dataesr/scientific-paragraphs-categorization huggingface dataset:
The differences observed at the two locations suggest that contrasting evolution of the weathering conditions affected emerged lands on the opposite sides of the Huincul Arch. Lastly, the clay-mineral dataset shows an overall trend of kaolinite with decreasing proportions during the Late Jurassic (Figure  5 ). If interpreted in term of paleoclimate control on clay minerals, this trend suggests aridity somewhat grew with time to some extent. Such a climatic trend is well known for the Jurassic-Cretaceous boundary and has been largely observed in (peri-)Tethyan sections, from France and UK to Tunisia and the Russian platform, based on clay minerals data, but on palynological data as well (e. g. ,  Deconinck, 1993; Abbink et al. , 2001; Ruffell et al. , 2002; Schnyder et al. , 2006; Grabowski et al.
Organic source(s) and productivity The Rock Eval data reveal that the organic matter of the studied samples, collected on outcrops, suffered from weathering. This is indicated by the abundance of samples with very low TOC and with OI values above 300 mg CO2/g TOC  (Espitalié et al. , 1985). In particular, it can be noted that most samples in section 2 and approximately half of samples in Covunco section were severely affected by weathering. From this observation, the results from "unoxidized" samples must be considered with caution, keeping in mind that even slight oxidation results in a decrease of TOC values of the rock and an evolution of organic matter bulk chemistry towards that of Type III kerogen  (Durand and Monin, 1980; Petsch et al. 2000). Nevertheless, and despite the slightly higher thermal maturity in Covunco area, the available data from the less oxidized samples allow discussing the source of organic matter.
Acknowledgments This paper is a part of the PhD studies of the first author, undertaken at the university of  Pau et des Pays de l'Adour (France)  and sponsored by TOTAL S. A. The authors thank TOTAL S. A for permission to publish this paper. The reviewers (and among them  Dr. J. Schnyder ) as well as the Editor in charge of the manuscript are warmly thanked because they helped us improve substantially our paper. Raw data are available in the supplementary material file.
These approaches have some limitations, especially when dealing with an object the scale of a heritage complex site with high resolution point cloud from heterogeneous sources. A naïve region-growing method applied to such datasets would require enormous resources, while a normal analysis may encounter problems with heterogeneous objects. Another approach  (Liu et al. , 2018)  suggested using 2D vectorial GIS data to aid the segmentation, but lacks the semantic annotation part and was performed on a projected 2. 5D point cloud. A similar approach was also used in segmenting 2D aerial images  (Kaiser et al. , 2017). The region-growing algorithm may also be used to perform segmentation and classification in larger scale objects  (Bassier et al. , 2017a; Spina et al. , 2011). Other approaches may also use machine learning in the image space  (Grilli et al.
PROPOSED METHOD The proposed approach uses 2D GIS layers in the form of the commonly used ESRI shapefiles. These shapefiles contain 2D overhead vectorial representations of objects within the heritage complex, each with its own semantic attributes stored inside the file's database part. In some cases, these shapefiles may already exist. In other cases where a 2D GIS of the site has not been developed, the shapefiles can be obtained from simple digitisation. This may be performed based on existing topographical maps, online maps, or from orthophotos generated by photogrammetry. The 3D point cloud may be obtained using any kind of tools; including laser scanning, photogrammetry, or a combination of these techniques. The point cloud should be georeferenced to the same system as the GIS, which often times translates into the common national projection coordinate system.
This procedure is well integrated in the classical heritage documentation workflow. However, in the case of the impossibility of an absolute georeferencing, a preliminary 3D transformation may also be performed beforehand. The proposed approach is developed in MATLAB R2018a using its Computer Vision Toolbox. The first step in the workflow involves the extraction of the ground. This approach used the Cloth Simulation Filtering (CSF) method  (Zhang et al. , 2016)  to extract the ground. Each polygonal vector in the shapefile was then used to segment the non-ground point cloud, using a "cookie-cutter" approach. Figure  1  displays the pseudocode of the proposed segmentation algorithm used at the aftermath of the ground extraction process, as written in the function shapeseg. Each shapefile represents one object class.
