esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
## 1 Introduction
Additionally, these methods may struggle to balance high fusion quality with computational efficiency, particularly when handling large or high-resolution images. Meanwhile, conventional GAs are susceptible to premature convergence, where the algorithm converges to a suboptimal solution due to a loss of diversity within the population. To address these issues, an improved Genetic Algorithm-Based Wavelet Image Fusion Technique can be employed. The Elite Strategy is a technique that ensures the best solutions in each generation are retained and passed on to the next iteration. This helps to preserve the most promising candidates, preventing the algorithm from losing high-quality solutions during the evolutionary process. By integrating the Elite Strategy into the genetic algorithm, we can improve the convergence speed and the overall fusion quality, leading to a more effective and efficient fusion process. The contributions of this paper are listed below:1. An elite strategy-enhanced genetic algorithm (ESGA) is proposed to improve the robustness and convergence efficiency of fusion weight optimization. By preserving high-quality individuals across generations and integrating an adaptive mutation mechanism, ESGA effectively mitigates premature convergence while maintaining solution diversity, enhancing fusion performance. 2. A synergistic framework combining the improved genetic algorithm with DWT is developed for multiscale image decomposition. Haar wavelet is selected as the basis function due to its computational efficiency and ability to balance localization in both spatial and frequency domains. The low-frequency subbands capture the overall structure of the image, while high-frequency subbands retain critical details such as edges and textures. This approach effectively integrates the strengths of both source images, avoiding deficiencies in overall appearance or detail retention. 3. A prototype system was developed and tested in a realistic environment to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed image fusion method significantly enhances the accuracy and efficiency of PCB detection, providing a reliable solution for automated defect detection and quality assessment.
# Characteristics of Aeolian Dune, Wind Regime and Sand Transport in Hobq Desert, China
## 2 Study Area
### Trend and Breakpoint Detection  
The Mann-Kendall (M-K) trend test [27,28] is based on the correlation between the ranks of a time seriesand their time order. For a time series \(X=\left\{x_{1},\,x_{2},\,\ldots,\,x_{\text{n}}\right\}\), the test statistic is given by  
\[S=\sum_{i<j}a_{ij}, \tag{6}\]  
where  
\[a_{ij}=\text{sign}(x_{j}-x_{i})=\text{sign}(R_{j}-R_{i})=\left\{\begin{array}[ ]{c}1,x_{i}<x_{j}\\ 0,x_{i}=x_{j}\\ -1,x_{i}>x_{j}\end{array}\right. \tag{7}\]  
and \(R_{i}\) and \(R_{j}\) are the ranks of observations \(x_{i}\) and \(x_{j}\) of thetime series, respectively. The mean and variance of the \(S\) statistic in Equation (6) above are given by [28]  
\[E(S)=0, \tag{8}\]  
\[V_{0}(S)=n(n-1)(2n+5)/18, \tag{9}\]  
where \(n\) is the number of observations. If \(n\) is greater than 8, the \(S\) values will follow an approximately normal distribution [29]. The Mann-Kendall \(Z\) is given by [30]  
\[Z=\left\{\begin{array}{c}(S-1)/\sqrt{V0(S)},S>0\\ 0,S=0\\ (S+1)/\sqrt{V0(S)},S<0\end{array}\right. \tag{10}\]  
A positive value of \(Z\) indicates an increasing trend, and vice versa. The null hypothesis of no trend is rejected if \(|Z|>1.96\) at the 5% significance level [29].  
After trend analysis, we use the Pettitt test [31], a non-parametric method that is widely applied to detect abrupt changes, to find out the breakpoint.
