esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Epileptic seizure detection classification distinguishes between epileptic and non-epileptic signals and is an important step that can aid doctors in diagnosing and treating epileptic seizures. In this paper, we studied the existing epileptic seizure detection methods in terms of challenges and processes developed based on electroencephalograph (EEG) signals. To identify the research deficiencies and provide a feasible solution, we surveyed the existing techniques at each phase, including signal acquisition, pre-processing, feature extraction, and classification. Most previous and current research efforts have used traditional features and decomposing techniques. Therefore, in this paper, we introduced an enhanced and efficient epileptic seizure technique using EEG signals, for which we also developed a mobile application for monitoring the classification of EEG signals. The application triggers notifications to all associated users and sends a visual notification should an EEG signal be classified as epileptic. In this research, we have used publicly available EEG data from the University of Bonn. Our proposed method achieved an average accuracy of 98% by utilizing different machine-learning algorithms for classification, and it has outperformed recently published studies. Though there have been other mobile applications for epileptic seizure detection, they have been based on motion and falling detection, as opposed to ours, which was developed based on EEG classification. Our proposed method will have an impact in the medical field, particularly for epilepsy seizure monitoring as well as in the Human-Computer Interaction fields, majorly in the Brain-Computer Interaction (BCI) applications.  
epilepsy; epileptic seizure; EEG; machine learning algorithms Article  
An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development  
[PERSON] 1, [PERSON] 1,* 2, [PERSON] 1, [PERSON] 2 and [PERSON] 3(c)
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 4 Partial Discharge Measurement Using Different Types of Sensors on the Power Transformer and GIS
### Mechanism in Electric-Electromagnetic Sensors
#### 4.1.2 PD Detection in Transformer  
The UHF PD detection technique is widely employed for routine maintenance and monitoring of high-voltage transformers. When applied to transformers, the UHF PD detection method benefits from low noise levels due to effective shielding. Additionally, the attenuation of signals in oil insulation is minimal, resulting in excellent measurement sensitivity in on-site conditions. In laboratory settings, the PD measurement setup for transformers typically involves a partially enclosed metallic tank measuring 1.0 m \(\times\) 0.5 m \(\times\) 0.5 m. The setup includes a needle sphere PD source and two similar disc sensors. The transient reading record has a bandwidth of approximately 3 GHz, and no additional amplification is necessary as the output quality from the pre-amplifier is satisfactory. The UHF technique has gained popularity for testing power transformers, offering superior sensitivity compared to AE methods [125]. A typical UHF monitoring system setup, shown in Figure 13a, consists of filtered and amplified signals from the sensors, which are then detected and digitized. The digitization process enables dynamic utilization of the signals, and the phase reference and clock information are logged along with the digitized data. The recorded PD pulses are considered to originate from a point source in real time, with the amplitude of the pulse calibrated against the UHF signal energy [62]. The coupling capacitor does this by enabling UHF transmissions to get through while filtering out the low-frequency noise based on Figure 13b. Since the testing tools are corona free, they can be connected to transformers of various power.
