esa-sceva/satcom-chunk-collection huggingface dataset:
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 3 Simulation and Analysis
### Sources Localization in Real Networks  
In addition to the synthetic network, we test the performance of the above five methods in real networks (Karate, Lesmis, Adjnoun, Football, Jazz and USAir). These networks are social networks, where propagation usually occurs.  
From Figure 5, we can find that PCL has the highest sources localization accuracy in all real networks. Meanwhile, the sources localization accuracy of PCL keeps robust with a different network structure. Moreover, it confirms that the average degree and the scale of the network have less effect on sources localization accuracy. All in all, PCL behaves best in sources localization accuracy of five different methods, that is, PCL, LPSI, DC, JC and UBC.
## 4 Fuzzy Fatigue Reliability Assessment of Welded A-Type Frame
### Finite Element Model of Welded A-Type Frame  
The finite element model of the welded A-type frame was built by software HYPERMESH, as shown in Figure 10. There were 84,948 elements and 134,564 nodes, which included 82,046 C3D8 elements and 2902 C3D6 elements. Gradual meshing simulated the weld metal was simulated. The connection sites in the welded A-type frame were marked in Figure 10 by A1 (front traction joint), A2 (right steering joint), A3 (left steering joint), and A4 (front lateral stabilizer joint), and the corresponding loads at fours joints were \(F_{1}\), \(F_{2}\), \(F_{3}\), and \(F_{4}\). There were four kinds of plates marked by lateral plate, based plate, roof plate, and tail plate, whose thickness was different from each other. Subsequently, in order to testify the accuracy of this model, the quasi-static analysis for simulating the mechanical behavior under the condition of random horizontal road running with full load was conducted to compare the calculated equivalent stresses with the tested ones. Figure 11 shows the simulated stress contour. The maximum stress located at the corner between the bottom roof plate and right lateral plate was 83.69 MPa, which was caused by the stress concentration at geometric transition position. The measuring points of welded A-type frame are displayed in Figure 12, and the layout of strain gauge at point A1-2 are also shown in Figure 13. Table 4 lists the experimental data for the same condition obtained from the mine road tests. The relative errors between the simulated results and the tested ones were around 10%, except the measuring point A2-1 and A2-2. Ignoring the steering loads at location A2 and A3 under the condition of random horizontal road running with full load in the simulation caused it. However, there were some loads at these two joints in the real truck due to goods partiality and residual pressure in steering power cylinder.  
Figure 11: Stress contour of welded A-type frame.  
Figure 12: Schematic diagram of measuring points.
## 3 Results and Discussion
### Characterization Analysis and Mechanism  
#### 3.3.1 SEM Analysis  
To elucidate the detailed reaction mechanism, physical chemical property changes of the sample powders were examined. Figure 3 shows the morphology of activated samples at different ball-milling times as observed by SEM. It can be seen that the surfaces of the particles became significantly rougher with the extended milling time. These outcomes imply that substantial energy was generated during the MC process and transferred to  
Figure 2: Degradation and debromination of BDE 209 at different ball milling conditions in 240 min. Conditions: (**a**) The effect of grinding reagent molar ratio between LiCoO\({}_{2}\) and Fe (\(n_{\mathrm{LiCoO}}/n_{\mathrm{Fe}}\)) with rotation speed of 600 rpm, and ball-to-powder mass ratio (\(m_{\mathrm{p}}/m_{\mathrm{p}}\)) of 100:1. (**b**) The effect of rotation rate with \(n_{\mathrm{LiCoO}}/n_{\mathrm{Fe}}/n_{\mathrm{Br}}\) = 1.5:1.67:1, and \(m_{\mathrm{p}}/m_{\mathrm{p}}\) of 100:1. (**c**) The effect of ball-to-powder mass ratio with \(n_{\mathrm{LiCoO}}/n_{\mathrm{Fe}}/n_{\mathrm{Br}}\) = 1.5:1.67:1, and rotation speed of 600 rpm.  
the samples, resulting in the samples being damaged and inducing the degradation and debromination of BDE 209.
