dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis procedure We jointly analysed GBM and LLE data with the pyXSPEC fitting software1. We also performed a joint spectral analysis using the "Multi-Mission Maximum Likelihood" (threeML) software2  [7] , which combines simultaneously the native likelihoods of different instruments. In this analysis, threeML offered the full accuracy of the LAT unbinned likelihood technique, which is lost during the binning in space and energy that is required by pyXSPEC. We considered the spectral models Band  [5]  and ISSM  [6] , both having four parameters. Additionally, we considered the models obtained by multiplying these functions by a high-energy exponential cutoff (∝ 𝑒 -𝐸/𝐸 𝑐𝑢𝑡 ). We called the resulting models BandExpCut and ISSMExpCut.
The spectral results obtained with threeML are fully consistent with the pyXSPEC analysis, confirming the cutoff detections. We performed the same fits on GBM+LLE+LAT data, limiting LLE data below 100 MeV, and considering the LAT standard data above 100 MeV. We detected spectral cutoffs in time bins B, C, and B+C with both BandExpCut and ISSMExpCut functions at values compatible within errors with the previous analyses.
Remerciements L'auteur exprime sa reconnaissance au  Dr. Serge Koutchmy  pour de fructueuses discussions et sa mise à disposition des références [30, chapitre 3], [39]. Il remercie aussi la bibliothèque de l'Observatoire de Paris pour son autorisation de reproduction de nombreuses pages de [4]. Enfin, il exprime sa gratitude à Lilie Pons (secrétaire éditoriale -Centre Mersenne ) pour son précieux travail technique améliorant la lisibilité des extraits de textes anciens.
Le carnet d'évaluation de la progression des palettes pré-calculées Pour commencer, nous avons voulu examiner la progression de luminosité des palettes pré-calculées existantes, ainsi que le niveau des différences de couleur entre chacune de leurs étapes. L'objectif est de vérifier si les progressions de composantes annoncées comme efficaces sont bien celles utilisées par ces palettes, et selon quelles proportions. Il existe une bibliothèque JavaScript qui fournit de manière très pratique une grande quantité de palettes pré-calculées, pour les mettre à la disposition des programmes ou carnets d'infographie de données : DicoPal, disponible sur son dépôt GitHub. Comme StatsBreaks évoquée plus haut et que l'on retrouvera pour le carnet principal, DicoPal a été produite par l'UMS RIATE. Grâce à cette bibliothèque, on peut donc passer en revue de nombreuses palettes, issues de ColorBrewer comme d'autres sources, par exemple celles de F.
Numerical setting and first results Problems (  24 ) and (  28 ) are solved by direct minimization algorithms over the Stiefel manifold  [1]   St(N , N b ) = {R ∈ R N ×N b | R T R = I N b }. The explicit computation of the gradients of J A and J E with respect to R is detailed in the Appendix. We used a L-BFGS algorithm (with tolerance 10 -7 on the norm of the projected gradient), as implemented in the Optim. jl package  [22]  in the Julia language  [4]. As initial guess, we picked the first N b Hermite functions introduced in Section 3. 3. In this subsection, we choose a probability distribution P supported in the interval I = [1. 5, 5] so as to retain the physics of interest that takes place around the equilibrium configuration a 0 1. 925 and all the way to dissociation. In particular a min = 1.
Acknowledgements The authors thank  Susi Lehtola  and  Etienne Polack  for fruitful discussions. This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement  EMC2 No 810367 ). This work was supported by the  French 'Investissements d' Avenir' program , project  Agence Nationale de la Recherche (ISITE-BFC)  (contract  ANR-15-IDEX-0003 ). The work of the second author was also supported by the  Ecole des Ponts-ParisTech.
Acknowledgments Many people helped me during the elaboration of this article. First of all, I thank my University teachers of the 1960ies and among them, the late F. Link who introduced me to the effects of refraction for the Earth's atmosphere and showed me the "vase atmosphérique" concept when rising together to the  Pic du Midi Observatory at sunrise. I thank  William Sheehan  for meaningful discussions that sometimes went far beyond Venus. Remarks of  Jay Pasachoff  were also greatly appreciated. Hadis Goodarzi ,  Paolo Tanga ,  François Sèvre  and  Nicolas Lefaudeux  provided material, advices and discussions for preparing the paper;  Bruno Sicardy  has been a meaningful and professional reviewer of the first draft of this paper. David Valls-Gabaud  and  Cyrille Bazin  worked on the topic together with the author and the Chinese team headed by  Zhi Xi , to perform the Yunnan experiment.
