esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, these approaches are unsuitable for evaluating system reliability measures of complex lifeline networks with sophisticated performance measures such as TSTT. This study proposes an accelerated methodology of system-level seismic risk assessment with a novel system performance measure. By adopting the performance measure of TSTT which considers the total travel times of the entire network system between multiple pairs of origin and destination nodes, the performance of a bridge transportation network can be measured more accurately. In addition, to reduce the computational time, an artificial neural network (ANN)-based surrogate model was employed because it is difficult to calculate the TSTT measurement directly for PSHA considering multiple epicenters and earthquake magnitudes. The surrogate model enables accelerated calculation of the novel performance index of complex networks without direct calculation of an entire framework. To demonstrate the proposed method with an ANN-based surrogate model, an actual bridge transportation network in Pohang, South Korea, was adopted, and the network map was reconstructed based on the locations of bridges and roads. In addition, the training data for the ANN-based surrogate model were set to the epicenters and magnitudes of historical earthquake data to generate random events, and the damage states and results of TSTT simulation of the bridge were utilized as input and output data, respectively. The performance of the proposed surrogate model was verified through the correlation coefficient of the generated surrogate function. The constructed surrogate model was able to estimate the mean total travel time and relative importance of the bridge transportation network according to the various earthquake magnitudes and epicenters.
## 3 Establishment of Geological Hazard Risk Assessment Model
### Evaluation Model
#### 3.3.2 Evidence Weight Model  
The evidence-weighting model is a method based on the Bayesian model, which integrates various evidence layers to assess the probability of an event occurring [35]. It combines multiple evaluation factors to assess the degree of hazard associated with geological disasters. The weight of each hazard factor reflects its degree of influence on geological disasters. By aggregating the weights of all factors and performing spatial correlation analysis between existing disaster points and hazard factors, the geological hazard risk index is obtained. The value of this index represents the probability of a geological disaster occurring in the study area [36]. The formula for calculating the evidence weight is as follows:  
\[W_{i}^{+}=ln\frac{P(F/L)}{P(F/\overline{L})} \tag{4}\]  
\[W_{i}^{-}=ln\frac{P(\overline{F}/L)}{P(\overline{F}/\overline{L})} \tag{5}\]  
\[C_{i}=W_{i}^{+}-W_{i}^{-} \tag{6}\]In the formula, \(L\) represents the total number of grid cells in the area affected by geological disasters; \(F\) represents the total number of grid cells in the evaluation factor where geological disasters occur; \(\overline{L}\) represents the total number of grid cells in areas without geological disasters; \(\overline{F}\) represents the total number of grid cells in the evaluation factor where geological disasters have not occurred; \(W_{i}^{+}\) represents the positive weight; \(W_{i}^{-}\) represents the negative weight; \(C_{i}\) represents the final weight value of the evaluation factor, i.e., the evidence weight. A larger \(C\) value (greater than 0) indicates that the factor promotes the occurrence of geological disasters; otherwise, it does not. When \(C=0\), it means that the influencing factor is unrelated to the occurrence of geological disasters.
