esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 3 Results and Discussion
### Isotherm Studies  
Figure 16 is the isotherm model of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\), and the correlation coefficients are shown in Table 6. We found that both the Langmuir adsorption isotherm model and Freundlich adsorption isotherm could describe the removal process of methylene blue (R\({}^{2}\) > 0.9). Therefore, it can be considered that the adsorption sites of AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) for methylene blue were uniformly distributed and had the same affinity, and the adsorption process was mainly represented by chemical adsorption [46].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**adsorption Model** & \multicolumn{3}{c}{**Laggergren Model**} & \multicolumn{3}{c}{**McKay Model**} \\ \hline Composite material & AC-CuO & AC-ZnO & AC-TiO\({}_{2}\) & AC-CuO & AC-ZnO & AC-TiO\({}_{2}\) \\ \hline Nonlinear expression & \multicolumn{3}{c}{\(Q_{t}=Q_{e}\big{(}1-e^{-K_{1}t}\big{)}\)} & \multicolumn{3}{c}{\(Q_{t}=\frac{K_{2}Q_{e}^{2}t}{1+K_{2}Q_{t}}\)} \\ \hline Fitting parameters & \(Q_{\rm e}\) = 4.3604 & \(Q_{\rm e}\) = 4.5795 & \(Q_{\rm e}\) = 4.7746 & \(Q_{\rm e}\) = 4.6903 & \(Q_{\rm e}\) = 5.0446 & \(Q_{\rm e}\) = 5.5352 \\  & \(K_{1}\) = 0.1573 & \(K_{1}\) = 0.0840 & \(K_{1}\) = 0.0437 & \(K_{2}\) = 0.0511 & \(K_{2}\) = 0.0245 & \(K_{2}\) = 0.0099 \\ \hline R\({}^{2}\) & 0.68 & 0.88 & 0.95 & 0.90 & 0.96 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of correlation coefficients for kinetic model fitting.  
Figure 16: Isotherm model of (**a**) AC-CuO, (**b**) AC-ZnO, and (**c**) AC-TiO\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**adsorption Model** & \multicolumn{3}{c}{**Intra-Particle Diffusion Model**} \\ \hline Composite material & AC-CuO & AC-ZnO & AC-TiO\({}_{2}\) \\ \hline Nonlinear expression & \multicolumn{3}{c}{\(Q_{t}\)= \(K_{d}t^{0.5}\)+\(b\)} \\ \hline Fitting parameters & \(K_{d}\) = 0.1689 & \(K_{d}\) = 0.2355 & \(K_{d}\) = 0.3270 \\  & \(b\) = 2.8412 & \(b\) = 2.2788 & \(b\) = 1.3115 \\ \hline R\({}^{2}\) & 0.83 & 0.80 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of correlation coefficients for intraparticle diffusion model fits.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 3 System Model
### Experimental Channels  
Two channels are considered in this paper, a tank channel for static performance tests and a replay channel reproducing real-world at-sea acoustic transmission. In both experiments, the CP duration is set much longer than the Root Mean Square (RMS) channel delay spread [17] in order to absorb most of the multipath energy.
