dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015a Croquer et al. , , 2015b;; Henzler, 1983; Huang et al. , 1999; Li and Li, 2011; Ruangtrakoon et al. , 2013; Tillner-Roth and Baehr, 1994; Yadzani et al. , 2012)  depending on the RANS model, which is usually required when using wall functions. • For the low-Reynolds number approach, the software ANSYS ICEM v. 15 was used to build the structured mesh grid. It is composed of tetrahedral elements, which allows a greater control over the near-wall elements and their growing rate toward the free flow area. A mesh grid with 645,000 elements proves to be sufficient to get grid independent solutions. The wall coordinate remains less than 0. 79, which ensures to have at least 18 mesh points to describe the linear and logarithmic regions of the velocity profile  (Croquer et al. , 2015b). Details of the chosen grids are shown in Fig.
It may be seen as a useful indicator of the deviation from the perfect gas model. Figure  5  displays the map of Z obtained by the model based on the REFPROP 7. 0 database equation for OP2. The factor Z varies from 0. 648 at the inlet of the primary nozzle to 0. 935 in the constant section area where the shock wave appears. As expected, the deviation from the perfect gas law is then maximum at the inlet of the primary nozzle where the highest pressure levels are reached. Z is equal to 0. 91 in the secondary fluid entrance region, close to the behavior of a perfect gas. The overprediction already evoked above in terms of the entrainment ratio is then related to inaccurate predictions of the heat and fluid flow inside the primary nozzle. Similar results have been obtained using the RKS equation of state with 0 672 0 967.
≤ ≤ Z. As a conclusion, the use of the perfect gas model to investigate supersonic ejectors working with R134a, though being more easy to use, is not advisable. The two real gas models perform quite well providing very similar results in terms of ejector performance and local flow features. It has been checked also that there is no noticeable difference between the two real gas models in terms of computational resources (CPU time and required memory size). Thus, they can be used confidently for supersonic ejector applications with R134a. For sake of clarity, only the REFPROP 7. 0 database equation will be considered in the following. For other ejector designs and other operating conditions, this model should nevertheless be used with a certain caution as the temperature range covered by this database (170-455 K) could be easily exceeded during the convergence process, which would cause the solution to fall in regions where the fluid properties are not defined.
Influence of the RANS turbulence model Four two-equation turbulence models described in Section 2. 4 have been compared to the experimental data of García del  Valle et al. (2014). For all calculations, the real gas model based on the REFPROP 7. 0 database has been used. Table  4  shows the relative deviation ε ω ω ω = pred exp exp between the predicted ω pred and the experimental ω exp = 0 398. values of the entrainment ratio for OP2. The standard k-ε model with standard wall functions performs the best with a deviation of 4. 27%. This model is indeed known to predict well wallbounded flows with relatively smooth mean pressure gradients, which is the case in the region confined between the primary nozzle exit and the appearance of the shock wave in the mixing chamber. The advanced versions of the k-ε model (realizable k-ε and k-ε RNG) do not improve the predictions of the entrainment ratio.
Another interesting quantity when speaking about turbulence modeling is the turbulence viscosity ratio defined as the ratio of the turbulence viscosity νT and the kinematic viscosity ν. As shown in Table  3 , the viscosity ν varies within the ejector when using the REFPROP 7. 0 database. It has been checked that the same contours are obtained for ν whatever the two-equation model used. The turbulence viscosity ratio is then a direct measure of the turbulence viscosity νT and so of the energy dissipated by the turbulence model. Figure  10a  and b report the iso-contours of the turbulence viscosity ratio for OP2 using the standard k-ε and k-ω SST high-Reynolds number models. Both models exhibit the same patterns. The turbulence models act mainly in the mixing layer at the trailing edge of the primary nozzle exit and then at the shock wave location.
