dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper presents a new deep-learning-based method for 3D Point Cloud Semantic Segmentation specifically designed for processing real-world LIDAR railway scenes. The new approach relies on the use of spatial local point cloud transformations for convolutional learning. These transformations allow an increased robustness to varying point cloud densities while preserving metric information and a sufficient descriptive ability. The resulting performances are illustrated with results on railway data from two distinct LIDAR point cloud datasets acquired in industrial settings. The quality of the extraction of useful information for maintenance operations and topological analysis is pointed together with a noticeable robustness to point cloud variations in distribution and point redundancy.
g. the OpenTopography 1 database). Several large scale high density open source LIDAR datasets -e. g. Hessigheim Benchmark  (Kölle et al. , 2021)  -address the issue of Point Cloud Semantic Segmentation (PCSS) i. e. labeling each point with relevant semantic information. Although this information is low-level, it is used to build meaningful representations for real-world applicatione. g. numerical terrain models, vegetation mass estimate, population density, infrastructures cartography -see the work of  Soilán et al. (2019)  for a comprehensive review in the railway field. Granting the railroad industry has been using LIDAR data for a while, especially for these type of applications, deep learning-based processing is yet at its early stages of development in this field. The use of reliable point cloud semantic information could simplify existing railway use cases and lead to new ones (vegetation growth estimate, automated site inventory, assisted building information modeling).
Railway LIDAR PCSS represents a first step towards optimizing maintenance. In this paper we address the issue of semantic segmentation on large scale railway LIDAR point clouds by proposing a simple partially symmetrical kernel operator supported by a deep-learning framework. We design our method to be specifically adapted to the characteristics of railway LIDAR data. Evaluation of the resulting approach is performed using railway data from two distinct LIDAR point cloud datasets. The paper is organized as follows: we first introduce the railway context and our datasets as to identify their specificity. We then discuss several PCSS methods relevant to the specifics of our use case. We present our deep-learning approach designed for railway environment then evaluate our results of segmentation, speed and robustness to point distribution together with several state-of-the-art methods.
Railway datasets To tackle our use case, we introduce two datasets acquired on French railroads, annotated for semantic segmentation evaluation purposes (see Table  1 ). The former (Paris-Lyon) is a handlabeled (high semantic quality) airborne LIDAR point cloud dataset of 5. 25km of linear tracks on a high-speed line. The later (Saint-Etienne) is a semi-automatically labeled (lower semantic precision) railborne LIDAR set of 13km on a regional line. railway related objects (beacons, antennas, electrical cabinet, etc. ) and punctual objects (lightning pole, high voltage lines, cars, etc. ). As we focus mainly on the railway infrastructure, we keep a reduced number of classes to facilitate our PCSS problem while still segmenting out important objects. Both datasets contain the same information per point collected from the LIDAR scanner: XY Z the 3D spatial coordinates, Intensity of the returning signal, Nret the number of returns per laser beam and R id the return index in a series of returns per beam.
Analysis Here we want to identify the characteristics of our datasets in the scope of our PCSS task. These characteristics will orient our choices in the development of our specific approach. As we focus on railway infrastructure, we notice in Figure  2  that objects of interest are mainly oriented vertically. The ground beneath the tracks being necessarily flat for complying with railroad standards, objects like antennas, catenary poles, signalization poles or electrical cabinets always show a major Z-oriented component. A usual inconvenient of the LIDAR scanning approach is the locally varying distribution of points in the resulting cloud. As we notice in Figure  1 , areas close to the sensor bear more point density than those away from it. We also notice that a lower point of view (railborne) leads to more local density variations than a higher point of view (airborne).
The methods shows very good results although on a very small dataset. Exploring deep-learning approaches,  Soilán et al. (2020)  apply PointNet  (Qi et al. , 2017a)  and KPCNN  (Thomas et al. , 2019)  to segmentation of railway tunnels point cloud data. Although the test environment is simple, their work shows hopeful results for full infrastructure segmentation using deep-learning-based approaches.
