dataesr/scientific-paragraphs-categorization huggingface dataset:
B. ) and GRAL, financed within the  University Grenoble Alpes graduate school (Écoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation pour la Recherche Médicale (FRM) , the fonds  FEDER  and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBiSA). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). The authors are very grateful to the staff of the animal facility of  Institut Gustave Roussy  for technical help. We thank  Didier Poncet  and  Pierre Bobé  for their critical reading of the manuscript. The data supporting the conclusions of the study are available from the corresponding authors. The EM map generated in this study has been deposited in the Electron Microscopy Data Bank under the number  EMD-14863.
Here we present a first investigation of dissolved oxygen in the Charente estuary, based on a combination of longitudinal studies along the estuary axis and instrumented sites to determine the intensity and spatial extent of deoxygenation. Oxygen and salinity sensors were deployed at several sites during the summers of 2018, 2019 and 2020 to record temperature, 35 salinity and dissolved oxygen every 15 minutes. This high-frequency dataset will be compared with a long-term low-frequency dataset  to determine whether or not there is a degradation of oxygenation in the Charente estuary. The highfrequency dataset shows a high variability of dissolved oxygen (DO), with a clear influence of tidal cycles. During summer, DO concentrations are often below 5 mg L -1 , and sometimes even below 2 mg L -1 , indicating the presence of a summer oxygen minimum zone with an extension of about 20-25 km.
Data sources The study of dissolved oxygen variability in the Charente estuary is based on three different datasets (Table  1 ). The first two dataset were acquired between 2018 and 2020, based on a strategy developed in the Loire and Gironde estuaries  (Schmidt et 120 al. , 2019). The principle is to combine instrumented sites and longitudinal investigations. In the course of this work, we discovered the existence of an institutional long-term low frequency monitoring of water quality, started in 1975, whose data were also used.
Low-frequency dataset Dissolved oxygen data collected from 2018 to 2020 revealed hypoxic situations, which was surprising as the Charente estuary was not known to have such a problem, unlike the Loire and Charente estuaries. We therefore searched for historical data, 160 which enabled us to discover the existence of a long-term, low-frequency monitoring of water quality, carried out by the French Water Agency, in particular to assess the ecological status of transitional waters. This monitoring is part of the water quality assessment carried out by the French Water Agency, in accordance with the guidelines of the European Water Framework Directive, which includes the measurement of a number of chemical species such as organic and metallic pollutants, as well as temperature, salinity and dissolved oxygen.
Long-term, low-frequency monitoring of dissolved oxygen The low-frequency dataset acquired at Rochefort covers 48 years, corresponding to very contrasting hydrological and climatic 180 conditions. During this period, river discharges varies between 7. 3 and 479 m 3 s -1 , covering the whole range of the Charente discharge. As river discharge decreases, salinity increases, ranging from 0. 25 to 17. 8 (Figures  2  & 3 ; Table  A2 ). The highest salinities are usually observed in summer and autumn, during the low water period of the Charente river. It is also during this period that the water temperature is the highest, typically between 20. 5 and 27. 0°C, while in winter it ranges between 3 and 10. 2°C. On the basis of the high summer salinity and temperature, it was already possible to speculate that these conditions 185 were not favourable for good oxygenation of the Rochefort waters, since solubility decreases as these parameters increase.
The periods during which hypoxia levels are measured are usually short, typically between 3 and 6 days, which calls into question the frequency of measurements to be able to capture them. The low frequency measurements have been plotted along the high-frequency records (Fig. 4 ). A first comment is the good agreement between the two datasets, which came as a pleasant 270 surprise. Indeed, one might have expected a problem with the quality of the low-frequency monitoring data, which is carried out by operators rather than scientific teams. This validates the use of these data, which are often discarded by research teams. However, the main problem is the inability to capture large variations in DO. This is not surprising with only two measurements in summer at the water quality station of Tonnay-Charente (July, September).
org/10. 5194/bg-2023-150 Preprint. Discussion started: 7 September 2023 c Author(s) 2023. CC BY 4. 0 License. recurrent hypoxia in summer. For a first estimation of the controlling factor of the DO variability using a path-analysis, we used the 2020 dataset, which has the interest to include also the concentrations of suspended particulate particles  (Schmidt and Diallo, 2023). It also covers a temporal period from April to November, which encompasses the deficit DO period and then the temporal variability of the quality parameters in different hydrological and meteorological contexts. First, the regression of the environmental parameters, i. e. discharge, tidal range, salinity, temperature, suspended particulate matter, with DO 385 allowed to distinguish two groups according to their influence on DO variability.
