esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Our Previous Work on Dimension Optimization
```
1:Input : Channel covariance matrices \(\left\{\textbf{R}_{g}\right\}_{g=1}^{G}\), number of users in the groups \(\left\{K_{g}\right\}_{g=1}^{G}\), SNR \(\gamma\), and feedback bit \(B\)
2:Obtain \(\textbf{U}_{g}\) and \(r_{g}\) for all \(g\in\left\{1,\ldots,G\right\}\) by SVD
3:Define \(s_{\min}\triangleq\max\{K_{1},...,K_{g}\}\) and \(s_{\max}\triangleq\min\{r_{1},...,r_{G}\}\)
4:Set \(s\in\mathbb{Z}^{+}\) and \(s_{\min}\leq s\leq s_{\max}\)
5:for\(s=s_{\min},\cdots,s_{\max}\)do
6: Construct outer precoder \(\textbf{T}_{g}\in\mathbb{C}^{M\times s}\) for all \(g\in\left\{1,\ldots,G\right\}\) (referring to Section 3.1)
7: Compute \(\mathcal{R}_{g}^{\prime}(s)\) based on (28) for all \(g\in\left\{1,\ldots,G\right\}\)
8: Compute \(\sum_{g=1}^{G}\mathcal{R}_{g}^{\prime}(s)\)
9:endfor
10:Obtain \(s^{\star}=\arg\underset{s_{\min}:s\leq s_{\max}}{\max}\mathcal{R}_{g}^{\prime} (s)\)
11:Output : The optimal dimension \(s^{\star}\)
```  
**Algorithm 1** Finding the optimal solution of the problem **P3**
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Elliptic Curve Cryptosystem  
Elliptic curve cryptosystem (ECC) is one of the three types of public key cryptosystems that have been proved to be safe and effective so far, and is known for its high efficiency. The security of ECC is based on the intractability of elliptic curve discrete logarithm problem (ECDLP), and it has theadvantages of short key, short signature, and small software implementation scale [35; 36; 37; 38]. The elliptic curve defined on the finite field \(F(q)\) is as follows:  
Assuming \(q>3\), and \(4a^{3}+27b^{2}\
eq 0\text{mod}\ q\), the curve  
\[y^{2}\equiv x^{3}+a\alpha+b(\text{mod})\ \ a,b\in F(q) \tag{1}\]  
is called an elliptic curve on the finite field \(F(q)\), which can be represented as \(E_{q}(a,b)\).
## 3 Methodology
### Hyper-Parameter Optimization
#### 4.2.1 ANN Hyper-Parameter Optimization  
In the ANN-based coefficient of the performance prediction model, the learning degree changes according to the number of hidden layers (Hidden Layer, HL) and the number of neurons in the hidden layer (Hidden Neuron, HN). As the number of hidden layers increases, the ANN is called \"deep\". In the case of a simple I/O relationship, high learning performance is possible even if the number of hidden layers and neurons is small. It is important to select the number of hidden layers and the number of neurons in the predictive model. In the hyper-parameter range of Table 18, the optimal number of hidden layers and neurons are derived through Bayesian optimization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Normalization** & **Train Time (s)** \\ \hline ANN & Min-Max & 1.43 \\ Standardization & 0.83 \\ SVM & Min-Max & 2.63 \\ Standardization & 6.64 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Computation time analysis of initial ANN model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine Learning Model** & **Hyper Parameter** & **Optimization Method** \\ \hline \multirow{4}{*}{ANN} & Training iterations & Grid search \\  & Number of hidden layer & Bayesian optimization \\  & Number of hidden node & Bayesian optimization \\  & Kernel & Grid search \\ SVM & Number of gamma & Random search \\  & Number of cost & Random search \\ \hline \hline \end{tabular}
\end{table}
Table 17: Hyper-parameter optimization method.  
Figure 7 shows the prediction model performance analysis according to 100 repetitions of learning. The performance of the performance coefficient prediction model was stabilized at 60 repetitions of learning. After a steady decrease in RMSE up to 40 times, unstable performance was analyzed during 40 to 50 repetitions. This required iterative learning at least 60 times to develop a performance coefficient prediction model using an ANN model.  