Finally, as the 2D vector geometry was taken from the shapefile, the associated attribute fields may be annotated directly to the segmented point clouds. This ensures that all the attributes of the 3D object as recorded in the 2D GIS is stored within the structure of each segmented point cloud. The function will therefore generate, as an output, a structure for each class which consists of the different objects (records or instances in the shapefile database). These objects themselves are also structures in which the segmented point cloud and attributes are stored. The function also gives as an output the remaining unsegmented data, which can then be used as input for the further segmentation of other shapefile classes by reiterating the function.
RESULTS AND DISCUSSIONS An experimental trial was conducted using the dataset from the Siti Inggil complex of the Kasepuhan Palace, Cirebon, Indonesia  (Murtiyoso et al. , 2018). This area dated to the 13 th century and includes several historical pavilions within its 1,200 m 2 brick-walled perimeters. Heavy vegetation was also present within Siti Inggil, often overlapping with the buildings. The site was digitised in May 2018 using a combination of terrestrial laser scanner (TLS) and photogrammetry (both terrestrial and UAV), and was georeferenced to the Indonesian national projection system. As part of the preliminary data acquisition, a UAV flight was conducted to obtain the global view of the site. A rough 2D GIS was therefore acquired by digitising the resulting orthophoto map into three layers (buildings, walls, and gates) for the purpose of this experiment.
The original point cloud dataset consisted of 10. 4 million points (Figure  2 ), and were segmented into four classes (the three layers and the ground) and 13 different annotated objects in about 10 minutes using an Intel (R) Xeon (R) E5645 2. 4 GHz CPU. 1. Segmentation statistics for the 13 object clusters of interest (trees are not included). The GIS shapefile data shown in Figure  3  was used to aid the segmentation process. The shapeseg function was then subsequently employed for each shapefile class, while taking into account the order of which shapefile is processed first. Generally speaking, lower objects were segmented first, moving towards higher objects (see Figure  7 ). In the case of Siti Inggil, after the classification of the ground using the CSF algorithm, the low brick walls were the first to be processed.
CONCLUSIONS The approach demonstrated in this paper has shown to be able to perform well in segmenting the 3D point cloud of heritage complexes. The resulting segmented point clouds were relatively clean despite the dense vegetation around the objects of interest. Using this method, each object was segmented individually (Figure  8 ), but retains a classification according to the input shapefile. Another advantage is that the semantic attribute present in the shapefile database is not lost. This information is annotated automatically to each object cluster, therefore facilitating further work on site management in the form of 3D GIS and/or HBIM. Some shortcomings are still present, however. The fine tuning of segmentation parameters is important in order to extract the correct objects. This is particularly true for the region growing segmentation parameters for two different objects which superposed closely.
It is implemented by using Matlab R on an IntelCore i5-8300H, 2. 30 GHz. The CPU time needed for the wave tracking algorithm is a small percentage of the total CPU time per time step. Depending on the discretization used (number of modes N tot and number of grid points N X ) this percentage ranges from 5-10 % of the total CPU time per time step. The difference in CPU time between the two proposed methods is insignificant. For the three test cases studied in this paper, the total CPU times per time step are approximately 0. 11 sec (Section 5. 1), 0. 1 sec (Section 5. 2 ), and 0. 23 sec (Section 5. 3).
Acknowledgements The authors thank  Prof. do Carmo  ( University of Coimbra, Portugal ) for providing the laboratory data for the dam-break problem,  Prof. J. T. Kirby  ( University of Delaware, USA ) for providing the laboratory data of regular waves breaking on a sloping beach and  Prof. S. Beji  ( Istanbul Technical University, Turkey ) for providing the laboratory data for waves breaking over a submerged bar. This work benefited from  France Energies Marines and French State financ-.
