esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impacts of Random Distributed Vacancy Defects in Steady-State Thermal Conduction of Graphene
## 4 Conclusions  
In this paper, we analyzed and discussed the impacts of vacancy defects in graphene for steady-state thermal conduction. Two boundary conditions as chirality in graphene are considered. The center  
Figure 19: Temperature results of random vacancy defected graphene with its heat source in zigzag edges (the percentages of vacancy defects in (**a**–**d**) are 0.2%, 0.5%, 1%, and 3%, respectively).  
Figure 17: The robustness of graphene with random distributed vacancy defects. (**a**) is for the equivalent coefficient, (**b**) is for \(\beta\).  
Figure 18: Temperature results of random vacancy defected graphene with its heat source in armchair edges (the percentages of vacancy defects in (**a**–**d**) are 0.2%, 0.5%, 1%, and 3%, respectively).  
concentrated, periodic, and random distributed vacancy defects in graphene are compared using thermal energy, the equivalent coefficient of thermal conductivity, and thermal field. The results show that:  
1. The center concentrated vacancy defects evidently destroyed the regularity and band characteristic of the temperature field in the steady-state thermal conduction. Moreover, the impacts of periodic and random distributed vacancy defects in temperature field of graphene is not convenient to track.
2. The graphene with periodic vacancy defects is sensitive to boundary conditions. The graphene with its heat source in armchair edges has a strengthening effect in the equivalent coefficient of thermal conductivity when periodic vacancy defects is distributed in the first mode. However, graphene with its heat source in zigzag edges is more robust to defend the reduction of the equivalent coefficient of thermal conductivity in the development of periodic vacancy defects. On the contrary, center concentrated and randomly distributed vacancy defects have a consistent equivalent coefficient of thermal conductivity under two boundary conditions.
3. Furthermore, the amount and randomness of the vacancy defects are more important than the chirality in stochastically vacancy defected graphene. When _Per_ equals to 0.2%, the reduction of thermal energy fluctuates from 0.3-1%, and from 13-17% when _Per_ is 5%. The thermal energy has discrete range for certain quantity of random vacancy defects. The range of thermal energy is useful when reflecting on the amount of vacancy defects designed as a control switch in devices or instruments.
4. The robustness of graphene to resist the fluctuation owing to the randomness of vacancy defects declines sharply when _Per_ is less than 1%. The variance caused by random dispersion of vacancy defects is amplified, according to the increment of _Per_.
5. Different than the center concentrated and periodic vacancy defects, the minimum, maximum, and mean values of thermal energy in graphene with random vacancy defects have a linear decreased tendency with the increase of vacancy amounts.
## 2 Materials and Methods
### Problem Description  
In an LEO satellite communication system, the problem of selecting the handover satellite is essentially a multi-criteria optimization problem. We address the optimization problem within a specific communication duration T, which is divided into M different time periods, denoted as \([(t_{0},t_{1}),(t_{1},t_{2}),\ldots,(t_{n},t_{n+1}),(t_{M-1},t_{M})]\). The satellite network topology remains fixed in each time slot. The UE measures information from the LEO satellite network to obtain the directed graph of visible satellites capable of covering the user in that time slot. Subsequently, it selects the handover path that satisfies the user's QoS in each time slot. The aim of this paper is to minimize the number of long-term switches over the entire communication duration T, select satellites with higher data rates and lower transmission delays, and maintain the load balance of the satellites. This constitutes a multi-objective optimization problem involving three handover factors. In this paper, we employ a normalized function of the remaining service time, data rate, and transmission delay of the satellite as the objective function for these three attributes.  
Assuming a switchover at the service cutoff moment of the current serving satellite, the candidate satellite can provide communication to the user for the length of \(t_{j}^{e}-t_{i}^{e}\), so the normalization function of the remaining service time is defined as:  
\[N(t_{i})=\frac{t_{j}^{e}-t_{i}^{e}}{t_{max}},t_{e}^{i}-t_{e}^{i}\leq t_{max} \tag{10}\]  
where \(t_{max}\) is the maximum service time of the candidate satellite. Both the transmission delay and data rate are obtained using the Min-Max normalization method to obtain the objective function, defined as, respectively:  
\[N(d_{i})=\frac{d_{i}-\min(d)}{\max(d)-\min(d)} \tag{11}\]  
\[N(r_{i})=\frac{r_{i}-\min(r)}{\max(r)-\min(r)} \tag{12}\]  
wherein \(d_{i}\) denotes the current transmission delay of the candidate satellite, and \(\min(d)\) denotes the minimum transmission delay of the candidate satellite. \(r_{i}\) denotes the current data rate of the candidate satellite, and \(\max(r)\) denotes the maximum data rate of the candidate satellite.  
