dataesr/scientific-paragraphs-categorization huggingface dataset:
Network and model contributions The reconstruction steps are further decomposed between data consistency and regularization in Figure  6  and illustrated in Figure  7. For both metrics, the two baselines shown in Figure  6  with brown (BASE R ) and gray (BASE ) curves are again significantly different. The BASE R curves show a slow start with oscillations between improving data consistency and degrading regularization but with a regular progression afterwards. The oscillations are explained by the constant wavelet threshold, too large during early F I G U R E 6 Testing scores evolution over reconstruction. The main pipelines are decomposed between data consistency ( i ) and regularization (R i ) steps to provide the classic and ΔB 0 -weighted structural similarity index measure (SSIM) scores over testing dataset over reversed logarithmic scales.
F I G U R E 7 Susceptibility-weighted imaging (SWI) images evolution over reconstruction. The intermediate SWI images with 4 mm minimum intensity projection are provided over reconstruction decomposed into data consistency ( i ) and regularization (R i ) steps for acquisition 106 from testing dataset. Various details are pointed out with arrows: coil sensitivity degraded by data consistency (green), off-resonance degraded by data consistency (blue) and off-resonance degraded by regularization (red).
DISCUSSION The diverse contributions of this article are discussed hereafter. First, we compared conventional methods  [29] [30] [31]  to perform, to the best of our knowledge, the first partial off-resonance correction study. Second, we analyzed the deep learning contributions in a multi-parametric acceleration pipeline. Finally, we conducted a deep learning study over a fairly large in vivo dataset consisting of model-based self-estimated off-resonance corrected volumes. 14.
Dataset generation The ΔB 0 map estimation technique developed in a previous publication  14  assumes the relationship between B 0 inhomogeneities and image phase to be dominant over other phase sources for large echo times (e. g. , 20 ms and higher) at 3T. It allows for a simple estimate of the ΔB 0 field map retrospectively with minimal error and no motion-related mismatch for SWI acquisitions and alike. It also avoids any assumptions on the trajectory and can, therefore, be used on any dataset matching the above-mentioned acquisition setup and providing either the raw k-space data or complex-valued MR images. The self-estimation method has been carefully assessed during the early stages of the study, and reached our expectations. However, two other competing methods were also considered, the first by Lee et al.
Various pipelines with I = 5, Q = 5 and L = 5 are summarized with data consistency and regularization terms and evaluated using classic and ΔB 0 -weighted SSIM and PSNR scores. Bold signifies best (i. e. highest) PSNR and SSIM score. T A B L E 1 Scores for different correction coefficients and pipelines over the test dataset Classic 𝚫B 0 -weighted Pipeline Data consistency Regularization SSIM PSNR SSIM PSNR BASE F 𝛺 UNet 0. 9421 33. 32 0. 8947 23. 50 BASE R F 𝛺,𝛴 (SVI) Wavelet 0. 9392 31. 32 0. 9249 23. 33 MFI F 𝛺,𝛴 (MFI) UNet 0. 9596 34. 96 0. 9475 26. 42 MTI F 𝛺,𝛴 (MTI) UNet 0. 9601 35. 13 0. 9452 26. 11 SVI F 𝛺,𝛴 (SVI) UNet 0. 9613 35. 24 0. 9498 26. 57 SVI 1∕2 F 𝛺,𝛴 (SVI) UNet 0. 9616 35. 19 0. 9541 27. 31 Note:.
The best performing pipelines (SVI and SVI 1∕2 ) are evaluated with and without buffers, along with the baseline BASE based on the non-correcting Fourier operator F. The classic and B 0 -weighted scores are averaged over the testing dataset, with statistical significance assessed through pairwise two-sided Wilcoxon signed-rank tests over the SSIM scores with a global Benjamini-Hochberg correction. Classic 𝜟B 0 -weighted Pipeline Buffers p SSIM PSNR p SSIM PSNR BASE - 0. 9427 33. 40 ** 0. 8979 23. 69 3 0. 9421 33. 32 0. 8947 23. 50 SVI - * 0. 9603 35. 11 * 0. 9490 26. 57 3 0. 9613 35. 24 0. 9498 26. 57 SVI 1∕2 - ** 0. 9575 34. 71 ** 0. 9493 26. 98 3 0. 9616 35. 19 0. 9541 27. 31 p > 0. 05. p < 0. 05. p > 0. 005. Note:.
