dataesr/scientific-paragraphs-categorization huggingface dataset:
Extraction of simulated spectrum in real data To validate the EXOSPECO method, we injected the contribution of a synthetic companion in existing SPHERE/IRDIS MRS data d of the star HIP 65426 observed on 2019-05-20. Although HIP 65426 star hosts a planet  (Chauvin et al. 2017; Carter et al. 2023) , the frame was selected for the de-rotation angles hiding the planet outside the slit. The off-axis PSF h ⊕ of the synthetic companion follows the model in Eqs. (  13 ) and (14c) with σ ⊕ set to match the diffraction limit of the telescope at the reference wavelength λ ref and with different angular positions ρ ⊕ on the side of the coronagraphic mask where no companion was detected. The "ground truth" SED of the synthetic companion is z gt = χ x flux , where χ > 0 is the mean contrast of the companion relative to the star (without a coronagraph) and x flux is the SED of the star HIP 65426 calibrated as explained in Appendix B.
A possible improvement be to use a more complex model of the on-axis PSF and consider more than one mode in the series expansion of Eq. (3). To summarize the performances of the current version of EXOSPECO for a single data frame of the HIP 65426 observations, Fig. 14  plots the minimal contrast needed to achieve a given relative error q as a function of the angular separation. The figure shows that by tolerating a relative error as high as q = 0. 3, a companion with a contrast up to χ ∼ 2 × 10 -5 can be characterized. In our conclusions, we explain how to extend EXOSPECO to jointly process several data frames in order to increase the sensitivity of the algorithm.
16. Comparison of the SEDs extracted from semi-synthetic data by EXOSPECO and by a standard TSVD method. The angular separation and the contrast of the injected companion are respectively ρ ⊕ -ρ ⋆ = 785 mas and χ = 2 × 10 -4. The green dashed curve represents the ground truth injected spectrum which is that of HIP 65426 multiplied by χ. the spectral regularization, the SED extracted by EXOSPECO is smoother and less noisy. At a coarser resolution, the two SEDs display quite different spectral features. However, without a known ground truth, the two SEDs cannot be ranked. For this reason, we also compared the results given by the two methods on a synthetic injection done as described in Sect. 5. 3. Figure  16  clearly demonstrates that not only does EXOSPECO produce less noisy results, but that they also offer a better reflection of reality.
Conclusion In this paper, we present a novel algorithm, EXOSPECO, to extract the spectrum of a companion from high-contrast longslit spectroscopic data. The most challenging part of such a processing is to disentangle the signal of interest from the stellar leakages since they are much brighter. Compared to existing methods, our algorithm avoids any transform of the data, whether it is to align the speckles of the stellar leakages at all wavelengths or to fix defective pixels. EXOSPECO has also the advantage of jointly extracting the parameters describing the stellar leakages (the star spectrum and the on-axis PSF), the companion spectrum, the off-axis PSF, and, optionally, some calibration parameters. By using non-uniform statistical weights for the data pixels, our approach is optimal in the maximum likelihood sense, namely, it takes into account all available measurements and consistently treats defective pixels as missing data.
This will typically gain at least 50% in telescope time while reaching (and even surpassing) the same contrast limit. This new method also paves the way to combining polarimetry and spectroscopic measurements with IRDIS LSS mode (R. Holstein, priv. comm. ). As it is based on an inverse problems framework, EXOSPECO is very flexible and can be adapted to various kinds of data (such as data sequences or data from other instruments). An example of such an extension of EXOSPECO is the joint processing of multiple frames that can be done as follows. Assuming T LSS exposures d 1 to d T of the same object are collected during a night, they can be combined into a single criterion that extends Eq. (  17 ): C(x, y 1 , y 2 ,. , y T , z, ν, µ) = T t=1 ∥d t -m(x, y t , z, ν)∥ 2 W t + µ x R x (x t ) + µ y T t=1 R y (y t ) + µ z R z (z), (37) where the statistical independence of noise between frames is considered (a natural assumption).
