esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Experiments
### Imaging Results
#### 3.2.2 Layer Models
Compared with the RTM (Figure 14e), there were less interferences beneath the interfaces in RTM but MVSS beamforming showed a higher vertical resolution. Still, the surface wave caused an intensive artifact at the top in the RTM result. Compared the imaging results of the three methods, the artifacts caused by direct waves and surface waves are weaker in the MVSS beamforming result. Similarly, the artifacts on both sides of the image and beneath the deeper interface are also weaker. The dislocations across the deeper interface are missing because the reflection angles generated at such dislocations are large, and thus, the signal could not be recorded by the receiver array. In summary, compared with the imaging results in model B-1, the shallower interface is complete, and the fault dislocation is well determined. However, the dislocation of the deeper interface is missing because the reflected waves were not received by the receiver array. Both model B-3 (Figure 15) and model B-4 (Figure 16) contained low-velocity areas near the dislocation. The imaging results of model B-3 are not very different from those of model B-2. The weak layer results in more S-wave interferences above the shallower interface (Mark 1). The reason is that when seismic waves arrive at the weak layer, the reflected waves are not recorded by the receivers. Thus, because of the shadow created by the weak fault layer, the layer beneath the shallower interface is distorted (Mark 2). Compared with Kirchhoff migration and DAS beamforming, MVSS beamforming still suppressed the S-wave interferences more effectively and provided a better contrast. In model B-4, the fault angle is shallower than that in model B-3. The shallow part of the weak layer can be observed, but the deep part could not because the imaging results were disturbed by the weak layer. Arcuate interferences (Mark 1) and artifacts along the fault (Mark 2) can be observed. Furthermore, the imaging results were disturbed by the complicated structure due to the fault dislocation (Mark 2). However, the position of the deeper interface was not influenced by the weak fault layer. Figure 14: Model B-2. (**a**) True velocity model. (**b**) Imaging result of MVSS beamforming. (**c**) Imaging result of Kirchhoff migration. (**d**) Imaging result of DAS beamforming. (**e**) Imaging result of RTM. Comparing MVSS beamforming with Kirchhoff migration and DAS beamforming reveals that the artifacts (Mark 2) in the latter two methods were more intensive than those in the former. For the deeper interface, Kirchhoff migration provided a better contrast than MVSS beamforming.
# Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
## 4 Simulation Results and Experimental Validations  
The post-processing functionality within the study directory facilitates the generation of various outputs from the offline PP process, thus allowing for a comprehensive examination of the performance of the NSGA-II-based optimal path planner.  
Figure 3: Flowchart of finding the shortest path through the application of the NSGA-II.  
Figure 4 illustrates the approximate Pareto optimality. The red dot represents the optimal reference point. Orange triangles and blue dots denote the solutions attained by the optimized NSGA-II and MOGA, respectively. In non-dominated results, the corner points represent the solution nearest to the optimal reference point. According to the comparative simulation results, the NSGA-II exhibits a feature as good as the MOGA does. Furthermore, the non-dominated results provided by the NSGA-II are more intense. Additionally, it can be observed from the figure that the best possible results can be presented by the nearest points to the optimal reference point.  
In this study, the NSGA-II is utilized for toolpath optimization. The NSGA-II integrates the CD metric to ensure diversity in the solution space, thus achieving more efficient cutting paths. The outcomes of the NSGA-II are discussed to better understand its results. CD, a strategy of the NSGA-II [41; 57], is employed at the Pareto front to ensure a diversity of solutions. This is achieved through assessing the distribution of solutions near each population, which serves as an indicator of diversity within the Pareto optimal solutions. CD is calculated via summing the normalized distances between each individual and its two nearest neighbors across all objectives [38]. The solution that registers the highest crowding distance is considered the least crowded. This method helps in the optimization of cutting paths for workpieces with complex geometries via balancing the development of solutions with regard to both quality and efficiency. The usage of CD ensures that the solution population covers a broad spectrum of solutions, thereby increasing both the speed and accuracy of the cutting paths. This approach leads to cost and time savings in the production processes while maximizing the quality of the workpiece.
