dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Florian Haas  kindly provided the photographs used for Fig. 1, in particular of  La Réunion, Dreitorspitze and Zwieselbach valley. The photo of Gampenalm was taken by an installed camera as part of the  ERC   SLIDEQUAKES  project. We greatly appreciate the constructive comments and detailed feedback by the three anonymous referees that helped to improve the quality of this paper. Financial support. Financial support for the fieldwork on La Réunion was provided by  ERC-CG-2013-PE10-617472SLIDEQUAKES. This is IPGP contribution no. 4204. The open-access publication of this article was supported by the  Open Access Fund of the Catholic University Eichstätt-Ingolstadt. Review statement. This paper was edited by  Yves Bühler  and reviewed by three anonymous referees.
Quantification of luminophore displacement in soil and casts In order to quantify tracers from each soil layer and cast sample, a weighted subsample was homogeneously spread in a black dish (4 × 4 cm), which was placed inside a dark box (50 × 40 × 40 cm) and illuminated with two UV light tubes (Philips TL 8W/08 T5/BLU; emission peak at 365 nm). One photo was taken for each subsample, using a digital camera (Olympus C-2500L) fixed at the top of the box. Finally, the surface (pixels number) occupied by each colored tracer was quantified with an image analysis software (Image-Pro® Plus, Media Cybernetics;  Gilbert et al. 2007 ) (see materials and methods in the Supplementary text for the detailed description: Fig. A. 4). The data obtained was used to produce tracer repartition profiles and calculate tracers found in casts.
PDC ¼ 10 3 ∑p i d i t -1 10 -3 cm d -1 À Á ; with∑p i ¼ 1 where the index i refers to the layer number. This formal measure of particle transport has been designed to be as general and simple as possible, thus allows comparing the transport intensity in very different situations. This databased analysis can be calculated when profiles of tracers are available with a time reference, without any extra assumptions.
Hydraulic Data Typically, the position of the water table is poorly known and it is often determined by a few points where the pore pressure is known, by the hydromechanical properties of the studied layers (permeability, porosity, Biot coefficient, dynamic viscosity) and by boundary conditions (null fluid flux, prescribed pore pressure field, inflow, outflow, leakage). Fortunately, the Lake Most site is very well instrumented: the piezometric heights of the water table have been recorded for 93 wells since 2014. The water table DMT has been built from 93 piezometric water levels and lake surface. For slope stability calculations, the position of the water table has been used to calculate the pore pressure and effective stress fields.
S3 ). Every subsequent formulation was therefore stored at -20 °C and thawed at 4 °C before use. The ability of the formulation to release Retro-2. 1 in a sustained fashion was estimated by the dialysis method at room temperature. The detailed procedure can be found in the experimental part. Although this model has been recognized as suboptimal to predict the sustained-release properties of a system in vivo, it can give precious information on the interactions between the drug and the carrier. 24 In this setting, a burst release of about 18% of the payload was observed in the first 40 minutes, followed by a slow release of about 5% of the payload in the following 6 hours (Fig. 2 ). After 6. 5 hours, a precipitate started appearing in the dialysis bag and the experiment was stopped. These data should be considered with precaution, as several studies showed that lipophilic drugs are quickly released from PEG-PLA micelles and then adsorbed on plasmatic proteins following an intravenous injection.
Dynamic Light Scattering Dynamic Light Scattering experiments were performed using a Vasco Flex particle size Analyzer equipped with a 450 nm Laser. Data were recorded at 20 °C using a refractive index of 1. 331 and a solvent viscosity of 0. 982 cP. For each sample, 10 acquisitions were realized using a noise limit of 1% and 200 channels with a time interval of 1 µs. All samples were analyzed at a concentration of 10 mg/mL in 4 mL glass vials (Lab File, Wheaton) unless otherwise noted and filtered on a 0. 2 µm membrane (Millex ® ) before analysis.