A77 , page 15 of 19 Thé, S. , et al. : A&A, 678, A77 (2023) Acknowledgements. This work was supported by the  Action Spécifique Haute Résolution Angulaire (ASHRA) of CNRS/INSU  co-funded by  CNES. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France) ,  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France) ,  LESIA (Paris, France) ,  Laboratoire Lagrange (Nice, France) ,  INAF -Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zürich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands)  in collaboration with ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE  also received funding from the  European Commission Sixth and Seventh Framework Programmes  as part of the  Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566 for FP6  ( 2004-2008 ), grant number  226604 for FP7 (2009 )(2010)(2011)(2012) and grant number  312430 for FP7 (2013 )(2014)(2015)( 2016 ).
Areal mass densities (AMDs) and molar surface densities of adsorbed biomolecules were determined by numerical fitting of the SE data using the software CompleteEASE (J. A. Woollam). A model with a stack of multiple isotropic layers relates the measured ellipsometric angles Δ and Ψ as a function of λ to the optical properties of the substrate, the adsorbed films, and the surrounding buffer solution. The semi-infinite bulk solution was treated as a transparent Cauchy medium (refractive index: n sol (λ) = A sol + B sol /λ 2 , where A sol = 1. 325 and B sol = 0. 00322 μm  2 47  ). The native oxide film on the Si wafers was modeled as a single and transparent Cauchy layer. Its optical properties were determined from the measurements acquired in the presence of bulk solution but in the absence of the biomolecular film, which were then fitted over the range of λ using the tabulated values for the underlying Si substrate (implemented in CompleteEASE).
Binding under Flow. We deployed quartz crystal microbalance (QCM-D) to follow the same binding process under flow. The QCM-D flow modules facilitated constant flow over time of the reagent solution across the sensor surface. A further benefit of QCM-D was that four experiments could be performed in parallel, thus increasing the data acquisition throughput compared to SE. We confirmed proper formation of the SAv monolayer on SLBs (Figure  S6A ) and then incubated b-ZZ on its own or mixed with free biotin as the competing anchor. Binding of biotin alone is not detectable by QCM-D, owing to the small size of biotin and its complete burial in the SAv binding pocket,  49  and the QCM-D responses shown in Figure  4A  thus represent exclusively b-ZZ binding. All binding curves show a similar initial binding phase, with a decrease in frequency (ΔF; Figure  4A , bottom), demonstrating binding.
Image processing To process and analyze the images in this work we used ImageJ, MATLAB (MathWorks, CA, USA) and Imaris (Bitplane, Switzerland). For RBC segmentation, several sequences of image operations were tested and successfully used to generate "Enhancement" maps. To segment flowing RBCs in 2 dpf embryos around the ROI shown in Fig. 3c  and in hypoxia experiments (Fig. 4b ), we used Enhancement = (S 401 + S 433 )/S 373. This definition allows the detection of RBC pixels through the normalization of an image containing resonant hemoglobin THG-TSFG signals by a non-resonant one. In experiments where the non-resonant signal at 373 nm was weak (as in Fig. 3d ), we used the alternative definition Enhancement = ( S 401 + S 433 ) -S 373 , in order to minimize noise propagation. For processing the large-scale mosaic image (Fig.
Kamiokande The Kamiokande II detector was installed in this context, in the old mines of the Kamioka region, and started to take data in 1986  [5]. The inner detector was composed of 3 kton (2140 tons fiduciary) of pure water, monitored by 948 20-inch PMTs, arranged in a 1 m 2 step grid covering the inner part of the detector. The outer part of the detector was covered by a 123 PMTs Cherenkov counter, used to veto against incoming particles, and absorb 𝛾-ray emissions from the radioactivity of the surrounding rock. Additionally, a slow muon monitor was installed to veto their potential decay in the detector. Neutrinos are detected through scattering interactions, such as 𝜈 + 𝑒 → 𝜈 + 𝑒, where the electron recoil kinematic allows for the tracking the neutrino characteristics. This channel leads to an angular reconstruction with an average error of 28°.
The energy threshold is about ∼ 300 GeV for IceCube, and ∼ 10 GeV for Deep Core  [2]. PMT pulses are sent to the surface, but only local coincidence ends in full waveform, in order to reduce the data throughput from noise hits. The data rate is about 100 GB/day, written on tapes. Online processing and analysis, performed on local computer farms, extracts the interesting events, such as: up-going muons, cascades, high-energy events, coincidences between IceTop and IceCube, follow-up events, etc. The refined data amount for about 20 GB/day and are sent via satellites communications to the outer world. Calibration can be achieved thanks to LEDs placed on the flasher board. The DOMs cannot be retrieved from the ice since they are frozen in it, however the main digital electronic board is made of a Field Programmable Gate Array (FPGA), where new functionalities can be uploaded.
