dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We acknowledge the  CNRS infrastructure RENARD  ( FR 3443 ) for EMR facilities. We acknowledge the  Institute of Materials of Paris (IMPC)  for the access to the NMR relaxometers (RELAXOME facility). The NMR relaxometers were funded by  Sorbonne-Université ,  CNRS , and  Région Ile de France. Anne-Laure Rollet  is grateful for the  COST Action CA15209 EURELAX "European Network on NMR Relaxometry ", supported by  COST (European Cooperation in Science and Technology). We also acknowledge the transverse  R&D program  from  Total-Energies  for full support and resources. The authors wish to thank  Jean-Marc Moron  from  TotalEnergies  for the kerogen isolate extraction. Review statement. This paper was edited by  Geoffrey Bodenhausen  and reviewed by two anonymous referees.
Thermodynamics Simulations are run on fcc, Ni-based alloys. The atom-vacancy interaction parameters are obtained from the energy of formation of vacancies in pure metals in the fcc crystal structure. These cannot necessarily be measured experimentally, and indeed experimental data of this type are scarce even when the pure metal is stable in the fcc structure. Instead, we use DFT results from Shang et al  [54]  (from their Supplementary Data), which provide a consistent dataset for most elements of interest. When the vacancy formation energy for a given element is not given in Ref. [54] , we use the value for Ni instead. Gibbs energies of pure metals and atom-atom interaction parameters are obtained from assessed descriptions of the relevant systems published in the literature. These are available as functions of atom fractions, not site fractions, i.
2017). The technical data collected during the surveys (Sect 4. 4. 1) were used to validate that the model gave accurate drying predictions. Deviations between predicted data and actual data were satisfactorily low (less than 10%) for moisture content of the end product, air temperature after drying, and energy consumption. The numerical model was applied to investigate optimum dryer designs that minimize energy consumption and maximize the quantity of water evaporated per unit mass of hot air (Sect. 2. 1). The main challenge was to identify a configuration where the hot air carried just enough energy to dry the product flow rate (thermodynamics design objective) and the particles stayed in contact with the hot air long enough for complete heat and water transfers (kinetics design objective).
Experimental design The energy performance of the dryer was first evaluated following the processing center's drying procedure, with no interventions other than sampling material and recording drying conditions. Based on the data collected, the feeding rate was adjusted to optimum value, and the data collection was repeated. Data were collected over six consecutive days (recording 5 hours/day), the first 3 days with the original feeding rate and the subsequent 3 days with the optimum feeding rate. Data collection started after the dryer had been in operation for 1 hour to ensure steady-state conditions. Statistical analyses Statistical analyses were performed with SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA) software, following a comparative experimental design  (Precoppe et al. 2016)  with three replicates and two treatments: before and after the adjustment on the feeding rate.
Data collection Temperatures, relative humidity, pressures, and air velocities were measured at various points on the dryer (Fig. 4. 7) with suitable sensors connected to a wireless data logger (LOG-HC2-RC, Rotronic) recording values at 1-minute intervals. Mass flow rates of wet and dry cassava grits were measured using a digital balance (AWB120; Avery Weigh-Tronix, Smethwick, UK) and a chronometer. Samples of wet and dried cassava grits were collected at 1-hour intervals for moisture content analysis (3 h at 103 ± 1 °C in a convection oven) according to AOAC 935. 29  (AOAC 1998).
This work is supported by the  NSFC  (Grant No. 51971178 ), the  Fundamental Research Funds for the Central Universities  (Grant No. D5000220034 ), the  Natural Science Basic Research Plan for Distinguished Young Scholars in Shaanxi Province  (Grant No. 2021JC-12 ). E. Pineda  and  D. Crespo  acknowledge financial support from the research project  PID2020-112975GB-I00  funded by  MCIN/AEI /10. 13039/501100011033  and from  Generalitat de Catalunya  ( AGAUR grant   2017SGR0042 ). Y. J. Wang  was financially supported by  NSFC  (Grant No. 12072344 ) and the  Youth Innovation Promotion Association of the Chinese Academy of Sciences. Y. Yang  acknowledges financial support from  Research Grant Council  (RGC), the  Hong Kong government through the General Research Fund (GRF)  with the grant numbers  CityU11200719  and  CityU11213118.
Computerized maintenance management systems (CMMS) aim to assist administration and maintenance agents in their asset maintenance tasks (e. g. , building, network, air conditioning, faucets maintenance). In this context, geospatial data can help to have a better understanding of the assets they represent by providing additional information: spatial, thematic, temporal, or inter-object relationships. Using such information often leads to interoperability issues as different domains describe them with different data models, like Building Information Modeling (using the Industry Foundation Classes format) and Geographic Information System (using the CityGML format). Spatial information, and particularly 2D or 3D geometry, are stored using heterogeneous representations (e. g. , triangle soup, boundary representation, sweep volume, composite solids).
