esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The discrete element software PFC2D was used for the numerical simulation of compression tests on an anchored rock mass to study the influences of factors, including the bolt length and diameter, the strength of the rock, and the fracture angle, on the mechanical properties of bolts support from two aspects: the stress distribution on, and the anchorage force provided by such bolts. The following conclusions can be obtained:  
1. In a homogeneous rock mass, the bolts show similar stress distributions under conditions of different bolt lengths and diameters, as well as the strength of the rock, while the stresses differ. The bolt stress increases at first and then decreases on the whole, and there is only one stress peak in front of the bolts. With the damage to the anchored rock mass, the peak bolt stress gradually shifts to greater depth;
2. With the enlargement of bolt length and diameter and the improvement in the strength of the rock, the maximum anchorage force of bolts gradually grows. Under conditions of rock mass in the same type, enlarging the bolt length and diameter enables anchored rock mass to withstand large damage and avoids premature failure;
3. The axial stress decreases abruptly in the zone with a fracture angle of 90\({}^{\circ}\), thus forming a valley and rendering the stress distributions M-shaped. The bolt stress increases suddenly in the zones with fracture angles of 60\({}^{\circ}\) and 45\({}^{\circ}\), thus forming peaks that do not shift to greater depth. The bolt stress does not increase or decrease suddenly in the zone with a fracture angle of 30\({}^{\circ}\); instead, it increases, then decreases, and the peak stress occurs in front of the fracture zone;
4. As the fracture angle decreases, the maximum anchorage force of bolts shows a decreasing, then increasing trend. When the fracture angle is 45\({}^{\circ}\), the maximum anchorage force is minimized, and this is only 75% of that in an intact rock mass;
5. It is necessary to strengthen the exploration of the internal conditions prevailing in a rock mass and consider the influence of structural planes, such as fractures, on the anchorage force in the subsequent support design. The axial force on the bolts can also be monitored for stability evaluation and early warning of anchored rock mass failure according to changes in the stress.  
Conceptualization, C.J.; Methodology, T.H. and C.J.; Software, G.L., Q.W. and T.H.; Writing--original draft, T.H. and L.H.; Writing--Review and Editing, C.J. and H.L. All authors have read and agreed to the published version of the manuscript. This research received no  
Data Availability Statement: All data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.  
Author [PERSON] was employed by the company Ansteel Cornerstone Mining Corporation, Limited. 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.
## 3 Methodology
### Component Fusion Steps  
The PAC-PCNN model is introduced into the image fusion process. The MSMDM values of the basic image and MS intensity component \(IC\), respectively, are used as the inputs of the PAC-PCNN model. The detailed design of the MSMDM is shown in Section 2.2.  
There are five free parameters in the PAC-PCNN model: \(\alpha_{f}\), \(\beta\), \(V_{L}\), \(V_{E}\), \(\alpha_{e}\). The weighted connection strength is denoted by \(\lambda=\beta V_{L}\). Thus, the weighted connection strength is expressed. According to the analysis in [16], all the free parameters can be calculated adaptively according to the input information, which solves the difficulty of setting free parameters in the traditional PCNN model. All the free parameters in the PAC-PCNN model are automatically calculated in accordance with Formulas (35)-(38):  
\[\alpha_{f}=\ln\biggl{(}\frac{1}{\sigma(S)}\biggr{)} \tag{35}\]  
\[\lambda=\frac{\frac{S_{\text{max}}}{S^{\prime}}-1}{6} \tag{36}\]  
\[V_{E}=e^{-\alpha_{f}}+1+6\lambda \tag{37}\]  
\[\alpha_{e}=\ln\left(\frac{\frac{V_{E}}{S^{\prime}}}{\frac{1-e^{-\lambda_{f}} }{1-e^{-\alpha_{f}}}+6e^{-\alpha_{f}}\lambda}\right) \tag{38}\]  
where \(\sigma(S)\) is the standard deviation of the normalized input image \(S\). \(S^{\prime}\) and \(S_{\text{max}}\) indicate the normalized Ostu threshold and the maximum intensity of the input image, respectively.  
When the maximum number of iterations is reached, the iteration is stopped. Then, the sum of the ignition times with the basic image and \(IC\) are obtained, respectively. That will provide the total number of ignitions \(T_{Basic}\) and \(T_{IC}\) for the basic image and \(IC\), respectively.  
