dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1997). WHISPER is a sounder which triggers resonances characteristic of the plasma within a region of a few kilometres surrounding the spacecraft. It measures local and radio waves at a good sensitivity, via an onboard fast Fourier transform (FFT) analyser, in the 2-80 kHz range. WHISPER data are available from the four spacecraft during most of the Cluster mission, while measurements from the wide-band data (WBD) instrument  (Gurnett et al. , 1997) , covering a similar frequency range with a better time and frequency resolution, have been by necessity restricted to selected time windows during the mission. No clear wide-banded NTC event has been pointed so far during these time intervals. In the course of this paper, we present detailed observations of wide-banded NTC emissions obtained from three chosen events by the WHISPER instruments on board the Cluster spacecraft.
A global view: the 26 August 2008 event The analysis of this case event, on 26 August 2008, forms the core of this paper. In a first part, we describe spectral characteristics obtained from data of the three Cluster spacecraft where the NTC waves are clearly present (Figs. 5 to 7). We question in particular the nature of the waves, electrostatic or electromagnetic. In a second part, we present and discuss global views derived from the spectral analysis (Figs. 8  and 9 ). These global views could be obtained thanks to the large dimension of the Cluster constellation during the event.
Acknowledgements. The authors thanks warmly the Cluster scientific and technical teams, in particular the Cluster operation team in ESOC, as well as the Cluster science mission centre in JSOC, for making the tilt campaign come true. They are grateful to the FGM team and the  Cluster Active Archive  for providing magnetic field data and to the  WDC for Geomagnetism, Kyoto , for providing environmental data. CNES and ESA funding supported WHISPER instrument construction and support WHISPER data analysis. The topical editor C. -P. Escoubet thanks  O. Santolik  and one anonymous referee for help in evaluating this paper.
We have separately measured the ablation threshold fluence for the particular type of glass we used (soda-lime glass microscope slides), for near-infrared laser pulses with various durations in the range from 1 to 10 ps. We found that the ablation threshold fluence steadily grows with pulse duration in that range, from 2. 5 to 5 J cm -2. Data for the diameter of the ablation mark versus pulse energy, for the case of 10 ps pulse, are shown in figure  2. The corresponding area of the mark, which is proportional to the diameter squared, scales linearly with the pulse energy, as evidenced by the agreement of the data with the dashed regression curve. Thus, the average fluence computed as the ratio of the pulse energy to the ablation area is approximately constant (or clamped) with respect to the pulse energy, at a given pulse duration.
Acknowledgments This material is based upon work supported by the  US Defense Threat Reduction Agency  under program number  HDTRA 1-14-1-0009  and by the  US Air Force Office of Scientific Research  under programs number  FA9550-12-1-0482  and number  FA9550-16-1-0013. The of the Jupiter Laser Facility was supported by the  US Department of Energy ,  Lawrence Livermore National Laboratory , under contract no. DE-AC52-07NA27344. Numerical simulations were performed at  Mésocentre de Calcul Intensif Aquitain (MCIA) ,  Grand Equipement National pour le Calcul Intensif  ( GENCI , supported under grants no. 2015-056129  and  2016-057594 ), and  The Partnership for Advanced Computing in Europe  ( PRACE , supported under grant no. 2014-112576 ). LB thanks  Patrick Combis  for the discussions on ablation thresholds in glasses.
Further complications stem from the fact that late addition of extra-terrestrial S to the mantle, post-core formation, should overwhelm any pre-existing S (isotope) signature (the "late veneer";  Holzheid et al. , 2000; Wang et al. , 2013). In an effort to investigate the role of S during Earth's differentiation, we have investigated the Cu isotope compositions of bulk Earth and BSE; this is because Cu is siderophile and strongly chalcophile (~2/3 of Earth's Cu is thought to be in the core;  Palme and O'Neill, 2014, McDonough, 2003)  but is less volatile than S, so is abundant enough in Earth's mantle to have been largely unaffected by a late veneer. To begin with, it was necessary to obtain robust Cu isotope compositions for both BSE and bulk Earth. To this end, we measured the Cu isotope composition of 88 extra-terrestrial and terrestrial samples using high precision multi-collector inductively coupled plasma mass spectrometry (MC-ICPMS; see Supplementary Information for methods and data tables) and combined these results with pre-existing literature data.
