dataesr/scientific-paragraphs-categorization huggingface dataset:
The cameras are placed opposite to the laser with respect to the sides walls and at each side of the measurement planes. Forward scattering mode is hence achieved. The calibration of the PIV images is realized using a LAVISION 0:3 Â 0:3 m 2 calibration body. The image acquisition is realized with the DAVIS 8. 2. 2 program of LAVISION at a resolution of 1280 Â 800 pixels with a pixel size of 20 Â 20lm 2. In each configuration and measurement plane, ten blocks of N ¼ 4096 images are taken at a sampling frequency of f s ¼ 3kHz, corresponding to a measurement time T ¼ N=f s % 1:37s. The reconstruction of the velocity field u ¼ ðu; v; wÞ ¼ ðu i Þ [i ¼ 1; …; 3 along the coordinates (x, y, z), respectively] uses the Lucas-Kanade method to solve the optical-flow problem in a multi-resolution Gaussian pyramid approach in order to prevent convergence to local extrema.
This is implemented in the FOLKI software of ONERA, which is also used to compute the streamwise vorticity x ¼ @u=@y À @v=@x. 21 Windows of 31 Â 31 pixels are used, which yields a spatial resolution of the PIV data of approximately 7 mm. Four different configurations are analyzed. We call no-jet configuration the reference case of the wing alone without the jet generators. Furthermore, three different jet-vortex configurations with relative spacing d jet =b 2 f0:44; 0:58; 0:80g as shown in Fig. 1  have been assessed. The position d jet =b ¼ 0:80 corresponds roughly to the position of the rolled-up tip vortex and, thus, deserves a special attention, as we confirm later on.
Data are continuously monitored on an hourly basis using a data logger. On the GISFI site, TDR probes are RIME-PICO32 sensors with internal TDR-electronics. They are set horizontally and record the water content in cm 3. cm -3 on an hourly basis. The calibration is performed on two measurements, one in dry and one in water-saturated condition. At the OPE site, the soil moisture sensors used (UMP-1Umwelt Geräte Technik GmbH) are based on a frequency domain reflectometry (FDR) method and measure local change in dielectric permittivity. Tipping bucket resolution is 0. 1 mm. h -1 on the two sites. The sets of atmospheric forcing variables (wind speed, precipitation rate, short-wave incident radiation, air temperature, air humidity, atmospheric pressure) used to force the ISBA LSM are observed in situ at an hourly time step by two local meteorological stations, one at OPE and one at GISFI.
The atmospheric forcing is assumed to be identical for all the lysimeters of each site. Annual precipitation is 20 % higher at the OPE site (876 mm. year -1 ) than at the GISFI experimental station (727 mm. year -1 ). Long-wave radiations are derived from atmospheric conditions using the equation of  Prata (1996). Atmospheric data gaps are filled by regressions on available data using two neighboring meteorologic stations. The gaps represent up to 12 % of the observations for the GISFI site. All soils for all lysimeters are described in Table  1  while all available observations and their characteristics are summarized in Supplement Table  S2.
Vegetation parameters For the lysimeters covered by vegetation (O1, O2, O3, G3 and G4), two additional soil parameters must be determined. The were derived form the TRY database  (Kattge et al. , 2020)  for alfalfa as this crop is not a standard vegetation type in ISBA. As there are no measurements of LAI for these lysimeters, the simulation of the LAI can only be compared to the literature. As expected, the simulated LAI is minimal between December and April and maximal in August, and they are variabilities between years (Supplement Figure  S4 ). For soils with grass (G4, O1, O2, and O3), the maximum LAI varies between 3 and 5, and the mean annual LAI varies between 1. 3 and 2. These values are similar to those found in other studies  (Calvet, 2000; Darvishzadeh et al. , 2008). LAI for alfalfa cover (G3) is larger (LAImax = 7.
