dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, R-band showed the highest standard deviations except for bathymetric values that ranged from 2 m to 2. 5 m. For G-band, the standard deviation was less than 1 m for all bathymetry lower than 4. 4 m. This value decreased to 4. 1 m, 3. 8 m, 3. 4 m, and 3. 2 m, for B-band, B/G ratio, R-band, and R/G ratio, respectively. B-band and R-band showed a lower sensitivity than the G-band. This confirms a comprehensive sensitivity analysis carried out prior to this study, which demonstrates that the best performance for LRM and CBR was obtained by using G-band, while for SM, it was obtained by using the combination of R-band and G-band. In the following, the presentation of LRM, SM, and CBR results will be focused only on these X parameterizations. When the filter of 1σ was applied to the calibration dataset, the number of matchups significantly decreased from 915,326 points to 337,607 points, 130,583 points, and 19,737 points, for CBR, LRM using the log-transformed G-band (LRM-G), and LRM using the log-transformed R-band (LRM-R) methods, respectively (Figure  6b ).
A total of 70 satellite images had more than 100 matchups for CBR, whereas LRM-G and LRM-R had only 44 and 31 satellite images, respectively. It was interesting to note that the number of satellites images with more than 1000 and 10,000 matchups for CBR filter, was 43 and 3, respectively. Satellite images with few match-ups generated a 1σ-filtered maximum bathymetry (Zmax-1σ) value of 0. For CBR, 80 satellite scenes show a Zmax-1σ value different of 0 (Figure  6c ). The mean value of Zmax-1σ was 7. 3 ± 2. 1 m. For LRM-G and LRM-R, the mean values were 5. 1 ± 1. 7 m and 3. 2 ± 1. 1 m, with a total number of images of 50 and 39, respectively. When the filter of 1σ was applied to the calibration dataset, the number of matchups significantly decreased from 915,326 points to 337,607 points, 130,583 points, and 19,737 points, for CBR, LRM using the log-transformed G-band (LRM-G), and LRM using the log-transformed R-band (LRM-R) methods, respectively (Figure  6b ).
A total of 70 satellite images had more than 100 matchups for CBR, whereas LRM-G and LRM-R had only 44 and 31 satellite images, respectively. It was interesting to note that the number of satellites images with more than 1000 and 10,000 matchups for CBR filter, was 43 and 3, respectively. Satellite images with few match-ups generated a 1σ-filtered maximum bathymetry (Z max-1σ ) value of 0. For CBR, 80 satellite scenes show a Z max-1σ value different of 0 (Figure  6c ). The mean value of Z max-1σ was 7. 3 ± 2. 1 m. For LRM-G and LRM-R, the mean values were 5. 1 ± 1. 7 m and 3. 2 ± 1. 1 m, with a total number of images of 50 and 39, respectively. Data filtering for CBR approach was applied on the different class-specific LRM-G. Each class was associated with specific optical conditions. The use of these classes allowed us to split the initial matchup dataset into class-specific matchup datasets for which the natural variability in the relationships between X and Z situ was significantly reduced.
Inter-Comparison of the Performance of Empirical SDB Approaches The performance of the LRM-G approach was evaluated using the 50 satellite images for which the Z max-1σ value was different than 0. The median values for r 2 , RMSE, Bias, and MRAD were 0. 58, 0. 54 m, 0. 40 m, and 9. 70%, respectively (Figure  7 ). The performance of SM using LRM-G and LRM-R was evaluated using a reduced dataset of 33 satellite images for which the Z max-1σ value was different than 0 for G-band and R-band. SM performance was slightly higher than LRM-G performance. The median values for r 2 , RMSE, Bias, and MRAD were 0. 64, 0. 54 m, 0. 39 m, and 8. 99%, respectively. The CRB approach allowed us to evaluate the performance using a significantly larger dataset (N = 80). The median values for r 2 , RMSE, Bias, and MRAD were 0.
90, 0. 53 m, 0. 40 m, and 7. 62%, which demonstrated higher performance than LRM-G and SA. When the analysis focused on a reduced dataset of the 33 best satellite scenes, performance became significantly higher. The median values for r 2 , RMSE, Bias, and MRAD were 0. 94, 0. 46 m, 0. 36 m, and 6. 15%. reduced. This reduction of variability allowed us to reduce the number of matchups removed by the 1σ-filter.
