esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 3 Results
### Performance of the Micro-Capsule Network Model  
To further evaluate the performance of the micro-capsule network model, the results for EEG signals corresponding to the normal state, inter-ictal and ictal state are calculated with evaluation metrics. Each group of experiments lists the average and standard deviation through paired EEG dataset cases (for example, AB vs. E denotes normal state vs. ictal, C vs. E and D vs. E stand for inter-ictal vs. ictal state, respectively, A vs. C vs. E represents normal state vs. inter-ictal vs. ictal state) under different epileptic states, and the performance results are shown in Table 2.  
It can be seen from Table 2 that the accuracy of the proposed model for paired EEG dataset cases are 99.94 \(\pm\) 0.07%, 99.49 \(\pm\) 0.41%, 99.62 \(\pm\) 0.02%, and 99.06 \(\pm\) 0.22%, respectively. Similarly, the indicators of sensitivity and specificity have a corresponding display. The micro-capsule network model can correctly and stably detect whether it is the binary classifications of normal state and epilepsy state or the three classifications of the normal state, inter-ictal, and ictal state. The results show that the proposed method has better performance of the micro-capsule network model through paired EEG dataset cases for epileptic recognition ability, an average recognition accuracy higher than 99.00%, and good robustness in the case of a small dataset. It can be seen that the proposed framework model has better classification accuracy, which further verifies the effectiveness of this research method.
## 5 Conclusions  
Low resistant and highly transparent ZnO doped thin films, used for transparent conductive oxide applications by an easy, low-cost spray pyrolysis technique, were successfully developed. The structural, morphological, optical, and electrical properties of the coating with composition Zn\({}_{1-x}\)M\({}_{x}\)O\({}_{1-y}\) (x = 0.03) were characterized. All film crystals were grown in a wurtzite structure with preferred c-axis orientation along 002 crystal plane. Layers presented an optical transmittance higher than 90% in the case of aluminium doping. An increase in the energy band gap of doped ZnO was observed; the values go from 3.32 to 3.36 eV, depending on the dopant and morphology, and decrease at 3.27 eV when only oxygen vacancies were generated. Good sheet resistance values of 229.69, 385.71, and 146.94 \(\Omega\)/sq were obtained for ZnO:Al, ZnO:Ga, and ZnO:In thin films.  
Conceptualization, J.B.C. and D.F.; methodology, S.P.; software, T.S.L.; validation, T.S.L.; investigation, S.P., G.S., and J.G.; writing--original draft preparation, S.P.; writing--review and editing, D.F. and T.S.L.; supervision, J.B.C. and D.F.; project administration, J.B.C.; funding acquisition, J.B.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Spanish Ministry of Economy and Competitiveness under the program Programa Estatal de I+D+I orientada a los retos de la sociedad (IGNITE Project Ref. ENE2017-87671-C3-3-R) and program Proyectos de I+D+i-y de los Programas Estatales de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i y de I+Di Orientada a los Retos de la Sociedad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020 (Ref. PID2020-116719 RB-C43).  
Not applicable.  
We also appreciate the characterization assistance of the Central Service of Scientific Instrumentation (SCIC) at the University Jaume I. We also thank the group of [PERSON] (INAM-UJI) for the analysis and characterization assistance.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# An Effective Hybrid Routing Algorithm in WSN:
## 5 Experimental Results and Analysis  
### Simulation Settings of the WSNs  
In order to evaluate the performances of ACOHCM algorithm, a simulation of WSN environment is run in C++. In our simulated network, the area of 100 m \(\times\) 100 m, 100 nodes are randomly generated. As shown in Figure 1, the sink node is assumed at the center of the whole area. The best combination of the parameters \(\alpha,\beta,\rho,Q\) in the algorithm is determined by experiments. These parameters in our simulation are shown in Table 1.
# Privacy Auditing in Differential Private Machine Learning:
## 3 Privacy Attacks
### Type of Attacks
#### 3.3.5 Property Inference Attacks  
Property inference attacks (also called distribution inference) [150; 151; 152; 153] aim to infer global, aggregate properties of the training data used in machine-learning models, rather than details of individual data points. These sensitive properties are often based on ratios, such as the ratio of male to female records in a dataset. The attack attempts to understand the statistical information of a training dataset from an ML model. In contrast to privacy attacks that focus on individuals in a training dataset (e.g., membership inference), PIAs aim to extract population-level features from trained ML models [60]. Existing property inference attacks can be broadly categorized as follows:The attacker attacks the training dataset and attempts to leak sensitive statistical information related to the dataset or a subset of the training dataset, such as specific attributes, which can have a significant impact on privacy in the model [150]. The attacker can also exploit the model's ability to memorize explicit and implicit properties of the training data [154]. This can be achieved by poisoning a subset of the training data to increase the information leakage [152]. There is an option where an attacker can maliciously control a portion of the training data to increase the information leakage. This can lead to a significant increase in the effectiveness of the attack and is then referred to as a property inference poisoning attack [155].  
