dataesr/scientific-paragraphs-categorization huggingface dataset:
In France, the development of InSAR services adapted to Sentinel-1 data for the Earth sciences research community is among the objectives of ForM@Ter, a data and services centre for the study of the Solid Earth. ForM@Ter is one component of the national research infrastructure Data Terra (https://www. data-terra. org), which aims to promote an integrated approach for the study of the Earth System through multi-type observation data and services, in particular by enforcing data interoperability. ForM@Ter helps developping high-performance tools and services to access, process and analyze satellite and in-situ data on the Solid Earth, as well as their value-added products, in a unified and simplified way. In particular, ForM@Ter sets up a meta-catalog for unified search and access to data as well as a web portal to make data, products and services available.
Two services dedicated to the measurement of ground motion by InSAR using Sentinel-1 data are being developed within ForM@Ter : the GDM-SAR and FLATSIM services. GDM-SAR (Ground Deformation Monitoring-Synthetic Aperture Radar) is an on-demand processing service, which will be accessible to the national and European scientific community as part of the Thematic Core Service Satellite Data of the European solid earth research infrastructure, EPOS (https://www. epos-eu. org). In this paper, we focus on the FLATSIM service. FLATSIM stands for ForM@Ter LArge-scale multi-TemporalSentinel-1 InterferoMetry. This service, dedicated to massive, automatic, multi-temporal InSAR processing over large continental areas (250,000 km 2 minimum per study site), with an emphasis on the recovery of large-scale deformation processes.
The FLATSIM service delivers interferograms, line of sight average ground velocity maps, time series of ground displacements, and numerous auxiliary information. The processed areas are selected through a series of call for scientific proposals presently open to the French scientific community. In the following, we first present the main processing steps implemented in FLATSIM, based on the MTInSAR NSBAS software  [39, 40]. The FLATSIM parallel implementation on the CNES High Performance Computing infrastructure is then described together with the production and delivery modules. Next, we present the main products, including the auxiliary data that are provided for assessment of the products' quality. Finally, two FLATSIM case studies among the first selected ones are presented to discuss the qualification of the results.
Main steps The FLATSIM service runs the New Small BASeline (NSBAS) processing chain. The NSBAS chain is versatile, allowing for the user to choose different processing options and parameters, for example for coregistration, selection of image pairs, multilooking, atmospheric or Digital Elevation Model (DEM) corrections, filtering, unwrapping, or to decide the order in which steps are performed. Here, we only describe the specific steps, workflow and parameters implemented in FLATSIM. They are included in the 2. 02 version of the NSBAS processing chain. NSBAS partly relies on a set of modified scripts and libraries originating from the legacy InSAR processing software Repeat Orbit Interferometry PACkage (ROI_PAC)  [41] , expanded to allow for the processing of a stack of SAR images, using a set of parallelized steps, as described in  [19, 39, 42].
Burst selection The Sentinel-1 standard acquisition mode (TOPS) consists in sweeping the antenna beam from the aft to the fore, thereby illuminating a portion of the ground called a "burst". Three sub-swaths are successively covered to broaden the swath. The Sentinel 1-A and 1-B satellites are tasked to maintain synchronicity of burst acquisition, which is essential to ensure an optimal interferometric coherence. However, Sentinel-1 acquisitions do not necessarily cover the same burst coverage. In other words, the acquisition start/stop times (w. r. t. the ascending node crossing time, or ANX time) may vary from one acquisition to another. As input, FLATSIM is given the north and south latitude limits of the area of interest. Then, starting from these a priori limits, an optimization step is required to select the best set of acquisitions that simultaneously maximizes the latitudinal extent of the data set and the number of acquisitions, without including gaps in the processing.