## 3 Results and Discussion
### Classification Results
Therefore, the use of a time series with at least one vegetation cycle is recommended, as  
Figure 11: Pixel-based classification result (**a**) and object-based classification result (**b**) for the study area in northern Greece/Turkey. Figure 12: Pixel-based classification result (**a**), object-based classification result (**b**) and validation mask (c) for the study area in China. the statistical range of the backscatter values of the vegetation stages is detected and thus sufficiently represented [8]. The accuracy values for the pixel-based and object-based classifications for the respective study areas are shown in Table 6. The OA for all study areas ranges between 80% and 87%. In comparison, in [8] the authors achieved a similar or even slightly lower OA for the pixel-based (75%) and object-based (82%) classification. The accuracy assessment for the study area in northern Greece/Turkey shows high values for the TOW class but strong misclassifications for TFV (PA: 28% for pixel-based and 6% for object-based) and DL (UA: 57% for pixel-based and 53% for object-based). This indicates a significant confusion between these two classes and can be explained by different environmental conditions (see Section 3.1). The accuracy values show no significant difference between pixel-based and object-based classification, except for the PA values of TFV. When applying object-based classification, it should be noted that the small-scale flood areas could be generalized by merging the pixels to objects. However, object-based classification can help to exclude pixels which can be confused with a flood-related increase in values due to dominant environmental conditions, such as soil moisture, topography, or surface roughness, which can cause a small-scale increase or decrease in the backscatter. In addition, objects are less susceptible to speckle noise than pixels [29].
## 2 Technological and Policy Solutions
### Policymaking Arenas
#### Italy  
It has been noted by a 2019-2020 study of the Civitavecchia port [45] that although EU-regulated emissions of NO\({}_{2}\), PM\({}_{10}\), and SO\({}_{2}\) met EU standards, there were also unregulated emissions that were problems, in particular, black carbon and ultrafine-to-coarse particles. The data indicated that such non-regulated pollutants showed 'the in-port, high polluting potential of some ship categories' including Ro-Ro passenger ships.' This finding underscores the potential significance of including emissions limits on black carbon and other particulate matter not currently covered by IMO or EU or Italian policies. It also suggests that Ro-Ro passenger ships should be a high-priority 'class' of ships for intensive emissions monitoring.  
That finding is also a useful point of departure for an analysis of Italy's policy issues, for it suggests that the status quo is not acceptable because of the impact of maritime shipping's emissions. Despite existing IMO limitations on the sulfur content of maritime fuel and despite EU limitations on some emissions, ships and their ports are nevertheless emitting gases and particulate matter that are damaging to human health and agricultural production. Maritime emissions are a threat to public health, especially near ports and coasts, and they are a threat to many kinds of agricultural crops [7; 8; 17; 18; 19; 20].  
In Italy, Venice is receiving the most attention among environmentalists, urban planners and policymakers as well as journalists, because of the damage done by large cruise ships to the area's physical, cultural, and economic sustainability [47]. In addition to the impact of the ships in the damage done by their wakes to the foundations of the buildings and thus the future existence of the city, there are also serious air pollution issues, including the damage done by the ships' emissions to public health. As indicated by the emissions of both particulate matter (PM) and nitrogen oxides (NOx) in 2017, Venice had the third highest emission level among Italian ports [47].  
The mix of impacts on public health and agricultural production is different in different regions and ports. In large northern and cruise-intensive ports everywhere, there is relatively more impact on the health of millions of nearby residents, while in smaller ports and where there are major agricultural areas nearby there is more impact on olives, grapes and other crops. Of course, there are both kinds of concerns, but the focus and results of technical effectiveness analyses and economic cost-benefit analyses of mitigation alternatives are quite different depending on the profiles of the ports and the volume and types of ships passing through them.  
Table 9 displays the list of 33 ports in the Italian system [81]. The 33 individual ports are subject to the authority of the regional authorities, which are in turn subject to national laws. The system thus has elements of both decentralization and centralization.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### PM Detection Methods
#### 2.3.1 TSP Detection Methods  
TSP in the form of free distribution in all corners of the air environment is very easy to be inhaled into the body. Some of the particles of small size enter into the blood and with the blood circulation flow gradually into the body to various tissues and systems, then threatening the health of the human body [38]. Because the components of TSP in the atmosphere are very complex (including PM2.5 and PM10) and vary greatly, the only method often used to detect and analyze TSP is the gravimetric method, which is a manual analysis method, and the sampler used for this method needs to have certain cutting characteristics. Through this sampler, a quantitative volume of air is extracted at a constant speed, and TSP with a particle size of less than 100 \(\upmu\)m is trapped on a constant weight filter membrane. The mass concentration of TSP is calculated based on the difference between the weight of the membrane before and after sampling and the volume of air collected [39].
