dataesr/scientific-paragraphs-categorization huggingface dataset:
Method Our objective in this section of the paper is to check whether radar data can be successfully used to describe the detailed structure of the low troposphere between two consecutive radiosoundings, even if the latter are 6 or 12 hourly. The two bordering soundings, called RS1 and RS2, are used as initial and final conditions. We first retrieved the radar humidity profiles closest to RS1 and RS2 on the basis of the application of the algorithm described in the previous sections. The algorithm provides the values of Hlim and the calibration coefficients at the launching times of RS1 and RS2. RS1 and RS2 also provided the initial and final bottom and top border conditions for q, which we interpolated at the radar times (every 15 min). Pressure and temperature values were also interpolated at the times between RS1 and RS2 (at all levels) to provide θ and P present in Eq.
(2001)  by using a key parameter from the radar, which is the level of the reflectivity peak value, Hlim, to split the calculations in two parts, with two different calibration coefficients accounting for two distinct vertical regions of different turbulence characteristics. The introduction of this level also mitigated the errors by replacing a long integration by two shorter ones. After assessing the algorithm at the time of the RS observations, we applied it between two RS profiles to obtain humidity profiles at a finer time resolution and to check the performance of the combined algorithm with respect to a simple RS time interpolation. We used, when available, simultaneous lidar data to assess the results. The set of data that enabled this comparison was collected during a period seldom characterized by the presence of clear-sky conditions, while cloudy conditions prevailed (HyMeX SOP1).
Acknowledgements. Data were obtained from the  HyMeX program , sponsored by grants from  MISTRALS/HyMeX  and  ANR-11-BS56-0005   IODA-MED  through the  Fonds Européen de Développement Régional  of the European Operational Program 2007-2013. BLLAST field experiment was made possible thanks to the contribution of several institutions and supports:  INSU-CNRS (Institut National des Sciences de l'Univers ,  Centre national de la Recherche Scientifique, LEFE-IDAO program ),  Météo-France ,  Observatoire Midi-Pyrénées  ( University of Toulouse ),  EUFAR (European Facility for Airborne Research)  and  COST ES0802  (European Cooperation in the field of Scientific and Technical). BLLAST field experiment was hosted by the instrumented site of  Centre de Recherches Atmosphériques, Lannemezan, France (Observatoire Midi-Pyrénées, Laboratoire d'Aérologie ).
We also thank the  CNRM team (Météo-France, Toulouse)  for operating the Candillargues radar and launching the radiosoundings and SEDOO ( Observatoire Midi-Pyrénées, Toulouse, France ) for their help in quick-look providing and database management for both BLLAST and HyMex experiments. Edited by:  Laura Bianco Reviewed  by: two anonymous referees.
Data availability. The data sets used in the present paper are available through the following references: RS from BLLAST and HyMex in  Derrien and Lothon (2011;   https://doi. org/10. 6096/BLLAST. MODEM) and Legain and Tzanos (2012; https://doi. org/10. 6096/mistralshymex. 389), respectively, and UHF wind profiler radars from BLLAST and HyMex in Saïd and Bezombes (2012; https://doi. org/10. 6096/BLLAST. UHF. SITE) and Donier and Saïd (2012; https://doi. org/10. 6096/MISTRALS-HyMeX. 568), respectively.
13, 14 To our knowledge, only one small-scale study from Guinea Bissau has estimated the prevalence of HCV co-infection according to HIV types and did not find any difference between HIV-1 and HIV-2. 15 he objectives of this study were to estimate the prevalence of HCV according to HIV types in the West Africa Database on Antiretroviral Therapy (WADA) HIV-2 cohort in two West African countries and to identify risk factors for HCV positivity.
Materials and Methods A cross-sectional survey was conducted from March to December 2012 in two countries (Burkina Faso and Côte d'Ivoire) within the WADA HIV-2 cohort. The method of this survey was described in details elsewhere. 16 his cohort is embedded in the International epidemiological Database to Evaluate AIDS (IeDEA) West Africa Collaboration, which is part of the global IeDEA network. 17 All patients aged 18 years and above, registered in the WADA cohort as HIV-2 or dually reactive, who attended one of the participating clinics during the study period and who agreed to participate were included in this survey regardless of ART initiation according to WHO 2010 guidelines. 18  standardized survey form was used to collect data on patients' demographics, clin-Acknowledgments: we thank the fieldwork team, study sites and participants for their effort.
