dataesr/scientific-paragraphs-categorization huggingface dataset:
The learned weight matrix is applied on the test data contained in the vector X test , in order to find the neural network prediction Y test ¼ WFðX test Þ: (3) Comparing the prediction to the test target Ỹtest allows to evaluate the prediction accuracy, i. e. , the fraction of times the data point is correctly classified, as well as the normalized root mean square error NRMSE ¼ 1 y max À y min ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P N i ðy i À ỹi Þ 2 N s : (4) We will use these measures to evaluate the performance of the reservoir, and thus the capacity of the chosen dynamical system to efficiently implement reservoir computing. It should be noted that there are neural networks capable of achieving higher accuracies than reservoir computing for the learning tasks investigated in this study  16.
Quantum simulations We simulate the dynamics of the coupled quantum oscillators using the library QuantumOptics. jl for simulating open quantum systems in Julia  38. The dynamics can be captured by the quantum master equation _ ρ ¼ Ài½ Ĥ þ Ĥdrive ; ρ þ Ĉρ Ĉy À 1 2 Ĉy Ĉρ À 1 2 ρ Ĉy Ĉ; (8) where ρ is the density matrix of the system. Ĥdrive is the resonant drive Hamiltonian  [39] [40] [41]  Ĥdrive ¼ iϵ a ffiffiffiffiffiffiffi 2κ a p ðâ À ây Þ þ iϵ b ffiffiffiffiffiffiffi 2κ b p ð b À by Þ (9) and ϵ a ¼ ϵ b ¼ ϵ 0 x i (10) are the drive amplitudes that encode the input data x i. For sinesquare waveform classification task we use ϵ a 0 ¼ ϵ b 0 ¼ 1:2 10 6 ffiffiffiffiffi ffi Hz p in order to populate with a significant probability the first 5 levels in each oscillator, and to have negligible probability to populate states above 8 (we truncate the Hilbert space at 8 photons in each oscillator).
Classical static reservoir simulations The simulations of the classical reservoirs, both static and dynamic, were performed in the library pytorch for training neural networks in Python. The state of the reservoir at time t is YðtÞ ¼ fðW in XðtÞ þ W res Yðt À 1ÞÞ (14)   where f is the ReLu function, W in is the vector that has the length of the size of the reservoir, and maps the input data on the reservoir, W res is a square matrix that has the dimension of the size of the reservoir and which gives the memory to the reservoir. Here the reservoir has the memory of a single step in the time, which is sufficient for the sine and square waveform classification task. In the simulations of the static reservoir, the size of the reservoir is equal to the number of measured neurons, shown in Fig. 4.
ACKNOWLEDGEMENTS This research was supported by the  Quantum Materials for Energy   Efficient Neuromorphic Computing  (Q- MEEN-C ), an  Energy Frontier Research Center  funded by the  U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) , under Award  DE-SC0019273 , by the  Paris Ȋle-de-France Region  in the framework of  DIM SIRTEQ  and by  European Union  (ERC, qDynnet,  101076898 ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the  European Research Council. Neither the  European Union  nor the granting authority can be held responsible for them.
The finite element software Code_Bright was used to investigate first the TH behaviour of an embankment, equipped with heat exchangers, during energy storage/retrieval phases through thermo-hydraulic numerical simulations. The analysis considered the heat conduction as well as the vapour and liquid water transfer mechanisms in the unsaturated porous medium. The van Genuchten Model for the water retention curve and a power law was selected for the unsaturated hydraulic conductivity. The parameters of these hydraulic constitutive models were fitted from laboratory tests except the power parameter which was estimated based on  [10]. The default procedure was used to handle the temperature-dependency of the thermal conductivity of the soil's solid phase with parameters fitted on laboratory measurements while the thermal parameters of the water and air phases were assumed to be constant.
