esa-sceva/satcom-chunk-collection huggingface dataset:
# _M\({}^{2}\)_Gf: Multi-Scale and Multi-Directional Gabor Filters for Image Edge Detection
## 3 The Proposed Edge Detection
### Proposed Edge Detection  
The outline of the proposed algorithm is as follows :  
1. Convert color images to \(L^{\prime}a^{\prime}b^{\prime}\) space.
2. Extract edge strength maps from the each channel by multi-directional Gabor filters with multi-scale, and the fused edge feature is attained by the computation of ESMs in terms of Equation (8).
3. Calculate the global and local average changes of the image, \(\overline{s}_{Global}(\mathbf{z})\) and \(\overline{s}_{Local}(\mathbf{z})\): \[\overline{s}_{Global}(\mathbf{z})=\frac{1}{M\cdot N}\sum_{\mathbf{z}}\xi( \mathbf{z}),\] (9) \[\overline{s}_{Local}(\mathbf{z})=\frac{1}{W^{2}}\sum_{\tau\in Q}\xi( \mathbf{z}+\tau)),\] (10) where \(M\cdot N\) refers to the dimension of the image, \(\xi(\mathbf{z})\) is the fusion of features in Equation (8). The \(Q\) is a matrix with side length \(W\), whose values are all ones. And the \(Q\) is convolved with \(\mathbf{z}\) while \(\tau\) is the stride. The visual system is simulated to perform contrast equalization on the improved fusion of ESM, which is defined as \[\xi(\mathbf{z})=\frac{\xi(\mathbf{z})}{\overline{s}+0.5\overline{s}_{Local} (\mathbf{z})}.\] (11)
4. Apply the non-maxima suppression for each pixel, the gradient modulus \(\xi(\mathbf{z})\) and orientation \(\theta(k)\) are used to determine whether it is the maximum of \(\xi(\mathbf{z})\).
5. Set the upper and lower limitations, which are determined by the histogram of the fused edge feature of the input image. The dimension of the image is \(M\cdot N\), and the coefficients \(\beta_{low}\) and \(\beta_{up}\in[0,1]\): \[T_{up}=\xi\big{(}\mathbf{z}_{\beta_{up}\cdot M\cdot N}\big{)},\] (12) \[T_{low}=\xi\big{(}\mathbf{z}_{\beta_{low}\cdot M\cdot N}\big{)},\] (13) where the symbol \"[]\" is down-integer operation, \(\mathbf{z}_{\beta_{up}\cdot M\cdot N}\) and \(\mathbf{z}_{\beta_{low}\cdot M\cdot N}\) represent the \([\beta_{up}\cdot M\cdot N]\)th pixel and the \([\beta_{low}\cdot M\cdot N]\)th pixel listed the ESM from smallest to largest.
6. Make the hysteresis decision. The determination of edge pixels is implemented in two stages. All the pixels whose value of the fused ESM exceeds \(T_{up}\) are recognized as edge pixels. A candidate edge pixel with values of fused feature between \(T_{up}\) and \(T_{low}\) is regarded as an edge pixel if it can be connected with strong edge pixels in the four- or eight-neighborhood criterion.  
A flow diagram of the proposed approach is depicted in Figure 7.  
Figure 7: The flow diagram the proposed method.
# Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
## 3 Cognitive Interference Alignment Schemes Design  
In this section, two self-organizing cognitive IA schemes are proposed to mitigate the cross-tier interference caused by the cognitive spectrum sharing in an autonomous way. First, we consider the strict interference-free cognitive scenario and propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences can be perfectly eliminated by aligning them into the orthogonal subspace of the intended signal. On the other hand, the interference nulling based IA algorithm can achieve its optimum only with negligible noise. Hence, when the interference-free constraint imposed on the femtocell users can be relaxed, we also propose a partial cognitive IA scheme that further improves the network performance under low and intermediate SNR conditions.
## 3 Kinetostatic Modeling and FEA
### FEA Verification  
To validate the analytical model from Section 3.2, FEA analyses are executed. Taking point \(E\) as the origin, the coordinates and beam width are defined as listed in Tables 2 and 3.  
