dataesr/scientific-paragraphs-categorization huggingface dataset:
Climatology West African climate depends on the position of the Intertropical zone of convergence (ITZC), which is the limit between a cool and humid marine air mass (Monsoon) and a warm and dry Saharan air mass   (Goula et al. , 2010). Meteorological parameters from 2018 to 2020 (monthly temperature and relative humidity) were provided by the SODEXAM (Society of exploitation and airport development, Aeronautics and Meteorology) for Abidjan and Korhogo, as well as long-term rainfall databases for the periods  1980-2020 and 1990-2020  respectively. From 2018 to 2020 in Abidjan, we used rainfall measured by the EVIDENCE project (Extreme rainfall events, vulnerability to flooding and water contamination) that installed tipping bucket rain gauge (tilting of the bucket for 0. 5mm) and Précis Mécanique® (rain interception cone 1.
5 m from the ground and with an area equal to 400 cm 2 ). Tipping bucket dates (day month year hour minute second) are recorded in a HOBO Pendant® UA-003-64 data logger. The rain gauge data were collected monthly. During each visit, the devices were cleaned and the tipping volume of the buckets was checked in the urban site of Abidjan. At the Lamto site, the long-term monitoring program (INDAAF) provides air temperature, humidity and rainfall data for the period  (Diawara et al. , 2014). Abidjan is characterized by a bimodal rainfall regime defined by two wet seasons and two dry seasons. A long-wet season lasts from March to July and a short-wet season from October to November. A long dry season lasts from December to February and a short dry season from August to September  (Leroux et al.
Considering data in Abidjan from 1980 to 2020, we have 20 years of surplus and 18 years of deficit compared to an average rainfall of approximately 1522 mm, with the maximum rainfall recorded in 1990 (3338. 9 mm) and the minimum in 2001 (784 mm). According to the classification of Sarr, 2020 (AII = + 0. 14) can be considered as a moderately wet period while the other two years 2018 (AII = -0. 08), 2019 (AII =-0. 31) can be classified as moderately dry years. For Lamto, from 1998 to 2020, there are 8 years of surplus and 13 years of deficit compared to an average rainfall of approximately 1229 mm. The maximum rainfall was recorded in 2007 (1548. 5 mm) and the minimum in 2014 (991. 9 mm). For the three years of the study period, 2019 (AII =+1. 7) can be considered as a strongly wet period while 2018 (AII =-0.
Lamto has good %TP values for all years. However, for comparison purposes, only 2019 and 2020 will be considered for computing the annual volume weighed mean (VWM) and Wet deposition fluxes (WD), and the average of the two years 2019-2020, where the mean collection rate is respectively 78 % ,94 %, 73 % and the PCL is 100% in Abidjan, Lamto and Korhogo. Data from January 2018 to December 2020 will be used to calculate monthly Volume Weighed Mean (VWM) and Wet Deposition (WD) according to the quarterly %PCL. In reference to the WMO international standards, we assume the precipitation collection at Abidjan, Lamto and Korhogo in 2019 and 2020 can be considered as representative of the studied period according to the parameters calculated in Table  1. https://doi. org/10. 5194/egusphere-2022-994 Preprint.
Satellite data We used version 1. 6 of the CrIS-Fast Physical Retrieval (CFPR)-NH 3 product  ( Shephard et al. 2015; Mark W. Shephard et al. 2020). CFPR is a physical retrieval of an atmospheric profile of NH3 derived from minimizing residuals between the measured and simulated spectra. The product compares well with in situ and ground-based FTIR observations  (Dammers et al. , 2017; Kharol et al. , 2018). The CrIS sensor has an NH 3 detection limit of ∼0. 3 to 0. 9 ppb  (Shephard et al. , 2020) , which varies depending on the atmospheric conditions. Comparing the NASA/NOAA SNPP satellite radiances from both CrIS and VIIRS instruments show that the stability of CrIS is very good. The VIIRS -CrIS daily mean brightness temperature difference shows a trend of -0. 40 ± 0. 03 mK per year at the wavelength of 10.