## Appendix A Proof of the Partition Range in Branch Phase  
Assume message comes from the \(k\)-th branch, and \(\mathrm{Relay_{I}}\) lies on the \(m\)-th branch. One \(\mathrm{Relay_{B}}\) is expected to be selected on the \(n\)-th branch. It is worth pointing out that \(m\
eq n\). Denote the distance between \(\mathrm{Relay_{I}}\) and the center of intersection (C) as \(l_{\mathrm{C}}\), and the distance between the optimal position \(\mathrm{P_{opt}}\) on the \(n\)-th branch and C as \(l_{\mathrm{B}}^{n}\). The relationship between \(l_{\mathrm{C}}\) and \(l_{\mathrm{B}}^{n}\) can be presented as  
\[\left\{\begin{array}{ll}l_{\mathrm{B}}^{n}=R+l_{\mathrm{C}},&\mathrm{when}\ n=m\\ R^{2}=l_{\mathrm{C}}^{2}+(l_{\mathrm{B}}^{n})^{2}-2l_{\mathrm{C}}l_{\mathrm{B}} ^{n}\cos(\theta_{n}-\theta_{m}),&\mathrm{when}\ n{\
eq}m\end{array}\right. \tag{1}\]  
The second sub-equation in the above equation can be represented as  
\[(l_{\mathrm{B}}^{n})^{2}-2l_{\mathrm{C}}\cos(\theta_{n}-\theta_{m})l_{\mathrm{ B}}^{n}+(l_{\mathrm{C}}^{2}-R^{2})=0 \tag{2}\]  
Since \(l_{\mathrm{C}}\leq R/2\), then  
\[R^{2}\geq\frac{R^{2}}{4}\geq l_{\mathrm{C}}^{2}\geq\left(1-\cos^{2}(\theta_{n} -\theta_{m})\right)l_{\mathrm{C}}^{2} \tag{3}\]  
So  
\[(2l_{\mathrm{I}}\cos(\theta_{n}-\theta_{m}))^{2}-4(l_{\mathrm{C}}^{2}-R^{2})\geq 0 \tag{4}\]  
From the above analysis, we can derive that \(l_{\mathrm{B}}^{n}\) obtained from (2) has a real value. Moreover, \(l_{\mathrm{B}}^{n}\geq 0\). Then \(l_{\mathrm{B}}^{n}\) can be presented as  
\[l_{\mathrm{B}}^{n}=\begin{cases}R+l_{\mathrm{C}},\mathrm{when}\ n=m\\ l_{\mathrm{C}}\cos(\theta_{n}-\theta_{m})+\sqrt{R^{2}-{l_{\mathrm{C}}}^{2}\sin^{2}( \theta_{n}-\theta_{m})},\mathrm{when}\ n{\
eq}m\end{cases} \tag{5}\]Since Relay is expected to be closer to the optimal position than Relay, the partition range for Relay is the range between Popt and Relay, when Relay and Relay on the same branch (i.e., \(m=n\)). Therefore, the partition range \(R_{\text{P-B}}^{n,m}\) in the branch phase on the \(n\)-th branch when Relay lies in the \(i\)-th final segment on the \(m\)-th branch is  
\[R_{\text{P-B}}^{n,i,m}=\begin{cases}R,\text{ when }n=m\\ l_{\text{C}}^{i}\cos(\theta_{n}-\theta_{m})+\sqrt{R^{2}-(l_{\text{C}}^{i})^{2} \sin^{2}(\theta_{n}-\theta_{m})},\text{ when }n\
eq m\end{cases} \tag{10}\]  
The expected location of the relay node is the point in the middle of the final segment. Thus, when Relay is in the \(i\)-th final segment, the distance \(l_{\text{C}}^{i}\) can be expected as  
\[l_{\text{C}}^{i}=\left(\sum_{b=1}^{i-1}\left(W_{\text{seg}(N_{\text{iter}},b)} \right)+\frac{1}{2}W_{\text{seg}(N_{\text{iter}},i)}\right)\times R \tag{11}\]  
where \(W_{\text{seg}}(N_{\text{iter}},i)\) can be obtained from (1).
# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 5 Discussion  
The defect in the tunnel structure is a key factor affecting the safe operation of the tunnel. Consequently, the regular inspection of the tunnel structure is essential for engineers. GPR has been widely used in structural health testing as the main nondestructive testing method. Engineers scan the tunnel structure by riding a lift truck with a handheld radar antenna (as shown in Figure 13a). It is evitable to interrupt the traffic and close the road for safety. This manual detection method is inefficient, and there are also potential safety hazards in the operation of climbing vehicles. For a 1 km long tunnel, the manual inspection method usually requires 4-5 experienced inspectors, and the operation would take 12-24 h.  
In order to improve the efficiency of defect detection, the DMDS is developed in this paper. The field experiment has been carried out to verify the ability to figure out the defects in the tunnel structure. The overall detection process takes only about 15 min for a 3 km long tunnel with a driver and an operator. Furthermore, the operator could stay in the vehicle during the detection (shown in Figure 13b). Safety is guaranteed, and the traffic does not have to be interrupted.  
