esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 3 Literature Review of GANS for SSL  
### Taxonomy  
Figure 2 shows a taxonomy of the surveyed papers. As Figure 2 shows, early approaches to SSL GANs generally involved extensions to existing GAN models by use of pseudo-labeling, or by adding a classifier component to the original GAN architecture. This approach was seen in numerous models such as CatGAN [30], SGAN [31], Improved GAN [32], GoodBadGAN [29], CT-GAN [19], and MatchGAN [33]. Many others used a conditional approach where the image as well as the label was fed into the GAN. This was seen in the case of EnhancedTGAN [34], MarginGAN [27], Triangle-GAN [35], Structured GAN [36], R\({}^{3}\)-CGAN [28], and EC-GAN [37]. A third approach consisted of models using encoder-based approaches where an encoder was added to the GAN architecture to map images into a latent space, which then subsequently helped in the training process. This approach was seen in BiGAN [38], ALI [39], and Augmented BiGAN [40] models. More recent approaches have used manifold regularization techniques in order to make the model more resistant to perturbations in the input. Laplacian-based GAN [41], Monte Carlo-based GAN [42], SelfAttentionGAN [43], and SSVM-GAN [44] all fall into this category. Other unique approaches involved using two GANs as seen in MCGAN [45], VTGAN [46], and IAGAN [47], and finally leveraging conditional GANs in a stacked discriminator approach, seen in SS-GAN [48], to discriminate between predicted attributes.
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 4 Simulation Experiments and Comparison of the Fusion Algorithm
### Experimental Setup  
To evaluate the efficiency and effectiveness of the NRBO-DWA fusion algorithm in dynamic environment path planning, a 3D spatial simulation was conducted using the actual size parameters of a circular werf knitting workshop production line in Guangdong as the experimental background. In addition, recent bio-inspired algorithms, specifically the GA-DWA and PSO-DWA are used to evaluate the efficiency and effectiveness of the NRBO-DWA fusion algorithm.  
Figure 7: Convergence diagrams of different algorithms on test functions, (**a**) \(F_{1}\); (**b**) \(F_{2}\); (**c**) \(F_{3}\); (**d**) \(F_{4}\); (**e**) \(F_{5}\); (**f**) \(F_{6}\).  
DWA, PSO-DWA, and SSA-DWA algorithms, were selected for comparison. The 3D models and algorithms were implemented using MATLAB, with the simulation environment running on MATLAB R2022b, an AMD 7950X CPU, 64 GB of memory, and the Windows 11 operating system. The simulation environment was set in a 6000 mm \(\times\) 8000 mm \(\times\) 4000 mm 3D space map, where 3D path planning simulations were performed under the same conditions to verify the effectiveness of the NRBO-DWA algorithm. Obstacles were uniformly represented by rectangular cuboids, and passable areas were marked in white. Due to the large number of points in the 3D spatial map, the map was voxelized to reduce the computational load, with each voxel measuring 10 mm \(\times\) 10 mm \(\times\) 10 mm. The starting position for the simulation was set at coordinates (1000,1600,500), and the target position was set at (5000,7600,1000). The population size for the four algorithms was uniformly set to 100, with a maximum of 100 iterations. Dynamic obstacles were represented by small rectangular cuboids moving back and forth along the Y-axis in the range [5000, 6000] at a linear velocity of 1.5 m/s. Other parameter values are shown in Table 3.
# Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Proposed Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Statistical indicators [6; 7; 8; 9]} & - & CC, ASLE, SD, SSE, MSE, & - & CC is not sensitive to changes in the \\  & - & CC, ASLE, SD, SSE, MSE, & - & SD takes place when there is a normal \\  & and MM are used to & - & slight shift in FRA peaks. \\  & - & distinguish two FRA traces. & - & Others, MSE, MD, SSE, also have similar \\  & - & CC and ASLE can give better & - & disadvantages to CC and SD. \\  & comparison results & - & Interpretation of the measured FRA still \\  & - & Can simulate the magnitude & - & Exact parameters should be identified by \\  & - & and phase of the FRA similar & - & measurements or imperial equations. \\  & - & This method can investigate & - & The model cannot consider all transformer \\ circuit [13; 14; 15] & - & major transformer faults & - & parameters, such as detailed mutual \\  & - & such as radial deformation & - & inductances and shunt conductance. \\  & - & and axial displacement. & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of various frequency response analysis (FRA) interpretation methods. This paper aims to take a step towards establishing guidelines for FRA interpretation. The proposed guidelines are designed to facilitate an uncomplicated way to identify fault types within power transformers. The proposed guidelines are developed by reviewing major studies and standards for FRA interpretation. The effect of various faults on a transformer FRA signature is categorized into three frequency bands: low, mid, and high. Classification is also categorized into three states: no variation, small variation, and large variation, through CC calculation. Non-expert users can use these guidelines to interpret the measured FRA responses. The proposed guidelines are validated through experimental testing on a three-phase 11/0.433 kV, 500 kVA distribution transformer with a short circuit fault. The step-by-step research progress of the proposed method is shown in Figure 1.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation
### Evaluation of Stair Removal  
For testing the robustness of our algorithm against different walking velocities in the stair removal component, we recorded data with three different walking velocities, approximately 1\(\times\), 1.2\(\times\) and 1.5\(\times\), while climbing five pairs of stairs on a building, as can be seen in Figure 13. As demonstrated in the results (Table 3), the algorithm scored a precision of 94%, recall of 93.8% and F-score of 93.9% on correctly identifying the stairs, with the same sliding window length for all datasets. The sliding window size was 50 samples long or approximately 10 s, while it slid for every sample or approximately every 250 ms.
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm
### Wavefield Image Damage Identification of Single Defects Based on NCA-RPCA Algorithm  
#### 5.2.1 Defects in Intermediate Positions  
A rectangular central defect with dimensions of 5 mm width, 7 mm depth, and 0.5 mm height was introduced on the three-dimensional aluminum plate. This is illustrated in Figure 15.  
Figure 14: The 3D plot of parameter selection for “a”.  
Figure 13: Illustrates the curve depicting the variation of the number of outliers under different defects as the threshold “a” changes.  
The receiving nodes deployed on the 3D aluminum plate can obtain the wavefield images at different output moments after the solution operation. Figure 16 shows that the Lamb wave completes its transmission at the defect location at 30 us, which visualizes the wavefield image of the Lamb wave that has been reflected and scattered after encountering the damage. It can be clearly seen that the Z-axis displacement component at the defect has changed. When the Lamb wave encounters a defect during propagation, it will change the propagation path of the wave and the upper and lower interface conditions, thus damaging the echo and scattering signals. The color table on the right side of Figure 16 can reflect the fluctuation of the Lamb wave in the process of propagation; the bigger the fluctuation, the closer the color is to the upper color, and vice versa, the closer the color is to the lower color, the modes of the Lamb wave will change accordingly, and the NCA-RPCA algorithm is very sensitive to the detection of the anomalous values. The algorithm processing can identify the damage more quickly and accurately; the results are shown in Figure 17, and the original image data matrix is decomposed into a low-rank matrix and a sparse matrix.  
Figure 16: Wavefield image of the middle defect.  
Figure 15: Structure of the middle defect.  
After processing by NCA-RPCA, the principal information in the image data matrix is retained in the low-rank matrix, while the outliers are put into the sparse matrix. Since there are noise and outliers in the sparse matrix, it is also necessary to detect the defects corresponding to them by selecting a suitable threshold to remove the other noisy parts through the adaptive thresholding method. In this way, defects can be successfully detected regardless of their size, number, and location. The sparse matrix after denoising is shown in Figure 18, and then the damage can be identified in the original image by processing the nonzero ranks of the sparse matrix for identification, as shown in Figure 19.
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
## 2 Satellite Overview
The navigation receiving unit (NRU) and the measurement and control unit (MCU) have low heat generation.
