dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike Eq. (  13 ), Eq. (  14 ) does not depend on the frequency, which means that one single correction holds for every frequency. In fact there are other smaller relativistic effects that must be taken into account when making barycentric corrections. One is the Einstein delay, ∆ E , due to the Earth motion and FIG. 2. Average spectrum of VSR4 data around Vela (top plot) and Crab (bottom plot) reference frequency (identified by the vertical black lines). For Vela two spectra are in fact shown in the same plot. The upper one (black curve) has been computed over the first 29 days of the run when the frequency region around Vela was affected by an instrumental disturbance. This was due to a non-linear coupling between the DARM control line at 379 Hz and calibration lines at 356 Hz and 356. 5 Hz and was moved away from the Vela region by shifting of 5 Hertz the frequency of calibration lines.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Adminis-tration, OTKA of Hungary , the  Lyon Institute of Origins (LIO) , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  National Science and Engineering Research Council Canada ,the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
Absolute In-Flight Calibration The calibration work during orbit will be performed in three steps. First, the counting rate of each pixel will be monitored during the ultra-high energy cosmic ray (UHECR) observation for a health check. Based on this information, dead pixels will be disabled online or ignored in the offline analysis. In the present design, a 'slow mode' is defined where the count rates are monitored every 3. 5 seconds. The slow mode data can be used for the calibration to derive average counting rate in each orbit. Second, more detailed calibration work will be performed every orbit, before or after the dark sky observations. In this stage, the efficiencies of each pixel of the focal surface detector with the light sources will be measured on the third lens at a few different light intensities allowing the efficiency to be measured in single photon counting mode and for strong light.
The signal in darkness will also be measured. Third, more detailed calibration data will be taken every month. The absolute gain of each pixel will be determined by photon counting, making use of the threshold scan method (i. e. taking 'S-curve'). The transmittance of the optics will be also examined at this stage. The average rate of the calibration data will be a few kbps, which is well below the allocated rate for calibration data. Two methods are foreseen: moon illumination, and calibrated man-made light from outside (GLS).
Extrapolations from the single laser frequency to the broadband emission of the shower signal is doable. The lasers in the GLS units will produce tracks in the JEM-EUSO observatory. The laser tracks will have optical similarities to the tracks produced by 100 EeV EASs. Unlike cosmic EAS events which are essentially random, the properties of GLS flashes and laser shots can be programmed in advance and measured independently at their source. The properties of the GLS signals include the absolute time, calibrated intrinsic luminosity for the flashers and calibrated beam energy and direction for the lasers. These independent measurements can then be compared, event by event, to the JEM-EUSO measurements of these GLS signals to monitor and validate key parameters of the detector and the data analysis chain.
In such cases, the GLS becomes a reference calibration source for the efficiency of JEM-EUSO. When measurements of the flasher show that atmospheric scattering is dominated by Raleigh scattering, the light scattered from the laser beam along its length can be calculated. Since the energy in the laser beam is known, the intrinsic luminosity of light scattered from the beam is also known and can be compared to the intrinsic luminosity reconstructed from measurements made by JEM-EUSO. GLS lasers will also be programmed to generate an artificial full sky map of potential cosmic accelerators by firing laser shots at astronomical objects of interest such as Cen-A, Virgo, and the galactic center. A sky map of laser track directions as reconstructed by JEM-EUSO will be accumulated over the mission to test the absolute EAS pointing accuracy reconstruction using the JEM-EUSO instrument, including the correct generation and transfer of absolute time stamps through the data acquisition and analysis chains.
Laser Systems The design and performance parameters draws on that of two laser facilities that have been in operation at the Pierre Auger Observatory since 2004 and 2009. The laser will be a frequency tripled YAG with an output wavelength of 355 nm. The relative energy of each shot will be measured by a pyroelectric energy monitor probe. The net polarization of the beam will be randomized so that the atmosphere scatters equal amounts of light about the direction of the beam axis. For calibration of the monitor energy probe, the beam steering system directs the beam downward to a calibration probe that will measure the absolute beam energy downstream of all optics. To facilitate separation of EAS and laser data collected by JEM-EUSO, the laser will be triggered at precise times using a custom GPS timing module  [22].
