esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.7 Numerical Methodology  
In the present work, the 3D pressure-based implicit unsteady solver available in ANSYS Fluent code was used to solve the basic governing equations (mass, momentum and energy). The equations were spatially discretized using the finite volume method using the standard scheme for pressure interpolation. The discretization scheme for the convective term of transport equations used the first-order upwind scheme. The pressure-velocity coupling in the discretized equations was performed using the semi-implicit method for pressure linked equations (SIMPLE) algorithm to solve the pressure field.  
In the biogas-DME hybrid dual-fuel engine, the fuel compositions can be varied to fit the operating conditions. Thus, in this study, the DME content, the biogas compositions, and the diesel injection were varied. The performance and emissions of the engine were examined as DME varied in the range of 0%-40%, CH\({}_{4}\) in biogas varied in the range of 60%-80%. The energy brought into the engine by different fuel compositions was described by the equivalence ratio of the hybrid fuel mixture \(\phi\)a/b, where \(\phi_{\text{gas}}\) = a was the contribution of the biogas and DME part into the global equivalence ratio b. The diesel contribution in the equivalence ratio was thus, \(\phi_{\text{die}}\) = b - a. In this study, \(\phi_{\text{die}}\) varied in the range of 0-0.2.
# Wrinkling Patterns and Stress Analysis of Tensile Membrane with Rigid Elements
## 2 Non-Uniform Stress Field
The static simulation of the non-uniform membrane structure shown in Figure 1 was carried out by using ABAQUS finite-element analysis software. The thin-shell element S4R with four nodes and six degrees of freedom was used for the membrane structure, and the hexahedral element C3D8R with eight nodes and three degrees of freedom was used for the rigid element. The rigid element and the membrane were connected by binding constraints. The material parameters of the membrane and rigid elements are shown in Table 1. \(R_{c}\) is the distance between the boundary of the membrane and the boundary of the rigid element. When \(R_{c}=40\) mm, the analytical solution and finite-element results of the membrane structure with four rigid elements are shown in Figure 2. The analytical solution to the first principal stress of the membrane is in good agreement with the finite-element results. The membrane structure used in this paper is a polyimide membrane with a side length of 300 mm, and the rigid element is a square aluminum sheet with a side length of 10 mm. The material parameters of the membrane and the rigid element are shown in Table 1.  
When the rigid elements are in different positions, the superposition of the tensile stress of the membrane and the disturbance stress of the rigid elements changes the compressive-stress distribution area of the membrane. The compressive-stress distribution area of the membrane is the area of the first principal stress \(\sigma_{1}>0\) and the second principal stress \(\sigma_{2}\leq 0\) of the membrane. Using the non-uniform stress-field calculation method mentioned above, the stress distribution of the membrane structures containing four rigid elements when the rigid element position \(R_{c}\) is 30 mm, 40 mm, 50 mm, and 60 mm were studied. The results are shown in \[D_{1122}(r_{n})=S_{1122}(r_{n})+\frac{1}{2(1-v)}\bigg{\{}\Big{[}(1-2v)x_{n}^{ 2}+y_{n}^{2}\Big{]}\frac{a^{2}}{r_{n}^{4}}-\bigg{(}y_{n}^{2}+\frac{1}{a^{2}}x_ {n}^{2}y_{n}^{2}\bigg{)}\frac{a^{4}}{r_{n}^{6}}+\frac{6a^{4}}{r_{n}^{8}}x_{n}^ {2}y_{n}^{2}\bigg{\}} \tag{25}\]0 It can be seen from the figure that the compressive-stress distribution area of the membrane changes with the change in the position of the rigid element. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Membrane** & **Rigid Element** \\ \hline Thickness (mm) & 0.025 & 1 \\ Poisson’s ratio & 0.34 & 0.34 \\ Young’s modulus (Mpa) & 2500 & 71,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Material parameters of membrane and rigid elements. Figure 2: The first principal stress of a film with four rigid elements: (**a**) analytical solution; (**b**) finite-element solution.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Energy Optimized Protocols
#### 11.3.3 E2E Delay  
In the Figure 16 it is visibly shown that both versions of AEDG have induced higher E2E delay as compared to RE-AEDG. This is due to the fact that data reaching at their respective gatherer stays there for the time until AUV approaches to them. Moreover for AEDG, AUV track is smaller as compare to RE-AEDG so; the intercommunication distance between the source and destination node (AUV) is enlarged. Whereas for RE-AEDG, threshold based neighbors assignment to a node leads to  
Figure 14: PAR of networks in energy optimized and non optimized protocols.  