This sub-category of events alone has evidenced over 6 years of data the existence of an astrophysical neutrino flux with more than 7 𝜎 of significance. IceCube is taking data in its final configuration since January 2011, with a duty cycle larger than 99%, detecting every year about 10 5 neutrino events, from which 99. 9% are atmospheric. The failure rate is of 1 DOM every year over the 5160 DOMs in total.
KM3NeT is considered as the European counterpart to IceCube, focusing on the Southern Hemisphere. In principle, a complete ARCA detector could detect IceCube astrophysical neutrino flux at a 5 𝜎 level in 1 year of operation. Thanks to its location, its best sensitivity is toward the Galactic center, where several 𝛾-ray TeV sources are detected. In less than 4 years, the predicted neutrino flux should be probed, and constraints set. Furthermore, the good angular resolution, as well as its field of view, should allow for multi-messenger followups. Finally, ORCA could determine the neutrino mass hierarchy with a significance of 3 𝜎 in 3 years of data taking. To complete the picture, let us mention P-ONE (Pacific Ocean Neutrino Experiment) that builds on a similar concept as KM3NeT and Baikal GVD but deployed in the Pacific Ocean.
(2022) , the [M/H] provided by GSP-Phot , together with its estimates of temperature and gravity, can still be used to select solar-like stars whose RVS spectra are in close agreement with those of known solar analogues. Therefore, GSP-Phot [M/H] estimates, in spite of their large systematic errors, still contain some exploitable information about the actual metallicity of the star, which is why they were not removed from the Gaia DR3 release. This issue of [M/H] discrepancies is likely due to the mismatch between observed BP/RP spectra and the models employed by GSP-Phot (see Sect. 3. 1). Given the large differences between observed and model BP/RP spectra shown in Fig. 1c  and d , it is not surprising that the metallicity estimates are of poor quality. Metallicity is the weakest parameter in GSP-Phot, in the sense that it has the lowest impact on the shape of BP/RP spectra and so is most affected by model-data mismatches.
Uncertainty validation It is conceptually very challenging to validate uncertainty estimates because we not only need reliable reference values but also reliable uncertainties on these reference values. In Fig. 10  we can validate our log g uncertainties assuming that measurement errors in asteroseismic gravities are negligible compared to GSP-Phot uncertainties (e. g. Creevey et al. 2013). We further validate our uncertainty estimates by employing the BP/RP split-epoch validation dataset introduced in De Angeli et al. (  2022 ): first, we go back to the epoch BP/RP spectra of each source 17  and randomly group them into two sets; second, for both sets of epoch spectra we compute a mean BP/RP spectrum. This procedure provides two statistically independent BP/RP spectra for each source. Each BP/RP spectrum now only has half of the epochs of the actual source, and so this procedure produces spectra with slightly lower signal-to-noise ratio.
4 for metallicity. However, we have to caution that the 17 994 sources from the BP/RP split-epoch validation dataset may not be large enough or representative enough, meaning that the values in Table  4  can only provide a rough indication. In particular, for asteroseismic gravities, Fig. 10  suggests that our uncertainties are too small by a factor of 10. Given this somewhat unclear situation, we do not apply any correction to the GSP-Phot uncertainties in the published Gaia DR3 data.
Temperature-extinction degeneracy As a final use case of the BP/RP split-epoch validation dataset, Fig. 19  clearly illustrates the degeneracy between effective temperature and line-of-sight extinction by comparing the differences in parameters for each pair. This degeneracy originates from the fact that low-resolution, optical BP/RP spectra are very similarly affected by both parameters. Figure  19  also shows that the temperature-extinction degeneracy affects dwarfs and giants in different ways: while dwarfs can exhibit temperature variations as large as several hundred Kelvin even for small extinction variations, giants usually exhibit smaller temperature variations that are accompanied with much larger extinction variations. We note in particular that the temperature-extinction degeneracy works both ways in Fig.