5, LAImean = 4. 8) which is expected for such well-developed vegetation  (Wolf et al. , 1976; Wafa et al. , 2018). Here, we present the main results for each model approach described in section 2. 3 in terms of simulated soil water and drainage water dynamics, water budget and intense drainage water events. We used four skill scores to assess the ability of each model approach to reproduce. The overall bias between simulatuion and observation, the centred root mean square error (CRM SE) computed by subtracting the simulated and observed annual means from their respective time series before computing a standard root mean square error, the coefficient of determination (R 2 ) or the Pearson correlation (R), and the Nash and Sutcliffe (1970) efficiency criterion (N ASH) that determines the relative magnitude of the residual variance compared to the measured data variance.
Soil water dynamics The dataset allows assessing the evolution of the total soil water mass derived from the mass of the lysimeter, of the soil volumetric water content at several depths, and of the drainage water flux at the bottom of the lysimeters. In the following analysis, periods when the meteorological forcing is reconstructed or when data is of poor quality (Supplemen Table  S1 ) are not taken into account in the scores computation. These periods are short, except for the drainage water of lysimeter G3 (23 % of the duration).
Drainage water Drainage at 2 m depth is measured at an hourly frequency. However, to compensate for the measurement limits associated with a 0. 1 m. h -1 threshold, the data is aggregated daily. The annual volume drained varies significantly between lysimeters of the same sites (Supplement Table  S2 ), although it is assumed they are exposed to the same atmospheric conditions. On the GISFI experimental station, the mean annual drained water is maximum on bare soil lysimeters G1 and G2 (>300 mm. year -1 ). The mean annual drained water is two to three times lower for the lysimeters covered by vegetation. At the OPE experimental station, all the lysimeters have a vegetation cover, and the mean annual drained water shows a variation of only 16 % from 300 to 363 mm. year -1. Such amounts are comparable to the volume drained on the bare soil of the GISFI experimental station, which is mainly due to higher mean annual precipitation at OPE.
We recognise that this last assumption has to be confirmed over many other experimental field sites. Indeed, this study is based on two experimental sites with similar climatic conditions, with low intensity precipitation events compared to other regions. It would be interesting to conduct additional studies in other contrasting climates. Finally, this study increases the confidence that LSMs are powerful tools to simulate the recharge of groundwater, in different environmental conditions, with many soils and vegetation covers, and therefore can be used for many applications in hydrology at both the regional and the global scales. The sensibility of our LSM to the soil heterogeneity or the value of some hydrodynamical parameters underline however that this remains a challenge even with the advent of global databases describing the vertical profile of the soil properties at depths greater than 1m  (Poggio et al.
Finally, in agreement with previous studies (Section 2. 2. 3) and our rock magnetic findings, SEM images of the samples and electron dispersive spectroscopy analyses show that iron oxides (magnetite) are present in larger quantities in heated samples. They are found in framboidal grains as precipitated grains and pseudomorphic rims around pyrite grains (Figure  3 ). Note that magnetite SD grains are <100 nm in size, which are abundant in the samples according to the FORC diagrams (Figure  S4  in Supporting Information S1), are not observable on regular SEM images. This plot was generated using the MAX UnMix software  (Maxbauer et al. , 2016). (c) First-order reversal curve (FORC) diagram obtained using the FORCinel software  (Harrison & Feinberg, 2008). (d) Hysteresis curves of the sample before and after heating.
Acknowledgments We thank  Dr. Y. Yu  and an anonymous reviewer for their fruitful comments. We thank  Prof. Charles Aubourg  for providing the BUR and SIG samples,  Prof. Pierre Rochette  for fruitful discussions of the results, and  Dr. François Demory  for assistance during the experiments and for providing the ISS samples. This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant  agreement No. 101027092.
They involve very different spacecraft, from a 12 Kg Cubesat (HaloSat) to major space-borne observatories (HST, Gaia, and Spektr-RG). I make use of updated 3D maps of dust and a recent massive star catalog. I distinguish arguments based on geometric similarity or dissimilarity from those derived from measurements and physical models. Considering all past and recent constraints, it is clear that there is no entirely local or entirely distant scenario that is free from contradictions with some of the data analyses or from improbable coincidences. I discuss a speculative scenario, partially inspired by recent BF and Milky Way gas models, combining near and far aspects and seeming to be able to meet the various constraints. However, new data and models are needed to bring the controversy to a close and we can still expect new episodes of this long story.
