esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 4 Damage Identification Experiment of a Submerged Cantilever Steel Beam
### Influence of Immersion Depth on the Natural Frequency of the Cantilever Beam  
In order to assess the effect of immersion depth on the natural frequencies of the cantilever beam, the dynamic experiment is carried out for an undamaged cantilever beam under the water in a tank of 0.8 m height (see Figure 6). The immersion depths of the cantilever beam are 0 m, 0.2 m, 0.4 m, 0.6 m, and 0.8 m. Acceleration responses are measured by knocking the top of the cantilever beam. When the mass is attached at the top substructure, and the immersion depth is 0.4 m, the corresponding time-domain acceleration response of the beam is shown in Figure 7. Then, the fast Fourier transform is used to transform measured acceleration responses. Natural frequencies can be obtained by extracting the peak of the frequency spectrum. The natural frequencies of the intact cantilever beam under different immersion depths measured experimentally are listed in Table 1. The FE model of the undamaged submerged cantilever beam can be developed using the adding mass method with the liquid-solid coupling. The theoretical natural frequencies of the beam under different immersion depths are shown in Table 1. Mode shapes of the FE model can be calculated, and the first two shapes of the cantilever beam with 0.4 m immersion depth can be seen in Figure 8.  
Figure 8: The first two shapes of the cantilever beam with 0.4 m immersion depth.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Natural Frequencies**} & **1** & **2** & **3** & **4** \\ \hline \multirow{2}{*}{0 m} & Experiment & 5.18 & 32.58 & 84.98 & 167.76 \\ \cline{2-6}  & Theory & 5.32 & 31.97 & 83.23 & 171.74 \\ \hline \multirow{2}{*}{0.2 m} & Experiment & 5.12 & 33.45 & 81.73 & 164.89 \\ \cline{2-6}  & Theory & 5.01 & 31.20 & 85.34 & 163.61 \\ \hline \multirow{2}{*}{0.4 m} & Experiment & 4.42 & 24.74 & 73.90 & 162.23 \\ \cline{2-6}  & Theory & 4.66 & 25.75 & 76.78 & 157.48 \\ \hline \multirow{2}{*}{0.6 m} & Experiment & 4.28 & 21.44 & 68.74 & 152.64 \\ \cline{2-6}  & Theory & 4.34 & 22.36 & 71.74 & 148.75 \\ \hline \multirow{2}{*}{0.8 m} & Experiment & 4.07 & 18.99 & 64.76 & 146.87 \\ \cline{2-6}  & Theory & 4.17 & 20.86 & 63.66 & 145.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: First natural frequencies (Hz) of the submerged cantilever beam.  
Figure 6: Experimental devices of the immersed cantilever.  
Figure 7: Acceleration response.
## 4 Evaluation and Results
### QPE Approximation  
The Monte Carlo simulation and analytical approximation model are illustrated in Figure 5. Figure 5a shows exactly the same result as that in Figure 4b, which confirms Equation (45) in the analytical approximation model. The results of \(\sigma\left[\mathrm{QPE}\right]\) in Figure 5b, however, are presented in a slightly complex form. This is because of the combination of the properties of \(\sigma\left[\mathrm{QPE}_{v}\right]\) and \(\sigma\left[\mathrm{QPE}_{a}\right]\).  
The dashed magenta line in Figure 5b is \(\sigma\left[\mathrm{QPE}\right]_{max}\). This maximum value does not equal the exact maximum of \(\sigma\left[\mathrm{QPE}\right]\); instead, it has a 1.80% relative error with the given simulation parameters. The most important advantage of \(\sigma\left[\mathrm{QPE}\right]_{max}\) is that researchers can calculate this value easily with the aid of only a calculator and the appropriate parameters, combined with Equation (47) and related equations, and an acceptable approximation maximum value can still be determined.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 4 Simulation Settings and Results
### GAN Model Architecture  
The architectures of discriminator and generator are illustrated in Figure 7a,b, respectively. As shown in this figure, both models are designed as fully connected networks with five linear layers. In the case of the discriminator, the input channel sample is flattened into a vector from the original matrix. Considering that the coefficient of the channel is with a complex form, an efficient way to handle this is to split it into the real and imaginary parts. In this experiment, the real part and the imaginary part are treated as two different samples. Hence, the input layer is still designed with \(N_{s}\times N_{f}=672\) neurons. The hidden layer consists of three downsampling layers, which contain 512, 256 and 64 neurons, respectively. The first four layers are each followed by a LeakyReLU active function and a LayerNorm normalized function. Finally, the output layer contains only one neuron followed by a sigmoid function, which outputs a probability \(p_{s}\) to judge the authenticity of the input samples.  