Inter-Comparison of the Performance of Empirical SDB Approaches The performance of the LRM-G approach was evaluated using the 50 satellite images for which the Zmax-1σ value was different than 0. The median values for r 2 , RMSE, Bias, and MRAD were 0. 58, 0. 54 m, 0. 40 m, and 9. 70%, respectively (Figure  7 ). The performance of SM using LRM-G and LRM-R was evaluated using a reduced dataset of 33 satellite images for which the Zmax-1σ value was different than 0 for G-band and R-band. SM performance was slightly higher than LRM-G performance. The median values for r 2 , RMSE, Bias, and MRAD were 0. 64, 0. 54 m, 0. 39 m, and 8. 99%, respectively. The CRB approach allowed us to evaluate the performance using a significantly larger dataset (N = 80). The median values for r 2 , RMSE, Bias, and MRAD were 0.
90, 0. 53 m, 0. 40 m, and 7. 62%, which demonstrated higher performance than LRM-G and SA. When the analysis focused on a reduced dataset of the 33 best satellite scenes, performance became significantly higher. The median values for r 2 , RMSE, Bias, and MRAD were 0. 94, 0. 46 m, 0. 36 m, and 6. 15%. and (c) the cluster-based regression model (CBR). Performance is quantified using the coefficient of determination (r 2 ), the root mean square error (RMSE), the bias (Bias), and the mean relative absolute difference (MRAD). N is the number of satellite images; MeV is the median value. The MeV value between parentheses is calculated using the 33 best satellite scenes.
Validation of the SDB Uncertainty Model Validation of the multi-scene SDB uncertainty model was carried out only for the CBR approach that showed the best performance for regression models and allowed exploiting more satellite images and generating bathymetry maps over a larger depth range. After applying the 1σ-filter, the matchup dataset (DS), associated with the six satellite images acquired in October 2017, was composed of 3188 sounding points. The random point extraction procedures generated a calibration dataset (DS-C) and a validation dataset (DS-V) composed of 2136 and 1052 points, respectively (Figure  8a ). The percentage of observations associated with an SDB error lower than the total vertical uncertainty, P(ε DS-V i < TVU i ), was computed for each of the 100 random samplings (Figure  8b ).
Validation of the SDB Uncertainty Model Validation of the multi-scene SDB uncertainty model was carried out only for CBR approach that showed the best performance for regression models and allowed ploiting more satellite images and generating bathymetry maps over a larger de range. After applying the 1σ-filter, the matchup dataset (DS), associated with the six s ellite images acquired in October 2017, was composed of 3188 sounding points. T random point extraction procedures generated a calibration dataset (DS-C) and a vali tion dataset (DS-V) composed of 2136 and 1052 points, respectively (Figure  8a ). The p centage of observations associated with an SDB error lower than the total vertical unc tainty, P( < TVUi), was computed for each of the 100 random samplings (Figure  8  The absolute frequency of P( < TVUi) displayed a normal distribution with a me value of 95.
Impact of the Multi-Scene Approach on Uncertainty Three periods of 1 month each were identified to derive SDB and uncertainty m from the multi-scene approach. The first period in October 2017 was covered by six s ellite images. The second and third periods were centered on the months of Septem 2019 and September 2020, including, respectively, five and four satellite images of h quality. Field bathymetry datasets associated with these two last periods were compo of 2148 and 2802 sounding points, respectively. In comparison to the 2017 bathyme survey, the spatial distributions of the bathymetric data points in 2019 and 2020 w smaller (Figure  9a, c, e ). In 2020, field bathymetry data were mainly located on the s platform, while the data collected in 2020, were gathered along the southern part of inlet.
Impact of the Multi-Scene Approach on Uncertainty Three periods of 1 month each were identified to derive SDB and uncertainty maps from the multi-scene approach. The first period in October 2017 was covered by six satellite images. The second and third periods were centered on the months of September 2019 and September 2020, including, respectively, five and four satellite images of high quality. Field bathymetry datasets associated with these two last periods were composed of 2148 and 2802 sounding points, respectively. In comparison to the 2017 bathymetric survey, the spatial distributions of the bathymetric data points in 2019 and 2020 were smaller (Figure  9a, c, e ). In 2020, field bathymetry data were mainly located on the spit platform, while the data collected in 2020, were gathered along the southern part of the inlet.
