly reconstruction for the construction period  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008). From a statistical point of view mean values and climate variability are represented by Regional Climate Models but they do not reproduce the chronology of fields. For this reason, in absence of hourly scenarios, validation of reconstructed series from the ENSEMBLES database cannot be done with a day by day comparison as shown in Section Validation and Application to climate scenarios from Regional Climate Models simulations and only a statistical analysis makes sense.
The k-clustering algorithm is implemented in a variety of free programming languages and can be run in a simple computer or laptop. The attribution method is based on variables that are also available at daily frequency in climate scenarios databases so it is directly applicable to this kind of climate projections. Hourly reconstructed series are obtained using the observed (or modeled) mean value. If maximum and minimum values are also available, the shape of the cluster diurnal cycle is modulated in amplitude accordingly allowing extremes higher than those observed during the construction period if those are represented in the climate projections. Even though interesting applications can be developed from the cluster attribution, for example to relate a kind of type of meteorological situation with impact factors in health as pollutants concentrations or thermal stress or to contextualize single field campaign days within a larger climatology.
ACKNOWLEDGMENTS This research was performed at the  GAME/CNRM (Météo-France, CNRS)  supported by the  French National Research Agency (ANR)  under grant  ANR-09-VILL-0003  and the  Scientific Cooperation Foundation STAE in Toulouse  in the context of the MUSCADE and the  ACCLIMAT  project respectively. Authors would like to thanks  Nowlen Huet ,  Samuel Somot , and  Michel Déqué  from the  CNRM  for their constructive advice in the statistical and correction methods and  Marien Lennart ,  Ralf Podzum , and  Daniela Jacob , from the  Climate Service Centre of the Max Planck Institute for Meteorology , that provided the hourly timeseries from REMO simulations.
O B S E RVAT I O N S A N D DATA R E D U C T I O N Spectropolarimetric observations were conducted in two runs in 2009 and in 2014 (runs ID 09AO01 and 14AC08) with the échelle spectropolarimeter ESPaDOnS  (Donati et al. 2006 ) located at the 3. 6m Canada-France-Hawaii Telescope (CFHT, Hawaii) and in one run with Narval (TBL, France), twin of ESPaDOnS, in 2014. In both instruments, the spectral range covers a large part of the visible domain, i. e. from 370 to 1050 nm over 40 spectral orders with a resolving power R ∼ 65 000. The unpolarised spectrum (Stokes I) and the circular polarized spectrum (Stokes V) were obtained in Queued Service Observing mode following the standard procedure in which each spectropolarimetric sequence consisted of four individual sub-exposures taken in different orientations of the half-wave Fresnel rhombs.
Stokes I and Stokes V spectra were then derived for each set of sub-exposures. The observations were mostly conducted under sub-arcsecond seeing conditions. The complete data reduction was performed with the CFHT's reduction pipeline UP- ENA which is based on the software LIBRE-ESPRIT  (Donati et al. 1997 ). This fully automatic reduction process performs classical operations on spectropolarimetric data: bias subtraction, flat-fielding, removal of bad pixels, wavelength calibration, spectrum extraction and extraction of the polarimetric information. We also looked for any previous spectropolarimetric observations of the stars in our sample in 'PolarBase' 5  (Petit et al. 2014). This data base contains a large number of reduced and calibrated spectropolarimetric data obtained with both ESPaDOnS and Narval.
Thus, we retrieved Stokes Q, U and V data for R Sct, whose analysis will be combined to our more recent data set (see Section 4. 2). Because most of PAGB stars are known to exhibit pulsations with a wide range of amplitudes, periods and cycle regularity, one of the main constraints of our programme was the photometric variability of our sample targets (see Section 2). We programmed the observations of the stars around maximum light (e. g. at their brightest phase), when their photosphere is the hottest. The time constraint for each target was derived based on literature data and light curves from the AAVSO and the GCVS. Table  2  presents the log of our Stokes V observations with the target ID, the date of observation, the number of collected V sequences, the total exposure time (in s) for a single Stokes V sequence and the peak S/N of a single Stokes V sequence per 2.
