esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging
## 2 Mathematics of Image State Decomposition and Enhancement
### \(\psi\)-Ensemble Matrices, for (RGB)\({}_{m,n}\)  
The \(\psi\)-ensemble matrices in (2), designed for an RGB image where \(\psi_{\text{m,n}}\geq 0\), are nonnegative ensemble matrices. For the sake of brevity in the notation, the ij index values will be removed from the following formula. The mathematics is applied pixel-wise and across the color channels to build the \(\psi\)-ensemble relations; it is of note for the sake of formalism to indicate the index notation. The m index in the following formula is the level of decomposition (Ln), and the integers 1 to 6 represent the first six levels of ISED decomposition demonstrated in this study. For clarity, the notation L6 is the sixth level of decomposition, and L3 would indicate the third level of decomposition. The \(n\) index in the following formula refers to the particular \(\psi\)-ensemble matrices \(\psi_{\text{m,n}}\) (state settings). The matrices in (2) are the possible ensemble relations between three color channels. R, G, and B. There can be between two to \(n\) channels for this method, but the \(\psi\)-ensemble matrices must be formulated for application-specific purposes [1]. The [0, 1] in our case is a binary decision in our study, 0 = not detected (off) and 1 = detected (on).  
\[\begin{split}\psi_{m,1,ij}&=[0,1](\text{B}_{m,ij}-\text{G}_ {m,ij})\\ \psi_{m,2,ij}&=[0,1](\text{R}_{m,i}-\text{G}_{m,i})\\ \psi_{m,3,ij}&=[0,1](\text{R}_{m,i}-\text{B}_{m,i} )\\ \psi_{m,4,ij}&=[0,1](\text{G}_{m,i}-\text{B}_{m,i} )\\ \psi_{m,5,ij}&=[0,1](\text{G}_{m,i}-\text{R}_{m,i} )\\ \psi_{m,6,ij}&=[0,1](\text{B}_{m,i}-\text{R}_{m,i} )\\ \end{split} \tag{2}\]  
To clarify the notation, the example \(\psi_{3,2j}\) (3) is the third decomposition of the ensemble matrix, recall the pixel-wise ij indices are dropped for brevity.  
\[\psi_{3,2}=[0,1](\text{R}_{3}-\text{G}_{3}). \tag{3}\]  
\(\psi_{3,2}=[0,1]\) (R\({}_{3}\)-G\({}_{3}\)) has different matrix values when compared to other Ln. To further illustrate, for this type of recursion, the L1 matrix notation would be \(\psi_{1,2}=[0,1]\) (R\({}_{1}\)-\(G_{1}\)). Additionally, lower value Ln ISED images contain more information in the \(\psi\)-ensemble matrix.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 4 Results
### Keypoint-Matching Comparison vs. SIFT/ORB
#### 4.2.2 Evaluation of the ARE
This robust geometric understanding by GeoTemplateKPNet is particularly vital for structured, feature-poor templates, where subtle shifts in keypoint prediction can significantly impact the transformation accuracy. To provide a more detailed understanding of the alignment performance, particularly for template geometries where the model might struggle or excel, we also examine the AREs broken down by individual template classes for each backbone. Table 6 presents the per-class AREs for the ResNet-50 backbone, and Table 7 presents the results for the VGG-16 backbone. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **K** & **Average ARE (\%)** & **Std. Dev. ARE** \\ \hline SIFT + RANSAC (\(N_{min}=3\)) & N/A & N/A & 0.207 & 0.193 \\ \hline ORB + RANSAC (\(N_{min}=3\)) & N/A & N/A & 0.387 & 0.253 \\ \hline GeoTemplateKPNet & ResNet-50 & 3 & 0.139 & 0.124 \\ \hline GeoTemplateKPNet & ResNet-50 & 4 & 0.530 & 0.239 \\ \hline GeoTemplateKPNet & ResNet-50 & 5 & 0.354 & 0.275 \\ \hline GeoTemplateKPNet & VGG-16 & 3 & 0.088 & 0.058 \\ \hline GeoTemplateKPNet & VGG-16 & 4 & 0.298 & 0.133 \\ \hline GeoTemplateKPNet & VGG-16 & 5 & 0.501 & 0.300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall average AREs in the synthetic test set for different methods. Tables 6 and 7 show the per-class average AREs broken down for the ResNet-50 and VGG-16 backbones, respectively. It is