esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
A sub-gallery comprises of a room-and-pillar structure and the remaining ore body for the extraction of minerals, as shown in Figure 1. The channel modeling for both nominated zones is conducted according to their environment and the signal-received power is analyzed by categorizing the LOS zones to inspect the performance of wireless links in complex areas. In summary, the proposed system makes the following contributions:  
Figure 1: Room-and-pillar architecture of coal mines. * This study investigates the influence of several deteriorating factors on a wireless link using the 2.4 GHz to 5 GHz frequency band in underground coal mines. * The characteristics of electromagnetic signal propagation are investigated for complex segments of a mine using line-of-sight (LOS) links. * A hybrid channel-modeling approach is proposed for communication in underground coal mines using a multimode waveguide model and geometrical optic (GO) model. * Performance evaluation of the proposed approach is done using several critical factors such as path loss, received power loss, delay spread, and shadow fading. Overview of Conventional Communication Techniques and Potential Communication Challenges in Underground Mines  
This section briefly describes the conventional communication techniques and potential challenges in underground mines that complicates the development of an efficient channel model for wireless communication.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 4 Verification of the Proposed Method
### Finite Element Model
The objective function takes the form of the residual sum of squares and is defined as Equation (37):  
\[U=\sum_{i=1}^{3}\[PERSON]{(}\frac{c_{i}^{t}-c_{i}^{a}}{c_{i}^{t}}\Biggr{)}^{2}+ \sum_{j=1}^{6}\[PERSON]{(}\frac{f_{j}^{t}-f_{j}^{a}}{f_{j}^{t}}\[PERSON]{)}^{2} \tag{37}\]  
where \(c_{i}^{t}\) is the actual displacement at the midpoint \(C_{i}\) of the \(i\)-th span, \(i=1\), \(2\), \(3\); \(c_{i}^{a}\) is the initial displacement value; \(f_{j}^{t}\) is the actual value of the \(j\)th order natural frequency, \(j=1\), \(2\), \(\ldots\), \(6\); \(f_{j}^{a}\) is the initial value of the natural frequency. The four parameters to be updated were divided into 21 levels in the range of \(-10\%\) to \(10\%\) with \(1\%\) as the step size, and the uniform design table [46] U\({}_{21}\)(\(21^{4}\)) was used for experimental design. The parameter values of the 21 levels were sequentially brought into the initial finite element model, and the corresponding model responses were obtained. Taking 21 sets of parameter test values as input samples and taking the corresponding model responses as output samples, the input and output samples were normalized and used for WNN training.
# Analysis of AC DC Four-Switch Boost-Buck Battery Charger Converter for EV Applications
## 6 Boost-Buck Converter Using Snubber RC Circuit
### State-Space Averaging and Small-Signal Model Analysis in Boost Mode with RC  
This section will discuss the state-space average equations for the converter shown in Figure 10 for the boost mode with the RC-damping circuit, which can be derived and expressed as follows:  
\[\begin{array}{lll}\frac{d\mu_{11}(t)}{dt}&=&\frac{v_{g}(t)+v_{c}(t)\left(d_{ 1}(t)-1\right)}{L_{1}},\\ \frac{d\mu_{12}(t)}{dt}&=&\frac{v_{g}(t)-v_{g}(t)+d_{1}(t)v_{c}(t)}{L_{2}},\\ \frac{dv_{c}(t)}{dt}&=&\frac{(1-d_{1}(t))\mu_{11}(t)-d_{1}(t)\mu_{12}(t)+\frac {v_{c}(t)-v_{g}(t)}{R_{d}}}{C},\\ \frac{dv_{g}(t)}{dt}&=&\frac{i_{12}(t)}{C_{0}}-\frac{v_{g}(t)}{R_{c}C_{0}},\\ \frac{dv_{g}(t)}{dt}&=&\frac{v_{c}(t)-v_{g}(t)}{C_{2}R_{d}}.\end{array} \tag{12}\]  
The DC equilibrium equations can be expressed as follows:  
\[\begin{array}{lll}V_{C}&=&V_{o}=&V_{cd}\;,\\ D_{1}&=&1-\frac{V_{g}}{V_{0}},\\ I_{L1}&=&\frac{D_{1}*V_{g}}{R_{o}*(D_{1}-1)^{2}}\;,\\ I_{L2}&=&-\frac{V_{g}}{R_{o}*(D_{1}-1)}.\end{array} \tag{13}\]  
The two transfer functions, the duty cycle to rectified input current \(G_{i_{g}d-RC_{\text{Do}}}(s)\) and the duty cycle to output voltage \(G_{pd-RC_{\text{Do}}}(s)\), are obtained after linearization around the equilibrium point and will be used for the numerical analysis in the next section.  
