esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Multiple Converter Configuration Versus Typical Configurations  
This section analyzes the performance of the multiple converter configuration (C-MC) against typical configurations: battery-ultracapacitor configuration (C-B/UC) and ultracapacitor-battery configuration (C-UC/B). The simulation results of the C-MC against the CB/UC and C-UC/B are shown in Table 4. In the comparison of configurations, \(\Delta\ SOC_{b}\) is taken into account, which represents the difference between the initial and end state of the battery \(SOC_{b}\) when the driving cycle concludes. The difference between the initial and final state of the \(SOC_{uc}\) of the UCs at the end of the driving cycle is represented by \(\Delta\ SOC_{uc}\). Information on the energy delivered by the batteries (\(E_{b}\)) and UCs during the driving cycle for each configuration (\(E_{uc}\)) is also presented.  
Table 4 shows that \(\Delta SOC_{b}\) and \(E_{b}\) remain the same for all configurations before the CITY II driving cycle, that is, in this cycle the same amount of energy is extracted from the batteries and therefore the state of charge of the batteries is the same at the end of the cycle in all configurations (CB/UC and C-UC/B). Table 4 shows that in the CITY II cycle the \(\Delta SOC_{uc}\) in the C-MC is greater than that of other configurations, which means that the state of charge of the ultracapacitors at the end of the cycle is lower compared to that of other configurations. This is reflected in energy consumption where it is observed that \(E_{uc}\) in C-MC is greater than in the other configurations. In the ECE driving cycle, \(\Delta SOC_{b}\) of the MC configuration is slightly higher compared to that of the other configurations. The result above means that the battery pack at the end of the C-MC cycle is less charged than the other configurations although the difference is not very significant. The above is reflected in \(E_{b}\) where the amount of energy consumed is greater compared to the other configurations. In the case of the ECE cycle, the C-MC shows a \(\Delta SOC_{uc}\) greater than C-B/UC and C-UC/B which means that the energy consumed throughout the cycle is less than the energy consumed in the other configurations.  
In summary, in the CITY II scenario, all the configurations (taking into account that the battery acts as the main power source) present the same energy consumption. In this particular case, any configuration is adequate to meet the need for power demand in a city. In the scenario with ECE, the C-UC/B shows less energy consumption compared to the C-B/UC and C-CM. However, in this case the variations in energy consumption between the configurations are not very significant, so you are free to choose any configuration. However, for both scenarios the C-MC has the advantage of having two converters, which allow better control of energy flow for both energy sources unlike the other configurations.
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## 4 Blind Energy Estimation (BEE)
### EM-Step (Lines 7 to 14)  
The EM is an iterative two-step algorithm in which the expectation phase (E-step) evaluates the responsibilities using the current parameters, and the maximization phase (M-step) updates the parameters using the current responsibilities. Specifically, in _E-Step_, the responsibilities \(r_{k}^{(l)}\) are evaluated for all \(k\)\((\in\{1,2,\ldots,M\})\) and \(l\)\((\in\{1,2,\ldots,L\})\) (line 8). As the name implies, each value of this indicates the responsibility of cluster \(k\) for observation \(l\). Then, in _M-Step_, each cluster's mean \(\mu_{k}\), variance \(\sigma^{2}\), and weight \(\pi_{k}\) are adjusted to match the observations it is responsible for (lines 9 to 13). Note that we consider a common variance for all clusters and draw it by averaging \(M\) variances. Then, the AP evaluates the log-likelihood and compares it to the previous log-likelihood to check convergence. If it does not converge, repeat from E-step.
