dataesr/scientific-paragraphs-categorization huggingface dataset:
It is worth noting that the plasma tensor typically requires more memory space compared to the metallic structure models. To address memory constraints, especially for a large number of simulations or 3D simulations, a dynamic creation of the plasma model is also useful. By maintaining the structure of the input and output of the F S and F P functions, it is possible to modify their contents without affecting the other phases of the process. This flexibility allows different users to define their own F S functions based on their specific requirements, while keeping the rest of the framework code intact. The final phase involves the creation of simulation scripts. The UTS scripts execute the simulations required to measure the UTS impulsive response. Each UTS simulation is associated with an element from the L S T R list.
Since the sources are assumed to be independent, these simulations can be run in parallel. The MCAL scripts run simulations for calibrating the group delay within the antenna setup corresponding to each element in the L S T R list. The PLS scripts execute simulations with the plasma models associated with the combinations in the L S I M list. The SRC scripts generate synthetic reference signals for each combination in the list of sources, L S RC. This includes creating phase-shifted signals for IQ detection. To handle a large number of simulations in high-performance computing (HPC) environments, the scripts execute simulations in intervals of indices, facilitating efficient management and execution. After the simulation script creation phase, verification scripts are also generated. These scripts include routines to check if the simulations have completed successfully.
These points can be easily determined using CAD software. Therefore, it is useful to express the translation vector T and the rotation matrix R in terms of these points. The translation vector is given by the origin of S 0 , which can be expressed as:  The unit vectors corresponding to the three axis of S 0 are given by T = p 0 = (p 0x , p 0 y , p 0z ), (75) v [x,y,z] = p [x,y,z] -p 0 |p [x,y,z] -p 0 | 2 , ( 76 ) and the R matrix is given by R = ⎡ ⎣ R xx R xy R xz R yx R yy R yz R zx R zy R zz ⎤ ⎦ = ⎡ ⎣ v xx v yx v zx v xy v yy v zy v xz v yz v zz ⎤ ⎦. ( 77 ) To produce metallic structures with realistic geometry, it is necessary to verify if each point of the FDTD grid is inside the metallic material, taking into account the respective ROI coordinates. In this context, we have developed CAD2RFM, a routine that converts CAD models into FDTD grids compatible with REFMUL codes.
If the calculated terms for both points have the same sign (positive or negative), it means they are on the same side of the plane. • if the side is the same for all four planes, then the point p is determined to be inside the tetrahedron. An example of an implementation in the DEMO PPR is illustrated in Fig. 7. We defined the tokamak's wall using the 2D 2017 DEMO geometry with a width of 1600 mm and a thickness of 100 mm. The entire model, composed of 16 antennas, was designed using CAD software. The mesh is calculated within the coordinate frame associated with the machine. By defining the simulation frame and selecting the Region of Interest (ROI), in this case at the top of the machine (blue box), the code creates the input metallic structure for REFMUL3 (shown on the right side of the figure).
Simplifying the input data definition In order to investigate the DEMO PPR system and demonstrate the automation of the simulations and data processing, a framework was developed based on the structure discussed in the previous sections. This framework specifically focused on the 2D O-mode case, enabling the execution of a large number of simulations. The CAD2RFM algorithm, adapted to a 2D triangular mesh, was utilized to create an additional F S function, which defined an array of 2D antennas with a variable setup around the DEMO wall. The DEMO 2017 geometry was employed to establish a system configuration comprising 100 reflectometers, divided into 16 regions and labeled with the code G[region][letter]. The official data from the EUROfusion database provided essential information, including the vacuum vessel geometry, the normalized poloidal flux, and the electron density profile for the baseline scenario (R 0 = 8.
The data analysis process for PPR systems was also reviewed using the same formalism. This enabled a comprehensive problem description and facilitated the development of a framework to simplify the simulation process for a set of multi-reflectometer systems. The synthetic signals and other variables and operations involved in the simulation process can be written as a function of a reduced number of main variables, the simulation dimension (D), region of interest (R), metallic structure (S), plasma (P), and source/band (B). This approach allows the definition of inputs through independent lists of variables, a list specifying dependencies between their elements, and configuration files on which the simulation scripts depend of in a generalized form. Various solutions were proposed to generate realistic plasma/reflectometer models and minimize the complexity of input definition.
