esa-sceva/satcom-chunk-collection huggingface dataset:
_applied_  
_sciences_  
_Article_  
**Time-Varying Wind-Resistance Global Reliability Analysis of In-Service Transmission Tower Using High-Order Moments-Based Improved Maximum Entropy Method**  
**[PERSON] 1, [PERSON] 2,3,*\(\copyright\), [PERSON] 1 and [PERSON] 1,4**  
1 School of Civil Engineering, Chongqing University, Chongqing 400045, China  
2 School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150040, China  
3 Chongqing Research Institute of Harbin Institute of Technology, Harbin Institute of Technology, Chongqing 401151, China  
4 Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University), Ministry of Education, Chongqing 400045, China  
*Correspondence: [EMAIL_ADDRESS]; Tel.:+86-15123044500  
**Abstract:** The transmission tower is an important infrastructure for transmission lines. To secure the operation of the power grid, it is particularly important to evaluate the safety of the in-service transmission tower under the action of random wind loads throughout their entire life cycle. Thus, this paper firstly establishes the time-varying equivalent performance function of the in-service transmission tower under the action of random wind loads. Then, in order to address the shortcomings of the traditional maximum entropy method, the high-order moments-based improved maximum entropy method (HM-IMEM) is proposed and extended to assess the wind resistance global reliability of the in-service transmission tower. Finally, the effectiveness of the proposed method is demonstrated evaluating the wind resistance global reliability of an in-service transmission tower in an engineering setting. Analytic results indicate that: (1) The proposed method can ensure a balance between calculation accuracy and efficiency. Compared with Monte Carlo simulation (MCS) method, the relative error is only 0.11% and the computational cost is much lower than that of the MCS method. (2) The reliability of the in-service transmission tower significantly decreased over time. In order to guide maintenance and reinforcement by predicting the time-varying performance of in-service transmission towers, it is of great engineering value to evaluate the wind resistance global reliability of the in-service transmission tower.  
**Keywords:** in-service transmission tower; wind resistance global reliability; time-varying system; HM-IMEM; time-varying equivalent performance function
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 4 Simulation and Numerical Results
### Bit Error Rate of Normalized Symbol Joint Decision
Figure 11 shows that when the demodulator uses the normalized symbol joint decision, the bit error rate is a function of the signal to noise ratio and the sub-path gain. Figure 9: Bit error rate comparison of normalized symbol joint decision and symbol joint decision after fixed main path gain. Figure 12 indicates that the BER is a function of SNR and sub-path gain as independent variables when the demodulator uses the symbol joint decision. Figure 11: BER for normalized symbol joint decision as a function of SNR and sub-path gain. Figure 10: Bit error rate comparison of normalized symbol joint decision and symbol joint decision after fixed sub-path gain. Comparing Figure 11 with Figure 12, considering the influence of signal-to-noise ratio and multipath gain, it can be seen that the normalized symbol joint decision proposed in this paper is superior to the symbol joint decision in reducing the bit error rate.
# Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach
## 5 Scenario and Simulation Report  
Simulation tools are essential to determine whether it would be feasible to use one or more LoRa gateway, and one or more 4G/LTE base stations, to efficiently address all nodes in an off-grid area. To study our network performance, we built two different simulation scenarios, an indoor LPWAN, and an outdoor SD-WAN approach.
## 3 Key Technology in Multiphase Vortex-Induced Vibration Detection
### Flow-Induced Vibration Signal Processing Algorithm  
In the actual industrial field environment, the data capture of a vibration signal is subject to high-frequency interference in the field environment, and the data, after interference removal and signal separation processing, still contain a large number of irrelevant signals. The fluid-induced vibration signal has a low energy and a high-frequency distortion nonlinear vibration component linked to the vortex generation process's critical state transition. These interference factors bring great difficulties to the design of a distortion detection and recognition algorithm for vortex-induced vibration signals. As a result, simple or single signal identification methods (such as FFT, cepstrum, etc.) are insufficient to meet the requirements, and it is necessary to optimize and integrate fluid-induced vibration signals according to their characteristics in order to ensure system detection effectiveness [119]. [PERSON] et al. [120] used the hidden Markov model to identify steel flow vibration signals and obtained beneficial results in field experiments. [PERSON] et al. [121] adopted the discrete wavelet transform to detect the vibration signals in the casting process, which could identify the typical pouring state and achieved good results. In recent years, [PERSON] et al. [1] carried out a preliminary study on the fluid-solid coupling vibration characters of multiphase vortices and analyzed the distortion features of vibration signals with time spectrum and cepstrum. In the critical penetration condition, especially in large flows, the vibration signal contains a nonlinear pulse component due to multiphase coupling, which is represented in an apparent step growing and reducing that is characteristic of the vibration wave. The discrete wavelet transform method is used to identify the signal distortion feature; however, it has to be enhanced in terms of recognition stability and judgment reaction time.  
