dataesr/scientific-paragraphs-categorization huggingface dataset:
Reconstruction of Structural Elements Planes composing walls, ceilings and floors were identified thanks to successive segmentations. These elements are still in form of point clouds. At this stage, a 3D reconstruction of the structural elements of the building (walls and slabs) must be performed. To reconstruct slabs and walls and to export the result into a BIM format, two steps are necessary. The 3D geometry of the elements is first described into the obj format  [61]  developed by Wavefront Technologies. This format is a simple data-format which is open and which is used by a plenty of 3D graphics applications. It has been chosen as a transition format towards IFC format  [62]. The second step consists in translating the 3D geometry of elements into an IFC file in order to be able to open results of the developed approach in BIM software.
File Generation into IFC Format The obj format is not a BIM format. Indeed, only the geometry of structural elements is described in a such a file. Neither object class is assigned to elements contrarily to what it is expected in a building information model. A conversion phase is thus necessary to be able to integrate results in BIM software. The Industry Foundation Classes (IFC) format was chosen as format for the output of the approach. The IFC format is a standardized object-based file format used by the AEC industry to facilitate the interoperability between the building actors. Both elements and relationships between elements are described in this type of file. It has the advantage not to be a proprietary format. In order to generate a file in IFC format from a file created in obj format, the open-source parametric 3D CAD modeller FreeCAD was used.
The Architecture module is particularly interesting since it provides BIM workflow to FreeCAD, with support for features like IFC support (thanks to IfcOpenShell, an open source library), fully parametric architectural entities such as walls, structural elements or windows. The FreeCAD software allows importing the created obj file. Once the file is imported, each object can be selected separately. The objects describing slabs are first selected. A tool provided in the Arch workbench is used to transform these objects into parametric structural entities for which a role property is set (beam, column, slab, roof, foundation, etc. ). Similarly, objects describing walls are selected and transformed into parametric wall entities. During the creation of wall entities, walls which are close from each other are joined automatically.
Assessment of the Developed Approach As depicted in the introduction of this paper, three items were considered for the development of the approach: the degree of automation, the transferability and the geometric quality. The assessment of the developed approach will consider these items. The developed approach is applied to two datasets with the same thresholds in order to conclude about the degree of automation and the transferability of the approach. The assessment of the approach is divided in two parts. First, results obtained for the segmentations into sub-spaces and planes and the classifications of points into several categories are studied. Then, results of wall and slab reconstructions are presented. The geometric quality of generated models of both buildings under study are analysed.
Datasets and Thresholds Used for the Assessment The approach was developed based on two datasets namely an individual house and the INSA building presented above. Despite two buildings with different characteristics, the developments may have been influenced by particularities of these datasets. Thus, the assessment of the approach has been performed with two other datasets not involved in the development of the approach. In this sub-section, the datasets used for the approach assessment are first presented. Then, a review of thresholds involved in the approach is proposed and default values used for the assessment are listed.
Datasets for the Approach Assessment During our project, a call for point clouds has been launched. Consecutively to it, four datasets were provided by French Land Surveyors Offices and a French company. It deals with indoor point clouds issued from laser scanning acquisitions. Among these datasets, two datasets were more specifically chosen for the assessment of the approach. They present the advantage of being relatively complete compared to the two others and they concern buildings with different functions and surfaces. Figure  11  presents the two selected datasets. The first dataset deals with an individual house composed of a garden level and a first floor for a total surface of about 200 m 2. Indoor point clouds of this house were acquired with a FARO Focus3D S120 laser scanner. The second dataset concerns an office building composed of two blocks and four floors.
Some thresholds are defined manually. Thresholds related to constraints and quality criteria have to be defined regarding the project specifications and thus they cannot be automated. These thresholds are notably involved in plane segmentations: the maximum distance between a plane and its inliers, the horizontal and vertical constraints and the threshold related to the RMSE of planes. Default values are proposed to the user for such thresholds. Other thresholds are defined manually and are intended to be applied to buildings in general as for example the smallest volume or the smallest plane which have to be taken into account. Default values of those thresholds are also proposed and should be adapted to several datasets of various buildings. By considering all information given by the user, some thresholds are defined automatically as for example for the minimum number of points of a 3D region.
