dataesr/scientific-paragraphs-categorization huggingface dataset:
Interferometry Two sets of near-IR interferometric data from two different VLTI beam-combiners were used to study Altair: 8 observations from PIONIER (H band; 1. 65 µm) and 6 observations from GRAV-ITY (K band; 2. 2 µm). The PIONIER observations (listed in Ta-   Marion et al. 2014). They were conducted with a compact configuration (AT) and three wavelength channels in the H band. This second part of PIONIER data was used despite the three-year gap with the first one, as the compact configuration will only be sensitive to large scales, such as the overall shape of the star, which does not change significantly in such a short time for Altair. The second set of data is made of two nights of GRAVITY observations. They were obtained during the first Science Verification Time (SVT) of the instrument (programme ID : 60.
A-9164(A)). They were reduced using the GRAVITY pipeline  (Lapeyrere et al. 2014) , with the star HD188310 used as calibrator, through reduction recipes made available by the GRAV-ITY consortium in their python toolkit 3. Six squared visibilities and four closure phases were obtained with each observation, for both the p and s polarisation directions. This yields two data sets at high resolution in the K band (R ∼ 4000), and two at low resolution for the fringe tracker. As there was no significant difference between the data sets for the two polarisations, we averaged them. Only the science detector data was used in our analysis. The uv-plane for both instruments is shown in Fig. 2 , and the observables are shown in Fig. 1  along with the theoretical data.
Atmosphere models Among the different existing atmosphere codes, we decided to use models from the PHOENIX code 5   (Husser et al. 2013 ), as it is the code that best matches our needs in terms of surface parameters (see Table  3 ), spectral range, and specific intensity. Indeed, these spherically symmetric atmospheres were used to produce specific intensities (low resolution, but for different values of the emission angle) and spectra (high resolution), from 500 to 26000 Å, which suits our study since we are analysing observational data in the visible range (ELODIE), the H band (PIONIER), and the K band (GRAVITY). Models computed for 0. 5 < log g < 6. 0, 2300K< T eff < 12000K and -1. 0 < [M/H] < 1. 0 were selected from the PHOENIX online library. To compute the intensity maps, as seen from Earth, we need the specific intensities as a function of the emission angle I(µ).
These are available in the PHOENIX database, but only with a 1 Å step. This kind of resolution is sufficient to analyse PIONIER and GRAVITY data, but as most lines are blended together by rapid rotation, initial high resolution spectra are needed to obtain an accurate broadened spectrum in the visible, which we need for the analysis of our spectroscopic data. Such high resolution spectra are only available in the database as fluxes, independent of the direction of emission. To compute the desired quantity, we used  Claret (2000) 's expression of limb darkening (Eq. 6), using the a k -coefficients found in the dedicated Vizier catalogue  (Claret 2018, most  recent catalogue for the wavelengths of interest). The expression I λ (µ) = I λ (µ = 1)         1 - 4 k=1 a k 1 -µ k 2         , (4) can be coupled with F λ = 2π 1 0 I λ (µ)µdµ (5) to yield F λ = I λ (µ = 1)π         1 - 4 k=1 a k k k + 4        .
ESTER models We now want to determine the mass M, the metallicity Z, and hydrogen mass fractions in the envelope and core, X and X c respec-. These results are listed in Table  5. tively. ESTER models are used, with i, PA and Ω fixed to the previously obtained values. Interferometric and spectroscopic data are analysed simultaneously. The metallicity of the stellar model must be addressed here. Ideally, this metallicity should match that of the model atmospheres unless some physical phenomena, such as diffusion, were to lead to a different composition in the surface layers. In the present case, we treat the two metallicities as independent parameters as they are subject to different constraints. Indeed, the atmospheric metallicity is especially sensitive to spectroscopic constraints as shown above.
