dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations The principal limitation of the study is its retrospective aspect with the reported food consumption by participants. Subjects responding to the questionnaire sometimes responded at a distance from the first lockdown. In this study, only self-reported data were collected, and each respondent could answer anonymously, with no checks possible. However, anonymity may have prevented misreporting. The cross-sectional nature of this study, which required participants to recall their behaviors prior to and then during lockdown, may have resulted in measurement bias due to memory lapses. A further limitation relates to the study design (online survey), which may have induced selection bias because participants were volunteers, and those who used the internet more frequently may have been more likely to participate.
IllustrisTNG simulation We use the outputs of the gravo-magnetohydrodynamical simulation IllustrisTNG 1   (Nelson et al. 2019) , which follows the coupled evolution of DM, gas, stars, and black holes from redshift z = 127 to z = 0. This simulation is run with the moving-mesh code Arepo  (Springel 2010) , and the values of the cosmological parameters are those of Planck Collaboration XIII (2016): Ω Λ,0 = 0. 6911, Ω m,0 = 0. 3089, Ω b,0 = 0. 0486, σ 8 = 0. 8159, n s = 0. 9667 and h = 0. 6774. We study the TNG300-1 simulation box, which is the best resolution run (m DM = 4. 0 × 10 7 M /h) of the largest volume of the IllustrisTNG simulation. This box, which consists of a cube of about 302 Mpc side length, allows both the statistically significant detection of long cosmic filaments and the study of processes down to small scales.
Analysed data sets The full snapshot of the TNG300-1 box at z = 0 contains a very large number of DM particles and gas cells (∼14 × 10 9 ), and ∼7 × 10 8 star particles. Running the analysis on this extremely large amount of information is computationally very expensive and time consuming, so in order to compute the density profiles in reasonable computing time, we sample the DM, star particles, and the gas cells by randomly selecting one out of 1000 (and we rescale the resulting densities by this factor). As shown in Appendix A, this sub-sampling factor turns out to be a good compromise between computational speed and precision, and it does not bias the density estimates with respect to the full particle distribution. Moreover, performing the analysis on different random extractions gives stable results, so this sub-sampling does not undermine the statistical conclusions presented in this work.
The filament catalogue We detect the filaments in the TNG300-1 simulation box by applying the DisPerSE code  (Sousbie 2011; Sousbie et al. 2011)  to the galaxy distribution at z = 0. DisPerSE identifies filaments from the topology of the density field, traced here by galaxies, using Discrete Morse theory. All the details and technicalities of the extraction of the skeleton used in this work can be found in  Galárraga-Espinosa et al. (2020) , where the catalogue of filaments was first constructed. It is important to mention that, following  Galárraga-Espinosa et al. (2020 , 2021) , a filament is defined as the set of straight segments connecting a point of maximum density (hereafter CPmax), to a saddle of the density field (see Fig. 2  of  Galárraga-Espinosa et al. 2020). Within this definition, the filaments of our catalogue have a maximum length of L f = 65.
Acknowledgements. We would like to thank the anonymous referee for his/her useful comments and suggestions. This research has been supported by the funding for the  ByoPiC  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2015-AdG 695561. (ByoPiC, https://byopic. eu). The authors acknowledge the very useful comments and discussions with all the members A115, page 12 of 16  D. Galárraga-Espinosa  et al. : Matter in cosmic filaments of the ByoPiC team (https://byopic. eu/team/). We also thank the Illus-trisTNG team for making their data publicly available, and for creating a user friendly and complete website.
This makes the FOC implementations almost exclusively specific to certain microcontroller architectures  (Belhamel et al. , 2020; Cheles & Sammoud, 2008) , motor drivers, current and position sensors, and motors  (Carey et al. , 2019; Castiglia et al. , 2018; Reddy & Murali, 2016). SimpleFOC has been developed in an effort to provide a more generic and easy-to-use implementation of the FOC method to bolster the rapid development of highly dynamic cyber-physical systems (e. g. , control theory experimental setup, dynamic robotic systems, etc. ). SimpleFOC implements the FOC algorithm routines, motion control strategies, generic hardware interfaces, and various configuration parameters encapsulated in an object-oriented C++ library. It provides the users with an intuitive way to develop their motion control application and the possibility to change all the hardware components (motor, sensors, drivers, microcontrollers) with relatively minor code modifications.
SimpleFOC supports various RISC-based microcontroller architectures such as Atmega328/2560, MK20 series, STM32 series, SAMD series, RP2040 series and Xtensa LX6, as well as different platforms such as Arduino UNO, Nucleo, ESP32, Teensy, Portenta, to name a few. Like many other open-source platforms  (Chitta et al. , 2017) , SimpleFOC takes advantage of using existing software toolchains to facilitate the interoperability of the hardware to use the FOC approach with different microcontrollers.
