esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 3 Imaging Method Based on MAF-MDF
### Maf-Mdf  
In practical distribution networks, noise present in the waveforms recorded by phasor measurement units (PMUs) can interfere with fault line selection. To tackle this issue, the zero-sequence current signals collected at each measurement point from 10 milliseconds before to 20 milliseconds after the fault event are first subjected to a moving average denoising process. These signals are then subjected to normalization, ensuring noise reduction while preserving the inherent pattern of signal variation. Subsequently, the normalized current sequences are transformed into two-dimensional images via the motif difference field (MDF) methodology.  
Figure 3 illustrates the zero-sequence current waveforms at the primary endpoints of each feeder in a four-terminal distribution network during a single-phase grounding fault on Feeder 1, along with the imaging results. On the left side of the figure are the original signals, the center portion displays waveforms after undergoing moving average denoising and normalization, and the rightmost section presents the MDF images.  
Observably from the figure, following denoising and MDF transformation of the zero-sequence current sequences captured at various points, the MDF fault images of non-faulty feeders exhibit nearly uniform color distributions, whereas there is a conspicuous distinction in color distribution between the faulty and healthy feeders. By mapping one-dimensional zero-sequence current data into a two-dimensional space, waveform disparities are visually translated into discernible color variations.  
Figure 3: Comparison of MDF images of feeders when single-phase ground fault occurs.  
Figure 2: Schematic diagram of MDF transformation principle.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 3 SSA-LSTM Based Dam Deformation Prediction Model
### Selection of Feature Factors and Model Parameters
The size of the batch size also affects the training speed and performance of the model, and it is usually appropriate to adjust the size of the batch size depending on the test. The number of neurons is usually determined by the complexity of the model and the complexity of the task, so it also needs to be sized depending on the different tests.
# Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
## 2 Materials and Methods  
### Governing Equations  
In this study, the large-eddy simulation (LES) method is used to solve the flow field. Turbulent flows consist of eddies of different scales, and the fundamental idea of the LES method is to directly solve large-scale eddies in the flow field while modeling small-scale eddies. The governing equations of the LES method are derived by spatially filtering Navier-Stokes equations. Each solved variable, denoted as \(f\), is decomposed into its large-scale component \(\widetilde{f}\), and its corresponding fluctuating component \(f^{\prime}\). The filtered continuity equation and momentum conservation equation are given by:  
\[\frac{\partial(\rho\widetilde{u}_{i})}{\partial t}=0, \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho\widetilde{u}_{i})+\frac{\partial}{\partial x _{j}}\big{(}\rho\widetilde{u}_{i}\widetilde{u}_{j}\big{)}=\frac{\partial}{ \partial x_{j}}\bigg{(}u\frac{\partial\widetilde{u}_{i}}{\partial x_{j}}\bigg{)} -\frac{\partial\widetilde{p}}{\partial x_{i}}-\frac{\partial\tau_{ij}}{ \partial x_{j}}, \tag{2}\]  
where \(u_{i}\) is the velocity component along the coordinate axis \(x_{i}\), \(\widetilde{u}_{i}\) is the filtered velocity component, \(t\) is time, \(\rho\) is the fluid density, \(\mu\) is the fluid dynamic viscosity, \(\widetilde{p}\) is the filtered pressure, and \(\tau_{ij}\) is the subgrid-scale Reynolds stress representing the influence of filtered small eddies on the flow field,  
\[\tau_{ij}=\rho\big{(}\widetilde{u_{i}}\widetilde{u}_{j}-\widetilde{u}_{i} \widetilde{u}_{j}\big{)}, \tag{3}\]  
the filtered subgrid-scale Reynolds stress \(\tau_{ij}\) is an unknown and requires modeling with a subgrid-scale model. The widely used Boussinesq eddy viscosity hypothesis equation is given by:  
\[\tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}=-2\mu_{t}\widetilde{S}_{ij}, \tag{4}\]  
where \(\mu_{t}\) is the subgrid-scale eddy viscosity and \(\widetilde{S}_{ij}\) is the strain rate tensor,  
\[\widetilde{S}_{ij}=\frac{1}{2}\Bigg{(}\frac{\partial\widetilde{u}_{i}}{ \partial x_{j}}+\frac{\partial\widetilde{u}_{j}}{\partial x_{i}}\Bigg{)}. \tag{5}\]  
In this study, the WALE (wall-adapting local eddy-viscosity) subgrid-scale model is utilized. The WALE model avoids the defects of the Smagorinsky model, and it does not require any form of near-wall damping, but automatically provides accurate scaling at the wall surface, where the eddy viscosity is defined as:  
\[\mu_{t}=\rho\Delta^{2}S_{w}. \tag{6}\]  
where \(\Delta\) is the length scale, \(\Delta=C_{w}V^{1/3}\), \(V\) is grid cell volume, \(C_{w}\) is a model coefficient (default value is 0.544), and \(S_{w}\) is given by:  
\[\begin{array}{c}S_{w}=\frac{\big{(}S_{i}^{d}S_{ij}^{d}\big{)}^{3/2}}{\big{(} \widetilde{S}_{ij}\widetilde{S}_{ij}\widetilde{S}_{ij}\big{)}^{5/2}+\big{(}S_ {ij}^{d}\big{)}^{5/4}},\\ S_{ij}^{d}=\frac{1}{2}\Big{(}\widetilde{S}_{ij}^{2}+\widetilde{S}_{ij}^{2} \Big{)}-\frac{1}{3}\delta_{ij}\widetilde{S}_{kk^{\prime}}^{2}\\ \widetilde{S}_{ij}=\frac{\partial\widetilde{u}_{i}}{\partial x_{j}}.\end{array} \tag{7}\]
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
## 4 Application
### Roller Bearing Vibration Signals  
The test was carried out for 35 days. Figure 5a-d shows the vibration signals for the normal state, a roller element failure, inner race failure, and outer race failure, respectively [23].  
