esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Integral Inequalities for Stability Analysis of Interval Time-Delay Systems
## 5 Numerical Examples
Moreover, the Wiringer-based inequality [19] and new integral inequality [17] are encompassed as particular cases of that in Lemma 2. From Table 5, the results by Theorem 1 outperform those by [14; 15; 17; 19; 37]. **Example 3. ** Consider the interval time-delay system:  
\[\dot{x}(t)=\left[ \begin{array}{cc}0&1\\ -100&-1\end{array} \right]x(t)+\left[ \begin{array}{cc}0&0.1\\ 0.1&0.2\end{array} \right]x(t-d(t)) \tag{47}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mu=0.3\) & \(\mu=0.5\) & \(\mu=0.8\) & \(\mu=1\) & **Number of Variables** \\ \hline [PERSON] [30] & 2.2160 & 1.1270 & 0.8710 & 0.8710 & \(3n^{2}+3n\) \\ [PERSON] et al. [14] & 2.2160 & 1.1272 & 0.8714 & 0.8714 & \(18n^{2}+8n\) \\ [PERSON] et al. [34] & 2.2850 & 1.2080 & 1.0200 & 1.0200 & \(3n^{2}+3n\) \\ [PERSON] et al. [35] & 2.3070 & 1.2330 & 1.0440 & 1.0440 & \(3n^{2}+3n\) \\ [PERSON] et al. [13] & 3.6962 & 1.7655 & 1.3123 & 1.3123 & \(25n^{2}+9n\) \\ Corollary 2 & 3.9872 & 2.0362 & 1.5110 & 1.4849 & \(8.5n^{2}+3.5n\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MADBs \(d_{2}\) for various \(\mu\) and \(d_{1}=0\) (Example 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \(d_{1}=0.3\) & \(d_{1}=0.5\) & \(d_{1}=0.8\) & \(d_{1}=1\) \\ \hline [PERSON] et al. [37] & 2.190 & 2.200 & 2.200 & 2.210 \\ [PERSON] et al. [14] & 2.263 & 2.285 & 2.307 & 2.316 \\ [PERSON] et al. [15] & 2.432 & 2.433 & 2.430 & 2.423 \\ [PERSON] and [PERSON] [19] & 2.570 & 2.572 & 2.576 & 2.579 \\ [PERSON] and [PERSON] [17] & 2.704 & 2.754 & 2.795 & 2.806 \\ Theorem 1 & 2.901 & 2.973 & 3.185 & 3.247 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MADBs \(d_{2}\) for various \(d_{1}\) and \(\mu=0.3\) (Example 2). This example is investigated specifically for comparison with the Auxiliary-function based integral inequalities [26]. For the unknown \(\mu\) and given \(d_{1}\), the MADBs by the approach in [26] and Corollary 1 are listed in Table 6. As pointed out in Remarks 1 and 2, the newly proposed inequalities have been proved to offer tighter bounds for both single and double integrals and make use of more system information. As a result, from Table 6, Corollary 1 achieves less conservatism and fewer variables than the method in [26], which demonstrate the advantages of Lemmas 2 and 3.
## 5 Simulations and Analysis  
In this section, simulations are implemented to illustrate the feasibility of the proposed detection scheme on the LFC system. As is shown in Figure 3, an IEEE 39-bus 10-unit power system is used as the tested system. The data of the system can be found in [27,28]. The red block represents the compromised area (Area _i_).  
Based on the Formulas (2)-(4), it is assumed that the uncertainties of the system studied in this simulation environment are as follows:  
\[\Delta A_{i}(t)=\begin{bmatrix}-0.065&0&-0.065&0\\ 0.043&-0.043&0&0\\ 0&0.012&-0.012&-0.012\\ 0&0&0.0025&0\end{bmatrix}\cdot sin(t)\]  
\[\Delta B_{i}(t)=\begin{bmatrix}-0.065&0&0&0\end{bmatrix}^{T}\cdot sin(t),\]
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.14 Sound Squatting  
Sound squatting, more commonly known as voice squatting or skill squatting, is another form of cyber squatting. In this method, the attacker uses a voice user interface (VUI) to exploit the usage of homonyms and errors caused by input. A homonym is a word that sounds similar to another word but is spelt differently. Virtual assistants make use of keywords activated by a person's voice to open third-party applications. To exploit this, a malicious hacker registers a fake third-party application with a voice keyword similar to an authentic application. Thus, when a user requests or calls for the particular application, the virtual assistant opens the fake third-party application instead. When opened, this fake application then proceeds to steal sensitive information from the user and install more malicious software. The main issue with these applications is that they can run for long periods in the background without the user's knowledge.  
