esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Case III: Binary Classification and Multiclass Classification (CSE-CIC-IDS 2018)  
Similarly, an up-to-date dataset CSE-CIC-IDS 2018 [16] is conducted in the third experiment. This data set was created by the Communications Security Establishment (CSE) and the Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services) in 2018. CSE-CIC-IDS 2018 contained 16,233,002 records covered over the network traffics within 10 days. It included recent known attacks for intrusion detection exercise with massive network traffic and system logs. It consists of seven types of attacks including Brute-force, Heartbleed, Botnet, DoS, SQL Injection, Web attacks, and infiltration of the network from inside. The dataset includes the captures network traffic and system logs of each machine, attack classes along with 80 features extracted from the captured traffic using CICFlowMeter-V3. About 17% of the instances were malicious traffics for 7 major attacks which can be summarized in five attack categories including Brute-force, Bot, DoS, SQL Injection, and infiltration. First, 80 features of CSE-CIC-IDS 2018 were ranked using Equations (5)-(8), the set of reduced features selected from the top 36 of the 80 total features using the C4.5 approach are selected (Table 11).  
First, 80% of the data files are used for training, while 20% are used for testing the model. Analyses were conducted using RF (\(n=150\)), and the SVC to evaluate the performance of the proposed model on CSE-CIC-IDS 2018. First, filtered out four descriptive metrics (unnecessary features) from 80 features in the classification process, i.e., label (output), src_port, dst_port, timestamp, protocol. According to the results, the binary classification accuracies for 99.98% and 97.05% for \(N_{f}=36\) on the training and testing data, respectively (Table 12).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SMOTE(Training/Testing)** & **Without SMOTE(Training/Testing)** \\ \hline RF & 87.35\% and 46.34\% & 84.01\% and 42.07\% \\ SVC & 72.87\% and 61.40\% & 77.86\% and 62.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Multiclass classification accuracy (23 features).  
In an evaluation of multiclass classification for six subcategories, the average accuracies for the RF and the SVC were listed as shown in Table 13.
## 4 Dynamics
### Linearization  
The set of six dynamic Equations (7) must be completed with six kinematical equations that link global position coordinates with velocity components and attitude angles with  
Figure 6: Coordinate systems and forces components.  
angular rates. These twelve nonlinear equations constitute a mathematical model of the aircraft's motion (flight), which was used to simulate selected maneuvers. In order to determine the dynamic stability characteristics, equation system (7) was extended with two kinematic equations that link the pitch angle and roll angle to angular rates. The set of eight equations was next linearized to the form [41]:  
\[\frac{ds_{8}}{dt}=A_{8}^{-1}\overline{F_{8}}(s_{8},t)\Longrightarrow\frac{ds_ {8}}{dt}=J_{8}s_{8} \tag{10}\]  
where:  
\[s_{8} =\{u,v,w,p,q,r,\varphi,\theta\}^{T} \tag{11}\] \[J_{8} =\{\frac{\partial(A_{8}^{-1}\overline{F_{8}})_{i,j}}{\partial s_ {j}}\} \tag{12}\]  
Then, the eigenvalue problem can be formulated:  
\[[J_{8}-I\lambda]s_{8}=0 \tag{13}\]  
Solving the eigenvalue problem gives the frequency and damping coefficients. Solving the problem of eigenvectors allows the identification of the form of motion [41].  
\[\lambda=\xi+i\eta \tag{14}\]  
where:  
\(\xi\)--damping coefficient;  
\(\eta\)--frequency coefficient.  
While the frequency and damping coefficients are given by directly solving eigenvalue problem (13), more useful measures--damping ratio and undampened natural frequency--can be obtained from the following formulas:  
\[\omega_{n}=\sqrt{\xi^{2}+\eta^{2}} \tag{15}\] \[\zeta=\frac{-\zeta}{\omega_{n}} \tag{16}\]  
The period and time of damping halftime (or double time) can be computed as follows:  
\[T=\frac{2\pi}{\eta} \tag{17}\] \[T_{1/2}=-ln(2)/\xi\quad\text{for }\xi<0,\quad\ T_{2}=ln(2)/\xi \quad\text{for }\xi>0 \tag{18}\]  
The quantities defined above constitute the basic measures of handling qualities presented later in the article.
# Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis
## 2 Methods  
### Karhunen-Loeve (KL) Method  
The characteristics of a random field are defined by its mean \(\mu\), standard deviation \(\sigma\), and autocorrelation function [14]. In this study, the adopted autocorrelation function expression is formulated as follows:  
\[\rho\big{(}\tau_{x},\tau_{y}\big{)}=\exp\Big{(}-(\tau_{x}/\delta_{x})^{2}-\big{(} \tau_{y}/\delta_{y}\big{)}^{2}\Big{)}, \tag{1}\]  
where \(\tau_{x}\) and \(\tau_{y}\) are the relative separation distances along the \(x\)-axis and \(y\)-axis, respectively, while \(\delta_{x}\) and \(\delta_{y}\) denote the corresponding fluctuation ranges along these axes.  
The cohesion (\(c\)) and friction angle (\(\varphi\)) are considered to be the uncertain characteristics of soil, exhibiting a close and inseparable interdependence that cannot be ignored. The cross-correlated lognormal random fields can be represented as follows [21]:  
\[H_{c}(x,\theta)=\exp\Bigg{[}\mu_{\ln c}+\sum_{i=1}^{M}\sigma_{\ln c}\sqrt{ \lambda_{i}}\varphi_{i}(x)\chi_{ci}(\theta)\Bigg{]}, \tag{2}\]  
\[H_{\varphi}(x,\theta)=\exp\Bigg{[}\mu_{\ln\varphi}+\sum_{i=1}^{M}\sigma_{\ln \varphi}\sqrt{\lambda_{i}}\varphi_{i}(x)\Big{(}\chi_{ci}(\theta)\cdot\rho_{c, \varphi}+\chi_{\varphi i}(\theta)\cdot\sqrt{1-\rho_{c,\varphi}^{2}}\Big{)} \Bigg{]}, \tag{3}\]where \(\chi_{i}(\theta)\) is random variable; \(\lambda_{i}\) and \(\varphi_{i}(x)\) are the eigenvalues and eigenfunctions of the autocorrelation function, respectively; \(M\) is the truncation term of the series expansion; \(\mu_{\text{ln}}=\text{ln}\mu\,-\,(\sigma_{\text{ln}})^{2}/2\) and \(\sigma_{\text{ln}}=(\text{ln}(1+(\sigma/\mu)^{2}))^{0.5}\) are the mean and standard deviation of lognormal random field, respectively; and \(\rho_{c,\varphi}\) is the cross correlation coefficient between \(c\) and \(\varphi\).
## 5 Detection in UWB Positioning Algorithms  
Generally, in a UWB IPS, the signals are classified as either a LoS or NLoS signal [27; 78; 79; 80; 81]. There are some papers where the signals are classified as having quasi-LoS (QLoS); see [82; 83] and references therein. The detection of the UWB positioning system is classified as LoS and NLoS in Figure 12. In LoS conditions, where there is a clear environment and there are no obstacles between the anchor and the tag, the estimated range between each anchor and tag (\(d_{1},d_{2},d_{3}\)) can be calculated accurately, allowing the trilateration theory to be applied and the position of the tag to be accurately obtained as shown in Figure 12c. However, in NLoS conditions, where the signal is attenuated or refracted by obstacles causing a positive bias, the distance measurement for anchor 3 (\(d^{{}^{\prime}}_{3}\)) is estimated inaccurately. This causes the circles to overlap as shown in Figure 12d, resulting in the tag's location being in any highlighted area rather than an accurate location point. As a result, the localization accuracy of the tag is seriously affected. Therefore, from Figure 12, it can be observed that the trilateration positioning algorithm suffers from positive bias NLoS errors. This positioning error can be solved by employing a joint approach of employing empirical and ML models. ML models can be used to classify these LoS and NLoS conditions to improve the accuracy of the positioning algorithm. Let us now look into ML models in detail for NLoS classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Year** & **Positioning** & **Algorithm** & **Description** \\ \hline
