esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 PPO-Attention Algorithm
### Incorporation of an Attention-Based Policy Network into the PPO Algorithm  
We have integrated the attention-based policy network into the Proximal Policy Optimization (PPO) algorithm, resulting in our PPO-Attention algorithm.  
As described in Section 3.2, PPO is a highly regarded policy optimization method in the field of RL, known for its remarkable performance across diverse RL tasks. It utilizes policy gradient methods to update the parameters of the policy network based on advantage estimation and trust region constraints.  
In our PPO-Attention algorithm, we exploit the attention mechanism to enhance the policy network's ability to capture the interdependencies among nodes in a road network. Specifically, the attention layer incorporated in the encoder section of the policy network assigns distinct weights to vertices, taking into account their significance and relevance for the charging station placement task. By doing so, the model becomes adept at identifying critical nodes and making informed decisions that incorporate the broader context of the road network.  
The PPO-Attention algorithm follows the core framework of PPO while substituting the conventional policy network with the attention-based policy network described above. During the training process, the algorithm gathers trajectories through interactions with the environment and computes advantage estimates for each state-action pair. Subsequently, the policy network is updated based on these advantage estimates and a trust region constraint, which ensures that the policy updates remain within predefined bounds, thus preserving stability.  
By incorporating attention mechanisms and reinforcement learning into the PPO-Attention algorithm, we can effectively optimize the placement of charging stations in road networks, considering various factors and capturing the interdependencies between network nodes.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Brillouin-Distributed Measurement with Phase Coding by Pseudo-Random Bit Sequence  
In the BOCDA technology described above, a continuous wave (CW) lightwave with analog frequency modulation on the pump and probe lightwave was used, which realized the position-selective generation of the stimulated Brillouin scattering along the fiber. Recently, a new concept of a CW wave technology has been introduced [77], in which the pump and probe CW lightwave are modulated in the phase by a common binary pseudo-random bit sequence (PRBS).  
In the technology, phase modulation is done by the 0 or \(\pi\) phase in PRBS both for the pump and probe lightwave. Then, the specific position appears where the two lightwaves that have exactly the same sequence meet each other, where the relative phase difference does not takes place to efficiently generate the stimulated Brillouin scattering. At the other portion, the two lightwaves do not have the same correlation due to the PRBS nature. Thus, the position-selective measurement can be done to perform the distributed measurement. The random accessibility can also be realized in the technology.  
The spatial resolution is decided by the correlation peak width, which is on the order of half the bit width. The measurement range, or the correlation peak interval, is decided by the PRBS length. PRBS generators can provide sequences even with 2\({}^{31}\)-1 bit length. This corresponds to a billion order of the ratio between the measurement range and the spatial resolution. In Ref. [77], a distributed measurement is demonstrated with about a one-cm spatial resolution and 200-m measurement range.  
This technology provides us with a long length and high spatial resolution distributed measurement with random accessibility. Position-selective Brillouin dynamic grating generation has also been demonstrated by this technology [78].
## 4 Discussion
but not the exact geographic location, it is not possible to combine the obtained charging profiles and the daily trips to find the optimal location for public charging points for users. Therefore, real and measured data can only be compared regarding the specific field of recharging and impact on the grid through the definition of hourly recharging profiles. Despite this limitation, these two types of data can be considered sufficient to have an initial validation of the data obtained through simulation, as well as showing the different characteristics of the reloads obtained, to refine the future use of predictive systems. Building on the results of this study, several future research directions emerge and could further enrich the field of e-mobility. First, future studies should explore the long-term impact of increased EV adoption on regional and national electricity grids, incorporating diverse geographic and climatic conditions. Second, there is a need to investigate the effectiveness of various incentive programs and policies in promoting the deployment and usage of EV recharging infrastructure. Third, research could focus on the integration of renewable energy sources with EV charging stations to enhance sustainability and reduce grid dependency. Fourth, the development of advanced predictive models using machine learning and AI could improve the accuracy of demand forecasting and grid impact assessments. Finally, examining user behavior and preferences in different urban and rural settings could provide insights for optimizing the design and placement of future recharging stations. As EV adoption continues to grow, such detailed understanding of charging patterns will be crucial for ensuring the sustainable and efficient integration of EVs into our transportation and energy systems.
