esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response of Loess-Mudstone Slope with High Anti-Dip Angle Fault Zone
## 4 Results of the Numerical Simulation and Analysis
### Distribution of Plastic Zone in Model Slopes  
The distribution maps of the plastic zone in the model slope with an HADAFZ in response to different input PGAs of the Minxian wave are shown in Figure 15. When a = 0.1 g, a small-scale shear plastic zone was first generated in the fault zone, a small-scale tension plastic zone appeared on the top surface of the slope, and other areas inside the slope were still elastic. When a = 0.2 g, the shear plastic zone in the fault zone expanded, extended to the loess stratum, and intersected with the striped shear plastic zone appearing at the front and middle of the L-W interface. In addition, the tension plastic zone at the top surface spread to the middle surface of the slope. When a = 0.3 g, the shear plastic zone at the front and middle of the L-W interface not only extended to the rear part, but also extended to the slope shoulder in an arc shape, forming a potential sliding surface. In addition, the tension plastic zone at the slope surface gradually extended to the slope toe of the loess stratum. When a = 0.4 g, the range of the plastic zone further expanded into one piece. Compared with the model slope without the fault zone (Figure 16), when a = 0.1-0.3 g, except for the fault zone, the appearance and expansion of the plastic zone in other areas were basically same. However, when a = 0.4 g, the tension plastic zone of the model slope without the fault zone further extended from the slope surface to the slope foot of the mudstone stratum, while the mudstone stratum of the model slope with an HADAFZ was still elastic. The dip of HADAFZ is opposite to the slope direction and sliding direction, which hinders the extension of the tension plastic zone to the mudstone, so it is different from the slope without a fault zone or with BFZ [6,32]. In comparison, the slope with an HADAFZ has a relatively higher stability under the action of an earthquake.  
Figure 14: Input and output acceleration time history curves.  
Figure 13: Comparison of AAFs between shaking table test and numerical simulation under influence of Minxian wave, with input PGA of a = 0.1 g at (**a**) slope surface, (**b**) vertical section, (**c**) L–W interface and (**d**) W–M interface.  
surface along the fault zone, and that at the L-W interface expanded, forming a potential arc sliding surface. Compared with the model slope without a fault zone (Figure 18), the max. shear strain increment of the model slope with an HADAFZ was mainly concentrated at the fault zone, while the level of the arc-shaped shear strain zone extending from the L-W interface to the slope shoulder was smaller than that of the model slope without a fault zone under any PGA. Therefore, the existence of the HADAFZ restrains the development of the conventional circular arc sliding surface of the slope. Under the action of an earthquake, the slope with an HADAFZ is relatively more stable than the slope without fault zone or with BFZ [6,32].
## 3 Routing Protocols for UWSN
### Protocols Facturing Node Mobility
#### 3.1.1 Vector Based Forwarding (VBF) and Hop-by-Hop VBF (HH-VBF)
If the threshold value is high, energy consumption will be less as the nodes involving in data packet delivery will be less but it will cause low packet delivery ratio. On the other hand, setting a higher threshold value will involve a larger number of nodes delivering the same packet data and eventually increase the energy consumption. This will result in a high packet delivery ratio. So, depth threshold value is the key to the trade off between the energy consumption and delivery ratio. Though it gives very good performances in dense network, in sparse areas packet delivery ratio degrades as it works only in greedy mode. Simulations show that, it can perform up to 95% of packet delivery ratio in dense network with reasonable amount of energy consumption [52]. Furthermore, DBR is improved in Depth Based Multi Hop Routing (DMBR) which includes multiple sink network structure [62]. DMBR divides  
Figure 4. Routing Technique of Depth Based Routing (DBR) [52]. the whole process into two steps--route discovery and send packets, which makes the routing scheme more energy-efficient and also reduce the channel conflicts, thus improving DBR. It was improved further in Energy Efficient Depth Based Routing (EEDBR) [55]. [PERSON] et al. suggested to improve the life time of the network by utilizing the sensor residual energy. In DBR, a node having smaller depth than neighboring nodes will always forward the data in each and every transmission whereas nodes having slightly larger depth may sit idle. As a result, same nodes will be used repeatedly and eventually die out earlier than the other nodes. This creates routing holes all over the network which partitioned the network into parts. As a result, sensors can not communicate which results in shorten the network life time. This was improved in EEDBR. In EEDBR every sensor node shares its residual energy and depth with its neighboring nodes. Besides, each sender also broadcasts the depth of its neighboring nodes during transmission. When forwarding nodes receive the data packets, depending on the residual energy it stores the data for a specific period and to maintain the required delivery ratio, nodes take decision whether to transmit or suppress the packet transmission. DBR reduces the delay and improves the network lifetime. Moreover, it gives a satisfactory delivery ratio of the data packets along with good power-efficiency.
