dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by the  Beijing Advanced Innovation Center for Tree Breeding by Molecular Design  and the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ) (to FM and YD). We also acknowledge grants from the  China Postdoctoral Science Foundation  ( 2019M660508 ) (to QZ),  Strategic Priority Research Program   of the Chinese Academy of Sciences  ( XDB31000000 ) (to ZY),  National Natural Science Foundation of China  ( 31970015  to GW and  U1802231  to YD),  Second Tibetan Plateau Scientific Expedition and Research Program  ( 2019QZKK0503 ), and  Novo Nordisk Foundation  ( NNF20OC0059697  to AL).
Availability of data and materials Raw sequencing data were deposited in the NCBI Sequence Read Archive database under the accession numbers PRJNA957854 (for rDNA ITS1), PRJNA957896 (for rDNA ITS2), and PRJNA955425 (for RNA reads). The datasets supporting the conclusions of this article are included within the article and its additional files (Supplementary Material). Scripts used for data analysis are available at GitHub: https:// github. com/ anleb reton/ Scrip ts_ from_ Zeng_ Lebre ton_ et_ al. _ 2023. git.
From 20 s on, the offered data rates are inverted. Hence, the best DC value in terms of throughput should now favor Wi-Fi when allocating transmission time. For that case, the algorithm selected 0. 3, which gives 70% of the window time to Wi-Fi and 30% to LTE. It is also important to note that the DC value of 0. 7 is selected fewer times than 0. 3, even though the simulation is evenly split into two parts. This behavior is due to the simulation software used. The ns-3 simulator allocates an initial time to configure the servers and start the services. As a result, in the first part of our simulation, when 0. 7 is the optimal DC value, the proposed framework has less time to act due to this warm-up period. However, this behavior does not impact the performance, as the results demonstrate that the algorithms effectively select the optimal DC value in each half of the simulation.
Context. Gaia's readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full two-dimensional (2D) information. This new pipeline produced half a million additional Gaia sources in the region of the omega Centauri (ω Cen) cluster, which are published with this Focused Product Release. We discuss the dedicated SIF CF data reduction pipeline, validate its data products, and introduce their Gaia archive table. Aims. Our aim is to improve the completeness of the Gaia source inventory in a very dense region in the sky, ω Cen.
Introduction On June 13, 2022, the Data Processing and Analysis Consortium (DPAC) of the European Space Agency (ESA) cornerstone mission Gaia published Data Release 3 (DR3), containing astrometry and photometry for more than 1. 8 billion sources  (Gaia Collaboration 2021 , 2023). This third Gaia data release provides incredible insights into our home galaxy and beyond. The Gaia catalogue has been used for several studies in the omega Centauri (ω Cen) cluster. Examples are the measurement of the cluster parallax and calibration of the luminosity of the tip of the red giant branch  (Soltis et al. 2021) ; investigations on stellar kinematics to describe the distribution of both the dark and luminous mass components in the cluster  (Evans et al. 2022) ; analysis of the kinematics of stellar sub-populations in the cluster  (Cordoni et al.
2020; Sanna et al. 2020 ) and identification of a stellar stream originating in ω Cen  (Ibata et al. 2019a,b) ; as well as combinations of Gaia data with other survey catalogues  (Scalco et al. 2021; Prabhu et al. 2022) , just to mention a few. Generally, the completeness of the Gaia catalogue is very high. A small number of densely populated regions, one of which is ω Cen, are an exception though. Gaia's astrometric crowding limit is around 1 050 000 objects deg -2 (Gaia Collaboration 2016), which is about one object every 12 square arcseconds. For denser areas, the readout window strategy reaches its limitations. Therefore, for the most crowded regions in the sky, clear holes are visible in the catalogue data. With this Focused Product Release (FPR), the hole in the core of the ω Cen cluster is now filled up with half a million additional sources.
Not being limited by a readout window, SIF CF processing uses the full 2D information about neighbouring sources to obtain accurate astrometry and photometry. The aim of this paper is to describe the implementation of the SIF CF pipeline and validate its performance. It starts with information on the SIF CF images, the main input of the pipeline, in Sect. 2, followed by a description of the SIF CF data reduction in Sect. 3. Most of the SIF CF pipeline is based on the nominal Gaia software systems that have been used to produce the Gaia catalogues released so far. For SIF CF processing, these software systems were adapted to process extended 2D images: The SIF CF image parameter determination (SIF CF IPD) registers all source detections in an image (Sect. 3. 1). The cross-match between these detections was done by SIF CF XM (Sect.
