dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Analysis The reconstruction of the X-ray tomography data acquired before and after the mechanical loading was performed using the X-Act software (RX-Solutions, Chavanod, France). The resultant 3D images were analysed using the open source DVC software TomoWarp-II (  [32] ) to provide the strain fields of the tested samples. While the full strain tensor was determined, only the volumetric and shear strain fields are presented in the following. The X-ray tomography images were also used to characterise the intrinsic inhomogeneity of the samples that could affect the fluid flow. This included computation of the 3D porosity field using an in-house code developed at Laboratoire 3SR. The algorithm starts by separating the solid and fluid phases with a pre-defined greyscale threshold, followed by the counting of the number of voxels corresponding to each phase over a set of cubes centred on a regularly-spaced grid.
Fitting the evolution of the greyscale data for all the voxels over the full 4D data-set provides five 3D volumes, one for each of the fitting parameters, plus the error between the real data and the fitted curve. A vertical central slice and a horizontal slice of the fitting results for VO01ME are represented in Figure  5. The values of A varied from 0. 23 to 0. 41, whereas B varied from 0. 40 to 1. This is coherent with the previous statement about the higher variation across the voxel greyscale values for voxels saturated with light water. The first high-speed tomography was acquired after the first arrival of the light water. Therefore, there is an absence of data in the bottom part, the black area. The early arrival of light water in the sample could also be the reason for the higher values of the A parameter in the bottom half of the sample.
The addition of CTI to plasma samples prevented the conversion of FXII into FXIIa (Figure  3F ). To demonstrate a direct effect of FXIIa on fibrin structure, we clotted hirudin-preincubated COVID-19 plasma with batroxobin in the presence of FXIIa and/or CTI and measured the maximum turbidity. As shown in figure  3G , FXIIa increased fibrin density and this effect was diminished by CTI thus ensuring direct, thrombin independent, role of proteolytically active FXII in the modulation of fibrin architecture. The elevated levels of factor XIIIa were not detected in plasma of critically ill COVID-19 patients (data not shown). Together, these results suggest that high levels of fibrinogen along with markedly increased rates of FXII activation in COVID-19 plasma create a specific procoagulatory microenvironment, which promotes the formation of particularly dense fibrin networks.
Acknowledgement We thank  E. Bieniek  for her excellent technical assistance. We also thanks  A. Seipp  ( Institute of Anatomy and Cell Biology, Justus Liebig University, Giessen, Germany ) for performing the scanning electron microscopy pictures. This study was funded by the  German Research Foundation  (DFG: SFB/ TR84  Project  A2  to M. W. and W. M. K. ), the  Else Kröner-Fresenius-Foundation  ( 2014_A179  to M. W. and P. M. ), the  Oskar Helene Heim Foundation  (to P. M. ), the  German Center for Lung Research  ( 82 DZL 005A1  to M. W. ) and the  University Medical Center Giessen and Marburg (UKGM  to M. W. and P. M. ).
Availability of a reliable cloud mask for optical satellite imagery is a prerequisite, when generating high-quality high-level geoinformation products. Creation of a reference (ground truth) cloud mask for moderate spatial resolution sensors, such as Operational Land Imager (OLI) aboard Landsat 8 and Multispectral Instrument (MSI) aboard Sentinel-2A/B satellites, is a challenging and time-consuming task. Existing reference datasets were mainly produced through photointerpretation of satellite images by an analyst, which can introduce subjectivity in detecting clouds. Therefore, other methods for generating cloud reference data shall be explored and evaluated that can complement existing datasets. In this paper, we document generation and provide the description of a new reference cloud dataset, named GSFC-Cloud, which is based on the extensive use of ground-based images of the sky.
The dataset is collected over the same area, covers various cloud conditions, and is available for six Landsat 8 and twenty-eight Sentinel-2 scenes spanning the period of September 2017 to November 2018. The dataset is available in the vector format, so cloud masks at various spatial resolutions can be validated. We also describe a system to automate the process of ground-based data collection using low-cost off-the-shelf parts with the long-term objective to replicate this set-up in multiple locations around the world. We use the proposed dataset to validate and improve the Land Surface Reflectance Code (LaSRC) for cloud detection in Sentinel-2 imagery. We show that adding a parallax feature to estimate a subpixel shift between red and green bands with a phase correlation method can reduce overdetection of clouds and improve performance of LaSRC.