D I AG N O S I S O F T H E M AG N E T I C F I E L D We applied the least squares deconvolution (LSD) procedure  (Donati et al. 1997)  as implemented in the ILSD code of  Kochukhov, Makaganiuk & Piskunov (2010). We employed line masks constructed from Vienna Atomic Line Database (VALD3,  Kupka et al. 1999 ) extract stellar requests from 3690 to 10 000 Å for temperatures and gravities corresponding to our targets, with a line depth threshold of 0. 2 and 2 km s -1 micro-turbulence. Masks were iteratively cleaned by direct comparison of the LSD model spectrum 5 http://tblegacy. bagn. obs-mip. fr/ with the observed spectrum, in particular removing broad lines (H lines, the Ca II H and K lines) as well as strong interstellar lines, lines in obvious emission and lines blended with strong telluric features.
A search for the neutral Higgs bosons predicted by the Minimal Supersymmetric Standard Model (MSSM) is reported. The analysis is performed on data from proton-proton collisions at a centre-ofmass energy of 8 TeV collected with the ATLAS detector at the Large Hadron Collider. The samples used for this search were collected in 2012 and correspond to integrated luminosities in the range 19. 5-20. 3 fb -1. The MSSM Higgs bosons are searched for in the τ τ final state. No significant excess over the expected background is observed, and exclusion limits are derived for the production cross section times branching fraction of a scalar particle as a function of its mass. The results are also interpreted in the MSSM parameter space for various benchmark scenarios.
5-20. 3 fb -1 of proton-proton collision data collected with the ATLAS detector  [34]  in 2012 at a centre-of-mass energy of 8 TeV. Higgs boson production through gluon fusion or in association with b-quarks is considered (see figure  1 ), with the latter mode dominating for high tan β values. The results of the search are interpreted in various MSSM scenarios. The ATLAS search for the SM Higgs boson in the τ τ channel  [35]  is similar to that described here. Important differences between the two searches are that they are optimized for different production mechanisms and Higgs boson mass ranges. Additionally, the three Higgs bosons of the MSSM, which can have different masses, are considered in this search. In particular the couplings to b-quarks and vector bosons are different between the SM and MSSM.
Data and Monte Carlo simulation samples The data used in this search were recorded by the ATLAS experiment during the 2012 LHC run with proton-proton collisions at a centre-of-mass energy of 8 TeV. They correspond to an integrated luminosity of 19. 5-20. 3 fb -1 , depending on the search channel. Simulated samples of signal and background events were produced using various event generators. The presence of multiple interactions occurring in the same or neighbouring bunch crossings (pile-up) was accounted for, and the ATLAS detector was modelled using GEANT4  [36, 37]. The Higgs boson production mechanisms considered in this analysis are gluon fusion and b-associated production. The cross sections for these processes were calculated using Higlu  [38] , ggh@nnlo  [39]  and SusHi  [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54].
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Acknowledgements. We thank  Mark Williams  and one anonymous referee for their constructive remarks that have greatly improved this paper. We thank  Pierre Sepulchre  for providing the IPSL CM4 GCM run. We are also grateful to  David G. Ferreira  for his constructive and helpful comments. This work was granted access to the HPC resources of TGCC under the allocation 2014-012212 made by  GENCI. Edited by:  A. Haywood.
The point-to-point distance between each spectral collection was about 50 m. Data acquisition was performed using HORIBA LabSpec 5 software. Chemometrics operations were carried out using OPUS version 7. 0 software from Bruker Optic. Partial Least Square (PLS) regression was used for the whole study and a crossvalidation procedure was used. All spectra were pretreated thanks to a Standard Normalization Vector (SNV) treatment to correct effects of the multiplicative interferences of scattering. The treatment consisted in subtracting the mean spectrum and scaling with the spectrum standard deviation  [7]. Processed spectra were fitted using Fityk 1. 2 software and the different peaks were attributed to the corresponding organic groups  [8].