In path screening, each objective function is assigned corresponding weights. These objective functions are then multiplied by their respective weights and summed to obtain a new objective function, which is subsequently used to address the multi-objective optimization problem as a single-objective task. To achieve a balance between the three objectives, we formulate the multi-objective optimization problem as follows:  
\[Z=max\{\sum_{i=1}^{n}(w_{1}N(d_{i})+w_{2}N(r_{i})+w_{3}N(t_{i}))\} \tag{13}\]where \(w_{1}\), \(w_{2}\), and \(w_{3}\) denote the weight values corresponding to different attributes, respectively, which are subsequently calculated based on the Analytic Hierarchy Process (AHP), and \(n\) denotes the number of candidate satellites for handover paths in the satellite handover directed graph.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation
### Data Analysis  
#### 3.3.1 Raw Data Analysis  
Over the course of the proposed system's operational period, the three-dimensional displacements obtained from the marker images processed in the cloud server are shown in Figure 10. To understand the effect of temperature on the structure, ambient temperature measurements were incorporated using weather data sourced from the Korea Meteorological Administration API. This data integration allowed for a comprehensive analysis of the environmental factors affecting bridge structure. Given that the temperature at the sensor installation site was significantly influenced by solar radiation, resulting in discrepancies compared to the ambient temperature, it was crucial to incorporate ambient temperature data for an accurate analysis. The ambient temperature data provided a more consistent baseline, allowing for a clearer understanding of the temperature's impact on the observed displacements. The maximum displacement levels measured over a 9-month period were approximately 62.5 mm, 15 mm, and 11 mm along the X, Y, and Z axes, respectively. The \(X\)-axis displacement exhibited the most significant response, indicating the expansion and contraction of the bridge bearing due to temperature variations. In contrast, displacements along the Y and Z axes were relatively small but showed a seasonal trend consistent with temperature changes. It is important to note that a data gap is visible in February 2022, which was attributed to a server issue that resulted in data loss. The software was subsequently stabilized to ensure consistent data logging.  
To analyze the effect of temperature on the structure, the correlation between temperature and displacements was analyzed. Among the three dimensions, the longitudinal direction (i.e., \(X\)-axis) exhibited the most linear behavior, with an R-squared value of 0.84, as shown in Figure 11a. Conversely, the \(Y\) and \(Z\) axes displayed a more dispersed displacement pattern, indicating a weaker correlation with temperature. These results indicate that temperature exerts less influence on the \(Y\) and \(Z\) axes, whereas the \(X\) axis is strongly associated with temperature variations. The analysis concluded that the displacement  
Figure 10: Field experiment results: (**a**) \(x\)-axis displacement; (**b**) \(y\)-axis displacement; (**c**) \(z\)-axis displacement; (**d**) temperature variations.  
in the longitudinal direction was predominantly influenced by long-term temperature changes due to seasonal cycles. This consistent pattern underscores the significant impact of ambient temperature on the structural behavior of the bridge, particularly in the \(X\)-axis displacement, which reflects the bridge bearing's expansion and contraction.
# Influencing Factors of Motion Responses for Large-Diameter Tripod Bucket Foundation
###### Abstract  
Large-diameter multi-bucket foundation is well suited for offshore wind turbines at deeper water than 20 m. Air floating transportation is one of the key technologies for the cost-effective development of bucket foundation. To predict the dynamic behavior of large-diameter tripod bucket foundation (LDTBF) supported by an air cushion and a water plug inside every bucket in waves, three 1/25-scale physical model tests with different bucket spacing were conducted in waves; detailed prototype foundation models were established using a hydrodynamic software MOSES with a draft of 4.0 m, 4.5 m, and 5.0 m and with a water depth of 10.0 m, 11.25 m, and 12.5 m. The numerical and experimental results are consistent for heating motion, while exhibiting favorable agreement for pitching motion. The results show that the resonant periods for having motion increased with increasing draft and water depth. The maximum amplitude for having motion first decreased and then increased with the increase of water depth and spacing between the buckets. The maximum amplitude for pitching motion first decreased and then increased with increasing water depth but decreased with increasing spacing between the buckets. The wider the spacing between the bucket foundations, the larger the heave response amplitude operators (RAOs). Simply improving the pitch RAOs by increasing the spacing between bucket foundations is limited and negatively affects motion performance during the transportation of LDTBF.  