Acknowledgements The authors thank  Eric Doris  and  Edmond Gravel  ( Université Paris-Saclay ,  CEA ,  INRAE ,  Département Médicaments et Technologies pour la Santé (DMTS) ,  SCBM , 91191  Gif-sur-Yvette, France)  for useful discussion as well as  Géraldine Carrot  ( Université Paris-Saclay, CEA IRAMIS, LICSEN ) for the access to a GPC system. This work has been funded by the  French National Agency for Research (ANR)  under Contracts  Anti-HUS ANR-14-CE16-0004  and  SMERSEC ANR-20-CE18-0016-01 , the  Swedish Research Council  ( K2015-99X-22877-01-6 ) and the  Joint ministerial program  of R&D against CBRNe risks. The Cintrat, Gillet and Pruvost teams are members of  LabEx LERMIT  ( ANR-10-LABX-33 ). R. V. was funded by the  CEA   Irtelis international PhD program.
LOI data showed that the 2001 organic layer samples contained a substantial amount of mineral material. The organic layer dry weight in 2001 was thus corrected (DW corrected ) for the sampling bias by assuming that the LOI in the 2001 samples should be equal to the LOI measured in 2019 as follows (Eq. 1) (Supplementary material 2) : (Eq. 1) We estimated that the excess dry mass measured in the organic layer in 2001 was caused by a 0-5cm organo-mineral soil layer inclusion of less than one millimetre of thickness. No correction on soil stocks was applied because i) this soil thickness was very small compared to the sampled 0-5cm layer and ii) the estimated Mg, Ca and K pools in a millimetre thick layer were much smaller than the Ca, Mg and K pool correction in the organic layer.
Quanti cation limits and date outliers We chose to replace values in the dataset that were below the analytical quanti cation limit by the quanti cation limit value. Changes in quanti cation limits can result in a considerable bias  (Hansson et al. , 2020). In order to avoid such biases, i) quanti cation limit values were set to the highest value between the 2001 and 2019 campaign and ii) we replaced the values below the analytical quanti cation DW corrected = DW uncorrected × [1 -(1 - )] LOI 2001 LOI 2019 limit by the quanti cation limit values. These changes affected 9. 4%, 0. 2%, 15. 4%, 55. 6% and 1. 4% of the exchangeable Fe, Mg, Ca, Na and H values in the initial dataset. Potential outliers were identi ed based on the distributions of each measured variable. Outliers were identi ed if they were outside the boundaries A (Eq.
In the Nordmann r stand, the increase in organic layer dry weight may partly be due to an important production of fruit recorded in 2017. However, for both the CwS and Nordmann r stands, an increase in carbon content in the topsoil layers was also recorded (Fig. 7 ) which suggests that the increase in organic layer dry weight is due to a slower organic matter decomposition. The strong increase in Ca stored in the organic layer of the Douglas r stand can be explained by the biological cycle in this stand: the Ca concentration in the litterfall was higher in this stand compared to all others (data not shown). The mineral soil often represents the largest proportion of available nutrients in forest ecosystems and is often the only compartment taken into account in fertility budget studies. However, the organic layer cannot be neglected because it directly contributes to the replenishment of the plantavailable pools in the mineral soil and represents in many cases an important pool of nutrients  (Jonard et al.
The Experimental Data The experiments were carried out using a cross-flow electrolyzer, which utilizes REM as a porous anode in inside-outside cross-flow filtration mode. The experimental data were taken from  [30]. The REM is a porous electrode (in our case, it is a tubular Magnelli phase sub-stoichiometric titanium oxide electrode). The transmembrane pressure was used as an independent parameter of the experiment. The feed solution contains a PCT of varying concentrations, which is the target pollutant, and a supporting electrolyte (0. 1 M Na 2 SO 4 ), which presumably does not participate in any chemical reactions and is used only to decrease the total resistance of the system. The following current mode was used during the anodic oxidation processes: the current density was set at -300 A/m 2 for 90 min, then, no current was supplied for 50 min.
). In late 2018, Mercator Ocean International (MOI) and the European Environmental Agency (EEA), as entrusted entities by the European Commission to implement the Copernicus Marine and Land services, developed a roadmap for the evolution of Copernicus marine and land services to better serve coastal users  (MOI and EEA, 2018 ). An improved monitoring of the coastal topography and bathymetry was included in this coastal roadmap, to complement existing services and data portals, such as EMODnet-Bathymetry, by further exploiting Copernicus Sentinel satellite data. In this context, MOI is studying the possibility to develop and distribute in the Copernicus Marine Service in the near future (post 2022) a pan-European, dynamic satellite-derived coastal bathymetry product at intermediate horizontal resolution.