7 Ga Belingwe (South Africa). The second were "fertile" orogenic lherzolites from Lanzo (Italy) and Horoman (Japan); that is, samples of the mantle that appear to have undergone little to no melt depletion. These data were augmented by Cu isotope analyses of mid-ocean ridge basalts (MORB), which are typically formed by fairly high (10-15 %) degrees of melting of upper mantle. We also include data from a variety of ocean island basalt samples to investigate the possibility of Cu isotope mantle heterogeneities (see Supplementary Information for all sample information). It is of course impossible to obtain a sample of the 'bulk Earth' and hence, like many studies before ours, we assume that bulk Earth formed from primitive (chondritic) meteorites (e. g. ,  Palme and O'Neill, 2014). The issue then is to decide which group(s) of meteorites best represents Earth, in terms of its Cu budget.
Acknowledgements This research was supported by the  Marie Curie IOF Fellowship "Isovolc" , funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement # 207467  (DECore) and under the  H2020 framework program / ERC  grant agreement # 637503  ( Pristine), as well as the financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). FM thanks the  ANR  through a chaire d'excellence  Sorbonne Paris Cité  and the  INSU  through the  PNP program. ISP acknowledges support from  NSF   EAR0946629. The authors would like to thank  Katharina Lodders ,  Bruce Fegley ,  Astrid Holzheid  and  Kevin Burton  for discussions,  Juliens Foriel and Moureau  for analytical support, and  Manuel Moreira ,  Euan Nisbet ,  Gary Byerly ,  Tim Elliott ,  Fang-Zhen Teng , and  Rosalind Helz  for sample provision.
(2) In all scans, we select the generated points which fulfil a set of constraints derived from flavour physics and lower energy searches at LEP2 and the Tevatron, as summarised below and discussed in more details in Ref. [26] , to which we refer also for further details on the pMSSM scans. These selected points are referred to as "accepted points" in the following. The pMSSM scans employ a number of programs and software tools. Only those most relevant to this study, are mentioned here, while further details are given in  [26]. SUSY mass spectra are generated with SOFTSUSY 3. 2. 3 [44]. Decay branching fractions are calculated using SDecay  [45, 46]. Higgs decay branching fractions are calculated with HDECAY (5. 0)  [47]. The flavour observables and dark matter relic density are calculated with SuperIso Relic v3.
2  [48]. Cross sections are computed with Pythia 6. 424  [49] , also used for event generation. The NLO k-factors are evaluated using Prospino v2  [50]. The physics object response for the LHC analyses is simulated using the Delphes 3. 0 fast simulation package  [51, 52] , tuned on the ATLAS detector performance and validated for the ATLAS multi-lepton neutralino and chargino analyses.
LHC As a first step, we combine the results on the branching fractions discussed in the previous section to the expected relevant cross sections for the associated chargino neutralino production. Through the decay χ± 1 → W ± χ0 1 , W ± → ± ν, this process produces a high p T lepton, which is very valuable for the event trigger and subsequent selection. The cross sections for chargino neutralino production at 8 and 14 TeV, followed by χ0  6  and 7 , respectively. Because of the small values of these cross sections and the large spread for a given neutralino mass, luminosity is as important as energy for probing electroweak SUSY particle production at the LHC. The high-luminosity LHC program (HL-LHC) bringing a tenfold increase in the data statistics will be extremely beneficial to these studies.
2,3 → h χ0 1 ; h → b b, τ τ and χ± 1 → W ± χ0 1 , W ± → ± ν are given in Figures The ATLAS and CMS experiments have conducted searches for electroweak production of SUSY particles in the two-and three-lepton + MET channels and the bb + MET channel. Accepted pMSSM scan points fulfilling the selection criteria discussed above and having an inclusive chargino and neutralino production cross section yielding at least ten signal events at 8 or 14 TeV collision energies are studied in details. A set of 20k SUSY events with inclusive neutralino and chargino production is generated for each of these points at both centre-of-mass energies. Simulated events are passed through a fast parametric simulation using the Delphes 3. 0 package which simulates the physics objects used in the subsequent analysis.
Jets are reconstructed using the anti-kt algorithm  [65] , implemented in the FastJet package  [66]. The selection criteria of the two-and three-lepton + MET  [17, 20]  and the bb + MET  [21]  analyses of ATLAS are applied. The exclusion of the pMSSM points is assessed using the CLs technique  [67] , where the number of expected signal events is obtained from the result of the selection on the generated events and that of background events is taken from that estimated for the ATLAS analyses, appropriately rescaled to the assumed luminosity and collision energy. The results of this simulation are validated by comparing the  95 % C. L. exclusion contours with those expected for the ATLAS analyses, under the same assumptions. These contours agree within 20-30%. We study the fraction of accepted pMSSM points for which the different channels and their combination lead to a 95% C.
