esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Theory
### Theoretical Analysis
#### 2.3.1 Working Mechanism of Wavelet Transform  
1. Frequency domain analysis characteristics: as a time-frequency analysis tool, wavelet transform can analyze signals in both time (or space) and frequency domains. Unlike Fourier transform, it has localization and multi-resolution properties, allowing it to extract image details at different scales, which is crucial for detecting rain streaks in deraining tasks. Rain streaks, as high-frequency noise, are concentrated in certain areas of the image and are captured in high-frequency components (HL, HH). Wavelet transform breaks the image into different scales, enabling focused processing of rain streaks while preserving background details by separating components from high-frequency small scales to low-frequency large scales.
2. Correlation between high-frequency components and rain streaks: since rain streaks are typically vertical or diagonal, they manifest as high-frequency features in the HL and HH sub-bands during wavelet decomposition, making them easier to capture. In contrast, the low-frequency sub-bands (LL, LH) retain horizontal details and background information. Deraining targets high-frequency subbands to remove or suppress rain streaks. The image is then restored using the inverse wavelet transform, combining all subbands to reconstruct the rain-free image. Since low-frequency subbands remain unaffected, the image structure is preserved, ensuring clarity and retention of original details.
3. Wavelet coefficient thresholding in practical applications: After extracting high-frequency rain streak features using wavelet transform, thresholding techniques can be further applied to suppress noise. Rain streaks, being sparse signals in high-frequency components, can be extracted, and irrelevant noise can be suppressed by setting an appropriate threshold. This method leverages the sparsity and locality of the signal, effectively reducing interference with the image background and enhancing deraining performance.
## 5 Simulation Results and Analyses
### Comparison and Analysis of the Number of Surviving Nodes  
Figure 4 shows the number of surviving nodes versus the number of rounds for the proposed algorithm and reference algorithms when 400 nodes were considered in the network, from which we can see that the number of surviving nodes decreases with the increase in the network rounds no matter which algorithm is used. However, by using the proposed ACOCR, the network always has the largest number of surviving nodes.  
Figure 4: The number of surviving nodes versus the number of rounds for the six algorithms.  
In order to further assess the network lifetime, this paper brings in some metrics, such as first node dead (FND), half of the nodes dead (HND), and last node dead (LND). Figure 5 illustrates the number of rounds when FND, HND, and LND arise for the six algorithms, from which we can see that the first node of the ACOCR, EOCA, CUWSN, LEACH-ANT, DUCS, and LEACH dies in about the 806 th, 686 th, 632 th, 569 th, 481 th, and 423 th round, respectively. That indicates that with respect to the FND metric, the efficiency of the proposed ACOCR is 17.5%, 27.5%, 41.7%, 67.6%, and 90.5% higher than that of EOCA, CUWSN, LEACH-ANT, DUCS, and LEACH, respectively. As for the HND and LND, the proposed ACOCR outperforms LEACH by 63.2% and 65.2%, respectively. In conclusion, the proposed ACOCR algorithm has the best performance in prolonging the network lifetime because it adopts the improved CHN selection scheme by comprehensively considering the residual energy of the nodes, the distance between the node and the SN, and the average distance between the node and the other nodes in the cube. The CHN selection is capable of distributing the network load equally and preventing the nodes with low energy from becoming CHNs so as to prevent the premature death of nodes. Additionally, the ACOCR employs the improved ACO to find the optimal paths between CHNs and the SN in order to reduce the energy consumption. The LEACH has the worst performance, as it randomly selects CHNs without considering the residual energy of the nodes, which makes some nodes with insufficient residual energy be selected as CHNs and thus die too early. In addition, it does not consider the multi-hop paths when the CHNs send data packets to the SN. The LEACH-ANT algorithm and the DUCS algorithm outperform the LEACH algorithm. This is because the LEACH-ANT algorithm employs ACO algorithms to look for the next hop node, and the DUCS algorithm selects the CHN according to the residual energy of the node. However, the LEACH-ANT algorithm does not optimize the CHN selection or improve the ACO algorithm, and the DUCS algorithm does not consider the optimal paths from CHNs to the SN. Hence, they are inferior to the proposed ACOCR algorithm.
