dataesr/scientific-paragraphs-categorization huggingface dataset:
From raw timestreams to maps The data were reduced using the Python-based BOlometer data Analysis Software package  (BOA v4. 1 -Schuller 2012) , following the prescription outlined in §10. 2 and §3. 1 of  Siringo et al. (2009)  and  Schuller et al. (2009) , respectively. We briefly outline the reduction steps below. -Timestreams for each scan were calibrated onto the Jy beam -1 scale using primary or secondary flux density calibrators. -Channels exhibiting strong cross talk with their neighbors, showing no signal or high noise were flagged, whilst the remaining channels were flatfielded. -Timestreams were flagged in regions where the speed and acceleration of the telescope are too severe to guarantee reliable positional information at every timestamp. -In an iterative manner, the following sequence was performed: 1.
Source extraction We used a custom-written Interactive Data Language (idl -Landsman 1993) source extraction algorithm to identify and extract sources in the beam-smoothed S/N maps, noting that the beam-smoothing step described above optimizes the detection of point sources. In a top-down fashion, we searched for pixels above 24  our floor S/N detection threshold Σ thresh > 3. 5σ. In Table  B1 , we catalog the peak flux density, noise and position determined from a three-parameter Gaussian fit made inside a box of width θ (i. e. ≈ 27 ′′ ) centered on a source. After removing the fit from the map, we searched for and cataloged subsequent peaks until no more could be found. During the extraction process we performed some additional steps: sources deemed too close to each other (∆r < θ/2) have their parameters re-evaluated, fitting multiple three-parameter Gaussians simultaneously; sources deemed too close (∆r < θ/2) to the map edges were rejected.
ACKNOWLEDGMENTS AJRL, RJI, JMS, IO, LD, VA and ZYZ acknowledge support from the  European Research Council (ERC)  in the form of Advanced Grant,  321302 , cosmicism. H. D. acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  under the  2014 Ramón y Cajal program  MINECO  RYC-2014-15686. JLW acknowledges support from an  STFC   Ernest Rutherford Fellowship. D. R. acknowledges support from the  National Science Foundation  under grant number  AST-1614213. GDZ acknowledges support from  ASI/INAF  agreement n. 2014-024-R. 1. We pay special thanks to the useful feedback provided on the draft version of this work to  D. Farrah ,  J. Greenslade ,  M. J. Michalowski  and  I. Valtchanov. This research has made use of data from  HerMES  project (http://hermes.
Acknowledgments: We thank  M. Poullain  for her help in producing fig. S1. Funding: This work was supported by  European Research Council  advanced grant  290853-XCHEM  within the  seventh framework program  of the  European Union. We also acknowledge the financial support from  MINECO  projects  FIS2013-42002-R  and  FIS2016-77889-R , and the  European COST (Cooperation in Science and Technology) Action   XLIC CM1204 , and the computer time from the  Centro de Computación Científica de la Universidad Autónoma de Madrid and Marenostrum Supercomputer Center. A. P. acknowledges a Ramón y Cajal contract from the  Ministerio de Economía y Competitividad (Spain). F. M. acknowledges support from the  "Severo Ochoa" Programme for Centres of Excellence in R&D (MINECO , grant  SEV-2016-0686 ) and the " María de Maeztu "  Programme for Units of Excellence in R&D  ( MDM-2014-0377 ).
An exception is, of course, represented by the case of SM portals which are completely specified by just two parameters, i. e. the DM mass and couplings; • Our analysis will be mostly focused on the comparison between DM relic density and Direct Detection, especially in light of the incoming data releases of next generation multi-TON detectors. Collider and Indirect Detection limits will be reported only when their are competitive or feature a clear complementarity with DD limits. Within these guidelines we will review a multitude of models which have been classified in five categories: 1. SM portals: Here the portal of DM interactions is represented by the Higgs or the Z-boson. As already pointed this scenarios are the most predictive since have only two free parameters. On the other hand this turns also in the most stringent limits so that these scenarios are strongly disfavored already by present limits; 2.