Skill squatting when coupled with smart speakers, which are becoming more common in homes throughout the world, can be used to create complex phishing attacks. Smart speakers can often interact with other devices through the internet of things and even be used to make financial transactions, thus creating a whole new area for phishers to target. As shown by [PERSON] et al., who demonstrated that by creating a skill that utilized the command \"Am X\" when an Alexa user attempted to use the American Express skill (with the skill name \"Amex\"), the user could easily be redirected to a phishing version of the login screen for American Express and their credentials stolen [69]. Some countermeasures have been proposed for this whereby before a skill is accepted it could be checked to see if the skill could be mistaken for an already existing command, although this does not seem to have yet been implemented.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 3 System Model
### Edge Computation Model  
(1) _Edge Computing:_ We denote \(F\) as the total computing capacity of the MEC server, while \(\kappa_{m}\) is the ratio of computing capacity allocated by the MEC server to the UE \(m\). Thus, the computation delay of the task \(D_{m}\) offloaded to the MEC server is  
\[t_{m}^{C}=\frac{c_{m}}{\kappa_{m}F}. \tag{6}\]  
The energy consumption generated by the UE \(m\) during edge processing is  
\[e_{m}^{C}=v\cdot(\kappa_{m}F)^{2}\cdot c_{m}, \tag{7}\]  
where \(v=10^{-27}\) is the effective switched capacitance of the CPU, determined by the CPU hardware architecture.  
(2) _Local Computing:_ For task \(D_{m}\) computed locally, we use \(F_{m}^{loc}\) to present the computing capacity of UE \(m\). Thus, the latency of the task \(D_{m}\) processed locally is  
\[t_{m}^{loc}=\frac{c_{m}}{F_{m}^{loc}}, \tag{8}\]  
Thus, the energy consumption of the task processed locally is  
\[e_{m}^{loc}=v\cdot\left(F_{m}^{loc}\right)^{2}\cdot c_{m}, \tag{9}\]
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 4 Simulation Results
### Performance of FDNN Receivers  
Figure 6 shows the performance of FDNN receivers under different \(\Delta_{SNR}\).  
When \(\Delta_{SNR}=1\) dB, user 2 struggles to reduce BER due to the small \(\Delta_{SNR}\), making it challenging for receivers to separate signals. So, the BER curve of user 2 is close to a parallel horizontal line without a downward trend. As the \(\Delta_{SNR}\) increases, BER descent accelerates, indicating improved signal distinguishability. When \(\Delta_{SNR}=3\) dB, BER curves show a linear decline from \(10^{-1}\) to \(10^{-2}\). Further increases in \(\Delta_{SNR}=5\) dB and \(\Delta_{SNR}=7\) dB lead to even faster BER reduction, reaching \(10^{-4}\) for \(\Delta_{SNR}=5\) dB and \(10^{-6}\) for \(\Delta_{SNR}=7\) dB at \(\text{SNR}=22\) dB. This demonstrates that a higher \(\Delta_{SNR}\) correlates with a lower BER.  
Figures 7 and 8 show the performance of FDNN and SIC receivers for user 1 and user 2.  
Table 2 presents the BER for two users at \(\text{SNR}=20\) dB. Both user 1 and user 2 exhibit lower BERs with FDNN receivers compared to SIC receivers. As user SNR increases, the BER advantage of FDNN receivers over SIC receivers becomes more pronounced. Additionally, the comparison between \(\Delta_{\text{SNR}}=3\) dB and \(\Delta_{\text{SNR}}=5\) dB reveals that FDNN receivers perform better than SIC receivers at \(\Delta_{\text{SNR}}=3\) dB, emphasizing the significant impact of SNR difference on NOMA user detection performance. FDNN receivers excel in scenarios with lower user differences.  
Figure 8: Performance of FDNN and SIC receivers for user 2.  
Figure 7: Performance of FDNN and SIC receivers for user 1.  
Figure 9 examines the influence of the SNR difference \(\Delta_{SNR}\) between users on the BER of NOMA systems. The greater the SNR of a channel, the more power it is allocated. We use this to simulate the effect of power allocation on system performance.  
