dataesr/scientific-paragraphs-categorization huggingface dataset:
NRT observations NRT observations (from satellite, ground-based and in situ platforms/instruments are provided by EUNADICS-AV partners, external data sources or agreements; see Tables  2  and 3 ). A prototype data portal has been implemented in a demonstration phase of the EUNADICS-AV project (see the exercise study of  Hirtl et al. , 2020a). In the case of a future operationalisation of EUNADICS-AV activity for TRL (technology readiness level; see H2020, Annex G of the General Annexes) of 5 and higher, i. e. system prototype validation and demonstration in operational environment, all the NRT observations will be visible on the EUNADICS-AV data portal. The routine data products, based on NRT products from eight satellite hyperspectral sensors (i. e. OMI, OMPS, GOME-2B, GOME-2C and TROPOMI in the UV-vis and AIRS, IASI-A and IASI-B in the IR range), related to the detection of volcanic eruptions, sandstorms or smoke from wildfires and can be consulted and monitored through the EUNADICS-AV-SACS web interface.
The currently operational SACS website (https://sacs. aeronomie. be, last access: 5 November 2021) is a self-contained system that allows for the visualisation of NRT satellite data and provides alerts to subscribed users in the case of the detection of elevated amounts or concentration of volcanic emissions. Within the EUNADICS-AV project, the development of a new SACS interface has started. This work in progress is based on modern visualisation methods and the handling of geophysical data. It is currently in the development phase and allows for monitoring user-selected satellite sensors and products in the form of zoomable/pannable maps, using GeoTiff, GeoServer and Web Map Service (WMS) facilities for serving geographical data. At the moment, all NRT observations linked to the EUNADICS-AV EWS rely on the current SACS web interface.
Email notifications Currently email notifications from the EUNADICS-AV EWS for public or governmental users take only place after volcanic and radionuclide cloud hazard detection. Data products are collected by the EUNADICS-AV data harvesting facility and transferred in NRT for analysis by our EWS. The automated EWS not only applies specific mechanisms to issue selective detection (extension of the SACS system to other kinds of alerts and instruments) but also takes into account inputs from existing systems, like NASA FIRMS, VONA messages and EURDEP/EMERCON messages. In the case of the detection of a natural airborne hazard in a specific SACS region (see Sect. 4. 5), a notification is sent internally to EUNADICS-AV partners (one notification per affected re- gion). In the case of detection of an exceptional SO 2 /ash concentration, a notification is sent to stakeholders (email) with relevant information (e.
Data file transfer The data integration in dispersion models is essential in the resilience and the decision-making process after a crisis in aviation related to airborne hazards. It is also critical for the ATM stakeholders to receive homogenised and easy-toread data to have a fast and clear view of the scene during such a crisis  (Bolić and Sivčev, 2011, 2012). This is why one of EUNADICS-AV objectives was the implementation of alert data products (with metadata, key information about the alert, flags and gridded data), allowing for a good dissemination of information. The data file transfer established by the EUNADICS-AV EWS consists of the creation of alert products in a homogenised, standardised format (netCDF), so-called NCAP files. The netCDF format has been chosen because this is a very common and convenient format (easy access), with relevant metadata information for users.
A routine alert product is implemented using a Python script that handle data collection, alerting and database maintenance functions. The content of the alert products is the following: data (all pixels from satellite, ground-based or in situ products), tailored products: alert pixels (position, values/columns densities/index and height), level of severity (LOW and HIGH), extended plume of hazard (completion of the hazardous plume), surface and mass loading, gridded data, contours (surface, mean, maximum and mass), information about the source, SACS region and maximum values, traceability and tracking of event (START → END), links (SACS images, SACS notification and quicklooks from EU or regional network). More details about the description of the content of the NCAP files is presented in Appendix A.