3. 2) and a coverage map was calculated from the image boundaries to identify reliable sources  (Sect. 3. 3). For these, Gaia's Astrometric Global Iterative Solution (AGIS) produced valid astrometry (Sect. 3. 4), and robust photometry was obtained with a dedicated ad hoc procedure using the Gaia DR3 photometry as a reference (Sect. 3. 5). In Sect. 3. 6, we list all filters that were applied to the SIF CF data. While a full description of the nominal Gaia software systems can be found in the corresponding, linked papers, here we concentrate on the adaptations implemented for the SIF CF processing. A validation of the SIF CF data products is provided in Sect. 4. It was done by comparing SIF CF data to nominal Gaia DR3 data and to a dedicated Hubble Space Telescope (HST) dataset that covers the core of ω Cen.
After basic information regarding the number of detections and sources created by the SIF CF pipeline (Sect. 4. 1), photometry was validated comparing SIF CF statistics to nominal Gaia (Sect. 4. 2). Astrometry was then examined in terms of positional (Sect. 4. 3), proper motion (Sect. 4. 4), and parallax (Sect. 4. 5) uncertainties. In the following section, we describe the HST dataset and its usage (Sect. 4. 6), address the small-scale completeness of SIF CF data compared to Gaia DR3 and HST data (Sect. 4. 7), the source density and depth  (Sect. 4. 8) , and the reliability and completeness of the SIF CF sample (Sect. 4. 9). Known issues are discussed in Sect. 4. 10. In Sect. 5, the SIF CF data product, available in the Gaia archive table crowded_field_source, is described. Finally, a summary is provided in Sect.
SIF CF images During commissioning of the Gaia satellite in 2014, data gaps were visible in the very dense centres of dense regions in the sky. While in the outskirts of ω Cen the star detections are usually well separated, multiple sources overlap in the core of the A short feasibility study was conducted and showed that the analysis of two-dimensional SM images could enhance the completeness of the Gaia catalogue in these regions by roughly two magnitudes. Indeed, this turned out to be a conservative estimate. Originally, full SM images were scheduled for transmission to ground for calibration purposes only, but the improvement was such that the DPAC Executive Board together with the Gaia Science Team decided to record SIF images in addition to the nominal readout windows for a few selected regions on the sky.
These regions include the surroundings of 230 very bright stars (VBS) and nine crowded fields (CFs). Table  1  lists the dense regions in the sky selected for Gaia SIF CF data acquisition together with the starting date of their observation, see  Sect. 1. 1. 3 in de Bruijne et al. (2022). From that date onward during most scans of one of the satellite fields of view (FOVs) containing one of the listed regions, SIF CF images were recorded. For ω Cen and Baade's Window data record started 18 months earlier than for the other regions, because they were selected as test regions. Additionally, Baade's window and Sgr I were both observed once on 15 October 2014, see again  Sect. 1. 1. 3 in de Bruijne et al. (2022). While acquisition of SIF CF images is still ongoing, the ω Cen sources presented here were observed in images obtained between 1 January 2015 and 8 January 2020.
A35, page 3 of 22 The limitation of the SIF CF processing is the absence of colour and spectral information for the sources. All information is based on the SM images only. The advantages of 2D SIF images compared to nominal readout windows are obvious: SIF CF data suffer neither from being collapsed to one dimension before download nor from truncation of data, as can happen for overlapping windows, and each SIF CF image contains sources from one FOV only.
SIF CF pipeline In March 2019, a small team was assembled to analyse the SIF CF data, and between September 2021 and August 2022 the SIF CF pipeline had its first operational run, the results of which are published in this FPR. It used a total run time of 22 days distributed across the various software systems involved. Initial investigation phase. The first step towards a SIF CF data pipeline consisted of choosing a software system to be used for source detection in the future pipeline. Well known software packages like DAOPHOT  (Stetson 1987)  and SExtractor  (Bertin & Arnouts 1996) , as well as an adapted version of Gaia's nominal 'Detection and Image Parameter Determination' (IPD) software  (Castañeda et al. 2015)  were tested on a set of selected SIF CF images. Five independent research groups from Bologna, Groningen, Padova, Edinburgh, and Potsdam participated in the tests.