Table 3 presents the BER for two users at different \(\Delta_{SNR}\). The \(x\)-axis represents \(\Delta_{SNR}\) between two users, and the y-axis represents BER. User 1 maintains a fixed SNR of 15 dB, while the SNR of user 2 is \((15-\Delta_{SNR})\) dB. We can see that when \(\Delta_{SNR}=3\sim 7\) dB, as \(\Delta_{SNR}\) increases, the BER for both two users decreases due to reduced interference. However, when \(\Delta_{SNR}=9\) dB, the BER of user 1 continues to decline, while the BER for user 2 increases, as the SNR of user 2 becomes too low. The comparison between the blue and red lines in Figure 9 indicates that FDNN receivers consistently outperform SIC receivers, reinforcing the superiority of FDNN receivers in MIMO-NOMA scenarios.
## 3 Results of Simulation Experiments and Discussion
### Performance Analysis with Small-Scale Problem  
#### 3.2.1 Analysis of Success Rate in Generating Feasible Solutions  
We first tested the success rate of the proposed approach with small-scale problems. Here, the success rate is defined as the number of experiments with feasible solutions for task assignment and the path plans generated divided by the total number of experiments tested. A total of 20 sets of experiments with the quantity of tasks varying from 10 to 100 and with 5 or 10 robots were generated. The time spent and energy consumed by machining centers for each task were randomly set as constants.  
Figure 6 shows the relationship between success rate and the number of tasks with different numbers of robots and time limits when comparing our proposed methods with a popular method for PC-TAPF problems: Sequential Next-Best Assignment Search-Incremental Slack-Prioritized Search (NBS-ISPS) [4]. In NBS-ISPS, task assignment and path planning were solved by the NBS solver and ISPS solver, respectively, and independently. The time limit means the maximum allowed runtime of the optimization solver in NBS-ISPS.  
Figure 6 indicates that the success rate of our proposed methods is 100%, while the success rate of the NBS-ISPS method decreases with an increasing number of tasks, which implies that the NBS-ISPS method may not be suitable for large-scale problems. When the number of tasks is \(n=50\), the success rate seems to be mainly influenced by path planning, since the ISPS solver sometimes cannot generate feasible solutions within a finite number of iterations and sometimes reaches no solution. Thus, the ISPS solver itself is incomplete. When \(n>50\), the success rate is mainly affected by task assignment. In this case, the number of tasks \(n\) is much larger than the number of robots \(r\), and the NBS solver may not be able to find a feasible solution in finite time. For example, in Figure 6a, when \(n=80,r=5(n\gg r)\), the success rate of NBS-ISPS is only 15%.  
By comparing Figure 6a,b, we find that increasing the time limit within a certain range can improve the success rate of NBS-ISPS. For example, when \(n=20,r=10\), and the time limit is 100 s or 200 s, the success rate of NBS-ISPS is 90% or 95%, respectively. Furthermore, we examined the effects of different numbers of robots on the success rate, and found that the success rate of NBS-ISPS grows as the number of robots increases. In summary, our proposed methods outperform the NBS-ISPS method in terms of success rate with small-scale problems.
## 3 Composition of the Sewer Monitoring System
The monitoring center calculated and processed the collected data according to the communication protocol and displayed the gas concentration information for each wellhead. The gateway was responsible for collecting the information transmitted by the sensor detection node in the area and forwarding it to the monitoring center. It consisted of a Zigbee communication module and an alarm device. Typically, sewer monitoring devices are installed in densely populated areas, dividing them into several small areas; each small area contains a gateway, which is installed in the center of the small area. The division of these small areas must guarantee that each sensor detection node within the area can communicate with the gateway. The gateway communicates wirelessly with the sensor detection node through the Zigbee network and communicates with the host computer of the monitoring center through a serial port with an RS-485 interface. If the concentration of methane in a sewer in an area exceeds the safety limit, the alarm device of the gateway emits an audible and visual alarm. The working software of the monitoring center was designed using Labview 2013, which can dynamically display the concentration of methane in the sewer of the monitored area. When the methane concentration in the sewer exceeds the alarm's threshold, an alarm prompt is issued. The location of the sewer can be determined based on the wellhead number, and the staff can be informed to adopt appropriate steps to prevent an accident from happening.
## 6 Results Comparison with Other Countries  
The findings of the current study were contrasted with other countries like China, the United Kingdom, Nigeria, and Pakistan. In addition, Table 6 elaborates the comparison of BIM implementation barriers with other countries.  
