dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The following National and International Funding Agencies funded and supported the  MIRI  development:  NASA ;  ESA ;  Belgian Science Policy Office (BELSPO) ;  Centre Nationale d'Études Spatiales (CNES) ;  Danish National Space Centre ;  Deutsches Zentrum fur Luftund Raumfahrt (DLR) ;  A121 , page  11  of  12 A&A 673 , A121 (2023) Enterprise Ireland;  Ministerio De Economiá y Competividad ; The  Netherlands Research School for Astronomy (NOVA) ; The  Netherlands Organisation for Scientific Research (NWO) ;  Science and Technology Facilities Council ;  Swiss Space Office ;  Swedish National Space Agency ; and  UK Space Agency. We thank  Fabiana Faustini  for providing the infrared data. We also thank  Roy van Boekel  for mid-infrared line diagnostic discussions. H. B. acknowledges support from the  Deutsche Forschungsgemeinschaft in the Collaborative Research Center  ( SFB 881 ) "The  Milky Way System " (subproject B1).
E. vD. , M. vG. , L. F. , K. S. , W. R. and H. L. acknowledge support from  ERC Advanced  grant  101019751 MOLDISK ,  TOP-1  grant  614. 001. 751  from the  Dutch Research Council (NWO) , The  Netherlands Research School for Astronomy (NOVA) , the  Danish National Research Foundation  through the  Center of Excellence "InterCat " ( DNRF150 ), and  DFGgrant 325594231 ,  FOR 2634/2. P. J. K. acknowledges financial support from the  Science Foundation Ireland/Irish Research Council   Pathway programme  under Grant Number  21/PATH-S/9360. A. C. G. has been supported by  PRIN-INAF   MAIN-STREAM 2017  " Protoplanetary  disks seen through the eyes  of newgeneration instruments " and from  PRIN-INAF 2019  " Spectroscopically tracing the disk dispersal evolution (STRADE) ". K. J. acknowledges the support from the  Swedish National Space Agency (SNSA).
These OIB samples also have resolvable negative u 182 W values down to -13. 8 ± 3. 3 ppm (refs. 16,55). Although fractional crystallization of Fe-Ti oxides can quickly lead to increasing δ  49  Ti values for evolved mafic lavas  3, 6, 7, 31, 32  , we argue that the mantle-derived rocks in this study are devoid of Fe-Ti oxide fractionation based on two observations: (1) although at fayalite-magnetite-quartz buffer Fe-Ti oxides normally start crystallizing at late stage of magma differentiation 67 (MgO < 5 wt%), the measured samples have high MgO contents of >5. 80 wt%, except for sample ICE-14-16 with MgO = 5. 02 wt%, and (2) the lavas from the same age groups or the same oceanic islands did not show resolvable increase in δ  49  Ti with decreasing MgO contents (Extended Data Fig. 1b ). We also note that some OIB samples contain the earlier crystallized olivine phenocrysts that would lead to much higher MgO contents, which-however-should have negligible effects on the Ti isotopic compositions of the studied samples in a whole-rock-scale owing to the low TiO 2 contents in olivine.
Each analysis of the standard or samples comprises 100 cycles with 8 s integration time. On-peak zeros were measured before each sample/ standard analysis in the same 0. 5 M HNO 3 + 0. 01 M HF solution used to dissolve the sample/standard for 75 cycles with 8 s integration time. The typical background for the measurements is about 2-4 mV on 48 Ti +. To evaluate data reproducibility, each sample has been normally analysed 4-8 times and four reference materials (that is, BHVO-2, BCR-2, AGV-2 and BIR-1) have been processed several times in parallel with the unknown samples.
d. ) or ±0. 004‰ (2 s. e. ) (refs. 3,4) (Extended Data Fig. 4 ). We note that data offset between laboratories also exists for the δ  49  Ti results from Archaean komatiites, with the substantially lower and more scattered δ  49  Ti values in Greber et al. 33  than those in this study and Deng et al. 4  (Extended Data Fig. 5 ). We emphasize that the presence of data discrepancy between digestion duplicates of the same komatiite powders in Greber et al. 33  probably points to a larger analytical uncertainty on the reported δ  49  Ti dataset for both whole-rock chondrites and Archaean komatiites than the claimed precision of ±0. 030-0. 034‰ (95% confidence interval) for individual samples.
