dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank the  JMARS team  for providing global data sets, and  Deanne Rogers  and  Olivier Gasnault  for their compositional provinces maps. We thank  Suniti Karunitillake  for assistance and verification of the multivariate analysis methods. Our analyses were made possible through the use of the  NAU Monsoon High Performance Computing cluster. This research was funded in part through a collaboration between the  Mohammed Bin Rashid Space Centre, Northern Arizona University , and the  University of Colorado, Boulder Laboratory for Atmospheric and Space Physics  and the 2001 Mars Odyssey Project.
Data Availability Statement All data sets used in this work are available through JMARS (https://jmars. mars. asu. edu/) or the NASA Planetary Data System (PDS) (https://pds-geosciences. wustl. edu/dataserv/mars. html). The Mars Odyssey Gamma Ray Spectrometer (GRS) derived chemical maps were derived from the spectral data archived on the PDS by applying the spectra-to-chemistry modeling methods described by  Boynton et al. (2007) ,  Evans et al. (2006) , and  Karunatillake et al. (2007). The derived chemical maps have also been described and analyzed in the works by  Hood et al. (2019)  and  Ojha et al. (2019) , among others.
Identification of the different contributions in the measured LEE enhancement Additional incoherent 3D-FDTD simulations in a pulsed regime were performed to evaluate light extraction efficiency for some of the fabricated structures to better understand our results. To perform those simulations, we use commercially available software (FullWAVE and LED Utility from the Rsoft Design Suite)  [32, 33]. The lateral dimensions of the computation domain were fixed at 10 lattice periods for computing time issues and surrounded by perfectly matched layers (PML). The input spectrum is Gaussian-shaped with a center wavelength at 640nm and a FWHM of 20nm as in our PL measurements. The MQW region was represented by a source plane of 50 transverse electric (TE)-polarized dipoles in the middle of the CCL (see Table  2 ).
PoS(ICRC2021)572 New cosmic ray MIN-MED-MAX benchmark for dark matter indirect signatures Pierre Salati at h 250 GV needs to be introduced to explain the hardening of the primary CR fluxes. The three high-rigidity break parameters ( h , h , h ) are set to the values reported in  [8]. Below l , the index is replaced by l to account for the spectral break seen in CR data below a few GV. This low-rigidity break can result from Galactic advection with vertical wind c , supplemented by diffusive reacceleration triggered by the Aflvénic speed , or from damping effects in the magnetic turbulence spectrum on small scales. In  [8] , three CR transport schemes were introduced. The most general scheme is BIG with 7 free parameters ( 0 , , l , l , c , , ) while l is set to 0. 05 for fast transition and = 1.
The configuration SLIM is a special case of BIG, with = c = 0, and it has 5 free parameters ( 0 , , l , l , ). The configuration QUAINT is also a special case of BIG with no low-rigidity break but a low-velocity tuning parameterised by , and it has 6 free parameters ( 0 , , , c , , ). We will be mostly concerned here with the SLIM scheme. The remaining columns combine 'Base' data (from first column) to 10 Be/Be low-energy data (third column), to 10 Be/ 9 Be low-and intermediate-energy (next-to-last column), or combine all the previous cases (last column). For more details, see  [1, 2]. At high energy, the abundance of secondary nuclei relative to their primay progenitors is proportional to /. At first order, the B/C and Li/C ratios cannot break the degeneracy between the diffusion coefficient and the height of the magnetic halo.
Defining the new MIN, MED and MAX models The DM signal is produced all over the magnetic halo and not solely inside the Galactic disk. It is approximately proportional to the ratio 2 / where the diffusion coefficient is taken at the rigidity of interest. From a few GV upward, B/C and Li/C data constrain the ratio / so that the DM signal is merely proportional to. The thicker the magnetic halo, the stronger the signal. At lower rigidities, and become independent. The more negative the low-energy index l , the larger at sub-GeV energies and the weaker the antiproton and positron fluxes. The CR parameters that crucially control the intensity of the DM signal are and l. From the best-fit values and covariance matrix of Tab. 1, we draw a collection of 10 5 SLIM (correlated) propagation parameters.
