esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 4 Header Analysis
#### 4.3.2 TCP Flag Anomalies  
An analysis of the behavior of the less frequent attackers may also return useful information, as they might have noted they were targeting a controlled environment or a host that did not have what they were expecting. On the contrary of what was observed in the collected data, each IP address shown in Figure 9 transmitted only four packets to the Honeynet and this was considered a strange behavior based on all other data analyzed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Port Number** & **Number of Packets** \\ \hline
23 & 8,209,474 \\
22 & 3,474,610 \\
445 & 209,689 \\
80 & 91,459 \\
443 & 53,950 \\
139 & 39,934 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The six most attacked ports.  
Figure 8: Classification of traffic according to its destination port.  
Although none of the IP addresses reached layer 5, some of them presented anomalies in the TCP flags such as 1.2 xx.202.209, that transmitted only SYN packets. This behavior may be associated to a port scan or a TCP SYN flood, but these activities usually have a much greater number of packets transmitted.  
Other IPs transmitted a significant amount of RST packets, which is not common in normal TCP connections. The performed analysis could not identify what they were trying to achieve due to the short amount of packets and none of them sent information using layer 5. Furthermore, the first octet of all IP addresses is identical, whereas the second seems to be increasing. All of the IP addresses were trying to connect to port 23 and are georeferenced to countries in Southeast Asia. The fact that the IPs are georeferenced to different countries indicates that maybe they are somewhat not part of the same source, but the similarities in the first two octets of the IPs and its behavior may provide evidence that they are somehow related, possibly belonging to the same attacker, who spoofed the source IP addresses.
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 5 Case Study
### Stage 1: Preliminary Preparation
However, the substantial upfront investment and energy usage could influence its long-term economic viability. The Multi-Functional Robotic Arm (A2) is designed for various inventory management functions, such as counting, sorting, and transferring materials. It highlights operational versatility, featuring programmable capabilities that allow it to cater to different types of inventory. Although it may be slower in purely counting tasks, its multifunctional abilities make it a valuable asset for intricate workflows. The Mobile Inventory Tracker (A3) facilitates mobile inventory tracking, prioritizing user-friendliness and mobility across various production areas. With IoT connectivity, it seamlessly integrates into digital transformation efforts by delivering real-time updates on inventory status. Its portability and relatively low upkeep costs are notable benefits, although it may not be as efficient in large-scale operations as fixed systems. The traditional counting machine, the Cost-Efficient Fixed Inventory Counter (A4), is designed for energy efficiency and robustness. It is optimized for managing high inventory volumes with minimal interruptions, making it ideal for more extensive operations. While it does not offer advanced features like IoT connectivity or multifunctionality, its lower initial and operational costs render it a practical choice for small and medium-sized businesses.
## 4 ITS Threats Analysis and Classification
### Classification of ITS Attacks and Cryptographic Countermeasures
#### 4.4.6 Attacks on Non-Repudiation/Accountability and Countermeasures  
Non-repudiation is defined as the impossibility for one of the ITS users involved in a communication to deny having participated in all or part of a communication event. This protects against false denials involved in the communication. Non-repudiation provides the receiver with proof that the sender is accountable for the messages it generated [82]. The main goal of non-repudiation consists of collecting, maintaining, making available and validating undeniable evidence about a claimed event or an action. Non-repudiation depends on authentication, but it generates solid proof, as the system can identify the attackers who cannot deny their crimes [83]. Violators or misbehaving users cannot deny their actions. Any car information (e.g., speed, trip route and violation) will be stored in a tamper-proof device (TPD), and any authorized official will be able to retrieve these data.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 3 Results
### Verification for the Symmetric Case  
In this section, we provide a detailed verification of the first ten eigenvalue squares for symmetric L-shaped membranes by comparing our results with those reported in existing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensionless Parameters** & _d/h_ & _a/h_ & _b/h_ \\ \hline Symmetric Case & 1.00 & 1.00 & 1.00 \\ Asymmetric Case & 0.57 & 1.24 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensionless parameters for selected cases of L-shaped membranes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(k_{i}^{2}\)} & \multicolumn{6}{c}{\(N\)} \\ \cline{2-7}  & **10** & **60** & **120** & **1000** & **1600** & **1800** \\ \hline
1 & 9.6558 & 9.6413 & 9.6403 & 9.6398 & 9.6397 & 9.6397 \\
2 & 15.1978 & 15.1973 & 15.1973 & 15.1973 & 15.1973 & 15.1973 \\
3 & 19.7392 & 19.7392 & 19.7392 & 19.7392 & 19.7392 & 19.7392 \\
4 & 29.5217 & 29.5215 & 29.5215 & 29.5215 & 29.5215 & 29.5215 \\
5 & 31.9521 & 31.9164 & 31.9142 & 31.9127 & 31.9127 & 31.9127 \\
6 & 41.5043 & 41.4774 & 41.4757 & 41.4745 & 41.4745 & 41.4745 \\
7 & 44.9511 & 44.9485 & 44.9485 & 44.9485 & 44.9485 & 44.9485 \\
8 & 49.3480 & 49.3480 & 49.3480 & 49.3480 & 49.3480 & 49.3480 \\
9 & 49.3480 & 49.3480 & 49.3480 & 49.3480 & 49.3480 & 49.3480 \\
10 & 56.7430 & 56.7128 & 56.7109 & 56.7097 & 56.7096 & 56.7096 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The square of the first ten eigenvalues versus truncation indices \(N\) for the symmetric case.  
