dataesr/scientific-paragraphs-categorization huggingface dataset:
Fluorescence detector The data acquisition for the FD telescopes is organized by site to insure against disruption of data collection due to possible communication losses between the CDAS and the remote detectors. The data transport for FD monitoring is organized via a database internal replication mechanism. This mechanism recognizes communication problems and recovers submitted database changes when the connection is reestablished. This guarantees completeness of the dataset on the central server, even if the information is not immediately available online during network failures. Figure  22  shows a schematic layout of the databases. The information collected for the supervision of the FD operation is organized into five sections. The calibration section contains the information from the different levels of calibration as described in Section 4.
Los Leones Los Leones Los Morados Los Morados Loma Amarilla Loma Amarilla HEAT HEAT Server@Campus Server@Campus  Their information helps judging the atmospheric conditions at the site, which is vital for the operation of the telescopes. The data collected in the database can be used to derive higher level quantities such as the on-time of the FD telescopes. This quantity is of major importance since it is a necessary ingredient of flux measurements. The dead time of each telescope is also recorded in the database. Together with the run information and other corrections retrieved from the database, the total on-time for each telescope can be determined individually. The on-time is calculated only for time intervals of ten minutes, balancing the statistical precision of the calculated on-time due to statistics with the information frequency.
2 and those for identifying and determining clouds and the general extinction above the Observatory in Section 9. 3. An extensive system of atmospheric monitoring devices has been installed (Figure  29 ). The types of measurements possible with these instruments are listed in Table 2. 29. Some of these stations are used for operational control of the nearby installations. The data from the weather stations at each FD site and at the CLF additionally serve as atmospheric ground information in several parts of the air shower reconstruction. Typically, those data are transferred via the central campus in Malargüe, processed and stored in our databases for atmospheric monitoring information (cf. Section 9. 5) within a couple of days. The weather stations are commercial products 3 equipped with temperature, pres-sure, humidity, and wind speed sensors recording data every 5 minutes.
GDAS is the result of atmospheric computer analyses and forcasts which are run serveral times per day and are based on meteorological measurements from all around the world. The data are available in 3-hourly, global, 1 chosen GDAS grid point is marked in Fig. 29. Each GDAS dataset consists of surface data together with data for 23 constant pressure levels reaching up to about 26 km. For the purpose of the Auger Observatory, maily the information for temperature, pressure and humidity are used. These GDAS data have been compared with according local radio soundings and the records from the ground-based weather stations. The agreement of the locally recorded data with the GDAS data for the given grid point is well enough for the application in air shower physics, c. f. Fig. 30. The variation for temperature is below ±1 K for altitudes between ground and about 20 km a.
Atmospheric databases The atmospheric monitoring data are organized into MySQL databases that are accessed by the Auger offline analysis package for air shower reconstruction, condition assessment, and aperture estimates. The cloud information includes the IR cloud camera distributions mapped onto the FD pixels and the hourly measurement of the fraction of the sky covered by cloud measured by the lidars. An hourly cloud coverage below 20% is required for hybrid events to be used in the analysis of the mass composition and energy spectrum of the cosmic rays  [21, 38]. The aerosol optical database contains, for each FD site, hourly τ aer (h) profiles in 200 m steps and derived from CLF or XLF laser shots. FD data used for cosmic ray publications is required to be collected during hours with τ aer (3 km a.
g. l. )< 0. 1. FD data recorded during hours without τ aer (h) profiles due to extremely poor viewing conditions or technical problems are not used for publications. This database spans nearly 10 years. The molecular database contains the atmospheric state variables measured on the ground by the weather stations and the vertical profiles derived from BLS weather balloons and more recently from the Global Data Assimilation System, provided from NOAA's  5  National Centers for Environmental Prediction (NCEP)  [77]. These atmospheric data are available for a position close to the Auger array in three hour intervals. Data updates can be obtained once per week and are filled automatically into the Auger atmospheric monitoring database. Such atmospheric state variables are applied as standard in the air shower reconstruction, mainly during the description of the fluorescence light emission and the Rayleigh scattering of that light on its path between emission point at the air shower and the fluorescence telescopes, since mid 2011.