The detected amplitude is determined by filtering the I/Q signals using a cutoff frequency corresponding to 1-2 times the full width at half maximum (FWHM) above the cutoff frequency of the maximum of the mean detected amplitude derivative. The developed techniques and algorithms were validated using the reference 2017 DEMO baseline scenario available in the official EUROfusion database. The results obtained from the algorithm were compared to the manual selection of the signal delay and filter parameters. In simulations without plasma-wall effects, the algorithm yielded similar results to the manual selection. However, when the wall effects were included, there were slight deviations from the results obtained through manual selection. These deviations occurred when the amplitude of the characteristic frequency of the I/Q signals was comparable to the reflections, or when the estimation of signal lag was not accurate due to losses at lower frequencies.
Acknowledgements IPFN activities received financial support from " Fundacão para a Ciência e a Tecnologia " through projects  UIDB/50010/2020  and  UIDP/50010/2020. This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No. 101052200-EUROfusion ). 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 Commission. Neither the  European Union  nor the  European Commission  can be held responsible for them.
Data analysis Data obtained from SPRi measurements were analysed using Origin 2018 and Matlab R2020a Software. Each injection results in an interaction between the sample and various previously chosen spots on the sensor surface. The reflectivity variation values were obtained considering the interaction of at least 10 replicates on the sensor surface for each type of immobilized molecule. Thus, it was possible to represent the variation values by the mean and the standard deviation. Finally, the mean reflectivity variation values were used to obtain the surface concentration density and the limit of detection (LOD), eq. 1. LOD = 3 x S blank B ( 1 ) Where S blank is the standard deviation of the measures with blank samples and B is the slope of linear fitting of the variation of reflectivity vs.
Data obtained by injecting CA-125 and mesothelin at increasing concentration were used to calculate the LOD of the biosensor. The analytical curve (Fig. 7 ) shows the average value of the reflectivity variation obtained in each injection of proteins at different concentrations. LOD values for detection of mesothelin and CA-125 were 3. 03 nM and 13. 62 nM, respectively. These values indicated the potential application of the biosensor to detect mesothelin and CA-125 at concentrations below the reference value for EOC. Biomarkers incubated in FBS were also tested in the biosensor and AuNPs capable of locally and selectively concentrating proteins incubated in a medium were used as signal amplifying agents. In order to enhance the sensitivity of the biosensor, gold nanoparticles were used to capture the proteins in a solution before the injection in the system.
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Erenildo Ferreira de Macedo and Lucca Silva Nascimento received financial support from São Paulo Research Foundation (FAPESP 2019/ 06803-4; 2016/07354-0; 2020/00807-5). This work was supported by FAPESP (2022/00662-2).
Acknowledgements The authors would like to thank the project Transport & Entreposage of the  French Institut Tripartite CEA-EDF-Framatome  for supporting this study. They also would like to thank  Agathe Navailles  and  Freddy Salliot  for their participation in the developpement of the C-ring technique and its application to DHC at  CEA ,  Frederic Datcharry  from  CEA  for the realisation of the hydrogen charging.
B. Circular holey structure For the second structure, we aim to verify the applicability of the code to find complex and evanescent modes. For this purpose, the geometry was chosen to compare the results with Fig. 8  from  [19]. Two meshes were generated to have ratio of wavelength to largest triangle edge of approximately 4 and 8 at 100 GHz, corresponding to 255 and 831 basis functions, as shown in Figs. 10(a ) and (b). Here, due to the curved surface, the entire structure is remeshed instead of splitting each triangle into four. For the generation of the dispersion diagrams, the frequency range considered is The meshing software used is Gmsh  [43]. 0. 5 GHz to 100 GHz with N init = 20 initial points in the interpolation scheme. For evanescent modes, the frequency ranges are set to coincide with the stopbands at Γ, X and M.