Figure 10: The modified boost-buck converter with RC snubber damping.
## 3 Results
### Density  
Figure 5 shows the strains-to-failure for maple samples impacted on the face grain and for ash samples impacted on the edge grain, respectively and Figure 6 shows the failure energy for the same wood species and impact-surfaces, respectively. These two combinations of wood species and impact-surface type are of primary interest because these are the same combinations that are currently prescribed for on-field bat/ball impacts. There is wide scatter among the data points in each of these four figures but this type of scatter is not unusual in wood testing. Despite the wide scatter, some trends and differences can be noted amongst the data for each of the wood species as a function density.  
Linear regressions (as denoted by the solid and dashed black lines) of the Charpy data are included in Figure 5. For the maple sample set (Figure 5, open data points), the strain-to-failure exhibits a much steeper response with respect to density than the slope of the ash data (Figure 5, solid data points). The flatter slope of the ash data suggests that the durability of ash bats is less sensitive to wood density than maple bats. As used in this paper, bat durability is defined as the relative bat/ball impact speed at specific axial location along the bat--the greater the relative bat/ball impact speed, the greater the bat durability [15]. On this basis and in terms of baseball bats, it is important when using a maple wood to maximize the wood density to achieve the best impact properties for the desired profile. For ash bats, the density, while still a concern, is not as essential to the impact properties as the impact face is, with edge grain impact being the preferred impact surface. An analogous interpretation of the failure energy plot (Figure 6) can be made.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Impact Surface**} & **Number** & \multirow{2}{*}{**Strain to Failure**} & \multirow{2}{*}{**Max Deflection in. (cm)**} & \multirow{2}{*}{**Energy ft-lbf (N-m)**} \\  & & **of Samples** & & & \\ \hline Ash & edge & 23 & 0.0265 \(\pm\) 0.002 & 0.148 \(\pm\) 0.0122 (0.376 \(\pm\) 0.0310) & 6.48 \(\pm\) 1.02 (8.79 \(\pm\) 1.38) \\ Ash & face & 12 & 0.0259 \(\pm\) 0.003 & 0.146 \(\pm\) 0.0139 (0.371 \(\pm\) 0.0353) & 7.19 \(\pm\) 1.14 (9.75 \(\pm\) 1.55) \\ Maple & edge & 21 & 0.0236 \(\pm\) 0.002 & 0.133 \(\pm\) 0.0134 (0.338 \(\pm\) 0.0340) & 6.30 \(\pm\) 1.33 (8.54 \(\pm\) 1.80) \\ Maple & face & 33 & 0.0250 \(\pm\) 0.004 & 0.140 \(\pm\) 0.0191 (0.356 \(\pm\) 0.0485) & 6.41 \(\pm\) 1.48 (8.69 \(\pm\) 2.01) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of mean values with one standard deviation for Charpy Impact testing of Major League Baseball (MLB)-quality Ash and Maple.
###### Abstract  
This paper proposes a set of field test technology system for layered settlement of composite strata based on weak reflectivity fiber Bragg grating sensing technology based on the shield project of \"Keyuan Station \(\sim\) Shenzhen University Station\" section of Shenzhen Metro Line 13, and through the comparison and verification of three-dimensional numerical simulation and field monitoring, the law and distribution characteristics of disturbance settlement of ground surface and overlying strata during shield tunneling are systematically analyzed, and the vertical and horizontal zoning (layer) system for the spatial and temporal evolution of layered settlement of composite strata during shield tunneling is constructed. On this basis, the targeted settlement control technical measures and recommendations are proposed. The findings show that the weak reflectivity fiber grating sensing technology can better perceive the evolution law and distribution characteristics of vertical and horizontal settlement of composite strata caused by shield tunneling, which is in good agreement with the numerical simulation results, and has the advantages of automation and high precision, it can be used as a supplement and alternative method for traditional measurement methods. The stratum deformation is small and layered settlement is not obvious in shield approaching stage (\(-\)5D\(-\)0), after shield crossing and shield tail falling (0\(-\)3D), the stratum is the longitudinal main deformation zone of shield tunneling disturbance, and the influence range of the whole tunneling disturbance is about (\(-\)1D\(-\)3D). Meanwhile, according to the influence degree of shield tunneling disturbance, the overlying strata of the tunnel can be divided into main disturbance layer and secondary disturbance layer, and the main disturbance layer is located in the range of 0.5D above the tunnel. In addition, based on the different stages of shield tunneling and the vertical and horizontal zoning (layers) of existing structures such as buildings (structures), the settlement control measures and suggestions are proposed. The research results demonstrate the feasibility of weak reflectivity fiber grating for distributed and continuous strata monitoring. It has important guiding value for improving the understanding of settlement law produced from shield construction in composite strata and analyzing and predicting potential risks resulting from shield construction. It also provides reference value for future subway design and construction.  
