esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 6 Conclusions  
With an emphasis on green energy and remote monitoring, smart technologies and agricultural systems have grown significantly in recent years. It controls the plants, soil characteristics, water pressure, and weather-related information, which benefits farmers and increases production. However, limited solutions have been proposed to enhance production in precision agriculture while reducing latency and information disturbance. Furthermore, protecting agricultural data while utilizing the insecure Internet is another significant research challenge. In this study, we presented a framework for providing a farmer communication system to enhance timely delivery through the cooperation of fog systems. This system also incorporates redundant information detection, which reduces network bandwidth inefficiencies. Additionally, the proposed framework employs security methods to counter privacy attacks on sensing data. The performance results showed the good outcomes of the proposed framework with maximum energy efficiency and delivery ratio. Furthermore, its performance has proven the improved processing usage in the existence of malicious devices. In the future, we intend to cope with the proposed framework's scalability and load-balancing issues with the support of a multi-cloud architecture. Moreover, we would like to integrate security to maintain cloud integrity from the point of users' perspective.  
Conceptualization, A.R. and K.H.; methodology, A.R. and I.A.; software, I.A. and K.H.; validation, J.L., L.P. and A.R.; formal analysis, A.R. and J.L.; investigation, L.P. and I.A.; resources, A.R. and I.A.; data curation, J.L. and L.P.; writing--original draft preparation, I.A. and A.R.; writing--review and editing, K.H., J.L. and L.P.; visualization, A.R. and L.P.; supervision, J.L. and I.A.; project administration, A.R. and I.A.; funding acquisition, J.L. and I.A. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the \"Ministerio de Ciencia e Innovacion\" through the Project PID2020-114467 RR-C33 and by \"Ministerio de Agricultura, Pesca y Alimentacion\" through the \"proyectos de innovacion de interes general por grupos operativos de la Asociacion Europea para la Innovacion en materia de productividad y sostenibilidad agricolas (AEI-Agri)\", project GO TECNOGAR. This work was supported by the research SEED project \"Intelligent and trusted metaheuristic optimization model for agriculture using ubiquitous sensors network\" Prince Sultan University, Riyadh Saudi Arabia, (SEED-CCIS-2022[109]) under Artificial Intelligence & Data Analytics Research Lab. CCIS.  
Not applicable.  
**Data Availability Statement:** All data are provided within the manuscript.  
Figure 9: (**a**) Network connectivity with varying sensors and (**b**) Network connectivity with varying distance.
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion  
### Experimental Setup and Parameters  
The performance metrics evaluated in this study for pest identification included accuracy, precision, recall, and the F1 score. Furthermore, the complexity of the model was assessed based on the number of model parameters and the volume of floating-point operations.  
Accuracy refers to the proportion of correctly predicted samples among all the samples, as shown in Equation (4).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100 \tag{4}\]  
Precision refers to the proportion of samples that were correctly predicted as positive among all actual positive samples, as shown in Equation (5).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100 \tag{5}\]  
Figure 8: Structure of BCBAM attention mechanism.  
Recall refers to the proportion of positive samples that were correctly predicted as positive, as indicated by Equation (6).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100 \tag{6}\]  
The F1 score is the harmonic mean of precision and recall, as shown in Equation (7).  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\times 100 \tag{7}\]  
where TP represents the number of actual positive samples that were correctly predicted as positive, TN represents the number of actual negative samples correctly predicted as negative, FP represents the number of actual negative samples incorrectly predicted as positive, and FN represents the number of actual positive samples incorrectly predicted as negative.
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 6 Conclusions  
This paper describes how a satellite swarm creates an ICNS with the theory of complex networks. We proposed the community-based construct method of ICNS and its improvement method. The results regarding constructing ICNSs have proven that it is necessary to improve the construct method of an ICNS, which helps an ICNS to maintain dynamic connectivity continuously. The results of dynamic connectivity simulations of an ICNS indicate that an ICNS cannot be dynamically connected in the last few slots of the last state period. Therefore, a satellite swarm should fulfill its task as soon as possible; otherwise, poor communication may occur at the end of the task time interval.  