Summary In Gaia DR3, one of the major new data products is a collection of 220 million low-resolution BP/RP spectra. In this paper, we explain how the CU8 Apsis module GSP-Phot provides a homogeneous catalogue of stellar-parameter estimates for 471 million sources with G < 19 based on these BP/RP spectra, parallax, and integrated photometry. We emphasise that GSP-Phot assumes that each source is a single star and that using combined BP/RP spectra implies that any intrinsic time variability is lost  (De Angeli et al. 2022). One of the main design features of GSP-Phot is to not normalise the BP/RP spectra but instead to exploit the apparent flux level of the BP/RP spectra as an observational constraint on the radius and distance of the star (see Eq. (  2 )). GSP-Phot also employs PARSEC isochrones in order to guarantee astrophysically self-consistent stellar temperatures, gravities, metallicities, radii, and absolute magnitudes (see Sect.
3 ). Despite the filtering, the GSP-Phot results remaining in Gaia DR3 can still suffer from several systematic effects (an overview of the main issues identified in the Gaia DR3 data is given by  Babusiaux et al. 2022 ): 1. GSP-Phot systematically underestimates distances for sources with low parallax quality, which applies to most sources in Gaia DR3. As discussed in Sect. 3. 7, this is due to an overly harsh distance prior employed by GSP-Phot. Sources with high-quality parallax measurements ( /σ > 20) should have reliable distances. 2. GSP-Phot imposes a non-negativity constraint on extinction. As a result, in low-extinction regions, GSP-Phot tends to overestimate extinction (e. g. in the Local Bubble as shown in Fig. 12 ). 3. The [M/H] estimates from GSP-Phot are dominated by large systematic errors which reduce them to the level of qualitative information.
(2022). Secondly, in high-extinction regions, the StarHorse2021 av50 can be substantially larger than the GSP-Phot A 0 (see Fig. 13 ). This effect cannot be explained by the different definitions of av50 and A 0 (which should work in the exact opposite direction). The systematic differences between GSP-Phot and StarHorse2021 extinction estimates are currently not understood. We caution that the uncertainty estimates from GSP-Phot tend to be much smaller than the typical differences from reference values. Validation of uncertainties is very difficult. In particular, a simple comparison to literature values is insufficient because the literature values also often have underestimated uncertainties. In this work, we circumvent this problem by producing two statistically independent incarnations of a limited sample of stars (the BP/RP split-epoch validation dataset discussed in Sect.
Gaia data are being processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https: //www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. The Gaia mission and data processing have financially been supported by, in alphabetical order by country: -.
Acknowledgements. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is through grant  NWO-M-614. 061. 414 , through a  VICI grant  ( A. Helmi ), and through a  Spinoza prize  ( A. Helmi ), and the  Netherlands Research School for Astronomy (NOVA) ; the  Polish National Science Centre through HAR-MONIA  grant  2018/30/M/ST9/00311  and  DAINA grant   2017/27/L/ST9/03221  and the  Ministry of Science and Higher Education (MNiSW)  through grant  DIR/WK/2018/12 ; the  Portuguese Fundação para a Ciência e a Tecnologia (FCT)  through national funds, grants  SFRH/-BD/128840/2017  and  PTDC/FIS-AST/30389/2017 , and work contract  DL 57/2016/CP1364/CT0006 , the  Fundo Europeu de Desenvolvimento Regional (FEDER)  through grant  POCI-01-0145-FEDER-030389  and its  Programa Operacional Competitividade e Internacionalização  ( COMPETE2020 ) through grants  UIDB/04434/2020  and  UIDP/04434/2020 , and the  Strategic Programme   UIDB/-00099/2020  for the  Centro de Astrofísica e Gravitação (CENTRA) ; the  Slovenian Research Agency  through grant  P1-0188 ; the  Spanish Ministry of Economy (MINECO/FEDER, UE) , the  Spanish Ministry of Science and Innovation (MICIN) , the  Spanish Ministry of Education, Culture, and Sports , and the  Spanish Government  through grants  BES-2016-078499 ,  BES-2017-083126 ,  BES-C-2017-0085 ,  ESP2016-80079-C2-1-R ,  ESP2016-80079-C2-2-R, FPU16/03827 ,  PDC2021-121059-C22 ,  RTI2018-095076-B-C22 , and  TIN2015-65316-P  (' Computación de Altas Prestaciones VII') , the  Juan de la Cierva Incorporación Programme  ( FJCI-2015-2671  and  IJC2019-04862 -I for F.
