dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantifying the migration of the Southern Junggar foreland basin We tracked all the seismic reflectors in profile AA' and identified northward onlap points of the foreland sequence in the SJFB based on the northern termination sites of the reflectors. The termination of a seismic reflector to the foreland (stratal onlaps in the foreland basin) represents the distal end of deposition at that time. Generally, the thinnest sedimentary sequences or no sediments occur in the forebulge of a foreland basin system, and thicken to the foredeep (DeCelles and Fig. 5. The interpreted north-northeast trending seismic profiles across the SJFB (a) and the NTFB (c). See Fig. 1b  for their locations. Vertical exaggeration is ~15 times. These boundaries and faults are interpreted from well logging data near the profiles and interpretations of other seismic reflection profiles near the profiles from previous publications  (Wang et al.
Numerical model The tectonic load of the thrust wedge is imposed onto a semi-infinite elastic plate floating on the asthenosphere in the model. We employ the finite difference solution for the flexure equation based on a Python script called 'Gflex'  (Wickert, 2016). Given that the critical angle of thrust wedges tends to remain stable over millions of years  (Davis et al. , 1983) , and no research has documented changes in the critical angle of the Tian Shan, we reduced the tectonic loads of the northern and southern Tian Shan thrust wedges to a triangular wedge imposed on the end of the plate with a fixed critical wedge angle (α + β in Fig. 6 ). Main parameters in the elastic plate model contain the Te, E, ν, ρ c , ρ m , g of the Tarim and Junggar blocks, and the wedge angle (α, β), density (ρ o ) and width (W) of the thrust wedges of the Tian Shan.
For the modern NTFB and SJFB (0 Ma), the Te and β are only two unknown variables in our modelling while the other parameters are known. The optimal values for these two parameters are determined by minimizing the misfit between the observed and modelled subsidence profiles across the Tarim Basin. There are certain trade-offs between the Te and β. We apply the downhill simplex algorithm  (Wright, 1996)  to determine the best combination of these two variables, and utilize Python's scipy fmin function to implement the downhill simplex algorithm. We model the best combination of the Te of the modern Tarim and Junggar blocks and the β of the modern thrust wedges using the trial-error method carrying out through a Python program based on the 'Gflex' and the 'fmin. ' For the historic NTFB and SJFB (~20 Ma, ~13-16 Ma, ~5 Ma), the historic Te and W are treated as variables in our modelling.
Remote sensing and reanalysis data The RS data set is composed of 140 images (acquired from 01 January 2005 to 31 December 2021) of Level 1B data from the MODIS sensor onboard the TERRA satellite. The images were acquired off the Amazon shelf where the presence of ISW signatures was identified in the sun glint region using the Band 6 centered at 1640 nm with a spatial resolution of 500 meters. Level 1B MODIS/TERRA images were collected from NASA's Earth Science Data System, ESDS (https://earthdata. nasa. gov/). The cloud coverage (especially during the months of MAMJJ) and the position of the sun glint area, which changes its position over the year, are limiting factors for our samples. The Global Ocean Ensemble Physics Reanalysis (EPR) data provides a 3D-gridded description of the global oceanic physical state at 0.
25-degree resolution, starting from January 1993 until December 2019. The data is produced by Mercator Ocean International as part of the Copernicus Programme (https://marine. copernicus. eu/), using a multi-numerical ocean model (GLORYS2V4 from Mercator Ocean, France, ORAS5 from ECMWF, GloSea5 from Met Office, United Kingdom, and C-GLORSv7 from CMCC, Italy) ensemble approach and data assimilation of satellite and in situ observations. The daily mean average of temperature, salinity, and currents variables was acquired from 2005 to 2019 for 75 vertical levels.
The underestimation of the ISW phase velocity calculated using the TGE is expected since the equation calculates linear wave phase speed and the nonlinear effects increase the speed of the linear waves  (Alford et al. , 2010; da Silva et al. , 2011). The nonlinear phase speed can be corrected using the Korteweg-De Vries (KdV) equation  (Hammack and Segur, 1974; Alford et al. , 2010) , however, this theory applies only to shallow waters with uniform depths which is not the case in our study area. The waves' velocities of all modes are calculated using Equations 8 and 9. The local values of stratification and shear were taken from daily and monthly reanalysis data for each location where ISWs were identified considering the entire period of time (from 2005 to 2019). The current velocities were decomposed in the ISW traveling direction.