The proposed method is capable of constructing an ICNS with a good dynamic connection quickly. It has many advantages, such as being a simple model, and has good flexibility, adaptability, and expansibility. In fact, the ICNS constructed by our method can be further improved with some intelligent optimization methods and reinforced learning algorithms in order to achieve a certain object.  
Besides community, path, and connectivity, other concepts and properties of complex networks may also be adopted in an ICNS, such as invulnerability, synchronization, and traffic dynamics, which will be the subject of our future work.  
Conceptualization, W.L. and Q.L.; methodology, W.L. and Q.L.; software, W.L.; validation, Q.L. and Z.Z.; formal analysis, W.L. and C.Z.; investigation, W.L. and C.Z.; resources, C.Z. and Z.Z.; writing\(-\)original draft preparation, W.L., Q.L., C.Z. and Z.Z; writing\(-\)review and editing, W.L., Q.L., C.Z. and Z.Z; supervision, Q.L. and Z.Z.; project administration, Q.L.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62003359).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Average Number of ISLs** & **APTL** \\ \hline The proposed method & 19.92 & 1.33 \\ RWM & 17.28 & 1.49 \\ RWMWE & 14.85 & 1.15 \\ DDE & 12.23 & 2.12 \\ GA & 9.63 & 1.38 \\ \hline \end{tabular}
\end{table}
Table 4: Results of the different methods.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Task time interval & 10,080 min (1 week) \\ State period & 1 min \\ Slot & 3 s \\ \hline \end{tabular}
\end{table}
Table 3: Main parameters of the simulation setup.
## 3 Simulation Results
### Single Objective Optimization
#### 3.1.2 With Considering the Limitations of Power Exchange with Upstream Network  
In this section, the power exchange with the utility grid is considered as finite. The distributions of power for different purposes are provided in Tables 9 and 10. Comparison of the results of the cost and pollution targets in both cases with and without network limitation shows that considering the power exchange constraint with the network increases the cost and pollution. A comparison is also made between the proposed method and the methods of PSO and DE and the results are shown in Figures 4 and 5 as well as Tables 11 and 12. The results indicate the superiority of the proposed method over other methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Best Answers** \\ **(kg)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Answers (kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Worst Answers** \\ **(kg)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation (kg)** \\ \end{tabular} \\ \hline PSO & 178.73 & 373.61 & 1297.41 & 327.62 \\ DE & 136.7 & 152.35 & 178.77 & 16.17 \\ IDE & 136.49 & 151.3 & 171.17 & 16.07 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the proposed method with other methods for reducing gas emission without considering the limitations of upstream network power exchange.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & \multicolumn{5}{c}{**Generation**} \\
**MT (kW)** & **FC (kW)** & **PV (kW)** & **WT (kW)** & **Storage (kW)** & **Grid (kW)** \\ \hline
1 & 6.01 & 15.03 & 0 & 0 & 2.64 & 27.93 \\
2 & 7.44 & 4.6 & 0 & 1.41 & 9.9 & 27.57 \\
3 & 17.22 & 4.76 & 0 & 0.1 & \(-\)0.73 & 24.11 \\
4 & 19.8 & 4.29 &
## 3 Results  
### Classification Accuracy Indices of Different Models  
Table 1 shows the quantitative evaluation metrics (OA, kappa, IoU, F1-score, Pre, and Rec) for all classification types, which were calculated using the test dataset to allow for quantitative comparisons. The accuracy metric showed that the MSG-GCN model performed best (kappa = 0.7808, OA = 0.8523), while U-Net++ (kappa = 0.7263, OA = 0.8143) and GCN (kappa = 0.7473, OA = 0.8240) performed well; FCN (kappa = 0.5209, OA = 0.6895), U-Net (kappa = 0.6706, OA = 0.8098), and ViT (kappa = 0.6942, OA = 0.7914) performed moderately. RF (kappa = 0.3373, OA = 0.5739) and SVM (kappa = 0.0028, OA = 0.6344) did not perform as well as the other models. In terms of the classification quantitative metrics for the three forest types, the MSG-GCN model performance was competitive relative to the other methods. Of the three forest types, NMX was the least accurate for all models because of dataset imbalance; few NMX areas were correctly classified. Although GCN had the best accuracy for NMX, the accuracies of NBL and CP were lower than that of MSG-GCN. Of the three forest types, CP was most accurately detected by U-Net, U-Net++, ViT, GCN, and MSG-GCN. RF and SVM detected NBL most accurately, followed by CP. Considering the parameter numbers and FLOPs required to process the images, our MSG-GCN architecture combining GCNs with CNNs has the largest model size (88.10 M parameters) and required more operations for processing and slightly fewer FLOPs (104.99 GMac). The FCN has the fewest parameters (3.93 M) and U-Net is optimal in terms of FLOPs (218.94 GMac).