New MIN-MED-MAX fluxes on selected examples In Fig. 3 , we present examples of DM-generated antimatter fluxes. The primary positron (left) and antiproton (right) fluxes are calculated for a subset of the SLIM models derived in Sec. 3 and Pierre Salati a DM mass of 100 GeV. The positron flux is calculated using the so-called pinching method in a 2D setup  [10] , which is the most up-to-date semi-analytical procedure to incorporate all CR transport processes. The DM halo profile is borrowed from  [11]  with a galacto-centric distance of 8. 21 kpc, a local DM density of 0. 383 GeV cm -3 and a scale radius set to 18. 6 kpc. different annihilation channels, i. e. ¯ in pink and + -in blue. The annihilation cross section has been respectively set to 1. 5 × 10 -24 and 3 × 10 -25 cm 3 s -1 , to obtain primary fluxes roughly at the same level as the A -02 data  [12] , just for illustration purposes.
We apply the technique to Wind/WAVES data during 1999 day of year 227-257, when the Cassini spacecraft flew past Earth and provided an opportunity to observe AKR from two remote locations. We examine the AKR flux and power, with observations made from LTs of 1700-0300 hr having an average power up to 10 4 Wsr -1 larger than those on the dayside and an increasing AKR power observed at higher magnetic latitudes. We perform a linear cross-correlation between the Wind AKR power and the spacecraft magnetic latitude, showing positive then negative correlation as Wind travels from the Northern to Southern magnetic hemisphere. Statistically significant diurnal modulations are found in the whole 30-day period and in subsets of the data covering different local time sectors, indicative of a predominantly geometrical effect for remote AKR viewing.
The reproduction of well-known features of the AKR verifies the empirical selection and shows the promise of its application to Wind/WAVES observations. Plain Language Summary Auroral Kilometric Radiation (AKR) is naturally occurring radio emission from the Earth's Northern and Southern polar regions, which becomes more intense as the aurora brightens. In this work, we examine data from the Wind spacecraft WAVES instrument from a 30-day interval in 1999 when a second spacecraft, Cassini, was also flying near Earth and measuring the AKR from a different viewpoint. In this work, we select the AKR using an empirical measure of the variability observed by the WAVES instrument, and compare the distribution and time profile of AKR intensity. Comparing measurements of this radio emission from different spacecraft positions help us to understand how the AKR is best viewed and illustrate the constrained beaming of the emission.
Although this is less true for the spin period than it is for the sweep duration, it is possible that the variability measured by the Z antenna for a spin is due to the more slowly varying intensity of separated sources. Given the generation mechanism of AKR, however, it is likely that a single source will have a highly varying amplitude on spin timescales of seconds. While it is nontrivial to discern between the two effects, it is important to note that this may also lead to an overestimation of the AKR selection, although this effect is negligible when data are averaged over the sweep duration. A statistical proxy for AKR can be derived from the Z antenna by modeling the 8 measurements made during a single spin as a normal distribution centered on a mean intensity, with any variability then being described by its standard deviation  E.
10. 1029/2021JA029425 7 of 26 shows similar data for an observation of a solar radio Type III burst, with the double-peaked modulation pattern in the received power clearly visible in the top panel and the Z antenna measuring nearly constant intensity emission; illustrating measurements of a radio source more appropriate for use with a GP inversion. While a comprehensive study of the  Z E distribution is not included, examination of the dynamic spectrograms show that the Z antenna consistently measures higher variability (using  Z E ) during periods of AKR bursts. To select regions that correspond to AKR emission, a numerical threshold is chosen based on the visual identification of the dynamic spectrograms from Wind during the Cassini flyby; here   0. 1 Z E. Given that the opposite limit of  Z E selects sources that are less variable,  Z E could be useful to select data that are more appropriate for a GP inversion with Wind.
Given that the retrieval of direction-finding parameters for radio sources is dependent on a strong signal, only observations with a signal-to-noise ratio (SNR) of 25 E dB are shown, highlighting the presence  The GP inversion is subject to a degeneracy in the wave vector direction, so angles that suggest an unphysical source direction for AKR have been transformed appropriately; Earth is at approximately  130 E for the data shown. For the AKR between 0800 and 2000 UT, the azimuthal angle is poorly constrained and has high variability during AKR observations due to the aforementioned variability during the spacecraft rotation. While  Z E acts as a proxy for the radio source here, we note that without access to the GP inversion and polarization information, we cannot make an exact physical inference and so the selection is empirical.