g. EARLINET, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e. g. selective ash detection, SO 2 column and plume height, dust clouds and smoke from wildfires), with identification and traceability of extreme events. To conclude, the EUNADICS-AV EWS has developed new tailored alert products for aviation, i. e. NRT observations, notifications and the implementation of netCDF alert data products (NCAP files). The EUNADICS-AV EWS achievements include the following: In situ: EURDEP Network Gamma radiation Selective Nuclear 15 min/every 1 min sensors the improvement of NRT discrimination between volcanic ash and other aerosols (dust or smoke) or meteorological clouds; -NRT retrievals of plume height (ash and SO 2 ); -NRT retrievals of volcanic-ash mass loadings with use of SEVIRI on board a geostationary platform; the use of polar-orbiting NRT measurements with high spatial resolution (under 10 km); the use of key measurements from the ground-based network, in particular lidars and ceilometers measurements, as well as near-source parameters from volcano observatories.
The EUNADICS-AV EWS passes with success the performance verification in a limited environment (TRL 4). Concerning future plans with regard to natural airborne hazards, collaborations are on-going not only with key stakeholders in charge of implementing data integration in dispersion model and providing advisories for aviation (i. e. VAACs and NMS) but also in collaboration with WMO SDS-WAS, in the frame of the inDUST (International Network to Encourage the Use of Monitoring and Forecasting Dust Products) COST (European Cooperation in Science and Technology) action (https: //cost-indust. eu, last access: 5 November 2021) with the use of EUNADICS-AV dust alert products. The functionality of the EUNADICS-AV EWS is also currently evolving thanks to Engage KTN (Knowledge Transfer Network) catalyst funding (https://engagektn.
Although in this case, unlike with the EURDEP data, the data will not be harmonised, they can be used as a triggering system or, at least, as an awareness system potentially for such events for which the gamma dose rate monitoring may not provide useful information (for very far-away sources or radionuclides that are pure beta emitters, for instance). We can clearly see the interest of the EUNADICS-AV consortium in proceeding with a future relay of radiological data (gamma dose rate and radionuclide concentrations in ground-level air), creating early warnings using homogenised critical dataset and being used to trigger data assimilation/inverse modelling for source term estimates.
Quality of information (a) Product delivery (timely and reliable manner) (b) Charts of tailored products (e. g. synergy between different kinds of observations) (c) Points of contact for data problems, issues and feedback (d) NRT data delivery (maximum 6 h after measurements) (e) Use of multi-source satellite data (on different platforms) is highly required (f) Development of quantitative ash concentration retrievals (combined with model data) (g) Communication (product generation and availability) to integrate novel development in user systems 2. System interoperability (a) Documentation (product characteristics, format, source of origin, algorithm and limitations) (b) Training for compatibility and homogenisation of dataset format (c) Global coverage from satellite data (not only over Europe) (d) Validation and certification of satellite products by other sources 3.
Improvement of the (a) Open and unrestricted access display (requirements from (b) NRT standardised volcanic-ash product with a repetition rate of 15 min or better pilots and airlines) (c) Practical dataset (universal format and colour patterns to highlight ash reports and forecasts) (d) Indication of at least three levels of ash or SO 2 contamination (e. g. not relevant, low and high) (e) 3D display of airborne hazard clouds, e. g. with the use of Google Earth KML files and/or similar format compatible with SkyVector charts (see https://skyvector. com, last access: 5 November 2021) or with an electronic flight bag (EFB), an electronic information management device helping flight crews to perform flight management tasks more easily and efficiently.
Acknowledgements. We thank  Mike Pavolonis  and the  VOL-CAT team  for providing detailed information about the status of their early warning system related to volcanoes. We thank  Ruslan Borodin  for providing an illustration of a chart (radioactivity advisory based on conditional data), as provided by the  Federal Environmental Emergency Response Centre (FEERC) of Russia. Financial support. This work has been conducted within the framework of the  EUNADICS-AV  project, which received funding from the  European Union 's  Horizon 2020 research programme for societal challenges -Smart, Green and Integrated Transport - under grant agreement no. 723986.