Acknowledgements A. Annihilation cross-sections ACKNOWLEDGEMENTS The authors thank  Werner Rodejohann ,  Miguel Campos ,  Alexandre Alves ,  Carlos Yaguna  and  Chris Kelso  for discussions. The authors also thank  Francesco D'Eramo  for his valuable comments. P. G. acknowledges the support from  P2IO Excellence Laboratory (LABEX). S. P. 's work was partly supported by the  U. S. Department of Energy  grant number  DE-SC0010107. This work is also supported by the  Spanish MICINN 's  Consolider-Ingenio 2010 Programme  under grant  Multi-Dark   CSD2009-00064 , the contract  FPA2010-17747 , the  France-US PICS  no. 06482  and the  LIA-TCAP of CNRS. Y. M. acknowledges partial support the  ERC  advanced grants Higgs@LHC and MassTeV. This research was also supported in part by the  Research Executive Agency (REA)  of the  European Union  under the Grant Agreement PITN- GA2012-316704  ("HiggsTools").
Discussion This work has been initiated to characterize the nature of the diffusion of molecules binding the inner leaflet of the cell plasma membranes by means of FRAP experiments. In a first attempt, we decided to compare experimental data obtained with the PH domain of EFA6 expressed in BHK cells to FRAP curves generated from anomalous subdiffusive particles numerically simulated. Then, we analyzed the recoveries with three different diffusion models, namely the free Brownian motion (Bm), the restricted Brownian motion (rBm) and the CTRW anomalous subdiffusion (aDm). Four parameters can be extracted from these diffusion models. The Brownian diffusion coefficient D and the mobile fraction M (M = 1 in the case of Bm) on one side, and the anomalous subdiffusion exponent α and its related anomalous diffusion coefficient D α on the other side.
Acknowledgements The content of Section 3 was conceived as I was contemplating the Charles River from the window of Pr. Richard P. Stanley 's office in the  Department of Mathematics at MIT. I want to express to him my gratitude for letting me use this spacious, quiet, and inspiring place. Partial support of the  ANR-DFG  grant  TurbMix  ( ANR-14-CE35-0031-01 ) is acknowledged.
Structural and magnetic characterization The structural properties of the Pr Co H x 2 7 samples were characterized using X-ray difraction (XRD). Diffraction patterns were recorded on a Bruker diffractometer with CuK α radiation. The counting rate was 22 s per scanning step and the step size was 0. 04°. An internal Si standard (NBS, SRM 640) was used to measure the unit cell parameter with an accuracy of ± × - 1 10 4 Å. Possible space groups and structural parameters for the hydride phases were determined using the Dicvol06  [16] , N-TREOR  [17, 18] , EXPO2004  [19]  packages. The XRD data was refined using Rietveld method  [20, 21]  and RIETAN-2000 programs  [22, 23] , with the assumption of a peak line profile of Thompson-Cox-Hastings allowing multiple phase refinement of each of the coexisting phases  [24, 25].
Multivariate selection Once the initial selections are implemented, a multivariate analysis (MVA) is applied to further discriminate between signal and combinatorial background. The implementation of the MVA is performed with the TMVA package  [41, 42] , using the B 0 → D 0 π + π - normalisation channel to optimise the selection. For this purpose only, a loose PID criterion on the pions of the π + π -pair is set to reject the kaon and proton hypotheses. The sPlot technique  [43]  is used to statistically separate signal and background in data, with the B 0 candidate invariant mass used as the discriminating variable. The sPlot weights (sWeights) obtained from this procedure are applied to the candidates to obtain signal and background distributions that are then used to train the discriminant.
Default fit and robustness tests The default fit to the data is performed, using the MINUIT/MINOS  [52]  and the RooFit  [53]  software packages, in the mass-range m D 0 h + h -∈ [5115, 6000] MeV/c 2. The fit results are given in Table  3. An unconstrained fit to the m D 0 π + π -distribution returns a negative B 0 s → D * 0 K -π + yield, which is consistent with zero within statistical uncertainties (-2167 ± 1514 events), while the expected yield is around 1. 8% that of the signal yield, or 540 events (see Table  1 ). The B 0 s → D * 0 K -π + contribution lies in the lower mass region, where background contributions are complicated, but have little effect on the signal yield determination. In the fit results listed in Table  3 , this contribution is fixed to be 540 events. The difference in the signal yield with and without this constraint amounts to 77 events, which is included as a systematic uncertainty.