76 um for a ∼273 K average scene (David Tobin, personal communication). The most recent update of the CFPR (v1. 6) identifies and accounts for non-detect values below the sensor's detection limit, which reduces the previously reported small positive bias for the lower range of total column concentrations (<5x10 molecules cm -2 ). For this study, only We used Level 3 (L3) data at 0. 25×0. 25resolution from the NASA tropospheric NO2 standard product from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite. OMI is a nadir-viewing spectrometer in sun-synchronous orbit with near-daily global coverage that measures solar backscatter in the UV-visible range  (Krotkov et al. , 2017). The OMI product relies on air mass factors calculated with the assistance of an atmospheric chemical transport model, and is sensitive to model representations of emission, chemistry and transport data.
Acknowledgements This work carried out in part within the framework of the  PASMU  project, financed by the  Ministry of Higher Education and Research of Côte d'Ivoire , in the framework of  Debt Reduction-Development Contracts (C2D ) managed by the  Research Institute for Development (IRD)  under funding program  5768A1-PRO/PRESED-CI C2D   PASMU  project whose main agreement is registered under number https://doi. org/10. 5194/egusphere-2022-994 Preprint. Discussion started: 14 November 2022 c Author(s) 2022. CC BY 4. 0  License. 305984/00. This work also received a contribution from the  INDAAF program , supported by the  INSU/CNRS , the  IRD  ( Institut de Recherche pour le Développement) , from the  Observatoire des Sciences de l'Univers EFLUVE , from the  Observatoire Midi-Pyrénées  and the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement No.
Data availability Raw data were collected in the framework of the PASMU project and are available on request from the coordinator Pr V. Yoboué (yobouev@hotmail. com). Data of the LAMTO site are available from the INDAAF project at the address http://indaaf. obs-mip. fr The pre-processed HYSPLIT trajectory data can be obtained from the corresponding author, and the trajectories can be freely calculated at the web page https://www. ready. noaa. gov/HYSPLIT_traj. php. MODIS burned-area data are available from https://doi. org/10. 5067/MODIS/MCD64A1. 006 (Giglio et al. , 2015). OMI L3 NO SP version 4 is available at 10. 5067/MEASURES/MINDS/DATA301 (Lamsal et al. , 2014). The CrIS CFPR version 1. 6. 3 NH3 VCD data is available upon request from Environment and Climate Change Canada (mark. shephard@ec.
(Zhou et al. , 2017)  propose a processing method for inspecting a railway tunnel from data acquired by a mobile laser scanner. Based on the extraction of the rails from a segmentation, a modeling of the alignment of the rails is carried out. From this modeling, a dynamic coordinate system is created. Sections are then extracted at a regular interval according to the local coordinates created earlier. Upstream of the application of these modeling or extraction methods, the pre-processing of point clouds must be carried out. Pre-processing refers to cloud consolidation, georeferencing and segmentation. Although these steps may seem relatively simple, the application of these steps to a linear structure such as the tunnel can pose some difficulties, especially for consolidation. Even if many acquisitions are made without recalibration spheres, this is not possible for the case of the tunnel.
Its geometry, which has many similarities from one section to another, makes the consolidation stage more difficult despite the use of recalibration spheres. For georeferencing, tunnels being underground infrastructures traditional geodetic surveying methods must be implemented in order to carry out this step. The software is also adapting to the development of the use of the laser scanner in tunnel modeling. This is particularly the case of the 3DReshaper software which offers many functions applicable to tunnels such as the calculation of the axis, the realization of profiles or sections, etc. For inspection, comparison functions are also available such as polyline comparisons to compare several sections, or profile comparison tools. The guide published by 3DReshaper presents many examples of how to use these functions in the case of a tunnel.
Point cloud processing After acquiring the entire tunnel, it was necessary to start by processing the data. The first step in processing is the consolidation of the point clouds. The raw point clouds extracted from the laser scanner were imported into the Faro SCENE software. Automatic sphere-based consolidation was performed for each portion of the tunnel acquired in one go. The difference in the detection quality of each sphere is due to a greater distance between the scanner and the target, to an angle too sharp during the acquisition or to a too low detection of the number of pixels belonging to the target. Some scans were assembled to the consolidated group through plan and point consolidation (measured manually). The remaining scans were added one by one to the consolidated group, again by consolidation from the spheres.