We used the CESAM code  (Morel & Lebreton 2008)  to get an estimate of the time it would take for a 1. 86M star with an initial hydrogen content X = 0. 739 to evolve from X c = 0. 739 to X c = 0. 710. The resulting age is ∼ 93 Ma. Since this age was evaluated with a 1D model taken outside its range of validity, as far as rotation is concerned, we estimate the age of Altair to be around 100 Myrs. [M/H] We note that the metallicity obtained for the best ESTER model does not match that of the model atmosphere used to reproduce the observed spectrum. Indeed, the correlation between M and Z (see Eq. 14) leads to Z = 0. 019 for X = 0. 739. Knowing that [M/H] = log X Z Z X , (16) we get a corresponding [M/H] = 0. 19 using the solar composition from  Asplund et al. (2005). This is lower than the metal-licity used in the model atmosphere (i.
Yet, this effect only amounts to 1% at maximum in the K-band, way below our uncertainty on the radius of the star. Furthermore, they also find that the difference in gravity darkening between both geometries is negligible for this kind of stars. A star like Altair has an effective gravity at the surface log g > 3. 0, with our best model showing 3. 8 log g 4. 3 from equator to pole. We should then be safe from any significant effect of the geometry of the atmospheres we used, and a spheroidal code for computing model atmospheres would not improve the accuracy of our results in a way that would justify the time and effort put into it. If surface convection and time evolution are one day successfully implemented into ESTER, then modelling evolved stars with extended atmospheres would become feasible, and this matter would have to be resolved before a work such as this one should be considered.
Conclusions We conducted a multi-technique analysis of the star Altair (HD187642) using interferometry, spectroscopy, and seismology. For the first time, this kind of analysis was performed by comparing observational data with intensity maps computed from a full two-dimensional model of the stellar interior (ES-TER) and surface (atmosphere models). PHOENIX atmosphere models were used when fitting interferometric observables, and were furthermore supplemented by Claret's 4-coefficients limb-darkening law when interpreting high resolution visible spectra from the ELODIE instrument. For the stellar structure, the ω-model (i. e. the GD model from  Espinosa Lara & Rieutord (2011)  applied to a Roche model) first allowed us to constrain Altair's equatorial radius, position angle, rotation velocity and inclination with interferometry, while the spectrum provided a preliminary value for the metallicity of the atmosphere.
Acknowledgements. This research made use of the  Jean-Marie Mariotti Center (JMMC) service OiDB 8. This research made use of the SIMBAD and VIZIER databases ( CDS, Strasbourg, France ) and  NASA 's Astrophysics Data System. KB, AD, MR, and DRR acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ESRR  ( ANR-16-CE31-0007-01 ), which made this work possible. MR and DRR also acknowledge the  International Space Science Institute (ISSI)  for supporting the SoFAR international team 9. We would also like to thank the referee for his useful insight and comments.
Even with fixed geometries, determining the detector position is still an issue for the majority of users. Hence, a new graphical user interface has been developed for the Python fast azimuthal integration library pyFAI  (Kieffer et al. , 2019) , focusing on the user experience to grant them autonomy in data analysis once they have returned to their home institutes. Laboratory source diffractometers are commonly equipped with (small) area detectors mounted on moveable goniometer arms for powder diffraction (for example, the Rigaku HyPix-3000 detector). Detectors at synchrotrons are often mounted on goniometer or translation tables to provide the degrees of freedom (hereafter DoFs) needed to align the beamline, although those DoFs are rarely used for data acquisition. One counter-example is reported by  Gao et al.
(2016) , where the beamline is equipped with a moving strip-detector, the Mythen detector (1D) from Dectris, but the Mythen is not an area detector. Pair distribution function (PDF) analysis typically requires very large area detectors and higher energies to be able to cover the needed high q-range in one single frame  (Chupas et al. , 2003). When speed is not critical, PDF experiments can be performed with small area detectors mounted on a motorized ISSN 1600-5775 arm, and moved in front of the sample during data acquisition to cover a larger solid angle. A new pyFAI module which handles goniometers and moving detectors is also presented. It offers the ability to exploit the positioning system of the detector (often readily available on beamlines) to acquire powder diffraction or PDF data on a larger q-range with no additional cost or investment.
