dataesr/scientific-paragraphs-categorization huggingface dataset:
Le rythme peut être pilotés par plusieurs modes de contrôle du temps de synthèse. Dans le mode « défilement » ou « frottement » (scrub) la pointe du stylet dans la dimension Y définit la position de lecture de l'échantillon. On peut ainsi jouer le son à l'endroit ou à l'envers, sauter d'une position à l'autre. Dans le mode « vitesse » (speed), l'appui du stylet sur la tablette déclenche la lecture des échantillons, avec une vitesse qui dépend de la position Y du stylet. Une vitesse nulle, au centre de la tablette lit les échantillons à leur vitesse nominale d'enregistrement ; une vitesse positive accélère la lecture alors qu'une vitesse négative ralentit la lecture. Ces deux modes sont surtout utiles pour la pratique musicale. Figure2: Schéma de principe de Voks. En haut le contrôle gestuel et l'interface utilisateur ; au centre le synthétiseur par échantillonnage ; à gauche la base de données d'échantillons étiquetés(Locqueville et al.
C Nous proposons dans cette section deux méthodes pour accroître la confidentialité des témoignages, qui s'inspirent de méthodes utilisées dans le domaine des bases de données. Lorsque des données stockées dans une base doivent rester confidentielles, la première étape est l'anonymisation en supprimant les informations permettant d'identifier un individu. Bien que l'anonymisation soit nécessaire, elle n'est cependant pas suffisante pour assurer la confidentialité car il peut être possible reconstruire les informations cachées, par recoupement par exemple avec d'autres sources d'information. Ainsi, une seconde étape de confidentialisation (4) doit être mise en place [1]. Il s'agit de modifier une base de données anonymisée pour empêcher la reconstruction des identités des individus associés aux données.
Intuitivement, anonymiser et confidentialiser un système de réputation est un problème analogue à anonymiser et confidentialiser une base de données. En effet, les témoignages des agents sont des éléments d'information qui peuvent être enregistrés dans une base centralisée ou distribuée entre les agents. Dans la littérature concernant les systèmes de réputation, de nombreux travaux ont proposé d'anonymiser les témoignages en se fondant sur des protocoles cryptographiques pour s'assurer de la non-répudiation des témoignages  [3, 11, 22, 28]. Toutefois, comme pour les bases de données, ceci ne permet pas de garantir la confidentialité des témoignages. À moins de restrictions particulières du système, un agent, sachant les interactions qu'il a eu avec d'autres agents, peut observer la dynamique de sa réputation et en déduire les témoignages qui ont pu être formulés envers lui.
Nous proposons de partir du constat de Hasan et al. et d'étudier l'effet d'un bruit appliqué aux témoignages sur les systèmes de réputation. Nous distinguons deux types de bruits : un bruit sur les témoignages eux-mêmes qui correspond au bruit classiquement utilisé pour la confidentialisation des bases de données et un bruit sur la diffusion des témoignages sous forme d'un délai. En effet, si un agent veut observer l'évolution de sa réputation pour déduire la valeur des nouveaux témoignages à son encontre, il peut supposer que ces nouveaux témoignages sont diffusés aussitôt ou presque après la transaction. L'introduction d'un délai vient décorreler ces deux événements.
B L'ajout de bruit est une méthode courante pour confidentialiser une base de données  [8, 21]. Cela consiste à altérer les valeurs numériques de cette base de données afin que les informations relatives à un même individu ne puissent plus être mises en corrélation. Toutefois, l'ajout de bruit modifie le résultat des requêtes : plus les données sont altérées, plus la confidentialité est forte mais moins l'information globale sur la base est conservée. Parmi les méthodes de bruitage, le bruit différentiel  [8]  permet d'éviter ce problème en calculant un bruit spécifique pour chaque requête possible sur la base tout en minimisant la dissimilarité entre les résultats des requêtes sur la base bruitée et non bruitée. Si cette méthode est particulièrement intéressante, elle s'accommode mal des données dynamiques comme peuvent l'être l'ensemble des agents et leurs témoignages dans un système de réputation.
