dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, normalization to C 248 nm, O 778 nm, and Norm 3 all agree well on bassanite composition. The normalized H signal predicted for gypsum falls outside of the observed ChemCam vein distribution for all calibration approaches except C 658 nm and Norm 1, and for these calibrations the mean of the measured distribution falls between bassanite and gypsum. We also use our data to examine the water content of Martian rocks, specifically the Murray formation, a finegrained, thinly laminated mudstone facies, which is the lowermost strata of the Mount Sharp group. We analyzed the RMI images by eye to exclude rough targets, those where ChemCam shot loose sediment (sand and dust), and diagenetic features that may have a different H signal. We also exclude points with bad focus and low major-element totals (<87%).
In total, we included 229 ChemCam targets in our analysis from sols 766-1815. Using the C 248-nm normalization, the range in H content for the middle 50% of the data is 2. 0-4. 1 wt. % H 2 O with a median of 3. 0 wt. % H 2 O. The O 778-nm normalization predicts a median value of 2. 3 wt. % H 2 O with the middle 50% ranging from 1. 3 to 3. 3 wt. % H 2 O. These values are consistent with the total oxide percentages derived from ChemCam, which have a median of 97. 0 wt. % for the bedrock points we considered. When we take the weight percent H 2 O calculated from our calibrations and add it to the ChemCam derived total oxide percentage, we get a median total of 99. 6 wt. % for the O 778-nm calibration and 100. 2 wt. % for the C 248-nm calibration. Some low water content samples (8 with the C 248-nm normalization and 17 with the O 778-nm normalization) plot with less than 0 wt.
Overall, these data indicate that the water content of samples can be successfully estimated using normalized univariate analyses of H peak area in ChemCam data. Ultimately, this laboratory study better informs both future ChemCam studies of hydration as well as future SuperCam studies. The SuperCam instrument, part of the Mars 2020 mission, will have improved spectral resolution in the VNIR range, which will aid in distinguishing the H and C 658-nm emission lines as well as the Fe 654. 8-nm and 659. 4-nm lines. SuperCam's temporally gated intensifier will also aid in distinguishing between peaks as well as boosting signal, and its infrared spectroscopy and Raman subsystem will also help to decipher the possible contributors to the LIBS H signal  (Wiens et al. , 2017). Journal of Geophysical Research: Planets.
4-0. 9 wt. % H 2 O for the C 248-nm and O 778-nm normalizations. • Normalization to C 248 nm and O 778 nm are favored because they have lowest RMSECV trends and best correct for distance and other experimental effects in the laboratory data. C (I) 248 nm was favored by  Rapin et al. (2017a) ; we slightly prefer normalization to O (I) 778 nm because it is a neutral emission line on the same (VNIR) detector as H. When applied to ChemCam data on Mars both perform similarly and predict and variation in results depending on normalization method used is typically <0. 1 wt. % H. • Comparison of sample hydration across different physical matrices is challenging because sample properties like surface roughness, cohesion, and grain size impact LIBS measurements of H. The natural samples we measured as both rock chips and pressed pellets document the effects of physical matrix on H emission, especially on the H peak shape even though these samples are all hard, coherent, and fine grained.
Acknowledgments We thank  Mark Davis , from  Caltech Chemical Engineering , and his lab group, in particular  Kramer Brand  and  Marat Orazov , for assisting us in using their thermogravimetric analysis instrument. The authors gratefully recognize the work of  Rhonda McInroy  in collecting LIBS measurements at LANL. We also thank  Agnes Cousin  for helpful discussions. Thanks to  George Rossman  for help in sample selection, acquisition, and preparation. The work at  Caltech  was supported by a  NASA   MSL Participating Scientist Program  grant to  B. L. Ehlmann  and a  National Science Foundation Graduate Research Fellowship  grant  DGE-1144469  to  N. H. Thomas. Work  at  LANL  and the instrument facilities were supported by the  NASA   MSL  project. O. F. , P. -Y. M. , J. L. , O. G. , and S.