Methods To search for stable Cr-H compounds with variable compositions under high pressure, we employed an effective and unbiased crystal structure searching method based on an evolutionary algorithm, as implemented in the USPEX code  [41] [42] [43] [44]. All compositions between pure Cr and pure H were allowed under constraint that the total number of atoms in the unit cell be up to 30. The first generation of 100 structures/compositions was produced randomly; all subsequent generations contained 80 structures and were produced using variation operators such as heredity (60%), softmutation (15%), transmutation (15%), 10% of each new generation was produced randomly. Geometry relaxations were performed based on density functional theory (DFT)  45, 46  with the Perdew-Burke-Ernzerhof (PBE)  47  version of the generalized gradient approximation functional (GGA), as implemented in the VASP code  48.
We used projector-augmented wave (PAW)  49  potentials for Cr and H atoms, with radii 2. 5 a. u. for Cr ([Ar] core) and 1. 1 a. u. for H. A plane-wave basis set with a kinetic energy cutoff of 600 eV was employed. We used uniform Γ-centered k-points meshes with a reciprocal space resolution of 2π × 0. 03 Å -1 for Brillouin zone sampling. All structural relaxations were done with a tight force convergence threshold (1 meV/Å). These settings enable excellent convergence of total energy differences, forces, and stress tensors. Electron-phonon coupling (EPC) calculations were performed using the Quantum Espresso package 50 with Vanderbilt-type ultrasoft pseudopotentials  51  with cutoff energies of 60 and 500 Ry for the wave functions and the charge density, respectively. The dynamical matrix was computed on an 8 × 8 × 5 mesh for CrH and 4 × 4 × 5 mesh for CrH 3.
In all calculations, force convergence threshold (1 meV/Å) for structural optimizations was applied. The McMillan-Allen-Dynes equation  37   , where ω log is the logarithmic average frequency, λ is the electron-phonon coupling constant and μ* is the Coulomb pseudopotential, which is taken to be between 0. 1-0. 13  28. Theoretical phonon spectra were calculated based on the supercell approach using the PHONOPY package  52  in order to probe the dynamical stability of the enthalpically preferred Cr x H y compounds at different pressures. Because of the very low mass of the hydrogen atom, the zero-point energy (ZPE) may well be large enough to affect the overall structural stability of the computed phases. Therefore, we estimated the ZPEs for each Cr x H y compound, pure H 2 and Cr under pressure within the quasi-harmonic approximation using the PHONOPY code.
Acknowledgements We thank the  Natural Science Foundation of China  (Grants No. 51372203  and No. 51332004 ), the  Basic Research Foundation of NWPU  (Grant No. JCY20130114 ), the  Foreign Talents Introduction and Academic Exchange Program  (Grant No. B08040 ), the  National Science Foundation  (Grants No. EAR-1114313  and No. DMR-1231586 ),  DARPA  (Grants No. W31P4Q1310005  and No. W31P4Q1210008 ), the  CNRS and Poitiers University, and the Government of the Russian Federation  (Grant No. 14. A12. 31. 0003 ) for financial support. The authors also acknowledge the  High Performance Computing Center of NWPU  for the allocation of computing time on their machines.
, 2010)  and TrOCA  (Touratier and Goyet, 2011)  -along the Meteor M51/2 section (November 2001). Vertical profiles on the right are for mean anthropogenic C T along the section estimated by each method. The vertical profile of the TrOCA method does not go below 3500 m depth because it requires measurements, some of which where not available at that depth. Meteor (M31/1), El Boukary estimated with two methods that the Mediterranean Sea had absorbed 3. 1 and 5. 6 Pg C, but he concluded that even the lower value was an overestimate. Later, data from a transbasin transect in 2001 (Meteor M51/2) was used by two independent studies to estimate anthropogenic CO 2. In the first,  Schneider et al. (2010)  used those data with the transit time distribution approach (TTD, from  Waugh et al. , 2006) , a back-calculation method based on CFC-12 derived mean ages of water masses.