Analysis of semantic segmentation quality This part presents the segmentation performances of the proposed method and compares them to the similar state-of-the-art methods ConvPoint  (Boulch, 2020)  and  ShellNet (Zhang et al. , 2019)  over the two railway datasets presented in Section 2. 1. Segmentation performances were evaluated using the widely used mean Intersection-over-Union (mIoU) metric as described by  Qi et al. (2017a). For fair comparison, the learning parameters of our approach have been chosen to be close to those originally used in the tested methods. To select the network inputs, the dataloader proceeds as follows: 8 points are randomly selected from the entire training dataset, used as centers for column-shaped areas of 10m × 10m wide. All points within those 8 areas are each randomly sampled to 8192 points and concatenated as inputin areas containing less than 8192 points, samples are repeated.
Each point is represented by its XY Z position and its base LIDAR information introduced in Section 2. 2 as normalized and centered input features. The inference split of Paris-Lyon dataset represents 14. 3% of the entire dataset (750m) and 19. 2% for Saint-Etienne (2. 5km). The network is trained using a cross-entropy loss function, an initial learning rate L0 = 1 × 10 -3 along with an Adam optimizer. Each epoch is composed of 100 iterations over the dataloader, i. e. 100 × 8 × 8192 points. The current learning rate is decreased every 50 epoch by a factor 0. 9. All methods use a dropout parameter of 0. Semantic segmentation performance results are summarized in Table  2. The proposed method shows comparable overall results with both tested state-of-the-art methods. It systematically outperforms both methods while segmenting large objects like buildings, concrete structures, vertical objects like catenary poles or environment objects (encompassing antennas, sidetrack beacons, signal poles, electrical cabinets or power-line poles) but, mostly underperforms while segmenting the ground, rails or vegetation (i.
e. classes without a dominant vertical component). We also notice that the strong class imbalance does not necessarily impact the segmentation performances. In Paris-Lyon results, all methods perform properly on the catenary poles class but not on the building class (respectively 0. 14% and 0. 11% of the dataset's points). ShellNet fails to segment the building class altogether. This result can be explained by the regular spatial distribution of catenary poles within the point clouds, contrary to the buildings. The batches being drawn randomly across the whole dataset, the building class is not seen often during training. We can conclude that in this configura- tion, the spatial distribution of objects within the dataset has a strong impact on the segmentation performances, especially in the case of statistically unbalanced dataset.
5 times than ConvPoint), and shows an inference time consistent with those of the other methods -although ConvPoint shows slightly better results. On Saint-Etienne however, the proposed method struggles to reach convergence within competitive time (ConvPoint is ∼4. 5 times faster than the other methods), the inference times are similar for all methods -although slightly better for ShellNet. The Saint-Etienne dataset scene complexity being greater, smaller networks (ShellNet and ours) might suffer from a lack of abstraction compared to ConvPoint (embedding a 13-layers-deep network), leading to a slower convergence. An other aspect of our kernel approach being oriented towards robustness to varying density, we evaluate our method's robustness to different point distributions. We test the methods (only at inference) with subsampled point clouds (randomly keeping one point per voxel of a 3D grid) and compare their segmentation performances (see Figure  8 ).
CONCLUSION In this paper we investigated the relevance of a deep-learning based approach for LIDAR railway PCSS with industrial standards. The railway environment is specific regarding the spatial disposition of infrastructure objects. We build on this specificity to design an axially symmetrical learnable kernel operator for integrating in a deep-learning framework. We experimented with a classic U-Net architecture on two distinct LIDAR railway datasets. Our methods shows similar or better segmentation quality results (especially on railway specific objects of interest) compared to other state-of-the-art methods (cf. Section 5. 2) and mitigated results in terms of convergence times on complex datasets (cf. Table  3 ). Finally, its ability to process strongly subsampled clouds without suffering too severe of a drop in segmentation performances opens industrial possibilities in terms of speed for specific maintenance use cases.
Future investigations into the network architecture could prove useful in scaling to industrial expectations as railway environments often resemble the Saint-Etienne in complexity. Several network architectures for PCSS show conclusive results as Res-Nets  (Thomas et al. , 2019)  or attention mechanisms  (Hu et al. , 2020)  and could support our kernel approach and speed up convergence on difficult datasets, hence bringing it closer to industrial standards. An other development to further evaluate our approach will consist in completing both datasets with additional data, compensating for the spatial and statistical class imbalances.