# Self-Assembled Multinuclear Complexes for Cobalt(II/III) Mediated Sensitized Solar Cells
## 3 Results and Discussion  
### Spectroscopic Properties  
#### 3.1.1 [Ru(TMAM)\({}_{2}\)CN\({}_{2}\)]TfSI\({}_{4}\) (1)  
The UV-Vis spectra of **1** (RuTMAM) compared with the dichloride precursor complex [Ru(TMAM)\({}_{2}\)Cl\({}_{2}\)]\({}^{4+}\) are reported in Figure 2a. Both complexes showed two broad mixed character MLCT-LLCT (Metal to Ligand Charge Transfer-Ligand to Ligand Charge Transfer) bands [22] spanning the visible range and a sharp and intense ligand centered \(\pi\)-\(\pi^{*}\) transition at ca. 300 nm. The blue shift of ca. 40 nm of the MLCT band maxima observed upon substitution of the chloride ligands with the CN\({}^{-}\) groups was consistent with the stabilization of d\(\pi\) orbitals of Ru(II) due to back bonding to \(\pi^{*}\) orbitals of the cyano ligands. This compound exhibits a quasi-reversible Ru(II)/Ru(III) oxidation process (\(\Delta\)E = 120 mV) with E\({}_{1/2}\) = 960 mV vs. SCE (Figure 2b). **1** (Ru(TMAM)) emits from the lowest triplet state (T\({}_{1}\)) in aerated fluid solution with an E\({}^{00}\) evaluated from the crossing of the normalized absorption and emission spectra of 2.13 eV.
# Adaptive Thermal-Aware Routing Protocol for Wireless Body Area Network
## 3 Proposed Method of ATAR
### Adaptive Thermal-Aware Routing Protocol
The steps of the initialization phase is mentioned in Algorithm 1 and the flowchart is shown in Figure 11.  
The basic responsibility of the initialization phase is to organize and get information about the distance of all available sensor nodes from the sink along with the current statistics of total temperature of the route, node with temperature above threshold value \(ntemp>thr\). The selection of appropriate route is made on the basis of route stability that is dependent upon the total route temperature and avoiding upon the node with above threshold temperature and energy. The multiple routes are discovered, probability is evaluated and selection of route depends upon probability value. To calculate the stability of a route we defined cost functions, i.e., Thermal-aware function. Before the destination node forwarded setup packet it sets its \(Cost\) field to zero, as shown in Equation (2)  
\[Cost(Ns)=0 \tag{2}\]  
The current node sends a data packet only to neighboring nodes whose distance to the destination is the least. Thus, only relay node \(Nr\) accepts the data packet which satisfies Equation (3). \[d(Ns,Nd)>d(Nr,Nd) \tag{3}\]  
Figure 10: ATAR: Initialization Phase. where \(d(Ns,Nr)\) is the distance between the sending node and the relay node. All of the nodes within the network are ranked on the basis of their temperature. These ranked nodes are then selected as next hop to forward the data packet. The thermal-aware function calculates the total temperature of the nodes on the route R by applying the following cost function (4)  
\[T_{R}=\sum_{i=1}^{n-1}T(i,R) \tag{4}\]  
where \(T_{R}\) represents the total temperature of route \(R\), \(i\) represents the nodes that path \(R\) constitutes. ```
1:global variables
2:Integer Nodelevel[9]
3:Integer Sinklevel
4:Integer Relaylevel
5:Integer RelayLevelValue
6:Integer SourceLevelValue
7:SinkLevel \(\gets\) 0
8:NodeLevel[] \(\leftarrow\) \(-1\)\(\triangleright\) For all Array values
9:\(S_{RL}\gets 0\)\(\triangleright\) Sink broadcast setup packet
10:end global variables
11:ifRelayLevel == \(-1\) OR RelayLevelValue \(>\) SourceLevelValue then
12:SourceLevelValue \(\leftarrow\) SourceLevelValue + 1
13:RelayLevelValue \(\leftarrow\) SourceLevelValue
14:Rebroadcast the message to neighbors
15:else
16:Rebroadcast the message to neighbors
```  
_The process of rebroadcasting continues until all nodes will aware of its ring level_  
Figure 11: Flow chart of initialization phase.