Acknowledgements We thank  S. Tian  and  F. Moynier  for providing the 47 Ti-49 Ti double spike. We thank  C. Cloquet  for sharing the OL-Ti standard. This project is supported by grants from the  Carlsberg Foundation  ( CF18-1105 ) and the  European Research Council  ( ERC Advanced Grant  agreement no. 833275-DEEPTIME ) to M. B. and grants from the  Villum Fonden  (no. 00025333 ) and the  Carlsberg Foundation  ( CF20_0209 ) to M. S. M. -A. M. acknowledges support from  NERC  standard grant  NE/R001332/1.
Dynamical calculations In Ref. [1]  we performed quantum dynamical calculations on the CO-N 2 potential energy surface (PES) of Liu et al. [13]. As shown in Paredes-Roibás et al. [1]  and Jó źwiak et al. [12]  this PES is accurate enough for producing pressure broadening coefficients. For the present work we have completed these calculations up to a total energy of ∼500 cm -1 using the close-coupling method 1  and by additional calculations at 60 0, 70 0 and 800 cm -1 using the IOS approximation as implemented in the MOLSCAT code  [14]. Our previous work  [1]  was devoted to CO perturbed by N 2. Rovibrational quantum numbers with subscripts 1 and 2 were associated with CO and N 2 respectively, in particular for the bistate to bistate kinetic energy dependent cross sections, σ ( j 1 j 2 → j 1 j 2 ; E kin ).
Since 1 In order to perform CC calculations between 315 and 500 cm -1 we used a varying step for the total angular momentum J from 2 to 4. the PES is the same for the present study we just have to affix subscripts 1 and 2 to the N 2 and CO molecules. The CO molecule is in its ground vibrational state while the N 2 molecule can be either in its ground or first excited vibrational states. However, because the N 2 molecule is quite rigid owing to its triple bond, the N 2 -CO PES depends only weakly on the N 2 vibrational state, thus scattering calculations consider both molecules in their ground state. We therefore disregard any vibrational effects. Since the MOLSCAT code provides us bistate-to-bistate cross sections at various kinetic energies we first introduce a partial pressure broadening cross section as: σ ppb ( j 1 , j 2 ; E kin ) = j 1 = j 1 j 2 σ j 1 j 2 → j 1 j 2 ; E kin.
Located at the western termination of the Carlsberg Ridge, this 330-km-long fault is part of the active plate boundary between India and Somalia. A key property of the Owen Transform Fault is that the transform fault valley is the far-end depocentre for the distal Indus turbidites (Figure  1 ), bearing resemblance to the Vema Transform Fault valley fed by the Amazon Cone and filled with ∼1,000 m of sediments  (Bonatti et al. , 2003 (Bonatti et al. , , 2005;; Perch-Nielsen et al. , 1977). The Owen Transform Fault however represents an end-member case of a sedimented transform fault, with more than 5,000 m of sediments piled in the valley at its deepest point, providing an exceptional sedimentary record of the tectonic events through time. The new set of marine data used here was collected in 2019 during the VARUNA and CARLMAG cruises aboard BHO Beautemps-Beaupré, an oceanographic ship operated by the French Naval Hydrographic and Oceanographic Services (SHOM) and the French Navy.
The New Data Set The Bathymetric grid used in this study is a compilation of bathymetric data from previous cruises on this region (Figure  2 ): AOC (https://doi. org/10. 17600/6090030), OWEN (https://doi. org/10. 17600/9090020), OWEN-2 (https://doi. org/10. 17600/12090050) plus the new VARUNA (https://doi. org/10. 17600/18001108) and CARL-MAG (https://doi. org/10. 17600/18000872) data. All data were acquired with the same Kongsberg-Simrad EM120 deep-water multibeam echo-sounder aboard BHO Beautemps-Beaupré oceanographic ship. Grids were built at 10-to 50-m horizontal spacing, with a vertical resolution of about 10 m. VARUNA seismic reflection profiles (Figure  2 ) were acquired at 10 knots ship speed (fast acquisition) using two coupled air-guns of 45/45 c. i. and 105/105 c. i. fired every 10 s allowing a 9 s record length.
Acquisition on a 300-m long 48-channel streamer allows a theoretical Common Mid-Point spacing of 3. 125 m. The entire seismic data set (5,000 km in total) was processed using the CGG Geovation software. Processing consisted of Normal Move-Out correction, stacking, bandpass filters (13-16-100-125 Hz) and migration (Kirchoff migration, mostly using water velocity, with local variations to improve imaging where needed). The imaged profiles show good penetration down to the basement, sometimes reaching the very limit of the record at 9 s two-way travel time. Some of the profiles acquired previously during the OWEN-2 cruise (Figure  2 ) were reprocessed to improve ties between the two surveys. In red, the Owen Fracture Zone active trace from  Rodriguez et al. (2011 Rodriguez et al. ( , 2018)). Multibeam is complemented with SRTM30_PLUS in the background  (Becker et al.