The four yellow triangles correspond to other additional measurements towards the NPS, however the longitudes of the probed directions are not +29 • (see text). A dual structure is contradicted by X-ray data. At variance with polarization measurements, X-ray data favor the continuity of the LNPS source properties from low to high latitudes, because there is no discontinuity nor sign of inhomogeneity of the recorded spectra. [1]  analyzed the entirety of Suzaku spectra in the direction of the LNPS and found that all spectra can be fitted with a 0. 3 keV gas, with no need for any discontinuity, nor even variation except for the emission measure. Such homogeneity is in agreement with the previous analyses based on Suzaku, XMM-Newton and Swift observations in various directions within both the LNPS and the northern FB contour (  [21, 22, 51] ).
Using global spectral shapes More quantitative estimates of the distances to the LNPS source regions can be obtained from spectral information on the absorbing foreground. Several X-ray spectra have been recorded towards NPS regions, and various conclusions have been drawn depending on the probed region and the spectral analysis. XMM-Newton then later on Suzaku were used to record spectra towards the NPS above b = +20 • (  [30, 54] ). The data analysis suggested a significant absorbing column on the order of the estimated full Galactic column. In the frame of a wider study of the whole X-ray emission supposedly associated with the Fermi bubbles,  [21]  recorded and analyzed the Suzaku spectra for 8 sightlines towards the high latitude NPS (43 • ≤ b ≤ 48 • ). Using a single component for the NPS and Galactic halo (in addition to the Local Bubble) the authors found that the column of the cold absorbing foreground is on the order of the full Galactic column.
Compared to the spectral modeling of  [25] , the analysis was made differently. [18]  used data recorded at the same latitude but out of the NPS to perform a preliminary determination of the emission from the LB and the one from the Galactic halo (at such latitudes the halo emission is a non-negligible contributor, which not was not the case for the NPS between 6 and 11 degrees latitude where the emission from the bulge is the main additional contributor). The authors built model spectra for various combinations of thermal+CX emission and absorbing columns of both neutral and ionized interstellar gas and found that a high absorbing column of hot (0. 17-0. 20 keV) ionized gas is necessary to reproduce simultaneously the enhanced OVII and Ne IX forbidden-to-resonance ratios and high OVIII Lyβ line relative to other Lyman series.
This is certainly not the end of the story and we emphasize that the suggested speculative scenario lacks modeling support. New large-scale models of the FBs and of their expansion are needed, using more complex gas distributions in the disk, like the one shown in Figure  3 , and in the halo, taking into account the density asymmetry. This is a particularly difficult task, however the results may show whether or not the observed radio/microwave spurs and Loop I can be reproduced in 3D, and not only their approximate directions as in Figure  3. More sensitive and numerous radio/submm data and especially Faraday tomography studies (with LOFAR) will be also crucial to confirm or not the proximity of the high latitude Loop I. Future Gaia data releases and resulting dust maps with higher resolution and larger extent are also expected to help, and in the far future Athena X-ray spectra should bring further constraints.
We present a measurement of the momentum spectra of π AE , K AE , p AE , Λ, Λ, and K 0 S produced in interactions of negatively charged pions with carbon nuclei at beam momenta of 158 and 350 GeV=c. The total production cross sections are measured as well. The data were collected with the large-acceptance spectrometer of the fixed target experiment NA61/SHINE at the CERN SPS. The obtained doubledifferential p-p T spectra provide a unique reference dataset with unprecedented precision and large phasespace coverage to tune models used for the simulation of particle production in extensive air showers in which pions are the most numerous projectiles.
The negatively charged hadrons (h -) produced in these interactions were transported through the H2 beam line to the NA61/SHINE experiment. A beam momentum of 158 GeV=c was requested for the first part of data taking and 350 GeV=c for the second part, each of which lasted one week. At these momenta the negatively charged beam particles are mostly π -mesons. They are identified by a differential ring-imaging Cherenkov detector (CEDAR)  [28]  and the fraction of pions was measured to be ∼95% for 158 GeV=c and ∼100% for 350 GeV=c (see  Fig. 2 in [37] ). The CEDAR signal is recorded during data taking and then used as an offline selection cut. Downstream of the CEDAR three proportional chambers are used as beam position detectors (BPDs) to measure the trajectories of the incoming particles. Two scintillation counters (S1 and S2) and three veto counters (V0, V1, and V1 0 ) define the beam trigger with the coincidence see inset in Fig.