shield tunnel; composite strata; layered settlement; weak reflectivity fiber grating; numerical simulation; field monitoring
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 1 Introduction
According to the performance evaluation results of this study, the proposed EGR outperforms GEEC, EEREG, QMOR, AB routing, and LEACH-C in terms of energy efficiency, end-to-end delay, packet delivery ratio, cluster formation time, and network lifetime. The proposed EGR exhibits 31.7%, 32.9%, 66.8%, 42.2%, and over 200% less end-to-end delay as compared to GEEC, EEREG, QMOR, AB routing, and LEACH-C, respectively. The contribution of this study can be summarized as follows:  
* The unique and intelligent method of CH election using EG makes the proposed EGR energy-efficient. A node with the highest level of residual energy in each cluster becomes the CH in each round. EG has been used in cluster-based routing protocols for WSNs in the literature; however, the proposed EGR is the first attempt to use EG for routing in WMSNs. * A technique for avoiding data redundancy based on the calculation of the region overlapped by two nearly placed sensors is introduced, which results in a significant increase in the network lifetime. * The proposed EGR makes the delivery of the sensed data faster and more reliable by effectively exploiting the multi-sink environment. * The proposed EGR outperforms the conventional routing protocols in terms of energy efficiency, end-to-end delay, packet delivery ratio, and network lifetime. A lot of symbols have been used in this paper to present different concepts and mathematical formulas. To increase the readability of the paper, we summarize all the symbols in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(S\) & Set of sensor nodes \\ \(K\) & Number of clusters \\ \(H\) & Set of CHs \\ \(M\) & Set of CMs \\ \(T\) & Minimum percentage of the overlapped region for FoV (Threshold) \\ \(\theta\) & Vertex angle created by an isosceles triangle \\ \(C_{s}\) & Congruent side of an isosceles triangle \\ \(\lambda\) & Orientation angle \\ \(\Delta V\) & Advertisement message \\ \(P\) & Set of players \\ \(U\) & Utility function \\ \(NH\) & Set of nodes having higher residual energy \\ \(NL\) & Set of nodes having lower residual energy \\ \(E_{T}\) & Energy threshold \\ \(T_{CH}\) & Strategy for being a CH \\ \(T_{NCH}\) & Strategy for not being a CH \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbols and their meaning. The remainder of this paper is organized as follows: In the following section, the related works are summarized and discussed in brief. In Section 3, the network model and problem statement imposed in this study are presented. In Section 4, the proposed EGR is presented in detail. That is, the cluster formation process, CH election process using EG, data redundancy avoidance algorithm, and routing algorithm are presented. In Section 5, the performance of the proposed EGR is evaluated via extensive simulation and compared with QMOR, AB routing, and LEACH-C protocols. Finally, this study is concluded in Section 6.
## 2 Materials and Methods
### Structural Analysis
#### 2.2.2 Digital Analysis  
Physical lace samples are abstracted as simple graphs and analysed in the FEM plugin Karamba3D (Figure 7) [20]. Each pattern is tested as a 10 x 20 cm sheet with identical cumulative point loads, and the results of the digital analysis are shown in Table 2. A correlation with results of mechanical tests can be observed. However, due to aforementioned issues related to the preparation of physical samples, minor discrepancies can be observed. For instance, _Quadrille Croise_ has the lowest maximum deformation given similar fibre count, which echoes the results of the physical tests, but _Grille_ shows much higher deformation in the digital simulation compared to the physical tests. This is likely due to lack of consideration of fibre-fibre interactions and its subsequent effects on resin impregnation. Fabrication variance and unfavourable support conditions, as discussed in the previous section, could also be a factor. For the purpose of informing the computational design process, the digital analysis results are calibrated based on the physical tests, further described in Section 2.3.2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Pattern**} & **Fibre** & **Weight** & **F Max** & **dL at Fmax** & **Strength** \\  & **(mm)** & **(g)** & **(N)** & **(mm)** & **(mPa)** \\ \hline (a) A la Vierge & 340 & 20 & 360 & 10 & 0.18 \\ (b) Grille * & 320 & 20 & 568 & 8.2 & 0.28 \\ (c) Quadrille Croise & 320 & 20 & 520 & 5.1 & 0.26 \\ (d) Quadrille Simple & 240 & 13 & 251 & 6.7 & 0.13 \\ (e) Toile * & 280 & 22 & 1090 & 18.1 & 0.55 \\ (f) Torchon & 270 & 19 & 489 & 17 & 0.24 \\ (g) Torchon Double & 540 & 34 & 1230 & 8.8 & 0.62 \\ (h) Tulle & 280 & 18 & 234 & 10.6 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of physical compression tests (Patterns marked with an asterisk are not considered for implementation in tailored lace designs.)