## 4 Results
### Extraction of Feature Bands Using SCARS Algorithm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spectral** & **Feature Band/nm** \\
**Transformation Form** & \\ \hline N & 946, 1034, 1038, 1045, 1055, 1199, 1214, 1344, 1375, 1428 \\ \hline FD & 1077, 1081, 1095, 1116, 1275, 1278, 1402, 1469, 1522, 1604, 1611, 1681 \\ \hline SD & 996, 1003, 1188, 1213, 1227, 1234, 1241, 1255, 1273, 1280, 1333, 1337, \\  & 1351, 1401, 1426, 1433, 1436, 1440, 1465, 1472, 1507, 1525, 1539, 1557, \\  & 1568, 1575, 1582, 1603, 1653, 1656, 1681, 1685 \\ \hline RFD & 1176, 1183, 1257, 1264, 1459, 1522, 1568 \\ \hline RSD & 971, 996, 1010, 1213, 1227, 1241, 1255, 1294, 1383, 1397, 1426, 1433, \\  & 1436, 1440, 1504, 1507, 1525, 1582, 1653 \\ \hline LRFD & 1095, 1116, 1119, 1179, 1183, 1275, 1278, 1324, 1399, 1423, 1430, 1459, \\  & 1469, 1522, 1529, 1604, 1607, 1681 \\ \hline LRSD & 996, 1003, 1188, 1220, 1255, 1273, 1305, 1337, 1372, 1397, 1426, 1433, \\  & 1436, 1440, 1472, 1507, 1525, 1568, 1582, 1653, 1656, 1681 \\ \hline RLFD & 971, 1073, 1112, 1176, 1183, 1194, 1257, 1264, 1275, 1278, 1459, 1469, \\  & 1505, 1522, 1568, 1611 \\ \hline RLSD & 996, 1213, 1241, 1255, 1266, 1397, 1426, 1433, 1436, 1440, 1472, 1525, 1568, 1582, 1653, 1681 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected feature bands for various spectral transformations after running the SCARS. Figure 9: SCARS feature band extraction process. (**a**) Variable count selection curve. (**b**) RMSECV change curve for variable selection. (**c**) Variable selection coefficient change curve. (Each line represents a regression path, and “*” indicates convergence at this point). transformation. This is because the reciprocal logarithmic first-order differentiation transformation emphasizes bands with larger logarithmic derivative values and is mainly used to extract broader spectral features. Conversely, the reciprocal logarithmic first-order differentiation transformation can better highlight local features in the spectrum, resulting in more detailed and accurate characteristic bands. The characteristic bands selected by the LRFD and LRSD transformations were mainly concentrated in the range of 1400-1600 nm, reflecting the detailed information and local changes in the spectrum. In this wavelength range, the absorption peak of the soil organic matter can be highlighted and accentuated by the logarithmic reciprocal differentiation transformation.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 4 Results
Figure 8 shows the impact of the number of tiers in the multi-tier system architecture on the unavailability of a dedicated endpoint service. Four cases with different numbers of tiers (3, 5, 7, 9) are compared. Figure 6: The availability of service at one level. \[\sum_{i\in z}p_{i}(t)=1,\ \ \ \ \ z=\overline{1,k-1},\ \overline{0_{f},(k-1)_{f}} \tag{4}\]0 The unavailability parameters for (**a**) \(\gamma=10^{-2}\) and (**b**) \(\gamma=10^{-3}\). Within each architecture, the unavailability is plotted as a function of the reliability of nodes across tiers. As observed, adding more tiers consistently increases the unavailability of the end-point service. The trend illustrates how additional tiers provide more points of potential failure that reduce overall service availability when aggregated end-to-end. The more layers data must traverse from the endpoint to the cloud, the lower the combined reliability. This demonstrates how the Markov model can provide insight into availability dependencies in complex multi-tier architectures in order to guide design tradeoffs between tier count and functionality factors. To quantitatively demonstrate the proposed modeling approach, we simulated a four-tier network with the following reliability parameters:  
* Tier 1 (IoT): 10 nodes, \(\gamma\) = 0.01
* Tier 2 (Edge): 15 nodes, \(\gamma\) = 0.02
* Tier 3 (Fog): 5 nodes, \(\gamma\) = 0.005
* Tier 4 (Cloud): Availability = 99.95%  
Applying the Markov model methodology, the calculated availability of an endpoint device was 98.51%. Changing the reliability of sensors at the first IoT level of topology to \(\gamma\) = 0.001 improved the availability to 99.88%. Analysis of the obtained values showed that by changing the reliability of the sensors at the first level, it was possible to reduce the unavailability of the end-to-end service by 3.725 times. These quantitative results showcased the use of the proposed technique to evaluate availability in multi-tier networks under different configurations. The model enables architects to set numerical availability targets and determine required reliability levels and nodes per tier to achieve set goals. Developers can quantify the impact of topology changes and identify upgrade priorities. The proposed Markov chain modeling approach provides a methodology backed by quantitative analysis capabilities to assess the availability in geographically distributed multi-tier networks. As connectivity expands across heterogeneous systems, this technique offers an effective tool for ensuring robust and reliable service.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 5 Application Design of ANN-Based Real-Time Scheduling in the Cloud Manufacturing Environment  
Mold casting is a common industry that applies the CMfg. Figure 13 shows its demand and supply distribution in the Zhejiang province.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset Group** & **MT Amount** & **Average Schedule Time (s)** & **Average Decision Time for MT (ms)** \\ \hline Exp-30 & 600 & 25.147 & 41.911 \\ Exp-60 & 1200 & 32.909 & 27.424 \\ Exp-90 & 1800 & 32.113 & 17.840 \\ Exp-120 & 2400 & 38.990 & 16.246 \\ Exp-mix & 1558 & 23.491 & 15.078 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average decision time of the ANN-based approach.  
