esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 2 Materials and Methods
### Applied HSI Procedure
Sensitivity, which sometimes is referred to as the true positive rate or recall, is a measure of a model's accuracy in identifying positive cases. A high sensitivity means that the test or model is good at minimizing false negatives and catching most true positives. Specificity, sometimes referred to as the true negative rate, evaluates how well a model can detect negative cases. A high specificity minimizes the inaccurate classification of negatives as positives, indicating that the model is good at preventing false positives.
## 1 Introduction
### Bailey Bridge Structural Characteristics
#### 1.2.3 Third Generation  
The Mabey Compact 200 and Acrow 700 came with increased panel depth, approximately 50% higher panel height, and 20% more potent in shear than the original Bailey Bridge demonstrated in Figure 1b,c. The depth improved its stability and potential to accommodate a wider roadway of 7.3 m. Mabey Compact 200 was used during the construction of the Baker bridge in 2009 in Cambria, Britain. The Acrow 500 was replaced with the Acrow 700, later adopted as Acrow 700 XS [11]. The Acrow 700 XS were assembled for recovery efforts at ground zero of the World Trade Center, during the terrorist attack, in record time. The structure was erected on prefabricated piers, moving a record 1.8 million tons of debris. Additionally, it was also operational during the rebuilding process. On the other hand, New Jersey Turnpike Authority employed the 700 series to create a diversion during the upgrade of the existing bridge in a record 15 min [11].  
Figure 1: (**a**) Typical Bailey Bridge, (**b**) Mabey Bailey Bridge, (**c**) Acrow Bailey Bridge and (**d**) M2 typical end shear/post.
## 3 Results and Analysis
### Inversion Results
Because of the underlying surface, there will be different surface reflectivities in the same vegetation growth conditions. Although there are more inflection points and a greater chance of outliers, the HJ-1 data can more realistically reflect the change in the surface during the test period. As the results indicate, the combination of MODIS data and HJ-1 data is expected to be a method of improving the data quality of HJ-1 30 m NBAR time series. Certainly, compared with MODIS NBAR products, there is still a shortage of large-scale data usage in surface reflectivity inversion, when using the HJ-1 and MODIS joint data. However, with the use of small-scale data, the HJ-1 fusion data can highlight more detailed real surface conditions. Figure 8: (**a**) Sun observation angle distribution, where the red circle, the green circle, and the pink circle are corresponding to the solar zenith angle, the original HJ-1 observation zenith angle, and the fusion HJ-1 data observation zenith angle, respectively; (**b**) directional reflectivity profile, where the red and blue lines are corresponding to the MOD43 and HJ-1 reflectivity, respectively.
# Geant4 Simulations of a Scintillator Cosmic-Ray Detector
## 1 Introduction  
The study presented in this article aims to estimate usefulness of simple flat scintillator detectors for measurements of cosmic rays (CRs) by registering secondary charged particles and photons created in the atmosphere in a phenomenon called an Extensive Air Shower (EAS). EASs are results of interactions of primary CRs coming from space into the atmosphere. While single EAS events have been studied for years by numerous large observatories around the globe [1, 2, 3], little is known about large-scale correlations of cosmic rays [4, 5]. The detection of such correlations in the form of Cosmic-Ray Ensembles (CREs) is the goal of the Cosmic Ray Extremely Distributed Observatory (CREDO) [6]. Such studies require a large network of detectors collecting cosmic-ray data. For this purpose, devices that are possibly inexpensive and easy to build in large numbers are needed [7, 8]. A good candidate is a small plastic scintillator detector based on the Cosmic Watch design [9].  
However, as such a device provides limited information, it is necessary to study its capability and efficiency in the detection of single EAS. This work aims to provide quantitative and practical knowledge about the potential performance of a class of cosmic-ray detectors based on plastic scintillators. The methods of analysis used in this analysis are similar to those commonly used in this field [10; 11], but the properties of such simple detectors have not been sufficiently studied yet. This study aims to estimate the sensitivity of detection for several types of particles from EASs with various momenta and to determine how it depends on the inclination angle of cosmic-ray particles, the parameters of the device, and different shields. To achieve this goal, computer simulations of particles hitting detectors of specific designs were performed. Details of the simulations are discussed in Section 2; the Geant4 software and the simulated detector are presented in Section 3. Then methods of estimation of the electronic signal produced by the detector are described. At the end, final results and conclusions from this analysis are presented.