## 2 Materials and Methods  
### Data Preparation  
For this study, a set of 128 Sentinel-2 tiles was used. Figure 2 illustrates the geographic distribution of these tiles. For each tile, a single date was used and the selection was random for the period from early 2016 to the end of 2020. The goal was to cover a wide range of geographic areas and seasons. For each acquisition, the data were obtained at two processing levels: 1C (from PEPS, CNES mirror of Sentinel data) and 2A (from Theia's catalog ), the latter having been produced by the MAJA processor. This allows us to use accurate masks at the pixel level for clouds, cloud shadows and saturation effects.  
For each acquisition, 100,000 pixels were sampled. Only non-saturated pixels were selected, regardless of their cloud or shadow status. Pixel positions were selected on the 20 m resolution grid. For each 20 m pixel position, the following information was recorded:  
* whether the pixel was detected as a cloud or a shadow (without distinction between these two states),
* the reflectance in the 20 m bands for levels 1C and 2A,
* the reflectance of the four corresponding pixels of each of the 10 m resolution bands for levels 1C and 2A,
* the reflectance at the 20 m pixel position of the 60 m resolution bands after bicubic resampling for level 1C,
* the solar and viewing angles for each pixel.  
For the analyses performed in the following sections, we split the data at the tile level. This means that all the pixels used for testing (measures of accuracy of the reconstructions) belong to tiles for which no pixel was used for training or even intermediate validation.  
Figure 2: Geographic distribution of the tiles used for the study.  
In the experiments carried out in this work, we randomly select 100 tiles and do a 80/20% split at the tile level for training and testing purposes. This means that training and testing pixels come from different tiles and dates. The training set is further split into proper training samples (80%) and validation samples (20%), the latter being used for monitoring the convergence of the training. For each experiment (i.e., set of predicted bands and set of predictor bands), the experiment is repeated 10 times by selecting a different set of 100 tiles among the 128 available. This allows checking for possible selection biases and allows further assessment of the robustness of the regressions.  
Further, only clear pixels (non-cloudy and not shadow) are used for training and validating models. This reduces the number of available pixels. On average, each experiment uses 3.86928 \(\times\) 10\({}^{6}\) training samples, 967,320 validation samples and 1.2582 \(\times\) 10\({}^{6}\) testing samples and is repeated 10 times.  
The dataset has been made public [25] and is available for other researchers to reproduce and improve the work presented in this paper.
## 4 Discussion
### Comparisons of Fusion Results with Different Methods
#### 4.4.2 Quantitative Comparisons  
The quantitative evaluation presented in Tables 2 and 3 demonstrates VCAFusion's superior performance across multiple metrics. Our method achieved the highest scores in visual information fidelity (VIF), spatial frequency (SF), average gradient (AG), and correlation coefficient (CC), while securing second place in both peak signal-to-noise ratio (PSNR) and QABF metrics. These results highlight the following two key strengths of our approach: (1) exceptional preservation of edge details and gradient information, as evidenced by its leading SF and AG scores, and (2) outstanding visual saliency representation, indicated by its top VIF performance. The consistent superiority of VCAFusion is further corroborated by the comparative line graphs in Figures 15 and 16, which show our method maintaining dominant positions in VIF, SF, and AG metrics while remaining competitive across all other evaluated criteria.  
Table 4 shows a comparative analysis of computational efficiency across different fusion methods. While deep learning-based approaches (e.g., DenseFuse, SeAFusion) achieve reasonable processing speeds with GPU acceleration, they incur substantial training overhead and lack interpretability due to their black-box nature. By contrast, the traditional methods exhibit higher computational efficiency without requiring pre-training. Our method strikes an optimal balance, delivering competitive processing speeds while avoiding the training burden and transparency limitations of deep learning. Although our approach involves multiple steps (retinex decomposition, SWGF, saliency weighting, and three-layer fusion), its computational complexity remains manageable for real-time deployment. Specifically, the runtime scales linearly with input resolution, and the memory footprint is dominated by intermediate feature maps rather than learned parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **CBF** & **DenseFuse** & **DRTV** & **PIAFusion** & **MLGCF** \\ \hline Times & 20.24 & 0.06 & 1.09 & 0.09 & 104.99 \\ \hline
**Methods** & **MSID** & **SeAFusion** & **PM** & **RGFTV** & **Ours** \\ \hline Times & 0.47 & 0.04 & 11.534 & 23.08 & 1.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean running time of nine fusion methods on the TNO and M3 FD datasets. (unit: second).  
