dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Van Steenkiste et al. , 2013; Tessens et al. , 2014) , and Proseriata  (Laumer et al. , 2014; Scarpa et al. , 2016). Accordingly, it has proven to be challenging to provide morphological apomorphies for many of the resulting clades. Therefore, phylogenomics based on much larger molecular datasets and advances in the study of morphological characters should be integrated to provide a more robust taxonomy for different microturbellarian groups. A general key for freshwater microturbellarians is missing at this date, and existing keys focus on specific U N C O R R E C T E D P R O O F U N C O R R E C T E D P R O O F 442 taxa or regions. A recent key to freshwater Platy-443 helminthes of the Nearctic extends to genus, and 444 includes a species list  (Noren ˜a et al. , 2015). At present, 445 there is no genus-level key to the Palearctic, which is 446 unfortunate, as the majority of collecting and taxo-447 nomic work has been done there.
The Turbellarian 448 Taxonomic Database  (Tyler et al. , 2006 (Tyler et al. , -2016) )  and the 449 World Register of Marine Species (WoRMS, 2020b) 450 includes worldwide coverage of marine, freshwater, 451 and limnoterrestrial Platyhelminthes, but does not 452 provide a key. 453 Ecology 454 Studies on gastrotrich and microturbellarian autoecol-455 ogy and synecology are not numerous  (Schwank, 456 1981b (Schwank, 456 , 1982a;; Heitkamp, 1982 Heitkamp, , 1988;; Ricci & 457 Balsamo, 2000; Kolasa, 2002; Nesteruk, 2016a Nesteruk, ,b , 458 2017)). Abiotic and ecological factors define the 459 qualitative and quantitative compositions of popula-460 tions, whose mean densities widely vary depending on 461 the characteristics of the habitat and seasonal dynam-462 ics, and can range from a few thousand up to 2.
975 A comparative summary of modes of reproduction 976 of freshwater gastrotrichs and microturbellarians is 977 reported in Table  5. 978 Global diversity and distribution 979 The majority of freshwater gastrotrichs are Chaeto-980 notida, with about 350 species in 5 families and 24 981 genera (72% of total chaetonotidan species). Only four 982 species of Macrodasyida, in one family and one genus 983 (except one species incertae sedis), occur in fresh 984 waters  (Kisielewski, 1987; K a ˚nneby & Kirk, 2017; 985 Garraffoni et al. , 2019). Diversity of freshwater 986 Gastrotricha in different geographic areas is not as 987 well known as that of marine species, and available 988 data are quite heterogeneous. Most research has been 989 carried out in Europe and the Americas. Data on 990 geographic distribution are usually limited to the sampling sites, especially in older literature, and occasionally include some ecological data (see  Balsamo et al.
Acknowledgements Julian P. S. Smith III was supported by grant  P20GM103499  ( SC INBRE ) from the  National Institute of General Medical Sciences, National Institutes of Health ;  Niels Van Steenkiste  and  Brian Leander  were supported by grants from the  National Science and Engineering Research Council of Canada  ( 2019-03986 ) and the  Hakai Institute ;  Maria Balsamo  and  Loretta Guidi  were supported by Scientific Research grants from the  Italian Ministry of University  (MIUR, 2019). The authors are grateful to  Dr. Rick Hochberg  for the free and open sharing of his ideas concerning feeding guilds in gastrotrichs, and to  Dr. Seth Tyler  for pointing us to historical literature on microturbellaria as parasites and hosts.
Participants Migrants undergoing the mandatory medical check-up were selected at five local migration offices in three different regions of France. The recruitment was carried out differently according the organization of the office. In two offices, the physician or the nurse invited every migrant during medical consultation to participate in the study. In three offices, the study was presented to all migrants present at the beginning of the day and volunteers were invited to come forward. Participants included were over 18 years of age and had a sufficient knowledge of the French, English, or Arabic language. We aimed to achieve diversity in terms of sex, age, country of origin, and education level. The sample size was chosen to ensure data saturation, i. e. when new interviews did not add any new relevant information.