In the case of the generator, the input latent variable \(z\) is designed as a standard Gaussian vector with \(N_{z}=16\) dimensions. To evaluate the compression degree of the original channel matrix by the generator, the compression ratio is defined as the ratio of the dimension of \(z\) to the dimension of \(h\), which is expressed as  
\[\zeta=\frac{N_{z}}{N_{s}\times N_{f}}. \tag{12}\]  
Based on (12), the compression ratio in this experiment is \(\zeta=2.4\%\). It means that the proposed method can also be used for CSI feedback services to reduce the communication overhead drastically. The hidden layer consists of three upsampling layers that are symmetric to the downsampling layers of the discriminator. Similar active and normalized functions are inserted between the first four layers. In the end, the output layer contains 672 neurons connected with the Tanh activation function.  
Other hyperparameters are summarized as follows. The ratio of training rounds between discriminator and generator is set as \(r=2\). To generate synthetic channel samples with high quality, it is recommended to train the GAN model with a large number of epoches [23; 29]. Thus, the parameters of the GAN model are updated by using the RMSprop optimizer with learning rate \(\lambda=0.0001\) for up to 1000 epoches. The loss function \(F_{T}\) is set as the binary cross entropy function. In addition, the loss function \(F_{u}\) in Algorithm 2 is set as the \(L_{1}\)-norm function.
# Part-Treatment Efficacy Prediction Using a CMA-ES-Based Dendritic Neuron Model
## 1 Introduction
To improve the classification performance of the model, [PERSON] et al. used the states-of-matter search algorithm to improve the performance of the model [29]. [PERSON] et al. also used a heuristic algorithm (a biogeography-based optimization algorithm) to train the dendritic neuron model [30]. [PERSON] et al. used a decision-tree-based algorithm to initialize the weights of the dendritic neuron model [31], which effectively prevented the backpropagation algorithm from converging prematurely. The development of dendritic neuron models in several application areas has also attracted significant attention. [PERSON] et al. applied dendritic neuron models to wind-speed prediction and achieved excellent results [32]. [PERSON] et al. improved the model structure based on the dendritic neuron model and applied the improved model to financial time-series prediction [33]. [PERSON] et al. proposed the evolutionary dendritic neuron model, which has demonstrated good performance in the field of computer-aided diagnosis [34]. The performance of the dendritic neuron model has been greatly improved and successfully applied in several fields. However, to the best of the authors' knowledge, applying the dendritic neuron model to wart-treatment efficacy prediction has not yet been well explored. This motivates us to use the dendritic neuron model to address the wart-treatment efficacy prediction problem. In this study, to further improve the performance of wart-treatment efficacy prediction, we used the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the dendritic neuron model (DNM). The CMA-ES is considered more interpretable than other heuristic algorithms and has powerful optimization performance. The experimental results show that the improved DNM outperforms other comparable machine learning models in six metrics. It is worth mentioning that the specific pruning mechanism of the DNM can simplify the structure of the trained model. The proposed method can provide appropriate decision support for physicians. The contribution of this paper is threefold. First, a novel machine learning model, the DNM, is proposed for wart-treatment efficacy prediction. Second, the CMA-ES is incorporated as the training method of the DNM. Third, the experimental results demonstrate the advantages of the proposed CMA-ES-based dendritic neuron model in wart-treatment efficacy prediction. The remainder of this paper is organized as follows. Section 2 presents a description of the DNM. Section 3 explains how the CMA-ES trains the DNM to address the wart-treatment efficacy prediction problem. Section 4 provides the experimental studies and discussion. Finally, the conclusions of this paper are presented in Section 5.