## 6 Conclusions  
In this work, the rail corrugation, vehicle interior noise and wheel-rail noise were tested in some sections of a metro line, and the relevant test sections were selected for transformation to explore the impact of rail corrugation on vehicle interior noise. The results show that:  
1. The rail corrugation of the metro system can cause abnormal noise inside the train, and the interior noise exceeds the limit in many areas. When the train passes through the rail corrugation section, the A-weighted sound pressure level of the train interior noise increases significantly, and the maximum increase can be higher than 25 dB(A).
2. There is an obvious rail corrugation problem from 31.5-63 mm in the test section. When the train passes through the corrugation, the wheel-rail vibration frequency is in good agreement with the main frequency of noise inside the train and the wheel-rail noise frequency.
3. After increasing the lateral stiffness of the track, the interior noise is reduced from 95.6 dB(A) to 87.9 dB(A), with a decrease of 7.7 dB(A). After rail grinding, the interior noise is reduced to 84.2 dB(A), and the sound pressure level of 400-1600 Hz (the main frequency of interior noise) is greatly reduced.
4. After six months of the treatments, the interior noise remains relatively unchanged. Compared with simple rail grinding, the proposed treatment measures are effective.  
Conceptualization, H.P. and X.C.; methodology, H.P.; formal analysis, Y.Y.; investigation, Y.Y. and T.S.; resources, X.C.; data curation, Y.Y. and T.S.; writing--original draft  
Figure 21: Changes of interior noise after the treatments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Name** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** \\ \hline Interior Noise & 91.2 dB(A) & 95.6 dB(A) & 87.9 dB(A) & 84.2 dB(A) & 84.4 dB(A) \\ Left Rail Irregularity & & 16.6 μm & & 9.5 μm \\ Right Rail Irregularity & & 29.8 μm & & 12.1 μm \\ Train Speed & 85 km/h & 85 km/h & 85 km/h & 85 km/h & 85 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of five tests.  
preparation, H.P. and Y.Y.; writing--review and editing, Y.Y., X.C. and Y.Z.; supervision, H.P. and X.C.; project administration, H.P. and X.C.; funding acquisition, H.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51778050 and Beijing Municipal Science & Technology Commission, grant number Z191100002519010.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Study
#### 4.3.1 MSAAGCN  
We first added adjacency matrices of different orders and observed the changes in the recognition accuracy of the model to prove the effectiveness of our proposed multi-scale adaptive aggregate GCN. Since the maximum distance between the two joints in the NTU-RGB-D dataset was 12, we chose the maximum number of scales N to be 12. As shown in Table 1, we have verified the recognition accuracy of the model after aggregation of 1, 2, 4, 8 and 12-order adjacency matrices on the NTU-RGB-D X-sub dataset. Obviously, the more adjacency matrices are aggregated, the better the recognition accuracy, which shows that each adjacency matrix is useful for model performance. However, when we aggregate to higher-order matrices, the performance improvement of the model becomes smaller. This may be due to the small dependence of the recognition of most actions on the distant joints. We used N = 12 for MSAGCN to conduct the following experiment.  
