esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Feature Identification and Trajectory Planning of Pavement Cracks
## 3 Intelligent pavement Crack Detection and Repair Algorithm
### Canny Marginalization and Hough Space Transformation
The Canny algorithm would firstly introduce the x-direction convolution operator \(S_{x}\) and y-direction convolution operator \(S_{y}\) as shown in Equation (1), and calculate \(d_{y}\), \(d_{x}\) by Equation (2) for each pixel in the image matrix. Then the algorithm would calculate the image gradient magnitude \(M(x,y)\) according to Equation (3), and finally get the gradient direction angle \(\theta_{M}\) by Equation (4). The specific mathematical model for calculating the image gradient \(\theta_{M}\) is shown in Equations (1)-(4), where Equation (1) represents the convolution operators in the x and y directions, respectively, and Equations (2)-(4) represent the process of calculating the image gradient direction angle through the convolution operator. \[S_{x}= \begin{bmatrix}-1&1\\ -1&1\end{bmatrix} ,\quad S_{y}= \begin{bmatrix}1&1\\ -1&-1\end{bmatrix}  \tag{1}\]  
\[d_{x}=f(x,y)\times S_{x},\quad d_{y}=f(x,y)\times S_{y} \tag{2}\]  
\[M(x,y)=\sqrt{d_{x}^{2}(x,y)+d_{y}^{2}(x,y)}=|d_{x}(x,y)|+d_{y}(x,y)| \tag{3}\]  
\[\theta_{M}=arctan(\frac{d_{y}}{d_{x}}) \tag{4}\]  
After obtaining the image intensity gradient, the algorithm performs non-maximum suppression on the amplitude along the gradient direction to eliminate the non-edge point set. The specific process is as follows:  
1. Through the image coordinate angle, the edge directions are divided into four directions: vertical, horizontal, \(45^{\circ}\), and \(135^{\circ}\). The corresponding gradients are also four directions orthogonal to the edge direction. 2. For a horizontal edge, its gradient is vertical, and the gradient direction angle satisfies: \(\theta_{M}\in[-22.5^{\circ},22.5^{\circ}]\cup[157.5^{\circ},180^{\circ}]\cup[- 180^{\circ},-157.5^{\circ}]\) ; for a \(135^{\circ}\)edge, its gradient is \(45^{\circ}\), and the gradient direction angle satisfies: \(\theta_{M}\in[22.5^{\circ},67.5^{\circ}]\cup[-157.5^{\circ},-112.5^{\circ}]\); for a vertical edge, its gradient is in the horizontal direction, and the gradient direction angle satisfies: \(\theta_{M}\in[67.5^{\circ},112.5^{\circ}]\cup[-112.5^{\circ},-67.5^{\circ}]\) ; while for a \(45^{\circ}\) edge, its gradient is \(135^{\circ}\), and the gradient direction angle satisfies: \(\theta_{M}\in[112.5^{\circ},157.5^{\circ}]\cup[-67.5^{\circ},-22.5^{\circ}]\). 3. As shown in Figure 11, along the above-mentioned four types of gradient directions, the algorithm compares the value of the traversed pixel with the values of its neighborhood. For each pixel traversed by the algorithm, if its pixel value is the maximum value in the neighborhood centered on it, its pixel value is retained, otherwise, its pixel value is set to 0. After processing by the Canny edge detection operator, the algorithm uses the Hough space transformation to perform a line fitting operation on the boundary of the image crack feature, and the obtained fitting trajectory is shown in Figure 12.
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 2 Data and Methodology
### Air Turbulence Measurement
#### 2.2.2 Devg  
DEVG focuses on the nature of atmospheric turbulence itself and its impacts on aircraft flying over it. It uses measured aircraft vertical acceleration, aircraft altitude, weight, and other factors to characterize the physical quantities of atmospheric turbulence. It is estimated by vertical acceleration, which takes into account information such as total aircraft weight, flight altitude, airspeed, and air temperature, etc.  
