dataesr/scientific-paragraphs-categorization huggingface dataset:
Altogether we can conclude that coma regions showing large emissions are affected by opacity effects by no more than 15% for CO 2 and 20% for H 2 O. We evaluated that the highest attenuation occurs at azimuth angles of 157 • ≤ az ≤ 180 • for H 2 O and 113 • ≤ az ≤ 180 • for CO 2 within 1 km of the nucleus where molecules are not excited because of shadowing. Considering these limits, about 6% of the available CO 2 spectra in the VIRTIS dataset listed in Table  A. 1 are affected by opacity, while the percentage is lower than 1% for H 2 O. Since the effect is marginally significant only for a very small fraction of the total, we decided to mantain them in the set of analysed data owing to the negligible net impact on the results obtained so far.
Because of the low CO 2 column densities retrieved in this region, the ratio is lower than 1. 5% at all the considered tangent altitudes. Conversely, above the Hatmehit-Bastet region, this parameter is about ten times greater, reaching a maximum value of 35% at about 3 km from the nucleus. The CO 2 to H 2 O ratio increases when moving away from the nucleus, because the water content decreases more rapidly than carbon dioxide. This could be caused by the water source region being localized in a small area in the neck, while CO 2 distribution is spread more over the southern latitudes. However, these results apply only along the considered tangent profiles, and care is required when considering the entire dataset. The variability of the H 2 O and CO 2 emissions is typical of these bodies and might reflect differences in composition of the cometary nucleus.
Acknowledgements. The authors would like to thank the following institutions and agencies that supported this work:  Italian Space Agency (ASI -Italy) ,  Centre National d'Études Spatiales (CNES -France) ,  Deutsches Zentrum für Luftund Raumfahrt  ( DLR-Germany ),  National Aeronautic and Space Administration (NASA-USA ). VIRTIS was built by a consortium from  Italy, France , and  Germany  under the scientific responsibility of the  Istituto di Astrofisica e Planetologia Spaziali of INAF, Rome (IT) , which also leads the scientific operations. The VIRTIS instrument development for ESA has been funded and managed by  ASI , with contributions from  Observatoire de Meudon  financed by  CNES  and from  DLR. The instrument's industrial prime contractor was former  Officine Galileo , now  Selex ES  ( Finmeccanica Group ) in  Campi Bisenzio , Florence, IT.
A 32-year global ensemble reforecast dataset has recently been developed at Météo-France that is approximatively consistent with the operational global ensemble forecast system PEARP. Unlike ECMWF or NCEP, Météo-France does not possess a reanalysis of its own operational forecast system. Therefore, the initial atmospheric state and boundary conditions of the reforecasts are from the ECMWF ERA-Interim reanalysis. This article presents a study of the sensitivity of the reforecasts to the method of land-surface initialization. To this end, two sets of short-range hindcasts using different land-surface initialization approaches are compared. The first set is initialized from interpolated ERA-Interim land-surface fields based on a transfer function. The second set is initialized from offline simulations of the Météo-France land-surface model (SURFEX) driven by the 3hourly near-surface atmospheric fields of the ERA-Interim reanalysis.
Each set is run from 1800 UTC initial conditions and up to +108 h. Because better results overall are found using offline SURFEX simulations, this latter approach was chosen to perform an ensemble reforecast dataset. Then, this ensemble reforecast database will be used to build a climatology of the operational ensemble prediction system of Météo-France, which will, in turn, help to better estimate systematic forecast errors and, more importantly, improve the forecasting of rare extreme weather events.
Introduction An ensemble reforecast dataset is a long set of hindcasts initialized from dates in the past using the same operational ensemble forecasting system. The goal of producing such a dataset is to build a model climatology of the operational forecast system, which then can be used in numerous applications. Several articles have demonstrated the need for such a dataset in weather forecasting  (Hamill et al. , 2004; Whitaker et al. , 2006; Hamill and Whitaker, 2006; Hamill and Juras, 2006). Systematic errors have become challenging to correct by increasing the model resolution, improving the physical parametrizations, or improving the production of the initial conditions using a better data assimilation system. The use of an ensemble reforecast dataset will allow us to reduce those errors by learning from the past using post-processing methods.
Providing reliable forecasts of intense weather events three or four days ahead is still challenging today. In most cases, such events have low predictability. Although they have considerably improved in the last decades, numerical weather prediction (NWP) models can still have difficulties to predict, beyond 48 h, the intensity and the trajectory of the strongest storms and high precipitation events. Therefore, the computation of the EFI and SOT indices using the ensemble reforecast dataset will greatly help stakeholders to take preventive protective actions to reduce losses of lives and property caused by those devastating events. The only two operational centres to have produced a global ensemble reforecast dataset are the National Centers for Environmental Prediction (NCEP;  Hamill et al.
