esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Research Framework  
The proposed framework follows a three-stage process: (1) propensity matching between factual (normal) and counterfactual (incident) scenarios, forming paired input data capturing both normal and disrupted conditions; (2) training of a dual-channel deep counterfactual prediction model; and, (3) once trained, this model allows predictions of spatiotemporal passenger flow dynamics and quantifies causal impacts of operational incidents on passenger distribution across the network. The entire framework is presented as in Figure 2.  
Figure 2: Framework for URT network OD matrix prediction under operational incidents. (GAT stands for Graph Attention Network; TCN stands for temporal convolutional network).  
To clearly illustrate the method principle of the proposed framework, we define the following terms first.  
Definition 1: Prediction Time Interval: The day is segmented into \(T\) intervals of length \(\Delta\tau\), typically equals to 10, 20, 30, or 60 min. Let \(t\in\{1,2,\ldots,T\}\) index each discrete interval.  
Definition 2: Urban rail transit network graph: The URT network is defined as an undirected graph \(G=(V,E)\), where \(V\) is the set of URT stations and \(E\) is the set of edges representing direct operational connections between stations. An edge \((v_{i},v_{j})\) exists if there is a direct connection between stations \(v_{i}\) and \(v_{j}\).  
Definition 3: Urban rail transit passenger trip: A passenger trip \(\xi\) is characterized by origin station \(o\), departure time interval \(t_{o}\), destination station \(d\), and arrival interval \(t_{d}\). Individual-level trips are recorded via Automatic Fare Collection (AFC) systems [26].  
Definition 4: Station-level passenger flow: The inflow \(I_{s}^{t}\) for station \(s\) at interval \(t\) is the count of passengers entering station \(s\) during time interval \(t\). Similarly, outflow \(O_{s}^{t}\) denotes the passengers exiting at station \(s\) during time interval \(t\). Mathematically, we have:  
\[I_{s}^{t}=\sum_{\xi}\mathbb{I}(o=s,t_{o}=t) \tag{1}\]  
\[O_{s}^{t}=\sum_{\xi}\mathbb{I}(d=s,t_{d}=t) \tag{2}\]  
Definition 5: OD flow matrix: The OD passenger flow matrix \(\mathbf{F}^{t}\in\mathbb{R}^{S\times S}\) for interval \(t\) captures passengers traveling between each OD pair:  
\[F_{ij}^{t}=\sum_{\xi}\mathbb{I}(o=v_{i},d=v_{j},t_{o}=t) \tag{3}\]
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 3 Problem Statement
### Preliminaries  
The following notations are used in our proposed DOW-PR scheme:  
* _Sink Node D:_ A UWSN sink node (also called destination node) is a type of node that is placed at the ocean surface or embedded inside water. Primarily, its function is to collect data from the  
Figure 1: Forwarder node selection scenario.  
sensor node and forward it to the base station through high speed radio link. These sinks or destination nodes can be static or mobile. Let D be a set of network sinks, then: \[D=(D_{1},D_{2},D_{3},D_{4},......,D_{8},D_{EM}).\] (1)
* _Transmission Range (\(T_{r}^{S}\)) of Node S_. Transmission range of node S is an omni-directional distance from source node S(\(x_{s},y_{s},z_{s}\)) that currently forwarded the packet p until where it can transmit the packet p.
