dataesr/scientific-paragraphs-categorization huggingface dataset:
Proposed Simulator The WSNet simulator has been adapted to BAN and BBN contexts in order to fulfill the upper layers' requirements (i. e. , MAC, network and application layers) as shown in Figure  1. Importantly WSNet is adapted with respect to the multi-scale dynamics of inter-BAN multi-link channels. In this paper, we describe two approaches computing the received signal strength (RSS) in which we focus on joint mobility and propagation models i. e. , deterministic bio-mechanical modeling (blue approach) and semi-deterministic stochastic mobility modeling (green approach). Once the RSS is computed according to the considered propagation models and parameters, the propagation modules return the evaluated RSS to the WSNet simulation core, which, according to the current concurrent transmissions, will evaluate the corresponding SINR (signal-to-interference plus noise ratio), BER (bit error rate) and PER (packet error rate).
The first step consists of extracting some important deterministic information (i. e. , positions, direction, speeds) from the mobility model, at every simulation iteration. These information are treated by the BAN environment in the second step to define link conditions between the transmitter and the receiver thanks to the BAN and physical simulation module. The channel is modeled through three modules in the WSNet simulator: path loss (Step 3), shadowing (Step 4) and fading (Step 5). Link conditions are the input parameters of body-to-body and on-body channel models to select the configuration of each model and generate realistic traces. Depending on the considered link condition, the parameters of the propagation model are selected in a dynamic way during the simulation. Once the received signal strength is computed according to the considered propagation models and parameters, the propagation modules return the evaluated RSS to the WSNet simulation core, which, according to the current concurrent transmissions, will evaluate the corresponding SINR, BER and PER.
BAN Environment The module called "BAN and physical environment" aims at modeling the environment in which wireless nodes are deployed and the attenuation resulting from the propagation of radio signals depending on the radio link conditions and the scenarios. As shown in Figures  2  and 3 , this module has a central position. The BAN and physical environment module exchanges information with the mobility model (e. g. , positions, directions, speeds, obstruction) and with BAN propagation models (e. g. , link conditions) in order to select the corresponding channel model in our database. As shown in Figure  4 , we assume that a BAN is formed by three nodes: belt, head and wrist. A multi-BAN network can be seen as multiple star networks. The belt can be considered as the collecting node and/or the coordinator.
Then, we need to calculate the rotation matrix and translation matrix of every joint relative to its parent joint. More details regarding biomechanical modeling can be found in  [69, 70]. The above biomechanical modeling and transformation help to introduce space and time variations into the IEEE 802. 15. 6 proposed channel models to make them more dynamic and realistic. As shown in Figure  10 , our proposed intra-and inter-BANs' mobility modeling works based on six main steps: Step 1: real motion capture measurements, which contain the actual human mobility traces according to different mobility scenarios (e. g. , walking, running, etc. ), are extracted into our MATLAB mobility modeling tool  [66]. Step 2: The complete human body skeleton is captured from the input motion capture measurements, which consist of a set of markers (i.
Simulation Setup and Scenario A packet-oriented network simulator, WSNet  [58] , is used (as shown in Figure  1 ) to evaluate the BAN and BBN application scenarios. WSNet contains various models for wireless sensor networks, wireless local area networks and ad hoc networks. However, previously, it did not contain BAN-specific modules. Therefore, we have enhanced the simulator (with the focus on IEEE 802. 15. 6 standard compliance) to accurately model body area networks using enhanced channel models, an accurate radio link and mobility models (as explained earlier in Sections 3. 2 and 3. 1). In order to replicate the following simulation results, Table  8  presents the details of each model being used along with the configuration parameters and their values. It is important to mention that, for the deterministic approach, any specific scenario depends on the mobility patterns and the corresponding trace files.
During volcanically quiescent conditions, the accurate detection of stratospheric aerosols becomes challenging as the aerosol scattering signal becomes small compared to the molecular scattering. In an effort to better characterize the evolution of the stratospheric aerosol load and its variability at northern midlatitudes during the post-Pinatubo era we utilize a continuous 22-year observation record from the Observatoire de Haute-Provence and a variety of satellite data sets. The paper is organized as follows: Section 2 provides information on the Observatoire de Haute-Provence (OHP) lidars, aerosol retrieval and satellite data sets exploited. Section 3 compares the OHP lidar and satellite aerosol records. Section 4 provides examples of volcanic plume detections and distinguishes volcanically perturbed and quiescent periods.