important to note that the \"NaN\" (Not a Number) entries in the ARE tables signify instances where the model failed to predict a sufficient number of geometrically distinct keypoints (typically a minimum of three non-collinear points required for affine transformation estimation) to compute a transformation matrix reliably. This situation is particularly evident for Class 2 (straight arrow) templates, as also reflected in its lower success rates in the keypoint-matching comparison. Consistent with the overall results, compared to ResNet-50 with K = 3, VGG-16 with K = 3 generally shows lower ARE values across the classes where alignment is possible, particularly for Classes 0, 1, and 2. Classes 0 and 1 consistently exhibit lower AREs for both backbones at K = 3, indicating accurate alignment for these shapes. Class 2 remains challenging, with NaN AREs for ResNet-50 with K = 3 and a calculated ARE of 0.112 for VGG-16 with K = 3, despite a low IMR. This suggests that VGG-16 is sometimes able to find enough points for alignment, even if they are close to or outside the mask. Class 4 consistently shows higher ARE values. For K = 4 and K = 5, the per-class ARE data become much more sparse, with many NaN values, indicating that alignment could not be performed. When calculated, ARE values for K = 4 and K = 5 can be both higher and lower than those for K = 3, depending on the class, supporting the notion that K = 3 likely provides the best balance for the overall accuracy and robustness across the classes. The data for the VGG-16 with K = 5 per-class AREs are missing.
## 3 Results
### Radionic Analysis  
A total of 112 original texture features and 744 wavelet-based texture features were extracted, resulting in 856 RFs per case. To address class imbalance, the SMOTE oversampling technique was applied, resulting in two balanced datasets: the BCR dataset with 92 samples (46 per class) and the tumor grade dataset with 76 samples (38 per class), respectively. The results are presented in two sections: BCR feature selection and tumor grade feature selection.  
In the BCR analysis, the feature selection process identified 7 optimal RFs for the BCR_CT model and 86 for the BCR_PET model. All seven features selected for the CT-based model were derived from high-frequency wavelet transformations. In contrast, among the features selected for the PET-based model, 85 were wavelet-based (spanning both low- and high-frequency components), while 1 belonged to the second-order texture category: GLSZM Large Area High Gray Level Emphasis.  
Regarding tumor grade prediction, statistical testing led to the selection of 34 and 24 robust RFs for the CT and PET models, respectively. Among the CT-derived features, two were original (Shape Maximum 2D Diameter Row and First Order Minimum), while the remaining 32 were wavelet-based. For the PET-based model, two of the 24 selected features--Shape Maximum 2D Diameter Row and GLDM Gray Level Non-Uniformity--were non-wavelet, with the rest derived from wavelet transformation.  
Complete details of the selected features and their associated \(p\)-values are provided in the Supplementary Materials.
## 3 Baseline  
Recently, based on the assumption that handcrafted features may not be optimal, data-driven systems fed by less processed features, such as spectrograms or raw waveforms, have been explored in audio domains [10; 27; 28; 29]. In the SV field, it is hypothesized that a model directly fed the raw waveform can appropriately aggregate various frequency bands of utterances and potentially extract discriminative features by the first one-dimensional convolution layer [29; 30]. Therefore, we used raw waveforms as input in all experiments and exploited RawNet2 [31; 32], a representative neural speaker embedding extractor based on raw waveform input, as the baseline system.  