The last link in the vortex-induced vibration detection technology is detecting and identifying vibration signals, which is directly connected to the efficacy of the vibration detection system. The information presented above is undoubtedly beneficial. However, the success of the vibration signal processing algorithm is primarily determined by an examination of the physical development of the multiphase vortex and the flow-induced vibration detection technique. The key concept will be to find a suitable signal processing technique to detect the critical transition stage of the multiphase vortex based on the relevant study results of the multiphase vortex formation mechanism and flow-induced vibration features.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes  
In the slice node provisioning phase, a physical node for hosting a slice node needs to be carefully selected in order to meet its required CPU capacity and provisioned location requirement. Many studies in the VNE research area only consider the local resource attributes of a network node such as its CPU capacity and its adjacent link bandwidth. These works map virtual network nodes according to node ranking results based on the local resource. However, these studies do not consider global resource attributes. Moreover, the topology properties of nodes also affect the evaluation of the importance of nodes, such as degree centrality, betweenness centrality, and closeness centrality [29]. Only considering local resource attributes cannot accurately rank nodes. Therefore, we take into consideration both the local and the global resource attributes and topology attributes to comprehensively evaluate the importance of nodes.
## 3 Periodic Wireless Sensor Network Model  
In this section, we introduce the network model, energy profile of a network node and network topology model. The notation used in the following sections is summarized in Table 1.  
We consider a wireless ad-hoc network consisting of a set of \(N\) sensors \(v_{i}\) (\(i\in\{1,\ldots,N\}\)) monitoring a selected environmental parameter at a uniform, constant sensor data reading rate and one base station (gateway) \(v_{0}\) (\(i=0\)) whose purpose is to collect measurement data.  
The running time of the sensory network was divided into a series of cyclically repeated time frames \(F=\{0,1,\ldots,T\},F^{\prime},F^{\prime\prime},\ldots\)--each of length \(T\) of consecutive time slots \(t\). The initial state \(t=0\) for the next frame \(F^{\prime}\) is the final state \(t=T\) of the previous frame \(F\). For simplicity, the collected and transmitted data are divided into equally sized packets. Each of these data packets can be transmitted over the network between two directly neighboring nodes \(v_{i}\) and \(v_{j}\) during one unit of time \(t=F\). Each node in the network has a data buffer that can store up to \(Q_{i}\) packets of data at a time. The base station's data buffer size is assumed to be unlimited \(Q_{0}=\infty\).  
Each time unit \(t\in F\) was assigned a variable \(q^{i}_{t}\) denoting the number of data packets in the buffer of node \(v_{i}\) at time \(t\in F\). It was also assumed that at the initial time \(t=0\), at least one packet of monitored data coming from the sensor (\(q^{i}_{0}>0\)) enters the buffer of each node \(v_{i}\).
## 3 Mathematical Model
### Moment Generated from Static Yawing Motion  
The moment due to the static yawing motion was also considered, and examined by rotating the model at a constant yaw rate (\(r\)) using a turning motor. The moment measured at different yaw rates is shown in Figure 11a. The results demonstrated that the moments were proportional to the square of the yaw rate. This suggests that we can obtain the yaw moment coefficient (\(N_{H}\left(0,0,r\right)\)) of constant value at any yaw rate. The yaw moment coefficient was obtained as in Equation (8). Figure 11b shows the relationship between the yaw moment coefficient and the yaw rate relationship. We can see that the coefficients were irrelevant to the yaw rate and matched well at roughly 0.1.  
\[N_{H\left(0,\ 0,\ r\right)}^{\prime}=\frac{N_{H\left(0,0,r\right)}}{\frac{1}{2} \rho r^{2}L^{4}d} \tag{8}\]
## 4 System Model and Problem Formulation
### Energy Model  
The energy model employed in the proposed work is the same as that presented in [59]. Mathematically, the energy dissipation of the network model can be represented using Equations (3) and (4). Equation (4) represents the energy consumption required to receive a \(k\)-bit data package.  