The Python fast azimuthal integration library pyFAI is a Python  (van Rossum, 1989 ) library used to transform 2D diffraction images into 1D powder diffraction patterns by re-binning the pixel positions in polar coordinates. The radial units are typically the scattering angle 2 or the momentum transfer q = 4 sin()/. pyFAI also provides additional tools to calibrate the detector position, i. e. to determine its location in space by means of Debye-Scherrer conical rings resulting from the intersection of the diffracted X-ray beam with the detector surface. The observed rings are deformed into ellipses when the detector is planar but slightly inclined. The azimuthal integration is performed in two steps. The first is a pixel-wise transformation corresponding to the image correction, I cor ¼ signal normalization ¼ I raw À I dark FPAI 0 ; ð1Þ where I raw is the raw signal of the detector, I dark is the dark current image (it may also be the background image for certain experiments), F is the factor accounting for the flatfield correction, is the solid angle subtended by a given pixel, P is the polarization correction term and A represents the detector's apparent efficiency due to the incidence angle of the photon on the detector (for integrating detectors, highenergy photons with larger incidence angles see larger sensor thickness, and thus have a higher detection probability).
, 1996)  and re-used in many software packages such as GSAS-II  (Toby & Von Dreele, 2013)  and DAWN  (Filik et al. , 2017)  which also offer user-friendly interfaces. The FIT2D geometry reaches its limits when the detector is heavily tilted, and it is not even possible to describe a detector mounted parallel to the beam (which is sometimes used on laboratory sources in reflection geometry). The geometry used in pyFAI is based on the definition of the PONI (Fig. 1 ), which is the orthogonal projection of the sample position (the origin in pyFAI) on the detector plane (or the plane z = d 3 = 0 when the detector is non-planar and z varies from pixel to pixel). This geometry is inspired by SPD  (Boesecke, 2007)  and is capable of describing any detector position in space. It is worth noting that the PONI is co-located with the beam centre when the detector is not tilted, i.
Calibration of a detector on a moving stage Calibration of a detector mounted on a translation stage along the direct beam has successfully been exploited by DAWN  (Filik et al. , 2017)  and GSAS-II  (Horn et al. , 2019)  to remove the correlation between the detector distance and the energy of the beam. While these two software packages offer an intuitive user interface for those experiments, the modelization of the translation stage remains hard-coded. This section explains how to describe, programmatically, a moving stage in pyFAI, calibrate the proposed model and use it to perform the azimuthal integration with many images. The calibration of the detector position on a fixed goniometer position can be performed with pyFAI as long as several rings of the chosen calibrant are present in the image and that five control points can be extracted from one ring and at least one point from another ring.
Acknowledgements We would like to thank all ESRF beamline teams for supporting the pyFAI development, and especially  David Flot  from the MX-group who provided the data of the translation table. We would also like to thank the other participants in the project, especially  Serge Cohen  from the  Ipanema institute , the DiffAbs and Cristal beamlines and Frederic  Emmanuel Picca  from  Synchrotron Soleil. In the instrumentation services and development division (ISDD) of the ESRF we would like to thank  V. Armando Sole  ´, head of the data analysis unit and leader of the silx project, and all our colleagues from the silx project:  Thomas Vincent ,  Henri Payno ,  Damien Naudet  and  Pierre Knobel  for their support and ideas. A great thank you to  Carsten Detlefs  for his contribution to the documentation of the geometry in pyFAI.
In this study, we provide the most comprehensive assessment of the distribution, status, and population dynamics of three sympatric apex predators to date. Using a unified analytical approach (Fig. 1 ), we mapped the densities and measured vital rates of brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo) across more than half a million square kilometers. We utilized the world's most extensive individualbased monitoring dataset on large carnivores collected annually.
Materials and Methods Large Carnivore Monitoring. We used data from the Scandinavian large carnivore database Rovbase 3. 0  (12). This database is used jointly by Norway and Sweden to record information obtained during large carnivore monitoring, NGS data, GPS search tracks, and dead recovery data. Detailed information about each of these components is provided in SI Appendix. Open-Population Spatial Capture-Recapture Analysis. The OPSCR model  (13) (14) (15)  is composed of three submodels described in the following sections: 1) a model for population dynamics and population size (Population dynamics), 2) a model for density and movements (Density and movement), and 3) a model for detections during DNA searches (Detections). Population dynamics. We used a multistate formulation for the population dynamics model  (38).