Test cases The simulation tool used is OpenFast v2. 4. 0 (https://github. com/OpenFAST/openfast) and the FOWT model considered is the IEA 15 MW wind turbine mounted over the UMaine VolturnUS-S semi-submersible floater  (Allen et al. , 2020). Initially simple constant wind and monochromatic waves are testes in order to verify the analytical developments of the previous section. Then a test case more representative of the industrial design of FOWT is considered by testing a DLC1. 1. For the latter, simulation consider only 1 seed of 3600 seconds with aligned wind and irregular waves. For this time simulation, this is statistically equivalent to 600 seconds and 6 seeds. After some tests, for all the considered simulations, ζ rot = 0. 6 and ν rot = 0. 01 are chosen for the PI controller's tuning.
The electronic structure of electrode/HZO interfaces was probed by electron energy-loss spectroscopy (EELS) in the scanning transmission electron microscope (STEM). The microscope used was a JEOL JEM-F200, equipped with a high-brightness cold field emission gun, and operated at 200 kV. Core-loss STEM-EELS data were collected with a Gatan Continuum S spectrometer using the spectrum-imaging technique implemented in the Gatan microscopy suite, with a dispersion of 0. 15 eV per channel. The HR-TEM images were recorded using a JEOL JEM-F200 microscope and a JEOL JEM 2100 (HT pole piece) microscope both operated at 200 kV. The crosssection specimens for STEM analysis were prepared by the Ga-focused ion beam (FIB) milling technique. Polarization versus electric field measurements (P-E) were carried out on 20 μm-diameter capacitors, using a Keithley 4200A-SCS semiconductor parameter analyzer.
Acknowledgements This work was realized on the NanoLyon technology platform and on the  Micro Nano Research Facility at RMIT University. It was funded by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 780302  ( 3eFERRO ), by the  I3E   ECLAUSion  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 801512 , by the  French Public Authorities  through the  NANO 2022 program , and by  International Associated Laboratory in Photonics between France and Australia (LIA ALPhFA). The STEM-EELS and HR-TEM work was performed at the  RMIT Microscopy and Microanalysis Facility, a linked laboratory of Microscopy, Australia. Complementary TEM and HR-TEM experiments were performed at the consortium  Lyon-St-Etienne de microscopie and at Institut Lumière Matière.
Process Model and Simulations The process simulations were performed with Aspen Plus ® (version 9). The complete flowsheet representation of the considered CLC power plant is shown in Figure  3. The temperature at the outlet of the FR is set at 950 • C to have a good compromise between a need for high temperature to obtain sufficient conversions (and therefore residence times leading to reasonable reactor dimensions) and an upper limit to maintain conventional refractory technologies. A value of 3% is chosen to account for the heat loss of the reactor systems, as in  [37]. The Aspen Plus ® built-in thermodynamic database is used to evaluate the heat of each chemical reaction. The streams' separations that take place in the splitter, in the CS, and in the FR cyclone are modeled by a Separator, whereas the AR cyclone is modeled by a Ssplit unit.
This paper addresses the lack of "push-button" software for optical marine imaging, which currently limits the use of photogrammetric approaches by a wider community. It presents and reviews an open source software, Matisse, for creating textured 3D models of complex underwater scenes from video or still images. This software, developed for non-experts, enables routine and efficient processing of underwater images into 3D models that facilitate the exploitation and analysis of underwater imagery. When vehicle navigation data are available, Matisse allows for seamless integration of such data to produce 3D reconstructions that are georeferenced and properly scaled. The software includes pre-processing tools to extract images from videos and to make corrections for color and uneven lighting.
Four datasets of different 3D scenes are provided for demonstration. They include both input images and navigation and associated 3D models generated with Matisse. The datasets, captured under different survey geometries, lead to 3D models of different sizes and demonstrate the capabilities of the software. The software suite also includes a 3D scene analysis tool, 3DMetrics, which can be used to visualize 3D scenes, incorporate elevation terrain models (e. g. , from high-resolution bathymetry data) and manage, extract, and export quantitative measurements for the 3D data analysis. Both software packages are publicly available.