PCR-amplification reactions were performed in 25 µl volume reaction and contained 0. 5-3 μl of eluted DNA, 1. 5 mM of MgCl 2 , 0. 2 mM of deoxynucleotide (dNTP) mix, 0. 3 μM of each primer and 0. 5 U of the hotstart Platinum Taq DNA Polymerase (Invitrogen, Carlsbad, CA). PCR reactions were carried out for 35 cycles (94°C for 30 s, 55-58°C for 30-45 s, 72°C for 90 s) preceded by 2 min denaturation at 94°C and followed by five additional minutes of polymerization at 72°C. To minimize PCR bias, five different PCR reactions were pooled for each sample. Amplicons were then purified using the QIAquick PCR purification kit (Qiagen, Hilden, Germany). Sequencing was done using Illumina Miseq (2x300 bp, paired-end) at Eurofins Genomics (Konstanz, Germany). Sequence data are available 16S rRNA gene amplicon Illumina sequences have been deposited in the GenBank (NCBI) Short Read Archive with BioProject number PRJNA1046993.
ecogenomic. org/) release 207, using an in-house qiime classifier, which was trained and tested following qiime documentation (function feature-classifier) for prokaryotes; The curated database PR2 was used for eukaryotes  (41). After the quality-filtering process, a total of 9,135 prokaryotic ASVs were obtained, 565 of which were archaeal ASVs. A total of 1,715 eukaryotic ASVs were determined across all samples. Sequences affiliating to chloroplasts, mitochondria and Metazoa were removed from the analysis. The relative abundance and taxonomic affiliation of ASVs are provided in Table  S3  and Table  S4 , respectively.
ACKNOWLEDGEMENTS This work was supported by the bilateral  French-Chilean ECOS   Sud-ANID cooperation program  (project  MMEX ), the  ERC   AdG PlastEvol  ( 787904 , DM) and the  Moore-Simons Project on the Origin of the Eukaryotic Cell, Moore Foundation  grant  GBMF9739  (PL -G ; https://doi. org /10. 37807/GBMF9739 ). PA received funding from  ANID -Millennium Science Initiative Program   -NCN2021-056.
Supply of aggregate materials for every construction requires mining of sand and gravel, which leads to the formation of a myriad of freshwater lakes, a now common feature of the landscape in the valleys of large rivers. Typically small in size and shallow, they are filled with waters from the adjacent aquifers and directly exposed to the atmosphere. The creation of gravel pit lakes has various and contrasting effects on their immediate environment. This article first provides a review of these impacts from the hydrodynamic point of view, and illustrates them on simple numerical test cases. It also introduces the gravel pit lake module developed for the occasion within the integrated modelling platform CaWaQS, which formulation was tested on the same test cases against the Lak package, its Modflow counterpart.
Numerical modelling of groundwater-gravel pit lake exchanges The next step was to provide the integrated modelling platform CaWaQS with such a tool capable of simulating the fluctuations in gravel pit lake water levels, in relation to their environment. We chose to develop a library dedicated to the hydrological simulation of gravel pits, given the modular architecture of CaWaQS and thus, on the basis of the one already available in Modflow  [Harbaugh, 2005]. This module is based on the calculation of the water balance of the gravel pit, taking into account precipitation, evaporation, runoff and exchanges with adjacent aquifers. It has therefore been validated on a simplified alluvial plain case by comparing its performances with those of its counterpart and precursor, the Lak3 package  [Merritt and Konikow, 2000].
