esa-sceva/satcom-chunk-collection huggingface dataset:
# Methodology for the Concept Design of Locally Reinforced Composites
## 3 Demonstration of the Approach
### Analysis of the Deviation Factor and Stress Threshold
Figure 13 illustrates the resulting angles for the 1 st principal stress clusters at \(\mu\approx\) 50%, \(\mu\approx\) 67.5%, and \(\mu\approx\) 85% for \(\text{c}_{\text{d,max}}=\) 5\({}^{\circ}\), \(\text{c}_{\text{d,max}}=\) 10\({}^{\circ}\), and \(\text{c}_{\text{d,max}}=\) 15\({}^{\circ}\). The principal stress direction 2 shows the same behavior, in a mirrored pattern, and is therefore not discussed here. The base laminate covers the entire surface, while the reinforced area increases with the increasing stress threshold. For smaller deviation factors, the mechanical model calculates numerous small clusters (a, b, c), whereas high deviation factors result in  
Figure 12: Relative cost, weight, and displacement for deviation factors of 5\({}^{\circ}\), 10\({}^{\circ}\), and 15\({}^{\circ}\) in relation to the reference laminate. Values below 1 indicate an improvement. The weight is equal for each case since it is defined only by the stress threshold, and therefore is independent from the deviation factor. fewer but larger clusters (g, h, i). The increasing number of small clusters is responsible for the increased costs. In the example (square plate with hole), a non-linear stress distribution results within the component [31], as shown in Figure 1b. Hence, the relationship between the stress threshold and the reinforced area is not linear, i.e., a stress threshold of \(\mu\approx 50\%\) does not imply that half of the component area is covered by reinforcements (see Figure 13). For values up to approx. 40%, only a small share of the stresses and corresponding areas are identified as reinforcements by the mechanical model. This results in a significantly higher displacement compared to the reference laminate. However, costs and weight are also significantly lower. For large stress thresholds (\(\mu>90\%\)), the increase in weight and corresponding costs due to overlapping reinforcements are disproportionately large. Although this reduces the displacement to approx. 60%, the weight and costs also increase by 50%. Furthermore, the results in Figure 12 show that the material costs of the considered  
Figure 13: Illustration of the clusters and homogenized angles for the demonstration example. Results for deviation factors of \(\mathrm{c_{d,max}=5^{\circ}}\), \(\mathrm{c_{d,max}=10^{\circ}}\), and \(\mathrm{c_{d,max}=15^{\circ}}\) for stress thresholds \(\mu=50\%\), \(\mu=67.5\%\), and \(\mu=85\%\) are shown in (**a-i**). Grey areas indicate reinforcement areas. Angles for small clusters in (**b,c**) are not displayed. carbon fiber are the main cost influence and are directly dependent on the stress threshold. This behavior can be derived from the strong correlation of costs with component weight. The second most critical cost factor are the stacking costs for the reinforcements, influenced by the deviation factor allocated to the clusters.
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 4 Simulation Analysis
### Simulation Tests for Asymmetrical Phase-to-Phase Faults
#### 4.1.2 Operating Performance of the Negative Sequence Power Direction Criterion  
In order to test the operating performance of the proposed negative sequence power direction criterion when the system operation mode changes, two-phase short-circuit faults are set at the midpoint of line segment AC in the forward area of PR4 and at the midpoint of line segment CD in the reverse area of PR4, with the fault resistance being 0.2 \(\Omega\). The influence of the system operation mode on the proposed direction criterion is compared. The simulation results are shown in Figures 7 and 8.  
Figures 7 and 8 show that the negative sequence power direction principle proposed in this work is less affected by the system operation mode, and can accurately determine the fault direction when the direction criterion is activated.  
Figure 7: Fault at line AC under different operating conditions (**a**) Positive sequence voltage at PR4. (**b**) Reactive current measured by PR4. (**c**) Negative sequence impedance angle measured by PR4.