The notion of convergence is given naturally by moving to BL σ and considering convergence in the Strong Operator Topology ( §6). 6. We apply our theory of kernel approximations to two practical applications ( §7). First, we show how Bayesian inference can be performed approximately by showing that the †-operation commutes with taking approximations. Secondly, we consider the case of ProbNetKAT, a language developed in  [14, 22]  to probabilistically reason about networks. ProbNetKAT includes a Kleene star operator (-) * with a complex semantics which has proved hard to approximate. We show that (-) * can be approximated, and that the approximation converges. All the proofs can be found in the Appendix. Related work. Quasi-Borel sets have recently been proposed as a semantic framework for higher-order probabilistic programs in  [24].
Approximating the Kleene star of ProbNetKAT ProbNetKAT (  [14, 22] ) is a probabilistic network specification language extending Kleene Algebras with Tests (  [18] ) with network primitives and a binary probabilistic choice operator ⊕ λ , λ ∈ [0, 1]. For the purpose of the example shown here we will not need to introduce the full syntax and semantics of ProbNetKAT, rather we will focus on a single ProbNetKAT program which we will call cantor and is given by: cantor := p; (dup; p) * where p := π 0 ! ⊕1 /2 π 1 ! (13) The program acts on sets of finite sequences of 0 and 1, which can be thought of as packet histories. We will write H for the set {0, 1} * of all packet histories and H n for the set of histories of length as most n. A ProbNetKAT program is always interpreted as a kernel 2 H → G2 H.
Estimating Stroke Incidence and Mortality We used estimates from the GBD Study 2016  1, 2  of the rate of first stroke, cause-specific mortality, and all-cause mortality at the global, regional (21 GBD regions nested within 7 GBD superregions), and national (195 countries) levels, stratified according to age and sex (Table  1 ; and Table  S2  in the Supplementary Appendix, available with this the full text of this article at NEJM. org). Analyses were performed separately for ischemic stroke and hemorrhagic stroke; the latter included intracerebral hemorrhagic stroke and nontraumatic subarachnoid hemorrhagic stroke. The GBD Study 2016 used all available representative population-based data on incidence, prevalence, case fatality, and mortality to produce estimates of disease burden in 195 countries according to sex and 5-year age categories.
Mortality was estimated by means of the Cause of Death Ensemble model, in which vital registration, verbal autopsy data (which included a written summary of events leading to a person's death as well as answers to standardized questions obtained by trained workers from families or other reliable informants in the local language), and country-specific covariates are used to estimate cause-specific mortality over time. Stroke incidence was estimated with the use of DisMod-MR, a Bayesian meta-regression diseasemodeling tool. Details of the methods that we used to estimate stroke incidence and mortality have been previously published  3  and are summarized in the Supplementary Appendix.
We did not differentiate between subarachnoid hemorrhagic and intracerebral hemorrhagic stroke; these were combined in the estimate of hemorrhagic stroke. There is significant variation in stroke burden within large countries, and our results represent only the mean national risk. Finally, we analyzed only the lifetime risk of first-ever stroke and not recurrent stroke. In conclusion, our study provided global, regional, and country-specific estimates of the lifetime risk of stroke according to sex and age, although the precision of the estimates is limited by insufficient data in some countries. The global lifetime risk of stroke is approximately 25% starting at the age of 25 years among both men and women, and there is large geographic variation, with a particularly high lifetime risk of stroke in East Asia, Central Europe, and Eastern Europe.
Characterization of the material and its variability In this study, two different materials were chosen: material A (55% AS4/PEEK) and material B (50% HTS45/PEEK). Rings were manufactured consecutively by using the CETIM deposition machine, located in the Technocampus EMC2 at Nantes (France). Each ring needed approximately 4. 4 m of tape, which correspond about 6 tape layers (Figure  1 ). Two main data were collected: temperature evolution, measured with a thermal camera (FLIR T660) and microscopic analysis of the cross-section of tapes before processing them. The process parameters were set to: circumferential velocity of 7 m/min, laser power of 1000 W and the pressure applied to the compression roller was set to 2 bars. The image analysis is used to observe the microstructure of the material to eventually define the actual properties based on the observation of different phases.