6°N and ∼11. 2°N) the axial ridge is only several tens of meters above the Somalian seafloor (Figure  6c ). However, the bathymetric step is preserved across the transform fault and the Somalian seafloor is higher than the Indian seafloor. The two Ridge-Transform Intersections (RTIs) of the Owen Transform Fault are morphologically very distinct: 1. To the north, the Wheatley Deep is a 1,400 m-deep depression, 15 km-wide and 30 km-long nodal basin that forms the current Sheba-Owen RTI  (Fournier et al. , 2010 , Figure  6a ). The deepest point of this basin is around 5,845 m depth. The basin recently escaped turbiditic sedimentation due to a slight basement uplift that deflected the sedimentary flux east of it. 2. To the south, a large 54 km-wide 18-24 km-long corrugated surface occupies most of the oceanic floor on the Indian side at the Carlsberg-Owen RTI (Figure  6d ), interpreted as the youngest OCC.
The Owen Transform Fault experienced a period of relative tectonic quiescence interrupted abruptly around 2. 4-1. 5 Ma by transpression. The switch to the new regime is marked by a ubiquitous unconformity recognized in seismic along the entire length of the fault, coeval with the uplift and folding of the sediments and the abandonment of a distributed off-ridge normal faults network. Transpression is still active today, the component of compression being accommodated by a series of secondary folds and reverse faults. Services (SHOM) coordinated pre-cruises logistics and took care of scientific instruments and data acquisition while at sea. Seismic acquisition was handled by GENAVIR (IFREMER) and seismic data were processed using CGG Geovation software. We thank Javier Escartin and Cédric Bulois for fruitful discussions and Yu Ren for his constructive review.
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Water and Ocean and Topography) altimeter mission. Operational systems, however, generally fail to retrieve mesoscale dynamics for horizontal scales below 100 km and timescales below 10 d. Here, we address this challenge through the 4DVarnet framework, an end-to-end neural scheme backed on a variational data assimilation formulation. We introduce a parameterization of the 4DVarNet scheme dedicated to the space-time interpolation of satellite altimeter data. Within an observing system simulation experiment (NATL60), we demonstrate the relevance of the proposed approach, both for nadir and nadir plus SWOT altimeter configurations for two contrasting case study regions in terms of upper ocean dynamics.
We report a relative improvement with respect to the operational optimal interpolation between 30 % and 60 % in terms of the reconstruction error. Interestingly, for the nadir plus SWOT altimeter configuration, we reach resolved space-timescales below 70 km and 7 d. The code is open source to enable reproducibility and future collaborative developments. Beyond its applicability to large-scale domains, we also address the uncertainty quantification issues and generalization properties of the proposed learning setting. We discuss further future research avenues and extensions to other ocean data assimilation and space oceanography challenges.
, 2017; Tandeo et al. , 2020) , and, more recently, deep Published by Copernicus Publications on behalf of the European Geosciences Union. learning schemes  (Fablet et al. , 2020; Fablet and Chapron, 2022; Manucharyan et al. , 2021; Beauchamp et al. , 2020). Here, we further explore this avenue and, more specifically, the 4DVarNet framework recently introduced in  Fablet et al. (2021). As it relies on a variational data assimilation formulation, it appears to be particularly suited to the spacetime interpolation of sea surface variables from irregularly sampled observations. We propose a parameterization of the 4DVarNet scheme dedicated to SSH interpolation from satellite altimeter data and report OSSE (observing system simulation experiment) results to support the relevance of the proposed scheme.
Our main contributions are as follows: -The proposed 4DVarNet-SSH scheme delivers an endto-end neural architecture using raw satellite altimeter data and optimally interpolated fields as input. We also address uncertainty quantification issues using an ensemble method. -For the OSSE on two case study regions, respectively, along the GULFSTREAM, and for an open-ocean area dominated by mesoscale eddy dynamics, the 4DVarNet-SSH scheme outperforms previous work and significantly improves the performance metrics with respect to the operational processing. We also support the relevance of wide-swath SWOT altimeter data to significantly improve the reconstruction of sea surface dynamics compared to nadir-only satellite altimeters. -We deliver an open-source code for the proposed 4DVarNet-SSH scheme.