Acknowledgments The authors would like to thank the anonymous referee for their useful comments that significantly improved the quality and clarity of the manuscript. The authors wish to thank  Magnus Woods ,  Alexander James ,  Francesco Zuccarello , and the attendees of the Flux Emergence Workshop 2017 for useful comments which improved the clarity of the paper,  Pascal Démoulin  and  Gherardo Valori  for insightful discussions and assistance with the PFSS modeling, and the efforts of the JHelioviewer team for their continued development of the JHelioviewer tool (Mueller et al. , 2017). JMJ thanks the  STFC  for support via funding given in his PhD Studentship. DML is an  Early-Career Fellow , funded by the  Leverhulme Trust. LvDG acknowledges funding  under  STFC  consolidated grant number  ST/N000722/1 ,  Leverhulme Trust Research Project  Grant  2014-051 , and the  Hungarian Research  grant  OTKA K-109276.
The π and φ emission rates They are given in Table  V. The neutral pion is emitted coherently from the intermediate pp system. The negatively charged pion may be emitted from the antiproton only. However, in the intermediate I = 0 state one has also nn component and the π -may be emitted by the neutron. These processes are coherent. Therefore the ratio Γ(pnπ -)/Γ(ppπ 0 ) = 1. 78 (0. 22) within error limits equals 2 minus the square of the relevant pion-nucleon coupling constants. This indicates that pions are emitted predominantly in the baryonic phase of decay and that final state interactions are not essential, the pp and pn interactions being different. The BES data indicate effects of N * resonances in the invariant mass distribution which, depending on the way of description, amount to some 25% of the total rate.
ACKNOWLEDGMENTS The authors wish to acknowledge the operator team of the EOLE reactor,  P. -G. Allinei  and  P. Chaussonnet  ( CEA/DEN/DTN/SMTA/LMN ) for their participations to the calibration campaign, as well as  L. De Padua  and  N. Makaryan  ( CEA/DEN/D2S/SPR/LRIR ).
Acknowledgements We thank  Justin Simon , an anonymous reviewer, and the editor for their careful and thorough input that vastly improved the end product. We are grateful to CAPTEM for provision of lunar samples. P. A. S. and F. M. acknowledge funding from the  European Research Council  under the  H2020 framework program / ERC  grant agreement # 637503  (Pristine) as well as financial support of the  UnivEarthS Labex program at Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ), and the  ANR  through a chaire d'excellence  Sorbonne Paris Cité. Parts of the analytical facilities were supported by  IPGP  multidisciplinary program PARI, and by  Paris-IdF region SESAME  Grant No. 12015908. P. A. S. is indebted to  Hugh O'Neill ,  Bruce Fegley ,  Marc Norman  and  Ryuki Hyodo  for discussions.
In section 3, we present the spectral behavior of the composite qubitinterferometer system and provide a detailed data analysis. In particular, we describe three fundamental regimes of operation and the mathematical model used for analyzing our data. Fitting the experimental data engineered environment Г Φ Г 1 S(ω 01 ) S(0) relaxation dephasing Figure 1. An artificial atom realized as a transmon qubit is placed in one arm of a Mach-Zehnder type interferometer (black structure). The decoherence properties of the qubit are determined by its coupling to the environment. We extract the relaxation rate Γ 1 and the pure dephasing rate Γϕ at different qubit transition frequencies ω 01 from microwave transmission experiments. From the derived Γ 1 values we extract the influence of high-frequency noise (blue) near ω 01 and conclude that the qubit predominantly interacts with the Ohmic bath provided by the transmission line.
Acknowledgments The authors thank all collaborators, especial  Edwin Menzel  for his efforts in sample fabrication. The authors acknowledge support from the  German Research Foundation  through  FE 1564/1-1 , the  IMPRS Quantum Science and Technology , from the  MINECO/FEDER  Project  FIS2015-70856-P , and  CAM PRICYT Research Network   QUITEMAD+ S2013/ICE-2801. Toms Ramos further acknowledges the  Juan de la Cierva  fellowship  FJCI-2016-29190. We also acknowledge support from the  doctorate program ExQM  and the  EU  project  PROMISCE.
( 7 ) where v is the relative velocity of the incoming dark matter particles, n c is the color number of the final states SM fermions. The Eq. 6 is not valid near the Z resonance, i. e. when m Z ∼ 2m χ. In this case, the Z width should be properly included. We handled this procedure numerically within the MicrOmegas package  [92] [93] [94]. With the results we found the region of parameter space that yields that correct dark matter relic density of Ωh 2 = 0. 12 according to Planck  [95]. The region of parameter that furnishes Ωh 2 = 0. 12 is displayed in the figures 1-3 with a dashed black curve. Concerning indirect detection, there is another quantity to be determined which is the gamma-ray yield. To compute it we implemented the model in Pythia 8  [96]  and took into account all relevant annihilation channels.