## 2 Basics Theory  
### C-Responses  
From the geomagnetic fields measured from ground geomagnetic observatories or satellites in the time domain, we can obtain frequency domain fields, and then transform these into C-responses. Assuming that we have the frequency domain magnetic data of the radial component \(H_{r}(\omega)\) and the geomagnetic south component \(H_{\theta}(\omega)\) at a particular site, \(\theta\) is the geomagnetic colatitude. We can define the C-responses at \(\omega\) as  
\[C(\omega)=-\frac{a_{0}\tan\theta}{2}\frac{H_{r}(\omega)}{H_{\theta}(\omega)}. \tag{1}\]  
where \(a_{0}=6371\) km, and C-responses (in km) is a complex variable (for the detailed derivation process, refer to [PERSON] et al., 2018 [34]). The data processing is generally similar for satellite and observatory data, used to estimate the spherical harmonic coefficients [35]. The real part of C-responses reflects the mean depth of magnetic fields penetrated into the Earth, and the imaginary part reflects the resistivity variation [11]. We can use real geomagnetic data to estimate the C-responses at different frequencies [34; 36]. The forward modeling solutions for C-responses are provided by [PERSON] and [PERSON] [36].
## 2 Problem Formulation
### Constraints  
#### 2.3.1 Power Flow Constraints  
Since the topology of distribution networks is radial, the commonly used branch flow formulation [33] can be utilized to describe the power flow. Note that the subscript s is omitted for convenience of expression.  
\[\left\{\begin{array}{l}\sum\limits_{i\in u(j)}P_{ij,t}-r_{ij}\left(P_{ij,t}^{2 }+Q_{ij,t}^{2}\right)/U_{i,t}^{2}=\sum\limits_{k\in\wp(j)}P_{jk,t}+P_{j,t}\\ \sum\limits_{i\in u(j)}Q_{ij,t}-x_{ij}\left(P_{ij,t}^{2}+Q_{ij,t}^{2}\right)/ U_{i,t}^{2}=\sum\limits_{k\in\wp(j)}Q_{jk,t}+Q_{j,t}\\ U_{j,t}^{2}=U_{i,t}^{2}-2(r_{ij}P_{ij,t}+x_{ij}Q_{ij,t})+(r_{ij}^{2}+x_{ij}^{2 })(P_{ij,t}^{2}+Q_{ij,t}^{2})/U_{i,t}^{2}\end{array}\right. \tag{8}\]  
and  
\[\left\{\begin{array}{l}P_{j,t}=P_{\mathrm{L},j,t}+P_{\mathrm{ch},j,t}-P_{ \mathrm{dis},j,t}-P_{\mathrm{DG},j,t}\\ Q_{j,t}=Q_{\mathrm{L},j,t}-Q_{\mathrm{DG},j,t}-Q_{\mathrm{SC},j,t}-Q_{\mathrm{ SVG},j,t}\end{array}\right., \tag{9}\]  
where \(P_{ij}\) and \(Q_{ij}\) are the active/reactive power flow from bus \(i\) to \(j\); \(u(j)\) and \(v(j)\) mean the set of all parents and children of bus \(j\), respectively; \(r_{ij}\) and \(x_{ij}\) are the elements of admittance matrix; \(U_{i}\) is the node voltage magnitude of bus \(i\); \(P_{j}\) and \(Q_{j}\) are the injected active/reactive power of bus \(j\); \(P_{\mathrm{L},j}\) and \(Q_{\mathrm{L},j}\) are the active/reactive load for bus \(j\); \(P_{\mathrm{ch},j}\), \(P_{\mathrm{dis},j}\), and \(P_{\mathrm{DG},j}\) are the charging power of ESS, the discharging power of ESS, and the output of DG, respectively; \(Q_{\mathrm{DG},j}\), \(Q_{\mathrm{SC},j}\), and \(Q_{\mathrm{SVG},j}\) are the reactive compensations of DG, CBs, and static Var generator (SVG), respectively.