## Appendix A  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Calibration Point \(k\)**} & \multirow{2}{*}{**Teaching Point \(j\)**} & **Rope Length** & **End Adapter Position** \\  & & \(L_{1y,k}\) **(mm)** & \(t_{bj}\) **(mm)** \\ \hline \multirow{8}{*}{1} & 1 & 804.29199 & [1295.78, \(-\)46.49, 1042.74] \\  & 2 & 881.30255 & [1295.80, 209.68, 1042.73] \\  & 3 & 841.0332 & [1295.84, \(-\)452.11, 1042.68] \\  & 4 & 1026.1182 & [1188.90, \(-\)393.30, 1226.34] \\  & 5 & 995.62207 & [1189.90, \(-\)5.61, 1203.27] \\  & 6 & 655.6239 & [1083.19, \(-\)160.56, 748.53] \\  & 7 & 524.83978 & [1185.55, \(-\)344.85, 617.74] \\  & 8 & 811.8349 & [1183.51, 486.36, 592.35] \\  & 9 & 511.94385 & [1183.51, 12.84, 592.32] \\  & 10 & 612.43469 & [1183.52, \(-\)399.40, 726.54] \\  & 11 & 684.37616 & [1183.53, \(-\)79.43, 860.12] \\  & 12 & 804.41364 & [1183.53, 261.18, 860.11] \\  & 13 & 811.26715 & [1183.54, \(-\)213.55, 1013.35] \\  & 14 & 909.52759 & [1183.55, \(-\)378.61, 1098.11] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Single-robot experiment record data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Calibration Point \(k\)**} & \multirow{2}{*}{**Teaching Point \(j\)**} & \multicolumn{2}{c}{**Rope Length**} & \multicolumn{2}{c}{**End Adapter Position**} \\  & & \(L_{\mathbf{ij},k}\) **(mm)** & \multicolumn{2}{c}{**\(t_{kj}\) **(mm)**} \\ \hline \multirow{8}{*}{1} & 1 & 603.26971 & [1384.59, \(-\)236.38, 819.10] \\  & 2 & 552.45648 & [1421.48, \(-\)97.68, 788.31] \\  & 3 & 567.33954 & [1380.72, \(-\)166.95, 788.28] \\  & 4 & 552.86206 & [1307.30, \(-\)265.06, 715.18] \\  & 5 & 571.84094 & [1256.81, \(-\)344.70, 670.12] \\  & 6 & 605.784 & [1186.12, \(-\)404.16, 614.38] \\  & 7 & 654.08289 & [1131.13, \(-\)435.67, 605.88] \\  & 8 & 611.13702 & [1140.43, \(-\)350.05, 605.81] \\  & 9 & 643.98511 & [1083.60, \(-\)398.44, 545.23] \\  & 10 & 603.83746 & [1137.74, \(-\)451.52, 488.08] \\ \hline \multirow{8}{*}{2} & 1 & 808.75287 & [1229.18, \(-\)423.74, 845.20] \\  & 2 & 742.69165 & [1264.41, \(-\)376.20, 804.28] \\  & 3 & 718.60303 & [1293.55, \(-\)463.75, 745.99] \\  & 4 & 702.50343 & [1327.68, \(-\)507.81, 713.20] \\  & 5 & 66.68207 & [1351.13, \(-\)574.71, 617.97] \\  & 6 & 573.62531 & [1308.20, \(-\)526.70, 480.69] \\  & 7 & 574.88245 & [1324.42, \(-\)559.91, 447.93] \\  & 8 & 656.67828 & [1229.56, \(-\)603.47, 450.41] \\  & 9 & 659.8009 & [1251.96, \(-\)631.34, 433.26] \\  & 10 & 678.8609 & [1251.95, \(-\)670.43, 398.73] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dual-robot experiment record data for robot \(i=1\).
## 3 The Fractal Model and the W-M Contact Model  
Modeling studies for the normal contact stiffness of joint surfaces are generally divided into two types: those based on statistical parameters and those based on fractal parameters.  
[PERSON] and [PERSON] [26] first established the GW model based on statistical methods, assuming that the height of the rough surface profile follows a Gaussian distribution. Statistical parameters are usually constrained by the measurement accuracy of the instrument and do not have scale independence or uniqueness [7; 8], whereas fractal parameters have scale independence. Therefore, the contact stiffness calculation model of the interface based on fractal parameters has certain advantages. The implementation of the W-M model is defined by Equation (15). 3D fractal contact model is shown in Figure 9.  
\[z(x,y)=L\frac{ln\gamma}{M}^{1/2}\sum_{m=1}^{M}\sum_{n=0}^{n_{max}}\gamma^{(D-3 )n}\times\left\{cos\varphi_{m,n}-cos\left[\frac{2\pi\gamma^{n}(x^{2}+y^{2})^{1/ 2}}{L}\times cos\Big{(}tan^{-1}\Big{(}\frac{y}{x}\Big{)}-\frac{\pi m}{M}\Big{)} +\varphi_{m,n}\right]\right\} \tag{15}\]  
Parameter explanation:  
\(z\): the height of the combined surface profile;  
\(x\),\(y\): profile coordinates;  
\(\varphi_{m,n}\): random phase;  
\(L\): sampling length;  
\(D\): fractal dimension;  
\(G\): scale parameter;  
\(\gamma\): spatial frequency of the profile;  
\(M\): number of overlapping surface protrusions;  
\(n\): frequency exponent, rank of micro-asperities.  