This is likely a result of the conservation of angular momentum during protoplanetary disk formation  (Albrecht et al. 2013). As of today, four multiple systems have been observed to be misaligned: Kepler-56  (Huber et al. 2013) , HD 3167  (Dalal et al. 2019; Bourrier et al. 2021 ), K2-290 A  (Hjorth et al. 2021), and π Men (Kunovac Hodžić et al. 2021). Planets in these systems have the common point to exhibit relatively short orbital periods (P < 50 days). The obliquity of long-period planets in multi-planetary systems remains relatively unexplored and is an important step forward in our understanding of their formation. This paper presenting the RM detection of HIP41378 d is organised as follows. The observations and data reduction are described in Sect. 2. Section 3 presents the analysis of the RM effect with two different methods.
To improve this coverage, we observed a second transit of planet d with both the HARPS-N (programme ID: A45DDT2) and ESPRESSO (programme ID: 0109. C-0414) spectrographs. ESPRESSO  (Pepe et al. 2021 ) is mounted on the 8. 2-m ESO-VLT (VLT) at Paranal Observatory in Chile. This second transit occurred on 2022 April 01. The target was continuously monitored with both instruments over a total of 7. 7 h near the expected mid-transit time. Out-of-transit data were also secured with ESPRESSO over 2. 9 h on the following night. Out-of-transit observations were not secured with HARPS-N because of clouds. To mitigate for possible high-frequency stellar noise, we used an exposure time of 900s on both instruments. The HARPS-N spectra were reduced following the method described in  Dumusque et al. (2021) , while the ESPRESSO data were reduced with the online pipeline  (Pepe et al.
2021 ). The derived radial velocities are reported in the Tables A. 3-A. 5. The first five, in-transit HARPS-N spectra of the 2019 transit were obtained at relatively high airmass (above X = 1. 5) through variable thin clouds. These spectra exhibit a significantly higher photon noise (in the range 4-7 m s -1 while the other data of the radial-velocity time series have a photon noise at the level of 2-3 m s -1 ), and they were discarded from the analysis. In 2022, the host star exhibited significant stellar variability at the level of a few m s -1 with a timescale of about a week (see  Fig. A. 1) , which superimposes with the Keplerian orbit of the various planets. This leads to significant night-to-night variability limiting the use of out-of-transit data taken the night after the transit as a reference baseline for the analysis.
Rossiter-McLaughlin Revolutions The classical analysis of the RM effect, based on the anomalous RV deviation of the disk-integrated stellar line, can yield biased and imprecise results for λ and v sin i * (e. g. Cegla et al. 2016b; Bourrier et al. 2017). We thus performed a complementary analysis using the RM Revolutions technique  (Bourrier et al. 2021) , which interprets the planet-occulted stellar lines directly. This technique, however, requires that a reference spectrum can be calculated for the unocculted star, which is not possible for the 2022 HARPS-N and ESPRESSO data. The stellar line shape changed significantly between 2019 and 2022, preventing us from using the 2019 out-of-transit data as a reference for the 2022 transit. We thus focussed on the 2019 HARPS-N transit, in which post-transit exposures are available to compute the reference stellar spectrum.
Acknowledgements. Partly based on observations made with the  Italian Telescopio Nazionale Galileo (TNG)  operated on the island of  La Palma  by the  Fundación Galileo Galilei of the INAF (Istituto Nazionale di Astrofisica ) at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofisica de Canarias. Partly based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programmes  0109. C-0414  and  5105. C-0596. A. S. is grateful to the astronomers on duties who performed the observations at the telescope, especially  Marco Pedani  (TNG) and  Camila Navarrete (ESO). The project leading to this publication has received funding from the  french government  under the " France 2030 " investment plan managed by the  French National Research Agency  (reference :  ANR-16-CONV-000X / ANR-17-EURE-00XX ) and from  Excellence Initiative of Aix-Marseille University  -A*MIDEX (reference  AMX-21-IET-018 ).
This work was supported by the  "Programme National de Planétologie" (PNP)  of  CNRS/INSU. DJA acknowledges support from the  STFC  via an  Ernest Rutherford Fellowship  ( ST/R00384X/1 ). This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalização by these grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953. This 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). The authors acknowledge the financial support of the  SNSF. This project has received 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 ).
J. L. -B. acknowledges financial support received from  "la Caixa" Foundation  ( ID 100010434 ) and from the  European Unions Horizon 2020  research and innovation programme under the  Marie Slodowska-Curie  grant agreement No  847648 , with fellowship code  LCF/BQ/PI20/11760023. This research has also been partly funded by the  Spanish State Research Agency (AEI)  Projects No. PID2019-107061GB-C61  and No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (INTA-CSIC ). O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by  FCT. C. D. acknowledges supported provided by the  David and Lucile Packard Foundation  via Grant  2019-69648. S. G. S acknowledges the support from FCT through Investigador FCT contract nr. CEECIND/00826/2018  and  POPH/FSE (EC).