g. ,  Carrassi et al. , 2018) , which aims at estimating the optimal trajectory of a system in a predefined DAW, given the additional constraint of model uncertainty in the objective function J. Regarding statistical data assimilation, many state-of-theart methods rely on optimal interpolation (OI), which is the basic block of all statistical methods, especially regarding SSH-related datasets. OI has been used for decades  (Taburet et al. , 2019)  for the interpolation of along-track nadir altimeter datasets and is still used today for the operational marine (Copernicus Marine Environment Monitoring Service, CMEMS) and climate (Copernicus Climate Change Service, C3S) production of the EU Copernicus program. It involves a significant smoothing for solving spatial scales up to 150 km. Extensions of OI schemes to multiscales to better account for mesoscale sea surface dynamics have recently been proposed  (Ardhuin et al.
g. ,  Carrassi et al. , 2018). It makes the 4DVarNet framework relevant for comparison with traditional DA methods. The BOOST-SWOT 2020 data challenge (https://github. com/ocean-data-challenges/2020a_ SSH_mapping_NATL60, last access: 2022) provides a representative benchmarking framework to assess the performance of SSH mapping schemes for nadir-only and nadir plus SWOT (denoted as nadir+swot in the figures) altimetry datasets. As detailed hereafter, we introduce a parameterization of the 4DVarNet scheme dedicated to SSH interpolation issues and demonstrate its relevance in the context of the benchmarking settings introduced in the BOOST-SWOT 2020 data challenge.
Method This section details the proposed learning-based framework for the interpolation of satellite altimeter data. We first briefly review 4DVarNet framework recently introduced in  Fablet et al. (2021)  in Sect. 3. 1 and present the proposed parameterization for SSH mapping from nadir and SWOT altimeter data in Sect. 3. 2. We describe the resulting PyTorch package and its associated implementation details in Sect. 3. 4 and the proposed learning setting in Sect. 3. 3.
4DVarNet framework The 4DVarNet framework introduced in  Fablet et al. (2021)  provides a generic approach for the training of 4D-Var models and solvers. They have been shown to outperform classic 4D-Var solvers for toy case studies, such as Lorenz-63 and Lorenz-96 dynamics, when considering partially observed systems. The 4DVarNet framework can be regarded as an extension that used trainable, gradient-based solvers of the deep learning scheme, which led to the best SSH interpolation performance in our previous work  (Beauchamp et al. , 2020). From a methodological point of view, the 4DVarNet framework derives an end-to-end neural architecture from an underlying variational data assimilation (DA) formulation (see Sect. 2 again) as follows: J (x, y, ) = λ 1 ||y -H(x)|| 2 + λ 2 ||x -(x)|| 2 , (2) where λ 1,2 are predefined or tunable scalar weights, and we replaced the Mahalanobis norms ||.
This iterative rule, based on a trainable LSTM operator, is similar to meta-learning schemes  (Andrychowicz et al. , 2016). Due to the ability of LSTM models to capture long-term dependencies, it results in a trainable gradient descent with momentum. Overall, a 4DVarNet scheme defines a neural architecture which runs a predefined number of iterative gradient-based updates (see Eq. 3). Let us denote , (x (0) , y, ) as the output of the 4DVarNet architecture for a given prior and solver , the initialization x (0) of the state x, and the observations y on the domain. The resulting neural architecture is referred as an end-to-end architecture, since it uses as input raw observation data and an initial guess and directly provides as output the reconstructed state. Then, the joint training of operators { , } is stated as the minimization of a reconstruction cost L(x, x ), which is typically the root mean squared error (RMSE) between the true state x and its reconstruction x and additional regularization terms (fully described in Sect.
4DVarNet-SSH parameterization The proposed 4DVarNet-SSH framework aims at exploiting and improving the mapping performance of current operational OI products. This section draws from the generic 4DVarNet formulation presented above and adapts its target components, given that OI products retrieve consistent largescale dynamics. Then, we rely on the following multiscale decomposition: x = x + dx + , ( 5 ) where the anomaly dx is seen as the difference between the true state x and the large-scale components x. Regarding the observation data, let us denote by y( ) = {y k ( k )} the partial and potentially noisy altimetry observations associated with masks = { k } ⊂ D, where k corresponds to the gappy part of the field and index k refers to time t k. We use the operational OI product as a gap-free observation data, denoted as y, for the state component x, whereas the observation data for anomaly dx are yy over domain.
25 0. 75 1 0. 75 0. 25 0 T. This training loss is used in Sect. 4 for the OSSE-based BOOST-SWOT data challenge framework. Regarding the training configuration, when the domain is small (see the GULFSTREAM and OSMOSIS regions defined in Sect. 4), we use a single GPU and Adam optimizer with a batch size of 2 over 200 epochs. The same set of parameters holds for larger domains (NATL and cNATL; see again Table  1 ), but we use the 4DVarNet-distributed version of the code over 4 GPUs. The computational time of the training procedure lies between 4 and 5 h for the smalldomain setup and between 7 and 8 h for the large-domain setup.
