esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Algorithm
### Underwater Energy Consumption Model  
Ref. [43] gives an in-depth analysis of the energy consumptions of underwater acoustic networks according to the conceptions of underwater acoustic channel attenuation model, noise model and bandwidth model. The total energy consumption associated to one hop (which includes both the transmit and the receive energy at the two ends of the link), is determined by the power \(P_{r}\), acoustic electric conversion power \(P_{t}^{el}(l)\) which is the power required to transform acoustic signal to electric signal and single hop transmission delay \(t_{hop}(L,l)\). Specifically, the energy consumed by each single hop, \(E_{hop}\) is calculated as:  
\[E_{hop}(L,l)=t_{hop}(L,l)\times(P_{r}+P_{t}^{el}). \tag{17}\]  
The single hop transmission delay \(t_{hop}(L,l)\) is determined by package length \(L\) and the distance \(l\) between two transmitting nodes as well as channel effective bandwidth \(\alpha B(l)\) where \(\alpha\) means channel utilization rate. So the formula becomes  
\[E_{hop}(L,l)=t_{hop}(L,l)\times(P_{r}+P_{t}^{el})=\frac{L}{\alpha B(l)}\times(P _{r}+P_{t}^{el}) \tag{18}\]  
\(P_{t}^{el}(l)\) is calculated as  
\[P_{t}^{el}(l)=\frac{P_{t}(l)\times 10^{-17.2}}{\eta}. \tag{19}\]  
Herein, \(10^{-17.2}\) is the conversion factor from acoustic power in \(dBre\mu Pa\) to electrical power in Watt, \(\eta\) represents electronic circuit conversion efficiency and \(P_{t}(l)\) is the transmitting power of underwater nodes, which is calculated as  
\[P_{t}(l)=B(l)\times A\bigg{(}l,f_{0}(l)\bigg{)}\times N\bigg{(}f_{0}(l)\bigg{)} \times SNR_{tgt}. \tag{20}\]  
\(N(f(l))\) and \(A(l,f(l))\) are noise parameter and attenuation coefficient, respectively. \(AN=A\bigg{(}l,f(l)\bigg{)}N\bigg{(}f(l)\bigg{)}\) is the channel parameter and can be plotted as a curve in terms of \(f\) for given \(l\). Therefore, for certain \(l\), we can obtain its best frequency \(f_{0}(l)\) and then find its corresponding \(AN\) product, \(N\bigg{(}f_{0}(l)\bigg{)}\times A\bigg{(}l,f_{0}(l)\bigg{)}\) from the \(AN\) curve.  
\(SNR_{tgt}\) denotes the target signal noise rate for the terminal to receive signals correctly. \(B(l)\) is the usable bandwidth given \(SNR_{tgt}\), which is \(B(l)=b\times l^{-\beta}\). The positive parameters \(b,\beta\) depend on the target SNR as well. Unlike the transmit power, the receive power \(P_{r}\) is independent of distance, and rather depends on the complexity of the receive operations. So it could be set as a constant value. More details can be found in [44].  
In conclusion, the energy consumed for each hop in the terms of package length \(L\) and distance \(l\) is calculated as:\[E_{hop}(L,l)\] \[=t_{hop}(L,l)\times(P_{r}+P_{t}^{el})\] \[=\frac{L}{\alpha B(l)}\times(P_{r}+\frac{B(l)N(f_{0}(l))A(l,f_{0}(l ))SNR_{tgt}\times 10^{-17.2}}{\eta}) \tag{21}\]  
Based on such a model, we can calculate the energy consumed by each node which participates the routing.
## 4 6G and Dependability
### Dependability in 6G Networks
#### 4.2.4 Security  
As one of the main concerns regarding modern networks, security in 6G is of paramount importance. Novel technologies in 6G networks will also introduce new security concerns. In this regard, we could mention teraHertz (THz) technology, which is believed to hinder the ability of malicious users to perform eavesdropping; however, recent research has shown it is still possible, although difficult, to intercept the signals, even when transmitted with narrow beams [59]. Quantum communications are also expected to make a significant contribution in 6G networks, mainly from the perspectives of communications security, such as quantum and post quantum cryptography [60]. Nevertheless, the technology is still at its infancy, and although many advances have been made in the quantum cryptography field, there are still issues regarding operation errors in long distance communications. Furthermore, quantum computing can raise significant challenges to existing cryptographic security protocols [61].  