Considering a spatial resampling of 1 cm and a minimum volume of 1. 8 cubic metres (1 metre by 1. 8 m height), the smallest 3D region would count 92,000 points. The approach was obviously developed so that it can be applied to all datasets having characteristics described in project framework. The approach was applied to the two presented datasets with the proposed default values of thresholds. The results will allow concluding about the generalisation of the use of thresholds defined manually but also about the adaptation of thresholds established automatically. In this way, the transferability of the approach is investigated.
Processing of Datasets The developed approach was applied to the two presented datasets with the default threshold values proposed Table  1. The automatic 3D reconstruction of walls and slabs required a processing time of 8 min for the individual house and 19 min for the office building. Figure  13  shows for each dataset the repartition of processing time regarding the main steps of the approach namely the loading of point clouds, the segmentation into sub-spaces, the segmentations into planes and the point classification and finally the description of walls and slabs into obj format. One can observe that the repartition is quite similar for the two datasets. The most time consuming step is the segmentations into planes and the point classification which represents almost 50% of the processing time.
Segmentation into Sub-Spaces Table  2  presents the results of floor segmentation for the two datasets and the results of room segmentation for one floor of each building. For both datasets floors are correctly identified. Results of room segmentation are also very satisfying. Indeed, 3D regions describing rooms are distinct from each other. Considering all the rooms of the two buildings, only two of them were falsely fused. This fusion is due to the fact that one of the room was not complete in the initial point cloud and a part of the wall between the rooms was missing. The analysis of remaining point clouds of segmentations into sub-spaces is also instructive. For the individual house, all rooms were extracted, whereas for the office building, two rooms out of forty one were not extracted and are in the remaining point cloud.
Moreover, for each building the precision and the recall of each category are calculated based on the following formulas  [63] , p. 138: precision = TP TP + FP (1) recall = TP TP + FN (2) Results of the assessment of point classification into several categories are presented Table  3. For both datasets, results obtained for the category "ceilings and grounds" are very satisfying since a high percentage of true positives is combined with a low percentage of false negatives. For "walls" and "occluding objects" categories, results are satisfying but some exchanges of points between the two categories are observed. These exchanges are mostly due to the fact that a lot of objects are very close or leaned against walls. For the individual house, points belonging to cupboards embedded in walls were falsely classified as walls because they are located in the borders of rooms.
Segmentation into Planes As explained in Section 3, several plane segmentations are necessary and the number of segmentations is determined automatically. In order to validate if this number is correctly determined, remaining point clouds were analysed for both datasets. At the end of plane segmentations of room point clouds, 6% of points classified as walls were not extracted for the individual house and 12% for the office building. It is interesting to compare remaining points with reference point clouds of "walls" and "occluding objects" categories. Among remaining points, 70% belong in reality to objects for the individual house whereas 30% for the office building. This means that quality criteria integrated in the approach allows rejecting points belonging to objects falsely classified as walls.
Inspection of Reconstructed Walls The results of 3D semi-automatic reconstruction of walls and slabs are shown Figure  14  for both datasets. For visualisation purposes exploded perspective views are used. For the individual house 44 objects were created (12 walls and 32 portions of walls) whereas for office building 127 objects were created (35 walls and 92 portions of walls). For both datasets, about a third of wall portions corresponds to indoor parts of façades since the approach considers indoor point clouds exclusively. A visual inspection of the results superimposed with input indoor point clouds allowed to determine that 93% of acquired objects were reconstructed automatically for the individual house and 94% for the office building. In order to assess the geometric quality of the reconstruction, deliverables created manually based on indoor point clouds of the buildings were exploited.
Attribution of Standard Deviations to Reconstructed Walls For each created wall, a quality index is calculated. It deals with the standard deviation of the unsigned distances between points and the reconstructed wall. Figure  16  presents wall point clouds colourized with the standard deviations associated to walls for one floor of each dataset. For the individual house, the mean of standard deviations associated to walls or portions of walls is 9 mm whereas for the office building the mean of standard deviations is 8 mm. This means that on average, the walls are reconstructed with a precision of 1 cm. The visualisation of standard deviations thanks to a colour-bar allows locating rapidly walls or portions of walls for which standard deviations are distinguished from those of the other elements.
Conclusions The aim of this article was to present a processing chain conceived for the 3D semi-automatic reconstruction of indoors of existing buildings from point clouds for their integration in BIM software. The developed approach is composed of two parts. Based on indoor point clouds, the first part consists in several segmentations into spaces and planes and in the classification of points into several categories. The second part of the approach deals with the reconstruction of walls and slabs of buildings from the element point clouds extracted in the first part. At the end of the approach, a file in a BIM format is generated and reconstructed walls and slabs can be opened in BIM software. The approach was assessed based on two datasets which were not used for the development of the approach.
