esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Model Interpretability Analysis
### Susceptibility Evaluation
#### 4.1.2 Comparison of Other Model Results  
BPNN, ResNet, CNN, and VGG-16 models were used to predict landslide susceptibility in the study area, yielding the landslide susceptibility assessment results shown in Figure 10.  
Figure 10: Landslide susceptibility assessment results: (**a**) Inception; (**b**) BP; (**c**) ResNet; (**d**) CNN; (**e**) VGG-16.  
To visually demonstrate the fitting effect of each model on landslide susceptibility prediction, the number of historical landslide points in different risk-level zones and the proportion of landslide points in moderately high-risk and high-risk areas were calculated and are summarized in Table 6.  
Landslide predictions were made for positive and negative sample points using the four models' results. The ROC curves were plotted with True Positive Rate (TPR) on the y-axis and False Positive Rate (FPR) on the x-axis (see Figure 11). The Area Under the Curve (AUC) of the ROC curve indicates the model's accuracy, with a larger AUC indicating higher accuracy.  
Combining Figure 10 and Table 7, as well as the ROC curve in Figure 11, it can be seen that the Inception model performed the best in predicting landslide susceptibility. A total of 90.5% of historical landslide points fall into the high and very high susceptibility areas predicted by the Inception model. In areas where landslides have not occurred, the susceptibility levels predicted by the Inception model are generally lower, demonstrating high fitting accuracy. Additionally, the Inception model has the highest AUC value, at 0.986.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Low** & \begin{tabular}{c} **Moderately** \\ **Low** \\ \end{tabular} & **Medium** & \
\begin{tabular}{c} **Moderately** \\ **High** \\ \end{tabular} & **High** &
\begin{tabular}{c} **Landslide** \\ **Ratio (\%)** \\ \end{tabular} \\ \hline Inception & 6 & 5 & 10 & 20 & 180 & 90.5\% \\ BPNN & 8 & 7 & 10 & 16 & 180 & 88.7\% \\ ResNet & 6 & 10 & 9 & 20 & 176 & 88.7\% \\ CNN & 6 & 10 & 6 & 35 & 164 & 90.0\% \\ VGG-16 & 8 & 12 & 29 & 73 & 100 & 78.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of historical landslide points in different risk-level zones.  
Figure 11: ROC curves.  
The ResNet model also exhibited excellent predictive performance, but more landslide points were predicted as low or moderate susceptibility, in contrast to the Inception model, indicating some shortcomings. The CNN and BPNN models performed slightly worse, with sound fitting effects, but still showing poor predictive accuracy in some areas.  
Among the four models, the VGG-16 model had the poorest fitting effect. It predicted large areas of moderate to high susceptibility in areas where landslides have not occurred, and only 78.3% of the landslide points fall into high and very high susceptibility areas. There are also many landslide points in the low to moderate susceptibility areas predicted by this model, resulting in the smallest AUC value.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 5 Experiment and Result Analysis
### Analysis of Vessel Trajectory Restoration Results
#### 5.4.1 Analysis of Model Restoration Results
The comparison results are shown in Table 5 and Figure 13. It is obvious from Table 5 that the BG2 BG model achieves the lowest values in all the indicators except for the MAE-LAT indicator, which is not the lowest. Further observations of Figure 13 show that the trajectory restoration model results of the BG2 BG model are more consistent with the trend of real vessel trajectories. This indicates that the model demonstrates optimal performance on this dataset. Combining the results of Tables 4 and 5 with those of Figures 12 and 13, we can conclude that the framework proposed in this study demonstrates significant effectiveness in vessel trajectory restoration. Furthermore, the BG2 BG model demonstrates the highest accuracy on both different datasets, fully proving that the model has good generalization ability and robustness. Figure 12: Comparison of trajectory restoration results of different models: (**a**) restoration results of straight trajectory-1; (**b**) restoration results of curved trajectory-1; (**c**) restoration results of straight trajectory-2; (**d**) restoration results of curved trajectory-2.