By contrasting Malaysia's current study with China, \"unavailability of standards and guidelines\" was ranked first, while in Nigeria, \"unavailability of standards and guidelines\" was ranked fourth. However, \"unavailability of standards and guidelines\" could not gain the attention of researchers in the United Kingdom and in Pakistan and thus was marked as not identified. The \"lack of BIM training\" was not ranked in the top five in the United Kingdom, Nigeria, or Pakistan. However, \"lack of BIM training\" was labeled as not identified in China. Furthermore, \"lack of expertise\" was ranked third in the current study in Malaysia as well as in the United Kingdom. The \"lack of expertise\" in Nigeria was perceived to be the most crucial barrier. Moreover, \"high cost\" was ranked fourth in the current study in both Malaysia and China, while in Nigeria, \"high cost\" was ranked third. In addition, \"lack of research and BIM implementation\" was the least consideration barrier in the United Kingdom, Nigeria, and Pakistan.  
These findings provide evidence that more attention needs to be paid to BIM research and implementation in Pakistan and that \"lack of research and implementation of BIM\" is a crucial barrier that is largely unidentified by researchers. It is also understandable  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
that variations in ranks are due to different cultures and environmental factors in different countries. From this analysis, the conclusion can be drawn that barriers are similar among different countries but rank distinctively.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 4 Discussion
### Key Bottlenecks and Future Directions  
Deep learning methods require a large amount of data to train models effectively, but acquiring and labeling datasets can be costly and challenging. Additionally, despite the inclusion of spectral and spatial features, the improvement in classification accuracy for artificial surfaces remains limited. This limitation suggests that the selected features are not sufficiently sensitive to artificial surfaces. Developing building index features could address this issue. However, constructing a normalized building index often requires a mid-infrared channel, which is absent in most high-resolution remote sensing images that only have red, green, blue, and near-infrared channels [78]. To overcome this limitation, future research could explore using hyperspectral remote sensing data or combining high-resolution satellite images with medium-resolution remote sensing images. This approach could compensate for the deficiencies in optical resolution satellite images for accurately extracting building features. By integrating these data sources, it may be possible to enhance the sensitivity and accuracy of artificial surface classification in complex environments.
# Failure Mode Analysis and Dynamic Response of a Coal Mine Refuge Chamber with a Gas Explosion
## The Finite Element Model
### Modeling Approaches  
Considering the thickness factor of different parts in the refuge chamber model, the SOLID164 element is selected to simulate cabin door, methane/air mixture and air, and the SHELL163 element is selected to simulate cabins and flanges. The size of the shell and solid elements which are used to simulate the refuge chamber are 40 mm, and the size of the solid elements which are used to simulate methane/air mixture and air is 160 mm. A coupling algorithm is employed to simulate the fluid-structure interaction. The fluid, such as a methane/air mixture and air, adopts an ALE fixed/mobile mesh method and the refuge chamber structure adopts a Lagrangian deformable mesh method. The coupling forces at the fluid-structure (the air and the refuge chamber) interface are computed by the coupling algorithm. Through an explicit finite element method, the coupling forces are, respectively, added to the fluid and the structure nodal forces. In order to obtain the local peak pressure on the refuge chamber structure, an accurate fluid-structure coupling algorithm is needed.
## 3 Overview of the Design Method  
The object of our study is a fifth-order hairpin filter, as shown in Figure 1. The hairpin filter achieves a more compact design by folding parallel-coupled line resonators into a \"U\" shape [37], essentially functioning as a coupled bandpass filter.  
Its operation is based on the characteristics of microstrip lines and the propagation principles of electromagnetic waves. A microstrip line is a conductive wire fabricated on a dielectric substrate, possessing certain inductance and capacitance. When multiple microstrip lines are arranged in parallel, electromagnetic coupling occurs between them. This coupling causes a phase difference in the propagation of electromagnetic waves between the microstrip lines, resulting in filtering effects. Specifically, when an input signal passes through the hairpin filter, it is influenced by the coupling between the microstrip lines. Signals of different frequencies propagate at different speeds between the microstrip lines, leading to changes in the phase difference at the output end. By properly designing the parameters and arrangement of the microstrip lines, the phase difference within specific frequency ranges can be selectively increased or decreased, achieving filtering effects on the signal. Additionally, the inductance and capacitance characteristics of the microstrip lines also contribute to signal filtering. The inductance and capacitance of microstrip lines are frequency-dependent, and the propagation speed and impedance of signals on microstrip lines vary with different frequencies. By appropriately designing the inductance and capacitance of the microstrip lines, signals within specific frequency ranges can be selectively impeded or transmitted, achieving filtering effects.  
