esa-sceva/satcom-chunk-collection huggingface dataset:
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 3 Methods
### Graph Convolutional Network  
The graph convolutional network was incorporated into the proposed network to leverage the distinctive features of graph structures. This permitted the recovery of details that were lost in defocus images. The specific process is illustrated in Figure 5. First, the encoder generates high-dimensional features, which are transformed into independent vertices. Then, these vertices are connected through a pre-generated graph. As a result, the feature maps are converted into a graph network that can be further processed by graph convolutions. Following multiple graph convolution operations, the nodes in the graph structure are restored to feature maps in the same order [18].  
In the context of GCN, the propagation process involves the use of aggregators to acquire the hidden states of nodes. Different GCNs employ a variety of aggregators to collect data from neighbouring nodes, along with specific updates for adjusting node weights. [PERSON] et al. [5] introduced an aggregator designed for spectral GCNs. The aggregator is defined as follows:  
\[\mathrm{T}=\mathrm{\widetilde{D}}^{-\frac{1}{2}}\mathrm{\widetilde{AD}}^{- \frac{1}{2}}\mathrm{X} \tag{2}\]  
where \(\mathrm{T}\) is the aggregator, \(\mathrm{\widetilde{A}}=\mathrm{A}+\mathrm{I}_{\mathrm{N}}\) is the adjacency matrix of the undirected graph with added self-connections, \(\mathrm{I}_{\mathrm{N}}\) is the identity matrix, and \(\mathrm{\widetilde{D}}\) is the degree matrix from [5]  
\[\mathrm{GraphConv}(\mathrm{X})=\mathrm{\widetilde{D}}^{-\frac{1}{2}}\mathrm{ \widetilde{AD}}^{-\frac{1}{2}}\mathrm{X}\mathrm{\Theta} \tag{3}\]  
Figure 4: Illustration of the Iterative Adaptive Convolution.  
Figure 5: By reassembling feature maps from different channels into a graph structure and performing graph convolution operations, the lost image information was restored.  
Therefore, we represent the graph convolution utilized in the proposed network using Equation (3), where \(\widetilde{\text{D}}\) is the degree matrix, \(\widetilde{\text{A}}\) is the adjacency matrix of the undirected graph, X is the convolved matrix, and \(\Theta\) is a matrix of graph convolution filter parameters.  
Throughout the process of the graph convolution operation, the Watts-Strogatz (WS) model [19] was implemented to transform the feature map and graph structure. During the graph convolution process, the pre-generated graph was utilized based on the Watts-Strogatz model to achieve better results while reducing computational complexity. To further enhance the performance, the Residual Graph Convolutional Networks (ResGCN) [20] were integrated to increase the depth of the network.
## Materials and Methods
### Statistical Analysis  
Daily satellite LAI and uncertainty information were retrieved from the three LAI products at the location of each subplot of the six sites (Figure S1). If the field subplots overlapped with multiple pixels for a given LAI product, then the satellite-based value was estimated using the weighted sum of the contributing pixels, weighted by the area of overlap (Equation (3)). Data were cleaned based on the products' quality assessment information, before aggregation into monthly mean values, and comparison with the mean monthly ground-based LAI measurements at each site and plot. We calculate R\({}^{2}\), bias, and RMSE between the field and satellite LAI time series to evaluate the accuracy and error of the three LAI products.  
\[LAI_{R}\ =\ \sum_{i=1}^{n}\frac{Ai}{A}*Pi, \tag{3}\]  
where \(LAI_{R}\) is the extracted remote sensing LAI value; \(n\) represents the total number of the contributing pixels which overlapped with the field plots; \(A\) is the total area for the plots, \(Ai\) is the overlapped area between pixel and plots; \(Pi\) is the remote sensing pixel value.  
LAI products' uncertainty information was evaluated using the ratio of LAI products' uncertainty (LAI\({}_{\text{U}}\)) with the bias against the field measurements, \(r\), as follows (Equation (4)),  
\[r=\frac{LAI_{U}}{|Bias|}, \tag{4}\]  
where the bias (Equation (5)) is defined as:  
\[Bias=LAI_{product}-LAI_{field}, \tag{5}\]  
The ratio, \(r\), can be used to evaluate whether or not the product uncertainty is robust. When r \(\geq\) 1, the bias in the LAI product is within the reported uncertainty range, indicating that the uncertainty estimate for LAI is robust; conversely, if r \(<\) 1, the reported uncertainty fails to capture the observed bias and therefore the uncertainty estimate is not robust. We calculate this ratio \(r\) for each month at different plots and sites.
