dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Giancarlo Ferrera  and  Francesco Tramontano  for providing a preliminary version of their NNLO HVNNLO code and for extensive discussion. We are also grateful to  Alexander Karlberg ,  Zoltan Kunszt ,  Paolo Nason , and  Carlo Oleari  for useful exchanges. WA, WB, and ER thank  CERN  for hospitality, and WA, WB, ER and GZ would like to express a special thanks to the  Mainz Institute for Theoretical Physics (MITP)  for its hospitality and support while part of this work was carried out. The research of WA, WB, GZ, and, in part, of  ER , is supported by the  ERC  grant  614577  " HICCUP -High Impact Cross Section Calculations for Ultimate Precision ".
Instrumentation and data reduction This study uses data from the Cassini magnetometer  (Dougherty et al. , 2004) , Cassini Plasma Spectrometer (CAPS)  (Young et al. , 2004) , Magnetospheric Imaging Instrument (MIMI)  (Krimigis et al. , 2004) , and Radio and Plasma Wave Science (RPWS)  (Gurnett et al. , 2004). Data from the magnetometer is taken from the fluxgate magnetometer at a cadence of 1s and is presented in spherical polar coordinates (Kronographic Radial-Theta-Phi -KRTP), based on the kronographic position of the spacecraft, where e r is along a vector from the planet to the spacecraft, e θ points in the direction of increasing co-latitude, and e φ points azimuthally around Saturn in a prograde direction. Plasma data come from the CAPS suite of instruments, specifically the Electron Spectrometer (ELS) and Ion Mass Spectrometer (IMS).
ELS is a hemispherical top-hat electrostatic analyzer measuring electrons between 0. IMS is a hemispherical top-hat electrostatic analyzer measuring positive ions between 1 and 50 280 eV/q with an energy resolution of 16. 7%. Each of the eight anodes has an angular resolution of 20º×8. 3º providing a 160º×5. 3º instantaneous field-of-view, which as for ELS, is also extended to almost ~2 sr total field-of-view with azimuthal scanning. The highest time resolution data available from IMS is 4s. IMS also has a time-of-flight section to obtain energy-resolved mass per charge spectra with a mass resolution of 12. 5%. Counts were scaled with energy-dependent efficiencies (H. T. Smith, private communication) for H + and W + (taken to be O + ) and with an average of H 2 + and He ++ since they cannot be separated in IMS.
Uncertainties in compositional ratios are based on counting statistics. The MIMI Charge Energy Mass Spectrometer (CHEMS) instrument is used to provide energetic ion composition with an energy range of 3 -220 keV/e, a mass per charge resolution of ~8% and a mass resolution of ~15%. The Low Energy Magnetospheric Measurements (LEMMS) sensor provides energetic ion and electron fluxes from 0. 03 -160 MeV for ions and 0. 015 -5 MeV for electrons. Plasma wave data are provided by the RPWS instrument which includes three nearly orthogonal electric field antennae, in order to detect AC electric fields between 1 Hz and 16 MHz. Calibrated high frequency (kilometric) emissions were produced using the method of  Lamy et al. (2008). Figure  2  presents an overview of the data from rev 37 and 38. In both cases Cassini starts in a region where the plasma electron data is at the instrument noise level, the field strength is high and smoothly varying, and the energetic ion and electron fluxes are at the instrument noise level.
Overview and interpretation Figure  4  presents an overview of the magnetic field, plasma/particle, and plasma wave observations during the 16 January 2007 event. The composition data were derived by summing counts in CHEMS, and in the IMS time-of-flight system, from the straight-through (ST) detector. To improve the counting statistics of the compositional data the counts were summed within the intervals identified in the CAPS/ELS data. Electron pitch angle distributions were analyzed during the course of this study but provide an ambiguous result due to the lack of full pitch angle coverage (the accessible pitch angle range is typically ~45º -170º) in the downward (precipitating) direction, and to significant temporal variability in the data which aliases computed pitch angle distributions.