The optimization of the number of hidden layers and the number of neurons proceeds through two steps. First, the coefficient of determination, CvRMSE, was analyzed when the number of hidden layers was increased from 1 to 4 and the number of neurons was fixed. With the number of hidden layers with the highest performance calculated using this method, the combination of the optimal number of hidden layers and the number of neurons was derived through the change in the number of neurons. The analysis result is shown in Figure 8. The best performance was shown when the number of hidden layers was two and the number of neurons in the hidden layer was 11.
# Polynomial Time Algorithm for Shortest Paths in Interval Temporal Graphs +
## 3 Shortest Paths
### Complexity Analysis
The remaining (larger start time) intervals are then examined serially until the first interval that has already been used for the extension of a previously considered path in \(P(v,k-1)\) is encountered. Let \(\delta\) be the maximum number of travel intervals on any edge. We see that \(O(in\log\delta)\) time is spent performing the binary searches for the up to \(in\) paths and \(O(\delta+in)\) time in examining the remaining intervals. The total time to process \((u,v)\) is therefore \(O(in\log\delta)\) (note that \(\delta\leq i\)). The number of \(k\)-hop paths generated by extending the paths in \(P_{new}(u,k-1)\) using the edge \((u,v)\) is \(O(in+\delta)=O(in)\). To compute \(P_{new}(v,k)\), we need to compute a list of extensions of the paths in \(P_{new}(u,k-1)\) for all \(u\) such that \((u,v)\) is an edge of the temporal graph and merge these lists together, eliminating dominated paths. There are at most \(\mathit{indegree}(v)\) such path lists to be merged. Each of these has \(O(in)\) paths and takes \(O(in\log\delta)\) time to compute. The time needed to compute all of these lists is therefore \(O(in\log\delta*\mathit{indegree}(v))\). Pairwise merging these path lists takes \(O(in*\mathit{indegree}(v))\) time (note that during the pairwise merge of two path lists, dominated paths are eliminated, so, from Theorem 3, it follows that the list size remains \(O(in)\); two ordered path lists may be merged in linear time eliminating dominated paths). An alternative to pairwise merging of the path lists is to merge the \(\mathit{indegree}(v)\) lists simultaneously using a loser tree. This reduces the merging time to \(O(in*\log\mathit{indegree}(v))\). Regardless, the total time needed to compute \(P_{new}(v,k)\) is \(O(in\log\delta*\mathit{indegree}(v))\). Hence, for any \(k\), all \(P_{new}(v,k)\)s may be computed in \(O(ine\log\delta)\) time, where \(e=\sum\mathit{indegree}(v)\) is the number of edges in the temporal graph. For any \(k\) and \(v\), \(P_{new}(v,k)\) may be merged with \(P(v,k-1)\), eliminating dominated pairs from both lists in \(O(in)\) time as both lists are ordered by arrival time and of size \(O(in)\) (Theorems 2 and 3). Thus, the overall time taken by Algorithm 2 for each \(k\) is \(O(ine\log\delta)\) and the time over all \(k\)s is \(O(in^{2}e\log\delta)\), which is polynomial in the number of inputs.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 8 Validation and Analysis of Overall Wear Model  
### Model Validation  
The proposed overall wear model from Figure 10 is validated in a cycle-wise manner, i.e., over consecutive closing cycles with time varying operating parameters illustrated in Figure 6, thus mimicking real-world clutch application. The quadratic wear rate variability model (21) is employed. The obtained experimental and model-based simulation responses of cumulative worn volume are shown in Figure 18 for two test plates, where Plate I experienced floating temperature and Plate II was for the constant temperature (see Section 3). They align very well with each other, with the simulation response falling within 95% CI over all closing cycles, except at the very beginning of the response characterized by uncertain run-in wear levels. The relative total worn volume modeling error amounts \(-3.76\%\) and \(5.95\%\) for Plates I and II, respectively, which is well within the estimated 95% CIs at the end of responses being equal to \(\pm 11.1\%\) and \(\pm 14.5\%\), respectively. The above results confirm the validity of the overall wear model under more realistic, dynamic conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Modeling Points**} & \multicolumn{6}{c}{**Validation Points**} \\ \cline{2-9}  & \(\mathbf{L}(\theta)\) & \(\mathbf{\varepsilon_{scaled,\mu}}\) [-] & \(\mathbf{\varepsilon_{scaled,\sigma}}\) [-] & \(\mathbf{p}\) Value & \(\mathbf{L}(\theta)\) & \(\mathbf{\varepsilon_{scaled,\mu}}\) [-] & \(\mathbf{\varepsilon_{scaled,\sigma}}\) [-] & \(\mathbf{p}\) Value \\ \hline Exponential & 267.1 & 0.066 & 1.003 & 0.456 & 58.3 & 0.582 & 1.191 & 0.123 \\ \hline Linear & 266.4 & 0.060 & 1.003 & 0.345 & 57.3 & 0.587 & 1.163 & 0.109 \\ \hline Quadratic & 265.7 & 0.046 & 1.004 & 0.478 & 56.5 & 0.590 & 1.131 & 0.084 \\ \hline Linear & 260.6 & 0.041 & 1.004 & 0.718 & 56.4 & 0.477 & 1.036 & 0.068 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative performance indices of different wear rate variability models.  
