esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of a Multiscale Wavelet Model
### Wavelet Adjustment of Data Samples  
Let \((t_{i},X_{i})\), \(i=1,2,\ldots,N\) be the statistical series, and its wavelet decomposition at a level \(J\),  
\[X^{l}=\sum_{k}a_{0,k}\varphi_{0,k}+\sum_{j=1}^{J}\sum_{k}d_{j,k}\psi_{j,k}. \tag{2}\]  
The missing values will be categorized into two categories.  
* A missing value, \(X_{k}\), is situated between two segments of known values, after and before it. Thus, we chose the one with greater length for the prediction of the missing value \(X_{k}\). Denote, for example, \(L_{k}\) the length of the greater segment \(I_{k}=\{i_{1}^{k},i_{2}^{k},\ldots,i_{L_{k}}^{k}\}\).
* Consider next the truncated time series \(\widehat{X}_{i}\) corresponding to \((t_{i},X_{i})\ i\in I_{k}\), and its wavelet decomposition as in (2).
* If \(I_{k}\) is an after-interval to \(X_{k}\), we estimate the value \(X_{i_{1}^{k}-1}\) by \[X_{i_{1}^{k}-1}=\sum_{l}a_{0,l}\varphi(i_{1}^{k})+\sum_{j=1}^{J}\sum_{l}d_{j, l}\psi(2^{l}(i_{1}^{k})-l).\] (3)* If \(I_{k}\) is a before-interval to the missing value, we estimate the value \(X_{\hat{I}_{k}}+1\) by \[X_{\hat{I}_{k}}+1=\sum_{l}a_{0,l}\varphi(\hat{t}_{k}^{k}+1-l)+\sum_{j=1}^{l}\sum _{l}d_{j,l}\psi(2^{j}(\hat{t}_{k}^{k}+1)-l).\] (4)
* Whenever the after and before intervals have the same length, we take the mean value of the two predicted ones.
* In the case where many successive values are missing, we take the extremities of the missing segments as starting points to be reconstructed.
* Finally, each predicted value is added to the series, and the new series is reconsidered for the next step.  
We recall again that the process necessitates knowing the values of \(\psi\) on the dyadic grid \(\{2^{j}(N+1)-l,l\}\) and those of \(\varphi\) on the integer grid \(\{N+1-l,l\}\) in the supports.
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 5 ELM3 Based on SLM
### Statistical Linearization Procedure
\begin{array}{ll}c_{eq}&=E\Big{\{}\frac{\partial\mathcal{G}}{\partial\dot{x}} \Big{\}}=E\Big{\{}0.5\mu m\frac{\partial}{\partial\dot{x}}\Big{(}\ddot{x}\big{(} sgn\big{(}\dot{x}\ddot{x}\big{)}+sgn^{2}\big{(}\dot{x}\ddot{x}\big{)}\big{)} \big{)}\Big{\}}\\ &=0.5\mu m\text{E}\big{\{}2\ddot{x}\text{sgn}\big{(}\ddot{x}\big{)}\delta\big{(} \dot{x}\big{)}\big{(}1+2\text{sgn}\big{(}\dot{x}\ddot{x}\big{)}\big{)}\big{\}} \\ &=\mu m\text{E}\big{\{}\big{|}\ddot{x}\big{|}\delta\big{(}\dot{x}\big{)}+2 \delta\big{(}\dot{x}\big{)}\text{sgn}\big{(}\dot{x}\big{)}\ddot{x}\big{\}}\\ &=\mu m\text{E}\big{\{}\big{|}\ddot{x}\big{|}\delta\big{(}\dot{x}\big{)}\big{\}} \\ &=\mu m\int\big{|}\ddot{x}\big{|}\delta(x)f_{\ddot{x}}\big{(}\dot{x}\big{)}f_{ \ddot{x}}\big{(}\ddot{x}\big{)}d\dot{x}d\ddot{x}\\ &=\frac{\mu m}{\sqrt{2\pi\sigma_{\dot{x}}}}\int_{-\infty}^{+\infty}\big{|} \ddot{x}\big{|}f_{\ddot{x}}\big{(}\ddot{x}\big{)}d\ddot{x}\\ &=\frac{\mu m}{\sqrt{2\pi\sigma_{\dot{x}}}}\sqrt{\frac{2}{\pi}}\sigma_{\ddot{ x}}=\mu m\frac{\sigma_{\ddot{x}}}{\pi\sigma_{\dot{x}}}\end{array}  \tag{58}\]  
where \(\delta\big{(}\dot{x}\big{)}\) is the Dirac function of \(\dot{x}\), \(\big{|}\ddot{x}\big{|}\) is the absolute value function of \(\ddot{x}\), \(f_{\ddot{x}}\big{(}\dot{x}\big{)}\) and \(f_{\ddot{x}}\big{(}\ddot{x}\big{)}\) are the probability density function of \(\dot{x}\) and \(\ddot{x}\), which are assumed to be independent from each other to simplify the calculation. They are all considered to be Gaussian distributions with a mean of zero. \(\sigma_{\ddot{x}}\) and \(\sigma_{\dot{x}}\) are the standard deviation of \(\ddot{x}\) and \(\dot{x}\).