Figure 12: Average Responsiveness of NSGA-II(\(\alpha\)) series and ANN.  
It can be seen that the demanders are surrounded by numerous providers, and the resource optimization and coordination are in desperate need. With the increase in diversity and complexity of requirements from demanders, the real-time scheduling becomes much more difficult. Figure 14 shows the procedure for the demander to use MR via the CMfg platform.  
The key step is the allocation of MR, which can be realized by the ANN. Figure 15 shows the functional design of the application of the proposed ANN-based approach in the CMfg environment.  
Figure 14: Procedure for demander to use MR via the CMfg platform.  
Figure 13: Provider and demander distribution in the CMfg environment.  
The \"Intelligent coordination\" module that implements the ANN will handle the real-time scheduling problem, and putting the design into practical application is an ongoing work.
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Fastloc GPS and Argos Position Data Filtering and Cleaning  
The satellite tags recorded both the Fastloc GPS and Argos positions. For analysis, we retained Fastloc GPS data along with Argos positions classified as 1, 2, and 3 due to their comparatively higher accuracy [30], which is essential for examining fine-scale movements, habitat use, and home ranges of this species [31]. The GPS data were processed using Wildlife Computers' LocSolve GPS processor (version 1.1.1.0) and subsequently refined in R (version 4.3.1). Outliers were manually removed by excluding GPS positions with residual values greater than 30, as determined by the LocSolve GPS processor. Given the superior accuracy of GPS data, we used only these data to calculate the straight-line distance traveled over land (in km) and the average speed of movements (in m/s) for each tagged individual using the \"move2\" R package [32]. Distances were calculated as straight-line distances between GPS positions and were reported as \"minimum distances traveled\". These calculations may underestimate the true distances traveled by the reef manta rays, as they did not account for over-water paths constrained by the spatial configuration of islands in the study area.
## 4 Data Fusion
### Multiple Thermograms  
In the previous sections, the case of the integration of a single thermogram on a point cloud was considered. There might be, however, the need to integrate more than one thermogram on the same point cloud, in order to have a larger set of points with an associated temperature.  
Figure 5: Application of the exclusion methods on a part of a hypothetical surface represented by eleven points, for two values of \(\xi\). The value of \(\xi\) in (**b**) is twice the value of \(\xi\) in (**a**). Green points: to be maintained; blue points: excluded with the first exclusion; yellow points: excluded with the second exclusion; red points: not correctly excluded with the two exclusions.  
Figure 6: Overall workflow of the data fusion process.  