In this study, the FEA simulations were conducted using ANSYS Workbench 2021 R1. In the simulation, the entire mechanism was modeled with large deflection. A tetrahedron elements method was employed, generating a mesh with an element size of 1 mm. The meshing is shown in Figure 5. Boundary conditions and input displacement were set by applying fixed support and displacement in the static structural analysis. Assuming that the reducer is made using \(3D\) printing technology with PLA material, the Young's modulus \(E\) is 3.5 GPa, [PERSON]'s ratio \(v\) is 0.33, and the density is 1.25 g/cm\({}^{3}\). Through finite element analysis, the input-output relationship of the displacement reducer was obtained, as shown in Figure 6a. Figure 6b depicts the overlay of the analytical model and the finite element simulation model. The finite element model predicts an average reduction ratio of 4.5, whereas the analytical model calculates an average reduction ratio of 4.45, resulting in a discrepancy of 1.1%. Figure 6b illustrates the relationship between input displacement and input force, with an error of 3%. Figure 7 shows the stress distribution and deformation results of the model under ANSYS Workbench 2021 R1 simulation; the scale of deformation results is 1:1, with the dashed line representing the structure before deformation. The research results indicate that the model exhibits uniform stress distribution and deformation, thereby alleviating the phenomenon of stress concentration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Before Optimization (mm)** & **Lower and Upper Limits of Width (mm)** \\ \hline \(w_{AD}\) & 1.5 & [1,2] \\ \(w_{DG}\) & 1.5 & [1,2] \\ \(w_{AC}\) & 1.5 & [1,2] \\ \(w_{CB}\) & 1.5 & [1,2] \\ \(w_{CE}\) & 1.5 & [1,2] \\ \(w_{BE}\) & 1.5 & [1,2] \\ \(w_{DH}\) & 1.5 & [1,2] \\ \(w_{HE}\) & 1.5 & [1,2] \\ \(w_{EG}\) & 1.5 & [1,2] \\ \(w_{EF}\) & 1.5 & [1,2] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original width parameters and constraints.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{x-Coordinates}\) & \(\mathbf{y-Coordinates}\) & **Lower and Upper** & **Lower and Upper** \\
**Node** & **Before Optimization** & **Before Optimization** & **Limits of the \(\mathbf{x-Coordinates}\)** & **Limits of the \(\mathbf{y-Coordinates}\)** \\ \hline A & 24 & 60 & [20,30] & [60,70] \\ B & 24 & 44 & [20,30] & [40,50] \\ C & 0 & 32 & [0,10] & [30,40] \\ D & \(-30\) & 30 & [\(-30,-20\)] & [20,30] \\ E & 0 & 0 & 0 & 0 \\ F & 24 & \(-21\) & [20,30] & [\(-20,-10\)] \\ G & \(-24\) & \(-33\) & [\(-30,-20\)] & [\(-40,-30\)] \\ H & \(-15\) & \(-8\) & [\(-20,-10\)] & [\(-10\),0] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Original geometric parameters and constraints.
# Blockchain-Based E-Voting Mechanisms: A Survey and a Proposal +
## 7 Conclusions  
We present a comprehensive exploration and innovative approach to enhancing electronic voting (e-voting) systems using blockchain technology. Our extensive analysis covers existing blockchain-based e-voting systems, emphasizing the importance of the integration of advanced counting methodologies, robust cryptographic security measures, and innovative architectural designs. The paper's additional contribution lies in proposing a novel e-voting system that combines the strengths of the Borda count and the Condorcet method for vote tallying, implemented on a branching blockchain structure.  
Our comparative analysis of various blockchain-based e-voting systems studies critical trends in blockchain types, cryptographic techniques, and counting methods. This analysis underscores the importance of voter anonymity, election auditability, vote verifiability, uniqueness, and fairness in designing e-voting systems. Our innovative design, termed the Borda-Condorcet chain, leverages smart markers to facilitate multiple voting methodologies, ensuring a robust and fair electoral process.  
The implementation of our system on a Raspberry Pi 3 Model B+ showcases its feasibility in low-resource environments, manifesting its potential for widespread adoption in diverse settings. The adaptability and scalability of our system makes it a viable and promising solution for secure, transparent, and reliable e-voting.  