## 2 Experimental Development
### Exposure to Environmental Conditions
#### 2.1.2 UV Radiation  
Samples were exposed to UV radiation in an accelerated aging chamber with UVA black light lamps (340 nm, 13 W/m\({}^{2}\), 40 \({}^{\circ}\)C) at PUJB (Figure 1b). Due to irradiance intensity degradation over time [40], a short exposure period was selected to ensure uniform radiation. Based on previous studies [41; 42]; degradation can occur within hours; thus, specimens were removed after 5 and 9 days.
# Stereotactic Positioning System: Towards a Mechanism Used in Thermal Ablation Therapy
## 2 Materials and Methods  
### Characteristics of the Stereotactic Positioning System to Located Micro-Coaxial Antennas  
The stereotactic positioning system developed in this work was designed to locate the antenna around the leg, a region where bone tumors are common. Therefore, the positioning system must hold and move the micro-coaxial antennas such as the onesdescribed in Figure 1a. These antennas work at 2.45 GHz; they were built in a micro-coaxial cable with outer and inner conductors of 2.19 mm and 0.27 mm, respectively. These antennas have been tested either in phantom or ex-vivo tissue by using 10 W of input power applied per 10 min. Moreover, the standing wave ratio (SWR) of the antennas were from 1.13-1.55, showing a maximum energy transference. Figure 1b shows the axes that must be covered by the displacements of the stereotactic positioning system over the human leg.  
Because the proposed stereotactic positioning system was designed to be used in a human leg, it was necessary to delimit its dimensions. Therefore, three aspects were considered to delimit the stereotactic positioning system's dimensions:  
* User height: It was considered that the length of the leg is linked with the height. The average height of Mexican people was considered, i.e., 1.64 m for men and 1.58 m for women [28]; by considering this information, a length system of 70 cm was proposed (See Figure 2a). Therefore, the Z-axis can be displaced and locate the antenna around that 70 cm mark.
* User weight: The user weight is also a factor to be considered to delimit the dimensions of the stereotactic positioning system. Diseases such as overweight and obesity can cause the leg's subcutaneous adipose tissue to become thicker, affecting its diameter [29]. Therefore, legs with a maximum diameter of 40 cm could be placed; in this case, a maximum antenna length of 17 cm was considered. Figure 2a shows a cylinder (1) representing a transversal view of a human leg placed in the stereotactic positioning system and the direction in which the antenna (2) could be displaced along the Theta-axis.
* Length of the micro-coaxial antennas for thermal ablation: The length of the designed antennas was considered to delimit the stereotactic positioning system's height, Y-axis (See Figure 1a). Although the typical antenna length is 11 cm, future antenna designs could be longer; therefore, 17 cm were chosen as a limit. The final dimensions of the entire system are presented in Figure 2a,p. Figure 2c shows a representation of a human leg in the system and the corresponding axes.  
Figure 1: Micro-coaxial antennas and description of the displacements that the stereotactic positioning system must cover. (**a**) Example of four different micro-coaxial antennas designed to treat bone tumors [27], (**b**) axis convention for the stereotactic positioning system (Z-axis, Theta-axis, and R-axis) referred to the human leg.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### PV-Powered Charging Station with V2G Service with Energy Cost Optimization
#### 2.2.2 Human-Machine Interface  
This layer interacts with the EV users, allowing them to choose their preferred charging mode \(M_{v}\) among slow, average, and fast. It should be noted that all EVs have the same energy capacity and can handle fast charging. The HMI obtains the SOC of the EVs at their arrivals \(SOC_{EV\_after}\), and assists the users in selecting their desired SOC at departure \(SOC_{EV\_desv}\), as well as their participation in V2G service \(V2G_{EV}\) in real-time. Therefore, the estimated charging time \(t_{est\_ch_{s}}\), which indicates the time required to reach the desired SOC, is determined in Equation (3):  
\[t_{est\_ch_{v}}=(SOC_{EV\_desv}-SOC_{EV\_arr_{v}})\cdot E/P_{EV\_max_{v}}, \tag{3}\]  
where \(E\) is the EV battery capacity, and \(P_{EV\_max_{v}}\) is the maximum power of EV charging based on the charging mode selected by the user. The HMI for the PVCS is explained thoroughly in [49], and once the user preferences are set in the HMI, these data are communicated simultaneously to the operation layer and to the energy cost optimization layer to actualize the optimization with these data.  