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Improvement of Computational Efficiency of Parallel Approach
#### 3.2.1 Improvement of Efficiency with Different Number of Satellite Systems  
The computational efficiency of GPS, GPS+GLONASS and GPS+Gallieo +BDS parallel POD are compared to analyze the influence of the number of satellites. Two processes and ten threads are used in the parallel scheme. The statistical results of computational time are shown in Table 5, and the comparison between non-parallel and parallel approach is shown in Figure 3.  
It can be seen from Table 5 that the more satellite systems that are involved, the longer it takes for POD. After using MPI parallelism, the calculation time of this process is significantly reduced, indicating that it is reasonable to optimize this process in parallel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Max** & **Min** & **Mean** & **STD** \\ \hline Difference of ATPA & \(1.019\times 10^{-9}\) & \(-1.019\times 10^{-9}\) & \(-9.633\times 10^{-18}\) & \(1.365\times 10^{-13}\) \\ Difference of ATPL & \(1.023\times 10^{-12}\) & \(-1\times 10^{-11}\) & \(-6.233\times 10^{-16}\) & \(1.22\times 10^{-13}\) \\ Difference of unknown parameters & \(2.913\times 10^{-5}\) & \(-2.525\times 10^{-5}\) & \(7.519\times 10^{-8}\) & \(3.274\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the difference between the matrix elements of the parallel method and those of the non-parallel method.  
Figure 3: Computational time of parallel and non-parallel orbit determination.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite System** & \begin{tabular}{c} **Non-Parallel** \\ **POD** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Orbit** \\ **Estimation** \\ \end{tabular} &
\begin{tabular}{c} **Parallel Initial** \\ **Orbit Estimation** \\ \end{tabular} & **Parallel POD** \\ \hline GPS & 28 & 12 & 6 & 23 \\ GPS + GLONASS & 60 & 46 & 22 & 46 \\ GNSS(G/R/E/C) & 178 & 160 & 46 & 122 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time-consuming statistical results of GNSS parallel and non-parallel POD (unit: min).  
It can be seen from Figure 3 that the efficiency of GPS, GPS+GLONASS and GPS+GLONASS+Galileo+BDS POD have been obviously improved by the proposed parallel approach. As the number of satellites increases, the efficiency of the parallel POD has been improved more significantly, and the efficiency of the four-system combined POD has been improved the most. The parameters to be estimated and the dimension of the equation both increase nonlinearly, so the advantages of the parallel POD are more obvious.
