dataesr/scientific-paragraphs-categorization huggingface dataset:
Wave and River Discharge Data For the Sfântu Gheorghe and Grand Rhône river mouth comparison, we used ERA5 wave data a state-of-the-art climate reanalysis  (Hersbach et al. , 2020)  made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), retrieved in front of each river mouth. More information on the ERA5 dataset can be found at https://www. ecmwf. int/en/ forecasts/datasets/reanalysis-datasets/era5. Daily river discharge was provided by the Global Runoff Data Centre https://www. bafg. de/GRDC/EN/Home/homepage_node. html.
Flood-Storm Interactions at Natural River Mouths The probability of interaction of flood jets and storm waves is analyzed at two natural river mouths. Daily discharge data for rivers is scarce, and at the moment only a comparison between the Danube (Sfântu Gheorghe mouth) and Rhône rivers is possible (Supplementary Figure  3 ). Also, whereas the river discharge is measured, the Hs data are derived from Era5 which has lower wave heights, especially near the coast, compared to wave buoys (around 20% lower for storms). Era5 shows wave heights that are generally higher for the Rhône river mouth, but the proportion of waves directed onshore, into the river mouth, is lower. Although the Rhône was hypothesized to have strong flood-storm interactions  (Boudet et al. , 2017) , it seems to be not much different from the Sfântu Gheorghe river mouth.
ACKNOWLEDGMENTS FZ undertook part of the work at Aix-Marseille University under a "Bourse du Gouvernement Français" Ph. D. cotutelle thesis awarded by the Institut Français de Roumanie and was supported by the MIKE Powered by the DHI thesis program which allowed the user to use the Mike numerical model free of charge. We would like to thank François Sabatier for providing the Rhône river mouth bathymetry. Ben Mitch is thanked for creating the Panel Toolbox which made possible the creation of tight plots (https://www. mathworks. com/matlabcentral/fileexchange/ 20003-panel). We would also like to thank the two reviewers and the editor AS for handling the manuscript.
Measurements of aerosol distributions and meteorology were used to investigate the relation between INPs and environment. In particular, laboratory studies of soil dust aerosols generated from topsoil from the same soil unit that underlies extensive croplands to the east of the site  (DeMott et al. , 2018; Steinke et al. , 2016)  have been extended by using single-particle mass spectrometry to characterize particle composition and mixing state, to explore the potential relationships to observed INPs. These analyses were used to evaluate the hypothesis that INPs feeding convective cloud systems over the central Argentinian region are dominated by biological particles and other organic ice-nucleating entities that emanate from surrounding regions. We also investigate seasonal cycles, the impact of rain upon emissions, what the data indicate about the organic and mineral sources of INPs in this region, and relations between INPs and other aerosol properties that can inform the development of parameterizations.
, 2019). Aerosols were dried upstream from the SMPS and UHSAS while the APS measured particles at the humidity within the aerosol inlet in the AOS trailer (see  Uin & Smith, 2021  for a description of the AMF1 AOS). The nephelometer system in AMF1 is a humidigraph, with two nephelometers in series. The first nephelometer sampled at ambient RH in the trailer, while the second measured at elevated RHs imposed by a drying/humidification system immediately upstream; this study used data from the first, ambient-RH nephelometer. No correlations were found between the scattering signal from particles larger than 1 μm and ambient relative humidity, suggesting that the overall coarse particles sampled at the AMF1 site (e. g. , by the APS) were fairly hydrophobic, with sizes close to their dry diameter.
Meteorological measurements associated with the AOS are ambient temperature, pressure, relative humidity, wind speed and direction, and precipitation (measured by a piezoelectric precipitation sensor;  Kyrouac, 2019). The aerosol size distribution was merged (i. e. , leading to a continuous size distribution that encompasses all size distribution measurements) following  Hand and Kreidenweis (2002)  and  Khlystov et al. (2004). The method is described in Supporting Information S1. Hereafter, we assume the resulting size distribution represents dry particles. Since APS data were only available from 23 to 31 December 2018, nephelometer measurements were used outside of this period to derive supermicron aerosol surface areas, as described in Supporting Information S1. Complementing ground-based observations, the Department of Energy Atmospheric Radiation Measurement Program Aerial Facility G-1 (Gulfstream-I) aircraft measurements provided the unique ability to capture INPs above the surface layer at the AMF1 site.