Using the FD energy resolution of 7. 6 %, the resulting SD energy resolution with its statistical uncertainty is σ E SD /E SD = (16±1)% at the lower energy edge in Figure  41  and (12±1)% at the highest energies. Due to the large number of events accumulated until December 2012, the systematic uncertainty on the SD energy due to the calibration is better than 2 % over the whole energy range. The systematic uncertainties in the energy scale, shown in Table 3, are dominated by the absolute FD calibration  [124]. Further consistency checks are performed by joint calibration campaigns with the Telescope Array  [125, 126]. The dataset recorded extends up to larger angles of 90 •. For the inclined events, with zenith angles larger than 60 • we employ a different reconstruction method. More details on the reconstruction of inclined events can be found in  [123, 127, 128].
Data losses and inefficiencies can occur on different levels, from the smallest unit of the FD, i. e. , one single photomultiplier (pixel) readout channel, up to the highest level, i. e. , the combined SD/FD data taking of the Observatory. The active time of the FD data acquisition is calculated using a minimum bias data stream with a less restrictive trigger condition. Since July 2007, the relevant information concerning the status of the FD detector has been read out from the Observatory monitoring system (see  Section 7. 4 ). An on-time dedicated database has been set up by storing the average variances and the on-time fraction of individual telescopes in time bins of 10 minutes. The information on the veto due to the operation of the lidar or to an anomalous trigger rate on FD together with the status of the CDAS needed to form a hybrid event are also recorded.
Data processing and Off line framework 8. 1 Physics modules. 8. 2 Data access. 8. 3 Configuration. 8. 4 Utilities, testing and quality control, and build system. 8. 5 Off line summary. Installations for atmospheric state variables. 9. 1. 1 Ground-based weather stations. 9. 1. 2 Balloon Launching Station. 9. 2 Installations for atmospheric transmission. 9. 2. 1 Aerosol optical depth profiles and clouds: CLF and XLF. 9. 2. 2 Aerosol Phase Function monitors. 9. 2. 3 Horizontal Attenuation Monitor. 9. 3 Installations for clouds and extinction. Rapid monitoring. 9. 5 Atmospheric databases. 9. 6 Interdisciplinary atmospheric measurements. Shower arrival direction. 11. 5 Energy calibration. 12 Performance characteristics of the Observatory 12. 1 Key performance parameters. 12. 2 Surface detector performance.
1. 05/2. 1. 00/03. 0058and the  Czech Science Foundation  grant  14-17501S ,  Czech Republic ;  Centre de Calcul IN2P3/CNRS ,  Centre National de la Recherche Scientifique (CNRS) ,  Conseil Régional Ile-de-France ,  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS ),  Département Sciences de l'Univers (SDU-INSU/CNRS) ,  Institut Lagrange de Paris ,  ILP LABEX ANR-10-LABX-63 , within the  Investissements d'Avenir Programme   ANR-11-IDEX-0004-02 , France;  Bundesministerium für Bildung und Forschung (BMBF) ,  Deutsche Forschungsgemeinschaft (DFG) ,  Finanzministerium Baden-Württemberg ,  Helmholtz Alliance for Astroparticle Physics (HAP) ,  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Ministerium für Wissenschaft und Forschung ,  Nordrhein Westfalen, Ministerium für Wissenschaft, Forschung und Kunst, Baden-Württemberg, Germany ;  Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) ,  Gran Sasso Center for Astroparticle Physics (CFA) ,  CETEMPS Center of Excellence, Italy ;  Consejo Nacional de Ciencia y Tecnología (CONACYT) ,  Mexico ;  Ministerie van Onderwijs, Cultuur en Wetenschap ,  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Stichting voor Fundamenteel Onderzoek der Materie (FOM) ,  Netherlands ;  National Centre for Research and Development , Grant Nos.