Table 1 describes the structure of the baseline with several modifications to improve the performance of the original RawNet2. First, we increased the number of residual blocks [20] in the model from six to eight. Second, instead of the gated-recurrent unit, we employed attentive statistics pooling [33] to aggregate frame-level features into an utterance level feature. Third, we reduced the output dimension (embedding size) of the fully connected layer from 1024 to 512. More specific details related to the system architecture are described in the literature [31] and the author's GitHub ([[https://github.com/[PERSON]/RawNet](https://github.com/[PERSON]/RawNet)]([https://github.com/[PERSON]/RawNet](https://github.com/[PERSON]/RawNet)), accessed on 13 October 2020).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Block** & **Block Structure** & **\# Blocks** & **Output Shape** \\ \hline
1D-Conv & Conv(3, 3, 128) & 1 & L/3 \(\times\) 128 \\ \hline \multirow{4}{*}{Res1} & BN and LeakyReLU & \multirow{4}{*}{2} & \multirow{4}{*}{L/3\({}^{3}\)\(\times\) 128} \\  & Conv(3, 1, 128) & & \\ \cline{1-1} \cline{2-3}  & BN and LeakyReLU & & \\ \cline{1-1} \cline{2-3}  & Conv(3, 1, 128) & & \\ \cline{1-1} \cline{2-3}  & Maxpool(3) & & \\ \cline{1-1} \cline{2-3}  & AFMS & & \\ \hline \end{tabular}
\end{table}
Table 1: DNN architecture of the baseline system. \(L\) is the length of the input sample. Conv refers to the convolutional layer, and Res means the residual block. For convolutional layers, numbers inside parentheses refer to the filter length, stride size, and number of filters. ASP and FC are the attentive statistics pooling and fully connected layers, respectively. AFMS represents the alpha feature map scaling module, proposed by [31].
## 4 Results  
### Surface Subsidence Result  
DInSAR technology was used to calculate the cumulative subsidence of superimposed every two months from 25 August 2017 to 11 August 2022. The result can be seen in Figure 11 of Appendix A. There are obvious subsidence areas in the three mines, and the subsidence amount increases year by year, which is consistent with the mining collapse area. The subsidence of every two months is superimposed to obtain the cumulative subsidence of each year (Figure 3), and the maximum subsidence is about 0.708 m, among which the cumulative subsidence of Yantonggou Mining area is the largest, followed by Wangwa Mining area, and Wangwa No. 2 mining area is the smallest. Most census and pre-census areas are relatively stable. The annual surface collapse range of each coal mine is shown in Table 4. With continuous mining, the surface subsidence values have been increasing, and the affected surface area has expanded year by year. The cumulative surface subsidence area from 2017 to 2022 is 35.48 km\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **2017** & **2018** & **2019** & **2020** & **2021** & **2022** & **Total** \\ \hline Yintonggou mining area & 0.17 & 2.42 & 2.76 & 2.79 & 2.80 & 2.80 & 2.80 \\ Wangwa mining area & 11.70 & 17.04 & 21.26 & 23.82 & 23.93 & 23.94 & 23.94 \\ Wangwa No. 2 mining area & 8.73 & 8.731 & 8.733 & 8.734 & 8.74 & 8.74 & 8.74 \\ Total & 20.60 & 28.19 & 32.75 & 35.34 & 35.47 & 35.48 & 35.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Annual surface collapse range of coal mine based on DInSAR (km\({}^{2}\)).
## 7 Evaluation and Discussion
### Discussion
#### 7.4.3 VM-Based Protection and Signature Comparison  
The traditional code signature algorithm is an alphanumeric string obtained by processing the information transmitted through a one-way function to authenticate the information source and verify whether the information changes during the transmission.  
There are three differences compared here to VM-based protection:  
1. The purpose of the design is different. The former is to protect the application from reverse engineering. It protects the code and its execution logic. The latter is used to verify whether the information has been tampered within the transmission process. It does not have the ability of protecting the application program.
2. The idea of the algorithm is different. The former replaces the machine code of the application and interprets it through the redesigned Handler. The latter uses a selected one-way function (such as the hash function) to process the information transmitted to get the processing result. Then, the information is sent together with the processing result. The receiver uses the same one-way function to process the received information, and compares the obtained processing result with the result from the sender to judge whether the received information is tampered with.
3. Based on the above two points, the former needs to make necessary changes to the application program, while the latter only uses the application program or information as an input to the processing function and does not need to change the program.