, 2013)  and the ECMWF  (Hagedorn et al. , 2012)   forecasts for only a few years, i. e. not long enough to generate a reforecast. Further, the assimilation and modelling system may have changed over that period. Hence, for a centre to initialize a longer reforecast, reanalyses from another centre must be utilised. MeteoSwiss is one of the first centres to have done so, producing a 30-year reforecast dataset of the limited-area model ensemble prediction system (COSMO-LEPS) initialized from ECMWF reanalyses  (ERA-40; Fundel et al. , 2010). However, for land-surface fields, the initialization from a reanalysis developed using a different NWP model is not straightforward due to many differences of land-surface parametrizations (e. g. the number of soil layers) and the physiographic databases that represent soil and vegetation characteristics between two land-surface schemes.
Experimental design The land-surface initialization from an analysis that is produced by another model is not straightforward because of the many differences of land-surface parametrizations and physiographic databases between the two land-surface schemes, which must be taken into account. For instance, the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) scheme of ERA-Interim uses four soil layers with fixed thicknesses; from top to bottom, they are 7, 21, 72 and 189 cm (each layer has its own water content). In contrast, the land-surface scheme of ARPEGE (Action de Recherche Petite Echelle Grande Echelle; the operational global NWP model of Météo-France) uses only two layers, the top layer with a fixed size of 1 cm and the second layer overlaps the first one and has a variable size.
The water saturation fraction depends on the soil type (e. g. coarse, medium, medium-fine, fine, very fine, organic, reserved), and the soil types are also very different between the two land-surface schemes. Therefore, using the raw land-surface datasets from ERA-Interim (i. e. without interpolation) as initial conditions would likely lead to strong biases in the near-surface atmospheric forecasts rising from an incorrect land-surface initialization. This  is due to the role that soil moisture plays in regulating land-surface heat fluxes.
Two sets of hindcasts Two sets of short-range hindcasts are produced in this study. One set is initialized from the interpolated ERA-Interim land-surface dataset (hereafter FCT ERA-I ) based on the transfer function described in section 2. 1. The other set is initialized from the offline SURFEX simulations (hereafter FCT SFX ) described in section 2. 2. In both sets, the initial atmospheric and sea-surface temperature (SST) states are provided by ERA-Interim. ERA-Interim uses a succession of different SST data products  (Dee et al. , 2011, give more details). The hindcasts of each set are run using the ARPEGE model. It is a spectral model that uses a variable horizontal resolution with an enhanced spatial resolution over France  (Courtier et al. , 1991). The SST data used in ARPEGE are the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) from the UK Met Office  (Donlon et al.
Forecast skill In this section, the skill of each set of hindcasts is calculated using the land SYNOP station network. Each skill is then compared to that of the reference set. Classical forecast skill verification measurements (scores) computed over different areas across the globe are used: the root mean square error (RMSE), the mean error (bias) and the error standard deviation (SDE). Figure  4  shows the SYNOP station locations and the different areas where the scores are computed in grey rectangles. The same meteorological variables as in section 3. 2 are analyzed in this section. We will first compare FCT SFX with FCT ERA-I. Then, those two sets of hindcast will be compared to the reference set. First of all, in all the results, one can note a diurnal cycle which may be due to the larger number of available SYNOP observation data in the daytime than in the night-time.
For sea-level pressure and 6 h precipitation amount, differences between the FCT ERA-I and FCT SFX are not as significant as for the two previous variables (Figures  7  and 8 ). Over most of the study areas, no impact is found. Nevertheless, for sea-level pressure, the bias of FCT SFX is smaller than that of ARPEGE hindcasts over most of the study areas, except over Europe and Africa/Arabia. In Figure  8 , the discontinuity found in the results over Australia/New Zealand is due to a lack of SYNOP data at 0000 UTC. Finally, all Figures 5-8 show another positive impact of using SURFEX simulations for land-surface initial conditions, that is, to bring the skill of FCT SFX closer to that of ARPEGE hindcasts. Tables  1  and 2  summarize the latter results (Figures  5 6 7 8 ) by counting how many times each set of hindcasts shows the better skill and how many times each set is closer to the skill of the ARPEGE hindcasts.
Conclusion This article compares two land-surface initialization approaches for the development of an ensemble reforecast dataset at Météo-France. Because no reanalysis of our operational NWP model is available, the ensemble reforecast is initialized from the ERA-Interim reanalysis produced by ECMWF. Nevertheless, the initialization of the land-surface using the ERA-Interim reanalysis is not straightforward. Two approaches for land-surface initialization are then analyzed: (i) the first one interpolates ERA-Interim fields based on a transfer function that preserves the surface fluxes as much as possible, and (ii) the second one uses offline SURFEX (the land-surface model of ARPEGE) simulations driven by the 3-hourly near-surface atmospheric fields from ERA-Interim. First, in general the results have shown a non-negligible sensitivity of near-surface forecasts to the land-surface initialization approach, which is in accordance with several past studies.