Figure 3: Supervisory control system for the PVCS [44].
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Thermal Decomposition Techniques  
Thermal decomposition, initially used to synthesise high quality semiconductor nanocrystals, is now commonly employed to synthesise monodisperse magnetic nanocrystals [34; 35; 36; 37]. Thermal decomposition involves the heating (often to temperatures in the region of 300 \({}^{\circ}\)C) of organometallic precursors in organic solvents that contain a stabilising surfactant. Examples of such organometallic precursors include, but are not limited to metal acetylacetonates (e.g., Fe(acac)\({}_{3}\)) and metal carbonyls (e.g., Fe(CO)\({}_{5}\)) [38].
## 4 Simulation Analysis and Measurement Verification  
In this section, the proposed electromagnetic coupling model is implemented and measured to compare with the theoretical calculated results and electromagnetic simulation results in the main lobe zone. Referencing the relative position in the specifications [24], we analyze the mutual inductance parameters between the OAU and the balise, the equivalent impedance of the circuit network, and transmission loss characteristics. The influence of the conducting medium on the S21 parameters is analyzed, and the I/O characteristics of the balise in underwater debris are predicted. In addition, experiments are carried out to validate the comparison of the I/O characteristics in free space and with underwater debris.
## 2 The Personalized Diagnosis Methodology
### The Personalized Diagnosis Method Using WPT and SVM
#### 2.2.2 A Brief Review of SVM  
Consider a training set S:  
\[\text{S}=\{x_{i},y_{i}\}_{i=1}^{l}, \tag{1}\]  
where \(x_{i}\)\(\in\)R\({}^{l}\), \(y_{i}\)\(\in\)\(\{-1,+1\}\) and \(l\) is the number of samples. The aim of SVM is to determine an optimal hyper plane for separating one from the others by using the training dataset. To get the ideal hyper plane, the dual optimization problem is often mentioned in SVM as:  
\[\text{min}-\frac{1}{2}\sum_{i=1}^{l}\sum_{j=1}^{l}\alpha_{i}\alpha_{j}y_{i}y_{ j}K(x_{i},y_{j})+\sum_{i=1}^{l}\alpha_{i},\ s.t.\ \sum_{i=1}^{l}y_{i}\alpha_{i}=0,\ 0\leq\alpha_{i}\leq C,\ i=1,2,\...\ l \tag{2}\]  
in which \(\alpha_{i}\) is the Lagrange multiplier coefficient obtained by dealing with the dual optimization in the process of the SVM training; \(K(x_{i},\ y_{j})\) is referred to as the kernel function; \(C>0\) is the error penalty parameter. There are many forms of the kernel function. The radial basis function (RBF) kernel is employed in this paper because of the highest accuracy rate of classification. How to choose the tradeoff parameters \(p_{1}\) (the width of RBF) and \(C\) is a difficult task in the application of SVM. A possible way is suggested by Xiang et al. [36] in which the simulation investigation should proceed first to obtain the relative best parameters \(p_{1}\) and \(C\), and hence, these parameters were employed for the real-world structures.  
Figure 2: The typical binary wavelet packet transform (WPT) decomposition structure (three layers).
## 3 In-Depth Analysis of Utilizing Machine Learning in Maritime Systems
### Challenges and Limitations of Using Machine Learning in This Context  
While ML presents numerous benefits to marine traffic management and prediction, it is also important to acknowledge the challenges and limitations associated with its application in this context:  
1. Data Quality and Availability: ML algorithms depend heavily on the quality and quantity of data available for training. Incomplete, inconsistent, or inaccurate data can significantly affect the performance of these algorithms [42].