## 2 Results and Discussion
### Profiling of Montespertoli EVOO
#### 2.2.1 Chemical, Sensory, and Nutritional Characterization
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**FA (\%\({}_{\text{oleic acid}}\))**} & \multicolumn{2}{c}{**PV (meq\({}_{02}\)/kg)**} & \multicolumn{2}{c}{**K\({}_{232}\)**} & \multicolumn{2}{c}{**K\({}_{279}\)**} & \multicolumn{2}{c}{**\(\Delta\)K**} & \multicolumn{2}{c}{**TTC**} & \multicolumn{2}{c}{**TTC**} \\  & **2023** & **2024** & **2023** & **2024** & **2023** & **2024** & **2023** & **2024** & **2023** & **2024** & **2023** & **2024** & **2023** & **2024** \\ \hline EVOO1 & 0.22 & 0.21 & 4.5 & 9.6 & 1.65 & 1.80 & 0.13 & 0.17 & 0.003 & 0.002 & 3867.7 & 268.8 & 618.8 & 532.8 \\ EVOO2 & 0.21 & 0.23 & 5.1 & 5.9 & 1.62 & 1.68 & 0.13 & 0.14 & 0.003 & 0.002 & 407.8 & 317.1 & 560.9 & 409.8 \\ EVOO3 & 0.27 & 0.18 & 7.2 & 6.5 & 1.70 & 1.73 & 0.14 & 0.14 & 0.004 & 0.002 & 373.0 & 326.8 & 596.3 & 396.1 \\ EVOO4 & 0.27 & 0.17 & 4.6 & 5.0 & 1.76 & 1.67 & 0.14 & 0.15 & 0.004 & 0.002 & 369.4 & 322.5 & 663.6 & 398.1 \\ EVOO5 & 0.24 & 0.21 & 4.6 & 4.8 & 1.62 & 1.80 & 0.12 & 0.21 & 0.003 & 0.001 & 357.6 & 544.9 & 434.2 & 569.7 \\ EVOO6 & 0.27 & 0.28 & 8.3 & 6.7 & 1.80 & 1.77 & 0.12 & 0.15 & 0.004 & 0.003 & 308.7 & 266.9 & 563.5 & 561.5 \\ EVOO7 & 0.22 & 0.23 & 5.6 & 5.3 & 1.73 & 1.68 & 0.15 & 0.14 & 0.003 & 0.002 & 340.9 & 341.6 & 572.9 & 420.3 \\ EVOO8 & 0.21 & 0.24 & 5.1 & 5.7 & 1.77 & 1.72 & 0.14 & 0.13 & 0.005 & 0.002 & 336.1 & 306.1 & 509.4 & 439.0 \\ EVOO9 & 0.20 & 0.17 & 4.8 & 4.8 & 1.71 & 1.65 & 0.15 & 0.15 & 0.005 & 0.002 & 459.7 & 437.9 & 569.7 & 456.8 \\ EVOO10 & 0.23 & 0.17 & 5.5 & 6.3 & 1.74 & 1.66 & 0.14 & 0.13 & 0.004 & 0.002 & 263.0 & 282.7 & 517.2 & 338.7 \\ EVOO11 & 0.23 & 0.20 & 4.4 & 5.6 & 1.67 & 1.75 & 0.13 & 0.15 & 0.004 & 0.002 & 467.6 & 300.9 & 423.1 & 432.3 \\ EVOO12 & 0.20 & 0.14 & 4.2 & 7.6 & 1.58 & 1.78 & 0.12 & 0.16 & 0.003 & 0.002 & 369.3 & 310.8 & 450.7 & 393.8 \\ \hline Minimum & 0.20 & 0.14 & 4.2 & 4.8 & 1.58 & 1.65 & 0.12 & 0.13 & 0.003 & 0.001 & 263.0 & 266.9 & 423.1 & 338.8 \\ \hline Maximum & 0.27 & 0.28 & 8.3 & 9.6 & 1.80 & 1.80 & 0.15 & 0.21 & 0.005 & 0.003 & 467.6 & 544.9 & 663.6 & 569.7 \\ \hline Mean & **0.23** & **0.20** & **5.5** & **6.3** & **1.70** & **1.72** & **0.13** & **0.15** & **0.004** & **0.002** & **369.3** & **345.6** & **540.5** & **447.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical data of the produced EVOOs in 2023 and 2024. TTC, totaltocopherol content; TTC, total phenolic content.
## 2 Preliminaries
### Column Generation  
A classical approach commonly employed for solving many non-trivial optimisation problems, and particularly the CSP, is the CG technique [5; 6]. The general idea is to initially consider only a subset of the cutting patterns of the original problem and iteratively add patterns that have the potential to improve the current value of the objective function. CG algorithms first define the continuous relaxation of model (2)-(4) by removing the integrality constraints on variables \(x\) and heuristically initialize it with a restricted set of patterns \(P\subseteq\{a_{1},a_{2},\ldots,a_{n}\}\) that provides a feasible solution. For the sake of simplicity, in the following we use \(P\) to define both the set of patterns and the set of patterns indices. The resulting optimisation problem, called the restricted master problem (RMP), is as follows:  
min \[\sum_{j\in P}x_{j}\] (5) s.t. \[\sum_{j\in P}a_{ij}x_{j}\geq d_{i} \forall i=1,\ldots,m\] (6) \[x_{j}\geq 0 \forall j\in P\] (7)  
Once model (5)-(7) has been solved, let \(\pi_{i}\) be the dual variable associated with the \(i\)-th constraint (6). The existence of a column \(a_{j}\) that could reduce the objective function value is determined by the reduced costs \(c_{j}=1-\sum_{i=1}^{m}a_{ij}\pi_{i}(j\in P)\). The column with the most negative reduced cost may be determined by solving a bounded knapsack problem in which the profits are given by the dual variables \(\pi_{i}\). Let \(y_{i}\) be the number of times item type \(i\) is used, the pricing subproblem can be written as:  
\[\max \sum_{i=1}^{m}\pi_{i}y_{i}\] (8) s.t. \[\sum_{i=1}^{m}l_{i}y_{i}\leq L \tag{9}\] \[y_{i}\leq d_{i},\quad y_{i}\in\mathbb{Z}^{+} \forall i=1,\ldots,m \tag{10}\]  
If the solution to the pricing subproblem has a value greater than 1 (one), then the corresponding cutting pattern has negative reduced cost and it is added to the RMP. The process is iterated until no column with negative reduced cost is found, thus providing the optimal solution value to the continuous relaxation of model (2)-(4). Usually the solution found at the end of the CG method is fractional and, therefore, an additional effort is required to obtain a feasible integer solution. Rounding heuristics are commonly employed to perform this task, but their efficiency strongly depends on the instances at hand.  