States of individuals recovered dead (and thus transitions) following the last live detection were thus no longer latent, even in the absence of further live detections. Dead recovery locations were used as the coordinates s it of the AC of the recovered individual in the year following its death, thus informing (through the movement model described above) the AC location during the last time step that the animal was alive. Analysis. We fitted our Bayesian OPSCR models using Markov chain Monte Carlo (MCMC) simulation with Numerical Inference for Statistical Models Using Bayesian and Likelihood Estimation (NIMBLE)  (44) (45) (46)  in R version 3. 6. 1  (47). NIMBLE provides an alternative implementation of the Bayesian Inference Using Gibbs Sampling (BUGS) model language coupled with the capability to add custom functions, distributions, and MCMC samplers to improve computing performance.
57. We refer interested readers to our R package nimbleSCR  (50, 58) , which includes functions for fitting SCR models using NIMBLE. The package includes a collection of methods that drastically improve MCMC estimation, thereby enabling application of SCR to large-scale problems. Parameter estimation. The OPSCR is a hierarchical model with many parameters and latent variables directly estimated or derived (SI Appendix, Tables  S4-S6 ). Unless otherwise indicated, we summarized posterior distributions using their median and 95% CrI. To obtain abundance estimates, we summed the number of OPSCR-predicted AC locations of alive individuals that fell within each geographic region of interest (Scandinavia-wide, country specific, or local administrative unit) for each iteration of the MCMC chains, thus generating a posterior distribution of the density in that spatial unit.
Results from the posterior sampling procedure were processed like those from the original model fitting. We provide only a simple illustration of the potential of OPSCR for making forecasts. Actionable predictions should take into account that different sources of mortality are mutually influenced by their magnitude and timing. Furthermore, our example relies on the same density covariates that were used during the period with data. These covariates would have to be updated if extensive distributional change (e. g. , range expansion or contraction) is to be expected in the future. Data Availability. The. RData file with input data for OPSCR model fitting and carnivore density raster files have been deposited in Github (https:// github. com/richbi/WildThings) (57). ACKNOWLEDGMENTS. This work was made possible by the large carnivore monitoring programs and the extensive monitoring data collected by Swedish (L änstyrelsena) and Norwegian (Statens naturoppsyn) wildlife management authorities, as well as the public in both countries.
The full BK-based RDO loop is described in the pseudo-code provided hereafter in Algorithm 1. Algorithm 1: BK RDO loop with the MOEI adaptation. 1. Initialization: LHS DOE with n init samples S DOE = s (1) , s (2) ,. , s (n init ) While i < n init Select design s (i) in S DOE Run UQ solver to compute the statistics µ s (i) ,σ 2 s (i).
For this purpose, we used the SALib Python library  [72]. Only the discrete GEK results (reported in Figure  4 ) are considered here, the MC, BK, and continuous GEK methods leading to similar conclusions. The figure reports the first-order Sobol indexes with respect to each uncertain parameter, as well as the sum of the higher order indexes, corresponding to the interaction between parameters when these are changed simultaneously. Sobol total indexes are also included in order to further evaluate the relative importance of the various input parameters. The inlet total pressure P T,in appears to be by far the most influential parameter, consistent with the much larger range of its pdf. Nevertheless, the interactions terms are very significant, due to the highly nonlinear nature of the compressible CFD problem, with geometric parameters c and d contributing by more than 10% to the total variance and parameters a and b by more than 2%.
Outlines of SApHESIA Tools for SoS Architecting As SoS architecting tools are domain-dependent and/or not available in the public domain, we propose a set of tools in order to model and run easily SoS experiment through the SApHESIA model. This set is mainly composed of two elements : • the SoS and Environment Generator that consists of a SML Parser & Compiler and a GUI generator which jointly enable the user to manually create and/or modify a SoS and/or its environment. • the SApHESIA core architecting that consists of SApHESIA Engine and Viewer which respectively runs a loaded SApHESIA model and visualizes results of the tests. Without going into the details, the GUI generator and the language called SML (SApHESIA Modeling Language) enable to translate a SApHESIA model into a computable model usable by our tools.