Single-Particle Mass Spectrometry The Fifth international workshop on Ice Nucleation -Phase 1 (FIN-01) was conducted as an intercomparison of the performance of single-particle mass spectrometers for online size resolved composition measurements of single aerosol particles  (DeMott et al. , 2018). Dry particle suspensions in air were created at the Aerosol Interactions and Dynamics in the Atmosphere (AIDA) chamber facility, Karlsruhe Institute of Technology (KIT), as was also done for FIN-02. The SDAr01 sample was also analyzed during FIN-01. These data are considered of special relevance to regional soil emissions in the CACTI region. The data presented were obtained by the University of Manchester version of the laser ablation aerosol particle time-of-flight (LAAPTOF) mass spectrometer  (Gemayel et al.
Data Availability Statement Data supporting the findings of this study are available from the following sources. All AOS data and original IS data files for the Cordoba AMF-1 deployment are available from the Department of Energy Atmospheric Radiation Measurement program website (https://www. arm. gov/data). Derived aerosol and ice spectrometer data products are available from the Mountain Scholar digital repository at Colorado State University (http://dx. doi. org/10. 25675/10217/229310). Availability Statement for access to data used in this manuscript.
The Earth's atmosphere has continually evolved since its formation through interactions with the mantle as well as through loss of volatile species to space. Atmospheric xenon isotopes show a unique and progressive evolution during the Archean that stopped around the Archean-Proterozoic transition. The Xe isotope composition of the early atmosphere has been previously documented through the analysis of fluid inclusions trapped within quartz and barite. Whether this evolution was continuous or not is unclear, requiring additional analyses of ancient samples, which may potentially retain remnants of the ancient atmosphere. Here we present new argon, krypton and xenon isotopic data from a suite of Archean and Proterozoic barites ranging in age from 3. 5 to 1. 8 Ga, with the goal of providing further insights in to the evolution of atmospheric Xe, whilst also outlining the potential complications that can arise when using barites as a record of past atmospheres.
5 Ga) barite from the North Pole formation (Australia). Meshik and Pravdivtseva (2017)  questioned this determination by arguing that 130 Xe could have been contributed by Xe isotopes produced by cosmic rays at the surface of the Earth. Using the same data set, Meshik and Pravdivtseva (2017) hence estimated the low limit for this half-life to be >2. 4 × 10 21 yr after correction for cosmogenic effects, more in line with previous estimates  (Meshik et al. , 2001). These discrepancies highlight the inherent uncertainty associated with the 130 Ba-130 Xe dating method, and underscore the need for all the potential secondary Xe productions in barites to be thoroughly described and quantified. Given that barites can be affected by secondary productions of Xe, using barites as an archive of ancient atmospheric Xe requires the careful deconvolution of different potential Xe components.
) light Xe isotopes 124,126,128,129 Xe relative to the modern atmospheric composition (Fig. 1 ). These excesses decrease systematically from 124 Xe to 129 Xe, which is consistent with a mass-dependent fractionation (MDF) of 3. 4 ± 0. 5‰. u -  (1σ, error-weighted linear regression using 124,126,128,129,130 Xe with IsoplotR© software,  Vermeesch, 2018). Assuming that the noble gases within the fluid inclusions were in isotopic equilibrium with the atmosphere during rock formation, then this MDF signature should be representative of the atmosphere at the time of Xe trapping  (Pujol et al. , 2011; Pujol et al. , 2013; Avice et al. , 2017; Avice et al. , 2018). When corrected for MDF, the heavy Xe isotopes ( 131-136 Xe) are consistent with secondary contribution of fissiogenic 131-136 Xe from 238 U (Fig.
Acknowledgments This work was funded by the  ERC  grant No. 695618  to B. M. We thank the S. A. R. M for providing elemental bulk analyses of the barites. We thank  Laurent Zimmerman  for technical mentorship and assistance. We thank  David Byrne  for fruitful discussions. We thank the three anonymous reviewers for their comments and suggestions, which significantly improved the paper. This is CRPG contribution 2786.