## 4 Performance Evaluation
### Evaluation Metrics  
The performance of the proposed model was evaluated by various metrics, including accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve, and area under the curve (AUC). Accuracy is the ratio of correctly classified samples to the total number of samples. Precision measures the proportion of true positive predictions over the total number of positive predictions, while recall is the proportion of true positive predictions over the total number of actual positive samples. F1 score [52] is the harmonic mean of precision and recall, indicating the balance between the two measures. The ROC curve [53] is a graphical representation of the true positive rate (\(TPR\)) against the false positive rate (\(FPR\)), which is defined as follows:  
\[FPR=\frac{FP}{FP+TN}, \tag{6}\]  
\[TPR=\frac{TP}{TP+FN}, \tag{7}\]  
where \(TP\) refers to the number of correct positive predictions, \(FN\) refers to the number of incorrect negative predictions, \(TN\) refers to the number of correct negative predictions, and \(FP\) refers to the number of incorrect positive predictions. The AUC is the area under the ROC curve, indicating the ability of the model to distinguish between positive and negative samples.
## 3 Methodology for the Numerical Simulation and Results Analysis
### Simulation of the Cladding Failure Effects  
The objective of this study is not to achieve an accurate simulation of the failure itself but to obtain information about wave propagation phenomena in the specific system corresponding to the test device. Hence, cladding failure is modeled in a rather simplified way. Neither material distortion nor fuel-coolant thermal interaction are simulated. We only reproduce the over-pressure resulting from these phenomena and propagating through the system. To reproduce this over-pressure, a cavity was created inside the rod. That cavity is initially filled with pressurized gas at a higher pressure than the surrounding fluid pressure. That gas can represent pressurized fission gas, internal steam in case of a water-logged rod, or the pressure surge induced by fuel-coolant interaction. The contact area between the gas in the cavity and the surrounding fluid is obtained with an aperture in the cladding. At the beginning of the simulation, pressurized gas is released in the surrounding fluid, creating a pressure wave that propagates along the system, in both directions (downstream and upstream). The cavity inside the rod is axisymmetrical, but the aperture in the cladding stretches only over a reduced part of the cladding circumference and is not axisymmetrical. Thus, it results in an asymmetrical source and enables the observation of 3 dimensional effects, which is necessary to estimate the validity of the plane wave assumption.  
In the axial direction, the cavity and the cladding aperture in the model have the same length. The results that are presented in this article come from a simulation computed with an arbitrary failure position, which is set at \(135.3\times 10^{-3}\) A.U.L. from the lower end of the rod. In real experiments, the position of the failure depends on the initial state of the rod and the experimental conditions.  
The influence of the failure length in the model is discussed in Section 4.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (kg \(\cdot\) m\({}^{-3}\))** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Steel & 7830 & 176 & 0.3 \\ Zircaloy-4 & 6560 & 78 & 0.4 \\ Fuel & 10,500 & 80 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure materials properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (kg \(\cdot\) m\({}^{-3}\))** & **\(P_{\infty}\) (Pa)** & \(\gamma\) \\ \hline Water (liquid) & 764 & 4500 & 1.896 \\ Water (gas) & 1 & 0 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fluid materials properties.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 4 Experimental Results and Analysis
### Comparison with Other Algorithms  
Edge detection is a fundamental task in computer vision, with numerous exemplary detection models emerging in recent years. In this section, we juxtapose the detection effects of other edge detection algorithms on the ice cover dataset with our proposed CG-UNet. The experimental results are delineated in Table 3.  
As depicted in Table 3, Canny, the sole non-deep-learning algorithm, exhibited the poorest detection performance and proved ineffective in disc  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **ODS** & **OIS** & **Parameter/M** & **GFLOPS** & **FPS** \\ \hline Canny & 0.323 & 0.323 & - & - & - \\ HED & 0.858 & 0.867 & 14.7 & 93.5 & 34.8 \\ RCF & 0.873 & 0.883 & 14.8 & 102.6 & 33.4 \\ BDCN & 0.900 & 0.905 & 16.3 & 143.6 & 25.6 \\ DexiNed & 0.901 & 0.912 & 35.1 & 140.4 & 21.2 \\ PidiNet & 0.912 & 0.920 & 11.4 & 78.2 & 38 \\ UAED & 0.924 & 0.927 & 9.1 & 97.3 & 26.3 \\
**CG-UNet** & **0.934** & **0.938** & **15.8** & **156.7** & **24.5** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results with other algorithms.  
