esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Morphological Study  
Morphological analyses were performed to evaluate the fibrous structures of the fabrics. These studies confirmed that the fibrous structure remained intact after the addition of the AgNPs. The fibrous structures at 5000\(\times\) magnification and the normal distribution of the fiber diameters in the PLA-based nanofibrous textiles are shown in Figures 2 and 3, respectively. The nanostructures of the membranes in samples M1, M2, M3, and M4 displayed uniform, smooth, and beadless fibers, suggesting that the electrospinning parameters were unaffected by the incorporation of AgNPs. The nanofibrous fibers examined in this study were notable for their smoothness and absence of beads or droplets. Using the built-in FE-SEM software (PC-SEM Ver2, 0, 0, 8.), the average diameters for M1, M2, M3, and M4 were determined to be 435, 590, 823, and 644 nm, respectively. Compared to the pure sample shown in Figure S1, the reduction in the mean diameter observed in M1 and M2 was attributed to the presence of AgNPs. This decrease was attributed to the repellent force between the nanoparticles, which decreases the entanglement of the polymer chains. In the M3 and M4 samples, the nanofiber surfaces were free of beads. Nevertheless, the fibers showed a propensity to stick together (highlighted by yellow circles in Figure 2). This adhesion occurred even though identical manufacturing conditions were employed for all samples. As a result, the fibers lacked proper orientation, became thicker, and exhibited an irregular distribution, as shown in Figure 3[31].  
Figure 3: Average diameters and normal distributions of samples M1, M2, M3, and M4.  
Figure 2: FE-SEM morphological images of the prepared PLA-based nanofibrous electrospun fabrics: (M1–M4).
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 3 Solution to the Problem
### Modified Transfer Matrix Method
The total transfer matrix is defined as:  
\[\mathbf{\mathrm{F}}= \begin{cases}\underbrace{\mathbf{\mathrm{A}}\mathbf{\mathrm{\bullet}} \mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{ \bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}}_{N}& \text{N is odd}\\ \underbrace{\mathbf{\mathrm{B}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{ \mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}} \mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}\mathbf{\mathrm{\bullet}}}_{N}&\text{N is even}\end{cases}  \tag{32}\]  
where \(\mathbf{F}\) is a \(4\times 4\) matrix with component \(F_{ij}\), (i, j = 1-4). Substituting Equations (30) and (32) to Equation (31) yields:  
\[\lambda_{0}kh\mathcal{C}_{1}^{(1)}-(\lambda_{0}+2\mu_{0})C_{4}^{(1) }=0,C_{2}^{(1)}+kh\mathcal{C}_{3}^{(1)}=0\] (33) \[(\lambda_{H}+2\mu_{H})\sum\limits_{j=1}^{4}F_{4j}C_{j}^{(1)}-kh \lambda_{H}\sum\limits_{j=1}^{4}F_{ij}C_{j}^{(1)}=0\] \[kh\,\,\,\,\,\,\,\,\,\,\,\,\,\,Equation (33) is a set of linear algebraic equations with respect to \(C_{j}^{\dagger}(j=1-4)\). In accordance with the sufficient and necessary condition that a nontrivial solution should exist, the determinant of the coefficient matrix should be equal to zero. This condition leads to the following dispersion relationship for Lamb waves in a layered FG nano copper wafer:  
\[\left|\overline{T}_{ij}\right|=0 \tag{34}\]  
where:  
\[\overline{T}_{11}=\lambda_{0}kh,\overline{T}_{14}=\lambda_{0}+2\mu_{0}, \overline{T}_{22}=1,\overline{T}_{23}=kh\]  
\[\overline{T}_{3j}=(\lambda_{H}+2\mu_{H})F_{4j}-kh\lambda_{H}F_{1j},\overline{T }_{4j}=khF_{3j}+F_{2j},j=1-4\]  
All other terms are equal to zero.