Microwaves can help access this record as demonstrated by the first analyses of the data collected by the RADAR on board the Cassini spacecraft  (Ostro et al. , 2006 (Ostro et al. , , 2010)). While it was initially designed to examine the surface of Titan through the veil of its opaque atmosphere, the Cassini RADAR was occasionally used to observe Saturn's airless moons, namely, the six intermediate-sized satellites (in order of distance to Saturn) Mimas, Enceladus, Tethys, Dione, Rhea, and Iapetus, and the two irregular-shaped satellites Hyperion and Phoebe. Probing their subsurface at a wavelength of 2. 2 cm (13. 8 GHz), the instrument opened a new dimension in the exploration of these moons. ©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
(2006 Ostro et al. ( , 2010) )  as the observation database has increased (and will not increase anymore) and a factor 2 error in the originally published values was found (see supporting information, SI). We focus on the active measurements (the passive observations will be the subject of a following paper) and what they tell us about the geological history of Saturn's icy moons and their interaction with their near environment and, in particular, Saturn's dust rings.
II. Methods The data set comprises 122 samples, 62 from Castellas and 60 from D19 outcrops, collected along the 4 transects. Porosity values have been measured on 92 dry plugs with a Micromeritics AccuPyc 1330 helium pycnometer. Characterization of microfacies and petrography have been determined on 92 thin sections. The impregnation with a blue-epoxy resin allows to decipher the different pore types. Thin sections were coloured with Alizarin red S and potassium ferricyanide to distinguish carbonate minerals (calcite and dolomite). The thin sections have been analyzed using cathodoluminescence to quantify how the diagenesis and the fault zone setup affected the initial rock properties. The paragenetic sequence has been defined based on superposition and overlap principles observed on thin sections using a Technosyn Cold Cathode Luminescence Model 8200 Mk II coupled to an Olympus_ BH2 microscope and to a Zeiss_ MR C5.
During recent years, these major regions have been confirmed by an upsurge in bioregionalisation studies. This upsurge was facilitated by the increase in quality and quantity of large-scale datasets, as well as the development of new analytical tools  (Kreft & Jetz, 2010; Vilhena & Antonelli, 2015; Edler, Guedes, Zizka, Rosvall, & Antonelli, 2016). Consequently, multiple studies have tried to identify the major biogeographical regions for birds  (Procheş & Ramdhani, 2012; Rueda, Rodríguez, & Hawkins, 2013; Holt et al. , 2013) , mammals  (Kreft & Jetz, 2010; Procheş & Ramdhani, 2012; Rueda et al. , 2013; Holt et al. , 2013) , amphibians  (Procheş & Ramdhani, 2012; Rueda et al. , 2013; Holt et al. , 2013; Vilhena & Antonelli, 2015; Edler et al. , 2016)  and reptiles  (Procheş & Ramdhani, 2012).
Consequently, whether Sclater-Wallace's biogeographical regions are also applicable to freshwater fishes and to other freshwater organisms remains unresolved, and a global-scale quantitative bioregionalisation would represent an important step forward. In this study, we aimed to define the major biogeographical regions for strictly freshwater fish species at the global scale. To delineate biogeographical regions, we capitalised on the recent development of a comprehensive dataset on freshwater fish distributions in drainage basins covering more than 80% of the Earth surface  (Tedesco et al. , 2017). First, we identified the large biogeographical regions of freshwater fishes using a recently developed hierarchical. CC-BY 4. 0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
Distribution data We based our bioregionalisation on the most comprehensive global database on freshwater fish species occurrence in drainage basins  (Tedesco et al. , 2017). This database comprises 110 331 occurrence records for 14 953 species in 3 119 drainage basins of the world. Species names in the database were validated according to FishBase  (Froese & Pauly, 2017)  and the Catalogue of Fishes  (Fricke, Eschmeyer, & van der Laan, 2017) , and occurrence records were screened by the team developing the database (see details in  Tedesco et al. 2017). We applied additional filters and corrections to the database. Since our aim was to describe the natural biogeographical regions resulting from long-term isolation of freshwater ichthyofaunas, we excluded documented records of introduced species, but included species considered to be recently extinct in their historical river basins.