\[E_{tx}(k,\ d)=\begin{cases}k\left(E_{dec}+\varepsilon_{fs}d^{2}\right),\ if\ d<d_{0}\\ k\left(E_{dec}+\varepsilon_{mp}d^{4}\right),\ if\ d\geq d_{0}\end{cases} \tag{3}\]  
\[E_{rx}(k)=k\ast E_{dec} \tag{4}\]where \(k\) denotes the size of data packets in bits; \(d\) denotes the communication range between the transmitter and receiver; \(E_{tx}\) and \(E_{rx}\) denote the total energy spent on data transmission and reception, respectively; \(E_{elce}\) is the radio electronics energy used by a sensor to transmit and receive a message; \(\varepsilon_{mp}\) denotes the transmission amplifier energy in free space; \(\varepsilon_{fs}\) denotes the factor of a multipath fading channel model; and \(r_{0}\) denotes the threshold value and can be computed using Equation (5):  
\[r_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{5}\]
## 5 Conclusions  
We have investigated a novel estimation method to optimize the position of a relay station of the S-band telemetry system in outdoor environments. The optimal relay station position was then determined using the estimation method by comparing the path losses from the rocket to the main station and some candidate positions of the relay station. The path loss at the four candidate positions had different rates of increase along the rocket altitude, and thus, it intersected with the path of the main station at different rocket height. Therefore, once the rocket altitude that the relay station would receive was determined, the optimal position of the relay station could also be decided. To verify the result, the received power at the optimum position along the rocket altitude was examined by comparing it with the gain of the transmitting antenna. From the results, it could be understood that the slow envelope variation of the received power arises from the antenna radiation pattern in the LoS direction, and the fast fluctuation of the received power came from the phase difference between the direct and reflected rays. The proposed method can also be applied to the cell planning applications in mobile communications because mobile communications require many repeaters at high heights.  
Figure 10: Received power and the phases difference between the direct and reflected rays.  
**Author Contributions:** Conceptualization, D.J., S.W., C.W.K., and H.C.; methodology, D.J. and S.W.; software, D.J.; validation, D.J., S.W., C.W.K., Y.B.P., and H.C.; formal analysis, D.J. and S.W.; investigation, D.J. and S.W.; writing--original draft preparation, D.J. and S.W.; writing--review and editing, D.J., Y.B.P., and H.C.; visualization, D.J.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Blending Ensemble Learning-Based Landslide Susceptibility Mapping and Risk Assessment
### Landslide Susceptibility Mapping and Risk Assessment Based on Weighted Information Entropy  
#### 4.4.1 Calculation of Information Values of Predisposing Factors  
The information value method is an important approach for landslide susceptibility analysis. By means of statistical methods, the information model is adopted to compute the information values of different subcategories of each predisposing factor, representing the extent to which they contribute to the occurrence of landslide. Initially, the information provided by each predisposing factor Xi for the occurrence of landslide events (L) is calculated separately as information i (Xi), using sample frequency calculation to determine the information value. The specific calculation formula is as follows:  
\[i(L,x_{i})=\ln\frac{x_{i}\times S}{S_{i}\times X} \tag{10}\]  
Specifically, Xi denotes the number of disaster points at level i, S denotes the total area of the grid, Si represents the area of the grid at level i, and X represents the total number of landslide points in the study area. According to Formula (10), the category-specific information entropy of each predisposing factor was computed individually. The final weighted landslide susceptibility index (\(S_{\omega}\)) can be expressed as follows:  
\[S_{\omega}=\sum\omega_{i}\cdot i\ (L,x_{i})=\sum\left[\omega_{i}\cdot ln\left( \frac{x_{i}\times S}{S_{i}\times X}\right)\right] \tag{11}\]  
Among them, \(\omega_{i}\) is the importance coefficient of the i-th evaluation factor (such as slope, road distance, etc.), which must satisfy \(\sum\omega_{i}=\)1; i(L,x\({}_{i}\)) is the original unweighted information value, and its calculation method remains consistent with the previous approach. \(S\omega\) is the weighted superposition landslide susceptibility index, reflecting the relative importance of different factors in contributing to landslides.
## 3 Case Analysis
### Analysis of Results
In hard rock formations, the perception of drilling parameters is more significant and its experimental results are highly accurate, while its perception of drilling parameters is weakened in soft rock formations, which can affect the experimental results. Finally, in terms of test parameter acquisition, the process of this method test (the process of deriving the results of surrounding rock classification) is simpler than the traditional method, which requires a large number of calculation parameters, and we list below the calculation equations for the various methods, as well as the calculation parameters required for this method, which require a lot of time in the field to obtain; the parameters required for the method proposed in this paper only require the drilling parameters to be derived from the digital drilling rig system, and then the rock classification can be carried out. Combining these three points we conclude that the method proposed in this paper has advantages over traditional methods. Traditional method Q method calculation formula [36]:  