## 3 Results and Discussion
### The Influence of \(pH\)  
It is known that the initial pH of the solution can affect the removal rate by adjusting the zeta potential of the adsorbent and the ionization degree of the dye solution [24]. Therefore, it is of great significance to study the effect of pH on the adsorption of anionic dyes. In this study, 0.050 g \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA @IM was added into 50 mL ultra-pure water using Zetasizer Nano ZS90 Nanoparticle Size and Zeta Potential Analyzer (MALVERN, UK). The pH of the suspension was adjusted to 2-10 using HCl solution and NaOH solution, respectively. The zeta potential of the sample was measured. Figure 8a shows the zeta potential of the ionic liquid-modified magnetic polymer at pH 2-10. Additionally, at pH 5, its zeta potential is the highest, reaching 30.4 mV, which may be caused by imidazoline-like basic groups, such as amino and imino. Of course, we need to consider the specific data and discuss it. It can be seen from the figure that the polymer is positively charged in the pH range of 2-10. Figure 8b shows different pH values. Dye removal at two initial concentrations. It can be seen that the removal efficiency of FCF dye hardly changes at pH 2-10. The results show that the polymer can adsorb FCF dye in a wide range of pH. Further combining the data in Figures 7 and 8a, it is shown that magnetic polymer adsorbs FCF mainly through electrostatic interaction between protic N on imidazole group and FCF.  
Figure 8: The zeta potential (**a**) and adsorption capacity (**b**) of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM at different pHs.  
Figure 7: The adsorption capacity of different materials for FCF.
## 2 Materials and Methods
### Methodology
#### 2.4.7 Definition of ACO Parameters  
The performance of ACO largely depends on the appropriate configuration of its key parameters, which govern the balance between exploration of the solution space and convergence toward optimal solutions. The following are the main components that determine the system's behavior.  
Number of ants (\(m\)): Each ant represents a tentative solution that visits all stations exactly once. One ant per station was used (\(m=n\)), allowing each ant to start from a different station to enhance diversity in graph exploration.  
Figure 5: Directed graph representation with workstations, heuristic costs, and pheromone levels for each trajectory.  
Initial pheromone (\(\tau_{0}\)): A uniform pheromone value was assigned to all edges at the start of the algorithm, typically ranging from 1 to 2. This neutral setting enables the evolutionary process to guide path selection based on accumulated performance.  
Evaporation rate (\(\rho\)): Controls how quickly pheromone values decrease in each iteration. High values of \(\rho\) (0.4-0.5) promote exploration, while low values (0.1-0.2) promote exploitation and faster convergence. In this work, values between 0.1 and 0.5 were evaluated.  
Heuristic (\(\eta_{ij}\)): Each edge (\(i_{j}\)) has a heuristic value based on the estimated travel time, considering robotic arm constraints such as maximum speed and acceleration. It is calculated as  
\[\eta_{ij}=\frac{1}{time_{ij}} \tag{11}\]  
Higher values indicate more favorable paths.  
Relative importance of pheromone and heuristic (\(\alpha\),\(\beta\)): These parameters control how much the ant's decisions are influenced by accumulated pheromone \(\alpha\) versus the local heuristic \(\beta\). In this implementation, values between 1 and 2 were used for both, promoting a balanced influence between past success and travel efficiency.  
Number of iterations: A maximum of 1000 cycles was set as the stopping criterion. Alternatively, the algorithm halts if no substantial improvement is observed over 20 consecutive iterations.  
A dual strategy combining intensification and diversification was implemented to guide the search process. Intensification reinforces the most promising paths by increasing pheromone levels on successful routes, while diversification promotes exploration through pheromone evaporation and the deployment of multiple ants with randomized initial routes. This balance is essential to prevent premature convergence to suboptimal solutions and to avoid excessive exploration without meaningful improvement.
## 1 Introduction
### Effective Proposed Approaches for Classifying Multiple PD Sources  
This study delves into the topics of classifying partial discharge (PD) signals in insulation systems, especially when multiple sources are present. Signal complexity, inconsistency in PD characteristics, and external noise exacerbate these difficulties. The research seeks to enhance PD classification using sophisticated signal processing and deep learning (DL) methods. Unique PD signatures from each insulation defect necessitate precise classification for maintenance planning. Conventional techniques struggle when PD signals overlap, but DL techniques such as CNN and LSTM show promise in extracting and analyzing PD signal features in time-frequency domains. Advanced signal processing, like time-frequency analysis (TFA), allows for dual analysis of temporal and spectral signal aspects, thus improving PD source identification. The study concludes that advanced techniques are superior to traditional methods in providing accurate diagnostics and maintenance for complex electrical systems. This research focuses on classifying PD sources using state-of-the-art signal processing and DL methods for better identification accuracy. A key contribution is combining DL and TFA, where TFA alongside CNN significantly enhances PD source classification accuracy, as depicted in Figure 1.  