Figure 4: Illustration of the bearing test rig and sensor placement [23].  
Figure 5: Vibration signal. (**a**) Normal state; (**b**) roller element failure; (**c**) inner race failure; (**d**) outer race failure.  
When the network was trained, this meant that an error had met a demand. Test data was inputted into the wavelet neural network, and the test results are shown in Table 2.  
As shown in Table 2, four states of the bearing can be well separated, with a 100% rate of recognition. This shows that: (1) the feature extraction is effective; (2) the wavelet neural network can be used for information fusion; and (3) good classification results can be achieved.  
In order to further verify the effectiveness of the proposed method, we compared it with the methods described in Reference [24] based on the same bearing data. The authors of Reference [24] used principal component analysis (PCA), local and nonlocal preserving projection (LNPP), linear discriminant analysis (LDA), and supervised-learning-based local and nonlocal preserving projection (SLNPP) to extract features, respectively, before using the features as the inputs of \(k\)-nearest neighbor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Sample** & **Idea Output** & \multicolumn{4}{c}{**Diagnosis Result**} \\ \hline No. & \(y_{1}y_{2}y_{3}y_{4}\) & \(y_{1}\) & \(y_{2}\) & \(y_{3}\) & \(y_{4}\) \\ \hline
1 & 1 0 0 0 & 1.0096 & -0.0001 & -0.0125 & 0.0030 \\
2 & 1 0 0 0 & 1.0025 & 0.0001 & 0.0041 & -0.0067 \\
3 & 1 0 0 0 & 0.9799 & 0.0000 & 0.0225 & -0.0024 \\
4 & 0 1 0 0 & 0.0027 & 0.9990 & -0.0118 & 0.0097 \\
5 & 0 1 0 0 & 0.0004 & 1.0163 & -0.0546 & 0.0382 \\
6 & 0 1 0 0 & 0.0081 & 1.0044 & -0.0011 & -0.0114 \\
7 & 0 0 1 0 & 0.0025 & 0.0000 & 0.9972 & 0.0003 \\
8 & 0 0 10 & 0.0010 & 0.0000 & 0.9983 & 0.0007 \\
9 & 0 0 1 0 & 0.0036 & 0.000 & 1.0044 & -0.0009 \\
10 & 0 0 0 1 & -0.0379 & -0.0099 & -0.0267 & 1.0751 \\
11 & 0 0 0 1 & -0.0567 & -0.0150 & 0.0639 & 1.0751 \\
12 & 0 0 0 1 & -0.0322 & -0.0012 & -0.0508 & 1.0844 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault diagnosis results.  
Figure 6: Diagnosis of the wavelet neural network.  
algorithm (KNN). These results are shown in Table 3, where the proposed method has a higher recognition rate.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
###### Abstract  
Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet).  
Keywords:visual tracking; correlation filter; real-time tracking; multi-domain convolutional neural network (MDNet) +
Footnote †: journal: Applied sciences
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 3 Taxonomy of Resilience Evaluation Methods
### Resilience Metric Computational Method
#### 3.3.3 Graph Theory Algorithms  
Resilience computation is uniquely based on performance measures obtained from network topology and calculated using graph theory algorithms [116].
# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 2 Theory
### Differential Invariants  
There are several types of geometric features, which are useful for segmentation applications. Typical interesting image features are blobs, filaments and corners. Notably, object boundaries can be represented in terms of edges, which can be approximated by steps in image intensity. All these features can be computed from the local differential structure of the image and notably from the differential invariants. The theory will be exemplified with the Gaussian derivatives, which in view of the duality property of Equation (1), can be used to compute the image derivatives. The first four differential invariants are given in Table 1.  
