dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The  Copenhagen Centre on Energy Efficiency (C2E2) , with the financial support of  Bitten and Mads Clausen foundation  has funded the time allocated to developing and authoring this study. C2E2 is institutionally part of  UNEP DTU Partnership  (UDP), a  United Nations Environment Programme Collaborating Centre  hosted by the  Technical University of Denmark (DTU). The authors would like to thank  Oddgeir Gudmundsson  ( Danfoss ),  Celia Martinez  ( UNEP ) and the  National District Energy Office (ONED) , the  Sustainable Energy Agency (ASE)  and the  Ministry of Energy (Minergia) in Chile , for their support throughout the development of the study.
Foremost, some tasks that are easy to achieve using mouse and keyboard, such as data introduction, are difficult to achieve through 3D menus and virtual keyboards or voice commands. Goddard et al. [GBS*18] state that "a virtual keyboard floating in the VR scene is feasible but tedious. " Many modern HMDs like the HoloLens 2, the Meta Quest 2, or the HTC Vive Focus 3 offer hand tracking using multiple cameras integrated into the HMD. While this technology is convenient and user-friendly as it does not require additional hardware, the accuracy is still limited and the hands of the user have to be within the field of view of the cameras. Furthermore, no haptic feedback-that is, letting the user feel the interaction with virtual objects-is available, which lessens the user experience. To obtain a fully immersive experience (i.
Paper collection methodology For our paper discovery process, we opted for a systematic procedure, combining database queries and manual searches, that went as follows: 1. We agreed on a list of keywords that describe the intended area of focus. These keywords were chosen from the three relevant areas: mixed reality, (molecular) biology, and visualization (see point 4 for the list of keywords coloured by area). 2. Then, we collected a list of databases and search engines potentially containing relevant papers (listed also in point 4). 3. Based on the keywords, we assembled various search queries and tested their compatibility and outcomes on the selected databases. 4. Next, we performed several iterations of the first three steps. During these iterations, we filtered the keywords and databases, and optimized the query to provide more relevant results.
In the end, we searched five databases (IEEE Xplore, Web of Science, ACM Digital Library, EG Digital Library, PubMed) and used the query ("virtual reality" or "virtualreality" or "virtual environment" or CVE or HMD or headmounted or "virtual collaborative environment" or "mixed reality" or "augmented reality" or "extended reality") and (molecular or molecule or biological or biomolecular or biochemical or chemical or bioinformatics or cell or cellular or microbiology or pharmacology or protein or DNA or lipids or membranes or ligands or QM or RNA or docking or biomaterials or drug design or atom or atomic or crystal or membrane) and (visualization or visualisation or "visual analytics" or "visual representation"). 5. We created a shared Zotero (https://www. zotero. org/) library for the storage of all discovered publications.
6. We performed the first iteration over the query results to collect an initial set of papers. 7. Furthermore, after this step, we defined several categories which were used for the classification of papers based on their application area, used technology, publication type or other characteristics (see Supplementary Material S1). Assignment of categories to the individual papers was realized via corresponding Zotero tags. 8. After the first iteration, we decided to narrow down the search results to papers published in the year 2010 or later, as we focus primarily on recent technologies. We then went back through the selected databases and reviewed all returned results to ensure that we had included all relevant publications. 9. In the end, our Zotero library contained about 250 selected articles after this step.
Research use cases Apart from education, another important domain is biology research, offering a variety of focus topics. For example, the study of the constituents and behaviour of molecules is of great importance in fields such as drug design, analysis of molecular dynamics simulations, and structural analysis. Immersive applications have emerged naturally, from traditional molecular modelling software packages [SSS16, GBS*18] to newly developed use cases and applications [KBL*19, OBD*19]. While much of the relevant work in the literature focuses on direct translation to the virtual environment [GBS*18, DWH*20, KSB*22], a small category exploits the additional degrees of freedom and modalities that MR offers to represent non-3D intrinsic molecular data, such as genome-wide association data  [WNM20].
Intrinsic and non 3D-intrinsic data Given the intuitive spatial perception in an immersive environment and the inherent 3D structure of molecules, traditional use cases in the molecular domain can be directly transferred from desktopbased approaches. Therefore the three-dimensional spatial awareness that the immersive devices intuitively add to any molecular structure dataset naturally leads to use cases that explore these spatial features and are very often cited as a major advantage. Natural candidates that rely on intuitive perception of spatial structures include interactive drug design [AW99, NGEB15], molecular dynamics simulation analysis [AF98, NMB*16, GHK19, DWH*20], and protein structure analysis [RBDR18, XLX*21]. Interestingly, several works use the added dimension in the immersive environment and the natural way of interacting to visualize molecular data to add elements that are not intrinsically 3D as depicted in Figure  4.