Data availability. A description of the data used by EUNADICS-AV EWS is presented in Tables  2, 3 , 5 and 6. Some data are publicly available. Some data have restricted access, as mentioned in the Abstract and in Sect. 4. 6. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-21-3367-2021-supplement.
1. Quality of information (a) Product delivery (timely and reliable manner) (b) Charts of tailored products (e. g. synergy between different kinds of observations) (c) Points of contact for data problems, issues and feedback (d) NRT data delivery (maximum 6 h after measurements) (e) Use of multi-source satellite data (on different platforms) is highly required (f) Development of quantitative ash concentration retrievals (combined with model data) (g) Communication (product generation and availability) to integrate novel development in user systems 2. System interoperability (a) Documentation (product characteristics, format, source of origin, algorithm and limitations) (b) Training for compatibility and homogenisation of dataset format (c) Global coverage from satellite data (not only over Europe) (d) Validation and certification of satellite products by other sources 3.
Deep Learning-Based Segmentation To create virtual proxies that replace real objects, we exploit the silhouettes of real objects. We plan to use deep learning-based methods to extract the segmentation mask, which is considered as the silhouette of real objects. Multiple research has been focused on semantic and instance segmentation. Mask R-CNN  [33] , DeepLabv3  [34] , and PSPNet  [35]  are state of the art on this task. The performances of these models are compared on datasets  [36, 37]. These models give astonishing results but need lots of computing power and are not running in real-time. PSPNet  [35]  is running at 1 FPS on a laptop equipped with a GPU. Other researchers have focused on real-time segmentation. ESPNet  [38]  and ENet  [39]  are targeting the inference speed and the size of the network to run inference in real-time and reduce memory consumption.
Extract the Silhouettes from Interactable Real Objects Our target AR platform is mobile devices equipped with a single RGB camera. To have seamless interactions, an AR system needs to understand the surrounding space (e. g. , to recognize objects and their positions). Thus, we implemented a fast object segmentation network that uses images from the camera of mobile devices. Our network can recognize and segment multiple real objects at high speed with only the smartphone computational resources. Below we describe the dataset we created to train our network and detailed network configurations to improve the performances.
Animal DOLLS Dataset We aim to implement an AR scenario where a user plays with toys. A virtual human will appear in the AR space and interact with the real toys as a playmate. For convenience's sake, here we used a small set of animal dolls as interactable real objects. However, we believe the method described here can be applied to other types of objects, such as exhibits in a museum for an interactable AR curation. Dataset creation: We prepared seven animal dolls with different appearances. The animals we chose were a jaguar, zebra, giraffe, wallaby, panda, deer, and a fox (see Figure  2 ). From this set of dolls, we shoot an average of 25 videos lasting 45 seconds per animal. For each video, we varied the position of the doll, the background selection, the lighting condition, and the appearance of the animal in the video by walking around, dynamically zooming in/out, and rotating the camera.
These choices improve the diversity in terms of positions and angles within the dataset and ensure the seamless interaction with deformable real objects. For annotation and labeling, we used the Interactive Video Object Segmentation model from  [40]  to extract the ground truth masks. Then, we extracted the frames to train the segmentation network. We sampled images at a rate of 5 frames per second to avoid duplicated images of the same position or appearance. Our dataset contains 37,990 images and each category of animals has 4856-6109 images. Our dataset is openly available at https://github. com/khseob0715/DollDataset (accessed on 18 March 2021).
Data augmentation: We applied data augmentation techniques to our dataset before training our segmentation network. We added 10 4 synthesized images into the dataset by (1) replacing the background using the Describable Textures Dataset  [41] ; (2) rotating images in the range of 360 degrees, to improve the predictability when the mobile device rotates; (3) randomly changing the ratio of the doll area in the image, to mimic different zoom in/out conditions; (4) randomly flipping images on the horizontal and vertical axis. As thus, our network started training with the dataset that was robust for variation.