This paper presents two open-source software tools designed to create and analyze 3D reconstruction models of underwater environments: Matisse and 3DMetrics. Matisse is designed for non-expert users to easily generate fully-textured high-quality 3D models from submarine optical imagery and associated navigation data (see Figure  1  for GUI overview). Meanwhile, 3DMetrics provides a comprehensive set of tools for analyzing the geometric properties of these models. These software tools fill an important gap in the marine science community by providing a user-friendly solution for photogrammetry-based reconstruction of benthic structures using optical marine imagery. They enable researchers to generate and analyze 3D models with ease, facilitating more accurate and efficient analysis of underwater environments.
3DMetrics: Visualization and Interpretation/Analysis Software To facilitate the scientific exploitation of the results, users need software that can handle large 3D georeferenced models (including tools to interact with the scene), extract quantitative information, and manage the observations while providing input and output options. Based on experiences gained from numerous projects in different fields, we developed 3DMetrics (see Figure  7 ) to accommodate some of the most common scientific needs and requirements. 3DMetrics can import both 3D and terrain models derived from optical and acoustic (multibeam bathymetry) surveys respectively. The 3DMetrics software is based on the opensource library OSG 4 and, hence, can open all formats supported by OSG. This includes the frequently used. ply and.
obj formats, georeferenced with an associated. kml file. Near-seafloor optical surveys are often associated with other data (in particular, seafloor bathymetry), which provide geological and regional contexts for higher-resolution optical surveys, which are often restricted to smaller areas. Digital terrain grids in the NetCdf file format (e. g. ,. grd), are common and supported by 3DMetrics. Once opened, models can be manually shifted to finely co-register different datasets, as vertical and horizontal shifts of data may be required due to scale and navigation differences among datasets. This allows for the integration of different datasets, and their analyses at larger spatial scales (acoustic data) than those of optical data (3D scene) alone. The 3DMetrics software can upload several 3D models, with limitations on the number and size depending on video card hardware.
To facilitate visualization of datasets, particularly with several 3D scenes viewed simultaneously, 3DMetrics constructs multi-resolution models from the original ones generated by Matisse. This feature reduces memory requirements during visualization and allows efficient data upload and rendering. Imported 3D models can be vertically projected to create both ortho-photos and digital elevation models, exported as standard GeoTIFF files. In the released datasets, we provide ortho-photos of each resulting 3D model. This option facilitates data analyses and correlations with other 2D datasets, and the manipulation of results in other software (e. g. , GIS). In addition to visualization, the user can perform measurements on the 3D models for quantitative studies in two different modes. The 'quick measurement' toolbar (area 2 in Figure  7 ) provides interactive measurements (line, surface, point measurements), with a screen display of values that are not saved.
This feature is useful to explore models, to obtain an overview of the size of the scene elements, or to pick the geographic coordinates of interest points. The second mode, best suited for quantitative studies, creates measurement layers (in area 5 in Figure  7 ), allowing the user to record, save, open, and modify data. As is the standard for other types of 2D GIS software (e. g. , ArcView, QGIS), the measurement layer is customizable and includes user-defined attributes: line, area, point, text, and category. The layer attribute values are stored and shown in the attribute table (area 7 in Figure  7 ). The strength of 3DMetrics software is its ability to save the entire user workspace in a variety of formats, including shapefiles, text, and custom formats. This feature allows users to easily pick up where they left off in previous sessions by reopening their workspace in the exact same state.
Examples and Datasets: Anthropogenic, Geological, and Biological Studies Here, we present four datasets, which were processed with Matisse to generate 3D reconstructions: a litter dump at the seafloor, a sub-vertical fault scarp, a hydrothermal vent edifice, and a shipwreck (Table  1 ). The datasets were acquired with different vehicles and camera types, and correspond to different survey patterns. The data are provided in open-access (Table  1 ) and include input data (images or videos and navigation files) and the 3D models generated by Matisse together with the corresponding ortho-photos GeoTIFF. Table  1  provides details for each study area, including survey information, input data, output models, CPU computation time, and computer information. The choice of examples seeks to illustrate (a) different survey strategies and patterns adapted to the scene of interest, (b) imagery of varying quality and with different illuminations, and (c) examples that are relevant to different scientific disciplines (geology, archaeology, environmental studies, and biology, among others).
