esa-sceva/satcom-chunk-collection huggingface dataset:
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Fuzzy Logic-Based Safe Energy Level Determination
#### 4.4.2 Fuzzification  
With the two variables \(L_{r}\) and \(E_{min}\), we denote the output as the value of \(\theta\). Each input is mapped into three linguistic variables that are _low, medium_, and _high_. Meanwhile, the output is mapped into five ones, namely _very low, low, medium_, and _high_. We leverage the triangular and trapezoidal fuzzy numbers, whose formulas are given below:  
\[Triangular(x,a,b,c)=\left\{\begin{array}{ll}0&,\quad x\leq a\\ \frac{x-a}{b-a}&,\quad a\leq x\leq b\\ \frac{c-x}{b-b}&,\quad b\leq x\leq c\\ 0&,\quad c\leq x\end{array}\right. \tag{7}\]  
\[Trapezoidal(x,a,b,c,d)=\left\{\begin{array}{ll}0&,\quad x\leq a\\ \frac{x-a}{b-a}&,\quad a\leq x\leq b\\ 1&,\quad b\leq x\leq c,\\ \frac{d-x}{d-c}&,\quad c\leq x\leq d\\ 0&,\quad d\leq x\end{array}\right. \tag{8}\]  
where \(x\) is the crisp input, and \(a,b,c,d\) are membership function ranges of the fuzzy variables. The values of \(a,b,c,d\) are represented in Tables 2 and 3. Figures 5 and 6 depict the Fuzzy membership functions of the input and the output variables, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variable** & **Linguistic Value** & **Membership Function** \\ \hline \multirow{3}{*}{\(L_{r}\)} & \(L\) & \([0,0,2,6]\) \\ \cline{2-3}  & \(M\) & \([2,6,10]\) \\ \cline{2-3}  & \(H\) & \([6,10,\infty,\infty]\) \\ \hline \multirow{3}{*}{\(E_{min}\)} & \(L\) & \(\left[0,0,\frac{1}{4}E_{thr},\frac{2}{4}E_{thr}\right]\) \\ \cline{2-3}  & \(M\) & \(\left[\frac{1}{4}E_{thr},\frac{2}{4}E_{thr},\frac{3}{4}E_{thr}\right]\) \\ \cline{2-3}  & \(H\) & \(\left[\frac{2}{4}E_{thr},\frac{3}{4}E_{thr},E_{thr}\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input variables with their linguistic values and corresponding membership function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Output Variable** & **Linguistic Value** & **Membership Function** \\ \hline \multirow{3}{*}{\(\theta\)} & _VL_ & \([0,0,\frac{1}{3}\delta]\) \\ \cline{2-3}  & \(L\) & \([0,\frac{1}{3}\delta,\frac{2}{3}\delta]\) \\ \cline{2-3}  & \(M\) & \(\left[\frac{1}{3}\delta,\frac{2}{3}\delta,\delta\right]\) \\ \cline{2-3}  & H & \(\left[\frac{2}{3}\delta,\delta,\delta\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Output variable with its linguistic values and membership function, \(\delta=\left(0.1-2\frac{E_{0}}{E_{max}}\right)\).
## 7 Maintenance and Evolution  
Domain engineering is an iterative process. As new requirements emerge or feedback from deployed systems is received, core assets and variability models are updated to remain relevant and aligned with the product line's goals. This ensures the product line evolves dynamically with changing data, technology, and operational needs.
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 4 Analysis of the Influence Mechanism on the Bearing Performance of Yielding U-Shaped Steel Support
### Analysis of the Influence Mechanism on Bearing Performance
#### 4.2.1 Influence Analysis of Load Forms
The main reasons for this are as follows: When the horizontal load borne by the support is greater than the vertical load, the maximum axial force is located at the middle shoulder position of the top and the maximum bending moment is located at the bottom foot. When the horizontal load borne by the support is less than the vertical load, the maximum axial force is located in the middle of the straight leg and the maximum bending moment is located at the support shoulder position. When the support bears an asymmetric loads, both the maximum axial force and the maximum bending moment are distributed at the bottom foot position on the side with greater pressure. When the support bears a uniform load, the maximum axial force is located at the bottom foot and the maximum bending moment is at the support shoulder position. 3. Analysis of Bearing Capacity-Displacement  
Taking the middle of the straight leg in the A54-H\({}_{1}\) scheme and the middle of the top in the A54-H\({}_{2}\), A54-H\({}_{3}\), A54-H\({}_{4}\), and A54-H\({}_{5}\) schemes, which are the dangerous deformation cross-sections, as examples, the relationship between the stress form of the support and the bearing capacity is analyzed. The bearing capacity-displacement relationship curves of the support are shown in Figure 15.  