8 Data processing and Off line framework 8. 1 Physics modules. 8. 2 Data access. 8. 3 Configuration. 8. 4 Utilities, testing and quality control, and build system. 8. 5 Off line summary. Installations for atmospheric state variables. 9. 1. 1 Ground-based weather stations. 9. 1. 2 Balloon Launching Station. 9. 2 Installations for atmospheric transmission. 9. 2. 1 Aerosol optical depth profiles and clouds: CLF and XLF. 9. 2. 2 Aerosol Phase Function monitors. 9. 2. 3 Horizontal Attenuation Monitor. 9. 3 Installations for clouds and extinction. Rapid monitoring. 9. 5 Atmospheric databases. 9. 6 Interdisciplinary atmospheric measurements. Shower arrival direction. 11. 5 Energy calibration. 12 Performance characteristics of the Observatory 12. 1 Key performance parameters. 12. 2 Surface detector performance.
This component will receive data through a fast I 2 C bus up to 100MHz, and will control with high accuracy the position of each motor through a full mosfet-based bridge system. The real position of each motor will be provided to the micro-controller thanks to a magnetic encoder system embedded in each motor. This information will allow the microprocessor to modulate in real time the velocity of each motor, accomplishing the time constraints demanded by the Master system in order to follow the collision avoidance algorithm. On the other hand, this information will allow the system to know the existence of real movements and registering each time the existence of movement errors (hard motor fault, arrival to a physical zero, etc). To optimise the consumption of the electronics, the microcontroller can run in two modes, i.
As hown in the figure, the CCD camera has been mounted with two macro expanders and a Cannon macro lens. The camera is connected to a PC where an automatic Matlab routine developed by J. Silber at LBNL is executed. This routine communicates with the user interface of the positioner, it sends a position target, wait for the end of the movement, and takes a picture. After each shot, the routine extracts the distance between the fiber moved by the positioner and a second optical fiber used as a reference. It then computes the difference between the fiber position and the theoretical one, establishing this as the system accuracy. On the other hand, this system allows to calibrate the positioner at the beginning of each test, establishing the actuator center, the real length of its arms (L1, L2), and configuring the allowed positions.
Acknowledgements. We acknowledge support from the  Spanish MICINNs  Consolider- Ingenio 2010 Programme  under grant MultiDark  CSD2009-00064 ,  HEPHACOS S2009/ESP-1473 , and  MINECO Centro de Excelencia Severo Ochoa Programme  under grant  SEV-2012-0249. We also thank the support from a  CSIC-AVS  contract trough MICINN grant  AYA2010-21231-C02-01 , and  CDTI  grant  IDC-20101033 ; and support from the  Spanish MINECO  research grants  AYA2012-31101  and  FPA2012-34694. JPK, PH and LM acknowledge support from the  ERC  advanced grant LIDA and from a  SNF   Interdisciplinary grant. We thank  J. Silber  and  R. Bessuner  at  LBNL  for helping to set-up a replica of their testing optical bench set-up at  EPS-UAM.
The aim of this paper is to summarise the sensitivity of the ATLAS experiment to R-parity-conserving 1  [38] [39] [40] [41] [42]  models including the direct pair production of stops and sbottoms using the full √ s=8 TeV proton-proton collision dataset collected during Run 1 of the LHC. 2  The third-generation squarks are assumed to decay to the stable lightest supersymmetric particle (LSP) directly or through one or more intermediate stages. The analyses considered are those previously published by the ATLAS collaboration on the topic, together with new ones designed to increase the sensitivity to scenarios not optimally covered so far. A wide range of SUSY scenarios are studied by combining different analyses to improve the global sensitivity. The paper is organised as follows: Section 2 briefly reviews the expected phenomenology of thirdgeneration squark production and decay; Section 3 reviews the general analysis approach followed by the ATLAS collaboration for SUSY searches; Sections 4 and 5 present the exclusion limits obtained in specific models by combining the results of several analyses.
