dataesr/scientific-paragraphs-categorization huggingface dataset:
The stability we found on global SFH parameters (SFR, M * ) is attributed to the very good photometric coverage of our sample. In a future work, we will examine the effect of adding more spectroscopic information in the SED fitting and in relation to the photometric coverage. We then compared the D n 4000 and H δ for AGNs and sources in the reference catalogue. The two populations have similar redshift distributions; therefore, for the comparison we accounted only for their different M * , which we did by assigning weights to the sources in the two datasets. Our analysis reveals that AGNs tend to reside in systems with intermediate stellar ages (D n 4000 ∼ 1. 4), whereas sources in the reference catalogue A145, page 12 of 14 presented a double-peaked D n 4000 distribution. The latter also has a flat H δ distribution, while an AGN peaks at H δ ∼ 3 Å.
Acknowledgements. We thank the referee for their careful reading of the paper. G. M. acknowledges support by the  Agencia Estatal de Investigación ,  Unidad de Excelencia María de Maeztu , ref. MDM-2017-0765. The project has received funding from  Excellence Initiative of Aix-Marseille University -AMIDEX , a  French 'Investissements d'Avenir' programme. This work was partially funded by the  ANID   BASAL  project  FB210003. M. B. acknowledges support from  FONDECYT  regular grant  1211000. Q. N. acknowledges support from a  UKRI   Future Leaders Fellowship  (grant code:  MR/T020989/1 ).
The information coming from these apps ends up in M. R. C. S. , SOAWE and RAM, a set of databases containing all the information related to the inspections and monitoring campaigns. These are just some examples of BMSs. From a general point of view, all the information collected during inspections is used to generate inspection reports. In addition, BMSs can calculate a priority index based on the state of deterioration of the infrastructure and list bridges in order of criticality to facilitate the organization of maintenance operations. The work  [9]  introduces a critical framework for a digital information model for bridges, providing an efficient platform for storing, processing and managing Unmanned Aerial System (UAS) inspection data. This methodology has a core focus on the integration of BIM in the Geographic Information System (GIS), providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS.
The research study  [17]  introduced a comprehensive methodology for a reliable quality analysis of digital point clouds generated via various techniques, such as imagery acquisition and laser scanning subjected to the implantation of a detailed 3D reconstruction model. With regards to the methodology proposed in this document, in order to obtain a digital twin representation of the bridge, two alternatives can be distinguished depending on the existence of an infrastructure digital model: 1. If a 3D model such as BIM or Bridge Information Modeling (BrIM) exists, then it is possible to work with an interoperable file of Industry Foundation Class type (IFC). This model, however, must be georeferenced. Integrating the BIM model in the 3D territorial context means using coordinates, GIS data, cartographic maps, orthophotos, topographic databases and technical maps.
2. When the only available information is the project in paper format, which always happens with old facilities, two solutions arise: a digitization of the project in paper format or a new photogrammetry. In the former case, it is necessary to manually build a CAD 3D model from which it is possible to obtain a BrIM. In the latter case, instead, a photogrammetry can be obtained by a single UAV mission equipped with LIDAR, aided by a Laser Scanner total station if necessary. Then, using dedicated software, such as 3DF Zephyr, it is possible to obtain a mesh that is already georeferenced. Finally, using CAD software such as Archicad, a BrIM is obtained from the mesh file. An important advantage of using such a technique is to have at the same time a survey of the geo-morphological and hydraulic area characteristics, aimed to identify the state of decay and main structural and geometric characteristics of the structure, as well the potential condition risks associated with landslides or hydrodynamic actions.