## 3 Proposed Framework
### Mechanism for Enhancing Privacy and Security  
Ensuring privacy protection and secure data transmission is critical in collaborated-learning scenarios, particularly in resource-constrained IoT environments where data often traverses insecure or unreliable routing paths. We utilize the Salsa20 stream cipher, a lightweight and cryptographically secure encryption method selected for its effective balance of robust security, computational efficiency, and low network overhead.
## 7 Conclusions  
In this study, the application and geometric model of the SDM are proposed, regarding the construction and operation of tunnels passing through large karst caves. The geometric parameter determination method and supporting treatment technology would provide assurance to select the karst tunnel implementation. The following conclusions are drawn from the results of this work:  
1. The SDM is proposed. The SDL is used to divide the space of the large karst cave into eight parts. Compared with traditional construction methods, this present method provides a means to conduct a spatial analysis which is more straightforward and clear for tunnels through large karst caves.
2. The geometric model of the SDM is established. Cartesian and cylindrical coordinate systems are used to describe the geometric parameters of the SDL. A method for the site investigation of tunnels and large karst caves is proposed. The geometric parameters of the SDL are determined to realize the spatial geometric description of the SDM.
3. According to the SDM, spatial treatment technologies for the top, bottom, and two wings of the tunnel are proposed. It is emphasized that the overall technology application should follow the principles of safety, flexibility, science, sustainability, and economy.  
Conceptualization, Z.S. and Q.W; methodology, J.L. and Z.Z.; investigation, R.S. and Y.L.; data curation, Y.Y. and F.W. and H.S.; writing--review and editing, C.P.; All authors have read and agreed to the published version of the manuscript.  
Figure 26: Monitoring of the tunnel vault settlement.  
**Funding:** This research was funded by the National Natural Science Foundation of China (NSFC) (No. 51778351) and SDUST Research Fund (No. 2018 TDJH101).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(\rho\): polar diameter  
\(\theta\): polar angle  
\(Z\): a plane parallel to plane XOY  
\(i\): a section is numbered \(i\)  
\(D_{1}\): spacing of sections  
\(\rho_{k}\): geometric expression of the cave contour  
\(\rho_{r}\): geometric expression of the surrounding rock stability edge line  
\(\rho_{t}\): geometric expression of the tunnel contour line  
\(y_{1}\): geometric expression of the tunnel top line  
\(y_{2}\): geometric expression of tunnel bottom line  
\(y_{3}\): geometric expression of the water level line  
\(y_{4}\): geometric expression of filling line  
\(d_{1}\): vertical distance from the laser equipment to the central axis  
\(d_{2}\): distance between the laser equipment and the proposed measurement point  
\(d_{3}\): height of the laser equipment tripod  
\(d_{u}\): depth of the unstable surrounding rock  
\(d_{p}\): depth of a borehole into medium-weathered rock mass  
\(d_{f}\): thickness of the filling in the cave  
\(d_{w}\): depth of the water in the cave  
\(\alpha\): angle between \(d_{2}\) and the horizontal direction  
\(\beta\): angle between \(d_{2}\) and the vertical direction  
\(\gamma\): angle between \(d_{2}\) and \(\rho_{k}\)
## 5 Damage Detection and Localization  
Here, the focus is on differentiating the interaction of ultrasonic guided waves (UGWs) with surface artificial damage and impact damage, specifically interlaminar delamination and debonding, in composite panels with varying thicknesses. The configurations of PZT transducers for damage detection and localization are shown in Figure 1. To simulate surface artificial damage, an added mass in the form of weighted Blu Tack was applied to the surface of the panels. This allowed for reusability of the panels before conducting the impact tests. The impact tests were then performed to create barely visible impact damage in the composite panels. These impact events resulted in interlaminar delamination and debonding, which are common types of damage observed in composite structures.  
For damage detection, two methods were employed: the correlation coefficient and the delay-and-sum (DAS) algorithm. The correlation coefficient measures the similarity between the baseline (undamaged) UGW signals and the current UGW signals, providing a measure of the damage index. The theory and calculation of the damage index using the correlation coefficient can be referred to [22]. The DAS algorithm, on the other hand, utilizes the differences between the baseline and current UGW signals to detect and locate the damage. The theory and implementation of the DAS algorithm for damage detection can be found in the references [39; 40]. By employing these damage detection techniques, the researchers aim to demonstrate the effectiveness of distinguishing between surface artificial damage and impact damage using UGWs. The comparison of results for panels with different thicknesses provides insights into the influence of thickness on the detection and localization of various types of damage in composite structures.