Figure 15: Evaluation results of the eight fusion metrics for the TNO dataset in eight state-of-the-art methods and our method.
## 3 Battery Mathematical Models
### Battery Degradation Model
#### 3.3.3 Multi Factor Model  
The multi-factor model comprises all the degradation factors (environmental temperature, charging/discharging rate, depth of discharge, taper voltage and number of cycles). The cycle's  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Description** & **Unit** \\ \hline \(Q_{loss}\) & Capacity loss & Ah \\ \(A\) & Pre-exponential factor & Ah \\ \(Ea\) & Activation energy & J*mol–1 \\ \(R\) & Gas constant & J*mol–1K–1 \\ \(T\) & Temperature & K \\ \(n\) & Number of cycles & \\ \(z\) & Cycles exponent & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Battery ageing parameters.  
exponent \(z\) is set 0.74. Over and above, activation energy \(E_{a}\) is calculated by Equation (14) and the pre-exponential factor \(A\) by Equation (18). Substituting (14) and (18) to (17) yields the final Equation for estimating the capacity loss (19):  
\[Q_{loss}\ =\ A_{DOD,C_{rate},V_{t}}e^{\frac{-E_{a}(C_{rate})}{RT}}n^{0.74} \tag{17}\]  
\[A_{DOD,C_{rate},V_{t}}\ =\ -\xi_{9}+\xi_{10}DOD+\xi_{11}C_{rate}+\xi_{12}e^{ \xi_{13}V_{t}} \tag{18}\]  
\[Q_{loss}\ =\ \left(-\xi_{9}+\xi_{10}DOD+\xi_{11}C_{rate}+\xi_{12}e^{\xi_{13}V_{t}} \right)e^{\frac{-\xi_{1}e^{\xi_{1}e^{\xi_{12}C_{rate}-\xi_{3}}}}{RT}}n^{0.74} \tag{19}\]  
The battery degradation model's parametric coefficients are displayed in Table 2.
## 4 Analog Image Signal Relay Transmission (AISRT)
### Composite Video/Image Signal  
Currently, there are three major analog video signal standards in the world, including phase alternating line (PAL), national television system committee (NTSC), and sequentiel couleur a memoire (SECAM) standards [26; 27]. Among them, PAL is a color encoding system for analog television used  
Figure 2: Entire architecture of AISRT system.  
in broadcast television systems in some regions such as Europe, Australia, and China. It can work at usually 625-line 50 fields (25 frames) per second. Another common color encoding system is NTSC. It has been mainly adopted by North America, South America, and Japan. It can work at 525-line 60 fields (29 frames) per second. The SECAM standard consists of three forms: SECAM-L, SECAM-B/G, and SECAM-D/K, which are mainly used in France, the Middle East, and Russia. Due to the relatively higher scanning rate (50 fields per second) of NTSC, in this research, a type of NTSC camera was used for the composite video analog signal transmission.  
Composite video, also known as baseband video, is a traditional image data transmission method for the NTSC television signal. It can be transferred through a single analog waveform. The composite video contains color difference (hue and saturation of video), brightness, and synchronization signal. Especially, the synchronization signal can control the scanning of signals on the display including field sync signals, line sync signals, and line blanking signals.  
In the waveform of the composite video signal, the brightness signal, and the synchronization signal are added together, which is called the luminance signal (\(\Upsilon\)). The hue and color saturation are converted into chromatic aberration signal through a certain conversion and then modulated on a color subcarrier. These modulated chromatic aberration signals can be considered as the chrominance signal (C), and its phase represents the color whereas its amplitude represents the color saturation.
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 3 Case Study and Discussion
In addition, the reduced average vehicle delay was as much as 17.95%, 12.32%, and 11.78%, respectively, when the traffic volumes were 2500, 3500, and 4500 veh/h.
