dataesr/scientific-paragraphs-categorization huggingface dataset:
All the results are reproducible by delivering our method via an open-source platform called 3DUSE (3D-Urban Scene Editor) (https://github. com/VCityTeam/3DUSE (accessed on 7 September 2022)) which also contains a collection of other components for manipulating, processing and visualizing urban data. 3DUSE is a desktop solution, and some of the developed components are now available as services for a web-based solution called UD-SV (Urban Data Services and Visualization) (https://github. com/VCityTeam/UD-SV (accessed on 7 September 2022)). Our goal is to incorporate the visibility analysis component into UD-SV in the near future. This work is developed in the context of the VCity (https://projet. liris. cnrs. fr/vcity/ (accessed on 7 September 2022)) project of the LIRIS (https://liris. cnrs.
Data Used for Our Study In order to test the tools presented in the previous section, we worked with practitioners from the Lyon Metropolis, using the freely available 3D data covering the entire metropolis territory (more than 550 sq km of data are available). These data are composed of 3D models and semantic information provided in CityGML format (with buildings, terrain, roads and watercourses). Additional data, such as vectorial 2D databases, describe the territory (more than 1000 different datasets are available in the Lyon open data). For instance, LiDAR data and orthophotos can be used to generate an additional layer for a 3D vegetal canopy over the entire territory (describing this work on vegetation is considered out of our scope). Figure  11  shows how the urban landscape is represented through CityGML files available on the open data portal of the Lyon Metropolis.
Characterization of Geometrical Accuracy for the Visual Impact Assessment of a Specific Building In our case study on the metropolis of Lyon (>550 sq km), using the visibility analysis tool of a common GIS software program (ESRI ArcGIS (http://www. esri. com/arcgis/ about-arcgis (accessed on 7 September 2022))), we could only perform a 2. 5D visibility analysis on a 1-m-resolution DEM, covering 48 sq km (only the City of Lyon). Figure  12  shows how the urban morphology is rendered through a 1-m-resolution raster DEM, while Figure  13  makes it possible to visually compare the representation of a given area through a 1-m raster DEM and a 3D vector model. This sheds light on the crucial importance of the processing of 3D vector data, as offered by our tool, so as to use the most precise description of building shapes input and to be able to take into account the masking effects of vegetation.
Currently, the existing 2. 5D raster analysis performed on raster data limited in size (by machine and/or software limitations) simplifies the geometry of the vegetation and buildings when carried out in large areas (districts, cities, metropolis, etc. ). On very large scales (metropolis, region, etc. ), 2. 5D raster data can hardly even take into account the geometry of built structures, let alone the masking effects of vegetation. In our work on the landscape of the metropolis of Lyon with ArcGIS, we had to downsize our DEM to a resolution of 25 m or work on a larger area than the city itself (70,000 sq km) (the analysis of such a large area was interesting as the work on landscape composition included distant mountains visible in specific meteorological conditions (in our case, the visibility of Mont Blanc from the city center of Lyon)).
In order to assess the benefit of our tool in terms of precision compared to raster analysis, we studied the visibility of a well-known Lyon monument, the Fourvière Basilica. Located on a hill in the city, it is visible from many places in the city, including from a distance. This allowed us to test the method and demonstrate its potential to our practitioner partners. We thus generated an analysis using a classic GIS tool (ArcGIS), using the raster Digital Elevation Model (with 1-m resolution) mentioned above, as well as an analysis using our tool from a 3D vector dataset in CityGML. The comparison of the results was carried out only on the area containing the city of Lyon (48 sq km) as it was not possible to produce precise results over a wider area with the ArcGIS raster analysis. At first glance, a 2D visual comparison of the results shows relatively similar results, with spatial definition of the visible areas appearing rather close.
Example of Uses of the Data Produced by Our Tool Our tool generates multiple results for each viewpoint: quantitative data, 3D georeferenced data and several images. Regarding quantitative data, it is possible to obtain dashboards. Dashboards can be constructed on demand, thanks to the data stored in the database. For instance, for a given area, the attributes linked to each point can allow for the differentiation of the buildings, noticeable buildings, vegetation and terrain categories. Regarding 3D georeferenced data, they may be used as colored 3D points with a list of attributes that can be highlighted in a 3D view (for instance, Figure  13 ). A mix can also be performed with polygonal (CityGML) information. Images can also be produced with ray-tracing. It is possible to obtain depth cards with dedicated colors, images with building categorization, etc.