Conclusion The flow structure and performance characteristics of a supersonic ejector working with R134a have been studied by 2D axisymmetric, steady-state numerical simulations and compared to the experimental data of García del  Valle et al. (2014). A numerical benchmark of some thermodynamic and twoequation turbulence models available within Fluent v. 15 have been performed to build the CFD model offering the best compromise between accuracy and computational efforts. The following main conclusions may be drawn: • The two real gas equations, the Redlich-Kwong-Soave equation of state and the REFPROP 7. 0 database equation, provide similar and good results, while the perfect gas law fails to predict the correct entrainment ratio with an overprediction of about 20% whatever the operating conditions.
As 3D calculations are not required, it appears as a very promising tool even from an engineering point of view to design future ejectors as converged states are achieved within 3 hours with standard computational resources. The CFD model based on the k-ω SST model in its low-Reynolds number formulation and the REFPROP 7. 0 database equation can be now used confidently to investigate, in the second part of this work, the local flow features and the exergy destruction within the ejector and discuss the different assumptions made by 1D thermodynamic models.
Acknowledgement This project is part of the research program of the  NSERC Chair in Industrial Energy Efficiency , established at  Université de Sherbrooke  in 2014, with the support of  Hydro-Québec ,  Canmet-Énergie ,  Rio Tinto Alcan  and the  Natural Sciences and Engineering Research Council of Canada. They are here gratefully acknowledged. The authors acknowledge also  Professor Nicolas Galanis  for fruitful discussions and critically reviewing the manuscript.
Each spot was overlaid with 2 μL of matrix solution, a saturated solution of α-cyano-4-hydroxycinnamic acid in 50 % acetonitrile mixed with 2. 5 % trifluoracetic-acid. The matrix-sample was crystallized by air-drying at room temperature for 5 min. Measurements were performed using an Autoflex II mass spectrometer (Bruker Daltonik) equipped with a 337-nm nitrogen laser. Spectra were recorded in the 2-20 kDa mass range. Data were automatically acquired using AutoXecute acquisition control software. The two first raw spectra obtained for each isolate were imported into the BioTyper software, version 2. 0 (Bruker Daltonik GmbH), and were analyzed by standard pattern matching (with default parameter settings) against 5625 references in the BioTyper database. When both spots yielded a score ⩾1. 9, identification was complete.
PCR consisted of a 15-min initial denaturation Taq polymerase Hot-Star at 95 °C followed by 30-s denaturation at 95 °C, 30-s hybridation at 60 °C and 1-min elongation at 72 °C. After 35 cycles, extension was performed for 5 min at 72 °C. Amplified products were visualized under UV illumination with Syber Safe ® staining after electrophoresis using a 1. 5 % agarose gel. PCR products were cloned by the pGEM® -T Easy Vector System Kit according to the manufacturer's instructions (Promega, Lyon, France). They were sequenced in both directions using the Big Dye® Terminator V1. 1 Cycle Sequencing Kit (Applied Biosystems). Original sequences have been submitted to GenBank.
Sequence alignment and phylogenetic analysis Sequencing products were resolved using an ABI PRISM 3130 automated sequencer (Applied Biosystems). Sequences were compared with the GenBank database using the online BLAST program (www. ncbi. nlm. nih. gov). The highest percentage of sequence similarity was used to identify isolates. Sequence similarity higher than 97 % with a described species was considered to be indicative of identification at the species level. Phylogenetic analysis was established by the neighbor-joining method using MEGA5 software (www. megasoftware. net). Phylogenetic construct was based on the 18S rRNA gene sequences aligned with 52 references.
We first excluded from our analysis the 43,364 NIEBs and flanking windows (2. 7 % of our sample) lacking sufficient mappability to perform the test (Methods). Then, from this comparison, for each MNase-seq data set, we classified the 1,537,892 mappable NIEBs into 3 groups (Methods): (G1) NIEBs significantly depleted in nucleosome tags at the 5 % confidence level as compared to flanking windows; (G2) NIEBs significantly enriched in nucleosome tags at the 5 % confidence level as compared to flanking windows; and (G3) the remaining NIEBs. For "Valouev" in vitro data, we found that 76. 7 % of the NIEBs are in G1 and correspond to regions where the experimental in vitro nucleosome tags density is in agreement with the NIEB predictions, and only 0. 2 % are in G2 and have a significant nucleosome occupancy signal over the predicted NIEBs (Additional file 1: Figure  S3A ).
