esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Background Map  
The longer-term observations (months or yearly data) under quiet space weather conditions are selected to build a background map. The global background map is built by the median or mean values for specific physical values routinely produced as Level 4 data products for every five days or one year or different seasons (dependent on the types of physical values). The global background map helps depict large-scale ionospheric structures or the electromagnetic field intensity distributions [31; 32].  
A residual map is built for the specific EQ influential area by the difference values between the background map and the current EQ days (every five days within one month). For example, Figure 6 shows the residual map about the magnetic field in the frequency range from 60 Hz to 70 Hz during the 29 July 2021 and 14 August, M 7.0 Alaska EQs. It is roughly seen from the residual maps that for these two EQs, the PSD values of the magnetic field became enhanced 10 to 5 days before the mainshock (see the map of 20 to 29 July, 4 to 13 August); then, they dropped to a lower level after the mainshock (see the maps of 30 July to 3 August and 14 to 18 August). Admittedly, the residual map always looks disturbed; it is difficult to obtain reliable seismic information most of the time, as shown in Figure 6.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 3 Proposed FRL-SR Algorithm
### Reinforcement Learning Model Setting
In multi-agent reinforcement learning, the learning process of the agent needs the information of the neighboring agents, which includes Q-table, connection status, and available resources. Therefore, we develop the neighborhood discovery part for agents to share information. During the offline training phase, we perform the initial training of the agents in a ground-based network environment. By randomly generating packets, the agents act by observing the state of the packets and the state of the neighboring nodes. To avoid local optimal solutions, we use the \(\epsilon\)-greedy strategy to explore a larger unknown space. Static network training results are not fully suitable for dynamic LEO satellite networks, so online training is required to fine-tune the Q-table. Agents make routing decisions for satellite networks based on the pre-trained model and update the Q-table with the feedback results. It is important to note that the \(\epsilon\)-greedy strategy is not used at this stage, as we only need to fine-tune the Q-table. The advantage of using the pre-trained method is that it saves onboard resources and improves the performance of the initial phase of the algorithm.
# A Comprehensive Review of Micro UAV Charging Techniques
## 8 Potential Challenges in UAV Charging Techniques
### Vulnerability to Weather Conditions  
Harsh weather conditions impose critical challenges for laser-powered UAVs. Weather conditions such as cloud, fog, dust, snow, fog, smog, winds and atmospheric turbulence affect the laser beam divergence, alignment and power at the UAV [128]. Thus, future research studies must investigate weather effects and mitigative solutions for UAV laser charging. As wind perturbation can cause misalignment with ground stations, fluctuation and positioning error in UAVs, future research should focus on design mechanisms which can withstand such harsh weather conditions. As laser-powered UAVs are prone to these atmospheric conditions, here rechargeable and tethered UAVs are regarded as energy efficient solutions. Moreover, weather conditions also impact the tracking system, so strategies for efficient real-time tracking of UAV mobility should be investigated.
## 6 Methodology of Sample Implementation
### Implementation of VR Conferencing
#### 6.1.4 Simple VoIP  
The traditional multiperson VoIP voice conference refers to a server that obtains the voice code stream of each client, synthesizes it, and sends it to each client. In order to  
Figure 7: View of a user.  
realize the sense of direction of each participant's voice, the voice data stream of each participant should not be merged on the server but, instead, separately distributed to each other. Without lowering the voice quality, this method requires more bandwidth than traditional voice conferencing. Moreover, as the number of participants increases, the pressure on network bandwidth grows rapidly. Both the server and the client have to bear the pressure of the network bandwidth, and the demand on server grows faster. The network pressure can be relieved by limiting the number of participants. As the local area network (LAN) environment is in good condition, we simplified the VoIP process into the following sequence: collect audio signals, encode and package them, serialize them, send them to the server, transmit them to other clients, and the client receives a packet, deserializes it, unpack and decode it, and play the audio. We used the User Datagram Protocol (UDP) for network transmission and the Speex open source library [54] for the encoding and decoding.  
Listing 2 is some pseudocode for the network transmission.  