The last period, from 1990 (176‰) to 2016 (222‰), shows weak Δ 14 C variation. The POC sources for these two latter periods were less subjected to these aging processes (particularly from 1961 to 1980) and broadly exhibit a δ 13 C signature of terrestrial C3 plants with a possible imprint of aerial aquatic plants in samples showing positive Δ 14 C values (Fig. 6 , Table  1 ). Marwick et al. , 2015). Atmospheric δ 13 C and Δ 14 C from atmospheric CO 2  (Graven et al. , 2017) , C3 plants:  (Kohn, 2010; O'Leary, 1988) , -C4 plants δ 13 C  (Cerling et al. , 1993) , Δ 14 C for C3 and C4 plants are in equilibrium with the atmosphere (e. g. Trumbore, 2009) -Aquatic plant: δ 13 C for submerged and aerial plants  (Chappuis et al. , 2017) , Δ 14 C (SYRACUSE IRSN database) -δ 13 C of kerogen  (Tyson, 1995)  (fossil OM,  Tyson, 1995)  is divided into three main type of organic matter: the type III encompasses all the terrestrial higher plants origin while the type I and II combine the lacustrine/bacterial and marine origins.
Nevertheless, to subtract this POC component from the POCb signal, we estimated the Δ 14 C-POCa by using additional data sets acquired from radiological monitoring and survey (SYRACUSE IRSN database). Before the beginning of nuclear energy production by NPPs (PWR) in 1980 (Fig. 1 , Dampierre NPPs) and assuming the absence of 14 C nuclear releases before this period of time, the Δ 14 C-POCa values would express a part of the FRE. Thus, the age given by FRE was required in order to estimate the DI 14 C content of the Loire River upstream NPPs. Even though FRE depends on hydrological conditions and tributary inputs, involving a large range of FRE values, we used a mean FRE value of 1194 ± 30 14 C yr  (Coularis et al. , 2016) , i. e. approximately -143‰. This mean FRE value characterizes the upper part of the Loire basin and overcomes the potential influence of NPPs.
To assert whether the nuclear releases were recorded in aquatic plants, we used the radiocarbon data from some aquatic mosses (n = 21) collected upstream and downstream of the Belleville-sur-Loire NPPs as these mosses do not exhibit aerial surfaces that promote 14 C atm fixation during photosynthesis. The Mean Δ 14 C values of aquatic mosses collected in 1999 and 2000 upstream and downstream of the Belleville-sur-Loire NPPs reached -164, and -176‰ respectively, while the Δ 14 C atm ranged from 121 to 115‰. With respect to the uncertainties associated with FRE, these data show that aquatic mosses in the Loire River are generally in equilibrium with the FRE. Importantly, these data show that the measured mosses do not record the 14 C liquid releases from nuclear activities. However, this statement should be considered with caution due to (i) the deficiency of the representativeness of the samples for radiocarbon measurement purposes and (ii) the 14 C plume from the nuclear releases, which depends on the local hydrodynamic conditions, remains unknown.
Transit time of POCt from the catchment to the river sediments Based on our dataset, the transit time of the OM route in the catchment can be assessed by comparing the Δ 14 C peak level in the atmosphere in 1963 with the Δ 14 C-POCt peak in the sedimentary archives. Indeed, the terrestrial OM has previously fixed atmospheric 14 C during its growth on the catchment following photosynthesis (e. g. Schurr et al. , 2016)  prior its route toward the deposition area and its burial in river sediments. As a consequence, this POCt has only kept in memory the atmospheric 14 C during its growth. Since there is no additional 14C incorporation in OM after its death, a mean transit time can be estimated by comparing the time lag between the well know Δ 14 C peak level in the atmosphere in 1963 with that preserved within the sedimentary OC (Fig.