research. The comparison is shown in Table 3, which highlights the eigenvalue squares computed in the present study alongside those reported by [PERSON] et al. [30], [PERSON] [31], [PERSON] et al. [32], and [PERSON] [33].  
The results indicate a strong consistency with the results of [33], demonstrating that our computational approach yields accurate and reliable eigenvalues. Notably, the current values of all ten eigenvalues closely match those documented by [PERSON] and [PERSON] [33], affirming the reliability of our methods.  
The results from [30,32] generally align with our results, although slight discrepancies are noted. For example, the first eigenvalue in the study by [PERSON] et al. [30] is 9.6814, slightly higher than our result of 9.6397. Such differences, though minor, may arise from variations in computational precision or the specific numerical methods used.  
Higher-order eigenvalues also agree with the literature, although minor discrepancies are observed. These discrepancies highlight the sensitivity of higher-order modes to various numerical approaches and emphasize the importance of employing high-precision methods for accurate computations.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 6 Numerical Study
### Results and Discussion
#### 6.2.2 Ground Support Vehicle Scheduling
Figure 9: Distribution of water truck distance and wait time using FCFS and LS-NSGA-II. (**a**) Distance of water trucks; (**b**) Wait time of water trucks. vehicles and passenger stairs is more pronounced. Constrained by the nature of the tasks, the optimization of vehicle numbers compared to the FCFS approach is relatively minor. Fuel trucks are influenced by ferry vehicle tasks. In this study, all fuel truck tasks are non-passenger fueling. Thus, the time windows for fuel trucks occur after the ferry vehicles servicing arriving flights have completed their tasks and before the ferry vehicles servicing departing flights begin. This provides a relatively ample timeframe. The Gantt chart for fuel trucks shows that they prefer to execute tasks consecutively at adjacent stands or within the same apron, significantly reducing both driving distances and waiting times. Water trucks, due to capacity limitations, must return to the resource point to replenish water after serving a certain number of flights. Their service time is short, and their time window is broader, allowing for the greatest operational flexibility. As a result, water trucks are the least numerous among all vehicles. Under conditions where resource capacity is met, water trucks, like fuel trucks, also prefer to perform tasks consecutively at adjacent stands or within the same apron. For baggage handling tasks, the related vehicles (baggage belt loaders and baggage tractor) must carry out both loading and unloading operations. Baggage tractors need to travel back and forth between the baggage handling center and the stand, resulting in longer driving distances. Baggage conveyor belts have to wait for the baggage tractors to perform their tasks together. Due to the lengthy loading and unloading times, there is significant demand for baggage belt loaders and baggage tractors.
## 3 Results and Discussion
### Vertical TPH Removal at Three Depths in Soil Column
The remediation of contaminated soils in unsaturated zones is challenging due to low access of remedial agents to contaminants, which is often limited by depth, soil anisotropy, and gravity in unsaturated soils [7]. The results of this study suggest that the coupling of oxidation and bioaugmentation foam spraying through vertical holes can be used for enhanced removal of TPH from various depths. Notably, these vertical holes comprise only 0.8% of the total soil volume and 3% of the soil surface in the column. Figure 4: Residual TPH concentrations at three depths after PS oxidation (2 days) followed by biodegradation (Bio) (30 days). The different letters (**a**–**h**) above the bars indicate significant differences (\(p<0.05\), [PERSON]’s test). There are several limitations in interpreting the results of this study, which evaluated the role of soil holes in the delivery of remediation agents to a 30 cm deep soil. An evaluation should be conducted on a larger scale to increase reliability of the hole impacts when foam spray is used. The delivery processes for liquid transport through holes after the foam breaks should be made clearer and explained in more detail. Making some soil holes would also enhance delivery of oxygen into the deeper area and might accelerate biodegradation. Finally, more removal mechanisms to use between pre-oxidation and biodegradation should be studied further.