As expected, the largest fraction of points has bino χ0 1 , since this is dominant for light χ0 1 states, followed by higgsino-gaugino mixed states which and their extrapolations at 14 TeV for 300 fb -1 (centre) and 3000 fb -1 (lower). 8 TeV 14 TeV 14 TeV 25 fb -1 300 fb -1 3000 fb become increasingly important as the search sensitivity expands with higher energy and larger data sets. If a signal is observed in the bb + MET channel in the 13-14 TeV LHC runs, some important measurements could be performed. In the case the χ0 2 and χ0 3 states are close in mass, as expected over a large part of the MSSM parameter space, they would be both produced at the LHC and may subsequently decay into h χ0 1 and Z χ0 1 , as discussed above. In this case, the b b + E missing T final state will receive contribution from both h → b b and Z → b b, which can be resolved, at least on a statistical basis, from an analysis of the invariant mass of the reconstructed bb system.
, 2007; Robert et al. , 2015). Topographic and bathymetric data are extracted from the ETOPO1 database  (Amante and Eakins, 2009)  and the geoid anomaly from EGM2008 global model  (Pavlis et al. , 2008). In order to avoid sub-lithospheric density variations, we filtered the geoid so that the signal corresponding to the lower spherical harmonics until degree and order 10 is ruled out. Our modeling approach assumes that (1) local isostasy is verified, considering a depth of compensation of 300 km, and (2) the system is in thermal equilibrium. It also considers a four-layered density model composed of atmosphere/sea water, crust, lithospheric and asthenospheric mantle. The crustal density linearly increases with depth, and the density of the lithospheric mantle is temperature dependent. Fig. 3  shows three models involving variable crustal densities (Table  1 ).
We compare the Moho depths with the dataset from  Chevrot et al. (2014) , which involves recent receiver functions data and reflection-refraction profiles from France and Spain (see references in  Chevrot et al. , 2014). We perform a new interpolation of the Moho surface from Chevrot et al. 's dataset, using the 3D GOCAD modeler  (Mallet, 2002). This interpolation reveals the main pattern of the Moho surface, avoiding short wavelength variations (Fig. 3 ). The three models are in agreement with the Moho depth determined from the dataset of  Chevrot et al. (2014). We observe that the mean elevation of the Pyrenees is generally compensated by crustal thickening, so that the variation of elevation across the chain matches that of the Moho depth. Similarly, the base of the lithosphere parallels the Moho surface, and does not show any anomalous thickening or thinning that could trigger the mean elevation of the chain.
Acknowledgements We are grateful to the  BRGM (Bureau de  recherches geologiques et minieres) for funding the Gemma V. Bosch PhD thesis (Project  RGF, ''Referentiel geologique de la France ''). We wish to thank constructive thorough reviews by  Antonio Casas-Sainz ,  Michel Se  ´ranne, and  Yves Lagabrielle. Annabelle Bernard  is thanked for field assistance.
Five-Layer Model The measured compliance is resampled into 32, 17, and 18 data points equally spaced in log frequency domain for EOS1, S002, and S005, respectively, for the inversion. To invert for crustal structure from the compliance measurements, we started with a simple model in which we divided sediments into two layers and crust into three layers. Sediment thickness h was estimated in  Kuo et al. [2014] , and the two layers within the sediment are assumed to be 200 m and h À 200 m thick, respectively. The first crustal layer (crust 1) is 1 km thick, overlying a 2 km thick second layer (crust 2) on top of a half-space. We systematically search for the density ( ρ), V P , and V S of the sediment and crust that best fit the observed compliance data. The compliance is more sensitive to V S than to V P and ρ, especially for structures with high V P /V S or equivalently high Poisson's ratio  [Crawford et al.
7 because porosity, faulting, hydration, and melting only increase the velocity ratio from the value for a Poisson solid. In the bottom half-space layer, V P is fixed as V P = V S × 1. 73 and ρ is fixed at 2. 8 g/cm 3 to speed up the search. There are a total of 13 free parameters to be determined, and two or three runs can lead to a final model with a variance reduction > 99. 5%. Table  1  summarizes the statistical performance of the best fit model for each OBS. Figure  3a  shows the best fit five-layer models of V S. The predictions in compliance from these models are shown with the data in Figure  2. The first-order feature among the three models is the decreasing V S in the crustal layers from EOS1 to S002 and to S005. The variation is most significant in the shallow  Geophysical Research Letters 10.