Differential privacy (DP) auditing uses property inference attacks to test whether the DP mechanisms are robust against the leakage of information about specific properties, features, or patterns within the dataset.
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 4 Discussions
The difference between the maximum achievable PSNR and PSNR at a specific number \(N_{\Delta}\) is calculated as  
\[\Delta=20\log_{10}S\sqrt{K}-20\log_{10}\frac{S}{\sqrt{\frac{N_{\Delta}+K+1}{N _{\Delta}}}} \tag{10}\] \[=20\log_{10}\left[S\sqrt{K}/\left(S/\frac{N_{\Delta}+K+1}{KN_{ \Delta}}\right)\right]=20\log_{10}\sqrt{\frac{N_{\Delta}+K+1}{N_{\Delta}}}.\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(S\)} & \multirow{2}{*}{\(K\)} & \multicolumn{2}{c}{**PSNR (dB) at**} & \multicolumn{2}{c}{**PSNR (dB) at**} & \multicolumn{2}{c}{**PSNR (dB) at**} \\  & & & \multicolumn{2}{c}{**\(P\) Propagations**} & \multicolumn{2}{c}{**\(N\) Accumulations**} & \\ \cline{5-8}  & & & \(P=10\) & \(P=100\) & \(N=10\) & \(N=100\) & \(N=\infty\) \\ \hline RP & 2.04 & 17.36 & 14.04 & 17.86 & 14.04 & 17.86 & 18.59 \\ \hline MIFA (5 iterations) & 3.11 & 11.4 & 9.13 1 & 16.93 2 & 16.93 & 19.91 & 20.42 \\ \hline MIFA (10 iterations) & 3.27 & 9.55 & NA & 15.21 3 & 16.98 & 19.68 & 20.09 \\ \hline LRDS (\(k=4\)) & 2.08 & 16.79 & 14.2 & 17.9 & 14.2 & 17.9 & 18.61 \\ \hline LRDS+AIA (\(k=4\)) & 2.24 4  & 42.4 4 & 12.47 & 20.54 & 16.04 & 21.39 & 23.28 4 \\ \hline \hline \end{tabular}  
* The PSNR at \(N=1\) because each iteration needs two propagations. 2 The PSNR at \(N=10\). 3 The PSNR at \(N=5\). 4 The applied model is inapplicable, but the fitting result is still listed here for reference.  
\end{table} 
Table 1: Comparison of different binary CGH methods. It is noted that \(\Delta\) is independent of \(S\). From Equation (10) and for a given \(\Delta\) and \(K\), we find \(N_{\Delta}\)  
\[N_{\Delta}=\frac{K+1}{10^{0.1\Delta}-1}. \tag{11}\]  
The needed accumulation numbers for different \(K\)'s and \(\Delta\)'s were calculated and are listed in Table 2. Normally, we can set \(\Delta=1\) dB to achieve a balance between quality and computing cost.
## 1 Introduction
### Complexes  
We will be dealing with weighted points and their Delaunay and Voronoi diagrams. A weighted point \(p\) with weight \(\omega_{p}\) is represented as a ball \(b=B(p,\omega_{p})\). The squared _weighted distance_ of any two points \(p\) and \(q\) is \(\hat{d}(p,q)^{2}=d(p,q)^{2}-\omega_{p}^{2}-\omega_{q}^{2}\) where \(\omega_{p}\) and \(\omega_{q}\) are the weights of \(p\) and \(q\) respectively. With this definition, an unweighted point \(x\in\mathbb{R}^{3}\) has a distance of \(\hat{d}(x,p)=\sqrt{d(x,p)^{2}-\omega_{p}^{2}}\) from \(p\). Under the distance metric \(\hat{d}(\cdot,\cdot)\), one can define weighted versions of Delaunay and Voronoi diagrams. For a weighted point set \(S\subset\mathbb{R}^{3}\), let \(\operatorname{Vor}S\) and \(\operatorname{Del}S\) denote the weighted Voronoi and Delaunay diagrams of \(S\) respectively. Each diagram is a cell complex where each \(k\)-face is a \(k\)-polytope in \(\operatorname{Vor}S\) and is a \(k\)-simplex in \(\operatorname{Del}S\). Each \(k\)-simplex \(\xi\) in \(\operatorname{Del}S\) is dual to a \((3-k)\)-face \(V_{\xi}\) in \(\operatorname{Vor}S\) and vice versa.  