Visual Impact of Buildings in Context Our first illustrative example shows how selection based on semantic data and basic statistics can provide useful insights into the visual impact of a building on a district scale. This is useful for practitioners in order to verify the preservation of some particularly significant viewpoints from public spaces (views of monuments accessible by all citizens). Our tool provides 3D points indicating a 3D location from where the target of our analysis is visible. Each point contains, in its attribute table, the nature of the corresponding CityGML element (whether terrain, vegetation, building, etc. ) and its original CityGML identifier so that any data from the original 3D model can be linked to it (through a simple attribute join). For instance, in the case of the visibility analysis of the Fourvière Basilica presented above (see Section 4.
2. 1), we can derive precise results about its visibility in a given district from the data provided on the city of Lyon as a whole. The 2. 5D raster visibility analysis provides a binary result (whether or not the target is seen) but does not provide data on what the resulting pixel entails (if it is a public space, a private building, etc. ). Practitioners often use a spatial join to try to create such information (Apur 2014), but the accuracy of the result depends on the size of the pixel and the density of the urban fabric. In our case, each resulting 3D point corresponds to a unique element from the CityGML database (terrain, vegetation or building, etc. ). It is then easy to count, for example, from how many buildings the target is seen and to precisely determine from which location the target is seen (which floor of a building or which area of a public space such as a square or a park).
Detecting the Visibility of a Building from Vantage Points Our second illustrative example developed with practitioners is a work on the qualification of public belvederes, i. e. , public spaces that offer vantage points with views of remarkable buildings and landscapes. This is of interest to practitioners since belvederes enable everyone to enjoy the urban scenery. As far as planning is concerned, identifying possible locations for public vantage points or belvederes and working on their accessibility and equipment (public benches, for instance) are ways to grant every citizen access to the urban scenery. In the case of Lyon, we have used the visibility analysis from several landmarks in a GIS tool, along with other data (public transport, roads, topography and orientation), in order to create a database of existing belvederes and provide a diagnosis of their accessibility.
For each vantage point, we have computed the number of landmarks seen, their altitude, their accessibility from the public transport network (distance by foot from the nearest public transport), the effort required to walk up to it given the slope and its exposure to the sun, according to its orientation. This database also contains the equipment of each belvedere, which was existing data and can be used to develop a public strategy to enhance citizens' access to the skyline. Figure  20  shows visualizations of the data in a GIS tool: belvederes classified by the number of landmarks that can be seen and by the effort required to access them on foot (calculated in calories). Our data were also used to work on pedestrian itineraries on the hills of Lyon in order to automatically determine how many landmarks can be seen from a tourist path and where particular vantage points are situated.
Discussion The study of the visibility of landmarks at a given viewpoint or at different viewpoints is important. The former ensures that certain landmarks remain visible from a given landmark, and the latter, as illustrated in Figure  20 , helps to identify key vantage points that could be used by urban planners and even proposed to tourists. The use of 3D data compared to 2D or 2. 5D data helped us to obtain a much finer visibility analysis. In addition, by making use of standards such as CityGML, our approach can be extended to any other city in a reproducible manner, ensuring that the entire computational process is repeatable. Delivering data and code along with complete documentation also helps towards reproducibility. Compared to traditional tools, our approach can also be used to study the visibility of far-away objects.
Conclusions Using 3D CityGML vector data, our tool provides accurate and reliable results in terms of visibility analysis and skyline assessment and can be used over very large areas. In this article, we demonstrated our proposed method of visibility analyses for the city of Lyon, especially for some of the major historical monuments by making use of 3D data models. Our analyses show a significant improvement compared to tools based on 2. 5D raster data. Our tool, called 3DUSE, an open-source solution, also allows for quantitative and qualitative (through images) analyses of the skyline on various scales, enabling users to adapt the ray tracing resolution to their needs. The originality of our method lies in its versatility, the accuracy and diversity of the analysis results, as well as the optimization of the processing time.
Our method opens up new opportunities for skyline assessment and can help practitioners in various areas (identifying accessible public spaces with remarkable views, quantifying privatization of access to remarkable views, producing quantitative and qualitative data for public debates on landscape projects affecting the skyline, etc. ). The ability for the visibility analysis to cover very large areas is particularly interesting for work on areas sharing a common landscape. It can, however, lead to time-consuming processing and large resulting datasets, which will need to be explored, sorted out and analyzed through specific queries, requiring some GIS technical knowledge. The images and tables produced by our tool for every analysis can be used directly to discuss with a wider audience. The accuracy of the results produced by our tool depends directly on the 3D vector data used as input (its geometrical precision and available semantic data).
Nevertheless, our process could be improved in order to provide additional results to complete the visibility analysis. For instance, complementary processes could be developed in order to take into account meteorological data or human vision limitations  [10]  affecting visibility at a given time and place. Working with a temporal 3D model would thus be relevant in order to produce variable analyses according to the time of day/year. This would add further complexity with regard to handling the results, while practitioners and citizens have expressed a great need for accessible data. For this reason, subsequent work may also focus on providing results structured in a database with predefined queries adapted to the user's objectives.