# Multi-Issue Negotiation Protocol with Pre-Domain Narrowing
## 4 Negotiation Protocol with Pre-Domain Narrowing
### Naive Agent Strategies for the Negotiation Domain Space Narrowing
#### 4.4.2 Strategy for Narrowing Issues Using the Pre-Negotiation Method  
When narrowing issues using the pre-negotiation method, each issue is negotiated in a domain where the options are \"subject to the main negotiation\", \"Agent A chooses\", and \"Agent B chooses\". An agent needs to define its utility function for this pre-negotiation domain. In a naive strategy, the utility function for pre-negotiation is defined by the utility expected to be finally obtained. Whenever an agent has the right to choose an issue, it can always choose the option that maximizes its utility. In this case, the expected utility is the maximum. Meanwhile, when the other agent has the right to choose, the expected utility is set as the minimum value, under the assumption that the most unfavorable choice will be made. If an issue is to be a subject in the main negotiation, then assuming that the probability that each option is selected follows a uniform distribution, the average is taken to be the expected utility. Based on the above, Equation (4) is the utility function for pre-negotiation.  
\[\begin{split} U_{pre}(\vec{b})=&\sum_{i\in(\text{ Issue chosen by oneself})}\binom{w_{i}\times\max_{j}eval(v_{j}^{i})}{+}\\ &+\sum_{i\in(\text{ Issue chosen by opponent})}\binom{w_{i}\times \min_{j}eval(v_{j}^{j})}{+}\\ &+\sum_{i\in(\text{ Issue to be determined in the main negotiation})}\binom{w_{i}\times\frac{1}{k_{i}}\sum_{j=1}^{k_{i}}eval(v_{j}^{j})}{\Biggr{)}}\end{split} \tag{4}\]  
In pre-negotiation, the concession strategy is achieved using a concession function wherein the target utility decreases with time. Equation (5) shows the relationship between time \(t\) during the pre-negotiation and the target utility.  
\[U_{target}(t)=1-(1-U_{min})\times t \tag{5}\]  
where \(U_{min}\) is a parameter representing the lowest utility to which an agent is willing to make a concession. At the start of pre-negotiations, the target utility is the maximum value of 1. The target value then decreases linearly so that it reaches \(U_{min}\) at the end of the negotiation. At a given time \(t\), an agent accepts an opponent's proposal if the utility is \(U_{target}(t)\) or greater; otherwise, it replies with a counterproposal that has a utility of \(U_{target}(t)\) or greater.
## 5 Results and Discussion
### Discussion
The high accuracies achieved in our study with the CNN demonstrate its effectiveness in this domain. Notably, the CNN model alone consistently outperformed the combined methods (CNN + HOG, CNN + LBP, CNN + saliency) in terms of accuracy, underscoring the robustness of CNNs in the classification of meat and fish products.
## 3 One-Dimensional Seismic Fragility Analysis
### Selection of the Damage Indexes  
According to real seismic damage to actual bridges, the plate rubber bearing and the pier are most likely to be damaged under different ground motions. The plate rubber bearing is usually damaged because of the shear force, while the pier is usually damaged because of the bending moment. Therefore, the displacement \(\Delta\) and the displacement ductility ratio \(\mu\) were chosen as the damage indexes for the plate rubber bearing and the pier, respectively [37]. The four seismic performance limit states of the entire bridge were defined to be slight damage, moderate damage, serious damage, and complete damage, respectively.  
The moment-curvature analysis of the fifteen groups of the pier bottom section was performed by XTRACT to determine the threshold values of the pier under different performance limit states, and the corresponding ductility coefficients were obtained as well. Following the Seismic Retrofitting Manual for Highway Structures [38], the average seismic thresholds of the pier under four performance limit states were obtained based on the calculation formula related to the local displacement ductility capacity of the pier and the value of the pier column displacement ductility ratio, as shown in Table 5.  
