esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 2 WENO-ZQ Scheme and Numerical Tests
### The One-Dimensional Burgers Equation  
The Burgers equation, a fundamental equation in fluid mechanics, is commonly used to evaluate the computational accuracy of numerical schemes for solving flow problems. In this section, the WENO-JS and WENO-ZQ schemes are applied to solve the Burgers equation, allowing for a comparative analysis of their computational accuracy. Previous studies [22] have validated the computational precision of the WENO-ZQ and WENO-JS schemes using the inviscid Burgers equation, demonstrating that both achieve the designed high-order accuracy, with the WENO-ZQ scheme exhibiting a smaller numerical error. Consequently, these two schemes are applied in this section to solve the viscous Burgers equation. The one-dimensional viscous nonlinear Burgers equation is expressed as follows:  
\[\left\{\begin{array}{c}u_{t}+uu_{x}=uu_{xx}\\ u(x,0)=\frac{1}{v}\sin x\\ u(0,t)=u(2\pi,t),t>0\end{array}\right. \tag{16}\]  
For this case, the simulation is conducted to a terminal time of \(t\) = 0.5 with a viscosity coefficient of \(v=0.5\). The time step is set as \(\Delta t=O(\Delta x)\), and time discretization is performed using a third-order explicit TVD-RK scheme. The numerical solution obtained on a fine grid with \(N\) = 2560 is defined as the \"exact solution.\" Numerical errors are evaluated using the \(L^{\infty}\) and \(L^{1}\) norms to assess the accuracy of the schemes. Table 1 presents the numerical errors and convergence orders for both schemes under different grid resolutions. The results indicate that the WENO-ZQ scheme achieves fifth-order accuracy. Moreover, WENO-ZQ exhibits smaller numerical errors compared to WENO-JS on the same mesh.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Schemes** & \(N\) & \(L^{\infty}\)**Error** & \(L^{\infty}\)**Order** & \(L^{1}\)**Error** & \(L^{1}\)**Order** \\ \hline \multirow{4}{*}{WENO-JS} & 20 & \(4.09\times 10^{-3}\) & & \(1.53\times 10^{-4}\) & \\  & 40 & \(1.29\times 10^{-4}\) & 4.9838 & \(2.09\times 10^{-5}\) & 2.8746 \\  & 80 & \(7.05\times 10^{-6}\) & 4.1977 & \(2.80\times 10^{-6}\) & 2.8994 \\  & 160 & \(3.33\times 10^{-7}\) & 4.4042 & \(4.11\times 10^{-7}\) & 2.7698 \\ \hline \multirow{4}{*}{WENO-ZQ} & 20 & \(2.70\times 10^{-3}\) & & \(7.69\times 10^{-5}\) & \\  & 40 & \(1.34\times 10^{-4}\) & 4.3309 & \(1.14\times 10^{-5}\) & 2.7530 \\ \cline{1-1}  & 80 & \(4.04\times 10^{-6}\) & 5.0576 & \(1.65\times 10^{-6}\) & 2.7885 \\ \cline{1-1}  & 160 & \(1.17\times 10^{-7}\) & 5.1094 & \(2.13\times 10^{-7}\) & 2.9535 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational errors and convergence order in the one-dimensional viscous Burgers equation.  
