esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Output** & **Voltage** **(rms, V)** & **Current** **(rms, A)** & **Power** **(rms, W)** \\ \hline w/o TENG Amp. & 0.7794 & 6.753 \(\times\) 10\({}^{-5}\) & 5.26 \(\times\) 10\({}^{-5}\) \\ w/ TENG Amp. & 0.9654 & 8.41 \(\times\) 10\({}^{-5}\) & 8.12 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of rms values of TENG output performance with and without the amplifier. Figure 4: Measurement of output of the TENG specimen using the pushing tester. the output of most TENGs can be amplified. The conducive sponge TENG specimen in this study had a low output (<50 V) that could be amplified due to the occurrence of plasma. Figure 5: Comparison of outputs with and without the amplifier in a TENG specimen: (**a**) graphs of the current and voltage outputs of the PDMS\(-\)based TENG specimen; (**b**) graphs of the voltage peak, current peak, and power peak of the PDMS\(-\)based TENG specimen according to load resistance; (**c**) graphs of the current and voltage outputs of the conductive sponge TENG specimen; (**d**) graphs of the voltage peak, current peak, and power peak of the conductive sponge TENG specimen depending on load resistance. Multiple LEDs were used to visualize the effect of the TENG output amplifier. A rectifier was applied to the output terminal, which was connected to LEDs. The LEDs were connected in series. A pushing tester generated kinetic energies by applying the same force. As illustrated in Figure 6c-e, the conductive sponge TENG specimen without the amplifier could drive approximately 20 LEDs, while the one with the amplifier could drive approximately 60 LEDs.
# Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method
## 4 Simulation Example
This figure provides a visual representation of the mode switching dynamics, showing how the system transitions between different states throughout the simulation. Figure 2 and Figure 3 illustrate the actual and estimated state trajectories \(\times 1\) and \(\times 2\) under different system modes. The close correspondence between the actual states (solid lines) and their estimates (dashed lines) validates the effectiveness of the proposed observer in accurately tracking system dynamics. Notably, the estimation of sensor faults is not explicitly visualized as a separate plot. Instead, its effects are inherently embedded within the state estimation process. Since sensor faults directly alter the system's measured outputs, they subsequently affect the estimated state trajectories. This influence manifests as deviations between the actual and estimated states, particularly during fault occurrences. Figures 4 and 5 present the estimation results of actuator faults \(fa_{1}(g)\) and \(fa_{2}(g)\), respectively. The comparison between the actual fault signals (solid lines) and their estimates (dashed lines) demonstrates the accuracy of the proposed observer. It is noteworthy that transient negative values appear at the initial occurrence of faults. This phenomenon arose due to the observer's adaptive response, where estimation oscillations were observed before stabilizing. Such behavior was particularly evident in the actuator fault estimation, as the observer dynamically adjusted to minimize estimation errors and improve convergence. These results confirm that the proposed method effectively captured actuator faults while ensuring robust fault estimation performance. Figure 1: System mode switching in the s-MJNNS. Figure 4: Actuator fault \(fa_{1}(g)\) and its estimation \(\hat{fa}_{1}(g)\). Figure 6 illustrates the estimated output signal \(\overrightarrow{z}(g)\), demonstrating the effectiveness of the observer in accurately tracking the system's measured output under different modes. When faults occur, transient deviations are observed due to the system's response to faults. These fluctuations gradually diminish as the observer adjusts to the new conditions, ensuring accurate and stable output estimation. The results demonstrate that the proposed observer can accurately estimate the system's output even in the presence of faults, providing reliable fault estimation for subsequent fault-tolerant control. Based on these results, it can be concluded that the proposed state and fault estimation algorithm is effectively applicable to discrete-time s-MJNNs with time-varying delays and demonstrates accurate fault estimation capabilities for the system.
