esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
## 2 Related Works
### HPC Service Network Environment  
The fifth national supercomputer (KISTI-5), established and operated by KISTI in 2018, consists of the main system, Nurion, and the auxiliary system, Neuron. Nurion and Neuron have theoretical performances of 25.7 and 3.5 PFlops, respectively. Both provide login nodes for terminal access to industrial and academic researchers and DataMover nodes for data transfer. Furthermore, web servers are operated to provide HPC services through Jupyter notebooks and web services. To safeguard the system from external access, network security is reinforced through redundant configurations of anti-DDoS, intrusion prevention systems, firewalls, and web application firewalls. In particular, the firewall uses the five-tuple policy rule to control traffic entering from external sources into internal networks and outbound traffic from internal sources to external sources. The diagram below in Figure 1 illustrates the KISTI-5 supercomputer service network structure.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Anomaly Detection** & **Firewall Log Analysis** & **Machine Learning** & **Deep Learning** & **Adversarial Attacks** & **Errors** \\ \hline
[1] & ✓ & & ✓ & & & ✓ \\
[4] & & ✓ & ✓ & & & ✓ \\
[5,7] & & ✓ & ✓ & & & ✓ \\
[6,9] & & ✓ & ✓ & & & ✓ \\
[6,10] & & & ✓ & & & ✓ \\
[11,29] & & & & ✓ & ✓ & ✓ \\
[30,32] & & & & ✓ & ✓ & ✓ \\
[33,34] & & & ✓ & ✓ & ✓ & ✓ \\
[28] & & ✓ & & & ✓ & ✓ \\
[31] & & & ✓ & & & ✓ \\ Ours & ✓ & ✓ & ✓ & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of various aspects of cybersecurity, including anomaly detection, firewall log analysis, machine learning, deep learning, and adversarial attacks, surveyed in previous studies.  
Figure 1: Diagram of KISTI-5 supercomputer service network.  
Nurion provides services to remote users via the Internet. Therefore, pass policy rules are applied to the service ports of each infrastructure node to accommodate several unspecified users. However, external cyberattacks are not limited to specific infrastructure nodes, but attempt to simultaneously target multiple nodes. Therefore, it is crucial to detect and block them promptly to prevent damage. To address this issue, we set the firewall's highest-priority drop policy rules. This allows us to register IP addresses for cyberattacks and prevent them from spreading to other infrastructure nodes.
## 1 Introduction
Each set of initial parameters consists of both code delay and Doppler frequency. These signal parameters are coded to serve as the initial individuals (code delay individual and Doppler frequency individual, respectively) in the proposed method. Then, each set of initial individuals (including code delay and Doppler frequency individuals) processed through correlation generates an output value, which serves as the fitness value of that set of initial individuals. Afterwards, the evolutionary process of selection, reproduction, crossover and mutation begins. During the search process, once the fitness value exceeds the initial threshold (set at noise floor level), the desired signal may fall within the vicinity of the corresponding parameters of that set of individual. In the next iteration, the signal search space is narrowed down, the sets of individual are decreased and the threshold is increased. If the fitness value exceeds the maximum threshold, the result of acquisition is successful. Otherwise, the acquisition result may simply contain noise, which calls for a repeated run of evolution process. Although this method speeds up signal search time, the acquired parameter value can be a local optimal solution. Thus, an adaptive scaling scheme is utilized to assure a global solution during the signal acquisition process. Simulations and experiment results reveal that this method not only saves signal search time but also finds more accurate signal acquisition parameters for GPS signal. Comparison results of the published methods are also demonstrated in this paper. The organization of this paper is as follows. Section 2 describes the signal acquisition process and the possible problems to be encountered. Section 3 depicts how to apply the proposed method to GNSS signal acquisition. Section 4 explains the simulation and experimental procedure of the proposed method and provides the performance evaluation and comparison of published methods with the proposed method. An example is listed to analyze the feasibility of this method. Finally, Section 5 summarizes the key points of this paper.