Figure 7: The flow chart of the multi-scale decomposition method based on WMCF and GF.  
The fusion results _Fused_ are acquired through using the large number of ignition times. The fusion rules are as follows:  
\[Fused(x,y)=\left\{\begin{aligned} IC(x,y),T_{IC}\geq T_{Basic}\\ Basic(x,y),T_{IC}\leq T_{Basic}\end{aligned}\right. \tag{39}\]  
where \(Fused(x,y)\) represents the fused value at the position (_x,y_) in the fusion image, \(IC(x,y)\) represents the pixel value at the position (_x,y_) in \(IC\), and \(Basic(x,y)\) represents the pixel value at the position (_x,y_) in the basic image.
# Effectiveness of LRB in Curved Bridge Isolation:
## 3 Parametric Study  
The design of LRBs is based on different standards provided in various specifications (AASHTO 1999, FEMA 356, IRC 2015) [41; 42] and literature ([PERSON], [PERSON] et al., [PERSON] et al.) [43; 44; 45]. The design of LRB can be affected by the isolated time period, damping ratio, and type of seismic loading. In turn, these depend on the size and design of the LRB. The sensitivity of the bridge response has been studied for 1.5 to 3 s time periods and 0.10 to 0.25 damping ratios, according to the guidelines available in the literature ([PERSON] et al., [PERSON] et al., FEMA-356) [46; 47].  
Three recorded ground motions from the PEER Strong Earthquake Database (PEER 2014) [48] have been considered without scaling viz. (1) Imperial Valley (1940), with perpendicular components (PGA 0.313 g and PGA 0.215 g), (2) Kobe (1995), with two earthquake components (PGA 0.83 g and PGA 0.63 g), and (3) Turkey (1992), as indicated in Table 2 and Figure 5. Figure 6 shows the response spectrum curve considered in the study. The conditions close to far-field, near-field, and forward directivity effects, respectively, were represented by the selected ground motions.  
The time history analysis was performed using the numerical integration technique Hilber-Hughes-Taylor (HHT), where the value of the parameter \(\alpha\) is specified as \(-0.33\). The sectional behavior of different elements has been controlled by considering the non-linear force and deformation behavior. In this study, the seismic isolation bearing was first designed for the selected earthquake, and then the isolated bridge performance was determined. Appendix A (Tables A1-A3) shows the different seismic loads (uni-directional and bi-directional) considered for ground motions corresponding to the Imperial Valley, Kobe, and Turkey earthquakes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Record** & **Event** & **Magnitude** & **Station** & **Orientation** & **PGA (g)** & **Distance to Fault** \\ \hline
1 & Imperial Valley (1940) & 7.0 &
\begin{tabular}{c} 117 El Centro \\ Array \#9 \\ \end{tabular} & IMP VALL/I-ELC180 & 0.31 & 8.3 \\ \hline
2 & \
\begin{tabular}{c} Imperial Valley \\ (1940) \\ \end{tabular} & 7.0 &
\begin{tabular}{c} 117 El Centro \\ Array \#9 \\ \end{tabular} & IMP VALL/I-ELC270 & 0.21 & 8.3 \\ \hline
3 & Kobe (1995) & 6.9 & KJMA &
\begin{tabular}{c} RSN1106\_K0 BE\_KJM000 \\ \end{tabular} & 0.83 & 0.96 \\ \hline
4 & Kobe (1995) & 6.9 & KJMA &
\begin{tabular}{c} RSN1106\_K0 BE\_KJM090 \\ \end{tabular} & 0.63 & 0.96 \\ \hline
5 & Turkey (1992) & 6.9 & Erzincan & Erzincan, EW & 0.50 & 4.3 \\ \hline
6 & Turkey (1992) & 6.9 & Erzincan & Erzincan, NS & 0.39 & 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Earthquake-recorded motions considered.  
