esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Numerical Mesh  
In this work, the rectangular prism shape of the minichannel enables the use of a topologically simple hexahedral computational mesh. Such mesh can provide a better computational accuracy and an improved convergence speed over tetrahedral and/or mixed element meshes in simple geometries [56]. The sensitivity of the predictions to the level of spatial mesh refinement was determined by comparing the numerical results from six computational meshes ranging from 0.24 million cells to 1.74 million cells. Predictions were obtained of the average minichannel bottom wall temperature and of the inlet to outlet pressure drop, at the coolant Reynolds number of 274, coolant inlet temperature of 303.15 K, base heat flux input of 16.76 \(\mathrm{W/cm^{2}}\), and with a suspension of 1% Al\({}_{2}\)O\({}_{3}\) nanoparticles in water. The single-phase approach and the two-phase mixture model were both used, the latter with a combination of second phase volume fraction \(\alpha_{nf}=10\%\) and of second phase nanoparticle volume fraction \(\Phi=\)10%, and with \(\left(\alpha_{nf}=1\%,\Phi=100\%\right)\). Figure 4a,b indicates differences among the models in the average minichannel bottom wall temperature and in the pressure drop, but for each model the predictions become substantially mesh independent above 1.12 million cells. Consequently, the mesh with 1.12 million cells is used throughout the remainder of this investigation. Figure 5 shows the three-dimensional structure of this mesh at the minichannel inlet.  
Figure 6 shows an example of the convergence history with the two-phase mixture model. In this run, the minichannel through flow at a Reynolds number of 455 is simulated with a second phase volume fraction \(\alpha_{nf}\) of 4% and with \(\Phi=25\%\). The residuals for continuity, x-velocity, y-velocity, z-velocity, and energy equations reduce monotonically below \(10^{-6}\) in less than 100 iterations. This indicates an appropriate convergence of the results for the purpose of comparing them against corresponding ones from the single-phase approach and other two-phase mixture model runs.  
Figure 4: Mesh dependence of (**a**) the average minichannel bottom wall temperature and (**b**) the inlet to outlet pressure drop.  
Figure 5: Three-dimensional mesh structure of the minichannel heat sink.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Dynamic Cluster Construction
#### 4.1.1 Cluster Head Selection  
This paper uses the weighted clustering algorithm (WCA) to select cluster heads, while considering different factors in Equation (5). Where the node with the lowest calculated score is selected as CH, the factors considered are as follows:  
(1) Specifying the reciprocal of node degree \(1/o_{\alpha}\) of node \(\alpha\);  
(2) Specifying the average transmission time \(T_{\alpha}\) from the node to all neighboring nodes in the node \(\alpha\) range;  
(3) The weighted sum \(H_{\alpha}\) of the neighbor jump distance of node \(\alpha\);  
(4) Specifying the cumulative power \(P_{\alpha}\) of node \(\alpha\) during the time it acts as CH.  
If the value of node \(\alpha\) is \(w_{\alpha}<w_{min}\), it is selected as a CH node and then added to the CH list. This is expressed as follows:  
\[w_{\alpha}=w_{1}\frac{1/o_{\alpha}}{M_{o}}+w_{2}\frac{T_{\alpha}}{\bar{T}}+w_ {3}\frac{H_{\alpha}}{\bar{H}}+w_{4}\frac{P_{\alpha}}{\bar{p}} \tag{6}\]  
where \(w_{1}\), \(w_{2}\), \(w_{3}\), \(w_{4}\) is the weight factor corresponding to different parameters,  
\[w_{1}+w_{2}+w_{3}+w_{4}=1 \tag{7}\]\(o_{\alpha}\) in Equation (8) is the degree of node \(\alpha\) expressed as:  
\[o_{\alpha}=\text{Neighbor}(\alpha)\sum_{q\in\bar{N}\
eq n}\text{dist}(\alpha,q )\in(0,1) \tag{8}\]  
where \(dist(\alpha,q)=1\) indicates that \(q\) is within the transmission range of node \(\alpha\), otherwise it is 0. The average reciprocal of the node degree of all network nodes \(\bar{M}_{o}\) is as follows:  
\[\bar{M}_{o}=\frac{1}{N}\sum_{\alpha=1}^{N}\frac{1}{o_{\alpha}} \tag{9}\]  
\(T_{\alpha}\) is expressed as follows:  
\[T_{\alpha}=\frac{1}{\psi_{\alpha}}\sum_{q\in\psi_{\alpha}}t_{q,\alpha} \tag{10}\]  
where \(\psi_{\alpha}\) is a collection of nodes \(\alpha\) within the scope of the cluster, and \(t_{q,\alpha}\) represents the transfer time from node \(\alpha\) to any node \(q\). Similarly, the average transmission time of all network nodes is:  
