dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This paper made extensive use of the SIMBAD Database at  CDS (Centre de Données astronomiques) Strasbourg , the  NASA/IPAC  Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, CalTech , under contract with  NASA , and of the VizieR catalog access tool,  CDS, Strasbourg, France. PF acknowledges support from the  Conseil Scientifique de l'Observatoire de Paris  and the  Programme National Cosmologie et Galaxies (PNCG). L. M. , A. P. , E. M. C. and E. D. B. acknowledge financial support from  Padua University  through grants  DOR1699945/16 ,  DOR1715817/17 ,  DOR1885254/18 , and  BIRD164402/16.
Space-borne missions such as CoRoT and Kepler have provided a rich harvest of high-quality photometric data for solar-like pulsators. It is now possible to measure damping rates for hundreds of main-sequence and thousands of red-giant stars with an unprecedented precision. However, among the seismic parameters, mode damping rates remain poorly understood and thus barely used for inferring the physical properties of stars. Previous approaches to model mode damping rates were based on mixing-length theory or a Reynoldsstress approach to model turbulent convection. While able to grasp the main physics of the problem, those approaches are of little help to provide quantitative estimates as well as a definitive answer on the relative contribution of each physical mechanism. Indeed, due to the high complexity of the turbulent flow and its interplay with the oscillations, those theories rely on many free parameters which inhibits an in-depth understanding of the problem.
Our aim is thus to assess the ability of 3D hydrodynamical simulations to infer the physical mechanisms responsible for damping of solar-like oscillations. To this end, a solar high-spatial resolution and long-duration hydrodynamical 3D simulation computed with the ANTARES code allows probing the coupling between turbulent convection and the normal modes of the simulated box. Indeed, normal modes of the simulation experience realistic driving and damping in the superadiabatic layers of the simulation. Therefore, investigating the properties of the normal modes in the simulation provides a unique insight into the mode physics. We demonstrate that such an approach provides constraints on the solar damping rates and is able to disentangle the relative contribution related to the perturbation (by the oscillation) of the turbulent pressure, the gas pressure, the radiative flux, and the convective flux contributions.
The solar simulation For our subsequent analyses we use data from a numerical, hydrodynamical simulation of the solar surface. The simulation was computed as part of a more extended research project devoted to solar physics and the adequacy of numerical tools used in modelling the solar surface (see  Grimm-Strele et al. 2015a ). One of the simulations computed for this purpose and called cosc13 was used for the present work. Its setup and some details of the simulation code are described in the following.
The ANTARES simulation code The solar 3D hydrodynamical simulation cosc13 has been computed with the ANTARES code  (Muthsam et al. 2010 ) which numerically solves the Navier-Stokes equations (NSE) for a fully compressible fluid and accounts for radiative heat transfer, viscous processes, and realistic microphysics. ANTARES uses a conservative, 5 th order weighted essentially non-oscillatory (WENO5) finite difference scheme  (Shu 2003; Jiang & Shu 1996)  to discretize pressure gradients and terms representing advection in the NSE and a fourth order conservative finite difference scheme  (Happenhofer et al. 2013)  to discretize dissipative terms. Time integration is done by an explicit three-stage, second order Runge-Kutta scheme known as SSP RK(3,2) originally proposed by  Kraaijevanger (1991)  and analysed by  Kupka et al.
simulation is computed in the Fourier domain using Eq. (  3 ) for the amplitude. Note that for the first mode, we used the spectral resolution instead of the linewidth since it is not resolved. For consistency, we thus compute a 1D solar model that matches both the solar gravity and effective temperature but also the mean temperature at the bottom of the 3D numerical simulation. This model has been obtained using the CESTAM evolutionary code  (Marques et al. 2013 ) assuming standard physics: Convection was included according to  Canuto et al. (1996) , with a mixing-length parameter α = 0. 67, and turbulent pressure is ignored. Microscopic diffusion was included. The OPAL equation of state is assumed. The chemical mixture of the heavy elements is similar to that of  Grevesse & Noels (1993) 's mixture.