## 2 Audio Features and Pre-Trained Neural Networks  
### Audio Signal Features  
Audio classification is typically achieved by extracting discriminative features that represent the underlying common characteristics of audio signals belonging to the same class. Similar to the DCASE challenge, it is assumed that the audio signals are recorded by microphone arrays placed at different locations (nodes) within a room. The recorded audio signals can then be represented as:  
\[y_{m}(t)=\sum_{i=1}^{K}h_{m,i}(t)*S_{i}(t)+v_{m}(t) \tag{1}\]  
where, \(y_{m}(t)\) is the signal recorded at time \(t\) by microphone \(m\) in the array at each node, \(S_{i}(t)\) is the \(i^{th}\) sound source signal (where \(K\) is the total number of sounds), \(h_{m,i}(t)\) is the room impulse response (RIR) from source \(i\) to microphone \(m\), and \(v_{m}(t)\) is additive background noise at microphone \(m\). The audio recordings used in this work are four-channel and are time-aligned.  
This section discusses several top performing features considered for multi-channel acoustic scenes and evaluates them in terms of their advantages and drawbacks according to the requirements of the system. The following subsections evaluate the possible features according to their relevant categories within the feature engineering process [8], as shown in Figure 1.  
As observed, features are sub-divided into three main categories, namely: temporal features, spectral features, and cepstral features. Temporal features are computed in the time-domain and have the least computational complexity [8]. Spectral features, on the other hand, are extracted starting from the frequency representation of the signal [8]. Cepstral features then represent the rate of change within the different spectrum bands [8].  
Figure 1: Taxonomy of features extracted from audio.  
Finally, the fusion between spectral and temporal features results in spectro-temporal features, which combine both time and frequency attributes of a signal [8].  
Since temporal features are directly extracted from the audio signal, they often deter from providing reliable descriptors for multi-channel audio classification, as they do not contain information about the frequency. Hence, in this work, we examine cepstral and spectral features only. Along with this, we also examine spectro-temporal features, which are a combination of temporal and spectral features.
# Seismic Behavior of Triple Tunnel Complex in Soft Soil Subjected to Transverse Shaking
## 4 Details of the Finite Element Analysis
### Constitutive Model and Boundary Conditions
The Elastic modulus is taken as 37 MPa while the unit weight of concrete and the Poisson's ratio are taken as 25 kN/m\({}^{3}\) and 0.2, respectively. The lateral boundaries are kept far apart at a distance of 400 m based on the sensitivity analysis so that they do not interfere with the passage of earthquake waves. The tied degree of freedom condition is assigned to the lateral boundaries so that the left and right nodes move together while absorbing the lateral waves. This condition is used to simulate the free-field phenomenon of the soil. The base of the model is taken as rigid, which ensures that the waves do not escape and are reflected back into the model. The base is kept at a depth of 75 m and the earthquake signals are applied at the base of the model. The schematic diagram of the defined model geometry can be seen in Figure 2a. The model is discretized using 15-noded triangular elements with the mesh size selected based on Equation (7) given by [PERSON] and [PERSON] [36]. \[\Delta l=\frac{\lambda}{10}\;\text{to}\;\frac{\lambda}{8} \tag{7}\]  
And  
\[\lambda=\frac{V_{s}}{f} \tag{8}\]  
where \(\Delta l\) is the length of the finite elements and \(\lambda\) is the wavelength,\(f\) is the frequency, and \(V_{s}\) is the least shear wave velocity among all the soil layers. The time-step also plays an important role in the overall accuracy of the results and, hence, is selected by the equation below. \[\delta t=\frac{t}{m*n} \tag{9}\]  
where \(\delta t\) is the time step, \(t\) is the total time duration of the seismic vibration. In addition, \(m\) and \(n\) are the maximum number of steps and the number of sub-steps, respectively. This ensures that the waves do not pass more than one element at each time step.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 6 Randomization in Low-Level Caches
### Choice of the Addressing Function  
In our experiments, we modelled an ideal address randomization. The motivation was to prevent any \"shortcut attacks\" [53] that could break the randomization function.  
