esa-sceva/satcom-chunk-collection huggingface dataset:
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 3 Experiments and Results
### Verification Results Based on Image Dataset from One Session  
In this section, we evaluate the performance of various approaches on the HKPU finger-vein dataset by considering vein images collected in each of the two sessions. First, the performance is evaluated in each session, individually. In one session, there are 630 images from 105 fingers. Therefore, the total number of genuine scores and impostor scores is 1575 (105 \(\times\)\(C_{2}^{6}\)) and 196,560 (105 \(\times\) 104 \(\times\) 36/2). To compute the impostor score, the symmetric matches are not executed. Second, the performance of combining scores from two sessions is reported. So, there are 3150 (1575 \(\times\) 2 sessions) genuine scores and 393,120 (196,560 \(\times\)2 sessions) impostor scores. Table 1 lists the verification error of various approaches for each session taken separately, and then for the two sessions, mixed. The receiver operating characteristics (ROC) curve for the corresponding performances is illustrated in Figure 10. The experimental results from Table 1 imply that the proposed SCNN-LSTM approach outperforms existing approaches including CNN [33] and achieves low errors, e.g., 1.12%, 0.62%, and 1.01% for data in the first session, second session, and two mixed sessions, respectively. The ERRs are further reduced to 1.08%, 0.58%, and 0.95% using the proposed encoding approach. We also observe from Figure 10 that the SCNN-LSTM-based approaches significantly improve FRR when the FAR is lower than 0.01%, which implies that our system achieve lower verification error than the methods considered in our work at high security level system.  
Figure 9: Experimental results from various approaches. (**a**) Original image; (**b**) Gabor filters; (**c**) Difference curvature; (**d**) Maximun curvature point; (**e**) Mean curvature; (**f**) Region growth; (**g**) Repeated line tracking; (**h**) Wide line detector; (**i**) CNN; (**j**) SCNN-LSTM + Unsupervised encoding; (**k**) SCNN-LSTM + Supervised encoding.
## 4 Computational Experiments and Results Analysis
### Comparison Between Models
#### 4.4.2 Multiple Instances Comparison  
Once again, a comparison is conducted among the four models: deterministic, stochastic, multiyear, and stochastic-multiyear. However, this time, multiple instances are run instead of just one.  
Initially, four instances were created using Providencia data, each instance varying the model parameters such as fuel cost and the utilization of tax incentives. The objective was to evaluate the models' behavior under these variations. The parameters for each instance are detailed in Table 8.  
The LCOE results are presented in Table 9. The primary result to highlight is the difference in sizing between the one-year and multi-year models. The installed generators  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(\delta\) = 1} & \multicolumn{4}{c}{\(\delta\) = 24} \\ \cline{2-10}
**Output** & **Dt + 1y** & **St + 1y** & **Dt + My** & **St + My** & **Dt + 1y** & **St + 1y** & **Dt + My** & **St + My** \\ \hline LCOE (nnp)/  
and batteries vary significantly, as the one-year model does not account for demand growth. Consequently, the multi-year model is more conservative and results in higher costs.  
Subsequently, new instances were executed, also varying \(\delta\) and ENS, in addition to the four previous instances. In this case, the stochastic multi-year model was not included in the comparison due to its longer execution time compared to the others. Given the numerous instances, evaluating the stochastic multi-year model would require significant computational resources. The instances are displayed in Table 10.  
The results of the instances in Table 11 indicate that between the deterministic and stochastic models, the stochastic model provides superior results. This is attributed to its ability to test a greater number of combinations.  
On the other hand, when examining the behavior of renewable generation, Table 12 indicates that in most instances, the stochastic model utilized more renewable energy than the deterministic model. This comparison is solely based on the one-year formulation.
## 4 Application to Rolling Bearings Fault Diagnosis
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fault Class**} & \multicolumn{2}{c}{**Non-Discriminative Features**} & \multicolumn{2}{c}{**Discriminative Features**} \\ \cline{2-5}  & **Training Accuracy** & **Testing Accuracy** & **Training Accuracy** & **Testing Accuracy** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 95.74 & 100 & 100 & 100 \\
2 & 87.23 & 91.67 & 85.11 & 100 \\
3 & 93.62 & 91.67 & 100 & 91.67 \\
4 & 100 & 100 & 100 & 100 \\
5 & 100 & 100 & 100 & 100 \\
6 & 100 & 100 & 100 & 100 \\
7 & 100 & 100 & 100 & 100 \\
8 & 100 & 100 & 100 & 100 \\
9 & 100 & 100 & 100 & 100 \\
10 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The bearing fault diagnosis results using non-discriminative and selected discriminative features. Figure 7: Misclassified testing samples with **(a)** the non-discriminative features and **(b)** discriminative features. From the average recognition rates in Figure 8, only classes 2 and 3 do not have 100% accuracy. The recognition rates for class 2 (i.e., ball fault mode with fault size of 0.014 inches) are especially low. For both classes, a higher average recognition rate is obtained with the discriminative features. Thus, the proposed method with discriminative feature selection is validated to demonstrate much better performance.