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 4 Machine Learning
### Classification Methods
#### 4.1.5 Logistic Regression  
Logistic regression uses the logistic function for classification. The reason for this lies in its S shape (in a two-dimensional space), which enables generating a classification model that gives the probability of class (from 0 to 1).  
By using classical linear regression for binary classification where we encode the first class as \"1\" and second as \"0\", we can get probability values that are outside of \([0,1]\) range as a result of the regression function. However, since such a solution does not make sense, there is a need for a function that will overcome this problem, one of which is the logistic function [44]:  
\[l(x)=\frac{e^{w_{0}+w_{1}x_{1}}}{1+e^{w_{0}+w_{1}x_{1}}} \tag{3}\]  
where \(w_{0}\) and \(w_{1}\) are weight coefficients and \(x_{1}\) is independent variable.  
In a linear regression model, weight coefficients \(w_{0}\) and \(w_{1}\) are calculated by the least squares' method based on the data in the training set. This method is not suitable in a logistic model, so the maximum likelihood method is used instead, which estimates \(w_{0}\) and \(w_{1}\) so that \(l(x)\) for positive instances is close to 1 and for negative instances close to 0.  
Logistic regression strengths are in its simplicity, probability outputs, and that it is less prone to overfitting in low-dimensional data sets. On the other hand, with high-dimensional datasets overfitting can be an issue.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 7 Technological Integration and Evaluation  
The integration of these advanced HRTF techniques into consumer products, as well as their evaluation in real-world scenarios is a crucial aspect in ensuring their practical applicability. With the continuous evolution of audio technologies, companies such as Apple and Dolby, alongside academic projects like EarFish from the Active Perception Lab at the University of Antwerp, have pioneered the use of personalised HRTF solutions. These efforts bridge the gap between research and consumer adoption, aiming to enhance spatial audio experiences in consumer devices, from headphones to home theatre systems.
# Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems
## 2 Materials and Methods
### Hammerstein Model and Pulse-Compression Identification Procedure
The \(\left[g(t)\right]\) kernels and the \(\left[h(t)\right]\) kernels are related through a linear transformation. The identification procedure is based on this correspondence between the vectors of the \(\left[h(t)\right]\) and \(\left[g(t)\right]\). Let the evolution law of the instantaneous frequency of the swept signal be the exponential type, that is, let \(\omega(t)=d\phi(t)/dt=2\pi\text{ f}_{\text{MIN}}\text{ Exp}(\text{t/L})\), where the constant \(L=T_{0}/ln(f_{MAX}/f_{MIN})\) describes how quickly the frequency changes over time between \(f_{MIN}\) and \(f_{MAX}\) in a signal whose duration is \(T_{0}\); then, the kth harmonic of the input signal corresponds to a simple shift of the signal \(x(t)\) by the quantity \(\Delta t_{k}=L\ ln(k)\). In fact:  
\[ \begin{split} f(t+\Delta t_{k})=&\,f_{MIN}\,\text{ Exp}[\frac{t+\Delta t_{k}}{L}]=f_{MIN}\,\text{Exp}[\frac{t+L\,\text{ln}(k)}{L}]=\\ &\,=k\,f_{MIN}\,\text{Exp}[t/L]=k\,f(t)\end{split}  \tag{6}\]  
This property is depicted in Figure 2 and is the basis of the Hammerstein model identification procedure described in [16; 17; 18; 19]. In the particular case where the signal used for pulse compression is a swept sine of the exponential type and if, in addition, the
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 4 Performance Evaluation
### In Karman Vortex Flow  
Fish often swim in water that is full of disturbances. Therefore, the control strategy of the robotic fish must adapt to the flow environment. The Karman vortex is a typical vortex flow field for the environment test of the robotic fish. We extend the analysis of the algorithm in a steady flow to the Karman vortex flow field. We generate the Karman vortex shown in Figure 12 in FLUENT and add the vortex to the swimming environment of the robotic fish.  