Figure 15: Packets drop rate in energy optimized and non optimized protocols.  
comparatively reduced queuing delay. This is because it specifies the number of predecessor nodes that could be attached to another node. Thus, the E2E delay of RE-AEDG is relatively lesser than both versions of AEDG. However, despite of the random movement of AUVs in U-LVRP the presence of two of mobile sinks normalizes the additional message latencies created by dynamic selection of communication routes between nodes and AUVs.
# Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River
## 3 Results
### Correlations between the Examined Water Quality Parameters along Kifisos River
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & pH & DO & DIN & Salinity & TN & TP & T & EC & NO3–N & NO2–N & NH4*-N & PO4+P & _E coli_ & HWQI \\ \hline pH & 1.00 & & & & & & & & & & & \\ DO & 0.61 & 1.00 & & & & & & & & & & \\ DIN & \(-\)0.06 & \(-\)**0.31** & 1.00 & & & & & & & & & \\ Salinity & \(-\)**0.42** & \(-\)**0.31** & 0.07 & 1.00 & & & & & & & & \\ TN & \(-\)0.04 & \(-\)**0.29** & \(-\)0.01 & 0.03 & & & & & & & & \\ TP & \(-\)0.07 & \(-\)**0.31** & 0.49 & \(-\)0.12 & 0.49 & \(-\)0.23 & & & & & & \\ T & \(-\)**0.25** & \(-\)**0.49** & 0.27 & 0.09 & **0.28** & 0.54 & & & & & & \\ EC & \(-\)0.39 & \(-\)**0.30** & 0.06 & **0.96** & 0.03 & \(-\)0.07 & 0.14 & 1.00 & & & & \\ NO3–N & 0.03 & 0.01 & **0.56** & 0.38 & **0.54** & 0.11 & 0.04 & 0.40 & 1.00 & & & \\ NO3–N & \(-\)0.17 & \(-\)**0.43** & 0.59 & 0.10 & **0.60** & 0.33 & 0.27 & 0.10 & 0.06 & 1.00 & & & \\ NH4–N & \(-\)0.12 & \(-\)**0.32** & **0.40** & \(-\)0.10 & **0.42** & 0.20 & 0.10 & \(-\)0.11 & \(-\)0.23 & 0.81 & 1.00 & & \\ PO4+P & \(-\)0.08 & \(-\)**0.32** & **0.46** & \(-\)0.10 & 0.46 & 0.99 & 0.53 & \(-\)0.05 & 0.11 & 0.30 & 0.18 & 1.00 & \\ _E coli_ & 0.00 & \(-\)**0.35** & 0.16 & \(-\)0.18 & 0.19 & \(-\)0.03 & 0.16 & \(-\)0.19 & \(-\)0.17 & 0.45 & 0.48 & \(-\)0.06 & 1.00 \\ HWQI & 0.05 & 0.57 & \(-\)**0.52** & \(-\)0.01 & \(-\)**0.54** & \(-\)0.57 & \(-\)0.44 & \(-\)0.07 & \(-\)0.09 & \(-\)0.76 & \(-\)0.69 & \(-\)0.55 & \(-\)0.29 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 2: [PERSON]’s correlation among the examined parameters (bold numbers are statistically significant with \(p<\) 0.05) *. Figure 2: Variation of temperature with orthophosphates (PO4-P) and total phosphorus (TP) in Kifisos river. human sewage in the river. Nevertheless, there are other sources of nutrients as well since the correlation between _E. coli_, NO3 - N and HWQI is not very strong. The very high correlations between dissolved inorganic nitrogen (DIN) and total nitrogen (TN) as well as between total phosphorus (TP) and PO4-P indicates anthropogenic pollution as the dominant source of nutrients in the river (Table 2). In Table 2 green-yellow-red color scale was applied to visually represent data values based on their relative magnitude compared to the rest of the values. The lowest values were colored with red shades, the middle values with yellow shades, and the highest values with green shades.
###### Abstract  
Sugarcane (complex hybrids of _Saccharum_ spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (\(\text{GPP}_{\text{EC}}\)) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and \(\text{GPP}_{\text{EC}}\) data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane \(\text{GPP}_{\text{EC}}\) and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the \(\text{GPP}_{\text{EC}}\) and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (\(\text{GPP}_{\text{VPM}}\)). The seasonal dynamics of the \(\text{GPP}_{\text{VPM}}\) and \(\text{GPP}_{\text{EC}}\) at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.  