ACKNOWLEDGEMENTS We thank the  High Performance Computing Center (NPAD)  at  UFRN  for providing computational resources. FSQ and DC acknowledge support from  MEC  and  UFRN. FSQ also thanks the financial support from  ICTP-SAIFR FAPESP  grant  2016/01343-7. Y. M. acknowledges partial support from the  European Union   Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie :  RISE InvisiblesPlus  (grant agreement No  690575 ), the  ITN Elusives  (grant agreement No  674896 ), and the  France-US PICS Mi-croDark.
Description of image acquisition and workflow The rendering of the 3D models is outsourced to a private company, Visible Patient TM (Strasbourg, France)  (6). This requires no extra work for the referent surgeon. This point is relevant because the 3D model used for surgery may be complex to build and time consuming. To protect the identity of the patient, data are anonymized. A CT scan is performed by the radiology department. Images (DICOM format) stored in the hospital picture archiving and communication system (PACS) are uploaded to the Therapixel TM server. Reconstructions are rendered by specialized radiological technicians (Visible Patient TM employees) who contour all anatomical structures (arterial, venous and bronchial up to the fifth level of division). Lobar and sublobar segmentation are performed automatically by the software.
Analysis During the physics runs between 2008 and 2012, OPERA collected data corresponding to 1. 8 × 10 20 protons on target. The electronic detectors recorded 19,505 neutrino interactions in the target fiducial volume. The search of the neutrino vertex in the first and second most probable bricks plus some additional selections (see  [18]  for more details) resulted in a sample of 5603 located events out of which 4406 have an identified muon. For the present measurement an unbiased sub-sample of 818 events occurring in the lead with a negatively charged muon identified by the muon spectrometer was selected in order to measure the track and vertex parameters in the target including a detailed check of the nuclear break-up and evaporation processes. It is further required that W 2 , the square of the invariant mass of the hadronic system measured with the electronic detector, is larger than 1 GeV 2 /c 4 in order to eliminate the quasi-elastic contribution.
The final data sample contains 795 events with an identified muon. The contamination from ν μ neutral-current interactions in the final data sample is estimated by MC simulations to be less than 1%. Selected ν μ CC events are inspected carefully and tracks are classified as being left by a minimum ionisation particle (mip), grey and black depending on their ionisation features. The mip tracks are left by the muon and by highly relativistic charged hadrons resulting from the cascade of interactions generated inside the target nucleus by the primary hadrons emitted with the muon at the neutrino-nucleon interaction. Black tracks are produced by low energy fragments (protons, deuterons, alpha particles and heavier fragments) emitted from the excited target nucleus. Black tracks are classified as backward or forward based on the emission direction.
Acknowledgements We acknowledge  CERN  for the successful operation of the CNGS facility and  INFN  for the continuous support given to the experiment through its  LNGS laboratory. We acknowledge funding from our national agencies:  Fonds de la Recherche Scientifique-FNRS  and  Institut Interuniversitaire des Sciences Nucleaires for Belgium ; MoSES for Croatia; CNRS and IN2P3 for France; BMBF for Germany; INFN for Italy; JSPS,  MEXT , the  QFPU-Global COE program  of  Nagoya University, and Promotion and Mutual Aid Corporation for Private Schools of Japan for Japan ;  SNF , the  University of Bern  and  ETH Zurich for Switzerland ; the  Russian Foundation for Basic Research  (Grant no. 12-02-12142  ofim), the  Programs of the Presidium of the Russian Academy of Sciences (Neutrino Physics and Experimental and Theoretical Researches of Fundamental Interactions) , and the  Ministry of Education and Science of the Russian Federation for Russia , the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT and Future Planning  (Grant no.
A. Tools and methods PyHEADTAIL is a tracking code developed at CERN and is the successor of the HEADTAIL program  [16]. A detailed description of the model and available features is given in Ref. [11]. PyHEADTAIL being a macroparticle tracking code, does not directly compute the coherent frequency shifts of the head-tail modes, but rather it outputs the bunch centroid signal in time domain. In the event of an instability, the amplitude of the bunch centroid oscillation rises exponentially. After a certain latency time, which depends amongst others on the initial particle distribution, there is usually one dominant (aka. most unstable) mode that establishes and is responsible for the amplitude growth. The growth rate of the exponential hence corresponds to the imaginary part of the coherent frequency shift of the most unstable mode.