Data collection Interviews were performed following a semi-structured interview guide of open-ended questions based on a literature review exploring the following themes: knowledge about HIV and hepatitis B and C and its treatments, attitude towards HIV and hepatitis testing, knowledge and opinion on rapid testing, and attitude towards discussing sexuality or drug use with health professionals. The guide was modified and enhanced after the first 10 interviews. Interviews were conducted confidentially in a quiet room and audio-recorded with the participant's permission. In addition to the interviews, socio-demographic data were collected: sex, age, education level, country of origin, and reason for immigration. Date of arrival in France was collected from the second half of the sample. Most interviews (N = 30) were carried out in French, three interviews were carried out in English, and one in Arabic by a researcher who is a native speaker of this language (IBN).
Data analysis Interviews were transcribed verbatim and then analyzed through thematic analysis. Six researchers (SB, IBN, FT, AFD, SL, MD) worked in pairs to code a total of two interviews, and then codes were reviewed by the rest of the team to ensure consistency. This triangulation was done to ensure the validity of the results, according to a method proposed by Burnard  [10]. NVivo10 Software was used (license n˚12). We present the results according to the COREQ guidelines  [11]  in S1 Appendix. The one interview carried out in Arabic was directly transcribed and analyzed in French by the interviewer (IBN). The three interviews conducted in English were transcribed and analyzed in English with no translation.
Ethics statement The STRADA study received approval from a Parisian Ethical Institutional Review Board (IRB n˚00003835, protocol 2016/43NI) and was registered at the data protection agency (CNIL n2 008669). The qualitative study was conducted anonymously (i. e. no identifying data were collected). Participants who accepted to be interviewed were systematically asked if they consented to be audio-recorded at the beginning of each interview.
Acknowledgments We want to thank all people who have agreed to participate in the study, as well as the medical and administrative staff from OFII centers for facilitating this study. We warmly thank  Dr James W Griffith  (https://sites. northwestern. edu/griffithlab/) for his help with translation and proofreading of parts of the manuscript. We also thank  Thanh Le Luong  from  OFII  for her help in facilitating the organizations of interviews.
2008; Madejski et al. 2016)  reported possible contamination of the hard X-ray spectra by the high-energy component (named hard tail hereafter), but unfortunately, no very high-energy (VHE, E>100GeV) data were taken at that time to further constrain the VHE γ-ray flux. In the past, only one multi-wavelength campaign with X-ray instruments, Fermi-LAT, and H. E. S. S. was conducted  (Aharonian et al. 2009). The gathered data were equally well reproduced either by a leptonic model such as the SSC model  (Aharonian et al. 2009)  or a lepto-hadronic model  (Cerruti et al. 2012). PKS 2155-304 was then the target of a multi-wavelength campaign from June to October 2013 by NuSTAR, H. E. S. S. , as well as the Neil Gehrels Swift Observatory and the Fermi-LAT. These instruments observed PKS 2155-304 to provide contemporaneous data for the first time in a very broad energy range, extending from ultra-violet up to TeV γ-rays and yielding a more complete coverage in the X-ray and γ-ray ranges than the previous campaign held in 2008  (Aharonian et al.
Data analysis PKS 2155-304 is an important calibration source in X-rays and was observed during a crosscalibration campaign with other X-ray instruments early in the NuSTAR mission  (Madsen et al. 2017). The multi-wavelength observations of the source in April 2013 including NuSTAR, XMM-Newton, and Fermi-LAT were reported by  Madejski et al. (2016)  Epoch 6 is presented, for sake of completeness, since the Swift data were found to be not usable (see Section 2. 4). NuSTAR and Swift also observed PKS 2155-304 during three extra epochs (labelled 7, 8 and 9): those are reported here also for the sake of completeness. For each epoch, Fermi-LAT data were analysed and the results are reported in Section 2. 2. Fig. 1  presents the overall light curve derived from all the epochs.
2015)  or in Mono mode in which the array triggers on events detected only with CT5. PKS 2155-304 was observed by the full H. E. S. S. phase II array during the present observational campaign. Table  1  gives the date of each observation and the results of the analysis described in the following Sections. To ensure good data quality, each 28-minute observation had to pass standard quality criteria  (Aharonian et al. 2006). For two nights (2013-08-03 and 2013-09-28, epochs 2 and 4), these criteria have not been met by the four 12 m telescopes. Therefore, only CT5 Mono observations are available for these nights. Data for each night have been analysed independently using the Model analysis (de Naurois & Rolland 2009) adapted for the five-telescope array (hereafter named Stereo analysis). In this case, Loose cuts (with a threshold of 40 photo-electrons) were used to lower the energy threshold.