Task 1: Overview The vast majority of techniques described in the surveyed papers provide the readers with an overview of the data by showing the whole molecule in 3D using common spatial representations, such as molecular surface or ball-and-stick. The reason is that, in the immersive environment, zooming out is a rapid way to get an overview of the entire data by reducing the overall size of all molecules. Nevertheless, one could still argue that some representations are better suited for overview purposes (e. g. , ribbon representation), while others give more details (e. g. , ball-and-stick or van der Waals representations). Only a few surveyed papers provide representations tailored explicitly for overview purposes. Probst and Raymond  [PR18]  represent every molecule in the dataset as a point in a point cloud, and only if the users get closer, the actual spatial representation of a molecule is used.
Task 5: Relate The relate task pertains to the understanding the relationship between the visual elements in the virtual scene. These visual elements can either encode the same data, different data, or the relationship itself. Within the papers, we found that a quite commonly represented task was to relate various parts of datasets to each other. We found that there are four distinct ways how this visual relationship can be formed, which we use to structure our following discussion: colocation, spatial, dynamic, and explicit visual elements. Colocation is used when visual elements appear at the same location and the user can switch between them. Because they appear at the same location the user will know that they refer to the same data. Tools such as UnityMol [LTDS*13], ChimeraX [GHM*18], or custom visual representation designs [FKH*20, KSB*22, MB21] thus enable users to switch between different representations of a molecule or its constituents, depending on the derived molecular property of interest (e.
In addition, the presenter can teleport all other avatars to his or her location, giving all participants the same view onto the scene. The 2D viewer is visible in the virtual scene as a video camera that is visible to other participants. Such users can freely move in the space and speak to other participants, but cannot become a presenter and are thus prevented from manipulating the scene. The ghost mode provides the users with a passive viewing experience from a fixed position and is intended for streaming the scene to a large audience. Narupa [ODD*18, OBD*19, DWH*20, JBOB*20] is an opensource software package where multiple users can cohabit in the same VR space and interact with real-time molecular simulations. It is based on the iMD-VR software framework and extends its functionality by enabling a multi-person VR experience, setting up and customizing interactive user manipulation with MD simulations, and running the application on a local network.
Collaborative VR was recently thoroughly studied by Martinez and Baaden  [MB21]. They examined options for sharing curated visualizations of structural biology, modelling, and bioinformatics datasets for interactive and collaborative exploration. Their goal is to enable easy sharing of the visual experience with others. In their setup, each participant is represented by an avatar and has their point of view, separated from the others. However, participants may choose to group to adopt a similar viewing angle. One of the latest approaches that was motivated by the communication issues caused due to the COVID-19 pandemic, is ProMVR by Xu et al. [XYTT21]. This tool aims to support the collaborative tasks of protein designers and provide them with a virtual environment containing the protein 3D model and intuitive interactions with peers.
Acknowledgements This work was supported by the  US DOE LLNL-LDRD 22-FS-026  and was performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344  ( LLNL-CONF-831245 ) and by project  TIN2017-88515-C2-1-R(GEN3DLIVE ), from the  Spanish Ministerio de Economía y Competitividad , by 839  FEDER (EU)  funds. The work received further support by the  French National Research agency  (Grant " PIRATE ",  ANR-21-CE45-0014 ) and by the " Initiative d'Excellence" program  from the  French State  (Grant " DYNAMO ",  ANR-11-LABX-0011  and grant " CAC-SICE ",  ANR-11-EQPX-0008 ), and from  Carl Zeiss Stiftung. Ministerio de Ciencia e Innovación/AEI  ( PID2021-122136OB-C21[10. 13039/501100011033/FEDER,UE] ).
X-ray Data Collection A single crystal of the studied compound was glued and mounted at 150 K on a D8 VENTURE Bruker AXS diffractometer. Through the usage of the APEX 3 software, graphite monochromated Mo K radiation (with a wavelength of 0. 71073) was employed. SAINT was used to process the data, including its collection, reduction, and analysis. The SADABS software was used to carry out the empirical absorption adjustments of the multi-scan kind. The crystal structure was determined to be in the monoclinic symmetry and the space group P2/c by using the automated search for space group offered in Wingx  [25]. The molecular solid state structure was solved by direct methods using the SHELXT software package  [26]. The graphite monochromated Mo Kα radiation (λ = 0. 71073 Å) was used through the program APEX 3.
Data collection and reduction as well as their analysis were processed using SAINT. The SADABS program was used for the empirical absorption corrections based on the automated search for space group available in Wingx  [23] , the crystal structure was solved in the monoclinic symmetry and the space group P2/c. The molecular solid state structure was solved by direct methods using the SHELXT software package  [26]. On F2 with SHELXL-2015, it was improved using full-matrix least-square methods  [27]. All non-hydrogen atoms were anisotropically refined. The hydrogen atoms that were Oand N-bonded were located using different maps, and they were then treated with Uiso (H) = 1. 5 Uiso (O) and U (H) = 1. 2 Uiso (N). Using software called DIAMOND, the drawings were created. Crystallographic data and the results of the least-squares structure refinement are given in Table  S1.