[72] & 2020 & 1D and & ToA & Improving the UWB IPS accuracy by proposing a modified leading edge \\  & & 2D & & detection with LS trilateration filtering. \\ \hline
[73] & 2023 & 3D & ToA & Proposed convolutional neural networks (CNNs) to estimate the range and \\  & & & then mitigated the errors by utilizing channel impulse responses (CIRs). \\ \hline
[74] & 2021 & 3D & TDoA & Proposed anchor selection theory for improving the accuracy of IPS. \\ \hline
[75] & 2022 & 3D & TW-ToA & A messaging framework that optimizes the usage of resources. The results \\  & & & showed an improvement in error to as low as 5.4 mm when using 6 anchors. \\ \hline
[76] & 2022 & 2D & AoA & A fusion positioning system based on BLE-AOA and UWB was developed. \\  & & & It enhances the accuracy reaching below the sub-meter level. \\ \hline
[77] & 2020 & 2D and & RSSI & An RSSI IPS based on neural network is designed. Positioning error is \textless{}1\text{ m}\) \\  & & & and the average positioning error is 0.4436 m. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Key positioning algorithms and their description.  
Figure 12: _Cont._
## 4 Evaluation and Results
### Approximation of the Velocity Vector Term  
Figure 2 shows the result of comparing the statistical parameters of \(\Delta f_{r,v}\) and QPE\({}_{v}\) between the Monte Carlo simulation and the analytical approximation model. \(\mathbb{E}\left[\text{QPE}_{v}\right]\) shows oscillations around zero, and these are due to the Monte Carlo simulation method. With more error samples processed in the numerical simulation, the oscillation would be much smaller, which confirms the effectiveness of the analytical approximation model. The mean differences in the results between the numerical simulation and analytical approximation model in Figure 2c,d are \(1.64\times 10^{-3}\) Hz/s and \(0.093^{\circ}\). The difference of \(\sigma\left[\Delta f_{r,v}\right]\) may be high for the algorithm method, but it is a satisfactory result for the approximate QPE calculation. The integration time is not a consistent value, which leads to the differently shaped curves in Figure 2c,d.
## 3 Distributed Hybrid Formation Control  
### Distributed Control Law Design  
According to \(\bar{X}_{i}\), the following information can be calculated, including \(\alpha_{i}^{+}\), \(\alpha_{j}^{-}\), \(\alpha_{i}^{+}\), \(\dot{\alpha}_{i}^{-}\), the angular velocity \(\omega_{i}\) of robot \(p_{i}\) relative to \(c\) (counterclockwise is positive), the velocity \(v_{i}(t)\) of target \(c\), \(\dot{\bar{r}}_{i}(t)\), the unit vector \(\vec{e}_{r}\) of \(\bar{r}_{i}(t)\), and \(\vec{e}_{\theta}\) (\(\vec{e}_{\theta}=\vec{e}_{z}\times\vec{e}_{r}\)). Further, we have  
\[X_{i}=\bar{X}_{i}\cup\{\alpha_{i}^{-},\alpha_{i}^{+},\dot{\alpha}_{i}^{-}, \dot{\alpha}_{i}^{+},\omega_{i},v_{c}(t),\vec{e}_{r},\vec{e}_{\theta}\}\]  
Therefore, a general control law is designed as  
\[u_{i}(t)=F(X_{i})=\mathbf{v}_{c}(t)+\mathbf{v}_{i}^{\theta}(t)+\mathbf{v}_{i }^{*}(t)+g_{i}(x_{i}(t),t) \tag{12}\]  
where \(\mathbf{v}_{i}^{\theta}(t)\) and \(\mathbf{v}_{i}^{*}(t)\) denote the circumferential control and the radial control function for the robot \(p_{i}\), respectively.  