```
1//voipserver
2 sendData(data)
3{
4//sendaudipackagetoeverclient
5 for(inti=0;i<client_count;i++){
6 if(hasClient[i]){
7 mySocket.SendTo(...,Remotes[i]);
8}
9}
10}
11
12//voipclient
13 startSocket()//startudpsocket
14{
15 mySocket=newSocket(UDP);
16 IPEndPointsender=newIPEndPoint();
17 Remote=(EndPoint)sender;
18 intrecv=mySocket.ReceiveFrom(data,Remote);
19 while(true){
20//receiveaudipackagefromserver
21 mySocket.Receive(data,Remote);
22 process(data);//processaudidata
23}
24}
```  
Listing 2: pseudocode of VoIP
## 4 Proposed Model: RBF-Refined SVM Model
### SVM-Based Selection: Model--RBF Kernel  
The trial traces of the traffic were mapped in the feature space for class Y (where Y denotes yes and confirms that the packets arrived at the receiver's end) in a specific area. Correspondingly, the trial traces of the traffic for class N (where N denotes no and assures that the packets did not arrive at the receiver's end) were mapped in a different area. In the RBF kernel, the entire trace of the traffic was later mapped onto the surface of the hypersphere. The cosine values indicated the similarities between the traces. As the cosine value approached 1, the traces became more similar in the feature space. When the cosine value approached 0, the traces became more dissimilar in the feature space. When the RBF kernel function (\(k\)) values approached 1 for samples \(i\) and \(j\), this signified that they were in the same class as those denoted in Equation (1). Similarly, when the kernel values approached 0, this signified that they were in different classes, and \(\sigma\) was approximated to 0, as denoted in Equation (2). Hence, \(\varnothing\) and \(\sigma\) could be adjusted to approach 1, so as to yield better results in our scenario.  
\[K(\{x_{i}\}i=_{1}^{n},\ \{x_{j}\}j=_{1}^{n},\sigma)\ \approx 1 \tag{1}\]  
\[K(\{x_{i}\}i=_{1}^{n},\ \{x_{j}\}j=_{1}^{n},\sigma)\ \approx 0 \tag{2}\]  
The complexity in polynomial kernels is also influenced by the number of hyperparameters, whereas in RBF kernels, this influence is considerably less. Moving forward, RBF kernels were chosen as the ultimate model for our research, due to a better optimality rating through fewer numerical difficulties, perfect accuracy which avoided overfitting, and fewer hyperparameters through the reduction of complexity. Other researchers also justified the use of RBF kernels by their recommendation of datasets that do not have a large number of features. The traffic dataset did not involve a large number of features, and therefore, RBF kernels were used.
## 3 The Optimization and Reconfiguration of UDNs Incorporating VPPs
### Objective Functions
#### 3.1.2 Minimizing Network Losses  
Network losses refer to the inherent losses within the system and are an important indicator of the economic efficiency of the UDN. The expression for network losses is as follows:  
\[\min\theta_{2}=\sum_{ij\in L}^{L}\gamma_{ij}I_{ij}^{2}R_{ij} \tag{24}\]  
where \(\theta_{2}\) is the total network loss; \(L\) is the total number of lines in the UDNs; \(I_{ij}\) and \(R_{ij}\) are the current and resistance of line \(ij\), respectively; \(\gamma_{ij}\) is the state variable of the tie switch, with a value of 1 indicating a closed switch and 0 indicating an open switch.