The speed of the robotic fish swimming in the Karman vortex is depicted in Figure 13. The VMM follows the given speed faster at the beginning, whose rise time is 0.4 s shorter than the PM. The overshoot of the VMM, meanwhile, is 18% greater than that of the PM. The VMM, which has the characteristics of muscles, can use the flow vortex to boost the speed. Consequently, the robotic fish loses some ability to adjust its speed, leading to a greater overshoot. At 15 s, the overshoot of the VMM still affects the response of the speed that the following of the speed is a little slower compared with the PM.  
The angles of the joints in Figure 14 show that the VMM adjusts the movements of the robotic fish to adapt to the vortex in the water. As a result, the robotic fish swims with higher efficiency than controlled via the PM. The actual angle of the first joint has phase lag and positive deviation. The phase lag of the waveform is 38\({}^{\circ}\), which is greater than the lag in the steady water; the offset is 1.5 rad because the vortex affects the hydrodynamic force on the robotic fish.  
Figure 12: The robotic fish in Karman vortex.  
Figure 13: The speed of the robotic fish in Karman vortex.  
In the Karman vortex, the VMM can improve the propulsive efficiency more significantly than it does in the steady flow. Figure 15 illustrates that the VMM increases the efficiency about 10% than the PM. When the speed is 0.4 m/s, the \(\eta\) of PM is 36% while the \(\eta\) of VMM reaches 47%. At 0.5 m/s, the \(\eta\) of PM is 33% and the \(\eta\) of VMM is 40%. The vortex in the water contains the energy to generate the hydrodynamic force on the robotic fish. The virtual muscular method takes the compliance into the joints. The compliant joints can use the energy of vortex street better; thus, the robotic fish consumes less energy to produce motive force for the forward motion.
## 2 Vision and Key Features for Future 6G Networks
### High Security, Secrecy, and Privacy  
To date, research attention in 4G and 5G communications have been concentrated on the core network metrics of throughput, reliability, latency, and the number of served users with little attention to security, secrecy, and privacy issues. As acknowledged and assimilated, the two most efficient schemes to enhance these parameters involve increasing the density of the network and deploying a higher frequency to send modulated symbols. In 5G networks, traditional encryption algorithms based on the Rivest Shamir-Adleman (RSA) public-key cryptosystems are still in use to provide transmission security and secrecy [19]. RSA cryptosystems have become insecure under the pressure of Big Data and AI technologies, but novel privacy protection mechanisms remain far from being full-fledged in the 5G era. Thus, improvements in security, secrecy, and privacy in 6G networks are expected through these novel technologies.  
PHY security techniques and quantum key circulation through VLC should address the data security issues in 6G networks [68; 69]. Sophisticated quantum computing and quantum communication schemes may be effective in the provisioning of a comprehensive shield against different cyber-attacks [70]. Nevertheless, communication/data service providers have legally amassed a huge cache of user information, and private data leakage occurrences happened sporadically. As expected, this aspect will become a soft spot in the human-centric 6G technology and might produce a calamitous outcome without appropriate countermeasures. To address this issue, attaining total anonymization, non-distributed, and untraceability is expected in 6G systems via the use of blockchain technology [71].  
Conversely, recent research efforts have focused on real-life practical deployments, including multiple access schemes [72], physical air interfaces [73], and 6G data centers [74]. 6G network configurations are outlined in [75; 76], in which deploying cell-less topology, distributed resource distribution, and 3D super-connectivity are highly envisioned in 6G networks. Machine-type communications (MTCs) and vertical-specific wireless network results for 6G are examined in [77]; the work suggests that 6G could hasten the development of the first-ever barrier-breaking standard to fully substitute prevailing industry explicit communication protocols and offer an integrated solution permitting effortless connectivity of all needs in vertical industries.
