esa-sceva/satcom-chunk-collection huggingface dataset:
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 6 Performance Evaluation
### Energy Expenditure for Report Generation  
The authors compare the performance of PDF, DSEDA, and PKAEF in terms of energy expenditure for report generation. Since how to select the \(t\) participating nodes is not mentioned in PDF, the authors adopted the same selection method used in PKAEF for PDF in the simulation and overlooked the energy expenditure incurred by selecting participating nodes for PDF. Moreover, PDF provides two methods of \(k\) sharing: one is to precompute and prestore the shares of \(k\) (PDF is denoted as PDF_NSk in this case), and the other is to distribute the shares of \(k\) for each event report (PDF is denoted as PDF_Sk in this case).  
Figure 2a,b shows the results of energy expenditure for generating a signed report in Scenario 1 and Scenario 2, respectively. It can be seen that the energy expenditure of PDF_Sk is the highest because it requires more energy to distribute the shares of \(k\). DSEDA has the lowest energy expenditure for report generation. This is because the participating nodes in DSEDA do not sign the report jointly and the energy expenditure for sending the endorsement information from the participating nodes to the CH is low. PKAEF is more energy efficient than PDF_NSk, which benefits from broadcasting \(X(R)\) rather than \(R\). These results are consistent with the previous analysis.
## 4 Sandstone Failure Criterion
According to Equation (3), the storage limit of the rock elastic strain energy \(U_{E\text{max}}\) is  
\[U_{E\text{max}}=\frac{1}{2E}\Big{[}\sigma_{1}^{2}+2(1-\mu)\sigma_{3}^{2}-4\mu \sigma_{1}\sigma_{3}\Big{]} \tag{7}\]  
The elastic strain energy density corresponding to the peak stress of the rock sample is \(U_{EP}\). When the sample is damaged, the elastic strain energy density satisfies  
\[U_{E\text{max}}=U_{EP} \tag{8}\]  
The combination of Equations (6)-(8) is  
\[\frac{1}{2E}\Big{[}\sigma_{1}^{2}+2(1-\mu)\sigma_{3}^{2}-4\mu\sigma_{1}\sigma_ {3}\Big{]}=b\sigma_{3}+\frac{\sigma_{c}^{2}}{2E} \tag{9}\]  
The rock failure criterion based on elastic strain energy density (hereinafter referred to as \"E-D failure criterion\") obtained by simplifying Equation (9) is  
\[\sigma_{1}=2\mu\sigma_{3}+\sqrt{(4\mu^{2}+2\mu-2)\sigma_{3}^{2}+2E\bigg{(}b \sigma_{3}+\frac{\sigma_{c}^{2}}{2E}\bigg{)}} \tag{10}\]  
Assuming that the volumetric strain during rock failure is zero [23], Equation (10) can be further simplified as  
\[\sigma_{1}=\sigma_{3}+\sqrt{2 Eb\sigma_{3}+\sigma_{c}^{2}} \tag{11}\]  
Equation (11) is the rock energy density failure criterion, which has a simple form and contains fewer parameters. The physical meaning of each parameter is clear. Further observation reveals that the form of this failure criterion is similar to the expression of the classic Hoek-Brown failure criterion [24], as follows:  
\[\sigma_{1}=\sigma_{3}+\sqrt{m\sigma_{c}\sigma_{3}+\sigma_{c}^{2}} \tag{12}\]  
where \(m\) denotes the Hoek-Brown failure criterion. When \(b=\frac{m\sigma_{c}}{2E}\), the energy density failure criterion can be transformed into the Hoek-Brown failure criterion such that the Hoek-Brown failure criterion is a special case of the energy density failure criterion. How  
Figure 8: Relationship between confining pressure and \(U_{EP}\) of of seven different types of rocks. ever, the use of the Hoek-Brown failure criterion has strict limitations, as its third principal stress should be less than half of the uniaxial compressive strength. The energy density failure criterion is derived theoretically based on the results of triaxial compression tests, and its application is not limited by the third principal stress. Therefore, compared with the Hoek-Brown failure criterion, its application range is wider. The accuracy of the energy density failure criterion was verified using sandstone triaxial compression test data, as shown in Figure 9.  
From the figure, it can be observed that the energy density failure criterion calculates the strength of sandstone, and the experimental results are consistent, with R\({}^{2}\) = 0.986, thus verifying the accuracy of the energy density failure criterion.