Concluding remarks Using a 3D hydrodynamical simulation of the Sun computed with the ANTARES code and with a time-duration of about 11   8. Cumulative work integrals contributions of the gas pressure associated with mode 1 (top panel) and mode 2 (bottom panel), starting from the bottom of the simulation, as a function of the logarithm of temperature. The total mode damping of each mode is used to normalize the work integrals. Note that due to boundary condition effects, the upper-most layers (log T < 3. 64) must be considered with care (see text for details). hrs, we have shown that it is possible to identify at least three radial normal modes in the simulation, two of which were usable for our purposes. Those modes have been shown to have properties similar to the normal modes of 1D solar models that happen to have a node at the bottom of the simulation domain.
range of some of the key measurements, such as mass spectrometer composition, made the data interpretation difficult at low altitudes. Since then, in-situ measurements in the LTI have been limited to short crossings by sounding rockets, which by nature give only a snapshot of the LTI over a single location, whereas, for example, to understand the spatial structure and temporal evolution of key processes in response to a multi-hour solar storm, longer-term observations are required, across different locations. Density measurements as low as 130 km have been inferred from the decay of low-altitude surveillance 5 satellites, and have been useful for understanding the gross features of the lower thermosphere, but the electrodynamics and composition of the transition region between 100 and 200 km remain obscure.
Some major species 15 composition information is obtained by a combination of UV, IR and FPI measurements, but there is a significant gap in the obtainable profiles at ~100-200 km due to lack of appropriate emissions for observation. It is also noted that different observation methods may produce large deviations (even orders of magnitude) in estimates of key parameters in the LTI, such as conductivity, ion drifts and neutral winds, with no baseline dataset for comparison. 2 Daedalus Science  Objectives 20  The main scientific objectives are two-fold: On one hand, Daedalus will quantify, for the first time, the key unknown heating processes in the LTI, and in particular the largely unknown Joule heating as well as energetic particle precipitation heating, investigating how these affect the dynamics and thermal structure of the LTI, and how density, composition and temperature of the LTI vary during periods of enhanced heating associated with extreme space weather events.
These 10 measurements, through the comparison of the two methodologies described above, will also allow a new estimate of collisional cross sections, which are needed and widely used in simulations, analysis of radar data etc. Daedalus will have a complete suite of instruments to compare the two methodologies and to resolve which approximations are valid. In summary, to evaluate all the parameters that go into Joule heating calculation in a local volume of space, the required measurements are: neutral winds, ion drifts (along track and cross-track), ion density, ion composition and ion 15 temperature, electron temperature, neutral density, neutral composition (primarily N 2 , O, O 2 , N, NO) and neutral temperature, magnetic field, DC electric fields and DC currents. In Fig. 7  simulation results for Joule heating are plotted based on two physics-based models: TIE-GCM (left) vs.
Ram Wind Sensor (RWS) and Cross-Track Wind Sensor (CWS) Studies of the kinetics of neutral particle flow in the free molecular flow regime of the satellite environment led early on to various concepts for neutral wind measurements. Based on these concepts, measurements have since resulted in a large body 15 of data of high spatial resolution. These data have revealed an unexpectedly complex and variable neutral atmosphere, a signature of the deposition of large and highly variable quantities of energy. To resolve neutral winds two sensors will be used on Daedalus: a Ram Wind Sensor (RWS) and a Cross-track Wind Sensor (CWS).
Accelerometer (ACC) The accelerometer on-board Daedalus will measure non-gravitational accelerations such as air drag, Earth albedo and solar 5 radiation acting on the satellite. The direct measurement of acceleration α due to air drag can in turn be used to derive the total atmospheric mass density ρ, through the fundamental relationship: α = ½ ρ V 2 C d A / m, where C d , A, m, and V are the s/c coefficient of drag, cross sectional area, mass, and velocity respectively  (Hedin, 1991). Accelerometer data can also be used to derive winds in the thermosphere  (Sutton et al. , 2007; Doornbos et al. , 2010; Dhadly et al. , 2018). Using expected values of these parameters and obtaining values for ρ from the NRLMSISE-00 atmosphere model, it is calculated that the 10 drag acceleration will be on the order of 10 -7 g at 500 km and 10 -3 g at 120 km.