## 4 Results and Comparisons with Other Surface Water Products
### Evaluation of the SAR NN Classification Method

Table 7: Confusion matrices (in numeric and percentage forms) of the SAR-predicted surface water maps and the Landsat-8 reference surface water maps, over the Tonle Sap Lake (**Left**) and over the Mekong River (**Right**). Figure 9: **(a,d)** SAR surface water maps; (**b,e**) Landsat-8 surface water maps; and (**c,f**) their differences; over the Tonle Sap Lake (left), and over the Mekong river (right), for February 2016. Blue color presents water pixels while orange color presents non-water pixels detected by both Sentinel and Landsat, green color is Landsat water/Sentinel non-water pixels, and light blue color is Sentinel water/ Landsat non-water pixels. * The SAR responses can be affected by complex interactions with the terrain and the vegetation, especially along small river banks. It can be difficult to account for this local complexity in the methodology. * In the SAR water detection method, as in any other classifications method scheme, different parameters were selected to optimize the overall performance of the method, but local ambiguities can still exist. * Sentinel-1 and Landsat-8 data are not always acquired on the same day. * Using Landsat-8 quality flags, we can remove cloud-covered pixels, but we cannot detect cloud-shadow pixels causing ambiguities in the NN training dataset. * Reference surface water maps derived from negative NDVI values on the Landsat-8 images are not always perfect. Water under vegetation can be difficult to detect with Landsat-8 observations. The NDVI values can also be impacted for highly turbid waters where the NIR reflectance can be higher than the red reflectance.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 2 Related Work
They implemented two algorithms for the comparison of our results: Cuda-deep neural network gated recurrent unit (CuDNN-GRU) and Cuda-bidirectional long short-term memory (CuBLSTM). Upon their comparison, they found their algorithm to perform both efficiently and accurately. [PERSON] [19] used a machine-learning-based approach to detect and classify different types of network traffic flows. The proposed approach is validated using a new dataset experiencing various modern types of attacks such as HTTP flood, SID DoS and normal traffic. They claimed that their algorithm produced the best results as compared to Random Forest and Naive Bayes algorithms. As mentioned, DL algorithms have shown enhanced results for detecting normal DDoS attacks. Furthermore, researchers improve DL algorithms or combine them with relevant technologies so that their solution can perform better in an SDN environment. However, these solutions encounter the problem of further increases in training data. That said, the authors of [20] have already impacted the problem of adversarial attacks, simply by applying the most basic GAN framework instead of improving it to make it applicable to the SDN environment. Moreover, [PERSON] et al. [21] proposed that the deep learning model's accuracy depends on the preprocessed data quality. Therefore, efficient training of the DL model requires suitable preprocessing techniques. To solve these problems, we proposed an adversarial DBN-LSTM method for detecting and defending DDoS attacks in SDN environments.
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on the Sample Type
#### 4.1.1 Diagnosis Using Blood Plasma  
Multiple approaches to diagnosing viral hepatitis using blood plasma as the body fluid test samples have been proposed. In 2017, [PERSON] et al. studied HCV-infected human blood plasma samples using Raman spectroscopy to identify the biochemical fingerprints associated with HCV infection [18]. Identifiable changes in the Raman spectra of infected and normal samples were found. These spectral changes were proposed to be linked with the presence of viral proteins and fatty acids in the blood plasma of infected patients and could be used for the diagnosis of HCV. Further validation was performed by applying PCA and PCA-LDA, which demonstrated that normal and diseased samples could be clearly differentiated between with more than 98% sensitivity and specificity. Further attempts to predict the severity of the disease were made by applying PLSR modeling, and the viral load predictions were found to be 99%, comparable to the clinical PCR results. Another similar attempt was made by [PERSON] et al. in 2019 by using blood plasma samples from healthy and HCV-infected volunteers to identify changes in the biochemical features using Raman spectroscopy [67]. Changes proposed to be linked with the nucleic acid, viral RNA, and lipid and proteins of HCV were demonstrated in the Raman spectra. PCA was applied to differentiating between healthy and infected samples for further validation of this method. Additionally, the infected samples were divided into three groups, low, medium, and high viral loads, and successfully differentiated between using PCA.  
