esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 6 Limitations of Existing Works  
However significant the progress in the domains of MFR, FMR, and FU, the current techniques still exhibit notable limitations. These limitations come from the challenges of data availability, model adaptability, and real-world performance, which hinder the practical application of these technologies. Below, we outline some key limitations:  
* Dependence on Synthetic Data: Many state-of-the-art MFR and FU models are trained on synthetic datasets that simulate the presence of masks. While these datasets are essential due to the scarcity of real-world masked face data, they often fail to capture the complex variations in mask types, lighting conditions, facial structures, and occlusions seen in practice. As a result, models trained on synthetic data tend to perform well in controlled environments but show significant performance degradation when applied to real-world settings, where facial occlusions may vary unpredictably.
* Lack of Robustness to Diverse Occlusions: Current FU and MFR methods struggle to generalize across a wide range of occlusions. For instance, while a model may perform reasonably well when dealing with standard medical masks, it may falter when confronted with different types of face coverings (e.g., scarves, transparent masks, or masks with varying shapes and patterns). This limitation restricts the versatility and scalability of existing models, as their performance heavily depends on the types of occlusions present in the training data.
* Weakness in Accurately Reconstructing Fine-Grained Features: Face unmasking models typically focus on restoring critical facial features such as the mouth, nose, and chin. However, accurately reconstructing fine-grained details like lip color, facial hair, skin texture, and irregularities (e.g., scars or spots) remains a challenge. These subtleties are crucial for applications requiring high fidelity, such as identification and authentication systems.
* Computational Complexity: Advanced techniques such as GAN-based (generative adversarial networks) or diffusion-based models often require substantial computational power. While these models excel in generating visually plausible face reconstructions, their resource-intensive nature makes real-time processing difficult, especially on edge devices or mobile platforms. This limits the applicability of these models in environments where computational resources are constrained, such as CCTV systems or on-device recognition systems.
* Generalization to Unseen Data: Transfer learning and domain adaptation techniques have been explored to enhance the generalization capabilities of MFR models. However, the effectiveness of these methods is limited when faced with real-world masked datasets that differ significantly from the training data. In many cases, the models are sensitive to variations in demographics, lighting, and pose, leading to a decreased performance when applied to diverse populations or environments outside of their training conditions.
## 3 Results and Analysis
### Reduction of Power Line Interference
#### 3.1.2 Analysis of EOG Recordings  
The analysis of spectrum and waveforms of the EOG signal for a representative subject acquired from Experiment 2 is shown in Figure 7. The waveform of the signal obtained by applying Method 1 is a series of approximate sine waves with a peak-to-peak value of 0.5 mV, and blink signals can be seen by the signal envelope; the signal obtained by Method 2 is approximately sinusoidal with a reduced peak-to-peak value of 0.1 mV; the peak-to-peak value of the curve of Method 3 is further reduced to 0.06 mV, and the EOG signal becomes more obvious; In Method 4, the obtained EOG signal has almost no sine wave sequence with a fairly clean blink signal. Thus, the EOG signals obtained via the proposed shielded drive methods are better in comparison to those obtained via the unshielded drive approach (Method 1), and the EOG signal of Method 4 is considered to be the best.  
Furthermore, it could be seen from the spectrum analysis results that the 50 Hz frequency of EOG signal acquired by Method 1 is as high as 23.1 dB; the 50 Hz frequency amplitude of Method 2 is 10.3 dB, which is 12.8 dB lower than that of Method 1; Method 3 has a 50 Hz frequency amplitude of 5.7 dB, which is 17.4 dB lower than that of Method 1; Method 4's 50 Hz frequency amplitude of  
Figure 6: The comparison of the time waveforms (**a**) and spectra (**b**) of the ECG signals (subject #11) under different conditions. The 50 Hz notch condition (2 nd row) was obtained by applying 50 Hz notch filter (software) to raw data of the No-Shield condition (1 st row).  
\(-23.4\) dB is 46.5 dB lower than that of Method 1, which is considered very good. Moreover, the 50 Hz harmonics in the EOG spectrum of Method 4, such as 100 Hz and 150 Hz, also disappeared. We can also conclude that using the shielded drive approach could significantly reduce power line interference when recording EOG signal especially with Method 4.  
