dataesr/scientific-paragraphs-categorization huggingface dataset:
According to the simulation, the background is largely dominated by bb events, while the contribution from the decays of W and Z bosons is of the order of 10 events. All simulated cc and tt events are rejected. The bb cross-section value measured by LHCb, 288 ± 4 ± 48 µb  [26, 27] , predicts (15 ± 3) × 10 3 events for the 7 TeV dataset, after selection. The value for the 8 TeV dataset is (52 ± 10) × 10 3. The extrapolation of the cross-section from 7 TeV to 8 TeV is obtained from POWHEG  [28] , while Pythia is used to obtain the detection efficiency. The candidate yields for the two datasets are consistent with a dominant bb composition of the background. This is confirmed by the study of the shapes of the distributions of the relevant observables. Figure  2  compares the distributions for the 7 TeV dataset and for the 135 simulated bb events surviving the selection.
From simulation, the veto introduces a loss of efficiency of 13% (42%) for the detection of LLPs with a 30 GeV/c 2 mass and a 10 ps (100 ps) lifetime. The radial (σ R ) and longitudinal (σ z , parallel to the beam) uncertainties provided by the LLP vertex fit are shown in Figs. 2 f ) and g). Larger uncertainties are expected from the vertex fits of candidates from bb events compared to signal LLPs. The former are more boosted and produce more narrowly collimated tracks, while the relatively heavier signal LLPs decay into more divergent tracks. This effect decreases when m LLP approaches the mass of b-quark hadrons. A multivariate analysis based on a multi-layer perceptron (MLP)  [29, 30]  is used to further purify the data sample. The MLP input variables are the muon p T and impact parameter, the number of charged particle tracks used to reconstruct the LLP, the vertex radial distance R xy from the beam line, and the uncertainties σ R and σ z provided by the LLP vertex fit.
The muon isolation value and the reconstructed mass of the long-lived particles are not used in the MLP classifier; the discrimination power of these two variables is subsequently exploited for the signal determination. The signal training and test samples are obtained from simulated signal events selected under the same conditions as data. A data-driven approach is used to provide the background training samples, based on the hypothesis that the amount of signal in the data is small. For this, a number of candidates equal to the number of candidates of the signal training set, which is of the order of 1000, is randomly chosen in the data. The same procedure provides the background test samples. The MLP training is performed independently for each fully simulated model and dataset. The optimal MLP requirement is subsequently determined by maximizing a figure of merit defined by / √ N d + 1, where is the signal efficiency from simulation for.
Determination of the signal yield The signal yield is determined with an extended unbinned maximum likelihood fit distribution of the reconstructed LLP mass, with the shape of the signal component taken from the simulated models, plus the background component. After the MLP filter, no simulated background survives; therefore a data-driven method is adopted to determine the background template. The data candidates are separated into a signal region with muon isolation below 1. 4 and a background region with isolation value from 1. 4 to 2. The signal region contains more than 90% of the signal for all the models considered (see e. g.
Determination of the signal yield The signal yield is determined with an extended unbinned maximum likelihood fit distribution of the reconstructed LLP mass, with the shape of the signal component taken from the simulated models, plus the background component. After the MLP filter, no simulated background survives; therefore a data-driven method is adopted to determine the background template. The data candidates are separated into a signal region with muon isolation below 1. 4 and a background region with isolation value from 1. 4 to 2. The signal region contains more than 90% of the signal for all the models considered (see   The MLP classifier can be biased by the presence of signal in the data events used as background training set. To quantify the potential bias, the MLP training is performed adding a fraction of simulated signal events (up to 5%) to the background set.
The signal PDF is taken from the histogram of the mass distribution obtained from simulation. The fit is performed simultaneously on events from the background region and from the signal region. In the latter the numbers of signal and background events are left free in the fit, while the slope values and the relative strength of the two exponential functions are in common with the background region fit. Examples of fit results are given in Fig. 3 , obtained from the 8 TeV dataset for two signal hypotheses, LV38 5 ps and LV98 10 ps. The fitted signal yields, given in Table  1 , for both datasets are compatible with the background-only hypothesis. The validity of using events with isolation above 1. 4 to model the background has been checked by comparing the relevant distributions from events in the background and in the signal regions, including the muon p T and impact parameter distributions, as well as the number of tracks, invariant mass, vertex R xy and vertex uncertainties of the LLP candidate.