Financial support. support This work this supported by CNES funding in the frame of the MIAMAZ TOSCA project. C. A. D. L. is funded by the Ministry of Science, Technology, and Innovation and the Brazilian Navy (CNPQ/MCTI 06/2020 -grant #440852/2020-0), and the research project AtlantECO (H2020 BG-08-2018-2019, grant agreement N°862923). M. C. B. R. is funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. J. C. B. d. S. is funded by the Portuguese funding agency Fundação para a Ciência e Tecnologia (FCT) under project IDB/04683/2020. J. M. M. is supported by FCT -Portuguese Foundation for Science and Technology under contracts UIDB/04423/2020 and UIDP/04423/2020.
I. INTRODUCTION Building a realistic 3D model for the world has attired a lot the attention of researchers since decades. Even it seems easy to create a simple 3D model using a software like Maya or 3D Max, however the generation of a photorealistic complex object needs more efforts. Automatically reconstructing such 3D models requires first acquisition devices that can extract a 3D information of the scene. Then further modeling, computation and algorithms are usually required. For a long time, Laser scans (or similar techniques) have been extensively used, for example to produce 3D models in computer graphics. Usually associated with geometry post-processing (such as recovering smooth polygonal surfaces from point clouds), they provided nice results that are still used nowadays as references  [1]  or ground truth data.
B. Camera calibration The calibration means the determination of the projection matrix of equation (  3 ) thus computing the different intrinsic and extrinsic camera parameters. Many standards methods exist for doing calibration  [7]  such as Tsai, Zhang, Faugeras and Toscani…the principle of these methods is using a calibration pattern, that disposes some features with known geometry and they should be easily extracted from their corresponding calibration images. Camera calibration for a single and stereo camera can be done by a Matlab toolbox  [17]. Since we are working to estimate the camera motion from the image sequence so at the beginning we need just a partial calibration by meant the intrinsic camera matrix K presented in equations (  1 ) and (  2 ) then from each image we estimate the camera motion , thus the corresponding rotation matrix and translation vector.
F. The overall SFM algorithm The overall problem of constructing 3D points out from multiple images can be broken down into two main steps: Initial 3D model from two images, and Refinement through adding more images. The initial 3D model is important as its accuracy will determine the Additional points are added relative to the initial model as it serves as the base where everything is built upon. Every addition is based on the previous result and the process is iterative. The overall problem broken up into steps is outlined SFM Algorithm V. MESH BUILDING AND TEXTURE MAPPING Once we have a colored 3D point cloud, we can proceed to mesh building or usually called model fitting which gives a polygon mesh from the point cloud that gives another type of scene representation. We used MeshLab software  [24] , which is widely used for 3D point cloud processing like selecting, smoothing and coloring meshes, surface reconstruction and texture mapping.
1, 3 and 4 and 46 specimens to bed SHC. 2. One plant-bearing bed and 445 specimens from the Saint-Jean-de-Ceyrargues section (Supplementary Table  S2 ) and 174 specimens from one plant-bearing bed from the Saint-Jean-de-Maruéjols-et-Avéjan section (Supplementary Table  S3 ) were also analysed. Semi quantitative taphonomic data were obtained from leaf assemblages of selected leaf beds. However, comminuted plant debris and fossil-species represented by an insufficient number of specimens (1% of the total assemblage) were only considered from a qualitative viewpoint. The main taphonomic features considered were the size of leaves and shoots, their orientation and the type and degree of plant organ preservation and completeness.
Acknowledgements This study is a contribution to project  IBERINSULA  ( PID2020 113912GB-100 ) funded by  MCIN/AEI/ 10. 13039 501100011033 ), and to the project  Geologia Sedimentària  ( 2022 SGR-349 ) of the  AGAUR (Catalan Research Agency). It has also benefitted from a ' Margarita Salas ' postdoctoral fellowship awarded to AT by the  Spanish Ministry for Universities. We thank the councils of  Saint-Hippolyte-de-Caton, Saint-Jean-de-Ceyrargues and Saint-Jean-de-Maruéjols-et-Avéjan as well as K. Carrière  for providing access to the plant beds. We also thank  C. Borrely ,  V. Poncet  and  N. Ruel  for allowing us to study the historical plant collection housed in the NHM in Marseille. Thanks are extended to  Dr. R. Jattiot ,  Dr. B. Latutrie  and  M. Wienin  for reporting us the rich fossil plant beds in the Alès Basin.