Figure 5: Selected recorded earthquake motion time histories.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 4 Process Scheme and Measurement  
### Process Program  
According to the antenna structure, a technological preparation scheme is developed. The implementation steps are as follows: The first step is to prepare the substrate and etch the holes. In this paper, Rogers RT5880 is selected as the substrate material and is deep etched using APEX SLR ICP2 equipment to a depth of 254 um as shown in Figure 11a. The holes are internal hidden structures that are not visible within the profile. In step 2, a 400 mm thick layer of Si\({}_{3}\)N\({}_{4}\) is prepared by plasma-enhanced chemical vapor deposition (PECVD) and etched to form the switch contacts, as described in Figure 11b. In step 3, a 500 nm aluminum (Al) layer is grown by magnetron sputtering and etched in phosphoric acid solution to form the switching driver electrode, as shown in Figure 11c. In step 4, a layer of Si\({}_{3}\)N\({}_{4}\) with a thickness of 400 nm is deposited on the driver electrode by PECVD to prevent crosstalk among the signals, as shown in Figure 11d. In step 5, a Ti/Cu seed layer is sputtered onto the substrate, and the top patch structure and bottom ground layer are simultaneously prepared by water bath plating. The seed layer is then removed by wet etching, as shown in Figure 11e. In step 6, the silicon nitride isolation layer on the pad is removed by photolithography, and then the polyimide sacrificial layer is prepared by the rotary coating method, as can be seen in Figure 11f. In step 7, as shown in Figure 11g, the anchor holes in the sacrificial layer are created by wet etching. In step 8, the top electrode of the RF MEMS switch is formed by sputtering a gold seed layer and plating a 2 um thick gold layer. The seed layer is removed as shown in Figure 11h. In step 9, the L-slot frequency  
Figure 10: Parameter optimization: (**a**) The effect of \(M_{1}\) on the S11 parameters; (**b**) The effect of \(P\) on the S11 parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Size (mm\({}^{3}\))**} & **Number of** & **Resonant Frequency \(f_{\theta}\)** & **Bandwidth (GHz)** & **Adjustable** & **Maximum** \\  & & **Switches** & **(GHz)** & **Status** & **Gain (dB)** \\ \hline
[15] & \(10\times 10\times\) & 2 & 14.25/14.5/16.3/16.4/16.65 & & & \\  & 0.5 & & /18.8/20.8 & 14-15.2/15.8–17.3/18.6–19.1/20.6–21 & 4 & - \\
[16] & \(30\times 36.7\) & 4 & 9.92/14.86/18.21/21.2 & 9.5–10.3/14.3–15.2/17.8–19/20.5–22 & 4 & - \\
[17] & \(1.1\times 1.7\) & 2 & 40 & 30–52.5 & 2 & 3.3 \\
[23] & \(31\times 42.04\) & 2 & /114/14/14.1/14.11 & & 3 & 2.27 \\ This & \(4.07\times 5.27\) & & 41.63–43.11/46.76–48.60/51.88–54.51 & 4 & 7.90 \\ work* & \(\times\) 0.254 & & /56.01–57.44 & & \\ \hline \hline \end{tabular}
* The data shown are simulated only.  
\end{table}
Table 3: Comparison of Frequency Reconfigurable Antenna.  
reconfigurable antenna is finally obtained by releasing the PI sacrifice layer by reactive ion etching (RIE) in Figure 11i.
# Review of sEMG for Robot Control: Techniques and Applications
## 4 Application of sEMG Interface in Robot Control
### Classification of Robot Control Strategies  
The use of sEMG signals to drive robots or prosthetics can assist individuals with movement disorders in activities or enhance their movement abilities. Rehabilitation robots using neural interfaces have promising potential for the treatment of post-stroke patients. The control strategies for robots using sEMG interfaces can be generally categorized into full-human continuous control (S1), semi-autonomous continuous control (S2), and discrete control (S3). Their characteristics and related research are detailed in the following paragraphs.
## 4 Methodology
### Dataset Description  
Selecting an appropriate dataset is essential for evaluating the performance of IDS. Several publicly available datasets have been widely used in IDS research, such as TON_IoT, KDD Cup99 [27], and UNSW-NB15 [28; 29]. The KDD Cup99 dataset was initially introduced at the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99) in 1998 as part of the Third International Knowledge Discovery and Data Mining Tools Competition. As one of the earliest benchmark datasets for intrusion detection, it played a foundational role in early studies. However, with the evolution of network threats and system complexity, this dataset is now limited by its outdated feature distributions, class imbalance, and lack of diversity in attack types, making it less suitable for evaluating modern Network-Based Intrusion Detection System (NIDS) models [30]. The UNSW-NB15 dataset was created using the IXIA PerfectStorm tool in the Cyber Range Lab of UNSW Canberra. It blends real-world background traffic with synthesized modern attack behaviors, offering a comprehensive representation of contemporary internet communication.  
