esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Rolling Bearing
#### 4.2.5 The Proposed Method  
In this method, the observation signal is decomposed with EEMD first, and then the number of fault signals can be estimated with SVD. The ratios of neighboring singular values are shown in Table 2.  
As shown in Table 2, the sequence number of the maximum NSVR is 2. Therefore, the number of fault signals is 2. According to the correlation coefficient of each IMF, the observation signal is reconstructed with high-correlation IMFs. Finally, the fault signals can be extracted from the new observation signal with KICA. The Hilbert envelope spectra of the extracted fault signals are shown in Figure 15.  
In Figure 15b, the fault frequency of the inner ring, \(f_{ic}=106\) Hz, is extracted. The fault frequency of the outer ring, \(f_{oc}=70\) Hz, can be seen in Figure 15a. Furthermore, the features are extracted clearly.
## 4 Blockchain-Based LWE-CP-ABE Data Sharing Scheme
### Data Sharing Scheme in Key Management Network and Blockchain Network
[PERSON] inputs the attribute information \(U\) and the MSK and runs the **KeyGen** algorithm to output the user attribute \(\text{SK}_{\text{GID}}\) corresponding to the attribute. The specific algorithm is shown in Algorithm 3. ```
Input: the MSK and own attribute information \(U\);  Output: the user attribute SK, 1. Select a random vector \(\hat{\mathbf{t}}\leftarrow\mathcal{X}_{1}\); 2. \(\mathbf{t}=(1,\hat{\mathbf{t}})\in\mathbb{Z}^{m}\); 3. for each \(u\in\mathbf{U}\)  Select \(\hat{\mathbf{k}}_{u}\leftarrow\mathcal{X}_{\text{h}g}^{m}\); \(\tilde{\mathbf{k}}_{u}\leftarrow\text{EnSamplePre}\)  Compute \( \begin{pmatrix}\mathbf{A}_{u},T_{A_{u}},\sigma,t\mathbf{H}_{u}^{\top}-\hat{k}_{u}\mathbf{A }_{u}^{\top}\end{pmatrix} \) then Compute \(\mathbf{k}_{u}=\hat{\mathbf{k}}_{u}+\tilde{\mathbf{k}}_{u}\);  Output the user attribute \(\text{SK}_{\text{GID}}\); \(\text{SK}=(\{\mathbf{k}_{u}\}_{u\in\mathbf{U}},\,\mathbf{t})\)
```  
**Algorithm 3**User Attribute Secret Key Generation Algorithm  
(4) Ciphertext decryption. When DU wants to access the data, he retrieves the ciphertext address from the blockchain BN, searches the corresponding CT from the TPS through the ciphertext address \(CT_{Address}\) and downloads it to the local through the transaction \(Tx_{DownloadCT}=\{\text{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{                                                            }}}}  
(6) Ciphertext policy update. When DO obtains the ciphertext C derived from the original CT, the updated access control policy ciphertext \((\mathbf{c}_{i}^{\prime},\mathbf{\hat{c}}_{i}^{\prime})\) is used to run the **AccUpdate** algorithm to generate the new ciphertext CT\({}^{\prime}\), which is still uploaded to the original ciphertext address to facilitate the decryption procedures for data users. \[\text{CT}^{\prime}= \begin{pmatrix}(\mathbf{M}^{\prime},\rho^{\prime}),\{\mathbf{c}_{i} ^{\prime}\}_{i\in[\ell]^{\prime}},\{\mathbf{\hat{c}}_{i}^{\prime}\}_{i\in[\ell]^{ \prime}}\\ C=\text{MSB}\big{(}\mathbf{sy}^{\top}\big{)}\oplus m\end{pmatrix}  \tag{18}\]  
To an extent, the users can quickly and efficiently retrieve the required information using the formatted transaction structure in the blockchain encryption protocol based on the LWE-CP-ABE. Meanwhile, the whole log of each node's process event is recorded into the ledger on blockchain BN, which can offer better auditability and accountability in the dynamic access control system.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
###### Abstract  
All-in-focused image combination is a fusion technique used to acquire related data from a set of focused images at different depth levels, which suggests that one can determine objects in the foreground and background regions. When attempting to reconstruct an all-in-focused image, we need to identify in-focused regions from multiple input images captured with different focal lengths. This paper presents a new method to find and fuse the in-focused regions of the different focal stack images. After we apply the two-dimensional discrete cosine transform (DCT) to transform the focal stack images into the frequency domain, we utilize the sum of the updated modified Laplacian (SUML), enhancement of the SUML, and harmonic mean (HM) for calculating in-focused regions of the stack images. After fusing all the in-focused information, we transform the result back by using the inverse DCT. Hence, the out-focused parts are removed. Finally, we combine all the in-focused image regions and reconstruct the all-in-focused image.  