The same thresholds were used for both datasets. The results obtained for the first part of the approach are promising in a geometric and a semantic point of view. Indeed, sub-spaces are almost all segmented and points are well classified into the different categories. The second part of the assessment was looked to the reconstruction of building elements. The results are also very satisfying for both datasets since, for individual house and the office building, more than 90% of objects located in the input point clouds were reconstructed automatically. Additionally, percentages of respectively 93% and 88% of walls and portions of walls detected automatically are correctly positioned and deviations from one millimetre to several centimetres are observed in relation to deliverables created manually.
The assessment of the approach demonstrates that the same default thresholds can be applied to several datasets of quite different buildings, in other words that the approach is transferable. Moreover, the high percentages of elements correctly extracted shows that thresholds determined automatically are well determined. Finally, low deviations indicate that the reconstructed elements accurately described the as-is states of the buildings. Thus, the developed approach responds positively to the three items considered namely the transferability, the degree of automation and the geometric quality. Tools for the inspection of results were also proposed. Standard deviations associated to reconstructed walls and points-to-model distances were proposed as quality indexes. These tools for the quality assessment are essential since a complete automation of reconstruction seems utopian and reconstruction errors can potentially occur.
Acknowledgments: The authors want to thank for its financial support the  French College of Chartered Surveyors (Ordre des Géomètres-Experts). They also want to thank the  French Land Surveyors Offices Pierre BLOY (Paris), David PIERROT (Mandelieu)  and  SCHALLER-ROTH-SIMLER (Sélestat)  and the  French company FUTURMAP (Lyon)  who provided datasets for the assessment of the approach.
Motivation/context As an international research facility, the W7-X project hosts many types of components designed not only by the Max Planck Institute for Plasma Physics (IPP) teams but also by external partners. At IPP the Design Engineering (DE) division is responsible for design tasks and spatial integration of all the components within the Torus Hall where W7-X is installed. The design tasks are performed using a Computer Aided Design (CAD) software, CATIA Version 5. W7-X is a concurrent engineering project as dozens of designers are working simultaneously on components sharing the same space or possibly having common interfaces. Previously the CAD models of W7-X were available but designers could never load the whole environment since the sheer number of CAD models caused CATIA to crash.
SmarTeam SmarTeam, a Product Data Management (PDM) software, is a central knowledge repository that focuses on managing and tracking the creation, change, and archive of all information related to products. In the case of DE the products are CAD models. SmarTeam was chosen for its deep integration with CATIA. The presence of a well documented programmable interface offers DE computer programmers the option to customize the database interface, automate tasks, and develop new tools based on the needs and feedback of the user. Designers can save their CAD models in the PDM with minimal formatting effort and have the possibility to store additional information regarding these models such as manufacturing tolerances and deformation of the real component during operation. CATIA and SmarTeam are the backbone of the design activities but the needs of the end users, designers, assembly teams, and customers extend far beyond simple storage and tracking functionalities.
Two specific optimization techniques are used in the DMU: first some CATIA products, marked by the designers in SmarTeam, are replaced by CATIA parts in the DMU. These CATIA parts display the same geometrical information as the CATIA product but in a single file. Second, for each sub-system the remaining CATIA products are replaced with CATIA components. As a result only about 11,000 models are necessary for the DMU instead of over 48,000 models. Every night, a Python script determines which models were modified by comparing the versions of the sub-systems in the DMU and SmarTeam. Then all sub-systems containing modified models are extracted from SmarTeam and copied onto the machine hosting the process. Each sub-system is recreated with the aforementioned optimization and replaces its previous version in the DMU.
Benefits and a limitation The DMU is an independent optimized copy of the CAD models in SmarTeam. This allows the designers to work on CAD models from SmarTeam with a fully detailed structure and have a lightweight version of the entirety of W7-X in CATIA at the same time. Thanks to the optimized structure there are nearly no performance penalties. As the DMU update script runs every night, the modifications performed by one designer are available to all designers the next day and the update process is transparent for the users. One limitation is that the DMU is currently available only to IPP personnel and not to the external partners since the amount of CAD data does not allow for a simple sharing solution.