It can also be seen from Figure 7 that the graph obtained after applying the 50 Hz-Notch filter reflects the waveform as well as spectrum of the raw signal (the output of Method 1) which is characterized by a relatively much lower amplitude. The waveform is similar to that of Method 2, but the spectrum has a large depression around 50 Hz, which eventually led to the loss of useful components of the target signal. Hence, the proposed hardware-based method would be better.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 7 Conclusions  
In this paper, we derive a guaranteed cost strategy based on a Lyapunov-like function and matrix analysis theory, and propose a time delay compensation method for the orbital pursuit-evasion problem based on the uncertainty set. By combining the time delay compensation method with the guaranteed cost strategy, we derive a compensation control strategy.  
The simulation results show the following:  
1. The proposed guaranteed cost strategy is valid in the orbital pursuit-evasion problem with perfect information. By adopting the guaranteed cost strategy, the pursuer can guarantee capture within \(t_{go}^{F}\), and the evader can avoid capture before \(t_{go}^{F}\).
2. The proposed compensation control strategy is applicable to the orbital pursuit-evasion problem with imperfect information. When the pursuer has imperfect information, the compensation control strategy can shorten the relative distance between the players and increase the possibility of intercepting the evader. When the evader has imperfect information, the compensation control strategy can prolong the time that the pursuer takes to intercept the evader.  
Conceptualization, J.Z. and L.Z.; methodology, J.Z., H.L., and J.C.; formal analysis, H.L.; investigation, S.W.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 61633008, 61773132, 61803115).  
Not applicable.  
The data presented in this study are contained in the article itself.  
The authors declare no conflict of interest.
## 2 Hybrid DC Transmission System
### Control Strategies of the Hybrid DC Transmission System  
When the system integrates large-scale wind power, the significant and frequent fluctuations in wind power pose stricter requirements for power system frequency control. Traditional frequency control methods are currently insufficient to meet the demands of safe and stable system operation. While wind-hydro bundling alleviates wind power fluctuations to some extent, the adjustment speed of hydroelectric units cannot promptly follow the changes in wind power output, leading to potential power fluctuations. Therefore, considering the fast controllability of DC power, the stability of the wind power integration system can be improved, fully exploiting the modulation characteristics of DC transmission systems.  
Conventional frequency control primarily relies on governor response to achieve the absorption of wind power imbalance increments, but the capacity for adjustment is quite limited. Therefore, according to the operating conditions and characteristics of the system, when the hybrid LCC-VSC DC system is applied to wind power transmission, in order to ensure the stable operation of the transmission system, the LCC station is set up to use the control strategy of AFC and AGC to coordinate and cooperate so that the AGC unit G1 balances most of the incremental wind power, and the AFC modulates the DC active power in order to balance the small-amplitude and short-period active power disturbance components to assist the primary frequency regulation.  
At the same time, the DC transmission line power modulation is introduced into the AGC to coordinate with the AFC to track the active power fluctuation and frequency deviation of the system automatically, and the AGC unit active output is adjusted to balance the active power disturbance component with a large amplitude and a long period and ultimately to realize the difference-free regulation of the frequency. VSC2 and VSC3 stations use an adaptive DC voltage droop control to adapt to the frequent changes in wind farm currents and to guarantee the stability of the DC voltage of the receiving end system.  
Figure 1: Hybrid LCC-VSC transmission system.
## 3 Theoretical Analysis  
In general, collected electric energy signals always contain noise components. The better these noise components can be filtered, the stronger the harmonics and interharmonics detection performance the subsequent Prony algorithm can deliver. The traditional SVD denoising method, when used for signal denoising, often suffers from poor adaptability to signals with different noise intensities, which may lead to insufficient or excessive filtering of noise components, damaging useful signals and resulting in poor denoising effect. Therefore, this paper proposes an improved method to enhance the denoising ability of SVD and overcome the drawbacks of traditional SVD denoising.