## 5 SCODE_plus_: Enhanced Secure Routing and Key Management Scheme
### Secure Data Dissemination Scheme to Mobile Sinks
#### 5.3.4. Coordination Election  
As aforementioned, the proposed scheme is based on the GAF to establish a coordination network. During the discovery phase, a node with the highest ranking will stay awake and play a role of a coordinator while the others fall into sleeping mode. The node ranking is determined by an application-dependent ranking procedure (it can be an arbitrary ordering of nodes to decide which nodes would be active, or it can be selected to optimize overall network lifetime). In GAF, a node with a longer expected lifetime is assigned a higher rank. This rule put nodes with longer expected lifetime into use first. However, this is very vulnerable. An adversary can use a compromised node to advertise the highest rank so that this node can be active all the time.  
Practically, it is very hard (perhaps not possible) to absolutely defend against node compromise attacks for GAF. Therefore, we modify the discovery process and the ranking rule of GAF in order to reduce the risk without lessening its advantages. A coordinator is orderly selected according to node  
Figure 6: An example of routing in SCODE_plus_.  
ID at each discovery round. For example, there are \(m\) nodes \(\{\mathit{ID}_{\mathit{1}}\), \(\mathit{ID}_{\mathit{2}}\),..., \(\mathit{ID}_{\mathit{m}}\}\) (\(\mathit{ID}_{\mathit{1}}\)\(<\)\(\mathit{ID}_{\mathit{2}}\)\(<\)... \(<\)\(\mathit{ID}_{\mathit{m}}\)) in a certain cell, if node \(\mathit{ID}_{\mathit{i}}\) is a coordinator at the current round, then \(\mathit{ID}_{\mathit{i}+\mathit{1}}\) would be a coordinator at the next round; if \(i=m\) then \"\(i+1\)\" would be 1. By doing this way, the adversary cannot make her node active all the time at will. The compromised node is active only if it is a coordinator at the given round. On the other hand, our enhancement reduces communication overhead compared with GAF since nodes do not need to broadcast discovery messages.  
Once a new coordinator is elected, the old coordinator forwards all current routing operations to the new coordinator before it falls into the sleeping mode. That includes routing packets (if the transition occurs during the routing operation), the routing caching table containing uplink and downlink nodes, etc. The information is encrypted by their pairwise shared key. By doing this, it will not effect to the existing dissemination flows.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 4 (M-C)-BKA-MLP Prediction Model
### Model Comparison Before and After Optimization  
The performance of the single MLP model was compared against that of the BKA-MLP model, with detailed results presented in Table 3. The analysis demonstrates that in comparison with the single MLP prediction model, the BKA-MLP prediction model exhibits reductions in MAE and RMSE by 0.68% and 2.3%, respectively. The relative improvements are 35.42% and 48.94%, respectively. This indicates that the optimized model has achieved substantial reductions in initial error. This indicates that the BKA-MLP model's predictions are closer to the true values and exhibit higher accuracy. The MBE values for the two models are \(-\)0.0019 and 0.0004, respectively, indicating a negative bias of 0.19% for the MLP model and a positive bias of 0.04% for the BKA-MLP model, resulting in an absolute bias reduction of 0.15%. R\({}^{2}\), which measures the proportion of variance explained by the model, increased by approximately 10.8% for the BKA-MLP model compared to the MLP model. This suggests that the optimized model has a superior fitting effect and can account for a larger portion of the data variability. Overall, the BKA-MLP prediction model based on the M-C strength criterion demonstrates superior performance across various metrics, showing enhanced stability and feasibility in multiple training tests.  