## 2 Methodology
### Coarse-to-Fine Spatiotemporal Correlation Computation Scheme
#### 2.3.3 Disparity Selection and Sub-Pixel Disparity Refinement  
The disparity selection is performed using the WTA strategy, wherein each disparity is selected corresponding to the maximum correlation coefficient, as follows:  
\[D(\text{p})=\underset{d\in S_{d}}{\text{argmax}}C_{stzncc}(\text{p},d). \tag{4}\]  
Now that the integer pixel position, \((x-d_{int},y,N)\), having the largest correlation value, is found in the target image as the best candidate point, a quadratic curve is fitted with five points centred on the matching point. The matched sub-pixel disparity, \(d_{sub}\), is computed according to the coordinates corresponding to the extreme points of the quadratic curve:  
\[C_{stzncc}(x,y,x-d_{sub},y,N)=a(x-x_{sub})^{2}+c,x\in[x_{\text{max}}-2,x_{ \text{max}}+2],x_{\text{max}}=x-d_{\text{int}}, \tag{5}\]  
where \(a\) and \(c\) are the fitting coefficients, \((x-d_{sub},y,N)\), representing the coordinate of the maximum value of the fitting curve and \(C_{stzncc}(x,y,x-d_{sub},y,N)\) is the new matching position point in place of \((x-d_{int},y,N)\). Finally, the desired disparity map is acquired through post-processing (e.g., filtering-out outliers and smoothing the surface).
# A Single Image Enhancement Technique Using Dark Channel Prior
## 3 Improved Image Defogging Method Based on Dark Channel Prior  
### Improvement of High Time Complexity and Edge Effects of Dark Channel Prior Algorithm  
Aiming at solving the problems of high time complexity and edge effect of traditional dark channel prior algorithms, in this paper, we propose an improved method. First, a four-point weighting algorithm is used to estimate the atmospheric light value accurately. Then, the rough transmittance is estimated by using the dark channel prior principle. In order to avoid edge effect and maintain the defogging edge, a guided filter is introduced to refine the coarse transmittance map. Finally, the fog-free image is restored by the atmospheric scattering model. The detailed improvements are described as below.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 4 Experimental Evaluation and Results Analysis
### Computation Time
#### 4.1.2 Performance Analysis: MFSPV  
Similar to the DLAPP proposal [13], MFSPV's authors [14] only considered the SHA-256 cryptographic operation to calculate the generation and verification times. They claim that MFSPV's protection takes 0.018 ms and that deprotection takes 0.006 ms. The MFSPV's performance results of this study are shown in Table 8 (from a total of \(\sim\)500 messages).  
By analysing the results, and as concluded in the DLAPP's performance analysis, the estimations provided in the proposal protocol [14] are higher than the ones obtained (for the same reasons). Moreover, smartphone X can protect up to 9343 and deprotect 7327 messages per second (Figure 14). The OBU, as before, presents a lower performance than the smartphone, protecting 5981 and deprotecting 6519 messages per second. Unlike the DLAPP protocol, the protection and deprotection operations in MFSPV are not so similar. Deprotection exhibits lower computation times (\(\sim\)8% to 53%) on all nodes. This occurs because the protocol performs more hash operations in protection than in deprotection.  
Figure 13: Total DLAPP operations (per second) in the developed applications for OBU and smartphone X.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **Protection Latency [ms]** & **Deprotection Latency [ms]** \\ \hline Smartphone X & 0.136 & 0.107 \\ OBU & 0.167 & 0.153 \\ RSU & 0.138 & 0.064 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Median security CT lency results using MFSPV in each node.  
Assuming the previous high-vehicle-density scenario, i.e., 1800 messages needing to be verified per second, the MFSPV was also computationally light enough to manage this high-node-density scenario on OBUs and smartphones.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
## 3 Naming Scheme of SWIM Data
### Overall Structure of the Naming Scheme  
The service resources are classified by tags, such as aviation, flight, weather, time, organization, and other category tags. Each label represents an attribute of the resource, and all the labels identified are collected. The multidimensional label is convenient for users to identify and select the service information they need. Through descriptions from different angles, subsequent search and matching can be maximized.  
The overall design of the resource naming scheme is shown in Figure 1.  
Figure 1: The overall design of the naming scheme.  
The steps involved in the naming scheme design are as follows.  
Step 1: First, set the attribute analysis dimension according to the content attributes of the resource and the needs of the user, and express it as a collection of multiple attribute tags: where x is the content and k is the k dimension tag extraction function.  
Step 2: After the content is processed by the label extraction function, a property set of the corresponding relationship is generated for each content of the query: Each corresponding label represents an attribute of the content, and this attribute set is used to represent the characteristics of the desired content.  
Step 3: The collection of attribute tags is used to generate the required TLC form through the processing of the encoding function. The generated form is processed according to the \"hierarchical-flat-attribute\" component to form the final naming scheme.  
