dataesr/scientific-paragraphs-categorization huggingface dataset:
2009; Bonaparte et al. 2013; Palla 2021). This sequence produces the characteristic 'loop' feature in the chemical evolution track of [X/Fe] versus [Fe/H]. In Fig. 2 , we also report the predictions of the two-infall ES21 synthetic model, including errors for ages younger than 1 Gyr and the whole age range for the [Si/Fe] and [Mg/Fe] versus [M/H] relations. We included Si and Mg because (i) in ES20 with they approximate the global α and (ii) in ES21 the chemical evolution of Mg was studied. Concerning the whole-ages case (reported in the right panels), the general trend of the data is well reproduced for the highα and low-α phases. This agreement is quite reassuring because A109, page 6 of 16 In the lower panels, the numbers of Gaia DR3 stars contained in the hexagons for Mg is smaller by a factor of two compared to the Si plots (upper panels).
In the left panels, only stars with ages younger than 1 Gyr are represented, whereas in the right panels the whole ages are considered. We also depict the chemical evolution line predicted by ES21 without considering any errors with the green lines. all of the parameters are the same as in ES20 and ES21 for the first two gas infall episode (see Table  1 ). However, it is clear from Fig. 2  that the ES21 model is not capable of reproducing the young population centred at undersolar [M/H] values, predicting SSPs with a too-high metallicity. The presence of a young metal deficient population in Gaia DR3 data is discussed in more detail in Section 4. 5. For the moment, we just mention that Gaia Collaboration (2023b) pinpointed low [Mg/Fe], [Ca/Fe], and [Ti/Fe] ratios in the range of -0. 5 dex < [M/H] < 0 dex for evolved massive stars near the disc plane.
3  by assuming a smaller SFE value of the low-α phase I: ν 2 =1 Gyr -1. In Fig. 5 , we note that a weaker chemical dilution is present in the [Si/Fe] versus [M/H] space compared to the model of Fig. 3  during the third infall phase, showing a chemical evolution track very similar to the ES20 and ES21 models. It is worth mentioning that the models presented in Figs. 3  and 5  are also able to reproduce the data studied in ES20 and ES21. The proposed three-infall model with strong dilution has been used to interpret the [M/H] versus [Ce/Fe] Gaia DR3 abundance ratios in  Contursi et al. (2023). Future Gaia data releases, as well as other surveys, might revise the properties and significance of the young metalimpoverished population of stars found by Gaia Collaboration (2023b). However, the three-infall model presented in this paper, constrained by the recent local SFH derived from Gaia data, would still remain a viable scenario for the local disc formation.
Acknowledgements. E. Spitoni  and  A. Recio-Blanco  received funding from the  European Union 's  Horizon 2020 research and innovation program  under  SPACE-H2020  grant agreement number  101004214  ( EXPLORE  project). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  agreement  N. 101063193. This work has 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.
We reconstructed one open-canopy and one closed-canopy forest scene and created a reference digital surface model/digital terrain model (DSM/DTM) using airborne small-footprint lidar points over the study sites. The VHR simulations were configured to match three independent WorldView stereopairs. The results showed that, compared to the reference DSM, the surface elevations derived using simulated and WorldView image data were consistent, with differences of <1. 6 m in vertical bias, < 1 m in root mean square error (RMSE), and < 0. 07 in correlation coefficient (R). We demonstrated that realistic 3-D RTM simulations could be georeferenced with a camera model for DSM generation from simulated stereopairs. This work will support a follow-up investigation that examines stereo-derived DSM quality over a broad range of surface types and acquisition parameters to suggest optimal configurations for actual VHR stereo data acquisition of vegetation canopy surfaces.
However, access to a large amount of spaceborne data from VHR platforms over a selected forest study site is limited. In addition, many existing VHR stereo datasets were collected with standard configurations (e. g. , ∼ 35 ∘ convergence angle for alongtrack stereopairs by WorldView to balance the accuracy and occlusion for most Earth surface targets, rather than forests), limiting the ability to study the range of configurations that influence the accuracy of stereoderived forest height and structure. Although cross-track stereo combinations (imagery collected on subsequent orbits/days) could fill certain gaps in the range of sensor configurations from along-track data collection, differences in sun-sensor geometry, atmospheric conditions, and environmental factors on the terrain would increase the uncertainty of the results  (Qin, 2019).