## 4 Simulation and Discussion  
### Relationship between the Number of Nodes in Each Layer and the Recognition Accuracy  
The number of hidden layers and nodes of DNNs has a strong importance on the prediction accuracy of DNNs. When the number of nodes is too small, the DNN training model cannot learn well, which increases the training times and affects the training accuracy. When the number of nodes is too many and the training time increases each time, the DNN training model is prone to an over-fitting phenomenon. According to the characteristics of yeast protein data, data is randomly selected for testing. The relationship between classification error and the number of nodes in the hidden layer is shown in Figure 2.  
Figure 2 shows that there are six hidden layers and the learning rate is 0.001. Under the condition that the number of hidden layers remains unchanged, the number of nodes of each hidden layer is constantly increased to test the accuracy of classification. As can be seen from Figure 2, in the beginning, with the increase of nodes in each layer, the  
accuracy of classification becomes higher and higher. However, when the number of nodes in each layer reaches ten, the accuracy of classification does not change regularly. When the number of nodes in each layer reaches 60, the accuracy rate of classification remains at approximately 52%. When the number of nodes reaches 89, the accuracy of classification increases suddenly. It can be seen that the prediction accuracy of the yeast protein is not linear to the number of layers of DNN and nodes of each layer, which is only determined by the data characteristics of the yeast protein. Therefore, in the following experiment, the architecture with the highest classification accuracy of 73% is adopted, that is, the hidden layer consists of six layers, and the node number of each layer is 30.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 7 FIT-IoT Performance Evaluation
### Controlled Experiments  
The first controlled experiments, MQTT-1 and CoAP-1, involve 1 sender, 1 receiver, and a small number of messages (5) with a large IAT. Experiments MQTT-2 and CoAP-2 experiments involve also just 1 sender and 1 receiver, but a higher number (5000) of very frequent messages (1 ms IAT). Results obtained for the initial set of experiments (control experiments) are illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **Senders** & **Receivers** & **Messages** & **IAT (ms)** & **TTC** & **Packet Loss** \\  & & & & & & **(ms)** & **(Percentage)** \\ \hline \hline \multicolumn{8}{c}{Controlled Experiments} \\ \hline MQTT 1 & 1 & 1 & 5 & 1000 & 35.17 & 0 \\ \hline CoAP 1 & 1 & 1 & 5 & 1000 & 185.17 & 0 \\ \hline MQTT 2 & 1 & 1 & 5000 & 1 & 19.61 & 0 \\ \hline CoAP 2 & 1 & 1 & 5000 & 1 & 246.33 & 0 \\ \hline \multicolumn{8}{c}{1 to Many Experiments} \\ \hline MQTT 3 & 1 & 94 & 5 & 1000 & 136.97 & 0 \\ \hline CoAP 3 & 1 & 94 & 5 & 1000 & 4220.02 & 0 \\ \hline MQTT 4 & 1 & 94 & 5000 & 1 & 78,897.46 & 65.16 \\ \hline CoAP 4 & 1 & 94 & 5000 & 1 & 4526.39 & 0.04 \\ \hline \multicolumn{8}{c}{Many to 1 Experiments} \\ \hline MQTT 5 & 94 & 1 & 5/sender & 1000 & 102.232 & 0 \\ \hline CoAP 5 & 94 & 1 & 5/sender & 1000 & 2631.62 & 0 \\ \hline MQTT 6 & 94 & 1 & 50/sender & 1000 & 5419.86 & 0 \\ \hline CoAP 6 & 94 & 1 & 50/sender & 1000 & 4351.83 & 0.003 \\ \hline \multicolumn{8}{c}{Many to Many Experiments} \\ \hline MQTT 7 & 50 & 47 & 100/sender & 1000 & 117,814.03 & 0.01 \\ \hline CoAP 7 & 50 & 47 & 100/sender & 1 & 3611.39 & 0.131 \\ \hline \hline \end{tabular}
\end{table}
Table 18: FIT-IoT scenarios with parameters and results obtained.  