Section 5 describes the variability, annual cycle and long-term change in background aerosol. Section 6 discusses the proposed interpretation and concludes the paper. 2 Instruments and data sets 2. 1 Observatoire de Haute-Provence lidars The Observatoire de Haute-Provence (OHP) located in southern France (43. 9 • N, 5. 7 • E; 670 m a. s. l. ) is one of the Alpine stations within the Network for Detection of Atmospheric Composition Change (NDACC). The site is characterized by a high rate of clear nights and offers an opportunity for frequent lidar observations. For over 3 decades two independent lidar systems have been operated at the OHP station: a differential absorption lidar (DIAL) for stratospheric ozone (hereafter referred to as LiO3S) and a Rayleigh-Mie-Raman lidar for middleatmosphere temperature measurements (hereafter referred to as LTA).
OHP lidars aerosol retrieval For retrieving vertical profiles of stratospheric aerosol we use LiO3S and LTA measurements spanning 1994 through 2015 with a total number of 3118 (LiO3S) and 2691 (LTA) nights of lidar acquisitions, lasting 3-5 h each. The retrieval is based on the Fernald-Klett inversion method  (Fernald, 1984; Klett, 1985) , which provides backscatter and extinction coefficients. The reference zero-aerosol altitude is set between 30 and 33 km. The scattering ratio (SR) is computed as a ratio of total to molecular backscattering: SR = β aero + β mol β mol , (1) where β aero and β mol are the aerosol and molecular backscatter coefficients at a given wavelength. LiO3S 355 nm backscatter β aero , extinction α aero and scattering ratio data are converted to 532 nm using Eqs. (  2 ), (  3 ) and (  5 ) as follows: β λ2 aero = β λ1 aero × λ 2 λ 1 κb , (2) α λ2 aero = α λ1 aero × λ 2 λ 1 κe , (3) where λ 1 = 355 nm, λ 2 = 532 nm and κb and κe are wavelength exponents (Ångström coefficients), respectively, for particle backscatter and extinction.
Assuming molecular backscatter β mol ∝ λ -4. 09 , (4) the wavelength conversion of scattering ratio can be performed: SR λ2 = 1 + (SR λ1 -1) λ 2 λ 1 4. 09+κb. (5) The wavelength exponents for the 355-532 nm pair were adapted from  Jäger and Deshler (2002, 2003)  and set to κe = -1. 6 and κb = -1. 3 after the year 1997. Similarly, the extinction-to-backscatter (lidar) ratio is set to 50 sr after 1997, which is a commonly assumed value for volcanically quiescent conditions and periods of moderate eruptions (e. g. ,  Trickl et al. , 2013; Ridley et al. , 2014; Sakai et al. , 2016). The molecular backscatter is calculated from the National Centers for Environmental Prediction (NCEP) daily meteorological data interpolated to the OHP location. The lidar raw signals and resulting aerosol data have been subjected to a thorough quality screening, accounting for the instruments' technical health log.
Satellite aerosol sounders Over the course of the last 2 decades stratospheric aerosol observations from space were conducted by various satellite missions, exploiting different measurement techniques: solar and stellar occultation and limb scattering as well as nadirviewing lidar. We use five satellite-based data sets, altogether covering the time span of OHP lidar observations. SAGE II (Stratospheric Aerosol and Gas Experiment)  (Russel and McCormick, 1989 ) is a seven-channel Sun photometer. It was launched onboard the Earth Radiation Budget Satellite in 1984 and provided solar occultation measurements of stratospheric aerosol extinction with a vertical resolution of 1 km until mid-2005. SAGE II fully covers the latitude range from 80 • S to 80 • N in 1 (Eq. 2) month with a typical rate of 32 measurements per day (reduced to 16 after 2000).