, 2007). Sensor perspective projection was implemented in DART  (Yin et al. , 2015)  to expand the model flexibility for image simulation, including the spatial resolution, number of bands, and view directions  (Yin et al. , 2013). The latest release of the DARTDAO (data access object) built-in tool (DART, 2023) facilitates scene creation using voxels of 3-D leaf area density (LAD) derived from airborne and terrestrial lidar data  (Kükenbrink et al. , 2021; Regaieg et al. , 2021; Wei et al. , 2020). DART-simulated images using lidar-derived scenes can be comparable to a broad range of actual VHR images based on the radiance value of each pixel  (Qi et al. , 2018; Schneider et al. , 2014a Schneider et al. , , 2014b)). These comparisons could be further extended to image processing and computer vision products derived from VHR images, such as DSM reconstruction from stereopairs using existing software packages.
DSM generation from simulated stereopairs We used the NASA Ames Stereo Pipeline (ASP,  Beyer et al. , 2018; Moratto et al. , 2010; Shean et al. , 2016)  to generate DSMs from each set of DART-simulated stereopairs. The software configurations followed the same settings as the workflow of mass-processing VHR images in the NASA Center for Climate Simulation's Advanced Data Analytics Platform (ADAPT)  (Montesano et al. , 2017 (Montesano et al. , , 2019)). The scene boundaries, coordinate systems, and geolocation accuracy were consistent between the DART-simulated stereopairs and lidar reference. The stereo correlation algorithm employs an iterative semi-global matching method and an affine-epipolar alignment approach for tie-point detection  (d'Angelo and Reinartz, 2012; Hirschmuller, 2008; Rothermel et al.
Simulation configurations The simulation results were compared to three cloud-free WorldView along-track panchromatic stereopairs as shown in Table  1  (acquisitions named View1 and View2 for each pair; one pair over SERC and two pairs over GPNA represented as GPNA1 and GPNA2). Maxar provides Level-1B images to federal government agencies and non-profit organizations through the NextView license agreement  (Neigh et al. , 2013)  (htt ps://cad4nasa. gsfc. nasa. gov/). The WorldView acquisition date over SERC was close to the G-LiHT campaign date, whereas those over GPNA were approximately seven years after the G-LiHT campaign because of the lack of data coverage. Nevertheless, the evergreen forests of pine have not been changed due to logging or hazard. The limited data showed that the lack of statistically significant datasets over the same study site for the sensitivity study was the major constraint of actual data and the primary reason for using simulated data.
263 and a Lambertian ground reflectivity of 0. 097, derived from the default DART reflectance spectrum database of "deciduous leaf" and "plantation soil". These generic format properties are suitable for the broad spectral domain (450-800 nm), which approximates the World-View panchromatic band. The TOA images were simulated by coupling a clear-sky atmosphere using the USSTD76 gas model and the RURAL aerosol (visibility of 23 km) model  (Berk et al. , 1987; Gastellu-Etchegorry et al. , 2017). The default GSD of the simulated images was set to 0. 5 m-the same as the voxel dimension. However, the spatial resolution could be larger than 0. 5 m owing to the approximated cubic voxel shape spanning over different pixels after projection along the view direction, and the point spread function of the WorldView instruments.
Evaluation metrics After running ASP with identical processing settings for both WorldView and simulated VHR stereopairs, DSMs of 1 m GSD were generated (DSM gen,WV and DSM gen,sim , respectively) to facilitate comparison with the reference DSM and DTM derived from G-LiHT data (DSM ref and DTM ref ). Indeed, the scenes constructed based on a G-LiHT dataset had exact correspondences of coordinates as the DTM ref and DSM ref , which were also derived from the same dataset without any geolocation bias. Relying on the matched texture (canopy, terrain, etc. ) identified from different views, the stereo-derived DSM exhibits feature losses compared to the references. For example, if only terrain textures were identified and all vegetation textures were missed, the DSM gen would be close to DTM ref instead of DSM ref.
The DSMs were reconstructed similarly from WorldView and simulated stereopairs, so that both their results can be compared against the references. The 3rd column of Fig. 5  shows the DSM gen,WV and DSM gen,sim derived from the WorldView data and simulations. The DSMs used the same colormap and scale as DTM ref and DSM ref shown in Fig. 2. The DSM gen,WV were cropped from the DSM product of a strip of ~10 km swath, so the entire study sites were covered. In contrast, the DSM gen,sim used the simulated stereopairs prepared for the small site areas, so areas near the resulting DSM margins have artifacts and/or missing values. Also, taking G-LiHT lidar data as the reference neglected the canopy growth of evergreen forests for seven years over GPNA until the WorldView acquisitions. In addition, the geolocation error of World-View, which could cause a vertical offset to the generated DSM, was not simulated.