In order to simulate the real UAV in this realistic digital model, it is necessary to create a 3D model of the aircraft itself by using a 3D-modeling software such as Blender. The steps necessary to create the aforementioned digital twin representations are outlined in Figure  1. In this study, for simulation purposes, we created a BrIM of the Morandi Bridge sited in Catanzaro, Italy (Figures  3  and 4 ). In this case, we started from a paper version of the project. In order to simulate the real UAV in this realistic digital model, it is necessary to create a 3D model of the aircraft itself by using a 3D-modeling software such as Blender. The steps necessary to create the aforementioned digital twin representations are outlined in Figure  1. In this study, for simulation purposes, we created a BrIM of the Morandi Bridge sited in Catanzaro, Italy (Figures  3  and 4 ).
The methodology of this document proposes the choice of waypoints directly on the digital twin representation of the bridge. This operation can be easily carried out by the end-user using dedicated CAD software. Once these points of interest have been established, the proposed methodology allows the automatic generation of UAV trajectories in In order to simulate the real UAV in this realistic digital model, it is necessary to create a 3D model of the aircraft itself by using a 3D-modeling software such as Blender. The steps necessary to create the aforementioned digital twin representations are outlined in Figure  1. In this study, for simulation purposes, we created a BrIM of the Morandi Bridge sited in Catanzaro, Italy (Figures  3  and 4 ). In this case, we started from a paper version of the project.
Numerical Simulation In this section we report the performance of the proposed control strategy obtained by using the MATLAB ® programming and numeric computing platform. An important aspect in the choice of such environment is the opportunity to generate executable code on the on-board computers of real UAVs from the high-level implementation of the numerical simulation (see in this regard the Model Predictive Control Toolbox™ and the EMBOTECH ® FORCESPRO solver  [37] ). As reported in Section 3. 3. 2, the chosen UAV is the Asctec ® NEO hexacopter, whose physical and dynamic parameters are retrieved from  [32]  and reported in Table  1. In this simulation, we consider a fleet of three UAVs ( = 3).
Numerical Simulation In this section we report the performance of the proposed control strategy obtained by using the MATLAB ® programming and numeric computing platform. An important aspect in the choice of such environment is the opportunity to generate executable code on the on-board computers of real UAVs from the high-level implementation of the numerical simulation (see in this regard the Model Predictive Control Toolbox™ and the EMBOTECH ® FORCESPRO solver  [37] ). As reported in Section 3. 3. 2, the chosen UAV is the Asctec ® NEO hexacopter, whose physical and dynamic parameters are retrieved from  [32]  and reported in Table  1. In this simulation, we consider a fleet of three UAVs (n = 3).
Results As expected, during the simulation the UAVs track their references despite the measurement noise. Figure  10 , in particular, shows how the tracking error norm never exceeds the safety radius µ and, therefore, the UAVs do not collide. Figure  11 , moreover, shows an example of input saturation handled by the NMPC. Finally, thanks to the MATLAB ® UAV Toolbox Interface for Unreal Engine ® Projects, we created a 3D video to better show the behavior of the UAVs during the inspection:  [VIDEO]. the safety radius µ and, therefore, the UAVs do not collide. Figure  11 , moreover, shows an example of input saturation handled by the NMPC. Finally, thanks to the MATLAB ® UAV Toolbox Interface for Unreal Engine ® Projects, we created a 3D video to better show the behavior of the UAVs during the inspection:  [VIDEO].
The operation ends with the automatic writing of the inspection report and the updating of the priority index for predictive maintenance operations as is the case in the current BMS. an example of input saturation handled by the NMPC. Finally, thanks to the MATLAB ® UAV Toolbox Interface for Unreal Engine ® Projects, we created a 3D video to better show the behavior of the UAVs during the inspection:  [VIDEO]. The operation ends with the automatic writing of the inspection report and the updating of the priority index for predictive maintenance operations as is the case in the current BMS. The operation ends with the automatic writing of the inspection report and the updating of the priority index for predictive maintenance operations as is the case in the current BMS.