Acknowledgments: The  SKYLINE  Project, which brought together researchers and practitioners, was funded by the  French National Research Agency. We wish to thank all participants in the consortium, and especially practitioners from  Lyon (François Brégnac ) and London ( Rosemarie McQueen  and  Jane Hamilton ). We are very grateful for their involvement and contributions. Masters students (from the  Universities of Saint-Etienne, Lyon , and  Marne-La-Vallée ) also helped us to work on methods and use cases ( Anne-Thérèse Auger ,  Maximilien Brossard ,  Sylvain Jourdana ,  Antoine Laurent ,  Geoffrey Mouret ,  Naïme Osseni  and  Sébastien Truel ): we would like to sincerely thank them for their work.
Funding: This work was performed within the framework of the  LABEX IMU  ( ANR-10-LABX-0088 ) of  Université de Lyon , as part of the  "Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) program operated by the  French National Research Agency (ANR). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Disk setup We carried out our disk simulations with the code DustPy 1  by  Stammler & Birnstiel (2022)  which builds on the dust evolution model of  Birnstiel et al. (2010). This code solves both the gas and dust evolution equations described in the previous section. We assume the disk to be around a Solar mass star and initially set up the gas surface density of the disk following the profile of Lynden-Bell &  Pringle (1974) : Σ g (r, t 0 ) = Σ 0 r R c -γ exp        - r R c 2-γ        , (16) with γ = 1 and the normalisation factor Σ 0 = M disk /(2πR 2 c ) is defined through the initial disk mass, which we set to be M disk = 0. 05M ⊙. We take the characteristic radius R c = 60 au, from where the profile gets exponentially tapered. The radial grid contains n r = 300 logarithmically spaced cells and spans from 5 to 1000 au, as we are only interested in the outer parts of the disk.
Radiative transfer calculations To infer the observational appearance of the disk, we perform radiative transfer calculations with the code RADMC-3D 2   (Dullemond et al. 2012). As input, we take the 3D dust density distribution (see Eq. (  12 )) for each grain size obtained from our DustPy simulations at an evolution time of 1 Myr. We assume the composition of the grains as stated in the Disk Substructures at High Angular Resolution Project (DSHARP) paper of  Birnstiel et al. (2018) , consisting of water ice  (Warren & Brandt 2008) , astronomical silicates  (Draine 2006) , as well as troilite and refractory organics  (Henning & Stognienko 1996). We then calculate the opacities for each grain size using the program Optool 3   (Dominik et al. 2021) , where we further assume the grains to be compact spheres.
Acknowledgements. We would like to thank referee  Clément Baruteau  for his very constructive feedback, as well as  Mario Flock  for the fruitful discussion regarding zonal flows. The authors acknowledge funding from the  Alexander von Humboldt Foundation  in the framework of the  Sofja Kovalevskaja Award  endowed by the  Federal Ministry of Education and Research , from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement no. 714769  and funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under grants  361140270 ,  325594231 , and Germany's  Excellence Strategy  - EXC-2094 -390783311. Software:  DustPy  ( Stammler & Birnstiel 2022 ),  Matplotlib  (Hunter 2007),  Numpy  (van der Walt et al.
SEED (Simulation of External Exposures & Dosimetry) is a numerical dosimetric reconstruction software restricted to internal use  (SPRA and IRSN). The aim of this tool using Geant4 Monte-Carlo code is to provide dose estimates capable of supporting the triage decisions made by specialized medical teams in case of external exposure. Its strength is also to be able to operate in a degraded situation; integrated into a militarized and hardened device, it is a stand-alone tool in terms of both power supply and simulations thanks to a powerful multi-core computer. This tool can be implemented by a doctor / physicist pair, projected within a limited time as close as possible to the irradiation accident and emergency response teams. To validate the performance of the SEED tool, two dosimetric protocols were followed.
The first protocol (validation step 1) consisted in comparing the dose value provided by SEED to that given by MCNPX Monte-Carlo code (reference calculation code usually used at IRSN in the context of accidental exposures) for identical geometric configurations, with the same number of simulated particles. The second protocol (validation step 2) aimed at comparing the reconstruction performed with SEED with an external exposure carried out under the conditions of a realistic accident that was simulated experimentally. This paper aims to present the SEED tool as well as the validation process of the tool.
 Mobile computer The mobile numerical tool is autonomous for its computing capacity: it does not require data transfer to a remote cluster. The computer used is based on a variant of the "Dell Precision® R7910 Workstation". Its main operating system is a Linux distribution. The computing station has 36 cores, allowing 72 simultaneous operations (hyper-threading) this ensures a greater number of operations in a shorter time. The design basis was intended to address potential requirements of processor, memory, graphics and storage. In order to ease and to secure its transportation and use on the ground all this equipment was built-in into a military durable polyethylene container. The computer is securely attached to an aluminum chassis, itself suspended thanks to eight anti-vibration mounts.