## 3 Results
### Non-Targeted LC-QTOF-MS Based Metabolomics Data
 \\ \hline \multicolumn{7}{c}{_Benzenoids_} \\ \hline Phenylacetaldehyde & C\({}_{8}\)H\({}_{\text{G}}\)O & 120.0575 & 3.33 & 5 & [M+H-H\({}_{\text{G}}\)O]\({}^{*}\) & ESI + & Level 3 & 0.43 \\ \hline Methylstyrene & C\({}_{9}\)H\({}_{10}\) & 118.0783 & 14.59 & 6 & [M+H]\({}^{*}\) & ESI + & Level 3 & 2.31 \\ \hline Phenylpropanol & C\({}_{9}\)H\({}_{10}\)O & 134.0732 & 14.59 & 5 & [M+H]\({}^{*}\) & ESI + & Level 3 & 0.65 \\ \hline Cyclointegrin & C\({}_{21}\)H\({}_{20}\)O\({}_{6}\) & 368.1260 & 14.87 & 0 & [M-H]\({}^{-}\) & ESI \(-\) & Level 3 & 0.61 \\ \hline Cresol & C\({}_{7}\)H\({}_{\text{G}}\)O & 108.0575 & 16.46 & 4 & [M-H]\({}^{-}\) & ESI \(-\) & Level 3 & 1.25 \\ \hline Levomethadyl Acetate & C\({}_{23}\)H\({}_{31}\)NO\({}_{2}\) & 353.2355 & 16.99 & 6 & [M+H]\({}^{*}\) & ESI + & Level 3 & 1.17 \\ \hline Hydroxy-(pentadecatrienyl)benzoic acid & C\({}_{22}\)H\({}_{30}\)O\({}_{3}\) & 342.2195 & 17.33 & 4 & [M-H]\({}^{-}\) & ESI \(-\) & Level 3 & 0.30 \\ \hline \multicolumn{7}{c}{_Fatty Acids_} \\ \hline Corchoionol C glucoseide & C\({}_{19}\)H\({}_{30}\)O\({}_{8}\) & 386.1941 & 6.17 & 0 & [M-H]\({}^{-}\) & ESI \(-\) & Level 2 & 0.46 \\ \hline Thhydroxy-octadecadienoic acid & C\({}_{18}\)H\({}_{20}\)O\({}_{3}\) & 328.2250 & 11.14 & 1 & [M-H]\({}^{-}\) & ESI \(-\) & Level 2 & 0.42 \\ \hline Octadecatrieneoic acid & C\({}_{18}\)H\({}_{20}\)O\({}_{2}\) & 276.2089 & 15.23 & 1 & [M-H]\({}^{-}\) & ESI \(-\) & Level 3 & 0.37 \\ \hline Hydroxyoctadecatrienic acid & C\({}_{18}\)H\({}_{30}\)O\({}_{3}\) & 294.2195 & 15.32 & 1 & [M-H-H\({}_{2}\)O]\({}^{-}\) & ESI \(-\) & Level 3 & 1.40 \\ \hline Palmitoleic acid & C\({}_{16}\)H\({}_{30}\)O\({}_{2}\) & 254.2246 & 16.89 & 5 & [M+H-H\({}_{2}\)O]\({}^{*}\) & ESI + & Level 3 & 0.79 \\ \hline \multicolumn{7}{c}{_Glycerolipids_} \\ \hline Gingerglycolipid A & C\({}_{33}\)H\({}_{86}\)O\({}_{14}\) & 676.3670 & 14.50 & 1 & [M-H]\({}^{-}\) & ESI \(-\)/+ & Level 3 & 0.72 \\ \hline LPC 16:0 & C\({}_{24}\)H\({}_{50}\)NO\({}_{7}\)P & 495.3325 & 15.94 & 6 & [M+H]\({}^{*}\) & ESI + & Level 2 & 0.43 \\ \hline LPC 8:0 & C\({}_{16}\)H\({}_{20}\)NO\({}_{8}\)P & 397.1866 & 16.21 & 2 & \ \begin{tabular}{c} [M+HC \\ OOH+H]\({}^{-}\) \\ \end{tabular}  & ESI \(-\) & Level 3 & 0.98 \\ \hline PI 41:7 & C\({}_{96}\)H\({}_{83}\)O\({}_{13}\)P & 922.5571 & 16.03 & 7 & \begin{tabular}{c} [M+HC \\ OOH+H]\({}^{-}\) \\ \end{tabular}
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 4 Result Analysis
### Network Analysis
#### 4.2.2 Thematic Analysis
This analysis can reduce the dimensionality of data by identifying the underlying relationships between different themes. In the context of the evolution of e-payment technology, each cluster represents a group of interrelated topics that can be interpreted in terms of their dimensional value, which indicates their influence or importance in the analysis. Cluster 1 (Red Cluster): This cluster is characterized by terms that are related to the customer experience and service quality aspects of e-payment technology, such as \"consumer trust,\" \"loyalty,\" \"satisfaction,\" \"quality,\" and \"service quality.\" The dimensional values, represented by Dimensions 1 and 2 on the axes, show the degree of variance each dimension explains in the dataset. For instance, the value of 58.11% for Dimension 1 indicates that this dimension accounts for most of the variance in the data for this cluster. The significance of this cluster lies in its focus on the subjective experience of the consumer using e-payment technology. Factors such as trust, satisfaction, and quality are crucial for adopting and continuing the use of e-payment systems. The high percentage in Dimension 1 suggests that these aspects are highly influential in e-payment research and development. Cluster 2 (Blue Cluster): On the right side, we have a cluster that focuses on the adoption and theoretical framework of e-payment systems, with terms like \"technology acceptance model,\" \"user acceptance,\" \"information technology,\" \"banking,\" and \"behavioral intention.