, 2012; Gascon et al. , 2017; Wulder et al. , 2019; Zhu et al. , 2019). Combination of data from OLI and MSI sensors improves frequency of observations reaching the temporal revisit required for many land applications  (Li and Roy, 2017) , and multiple studies have shown efficiency of their combined usage  (Bolton et al. , 2020; Griffiths et al. , 2019; Pahlevan et al. , 2019; Roy et al. , 2019; Skakun et al. , 2019a ). NASA's Harmonized Landsat Sentinel-2 (HLS) product  (Claverie et al. , 2018)  is an example of harmonizing data from several complementary sensors into analysis ready dataset, as if data were acquired by a single sensor. In order to effectively combine data from OLI and MSI sensors it is crucial to perform an accurate cloud detection and screening. Cloud detection in optical satellite imagery has been an area of active research for the past decades  (Foga et al.
, 2017; Frantz et al. , 2018; Hagolle et al. , 2010; Hollingsworth et al. , 1996; Irish et al. , 2006; Qiu et al. , 2019; Scaramuzza et al. , 2012; Zhu et al. , 2015; Zhu and Woodcock, 2012). Validation of the produced cloud masks requires the development of reference validation datasets. Currently, several widely used validation datasets exist that aim at validating moderate spatial resolution sensors, such as OLI and MSI. This includes Landsat 8 Cloud Cover Assessment Validation Data (L8Biome)  (Foga et al. , 2017) , Spatial Procedures for Automated Removal of Cloud and Shadow (SPARCS) Validation Data  (Hughes and Hayes, 2014) , Hollstein  (Hollstein et al. , 2016) , and CESBIO  (Baetens et al. , 2019). The L8Biome dataset was generated for 96 unique globally distributed Landsat 8 scenes, which were selected based on biomes distribution.
There were eight biomes selected: barren, forest, grass/crops, shrubland, snow/ice, urban, water and wetland. The reference scenes were also selected in relationship to the cloud cover in order to have a representative ensemble of cloud conditions. Pixel-level classification was performed by a single analyst who manually labelled Landsat 8 scenes into the following classes: clear, cloud shadow, thin cloud and thick cloud. The SPARCS validation dataset contains 80 subsets of Landsat 8 scenes (with the size of 1,000 × 1,000 pixels) manually classified into the following classes: cloud shadow, water, snow/ice, land, cloud and flooded. The Hollstein dataset consists of manually classified polygons for globally distributed Sentinel-2A scenes with the objective to train machine learning algorithms (the reference dataset does not extend to the full Sentinel-2 scenes, though).
They provide a ready-to-use decision tree for surface reflectance with a set of rules for detecting clouds in Sentinel-2 imagery. The nomenclature of classes consists of cloud, cirrus, snow/ice, shadow, water, and clear sky. The CESBIO dataset follows the same approach, namely manually photo-interpreted polygons are used for training a machine learning algorithm, in this case using an active learning method based on Random Forest. The algorithm, trained with manually selected polygons, is used to classify the whole scene. The dataset includes 38 Sentinel-2 scenes, which are classified into the following classes: low/ high clouds (based on the cirrus band at 1. 38 µm), cloud shadow, land, water and snow. They provide also a per-pixel confidence map-a quality layer that shows reliability of the resulting reference map based on the classifier outputs.
These cloud validation datasets have the following common characteristics: global distribution of scenes, mostly single-date scenes (except for Hollstein and CESBIO, which provide up to 4 scenes over the same region for selected locations), similar class nomenclature and manual-based interpretation of clouds. The latter can be a source of subjectivity, especially in the case of cirrus and thin/transparent clouds  (Scaramuzza et al. , 2012). Also, only single-date reference scenes are available over the same area, which makes it difficult to analyze cloud detection algorithms for systematic errors and under various seasonal conditions (over the same region). While these validation datasets provide valuable labelled data to train and validate cloud detection algorithms, they rely purely on the visual, hence subjective, interpretation of the satellite imagery.