## 3 Methods
### Identification of Unpowered Areas
#### 3.1.2 Shaded Areas  
The position of the sun and year-round shading created by the terrain surrounding the expressway over time were modeled. The shaded area applied in this study corresponded to the height of the solar roof of an EV traveling on an expressway. Therefore, expressway data were generated by adding the height of the expressway (which varied by section) and that of the EV solar roof (1.7 m) and modeling the shading for the height-corrected expressway sections. To this end, 15 topographic maps (scale 1:5000) covering the Donghae expressway section from the Geospatial Information Platform ([[https://www.vworld.kr/](https://www.vworld.kr/)]([https://www.vworld.kr/](https://www.vworld.kr/)) (accessed on 15 July 2024)) [38] were acquired, and the maps were spatially integrated using the union analysis method in ArcGIS Pro 3.2 software. The topographic maps included contour data within a radius of 1-10 km from the centerline of the expressway. Therefore, they can reflect the surrounding environment, which forms the shaded areas. To understand the terrain undulations around the expressway, contour lines were extracted from the integrated topographic maps. Then, a triangulated irregular network was created and converted into digital elevation model (DEM) data with a spatial resolution of 30 m \(\times\) 30 m (Figure 3a). To accurately model the original shaded area, a digital surface model (DSM) that considers the surrounding terrain and buildings should be employed. However, as aforementioned in the study area section, the expressway in this study area was mostly constructed by cutting through the middle of a mountainous area. Therefore, a DEM, rather than a DSM, was adopted as the input because few buildings are constructed in the surrounding mountains or the altitude is low, which has little effect on expressway shading.  
The shaded areas were modeled using the Hillshade analysis method [39] in the ArcGIS software (Figure 3b) based on the DEM data around the expressway and the hourly position of the sun throughout the year. This algorithm displays the degree of shading by considering the position (altitude and azimuth) and shadow of the sun (light source) from gridded raster data showing terrain undulations. A database was built for the modeling results of the hourly shading area for all sections of the Donghae expressway. Shaded areas appear in the form of black grid cells, as shown in Figure 3c, and if the location of the shaded area and expressway overlap, the solar EV cannot generate power because of sunlight obstruction.  
Figure 3: Process of shadow analysis by time. (**a**) DEM; (**b**) hill-shade analysis at 3 p.m., winter solstice; and (**c**) an example of shaded pixels over the study area at 3 p.m., winter solstice.
## 5 Conclusions  
In this study, a novel ODAE-WPDC model was introduced for the recognition and classification of WS phishing to accomplish cybersecurity. At the primary stage, the proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. This is followed by feature extraction, and the AAA based FS process is utilized. Finally, the IWO with the DAE model is applied for the classification process, where the IWO algorithm assists in attaining maximum outcome. The performance validation of the ODAE-WPDC model is tested utilizing the benchmark Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model over recent DL models. Thus, the presented ODAE-WPDC model can be utilized for security in the digital era. In future, the presented ODAE-WPDC model can be extended to the design of a weighted ensemble voting process.  
Figure 9: \(Accu_{y}\) and \(F_{score}\) analysis of ODAE-WPDC approach with existing methodologies.
# Fault Classification and Localization Scheme for Power Distribution Network
## 2 Wavelet Transforms and Feature Extraction  
Signal processing and tracking form an integral part of the whole protection value chain. The DWT and WPD have emerged as powerful signal-processing tools. These tools have been used numerously in power systems to analyze signals of interest [19]. The DWT and WPD are orthogonal wavelets where a signal is passed through numerous filters. Considering the signal \((S)\), if DWT is employed, the signal is passed through the low- and high-pass filters simultaneously. The output of the high-pass filter (HPF) and low-pass filter (LPF) correspond to the detail and approximation coefficients, respectively, as depicted in Figure 1. The figure indicates that the detail coefficient information is lost at every level of decomposition. For instance, if there are \(l\) levels of decompositions, there would be \((l+1)\) possible ways to decompose the signal.  
However, unlike DWT, in the case of WPD, the signal is passed through multiple filters on both approximation and detail coefficients. Considering the signal \((S)\), if WPD is employed, the signal is passed through the low- and high-pass filters simultaneously, as depicted in Figure 2. Thereafter, the approximation and detail coefficients are further decomposed to level \(k\). The approximation and detail coefficients are further decomposed simultaneously at every level, resulting in more information, and a high-frequency resolu  
Figure 1: Discrete wavelets transform the decomposition tree.  