Therefore, to perform address randomization, we used a 128-bit AES cipher to perform the address randomization. When a request was made to the cache, the address went through an AES instance and the low bits of the output were selected to determine the set. The 128-bit AES key was private to each cache and to perform a remapping, a new pseudo-random key was generated by the simulator.  
We stress that using such a strong randomization function in L1 caches may not be realistic in practice. Any delays introduced into L1 requests would turn into significant slowdowns. The design of a lightweight, secure and optimized hardware address randomization function is a complex topic that is not covered in this paper. As an illustration, the low-latency cipher designed in the CEASER architecture [20, 58] is completely unsafe to use [17]. Using an AES-128 function to randomize the cache mapping deferred the problem of designing a safe and lightweight permutation function, but its integration into a hardware cache and the design of a lightweight alternative with similar properties are still open problems.
## 3 Results: Numerical Analysis
### Effects of Block or Column Height and Material on Load-Deflection Curve  
In practice, to create a cubic spring, the beam needs to be mounted on top of two columns or blocks, as shown in Figure 15. The height of the columns or blocks are shown as \(h\) in Figure 15. The rigidity of the columns or blocks is very important. A detailed \"2D plane stress with thickness\" finite element analyses in ANSYS was conducted to see the impact of the support structure (columns or blocks) height and rigidity on the nonlinear stiffness curve.  
The columns of this paper were 16 mm by 16 mm, but the column height was varied from 0 to 40 mm, and the column material was changed from aluminum to steel to see the effects of column height and material on the nonlinear stiffness. As for the beam that sits on the columns, just like the FE analysis of Figure 16, it has a thickness of 1 mm, width of 16 mm, and length of 90 mm. It is made from aluminum, with the piston shaft attached to the center of the beam having a diameter of 10 mm.  
Figure 18 shows the force-deflection curve for the beam at different column heights and materials. Figure 18 clearly shows that even though the columns are made of metal and may appear rigid, if the columns are too tall, the nonlinear stiffness behavior can significantly reduce. Figure 18 indicates that the beam should be mounted on steel columns or blocks and the height of the columns should be kept below 5 mm, as shown in Figure 19. The ANSYS results clearly show that the rigidity of the columns and the substrate to which the columns are to be attached are very important.  
Figure 17: Load–deflection curve of two 1 mm thick, 16 mm wide, and 90 mm long beams.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 4 Discussion
### Related Work
#### 4.1.4 Wireless Sensing Based on LoRa  
In previous work, human behavior perception based on LoRa mainly utilizes its long-distance communication capability. [PERSON] et al. [31] used LoRaWAN to transmit human activity data monitored by acceleration sensors, classified the four activities of staying, standing, walking, and running by extracting characteristic values, and further explored the relationship between data packet loss rate and recognition accuracy. The LoRaMote [32] system fixed the armband equipped with body temperature and pressure transducers on the human arm, and took advantage of the LoRaWAN long-distance transmission to transmit sensor data to the monitoring base station to remotely monitor the health status of the human target. [PERSON] et al. [33] deployed LoRa nodes on Windows, corridors, and rooftops of university campuses, forming a mesh network to transmit data collected by sensors for monitoring campus activities.  
In recent years, researchers conducted non-contact human behavior perception based on the LoRa signal itself. The Widesee (1995) system utilizes the mobility of an unmanned aerial vehicle (UAV) and carries a pair of LoRa transceivers to detect and locate human targets in high-rise buildings. The Widesee prototype system has a broad application prospect in the field of disaster rescue. [PERSON] et al. (2016) built a relationship model between human movement and LoRa signal changes, and verified its feasibility in the field of coarse and fine-grained behavior perception. The system can sense breathing when the target is 25 m away from the LoRa equipment or 15 m away from the wall, and track the walking distance and direction of the human body when it is 30 m away. However, this approach is limited to a single target scenario. Recently, the team (2016) proposed using beamforming to simultaneously monitor the respiratory rates of five human targets, achieving an average accuracy of 98.1%. [PERSON] et al. (2016) proposed the concept of a virtual fence to reduce the interference of LoRa signals in the process of long-distance transmission and realize multi-target perception. However, this method needs to know the direction information of targets, which hinders its application in real scenes.
