dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, two commercial wind farms with a power capacity of over 250 MW each will be in operation by 2029. (Seity et al. , 2011). It was designed to improve short-range forecasts of severe events such as intense Mediterranean precipitations, severe storms, fog or urban heat during heat waves. The physical parametrizations of the model come mostly from the Méso-NH research model, whereas the dynamic core comes from the non-hydrostatic model ALADIN  (Termonia et al. , 2018). The resolution of the AROME grid is 1. 3 km. The model is initialized from data assimilation derived from the ARPEGE-IFS variational assimilation system  (Courtier et al. , 1994)  and adapted to AROME's finer resolution. For each area of interest, the 10 m zonal (u) and meridional (v) wind speeds are extracted from AROME.
The opensource MeteoNet data set only contains surface parameters of temperature, humidity, pressure and precipitation, as well as 10 m wind speed (u 10 , v 10 ), which are considered in this study. The assumption is then made that relevant measurement points at 10 m are equally relevant for hub-height estimation, though this assumption should be tested with a suitable data set. Since the focus is on offshore wind, grid points on land were excluded from the analysis. The characteristics of each area are then the following: -Normandy -4272 grid points (∼ 7000 km 2 ); -Southern Brittany -1837 grid points (∼ 3000 km 2 ); -Mediterranean Sea -3571 grid points (∼ 5800 km 2 ). A total of 65 d (∼ 6 %) of the 3-year data set are unusable due to largely missing data. The missing data days are similar for each area and were removed from the analysis.
Problem statement The problematic of the presented work is to find D measurement points out of K NWP grid points to minimize the reconstruction error in the offshore wind field. A formalism for this sparse sampling problem is proposed in this section. In all that follows, the full state matrix X refers to the concatenation of zonal and meridional wind speeds on the K grid points of the model for all time steps. The list of sensor locations γ , which is the output of the methods described in this paper, contains the locations of the D sensors to sample the offshore wind field. The associated sparse measurement matrix Y γ corresponds to the measured zonal and meridional wind speeds at the γ locations for every time step. The formalism developed in this section is applied to the MeteoNet data set presented in Sect.
A follow-up of this work would be to include model data for each of the variables of interest and perform the clustering on the stacked first 10 EOFs of each variable for the design of a multiparameter observation network. Getting even closer to the industrial reality of the sensor networks, it would be of great interest to include a cost function dependent on the location (depth, distance from shore, other constraints). This method could be declined to find the Pareto optimal sensor network. The optimal number of sensors would therefore become the number at which the sensors' marginal cost exceeds the reconstruction gain. The data in this study are derived from the NWP model AROME data, with a 1. 3 km grid size and hourly definition. The parametrization of the model offshore is not perfect, in particular for the sea-atmosphere coupling that can lead to discrepancies in surface parameters, as shown during the Mediterranean HyMex campaign  (Rainaud et al.
, 2016). The dynamics learned might then be a coarse description of the https://doi. org/10. 5194/wes-8-771-2023 Wind Energ. Sci. , 8, 771-786, 2023 reality, and the derived sensor locations might be limited for real-time wind reconstruction, since they were only trained on low-spatiotemporal-resolution patterns. The obtained locations are in this case optimal only for reconstructing the dynamics of the NWP model, and the representativeness of the data used compared to the reality needs to be questioned. It could then be of interest to run such a study on higherresolution data, from either synthetic aperture radar measurements or large eddy simulations, though on shorter periods, or comparing the reconstruction to a set of measurements offshore. Publicly available through the MeteoNet data set, only 10 m wind speed is used in this paper.
Conclusions A method for finding an optimal sensor network for offshore wind reconstruction is presented in this paper and applied to three of the main offshore wind energy development areas in France. The sparse sensor placement problem is stated on a reduced basis of the 3-year AROME prediction of wind from the MeteoNet data set. State-of-the-art techniques of sparse sensor placement for reconstruction (QR pivoting and extrema methods) are compared to the proposed method, based on the Gaussian mixture model clustering of empirical orthogonal functions of zonal and meridional wind speeds offshore. By selecting the cluster centroids as proposed locations for sensors, the GMM method homogeneously par-titions the domain into spatially correlated clusters. In this way, the reconstruction error in the whole domain is minimized, leading to a 20 % decrease in wind reconstruction error compared to the median Monte Carlo case.
Code and data availability. Meteorological data used in this study are available online through the MeteoNet data set. The code developed for offshore wind resource sparse sampling using Gaussian mixture models can be accessed through https://github. com/rmarcille/gmm_sparse_sampling. git (last access: May 2023; https://doi. org/10. 5281/zenodo. 7835596,  Marcille and Thiébaut, 2023).