In addition to the VIMOS spectroscopic data, a large set of imaging data is available in the three fields covered by our pair search. The COSMOS field  (Scoville et al. 2007 ) has a full coverage with the HST/ACS F814W filter  (Koekemoer et al. 2007 ) and includes, among other data, ugriz photometry from Subaru  (Taniguchi et al. 2007) , and more recently YJHK photometry from the UltraVista survey  (McCracken et al. 2012). Spectroscopic redshifts from the zCOSMOS survey are also available  (Lilly et al. 2007 ). The ECDFS is covered by the MUSYC survey in UBVRIz'  (Gawiser et al. 2006 ) and partly by the CANDELS survey with the ACS and WFC3 on HST  (Koekemoer et al. 2011). The VVDS-02h field has deep CFH12K BVRI photometry  (Le Fèvre et al. 2004) , and even deeper CFHTLS ugriz photometry (e.
Pair properties From the multi-wavelength dataset, we have derived the main properties of the galaxies in the identified pairs. The stellar mass of each galaxy of a pair has been derived from SED fitting of the available photometric data at the measured redshift (see e. g. Ilbert et al. 2010)  and range from 10 9 to 10 11 M ⊙ (Table  2 ), with uncertainies by up to a factor 2 (e. g. Bolzonella et al. 2009 ) depending on the number of bands used, or the IMF. The stellar mass ratio are used to separate "major-merging pairs", with a mass ratio between the two galaxies of 1 ≤ M 1 /M 2 ≤ 4, from "minor-merging pairs", for which M 1 /M 2 > 4. We have checked that the SED-derived mass ratio for each pair is consistent with the K-band or H-band flux ratio of the two galaxies, as these fluxes probing above λ rest 4000Å can be considered as a rough proxy for stellar mass  (Bruzual and Charlot, 2003) , and we have used the error on the K-band or H-band flux ratio as a proxy for mass ratio errors.
Introduction Advances in information and storage technology have promoted the interest of companies for research works like Knowledge Discovery from Databases (KDD). Particularly, the generalization of the ERP (Enterprise Resource Planning) in industrial environments, make available a large amount of information. Hence, data mining techniques can be used to process this information and extract new knowledge, potentially useful to support decision-making. Nevertheless, this extraction should include a postprocessing phase assessing the usefulness and reliability of the results, before their validation  [1]. We propose in this paper an interactive approach for this post-processing phase, controlled by an industrial domain expert and a data mining expert.
Knowledge Discovery from Databases (KDD) The knowledge extraction approaches have developed new intelligent tools, more efficient than traditional data analysis methods for discovering new knowledge in an industrial context. Knowledge Discovery from Databases is defined as a "non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data"  [2] , in order to create a significant competitive advantage in companies. Given the great potential of the available data as a source of new knowledge  [3] , KDD has become essential in many industrial fields, including product and process design, materials planning, quality control, scheduling, maintenance, customer relationship management, etc. The general process involves three main phases: pre-processing, data mining, and postprocessing.
Data mining phase Data mining consists in applying data analysis and discovery algorithms to find hidden knowledge (relations or patterns) in large volumes of information  [3, 4]. Our focus is on the association rules mining approach  [5]  to discover relationships between a set of attributes (or items) in a database. The obtained relationships are based on the co-occurrence of attributes  [6]  showing correlation, but not a cause. An association rule is formally defined as a relationship between two itemsets through relations of the form "If X, then Y", denoted as ( → ), where ,  ∈  and  ∩  = ∅. is usually called hypothesis and  conclusion, i. e. the presence of  allows to conclude on the presence of. Two classical measures are usually related to assess the discovered association rules: support and confidence.