Alternative approaches therefore shall be explored to provide a more objective means in generating cloud reference data and to be able to do it in a consistent manner across different sensors. Among these approaches is the use of ground-based images of the sky, which can provide an independent and objective way of collecting data on cloud cover and can substantially assist the expert in labelling satellite imagery. Furthermore, the process of classifying ground-based photos and co-registering with satellite data can be automatized  (Ghonima et al. , 2012; Shields et al. , 1993; Yamashita and Yoshimura, 2012) , and cloud bottom height can be estimated using stereo images  (Beekmans et al. 2016; Hu et al. , 2009; Öktem et al. , 2014). One of the main limitations of using sky images is the reduced field view, which, with a single camera can be of around 30 km in the diameter and does not allow full coverage of Landsat 8 or Sentinel-2 scenes.
The study, concluded in 2002, showed a reasonable agreement between ground-and satellite-based cloud fraction, with the MODIS one overestimating cloud fraction. Murdock et al. (2013)  trained a machine learning algorithm, in which an input was a ground-based imagery of the sky and a target output was a satellitederived cloud mask. Applying this algorithm to sky images can produce a ground-based cloud map (as if it would be seen from the space). However, no previous studies attempted to use sky imagery to generate reference cloud masks aiming at moderate spatial resolution (10-30 m) satellites, such as Landsat 8 and Sentinel-2. The present study aims to fill this gap and has the following objectives: (i) to document the process of generating a cloud reference dataset for Sentinel-2 and Landsat 8 images over the NASA Goddard Space Flight Center (GSFC) site and make it publicly available; (ii) to develop a system architecture for automatic collection and processing of sky images, so it can be installed on multiple sites; (iii) to apply the developed dataset to validate and improve the cloud mask generated within the Land Surface Reflectance Code (LaSRC).
GSFC-Cloud reference data description Cloud reference data were collected over the NASA GSFC (Fig. 1 ). The land cover map was produced using a time series of cloud-free Sentinel-2 images and previously developed neural network models for classification  (Skakun et al. , 2015; Zhang et al. , 2020). The area is quite heterogenous with major land cover classes being forest (~52%) and impervious surfaces (31%) with patches of natural vegetation and cultivated areas (totaling 17%). NASA GSFC also has an AERONET station  (Holben et al. , 1998) , which provides aerosol optical thickness (AOT) and water vapor. Ground-based images of the sky were collected from 2017 through 2019 using a smartphone camera with a fisheye lens. These data were collected manually during the Landsat 8 and Sentinel-2 overpasses (description of the system for automatic collection is given in Section 2.
2). It should be noted that it was essential to collect data coincidentally with satellite overpasses; otherwise, there would be mismatches between ground images and satellite data. The same stands for the sameday acquisitions from Landsat 8 and Sentinel-2: the difference in time can be 10-15 mins and therefore would make cross-comparison of masks from those satellites impractical (Fig. 2 ). Reference data were collected for 6 Landsat 8 and 28 Sentinel-2 scenes (the full list of scenes along with AERONET measurements of AOT is provided in Appendix 1). The objective was to capture various cloud conditions (Fig. 3 ) as well as seasonal cycles. Ground-based images were used to assist with manual labelling of clouds in Landsat 8 and Sentinel-2 imagery. In order to facilitate the labeling process, some of the of ground-based images were manually geo-referenced to satellite imagery by manually selecting control points (CPs) using the shape of clouds and referencing a ground-based image to the satellite one (Fig.
The images are then compressed and transmitted to a remote server for further processing. Data collection scheduling, preprocessing, and transmission is managed by a single-board computer (Rasbperry-Pi). Because the module is intended to be installed under different conditions, several options are available for power (power over ethernet, 5 V DC, or solar) and connectivity (ethernet, Wi-Fi, radio, or cellular network). To provide cloud base height estimation capabilities, two modules should be installed per site for stereo imaging  (Beekmans et al. , 2016). There are currently two prototype modules installed at NASA GSFC with the baseline 500 m. According to  Beekmans et al. 2016) , a baseline of 500 m can provide a compromise between the accuracy of height estimation and similarity and mutual visibility of clouds in both images.
The modules are synchronized and provide real-time cloud cover data every 5 min during the day and every 1 min during satellite overpasses. Fig. 6  shows examples of coincident Sentinel-2 and SkyCam observations for various cloud conditions. During the period October 2019 to June 2020, we have collected 5594 ground-based images with 49 matches with Sentinel-2 and 12 matches with Landsat 8.