Note that this procedure requires in the first place thermograms acquired in a temporal window in which no measurable changes in the thermal state occur, so as to guarantee that the integration is not carried out on discordant data. Since each thermogram can be individually integrated on the point cloud by the methodology described previously, the problem comes down to handling the overlapping zones, namely the points to which more than one temperature value is assigned. To give these points a single final temperature, the method utilized in [9] was followed. The method relies on the fact that the relative orientation between the surface of the inspected object and the camera affects the measurement accuracy, and, more specifically, the lower the viewing angle is, the higher the accuracy is. As shown in Equation (6), the temperature \(T_{i}\) assigned to the point \(P_{i}\) is computed as a weighted average of the temperatures \(T_{ij}\):  
\[T_{i}=\frac{\sum_{j=1}^{N_{i}}c_{ij}T_{ij}}{\sum_{j=1}^{N_{i}}c_{ij}} \tag{6}\]  
where the weight is the confidence factor \(c_{ij}\), the index \(i\) refers to the point of the points cloud and ranges from 1 to \(N\), whereas the index \(j\) refers to the thermograms that overlap in the point \(i\) and ranges from 1 to \(N_{i}\). The confidence factor \(c_{ij}\) is computed as a function of the viewing angle \(\theta_{ij}\) as shown in Equation (7):  
\[c_{ij}=e^{-\kappa\theta_{ij}} \tag{7}\]  
where the viewing angle \(\theta_{ij}\) is the angle from which the thermal camera sees the point \(P_{i}\), considering the thermogram \(j\), and can be computed as shown in Equation (8):  
\[\theta_{ij}=\arccos\frac{\left(O_{j}-P_{i}\right)\cdot\hat{n}_{i}}{\|O_{j}-P_{ i}\|} \tag{8}\]  
with point \(O_{j}\) identifying the optical center for the thermogram \(j\).  
In this way, a greater weight is assigned to the rays with smaller viewing angles, which allows more accurate measures. More precisely, the weight decreases with an exponential law, depending on a parameter \(\kappa\), that was set equal to 2 according to experimental evaluations.
# Full-Reference Image Quality Assessment Based on Multi-Channel Visual Information Fusion
## 2 Proposed Method
### Chromaticity Similarity Image Calculation  
The change in image colors affects the quality of the image, so chromaticity similarity is introduced as the third feature of the objective method, such as FSIM, VSI, and PerSIM. According to Table 1, the RGB image is mapped to the LMN uniform color space, where \(M^{*}\) represents the red-green channel and \(N^{*}\) represents the blue-yellow channel [17]. The chromaticity similarity can be calculated as:  
\[SIM_{MN}(x)=\frac{2M_{1}(x)\cdot M_{2}(x)+T_{3}}{M_{1}{}^{2}(x)+M_{2}{}^{2}(x )+T_{3}}\cdot\frac{2\cdot N_{1}(x)\cdot N_{2}(x)+T_{3}}{N_{1}{}^{2}(x)+N_{2}{}^ {2}(x)+T_{3}} \tag{8}\]  
where \(T_{3}\) is a constant for increasing the stability of the equation, \(M_{1}\), \(M_{2}\) and \(N_{1}\), \(N_{2}\) are the chromaticity channels of \(f_{1}\) (original image) and \(f_{2}\) (distorted image), respectively.
## 4 ADR Algorithms
### Hot Spot Detection Based on Localised Segmentation  
As shown in Figure 17, the refined ROI is sent to a z-score analyzer, which then determines the existence of hot spots induced by partial wire breaks within the refined ROI.  
The z-score analyzer analyses the z-score defined by the equation below:  
\[Z\_Score=\frac{I_{n}-Mean(I_{n})}{\sigma} \tag{4}\]  
Figure 16: Effectiveness of 0-1 scaler: (**a**) Intensity plot prior to scaler; (**b**) Scaled area within ROI of [300; 400].  
Figure 17: Schematic diagram of hot spot detection and ROI drawing.  
where the s represents the standard deviation of the pixels. The z-score indicates how far away each pixel within the refined ROI is deviated from the mean value. If a hot spot exists within the ROI, then the z-scores for the pixels associated with hot spot will be severely deviated from normal pixels. The thresholding to determine the existence of hot spot in this paper is set to z-score of six, acquired from DLIT images obtained from lab-based experiments.  
If the hot spots are detected, the corresponding ROI will be painted as red using a painter provided by OpenCV library. If not detected, the corresponding ROI will be painted as green. In spite of whether hot spots are detected, all detected refined ROI will be counted into the number of wires detected.