The first objective is to study the minimum number of static positions required for respecting the required accuracy. A solution is also proposed for compensating the drift of the mobile device. Secondly, the pertinence to use static stations for the principal loop and mobile system for adjoining rooms is investigated. The datasets chosen allow, on the one side, to quantify the limits of the mobile system for the acquisition of indoor buildings and, on the other side, to give recommendations regarding the configuration of static stations as a reference for mobile point clouds. Based on these experiments, a methodology is proposed for indoor environments to combine the use of the two acquisition systems and thus to save time in the field while still providing a good registration quality.
For each mobile acquisition dataset, registration with static scanner point clouds is thereafter made. Lachat et al. (2018)  suggested to perform a manual registration with common points to align the two point clouds followed by an adjustment using the generalized least squares method. In our study, an acquisition and also a processing methodology is sought. A robust comparison method between two point clouds is needed. The study of  Toschi et al. (2015)  presents a two steps comparison of point clouds acquired by different devices. First, an ICP (Iterative Closest Point) algorithm is launched in order to register one on the other. Following this, an M3C2 (Multiscale Model to Model Cloud Comparison) distance calculation is performed for quantifying the differences between the clouds  (Lague et al.
𝑆𝑘𝑒𝑤𝑛𝑒𝑠𝑠 = ∑(# ! $ &) " (($))* " and 𝐾𝑢𝑟𝑡𝑜𝑠𝑖𝑠 = ∑(# ! $ &) # (($))* # (1) The skewness parameter is related to the asymmetry of the histogram; the value obtained must be as close as possible to 0. The kurtosis parameter shows whether the histogram of the data has too flat or too hollow areas. This value should be around 3  (Toschi et al. , 2015). If the distribution does not follow a normal distribution, a non-parametric model based on the median of the data and the median absolute difference is used. This methodology is based on statistical methods and a robust nonparametric model. Both statistical parameters will be used to assess the proposed methodology. After a short description of the systems and datasets (section 3), the developed comparison workflow and the results obtained after its application on the datasets will be described (section 4).
Static and mobile acquisition systems used In order to conduct the study, two devices were used: the static laser scanner FARO FOCUS X330 and the handheld mobile mapping system GeoSLAM ZEB REVO RT. The characteristics of both devices are listed in Table  1. In the next sections, the mobile mapping system will be called "MMS" and the static laser scanning system "SLS". The MMS is composed of a "head" scanner for measurement and an inertial unit for direct positioning. The scanner is connected with an embedded computer that stores and synchronizes the data in real time. It operates using the SLAM technology. This process makes it possible to obtain a 3D representation of an environment and simultaneously to locate oneself in it. As already mentioned, unlike the FARO FOCUS X330, the ZEB REVO RT allows faster acquisitions and is easier to use.
Study areas and datasets In this work, two datasets are considered: SLS and MMS point clouds acquired in the Zoological Museum of Strasbourg and in several rooms of the graduate school INSA Strasbourg (France). This first dataset, the Zoological Museum, was chosen to test the robustness of the MMS against the SLS, along large loops. Indeed, the Zoological Museum is a building of about 50m x 50m constructed around an interior courtyard. This specific architecture allows to acquire easily horizontal loops (along each floor) and vertical loops (on the way to another floor). The horizontal loop is interesting because it is wide (about 140 m) and therefore the drift of the MMS will be easier to study. The vertical loop has been acquired in a stairwell of 18m height. This enables to study the drift in Z.
The first step will allow to quantify the loss of accuracy when reducing the number of static stations on which the mobile point clouds are registered. Then, in a second step, a solution to handle the drift observed in mobile acquisitions is proposed. The second dataset consists in several point clouds acquired in the INSA Strasbourg building. Many classrooms are adjoining and overlook the corridor. This dataset has been constructed to test the possibility to merge individual loops obtained in independent rooms with a principal acquisition of the SLS. In this regard, the static acquisitions act as a reference network.
Dataset 2: INSA Strasbourg building: For this dataset, two point clouds were acquired with the MMS in a corridor and several classrooms. In parallel, 9 SLS stations were carried out to cover the same area. Figure  2  shows the SLS stations, the trajectories (loops1 is about 43 m and loop2 is about 74 m long) of the handheld scanner as well as an horizontal slice of the MMS point cloud.