We adopted the half-space as shallow as possible while retained high data fitting: 4 km for EOS1 and 8 km for both S002 and S005. The inversion has 24, 30, and 30 unknowns, corresponding to the numbers of layers, for EOS1, S002, and S005, respectively. While these numbers are comparable to or exceed the numbers of data, the true degree of freedom for each model is necessarily suppressed by the requirement of minimum curvature in the Occam's inversion. Figure  3b  exhibits the smooth models for EOS1, S002, and S005. With all the discontinuities eliminated (Figure  S6  in the supporting information), the smooth models explain the data equally well as the five-layer model (Table  1 ). Because the Occam's inversion iterates to locate the minimum curvature solution that fits the data to within the given error, it may not surpass the performance of the grid search.
Interpretation The five-layer and the smooth models represent results from two end-member parameterizations that yield nearly equal fit to the data (Table  1 ). Both show the general pattern of V S in the crust decreasing from EOS1 to S005. Nonetheless, our interpretation is focused on the 1 km thick crust 1 of the five-layer model, which is likely to extract the properties of the shallow crust while preserving the possibility of the presence of the sediment-crust discontinuity. We performed a resolvability test in which velocities of each of the five layers are perturbed around the best fit values and inspected how data fitting degrades (Figure  4 ). A tight peak in variance reduction distribution indicates a good constraint of the best fit model, whereas a plateau implies a poor one. Separate narrow peaks for the three OBSs represent distinguishable solutions among them.
We tested whether this high-frequency data are necessary in order to draw the inference above, by removing the EOS1 compliance values at frequencies higher than the high-frequency limit at S005 and rerunning the inversion. The resulting model had higher V S in the sediment and lower V S in the crust (Figure  3 ), but the trend of increasing crust 1 V S persists. In the smooth model calculated using these "muted data," V S is reduced accordingly and the general trend holds. In summary, the most prominent pattern of this study is consistent with a scenario in which the elastic properties of the shallow crust of the OT are controlled by rifting-induced faulting and hydration.
Acknowledgments We thank critical comments from two reviewers. The portable OBSs were cobuilt in  WHOI OBS laboratory  under a collaborative project with  John Collins  and  Ken Peal. These data can be requested from IES data management center at http://dmc. earth. sinica. edu. tw or from B. Y. K. on a collaboration basis. The EOS1 data can be requested from the  CWB. The research was supported by the  Ministry of Science and Technology (MOST) of Taiwan, Republic of China , under grant  MOST-102-2116-M-001-026. L. C. is supported by  MOST   104-3113-M-002-004. The Editor thanks two anonymous reviewers for their assistance in evaluating this paper.
Introduction Near--Earth Objects (NEOs) are solar system bodies with a perihelion distance q<1. 3 AU. As with other small bodies in the solar system like main belt asteroids, comets or trans--Neptunian objects, NEOs are residuals of the early solar system and provide important constraints on its formation and evolution. The interest in NEOs also resides in the fact that they come close to the Earth's orbit and are thus a potential threat to Earth. More than 12 500 Near--Earth Objects (NEOs) have currently been detected but only 10% of them have been observed in the mid--infrared (5--25 μm) from ground or space--based telescopes, mainly with WISE through the NEOWISE program  (Mainzer et al. 2011 ) and with Spitzer through the ExploreNEOs program  (Trilling et al. 2010 ). However, none have been spatially resolved or observed in situ in the mid--infrared window.
Acknowledgements The contribution from France to the THERMAP instrument was funded by the  Centre National d'Etudes Spatiales (CNES). The contribution from Spain to the THERMAP instrument was funded by the  Spanish "Ministerio de Economía y Competitividad " projects  AYA2011--29489--C03--02  and  AYA2012--39115--C03--03. M. Delbo  acknowledges support from the  French "Agence Nationale de la Recherche " ( ANR--SHOCKS ).