Additionally, to exclude most species that could disperse through marine waters, we retained only families having less than 10% of their species occurring in marine waters. This filter retained all "primary" and almost all "secondary" families of fishes (only Pseudomugilidae and Fundulidae were excluded), i. e. families with respectively no or limited salt tolerant species, as well as 22 families that had never been classified (based on Table  2  of  Berra, 2007). It also included eight families with marine ancestors, seven of which had no species classified as tolerating salt water. Finally, we removed all diadromous species, according to FishBase. Additionally, we detected a few errors that were corrected in the database, mostly related to the native/introduced status for some species. The database used in this publication is available in Appendix S1 in Supporting Information.
The resulting dataset included 59 373 records of 11 295 species in 2 581 basins (Figure  1 ). To define our bioregions, we worked at the species level and used drainage basins as geographical units. Indeed, (1) in the absence of a unified phylogeny for actinopterygian fishes, species is the most standard unit available and (2) contrary to terrestrial vertebrates, (for which gridded distribution data of reliable quality are is available), the most precise distribution data available for actinopterygian fishes is at the drainage basin unit. However, it is important to note that even if drainage basins are uneven in size, they are biogeographically meaningful for freshwater organisms because water bodies are generally connected within basins but not between basins  (Hugueny et al. , 2010). CC-BY 4.
0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint. http://dx. doi. org/10. 1101/319566 doi: bioRxiv preprint first posted online  May. 11, 2018;  Figure  1. Global distribution of freshwater fish species richness per drainage basin based on the global database on freshwater fish species occurrence in drainage basins  (Tedesco et al. , 2017). Grey-shaded areas correspond to basins without records of native strictly freshwater species.
A site cannot be connected to another site, and a taxon cannot be connected to another taxon. By definition, site-site and species-species links are not allowed in this type of analysis. Our final network had 13 876 nodes (11 295 species and 2 581 basins) and 59 373 links. We handled the network under Gephi 0. 9. 2, with the ForceAtlas2 algorithm. This software groups nodes that are tightly interconnected (such as groups of sites and species from the same biogeographical region) and separates groups of nodes that are not interconnected (distinct biogeographical regions). Such a graphical representation is useful for analysing and exploring the network. We applied a community-detection algorithm to the entire network in order to group nodes into clusters (i. e. biogeographical regions). We applied the Map Equation algorithm (www.
g. for online databases such as FishBase) and analyses. Lastly, the Map Equation algorithm is robust to differences in sampling intensities, making the removal of basins with low species richness unnecessary. On the other hand, distance-based approaches have limitations (see e. g. ,  Leprieur & Oikonomou, 2014)  and can produce inconsistent results when transforming such large occurrence datasets into a single dimension during the clustering procedure (see Appendix S2). However, we provide clustering results using two additional methods in Appendix S2 for comparison: another network-based algorithm (Simulated Annealing,  Bloomfield et al. , 2017)  and a distance-based method (following the framework of  Kreft & Jetz, 2010).
Sensitivity analysis We analysed the robustness of the identified regions by randomly extirpating a percentage of species (random value between 0. 01 and 10. 00% of the total number of species in the database) and re-running the whole bioregionalisation process. This process was repeated 200 times. Then, for each region, we quantified the percentage of each region initial area that was retrieved in each simulation (Appendix S3).
Robustness of findings This first global quantitative analysis of the biogeography of freshwater fishes is based on a large scale database compiling occurrence data from thousands of sources and is thus inevitably subject to errors and incomplete data  (Tedesco et al. , 2017). To minimise errors, a careful screening and correction procedure has been implemented for this database (see  Tedesco et al. , 2017). Reassuringly, the results obtained from other clustering methods as. CC-BY 4. 0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint. http://dx. doi. org/10. 1101/319566 doi: bioRxiv preprint first posted online  May. 11, 2018;  well as our sensitivity analysis suggested that the regions we identified are robust.
Furthermore, regions were all spatially coherent (even though no spatial information was provided at any stage of the process) for the first two levels, with high degrees of endemism, indicating the quality of both the dataset and the bioregionalisation approach. The network method assigns clusters to species (as explained in the methods), which is an asset over distance-based clusters. However, one major caveat needs to be acknowledged. Species are assigned to the region where their present-day distribution is largest -this region is not necessarily the region where they originated from. A perfect example is the Characidae family at the transition between Nearctic and Neotropical: Astyanax mexicanus was assigned to the Nearctic, although its lineage is assumed to have colonised Mesoamerica during the Panama Isthmus formation  (Smith & Bermingham, 2005).