Once the "outliers" are removed, the comparison of the suggested combinations can begin. For that purpose, a workflow has been developed and is described in Figure  4. In a first experiment, all SLS stations are compared to the MMS loops after registration of both datasets. In a second experiment, the MMS loops are registered to a reduced number of SLS stations. For each stage, the registration is done in two steps. A first manual coarse registration, followed by the ICP algorithm. The reference results are given by a comparison of the MMS loop with all SLS stations of the same area. After a registration step providing a reference result (1), the number of static stations is progressively reduced, and the uncertainty of the registration (2) is analysed. The objective is to find the minimum number of static stations needed to register each loop, i.
The minimum number and the most appropriate position of the SLS stations in the corridor can therefore be studied. The minimum overlap to be considered between MMS and SLS acquisitions to ensure high accuracy in the room registration is also investigated. The results of each step are obtained via the analysis of the histogram of signed distances provided by the M3C2 algorithm. In order to perform these comparisons, the ICP and M3C2 algorithms are used via the CloudCompare (http://www. cloudcompare. org/ -Version 2. 9. 1) software. Afterwards, a Matlab script is used to calculate statistical parameters from the signed distances.
Horizontal loop of dataset 1: The first loop studied is the horizontal loop in the basement of the Zoological Museum. The trajectory of the MMS follows a closed corridor with 30 distributed static stations. In addition, a passage through an individual room is made coupled with 2 additional static stations (Figure  1 ). In order to register both clouds an ICP algorithm is launched, followed by an M3C2 distances calculation to compare their deviations. The result of the comparison is presented Figure  5. Additionally, a statistical study is performed with the calculated deviations, as presented in Table  2. The range (-3 cm; + 3 cm) was chosen regarding the accuracy of the MMS and is therefore considered as reference value. The Skewness and Kurtosis parameters allow to validate that the normal distribution is respected and that it makes sense to use the mean and standard deviation as statistical parameters.
6 ), so it has been decided to keep six SLS stations, one per corner, i. e. at the positions where the trajectory of the MMS varies suddenly. Additional intermediate stations between the corners are added for direct georefencing. An ICP algorithm is launched on the MMS data in order to register it with the reduced number of SLS stations. Then M3C2 is applied to calculate the distances separating both clouds. 3 , compared to the reference result, the percentage of distance between -3 and 3 cm decreases by 4% for 15 selected stations and by 6% for 6 stations. In addition, the RMS from the ICP algorithm is 2. 1 cm for 15 stations and 2. 3 cm for 6 stations. There is a slight loss of accuracy. This leads to the conclusion that it is acceptable to reduce the stations and to make this compromise between MMS acquisition and SLS stations for the acquisition of a linear of this type.
Vertical Loop of dataset 1: The processing steps carried out for the vertical loop are similar to those considered for the horizontal loop. After manual registration and segmentation, an RMS of 7 cm is obtained after ICP. This deviation is too high, and the bad results are confirmed by the M3C2 distances obtained (Figure  8 ). 5. References results for the vertical loop with all SLS stations and non-segmented MMS loop. Based on the results presented in Figure  8 , it can be noticed that the first (in red) and last landing (in blue) of the staircase present large deviations to the reference. It looks like a compression of the mobile point cloud compared to the static one. It is assumed that the inertial unit has encountered some kind of problem during the acquisition like a sudden movement.
Water samples were collected in each well at each sampling date. Temperature, pH, electrical conductivity, and dissolved oxygen were measured directly in the field with a mercury thermometer, a portable pH-meter (CG 818, Schott instruments), and a portable conductivity meter and optical dissolved oxygen meter (HQ30d, Hach Lange), respectively. The water was transported directly to the laboratory using polyethylene bottles under cool storage for additional analyses: alkalinity, dissolved carbon dioxide, calcium, magnesium, chemical (CDO) and biological (BDO5) oxygen demand, turbidity, and nutrient contents. The differences in physico-chemical parameters of water in wells with or without the new species were assessed using non-parametric Mann-Whitney U Tests using the STATISTICA v12 (StatSoft) software.