The concepts and information presented in this article are based on research results that are not commercially available. Future availability cannot be guaranteed. This work was granted access to the HPC resources of IDRIS under the allocation 2021-AD011011153 made by GENCI. We thank Dr. Zaccharie Ramzi and Chaithya G R for their previous works and open-source contributions. Additionally we thank Cecilia Garrec for the scientifc editing in English. Philippe Ciuciu received funding from Siemens Healthineers (France) to support this research. ENDNOTES * https://github. com/andyschwarzl/gpuNUFFT. † https://github. com/CEA-COSMIC/pysap-mri. ‡ https://github. com/andyschwarzl/gpuNUFFT. § https://github. com/chaithyagr/gpuNUFFT.
A. Characterization Accuracy The success of the phaseless characterization is evaluated by comparing the retrieved Far Field (FF) in y from the phaseless NF data to the reference one, y. This comparison is achieved point-wise by the error signal defined as follows Error(y, y) = ||y| -| y|| max i |y i |. ( 3 ) The mean of this error signal is the Equivalent Noise Level (ENL). Both are given in dB. The comparison of the FF magnitudes provides a single, straightforward, indicator of a coherent retrieved phase in NF as faithful FF reconstruction is not possible otherwise. It also avoids the comparison of phase patterns, which is tedious for phase reference reasons.
6  for various dataset sizes. Finally, the proposed nm-MT filter yields significantly better results in a more stable way. A possible explanation is that the nm-MT filter also acts on the order m. Since there is a multiplicative term e jmφ in the SW expression, which has constant modulus, the order m is highly critical for phase ambiguities. The lower error signal in the various 80 82 84 86 88 90 92 94 96 98 P 0 (%) cutting planes also illustrates the claimed improvements.
E. Data from Measurements The NF measurements have been performed in the StarLab for the horn antenna previously simulated and a metasurface antenna  [21]. The (complex) SW coefficients returned by the MVG software are used to generate the phaseless samplings. The ENL values between the FF reconstructions from the phaseless NF and the reference are given in Table  II. 1) Horn antenna at 10 GHz: The data parameters are the same as in the simulation except for the sampling distances, of 320 and 450 mm (the StarLab radius), or 20 and 30 % of the FF distance, respectively. The FF reconstructions of one polarization in two planes are shown in Fig. 10. 2) Metasurface antenna at 17 GHz: This antenna has a minimum sphere radius a = 130 mm, N = 56. The two NF spheres are located at 450 and 900 mm, or 6 and 11 % of the FF distance, respectively.
Conversely, new data from upcoming Venus missions should give us a constraint on the longevity of water on Venus and encourage the planetary and exoplanetary science communities to search for such worlds in exoplanet databases in the coming decades. Yet the latter is not unambiguous; for instance, key observations that the DAVINCI descent probe analytical instruments within and below the clouds (Sect. 5. 3) and the EnVision VenSpec-H spectrometer (Sect. 6. 3) are related to the D/H ratio and the heavy noble gas isotopes. Work by  Avice et al. 2022 (this collection)  demonstrates that the D/H ratio in itself is insufficient to determine when Venus lost its water and the time-scale of that loss, as implied in the published Pioneer Venus D/H measurements by  Donahue et al. (1982). The heavy noble gas isotope measurements by DAVINCI will be crucial to understand the epoch and timescale of the loss on Venus  (Garvin et al.
Science Phase I (SP1) occurs while aerobraking is paused, about 6 months after insertion into a polar elliptical orbit. Science Phase II (SP2) starts after further aerobraking has placed VERITAS in a near-polar, circular, low-altitude orbit that allows global observations throughout the mission. Over ∼3. 5 Earth years, the mission will return synergistic, global datasets with unprecedented coverage, resolution, and accuracy to meet its science goals: high resolution topography, X-band radar imagery, targeted surface deformation, near-IR spectroscopy and gravity. The VERITAS spacecraft downlinks a total of 20. 9 terabits of data to the DSN stations using CCSDS packets. The total volume of science data products to be archived (raw, reduced, and derived) is estimated to be 134 terabytes. These rich datasets will allow VERITAS to meet key required investigations and science objectives.