all-in-focused image; sum of updated modified Laplacian; frequency domain +
Footnote †: journal: Applied Sciences  
_Article_  
All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 5 Conclusions  
This paper proposes a Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for the semantic segmentation of mangrove remote sensing images. The main conclusions are as follows:  
(1) The construction of the MSSDBG dataset based on multi-source high-resolution remote sensing images represents a significant contribution to mangrove monitoring research. By incorporating multi-temporal and multi-scale characteristics from multiple satellite sources, this dataset provides essential support for algorithm development and validation in mangrove semantic segmentation tasks.  
(2) The proposed MFA-UperNet demonstrates innovative architectural design through its key components: a ConvNeXt-based feature encoder for superior feature extraction, a Cascade Pyramid Fusion Module (CPFM) for effective multi-scale feature handling, a Multi-scale Selective Kernel Attention Module (MSKAM) for adaptive feature enhancement, and an Auxiliary Edge Neck (AEN) for improved boundary accuracy. This comprehensive design effectively addresses the challenges of complex backgrounds and scale variations in mangrove remote sensing images.  
(3) The experimental results convincingly validate the effectiveness of MFA-UperNet, achieving outstanding performance with 94.54% mIoU and 97.14% mPA on the MSSDBG dataset. The model significantly outperforms existing methods, including Swin-UperNet, DeepLabv3+, and PIDNet, while ablation studies confirm the substantial contribution of each proposed module to the overall performance improvement.  
The practical applications and implications of this research extend beyond technical achievements, demonstrating the feasibility of high-precision mangrove mapping using deep learning approaches. This work not only contributes to improving regional mangrove monitoring and assessment systems but also provides valuable technical support for mangrove protection and management efforts, advancing the field of remote sensing-based coastal ecosystem monitoring.  
X.W.: Conceptualization, Supervision, Funding acquisition. Y.Z.: Data curation, Methodology, Software, Writing. W.X.: Methodology, Software, Writing--original draft. X.W.: Formal analysis. H.W.: Supervision, Writing--review and editing. J.C.: Methodology. Q.Q.: Formal analysis, Investigation. Q.W.: Investigation. J.Z.: Formal analysis. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Guangxi Science and Technology Major Project [grant number AA19254016]; the Guangxi Graduate Student Innovation Project [grant number YCSW2021174]; the Beihai City Science and Technology Planning Project [grant number 202082033]; and the Beihai City Science and Technology Planning Project [grant number 202082023].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We would like to thank the anonymous reviewers for their valuable suggestions. We also thank [PERSON] for her contribution to this work.  
The authors declare no conflicts of interest.
## 4 Intelligent Identification and Information Extraction of Rock Discontinuities
### HSV 3D Reconstruction and Clustering Analysis  
Using point cloud normal vectors, combined with Equations (11) and (12), the rock yield can be calculated quickly, but due to the significant number of steep cliff point clouds, hundreds or thousands of sets of yields can be obtained by this method, and all yields show the same color, which cannot visualize the spatial distribution of structural surfaces. Therefore, we implemented the hexagonal pyramid model (hue saturation lightness value, HSV) spatial coloring of point clouds based on Matlab, which assigns a specific color to each discontinuous in space. HSV is a combination of the Schmidt projection network and HSV color, and its color space is a cone. Using the circumference of the base of the cone represents the hue (H) and takes values from 0 to 360\({}^{\circ}\). Cyan is 0\({}^{\circ}\), blue is 60\({}^{\circ}\), magenta is 120\({}^{\circ}\), red is 180\({}^{\circ}\), yellow is 240\({}^{\circ}\), and green is 300\({}^{\circ}\). S is saturation, where the greater the proportion of the spectrum, the closer the color is to the spectral color, and the greater its saturation; luminosity (V) indicates the shade of the color (Figure 12). In our study, H corresponds to the dip direction, S corresponds to the dip angle, and V is the transparency set to the maximum value of 1. Among them, there are 361 colors in the color phase H corresponding to direction, and the distribution of rock production (dip angle and dip direction) can be effectively identified by observing the changes of different colors.  