The circumferential control \(\mathbf{v}_{i}^{\theta}(t)\) is essentially related to the distance from the robot \(p_{i}\) to the center point \(c\). One simple form of this function can be represented as  
\[\mathbf{v}_{i}^{\theta}(t)=\omega_{i}^{*}(X_{i})\bar{r}_{i}(t)\vec{e}_{\theta}\]  
The radial control function \(\mathbf{v}_{i}^{*}(t)\) is considered such that it equals to exchange information whenever any robots get contact with robot \(p_{i}\)  
\[\dot{\mathbf{v}}_{i}^{*}(t)=\alpha_{i}^{*}(X_{i})=(\alpha_{r}^{*}( X_{i})-\bar{r}_{i}(t)\omega_{i}+\bar{r}_{i}(t)\omega_{i}\omega_{i}^{*}(X_{i})) \vec{e}_{r}\] \[+(\alpha_{\theta}^{*}(X_{i})\bar{r}_{i}(t)+\omega_{i}^{*}(X_{i}) \bar{r}_{i}(t))\vec{e}_{\theta}\]  
where \(\omega_{i}^{*}(X_{i})\), \(\alpha_{r}^{*}(X_{i})\) and \(\alpha_{\theta}^{*}(X_{i})\) are three independent variables without specific forms.
## 2 Methods: Sparse Regularization and Identification
\right), \tag{5}\]  
or in a more compact form:  
\[\mathbf{Ga}=\mathbf{Y}. \tag{6}\]  
If it is expected that only N functions (N \(\leq\text{D}\ll\text{M}\)), D \(\sim\) N suffices for approximating, with adequate accuracy, solution \(y=y(\mu)\); then, only N non-zero coefficients are expected in vector \(\mathbf{a}\). Thus, one would prefer solving Equation (6) under the sparsity constraint. It is well known that standard procedures are unable to produce such sparse solutions, as it is the case when using the usual pseudo-inverses or the most common optimization based on the use of the L2-norm, and the last procedure leads to:  
\[\mathbf{a}=\texttt{argmin}_{\mathbf{a}^{*}}\|\mathbf{Ga}^{*}-\mathbf{Y}\|_{2}^ {2}, \tag{7}\]  
that results in  
\[(\mathbf{G}^{T}\mathbf{G})\mathbf{a}=\mathbf{G}^{T}\mathbf{Y}, \tag{8}\]  
for which its solution becomes almost fully populated, rendering the procedure inefficient to account for sparsity. In order to enforce sparsity, the use of the L0-norm is the best choice; however, its numerical treatment is extremely difficult. For that reason, intermediate norms are considered. The L1-norm has been widely considered and different procedures are used for enforcing it, as was the case for the one used in [7] for performing sparse identification. The so-called _elastic net_ regularization combines the _ridge_ regularization [28], which alleviates overfitting, with the _lasso_ regularization [29] that enforces sparsity. The elastic net regularization is described as follows:  
\[\mathbf{a}=\texttt{argmin}_{\mathbf{a}^{*}}\Big{\{}\|\mathbf{Ga}^{*}-\mathbf{ Y}\|_{2}+\lambda\Big{\{}\alpha\|\mathbf{a}^{*}\|_{2}^{2}+(1-\alpha)\|\mathbf{a}^{*}\|_{1} \Big{\}}\Big{\}}, \tag{9}\]and it allows recovering the usual L2-norm problem when \(\lambda=0\); for \(\lambda\
eq 0\), the resulting _elastic net_ regularized formulation reduces to _ridge_ regularization for \(\alpha=1\) and to the _lasso_ regularization for \(\alpha=0\). These regularizations were adapted to variable separations for addressing highly multi-parametric models in [8]. The _lasso_ regularization was extensively employed in the context of compressed sensing [30; 31]. Smaller norms can be also considered, as for example the p-norm, from which the minimization reads as follows:  
\[\left\{ \begin{array}{l}\texttt{min}_{\mathbf{a}}\sum\limits_{i=0}^{\mathbf{ M}-1}|a_{i}|^{p}\\ \text{subjected to : }\mathbf{Ga}=\mathbf{Y}\end{array} \right., \tag{10}\]  
which can be efficiently solved by using the FOCUSS algorithm [32]. In the next section, we show firstly an optimization procedure that uses the L1-norm together with the data when sparsity constrain has to be satisfied. Secondly, the classic L2-norm minimization procedure is presented when the sparsity constraint is no longer required by the problem.