VERITAS' rich global datasets will provide an invaluable resource for a new generation of Earth, planetary, and exoplanet scientists, and reveal the truth about how Earthlike Venus really is. These datasets are highly synergistic with DAVINCI (Sect. 5) and EnVision (Sect. 6), providing information about Alpha Regio and identifying key targets for EnVision exploration. These missions also enrich VERITAS contributions by, for example, providing high resolution visual and near-IR images of Alpha Regio (i. e. , DAVINCI flyby near-IR emissivity and sub-cloud near-IR imaging and high-resolution local topography before the VERITAS launch) and continuing the search for volcanic activity (EnVision).
2018 (Garvin et al. , 2022a)). SfM processing of bundles of descent images produces first 5 -60 m scale topography of tesserae and establishes boundary conditions for tectonic and erosional models. Final imaging scales from VenDI below ∼1. 5 km produce unblurred images at scales finer than 1 m, permitting feature-identification resolution of key indicators of sedimentary processes at scales that connect to those observed by prior Venera landers (e. g. ,  Garvin et al. 1984). Evaluation of Earth-based analogue datasets (Pilbara, Zagros mountains) that emulate VenDI near-IR bandpasses and spatial scales have demonstrated discrimination of felsic surfaces at <100 m (down to 5-10 m) providing confidence that sub-cloud descent imaging will complement 50-100 km scale orbital near-IR emissivity mapping by multiple missions (see Sects.
DAVINCI first and second Venus gravity-assist flybys, with a closest approach on the night-side hemisphere near Equator at 00:00 LT, are scheduled on January 30 and November 15, 2030 for the planned June 2029 Launch Readiness Date (Table  1 ). Thousands of images are acquired during the two flybys, potentially identifying felsic regions, "calibrated" by local-scale, sub-cloud VenDI band-ratio mapping at Alpha Regio, as well as with VASI lapse rate information. The fourth VISOR camera will provide global, dayside coverage of Venus in the unknown UV absorber band (355-375 nm). Dayside UV imaging (single band) will measure cloud dynamics as the spacecraft approaches and recedes from pericenter, allowing ultraviolet feature tracking at 355-375 nm at a frequency that exceeds any existing Venus orbital imaging dataset.
CUVIS (Compact Ultraviolet Imaging System) Technology Demonstration Opportunity (TDO) experiment CUVIS (Compact Ultraviolet Imaging System) acquires 0. 2 nm resolution spectra and hyper-cubes of images from 0. 2 to 0. 4 µm, concurrent with VISOR UV imaging on the dayside flybys of Venus, in a technology demonstration of a new class of small planetary instruments. This UV hyperspectral sensor (CUVIS) will perform upper atmosphere SO 2 and SO chemistry and gather spectral information on the unknown UV absorbing species at a spectral resolution of 0. 2 nm in the UV from 0. 20 µm to 0. 40 µm  (Pollack et al. 1980; Wilson et al. 2023, this collection). CU-VIS can be accommodated on the CRIS spacecraft and is implemented in a fully separable, do no harm fashion. It will further employ Machine Learning to identify key species and test approaches to accommodate effective data transmission for rich datasets such as those delivered by CUVIS.
VenSAR on EnVision A Synthetic Aperture Radar, VenSAR, will image pre-selected regions of interest at a resolution of 30 m/pixel, and subregions at 10 m/pixel. An order of magnitude better than Magellan and with a better sensitivity, these images are the key to understanding geological processes from local to global scale, discriminating relationships between units of different age, and identifying the changes caused by geological activity. Topographic information at 300 m spatial and 20 m vertical resolution across these regions, derived from stereo imaging at two different incidence angles, is complemented by a global network of altimetry mode tracks with a vertical resolution of 2. 5 m. This enables to map the surface at a better resolution than any previous dataset, essential for resolving the geometry of faults, folds and other features, and enabling the quantitative analysis of geological processes.
To achieve its science objectives, EnVision must return 210 Tbits (26. 25 Terabytes) of science data to Earth, using a Ka/X-band comms system with a fixed diameter hight-gain antenna, with a large dynamic range of distance to Earth (from 0. 3 to 1. 7 AU), from a low Venus polar orbit, in the hot Venus environment (exacerbated by the operation of highly dissipative units), while operating three spectrometers in a near cryogenic environment. Achieving the science objectives under these multiple constraints without oversizing the spacecraft requires careful planning of the science operations, making the science planning strategy a critical driver in the overall mission design against which the spacecraft and ground segment are then sized (Sect. 6. 2 and Fig. 15 ). EnVision science operations strategy is to obtain the widest range of data types that enables us to put the highest resolution datasets into regional and global context.