tion compared to the DWT technique [20]. The low-pass filter \(f(x)\) and the high-pass filter \(h(x)\) are mathematically expressed as:  
\[W_{2n}(x)=\sqrt{2}\sum_{k=0}^{2N-1}f(x)W_{n}(2x-k) \tag{1}\]  
\[W_{2n+1}(x)=\sqrt{2}\sum_{k=0}^{2N-1}g(x)W_{n}(2x-k) \tag{2}\]  
where \(n\) is the number of decomposition coefficients, \(W_{0}=\Phi(x)\) is the scaling factor, and the wavelet function is given by \(W_{1}=\psi(x)\). Feature extraction is a mathematical technique used to transform a large data spectrum into a small data spectrum without losing critical information. In power system applications, feature extraction is mostly used to improve computation processing time. In the present work, we extract statistical features from the fault signal to improve the protection scheme efficiency. The features are extracted at each level of decomposition, and a feature matrix is then developed. The statistical features extracted from the fault signal are defined mathematically as:
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### Fair C4.5 Algorithm  
The authors of [12] introduced FAir-C4.5, an enhanced fairness-aware Decision Tree algorithm that extends the widely used C4.5 model by integrating multiple fairness criteria, including Disparate Impact, discrimination score, consistency, and Disparate Treatment, to mitigate both group and individual discrimination. They propose three fairness-aware attribute selection strategies: (1) the lexicographic strategy, which ranks attributes based on a predefined priority order of fairness metrics; (2) constraint-based strategy, where attributes are selected only if their fairness values fall below a predefined threshold; and (3) the gain ratio with fairness (GRXFR) Strategy, which balances information gain and fairness metrics by multiplying entropy rank with fairness ranks. The algorithm is evaluated on 14 real-world benchmark datasets, including Adult, German Credit, ProPublica Recidivism, NYPD datasets related to racially biased policies, Portuguese Student Performance, Drug Consumption, Ricci, Wine Taste, Bank, Dutch Census, Law School Admission, and UFRGS GPA data. The results demonstrate that FAir-C4.5 significantly reduces discrimination scores across all fairness metrics, outperforming FFTree while maintaining competitive accuracy (0.5-2% lower than the original C4.5). Although FAir-C4.5 incurs higher computational costs than C4.5, it is twice as efficient as FFTree, making it a scalable and effective solution for fairness-aware decision-making.
## 4 Conclusions  
The removal of metal ions and turbidity from synthetic wastewater were investigated using a hybrid ion-exchange fabric/ceramic membrane system. The removal efficiency of As(V) and Zn(II) by the ceramic membrane was increased as the solution pH increased to 8, while the turbidity was completely removed regardless of the solution pH. The predominant reaction mechanisms of As(V) and Zn(II) were precipitation and phase-change, respectively. The removal efficiency of the ion-exchange fabric was affected by the solution Ph, and the maximum removal capacity of As(V) occurred at solution pH 4. Adsorption of As(V) on the ion-exchange fabric quickly reached equilibrium in 120 min, with the adsorption capacity being around one-third of other commercial ion-exchange fibers. Over 80% of As(V) and Zn(II) was steadily removed from the As + Zn + Kaolin solution by the hybrid ion-exchange fabric/ceramic membrane system at solution pH 6. The decreased flow rate and As(V) removal capacity were recovered through the backwashing process during continuous treatment with the hybrid ion-exchange fabric/ceramic membrane system. This compact hybrid system can be used for the treatment of various industrial wastewater sources containing ionic contaminants and turbidity.  
Conceptualization, methodology, supervision, N.-C.C.; software, validation, formal analysis, investigation, visualization, K.-H.C. and M.-S.K.; writing--original draft preparation, writing--review and editing, P.-S.J. and C.-G.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Ministry of Environment (MOE), South Korea, as an Advanced Industrial Technology Development Project (No. 2017000170009).  
The authors declare no conflict of interest.  
Figure 7: Effect of solution pH on the flow rate and Zn and As removal rates during the hybrid ion-exchange fabric/ceramic membrane processing of As + Zn + Kaolin solution: (**a**) pH 4, (**b**) pH 6, (**c**) pH 8, and (**d**) pH 10.