Figure 17: Histograms and approximative normal probability distributions of normalized residuals shown against standard zero-mean unit-variance normal distributions, for four-input linear wear rate variability model and modeling (**a**) and validation points (**b**).
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 2 Energy Constraints--A Persistent Problem
### Enhancing the Lifetime of Devices--An IoT Perspective  
The concept of IoT encompasses resource-constrained devices (such as tiny, battery-powered wireless temperature sensors, humidity sensors, movement detectors, etc.). To address the energy needs in IoT, an energy-efficient network architecture was proposed. This architecture avoids the energy hole problem (uneven energy drainage) using sensor and relay nodes [26]. The Quark operating system (OS) was introduced for low power IoT devices, with very small energy buffers [27]. The Quark OS saves power by scaling down every task of communication, sensing, and computation into small quanta (e.g., one pixel, half a bit) by fragmentation. The Quark OS has better performance than other traditional OSs for IoT. [PERSON] et al. suggested an energy-efficient multi-sensing platform WuLoRa (wake-up long range) for IoT by combining wake up radio and LoRa (long range) [28]. The wake-up radio technique, made for self-sustaining sensing application, enhances the operative lifetime of outdoor and indoor devices by utilizing low power hardware construction in IoT networks. The power consumption and latency is reduced by using ultra-low power wake up radio, supporting long-range communications.  
**Discussion:** Table 1 summarizes Section 2 and covers limited battery life as a long-lasting problem in cellular networks, D2D communications in disaster management, and IoT devices. Various steps have been taken to address the energy problems mentioned above, some of which are discussed below.  
1. Energy harvesting from the base station can be used for energy-efficient communications in cognitive cellular networks [18]. This technique is useful to address energy problems while considering busy channels.
2. mm-waves enable devices to harvest more power compared to conventional mobile networks [19]. mm-waves can also address energy crises in dense network areas to fulfill power demands.  
D2D-based survival in sharing techniques [4], D2D discovery mechanisms [24], and D2D-based messaging [25], perform well in disaster scenarios to combat power issues.
* An energy-efficient network architecture [26], a small power OS (Quark OS) [27], and low energy sensing platforms [28] are preferred in IoT to fulfill energy needs.  
* Survival in sharing mechanisms uses D2D-based communication to overcome battery power limitations during disasters.
* A D2D discovery mechanism takes advantage of the proximity area concept to establish energy-efficient D2D communications.
* A D2D-based messaging solution was proposed to overcome the battery power limitation in disaster situations.
* An energy-efficient network architecture based on sensor and relay nodes was discussed to avoid the energy hole problem.
* The Quark OS was introduced for low power IoT devices with tiny energy buffers.
* An energy-efficient multi-sensing platform WuLoRa (Wake up Long Range) was proposed for an IoT network.
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 3 Results  
### Synthetic Example  
In order to verify the validity of the coherent noise removal method based on the NSST, the synthetic data example is tested in this article. The synthetic seismic data is shown in Figure 4a. The seismic data to be tested includes 100 channels, each channel contains 1000 sampling points, and the sampling interval is 1 ms.  