Visible light communication (VLC) can improve wireless communications, as it offers a high bandwidth and is immune to electromagnetic interference. Moreover, VLC faces threats coming from attackers that are capable of positioning themselves within line-of-sight of the target. Physical security is also important, as the nodes of a highly distributed network can be easily targeted by malicious users and damaged as part of a cyberattack [56]. EdgeAI can help provide timely monitoring and response procedures, such as intrusion detection and prevention systems (IDS/IPS), deployed in the vicinity of where the threat originates [22]. Moreover, ML techniques [34] such as federated learning (FL) that enable the applications of AI in a distributed manner, as in the case of EdgeAI, can enable predict and deploy security procedures before a security attack or incident happens. Therefore, in the following section, we discuss the application of FL in 6G networks.
# Improving Adversarial Robustness via Distillation-Based Purification
## 3 Method
### Teacher Models  
Next, we describe the training of two teacher models, as depicted in Figure 2. The teacher models are of the same Convolutional Autoencoder structure, but one is trained adversarially using the PGD attack (AT teacher model) and the other is trained using the original image (NT teacher model).  
The objective function \(L_{AT}\) for training the AT teacher model consists of two loss terms, \(L_{p}\) and \(L_{adv}\), as follows:  
\[L_{AT} =L_{p}+L_{adv}=MSE(f_{AT}(x^{\prime}),\ x)-\log(\sigma(p_{out}-adv _{out}))\] \[=\frac{1}{n}\sum_{i=1}^{n}(f_{AT}(x^{\prime})_{i},\ x_{i})^{2}- \log\Bigl{(}\frac{1}{1+e^{-(p_{out}-adv_{out})}}\Bigr{)} \tag{2}\]  
Here, \(n\) is the number of pixels in an image. \(L_{p}\) computes the Mean Squared Error (MSE) of the original image \(x\) and the purified image \(f_{AT}(x^{\prime})\) (\(x^{\prime}\) is the adversarial example). \(L_{adv}\) is the adversarial loss function, where \(p_{out}\) and \(adv_{out}\) denote the output of the classification model with the purified image \(f_{AT}(x^{\prime})\) and with the adversarial sample \(x^{\prime}\), respectively. These two outputs should be maximized while minimizing the MSE term for training the AT teacher model.  
Next, the NT teacher model is trained to minimize the difference between the restored image and the original image. For this, the loss function \(L_{NT}\) uses the mean square error as shown below:  
\[L_{NT} =MSE(f_{NT}((x),\ x)\] \[=\frac{1}{n}\sum_{i=1}^{n}(f_{NT}(x)_{i}-x_{i})^{2} \tag{3}\]  
Figure 2: Training of the two teacher models, one with adversarial training (NT) using the PGD attack and the other with the normal training (NT) with original images. The \(\epsilon\) value for PGD is set to \(\frac{8}{255}\).  
where \(f_{NT}(x)\) is the image restored by the NT teacher model.  
As a result, the AT teacher model learns to remove noise by restoring the original images from the adversarial images, and the NT teacher model learns to restore original images by extracting the important features. The respective abilities of the two teachers are distilled to a purifier (student model).
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods
### Microbubble Localization
#### 2.2.3 Localization Uncertainty  
Localization uncertainty works as an evaluation metric measuring the accuracy of microbubble positioning. Let \(\sigma\) be the standard deviation of a fitted Gaussian PSF, \(a\) is the super-resolution reconstructed pixel size, \(N\) is the number of microbubbles detected for a given location, and \(b\) is the background signal level in microbubbles calculated as the standard deviation of the residuals between the raw data and our fitted PSF model [26]. The uncertainty in the lateral position of a microbubble can be approximated by the following formula [27]  
\[\langle\Delta x^{2}\rangle=\frac{\sigma^{2}+a^{2}/12}{N}+\frac{8\pi\sigma^{4} b^{2}}{a^{2}N^{2}}. \tag{7}\]  
As for the integrated Gaussian function, this formula still holds. Meanwhile, the \(\sigma\) is reduced and the localization accuracy increased.