Fe Isotope Data Four samples representative of banded (TAK51 and TAK52), nodular (TAK24) and disseminated (TAK53) ore types were selected for Fe and O isotope analyses. Fe isotopic compositions are given in Table  5  and Table  S2  and displayed in Figure  6. shows continental crust normalized lithophile element patterns with a peak in Mn, but variable Ti (Figure  4d ). 2. Trace element abundances in bulk continental crust are from  [30].
Fe Isotope Data Four samples representative of banded (TAK51 and TAK52), nodular (TAK24) and disseminated (TAK53) ore types were selected for Fe and O isotope analyses. Fe isotopic compositions are given in Table  5  and Table  S2  and displayed in Figure  6. In the disseminated ore (TAK53) magnetite forms euhedral grains (~400 µm-1. 5 mm). The grains are slightly hematized on the rims and in small cracks. Magnetite ∂ 56 Fe values (n = 14) range from 0. 76 ± 0. 26‰ to 1. 86 ± 0. 22‰. In the banded ore (TAK51 and TAK52), magnetite forms individual grains (~50 µm to several hundreds of µm) altered to hematite on the rims and in cracks. Magnetite ∂ 56 Fe values range from -0. 28 ± 0. 20‰ to 1. 12 ± 0. 21‰ (n = 28). However, only four points have a negative ∂ 56 Fe value. In the nodular ore (TAK24), magnetite forms mm-sized agglomerates partly elongated and disrupted in the quartz matrix.
Mapping process We started mapping H13 in ArcMap 10. 5. 1 in 2019 and migrated to ArcGIS Pro in 2022. We started by making a project file with a dedicated geodatabase for the feature classes. Initially, we created four feature classes: (1) linear features (polylines); (  2 ) contacts (polylines); (3) units (points) and (4) surface features (polygons). We began by digitising linear features, such as crater rims, then we mapped all tectonic structures. Next, we mapped crater material contacts while placing unit points and assigning crater degradation classifications to the attribute fields of those points. We then completed all remaining contacts for plains materials and their corresponding unit points. Finally, we digitised superficial units. We then generated polygons from the polylines of the geological 'contacts' and points of the 'units' feature classes to generate two versions (3 crater classes and 5 crater classes) of our map.
Acknowledgements Gratitude is given to the  UK Research and Innovation (UKRI) Science and Technology Facilities Council (STFC)  and the  Open University 's  Strategic Research Area in Space  for making this research possible. Thanks are also given to GMAP (Geologic Mapping  of Planetary Bodies ) formerly  PLANMAP  ( PLANetary MAPping Project ) for guidance and resources that helped with this work. SJC is grateful for the support of the  French Space Agency ,  CNES  for her BepiColombo related work. JW acknowledges support from the  European Space Agency (ESA)  as an  ESA Research Fellow  received during the write-up of this work. SB received funding from the  Italian Space Agency (ASI)  under  ASI-INAF  agreement  2017-47-H. 0.
The parameters were obtained from global fits of 11,376 and 3355 lines distributed over eight and three vibrational states (including the ground state), respectively, with a root mean square of 12 MHz. In a second step, the middle resolution spectrum of 2-FF recorded at 298. 15 K and available in the HITRAN database was reconstructed by extrapolating the data derived from our low-temperature high-resolution analyses to determine the cross sections of each vibrational band of both 2-FF conformers in the 700-1800 cm -1 region. Finally, we clearly demonstrated that the contribution of hot bands observed in the room temperature 2-FF spectrum, estimated between 40 and 63% of the fundamental band, must be imperatively introduced in our simulation to correctly reproduce the HITRAN vibrational cross sections of 2-FF with a deviation smaller than 10%.