2017). This is done to correctly capture solenoidal motions which need more resolution elements to be resolved than compressive motions  (Federrath et al. 2011). The evolution of this ratio is rather noisy, typically between 20 and 40% with rapid variation between snapshots. At energy equipartition, one expects ζ = 33% from a dimensional argument (compressive motions are along one direction, whereas solenoidal motions are along two dimensions, see e. g. Hennebelle & Falgarone 2012). During the first 10 Myrs after the activation of level 20, ζ is typically above that value in F10, and below it in F65. However, given the noise in the measurements, the data does not allow us to conclude that compressive motions are stronger in either of the simulations.
Furthermore, both power spectra get steeper at scales smaller than 1 pc, similar to the velocity power spectra shown in Fig. 10. This result is rather surprising, given that several studies have shown a dependence of the power-law exponent on the Mach number, combined with the fact that the Mach number is different in the two simulations (from 6. 5 to 9, see Section 3. 2). Note that those studies did not include self-gravity. To study further the density structure inside the zoom region, we use the python package astrodendro 1 to detect bound substructures. We detect all structures with a peak density above 10 4 H cm -3 and with an area of at least 20 pixels square on the density map, that is 0. 18 pc 2. We note that this density threshold is lower than the one at which cells are affected by the pressure support from the Jeans polytrope (see Section 2.
1). We then compute the virial parameter α vir of each clump, defined as (see e. g. Bertoldi & McKee 1992) : α vir = 5 3 σ 2 3D R 1/2 GM , (2) 1 accessible at this address: https://github. com/dendrograms/astrodendro/ where R 1/2 is the half-mass radius of the clump, M its gas mass and σ 3D its internal 3D velocity dispersion. In order to account for the non-homogeneity and non-sphericity of the clumps we use the half-mass radius instead of the detection radius, and we consider that a clump is gravitationally bound if α vir < 2. The following results are not qualitatively affected by using α vir < 1 or α vir < 5, as will be described in the following. Examples of gravitationally bound clumps in both simulations are shown in Fig. 12. There are several hundreds of such clumps in each simulation.
Acknowledgements. We thank the anonymous referee for their careful reading and detailed comments which improved the paper. We thank  Eva Ntormousi  and  Elliot Lynch  for very stimulating discussions. Simulations were produced using PRACE (grant  2020225366 ) and  GENCI  allocations (grants  A0090411111  and  A0110411111 ). We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS de Lyon  for the computing resources. JF, FB, NB and PH acknowledge support from PNCG. NB and PH acknowledge financial support from the  European Research Council (ERC)  via the  ERC Synergy Grant  " ECOGAL: Understanding our Galactic ecosystem -From the disk of the Milky Way to the formation sites of stars and planets " (grant  855130 ). We made use of Astropy (Astropy Collaboration et al.
Acknowledgements The authors wish to acknowledge  L. Delbes  from the X-ray diffraction facilities at IMPMC. Special thanks go to  Hicham Moutaabbid  for his help during the sample preparation with the Ar-Glove box operating at IMPMC and to  I. Criouet  for providing the synthetic smectite. The authors thanks the meteorite collection of the  Muséum National d'Histoire Naturelle  for providing the grains of the Orgueil meteorite (Inventory number 234). We gratefully acknowledge financial support from the  Paris Ile-de-France Region DIM ACAVþ funding  ( PARYUGU ), from the  ATM 2022_project "Orgueil " ( OE-7590 ) from the  Muséum Nationale d'Histoire Naturelle, in France , from the  CNES  ( MIN-PET Hayabusa2 ), and from Financial support provided by The  H2020 European Research Council (ERC)  ( SOLARYS ERC-CoG2017_771691 ).
This work has been funded by the  Centre National d'Etudes Spatiales (CNES-France)  and by the  ANR  project  CLASSY  (Grant  ANR-17-CE31-0004-02 ) of the  French Agence Nationale de la Recherche. This research used resources of the  FACCTS programme , and funding from the  U. S. DOE Office of Science-Basic Energy Sciences , under Contract No. DE-AC02-06CH11357. Finally, this manuscript has benefited from constructive comments by  Mike Zolensky  and two anonymous reviewers, as well as from associate editor  Francis McCubbin. Editor:  Francis McCubbin.