In the following, we will omit the isometry f in the identification between Σ and ∂Ω. Then, a fill-in (Ω, g, k) of (Σ, γ, H, α, h) satisfies the dominant energy condition, or is a DEC fill-in, if µ ≥ |J| g where µ and J are respectively the mass density and the current density defined by µ = 1 2 R g + (Tr g k) 2 -|k| 2 g and J = div g k -(Tr g k)g. Here R g and Tr g denote respectively the scalar curvature and the trace operator of (Ω, g). In this situation, a natural generalization of the mean curvature is given by the function H g := H g -|k( ν, •) T | 2 g + (Tr g |∂Ω k) 2 which corresponds, for a spacetime Bartnik data, to H := f * H g = H -|α| 2 γ + h 2. ( 2 ) When Σ is endowed with a spin structure, we will say that (Ω, g, k) is a DEC spin fill-in of the spacetime Bartnik data (Σ, γ, H, α, h) if (Ω, g, k) is a DEC fill-in and if Ω is a spin manifold which induces the given spin structure on Σ.
As mentioned above, the proof is a direct consequence of an eigenvalue estimate for the first eigenvalue of the Dirac operator of a spacetime Bartnik spin data which admits a DEC spin fill-in. This lower bound is stated as follow. Theorem 2. If a spacetime Bartnik spin data (Σ, γ, H, α, h) admits a DEC spin fill-in (Ω, g, k) with H > 0, then the first eigenvalue λ 1 (Σ, γ) of the Dirac operator of (Σ, γ) satisfies min Σ H ≤ 2|λ 1 (Σ, γ)|. One can consider F spin (Σ, γ), the set of the DEC spin fill-ins of the Riemannian spin manifold (Σ, γ) without specifying the data H, α and h. Then Theorem 2 implies that if F spin (Σ, γ) = ∅, it holds that sup (Ω,g,k)∈F spin (Σ,γ) min Σ H ≤ 2|λ 1 (Σ, γ)| < ∞. The proof of Theorem 2 relies on spin geometry and we refer especially to [BHM + 15, Fri98, Gin09] for more details on this subject.
The Polarimeter To measure the polarization of the light emitted by the Planeterrella, we use the same polarimeter (called Grand Cru) as that used in a previous study on the auroral polarization. A detailed description is provided in  Bosse et al. (2020) , with the instrumental characteristics and the data analysis procedure. We therefore only recall here its basic principles and the main instrument design. The light first passes through a polarizing filter rotating regularly at 5 rotations per second. We then select the wavelength of interest with an optical filter of 10 nm width. Finally, a photomultiplier measures the remaining BOURGEOIS ET AL. AoLP. It is also possible to average the signal over multiple rotations of the polarizing filter to increase the S/N ratio. However, this assumes that the signal is constant over the averaging window T 1.
Acknowledgments We thank  Baptiste Falque  and  Olivier Katz  for their help with the experimental setup and their mastering of the Planeterrella. This work was partially funded by the  IPEV  project on the high latitude polarization of the auroral emissions  POLARLIS  (project number  1026 ). It was also funded by the  Prematuration CNRS program  and by the Maturation program CM180023, project  PTCU  Number  180018M  from the  SATT Linksium. This work was supported by the  Programme National PNST of CNRS/INSU  co-funded by  CNES  and  CEA. The polarimeter is patented by  CNRS  and valorised by  Linksium  (international patent number  1873378 ). The Planeterrella experiment is a property of  CNRS, France.
The angles   and   are measured from the zenith. The emission angle is fixed by the geometry of the rover with the primary and secondary targets seen by Mastcam-Z under emission angles of approximately 58  • and 54  • , respectively. The overall distributions of the incidence and azimuth angles of the cal-target images are reported in Figure  3. A total of 197 cal-target sequences (which include images in only L0/R0 or all filters) were acquired by Mastcam-Z over the first 350 sols, each of which can be identified by its incidence and azimuth angles. The left plot in Figure  3  shows the combinations of incidence and azimuth angles of those 197 sequences. Summing the data points along the two axes of the plot yields the two histograms of azimuth and incidence in Figure  3  on the right. Given the changing orientations of Perseverance, the azimuth angles are evenly distributed, though with a larger frequency in the 5  • -10 𝐴𝐴 • interval, which corresponds to the Sun being approximately behind Mastcam-Z during the observation (Sun in front of the rover), and a much lower frequency of observations at opposite azimuth.