In order to accurately obtain the information about the discontinuous surface of the rock mass, a clustering analysis of the point cloud is required before information extraction. Therefore, in this paper, a combination of kernel density estimation (KDE) and the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to complete the rock discontinuity clustering. First, the KDE algorithm was used to identify the peaks representing the direction of the 3D point cloud and its adjacent point clouds, and then the DBSCAN algorithm was used to perform the clustering operation for further clustering analysis. In addition, we adopted the research results of [PERSON] et al. [22; 23]. The two parameters of DBSCAN were set as _Minpts_ = 4, and _Eps_ was the average distance between each point and the surrounding 4 neighboring points plus 2 standard deviations.  
Figure 12: HSV color space and HSV color wheel.
# Guided Self-Accelerating Airy Beams--A Mini-Review
## 1 Introduction
And the field distribution of the Airy beam varies differently in different NL media [36; 39]. As is well known, the beam focusing is an efficient way to improve laser power density. Thanks to the self-accelerating feature of Airy beams, in 2010, [PERSON] and [PERSON] proposed a novel abruptly autofocusing (AAF) beam [40]. The beam possesses a ring-shaped initial transverse Airy amplitude pattern, and accelerates in either the inward or outward direction, determined by the Airy wave function tail [41]. During propagation, the AAF beam can keep a low intensity profile initially, and then abruptly converge to a focal point where the intensity grows by orders of magnitude [42; 43; 40; 44; 45; 46]. This abruptly autofocusing property of an AAF beam avoids the possible interaction of the beam with the transmission medium before focusing. It can be used in biomedical treatments, bottle beams, light bullets, and in other nonlinear settings. To date, immense research work has been devoted to Airy beams, from theoretical predications to experimental verifications, from fundamental research to potential applications. Without doubt, it has become one of the hottest developing fields in linear and nonlinear optics [47; 48; 49; 50]. In this short review, based on our previously published work, we discuss some robust and flexible manipulation techniques applied to Airy beams. The organization of the paper is as follows. Section 2 provides results and discussion. In Section 2.1, we describe the interaction of two Airy beams as they propagate simultaneously in a NL medium; in Section 2.2, we summarize Airy beam propagation in a harmonic potential; in Section 2.3, we discuss the controllable spatial modulation of AAF beams, under an action of different dynamic linear potentials. In Section 3, we conclude the paper.
## 4 Application Example
### Brief Summary  
The influences of the structural factors on the air velocity and pressure in the spillway tunnel are summarized in Table 2. Since the air velocity and air pressure are the main indicators to evaluate the ventilation characteristics of the air supply system, then the influences of the structural factors on the ventilation characteristics could be qualitatively evaluated. Generally, the structural factors of spillway tunnels and air intake wells influence the ventilation characteristics to different degrees. The factors of air intake wells (including the cross-sectional area, location and number) have large influences, while the factor of air intake well length has a slight influence on the ventilation characteristics. Moreover, the factor of spillway tunnel height also greatly influences the ventilation characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Factor**} & \multicolumn{3}{c}{**Result**} \\ \hline
**Name** & **Variation** & **Air Velocity** & **Air Pressure** & **Degree** \\ \hline Spillway tunnel height & Increase & \(\downarrow\) & \(\uparrow\) & Notable \\ Air intake well number & Decrease & Upstream \(\uparrow\) & \(\uparrow\) & Extreme \\  & & Downstream \(\downarrow\) & Upstream \(\downarrow\) & Extreme \\ Air intake well location & Move upstream & Upstream \(\uparrow\) & Upstream \(\downarrow\) & Extreme \\  & & Downstream \(\downarrow\) & Downstream \(\uparrow\) & Extreme \\ Air intake well & & Increase & \(\uparrow\) & \(\downarrow\) & Notable \\  & & & & \\ \hline \hline \end{tabular}  
* Notes: (1) \(\uparrow\) means the increase in air velocity or air pressure; (2) \(\downarrow\) means the decrease in air velocity or air pressure.  