Our work marks a significant stride towards modernizing electoral processes through blockchain technology. The ongoing evolution of blockchain technology and its application in e-voting systems will continue to be a dynamic and critical area of research, offering profound implications for the future of democratic governance. While our designed system has addressed several challenges faced by current e-voting systems, we envision that future research directions should focus on further enhancing security and privacy features, such as exploring the possible integration with post-quantum cryptographic techniques, and extending the accessibility and usability of such systems to ensure a more inclusive and democratic electoral process for wider adoption worldwide.  
Conceptualization, M.S., C.-T.H. and T.G.; methodology, M.S.; formal analysis, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S., L.N., C.-T.H. and T.G.; supervision, C.-T.H.; funding acquisition, L.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Air Force Office of Scientific Research through SystemsPlus, Inc. contract number AFRL-2024-3790 and the Air Force Research Laboratory through the Information Directorate's Information Institute(r) contract number AFRL-2024-3790.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 3 Design of Data Collaboration Mode Based on Improved Framework
### Data Collaboration Method Based on ITS
#### 3.1.2 Collaboration between BIM and UAV  
The integration of IoT sensors and BIM makes real-time information exchange and exchange possible. Object perception using unmanned aerial vehicle (UAV) image information improves the fault-tolerance of eye-movement level BIM modeling, making it possible to build high-precision and high-quality traffic models.  
As a video sensor in the IoT [31], UAV is a convenient tool for video data acquisition. After the first UAV was launched from the US Navy Research Office, it is mainly used for boring and dangerous work [32]. Combined with cameras, UAVs can obtain high-resolution images from different angles and transmit information to the ground through routing. In order to solve the problems caused by bad weather and the existence of blind spots, this paper combines the UAV technology with the BIM model to model the traffic scene of the corresponding intersection, and imports the cooperation information into the UE4 engine for modeling. The model includes the external information of the building, 3D scene model of road condition information and macro traffic information, and carries out feature extraction and visualization of intersection traffic flow information according to algorithm model.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Quantitative Metrics of Imaging Results  
To compare different imaging methods, two quantitative metrics--the Array Performance Indicator (API) [29] and SNR [30]--were introduced to evaluate the performance of array imaging. The API is a dimensionless parameter that represents the point spread function of the spatial dimension of the image resolution.  
\[\text{API}=\frac{S}{\lambda^{2}} \tag{12}\]  
where \(S\) is the area of the region where the point spread function is greater than the threshold amplitude, with its value taken as the cross-sectional area at the normalized amplitude intensity of \(-6\) dB, and \(\lambda\) is the wavelength at the center frequency of the Lamb wave. A smaller API predicts a higher image resolution. The SNR reflects the relationship between the damage signal and the noise signal, characterizing the overall quality of the ultrasound detection image. It is expressed as follows:  
\[\text{SNR}=20\cdot\log\frac{I_{\text{max}}}{I_{\text{ave}}} \tag{13}\]  
where \(I_{\text{max}}\) is the maximum value of the defective signal and \(I_{\text{ave}}\) is the average value of the background noise. A higher SNR indicates better image quality, as it implies a stronger damage signal relative to the noise.
# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 4 Real-Time Performance Monitoring and Performance Evaluation
### Performance Evaluation and Classification  
During actual operation, the operation of the system often goes through three stages, normal operation, degradation and failure. Among them, the degradation can be divided into three stages, that is, the tiny degradation that can return to normal, the medium degradation that can cause poor control performance but does not cause failure, and the severe degradation that is prone to failure. Different measures should be taken in time for different performance de-flowering to prevent major safety accidents. Taking the maglev system as an example, maglev trains are often affected by multiple uncertain factors (for example: degradation/failure of the component, wearing out of the windings, aging of the IGBTs, variation of the load, demagnetization of the magnet caused by collision, changing of the operation point, humidity), resulting in the degradation of system performance. Therefore, it is necessary to classify the degradation of the system and adopt different measures.  