Creation of the report The creation of a CAR can be split into three distinct phases. The first phase, called pre-processing (Fig. 1 ), consists in manually defining the scope of the analysis. It begins when the designer opens the DMU and selects the models to investigate. This serves as input for an embedded function of CATIA, Spatial Query, that returns all the CAD models found in the DMU within a given distance. Next, another embedded CATIA function, Clash, is used to calculate the minimal distances between each of the selected models and each model returned by Spatial Query. CATIA creates a Clash Report which contains the distance, position, and model information for each pair. During the second phase, called processing (Fig. 1 ), a Python script uses the previously generated Clash Report as input to generate an Excel table.
This table displays various information for each pair of CAD models retrieved from SmarTeam: minimal distances, affected sub-system, version number, manufacturing, and deformation in operation. For each pair the remaining distance is calculated by subtracting the manufacturing tolerance and deformation from the minimal distance. A color code is assigned depending on the result to highlight the collisions. In the third phase, called post-processing (Fig. 1 ), the designer focuses on the highlighted collisions. Some collisions are indeed components which are rightfully interfacing and can be ignored once properly documented in the CAR. Additional comments and tolerances can be added to the CAR to accurately assess each possible collision. If after this phase no collision is remaining, the investigated components are considered collision free.
Benefits and limitations The CAR is a centralized document where all the information related to the CAD models is displayed. The attention of the designer is focused on the components that are colliding and the rest can be safely ignored, investing time where it matters most. The use of Excel allows for a convenient formatting and sorting of the information as well as ensuring that the results can be visualized on any computer running a spreadsheet software. The transport and assembly operations of the components are the responsibility of the assembly teams, including the design of the tooling and lifting equipment. As DE does not have this information during design phase, it is not possible to guarantee through the CAR the absence of collisions during the assembly phase. Meeting with the assembly teams early in the design process helps reduce the risk of collisions during the assembly operations.
Conclusion and next steps As a result of the implementation of SmarTeam, the DMU and the CAR, the design lead time was optimized by detecting possible collisions early on, avoiding costly iterations. The communication, a key point in concurrent engineering projects has been greatly improved thanks to the ease of sharing information regarding the design. The next steps on the CAR may be to automate the preprocessing phase to avoid human errors. Another planned development is to provide to our external partners a region of the DMU that correspond to the location of their equipment. The update frequency may be less frequent than one day due to technical limitations and the need to convert the data to a neutral format, since many partners do not use CATIA. Another future project is to imple-.
The reconstructed 3D tomogram had a resolution of about 10 µm (voxel size of 3. 8 µm) using the CTAgent software. Image visualization and segmentation were carried out using VGstudio and Fiji  [12]  software. Segmentation of the carbonated zones and the precipitated calcium carbonate from the µ-CT image was done under the assumption that the calcium carbonate containing layer is denser, and therefore brighter and more homogeneous in the (inverted) µ-CT images. The mineral precipitation was segmented by using a threshold of the gray-scale histogram in the microcavity section.
Modelling Reactive geochemical modelling was carried out using the numerical code PHREEQC  [13]  with the Thermoddem database  [14]  in order to be able to represent the cement in the model. In order to simulate chemical reactions taking place at the water/cement interface, two 1D transport models where set up. In both cases, the numerical grid represented a cross-section across the interface, i. e. the water/cement interface was present at one end of the model while the other end represented the interior of the cement. At the cement end of the model grid, the boundary was simulated as a closed boundary (Neumann boundary condition) while at the interface end of the model grid, the boundary condition varied in the two conceptual models. In one model, the boundary condition was of Dirichlet type (open boundary) obtained by keeping the water in equilibrium with a constant CO 2 pressure of 50 bars in order to model a situation with unlimited supply of CO 2 to the water phase.
Acknowledgements This study has been carried out through the projects "Closing the gaps in CO 2 well plugging" and " Ensuring well integrity during CO 2 injection " funded by the  Research Council of Norway  ( 243765/E20 ). Theese projects are administered as an integrated part of the  BIGCCS Centre  funded by  Gassco ,  Shell ,  Statoil ,  Total , Engie and the  Research Council of Norway  ( 193816/S60 ). The  Research Council of Norway  funded the tomography equipment through the  National Research Centre for X-ray Scattering and Imaging (RECX). H. O. S. and Y. Y. acknowledge support from  Innovation Fund Denmark  via the  CINEMA  project and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  653241. Partial funding for this work was obtained from the  Norwegian PhD Network on Nanotechnology for Microsystems , which is sponsored by the  Research Council of Norway, Division for Science,  under contract no.