_Applied sciences_  
_Article_ Shannon (Information) Measures of Symmetry for 1D and 2D Shapes and Patterns  
**[PERSON] 1,*\({}^{\text{\textregistered}}\)**, [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 2 and [PERSON] 3\({}^{\text{\textregistered}}\)  
\({}^{\text{\textregistered}}\) 1 Chemical Engineering Department, Engineering Faculty, Ariel University, P.O. Box 3, Ariel 407000, Israel; [EMAIL_ADDRESS] (L.L.); [EMAIL_ADDRESS] (M.F.)  
2 Department of Mechanical Engineering and Mechatronics, Faculty of Engineering, Ariel University, P.O. Box 3, Ariel 407000, Israel; [EMAIL_ADDRESS]  
3 Department of Industrial Engineering and Management, Faculty of Engineering, Ariel University, P.O. Box 3, Ariel 407000, Israel; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract** In this paper, informational (Shannon) measures of symmetry are introduced and analyzed for patterns built of 1D and 2D shapes. The informational measure of symmetry \(H_{sym}(G)\) characterizes the averaged uncertainty in the presence of symmetry elements from group \(G\) in a given pattern, whereas the Shannon-like measure of symmetry \(\Omega_{sym}(G)\) quantifies the averaged uncertainty of the appearance of shapes possessing a total of \(n\) elements of symmetry belonging to group \(G\) in a given pattern. \(H_{sym}(G_{1})=\Omega_{sym}(G_{1})=0\) for the patterns built of irregular, non-symmetric shapes, where \(G_{1}\) is the identity element of the symmetry group. Both informational measures of symmetry are intensive parameters of the pattern and do not depend on the number of shapes, their size, and the entire area of the pattern. They are also insensitive to the long-range order (translational symmetry) inherent for the pattern. Additionally, informational measures of symmetry of fractal patterns are addressed, the mixed patterns including curves and shapes are considered, the time evolution of Shannon measures of symmetry are examined, the close-packed and dispersed 2D patterns are analyzed, and an application of the suggested measures of symmetry for the analysis of the chemical reaction is demonstrated.  
**Keywords**: informational measure of symmetry; 1D shapes; 2D shapes; fractal patterns; time evolution; symmetry; pattern
## 2 Materials and Methods
### Statistical Methods  
The obtained results underwent rigorous statistical analysis to provide a comprehensive understanding of the data. Various statistical measures were employed, including arithmetic means (\(\overline{\text{x}}\)), standard deviations (SD), minimum (min), and maximum (max) values, to describe the central tendency and dispersion of the observed variables.  
Furthermore, the relationship between subjectively and objectively measured physical activity was assessed using the Spearman correlation, which measures the strength and direction of the association between two variables. This allowed us to investigate the extent to which subjective and objective measures of physical activity align with each other.  
The Wilcoxon signed-rank test was utilized to determine the differences between physical activity data collected from questionnaires and accelerometers. This non-parametric test takes into account the paired nature of the data and provides insights into whether there are significant disparities between the two measurement methods. Additionally, the matched-pairs rank biserial correlation coefficient effect size for the paired Wilcoxon signed-rank test was calculated, offering a standardized measure of the effect observed.  
Moreover, the association between the subjective and objective assessment tools and the extent to which participants met physical activity recommendations was examined using the chi-squared test of independence. This statistical test enabled us to assess whether there is a significant relationship between the assessment tools and the achievement of physical activity guidelines.  
A significance value of \(p=0.05\) was set to determine the statistical significance of the results. This threshold indicates that findings with \(p\)-values less than 0.05 were considered statistically significant, providing evidence to reject the null hypothesis.  