Figure 1: Proposed approach.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 4 Inversion Experiments
### Small-Scale Inversion Using Active Sources
The S-wave velocity structure was resolved, albeit with a low resolution, and only  
appeared to converge at the price of losing resolution by discarding the phase information. Finally, we proposed a full-waveform joint inversion of the phase and Z/H ratio in the same multiscale framework. Figures 10c and 11c show the results obtained using the joint-inversion philosophy. The addition of the Z/H ratio to the joint inversion helped constrain the S-wave velocity structure in the near-surface region, so the low-velocity layer was correctly corrected and converged to the target model. Moreover, the mitigation of the cycle-skipping problems contributed to the Z/H ratio and could help the phase information better constrain the principal features of the deeper structure. The LVZ and HVZ in the middle were well covered with correct amplitudes. In addition, the interfaces between the velocity zones were better defined in the vs. model. In the Vp model, although only the very top portion of the P-wave velocity model was acceptably imaged, it can be seen that the inclusion of the phase information in the joint inversion provided more additional structure to the P-wave velocity model compared with the inversion results obtained using on obtained using only the Z/H ratio. Figure 10: The recovered S-wave velocity models for the three types of inversion. (**a**) The final inverted model using only the Rayleigh wave phase. (**b**) The final inverted model using only the Rayleigh wave Z/H ratio. (**c**) The final inverted model using the joint inversion of the Rayleigh wave phase and Z/H ratio. In Figure 12, we show how well the waveform data were fit under phase and joint inversion as described in this paper. The left column shows the vertical displacement components (Uz), and the right column shows the horizontal components (Uh) for three traces of stations at X = 150, 300, and 450 m from one shot gather located at X = 50 m. The black solid lines represent the target data, red dashed lines represent the initial predictions, and blue solid lines and green solid lines represent the final models obtained by phase inversion and joint inversion, respectively. We can see that the phase inversion suffered from a cycle-skipping problem, creating local minima in the FWI objective function, so the final traces were badly matched to the target traces (Figure 12a). Joint inversion incorporating Z/H ratio improved the vs. model by mitigating cycle-skipping issues, so the traces of joint inversion were matched to almost-perfect agreement with the target traces (Figure 12b). Figure 11: The recovered P-wave velocity models for the three types of inversions. (**a**) The final inverted model using only the Rayleigh wave phase. (**b**) The final inverted model using only the Rayleigh wave Z/H ratio. (**c**) The final inverted model using the joint inversion of the Rayleigh wave phase and Z/H ratio.
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 3 ANC Control Algorithm Based on an Adaptive Tracking Method
 \tag{11}\]  
Add perturbations \(\mu_{k,A}\) and \(\mu_{k\text{-}\varphi}\) to \(A_{k\text{-pur}}\) and \(\varphi_{k\text{-pur}}\), respectively, and then add them to \(A_{k\text{-ref}}\) and \(\varphi_{k\text{-ref}}\), respectively. \[\left\{ \begin{array}{c}\Delta A=A_{k\text{-ref}}-\left(A_{k\text{-pur}}+\mu _{k\text{-}A}\right)\\ \Delta\varphi=\varphi_{k\text{-ref}}-\left(\varphi_{k\text{-pur}}+\mu_{k \text{-}\varphi}\right)\end{array} \right. \tag{12}\]  
Judge the change of \(P_{\text{mix}}(n)\) before and after adding perturbations through the Equation (11), we adjust the perturbation \(\mu_{k\text{-}A}\), \(\mu_{k\text{-}\varphi}\) to make \(P_{\text{mix}}(n)\) change towards to the minimum. When the constraint \(\Delta A=0\), \(\Delta\varphi=2l\pi+\pi\), \(l\) = 0,1,2... or the number of adjustments is reached, the system outputs an adaptive tracking signal with current \(A_{k\text{-pur}}+\mu_{k\text{-}A}\) and \(\varphi_{k\text{-pur}}+\mu_{k\text{-}\varphi}\). Figure 6 is a detailed amplitude adjusting process, and it is the same with phase adjusting process.