2. Complexity of Maritime Systems: Maritime systems are highly complex and dynamic, with many influencing factors like weather, regulations, and human behavior. Modeling these complex relationships can be challenging for ML algorithms.
3. Interpretability and Transparency: ML models, especially complex ones like deep learning [43] networks, are often described as \"black boxes\" due to their lack of interpretability. This can be a challenge in situations where understanding the reasoning behind a decision or prediction is crucial.
4. Security and Privacy Concerns: With increasing digitalization and data sharing, ensuring the security and privacy of sensitive data becomes crucial. Any breach could have serious implications, including potential threats to maritime security [41].
5. Regulatory and Ethical Considerations: As AI and ML continue to evolve, there is a need for regulations that address their ethical use, accountability, and potential impacts on jobs and skills. Such regulatory frameworks are still in their nascent stages, and their absence can pose challenges.
6. Reliance on Technology: Over-reliance on technology can pose risks, especially in scenarios where ML predictions are wrong or when technical failures occur. There is always a need for a balance between human judgment and automated decisions [44].  
Despite these challenges, the potential benefits of ML in maritime systems far outweigh its limitations. With the ongoing advances in technology and data science, many of these challenges can be addressed, making ML an invaluable tool in the future of marine traffic management and prediction. The subsequent sections of this review will further explore the practical applications, case studies, and future prospects of ML in maritime systems.
# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 2 Our Model Architecture  
### Re-ID Network Based on Attention Module  
SDLAU can be applied to different layers of standard CNN architecture to extract salient information from different scales. Each SDLAU takes the feature map generated by the convolutional layer and the attention state generated by the previous SDLAU as input. Afterwards, the unit generates a new state for the next SDLAU. The mechanism is not only to extract authentication information from different layers but also to combine them. This process allows the model to recognize an object by evaluating discriminative features at different resolutions. The differences between different car models are quite subtle, and feature maps of different scales can provide different levels of information. Therefore, it is useful to collect features and combine sufficient discriminative features from multi-scale regions. Our Re-ID network based on attention module is shown in Figure 1.  
In order to extract more significant and discriminative vehicle features, more targeted vehicle global features are obtained by fusing the spatial attention mechanism and the  
Figure 1: Re-ID network framework based on attention module.  
channel attention mechanism. The network model in this paper is composed of a backbone network, an attention network, and a fully connected layer, which is shown in Figure 1. The network architecture of re-identification is the global branch vehicle feature extraction model fused with attention mechanism. The backbone network adopts ResNet-50 to extract the basic network of image features. After preprocessing, the vehicle image is sent to the ResNet-50 network to extract the second-layer shallow features and the third-layer deep features of the vehicle. Then, the attention mechanism network is adopted respectively. It is fused with the second and third-layer vehicle features. Finally, the global features extracted by ResNet-50 are subjected to a weighted fusion operation, and the obtained global features of the vehicle are the features of the fusion attention mechanism. By combining the features extracted by the two-layer attention mechanism network, the more salient features of the vehicle are obtained by fusion. After that, the extracted features are input to the fully connected layer for ID classification and vehicle identification.