Service resources are divided into two levels: static entity domain tags and dynamic behavior domain tags. Static indicates that the content information does not change with time and scene changes and is a basic feature label of content information. Dynamic means that the information will change dynamically with the change of the network. It is a behavioral domain label. Resource attribute tags are not limited to these and can be revised as needed. Table 1 is an example of an attribute label classification.  
Based on the division of the attributes of \"entity domain label\" and \"behavioral domain label\", resources can be effectively unclassified, which is convenient for later maintenance and management to meet user needs.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.3 Histogram of Oriented Gradients (HOG)  
In HOG, each pixel's magnitude and direction are computed from its \(x\) and \(y\) gradients. The image is then divided into cells, and histograms are computed for each cell based on gradient values. An aggregation of cells forms a block, and normalization is applied to each block. A combination of the normalized histograms from the blocks forms the HOG features [48]. In this work, the cells are 8 by 8 pixels, and tyeblocks are 2 by 2 cells. The number of orientation bins for the histogram is 9.
## 3 Interference Mitigation Schemes  
Inter-WBASN interference has been investigated in terms of its effect on the degradation of system performance. In this section, we review existing algorithms to mitigate inter-WBASN interference. The interference mitigation schemes can be categorized as shown in Figure 2.
## 5 Experiments
### Next Challenges  
The developed AR system is useful in a real shipyard workshop, but future AR developers should consider the following challenges for their future work:  
* As of writing, Microsoft is working on its own official framework to implement shared experiences. This could be an important step forward as it is thought that this official implementation would solve the compatibility problems detected during the development of the solution presented in this paper.  
* Another important aspect that can be improved is the visualization of traditional documentation (or digital PDF files) through panels, which are shown as mere 2D objects that do not take advantage of all the potential of the used AR devices. In addition, documentation panels make it more difficult to navigate the information than in a computer or tablet. To tackle this issue, the documentation could be shown in a contextual way, associated with the parts that are being visualized in 3D and, whenever possible, using elements like arrows, gauges, tools (e.g., screwdrivers, drillers), or animations to enhance the information provided by the documentation.
* During the tests at Navantia's Turbine workshop, it was identified that the small details in the assembly steps were the most useful for the operators when using the developed AR application, since the possibility of watching the parts from different angles allows for highlighting specific details in a more effective way than a printed document. Therefore, future developers should consider adding more detail in each step of the assembly sequence, giving the possibility of moving back and forth in the animations.
* Due to the problems discussed in the previous Section 5.4 about the size of the anchors, it would be interesting to implement an error detection system during synchronization that would allow for restarting the synchronization and thus enabling automatically recovering from this type of malfunction.
* The option of adding speech recognition could be considered to facilitate the interaction with the application without the need for hand gestures, which would offer greater freedom when working. Nonetheless, developers should consider that speech recognition may be difficult or even impossible in noisy industrial environments.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Square Coaxial Line  
#### 2.2.1 Design of Square Coaxial Line  
Butler matrices are designed using various transmission lines, such as waveguides, microstrip lines, and coaxial lines [20]. When the operating band is in the C-band, the rectangular waveguide size of BJ40 is significantly larger than the square coaxial structure of our design. The coaxial line has the advantage that the signal is shielded by the outer conductor. Therefore, the coupling to the adjacent component can be avoided. A suppression multipactor concept is considered to use the TEM mode in the coaxial line. A square coaxial line is easier to machine than a cylinder coaxial line in a Butler matrix.  
Two conditions are met in the design: (1) characteristic impedance should be 50 \(\Omega\) to match the input and output terminals of the system; and (2) low attenuation. The design of a beam former begins with evaluating the inner and outer diameter to achieve the distance between them for handling higher microwave power levels. The characteristic impedance depends on the ratio of outer and inner conductor diameters. For a 50 \(\Omega\) square coaxial, the inner conductor used in the square coaxial line is 4.8 mm, and the outer conductor is 12 mm. The cross-section of a square coaxial line is shown in Figure 2. The white is the vacuum, and the orange is aluminum.  
Figure 1: Schematic of a 4 \(\times\) 4 Butler matrix.  