\" The placement of these terms along the two dimensions indicates their relative importance in explaining the data variance. These terms are more related to the theoretical and practical understanding of how and why consumers and businesses adopt e-payment technologies. The significance here is in understanding the factors that drive the adoption of e-payment technology. Models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are foundational to this understanding. These factors are essential for guiding the design, implementation, and improvement of e-payment systems to enhance user acceptance and usage. Together, these clusters and their dimensional values highlight two critical aspects of e-payment technology evolution: the user experience and the theoretical understanding of technology adoption. The user experience is key for customer retention and loyalty, while understanding the factors that drive adoption is essential for the initial acceptance of  
Figure 8: Factorial analysis. e-payment technologies. The factorial analysis demonstrates the interplay between these aspects and their combined influence on the direction and focus of e-payment technology development and research.
## 4 Experimental Results  
In order to demonstrate different aspects of the performance of RC_LCI, it was first applied to segment images with noise. Then, it was applied to correct inhomogeneous intensity images with different angles. Finally, images with noise and intensity inhomogeneity were used to demonstrate the effectiveness of RC_LCI. Experimental results were tested in Matlab R2015b on a computer with Core(TM) i7-8700, 8 GB of RAM, and Windows 10 operating system.
## 2 Materials and Methods
### Wireless Sensor Network Description  
A WSN comprises 'nodes', ranging from a few to hundreds or thousands, where each node connects to other sensors. Each node consists of several parts: a sensing unit, a processing unit, a communication unit, and a power unit (Figure 2).  
The sensing unit includes the sensors themselves, which capture environmental data. These data are then processed locally by the processing unit, consisting of a microcontroller or microprocessor, an electronic circuit for interacting with the sensors, and an energy source.  
Batteries, or an embedded energy harvesting system, typically power the power unit.
## 4 Experiments and Results  
### Experimental Methodology  
We use real-world applications to understand and compare the performance of big data processing platforms in each computing layer. Specifically, we use image classification  
Figure 2: Overview of edge/fog computing layers.  
as batch processing, object detection as stream processing, and image processing as function processing. Although our experimental design is centered around image-related applications within three fundamental data processing paradigms, it is applicable to a broad spectrum of use cases. The image-related applications serve as representative examples to showcase the platforms' performance across diverse scenarios. The findings and recommendations derived from our study are generalizable to a variety of applications within the selected data processing paradigms. In the following subsection, we present (a) detailed description of the experimental environment, including hardware configurations, software versions, and any relevant settings; (b) the tools, frameworks, and techniques utilized during the research; and (c) the metrics used to evaluate the performance of real-world applications deployed on big data processing platforms.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
###### Abstract  
Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models.  