As can be seen from Figure 4a, the effective signal with weak energy in the in-phase axis of the seismic record is covered by the coherent noise, which brings the interference to the subsequent processing of the seismic data. We apply the denoising method to the synthetic seismic data. Among the process, the decomposition scale of the NSST is 2, and the direction matrix is [12]. Figure 5 shows the coefficients after the decomposition of the NSST. There is an overlap of the frequency band between the effective signal and the noise. NSST can map the data into several sub-domains, which can represent the data sparsely, so that the noise and the signal can be separated better and the processing effect is better. Figure 4b shows the removed noise. Figure 4c is the result of the denoising method in this article. This method removes most of the coherent noise. This method can not only suppress the coherent noise but can also protect the effective signal to a great extent, restore the in-phase axis of the seismic record, enhance the continuity of the seismic record, and improve the SNR of the seismic record. The denoising results proves the feasibility of the denoising method in this article.
## 3 Multi-Parameter Optimization and Sensitivity Analysis Method  
Mathematical Model for Multi-Parameter Optimization of the Three-Dimensional Seismic Isolation Device  
The objective of parameter optimization for the seismic isolation device is to find a set of solutions that provide an overall optimal isolation effect against a series of vibrational loads.  
According to the Fourier series, any load can be decomposed into a linear combination of several harmonic loads. For a seismic design intensity of 8 degrees, the peak acceleration value of the designed harmonic vibrational load is set at 0.20 g, with design load frequencies \(\omega\) ranging from 1 Hz to 20 Hz, resulting in 20 sets of harmonic loads (independently applied in the horizontal and vertical directions), expressed as:  
\[\mathbf{a}_{i}(t)=0.2g\sin\omega_{i}t\ (\omega_{i}=1,2,\ldots 20) \tag{2}\]  
Under the \(i\)-th load set, the acceleration response at the top center node of the isolated object is calculated as \(\widetilde{\mathbf{a}}_{i}(t)\). The acceleration-transmission ratio of the isolated object at any given moment is defined as:  
\[\eta_{i}(t)=\frac{\widetilde{\mathbf{a}}_{i}(t)}{\mathbf{a}_{i}(t)}\times 100\% \tag{3}\]  
Thus, the mathematical model for this problem is to find a set of design parameters such that the following objective function is satisfied:  
\[\left\{\begin{array}{l}obj:\min W(\mathbf{X})=\max\{\eta_{1}(t),\eta_{2}(t), \ldots,\eta_{20}(t)\}\\ \quad\quad\quad\quad\mathbf{X}(x_{i})=\{h_{1},h_{2},l,r\}\\ s.t.:x_{\min}\leq x_{i}\leq x_{i\max}\end{array}\right. \tag{4}\]  
Figure 4: Schematic diagram of parameters to be optimized for the rubber seismic isolation bearing.  
Figure 3: Topological optimization model for the rubber seismic isolation bearing.  
where \(W(\mathbf{X})\) represents the objective function, and \(\mathbf{X}\) denotes the discrete design variables.  
The physical significance of this objective function is that when the design load frequencies are taken as specified, the acceleration-transmission ratio \(\eta_{i}(t)\) of the isolated object can be obtained at any moment, leading to the determination of the maximum acceleration-transmission ratio \(\max\{\eta_{i}(t)\}\). The objective function seeks to find the optimal set of parameter solutions \(\mathbf{X^{*}}\) that minimizes the maximum acceleration-transmission ratio \(\max\{\eta_{i}(t)\}\) corresponding to the 20 different load frequencies under their respective working conditions.
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 2 Related Work
### Focus Measures  
A focus measure operator calculates the focus quality of each pixel in the image sequence, and is evaluated locally. As the image sharpness increases, the value of the focus measure increases. When the image sharpness is maximum, the best focused image is attained. Some of the popular gradient-based, statistical-based, and Laplacian-based operators are briefly given in [12].  