Energized electrons are observed in ELS and an increase in flux of energetic electrons is recorded by LEMMS. The magnetometer data shows no evidence of field-aligned currents. From 1851 -2341 UT hot electrons and substantial fluxes of energetic electrons are found. The magnetometer data shows some evidence for FAC in a brief interval immediately after 1851 UT where rotations in B φ show evidence for an upward current layer is found, and a more distributed apparent downward layer towards the end of this region, but in light of the cusp motion identified in Figure  4  this downward layer is more likely to originate from the spacecraft moving back through the upward current layer in the opposite direction. The plasma composition is dominated by H + with m/q=2 to H + ratio of 5. 7±0. 9%, although the energetic ion composition is consistent with magnetospheric plasma with W + /H + of 29±1%, H 2 + /H + of 14.
Implications and further work General statements on the physics of the cusp in rapidly rotating giant planet magnetospheres were made in section two. The findings in this report have implications for the study of the jovian high latitude magnetosphere with Juno. Further study and modeling of the cusp in the jovian magnetosphere will be required in order to analyze Juno observations. A detailed survey of this dataset including cusp encounters in the northern and southern hemispheres will be reported in a future paper  (Jasinski et al. , in preparation). situ Ulysses/URAP radio measurements in the solar wind, J. Geophys. Res. 113, A08111, doi:10. 1029/2007JA012979. Tables  Table 1:  Field-aligned distance to the reconnection site during the 16 January 2007 event as estimated from observed ion energy-pitch angle dispersions and a fit to Equation  1.
Acknowledgements All the data used in this paper can be obtained from  NASA 's Planetary Data System at http://pds. jpl. nasa. gov. We thank  Lin Gilbert  and  Gethyn Lewis  for data processing and software support for CAPS at  MSSL/UCL , and  Steve Kellock ,  Leah Nani-Alconcel  and  Peter Slootweg  at  Imperial College  for MAG data processing, and engineers at  University of Iowa  and LESIA for RPWS data processing. CSA thanks  Frank Crary ,  Tom Hill ,  Jared Leisner ,  Hazel McAndrews ,  Don Mitchell ,  Mark Shappirio ,  Todd Smith  and  Rob Wilson  for useful comments and discussions. CSA was supported in this work by an  STFC   Postdoctoral Fellowship  and  Royal Society University Research Fellowship , AJC was.
ACKNOWLEDGMENTS This work was supported by the laboratory  LAMIH CNRS UMR 8201  ( University of Valenciennes), the laboratory LESTE (ENIM, Monastir, Tunisia ) and the  European Commission  within the  International Research Staff Exchange Scheme (IRSES)  in the 7th Framework Programme FP7/2014-2017/ under REA grant agreement n°612230. These supports are gratefully acknowledged.
A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a Massive Gas Injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electrons beam show that if the background electron density is too high, the gas may not penetrate, offering a possible explanation to the recent results of Reux et al.
Still, n e increases by a factor 6 at r = 0. 9 m, as can be seen in Figure  9  (bottom left plot). Looking at Figure  8  again, this simulation (plain line) appears much more consistent with interferometry data than the previous one: the n el increase now has the right order of magnitude. Figure  9  (bottom right) indicates that the cold front is less sharp and penetrates much more slowly than in the previous simulation. It takes about 5 -10 ms for it to reach the q = 2 surface (r = 0. 9 m), which is compatible with the experimental TQ onset time of 11 ms. In conclusion, when including charge exchange and recombination, IMAGINE simulations are rather consistent with experimental measurements. Let us now interprete these results.
VI. ACKNOWLEDGEMENTS We would like to thank  H. Bufferand ,  E. Joffrin ,  B. Pégourié  and  C. Sommariva  for helpful discussions or comments on the manuscript. 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 ITER Organization or the  European Commission.
They also concluded that 7-day travel survey data is a good sampling for individuals' long-term travel-activity patterns. Pas and Koppelman  [4]  applied the Uppsala data to analyse the determinants of day-to-day variability of travel behaviour. They argued that an individual's travel-activity pattern variability is influenced by the variation in their activity needs and by their resources and time constraints. The authors utilized a 5-day travel diary survey data to conduct an empirical analysis of the effect of socio-demographic characteristics on intrapersonal variability in the trip generation rate. They found a large intrapersonal variability in trip generation rates for which there were significant differences between different socio-demographic homogenous groups. Pas and Sundar  [2]  analysed the day-to-day variability of urban travel behaviour based on 3-day travel survey data.