# Review of Functionally Graded Thermal Sprayed Coatings
## 4 Major Applications of Functionally Graded Coatings
### Photo-Catalysis  
Photo-catalysis is an important issue in the chemical industry being related to degradation and destruction of organic pollutants. [PERSON] and [PERSON] [327] were the first to describe this phenomenon. More details are presented in the studies [328; 329]. Thermal spraying technology works on three semiconducting oxides, namely TiO\({}_{2}\), ZnO, and SnO\({}_{2}\). TiO\({}_{2}\) seems to be most frequently tested [1]. An important point is the fact that at the photoreaction the surface of oxide remains unchanged [330]. The possible applications of the photocatalytic thermal spray coatings is the self-cleaning of surfaces (e.g., glass building). The photo-catalysis was also tested for water and air purification, for anti-fogging surfaces, for photo-catalytic lithography, and for many other applications [331; 332].  
An example of composite photo-catalytic coating used against air pollution is TiO\({}_{2}\) doped with Fe\({}_{3}\)O\({}_{4}\) coating sprayed on mild steel substrates [333; 334]. Because of the presence of intermediate phases, such as FeTiO\({}_{3}\), the band gap is narrower, than in pure TiO\({}_{2}\) which resulted in better photocatalytic activity with reported efficiency of more than 90%. Inversely, the nanostructured TiO\({}_{2}\)/Fe\({}_{3}\)O\({}_{4}\) plasma sprayed coatings did not exhibit satisfactory photocatalytic properties. Their best photocatalytic efficiency was as low as 23%. [PERSON] et al. [335] sprayed the coatings of TiO\({}_{2}\) doped with ZnO or CeO\({}_{2}\) or SnO\({}_{2}\) on the foamed aluminum substrate and used them for benzene degradation. The coatings doped with CeO\({}_{2}\) and SnO\({}_{2}\) degraded better than that doped with ZnO. Nevertheless, their efficiency was greater than 90%. [PERSON] et al. [336] sprayed titania doped with iron coatings. The coatings were used for butane decomposition under visible and UV radiation. The photocatalytic activity was also observed in plasma-sprayed TiO\({}_{2}\) + ZnO.Fe\({}_{2}\)O\({}_{3}\) coatings [337]. The photocatalytic efficiency was promoted by FeTiO\({}_{3}\) phase being present in the coatings. On the other hand, the large amount of ZnFe\({}_{3}\)O\({}_{3}\) phase was not favorable for the photoactivity. The innovative plasma-sprayed composite of TiO\({}_{2}\) with carbon nanotubes had photo-catalytic activity greater than pure TiO\({}_{2}\) deposits [338].  
[PERSON] et al. [339] deposited TiO\({}_{2}\) with ECTFE polymer composite coating by LPCS method. The coating was used to degrade of NO and NO\({}_{2}\) pollutants. The photocatalytic activity of obtained coatings were much better than that of commercial paint.  
The group of [PERSON] from Ningbo, China [340; 341], manufactured nanocomposite coatings of TiO\({}_{2}\)/HA and TiO\({}_{2}\)/HA/reduced graphene oxide obtained by flame spraying and tested for water disinfection and air purification.
## 4 Experimental Results and Analysis
### Results of Three-Perspective Adaptive Cheating Behavior Detection
To measure the probability of non-cheating students being detected for cheating, using the false positive rate (\(FPR\)) as the standard, Formula (8) is calculated as follows:  
\[FPR=FP/(FP+TN) \tag{8}\]  
This study uses overhead perspective and horizontal perspective classifiers to detect cheating behavior based on the gaze direction. Therefore, the FPR of the classifier in the two perspectives is calculated separately, and the FPR is 1.27% in the overhead perspective and 0.05% in the horizontal perspective.
Without employing the rotation-invariant description method, it limits the robustness and distinctiveness of the learned descriptor. In this study, we aim to tackle these challenges in a joint learning framework that is able to not only accurately detect keypoints from 3D point clouds, but also derive compact and representative descriptors for matching. To this end, we find inspiration in the MDGAT-matcher [4] for a GAT-based two-stage descriptor for point cloud registration. However, the extension of our work is non-trivial. A reliable network for the extraction of robustness features against noise and occlusion is needed instead of using hand-crafted counterparts. In this work, we adopt joint learning of dense detection and description of 3d local (D3 Feat) [28] to extract dense features from the kernel point convolution (KPConv) network [36] and achieve density invariance on irregular 3D point clouds. Based on these density-invariant saliency scores, we detect keypoints in their local neighborhood. Finally, these dense features are further fed into the attention network, DFGAT, which fully exploits the local information and generates a generalizable and distinctive feature vector. Overall, the proposed DFGAT has the following key features:  
* It is the learning pipeline using joint keypoints descriptors and detectors, is density invariant, and generalized across all point clouds. * It can build the global spatial relationship and enrich characteristics for matching features. * It helps to save time and computing resources but still achieves the highest matching scales and success ratio in the registration experiment.