These three versions of the system have been used to quantify the impact of some updates. In a previous paper  (Lellouche et al. , 2013) , the main results of the scientific evaluation of MyOcean global monitoring and forecasting systems at Mercator Ocean showed how refinements or adjustments to the system impacted the quality of ocean analyses and forecasts. The primary objective of this paper is to describe the recent updates applied to the system PSY4V3 and show the highest impact on the product quality. Updates resulting from routine system improvements are not separately illustrated and discussed (bathymetry, runoffs, assimilated databases, mean dynamic topography, etc. ). So, particular focus was given to the initialization, correction of precipitation, assimilation of climatological temperature and salinity in the deep ocean, con-struction of background error covariance and the adaptive tuning of observation error.
5) adapted by  Blanke and Delecluse (1993). The lateral friction condition is a partial-slip condition with a regionalization of a no-slip condition (over the Mediterranean Sea), and the elastic-viscous-plastic rheology formulation for the LIM2 ice model  (Fichefet and Maqueda, 1997)  has been activated  (Hunke and Dukowicz, 1997). Instead of being constant, the depth of light extinction is separated in red-greenblue bands depending on the chlorophyll data distribution from mean monthly SeaWIFS climatology  (Lengaigne et al. , 2007). The bathymetry used in the system is a combination of interpolated ETOPO1  (Amante and Eakins, 2009)  and GEBCO8  (Becker et al. , 2009)  databases. ETOPO1 datasets are used in regions deeper than 300 m and GEBCO8 is used in regions shallower than 200 m with a linear interpolation in the 200-300 m layer.
, 2016). However, this statement applies to a fully coupled ocean-atmosphere system, which is not the case for the present system PSY4V3. Based on sensitivity experiments and following the results obtained by  Bidlot (2012) , we pragmatically consider only 50 % of the surface model currents in the wind stress computation. -The monthly runoff climatology is built with data on coastal runoffs and 100 major rivers from the  Dai et al. (2009)  database (instead of  Dai and Trenberth, 2002,  for the system PSY4V2). This database uses new data, mostly from recent years, and streamflow simulated by the Community Land Model version 3 (CLM3) to fill the gaps, in all lands areas except Antarctica and Greenland. In addition, we built mean seasonal freshwater fluxes representing Greenland and Antarctica ice sheet and glacier runoff melting.
For this purpose we have distributed the following mean values: 545 Gt yr -1 for Greenland and 2400 Gt yr -1 for Antarctic (corresponding to freshwater fluxes of 1. 51 and 6. 65 mm yr -1 , respectively). These values are in the range of estimations given by the IPCC-AR13  (Church et al. , 2013). They have been applied along the Greenland and Antarctica coastlines and over an open ocean domain varying seasonally and defined by the climatological presence of icebergs observed by the Altiberg iceberg database project  (Tournadre et al. , 2016). Domain covered by giant icebergs from  Silva et al. (2006)  complements southernmost areas not covered by Altiberg data. Onethird of these quantities is applied offshore and twothirds along the Greenland and Antarctic coastlines. We also used negative variations of water masses estimated from GRACE  (Bruinsma et al.
In addition, a 3D-VAR scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity  (Lellouche et al. , 2013). Compared to the previous HRG system PSY4V2, the following updates were done on the data assimilation part (see As OSTIA provides the foundation SST (considered nominally at 10 m of depth), the SST model equivalent is performed by calculating the nighttime average of the first level of the model temperature. Moreover, only one SST map is assimilated on the fifth day of the 7-day cycle. Cloudy regions are filled by the analysis performed in the OSTIA product. -In addition to the quality control based on temperature and salinity innovation statistics (detection of spikes, large biases) already present in the previous system, a second quality control has been developed and is based on dynamic height innovation statistics (detection of small vertically constant biases).