In terms of source detection, all three software tools delivered comparable results. Roughly 80 000 sources were found in a very crowded SIF CF area. As none of the methods outperformed the others, it was decided to use the adapted IPD code as the basis for the SIF CF pipeline. With comparable detection numbers, the reason for this choice was the most realistic description of the Point Spread Function (PSF) that Gaia's IPD provides. A good description of the PSF is crucial for accurate astrometry and photometry, and for IPD the PSF is based on all SM data available and not limited to the SIF CF images themselves. The SIF CF source detection and Image Parameter Determination software (SIF CF IPD) was then revised and improved regarding run time and performance for SIF CF images (see Sect. 3.
Validation In order to assure the quality of the SIF CF data product, a scientific validation has been performed. For this, we defined a fixed colour for each dataset that we use. Except for the last figure, throughout the whole document we use yellow for Gaia DR3 data, violet for SIF CF data published in this FPR, and dark blue for the combination of these two. Additionally, there are unpublished SIF CF data, which were used for photometric calibration only, in blue and HST reference data in teal.
′′ 57 separation, which roughly corresponds to 10 pixels in AL or 3 pixels in AC. This is consistent with the smallscale completeness reported for Gaia in  Fabricius et al. (2021). Unlike the nominal Gaia pipeline, the SIF CF pipeline can be applied to full images and profits from iterative source detection and parametrisation. The SIF CF dataset therefore reaches 50% density for a separation of 0. ′′ 22 -this is a factor of 2. 6 better than Gaia DR3. It corresponds to 1. 7 SM samples in AL and 0. 6 samples in AC. A more detailed study has shown that the SIF CF pipeline can separate sources down to 0. ′′ 2 distance in case their brightness differs by less than three magnitudes, which is the case for the biggest part of the data. If their brightness differs by more than three magnitudes though, the SIF CF resolving power decreases.
Reliability and completeness In the core of the ω Cen cluster, nominal Gaia DR3 sources become less numerous and less accurate. This is, where SIF CF data are expected to be deeper than nominal data. To probe the reliability and completeness of the SIF CF pipeline results, we selected an area in the ω Cen cluster core for which data are available in  Bellini et al. (2017). The preprocessing applied to this dedicated dataset is described in Sect. 4. 6. We should bear in mind though, that absolute astrometry with HST is either poor or tied to Gaia data, and should thus be used with care in this validation. The HST photometry can be used to some extent, as the HST F606W magnitudes are with a certain offset close to the Gaia G-band magnitudes. To make a proper comparison, we supplemented SIF CF sources with Gaia DR3 sources, creating a complete Gaia-and-SIF-CF source list.
Acknowledgements. This work made use of data from the  European Space Agency (ESA)  mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. All of the authors are current or past members of the  ESA Gaia  mission team and of the  Gaia DPAC , and their work has been supported by the  German Aerospace Agency  ( Deutsches Zentrum fur Luft-und Raumfahrt e. V. ,  DLR ) grant number  50QG1904  and  50QG1404 ; the  Spanish Ministry of Economy (MINECO/FEDER, UE)  through grants  ESP2016-80079-C2-1-R ,  RTI2018-095076-B-C21  and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu')  through grant  MDM-2014-0369  and  CEX2019-000918-M ; the  Tenure Track Pilot Programme  of the  Croatian Science Foundation  and the  Ecole Polytechnique Fédérale de Lausanne  and the Project  TTP-2018-07-1171  Mining the variable sky, with the funds of the  Croatian-Swiss Research Programme ; The authors thankfully acknowledge the computer resources from  MareNostrum , and the technical expertise and assistance provided by the  Red Española de Supercomputación at the Barcelona Supercomputing Center, Centro Nacional de Supercomputación.