The GUI generator also enables to change dynamically the SoS composition by adding or removing component systems or entities and also by changing the different properties of a component system (resources, links, etc. ). Thanks to the viewer, user can display useful data to validate our cooperation-based SoS architecting heuristic through different scenarios. SML is the XML-based language used to save and load SoS experiments through the SML Parser & Compiler. It enables to declare each type of elements (component system, entity, rule and so on) of the SApHESIA model. Each element of the model is presented through a hierarchical tree where each node represents a XML keyword usable to declare a property of a SApHESIA element. SApHESIA has been implemented in JAVA language because it is a wellmaintained programming language based on the Object paradigm close to the agent paradigm.
Instantiation of SApHESIA and Coupling Evaluation This section presents experimentation about reuse and interdependency between existing SoSs. The aim is to evaluate that two SoSs independently built using SApHESIA can serve each other (cooperate) and then together increase their own efficiency. As we did not find any case studies with real and usable data to evaluate our propositions, we evaluate them through a case study that we developed and another one found in the literature. The first case study (CoCaRo) is a resources transportation system we have defined to validate two contributions: (i ) the use of SApHESIA to define an agent and (ii ) the metric of criticality enabling cooperation between agents. The second case study concerns the Missouri Toy problem, a communication transportation problem.
DNA barcoding is a valuable tool to support species identification with broad applications from traditional taxonomy, ecology, forensics, food analysis, and environmental science. We introduce Microfluidic Enrichment Barcoding (MEBarcoding) for plant DNA Barcoding, a cost-effective method for high-throughput DNA barcoding. MEBarcoding uses the Fluidigm Access Array to simultaneously amplify targeted regions for 48 DNA samples and hundreds of PCR primer pairs (producing up to 23,040 PCR products) during a single thermal cycling protocol. As a proof of concept, we developed a microfluidic PCR workflow using the Fluidigm Access Array and Illumina MiSeq. We tested 96 samples for each of the four primary DNA barcode loci in plants: rbcL, matK, trnH-psbA, and ITS. This workflow was used to build a reference library for 78 families and 96 genera from all major plant lineages -many currently lacking in public databases.
g. , trnL, nuclear rDNA [ITS, 16 s], or mini-barcodes) that recover a higher number of taxa from degraded or mixed DNA samples  [14] [15] [16]. Approximately twelve different primer pair combinations are necessary to amplify DNA barcode sequences for the four most widely used markers (rbcL, matK, trnH-psbA, and ITS) across all major lineages of vascular plants, from seedless, non-vascular plants to angiosperms (Table  1 ). The use of multiple loci in plant DNA barcoding increases sample handling, preparation time, and costs. However, the use of multiple markers does not always result in complete and accurate species identification  [17] [18] [19] [20]. Such limitations, along with a general lack of sampling numerous plant taxa, have contributed to an underrepresentation or absence of plant groups in most of the publicly available barcode data and genetic data repositories compared with other branches of life, thus creating a gap in the reference libraries, which are often overrepresented by flowering plants (Table  2 ).
Other approaches have turned away from the traditional DNA barcode regions and aim to sequence large portions of genomes (genome skimming) or whole genomes (organellar or otherwise  28  ). Currently, some common high-throughput sequencing platforms are limited by shorter sequence read length (e. g. , the longest reads from Illumina MiSeq are 600 bp) and this limitation is important for plant DNA barcoding, which leverages some loci (e. g. , matK, approximately 1,000 bp) that exceed this read length limitation. Therefore, the barcoding community should anticipate some challenges in sequences that provide full coverage of these loci. The advancement of high-throughput sequencing technologies has expedited the progress of plant genomics, particularly chloroplast genomics. To date, the National Center for Biotechnology Information (NCBI) organelle genome database harbors more than 1000 chloroplast genomes.