\[Q= \frac{RQD}{J_{n}}\times\frac{J_{r}}{J_{a}}\times\frac{J_{w}}{SRF} \tag{9}\]  
Parameters to be obtained for the Q method: \(RQD\) for rock quality index, \(J_{n}\) for number of joint groups, \(J_{r}\) for joint roughness coefficient, \(J_{a}\) for joint alteration impact factor, \(J_{w}\) for joint water discount factor, and \(SRF\) for stress discount factor. Traditional method RMR method calculation formula [37]:  
\[RMR=\left(A_{1}+A_{2}+A_{3}+A_{4}+A_{5}\right)+B \tag{10}\]  
Parameters to be obtained for the RMR method: \(A_{1}\) for rock strength, \(A_{2}\) for RQD values, \(A_{3}\) for joint spacing, \(A_{4}\) for discontinuous structural face characteristics, \(A_{5}\) for groundwater conditions, and \(B\) for the joint orientation correction factor. Traditional method BQ method calculation formula [38]:  
\[BQ=90+3R_{c}+250\;K_{v} \tag{11}\]  
Parameters to be obtained for the BQ method: \(R_{c}\) represents the uniaxial saturation compressive strength of the rock, and \(K_{v}\) represents the rock integrity index. Formula for calculating \(R_{c}\): \(R_{c}=22.82\beta_{(50)}^{0.75};I_{s(50)}\) represents the measured rock point load strength index. Formula for calculating \(K_{v}\): \(K_{v}=\left(V_{vm}/V_{pr}\right)^{2}\); \(V_{pm}\) represents the elastic longitudinal wave velocity of the rock mass; \(V_{pr}\) represents the elastic longitudinal wave velocity of the rock. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **PSO-LSSVM** & **Q Method** & **RMR Method** & **BQ Method** \\ \hline
1 & IV4 (3.98) & IV4 (0.28) & IV4 (33.16) & IV4 (335.33) \\
2 & III3 (3.02) & III3 (5.31) & III3 (59.01) & III3 (359.39) \\
3 & III3 (3.26) & III3 (4.95) & III3 (58.3) & III3 (360.26) \\
4 & IV4 (3.98) & IV4 (0.46) & IV4 (37.76) & IV4 (330.89) \\
5 & IV4 (3.98) & IV4 (0.73) & III3 (40.79) & IV4 (333.95) \\ \hline \end{tabular}
\end{table} 
Table 3: Prediction results of PSO-LSSVM compared with traditional methods.
## 3 Results and Discussion
### Cyclic Response  
The main purpose of this part of the research was to find out the response of material to various cyclic loading schemes. According to the author's knowledge, the cyclic response of this material was not thoroughly investigated and there is a need to calculate the unloading and reloading response of the material. Figure 5A,B show the results obtained from the single and three unloading/reloading cycles, respectively. It can be seen that the geopolymer specimens reinforced with PVA fibers under three unloading/reloading cyclic compressions exhibited analogous unloading and reloading responses with little differences. However, when the specimens were subjected to a single unloading/reloading cyclic compression, the unloading and reloading response difference was slightly more obvious [28]. The specimen GP0 (with no fiber) gained the lower peak load values and lost its integrity after reaching the displacement level of 2 mm in both the cases, as compared to the other specimens reinforced with PVA fibers. The specimen GP5 considerably showed a better response under both cyclic loading schemes, as compared to all the other specimens. The specimen GP5 attained peak loads of 21.32 KN and 27.80 KN for single and triple cyclic loading, respectively, showing an increase of 51.85% and 78.1%, respectively, in comparison with the GP0 specimen. It was also noticed that when specimens reached an onset of displacement level, the parabolic unloading occurred. The parabolic portion showed the permanent deformation that happened during the softening of the material in compression. When the parabolic unloading reached a desired target load, the reloading of the specimen took place, which was also parabolic until reached a displacement level where unloading was started. After passing the peak load, the specimen persistently bore the strength reduction along the hysteretic curve. The failure observed in the PVA fibers induced geopolymers after reaching the peak load was a gradual process; at every step of the failure, the axial load was declined. The failure modes of GP5 and GP0 specimens are shown in Figure 6. It can be seen that the GP0 specimen was crushed into pieces, while the GP5 specimen bore debonding of fibers and spalling/cracking of concrete surfaces.  
Figure 5: Uniaxial cyclic compression: (**A**) Single unloading/reloading cycle (**B**) three unloading/ reloading cycles.  
Figure 6: Failure modes of GP0 and GP5 specimens.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### MADC Types
#### 4.3.1 Measure  
The security measure is essential in CPS security and mainly includes misconfiguration detection, botnet discovery, and vulnerability analysis for apps, firmware, or ICSs. Security researchers have conducted large-scale measurements of existing security risks and potential program vulnerabilities in the CPS ecosystem. All of these studies help us to improve the security of CPS.  
Security configuration mistakes are typical but cause severe security threats. The common situations are the misuse of cryptographic functions [100], hidden commands (backdoor) [112], and the lack of common exploit mitigation (e.g., ESP, ASLR, and stack canaries) [135]. Botnets have become one of the greatest threats to CPS security. Researchers have surveyed powerful botnets, such as Mirai [116] and Hajime [117]. They have also proposed analytical models to study the propagation mechanisms of botnets [118] and malicious behaviors on an internet scale [119]. Of these analyses, realistic honeypot [120] and efficient traffic analyses [121] are very useful. Interestingly, in [84], the authors leveraged a white botnet to recover compromised devices, which is a novel but efficient solution.