The results of this check are also consistent with zero signal for the two datasets. Both the LLP mass fit and the ABCD methods are tested with W and Z/γ leptonic      decays. Isolated high-p T muons are produced in such processes with kinematic properties similar to the signal. By removing the minimum R xy requirement the candidates can be formed by collecting tracks from the primary vertex. As before, the background is taken from a region of muon isolation above 1. 4, which contains a negligible amount of signal. For both datasets the number of events obtained from this study is compatible with the cross-sections measured by LHCb  [32] [33] [34].
e. the requirement of a minimum R xy disfavours heavy LLPs. The cut on R xy is more efficient at selecting LLPs with large lifetimes, but for lifetimes larger than ∼ 50 ps a considerable portion of the decays falls into the material region and is vetoed. Finally, a drop of sensitivity is expected for LLPs with a lifetime close to the b hadron lifetimes, where the contamination from bb events becomes important, especially for low mass LLPs. A breakdown of the relative systematic uncertainties for the analysis of the 8 TeV dataset is shown in Table  2. The table does not account for the uncertainties associated with the fit procedure, which, as described below, require a specific treatment. The uncertainties on the integrated luminosity are 1. 7% for 7 TeV dataset and 1. 2% for 8 TeV data  [35].
For R xy from 4. 5 mm to about 12 mm a study performed with inclusive bb events finds differences between data and simulation of less than 5%. The corresponding systematic uncertainties are determined by altering the efficiency in the simulation program as a function of the true vertex position. A maximum of 1% uncertainty is obtained for all the signal models. An alternative procedure to asses this uncertainty considers vertices from B 0 → J/ψ K * 0 decays with J/ψ → µ + µ -and K * 0 → K + π -. The detection efficiency in data and simulation is found to agree within 10%. This result, obtained from a four-particle final state, when propagated to LLP decays with on average more than 10 charged final-state particles for all modes, results in a discrepancy of at most 2% between the LLP efficiencies in data and simulation, which is the adopted value for the respective systematic uncertainty.
The uncertainty on the position of the beam line is less than 20 µm  [39]. It can affect the secondary vertex selection, mainly via the requirement on R xy. By altering the PV position in simulated signal events, the maximum effect on the LLP selection efficiency is in the range 0. 2-1%. The imprecision of the models used for training the MLP propagates into a systematic difference of the detection efficiency between data and simulation. The bias on each input variable is determined by comparing simulated and experimental distributions for muons and LLP candidates from Z and W events, and from bb events. The effect of the biases is subsequently estimated by testing the trained classifier on altered simulated signal events: each input variable is modified by a scale factor randomly drawn from a Gaussian distribution of width equal to the corresponding bias.
Conclusion Long-lived massive particles decaying into a muon and two quarks have been searched for using proton-proton collision data collected by LHCb at √ s = 7 and 8 TeV, corresponding to integrated luminosities of 1 and 2 fb -1 , respectively. The background is dominated by bb events and is reduced by tight selection requirements, including a dedicated multivariate classifier. The number of candidates is determined by a fit to the LLP reconstructed mass with a signal shape inferred from the theoretical models. LHCb can study the forward region 2 < η < 5, and its low trigger p T threshold allows the experiment to explore relatively small LLP masses. The analysis has been performed assuming four LLP production mechanisms with the topologies shown in Fig. 1 , covering LLP lifetimes from 5 ps up to 100 ps and masses in the range 20-80 GeV/c 2.
Figure5: Expected (open dots and 1 and 2 bands) and observed (full dots) cross-section times branching fraction upper limits (95% CL) for the processes indicated in the bottom left corner of each plot, ⌧ LLP is always 10 ps. The results correspond to the 8 TeV dataset. a) upper limits as a function of the LLP mass for process PA; b) as a function of the LLP mass for process PB, with m "g" = 100 GeV/c 2 ; c) as a function of m h 0 for process P C for m LLP of 20, 40, and 60 GeV/c 2 , from top to bottom (the single point at 130 GeV/c 2 with m LLP = 60 GeV/c 2 has been shifted to the right for visualisation); d) upper limits as a function of the LLP mass for process PD with m q = 60 GeV/c 2.
Summary of the contributions to the relative systematic uncertainties, corresponding to the 8 TeV dataset, (the sub-total for the 7 TeV dataset is also given). The indicated ranges cover the fully simulated LV models. The detection efficiency is affected by the parton luminosity model and depends upon the production process, with a maximum uncertainty of 7% for the gluon-gluon fusion process P C. For the fast simulation based analysis there is an additional contribution of 5%. The systematic effects associated with the signal and background models used in the LLP mass fit are not shown in the table.
2 -1. 0 1. 5 -3. 6 2. 2 LLP mass scale Models statistics Sub-total 8 TeV dataset (Sub-total 7 TeV dataset Parton luminosity Analysis with fast simulation 0. 8 -1. 5 1. 7 -2. 5 4. 9 -6. 5 4. 9 -6. 1) 3 -7 5.