Figure 5 illustrates the prediction outcomes and residual analysis graphs for both the MLP model and the BKA-MLP model on the test set. From Figure 5a,c, it is evident that among approximately 140 prediction samples, the predicted safety factor values of the MLP model are notably higher than the actual values around sample numbers 10 and 30, with localized deviations observed around sample numbers 40 and 60. In contrast, the predictions from the BKA-MLP model align closely with the true values, exhibiting minimal error and superior fitting performance. Analysis of the residual plots for both models reveals that most residual errors are randomly distributed around zero, indicating no discernible trend or pattern, thus supporting the reasonableness of the linear assumption and the absence of significant heteroscedasticity. Among the 138 prediction samples, only one data point in the BKA-MLP model's residuals exceeds \(\pm\)0.1, a reduction of seven data points compared to the MLP model, resulting in a residual distribution that is more tightly clustered around zero with fewer outliers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **MAE** & **MBE** & **RMSE** & **R2** \\ \hline MLP & 0.0192 & \(-\)0.0019 & 0.0472 & 0.8416 \\ BKA-MLP & 0.0124 & 0.0004 & 0.0241 & 0.9499 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MLP and BKA-MLP index results.  
Figure 5: Comparison of MLP and BKA-MLP results.
# Detection of DDoS Attacks in Software Defined Networking Using Entropy
###### Abstract  
Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.  
software defined networking; entropy; distributed denial of service attacks +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Cell Viability Assay  
Due to their different sizes and growth rates, SiHa and Ca Ski cells were seeded at a density of 10,000 cells; HeLa, 8000 cells; HEK 293, 5000 cells; and RPE-1, 4000 cells per well in 96-well plates. The next day, the cells were exposed to the tested ferrocenes diluted in dimethyl sulfoxide (DMSO; Sigma-Aldrich, St. Louis, Missouri, USA) in concentrations from 0 to 100 \(\upmu\)M (each in pentaplicate) for 24 h. Cell viability was measured using colorimetric MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) assay as described previously [25]. All experiments were performed in three independent runs (twice,when cytotoxicity was over 100 \(\upmu\)M). Data from cytotoxicity assays were measured using a Microplate Reader: Infinite(r) M1000 PRO (Tecan, Mannedorf, Zurich, Switzerland) and analyzed using GraphPad Software (San Diego, California, USA) as IC\({}_{50}\) values (concentrations of compounds that cause metabolic inhibition of 50% of cells).
## 4 Data Reducing Methods
### Induced Velocity Correction for Determination of the Effective Angle of Attack  
The inflow speed and direction measured by probe, \(V_{p}\) and \(\alpha_{p}\) are shown in Figure 9. \(V_{p}\) comprises the induced velocity of lift circulation \(V_{i}\) and the undisturbed inflow velocity \(V_{e}\). Effective angle of attack, \(\alpha_{e}\), is the angle between the undisturbed inflow velocity \(V_{e}\) and the chord line. Thus, the effective angle of attack at the probe section is corrected by Equation (7):  
\[\alpha_{e}=\alpha_{p}-\alpha_{i} \tag{7}\]  
Figure 8: Sketch diagram of the body-axis and air-axis force.  
The iteration steps to calculate \(\alpha_{e}\) are shown in Figure 10.  
Step 1: Assume the angle of attack measured by probe, \(\alpha_{p}\), is the initial angle of attack, and calculate the sectional lift by integration of the pressure distribution and Equation (6).  
Step 2: Compute the lift circulation by the Kutta-Joukowski theorem, as shown in Equation (8), where \(V_{p}\) is the inflow speed measured by probe, \(\beta\) is sideslip angle measured by probe, \(L\) is the sectional lift calculated by Step 1, and \(\rho\) is the air density measured by atmospheric pressure and temperature meters:  
\[\Gamma=\frac{L}{\rho V_{p}\cos\beta} \tag{8}\]  
Step 3: Let us consider the blade as a lift line and compute the induced velocity, \(V_{i}\), by the Biot-Savart integral law, as shown in Equation (9), where \(|AB|\) is the distance between probe tip to lift line, and \(a_{1}\), \(a_{2}\) are shown in Figure 11:  
\[V_{i}=\frac{\Gamma}{4\pi|AB|}(\cos a_{1}+\cos a_{2}) \tag{9}\]  
Figure 10: The flowchart to calculate the effective angle of attack.  
Figure 9: Effective angle of attack measured by leading-edge probe.  