## 2 Materials and Methods
### Characterization of Essential Oils
#### 2.3.1 Chemical Composition  
The essential oil was analyzed by GC/MS on a 7890B gas chromatograph coupled to a 5977A mass detector (Agilent, Santa Clara, CA, USA), equipped with a HP-5 ms capillary column (5% phenylmethylsiloxane, 30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m). The operating conditions were as follows: carrier gas, helium at a flow rate of 1.2 mL/min; the temperature program 2 min at 45 \({}^{\circ}\)C, then increasing to 250 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, finally held at 250 \({}^{\circ}\)C for 2 min; injector temperature 240 \({}^{\circ}\)C; injection volume 1 \(\upmu\)L; split ratio, 1:100. Mass spectra were recorded at 70 eV and were scanned in the range 40-400 m/z. The individual components of the essential oil were identified by comparing their retention indices with those of the literature. The retention indices were determined in relation to a homologous series of n-alkanes (C9-C23) under the same operating conditions. Further identification of the essential oil components was carried out by comparing their mass spectra either with the spectra stored in spectral data libraries or with mass spectra from the literature. The relative amounts of the essential oil components were determined by normalizing the peak area [18].
## 2 Methodology
### System Modeling  
#### 2.3.1 TRNSYS Model  
Natural ventilation heat exchange through an active interior window is dependent on the pressure difference between the attached sunspace and indoor and the effective opening area of windows. This model only considers the buoyancy-driven ventilation [20]. It arises due to density differences of the indoor and attached sunspace's air. The density of warmer air is lower than the density of colder air and tends to rise above the cold air [21]. The maximum potential air change (ACRmax.pot) is defined by Equation (1).  
\[ACR_{max.pot}=\eta\frac{A_{top}}{V}9.81H\sqrt{\left(\frac{\left|T_{air.room}- T_{air.sunspace}\right|}{T_{air.sunspace}+273.15}\right)\left(\frac{{A_{top}}^{2}+{A_{ bot}}^{2}}{{A_{bot}}^{2}}\right)} \tag{1}\]  
where \(\eta\) denotes the stack effect efficiency; \(A_{top}\) denotes the upper opening area, m\({}^{2}\); \(A_{bot}\) denotes the lower opening area, m\({}^{2}\); H denotes height between the openings, m; \(T_{air.room}\) denotes the room air temperature, \({}^{\circ}\)C; \(T_{air.sunspace}\) denotes the sunspace air temperature, \({}^{\circ}\)C.  
TRNSYS (transient system simulation program) is a program that simulates energy systems (particularly building energy systems) and other dynamic systems. It is now widely utilized by engineers all over the world in low-energy buildings, HVAC system design optimization, renewable energy (such as wind energy and solar hydrogen) system design  
Figure 4: Layout of measuring points in the living lab: (**a**) Layout of measuring points in the attached sunspace; (**b**) Layout of measuring points of the interior.  
optimization and other applications. It is particularly useful for solar system simulation research. Therefore, TRNSYS software is used to simulate the natural ventilation heat exchange model [22].  
The natural ventilation heat exchange model in TRNSYS is primarily made up of three modules: the building module, natural ventilation control module and weather reading module, as shown in Figure 5. Through TRNBuild, the building module is modeled based on the current circumstances, the natural ventilation module is used to mimic natural ventilation in the attached sunspace and inside the building, and the weather module is used to read the current weather parameters [23]. Considering that the weather changes usually take an hour to change significantly, time step sets as one hour in the simulation process [8].
## 3 Data Description and Methods
### Proposed Model
These two datasets are horizontally concatenated (hstack), resulting in a unified 2D tensor of the form (batch size, sequence length + LSTM units). This integration merges the temporal patterns learned from the S&P 500 data and the inherent characteristics of the VIX data into a single feature representation. The concatenated input enables the second LSTM layer to effectively model interactions between market trends and volatility patterns, enhancing the overall prediction capability of the model. By combining the temporal patterns learned from the S&P 500 data and the emphasized features from the VIX data, the second LSTM layer generates a refined output that predicts VIX volatility with greater stability and precision. This cascaded approach ensures that the model effectively captures both market-wide trends and volatility-specific patterns. The proposed model integrates the strengths of VMD for multi-scale feature decomposition, the hierarchical learning of Cascaded LSTM, and the feature enhancement capabilities of Multi-Head Attention. Together, these components form a robust framework designed to provide reliable and accurate predictions of VIX volatility.