All statistical analyses were performed using IBM SPSS software version 21, a widely recognized and robust tool for statistical analysis in the scientific community.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 1 Introduction
Therefore, to capture the entire waveform generated from damage, its window length should be set equal to the rise-time of a sloshing impact that has the potential to introduce damages. The sampling frequency of the waveform can be set larger than or equal to the 'Nyquist frequency' of the waveform generated during damages in austenitic stainless steel. According to the studies [14; 22], an AE waveform from damages in austenitic stainless steel contains frequencies of 133 kHz to 270 kHz. Therefore, a default sampling frequency of 5 MHz (in the AMSY-6 AE data acquisition system) is sufficient to provide an actual representation of the waveform. Provided that a fixed window length setting and a sampling frequency is used, the discrete voltage values in a waveform will be independent of threshold and timing settings. Therefore, a burst waveform processed with two different thresholds and timings will have a similar probability distribution. The similar probability distributions will result in similar disorderness. In other words, the disorderness of the waveform will remain unaffected by the threshold and timing settings. Unlike the window length and sampling frequency, there is no rational means to predict an appropriate threshold and timing setting prior to the experiment. As a result, independence of the feature extraction process from the threshold and timing settings can be considered as a significant advantage to AE monitoring. The proposed idea was evaluated by analysing the transient waveform (recorded in an AE data acquisition system) generated from a simulated AE source on a metal plate. The idea was validated by conducting tensile and fatigue tests on austenitic stainless steel (316L) with simultaneous AE monitoring. A background to AE monitoring is provided in Section 2. It is followed by the methodology of the experimental evaluation of the proposed idea in Section 3. After that, results and discussion are presented in Sections 4 and 5, respectively. Section 6 concludes with key findings of the research.
## 5 Comparison and Discussion
It can be seen from Figure 13, the target displacement for both frames is increased as the PGA increases. Generally, fixed models have lower target displacement demands compared to coupled systems on the soil. Again, the model with the uniform lateral load distribution is showing lower demand for the same required acceleration for models with modal distribution. Generally, the uniform load distribution is less accurate than the modal distribution since it does not consider the relationships between the modal displacements of each story. Interestingly, larger increase of the demanded target displacement for models on the soil is found for BF compared to MRF. This could potentially lead to the conclusion that frames with higher stiffness are more sensitive to SSI effects. In Figure 14 the results plotted on the site hazard curve expressed as the return period (T\({}_{\text{R}}\)) vs. the PGA are presented. The points on the curve indicate the PGA value and the corresponding seismic return period when a specific structural model reaches the SD and DL limit state. SSI models resulted in lower peak acceleration and shorter return period which leads to conclusion that fixed-base models are underdesigned. When modal lateral load distribution for MRF is observed, the numerical models for SD limit state show a decreased peak acceleration of 12% and a return period of 33% compared to the uniform load distribution for MRF where peak acceleration increased 5% and return period 17% for the models on the soil compared to fixed-base cases. If the DL limit state is considered, the peak acceleration is decreased 39% and the return period 78% for the modal distribution, while the uniform distribution shows 29% lower peak acceleration and 64% return period. This goes in hand with the higher story drifts for the modal distribution presented before. Figure 12: Comparison of the story drifts (PGA = 0.30 g). Figure 13: Target displacements for incremental N2 method. Site hazard was further calculated for each BF model. For the SD limit state, BF on the soil show a decreased peak acceleration of 37% and return period of 75% for uniform distribution compared to fixed-base models while in the case of the modal load distribution the peak acceleration decreased 44% and return period 83%. DL limit state shows a decrease in peak acceleration of 51% and a return period of 89% for uniform load distribution. Modal distribution for BF DL limit state resulted with a decrease of peak acceleration 58% and a return period of 193%. Overall results show that most of the SD model cases on the soil did not exceed the return period of 475 years, which leads to the concerned conclusion of underdesigned frames. Similar conclusion can be derived for the soil DL cases which drawback the return period of 95 years obligated by Croatian annex.
## 5 Evaluation of the Proposed Monitoring Approach
### Analysis of Environment Setup Constraints  
In the following, we assess the dependence of classification accuracy on the apparent size of the subject in the image frame. We hypothesize a drop in performance of action recognition when the subject's image is not large enough. The objective is to quantify this intuition, to eventually lay out some guidelines on the allowed distance between camera and monitored subject.  
The test set instances are partitioned according to the size (in pixels) of the detected subject, and each block is evaluated in terms of average accuracy. Figure 7 presents the results of this analysis: the accuracy is reported as a stacked bar plot showing the ratio between correctly identified examples and misclassifications. The analysis could be biased by the effective number of samples in each block, as fewer data lead to less reliable estimates. To compensate for this, we also report the samples distribution, encoded both in the opacity of the stacked bar plot, as well as a separate bar plot.  