Figure 6: Attention comparison results.  
within complex environments. Other deep learning algorithms attained a recognition accuracy of 85%. Notably, the CG-UNet model demonstrated substantial advantages over other edge detection algorithms, owing to the incorporation of the CGM and joint loss function. Due to the CGM, the CG-UNet has more parameters than other models, but its detection speed has not decreased significantly, maintaining above 20 FPS on the RTX3050 GPU. Figure 7 provides a visual representation of the performance of various algorithms on the ice-covered transmission line dataset.  
As depicted in Figure 7, the edges in the image generated by RCF appeared thicker, and the pixels within the bumps were not effectively processed. PidiNet, after redesigning the convolution operator and integrating traditional methods, achieved initial differentiation of pixels near real edges. Conversely, CG-UNet excelled in detecting uneven areas, particularly with the aid of the CGM. This module enables the model to effectively leverage encoder features to distinguish easily confused pixels during edge generation. Moreover, the joint loss function contributes to thinner edge generation.
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
###### Abstract  
Ultrasound breast tomography (UBT) is a promising quantitative imaging method. It allows for precise analysis of ultrasound velocity distribution, which is related to tissue density and elasticity, enabling cancer detection. Only a few centers around the world have a prototype of the device for in vivo breast ultrasound tomography imaging. The quality of images reconstructed from measurements of ultrasound pulse transit times is adversely affected by the refraction of beam rays on the breast immersed in water. Refraction can be reduced using waveform tomography, ray-tracing, and ray-linking methods. However, this requires the acquisition of a pre-reconstructed pattern and is limited by extreme computational costs. In this study, the effect of refraction on transit time measurements of ultrasound passing through the female breast was analyzed under immersion conditions in water. It was found that the refraction causes the highest measurement errors in the area of the water/breast interface, and these can be reduced by adjusting the water temperature and changing the breast geometry. The results allow us to improve the quality of breast images reconstructed using an efficient transformation algorithm that assumes rectilinear ultrasound propagation paths between transmitters and receivers. In vivo breast studies were performed on the developed hybrid UBT scanner.  
refraction of ultrasonic beam rays; ultrasound transmission method; ultrasound breast tomography; ultrasound speed +
Footnote †: journal: Medical Imaging  
Article Analysis of the Refraction Effect in Ultrasound Breast Tomography
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 8 Conclusions  
This work presented a novel method to create a covert channel leveraging wired Ethernet cables, commonly used in ICSs and other networked devices. Malware residing in the target's computer deliberately sends packet bursts to the Ethernet cable, encoding the desired data to exfiltrate by modulating the packet patterns used. The attacker captures the EMR from the Ethernet cable and subsequently demodulates it to recover the transmitted data. The covert channel is evaluated from multiple aspects, revealing that it can operate reliably under low data rates, i.e., 4 bps, from a distance of 2 m. The emitting frequency finding phase generated a massive EM dataset that is feature-rich and can be used in further research. The volume of network generated EM data gathered as part of this approach opens up several avenues for further research leveraging artificial intelligence on the volume of network traffic collected [25].  
Formal analysis, S.S.; Writing--review & editing, N.-A.L.-K. and M.S.; Supervision, A.P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Theory  
### Grubbs Test  
The coefficient of determination, \(R^{2}\), and the error vary according to the number of measured points. As the number of measured points increases, the coefficient of determination value tends to become closer to 1 and the error decreases. In Figure 3, the coefficient of determination was above 0.9 from 48 points at a wavelength of 247 nm in C. When the coefficient of determination is above 0.9, the reliability of the data can be considered to be high. To eliminate the suspicious data, the maximum and minimum values were subjected to Grubbs test to determine whether the value was an outlier.  
From Equation (1), the average of the signal intensities of the carbon for each value of \(G\) in the data was calculated from 1.75 to 3.19. For the 100 measurement points, the critical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Coal Name**} & \multicolumn{4}{c}{**Concentration (wt\%)**} \\  & **C** & **H** & **O** & **S** \\ \hline GUNVOR & 74.4 & 5.21 & 12.46 & 0.71 \\ MSJ-1 & 69.8 & 5.16 & 16.20 & 0.93 \\ MSJ-2 & 67.5 & 5.27 & 17.27 & 0.84 \\ LANNA HARITA & 68.3 & 5.08 & 18.03 & 0.53 \\ MACQUARIE & 67.5 & 4.81 & 18.27 & 1.10 \\ PEABODY & 66.3 & 5.01 & 17.96 & 0.67 \\ KPU & 65.6 & 5.25 & 20.97 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of coal sample.  