Keywords: offshore wind turbine; large-diameter tripod bucket foundation; air floating; response amplitude operator; resonant period +
Footnote †: journal: Applied Sciences
## 3 Data Description and Preprocessing
### Feature Selection  
The feature selection process involves selecting features that significantly impact the output parameter. It is an essential step in model development, as irrelevant and noisy dataset features can negatively affect the model's performance [60]. Feature selection was undertaken using the XGBoost algorithm, with K-fold cross-validation (K = 5) applied to the 45 features from the dataset. During each fold, 80% of the data were allocated for training, while the remaining 20% were used for testing. Five-fold cross-validation ensured that every observation was validated exactly once, balancing the avoidance of overfitting  
Figure 1: Frequency distribution histograms of incident clearance time categories.  
with reduced computational demands. The top 37 features were extracted from the best fold using the information gain (IG) method [61]. The IG estimator assesses the value of an attribute by quantifying the information gain entropy for the class in a decreasing sequence [62]. High-dimensional data are frequently processed using this methodology [63]. In the IG estimator, each attribute is assigned a score between 1 and 0, signifying the degree of relevance from most to least relevant. Subsequently, the features with the highest scores are used as inputs in the subsequent dimensionality reduction phase. The selected features are seen in Figure 2 below.  
Figure 2: Features importance.
## 5 Experimental Micro-Doppler Signatures  
### Data Collection Process  
Outdoor data were collected by placing the drone 3-15 m away from the radar system and performing different flight motions, as shown in Figure 13. There were a few difficulties when trying to obtain the micro-Doppler signatures of the drones. The RCS of the blades was too low; therefore, methods for increasing the detectability were conducted.  
The micro-Doppler signature was enhanced by adding reflective tape onto the blade tips of the quadcopter rotors, as shown in Figure 14. This significantly improved its detectability.  
Figure 14: Akaso UAV with reflective tape on the blade tip.  
Figure 12: 30\({}^{\circ}\) LOS antenna setup.  
Figure 13: Outdoor drone data collection setup.
# HiGoReg: A Hierarchical Grouping Strategy for Point Cloud Registration
## Appendix B Algorithm of Hierarchical Grouping Strategy  
Based on the theoretical development in Section 2.2, we present the HiGoReg algorithm in pseudocode to clarify its computational flow. This formal representation highlights the algorithmic structure, including initialization, iterative refinement, and convergence criteria, and serves as a reference for implementation and analysis.  
```
Input: Source point cloud and target point cloud Output: Estimated transformation parameters
1 Calculate the incipient RMSE;
2 Initialize \(\xi\) with \(\xi^{0}\);
3 Set the maximum number of iterations maxIter;
4 Initialize iteration counter \(j=1\);
5 while\(j\leqslant\) maxIterdo
6\(OLD\_RMSE\leftarrow\) RMSE;
7 Matching:
8 Multi query k-Nearest Neighbor (KNN) search point pairs;
9 Reject incorrect pairs and retain eligible pairs;
10
11 Calculating:
12 Initialize \(e_{f},e_{g}\) with \(e_{f}^{0},e_{g}^{0}\), respectively;
13 whileTruedo
14 Populate matrices \(\mathbf{A}_{k}\), \(\mathbf{J}_{k}\), \(\mathbf{L}_{k}\), and \(\mathbf{Q}_{\mathbf{I}_{k}}\);
15 if\(k=1\)then
16\(\mathbf{Q}_{\xi_{k}}\gets A_{k}^{\top}\mathbf{Q}_{\mathbf{I}_{k}}^{-1}\mathbf{A}_{k}\);
17\(\mathrm{d}\hat{\mathbf{x}}_{k}\gets\mathbf{Q}_{\xi_{k}}^{-1}\mathbf{A}_{k}^{\top}\mathbf{Q }_{\mathbf{I}_{k}}^{-1}\mathbf{L}_{k}\);
18
19 else
20\(\mathbf{Q}_{\xi_{k}}\gets\mathbf{Q}_{\xi_{k-1}}+\mathbf{A}_{k}^{\top}\mathbf{Q}_{\mathbf{I}_{k} }^{-1}\mathbf{A}_{k}\);