## 5 Optimization Method
### Price-and-Branch Algorithm  
The PBA method consists of the following steps, shown in the flowchart in Figure 3:  
1. Generating initial solution: This step generates an initial solution necessary to initialize the set of paths in the column-generation procedure in Step (2). The solution is found using a greedy heuristic algorithm presented in Section 5.2.
2. Generating paths: The goal of this step is to find a subset of the complete set of candidate paths \(\mathcal{P}\) that provides a good-quality solution to the problem and, at the same, decreases the computational effort of solving it. For this purpose, column generation (CG) is applied, which is an optimization technique allowing for a reduction of the number of variables (referred to as columns) in linear programming formulations [18; 19]. The CG procedure dedicated to problem (2)-(24) is described in detail in Section 5.3.
3. Solving MILP: In this last step, the set of candidate paths resulting from Step (2) is applied in MILP model (2)-(24), and the model is solved using a branch-and-bound solver (e.g., such as CPLEX v.12.9 [56]).  
The main processing steps of the optimization method are described in detail below.
## 3 Case Study
### The Practice of Wetland Elevation Monitoring and PPGIS
The PPGIS served as an important communication platform or medium that allowed the community residents with their lay knowledge to assist in the design, amendment, and execution of the survey and measurement; to improve the contribution and value of the lay knowledge, and to strengthen the legitimacy of the participation of community residents in the decision-making for wildlife refuge management. Further, the monitoring results presented by PPGIS revealed the spatial distribution of elevation and silt, which promoted the participation and broadened and deepened the discussions of participants at the platform meetings. Also, based on materials and messages displayedby PPGIS of different spatial scales, the stakeholders were able to understand the potential relationship between substratum elevation, tidal currents, and other hydrological factors, which contributed to developing the follow-up tidal current investigation. These monitoring results by PPGIS were able to assist the stakeholders (participants) in amending the management objectives and scope of this refuge and in dividing responsibilities according to their respective levels of authority in the platform meeting. Finally, all related monitoring data and results of discussion and consultation were incorporated into the conservation plan, i.e., the official management plan of the wildlife refuge, thus becoming formal management items. Furthermore, we seek to systematically describe how PPGIS assists community monitoring and helps stakeholders in performing better communication and decision-making in refuge operation and management. To achieve this, the effects of PPGIS on improving the processes of problem identification, monitoring method design, monitoring implementation, data analysis, and decision-making are represented by Figure 4 in this study. Among the different processes mentioned previously, PPGIS can help the stakeholders to better identify the changes in the wetland with 3-D environment visualization as well as to better design the monitoring method with lay knowledge. By contrast, the results collected from community monitoring can be used by PPGIS to help spatial interpolation, overlapping, and transparent layers. These functions allow the stakeholders to assess the results and information with greater flexibility. Meanwhile, the information extracted from the profile analysis assists in depicting the elevation profile at the bottom of the wetland. This profile allows the stakeholders to explore the causes of environmental change through simulation views and thereby to redetermine their management targets. Furthermore, we hope that the research framework presented in this study can facilitate further discussion on how the application of PPGIS can influence the participation and governance quality of the stakeholders associated with the PA.  
Figure 3: Elevation monitoring results of the wetland.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
###### Abstract  
Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky-Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment.  
elecroencephalogram; epilepsy; seizure detection; signal processing; machine learning Article
## 3 Methodology
### Preprocessing  
In the data preprocessing stage of this study, rigorous checks and transformations were performed to ensure the quality and suitability of the combined dataset. Firstly, a comprehensive search for missing values was conducted, revealing that the dataset was complete with no instances of missing data. This indicated the reliability and integrity of the dataset. Secondly, as the dataset was derived from a combination of three separate datasets, a thorough examination of duplicate values was carried out to ensure data consistency. This analysis confirmed the absence of any duplicates, reinforcing the accuracy of the dataset. Additionally, an outlier analysis was conducted to identify any extreme values that could potentially skew the analysis. Remarkably, no outliers were detected, underscoring the robustness of the dataset. To promote uniformity and facilitate meaningful comparisons among features, the data were scaled to a standardized range of 0 to 1. This scaling process enhanced the interpretability and analysis of the data. These thorough data preprocessing steps established a solid foundation for the reliable and high-quality analyses in this study.