FLATSIM architecture The goal of the FLATSIM project was to integrate the NSBAS chain in the CNES High Performance Computing (HPC) infrastructure to benefit from its computing power and from the fact that CNES mirrors locally the Sentinel-1 data from ESA. FLATSIM is thus able to process up to 6 years of Sentinel-1 data over large geographical areas (up to 7 million km² per year in total). FLATSIM is designed to automatically access and process Sentinel-1 radar images and to distribute interferometric products which cover worldwide areas of interest. As shown in Figure  7 , FLATSIM is composed of two main components: a Production Module and a Delivery Module. The strategy behind the integration of NSBAS in the FLATSIM project was to make NSBAS processing massively parallel. Even if most of its steps can be run in a parallel way, NSBAS was conceived as a global processing chain running on a single-node, monolithic workspace, in which all the data generated during the NSBAS workflow are stored, from acquisition of the Sentinel-1 SLC (Single Look Complex), to production of the time-series and formatting of distributed data.
Architecture of the CNES HPC infrastructure The CNES HPC infrastructure is a shared facility at the disposal of CNES projects and partners. It is composed of more than 480 batch servers for a total of 12288 cores (725 Tflops) and a 8. 2 PB General Parallel File System (GPFS) with 300TB burst buffer offering a bandwidth up to 100GB/s. This large computing capacity should enable FLATSIM to meet its production objectives. The integration of NSBAS should also be optimized to the CNES HPC, to avoid FLATSIM deteriorating the level of service for all the other users of this computing center. FLATSIM also relies on various services offered by CNES computing division, such as different types of data storage, database management system (DBMS) hosting services and web-site hosting mutualised services.
Architecture of the FLATSIM Production Module The technical environment of the production module includes the HPC and the GPFS, a shared postgresql DBMS, and the mutualized datalake (composed of several data storage spaces). The architecture of the Production Module (Figure  8 ) is based on 2 sub-components: • The FLATSIM application server which includes catalog and orchestrator capabilities, • The FLATSIM unitary treatments that integrate NSBAS as a library and run as unitary jobs on nodes of the HPC center. The FLATSIM application is in charge of referencing data, orchestrating processing steps and submitting them as unitary jobs to the scheduler. The data cataloged by the application server are stored on the GPFS, while their metadata and status of processing steps' jobs are registered in the postgresql database.
At any time during the production, the operators can interrogate the FLATSIM application server to request information on the production status, and act on products or jobs if necessary. During the first step of the production, FLATSIM gets SENTINEL-1 Level-1C products via direct privileged access on the PEPS platform (developed by CNES in the context of collaborative ground segments of Copernicus program) and stores them in the application server catalog. After the packaging step, the FLATSIM output products are transferred on the mutualized datalake, which is accessible by both the production module and the delivery module. At the same time, these products metadata are sent to the delivery module to be registered in its database. By default, the scheduling system in the CNES HPC allows a project to submit jobs on up to 800 cluster cores.
Architecture of the FLATSIM Delivery Module The role of the Delivery Module is to diffuse the FLATSIM products to the scientists and public community. The products are first made available to scientific experts for validation before being distributed to end users. The FLATSIM Delivery Module is composed of Web services and Websites. The web services and the expertise website are packed in an Apache HTTP server hosted in the CNES infrastructure in a VM (Virtual Machine) provided by the CNES web-site hosting mutualised services called Web-NG. In the CNES infrastructure, the delivery module uses a shared postgresql DBMS and the mutualized datalake to store the data. Depending on their types, the data are stored on different technical means: • Downloadable products on the GPFS part of the datalake, • Visualization data on the NAS (Network Attached Storage) part of the datalake, • Metadata in the postgresql database.
User access interface The public data access is made via the ForM@Ter Website, on the "Terre Solide" metacatalog page (https://www. poleterresolide. fr/), which is a Vuejs (javascript) application embedded on a page of a Wordpress site. The CNES public delivery web service provides an Opensearch Application Programming Interface (API) which allows access to the catalog of distributed products. The metadata of the FLATSIM collections (for each study area) are recorded by the ForM@Ter metacatalog in a Geonetwork catalog under the ISO19139 standard. Downloading and viewing products with WMS ( warehouse management system) is reserved for authenticated ForM@Ter users. Authentication is done with the ForM@Ter Single Sign-On (SSO). In Figure  10 , we display the requests made at the interface between the ForM@ter portal, the ForM@ter SSO, the Form@ter Catalog and the CNES FLATSIM services : • Request A : Geonetwork's Q Search API is used for user searches among collections.