# Research on Control Technology of Large-Section Water-Bearing Broken Surrounding Rock Roadway
## 5 Numerical Simulation Analysis
### Analysis of On-Site Monitoring Effect  
In order to verify the effectiveness of the optimized support scheme on the stability of the surrounding rock, a reliability analysis was conducted by monitoring the displacement of the surrounding rock in the Y1 belt conveyor roadway on-site. This study also accumulates essential data and references for future support schemes in the mine. The displacement monitoring primarily focuses on the two sides, as well as the roof and floor of the roadway. The monitoring method utilizes a cross-point distribution approach. Three monitoring sections were established, designated as Monitoring Section NO. 1, Monitoring Section NO. 2, and Monitoring Section NO. 3. Each section is spaced 20 m apart and contains four monitoring points. The monitoring period lasts for 120 days, with measurements taken approximately every five days.  
The displacement monitoring curve for Monitoring Section NO. 1 is shown in Figure 12. The diagram indicates that the rate of displacement change for the roof, floor, and both sides is relatively high during the initial 25 days. Following this period, the rate of displacement change gradually decreases from 25 days to 90 days, ultimately stabilizing after 90 days. Notably, the displacement of the roof and floor exceeds that of the two sides, with the maximum displacement of the roof and floor reaching approximately 75 mm, while the maximum displacement of the two sides is around 40 mm. This clearly demonstrates that the optimized support scheme is effective and can ensure the long-term stability of the roadway.  
Figure 13 presents the monitoring data for Section NO. 2. The diagram indicates that the displacement of the surrounding rock experiences significant changes during the first 35 days. Following this period, the displacement gradually stabilizes from day 35 to day 90,  
Figure 11: Displacement monitoring curve diagram: (**a**) old support displacement monitoring curve; (**b**) optimized support displacement monitoring curve.  
Figure 12: Monitoring Section NO. 1.  
after which it remains relatively constant. This pattern closely resembles the overall trend observed in the data from Monitoring Section NO. 1.  
Figure 14 presents the monitoring data for Section NO. 3. The diagram indicates that the displacement of the roof and floor experienced significant changes for approximately 35 days. This displacement gradually decreased from 35 days to 80 days and stabilized thereafter. Additionally, the displacement on both sides showed considerable variation for about 25 days, after which it gradually stabilized, ultimately reaching a state of relative stability by 75 days.  
Overall, the data from the three monitoring points show that the deformation of the tunnel perimeter rock is basically stable after 90 days and within the controllable range. It shows that the optimized support scheme has been effectively verified.
# Structural Topology Design Optimization Using the Binary Bat Algorithm
## 5 Numerical Examples
### The Cantilever Plate Problem (Case 1)  
The problem is illustrated in Figure 3, which shows a cantilever plate subjected to a point load. That is, the left boundary is fixed, and a unit point load is applied vertically downward at half the height of the right boundary. The problem was previously solved by [PERSON] and [PERSON] [3] and [PERSON] and [PERSON] [4] using GA and a mesh size of \(24\times 12\) (288 elements).  
Table 1 shows the topology designs obtained by the SIMP method, the BBA without symmetry (5000 design variables), and the BBA with symmetry (2500 design variables), as well as their corresponding compliances. The symmetry is taken around an axis parallel to the \(x\)-axis and passing through 0.5 on the \(y\)-axis. Although the SIMP method with a mesh size of \(100\times 50\) produced distinct boundaries, many gray elements were found due to the smoothing filtering across the edges. The mass density is taken as a continuous design variable rather than a discrete binary variable [3,4]. Using the SIMP method, the minimum compliance of 63.897 is obtained at iteration 184. With a mesh size of \(140\times 70\) (9800 elements), the minimum compliance of 63.2 is obtained at iteration 557. However, the computation time increased about 33 times. This result shows that a mesh size of \(100\times 50\) is resealable for the types of problems discussed in this study. Topology optimization using the present bit-array with the BBA is performed without and with symmetry. In both cases, it can be seen that the distinct black (material)  
Figure 3: Problem definition of the cantilever plate (case 1): design domain, loading, and support.  
and white (void) design topology are obtained because discrete binary design variables are employed. The minimum compliances of 63.249 and 62.162 are obtained from the BAA without symmetry and the BBA with symmetry, respectively. Minimum compliances of 64.44 and 65.26 were found by [PERSON] and [PERSON] [3] and [PERSON] and [PERSON] [4] using GA, respectively. Therefore, better compliances were found using the present bit-array with BBA but with different configurations (see Table 1).