esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
## 3 Proposed Method  
### Gabor Filter  
The texture of the fabric is usually composed of periodic patterns; by identifying and analyzing these periodic textures, defects in the fabric can be easily detected. A Gabor filter is a specialized wavelet filter that decomposes images into features corresponding to different scales and directions. By selecting specific frequencies and directions, the local features of textures can be extracted. [PERSON] et al. [35] proposed that many convolutional kernels similar to the Gabor filter appear in the low-level filters of AlexNet. Additionally, [PERSON] et al. [36] found that the receptive profiles of simple cells in the mammalian visual system resemble those of Gabor filters. Therefore, Gabor filters are widely used to simulate simple cells in the visual cortex, providing unique advantages in extracting low-level texture features. In traditional algorithms for fabric defect detection, a series of wavelet filters, particularly Gabor filters, have demonstrated significant effectiveness in fabric texture extraction.The 2D Gabor filter definition can be expressed as follows:  
\[\begin{split} G_{\lambda,\theta,\varphi,\tau}(x^{\prime},y^{ \prime})&=\exp\left(-\frac{{x^{\prime}}^{2}+\gamma^{2}y^{ \prime 2}}{2\sigma^{2}}\right)\exp\left(i\left(2\pi\frac{x^{\prime}}{\lambda}+ \varphi\right)\right)\\ x^{\prime}&=x\cos\theta+y\sin\theta\\ y^{\prime}&=-x\sin\theta+y\cos\theta\end{split} \tag{1}\]where \(x\) and \(y\) represent the two directions (horizontal and vertical) of a pixel, \(\lambda\) and \(\theta\) control the wavelength and orientation of the Gabor filters, respectively, \(\sigma\) is the Gaussian standard deviation of the filter, \(\varphi\) represents the phase, and \(\gamma\) is the aspect ratio of the space.
## 4 Proof of Concept  
As a proof of concept for the proposed architecture, five WiSe nodes and one Mobile Node were deployed in a 14 m \(\times\) 20 m home, in order to mount a WSN infrastructure and ensure coverage throughout the entire home. A 128-bit AES encrypting mechanism was also used to secure all data transmitted throughout the network.  
A total of 40 falls were carried out to validate the fall detection algorithm. The 40 falls were equally represented by 10 falls backwards, 10 falls forward, 10 falls to each side (five falls to the left and five falls to the right) and 10 falls to the knees, as shown in Figure 7.  
Figure 6: Web interface and alerts flow chart.  
A mobile node was programed to send the events (DATA) to the WSN. The monitored data sent to the WSN was also sent to a second computer possessing graph processing software every 500 ms or upon occurrence of an event, which came first. This was done in order to validate the data actually received with the values of the proposed algorithm. To validate the established values for heart rates, 10 stress-related tests were conducted, all of which generated events that were sent throughout the network and stored in the monitoring and alert interface. Figure 8 presents the scenario in which the test was performed.