## 4 Conclusions  
Due to limited node resources, intelligent oceans require the highest possible coverage rate to ensure reliable networks in UWSNs. This study proposed a three-dimensional coverage hole recovery algorithm using improved PSO and VFA in UWSNs. To avoid the problem of search optimization algorithms falling into local optima and slow improvement of coverage in the later stages of iteration, pre-coverage of underwater nodes is first performed under improved particle swarm optimization; the nodes are then moved to a position with higher coverage under the action of virtual forces, including global mutation operations. The analysis of experimental data generated from 45 nodes showed that the coverage rate increased from 72.02% to 91.36% after using the proposed algorithm, effectively reducing the coverage hole. With 50 nodes deployed, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed coverage algorithm increased to 87.69%, 90.08%, 89.80%, and 94.60%, while their corresponding coverage efficiencies were 53.62%, 53.79%, 52.36%, and 56.49%. In addition, we also verified that the clustering routing method based on the optimal location of nodes obtained using the coverage algorithm had the effect of prolonging the network life cycle. Based on a series of experiments, the proposed algorithm was shown to be superior to the relevant algorithms in terms of coverage and coverage efficiency and effectively improved the performance of UWSNs. In future research work, we plan to study sensor nodes with different sensing radii and networks with barriers to further optimize and improve the deployment strategy for underwater wireless sensor networks to meet more underwater sensor network design goals such as network lifetime and network connectivity.  
Conceptualization, L.Z. and C.L.; methodology L.Z. and C.L.; software, L.Z.; validation, L.Z.; formal analysis, L.Z. and C.L.; investigation, L.Z.; resources, L.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, C.L.; visualization, X.G., Y.C., and H.Z.; supervision, C.L.; project administration, C.L.; funding acquisition, G.X. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62001195), the Basic Science (Natural Science) Research Project of Jiangsu Higher Education Institutions (18 KJB460003, 21 KJB460030), and the Applied Basic Research Programs of Changzhou (CJ2020026). The authors would like to thank the anonymous reviewers for their helpful comments, which have improved the quality of the paper.  
The data presented in this paper are available upon contacting the corresponding author.  
Figure 11: Relationship between the average residual energy of nodes and network simulation time.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 8 Measurement in Real Topology--Evaluation of Simulation
### Comparison with Simulation Results  
For the simulations, the background noise was set up to very noisy conditions, and therefore according to the results of simulation the 6 repeaters are necessary to achieve BPL communication. According to real on-field measurements, four repeaters are necessary (see Table 5 and Figure 11). The difference of the numbers of repeaters is caused by the set-up parameters for simulation (e.g., background noise \(-\)70 dBm/Hz), which could be different in the real power grid and also the minimum throughout or minimum SNR level could be different for establishing BPL communication in noisy environments. Therefore, the simulation for the background noise PSD of \(-\)90 dBm/Hz (on-field good condition) was performed with the result of 2 repeaters.  
The key parameters for the evaluation of Smart Metering network behaviour and performance are the availability and stability [6]. The simulation results for very noisy conditions with results of 6 repeaters provide a proposal for high availability and stability, also in the time with high noise or short-time noise conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Measuring Point**} & \multirow{2}{*}{**Distance from TS [m]**} & \multicolumn{2}{c}{**Possibility of Communication**} \\ \cline{3-3}  & & **BPL** \\ \hline Smart Meter no. 32 & 835 & YES \\  & & (repeaters: n17, n19, n21, n27) \\ \hline Smart Meter no. 26 od & 705 & YES \\  & & (repeaters: n17, n19, n21) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Possibility of BPL communication with repeaters.  