Several clusters, groups of scans consolidated, had to be used and then consolidated relatively to each other. Georeferencing has also made it possible to simplify the process because it has the effect of fixing the position of the checkerboards present on certain scans. This makes it possible to constrain consolidation. Faro SCENE software provided a RMS on the consolidated cloud of 6. 8 mm. This value seems quite acceptable and consistent in view of the linear geometry of the point cloud which is conducive to error drift. To obtain a point cloud that was easier to manipulate during modeling, a resampling was performed on the project. In order to reduce the number of points while maintaining sufficient density to allow good visualization, it was decided to keep one point every 1 cm. As a result, the initial point cloud of 2,420,635,327 points has been reduced to a cloud of only 259,130,807 points.
The cloud cleanup was performed on the CloudCompare software. A translation had to be applied to the coordinates of the points to be able to maintain the accuracy of the initial cloud and allow a smoother use of the software. The resampling was carried out directly on Faro SCENE when exporting the point clouds thanks to the Uniform Filter application. It is therefore possible to choose the value of the sampling to be applied to the exported files. The final cloud thus obtained is shown in Figure  2. It is displayed in real colors; the color scan setting having been selected during the acquisition. The color effects are due to the position and colors of the lights present inside the tunnel.
EXTRACTION OF STRUCTURAL LINES To model the structural lines representing the planimetric footprint of the tunnel, i. e. the contours of the tunnel seen from above, two methods were defined, applied and then compared. The first uses the 3DReshaper software and its feature contour function. The second is a Python script that allows you to directly extract the extreme points of a cross-section from a point cloud in ASCII format and an axis of the tunnel. Only this second method makes it possible to realize the structural line that corresponds to the summit tangent of the tunnel.
Contour line extraction methods The first step is to generate a mesh of each of the portions of the point cloud after having carried out a regular re-sampling while keeping only the outer edges. In 3DReshaper, software used for these treatments, the parameter of the average distance between the points can be changed. To determine the most suitable value for this point cloud, tests were carried out with the values 0. 56which is the default value, 0. 1 and 0. 01. The mesh made with a value of 0. 01 is the most faithful. It is possible to distinguish the holes allowing the assembly of the tunnel voussoirs between them. The mesh now available it is necessary to carry out a smoothing of its surface. Indeed, the objective is to obtain the contour of the mesh, but it must be as smooth as possible so that the contour also has the least possible roughness.
Python scripts This method consists of writing a Python script that uses as input a cloud in ASCII format and a directional line following the tunnel. It makes it possible to extract successive sections of the point cloud in order to extract from each of them the maximum altitude point, and the two points located outermost of the tunnel, the tangency points defined above. The point clouds of the different parts of the tunnel are those that have already been worked on (assembled, cleaned and sampled). To limit calculation times, the circular portions have been subdivided into several sections. Exports in ASCII format can be made directly from the 3DReshaper software. The software also has a function to extract the neutral axis from a tubular shape. This axis corresponds to the line passing through the geometric centers of gravity of the sections.
The function determines a first help line roughly passing through the centers of the point cloud sections. This line can then be worked to remove aberrant sections. The neutral axis is then calculated from this line, portion by portion by selecting the appropriate type of section, namely circular or rectangular. A smoothing can also be applied to the created axis. Before launching the algorithm, it is necessary to enter a certain amount of data to the program: (i) the ASCII file containing the points of the neutral axis of the tunnel, (ii) the ASCII file of the point cloud to be processed (iii) the half thickness of a section, (iv) the verticality or not of the cross sections. The first step of the script consists of the realization of cross-sections of the tunnel. These sections are defined by normal vectors calculated from the coordinates of the points composing the neutral axis of the tunnel.