## 4 Experiments And Analysis
### Algorithm Performance Evaluation
Figure 9a,c shows the trajectory diagrams for the improved algorithm, from which it is obvious that the trajectories of the improved algorithms were shorter and smoother than those of the traditional algorithms. A total of 500 test trials were performed. The final results are presented in Table 3, which includes the average success rate, average reward, and average number of steps, as well as the reward variance and step variance for both the traditional algorithms and their improved versions under the condition of two randomly moving obstacles. The results indicate that the SAC algorithm is more suitable than the DDPG algorithm for the obstacle-avoidance path-planning of robotic arms in high-dimensional spaces. However, the method proposed in this paper enhanced the performance metrics of both algorithms, thereby validating the stability and robustness of the proposed approach. In particular, the reward variance and step variance further confirm the improvements in algorithm stability and efficiency. For SAC, the reward variance decreased from 8.47 to 4.13, a reduction of approximately 51.3%, and the step variance dropped from 20.55 to  
Figure 9: The robot manipulator and obstacle trajectories: (**a**) improved SAC algorithm; (**b**) traditional SAC algorithm; (**c**) improved DDPG algorithm; and (**d**) traditional DDPG algorithm. 8.27, a reduction of about 59.8%. For DDPG, the reward variance decreased from 11.87 to 6.63 (a 44.2% reduction), and the step variance was reduced from 27.57 to 11.90 (a 56.8% reduction). These significant decreases in variance clearly demonstrate that the improved methods produce a more stable, consistent performance across multiple trials. In our experiments, the obstacle speed was reasonably set to within a realistic range. The robot joint velocity during obstacle avoidance remained smooth and complied with both the joint position and velocity constraints, making the entire motion process feasible for future real-world implementations. These findings demonstrate that the newly proposed reinforcement learning-based obstacle-avoidance path-planning method is not only practical but also more robust and reliable. By significantly improving both the average performance and its stability, the method proves effective in accomplishing real-time obstacle avoidance tasks for robotic arms operating in dynamic, high-dimensional environments.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 7 Conclusions
The performance of micro-supercapacitors is also impacted by the fabrication technology, which influences the electrode microstructure, such as electrode dimension and inter-space, and the precision of those. Four main categories of fabrication technologies have been covered in this review, namely deposition, coating, etching, and printing methods. In terms of deposition methods, chemical vapour deposition is rarely used for micro-supercapacitors since flexible polymers cannot withstand high temperature. Electrophoretic and electrolytic deposition is more commonly used, and they have been reported for graphene coating and doping with carbon nanotubes. Coating with carbon nanotubes, graphene, and active carbon can be achieved through (sequential) spray coating, layer-by-layer coating, and vacuum filtration. Generally, custom-made masks are needed with this technique to generate the interdigitated patterns. Both laser and plasma etching techniques have been employed in the fabrication of micro-supercapacitors, e.g., laser etching to reduce graphite oxide to obtain graphene porous structure and plasma etching for carbon nanotubes. Finally, several printing techniques have been reported, including inkjet, screen, gravure, extrusion, and 3D printing. Inkjet and 3D printing, especially, are receiving increasing attention for the fabrication of micro-supercapacitors. These are simple, fast, cost-effective methods which can be up-scaled. This research was funded by KU Leuven Internal Funds Bijzonder Onderzoeksfonds 2020, grant number STG/20/013. Not applicable. This is a review paper and presents an overview of previously published data. The authors declare no conflict of interest.
# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 2 The Development Methods of CGAN  
### The Principle of CGAN  
CGAN is directly derived from GAN. In order to better introduce CGAN, the principle of GAN is herein briefly reviewed. In GAN, there are two networks--generative network \(G\) and discriminative network \(D\), in which \(G\) is used to establish the data distribution, and is used to estimate the probability that a sample comes from the dataset or \(G\). In order to obtain the distribution, \(p_{g}\), of the dataset, \(\mathbf{x}\), \(G\) will establish a mapping from the prior noise distribution, \(p_{z}(z)\), to the data space, \(\mathbf{x}\), \(G\) outputs a scalar, \(D(x;\theta_{d})\) to represent the probability that \(\mathbf{x}\) comes from the dataset. During the training, the parameter updates of \(G\) and \(D\) are carried out simultaneously. When adjusting the parameters of \(G\), the goal is to minimize \(\log(1-D(G(z)))\), and when adjusting the parameters of \(D\), the goal is converted to \(\log D(G(z))\). They counteract each other and optimize the following functions in the opposite direction:  