Figure 8: Controlled Experiments.  
For both sets of experiments, MQTT attains a lower TTC on average, compared to the one of CoAP. This difference is possibly due to the stop-and-wait retransmission reliability with exponential back-off process of CoAP.
## 3 Results and Discussion
### Oxidative Stability Study of Packaged Fried Almonds
#### 3.2.2 Fatty Acid Profile Determination  
Fatty acid composition of fried almonds packaged with neat PCL and the active materials as a function of storage time is shown in Table 3. The most abundant fatty acid at day 0 was oleic acid followed by linoleic acid and smaller amounts of palmitic, stearic and palmitoleic acids. These results are in agreement with previous literature reported for different almond cultivars ([PERSON], 2016; [PERSON] et al., 2016).  
A general increase in monounsaturated palmitoleic and oleic acids and saturated stearic and palmitic acids content with a parallel decrease in linoleic acid content was observed with increasing oxidation time. This behaviour was more significant for neat PCL compared to the active films (Table 3). Unsaturated fatty acids have different susceptibilities to the abstraction of hydrogens depending on the dissociation energies of the labile C-H bonds. Bonds adjacent to a double bond or to a tertiary carbon atom are weak and easy to break. For the fatty acid series -CH\({}_{2}\)-(CH=CH-CH\({}_{2}\))n-CH\({}_{2}\)- (\(n=1\)-3), the relative oxidation rate increases in a ratio of 1:40:80, respectively, with the oxidation of monounsaturated fatty acids being much less than that of polyunsaturated fatty acids ([PERSON] et al., 2016). For this reason, a decrease in linoleic acid as a consequence of oxidation reactions was observed. Since hexanal is a secondary metabolite of lipid oxidation which is formed from the breakdown of linoleic acid hydroperoxides, the higher PV, AV and hexanal contents obtained for samples packaged with neat PCL films could be directly related with the higher linoleic acid oxidation rate shown in Table 3. A decrease in linoleic acid content of 30% was obtained for neat PCL at 56 days of study compared to PCL3 and PCL6 samples which showed lower values (9 and 3%, respectively). These results are in accordance with several reports that linked a higher linoleic acid oxidation rate to a higher formation of peroxides, secondary oxidation products and hexanal content during the study of the oxidative stability of shelled walnuts ([PERSON] et al., 2016) and frozen cod ([PERSON] et al., 2016).
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Methodology  
Our aim is to formulate a swarm of drones in a disaster affected area to facilitate establishment of communication network without forming voids. A first-hand strategy to this end is the use of an AI-based method such as Particle Swarm Optimization (PSO) [37; 38]. However, PSO-based algorithms are prone to falling into local optima, and the rate of convergence in the iterative process may be low [39]. Other AI-based algorithms, such as Genetic Algorithms and Simulated Annealing, face the same issues, apart from being too slow. As concerns to neural networks and deep learning, they are computationally expensive and often require frequent training to produce accurate results [40].  
In our case, we have to produce deterministic positions for the drones in order to fully ensure network coverage in the disaster affected location while at the same time ensuring maximum average leftover energy to service the network. This needs to happen in a time-bound way using available computational resources. Therefore, we aim to develop a terrain-aware region-filling algorithm which is less computationally intensive. Here, we find the number and positions of drones and base stations required to create a robust temporary network in the target region. We accomplish this by considering the terrain, intending to cover the entire region without voids while minimizing the number of drones and maximizing their average leftover energy.  
The drone deployment algorithm we propose consists of a _preprocessing module_ and a _region-filling module_. Preprocessing involves obtaining terrain data on the disaster-struck region and then converting this into a suitable data structure for the region-filling algorithm. The region-filling algorithm provides a powerful solution to the above problem. The output of the region-filling algorithm contains a list of three-dimensional coordinates of the positions of drones, with base station indication, leftover energy, and hovering time. Finally, we simulate the deployment of drones based on the positional information obtained from the Region-Filling algorithm. An outline of the process is shown in Figure 2.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 5 Case Study  
### The GSPN Model for the Spread and Evolution of Grape Downy Mildew  
Grape downy mildew is a common plant disease caused by Plasmopara viticola, widely distributed in grape-growing areas, and especially severe under humid and warm climatic conditions. The disease is transmitted through airborne spores that infect grape plants, often initially attacking the leaves and forming typical white mold layers on the leaf surfaces. If not promptly controlled, downy mildew can cause significant losses to both the yield and quality of grapes [33]. As the spread of downy mildew is influenced by environmental factors such as temperature and humidity, establishing an effective early warning system is crucial for the prevention and control of this disease.  