## 6 Experimental Evaluation
### Experiment I: Service Classifier  
#### 6.1.1 Machine Learning Model  
We used the Wireshark network traffic dataset [30], which simulates the traffic directed to a Kali machine internally and externally. This traffic includes pings from the source to the destination and browsing activity recorded comprehensively by Wireshark. The.csv file contains detailed information such as the sequence number, timestamp, source and destination IP addresses, protocol, packet length, and additional information. The information available in the chosen dataset allows for precise analysis of network behavior and performance. This type of classification can be applied in various areas, such as real-time network monitoring, traffic pattern analysis, and network usage optimization.  
The simulation of the Kali machine's interactions with external sources models dynamics typical of edge computing systems, where communication between devices and servers is crucial. Diverse data, such as pings and browsing activity, enable the detailed analysis of latency and bandwidth, essential factors for edge computing performance. The simulated traffic offers valuable insights for developing and validating classification models, contributing to improved resource usage and service quality in edge computing.  
Our study uses the Gaussian naive Bayes model to classify network traffic into low latency and high bandwidth. The focus is on differentiating between traffic packets that require fast responses (low latency) and those that occupy more bandwidth (high bandwidth) based on packet size.  
Figure 6: Stopping criterion and simulation execution.  
The Gaussian naive Bayes model is based on [PERSON] theorem, assuming that the attributes are conditionally independent given the class [31]. This independence assumption greatly simplifies the calculations, allowing the model to be fast and efficient, even with large volumes of data. Despite this simplicity, Gaussian naive Bayes is highly effective in various applications, especially when the input variables follow a normal distribution [32]. In addition, its robustness in dealing with small data and its ability to provide direct probabilities make it a popular choice in many classification services, such as pattern recognition and spam filtering.
## 2 Materials and Methods
### The Conception of IFCS and its Preliminary Design in Dongting Lake  
Besides the existing dike surrounding a lake, the IFCS needs one more dike (the inner dike) parallel to the existing dike to form a by-pass canal that mainly consists of the inner dike in the lake; shuice gates are installed in the inner dike (see Figure 4). The inner dike could separate the inner lake and main channel. Thus, the excess floodwater could be diverted into the inner lake for storage, while the safe floodwater will be discharged to downstream without storage by closing the gates when the safe flood appears. Therefore, effective flood control capacity could be greatly increased, since the safe flood would not occupy the inner lake's capacity.  
The application of the IFCS in Dongting Lake needs to require designing the inner dikes and shuice gates in the lake, and Figure 5a shows the preliminary design, where the inner dike is represented by thick and solid blue lines and the hydraulic gates are marked by numbers with void red circles with a cross inside. The inner dikes are built along the main channel, which can easily be recognized according to the water body in the dry season of Dongting Lake (see Figure 5b). The area of the inner Lake of WDL, SDL, and EDL is 190 km\({}^{2}\), 560 km\({}^{2}\), and 969 km\({}^{2}\), respectively. The water level in the inner lake should drop to its ecological water level, which is corresponding to basic water requirement [21] (i.e., 29.0 m in WDL, 27.5 m in SDL, and 26 m in EDL, which are calculated while using the lake morphology [22]) before the arrival of the flood wave in the wet season [11]. Therefore, the Dongting Lake could be nearly empty and thus the lake has sufficient effective capacity to store the excess floodwater. The shuice gates will be opened to let the excess floodwater enter into the inner lake when the peak flood wave arrives [11], and the safe floodwater could be discharged to the downstream through the main channel by closing the shuice gates.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stations**} & \multirow{2}{*}{**NSE**} & \multicolumn{3}{c}{**Maximum Water Level (m)**} & \multirow{2}{*}{**Difference (m)**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**RMSE**} \\ \cline{3-3} \cline{5-6}  & & **Measured** & & & & \\ \hline Nanzui & 0.890 & 35.36 & 35.52 & 0.16 & 0.928 & 0.478 \\ Xiaohezui & 0.873 & 35.14 & 34.59 & \(-\)0.15 & 0.929 & 0.484 \\ Xiangyin & 0.936 & 34.28 & 34.37 & 0.09 & 0.961 & 0.376 \\ Yingtian & 0.949 & 34.26 & 34.36 & 0.10 & 0.967 & 0.363 \\ Luijao & 0.986 & 34.14 & 34.26 & 0.12 & 0.992 & 0.239 \\ Chenglingji & 0.981 & 34.00 & 33.98 & \(-\)0.02 & 0.995 & 0.308 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Nash–Sutcliffe efficiency coefficients (NSE), R\({}^{2}\) and the root mean square error (RMSE) calculated at the main hydrological stations in Dongting Lake.  