## 4 Theoretical Calculation and Analysis
### Analysis of the Calculation Results  
#### 4.5.1 Analysis of Temperature Contours  
The temperature contours corresponding to different test times were generated by the finite element software. The contours at 39 h, 71 h, and 254 h were taken as examples for detailed analysis, as shown in Figure 13a-c.  
Figure 11: Convection coefficient curve of top surface.  
Figure 12: Boundary conditions for the foundation slab.  
Figure 13: Temperature contour map corresponding to different times. (**a**) 39 h; (**b**) 71 h; (**c**) 254 h.
## 5 Experimental Validation
### Extraction Results of the Outer Race Fault  
In this subsection, the filtered signal is used for the outer race fault feature extraction. Figure 15a shows that the OFSC of the filtered signal can remain not to identify the BPOO, which is covered by the strong noise. The EEOS of the filtered signal's OFSC is displayed in Figure 15b. The \(\alpha_{\text{BPOO}}\) and \(2\alpha_{\text{BPOO}}\) is clear, while \(3\alpha_{\text{BPOO}}\) covered by the noise is hard to identify. The OFSC and its EEOS of the proposed method are displayed in Figure 15c,d. Figure 15c illustrates that the fault feature in the OFSC is effectively extracted. Although the component of \(3\alpha_{\text{BPOO}}\) is weak overall, it is still stronger than the nearby components. This point of view can be also be confirmed in Figure 15d. Moreover, the noise in Figure 15c is also weakened by the joint sparsity and low-rankness constraint, which can make the target fault component more obvious. Compared with the other two methods, the noise components in OFSC are still greater than the denoised OFSC obtained by the joint sparsity and low-rankness constraint. The noise components in OFSC make the \(3\alpha_{\text{BPOO}}\)s hard to identify, which are covered by noise-generated cyclic orders nearby.  
According to the experiment results, it turns out that the proposed method is capable of extracting the rolling bearing fault feature in OFSC. Moreover, the noise components in not only OFSC but also EEOS can be weakened, which is consistent with the results of simulation studies.  
Figure 14: The time domain waveform and spectrum of the measured signal before and after band-pass filtering. (**a**) the time domain waveform and rotating speed before band-pass filtering (line in blue: the signal waveform adhering the left \(y\)-axis and line in black: the shaft rotating speed adhering the right \(y\)-axis); (**b**) the spectrum before band-pass filtering; (**c**) the time domain waveform and rotating speed after band-pass filtering (line in blue: the signal waveform adhering the left \(y\)-axis and line in black: the shaft rotating speed adhering the right \(y\)-axis); (**d**) the spectrum after band-pass filtering.
## 4 Coverage Probability of MHCP-MHCP Network
Since we use the ASAPPP method, each tier of the MHCP network is approximated to the PPP network with the same density by SIR threshold scaling. \(\lambda_{k}\) is numerically equal to \(\lambda_{k\text{-}MHCP}\). Substituting (8) into (7), the coverage probability of a tagged MUE in the MHCP-MHCP network can be derived as  
\[ \begin{array}{l}\mathrm{P}_{c}(\{\lambda_{k-MHCP}\},\{\beta_{k}\},\{P_{Tk} \})\\ =\underset{k=1}{\overset{2}{\sum}}\lambda_{k-MHCP}\int_{\mathbb{R}^{2}}\exp \Bigg{(}-\bigg{(}\frac{\beta_{k}/G_{k}}{P_{Tk}\|x_{k}\|^{-\alpha}}\bigg{)}^{2 /\alpha}C(\alpha)\overset{2}{\underset{k=1}{\overset{2}{\sum}}}\lambda_{k}P_{ Tk}^{2/\alpha}\Bigg{)}dx_{k}\\ \overset{(a)}{=}\underset{k=1}{\overset{2}{\sum}}\lambda_{k-MHCP}\cdot 2\pi \int_{r}\exp\Bigg{(}-C(\alpha)\bigg{(}\frac{\beta_{k}/G_{k}}{P_{Tk}}\bigg{)}^{ 2/\alpha}r^{2}\overset{2}{\underset{k=1}{\overset{2}{\sum}}}\lambda_{k}P_{ Tk}^{2/\alpha}\Bigg{)}rdr\\ =\underset{k=1}{\overset{2}{\sum}}\lambda_{k-MHCP}\cdot 2\pi\int_{r}r\exp\Bigg{(}-C( \alpha)\bigg{(}\frac{\beta_{k}/G_{k}}{P_{Tk}}\bigg{)}^{2/\alpha}\overset{2}{ \underset{k=1}{\overset{2}{\sum}}}\lambda_{k}P_{Tk}^{2/\alpha}\Bigg{)}r^{2} \Bigg{)}dr\\ \overset{(b)}{=}\frac{\pi}{C(\alpha)}\frac{\underset{k=1}{\overset{2}{\sum}} \lambda_{k-MHCP}\frac{P_{Tk}^{2/\alpha}(\beta_{k}/G_{k})}{\underset{k=1}{ \overset{2}{\sum}}}\lambda_{k-MHCP}\frac{P_{Tk}^{2/\alpha}}{\underset{k=1}{ \overset{2}{\sum}}}\beta_{k}/G_{k}>1\end{array}  \tag{9}\]  