## 3 Results
### Correlation between Physical Features and Visual Impact Assessment  
A regression analysis was conducted to examine the correlation between the five physical features and the visual impact assessment. The exterior contour of the entrance pavilion (M), facade permeability (D), cultural characteristics (C), decorative materials (E), and the number of exterior colors (P) were taken as factors, and the average score (S) of each photo was taken as the dependent variable. The analysis results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} & \multicolumn{2}{c}{**Collinearity Statistics**} \\ \cline{2-2} \cline{5-8}  & **B** & & & & **Std. Error** & & **Beta** & & **Tolerance** & **VIF** \\ \hline (Constant) & 1.953 & 0.780 & & 3.504 & 0.057 & & & \\ M & \(-\)0.043 & 0.140 & \(-\)0.107 & \(-\)2.305 & 0.030 & 0.222 & 4.513 \\ D & \(-\)0.180 & 0.217 & \(-\)0.287 & \(-\)1.828 & 0.049 & 0.225 & 4.447 \\ C & 0.429 & 0.160 & 0.605 & 3.885 & 0.025 & 0.534 & 1.873 \\ E & \(-\)0.376 & 0.161 & \(-\)1.113 & \(-\)6.330 & 0.002 & 0.119 & 8.429 \\ P & 0.339 & 0.077 & 1.325 & 4.384 & 0.022 & 0.296 & 3.375 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Regression analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Demographic Characteristic** & **Variables** & **Number of Respondents** & **Proportion (\%)** \\ \hline Gender & male & 180 & 56.25 \\  & female & 140 & 43.75 \\ \hline  & 18–34 & 98 & 30.62 \\ Age (years) & 35–59 & 93 & 29.06 \\  & \(\geq\)60 & 129 & 40.31 \\ \hline  & Received higher education & 137 & 42.81 \\ Education level & Did not receive higher education & 183 & 57.19 \\ \hline  & With & 126 & 39.34 \\  & Without & 194 & 60.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of demographic characteristics of survey respondents.  
Based on the regression analysis, all five physical features influenced the average score for each photo. The statistical results indicate significant differences among all five factors: M: F = \(-\)2.305, \(p\) = 0.030; D: F = \(-\)1.828, \(p\) = 0.049; C: F = 3.885, \(p\) = 0.025; E: F = \(-\)6.330, \(p\) = 0.002; and \(p\): F = 4.384, \(p\) = 0.022.  
Hence, it was concluded that when the average score was used as the dependent variable, there were significant differences among all five physical features. These physical features affected the average scores of the photos.
## 4 Experimental Principles
### GA-DQN Anti-Jamming Decision-Making Method
#### 4.2.2 DQN Algorithm  
For the anti-jamming decision-making problem in small-sample conditions, firstly, LACGAN is used to complete the data augmentation and concatenation of the small-sample jamming signals. Then, the augmented samples are input into the DQN model as training samples. The entire training process for generating jamming samples is shown in Algorithm 1.  
```
1:Initialize \(G\), \(D\) with random weights;
2:Initialize Adam optimizers for \(G\) and \(D\);
3:Initialize learning rate, epochs \(N\), batch size \(B\);
4:forepoch\(\gets 1\)to\(N\)do
5:forbatch\(\leftarrow\)\(1\)to\(B\)do
6: Sample real jamming batch \(x_{\text{real}}\); sample noise batch \(z\);
7: Generate fake data \(x_{\text{fake}}\leftarrow\)\(G(z)\);
8: Train Discriminator\(D\);
9: Update \(D\) parameters via Adam to minimize \(\mathcal{L}_{D}\);
10: Sample new noise batch \(z^{\prime}\); generate new fake data \(x^{\prime}_{\text{fake}}\);
11: Train Generator\(G\);
12: Update \(G\) parameters via Adam to minimize \(\mathcal{L}_{G}\);
13:endfor
14: Compute Pearson correlation coefficients \(r\); concatenate selected samples to form augmented dataset;
15:endfor
16:return Augmented dataset.
```  
**Algorithm 1** GAN Algorithm for Synthetic Sample Generation  
To accelerate the convergence speed and enhance the decision-making accuracy of the DQN anti-jamming algorithm, this paper proposes a fast anti-jamming algorithm based on a Generative Adversarial DQN. The DQN model in this algorithm adopts the classical conventional architecture [29], as shown in Figure 3, and it incorporates Algorithm 1, enabling the GAN to generate additional experience samples every \(K\) time slots. This approach improves the efficiency of sample collection during the training phase of the algorithm, thereby accelerating the learning process. The specific procedure is detailed in Algorithm 2.  
```
1:Initialization:\(Q\), \(Q\) (set \(Q=Q\)), and parameter \(K\).
2:for\(T=1,2,\cdots,\infty\)do
3: The transmitter executes the transmission action \(\mathbf{a}_{T+1}\) according to the action instruction.
4: Calculate the transmission action reward \(r(\mathbf{s}_{T},\mathbf{a}_{T+1})\).
5: Obtain the channel state set \(\mathbf{s}^{T+1}\) of the next time slot and collect channel states.
6:if\(T\%K=0\)then
7: Execute Algorithm 1, generate additional experience samples. After pre-training by the DQN, store them in the experience pool \(\mathcal{E}\).