Acknowledgements This work was supported by the  Ministère de l'Enseignement Supérieur et de la Recherche  (grant to O. D. ). The authors are grateful to  Bertrand Lefeuvre  from  UMR-CNRS 6226, Université de Rennes 1  for ICP analysis and to  Sandra Casale  from the  Service de Microscopie Electronique at Fédération de Chimie et Matériaux de Paris-Centre FCMat -Sorbonne   Université for Transmission Electron Microscopy  analyses and discussions.
Protein identification Protein identifications were performed using PEAKS ® X-Pro software (64 bits version, 2020, BioInformatics solutions). It allows database search assisted de novo sequencing against the protein coding sequences from H. salinarum NRC-1 (8,533 entries from NCBI, download date 2021/08/03). Spectral peptides matching was carried out with the following parameters: (1) mass tolerance of 10 ppm on the parent ion, (2) mass tolerance of 0. 005 Da for fragment ions from MS/MS, (3) carbamidomethylated Cys (+57. 0215) and iTRAQ ® isobaric tag Lys and N-terminal (+304. 2054) as fixed modifications; and (4) oxidized Met (+15. 9949), deamidated Asn and Gln (+0. 9840) and iTRAQ ® isobaric tag Tyr (+304. 2054) as variable modification. The false discovery rate (FDR) was estimated with decoy-fusion option included in the software.
Protein iTRAQ ® quantitation The eight labeled samples (four replicates each of proteins from liquid stationary cultures and from halite brine inclusions) were mixed in equimolar ratios and injected in nanoLC-MS/MS in triplicate to reduce instrument variability as previously described. Quantitation was performed using PEAKS Q (quantitation program) iTRAQ 8-plex type with 10 ppm mass tolerance and only peptides with a score above FDR threshold 1% are used to quantify the identified proteins. Resulted quantitation was filtered accepting only proteins groups with fold change ≥2, at least two unique peptides and FDR adjusted ≤1% for both proteins identification and fold change significance using ANOVA significance method. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037167 (project DOI: 10.
Proteomic shifts in Halobacterium salinarum induced by acclimation to halite brine inclusions Total proteins were extracted from the brine inclusions of halite after two months of complete dryness, with surface-bound proteins removed using the new protocol detailed above. These brine inclusions extracts were compared to total proteins extracted from H. salinarum cultures in stationary growth phase, representing conditions prior to halite formation. The proteins unique to each condition, as well as the common proteome between liquid cultures and from halite interiors were analyzed to determine how H. salinarum acclimates to halite brine inclusions. Using the mass spectrometry data of four replicates from each condition, 1,249 total proteins were identified from stationary phase liquid cultures and 1,052 from halite brine inclusions, representing 1,342 unique proteins.
Although peripheral membrane proteins were identified using this approach, transmembrane peptides were absent in the mass spectrometry dataset. We hypothesize that this was due to retention of transmembrane protein domains with the lipid membrane fraction during TRIzol extraction in residual phenol phase after protein precipitation with isopropanol (see Supplementary Figure  2-1 ). Importantly, some transmembrane proteins could be identified by peptides outside the membrane-spanning domains, e. g. , the ArcD arginine-ornithine antiporter protein, for which seven cytosolic-and extracellular-facing peptides were identified (see Supplementary Figure  7-1 ). However, not all transmembrane proteins were identified as evidenced by the lack of identified peptides for bacteriorhodopsin in any samples (stationary phase liquid cultures and brine inclusions).
(Kanehisa et al. , 20160)of cold plasma sources used to remove surface-bound proteins. wasmeasuredasapercentage of total proteins removed (as determined by modified BCA assay) compared to untreated halite. DBD means dielectric barrier device; APPJ for atmospheric pressure plasma jet. The abbreviations "slm" and "sccm" are used for standard liters per minute and sccm standard centimeters cube per minute, respectively. multipleannotations in the NCBI database. To do so, we developed custom BASH and PYTHON scripts to merge all descriptions for one proteins identifier (favoring in order AAG-type, DAC-type, WP-type and then Q9H-type identifier). Venn diagrams were computed with VennDiagram and ggplot2 packages in R script. Functional proteins annotation was done with Kyoto Encyclopaedia of Genes and Genomes (KEGG,Kanehisa and Goto, 2000)using blastKOALA tool(Kanehisa et al.