[27]  and was previously successfully applied to thin layers and nanostructures  [28] [29] [30] [31] [32]. Employing an additional sample rotation, a five-dimensional data set I(x, y, Q x , Q y , Q z ), comprising two real space and three reciprocal space coordinates, is obtained. Note that the absorption depth of X-rays for the used energy in GeTe is around 20 µm, making this method bulk sensitive. For the presented studies, we recorded the diffraction intensity in the vicinity of the 222 and 444 Bragg reflections. Note that all specified Miller indices refer to the rhombohedrally distorted unit cell derived from the rock-salt structure. Considering the (111) facet/surface of the studied bulk crystal and thin film samples, this corresponds to an incidence angle of around 18 • and 39 • to the surface.
X-Ray Absorption Fine Structure Spectroscopy and Anomalous Diffraction Extended X-ray absorption fine structure measurements (EXAFS) were carried out at the XAS beamline at the Synchrotron Light Source ANKA in Karlsruhe, Germany. Data were acquired at the Ge-K edge in fluorescence mode. The obtained spectra were analyzed using Artemis  [33]  and Mkfit  [34] , which calculate the EXAFS signal using an ab initio multiple scattering theory. The combination of the mentioned software packages allows for directly refining the unit cell structure of GeTe together with the position of the atoms inside the unit cell. In contrast, Artemis alone only allows for refining the coordination shell radii. Anomalous diffraction was performed at beamline ID01 at the ESRF around the Ge-K edge in symmetric coplanar diffraction geometry.
Here, h represents the three-dimensional, reciprocal lattice vector of the respective Bragg reflection. In the simulation, we use the well-known approximation of the Thomson (classical) form factors by a sum of four Gaussians  [38]. The energy dependent dispersion (Hönl) corrections f (1) (E) + i f (2) (E) can be decomposed into a smooth part related to the isolated atom and an oscillatory part (the fine structure) that depends on the chemical environment of the atom and becomes important close to its absorption edges. Since there is only one unique resonant atom in the crystal structure, the imaginary part f (2) can be directly extracted from the EXAFS data. The smooth parts of f (1) and f (2) can be taken from databases  [39, 40]. The fine structure for f (1) was then obtained using the Kramers-Kronig relations applied to the spectra of f (2) (E)  [41].
In our case, the PFM amplitude signal is proportional to the projection of the FE polarization perpendicular to the sample surface. Therefore, it is proportional to the strength of spontaneous polarization along the α-GeTe(111) surface normal. In addition to simultaneous mapping of sample topography and FE amplitude and phase, local spectroscopy can be recorded; i. e. , the response of the PFM signal to poling. This enables measuring hysteresis loops, where our data systematically show asymmetric P-E loops and data from films on InP(111) show coercive fields less than 10 mV. In light of this small coercive field, we note that the PFM-phase domain mapping on films remains unchanged even after two days, thus ruling out charging artifacts induced by the PFM setup. For a sufficient signal-to-noise ratio in data acquisition, the excitation during PFM measurement was higher for bulk crystals (4V) than for thin films (1V), both with frequencies around 270 kHz.
Although PFM is measured close to resonance, no cross-talk with topography is visible which validates the quality of our PFM data discussed below. In order to ensure consistency, the PFM characterization of our GeTe films was compared to data obtained with a Cypher AFM from Asylum Research at the PSCM AFM Laboratory at the ESRF. The P-E loops were measured in dual AC resonance tracking. It follows that both instrumental setups deliver consistent results, in agreement with published results  [6, 7, 23].
Acknowledgments: The authors acknowledge the support of  Alain Panzarella  and  Diego Pontoni  at the PSCM facilities at the  ESRF  as well as  Stefan Mangold  at the  XAS-beamline at ANKA  for their support. Scanning XRD experiments were performed on beamline ID01 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. As well as Open Access Funding by the  Austrian Science Fund (FWF).