Modular Architecture As shown in Figure  2 , SimpleFOC provides a modular implementation of the FOC control architecture by intuitively dividing the code into the generic blocks representing different hardware and software components, such as motors, drivers, position sensors, current sensing, user interfaces, and finally the microcontroller specific code. Each block in Figure  2  represents the operational necessities of the FOC and motion control ( see Figure  1  ), as well as all the initialization, calibration, communication functionalities specific to the hardware. For each of the generic blocks, SimpleFOC provides multiple cross-platform implementations based on different hardware specifications and in this way supports many motor, sensor, driver, and microcontroller combinations. The position sensor blocks are based on the Sensor class, and SimpleFOC provides initial support for hall sensors (HallSensor ), magnetic sensors (MagneticSensor ), and quadrature encoders (Encoder ).
This paper aims to explore the deformation of the collided bodies in multibody systems and to effectively simulate the motion path of colliding bodies. First, we describe the geometrically nonlinear problems of materials by the total Lagrangian formulation. Second, a first-order integration scheme is used to solve the dynamics equations. An algorithm combining the bi-potential method with the node-to-point contact identification is proposed to solve the interface problems of rigid-flexible interaction collision. To observe the collision process more intuitively, the internal software FER/VIEW is introduced to visualize the results. The accuracy is proved by comparing the proposed method with the analytical solution or another numerical solution. Moreover, the proposed method has more numerical robustness, such as occupying less computer storage, saving the computational cost, and broadening the application range of the bi-potential method.
Then, it is brought into the global algorithm to compute the displacements and velocities. In Section 4, the bi-potential method is extended to the nonsmooth dynamic analysis of the rigid-flexible interaction collision, which is verified by four numerical examples. Example 1 is the collision of a rigid body with an elastomer. Examples 2 and 3 are the collisions between a composite plate and rigid bodies. Here, the composite plate is composed of a hyperelastic block and a rigid plate. In the meantime, the internal software FER/VIEW  [29]  , developed by Feng's research team, is used to visualize the numerical simulations so that the motion, stress, etc. can be observed more intuitively. Last, Section 5 draws some conclusions. Additionally, Example 4 is to solve the collision of a hyperelastomer, and verifies the conservation or dissipation of energy for multiple collision cycles, as shown in Appendix A.
Numerical results The algorithms presented above have been implemented in C++ and tested in the in-house code FER/IMPACT. The results are visualized by the postprocessor FER/VIEW. This section further verifies that the bi-potential method is extended to solve the collision between the moving rigid bodies and flexible bodies. In addition, the node-to-point contact recognition technology is adopted. The advantages of the proposed method are that the contact detection is only affected by the flexible bodies and does not need to calculate the interpolation matrix. We use four examples (one in Appendix A  [35]  ) with the nonsmooth factors to verify this issue. The time interval Δt = 10 -5 s.
Therefore, before the separation of the colliding bodies occurs, the analytical solution equations for the displacement and collision force are q(t) = -V 0 t, ( 35 ) R c (t) = ⎧ ⎪ ⎨ ⎪ ⎩ EA 0 V 0 C , 0 t 2h C , EA 0 V 0 C 1 + 2n INT * tC 2h , t > 2h C , ( 36 ) where n INT * means the number after the decimal point is omitted, the longitudinal wave propagation velocity C = E/ρ, and 2h/C denotes a period of wave propagation. In Figs. 3  and 4 , the longitudinal coordinates R c and d y are, respectively, the collision force and the vertical displacement. The results obtained by the proposed method are in good agreement with the results obtained by the finite element software MARC. However, the collision forces from MARC have a more obvious jump point at the initial time. In addition, the numerical results of the compression phase are also compared with the analytical solution, and the trend of the force is consistent.
2. In Fig. 8 , d y is the displacement in the y-direction, which reaches the lowest position when t = 0. 001 7 s. The node C recovers to the initial position at t = 0. 006 23 s, and the sphere recovers to the initial height t = 0. 004 85 s. Furthermore, the hyperelastic body gradually separates from the rigid plate, and starts to separate from the rigid plate at the node D first. Figure  9  depicts the stress generated by the collision, using the internal software FER/VIEW. When the rigid sphere collides with the hyperelastic body, the transverse stress waves are transmitted to the left and right ends. The longitudinal stress waves are transmitted downward. In the meantime, the hyperelastic body is firstly separated from the rigid plate from the node D, and its contact gap gradually expands to both the left and right ends.