These datasets are suitable for training and testing the different parameters and options of Matisse, and to allow non-expert users to further explore the functionalities and parameters. All datasets were processed using color and illumination correction, the 3D dense algorithm, the SIFT CPU implementation, and the exhaustive matching strategy (i. e. , brute-force matching). The first dataset was both processed with and without pre-processing to demonstrate how this correction impacts the result. The hydrothermal vent dataset includes already pre-processed images.
Anthropogenic Seafloor Litter Dump The first example corresponds to anthropogenic litter and debris off the French Mediterranean coast (43. 078°N; 6. 458°E), at a water depth of ≈600 m, acquired during the technical CARTOHROV-GEN cruise. With Matisse, we generated two final dense 3D-textured models, with uncorrected and corrected color and illumination, and an additional sparse model (see Table  1 ). The corrected imagery greatly improves the quality of the resulting 3D terrain model, with a rendering that facilitates its interpretation relative to the model derived from uncorrected data. Figure  8  shows the orthophotomosaic, and a scene detail shows the difference between using images that are uncorrected and corrected for illumination. This dataset corresponds to a sub-horizontal scene and may be applicable to many geological, biological, and environmental studies (e.
g. , distribution of seafloor ecosystems, fluid outflow at hydrothermal vents and cold seeps, etc. ). Numerous studies have investigated seafloor litter from visual observations  [52] [53] [54] , and quantification is typically based on the occurrence of objects along deep-sea vehicle tracks  [55, 56]. Proper quantitative studies can only be achieved with 2D and 3D photomosaics, which provide a comprehensive view of selected seafloor areas and where a proper quantification of the density of objects can be established from extensive image surveys. These datasets are also suitable for temporal studies that are not feasible with along-track imagery alone, owing to the difficulty of precisely collocating individual ROV tracks acquired at different times. Instead, repeated surveys of a given area ensure both the overlap of imaged areas, and the adequate co-registration required for such quantitative studies.
Fault Scarp and Associated Tectonic Markers Our second example corresponds to a video sequence of a submarine fault outcrop displaying markers of a past seismic event. This outcrop is located along the Roseau Fault, between the Les Saintes and Dominica islands (Lesser Antilles), which ruptured during the 2004 Les Saintes Earthquake. Data were acquired during the SUBSAINTES 2017 cruise, using an HD video camera mounted on the Victor 6000 ROV. The 3D model revealed the traces of this seismic event in the fault scarp, which were not readily identifiable in the original video imagery  [58]. Figure  9  shows the overall fault scarp, where the well-preserved fault plane is clearly identifiable as a smooth, sub-vertical surface emerging from the seafloor. On the flanks and above this plane, the irregular surface texture is the associated fault plane degradation.
Inpainting is preferred to image cropping as it keeps most of the information that was within the original image. It also ensures that the center of the image before and after is not modified and, therefore, that the optical center of the image stays close to the image center. Cropping is not recommended as it usually modifies the image and optical centers. The Eiffel Tower input dataset includes 4875 images from video frame extractions, with a subset of images associated with navigation, and a subset without navigation. The model extends ∼30 × 20 m horizontally and 20 m vertically, over a total surface of 920 m 2. Ecosystems are often hosted in geometrically complex habitats, which can be linked to a wide range of faunal and microbial communities. Moreover, 3D models are critical to understanding these habitats, communities, species distributions, and associated environmental drivers.
The resolution of this texture-mapped model (Figure  10 ) allows for the identification of fauna assemblages and substrate types, which can be mapped to understand community structures at small spatial scales  [1, 59]. Successive surveys and resulting 3D models at adequate time scales provide a unique opportunity to study fine temporal variations. The Eiffel Tower hydrothermal chimney, and its faunal and microbial communities, were mapped in 2015 (this dataset) and 2016, with additional models acquired in 2018 and 2020 being processed for temporal studies. Similar 3D temporal studies have been conducted in other ecosystems  [57, 60]. Finally, the 3D model was also used from year to year to relocate the ROV through the comparison with live video streams and facilitate the installation of measurement equipment [61].