With average  Z E spectra for each sweep, a mask can then be created and applied to the calibrated flux densities to select data that meet this criteria; here the flux densities are also averaged across the 3-min sweep cycle. In this case, the consideration of multiple spatially separated sources cannot be ignored, and the flux spectra represent the spatial average of AKR emission across a relatively wide longitude as well as a temporal average. (middle panel) and the application of the resulting mask (bottom panel). Wind observations in both (a) and (b) contain many Solar Type III bursts with various drift rates that at times cover the entire RAD1 frequency range, as well as increases in QTN and emission at the plasma frequency at frequencies  E 100 kHz. The middle panel shows the effectiveness of  Z E as an identifier of AKR emission, with observations of Type III bursts exhibiting a standard deviation less than that of the AKR by about an order of magnitude.
AKR Flux Density and Power Figure  5  shows the AKR flux density of both Wind and Cassini for the entire 30-day period studied here. The AKR flux density with Wind is obtained using the calibration and selection outlined in Section 2. The AKR flux density with Cassini contains that of both LH-and RH-circularly polarized AKR, obtained using a GP inversion technique and selecting data with | |. V  0 2 , where E V is the normalized Stokes parameter describing circular polarization  (Lamy et al. , 2010). The maximum of either the LH or RH AKR is shown at 90 s resolution in the Cassini spectrogram. Both flux densities are scaled for the distance from the approximate source (Earth's center) and normalized to 1 AU to enable a comparison between the two data sets. The general effect of viewing position on the AKR observations from each spacecraft can be seen here; emission is stronger and more consistent for Cassini as it travels away from Earth downtail, remaining on the nightside, while a periodic variability is seen in the Wind spectrogram as it passes the nightside during its perigees.
(2010). The middle panel shows Wind flux densities returned by the selection (described in Section 2. 3) applied to data from 1999 DOY 227-257. Given the anisotropy of the AKR beaming and the longitudinal distribution of AKR source regions, it cannot be assumed that each spacecraft will observe the same emission region at a given time from different viewing positions in space. To elicit a valid comparison of the reduced spectra between the spacecraft, we select Wind data such that only observations made from a similar viewing position are included; Cassini was in the region with a magnetic latitude       23 16 m E and within 0000-0200 hr LT during its flyby. A complete discussion of the flux densities observed by Cassini can be found in  Lamy et al. (2010) , but here we compare the main features with those of Wind.
The peak of the primary Type III burst at 272 kHz is seen close to 18:10 UT in this example, and while good agreement is seen between the Type III GP inversions with Cassini and Wind, the Z antenna-calibrated data have an approximately constant offset following the peak. By defining the bounding UT of the Type III burst as well as its frequency limits following visual examination of the relevant dynamic spectrogram, we can track the peak flux of the Type III burst for each set of fluxes and produce a spectrum of the ratio  Z T III S E S , where Z E S is the flux from Equation  B1 and  T III E S is the flux from the Type III GP inversion with Wind. The example in Figure  B1  shows the entire Wind/WAVES RAD1 spectrum, including the lower frequencies that the Type III emission does not reach and showing the discrepancy between the two data below 70 E kHz.
Taking the mean of the distributions here gives an average that is skewed toward higher values and represents more closely the larger extreme of the total distribution of data, as seen by its position with respect to the 75% quantile in most panels of Figure  D1. The median of these distributions would be a more statistically rigorous measure of the average. Here, however, where the aim is to demonstrate the empirical selection of AKR data and not to rigorously define the average AKR power of these observations with LT, the mean is sufficient.
Acknowledgments  J. E. Waters 's work was supported by the  EPSRC   Centre for Doctoral Training in Next Generation Computational Modelling Grant  No. EP/L015382/1. C. M. Jackman 's work is supported by the  Science Foundation Ireland  Grant  18/ FRL/6199. D. K. Whiter  was supported by the  Natural Environment Research Council of the UK  under grant  NE/ S015167/1. The authors acknowledge  CNES  ( Centre National d'Etudes Spatiales ),  CNRS  ( Centre National de la Recherche Scientifique ), and  Observatoire de Paris  for support to the  Wind/ Waves team  and the  CDPP (Centre de Données de la Physique des Plasmas)  for the provision of the Wind/Waves  RAD1 L2  data. The authors acknowledge support from  Paris Astronomical Data Centre (PADC)  for the preparation and distribution of the data collection.