The combination of chemical and mineralogical data from both the plains and tesserae would significantly advance our understanding of the Venusian crust, mantle and igneous processes, the evolution of volcanism with time and the range of surface-atmosphere interactions on modern Venus. Atmospheric measurements from the Venera-D orbiter would complement the volatile mapping carried out by EnVision, in complement to the geophysical studies and atmospheric descent probe measurements which are at the heart of EnVision, DAVINCI and VERITAS science questions (Sects. 4-6).
2 mm. See also Sect. 7. 3. 2 -Lander science, and Fig. 24. Harsh conditions at the surface of Venus require significant resources to extend the expected lifetime of spacecraft after landing. This lifetime should be sufficient to conduct all planned experiments and upload the obtained data to the Orbiter. As our analysis has shown, the optimum duration of the surface operations in this case is at least 2, maximum 3 hours. Lander descent time in the atmosphere: ∼50 min (from 125 km to the surface). Lander instrumentation will be operative during the descent and will collect both meteorological data and information about composition of atmospheric gasses and cloud aerosols. The ISKRA-V spectrometer (see Sect. 7. 3. 2, and Table  6 ) will study chemical composition of the atmosphere including abundances of gases SO 2 , CO, COS, H 2 O, NO 2 , HCl, and HF, and their isotopologues and isotopic ratios D/H, 13 C/ 12 C, 18 O/ 17 O/ 16 O, and 34 S/ 33 S/ 32 S during descent from 65 km and after landing; a suite of sensors (MTK-V) will determine temperature, pressure, wind speed, temperature gradient, acceleration from 120 km altitude to the surface and at the surface; at the surface, the package measures chemical composition of rocky sample (which must be delivered inside the lander) and continues measurements of chemical composition of the atmosphere: abundance and isotopic ratio of noble gases in the atmosphere (Sect.
VSAR on Shukrayaan-1 Global mapping by Magellan at resolution of 100-200 m was performed in single polarization (HH). Complete polarimetric data of selected sites are only available from earth-based radar experiments. The main instrument onboard, the S-Band Synthetic Aperture Radar (VSAR) targeting high-resolution (40 m), fully-polarimetric global mapping, would provide a major step in Venus surface studies, complementary to the VISAR and VenSAR operations and performance (Table  7 ). VSAR will be capable of polarimetric decomposition by retrieval of the complete scattering matrix (all four Stokes parameters). It is well known that the cross-pol channels (HV or VH) are more sensitive towards surface roughness and volumetric inhomogeneities within the medium and are not optimally used for dielectric constant estimation  (Fung et al.
The possibility of cycle-to-cycle interferometry is foreseen. In the radiometer mode, SAR will estimate the brightness temperature with an accuracy better than 2 K. The science objectives of Shukrayaan-1 significantly differ and complement those of EnVision and VER-ITAS, and therefore the overall mission operations and VSAR capabilities, operations and performances are different when compared to VISAR and VenSAR. For each of the three orbiter missions, payload instruments were chosen to meet the broad spectrum of measurement requirements needed to support mission science investigations. Combining the data from all three missions would provide us with deep scientific insights into the geology and evolution of Venus with topographic, polarimetric and global information available at a high resolution.
Future Mission Concepts and Measurements The data acquired with the VERITAS, DAVINCI, and EnVision missions from the end of this decade, in addition to other missions concepts described in Sects. 7-8, will fundamentally improve our understanding of the planet's long term history, current activity and evolutionary path. Yet even with the discoveries that await those missions, compelling science questions will likely remain-such as those regarding the chemical and physical cycles of the atmosphere, the interactions between that atmosphere and the surface, and the make-up and structure of the planet itself. Although new missions may be proposed in the nearterm using capabilities currently in development, investments in future technologies and new techniques will enable both long-term surface science and missions that take advantage of mobility in the surface, near-surface, and atmospheric environments (Fig.
Future Surface Investigations One of the fundamental measurements that is needed is the bulk elemental composition and mineralogy of the surface from key locations, especially tesserae. Tesserae are thought to be Fig. 25  The Venus In Situ Transfer and Analysis mission concept (VISTA) provides an opportunity to obtain measurements that cannot be obtained by a simple, short-term mission to Venus  (Izenberg et al. 2023 ; see also  Sect. 10. 2. 1). This illustration is provided as an example. Data acquired with the VERITAS, DAVINCI, and EnVision missions from the end of this decade will fundamentally improve our understanding of the planet's long-term history, current activity and evolutionary path (top left). Although new missions may be proposed in the near-term using capabilities currently in development, investments in future technologies and new techniques will enable both long-term surface science and missions that take advantage of mobility in the surface, near-surface, and atmospheric environments.