9. Comparison of LaSRCv3. 5. 5 vs LaSRCv3. 5. 6 (using r vis and parallax) for cloud detection in Sentinel-2 imagery by land cover type. Only scenes with cloud cover between 0% and 100% were used. metrics were estimated for three sets of data: (1) fully clear, when reference data has 0% of cloud cover; (2) fully cloudy, when reference data has 100% of cloud cover; (3) when cloud cover was between 0 and 100%.
Discussion and conclusion Validation of cloud mask algorithms for optical satellite imagery is a challenging and time-consuming task, because of the need to generate a reference (validation) dataset. Most existing approaches are based on photointerpretation of satellite imagery by an analyst(s) with little or no ancillary information, and thus they are lacking objectivity. Existing cloud validation datasets (L8Biome, SPARCS, Hollstein, CESBIO) for moderate spatial resolution sensors, such as Landsat 8/OLI and Sentinel-2/MSI, were generated in such a way. The advantage of this approach is that these datasets are distributed globally to represent various land conditions. On the other hand, a particular location is covered by a Landsat 8 or Sentinel-2 scene just once or few times, limiting the validity of the relevant dataset for the considered season and time of the year (up to 4 times for CESBIO/Hollstein daatsets).
Therefore, other approaches enabling creation of a network of sites for generating cloud reference data should be explored. In this study, we provided a proof of concept of using ground-based images of the sky to generate cloud reference data. Those ground-based images were manually collected using fisheye lens with a smartphone camera and used to create a cloud reference dataset, called GSFC-Cloud. It should be noted though ground-based images can greatly assist the analyst when deriving reference cloud mask, it cannot completely remove subjectivity in detecting clouds, particularly in boundaries of the clouds. This is especially the case of Sentinel-2 images because of parallax-induced subpixel shifts and various spatial resolutions. The developed dataset, provided in the form of vector polygons, covers various meteorological conditions over the same region.
The dataset includes both extreme cases of cloud cover (fully cloudy and fully clear), as well as scenes with partial cloud cover. The dataset contains 6 Landsat 8 and 28 Sentinel-2 scenes covering the period of September 2017 to November 2018 (the full list of scenes is provided in Appendix A). The limitation of the ground-based sky images is the field of view, which is ~30 km in diameter and cannot cover the whole extent of a Landsat 8 (~185 km) or Sentinel-2 (290 km) scene. Therefore, to maximize the use of ground-based images, more sites shall be developed, where the process of ground-based data collection and processing would be automized. We described such a system, which was developed using inexpensive off-the-shelf parts, and is currently installed at NASA GSFC. We plan to implement algorithms  (Beekmans et al.
2016; Ghonima et al. , 2012; Hu et al. , 2009; Öktem et al. , 2014; Shields et al. , 1993; Yamashita and Yoshimura, 2012)  for automatic processing of ground-based images (so the process of co-locating ground-based and satellite images, their classification and labeling will be done automatically) and to install this system at multiple sites around the world. We also showed the utility of the developed dataset to validate a cloud detection algorithm LaSRC for Sentinel-2 data  (Skakun et al. , 2019b; Vermote et al. , 2016). Several improvements were made with major one being incorporation of the parallax feature  (Skakun et al. , 2017). Compared to Landsat 8, Sentinel-2 does not have a thermal band and hence clouds can be confused with bright targets. However, Sentinel-2/MSI data are acquired at slightly different angles for different spectral bands  (Gascon et al.
Sample collection and study sites Fecal samples and rectal contents were collected from live or dead animals from five different populations. For free-ranging populations, six European mink were sampled in the Navarra region (Spain), twelve American mink were sampled in the Nive watershed (Southwest, Pyrénées-Atlantiques, France) and sixteen American mink from Brittany (Tomé island and close mainland; Figure  1 ). In order to investigate habitat variation from each free-ranging sampled populations, a map was created using QGIS 3. 16. 6-Hanover with GPS coordinates for each sample. Layers documenting landscape use, were simplified to agricultural, built, natural and water surfaces from datasets originating from IDENA (Spatial Data Infrastructure of Navarre) and data. gouv. fr from Open Street Map (Alexandre Lexman).