Final processing of contour lines: To achieve a 3D usable result, the 3D edges of the rectangular section portion were added to the outline from the Python script. However even if the result is right in terms of position, the first visual obtained can still be worked. An additional data can be integrated on these contours, it is the thickness of the voussoirs (concrete wall of the circular parts). Indeed, this thickness is visible in certain places of the tunnel and can therefore be measured on the point cloud using the Faro SCENE software (Figure  5 ). In order to confirm the value, the thickness was measured several times at different locations. This value was therefore set at 35 cm. All of these elements could be placed on a plan manually, but this method is very time-consuming. The goal here was to automate the production of this plan from the tunnel's point cloud (Figure  6 ).
Following the field measurements, the reflection focused on three main types of rendering to be proposed to the CTS. To obtain the positioning of the tunnel, it was agreed with the CTS that only the tunnel structural lines would be extracted from the point cloud at first. To do this, two methods were put into practice and compared. The first method consisted of the use of functions of the 3DReshaper software, which allow the extraction of the external contours of the tunnel in the form of polylines easily integrated into a GIS software. This method turned out to be fast and offers a relatively smooth result. On the other hand, it does not allow to extract the high line of the tunnel and the external contours are not known in altimetry. To overcome this limitation, a second method, based on the automatic realization of sections in the point cloud and then the search for characteristic extreme points in each of these sections was developed in the form of Python scripts.
The enrichment of CO 2 and bicarbonates in groundwater leads to an acidification of groundwater. Hence, CO 2 -rich groundwaters are generally more corrosive and more mineralized. However, the waters of this dataset keep the same relative proportions in cation composition, whatever their carbonate contents (Fig. 4 ). This observation is not really in line with the hypothesis that carbonate dissolution explains the main origin of this dissolved CO 2. As discussed in  Barros et al. (2021) , this hypothesis was until now the most commonly accepted one for the CO 2 origin. Indeed, if the presence of dissolved CO 2 in carbo-      gaseous waters was due to carbonate dissolution, it would be expected that this groundwater would also be enriched in Ca 2+ and possibly Mg 2+ ions in comparison to noncarbogaseous waters, but this is not the case in these Spa-Bru waters.
Acknowledgements. This work is part of the  ROSEAU  project, as part of the  Walloon program  " Doctorat en Entreprise ", co-funded by the  SPW Région Wallonne of Belgium and the company Bru-Chevron S. A. (Spadel S. A. ) , under grant number  7984. The collaboration with the CRPG of Nancy was made possible thanks to the  Europlanet Transnational Access Program , as this project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 871149. The authors acknowledge these two institutions for their support. Financial support. This research has been supported by the  Service Public de Wallonie  (grant no. 7984 ) and the  Horizon 2020  ( EPN-2024-RI (grant no. 871149 )). Review statement. This paper was edited by  Brian Berkowitz  and reviewed by two anonymous referees.
A C K N O W L E D G M E N T S HI is grateful for the support of the Institut de Physique du Globe de Paris, France, for a visiting fellowship in 2019, that kicked off this project. We acknowledge support from the ERC-Adv project ROMY (grant number 339991). Furthermore we acknowledge support through the PIONEERS project funded through the European Union's Horizon 2020 program under grant number 821881.
e. , "gravity-settling shadows" in which dips are overturned  30, 31  ) (Extended Data Fig. 1 ). An intriguing challenge here is to decipher how in situ growth of chromite is recorded in the texture of massive chromitites themselves. We have re-visited the UG1 chromitite from the classical Dwars River locality  41  (Fig. 1b ) where it is composed of 25-50 vol% of cumulus chromite that occurs as separate idiomorphic grains or clumps of grains that are smaller than 0. 1 mm in size (Fig. 1c, d ). The small grain sizes are emphasized by the fact that 1 cm 3 of the rock contains, at least, 500,000 individual chromite crystals. The chromite grains are enclosed within much larger oikocrysts of plagioclase (up to 5-10 cm in size) that are clearly visible in outcrops (Fig. 1c ). The traditional interpretation of such layers in the frame of gravity settling models is that chromite was the first to settle on the chamber floor  [32] [33] [34] [35]  followed, after some period of post-depositional cooling, by in situ growth of plagioclase oikocrysts from the interstitial melt in a mushy chromitite.