41 m, respectively. In 2017, all TVU averaged values were lower than 1. 1 m, with a minimum observed for bathymetric values ranging from 1 to 1. 5 m and a maximum value for the ones ranging from 4 to 4. 5 m. In 2019, the maximum value of TVU was observed for bathymetric values comprised between 2 and 2. 5 m, while the minimum value was observed for deeper waters. In 2020, the maximum TVU values were observed for deeper waters. These results may directly be associated with the specific spatial distribution of sounding points in 2019 and 2020, reducing the quality of the representativeness of the calibration dataset. To better illustrate the impact of the spatial distribution of sounding points on the uncertainty model performance, three examples of the contrasted spatial distribution of sounding points for the 2017 dataset are discussed (Figure  11a ).
The first case (case 1) was associated with a spatially homogeneous point extraction from DS. One point out of three To better illustrate the impact of the spatial distribution of sounding points on the uncertainty model performance, three examples of the contrasted spatial distribution of sounding points for the 2017 dataset are discussed (Figure  11a ). The first case (case 1) was associated with a spatially homogeneous point extraction from DS. One point out of three was extracted in order to generate DS-V. This selection ensured that DS-C was optically representative of DS-V. The second (case 2) and third (case 3) cases were associated with a geographic segmentation of DS-C and DS-V. For case 2, DS-C points were located on the northern part of the study area, while DS-C points were located on the southern part of the study area for case 3.
The supplementary use of uncertainty maps offers the opportunity to consider the analysis of vertical and volumetric morphological changes observed along the tidal inlet. The comparison of the 2017 and 2019 SDB maps (Figure  12d ) showed that absolute vertical changes ranged from 0 to 4 m, while the sum of the uncertainties of 2017 and 2019 ranged from 0 to 3 m (Figure  12e ). Despite high TVU values in 2019 due to low representativeness of the calibration dataset, several regions showed lower uncertainty values than bathymetric changes (Figure  12f ). This result allows these changes to be assigned a high degree of confidence. For other regions, uncertainties were in the same order of magnitude as the vertical changes. However, they were meant to be significantly reduced in the future with the increase in the number and quality of the satellite images, along with enhancements of the bathymetry survey strategies and SDB algorithms.
The total vertical uncertainty (TVU) associated with SDB empirical models depends on both the uncertainty generated by the regression model and the uncertainty due to the extrapolation of the SDB model beyond the calibration domain. The multi-scene approach developed in this study allows us to significantly reduce the uncertainty related to the regression models. The uncertainty associated with out-of-scope optical conditions is sensitive to the sampling of optical variability of the study area and consequently to the spatial distribution of sounding points. Furthermore, this component of TVU is impacted by the number of satellite images used for the multi-scene approach and by the associated hydrodynamic conditions. In the context of multisource bathymetric data merging approaches, these results demonstrate the need to adapt the sampling strategy of sounding points of bathymetric surveys to the optical conditions of the water column in order to generate a calibration dataset representative of the optical variability of the study site.
Remote Sens. 2022, 14, x FOR PEER REVIEW 19 of 25 values of TVU for each bin of  were displayed for the three years (Figure 10g-i). The average values were 0. 89, 1. 21, and 1. 41 m, respectively. In 2017, all TVU averaged values were lower than 1. 1 m, with a minimum observed for bathymetric values ranging from 1 to 1. 5 m and a maximum value for the ones ranging from 4 to 4. 5 m. In 2019, the maximum value of TVU was observed for bathymetric values comprised between 2 and 2. 5 m, while the minimum value was observed for deeper waters. In 2020, the maximum TVU values were observed for deeper waters. These results may directly be associated with the specific spatial distribution of sounding points in 2019 and 2020, reducing the quality of the representativeness of the calibration dataset.
Acknowledgments: The authors would like to thank the  Direction Départementale des Territoires et de la Mer 33  and the  Syndicat Intercommunal du Bassin d'Arcachon  for kindly providing the bathymetric datasets. This research also significantly benefited from the high-quality of open-access satellite imagery provided by the  European Space Agency  (Sentinel-2 images) and the  United States Geological Survey  (Landsat-7 and Landsat-8 images).