Geospatial data aims to describe features, abstractions of a real world phenomena, by providing spatial (such as location, topology and geometry), thematic (such as textual description, relationships with other features, etc. ) and temporal information. They have their own sources and exist in their own well-developed environments, such as : Computer-Aided Design (CAD) that aims to assist in the design, layout and technical documentation of object, Building Information Modeling (BIM) that allows the creation of digital representation of physical, operational and functional information of a facility (building, bridge, tunnel, etc. ), as well as GIS. They may follow different standard formats, for instance : CityGML or GeoJSON for GIS, Ifc for BIM, or DXF for CAD. Natively built for their respective environments, the use of such multisource and heterogeneous data naturally leads to interoperability problems.
Moreover, understanding their data construction philosophy, modelling design (if it exists), and usages are es-sential to use these data together. Furthermore, geospatial data models describe features at different scales, with more or less detail, as shown in Figure  1 : city scale, building scale, object scale. Thus, they can provide different information about the same feature. Therefore, it must be possible to navigate between those scales and gather information found in geospatial data to help understand an asset using its context: • Spatial information may help to better understand an asset: for example, it is easier to apprehend the topology of a building water network using 3D geometry because it allows visualizing the network through different floors. • Thematic information could enrich the existing description of an asset in a CMMS software.
In this paper, we propose a methodology and a logical architecture to integrate the geometric data of various geospatial data in the same Web viewer, by extracting and converting it into a unified and standardized model while keeping a link to its sources, to retrieve semantic data if needed. This work aims to help the user achieve a better understanding of assets to manage by making it possible to create contextual views of an asset by navigating in the spatial and thematic representations brought by heterogenous geospatial data. Another underlying difficulty of this work is the need to understand and align the different concepts brought by each domain (GIS, BIM, CAD) to visualize them efficiently in the same viewer. Our approach makes use of generic components and standards as a set of services as a reproducible solution, since it is intended to be implemented and demonstrated in existing CMMS software 1.
RELATED WORK Geospatial data can offer numerous details about features that can be thematic, temporal, or spatial. Mobilizing this information, such as 3D geometry, can help to achieve a better understanding of CMMS assets. For example,  (Verghote et al. , 2019)  showed that visualizing objects in 3D helps to understand the indoor routing of a building. There is also a need to take into consideration to the way this information is presented. Indeed,  (Neuville et al. , 2019)  showed that to help the user understand 3D geometry, the chosen point of view for visualizing the geometry plays a significant role in its comprehension. Mobilizing multiple features of the same object, especially in the same environment, is a challenge that many researchers and developers are trying to solve. They can represent the same object at different scales, through different domains: city-scale (for example, 2D and 3D GIS), building scale (e.
The 3DTiles standards allows to share, visualize, and interact with massive heterogeneous 3D geospatial content across desktop, web, and mobile applications. This standard aims to descaribe and visualize 2D and 3D triangulated geometry, stored using the GLTF 5  format, or point clouds on the web. We decided to use these standards as they are commonly used to represent 2D and 3D geometry from spatial data on the web. Using these standards, some researchers focused on visualizing city objects by making use of CityGML data. For example,  (Gaillard et al. , 2015)  proposed visualizing CityGML data on the web by creating 3DTiles, allowing the user to navigate through a city in a 3D environment. One interesting part of this approach is that they succeeded in keeping a link between a CityGML object and its 3D representation in the 3DTiles, i.
e. , making it possible to visualize geometric and thematic information of a building on the same context. This feature may help to understand a city object by completing geometric information with thematic one. Following the same approach,  (Jaillot et al. , 2020)  proposed a solution to detect changes in the city between various periods using CityGML and store them using an extension of 3DTiles 6  , allowing the user to navigate through a city in 4D, through time. Other works focused on building data visualization by converting Ifc files into 3DTiles. (Chen et al. , 2018)  proposed a solution using open source tools. However, this solution creates only one geometry for a whole Ifc file and does not allow retrieval of the associated semantic data. The solution of  (Xu et al. , 2020)  allows for the creation of one geometry per Ifc object and keeps the associated semantics directly in the generated 3DTiles.
Nevertheless, the link to the original Ifc instance is lost; storing semantic data in a 3DTiles file next to the geometry implies either enlarging the 3DTiles file size, making it longer to load in a web solution, or losing information to avoid this drawback. Some works focused on extracting new information from building data that may help CMMS agents. For example,  (Lin et al. , 2013)  and  (Khan et al. , 2015)  worked on producing indoor routing using BIM data. In their literature review,  (Fosu et al. , 2015)  classify approaches of the use of BIM and GIS data in five categories: BIM to GIS conversion  (Stouffs et al. , 2018) , GIS to BIM conversion  (Donkers, 2013) , BIM-GIS integration  (Karan et al. , 2016 , Hor et al. , 2016 , Huang et al. , 2020) , Unified Building Model (a data model able to represent objects that can be found in BIM and GIS)  (El-Mekawy et al.