Figure 11: Comparison of simulation with measurement.
## 3 Results and Discussion
### Effects of Resin Type
#### 3.3.3 Compression Strength  
It can be seen from Figure 12b that the adhesive type significantly affected the compression strength. Particleboard bonded with adhesive material had better compression strength than those made without adhesive material, where compression strength increased from 0.27 to 0.58 MPa. The particleboard made with urea formaldehyde had higher compression strength than the one made with cornstarch adhesive, where compression strength increased from 0.58 to 1.74 MPa.
Developing a Hybrid Detection Approach to Mitigating Black Hole and Gray Hole Attacks in Mobile Ad Hoc Networks  
[PERSON]  
1 Department of Electrical and Computer, University of Technology of Shiraz,  
Shiraz P.O. Box 71555-313, Iran; [EMAIL_ADDRESS]  
[PERSON]  
2  
[PERSON]  
2  
enhance the security posture of MANETs without compromising their inherent advantages of flexibility and efficiency.  
The primary contributions of this research are threefold:  
1. The development of an innovative hybrid detection method that leverages the strengths of anomaly detection, data mining techniques, and cryptographic verification to accurately identify and isolate malicious nodes within MANETs.
2. An enhancement of the dynamic source routing (DSR) protocol through the integration of our hybrid detection method, thereby improving its resilience against both known and emerging security threats.
3. A rigorous empirical validation of the proposed approach through extensive simulations, demonstrating significant improvements in key network performance metrics under various attack scenarios.  
The remainder of this paper is structured as follows: Section 2 explores the current state of the art in MANET security, focusing particularly on the existing strategies for combating black hole and gray hole attacks. This review sets the stage for our own contributions by highlighting the strengths and limitations of current approaches. Section 3 presents a detailed discussion of our proposed hybrid detection method. The results of the experimental evaluations are discussed in Section 5. Moreover, this section presents not only our experimental findings but also a comparison of our hybrid method with traditional approaches, highlighting its strengths and potential areas for further refinement. Section 6 summarizes the study's outcomes, highlights its contributions to the field, and suggests directions for future research. This structure ensures a comprehensive presentation of our study from its foundational concepts to its practical implications.
## 2 Temperature Field Coupling Theory (Airflow to Lining/Heat-Insulating Layer to Surrounding Rock)  
The calculation domains used for analyzing the temperature fields of a cold region tunnel under ventilation conditions usually involve two physical domains. One domain is a fluid domain formed by the airflow in the tunnel, while the other one is a solid domain formed by the tunnel lining (heat-insulating layer) and the surrounding rock. In the fluid domain, the governing differential equations used to analyze the motion fields and the temperature fields should consist of the energy conservation Equation (1), the mass conservation Equation (2), and the turbulence Equation (3). In the solid domain, the governing differential equation used to analyze the temperature fields is the energy conservation equation. The heat transfer between different physical domains is realized by means of convection through adjacent domain interfaces.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 5 Analysis of Node Transmission Capacity
### Calculation of Capacity of Nodes for Fixed Capacity  
Similarly, it is assumed that in a sea area of 20 \(\times\) 20 square nautical miles, the OS's position is \(X_{OS}=2,\;\;Y_{OS}=5\). The complete experimental parameters are shown in Table 4. When the channel capacity can only be fixed due to environmental constraints, we need to know whether the ship can connect to all nodes that need to be connected. Take the channel capacity of OS as three fixed values, assuming 1.5, 4.5, and 7.5. In addition, another nine target nodes in the sea area are randomly distributed, and their power is also random within one to ten. The setting of the location and the number of other ships well simulate the scene of an encounter at sea under random conditions. The random power of other ships can represent the heterogeneous communication equipment of ships, thus conforming to the multimodal characteristics of ship MANET. The calculation is based on Formula (19).  
As shown in Figure 11, according to the three experiments, it can be seen from that under the fixed channel capacity of the ship, it is not necessarily connected to all nodes.