\[\begin{split}\min_{G}\max_{D}V(D,G)=&\mathbb{E}_{ \mathbf{x}\sim p_{data}(\mathbf{x})}[\log D(\mathbf{x})]\\ &+\mathbb{E}_{\mathbf{z}\sim p_{z}(\mathbf{z})}[\log(1-D(G(\mathbf{z})))]\end{split} \tag{1}\]  
The generic framework of GAN could be seen in Figure 1. If the above generative network and discriminative network are conditional on some additional information \(\mathbf{y}\), then GAN will be extended to CGAN. Here, \(\mathbf{y}\) can be any additional information, such as category labels or other modal data. In the design of network architecture, \(\mathbf{y}\) is often used as the input for both the generative network and discriminative network. For \(G\), the prior noise \(p_{z}(\mathbf{z})\) and the condition \(\mathbf{y}\) are concatenated for the hidden layer; for \(D\), sample data or generated data \(\mathbf{x}\) and condition \(\mathbf{y}\) are concatenated as the input. Therefore, the objective function becomes:  
\[\begin{split}\min_{G}\max_{D}V(D,G)=&\mathbb{E}_{ \mathbf{x}\sim p_{data}(\mathbf{x})}[\log D(\mathbf{x}|\mathbf{y})]\\ &+\mathbb{E}_{\mathbf{z}\sim p_{z}(\mathbf{z})}[\log(1-D(G(\mathbf{z}|\mathbf{y}) ))]\end{split} \tag{2}\]
## 4 Results and Discussion
### Analysis of the Effect of Different Parameters on the Performance of ANN Models  
In order to comprehensively assess the role of different numbers of hidden neurons on the accuracy of model prediction, this study conducted a comprehensive comparative analysis of a series of training datasets of different sizes. Figure 8 indicates that as the size of the training dataset expands, the failure probability predicted by the ANN model gradually approximates the actual observed value, and the model prediction is more reliable. This phenomenon verifies that sufficient data can increase the ability to improve the generalization of the ANN model.  
The increase in dataset size also inevitably leads to an increase in computational resources and time, so the balance between computational cost and model performance needs to be considered in model design. Weighing the model performance and practical requirements, the training dataset containing 800 samples is selected for this study. With this dataset size, the MSE of the test set is reduced to 1.23 \(\times\) 10\({}^{-6}\), a result that not only proves the high accuracy of the model prediction but also shows the reasonableness of the model prediction results.  
For training datasets of different sizes, choosing the right number of hidden layer neurons is crucial to obtain the best fitting effect. As shown in Figure 9, by comparing the model performance under different configurations, it is found that the appropriate number of hidden neurons can significantly improve the predictive ability of the model and effectively avoid the overfitting problem. In particular, when the number of first hidden layer neurons is set to 8 and the number of second hidden layer neurons is set to 3, the  
Figure 8: Comparison of MSE (mean squared error) for different training datasets.  
model exhibits the best fitting effect, which is consistent with the rule of thumb presented in Section 2.4.1.
## 4 Method
### Evaluation Metrics  
GRACE TWSA data are used as reference points to evaluate the performance of the reconstructed solutions. Three metrics are employed to quantify the accuracy of the reconstructions: the root-mean-square error (RMSE) and Mean Absolute Error (MAE) coefficient. The formula of correlation coefficient is expressed as follows:  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{(X_{i}-Y_{i})^{2}}{n}} \tag{9}\]  
The \(RMSE\) provides a measure of the average magnitude of the discrepancies between \(X_{i}\) and \(Y_{i}\). By calculating the square root of the average of the squared differences, \(RMSE\) emphasizes larger errors, making it particularly sensitive to outliers. This metric serves as an essential indicator of the accuracy of the reconstruction, as it directly reflects the deviation from the observation.  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}|X_{i}-Y_{i}| \tag{10}\]  
The MAE quantifies the average magnitude of errors between observed \(X_{i}\) and predicted values \(Y_{i}\), without considering their direction. By calculating the average of the absolute differences between the predicted and observed values, MAE provides an intuitive measure of predictive accuracy. This metric yields a value in the same unit as the target variable, which makes it particularly interpretable. Lower MAE values indicate better model performance, suggesting that the predictions closely match the actual observations. MAE is pivotal for assessing the reconstruction reliability, as it reflects the model's capability to minimize overall prediction errors.
## 4 Results
### Analysis of Trends in Producer Accuracy and User Accuracy  
In this study, the PA and UA were used to evaluate the recognition of each crop. The time at which the PA and UA reached above 85% was considered the earliest that each crop was identifiable. The PA and UA recognition analysis for each crop is as follows.  