For most of the data taking period, the pre-scaling was 0. 4% for the zerobias beam trigger and 100% for the minimum-bias interaction trigger. The target used for this study was an isotropic graphite plate with a thickness along the beam axis of 2 cm and a density of ρ ¼ 1. 840 g=cm 3 , equivalent to about 4% of a nuclear interaction length. 90% of data was recorded with the target inserted and 10% with the target removed. The latter data was used to subtract interactions that took place in the material upstream and downstream of the target. In total, 5. 5 million events were recorded with the target inserted at a beam momentum of 158 GeV=c and 4. 5 million events at 350 GeV=c.
A. Reconstruction The raw data recorded during data taking are processed with the standard NA61/SHINE reconstruction chain (based on tried-and-tested algorithms developed by the NA49 collaboration) to obtain high-level physics information such as the charge and momentum of the produced particles. First, charge clusters are identified in the raw data of the TPCs and their three-dimensional positions are reconstructed from the centroids in drift time and in position on the TPC readout pads. A pattern recognition combines these clusters to form local track segments in each TPC separately. The local track segments are matched to global tracks for which the track parameters (track position at reference plane, charge, and three-momentum) are fitted. The beam trajectory before the target is determined using the position measurements from the BPDs.
B. Simulation Through this analysis, we will use simulations of the measurement to correct the raw-data spectra for various distortions originating from the detector acceptance, reinteractions with the detector material and within the target, feed-down from weak decays etc. For this purpose we simulated π --C interactions at both beam momenta with the hadronic event generators EPOS 1. 99  [31]  and QGSJet II-04  [32]  with the CRMC program  [33]. The 1. 99 version of the EPOS model was used rather than its newer "LHC" variant as the former is better tuned to interactions at SPS energies  [34]. The particles produced by these generators are then passed to a simulation of the passage of particles through the material and magnetic field of the NA61/SHINE setup using the GEANT3. 21 package  [35].
Furthermore, we reject events with an interaction vertex reconstructed far from the center of the target (jΔzj > 17 cm), since such events mostly originate from interactions outside of the target. With these criteria we select 2. 8 × 10 6 and 2. 6 × 10 6 minimum-bias triggers recorded with an inserted C-target at 158 and 350 GeV=c, respectively. By construction, due to the restriction to events with a reconstructed vertex close to the target position, only very few events recorded with a removed target survive the selection (≲7 × 10 3 events in each dataset). The sum of simulated events with an inserted target is 7. 2 × 10 6 and 6. 0 × 10 6 for beam momenta of 158 and 350 GeV=c, respectively.
The former group is defined as the tracks that bend away from the beam axis, while the latter as the tracks that bend toward the beam axis [40], i. e. , p x q > 0 (RST) and p x q < 0 (WST), where p x ¼ p sin θ cos ϕ and p and q are the momentum and charge of the track. These conditions simplify to q cos ϕ > 0 for RSTs and q cos ϕ < 0 for WSTs for the usual case of forward production in the laboratory frame, θ > 0. The distinction between the two track topologies is motivated by the fact that a right-and a wrong-side track with the same p and p T , cross different regions of the detector, which has important implications for the particle reconstruction and identification step based on the dE=dx measurements (see Sec. VI). Furthermore, this subdivision defines two independent datasets that can be compared to estimate the systematic uncertainties of the measured particle multiplicities, see Sec.
2. 9  [50] , EPOS 1. 99  [31] , QGSJet01  [51] , QGSJet II-03  [32] , Venus 4. 12  [52] , SIBYLL 2. 1  [53] , UrQMD1. 3. 1  [54, 55]  using the INTTEST mode of CORSIKA  [56]. The arithmetic mean of the different predictions of f prod is used to correct the data, and the maximum and minimum values as an estimate of the systematic uncertainty. In previous cross-section analyses within NA61/SHINE we used the absence of a signal in the S4 counter to define an interaction. But, as can be seen in Fig. 20  in the Appendix A, especially for the 350 GeV=c data studied in this paper, the radius of 1 cm of the S4 would lead to a large model-dependent correction for σ prod with Eq. (  6 ). Therefore, we instead use the GTPC to define an offline interaction trigger by requiring the absence of a track within a radius r trig from the beam extrapolation at 3.