Radius [R ] Fig. 1. Radius-insolation flux figure of known exoplanets. The colorcoding used is as follows: yellow, KELT-10b; green, HEARTS and SPADES surveys' sodium detections (SPADES is a twin survey using a similar setup as HARPS-N at the 3. 58 m telescope at La Palma); and red, WASP-127, the only nondetection from HEARTS. WASP-127 b has not been detected in HARPS data  (Seidel et al. 2020c ). However, by using VLT/ESPRESSO,  Allart et al. (2020)  established a tenuous signal below the detection limit of  Seidel et al. (2020c). The target of this paper is KELT-10 b  (Kuhn et al. 2016 ). This hot Jupiter has many properties similar to the benchmark planet system HD 209458 b, providing a valuable opportunity to compare these two planets. It notably features a sodium detection obtained by  McCloat et al.
Photometry analysis While the overall precision of ECAM light curves is better than those used in  Kuhn et al. (2016) , using only the two transits without the  Kuhn et al. (2016)  set of data provides compatible yet less precise system parameters. For that reason, we opted to analyze the entire collection of publicly available follow-up photometry (except for the discovery light curve from the KELT-South telescope), using the CONAN code  (Lendl et al. 2017). The first step in CONAN is to find a proper baseline model for the light curve. We searched for different trends, including airmass, sky, x and y coordinate shift, Full Width Half Maximum (FWHM), and sinusoidal pattern. Both ECAM light curves were detrended separately, and the appropriate model was selected using the difference in Bayesian Information Criterion (∆BIC) value and checking by eye (essential to avoid over-fitting).
The 26 June 2016 light curve has been detrended for time and FWHM trends, while the 21 July 2016 light curve has been detrended for time and sky trends. For the set of limb-darkening coefficients, we used the publicly available code LDCU 3   (Deline et al. 2022). The stellar parameters necessary for the limb-darkening computation come from the UVES analysis by  Sousa et al. (2018). Once we established a proper baseline, we ran CONAN on the entire set, using the differential evolution Markov chain Monte Carlo (DEMCMC) method described in  Lendl et al. (2017). We ran the code multiple times, and it varied whether or not we fixed an initial parameter between the different usable jump parameters. In the end, we fixed semi-amplitude K to the value from  Kuhn et al. (2016). We found better results with eccentricity set to 0, adopting these values.
Data reduction We exploit one dataset obtained with HARPS during the transit of KELT-10 b on 26 June 2016. Forty exposures were acquired (with exposure times of 900 s), among which 14 are in transit, seven are before the transit, and 19 are after the transit. KELT-10 was observed on fiber A, while fiber B was on the sky. The spectra were extracted from the detector images, corrected for, and calibrated using version 3. 5 of the Data Reduction Software (DRS) pipeline 4. One of the DRS corrections concerns the color effect caused by the variability of extinction induced by Earth's atmosphere (e. g. ,  Bourrier & Hébrard 2014; Wehbe et al. 2020 ). A134, page 3 of 13 A&A 672, A134 (2023) The flux balance of the KELT-10 spectra was reset to a G2 stellar spectrum template, before they were passed through weighted cross-correlation  (Baranne et al.
1996; Pepe et al. 2002)  with a G2 numerical mask to compute cross-correlation functions (CCFs) with a step of 0. 5 km s -1. This mask is part of a new set that was built with weights more representative of the photonic error on the line positions, as described in  Bourrier et al. (2021). As stated in Sect. 2, another transit was recorded with HARPS on 21 July 2016. This visit was discarded from the analysis because of the poor S/N. In fact, including this transit introduces spurious line variations that bias the results and for which we could not correct. We refer to Appendix C for more details. Throughout the RM analysis, the posterior probability distributions (PDFs) of free parameters describing models fitted to the data are sampled using emcee  (Foreman-Mackey et al. 2013 ). The number of walkers and the burn-in phase are adjusted based on the degrees of freedom of the considered problem and the convergence of the chains.