Its characteristic impedance [21] can be approximately calculated by  
\[Z_{0}=\frac{136.7}{\sqrt{\varepsilon_{r}}}\mathrm{lg}\bigg{(}0.9259\frac{O_{d}}{I _{d}}\bigg{)} \tag{1}\]  
Its attenuation constant [21] can be approximately calculated by  
\[\alpha_{c}=\frac{59.37R_{s}}{\eta_{0}Z_{0}}\bigg{(}1+\frac{I_{d}}{O_{d}}\bigg{)} \frac{1}{O_{d}} \tag{2}\]  
where \(O_{\mathrm{d}}\) and \(I_{\mathrm{d}}\) are the outer and inner conductor diameters, respectively, and \(\varepsilon_{r}\) is the relative dielectric constant of the vacuum. \(O_{\mathrm{d}}\) is 12 mm, and \(I_{\mathrm{d}}\) is 4.8 mm, as presented in Figure 2, which leads to \(O_{\mathrm{d}}/I_{\mathrm{d}}\) is 2.5.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results
#### 4.1.2 Area of the Region  
It is interesting to see how the area of the region affects the number of drones required. Our experiments yield the plot shown in Figure 18c. The graph shows a linear relationship with the parameters.
# Improved CycleGAN for Mixed Noise Removal in Infrared Images
## 3 Experiments  
### Experiment Settings  
**Dataset:** We chose the public dataset M3 FD [27] as the experimental data to train and test the improved network proposed in this paper. The M3 FD contains 4200 high-quality infrared images with a variety of typical targets with different infrared radiation characteristics, such as pedestrians, vehicles, trees and buildings. We first resized the original images into 256 \(\times\) 256 pixels and formed them as the ground truth group: GT. Then, we added the mixed noise consisting of Gaussian noise (\(\sigma=10\)) and Poisson noise (\(\lambda=4\)) to GT to form the noisy group: NO\({}_{\text{M}}\). Finally, we divided the groups into train sets and test sets according to 7:3. Meanwhile, we also set up two additional test sets named NO\({}_{\text{L}}\) (\(\sigma=5\), \(\lambda=2\)) and NO\({}_{\text{H}}\) (\(\sigma=15\), \(\lambda=8\)), which were, respectively, used to test the network's detail retention ability under lower intensity mixed noise and denoising effect under higher intensity mixed noise.  
**Environment and training details:** Our experiments used the PyTorch deep learning framework for model training on a single NVIDIA GeForce RTX 3090 graphics card. The specific experimental configurations are shown in Table 1.  
Before starting the training, we set the epoch and batch size to 200 and 8, respectively, the value of \(\eta\) in \(L_{PSNR}\) to 0.01, the values of \(v_{1}\) and \(v_{2}\) in Equation (12) to 0.8 and 0.2, respectively, the value of \(\mu\) in Equation (13) to 10 and the slope of the LeakyReLU function in the discriminator to 0.2. During the training process, we used the Adam solver [28] to optimize the model. We set the initial learning rate to 0.0001 and kept the learning rate the same for the first 100 epochs and made the learning rate decay linearly to zero over the next 100 epochs.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Name** \\ \hline operating system & Windows11 \\ CPU & AMD Ryzen 9 5900X \\ GPU & NVIDIA GeForce RTX 3090 \\ RAM & 32G \\ deep learning framework & PyTorch(1.13.1) \\ interpreter & Python(3.10) \\ cuda version & CUDA (11.7) \\ \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
###### Abstract  
Power systems are rapidly evolving to face the increasing penetration of renewable inverter-based generation units and to improve their reliability and safety. A power system is constantly exposed to sudden changes or disturbances that may affect its stability. In this paper, a comparative analysis of solutions to improve transient stability, both rotor angle and frequency stability, is performed. These solutions are SVC, STATCOM, a fast excitation system, and an additional parallel transmission line. Sensitivity analyses were performed to evaluate the effects of the location of the three-phase fault line and the most effective SVC or STATCOM installation bus. Based on these analyses, the worst-case fault is considered, and the critical fault clearing time is determined as an engineering parameter for comparing the different solutions. For the numerical analysis, the IEEE 9 bus system is considered, and the PowerWorld software tool is used. Rotor angle and frequency stability analyses were performed. Moreover, specifically for SVC and STATCOM, the effects of different values of short-circuit ratios were considered in the context of rotor angle stability analysis. As part of the frequency stability analysis, the use of the remuneration for load shedding service in Italy was considered to perform an economic analysis for SVC and STATCOM.  