* _Eligible Neighbors (\(EN_{i}\)) of Node i_: Nodes that are in transmission range of a node i. Let N be a set of nodes in a network \[N=\{n_{1},n_{2},n_{3},n_{4},\cdots,n_{k}\}.\] (2) Then, Eligible Neighbors of Node i can be expressed as \(EN_{i}\subseteq\) N \[EN_{i}=\{j\in N\quad\wedge\quad DIST_{j}^{i}\leq T_{r}^{i}\},\] (3) where \(DIST_{j}^{i}\) is the Euclidean distance between node i (\(x_{i}\),\(y_{i}\),\(z_{i}\)) and node j (\(x_{j}\),\(y_{j}\),\(z_{j}\)) in three-dimensional Euclidean space: \[DIST_{j}^{i}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}+(z_{i}-z_{j})^{2}}.\] (4)
* _Potential Forwarders (PF\({}_{i}\)) of Node i_: Potential Forwarders of node i are those nodes that are in transmission range \(T_{r}^{i}\) and their depth (\(d_{j}\)) is less than depth (\(d_{i}\)): \[PF_{i}\subseteq EN_{i},\text{where}\] \[PF_{i}=\{j\in EN_{i}\quad\wedge\quad d_{j}\leq d_{i}\}.\] (5)
* _Potential Forwarding Zone (PFZ)_: Potential Forwarding Zone (PFZ) is the hemispherical region whose radius is equal to \(T_{r}^{S}\) and each point in PFZ has lesser distance to the sink as compared to source node. PFZ is the subregion of \(T_{r}^{S}\) of node S and the nodes in the region are called potential forwarder nodes (PFNs), which are next forwarders of packet p. Any point in 3D Euclidean space q(\(x_{q}\),\(y_{q}\),\(z_{q}\)) is considered to be in the PFZ of S, if it satisfies the following conditions: \[DIST_{D}^{q}<DIST_{D}^{S},\,DIST_{S}^{q}<T_{r}^{S}\,\text{where}\] \[DIST_{D}^{q}\] is the Euclidean distance between point q(\(x_{q}\),\(x_{q}\),\(x_{q}\)) and Sink D(\(x_{D}\),\(y_{D}\),\(z_{D}\)) in three-dimensional Euclidean space: \[DIST_{D}^{q}=\sqrt{(x_{q}-x_{D})^{2}+(y_{q}-y_{D})^{2}+(z_{q}-z_{D})^{2}}.\] (6)
* \(DIST_{q}^{S}\) is the Euclidean distance between point q(\(x_{q}\),\(x_{q}\),\(x_{q}\)) and Source S(\(x_{s}\),\(y_{s}\),\(z_{s}\)) in three-dimensional Euclidean space: \[DIST_{q}^{S}=\sqrt{(x_{s}-x_{q})^{2}+(y_{s}-y_{q})^{2}+(z_{s}-z_{q})^{2}},\] (7) \[Z_{q}\leq Z_{s}.\]Neighbors of node i that are in PFZ of S : \[X_{i}=\{n_{i}\in PF_{i}\;\mid\;DIST_{n_{i}}^{i}\leq T_{r}^{i}\wedge\;\;Z_{n_{i}} \leq Z_{s}\}.\] (8)
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Effects of Transmission Power, Number of Hops and Redundancy  
Figure 7 shows the transmission redundancy \(\eta\) and average delay \(\phi\) for different transmitting sound level \(SL\). In this simulation, we assume all nodes are fixed in position. The coordinates of Node 0 (source node) and Node \(N\) (destination node) are (0, 0, 0) and (2500, 2500, 2500), respectively. Thus, the distance between each node will become closer as the number of nodes increases. We perform 2000 trials for each transmitting sound level \(SL\). From Figure 7a, we can see that the transmission redundancy \(\eta\) becomes lower with increasing \(SL\), and there are three ways to achieve high data transfer reliability: increasing the transmitting power, sending more redundancies, or reducing the distance between each node. Although increasing the number of nodes is helpful for reducing the packet error rate between each node and thus we can reduce the number of sent packets at each node, the packets in the network may have to travel through more nodes and result in a incremental number of sent packets. Thus, the transmission redundancy \(\eta\) does not have significant change when the number of nodes is larger than seven. The average number of packets for transmitting a data packet reduces by about one when \(SL\) increases 1 dB. Figure 7b shows the average delay \(\phi\) for different number of hops and \(SL\). It can be seen clearly that the average delay for transmitting a packet reduces largely when we increase the number of nodes from four to six. It is because the packet error rate between each node becomes lower, and we could send fewer packets to achieve high throughput. However, when the number of nodes is larger than seven, the average delay is flat or even higher for increased \(N\). This is because each node brings a state transition delay (the time for changing state between transmitting and receiving) and each node has to wait for an acknowledgement packet before sending a new block. From the above analysis, we can see that our protocol can work better with a larger number of nodes with the help of nodes' network coding.  