These searches can be spatial, temporal, by data center, open term, variable or platform. The requests are xhttp requests, invisible to the user. • Request B : The metacatalog's Opensearch API allows searches among the granules of a FLATSIM collection. The requests are xhttp requests, invisible to the user. • Requests C1 and C2 (User authentication for the metacatalog) : C1, the user authenticates with the Keycloack SSO and is redirected with a code to the metacatalog page. C2, the Vuejs metacatalog application queries the SSO with the code to obtain an access token and user information (invisible to the user). • Requests D1 to D4 (Authentication with FLATSIM) : D1, the user authorizes FLATSIM to access his user data and is redirected to the metacatalog page with a code for FLATSIM Authentication.
D2, the metacalog application transmits the code to the FLATSIM authentication service (invisible to the user). D3, FLATSIM's authentication service queries the SSO with the code, to get user data (invisible to the user). D4, the authentication service transmits a FLATSIM data access token to the metacatalog application (invisible to the user). • Request E : The user now has a FLATSIM access token, he can query the WMS Flatsim service by transmitting the token. The service checks that the token is valid with the FLATSIM authentication service and sends the images back (invisible to the user). • Request F : The user now can request a download by transmitting the token. The service checks that the token is valid with the FLATSIM authentication service and then returns the archive (invisible to the user).
The success of the implementation relies (1) on the algorithm of the NSBAS stack processing, with a single reference image for the whole image stack, avoiding redundant copies of input data, together with optimizations for a large number of steps, but more importantly, (2) on the specific, parallel NSBAS chain implementation on the CNES HPC infrastructure, with local transfer of input-output data on cluster nodes, allowing very fast I/O between disk and processors. Since the goal of the service is to serve scientific purposes, the processed regions are selected according to scientific criteria. FLATSIM thus provides a set of products (like e. g. the DTs product) that allows the end-users that do not have large computational capabilities to take advantage of the MTInSAR methodology, making an optimal use of the wealth of data provided by the Sentinel-1 mission.
Acknowledgments: The development, automation and optimization of the NSBAS chain necessary for this work was supported by  CNES , focused on Sentinel-1 mission, under the project " DAT-S1 : Déformation actuelle au Tibet mesurée par Sentinel-1 ". Copernicus Sentinel data 2014-2020, processed by  ESA , were retrieved from CNES PEPS platform. We wish to thank  Michel Diament  who supported this work on behalf of the ForM@Ter structure. We thank two anonymous reviewers for their helpful comments.
X-Ray Diffraction (XRD) The X-Ray Diffraction (XRD) data of the Fh and Fh_Sb series and the biogenic minerals were collected using a Bruker D8 diffractometer equipped with a monochromator and position-sensitive detector. The X-ray source was a Co anode (λ = 0. 17902 nm). The diffractogram was recorded in the 3 -84° 2θ range, with a 0. 0359° step size and collection time of 3 seconds per point. Crystalline size was calculated using the Scherrer equation (  2 ) D = K/cos (2) where D is the mean size of the crystalline domains, K is a dimensionless shape factor (0. 94),  is the wavelength of the X-ray radiation,  is the line broadening at the full width at half maximum (FWHM) and  is the Bragg angle of the particular peak of interest.
This approach is useful for the unambiguous identification of iron oxides at room temperature and low-temperature studies by comparing the hyperfine split obtained at low temperature with the spectra measured at room temperature. The hyperfine interaction parameters were determined by fitting the experimental spectra by a least-squares method using Recoil software  (Lagarec and Rancourt, 1998)  with Voigt-based fitting analysis or the Lorentzian model, except in the case of the appearance of a magnetic order Fe(II) phases (siderite), whereas the full static Hamiltonian site model was required at very low temperature (15 K) to solve the mixed hyperfine interactions. Center shifts were reported with respect to that of 25 µm-thick α-Fe foil at room temperature.