transient stability; power system stability; three-phase fault FACTS; PowerWorld Simulator 2021 11, 11359. [[https://doi.org/10.3390/app112311359](https://doi.org/10.3390/app112311359)]([https://doi.org/10.3390/app112311359](https://doi.org/10.3390/app112311359))
## 3 Results  
### Pix2 Pix Performance  
#### 3.1.1 Quantitative Evaluation  
The quality of the images generated by Pix2 Pix was assessed using three main metrics. The PSNR (Peak Signal-to-Noise Ratio), which measures the similarity between the generated images and the real ones, yielded a value of 18.04, indicating moderate distortion. The SSIM (Structural Similarity Index), evaluating the structural similarity between the images, scored 0.64, reflecting a reasonable correspondence in structure. The FID (Frechet Inception Distance), which measures the difference between the distributions of characteristics in the generated and real images, resulted in a value of 123.32, suggesting improvement in aligning the generated images with real ones.
# A Cross-Layer Media Access Control Protocol for WBANs
## 4 The Proposed Cross-Layer MAC Protocol
### Priority-Based Transmission Power Adjustment
Then, we can see that \(\mathcal{W}\) equals zero when the lost data are within the health range and all the periodic data have been received successfully during the last \(M\) superframes. When the value of \(\mathcal{W}\) is bigger than a threshold value \(\mathcal{W}_{\delta}\), the generated data need to be re-transmitted during the CBP if a transmission failure occurs. The adjusted transmission power when re-transmitting period data is  
\[P_{i}^{t}=min\{P_{i}^{s}+2^{t}\mathcal{W}P_{i}^{s},\ P_{max}\}, \tag{9}\]  
where \(P_{i}^{\delta}\) denotes a step value for power adjustment and \(t\) is the number of transmission failures for this same data. For event-triggered data, transmission power adjustment scheme is described as  
\[P_{i}^{t}=min\{P_{i}^{s}+2^{t}\theta P_{i}^{\delta},\ P_{max}\}, \tag{10}\]  
where \(\theta\) is a fixed-weight value corresponding to the data type. Through power adjustment, we hope to balance the trade-off between the network lifetime and the transmission reliability. Since heterogeneous data have different QoS requirements, an adaptive transmission power adjustment scheme is a good partner to work together with the MAC protocol to improve network performance.
# DVCW-YOLO for Printed Circuit Board Surface Defect Detection
## 2 Methodology
### WIoI Loss Function  
The CIoU loss function [21] in the YOLOv8 model performs suboptimally when detecting low-quality samples, such as small defects, and exhibits limited generalization ability. To address these issues, the WIoU loss function [22,23] in the DVCW-YOLO model incorporates dynamic and non-monotonic focusing mechanisms. These mechanisms enhance the model's ability to handle small objects or complex backgrounds by improving generalization and detection quality. The WIoU function assesses anchor frame quality by considering outliers in the dataset and adjusting sample weights to reduce the impact of low-quality samples.  
\[L_{WIoU}=R_{WIoU}L_{IoU} \tag{3}\]  
\[R_{WIoU}=\exp(\frac{(x-x_{gt})^{2}+(y-y_{gt})^{2}}{(W_{g}^{2}+H_{g}^{2})}) \tag{4}\]  
\[L_{IoU}=1-IoU \tag{5}\]  
Specifically, \(LWIoU\) denotes the modified loss function, while (\(X\), \(Y\)) are the horizontal and vertical coordinates of the predicted bounding box center, respectively. (\(Xgt\), \(Ygt\)) represent the center coordinates of the true bounding box, and (\(Hgt\), \(Wgt\)) denote the height and width of the true bounding box, respectively. The loss \(RWIoU\) is computed using an exponential function, which increases the weight of anchor boxes that are closer to the true bounding box while reducing the impact of those farther away. This loss \(IIoU\) complements the \(IoU\) between the predicted bounding box and the true bounding box, focusing on improving the overlap quality.