In the aircraft meteorological data relay reference manual [28], the World Meteorological Organization (WMO) defined the DEVG estimation method based on the formulations of [PERSON] [29] and [PERSON] [30], as shown in Equation (2):  
\[DEVG=\frac{Am|\Delta n|}{V} \tag{2}\]  
where \(\Delta n\) (unit: g) is the peak value of the aircraft's vertical acceleration deviating from 1g, \(m\) (unit: ton) is total weight of the aircraft, \(V\) (unit: \(\frac{m}{s}\)) is the air flow rate, and \(A\) is a specific parameter mainly dependent on the aircraft type, weakly affected by its weight, altitude, and Mach number. It can be approximated via the following equations, Equations (3) and (4),  
\[A=\overline{A}+c_{4}\Big{(}\overline{A}-c_{5}\Big{)}\Big{(}\frac{m}{m}-1\Big{)}, \tag{3}\]  
\[\overline{A}=c_{1}+\frac{c_{2}}{c_{3}+H}, \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Turbulence Intensity** & **Overload Increment Range (g)** \\ \hline
**steady flight** & \(|\Delta n|\leq 0.2\) \\
**weak turbulence** & \(0.2<|\Delta n|\leq 0.5\) \\
**moderate turbulence** & \(0.5<|\Delta n|\leq 0.8\) \\
**strong turbulence** & \(|\Delta n|>0.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Air turbulence intensity and the corresponding overload increments.  
where parameters \(c_{1},c_{2},c_{3},c_{4},\) and \(c_{5}\) refer to specific values could be found in the aircraft meteorological data relay reference manual, referring to \"Table 1 Parameters used in the estimation of DEVG\" on Page 31 of [28]. Their calculations depend on a specific flight profile, which could be expressed as \"take-off at speed \(V_{1}\), accelerate to airspeed \(V_{c}\) by height \(h_{1}\), and maintain the lesser of \(V_{c}\) or Mach \(M_{c}\) to cruise altitude \(h_{c}\). Reverse the procedure in the approaching phase\" [28] (Page 28); \(\overline{m}\) refers to the reference aircraft weight in tons; \(H\) (unit: thousand feet) is the flight altitude.  
DEVG provides another effective measurement of AT, and the relationship between the AT intensity and DEVG range is shown in Table 5. Although DEVG is roughly related to EDR, it does not represent a true measure of atmospheric turbulence intensity; thus, it is not as widely used in atmospheric turbulence measurement applications as EDR.
## 3 Numerical Results
Nevertheless, the curves of \"\(N=21\)\" and \"\(N=25\)\" have smaller slopes in the range [0, \(2^{\circ}\)] compared to the slope of \"\(N=31\)\" curve, which means that lower quality factors result in lower measurement sensitivity for \(\theta_{inc}\). Figure 5 serves to demonstrate the impact of \(\Delta_{f}\) on the device's performance. One of the two curves in Figure 5 is the same as that in Figure 3(c), where \(\Delta_{f}=\)0.88%. The other curve in Figure 5 is generated by increasing \(\Delta_{f}\) to 1.03% and with all the other parameters unchanged. It is observed that, the increase of \(\Delta_{f}\) enlarges the detectable range from [\(-2^{\circ}\), \(2^{\circ}\)] to [\(-4^{\circ}\), \(4^{\circ}\)]. As a price, the coupling efficiencies around \(\theta_{inc}=0\) drop with the increase of \(\Delta_{f}\). To be specific, the coupling efficiencies of both detectors at surface normal incidence are 17% when \(\Delta_{f}=0.88\%\); and they drop to 11% when \(\Delta_{f}\) increases to 1.03%. In order to visualize our device's behavior better, field distributions are plotted in Figure 6 for several incident angles. The field distribution plots in Figure 6 correspond to the \(\Delta_{f}=1.03\%\) curve of Figure 5. The device configuration is shown in Figure 6(a). The incident wave is a continuous wave at \(f_{inc}=0.645\)\(c/a\). In Figure 6(b-e), electric field \(E_{y}\) at a certain time moment is plotted, with four different incident angles respectively. In Figure 6(b-e), the strongest positive field intensity is represented by dark red color, the strongest negative field intensity is represented by dark blue color, and white color stands for zero field intensity, as specified at the end of Figure 6. Two guided modes (which are traveling toward \(+x\) and \(-x\) directions, respectively) can be clearly identified in the waveguide. When \(\theta_{inc}=0\), the two modes are equally strong. With the increase of incident angle, the two modes become more and more unbalanced. When \(\theta_{inc}=3^{\circ}\), the \(-x\) mode is much stronger than the \(+x\) mode. Figure 4: Numerical results for the angle detector with three different values of \(N\).