Conclusions and perspectives We aim to decipher the Δ 14 C-OC values in sediments from one of the most nuclearized rivers in Europe. For a similar period and as expected, the 14 C levels in these materials are far different from those in the atmosphere. There are several reasons for this discrepancy. The first is the dilution of the radiocarbon contents by dead-carbon inherited from the erosion of outcropping sedimentary rocks and aged superficial formations in the catchment. The proportion of the dead-carbon can be easily determined with a linear modelling. Due to the combination of previously published optical measurements, a mean FRE value and a radiocarbon dataset for aquatic organic matter sampled along the river corridor, the contribution of aquatic POC to the Δ 14 C-OC was also calculated.
Acknowledgments This work was funded by the  Institute for Radioprotection and Nuclear Safety (IRSN-NEEDS , project  PALYNO ) and a part of the  TRAJECTOIRE  project from the  French National Research Agency  ( ANR-19-CE3-0009 ,  2020-2024 ). Warm thanks are addressed to  J. P Bakyono ,  F. Giner ,  D. Mourier  and  C. Le Corre  for their precious help during field sampling, sample preparation and sample analyses. The authors also sincerely thank the three anonymous reviewers and the editor for their appreciated help in the improvement the manuscript.
The Mediterranean region is experiencing rapid population growth, increasing water demand, and faster climate warming. Groundwater plays a central role in the development of the Mediterranean by ensuring food security, buffering climate changes impacts and maintaining socio-economic stability and ecosystems integrity. However, despite groundwater being recognised as a powerful catalyser against climate-and human-induced effects, its status remains unknown, and its budget is uncertain. Moreover, the lack of appropriate groundwater monitoring programs at a regional scale, data sharing policy, and fragmented water policy and governance have complicated this vital resource's sustainable utilisation and protection. To this end, an international and multidisciplinary team of scientists with complementary skills have joined efforts through two PRIMA-funded projects (Sustain-COAST and InTheMED) towards better groundwater management and governance in the Mediterranean.
In this respect, the Sustain-COAST Living Labs provide the setting to explore innovative governance approaches that could be scaled to increase resilience and adaptation capacity across the Mediterranean. At a regional scale, efforts target detailed water policy examination, pathways exploration towards the reinforcement of in-situ data collection, and groundwater modelling. Examination of water policy of the Mediterranean revealed an unbalanced situation between South Europe, characterised by complete, harmonised and straightforward water policy. Conversely, in North Africa and Middle East, water policy was found complex, not regionalised and fragmented in various initiatives. Results also showed the Mediterranean has become increasingly dependent on cereal importation over local food production, which poses a severe threat to its best diet overall and food security.
Acknowledgement: This work was supported by  Sustain-COAST and InTheMED projects. Sustain-COAST  was funded by the  German Federal Ministry of Education and Research (BMBF, Germany , Grant  01DH19015 ) under the  EU PRIMA 2018 programme. InTheMED is part of the PRIMA 2019 programme supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  1923.
Sequence processing Sequences were processed using the dada2  (Callahan et al. 2016 ) package within R (R Core Team 2021). Reads were filtered and trimmed using the filterAndTrim function with the following parameters: truncLen = c(250, 240), trimLeft equal to each primer length (for primer removal), maxN=0, maxEE=c(2, 2), and truncQ=10. Merging of forward and reverse reads with the mergePairs function and chimeric sequences removal with the removeBimeraDenovo function were both performed with default parameters. Resulting ASVs were taxonomically assigned using assignTaxonomy function with PR2 database  (Guillou et al. 2013 ) version 4. 14 (https://pr2-database. org/). Samples with less than a total of 3,000 reads were excluded, and the number of reads for each sample was normalized by the median sequencing depth.
Autotrophic taxa were selected by filtering-in divisions Chlorophyta, Cryptophyta, Haptophyta, Ochrophyta and Cercozoa. Classes known to comprise only heterotrophic members (Chrysophyceae, Sarcomonadea and Filosa-Thecofilosea) were excluded. Dinoflagellates were not considered because they contain both autotrophic and heterotrophic taxa, and the high number of 18S rRNA gene copies per genome makes them dominate read numbers, obscuring patterns of other autotrophs. Processing script can be found at https://github. com/vaulot/Paper-2021-Vaulot-metapr2/tree/ main/R_processing.