We used SAGE II version 7. 0 aerosol extinction data at 525 nm. GOMOS (Global Ozone Monitoring by Occultation of Stars)  (Bertaux et al. , 2010)  is a UV-visible-near-infrared spectrometer launched in 2002 onboard ENVISAT and op-erated until April 2012. The instrument performed occultations of selected stars by means of four spectrometers. We use aerosol extinction profiles at 550 nm retrieved by the Aer-GOM algorithm, which was developed using an improved aerosol parameterization  (Vanhellemont et al. , 2016). OSIRIS (Optical Spectrograph and InfraRed Imaging System) is a limb scatter instrument launched onboard the Odin satellite in 2001 and providing measurements of various chemical species and aerosol extinctions  (McLinden et al. , 2012). The primary instrument is an optical spectrograph (OS) operating in the 284-810 nm range and providing between 100 and 400 vertical profiles per day depending on the time of year.
00 product, post-processed using a treatment described by  Vernier et al. (2009). The total attenuated backscatter profiles from CALIOP are corrected for molecular attenuation and ozone absorption after adjusting the calibration altitude to 36-39 km. The attenuation by aerosol, constituting less than 1 % at 15 km during background aerosol conditions, is neglected. Data below clouds are removed from the analysis. The scattering ratio profiles are obtained using molecular backscatter computed using NASA Global Modeling and Assimilation Office (GMAO) data. The backscatter data of CALIOP are cloud-cleared in the upper troposphere using a depolarization ratio threshold of 5 %. The conversion of backscatter to extinction is done using a lidar ratio of 50 sr. OMPS (Ozone Mapping Profiling Suite) LP (Limb Profiler) onboard the Suomi NPP (National Polar-orbiting Partnership) satellite, launched in 2012, measures limb-scattered light with a sampling rate of up to 7000 measurements per day  (Jaross et al.
, 2014). Regular observations of aerosol extinction have been available since April 2012. We use OMPS V0. 5 extinction data at 675 nm  (DeLand et al. , 2016). It should be noted that among the passive satellite sounders, SAGE II and GOMOS measure aerosol extinction, whereas OSIRIS and OMPS measure limb-scattered radiation, from which aerosol extinction is then retrieved. In contrast, the CALIOP instrument, based on an active sounding technique, measures aerosol backscatter. In order to compare OHP lidars and satellite instruments, all data sets were converted to extinction at a common wavelength of 532 nm. Ta-ble 1 summarizes the wavelength exponents κe used for conversion (Eq. 3) and the time spans of data sets involved in the present analysis.
These periods are also characterized by a stable aerosol load that is without strong enhancements due to volcanic eruption. The comparison reveals close agreement between the OHP lidar, SAGE II, GOMOS and OSIRIS (Fig. 2a ) above 15 km and somewhat poorer agreement below. Figure  2b  suggests a good agreement between the OHP lidar and CALIOP (relative difference 5-10 %) throughout the entire range of altitudes except the uppermost layer above 25 km, where the OHP lidar is 15-20 % lower than CALIOP. This feature may be related to an error in lidar calibration, relying on the assumption of the absence of aerosol above 30 km, which -as suggested by CALIOP data calibrated at higher altitudes -may not always be the case. The other two satellite sounders covering the 2013-2014 period -OSIRIS and OMPS -show somewhat larger discrepancies with the OHP lidar and CALIOP, reaching 30 % in the uppermost and lowermost layers.
Post-Nabro period By early mid-2013 all data sets report sAOD 1730 stabilizing around 2. 8 × 10 -3 ± 3. 0 % (mean lidars) or 2. 8 × 10 -3 ± 3. 5 % (mean satellites) and remaining at that level for almost 2 years, through December 2014. In January 2015, the plume of the Kelud eruption  (Kristiansen et al. , 2015)  reached OHP latitude as inferred from CALIOP sAOD 1730 time-latitude section (not shown). The signatures of the Kelud plume were observed at OHP until April 2015, after which sAOD 1730 returned to near background levels.
This way, the presence of a plume at OHP and the temporal extent of the corresponding volcanic period were determined. In other words, the satellite data were used to detect a plume, whereas the OHP lidar data were used to determine the duration of the respective volcanic period at OHP latitude. Thus, a period is considered as volcanically perturbed if a plume occurs in the Northern Hemisphere and if both of the following two conditions are fulfilled in OHP observation posterior to the eruption: i. monthly mean sAOD 1730 value exceeds the 1-σ range of the reference quiescent period of 1997-2003 (grey band in Fig. 3 ); ii. monthly mean SR profile exceeds the 1-σ range of the "background" SR profile -an average over the entire reference quiescent period of 1997-2003 (grey-filled in Fig. 5 ) in a layer > 2 km thick.