Building on the previous work, the TON_IoT dataset was collected in a realistic, large-scale test environment at the UNSW Canberra Cyber Institute's IoT Lab in 2019. It was specifically designed for research into the security of IoT and IIoT systems. This dataset integrates heterogeneous data from multiple system platforms, including operating system logs, telemetry from sensors, network traffic records, and behavioral sequences, comprehensively capturing both normal and malicious patterns found in typical industrial intelligent environments [31; 32]. Given its rich data structure and practical relevance--closely resembling the complex communication interactions between devices and platforms in the T2G environment of EMU/DMU--it features high dimensionality, strong temporal characteristics, diverse attack types, and controllable sample distribution. In this study, the Processed Network subset of TON_IoT was selected as the primary experimental dataset to support the construction and validation of the proposed intelligent intrusion detection and response framework. In this work, we utilize a 10% subset of the TON_IoT dataset to reduce computational overhead while preserving sufficient data representativeness. This subset was selected using stratified random sampling to maintain the original class distribution proportions. Table 1 shows the data structure of the TON_IoT dataset we used.
# Review of Acoustic Sources Alternatives to a Dodecahedron Speaker
## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Sources** & **Omnidirectionality** & **Repeatability** & **Even Frequency** & **Adequate Sound** & **Accuracy in** & **Fulfillment of** & **Featureed** \\  & & & & & & & & **ISO 382E** & **References** \\ \hline
1 & Balloon & No & No (yes under conditions) & No & Yes (no in the low-frequency range) & No (yes for RF except low-frequency range) & No & [(7,23,5,6,6,7,8,9,10,11,12,13,14,15,16)] \\ \hline
2 & Gunshot & No & Yes under conditions) & No & Yes (no in the low-frequency range) & No (yes for RF except low-frequency range) & No & [(33,4,7,1,7,9,10,11,12,13,14,15,16)] \\ \hline
3 & Firecacker & Possibly Yes & Yes (light deviations) & No & Yes (no in the low-frequency range) & Possibly yes & Possibly yes & [(7,44,5,9,10,11,12,13,14,15,16)] \\ \hline
4 & Handlap & No & No & No & No & No & [(7,7,9,10,11,12,13,14,15,16)] \\ \hline
5 & Inverse horn design & Yes & Yes & Yes (through inverse filtering) & Yes & Yes & Yes & [(105,11,10,11,12,13,14,15,16)] \\ \hline
6 & Woodens clapper & No & Yes (under conditions) & No & Yes (no in the low-frequency range) & - & No & [(60,102,11,11,12,11,13,14,15,16)] \\ \hline
7 & Should primed & No & Yes & No & Yes (except low-frequency range) & - & No & [(99,119,120)] \\ \hline
8 & Rotation of directional speaker & - & Yes & Yes & Yes & Yes (for RF) & - & [(43,121)] \\ \hline
9 & Firecacker & No & Yes & No & No & - & No & [(33,122,123,124)] \\  & Transducer & Possibly yes (under conditions) & Yes & No & Yes & - & - & [(23,124)] \\ \hline
10 & Ring indicator & Possibly yes (under conditions) & Yes & No & Yes & - & - & [(23,124)] \\ \hline
11 & Explosive mixture of actor/geng sets with air & - & - & Yes & Yes & - & - & [(91)] \\ \hline
12 & Compressor Nozzle & - & - & No & No & No & - & [(84)] \\ \hline
13 & Directional & No & Yes & Yes & Yes & No (yes for RF under conditions) & No & [(75,127,128,129,130,131,14,15,16)] \\  & Loadpower & & No & Yes & No & Yes (no in the low-frequency range) & - & - & [(136,141)] \\ \hline
14 & Laser-induced air & Possibly yes & Yes & No & Yes (no in the low-frequency range) & - & - & [(136,141)] \\  & broadband & & & & & & & \\ \hline
15 & Electric spark source & No (depends on the implementation) & Yes & Yes (depends on the implementation) & No (depends on the implementation for low-frequency range) & No & [(142,143,144,15,16)] \\ \hline - & Dodecheroden & Yes & Yes & Yes (depends on the implementation) & Yes (depends on the implementation for low-frequency range) & Yes & Yes & [(26,17,18,1 Data from this review indicate that besides dodecahedron speakers, other sound sources that fulfill ISO 3382-1 requirements are inverse horn designs and possibly firecrackers. However, for firecrackers there is no research that has been carried out according to the specifications of ISO 3382-1 for the measurement of omnidirectionality. Other sound sources that can potentially fulfill ISO 3382-1 source requirements are the rotation of a directional speaker, ring radiator and laser-induced breakdown.