Figure 3: Spectral properties of the 5 th-order accuracy schemes.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Comparisons
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Electric Equipment** & **Models** & **Features** & **Ref.** \\ \hline
1 & Transformers & FRSNPS & Fuzzy production rules in the framework of FRSNPS; & [84] \\  & & & Fuzzy reasoning algorithm based on FRSNPS; Proposition neuron: & \\  & & & _and_ and _or_ rule neuron & \\  & & & IEC ratio of gases are given as input & \\ \hline
2 & Transformers & LSNPs & Learning model in the framework of SNPS with belief & [91] \\  & & & AdaBoost; Use LMS algorithm & \\  & & & for training of the weights in the network; & \\  & & & Identify faults using DGA & \\ \hline
3 & & FDSNPS & FRSNPS with trapezoidal & [89] \\  & & & fuzzy numbers; Effectively & \\  & & & analyze uncertain and incomplete & \\  & & & messages from SCADA; & \\  & & & Proposition neuron; _general_, & \\  & & & _and_, _or_ rule neuron; Reasoning & \\  & & & algorithm based on FRSNPS with trapezoidal fuzzy numbers; & \\ \hline
4 & & Traction & FRSNPS with weights & [88] \\  & & & associated with synapses; Weighted & \\  & & & matrix-based fuzzy reasoning algorithm; & \\  & & & Proposition neuron; _simple_, _general_ & \\  & & systems & _and_, _or_ rule neuron; & \\ \hline
5 & & & MFRSNPS & FRSNPS with modified & [82] \\  & & Metro & threshold condition \(E=\{a^{\theta},\theta>\lambda_{i}\}\); & \\  & & & Reasoning algorithm based on & \\  & & & MFRSNPS; Proposition neuron; _general_, & \\  & & & _and_, _or_ rule neuron; & \\ \hline
6 & & & Combination of extended SNPS; & [130] \\  & & & Capable of searching and output fault & \\  & & & sections automatically; Effectively & \\  & & & identify faults with uncertain and & \\  & & & incomplete messages from SCADA; & \\  & & & Used for FSE problem; & \\  & & & probabilities are associated with & \\  & & & the rules; Guider algorithm associated & \\  & & & with OSNPS & \\ \hline
7 & & & SNPS with spikes and anti-spikes; & [86] \\  & & & Matrix fuzzy reasoning algorithm & \\  & & & based on IASNPS; Effectively & \\  & & & analyze uncertain and incomplete & \\  & & & messages from SCADA; Neurons & \\  & & & are divided into node and region neurons; & \\ \hline
8 & & Small & FRSNPS with real numbers; & [85] \\  & & & Reasoning algorithm based on rFRSNPs; & \\  & & & Proposition neurons; & \\  & & & rule neurons; Rough set theory is & \\  & & & used to reduce the feature of the faults; & \\ \hline
9 & Electric & rFRSNPs & FRSNPS with real numbers; & [87] \\  & & & Proposition neurons; rule neuron; & \\  & & & Fault diagnosis for SS4 locomotive & \\  & & & systems; & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of fault diagnosis models with spiking neural P systems.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Challenges
#### 5.1.1 EMF Exposure from 5G Technologies  
The complexity of 5G network features, such as densification, massive MIMO, and mmWave technology, introduces significant challenges in understanding and managing EMF exposure. The long-term health effects remain largely unknown, requiring extensive research to provide conclusive evidence. Additionally, accurate measurement and assessment are hindered by factors such as proximity to transmission sources, diverse antenna types, and the lack of standardized measurement protocols. Public concern and widespread misinformation regarding 5G-related health risks further complicate efforts to provide clear, science-based information.
## 4 The Role of Dielectrophoresis in the Characterization of Skin Tumor Cells
### Theoretical Background
The differences between malignant and benign skin cells also consist of distinct molecular profiles, as recent reviews suggest [15]. Thus, mutations in _TP53_, _CDKN2A_, _NRAS_, _BRAF_, or _KIT_ genes have already been demonstrated to play a significant role in malignant melanoma, becoming targets for newly developed therapies [88,89]. _CDKN2A_, _TP53_, and _NRAS_ mutations were also detected in SCC [90,91], explaining the effectiveness of novel MM-employed therapeutic approaches also in SCC. Regarding BCC, the _PTCH1_, _GLI1_, _SMO_, and _E2F5_ mutations were detected, with specific inhibitors and antagonists being already available in clinical practice, while other molecules are still under investigation within clinical trials [92]. It is clear, however, that there are still many unknown gene mutations involved in skin cancers, as their potential influence on the dielectric properties of skin cells remains to be demonstrated. Finally, recent research increasingly emphasizes the importance of buffer solutions in DEP measurements, as the liquid medium is highly important for the cell's behavior in the nonuniform electrical current field, as stated in Maxwell-Wagner equation. On the one hand, electrolyte ionic composition is essential for tumor cell trapping. Low conductivity media, containing a lower amount of salts, are preferred, as lower extracellular ionic concentrations induce stronger polarization in the cell's cytoplasm [93]. Low buffer conductivity is helpful in both pDEP and nDEP for cell discrimination, also reducing the possibility of a possible Joule heating effect [94]. On the other hand, extracellular media is directly influencing living cells' viability. Especially in frail skin tumor cells and CTCs, the choice of an appropriate suspension medium is all the more relevant, the loss of cell viability leading to erroneous final results [95]. There is an obvious need to develop better suspense solutions for biological particles, both cell-friendlier and more suitable for DEP.
