dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of ICESat-2 Seabed Ellipsoidal Heights The Litto3D ® dataset was used to validate the seabed photon ellipsoidal heights corrected from the refraction bias. While the ICESat-2 photons' geographic coordinates are projected onto a local tangent plane (ENU) during the refraction bias correction, the vertical references are different. Soundings measured by the SHOM bathymetric LiDAR are provided with orthometric heights. The latter were converted into ellipsoidal heights (relative to the IAG GRS 80 ellipsoid) using Circe 5. 2. 1 (IGN free software). Data visualization and extraction were conducted in GlobalMapper software, and the points were processed using Spyder 4. 15 python interpreter (an open-source MIT environment) to qualify the accuracy. The point density from Litto3D ® is considerably higher compared to the ICESat-2 dataset, it was interpolated to allow a comparison with the ICESat-2 dataset.
The closest tide gauge from the study site was also located at Dzaoudzi and the measurements were available from the SHOM website. The tide gauge recorded a water height of 1. 02 m above the chart datum on 25 May 2020, at 07 h 24 min UTC. Figure  4  illustrates the different variables involved to compute the bathymetry from ICESat-2 ellipsoidal heights. Relative bathymetry points from the MS imagery were collected at the exact same location as the measurement points of ICESat-2 and an equation linking the two datasets Relative bathymetry points from the MS imagery were collected at the exact same location as the measurement points of ICESat-2 and an equation linking the two datasets was determined. Finally, the equation was applied to the relative DDM using the ENVI band math tool to generate the final DDM.
Digital Depth Model Validation We compared different regression models. The aim was to find the model that best matches the bathymetry measurements of ICESat-2 with the remotely sensed spectral values of Pleiades-1A. The best regression was chosen based on the RMSE and based on the coefficient of determination, R 2. The final DDM was validated in comparison to the Litto3D ® reference dataset by computing the root mean square error (RMSE) and the maximum absolute error (MAE) statistical indicators.
A better solution was to generate a DAM. Bathymetric data are necessary to quantify the loss of light in the water column, to compensate for this loss, and finally to obtain a DAM  [25, 50]. Benthic albedo values were obtained with Equation (  5 )  [25] : A b = (R w -R ∞ )e 2K d z + R ∞ (5) where A b is the bottom albedo, R w is the water column reflectance, R ∞ is the reflectance in deep water, and K d is the diffuse attenuation coefficient. The TOA reflectance value for each spectral band was obtained after processing. First, the 2 m pixel size MS imagery was cropped to the area of interest and then orthorectified. Second, the image was converted into bottom of atmosphere (BOA) reflectance values using the FLAASH algorithm (see  [50]  for further details). At this point, it was possible to enhance the spatial resolution using pan-sharpening.
Validation of the Supervised Classification A validation dataset based on the MS imagery was produced with the same knowledge as for the calibration phase. A post-classification accuracy assessment using a confusion matrix provided information on overall accuracy (OA) and the kappa coefficient (κ)  [52, 53]. The ML and the SVM classifiers were set with the default parameters. A neural network classification was implemented with one and three hidden neurons in one hidden layer, in order to test the depth of the neuronal architecture.
DDM 3. 1. 1. Correction of ICESat-2 Dataset The ICESat-2 gt1l transect from the 2020 dataset corresponds to the strong beam and presents some variability in the depth range. The results obtained in this study, after removing the noise photons and correcting the signal for refraction bias, are presented in Figure  6. For comparison, the official signal detection and classification provided with the downloaded L2 ATL03 dataset were presented in Figure  2. A first DAM was created for depths in the range of 0-3. 8 m using three spectral bands (Red, Green, and Blue). A second DAM was created for depths in the range of 3. 8-15 m using two spectral bands (Green and Blue), as the extinction depth of the red band was exceeded. Due to the fact that some habitats are not present in the studied depth range, two sets of regions of interest (ROI) were created for each DAM.