## 2 Heterogeneous Systems: TiO\({}_{2}\) Photocatalysis  
The estimation of intrinsic kinetic parameters requires models capable of capturing the relative influence on the measured outputs of different phenomena occurring in microreactors, namely chemical reactions, adsorption, light absorption, mass transfer limitations between the phases and within each phase, fluid dynamics, etc. Selection of the right model is of crucial importance and it is highly dependent on the specific process investigated.  
Photocatalytic processes using TiO\({}_{2}\) are among the most commonly investigated heterogeneous photocatalytic systems. Despite the great variety of devices, materials, and deposition techniques, these microreactors present some common characteristics and mechanisms. The general scheme of a photocatalytic microreactor for degradation studies is reported in Figure 1.  
The internal surface of the microchannel is usually covered with a thin layer of photocatalytic material. The light is often irradiated on the opposite side, where no catalyst is present. In this case, the main factors controlling the degradation are (_i_) adsorption of species on the catalytic surface and surface reaction; (_ii_) advection and diffusion of the species in the liquid phase; (_iii_) diffusion of the species in the solid catalyst for porous materials; (_iv_) light absorption of the liquid solution, containing the pollutants and degradation products; (_v_) light absorption of the solid catalytic layer, resulting in the generation of photoexcited electron/hole pairs and oxidizing species.
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 4 Conclusions  
In this paper, the Rmap algorithm that improves the performance of the grid map was proposed. The Rmap can represent an area of similar quality to the grid map with a relatively small number of rectangles. The Rmap also can guide an exploration path for obtaining new information in the unknown area. This study also showed an applied case of the Rmap for the coverage application. Compared to the previous mapping algorithms, the Rmap showed better memory efficiency and coverage performance for the unknown area.  
This experiment results mean that our Rmap scheme can improve the overheads caused by the grid map in SLAM. Additionally, it demonstrated that the map representation structure could reduce the computation required to calculate the path in the SLAM process. Moreover, the Rmap achieved a high covered area by reducing redundant visits in coverage cases. Finally, the Rmap algorithm was integrated with DP-SLAM to evaluate the performance, and this study found that The Rmap has the potential to combine with other SLAM algorithms to get better results.  
Currently, the Rmap does not consider dynamic obstacles like people or pets. The detection and representation method for the dynamic obstacles should be researched in the future. Furthermore, it is possible that the Rmap can be extended to represent the 3D area with vision sensors.  
Conceptualization, H.S.J.; Formal analysis, J.A.; Funding acquisition, H.S.J.; Methodology, C.W.L. and J.K.P.; Resources, H.J.O.; Supervision, H.S.J.; Validation, J.D.L.; Writing - original draft, C.W.L.; Writing - review & editing, H.S.J.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07043417). The authors declare no conflicts of interest.
## 3 Results and Analysis  
### Descriptive Statistics of Peanut SPAD Data  
A total of 40 sample data were obtained. The cross-validation method was used to randomly divide the data into two groups, of which 32 were used as training samples for analysis and modeling and 8 were used as verification samples to verify the accuracy of the model. SPAD values of the training sample were between 35 and 49.35 (Table 2), including the minimum and maximum values for the whole sample. The interval distribution was reasonable and degree of variation relatively large, which is guaranteed in a certain sense the applicable scope of the constructed peanut leaf SPAD estimation model. The descriptive statistics of the verification sample and training sample were similar, therefore verifying the reliability of the established model.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperspectral Index** & **Formula** \\ \hline Normalized difference spectral index & \(R_{14}-R_{32}\) \\ (NDSI) & \(\frac{R_{1}}{R_{1}+R_{12}}\) \\ Ratio spectral index & \(\frac{R_{1}}{R_{12}}\) \\ (RSI) & \(R_{1}-R_{12}\) \\ Difference spectral index & \(R_{14}-R_{32}\) \\ (DSI) & \(R_{14}-R_{32}\) \\ Soil adjust spectral index & \(\frac{R_{14}-R_{32}}{R_{14}+R_{12}+L}\) \\ (SASI) & \(\frac{R_{14}}{R_{14}+R_{12}+L}\) \\ \end{tabular} \(R_{14}\) and \(R_{12}\) refer to the canopy spectral reflectance of 2 wavelengths, \(L\) is the soil correction parameter and \(L=0.5\) is selected in this paper.  