In Appendix A we provide the efficiency map corresponding to the two beam energies such that in the future it will be possible to recalculate f prod with a different set of models, and possibly smaller systematic uncertainty. The obtained cross sections are presented in Fig. 4  along with the theoretical prediction using the Glauber theory  [43]  for several assumptions on the inelastic screening parameter λ  [45]  (from top to bottom λ ¼ 0. 1, 0. 3, 0. 5, 0. 7, and 0. 9) and previous measurements  [58, 59, [61] [62] [63]  adjusted to σ prod  [60]. As can be seen, the only previous dataset on the production cross section in π --C interactions from Ref. [58]  agrees well with our measurement and the total uncertainty of the cross sections presented here matches the statistical precision of this old measurement (no systematic error was quoted in this reference).
VI. dE=dx ANALYSIS The spectra of identified particles produced at the main vertex are obtained by extracting the average particle yields in each kinematic bin from the measured distributions of the energy loss of tracks in the TPCs. For this purpose, FIG. 5. Examples of the fitted m inv distributions for the 158 GeV=c dataset with target inserted for Λ (left), Λ (middle), and K 0 S (right). The black markers show the measured m inv distributions. The curves show the results of the fit with the signal in blue, the background in red and the total in gray. On the bottom of each plot we show the residual distributions, i. e. , the difference Δ between the observed and fitted number of entries in units of the uncertainties σ of the observed number. The hpi and hp T i of the phase space bin are indicated on the top of each panel.
C sim is a correction factor derived from simulations that will be discussed in the next section. The indexes I and R refer to target inserted and removed, respectively, and N is the number of selected minimum-bias interaction-trigger events. The factor b is the so-called target-removed factor, and it normalizes both target inserted and removed dataset to the same number of beam particles. Given the number of events, N T int , and the probability of a beam particle to produce an interaction trigger, P T int , the number of beam particles for a given dataset can be estimated as N beam ¼ N T int =P T int. P T int is measured from zero-bias beam triggers, see Eq. (  3 ). The target-removed factor is given by b ¼ N I beam =N R beam and its value is about five, corresponding to the time spent in target-removed configuration during data taking.
A. Correction factors The selected number of events N and the estimated number of particles n in a given phase space bin are biased estimators of the true number of production events N prod and the true number of produced particles n. These biases are corrected for with the help of simulations for which it is easy to determine the ratio of generated and measured spectra, C sim ¼ n gen N gen n N ; ð13Þ where n and N prod are the values from the event generator, n and N T int are obtained after the detector simulation, event reconstruction, and event and track selection. Two simulated datasets generated with different hadronic interaction models (see Sec. III) are used for systematic studies. To gain further insights into the different contributions to the correction factor it is useful to split C sim into event-and particle-contribution factors, referred to as α and β in the following, C sim ¼ N N gen n n gen ¼ α=β: ð14Þ The α factor is a property of the dataset as a whole and thus depends only on the beam energy, whereas the β factor depends on the phase space bin and on the particle type.
We get α ¼ 0. 872 AE 0. 004 and 0. 732 AE 0. 012 for beam momenta of 158 and 350 GeV=c, respectively. These values were determined from the arithmetic average of the two simulated datasets, with the upper uncertainty range corresponding to the result predicted by EPOS 1. 99 and the lower range to the one from QGSJet II-04. These values give the product of the efficiency of the vertex-z cut (cf. Sec. III C) and the efficiency of the minimum-bias interaction trigger (cf. Sec. II). The former amounts to approximately 0. 975 with a good agreement between real and simulated data. Therefore, α is dominated by the trigger efficiency and is thus mainly a model-dependent correction, since the efficiency depends on the fraction of events with a high-momentum charged particle triggering the S4 scintillator.