They concluded that there is considerable day-to-day variability in individuals' trip chaining, daily travel time and trip generation rate. Pendyala  [3]  utilized GPS devices to study travellers' day-to-day variability over a six-day period. The author applied the measurements developed by Pas and Sundar  [2]  and Pas and Koppelman  [4]  to quantify the variability of individual travel/activity behaviour. The author found that the degree of intrapersonal variability is influenced by the duration for which travel information is collected. The longer the surveyed period, the greater the level of intrapersonal variability. Apart from these earlier studies, the Mobidrive six-week travel diary dataset  [15]  provides a rich source for several studies  [9] [10] [11] 13]. Schlich and Axhausen  [16]  utilized the Mobidrive data to study variability in day-to-day travel behaviour.
The data The data for the analysis is based on a 7-day mobility survey, conducted in the city of Ghent in 2008  [29]. The objective of the survey was to understand how individuals organize their daily activities and to investigate temporal variations in individual travel-activity patterns. As a 7-day mobility survey is a much more burdensome task compared to a traditional oneday mobility survey, the survey protocol has been carefully designed in order to make it successful. Given the available time and budget constraints, only individuals were surveyed instead of all members of the household. The surveyed individuals were randomly drawn from the population in the city of Ghent based on the stratification of household size, gender, and age of the head of the household (12 to 75). The survey methodology is based on a paper and web survey followed by phone support.
Statistical Analysis Differences in baseline clinical characteristics between low, medium, and high clinical risk groups were assessed by chisquare tests in the case of categorical data or 1-way ANOVA in the case of continuous data. A multivariate Cox proportional hazards regression model was fitted with the following covariates: clinical risk score; PGXscore; treatment; and treatment9PGXscore interaction (full model). The baseline hazard function, H 0 (t), was estimated by dividing the cumulative hazard at the end of follow-up through the exponential function of the mean of the covariates. The cumulative survival under perindopril treatment versus placebo at the median follow-up of 4. 2 years was calculated for each clinical risk score within the separate pharmacogenetic risk strata as follows: S (4.
15  Secondly, the difference in model-based discrimination slopes was evaluated through integrated discrimination improvement (IDI). 16 Calibration of both the model consisting of clinical risk score only and the full model was tested with the Hosmer-Lemeshow (H-L) goodness-of-fit test. All statistical tests were 2-sided with a type I error level of 0. 05, except for the IDI, for which a conservative significance level of 0. 01 was maintainted. 16 e performed a cross-validation within our own data set by bootstrap methods, as suggested by Harrell et al. 17.
Results Complete data on baseline clinical patient characteristics and (pharmaco)genetic profile were obtained for 8726 patients (of which 4338 were allocated to perindopril and 4388 to placebo). Median follow-up was 4. 2 years (interquartile range: 4. 0-4. 5), during which 785 patients (9. 0%) experienced the primary endpoint of cardiovascular mortality, nonfatal MI or resuscitated cardiac arrest. Treatment with perindopril was protective in the overall study population; the number of patients on perindopril treatment that experienced the primary endpoint was 346 (8. 0%) versus 439 (10. 0%) on placebo (adjusted HR: 0. 80; 95% CI: 0. 68-0. 92). Baseline characteristics of the overall study population and various subgroups according to the clinical risk level are provided in Table  1. Interaction between study treatment and clinical characteristics (including concomitant medication) was not found.
Confirmation of our findings in other large data sets would invigorate the presented conclusions and derived clinical implications. The EUROPA trial was powered for detection of treatment benefit for the entire study population irrespective of clinical or pharmacogenetic risk categories. Thus, lack of power cannot be excluded as an explanation for the observed nonsignificant treatment benefit in the higher PGX scores. On the other hand, it must be noted that the absolute numbers of study participants in PGXscores ≥2 are higher than those below (Table  3 ). Patients enrolled in the EUROPA trial primarily consisted of Caucasian males without overt heart failure, who were randomized to placebo or perindopril 8 mg daily. The generalizability of the presented results toward other patient groups, for example, those with a higher proportion of women, heart failure, patients of other ethnicities, or those using other ACE inhibitors or lower dosages of perindopril, may therefore be limited.