## 3 Results and Discussion
### Building Consumption Vs. On-Site PV Generation Under Net Billing  
However, when the billing policy changes from NEM to NEB with a much lower \"avoided cost\" pricing scenario, a smaller PV installation reduces \"avoided cost\" rate payments in those incidences when PV generation exceeds real-time consumption (for example, excess electricity generation around noon hours in cooler months in Figure 9a). A smaller PV installation of 35 kW\({}_{\text{p}}\) resulted in an 85% self-consumption ratio (Table 3), which is much higher than those of the 70 kW\({}_{\text{p}}\) and 105 kW\({}_{\text{p}}\) PV systems (59% and 42%, respectively).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**PV System Size, kW\({}_{\text{p}}\)** & **Annual Electricity Consumption Before PV, kWh** & **PV Generation, KWh** & **Annual Electricity Consumption After PV, kWh** & **Self-Consumption** & **Self-Consumption** \\ \hline
35 & 127,117 & 56,721 & 70,396 & 48,028 & 85\% \\
70 & 127,117 & 109,057 & 18,060 & 63,807 & 59\% \\
105 & 127,117 & 162,868 & \(-\)35,751 & 68,435 & 42\% \\ \hline \end{tabular}
\end{table}
Table 3: Simulation results of annual electricity consumption (kWh/yr) without and with PV installation and self-consumption ratios of three system sizes.  
Figure 10: Monthly energy balance in the case of 70 kW\({}_{\text{p}}\) PV installation with net metering billing arrangement.  
Figure 9: **(a)** Hourly energy demand and PV generation with system size of 70 kW\({}_{\text{p}}\) and 35 kW\({}_{\text{p}}\) in December. Both PV sizes of 35 and 70 kW\({}_{\text{p}}\) produce more electricity than farm electric demand during a sunny day. The 35 kW\({}_{\text{p}}\) array will have significantly less export than the 70 kW\({}_{\text{p}}\) array. **(b)** Hourly energy demand and PV generation with system size of 70 kW\({}_{\text{p}}\) and 35 kW\({}_{\text{p}}\) in June. The 35 kW\({}_{\text{p}}\) array will not produce enough in any hour of a day in June, while the 70 kW\({}_{\text{p}}\) array has a smaller net export to the grid than in winter months.  
Annual costs of farm electricity before and after the solar PV installation of different system sizes under the NEM and NEB are shown in Table 4. Under NEM arrangements, annual farm net electricity costs are greatly reduced from USD 15,254 before PV installation to USD 8448, and USD 2167 with the 35 and 70 kW\({}_{\text{P}}\) PV installations, respectively (Table 4). These correspondingly increased net annual benefits and led to similar simple payback periods around 7 years.  
The NEB arrangements had different impacts on the financial performance for the three system sizes. The larger PV system (i.e., 105 kW\({}_{\text{P}}\) system) resulted in a longer simple payback of 9.9 years, although it still had higher net annual savings on electric bills (Table 4) compared to a 35 kW\({}_{\text{P}}\) or a 70 kW\({}_{\text{P}}\) system.  
Without the avoid cost rate penalty, NEM pricing systems make the PV installation more profitable than the NEB scenario, especially for larger systems that aim to offset more annual farm electricity consumption (Figure 11).