These maps can be integrated into flood databases to identify risk zones and determine levels of vulnerability. Floodable zones have become a considerable socio-economic issue at the local level of the Mekong Delta, which is ecologically, economically and socially important, thus many studies have been conducted over the years using Earth Observation data specially to characterize the rice crop. The majority of these studies have focused on delineating the distribution of rice crops using either optical satellite imagery (passive sensors)  [5, 6]  or radar satellite images (active sensors)  [7] [8] [9]. Active or passive sensors used for flood risk applications cover a very wide range of the electromagnetic spectrum, and information from these spectral ranges or their combination contributes significantly to forecasting and risk management.
Data Set 2. 2. 1. Satellite Data The data used in this study are images from the Sentinel-1 radar satellite of the European Space Agency (ESA). Launched in 2015, Sentinel-1A and Sentinel-1B, share the same orbital plane, generatinge data in the microwave (long wavelength or microwave) in C band with a wavelength of about 4. 5 cm. The images have been uploaded onto the ESA Hub Copernicus platform. They are in Ground Range Detected (GRD) (intensity and amplitude) and are obtained from SLC images (Table  1 ). The images are downloaded over a period of six months (from 6 June to 30 November), which corresponds to the period of flooding in the Mekong Delta region. The temporal resolution of the Sentinel-1 data is 6 days, which enables the acquisition of 30 images over the study period. The two Sentinel-1 satellites are located in the same orbit at about 900 km above the Earth's surface.
Environmental Data Water level data of the Mekong River and diurnal precipitation data from 2019 to 2020 were downloaded from the NOAA (https//www7. ncdc. noaa. gov, accessed on 18 June 2022) and the Mekong River Commission platforms (https://www. mrcmekong. org/, accessed on 18 June 2022) corresponding to the measurement of the hydro-meteorological stations (Figure  2 ). These data were compared with the backscatter values from radar images acquired for the two years.
In general, it is best to choose the sigma0 calibration for terrain with little relief, such as the Mekong Delta. The calibration allows the output images to be calibrated in sigma0 VH and VV. "Terrain correction" processing was applied to allow orthorectification of the image to correct for distortion effects that occurred during image acquisition. This operation also allowed the image to be georeferenced so that it could be projected onto a known terrestrial reference frame (geographic [WGS] or cartographic [UTM]). This operation re-projects the image in the right direction, assigns a projection and corrects any effects related to the terrain. To do this, SNAP software used a DTM (by default, SRTM 3 s) that it downloaded automatically.
In general, it is best to choose the sigma0 calibration for terrain with little relief, such as the Mekong Delta. The calibration allows the output images to be calibrated in sigma0 VH and VV. "Terrain correction" processing was applied to allow orthorectification of the image to correct for distortion effects that occurred during image acquisition. This operation also allowed the image to be georeferenced so that it could be projected onto a known terrestrial reference frame (geographic [WGS] or cartographic [UTM]). This operation re-projects the image in the right direction, assigns a projection and corrects any effects related to the terrain. To do this, SNAP software used a DTM (by default, SRTM 3 s) that it downloaded automatically. A pixel on the SAR image contains a signal with an intensity that corresponds to the backscatter of many reflectors present on the surface imaged by the sensor (Lee et al.
, 1994a). The total signal contained in a pixel is therefore a coherent superposition of all the contributions of the signals backscattered by each of the surface's reflectors. The phase of each reflector is correlated with the distance between these reflectors and the satellite as well as with the physical and electromagnetic properties of the reflectors. Thus, this large number of reflectors produces interference between the signals backscattered by each reflector, causing the salt and pepper effect and the salt effect (speckle) observed on SAR images. The filter applied is a convolution window of variable size that reduces noise by smoothing the image values. The last pre-processing step is the logarithmic transformation of the unit backscatter coefficient converted to dB. As a result of data preprocessing, there are two datasets for the years 2019 and 2020.
Machine Learning Algorithms (CNN, MLP and RF) and Classification of Flooded Areas and Flood Zones Image Segmentation and Feature Extraction Segmentation is defined as the process of dividing images into discrete regions or objects that are homogeneous with regard to their spectral and spatial characteristics  [55, 56]. In this study, multi-resolution segmentation (MRS;  [57] ) was applied. Implemented in the eCognition ® software (Trimble Geospatial Imaging), MRS has recently become the best-known algorithm for segmentation. MRS is a technique for merging regions of interest in order to achieve separations that maximize inter-object variability while minimizing intra-object variability. The fusion parameter of these regions is based on a homogeneity criterion resulting from the combination of a spectral criterion and a shape criterion.