The Extraction of Solar Irradiance Through Linear Fits Following the formal approach of Section 2. 1, the solar irradiance   was computed as the  RAD∕IOF ratio of Equation 1, which in practice translated into a linear fit. For every image of the cal-targets in some filter, we extracted the values of radiance of the eight clean spots as the averages over the pixels of their ROI selections. Simultaneously, the corresponding values of reflectance of the clean spots under the same illumination geometry as the observation were computed. These reflectances were known from laboratory measurements of the eight color and grayscale samples at different geometries across the visible and near infrared spectrum (400-2,500 nm),  (Buz et al. , 2019; Kinch et al. , 2020). The values observed for the clean spots were plotted as radiance-versus-reflectance data points, and were fitted with a straight line passing through the origin, in the form  =  ⋅  (we refer to this model as "one-term fit," because it only has one multiplicative term).
2 was also included in the RC-files, along with three Boolean arrays of flags (expressed in terms of 0 and 1) that specified which regions had been selected, which ones were "bad" and should not be used (usually due to unwanted shadows), and which should be used to make the linear fits for calibration. We used a graphic interface to mark the regions appropriately. Whenever a region had not been selected, its place in the data arrays of the RC-files did not show any numerical value. An example of an RC-file is given in Appendix A.
Data Availability Statement The methods and results described in this manuscript are based on the radiometric calibrated images from the Mastcam-Z instrument. All image data presented here are available through the Planetary Data System Imaging Node (https://pds-imaging. jpl. nasa. gov/portal/mars2020_mission. html) and GeoSciences Node (https://pds-geosciences. wustl. edu/missions/mars2020/), under the Mars 2020 Mission. The Radiometric Coefficient files are second-order products and will also be available as part of the Mastcam-Z Science Calibrated Bundle (https:// pds-imaging. jpl. nasa. gov/data/mars2020/mars2020_mastcamz_sci_calibrated/), in a calibration collection, in the summer 2023 at the latest. Before then, the RC-files can be requested directly to the corresponding author.
Acknowledgements This work was partly supported by the  IPGP analytical platform PARI, Ile-de-France SESAME  Grants  12015908 ,  DIM ACAVþ , the  ERC  grant  101001282  (METAL),  Labex UnivEarth (FM) ,  JSPS Kaken-hi grants (ST, HY, TY)  and the  CNES. We thank  Dominik Hezel  and one anonymous reviewer for constructive comments that helped improve the manuscript. Editor:  Anat Shahar.
Remerciements  (3. 25)     où σ(E dep ) est l'écart-type du dépôt d'énergie. Si S(E clus ) < -1 on va additionner les énergies des topo-clusters, qui peuvent être associés à la trace, dans un cône de ∆R = 0. 2 autour de la position de la trace. Dans le cas où le dépôt d'énergie attendu est toujours plus grand que l'énergie de l'ensemble des topo-clusters, les cellules sont enlevées de l'algorithme an d'éviter le double comptage. Dans le cas où le dépôt d'énergie attendu est plus petit, l'énergie cellule par cellule est enlevée pour se rapprocher le plus du dépôt d'énergie attendu. Ceci est fait en paramétrisant une gerbe et en calculant l'énergie du cluster dans des anneaux autour du centre de cette gerbe. On va augmenter la taille de l'anneau et enlever du cluster les cellules dont l'énergie fait dépasser le dépôt d'énergie attendu.
It can be generalized to all times of the year and to all areas, whether mountainous or not. The objective of this work is to identify three characteristics of inversions (intensity, frequency, and duration) and their variations over time. This analysis, based on a large documentary database, is essential to the understanding of a phenomenon that is often studied in small areas. After presenting the area of study, continental France, the first section of this work sets out the data and the method. In the results section, variations of these three characteristics are analysed separately and then synthetically via an ascending hierarchical classification. The discussion section examines the criteria used for defining inversions in this paper. Lastly, inversion characteristics are analysed with respect to weather conditions summarized by daily temperature amplitudes.
