dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics. Sample sizes were estimated from the expected effect size based on previous or preliminary experiments. No data was excluded, with the exception of obvious technical failure. Statistical analysis of experimental results were carried out using GraphPad Prism6 software. We utilized one-way analysis of variance and Tukey's post hoc tests. P values o0. 05 were considered significant. The coefficients of determination (R 2 ) were calculated using software R for comparing numerical simulation results with experimental measurements.
Acknowledgements We thank  Pierre Vincent  and  Kees Jalink  for providing Sindbis AKAR3 and T-EPAC-VV viruses,  David A. DiGregorio ,  Fiona Francis ,  Nicolas Le Nove  `re and  Manuel Mameli  for critical reading of the manuscript and helpful suggestions, and The Virtual Cell team for providing technical support. The work was supported in part by  INSERM , the  UniversitePierre et Marie Curie  ( UPMC, Paris 6 ),  Region Ile-de-France DIM NeRF, Fondation pour la recherche sur le cerveau (FRC)  and the  EC FP7.
Industrial Context In order to fulfill the need for as-built datasets to help workers in complex buildings in their daily jobs, large and multi-sensor surveys now have to be considered at the whole building size. Unfortunately, many current tools (including sensors, processing programs and visualization applications) have not been designed for such large surveys of complex indoor facilities. Until today, the major uses with as-built data in the industry are related to the description of the actual shape of only a part of the facility, with its obstacles and free spaces with centimeter accuracy, to help maintenance planning, handling, storage, replacement or changing important components in that specific part of the plant; see Figure  1. The next step in the field of 3D surveying of facilities consists both of scaling up the current state of the art, without compromising data quality, and in dedicating as-built datasets to new users, who are not experts in CAD or terrestrial laser scanner (TLS) data.
shape of only a part of the facility, with its obstacles and free spaces with centimeter accuracy, to help maintenance planning, handling, storage, replacement or changing important components in that specific part of the plant; see Figure  1. The next step in the field of 3D surveying of facilities consists both of scaling up the current state of the art, without compromising data quality, and in dedicating as-built datasets to new users, who are not experts in CAD or terrestrial laser scanner (TLS) data.
‚ Recommendations and examples for developing dedicated applications for virtual tours of complex architectures using multiple data types in order to increase the value of the dataset and answer users' requirements, Section 5; ‚ An overview of the remaining bottlenecks and challenges in view of the generalization of large, dense, multi-sensor scanning surveys, Section 6. • Recommendations and examples for developing dedicated applications for virtual tours of complex architectures using multiple data types in order to increase the value of the dataset and answer users' requirements, Section 5; • An overview of the remaining bottlenecks and challenges in view of the generalization of large, dense, multi-sensor scanning surveys, Section 6.
Scanning Industrial Interiors, a 20-Year-Old Challenge As shown in Figure  3 , power facilities are a specific subset of industrial environments, in their shapes (congested, with a huge number of objects, whose manufactured geometries correspond to a set of 3D Figure  2. Global framework of as-built data production in the experiment. White boxes are the initial data sources; grey boxes are intermediate data versions; and black boxes are the datasets that composed the virtual replica of a complex building. Orange outlines represent stronger control procedures in the data production process.
58 global geometric tolerance is ±2 cm) and, also, the productivity of 3D reconstruction (up to 100. 000 objects for one single building). In an industrial context, many users are not experienced users of laser scanner or CAD data. Furthermore, geometry alone is not sufficient to meet all maintenance needs and to represent all of the complexity of the architecture and the details of a power plant. These observations lead to the following requirements for the tools and methods used in these new standards in the production and use of as-built datasets for the maintenance of facilities: (1) Multi-sensor datasets that describe both the geometry and appearance of a facility; (2) Fast and accurate sensors, responsive to harsh albedos or very reflective surfaces; (3) Automated pipelines for conversions and formatting; (4) Large-scale efficient tools for registration and processing; (5) User-friendly diagnostic tools to achieve high quality requirements on large and complex datasets; (6) Dedicated solutions for the intuitive exploration and use of multi-sensor datasets.
In an industrial context, many users are not experienced users of laser scanner or CAD data. Furthermore, geometry alone is not sufficient to meet all maintenance needs and to represent all of the complexity of the architecture and the details of a power plant. These observations lead to the following requirements for the tools and methods used in these new standards in the production and use of as-built datasets for the maintenance of facilities: (1) Multi-sensor datasets that describe both the geometry and appearance of a facility;  (2)  Fast and accurate sensors, responsive to harsh albedos or very reflective surfaces;  (3)  Automated pipelines for conversions and formatting;  (4)  Large-scale efficient tools for registration and processing;  (5)  User-friendly diagnostic tools to achieve high quality requirements on large and complex datasets;  (6)  Dedicated solutions for the intuitive exploration and use of multi-sensor datasets.