(1) Analysis of trends in producer accuracy and user accuracy for rice identification  
Table 7 shows the maximum PA and UA achieved by each classifier at different periods for rice identification. The maximum PA was achieved on 8 May (87.9%), and the corresponding UA (80.1%) was lower, so the rice could not be effectively monitored at that time. The RF classifier performed best on 13 May, with its PA and UA being about 95%, followed by the GBDT (about 93%). In contrast, the Cart and SVM did not perform as well as the first two, but both could identify rice effectively, with PA and UA above 90%.  
(2) Analysis of trends in producer accuracy and user accuracy for corn identification  
Table 8 shows the maximum PA and UA that each classifier could achieve at different times for corn identification. It can be seen that both PA and UA increase with time. In particular, the maximum PA and UA achieved on 12 June was about 81% using GBDT and RF. Corn was able to be identified earliest in the period to 7 July using any of the four classifiers. Their performance was comparable, with the maximum PA and UA both being above 97%.  
(3) Analysis of trends in producer accuracy and user accuracy for soybeans identification  
Table 9 shows the maximum PA and UA achieved by each classifier for soybeans at different times. As with soybeans identification, the PA and UA of soybean identification also increased over time. The maximum PA and UA on 12 June was also around 81%. On 7 July, all four classifiers could effectively identify soybeans, with GBDT and RF having maximum PA and UA of 97%. The SVM and Cart are somewhat lower at 96% and 94%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Cart**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-9}  & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** \\ \hline
8 May & 77.1\% & 69.7\% & 87.9\% & 80.1\% & 86.4\% & 80.2\% & 86.3\% & 79.2\% \\
13 May & 90\% & 91.9\% & 93.6\% & 95.6\% & 95.9\% & 97.7\% & 91.3\% & 92.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum PA and UA of rice in 2020 different periods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Cart**} & \multicolumn{2}{c}{**GBDT**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**SVM**} \\ \cline{2-9}  & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** & **PA** & **UA** \\ \hline
8 May & 57.5\% & 58.1\% & 69.5\% & 64.9\% & 69\% & 64.8\% & 57.5\% & 62.8\% \\
13 May & 64.5\% & 63.2\% & 71.5\% & 71.9\% & 71\% & 69.5\% & 87\% & 68.8\% \\
28 May & 72.5\% & 69.4\% & 77.5\% & 74.9\% & 78\% & 75.7\% & 73.2\% & 72.
## 2 Related Work
They partition a network in a manner that not only expands the capacity regions of sub-networks, but also allows distributed algorithms to achieve the capacity regions. However, they will need to allow dynamic channel allocation that will require the channel allocation algorithms for online and distributed operation. A distributed gateway centered multi-radio multichannel approach has been developed by [43] and [44] where mesh gateways are considered as sinks and sources of data. These approaches consider the coexistence of more than one radio interface of the same homogenous standard on a mesh router and use more than one available orthogonal channel. In [41], its authors have addressed co-existence of heterogeneous interfaces and introduced a radio based novel graph model which captures the heterogeneity of interfaces. They have also formulated scheduling, routing and channel assignment as an optimization problem. Their results show improvement in network capacity while preserving node level fairness. In [45] the given network consists of a set of stationary wireless routers where some of them also act as gateways to the Internet. They assume that the paths between the routers and the gateways have been pre-determined, for example, the neighbor-to-interface binding mechanism in [43] which can be used to determine the paths and the logical topology of the network. In their work the implementation can either be centralized or distributed. For distributed implementation, each node is responsible for assigning the optimal channels to some links. One of the distinct advantages of this algorithm is that it has the ability to assign the non-overlapping channels and also the partially overlapping channels. This allows the IEEE 802.11 frequency band to be fully utilized.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