Figure 9: 3D fractal contact model diagram (blue represents moving contact points, while white represents stationary contact points).  
Based on the theory of three-dimensional anisotropic fractal geometry, this paper establishes a fractal model for the normal contact stiffness of the interface, compares it with other models in the literature, and verifies the correctness of the model. The model uses a distribution function to express the distribution of contact points on the interface, superimposes frequency micro-asperities, and includes three contact deformation mechanisms of micro-asperities: elasticity, elastoplasticity, and fully plasticity. It considers the anisotropy of rough surfaces, making the model more closely aligned with the actual contact process of contact groups. The base shape of the relay contacts selected in this paper is close to an arc surface, so the base shape is added to the differential plane W-M model, resulting in Equation (16).  
\[\begin{split} z(x,y,\theta)&=r\times cos\theta+L \bigg{(}\frac{G}{L}\bigg{)}^{D-2}\bigg{(}\frac{ln\gamma}{M}\bigg{)}^{1/2} \sum\limits_{m=1}^{M}\sum\limits_{n=0}^{n_{\text{max}}}\gamma^{(D-3)n}\\ &\times\bigg{\{}cos\varphi_{m,n}-cos\bigg{[}\frac{2\pi\gamma^{n} \big{(}x^{2}+y^{2}\big{)}^{1/2}}{L}\times cos\Big{(}tan^{-1}\Big{(}\frac{y}{x} \Big{)}-\frac{\pi m}{M}\Big{)}+\varphi_{m,n}\bigg{]}\bigg{\}}\end{split} \tag{16}\]  
In which, \(r\) is the base radius, and \(\theta\) is the horizontal angle of the base, and the model is shown in Figure 10.  
Equations (15) and (16) are from references [12; 26].
# Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids
## 1 Introduction
They provide a structured framework for considering various constraints and optimization objectives including grid capacity, user preferences, and energy prices. They achieve good results; however, their main drawbacks are related to the model's assumptions and structure, which need to be continuously updated to meet the actual changing conditions in grid states, charging station deployments, and EV availability [19]. Moreover, the model solving method is computationally complex, being less suitable for large-scale dynamic features involved in EV scheduling [20]. In this paper, we address the limitations by proposing a model-free solution for EV participation in DR programs, leveraging deep Q-learning. We aim to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the grid operator for a specific interval. The deep Q-learning solution determines optimal EV scheduling actions based only on the current state, which is represented by the state of charge of EVs and the availability of charging stations in the microgrid. We adapted the Bellman equation, which plays a crucial role in assessing the value of a specific state, considering specific rewards for EV scheduling actions and Q-values to evaluate the effectiveness of each action. The methodology typically involves the estimation of Q-values for available EV scheduling actions and then the selection of the actions with the highest reward. Traditionally, the Q-function is implemented as a look-up table, updated after each transition. However, in dynamic scenarios with a multitude of complex states, rendering a table insufficient, we opt for representing the Q-function using a neural network in which the input variables signify the state, and the output provides the Q-value for every possible action. For action selection, we employed the epsilon-greedy algorithm to balance the exploitation of the current best action or explore new possibilities for EV scheduling to meet the target DR energy profile. The main advantage of our model-free solution compared to model-based solutions is that it can learn from previous experiences, which allows it to make better decisionsover time about scheduling EVs at charging stations. Also, model-free solutions can learn optimal scheduling policies that yield good results when operating in complex and uncertain environments or with frequent changes. Finally, model-free solutions do not require prior knowledge and are consequently able to dynamically adapt to different scheduling scenarios without having to have a predefined model. The rest of this paper is structured as follows: Section 2 reviews the literature on EV scheduling and coordination solutions; Section 3 presents our deep Q-learning solution for EV participation in DR programs; Section 4 provides experimental validation, demonstrating the approach's effectiveness in meeting target energy profiles and evaluating the quality of the learned EV scheduling solutions; and Section 5 concludes the paper and presents future work.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Machine Learning
The SVM algorithm has become a reference found in many of the comparisons described in SQI papers. [PERSON] et al. [46] described a benchmark over several machine learning methods (SVM, classification and regression tree, ensemble decision tree, and threshold optimization) and found that SVM offers less overfitting and maintains high performance over several datasets. They also compared their method using SVM with two other algorithms [43; 77] on the Capnobase dataset [83]. [PERSON] et al. [52] also found the best performance with their SVM classifier against seven representative methods [42; 43; 68; 70; 77; 89; 90]. [PERSON] used the SVM algorithm to find the most discriminant feature to classify the quality of a PPG pulse [51]. [PERSON] et al. [36] compared their method with a smaller SQI including an SVM using time-domain features. [PERSON] et al. [55] compared the performance of different classic deep learning architectures but included the SVM in their comparison. Designing supervised learning methods for the quality assessment of a PPG signal makes the step of signal annotation necessary (whose issues are highlighted in Section 3.1.3) and adds the difficulty of hyperparameter tuning. For example, [PERSON] et al. [42] performed an ablation to find the best parameters of their network. The authors tested the use of either four inputs (three correlations with a template and clipping detection) or six inputs (four previous entries with number of detected pulses and a combination of the four previous entries) and the number of neurons in the hidden layer of a multi-layer perceptron (MLP). A summary of the different SQI based on machine learning methods is presented in Table 6.