## 4 Empirical Analysis
### The Analysis of the AIA-NRM Approach  
The analysis process of AIA-NRM includes two stages. The first stage is the AIA approach and the second stage is the analysis of the NRM approach. The AIA approach determines the acquisition and importance level of aspects/criteria for digital transformation; the AIA (acquisition-importance analysis) approach can help decision makers identify aspects that should be developed when the standard importance level is more than the average importance level. There are four development strategies presented in Table 11. Development strategy B (immediate improvement) can apply to the aspects of PC and RS (regulatory system), development strategy C (maintain status) can apply to the aspects of CR (critical resources) and ME, and development strategy D (status monitoring) can apply to the OM (operation management) aspect.  
The AIA-NRM approach determines the criteria that should be developed based on the AIA approach and the development path using the NRM (network relation map). We can determine that the aspects of PC and RS should be developed, and the aspects of RS are the aspect that is the primary aspect with net influence. So, we can develop the PC aspect by addressing the aspect of RS. The ME aspect is the primary aspect being influenced; therefore, the ME aspect can be influenced by the aspects of RS, CR, OM, and PC, as shown in Table 11 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Aspects** & **PC** & **OM** & **CR** & **RS** & **ME** \\ \hline Professional competence (PC) & - & & & & \\ Operation management (OM) & 0.071 & - & & & \\ Critical resources (CR) & 0.300 & 0.224 & - & & \\ Regulatory system (RS) & 0.283 & 0.225 & 0.006 & - & \\ Market expansion (ME) & \(-\)0.247 & \(-\)0.295 & \(-\)0.517 & \(-\)0.521 & - \\ \hline \hline \end{tabular}
\end{table}
Table 10: The net influence matrix.  
Figure 4: The network relation map (NRM) of digital transformation for Med-Tech industry.
## 5 Analysis of Influence Factors
### Influence of Anchor Cable Parameters
#### 5.3.1 Influence of Prestress  
As can be seen from Figure 25, when the prestress increases from 0 to 250 kN, the maximum horizontal displacement of the pile decreases from 31.87 mm to 13.53 mm, with  
Figure 23: Influence curves of different pile diameters: (**a**) Horizontal displacement of piles, (**b**) Ground settlement, (**c**) Axial force.  
Figure 24: Influence curve of different pile spacing: (**a**) Horizontal displacement of piles, (**b**) Ground settlement, (**c**) Axial force.  
a decrease of 57.5%. The ground settlement decreases from 16.61 mm to 8.72 mm, with a decrease of 47.5%. It shows that increasing the prestress value can significantly decrease the horizontal displacement of the pile and control the ground settlement.  
When the prestress is 0 and 50 kN, the maximum horizontal displacement exceeds the maximum value allowed. As the prestress increases from 150 to 250 kN, the horizontal displacement curve of the pile and the settlement curve fit tightly. It indicates that the effect of increasing prestressing force is limited in reducing the pit deformation. This is uneconomical if the prestressing force keeps increasing and it may cause the displacement of the supporting structure to the back of the pit, which may have a bad effect on the surrounding environment of the pit. Therefore, the reasonable range of prestressing is 100-150 kN.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Algorithm Comparison  
The model proposed in this study aims to minimize the total cost of the vehicle fixed cost, transport costs, and carbon emission costs, and integrates the effects of time window constraints and vehicle load constraints on the total distribution cost. For the solution algorithms for this class of problems, studies have shown that the Ant Colony Algorithm (ACO) exhibits strong robustness and effectiveness. Therefore, in order to assess the performance difference between the algorithm designed in this study (INNC-GA) and the ACO algorithm in solving this type of problem, combined with the solution results of 5.3 for the model considering carbon emission cost, the ACO algorithm is used to simulate the six example sets of C101, C201, R106, R206, RC108, and RC208, respectively, with the specific  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Number of Vehicles** & **Customer Points** &
\begin{tabular}{} \end{tabular} \\ \hline
1 & 10 & 0-22-24-33-31-35-37-38-9-11-10-0 \\
2 & 13 & 0-5-2-1-7-3-4-40-44-46-45-50-43-8-0 \\
3 & 9 & 0-6-32-49-47-42-41-48-0 \\
4 & 11 & 0-20-30-29-34-28-26-23-18-19-15-25-0 \\
5 & 7 & 0-27-39-36-16-14-12-17-13-21-0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Considering the cost of carbon emissions cost.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Number of Vehicles** & **Customer Points** &
\begin{tabular}{} \end{tabular} \\ \hline
1 & 10 & 0-50-45-44-40-49-46-41-42-43-48-0 \\
2 & 13 & 0-6-31-34-35-39-38-37-36-29-27-22-21-8-0 \\
3 & 7 & 0-32-33-28-26-23-30-24-0 \\
4 & 11 & 0-25-18-19-16-14-12-15-17-13-9-20-0 \\
5 & 9 & 0-47-4-3-7-1-2-10-11-5-0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Without considering the cost of carbon emissions cost.  