The results obtained for the other backgrounds are consistent with the estimated relative yields computed in Sect. 4. 1. The fit uses Gaussian constraints in the fitted likelihood function for the yields of the modes Λ 0 b → D 0 pK -, Ξ 0 b → D 0 pπ -, and Λ 0 b → D 0 pπ -, as explained in Sect. 4. 5. The fitted signal yields are N B 0 →D 0 π + π -= 29 943 ± 243, N B 0 →D 0 K + K -= 1918 ± 74, and N B 0 s →D 0 K + K -= 473 ± 33 events respectively, and the ratio r B 0 s /B 0 ≡ N B 0 s →D 0 K + K -/N B 0 →D 0 K + K -is (24. 7 ± 1. 7)%. The ratio r B 0 s /B 0 is a parameter in the fit and is used in the computation of the ratio of branching fractions thus observed with an overwhelming statistical significance. The χ 2 /ndf for each fit is very good. The data distributions and fit results are shown in Figs.
There is some possible accumulation of candidates in a broad structure around 1. 7 GeV/c 2 , which may correspond to the φ(1680) state. In addition, in the D 0 K -system, the D * s2 (2573) -resonance is identifiable. An analysis with additional LHCb data will enable the study of D * * s spectroscopy, particularly those resonances that are natural spin-parity members of the 1D and 1F families. The differences between the B 0 and B 0 s modes are also interesting. In addition, different resonances can contribute strongly with respect to B 0 s → D 0 K -π + decays  [23, 62].
The HRTP profiles are retrieved with very good vertical resolution of ~200 m and high accuracy of ~1-3 K for altitudes of 15-32 km and with a global coverage. The best accuracy is achieved in in-orbital-plane occultations, and the accuracy weakly depends on star brightness. The whole GOMOS dataset has been processed with the improved HRTP inversion algorithm using the FMI's Scientific Processor; and the dataset (HRTP FSP v1)  is in open access. The validation of small-scale fluctuations in the retrieved HRTP profiles is performed via comparison of vertical wavenumber spectra of temperature fluctuations in HRTP and in collocated radiosonde data. We found that the spectral features of temperature fluctuations are very similar in HRTP and collocated radiosonde temperature profiles. HRTP can be assimilated into atmospheric models, used in studies of stratospheric clouds and in analysis of internal gravity waves activity.
The main advantage of HRTP is its vertical resolution, which is ~200-250 m. Such resolution allows probing gravity wave (GW) spectra. The validation of the small-scale structure of HRTP is therefore an important issue before using the data in GW research. The validation of the small-scale structure is a challenging task, because temperature fluctuations are rapidly varying due to gravity wave activity. Sofieva et al. (2008 Sofieva et al. ( , 2009c) )  proposed to use spectral analysis for validation of small-scale structure in temperature profiles, as this approach allows using measurements separated by several hundreds of kilometers and by several hours. The previous validation has been performed on HRTP processed by the FMI scientific processor (analogous to the ESA IPF v6 algorithm) using collocated radiosonde data.
It has shown that the small-scale fluctuations in HRTP have similar rms as in collocated radiosonde profiles, for vertical (in orbital plane) occultations of bright stars  (Sofieva et al. , 2009c). In case of oblique occultations or dim stars, the HRTP fluctuations are of larger amplitude than those of in-situ measurements. An analogous study with the method  (Sofieva et al. , 2009c)  but applied to much larger datasets of GOMOS HRTP IPF v6 and collocated radiosonde profiles has shown that fluctuations in HRTP v6 are nearly always larger than in the collocated radiosonde data (for both vertical and oblique occultations). Therefore, IPF v6 HRTP data were not recommended for gravity wave analyses. This will be illustrated in Section 4 of this paper. In this paper, we introduce an improved version of the GOMOS HRTP algorithm and present the whole GOMOS HRTP dataset processed using the FMI's Scientific Processor, HRTP FSP v1.
The weak singularity of the integrand at the lower limit does not cause problems for a numerical realization: the singularity can be estimated or the midpoint product integration method can be applied. The upper limit should be chosen high enough (~120 km), therefore the refractive angle profile calculated using ECMWF&MSIS data is used at altitudes above HRTP range in the processing. Application of Abel integration requires monotonous impact parameter. This requirement can be violated, because the impact parameter is computed using measured (noisy) refractive angle. In the current implementation, the impact parameter is computed using the smoothed refractive angle and its monotonicity is checked. Real geometric (tangent) altitudes can be determined as () p hR np  ( 14 ) where R is the local radius of curvature of the Earth surface.