## 4 Model Establishment
### The Correlation between Selected IM Indicators and EDP  
The choice of intensity measure (IM) directly affects the accuracy of the vulnerability analysis results. This study analyzed and compared four IM indicators: PGA, PGV, PGD, and Sa(T1) (spectral acceleration at the structure's first mode period).The peak inter-story drift angles of the structure under different amplitude inputs were calculated, and the correlation between IM indicators and EDP was analyzed.  
In this study, the Pearson correlation coefficient was used to characterize the correlation between different variables. It can be calculated according to Equation (19). In this equation, \(X\) and \(Y\) are the two variables for which the correlation is to be analyzed. \(X\) takes \(\ln(\mathrm{PGA})\), \(\ln(\mathrm{PGA})\), \(\ln(\mathrm{PGD})\), and \(\ln[\mathrm{S_{a}(T_{1})}]\), respectively, while Y takes \(\ln(\mathrm{EDP})\). Cov(X,Y) is the covariance of X and Y, and D(X) and D(Y) are the variances of the two variables. The closer the correlation coefficient is to 1, the stronger the positive correlation between the variables, the closer it is to \(-1\), the stronger the negative correlation between the variables, and the closer it is to 0, the weaker the correlation between the variables.  
\[\rho(X,Y)=\frac{Cov(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}} \tag{19}\]  
Here, pure frame structure: FR structure, rocking wall-frame structure: ND structure, and energy dissipating rocking wall-frame structure: BD structure.  
The correlation coefficients between \(\ln(\mathrm{EDP})\) and \(\ln(\mathrm{IM})\) for FR, ND, and BD structures were calculated sequentially, and the results are shown in Table 2.  
From Table 2, it can be observed that the correlation between PGV and EDP was the strongest, while the logarithmic correlation between \(\mathrm{S_{a}(T_{1})}\) and EDP was the weakest, with PGA falling between the two. From Figure 5, it can be observed that the predominant period of the average acceleration response spectrum for the 15 records was around 0.3 s. The fundamental period of the BD structure was approximately 0.55 s, while the fundamental periods of the ND and FR structures exceeded 1 s. After the structures entered the plastic range, the periods extended further. Therefore, it was more reasonable to use PGV for scaling in the long-period range, which is consistent with the relevant requirements of Japanese standards. However, the Chinese code expresses the response spectrum in terms  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Calculate the Structure** & **PGA** & **PGV** & **PGD** & \(\mathbf{S_{a}(T_{1})}\) \\ \hline BD & 0.81 & 0.95 & 0.80 & 0.66 \\ ND & 0.73 & 0.91 & 0.82 & 0.66 \\ FR & 0.74 & 0.93 & 0.82 & 0.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The earthquake records.  
of acceleration, and the correlation between PGA and EDP was significantly better than that of \(\mathrm{S_{a}(T_{1})}\). Hence, it was reasonable to use PGA as the IM for incremental dynamic analysis in this study.
## 4 Formulation of an Operation and Maintenance Strategy for Equipment with a High Failure Rate
### Data Processing and Fault Prediction
#### 4.1.1 Data Carding and Preprocessing  
In the three sections of Urumqi Round City Expressway (East) (G3003), Xiaocahu Urumqi Expressway (G30), and the reconstruction and expansion project of Dahuangshan-Urumqi Section of Jingxin Expressway (G7), there are 5196 sets of expressway mechanical and electrical equipment. Among them are 981 pieces of video surveillance equipment, and the amount of fault recording data related to this kind of equipment is 29. In the above three road sections, the fault recording data are as high as 17.6%, which belongs to the equipment with high fault incidence.  