Let \(S\) be a point set sampled from \(|\mathcal{D}|\). For any sub-collection \(\mathbb{X}\subset\mathcal{D}\) we define \(\operatorname{Del}S|_{\mathbb{X}}\) to be the Delaunay subcomplex restricted to \(\mathbb{X}\), i.e., each simplex \(\xi\in\operatorname{Del}S|_{\mathbb{X}}\), called a restricted simplex, is the dual of a Voronoi face \(V_{\xi}\) where \(V_{\xi}|_{\mathbb{X}}=V_{\xi}\cap|\mathbb{X}|\
eq\emptyset\). By this definition, for any \(\sigma\in\mathcal{D}\), \(\operatorname{Del}S|_{\sigma}\) denotes the Delaunay subcomplex restricted to \(\sigma\), and \(\operatorname{Del}S|_{\mathcal{D}_{i}}=\bigcup_{\sigma\in\mathcal{D}_{i}} \operatorname{Del}S|_{\sigma}\) and \(\operatorname{Del}S|_{\mathcal{D}}=\bigcup_{\sigma\in\mathcal{D}} \operatorname{Del}S|_{\sigma}\). An \(i\)-face \(\sigma\in\mathcal{D}_{i}\) should be meshed with \(i\)-simplices. However, \(\operatorname{Del}S|_{\sigma}\) may have lower dimensional simplices not incident to any restricted \(i\)-simplex. Therefore, we compute special sub-complexes of restricted complexes. Define the following \(i\)-dimensional subcomplexes (see Figure 1):  
\[\operatorname{Skl}^{i}S|_{\sigma}=\{t\,|\,t\in\operatorname{Del}S|_{\sigma}\]  
is an \(i\)-simplex or a sub-simplex of an \(i\)-simplexand  
\[\operatorname{Skl}^{i}S|_{\mathcal{D}_{i}}=\bigcup_{\sigma\in\mathcal{D}_{i} }\operatorname{Skl}^{i}S|_{\sigma}.\]
## 4 Experimental Setup
### Performance Metrics
#### 4.4.2 Spacing (SP)  
The spacing (SP) metric is a widely used measure in MOO to assess the uniformity of the distribution of solutions in the Pareto front. It evaluates how evenly the solutions are spread along the Pareto front. A lower value of SP indicates a more uniformly distributed set of solutions, which is desirable in MOO as it ensures better coverage and diversity [31]. The SP metric is calculated using the following steps and formula:* Calculate the distance: For each solution \(i\) in the obtained Pareto front, compute the distance \(d_{i}\) to its nearest neighbor solution in the objective space. This distance is typically calculated using the Euclidean distance. \[d_{i}=\min_{j\
eq i}\sqrt{\sum_{k=1}^{M}\big{(}f_{k}(x_{i})-f_{k}(x_{j})\big{)}^{ 2}}\] (13)
* \(M\) is the number of objectives,
* \(f_{k}(x_{i})\) is the value of the \(k\)-th objective for solution \(x_{i}\),
* \(f_{k}(x_{j})\) is the value of the \(k\)-th objective for solution \(x_{j}\).
* Compute the average distance: Calculate the average of these minimum distances. \[\overline{d}\,=\,\frac{1}{|A|}\sum_{i=1}^{|A|}d_{i}\] (14) where \(A\) is the Pareto front. \(|A|\) is the number of solutions in the Pareto front.
* Calculate the SP metric: Finally, compute the SP metric as the standard deviation of the distances \(d_{i}\) from their mean \(\overline{d}\). \[SP=\sqrt{\frac{1}{|A|-1}\sum_{i=1}^{|A|}\big{(}d_{i}-\overline{d}\big{)}^{2}}\] (15)  
By using the SP metric, we can quantitatively evaluate and compare the distribution quality of solution sets obtained by different multi-objective optimization algorithms. Lower SP indicates that the solutions are more evenly distributed along the Pareto front, which is desirable. Higher SP indicates uneven distribution of solutions, suggesting gaps or clusters in the Pareto front.