In the early warning system for grape downy mildew, the leaves are the most critical observation area, while the monitoring of young shoots also holds certain significance. In comparison, the observation of roots, stems, flowers, fruits, and buds has a lesser role in disease assessment. To better align the GSPN model with the actual prevention and control  
Figure 10: Steady-state probability distribution of the plant pest and disease diagnosis model under variation in \(\lambda_{21}\).  
needs of grape downy mildew, the model of plant pest and disease spread evolution is simplified in this case study. Based on practical considerations, we set \(\lambda_{2}=\lambda_{3}=\lambda_{5}=\lambda_{6}=\lambda_{7}=\lambda_{8}=0\), thereby creating a more simplified GSPN model focused specifically on the leaf disease analysis of grape downy mildew.
## 3 Results
### Trapped Modes  
The works of [7; 8; 9] discuss the existence of trapped modes in the context described here. At the Lame frequency \(k_{t}=\sqrt{2}\tau\), the propagating mode becomes orthogonal to the evanescent Lamb modes. In this case, the single propagating mode travelling leftwards can be trivially combined with the corresponding rightwards travelling mode to satisfy the traction-free condition, and hence the coefficients on all other modes are trivially zero. However, it has been observed that for two particular values of the Poisson ratio, \(\
u^{*}=0\) and \(\
u^{*}\approx 0.2248\), there also exist a combination of evanescent Lamb modes that sum to satisfy the traction-free boundary conditions. In the case of \(\
u^{*}=0\), this trapped mode has formally been proven to exist; however, for \(\
u^{*}\approx 0.2248\), its existence has only been established numerically.  
Our modal expansion, which includes the new corner modes, can easily be employed to accurately determine the value of Poisson's ratio for the latter trapped mode solution. We search for a homogeneous solution of the problem solely in terms of the modes which decay as \(x\to\infty\). In order to find the homogeneous solution, we construct a collocation matrix as before, with the propagating mode term removed, for a given \(\
u\) at the Lame frequency. We then vary \(\
u\) and observe where the value of the determinant nearly vanishes. Figure 10 shows the resulting absolute values of the determinant, normalised on the magnitude at \(\
u=0.22479\) for ease of comparison, found when using this process for a range of numbers of corner modes. This clearly demonstrates that the modal expansion presented in this paper does find this trapped mode and that, for \(M>6\), its value has converged to six significant figures, at \(\
u\approx 0.224798\).  
Figure 9: For \(k_{t}=8\) and \(\
u=0.3\) (the most challenging case of Figure 8), we illustrate the convergence of the energy and of the reflection coefficients of the three propagating modes. We display the absolute error between the computed reflection coefficients and a reference value, i.e., that obtained for \(N=10\) and \(M=12\), and the reference value for the energy is 1. The continuous lines correspond to the pure Lamb mode approach \((M=0)\) as the number \(N\) of Lamb modes increases. The dashed lines correspond to our new approach for a fixed number \(N=10\) of Lamb modes as the number \(M\) of corner modes increases.
## 4 Empirical Study
### Model Validation
* **CTE:** The final aspect of this validation process is assessing risk within the predictive distributions, specifically through the Conditional Tail Expectation (CTE), defined as follows: \[\text{CTE}(q)=E[\ \bar{Y}_{h}|\bar{Y}_{h}>Q_{q}(\bar{Y}_{h})\ ],\quad q\in(0,1)\] (42) where \(Q_{q}(\bar{Y}_{h})\) is the \(q\)th quantile of the predictive distribution. The CTE analyzes the tail behavior of predictive distributions to provide insights into expected aggregate losses (risk premium) under extreme conditions [35]. A lower CTE value indicates the model predicts less severe losses in extreme scenarios. We outline several criteria for model validation, including (1) LPPD, (2) \(D_{KL}\), (3) SSPE, (4) SAPE, and (5) CTE. In the upcoming section, these metrics are used to evaluate the performance and accuracy of our proposed models in predicting risk premiums.