, 2009c)   In this section, we show illustrations of applications of HRTP for analyses of gravity waves. The spatio-temporal distributions are presented only for illustrations that new HRTP dataset provide valuable geophysical information, which is in agreement with analyses using other datasets. The gravity wave potential energy per unit mass is defined as In our analysis, smooth profiles  radiosonde, lidar and GPS radio-occultation measurements, both qualitatively and quantitatively  (Allen and Vincent, 1995; de la Torre et al. , 2006; Sofieva et al. , 2009a; Tsuda et al. , 1991 Tsuda et al. , , 2000)). The overall distribution reproduces the known features: strong GW activity close to the edge of polar vortex, a peak near Antarctic Peninsula in local winter. Enhancements in equatorial regions are clearly observed, analogous to those found in global analyses of radio-occultation data (de la Torre  et al.
Detailed analyses of gravity wave distributions using HRTP might be the subject of future works and publications. We described the improved algorithm for high-resolution temperature and air density profiling using the bi-chromatic scintillation measurements. This method allows temperature profiling in the altitude range 10-35 km with the vertical resolution of ~ 250 m and accuracy in the stratosphere of 1-2 K. The retrieval algorithm is applied to the whole GOMOS/Envisat dataset, and the new GOMOS HRTP FSP v1 data set is now available. The best accuracy is achieved for vertical occultations bright stars. The uncertainty of HRTP retrievals depends weakly on stellar brightness. The spectral analysis of HRTP and collocated radiosonde profiles has been applied for validation of small-scale fluctuations.
HRTP can be assimilated into atmospheric models, used in studies of stratospheric clouds and in analysis of internal gravity waves activity. As an illustration of application of HRTP for gravity wave research, GW potential energy has been evaluated using the GOMOS HRTP dataset. The obtained spatio-temporal distributions of GW potential energy are in good agreement with previous analyses using other datasets. This paper is dedicated mainly to the retrievals, validation and geophysical assessment of small-scale fluctuations in the retrieved GOMOS high-resolution temperature profiles. However, HRTP can be smoothed down to lower resolution, and used in other analyses, including analyses of temperature trends, in combination of 10-years GOMOS HRTP data with other limb profile temperature measurements.
Acknowledgements The authors thank  ESA  for the GOMOS data and support through a dedicated project on improvement of HRTP data. The thank the whole GOMOS team for many years of fruitful collaboration. The work of  V. F. Sofieva  has been supported by the  Academy of Finland ,  Centre of Excellence of Inverse Modelling and Imaging. The work of V. Kan has been supported by the  Russian Foundation for Basic Research , grant  16-05-00358. We thank the  Academy of Finland , project  TT-AVA , for supporting the collaboration between FMI and A. M. Obukhov Institute of Atmospheric Physics, Russia. The authors thank the FMI summer student  Teemu Tiinanen  for his contribution to HRTP analyses.
This persistent auroral observational disparity with the PPO model is intriguing, because the perturbation field model continues to fit with independent, long-time SKR and magnetic FAC signatures measured by Cassini (e. g. Provan et al. , 2016) , which we expect to be closely associated with auroral emission production. As expected we found the total northern UV intensity in early 2014 to peak around an SKR phase of ΦNSKR = 0°. It is likely that non-PPO dynamics have affected the observed modulation pattern in this limited dataset, which we suggest may also be an important factor to consider in similar analyses. For example, 10 out of 60 2014 HST images show, atypically, absence of any dawn arc emission coincident at magnetic longitudes around ΨN = 90° (see annotated images in Supporting Information S1 and  Kinrade et al.
Acknowledgements This work is based on observations made with the NASA/ESA Hubble Space Telescope (observation ID: GO13396), obtained at the Space Telescope Science Institute (STScI), which is operated by AURA, Inc. , for NASA. The Hubble observations are available from the MAST (https://archive. stsci. edu/hst/) or APIS (https://apis. obspm. fr) repositories, supported by the STScI (http://www. stsci. edu/hst. SKR phase data can be retrieved from publications and are also available from the webpage http://www. lesia. obspm. fr/kronos/skr_periodicity. php. All PPO phase data  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017).