comparison graphs shown in Figure 6. It can be seen that the INNC-GA algorithm designed in this study outperforms the ant colony algorithm in terms of transport cost, carbon emission cost, and total cost when satisfying the time window constraints and vehicle loading constraints. This indicates that the INNC-GA algorithm designed in this study has significant advantages in solving the vehicle path optimization problem with time window constraints, which can help logistics enterprises to reduce the cost expenditure, provide more environmentally friendly operations, and create greater economic and environmental benefits for the enterprises.  
Figure 6: Comparison of results. (**a**) Transport cost; (**b**) Carbon emission cost; (**c**) Total cost.
## 2 Materials and Methods
### CO\({}_{2}\) Removal  
The CO\({}_{2}\) removal set-up requires solution preparation and the following solutions were prepared [19]. A solution of 1.2 L NaOH (3 M) was prepared by mixing 144 g of NaOH in 1.2 L distilled water. pH indicator thymolphthalein solution (0.4%) was prepared by mixing 40 mg of thymolphthalein in 9 mL of ethanol (99.5%) and 1 mL distilled water. A total of 6 mL of the thymolphthalein solution was added into 1.2 L NaOH solution. The resulting mixture was transferred to CO\({}_{2}\) removal bottles, one for each batch test and each with an 80 mL mixture. The thymolphthalein-NaOH mixture has a bright blue color and when enough CO\({}_{2}\) is absorbed, the blue color fades and becomes colorless. Thymolphthalein is bright blue in basic solutions but it turns colorless in acidic or neutral solutions. At this point, a new mixture has to be used. According to the CO\({}_{2}\) removal manual, this method absorbs more than 98% of CO\({}_{2}\) produced during the biogas production process. The removal is based on the following reaction [20]:  
\[\text{CO}_{2\ (\text{g})}\rightarrow\text{CO}_{2\ (\text{aq})} \tag{4}\]  
\[\text{CO}_{2\ (\text{aq})}+\text{OH}^{-}_{(\text{aq})}\rightarrow\text{ HCO}_{3\ (\text{aq})}^{-} \tag{5}\]  
\[\text{HCO}_{3\ (\text{aq})}^{-}+\text{OH}^{-}_{(\text{aq})}\rightarrow\text{ CO}_{3\ (\text{aq})}^{2-}+\text{ H}_{2}\text{O}_{(\text{I})} \tag{6}\]
## 4 Discussion
### Uncertainty Analysis  
The errors and uncertainly of the input data, and the method based on multi-scale remote sensing data would have some influence on the accuracy of the result in detecting forest degradation. Although MODIS VI, MODIS LAI/FPAR and MODIS NPP products have high accuracy [27; 32; 45], errors would certainly have some impact on the forest degradation model. Besides, results of this paper were cross validated by Landsat data. The scale difference between MODIS images (500 m) and Landsat images (30 m) would also bring some uncertainly when validating the degradation forest. Moreover, most MODIS 500 m pixels are mixed pixels, some other land-cover types existed in a mixed MODIS pixel. Land-cover changes from forest to a high vegetation density types (such as cropland and grassland), could not be detected using the coarse MODIS products [1]. Therefore, the changes of the land-cover could lead to some uncertainly when using MODIS products to describe the forest degradation. Finally, MODIS products were used to locate the forest degradation pixels, then Landsat ETM+ data were used to analyze the details in the degraded areas from MODIS datasets in this paper, leading to that some degraded areas outside the MODIS pixel were ignored, which could bring some omission of the degraded forest.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.20. Multi-Access Edge Computing (MEC)  
The potential to deploy cloud computing capabilities within Radio Access Network closer to 5G end users can increase the performance of 5G applications and minimize network congestion. Mobile users can utilize MEC to access cloud computing and IT services at the nearest position, such as the edge of the base station [76]. This enables it to precisely meet the criteria of the 5G standard for low latency and high bandwidth.  