Figure 4: The schematic of the innovative flood control scheme (IFCS).
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 2 Proposed Methods
### The Harmonic Fading-Out Module  
Due to the absence of channel interaction in the harmonic repair module, there is redundancy in the restoration process of mid-to-high frequency harmonics, negatively  
Figure 2: The structure of the harmonic repair module.  
impacting the fidelity of speech timbre restoration. The harmonic fading-out module extracts spectral information from multiple angles and filters out potentially over-restored comb-like waveforms. It connects the harmonic repair module to two stacked multi-head attention [21] modules, one unfolding along the channel dimension and the other along the frequency dimension.  
As illustrated in Figure 3, this module first reshapes the input data \(X\) into \(\mathbb{R}^{C\times F\times L}\) and uses three linear layers to obtain the \(Q\), \(K\), and \(V\) keys. \(L\) and \(F\), respectively, represent the number of time frames and frequency bins. After rectification via Scale, \(Qc\) and \(Kc\) are multiplied and then combined with \(Vc\), and finally, the result is concatenated with \(Xc\) to form a residual connection. The output is then used as the input for the next layer of the frequency attention module. The frequency attention also operates in a similar structure, ultimately producing the output of this module \(X\in\mathbb{R}^{C\times L\times F}\).
## 3 Methodology
### Adaptive Termination Condition  
Second, to solve the problem of over- or under-fitting, we adopted the previously proposed NLE module [22], which can assess the severity of the noise interference and obtain the noise level value of the noisy image to allow us to set a more reasonable adaptive termination condition. Specifically, the residual image \(\hat{n}^{i}=\left(x_{0}-\hat{x}^{i}\right)\) can be obtained by subtracting the \(i_{th}\) output image of the deep generative network from the noisy image \(x_{0}\). In the early stages of the network iterations, the network output image \(\hat{n}^{i}\) is far from the undistorted image so the standard deviation \(std\big{(}\hat{n}^{i}\big{)}\) of the residual image \(\hat{n}^{i}\) is relatively large. When arriving at the appropriate \(i_{th}\) iteration, the standard deviation \(std\big{(}\hat{n}^{i}\big{)}\) of the residual image \(\hat{n}^{i}\) should be close to the noise level value \(\sigma\) of the noisy image measured by the previously proposed NLE module. Therefore, \(std\big{(}\hat{n}^{i}\big{)}\approx\delta\) is used in our work to adaptively terminate the iteration process. In short, with an accurate noise level value, \(\sigma\), we can determine when to terminate the iteration process after the appropriate number of iterations has been completed. As shown in Figure 5, the adaptive termination point \(\hat{x}^{*}_{proposed}\) of the proposed method is closer to the optimal point \(x_{gt}\) than that of the DIP method. Meanwhile, Figure 6 also shows that our adaptive termination condition set the termination step number at 2801, which is very close to the optimal iteration step, 2835, in the iterative process. Thus, the proposed approach can resolve the early stopping problem and achieve more optimal denoising performance.
# Detection of Thermal Strain in Steel Rails with BOTDA
## 3 Results and Discussion
### Measurement Accuracy  
In this test, measurement accuracy is indicated using measurement repeatability. It took about 9 min for every measurement with the BOTDA system, and more than 20 data points could be collected during every temperature step. Standard deviation of the measured Brillouin frequency shift at each  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Rail** & **Fiber A** & **Fiber B** & **Fiber C** & **Strain Gauge** \\ \hline \(dv_{\mathrm{B}}/d\tau\) (MHz/\({}^{\circ}\)C) & 1.53 \(\pm\) 0.04 & 1.47 \(\pm\) 0.06 & 1.51 \(\pm\) 0.02 & \\ Strain(\(\mu\epsilon\)) & 10.83 \(\pm\) 0.83 & 9.58 \(\pm\) 1.25 & 10.42 \(\pm\) 0.42 & 10.7 \\ \hline \end{tabular}
\end{table}
Table 2: Brillouin frequency-shift variation with the temperature of different fibers on the rail.  