## 4 Experimental Results
### Hyperparameters Turning  
The GA developed here is employed to generate the optimal hyperparameters for the CNN model using the augmented version of the HAM10000 dataset, which is split into 80% and 20%, for training and testing, respectively. The other augmented datasets are not used to generate the optimal hyperparameters; instead, they are used to train and test the optimized CNN model only.  
The GA starts the process using the initial values of the hyperparameters, which are shown in Table 2. The outcome of the algorithm is the model configuration with the highest fitness values. As a result, the optimized CNN model generated by the genetic algorithm has 13 layers consisting of one input layer, three convolutional layers, three Relu layers, three max-pooling layers, and three fully-connected layers (FCL). The FCL consists of one flattened layer, one dense layer, one softmax layer and one classification layer. Furthermore, the learning rate and epochs provided by the GA were 0.0001 and 50, respectively. The other best values for the hyperparameters of the genetic algorithm are represented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Droupout** & **Learning Rate** & **Epoach** & **Activatin Function** \\ \hline HAM10000 & 0.3 & 0.0001 & 50 & Relu and Softmax \\ \hline
**Optimizer** & **Momentum** & **Batch Size** & **Kernel Size** & **Nodes** \\ \hline Adam & 0.95 & 64 & (3, 3) & 256 128, 64, 256, 32, 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best values of hyperparameters of genetic algorithm
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 2 Models and Setup
### Geometry Model  
In this section, a Cartesian 3D geometry model for the transport aircraft is presented as shown in Figure 2. In Figure 2, the geometric aircraft model has been constructed based on the typical transport aircraft, Lockheed Martin's LM-100J [3]. In the case of events such as the Chernobyl nuclear power plant accident, it can act as a civil multi-purpose air-fighter capable of a rapid and efficient transportation of cargo. The detail parameters of the LM-100 aircraft plane can be accessed easily. This section mainly focuses on the main structure parameters as shown below.  
In order to maintain the basic structure of the aircraft, the study ignores unnecessary details such as windows. Additionally, the model is composed of all-metal materials and is therefore defined as an ideal conductor. Hence, the field strength is mainly focused and simulated around and inside the aircraft. For the dimension sizes of the geometry model of the transport aircraft, the fuselage length (\(x\)), wingspan width (\(y\)) and height (\(z\)) are \(x\times y\times z\) = 35.07 m \(\times\) 39.48 m \(\times\) 10.89 m respectively.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 3 Existing Methods for Vbda
### Vary Beam Divergence by Adjusting the Focal Length F of the Collimating Lens  
#### 3.1.1 Using Zoom Group  
Figure 10 illustrates the operating principle, which involves adjusting the distance between two or more zoom lenses to alter the focal length of the lens group, subsequently changing the beam divergence angle. The technique offers several advantages, including a wide range of continuous divergence angle changes and the relatively mature technology of the zoom lens group. However, designing a zoom lens group with a large zoom ratio is costly, and high-quality guide rails or actuators are necessary, which can be a potential disadvantage.  