The allowable shear strain \(\gamma_{\alpha}\) of the plate rubber bearing can be calculated using Formula (9). In accordance with the provisions of the Japanese Specifications for Seismic Design of Highway Bridges [39], this paper takes 100%, 150%, 200%, and 250% of the allowable shear strain as the threshold value of the plate rubber bearing under the four performance limit states. The corresponding displacement thresholds are calculated based on Formula (9), and are shown in Table 5:  
\[\frac{\Delta_{\max}}{\sum t_{e}}\leq\gamma_{\alpha} \tag{9}\]  
where \(\Delta_{\max}\) is the peak displacement of the plate rubber bearing and \(\sum t_{e}\) is the total thickness of the rubber layer.
## 2 Proposed Method
### Appliance States Identification and Load Disaggregation  
#### 2.2.1 Aggregation Power Clustering and Appliance Identification through DTW  
As we mentioned in the previous sections, each turned-on appliance contributes to the total power; the larger the contribution, the greater the impact is on the aggregation power. Therefore, clustering the values in total power time series, the largest contribution appliance will be the first division.  
Given time window length \(p\), we measured the total power readings, and the set of \(p\) samples is represented as \(\left\{x_{1},x_{2},\ldots,x_{p}\right\}\). Then, the total power readings were divided into \(k\) clusters via the following simple version of K-median clustering [34]:  
\[\min_{\left\{C_{1},\ldots,C_{k}\right\}}\sum_{i=1}^{k}\sum_{x\in C_{i}}|x- \widetilde{c}_{i}|, \tag{1}\]  
where \(C_{i}\) is the set of total power readings in the i-th cluster and \(\widetilde{c}_{i}\) is the median of the values in \(C_{i}\).  
The value of k is determined by the minimum sum of the interclass distance. When k changes from 2 to 10, the total power values are clustered into k class, and the sum of the interclass distances is calculated, respectively. The k value corresponding to the minimum sum of the interclass distance is obtained. The largest contribution to total power makes the largest total power values, so the cluster with the maximum cluster center is when the largest contribution appliance is running. Finding the cluster \(C_{i}\) with the maximum cluster center, each \(C_{i}\) subsequence is taken from the time series, then the DTW distance between these subsequences and the signatures of each appliance are respectively calculated according to literature [35] to give the similarity of the two trajectories. As a larger DTW distance means a lower similarity, the subsequences are respectively labeled according to the DTW distances.
## 2 Preliminaries and Related Works
### Network Coding and Its Fundamental Concepts
\right]\) are full rank, we say, the network coding is decodable. Feasible network coding: If all the sinks can perform a successful decoding, we say, the constructed network scheme is a feasible network coding scheme. That means \(\left[ \begin{array}{c}afaeik+cgjk\\ bfbeik+dgjk\end{array} \right]\) and \(\left[ \begin{array}{c}aeil+cgjlch\\ beil+dgjldh\end{array} \right]\) need to both be full rank simultaneously which is not easily satisfied and we have to select the values of \(a\), \(b\),..., \(l\) delicately in the field \(F_{q}\). Transfer matrix: The propagation effect of the network coding between two nodes, whose distance is multi-hop distance, will results in a matrix transformation. The generated matrix is called transfer matrix. For example, while the transfer matrix from source node 1 to sink 6 is considered, it is \(T=\left[ \begin{array}{c}afaeik+cgjk\\ bfbeik+dgjk\end{array} \right]\) with respect to coded message vector \(X\), and it is \(T\cdot G\) with respect to the original message \(u\). The computing method is complicated and readers are invited to the referred paper [28]. Propagated error: we illustrate the concept of \"propagated error\" with red and blue symbol \"\(\times\)\". In the link from node 2 to node 4, where an original error \(Z\) occurs, and it is considered a \(1\times 1\)  
Figure 1: Network coding on butter-fly network. We illustrate the concept of “propagated error” with red and blue symbol “\(\times\)”. The red part shows the concepts about block transmission in network coding. error vector, then it is marked with a red \"\(\times\)\". Because node 4 and 5 both select randomly coding coefficients, the original error \(Z\) will be combined into the messages along the downstream links which are link 4-5, 5-6 and 5-7. The errors in the downstream links due to the original error are marked with blue \"\(\times\)\" in Figure 1. With respect to link 2-4 and sink 6, the transfer matrix is the left part of matrix \(K_{w}\times K_{x}\) which is \([ik]\in 1\times 1\). Thus, the error transfer \(T_{Z\to Y}\) is \([ik]\in 1\times 1\). With respect to original error \(Z\), the propagated error in sink 6 is \(T_{Z\to Y}\cdot Z\). In a similar way, with respect to link 2-4 and sink 7, the transfer matrix is \([jl]\in 1\times 1\). _Block transmit_: the red part in Figure 1 shows the concepts about block transmission in network coding. Block transmission will bring down the overhead of network coding.