Genome-wide nucleosome positioning data We used genome-wide maps of nucleosome positioning in vivo in resting human CD4 + T cells and in vitro obtained by Valouev et al. [42]  using micrococcal nuclease (MNase) digestion of chromatin and massive DNA sequencing technique (MNase-seq). Data files were downloaded from Gene Expression Omnibus (GEO, www. ncbi. nlm. nih. gov/ geo/) under accession number GSE25133. For every read on the positive strand, we count a score of '1 read' at the corresponding dyad locus, i. e. 70 bp further. We do the same for the negative reads, we count '1 read' 70 bp before the read match. We also used for comparison, the in vivo genome-wide map of nucleosome positioning in resting human CD4 + T cells obtained by Schones et al. [57]  using MNaseseq technique. We downloaded the tag coordinate bed files of the resting nucleosomes from dir.
Interspecies divergence rates We used the point-mutation data obtained by Chen et al. [6]. Nucleotide substitutions were tabulated in the human lineage since its divergence from chimpanzee using both the orangutan and macaque as outgroups. To minimize the effects of alignment artifacts, only isolated substitutions defined as those flanked by sites that are identical in the four species were tabulated. Sequences were divided into CpG and non-CpG sites. CpG sites were defined as the sites having the following human/chimpanzee/orangutan/macaque pattern: NG/CG/CG/CG or CG/NG/CG/CG or CN/CG/CG/CG or CG/CN/CG/CG, where N is any nucleotide. CpG sites were excluded for the computation of C → W/S substitution rates. Substitution rates were calculated by dividing the number of substitution events of the appropriate type by the number of potentially mutable sites that meet the same criteria.
Meiotic recombination Crossover rate data of the human genome were retrieved from the International HapMap Project (http://www. hapmap. org) [86]. The crossover rate for a given 100 kb window (Table  1 ) was computed by taking the two most distant markers belonging to this window and dividing their distance in the genetic map in centimorgans (CM) by their distance in the sequence map in megabases (Mb). Note that only the 100 kb windows with at least 5 markers were considered. We used the genome-wide recombination initiation maps of Pratto et al. [79]  (Additional file 1: Figure  S14 ). We downloaded the double strand break (DSB) hotspots in human individuals data (the GSE59836 peak data file) from http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSM1447330.
Phylogenetic analysis PCR products were directly sequenced in both directions or sequenced after cloning into pT7Blue-2 T-vector (Novagen) using the Applied Biosystems 3130xl Genetic Analyzer (Applied Biosystems). All sequences were compared with the GenBank database using a standard nucleotide-nucleotide BLAST search  (Altschul et al. , 1990)  and were aligned with their relatives by using GENETYX software (Software Development Co). The hhyL gene sequences were translated in silico by using both NCBI Open Reading Frame (ORF) Finder and ExPasy translation tool  (Gasteiger et al. , 2003) , and were also aligned. A phylogentic tree was constructed with the neighbour-joining method  (Saitou and Nei, 1987). The robustness of tree topology was assessed by bootstrap analysis  (Felsenstein, 1985)  based on 1000 replications.
Acknowledgements The authors are grateful to  Daisuke Todaka  and  Kazuko Shinozaki  ( Japan International Research Center for Aglicultural Sciences ) for providing plant samples. We also thank  Naomi Yasuda  ( AIST ) for useful advice about cultivation techniques of plants and  Mayako Kutsukake  ( AIST ) for help in microscopy observation. This work was supported by  JSPS KAKENHI  Grant Number  25660282  and a research grant from  Asahi Group Foundation. Part of this work was done while the principal author (MK) was a visitor at  INRS-Institut Armand-Frappier  under the  JSPS Institutional Program for Young Researcher Overseas Visits. Research on H2 ecophysiology in the laboratory of PC is funded by  NSERC-Discovery  grant.