, 2012 , Yan et al. , 2021). The last category is the use of a Web Viewer to use this data, like  (Hijazi et al. , 2020)  done by using 3DcityDB  (Yao et al. , 2018)  and Bim-Server  (Beetz et al. , 2010)  to transform the geometry as GLTF, that allows users to directly query the geometry they need to visualize in databases. BIM-GIS conversion or integration implies different challenges  (Zhu et al. , 2018)  that still needs to be solved: • at the geometrical level: with the storage of geometry, geo-referencing and LOD harmonization • at the semantic level: since BIM and GIS models were designed for different purposes, major differences exist between them  (Fosu et al. , 2015). Finally, considering IFC and CityGML data as the most used models currently, there is a need for a solution to visualize and navigate heterogeneous geospatial data from the city to building interiors.
These data offer different representations of CMMS assets, and thus bring different key information about them. In order to visualize them, using standards such as WMS/WFS and 3DTiles seems to be mandatory if we want to propose a reproducible, usable, and interoperable solution for all. We will focus on using data directly from their sources as our purpose is to let data evolve in their own domains and gather information about an asset only when it is needed. A contextual view is the visualization of one or more representations of an asset and its context. We can distinguish two types of representations. The first, spatial, represents the location, the topological or geometric information. This representation can be either realistic or symbolic, such as an icon, in 2D or 3D. For example, when a user does not need a realistic and detailed visual representation of an asset and only needs to find its location, its topology, or the surrounding objects, a symbolic representation, would be sufficient.
The logical architecture of our methodology is presented in Figure  2. We represent georeferenced semantic data objects that can be stored in databases or files (Figure  2 , left). They can be either geospatial or CMMS data. They are divided into two categories: those that have 2D or 3D geometrical information and those that do not. The main idea behind this division is that if a non-spatial object, such as an event (e. g work order, disorder. ) or a document (e. g technical documentation, photograph) does not have a spatial representation, or if one data object of an asset is only thematic, it is not visible in a 2D or 3D environment. For example, work orders need to be visible on the map to allow administrators to understand the current status of their asset fleet. Since they are not physical objects, they are not described by geometry but by a spatial representation is needed to locate them.
By providing a link between a spatial representation and its original data sources, the viewer requesting and displaying the data will know where to find the semantic data related to the spatial representation. Providing a link between an asset and its various representations described in multiple data sources allows one to navigate in its context by having multiple spatial and thematic representations using both geometric and semantic data. This can help in better understanding of an asset by using complementary information that exists in different domains. As data may come from heterogeneous sources, they can evolve on their own. They can be used and modified by many other people. They can exist in different databases. As we want to be able to display all geometric data, we propose to convert them to a homogeneous standard, to let any kind of viewer use them while supporting only one standard.
At this point, we then have another representation of the geometric information available in addition to the source representation. That is why we chose to keep the link between a geometry and its semantic data, so that it is possible to retrieve any kind of information on an object brought by the same source. As a geospatial dataset may describe many objects, each created geometry must correspond to an object. The link is not simply between a geospatial dataset and the geometry created from it. A link is created for each described object between its geometry and source, using its identifier (for example, GUID for Ifc and GMLID for CityGML) in the source. As keeping just the identifiers may lead to problems such as two objects from two sources having the same identifier, we also keep a reference to the source in the link.
Additionally, as we want to use geospatial dataset without modifying them, the link must then be stored in the geometry. The homogeneous format used to store geometry must allow us to do so. As we wish to propose the user to create a contextual view of an asset using semantic and geometric data, there is a need for a Web viewer to be able to navigate all those heterogeneous data in one scene. It must also let the user interact with an asset in the same way as it does in a classic CMMS software. For example, the user must be able to declare a disorder, or assign a work order. Using the link between the geometry and semantic data allows to navigate in all information brought by a single source. Using the link between data with and without geometry allows to navigate between many semantic information about the same object, from its spatial representation.
Technical Architecture This section explains the technical implementation, the components used, the processing of geometry (e. g. , how to extract and transform geometry for each source), and the navigation in the context of an asset. More particularly, the experimentation done focuses on extracting geometry from data sources to standardized geo-referenced geometry while keeping a link with the original data. The use of standardized streams (WMS and WFS) allow the chosen viewer, UD-Viz, to use various 2D geometry independently from their format. For example :ground photography for background visualization or 2D geometry of cadastral map. UD-Viz is a web-viewer, based on Itowns, a Three. js-based (JS/WebGL) framework for geospatial data visualization. 3D geometries are extracted from their sources and then stored using 3DTiles and served to the viewer with 3D Tiles Samples, a simple NodeJS server.