## 4 Results
### Model Evaluation
#### 4.1.2 Performance Metrics  
To evaluate the model's effectiveness, we computed [27]:  
* Precision, recall, and F1-score: these metrics assess the classification accuracy and misclassification rates for different anomaly types.
* Confusion matrix: this metric helps analyze false positives, false negatives, and overall model performance.  
The model achieved an overall accuracy of 95%, with high precision, recall, and F1-scores across all three classes, indicating a robust classification performance. The classifier correctly identified intrusions with a recall of 100%, ensuring minimal false negatives for intrusion detection.
## 4 Results and Discussion  
### Experiment Repeatability  
Table 3 shows representative data (Tests A and B) that were repeated under certain measurement conditions. After 48 h of the preliminary test, the pressure in both cells  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Scales/Types** & **Error** \\ \hline Humidity (RH) & 3–95\% RH & \(\pm\)2\% FS (full-scale) \\ Temperature (T) & T-type thermocouple & \(\pm\)0.5 \({}^{\circ}\)C \\ Pressure (p) & 0–200 kPa, abs & \(\pm\)0.1\% FS \\ Mass (m) & 0–600 g, d = 0.01 g & \(\pm\)0.1 g \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the sensors used in the experiments.  
increased by ~40 kPa, which was used to measure the amount of nitrogen that permeated through the membrane. In addition, the weight of the absorbent tray increased due to the permeation of water vapor.  
Figure 5 shows the change in pressure within the cells over the measurement period. Since it was assumed that all of the water vapor that permeated the cell was absorbed by the absorbent, the increase in pressure was solely attributed to the permeation of nitrogen. The slope of the graph decreased over time, which is interpreted as a weaker driving force as the absolute pressure difference decreases. Therefore, it was confirmed that there exists a sufficient driving force for the permeation of nitrogen (or air) when testing dense membranes for water vapor permeation, which should be considered in practical applications instead of assuming ideal behavior.
## 6 Conclusions
### Future Work  
(1) SimKGC achieves the best performance on the WN18 RR dataset, mainly due to the design of a high-quality negative sampling strategy. In future work, we will focus on constructing high-quality negative samples to further improve the performance of CAKGC on WN18 RR. (2) Recent studies have shown that MLP architectures perform comparably to attention architectures in the field of image processing, while attention mechanisms have significantly higher time complexity than MLP. To reduce the model's time complexity, future work will explore designing an MLP architecture to replace the attention mechanism in MRAN, with the expectation of achieving comparable or even better performance. (3) The performance improvement of CAKGC on FB15k-237 is not as significant as on WN18 RR, possibly due to its text-based nature, which fails to consider the rich contextual information of entities. In future work, we plan to incorporate graph neural networks to capture the neighborhood information of entities and further enhance the model's performance by effectively integrating the entity's neighborhood information with text descriptions and category information.
# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 2 Background and Related Work
Some examples of algorithms modified for dealing with imbalanced dataset are discussed in existing literature [27; 28; 29; 30]. A large number of ensemble solutions have been proposed in the literature to deal with the imbalanced dataset problem in the classification task [31; 32; 33; 34]. In this approach, bagging and boosting algorithms are combined with resampling techniques or cost-sensitive learning methods. [PERSON] and [PERSON] reviewed state-of-the art data-balancing strategies used prior to the learning phase. The study discussed the strengths and weaknesses of the techniques and also reported intelligent sampling procedures based on their performance, popularity, and ease of implementation [35]. [PERSON] and [PERSON] proposed a data-processing sampling technique named Split Balancing (sBal) for ensemble methods. The proposed method creates multiple balanced bins and then multiple base learners are induced on balanced bins. It was found that the sBal method improves classification performance considerably compared to existing ensemble methods [36]. [PERSON] et al. used SMOTE, NearMiss, cost-sensitive learning, k-Means SMOTE, TextGan, LoRAS, SDS, Clustering-Based Under Sampling, and a VL strategy to balance the imbalanced dataset for automatic detection of online harassment [37]. [PERSON] et al. proposed a novel SVDD boundary-based weighted over sampling approach for dealing with imbalanced and overlapped dataset classification issues [38]. [PERSON] et al. proposed a