This equation also implies that computed A T varies temporally with simulated salinity. For VAR to take into account variable salinity (total alkalinity) as well as variable temperature, while maintaining adequate precision, we made two types of modifications to the standard equations. First, we replaced Eq. (  6 ) with a direct relationship between δpCO 2,o and δC T but with a cubic formulation instead of a linear formulation, i. e. , implying an additional coefficient. δpCO 2,o = 0 + z 1 δC T + z 2 δC 2 T + z 3 δC 3 T (12) Then for each of three coefficients, we replaced the former two equations, quadratic in temperature T , with three equations, cubic in T and S. z 1 = a 0 + a 1 T + a 2 S + a 3 T 2 + a 4 S 2 + a 5 T 3 + a 6 S 3 + a 7 T S + a 8 T 2 S + a 9 T S 2 z 2 = b 0 + b 1 T + b 2 S + b 3 T 2 + b 4 S 2 + b 5 T 3 + b 6 S 3 + b 7 T S + b 8 T 2 S + b 9 T S 2 z 3 = c 0 + c 1 T + c 2 S + c 3 T 2 + c 4 S 2 + c 5 T 3 + c 6 S 3 + c 7 T S + c 8 T 2 S + c 9 T S 2 (13) The associated coefficients are listed in Table  1 , while the R program used to make these calculations, which exploits the seacarb software package for the carbonate system (Lavigne and Gattuso, 2011), is given in the Supplement.
This exercise yields a surface map of δpH. For deep waters, we consider changes only along one transbasin section, Meteor M51/2. Exploiting total alkalinity, C T , temperature, and salinity measured along from this section in November 2001  (Schneider et al. , 2007 (Schneider et al. , , 2010)) , we computed corresponding pH for all data points along the section and throughout the water column. Then we subsampled the simulated δC T in 2001 at all station locations and sample depths. After removing those simulated results from the measured C T , we recalculated pH. The difference is the δpH along the same section. For comparison, we repeated this exercise, but instead of simulated δC T , we used the TTD databased estimates of anthropogenic C T from  Schneider et al. (2010) , already available along the same section.
Comparison with TTD data-based results To go beyond model comparison of simulated uptake of anthropogenic CO 2 , we also compare model results to databased estimates. In particular, we focused on data-based estimates of anthropogenic carbon deduced with TTD because that method requires only measurements of temperature, salinity, and CFC-12, all of which were simulated, thereby allowing us to test the approach (see Sect. 4. 1). For now though, let us simply compare model results to the TTD estimates of δC T based on observations collected on the Meteor M51/2 section in 2001  (Schneider et al. , 2010). A first comparison reveals that the modeled δC T is lower everywhere than the TTD estimates of the inventory along the Meteor M51/2 section (Fig. 9 ). While the TTD inventory along this section averages 83 mol m -2 (ranging from 21 to 153 mol m -2 ), the model average is 50 mol m -2 , 40 % less.
δC T in the Mediterranean Sea Our comparison of modeled to measured CFC-12 indicates that the model adequately represents ventilation of nearsurface and intermediate waters but underestimates ventilation of deep waters. This CFC-12 evaluation alone implies that our simulated δC T is likewise too low in Mediterranean Sea deep waters and hence that our simulated total anthropogenic carbon inventory of 1. 03 Pg C is a lower limit. Yet even in the top 400 m, where there is tight agreement between simulated and observed CFC-12, the databased estimates of δC T from the TTD method are 20 % larger than those simulated (Fig. 5 ). Hence it is unlikely that the modeled circulation is the primary cause. Simplifications with the perturbation approach, e. g. , its steady-state assumption, could be partly to blame, although errors due to circulation-induced changes in biological productivity appear small for the global ocean  (Siegenthaler and Sarmiento, 1993; Sarmiento et al.