Figure 6: Brillouin frequency-shift variation with the temperature of different fibers glued on the same rail.  
data point was considered, and the error bars in Figures 5 and 6 were calculated based on the standard deviation from 10 Brillouin frequency-shift measurements made at each fiber position along the rail specimen. According to Table 2, the standard deviation of the strain captured by three types of fibers was 0.83 \(\upmu\)e, 1.25 \(\upmu\)e, and 0.42 \(\upmu\)e, respectively. Compared with the OFS fiber (Fiber C), such higher measurement uncertainty of the two tight buffer fibers was caused by the jacket effect.
# A Novel Encryption Scheme in Ship Remote Control against Differential Fault Attack
## 4 Results and Discussion
### Safety Suggestions for CPS  
To ensure the data security of ship CPS, this paper also puts forward the following suggestions from hardware and management.  
Hardware. The safety gateway, switch, and other equipment already equipped on the ship's CPS can detect, intercept and distribute the data flow. Among them, the border security gateway and the ship shore communication behavior management server responsible for data encryption and decryption must be physically protected to prevent power consumption analysis and side channel attacks against hardware. If necessary, a hardware encryption device with mask technology is required. In addition to using cryptographic technology, it is also essential to use encrypted channels for data transmission to ensure data security and redundancy.  
Management. The management of keys, including generation, storage, distribution, use, backup or recovery, update, revocation and destruction, should be improved, as should the authority management of communication-related personnel. Although these contents have been improved and relevant standards have been issued in other fields, such as intelligent factories, adaptive modifications should be made to the existing standards for intelligent ships to adapt to the ocean-going operation environment of ships.
## 3 Theoretical Background
### Elliptic Envelope  
The EE algorithm is an unsupervised machine learning technique that fits a robust covariance estimate to the data. Based on the assumption that the inlier data are normally distributed, it finds the inlier location and covariance in a robust manner without being affected by outliers. The EE algorithm tries to fit an ellipsoid around the data using the minimum covariance determinant [49; 50]. The radii of the ellipsoid along each axis are measured in terms of the Mahalanobis distance. The calculated Mahalanobis distances are then used to derive a measure of outlyingness. Instances that fall outside the ellipsoid will be identified as outliers. The algorithm requires a contamination parameter to define the expected proportion of outliers to be detected. In the current study, we applied the EllipticEnvelope function of the scikit-learn covariance module [47] to implement the EE algorithm.
The efficiency of TBM tunneling depends on factors such as disc-cutter size, spacing, and penetration, all of which affect the rock-breaking performance. [PERSON] [18] used ABAQUS to establish a finite element model of disc-cutter rock breaking, examined the effects of cutter diameter on rock-breaking forces, and generated comprehensive relationship curves. [PERSON] et al. [19] employed MatDEM software to create a large-scale 3D model for disc-cutter rock breaking, analyzing the changes in cutting forces and specific energy under varying cutter spacings and penetrations. They identified an optimal cutter spacing-to-penetration ratio that minimized specific energy for rock breaking in uniform rock materials. Through experiments and finite element simulations, [PERSON] [20] explored the effects of cutter spacing and penetration on rock-breaking performance and established a mechanical model for optimal cutter spacing during cooperative rock breaking. The study of TBM disc-cutter rock-breaking mechanisms, particularly through discrete element modeling, offers valuable insights for cutter design and tunnel parameter optimization, ultimately improving efficiency and safety. Discrete element techniques are particularly effective for analyzing rock-cutter interactions. These simulations help to understand the dynamic processes of rock breaking, including fracture modes and cutter wear mechanisms, and are essential for cutter design and parameter selection. Optimized cutter designs and tunneling parameters can reduce construction durations, lower costs, and decrease cutter replacement frequencies, thereby enhancing the economic and social benefits of large-scale infrastructure projects. Despite significant progress, the existing studies lack systematic investigations into the factors influencing disc-cutter rock-breaking performance. Using validated numerical models can provide more objective and reliable insights. In this study, we developed a 3D discrete element model for rock breaking using disc cutters and validated it by comparing the simulation results with the experimental and finite element data from the literature. The effects of disc-cutter diameter, cutter spacing, and penetration on rock-breaking performance were systematically analyzed.