Introduction With the advent of Internet technologies, we have witnessed the emergence of largescale collaborative platforms that enable the creation of open-source content through the voluntary participation of large numbers of users. These platforms have fundamentally changed the practices of knowledge production and consumption, turning into public goods a number of cultural and scientific resources. The best-known example of these collective cultural platforms is the online encyclopedia Wikipedia, which has become one of the most important and trusted sources of information worldwide  (Anthony et al. 2009; Fallis 2008). Other examples of online collaboration can be found in software production, from the Linux movement, to GitHub, to Q&A systems, like Stack Overflow, and in scientific task-sharing projects, like Galaxy Zoo.
Related work There is an extensive and multidisciplinary literature on online collaborative systems, and on interaction patterns in large-scale collaborations: the case of Linux is, for example, analyzed in  Maillart et al. (2008) , Wikipedia in  Voß (2005) ,  Yasseri and Kertész (2013) ,  Ciampaglia and Vancheri (2010) ,  Yasseri et al. (2012) ,  Kittur and Kraut (2008)  and GitHub in  Thung et al. (2013) ,  Guzman et al. (2014) ,  Sornette et al. (2014). However, few works focus on large-scale scientific collaboration and even less on Polymath projects. In addition to the reflections by Gowers himself  (Gowers and Nielsen 2009) , a descriptive analysis of Polymath 1 project can be found in  Barany (2010) , where the authors provide a qualitative discussion of the rules that Polymath contributors developed to organize their work.
Data and methods We collected all the posts from Polymath projects 1, 4, 5, 8, and 15, starting from the links listed on the Polymath project wiki page (The Polymath Wiki 2021). The corpus for each project consists of a collection of posts identified by publication date, author, text, and parent post (for posts written in response to another contribution). The posts were published primarily on three blogs: Timothy Gowers's blog (2021), Terence Tao's blog (2021) and The Polymath blog (2021). Each of these blogs entails different technical restrictions on author interaction. On Gowers's blog, comments can only be posted in the main threads, limiting the depth of discussion and preventing authors from responding to comments in sub-threads. In contrast, Tao's blog allows comments up to a depth of 4.
Similarly to  Roth et al. (2013) , we hence simulate K networks {Ŵ k = (V, E k , Q k )} k∈{1,. ,K } with an expected degree for each node equal to the one of the authors of our dataset, keeping the same number of nodes n = |V| and edges m = |E| = |E k | for all k ∈ {1,. , K }. To do so we draw the weights Q k ij from a multinomial distribution with parameters m and p = {p ij } i,j∈V such that where d out i is the out-degree of node i and d in j is the in-degree of node j in the comments interaction network. Figure  3A  shows the distribution of the fraction of in-core links (i. e. , the fraction of messages from elite contributors to other elite contributors) in our K = 100 simulations and compares these distributions with the actual fraction of in-core links in our dataset. Figure  3B  shows the same comparison for the in-periphery links, i.
e. , the fraction of messages written by peripheral contributors directed to other peripheral contributors. Both plots show a peculiar division of labor in the Polymath project: both core-to-core and periphery-to-periphery links are more represented than in random simulations, underlining that authors are more likely to reply to contributors who participate in the discovery process to a similar extent. p ij = d out i • d in j m 2 As mentioned in the Data and Methods section, some of the blogs we studied limit the depth of response structures. We qualitatively observed a shift from a non-hierarchical structure in the very first project (i. e. , only the presence of second-level comments and no deeper structures) to a more structured organization of posts in later projects. To evaluate the robustness of the results presented in Fig.
Our results are similar to those of  Sornette et al. (2014)  for GitHub. Figure  5B , C suggest that contributions have positive super-linear effects, even when they are relatively marginal. In Fig. 5B , we show that the average individual daily production (for all contributors with more than 10 posts in all the projects) grows with the number of users active on that day. Figure  5C  displays the average daily productivity of the active core as a function of the number of users in the peripheral shells. We observe that an important presence of peripheral users boosts the productivity of the most active users. In Fig. 5 , we show the results obtained by aggregating all the projects. The individual analysis of each blog shows similar trends with very small variations in the growth exponents (blog1: γ = 1.