\[\bar{T}=\frac{1}{N}\sum_{\alpha=1}^{N}T_{\alpha} \tag{11}\]  
In order to reduce the communication load, CH should choose nodes with few hops to establish communication. The \(H_{\alpha}\) value is as follows:  
\[H_{\alpha}=\sum_{k=1}^{\infty}\frac{\gamma_{k}\times k}{\text{count}_{k}} \tag{12}\]  
where \(count_{k}\) is the number of \(k-hop\) neighbors of node \(\alpha\) and \(\gamma_{k}\) is the weight coefficient. The average weighted sum of hops in the range is as follows:  
\[\bar{H}=\frac{1}{N}\sum_{i=1}^{N}H_{i} \tag{13}\]  
\(P_{\alpha}\) is mainly related to the maximum power value of nodes. Compared with other cluster nodes, it is assumed that the CH power value is higher. With this in mind, the average power of network nodes is calculated as follows:  
\[\bar{P}=\frac{1}{N}\sum_{\alpha=1}^{N}P_{\alpha} \tag{14}\]
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 3 Investigation of the Relationship Between Vertical Curves and Track Irregularities
### Impact of Vertical Curve Radius and Length on Detection Accuracy  
In this section, the vertical curve gradient remains constant while the radius and length are varied to examine their effects on detection accuracy. The radius values are consistent with those in the previous section, and the specific test conditions are outlined in Table 4.  
After obtaining virtual dynamic responses under varying vertical curve radius and length conditions, virtual irregularities were computed. As in the previous section, a 500 m segment in both directions from the curve centre was selected for the time-frequency domain analysis, as shown in Figure 7.  
In conjunction with the findings from Sections 3.1-3.3 and Figure 7, it is further confirmed that the vertical curve radius is the primary factor affecting detection accuracy for vertical irregularities in the longitudinal profile. While the radius does not impact the wavelength composition of the virtual irregularities, it has a subtle effect on the power spectral density amplitude. Additionally, the location of peak detection errors is determined solely by the vertical curve length.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operating** & **Train Speed** & **Radius \(R\)** & **Length** & **Gradient** & **Single-Side** \\
**Conditions** & \(V\) (km\(\cdot\)h\({}^{-1}\)) & **(m)** & **L (m)** & **Difference \(\Delta i\) (\%o)** & **Gradient \(i\) (\%o)** \\ \hline \hline
1 & & 6000 & 240 & & \\
2 & 120 & 10,000 & 400 & 40 & \(\pm\)20 \\
3 & & 14,000 & 560 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental conditions for variations in vertical radius and length.  
Figure 6: Time–frequency domain comparison of virtual and excitation irregularity metrics under varying vertical curve gradients and radii (**a**) Comparison of error standard deviation (**b**) Comparison of power spectral density.
## 4 Conclusions  
A Spidron fractal dipole antenna with a ferrite-loaded AMC is proposed in this paper. The proposed antenna operates in the HF/VHF/UHF bands with the application of a Spidron fractal shape and a broadband miniaturized AMC. By implementing MP2106-0M0 ferrite with high permeability in the AMC, the antenna can be operated in the HF/VHF/UHF bands and has a  
Figure 10: (**a**) Recived power measurement setup of the manpack; (**b**) Measured received power levels of the Spidron fractal dipole antenna with the AMC and a reference antenna.  
wide operating frequency bandwidth, despite its small size. The dipole antenna is designed with a quasi-self-complementary Spidron fractal structure to have broadband operating characteristics when combined with an AMC. The VSWR characteristics (<3.5) of the proposed Spidron fractal dipole antenna with the AMC satisfy the requirements of a bandwidth of 20-500 MHz. The Spidron fractal dipole antenna with the AMC has a directional radiation pattern along the \(+z\)-axis due to the influence of the AMC, with a distance between the antenna and the AMC of only 0.0001 \(\lambda_{L}\) (\(\lambda_{L}\) is the wavelength at the lowest operating frequency). The size of the proposed antenna with the AMC is \(0.03\times 0.026\times 0.001\,\lambda_{L}^{3}\), and the performance of the antenna with the AMC was verified by measurements. These results demonstrate that the proposed antenna with the AMC is applicable to relatively small applications that require an HF/VHF/UHF antenna with low-profile characteristics, such as a manpack.  