## 4 Results
### Discussion  
#### 4.3.1 Sensitivity Analysis for Strain Measurement Points  
The performance of the strain-based displacement reconstruction methods is significantly affected by the arrangement of strain measurement points. In this section, the sensitivity of the reconstruction accuracy of the PI_DeepONet to the locations and number of strain measurement points is analyzed.  
In the numerical examples we presented in Sections 4.1 and 4.2, the strain measurement points are uniformly distributed and the distance between adjacent strain measurement points is constant. To investigate whether PI_DeepONet relies on uniformly arranged measurement points, we performed the same reconstruction task for the beam via randomly arranged strain measurement points. The relative errors at the maximum response are compared in Table 10. The results show that the reconstructed accuracies for the randomly and uniformly arranged points are essentially consistent, indicating the low sensitivity of PI_DeepONet to the locations of strain measurement points.  
The sensitivity of the reconstruction accuracy of PI_DeepONet to the number of strain measurement points was analyzed utilizing the beam with two ends clamped. The reconstruction accuracy of the PI_DeepONet was evaluated for the number of strain measurement points ranging from 2 to 18. The evolution of the relative error at the maximum response with the number of measurement points is shown in Figure 23. The results show that the relative error decreases as the number of measurement points increases. However, the relative error remains stable after the number of measurement points exceeds a certain threshold. The threshold of the number of strain measurement points for the complex load is higher than that for the simple load. Therefore, an adequate number of strain measurement points is essential to perform the reconstruction task via PI_DeepONet.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Restrictions**} & \multicolumn{3}{c}{**Relative Errors**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Uniformly Arranged Strain**} & \multicolumn{3}{c}{**Randomly Arranged Strain**} \\  & \multicolumn{3}{c}{**Measurement Points**} & \multicolumn{3}{c}{**Measurement Points**} \\ \cline{2-7}  & \(T_{P}\) & \(T_{U}\) & \(T_{S}\) & \(T_{P}\) & \(T_{U}\) & \(T_{S}\) \\ \hline one end clamped & 2.5\% & 2.2\% & 1.0\% & 2.7\% & 1.4\% & 0.055\% \\ \hline simply supported–simply supported & 0.32\% & 0.29\% & 2.9\% & 0.78\% & 0.013\% & 1.1\% \\ \hline clamped–simply supported & 0.78\% & 0.013\% & 1.7\% & 1.1\% & 0.024\% & 1.3\% \\ \hline clamped–clamped & 1.0\% & 0.088\% & 0.74\% & 0.30\% & 0.44\% & 1.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Relative errors in the maximum response calculated by the PI_DeepONet via uniformly arranged measurement points and randomly arranged measurement points for the beam with different restrictions.  
Figure 23: Evolution of the relative error at the maximum response with the number of strain measurement points for the beam with the restriction of two clamped ends.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Resource Observation and Discovery
#### 3.5.1 Resource Observation  
Ref. [19] describes briefly about using CoAP protocol for observing resources in IoT environment and WSNs. A simple application of observing the temperature form a temperature sensor is exhibited. The temperature sensor acts as a CoAP server which can be queried by any CoAP client to observe the resource i.e., temperature in this case. The CoAP client can exploit the use of RESTful methods for query. An example of how resource observation is implemented in CoAP is shown in Figure 5. Here a CoAP client sends a request a CoAP server as an observer of a particular resource i.e., value of power in wattage in this case. This establishes an observational relationship between CoAP client and the resource. The client is then provided with the current state of the resource in response to the GET request generated by client.