Gamma-and XRF-based spectroscopic measurements were acquired by the Venera and VeGa landers, but modern equivalents of these instruments would provide much improved measurement accuracy. Moreover, carrying out compositional analyses in a tessera region (none of which has been sampled before) would reveal whether these terrains are chemically distinct from the lava plains where previous analyses have been conducted (e. g. ,  Gilmore et al. 2023, this collection). Collecting imaging data from the surface is not straightforward (e. g. ,  Garvin et al. 1984 ), as silicon-based detectors typically used in virtually all modern imagers will not work at Venus surface temperatures. For short-lived landers, one can use silicon imaging chips inside insulated housings (e. g. ,  Kremic et al. 2020). For prolonged operation at the surface, however, imagers capable of sustained operations at high temperatures are needed.
2023, this collection) , current radar data indicate rock surfaces over most of the planet. Heat flow is typically measured in a borehole, as done on Earth; and as was done using holes drilled by Apollo astronauts on the Moon's landing sites. Technologies to install sensors at depth include drilling (e. g. ,  Vago et al. 2017) , or penetrators or hammering mechanisms such as on the Rosetta or InSight missions  (Spohn et al. 2007 (Spohn et al. , 2018 (Spohn et al. , 2022)) ; or pneumatically, as has been studied for the Moon  (Zacny et al. 2013). However, drilling into basalt for constraining the heat flow value at Venus is very challenging. Alternatively, the temperature gradient and thermal conductivity could be measured with the "thermal blanket" technique used for heat flow studies at ocean bottoms (e.
Earth-based polarimetric mapping using the Arecibo radar shows that information on small-scale roughness correlates Venus lava flows with those in terrestrial settings  (Campbell and Campbell 1992) , and may reveal deposits formed during recent, volatile-rich eruptions  (Campbell et al. 2017). New methods for combining Earth-based hybrid astronomical radar polarimetry with refined Magellan radar measurements can extend the scientific value of existing datasets while we await the arrival of the next wave of orbital radar mapping missions, VERITAS and EnVision, as well as other radar missions considered for selection described in Sect. 3. Recent work by  Garvin et al. (2022b Garvin et al. ( , 2023 in prep. ) in prep. )  demonstrated how multiple viewpoint Arecibo polarimetric radar data could be used via Shape-from-Shading together with reanalyzed Magellan radar altimeter radagrams to produce km-scale topography of Alpha Regio as a test-case.
Sects. 4-6 captured the considerable advances that the three newly selected missions VERITAS (Sect. 4), DAVINCI (Sect. 5) and EnVision (Sect. 6) will bring to these science goals. These rich, highly synergistic datasets will provide an invaluable resource to assess the long-term history, stability of water reservoirs in the mantle and atmosphere, current levels of activity, divergent pathways and evolution toward habitability. Together they will reveal whether Venus-like and Earth-like planets can potentially transition into one another over time, which would imply that Earth-like exoplanets may be common among Earth-sized exoplanets.
Acknowledgements T. W. and G. A. acknowledge  France 's  Centre National d'Études Spatiales (CNES)  for funding support of Venus related studies. J. B. G. (as well as S. G. G. A. N. J, and E. K) acknowledges  NASA 's  Discovery program  for support of Venus related studies and the DAVINCI mission. Work by S. E. S. , S. H. , and D. N was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. Members of the VERITAS team are grateful for funding by  NASA 's  Discovery Program  and  JPL 's proposal support. C. G. acknowledges the support of  Rice University  and the CLEVER planets group (itself supported by  NASA  and part of  NExSS)  and  ET-HoME Excellence of Science programme. A. S. acknowledges support from  NASA 's  Habitable Worlds Program  (No.
Acknowledgments We thank  Luca Silvestrini  for a helpful suggestion, and  Ann-Kathrin Perrevoort  for helpful comments about observables in µ → eēe. MA was supported by a doctoral fellowship from the  IN2P3. The work of SL is supported in part by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  agreement No. 860881-HIDDeN.