Writing, O.H.K.; Antenna design and simulation, O.H.K.; Laboratory tests, O.H.K.; Methodology, K.C.H.; Writing Review and Editing, O.H.K. and K.C.H.; Conceptualization, O.H.K. and K.C.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2018R1C1B6005024).  
The authors declare no conflict of interest.
# Evaluation of Accuracy of Traffic Flow Generation in SUMO
## 3 Results and Discussion  
### Real-Time Traffic in AutoNavi Map  
To verify the traffic situation created by software, we compared it to the real traffic data. In this article, a macroscopic scale verification method was used because of the limitation of the verification data. AutoNavi map is supported by AutoNavi Software Co., Ltd. (Beijing, China), which provides real-time traffic in three colors. Red indicates congestion and the average speed in this area is less than 5 km/h, yellow indicates low speed, the average speed is 5-30 km/h, green indicates smooth traffic with average speeds over 30 km/h. Compared to the strict data protection guidelines in Europe, the handling of private data in China is much less regulated. Therefore, the collection of real-time traffic data is much easier. For instance, the traffic data of the AutoNavi map are collected in real-time from different sources; 15% of data are from the traffic control department of the city, mostly their data are from fixed-point detections like camera and induction loops. AutoNavi map users offer the remaining 85% of data. In most cities, AutoNavi cooperates with taxi companies to gain their FCD. AutoNavi also has a close cooperation with transportation network companies such as Uber and Didi. What is more, as the most popular map app (also Apple Maps' provider), AutoNavi obtain FCD from each user while they are uploading their positions during navigation. After acquired by Alibaba Group, AutoNavi synchronized other staff vehicle FCD such as logistics vehicle data and food delivery data. Though these methods together, AutoNavi get real-time traffic data with high accuracy and fidelity.
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 2 Materials and Methods
### Satellite Imagery  
In Spring 2020, we systematically queried archives of GeoEye-1, LandSat-8, PlanetScope, Sentinel-2, and WorldView for imagery of the Point Lay haulout area, spanning roughly 9 km of barrier island beach (bounding geographic coordinates: 162.7\({}^{\circ}\)W, 69.6\({}^{\circ}\)N,163.4\({}^{\circ}\)W, and 69.9\({}^{\circ}\)N) during 25 July-15 November 2018 and 2019 and obtained all available imagery. We also systematically queried the ground range detected SAR imagery archive of Sentinel-1 C-band 10 m resolution VH cross-polarization IW (interferometric wide) and the 40 m resolution HV cross-polarization EW (extra wide) swath modes. We arranged with the TerraSAR-X mission to collect X-band SAR imagery of Point Lay during the 2020 walrus haulout season, after preliminary exploration of the Sentinel-1 SAR imagery indicated the operational value of SAR for haulout monitoring. For this purpose, TerraSAR-X representatives recommended the instrument's highest resolution image mode (\"staring spotlight\", abbreviated as TerrSAR-X ss in figure legends) with ~1.1 m resolution in a single polarization, or the \"stripmap\" (abbreviated as TerrSAR-X sm in figure legends) mode with ~3.5 m resolution and a more extensive swath width [27]. TerraSAR-X delivered multi-look ground range detected SAR imagery processed with a radiometrically enhanced mode to reduce speckle and science-grade orbital parameters to optimize geographic registration. We then obtained all available imagery from the Sentinel-1, Sentinel-2, and LandSat-8 public archives during the 2020 study season to compliment the TerraSAR-X imagery. We also examined browse imagery in the Planet 2020 archive to assess the frequency of clear sky conditions, however we did not purchase 2020 PlanetScope imagery for analysis. Use of Google Earth Engine [28] enabled efficient access to Sentinel and Landsat imagery.