Conclusions A first simulation of anthropogenic carbon in the Mediterranean Sea suggests that it accumulated 1. 0 Pg C between 1800 and 2001. That estimate provides a lower limit based on comparison of observed vs. simulated CFC-12 in the same model, which reveals that modeled deep waters are poorly ventilated. Furthermore, we demonstrate that a previous databased estimate of 1. 7 Pg C  (Schneider et al. , 2010 ) is an upper limit after testing the associated TTD approach in our model. In 2001 in the reference model, a total of 1. 5 Pg C of anthropogenic carbon had entered the Mediterranean Sea with 52 % from the air-sea flux and 48 % from Atlantic Water inflow; however, 31 % of that total had also left via the deep Mediterranean Outflow Water. Out of the net accumulation of 1. 0 Pg C, 75 % comes from the air-sea flux and 25 % from net transfer across the Strait of Gibraltar.
Acknowledgements. We thank  Samar Khatiwala  for providing his data-based estimates for anthropogenic carbon, which we used as a lateral boundary condition for the Atlantic portion of our model domain. We thank  Météo-France / CNRM  and in particular  Michel Déqué  and  Florence Sevault  for running and providing the ARPERA data set. We also thank  Marte Álvarez  and an anonymous referee for their insightful reviews, which have helped to improve this manuscript. This work was supported by the  French SiMED project (Mercator Ocean) , the  MORCE and MED-ICCBIO projects (GIS) , the  EU   FP7  project  MedSeA  (grant  265103 ), and the  French ANR  project  MACROES (MACRoscope for Oceanic Earth System   ANR-09-CEP-003 ). This work is a contribution to the HyMeX and MERMEX programs, and access was granted to the  HPC resources of IDRIS (Institut du Développement et des Ressources en Informatique Scientifique ) of the  Centre National de la Recherche Scientifique (CNRS)  under allocations for years 2010, 2011, and  2012  (project  1010227 ) made by  Grand Equipement National de Calcul Intensif (GENCI).
, 1991; Carter, 2009; McInerney et al. , 2011) , or used as a dating tool  (McClaran and Umlauf, 2000; Piperno and Stothert, 2003; Parr and Sullivan, 2005; Piperno, 2006). Motivated by anthropogenic emissions of carbon dioxide (CO 2 ) (Mauna Loa Observatory; NOAA-ESRL data at http://www. esrl. noaa. gov/) and their direct association with climate change, a set of recent studies has advanced the idea that many monocotyledonous crop species (bamboo, sugarcane, maize, rice, etc. ) as well as grasslands in general (among the largest ecosystems in the world -  Suttie et al. , 2005)  may play a significant role in C sequestration through a newly evidenced mechanism: CO 2 biosequestration in grass biosilica particles  (Parr and Sullivan, 2005, 2011; Parr et al. , 2009 Parr et al. , , 2010;; Song et al.
Additionally, confirmation of a dual origin (soil organic matter (SOM) and photosynthetic) of phytC would open new questions regarding plant-soil interactions and SOM recycling, relevant for our understanding of the role of terrestrial ecosystems in the C cycle. To unequivocally establish that a fraction of phytC is indeed from soils, a robust data set is produced here by considering and ruling out all other factors that can possibly bias the isotopic signatures of phytC. We reassess the old soil C contribution to phytC hypothesis  (Santos et al. , 2012a)  on the basis of > 200 isotopic results (δ 13 C and/or 14 C) of phytoliths and associated materials (grass tissues, SOM fractions, amendments and hydroponic solutions, CO 2 respired from substrates or extracted from air). Pure phytolith concentrates were acquired from sets of above-and below-ground C manipulation experiments.
At the sorghum site, the Fm 14 C value of the bulk biomass harvested at the ambient CO 2 plot matched with the Fm 14 C value of the CO 2 of the year of harvest (e. g. , Fm 14 C ≈ 1. 097, equivalent to the atmospheric 14 CO 2 signature measured from clean air in 1999 -http://calib. qub. ac. uk/CALIBomb/ database and calibration software). This 14 C signature is higher than the present-day ambient CO 2 due to nuclear weapon tests carried out during the 1950s and 1960s  (Levin, 1997; Levin et al. , 2013). The nuclear weapon tests doubled the 14 C content in the atmosphere, which created an isotopic chronometer (the 14 C bomb peak) during the last 60 years for all living organisms. At the durum wheat site, however, the 14 C signature of the biomass harvested at the ambient CO 2 plot was slightly depleted (Fm 14 C ≈ 1.