\end{table}
Table 1: Names and algorithms of the spectra indices used in this paper.
## 3 Optimal Efficiency Tracking Method in Full Current Mode  
### Secondary-Side Impedance Matching Topology and Analysis  
Active impedance matching and passive impedance matching are key techniques for optimal efficiency tracking in wireless power transfer systems. Researchers have introduced active components or adjusted circuit parameters to achieve impedance matching, improving power transfer efficiency and system stability. However, different methods and techniques have variations in cost, efficiency, and stability, requiring selection and optimization based on specific application requirements.  
Due to the rapid and wide-ranging load variations in the designed system, optimal efficiency tracking through passive impedance matching is difficult to achieve. Therefore, active impedance matching was adopted in this system. The secondary-side impedance matching topology is shown in Figure 3.  
In this figure, Vin represents the AC voltage source; S1, S2, S3, and S4 are the four switch transistors; and Lp and Ls and Cp and Cs are the resonant inductors and capacitors on the primary and secondary sides, forming the resonant network of the entire system. Rp and Rs are the internal resistances of the primary and secondary coils, respectively, AIM (Active Impedance Matching) denotes the active impedance matching module, and RL represents the system's equivalent resistance.  
Active impedance matching, also known as active matching or active impedance control, is a technique using active electronic components to match the source or load impedance to the desired impedance. It involves active devices such as operational am  
Figure 3: Secondary-side impedance matching topology.  
Figure 2: Two-port equivalent topology.  
plifiers (op-amps), transistors, or integrated circuits to actively adjust the impedance and achieve maximum power transfer or signal integrity.  
In active impedance matching, a DC-DC equivalent converter is introduced, and the load resistance is transformed by controlling the duty cycle in the circuit. This technique allows control over the maximum power point and maximum efficiency point. Active impedance matching circuits have two forms: buck and boost circuits. The following analysis focuses on buck and boost circuits.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### Node Extraction and Edge Construction
#### 2.3.3 Node Duplicate Detection for Doors  
According to the file tree structure shown in Figure 5, point cloud files representing the same door can appear simultaneously in both general space (e.g., office) and transition space (e.g., hallway) directories. This setup introduces the potential issue of duplicate node extraction when processing door data, as a newly extracted door node may have already been processed and saved in the navigation network. To address this issue, we used a node duplicate detection algorithm that filters out duplicate nodes extracted from the door point cloud data.  
Since the semantically segmented point cloud files representing the same door in the general space (e.g., office) and transition space (e.g., hallway) directories may have slight differences in detail, but are generally consistent, such as points distribution or number of points, the extracted centroids for the same door may be different. To accurately identify duplicate nodes, we employed a distance threshold-based method.  
To ensure that each node uniquely represents a specific door, for each new door point cloud file, we calculate its centroid coordinates \(\mathrm{C_{new}}=\left(x_{\mathrm{new}},y_{\mathrm{new}},z_{\mathrm{new}}\right)\). For each existing door node in the extracted navigation network, we directly iterate through the centroid coordinates \(\mathrm{C_{e}}=\left(x_{\mathrm{e}},y_{\mathrm{e}},z_{\mathrm{e}}\right)\) of each door, then calculate the Euclidean distance \(d\) between the new door centroid and each door centroid using the following equation:  
\[d=\sqrt{\left(x_{new}-x_{e}\right)^{2}+\left(y_{new}-y_{e}\right)^{2}\;+\left( z_{new}-z_{e}\right)^{2}} \tag{5}\]  
If the calculated distance \(d\) is less than the threshold, the nodes are considered duplicates and only the existing node is retained. Based on our prior knowledge, we set the threshold to 1 m. By using this threshold range, we can identify whether nodes are duplicates. This method ensures that each node is unique and accurately represents a door location within the indoor space.