5 °C per cycle during the first 8 cycles). The next 27 cycles were performed at 51 °C. The PCR results were checked by gel electrophoresis, then the PCR products were purified and sequenced in both directions by the Eurofins sequencing facility or with an Applied Biosystem 3130 XL sequencer in the DNA sequencing facility of the Institute of Genetics and Development of the University of Rennes (https://igdr. univ-rennes1. fr/en). All COI sequences obtained in four wells are deposited on Genbank (assession numbers OL514108; OL514109; OL514110; OL51411 and OL51412) and on the Barcode of Life Data systems (BOLD) (BOLD process id: METAF001-21, METAF002-21, METAF003-21, METAF004-21 and METAF005-21). The new COI sequences were supplemented by COI sequences downloaded from Genbank: two sequences of Metastenasellus species from Benin (accession numbers KY623773.
1 and KY623774. 1); four from M. camerounensis from Cameroon (accession numbers KY623769. 1; KY623770. 1; KY623771. 1; KY623772. 1), and two from an unknown Metastenasellus species from Cameroon (accession numbers KY623775. 1; KY623776. 1). All sequences were aligned with the MUSCLE algorithm  (Edgar 2004 ) implemented in SEAVIEW ver. 5 (Gouy et al. 2021) to explore the Metastenasellus species diversity using different species delimitation methods implemented in iTAX-OTOOLS 0. 1  (Vences et al. 2021). Firstly, according to distance-based methodologies: the Assemble Species by Automatic Partitioning (ASAP)  (Puillandre et al. 2021 ) and the distance-based Automatic Barcode Gap Discovery (ABGD)  (Puillandre et al. 2012). Secondly, the results of the genetic distance-based species delimitation were cross-validated with the phylogenetic tree-based delimitation methods, namely: Generalized Mixed Yule Coalescent (GMYC)  (Pons et al.
Distribution of Stenasellidae in Africa An exhaustive survey of the literature on Stenasellidae (species descriptions, identification keys, monography, PhD theses, books, papers) was carried out for all the species known in continental Africa. All maps were drawn with Arcgis Desktop 10. 4 software (Esri) and using GIS data available on DIVA website (http://www. diva-gis. org) in WGS 84 datum. Diagnosis. Metastenasellus boutini n. sp. is characterized by pleonites 1 and 2 around 50% as long as pereonite 7, the presence of sternal spine on each dactylus of pereopods 2-7 and endopodite of pleopod 2 in males long and large with a helicoidal spermatic duct.
Molecular analysis We sequenced and analyzed DNA from five individuals from four wells, including the type locality at Douala. Based on the Folmer's fragment of COI marker, the new species is clearly distinct from the other species sequenced in Cameroon and in Benin. The pairwise genetic distances between M. boutini and all other species varied between 22. 4 and 27. 8% for Cameroonian species and even 28. 2% for the species in Benin (Fig. 5 ). In addition to genetic distance and morphological distinctness, all delimitation methods clearly highlighted the existence of at least five distinct lineages of Metastenasellus in the material available for Africa (Fig. 5 ). All delimitation methods confirmed that individuals from wells at PK21 belong to the same lineage. Consequently and in addition to morphological distinctness, molecular data support the hypothesis that M.
However, and despite their wide environmental tolerance, there is no mention of stenasellids in brackish water and their known localities do not exceed a salinity of 3 g. L -1 for S. ruffoi  (Messana, 1993). Stenasellids can also colonize all kinds of groundwaters (karst, interstitial and phreatic waters)  (Magniez 1999 ). The wide "tethyan" distribution of stenasellids suggests the presence of an ancestor at least during the Upper Cretaceous. This hypothesis is well supported by the molecular phylogeny of Asellota  (Morvan et al. 2013)  showing that stenasellids were already present on Pangaea during the late Paleozoic (≅ 250 MYA), while African and Nearctic stenasellids were geographically separated. The lack of data about the distribution of stenasellids in Africa does not allow us to draw a clear conclusion about their biogeography in Africa.
insularis, showed close affinities to the extant genus Mus and pointed to a 6. 9 Ma divergence date (genetic data were obtained by means of the traditional polymerase chain reaction method). Hot and humid thermal conditions hamper the retrieval of ancient genetic data  [6]. Without this information, it is difficult to unravel the affinities of highly modified extinct species such as †C. bravoi to their mainland smaller relatives. Here, we managed to retrieve partial nuclear and mtDNA data from two †C. bravoi specimens; we subsequently used this information to provide divergence age estimates and phylogenetic relationships for this lineage and determine the rate of increase in body size of this insular rodent.