8:endif
9: Batch sample from the experience pool \(\mathcal{E}\).
10: Input samples into the target network and the policy network. The former outputs \(\max Q(\mathbf{s}^{\prime},\mathbf{a}^{\prime};\theta)\), and the latter outputs \(Q(\mathbf{s},\mathbf{a};\theta)\).
11: Use stochastic gradient descent + backpropagation to update the network parameters \(\theta\).
12: At the end of the time slot, sense the spectrum to obtain the state \(\mathbf{s}_{T+1}\) of the next time slot.
13: Continue to execute the policy according to the previous time slot.
14:endfor
```  
**Algorithm 2** DQN Anti-Jamming Decision-Making Algorithm
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
## 3 Results and Discussion
### Air Contents  
Figure 5 shows the air contents of the different concrete mixtures used in this study. For all mixtures, the air content ranged between 0.4 and 1.3%. It should be noted that no significant correlations were observed between a change in air content and those of water content or DS/FA ratio.  
It has been reported that FA with particle sizes of 0.15-0.06 mm lead to a higher air content in the concrete mixture than those with larger particle sizes; while, FAs with particle sizes less than 0.15 mm lead to a decrease in air content [19]. Among the DS in this study, particles with a size of 0.15-0.06 mm were approximately 65% by weight, whereas those  
Figure 4: Slump/AD ratios calculated for the different concrete mixtures used in this study.  
Figure 5: Air contents of the different concrete mixtures used in this study.  
with a size of less than 0.15 mm accounted for approximately 35%. Therefore, as the DS/FA ratio increased, the air content could be expected to increase because the amount of FA with particle sizes of 0.15-0.06 mm increased. However, the experimental result of air content did not show the tendency. This result may be due to the difference between DS and fine aggregates used in previous studies. Particularly, due to differences in physical properties such as particle shape, surface texture, etc., the effect of DS particles on the air content may be different. In addition, the magnitude of the influence of the unit weight of fine aggregates with a specific size range on the increase or decrease in the air content may be different. Therefore, even if the weight of the particles increasing air content increases more than the weight of the particles reducing air content, the air content may not increase. For a more accurate analysis of the DS particle size effect on air content additional experimental studies are needed.
# Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon
## 2 Materials and Methods
### Dataset Preparation  
All the input and output variables were compiled into a structured dataset that contained 150 observations, where each corresponded to a final configuration obtained from the MD simulations under specific values of \(R_{c}\), \(T\), and \(P\). This dataset is provided in the Supplementary Materials to facilitate reproducibility and further analysis. Given the diverse nature of the variables spanning different orders of magnitude and units (e.g., pressure in GPa, temperature in K, energy in eV, and density in g/cm\({}^{3}\)), it was necessary to preprocessthe data to ensure numerical stability and enhance the performance of the predictive models. To achieve this, standardization was applied to all the variables, transforming them to have a mean of zero and a standard deviation of one. This step mitigated potential biases arising from variables with significantly different scales, allowing the machine learning algorithms to assign equal importance to all the features. The standardization process was carried out using the following expression:  
\[z_{i}^{s}=\frac{z_{i}-\mu}{\sigma}, \tag{1}\]  
where \(z_{i}^{s}\) is the standardized variable; \(z_{i}\) is the original variable from the MD simulations; and \(\mu\) and \(\sigma\) are the mean and standard deviation of \(z_{i}\), respectively. This transformation ensured that the dataset was centered and scaled appropriately, which improved the convergence and interpretability of the subsequent statistical and ML models. The data handling and transformations were implemented using the Pandas library in Python [33], which provides efficient tools for managing and preprocessing large datasets. While alternative data preprocessing techniques exist, such as min-max normalization, robust scaling, and log transformations, standardization was chosen based on its superior performance in preliminary predictive modeling trials.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Non-Repudiation, Accountability, and Auditability  
A set of additional key properties integral to the provision of security are non-repudiation, accountability, and auditability. Non-repudiation refers to the \"ability to prove the occurrence of a claimed event or action and its originating entities\" (ISO/IEC 27000:2018 [48]). Auditability from a security perspective requires the ability to recognize, record, store, and analyze information related to security relevant activities, producing \"audit records that can be examined to determine which security relevant activities took place and whom (which user) is responsible for them\" (ISO/IEC 15408-2008 [29]). Finally, accountability, as the name suggests, refers to the ability of assigning actions and decisions to entities, while having the means (e.g., using authentication, authorization, auditing, and non-repudiation mechanisms) to hold said entities accountable for those actions and decisions.  