## 4 Digitalization of MGs
### Digital Twin Technology
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Concept** & **Explanation** \\ \hline
**Optimization of energy management strategies** & Digital twins enable simulation of scenarios and testing of energy management strategies in MGs to optimize energy usage, reduce costs, and increase efficiency. \\ \hline
**Predictive maintenance** & Digital twins can prevent system failures and reduce maintenance costs and downtime by enabling operators to identify potential issues through analysis of data from sensors and other sources. \\ \hline
**Improved reliability** & Digital twins can improve the overall resilience of MGs by identifying potential weaknesses and enabling operators to implement corrective measures to ensure operational continuity during power outages or other disruptions. \\ \hline
**Real-time monitoring** & Digital twins can provide real-time monitoring of microgrid performance, allowing operators to detect issues promptly and prevent system failures to ensure peak efficiency. \\ \hline
**Cost savings** & Optimizing energy management strategies and implementing predictive maintenance measures can lower maintenance and downtime costs for microgrid operators. \\ \hline
**Increased efficiency** & Digital twins can help to optimize energy usage, reduce waste, and increase efficiency, resulting in lower costs and improved performance. \\ \hline
**Better planning** & Digital twins enable simulation of scenarios and testing of energy management strategies to prepare for future developments and ensure operational continuity in changing conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Key concepts for the use of digital twin technology in MGs. Figure 9: A general overview of the important digital twinning services in MGs [6].
## 2 Improvements to the A* Algorithm
### Improvement to the A* Algorithm
#### 2.2.2 Selection of Critical Points  
Traditional A* algorithms frequently generate a considerable quantity of redundant nodes and avoidable corners in path planning. This not only results in insufficiently smooth paths and an increase in the path length, but the path may even pass through obstacles, thereby influencing the motion efficiency and performance of robots in complex environments. Hence, eliminating these redundant nodes constitutes a critical step in path optimization. Through path optimization, the robot can move in a smoother and more efficient manner, thereby enhancing the overall operational performance and environmental adaptability. This optimization not only alleviates the computational load but also significantly enhances the robot's navigational accuracy. The optimized path is presented in Figure 1.  
As shown in Figure 1a, the black line segments represent the robot's initial path. In Figure 1b, the blue line represents the path after the first optimization, and in Figure 1c, the red line represents the path after the second optimization.  
First, the A* algorithm is employed to generate the initial paths p1, p2...p7 and to record all the nodes passed on the path, encompassing redundant nodes and turning points. These nodes represent the planned trajectory of the robot from the origin point to the goal point. In the next step, each node pi along the path is traversed, and its front and rear neighboring nodes pi \(-\) 1 and pi + 1 are examined. For this purpose, the vector from \(pi\) to \(pi-1\) and the vector from \(pi\) to \(pi+1\) are computed, and the angle between them is computed by the dot product and the modulus length of these two vectors \(\theta\). Specifically, the angle between the two vectors \(\theta\) can be calculated by the following formula:  
\[\cos\theta=\frac{\overrightarrow{pipi-1}\cdot\overrightarrow{pipi+1}}{ \left|\overrightarrow{pipi-1}\right|\cdot\left|\overrightarrow{pipi+1}\right|} \tag{6}\]  
If the angle \(\theta\) is equal to 0, then pi, pi \(-\) 1, and pi + 1 are collinear. In this case, node pi can be regarded as redundant and can be deleted, and its parent node is modified to connect both pi \(-\) 1 and pi + 1. If the pinch angle is not 0, the linear equations of pipi \(-\) 1 and pipi + 1 are established, respectively. The safety distance f is introduced; the value d denotes the minimum distance between the obstacle point and the straight line. If d \(\leq\) f, there is an obstacle between the two points, and then the current point is set as the parent node; otherwise, if there is no obstacle between the two points, the intersection of the two straight lines is the parent node, and other redundant nodes are deleted. The above steps are repeated. As shown in Figure 1, in the first optimization, nodes p2, p3, and p6 are deleted, and p1p4 and p5p7 are connected because the line of p4p7 passes through the obstacle, so p4 is retained. The second optimization extends p1p4 and p5p7 to intersect at point p8, which is the new parent node. The final path is p1p8p7.