The full acknowledgements are available in Appendix C. Agence  Nationale de la Recherche  (ANR) through grant  ANR-10-IDEX-0001-02  for the ' Investissements d'avenir' programme , through grant  ANR-15-CE31-0007  for project ' Modelling the Milky Way in the Gaia era' (MOD4Gaia) , through grant  ANR-14-CE33-0014-01  for project 'The  Milky Way disc formation in the Gaia era' (ARCHEOGAL) , through grant  ANR-15-CE31-0012-01  for project ' Unlocking the potential of Cepheids as primary distance calibrators' (UnlockCepheids) , through grant  ANR-19-CE31-0017  for project ' Secular evolution of galaxies' (SEGAL ), and through grant  ANR-18-CE31-0006  for project ' Galactic Dark Matter' (GaDaMa ), the  Centre National de la Recherche Scientifique (CNRS)  and its  SNO Gaia  of the  Institut des Sciences de l'Univers (INSU) , its  Programmes Nationaux :  Cosmologie et Galaxies   (PNCG) ,  Gravitation Références Astronomie Métrologie (PNGRAM) ,  Planétologie (PNP) ,  Physique et Chimie du Milieu Interstellaire (PCMI) , and  Physique Stellaire (PNPS) , supported by  INSU  along with the  Institut National de Physique (INP)  and the  Institut National de Physique nucléaire et de Physique des Particules (IN2P3) , and co-funded by  CNES ; the ' Action Fédératrice Gaia' of the Observatoire de Paris , and the  Région de Franche-Comté ; -the  German Aerospace Agency  ( Deutsches Zentrum für Luft-und Raumfahrt e.
Anders), the Severo Ochoa Centre of Excellence Programme (SEV2015-0493) and MCIN/AEI/10. 13039/501100011033/ EU FEDER and Next Generation EU/PRTR (PRTR-C17. I1); the European Union through European Regional A35, page 19 of 22 Gaia Collaboration: A&A, 680, A35 (2023) Development Fund 'A way of making Europe' through grants PID2021-122842OB-C21, PID2021-125451NA-I00, CNS2022-13523 and RTI2018-095076-B-C21, the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu') through grant CEX2019-000918-M, the University of Barcelona's official doctoral programme for the development of an R+D+i project through an Ajuts de Personal Investigador en Formació (APIF) grant, the Spanish Virtual Observatory project funded by MCIN/AEI/10. 13039/501100011033/ through grant PID2020-112949GB-I00; the Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC), funded by the Xunta de Galicia through the collaboration agreement to reinforce CIGUS research centers, research consolidation grant ED431B 2021/36 and scholarships from Xunta de Galicia and the EU -ESF ED481A-2019/155 and ED481A 2021/296; the Red Española de Supercomputación (RES) computer resources at MareNostrum, the Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS) through activities AECT-2017-2-0002, AECT-2017-3-0006, AECT-2018-1-0017, AECT-2018-2-0013, AECT-2018-3-0011, AECT-2019-1-0010, AECT-2019-2-0014, AECT-2019-3-0003, AECT-2020-1-0004, and DATA-2020-1-0010, the Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya through grant 2014-SGR-1051 for project 'Models de Programació i Entorns d'Execució Parallels' (MPEXPAR), and Ramon y Cajal Fellowships RYC2018-025968-I, RYC2021-031683-I and RYC2021-033762-I, funded by MICIN/AEI/10.
2000), the construction of which was supported by the Velux Foundation of 1981 and the Danish Space Board; -The Tycho double star catalogue (TDSC, Fabricius et al. 2002), based on observations made with the ESA HIPPAR-COS astrometry satellite, as supported by the Danish Space Board and the United States Naval Observatory through their double-star programme; -data products from the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006), which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center (IPAC) / California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA) and the National Science Foundation (NSF) of the USA; -the ninth data release of the AAVSO Photometric All-Sky Survey (APASS, Henden et al.
Precipitation Data Our data set consists of ECMWF ensemble forecasts of daily accumulated precipitation and the corresponding observations at seven meteorological stations spread across the Netherlands (De Bilt, De Kooy, Eelde, Schiphol, Maastricht, Twente and Vlissingen) 1. We use about 9 years of data, from January 1st, 2011, until November 30th, 2019, with sample size n = 3256. We fit separate models for each station with response variable Y equal to the observed precipitation at the station between 00 UTC and 24 UTC. As for the covariates, we use ECMWF ensemble forecasts of daily accumulated precipitation that is computed the day before at 12UTC. The ensemble forecast contains 51 members. For efficiency, we use two summary statistics, namely the standard deviation of the ensemble members and the upper order statistics (the maximum of the ensemble members).