In 2020, [PERSON] et al. tested the potential of Raman spectroscopy for the diagnosis of hepatitis B using blood plasma samples [20]. The Raman spectra were recorded for 10 normal and 24 HBV-infected samples, and PCA-SVM multivariate data analysis techniques were applied to differentiating between the healthy and diseased samples. The models showed greater than 98% sensitivity, specificity, and accuracy in distinguishing between the healthy and HBV-infected plasma samples. Further validation of the model was performed using PLSR, showing a significant percentage of agreement with the clinical methods. [PERSON] et al., in the same year, studied 1000 blood plasma samples, including 600 HBV-infected and 400 healthy samples, using Raman spectroscopy combined with pretrained convolutional neural networks based on ResNet101 [34]. It was claimed that the developed model was capable of detecting minute differences between diseased and healthy spectral data with >99% sensitivity, specificity, and accuracy. The developed model was claimed to have a 7% higher accuracy compared to that of PCA-SVM and PCA-LDA convolutional approaches.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 2 Partitioned Step Particle Swarm Optimization
### Performance Tests
#### 2.3.2 Benchmark Functions  
The list of unconstrained functions can be found in Table 2, with its corresponding property taken from [24]. Moreover, five benchmarks constrained optimization problems were also used for the testing, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & \multicolumn{2}{c}{**Parameter Setting**} \\ \hline PSO & \(c_{1}=c_{2}=2\), \(\omega_{\min}=0.4\), \(\omega_{\max}=0.9\) \\ CPSO & \(c_{1}=c_{2}=2\), \(\omega_{\min}=0.4\), \(\omega_{\max}=0.9\) \\ PSOCO & \(c=1.49618\), \(\omega=0.7298\), CR = 0.05, G = 7 \\ WICPSO & \(c_{\max}=2.2\), \(c_{\min}=1.5\), \(c_{\max}=2.2\), \(c_{\min}=1.5\), \(\omega_{\min}=0.4\), \(\omega_{\max}=0.9\) \\ APSO & \(c_{1}=c_{2}=2\), L = 3, C = 1 \\ ScPSO & \(c_{1}=c_{2}=2\), \(\omega_{\min}=0.4\), \(\omega_{\max}=0.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for compared algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Function Name** & **Characteristics** & **Range** \\ \hline f1 & Sphere & C, D, S, Sc, U & \([-10,10]\) \\ f2 & Rosenbrock & C, D, NS, Sc, U & \([-30,30]\) \\ f3 & Chung Reynolds & C, D, PS, Sc, U & \([-100,100]\) \\ f4 & Dixon and Price & C, D, NS, Sc, U & \([-10,10]\) \\ f5 & Powell Singular & C, D, NS, Sc, U & \([-4,5]\) \\ f6 & Step & DC, ND, S, Sc, U & \([-100,100]\) \\ f7 & Cosine Mixture & DC, ND, S, Sc, M & \([-1,1]\) \\ f8 & Ackley 1 & C, D, NS, Sc, M & \([-35,35]\) \\ f9 & Alpine & C, ND, S, NSc, M & \([-10,10]\) \\ f10 & Egg Holder & C, D, NS, Sc, M & \([-512,512]\) \\ f11 & [PERSON] & C, D, NS, Sc, M & \([-100,100]\) \\ f12 & [PERSON] & C, D, NS, NSc, M & \([-100,100]\) \\ f13 & [PERSON] & C, D, S, Sc, M & \([-05,0,5]\) \\ f14 & [PERSON] & C, D, S, NSc, M & \([-500,500]\) \\ f15 & [PERSON] & C, D, NS, Sc, M & \([-100,100]\) \\ f16 & [PERSON] & C, D, S, Sc, M & \([-500,500]\) \\ f17 & [PERSON] & C, D, S, NSc, M & \([-10,10]\) \\ f18 & Stretched V Sine Wave & C, D, NS, Sc, U & \([-10,10]\) \\ f19 & [PERSON] & C, D, NS, NSc, M & \([-5,5]\) \\ f20 & Trigonometric 2 & C, D, NS, Sc, M & \([-500,500]\) \\ \multicolumn{2}{c}{} & Nomenclature: Continuous (C), Differentiable (D), Discontinuous (DC), Multimodal (M), Non-Differentiable (ND), Non-Separable (NS), Non-Scalable (NSc), Partially Separable (PS), Separable (S), Scalable (Sc), and Unimodal (U) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Benchmark functions (Unconstrained).
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 1 Introduction
#### What Makes Multilayer Networks Different Than Single-Layer in Transportation  
Having given the broader context for our work, we now highlight the main features of transport on multilayer networks. The presence of edges between layers (inter-layer edges) makes a multilayer network fundamentally distinct from a standard single-layer one, as these edges allow passengers to switch between transportation modes. However, this is not the only difference. In fact, in a multilayer network, the various layers have different characteristics. The main one is that the type of transportation cost varies across layers. For example, the cost to build and maintain the infrastructure differs depending on the transportation mode, with subway or rail tracks costing more than a road network. Moreover, the cost assigned to traffic congestion is also different, as road networks are more sensitive to traffic bottlenecks than rail ones. In addition, the power dissipated differs depending on the means of transportation, as running a tram generally produces fewer CO\({}_{2}\) emissions than running a bus. All these different features impact the results of an optimal transport problem, as the network features contributing to the cost function to be optimized vary with layers, and thus also the optimal solution.  