The research on this method is as follows: [PERSON] [36] and [PERSON] et al. [37] used two positive lenses combined as a VBDA mechanism. [PERSON] et al. [38] varied the beam divergence angle from 0.7 mrad to 1.2 mrad by moving the rod lens connected with optical fiber along the axial direction. [PERSON] et al. [39] designed a VBDA mechanism by altering the distance between the primary mirror and the secondary mirror of a Cassegrin transmitting antenna. The VBDA mechanism designed and fabricated by [PERSON] et al. [40] is shown in Figure 11. Its beam divergence angle can be continuously adjusted between 90 and 6250 \(\upmu\)rad (full width at half maximum, FWHM), with optical axis stability of less than 5% during the transformation process. The system's optical aberrations were evaluated using a shear interferometer, and the outgoing beam exhibited good wavefront quality.  
Figure 11: (**a**) 3D model of the beam divergence angle control system, (**b**) first prototype of the beam divergence angle control system [40]. Reprinted/adapted with permission from Ref. [40]. 2022, [PERSON], [PERSON].  
Figure 10: The principal diagram of VBDA by using zoom group. (**a**) Collimation state, (**b**) Large divergence angle state.  
Figure 9: Existing methods of varying divergence angle.
## 5 Real-Time Experimental Validation
### Case Two: Experimental Results of the Proposed FDSC Capability during Resistive Load Variations  
In this scenario, a resistive load, \(R_{L_{1}}\) = 10 \(\Omega\), is initially connected to the bus, and the secondary control remains inactive. As shown in Figure 27, the bus voltage deviates from the rated DC voltage and decreases to 40 V. Upon activation of our proposed FDSC at 2 s, the bus voltage swiftly returns to the nominal voltage value of 48 V. To assess the performance of our control technique under load variations, another resistive load,  
Figure 24: DC microgrid real-time simulation system. (**a**) DC microgrid real-time system; (**b**) Speed goatTM real-time test bench.  
Figure 25: DC microgrid common bus voltage.  
\(R_{L_{2}}=15\ \Omega\), is connected to the microgrid at \(t=6\ \mathrm{s}\) and disconnected at \(t=14\ \mathrm{s}\). From Figure 27, it can be observed that the bus voltage remains stable at the rated voltage value, despite fluctuations in the load conditions. Furthermore, Figure 28 demonstrates that the current distribution among the four converters is in accordance with the intended design, demonstrating consensus in nature. The experimental results for this case study align with our theoretical analyses, confirming their consistency.  
Figure 27: DC microgrid common bus voltage under load variations.  
Figure 26: Current output measurement of the DC microgrid; (**a**) Current output for DER 1; (**b**) Current output for DER 2; (**c**) Current output for DER 3; (**d**) Current output for DER 4
## 2 Review and Evaluation of Solar Irradiance Forecasting Sensors  
To ensure a comprehensive and relevant review, we established specific inclusion and exclusion criteria for the selection of studies. The inclusion criteria were as follows:  
* Studies published in peer-reviewed journals and conferences from 2000 to 2024.
* Research focusing on solar power forecasting using imagery-based methods, including ground-based sensors, satellite data, and all-sky cameras.
* Research that provides quantitative performance evaluations using standard error metrics.  
The exclusion criteria included the following:  
* Studies not available in English or Spanish.
* Papers that did not provide sufficient methodological details or performance metrics.
* Research focused solely on theoretical models without practical implementation or validation. These criteria ensured that the review covered a wide range of relevant and high-quality studies, providing a robust basis for the comparative analysis presented.  
Solar irradiance forecasting systems can be broadly classified into three categories based on the technology used. The first category consists of sensor-based systems that directly measure solar irradiance using pyranometers and pyrheliometers, or indirectly using photovoltaic systems, ceilometers, and temperature sensors. The second category comprises systems that process images taken from space, such as satellites, to make predictions. The third category includes systems that use all-sky cameras on the Earth's surface to capture images for prediction purposes.  