Alternatively, one can embed the column generation lower bound into an enumeration tree, thus obtaining a branch-and-price algorithm [15]. However, degeneracy difficulties and long-tail effects are known to occur as problems become larger. In order to accelerate the convergence to the continuous optimal solution and stabilise the column generation approach, additional dual cuts, and methods to tighten lower and upper bounds on the dual variables were proposed in [20; 21]. In [22], an exact solution approach based on cutting plane generation was introduced. The algorithm computes a lower bound by solving the continuous relaxation of the set covering formulation, and an upper bound by using heuristics. The method was embedded into a branch-and-price algorithm in [23].
# Defects in Silicon Carbide as Quantum Qubits: Recent Advances in Defect Engineering
## 3 Defects in Silicon Carbide
Laser writing utilizes ultrafast femtosecond laser pulses to generate point defects at targeted positions within the SiC lattice, and has demonstrated high spatial precision in defect creation [14]. In contrast to conventional broad-beam ion implantation, FIB implantation allows for the delivery of individual ions to specific locations, enabling precise control over defect placement with minimal collateral damage [17; 18]. Despite its advantages in spatial accuracy, FIB implantation typically requires post-implantation annealing to activate the desired defect states and repair lattice damage.
## 3 Results and Discussion
### Comparison between Steady and Unsteady Results  
Figure 11 represents the centrifugal compressor performance map, including adiabatic efficiency and total pressure ratio for radial gap ratios of 1.08 and 1.14 at \(\rm{M_{u}}=0.93\). These were calculated by steady and unsteady simulations. The same operating points are shown in the figure.  
It can be seen clearly that for the radial gap ratio of 1.08, the mass flow rate resulting from the unsteady simulation is smaller than that from the steady simulation at OPT-1 and OPT-2. The discrepancy decreases to its minimum value at OPT-3. Following this, at near peak efficiency (OPT-4), peak efficiency (OPT-5) and near surge condition (OPT-6), the mass flow rate resulting from the unsteady simulation is larger than that from the steady simulation. Moreover, steady simulations compared to unsteady simulations have a higher adiabatic efficiency of 0.1, 0.24 and 0.21 points at OPT-4, OPT-5 and OPT-6, respectively. The pressure ratio is almost the same for both the steady and unsteady simulation.  
However, at a radial gap ratio of 1.14, the mass flow rate ensuing from the unsteady simulation is larger than that from the steady simulation for all operating points. This difference is about 1.63% and 1.95% at the peak efficiency point (OPT-4) and near surge condition (OPT-5), respectively. Furthermore, the steady simulation has a higher adiabatic efficiency of 0.32 points compared to the unsteady simulation at near peak efficiency condition (OPT-3). This discrepancy reduces to 0.18 at OPT-4 and 0.12 point at OPT-5. The discrepancy in the total pressure ratio is negligible. These differences between the steady and unsteady results are mainly because the wake recovery is accurately reproduced in unsteady simulations [34].  
The results gained by the steady simulation in this are consistent with the unsteady simulation. Therefore, the results are reliable and can predict the compressor performance and flow behavior inside the compressor accurately.  
Figure 11: Compressor performance with steady and unsteady simulation for radial gap ratios of 1.08 and 1.14; \(\rm{M_{u}}=0.93\).