Microbial bio-reduction of Sb-bearing ferrihydrite We quantitatively monitored the bio-reduction of Sb-bearing ferrihydrite series over time. Bio-reduction started without a lag phase and reached a plateau after 25 days (Fig. 2 ). No measurable Fe(III) reduction took place in the non-inoculated treatments (data not shown). The apparent rate of reduction was 16. 40 ± 0. 07% (Fe II tot /Fe tot ) day -1 for Fh, while the rate was less than 50% slower for Fh_Sb (Table  1 ). Additionally, the extent of reduction reached ~ 35% for pure Fh, and ~ 30% for Fh_Sb(0. 04) and Fh_Sb(0. 06), whereas it plateaued at ~ 50% for Fh_Sb(0. 1) (Table  1 ). After 25 days of incubation, only a minor fraction -less than 0. 5% -of the Sb ions were present in the aqueous phase irrespective of the molar ratio Sb/(Sb + Fe) of the initial Fhs.
, 2015). Modelling of this spectrum component, using the software package Recoil  (Lagarec and Rancourt, 1998) , is intricate, owing to the mixing of states, particularly given the fact that, for Fe(II) compounds below magnetic transition, the electric quadrupole and magnetic hyperfine interactions are generally of the same order of magnitude. Thus, it is not likely that this component can be analyzed in terms of six-line patterns (sextets) as is the case for the magnetism of ferric compounds  (Duboscq et al. , 2020). The octet comprises eight lines that have to be computed fully by solving the eigenvalues of the full Hamiltonian. The octet is characterized by hyperfine parameters (Table  2 ), a CS of 1. 76 mm s -1 , electric quadrupole interaction (e 2 qQ/2) of 2. 72 mm s -1 , and a hyperfine field H of 18.
1) transformation  (Williams and Scherer, 2004; Masue-Slowey et al. , 2011; Frierdich et al. , 2012). The inhibitory effect of a high Sb(V) concentration (0. 25 mmol of Sb(V) per gram of goethite) was also observed during the Fe(II)-catalyzed recrystallization of goethite  (Burton et al. , 2020). The amount of Sb(V) in the Fh_Sb(0. 1) probably increased the extent of reduction by likely retarding the mineralogical transformation of the Fh_Sb(0. 1) and preserving the Fh_Sb(0. 1) surface area for further reduction  (Masue-Slowey et al. , 2011). At the end of the bio-reduction period, a minor fraction of Sb(V) was measured in the solution. Our results indicate that a substantial amount of Sb(V) was probably associated with secondary minerals, in line with the data observed elsewhere under biotic and abiotic conditions  (Mitsunobu et al.
ACKNOWLEDGEMENTS: This work has been supported by the  French National Research Agency  through the national " Investissements d'Avenir" programme  under the reference  ANR-10-LABX-21-01 / LABEX RESSOURCES21  and  EC2CO 2017. We would like to thank  Aurélien Renard  of the Spectroscopy and  Microscopy Core Facility of SMI LCPME (Université de Lorraine , CNRS, LCPME -http://www. lcpme. cnrsnancy. fr) for XPS analyses and  Renaud Gley  of the  Université de Lorraine, CNRS , LIEC for XRD analyses.
] Unit number amount ratio, and the respective atomic masses  [38]. Measurement uncertainties were evaluated according to the Guide to the Expression of Uncertainty in Measurement (GUM)  [39]  using the GUM Workbench© software  [40]. A mass fractionation correction using measurements of a CRM on the same magazine as the sample is recommended following ASTM C1672-17, but not mandatory  [28]. This correction was not possible to perform for the Am measurements for this project, since there was no suitable Am isotopic standard available. Due to similarities in the chemical behaviour and similar ionization energies, it can be assumed that Am behaves similar to U or Pu during the total evaporation measurement and that the bias statements for U and Pu can equally be applied for americium. For the n( 241 Am)/n( 243 Am) ratio the uncertainty component was calculated as 0.