The Status group shows excellent results at all scales, therefore not putting any concrete constraint on subject size. The Alerting group presents relatively stable results for all size blocks for which there is a significant amount of data, i.e., from 100 pixels on. Finally, the Daily-life group establishes a possible constraint on the subject size to be at least 300 pixels, to guarantee a high enough recognition level.  
By knowing or estimating the intrinsic parameters of an acquisition device, it is possible to exploit the following correspondence between the subject's apparent size in pixels, and their distance from the camera [59]:  
\[distance=\frac{real\_size}{apparent\_size}\left(\frac{image\_size}{sensor\_size}F_{ metric}\right)=\frac{real\_size}{apparent\_size}F \tag{1}\]  
As an example, with a consumer device such as the Microsoft LifeCAM HD 3000 working at \(1280\times 800\) pixels resolution, the estimated focal length \(F\) is 1165 pixels. If we assume an average person to be 1.65m tall (considering the average between mean height of adult males and females in year 2014, from a global study published by NCD Risk Factor Collaboration in 2016 [60]), putting a constraint on the subject being at least 300 pixels large means imposing the camera to be no farther than 6m from the monitored subject.
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Block Diagram of Stream Ciphers Based on Secure PRNGs-CS  
Figure 5 depicts a stream encryption/decryption system's block diagram. The stream cipher method uses the XOR function for both encryption and decryption, by combining the keystream with the plaintext for encryption and with the ciphertext for decryption.  
The security of such a system relies entirely on the PRNG-CS keystream. An encryption/decryption system is considered unconditionally secure when the keystream is perfectly random and the period is infinite (a so-called one-time pad).  
PRNG-CS takes as input a secret key (K) and an initial value (IV) utilized to defeat a known plaintext attack. The IV changes with each new session and can only be used once. Therefore, generated sequences in different sessions using the same secret key will differ. Note that stream ciphers are used to continuously cipher data, such as selective video encryption or network communications. Below we will describe the proposed secure PRNGs-CS in detail.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \begin{tabular}{c} **Logistic** \\ **Map** \\ \end{tabular} & \begin{tabular}{c} **STM** \\ \end{tabular} & \begin{tabular}{c} **PWLCM** \\ \end{tabular} & \begin{tabular}{c} **3D-Ch** \\ **Map** \\ \end{tabular} & \
\begin{tabular}{c} **32-Bit** \\ **LFSR** \\ \end{tabular} &
\begin{tabular}{c} **3D-Ch** \\ **with LFSR** \\ \end{tabular} \\ \hline \multirow{4}{*}{Resources used} & \multirow{2}{*}{Area} & LUTs & 77/0.14\% & 2830/5.32\% & 7374/13.86\% & 286/0.05\% & 2/\textless{} 0.01\% & 319/0.60\% \\  & & FFS & 49/0.05\% & 57/0.05\% & 63/0.06\% & 47/0.04\% & 62/0.06\% & 71/0.07\% \\  & & Slices * & 33/0.25\% & 853/6.41\% & 2171/16.32\% & 87/0.65\% & 16/0.12\% & 99/0.74\% \\ \cline{2-8}  & DSPs & 4/1.82\% & 0/0.00\% & 0/0.00\% & 12/5.45\% & 0/0.00\% & 12/5.45\% \\ \hline \multirow{4}{*}{Speed} & WNSi (ns) & 0.102 & 0.059 & 0.108 & 0.031 & 6.078 & 0.096 \\  & Ti (ns) & 12 & 28 & 31.2 & 22 & 8 & 21.6 \\ \cline{1-1}  & Max. Freq. (MHz) & 84.04 & 35.78 & 32.16 & 45.51 & 520.29 & 46.50 \\ \cline{1-1}  & Throughput (Mbps) & 2689.52 & 1145.27 & 1029.20 & 1456.59 & 16,649.32 & 1488.09 \\ \hline Efficiency (Mbps/Slices) & 81.500 & 1.342 & 0.476 & 16.742 & 1040.58 & 15.031 \\ \hline Power (W) & 0.083 & 0.070 & 0.105 & 0.048 & 0.118 & 0.055 \\ \hline \end{tabular}  
* Note: Each slice contains four 6-input LUTs and 8 flip-flops.  
\end{table}
Table 2: Hardware metrics of studied chaotic maps, the 32-bit LFSR, and the 3D-Chebyshev map coupled with the 32-bit LFSR.