Thereby, there is an optimal shock absorption and an efficient cooling by natural circulation of the surrounding air flow.  SEED software SEED is dedicated to the numerical dosimetric reconstruction of external exposure. Its main features are: -to model an irradiation accident situation, called "scene", from the graphical user interface (GUI); -to generate an input file in GATE format  (Rodrigues et al 2004 , Sarrut et al. 2014 , Jan et al. 2011 ) containing all the necessary information (geometry, physical parameters, etc. ) and to launch the calculation; -to display the results of Monte-Carlo calculations as coloured layers that can be superimposed on the elements of the scene; -to save / load a scene; -to create an exposure scenario combining different scenes. SEED is programmed in C++ language and offers an interface based on an OpenGL 3D display in order to achieve a rapid modelling of the accident scene.
From the GUI it is also possible to launch a Monte-Carlo calculation corresponding to the modelled scene. Then it automatically creates the input file in GATE format. To reduce the simulation time, the user can also allocate the calculation over several cores and define the number of cores. Only one calculation per scene is allowed, but it is possible to create a new scene from the elements of a previously modelled scene that has been saved. This calculation can be computed in the background while the user can work on the modelling or analysis of other scenes simultaneously. Thus, a scene can have three different status, which can be viewed on the icon relating to the scene: calculation in progress, calculation completed, design (no calculation launched).  Calculation code SEED uses the Geant4-based GATE (Geant4 Application for Tomographic Emission) version 8 Monte-Carlo platform to simulate the interaction between radiation and matter.
 Protocol The objective was to benchmark the SEED tool in order to assess its accuracy and its performances. Numerical simulations were carried out for several irradiation configurations with both the SEED tool and MCNPX reference calculation code  (Hendricks et al. 2008 ). Then the doses estimated with the two codes were compared. The principal aim was to check that the GATE input file automatically created by SEED was correct in terms of description of the geometry, description of the source, definition of the physics, etc. Recent radiological accidents  ((IAEA 2004 ((IAEA , 2009 ((IAEA and 2018) )  and foreseen malevolent scenario mainly involve gamma sources. Thus, this benchmark, the first of many to come, was restricted to gamma emission. Two types of geometries were defined to perform this benchmark: "simple" geometries and "complex" geometries (Table  1 ).
 Physics list Before comparing the SEED software with the MCNPX calculation code, it was necessary to ensure that the two software programs used the same models for simulating the interactions of ionizing radiation with matter, in particular for photons and electrons that were the only particles transported in the calculations performed in this study. The energy range defined by default in MCNPX (from 1 keV to 100 MeV, for both photons and electrons) has been set in SEED. Although some physics lists are already available in Geant4 (e. g. , the "emstandard_opt3" constructor, that is commonly used in the medical field), it was decided at the design of the SEED software to be able to control the physical models and their parameters to be used in the simulations. This allows some interactions to be selected according to the time allowed for the simulation as well as the required degree of precision.
In order to compare the doses measured on the slabs and on the ATOM CIRS ® dummy by the two types of dosimeters expressed in air kerma and Hp(10) for LiF and OSL respectively, the air kerma values obtained with the thermoluminescent dosimeters were converted into Hp(10). The conversion factor for air kerma into Hp(10) was established using the data supplied by the International Commission on Radiation Units & Measurements (ICRU 1998). The conversion factor for air kerma/Hp(10) obtained for 367 keV was 1. 316 (+/-9. 70%). An uncertainty budget was performed (Table  2 ). Then uncertainty for thermoluminescent measurements was evaluated using error propagation as described in GUM (ISO 2008). It takes into account: the reproducibility of the measurements performed with the powder of the same tube, the uncertainty on the calibration, the uncertainty due to the difference in response of the LiF dosimeters between the calibration energy and the energy of the Iridium-192 and the uncertainty on the conversion factor from air kerma into kerma in the material or Hp(10).
The following procedure was adopted:  Use of 3DSlicer ® software to convert the DICOM ® format images into a mesh (Fedorov et al. 2012)  Export of this mesh in. STL (STereo-Lithography) format according to the coordinates system created by 3DSlicer;  Use of Meshlab ® software in order to convert the binary STL file generated by 3DSlicer into an ASCII STL file. This software also provides the reduction of the number of mesh vertices while keeping its geometry. In order to limit the use of the resources of the computing station when displaying geometries, the number of vertices of the different meshes is reduced before their integration into SEED, taking care not to alter their geometric quality.  STL ASCII file upload in SEED and mesh display.  Mesh volume calculation in order to be able to calculate the dose from the energy deposited in the mesh volume.