Figure 6: (**a**) an illustration for movement path; (**b**) the convergence procedure of estimating the block window size for moving nodes.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Delay Optimized Protocols
#### 11.2.2 E2E Delay  
From Figure 9, it is clear that the delay optimized versions of the protocols show lower delay than their respective non-optimized versions. In both versions of AEDG and RE-AEDG, the GWs are the major cause of delay. RE-AEDG-Dopt shows the highest computational and holding delays as the nodes in RE-AEDG carry out the cooperation.  
Whereas, U-LVRP's random AUV motion causes an increased overhead on the nodes to dynamically select their routing tracks. At every sojourn stop, anchor node reselection also results in higher computational delays. Thus these are the major delay creation factors for both versions of U-LVRP. The steep behavior for both curves of U-LVRP is because they initially lead to rapid network  
Figure 8: Packets received at sink in delay optimized and non optimized protocols.  
Figure 7: PAR in delay optimized and non optimized protocols.  
sparseness. Sparseness influences delays because nodes need to compromise their priority for neighbor node selection. However, with the passage of time nodes in U-LVRP start energy conservation causing rapid reduction in delays as compared to the other two protocols.
# Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems
## 4 Experimentation and Results Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multicolumn{6}{c}{**Classification \% (std.)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Proposed**} & \multirow{2}{*}{**FCM**} & \multirow{2}{*}{**Subtractive**} & \multirow{2}{*}{**Decision**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**KNN**} & \multirow{2}{*}{**Naive**} \\  & \multicolumn{2}{c}{**Hybrid Learning**} & & & & & & \\  & \multicolumn{2}{c}{**Technique**} & & & & & & \\ \hline \multirow{2}{*}{Iris} & 95.7880 & 85.5004 & 96.1394 & 95.0559 & - & 94.8267 & 94.8515 \\  & (1.4738) & (4.4088) & (1.7889) & (3.1016) & - & (1.8996) & (2.4019) \\ \hline \multirow{2}{*}{Wine} & 87.5556 & 67.2222 & 74.2222 & 88.2963 & - & 70.2593 & 96.5926 \\  & (8.0889) & (6.2489) & (13.09) & (5.5401) & - & (4.4034) & (2.1084) \\ \hline \multirow{2}{*}{Breast} & 95.5861 & 93.2998 & 94.1186 & 91.6426 & 94.9867 & 93.4189 & 94.7079 \\  & (1.1888) & (1.6142) & (1.121) & (2.4014) & (1.2044) & (1.5414) & (1.3952) \\ \hline \multirow{2}{*}{Glass chemical} & 90.6983 & 89.5373 & 90.6164 & 90.7540 & 91.3606 & 93.0778 & 89.8460 \\  & (1.9856) & (2.9519) & (2.9945) & (2.3476) & (3.165) & (2.8169) & (3.055) \\ \hline \multirow{2}{*}{Crab gender} & 95.5000 & 50.3000 & 90.7667 & 79.1333 & 94.1000 & 83.7000 & 63.4667 \\  & (1.6132) & (3.2641) & (3.3211) & (4.3366) & (2.1804) & (3.8411) & (10.7027) \\ \hline \multirow{2}{*}{Huberman survival} & 74.4116 & 72.4723 & 71.0549 & 6.4893 & 72.2641 & 66.7862 & 73.8685 \\  & (2.1708) & (2.5779) & (2.8901) & (4.6845) & (2.7463) & (3.8423) & (2.8525) \\ \hline \multirow{2}{*}{Qualitative bankruptcy} & 99.2404 & 82.3181 & 98.1105 & 98.4230 & 98.6156 & 99.1158 & 98.0451 \\  & (0.6214) & (4.1822) & (1.4148) & (1.0181) & (0.639) & (0.9828) & (1.04) \\ \hline \multirow{2}{*}{Fertility} & 87.1333 & 86.9333 & 80.2667 & 82.4667 & 86.6000 & 80.2000 & 85.0000 \\  & (2.6261) & (3.2582) & (4.3676) & (7.2603) & (3.8721) & (5.3653) & (3.4157) \\ \hline \multirow{2}{*}{Vertebral column} & 80.8676 & 61.7257 & 80.8731 & 80.2976 & - & 80.0110 & 77.0305 \\  & (2.9845) & (3.8218) & (2.8185) & (3.3082) & - & (3.1363) & (3.6083) \\ \hline \multirow{2}{*}{Indian liver patient} & 70.5602 & 70.4214 & 68.7966 & 66.2205 & 70.5329 & 64.6039 & 57.2955 \\  & (3.6878) & (2.1655) & (2.7519) & (2.8477) & (2.1469) & (2.4738) & (3.5323) \\ \hline \multirow{2}{*}{Seeds} & 94.3082 & 89.3711 & 93.1132 & 90.5975 & - & 89.7484 & 90.8805 \\  & (2.1189) & (3.1082) & (2.9531) & (4.0337) & - & (2.5469) & (2.8962) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for classification benchmarks.