The value of the local variance parameter is computed at each iteration and serves as a condition for terminating the segmentation process: when the LV value of a layer under consideration is equal to or less than the value of the previous iteration, the iteration terminates, and the objects in the layer are segmented  [55]. A fusion image after preprocessing has a size of 11,961 × 9292 pixels. In this study, MRS extracted geographic image features with the parameters scale = 15, shape = 0. 2, and compactness = 0. 5 based on an optimization process. The geographic object had characteristic values, in this case the average value per signal band of all pixels belonging to this object. The dataset of objects had hundreds of thousands of objects that were generated after the application of MRS on fusion images.
For the training set, labeling objects around the observation points have been collected. Algorithm MLP, RF used this training set for constructing and testing the models. Table  2  shows the training dataset. The fusion images with a size of 11,961 × 9292 pixels from the site web of The European Space Agency (https://scihub. copernicus. eu/dhus/, acceded 13 January 2021) has used to train the model; the authors divided the original image into a patch-images set with size 8 × 8 by a function chess-board segmentation of Ecognition@trimple 9. 5. Thus, a data set created from an original image has 1,735,695 patch-images. The training dataset is selected based on a set of observation points at the terrain. Each selected patch image must have more than 70% of the pixels whose signal values correlate with one of the observed points (correlation approaches 1).
Random Forest The Random Forest (RF) algorithm has been widely applied to the classification of floods and floodable areas. It is a non-parametric ML algorithm developed by Breiman  [63]. An RF algorithm is constructed with several decision trees based on the bootstrap technique, a statistical inference method that allows for the approximation of the distribution of an estimator when the distribution of the sample is not known. The most important parameters of an RF classifier are the tree depth and the minimum sample size. In this work, 500 trees were established with a subset as the number of features calculated as the square root of the total number of features. The free software Orfeo ToolBox (OTB), in.
Random Forest The Random Forest (RF) algorithm has been widely applied to the classification of floods and floodable areas. It is a non-parametric ML algorithm developed by Breiman  [63]. An RF algorithm is constructed with several decision trees based on the bootstrap technique, a statistical inference method that allows for the approximation of the distribution of an estimator when the distribution of the sample is not known. The most important parameters of an RF classifier are the tree depth and the minimum sample size. In this work, 500 trees were established with a subset as the number of features calculated as the square root of the total number of features. The free software Orfeo ToolBox (OTB), in the version integrated with QGIS, was used for this application. In order to train and validate the model on independent data sets, we randomly divided the database into 70% for training and 30% for validation.
Backscatter Profiles of Rice Fields in the Dry Season and the Wet Season Based on the principle that the radar signal is sensitive to surface moisture, we analyzed the backscatter coefficient of a few different rice paddies for reference during the dry and the wet seasons (flooded paddies and non-flooded paddies) in correlation with the daily rainfall for two years (2019, 2020) recorded at the Tan Chau hydro-meteorological station located in the study area. These data were interpreted in relation to the rice seasons, the SAR Sentinel-1 satellite observations (60 images per year, 5 images per month) and the water levels of the Mekong River as recorded at the same station over the two years. The water levels of the Mekong River started to increase in June along with the increase in rainfall with a maximum of 70 mm on August 1, 2020.
By testing the accuracy of the ML algorithm, the first objective of this study were to define an effective and validated method for detecting flooded and non-flooded areas in the Mekong Delta. This type of study could provide a large panel of users with the possibility and choice to reproduce the exact method in an automatic and standard way, which should allow the updating of a possible database shared between local institutes in an efficient way  [71]. Thus, it was important not only use software algorithms and data that provide a reliable and accurate result but also that are available and reproducible, in order that the mapping can also be undertaken by the partners in Vietnam or in other concerned study areas. Regarding the data, we noticed that, although Sentinel-1 SAR data are widely used for flood monitoring due to their high spatial and temporal resolution and free availability, very few studies using Sentinel-1 SAR data for flood mapping and monitoring have been conducted in the Mekong Delta.
For the algorithm choice, we have provided detailed results and the accuracy of three ML algorithms for image classification, via a comparative study. In addition, supervised and unsupervised classification methods have been widely applied for surface water mapping using satellite images  [72]  and have compared supervised and unsupervised machine learning methods, revealing an overall accuracy of the unsupervised water classification method of 89. 3%  [73] , while in the present study, the accuracy has achieved higher values (globally higher than 95%). Although the supervised classification method is able to map water bodies efficiently, it could be a tedious task because of the creation of the training and the validation dataset, which could be time-consuming. The flood maps derived from the algorithms tested here in our work were validated by overlaying them with metrological and hydrographical data.