In order to verify the effectiveness of the various modules in our proposed spatio-temporal network, including MSAGCN, MSATCN and STCAtt modules, we conducted experiments on a NTU-RGB-D X-sub benchmark to show the performance of the model with and without these modules. As shown in Table 2, we used Js-AGCN (Joint stream from 2s-AGCN [15]) as the baseline for comparison experiments. In order to ensure the fairness of the experiment, we first used the original spatial convolution and temporal convolution modules in Js-AGCN to test the recognition accuracy of the network model. Then we replaced the original spatial convolution module with the MSAGCN modules,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Number of Scales**} \\ \cline{2-6}  & **N = 1** & **N = 2** & **N = 4** & **N = 8** & **N = 12** \\ \hline MSAGCN(Ours) & 86.2 & 86.7 & 87.1 & 87.6 & 87.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recognition accuracy (%) with different scale aggregation on X-sub benchmarks of the NTU RGB+D dataset.  
and we found that the accuracy of the model improved 1.1%. This shows that the multi-scale aggregate graph convolution operation can extract more features from the input skeleton sequence, which is helpful for better classification. Next, we used the MSATCN module with kernel size \(T_{1},T_{2},T_{3}=\{3,5,9\}\) to replace the original temporal convolution module, which improves the accuracy of the model by 0.6%. This shows that the use of a fixed temporal convolution kernel will make the temporal features indistinct, and the multi-scale temporal convolution can effectively capture the features of different temporal ranges. Finally, we used both the MSAGCN and MSATCN modules to replace the original modules, and the accuracy of the model increased to 87.8%, which shows that our proposed MSAGCN and MSATCN can more effectively capture the spatio-temporal features of the skeleton data.
## 2 Materials and Methods  
### Data  
A series of land surface variable datasets were chosen to evaluate the reconstruction performance of the new model. The NDVI (produced by MOD09Q1), LAI (MCD15A3H), GPP (MOD17A2H), and MSR (MOD09Q1) datasets from 2001 to 2005 in the global region are selected. 183 NDVI images, 183 GPP images, 183 MSR images, and 228 LAI images are collected in the global region. The detailed information is shown in Table 1. Among them, the NDVI and MSR datasets have temporal and spatial resolutions of 8 days and 250 m, the LAI dataset has temporal and spatial resolutions of 4 days and 500 m, and the GPP dataset has temporal and spatial resolutions of 8 days and 500 m.
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 2 Materials and Methods
### Improved ShuffleNet_wogan Model
#### 2.5.1 Mish Activation Function  
In the original ShuffleNet V2 model, the ReLU activation function is used. When ReLU is employed in a neural network, the output is either 0 or 1, regardless of the input value. This adds sparsity to the network and results in high computational efficiency. However, a drawback is that during training, when negative values or large gradients pass through ReLU, its neurons may become inactive and cannot be reactivated by any input.  
To address this limitation, this study replaced ReLU with the Mish activation function. Mish is a smooth and non-monotonic activation function, with its mathematical expression shown in Equation (1). Compared to ReLU, Mish offers better gradient flow, especially in the negative value region, where the output does not completely drop to zero. This enhances the model's feature representation capability and allows the model to converge faster. Mish demonstrated superior performance over ReLU and Swish in many deep learning tasks, making it a well-balanced choice in terms of stability and performance.  
\[Mish(x)=x\times\tanh(\ln(1+\varepsilon^{x})) \tag{1}\]  
Figure 10: Module architecture of ShuffleNet V2. (**a**) Basic unit; (**b**) down-sampling unit.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 2 BDCOA Based Optimum Control Signal
### FSO System Model  
Figure 1 shows the architecture of the FSO-based communication model, which comprises different modules such as Single-Mode Fiber (SMF), SLAO system, receiving terminal, and laser point source. A laser beam is utilized as an optical point source to emit the Gaussian beam in FSO. Next, the discharged signal is broadcast via the atmosphere with the laser carrier signal. The phase consistency is affected by the atmospheric turbulence caused by the WA. The utilization of sensor-less AO is employed to compensate for the WA. The coupling over the calibrated laser beam is ensured by using the SMF. In the reception terminal, the laser beam's coupling efficiency is highly enhanced based on WA compensation, which further enhances the FSO performance.  
Figure 1: Compensation of WA using BDCOA in FSO system.