L'outil de calage bayésien Les modèles ont été implémentés dans un outil statistique bayésien BaM ! et peuvent être utilisés sur toute station hydrométrique respectant les conditions présentées ci-dessus. Les modèles temporel et dynamique sont disponibles en open source à l'adresse suivante: https://github. com/BaM-tools/BaM. L'outil BaM ! permet d'estimer les paramètres d'un modèle quelconque (ici, des modèles de courbe de tarage) accompagnés de leurs incertitudes. Dans notre cas, il utilise des a priori sur les contrôles hydrauliques et des observations pour l'estimation de la courbe de tarage. Il permet en outre d'effectuer des prédictions à partir du modèle estimé, notamment calculer des séries de débits accompagnées de leurs incertitudes. En plus des jaugeages (couples h-Q) traditionnellement utilisés pour caler les courbes de tarage, il est possible d'utiliser des observations liées à l'état de développement des plantes.
Remerciements Ces travaux bénéficient du soutien du ministère chargé de la transition écologique par décision de subvention n°  2102897179, dans le cadre du Programme MTE (DGPR/ SRNH) -INRAE 2020 , Connaissance et prévention des risques naturelles et hydrauliques, Action 2 -Prévision des inondations, HYDROM A1 : Courbes de tarage dynamiques -végétation aquatique et influence de la marée. Les auteurs remercient la DREAL  Nouvelle-Aquitaine  ( Isabelle Levavasseur  et  Amilien Bertrand ) pour nous avoir fourni les données utilisées dans ce papier. Nous remercions également EDF-DTG ( Sophie Marcucci ) pour nous avoir transmis des informations et données sur une station voisine de celle étudiée dans ce papier. L'ensemble de ces données a été d'une grande aide pour affiner nos modèles.
IoT, big data et intelligence artificielle Le multiusage est une clé essentielle pour optimiser les services rendus et, in fine, pour optimiser la rentabilité économique globale des aménagements. La difficulté réside dans la juste allocation des ressources, en fonction des besoins réellement exprimés : -Quel débit relâcher pour optimiser l'utilisation agricole ? -Quelle puissance turbiner pour s'adapter aux contraintes du réseau (ou au signal prix) ? -Quel débit relâcher pour les besoins des écosystèmes ? -Comment gérer au mieux les ouvertures de vannes en crue ? Actuellement, et de manière classique, les données et outils utilisés à cet effet restent très sommaires, et ne permettent pas de gestion fine. Mais cela change : données satellites sur les niveaux des rivières et l'état de saturation des sols  [20] , appareils de mesure connectés et/ou bases de données qui fournissent des données (état du système : débits, cotes, fuites ; météo, hydrologie, état des sols ; besoins réseau, prix du marché) à des pas de temps et d'espaces de haute fréquence, nouvelles mesures à caractère environnemental (suivi des poissons, ADN environnemental par exemple) ; mesures sur les ouvrages permettant d'optimiser l'entretien et la maintenance.
Box 1: Methods and data collection used The LIFT quantitative large-scale farmer survey was administered to participants across 12 European countries or regions, namely Austria, Germany, Greece, England, France, Hungary, Ireland, Italy, Poland, Romania, Scotland and Sweden in the autumn of 2019 to the spring of 2020. We received responses from over 1,600 farmers. The questionnaire consisted of a number of sections which covered: the characteristics of farming systems, the adoption of ecological practices and detailed motivations and attitudes towards their adoption. The survey also included an evaluation of the economic impacts of adoption. See  Tzouramani et al. (2020)  for further details. An in-depth qualitative study of Swedish, French and Irish conventional and organic farmers was undertaken to study farmers' decision-making using the Means-End chain (MEC) approach.
MEC assumes a hierarchical relationship within decision-making which links the perceived attributes of a practice to the consequences of these attributes and, finally, to the desired end-states achieved, taking the consequences of the attributes into account. MEC was used here to study the drivers of farmer's decisions to run conventional or organic farms. Individual farmer interviews were used to elicit a detailed understanding about which attributes of farmers characterise their decision to run either a conventional or organic farm, what consequences they perceive from those attributes and why those consequences are important to the farmers. Based on this study we gained in-depth understanding about drivers of conventional and organic farming. We were also able to contrast findings across the three countries where data were collected.