The ESM approach also includes modeling of the temporal domain through the inclusion of which entities and relationships "exist" at various time steps. The ESM approach explores several topological metrics of the resulting graph in an effort to characterize a complex system in order to support decisions, such as where to exert influence in the system to make the most improvements or to determine which parts of the system are more important than others. The author highlights challenges in the collection and the mapping of the data in order to construct a graphical model of the project (eg, relying on qualitative interviews to establish relationships between some entities). Several topological assessments were made from the resulting graph at different times; number of vertices, number of edges, average degree, average path length, and clustering coefficient.
, 2004)  are designed to observe lightning in the very high-frequency (VHF; 30-300 MHz) range, which allows detection of the lightning leader phases and mapping of the lightning branches inside the cloud. This provides information on lightning closely linked with the microphysical structure of the cloud in which the discharges propagate. In the frame of the HyMeX program  (Ducrocq et al. , 2014) , which aims at documenting the water cycle in the Mediterranean Basin that is considered a climatic hot spot  (Giorgi, 2006) , we plan to monitor the convection to address the question about the evolution of high-precipitation events and deep convection in response to climate change. For this purpose, the Collectivité Territoriale de Corse -via the PCOA (CORSiCA Atmospheric Observations Platform, https://corsica.
In the following, we first present the SAETTA network and its performances in Sect. 2, then describe the overall observations performed from 2014 to 2016 in Sect. 3, describe insight into specific and unusual events detected so far at storm and lightning scales in Sect. 4, and discuss the perspective of such an instrument with respect to the scientific questions addressed and to the operational needs for ground-based and spaceborne lightning observations. Several results presented benefited from the use of the XLMA software developed by Ron Thomas  (Thomas et al. , 2003) , which was used as an analysis and display tool.
, 2004). As a matter of fact, a minimum of six stations are required in the data processing although only four unknowns (x, y, z, t) are to be determined for each VHF source. This consideration of redundant measurements is useful for checking the solution's validity. Several advantages arise from a 12-station network: for instance, the redundancy/reliability in the case of shortterm and long-term failures, the effect of localized high-rate storms on a sensor's contribution to more-distant activity, and the improved geometry for geo-location of distant lightning while maintaining height accuracy for nearby low-altitude lightning channels. Another advantage would be that more VHF sources can be located during a given discharge because two different sets of six stations can detect sources in the same time window.
Typical lightning observation SAETTA is able to detect flash activity up to approximately 350 km from the center of the network (Pinerole station). An example of this large-scale detection is displayed in Fig. 5  as VHF source density for the 28 June 2017 event. The left graph corresponds to minimally filtered data (minimum of six stations and reduced chi-square χ 2 ≤ 5; see  Thomas et al. , 2004). It is composed of various panels according to the conventional XLMA format: altitude vs. time (top panel); altitude vs. longitude or view from the south (center left panel); VHF sources altitude histogram (center right panel); latitude vs. longitude or geographical projection (bottom left panel); altitude vs. latitude or view from the west (bottom right panel). One can see in the vertical projections (center left and bottom right panels) that lower layers are poorly and even undocumented far from the network because of the Earth roundness, as it is with meteorological radars.
Short lightning climatology over Corsica In order to assess the behavior of convection over Corsica, overall data for all years of observation are accumulated. The data analysis focuses on the 240 km × 240 km domain of better detection accuracy centered on Corsica with filtered data (VHF sources determined with a minimum of seven stations and with a reduced-chi square χ 2 ≤ 0. 5). At the present time the data processing and analysis of 3 years of observation have been completed  (2014; 2015; 2016). The sample size is not yet large enough to call this climatology but it gives the first details of the stormy behavior in this region. The overall cumulative number of filtered VHF sources is displayed in Fig. 7a. The VHF sources are counted on 1 km × 1 km pixels. Not clearly visible in this top left figure with a log scale in base 10, the highest values (dark red) are located above the main relief of the island of Corsica, and more specifically in the center of the northern part at the crossroads of three large valleys, roughly between the Pinerole and Compoli stations (see Fig.