Acknowledgements. We thank the personnel of OHP for conducting lidar measurements. The work was done with the support of the  French Institut National des Sciences de l'Univers (INSU)  of the  Centre National de la Recherche Scientifique (CNRS)  and of the  Centre National d'Etudes Spatiales (CNES). We thank  Laurent Blanot  ( Acri ST)  and  Nickolay Kadygrov (IPSL)  for their help with satellite data handling. OMPS LP Version 0. 5 aerosol extinction coefficient data are produced by the LP processing team (https://ozoneaq. gsfc. nasa. gov/data/omps/). The AerGom project was financed by the  European Space Agency  (contract number  22022/OP/I-OL ). Charles Robert 's research was supported by a  Marie Curie Career Integration Grant  within the  7th European Community Framework Programme  under grant agreement no.
Acknowledgements. This study has been supported by  CNRS  and has been founded by the  French ANR  project  SMOC. Supercomputing facilities were provided by  PRACE  project  RA1616  and  GENCI  project  GEN1140. The authors wish to thank  Y. Cuypers ,  E. Pallàs-Sanz ,  L. Debreu ,  F. Lemarié  and  P. Marchesiello  for useful discussions. A special thanks to  S. Masson  for his assistance in porting NEMO configurations to Tier0 systems. Interactions with the  Communauté de Modélisation (COMODO, ANR funding)  are also acknowledged. We are grateful to three anonymous reviewers for helpful comments on the manuscript. Edited by:  M. Hecht.
The mistag rate has been measured in an inclusive jet sample. The calibration results are presented as data-to-simulation scale factors, derived from the ratio of the efficiency or mistag rate measured in data to that obtained in simulated events. Where more than one calibration method exists the results from the various analyses have been combined taking into account the statistical and systematic correlation. This paper is intended to provide a complete description of almost all the b-tagging developments in ATLAS during Run 1 of the LHC in the years 2010 -2012. The results are illustrated with data taken in the year 2011 at a centre-of-mass energy of 7 TeV. As these developments extended over a period of years, there is some variation between the simulated samples and systematic uncertainties used for the data efficiency measurements depending on the chronology.
Data and simulation samples, object selection The studies presented in this paper are generally based on a data sample corresponding to approximately 4. 7 fb - 1  of 7 TeV proton-proton collision data, after requiring the data to be of good quality; slight differences exist due to variations in data quality requirements. The data have been collected in 2011 using the ATLAS experiment. The ATLAS detector is a large, general-purpose collider detector and is described in detail elsewhere  [1]. Its most prominent features, as relevant to b-jet identification and its performance estimation, are: • An Inner Detector (ID)  [2] , providing tracking and vertexing capabilities for |η| < 2. 5. 1 It is immersed in an axial 2 T magnetic field and features three subdetectors employing different techniques.
Comparison procedure and sample selection Although there are a reasonable number of decay channels of b hadrons that would be suitable for the selection of the b-jet-enriched sample, in practice only the decay mode B ± → J/ψ(µ + µ -)K ± is chosen for this analysis. This decay is characterised by both a clear signature and a high branching fraction (≈ 10 -3 ), compared to other decays involving a J/ψ. A logical OR of J/ψ triggers has been used for the event selection, applied to the full 2011 dataset and to all simulated samples. In the simulated signal sample, which is generated using PYTHIA, true B ± → J/ψ K ± decays are required, matched in ∆R to the reconstructed candidate. The simulated jet kinematic (p T , η) spectra are reweighted to match those in the data. The J/ψ candidate is selected requiring two muons with p T > 4 GeV and invariant mass within 200 MeV from the J/ψ mass.
Data and simulation samples The events used in the analyses were collected with triggers that require a muon reconstructed from hits in the Muon Spectrometer and spatially matched to a calorimeter jet. In each jet p T bin of the analyses, the muon-jet trigger with the lowest jet threshold that has reached the efficiency plateau is used. In the lower jet p T region (up to 60 GeV) jets with E T > 10 GeV at the EF level are required. Starting from 60 GeV up to 110 GeV the analyses use events with at least one jet with E T > 10 GeV at the first trigger level, while for jet p T above 110 GeV the trigger threshold is increased to 30 GeV. During data taking each of the muon-jet triggers was prescaled to collect data at a fixed rate slightly below 1 Hz. For quantities related to b and c jets, the analyses make use of a simulated muon-filtered inclusive jet sample, referred to below as the µ-jet sample, where the events are required to have a muon with p T > 3 GeV at generator level.