## 4 Results and Discussion
### Impact Mechanism of Urban Spatial Form on Outdoor Environmental Performance
#### 4.2.3 Impact Mechanism of Urban Spatial Form on SHs  
In Model 3, the results of explainable machine learning (Figure 12a) show that the variables influencing SHs include AH, AV, FAR, and SC, while MA, StdH, CVH, PO, OSR, SCD, and other shape indicators have little effect on SHs. The SHAP summaryplot (Figure 12b) reveals that shape indicators that significantly promote UTCI-S are not apparent in the model, while AH, MA, and FAR significantly suppress SHs.  
These results demonstrate the impact of urban spatial form on sunlight hours. Specifically, the higher the average building height, the larger the average building footprint, and the higher the floor-area ratio (FAR), the shorter the sunlight duration. This may be because increasing the building height, footprint, and FAR leads to more buildings on the same block scale, resulting in outdoor areas being in the shadow of buildings, thereby reducing sunlight hours per unit area.
# Review: Facial Anthropometric, Landmark Extraction, and Nasal Reconstruction Technology
## 5 Discussions and Evaluations
Several authors have conducted studies to collect facial data for research in this area, such as 300W [113], CMU MultiPIE [114], AFLW [115], Helen [116], LFPW [15], FRGC [117], XM2 VTSDB [118], BioID [119], PUT [120], AR Purdue [121], MUCT [122]. The Multi-PIE dataset [114] with the number of 755,370 images was also investigated in this work. The number of images in the datasets published by the authors is shown in Figure 3(b). Normalized Mean Error (NME) is an error evaluation method defined by Formula (1). The diagram in Figure 3(a) shows the NME of the process of identifying landmarks automatically on the 300W dataset [113] in all three parts of the dataset, including 300W-Common, 300W-Full, and 300W-Challenging. \[NME=\frac{1}{N}\sum_{n=1}^{N}\frac{\left\|y_{n}-\widetilde{y}_{n}\right\|_{2} }{d_{n}} \tag{1}\]  
where \(N\) is number of sample size; \(y_{n}\) is the ground truth landmark; \(\widetilde{y}_{n}\) is the predicted landmark; \(d_{n}\) is the square-root of the ground truth bounding box. on 3D imaging technology, computer-assisted surgery, and 3D printing. However, they only provide a general review of previous studies not in-depth comments on each technology. In another review, [PERSON] [124] reviewed studies on facial landmark extraction on 2D images and video. The author updated papers on facial landmark recognition in 2018 and the article provides an overview of the proposed methods, however, the article only focuses on evaluating the structures of models in the field of technology and does not refer to studies based on any sustainable medicine theories. A review with a similar purpose was carried out by [PERSON] et al. [125] in 2018. Unlike previous reviews, we reviewed previous studies in both technology and medicine. The three issues evaluated included facial anthropometrics, landmark extraction, and nasal reconstruction technology. Anthropometric measurements of the human face are carried out in studies to collect anthropometric data of people in different regions. Facial landmarks are defined according to sustainable medicine theories. Research on landmark extraction has achieved many achievements in recent years, but their applications in medicine are still limited. The purpose of this review is to give readers an overview of both the technique and medical aspects. The nasal reconstruction technology studies are reviewed in Section 4, which updates new research in the field that can serve as a reference for future research. In this work, we only focus on reviewing the reconstruction techniques without evaluating many of the articles on materials; this is a small limitation. Despite what we have analyzed, research in these fields has been continuing. To our knowledge, applications in medicine are expected to be considered more in future studies, such as automatic anthropometric measurement systems, novel materials to replace damaged organs, 3D printing technologies suitable for different materials, and high resolution.