The shift is measured with respect to the unstable synchrotron side band. BimBim is another accelerator physics code developed at CERN and its main purpose is to model beam-beam and wakefield effects. The underlying scheme is the circulant matrix model described in Refs. [9, 10]. BimBim solves the circulant matrix and outputs directly the complex coherent frequency shifts of "all the possible" (stable and unstable) modes. Naturally, the chosen number of slices and rings for the discretization of the longitudinal phase space puts a limit on the maximum order of the unstable mode that can be described by the model. Since the formalism is based on matrices, it only accounts for linear dependencies on the transverse phase space variables, and hence it cannot model, for instance, detuning with transverse amplitude as introduced by Landau octupoles.
[2, 7]. It is due to the fact that the specific azimuthal mode has a negative real coherent frequency shift combined with the one-sided frequency spreads (and stability boundary diagrams) that are introduced by ξ ð2Þ as illustrated, e. g. , by Fig. 4. In that case, ξ ð2Þ < 0 provides much more effective Landau damping. Analytical solutions (dashed lines) are shown alongside the tracking results. They deliberately exclude the frequency spread from ξ ð2Þ to remove the effect of Landau damping and to illustrate solely the implications of a change of effective impedance. Unfortunately, this decoupling of the two effects cannot be achieved in a macroparticle tracking code as Landau damping is intrinsically included in this model once nonlinear chromaticity is added. At ξ ð2Þ ¼ 0, PyHEADTAIL and the analytical prediction are expected to be identical.
Note that we will refer to the LiteCORE-80 forecasts as those representative for a possible downscoped version of CORE-M5, called MiniCORE  [7] , with a 80 cm telescope and covering the reduced 100-600 GHz frequency. For all these configurations, we consider an inverse noise weighted combination of central frequency channels, always under the assumption that lower and higher frequency channels remove completely the foreground contamination. Table  3  reports the experimental specifications for the central frequency channels of these configurations. This comparison sheds light on the role that angular resolution and noise detector sensitivity plays in determining what new conclusions concerning inflation can be extracted from the data of a particular concept for a future CMB space mission. See also  [169] [170] [171]  for previous forecasts for inflation science comparing different future experiments dedicated to CMB polarization.
See  [9]  for an analogous discussion of the larger expected improvement on the uncertainty on σ 8 , whose value obtained from the CMB currently disagrees by about 2σ with the measurement obtained using galaxy shear measurements  [49, 50]. CORE-M5 will achieve an accuracy in the determination of n s more than three times better than the current Planck uncertainty. The CORE-M5 error on n s will be comparable to the standard theoretical uncertainty arising from the entropy generation stage after inflation  [3] , which enters into the comparison of CMB data with specific inflationary models. The predictions for a given inflationary model will be sensitive not only to the form of the potential during inflation but also to the subsequent reheating stage during which the inflaton decays, leading to a fully thermalized Universe.
In the next Section this aspect will be explored quantitatively for the inflationary models studied in Ref. [102]. Inflation as modelled by a slowly rolling scalar field generically predicts a small running of the spectral index of the primordial spectra  [195] , which in terms of the slow-roll parameters is of higher order than the deviation of the scalar spectral index from unity. Although a theoretical prior based on the slow roll approximation makes the running of the scalar spectral index undetectably small for the precision of cosmological observations achieved so far  [196, 197] , its value inferred by CMB data was not merely a consistency check. Negative values of the running could be easily accommodated by pre-Planck data since the WMAP first year data release. See for example the WMAP 7 year data combined with SPT result dn s /d ln k = -0.
024 ± 0. 011  [198]. With the most precise measurement of the CMB anisotropies in the region of the higher acoustic peaks, the Planck data are compatible with a vanishing running of the spectral index. See the Planck 2015 temperature and polarization (TT,TE,EE + lowP) constraint dn s /d ln k = -0. 006 ± 0. 007 at 68% CL  [5]  or the most recent result dn s /d ln k = -0. 003 ± 0. 007 at 68% CL, obtained with the latest measurement of the reionization optical depth τ  [178]. Table  5  reports the forecasts for the cosmological parameters when dn s /d ln k is allowed to vary in addition to the parameters of the standard ΛCDM model for a fiducial cosmology in which dn s /d ln k = 0. As indicated in Table  8 , the LiteBIRD forecast error on the running does not improve significantly on the current Planck constraints.