Modified TIE reconstruction procedure Basic TIE The TIE was first described by Teague as an intensity conservation equation which was later on solved within various approaches by Paganin and Nugent  [30, 38]. A detailed derivation of the TIE (Eq. 4) is given by De Graef  [39]. The TIE relates the intensity derivative of several images at different defoci to the lateral phase variation (Eq. 4), where (φ(x, y, z)) is the lateral phase variation, ( δ δz I 0 ) is the intensity derivative at z = 0, I 0 is the inten- sity at z = 0, is the electron wave length, and ∇ x,y the two-dimensional gradient operator in x and y, resp. ∇ -2 x,y its inverse. (4) φ(x, y, z) = - 2π 1 I 0 ∇ x,y ∇ -2 x,y δ δz I 0. In this work, the simple TIE was implemented as a script in Digital Micrograph. Then the algorithm was further adapted and improved as described in the following.
Acknowledgements This work was supported by the  Swiss National Science Foundation (SNF)  (Project No. 200020_132377 ), by the  European Union  under the  Seventh Framework Program  under a contract for an  Integrated Infrastructure Initiative  (Reference 312483-ESTEEM2) and by the  Scientific Center for Optical and Electron Microscopy (ScopeM)  of the  Swiss Federal Institute of Technology ETHZ. PR acknowledges the  EU-FP7   R2R-CIGS  project  283974. ES and CG acknowledge the " Conseil Regional Midi-Pyrnes " and the  European FEDER  for financial support within the  CPER program , and the  French National Research Agency  under the " Investissement d' Avenir" program  reference No. ANR-10-EQPX-38-01.
III. INTEGRATION OF THE BGA-MODULE WITH THE EYEWEAR DEVICE In order to validate the GO/PO based lens design and to evaluate the antenna in the realistic use-case scenario, full wave electromagnetic simulations were also carried out, using the commercial software Empire XCcel  [14]. The simulation model included the BGA-module integrated with the dielectric lens, mounted in the left-hand side of a dedicated ABS eyewear frame (Fig. 7  left part). An FR4 substrate which might be needed in a realistic product as the application PCB and a backing ground plane were also included in this model, behind the Antenna-module (Fig. 8 ). The frame includes a curved region on the right-hand side of the head to emulate visually the screen of a smart eyewear device. It also includes on the two sides of the frame, two parallelepiped casing-like structures for housing the application PCBs for WLAN/Bluetooth and WiGig standards, respectively.
A. Simulations with Homogeneous Head Model Simulations with the homogeneous head model were performed to obtain the power density level at the surface of the skin. According to ICNIRP guidelines, the power density level on the tissue, averaged over 20cm 2 should be lower than 1mW/cm 2 (or 10W/m 2 ) around 60 GHz frequency. The simulated power density with EMPIRE XCcel software on the skin surface can be observed in Fig. 19  for 60 and 66GHz. It should be noted here that the values presented in the figure legends are not averaged either over time or space, so they are the worst-case levels. Moreover, the input power to the antenna was set as 1W in the simulations so the power density levels shown in the figures need to be divided by 100 to comply with the typical input power level of 10dBm for WiGig devices.
A search is presented for a new, light boson with a mass of about 1 GeV and decaying promptly to jets of collimated electrons and/or muons (lepton-jets). The analysis is performed with 20. 3 fb -1 of data collected by the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a centre-of-mass energy of 8 TeV. Events are required to contain at least two lepton-jets. This study finds no statistically significant deviation from predictions of the Standard Model and places 95% confidence-level upper limits on the contribution of new phenomena beyond the SM, incuding SUSY-portal and Higgs-portal models, on the number of events with lepton-jets.