3. 1. Including a lake module in the CaWaQS hydrosystems modelling platform 3. 1. 1. The CaWaQS platform CaWaQS (CAtchment WAter Quality Simulator) is a distributed and modular modelling platform for regional hydrosystems  [Flipo, 2005 , Labarthe, 2016]. This tool couples specific packages to simulate water transfers within and between the different reservoirs of the water cycle, from the surface to the underground compartment. The platform is conceptually divided into three components representing the surface, the unsaturated and saturated zones. Among its various libraries, it includes a package for the calculation of hydraulic heads in multi-layer aquifer systems, applying a semi-implicit finite volume scheme for the numerical resolution of the groundwater flow equation. We relied on the functionalities already existing in this library to develop a new module to simulate the interactions between the aquifer and a surface water body in order to estimate the hydrodynamic impacts of gravel pits.
The water budget of the gravel pit is detailed in Table  3. The atmospheric moisture deficit E -P of 35 mm•year -1 results in a net inflow to the gravel pit of the same magnitude, of which nearly three quarters are from the alluvial aquifer. There is no difference between the flows calculated by each code. When using the explicit procedure at a daily time step, the simulated lake level evolution was almost identical between the lake module of CaWaQS and Neumann q lat (m 3 •s -1 ) 1. 85 × 10 -3 Recharge I (m•s -1 ) 6. 9 × 10 -9 Precipitations P (m•s -1 ) 2. 14 × 10 -8 Evaporation E (m•s -1 ) 2. 25 × 10 -8 V and H indicate flow through vertical interfaces between the gravel pit and the first layer and through horizontal interfaces between the gravel pit and the deeper layer, respectively. Lak, despite some differences at the beginning of the simulation, maximum on the first day (0.
Edifice Landform Morphology As well as studying the temporal evolution of drainages on edifices, we also consider the broad geometry of the volcanoes. Grosse et al. (2009 Grosse et al. ( , 2012) )  developed the initial MorVolc algorithm in IDL, which quantifies edifice morphologies through a series of size, shape, slope, orientation, peak, and summit parameters. Using the same framework as DrainageVolc, we redeveloped the IDL code in Matlab, also utilizing the TopoToolbox DEM analysis package  (Schwanghart and Scherler, 2014). Both DrainageVolc and the updated MorVolc scripts are available for use on GitHub (https://github. com/danjohara/Volc_Packages). We analyze simple edifice geometry measurements with this updated version of MorVolc, including effective radius, height, height-radius ratio, and mean slope of the main flank (edifice region between the lowest closedcontour that encompasses the edifice and the edifice's summit contour).
7°N). In 2022, in the frame of the AsA 2022 campaign (https://www. hemera-h2020. eu/small-sensors-campaign/), the resulting measurements of water vapour were compared to the NOAA ESRL frost point hygrometer (NOAA FPH) from the ground up to few kilometres below the balloon burst altitude and to meteorological radiosondes (M20 and iMet-4) in the lower troposphere. Section 2 describes quickly Pico-SDLA H 2 O, predecessor of the present Pico-Light H 2 O hygrometer. Section 3 describes in detail the Pico-Light H 2 O hygrometer, its performances and a discussion around instrumental uncertainties. Section 4 describes the NOAA FPH hygrometer. The M20 sondes are described in section 5. Sections 6 and 7 overview the datasets and flight conditions, then Sections 8 and 9 present the results of the intercomparisons, in volume mixing ratio between Pico-Light H 2 O and NOAA FPH (section 8) and in relative humidity in the troposphere between the in situ hygrometers and meteorological sonde (section 9).
The absolute frequency axis of the spectrum is calculated using spectroscopy: the spectrum peak position coincides with the absolute line center frequency found in the HITRAN spectroscopic database  (Gordon et al. , 2017). Knowing the ambient pressure and temperature and using the first approximation of the volume mixing ratio allows one to simulate a synthetic spectrum from which the absolute frequencies of the points at the half maximum are known. The frequency step between each data point is then equal to the absolute frequency width at half maximum (obtained from the synthetic spectrum) divided by the number of experimental points at half maximum (estimated from the approximate experimental transmittance, as describe above). The absolute frequency step is assumed to be constant over the full scan.