A scintillator-tile calorimeter provides hadronic coverage for |η| < 1. 7. The end-cap and forward regions, spanning 1. 5 < |η| < 4. 9, are instrumented with LAr electromagnetic and hadronic calorimeters. The muon spectrometer has separate trigger and high-precision tracking chambers which provide trigger coverage for |η| < 2. 4 and muon identification and momentum measurements for |η| < 2. 7. The data sample used in this analysis was taken during the period from March to December 2012 with the LHC operating at a pp centre-of-mass energy of √ s = 8 TeV. 11 Following requirements based on beam, detector conditions and data quality, the complete dataset corresponds to an integrated luminosity of 20. 3 fb -1 , with an associated uncertainty of 2. 8%. The uncertainty is derived following the same methodology as that detailed in Ref.
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; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; RGC, Hong Kong SAR, China; ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America.
Acknowledgments. We thank  I. Affleck ,  N. Andrei ,  J. Dubail ,  F. H. L. Essler ,  A. M. Tsvelick  and  A. B. Zamolodchikov  for useful discussions. HS gratefully acknowledges the hospitality of the Rutgers Physics Department where this work was started. The work of HS and JJ was supported by the  French Agence Nationale pour la Recherche (ANR Projet 2010 Blanc SIMI 4: DIME) ; the work of HS was also supported by the  DOE  under grant number  DE-FG03-01ER45908. The work of RV was supported by the  Quantum Materials program  of  LBNL , and the work of SL was supported by the  NSF  under grant number  NSF-PHY-1404056.
Computational details Within the framework of the density functional theory (DFT)  [32, 33]  two computational methods were used in a complementary manner. For the purpose of firstly obtaining energy (total and cohesive) and charge transfer quantities, the Vienna ab initio simulation package (VASP) code  [34, 35]  was used with the projector augmented wave (PAW) method  [35, 36]. DFT exchange and correlation effects were accounted for in the framework of the generalized gradient approximation (GGA) scheme following Perdew, Burke and Ernzerhof (PBE)  [37]. Atoms were relaxed and the conjugategradient algorithm  [38]  is used in this computational scheme. The tetrahedron method with Blöchl corrections  [39]  as well as Methfessel-Paxton  [40]  Brillouin-zone (BZ) integrals were approximated using the special k-point sampling of Monkhorst and Pack  [41].
VI. CONCLUSION To measure the temperature profile of the cantilever, we propose to use the frequency shifts triggered by the softening of silicon upon heating. The proposed model adequately describe the experimental data in vacuum below 200 • C, and could be quantitative at higher temperature provided the Young's modulus of silicon is better characterized in the adequate temperature range. Our approach is robust for all flexural modes of the cantilever, and relates the frequency shift to the average of the temperature profile weighted by the local curvature. Coefficients to apply to modes 1 to 6 are given in table I for direct application to linear temperature profiles. As for observations in air, a qualitative description of the measured frequency shift has been proposed through the dependence of inertial effects due to the surrounding fluid in its temperature.
ACKNOWLEDGMENTS We thank  F. Vittoz  and  F. Ropars  for technical support,  A. Petrosyan ,  S. Ciliberto  for stimulating discussions. This work has been supported by the  ERC  project  OutEFLUCOP  and the  ANR  project  HiResAFM  ( ANR-11-JS04-012-01 ) of the  Agence Nationale de la Recherche in France.
We consistently analyse for the first time the impact of survey depth and spatial resolution on the most used morphological parameters for classifying galaxies through non-parametric methods: Abraham and Conselice-Bershady concentration indices, Gini, M20 moment of light, asymmetry, and smoothness. Three different non-local datasets are used, ALHAMBRA and SXDS (examples of deep ground-based surveys), and COSMOS (deep space-based survey). We used a sample of 3000 local, visually classified galaxies, measuring their morphological parameters at their real redshifts (z ∼ 0). Then we simulated them to match the redshift and magnitude distributions of galaxies in the non-local surveys. The comparisons of the two sets allow to put constraints on the use of each parameter for morphological classification and evaluate the effectiveness of the commonly used morphological diagnostic diagrams.