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 4 Secondary Development of Creep Damage Constitutive Model
### Verification of Axial Stress-Strain
#### 4.2.2 Numerical Modeling Results  
Figure 13 illustrates the comparison between the laboratory tests and the simulated tests. In Figure 13a, it can be observed that the axial strain calculated by the model in this paper generally aligns with the axial strain curve from the uniaxial laboratory tests documented in the literature: at an axial load of 5.5 MPa, the axial strain is approximately 0.3%; at 6.3 MPa, the axial strain is about 0.33%; at 7.1 MPa, the axial strain is about 0.38%; at 7.9 MPa, the axial strain is about 0.44%; and at 8.7 MPa, the axial strain is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(G\)/\(G\)Pa & \(K\)/\(G\)Pa & \(\eta^{\text{ve}}\)/\((G\)Pa\) \(\cdot\)h & \(\eta^{\text{vp}}\)/\((G\)Pa\) \(\cdot\)h & \(C\)/\((\)MPa\()\) & \(\varphi\)/\((^{\circ})\) \\ \hline
2.24 & 3.1 & 0.54 & 7.08 & 4.2 & 24.3 \\ \(e_{start}\)/\((\)GPa\()\) & \(e_{md}\)/\((\)GPa\()\) & \(\sigma_{t}\)/\((\)MPa\()\) & \(\lambda\) & \(\alpha\) & \(L\) \\
2.03 & 1.56 & 18.3 & 0.43 & 0.35 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Verification model parameter for triaxial simulations.  
Figure 12: Program flow chart.  
about 0.6%. The average deviation remains within 5%, exhibiting high consistency with experimental values. According to Figure 13b, the simulated fracture angle is 76.5\({}^{\circ}\), while the experimental fracture angle is 74.1\({}^{\circ}\), with a discrepancy of 2.4\({}^{\circ}\). This indicates that the failure modes are consistent, demonstrating that the weakly cemented soft rock creep model considering damage, developed based on FLAC3D in this paper, can essentially represent the actual creep curve of the rock under uniaxial conditions.  
To further validate the model's applicability, simulations were also conducted under triaxial conditions. Figure 13c presents a comparison between laboratory-measured and numerically simulated axial strain over time under triaxial creep conditions. Both curves exhibit remarkably similar trends, demonstrating the model's ability to accurately replicate experimental creep behavior. Throughout the entire duration, the maximum deviation between experimental and modeled results remains within 5%, indicating excellent agreement. Moreover, the simulated fracture angle is 56.1\({}^{\circ}\), while the experimental fracture angle is 57.3\({}^{\circ}\), with a discrepancy of 1.2\({}^{\circ}\).