Then, over the study period, the hindcasts have most of the time shown better skill using SURFEX simulations than the interpolated ERA-Interim land-surface initial conditions. Moreover, the use of SURFEX simulations allows production of near-surface hindcasts that are much closer to ARPEGE hindcasts. A positive impact using SURFEX simulations has also been found for the boreal winter season of 2009/2010 (not shown), but this is not as significant as for the boreal summer season reported. Therefore, we have have decided to develop a Météo-France ensemble reforecast dataset using a blended initialization approach in which ERA-Interim provides the initial atmospheric state and boundary conditions and the SURFEX simulations provide the initial land-surface state. The ensemble reforecast dataset is a long set of retrospective weather forecasts to build a climatology of the operational ensemble prediction system PEARP (Prévision d'Ensemble ARPege).
Thus, we have produced hindcasts over 32 years (from 1981 to 2012), run once every four days at 1800 UTC up to 4. 5 days. For each date, a ten-member ensemble hindcast is run, each member using a different physical package. All physical packages are validated separately by experts on the physics of the ARPEGE model to verify using statistical tests that the results provide valuable weather predictions. The same resolution as in the operational Météo-France ensemble forecast system is used (T538L65 ∼ 15 km grid spacing over France, 65 levels). This initiative will allow us to improve weather prediction by correcting systematic forecast errors using statistical postprocessing methods, because such a long training ensemble reforecast dataset will document and therefore better detect relatively rare past events, particularly those most extreme wind or rainfall events that have caused losses of lives and property.
1%; the interference is significant only with the gg → W + W -process. The WZ, ZZ, VVV (V = W/Z), Z/γ * → + -, Wγ * , and W+jets processes are generated using MADGRAPH. All other background processes are generated using PYTHIA 6. 4. The set of parton distribution functions (PDF) used is CTEQ6L  [25]  for leading order (LO) generators and CT10  [26]  for next-to-leading-order (NLO) generators. All the event generators are interfaced to PYTHIA 6. 4 for the showering and hadronization of partons, except MC@NLO, which is interfaced to HERWIG 6  [27]. The TAUOLA 2. 7 package  [28]  is used in the simulation of τ decays to account for polarization effects. In order to suppress the top quark background processes, the pp → W + W -cross section is measured with events that have no more than one high-p T jet.
This information is combined using a multivariate algorithm that exploits the differential energy deposition in the isolation region to discriminate between the signal of prompt muons and muons from hadron decays inside a jet. The exact threshold value depends on the muon η and p T  [36]. Jets are reconstructed using the anti-k T clustering algorithm  [37]  with a distance parameter of 0. 5, as implemented in the FASTJET package  [38, 39]. The properties of the jets are modified by particles from pileup interactions. A combinatorial background arises from low-p T jets from pileup interactions, which are clustered together with high-p T jets from the primary interaction. A multivariate jet identifier is applied to separate jets from the primary interaction and those reconstructed from energy deposits associated with pileup interactions  [40].
Other contributions from Wγ, Wγ * , and other subdominant diboson (WZ and ZZ) and triboson (VVV) production processes are estimated partly from simulated samples. A common scale factor is estimated for the tt and tW simulated samples. The top-quark back-   ground is suppressed using a top-tagging veto that eliminates visible top-quark decays. After the full event selection described in Table  1  but before the top-quark veto, the remaining topquark background contribution (B t-tag ) is estimated as: B t-tag = N t-tag (1t-tag )/ t-tag , where N t-tag is the number of t-tagged events before the top-quark veto, and t-tag is the corresponding t-tagged efficiency. The number of t-tagged events (N t-tag ) is determined in the signal data sample by counting the number of events passing the t-tagging requirements described in Section 4 and subtracting any remaining background on the basis of simulations or data, as described in the present section.
The t-tagged efficiency ( t-tag ) is obtained from a measurement of the efficiency to tag a b-quark jet or soft muon in a top-enriched sample that consists of events with one (two) jet and exactly one b-tagged jet with p T > 30 GeV, which isolates one b quark in a sample that is primarily tt or tW events. Any remaining background is subtracted from the measured data in the top-enriched control sample. After excluding this b-tagged jet, the t-tagging efficiency is determined by counting the number of events that have an additional b-tagged jet or a soft muon. The measured efficiency is defined per b-quark decay and the value measured in the top-enriched sample is converted to a top-tagging efficiency in the signal region by taking into account the relative difference in the number of b-quark jets between the two samples after excluding the high-p T b-tagged jet used to select events in the control sample.