K-nearest neighbor over sampling approach (KNNOR) for augmentation and to generate synthetic data points for the minority class [39]. Few papers talk about the performance of resampling techniques. The following are some observations based on these papers. The US technique works well when the number of minority class instances is large (in the hundreds). The OS technique works well when the number of minority class instances is small. When the data size is too large, a combination of SMOTE and US techniques works well. The study conducted by [PERSON] et al. [40] compared preprocessing techniques against cost-sensitive learning and it was found that there are no differences among the data-preprocessing techniques. Both preprocessing and cost-sensitive learning found good and equivalent approaches. The study conducted by [PERSON] et al. [4] tested three DBT, namely US, OS, and SMOTE, using four different classifiers. The study found that SVM combined with SMOTE gives better results. The study conducted by [PERSON] et al. [41] found that US leads to improved prediction accuracy. As the focus of this study was to assess the performance of the resampling techniques, we assessed the following most-used resampling techniques: (i) Under Sampling (US); (ii) Over Sampling (OS); (iii) Hybrid Sampling (HS); (iv) Clustering-Based Under Sampling (CBUS); (v) Random Over Sampling Examples (ROSE); (vi) Synthetic Minority Over Sampling technique (SMOTE) and (vii) Clustering-based US (CBUS) technique.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 6 Open Research Issues and Trends in 5G
### Network Scalability  
The channel state information (CSI) has been always demanded improvement. Though the stochastic beamforming scheme has been discussed in the previous literature as a way to minimize CSI acquisition excess, it still requires a more effective algorithm for large-scale networks. Moreover, the uplink compression techniques can be improved to enhance the sum-rate capacity. Heuristic algorithms should also be developed for effective Infrastructure Deployment and Layout Planning (IDLP) on a large scale. Furthermore, heuristic algorithms for time efficiency demand more attention for minimizing the complex challenges of network scalability.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 4 Modulation Technology in VLC System
### Orthogonal Frequency Division Modulation (OFDM) Technique  
OFDM uses multiple subcarriers with orthogonal frequencies to utilize the available bandwidth efficiently. The OFDM diagram transmission and reception is shown in Figure 12 [119]. The serial data stream is converted to a parallel stream and mapped using a modulation scheme, such as PSK or QAM. The data flow is then imposed with Hermite symmetry to ensure that the inverse fast Fourier transform (IFFT) output in the following block is true valued. The use of PSK or QAM is therefore possible when OFDM is employed. If the size of the IFFT is N, N/2 symbols going into the IFFT will represent the data to be transmitted, and the other N/2 symbols will be their conjugates. This ensures that the IFFT  
Figure 11: Constellation diagram of (**a**) BPSK, (**b**) 4-QAM (QPSK) and (**c**) 16-QAM.  
Figure 10: Signal-space constellation of discrete PM: (**a**) binary PSK, (**b**) quadrature binary PSK, (**c**) eight PSK and (**d**) phase of the carrier under modulation with \(\pi\) phase shift of the BPSK at the edge of the pulse period. From: [119], Copyright © 2015 and Imprint. Reproduced by permission of Taylor & Francis Group through PLSclear.  
generates only real values. The analytical expression between the BER and SNR of M-QAM OFDM can be determined by Equation (17) [122]:  
\[BER=\frac{4\sqrt{M}-1}{\sqrt{M}\log_{2}(M)}Q\Bigg{(}\sqrt{\frac{3\log_{2}(M)}{(M -1)}SNR}\Bigg{)}+\frac{4\sqrt{M}-2}{\sqrt{M}\log_{2}(M)}Q\Bigg{(}3\sqrt{\frac{3 \log_{2}(M)}{(M-1)}SNR}\Bigg{)} \tag{17}\]
## 6 Influence of Floods on GPS, GLONASS, and BDS CNR
### Influence of floods on BDS with 1 s Data Set
#### 6.4.1 BDS MEO Satellite  
In order to analyze the influence of floods on the BDS MEO satellite, the data set collected seven days prior to the flood should be used. The comparison results are presented in Figure 14. The blue and red dots denote the original CNR observations on DOY:196 (no flood) and DOY:203 (flood), respectively. It can be seen that the CNR on DOY:203 performs lower than that of on DOY:196, from a low elevation to a high elevation as a whole. This result is consistent with previous experiments, which indicates that the flood can reduce the CNR of the BDS MEO satellite. In addition, to further investigate the extent of the impact,  
Figure 13: CNR of GPS G32 satellite L1 frequency comparison results with elevation as the horizontal axis. The blue dots denote the CNR collected on DOY:200 (no flood), and the red dots denote the CNR collected on DOY:203 (with flood). The green line is the elevation angle.  