MEC further enables 5G networks to properly evaluate data collected from several sources and offer locally relevant information for URLLC use cases, such as smart driving services [114]. This capability will allow 5G applications to embrace improved services that need very low latency and large bandwidth effortlessly. These programs will have rapid, real-time access to radio network data. High bandwidth and extremely low latency computers are required for current automobile applications and services. Because MEC extends cloud infrastructure to where people and things connect to the network, additional processing and storage equipment is required, which significantly influences base station design and manufacturing. Core networks can be made less crowded from their perspective by providing numerous services and storing data at the network edge [64].  
MEC acts as a barrier between the end user and the cloud server, allowing the user to move closer to the cloud. This edge now offers all services, including virtual software, video conferencing, and other services, thereby boosting the efficiency of cloud computing. In addition, because MEC can be viewed remotely, it reduces the requirement for the user to download the complete program on his device, thereby improving device performance. [PERSON] et al. [115] offered a taxonomy for mobile edge computing applications and evaluate technological potential and restrictions. There is a list and explanation of the many application categories that benefit from edge deployment. These applications were carefully classified on the basis of technical characteristics. The work established that effective MEC deployment in 5G networks raises the overall performance of the 5G architecture.  
The 5G network and the concept of edge computing in MEC allow real-time, on-demand delivery of IoT applications and services, such as telemedicine, autonomous vehicles, industrial automation, and data processing from IoT sensors [116]. Because of thedense cell location, inter-cell interference is conceivable. For example, interference from surrounding cells and the macro-cell may affect the micro-cell. A 3 GPP project answer to these challenges is an inter-cell collaboration [115; 117].
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Feature Extraction Techniques  
Once the malware analysis method is chosen, it is important to identify the most appropriate technique for extracting the features that the malware analysis produces. These features are then used in a model with the objective of identifying the nature of the file. The following techniques are commonly used for feature extraction [34].  
* N-gram: This technique extracts consecutive sequences of n elements from a file or data stream, grouping them by n-values (e.g., 2-g, 4-g) For example, if the system outputs the following result P = (1, 2, 3, 4, 5), then 2-g and 4-g will be ([1, 2], [2, 3], [3, 4], [4, 5]) and ([1, 2, 3, 4], [2, 3, 4, 5]) respectively. This technique can be applied to both static and dynamic analyses. Despite being widely used in malware detection, the n-gram technique has the limitation that the attributes, whether static or not, may not have a relational sequence, making classification and grouping more challenging [41].
* Graph-based Technique: Uses graph structures to represent interactions between file components. For instance, Control Flow Graph (CFG) analysis is a specific graph-based approach used for detecting malware behavior, particularly in environments like IoT [32]. Subgraphs can be created for larger datasets, aiding analysis.
* Vision-based Techniques: Feature extraction based on images in malware analysis involves converting the malware or binarized file into a visual representation. In this approach, the binarized malware is grouped into 8-bit sets and represented in a two-dimensional matrix (2D). This technique can be used in both static and dynamic analyses [42]. The main advantage of image-based visualization in malware analysis is the ability to extract information from the file, such as operation code (opcode), byte sequence, API calls, and system calls.