## 5 Stage-Wise Role of SiL and HiL Across Development Phases
### Test Coverage Across Development Stages
#### 5.4.3 Phase III--Post-Deployment (G120)  
In G120, the DT plays a monitoring role, tracking live system performance, detecting anomalies, and enabling predictive maintenance, eliminating the need for redundant physical revalidation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gate** & **Activity** & **Dominant** & **SiL** & **HiL** & **Digital Twin** \\  & **Focus** & **Test Level** & **Role** & **Role** & **Role** \\ \hline G10–G30 & System definition, design & L0 & — & — & Initial requirement mapping \\ G40–G50 & Logic and software validation & L1 & ✓ & — & Simulation support \\ G60 & Traceability and requirements & L1 & ✓ & — & Documentation and linkage \\ G70 & DT calibration & L2 & ✓ & ✓ & Digital modeling foundation \\ G80-G90 & Real-time validation & L2 & ✓ & ✓ & Environment integration \\ G100 & Integration pipeline & L3 & ✓ & ✓ & Scenario orchestration \\ G110–120 & Launch and operation & L4 & — & — & Monitoring and analytics \\ \hline \hline \end{tabular} \(\checkmark\): Method applicable/used at this development gate. \(\dashv\): Method not applicable/not used at this development gate.  
\end{table}
Table 5: Test method distribution across development gates.
## 3 Numerical Results and Discussion  
Following to the developed, unified analytical model of the vibro-acoustic properties of the coupling system between the composite laminated rotationally stiffened plate and the acoustic cavities, numerical discussions and results analyses were conducted in this section to further study its vibro-acoustic coupling characteristics [27]. This part primarily validated the convergence and accuracy of the coupling system model, investigated the factors that influence the natural frequencies of free vibration, and investigated the steady-state response analysis of the coupling system while being affected by the source of point sound and point force. The materials utilized for the laminated plate and laminated beams are listed in Table 2. The acoustic cavities in the coupled system are primarily modeled using air as the acoustic medium, with air density defined as pair = 1.21 kg/m\({}^{3}\). The speed of sound in air is specified as air = 340 m/s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\(\rho\) (kg/m\({}^{3}\))} & \multicolumn{6}{c}{**Material Property Parameter**} \\ \cline{2-7}  & & \(\mathbf{\textit{E}_{1}}\) (Pa) & \(\mathbf{\textit{E}_{2}}\) (Pa) & \(\mathbf{\textit{G}_{23}}\) (Pa) & \(\mathbf{\textit{G}_{12}}\)(Pa) & \(\mathbf{\textit{G}_{13}}\) (Pa) & \(\mathbf{\textit{\mu}_{12}}\) \\ \hline Graphite fiber resin & 1600 & 1.85 \(\times\) 10\({}^{11}\) & 1.09 \(\times\) 10\({}^{10}\) & 7.3 \(\times\) 10\({}^{9}\) & 7.3 \(\times\) 10\({}^{9}\) & 7.3 \(\times\) 10\({}^{9}\) & 0.28 \\ Glass epoxy resin & 1810 & 3.9 \(\times\) 10\({}^{10}\) & 8.4 \(\times\) 10\({}^{9}\) & 4.2 \(\times\) 10\({}^{10}\) & 4.2 \(\times\) 10\({}^{10}\) & 0.26 \\ Q235 steel & 7800 & 2.16 \(\times\) 10\({}^{11}\) & 2.16 \(\times\) 10\({}^{11}\) & 8.31 \(\times\) 10\({}^{10}\) & 8.31 \(\times\) 10\({}^{10}\) & 8.31 \(\times\) 10\({}^{10}\) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The material parameters used in the numerical calculations in this paper.
## 1 Introduction  
Lithium-ion batteries (LIBs) are used for energy storage in various electronic devices, owing to their high energy density and excellent cycling stability [1, 2, 3]. However, the increasing demand for electric vehicles has increased the consumption of lithium [4, 5]. Moreover, only a few lithium reserves are available for mining, and the cost of lithium is increasing because of its uneven distribution around the world. These drawbacks limit the applications of LIBs in future electric devices [6, 7].  