Data Availability Statement: Landsat-8 Collection 1 Level 1 products were downloaded freely on https://earthexplorer. usgs. gov accessed on 16 March 2022. Sentinel-2 Level 1C products associated with the T30TXQ tile were downloaded freely from https://scihub. copernicus. eu/ accessed on 16 March 2022. Bathymetry data can be ordered from the Direction Départementale des Territoires et de la Mer 33 and the Syndicat Intercommunal du Bassin d'Arcachon (France).
The umbrella cloud radius was estimated between 4. 5 and 16. 5 km, a 40% uncertainty reduction compared to its prior. The umbrella cloud height (i. e. , height at the base of the cloud) is a highly uncertain parameter and we assign a uniform prior search domain between 10 and 30 km above vent. The inversion estimates a mean of 20 km a. s. l. with a 95% confidence interval between 8 and 32 km, indicating a poorly constrained parameter. This is a natural result in ill-posed inverse problems; the deposit data do not have sufficient unique information to condition this ESP. The umbrella cloud height and diffusion coefficient show only minor differences between the prior and the posterior estimates indicating two dependent parameters. The uncertainty in diffusion coefficient is reduced by ∼15%.
Practical questions of model map calculations After the first macromolecular refinement program  (Diamond, 1971) , real-space refinement in macromolecular crystallography has been substituted by reciprocal-space refinement starting end of 70ths. Being limited for a while by its use in computer graphics programs, e. g. , coot  (Emsley & Cowtan, 2004) , real-space refinement got a new interest with the recent progress of cryo EM  (Chapman et al. , 2013; Murshudov, 2016; Afonine, Poon et al. , 2018)   (Ten Eyck, 1977; Navaza, 2002). The tests above show that a direct map composed of the individual atomic images can be calculated instead avoiding Fourier transforms at all. However, the truncation distance of atomic images should be larger than the traditional values used for atomic densities (2.
Calculations of maps of inhomogeneous resolution Crystallographic experimental maps are usually calculated as the discrete Fourier transform, in all grid points at a time, and supposed to have the same local resolution in all their regions. The situation is different for the maps of electrostatic scattering potential in cryo EM  (Cardone et al. , 2013). The variation of the local resolution is traditionally represented by a color map and is hardly available from structural data bases such as EMDB  (Lawson et al. , 2016)  or EMPIAR  (Iudin et al. , 2016). To calculate the respective model inhomogeneous-resolution maps, the resolution of each atomic image should fit the local resolution and is eventually could be refined in the same way as the displacement parameter. Presenting an atomic image as a double convolution (20) with   (; ) defined by (  14 ) suggests that the shell decomposition of () = �  () �;  () ;.
Dielectric equipment and measured quantities The dielectric equipment, which is available at ILL for simultaneous n-DE experiments  [12] , is based on a Novocontrol Alpha-AT Analyser, controlled by a PC running WINDETA software  [8]  and which can be triggered by the ILL instrument software NOMAD [9] to run simultaneous with the instrument data acquisition. With dedicated dielectric equipment the Novocontrol analyser covers a wide frequency range in the µHzto above-MHz region. Our test measurements were carried out in a restricted frequency range from 0. 27 Hz to 1 MHz. The DE-analyser is connected via a dispatch box to an ILL built sample stick, which is more than 1 m high and is usable with standard ILL top loading 'Orange cryostats' having a 50 mm diameter bore. For our tests we have used the standard IN16B cryofurnace.
Certainly the sample holder, originally designed for neutron scattering only, adds a non-negligible contribution as we have shown. Another important contribution comes from imperfect sample filling, which is nearly unavoidable with the present design but which could have been further optimised for the glycerol data shown. The sample is filled in the liquid state near room temperature and the needed sample quantity is calculated from the nominal sample holder dimensions. However, mechanical precision and the necessary play between the top and bottom pieces, uncertainties in the sample thickness due to the squeezed Indium seals and Teflon or minor deformation of the thin Aluminium neutron windows will contribute mostly to underestimate the sample quantity needed. Thus some void or gaz filled volume and an imperfect sample geometry are difficult to avoid and cause more problems for highly viscous samples.