Conclusion Over the past few decades, we have witnessed the rise of large online collaborations such as Linux, GitHub, and Wikipedia. In 2009, the first Polymath project was launched with the goal of exploiting online collaborative environments to solve mathematical problems. In this work, we have investigated how the path to scientific discovery develops in this collaborative environment, how the labor is organized between authors, and which actors are the main innovators. Our results, which are consistent with previous works, show that productivity is highly skewed between contributors and that there is a small hyper-productive elite that publishes the bulk of the contributions. Nonetheless, peripheral contributors also play a significant role, as content production grows super-linearly with the number of discussants.
We conducted only a limited analysis of the content of the posts and of the semantic relationships between them. In a follow-up study, it would be interesting to examine the relationships between contributions based on the similarity of the content they produced rather than considering only their direct interactions in the response network. This structure would allow us to analyze the thematic cooperation patterns between contributors. This similarity network would also allow us to characterize the internal composition of users' "opinions" on solution techniques and their complex dynamics. Finally, it would be interesting in future works to compare the results obtained from the Polymath dataset with other online collaborative environments, in particular, to analyze the relationship between the level of participation and innovation, which remains largely unexplored in the literature.
Once the grains are segmented into several sub-clouds, each grain-scale morphology is determined by fitting a 3D geometrical model applied to each sub-cloud. If different geometrical models can be tested, this study focuses mostly on ellipsoids to describe the geometry of grains. G3Point is a semi-automatic approach that requires a trial-and-error approach to determine the best combination of parameter values. Validation of the results is performed either by comparing the obtained size distribution to independent measurements (e. g. , hand measurements) or by visually inspecting the quality of the segmented grains. The main benefits of this semi-automatic and non-destructive method are that it provides access to (1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to meters; (2) a very large number of data, mostly limited by the number of grains in the point cloud data set; (3) the 3D morphology of grains, in turn allowing the development of new metrics that characterize the size and shape of grains; and (4) the in situ orientation and organization of grains.
Method G3Point is a Matlab program which aims at measuring the size, shape and orientation of a large number of individual grains as detected from 3D point clouds describing the topography of surfaces covered by sediments. The main functions of G3Point are described in the following and Fig. 1. Compared to 2D digital elevation models (DEMs), where elevation z is defined as a function of the horizontal coordinates (x, y), 3D point clouds can include several points located at the same horizontal position (e. g. , the face above and below a grain), allowing a better description of geomorphological features such as grains. In the following, we assume that the considered point cloud is already denoised and cleaned of any geometrical feature not corresponding to pebbles. These features include trees, trunks, vegetation, the water surface, human-made objects and patches of fine grains (i.
Acknowledgements. We thank  Thomas Croissant ,  Edwin Baynes ,  Benjamin Bruneau ,  Benjamin Guillaume ,  Lucas Pelascini  and  Romy David  for their help acquiring data. We thank  Edwin Baynes  for proofreading the paper. We are grateful to  Rebecca Hodge ,  Benjamin Purinton  and an anonymous reviewer for their constructive comments and their work on this paper. Financial support. This research has been supported by the  H2020 European Research Council  (grant agreement no. 803721 ). We also acknowledge support by  Université Rennes 1 and CNRS. Review statement. This paper was edited by  Rebecca Hodge  and reviewed by  Benjamin Purinton  and one anonymous referee.
Code availability. A MATLAB version of the algorithm can be accessed through a GitHub and/or a Zenodo repository: https: //github. com/philippesteer/G3Point/ (last access: 18 March 2022) and https://doi. org/10. 5281/zenodo. 6368501 (Steer, 2022). Data availability. The data that support the findings of this study are available from the corresponding authors, Philippe Steer and Laure Guerit, upon reasonable request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esurf-10-1211-2022-supplement.