the original CNR is fitted by a third-order polynomial fitting method. The green line means the fitted result of DOY:196, and the yellow line denotes the fitted result of DOY:203. From the fitted result, it is evident that the CNR on DOY:203 performs lower than that of on DOY:196. Although the average decrease is only approximately 0.99 dB-Hz, the influence of floods on the CNR is noteworthy in and of itself. In addition, it should be noted that the above phenomenon not only appeared on the B20 satellite, but also appeared on other BDS MEO satellites.
# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 2 Related Work
### Contributions  
AI methods characterizing WL from EEG signals must face several challenges. First, in order to properly be trained and tested, it is mandatory to have data with unambiguous annotations (known as ground truth, GT). The collection of this annotated data is complex because the concept of WL itself is multifaceted and difficult to determine in an objective, systematic manner. Second, for optimal performance of the system, it should properly combine the signals recorded from the different EEG electrodes. Finally, a main issue that a machine learning (ML) system involving humans should consider is its generalization power, including reproducibility of results and the capability of transfer learning--that is, to what extent a general model trained over a set of individuals can successfully predict a new unseen individual performing a different task than the ones used for training the system [35].  
This work contributes to the three challenges as follows:  
1. Unambiguous Annotated Dataset. In order to generate data with unambiguous annotation, we have designed serious games and flight scenarios in an A320 simulator. The serious game was a modified n-back-test [36] with increasing memory demand. The level of difficulty of the test is our GT for training models. Such level of difficulty was cross-checked with the difficulty perceived by the player assessed using NASA TLX questionnaire. Models were trained using n-back-test data recorded from a population that did not include pilots. The task and population transfer of systems were validated in cockpit simulation exercises designed to have different levels of complexity, and unexpected unsaved situations known to substantially drop pilots' performance.
2. Models able to recognize two levels of workload with high generalization capability. Two different architectures are proposed for the fusion of EEG sensor signals (channels) at two different levels [37]: input data (labeled input projector model) and convolutional feature (labeled feature projector model) models. Both architectures consist of an input unit managing fusion at the input level, a convolutional unit, and an output unit for fusion of convolutional features. For each architecture, several classification problems (including an increasing number of WL classes) were trained on n-back-test data using a one-subject-out scheme and tested in binary problem for detection of WL on flight simulations.  
The results show that between the two models, projecting convolutional feature channels achieved higher performance, with 76.25% sensitivity and 87.81% specificity in WL detection in n-back-test leave-one-out subject evaluation, and good task transfer with the detected WL increasing with the number of interruptions.
## 5 Simulation Results
### Result Discussion  
The statistical performance of the ANN, LSVM, CGSVM, and VEL algorithms is presented in Table 1, which also offers insights into the attack detection capabilities of the trained models. Among these, the LSVM algorithm demonstrates the highest sensitivity, indicating its robustness in detecting potential DoS attacks. On the other hand, the VEL algorithm achieves the highest specificity, demonstrating its strong ability to minimize false positive detections. The CGSVM classifier stands out with the highest NPV of 98.86%, showcasing its effectiveness in correctly identifying normal traffic. Additionally, the VEL model reports the lowest false positive rate (FPR) of 1.87%, reflecting its capacity to reduce the incorrect classification of normal traffic as attacks. Moreover, VEL achieves the lowest false negative rate (FNR), highlighting its proficiency in detecting attack traffic without leaving a significant portion undetected. Furthermore, the VEL model attains the highest precision value of 99.64%, confirming its ability to resist being overwhelmed by false alarms.  
The VEL scheme enhances prediction performance by combining the prediction ability of each classifier model. VEL's prediction performance is compared with the existing techniques in the literature in Table 2. VEL exhibits a higher classification accuracy of 99.16%, with a minimal misclassification rate of 0.83%, making it a suitable candidate for the deployment of the intrusion detection system in AVs, especially in a Mode 3  
Figure 14: Confusion matrix of VEL testing.  
Figure 13: Confusion matrix of ANN algorithm testing.  
communication environment. Gossip learning (GL) provides a better approach by retaining the appropriate classifier for IDSs in AV systems [43].