It covers |η| < 2. 7 and provides precision tracking and triggering for muons. ATLAS has a three-level trigger system. The Level 1 (L1) trigger is implemented in hardware, and it uses information from the calorimeters and muon spectrometer to reduce the event rate to 75-100 kHz. The software-based Level 2 (L2) trigger and the Event Filter (EF) reduce the event rate to 300-500 Hz of events that are retained for offline analysis. The L1 trigger generates a list of region-of-interest (RoI) η-φ coordinates. The muon RoIs have a spatial extent of 0. 2 in ∆η and ∆φ in the MS barrel, and 0. 1 in the MS endcap. Electromagnetic calorimeter RoIs have a spatial extent of 0. 2 in ∆η and ∆φ. For the L2 trigger the reconstruction is mostly based on simplified algorithms running on data localized in the RoI which was reported by L1.
Signal models Two benchmark models are used to interpret the data. In the SUSY-portal model (Section 3. 1), a pair of squarks is produced and the cascade decays of the squarks include dark-sector particles and one or more dark photons. In the Higgs-portal model (Section 3. 2), the SM Higgs boson decays into a pair of dark fermions, each of which decays into one or more dark photons in cascades. For both models, the dark photons decay into lepton pairs, that can be reconstructed as a lepton-jet, or light hadrons, depending on the branching fractions. Monte Carlo (MC) simulated samples are produced for the two models. All signal MC events are processed with the Geant4-based ATLAS detector simulation  [38, 39]  and then analysed with the standard ATLAS reconstruction software. The branching ratio (BR) values for the darkphoton decays to leptons are taken from Ref.
The contribution from WZ and ZZ backgrounds, when one of the bosons is off-shell, is modelled with Sherpa  [54]. Of the backgrounds considered, only the hadronic multijet, γ + jets and Z(→ + -) + multijet events contribute significantly. The contribution from t t is negligible. The hadronization of the multijet, photon + jets and W/Z + jets samples is done with Pythia 8 using the CT10 NLO  [55]  PDF set. For the underlying event, the AUET2 set of tuned parameters  [48]  is used. As with the signal MC samples, the SM MC samples included the effect of multiple pp interactions per bunch crossing and are assigned an event weight such that the distribution of the number of pp interactions matches that in data. All MC events are processed with the Geant4-based ATLAS detector simulation  [38, 39]  and then analysed with the standard ATLAS reconstruction software.
While other experiment's results are independent of the topology of the dark-photon production, the ATLAS results depend on the topology, i. e Higgs boson mass and its production mechanism, and its decay into dark photons. The results from CMS are shown elsewhere  [15]. The values are evaluated using Equation (1) combined with R-ratio measurements of e + e -collider data  [28] [29] [30] [31] [32] [33] , where the R-ratio is the ratio of the hadronic cross section to the muon cross section in electron-positron collisions. The expression for with respect to these variables is 2 = c cτ(Γ e + e -+ Γ µ + µ -+ RΓ µ + µ -) , (5) where is the reduced Planck constant, R is the R-ratio, while Γ e + e -and Γ µ + µ -are given by Equation (1).
Conclusions A search for prompt lepton-jets using 20. 3 fb -1 of pp collision data collected with the ATLAS detector at the LHC at a centre-of-mass energy of 8 TeV is performed. Such lepton-jets are expected from decays of low-mass dark photons in some model extensions to the SM. The analysis requires events with at least two lepton-jets. No significant excess of events compared to the SM expectation is observed in any of the analysed channels, and 95% confidence-level upper limits are computed on the production cross section times branching ratio for two prompt lepton-jets in SUSY-portal and Higgs-portal models. The results are also interpreted in terms of a 90% confidence-level exclusion region in kinetic mixing and dark-photon mass parameter space, based on combined results from the H → 2γ d + X topology.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  RCN, Norway ;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ;  EPLANET ,  ERC ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Region Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF, GIF  and Minerva, Israel; BRF, Norway; the  Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN  and the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC (Taiwan) ,  RAL (UK)  and  BNL (USA)  and in the Tier-2 facilities worldwide.