For s = -1 the bifurcation is subcritical (discontinuous) and the equation must be supplemented by a quintic term. It is worth remarking that, once an additional parameter is eliminated by the condition of being at the instability threshold, the original five-parameter problem gets reduced to a two-parameter problem. Accordingly, since the phase diagram of Eq. (  30 ) is well-known  [41] , to classify all possible nonlinear scenarios (near threshold) it suffices to obtain the map of the physical parameters of the original problem into the parameters of the amplitude equation c and b. This mapping always exists but it may be difficult to obtain in practice. We have performed this analysis following standard methods and with the help of the software Mathematica. The extent to which the results obtained through this weakly nonlinear analysis apply to situations more deeply in the nonlinear regime is not guaranteed, but it is plausible to expect that the qualitative behavior will be similar.
The need of at least two sources of activity, and a coupling between polarity and flow seem also well established. We expect that further experimental inquiry will eventually test the ideas here developed and clarify the appropriate mechanical framework for a continuum description for collective cell migration in tissues. We thank R. Alert, F. Graner, V. Hakim, J. -F. Joanny, P. Marcq, J. Prost, X. Trepat, R. Vincent and S. Yabunaka for useful discussions. We acknowledge financial support from MINECO under projects FIS2013-41144-P and FIS2016-78507-C2-2-P from Generalitat de Catalunya under project 2014-SGR-878. CBM also acknowledges a FPU fellowship from the Spanish Government.
Spectra were processed by using aL orentzian line broadening of 100 to 300 Hz for 2 HNMR spectra before Fourier transform from the top of the echo. Samples were equilibrated for 30 min at ag iven temperature before data acquisition. All spectra were processed and analysed by using Bruker To pspin 3. 2 software. Spectral moments were calculated for each temperature by using the NMR Depaker 1. 0rc1 software [Copyright (C) 2009 SØbastien Buchoux]. O rientational order parameters (S CD )w ere calculated from experimental quadrupolar splittings (Dn Q )a ccording to Equation (1): Dn Q q ðÞ¼ 3 2 A Q 3cos 2 q À 1 2 S CD ð1Þ in which A Q = (e 2 qQ)h À ,t he quadrupolarc oupling constant for methyl moieties is 167 kHz, and q is the angle between the magnetic field and the bilayer normal. [38] We calculated twice the value of the orderp arameter as am ean for structural order along the bilayer normal.
Acknowledgements This work was further supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 researcha nd innovation program  ( ERC Starting Grant  to A. L. ,a greement no. 639020 ), the  ANR  ( ANR-14-CE09-0020-01  to A. L. ),  IdEx Bordeaux  (Chaire d'Installationt oB. H. ), the  EU COFUND program (  FEL-2012-1t  oS. B. ), and the  Swiss NationalS cience Foundation  (grant  144444  to R. R. ). This work has benefited from the facilities and expertise of the  Biophysical and Structural Chemistry Platform (BPCS)a tI ECB ,  CNRS UMS3033 ,Inserm  US001 ,  Bordeaux University ,a nd financial support from the  TGIR-RMN-THC Fr3050 CNRS  for conducting the research is gratefully acknowledged.
A dedicated heating power during ramp-up, in addition to the one required during flat-top, appears to be necessary for reliable access to H-mode. The ramp-down phase poses specific issues on vertical stability due to the complex plasma evolution. Ramp-down trajectories with controllable plasma boundaries generated by the CREATE NL code have been coupled to 1. 5D transport simulations by means of the JINTRAC transport suite of codes. The results show the necessity of additional ramp-down heating power to avoid a radiative plasma collapse and to control the current density profile. Reducing the current ramp-rate in the ramp-down phase can help the controllability of the plasma. © 2017 The Author(s). Published by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.
A parametric study for DEMO1 ramp-up (RU) To explore different RU options, a parametric study by means of the METIS code has been performed. METIS (Minute Embedded Tokamak Integrated Simulator), developed as part of the CRONOS suite  [9] , is a fast integrated tokamak simulator which models the plasma using scaling laws coupled with simplified source models (0. 5D). The aim of the work is to arrive to a RU optimized in terms of a combination of several quantities as for example RU duration, flux consumption, total installed H&CD power and internal inductance li. Different RU options have been investigated and compared. In this work the FT additional power (P add,FT ) provided by a NBI system (1 MeV) is switched on approximately at the end of the RU to avoid shine through problems (density limit scaled from ITER  [10] , as a conservative estimate: n e,avg > 3*10 19 m -3 ).
A trajectory validation for DEMO1 ramp-down (RD) The aim of this task is to validate a ramp-down trajectory consistent in terms of vertical stability. Plasma boundaries produced by the CREATE NL free boundary equilibrium code  [12]  have been given as input to a plasma transport simulation done by means of the JINTRAC suite of codes (1. 5D)  [13]. The CREATE NL code has been successively used to assess the feasibility of the trajectory investigated in terms of coil capabilities. In order to simplify the complex JINTRAC ramp-down simulation, the effective charge Z eff has been prescribed to be radially constant. Line radiation has also been prescribed to reach a convergent simulation. A ramp-down trajectory has been simulated running from 19. 6 MA to 5 MA (the end of the diverted plasma phase).