Low-level easterly winds encircling Antarctica help drive coastal currents which modify transport of circumpolar deep water to ice shelves, and the formation and distribution of sea ice. Reanalysis datasets are especially important at high southern latitudes where observations are few. Here, we investigate the representation of the mean state and short-term variability of coastal easterlies in three recent reanalyses, ERA5, MERRA-2 and JRA-55. Reanalysed winds are compared with summertime marine nearsurface wind observations from the Advanced Scatterometer (ASCAT) and surface and upper air measurements from coastal stations. Reanalysis coastal easterlies correlate highly with ASCAT (r = 0. 91, 0. 89 and 0. 85 for ERA5, MERRA-2 and JRA-55, respectively) but notable wind speed biases are found close to the coastal margins, especially near complex orography and at high wind speeds.
To characterise short-term variability, 12-hourly reanalysis and coastal station winds are composited using self-organising maps (SOMs), which cluster timesteps under similar synoptic and mesoscale influences. Reanalysis performance is sensitive to the flow configuration at stations near steep coastal slopes, where they fail to capture the magnitude of near-surface wind speed variability when synoptic forcing is weak and conditions favour katabatic forcing. ERA5 exhibits the best overall performance, has more realistic orography, and a more realistic jet structure and temperature profile. These results demonstrate the regime behaviour of Antarctica's coastal winds and indicate important features of the coastal winds which are not well characterised by reanalysis datasets.
, 2017; Stewart et al. , 2019). Antarctic coastal winds also have a large non-easterly component, especially on short timescales. Meridional winds could act as a control on ice shelf stability in some regions  (Hazel and Stewart, 2020)  and have a major impact on sea ice concentrations, for example off the Ross Ice Shelf  (Kurtz and Bromwich, 1985; Petrelli et al. , 2008; Mathiot et al. , 2012; Silvano et al. , 2020) , and in other prominent regions of sea ice production  (Wang et al. , 2021). Given the sparsity of nearby observations, reanalysis datasets are critical for characterising Antarctic coastal winds, and often provide boundary conditions for atmospheric and ocean models. As an observational benchmark they are also important for evaluation of coupled models used for future projections.
Published by Copernicus Publications on behalf of the European Geosciences Union. In part due to the paucity of assimilated observations, historical trends of coastal winds from reanalysis are uncertain and differ among datasets, though all indicate an increase in the seasonality of the easterlies over the last few decades  (Hazel and Stewart, 2019). Over Antarctica itself, the magnitude of the late 20th century trend in near-surface winds differs considerably in its pattern and magnitude between reanalyses, especially in the coastal region  (Dong et al. , 2020). On average, reanalyses exhibit higher wind speed correlation coefficients with surface station measurements in summer compared to winter and over inland regions compared to the coast  (Tetzner et al. , 2019; Dong et al. , 2020). The realism of tropospheric wind profiles is improved in the more recent ERA5 reanalysis with respect to the earlier ERA-Interim at coastal stations, but large deficiencies remain  (Vignon et al.
2. To what extent is reanalysis representation of Antarctic coastal winds sensitive to variations in the driving flow regime? 3. Which reanalysis most accurately represents Antarctic coastal winds? Our analysis has two components; firstly, to quantify reanalysis performance across the entire coastal region we compare with observations from the EUMETSAT MetOP-ASCAT sensor, which has near-coastal data available during austral summer. Next, to evaluate reanalysis representation of variability on short timescales, a self-organising map (SOM) methodology is used to composite surface and upper air wind data available from four coastal surface stations which have high-resolution radiosonde data compiled by  Vignon et al. (2019). Section 2 details the datasets used as well as the collocation and SOM approach.
Reanalysis datasets For this analysis, we evaluate ERA5, MERRA-2 and JRA-55, which belong to the latest generation of reanalyses which have been widely adopted for studies of the Antarctic circulation and for quantifying historical trends and evaluating climate models. Near-surface winds are evaluated against in situ and scatterometer-derived winds for the period January 2010 to December 2017. For the comparison with scatterometer winds, the reanalysis neutral 10 m winds are used. This is because the same assumption of a neutral profile is made when deriving ASCAT 10 m winds from surface stress (the feature directly observed by scatterometers) so, as in the ECMWF assimilation scheme  Hersbach (2010) , we consider them to be more comparable.