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 5 Results and Discussion
### Comparative Evaluation with Periodic Sampling Approaches
Nevertheless, if a dedicated communication infrastructure is used for exchanging context information, similar communication costs will still be incurred on the dedicated network, which includes extra hardware implementation for its realization. Tables 3 and 4 provide a broader comparison of makespan outcomes under various context exchange strategies. The periodic sampling methods reveal a consistent pattern: shorter intervals, such as 2 TU, introduce substantial communication overhead, leading to inflated makespan values. Conversely, longer intervals, such as 10 TU, lessen this overhead, resulting in comparatively lower makespans. Nonetheless, these periodic strategies still underperform when contrasted with our proposed algorithm, which seamlessly incorporates context information exchange during scheduling. For example, in the case of 30 jobs, the proposed method completes scheduling in 210,652 time units, outperforming even the most efficient periodic method (10 TU) by a considerable margin. While the no-context-exchange setup yields the lowest makespan (e.g., 177,685 for 30 jobs), it entirely omits adaptivity, rendering it unsuitable for dynamic environments. These comparative results underscore the practical advantage of our algorithm in managing communication overhead while maintaining system responsiveness. \begin{table}
\begin{tabular}{r r r r r r r} \hline \hline
**Job Size** & **Prop. Alg.** & **2 TU Samp.** & **4 TU Samp.** & **6 TU Samp.** & **8 TU Samp.** & **10 TU Samp.** \\ \hline
10J & 490 & 4845 & 2423 & 1610 & 1207 & 971 \\
20J & 1005 & 188,330 & 94,164 & 62,777 & 47,083 & 37,660 \\
30J & 7690 & 444,216 & 222,116 & 148,073 & 111,064 & 88,846 \\
40J & 39,000 & 783,236 & 391,627 & 261,136 & 195,834 & 156,618 \\
50J & 74,200 & 1,978,093 & 989,038 & 659,387 & 494,580 & 395,609 \\
60J & 147,420 & 4,862,588 & 2,431,259 & 1,620,815 & 1,215,552 & 972,574 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Communication cost measured in bytes per period for different sampling methods and job sizes. Figure 7: Total communication cost for different sampling intervals as the number of jobs increases.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 6 Conclusions  
In this paper, we proposed a spaceborne target detection and compression storage system that can detect intended targets and send data to the ground. It can improve transmission efficiency, especially for high-resolution satellite image transmission systems. We proposed an FGPA and ASIC method to realize image compression and used the Jetson Xavier NX to realize target detection. We modified the Yolov5 algorithm to improve detection performance for small targets. In order to restore the real application scenario, raw satellite images were used for tests, instead of an open-source dataset that has been pre-processed at high quality. The experimental results showed that Yolov5 performs well in real satellite image detection with appropriate revisions and is feasible for implementation  
Figure 19: Detection results based on the original image.  
Figure 20: Detection results based on the compressed image.  
on a satellite with few hardware additions. In addition, image compression has little effect on target detection, enabling the detection of targets on compressed images, which can reduce the data rate requirement of the interface and increase efficiency. In the future, we will try to use the neural network to detect a large target on a satellite image with low-resolution, such as an airport.  
Conceptualization, Q.S.; methodology, Q.S.; software, W.Z.; validation, J.Z., G.L. and W.Z.; formal analysis, Q.S.; investigation, D.W; resources, D.W.; data curation, Q.S.; writing--original draft preparation, Q.S.; writing--review and editing, Q.S.; visualization, W.C.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
The authors received no specific funding for this work.  
Not applicable.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials
### Commonly Used Protocols
#### 2.7.12 Z-Wave  
Z-Wave is a subGHz wireless communication protocol used by different IoT applications. It is an ultra-low-power, mesh network protocol that lets devices communicate with each other over long distances (has a range of 100 m). Its data rates are 9.6 kbps, 40 kbps, or 100 kbps, and it uses a frequency of 908.42 MHz in the United States and 868.42 MHz in Europe [45]. Z-Wave deployments can be scaled by linking together Z-Wave networks. Z-Wave is well suited for applications that require reliable, secure, and low-power communication, such as control smart home devices (lights, locks, thermostats, and security systems) [46].
