dataesr/scientific-paragraphs-categorization huggingface dataset:
migration processes. Information related to interventions are collected and integrated into a GIS database. It uses spatialised polygons, polylines or points depending on their characteristics. Datasets are organised on a year-to-year basis and according to intervention type. Management data used in this study relates to years 2011 to 2017 in order to meet available LiDAR datasets. Information related to ONF intervention on dunes during this period are extracted using boxes division. For each box, the number of interventions listed is computed using "select by location" tools. Only interventions related to sediment flow management are considered. They correspond to marram grass planting, wind fence set up, branches cover and dune front cut off. Pathways maintenance and waste collection are not considered in the present analysis.
Acknowledgements The authors would like to thank the ONF agents interviewed during this study, in particular  David Rosebery ,  Benjamin Sirren ,  Bernard Capdeville. The authors are grateful to the  Observatoire de la Côte Aquitaine (OCA)  for providing data resources in partnership with the ONF and the IGN for LiDAR Data. All cofunders of the  OCA  project are thanked, namely, the  European Union  via the FEDER fund, the  SGAR , the  Région Nouvelle-Aquitaine , and the  department of Gironde, Landes and Pyrénées-Atlantiques. Thanks also to  Bruno Castelle ,  Vincent Marieu  and  Patrick Hesp  for their constructive remarks. Finally, the  BRGM  is also thanked for the support of the publication and  Nicolas Bernon  and  Sophie Lecacheux  for the first discussions. The authors want to gratefully acknowledge the anonymous reviewers for their kind remarks, constructive criticism and insightful comments that greatly improved the manuscript.
Specifications The smart solution of the utility tunnel aims at the construction of a comprehensive system that uses smart technology to cope with the challenges presented in Figure  2. Figure  3  summarizes the specifications for the smart system. It should collect data concerning the utility tunnel assets and operation. Asset data are integrated into a three-dimensional (3D) digital model including both the tunnel and the hosted utilities, with identification and geo-localization attributes for all components. Operating data are collected via sensors used in monitoring the tunnel and hosted utilities. The smart system should also include applications for online access and update of inspection and maintenance data. Management and liability also constitute serious challenges for companies in charge of hosted utilities  [7, [11] [12] [13].
Specifications The smart solution of the utility tunnel aims at the construction of a comprehensive system that uses smart technology to cope with the challenges presented in Figure  2. Figure  3  summarizes the specifications for the smart system. It should collect data concerning the utility tunnel assets and operation. Asset data are integrated into a three-dimensional (3D) digital model including both the tunnel and the hosted utilities, with identification and geo-localization attributes for all components. Operating data are collected via sensors used in monitoring the tunnel and hosted utilities. The smart system should also include applications for online access and update of inspection and maintenance data. The smart system should conduct real-time and historical data analysis using engineering and security software, as well as advanced ICT (information and communication technology) tools such as machine learning and artificial intelligence.
Smart System Architecture The architecture of the smart management system is described in Figure  4. A smart management platform ensures the coordination of the system components, which include the following layers: manager layer, physical layer, monitoring layer, inspection and maintenance layer, information system layer, analysis layer, and control layer. The platform ensures coordination and interaction among the layers of the smart system. The physical layer concerns the physical components of the tunnel and hosted utilities. Information about the functioning of these components is collected via sensors in the monitoring layer, while information about their maintenance and inspection is collected from maintenance operators via the maintenance layer. Collected data are stored in the tunnel information system, transmitted to the Physical layer : Tunnel and hosted utilities  The platform ensures coordination and interaction among the layers of the smart system.
The physical layer concerns the physical components of the tunnel and hosted utilities. Information about the functioning of these components is collected via sensors in the monitoring layer, while information about their maintenance and inspection is collected from maintenance operators via the maintenance layer. Collected data are stored in the tunnel information system, transmitted to the tunnel managers, and analyzed using data analysis tools and software. The results of analysis are transmitted via the platform to the tunnel managers, who can, according to these results, operate manual actions related to safety and system performances. Results can also be used to operate automatic actions via the control layer (switches, valves, motors, pumps, etc. ).