Data Availability Statement The Cassini RPWS data from the GP inversion during the period studied here are included in  Lamy et al. (2010)  and can be found online (https://doi. org/10. 25935/5JFX-DH49 (Cecconi et al. , 2017) ). The data used in this study can be found online (https://doi. org/10. 25935/wxv0-vr90), as can the code used to calibrate and apply the AKR selection (https://github. com/WatersJE/WindWaves_AKR_calibration_selection).
Acknowledgments Y. Prezado  warmly thanks the work of the current and former members of her team " New Approaches in Radiotherapy ", the personnel of the Orsay proton therapy centre and the  Experimental Radiotherapy platform of Institut Curie. Y. Prezado  acknowledges the funding from  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  817908 ). Remerciements  Y. Prezado  remercie chaleureusement le travail des membres actuels et anciens de son équipe « Nouvelles approches en radiothérapie »,  le personnel du centre de protonthérapie d'Orsay et la plateforme de radiothérapie expérimentale de l'Institut Curie. Y. Prezado remercie le Conseil européen de la recherche (ERC) pour son financement dans le cadre du programme de recherche et d'innovation Horizon 2020 de l'Union européenne  (convention n • 817908).
Their investigations also suggested the absence of a sunward motion of the magnetopause with an increasing IMF B x component. In this third paper of a series of papers dedicated to the magnetopause, we develop a new asymmetric, non-indented analytical magnetopause model that takes into consideration these latest findings. The outline of this paper is as follows: Section 2 presents the magnetopause crossings dataset we use and the associated upstream solar wind and IMF conditions, Section 3 focuses on the development of the magnetopause model, Section 4 compares it with other existing magnetopause models and Section 5 presents its differents characteristics.
Dataset We use the same magnetopause crossings dataset as that presented in  Nguyen et al. (2020b). This dataset consists in the combination of 13 181 magnetopause crossings detected in the data of the most recent near-Earth missions (THEMIS, Cluster, Double Star, ARTEMIS and MMS) with a gradient boosting classifier (see  Nguyen et al. (2020a)  for more details) 1  together with the 2168 events manually identified by various experts in the data of older missions (OGO, Geotail, Hawkeye, AMPTE and IMP). 2 The solar wind upstream conditions associated to each crossings were obtained from OMNI data by applying the two-step propagation algorithm exposed in  Šafránková et al. (2002). In order to remove the aberration caused by the Earth's revolution, we correct the GSM position of each crossing by using a similar approach than  Lin et al.
(2010)  and  Boardsen et al. (2000) , assuming a revolution velocity of 30 km/s. In addition to the non-aberrated GSM (X, Y, Z) cartesian coordinates, we use the spherical counterpart (R, θ, φ) as in  Nguyen et al. (2020b) :    X = R cos(θ) Y = R sin(θ) sin(φ) Z = R sin(θ) cos(φ) (1) Similarly to  Wang et al. (2013)  and  Nguyen et al. (2020b) , we balance the hemispheric distribution of the dataset by assuming a symmetry between the summer northern hemisphere and the winter southern hemisphere and thus by reverting the GSM Z coordinate and the Earth dipole tilt angle γ : : r(X, Y, Z, γ) = r(X, Y, -Z, -γ). Following the suggestions of  Nguyen et al. (2020b)  we also assume a dawn-dusk symmetry of the magnetopause and thus revert the Y component of each event. Different observations of the polar cusps crossings led to different conclusions regarding the shape of the boundary in the near-cusp region.
Boardsen et al. (2000) ;  Šafránková et al. (2002); afrnkov et al. (2005)  suggested the magnetopause is indented while  Zhou and Russell (1997) ;  Lavraud et al. (2004)  suggested a non-indented boundary. This question is investigated in the fourth paper of our study  (Nguyen et al. , 2020c). For now, we restrict the dataset to the 58154 so-called "out of cusp" events. Those events are defined as those falling outside of the cusp indentation as defined by  Lin et al. (2010) , for which θ satisfies:      (θ -θ n ) 2 + φ 2 ≥ -1 dn 2 a 21 if Z ≥ 0 (θ -θ s ) 2 + φ 2 ≥ -1 ds 2 a 21 if Z ≤ 0 (2) Where d n,s = a 16 ± a 17 γ + a 18 γ 2 , θ n,s = a 19 ± a 20 γ, and a 21 represent the scope, the zenithal position and the shape of the polar cusps, γ is the Earth dipole tilt angle and a 16 , a 17 , a 18 , a 19 , a 20 and a 21 are the corresponding coefficients fitted by  Lin et al.