Figure 12: Structural defects are visible at the corresponding anomalous locations in the GPR profile.  
Figure 13: (**a**)Traditional testing methods require inspectors to work on ascending equipment; (**b**) In the proposed method the inspector can stay in the vehicle to conduct the detection.  
Table 2 shows the comparison between the proposed DMDS and the traditional detection method. The method proposed in this paper has significant efficiency, labor, cost, and safety optimization effects compared with traditional methods.  
The method mentioned in this paper is mainly used for the rapid detection of highway tunnels. The detection speed can reach up to 50 km/h according to the radar sampling frequency. However, for safety reasons, it is recommended to control the detection speed within 30 km/h. Due to the rapid detection work moving speed, and there are more auxiliary facilities in the tunnel, before the implementation of the detection work should be based on the tunnel section design drawings of the layout of the measurement line for the reasonable arrangement to avoid the occurrence of antenna collision accidents. How to quickly avoid obstacles in high-speed driving conditions and the protection of radar antennas during detection are the key research directions in the future. Furthermore, it is imperative to use the method of machine learning instead of manual identification of diseases and obtain the quantitative parameters of defects. A sufficient number of sample sets is the basis of machine learning methods. In the future, we will achieve this goal through the proposed GPR fast detection method.
# An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions
## 4 Experiments
### Parameter Settings
The relationship between \(k_{1}\) and \(ESR\) is shown in Figure 3c. When the value of \(k_{1}\) decreases to 30% of the total dimension, the value of \(ESR\) reaches its highest point. \(k_{1}\) is just an intermediate variable, and retaining too few of its dimensions may exclude important features. Therefore, we choose the sub-optimal value of \(ESR\), which is to reduce the value of \(k_{1}\) to 50% of the total dimensions (\(k_{1}=7\)). When the value of \(k_{0}\) is between 2 and 6, it can be seen from Figure 3d that, when \(k_{0}=2\), the values of \(SAR\), \(LFR\), and \(ESR\) are optimal. The optimal values of the main parameters in SWAT and WADI are shown in Table 1.  
From Table 2, it can be seen that the four features most likely to be affected by the attack in the interpretation of sample 1892 are P-203, FIT-201, LIT-101, and MV-101. The actual attack point is MV-101, which was originally in a closed state (state 1) and later changed to an open state (state 2). It can be seen that the value of MV-101 has increased under the attack. This is consistent with the predicted situation. For sample 280322, the actual attack points LIT-101, P-101, and MV-201 show decreasing, increasing, and increasing trends, respectively. Our method predicts that both P-101 and MV-201 have increased in size, which is consistent with the actual situation. LIT-101 did not appear in the explanation  
Figure 3: Relationships between \(k_{0}\), \(k_{1}\), and \(k_{2}\) and system performance, respectively. table, possibly due to the pumping of water from the first tank to the second stage after P-101 was attacked, resulting in insignificant changes in the water level detected by LIT-101.
## 3 Results and Discussion
### Experiment of Noise Source Separation
#### 3.2.2 Analysis of Experimental Results  
Firstly, the complex sound pressures and their gradients measured from the line array are processed using phase conjugation to obtain noise source information. Secondly, we separate the sound pressure signals caused by loudspeaker A and B from the sound pressure signal measured by test microphones at three test points when two loudspeakers act simultaneously using the proposed noise separation technique.  
Tables 2 and 3 compare the SPLs obtained by the noise separation technique, measured by three test microphones when loudspeaker A or B works alone. All SPL absolute errors between the measurement values and values from the noise separation technique at the three test points for loudspeakers A and B are less than 2 dBA. Therefore, we think that it is accurate and feasible to separate the noise components of multiple sources using the noise separation technique based on the phase conjugation method within the error tolerance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No. of Test Point** & **Experiment** & **Noise Separation Technique** & **Absolute Error** \\ \hline
1 & 35.13 & 36.95 & 1.82 \\
2 & 36.82 & 35.61 & 1.21 \\
3 & 37.93 & 38.25 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 2: A-weighting SPL of experiment and noise separation technique at 3 test points/dBA (loudspeaker A with 400 Hz frequency).  