## 3 Application and Comparison of Various Classification Methods
### Standard Extreme Learning Machine  
A neural network is a set of connected input/output units where each connection has a weight associated with it. The network learns by adjusting the weights and biases iteratively to minimize the approximate mean square error. In supervised learning, a gradient descent-based learning algorithm is a typical back-propagation (BP) learning algorithm, which updates the network weights and biases in the direction (i.e., gradient direction) in which the error function decreases most rapidly [42]. However,  
\begin{table}
\begin{tabular}{l} \hline \hline
**Definition:**\(X\): training data, \(Y\): class labels of \(X\), \(x\): unknown sample \\ _Initialize:_ Set variables \(X\), \(Y\), \(x\) \\ _for i = 1 to m \\  Compute distance \(d(X_{i},x)\) \\ _end_ \\  Compute set \(l\) containing indices for the k smallest distances \(d(X_{i},x)\) \\ _Return_ majority label for \(\{Y_{i}\) where \(i\in l\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pseudocode of the k-Nearest neighbor algorithm.  
Figure 2: Schematic Concept of SVM.  
the learning speed of gradient-based algorithms is generally slow, and the selection of a learning rate is also tedious. These algorithms will be unstable if too large a value is chosen and will converge too slowly if the value is too small. Hence, the Extreme Learning Machine (ELM) algorithm was proposed to overcome these issues.  
The ELM [43, 44] was originally inspired by biological learning and proposed a modification of the single-layer feedforward network (SLFN), wherein weights and biases are chosen randomly to overcome the challenging issues faced by BP learning algorithms. Unlike other so-called randomness (semi-randomness)-based learning methods/networks [45], all the hidden nodes in the ELM are not only independent of the training data but are also independent of each other. [PERSON] et al. [18] established the universal approximation capability of ELM and its capability for biological learning. Based on the proven theory, the input weights and hidden layer biases of SLFNs can be randomly assigned if the activation functions in the hidden layer are infinitely differentiable, and the standard ELM process is given as follows [46]:  
Given a training set \(\textbf{N}=\{(x_{i},\,t_{i})|x_{i}\in R^{n},\,t_{i}\in R^{m},\,i=1,\,...,\,N\}\), activation function \(g(x)\), and the number of hidden nodes \(N\).  
1. Randomly assign input weight (\(w_{i}\)) and bias (\(b_{i}\), \(i=1,\,...,\,N\)).
2. Calculate the hidden layer output matrix (\(H\)).
3. Calculate the output weight (\(\beta\)),  
Where \(\beta=H^{+}\,T,H^{+}\) is the Moore-Penrose generalized inverse of matrix \(H\), and \(T=[t_{1},\,...,\,t_{N}]^{T}\).
## 3 Mathematical Model of Geolocation and Tracking Scenarios  
### Conventional Geolocation Measurements  
The signal transmitted from a high-speed dynamic emitter received at multi-sensors has time delay and frequency Doppler. Therefore, we can compute TDOA and FDOA between the emitter and the \(i\)th sensor [20].  