## 4 Experiments and Discussion
### Evaluation in Unfamiliar Environments  
Here, we will further investigate the generalization ability and the performance of trained models in unfamiliar environments, in which the UGVs have never encountered these types of obstacles during training runs. Figure 5a shows a new dense setting (env5), where the obstacles have different shapes and sizes compared to env1 to env4, while Figure 5b presents another unfamiliar factory setting (env6), where UGVs may encounter more complex obstacles, such as shelves with slender legs.
## 3 Results and Discussion
### Changes in Transmittance of Skin In Vitro with Different Treatments  
In this study, the reflectance and transmittance of in vitro skin were measured by a spectrophotometer with an integrating sphere setup. Figure 5a,b show typical spectra of the reflectance and transmission of in vitro skin with different treatment in 60 min, respectively. Oscillations at 800 nm in spectra are created by light source change of the spectrophotometer. The reflectance and transmittance are used to measure the optical clearing efficacy: the lower the reflectance, the higher the transmittance and the better the optical clearing efficacy. The combined penetration enhancement method at 0.7 J/cm\({}^{2}\) achieves the best optical clearing effect. In Figure 5a, the reflectance of the skin decreases significantly and by 33.9%, 35.9%, and 33.4% relatively at 475, 575, and 615 nm, respectively, and this can match the in vivo results approximately. In Figure 5b, the transmittance increases 70.0%, 167%, and 101% at 475, 575, and 615 nm respectively. These conclusions are also matched: (1) the mono-treatment of thiazone causes insignificant optical clearing effect; (2) the laser dose of 0.9 J/cm\({}^{2}\) causes the worst optical clearing effect; (3) the efficacy of combined treatment is a little better than the mono-treatment of laser-irradiation in 60 min.  
Figure 4: Reflectance spectra of in vivo rat skin with combined treatment after different laser irradiation doses at 60 min.  
Figure 5: Typical reflectance (**a**) and transmittance (**b**) spectra of in vitro rat skin with different treatment in 60 min.
## 3 Numerical Simulation of Warm Forging-Finishing of Spiral Bevel Gear
### Simulation and Result Analysis of Warm-Forging-Finishing of Spiral Bevel
#### 3.3.4 Influence of Workpiece Heating Temperature on Forging-Forming  
The heating temperature of the workpiece was selected to be 923 K, 1023 K and 1073 K, respectively; the effective stress and strain at each reference point positions of the forged workpiece were analyzed, and the simulation results are shown in Figure 14.  
As can be seen from Figure 14, from the overall trend of the curve, it can be clearly observed that the higher the heating temperature of the workpiece, the smaller the effective stress value of the forging. The reason is that the higher the temperature of the workpiece, the stronger the fluidity of the material and the lower the deformation resistance. From the whole gear, the effective strain and effective stress of the big end of the gear are significantly greater than those of the middle and small end of the gear, which is mainly caused by the flash of the forging. From the various parts of the gear, the effective strain at the reference point of the tooth root of the forging is significantly greater than that at the reference point of the tooth surface and the tooth top, which is due to the fact that the forging flash is produced by the flow of excess metal material from the tooth root along the direction of the back cone of the gear.  
Therefore, the increase in the forging temperature of the workpiece can effectively reduce the stress generated during the forging of the spiral level gear, especially the equivalent stress at the tooth root of the big end, so as to improve the stress condition at the tooth top of the big end of the forging die and reduce the probability of fracture failure of the tooth top of the tooth die.  
By using DEFORM-3D software, the changes in four process parameters, namely forging speed, die preheating temperature, friction factor and workpiece heating temperature, in the warm-forging process of spiral level gears are simulated and analyzed, and the changing laws of the metal material flow and forming load in the warm-forging process of spiral level gears are obtained. The forging-forming load will affect the precision of the forging gear, die life, forging equipment, etc. Reducing the forming force can reduce costs and improve quality, which is of great significance for actual production. Therefore, it is necessary to further optimize the warm forging-process parameters of spiral level gear to obtain higher-quality parts.  
Figure 14: Effective strain at the reference point of the final forgings of the spiral level gear at different workpiece heating temperatures: (**a**) effective stress and (**b**) effective strain.