Acknowledgements We thank  Céline Bellard  and  Philippe Keith  for useful discussions, and  Aldyth Nyth  and  Lissette Victorero  for English editing. We thank  François-Henri Dupuich  from derniercri. io for figure editing. Laboratoire Evolution et  Diversité Biologique  is part of the  French Laboratory of Excellence projects ''LABEX TULIP'' and ''LABEX CEBA''  ( ANR-10-LABX-41 ,  ANR-10-LABX-25-01 ).
The electronic supplementary information contains a catalogue of rest frequencies for imidazole that were directly obtained with the SPCAT programme  (Pickett 1991)  without any further editing. This data listing exactly matches the file format of CDMS 3   (Müller et al. 2005; Endres et al. 2016 ) and JPL 4   (Pickett et al. 1998)  databases, and can thus readily be implemented in some of the most frequently used astronomy analysis tools, such as CASSIS 5  and MADCUBA  (Rivilla et al. 2016). The results of two separate computations are provided. The file imidazole. cat contains a compilation of pure rotational frequencies extending up to 600 GHz, and the file imidazole_ hfs. cat provides a list of hyperfine components limited to 50 GHz. Both catalogues were obtained from fit I results (see Table  2 ), and following the CDMS standard, the integrated intensity of each transition is computed at 300 K.
Astrophysical implications We used the best-fit model of the rotational spectrum of imidazole obtained in this work to search for this molecule in the hot molecular core Sgr B2(N2). This hot core is located in the Sagittarius B2 molecular cloud, at a distance of 8. 3 kpc  (Reid et al. 2014). We used the EMoCA imaging spectral line survey obtained with the Atacama Large Millimeter/submillimeter Array between 84. 1 GHz and 114. 4 GHz with a median angular resolution of 1. 6. A detailed account of the observations and data reduction can be found in  Belloche et al. (2016). We used Weeds  (Maret et al. 2011)  to generate a synthetic spectrum of imidazole under the approximation of local thermodynamic equilibrium (LTE) and searched for matching lines in the observed spectrum of Sgr B2(N2). We did not find evidence for imidazole in the EMoCA spectrum of Sgr B2(N2).
Acknowledgements. M. Schnell acknowledges the  ERC Starting Grant ASTROROT (Grant Agreement Number   638027 ). B. E. Arenas acknowledges The  International Max Planck Research School for Ultrafast Imaging and Structural Dynamics, IMPRS-UFAST. A. L. Steber is grateful for The  Louise Johnson Fellowship  of the  Hamburg Centre for Ultrafast Imaging, CUI. B. J. Harris  and B. H. Pate are supported by  National Science Foundation. J. -C. Guillemin  thanks the  Centre National d'Etudes Spatiales (CNES)  for financial support. A. Pietropolli Charmet  gratefully acknowledges financial support by  University Cà Foscari Venezia  (ADiR funds) and the  SCSCF ("Sistema per il Calcolo Scientifico di Cà Foscari ", a multiprocessor cluster system owned by  Università Cà Foscari Venezia). This work has been in part supported by the  Deutsche Forschungsgemeinschaft (DFG)  through the collaborative research grant  SFB 956  " Conditions and Impact of Star Formation ", project area  B3.
CFOUR, Coupled-Cluster techniques for Computational Chemistry, a quantum-chemical program package written by J. F. Stanton, J. Gauss, L. Cheng, M. E. Harding, D. A. Matthews, P. G. Szalay et al. , and the integral packages MOLECULE (J. Almlöf and P. R. Taylor), PROPS (P. R. Taylor), ABACUS (T. Helgaker, H. J. Aa. Jensen, P. Jørgensen, and J. Olsen), and ECP routines by A. V. Mitin and C. van Wüllen. For the current version, see http://www. cfour. de.