## 4 Practical Algorithms for Action Masking
### On-Policy Algorithm  
In the On-Policy Invalid Action Masking (On-PIAM) algorithm [23], the target and behavior policies are both masked policies (\(\pi_{\theta}^{\omega}\)). Since \(\pi_{\theta}^{\omega}\) is differentiable to its parameters \(\theta\), the policy gradient algorithm can be directly applied to train \(\pi_{\theta}^{\omega}\):  
\[\
abla_{\theta}J(\pi_{\theta}^{\omega})=\mathbb{E}_{\tau\sim\pi_{\theta}^{ \omega}}\Bigg{[}\sum_{t=0}^{H}\
abla_{\theta}\log\pi_{\theta}^{\omega}(a_{t} |s_{t})\hat{A}_{t}\Bigg{]}. \tag{23}\]  
To train the agent more robustly, we can take Proximal Policy Optimization (PPO) algorithm [27] as the training algorithm:  
\[L_{\text{On-P}}^{CLIP}(\theta) =\mathbb{E}_{\tau\sim\pi_{\theta}^{\omega}}\big{[}\min\big{(}r_{t} ^{\omega}(\theta)\hat{A}_{t},\text{clip}\big{(}r_{t}^{\omega}(\theta),\epsilon \big{)}\hat{A}_{t}\big{)}\big{]}, \tag{24}\] \[\text{where }r_{t}^{\omega}(\theta) =\frac{\pi_{\theta}^{\omega}(a_{t}|s_{t})}{\pi_{\theta_{\text{ old}}}^{\omega}(a_{t}|s_{t})},\text{ and clip}(r,\epsilon)=\left\{\begin{array}{ll}1+\epsilon,&r>1+\epsilon\\ r,&1-\epsilon\leqslant r\leqslant 1+\epsilon\\ 1-\epsilon,&r<1-\epsilon\end{array}\right.. \tag{25}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **Behavior Policy** & **Target Policy** & **Objective** & **Policy Gradient** & **PPO Ratio** \\ \hline On-PIAM [23] & \(\pi_{\theta}^{\omega}\) & \(\pi_{\theta}^{\omega}\) & \(J(\pi_{\theta}^{\omega})\) & Equation (23) & \(r_{t}^{\omega}(\theta)=\pi_{\theta}^{\omega}(a_{t}|s_{t})/\pi_{\theta_{\text{ old}}}^{\omega}(a_{t}|s_{t})\) \\ Off-PIAM & \(\pi_{\theta}^{\omega}\) & \(\pi_{\theta}\) & \(J(\pi_{\theta})\) & Equation (27) & \(r_{t}^{\text{IS}}(\theta)=\pi_{\theta}(a_{t}|s_{t})/\pi_{\theta_{\text{ old}}}^{\omega}(a_{t}|s_{t})\) \\ CO-IAM & \(\pi_{\theta}^{\omega}\) & \(\pi_{\theta}^{\omega},\pi_{\theta}\) & \(J(\pi_{\theta}^{\omega})-c_{\theta}\Phi(\pi_{\theta})\) & Equation (40) & \(r_{t}^{\text{CO}}(\theta)=\pi_{\theta}(a_{t}|s_{t})/\pi_{\theta_{\text{ old}}}(a_{t}|s_{t})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of On-PIAM, Off-PIAM, and CO-IAM algorithmsDuring training, the agent also maximizes the policy's entropy to encourage exploration. We combine these optimization terms and obtain the following optimization objective  
\[L^{PPO}_{\text{On-P}}(\theta)=L^{CLIP}_{\text{On-P}}(\theta)+c_{e}\mathcal{H}( \pi^{\varnothing}_{\theta}). \tag{26}\]  
where \(\mathcal{H}(\cdot)\) is an entropy bonus and \(c_{e}\) is a coefficient for the entropy item.  
After the training, we obtain a trained policy \(\pi^{\varnothing}_{\theta}\) rather than \(\pi_{\theta}\). Note that we cannot remove the masking when using the policy because the target policy is the masked policy \(\pi^{\varnothing}_{\theta}\).