Moreover, the LiteBIRD uncertainty on the spectral index increases by 44% when the running is allowed to vary. By contrast, CORE-M5 will be able to reduce the current uncertainty by approximatively a factor of 3. The forecast error on the spectral index increases by only 7% when the running is allowed to vary. COrE+would only marginally improve on the CORE-M5 forecast uncertainties for the running. It is now understood that the negative running spectral index allowed by the pre-Planck data was driven by the low amplitude of the temperature power spectrum at 40 for the WMAP data. The relatively larger impact of this anomaly at 40 for WMAP data could have then conspired through calibration uncertainties in the combination with complementary CMB data at higher angular resolution such as SPT to accommodate a larger negative running.
8 × 10 -4 and 4. 3 × 10 -4 as the CORE-M5 and LiteBIRD 95% CL upper bounds on r taking into account noise sensitivity only, respectively. CORE-M5 could improve by more than a factor of 2 on the 95% CL upper bound on r obtained with LiteBIRD. Table  7 : Forecast 68% CL constraints on primary and derived cosmological parameters when r is allowed to vary for LiteBIRD, LiteCORE-80, CORE-M5, and COrE+configurations. These forecasts assume r = 10 -3 as the fiducial value. The forecast CORE-M5 and LiteBIRD 68% and 95% CL 2D marginalized regions in (n s , r) for r = 0. 0042, r = 0. 001 and r = 0 are displayed in Fig. 8. These forecasts are compared with the corresponding regions for the real Planck 2015 data combined with the BKP joint cross-correlation for B-mode polarization  [5]. Theoretical predictions for a few inflationary models in agreement with current data are also shown: natural inflation (purple band), the hilltop quartic model (red).
2) and the correlation fraction cos ∆ defined in Eq. (7. 1). 7. 2 Adiabatic fiducial data 7. 2. 1 Fitting a generally correlated mixture of adiabatic and CDI modes Figure  25  presents the constraints on primordial curvature, isocurvature, and correlation powers if the true primordial perturbations were purely adiabatic without a tensor contribution. We fit an extension of the standard ΛCDM model with three isocurvature parameters to these data. All datasets use TT, TE, and EE, but for CORE-M5 and COrE+ we also give the results with the CMB lensing potential (PP) data included. BB and delensing analysis is not applied in this Subsection. In addition to forecasts for 'future experiments,' for comparison we show the constraints for simulated Planck TT, TE, EE data assuming the bluebook  [283]  white noise sensitivities divided by √ 2 using the 100, 143, and 217 GHz channels in inverse noise weighting (the factor 1/ √ 2 takes into account that Planck operated at least twice as long as the nominal mission described in the bluebook).
Fiducial data with a fully (anti)-correlated CDI contribution Isocurvature perturbations do not produce B modes except through lensing exactly as in the adiabatic model without tensors. Therefore BB and delensing information would not significantly help for the studies described in the previous Subsections. However, in this Subsection we utilize BB, since we fit to our fiducial data a model where r is a free parameter, while n t is fixed by the inflationary consistency condition to n t = -2 = -r/(8(1cos 2 ∆)) = -r/8. We create two fiducial datasets, one assuming "Curvaton I" (i. e. , 100% correlated ADI and CDI) with β iso = 0. 1%, and another assuming "Curvaton II" with the same isocurvature fraction (but setting 100% anticorrelation between ADI and CDI). As explained in the previous Subsection, these fiducial data do not have any primordial tensor contribution.
One-parameter isocurvature extensions to the adiabatic ΛCDM model. Left: an "Axion" model (uncorrelated adiabatic and isocurvature modes, with n II = 1, cos ∆ = 0). Middle: "Curvaton I" (fully correlated adiabatic and isocurvature modes, with n II = n RR , cos ∆ = +1). Right: "Curvaton II", (fully anti-correlated adiabatic and isocurvature modes, with n II = n RR , cos ∆ = -1). The simulated Planck and LiteBIRD datasets contain only TT,TE,EE, but the simulated CORE-M5 is presented both with and without the lensing potential PP (but not with the BB nor delensing information). The real Planck data contain also a noisy B-mode at low-, consistent with zero. CORE-M5 has capability for an unprecedented constraining power in the curvaton models. RR σ(n RR ) 100β iso n RR σ(n RR ) 100β iso n RR σ(n RR ) n RR σ(n RR ).