1970). Our international author group with country-specific expertise responded to the same set of questions about indicator systems for all case studies (see Appendix S2). After receiving the first set of answers, we discussed the answers (bilaterally and within country groups) and then compared answers across countries, including more questions and dropping off others. In terms of data sources, we primarily used publicly available documents, indicator databases, and web portals describing indicator development on the international and national level (Appendix S3). Further steps of validation are also described in Appendix S3.
However, these are only a means to an end. What is ultimately important for the political debate, however, are the targets that determine the level of ambition for sustainable development [''Ambitionsniveau''], as well as the corresponding measures defined in the German National Sustainability Strategy (DNS). The number of sustainability indicators in the strategy is deliberately limited in order to provide an overview of the state of sustainable development with as little data as possible and thus to create a somehow manageable compass for sustainability policy. '' (DNS 2021, our translation). We obtained very similar results when surveying the opinion of indicator experts, who argued in interviews that there is currently too much focus on the monitoring and reporting system of SDGs in Germany according to the motto ''We can only manage what we measure'' (Interview with BMUB 2020).
Acknowledgements We would like to thank all PEER TRISD institutes for support of this project:  UKCEH -UK Centre for Ecology and Hydrology, CIENS -Oslo Centre for Interdisciplinary Environmental and Social Research, DCE -Danish Centre for Environment and Energy ,  JRC -European Commission -Joint Research Centre, SYKE -Finnish Environment Institute ,  UFZ -Helmholtz Centre for Environmental Research ,  INRAE -National Research Institute for Agriculture, Food  and the  Environment, WENR -Wageningen Environmental Research. https://www. peer. eu/projects/peer-researchon-sustainable-development-goals-peer-trisd.
We use this database to estimate new parent/daughter ratios for the lower continental crust: 138 La/ 142 Ce = 0. 00380 ±0. 00007 and 147 Sm/ 144 Nd = 0. 128 ±0. 007. Measurements and models are finally reconciled when considering massive recycling of lower continental crust through Earth's history, totaling 3 to 4 present-day, continental crust masses.
To address this, we explore the Ce-Nd isotopic composition of the deep continental crust by analyzing 35 middle to lower continental crust samples from three localities: (1) xenoliths of Permo-Triassic rocks in basaltic breccias from Neogene maars in Massif Central, France; (2) uplifted deep Permian crust from the Ivrea-Verbano zone, Italy, and (3) Neoarchean to Paleoproterozoic xenoliths from the Devonian Udachnaya kimberlite in the central Siberian craton, Russia. Our UCC dataset is complemented by new analyses of Precambrian composites from the Canadian shield. Our data are compared to a large dataset of REE contents in worldwide granulites to evaluate their significance in the lower crust budget. In particular, we investigate the distribution of 138 La/ 142 Ce and 147 Sm/ 144 Nd ratios in lower crust samples and their impact on the isotope evolution of the lower crust.
Major and trace element compositions Major and trace element compositions measured in this study are reported in Supplementary data 1. All metaigneous and metasedimentary samples in our study are presented on a total alkali (Na 2 O + K 2 O) versus silica diagram and compared with literature granulites (Fig. 1 ). Samples from Bournac and Canada have the highest SiO 2 content, with values ranging from 58 to 71 wt%. Ivrea and Beaunit samples are highly variable in SiO 2 (32 to 68 wt% and 44 to 62 wt%, respectively) but they contain the most mafic samples of our dataset (BPG2 and IV9). Siberian samples have 47 to 55 wt% SiO 2 with one exception (62 wt%). The more felsic samples show high Na 2 O + K 2 O values (up to 9. 5 wt%, SiO 2 = 53 wt%). Loss on ignition (LOI) values in the Siberian samples range from 0.
Data from this study compared to crustal reservoirs Recent publications, such as  Rudnick and Gao (2014)  and  Hacker et al. (2015) , provided estimates for the chemical composition of the deep crust. These compositions differ due to the relative weight given to measurements (e. g. , abundance of mafic and felsic components) and geophysical constraints. Fig. 1  reports the SiO 2 and alkalis for these reference compositions but also a large literature dataset composed of Archean and post-Archean granulites (grey and pink squares in Fig. 1 ).