Descriptions of the compared datasets The water vapour datasets originate from three different types of instruments: an optical hygrometer (Pico-Light H 2 O) and a frost point hygrometer (NOAA FPH), with both able to provide reliable measurements of water vapour up to the middle stratosphere, and meteorological radiosondes sondes from Intermet (iMet-4) and Meteomodem (M20) that provide accurate RH measurements up to about 6 to 13 km respectively. Flight chains schematics are shown in Fig. 4. The Pico-Light hygrometer is able to provide reliable measurements during the balloon ascent and the descent under parachute. The flight chain separates the instrument from the balloon by 17 m. In this case, the ascent measurements become affected by outgassing from the balloon envelope at about 14 km.
The differences decrease with altitude, as does the variability of water vapour. Considering pairs of profiles from the same flight segment (i. e. ascent or descent) should reduce the differences. In the troposphere, we only consider ascent datasets since both instruments were flown under their own balloons. 495 The relative difference in mixing ratio is highly variable, strongly correlated with vertical structures and therefore to the variability of tropospheric water vapour. The mean tropospheric relative differences average is of (3. 84 ± 23. 64)%. The comparison between Pico-Light H 2 O and the NOAA FPH above the LRT is illustrated in Fig. 9. In the TR, for an altitude range between the lapse rate tropopause (around 11. 5 km) and cold point tropopause (around 16. 5 km), the mean relative difference is then of about (1.
Acknowledgement This work is based on observations with Pico-Light H 2 O under a balloon operated by  CNES , under the agreement between  CNES  and  CNRS/INSU , within the WP11 of the European project  HEMERA H2020  (Integrated access to balloon-borne platforms for innovative research and technology). The  CNES  staff at  Aire-sur-l'Adour  is highly acknowledged for its efficiency and valuable support in the organization and performance of the AsA2022 campaign. The Aerodrome Staff is also acknowledged for granting access to their facility, thereby permitting the launch of the  NOAA  FPH balloons very close to the  CNES balloon launch facility in Aire-sur-l'Adour. Time and effort of the FPH team that participated in this campaign was supported by the  NASA   Upper Atmospheric Composition Observations program.
Acknowledgements. We acknowledge the  Institut Pierre Simon Laplace  for hosting the iLOVECLIM model code under the LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus). This work was supported by  ANR PIA  funding:  ANR-20-IDEES-0002. It also received funding from the  French National Research Agency  under Grant  ANR-19-CE01-0015  (EIS). 13 https://doi. org/10. 5194/egusphere-2023-1618 Preprint. Discussion started: 15 August 2023 c Author(s) 2023. CC BY 4. 0  License.
Data availability. Archiving of source data of the figures presented in the main text of the manuscript is underway. Data will be made publicly available upon publication of the manuscript on the Zenodo repository with digital object identifier 10. xxxx/zenodo. xxxxxxx. They are temporarily available for review purposes upon request. Author contributions. A. Q. designed the project and performed the simulations. A. Q. and D. M. R. have contributed to the model developments necessary to perform this work. A. Q. and D. M. R. participated in the analysis of model outputs and the manuscript writing.
Acknowledgements. This work is part of the  French scientific community   EECLAT  project ( Expecting EarthCARE , Learning from A-train). The EECLAT community and research activities are supported by the  National Center for Space Studies (CNES)  and the  National Institute for Earth Sciences and Astronomy (INSU). Financial support. This research has been supported by the  National Institute for Earth Sciences and Astronomy (INSU  grant).
( 17 ) 3 Results Full results are available in the interactive Jupyter notebook at https://gitlab. com/M Arran/simulated-slidequakes  (Arran et al. , 2023). Here, we provide a summary, describing the depth profiles of kinematic properties (volume fraction φ, downslope velocity u x , and granular temperature T ) and of collisional properties (the probability P c that a particle is connected to the base, the mean squared normal and tangential impact velocities δ2 n (z) and || δt || 2 (z), and the rate n I of interparticle collisions per unit volume). We describe the power spectra P F• of the components of the basal forces exerted by simulated flows and, for the base-normal components, we compare the high-frequency components of the power spectra to the predictions listed above.