Each 3DTiles from a data source that has additional semantic data must include the link between the geometry and the original data sources. It allows the viewer to query additional information (such as specific attributes or relationships with other objects) about Ifc objects using BIMserver (used only to store semantic data), about CityGML objects using 3DCityDB and about CMMS data using its database. The viewer will only query the geometry of the objects visible by the camera. It will query 3D geometry from 3D Tiles Samples and 2D geometry from Geoserver using WMS. Using the link embedded in the geometry, the user can then query additional thematic data using either 3DCityDB, BimServer or CarlSource. The link between geometries and their sources can be ensured when using 3DTiles: each tile can contain multiple objects and is stored in the B3DM format.
Results As shown in Figure  4  and 6, our method can handle heterogeneous and complex geospatial data. Those figures demonstrate the capacity to visualize spatial representations from various sources, models, and formats: 3D, with the CityGML of the city of Limonest, and an Ifc file of a building in Limonest; 2D with the background or the roads that are served using WFS by the open data of Lyon; as Point Cloud, still using the open data of Lyon. The use of 3D Tiles allows using the existing methods of viewers that are efficient to load and visualize the geometries as 3D Tiles. It also allows avoiding further development in the viewer to handle geometry described by other data models. Also, by making loose couplings between used components and using different components for local problems, such as using py3Dtiles to extract the geometry, we ensure that our solution is modular.
Such a modular approach allows us to test and use The current solution allows to navigate in an asset's context and create contextual view in different ways. As shown in Figure  7 , it is possible to go through 3D visualization and CMMS data. As shown in Figure  8 , it is possible to ask for more thematic information about an asset, coming from either the original data source or documents linked to it. Internal users validated the solution, both from the usage and performance point of view and the feedback obtained was used to modify it. However, the current solution is limited by data quality and quantity issues. Indeed, as BIM and GIS domains are rather new, data produced vary from sources and software used to create them, even when following a specification such as CityGML or Ifc. Also, key information may be missing, such as the projection system used.
https://github. com/CesiumGS/3d-tiles/tree/main/extensions;online accessed April 20, 20223. CONTEXTUAL VIEW OF AN ASSETOur goal is to provide contextual views of an asset to the users, thus allowing them to visualize the many features in geospatial data describing CMMS assets. To this end, we propose a solution to visualize and navigate heterogeneous geospatial data on the web by reading and extracting geometric information from this data. Moreover, this solution ensures that the extracted geometric information is always linked to its sources for retrieving additional information using semantic data. By doing so, we ensure that the users can access all information, geometric and semantic, of an asset from geospatial data.
The clock signal is provided by a wideband generator (R&S SMA 100A), which can generate low-jitter sine waveform (50 fs rms in the bandwidth of interest). The modulator and decimator outputs by the prototype ADC are acquired by of a high-speed logic analysis system (Agilent 16901) that has a memory depth of 4 MB. Stored data are then post-processed with MATLAB on a PC. The RC time constant of the integrators and the offset of the comparators are auto-calibrated once after the test setup power up. No calibration is redone during the measurement procedure.
Microscopy Fluorescent slides were analyzed by microscopy (upright Nikon microscope). To analyze standard coloration (HPS), specific coloration and immunohistochemistry, the Primovert microscope Zeiss was used, and images were collected using Axiocam 105 color as Camera and ZEN Lite 2012 as software. For global analyses, sections were scanned with panoramic scan II (3D Histech, Hungary) at 40× for IHC and using the Vectra POLARIS (Akoya) device. Captured images were processed using HALO software (Indica Lab, V3. 2. 1851) and adapted algorithms for quantification: area quantification v2. 1. 3, cytonuclear v2. 0. 8 and cytonuclear FL v2. 0. 6. Images from scanned slices, were generated using softwares: Caseviewer (3Dhistotech 2. 3. 0. 99276) for coloration and IHC, and Phenochart (Akoya 1. 0.
2C  and D ) and was further validated owing to an enrichment in cells expressing GATA2 in this sorted subpopulation (Fig. S4D † ). The production following culture in the 3D-system of more mature cells was also observed by flow cytometry analysis of indicators such as CD14 (monocytes) (Fig. S4E  and F † ). At the functional level, we evaluated the capacity of 3D-seeded cells to support maintenance and production of progenitors and HSC in situ using respectively the colony-forming cell assay (CFC) and long-term initiating cell (LTC-IC) assay that quantify their derived progenitors by colony-forming cell assay (W5-CFC). Data obtained highlighted a trend towards an increase in the total number of CFC colonies formed when cultured in the presence of stroma (Fig. 2E ). More importantly, identification of the different hematopoietic subtypes  45  revealed the ability of the 3D model to maintain and favor the generation of all early and late hematopoietic progenitor subsets (Fig.