# CYRA: A Model-Driven CYber Range Assurance Platform
## 1 Introduction
This study presents CYRA, a model-driven CYber Range Assurance Platform that allows (i) the continuous security assurance of the actual operating system, and (ii) the dynamic adaptation of the training procedures in the virtual cyber ranges environment. Initially, the organisation's technical assets and operational aspects are evaluated via automated and semi-automated mechanisms. Then, the actual security posture is disclosed and security Key Performance Indicators (KPIs) are defined, representing the desired level of protection that we want to accomplish through training. A relevant Cyber Threat and Training Preparation (CTTP) Programme is developed and the main training is begun. Throughout this process, the installed analysis mechanisms, from the initial phase, are continuously assessing wherever the trainees are applying the learned concepts in the actual system. If not, indications are provided to the trainer and the training procedures are adapted based on fully- or semi-automated techniques. Meanwhile, the deployed mechanisms may discover new threats that were not recognised before and drive the creation of additional training content. The proposed solution has been successfully deployed in three Information and Communications Technology (ICT) domains of smart transportation, smart energy, and healthcare. For each of these pilots, three complete Training Programmes have been created, namely: \"security awareness\" for staff with no or low-security knowledge, \"edge system security administrator\" for personnel that require main security knowledge concerning the setting and usage of edge systems, and \"backend security manager\" for security and privacy experts. The platform has been evaluated under real operational conditions where cyber-range training was provided to actual employees. The security KPIs were set in advance, and the trainers guided the trainees to accomplish them and adapt this knowledge back in their workplace. The main training life-cycle has been presented in our previous works [20], while this paper concentrates on the aforementioned modules for CTTP Models and Training Programmes adaptation and continuous security assurance in the operational system. The rest of the paper is organised as follows. Section 2 overviews the related studies in this field. Section 3 describes the Cyber-Range Assurance Platform. Section 4 details the creation of training programmes and their underlying elements. Section 5 presents the proposed adaptation tool of the platform and Section 6 a demonstration example. Section 7 outlines an evaluation analysis, where external trainers utilise the CTTP models and programmes editor and assess its usability and user-friendliness. Finally, Section 8 provides the concluding remarks and pointers to future work.
# Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm
## 4 Conclusions and Future Works  
In this paper, a load identification features selection method was proposed and validated by using the laboratory dataset and REDD. The experiment showed that our method has the ability to extract the desired features, which may accurately match the features of the device recorded in the database. In the experiment, the performance of load event detection was also carried out. A bilateral CUSUM combined with the minimum inner-class variance approach that we proposed also promoted the detection effectiveness, especially for the climbing character of a load event. Besides, the resampling method incorporated in our method was used to find the stable characteristics of the load event, thus obtaining the desired features. Through the 2-D fuzzy membership measurement, it was found that the feature extracted by the resampling method is closer to that of the actual device, and can be applied to load identification. However, this resampling method requires a relatively stable switching period of the device. In practice, the device being turned on or off is random, thus the obtained features may fail to match the features of devices in the database.  
In the future, the limitations mentioned above should be carefully considered. For example, some devices have a short duration after state switching, and the corresponding steady-state time is also very short. In this case, the method combining the transient features may work better. Moreover, different algorithms, based on different features, may be valid for certain types of devices. Therefore, it is necessary and feasible to integrate the proposed method with other complementary NILM models.  
Methodology, H.H.; Validation, Y.X.; Formal analysis, B.Q.; Data curation, X.L. and H.Z.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 9: Features performance on REDD based on the resampling method.
## Appendix A SINR Analysis for Asynchronous CDMA Systems  
In this appendix, the analysis of the SINR for asynchronous CDMA systems is presented.  