Step 4: Compute the new inflow velocity, \(V_{n}\), by velocity resolution of \(V_{p}\) and \(V_{i}\), and compute the new angle of attack, \(\alpha_{n}\).  
Step 5: Calculate the error between the new angle of attack and the initial angle of attack used in Step 1. If the error is less than 0.1\({}^{\circ}\), the effective angle of attack is equal to the new angle of attack. If not, repeat Steps 1 to 4, replacing the initial angle of attack with the new angle of attack.
## 3 Problem Description and Proposed Solution
### Method of the Proposed Approach
#### 3.2.3 Optimized PC Algorithm Based on Knowledge Graph  
As mentioned in Section 1, solving the problem of abnormal diagnosis in the application of a complex MSA is very challenging because the O&M personnel are highly dependent on their domain knowledge when diagnosing the root cause. In addition, it is very labor-intensive to rely on manual diagnosis only. Therefore, we propose an approach to assist root cause diagnosis through a knowledge graph. The specific implementation is to improve the causality graph described in Section 3.2.2 by using a knowledge graph. We refine the causality graph based on a PC algorithm combined with a knowledge graph. The basic goal of the causality graph is to find the parent node and child node set PC (X) of node X and to find the V-structure to learn the DAG structure. Algorithm 1 details the implementation.
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
## 3 Derivation of Probability-Stress-Life Curves
### Statistics Method for P-S-N Derivation
\[x_{c}(\log C)=x_{m}(\log C)-k\cdot Std(\log C) \tag{5}\]  
\[k=t_{p,n-1}/\sqrt{n}+\Phi_{\alpha}^{-1}.\sqrt{(n-1)/\chi_{(1+\beta)/2,n-1}^{2}} \tag{6}\]  
where \(x_{c}\) is the characteristic value; \(x_{m}\) and \(Std\) stand for the sample mean and deviation, respectively; \(n\) is the number of tested specimens; \(t_{p,n-1}\) is the critical value of t-distribution with \(n-1\) degrees at the confidence level \(p\); \(\Phi_{\alpha}^{-1}\) is critical value of standard normal distribution at the survival rate \(\alpha\); \(\chi_{(1+\beta)/2,n-1}^{2}\) is the critical value of chi-square distribution with \(n-1\) degree at the confidence level \((1+\beta)/2\). As per the suggestion by IIW [36], the survival rate \(\alpha=95\%\) can be applied with the confidence levels \(p=\beta=75\%\) in deriving the characteristic value of \(\log C\). It can be also found that the IIW method takes into consideration the influence of the number of specimens on fatigue strength, through incorporating the uncertainty in estimators.
# Smart Antenna for Application in UAVs
## 3 P-ESPAR Antenna Study  
In this article, we present the development of a low-cost prototype of a P-ESPAR antenna with two layers of air and one layer of FR-4. This antenna arises in the context of a previous work, where the design of an antenna with substrate RT Duroid 5870 was carried out by [PERSON] [12].  
In this sense, the configuration and simulation of the antenna is presented as well as the conclusions regarding the comparison of the results obtained through the CST Microwave Studio simulator.  
The sizing of the P-ESPAR antenna took as mandatory requirements the parameters presented in Table 1.
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Congestion Control
#### 3.1.1 Congestion Control with Predefined Rules
In the first phase, DBCCA minimizes the delay gap of different paths and obtains a rate distribution vector. In the second phase, the congestion window is adjusted based on the rate distribution vector acquired in the first phase and RTT. DBCCA also shifts traffic to paths with lower losses to improve overall throughput in heterogeneous networks. [PERSON] et al. [56] proposed CWA-MPTCP, a congestion control algorithm for MPTCP. CWA-MPTCP takes goodput as an important metric to adjust the congestion window. Since packets have a fixed sequence in TCP transmission, the delay difference of the paths would result in out-of-order packets arrival in receiver buffer, which will reduce the goodput. This is because the out-of-order packets need to take some time to be reordered in receiver buffer. CWA-MPTCP aims to improve the throughput of MPTCP in a condition where the delay difference of different paths is large.