Figure 14: Distribution of axial forces and bending moment under different load forms. It can be known through the analysis of the bearing capacity-displacement curves of the support that:  
The support failure has gone through three stages: linear elasticity (OA), plasticity (AB), and yield (BC). In the OA stage, the displacement of the support has a linear relationship with the load, the support deforms uniformly and slowly with the increase in the load, the support enters the plastic deformation stage when it reaches point A, and the support begins to yield when it reaches point B; meanwhile, the load rising speed is greatly reduced and the deformation speed of the support is rapidly accelerated. Among them, the maximum bearing capacity of the support in the A54-H1, A54-H2, A54-H3, A54-H4, and A54-H5 schemes is 559.6 kN, 326.4 kN, 11.1 kN, 213.9 kN, and 854.4 kN, respectively. Among them, the size and sectional mode of the U-shaped steel support in scheme A54-H1 and the laboratory test are the same, and the difference rate of the bearing capacity between the two is 8.5%, while the difference rate is mainly due to the difference in loading forms, which verifies the correctness of the numerical model. In conclusion, under the action of a uniform load, the supports have the highest bearing capacity, which is 16.97 times and 3.99 times that of the support under a concentrated load and an asymmetric horizontal load, respectively, and 1.47 times and 2.62 times that of the support under a symmetric load, respectively.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
###### Abstract  
The intelligent reflecting surface (IRS) is a cutting-edge technology for cost-effectively achieving future spectrum- and energy-efficient wireless communication. In particular, an IRS comprises many low-cost passive devices that can independently reflect the incident signal with a configurable phase shift to produce three-dimensional (3D) passive beamforming without transmitting Radio-Frequency (RF) chains. Thus, the IRS can be utilized to greatly improve wireless channel conditions and increase the dependability of communication systems. This article proposes a scheme for an IRS-equipped GEO satellite signal with proper channel modeling and system characterization. Gabor filter networks (GFNs) are jointly proposed for the extraction of distinct features and the classification of these features. Hybrid optimal functions are used to solve the estimated classification problem, and a simulation setup was designed along with proper channel modeling. The experimental results show that the proposed IRS-based methodology provides higher classification accuracy than the benchmark without the IRS methodology.  
m _Article_  
Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals.  
**Keywords:** modulation classification; Gabor filter network; intelligent reflecting surface; GEO satellite signals; global and local search methods +
Footnote †: journal:
## 3 Our Approach
### Dynamic Attestation Based on the Node's Risk Level
```
1:procedureChallengeGeneration(\(R_{i}\), \(\varphi\), \(\beta\))
2:for all\(i\in[1,N]\)do
3: Generate attestation interval \(T_{i}^{p}=\frac{R_{i}^{p}+\varphi}{R^{p}+\varphi}\cdot T\cdot\beta\)
4: Generate attestation time \(t_{i}=\sum\limits_{j=1}^{p-1}T_{i}^{j}+t_{random},\qquad t_{random}\in[0,T_{ i}^{p}]\)
5:\(Challenge:key=hash(T_{Verifier})\)
6: Attached with \(RC4\) seed \(m=\{key\|W_{k}\}\)
7: Send challenge message \(Verifier\to n:request=\{m\|MAC_{n}(m)\}\)
8: Record sent time \(Ts_{Verifier}=Tcurrent_{Verifier}\)
9:endfor
10:endprocedure
```  
**Algorithm 1** The verifier process (Part I). ```
11:procedureChecksumVerification(\(response\), \(\Delta T_{i}\), \(Ts_{Verifier}\))
12: Record received time \(Tr_{Verifier}=Tcurrent_{Verifier}\)
13:if\(MAC_{n}(C)=MAC_{n}^{\prime}(C)\)then
14: Generate another checksum \(C^{\prime}=\{C_{1}^{\prime}\|C_{2}^{\prime}\|\cdots\|C_{k}^{\prime}\}\)
15:if\(C=C^{\prime}\)then
16:\(\Delta T_{Verifier}=Tr_{Verifier}-Ts_{Verifier}\)
17: Delay on each hop \(\left\{ \begin{array}{l}D_{1}=\frac{\Delta T_{1}-\Delta T_{Verifier}}{2}, \\ D_{i}=\frac{\Delta T_{1}-\Delta T_{i-1}}{2}\qquad(1<i\leq n-1)\end{array} \right.\)
18: Get average delay \(\bar{d}=\frac{1}{n^{\prime}}\sum\limits_{i=1}^{n^{\prime}}D_{i},\qquad(|\)\(D_{i}-\bar{D}\)\(|\leq 3s)\)
19:\(T_{checksum}^{\prime}=\Delta T_{Verifier}-n\cdot\bar{d}\)
20: Bayesian classifier decision \(R(c|\mathbf{x})=\min_{i=1,2}R(c_{i}|\mathbf{x})\)
21:else
22: Checksum Inconsistency Error
23:endif
24:else
25: MAC Inconsistency Error
26:endif
27:endprocedure