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Simulation Results and Analysis of UAV Path Planning in Complex Terrain
#### 5.3.2 Simulation Results and Analysis  
Figure 10 demonstrates the trajectory of the UAV in complex terrain. The comparison of the basic SSA, AO, and the proposed IHSSAO in the UAV planning path map shows that although the basic SSA can avoid the threat area as well as the AO and IHSSAO algorithms, its planned path is not only too high in the flight altitude, but also too long in the flight path, and consumes more energy. On the other hand, although the UAV path planned by the AO met all aspects of the UAV constraints in the early stage, the flight altitude variation was large and the pitch angle was too large at the location near the back row of peaks. Compared with the basic SSA and AO, the IHSSAO proposed in this paper can plan an optimal trajectory that avoids mountainous terrain and threat areas, and can fly as close to the ground as possible while satisfying the physical constraints of the UAV.  
Furthermore, Figure 11 shows the convergence cures of the SSA, AO, and IHSSAO. According to the simulation results, we can observe that IHSSAO not only has a rapid convergence rate but also has the best convergence effect. Although it falls into a local optimum in the early stage, the proposed algorithm can escape from the local optimum due to the fact that we introduced the pinhole imaging opposition-based learning strategy into it. In conclusion, the proposed IHSSAO has superiority over the basic SSA and AO in solving the UAV path planning problem in complex terrain.  
\begin{table}
\begin{tabular}{c c c} \hline
**Threat Sequence** & **Center Coordinates (km)** & **Radius (km)** \\ \hline
1 & (60, 160) & 20 \\
2 & (80, 100) & 20 \\
3 & (100, 40) & 20 \\ \hline \end{tabular}
\end{table}
Table 11: Parameter settings of the threat area.  
Figure 11: The convergence cures of the SSA, AO, and IHSSAO.  
Figure 10: The best path of SSA, AO, and IHSSAO. (**a**) Path comparison in three-dimensional space. (**b**) Path comparison of paths in the X-Y plane. (**c**) Side view of path comparison in the X-Z plane.
# Determination of the Depth of Cut via Surface Integrity
## 1 Introduction and Literature Review
Table 1 presents an overview of optimization methods used by certain authors. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Paper** & \multicolumn{2}{c}{**Optimization Mathematical Methods**} \\ \hline  & **Traditional** & **Non-Traditional** \\ \hline
[16] & Dynamic programming & \\ \hline
[17] & & Neural network \\ \hline
[18] & Integer programming & \\ \hline
[19] & & Simulated annealing, Hooke–Jeeves pattern search \\ \hline
[20] & Genetic algorithm \\ \hline
[21] & Scatter search \\ \hline
[22] & Genetic algorithm \\ \hline
[23] & Genetic algorithm \\ \hline
[24] & genetic algorithm, simulated annealing, ant colony optimization \\ \hline
[25] & Evolutionary strategy \\ \hline
[26] & Particle swarm optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Optimization techniques for machining parameters. Figure 1: Components of the surface integrity. Most optimization methods from the analyzed papers consider manufacturing cost maximization as the objective function. However, other papers use other optimization criteria, such as maximization of material removal rate, minimization of production time, minimization of surface roughness, and minimization of cutting temperature. Based on the current literature, it might be appreciated that the optimization mathematical models do not contain enough elements regarding the insurance of the machined-surface quality because they do not consider surface integrity. From this viewpoint, the depth of cut must be understood differently from the speed and feed rate. The depth of cut is directly connected to the surface integrity (Figure 1), and surface integrity is a very important concept used for appraising the machined-surface quality. The present paper develops a method focused on the concept of surface integrity for determining the depth of cut related to the routing sheet for machining front faces of cast iron work pieces.
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 4 Results and Discussions
### Comparison between the Irregular Wave and the Regular Waves  
To analyze the influence of irregular waves on the hydrodynamic performance of the OWC, a comparison between the surface elevation, pneumatic pressure and hydrodynamic efficiency under irregular and regular wave conditions are performed. For the regular wave, the wave heights \(H\) at the chamber mid-point and the pneumatic pressures \(P\) in the chamber are normalized as follows:  
\[H=\frac{1}{N}\sum_{i=1}^{N}\left(\eta_{\textit{crest}}^{i}-\eta_{\textit{ trough}}^{i}\right) \tag{14}\]  
\[P=\frac{1}{N}\sum_{i=1}^{N}\left(P_{\textit{crest}}^{i}-P_{\textit{ trough}}^{i}\right) \tag{15}\]  
where \(\eta_{\textit{crest}}^{i}\), \(\eta_{\textit{ trough}}^{i}\), \(P_{\textit{crest}}^{i}\) and \(P_{\textit{ trough}}^{i}\) are the crest and trough values of free surface elevations and pneumatic pressures for the \(i\)th wave respectively, \(N\) is the number of wave crest and trough values selected from stable and continuous wave periods. In the present model, \(N\) is set to \(10\).  