Requirement Engineering for As-Built Datasets of Complex Architectures In order to represent a complex facility, the requirement engineering of several jobs, through meetings and discussions, to estimate the optimal value of the dataset produced, has led to the conclusion that the following data types must be produced together; see Figure  4:  (1) Panoramic images, in order to be able to read each equipment tag that might be visible up to 5 m from the main pathways in the facility, as well as to understand the overall architecture of the building;  (2)  Georeferenced terrestrial laser scans, in order to deliver local sub-centimeter geometric accuracy on distance measurements, as well as global 3D locations with less than 2 cm of deviation from the ground truth (2. 58σ tolerance);  (3)  2D floor maps, with all elements relative to the structure of the building (walls, floors, ladders, stairs, etc.
Then, the tools and methods that might meet all of these criteria were selected using the experience of survey teams that should submit sample datasets to prove their expertise (from either previous surveys or small-scale experiments in similar conditions). The initial estimates of costs and delays were extrapolated to the building scale thanks to a deep analysis of these previous acquisitions. Finally, to ensure the consistency of results for initial user needs, the dataset was fully checked, using a Level 2 control procedure and then reworked until quality standards were achieved. Finally, user feedback showed the validity of such approaches. In order to match these requirements, one must define relevant specification criteria for each type of data, as presented in Table  1. Then, the tools and methods that might meet all of these criteria were selected using the experience of survey teams that should submit sample datasets to prove their expertise (from either previous surveys or small-scale experiments in similar conditions).
The initial estimates of costs and delays were extrapolated to the building scale thanks to a deep analysis of these previous acquisitions. Finally, to ensure the consistency of results for initial user needs, the dataset was fully checked, using a Level 2 control procedure and then reworked until quality standards were achieved. Finally, user feedback showed the validity of such approaches. Recent breakthroughs offer the prospect of large-scale and multi-sensor scanning surveys that comply with the previously defined technical requirements and tolerances, from acquisition and processing to data integration in dedicated software, developed specifically for maintenance work in a power plant. As will be shown in this section, new developments in data acquisition in the past five years have mainly improved the speed rate, with almost no gain in accuracy.
Regarding global accuracy, the fine registration of large laser scanner datasets is an open and active research topic. For complex buildings, cloud-to-cloud automatic approaches, such as ICP and variants, cannot be used for several reasons. First, due to the proximity of objects, a small relative change in scanner position induces strong differences on point clouds. Thus, cloud-to-cloud constraints cannot, by definition, lead to accurate and trustworthy results in complex scenes. Secondly, these approaches cannot take into account datum constraints for data referencing without a great loss of automation. Finally, these approaches are neither efficient nor robust for very large datasets (many hundreds of stations)  [17]. Currently, only target-based registration paired with a total station survey for datum referencing allows the fine registration of hundreds of laser scans (3σ = ˘2 cm) on the scale of 10-floor buildings.
3D CAD Reconstruction Recent improvements in processing tools for 3D reconstructions from large point clouds (tens of billions) have made possible the "as-built" reconstruction of a full mock-up. First, the data storage cost has increased by a factor of 25 in the last decade  [18] , while the data USB external transfer rate has increased by a factor of 20  [19] , and internal SSD now reaches 500 Mb/s. However, if dozens of laser scans requires gigabytes, thousands of laser scans require terabytes. To improve both file storage, versioning and processing, data structures have been developed  [20]  and implemented in many commercial software. Other great improvements have been made for visualizing and manipulating a billion point clouds, for example using QuadTrees  [21] , such as implemented in the WebGL renderer PoTree  [22] , with real-time shaders, like Eye Dome Lightning  [23] , also implemented in the open source software CloudCompare.
Goals and Context of the Experiment In the wake of several research projects that contributed to the development of tools and methods for dense scanning  [33] , reconstruction  [34]  or registration  [17]  and also tools for the visualization of complex datasets  [23] , EDF carried out the first very large-scale experiment in the most complex building in a 1300-MW nuclear power plant facility: a reactor building. The goal was to assess the effective performance (quality, cost and speed) of current tools and best practices through a major research project launched in 2013, in order to make a decision on the generalization of the multi-sensor scanning of power plants and highlight remaining bottlenecks to target lock ups. Another goal was to maximize the benefits of this dataset to the company, especially by sharing it with as many users as possible, to assist them in their daily work.
A Level 2 Procedure for the Quality Control of Large Datasets We mentioned several definitions of tolerances and quality requirements to reach high-quality data production. In order to reach these requirements, it is necessary to use specific procedures for the quality control of large, as-built datasets (laser scans, RGB panoramic or 3D CAD models). These Level 2 procedures are a standard for quality monitoring and for detecting non-conforming materials. Our experiment demonstrated their usefulness. To enhance dataset quality, we therefore implemented it in the following manner:.