# High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis
## 6 Results and Discussion
### Comparison with Four Popular Methods  
#### 6.2.1 DBSCAN  
In this experimental analysis, we implement the popular DBSCAN algorithm with two higher-dimensional datasets. The performance of DBSCAN depends on two supplied parameters (Eps and MinPts). It is evident that a small change in Eps or MinPts can result in a different number of clusters and a varying amount of noisy data. When Eps is too small or MinPts is too large, DBSCAN is unable to separate nearby clusters (e.g., MinPts = 3, Eps = 0.5 vs MinPts = 3 and Eps = 0.7). Therefore, it is necessary to search for different parameter values to find the most optimal ones, where the algorithm provides the optimal number of clusters without noise from the dataset. Initially, Eps is set to 0.5, and MinPts is set to 3. In this case, DBSCAN produces a total of 89 clusters, but the noise is high (440). For (MinPts = 4 and Eps = 0.5), DBSCAN provides 81 clusters, but the noise remains high. Hence, it is crucial to analyze different parameter combinations to achieve optimal noise-free clusters. For example, when the parameters (MinPts = 3 and Eps = 1.0) are used, the algorithm produces a total of 8 clusters with no noise. In some cases, the noise is also zero, but the algorithm fails to determine the clusters from the dataset (e.g., Eps = 1.1 and MinPts = 3). In a similar way, we obtain a total of 50 clusters for dataset 2. In addition, we evaluate the performance using the silhouette coefficient. By employing DBSCAN, we obtain a coefficient value of 0.288 for dataset 1, whereas, for dataset 2, the coefficient value reaches 0.832, which is the same as the proposed method. Although the algorithm provides optimal outcomes with the dataset, searching and analyzing different parameter values can  
Figure 3: Performance comparison of the algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dataset 1** & \multicolumn{3}{c}{**Dataset 2**} \\ \hline Methods & Cluster Number & Silhouette Coefficient & Cluster Number & Silhouette Coefficient \\ Mean Shift & 1 & N/A & 1 & N/A \\ K-means & 8 & 0.226 & 50 & 0.832 \\ Affinity Propagation & 976 & 0.195 & 1525 & 0.0745 \\ DBSCAN & 8 & 0.288 & 50 & 0.832 \\ Fuzzy K-means & 8 & 0.158 & 50 & 0.832 \\ JBOS & 8 & 0.259 & 50 & 0.584 \\ K-Mean++ & 8 & 0.262 & 50 & 0.60 \\ ECA\({}^{\star}\) & 8 & 0.001 & 50 & \(-\)0.12 \\
**DPPA** & **8** & **0.288** & **50** & **0.832** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of the algorithms for two datasets.  
be challenging and time-consuming. In our experiment, we evaluated nearly 40 parameter combinations to achieve optimal noise-free clusters.
# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 3 DM Evaluation
### Analysis of the Selection of Regression Data Mining Technique  
#### 3.2.1 Artificial Neural Network (ANN)  
An artificial neural network is a type of mathematical model that learns to create and optimize a function (or distribution) that defines a set of input (learning) characteristics. The network's learning process occurs through modifying the weighting parameters of the network of nodes, which is possible by applying certain network learning performance measures. Weight tuning parameters are generated by the learning function. The network consists of a set of neurons, each of which has an activation function (weight function) that processes the input data. The behavior of a neuron (and the entire neural network) is strongly dependent on the type of activation function used. These weights must be updated in the learning process by minimizing the estimation error. Layer node structures can be created in which data must flow in a certain direction. Different ways of connecting network nodes affect the network's capabilities. Neurons in a network can be connected in various ways to form various topologies, which has a major impact on the network's learning capabilities. One of the most popular types of networks in supervised learning is the multilayer, layered, forward-coupled perceptron network [45].  