Correction of ICESat-2 Dataset The ICESat-2 gt1l transect from the 2020 dataset corresponds to the strong beam and presents some variability in the depth range. The results obtained in this study, after removing the noise photons and correcting the signal for refraction bias, are presented in Figure  6. For comparison, the official signal detection and classification provided with the downloaded L2 ATL03 dataset were presented in Figure  2. In Figure  6 , photon positions (latitude and longitude) were projected onto a local geographic plane. Therefore, the horizontal axis corresponds to the along track distance. The origin point corresponds to the northernmost location of the trajectory. For the current study, the analysis indicates that (1) points from the seabed detected in this study are following the bottom topography, distinguishable on the satellite imagery; (2) while the ATL03 data classified with a high and medium confidence level (corresponding to the blue and green points in Figure  2 ) are located at or close to the surface, our bathymetry algorithm correctly identifies the bottom topography from low and buffer confidence points, a much smaller portion of the returned signal; and finally (3) the ATL03 dataset removed only a small fraction of the noise photons in comparison to the results generated with the DBSCAN.
Validation of ICESat-2 Data The comparison between ICESat-2 ellipsoidal heights and the Litto3D ® ellipsoidal heights is presented in Figure  7. ATL03 data classified with a high and medium confidence level (corresponding to the blue and green points in Figure  2 ) are located at or close to the surface, our bathymetry algorithm correctly identifies the bottom topography from low and buffer confidence points, a much smaller portion of the returned signal; and finally (3) the ATL03 dataset removed only a small fraction of the noise photons in comparison to the results generated with the DBSCAN. The DBSCAN algorithm was configured based on an empirical approach with the value ϵ = 0. 65 m. This value, valid for our study area, allowed us to retrieve a majority of the seabed signal while eliminating most of the noise photons.
Validation of ICESat-2 Data The comparison between ICESat-2 ellipsoidal heights and the Litto3D ® ellipsoidal heights is presented in Figure  7. Scaled pixel values were extracted from the relative water depth map derived from the 0. 5 m MS imagery at the same location than ICESat-2 bathymetric points using QGIS 3. 18. 3 (open-source geographic information system). Several regression models were  Scaled pixel values were extracted from the relative water depth map derived from the 0. 5 m MS imagery at the same location than ICESat-2 bathymetric points using QGIS 3. 18. 3 (open-source geographic information system). Several regression models were tested, and the corresponding equations and their performance (in terms of the RMSE) are presented in Figure  A1 (Appendix A ). The regression chosen is a 2nd degree polynomial (Equation (  6 )).
Digital Depth Model Validation The reliability of the DDM was quantified by comparing the estimated bathymetry to the Litto3D ® reference dataset. Points from the bathymetric LiDAR point clouds were extracted, along the path of ICESat-2, from the DDM. The predicted RMSE was 0. 895 m and the observed RMSE was 0. 874 m along the ICESat-2 path. The predicted R 2 coefficient was 0. 931 and the observed R 2 coefficient was 0. 97. In addition, we report a MAE of 0. 701 m.
Benthic Habitat Classification Results from the four classifiers tested using different combinations of predictors are summarized in Figure  10 , presenting both the overall accuracy and the kappa coefficient values. The reliability of the DDM was quantified by comparing the estimated bathymetry to the Litto3D ® reference dataset. Points from the bathymetric LiDAR point clouds were extracted, along the path of ICESat-2, from the DDM. The predicted RMSE was 0. 895 m and the observed RMSE was 0. 874 m along the ICESat-2 path. The predicted R 2 coefficient was 0. 931 and the observed R 2 coefficient was 0. 97. In addition, we report a MAE of 0. 701 m.
Bathymetric Errors RMSE values comparing the DDM to the Litto3D ® dataset were obtained along the ICESat-2 ground track, thus at the same place used for the calibration. Here, we discuss the impact of both the depth values and the location of the validation transect on the results. Figure  12  shows the DDM from the Litto3D ® dataset. While the DDM produced in our study reaches 33 m depth, the Litto3D ® survey of this area indicated depths reaching at least 82 m.