\end{table}
Table 2: The influences of the structural factors on the ventilation characteristics.  
Figure 13: Influence of the length of air intake wells on the ventilation characteristics.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles
### Robust Estimates for Regression Problems
#### 2.2.2 M-Estimator  
The M-estimate of regression is defined as  
\[\hat{\mathbf{x}}_{M}=\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\rho\left(\frac{r_{i}( \mathbf{x})}{\hat{\sigma}}\right)\,, \tag{15}\]  
with \(\hat{\sigma}\) an auxiliary estimate of the scale of errors, or, equivalently, as the solution to  
\[\sum_{i=1}^{n}\psi\left(\frac{r_{i}(\mathbf{x})}{\hat{\sigma}}\right)\frac{ \partial\left(r_{i}(\mathbf{x})/\hat{\sigma}\right)}{\partial\mathbf{x}}= \mathbf{0}\;, \tag{16}\]  
which is commonly solved by an Iteratively Reweighted LS (IRLS) procedure, with an instrumental weight function defined as  
\[W(x)=\left\{\begin{array}{l}\psi(x)/x,\text{ if }x\
eq 0\\ \psi^{\prime}(0),\text{ if }x=0\end{array}\right.\,, \tag{17}\]  
to provide the convenient alternative formulation,  
\[\sum_{i=1}^{n}W\left(r_{i}/\hat{\sigma}\right)\;\frac{r_{i}}{\hat{\sigma}}\; \frac{\partial\left(r_{i}/\hat{\sigma}\right)}{\partial\mathbf{x}}=0\;. \tag{18}\]  
Solving such system requires finding the estimate as well as the weights for each of the observations according to the corresponding weighting function. Notice that a normalization using the dispersion of the residuals \(\hat{\sigma}\) is included in the formulation, because these estimates are not scale equivariant. An estimate of the residuals dispersion must be used, for instance, the normalized median absolute deviation (MAD), defined as  
\[\partial_{\text{MAD}}(\mathbf{x})=c_{m}\;\text{Med}(|\mathbf{x}-\text{Med}( \mathbf{x})|) \tag{19}\]  
being Med\((\mathbf{x})\) the median of \(\mathbf{x}\), and \(c_{m}\) a normalizing constant (\(\approx\)1.4815 to make MAD consistent with the usual parameter \(\sigma\) at Gaussian distributions) [49]. Instead of using an auxiliary scale estimate for the M-estimation, it is also possible to perform a joint regression of the vector of unknown parameters and the scale [42].  
Notice as well the relevance on the choice of monotone against redescending loss functions. Monotone estimators constitute a convex optimization problem, for which the uniqueness of the solution is guaranteed and the starting point only influences the convergence rate [44]. Contrarily, redescending estimators suffer from the defect of requiring regularity conditions for their uniqueness and continuity [47]. A more extensive discussion on this matter can be found in Section 5.1 with the pictorial support of a GNSS-related example.
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 5 Evaluation
### Methods
From the preliminary tests, we found that the performance such as delivery ratio significantly improves if this value is +4 compared to +2 and 0, and that the values larger than +4 does not improve the performance but increasing the load of packet copies. We transmit data packets for 1000 s after a 3600-s wait to obtain stable routing tables. In the first scenario, in which we measure the communication performance, we generated 1000 packets during the simulation time between randomly-selected static node pairs with a constant time interval. We generate 3-8 vehicles per minute per road-end, which is a relatively sparse traffic state. Note that three vehicles per minute would be sparse enough to demonstrate the case of rural area or right-time scenarios. We take the average of 10 repetitions to show the results. To consider the uncertainty of vehicle behavior under sparse deployment of static nodes, we make vehicles pretend to leave major roads for entering minor streets or parking lots along the road, and again join in to major roads. Because we do not have static nodes at minor intersections in the sparse scenario, we cannot trace a part of vehicles that escapes into minor streets, etc. Thus, we treat the uncertain behavior of vehicles as a probabilistic phenomenon. Specifically, we probabilistically select vehicles at each section of roads between two static nodes (i.e., at each links), and flush (i.e., delete) all packets and messages held on them. This _flush operation_ pretends that a vehicle leaves the road and simultaneously another vehicle joins. We let the probability of flushing at each road section be 0%, 1.0%, and 2.5% to measure the effect of uncertainty of vehicle movement. On the other hand, in the second scenario in which we measure the fault tolerance, we block the intersections a, b, c, and d shown in Figure 10 to pretend a traffic accident in the intersection. This traffic accident occurs at 500 s after the starting time of data transmission. With this scenario, we observe the degree of packet loss during the time period before converging to the new shortest paths set. Figure 10: Kyoto City Map used in simulation.