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### Comparative Performance Analysis with Other Forecasting Models
While the Transformer model achieves the second-best predictive performance, it requires 33% more training time and shows inferior scalability with increasing sequence length due to its quadratic complexity. The xLSTM model requires moderately more computational resources than standard recurrent models, with a 42% increase in training time compared with LSTM. However, this computational investment yields substantive performance improvements, with a 6.8% increase in \(R^{2}\) and a 24.6% reduction in MAPE. This represents an efficient trade-off between computational cost and predictive accuracy, particularly in operational contexts where forecasting errors translate directly to economic costs through suboptimal energy dispatch or unnecessary reserves. These comparative results validate the architectural innovations of the xLSTM model, demonstrating quantifiable improvements over both traditional statistical approaches and contemporary deep learning architectures for wind power forecasting.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 2 Wireless Power Transfer System Model
### Rectenna Model  
The utilized rectenna model [9; 10] is depicted in Figure 3. It comprises an antenna, a single diode rectifier, and a load. The receiver antenna model also includes a voltage source \(v_{s}(t)\) and a series impedance (\(R_{s}=50\;\;\Omega\)). The rectifier's input impedance \(R_{in}\), as well as its input voltage \(v_{in}(t)\) is also depicted in Figure 3. In order to simplify the rectenna model, a lossless antenna and a perfect matching between the antenna and the rectifier (\(R_{s}=R_{in}=50\;\;\Omega\)) is considered. Moreover, the antenna noise and the diode's series resistance are omitted. The value of the load is set to \(R_{L}=1.6\;\mathrm{k}\Omega\) and the value of the capacitance \(C\) is equal to \(C=50\frac{T_{0}}{R_{L}}\), resulting in a mitigation of the output fluctuation \(v_{out}\).
## 6 Toxicity and Health Risk Assessments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Nap**} & \multicolumn{1}{c}{**Sempling Location**} & \multicolumn{1}{c}{**ECR**} & \multicolumn{1}{c}{**HQ**} & \multicolumn{1}{c}{**BaP**} & \multicolumn{1}{c}{**Sampling Location**} & \multicolumn{1}{c}{**ECR**} & \multicolumn{1}{c}{**HQ**} \\ \multicolumn{1}{c}{**(ng\(\cdot\)m\({}^{-3}\))**} & \multicolumn{1}{c}{**Sampling Location**} & \multicolumn{1}{c}{**(\(\times\)10\({}^{-6}\))**} & \multicolumn{1}{c}{**HQ**} & \multicolumn{1}{c}{**(ng\(\cdot\)m\({}^{-3}\))**} & \multicolumn{1}{c}{**Sampling Location**} & \multicolumn{1}{c}{**(\(\times\)10\({}^{-6}\))**} & \multicolumn{1}{c}{**HQ**} \\ \hline
3879.4 [67] & Haidian, Beijing & 20.33 & 0.20 & 52.58 [67] & Haidian, Beijing & 4.86 & 4.05 \\
38.5 [72] & Harbin Winter & 0.20 & 0.00 & 4.61 [72] & Harbin Winter & 0.43 & 0.36 \\
174 [69] & PRD & 0.91 & 0.01 & 1.33 [105] & Lianhu, Xi’an & 0.12 & 0.10 \\
224 [69] & Rural NCP & 1.17 & 0.01 & 2.04 [106] & Jinan, China & 0.19 & 0.16 \\
596 [107] & Classroom, German & 3.12 & 0.03 & 155 [108] & Svirsk, Russia & 14.34 & 11.95 \\
378.6 [109] & Assiut, Egypt & 1.98 & 0.02 & 73.6 [109] & Assiut, Egypt & 6.81 & 5.67 \\
41 [74] & North Atlantic & 0.21 & 0.00 & 2.91 [110] & Tokyo, Japan & 0.27 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Related values of Nap and BaP in IVOC species for human health risk assessment. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Toxicity Parameters** & **Nap** & **BaP** & **Units** \\ \hline IARC Carcinogenic Classes & \begin{tabular}{l} Possibly carcinogenic to \\ humans \\ \end{tabular} & \
\begin{tabular}{l} Carcinogenic to humans \\ \end{tabular} &
\begin{tabular}{l} \\ \end{tabular} \\ Reference Concentration (RFC) & \(3.0\times 10^{-3}\) & \(2.0\times 10^{-6}\) & \(\text{mg}\,\text{m}^{-3}\) \\ Inhalation Unit Risk (IUR) & \(3.4\times 10^{-5}\) & \(6.0\times 10^{-4}\) & \((\text{ug}\cdot\)m\({}^{-3})^{-1}\) \\ Reference Dose (RfD) & \(2.0\times 10^{-2}\) & \(3.0\times 10^{-4}\) & \(\text{mg}\,\text{kg}^{-1}\,\text{day}^{-1}\) \\ Oral Slope Factor (CSFo) & 0.12 & 1 & mg kg\({}^{-1}\,\text{day}^{-1}\) \\ Maximum Contaminant Levels (MCLs) & 0.2 & 0.2 & \(\text{\mu}\,\text{g}\,\text{L}^{-1}\) \\ Median Lethal Dose (LD50) & 150 & 250 & mg kg\({}^{-1}\) \\ (Intraperitoneal, Mouse) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A toxicity comparison of Nap and BaP. in the spatial differentiation of BaP. It is recommended to establish a dynamic monitoring mechanism for BaP in key areas, focus on complex exposure scenarios, especially in urban built-up areas where ECR values exceed safety thresholds, and implement precise control strategies for BaP based on source apportionment and human health risk assessments.