Keywords:CO\({}_{2}\) eddy covariance flux tower; MODIS images; vegetation photosynthesis model; vegetation index +
Footnote †: journal: remote sensing
# Towards a Reliable Evaluation of Local Interpretation Methods
## 3 Human-Friendly Evaluation
### Preliminaries  
Gradient-Weighted Class Activation Mapping (Grad-cam) is the most popular interpretation method that uses a gradient-weighted linear combination of activation from convolutional layers. This approach takes the internal network state into account and produces a coarse FSM that is most responsible for model decision.  
First of all, the importance of k-th channel feature map for c-th class \(\mathbf{\alpha}_{k}^{c}\) can be calculated based on the last fully-connected layer of fine-tuned convolutional neural network (CNN) module. After, the feature maps on the last convolutional layer of fine-tuned CNN models are combined with different weights and Rectified Linear Unit (RELU) function so as to eliminate all negative values in Grad-cam\(=Relu(\sum_{k}a_{k}^{c}A^{k})\).  
Meaningful image perturbations (Mask) is a perturbation-based interpretation method that perturbs the input with optimization to estimate the importance of each input pixel. This method is model-agnostic and testable because of explicit and interpretable image perturbations. The perturbed image Mask is defined by \(Mask=m\odot x+(1-m)\odot r\), where m is a mask, x is the input image, and r is a reference image. \(\bigcirc\) denotes element-wise multiplication.  
Randomized input sampling (Rise) is a black-box-oriented interpretation method. The main idea behind this approach is to sample the input via random masks and then monitor the model class probability. Rise generates the final visual interpretation by a linear combination of the random binary masks. This method is defined by \(Rise=\sum_{m}f(x\odot M)\odot M\), where M is a random binary mask with distribution D. \(\bigcirc\) denotes element-wise multiplication.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 3 Materials and Methods  
The proposed historical map registration framework is illustrated in Figure 1. The original map images are input to a map segmentation module for noise suppression. Then, we train a basic feature extraction model with dual-branch decoders--one dedicated to detecting feature points and the other to generating discriminative descriptors in our generated geometric shape dataset. The preprocessed images are then passed through this model to be fine-tuned. Within the graph attention module, a custom graph neural network aggregates contextual information across feature points through multi-hop message passing, significantly improving descriptor consistency. The Sinkhorn algorithm is first applied to compute dense correspondences, followed by RANSAC-based outlier rejection to estimate the optimal transformation model, yielding the final registered image pair.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### Secure Communication  
The secure communication procedure is performed between two trusted vehicles \(OBU_{i}\) and \(OBU_{j}\) when they intend to interact with each other.  
1. After completing the login procedure, \(OBU_{i}\) generates a random number \(r_{i}\) and computes the messages \(AID_{i}=ID_{i}\oplus r_{i}\), \(M_{1}=PSK\oplus r_{i}\) and \(M_{2}=PSK\oplus h(AID_{i}||r_{i})\), where \(PSK\) was obtained in a previous authentication procedure.
2. \(OBU_{i}\to OBU_{j}\): A secure communication request (i.e., \(AID_{i}\),\(M_{1}\),\(M_{2}\)) is sent to \(OBU_{j}\) by \(OBU_{i}\).
3. Upon receiving (i.e., \(AID_{i}\),\(M_{1}\),\(M_{2}\)), \(OBU_{j}\) uses \(PSK\) to retrieve \(r_{i}\) from \(M_{1}\) and then computes \(PSK\oplus h(AID_{i}||r_{i})\) and checks whether it is equal to \(M_{2}\). The request will be rejected if this equality does not hold. Otherwise, \(OBU_{i}\) randomly chooses \(r_{j}\) and computes \(AID_{j}=ID_{j}\oplus r_{j}\), \(M_{3}=PSK\oplus r_{j}\), \(M_{4}=PSK\oplus h(AID_{j}||r_{j}||h(r_{i}))\) and a session key \(SK_{ij}=h(r_{i}||r_{j}||PSK)\).
4. \(OBU_{j}\to OBU_{i}\): \(OBU_{j}\) returns the response messages (i.e., \(AID_{j}\),\(M_{3}\),\(M_{4}\)) \(\circ\)\(OBU_{i}\).