Including the experimental uncertainties gives a systematic uncertainty of around 10% for WZ and ZZ processes. In the case of Wγ ( * ) backgrounds, the variation in PDFs gives a systematic uncertainty of 4%. A summary of the relative uncertainties in the W + W -cross section measurement is given in Table  3 , where the jet counting model uncertainty includes the renormalization and factorization scales, and underlying event uncertainties. 7 The W + W -cross section measurement The inclusive cross section is determined as σ W + W -= N data -N bkg L 3 B(W → ν) 2 , (1) where N data and N bkg are the total number of data and background events, is the signal efficiency, L is the integrated luminosity, and B(W → ν) is the branching fraction for a W boson decaying to each lepton family B(W → ν) = (10.
(3) The parameter Λ is the mass scale that characterizes the coefficients of the higher-dimension operators, which can be regarded as the scale of new physics. The three operators in Eq. (  3 )  generate both ATGC and Higgs boson anomalous couplings at tree level and modify the pp → W + W -cross section. In the absence of momentum-dependent form factors, the traditional LEP parametrization of ATGCs can be related to the values of the coupling constants of the dimension-six electroweak operators  [10]  as summarized in Eq. 4: δ(c WWW /Λ 2 ) = 2 3g 2 M W 2 δλ γ , δ(c W /Λ 2 ) = 2 M Z 2 δg Z 1 , δ(c B /Λ 2 ) = 2 δκ γ M W 2 2 + δg Z 1 M Z 2 2. ( 4 ) The dataset selected for the W + W -cross section measurement is used to bound c WWW /Λ 2 , c W /Λ 2 , and c B /Λ 2. For this measurement, we require the events to have zero reconstructed and identified jets with p T > 30 GeV and |η| < 4.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the  Austrian Federal Ministry of Science, Research and Economy  and the  Austrian Science Fund ; the  Belgian Fonds de la Recherche Scientifique , and  Fonds voor Wetenschappelijk Onderzoek ; the  Brazilian Funding Agencies (CNPq, CAPES ,  FAPERJ , and  FAPESP) ; the  Bulgarian Ministry of Education and Science ; CERN; the  Chinese Academy of Sciences, Ministry of Science and Technology , and  National Natural Science Foundation of China ; the  Colombian Funding Agency (COLCIENCIAS) ; the  Croatian Ministry of Science, Education and Sport , and the  Croatian Science Foundation ; the  Research Promotion Foundation , Cyprus; the  Ministry of Education and Research, Estonian Research Council  via  IUT23-4  and  IUT23-6  and  European Regional Development Fund , Estonia;.
Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies.
The difficulty of IIF diagnosis technique is related to the distinction of very similar fluorescence patterns (such as Fine Speckled and Coarse Speckled patterns) and to the subjectivity of the observer. For that reason, two Senior Immunologists (double reading) with strong experience in fluorescent image interpretation are quite often needed. However, this condition is not respected in all immunology laboratories involved in diagnosis. The introduction of new modern approaches, based on computer systems, is an economic and effective support for the diagnosis of autoimmune diseases  [7, 8]. Nowadays the need within the scientific community for a large database of IIF images reported out by medical experts is on the increase. Its use could be related to various purposes: training of young Immunologists, epidemiological studies, diagnosis, and so forth.
The AIDA Project The AIDA (autoimmunity: diagnosis assisted by computer) Project has been financed by a EU cross-border cooperation Italy-Tunisia, involving four teams in Sicily and four teams in Tunis, as presented below. The aim of the AIDA Project is the applications of ICT for the analysis and interpretation of IIF images. The AIDA Project has two main specific objectives: (i) the creation of a large database of IIF images, interpreted with the contribution of Italian and Tunisian hospitals; (ii) the evaluation of the added value of CAD, with cooperation from an Immunologist as a second Reader in detection of autoantibodies. These specific objectives are being achieved through the production of a set of expected results: (i) the improvement of organizational and decisional processes of public health policies that will lead to construction of an interinstitutional horizontal partnership (between health facilities) and vertical partnership (region/province/university/ASP), (.
Database. Using a uniform approach, three Tunisian immunology services (Pasteur, Charles Nicolle, and Ariana) and four Sicilian ones (ASP-Trapani, Buccheri La Ferla, Civico, and Sciacca) contributed to collection of images of IIF test on HEp-2 cells. These images correspond to the routine IIF technique performed in the different hospitals for autoimmune diseases diagnosis and were thus reported by Senior Immunologists. A total of 5762 sera of patients addressed for diagnosis of autoimmune disease were involved in this study, as indicated in Table  1. Each image and related report was stored in a common database created in the context of AIDA Project. The database stored the contents of two years' activity and reached a total amount of 14393 stored images. This number is very high compared to public IIF images databases available worldwide (public dataset can be downloaded after proper registration, on site: http://i3a2014.