Intercomparisons of DSMs derived from the simulations and WorldView data Intercomparison between DSM gen,WV against DSM gen,sim revealed the spatial distributions of the differences (Fig. 6 ). For SERC, the differences were relatively consistent, except for the boundaries. At the lower edge, where the dense canopy transits to a flat region, DSM gen,sim < DSM gen,WV indicated that less smearing effect was obtained for the DSM derived from simulated data. At the top-right corner, where the dense canopy is converting to a partially open canopy as shown in Fig. 1 , DSM gen,sim > DSM gen,WV indicated that the simulated images had more matched textures from the open canopy. This effect was also observed for GPNA1, DSM gen,sim > DSM gen,WV for the lower canopy (younger pines) where slightly more textures were captured from the simulated data.
Discussions and conclusions To examine estimates of forest canopy surfaces from spaceborne stereogrammetry, we developed a VHR stereopair image simulation method using the DART 3-D radiative transfer model and evaluated the quality of the DSMs built from these simulated stereopairs against those generated from real VHR spaceborne stereopairs. Throughout the process, actual data from different sources were used for scene construction (G-LiHT point cloud), references dataset preparation (DSM and DTM derived from G-LiHT), and comparison and evaluation of results (simulated and WorldView along-track stereopairs). Two sites of closed (SERC) and open (GPNA) canopies were evaluated using three independent stereopairs. The DSMs derived from WorldView and simulated stereopairs generated promising results of comparisons.
Factors influencing generated DSM accuracy The sun-sensor geometry and forest type can strongly influence the characteristics of the DSMs from stereogrammetry for complex forest vegetation. In this study, the WorldView stereopairs illustrated the importance of image acquisition conditions for stereogrammetry over forests. The three stereopairs of both Worldview and simulations demonstrate the resulting variation in surface estimates, which range from surfaces from the terrain (DTM ref ) up to the reference canopy (DSM ref ). The dense canopy site (SERC), where the 20-100 percentile of the canopy height is above 25 m, was the only case for which the generated DSMs were close to DSM ref. For the open canopy site, GPNA1 featured the acquisition configurations typical of stereo data in existing commercial archives (∼ 35 ∘ convergence angle).
Prospects: An evaluation tool to investigate optimal configurations and algorithms for VHR satellite missions The developed simulation method can serve as a prototype for an indepth study to optimize future VHR satellite configurations and algorithms to retrieve DSM and DTM with different vegetation structures and fractional covers based on realistic scenes created for forest research sites, such as NEON  (Elmendorf et al. , 2016). Further development of DART will incorporate spectral variability influence of narrower bands ranging from visible to near-infrared in stereo capacity, such as by simulating the acquisition of the CO3D Earth Observation program  (Lebègue et al. , 2020; Michel et al. , 2020; Youssefi et al. , 2020). In addition, tree delineation and species classification can further enhance scene construction using various physiological parameterizations.
In the current study, the PVlad model played an essential role in constructing realistic scenes of 3-D plant area density distribution from dense, multi-path G-LiHT lidar data, with georeferencing of the DART local coordinate system. >860 G-LiHT campaigns have been conducted in the USA, providing a broad range of multi-path campaigns that would be suitable for generating realistic scenes for future studies of VHR stereogrammetry. The advantages of using realistic scenes are apparent in three aspects: 1) a broad range of validation data from actual remote sensing measurements; 2) extensive field and tower-based measurements to parameterized scenes from forest research sites, such as SERC, which has a ForestGeo site  (Anderson-Teixeira et al. , 2015) , a NEON tower, and continuous litter-collection LAI measurements; and 3) building high-fidelity realistic training data for deep learning approaches for remote sensing data interpretation.
Acknowledgments This research was supported by ( 1)  NASA Goddard Space Flight Center 's  Internal Research and Development Program , (2)  NASA 's  Commercial SmallSat Data Acquisition Program (CDSA) augmentation  to the  Terrestrial Ecology Program  [grant number  NNH16ZDA001N-CARBON ], and (3)  NASA 's  Earth Science Technology Office  [grant number  NNH21ZDA001N-DSI ]. Maxar data were provided by  NASA 's Commercial Archive Data for NASA investigators under the  National Geospatial-Intelligence Agency 's NextView license agreement. The authors are also thankful to  Guoqing Sun  at the  University of Maryland ,  Rongjun Qin at Ohio State University , and  Oleg Alexandrov  at  NASA Ames Research Center  for their valuable suggestions. The authors also thank the anonymous reviewers for their valuable suggestions.