(2010). The crossings that are found inside of this so-defined cusp-indentation will constitute the core of the dataset used in  Nguyen et al. (2020c). The obtained symmetrized dataset is randomly split into a training set 43 664 events that will serve for the fit performed in section 4 and into a test set of 14 490 events used to evaluate the accuracy of the resulting model in section 5. The distribution of the cartesian position and the associated solar wind physical parameters of the events that constitute the different sets is shown in Figure  1. In each panel, we notice similar distributions of both the train and the test sets. This indicates that there is no particular bias between the two sets.
This finding gives clues on the influence of the Y component of the IMF on the magnetopause and is in agreement with  Liu et al. (2015). However, for statistical reasons, we limited our study to the (X -Y ) and the (X -Z) and symmetrized our dataset. As a consequence, neither the statistical investigations we performed in  Nguyen et al. (2020b)  nor the model we developed in this paper considered a continuous twist of the magnetopause with the IMF clock angle as suggested by  Liu et al. (2015)  and  Lavraud and Borovsky (2008). Future investigations, using more data, should address this delicate point. Finally, all of the crossings we used in this paper were located outside of the nearcusp regions and there is thus no clue on how well our non-indented model performs and the extent to which it has to be adapted in order to provide a precise description of the magnetopause in this specific region of the near-Earth environment.
Naturally, the shifting time increases with the zenith angle and the left boundary of the grey interval represented in the two left panels then corresponds to the magnetopause computed at the left border of the two right panels. The obtained magnetopause then considers an IMF shift from a negative to a positive B z and the propagation of this transition is reproduced through the propagation of the erosion we notice in the (X -Z) plane. Adapting the two step propagation algorithm, we then elaborated a process useful for providing a dynamical view of the magnetopause at any time. Additionally, this process is independent from the used static magnetopause model and thus easily adaptable to the models of  Shue et al. (1997) ,  Lin et al. (2010)  and  Liu et al. (2015). The numerical implementation of this models can be found at: https://github.
Ce jeu de données, SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset)  (Caillouet, 2019) , a ensuite servi d'ébauche (ou information a priori) pour l'assimilation des observations historiques de Météo-France à l'aide d'un filtre de  Kalman d'ensemble (EnKF, Evensen, 2003) , permettant de créer la réanalyse de surface ensembliste FYRE Climate (French hYdrometeorological REanalysis of Climate) sur la période 1871-2012  (Devers et al. , 2020 (Devers et al. , , 2021)). Une modélisation hydrologique continue ainsi que l'assimilation d'observations historiques de débit à l'aide d'un filtre de Kalman d'ensemble ont ensuite permis de créer FYRE Hydro, une réanalyse fournissant des débits journaliers, disponible sur 661 bassins faiblement anthropisés  (Devers, 2019). Cet article présente une reconstruction ensembliste des évènements spatio-temporels d'étiage extrême en France depuis 1871, à partir des réanalyses météorologiques Safran et surtout FYRE Climate, sur un grand nombre de bassins versants jaugés et faiblement anthropisés en France.
Ces méthodes partent d'une base de données pluviométriques. Le raisonnement est inductif, reposant sur la moyenne d'eau tombée lors d'une précipitation. D'autre part, les défenseurs de la méthode dite « rationnelle » reprochent à leurs interlocuteurs de sous-estimer les fluctuations d'intensité de la pluie au cours d'une même averse. Pour calculer le débit maximal, ils prennent en compte non pas la moyenne, mais l'intensité maximale d'un épisode. Le postulat est d'imaginer un épisode de pluie d'une intensité maximale constante, se déversant uniformément sur l'ensemble du bassin. En simulant une précipitation qui n'existe pas à partir d'un raisonnement maximaliste et déductif, l'approche rationnelle permet de créer un système d'assainissement pluvial avec moins de données empiriques 6. Elle devient l'une des méthodes majeures de la modélisation en hydrologie par la suite, encore utilisée aujourd'hui.