5. After receiving the messages \(\{AID_{j}\),\(M_{3}\),\(M_{4}\}\), \(OBU_{i}\) verifies whether \(OBU_{j}\) is trusted: \(OBU_{i}\) uses \(PSK\) to retrieve \(r_{j}\) from \(M_{3}\) and checks whether \(M_{4}=h(AID_{j}||r_{j}||h(r_{i}))\) holds. If so, \(OBU_{i}\) computes a session key \(SK_{ij}=h(r_{i}||r_{j}||PSK)\) and a reply message \(M_{5}=SK_{ij}\oplus h(r_{j})\). Otherwise, the process is terminated.
6. \(OBU_{i}\to OBU_{j}\): \(OBU_{i}\) sends \((M_{5})\) to \(OBU_{j}\).
7. After receiving the message \(M_{5}\), \(OBU_{j}\) computes \(SK_{ij}\oplus h(r_{j})\) and then checks whether it is equal to \(M_{5}\). If this quality holds, then the two trusted vehicles can communicate securely using \(SK_{ij}\). Otherwise, \(OBU_{j}\) terminates the process.
# DTFS-eHarris: A High Accuracy Asynchronous Corner Detector for Event Cameras in Complex Scenes
## 4 Experiments and Results
### Accuracy Performance  
The False Positive Rate (FPR) and Accuracy are reported in Tables 2 and 3, respectively, using the same definition as [9; 25]. For the ground-truth corners, the trajectories of the real corners have been obtained using the Harris and LK algorithms in Section 4.1. The confusion matrix is shown in Table 4.  
The TPR is calculated as Equation (12), the FPR is calculated as Equation (13) and the Accuracy is calculated as Equation (14).  
\[TPR=TP/(TP+FN) \tag{12}\]  
\[FPR=FP/(FP+TN) \tag{13}\]  
\[ACC=TP/(TP+FP) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Shapes** & **Dynamic** & **Poster** & **Boxes** \\ \hline eFAST\(\dagger\) & 11.08 & 12.12 & 27.85 & 28.22 \\ G-eHarris\(\dagger\) & 7.91 & 16.9 & 42.50 & 45.21 \\ Arc\({}^{*}\) & 19.70 & 34.00 & 58.11 & 56.32 \\ \hline FA-Harris & 7.05 & 7.67 & 19.27 & 18.00 \\ Ours & **4.77** & **3.45** & **5.31** & **5.15** \\ \hline \end{tabular}
\end{table}
Table 2: False Positive Rate (%) of event-based corner detectors. The best results are in bold.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Shapes** & **Dynamic** & **Poster** & **Boxes** \\ \hline eFAST\(\dagger\) & 76.79 & 68.61 & 60.67 & 60.53 \\ G-eHarris\(\dagger\) & 77.12 & 68.18 & 59.78 & 59.30 \\ Arc\({}^{*}\) & 76.58 & 63.87 & 59.00 & 58.33 \\ \hline FA-Harris & **79.74** & 72.34 & 61.88 & 62.36 \\ Ours & 78.82 & **75.4** & **64.95** & **67.38** \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy (%) of event-based corner detectors. Two hybrid methods are listed separately. The best results are in bold.  
Figure 9: Corners detected by different detectors between two adjacent images on poster (the most complex scene), in which green is positive and red is negative.  
The proposed method shows the best performance in FPR and better performance in Accuracy. With the increase in scene complexity, the advantage gradually expands, which means that our detector is more robust for complex scenes (poster and boxes).
## 3 Results and Discussion  
Table 3 summarizes the workability (slump flow/flow time (T50)/flow velocity/fresh density) and hardness (28-day compressive strength/density/UPV) properties of all 32 AAECs. The following subsections present the influence of the MgO, nanomaterials (MWCNT/rGO), and reagent types on the workability, compressive strength, UPV, and microstructural characteristics of the AAECs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**AAECMix** & **Fresh Density (kg/m\({}^{3}\))** & **Slump Flow (mm)** & **Slump Flow Time (s)** & **Slump Flow Velocity (mm/s)** & **Slump Flow (mm/s)** & *** Hard/Dry Density kg/m\({}^{3}\)** & *** 28-Day Compressive Strength (MPa)** \\ \hline B1 & 1921 & 700 & 4 & 175.0 & 2000 & 3432 & 38.5 \\ \hline B2 & 1924 & 705 & 5 & 141.0 & 2032 & 3067 & 40.8 \\ \hline T1 & 1930 & 710 & 3 & 236.7 & 2120 & 3231 & 30.5 \\ \hline T2 & 1914 & 750 & 3 & 250.0 & 2064 & 3717 & 33.4 \\ \hline B1M5 & 1924 & 690 & 5 & 138.0 & 2016 & 3845 & 46.2 \\ \hline B2M5 & 1923 & 685 & 5 & 137.0 & 2072 & 3896 & 48.5 \\ \hline T1M5 & 1927 & 700 & 4 & 175.0 & 1984 & 3762 & 38.2 \\ \hline T2M5 & 1933 & 705 & 4 & 176.3 & 2056 & 3954 & 41.1 \\ \hline B1C3 & 1930 & 720 & 4 & 180.0 & 2008 & 3566 & 41.5 \\ \hline B2C3 & 1930 & 660 & 4 & 165.0 & 2080 & 3436 & 44.6 \\ \hline \end{tabular}
\end{table}
Table 3: Physical workability and hardened properties of AAECs.