According to the residual generator constructed in Section 3.2, the thresholds are set by analyzing the residual signal during normal operation and without degradation. According to the thresholds \(R_{th1}\) and \(R_{th2}\), the degradation of the suspension system is divided into three levels [38]:  
* Tiny degradation:\(-R_{th1}<r(k)<R_{th1}\). The degradation may be caused by parameter changes or external interference. Through the feedback control of the nominal controller, the train can still run safely and stably without any additional measures;
* Medium degradation: \(-R_{th2}<r(k)\leq-R_{th1}\) or \(R_{th1}\leq r(k)<R_{th2}\). This may be caused by changes in the component parameters of the suspension system (such as large load fluctuations, track irregularities, etc.). If medium degradation occurs, there is no need to stop the maglev train for overhaul, and one only needs to activate the online performance optimization algorithm;
* Severe degradation: \(r(k)\leq-R_{th2}\) or \(r(k)\geq R_{th2}\). This may be caused by component failure of the suspension system. At this stage, the train is very prone to breakdowns. At this time, the maglev train should be stopped and overhauled as soon as possible to prevent major safety accidents.
## 3 Comparing SESO and SERA with Other Topology Optimization Methods
### Comparing Topology Optimization Algorithms with a Mesh-Independency Filter  
A long cantilever, Figure 2, is selected as a test example. It involves a series of broken bars during the optimization procedure. A concentrated load of F = 1 KN is applied in the middle of the free edge. The cantilever has dimensions L = 160, h = 40 and b = 4. [PERSON]'s modulus E = 1 MPa and [PERSON]'s coefficient 0.30. The design domain was discretized with a fine mesh of \(160\times 40\times 4\), totaling 25,600 cubic elements of eight nodes, and the volume constraint for this structure is 0.30 of the initial volume. Table 1 shows the optimization parameters used in the four methods presented. Using the filter, it is noted that the topologies obtained with the SESO, SERA, and ESO methods are similar since the topology achieved with the SIMP method is quite different. It is noteworthy that the ESO and SESO methods obtained equal compliances, with a lower value than the SERA, which computationally, for this problem, proved to be more efficient.  
In most cases, discrete evolutionary methods, when using a small evolutionary ratio (ER) and a finer mesh, reach optimal settings in the solution domain. This is one of the advantages of these methods. However, its computational efficiency is extremely dependent on the selected parameters, such as its mesh and ER parameter. Compared to these methods, the modified SIMP is more stable and less dependent on optimization parameters provided that exponent \(p\) of the penalty is correctly calibrated; in this article, \(p\) = 3. This method will produce optimal solutions because the optimization criteria are met. The optimum solution presented by SIMP has approximately 41.5% greater compliance, and the manufacture of the resulting structure is more difficult.  
Figure 2: Long Cantilever beam subjected to tip point load.
# Angle-Resolved Fluorescence of a Dye Coupled to a Plasmonic Nanohole Array
## 3 Results
The first row of Figure 5 (panels (a) and (b)) results from the TE and TM R spectra acquired impinging and collecting from the front side, in contrast to panels (c) and (d) from the back side, respectively. The dispersion behavior of the spectral features can be clearly observed, and several different polaritonic branches can be recognized. The same behavior can also be recognized in transmittance (T), as shown in Appendix A.1. To identify SPP dispersion, the calculated dispersion curves were compared with the R spectra measured in the TM configuration. On the other hand, the R spectra in the TE configuration provide a better visualization of the LSPR mode features [31]. At the lowest angles, below 15\({}^{\circ}\), the superposition of both LSPR and SPP modes is noticeable, together with the photonic band gap opening at about 680 nm, corresponding to a simultaneous minimum in T and a maximum in R. Further, around 0\({}^{\circ}\) at 800 nm, an evident optical branch can be observed with almost no dispersion until 10\({}^{\circ}\) and an evolution that coincides with the lowest SPP mode trend for larger angles. It is worth noticing how the laser excitation wavelength, indicated with the dashed red line in Figure 5, crosses the GNA plasmonic modes around (15\(\div\)20)\({}^{\circ}\) and (30\(\div\)40)\({}^{\circ}\). Figure 4: Variable-angle fluorescence spectra of ATTO700 dye measured on the PEF platform by impinging and collecting from the same side. Panels (**a,b**) report the spectra corresponding to the front-side measurements. Panels (**c,d**) report the spectra corresponding to the back side ones. Spectra are shown as a function of the pumping angle \(\alpha\) in panels (**a,c**), in contrast to the collection angle \(\beta\) in panels (**b,d**).