Finally, the network topologies themselves vary with layers [41], as a bus network has many edges with short lengths, while a rail network tends to have fewer but longer edges. In addition, the weights assigned to each edge differ based on the layer, which can induce coupling between layers [42].
## 4 Simulation Model of the ISP Servo System
### Simulation Parameter Settings  
In order to study the influence of three internal disturbance factors, such as friction, transmission error, and backlash, and three external disturbance factors, such as road disturbance torque, equivalent inertia, and mass imbalance torque, on the servo performance of the system, it is necessary to set the reference value of each disturbance factor.  
1. Internal disturbance factors  
According to the experimental platform, the friction torque, transmission error, and backlash are tested, and the model is established according to the modeling method in Section 3. The experimental results are taken as the reference value, and the three internal disturbance factors are evenly distributed in the range of 0.5 times to 2 times the reference value.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Voltage conversion factor \(K_{g}\) (A\(\cdot\)V\({}^{-1}\)) & 1.3 \\ Motor moment coefficient \(K_{m}\) (N\(\cdot\)m \(\cdot\)A\({}^{-1}\)) & 0.1538 \\ Motor rotor inertia \(J_{m}\) (kg\(\cdot\)m\({}^{2}\)) & 2.74 \(\times\) 10\({}^{-4}\) \\ Equivalent load inertia \(J_{l}\) (kg\(\cdot\)m\({}^{2}\)) & 5.76 \\ Transmission ratio \(N\) & 101 \\ Number of incremental encoder lines & 4096 \\ Incremental encoder subdivision factor & 4 \\ Number of encoder lines & 36,000 \\ Encoder subdivision factor & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental system parameters.  
Figure 4: Simulation model of the ISP servo system.  
2. Road disturbance torque  
The random disturbance torque caused by driving on the D-class road at a speed of 10 m/s is used as the reference of the road disturbance torque, and the road disturbance torque is uniformly changed in the range of zero torque and two times the reference value.
3. Equivalent inertia  
Taking the inertia of the lightest pointing device as the reference value, the inertia of the heaviest pointing device is about 1.6 times the reference value, so the equivalent inertia is uniformly changed in the range of the reference value and 1.6 times the reference value.
4. Mass unbalanced torque  
The mass imbalance torque is based on the torque generated by the 1.5 mm center of gravity offset, and the mass imbalance torque is uniformly changed in the range of 0.5 times to 2 times the reference value.  
In order to reduce the amount of data and shorten the simulation time, the parameters of the six factors are divided into seven equal parts, and seven levels of influencing factors are set. The simulation parameter settings for the six factors relative to the reference value are shown in Table 2.
## 3 Results
### Enzymatic Activity of Antioxidants  
ROS can induce skin cell oxidative damage. An increase in ROS levels can lead to DNA damage, oxidative stress, tumor progression, and disruption of redox homeostasis, which are linked to the emergence of numerous diseases. The function of antioxidants is essential to preventing oxidative cell damage [36]. Antioxidant mechanisms rely on enzymes (SOD, CAT, GPx, GST, etc.) that inhibit excessive ROS production [44]. SOD slows the aging process by reducing the generation of anion superoxide, which is produced during the early stages of oxidative damage [45]. CAT transforms superoxide into oxygen and hydrogen peroxide. Cells that use CAT and GPx often catalyze the quick breakdown of hydrogen peroxide. GSTs primarily function as cytoprotectors by inducing the association of reduced glutathione (GSH), which results in reactive electrophiles.  
To investigate the antioxidant enzymatic activity caused by tatheebo and roasted tatheebo, HaCaT cells were treated with both extracts, and GPx, SOD, CAT, and GST levels were examined. Roasted tatheebo increased the protein expression of GPx, SOD, CAT, and GST more than unroasted tatheebo extract. H2O2 reduced the expression of these proteins, and treatment with roasted tatheebo reversed that effect of H2O2. Figure 3 shows that after treatment with H2O2 (100 mM), the activities of the four enzymes were downregulated. Treatment with tatheebo or roasted tatheebo recovered the enzyme activities compared with the control. Moreover, roasted tatheebo appeared to have more antioxidant properties because it increased the activity of the antioxidant enzymes more than unroasted tatheebo did (Figure 3).  