Globally, water is unequally distributed, and water volumes are not constant due to unequal volumes of water-replenishment and water-depletion. Freshwater is replenished through direct rainfall, whereas its consumption is mostly the sum of evaporation, ground seepage, outlet flow and anthropogenic activities, such as irrigation. Therefore, for proper management of freshwater from lakes, rivers, and reservoirs, the monitoring of water volumes and water levels is essential. In general, water surfaces are monitored using in-situ gauge observations. These gauges measure the temporal variations of water levels in lakes, reservoirs, and rivers. However, in-situ monitoring of water levels is scarce and sometimes impractical in many regions of the world due to several reasons:  (1)  the decline of gauging networks globally due to the cost of installation and maintenance, and their sparsity in developing countries  [6] ;  (2)  the limited accessibility and costly charges for acquiring in-situ water level data as they are considered sensitive information  [7] ; and, finally, (3) the difficulty to monitor water levels across any free water surface, especially in areas where the channel network is not well defined, such as in the case of floodplains and wetlands.
89 cm) and ~20 cm (bias = 0. 26 cm). Huang et al. [21]  used Sentinel-3A data to monitor water levels along the Brahmaputra River (rived bed width varies between ~100 m to more than 1000 m). They reported that the standard deviation of the difference between the gauged station recorded water levels and Sentinel-3A derived water levels ranged from 41 to 76 cm. Normandin et al. [22]  compared 18 water levels derived from Sentinel-3A with gauge records from 5 in-situ stations in the Inner Niger Delta. Their results showed that with only taking into account the closest ones, the RMSE ranged from 16 cm to 70 cm. Finally, Bogning et al. [23]  found that the RMSE on the estimation of Ogooué river (river bed width varies from ~300 m to ~1000 m) water levels using Sentinel-3 data varied from 41 cm to 89 cm.
The average water level elevation for the majority of the lakes is between 405 and 433 m, while Lake Geneva has an average water level elevation of 372. 05 m, and Lake Sempach and Lake Thun have an average water level elevations of 503. 66 m and 557. 67 m, respectively. Table  1  lists the associated information about the gauge stations, and the number of GEDI acquisitions over each of the eight case study lakes. Lake boundaries have been provided by the Global Lakes and Wetland Databases (GLWD)  [29]  and will be used to extract the GEDI footprints that were acquired over each of the eight lakes. Water level records from in-situ gauge stations over lakes were obtained free of charge from the Hydrology Department of the Federal Office for the Environment (FOEN) (www. hydrodaten. admin. ch).
GEDI Data Products The Global Ecosystem Dynamics Investigation (GEDI) onboard the International Space Station (ISS), which commenced operations in early 2019, uses three onboard lasers that produce eight parallel tracks (beams) of observations. GEDI lasers illuminate a surface or footprint on the ground with a 25 m diameter, at a frequency of 242 Hz, over which 2D or 3D structures are measured. The footprints are separated by ~60 m (center to center) along the beam, and the beams are separated by ~600 m. As the ISS is not maintained in a repeating orbit  [25] , the repeat cycle of GEDI acquisitions are not guaranteed. However, GEDI has the ability to rotate the instrument up to six degrees, allowing the lasers to be pointed as much as 40 km on either side of the ISS's ground track  [25]. Over our studied lakes, there are on average two acquisition series per lake during the first two months of available GEDI data (Table  1 ), which correspond to the time period between mid-April 2019 and mid-June 2019.
Algorithm Smooth Width Smoothwidth_Zcross Front_ Threshold Back_ Threshold 1 6. 5 6. 5 GEDI data used in this study are already processed and published by the Land Processes Distributed Active Archive Center (LP DAAC). Currently, there are three data products (L1B, L2A, and L2B) that are available for download. The L1B data product  [30]  contains detailed information about the transmitted and received waveforms, the location and elevation of each waveform footprint, and other ancillary information, such as mean and standard deviation of the noise, and acquisition time. The L2A data product  [31]  contains data of elevation and height metrics of the vertical structures within the waveform. These height metrics are issued from the processing of the received waveforms from the L1B data product.