2019) , vegan (NMDS)  (Dixon 2003 ), ggplot2 (plotting, Wickham 2016) , ComplexUpset (upSet graphics, Krassowski 2020). Abundant ASVs for each size fraction were selected by keeping only ASVs which were among the top 90% most abundant sequences in at least one sample. Abundant taxa for the whole community (i. e. considering all size fractions) had to be among the top 90% most abundant sequences in at least 10% of the samples, except for the intersection analysis (upSet graphic), where taxa present in the top 70% of sequences in at least one sample were filtered. This was done with the topf and genefilter_sample functions of phyloseq. NMDS analysis was performed using Bray-Curtis distance with the metaMDS function of the package vegan, and statistically significant environmental parameters (p-value ≤ 0.
001) and genera (p-value ≤ 0. 05) were mapped against it using the function envfit. Indicator species analysis (indicspecies package, De Cáceres et al. 2010) was performed with abundant taxa (selected as described above) within each size fraction in order to find significant association between taxa and a given sector (or combination of sectors), using the default IndVal index as statistic test and 9999 random permutations. Global distribution of ASVs was performed using the metaPR 2 database (https://shiny. metapr2. org, Vaulot et al. 2022 ) which contains metabarcodes from 41 public datasets representing more than 4,000 samples distributed over a wide range of ecosystems. ASV sequences from the present study were entered in the "Query" panel, and matching metaPR 2 ASVs (100% similarity) were displayed in the "Map" panel.
Phytoplankton microdiversity Taxa grouped by genera mask variability at the species and ASV level. Looking at all the genera with more than two ASVs in the whole dataset, the ASV-level distribution of taxa yields potential information on nichepreference. In general, most genera had more ice-associated ASVs compared to OW (Table  3 ). Interestingly, several low-abundance taxa harbored high numbers of ice-associated ASVs, for example, the Dictyochophyceae genus Pseudochattonella and the environmental clade 2 of Bolidophyceae. Two groups presented a surprisingly large number of ASVs: the B clade of Dolichomastigaceae (Mamiellophyceae) with a total of 29 ASVs, and the centric diatom genus Chaetoceros with 35 ASVs (Table  3 ). Alpha diversity indices indicate that, in general, diversity was higher in the smallest size fraction in the UI and MIZ sectors, and decreased towards bigger size fractions.
Distribution of abundant ASVs The distribution of the ten most abundant ASVs within each division demonstrated that although the dominant community might be comprised of few genera, ASV-level distribution follows distinct patterns within these genera and even within the same species (Figure  6 ). It is also notable that some ASVs were particularly adapted to distinct environments, regardless of differences between sectors, since they were present (and abundant) throughout the dataset, such as M. polaris (ASV_0003), Teleaulax glacialis (ASV_0038) and P. pouchetii (ASV_0001) (Figure  6 ). Chlorophyta top 10 ASVs belonged to five genera: Bathycoccus, Mantoniella, Micromonas, Pterosperma and Pyramimonas, from which five ASVs were significantly correlated to ice-associated sectors. M. polaris ASV_0154 had a single base pair difference with M.
polaris ASV_0003 (Figure  S6 ) and was less abundant than the latter in our dataset (Figure  6 ), as well as in other Arctic datasets (Figure  S7 ). M. squamata (ASV_0104) and Pterosperma sp. (ASV_0244) were present in UI samples, mainly within 0. 2-3, but also in the 3-20 µm size fraction. Within the top 10 most abundant Cryptophyta ASVs, three were assigned as F. daucoides. While ASV_0041 F. daucoides was strongly associated with MIZ+UI samples for both 0. 2-3 and 3-20 µm size fractions but also found in the OW sector, ASV_0055 was exclusively found in ice-associated sectors in the smaller size fraction (Figure  6 ). T. gracilis ASV_0038 was highly abundant at all sectors, but was flagged as indicator ASV for the MIZ+UI sector group in the > 20 µm size fraction. Five Chrysochromulina and three Phaeocystis ASVs comprised the ten most abundant Haptophyta, three of them only found in ice-associated sectors.