Manuscript received ; revised. This publication is based upon work from  COST Action SyMat  ( CA18223 ), supported by  COST (European Cooperation in Science and Technology). It is supported in part by the  Horizon Europe Research and Innovation Program  under the  GENIUS Project ,  Marie Skłodowska-Curie Grant  under Agreement  101072560 , in part by the project  PON Research and Innovation "Microwave Imaging and Detection powered by Artificial Intelligence for Medical and Industrial Applications "( DM 1062/21 ), funded by the  Italian Ministry of University and Research (MUR) , and in part by " Unite! -University Network for Innovation, Technology and Engineering".
Our objective is to provide a detailed description of the parameters that control the Fe-OM aggregate as regard to REE. Aggregates of Fe-OM-Ca were synthesized with a constant Ca/Fe ratio of 0. 5 (mol mol -1 ) and various Fe/OC ratios (ranging from 0. 1 to 0. 0008 mol mol -1 ). Adsorption experiments were carried out to investigate the distribution of REE relative to structure of the Fe-OM aggregates in order to evaluate the involved mechanisms and the controlling parameters. For this, experimental datasets were analysed to investigate the REE patterns variation relative to the REE concentrations, Fe/OM ratio, presence of Ca and pH. The surface interactions of REE with the Fe-OM aggregate was investigated by X-ray absorption spectroscopy (XAS), carried out at the Ce K-edge.
For each sample, the recorded spectra (up to four) were superimposed, demonstrating that no beam damage had occurred as a result of these measurement conditions. XANES and EXAFS spectra were extracted using the Athena software  [45]  including the Autbk algorithm (Rbkb = 1, K-weight = 3). Normalized spectra were obtained by fitting the pre-edge region with a linear function and the post-edge region with a quadratic polynomial function. EXAFS spectra were analysed by shell fitting. A theoretical back scattering paths were calculated from the crystal structure of Ce (HCOO) 3  [46]. Theoretical phase and amplitude functions were calculated using FEFF. The paths were carefully considered and selected for the final fits using the Artemis software  [45]. The Fourier transform of the k 3 -weighted EXAFS spectra were calculated over a range of 2-9 Å -1 using an Hanning apodization window (window parameter = 1).
Machine learning approach to identify small craters on high-resolution images: We adapted an automated Crater Detection Algorithm (CDA)  [15]  to identify craters smaller than 1 km in diameter across the entire surface of Mars. The algorithm was trained using High-Resolution Imagery System Experiment (HiRISE) images (25 cm/pixel) and applied on the global Context Camera (CTX) mosaic  [16]  (6 m/pixel), thus generating a database of more than 90 million detections (Figure  1 ). Figure  1 : Crater Size-Frequency Distribution of the automatic crater database (in red) compared to the manual catalogue (in blue) from  [11] [12]. We evaluated the performance of our model against ~1000 impact craters >60 m (10 pixels in diameter on CTX imagery) manually mapped on 6 different geological units, thus constituting the ground truth (GT).
The ejection site of a group of Martian meteorites: We analyzed the spatial and size distribution of craters smaller than 300 m by computing a crater density map from this dataset. The figure  3  presents the results obtained by combining three density maps with red, green, and blue channels corresponding to local crater density (in a 0. 05°grid) for three diameter size ranges, in order of decreasing range of diameter, respectively 150 < D < 300 m, 75 < D < 150 m and < 75 m. Using this map, we identified 19 secondary ray systems associated with large and recent primary craters (Figure  3 ). Using crater counts on the ejecta blanket of each of those 19 impact craters and their surrounding terrains, we derived respectively the model age of those impacts and the age of the impacted material. Those results are compared with the ejection age of Martian meteorites as well as their crystallization ages.
Data points were collected with an integration time of 1 -2 s at each angle, over an incident angle range of θ i = 0. 06 -2. 60 • , corresponding to a momentum transfer vector perpendicular to the surface, Q z , range of 0. 015 -0. 64 Å -1 , where Q z = (4πsinθ i )/λ. The data were normalised and corrected to account for both the sample width (9 -10 mm) and the Gaussian intensity profile of the beam (derived from a spline fit to a fine height scan of the sample at an incident angle θ i = 0 • ). Analysis of XRR data on mica must consider the contribution of the mica crystal truncation rods (CTR) to the overall reflectivity; the first mica CTR peak occurs at Q z ≈ 0. 63 Å -1 , which is within the typical Q z range where the Kiessig fringes from the sample are seen. The XRR data were fitted using a Python software package employing the standard Parratt algorithm  [37]  and incorporating a 'dual layer' approximation to account for the first CTR of the mica substrate as described in detail previously  [31].