Implementation aspects We provide an open-source PyTorch implementation of the 4DVarNet-SSH scheme. (The code is available at https://doi. org/10. 5281/zenodo. 7186322, hgeorgenthum et al. , 2023). PyTorch is a state-of-the-art deep learning framework. We benefit from the associated packages such as lightning and hydra to provide a high-level environment and make the reproduction of the experiments and the development of other applications easier. Through the lightning package, our implementation supports multi-GPU distributed learning configurations. This may be highly relevant for speeding up the training process. Regarding computational issues, the OSSE-based applications (see Sect. 4) involve the processing of 7 × 240 × 240 tensors (i. e. , 7 d time series over a 12 • × 12 • domain with a 1/20 • resolution).
GPUs with a significant RAM (typically above 30 Gb), such as NVIDIA V100, A40, and A100, can process such tensors through the proposed 4DVarNet architecture. The direct training 4DVarNet models over larger spatial domains is, however, limited by the GPU memory. To address this issue, we develop a specific data management module, through so-called data loaders. Our data loader module automatically extracts patches of a predefined size (typically, 7 × 240 × 240 in the reported experiments) from the considered training dataset according to stride parameters (as sketched in Fig. 2 ). One can exploit the same approach to apply a learned model to a large domain during the evaluation or production stage. In both cases, we benefit from the fully convolutional feature of the considered neural architecture.
This guarantees that, up to border effects, the 4DVarNet processing is translation invariant. (-65 • , -55 • ) (33 • , 43 • ) 10 • × 10 • GULFSTREAM2 (-45 • , -35 • ) (42 • , 52 • ) 10 • × 10 • OSMOSIS (-19. 5 • , -11. 5 • ) (45 • , 55 • ) 8 • × 10 • OSMOSIS2 (-28. 5 • , -20. 5 • ) (50 • , 60 • ) 8 • × 10 • cNATL (-50 • , -10 • ) (33 • , 53 • ) 20 • × 40 • NATL (-79 • , 7 • ) (27 • , 65 • ) 38 • × 88 • Let us specify that the size of the tensors relates to the maximal space-time distance with significant autocorrelation of the SSH. Then, we use a data assimilation window of length N = 2L + 1, meaning that we use temporal sequences of the state space x k±L = x k-L:k+L. The idea is to optimize the results for the time t k at the center of the window [t k-L ; t k+L ], and the value of N has to be chosen according to the dynamics of the geophysical field considered, which is the SSH here.
In the following experiments, we use a value of N = 7, which seems to be enough to describe the spatiotemporal correlations of the anomaly between the ground truth and the DUACS (Data Unification and Altimeter Combination System) OI scheme. To apply 4DVarNet directly on the SSH dataset, without providing the DUACS OI baseline as a coarse version of the reconstruction, the value of N shall increase up to ∼ 20 d (see, e. g. ,  Beauchamp et al. , 2022). In the same vein, the spatial size of the patches is 240 × 240 pixels, with a corresponding 0. 05 • resolution, where 12 • is large enough to consider the spatial autocorrelation as negligible outside of the patch.
NATL60 dataset and case study regions In our study, the nature run (NR) corresponds to the NATL60 configuration  (Molines, 2018)  of the NEMO (Nucleus for European Modelling of the Ocean) model. It is one of the most advanced, state-of-the-art, basin-scale, high-resolution (1/60 • ) simulations available today, whose surface field effective resolution is about 7 km. In this work, we will use the following five different subdomains of the North Atlantic basin (see Fig. 3 ): The GULFSTREAM and OSMOSIS domains (solid blue and red lines in Fig. 3 ) are the domains used by the BOOST-SWOT project in the framework of the NATL60 OSSE throughout the different related studies (see their 2020 ocean data challenges in Le  Guillou et al. , 2020). Because we aim at exploring the capabilities of 4DVarNet to deploy at the basin scale, we also propose the two alternate GULF-STREAM2 and OSMOSIS2 domains (dashed blue and red lines in Fig.
Over these regions, the SSH resolution of the nature run is downgraded to 1/20 • , which is enough to capture both mesoscale dynamical regimes and the OSMOSIS-related smaller scales, while avoiding unnecessary heavy computation time. The NATL60 nature run will then be used as the reference ground truth (GT) in an observing system simulation experiments (OSSEs). The pseudo-altimetric nadir and SWOT observational datasets will be generated by a realistic subsampling of satellite constellations.