COASTAL BATHYMETRY FOR COASTAL MARINE APPLICATIONS Accurate and timely information on the state of marine resources and ecosystems remains a challenge. The United Nations Decade of Ocean Science for Sustainable Development (Ocean Decade) 2021-2030 is a global framework that seeks to generate and use knowledge for transformative actions needed to achieve a healthy and resilient, safe and productive ocean. The next 10 years will also be the implementation of S-100, the universal hydrographic data model launched by the International Hydrographic Office (IHO) to develop the future generation of digital products and services. In this section, the importance of bathymetric information for a range of coastal marine applications is illustrated. • Aquaculture: About 70% of the seafood consumed in the EU is imported.
, 2019). In the coastal zone, satellite-derived bathymetry (SDB) can reach much finer resolutions thanks to two approaches  (Pleskachevsky et al. , 2011). The first one exploits the attenuation of light with depth in the water column as a function of the wavelength. Bathymetry can be estimated by combining optical satellite data and radiative transfer models or empirical models. The second SDB method is based on the influence of topography on waves in shallow water. Each method and sensor comes with its own strengths, limitations, and scope of applications  (Gao, 2009; Jawak et al. , 2015). Specifically, methods based on radiative transfer perform best in clear, calm waters, while wave-based inversion techniques require waves and perform well in turbid waters. Intertidal topography and its variation also benefit from the use of satellite remote sensing  (Sagar et al.
013 km  (Ramaseri Chandra et al. , 2020). This can be applied to measure bathymetry for a depth range of 0-45 m along beam tracks in coastal waters. ICESat-2 data have shown to have as high as 95% accuracy when compared to in situ airborne LIDAR surveys  (Parrish et al. , 2019). These data provide a worldwide dataset that is currently being used to validate SDB depth retrievals  (Thomas et al. , 2021).
SHOM openly shares on its data platform the composite digital terrain models (DTM) of its coasts with a spatial resolution of 0. 001 • (∼111 m), as well as some very high resolution local DTMs with a resolution of 0. 0002 • (∼20 m). The IHO Digital Bathymetry (DCDB), hosted by the U. S. National Oceanic and Atmospheric Administration (NOAA) on behalf of the IHO Member States, freely distributes unedited single and multibeam bathymetric data provided by hydrographic offices, industry and crowdsourcing. Several free and open initiatives then procure composite maps based on those multi-sensor data, such as NOAA-ETOPO1, GEBCO or EMODnet (Table  1 ). Although these initiatives ensure that global bathymetric data are freely available in the open ocean, these data are less accurate in shallow waters where vertical and spatial resolution is insufficient.
Furthermore, these gridded composite data are often only interpolated with coarser resolution data, or may even include Nineteenth century lead line measurements. Local variations in depth such as sandbanks and coral reefs on the one hand and temporal morphodynamic changes in seabed structures on the other hand can be significant in nearshore areas. The General Bathymetric Chart of the Oceans (GEBCO) aims to provide the most authoritative and publicly accessible bathymetry of the world's oceans. It operates under the joint auspices of the IHO and the Intergovernmental Oceanographic Commission (IOC) of UNESCO. The latest version of GEBCO, GEBCO_2020, is based on Version 2 of the SRTM15+ data set  (Tozer et al. , 2019)  and is then merged with regionally compiled gridded bathymetric data sets using many different sources of data with varying quality and coverage (Figure  3 ), including EMODnet for Europe.
As GEBCO is an information product created by interpolating measured data, the resolution of the grid can be significantly different from that of the data used. The GEBCO_2020 grid is the latest published global bathymetric product and provides global coverage on a 15 arc second (∼463 m) grid. The grid was developed as part of the Seabed 2030 project, which is a collaborative project between GEBCO and the Nippon Foundation to facilitate comprehensive mapping of the global seafloor by 2030  (Mayer et al. , 2018). The project is aligned with UN Sustainable Development Goal #14 to conserve and sustainably use the oceans, seas and marine resources. The European Marine Observation and Data Network (EMODnet) combines data products from over 100 marine research bodies on the bathymetry, geology, habitats, physics, chemistry and marine life of the seas around Europe.