# Reconfigurable Robotic Exercising Companion
## 3 Emotion-Based Feedback per Performance of Exercise
### Emotion Generation
Two threshold values are introduced to define the emotion regions based on the range of \(\hat{C}_{t}\). Algorithm 1 shows the flow of the emotion generation based on the average correctness score. According to the output emotion, the robot motion profile is derived. The robot moves on a jerky path for the sad emotion expression, and for the happy emotion, it follows a smooth circular path. Furthermore, the robot's movement speed is calculated based on the degree of each emotion level. This process is given in Algorithm 2. \[\hat{C}_{t}=\frac{1}{3}\sum_{t-2}^{t}C_{t} \tag{7}\]  
Figure 5: Emotion space model configured for the proposed system. Figure 6: Smorphi configurations with labeled top for emotion expression. (**a**): happy expression with O shape and (**b**): sad expression with S shape. ```
\(\hat{C}_{t}\); \(upperthreshold\); \(lowerthreshold\); \(emotion\gets none\) \(emotionlevel\gets 0\) if\(\hat{C}_{t}\geq upperthreshold\)then \(emotion\gets happy\) \(emotionlevel\leftarrow(\frac{\hat{C}_{t}-upperthreshold}{100-upperthreshold})*100\) else if\(\hat{C}_{t}\leq lowerthreshold\)then \(emotion\gets sad\) \(emotionlevel\leftarrow(\frac{lowerthreshold-\hat{C}_{t}}{lowerthreshold-0})*100\) endif endif
```  
**Algorithm 1** Emotion derivation  
```
\(\hat{C}_{t}\); \(upperthreshold\); \(lowerthreshold\); \(emotion\gets none\) \(upperthreshold\); \(emotion\gets maxspeed\); \(motionpattern\gets none\) \(speed\gets 0\) if\(emotion= happy\)then \(motionpattern\gets circular\) \(speed\leftarrow(\frac{emotionlevel}{100})*maxspeed\) else \(motionpattern\gets jerky\) \(speed\leftarrow(\frac{emotionlevel}{100})*maxspeed\) endif
```  
**Algorithm 2** Motion pattern, robot speed calculation  
The robot is commanded to follow the motion profile and the speed determined by the algorithms. These motion cues are predefined for both cases of emotions. The navigation controller of the robot derives linear velocity components of the robot in x and y directions at time \(t\) (defined by \((v_{x_{t}},v_{y_{t}})\)). Here, the robot's movement is holonomic. Hence the angular displacement of the robot is not required for circular loop motions. Let the motion pattern be circular with a fixed radius and speed is \(v_{t}\) at time \(t\). Let \(\phi\) be the current position on the path. Thus, directional linear velocity components of the robot \((v_{x_{t}},v_{y_{t}})\) can be calculated according to (8). The directional velocity components are derived according to (9) for the jerky motion. Here, \(\alpha\) is a constant that controls the amount of jerkiness. The wheel angular velocities corresponding to the linear velocity components are calculated based on the kinematic model of the robot. The motors are commanded to follow the derived wheel velocities. \[ \begin{bmatrix}v_{x_{t}}\\ v_{y_{t}}\end{bmatrix} =v_{t} \begin{bmatrix}\cos(\phi)\\ \sin(\phi)\end{bmatrix}  \tag{8}\]  
\[ \begin{bmatrix}v_{x_{t}}\\ v_{y_{t}}\end{bmatrix} =v_{t} \begin{bmatrix}\cos(\phi)+\sin(\alpha\phi)\\ \sin(\phi)+\cos(\alpha\phi)\end{bmatrix}  \tag{9}\]