As a result, the number of parameters was reduced to 2. 4 million from 7. 76 million (cf. [42] ). Finally, we trained the network for 5 epochs using Adam [43] optimizer with a learning rate started from 10 -4 , and was divided by 10 after every two epochs. Figure  3  shows the recognition accuracy of our dataset. We trained the model with TensorFlow considering portability to mobile platforms.
In detail, our process accesses the CPU of the device and obtains the raw image data using ARCore API. The raw image data is in the YUV-420-888 format with 640 × 480 pixels. This raw data is converted to the input format of the segmentation network model-RGB with 192 × 192 pixels. Up to here is the pre-processing in our evaluation. Once the converted data is fed into the segmentation module, the segmentation network runs on the mobile CPU to generate a segmentation map and classes of the recognized objects in the scene. Next, our system executes the 3D silhouette mesh creation procedure using the results from the segmentation module. The results of our performance evaluations are summarized in Table  2. On all tested devices, our entire process ran below 600 ms (roughly equivalent to about 20 frames).
Participants We recruited 30 participants from a local community. However, we omitted data from 6 participants who did not pass our screening criteria; 1 by trick questions and 5 by short survey time. Thus, we had 24 valid participants (7 females, 17 males, age 20-31, M = 26. 04, SD = 3. 12) for the analysis. Participants had a low familiarity with AR (M = 2. 62, SD = 0. 87), measured on a five-point Likert scale (1: Not at all, 2: Slightly, 3: Moderately, 4: Knowledgeable, and 5: Expert). Lastly, 13 of them had a previous experience of participating in an AR experiment. Photo illustrations of interaction scenarios with the silhouette meshes applied. (a) One doll that is similar in size to a virtual human, (b) two dolls that are similar in size to a virtual human, and (c) one doll that is larger than a virtual human.
Limitation 1: Limited Pilot Study Due to the unprecedented pandemic situation, in-person and lab-based typical humansubjects experiments were out of option until now. Thus, we collected our data through the online video-based survey for this study. One might raise the question of the validity of the online video-based data collection for user studies conducted in this paper. In addition, compared to the existing researches that collected data via online surveys, the number of participants in our survey seemed a bit small  [53] [54] [55]. Thus, we plan to extend our present study to a face-to-face experiment, where participants actually experience the AR contents in person instead of observing pre-recorded video clips. We will compare the results of the online-based survey presented in this paper with the data collected through the face-to-face experiment to confirm the validity of the video-based survey and/or to see how user's perception differs.
Conclusions In this paper, we proposed the silhouette mesh, a practical method that allows virtual objects to perform realistic interactions with real objects without pre-modeled virtual proxies. Our silhouette mesh creation requires no user intervention, even on deformable real objects, and it uses only a single mobile device. In our case study, we prepared the animal dolls dataset to train the segmentation network. We then applied it to the multimodal-based AR system to demonstrate seamless and realistic interaction between the virtual human and real-world objects. The result of our pilot study indicated that our approach, silhouette mesh, significantly improves the user experience and perception with the virtual human in the physical-virtual interaction in the shared space. Although our work has limitations and has realized only four types of interactions to pursue the case study, our approach can be used in touch-driven AR contents to interact with real-world objects and extend the interaction type depending on the AR scenarios.
The potential rocksalt ordering on (001) oriented substrates was addressed by performing scans in the [111] direction. Additionally, films were grown on (111) oriented substrates, where any rocksalt would be observable in out-of-plane scans. In both cases, absence of higher order peaks excluded presence of any rocksalt ordering (see Supplementary Figure  1  and 2 ). The high structural quality and absence of B-site order where confirmed by Scanning Transmission Electron Microscopy (STEM), as shown in Fig. 1d. Finally, the oxidation states of Fe and Ti where examined by means of X-ray Photoelectron Spectroscopy (XPS). The spectrum of the (001) oriented La 2 TiFeO 6 film shown in Fig. 1b  reveals that the Fe ions are partly in the 2+ oxidation state, while Ti is completely 4+ (data not shown).