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 1 Introduction
The power allocation scheme is investigated in [16] for maximizing the secure energy efficiency (SEE) for the DF relay network, but only for a single subcarrier. Considering the aforementioned discussions, in this paper we jointly investigate both secure transmission and EE maximization issues for a dual-hop DF relay network, which consists of one source node, one destination node, and one relay node with an eavesdropper between the relay node and the destination node. The key contributions are highlighted as follows:  
* In contrast to [13; 14; 15; 16; 17], where the secure sum rate/EE maximization for a single subcarrier is served as the main design goal, in this paper, a joint optimization problem is formulated to maximize the secure EE of the network, subject to a total transmit power budget. The original optimization problem is NP-hard. We first transform the secure EE problem into an equivalent convex problem for finding the optimal power allocation under given subcarrier pairing. The optimization problem then becomes a mixed-integer binary non-linear programming problem. Instead of searching over all possible subcarrier pairing combinations, a near-optimal EE maximization (EEM) algorithm is found by using the Hungarian method to reduce the computational burden. To get more insights, we derive a closed-form expression for the source and the relay power allocation. * Furthermore, a suboptimal algorithm with even less complexity but acceptable performance degradation is investigated. Next, we analyze the complexity of the proposed algorithms, and evaluate the performance and compare with an EEM algorithm without an eavesdropper (EEM w/o E) by computer simulation for understanding the trade-off between them. The organization of this paper is as follows. The system model and problem formulation are described in Section 2. A near-optimal subcarrier pairing and power allocation algorithm for maximizing the EE is presented in Section 3, whereas the suboptimal algorithm is illustrated in Section 4. The complexity analysis is presented in Section 5, followed by the numerical results in Section 6. Finally, Section 7 concludes the paper.
## 4 Sensitivity Analyses on Boundary Conditions and Model Closures
### Sensitivity Analyses on Boundary Conditions and Model Closures  
It is interesting to perform sensitivity analyses on the inlet BSD and the constant \(K\) (see Equation (33)). When using a uniform BSD as an inlet, the results for the complete range of gas velocities are displayed in Figures 7-11. As expected, the model is influenced by the inlet BSD: different initial conditions led to different results. Nevertheless, acceptable results can still be obtained: the proposed model can be used with a uniform inlet BSD as a preliminary tool to support the design of a bubble column. When using the experimental BSD at the sparger along with the original constant proposed by [PERSON] et al. (2013) (\(K=0.25\)), the model is not able to predict the experimental (developed) BSD (Figure 12). The reason is that K determines the breakup frequency and, thus, erroneous modeling determines a different equilibrium point between the coalescence rate and breakup. In Figure 12, for example, the breakup rate is too small to counter-balance the coalescence rate: this issue is the main drawback of the applicability of the present model for performing a scaling-up procedure, as the fitted expression for K is different for different gas distributors and/or different column configurations. Thus, future work should focus on an improved validation of the proposed approach under relevant operating conditions. Possibly, a fitted multi-dimensional map of K-values may be applied in future work. Finally, when using a uniform inlet BSD and the original value of the constant, the model is able to provide first-attempt results, as displayed in Figures 13-17. The agreement is lower compared with the modified constant but still acceptable considering the complex gas-liquid flows within the range of gas velocities considered. This result was expected because the model of [PERSON] et al. (2013) was derived for uniform inlet BSD.  
Figure 8: Comparison between PBM and experimental data: uniform BSD at the inlet: \(U_{G}\) = 0.0074  m/s.  
Figure 10: Comparison between PBM and experimental data: uniform BSD at the inlet: \(U_{G}\) = 0.0149  m/s.  
Figure 9: Comparison between PBM and experimental data: uniform BSD at the inlet: \(U_{G}\) = 0.0111  m/s.  
Figure 11: Comparison between PBM and experimental data: uniform BSD at the inlet: \(U_{G}=0.0188\) m/s.  
Figure 12: Comparison between PBM and experimental data: Martinez constant and real BSD at the inlet: \(U_{G}=0.0033\) m/s.  
Figure 13: Comparison between PBM results and experimental data: Martinez constant and uniform BSD at the inlet: \(U_{G}=0.0037\) m/s.  
Figure 14: Comparison between PBM results and experimental data: Martinez constant and uniform BSD at the inlet: \(U_{G}\) = 0.0074 m/s.  
Figure 15: Comparison between PBM results and experimental data: Martinez constant and uniform BSD at the inlet: \(U_{G}\) = 0.0111 m/s.