###### Abstract  
Large numbers of seismic channels and high-density energy-efficient acquisition systems are the development trend of seismic instruments and have attracted high R&D interests in recent years. The combination of remote sensing and wireless sensor network technology provides superior observation capabilities for high-density seismic exploration. However, large-scale and multi-node acquisition methods place higher requirements on time synchronization performance. Seismic data with poor time synchronization will cause considerable errors in the interpretation of seismic data and even have no practical significance. Thus, the strict time synchronization performance is the prerequisite and basis for the application of cable-less storage seismograph in high-density seismic array applications. The existing time synchronization methods have high power consumption and poor time synchronization accuracy, which is not suitable for the long-time task. In addition, these methods are affected by the number of nodes and the distance. This paper presents an energy-efficient time-sharing indexed interpolation intercept method for the seismic data synchronization. The time synchronization method uses the high-precision TCXO as the main clock and records GPS time in the SD card at intervals to achieve the high-precision time-stamp for the seismic data. Then the seismic data is intercepted intermittently based on precise time stamps, which achieves the strict seismic data synchronization. Performance analysis shows that the time synchronization accuracy of the proposed method is 0.6 us and saves 73% energy of the time-sync periods compared to the common GPS timing method. The field measurement results indicate that the time synchronization accuracy is not associated with the working time and the distance between nodes so that the proposed synchronization method is suitable for the high-density seismic survey.  
Keywords:high-precision; energy-efficient; time synchronization; high-density seismic survey +
Footnote †: journal: Applied Sciences
# Simple and Efficient Non-Contact Method for Measuring the Surface of a Large Aspheric Mirror
## 3 Experiment and Results  
The preliminary experiment was carried out on a \(\mathcal{O}\)3 m concave off-axis polished mirror, with a radius of curvature of 13 m. An API laser tracker model Radian was set up at the center of curvature of the mirror on the test tower.  
The non-contact probe is made with a \(\mathcal{O}\)25 mm flat round air bearing and preloaded at 0.4 kg. A 0.5-inch hollow SMR is placed on the center of the pad, and the 'radius' of the probe is 12.55 mm. The non-contact probe works under a pressure of 2.5 bar, and 16 uniformly spaced radial lines were sampled. The scanning speed is approximately (0.3-0.5) m/s, and it takes about 3 min to measure the entire surface.  
The preliminary data from this testing are quite encouraging; the optical measurement data (surface measured with interferometer and CGH) show that the mirror surface has 20 nm RMS figure error. The measurement result of the laser tracker is 4.4 \(\upmu\)m in peak-to-valley (PV) and 0.8 \(\upmu\)m in RMS, respectively, as shown in Figure 6, confirming that high precision of the non-contact scanning method in this geometry is achieved.
## Challenges and Potential Solutions
### 3D Networking Reliability-Latency Fundamentals  
The 6G is expected to support 3G applications and deployment. For instance, 3D base stations will be presented. Research into measurement and data-driven modeling of the propagation environment is essential. Network planning and 3D frequency utilization should also be developed where 3D networks are significantly different from the conventional 2D networks because of the new altitude dimension and degrees of freedom. Moreover, the fundamental 3D performances of 6G systems, i.e., the rate-reliability-latency tradeoffs and SE, are required. Such analysis must quantify the spectrum, energy, and communication requirements that 6G must support the identified driving applications. Recent research in [88, 89] provides the related fundamental information in that direction.  
Sequentially, the Poisson point process (PPP) has a major benefit in the mathematical tractability and is used in modeling network deployment and coverage probability. However, the mathematical objects are randomly distributed in the space. In real applications, multi-tier heterogeneous applications have random distributions. Actual interactions between base station locations could appear in terms of repulsion and attraction. Small cell networks tend to involve attraction reflecting user-centric hotspot areas, whereas repulsion is observed at the macro cell level to represent rural and urban deployments. This distinction will be more emphatic in very small cell environments. However, the 6G networking environment will not be presented by the trusted PPP because the latter is mostly limited to the 2D plane. In the future, emerging deployment scenarios will be conducted in 3D. However, such scenario is complicated at the THz frequency band because of the blockages and the highly directional beam patterns.