Acknowledgments UHPLC MS/MS data were acquired at the  Plateau Technique de Spectrométrie de Masse Bio-organique, UMR 7245 Molécules de Communication et d' Adaptation des Microorganismes, Muséum national d'Histoire naturelle, Paris, France. The nanoLC-MS/MS data were acquired at ProGenoMix MS-platform, IBiSA labeled, at  CEA/SPI/Li2D. Thanks to  Imène Esteve  of the.
Funding This work was supported by the  X-life program  of  CNRS-MITI , the  ATM program  of the  Muséum National d'Histoire Naturelle , the  French National Research Agency ANR-PRCI  " ExocubeHALO" project  ( ANR-21-CE49-0017-01_ACT ), and the  Sorbonne Université  (graduate stipend CF).
As ab initio as these methods are, they need to be constrained by experimental data. In principle, x-ray absorption near-edge spectroscopy (XANES) is a relevant measurement, since it gives access to atomic scale information, especially to the electronic structure, even in the partially disordered situations encountered in WDM. In this context, performing timeresolved XANES (Tr-XANES) measurements in samples heated by a femtosecond laser up to the WDM regime, provides valuable data to unravel the respective dynamics of the electronic structure and that of the nuclei, and thus to understand their interaction. On the other hand, such extreme conditions of matter require revisiting the "standard" interpretation (i. e. , near room temperature) of XANES features.
For similar reasons, the x-ray pulse duration is difficult to evaluate precisely from calculations. That would require complex radiationhydrodynamics codes. Its measurement is also a real experimental challenge. One can use x-ray streak cameras providing sub-picosecond time resolution, but it is an experiment in itself. 37  Most of the time, the Tr-XANES experiment itself provides an upper value of the x-ray duration. With solid conversion targets, we observed that the global emission level increased with the laser pulse duration from sub-ps to $10 ps (see, for example, Fig. 7  in the case of CsI target). The x-ray pulse duration was observed to increase as well (unpublished x-ray streak measurements). Therefore, in order to keep a good time resolution, a compromise is necessary. In Table  I , several x-ray source data are reported when using a 3 ps laser pulse duration FWHM (full width at half maximum), corresponding to 1.
The characteristic time was observed in the picosecond or even sub-picosecond timescale. 53  The overall experimental data were well reproduced with two-temperature hydrodynamic simulations, supporting a thermal phase transition. The interpretation of XANES spectra is of course highly material dependent, and a calculation support is particularly required in the warm dense matter regime where exotic and original situations are expected. However, the investigation carried out on warm dense copper could be a priori generalized to other elements. For example, the van Hove singularities are general features in most of the periodic crystalline phases. As a consequence, corresponding post-edge modulations are expected in a wide range of materials. In the same way, the effect of the electron temperature (T e ) on the frontier between  occupied and vacant electron states, should be observable on a wide range of x-ray absorption edges, as far as the T e value exceeds the natural bandwidth of the edge.
The pulse duration obtained using particle-incell simulations is $10 fs. 28, 83  By combining these numbers, the peak brightness of a betatron source reaches 10 22 photons per second, mm 2 , and mrad 2 in 0. 1% of the bandwidth (BW). While this peak brightness is greater than in a synchrotron (essentially thanks to the short pulse duration),  71  betatron sources could work at 10 Hz, but are usually used at a fraction of Hz in practice. This means that these sources should be used in single-shot or several-shots experiments not requiring too much data accumulation. In addition to the interesting features for Tr-XANES applications (femtosecond pulse duration, broad and smooth continuous spectrum, collimation, and flux), the betatron source has also the advantage of not producing debris (interaction with a gas target).
The photon counting statistics is reported in the error bars (shaded areas). The pre-edge is clearly visible and reaches a maximum level around 1 ps. The deduced dynamics of T e was then compared with three different simulations using the two-temperature model implemented in the hydrodynamic code ESTHER. 88  The first one (BT) assumed a uniformed heating over the whole sample to simulate a ballistic transport. Another one (DT) considered the heating in the laser skin depth only, followed by a diffusive transport in the depth of the sample. The last one (CT) was a combination to simulate the saturation of the ballistic transport. More detail is given in the reference. 89  An example of a DT simulation is presented in Fig. 15  (a). The average electronic temperature was then compared with the experimental results in Fig.
Data Processing Steps The experiment requires that we record the raw data and process them as follows. The average temperature, feature size and feature velocity are calculated, and the corresponding physical parameters are obtained. (1) The average temperature t0 for convective heat transfer flowing in the channels of the electronic heat sink parts is as follows: 𝑡 = (𝑡 𝑡 𝑡 ⋯ 𝑡 )/𝑛. ( 12 ) where 𝑡 is the average temperature of the radiator surface; t1, t2. tn are the radiator surface temperature, °C.