Step (a) follows from the transformation from Cartesian coordinate to polar coordinate. Step (b) follows from \(\int x\epsilon^{x^{2}}=\frac{1}{2c}\epsilon^{x^{2}}\). In the above derivation, we approximate each MHCP tier to PPP tier, so we call this approximation method \"ET-ASAPPP\".
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 5 Simulation Experiment and Result Analysis
### Simulation Experiment Parameter Setting  
Cellular users and D2D cluster users are randomly distributed within the service area, the UAV is initially deployed in the vicinity of the cellular users, and the initial flight altitude is set to 100 m. The neural network architecture contains 3 hidden layers, and each hidden layer contains 256 neurons; the activation function is selected as ReLU; and the loss function is MSE. The training process is optimized by using the Adam optimizer, and the value of \(\delta\) in the greedy strategy decreases linearly from 0.9 to 0. The simulation parameters are shown in Table 1.
## 5 Discussion

Table 3: Average rates of deformation in the eastern and western sector in the two 2007–2010 and 2014–2016 time intervals. One goal of the present analysis is to confirm/corroborate our previous results, taking into account the shape of the (vertical) deformation time-series in recent years (2013-2016), as captured by the CSK constellation sensors. As a matter of fact, the comparison between the predicted cumulative displacement maps related to 2015 and the one obtained in March 2016 (see Figure 6) shows that the newly acquired CSK data chiefly confirm the validity of previous best-fit models (see Figure 8 of [19] for a direct comparison). The deviation of the measured deformations from best-fit models, as retrieved using only ASAR/ENVISAT data (covering the period from 2007 to 2010), and the ones recovered using the combined ASAR/ENVISAT and CSK data, is minimal, as testified by the average RMSE values listed in Table 4. The differences between the newly derived model and the old models are more appreciable in the eastern sector of the city than those in the western sector. This is due to the different types of soils used and the various stages of soil consolidation that the two areas experience. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **WS** & **ES** \\ \hline ENVISAT vs. ENVISAT/CSK & 3.9 mm & 6.4 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average RMSE values of the differences between the best-fit models retrieved by using only ENVISAT data (2007–2010) and the ones extracted from the combined ENVISAT/CSK data (2007–2016). RMSE values are computed for both the western and the eastern sectors of Lingang New City. Figure 6: Map of the entire cumulative displacements on 18 March 2016, as obtained by the combination of ASAR/ENVISAT and the CSK time-series.
## 2 Model Development
### Emission Module
#### 2.2.1 Sound of Tires  
The emission signal of the sound of tires is assumed to consist of broadband noise only, _i.e._, discrete tones due to, e.g., tire tread resonances or discrete vibrational tire resonances are not taken into account.  
The spectral shaping of the broadband noise components is performed in \(1/3\) octave bands. For each \(1/3\) octave band \(i\), white noise is generated and filtered by a digital pink filter. This pre-shaping helps to produce a smoother spectrum of the resulting signal [6]. The output of the pink filter is bandpass filtered by an eighth order Butterworth filter (Class 0 according to the standard IEC 1260:1995 [33]) and normalized to unit signal power to obtain the signal \(\xi_{i}(t)\). For stability reasons, also at low frequencies, the filters are implemented as cascaded second-order sections (SOS).  