-A consistent along-track SLA dataset (http: //marine. copernicus. eu/documents/QUID/ CMEMS-SL-QUID-008-032-051. pdf, last access: 20 September 2018), with a 20-year altimeter reference period, is assimilated all along the simulation performed with the system PSY4V3. Reprocessed observations are assimilated until the end of August 2015. Near-real-time observations are assimilated afterward. -The CORA 4. 1 CMEMS in situ reprocessed database  (Szekely et al. , 2016 ; http://marine. copernicus. eu/ documents/QUID/CMEMS-INS-QUID-013-001b. pdf, last access: 20 September 2018) has been assimilated for the 2006-2013 period. In addition to Argo and other in situ datasets, this database includes temperature and salinity vertical profiles from the sea mammal (elephant seals) database  (Roquet et al.
These last conditions (Eq. 3) prevent the threshold from being reached too quickly in shallow areas. The average and standard deviation of the thermal or haline components of dynamical height innovation have been calculated from a global simulation at 1/4 • , which is a twin simulation of the PSY4V3 one. Note that the simulation at 1/4 • also assimilates the CORA 4. 1 CMEMS in situ database. The temperature and salinity threshold twodimensional fields used by QC2 are then computed as the average plus 6 times the standard deviation of the dynamical height innovations (Fig. 2 ). With these temperature and salinity thresholds, the system will more easily reject biased salinity profiles in the tropics and biased temperature profiles in strong currents. It should also be noted that the QC2 quality control rejects the entire vertical profile, while the QC1 quality control only rejects aberrant temperature and/or salinity values at some given depths on the vertical profile.
Initialization of oceanic simulation One way to initialize physical ocean model simulations is by using climatological values of temperature and salinity from databases and assuming the velocity field is zero at the start. The model physics then spins up a velocity field in balance with the density field. Another common way to initialize a model is with fields from a previous run of that model or with the results from another model. Given that data assimilation of the current observation network rapidly (in about 6 months) adjusts the model state in the first 1000 m, the first solution has been chosen to minimize potential drifts occurring after some years of simulation. Compared with the previous system PSY4V2 starting in October 2012 from the WOA09 three-dimensional climatology (see Fig.
Correction of precipitation Many studies (e. g. ,  Janowiak et al. , 1998 Janowiak et al. , , 2010;; Kidd et al. , 2013)  have compared reanalysis and atmospheric model precipitation fields with observation-based datasets and have shown that atmospheric model products always bring significant and systematic errors and are not able to close the global average freshwater budget. For instance,  Janowiak et al. (2010)  found that the IFS operational model and ERA-Interim reanalysis  (Dee et al. , 2011)  from ECMWF perform well for temporal variability with respect to observational datasets, but they globally overestimate the daily precipitation. Although progress has been made in the ECMWF fore-    cast model, substantial errors still occur in the tropics  (Kidd et al. , 2013). The correction of atmospheric forcing within ocean applications has already been successfully explored by adjusting atmospheric fluxes via observational datasets in global applications  (Large and Yeager, 2009; Brodeau et al.
This latter assertion is a limitation of the method as it assumes the operational ECMWF forecast model has a constant bias. In order to avoid discontinuities when either PMWC or ECMWF products exhibit zero precipitation, e. g. , in arid areas, we do not apply any correction in monthly mean values less than 1 mm for rainfall fluxes. Also, in order to keep the more accurate small-scale signal from the high-resolution forcing, the correction is only applied to the large-scale component obtained by a low-pass Shapiro filter. Hilburn et al. (2014)  provided the accuracy of RSS over ocean rain retrievals validated against well-established long-term in situ datasets such as observations from Pacific Marine Environment Laboratory rain gauges on moored buoys in the tropics. They found that on monthly average, the standard deviation between satellite and buoy is 15.