Bathymetric Errors RMSE values comparing the DDM to the Litto3D ® dataset were obtained along the ICESat-2 ground track, thus at the same place used for the calibration. Here, we discuss the impact of both the depth values and the location of the validation transect on the results. Figure  12  shows the DDM from the Litto3D ® dataset. While the DDM produced in our study reaches 33 m depth, the Litto3D ® survey of this area indicated depths reaching at least 82 m. The map of the differences between the Litto3D ® and the DDM calibrated with ICESat-2 dataset is shown in Figure  13. The extrapolation works very well over the range of depths used in the calibration. However, the error increases at deeper depths. Once again, in this figure, the hatched areas correspond to depths greater than the maximum calibration depth of the model.
Large error values seem to appear in deeper waters (over 15 m depth), probably due to the fact that the bathymetry was calibrated with a limited depth range (the ICESat-2 dataset does not exceed a depth of 15 m). On the other hand, the fact data sampling was restricted to the ICESat-2 track is not ideal for the calibration. Most of the soundings represent a depth lower than 5 m (this concerns nearly 85% of the total amount of points for the 2020 dataset). The dataset only has a few points representing higher depth values. This was confirmed by the study of three other transects, taken at different places over the area (a transect along the ICESat-2 swath, a transect perpendicular to the swath, and a transect extracted far from the swath). The results of those tests are visible in Figures  A2-A4 (Appendix B ), showing that depth strongly affects the quality of the bathymetry.
Consequently, errors increase for depth exceeding around 15 m. However, for shallower depth values, the results are similar, regardless of the transect location. The map of the differences between the Litto3D ® and the DDM calibrated with I Sat-2 dataset is shown in Figure  13. The extrapolation works very well over the rang depths used in the calibration. However, the error increases at deeper depths. Once ag in this figure, the hatched areas correspond to depths greater than the maximum calib tion depth of the model. The map of the differences between the Litto3D ® and the DDM calibrated with ICE-Sat-2 dataset is shown in Figure  13. The extrapolation works very well over the range of depths used in the calibration. However, the error increases at deeper depths. Once again, in this figure, the hatched areas correspond to depths greater than the maximum calibration depth of the model.
Conclusions This study aimed to evaluate the quality of VHR DDM and DAM generated from satellite data. A DDM calibrated with data from the satellite ICESat-2 presented a RMSE of 0. 89 m along ICESat-2 ground track, i. e. , around 6% of the maximum depth retrieved by ICESat-2. Bathymetric results were generally satisfying down to a depth of around 15 m, which is close to the maximum depth of the calibration data used. Marine habitat classification results were very heterogeneous, depending on the number of predictors used, the type of predictors, and the algorithm used. However, some combinations of parameters provided satisfactory results. The classification with the ML classification using Blue and Green spectral bands with the three geomorphic predictors provided an overall accuracy of 96.
In the prospect of future studies, it would be relevant to consider several ICESat-2 ground tracks from the area of interest and even to add the points from other times that ICESat-2 surveyed the area. This would provide a better variability of depths and a better spatial distribution of the data for the calibration process. Moreover, this increase in the number of points opens prospects for the use of deep learning methods to generate DDMs. Developing an algorithm dedicated to the processing of seafloor data generated from ICESat-2 datasets would be important. The correction for the refraction effect has proven necessary and reliable, but could be further enhanced. The water column properties are changing with depth and the refraction correction should also adapt according to the water column properties.
Data Availability Statement: The Pleiades-1A imagery provided by CNES-AIRBUS through the DINAMIS platform is not publicly available. The ICESat-2 L2 ATL03 geolocated photons analyzed during this study are publicly available at https://search. earthdata. nasa. gov/search, last accessed: 28 December 2021. The Litto3D ® dataset used for validation is publicly available from https://diffusion. shom. fr/, last accessed: 28 December 2021. The diffuse attenuation coefficient of 490 nm measured at 4 km resolution by MODIS is available publicly from https://oceancolor. gsfc. nasa. gov/l3/, last accessed: 28 December 2021. Altimetric information over Mayotte island are available from the SHOM website, https://data. shom. fr/, last accessed: 25 October 2021.
Acknowledgments: The authors gratefully thank the NASA for distributing the ICESat-2 data and for the support of the ICESat-2 Applied Users group, and the SHOM for providing us with the airborne LiDAR survey data. The authors would like to thank the DINAMIS platform for access to the Pleiades images and, the CNES "Pléiades © CNES 2020, Distribution Airbus DS". Pleiades-1 imagery is a courtesy from CNES-AIRBUS. Thank you to Thomas Claverie (UMR Marbec) and Aline Aubry (UMR Espace-Dev) from CUFR Mayotte for providing expert knowledge used for habitat mapping. The authors would also like to thank the reviewers for improving the manuscript with their comments and corrections.