Acknowledgments This work was primarily funded by project  ERC-CG-2013-PE10-617472 SLIDEQUAKES , with supporting funds from  La Société des Amis de l'ESPCI  and from  IPGP. RT acknowledges the support of the  France-Norway IRP D-FFRACT programs , and of the  Research Council of Norway  through its  Centres of Excellence  funding scheme, Project No. 262644. The authors are aware of no conflict of interest. Numerical computations were performed on the S-CAPAD/DANTE platform,  IPGP, France , with code using NumPy (Harris et al. , 2020), Matplotlib (Hunter, 2007), and pandas (McKinney, 2010).
, 2000; Philpot, 2019). Associated TB LiDAR datasets are bi-spectral, consisting of one point cloud (PC) per wavelength. Their typical shot densities above 10 pts/m² result in submerged topographies being as detailed as emerging parts (see Figure  1 ). Shallow submerged parts usually represent a significant source of uncertainty when using only topographic LiDAR data to study land-water interface dynamics  (Lague and Feldmann, 2020). Combining high-resolution data about the submerged and emerged surfaces offers new opportunities to map habitats in fluvial  (Fernandez-Diaz et al. , 2014; Mandlburger et al. , 2015; McKean et al. , 2009; Pan et al. , 2015)  or coastal  (Chust et al. , 2010; Hansen et al. , 2021; Launeau et al. , 2018; Parrish et al. , 2016; Smeeckaert et al. , 2013; Wilson et al.
, 2019)  environments, improve high-resolution modeling of flood inundation  (Lague and Feldmann, 2020; Mandlburger et al. , 2015)  or track sediment transport at the land-water interface. Nevertheless, to fully use these datasets and leverage the scientific potential of extensive datasets made of billions of points, automatic classification of green LiDAR data directly at the 3D PC level is essential. Several methodological challenges complexify the development of adapted classification workflows. First, the refraction of the green laser in water makes it critical to detect all green points below water during data production to subsequently perform accurate refraction correction. This correction requires accurately knowing the spatial extent of water in the scene and the local water elevation, which the NIR channel gives when data are available in the area.
5 m over dark rocks and will typically only reach depths of 1-4 m in rivers owing to the reduced water clarity  (Lague and Feldmann, 2020). Thus, it is commonplace in inland water surveys that deeper parts of rivers or lakes are locally not detected due to green laser extinction. Consequently, as for ground detection below a dense vegetation canopy, one cannot assume that a simple operation such as picking the lowest point over a specific area or extracting the last LiDAR recorded echo will systematically isolate the bathymetry. Similarly, because volume echoes can occur up to 1 m below the water surface, removing all green points below this depth is impossible as a large part of very shallow bathymetry will be discarded. There is currently no proposed method to automatically separate bathymetric echoes from volume echoes over large PC datasets in complex inland water environments.
As a result, vertically highly dense data is condensed into equally spaced punctual values, losing the spatial point pattern information. Few studies provide 3D classifications of underwater environments using bathymetric lidar  (Hansen et al. , 2021; Letard et al. , 2022b Letard et al. , , 2022a)). Additionally, most of them require full-waveform information  (Letard et al. , 2022b (Letard et al. , , 2022a)) , which is complex to process and often unavailable or unpublished. On the land covers, TB lidar datasets offer potentially new classification opportunities using the bispectral backscattered intensity. Research using multispectral lidar to study urban environments already leverages this opportunity by emphasizing the exploitation of the backscattered signal on top of geometrical features evaluated in 2D or 3D  (Morsy et al.
, 2022 (Morsy et al. , , 2017a;; Teo and Wu, 2017). However, there have been, to date, no applications attempting to classify topo-bathymetric 3D PCs directly. This work presents an original framework called 3DMASC for 3D point classification with Multiple Attributes, Multiple Scales, and Multiple Clouds and its application to coastal and fluvial TB airborne lidar datasets. 3DMASC combines proven classical elements of single PC semantic classification, such as geometric feature extraction from multi-scale spherical neighborhoods or k-nearest neighbors  (Thomas et al. , 2018)  and a random forest machine learner  (Breiman, 2001). In addition, it adds new features specifically engineered to leverage the NIR and green PCs. Our contributions consist in: The paper is organized as follows: the next section introduces the related works on the processing of 3D point clouds; our methodology is then introduced in section 3, and experimental results are shown in section 4, associated with a discussion in section 5.