Keywords:pan-sharpening: Swin transformer; multi-scale residuals and dense blocks; residual feature fusion block +
Footnote †: journal: Applied
## 3 Application Areas of IoT and UAV in Smart Farming
### Mapping  
UAVs are used to create useful 2D or 3D maps of an agricultural field. For example, the area of the farmland, soil conditions, and status of the crops, infestation within the crop can be detected in these maps [44]. For instance, the authors in [45] obtained high-resolution maps delineating the spatial variations of radiation interception using UAV images. These maps are used for profitable precision agriculture tasks, such as the separation of fruit quality areas, identifying the area of deforestation and the agronomic control of homogeneous zones.  
Figure 3: Different types of agricultural UAVs (Harvesting UAV, Spraying UAV, Mapping UAV, Sensing UAV) [6].
# Rankine Active Earth Pressure of Unsaturated Filling under Earthquake
## 3 Analyses and Results
### Distribution of Seismic Active Earth Pressure
#### 3.1.2 Effect of Unsaturation Parameters  
(1) Tables 1 and 2 show that the intensity of Rankine active earth pressure increases along the wall back downwardly. The existence of matric suction as well as the attributed friction angle results in shrinkage of the intensity of Rankine earth pressure. On the other hand, the range of \"zero intensity\" amplifies. Given \(b=0.5\), the earth pressure at a depth of 4 m reduces from 44.75 kPa to 0 kPa when the matric suction increases from 0 kPa to 90 kPa, and the depth of zero intensity increases to be deeper than 4 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**H/m**} & \multicolumn{8}{c}{**(\(\mu_{a}-\mu_{m}\))/kPa**} \\ \cline{2-13}  & **0** & **30** & **60** & **90** & **120** & **0** & **30** & **60** & **90** & **120** & **0** & **30** & **60** & **90** & **120** \\ \cline{2-13}  & \multicolumn{8}{c}{\(b=0\)} & \multicolumn{8}{c}{\(b=0.5\)} & \multicolumn{8}{c}{\(b=1\)} \\ \hline
1 & 3.43 & 0 & 0 & 0 & 0 & 2.01 & 0 & 0 & 0 & 0 & 1.64 & 0 & 0 & 0 & 0 \\
2 & 16.13 & 6.06 & 0 & 0 & 0 & 10.25 & 4.05 & 0 & 0 & 0 & 9.30 & 0 & 0 & 0 & 0 \\
3 & 29.84 & 23.89 & 12.61 & 0 & 0 & 25.50 & 21.80 & 10.88 & 0 & 0 & 19.76 & 16.43 & 9.69 & 0 & 0 \\
4 & 46.54 & 41.71 & 29.21 & 15.35 & 2.08 & 44.75 & 39.55 & 27.38 & 0 & 0 & 32.33 & 27.94 & 17.03 & 0 & 0 \\
5 & 65.24 & 59.53 & 45.81 & 32.86 & 12.94 & 60.4 & 55.30 & 40.88 & 16.56 & 9.79 & 46.89 & 37.44 & 24.37 & 12.99 & 6.23 \\
6 & 85.39 & 77.36 & 62.40 & 50.37 & 28.26 & 77.25 & 73.05 & 58.39 & 33.28 & 24.41 & 57.45 & 46.94 & 31.71 & 18.36 & 11.62 \\
7 & 104.1 & 95.18 & 79.00 & 67.88 & 46.14 & 101.4 & 91.80 & 72.89 & 50.00 & 39.02 & 75.02 & 56.44 & 39.05 & 23.73 & 18.15 \\
8 & 131.3 & 113.0 & 95.60 & 85.38 & 67.55 & 129.7 & 109.5 & 92.39 & 66.73 & 53.63 & 90.58 & 65.95 & 46.39 & 29.10 & 21.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intensity values of Rankine active earth pressure and matric suction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**H/m**} & \multicolumn{8}{c}{\(\varphi^{bF}\)} \\ \cline{2-13}  & **10** & **13** & **16** & **19** & **22** & **10** & **13** & **16** & **19** & **22** & **10** & **13** & **16** & **19** & **22** \\ \cline{2-13}  & \multicolumn{8}{c}{\(b=0\)} & \multicolumn{8}{c}{\(b=0.5\)} & \multicolumn{8}{c}{\(b=1\)} \\ \hline
1 & 4.66 & 0 & 0 & 0 & 0 & 3.12 & 0 & 0 & 0 & 0 & 1.29 & 0 & 0 & 0 & 0 \\
2 & 11.4 & 5.18 & 3.94 & 2.6 & 1.54 & 14.39 & 4.17 & 2.49
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 5 Emission Characteristics