Derivation of the Desired Signal \(v_{1}(t)\) at \(SU_{1}\)  
The desired signal term is expressed as  
\[v_{i}(t)=\sqrt{\frac{2E_{p}}{T_{p}}}\int_{0}^{T_{b}}m_{i}(t-\tau_{i})c_{i}^{2} (t-\tau_{i})\cos^{2}(\omega_{c}(t-\tau_{i}))dt, \tag{10}\]  
and, as the message \(m_{i}(t-\tau_{i})\) is constant over time duration \(T_{b}\),  
\[v_{i}(t)=\sqrt{\frac{2E_{p}}{T_{p}}}m_{i}(t-\tau_{i})\int_{0}^{T_{b}}c_{i}^{2} (t-\tau_{i})\cos^{2}(\omega_{c}(t-\tau_{i}))dt, \tag{11}\]  
The desired signal in terms of the original message \(m_{i}(t)\) is expressed as  
\[v_{i}(t)=\frac{T_{b}}{2}\sqrt{\frac{2E_{p}}{T_{p}}}\ m_{1}(t-\tau_{i}). \tag{12}\]  
Derivation of \(v_{MAI}(t)\) Due to the Effect of U-1 Users  
The multiple-access interference term is due to the effect of the transmission of user \(SU_{2}\) up to user \(SU_{U}\), which is the assumed interference term with respect to user \(SU_{1}\); therefore, \(v_{MAI}(t)\) can be formulated as  
\[v_{MAI}(t) = \int_{0}^{T_{b}}\sum_{\begin{subarray}{c}u=0\\ u\
eq i\end{subarray}}^{U}\sqrt{\frac{2E_{p}}{T_{p}}}m_{u}(t-\tau_{u})c_{u}( t-\tau_{u})cos\ (\omega_{c}(t-\tau_{u})\] \[+ \phi_{u})c_{i}(t-\tau_{i})cos(\omega_{c}(t-\tau_{i}))dt=\sum_{ \begin{subarray}{c}u=0\\ u\
eq i\end{subarray}}^{U}I_{u},\]where  
\[I_{u}=\int_{0}^{T_{b}}\sqrt{\frac{2E_{p}}{T_{p}}}m_{u}(t-\tau_{u})c_{u}(t-\tau_{u })cos(\omega_{c}(t-\tau_{u})+\phi_{u})c_{i}(t-\tau_{i})cos(\omega_{c}(t-\tau_{i} ))dt, \tag{10}\]  
Next, the multiple-access interference power \(\sigma_{MA1}\)2 is defined, which is the second order of analysis, i.e., the expectation of \(v_{MA1}\)2\((t)\):  
Footnote 2: The \(\sigma_{MA1}\) is defined as the sum of the \(\sigma_{MA1}\) and \(\sigma_{MA1}\).  
\[\sigma_{MA1} \tag{11}\]  
The expectation in Equation (11) is derived deeply, as per [41], and is represented as \[\sigma_{MA1}\] (12)  
Assuming the variety of the channel gain of each interferer (i.e., \(\alpha_{u}\)) within the operating system, Equation (12) can be rewritten and represented as  
\[\sigma_{MA1} \tag{13}\]  
where \(i\) is the desired user.
## 1 Introduction
The simulations were conducted under contingency scenarios involving the severe failure of high-voltage transmission lines, demonstrating that it was applied effectively in the Korean power systems. The proposed method provides an insight to decision makers to perform a suitable detection of the voltage stability/instability status for their systems. This allows for the system to be monitored with more accurate measurements; further, wide-area blackouts may be counteracted and more efficient power systems can be operated.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 4 Challenges of Predictive Maintenance
### Data Source Limits  
The availability of relevant data is essential for the creation of a production process management model. However, companies rarely have all the relevant data at the beginning of the introduction of production process management [30]. After using the data that is already available, it is necessary to identify the gaps and aim to resolve them. Furthermore, the quality of the existing information sources may not meet the required needs. If only part of the data is affected by unsatisfactory quality, this can be overcome during data preparation, as long as the amount of data points is sufficient to achieve statistical significance and as long as defect detection can successfully isolate machine-critical points [52]. The company using predictive maintenance methods may then face challenges when the necessary confidence in the data does not hold true, i.e., if sensors, controllers, or other data sources provide inaccurate or incorrect measurements. This can result in incorrect predictions and missed maintenance urgency or false alarms. An additional challenge for sensor technology is that sensors currently tend to operate offline without contributing to online data. In addition, sensors are subject to downtime, instrument degradation, noise, or simply the sensor may fail. It is then important to clean the data before applying the predictive maintenance algorithm in order to predict the true reality and not distort the results.