Data Processing Steps The experiment requires that we record the raw data and process them as follows. The average temperature, feature size and feature velocity are calculated, and the corresponding physical parameters are obtained. (1) The average temperature t 0 for convective heat transfer flowing in the channels of the electronic heat sink parts is as follows: t 0 = (t 1 + t 2 + t 3 + • • • + t n )/n. ( 12 ) where t 0 is the average temperature of the radiator surface; t 1 , t 2. t n are the radiator surface temperature, • C. (2) Feature dimensions refer to standard-like geometric dimensions. Convective heat transfer within the electronic heat sink is dimensioned as a microchannel. This experiment is a rectangular cross section, so the equivalent diameter d e is as follows: d e = 4 S U = 2 ab a + b.
Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et     al. , 2020(Petrescu et     al. ,  , 2021b)). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems.
The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models.
By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr -1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties.
For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr -1 (926 ± 13 Tg C yr -1 ), while eight other BU sources report a mean value of 3340  [3238, 3401]  [25th,75th percentile] Tg CO2 yr -1 (948  [937, 961]  Tg C yr -1 ). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr -1 (1038 Tg C yr -1 ), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr -1 while six other BU approaches reported a mean sink of -62  [-117,-49]  Tg C yr -1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69  [-152,-5 ] Tg C yr - 1.
The current work has a strong focus on the EU27, and therefore sits within the context of recent legislation passed by the European Parliament concerning commitments for the LULUCF sector to achieve the objectives of the Paris Agreement and the reduction target for the Union (EU, 2018a and the proposed amendments, EU, 2021a). This legislation requires that, "Member States shall ensure that their accounts and other data provided under this Regulation are accurate, complete, consistent, comparable and transparent". The TD and BU methods discussed below include the most up-to-date publicly available spatially explicit information, which can help provide a quality check and increase public confidence in NGHGIs. The work presented in this paper covers dozens of distinct datasets and models, in addition to the individual country submissions to the UNFCCC of the EU Member States and the UK.
As Annex I Parties, the NGHGIs of the EU Member States and the UK are consistent with the general guidance laid out in  IPCC (2006)  yet still differ in specific approaches, models, and parameters, in addition to definitional differences in the underlying system boundaries and activity datasets. A comprehensive investigation of detailed differences between all datasets is beyond the scope of this paper, though systematic analyses have been previously made for specific sectors (e. g. AFOLU 3  -  Petrescu et al. , 2020; previous synthesis to this work -Petrescu et al. , 2021b; FAOSTAT versus UNFCCC NGHGIs -Tubiello et al. , 2021 , Grassi et al. , 2022a ; UNFCCC versus bookkeeping models -  Grassi et al, 2022b; and UNFCCC versus inversions -Deng et al. , 2021)  and by the Global Carbon Project CO2 syntheses (e.
g. ,  Friedlingstein et al. , 2022). Every year (time "t") the Global Carbon Project (GCP) in its Global Carbon Budget (GCB) quantifies largescale CO2 budgets up to the previous year ("t-1"), bringing in information from global to large latitude bands, including various observation-based flux estimates from BU and TD approaches  (Friedlingstein et al. , 2022). The current manuscript, given the focus on a single region ("Europe'') with extensive data coverage, dives into more detail than the GCB, including sector-specific models related to LULUCF (e. g. , Forest land, Grassland, Cropland) and making heavy use of the EU27+UK NGHGI in an effort to build mutual trust in the various approaches. Compared to  Petrescu et al. (2021b) , the current work updates datasets, methods, and uncertainties.
The methodological and scientific challenges to compare these different estimates have been partly investigated before  (Pongratz et al. , 2021 , Grassi et al. , 2018a, for LULUCF; Andrew, 2020, for  fossil sectors) but such comparisons were not done in a systematic and comprehensive way, including both fossil and land-based CO2 fluxes, before  Petrescu et al. (2021b). As  Petrescu et al. (2021b)  is the most comprehensive comparison of the NGHGI and research datasets (including both TD and BU approaches) for the EU27+UK to date, the focus of the current paper is on improvement of estimates in the most recent version in comparison with the previous one, including changes in the uncertainty estimates and identification of the knowledge gaps and added value for policy making. Official NGHGI emissions are compared with research datasets, including necessary harmonization of the latter on total emissions to ensure consistency.