Aliquots of pre-baked (900 • C 3 h -1 ) silicon dioxide powder (SiO 2; mesh# -325, Sigma Aldrich, St. Louis, MO, USA) were chemically pretreated in parallel with the plant samples, and later analyzed as phytolith extract to provide independent blank data for each laboratory following the procedures described in  Santos et al. (2010). Due to the limited amount of plant biomass produced by the below-ground C manipulation experiment (Sect. 2. 1. 2), only two protocols were tested (1a and 2b) at only three of the laboratories (UCI, CEREGE, and LacCore), followed by blank sample materials as required.
The Supplement related to this article is available online at doi: 10. 5194/bg-13-1269-2016-supplement. Acknowledgements. The authors gratefully acknowledge the support of the  US National Science Foundation  ( DEB-1144888  to GMS), the  French FIR 2010  ( Aix-Marseille Université ), ECCOREV 2011, AIR Archéométrie 2011(CNRS) and  Labex OT-Med 2013. PER wishes to thank  J. A. Mason  ( University of Wisconsin-Madison ) and the  National Lacustrine Core Facility  ( University of Minnesota ) for lab space usage. GMS thanks  M. J. Ottman ,  B. Kimball ,  S. W. Leavitt ,  E. Pendall ,  P. Pinter ,  G. Hendrey ,  H. L. Cho  and  R. Rauschkolb  for providing the archived Maricopa FACE samples. Financial support provided by the  Durum experiment  via the "Fondazione in rete per la ricerca agroalimentare" with the AGER program: agroalimentare e ricerca is gratefully acknowledged.
Ozone evaporation-condensation cycles Using the liquid ozone trapped in the bottom of the ozone generator, a series of evaporation-condensation cycles was performed so as to obtain about nine different ozone pressures in the gas cell to cover the range 0. 2-1 mbar, whilst measuring the absorbance A e at a fixed laser wavelength to determine the ozone cross-section. The process followed during those cycles was optimized during extensive testing to maximize the purity of the gaseous ozone samples released into the gas cell. The key parameters to monitor were the cryostat temperature T cryo , the sample total pressure P T measured with the Baratron, and the ozone partial pressure P (O 3 ) which at this point was used as an indicative real-time value determined using the following equation: P (O 3 ) = A e RT cell σ N A L opt , (1) where A e is the absorbance, T cell the gas cell temperature, N A the Avogadro constant, R the gas constant and σ a value of the ozone cross-section chosen from the literature (the so-called BDM data set was used in this instance).
Ozone absorption cross-section values at the three laser wavelengths Three series of measurements, each including nine points at different pressures covering the range 0. 2-1 mbar, were performed at each of the three laser wavelengths: 244. 06, 248. 32 and 257. 34 nm, constituting data sets of 27 points. Each measurement point was formed by the values X = CL opt and Y = A e recorded during one ozone evaporation-condensation cycle. Each set of 27 (X, Y ) values was fitted using a linear regression model with uncertainties in both axes, following the generalized least-squares approach as described for example in  Riu and Rius (1995). Calculations were performed with the software B_Least, detailed of which can be found in the ISO standard 6143  (ISO, 2001). One possible drawback of such a method is an underestimation of the slope uncertainty due to the averaging effect the regression when all points are considered independent.
Acknowledgements. The authors would like to acknowledge the contributions of Edgar Flores (BIPM) in the FTIR analysis,  Lennart Robertsson  (BIPM) in the absorption cell path length measurement, and  Richard Davis  ( BIPM ) for advice on pressure measurements. All data used to deduce the ozone cross-sections were acquired experimentally and saved in ASCII format files. All files will be kept at the BIPM and can be communicated on demand. Data not acquired at the BIPM are published values of the ozone cross-section and in that case the sources are cited in this paper. Edited by:  Dr. M. Weber.