First, there are Modified Laplacian (ML) and Sum of Modified Laplacian (SML) as Laplacian-based operators. When Laplacian is used in textured images, \(x\) and \(y\) components of the Laplacian operator may cancel out and provide no response. ML is calculated by adding the squared second derivatives for each pixel of the image \(I\) as:  
\[F_{ML}(x,y)=\left(\frac{\partial^{2}I(x,y)}{\partial x^{2}}\right)^{2}+\left( \frac{\partial^{2}I(x,y)}{\partial y^{2}}\right)^{2} \tag{2}\]  
If the image has rich textures with high variability at each pixel, focus measure can be evaluated for each pixel. In order to improve robustness for weak-textured images, SML is computed by adding the ML values in a \(W\times W\) window as:  
\[F_{SML}(i,j)=\sum\
olimits_{x\in W}\sum\
olimits_{y\in W}\left\{\left(\frac{ \partial^{2}I(x,y)}{\partial x^{2}}\right)^{2}+\left(\frac{\partial^{2}I(x,y) }{\partial y^{2}}\right)^{2}\right\} \tag{3}\]  
Figure 2: Image formation in optical lens.  
where, \(i\) and \(j\) are the \(x\) and \(y\) coordinates of center pixel in a \(W\times W\) window, respectively.  
Next, there is Tenenbaum (TEN) as a gradient-based operator. TEN is calculated by adding the squared responses of horizontal and vertical Sobel operators. For robustness, it is also computed by adding the TEN values in a \(W\times W\) window as:  
\[F_{TEN}(i,j)=\sum\
olimits_{x\in W}\sum\
olimits_{y\in W}\left\{\left(G_{x}(x, y)\right)^{2}+\left(G_{y}(x,y)\right)^{2}\right\} \tag{4}\]  
where, \(G_{x}(x,y)\) and \(G_{y}(x,y)\) are images acquired through convolution with the horizontal and vertical Sobel operators, respectively.  
Finally, there is Gray-Level Variance (GLV) as a statistics-based operator. It has been proposed on the basis of the idea that the variance of gray level in a sharp image is higher than in a blurred image. GLV for a central pixel in a \(W\times W\) window is calculated as:  
\[F_{GLV}(i,j)=\frac{1}{N^{2}}\sum\
olimits_{x\in W}\sum\
olimits_{y\in W}\left\{ \left(I(x,y)-\mu\right)^{2}\right\} \tag{5}\]  
where, \(\mu\) is the mean of the gray values in a \(W\times W\) window.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Solution Blueprint
#### 3.3.8 Response Module  
The response module is responsible for reacting to timing side-channel attacks, which occur based on the rules and policies generated via the data analysis module. Real-time responses are enabled by utilising specific response mechanisms, particularly OpenFlow messages. A key characteristic of the response module is its ability to customise the response to each attack attempt, ignoring a generalised one-size-fits-all method in favour of a more tailored and efficient approach. The functionality of the response module is supported by two distinct components. The first component is activated when the data analysis module identifies a previously determined source as the attacker. In this case, the component builds a customised fixed-drop rule, which is then sent to the controller. The creation of this flow is a complex procedure and is the direct result of comprehensive data analysis. It targets specific parameters, such as the source, destination, and protocol, as opposed to hindering all the traffic from the host. When the data analysis module detects an attack launched from a previously undetected source, it triggers the second component of the response module. In response, a temporary drop rule is generated. The transient nature of the rule ensures that it is removed immediately if no further malicious activity is detected from the source. The primary purpose of this rule is to validate and provide feedback on prospective reoccurrences in future network encounters. These two components of the response module represent an advanced and dynamic real-time network threat response strategy for timing side-channel attacks.
## 4 Result and Discussion
### Model Comparison
On the other hand, the predictive performance of the multi-output RF and XGBoost models is in close agreement with that of the single-output models. This demonstrates the strong generalization performance of the WAOA-ANN model, as well as its effective utilization of the potential connection between borehole breakout angle and depth. In comparison to the single-output ANN model, the WAOA-ANN model demonstrates superior prediction performance and requires fewer training resources for predicting the angle and depth of borehole breakout. For the WAOA-RF and WAOA-XGBoost models, there is no significant improvement compared to the single-output RF and XGBoost models. Additionally, their hyperparameters result in large model sizes, making the training process time-consuming and memory intensive, offering no practical advantage over the single-output RF and XGBoost models. Based on the comparative analyses of the models, the WAOA-ANN model is recommended for predicting borehole breakout size. Furthermore, the WAOA-ANN model undergoes further sensitivity analyses to explore the impacts of various input features on borehole breakout size.