The sound pressure emission signals of the sound of tires component are thus calculated by [6]:  
\[s_{\text{tire}}(t)=\sum_{i=1}^{N_{b}}p_{0}10^{t_{\text{tire}}(v(t),\varphi(t))/ 20}\cdot\xi_{i}(t) \tag{3}\]  
with \(N_{b}\) being the number of considered \(1/3\) octave bands, the reference pressure \(p_{0}=20\)\(\mu\)Pa and normalized bandpass filtered pink noise signals \(\xi_{i}(t)\). A total of \(N_{b}=29\) bands from 20 Hz to 12.5 kHzare used. For the sound pressure level \(L_{\text{tire},i}\) of band \(i\), a common logarithmic speed relationship [22] with additive correction terms is assumed:  
\[L_{\text{tire},i}(t)=A_{i}+B_{i}\cdot\log\left(\frac{v(t)}{v_{0}}\right)+\Delta L _{\text{road},i}(v(t))+\Delta L_{\text{dir},i}(\varphi(t)) \tag{4}\]  
with reference speed \(v_{0}=70\) km/h, regression parameters \(A_{i}\) and \(B_{i}\), the road surface correction \(\Delta L_{\text{road},i}\) and a horizontal directivity \(\Delta L_{\text{dir},i}\). For the road surface correction, the Swiss \"sonRoad\" model [18] offers the parameter \(\Delta_{\text{BG}}\) for 10 surface types. However, \(\Delta_{\text{BG}}\) does not depend on frequency or vehicle speed. The recently-published EU directive on establishing common noise assessment methods (CNOSSOS-EU) [27] contains spectral corrections in octave bands in the form of:  
\[\Delta L_{\text{road},i}(v(t))=\alpha_{i}+\beta\cdot\log\left(\frac{v(t)}{v_{0 }}\right) \tag{5}\]  
with experimental regression parameters \(\alpha_{i}\) and \(\beta\), which are tabulated for 15 different road surface types. The horizontal directive simulates the horn effect [26] and only applies for signal \(s_{1}(t)\), _i.e._, for the lower source position (S1). The empirically-obtained relationship [21]:  
\[\Delta L_{\text{dir},i}(\varphi(t))=\left\{\begin{array}{ll}-2.5+4\left|\cos \varphi(t)\right|+C,&800\text{ Hz}\leq f_{c,i}\leq 6.3\text{ kHz}\\ 0,&\text{otherwise}\end{array}\right. \tag{6}\]  
with the \(1/3\) octave band center frequencies \(f_{c,i}\) and the correction \(C\) is employed. \(C\) accounts for a limited emission angle range during emission measurements, e.g., it amounts to 0.9 dB for an angle range \(45^{\circ}<\varphi<135^{\circ}\).
[PERSON] [17] developed a neural network to monitor tool wear in MDF milling based on machined surface temperature, cutting force, thrust force, and power consumption. The article of [PERSON] et al. [18] proposes an automatic system to detect the drill bit condition by measuring various signals during machining. It uses k-NN (k-nearest neighbor algorithm) classifier in MATLAB to classify the drill bit as Green (tool can still be used), Red (tool change is necessary), or Yellow (intermediate, warning state). It reports a 76% accuracy of classification. Based on the information presented above, one could observe that there is a lack of studies regarding the application of ANN and RSM in modeling of the drilling process ofplywood. Therefore, the objective of this research is to apply the ANN modeling technique and RSM to model and optimize the drilling process ofplywood. The analyzed factors (independent variables) that are considered in this work are drill tip angle, tooth bite, and drill type (flat or helical). The responses (dependent variables) are the thrust force and drilling torque--two dynamic parameters that affect both quality and the energy consumption during drilling of wood and wood-based materials.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 5 Prospects  
The electrical contact, serving as the core of the high-voltage circuit breaker, is a crucial factor influencing its breaking capacity. As the increasingly serious problem of over-standard short-circuit currents, circuit breakers are evolving towards higher voltage ratings and larger breaking capacities. Additionally, electrical contacts used in reactive power compensation circuit breakers and filter group circuit breakers must adapt to frequent switching conditions, necessitating enhanced resistance to arc erosion and mechanical wear. Traditional copper-tungsten alloy materials are gradually becoming inadequate in meeting these demands.  
Graphene-enhanced electrical contact technology, utilizing graphene's high strength, conductivity, and thermal conductivity, is expected to significantly reduce the failure rate of high-capacity circuit breakers, mitigating the problem of over-standard short-circuit currents. This holds significant importance in ensuring the safe, stable, and reliable operation of high-voltage transmission system, and it will drive advancements and innovations in electrical contact materials for high-voltage circuit breakers.  