## 3 Materials and Methods
### Validation of the Modified Miami Model
The effect of the different BM\({}_{\text{max}}\) and of the MAT\({}_{\text{nat}}\) with a later start of biomass production in combination with a higher grade led to strong differences between the graphs for the two species. **Figure 4.** ABM of **(a)** Norway spruce and **(b)**_Miscanthus_ as functions of MAT and MAP; the species-specific parameters (BM\({}_{\text{max}}\), MAT\({}_{\text{nat}}\)) result in different starts, different increases and, finally, in different amounts of ABM. ABM, modeled above-ground biomass production of a species in dry matter (Mg ha\({}^{-1}\)a\({}^{-1}\)); MAP, mean annual precipitation (mm); MAT, mean annual temperature (\({}^{\circ}\)C); BM\({}_{\text{max}}\), maximal above-ground biomass production in dry matter of a species in the literature (Mg ha\({}^{-1}\)a\({}^{-1}\)); MAT\({}_{\text{nat}}\), mean annual temperature of the native distribution area of a species (\({}^{\circ}\)C). Choice of Species-Specific Parameters for the Modified MIAMI Model to Estimate the Above-Ground Biomass Production in Dry Matter for Different Selected Species  
The modified Miami model was used to estimate the above-ground biomass production of a species in the dry matter (ABM) of different species on the two sites in Sweden. To compare the ABM of different species on different sites using the modified Miami model, the species-specific parameters for all the species and climate data for all the sites were needed. Table 2 shows the species-specific variables (BM\({}_{\text{max}}\) and MAT\({}_{\text{nat}}\)) in the literature for the different species. The climate data for the two sites are (1) for southern Sweden a MAT of 6 \({}^{\circ}\)C and MAP of 750 mm and (2) for northern Sweden a MAT of 3 \({}^{\circ}\)C and MAP of 601 mm. The site in northern Sweden lies in the fifth hardness climate zone, and the site in southern Sweden in the seventh climate zone [45]. On both sites, three species, Norway spruce, Scots pine (_Pinus sylvestris_ L.) and birch (_Betula_ spp. ), have together more than 90 percent of the standing volume of all land use classes [46]. In the region of the northern site, the total number of different tree species was 10. Of these, the most frequent was Scots pine with 43.1%vol. In the region of the southern site, sixteen species were found, the most frequent species being Norway spruce with 48.5%vol. The mean volume increment, including the growth of felled trees on forest land, was 3.0 and 5.7, respectively [46]. The soil on both sites is brown soil, and the dominant humus layers are duff and mull in the two regions. The regions are further characterized mainly by Podzols with high frequencies of clay [47]. For BM\({}_{\text{max}}\), the averages of the relatively higher biomass production values found in the literature were chosen. The MAT\({}_{\text{nat}}\) values are either mean annual temperature values for the native region, when available, or averages of the mean annual temperature values from growing tests, plantations and forestry documented in the literature.
## 3 Results and Discussion
### Future Work  
MI directional filters can also be designed for use at UHF. Potential research could concentrate on counteracting ferrite losses or, alternatively, minimising non-nearest-neighbour coupling and radiation in open-loop designs. Furthermore, the device wave operating principles can be extended to other metamaterial types, such as elastic or acoustic [54]. In particular, it has already been shown by us that MEMS could be used to generate high-performance two-port notch and comb filters [55]. Once again, wave analysis was used to simplify the coupled dynamical equations. Extension to multiport devices can be a fitting research target. Higher performance is expected due to inherently higher Q-factors of MEMS resonators.
# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
###### Abstract  
In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle's super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.  
vehicle cooperative network; vehicular fog computing; vehicular cooperation network; super-degree distribution; cooperation mode of vehicles +
Footnote †: journal: Sensors