Finally, the L2B data product  [32]  provides footprintlevel vegetation metrics, such as canopy cover, vertical profile metrics, Leaf Area Index (LAI), and foliage height diversity (FHD). In this study, the received waveforms, their geolocation (longitude, and latitude), as well as their acquisition times, were extracted from the L1B data product. In the L2A GEDI data used in this study are already processed and published by the Land Processes Distributed Active Archive Center (LP DAAC). Currently, there are three data products (L1B, L2A, and L2B) that are available for download. The L1B data product  [30]  contains detailed information about the transmitted and received waveforms, the location and elevation of each waveform footprint, and other ancillary information, such as mean and standard deviation of the noise, and acquisition time.
The L2A data product  [31]  contains data of elevation and height metrics of the vertical structures within the waveform. These height metrics are issued from the processing of the received waveforms from the L1B data product. Finally, the L2B data product  [32]  provides footprint-level vegetation metrics, such as canopy cover, vertical profile metrics, Leaf Area Index (LAI), and foliage height diversity (FHD). In this study, the received waveforms, their geolocation (longitude, and latitude), as well as their acquisition times, were extracted from the L1B data product. In the L2A data product, the derived metrics are also grouped by algorithm. Therefore, for each beam, the metrics derived from each of the six algorithms, as well as the parameters used for each algorithm, are available. Therefore, we extracted from L2A for each beam, and for each of algorithms 1 and 2, the following variables: (1) the position within the waveform, as well as the elevation of toploc and botloc, (2) the latitude and longitude, as well as the elevation of the lowest peak or mode, (3) the amplitude of the smoothed waveform's lowest detected mode (zcross_amp), (4) the width of Gaussian fit of the received waveform (rx_gwidth), and (5) the number of detected modes (num_detectedmodes).
Filtering of GEDI Waveforms Not all GEDI acquisitions are viable, as atmospheric conditions and clouds can affect them (Figure  2c ). Therefore, two filters were applied to remove erroneous lower quality returns. The first filter applied removes waveforms with reported elevations that are significantly higher than the corresponding Shuttle Radar Topography Mission (SRTM) DEM elevation  [33]  (i. e. , we removed all waveforms were |GEDI elevation-SRTM| > 100 m). Since we are only interested with waveforms that are acquired over water, we removed all waveforms having two or more peaks or modes. A multi-modal waveform is a strong indication that the waveform was acquired over areas with complex geometry (e. g. , vegetation or considerable relief). Information regarding the number of detected modes for each waveform were acquired from the L2A data product.
Over the eight studied lakes, 21242 GEDI shots were available for comparison with the lake gauge data (Figure  1 , Table  1 ). From these shots, only 4637 (21. 8%) provided exploitable waveforms. GEDI data accessible through NASA's LP DAAC contain a quality flag (quality_flag) for each acquired waveform. A waveform with a quality flag set to '1 indicates that the waveform meets certain criteria based on energy, sensitivity, amplitude, and real-time surface tracking quality, and thus can be processed further. However, in this study, after the application of the SRTM DEM filter, waveforms with either value of the quality_flag (0 or 1) showed similar characteristics (e. g. , defined single peak, high signal to noise ratio, etc. ). Therefore, all waveforms were analyzed regardless of the value of the quality_flag.
Transformation of GEDI Elevations In order to conduct a consistent analysis between the elevations provided by GEDI and water elevation from gauge stations, the heights from both datasets must refer to the same vertical datum. In this study, the geolocated GEDI waveform elevations are relative to the WGS 84 ellipsoid, while gauge stations are provided as orthometric heights with reference to the French height system (NGF-IGN69) for lakes Geneva and Neuchâtel, and the Swiss height measurement reference system (LN02) for the other lakes. The tide gauge at Marseille determines the 'zero level' for all elevations in France, while the reference for all height measurements in Switzerland is the "Repère Pierre du Niton" in the harbor of Geneva (stone). The elevation of this stone was evaluated in 1902 to be 373.