* Hashing Technique: A feature extraction method for malware identification and analysis. It generates a unique hash value (e.g., MD5, SHA-1, SHA-256) from binary data, providing a quick identifier for malware detection and classification [43].  
Figure 5 summarizes the main feature extraction techniques identified. These include approaches such as n-grams, graphs, visual representations, and hash indicators, which are essential for transforming malware data into useful attributes for detection and classification. This representation allows us to visualize the methodological diversity and its relevance depending on the type of analysis.
## 2 Decomposition Mechanism of SF\({}_{6}\) and Influencing Factors
To ensure normal operation, the moisture content of the buffer gas in the equipment shall be controlled below 800 ppmv. [PERSON] et al. (2018) reported that SF\({}_{6}\) decomposition characteristics under partial discharge (PD) are not only associated with insulation fault type and its severity but also closely related to the H\({}_{2}\)O content in the equipment. Higher humidity caused more favorable generation of SO\({}_{2}\) and HF (hydrogen fluoride), while H\({}_{2}\)O had no significant effect on CO\({}_{2}\), but the water depleted intermediate by-products (CF\({}_{2}\) and CF\({}_{3}\)) and inhibited CF\({}_{4}\). These results show that the O\({}_{2}\) and water content have some effects on the decomposition products of SF\({}_{6}\) and the internal insulation state of the equipment under high pressure. The degree of insulation damage and the development trend of the insulation situation affect decomposition composition, content, and change in the SF\({}_{6}\) gas. Previous experimental studies demonstrated that the ratio of SO\({}_{2}\), SOF\({}_{2}\), and SO\({}_{2}\)F\({}_{2}\) is advantageous for determining the degree of discharge in electrical equipment. Under higher-intensity discharge conditions, the volumetric fraction ratio of SOF\({}_{2}\)/SO\({}_{2}\)F\({}_{2}\) and SO\({}_{2}\) content increases more rapidly ([PERSON] et al., 2018). The discharge decomposition of SF\({}_{6}\) involves not only complex physical changes but also complex chemical processes. Defect type, solid insulation material, water content, micro-oxygen original content, discharge voltage, electrode material, and discharge current can affect the final generation ([PERSON] et al., 2018).
## 3 Proposed BC-UAC Method
### The Frame Structure  
Figure 5 illustrates the frame structure of BC-UAC based on the bottlenose dolphin whistle, which is composed of the synchronization signal, the guard interval and the bionic modulated information bits.  
Due to its good autocorrelation characteristic, the original whistle signal is used as the synchronization signal. By performing the correlation between the received whistle signal and the original whistle signal stored locally, the location of the correlation peak is obtained to realize the synchronization of the frame and the recognition of the whistle signal.  
A blank space is used as the guard interval to prevent the interference between the synchronization signal and the bionic modulated information bits caused by the multipath effect of the underwater channel.  
The bionic modulated information bits generate a synthetic whistle modulated by time-delay or frequency-shift.
# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 5 Analysis  
Considering the process described in Section 4, this section analyses the results of the SLR method. According to the research questions defined in Section 4.1, we respond to RQ1, RQ2, and RQ3, as indicated in Table 2. In addition, these research questions are discussed in separate Sections 5.1-5.3.  
RQ1: What Machine Learning Approaches Are Applied in Physical Layer Authentication to Secure Wireless Networks?  
As artificial intelligence technologies advance, machine learning and deep learning are progressively expanding in all parts of people's lives. Recently, advances in ML and DL have enhanced the reliability of classification in physical layer authentication. Therefore, we present various learning approaches for physical layer authentication to secure wireless networks and discuss the algorithms utilised. Figure 7 represents the percentage of ML and DL algorithms in the reviewed papers. In the research papers, we observed 12 algorithm types; 45% of the studies applied ML algorithms, and 55% applied DL algorithms to implement their solution. In addition, we compared the chosen studies, as shown in Table 6. This comparison reflects approaches, algorithms, evaluation types, features, performance metrics, tools, and platforms. The performance results of selected studies are shown in Figure 8.