Finally, our risk model does not contain data on circulating serum biomarkers other than total cholesterol and creatinine levels. A prespecified substudy of the EUROPA trial, called PERTINENT, actually did study bradykinin and angiotensin II, but also markers of endothelial function (nitric oxide synthase) and inflammation (C-reactive protein, tumour necrosis factor alpha, and von Willebrand factor). Use of perindopril was reflected in various circulating biomarker levels, which were interpreted as a biochemical indication of normalization of the angiotensin II/bradykinin balance, reduction of inflammation, and prevention of endothelial apoptosis. 24, 25 Unfortunately, the cohort in which these biomarkers were assessed was too small in order to properly study the interaction between the various serum biomarkers and treatment effect by perindopril on clinical endpoints.
Introduction Latest measurements of cosmic microwave background (CMB) anisotropies in temperature and polarization from Planck satellite, a1 complemented at smaller scales by recent ground-based experiments (see e. g. Refs. [2, 3, 4, 5]) and combined with other cosmological information coming from e. g. type-Ia supernovae, galaxy and galaxy cluster surveys, have reached high precision in estimating all the parameters that describe the current so-called standard cosmological model. Far from representing a fully, physically exhaustive interpretation of the Universe properties, the cosmological constant plus cold dark matter (ΛCDM) model phenomenologically describes reasonably well existing data with a simple set of six parameters (see e. g. the lectures by M. Bersanelli and J. -L. Puget on Planck results b in this Meeting).
b  This paper is based largely on the products available at the ESA Planck Legacy Archive and publicly available publications by ESA and the Planck Collaboration, for what concerns the related aspects. Any material presented here that is not already described in Planck Collaboration papers represents the views of the authors and not necessarily those of the Planck Collaboration. Cosmology and fundamental physics with microwave surveys 3 scale pattern (i. e. low multipole region) of CMB anisotropy angular power spectrum (APS) is investigated to link the inflationary phase to the string theory while CMB data and galaxy surveys are jointly analyzed in Sect. 4 to constrain inflationary models predicting localized 'features' in the primordial power spectrum (PPS). Going beyond power spectrum (PS) analyses, the study of primordial non-Gaussianity (PNG), discussed in Sect.
5, allows to test mechanisms for the generation of primordial perturbations in the early Universe. Sects. 6 and 7 discuss two important topics in dark matter studies, respectively the bounds on neutrinos masses and parameters and on thermal axion mass from cosmological data while Sect. 8 summarizes the state of the art on the neutron lifetime, τ n , a fundamental quantity in nuclear physics. Attention is given to the comparison with laboratory experiment results. Sect. 9 is devoted to the test of the Einstein equivalence principle (EEP), at the basis of general relativity (GR), through the analysis of the cosmic polarization rotation (CPR) and to the comparison of results from astronomical and CMB based analyses. Finally, Sect. 10 discusses the main cosmological and fundamental physics information contained in the CMB spectral distortions in the light of the contribution expected from the Square Kilometre Array (SKA).
The late ISW effect is active at more recent times: focusing on scales corresponding to galaxy clusters, the CMB photons get redshifted by the time-dependent gravitational potentials. The potentials causing the late ISW also give rise to the weak lensing distortions: the interplay between these two effects results in a non-Gaussian correlation between small and large angular scales, which is encoded in the lensing-induced bispectrum. The optimal detection 9 of the ISW effect is made by the cross-correlation of the CMB temperature anisotropies with tracers of the gravitational potential, like, for instance, galaxy catalogues. The first detection  12  was made using Wilkinson Microwave Anisotropy Probe (WMAP) data and radio and X-ray galaxy catalogues. The ISW signal is very weak (an ideal LSS tracer could provide a detection of up to ≈ 8 σ), and, therefore, its capability to constrain cosmological parameters, is very limited.