The   EMODnet Bathymetry portal provides a harmonised digital bathymetry on its web platform for Europe's sea basins with a grid resolution of 1/16 arc minutes (∼115 m). Over 50 different data providers consisting of national hydrographic services, marine research institutes, and private companies, from 26 countries contribute to the development of this composite (Figure  4 ). Individual datasets at their actual resolution are, however, only accessible on request from the providers. Since 2019, EMODnet Bathymetry and the Seabed 2030 Project have shared a Memorandum of Understanding with the common aim of improving the bathymetric knowledge, while promoting the distribution of this knowledge to all potential users. Commercial hydrographic companies also hold large amounts of bathymetric data.
For the most part, these data are owned by the customers of the survey companies that collect them. Considering the cost of acquisition and their potential market sensitivity, these survey data are only shared in the public domain and mapping projects with the permission of the hydrographic company and the customers  (Wölfl et al. , 2019). For instance, Fugro, a world leading offshore survey company serving the oil and gas, infrastructure, renewables, power, nautical and mining market sectors, have conducted bathymetric surveys and has contributed over 1. 1 million square kilometres of in-transit bathymetry data to Seabed 2030 to date. New players, such as the marine robotics companies Ocean Infinity with its "Armada fleet" or iXblue with its "DriX, " are now capable of deploying autonomous USVs for seabed mapping.
Thanks to the considerable advances in SDB methods in recent years various research projects have been carried out, such as the Allen Coral Atlas which provides satellite-derived bathymetry in global coral reef regions  (Li et al. , 2021). A number of private companies have also emerged to exploit this potential (Supplementary Appendix 1). In the United States, TCarta Marine is developing a system (Figure  5 ) which brings to bear modern computational resources, multiple satellite platforms, and machine learning to combine multispectral inversion with wave-based inversion "ultimately producing a persistently updated global shallow water bathymetry dataset, " as presented. To further enhance the accuracy of the SDB measurements, TCarta has developed an AI-based technique for leveraging ICESat-2 data to train the SDB algorithm and validate results.
TCarta expects to present a worldwide Global-SDB dataset completed by late 2021 to its customers, with depths reaching more than 30 meters, depending on water clarity, at a 10meter resolution and an accuracy within 10% of depth or less  (Ocean News, 2021). In Europe, companies specialising in SDB are notably supported by projects funded by ESA and by the European Commission within the framework of the H2020 programmes (Supplementary Appendix 2). For the latest one, the German company EOMAP recently joined forces with Fugro and the Hellenic Centre for Marine Research in the H2020-EU project named "4S" (Satellite Seafloor Survey Suite), which started in 2020. The 3-year project will develop an online cloudbased solution that will use highly automated Earth observation algorithms and workflows to remotely map and monitor seafloor habitats, morphology and shallow water bathymetry.
Hydrographic Data Standards So far, SDB data are not considered as hydrographic data because of their lower accuracy and the difficulty of estimating uncertainties compared to data from conventional sensors (such as echo sounders or LIDAR). Only the latter are plotted on  From IHO S-44 (2020). THU is the total horizontal uncertainty and TVU is the total vertical uncertainty. From IHO S-44 (2020). Total horizontal uncertainty (THU): Component of total propagated uncertainty (TPU) calculated in the horizontal dimension. THU is a twodimensional quantity with all contributing horizontal measurement uncertainties included. Total vertical uncertainty (TVU): Component of total propagated uncertainty (TPU) calculated in the vertical dimension. TVU is a one-dimensional quantity with all contributing vertical measurement uncertainties included.