To further demonstrate the specificity of the EPICOVID signature for COVID-19, the authors determined if the identified epigenomic profile was overrepresented in DNA methylation datasets for other respiratory diseases (especially those that involve hyperactivation of the inflammatory cascade) available from the public genomics data repository Gene Expression Omnibus (GEO). They observed that the EPICOVID signature was neither enriched in other respiratory diseases such as tuberculosis, chronic obstructive pulmonary disease, asthma, and other respiratory allergies nor in systemic inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Sj€ ogren's syndrome, and inflammatory bowel disease. Concerning viral infections, the EPICOVID signature was not enriched in hepatitis C infection and likewise among HIV-infected individuals.
Sample collection During the sampling period (winter 2018 to spring 2019 and winter 2019 to spring 2020), each farm was visited once per season (autumn, winter and spring), for a total of three visits per farm (Fig. S1 ). Farm characteristics, sampling dates and Listeria spp. isolated are indicated in Tables  S1  and S3. Farm 'Sheep B' was sampled 11 times during seven consecutive seasons from autumn 2018 to spring 2020 for Listeria genomic diversity analysis due to the discovery of the new species L. valentina in the 27-Feb-2019 sampling  (Quereda et al. , 2020). For consistency with data from other farms, only data from three consecutive seasons (autumn 07-Nov-2018, winter 27-Feb-2019 and spring 10-Apr-2019) in this farm were considered for prevalence and statistics calculations. On each farm visit, 50 samples [32 samples of faeces from individual animals, three samples of feed, three samples of bedding, three MFS, and nine surface swabs (three from milking station floor, three from water troughs, three from food troughs)] were collected during three consecutive seasons (autumn, winter and spring) amounting to 150 samples per farm.
, 2016). Minimum spanning trees and single linkage dendrograms were built from cgMLST profiles using Bionumerics 7. 6 software (Applied Maths, Belgium) and annotated with iTol v. 4. 2  (Letunic and Bork, 2021). Assemblies were also screened for antimicrobial resistance genes and the presence of plasmids using ABRicate v. 1. 0. 1 (https:// github. com/tseemann/abricate) and MOB-suite v. 2. 0. 1  (Robertson and Nash, 2018)  respectively.
Statistical analysis For DIM analysis cows were grouped into different categories considering the dairy cattle lactation curve and classified into 'early lactation' (0-120 days), 'mid-lactation' (121-240 days), 'late lactation' (241-360) and 'end of lactation' (>361 DIM). DIM were not analysed in sheep farms since all the ewes in the same farm were synchronized using intravaginal sponges and delivered approximately the same day. Shannon diversity indices and Hutcheson T-test  (Hutcheson, 1970)  were calculated using the web https://www. dataanalytics. org. uk/ comparing-diversity/. The rest of the statistical analyses were conducted with IBM SPSS Statistics version 25. The significance level for all statistical tests was P < 0. 05. Chi-square tests were performed to determine the effect of season, the number of lactation and DIM on the number of L.
Acknowledgements We are grateful for the willingness of the farmers to participate in this study. This work was supported by  Generalitat Valenciana  (Project references  GV/2018/A/183 ,  AICO/2021/278 ), the  Spanish Ministry of Science and Innovation  (Project reference  PID2019-110764RA-I00/AEI/10. 13 039/501100011033 ) and  Universidad CEU Cardenal Herrera Programa INDI 20/40  (J. J. Q. ), the  Spanish Ministry of Science and Innovation  (Project reference  PID2020-119462RA-I00/AEI/10. 13039/ 501100011033 ) (A. G. -M. ),  Institut Pasteur ,  Inserm ,  Santé Publique France , and  Fondation Le Roch-Les Mousquetaires  (M. L. ). J. J. Quereda  is supported by a 'Ram on y Cajal' contract of the  Spanish Ministry of Science, Innovation, and Universities  ( RYC-2018-024985-I ). C. Palacios-Gorba  is supported by a Predoctoral contract from the  Universidad Cardenal Herrera-CEU.