```  
**Algorithm 2** The verifier process (Part II). ```
1:procedureRelayNodeForwarding(\(ReceivedPacket\))
2:if\(ReceivedPacket\) =\(request\)then
3: Forward packet \(i\to i+1:request\)
4: Record forwarding time \(Ts_{i}=Tcurrent_{i}\)
5:else
6: Record forwarding time \(Tr_{i}=Tcurrent_{i}\)
7: Forward packet \(i\to i-1:response\)
8: Get time difference \(\Delta T_{i}=Tr_{i}-Ts_{i}\)
9: Generate report message \(m=\{\Delta T_{i}\|MAC_{i}(\Delta T_{i})\)
10:endif
11:endprocedure
```  
**Algorithm 3** The relay node process. ```
1:procedureRemoteDeviceChecksum(\(key\), \(W\))
2: Set \(j=1\)
3:while\(j\leq k\)do
4:\(A_{j}=RC4(key,W_{j})\)
5:\(C_{j}\gets Mem[A_{j}]\oplus C_{j-1}+RC4_{j}\)
6:\(C_{j}\gets rotate\ left\ one\ bit(C_{j-1})\)
7:\(j\gets j+1\)
8:endwhile
9: Generate checksum \(C=\{C_{1}\|C_{2}\|\cdots\|C_{k}\}\)
10: Generate and send response message \(response=\{C\|MAC_{n}(C)\}\)
11:endprocedure
```  
**Algorithm 4** The remote device checksum generation process.
## 2 Overview of Internet of Things Applications
### Internet of Things and Next Generation Networks  
The current 4G/LTE networks in comparison with the previous generation of networks is designed using Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) technology to specifically offer higher data transmission rates in addition to enhanced system capacity due to the high demands for the sparely available radio frequency spectrum. As earlier mentioned, it is estimated that the number of internet-connected devices will exceed 50 billion by 2020. Hence, it is required that contemporary network technologies with the capability of offering higher data rate applications, ultra-low/reliable end-to-end latency, very high reliability/scalability, improved security features in addition to low power consumption capacity be developed so as to enhance the quality of service (QoS) delivery in addition to the quality of experience (QoE) requirements of network users. As such, next-generation (xG) networks are carefully designed to meet the above-mentioned fundamental requirements. It is important to mention that the emerging xG network is the 5G network of technologies which are designed to offer about 10-100 times higher data rates in addition to lower energy consumptions as compared to the current LTE-based networks. Consequently, about 10 enabling technologies were proposed as promising air interface for realizing xG networks [33]. These technologies include SDN, NFV, massive Multiple Input Multiple Output (MIMO), radio access techniques, mobile cloud computing, millimetre-wave communication, IoT, Device-to-Device (D2D) communications, green communications and ultra-densification. 5G networks generally possess the capabilities of managing and supporting the high demanding requirements of D2D-based IoT communication applications by integrating numerous heterogeneous access technologies for seamless connectivity of devices/objects with the internet. Refs. [34; 35; 36; 37] provide a comprehensive review of architecture, business models, technology drivers, and applications of IoT for xG networks.
## 6 Conclusions  
1. Field investigation and drilling exploration were carried out for the mining roadway of ZF3806's working face, and rock mechanics experiments were carried out. The analysis results show the coal and rock masses have significant fragmentation, the roof subsidence is severe, and the support failures are more frequent. During mining, accidents, such as the roof falling and anchor bolt failure, occur, and there are significant potential safety hazards.
2. The layered grouting reinforcement method of \"shallow bolt grouting + deep cable grouting\" is proposed, and the bending moment equation of roof rock beam is established. Through layered grouting, the strength of the surrounding rock is increased, and the bending moment of roof rock is reduced so the roadway roof forms a composite beam support structure, and its ability to resist bending deformation is strengthened.  
Figure 14: Effect of grouting support.  
Figure 13: Distance variation curve of ZF3806 roof and floor of mining roadway.  
3. The layered grouting of \"shallow bolt grouting + deep cable grouting\" is adopted to reinforce the support, and the field application effect is significant. According to the field-monitoring results, the stability of the roadway is improved, the roof displacement is reduced, and the integrity is better. The displacement of both sides of the roadway is within the controllable range, which ensures safe mining and reduces the later maintenance cost of the roadway.