Moreover, with the increasing demand for low-carbon, clean, and environmentally friendly technologies, high-voltage circuit breakers are gradually shifting towards vacuum or SF\({}_{6}\) alternative gas medium environments. Therefore, there is an urgent need for further research on novel electrical contact materials suitable for these environmentally friendly atmospheres.  
Conceptualization, T.L., Y.H. and Y.D.; methodology, T.L., Z.P., D.J. and Y.F.; formal analysis, T.L., Z.P. and Y.F.; investigation, T.L., Z.P. and Y.F.; writing--original draft preparation, T.L. and D.J.; writing--review and editing, Y.F., Z.S., Y.H. and Y.D.; supervision, Z.S., Y.H. and Y.D.; funding acquisition, T.L., Z.P., Y.H. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Grid Corporation of China Science and Technology Foundation, grant number 5500-202158363A-0-00.  
Not applicable.  
The data presented in this study are shown in the paper.  
Authors Dr. [PERSON], Prof. Dr. [PERSON], Mr. [PERSON], and Prof. Dr. [PERSON] were employed by the company State Key Laboratory of Advanced Power Transmission Technology, State Grid Smart Grid Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results and Discussion
### Adding Physical Information
Specifically, the NN that solely relies on the provided input without any additional physical information achieves an accuracy of 93.56%. In contrast, the network utilizing velocity vectors achieves a slightly lower accuracy of 91.48%. This suggests that while the inclusion of velocity vectors may provide some useful information, it does not necessarily translate into a significant boost in classification performance.
###### Abstract  
Shield tunnels are increasingly monitored using 3D laser scanning technology to generate high-resolution point cloud data, which serve as a critical foundation for precise deformation analysis. This study introduces an advanced methodology for analyzing tunnel cross-section displacements, leveraging point cloud data captured by the Self-Mobile Intelligent Laser Scanning System (SILSS), a Mobile Laser Scanning (MLS) platform capable of rapid and detailed 3D mapping of shield tunnels. The preprocessing pipeline includes the precise extraction of cross-sectional linings through local point density outlier removal techniques to enhance data accuracy. A custom segmentation algorithm partitions the tunnel cross-section linings into individual shield rings, enabling detailed and time-resolved displacement tracking. Aligned point clouds from different times were processed using the Iterative Closest Point (ICP) algorithm to achieve high-accuracy displacement analysis. Key displacement metrics, including average shield ring point cloud displacement and centerline shift, were computed to quantify displacement. Additionally, ovality analysis was employed to detect shield ring shape changes, providing critical insights into structural deformations. The findings are visualized in 3D, highlighting significant displacement areas in the tunnel cross-section. An analysis of the corresponding data obtained from the Leica Pegasus Two Ultimate scanner system shows that the data collected by SILSS are accurate. This methodology offers a robust tool for continuous tunnel monitoring, supporting the development of safer and more resilient underground infrastructure systems.  
mobile laser scanning; point cloud data; point cloud processing; tunnel cross-section; point cloud displacement; deformation Article
# Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN
## 3 Problem Formulation
### Local Computing Resource Optimisation  
For a fixed transmission energy and time allocation, the optimal local CPU frequency for node \(k\) can be derived by solving the subproblem P(2.1), as shown in Equation (28). P(2.1).  
\[\begin{array}{rl}\text{P(2.1)}:\max\limits_{\{f_{k}^{t}\}}^{K}\sum\limits_ {k=1}^{K}Q_{k}^{t-1}\bigg{[}\frac{T_{k}^{t}}{\Phi_{k}}\bigg{]}+\sum\limits_{k=1 }^{K}E_{k}^{t-1}\Big{[}\kappa_{mob,k}\big{(}f_{k}\big{)}^{3}T\Big{]}+V\left[ \sum\limits_{k=1}^{K}\bigg{[}\frac{T_{k}^{t}}{\Phi_{k}}\bigg{]}-\eta_{EE}^{t} \sum\limits_{k=1}^{K}\Big{(}\kappa_{mob,k}\big{(}f_{k}^{t}\big{)}^{3}T\Big{)} \right]\\ \hskip 113.811024 pt\text{C5}:f_{k}^{\min}\leq f_{k}\leq f_{k}^{\max}\end{array} \tag{28}\]  
Since the objective of problem P(2.1) is convex and its constraints are linear, its optimal solution can be obtained at the boundary or stationary points of the objective function, and the optimal local CPU frequency can be calculated from Equations (5)-(26).  
\[f_{k}^{t*}=\sqrt{\frac{Q_{k}^{t-1}+V}{3\delta_{k}\kappa_{mob,k}(V\eta_{EE}^{t -1}-E_{k}^{t-1})}} \tag{29}\]