However, as mentioned above, we expect to have only half the time to carry out the survey, i. e. , in the future, twice the number of sensors and operators will have to be sent to a site, creating additional stress on current methodologies and synchronization between operators. Discussion on processing TLS data: This experiment also underlined some constraints on scaling up the current post-processing tools and methods in terms of quality, time or cost (fine registration of large laser scanner datasets, quality monitoring and validation tools, issues for formats, storage and data sharing). To illustrate the complexity of the fine registration of laser scans in complex architectures, we show in Figure  5a  view of the 3D network used in the adjustment. This experiment, mostly performed with a Leica Cyclone, underlined the lack of robustness of weighted least squares in such large blocks and the need to work with 3D topographic networks instead of 2.
• Without better algorithms for the fine referencing of TLS dataset and large 3D networks, the only way to comply with a global 2. 58 tolerance of 2 cm accuracy overall, the dataset consists of creating sub-networks with fewer than 200 stations, independently referenced to the external reference system. This constraint implies thorough planning on site for placing and surveying targets. Recent developments have shown that we can expect better results in automation and Figure  6. Key numbers in the multi-sensor scanning survey of a nuclear reactor building  [1] ; the main order of magnitude that may be highlighted is the number of stations: more than 1000 stations (for more details, see  [35] ). The relative time allocation is shown on the graph; CAD reconstruction is the main budget. Discussion on on-site acquisition: As detailed in  [35] , this acquisition phase demonstrated the feasibility of such surveys thanks to some specific evolutions and developments of the tools and methods to take into account the up scaling.
To succeed, a large number of sensors and operators (five laser-scanning surveyors with Leica HDS 6100 and Leica HDS 7000 and three photographers with Canon EOS 5D Mark III with motorized panoramic heads) is required. However, as mentioned above, we expect to have only half the time to carry out the survey, i. e. , in the future, twice the number of sensors and operators will have to be sent to a site, creating additional stress on current methodologies and synchronization between operators. Discussion on processing TLS data: This experiment also underlined some constraints on scaling up the current post-processing tools and methods in terms of quality, time or cost (fine registration of large laser scanner datasets, quality monitoring and validation tools, issues for formats, storage and data sharing).
These points are certainly an issue for 3D reconstructions, but mostly for taking measurements of so-called "bubble views" or station-based views where no feedback on the real position of 3D points is given. Our solution consisted of developing an efficient interface to perform a manual segmentation of these outliers Figure  7. ‚ Without better algorithms for the fine referencing of TLS dataset and large 3D networks, the only way to comply with a global 2. 58σ tolerance of 2 cm accuracy overall, the dataset consists of creating sub-networks with fewer than 200 stations, independently referenced to the external reference system. This constraint implies thorough planning on site for placing and surveying targets. Recent developments have shown that we can expect better results in automation and quality by bundler snooping (moving targets) and the use of robust estimation well known from geodesists  [36].
quality by bundler snooping (moving targets) and the use of robust estimation well known from geodesists  [36]. Discussion on processing panoramic images: The level of automation and the quality of output using modern stitching software, such as the one we used, Kolor AutoPanoPro, are very good. However, some issues still have to be addressed: • When dealing with 450 Mpixel images of indoor scenes, the field of view of a single picture is quite small and can lead to a lack of feature points when only uniform objects, such as a painted wall, are visible in the picture. The Level 2 control procedure has led to the reopening of 20% of the panoramic stitches for editing; • Another recurrent error in panoramic images consists of estimating verticality, based on the images. In congested environments, default settings can lead to errors of up to 20° in that estimate.
We recommend either the use of leveled panoramic heads with custom settings for estimating the relative position of unit images or the use of vertical definition tools through the software interface; • A major issue is the registration of the panoramic images on the external reference frame; see Figure  8. As will be discussed later on, the fine registration of panoramic images is valuable in assisting with the quality control of 3D reconstructions and offers a better user experience when browsing the dataset. We will address this issue later in this paper. Discussion on restructuring and updating 2D floor maps: A global model of the plant is key data for designing the structure of the dataset, unifying the names of the objects and performing an analysis on it. By constructing a graph of the various objects and their relationships (adjacency, verticality, inclusion, etc.
), it makes it possible to answer questions like "What are the panoramic images taken in the rooms adjacent to a specific one?" or "Can I access a specific location in the building without taking ladders?" This entire graph database was built using structured 2D floor plans, updated using the 3D dataset: walls, grounds, ladders, stairs and many others; cf. Figure  9a. These as-built floor plans were then automatically processed to extract all of the required information and to build a "topological graph", Discussion on processing panoramic images: The level of automation and the quality of output using modern stitching software, such as the one we used, Kolor AutoPanoPro, are very good. However, some issues still have to be addressed:.