# Chaotic Evolutionary Programming for an Engineering Optimization Problem
## 7 Conclusions  
In this manuscript, chaotic evolutionary programming and pattern search have been proposed as a solution for the economic load dispatch problem of multi-fuel power generators. The CEPPS employs chaotic map based stochastic population initialization. Secondly, the chaos based mutation and selection operators have been proposed. In order to enhance the exploration capability, Powell's pattern is introduced in the search process under stochastic control. The well accepted chaos map, viz. the Gauss map and the tent map based CEPPS variants, were analyzed for performance. Computer simulations have been performed on generalized test functions and a load dispatch problem to verify the effectiveness of the CEPPS variants. The numerical results of the generalized benchmark problems reveal that tent map based CEPPS has a better search capability to find the optimal solution in the majority of the test problems. The experimentation on the ELD problem of multi-fuel generators clearly indicates that CEPPS variants have the ability to provide very competitive results in terms of generation cost as compared with the existing literature. Lastly, it has been observed that the Gauss map based CEPPS variants have an advantage in providing a better solution for multi-fuel systems.  
All authors contributed equally to this work. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Cost (\$/h)**} \\ \cline{2-7}  & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** \\ \hline BBO [31] & 624.51 & – & – & – & – & – \\ CPSO [32] & – & 623.82 & – & – & – & – \\ CGA-MU [33] & 623.80 & 624.71 & – & – & – & – \\ DE [33] & 623.80 & 624.46 & – & – & – & – \\ DEBBO [34] & 624.51 & – & – & – & – & – \\ ELHN [35] & 624.51 & – & – & – & – & – \\ IGA [30] & 624.51 & – & – & – & – & – \\ IGA-MU [30] & 623.80 & 624.51 & – & – & – & – \\ KHA [36] & 624.51 & – & – & – & – & – \\ PSO [33] & 623.80 & 624.24 & – & – & – & – \\ QP-ALHN [37] & 623.80 & – & 624.32 & – & – & – \\ SPPO & 623.80 & 623.82 & 624.32 & 700.29 & 700.77 & 700.48 \\ CEPPS-1 & 623.75 & 623.87 & 623.76 & 699.70 & 699.54 & 704.94 \\ CEPPS-2 & 623.75 & 623.88 & 623.77 & 699.77 & 699.73 & 700.60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test Power System 1, comparison of economic load dispatch (ELD) (\(P_{D}=2700\) MW). BBO, biogeography based optimization; DE, differential evolution; ELHN, enhanced augmented Hopfield neural network; IGA, improved gravitational search algorithm; KHA, krill herd optimization; QP-ALHN, quadratic programming augmented Hopfield neural network.
# Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint
## 4 The Proposed GTSC Method
### Optimal Cluster Head Selection Algorithm
The relative mobility factor is normalized as  
\[f_{M_{i}}=\frac{\sum\limits_{j=1}^{N_{CH}}sim_{-}v_{ij}}{N_{CH}} \tag{29}\]  
As mentioned in Section 3.2, we convert the performance constraint of end-to-end delay into the number of forwarding hops. The factor of inter-cluster forwarding hops can be normalized as  
\[f_{Hop_{i}}=\frac{\sum\limits_{j=1}^{N_{CH}}hop(i,j)}{N_{CH}\cdot hop_{\text{ max}}} \tag{30}\]  
where \(hop(i,j)\) represents the number of forwarding hops between cluster head \(i\) and \(i\), \(hop_{\text{max}}\) denotes the maximum hops between cluster heads. Based on the definition of above factors, the cluster head selection algorithm is presented in Algorithm 2. ```
1:Cluster set \(\{Cluster_{1},Cluster_{2},\ldots,Cluster_{K}\}\)
2:Cluster head set \(\mathbf{S}=\{CH_{i}^{1},CH_{i}^{2},i=1,2,\ldots,N_{c}\}\)
3:for\(i=1\) to \(K\)do
4:for each node \(j\in Cluster_{i}\)do
5: calculate \(f_{Intra_{j}}\)
6:endfor
7: select \(n\) nodes as cluster head candidates
8: cluster head candidate set \(\mathbf{S}_{Rough_{i}}=\left\{CH_{i}^{1},CH_{i}^{2},\ldots,CH_{i}^{n}\right\}\)
9:endfor
10:for\(i=1\) to \(K\)do
11: for each cluster head candidates \(j\in\mathbf{S}_{Rough_{i}}\)do
12: calculate \(F_{node_{j}}\)
13: endfor
14: select two nodes as double cluster heads
15: construct cluster head set \(\mathbf{S_{i}}=\left\{CH_{i}^{1},CH_{i}^{2}\right\}\)
16:endfor
17:return\(\{S_{1},S_{2},\ldots,S_{K}\}\)
```  
**Algorithm 2** The Optimal Cluster Head Selection Algorithm