4. The research of this paper is mainly field oriented. This paper mainly studies the rock mechanics failure characteristics of the roadway roof and the mechanism of grouting reinforcement support and carries out field application. Due to the influence of objective factors, such as experimental conditions, further research will be carried out on the performance of grouting materials and the comparison before and after grouting in broken rock mass.  
Writing--original draft preparation, Z.L.; data curation, Z.L. and T.F.; investigation, Z.L. and T.F.; formal analysis, Z.L.; funding acquisition, Z.L. and T.F.; methodology, Z.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 (51974117, 52174110) and the Natural Science Foundation of Hunan Province (2020)J4027).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding authors upon request.  
The authors are thankful for the support and help of [PERSON] from Shanxi Binxian Shuilandong Coal Mine Co., Ltd.  
The authors declare no conflict of interest.
## 2 Related Works  
### Related Internet Traffic Classification Systems  
This section discusses some of the previous Internet traffic classification work. Several research papers [9; 15; 16] have considered the ML classifier, which was used to classify datasets in different ways, such as packet traffic features, flow traffic features, statistical packet features, etc. made a comparison between five ML algorithms (MLP, RBF, C 4.5, Bayes Net and Naive Bayes). The authors developed a real Internet traffic dataset which included seven applications: web, e-mail, web media, P2P, FTP data, instant messaging and VoIP. In their work, they used Wireshark as the capturing tool. The results showed that, in the case of a full features dataset, Bayes Net classifier provides the best accuracy at 85.33%; when the authors applied the approach of reduced features, C4.5 provided the highest accuracy at 93.66%.  
In [17] the authors proposed LASER (Longest Common Subsequence (LCS)-based Application Signature ExtRaction) technique to classify P2P traffic. The proposed method is a hybrid algorithm which investigate packet header to extract a specific signature from the payload information. Four of P2P application (Storm, Waledac, BitTorrent, eDonkey) was used to be classified by this classifier which is shows accepted accuracy results (more than 91%).  
In [18], based on the analysis of P2P traffic classification technologies, a combination of packet-level classifier and flow level classifier was proposed. The first level was a deep packet inspection-based classifier which works at the packet level to identify the specific P2P traffic. The second step was a machine learning approach, which classified the remaining unknown P2P traffic at the flow level.  
[PERSON] et al. in [14] Proposed traffic classification method using payload size sequence signature (PSS). The proposed classifier generates unique sequence PSS for each internet application from the first N packets of the flow. The v and groups PSS vectors on the basis of the similarity between PSS vectors to identify flow patterns of the application. The proposed methods were evaluated by identifying some internet applications such as Skype, GomTV, PuTTY and other such applications.  
The authors in [19] proposed an algorithm (named Skype-Hunter) to identify Skype traffic. The proposed algorithm relied on both signature-based and statistical traffic features. The experimental part of this work considered different scenarios, such as: (1) no restrictions on the transport protocols; which means using the direct connection between the Skype clients; (2) the presence of a NAT IP; this means using the IP network address translator, which is a router function that can be configured to allow the addresses of a stub-domain to be reused by any other stub-domain; (3) the presence of a firewall, which does not allow the use of UDP.  
The authors of [20] used a hybrid approach to classify network traffic using the SVM and NAIVE Bayes algorithms. The paper used a flow statistical feature to enhance feature discretization.
## 3 Results and Discussion
### Treatment Costs  
Economic efficiency is an important indicator of the feasibility of a technology [35]. In this study, we compared the COD removal, biochemical properties (B/C), and cost analysis of the two oxidation methods after treatment.  
After Fenton and ozone oxidation treatment, the COD in wastewater was significantly reduced and the B/C value reached above 0.3, indicating significant improvement in biochemical properties. It is worth nothing that the COD removal effect and biochemical properties of the Fenton oxidation treated wastewater were better than ozone oxidation (Figure 5).  
The economic value of two methods was compared in terms of cost. For Fenton oxidation, the optimal dosing amounts of H\({}_{2}\)O\({}_{2}\) and Fe\({}^{2+}\) were 500 mg/L and 120 mg /L, respectively, while the cost of Fenton oxidation was approximately 1.77 $/kg\({}_{\text{COD}}\) (30% H\({}_{2}\)O\({}_{2}\):6.71 $/kg; ferrous sulphate heptablydrate: 4.74 $/kg); For ozone oxidation, the ozone rate of 80 mL/min is the optimal condition, and the cost is about 1.96 $/kg\({}_{\text{COD}}\) (liquid oxygen: 149.1 $/t) (Figure 5). However, the ozone oxidation method is limited by the high operating cost of the ozone generator and treatment cost, so the cost of ozone oxidation is relatively higher than the Fenton method. In summary, in terms of the overall COD removal and biochemical analysis, the Fenton method has better economic value of than the ozone oxidation method.