Extraction and analysis of individual exposures We employed a novel technique, the Rossiter-McLaughlin Revolutions (RMR,  Bourrier et al. 2021)  technique, to analyze the data. This method fits the spectral line profiles from all planetocculted regions at once with a joint model, which boosts the S/N by the number of in-transit exposures at first order, allowing access to fainter RM signals. The RMR is optimal to fully exploit a RM dataset, to assess biases due to line shape variations, and to measure obliquities from all types of systems in a more comprehensive way than traditional techniques. Indeed, a classical RM analysis only relies on the anomalous RVs of the disk-integrated lines, which condense the information contained in CCF profiles into a single measurement and limit our ability to detect the occultation of the stellar surface by the planet.
The master-out CCF is fitted with a Gaussian model, and all CCF DI are shifted to the star rest frame using the derived centroid. Aligned CCF DI are then scaled to a common flux level outside of the transit and to the flux expected from the planetary disk absorption during transit using a light curve computed with the batman package  (Kreidberg 2015). Cross-correlation functions from the planet-occulted regions are retrieved by subtracting the scaled CCF DI from the master-out CCF and are then reset to a common flux level to yield intrinsic CCF intr that allow for a more direct comparison of the local stellar lines (Fig. 4 ). Following the RMR approach, we first analyzed individual exposures to evaluate the quality of each CCF intr and assess the possibility for line variations along the transit chord.
Transmission spectroscopy This section describes the transmission spectroscopy performed on the HARPS dataset, including all the corrections done before the calculation. We followed the methods by  Mounzer et al. (2022) , which further refine methods by  Seager & Sasselov (2000) ,  Redfield et al. (2008)  and  Wyttenbach et al. (2015). We used the e2ds format of HARPS spectra (DRS version 3. 5), which includes a 2D grid of 72 and 71 echelle orders (for fiber A and B, respectively), and each order has 4098 pixels. The first step in the analysis is deblazing, using the blaze calibration obtained for each night of observation. This study's primary focus is orders 56 and 67 (using the order numbering by DRS, 55 and 66 for the fiber B data). The difference in order numbering is due to a gap in fiber B on the order of number 45 (at 5277 Å) 5.
Correction for Earth's atmosphere After correcting for the instrumental effects, we proceeded to mitigate the impact of Earth's atmosphere on the observed spectra. To do so, we used several corrections. First, we used molecfit  (Smette et al. 2015; Kausch et al. 2015)  to correct for the H 2 O and O 2 lines from the atmosphere, following the procedures described in  Allart et al. (2017)  and  Seidel et al. (2020c). In a few words, molecfit takes an input scientific spectrum and fits it with a synthetic high-resolution (R ∼ 5 000 000) transmission spectrum in selected wavelength regions with prominent telluric absorption. It runs a line-by-line radiative transfer code using an extensive line list of atmospheric lines and calculates the transmission spectrum during the given observation. Finally, it corrects the scientific spectrum, significantly mitigating the effect of the telluric lines.
Finally, molecfit does not correct for sodium emission in Earth's atmosphere. We monitored the fiber B data (aimed at the sky background) and found significant sodium emission. Our attempts to subtract this emission were unsuccessful because of the fiber A/B efficiency ratio variability at night. Due to this variation, we observed the remainder of the emission feature at high airmass, creating a spurious signal in the transmission spectrum. As a result, we thus opted to mask a small part (around 8 pixels) of the spectrum. Afterward, we shifted all the spectra into the rest frame of the star, using the BERV (approximately 2. 7 km s -1 for night 1) and systemic velocity value calculated from the analysis of the master-out CCF (Sect. 4; 31. 96 ± 0. 02 km s -1 ). The value of systemic velocity calculated from HARPS is compatible with both  the values from  Kuhn et al.
Correction for stellar effects Once we shifted to the stellar rest frame, we could correct for the stellar lines. At first order, we could calculate the average out-oftransit spectrum (M out ) and divide our dataset by it  (Wyttenbach et al. 2015; Seidel et al. 2019). However, doing so disregards the RM and center-to-limb variation (CLV) effects that can affect our spectra. This approach is the one  McCloat et al. (2021)  took, as the lack of precise wavelength calibration in their UVES data disallowed the measurement of RVs from the spectra, preventing an RM correction. HARPS, however, is designated to provide accurate RVs, hence we could measure and correct for the RM effect and simulate its impact on the final transmission spectrum. To correct our spectra for the RM effect, we adapted the formula by  Mounzer et al.