Since the aforementioned properties are considered to refer to high-level problems, they depend on lower-level patterns, creating the context for them. Their relationships to those patterns are depicted in Figure 9, in the form of a graph. Signed Message and Audit Log patterns can be utilized to provide non-repudiation, accountability, and auditability.  
Figure 9: Hierarchical graph of Non-repudiation, Accountability, and Auditability properties and relevant patterns.
## 1 Introduction
Therefore, we investigate various scenarios in HetNets and SINR values that might occur at the receiver of each UE [8] within the area of the serving tier, served by a single BS, and a single candidate-serving BS. The SINR analysis points to the network spectral efficiency (SE) and deployment efficiency (DE). Basically, two approaches have been used in the analysis of large networks:  
* hexagonal grid-based, which assumes that the cellular network is composed of a grid of hexagons representing cells with a BS placed in the center of each cell [9; 10];
* stochastic geometry-based, which not only captures the topological randomness in the network geometry but also enables tractable and accurate analysis [9; 11; 12]. The HetNet topology structure can be presented in the form of Poisson Point Process (PPP) [8], as the BSs are stationary within a network area. Such a model can well describe irregular placements of BSs within a real network, rather than the traditional hexagonal model [9]. The topology analysis in the PPP form dates back to quite a while ago [10; 11; 13], but only recently have the network coverage and capacity been modeled as PPP [9] and the PPP-distributed BSs applied to various network scenarios including heterogeneous mobile networks [12; 14; 15; 16; 17], MIMO mobile networks [18; 19], and MIMO HetNets [20; 21]. The contributions of this work can be summarized as it follows:  
1. We characterize the distribution of BSs in a certain network area, where the locations of BSs and UEs are considered to be PPPs. We consider the UEs from open-access (OA) small cells, and not the ones belonging to closed subscriber groups (CSG). In addition, the serving BS is selected on the basis of the cell range extension (CRE), and the analytical expressions for EE and SE are derived for the two scenarios of interest. 2. We introduce the joint analysis of SE, EE, and cost-oriented DE. This holistic approach has already been applied [22], but in this investigation, we enhance the model to exhibit the trade-offs among the three parameters. 3. Finally, the simulation results validate the benefits of cell planning according to the proposed model and recommended simulation configuration set-up [23]. It is possible to further improve the analysis by involving complexity and fairness, if the model is enhanced by various mechanisms of network resources' management [24]. In Section 2, we first review some basic theories and the trade-off between spectral and energy efficiency in terms of theoretical transmission performance boundaries. Then, we present the stochastic geometry-based model for the analysis of large HetNets with randomly distributed candidate-serving and serving BSs within the serving-tier area, while taking into consideration three major issues: SINR, SE, and DE. Furthermore, to validate the analytical model, in Section 3, we report the results of the simulation conducted by means of ns-3 tool to get the instantaneous SINR values at each UE receiver of the network under test. In Section 4, discussion of the obtained results is provided, whereas the conclusions are drawn in Section 5.
# Optimizing Energy Conversion in a Piezo Disk Using a Controlled Supply of Electrical Load
## 1 Introduction
The application of model situations for the optimization of technological processes related to energy management and conversion is considered in works [12; 13; 14; 15]. In works [16; 17], methods and solutions of mathematical models described by the corresponding systems of equations are investigated. Disk piezoelectric devices are widely used in elements of information systems: in wireless communication, elements of satellite communication, global positioning systems. Among such devices, microwave piezo motors on traveling waves of various types are distinguished, including a motor built on an electromechanical disk converter, which performs non-axisymmetric oscillations under the action of a harmonic electric load. In this study, we perform separation of the electrode coating of the plate with thin diametrical dielectric sections and introduce a time-varying electric potential difference of different amplitude or phase to individual parts of the plate. We study the problem of non-axisymmetric planar oscillations of a piezoceramic disk with thickness polarization and the problem of optimization of the EMCO value as a function of an infinite number of parameters in space \(l_{2}\) of square summable sequences.