19 Fig. 1  shows the ISW fluctuation maps obtained from the full cross-correlation of the Planck SEVEM CMB map with NVSS, WISE-AGN, WISE-GAL, SDSS-CMASS/LOWZ, SDSS-MphG, and the Planck lensing LSS tracers. All these results were obtained without using CMB polarization information (except for the estimation of the Planck lensing map). In principle, including polarization could increase the ISW detection 20 around a 15%, however, the current CMB polarization data from Planck is high-pass filtered at the largest angular scales ( 5 • ), which are the most important ones in this context. Including the large scale polarization is, perhaps, the most important remaining aspect within the context of the ISW study, at least, from the CMB side. On the other side, future galaxy surveys like Euclid, 21 J-PAS  22  or LSST,  23  among others, will provide accurate galaxy catalogues, probing very large volumes, allowing to perform, for instance, a tomographic ISW detection.
A complementary approach consists in stacking the CMB fluctuations in the position of known structures, such as voids and clusters, as done initially on WMAP data 24 using a catalogue (GR08) of super-structures from SDSS. An anomalous ISW signal, incompatible with the standard ΛCDM model was found, confirmed in the Planck analyses, showing that the intensity of the detected signal (≈ -11 µK for voids, and ≈ +8. 5 µK for clusters) and the scale at which that signal is maximum (≈ 3. 5 • for voids, and ≈ 4. 5 • for clusters) are, indeed, unexpected. At these scales, the current CMB Planck polarization map still retains certain signal, despite the high-pass filtering and, therefore, it can be used to test further the nature of this anomalous signal. The key point is that, if this signal is caused by the ISW effect, and, therefore, originated by a gravitational secondary CMB anisotropy, a negligible contribution of the CMB polarization is expected.
In fact, no associated polarization is found in Planck data, although the diminishing of the signal caused by the high-pass filtering limits any strong conclusion. Anyway, the current polarization data are not in contradiction with assuming that the emission coming from these GR08 structures provides an anomalous ISW signal. Studying the stacked fluctuations of the Planck lensing map on the GR08 positions also supports this hypothesis. In fact, at least for the voids, a clear correlation between the lensing gravitational potential and the position of the super-structures is found. This kind of studies could be further extended once Planck provides its next and final release, which will include polarization information at all the angular scales.
Parametrization of early and late ISW and data analysis The ISW amplitude can be parametrized in terms of A eISW and A lISW , which rescale the contribution of the ISW to the temperature anisotropies. A Markov-chain Monte-Carlo (MCMC) analysis was performed with a baseline standard ΛCDM model and flat priors on the parameters. 25 We also check the impact of a Gaussian prior A lISW = 1. 00±0. 25, consistent with the 68% confidence level (C. L. ) bounds on the same parameter from the estimation of the ISW-lensing bispectrum, which has been obtained by cross-correlating the Planck CMB maps with the Planck map of the lensing potential. Various datasets were tested: the high-Planck temperature and temperature+polarization APS in the range 30 ≤ < 2500 (hereafter T T and T T, T E, EE, respectively) in combination with the low-Planck temperature and polarization APS in the range 2 ≤ < 30 (lowP).
We also tested the WMAP APS including both temperature and polarization up to = 1200. Planck T T + lowP data provide tighter constraints than WMAP on the early ISW (A eISW = 1. 064 +0. 042 -0. 043 vs. A eISW = 1. 007 +0. 056 -0. 058 at 68% C. L. ), and present a 1σ evidence of A eISW = 1 that is stable when considering the extensions of the ΛCDM model shown in Fig. 2. Regarding the late ISW, Planck data place a constraint A lISW < 1. 14 at 95% C. L. : Planck alone does not improve significantly the constraint on A lISW with respect to WMAP data (which give A lISW = 0. 958  +0. 391  -0. 317 at 68% C. L. ). In fact, the late ISW affects angular scales that are dominated by cosmic variance, rather than by instrumental noise. Adding the prior on A lISW coming from CMB temperature anisotropies-weak lensing correlations, we find a ∼ 4σ detection A lISW = 0.