Let the emitter position at time index \(k\) be,  
\[\mathbf{u}_{k}^{o}=\mathbf{u}_{0}^{o}+\zeta k\mathbf{u}_{k}^{o}\, \tag{1}\]  
where \(\mathbf{u}_{0}^{o}=[x_{0}^{o},y_{0}^{o},z_{0}^{o}]^{\prime}\) and \(\mathbf{\dot{u}}_{k}^{o}=[\dot{x}_{k}^{o},\dot{y}_{k}^{o},z_{k}^{o}]^{\prime}\) are initial location vector and velocity vector of emitter, respectively, \(\zeta\) is time between successive measurements, \((\cdot)^{\circ}\) represents the true value without noise, and \([\cdot]^{\prime}\) denotes the matrix transpose.
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 3 Methodology
### Generating the Training Data  
The evaluation is based on the Rosenbrock function, which is a non-convex function forming a valley used by several papers to perform tests for metaheuristics. Because of its shape, finding a good direction is trivial; however, finding the optimal step size is hard. Therefore, the fitness function is defined as follows:  
\[f(x,y)=(a-x)^{2}+b(y-x^{2})^{2} \tag{3}\]  
The standard parameters (\(a=1\), \(b=100\)) have been used; therefore, the global minimum is at the \((a,a^{2})\), and the value of the function is \(f(a,a^{2})=0\) here.  
The training dataset is generated by the execution of 20,000 HC algorithms from random starting positions. As a constraint, the starting positions \((start_{x},start_{y})\) are chosen randomly from a predefined area around the global optimum.  
\[-49\leq start_{x}\leq 51 \tag{4}\]  
\[-49\leq start_{y}\leq 51 \tag{5}\]  
The training database generator algorithm is based on HC using the BF method for \(\epsilon\) determination, where the set of possible step sizes is as follows:  
\[E=\{10^{-9},10^{-8},10^{-7},10^{-6},10^{-5},0.0001,0.001,0.1,1.0,10.0\} \tag{6}\]  
The number of possible directions (\(\delta\)) was four, as follows:  
\[\{(x-\epsilon,y),(x+\epsilon,y),(x,y-\epsilon),(x,y+\epsilon)\} \tag{7}\]  
According to the BF method, the algorithm examines all \(\epsilon\) values and all directions, and it chooses the best move for all iterations. In total, 11 different \(\epsilon\) values were tested, with 4 directions for each case, so choosing the best move requires 44 fitness calculations. This is quite computationally intensive but gives the most accurate dataset for training. The HC algorithm has been extended with some monitoring operations as follows: it continuously saves the fitness value of the current \(p\) position and the best \(\epsilon\) size from this location for each iteration. As visible from Table 1, all HC runs could reach a good fitness value; however, the standard deviation of the required steps is very high.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.17 Speed Based on Demand Vector (SODV) Link Routing Protocol  
In [43] SODV was presented, an AODV-based routing protocol. SODV improves the routing process and makes the selection of neighboring vehicles incresingly relevant by taking into account vehicle velocity in the packet-transmission process.  
The authors selected the geometric average velocity to calculate the vehicle's average velocity because it is more reliable and better reflects reality than arithmetic average velocity. Geometric average velocity is calculated as shown in Equation (22), and the number of nodes is variable from \(i\) to \(n\).  
\[\text{Geometric average velocity}=\sqrt[n]{\prod_{i=1}^{n}} \tag{22}\]  
Simulation analysis showed that SODV improved the AODV routing protocol, especially with regard to transmission delay.
## Appendix A Numerical Shadowgraphy Functions  
CFD results have been converted in the functions shown in Figure 9 based on the following physical considerations that follow the equations in Settles [20] and applied to asymmetric cylindrical coordinates.  