In the system8 measurement, the fake muon contribution is varied in both b and c jets, again with a negligible impact on the final result. p rel T light-flavour-template contamination In the p rel T method, the templates for light-flavour jets are obtained from a light-flavour-enriched data sample. A measurement bias can arise from b-jet contamination in the light-flavour template. This bjet contamination in the light-flavour template is estimated from simulation to be between 4% and 10%, depending on the jet p T bin. The bias introduced by this contamination is corrected for in the final result, and the result of a 25% relative variation of the b-jet contamination is taken as a systematic uncertainty. p rel T cut variation The system8 analysis uses a cut on the p rel T of the muon associated to the jet to arrive at a sample with enhanced heavy flavour content.
The b-jet tagging efficiency is measured with the jet assigned by the fit to be the b jet on the leptonic side of the event (i. e. , associated with the leptonic W-boson decay). Choosing the lowest χ 2 permutation of the kinematic fit selects the correct jet association in about 60% of the t t events. In addition to the combinatorial background the sample still contains backgrounds from other processes, such as single top and W+jets events. Nevertheless, the full b-jet weight distribution can be obtained from data by using a statistical background subtraction. This subtraction is done by dividing the sample into two orthogonal sub-samples based on the information about the jets associated to the hadronic side of the event (i. e. , associated with the hadronic W-boson decay): the first sub-sample ("signal sample") is selected by applying additional cuts to increase fraction of correct mappings, while the second subsample ("background sample") is enriched in incorrect mappings.
Background flavour composition The flavour composition of all background samples except W+jets and in some cases Z+jets is taken from simulation. No systematic uncertainty on the flavour composition for these samples is assigned. For the W+jets background the normalisations of heavy flavour (HF) events (W b+jets, Wcc+jets and Wc+jets) are varied within their uncertainties. Sources of systematic uncertainty that affect the HF scale factors in W+jets events often also affect the calibration methods described in this paper directly. Examples of such systematic uncertainties are the uncertainties on the t t cross section and W+jets normalisation. To account for such correlations, these uncertainties are evaluated by coherently evaluating their impact on all components of the analysis. In the combinatorial likelihood method the heavy flavour component of the Z+jets background is varied up and down by 100% and 50% respectively, which is conservative compared to the variations between data and simulated events observed in Z + b measurements  [55].
The multijet templates used to derive the number of b-tagged multijet events are extracted from data control samples defined in Section 12. 3 with the additional requirement that the selected SMT jet is b-tagged. Since the multijet tagging rates computed using the fit results for the OS and SS samples lead to compatible results, the final multijet tagging rate is obtained from the fit results derived for the sum of the OS and SS samples. The fit results before and after applying the MV1 b-tagging requirement corresponding to the ε b =70 % operating point are shown in Fig. 44. The multijet tagging rates for the different operating points vary between 26 % and 55 % indicating that the multijet sample has a large heavy flavour component. The relative total uncertainties range between 15 % and 23 % accounting for the dominating statistical uncertainties of the E miss T fits and systematic uncertainties related to the choice of the fit range and variations of the shapes of the multijet and non-multijet templates, obtained by modifying the inverted electron identification requirements or the relative fractions of the different background processes, respectively.
Data and simulation samples The data sample used in the D measurement was collected using a logical OR of inclusive jet triggers. These triggers have been heavily prescaled to a constant bandwidth of about 0. 5 Hz each and reach an efficiency of 99% for events having the leading jet with an offline p T higher than the corresponding trigger thresholds by a factor ranging between 1. 5 and 2. Events with at least one jet with p T above a given threshold at the highest trigger level are selected, and using a combination of the inclusive jet triggers, the data set covers the entire 20-200 GeV jet p T range used in the analysis. The analysis makes use of a Monte Carlo simulated sample of multijet events. The samples used are equivalent to those used in the muon-based b-jet tagging efficiency measurement (see Section 8) with the exception that each event in the sample used in the D analysis is required to contain a D + meson, in the decay mode D 0 (→ K -π + )π +.