H. E. S. S. observations and analysis The High Energy Stereoscopic System (H. E. S. S. ) is an array of imaging atmospheric Cherenkov telescopes located at 1800 m altitude in the Khomas highlands of Namibia. The H. E. S. S. array is designed to detect and image the brief optical Cherenkov flash emitted from air showers, induced by the interaction of VHE gamma rays with the atmosphere of the Earth. In the first phase of H. E. S. S. , during which the data used here were recorded, the array consisted of four 13 m telescopes placed on a square of 120 m side length. Gamma-ray events were recorded when at least two telescopes in the array were triggered in coincidence  (Funk et al. 2004) , allowing for a stereoscopic reconstruction of gamma-ray events (for further details see  Aharonian et al. 2006a).
Recently H. E. S. S. has entered its second phase with the addition of a fifth, large 28 m telescope at the centre of the array. The addition of this telescope, which is able to trigger both independently and in concert with the rest of the array, increases the energy coverage of the array to lower energies. The work presented in the following sections does not use data recorded with the large telescope. Table  1 : Overview of the H. E. S. S. observation campaigns. The livetime given in hours corresponds to the data fulfilling quality requirements. year mean offset (1)  (1) Mean angular distance between the H. E. S. S. observation position and the nominal centre of the SNR taken to be at R. A. : 17 h 13 m 33. 6 s , Dec. : -39 d 45 m 36 s. The RX J1713. 7-3946 data used here are from two distinct observation campaigns.
This procedure yields a total dead-time corrected exposure time of 164 hours for the source morphology studies. For the spectral studies of the SNR, a smaller data set of 116 hours is used as explained below. The data analysis is performed with an air-shower template technique (de Naurois & Rolland 2009), which is called the primary analysis chain below. This reconstruction method is based on simulated gamma-ray image templates that are fit to the measured images to derive the gamma-ray properties. Goodness-offit selection criteria are applied to reject background events that are not likely to be from gamma rays. All results shown here were cross-checked using an independent calibration and data analysis chain  (Ohm et al. 2009; Parsons & Hinton 2014).
The statistical quality of the XMM-Newton data is in any case very high such that the uncertainties (well below 1%) are negligible compared to those of H. E. S. S. The two dominant sources of systematic effects when measuring r 0 in the XMM-Newton data are uncertainties in the true angular resolution of H. E. S. S. (relevant for the PSF smearing), and the estimation of the level of the Galactic diffuse background in the X-ray map. The first issue is addressed using a conservative H. E. S. S. PSF model that is broadened to cover systematic uncertainties. The latter issue is again taken into account by the constant c in the fit function (Eq. 1) that compensates for flat homogeneous offsets in the map. An additional parameter, δr 1/e , is listed in Table  2  for regions 2, 3, and 4. This parameter is defined as the radial distance between the peak shell emission, which is assumed to be the position of the shock, and the position where the peak emission has dropped to 1/e of its value.
GeV data analysis For the Fermi-LAT analysis, 5. 2 years of data (4 August 2008 to 25 November 2013) are used. The standard event selection, employing the reprocessed P7REP SOURCE V15 source class, is applied to the data, using events with zenith angles less than 100 • and a rocking angle of less than 52 • to minimise the contamination from the emission from the Earth atmosphere in the Fermi-LAT field of view  (Abdo et al. 2009). Standard analysis tools, available from the Fermi-LAT Science support centre, are used for the event selection and analysis. In particular, a 15 • radius for the region of interest and a binned analysis with 0. 1 • pixel size is used. The P7REP diffuse model  (Acero et al. 2016a)   3  and background source list, consistent with the official third source catalogue (3FGL,  Acero et al.
Derivation of the present-age parent particle distribution To understand the acceleration and emission processes that produce the observed data, the present-age particle distribution can be derived by fitting the observed energy spectra. We present below the results of the derivation of the parent particle population for both a hadronic and leptonic model for the whole remnant, for the two halves of the remnant, and for the spatially resolved spectra extracted from the 29 regions described above in Sect. 4. 2. For the full and half remnant spectra, the gamma-ray spectra from Fermi-LAT and H. E. S. S. are used in the fitting, whereas only the H. E. S. S. spectra are used for the 29 spatially resolved regions because the Fermi-LAT data lacks sufficient statistics when split into these regions.