3 ): firstly, the packing densities are greatly different, being much higher in the synthetic pack than in the natural sample (60% vs. 27%). Secondly, the distribution of coordination numbers is significantly different (Fig. 3b, c ). In the random pack, coordination number increases exponentially with the grain size. This happens because, for geometrical reasons, larger grains have a larger surface area and hence are likely to be in contact with a larger number of smaller grains filling space between them. In the natural UG1 data set, however, the coordination number flattens out and remains roughly constant at about 10 for grains larger than 150 microns in size (noting that only a small proportion of the total number of grains falls in this size range). This results from the chromite grains forming an open cage-like or chain-like structure where gaps in the framework are not occupied by other grains, causing lower coordination numbers at larger grain sizes.
The separation of touching chromite crystals in 3D was done using a modified version of the algorithm used to separate touching chromite in komatiites  89  and chromite from the normal Merensky Reef 90 using Avizo2020 and Matlab software. The shape and size characteristics of chromite network and individual grains were computed to provide quantitative measure of chromite grains above 15 µm equivalent sphere diameter (ESD). Chromite grains were defined as touching each other using an 18-voxel connectivity threshold (i. e. , voxels are connected if their faces or edges touch). The coordination number of each chromite grains (i. e. , the total number of other chromite grains touching in 3D a given grain) was also calculated. All results are summarized in Figs. 2  and 3.
Acknowledgements Several versions of this manuscript benefited from the constructive peer reviews by  Catherine Annen ,  Allen Glazner ,  Chris Hawkesworth ,  Olivier Namur ,  Jill VanTongeren ,  Zoja Vukmanovic ,  Steve Prevec ,  Willem Kruger ,  Richard Hornsey ,  Alan Boudreau ,  James Mungall ,  Richard Naslund ,  Ilya Veksler  and  Jean Bédard. Official reviews by  Richard Naslund  and  James Mungall  helped to clarify some points in the final version of this manuscript. The study was supported by research grants to R. L. from the  National Research Foundation (NRF)  and  Department of Science and Technology (DST)-NRF Centre of Excellence for Integrated Mineral and Energy Resource Analysis  (CIMERA) of South Africa. V. J. R. was supported by the  DST-NRF Centre of Excellence in Palaeosciences (CoE-Pal) , and the  Palaeontological Scientific Trust (PaST).
After removing the instrumental drift, spikes, steps and tares, both gravimeters have been calibrated by fitting the output signal against a synthetic reference signal based on the body tidal gravity response due to Wahr-Dehant Earth model and FES2014 ocean tidal loading model. The calibrated signals have been analyzed with ET34-X-V80 software for tidal analyses. This enabled us to retrieve a set of frequency-dependent gravity factors (amplitude and phase) for the main tidal waves, as well as to obtain gravity residuals. It turns out that the accuracy of the amplitude estimates for the main tidal waves is 0. 2 7 1% for LCR_ET16 in Aswan and 1 7 10% for the LCR_D-218 in Abu Simbel. To improve the tidal model at Abu Simbel, LCR_ET16 was stopped in Aswan and relocated there. The first 90 days of gravity recordings from ET16 at Abu Simbel provide promising results, with an accuracy of the order of 0.
16). Almost all wave groups had phase shifts close to zero. The highest standard deviations were found for waves K1 and S2, leading the authors to invoke an effect of air pressure and solar variation during the day, which is expected in tropical areas such as Aswan or alternatively attributed to a problem in the instrumental chain, i. e. the quartz clock drift of the Data AcQuisition (DAQ) system. Even more interesting were the results in terms of gravity residuals, which showed noise levels in the diurnal and semidiurnal at 16 nm/s 2 and 9 nm/ s 2 respectively. This clearly proves that the tidal waves were not completely separated. All these outcomes claim for a need of a more accurate tidal model for the region. Two methods were used by  Hassan et al. (2010b)  to evaluate the gravity variations in the tidal records due to variations of water level at Lake Nasser: (a) block analysis; (b) correlation analysis between water levels and gravity residuals.