The optimal topology of the ANN was determined using an iterative algorithm [42]. As a result the optimal ANN topology was obtained as follows:  
* ANN type: MLP 25-6-1 (multilayer perceptron of 25(input variables)-6(hidden layer perceptron)-1(output variable);
* Hidden layer activation function type: exponential;
* Output activation function type: exponential.
## 3 Income Model of Roadside Units
### Positive Income of Roadside RSU Coverage
#### 3.1.1 Construction of an Instantaneous Transmission Model under Different Permeabilities  
The expressway scene studied in this paper can be approximated as a one-dimensional scene in which intelligent vehicles act as important nodes in the information transmission process in a vehicle self-organizing network. As shown in Figure 2, when the distance between two adjacent vehicles is less than or equal to the perception radius \(R_{v}\) of the intelligent connected vehicle, instantaneous transmission will occur and the information will be transmitted directly between vehicles through wireless communication.  
In Figure 2, when the distance \(x_{i,i-1}\) between vehicle \(i\) and vehicle \(i-1\) and the distance \(x_{i-1,i+1}\) between vehicle \(i-1\) and vehicle \(i+1\) are less than \(R_{v}\), the information carried by vehicle \(i\) can be transmitted through vehicle \(i-1\) by two jumps and then transmitted to vehicle \(i+1\). When the distance between two adjacent vehicles is greater than \(R_{v}\), the ferry transmission will occur. At this time, the vehicle will continue to move along the traffic flow carrying information until the distance between it and the next target vehicle is equal to \(R_{v}\), at which point a new round of instantaneous transmission will begin.  
In the process of information transmission, when W, instantaneous transmission occurs between vehicles. According to the analysis of vehicle cluster characteristics above, the expected distance of single-hop instantaneous transmission is as in Equation (8), the instantaneous transmission distance is as in Equation (9), and the transmission time is as in Equation (10).  
\[E(x|x<R_{v})=\frac{\int\limits_{0}^{R_{v}}x\alpha\lambda e^{-\alpha\lambda x} dx}{\int\limits_{R_{v}}^{R_{v}}\alpha\lambda e^{-\alpha\lambda x}dx}=\frac{1-e^{- \alpha\lambda R_{v}}(\alpha\lambda R_{v}+1)}{\alpha\lambda(1-e^{-\alpha\lambda R _{v}})} \tag{8}\]  
Figure 2: Instantaneous transmission.  
\[E(X)=\overline{k}E(x|x<R_{v})=\overline{k}\frac{1-e^{-\alpha kR_{v}}(\alpha\lambda R _{v}+1)}{\alpha\lambda(1-e^{-\alpha kR_{v}})} \tag{9}\]  
\[t(X)=\overline{k}\tau_{a} \tag{10}\]  
In the equation, \(\overline{k}\) represents the expected number of hops of a set of information instantaneous transmission and \(P\) represents the probability of instantaneous transmission of information between vehicles, whose expressions are Equations (11) and (12), respectively. \(\tau_{a}\) represents the single-hop transmission time.  
\[\overline{k}=\sum_{k=1}^{n}k\frac{(n-k)P^{k}(1-P)+P^{k}}{n+1} \tag{11}\]  
\[P=p(x<R_{v})=\int\limits_{0}^{R_{v}}\alpha\lambda e^{-\alpha\lambda x}dx \tag{12}\]
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 4 QKD Applications
### Quantum Applications Summary  
Application areas with various performance and cost requirements have been analyzed. For long-range fiber communication, compatibility with classical switched optical networks is a must, as well as the use of cost-effective photonic integrated technologies. CV-QKD is more promising under this context [59] but it suffers from performance issues that may be mitigated using phase- or spin-modulated DV-QKD with decoy states. Since repeaters will not be available for a long time, trusted nodes will be used. Communication between data centers has less stringent distance requirements and could be the first application where QKD systems in fiber will be tested in the field. Similarly, the aerospace industry is less cost-sensitive than the fiber communication industry, meaning this field may be used as a test bench for technologies that will be engineered further before being moved to other areas. Cost and operation at ambient temperature are even more important for sensors used in the IoT, so we expect quantum technologies will be adopted in that area when integrated quantum devices are available for fiber communication.