COSMOS The Cosmic Evolution Survey (COSMOS;  Scoville et al. 2007 ) is a deep, 2 deg 2 multiwavelength survey. In this work we use the observations from the HST ACS survey, where more than 1. 2 × 10 6 sources were detected down to a limiting magnitude 26. 5 in the filter F814W. The ACS data reduction and images are described in  Koekemoer et al. (2007) , while the photometric catalogue is presented in  Leauthaud et al. (2007). The photometric redshifts used in this work are presented in  Ilbert et al. (2009) , and were measured using the Le Phare code and photometric information from 30 broad, intermediate, and narrowband filters from UV, optical, NIR and MIR bands. The obtained measurements show accuracy of ∼ δz / (1 + z) = 0. 007 for galaxies brighter than i + = 22. 5. At magnitudes fainter than i + < 24.
COSMOS sample selection The COSMOS 2 deg 2 field is covered with 81 HST/ACS images  (Koekemoer et al. 2007). Taking into account the number and size (20,480 pixels square) of the images, and the computing time that we would need to process all the data with the galSVM code (Sec 3), we are finally using an area of 0. 5 deg 2. Considering the depth of the COSMOS data, for the study presented in this paper, the selected area provides us with the statistical information completely comparable with the other two surveys. Moreover, we checked that the magnitude and redshift distributions of galaxies in the selected field correspond to the total ones. Our main criteria for the COSMOS sample selection is again based on the accuracy of the photometric-redshifts. We selected only sources with i + < 24.
Methodology applied on the local sample We measured the reference morphological parameters of the local sample, at their real redshifts z ∼ 0, in three different SDSS bands: g, r, and i. This is needed in order to compare in a consistent way the morphologies of local and simulated galaxies in Sec. 4. 2 and 4. 3, since the corresponding k-correction will imply a different filter for such a comparison. We used the function implemented in the galSVM 5  public code of galaxy classification  (Huertas-Company et al. 2008 , 2009)  for measuring the parameters. The input information that the code needs, related with the source position, size, ellipticity parameters, and background, was obtained by running SExtractor in all three bands.
Methodology applied at higher redshifts To simulate the conditions of non-local surveys (case 2 in Sec. 3), we used again the galSVM code  (Huertas-Company et al. 2008 , 2009) , performing the following three steps for each of the three analysed surveys: -First, we constructed the correspondent magnitude and redshift distributions of non-local galaxies (see Sec. 4. 2 and Fig. 7 ). We randomly redshifted and scaled in luminosity the selected sample of local galaxies to match the distributions of non-local ones (see Tab. 1 fot the used photometric band in each survey). In this way, when scaling the galaxy in flux, the surface brightness dimming was directly taken into account. Moreover, we re-sampled the local galaxies with the corresponding pixel-scale for each selected non-local survey, and convolved with its PSF to match the spatial resolution.
These images represent the output of the first step explained in this section, that we use in the second step to drop them in the real background of the non-local survey images, and to measure their morphological parameters in the third step. The colour images were gathered from the EFIGI 6  project, by combining the gri images  (Baillard et al. 2011) , while the original images were downloaded from the SDSS DR4 database and correspond to the images used by N&A in their visual classifi- cation. We can observe in each survey how the galaxy information changes when going from brighter to fainter magnitudes (in general from lower to higher redshifts), but even more, how the role that the spatial resolution plays when classifying galaxies, where in COS-MOS survey the galaxy information can be conserved up to much higher redshifts in comparison with the ground-based surveys.
Finally, we measured the morphological parameters of simulated galaxies, again in the same way as we did at z ∼ 0. As mentioned above, in each survey, depending on the used photometric band and the redshift to which the galaxy was moved, we used the corresponding rest-frame SDSS images to carry out all the measurements. In each non-local survey, we had to repeat all the procedure a number of times, for each separate image, in order to cover the analysed survey areas (see Table  1 ). In case of ALHAMBRA, we were dealing with 48 sub-fields (4 CCD images per pointing, with 12 pointing in total (1 or 2 per each field); see  Pović et al. 2013). In SXDS we repeated the procedure in each of five SXDS fields (see Sec. 2. 2. 2), while to cover the selected area of COSMOS we run the code on 17 images.