Different readily available software packages are often optimized towards one or more parameters, reducing the ability of the user to mine different data measurements and the versatility of the software  [19]. CellProfiler represents a robust, user-friendly and open-access software platform with algorithms and features that facilitate high-throughput work in biological research  [20]. Advanced algorithms for image analysis are organized in individual modules that can be inserted in a sequential order to generate a customizable pipeline to identify or measure biological elements, named "objects", or quantify positive areas in acquired images  [20] [21] [22]. In the current manuscript, we propose a method to perform image analysis of muscle sections by using pipelines built with CellProfiler software, which have been recently implemented and updated to CellProfiler 4  [23].
Specifically, we present the data obtained by using a pipeline, which we named MyoProfiler, to measure MFD, CNF, PNF, cell localization and the number of macrophages in muscle sections from mdx mice compared with wild-type ones. We also developed another pipeline, which we named SiriusProfiler, for the precise quantification of extracellular matrix deposition. Moreover, with the goal of validating the performance of our method, we compared automatic quantification, performed using CellProfiler, with manual quantification, performed using Fiji software. The results show that these pipelines allow the automatic analysis of multiple images in a quick and reliable manner by using a single software package for multiple outputs, thus representing useful tools for the quantification of key muscle parameters in both physiological and non-physiological conditions.
Representative images of TA muscle immunofluorescences were acquired using an Olympus confocal microscope (Olympus FV1200, Olympus, Tokyo, Japan) with 40X magnification and visualized with FV10-ASW software (version 4. 2; Olympus, Tokyo, Japan). Images for histological analysis were acquired using an Olympus BX53 microscope mounting an XM10 cam (Olympus, Tokyo, Japan) and using "cellSens Standard" software (version 1. 17; Olympus, Tokyo, Japan). We acquired adjacent images at 10X magnification of the whole muscle section from both WT and mdx mice. A few fields with evident histological defects were removed before the analysis in order to avoid artifacts. Images were saved and exported as 16-bit images (grayscale images).
CellProfiler-Based Pipelines for Muscle Analysis CellProfiler, developed by the Carpenter Lab at the Broad Institute of Harvard and MIT, is open-access software and available for Windows and macOS  [20, 21]. CellProfiler code was written using Python  [22] , and an updated, faster version of CellProfiler was recently released (CellProfiler 4)  [23]. Java (www. java. com, accessed on 20 October 2018) installation and update are required prior to CellProfiler installation. Inexperienced users are encouraged to read the CellProfiler manual before using it. For the analysis of data described in this paper, we used the latest version (4. 2. 1) of CellProfiler downloaded from the official CellProfiler website (www. cellprofiler. org, accessed on 11 October 2021) and installed it on a laptop computer (Intel ® Core™ i7 4500 U CPU @1.
80 GHz 2. 40 GHz, 8. 00 GB RAM, and 64 bit Windows 10 Home operating system). CellProfiler can process a wide range of image formats using the BioFormats library (complete list of formats permissible here). To use a pipeline, the user has to run CellProfiler (version 4. 2. 1), go to "file", select "open project" and run the project corresponding to the pipeline of interest. Alternatively,. cpproj or. cppipe files can be run. Then, a list of images can be dropped into the Images module. Image processing and data extraction can be performed through Metadata, NamesAndTypes and Groups modules: for each module, a caption with detailed information is available. Together with these four standard modules, custom modules are displayed as soon as the pipeline/project is opened. We also added captions for each module of both pipelines that we designed.
Validation of CellProfiler-Based Pipelines by Fiji Quantifications obtained with pipelines designed with CellProfiler were validated using Fiji software  [25, 26]. The quantification of the minimum Feret diameter (MFD) of muscle fibers began using anti-laminin, α1-labeled images. DAPI-labeled nuclei and anti-F480-stained macrophages were manually counted using the Cell Counter plugin. CNFs were quantified by combining anti-laminin, α1-labeled images and DAPI-labeled images. All numerical data were exported to Excel files and used for final quantifications. Sirius red quantifications with Fiji were performed using the Color Deconvolution plugin, and the red image was thresholded using the Otsu threshold method  [27]. All analyzed images and samples used for quantifications in CellProfiler were used for the validation with Fiji.