ACKNOWLEDGMENTS We thank  T. Lecuit ,  F. Pichaud ,  M. Suzanne ,  Bloomington Stock Centre, Vienna Drosophila Resource Center, Transgenic RNAi Project at Harvard Medical School , and  Kyoto Stock Center  for fly lines;  L. Alpar ,  M. Balakireva , and  F. Bosveld  for critical comments on the manuscript;  F. Macaluso  for help with experiments characterizing RhoGEF4 function;  A. F. Santos  and  A. Barros-Carvalho  for help with the production of RhoGEF lines; the  PICT-IbiSA@BDD imaging facility of Institut Curie  (member of the  French National Research Infrastructure France-BioImaging ,  ANR-10-INBS-04 ); the  i3S Scientific Platform ALM , member of the national infrastructure PPBI,  Portuguese Platform of Bioimaging  ( PPBI-POCI-01-0145-FEDER-022122 );  ERC Advanced  (TiMorph, 340784);  ARC  ( SL220130607097 );  ANR  ( TiMecaDiv   20CE13000801 ),  CAN-CERO-INCA  ( PLBIO2020/BELLAICHE );  ANR Labex DEEP  ( 11-LBX-0044 ,  PSL ANR-10-IDEX-0001-02 ); Fundaca ˜o para a Ciencia e a Tecnologia, I.
Aims. High-contrast long-slit spectrographs can be used to characterize exoplanets. The resulting spectroscopic data are, however, corrupted by stellar leakages that largely dominate other signals and make the process of extracting the companion spectrum very challenging. This paper presents a complete method to calibrate the spectrograph and extract the signal of interest. Methods. The proposed method is based on a flexible direct model of the high-contrast long-slit spectroscopic data. This model explicitly accounts for the instrumental response and for the contributions of both the star and the companion. The contributions of these two components and the calibration parameters are jointly estimated by solving a regularized inverse problem. As this problem has no closed-form solution, we propose an alternating minimization strategy to effectively find the solution.
(2016)  adapted the strategy implemented in TLOCI  (Marois et al. 2013)  to the LSS data. In spite of these improvements, existing extraction methods suffer from a number of faults, most of them stemming from the requirement to geometrically transform the LSS data to align the dispersed speckles. In particular, they provide, at best, a leastsquares estimation of the stellar leakages which is sub-optimal as the noise is not independent or identically distributed  (i. i. d. )  in the geometrically transformed images  (Thiébaut et al. 2016 ). To overcome the drawbacks of existing methods, we propose formulating the extraction of the spectrum of a companion as an inverse problem. The inverse problem corresponds to the joint estimation of the contributions of the star and of its companion from the LSS data.
Low-rank approximation of the stellar leakages Devaney & Thiébaut (2017) have shown that, except in the vicinity of the coronagraphic mask, the chromatic PSF can be written in the form of a series expansion. Applying their model to the star and taking into account that our data have one angular dimension instead of two yields:   h ⋆ (ρ, λ) = k≥1 γ(λ) k h ⋆,k γ(λ) (ρ -ρ ⋆ ) , (3).
Extracting the SED of the companion also requires the estimation of the on-axis PSF and the SED of the host star. The label "available via calibration" denotes components that may be self-calibrated by EXOSPECO directly from the science data (see Sect. 3. 5 for details). f ⋆ (λ) = N x j=1 φ ⋆ λ -λ grd ⋆, j f ⋆ λ grd ⋆, j x j , ( 11a ) h ⋆ (ρ) = N y j=1 ψ ⋆ ρ -ρ grd ⋆, j h ⋆ ρ grd ⋆, j y j , (11b) f ⊕ (λ) = N z j=1 φ ⊕ λ -λ grd ⊕, j f ⊕ λ grd ⊕, j z j , (11c) grid of wavelengths to sample the star SED f ⋆ , ρ grd ⋆ ∈ R N y is an evenly spaced grid of angles to sample the on-axis PSF h ⋆ , and λ grd ⊕ ∈ R N z is an evenly spaced grid of wavelengths to sample the companion SED f ⊕. At the coordinates (ρ n , λ n ) of any pixel n ∈ ⟦1, N⟧ of the detector, our linear interpolation yields: f ⋆,n = f ⋆ (λ n ) = N x j=1 φ ⋆ λ n -λ grd ⋆, j F ⋆,n, j x j = (F ⋆ x) n , (12a) h ⋆,n = h ⋆ γ n (ρ n -ρ ⋆ ) = N y j=1 ψ ⋆ γ n ρ n -ρ ⋆ -ρ grd ⋆, j H ⋆,n, j y j = (H ⋆ y) n , (12b) f ⊕,n = f ⊕ (λ n ) = N z i=1 φ ⊕ λ n -λ grd ⊕, j F ⊕,n,i z i = (F ⊕ z) n , (12c) with γ n = γ(λ n ), and the matrices  12a )-(12c) represent interpolation operators 5.