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 5 Conclusions  
Load disaggregation is an important part of smart grids. At present, existing non-intrusive load disaggregation methods based on deep learning have some problems; for example, they easily lose features and have difficulty in detection, they do not identify low-use electrical appliances well, and their networks degrade easily due to gradient disappearance. The disaggregation effects of traditional methods are also very poor. In order to solve these problems, the MSA-Resnet is proposed for NILD. The residual network deepens the network structure, avoids the gradient, and reduces the optimization difficulty. Multi-scale convolutions obtain richer load characteristics and avoids feature simplification. The attention mechanism is used to enhance the ability of the network to learn load characteristics and improves the performance of the network. With its excellent performance on the WikiEnergy and UK-DALE datasets, the MSA-Resnet is shown to be an effective way to solve non-intrusive load disaggregation. In future work, we will conduct further experiments on public datasets such as REDD and real household data of the State Grid to verify the generalization performance of the model.  
Conceptualization, L.W., X.Z., and J.Q.; methodology, M.X. and Y.X.; software, K.W.; validation, X.Z. and M.X.; formal analysis, L.W., X.Z., and J.Q.; investigation, X.Z. and M.X.; resources, M.X. and K.W.; data curation, X.Z. and M.X.; writing--original draft preparation, X.Z.; writing--review and editing, L.W. and M.X.; visualization, X.Z.; supervision, M.X. and Y.X.; project administration, L.W.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the State Grid Corporation of China Project 'Fundamental Theory of Dynamic Demand Response Control Based on Large-Scale Diversified Demand Side Resources'. The authors declare that there is no conflict of interest regarding the publication of this paper.
## 3 Results and Discussion
### Construction of Quality Score Prediction Model
#### 3.3.2 PCA of Selected Characteristic Spectral Intervals  
Principal component analysis (PCA) was performed on the selected characteristic spectral intervals, and the contribution rates of each principal component (PC) are summarized in Table 3.  
As shown in Table 3, PCA of the characteristic spectral intervals reveals that the contribution rates of the first seven PCs decrease rapidly. PC1 accounts for 92.45%, PC2 for 4.85%, and PC3 for 1.75%, while the contribution rates of PC4 through PC7 are all below 1.00%. The cumulative contribution rate of the first three PCs is 99.05%, indicating that these components effectively capture nearly all the information within the characteristic spectral intervals [32]. These three components can be utilized in the next step to develop an artificial neural network (ANN) prediction model for YCXY tea quality.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 5 Simulation and Analysis
### Simulation Analysis
In order to simulate both conventional band communication rates and possible future laser communication rates, the bandwidth was set between 100 Mb/s and 1000 Mb/s. This allows for an accurate assessment of the transmission capabilities of the link in relation to the offloading \(Cost\). The Local algorithm has remained largely unchanged, as it excludes the possibility of offloading tasks to other satellites for computational assistance. Changes in the bandwidth of ISLs do not have an impact on this algorithm. However, the other three algorithms have undergone various modifications. Specifically, the Greedy algorithm changes most significantly because it prioritizes offloading tasks to the satellite with the smallest queue for processing. Additionally, the DOALH algorithm still performs best, as depicted in Figure 7.  
It is important to note that while the DQN algorithm showed some level of performance, it was not outstanding in comparison with the DOALH algorithm. Throughout the simulation, we made several parameter modifications to observe its performance, but it still fell short of the DOALH algorithm. Reinforcement learning algorithms typically require a large amount of historical data for training and a long training time to converge to a better solution. Additionally, their performance heavily depends on the algorithm parameter settings, making it challenging to implement in practical engineering applications. Figure 6: \(V=10\), \(\beta=0.1\), \(B=500\) (Mb/s) Average \(Cost\) vs. \(M\). Figure 7: \(M=50\), \(\beta=0.1\), \(V=10\) Average \(Cost\) vs. ISL bandwidth. The DOALH algorithm, on the other hand, can quickly obtain optimal solutions without requiring any of the aforementioned information. As a result, it holds significant reference value for practical engineering applications.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Wavelet Transform Techniques
#### 2.4.2 Continuous Wavelet Transform (CWT)  
The continuous wavelet transform (CWT) provides a time-frequency analysis of the ECG signal useful for non-stationary signals. _CWT_ decomposes the signal into scales and translation, allowing a continuous representation of the signal in the time-frequencydomain. The _CWT_ is defined as the integral of the convolution of the ECG signal with a continuously scaled and translated wavelet function:  
\[CWT_{x}(a,b)=\frac{1}{\sqrt{|a|}}\int\limits_{-\infty}^{\infty}x(t)\psi\bigg{(} \frac{t-b}{a}\bigg{)}dt\]  
where \(a\) is the scaling factor, \(b\) is the translation factor, \(x(t)\) is the ECG signal, and \(\psi\) is the mother wavelet.  