# Ultrafast Modulation of Magnetization Dynamics in Ferromagnetic (Ga, Mn)As Thin Films
## 2 Materials and Methods  
Ferromagnetic Ga\({}_{0.964}\)Mn\({}_{0.036}\)As films with thickness of 97 nm were grown by means of low-temperature molecular-beam epitaxy (LT-MBE). The layers were grown on [001]-oriented GaAs substrates. The hole concentration \(p\) of the as-grown film was estimated to be \(\sim\)2 \(\times\) 10\({}^{20}\) cm\({}^{-3}\). After annealing at 250 \({}^{\circ}\)C in N\({}_{2}\) for 1 h, \(p\) was estimated to increase to \(\sim\)3 \(\times\) 10\({}^{20}\) cm\({}^{-3}\). Superconducting quantum interference device (SQUID) measurements showed that the Curie temperatures T\({}_{\text{C}}\) of the annealed and as-grown samples were \(\sim\)80 K and \(\sim\)60 K, respectively. The magnetic anisotropy properties of the investigated (Ga,Mn)As layers were characterized by ferromagnetic resonance (FMR) measurements at different temperatures. Time-resolved pump-probe magneto-optical Kerr effect was measured using a Ti:sapphire laser with repetition rate of 80 MHz and spectral width of 10 nm. Pump-induced magnetization dynamics of Mn spins were studied via time-delayed probe pulses. Both the pump pulse and the probe pulse were linearly polarized with the same photon energy ranging from 1.53 eV (810 nm) to 1.58 eV (785 nm). The pump intensity was varied from 1.8 ul/cm\({}^{2}\) to 28 ul/cm\({}^{2}\), and the probe intensity was fixed at 0.6 ul/cm\({}^{2}\). The samples were mounted in a Janis subcompact cryostat. The experiments were performed at 10 K unless noted otherwise. There is no external magnetic field applied in the experiments.
# Exploring the Key Characteristics and Theoretical Framework for Research on the Metaverse
## 6 Discussion
### Other Areas of Impact  
The advent of the Metaverse carries with it potentially transformative implications across a variety of fields, including education, healthcare, and entertainment, fundamentally reshaping the way we live, learn, heal, and play.  
In the field of education, the Metaverse could revolutionize learning experiences. It can offer immersive, interactive digital environments that mimic real-world settings, making learning more engaging and practical. Students around the globe could have access to a more equitable education by virtually attending any school or lecture, regardless of geographic location. Furthermore, special needs education could be significantly enhanced with personalized learning environments tailored to each student's unique needs.  
The potential impacts of the Metaverse on healthcare are equally profound. Telemedicine could take a leap forward by utilizing the immersive environments of the Metaverse, allowing for more comprehensive virtual health assessments and treatments. Rehabilitation programs could also be transformed with the application of immersive virtual reality therapies, enabling more effective and personalized treatment plans. The Metaverse could even help reduce feelings of isolation in long-term patients by providing a virtual social environment.  
As for entertainment, the Metaverse can offer a new level of interactivity and immersion. This could lead to more engaging and diversified forms of entertainment, from gaming to live concerts to social interactions, all in a virtual setting. Additionally, it has the potential to democratize content creation, providing users with the tools to create their own unique experiences.  
These are just speculations and possibilities of the impacts of the Metaverse on these fields. As technology continues to evolve, we might witness even more transformative changes that are currently beyond our imagination.