Figure 8: Experimental scene in semi-anechoic chamber: (**a**) experimental equipment setup, (**b**) loudspeaker, (**c**) line array, (**d**) DAQ system.
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 3 Construction of Vehicle Scheduling Optimization Model for Coal Mine Enterprises
### Establishment of a Multi-Customer Vehicle Scheduling Model  
#### 3.2.1 Problem Description  
The MCVS problem in the coal intelligent loading system is outlined as follows: On the \(K\) loading bunkers, there exist \(N\) customers requiring loading. Each customer possesses several vehicles that coal can be loaded into at different loading bunkers. Efficient reduction of the entire loading cycle and prevention of resource wastage is achieved through the provision of an explicit loading notification. This paper addresses a half-day cycle in which each customer's driver arrives at the loading site based on the communicated date provided by the enterprise's staff for loading. Owing to distance variances and departure times, the arrival times of drivers will vary accordingly. Hence, the driver shall inform the person in charge of the expected arrival loading time window in the previous cycle to allow for proper scheduling of the loading operation notice. Based on the present loading process conditions, the following assumptions are posited for addressing the MCVS problem:  
1. All loading bunkers are of the same type and have the same loading rate;
2. The total amount of coal required by each customer's vehicle is less than the total capacity of the \(K\) loading bunkers;
3. Each vehicle can only be loaded under one loading bunker;
4. The arrival time of all customer vehicles is known;
5. Loading of any vehicle cannot be interrupted.
## 4 Discussion
### Use of Auxiliary Geometry Part to Improve the Accuracy of IOSs
A standard resin model of the dedentulous maxilla with six bone-level dummy implants (NobelActive internal RP, e4.3/10 mm) was used as the master model. The Trios 3 (3 Shape) scanner was used for all scans. The linear distortions ranged from \(-\)94.2 to 363.0 \(\upmu\)m in group 0, \(-\)367.6 to 72.6 \(\upmu\)m in group 1, \(-\)279.0 to 86.2 \(\upmu\)m in group 2, and \(-\)243.7 to 75.9 \(\upmu\)m in group 3. The angular distortions ranged from \(-\)0.897 to 0.526\({}^{\circ}\) in group 0, \(-\)0.736 to 0.418\({}^{\circ}\) in group 1, \(-\)1.125 to 0.364\({}^{\circ}\) in group 2, and \(-\)0.881 to 0.332\({}^{\circ}\) in group 3. For group 0, the trueness ranged from 0.6 to 363.0 \(\upmu\)m/0.041 to 0.630\({}^{\circ}\), while the precision ranged from 5.1 to 290.3 \(\upmu\)m/0.023-0.666. For group 1, the trueness ranged from 0.6 to 206.9 \(\upmu\)m/0.021-0.983\({}^{\circ}\), while the precision ranged from 1.8 to 184.2 \(\upmu\)m/0.012-0.732\({}^{\circ}\) For group 2, the trueness ranged from 7.4 to 162.6 \(\upmu\)m/0.036-0.918\({}^{\circ}\) while the precision ranged from 16.6 to 181.2 \(\upmu\)m/0.059-0.929\({}^{\circ}\). For group 3, the trueness ranged from 1.0 to 135.4 \(\upmu\)m/0.020-0.680\({}^{\circ}\), while the precision ranged from 3.7 to 103.4 \(\upmu\)m/0.037-0.446\({}^{\circ}\). With the devices, the number of linear and angular measurements with bias beyond the clinical threshold was significantly reduced. The authors found that the three types of auxiliary devices significantly improved the scanning accuracy of the IOS in complete-arch implant scanning; however, the fiducary spheres might compromise the precision of the scanning [34].