Acknowledgments This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom   Research and Training Programme   2014-2018  under Grant agreement no. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. The author (PV) gratefully acknowledges his colleague  Oisin McCormack  for the help with the language check.
Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10. 7 cm radio flux (F10. 7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis.
Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multiwavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10. 7 cm flux, performs better than NOAA's present prediction of the 10. 7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10. 7 flux.
Here, in addition to the classical 10. 7 cm radio flux from Penticton Observatory, we consider daily radio observations made between 3. 2 and 30 cm at the Nobeyama Radio Observatory in Japan. Although these datasets are publicly available, they are provided in different formats and are occasionally affected by data gaps or corrupted by solar flares, which hinders their immediate use. Therefore, our first objective is to correct for these and deliver one single homogeneous dataset with daily values from 1951 to today. The different data-processing steps take advantage of the simultaneous measurements at the different wavelengths and are described in Section 2. Our second objective is to deliver a one-month forecast. While physical models perform better for forecasting the solar radiative output on time scales of weeks (e.
Data description and preprocessing Radio emissions at centimetric wavelengths and EUV emissions are generated by different physical processes but originate from the similar regions of the solar atmosphere; as a result, radio emissions (which can be measured from the ground) can provide reasonable proxies for solar EUV and UV emissions (which must be measured from space); see for example  (Nicolet and Bossy, 1985; Tapping, 2013). Best known is the radio flux at 10. 7 cm wavelength, i. e. the F10. 7 index, which is used as a solar input in many space weather applications. Radio emissions at other wavelengths have a different blend of chromospheric and coronal contributions, which is one of the prime motivations for performing a multiwavelength monitoring of the Sun. The F10. 7 index has been measured continuously since 1947, first at Ottawa, and subsequently at the Penticton Radio Observatory in British Columbia.
Data collection All the observations from Ottawa/Penticton are available in one single file at the NOAA's National Center for Environmental Information (ftp://ftp. ngdc. noaa. gov/STP/ space-weather/solar-data/solar-features/solar-radio/noontimeflux/penticton/penticton_adjusted/listings/) while observations from Toyokawa and Nobeyama are provided in monthly files by the Nobeyama Radio Observatory (http://solar. nro. nao. ac. jp/norp/data/). This dispersion of the information may partly explain why these data have received little attention so far. The first step thus consists in collecting all the data in one single file, with daily values since November 1, 1951. Notice that the complete set of wavelengths is accessible only from 1957 onwards and so earlier values are extrapolated (see below).
Introduction The initial objective of our model was to forecast the F30 proxy up to 7 days ahead. We later extended the horizon to 30 days for all five wavelengths. The results of these investigations were obtained by means of the "R" software (R Core Team, 2017). Our dataset of solar fluxes contains five records of daily values from November 1st, 1951 till today, including complete solar cycles 19-23, and most of cycle 24. We separated this dataset into two subsets: the first four solar cycles  (years 1951-1995) , representing 70% of the data, were used as the training set, and the last two cycles (years 1996-2016), as the validation set. Both subsets cover a complete range in solar activity, from cycle minimum to maximum and even varying levels of maximums, with moderate and strong solar cycles.
Thermosphere density model Semi-empirical thermosphere models are used in satellite orbit determination and prediction programs to compute the atmospheric drag force, as well as in upper atmosphere studies. They predict temperature and (partial) density as a function of the location (altitude, latitude, longitude, local solar time), solar and geomagnetic activities, and day-of-year. The first Drag Temperature Model, DTM-78  (Barlier et al. , 1978) , was developed in the seventies; its most recent version is DTM-2013  (Bruinsma, 2015). The DTM models are constructed by fitting to the underlying density database, which is considerably larger and more accurate for DTM-2013 than for DTM-78, to reproduce the climatology of the thermosphere. The spatial resolution of the models is of the order of thousands of kilometers.
Conclusions A prototype service delivering nowcasts and forecasts of radio solar fluxes at five wavelengths, from 3. 2 cm (F3. 2) to 30 cm (F30), has been operational at CLS since May 2016 and accessible at ftp://ftpsedr. cls. fr/pub/previsol/solarflux. The archive starts on November 1st, 1951, and new values are added on a daily basis, at 04:00 UT. The processing consists first in filling the gaps and correcting the spurious data based on an expectation-maximization method. Next, we forecast the five fluxes up to 30 days ahead by using an analogue neural network. The relative performance for F30, in terms of std of the error, can be as much as 25% better after 7 days than that of the present F10. 7 forecast by NOAA, with a marked improvement at high solar activity. When applied to the F10.