## 4 Performance Evaluation and Discussion
### Complexity Analysis  
Compared with the KF-based tracking, the extra computational load in the proposed method is mainly the computation of \(\beta_{k}\) and \(\lambda_{k}\), as shown in Figure 3 marked with the orange blocks. Note that \(n\) represents the dimension of the state vector, and \(m\) denotes the dimension of the measurement vector. Firstly, the computation of \(\beta_{k}\) requires three equations shown in Figure 3, and then the computation complexity of \(\beta_{k}\) is \(O(n)\). Three variables \(d_{k}\), \(\beta_{k}\), and \(\hat{C}_{k}\) are scalars, and Table 3 shows the details of three equations for one iteration. For the computation of variable \(\beta_{k}\), \({d_{k}}^{2}\) has been obtained in the computation of \(\hat{C}_{k}\), so the multiplication number of the variable \(\beta_{k}\) is 1. Secondly, the calculation of \(\lambda_{k}\) also needs three equations shown in Figure 3. The computation complexity for \(\mathbf{A}_{k}\) is \(O(n^{3})\). However, because \(\mathbf{\Phi}\) is a \(3\times 3\) upper triangular matrix, the computation of \(\mathbf{A}_{k}\) needs 24 multiplications and 14 additions, which is the most time-intensive part. The computation of \(\mathbf{B}_{k}\) needs 12 multiplications and 8 additions. The total operations are 44 multiplications and 28 additions, as demonstrated in Table 3.  
Figure 10: The results of the adaptive factor \(\lambda_{k}\) in Example 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Variable** & **Number of Multiplications** & **Number of Additions** \\ \hline \multirow{2}{*}{Calculation of \(\beta_{k}\)} & \(d_{k}\) & 3 & 3 \\  & \(\hat{C}_{k}\) & 3 & 2 \\  & \(\beta_{k}\) & 1 & 0 \\ \hline \multirow{2}{*}{Calculation of \(\lambda_{k}\)} & \(\mathbf{A}_{k}\) & 24 & 14 \\  & \(\mathbf{B}_{k}\) & 12 & 8 \\  & \(\lambda_{k}\) & 1 & 1 \\ \hline \multicolumn{3}{c}{Total requirements} & 44 & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational complexity of \(\beta_{k}\) and \(\lambda_{k}\) for one-step iteration.  
According to [PERSON] et al. [34], the multiplication number \(M(n,m)\) and the addition number \(A(n,m)\) for each KF iteration are as follows:  
\[M\Big{(}n,m\Big{)}= 2n^{3}+3n^{2}m+n^{2}+2 nm^{2}+2 nm \tag{32}\] \[A(n,m) =2n^{3}+3n^{2}m+2 nm^{2}-nm-n \tag{33}\]  
For the KF tracking, \(n=3\) and \(m=1\). Hence, \(M(n,m)\) and \(A(n,m)\) are 102 and 81, respectively.  
Accordingly, in comparison with KF tracking, the calculation load of the proposed AKF method increases about 43% of multiplications and 34% of additions. However, the calculation of \(\lambda_{k}\) is only used when the system works abnormally. When the system operates in the steady state, only \(\beta_{k}\) needs to be computed, the computational load of which is small, as shown in Table 2. Therefore, in comparison with the KF-based tracking, the extra calculation burden in the proposed AKF method is not heavy.