21\(\mathbf{G}_{k-1,k}\gets\mathbf{Q}_{\xi_{k-1}}^{-1}\mathbf{A}_{k}^{\top}\Big{(}\mathbf{Q}_{ \mathbf{I}_{k}}+\mathbf{A}_{k}\mathbf{Q}_{\xi_{k-1}}^{-1}\mathbf{A}_{k}^{\top}\Big{)}^{-1}\);
22\(\mathbf{L}_{k-1,k}\gets\mathbf{L}_{k}-\mathbf{A}_{k}\mathbf{\delta}_{k-1}^{\top}\);
23\(\mathrm{d}\hat{\mathbf{x}}_{k}\leftarrow\mathrm{d}\hat{\mathbf{x}}_{k-1}+\mathbf{G}_{k-1,k }\mathbf{\tilde{L}}_{k-1,k}\);
24
25 end if
26 Update the parameter vector: \(\hat{\mathbf{x}}^{j}\leftarrow\hat{\mathbf{x}}^{j-1}+\mathrm{d}\hat{\mathbf{x}}_{k}\);
27 Compute the error vectors \(\mathbf{e}_{k-1}^{j}\) and \(\mathbf{e}_{k}^{j}\);
28 if\(||\mathrm{d}\hat{\mathbf{x}}_{k}||<(\varepsilon_{R},\varepsilon_{T})\to 0\)then
29 break;
30
31 end if
32
33 end for
34 Calculate the RMSE at the current pose;
35\(j\gets j+1\);
36 if\(|\text{RMSE}-\text{OLD\_RMSE}|<\varepsilon_{\text{rms}}\)then
37 break;
38
39 end if
40
41 end for
42
43 end for
44
45 end for
46
47 end for
48
49 end for
50
510 end for
```  
**Algorithm A1**HiGoReg
## 5 Numerical Simulation and Verification
### Simulation Investigation and Verification
#### 5.3.1 Finite Element Model  
As displayed by the three-dimensional schematic diagrams in Figure 10a,b, a sample pipe with an inner radius of \(r\), an outer radius of \(J\) and a length of \(B\) is used. Circumferential grid nodes on one end of the pipe are chosen as the excitation nodes. Receiving nodes of guided-wave signals are \(E\) away from the excitation nodes. The reflector, whose front-edge is located \(F\) away from the excitation nodes, is characterized by the parameters of the axial length \(Z\), the slope angle \(\alpha\), the radial depth \(Q\) and the circumferential extent \(\beta\), respectively. Three test cases with different types of models depicted in Figure 10b were investigated. The geometrical and physical parameters of the sample pipes are listed in Table 3. The physical parameters in all models include the Poisson's ratio, Young's modulus and density, which are set with the values for a steel pipe [6]. Model 1 and Model 2 represent reflectors with different structures, and their slope angles are different (typical values: \(90^{\circ}\) and \(120^{\circ}\)) in Cases 1 and 2. Considering that Case 3 contains multiple reflectors, to show the results clearly, the values of geometrical parameters in Case 3 are larger than those in Cases 1 and 2. The types of the reflectors in Model 1 and Model 2 are defect features, while Model 3 contains a geometric feature. The predicted results using the proposed method were presented and discussed below.  
Figure 10: Schematic illustration of the finite element models in the sample pipe: (**a**) three-dimensional schematic diagram; (**b**) schematic of a sample pipe with different types of models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Outer** & **Imer** & **Axial** & **Circumferential** & **Slope** & **Depth** & **Length** & **Length** & **Density** & **Distance** & **Poisson’s** & **Young’s** & **Ratio** & **Modulus/CPa** & **Reflector’s Type** \\  & **Radius** & **Radius** & **Length** & **Length** & **Exten** \& **(Q)/mm** & **Exten** \& **(Q)/mm** & **Exten** \& **(Q)/mm** & **Exten** \& **(Q)/mm** & **Ratio** & **Modulus/CPa** & **Reflector’s Type** \\ \hline Case 1 & 204 & 200 & 2 & 5 & 90 & 2 & 650 & 390 & 7800 & 230 & 0.28 & 210 & \begin{tabular}{c} Defect feature \\ (Model 1) \\ \end{tabular} \\ \hline Case 2 & 204 & 200 & 2 & 5 & 120 & 2 & 650 & 390 & 7800 & 230 & 0.28 & 210 & \
\begin{tabular}{c} Defect feature \\ (Model 2) \\ \end{tabular} \\ \hline Case 3 & 204 & 200 & 4 & 10 & 90 & 4 & 650 & 7800 & 230 & 0.28 & 210 &
\begin{tabular}{c} Defect feature \\ (Model 3) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometrical and physical parameters of the sample pipes.