## Appendix A Granger Causality Test  
As described in Section 4, the Granger causality test [41] was performed on the converted time series data, considering the time scales (\(t_{s}=60\) and \(120\) s). The Granger causality test determines whether one time series is good for forecasting another. Table 13 shows the Granger causality test result to forecast normal and attack events. We found that about 90% of the temporal features showed their significance to forecast network events when analyzing the monthly data (i.e., January 2015 dataset).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Events**} & \multicolumn{3}{c}{**Normal**} & \multicolumn{3}{c}{**Attack**} \\ \hline \multicolumn{2}{c}{**Data**} & \multicolumn{2}{c}{**1 January 2015**} & \multicolumn{2}{c}{**January 2015**} & \multicolumn{2}{c}{**1 January 2015**} & \multicolumn{2}{c}{**January 2015**} \\ \hline \multicolumn{2}{c}{**Features**} & \multicolumn{1}{c}{**Time (\(t_{s}=60\) s)**} & \multicolumn{1}{c}{**Time (\(t_{s}=120\) s)**} & \multicolumn{1}{c}{**Time (\(t_{s}=60\) s)**} & \multicolumn{1}{c}{**Time (\(t_{s}=120\) s)**} & \multicolumn{1}{c}{**Time (\(t_{s}=120\) s)**} & \multicolumn{1}{c}{**Time (\(t_{s}=120\) s)**} \\ \hline \multirow{6}{*}{\(\mathcal{P}\)} & 0.28 & 0.01 & 0.00 & 0.01 & 0.15 & 0.13 & 0.00 & 0.00 \\  & 0.01 & 0.23 & 0.00 & 0.00 & 0.00 & 0.01 & 0.00 & 0.00 \\  & 0.00 & 0.25 & 0.00 & 0.00 & 0.00 & 0.29 & 0.00 & 0.00 \\  & 0.68 & 0.01 & 0.00 & 0.00 & 0.72 & 0.13 & 0.00 & 0.01 \\  & 0.33 & 0.09 & 0.00 & 0.00 & 0.04 & 0.25 & 0.00 & 0.00 \\  & 0.23 & 0.29 & 0.00 & 0.00 & 0.01 & 0.12 & 0.00 & 0.59 \\  & 0.18 & 0.07 & 0.05 & 0.13 & 0.03 & 0.08 & 0.00 & 0.18 \\  & 0.07 & 0.28 & 0.49 & 0.30 & 0.29 & 0.15 & 0.00 & 0.23 \\  & 0.06 & 0.64 & 0.00 & 0.02 & 0.12 & 0.19 & 0.00 & 0.03 \\  & 0.01 & 0.34 & 0.00 & 0.00 & 0.00 & 0.13 & 0.00 & 0.00 \\  & 0.09 & 0.25 & 0.02 & 0.39 & 0.05 & 0.10 & 0.00 & 0.24 \\  & 0.20 & 0.53 & 0.69 & 0.19 & 0.01 & 0.02 & 0.00 & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Evaluating the predictability of all measured variables with the Granger causality test on the data on 1 January 2015 and the entire January 2015. The variables are analyzed with the time scales (\(t_{s}=60\) and \(60\) s). \(\mathcal{P}\), \(\mathcal{W}\), \(\mathcal{E}\), and \(\mathcal{N}\) indicate statistical features, wavelet features, first-moment features, and nominal features, respectively. The gradient color is used to show the scale of the \(p\)-values. Statistically significant (\(p<0.05\)) features are highlighted in a solid red color.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
###### Abstract  
Cross-tensioned concrete pavement can reduce transverse joints and cracks and improve the durability of the pavement, and the decrease in slab thickness can be achieved without damaging the performance of the pavement. However, the corrosion of the steel can cause serious damage to the pavement structure, resulting in higher maintenance costs and shorter service life. Basalt fiber-reinforced polymer (BFRP) has been proven to be an effective alternative in both jointed plain concrete pavement (JPCP) and continuously reinforced concrete pavement (CRCP) due to its lightweight and corrosion-resistant properties. In this paper, a systematic theoretical method for determining the prestress loss of BFRP tendons in cross-tensioned concrete pavement was proposed, with the impact of the slab width and distribution angle of the prestressed tendon on the prestress loss being studied and compared to the results of traditional steel strands. Results showed that the proportion of the prestress loss due to anchorage deformation and prestress retraction in the prestressing stage rose with the increase in distribution angle and the decrease in slab width, while the prestress loss during the in-service stage was a constant value for both BFRP tendons and steel strands. The prestress loss of BFRP tendons was far lower than that of steel strands in both prestressing stage and in-service stage for a given slab width (3 m, 4.5 m, 9.0 m, 12.75 m) and distribution angle (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), 45\({}^{\circ}\)), and the difference ranged from 6.4% to 16%, signifying the feasibility of BFRP tendons in cross-tensioned concrete pavement. Overall, the smaller the slab width, the greater the difference of the prestress loss between BFRP tendons and steel strands.  
cross-tensioned concrete pavement; basalt fiber-reinforced polymer (BFRP); prestress loss; steel strands; distribution angle; slab width Article