Figure 2: An assessment of the cytotoxicity of tatheebo (TA) and roasted tatheebo (roasted TA) to cells (**A**) on HaCaT cells, (**B**) on RAW264.7 cells, and (**C**) on lung cancer A549 cell lines versus non-treated cells. The mean as well as the standard deviation for four different replicates are shown in the graph. **\(\mathbf{\mathrm{\text{{++}}}}\)\(\mathbf{p}\)\(<\) 0.001 denotes significant deviations from control sets.
# Reducing the Complexity of Musculoskeletal Models Using Gaussian Process Emulators
## 2 Materials and Methods
### Model Reduction  
#### 2.3.1 Sobol's Sensitivity Analysis  
In order to assess the model-dependency of SA, a global Sobol's sensitivity analysis [40] was performed independently on SGM and SSM. This allowed the evaluation of the contribution of individual muscle \(\mathrm{F_{max}}\) variations to overall variations in the output JCFs. SA decomposes the output variance and ranks the contribution of individual inputs by mean of the Sobol's indices, real numbers ranging from 0 to 1, with 1 signifying that the entire variability of the output can be ascribed to the variation of a single input. The [PERSON] algorithm [24] was used to sample the input parameter space and generate 30,720 virtual subjects. For each virtual subject, the values of each \(\mathrm{F_{max}}\) were sampled from a normal distribution with mean and standard deviation derived from the measured distributions. The number of virtual subjects was chosen to guarantee convergence of the Sobol's algorithm, which was implemented through the SALib Python library [41]. The trained GPs were used to predict the hip, knee, and ankle JCF waveforms for each virtual subject.  
Sobol's indices ascribe fractions of the output variance to variations of individual outputs, but do not account for the size of the output variance. To account for this a new metric, VRSI (variance renormalised Sobol's indices), was defined in order to normalise the Sobol's indices based on the size of the output variance:  
\[VRSI_{it}=\frac{S_{it}*V_{t}}{\sum_{i}S_{it}*V_{t}}\]  
where \(S_{it}\) is the Sobol index of input \(i\) with the JCF at time \(t\), and \(V_{t}\) is the variance of the JCF at time t. By means of this normalisation process, we deemed as less important those inputs that showed high Sobol's index in a region of the gait cycle where the output variance was small.  
A further analysis was conducted on the SSM data using the emulated dataset and the VRSI values. Pearson's Product-Moment correlation (alpha = 0.05) was calculated between the input \(\mathrm{F_{max}}\) and the peak values of JCF only for those muscles presenting a VRSI above 0.1 at the peaks. Significant correlations, with either moderate ( \(\mathrm{I\,R}\) \(>\) 0.5) or strong ( \(\mathrm{I\,R}\) \(>\) 0.7) correlation coefficient, were presented and discussed.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 7 Numerical Results
### P2P System with Countermeasures for the Malware Attack  
In this case, the system has incorporated different countermeasures to avoid infections of the malware. However, no countermeasures are completely effective, and some peers still get infected. This vulnerability of cybersecurity countermeasures is modeled with the probability \(P_{I}\). Hence, low (high) values of \(P_{I}\), correspond to the case in which either  
Figure 10: Average number of healthy and infected peers under a malware attack for different values of \(\gamma\), \(\eta\), and \(\lambda\). (**a**) Variation of infected nodes and \(\gamma\). (**b**) Variation of infected nodes and \(\theta\). (**c**) Variation of infected nodes and \(\lambda\).  
the countermeasures are very effective (inefficient) or the malware attack is weak (strong) and cannot (can) infect nodes. This effect is clearly seen in Figure 11 where infected peers drastically diminish as \(P_{l}\) approaches the value of 0, avoiding most of the potential infections in the system. These results were obtained using \(\gamma=1\), \(\eta=0.85\), and the new user arrival rate \(\lambda=1\). The running time was 1 hour and 33 minutes, which exceeded the previous execution times, accounting for the added complexity of the system. Note that the number of initial nodes no longer has any effect on the number of infected nodes. As such, the introduction of countermeasures is an effective way to control the spread of malware. From the practical side, the system administrator can clearly see from the number of infected nodes the effectiveness of the countermeasures to produce more effective tools if required. Moreover, as time passes, some countermeasures that were efficient in the beginning may become obsolete as time passes by, which would be reflected in a higher portion of infected nodes.