Conclusions In this paper, the bi-potential method is combined with the node-to-point contact interface to study the nonsmooth dynamic problems of rigid-flexible interaction collision, and the small and large deformations in the multibody systems are both considered. The trajectory of the colliding body and the deformation of the collided body are analyzed. Finally, it is compared with the analytical solution or commercial software MARC. It can be proved that the present method in this paper fits well with MARC. Moreover, it is closer to the analytical solution and easier to converge, and the selection of the parameters is simpler. At the same time, the application range of the bi-potential method is broadened, the smooth curve of the geometric shape of the rigid body does not need to be divided into nonsmooth segments like MARC, and the interpolation matrix does not need to be calculated.
Besides, the proposed method keeps better energy conservation during the numerical simulation. Therefore, it can be concluded that the proposed method is more accurate and robust, saves the calculation cost, and occupies less computer storage. Further, we will optimize the program written in C++ so that the proposed method has more obvious advantage in computational time than MARC. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.
Methods and tools for assessing the visual impact of objects such as high-rises are rarely used in planning, despite the increase in opportunities to develop automated visual assessments, now that 3D urban data are acquired and used by municipalities as well as made available through open data portals. This paper presents a new method for assessing city visibility using a 3D model on a metropolitan scale. This method measures the view composition in terms of city objects visible from a given viewpoint and produces a georeferenced and semantically rich database of those visible objects in order to propose a thematic vision of the city and its urban landscape. As far as computational efficiency is concerned and considering the large amount of data needed, the method relies on a dedicated system of automatic data organization for analyzing visibility over vast areas (hundreds of square kilometers), offering various possibilities for uses on different scales.
5D data without semantics. Using open data in a standardized format allows us to provide a generic method usable on other available datasets in other cities. Our method offers analytical tools for quantitatively and semantically assessing the visual impact of city objects taking into account topography, but also building types and forms, vegetation, hydrology and any other types of city features included in the 3D model over large areas (several hundred square kilometers). It measures the visual composition of a landscape from a given viewpoint: by using the 3D model composed of different types of city objects, we detect those that are visible and store their identification number and geographic coordinates in a 3D georeferenced database. We can then provide the list of visible objects and the semantic information linked in the 3D city model (name, address of a building, botanical species, etc.
) to generate a database that can be queried and used for further analyses beyond a visual representation of the view in question. Moreover, thanks to the 3D coordinates of the visible objects, these results can be used together with external georeferenced data, such as socio-economic data describing the corresponding area, to perform specific analyses corresponding to user needs. Thanks to geometry and semantics, city view composition studies can be conducted. Figure  1  illustrates this purpose with a view of Lyon (France). Landscape analysis can provide additional information, such as vegetation composition  [5]  and the location of significant buildings or roads  [13] [14] [15]. Visual analysis can be carried out according to urban planners' needs  [16, 17]  (Figures  2  and 3 ). This paper focuses on the production of these new methods to assess the visibility and visual impact of existing or planned features of the city described in a 3D geometrical and semantic city model in order to provide local authorities and associations with quantitative and qualitative data (through georeferenced geometrical and semantic data) for a fairer debate between stakeholders and decision-makers.
[30]  also use 2. 5D models to develop their concept of "viewsphere analysis" consisting of estimating the visibility of a scene, defined by a viewpoint and its spherical surroundings, by computing volume-based indices. Visibility analyses were also performed for smaller cities based on 2. 5D data using tools such as the Line of Sight Analyst toolbox  [31]  and Viewshed  [32]  tools in ArcGIS. We will also compare the results obtained from our proposed approach to those obtained from the ArcGIS tool in Section 4. The isovist approach  [26, 27]  is a vector method based on ray casting strategies (GearScape Geoprocessing Language (GGL)  [33] , Isovist Analyst  [34] ). While 2. 5D raster analyses do provide information on the distance up to which a building can be seen and indications as to which areas may be affected by its visual impact, they do not address vertical surfaces, especially facades, as noted by Suleiman et al.
While the use of 3D models can improve the precision and relevance of the analyses produced, it is also important to note that they can also provide more immersive visibility analyses in which non-expert users can easily identify their surroundings and understand the corresponding results. Practitioners stated that 3D visualizations are often used in association with 2. 5D raster analysis, especially through photomontages. The vast majority of such photomontages are not produced from accurate spatial data, and some have been accused of conveying a wrongful image of a project, spoiling public debate and sometimes giving rise to legal action  [37]. Other practitioners use 3D models provided by Google by inserting models representing projects into Google Earth (Figure  3 ). In this case, users have no control over the 3D data available in their area of interest, little control over what can be displayed in the Google Earth viewer and no way to perform analysis, limited to only visualization.