Conclusions and Perspectives Matisse provides a complete suite to pre-process and process underwater imagery (video and still images), and to generate 3D textured scenes. With 3DMetrics, users can visualize these 3D scenes, integrate additional data, such as other scenes and bathymetry, and extract quantitative information while providing data management capabilities and standard output options for further analyses. The processing pipeline in Matisse is specifically tailored for underwater applications, and the software development has benefited from extensive testing on numerous cruises and under various surveying scenarios. This software is designed with flexibility in mind, allowing it to be applied to various instruments and vehicles, including legacy data. We additionally provide four open-access datasets, with explanations of the processing steps in Matisse, and the resulting 3D textured models.
These different datasets are relevant for environmental impacts and pollution studies, tectonics, geology, biology, and archaeology, and this software may be used in other fields. These datasets also provide different survey configurations, illumination characteristics, and types of imagery, to showcase the flexibility and robustness of Matisse, and facilitate training. We released a stable version of Matisse as open-source code available on GitHub, which can be installed easily on Linux and Windows systems. Future versions and updates will also be distributed through the GitHub repositories, including an OSX version, and a version specific for servers, to provide remote (client/server approach) processing capabilities. The processing time will be updated to reflect the latest hardware architecture performance as soon as possible.
Currently, 3D reconstructions are limited to scenes of a few hundred square meters due to computational and memory constraints. However, we plan to expand the scale of feasible reconstructions and increase speed by implementing smart sub-scene segmentation and fusion. As we move forward, we anticipate better integration of optical mapping with other techniques, such as multibeam high-resolution terrain data, hyperspectral imaging  [64] , structured light, and underwater lidar to better characterize seafloor texture and nature. It is important to note that the limitations of reconstruction will vary depending on the survey techniques and instrumentation used, requiring a case-by-case evaluation  [26]. Nevertheless, our datasets demonstrate the flexibility and robustness of our approach.
Acknowledgments: The  French National Oceanographic Fleet, Ifremer , and the  French Ministry of Research  supported the acquisition of data during several cruises (DOIs  10. 17600/17005800  (https://doi. org/10. 17600/17005800 ), 10. 17600/13030070  (https://doi. org/10. 17600/13030070 ), 10. 17600/15000200  (https://doi. org/10. 17600/15000200 ), 10. 17600/16012300  (https://doi. org/10. 17600/16012300 )). We thank the crew, officers, technical staff, and scientific staff from these cruises for their work and dedication in facilitating the work we present here. We also thank  Graham Douglas  for his assistance in revising the manuscript.
Funding: The development of the software and processing of the data were partially supported by the  Ifremer PSI  grant scheme (3DMetrics) awarded to A. A. and M. M. , as well as the  ANR Sersurf  ( ANR-17-687 CE31-0020 , PI JE) granted to A. A. and J. E. Furthermore, J. E. received support from  CNRS/INSU  for data exploitation during the SUBSAINTES'17 cruise. Part of the work was supported by the  European Union 's  Horizon 2020 research and innovation project iAtlantic  under Grant Agreement No. 818123.
The numerical simulations are implemented in the LAMMPS package  86  in which the natural time scale is t 0 ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ml 2 =k B T p , this means that t b /t 0 = l/24 E 0. 42 for the parameters used here. The integration time step is 10 À4 t 0. The natural unit of both pressure and stress is k B T/l 3 ; all results for such quantities are quoted relative to this baseline. To compare to relevant experimental units, we realize that corresponding experimental systems of sticky colloidal gels have typical diameters c between 0. 1 and 3 mm. 61, 87  This allows for a variation of corresponding Brownian times between t b B 0. 005-7 s and pressure units between k B T/s 3 B 10 À4 -10 1 Pa.
Acknowledgements We thank  Daan Frenkel ,  Sylvain Patinet ,  Camille Scalliet ,  Amin Doostmohammadi ,  Abraham Mauleon-Amieva ,  Rui Cheng , and  Malcolm Faers  for helpful discussions. This work was supported by the  EPSRC  through grants  EP/T031247/1  (KT and RLJ) and  EP/T031077/1  (CPR and TBL). In the later stages of the project, KT also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska Curie grant  agreement no. 101029079.