By adjusting the geometric parameters and spacing of the microstrip lines, the resonant frequency and bandwidth of the hairpin filter can be controlled, selectively impeding or  
Figure 1: This is the topology structure of the hairpin filter used for the experiment.  
transmitting signals within specific frequency ranges, achieving filtering effects. As shown in Figure 1, the arm lengths \((L_{1},L_{2},Lt)\), gaps \((d_{1},d_{2})\), and line width \((W)\) are all related to the frequency response of the filter, affecting its stopband attenuation and selectivity.
# Investigation of Rock Joint and Fracture Influence on Delayed Blasting Performance
## 1 Introduction  
Because of the rapid development of mine operations, mining technology has become a process that adapts to changes in ore body, rock conditions, and operational requirements. Currently, large-scale blasting is a challenging topic in open-pit operations. This is because of the existence of unpredictable geological structures, including joints, fissures, faults, and muddy-interbedding planes in rock mass. Hence, it is critical to improve the blasting technology to satisfy the operational environments.  
As joint structures are naturally formed with high randomness, it is difficult to study a universally applicable joint structure. In the past, studies only focused on joint behavior under static loads. In this paper, a dynamic analysis of joint behavior under blasting is carried out.  
The attenuation index of shock and stress waves in this study was determined by determining the degree of rock mass damage in conjunction with stress and displacement field in fracture mechanics type I and II cracks. By analyzing the change in vibration velocity of incident and transmitted particles at the joint interface, the propagation velocity of the detonation wave can be calculated. This can, in turn, simplify the analysis of the incident particle vibration velocity at the interface of joints from the detonation wave attenuation to the stress wave. After the fracture stress wave changes, the particle stress change around the fracture is deduced, and the stress state of the surrounding rock during the blasting and mining process of the fractured rock mass can be determined. This result can provide a technical basis for large-scale open-pit blasting.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 2 ETC-SE Model
### Measurement Functions of the ETC-SE Model  
With the consideration of the line temperature, the measurement functions are modified in the proposed ETC-SE model, and can be listed as,  
\[\left\{\begin{array}{l}U_{i}=U_{i}\\ \theta_{i}=\theta_{i}\\ P_{i}=U_{i}\sum\limits_{j\in I}U_{j}[G_{ij}(T)\cos\theta_{ij}+B_{ij}(T)\sin \theta_{ij}]=0\\ Q_{i}=U_{i}\sum\limits_{j\in I}U_{j}[G_{ij}(T)\sin\theta_{ij}-B_{ij}(T)\cos \theta_{ij}]=0\\ P_{ij}=U_{i}^{2}(g+g_{c})-U_{i}U_{j}g\cos\theta_{ij}-U_{i}U_{j}b\sin\theta_{ij} \\ Q_{ij}=-U_{i}^{2}(b+b_{c})-U_{i}U_{j}g\sin\theta_{ij}+U_{i}U_{j}b\cos\theta_{ij} \end{array}\right. \tag{6}\]  
with  
\[G_{ij}(T)=-g_{ij}(T)=-\frac{R_{ij}(T)}{R_{ij}(T)^{2}+X_{ij}{}^{2}} \tag{7}\]  
\[B_{ij}(T)=-b_{ij}(T)=\frac{X_{ij}}{R_{ij}(T)^{2}+X_{ij}{}^{2}} \tag{8}\]  
The measurement functions are the nonlinear functions of line temperatures, which can be calculated via the HBE. The meteorological data \(\mathbf{z}_{meteo}\) are used to calculate the heat parameters, which can be represented as [10],  
\[h_{HBE}=I^{2}R(T)+q_{s}-q_{c}(T)-q_{r}(T)=0 \tag{9}\]  
\[q_{e}=A_{e}(T-T_{\mathbf{a}}) \tag{10}\]  
\[q_{r}=A_{r}[(T+273)^{4}-(T_{\mathbf{a}}+273)^{4}] \tag{11}\]  
\[R(T)=R_{r}[1+\alpha_{l}(T-T_{d})] \tag{12}\]  
where \(R\) is the unit resistance of transmission line; \(q_{s}  
convective heat loss and radiated heat loss, respectively; \(\alpha\) is the temperature coefficient of resistance; \(T_{d}\) is the reference temperature; \(R_{r}\) is the rated resistance at temperature \(T_{d}\).