### Unregulated Emissions
#### 5.2.2 Polycyclic Aromatic Hydrocarbons  
Polycyclic aromatic hydrocarbons (PAHs) are organic substances composed of carbon and hydrogen atoms. They are composed of at least two condensed or molten aromatic ring structures [123]. Low molecular weight compounds containing no more than four rings are mostly distributed in the gas phase, and higher molecular weight compounds containing more than six rings are mainly found in the particles [124]. Their distribution between the two phases depends on their molecular weight, ambient temperature, PAH concentration and particle composition [13]. Although PAHs are unregulated gases, the emissions can be harmful to humans. The American Conference of Governmental Industrial Hygienists (ACGIH) indicates that short-term exposure to PAHs can lead to impaired lung function, and certain patients may even show a thrombotic effect. Prolonged exposure to PAHs may cause cancer, cell malformations and even inheritance of toxicity. At a certain concentration, adverse symptoms such as vomiting and dizziness may also occur [123].  
Most experimental studies have found that biodiesel can reduce the release of some PAHs. [PERSON] et al. tested rapeseed methyl esters, waste cooking oil methyl esters and waste cooking oil ethyl esters biofuels in urban and extra-urban modes [13]. The results showed that the emissions of heavy PAHs are generally higher than that of light PAHs, and biodiesel can reduce total PAH emissions as shown in Figure 15. [PERSON] et al. investigated the PAH emissions of waste cooking oil biodiesel (WCO) on Cummins heavy-duty engines and found that the average reduction rates of total PAHs of WCOB5, WCOB10, WCOB20 and WCOB30 were 8.1%, 24.0%, 32.0% and 37.7% compared with ultra-low sulfur diesel [125]. Some researchers also tested blends of ultra-low sulfur diesel and soybean biodiesel in different concentrations and combined them with the SCR (Selective Catalytic Reduction) system to analyze diesel engine emissions results. After replacing diesel with biodiesel, total PAHs emissions were reduced by 28% [126]. This conclusion is similar to findings of other studies [127; 128; 129].  
The PAHs produced by diesel engines mainly come from PAHs formed from fuel during combustion, PAHs accumulated from fuel to lubricating oil, release of PAHs deposited by the engine, and PAHs generated from the fuel pyro-synthesis process [130]. Biodiesel itself has fewer polycyclic aromatic hydrocarbons, which can reduce granular phase aromatics generated during combustion and indirectly reduce the release of lubricants and sediments [126; 130]. The higher oxygen content in biodiesel is more likely to result in complete combustion, thereby reducing PAH emissions [130].  
Figure 14: Volatile organic carbon (VOC) emissions from a diesel engine fueled with conventional diesel fuel, B20, and canola oil biodiesel fuel (adapted with permission from [119], Elsevier, 2018).
# Development, Implementation and Evaluation of An Epidemic Communication System
## 3 Implementation Method for Epidemic Communication
### Change in Amount of Data Transfered Due to Movement of Radio Stations  
In epidemic communication, as depicted in Figure 7, wireless stations establish connections to receiving stations and transfer data while moving within the communication range of other wireless stations. The communication range is utilized for data transfer while moving, and the connection is terminated when leaving the communication range. When approaching other wireless stations, these operations are repeated.  
However, when passing through this communication range, there is overhead involved in connection time and the procedures for sending and receiving packet data. The actual time available for data transmission is the time remaining after deducting these overheads.  