Comparison with previous sodium detection Recently,  McCloat et al. (2021)  have claimed sodium detection from a VLT/UVES single transit dataset. However, these data could not allow these authors to obtain RV measurements precise enough to correct for the RM effect (which was attributed to molecfit being unable to fine-tune the wavelength grid -see  Sect. 4. 6 in McCloat et al. 2021). Since the authors could not measure the RVs during the transit, a potential RM effect could have created a significant signal, as in the case of  Casasayas-Barris et al. (2021). Furthermore, the tentative sodium detection had a significant redshift, likely due to the wavelength calibration issues mentioned above. We modeled the effect of RM and CLV on the transmission spectrum, finding that the distortion by planet-occulted stellar lines cannot explain the measured sodium detection.
2021). On the other hand, atmospheric variability can significantly affect the dataset, especially for slit-based spectrographs such as UVES. We conclude that to properly address the question on the presence of chemical species in the atmosphere of KELT-10b (and confirmation of the previous Na I detection), we need additional data from larger telescopes than the 3. 6 m telescope at La Silla to obtain a good S/N and temporal sampling and to correct for the stellar effects such as RM that cause a significant impact (∼0. 2%) on the transmission spectrum.
A134, page 2 of 13 M. Steiner et al. : A&A proofs, Acknowledgements. We would like to thank the anonymous reviewer for the insightful comments. We would like to thank  Sean McCloat , who was kind enough to provide reduced UVES data for comparison purposes with the analysis presented in this paper. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES ; grant agreement No  724427 ). It has also been carried out in the frame of the  National Centre  for Competence in Research PlanetS supported by the  Swiss National Science Foundation (SNSF). DE and MS acknowledge financial support from the  SNSF  for project  200021_200726. O. A. and V. B. 's work has been carried out in the frame of the  National Centre  for Competence in Research PlanetS supported by the  Swiss National Science Foundation (SNSF).
O. A. and V. B. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  SPICE DUNE ; grant agreement No  947634 ). This project has also been carried out in the frame of the  National Centre  for Competence in Research PlanetS supported by the  SNSF. M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 742095  ; SPIDI: Star- Planets-Inner Disk-Interactions ; http://www. spidi-eu. org). S. G. S. acknowledges the support from  FCT  through the contract nr. CEECIND/00826/2018  and  POPH/FSE  (EC).
Il existe des méthodes génériques de calcul des LCZs à partir de données satellitaires, telles que Landsat  (Ching et al. , 2018). La classification à partir de cette imagerie induit une dégradation de la résolution spatiale de la classification et une sousreprésentation des zones urbanisées qui, dans le cas des villes de taille moyenne, va influer l'identification des LCZ  (Hidalgo et al. , 2019). Pour cette raison, nous avons retenu l'outil GeoClimate qui transforme des données géographiques provenant de bases de données telles que OpenStreetMap ou BD Topo® de l'IGN en centaines d'indicateurs géospatiaux utiles pour les applications climatiques, comme par exemple le facteur de vue du ciel ou la fraction de végétation et de bâtiment. A partir de ces indicateurs, GeoClimate propose une méthodologie pour extraire les LCZ (pour plus d'informations, le code de GeoClimate est en accès libre).
Cartographier les zones végétalisées avec de l'imagerie satellitaire La base de données BD Topo® (IGN, 2016) permet de bien décrire les zones bâties, en revanche les zones végétalisées sont très mal renseignées, notamment la végétation des cours et jardins privés. Pour identifier les îlots de fraîcheurs, dans le cadre du programme SAVE-IFU, une cartographie des zones végétalisées a été réalisée à partir d'images Pléiades acquises au cours de l'année 2021 sur les six territoires étudiés. Trois phases d'acquisition ont été planifiées, à savoir mars (début du printemps), mai (fin du printemps) et une acquisition estivale qui était prévue. Les conditions météorologiques (couverture nuageuse et précipitations fréquentes) ont empêché l'acquisition d'images lors de l'été 2021 (tableau 1). Les images de la bande panchromatique sont acquises à la résolution de 70 cm, tandis que les images des autres bandes spectrales ont une résolution de 2,8 m.