5 ns, Table  2 ) provide more precise elevations in comparison to algorithm a2 (Smoothwidth_zcross of 3. 5 ns, Table  2 ). Using the entire database from all the lakes in this study (8 lakes and 4637 viable waveforms), GEDI footprint elevations in comparison to in-situ gauge station elevations showed a mean elevation difference of 0. 61 cm with a1 and 7. 8 cm with a2. The standard deviation of the mean difference between GEDI footprint elevations and gauge station readings is 22. 3 cm using a1 and 23. 7 cm using a2. The root mean square error (RMSE) on GEDI elevations is slightly higher using a2 with a value of 24. 9 cm against 22. 3 cm using a1.
Analysis of GEDI Waveforms for Each Lake The precision of elevations estimated from GEDI waveforms was studied separately for each lake using all GEDI beams from all acquisition dates. Table  3  shows a mean difference (MD) between GEDI and in-situ elevations that varies between -13. 8 cm (under-estimation by GEDI) and +9. 8 cm (over-estimation by GEDI). The reported standard deviation from MD varied between 14. 5 cm and 31. 6 cm. Figure  4a  shows an example of GEDI data for a transect with its 8 beams, acquired on May 29th 2019 at 9:25 p. m. over lake Neuchâtel in Switzerland (GEDI elevations for all lakes can be found in Appendix A, Figure  A1 ). This example shows what has also been observed over the other lakes, albeit with different elevation precision depending on the acquisition date, or beam.
Analysis of GEDI Waveforms by GEDI Beam Figure  5  shows the summary of the statistics calculated from the difference between GEDI and hydrological gauge elevations for each date and each beam, using data from all lakes. These statistics were first calculated for each GEDI beam and for each date. Only the statistics with at least 30 GEDI shots for each date/beam pair are reported in this section. Results show that the bias (elevations from GEDI-elevations from gauge stations) varied depending on the acquisition date, and the beam. For certain beams, at certain dates, elevation from GEDI had the same order of magnitude as in-situ elevations, while, for other dates, and even for the same beam, the bias was very significant (Figure  5a ). For example, on 20 April 2019, elevations from GEDI acquired over beam 1 showed a bias of -2.
Modelling GEDI Estimation Erorrs In the previous sections, we showed that there were several instrumental and environmental factors affecting the acquired GEDI waveforms, thus producing an important difference between in-situ and GEDI estimated elevations. Among these factors, the provided zcross_amp, rx_gwidth from the L2a data product, and the derived GEDI viewing angle (θ) has been examined. zcross_amp and rx_gwidth were chosen as they are an indicator of saturation as seen in the previous section, whereas the viewing angle has been demonstrated in Urban et al. [37]  to increase elevation errors for ICESat-1 GLAS when the viewing angle deviates from nadir due to precision attitude determination. In this paper, the GEDI viewing angle (θ) has been estimated using the following equation: θ = tan -1 d H ( 5 ) where d is the distance between an acquired GEDI shot and the position of GEDI projected at nadir onto the WGS84 reference ellipsoid and H is the elevation of GEDI over the referenced ellipsoid.
In addition to the previous factors, several additional environmental factors have also been considered since in-situ water levels do not necessarily provide water elevation across the surface of lakes as standing waves (seiches), and wind-generated waves are commonly present over lakes. Over the studied lakes, no direct information about waves were available, therefore, they were substituted by proxy variables. In essence, we chose the factors that influence the creation and the form of standing, and wind-generated waves (e. g. , wave heights and wave direction). These factors include wind speed, wave direction, and lake depth. Wind speeds were acquired at each GEDI acquisition date using meteorological data from the nearest weather stations. Wave direction and average lake depth at each GEDI footprint were acquired from the LATLAS project (swisslakes.
Finally, GEDI estimation errors were modeled using the previously mentioned factors in a Random Forest regressor (RF). Random Forests are an ensemble of machine learning algorithms used for classification or regressing by fitting a number of decision trees on various sub-samples of the dataset, and uses averaging to improve the predictive accuracy and control over-fitting  [38]. Compared to linear models, RF is advantageous for being able to model also nonlinear relationships (threshold effect) between the variable to explain and the explanatory variables. For this study, the number of trees in the RF were set to 100 trees, with a tree depth equal set to the square root of the number of available factors. In order to train and assess the model accuracy, we randomly split the database into 70% for training and 30% for validation (and accuracy estimation).