The EXOSPECO algorithm FITSTAR (Algorithm 1) and FITCOMPANION (Algorithm 2) are the building blocks of the EXOSPECO method given in Algorithm 3 for estimating all unknowns. A few additional remarks are given below. First, for the first estimation of the stellar leakage parameters, it is beneficial to define a masked version, W ⋆ , of the precision matrix of the data to avoid a significant bias of the first estimates due to the signal from the companion, which would slow down the convergence of Algorithm 3. The masked precision matrix is simply given by: W ⋆ = diag(w ⋆ ), (28) where the weights, w ⋆ , are those of the precision matrix, W, of the data except that they are set to zero for the pixels that are the most impacted by the companion: ∀n ∈ ⟦1, N⟧ : w ⋆,n = 0 if γ n |ρ n -ρ ⊕ | ≤ τ w n otherwise (29) with τ > 0 the angular half-width at the reference wavelength of the impacted region.
Comparison of the calibration models To compare the calibration models considered in Sect. 4. 2, we applied EXOSPECO (Algorithm 3) on a scientific dataset of the star HR 3549 observed in MRS mode of SPHERE/IRDIS on 2015/12/28. Figure  7  shows the residuals r = dm, that is the difference between the data and their model, computed for the different spectro-angular dispersion laws. This figure shows that the root mean square (rms) of the residuals are significantly reduced when using more flexible calibration models than the standard one. The improvement brought by the medium complexity model compared to the standard model proves that accounting for a slight angular misalignment between the spatial and spectral directions and the detector axes is important. Compared to the medium model, the complex model is able to account other image distortions than a simple rotation and thus achieves a better suppression of the stellar leakages.
Reduction of the self-subtraction One of the main features of EXOSPECO is that it jointly estimates the contributions of the star and of the companion whose parameters are iteratively refined until convergence. Figure  8  shows the residuals close to the companion (in the region outlined by the A77, page 11 of 19 Fig. 8. Residuals between the HR 3549 scientific data and the model of the stellar leakages near the companion (defined by the black rectangle in the bottom of Fig. 7 ) and for the complex model of the spatial and spectral dispersion laws after one iteration (top) and after convergence (middle) of EXOSPECO. The rms of the residuals in this region are significantly reduced after convergence. The orange dashed line indicates the position of the companion at the different wavelengths.
Tuning of the regularization parameters As described in Appendix C, the fact that the model of the star leakages is bi-linear makes it possible to tune the regularization of this component by a single hyper-parameter, that is, the other hyper-parameter being held fixed. Thanks to this, the solution found by EXOSPECO only depends on two hyper-parameters: one for the star, say, µ x (while µ y = 1 is imposed) and one for the companion, µ z. In this section, we highlight the incidence on the companion SED extracted by EXOSPECO of these remaining hyper-parameters using the same scientific data set as in Sect. 4. 5. Figure  9  shows the SEDs of the companion estimated by EXOSPECO for different values of the star regularization hyperparameters (µ y = 1 and µ x = 10 -3 , 10, and 10 5 ). For such a Fig.
The tuning of µ x can thus reasonably be done by visual inspection. On the contrary, as Fig. 10  shows, the hyper-parameter µ z has a strong impact on the resulting companion SED. This is expected as µ z directly tunes the strength of the smoothness constraint for the companion SED z. This hyper-parameter has thus to be carefully chosen to find the best compromise between a solution that is too smooth (e. g. , for µ z = 10 7 in Fig. 10 ) or too noisy (e. g. , for µ z = 10 2 in Fig. 10 ). It is worth noticing that the correct value of µ z strongly depends on the considered data, so µ z = 10 5 , which seems to be a good choice for the HR 3549 data (see Fig. 10 ), should not be considered as a universal value. mention a few that could potentially be used with our extraction algorithm. Implementing and testing these methods for EXOSPECO is beyond the scope of this paper.