Fermi-LAT data analysis and results The Fermi-LAT is a γ-ray pair conversion telescope  (Atwood et al. 2009 ), sensitive to γ-rays above 20 MeV. The bulk of LAT observations are performed in an all-sky survey mode ensuring a coverage of the full sky every 3 hours. Data and software used in this work (Fermitools) are publicly available from the Science Support Center 1. Events within 10 • around the radio coordinates of PKS 2155-304 (region of interest, ROI) and passing the SOURCE selection  (Ackermann et al. 2012)  were considered corresponding to event class 128 and event type 3 and a maximum zenith angle of 90 •. Further cuts on the energy (100 MeV<E<500 GeV) were made, which remove the events with poor energy resolution. To ensure a significant detection of PKS 2155-304, time windows of 3 days centred on the campaign nights (Table  1 ) were considered to extract the spectral parameters.
(2005)  of 1. 4 × 10 20 cm -2 was tested, and the best-fit spectral parameters of the source were entirely consistent with results obtained by using  Dickey & Lockman (1990)  values. Data for both NuSTAR detectors were fitted simultaneously, allowing an offset of the normalization factor for the focal plane module B (FPMB) with respect to module FPMA. Regardless of the adopted models, the normalization offset was less than 5%. The resulting fit parameters are given in Table  3. More complex models for fitting to the datasets obtained during joint NuSTAR and Swift-XRT pointings were considered, and those are discussed in Section 3. 2. The source exhibited significant variability in one of the pointings, on August 26 (epoch 8); the NuSTAR X-ray count rate for the FPMA module dropped by almost a factor of 2 in 25 ks clock time (Fig.
2016 ), here, a joint fit to the lower-energy Swift-XRT and the higher-energy NuSTAR data was performed to investigate the need for such more complex models. Since the source is highly variable, only the strictly simultaneous Swift-XRT and NuSTAR data sets were paired. To account for possible effects associated with variability or imperfect Swift-XRT -to -NuSTAR crosscalibration, the normalizations of the models for the two detectors were allowed to vary, but the difference was in no case greater than 20%, consistent with the findings of  Madsen et al. (2017) , with the exception of the August 26 observation (epoch 8) where NuSTAR revealed significant variability (see note in Section 2. 3). To explore the spectral complexity similar to that seen in April 2013, the following models were considered 6  : (1) PL: a simple power-law model; and (2) LP: a log-parabola model.
The resulting joint spectral fits are given in Table  5. In four observations (epochs 1, 3, 4 and 9), the model consisting of a simple PL absorbed by the Galactic column fits the data well: no deviation from a simple power-law model is required. However, for epochs 2, 7 and 8, a significant improvement (∆χ 2 > 20 for one extra parameter) of the fit quality is found by adopting the LP model. Thus, at these epochs, the spectrum steepens with energy. In conclusion, there are not only spectral index changes from one observation epoch to another, but there is also a significant change of the spectral curvature from one observation to another. Bhatta et al. (2018) , using only NuSTAR data, reported results on the same observations and also found a change in the spectral shape for epoch 8 but not for epoch 2 and 7.
Broad-band X-ray spectrum In the energy range from 0. 3 keV to 10 keV, the spectrum is usually assumed to be the highenergy end of the synchrotron emission. Indeed, the measured spectral index of PKS 2155-304 in the X-ray regime is generally in agreement with the value expected for a HBL, for which a powerlaw spectral index, Γ, is typically steeper than 2 (hereafter "soft component"). Nevertheless a single power law is too simple a representation of the spectrum when measured with sensitive instruments affording a good signal-to-noise ratio. As already pointed out by  Perlman et al. (2005) , the soft Xray spectra of HBLs are well represented as gradually steepening functions towards higher energies. In the data presented here, the spectral index measured by Swift-XRT is always harder than the one measured by NuSTAR.