Shear forces between the LB films Shear force measurements were also made as the LB films were progressively compressed and decompressed (i. e. as the surface separation D was decreased and increased) in n-dodecane. Fig. 6a  shows the shear trace with a saw-tooth form, thus at a constant shear velocity, v s between the two extremes of the shear trace, applied to the top surface as a function of time. An example of the corresponding shear response is also shown in Fig. 6a , where the displacement from the midpoint to the plateau is used to calculate the shear force, F s , transmitted across the FOCP LB films. Plots of F s against F n obtained for all four values of Π dep are shown in Fig. 6c. The data obtained between FOCP surface layers formed by physisorption from solution in n-dodecane (c pol = 0.
002 -0. 011). From the corresponding normal force profiles measured in n-dodecane, the calculated values of 0. 5D min /L 0 (the ratio of hard-wall thickness (for a single layer) to the unperturbed layer thickness) are 0. 10 at Π dep = 5 mN m -1 and 0. 15 at Π dep = 10 mN m -1. This indicates that the FOCP LB films at Π dep = 10 mN m -1 are less compressible and so provides evidence for a higher monomer packing density. The SLD values obtained from fitting the dry-film XRR data (Table  1 ) also support this hypothesis, with ρ = 2. 84 and 3. 34 × 10 -6 Å -2 obtained at Π dep = 5 and 10 mN m -1 respectively, suggesting a higher polymer volume fraction at Π dep = 10 mN m -1. Comparable shear measurements were also performed between the dry LB films in air, prior to injection of the n-dodecane solvent; in all cases, a rigidly coupled shear response was seen, where the maximum applied shear amplitude could not overcome the static frictional force required to initiate sliding of the strongly adhered surfaces.
Acknowledgements N. M. T. gratefully acknowledges funding from  Infineum UK Ltd , the  EPSRC  and the  University of Bristol  (The  Everett Bequest Fund ). W. H. B acknowledges funding from the  EPSRC  ( EP/H034862/1 ) for the SFA experimental setup. Synchrotron XRR was performed at beamline BM28 at the  ESRF  (experiment number BM28-01-1193), a mid-range facility supported by the  EPSRC , and we are grateful to all the beamline staff for their support. We are indebted to  Anthony Strong  ( Infineum ) for the synthesis of the polymers used in this work.
Acknowledgment This work was financially supported by the  French "Investissements d'Avenir" program , project  ISITE-BFC  (contract  ANR-15-IDEX-0003 ), the " Conseil Régional Bourgogne Franche-Comté" , the  FEDER (European Funding for Regional Economic Development) , the  French National Research Agency  (contract  ANR-19-CE28-0009 ), and the  European Research Council  (contract  HUMANFACE 101055175 ). The authors are grateful to the participating parents and infants. They also thank  Sylviane Martin  for her help in recruiting them and  Vincent Gigot  for his assistance with EEG preprocessing.
For the day-ahead market, numerical results are conducted over the simulation horizon of 24 h with a sampling time of 1 h for the utility grid interaction. Table V-A summarizes the main characteristics of the device parameters. Since the control strategy implemented in this paper will be integrated into the Khalifa University laboratory microgrid, which is currently under construction in Abu Dhabi, United Arab Emirates, data from the literature are adopted for the parameters not yet defined. The simulations have been carried out in MATLAB with YALMIP tool and GUROBI optimizer, and the optimization problem of the microgrid under investigation is solved in 25 s on a PC with an Intel Xeon (R) W -2245HQ 3. 9 GHz with 128 GB RAM. Fig. V-B  shows the requested load, the RES power, and the price profile considered in the scenario.