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
###### Abstract  
This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. This study implemented a systematic experimental framework to test various UAV flight patterns, including circular, surface, and aerial configurations. Under controlled environmental conditions on representative building structures, key variables were manipulated, and all collected data were processed through a consistent SfM pipeline based on the SIFT algorithm. Quantitative evaluation results using various analytical methodologies (multiple regression analysis, Kruskal-Wallis test, random forest feature importance, principal component analysis including K-means clustering, response surface methodology (RSM), preference ranking technique based on similarity to the ideal solution (TOPSIS), and Pareto optimization) revealed that the basic shooting pattern 'type' has a significant and statistically significant influence on all major SfM performance metrics (reprojection error, final point count, computation time, reconstruction completeness; Kruskal-Wallis \(p<0.001\)). Additionally, within the patterns, clear parameter sensitivity and complex nonlinear relationships were identified (e.g., overlapping variables play a decisive role in determining the point count and completeness of surface patterns, with an adjusted R\({}^{2}\)\(\approx\) 0.70; the results of circular patterns are strongly influenced by the interaction between radius and tilt angle on reprojection error and point count, with an adjusted R\({}^{2}\)\(\approx\) 0.80). Furthermore, composite pattern analysis using TOPSIS identified excellent combinations that balanced multiple criteria, and Pareto optimization explicitly quantified the inherent trade-offs between conflicting objectives (e.g., time vs. accuracy, number of points vs. completeness). In conclusion, this study clearly demonstrates that hierarchical strategic approaches are essential for optimizing UAV-SfM data collection. Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches.  
3D reconstruction; UAV; SfM; path planning
## 3 Results and Discussion
### Grain Area Distributions  
To generate the desired micro-structure, a uniform random point generator was employed. The 2D quasi-random generator (Sobol's sequence) was chosen. Extensive tests and comparisons were conducted between the quasi-random generator and a pseudo-random point generator. The simulations consistently demonstrated that the quasi-random generator yielded superior grain morphology compared to the pseudo-random generator. Specifically, the quasi-random generator exhibited a higher proportion of equiaxed grains compared to the pseudo-random generator, as evident in Figure 9. Furthermore, the standard deviation of the mean grain area obtained from the quasi-random generator was consistently lower than that of the pseudo-random generator, as depicted in Figure 10.  
The mean grain area distribution for different micro-structures was evaluated. Additionally, by employing Image) software version 1.530 [40], it was possible to obtain the following:  
* A plot of the grain area vs. the # of grains;
* A plot of the grain area vs. the cumulative # of grains.  
Figures 10 and 11 present the resulting grain area distribution for a case of 500 grains with an average grain area of \(A_{gr}=30.52\times 10^{-6}\) [mm\({}^{2}\)] (\(ASTM\) \(G=12\)[39]).  
Figure 10: A graph of 500 grains: quasi random, pseudo random, and the mean grain area vs. the # of grains.  
Figure 9: Voronoi tessellation (VT) generators. (**a**) Pseudo random generators. (**b**) Quasi random generators (Sobol).
## 4 Inhibition of Alternative \(\sigma\) Factors by Anti-\(\sigma\) Factors
### Anti-\(\sigma\) Factors that Insert between \(\sigma_{2}\) and \(\sigma_{4}\)  
Up to one third of ECF \(\sigma\) factors are controlled by anti-\(\sigma\)s that share an N-terminal anti-sigma domain (ASD) structural motif [61]. The structure of _E. coli_\(\sigma^{E}\) in complex with the N-terminal cytoplasmic domain of its anti-\(\sigma\) RseA revealed that the first three RseA helices (ASD-\(\alpha\)1-3) form a three-helix bundle that is sandwiched between the \(\sigma_{2}\) and \(\sigma_{4}\) domains and occupies a groove in \(\sigma\)4 that occludes the \(\beta\)-flap-tip-helix binding determinant (Figure 3) [62]. The fourth helix, ASD-\(\alpha\)4, interacts with \(\sigma\)R2.2 thereby blocking \(\beta\)' coiled-coil interactions. Despite lacking sequence similarity, ChrR, a two-domain anti-\(\sigma\) factor that controls the _R. sphaeroides_\(\sigma^{E}\)-dependent response to singlet oxygen, has a highly similar ASD three-helix bundle buried between the cognate \(\sigma_{2}\) and \(\sigma_{4}\) domains (albeit with different juxtaposition to \(\sigma^{\text{E}}\)/RseA) with ASD-\(\alpha\)4 also blocking \(\beta\)' coiled-coil interactions [61]. Interestingly, whereas the RseA ASD fold is stabilized by extensive hydrophobic interactions, ChrR uses a structural zinc that is coordinated by a common ZASD (zinc binding anti-sigma domain) motif that was initially recognized in the _S. coelicolor_ redox sensing anti-\(\sigma^{\text{R}}\) factor RsrA [61, 63]. The ZASD motif includes a HisXXXCysXXCys sequence that includes three of the four zinc ligands, with the fourth ligand being either cysteine or histidine.