## 4 Discussion  
The experimental simulations validate the advantages of the hybrid algorithm. During the dynamic response phase, the algorithm exhibits significant disturbance rejection capability and fast convergence, particularly under the influence of airflow disturbances, where the UAV quickly returns to the desired trajectory. In the steady-state phase, the steady-state error of the algorithm's output is almost zero, demonstrating excellent tracking performance. Compared to traditional PID control, the hybrid algorithm shows significant advantages in  
Figure 12: UAV attitude angular velocity experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PID** & **ADRC** & **ST-SMC** \\ \hline The maximum amplitude of the pitch angle (in radians) & 0.016 & 0.019 & 0.008 \\ Time required for the pitch angle channel to stabilize (in seconds) & 5.516 & 3.687 & 2.763 \\ The maximum amplitude of the roll angle (in radians) & 0.088 & 0.083 & 0.034 \\ Time required for the roll angle channel to stabilize (in seconds) & 9.631 & 7.219 & 6.764 \\ The maximum amplitude of the yaw angle (in radians) & \(-\)0.00712 & 0.00834 & \(-\)0.00467 \\ Time required for the yaw angle channel to stabilize (in seconds) & 17.267 & 8.873 & 6.326 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results comparison and analysis.  
response speed, disturbance rejection capability, and control precision, providing reliable technical support for stable UAV operation in complex dynamic environments.
## 5 Conclusions  
Recent advances in MEMS and microprocessor and wireless communication technologies have enabled the deployment of large-scale sensor networks, where thousands or even tens of thousands of small sensors are distributed over a vast area in order to collect sensing data. A large amount of data from a large number of sensor nodes can provide very important information to the users; however, a very large amount of data might cause large-scale congestion in a network.  
Conventional congestion control protocols usually use a back-pressure scheme that reduces the congestion by reducing the transfer rate of the child nodes of the congested node and by decreasing the packet generation rate of the sensor node that causes heavy traffic. However, packet drops may still occur because the back pressure is propagated slowly due to the collision among sensor nodes in case of serious congestion.  
In this paper, we proposed a new congestion control technique, named ACT, based on an adaptive compression scheme for packet reduction in case of congestion. Compression techniques used in the ACT are DWT, ADPCM, and RLC. ACT first transforms the data from the time domain to the frequency domain, reduces the range of the data with the help of ADPCM, and then reduces the number of packets by using RLC before transferring the data to the source node. It then introduces the DWT for priority-based congestion control because the DWT classifies the data into four groups with different frequencies. Subsequently, it assigns priorities to these data groups in an inverse proportion to the respective frequencies of the data groups and defines the quantization step size of ADPCM in an inverse proportion to the priorities. RLC generates a smaller number of packets for a data group with a low priority. In the relaying node, the ACT reduces the number of packets by increasing the quantization step size of ADPCM in case of congestion. Moreover, in order to facilitate the back pressure, the queue is controlled adaptively according to the congestion state. We experimentally demonstrated that the ACT increases the network efficiency and guarantees fairness to sensor nodes, as compared with the existing methods and that it exhibits very high ratio of the overall available data in the sink.
# Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
## 5 Results and Discussion
### Scope for Future Research  
This section notes some of the challenges that need to be addressed to enhance the existing systems. The likelihood-based strategy can improve the security and privacy of context-driven service deployment in IoT environments by actively addressing the following problems:  
* _User Consent and Control._ Users should have authority over the gathering and use of their contextual data during the process of deploying context-driven services. Ensuring user privacy and trust requires transparency in data collection processes, explicit permission mechanisms, and the provision of opportunities for users to opt out or modify their data preferences. The insufficient provision of user consent and control methods may result in privacy breaches and erode user trust in the system.
* _Data Privacy._ The deployment of services driven by context relies primarily on the collection and analysis of contextual data from an IoT device and their users. This often contains sensitive information such as location data, behavioral patterns, and user preferences. The acquisition and manipulation of such data give rise to issues relating to the violation of privacy. Any unauthorized attempt to access this data--whether by individuals with malevolent intent or to exploit weaknesses in the system--has the potential to result in privacy breaches and undermine the confidentiality of users.
* _System Vulnerabilities._ Similar to other software systems, the likelihood-based approach may have weaknesses that can be exploited by attackers to compromise the system's security. If vulnerabilities such as buffer overflows, injection attacks, or insecure authentication techniques are not swiftly recognized and corrected, they could provide substantial hazards. Performing regular security assessments, code reviews, and vulnerability scans is crucial to detecting and addressing any potential vulnerabilities in the system.