## 1 Introduction
In particular, BERT, a pre-trained model based on the Transformer, has brought new breakthroughs to various natural language processing tasks, including text classification, fully demonstrating the value of the Attention mechanism [32; 33; 34; 35; 36] in global feature processing. However, Transformer-based models tend to be more complex and require more data and higher computing resources. This paper proposes a CAC classification model based on CNN and the Attention mechanism, which fully leverages the local feature extraction capabilities of CNNs. By introducing the Attention mechanism, the model avoids the limitations of CNN in global dependency modeling and achieves a perfect combination of the advantages of both approaches. While RNN networks can also be used for global feature extraction, there have been many works that combine RNN with CNN [37; 38; 39] or RNN with Attention mechanisms [40; 41; 42] to model and classify textual data. However, considering the issue of feature forgetting and poor parallelization of RNN and the superior feature extraction capabilities of attention compared to RNN, we have chosen to build the CAC model by combining CNN with Attention. Unlike other CNN and Attention-based models [43; 44; 45; 46], our CAC model has a simpler structure and lower complexity. In the design of the model's structure, we draw inspiration from the computational principles of membrane computing [47; 48]. Membrane computing is a hierarchical and parallel computational model where individual computing units are encapsulated within different membranes and communicate and interact through the membranes to exchange information and perform computations, thus achieving highly parallel computing processes. Building upon this, we incorporate the hierarchical structure, isolated computation, and parallel computation features of membrane computing into the design of our model,combining the advantages of CNN and Attention. This enables the model to handle both local features and extract global relationships. Furthermore, we simplify the stacked computational Attention structure to support multi-layer computations, significantly reducing the computational complexity of the model. Experimental results on multiple text classification datasets demonstrate that compared to CNN and Transformer, our novel model not only converges faster but also achieves significantly improved accuracy. The remainder of this paper is organized as follows. Section 2 focuses on the implementation process of our model, including its structure, parameter settings, and other details. Section 3 presents the performance of our model on different datasets. In Section 4, we performed ablation experiments to investigate the role of each component of our model. Finally, we summarize the main contributions and limitations of this paper and suggest future research directions.
## 1 Introduction
As an alternative to a speaker-based AVAS, research is being conducted on an actuator-based system that forces the vibration of a panel. The panel actuator system does not require a vehicle structure design change because it can be simply attached to an existing panel or vehicle. Moreover, it is more durable than the conventional loudspeaker-based system because of no exposure to the external environment [18; 20]. [PERSON] et al. reported that an actuator system mounted on a panel reproduces the sound performance of a loudspeaker-based system and outperforms the sound characteristics of the speaker-based system at a certain frequency [21]. Given that the panel vibration AVAS has an omnidirectionally radiated sound field, the main focus of research has been on the sound field and control of the system by structural vibration [18; 20; 22; 23; 24; 25], and little research has been dedicated to the shape and performance optimization of the actuator of the panel vibration type. In general, electrical energy is converted into vibration (acoustic) energy in a vibration actuator or loudspeaker system, and research has been conducted to improve its performance [26; 27; 28; 29; 30]. [PERSON] et al. studied the electromagnetic force characteristics and vibration performance of a permanent magnet according to its magnetization direction [31]. Since the basic architecture of a panel-type actuator is similar to that of a general actuator and loudspeaker system, its excitation can be improved by optimizing the electromagnetic circuit. The size and shape of the actuator are factors that affect panel excitation and vibration, and their performance can be improved by optimizing the magnetic circuit considering the design boundaries. Again this background, this study compares the electromagnetic force performance of an AVAS actuator mounted on a vehicle hood depending on the magnetic circuit structure, selects the optimal magnetic circuit structure, and optimizes the design parameters and design tolerances for the electromagnetic force performance and slimming of the selected magnetic circuit structure. As a result of design factor analysis using design of experiment (DOE), the magnetic circuit design parameters applicable to electromagnetic force performance were set as the design parameters of each magnetization structure. With the selected design parameters, we optimized the magnetic circuit using the desirability function as a multi-objective function after deriving a nonlinear regression model using response surface methodology. Subsequently, we presented an optimum tolerance design capable of securing the electromagnetic force characteristics by minimizing the production and assembly process deviations, using the Monte Carlo simulation (MCS), to ensure a 3\(\sigma\) quality-level process capacity.