Statistical analyses All statistical tests were conducted separately for the different Leptoseris and Sinularia species, since they were collected at two different depths and could not be compared. Differences in Pg, Pn, and R rates between the two mesophotic Leptoseris coral species were analyzed using individual Student's t-tests. Data were beforehand checked to meet t-test assumptions (normal distribution, homogeneity of variances). Differences in Pg, Pn, and R rates (normalized to AFDW) between the different mesophotic Sinularia species were analyzed with a one-factor permutational multivariate analyses of variance (PERMANOVA) using Primer-E version 6 software  (Clarke and Gorley 2006)  with the PER-MANOVA+ add on  (Anderson 2001). Euclidean distance of square-root transformed data and type III partial sums of squares were used with unrestricted permutation of the raw data (9999 permutations).
Differences in DIC and DIN assimilation rates, C and N content, and in C : N ratio were analyzed with a twofactor PERMANOVA with either Leptoseris or Sinularia species and coral compartment (host tissue and symbiont) as fixed effects. Analysis was based on Euclidean distance of square-root transformed data and type III partial sums of squares were used with permutation of residuals under a reduced model (9999 permutations). The significance for all main tests and the pair-wise comparisons was based on Monte Carlo tests (significance level, p < 0. 05).
Acknowledgments The authors would like to thank the staff of the  R/V Sam Rothberg and of the Israel Nature and Parks Authority , who greatly contributed to the success of this field trip. They would also like to thank  Gal Eyal  and an anonymous reviewer, for constructive comments on this manuscript. This research was supported by the  "Explorations de Monaco/Monaco Explorations. "Joerg Wiedenmann acknowledges funding from the  Natural Environment Research Council NERC  ( NE/S003517/1  and  NE/T001364/1 ).
The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic.
Airborne Data The acquisition of aerial hyperspectral data and water quality measurements (10 stations) were carried out over the study area on 1 July 2019. The aerial hyperspectral images were acquired by Hytech Imaging company at a spatial resolution of 1 m using the HYSPEX sensor flying at an altitude of 2400 m  [52]. The sensor records 160 spectral bands in the range of 400-1000 nm with a 3. 6 nm bandwidth. The ATCOR algorithm  [53]  was used to perform the atmospheric correction. Black, grey, and cream-colored ground targets of an area of 5 m 2 were positioned to facilitate atmospheric correction. The time slot of the data acquisition was between 11:45 a. m. and 3:45 p. m. Thirty-two flight lines were acquired and merged to obtain a mosaic (Figure  2 ). Radiometric calibration was carried out for the visible-near infrared data using the manufacturer's static calibration parameters; in particular, the quantum efficiency, the relative response of the pixel matrix, and spectral calibration were considered.
The black current measurement of each image, which was integrated into the raw data headers, was also taken into account. Some of the lines acquired in the north-south direction have been contaminated by the sun glint, which were removed from the raw data as follows. A pixel-by-pixel analysis was carried out to check whether the saturation due to sun glint did not exceed 16 adjacent spectral bands. If not, a spectral interpolation was performed on saturated zones by an Akima-spline interpo-.
Data 2. 2. 1. Airborne Data The acquisition of aerial hyperspectral data and water quality measurements (10 stations) were carried out over the study area on 1 July 2019. The aerial hyperspectral images were acquired by Hytech Imaging company at a spatial resolution of 1 m using the HYSPEX sensor flying at an altitude of 2400 m  [52]. The sensor records 160 spectral bands in the range of 400-1000 nm with a 3. 6 nm bandwidth. The ATCOR algorithm  [53]  was used to perform the atmospheric correction. Black, grey, and cream-colored ground targets of an area of 5 m 2 were positioned to facilitate atmospheric correction. The time slot of the data acquisition was between 11:45 a. m. and 3:45 p. m. Thirty-two flight lines were acquired and merged to obtain a mosaic (Figure  2 ). Radiometric calibration was carried out for the visible-near infrared data using the manufacturer's static calibration parameters; in particular, the quantum efficiency, the relative response of the pixel matrix, and spectral calibration were considered.