Data In the following experiments, we use the ETAPE 2012 data  [18]. The data consists in 7 different shows from French TV channels and French Radios, split according to the official train, development and test sets. The development corpus corresponds to 15 recordings (6h53 hours) and is employed to determine the various thresholds of the systems. The evaluation corpus contains 15 recordings (6h57 hours) and is employed to evaluate model performance. For training, we use ESTER1, ESTER2, REPERE and ETAPE training corpora. I-vectors, speaker embeddings and GMM-UBM models are all learned from the training corpus. We note that on the dev corpus 28. 57% speaker are present in the training corpus and on the test corpus 35. 25% speakers are present in the training corpus.
Piomelli, Balint & Wallace (1989)  present a detailed computation of the order of magnitude of the terms that may be responsible for departure from Taylor's hypothesis, which they conclude is valid above the buffer layer in channel and boundary layer flows at moderate Reynolds numbers. Consequently, a scale-dependent value U c (λ x ) of the convection velocity for which the validity of Taylor's hypothesis is as good as possible is sought, which may be different from the mean velocity (see  Fisher & Davies 1963 ). An estimation of the validity of the frozen turbulence hypothesis is simultaneously sought. Some of the turbulent convection velocity datasets available in the literature are first reviewed in the next section.
A short literature review of convection velocity datasets in turbulent shear flows The present non-exhaustive review focuses on advection of a hydrodynamic nature (as opposed to acoustic waves), mainly using methods that are in some way connected with the new method presented in this study. A statistically stationary and planar flow spatially developing along the streamwise coordinate x is considered (e. g. a boundary layer). The mathematical expectation E(a) of a quantity a may be obtained by averaging in the statistically homogeneous spanwise (z) and time (t) directions, and by averaging over several independent realizations of the same turbulent flow. The expectation E consequently verifies the axioms of a Reynolds average, and more specifically commutes with the time and space (in all directions) derivation.
Only the streamwise length scale is considered here, even though the dependence on the spanwise length scale is sometimes addressed, for example by  Kim & Hussain (1993) and del Álamo & Jiménez (2009). The streamwise velocity fluctuation u(x, t) = U(x, t) -U(x, t) is defined from the Reynolds average • , where the dependence on y and z is omitted. If the fluctuation is exactly convected at the convection velocity U c , the following equation holds: ∂ t u + U c ∂ x u = 0, (1. 1) where U c > 0 corresponds to a convection towards x > 0. From table 1, it appears that even though many studies have been devoted to the topic, including studies of boundary layers, there is no available dataset, to the authors' knowledge, of the scale-dependent convection velocity of streamwise max x R uu ( x, τ ) U c (τ ) yes U c (τ , f ) Wills (1964) RJ n/a EXP 3 (x, t) max x R uu ( x, τ ) U c (τ ) yes max C ∞ -∞ R uu (Cτ , τ ) dτ U c max C S uu (k x , f = -k x C/(2π)) U c (k x ) S uu (k x , U c ) Favre, Gaviglio & Dumas (1967) APGTBL Re θ = 8700 EXP 4 (x, t) max τ R uu ( x, τ ) U c ( x) yes U c (λ x ) Blackwelder (1977) ZPGTBL Re θ = 2500 EXP 1 (t) max τ 1 T T/2 -T/2 u(x, t + s)u(x + x, t + s + τ ) ds U c ( x, t) no min τ 1 T T/2 -T/2 (u(x, t + s) -u(x + x, t + s + τ )) 2 ds U c Goldschmidt, Young & Ott (1981) PJ Re D = 10 4 EXP C (t) max τ R uu ( x, τ ) U c no U c ( f ) Kim & Hussain (1993) C Re τ = 180 DNS C (x) max x R uu ( x, τ ) U c (k x , τ ) yes Romano (1995) C Re τ = 306 EXP 3 (t) max τ R uu ( x, τ ) U c ( x) no U c ( f ) = - 2πf x arg S u(x)u(x+ x) ( f ) U c ( x, f ) max τ R uu ( x, τ ) U c ( x) no del Álamo & Jiménez (2009) C Re τ = 1900 DNS C (k x , ∂ t ) U c (k x ) = - Im E(û * (k x )∂ t û(k x )) k x E(û * (k x )û(k x )) U c (k x ) yes min C E (∂ t u + C∂ x u) 2 U c Chung & McKeon (2010) C Re τ = 200 000 WMLES 6 (x, t) kx/(2π) -∞ ∞ -∞ S uu k ′ x 2π , f ′ df ′ dk ′ x 2π = ∞ f ∞ -∞ S uu k ′ x 2π , f ′ dk ′ x 2π df ′ U c (k x ) yes Davoust & Jacquin (2011) RJ Re = 2.