In imaging spectroscopy, especially in HSI, the spectrum for each pixel is measured at different wavelengths as shown in Fig. 1. In order to provide more information on what is imaged, the radiation striking each pixel is broken down into many different spectral bands  (Armin Schneider, 2017). Fig. 1 (a) presents a hyperspectral cube and each slice of the cube represents images captured at different wavelengths, Fig. 1(b ) presents a color image from the hyperspectral cube, and part (c) presents a pixel-wise classification map. The original image is from the hyperspectral dataset Jasper Ridge, captured using AVIRIS  (Green et al. , 1998)  air-borne spectrometer  (Kruse et al. , 1993). Part (d)  and (e) of Fig. 1  show the unique spectral signature and spectrum score of each entity respectively.
The use of machine learning and deep learning for hyperspectral image classification is presented in  Datta et al. (2022). While earlier reviews have provided valuable insights, the field of imaging spectroscopy has experienced significant advancements in recent years. For example, there has been substantial and rapid progress in HSI imaging technology. Similarly, the availability of new HSI datasets, and their usage in new applications, as shown in Figs. 2  and 3. Fig. 2  illustrates that the research landscape and trends in the field of HSI during 2015-2022 have rapidly evolved. In terms of source comparison (i. e. , platforms publishing HSI-related research), IEEE and ScienceDirect are leading the HSI-related research production with larger impact and visibility (Fig. 3 ). Since the field is continuously evolving at a faster pace, it is important to review the recent advancements, developments, and challenges to reflect the state of the field.
Thus, a comprehensive review of the recent advancements in terms of applications, databases, software, and future prospects could progressively benefit the community. In connection with this, we present an updated comprehensive review of the state of HSI and its state-of-the-art applications across various scientific and professional fields. Such a review could potentially act as a roadmap and would progressively provide useful references to research and the professional community in terms of literature, databases, tools, and applications of imaging spectroscopy. The rest of the paper is organized as follows. We first discuss imaging spectroscopy acquisition systems with camera models and lens specifications. Within the spectroscopy, our focus is mainly on HSI. This is followed by the discussion on pre-processing challenges and methods for processing hyperspectral images, including spikes removal, dead pixels, compression, and spectral processing in Section 3.
Applications of HSI in modern-day societies, such as medicine, precision agriculture, remote sensing, food quality control, material classification, document image analysis, and face recognition are discussed in Section 4. Next, we present open-source hyper-spectral databases and software libraries for processing hyperspectral images in Section 5. Finally, Section 6 summarizes the key insights from this study. A list of acronyms used in the paper is given in Table  1.
Temporal scanning Temporal resolution is defined as the amount of time needed to revisit and acquire data for the exact same location by the hyperspectral sensor  (Ma et al. , 2021). The fundamental trade-off here is between spectral and temporal resolution, with spectral filtering performed through mechanical filter wheels (usually restricted to MSI) or acustooptical or liquid-crystal tuneable filters (enabling HSI at a higher cost). The ability to obtain a spectrum image by simply taking a snapshot is very appealing for time-constrained applications, and this has sparked a lot of study  (Hagen & Kudenov, 2013).
Ground-based hyperspectral systems Imaging spectroscopy is a versatile technology that finds applications in various fields such as medical, forensic image analysis, agriculture, and material classification. In ground-based applications, a hyperspectral imaging system typically comprises a light source, a chargecoupled device (CCD) or complementary metal oxide semiconductor (CMOS) camera, and a spectrograph. Fig. 5  shows a typical ground-based hyperspectral imaging system. The system collects data over a range of wavelengths, typically spanning from the visible to the near-infrared region of the electromagnetic spectrum. The resulting hyperspectral data cube can be analyzed using various techniques, such as spectral unmixing, classification, and feature extraction, to extract valuable information about the scene or object being imaged.
Data enhancement, super resolution, and fusion The physical limitations of hyperspectral sensors may compromise the quality of acquired hyperspectral images. Image enhancement methods like super-resolution can be a good choice to improve the quality of hyperspectral data  (Farsiu, Robinson, Elad, & Milanfar, 2004). High-resolution hyperspectral pictures are generated from lower-resolution HSI data by utilizing high spatial-resolution information from another imaging source.