2 and propagates upward over a vertical distance varying from 3 km (flashes no. 2 and no. 3) up to 5 km (flash no. 5). Then the positive leader spreads in a negative charge layer almost horizontal but with a tilt parallel to that of the positive charge layer below, at altitudes ranging from a maximum of about 12 km to a minimum of about 8 km, and at horizontal distances varying from 10 km (flash no. 3) to 33 km (flash no. 5), in several directions but mainly downwind. Those uncommon flashes can be illustrated by flash no. 5 that appeared just before 13:21:43. 5 UT. The corresponding VHF sources are displayed in Fig. 13  versus time (top left), altitude (top right), power (bottom left), and inferred cloud charge polarity (bottom right). The polarity of the VHF sources (selected by hand with the XLMA software) is deduced from the intensity of the power with which they are detected.
A more comprehensive assessment of SAETTA's performance could undoubtedly benefit from comparisons with trajectories of airliners that can be detected by the network. We plan to perform this kind of comparison with GPS data of commercial flights. Combining observations from the years 2014 to 2016, we have elaborated a preliminary climatology of total lightning activity on Corsica, which has never been done so far, in a 240 km × 240 km domain. The number of lightning days per square kilometer is dominated by daytime convection over the relief from June to July, with a local maximum at the north center of the island produced in July between 11:00 and 14:00 UT. Tidiga et al. (2018)  showed -via a numerical study using the cloud-resolving model Meso-NH at high resolution -that in absence of synoptic forcing this local maximum is likely due to the low-level convergence of moist air fluxes originating from sea breezes channeled through three main valleys that converge towards each other at this location.
Subsequent studies envisaged should analyze the fine-scale impact of synoptic forcing on this scenario. Additionally, the monthly number of lightning days undergoes two maxima: (i) one in June due to daytime convection in phase with the maximum of solar flux at the summer solstice and (ii) one in September associated with numerous small storms over the sea or with some high-precipitation events. Those last events may be associated with high precipitation and flash floods  (Scheffknecht et al. , 2016)  and are the focus of the HyMeX program  (Ducrocq et al. , 2014). The present paper also reports unusual lightning events that occurred in a mesoscale convective system on 8 June 2015. Produced during convective surges, the first type of lightning events consisted of numerous VHF sources concentrated on a small perimeter (5 km by 5 km) and protruding from the top of the cloud located at about 11.
Acknowledgements. Acknowledgements are addressed to SAETTA main sponsors:  Collectivité Territoriale de Corse  through the  Fonds Européen de Développement Régional  of the European Operational Program 2007-2013 and the  Contrat de Plan Etat Région  that funded the  CORSiCA  project;  Collectivité de Corse  through the CORSiCA 2017-2019 project;  CNRS-INSU  through the  HyMeX/MISTRALS program ;  ANR IODA-MED ;  CNES  through the  SOLID  project;  UPS/Observatoire Midi-Pyrénées ; and  Laboratoire d'Aérologie. We also sincerely thank the many individuals and regional institutions in Corsica (including the  Conservatoire du Littoral ,  Qualitair Corse , and  INRA San Giuliano) , who host the 12 stations of the network, who helped us to find sites, or who bring us assistance for the logistics during missions in the field.
We acknowledge the use of imagery from the  NASA  WorldView application (https://worldview. earthdata. nasa. gov,last access: 23 October 2019), part of the NASA Earth Observing System Data and Information System (EOSDIS). We thank both reviewers for their constructive comments and suggestions that helped us to improve the paper. Financial support. This research has been supported by the  Collectivité Territoriale de Corse  (FEDER 2007-2013  CPER CORSiCA  grant), the  ANR  (grant no. IODA-MED ANR-11-BS56-0005 ), the  MISTRALS/HyMeX  (grant no. TS4a ), the  CNES  ( SOLID grant ), and the  Collectivité de Corse  ( CORSiCA 2017-2019  grant). Review statement. This paper was edited by  Christian Barthlott  and reviewed by two anonymous referees.