Features in shadowgraphy images are driven by changes in the fluid's refractive index \(n\). Such an index varies with the gas density \(\rho\) as described by the following linear relationship:  
\[n-1=k\rho, \tag{1}\]  
where \(k\) is the Gladstone-Dale coefficient (about \(0.23\) cm\({}^{3}\)/g for air at standard conditions). The angular ray deflection for light collimated in the \(z\) direction crossing a medium can be decomposed into \(x\) and \(y\) components and is obtained by integrating the respective gradients along the \(z\) direction:  
\[\varepsilon_{x}=\frac{1}{n_{0}}\int_{z}\frac{\partial n}{\partial x}\mathrm{ d}z, \tag{2}\]  
\[\varepsilon_{y}=\frac{1}{n_{0}}\int_{z}\frac{\partial n}{\partial y}\mathrm{ d}z, \tag{3}\]  
where \(n_{0}\) is the refractive index of the surrounding medium.  
For an asymmetric geometry in cylindrical coordinates with \(x\) being the axial direction, \(y\) the radial direction, and \(z\) the direction of the collimated rays, Equations (2) and (3) correspond, respectively, to the following two equations:  
\[\varepsilon_{x}(x,y)=\frac{2}{n_{0}}\int_{y}^{R}n_{x}(x,y^{\prime})\mathrm{ cot}\bigg{(}\mathrm{acos}\bigg{(}\frac{y^{\prime}}{R}\bigg{)}\bigg{)}\mathrm{d}y^{ \prime}, \tag{4}\]  
\[\varepsilon_{y}(x,y)=\frac{2}{Rn_{0}}\int_{y}^{R}n_{y}(x,y^{\prime})y^{\prime }\mathrm{cot}\bigg{(}\mathrm{acos}\bigg{(}\frac{y^{\prime}}{R}\bigg{)}\bigg{)} \mathrm{d}y^{\prime}. \tag{5}\]  
In shadowgraphy images, the pixel intensity shifts with changes (gradients) in this ray deflection. Therefore, the divergence of ray deflection,  
\[f_{S}(x,y)=\frac{\partial\varepsilon_{x}(x,y)}{\partial x}+\frac{\partial \varepsilon_{y}(x,y)}{\partial y}, \tag{6}\]  
is typically used to compare numerical results to shadowgrams. The variable \(f_{S}(x,y)\) is used to convey the results in Figure 9. The figure also shows a simplified function that is often adopted in the literature, consisting of the Laplacian of the refractive index,  
\[f_{L}(x,y)=\
abla^{2}n. \tag{7}\]  
This function implies that the target has a thin planar geometry. Although this is an incorrect assumption, results using this variable are still provided for the reader's benefit due to the ease of reproduction.
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 3 Proposed Method  
### Framework Design  
The proposed digital image watermarking framework is illustrated in Figure 1. Our framework adopts an encoder-decoder architecture, where the encoder network is responsible for watermark embedding while the decoder network handles watermark extraction.  
Specifically, the cover image \(I_{cov}\) and the preprocessed secret message \(I_{sec}\) are fed into the edge-aware secret hiding module (EASHM), which leverages a maximal parameter-shared dual-task encoder that simultaneously performs edge detection and watermark embedding to ensure structurally aware and minimally perceptible information hiding. During the embedding process, guided by the edge detection teacher, the EASHM produces an edge-fitting watermark \(I_{hide}\) that is subsequently combined with \(I_{cov}\) to generate the watermarked image \(I_{um}\). Additionally, to enhance the watermark imperceptibility, \(I_{um}\) and \(I_{cov}\) are passed through a discriminator. The watermarked images \(I_{um}\) are then processed through a social network noise simulator (SNNS) to simulate real-world distortions, resulting in noised watermarked images \(NI_{wm}\). Finally, as the decoder component, the secret recovery module (SRM) implements image shape correction, enabling robust watermark retrieval under various spatial deformations, and recovers the secret message \(M_{rec}\) from \(NI_{wm}\).