## 3 System Model  
The scenario considered is a 3D UWSN where nodes are deployed at different depths in an underwater cubic region. A channel model for underwater medium is also implemented considering the environment factors of USWNs that include transmission loss, absorption loss, signal to noise ratio, various noises, and energy consumption during a packet transmission and reception. It is also important to consider the energy consumption parameter while designing protocols for UWSN due to the limited energy available at the nodes.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 4 Numerical Simulation
### Comparison and Analysis  
A variety of disturbance factors were applied to the system, including wind disturbance, centroid deviation, rudder effect deviation and drifts error. Then, the ESO and PID control effects were verified.  
The disturbance factors imposed are as follows: the disturbance wind speed is 10 m/s, the centroid deviation is 0.1\({}^{\circ}\)/s, and the rudder effect deviation is 10.  
As can be observed from Figures 14 and 15, under the condition of comprehensive disturbance, the controller proposed in this paper is effective. The influence of disturbance, model deviation and noise is suppressed, and the control effect has no static error. The steady-state performance is obviously better than PID control, and the attitude angle error is smaller than PID control, which significantly improves the missile attitude maneuverability and stability. They satisfy the design requirements of the controller, and further verify the effectiveness of the controller.  
Figure 12: Change curve of roll angle rate under drifts error condition.  
Figure 13: Roll angle response curve under drifts error.  
In order to verify the effectiveness of this algorithm and other adrc algorithms, the system is compared under the same experimental conditions. It can be observed from Figure 16 that compared with other ADRC, the ESO controller proposed in this paper has smaller amplitude of initial disturbance, tends to be stable faster, and has good control performance.  
Figure 14: Comparison of roll angle speed change curve between PID and the controller.  
Figure 15: Comparison of roll angle response curves between PID and the controller.
## 3 Experimental Results
### Bending Moment of Piles
#### 3.4.2 The Analyses of Bearing Ratio  
The bearing ratio (\(v\)) is used to evaluate the soil-pile interaction of the pile foundation, and it was calculated by the following equation:  
\[v=M/M_{0} \tag{2}\]  
\[M_{0}=Fl \tag{3}\]  
where \(M\) is the bending moment calculated by equation 1; \(M_{0}\) is the external moment, neglecting the soil effect and assuming that the pile is a cantilever beam; \(F\) is the lateral force imposed by the actuator; \(l\) is the lever arm length. Table 5 presents the bending moments and the bearing ratios of the CP-1 to CP-5, PC-6 to PC-7, RP-8 and HP-9 specimens at 0.7 m of buried depth for 5, 8, 10 and 15 mm displacement loadings. The force \(F\) was applied at 0.3 m above the soil surface, thus the external lever arm length was 1.0 m.  
As Table 5 indicates, the pile specimens were subjected to the larger external load with the increase of the bearing ratio. However, the bearing ratio of pile decreased after concrete cracked and its surrounding soil bore a larger external load. For the CP-1, CP-4 and PHC-2 to PHC-4 specimens, the bearing ratio reached the maximum value at 8 mm displacement loading and decreased after the concrete cracked. The bearing ratio of the CP-2, RP-8 and PHC-1 specimens reached the maximum value at 10 mm displacement loading, and then it was decreasing. For the CP-3 and CP-5 specimens,  
Figure 16: Maximum moments of the piles at 10 mm loading step.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**5 mm Loading**} & \multicolumn{3}{c}{**8 mm Loading**} & \multicolumn{3}{c}{**10 mm Loading**} & \multicolumn{3}{c}{**15 mm Loading**} \\ \cline{2-13}
**Specimen** & \(M\) & \(M_{0}\) & **(kN-m)** & \(v_{5}\) & \(M\) & \(M_{0}\) & \(  
the bearing ratio slightly decreased at 15 mm displacement loading. The bearing ratio of the PC-6 and PC-7 specimens reached the maximum value at 15 mm displacement loading. It can be concluded that the soil-pile interaction increases with the increase of reinforcement ratio, buried pile depth and prestressing level. Moreover, the bearing ratio of the HP-9 specimen was slightly decreased as the loading increased, indicating that the HP-9 specimen remained in the elastic range and the surrounding soil was subjected to increasing load.