As will be discussed later on, the fine registration of panoramic images is valuable in assisting with the quality control of 3D reconstructions and offers a better user experience when browsing the dataset. We will address this issue later in this paper. Discussion on restructuring and updating 2D floor maps: A global model of the plant is key data for designing the structure of the dataset, unifying the names of the objects and performing an analysis on it. By constructing a graph of the various objects and their relationships (adjacency, verticality, inclusion, etc. ), it makes it possible to answer questions like "What are the panoramic images taken in the rooms adjacent to a specific one?" or "Can I access a specific location in the building without taking ladders?" This entire graph database was built using structured 2D floor plans, updated using the 3D dataset: walls, grounds, ladders, stairs and many others; cf.
This graph is then used in the software applications that we developed, in order to answer queries on the semantic, geometric or topological properties of the building and its components; cf. Regarding the processing steps for the floor maps, the pipeline is as follows: Figure 9b. (1) Redraw existing floor maps in AutoCAD, following specific drawing rules and using only two types of objects: polylines and blocks (manual); (2) Compare the 3D as-built model and panoramic images to floor plans in a specific tool developed in Unity3D, to update them in AutoCAD (manual); (3) Convert polygons of floor maps to Scalable Vector Graphics (SVG) files (automatic) and export the blocks to XLS using EXTATTBE in AutoCAD (automatic); (4) In a dedicated C# tool, instantiate a class model of objects of the building: a.
), including controls to check with respect to the drawing rules, ii using a Clipper Library clipping algorithm  [38]  and threshold, compute relations between objects, such as adjacency, inclusion, etc. b For each pair of floors: i Connect objects, such as elevators, ladders, stairs, ii Using Clipper Library, compute vertical relationships between objects. (5) Export the instantiated building model to a topological graph in XML, with a description of the building model in XML Schema Definition (XSD). Discussion on the 3D CAD reconstruction based on the TLS point cloud: A large dataset of 40 billion 3D points was used to reconstruct an as-built 3D CAD model of the facility (see Figure  10 ). The main part of the 3D reconstruction was produced using Trimble RealWorks Version 8. x (80% of the final CAD model); some objects from a pre-existing CAD model were adjusted in Dassault Systems SolidWorks, as well as other equipment that could not be modeled as a combination of primitive shapes (see Subsection 2.
3. 3). As illustrated in Figure  6 , the 3D reconstruction was the main line in the budget of the data production. In order to meet end users' needs, the quality levels of the 3D data produced had to be defined in accordance with their intended uses: maintenance task planning (including the associated logistics: access, scaffolds, handling areas), worker safety and virtual tour for low accessibility rooms by inexperienced professionals. Discussion on the 3D CAD reconstruction based on the TLS point cloud: A large dataset of 40 billion 3D points was used to reconstruct an as-built 3D CAD model of the facility (see Figure  10 ). The main part of the 3D reconstruction was produced using Trimble RealWorks Version 8. x (80% of the final CAD model); some objects from a pre-existing CAD model were adjusted in Dassault Systems SolidWorks, as well as other equipment that could not be modeled as a combination of primitive shapes (see Subsection 2.
In order to deal with the huge amount of work and to reduce the time of data delivery, the 3D CAD reconstruction had to be split and parallelized by sectors and then allocated to half a dozen CAD operators for almost a year (10 months). To achieve and certify the quality of the reconstructed as-built model, a Level 2 check procedure was carried out by two independent operators and led to further iterations and re-working (approximately 10% of the total effort). This validation was performed using mesh-cloud distance computations, by using software originally developed by EDF R&D and Telecom ParisTech (CloudCompare  [33] ) and "out-of-core" technologies to display a maximum number of points for the visual inspection in Trimble RealWorks 8. x, guided by the analysis of an SQL database storing the standard deviation of each reconstructed geometry (~80,000 objects).
Summary of Tool Development during the Project Many tools (hardware and software) were used to produce both the data and end-user applications. Some of them were satisfactory, but many had to be improved during the project. Acquisition and stitching of RGB panoramic images: Canon EOS 5D cameras with a Dr Clauss motorized head were used. For stitching, mapping and blending, Kolor AutoPano was used, and XNview MP was very useful for batch conversions, resampling and renaming. None of the tools were improved. Acquisition and processing of the TLS dataset: Leica HDS 6000 and 7000 with total stations were used. The Z+F software was used to convert and filter scans. Specific settings were added to that software for improve the results. For registering scans, Leica Cyclone was used. For manual segmentation, we had to add new features in Trimble RealWorks, used to remove noise that could not be filtered out (mirrors).