## 3 Conceptual Approach
### Process Discovery Algorithm  
In this subsection, we introduce an extended algorithm for process model discovery. First, it is necessary to provide basic definitions and an overview of the task. Every process-aware information system that records run-time behavior has an event log. An event log is a file that contains information about process execution. Each record is an event with associated data: a timestamp of its start and completion, an activity and resource that executes this activity, and a process case id (instance) the record belongs to. They are the minimal items for compiling a log. However, if activities are considered to be atomic, i.e., have no duration, the last item is only needed for defining the order of activities and can be skipped if we know a priori that data are stored according to a timeline. We group an ordered set of events only containing activity names into cases that represent single process runs. This \"flat\" event log is used as an input for process mining in our discovery algorithm.  
While an event log is an input, the algorithm's output is a (_business_) _process model_, or a _process map_. In our case, a process model represents a formal graphical description of the actual process flow, i.e., the precedence of events, where nodes are activities, and edges are ordered relationships between them. In Figure 2, we provide a scheme of the solution for obtaining such a result, where dashed arrows are optional discovery steps, and main parameters are listed in callouts.  
Figure 2: Scheme of the algorithm workflow.  
The next section presents the precise details of the proposed algorithm implementation. Steps 1-3 are the basics of the algorithm's workflow and described in the following Section 4.1. Within step four, we define an optimization problem in Section 4.2 and propose an approach to model abstraction by folding significant cycles in Section 4.3.
## 3 Detouring Service via Multiple Paths
### Basic Detouring Service
\begin{table}
\begin{tabular}{r l} \hline
1: & **IF** (\(\mathbf{n_{next-hop}}\in\mathbf{N_{jamming}}\)) \\
2: & \(\mathbf{Z_{forward}}\)\(=\mathbf{N_{forward}}\)\(=\)\(\Phi\) \\
3: & **FOR**(each \(z\) in \(\mathbf{Z_{neighbor}}\)) \\
4: & **IF**((\(\mathbf{fn}\) in \(z\in N_{jamming}\)) AND (\(\mathbf{cz}_{Dist}\)\(>=\)\(\mathbf{z_{Dist}}\)) AND (\(\mathbf{z}\)\(\
eq\)\(\mathbf{dz}\) ) ) \\
5: & add \(z\) to \(\mathbf{Z_{forward}}\) \\
6: & **ENDIF** \\
7: & **ENDFOR** \\
8: & sort \(\mathbf{fn}\) _in_\(\mathbf{Z_{forward}}\) according to the distance value \\
9: & \\
10: & **WHILE**(\(|\mathbf{N_{forward}}|=\mathbf{u}\) ) \\
11: & pop the first node \(\mathbf{fn}\) in the sorted list \\
12: & add \(\mathbf{fn}\) to \(\mathbf{N_{forward}}\) \\
13: & **ENDWHILE** \\
14: & detour normal traffic evenly to \(\mathbf{fn}\) in \(\mathbf{N_{forward}}\) \\
15: & \\
16: & **ELSE IF** (\(\mathbf{n_{next-hop}}\
eq\mathbf{N_{jamming}}\)) \\
17: & route the traffic according to the general routing protocol \\
18: & **ENDIF** \\ \hline \(\mathbf{N_{jamming}}\) : Set of nodes in jamming area \\ \(\mathbf{N_{forward}}\) : Set of forward nodes \\ \(\mathbf{Z_{neighbor}}\): Set of neighbor zones \\ \(\mathbf{Z_{forward}}\) : Set of forward zones \\ \(\mathbf{n_{next-hop}}\): Next-hop node in general route \\ \(\mathbf{z}\) : A zone \\ \(\mathbf{cz}\) : A current zone where the detour node is performing DMP \\ \(\mathbf{dz}\) : A zone in which the pervious detour node is included, when the traffic is forwarded to the current detour node \\ \(\mathbf{fn}\) : A forward node \\ \(\mathbf{z_{Dist}}\) : Distance of zone \(\mathbf{z}\) from the destination zone \\ \(\mathbf{a}\): The number of forward nodes given as a system parameter. The detour nodes evenly forward normal traffic on the determined \(\mathbf{a}\) forward nodes. \\ \hline \end{tabular}
\end{table} 
Table 2: Algorithm selecting forward nodes at detour node.