A Kolmogorov-Smirnov test was performed on both Swift-XRT and NuSTAR spectral index distributions. It rejects the hypothesis that they are sampled from the same distribution with a P-value of 3%. This suggests that such steepening takes place for PKS 2155-304. At the end of the X-ray spectrum (roughly above a few keV),  Urry & Mushotzky (1982)   (2008)  and  Foschini et al. (2008). Fitting jointly the strictly simultaneous XMM-Newton data with the NuSTAR data, a more complete picture emerged, with a log-parabola describing the soft (E < 5 keV) spectrum, and a "hard tail," which can be described as an additional power-law. Concerning the observations presented in this work, adding an extra hard tail (LPHT model) does not significantly improve the χ 2. However, it is important to note that the flux of the object during the April 2013 pointing was relatively low, and the observations were fairly long (about 4 times longer than any single pointing during the campaign reported here).
As noted by  Madejski et al. (2016) , the hard tail becomes more easily detectable only during low-flux states of the softer, low-energy spectral component. To detect a possible hard tail in the data set of the present campaign, a spectral fit of all data sets simultaneously was performed. Due to the spectral variability of the soft, low-energy component (Table  3 ), stacking (or just summing) all simultaneously is inappropriate. Instead, a simultaneous fit of seven individual datasets from Epochs 1, 2, 3, 4, 7, 8, and 9, was considered, allowing the spectral parameters of the soft component (described as a log-parabola) to vary independently. Each epoch was described by a LPHT model (see Section 2. 5), and with common normalization of the "hard tail" for all data sets 9. Formally, the fit returns zero flux for the hard tail component.
The 99% confidence upper limit of 1. 8 × 10 -4 ph keV -1 cm -2 on the normalization of this component (at χ 2 + 2. 7) corresponds to a 20-40 keV flux limit of 2. 5 × 10 -13 erg cm -2 s -1. The normalization of this hard tail in epoch 0 data was 8 × 10 -4 ph keV -1 cm -2 (corresponding to a 20-40 keV flux of 12. 0 × 10 -13 erg cm -2 s -1 ), or more than 4 times higher than the upper limit measured during the other epochs. In conclusion, the hard tail is also variable on the time scale of months, but no conclusions on the shorter time scales from the presented NuSTAR data can be drawn. Note that the source does exhibit a similar flux level in X-rays with respect to the April 2013 data set while in optical, the flux is significantly lower. In an SSC framework, this photon field might be scattered by low energy electrons to produce hard X-ray photons, accounting for the hard tail visible in epoch 0.
Nevertheless, when the Fermi measurement is extrapolated down towards the NuSTAR energy range, it always overshoots the X-ray measurement. This can be due to a lack of statistics in the LAT range preventing the detection of spectral curvature as the one reported in the 3FGL catalog, since only 3 days of data were used in each epoch. The extrapolation of the 3FGL spectrum of PKS 2155-304 does not violate the upper limits derived here on the hard tail component but cannot reproduce epoch 0.
The radio emission could originate from another location in the jet, or from the emission zone, and is therefore considered as upper limits in the model. Historical data taken between 10 -2 eV and 1 eV (Infra-red range) are found to be quite stable in time with variation less than a factor 2. Such data have been collected using Vizier 11  and shown in the SEDs. For each epoch, a mathematical minimization  (Nelder & Mead 1965)  was performed to find the model parameters R, B, N, log(γ min ), log(γ b ) and log(γ max ) that best fit the data. The values of p 1 and p 2 were constrained by the UV and X-ray data, respectively, and not let free in the fitting procedure. Given the little spectral variability found in UV and GeV, p 1 was set to 2. 5 and  Rybicki & Lightman 1986). The minimization was performed using a Markov Chain Monte Carlo (MCMC) implemented in the emcee python package (Foreman-Mackey et al.
2013). For epochs 1-4, the upper limit on the hard tail flux (Table  5 ) is taken into account by forcing the inverse-Compton (IC) component of the model to be below this limit. The resulting parameters are given in Table  7  with their corresponding realizations in Fig. 3. The model parameters are consistent with previous studies by  Kataoka et al. (2000) ,  Foschini et al. (2007) ,  Katarzynski et al. (2008)  and  Aharonian et al. (2009). As in these previous studies, as well as for other BL Lac objects (e. g. Mrk 421  (Abdo et al. 2011b)  The data from epochs 1-5 are well reproduced by the simple SSC calculation presented here. In contrast to  Gaur et al. (2017)  for this object or Chen (2017) for Mrk 421, there is no need to invoke a second component to reproduce the SED without over-predicting the radio flux.