## 3 Influence Factor Analysis for Water Jet Slotting Depth
### Nozzle Diameter  
The nozzle diameter determines the flow rate of the water jet and affects the impact energy to a certain extent, as expressed by Equations (5) and (6) [27]. As the nozzle diameter increases, the jet flow rate and impact energy become larger. However, when the nozzle diameter is too large, the jet energy consumption becomes severe, which results in a waste of energy. To improve the impact capacity of the water jet and ensure the smooth discharge of slag during the water jet slotting process, the nozzle diameter should be appropriately increased.  
\[Q=\frac{1}{4}\pi\varphi vd^{2} \tag{5}\]  
\[\omega=\frac{\rho_{w}\pi\mu d^{2}\
u^{3}}{8}, \tag{6}\]  
where \(Q\) and \(\omega\) are the jet flow rate and impact kinetic energy, respectively; \(\mu\) is the flow rate coefficient; \(v\) is the water jet velocity; \(d\) is the nozzle diameter; and \(\rho_{w}\) is the water density.  
[PERSON] conducted a computational, numeric, fluid dynamics simulation to investigate the influence of nozzle diameter on jet flow characteristics, such as the axial velocity, and the axial dynamic pressure and its attenuation coefficient [28]. As shown in Figure 2, the axial velocities and dynamic pressures of the water jet were obtained with different nozzle diameters. As the nozzle diameter increased, the attenuation coefficient of the jet axial dynamic pressure first decreased and then increased. When the nozzle diameter was 3 mm, the attenuation coefficient was the minimum because the increase of the nozzle diameter affected the attenuation coefficient of the axial dynamic pressure, although the jet energy increased. Moreover, the influence of the nozzle diameter on the slotting depth was not too large. The jet pressure and flow rate of common high-pressure pumps satisfy the requirement for a 3 mm nozzle. Therefore, to investigate the effect of other parameters on the slotting depth, the nozzle diameter was set as a constant of 3 mm.
## 4 Experiments
### Color Image Denoising  
To test our proposed method on color image denoising, we conducted experiments on the Set5 dataset and compared its denoising results with those of the DIP, FFDNet, VDNet [33], SwinIR, CBM3D, DRUNet, DnCNN, and N2V methods. Table 5 reports the color image denoising performance of different methods with noise levels \(\sigma=15,\ 25,\ 50\) on the Set5 dataset. The three best methods are highlighted in bold font. One can observe that the proposed method significantly outperforms the unsupervised methods DIP and N2V and achieves a higher PSNR than most of the supervised methods.
# Online Sparse DOA Estimation Based on Sub-Aperture Recursive LASSO for TDM-MIMO Radar
## 2 Materials and Methods
### Proposed Method  
Based on model (4), the LASSO method attempts to recover \(\mathbf{s}\) by solving the following convex optimization problem [22]  
\[\underset{\mathbf{s}\in\mathbb{C}^{K}}{\arg\min}\|\mathbf{y}-\mathbf{A}\mathbf{s }\|_{2}^{2}+\lambda\|\mathbf{s}\|_{1} \tag{6}\]  
where \(\lambda\) is a parameter that weighs the sparsity of \(\mathbf{s}\). Equation (6) can be solved directly by convex optimization, such as the CVX tool box in MATLAB. However, the source code is not open, which cannot be ported to a hardware platform. In recent years, many iterative methods, such as split Bregman [38], IRLS [39], etc., have been proposed. For example, the split Bregman solution can be written as [38]  
\[\mathbf{\hat{s}}^{q+1}= (\mu\mathbf{A}^{*}\mathbf{A}+\lambda\mathbf{I})^{-1}(\mu\mathbf{ A}^{*}\mathbf{y}+\lambda(\mathbf{d}^{q}-\mathbf{b}^{q}))\] \[\mathbf{d}^{q+1}= \text{shrink}\left(\mathbf{\hat{s}}^{q+1}+\mathbf{b}^{q},1/ \lambda\right)\] \[\mathbf{b}^{q+1}= \mathbf{b}^{q}+\mathbf{\hat{s}}^{q+1}-\mathbf{d}^{q+1} \tag{7}\]  
where \(\mathbf{d}\) and \(\mathbf{b}\) denote the introduced temporary vectors, \(\mu\) an additional regularization parameter, \(q\) the iteration number, \(\mathbf{I}\) the identity matrix. The function \(\text{shrink}(x,\eta)=\text{sign}(x)\max(|x|-\eta,0)\), and \(\text{sign}(\cdot)\) denotes the sign function.  
It is seen that matrix inversion, matrix multiplication, and a considerable number of iterations are required, so the computational complexity is quite high, which is not suitable for engineering applications. To solve this problem, starting from the cyclic minimization [28; 40; 41], we propose a subaperture recursive online processing method.