We next describe the precise mechanisms i-iii) for GRF. For resampling, GRF uses subsampling rather than bootstrap, which facilitates the theoretical analysis of the model and ensures asymptotic normality for a small subsampling rate. Subsampling means that a smaller data set is produced by selecting randomly s observations out of n (without replacement unlike the bootstrap). The ratio s/n is called the subsampling fraction. In practice, the default subsampling fraction used in the grf package is 50%. The construction of trees in GRF is similar to Breiman's random forest: recursive binary splitting is used to optimize greedily some criterion and, at each step, the set of possible predictors for the split is reduced by a random selection of m predictors out of p. In GRF, different criteria are designed specifically for different tasks.
and Agogonia voluta gen. et sp. nov. Ophirina chinija differs from O. amphinema in having rounded cell ends, subapically emerging flagella and a posterior cell protrusion. The much more distantly related A. voluta has several unique ultrastructural characteristics, including sac-shaped mitochondrial cristae and a complex "B" fiber. Phylogenomic analyses with a large conserved-marker dataset supported the monophyly of Ophirina and Agogonia within the Ophirinina and, more importantly, resolved the conflicting position of ophirinids as the sister clade to all other jakobids. The characterization of the mitochondrial genomes showed that Agogonia differs from all known gene-rich jakobid mitogenomes by the presence of two group II introns and their corresponding maturase protein genes. A phylogenetic analysis of the diversity of known maturases confirmed that the Agogonia proteins are highly divergent from each other and define distant families among the prokaryotic and eukaryotic maturases.
One notorious example is the Malawimonadida, which has most of the "typical excavate" features and branched with some or all other excavates in molecular phylogenies reconstructed with few markers, though usually with modest support  (Simpson et al. 2006; Parfrey et al. 2010; Pánek et al. 2015; Zhang et al. 2015) , but disconnected from other excavates in most recent phylogenomic analyses based on larger datasets  (Hampl et al. 2009; Derelle and Lang 2012; Derelle et al. 2015; Katz and Grant 2015; Burki et al. 2016; Heiss et al. 2018). The recently described Ophirina amphinema (Suborder Ophirinina)  (Yabuki et al. 2018)  provides another example of an enigmatic lineage. This species branched either as sister clade to all jakobids in a multigene phylogeny or as sister clade to all Discoba in 18S rDNA phylogenies, in both cases with low support  (Yabuki et al.
2018). Not only its phylogenetic position within Discoba remains uncertain but Ophirina has both typical jakobid morphological features and also non-jakobid/discobid characteristics. Among the former are three microtubular roots associated with the posterior basal body, a dorsal fan of microtubules associated with the anterior basal body, and a multi-layered "C" fiber. Among the atypical features are the mitochondrial cristae morphology (discoid-flattened cristae) and the presence of two flagellar vanes  (Yabuki et al. 2018). More recently, an unrooted phylogeny reconstructed with a dataset of 14 mitochondrial genes placed Ophirina in an intermediate position between jakobids and other discobids  (Ettahi et al. 2021). In this context, the inclusion of sequence data from new Ophirinina species might help to resolve the phylogenetic position of this group in the eukaryotic tree.
Phylogenomic Analyses We used a large dataset of 350 conserved markers (112,726 amino acid positions) to reconstruct Bayesian inference (BI) and maximum likelihood (ML) phylogenomic trees with the CAT-GTR and the LG + C60 + F + R7 (PMSF) models, respectively. We obtained an overall identical topology with both methods, only with small differences in terminal branches. In both trees, the monophyly of Discoba, Jakobida, and Ophirinina and the sister relationship of Ophirinina to all other jakobids were recovered with high (≥95%) to full (100%) ML bootstrap support and full BI posterior probability (fig. 4A ; supplementary figs. S3 and S4, Supplementary Material online). Ophirina chinija and O. amphinema form a fully supported clade sister to Agogonia voluta. To measure the robustness of this result, we carried out an analysis of the progressive removal of fast-evolving sites of the alignment using ML reconstruction (fig.