Forecasting systems that predict solar irradiance are widely used in industries and plants that involve solar thermal and photovoltaic systems. These systems typically utilize satellite images and irradiance sensors to obtain data on global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI).  
However, the cost of acquiring satellite images and maintaining well-calibrated sensors is high. This is not only due to the required communication networks for data centralization and storage but also to the installation and maintenance of these sensors. Furthermore, these systems lack spatial-temporal resolution. Given these economic and technical limitations, the research and implementation of all-sky camera systems represent a promising opportunity.  
The following sub-sections will review the research conducted and categorized by the different technologies mentioned above. A particular emphasis will be placed on the systematic aspect of all-sky cameras. A preceding section is also included to cover the most commonly used error metrics and persistence models. This will aid in demonstrating how researchers measure, quantify, and compare the quality of the forecasts performed.
# Time-Variant Seismic Fragility of Offshore Continuous Beam Bridges Based on Collapse Analysis
## 4 Case Study
### Finite Element Model  
The OpenSees platform is used to build a finite element model to simulate the bridge seismic response. In the seismic analysis, the concrete box girder as the superstructure does not occur plastic deformation. Therefore, the main girder in this paper is modeled using elastic beam-column element, and the element mass per unit length includes the self-weight and secondary loads. The plastic hinge region of the pier is considered to simulate its nonlinear characteristics better. Bottom of 1\({}^{\#}\), 4\({}^{\#}\) and 5\({}^{\#}\) piers set up plastic hinge area and top and bottom of 2\({}^{\#}\) and 3\({}^{\#}\) piers set up plastic hinge area. The pier is modeled using nonlinear beam-column element, and the whole pier element is divided into several fibre sections, as shown in Figure 3. Concrete01 in OpenSees is used to simulate the concrete behaviour, refer to the [PERSON] et al. [35], to simulate the stirrup-confined concrete. The constitutive model of unconfined concrete and confined concrete are shown in Figure 3. Steel02 in OpenSees is used to simulate reinforcement bar, and its constitutive model is shown in Figure 3. Zero length section element are set at the bottom of the pier, and the strain penetration model of fully anchored steel reinforcement bars (Bond-SP01) is adopted to simulate the bond-slip effect in the plastic hinge area of each pier. \(S_{y}\) is the yield slip, and is determined based on [PERSON] and Sritharan [36] as follows.  
\[S_{y}=2.54\times\left[\frac{d_{b}}{8437}\frac{f_{y}}{\sqrt{f_{c}^{\prime}}} \times(2\alpha+1)\right]^{1/\alpha}+0.34 \tag{31}\]  
where \(d_{b}\) is the diameter of reinforcement bar, \(f_{y}\) is the yielding strength of reinforcement bar, \(f_{c}^{{}^{\prime}}\) is the concrete compressive strength, \(\alpha\)= 0.4 is the parameter of the local bond-slip relation CEB-FIP Model 90 [37], \(S_{u}\) is the loaded-end slip when reinforcement bar reaches ultimate strength, and \(S_{u}\) = 30-40 \(S_{y}\). The top of 1\({}^{\#}\), 4\({}^{\#}\) and 5\({}^{\#}\) piers are installed the bearing, which is QZ12500 and QZ6000, respectively. This paper adopts the restoring force model of PTFE slide bearing (Figure 3) to carry out the simplified simulation. The sliding displacement calculation is shown in Equation (32).  
\[x_{y}=\frac{f\cdot N}{K} \tag{32}\]  
where \(f\) refers to the coefficient of sliding friction, and the unit is taken as 0.02; \(N\) refers to the load of superstructure; \(x_{y}\) refers to the elastic displacement of the PTFE sliding bearing, and taken as 2 mm; \(K\) refers to the stiffness of horizontal shear, the shear stiffness of QZ12500 bearing is 125,000 kN/m, and that of QZ6000 be is 60,000 kN/m.