## 4 Performance Probability Analysis
### Ergodic Capacity  
According to [PERSON], ergodic capacity is a crucial performance metric because it quantifies the maximum achievable transmission rate at which errors can be recovered. In this paper, the DF relay protocol was selected. Specifically, the received signal of each relay station was fully decoded, re-encoded, and then transmitted to the next relay station. The code words transmitted from the source node \(S\) were selected from the Gaussian codebook. The minimum capacity of the link determines the capacity of the entire system. On the other hand, according to the minimum cut maximum traffic theorem [30], the overall capacity of the system is less than or equal to the capacity of each link. Then, the ergodic capacity of the \(i\)th (\(i\in N\)) relay can be expressed as  
\[C_{i}=\log(1+\mathrm{Y}_{i}). \tag{24}\]  
Therefore, the ergodic capacity of multidirectional multi-hop networks can be given as:  
\[\mathbf{C}=\min(C_{1},C_{2},...,C_{N}). \tag{25}\]  
According to [PERSON]'s inequality, the upper bound of the ergodic capacity in (25) is  
\[\mathbf{C} >\frac{1}{N}\mathbb{E}(\log(1+\mathrm{Y}_{i})) \tag{26}\] \[=\frac{1}{N}\mathbb{E}(\min\{\log(1+\mathrm{Y}_{1}),\cdot\cdot \cdot,\log(1+\mathrm{Y}_{N})\})\] \[=\frac{1}{N}\mathbb{E}(\log(1+\min\{\mathrm{Y}_{1},\cdot\cdot \cdot,\mathrm{Y}_{N}\})),\]where \(\mathbb{E}(\cdot)\) denotes mathematical expectation. Considering the Taylor series expansion, utilizing partial integration, we can rewrite the traversal capacity as  
\[\mathbf{C}=\frac{1}{N\ln(2)}\int_{0}^{\infty}\frac{1}{1+\mathrm{Y}_{DF}}(1-F_{ \mathrm{Y}_{i}}(Q))d_{\mathrm{Y}}. \tag{27}\]  
Combined with the connection probability, the ergodic capacity can be calculated as  
\[\begin{split}\mathbf{C}=&\frac{1}{N\ln(2)}\int_{0}^{ \infty}\frac{1}{1+\mathrm{Y}_{DF}}\times\\ &\prod_{i=1}^{N}\exp(-A_{\mathrm{Y}}T_{0})Det(\mathbf{I}+\beta \mathcal{K}_{i})^{-}\frac{1}{\beta}d_{\mathrm{Y}}.\end{split} \tag{28}\]  
The ergodic capacity is derived from the connection probability; thus, we can improve the ergodic capacity while considering the connection probability.
# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## 4 Fault Diagnosis Method Based on Digital Twin
### Fault Diagnosis Model  
Based on the theory outlined in the previous section, we designed a hybrid IVMD-PSO-LSSVM method for diagnosing faults within the wind turbine drive train. Taking gearbox bearings as an example, the program flow of the method is shown in Figure 11 with the following steps:  
**Step 1**: The vibration signals of wind turbine bearings are collected under different fault conditions.  
**Step 2**: The kurtosis index and the energy loss coefficient are used to determine the optimal parameter pairs (\(K_{best}\), \(\alpha_{best}\)) of the VMD, and then the raw vibration signal is decomposed into several IMF components.  
**Step 3**: Based on the minimum envelope entropy criterion, the optimal IMF component is selected for subsequent analysis.  
**Step 4**: Feature vectors containing rich fault information from optimal IMF components are extracted using the NCMDE algorithm.  
**Step 5**: The optimal combination of penalty factor c and kernel function parameters are determined for LSSVM using the PSO algorithm.  
Figure 10: Flowchart of PSO optimized LSSVM algorithm.  
**Step 6**: The extracted feature vector is randomly divided into training and test samples. The training samples are used to train the LSSVM after optimizing the parameters, and the test samples are used to test the trained LSSVM, which ultimately verifies the effectiveness and superiority of the method proposed in this paper.  
Suppose one wants to apply the above-proposed fault diagnosis model to an already-built wind turbine model. This requires connecting MATLAB and Unity3D; this problem is a call to the DLL file generated by MATLAB. The communication between MATLAB software and Unity3D software is achieved by calling the MATLAB program in C# language. First, the MATLAB program is compiled into a DLL program, then the DLL call is made using the C# language, and finally, by clicking on the fault diagnosis UI button, a C# script can be triggered to use the functions in the DLL program.