Sodium-ion batteries (SIBs) are considered to be the future energy storage devices because they are inexpensive and their operation is similar to that of LIBs [8]. However, the electrochemical performance of SIBs is insufficient, owing to their low first-cycle Coulombic efficiency and the limitations of the carbon material [9, 10]. This insufficient electrochemical performance is due to the fact that the ionic radius of sodium is larger than that of lithium, which decreases the diffusion rate to the electrode material. Moreover, sodium is thermodynamically unstable when forming compounds with graphite, which results in its negligible insertion into the graphene layer [11, 12]. Thus, the reversible capacity of a sodium-ion cell is determined by the adsorption onto the graphite surface, with ions stored in the void space in the disordered region, rather than being inserted.  
Therefore, the development of suitable electrode materials that offer high energy density, reversibility, and rapid ion insertion/extraction is essential.  
In this study, we aim to improve the electrochemical performance of carbon materials by co-doping heteroatoms, such as S and P, within the carbon matrix. S-doping increases the reversible capacity by widening the distance between the carbon layers, and causes a reversible reaction with sodium ions [13; 14; 15]. P-doping improves the electrochemical performance by inducing defects on the material surface and forming abundant active sites [16; 17]. In addition, a simple plasma-in-liquid process is used for doping the heteroatoms, rather than complex processes. The S and P co-doped carbon material (SP/C) exhibits high initial Coulombic efficiency and excellent reversible capacity with a long cycling life, even though SP/C is a nanomaterial with a large specific surface area.
# Thermal Exchange and Skid Resistance of Chip Seal with Various Aggregate Types and Morphologies
## 3 Results and Discussion
### Properties of Chip Seal  
#### 3.2.1 Thermal Exchange of Chip Seal  
Infrared images, taken every ten minutes, of the chip seal samples during the cooling process are presented in Figure 7, where the temperature scales also averaged from 20 to 200 \({}^{\circ}\)C. The thermal exchange performance began to be supervised by infrared camera after uniformly graded aggregates were spread on the hot asphalt and compacted. At the beginning, the color difference between the cold aggregate and the hot asphalt was extremely significant. As the cooling time increased, the temperature of the aggregate and asphalt tended to be consistent. The chip seal sample made of FER had the fastest cooling rate among the three samples, since its infrared images at 50 and 60 min are basically the same, indicating that the cooling process ended before 50 min, while this phenomenon was not found in the BS and BOF samples.  
Figure 8 presents the cooling curves of different chip seal samples based on the average temperature of the infrared images. It is found that ranking the chip seal samples according to cooling rate places FER slag as the fastest and BOF slag as the slowest, with the BS falling in between. Moreover, the temperature decreased first and then flattened with time, indicating that the cooling rate was getting smaller. The times at which the temperatures of the FER and BS samples stabilized were 45 and 55 min, respectively, as shown in Figure 8, while the time required for the BOF sample was more than 60 min, which indicates that the use of FER can make a chip seal that can resume traffic about ten minutes earlier than the original samples. [PERSON] [25] also found that BOF slag has a negative effect on the cooling of the asphalt mixture, and the cooling rate is 0.97 \({}^{\circ}\)C/min, which is similar to the 0.91 \({}^{\circ}\)C/min measured in this study. This slight difference in cooling rate is because a part of the steel slag in the chip seal was not covered by asphalt, which has a larger contact area with the air compared to the asphalt mixture.  
Figure 7: Infrared images of chip seal samples during cooling process.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality
### Instrument Configuration and Signal Detection  
Figure 2 illustrates the high-level setup schematic of the spaceborne LiDAR instrument which consists of three main subsystems: transmitter, receiver, and control electronics + thermal control. The transmitter includes a laser light source and optical elements (such as mirrors, lenses, or beam expanders) used to shape, collimate, or split the beam. The optical receiver consists of a telescope that collects the backscattered photons from the target, spatial and spectral filters that select the desired wavelengths, and a photodetector. The designs and choice of these elements vary among systems and depend on the application. If the transmitter and receiver share optical elements, the setup is called monostatic; if they have separate paths, it is bistatic. For example, the CALIOP instrument onboard the CALIPSO satellite is a bistatic LiDAR, using two lasers aligned parallel to the receiving telescope [24; 28].  
Figure 2: Schematic diagram of the LIDAR instrument architecture. The system includes a transmitter with a laser and beam expander, a telescope for signal collection, and a receiver equipped with optics, filters, a detector, and data acquisition components. The control electronics and thermal control unit manage system operations and maintain temperature stability.