Previous gas-phase spectroscopic investigations of FF mainly dealt with the orthoisomer (2-FF) with vibrational analysis in the far-IR region using an infrared Fouriertransform interferometer (FTIR), in the mid-IR region using a Raman spectrophotometer  [7] , and in the VUV region using a synchrotron-based photoabsorption spectrometer  [8]. Mid-IR vibrational cross sections are available in the HITRAN/PNNL atmospheric databases according to the medium resolution FTIR measurements (0. 112 cm -1 ) of Johnson et al. , who created a specific database for the quantitative IR spectroscopy of gases emitted by biomass burning  [9]. In these studies, only the strong Q-branches and the P and R patterns were observed and no rovibrational analysis could be undertaken due to the limited spectral resolution.
Rovibrational Analysis Seven bands of trans-2-FF and two bands of cis-2-FF recorded at a high resolution in the mid-IR range in jet-cooled conditions were analyzed. Each band was firstly fitted individu-ally with the PGOPHER program  [16]  using a Watson-type semirigid model for asymmetric tops (a reduction in the I r representation) developed up to the quartic centrifugal distortion (CD) terms. From the energy difference of Durig et al. between both conformers and our reservoir temperature (370 K), using a Boltzmann distribution, the sample before expansion was composed of 75% trans-2-FF and 25% cis-2-FF. It was difficult to assess precisely how it relaxed in the expansion but, in a high barrier approximation, we can assume that the ratio was roughly the same in the probed jet. Initially, GS parameters of both conformers were fixed to the values obtained by Motiyenko et al.
Mid-IR Cross Sections Two infrared spectra of FF were measured by Johnson et al. in the 550-6500 cm -1 spectral range  [9]. These spectra, which are available in the HITRAN atmospheric database  [21] , were measured using FTIR spectroscopy in a systematic study of biomass burning compounds with a resolution of 0. 112 cm -1 at 760 Torr in N 2 , at 298. 15 K, and at 323. 15 K. Medium-resolution vibrational cross sections were deduced for quantitative spectroscopy applications. In this section, we extrapolated the data obtained from our low-temperature high-resolution analysis to understand and predict these mid-IR cross sections at 298. 15 K. First of all, we reconstructed the cross sections of each vibrational band of the trans and cis conformers including the parameters of our global fit summarized in Tables  2  and 3.
The results are shown in Figure  8  (top, red curve). The intensities of each band, depending on both the relative abundance trans/cis and dipole moment value were adjusted individually to reproduce the experimental cross sections to the best of our ability. Noticeably, the presence of many hot bands should be considered, in particular, for transitions starting from low-frequency vibrational states (hereafter, designated by lf) up to 600 cm -1 , as each of them contributes more than 5% of the fundamental band intensity at room temperature. In the first step, the contribution of hot bands, for which Q branches were clearly visible in the room temperature spectrum, was added. The positions and the intensities of these hot bands were adjusted to optimize the agreement with the database cross sections.
An accurate model of these hot bands is required to perfectly simulate the vibrational cross sections. The position of these hot bands allowed us to make a preliminary estimation of the χ i,j anharmonic coefficients. The detailed simulation with the PGOPHER software  [16]  of the ν 17 and ν 23 bands around 750 cm -1 is given as example in Figure  8  (bottom). It includes the asymmetric top band contour with J max = 140 of four fundamental hot bands: (ν 17 ) t , (ν 17 ) c , (ν 23 ) t , and (ν 23 ) c , and eight additional hot bands: four hot bands for (ν 17 ) t with two sequences with χ 17t,l f 1t = -0. 6 cm -1 and χ 17t,l f 2t = -0. 35 cm -1 ; one for (ν 17 ) c with χ 17c,l f 1c = -0. 3 cm -1 ; one for (ν 23 ) t with χ 23t,l f 1t = -0. 45 cm -1 ; two for (ν 23 ) c with χ 23c,l f 1c = -0. 6 cm -1.
Theoretical Methods Calculations were performed using the Gaussian 16 rev. C. 01 software  [23]  on the computing clusters of the PhLAM laboratory. The frozen-core approximation was used throughout. Dunning and coworkers' augmented correlation consistent basis set aug-cc-pVXZ (X = D, T, Q) was used (denoted aVDZ, aVTZ, and aVQZ)  [24]. All geometries were fully optimized at the MP2 and B98 levels using the tight convergence criterion. Extrapolations to complete the basis set (CBS) for energies (including ZPE corrections), band centers, and rotational constants were performed from aVDZ, aVTZ, and aVQZ results using Dunning's formula  [25]. Frequencies and rotational constants in relevant vibrational states were calculated at the anharmonic level (VPT2 calculations as implemented in the Gaussian software)  [26]  with a tight SCF convergence criterion and the ultra-fine integral grid option.