In fact, under simplifying assumptions (namely constant density and spherical shape), the radar reflectivity in the Rayleigh regime is proportional to the sixth moment of the particle size distribution of snow particles. Because the original data set is 3-D (4-D for the WRF data set), we extract variograms (along any direction) that are also 3-D (4-D). Therefore, the mean and percentiles displayed (and the calculation of the decorrelation length) are computed based on the averaged signal along one (or two) dimension(s) (unless specified, we prefer to temporally average the signal). The variograms are plotted in Fig. S1  for EV1 and in Fig. S2  for EV2 in the Supplement. The obtained values for U , W , L t,Z H , L x,U , L x,Z , L z,Z , L x,Z DR , L z,Z DR and L z,w are summarized in Table  1.
These conditions, verified at the scale of the entire event, are sufficient conditions to reliably use PIVSs. We apply and illustrate PIVSs on two frontal snowfall cases, i. e. , one at Dumont d'Urville, Adélie Land, Antarctica, and one in the Taebaek mountains, South Korea. The robustness of the method is assessed by successfully comparing the output with two complementary data sets, namely snowflake observations from a MASC at the ground and products from a hydrometeor classification based on polarimetric radar data. This comparison highlights the local limitations of the method, especially in strong updrafts or fall streaks, i. e. , when the three conditions, albeit respected over the bulk of the event, do not hold locally. We derive characteristic metrics of the microphysical processes, in particular the altitude of layers in which each process is dominant, and the vertical extension thereof.
Acknowledgements. We are grateful to  Nikola Besic , for running the demixing algorithm over the two case studies. We are greatly appreciative of the participants of the  World Weather Research Programme Research Development Project and Forecast Demonstration Project and the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games  ( ICE-POP 2018 ) hosted by the  Korea Meteorological Administration. We also acknowledge the support of the  French National Research Agency (ANR)  and the  French Polar Institute (IPEV)  for the  APRES3  project (http://apres3. osug. fr,last access:  31 May 2021 ). We finally thank the two anonymous reviewers, who helped improve an earlier version of the paper. Financial support. Josué Gehring  received financial support from the  Swiss National Science Foundation  (grant no.
Data availability. The APRES3 campaign data are freely distributed on the PANGAEA data repository (https://doi. org/10. 1594/PANGAEA. 883562; Berne et al. , 2017). Further information on the ICE-POP data set and its distribution is given in Gehring et al. (2021Gehring et al. ( , https://doi. org/10. 5194/essd-13-417-2021)). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-14-4543-2021-supplement.
Before the dry runs and deployment, a register of the timing and track of overpasses from polar orbiting satellites (e. g. Terra, Aqua) that were in the vicinity of Ascension Island was made, taking care to exclude areas where the satellite was influenced by sun glint. This allowed scientists to decide on the relative priorities of flights. In the case that forecasted AOD and cloud-cover conditions were expected to be consistent for several days, priority was given to those days with local satellite overpasses, so the aircraft measurements could provide data for satellite validation and allowing the satellite measurements to put the aircraft measurements into a wider geographical context. Further information from ground-based instrumentation was utilized for nowcasting. This included information on the aerosol optical depth from two Cimel sun photometers based at the AERONET and AMF sites, a handheld Microtops sun photometer based at the operations centre in Georgetown, and a Leosphere depolarizing lidar, operated by KNMI at the airfield.
Scientific outreach showcasing the aircraft and our science was via guided tours of the aircraft and talks on the scientific research being performed to the general population of Ascension Island (approximately one-third of the island's entire civilian population was present). Depending on the aerosol and cloud conditions determined from forecast products, satellite retrievals, and ground-based observation data, the FAAM aircraft flights were designed to characterize the main aerosol and cloud state in clean and polluted conditions and to study properties and processes rather than to build a spatially and temporally representative mapping of the region (see  Redemann et al. , 2020 , for OR-ACLES flight plans for building such a representative mapping). This strategy for flight planning ensured that suitable data sets were collected to facilitate meeting the key objectives described in Sect.