The main difference is that the hard tail above ≈ 10 keV seen in the previous observations is not observed in the present data set. The SSC model was applied to the data of epoch 0 with results also presented in Table  7. 2. 8 +2. 9 -0. 8 7. 4 +0. 4 -1. 0 1. 6 +1. 0 -0. 9 5. 6 4. 2. Emergence of a hadronic component in hard X-rays ? Following the detection of a γ-ray flare from TXS 0506+056 in coincidence with a high-energy neutrino (IceCube Collaboration et al. 2018), several authors have independently shown that, while pure hadronic models cannot reproduce the multi-messenger dataset, a scenario in which the photon emission is dominated by an SSC component with a sub-dominant hadronic component is viable (see, e. g. ,  Ansoldi et al. 2018; Cerruti et al. 2018; Gao et al. 2018; Keivani et al.
2018). The hadronic component emerges in the hard-X-rays as synchrotron radiation by secondary leptons produced via the Bethe-Heitler pair-production channel in this scenario. With this result in mind, it was investigated whether the hardening seen in the NuSTAR data of PKS 2155-304 could be due to sub-dominant hadronic emission. Starting from the simple SSC model for epoch 0 (see Table  7 ), a population of relativistic protons was added. It was assumed that p p = p e,1 (i. e. , protons and electrons share the same acceleration mechanism, resulting in the same injection spectral index) and that the maximum proton Lorentz factor γ p,max is determined by equating acceleration and cooling time-scales. The proton distribution was normalized such that the hadronic component emerges in hard X-rays.
This research has made use of the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (USA). This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This research made use of Enrico, a community-developed Python package to simplify Fermi-LAT analysis (Sanchez & Deil 2013). This research has made use of the VizieR catalogue access tool,  CDS, Strasbourg, France. The original description of the VizieR service was published in A&AS 143, 23 This work has been done thanks to the facilities offered by the  Universit Savoie Mont Blanc MUST  computing center.
M. Cerruti  has received financial support through the  Postdoctoral Junior Leader Fellowship Programme  from  la Caixa Banking Foundation , grant n. LCF/BQ/LI18/11630012  M. B. gratefully acknowledges financial support from  NASA Headquarters  under the  NASA Earth and Space Science Fellowship Program  (grant  NNX14AQ07H ), and from the  Black Hole Initiative at Harvard University , which is funded through a grant from the  John Templeton Foundation.
With increasing mean and extreme temperatures due to climate change, it becomes necessary to use-not only future typical conditions-but future heatwaves in building thermal simulations as well. Future typical weather files are widespread, but few researchers have put together methodologies to reproduce future extreme conditions. Furthermore, climate uncertainties need to be considered and it is often difficult due to the lack of data accessibility. In this article, we propose a methodology to re-assemble future weather files-ready-to-use for building simulations-using data from the European Coordinated Regional Downscaling Experiment (EURO-CORDEX) dynamically downscaled regional climate multi-year projections. It is the first time that this database is used to assemble weather files for building simulations because of its recent availability.
Introduction Using future climate data for building energy simulation has been of interest to the research community for the past twenty years. However, building practitioners, nowadays, continue to use climate data based on historical weather datasets. Considering warming temperatures as a result of climate change in the near future, using only historical datasets for building thermal simulations will soon be outdated. In fact, global mean temperature has already increased by around +1. 5 • C-2 • C compared to pre-industrial conditions  [1]. The use of historical typical weather years becomes questionable, as heatwaves have already proven to happen more often. Unfortunately, it is expected that their frequency and duration will only intensify towards the end of this century  [2] [3] [4] [5].
With the increase in the frequency of extreme temperatures  [3, 24] , not only future typical weather files are needed, but future weather files, including consecutive hot days in the extremes of the distribution, such as extreme hot years or years containing heatwaves. These future weather files should help building practitioners at the design stage of the building to assess its capability to maintain or not maintain safe indoor temperatures under future climate without the use of air conditioning. In countries where air conditioning is already a common practice, these weather files could help to better size cooling systems. This article proposes a methodology for assembling future climate data in order to provide ready-to-use future weather files for thermal building simulations from European Coordinated Regional Downscaling Experiment (EURO-CORDEX) multi-year climate datasets.