Bioinformatics The quality controls of the paired-end sequence reads were performed through the software FastQC  (Andrews, 2010). Sequence reads demultiplexing, denoising and amplicon sequence variants (ASVs) picking steps were done with the QIIME2 tool  (Bolyen et   Analysis of a negative control showed the presence of bacterial sequences that probably derived from contamination during laboratory sample handling. However, the diversity of this control was dissimilar from those of all mink samples (Bray-Curtis dissimilarity > 70. 8%). For subsequent analysis of sequences associated with mink samples, negative control sequences were trimmed from the whole dataset. The cumulative sum scaling (CSS) method was used to normalize the data using the metagenomeSeq package  (Paulson et al. , 2013)  in R (R version 3.
Statistical analysis for comparison of β-diversity of gut bacteria between groups and differential abundance Unweighted and weighted UniFrac distance matrices between samples  (Lozupone et al. , 2010)  were used to investigate differences in gut microbial communities between population, host sex, host environment, and host species with all bacterial taxa. A PERMANOVA model Adonis from the vegan package was constructed with 9,999 permutations with reported F, R2, and p-values to determine whether there were significant differences between the mink populations, host species, and sex as main effects  (Oksanen et al. , 2019)  after testing the homogeneity of groups variances using the betadisper function from the same package. Pairwise PERMANOVAs were then conducted to investigate variations between groups with 9,999 permutations.
A principal component analysis (PCoA) using Unifrac distance measures between samples was conducted to visualize the potential similarities between groups. Finally, a UPGMA dendrogram was constructed using the qiime diversity beta-rarefaction function in QIIME 2 by mink populations with weighted Unifrac distances with 20 iterations with mean ceiling at 10,000 sequences rarefaction. The differential abundance analysis was conducted on the raw ASVs count, using the DESeq2 package  (Love et al. , 2014) , with a negative binomial Wald test to test significance between each group. Only phylotypes with a significance level (α) below 0. 001 after false discovery rate (FDR) corrections were considered using the Benjamin-Hochberg method. All phylotypes were tested in contrast, meaning that differential abundance was done pairwise between each mink population.
Microsatellite markers genotyping, and analysis A total of 94 hair and tissue samples were extracted from a larger dataset of samples from European and American mink over a ten-year period between 2000 and 2019 (unpublished data). All samples derived from the same population that the fecal samples originated from, but many from different individuals. Eighteen free-ranging American mink were sampled in Brittany (Côtes d'Armor), thirty American mink were sampled in the Pyrenees Atlantiques (Southwest region of France), as well as thirty individuals from the same fur farm in the southwest of France. Finally, ten captive European mink were sampled in captive settings at the FIEB breeding center and six free-ranging European mink were sampled in Navarra (Spain). Genomic DNA was isolated using the DNeasy Blood and Tissue Kit (QIAGEN) from tissue and hair samples.
Comparison of α-diversity in gut bacterial Samples of a mock community containing known concentrations of genomic DNA from 20 bacterial strains were sequenced. Nineteen of the twenty different strains originally included in the sample were detected. Therefore, our protocol allowed bacterial DNA detection and identification to the genus level as long as its concentration in the DNA extract was at least 2. 8 pg/μl, and provided that the sequence was included in the reference database. Following the raw data processing, we obtained 1,947,964 sequences belonging to 3,036 distinct bacterial phylotypes (ASVs) after removal of negative control sequences, for 52 samples (other samples were removed during CSS normalization due to low sequencing depth). Gut bacterial phylotypes richness did not significantly vary according to host species or host sex, when considering three richness measures (Table  2 ).
al. , 2019; v. 2020. 8), using the DADA2 pipeline (Callahan, McMurdie, & Holmes, 2017; Callahan et al. , 2016). ASVs -or also referred to as bacterial phylotypes-were then screened to the 97% 16S rRNA gene full-length reference sequences from the Silva RDP v. 138. 1 database (Pruesse et al. , 2007) for taxonomical association using the VSEARCH classifier implemented in QIIME2 (Bokulich et al. , 2018). Sequence alignment and phylogeny building were also conducted in QIIME2.