Among these kinds of equipment's fault records are 27 minor fault status records, 2 general fault status records, and 0 serious fault status records, of which 2 minor fault repair works have not been completed. In addition, if the equipment failure state is not transferred to a more serious state, the transfer rate is set to 0.1%, the corresponding  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type of Fault** & **Number of Fault Records** & **Proportion of Quantity** & **Fault Performance** \\ \hline Video & 29 & 17.6\% & The picture is not clear; identify fault, etc. \\ surveillance equipment & & & \\ Automatic & 20 & 12.1\% & Bar rise/fall fault; railing arm fracture; railing machine \\ railing machine & & & does not have power, etc. \\ Weighing equipment & 15 & 9.1\% & Axis identification error; weighing platform collapse; weighing platform overweight; no axis information, etc. \\ \hline \end{tabular}
\end{table}
Table 3: Overview of the main failures of the toll system.  
maintenance state transfer rate is 0.1%, and the repair completion rate is 99.9%. In the case of severe failure, the repair completion rate is 100%, which indicates that the equipment is replaced or the core components are replaced to ensure that the equipment is completely repaired.  
Based on the above data, the initial state transition probability of video surveillance equipment is \(\lambda_{01}=2.75\%\), \(\lambda_{12}=7.40\%\), \(\lambda_{2F}=0.10\%\). The repair completion rate of the minor fault state \(S_{1}\) is 92.59%, the general fault state \(S_{2}\) is 99.90%, and the serious fault state \(S_{\text{F}}\) is 100%.  
As in Section 3.2, combined with the number of equipment items, the amount of fault data, and the state transfer rate in the selected section, the initial probability and transfer rate matrices of each state of the video surveillance equipment are shown in Equation (12) and matrix (13):  
\[X=[0.8985,0.0275,0.0730,0.0010,0,0,0] \tag{12}\]  
\[P=\begin{bmatrix}0.9725&0.0275&0&0&0&0\\ 0&0.9270&0.0730&0&0&0&0\\ 0&0&0.9990&0.0010&0&0&0\\ 1&0&0&0&0&0&0\\ 0.9725&0.0275&0&0&0&0&0\\ 0.9270&0&0.0730&0&0&0&0\\ 0.9990&0&0&0.001&0&0&0\end{bmatrix} \tag{13}\]
## 2 Deep-Learning-Based Single-Ended Speech Quality Measures
### Model Network Architecture  
Early work on machine-learning-based non-intrusive speech quality evaluation was based on Gaussian mixture probability models (GMMs) [14]. In [15], a model based on the standard ASR system that combines a feed-forward DNN (which serves as an acoustic model) with a hidden Markov model (HMM) was presented, and the model reached an average correlation of r = 0.87. A model based on a bidirectional LSTM network was proposed in [16]. The model is focused towards speech enhancement. The DNN model based on a combination of a CNN and RNN (LSTM) [17] showed promising results in predicting the quality of super-wideband speech transmission and the impact of packet loss concealment. In [18], the convolutional networks (TCNs) used learned representations while maintaining the temporal structure of the signal for quality evaluation.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 2 Research and Development of Unsealed Borehole Leakage Detection Technology Based on Flow Method
### Selection of the Main Components of the Air Leakage Detection Device
#### 2.2.3 Selection of Pressure Limiting Valve  
The expandable capsule is inflated through the underground press-air system, and the expansion of the capsule generally requires a gas charge in excess of 0.1 MPa. The pressure of the underground press-air system in coal mines is generally 0.6-0.8 MPa. Since the selected capsule can withstand a maximum pressure of 0.4 MPa, in order to prevent the capsule from rupturing directly after it is connected to the press-air system, a pressure limiting valve (Figure 4) needs to be installed between the press-air system and the connecting rod equipped with the expandable capsule. In order to keep a certain affluence coefficient, the final selection of the pressure limiting valve pressure is 0.25 MPa.