The connection procedure and the sequence of data transmission are illustrated in Figure 8. In this study, the transmitting station uses passive scanning to find surrounding available receiving stations by receiving beacon signals sent by receiving stations for a certain period. Subsequently, as an authentication procedure, the transmitting station sends an authentication request to the receiving station, and the receiving station responds with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Transmission Rate**} & \multicolumn{4}{c}{**Distance between A and B**} \\ \cline{2-5}  & 0.3 m & 0.6 m & 0.9 m & 1.2 m \\ \hline
6 Mbps & 0.9 m & 1.7 m & 2.5 m & 3.4 m \\
9 Mbps & 1.0 m & 1.9 m & 2.8 m & 3.8 m \\
12 Mbps & 1.2 m & 2.4 m & 3.6 m & 4.7 m \\
18 Mbps & 1.5 m & 3.0 m & 4.5 m & 5.9 m \\
24 Mbps & 2.1 m & 4.2 m & 6.3 m & 8.4 m \\
36 Mbps & 3.3 m & 6.6 m & 9.9 m & 13.2 m \\
48 Mbps & 5.3 m & 10.5 m & 15.7 m & 21.0 m \\
54 Mbps & 5.9 m & 11.8 m & 17.6 m & 23.5 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distance between B and C with transmission rate achieved depending on distance between A and B.  
Figure 6: SINR and transmission rate evaluation.  
an authentication response to confirm its availability. Once the authentication procedure is completed, an association request is sent to initiate the connection procedure, and the connection is completed when the receiving station responds with an association response. In addition to these procedures, data transmission and reception involve carrier-sensing procedures and transmission-reception periods as specified in the IEEE 802.11g standard.  
For the evaluation of communication time and transmitted data volume, the beacon interval is set to 50 ms, and other overhead times for transmission and reception periods comply with the IEEE 802.11g standard [17].
# A Critical Review on Obesity: Herbal Approach, Bioactive Compounds, and Their Mechanism
## 4 Herbs That Control Obesity
### Capsicum annum  
The herb's common name is chili pepper; it belongs to the Solanaceae family. The herb is a native plant of Mexico [100]. The major bioactive compounds of this herb are flavonoids, phenolic acids, ascorbic acid, and carotenoids [101]. These compounds possess various pharmacological activities such as antioxidant, antimicrobial, anti-obesity, anti-cancer, anti-neoplastic, anti-ulcer, and anti-inflammatory [102]. The main active component responsible for its anti-obesity activity is capsaicin. It triggers the activation of the TRPV1 mechanism that can reduce abnormal glucose homeostasis by promoting the release of insulin and increasing the levels of glucagon-like peptide-1 (GLP-1). It promotes lipid oxidation, inhibits adipogenesis, activates thermogenesis, decreases appetite, and increases satiety, controlled by neuronal circuits in the hypothalamus [103]. In addition to inhibiting the expression of PPAR-\(\gamma\), C/EBP-\(\alpha\), and leptin, capsaicin dramatically reduced the glycerol-3-phosphate dehydrogenase (GDPH) activity and intracellular triglyceride in 3T3-L1 adipocytes [104].
## 2 Safe and Efficient UAV Route Construction Method over Wireless Multi-Hop Network  
We have established a distributed management system for UAVs that was inspired by the virtual circuit and datagram methods of packet switching networks [31]. We investigate a method to construct safe and efficient navigation routes for UAVs over a multi-hop network with radio terminal nodes as shown in Figure 1. In this method, UAVs are treated as data packets in a network, ground wireless devices as routers, and the connection between wireless devices as links, and the control of a packet switching network is applied to UAV flight path control.  
1. Each node exchanges routing protocol packets to construct the UAV flight route.
2. Each node creates a routing table for the UAV flight route from the source node to the destination node.
3. When the UAV arrives over a relay node on its flight route, it queries the node for information about the next hop node.
4. The UAV moves towards the next hop node. Repeating (3) and (4), the UAV follows the relay nodes and arrives at the destination node.