## 4 Experiments
### Quantitative Comparison with State-of-the-Art Methods  
#### 4.2.1 Result on NRGD  
As shown in Table 1, On the NRGD, our proposed method demonstrates significant improvements across all quantitative metrics compared to existing state-of-the-art methods. Specifically, our method achieves a PSNR of 26.4192 dB, which is substantially higher than the best competing method IAT (16.4169 dB) and significantly outperforms all other methods, indicating superior fidelity in restored images. In terms of structural similarity, our method attains an SSIM of 0.9602, which exceeds the next best method IAT (0.6588) by a considerable margin, highlighting the effectiveness of our approach in preserving structural details and consistency under severe nighttime glare conditions. Regarding perceptual quality, our method achieves a LPIPS of 0.0792, substantially lower than IAT (0.3671) and other methods, demonstrating the enhanced perceptual quality and naturalness of the restored images in alignment with human visual perception. Furthermore, our method obtains an MSE of 400.5131, which is notably lower than all baseline methods, indicating reduced pixel-wise reconstruction errors. These results collectively demonstrate that our proposed method not only enhances the visual quality of glare-affected nighttime images but also effectively preserves structural and perceptual consistency while minimizing pixel-level errors, thus validating the effectiveness and superiority of our approach under challenging real-world nighttime glare scenarios.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 3 Dual-Code Group Network Plan Diagram Model with Time Line
### Automatic Coding of Mission Dual-Codes  
Based on the topological sorting of the missions obtained in Algorithm 1 and the deterministic plan obtained in Section 3.3, dual-code coding can be performed for each mission. We design the automatic dual-code coding algorithm for the missions (see Algorithm 3).  
```
0: Sorted mission set \(\mathcal{M}_{s}\) Output: Critical path set \(\mathcal{\bar{K}}\)
1 Initialize:\(\max=0\)
2\(S_{m_{i}}\), \(F_{m_{i}}\)\(\triangleright\)\(S_{m_{i}}\) and \(F_{m_{i}}\) are start-code and end-code of \(m_{i}\), respectively \(m_{k}\)\(\triangleright\)\(m_{k}\) is the prior-mission of \(m_{i}\) and both belong to the same executor
3 for\(m_{i}\) in \(\mathcal{M}_{s}\)do
4 for\(m_{i}\) in \(\mathcal{F}\mathcal{M}_{m_{i}}\)do
5 if\(E(m_{i})=E(m_{j})\)then
6\(m_{k}\gets m_{j}\)break
7
8 endif
9
10 endfor
11 if not \(m_{k}\)then
12\(m_{k}=m_{i}\)
13\(S_{m_{i}}\leftarrow\max+1\)
14\(F_{m_{i}}\gets S_{m_{i}}+1\)
15 else
16 if\(T_{PF}^{m_{k}}=T_{PS}^{m_{i}}\)then
17\(S_{m_{i}}\gets F_{m_{k}}\)
18\(F_{m_{k}}\gets S_{m_{i}}+1\)
19 else
20\(S_{m_{i}}\leftarrow\max+1\)
21\(F_{m_{i}}\gets S_{m_{i}}+1\)
22 endif
23
24 endfor
25 return\(\mathcal{\bar{K}}\)
```  
**Algorithm 3** Automatic Coding for Mission Dual-Codes  
The dual-code encoding of a mission obtained by Algorithm 3 must satisfy the following conditions. One is that for every mission \((s,f)\) there is \(s<f\). Second, each mission \((s,f)\) in the sorted mission set \(\mathcal{M}_{s}\) is sorted according to the arrow tail \(s\) from smallest to largest. Third, each mission of the same executor is ordered by the arrow \(f\) from smallest to largest,and for two adjacent missions on the critical path, the node number of the prior-mission end is equal to the node number of the post-mission startnode. Fourth, the first node of the sorted mission set \(\mathcal{M}_{s}\) must be the global start node \(S\) and the last node must be the global end node \(F\). With Algorithm 3, we have an automated dual-code numbering method.
## 3 Methodology
### Proposed ML Models
#### 3.7.3 Support Vector Machine (SVM)  
Support vector machine (SVM) is a powerful supervised learning algorithm used for both classification and regression tasks. It constructs one or more hyperplanes in a high-dimensional space to separate data into distinct classes. The optimal hyperplane maximizes the margin between classes, improving model generalizability and reducing classification errors. For non-linearly separable data, SVM employs kernel functions to transform the input space, enabling effective separation in a higher-dimensional space. This approach enhances the algorithm's flexibility in handling complex datasets [33].