## 2 Methodology
### Viewpoint Transformation
The architecture for viewpoint correction of pavement images, combined with depth estimation, is illustrated in Figure 5.  
Before inputting pavement images into the pavement distress recognition model, the images first undergo preprocessing in a pre-trained domain prior knowledge generator. This generator generates prior knowledge specific to the current image, obtains some proposal line points, and then detects lines in the images. Based on the detected lines, the module utilizes perspective transformation algorithms to restore the image to a bird's-eye view perspective. The pseudocode of the viewpoint transformation module is presented as Algorithm 2. Under a unified and fixed bird's-eye view perspective, the features of pavement distress are more effectively presented. A fixed viewpoint is advantageous for the model to eliminate irrelevant interference factors, improving the accuracy of pavement distress recognition. ```
Input: The Proposal Line Points \(P\), the Input Image \(X\). Output: The Bird's-eye View Image \(X^{\prime}\). 1:for\(p_{i}\) in \(P\)do
2:\(l_{i}\) = Hough Transform (\(p_{i}\))
3:\(L\).append (\(l_{i}\))
4:for\(l_{i}\) in \(L\)do
5:for\(l_{j}\) in \(L\)do
6:\(p\) = line_intersection (\(l_{i}\), \(l_{j}\))
7:\(P^{\prime}\).append(\(p\))
8:\(k\) = KMeans (\(P^{\prime}\))
9:\(l_{1}\), \(l_{2}\) = Hough Transform (\(P\), \(k\))
10:\(p_{1}\), \(p_{2}\) = line_intersection (\(l_{1}\), \(x\) = 0), line_intersection (\(l_{2}\), \(x\) = 0)
11:\(p_{3}\), \(p_{4}\) = line_intersection (\(l_{1}\), \(x\) = height(\(X\)), line_intersection (\(l_{2}\), \(x\) = height(\(X\)))
12:\(X^{\prime}\) = Perspective Transformation (\(X\))
```  
**Algorithm 2**Viewpoint transformation
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Industrial Application
#### 4.1.1 Computational Configurations  
An electric potential difference \(\varphi=V_{\mathrm{s}}\) was imposed between the terminals of the conductor of the windings marked in red, as shown in Figure 4. A single-phase common mode test was considered in this example, which means that \(\varphi=V_{\mathrm{s}}\) was imposed on one terminal of the conductor and \(\varphi=-V_{\mathrm{s}}\) on the other, as shown in Figure 3. The frequency interval was [0:10\({}^{6}\)] Hz. To handle the skin effect, the considered mesh was well-refined and suitable for all frequencies up to 1 MHz, which featured 29,832,477 tetrahedrons including 5,257,323 nodes and 35,093,396 edges. For each frequency, the computing time using the Darwin model took about 9 days for 40,338,477 DoFs using the BiCGSTAB solver. Notably, for a system with a large DoFs, the BiCGSTAB is always preferred to direct solvers because the latter may fail due to the relative need for memory, which is much larger than that required for the iterative solvers. The mesh of the winding and the toroidal core are presented in Figure 5.
## 3 Results and Discussion
### Model Characteristics
#### 3.1.2 Biomass Equation  
The biomass equation (Table 2) includes the composition of proteins, DNA, RNA, lipids, carbohydrates, and cofactors. For the composition of DNA, the whole genome sequence was used to estimate the amount of each deoxyribonucleotide as described in [52], while mRNA, rRNA, and tRNA were used to estimate the total RNA in the cell as described in [14]. For the amino acid composition, the percentage of each codon usage was calculated from the translated genome sequence [52], using the e-BiomassX tool [53].  
Carbohydrate [54], Lipid [55], Sterol [55], Phospholipid [56], and Fatty acid [57] compositions were inferred from literature data. Essential metabolites were included in the biomass composition to qualitatively account for the essentiality of their synthesis pathways [41; 58]. The growth and non-growth ATP requirements were adopted from _S. cerevisiae_[59].