The first product is the CMEMS/DUACS (Data Unification and Altimeter Combination System) merged-gridded sea level anomaly heights in delayed time on a 1/4 • regular horizontal grid with a 1-day temporal resolution  (Pujol et al. , 2016). The second one is the Roemmich-Gilson Argo monthly climatology on a 1 • regular horizontal grid  (Roemmich and Gilson, 2009) , which is commonly used in the oceanographic community. Figure  14a, b  and c  show the 2007-2015 SSH variability for the three simulations (subsampled in a similar way to DUACS). SSH variability difference is defined as the difference of SSH standard deviations from PSY4 simulations and the DUACS product (Fig. 14d, e, f ). Compared to the variability of the DUACS product, the fronts in highmesoscale-variability regions such as the Gulf Stream, the Kuroshio current, the Agulhas current or the Zapiola eddy are misplaced in the FREE simulation.
Adaptive tuning of observation errors In order to refine the prescription of observation errors (instrumental and representativeness errors), adaptive tuning of errors for the SLA and SST has been implemented in PSY4V3. We use the Talagrand method  (Talagrand, 1998)  to adjust the background error. Instrumental error does not change with time. On the contrary, the representativeness error is really flow dependent. Taking into account the representativeness error is particularly important for assimilated OSTIA SST because the sky is clear only 30 % of the time on average. The method has not been used for temperature and salinity vertical profiles because of the reduced number of in situ data compared with satellite data. Three-dimensional fixed observation errors are then used for the assimilation of in situ temperature and salinity vertical profiles.
The drifting buoy SST data are present in the CMEMS in situ database used by Mercator but they have not been assimilated in the system because the depth of these data is a nominal value and we chose to assimilate only data with a measured depth value. Although we plan to assimilate these data in the future system, we use currently these data as independent information. This allows us to see that SSTs from in situ vertical profiles and SSTs from drifting buoys are coherent with each other. We thus again find the cold bias highlighted by the comparison with SST from in situ vertical profiles in the North Pacific. It is a lack of stratification in the model that causes midlatitude cold surface biases during (boreal) summer and a warm bias between 50 and 100 m. We also checked the time series of the mean and the RMS of the misfit (innovation) between the observed SSTs and the model.
For OSTIA SST, which is the gridded SST assimilated into PSY4V3, we obtain a mean warm bias of -0. 1 • C and an RMS error of 0. 45 • C (Fig. 21 ). Time series of the differences between the model and NOAA AVHRR SST, which was assimilated into the previous PSY4V2 system, are also shown in Fig. 21. This allows us to compare both gridded SST products. For in situ SST, the bias is smaller, suggesting that OSTIA and AVHRR are colder than in situ near-surface observations on global average. We can notice a drop in the RMS of in situ surface data in January 2014, which is due to the use of near-real-time observations for which most of the surface observations do not have a sufficient quality flag.
Comparison with a high-resolution SST external product The CLS (Collecte Localisation Satellites) has operated a near-real-time oceanography data service named CATSAT since 2002 for scientific, institutional or private users (support to fishery management or to the offshore oil and gas industry). These data include satellite observations such as chlorophyll a, SST and altimetry. Maps of SST are computed from Aqua/MODIS, S-NPP/VIIRS and Metop/AVHRR infrared sensors at 2 km resolution using nighttime data only to avoid diurnal warming effects. We can then evaluate the system ability to produce mesoscale features by comparing with the CATSAT daily SST product. In Fig. 22 , the CATSAT daily snapshot can be considered as an independent dataset since the OSTIA SST assimilated into the system has mostly seen microwave measurements during 2 weeks, as it was very cloudy in the Gulf of Mexico.
Comparison to tide gauge data The system PSY4V3 produces hourly outputs at the surface that can be compared with tide gauge measurements. For that, we used the BADOMAR product  (Lefevre et al. , 2005) , which is a specific processed tide gauge database developed and maintained at CLS that consists of filtered tide gauge data from the GLOSS/CLIVAR (Global sea Level Observing System/Climate Variability and Predictability) "fast" sea level data tide gauge network (GLOSS Implementation Plan, 2012). These tide gauge data are corrected for inverse barometer effect and tides. High-frequency model SSH compares well with tide gauges in many places, with a slight improvement in PSY4V3 with respect to PSY4V2 (not shown). The best agreement between the system PSY4V3 and tide gauges is found in the tropical band, as can be seen in Fig.