About 8% of the total power is used by an etalon consisting of a 0. 025 cm -1 free-spectral-range confocal Fabry-Perot cavity, to provide a relative frequency scale. Absolute frequency calibration is obtained by passing about 8% of the IR total radiation through a 10 cm length cell containing a known reference gas. A linear interpolation of the positions of the etalon maxima establishes the relationship between the voltage applied to the PT and the relative frequency. This new frequency scale enables one to correct the free-spectral-range value of the reference fixed at the beginning of each experiment. A typical frequency accuracy of about 0. 0005 cm -1 was achieved by comparing the frequency deviation of our measured lines of methanol (in the 975-1035 cm -1 range) and NH 3 and H 2 O (in the 1620-1720 cm -1 range) with frequency standards from the HITRAN2020 database  [33].
In particular, we tried to reproduce the vibrational cross sections referenced in the HITRAN atmospheric database measured at room temperature in a N 2 dilution. Using this approach, we clearly demonstrated the importance of hot bands in the room-temperature mid-IR spectra of 2-FF, which contribute between 40% and 63% of the fundamental bands. Experimental values presented here can be used to calibrate higher level calculations in order to obtain more accurate predictions of the anharmonic constants. Indeed, this work highlighted the necessity of a reliable anharmonic force field that is able to provide sufficiently accurate anharmonic coefficients χ i,j , allowing the hot band pattern to be assigned and the reconstruction of the room-temperature rovibrational cross sections to be improved for this kind of medium-sized VOCs, for which it is not possible to resolve the rovibrational structure at room temperature.
Funding: This work was supported by the  CaPPA project (Chemical and Physical Properties of the Atmosphere)  funded by the  French National Research Agency  ( ANR-11-LABX-0005-01 ) and the  ECRIN program  supported by the  Hauts-de-France Regional Council , the  French Ministry of Higher Education and Research  and the  European Regional Development Fund. The Ph. D. work of  S. Chawananon  was supported by the  MESRI (Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation) within the doctoral school of Chimie Physique et Chimie Analytique de Paris Centre  ( ED 388 ). AC is part of the  ATMOS  project n o 872081 funded by the  H2020-MSCA-RISE-2019 program. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Description of the dataset The methodology developed is presented for the male giant, but it is applicable to all disciplines (Slalom, Downhill, Giant and Super-G) and genders (female, male). The dataset includes all the performances achieved on the FFS (Fédération Française de Ski) and FIS (Fédération Internationale de Ski) circuit from the 2004/ 2005 season to the 2021/2022 season on male giant. 522,098 performances have been achieved by 25,083 skiers aged 10 to 25. Alpine skiing performance is quantified using FFS points. The number of points scored on D-day depends on the overall level of the race and the time achieved in relation to the winner of the race. The lower the number of points, the better the performance.
Model features To overcome counter-performances, the model is trained on the best performance by age per individual (14,838 performance achieved). The fixed and random parameters are estimated with a Bayesian model based on a Markov Chain Monte-Carlo Method (MCMC) with the No U-Turn Sampler algorithm (NUTS)  (Hoffman and Gelman, 2011)  proven on real simulated genetic data  (Nishio and Arakawa, 2019). The a priori fixed parameters of the model are estimated using the ordinary least squares method on all performance. The model has four parallel chains of 200 iterations each. Such choice of the number of iterations was made to minimize both the complexity by reducing the number of iterations and to maximize the quality of the results by adding iterations. There will therefore be a total of 800 quadruplets of estimated parameters {a+a i , b + b i , c + c i , d + d i } for each individual.
Conclusion We propose a potential estimation method based on the relationship between age and performance to obtain the potential performance curve and the type of progression of each individual. We then estimate four progression typologies, and the probabilities associated with each of them for all individuals. The purpose of the method is to help those involved in French winter sports to better detect young talents, but also to show the relationship between age and performance in alpine skiing with regard to the physical specificities of the skiers, relying  (Bacharach and Duvillard, 1995)  on the analysis of large-scale longitudinal data  (Müller et al. , 2015). The method developed here can be extended to all sports with quantifiable performance.