However, in the scientific field, multiple works exist for using 3D models to perform visibility analyses. For example, Morello and Ratti  [38]  and Suleiman et al. [39]  propose 3D extensions for the isovist concept on Digital Elevation Models (DEM) defined in a raster voxel model and a polygonal model, respectively. Caldwell et al. [40]  chose a different approach, precomputing a Complete Intervisibility Database on a Digital Elevation Model in order to be able to directly compute metrics supporting specific decision-making needs. For each point of the DEM, the viewshed (a raster approach), which is a concept close to that of the isovist (a vector approach)  [41] , is computed and stored for rapid access when needed by specific processes. These visibility results can, for example, be used to compute the least and most visible paths between two positions on the ground.
[15] , Czy ńska  [44] , which propose a method to measure the visual impact of rural buildings on natural landscapes and compares the results to a public survey. Similarly, Danese et al. [45]  examine the Visual Impact Assessment of new projects in various Italian cities by computing viewshed analyses before and after the addition of buildings and comparing the results in order to evaluate the impact on the visibility of landmarks. Most works propose visibility studies of 3D models but do not address the scaling problem inherent to the applications related to these data or acknowledge that their process can only address a limited area. We thus opt for a generic method that can be used on large 3D vector datasets available for numerous open data cities. As a result of the visibility analysis being stored as 3D georeferenced vector entities, any necessary post-processing of the generated data can be conducted in order to include visibility distances/range, meteorological conditions, etc.
These works also focus on analyzing the impact of buildings but rarely address other types of city features such as vegetation, landmarks or other urban features that may have a certain impact in terms of visibility. Since these kinds of data are now correctly managed by international standards, such as CityGML, it is necessary to enhance methods to take them into account semantically and geometrically. In order to overcome the shortcomings of available tools so as to provide practitioners with precise and useful tools, we worked with practitioners to provide a list of requirements a visibility tool should meet and then developed a method that could meet different practitioners' needs. This approach is intended to be more general than scientific works proposing one tool or one visibility indicator to answer a particular need  [44].
We thus aim to produce precise and complete results that can be stored as 3D georeferenced vector data with linked attributes, allowing for a wide range of GIS-based analyses to extract the necessary information, depending on the context of use. Based on the needs expressed, a few requirements were listed: • the need for precise geometrical analysis, which requires the use of a 3D vector city model instead of simplified DEM or rasters, • the need for semantic data to identify as precisely as possible which feature is seen from a vantage point; if a building or landmark is concerned by the visual impact of a proposed building (concerned only by its roof or its facades and which floors may be impacted, etc. ), • the need to be able to process large amounts of data, and especially rich, 3D vector data so as to obtain precise results on any area (a whole metropolis or region if needed), • the need for numerous outputs that can be used to generate multiple results (images, georeferenced databases and data quantification stored in spreadsheets), some of which may be used as is and others opening possibilities in terms of spatial analysis (i.
Currently, 3D city models may cover several hundred square kilometers, and numerous objects and polygons must, therefore, be tested for intersection computations. For example, the Lyon open data in France (https://data. grandlyon. com/, (accessed on 7 September 2022)) provides a territory of 550 sq km (an example of 1 × 1 km of Lyon is shown in Figure  6 ). Table  1  shows the number of triangles regarding the four different kinds of objects (buildings, roads, terrain and vegetation). Due to the density (urban, peri-urban or rural areas), the number of triangles may also evolve for these kinds of objects. Table  1. Different types of objects shown in Figure  6  and the associated number of triangles for a 1 × 1 km tile of Lyon. Due to visual accuracy, computing all the ray intersections in the 3D space is irrelevant and time-consuming.
Proposed Data Structure for a Large-Scale Study Managing a large dataset with numerous rays is only possible with a special data structure. The entire dataset cannot be loaded at the same time. A tiling decomposition is necessary to manage such a large dataset. The city model is decomposed into a regular configurable grid (Figure  8 ) with an automatic process. Thanks to this organization, only the necessary tiles are loaded, which makes it possible to manage a large area. An accelerating structure is then provided with a bounding box hierarchy based on a quadtree structure. More information can be found in  [48]. Each ray is sorted according to the bounding box intersection in order to load each tile one at a time.
Generating the Database (Step C and D) The given intersection of each ray is the starting point for producing a database composed of georeferenced 3D points with a link to the intersected object and its attributes. It is then easy to provide simple query information for a given viewpoint, such as visible buildings, or advanced information with linked attributes (tree species, number of road lanes, etc. ). Another application may be to compute the view composition in terms of percentages for a given viewpoint and the sky view factor (the sky view factor is the definition of the delimitation between the sky and the other city objects for a given viewpoint  [49, 50] ) with additional semantic information. An example of a synthetic view is provided in Figures  9  and 10    To summarize this section, the method provides a rich panel of output data.