Figure 9 shows the significant wave height \(H_{1/3}\) at the chamber mid-point and the significant pneumatic pressure \(P_{1/3}\) in the chamber versus \(k_{p}h\) (\(kh\) for regular wave conditions). In general, the regular and irregular wave results exhibit the similar trend. However, there are some differences between the two curves. Considering the free surface elevations at the chamber center \(H_{1/3}\), the results for the irregular-wave are lower than the regular-wave results in the low-frequency region, and the discrepancy between the trends becomes to reduce with the increase of \(k_{p}h\). The irregular wave results are marginally greater than the regular wave results in the high-frequency range. Considering the pneumatic pressures in the chamber, the irregular-wave results are lower than the regular-wave results in the mid-frequency region, most notably between \(0.9\leq k_{p}h\) (\(kh\)) \(\leq 2.7\).  
The comparison of the hydrodynamic efficiencies \(\xi\) in the regular and irregular wave conditions is shown in Figure 10. The general trends of regular- and irregular- wave results are similar. The hydrodynamic efficiencies \(\xi\) in irregular wave are higher than that in regular waves in the low-wave-frequency environment. Nevertheless, the hydrodynamic  
Figure 9: Comparison of (**a**) significant wave heights at the chamber mid-point and (**b**) significant pneumatic pressures in the chamber versus \(k_{p}h\) between regular and irregular wave conditions.  
efficiencies for the irregular waves are lower than those of the regular waves over a relatively wide frequency band including the resonant frequency.
# An Intelligent Condition Monitoring Approach for
## 4 Application and Results  
In order to check the performance of the proposed approach, a shearing experiment was carried out in lab environments, and the approach was validated using the noise signals that were acquired in the experiment.
## 3 Experiments and Results
### Rigid Body Displacement Experiment  
One of 3D-DIC's major advantages is its robustness and high precision in spatial displacement measurements. In-plane and out-of-plane rigid-body movement tests were performed to validate the feasibility and accuracy of the proposed system in displacement measurement. A 100 mm \(\times\) 100 mm flat plate with speckles sprayed on the surface was fixed on a piezoelectric-drive micro-displacement platform provided by Winner Optics with a resolution of 13 nm. The flat plate was displaced from 0 mm to 0.2 mm in 0.02 mm intervals in the in-plane (X) and out-of-plane (Z) directions. After setting up the experiment system, illumination intensity was adjusted to ensure similar brightness of the gray image from each channel.  
While calibrating this experiment, the Z direction of the established coordinate system was parallel to the Z direction of the displacement platform. A subset size of 31 \(\times\) 31 pixels and a grid step of 5 pixels were adopted in the correlation calculation. The displacement results was expressed by the average displacement of 5 \(\times\) 5 subsets at the image center. Figure 7 shows the measured displacements and errors in the X and Z directions. As ensuring that the X and Z directions of the movement were exactly the same with the directions of the selected calculation coordinate was difficult, the synthesis values of X and Z directions displacements were selected to be the measured value, which were approximately coincident with the real values. Errors from the measurement were less than 0.005 mm; standard deviations of the X and Z direction synthetic displacement were 0.0025 and 0.0033 mm, respectively. Results show that the proposed system has high accuracy in displacement measurement. The displacement error map of the final step in the X displacement test is shown in Figure 8.  
Figure 6: The reprojection errors: (**a**) blue channel, (**b**) green channel, and (**c**) red channel.  
Figure 7: The displacement results: (**a**) X direction and (**b**) Z direction.