and rock-salt order in different magnetic orientations. As the magnetic orientations of all the building blocks is G-type, it is reasonable to believe that it will be maintained also in the heterostructures. It has to be pointed out that in the case of La 2 VCuO 6 the situation is not as clear, since LaCuO 3 has never been observed experimentally and ab-initio studies debate whether or not it is non-magnetic with a formally Cu 3+ in a d 9 -L state  [43] [44] [45]. The AIRSS package interfaced with Quantum Espresso was used for the random structure sampling. All searches were performed at 0 GPa, targeting an initial volume of about 125 Å 3 ± 20%. Distance constraints where imposed by loosing of 20% the interatomic distances in an ordered LaFeO 3 perovskite. This allowed to explore a vast number of random sensible structures while respecting the relevant chemistry expected in double perovskites.
ACKNOWLEDGMENTS Part of this work was performed at the  Stanford Nano Shared Facilities (SNSF) , supported by the  National Science Foundation  under award  ECCS-1542152. CW was supported by grant [ EP/R02992X/1 ] from the  UK Engineering and Physical Sciences Research Council (EPSRC). This work was performed using resources provided by the  ARCHER UK National Supercomputing Service  and the Cambridge Service for Data Driven Discovery ( CSD3 ) operated by the  University of Cambridge Research Computing Service  (www. csd3. cam. ac. uk), provided by  Dell EMC  and Intel using Tier-2 funding from the  Engineering and Physical Sciences Research Council  ( capital grant  [ EP/P020259/1 ]), and DiRAC funding from the  Science and Technology Facilities Council  (www. dirac. ac. uk). CW and DB are grateful to  Antoine George ,  Nicola Bonini  and  Francesco Macheda  for insightful discussions.
Computational details All DFT calculations were performed within the Quantum Espresso code (version 6. 5) employing the PBE functional and the Hubbard-U local correction in the d-shells of the transition metals. The values for the Ti and Fe were chosen in accordance to the vast literature on DFT+U for similar materials, i. e. U(Ti) = 3. 0 eV, U(Fe) = 4. 8 eV. For La 2 VCuO 6 our choice of U-values differs from the referred literature values as they were obtained by means of the self-consistent linear response density functional perturbation theory (DFPT) method proposed by Cococcioni et al. 42  as implemented in Quantum Espresso. The U on vanadium was found to be practically indifferent to the 4+/5+ oxidation states, yielding values of 3. 5-4. 0eV. On the other hand, the U obtained on Cu 2+ was around 8.
ARC 3. 0 is a modular, object-oriented Python library combining data and algorithms to enable the calculation of a range of properties of alkali and divalent atoms. Building on the initial version of the ARC library [N. Šibalić et al. , Comput. Phys. Commun. 220, 319 (2017)], which focused on Rydberg states of alkali atoms, this major upgrade introduces support for divalent atoms. It also adds new methods for working with atom-surface interactions, for modelling ultracold atoms in optical lattices and for calculating valence electron wave functions and dynamic polarisabilities. Such calculations have applications in a variety of fields, e. g. , in the quantum simulation of many-body physics, in atom-based sensing of DC and AC fields (including in microwave and THz metrology) and in the development of quantum gate protocols.
Having precise and accurate values of the relevant atomic parameters (e. g. , transition dipole moments, Stark shifts, etc. ) is crucial for developing new experiments, comparing to theory, or simply for interpreting the data. As the number of potentially relevant parameters vastly exceeds what can be tabulated, there is a need for an atom-calculator computing these parameters on demand. ARC 1. 0  [18]  aimed to provide such a research tool for alkali metals by combining the best available algorithms and experimental data into a modular, object-oriented Python library. The open source nature of the package and the popularity of this programming language facilitated its rapid adoption by the community and the inclusion of newly developed methods. However, a growing number of research groups are now working on Rydberg states of atoms with two valence electrons  [19] , such as the alkaline earth elements (e.