# Visible Spectrum and Infra-Red Image Matching: A New Method
###### Abstract  
Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. In the third step, the regression model is used to process the MN-SIFT descriptors of test VS images in order to remove misalignment with the MN-SIFT descriptors of test IR images and to overcome textural and intensity changes. Experiments are performed on two different VS-IR image datasets. The experimental results show that the proposed method works really well and demonstrates on average 14% and 15% better precision and matching scores compared to recently proposed Histograms of Directional Maps (HoDM) descriptor.  
Keywords:feature point detectors; feature point descriptors; regression; brute force descriptor matcher; visible spectrum images; infra-red images; image matching +
Footnote †: journal: Medical Image Analysis
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration  
The emerging field of networked microgrids holds the potential to revolutionize traditional power grids, offering increased flexibility, sustainability, and resilience. Utilizing advanced configuration techniques, these networked microgrids can transform the way electricity is generated, distributed, and consumed in the future.  
The configuration of networked microgrids encompasses three key aspects: formation, power distribution, and operation. Formation involves allocating distributed energy resources (DERs) in each microgrid, establishing boundaries, and determining the physical and operational connections between microgrids to shape the overall structure of the networked microgrids. Power distribution involves conducting power flow analysis, calculating voltage magnitudes, phase angles, and power flows at different points in the system. The integration of power flow analysis, also known as load flow analysis, is crucial for understanding and managing the distribution of electrical power within microgrids, incorporating various elements such as distributed energy resources, energy storage, and loads. Operation defines the behavior of networked microgrids over time under different conditions.  
The following sections will explore these concepts in depth, offering a thorough examination of methodologies applied within each domain. Emphasis will be placed on exploring their problem, modeling, objectives, constraints, and comparing the advantages and disadvantages of each method. Although each paper's central idea and contributions will be summarized in a dedicated subsection, it is important to note that a paper may cover more than one aspect.
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 3 Comparative Analysis of Steady Responses Based on Different Damping Models
### Comparative Analysis of the Steady Response  
In order to compare the steady responses of the viscous damped system and hysteretic damped system, the vibration amplitude ratio of the two damping models is  
\[\delta_{Q}=\frac{Q_{\text{v}}}{Q_{\text{h}}}=\sqrt{\frac{\left(\omega^{2}- \theta^{2}\right)^{2}+4\xi^{2}\omega^{4}}{\left(\omega^{2}-\theta^{2}\right)^ {2}+4\xi^{2}\omega^{2}\theta^{2}}} \tag{31}\]  
The frequency ratio is assumed as  
\[\beta=\frac{\theta}{\omega} \tag{32}\]  
Equation (30) is rewritten as  
\[\delta_{Q}=\sqrt{\frac{\left(1-\beta^{2}\right)^{2}+4\xi^{2}}{\left(1-\beta^{ 2}\right)^{2}+4\xi^{2}\beta^{2}}} \tag{33}\]  
The maximum and minimum values of the vibration amplitude ratio are expressed as  
\[\left\{\begin{array}{l}\delta_{Q,\text{max}}=\sqrt{\frac{1}{1-\xi}}\\ \delta_{Q,\text{min}}=\sqrt{\frac{1}{1+\xi}}\end{array}\right. \tag{34}\]  
When the relative error of the maximum and minimum values of the vibration amplitude ratio for the two damping model are less than 10%, Equation (33) is rewritten as  
\[\left\{\begin{array}{l}\delta_{Q,\text{max}}\leq 1.10\\ \delta_{Q,\text{min}}\geq 0.90\end{array}\right. \tag{35}\]  
Figure 4: The hysteresis loops of hysteretic damping model: (**a**) hysteresis loop of resisting force; (**b**) hysteresis loop of damping force.  
By solving Equation (34), it is obtained as  
\[\left\{\begin{array}{l}\xi\leq 0.1736\\ \xi\leq 0.2346\end{array}\right. \tag{36}\]  
According to Equation (35), when the relative error of the maximum and minimum values of the vibration amplitude ratio for the two damping models are less than 10%, the damping ratio is less than 0.1736. The relationships between vibration amplitude ratios and frequency ratios with different damping ratios are shown in Figure 5. The amplitude ratio is an important index of comparative analysis of the steady responses based on viscous damping model and hysteretic damping model. The threshold of the damping ratio was selected as 0.1736. When the damping ratio is less than 0.1736, the steady responses based on the two damping model are approximately equal.