CWT allows detailed analysis of non-stationary ECG signals, facilitating the detection of dynamic changes associated with ventricular fibrillation.  
In conclusion, wavelet transform techniques, such as DWT and CWT, provide powerful methods for multiresolution analysis of the ECG signal, essential for the detection and prediction of ventricular fibrillation. The literature studies demonstrate the ability of these methods to extract relevant features and provide detailed representations of the ECG signal, helping to improve the accuracy and reliability of cardiac monitoring systems [42]. Future research could explore the integration of wavelet transforms with other machine learning techniques to develop hybrid models that combine the advantages of multiple signal analysis methods for optimal performance.
# ARMONIA: A Unified Access and Metro Network Architecture
## 2 ARMONIA Architecture
### ARMONIA Access Networks  
Passive optical networks (PONs) are adopted worldwide as the most efficient way to extend the optical fiber deep into the access network. Using PONs, multiple clients share the interface between the central office (CO) and the provider's metro network, as well as the optical fibers of the distribution network. PONs reduce the capital and the operational (energy) costs of the equipment, since they are based on passive components. The ARMONIA architecture supports a number of different types of PONs, handling the technological heterogeneity of the existing infrastructures and supporting their smooth upgrade. In particular, ARMONIA leverages PON networks based on optical line terminal (OLT) devices that support either the NETCONF protocol or can be controlled through a dedicated access network controller that exposes the appropriate northbound interfaces (e.g., through NETCONF, RESTCONF). In addition, ARMONIA is able to integrate mobile access networks that support software defined networking principles.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
## 6 Simulation Results and Discussion
### Discussion of Chattering  
From the results shown in Figures 5-7, those with an SMC structure have better performance than those with only integral state feedback control applied. The defined control law includes two parts: the linear part, \(u_{L}=-\mathbf{k}_{1}^{\text{T}}\mathbf{z}\), and the switching part, \(u_{smc}~{}=~{}-q\frac{\sigma}{|\sigma|+\varepsilon}\). From (27)-(29), it can be seen that the state z1 can cancel the effect from the reference input \(r\) and external load \(w\) in the steady state. The sliding surface \(\sigma\) and the control law \(u_{smc}=-q\frac{\sigma}{|\sigma|+\varepsilon}\) compensate for the parameter variations' effect. Moreover, the sliding surface usually equals 0, as Figure 8b shows, i.e., the switching control has the minimum value of \(q\) to ensure the system states, and \(\mathbf{z}\) or \(\mathbf{x}\), are kept on the sliding surface. The problem caused by the switching control and chattering is then solved.  
Figure 8: The sliding surface and load: (**a**) original picture and (**b**) zoomed-in picture in the vicinity of 0.
## 5 Discussion
### Discussion on Redundant Motion Representation  
This study's explicit objective for running was set at a reference speed of 2.22 m/s. As speed is expressed as the product of stride and cadence, the balance between these two parameters can be freely adjusted, even at a constant speed. Figure 4 plots the stride and cadence for each of the simulations, which were run for 60 s. This figure illustrates that many of the plots are distributed near the curve where cadence = 2.22/stride; only some plots deviate from this curve. The plots that deviate from the curve correspond to simulations where the gain \(K_{k}\) is small, indicating that these simulations did not employ tactics to adjust the speed to the reference, failing to achieve the running objective. In contrast, the plots near the curve demonstrate that multiple combinations of stride and cadence can be achieved by adjusting the tactics within the proposed model. This suggests that the simulation can reproduce various motion states, offering the flexibility to freely select the balance between stride and cadence.  
Therefore, the results indicate that the proposed model can generate redundant human motions, including cases where the objective is not achieved. The model's ability to reproduce experimental data within the representable range of motion supports its validity. Furthermore, the combination of tactics was able to generate unique motions from redundant tasks, suggesting that the proposed model has the potential to capture the individual characteristics of human motions.  
In this paper, we designed a motion tactics model that incorporates stability, speed, and energy as a key strategy and successfully achieved multiple stable runs at 2.22 m/s, extending beyond the stability range established in previous studies [28; 29]. From Figure 4 and Table 4, it was confirmed that changes in control gain can represent individual differences in motion, even when performed under the same conditions and for the same purpose. Therefore, the proposed model can be considered viable for representing the motion characteristics necessary for motion evaluation.