The time of the acquisition during the day, as well as the day of the week on which an acquisition took place, both can have effects on the echoed waveforms as seen previously. The effect of these two factors is related to the noise from the sun on the GEDI receiver, and the increased human activities over each lake during certain days. Finally, the provided quality_flag from the L1B data product could help, in theory, to select GEDI data with higher accuracy on elevations. Using data issued after the application of our filter (|SRTM elevation-GEDI elevation| > 100 m), we observed only a slight decrease of the root mean square error on GEDI elevation estimates when we considered only the waveforms with a quality_flag = 1. For algorithm a1, we observed a decrease in the RMSE of around 1. 4 cm (from 22.
Conclusions In this study, we analyzed GEDI data in order to determine its accuracy of elevation estimation over lake surfaces using algorithms provided by the Land Processes Distributed Active Archive Center (LP DAAC). The objective was to study the quality of the first two months of GEDI data (min-April until mid-June 2019) acquired over several lakes in Switzerland. Overall, 4367 GEDI shots out of 21,242 available shots were exploitable and analyzed over the eight studied lakes. This first analysis of GEDI data from the first two months of acquisitions showed a very low mean elevation difference between GEDI and in-situ gauge station elevations, in the order of 0. 61 ± 22. 3 cm for one standard deviation. While GEDI's reported vertical accuracy in this study was well below the 50 cm vertical accuracy provided in Dubayah et al.
[31] , it still remains higher than what was previously obtained using the ancient LiDAR satellite ICESat-1. In fact, the vertical accuracy of GLAS onboard ICESat-1 was better than 10 cm, as demonstrated by Baghdadi et al. [29]. The analysis of GEDI data by lake showed that the vertical precision varied from under-estimation by GEDI of -13. 8 cm for certain lakes, to over-estimations of +9. 8 cm for others, with a varying standard deviation between 14. 5 and 31. 6 cm. The investigation of GEDI's vertical accuracy by date showed a mean difference between GEDI and in-situ gauge station elevations varying between -26. 8 and +15. 2 cm. The lowest bias corresponded to data acquired in the morning or late at night. The highest recorded bias was observed on acquisitions that were made around noon, in the early evening, and over the weekend.
Lake GEDI Acquisition Dates dd/mm (Hours) GEDI Shots Count Average Water Level in 2019 (Gauges) Approximate Size (km 2 ) Geneva 20/04 (08:37); 04/05 (03:12); 28/05 (17:23) 7451 372. 05 m 584 Neuchâtel 21/04 (12:37); 28/04 (09:54); 29/05 (21:25) 5089 429. 30 m 429 Zürich 02/05 (08:08); 04/05 (03:12) 11/05 (00:30); 08/06 (13:10) 1711 405. 91 m 49 Obersee (Zürich) 02/05 (08:08) 967 405. 91 m 21 Lucerne 20/04 (08:37); 22/05 (23:59) 2523 433. 51 m * 113 Walensee 20/04 (08:37); 02/05 (08:08) 605 419. 05 m 24 Sempach 04/05 (12:37); 22/05 (23:59) 08/06 (13:10) 592 503. 66 m 14 Thun 20/04 (08:37); 21/04 (12:37) 2303 557. 67 m 48 2. 2. Datasets 2. 2. 1. In-Situ Water Levels from Gauge Stations.
The PTAL project aims at building and exploiting a database involving several analytical techniques, the Planetary Terrestrial Analogues Library, to help characterizing the mineralogical evolution of terrestrial bodies, starting with Mars. A total of 94 natural Earth rocks have been collected from selected locations around the world to get a selection of analogues for Martian geology. The PTAL library gathers a variety of igneous rocks from different plutonic and volcanic origins and compositions, associated to different environments and levels of aqueous alteration. Samples of impact craters and sedimentary units are also present. The full set of samples will be characterized with optical thin section analysis and XRD (Oslo University, Norway), NIR spectroscopy (Paris-Saclay University, France), Raman spectroscopy (Valladolid University, Spain) and LIBS (Paul Sabatier University, France).