USER SURVEY ANALYSIS Method The accuracy of SDB is expected to increase in the coming years to fill the world's coastal databases, achieving full bottom coverage in coastal areas with periodic updates. In this context, Mercator Ocean, as the entrusted entity by the European Commission for implementing the Copernicus Marine Service, is studying the possibility to develop and distribute in the near future (post 2022) a pan-European, dynamic satellite-derived coastal bathymetry product at intermediate horizontal resolution. This new dynamic product would be developed in cooperation and complementarity with the EMODnet initiative of DG MARE, using data from the Copernicus Sentinel missions. To identify users' interest in coastal bathymetric data, their usage and their requirements, a survey form has been sent out to CMEMS regular users and partners.
, 2016; Bergsma et al. , 2019)  and human interventions. The results of the CMEMS user needs analysis show a strong demand for new coastal bathymetric data. The requirements for very high resolution and very high accuracy can be met with commercial solutions, but with a trade-off for very local coverage and shallower depth. The results of this survey also show a clear interest for lower resolutions achievable with Sentinel-2, especially for scientific research applications where an update frequency would also be highly appreciated. This latter demand encourages the integration of an intermediate resolution SDB product into the CMEMS service catalogue. Further user surveys will be required to define the precise performance parameters to be adopted. To conclude, even if SDB data cannot provide sufficient quality to be recognised as hydrographic data as of today, it already has the great potential to provide a first-pass tool in unmapped areas or dynamical areas for survey planning, risk reduction, change detection for a wide range of applications (discussed in section "Coastal Bathymetry for Coastal Marine Applications").
The purpose of the ForM@Ter LArge-scale multi-TemporalSentinel-1 InterferoMetry service (FLATSIM) is the massive processing of Sentinel-1 data using multi-temporal Interferometric Synthetic Aperture Radar (InSAR) over large areas, i. e. greater than 250,000 km 2. It provides the French ForM@ter scientific community with automatically processed products using a state of the art processing chain based on a Small BAseline Subset approach, namely the New Small BASeline (NSBAS). The service results from a collaboration between the scientific team that develops and maintains the NSBAS processing chain and the French Spatial Agency (CNES) that mirrors the Sentinel-1 data. The proximity to Sentinel-1 data, the NSBAS workflow and the specific optimizations to make NSBAS processing massively parallel for the CNES High Performance Computing infrastructure ensures the efficiency of the chain, especially in terms of Input/Output, which is the key for the success of such a service.
The FLATSIM service is made of a production module, a delivery module and a user access module. Products include interferograms, surface Line Of Sight velocity, phase delay time series and auxiliary data. Numerous quality indicators are provided for an in-depth analysis of the quality and limits of the results. The first national call in 2020 for region of interest ended up with 8 regions spread over the world with scientific interests including seismology, tectonics, volcano-tectonics and hydrological cycle. To illustrate the FLATSIM capabilities, an analysis is shown here on two processed regions, the Afar region in Ethiopa, and the eastern border of the Tibetan Plateau.
However, due to the limited revisit time or the limitations of the acquisition strategies, the processed time series were in most places made of typically 15 to 20 acquisitions for ALOS-1 (e. g. ,  [20]  ) and up to about 40 to 80 acquisitions for ERS/Envisat (e. g. ,  [11, 18] ). Data gaps, and/or limited access to the whole data archive, made the continental-wide processing with these data quite challenging. However, a few large-scale studies have shown the feasibility of large-scale InSAR processing for tectonic applications  [21] [22] [23]. Since 2014, the radar satellite constellation of the European Copernicus Sentinel-1 mission (S-1) provides massive amounts of SAR data available free of charge (4 Peta-bytes/year today). Continental regions are systematically covered by acquisitions in the Interferometric Wideswath (IW) mode, with continuous 250 km-wide tracks.
With the launch of Sentinel 1-B in April 2016, the revisit time in Europe has dropped to 6 days. Therefore, beginning of 2021, after 5 years of mission, about 300 images per viewing geometry are available. In tectonically active regions outside Europe the sampling frequency is about 12 to 24 days. The retrieval of ground deformation at continental scale, with unprecedented temporal sampling, is now providing new observables leading to major scientific discoveries and enabling operational applications in the fields of environment, risk and asset management. However, taking advantage of this extraordinary data set requires the development of fully automated processing chains, as well as large computational and storage facilities coupled with the ability of software to scale up while processing thousands of SAR scenes, all of which require significant human resources.