Figure 3: Variation in coefficient of determination and error.  
Figure 2: Pellet sample of pulverized coal.  
values of \(G\) were 3.38 and 3.21 at the confidence levels of 95 and 97.5%, respectively [21]. The maximum \(G\) value, 3.19, satisfied the above criterion at the confidence levels of 95 and 97.5%:  
\[G=\frac{\left|\overline{\text{X}}_{\text{max,min}}-\overline{\text{X}}\right|}{s} \tag{2}\]  
where \(s\), \(\overline{\text{X}}\), and \(\overline{\text{X}}_{\text{max,min}}\) are the standard deviation, sample mean value, and sample maximum or minimum value, respectively.
## An Application Example
### Application  
It is vital to evaluate or analyze the research data before using the method of calculating facies proportions based on the confidence interval theory since the quality of the data may influence the outcome of the calculations. Declustering and normality tests are among the test methodologies. Since the premise of the approach relies on the statistics of a normal distribution for all the data, the distribution of the data will have an impact on how well the calculations turn out. This method gives values of the three levels directly from the data, so there are certain requirements to ensure data quality. The result might be distorted if certain huge clusters of high or low values exist. A similar occurrence happens frequently when developing reservoirs. For example, in the elastic sedimentary reservoir of continental facies, the facies transition is rapid, and the reservoir heterogeneity is serious. The characteristics of uneven reservoir distribution and severe heterogeneity lead  
Figure 2: Structural backdrop (**a**), paleogeography (**b**), and lithology distribution in different wells (**c**).  
to irregularity of well location design, and a favorable reservoir is the priority target. This results in the irregularity of the distribution of the obtained geological data. In the favorable part of the reservoir, the development wells will be dense, so that a lot of relevant data can be obtained. Due to inaccurate reservoir prediction and other reasons, there are only a few development wells and a few exploration wells in the distribution range of unfavorable reservoirs, so the geological data obtained is very limited. This uneven distribution of geological data may result in statistical results that are not representative of the actual data distribution. More favorable reservoir data skew the results. In less heterogeneous reservoirs, well distribution may primarily explore the edges of the reservoir, but this artificial tendency can also result in uneven well-data distribution. Generally speaking, the more data, the better the statistical results, and the smaller the error. The idea that we want the data distribution to be as even as possible is the underlying assumption. In this example, the number of wells in the studying area is small and the distribution is uniform, so only the normality test is carried out, and no declustering is carried out.
# A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis
###### Abstract  
The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called \(U_{\text{A}}\), which takes the correlation between training points and samples into account. The \(U_{\text{A}}\) function has been developed from the U function but is distinct from it. The \(U_{\text{A}}\) function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since \(U_{\text{A}}\) and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.  
failure probability; active learning; semi-parallel; Kriging; Monte Carlo +
Footnote †: journal: _Applied Sciences_
###### Abstract  
Vehicular ad-hoc networks (VANETs) are the specific sort of ad-hoc networks that are utilized in intelligent transportation systems (ITS). VANETs have become one of the most reassuring, promising, and quickest developing subsets of the mobile ad-hoc networks (MANETs). They include smart vehicles, roadside units (RSUs), and on-board units (OBUs) which correspond through inconsistent wireless network. The current research in the vehicles industry and media transmission innovations alongside the remarkable multimodal portability administrations expedited center-wise ITS, of which VANETs increase considerably more attention. The particular characteristics of the software defined networks (SDNs) use the vehicular systems by its condition of the centralized art having a complete understanding of the network. Security is an important issue in the SDN-based VANETs, as a result of the effect the threats and vulnerabilities can have on driver's conduct and personal satisfaction. This paper opens a discourse on the security attacks that future SDN-based VANETs should confront and examines how SDNs could be advantageous in building new countermeasures. SDN-based VANETs encourage us to dispose of the confinement and difficulties that are available in the traditional VANETs. It helps us to diminish the general burden on the system by dealing with the general system through a single wireless controller. While SDN-based VANETs provide us some benefits in terms of applications and services, they also have some important challenges which need to be solved. In this study we discuss and elaborate the challenges, along with the applications, and the future directions of SDN-based VANETs. At the end we provide the conclusion of the whole study.  
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