The eigenvalues of the Hessian tensor are solutions of the characteristic equation \(\det\left(\mathbb{H}-A\mathbb{I}\right)=0\), where \(\mathbb{I}\) is the identity matrix. This is a square equation with two real roots \(\lambda_{1,2}\), such that \(\lambda_{1}+\lambda_{2}=\Delta_{G}\) and \(\lambda_{1}\lambda_{2}=det\ \mathbb{H}_{G}\). In addition, by the symmetry of the Hessian, the eigenvectors form 2 mutually orthogonal vector fields.  
If both eigenvalues are negative, this is an indication for a bright blob-like feature around the point of reference. In a similar way, if both eigenvalues are positive, there is a dark blob-like feature around the point of reference. If the eigenvalues have opposite signs this is an indication of a saddle point at this point. Therefore, the zero-crossing of the Laplacian operator can be used to delimit regions, encompassing blobs. The zero-crossings form the so-called \"zero space\". This can be applied also to the normal component of the ALoG and can be used to segment blobs [21].  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **First Order Invariants** \\ \hline Gradient amplitude & \(A=\sqrt{G_{x}^{2}+G_{y}^{2}}\) \\ Gradient orientation & \(\sin\phi=G_{y}/\sqrt{G_{x}^{2}+G_{y}^{2}}\) \\  & \(\cos\phi=G_{x}/\sqrt{G_{x}^{2}+G_{y}^{2}}\) \\ \hline \multicolumn{2}{c}{**Second order invariants**} \\ \hline Laplacian & \(\Delta_{G}=\mathbb{T}\,\mathbb{H}_{G}=G_{xx}+G_{yy}\) \\ Determinant of the Hessian & \(\det\mathbb{H}_{G}=G_{xx}G_{yy}-G_{xy}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Low-order differential invariants.  
The number of differential invariants increases with the increase of the image dimensions, but the theory can be extended along similar lines.
# Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
## 2 Methodology and Validation
### Model Validation for CHS Columns under Fire
#### 2.3.2 Load Bearing Capacity at Room Temperature (GMNIA)  
For the nonlinear buckling analysis, the incremental and iterative method known as Geometrically and Materially Nonlinear Analysis with Imperfections (GMNIA) was employed. This method involves a structural analysis which explicitly includes imperfections, based on the principles of shell bending theory applied to imperfect structures that incorporate the theory of large displacement nonlinearities.  
The geometric imperfections were based on the combination of two different nodes of instabilities (local and global).  
Additionally, in terms of material, it was essential to incorporate an isotropic multilinear nonlinear inelastic behaviour into the material constitutive model, using the von Mises criterion to detect plastification. However, it is important to note that the thermal creep effect on the material was not considered explicitly in these analyses. This effect relates to the permanent deformation caused by the duration of exposure to high temperatures and may be omitted [10].  
The incremental solution was based on the arc length method, which is normally used to solve nonlinear systems of equations when the structural problem has one or more critical points. In the context of structural behaviour, a critical point can be interpreted as the point at which the loaded element cannot withstand an increase in external forces, resulting in instability [19]. In this case, the force applied to the column may be reduced during the simulation. This increment continues to vary between the initial incremental force multiplied by the minimum radius \(R_{min}\) and the maximum radius \(R_{max}\) as defined  
Figure 5: Imperfections for the CHS column.  
in the solution options. The convergence criterion is based on the displacement value of 3 mm.  
After conducting the GMNIA analysis at room temperature, it became possible to compare the results obtained in Ansys with the experimental and numerical results presented by [PERSON] et al. [11], as illustrated in Figure 6. In this graph, C corresponds to the axial displacement at the central node of the bottom plate (contraction). The comparison shows that the simulation performed in Ansys exhibited good agreement with the compared results, displaying only a minor difference in its final curve region. The maximum force obtained was 917.91 kN, representing a relative error of \(-1.93\%\) compared to the experimental result.
## 3 Parametric Investigation
### Varying the TE Noise Source Location and/or Frequency
#### 3.2.2 Interpretation of the Installation Effects
Oppositely, the RMS delta will be maximum for source frequencies such that \(|\Delta r/\lambda|=(2m+1)/2\), i.e., \(\lambda=4h/(2m+1)\), with \(m=1\), \(2\), \(\ldots\), etc. The lowest one corresponds to \(m=1\), i.e., \(\lambda=4h\), i.e., \(St=1/10=0.1\), which matches with the above observations (\(St_{max}\sim 0.3=0.1\) mod(\(\Delta S_{t}\)). Obviously, the above relationships become less trivial when the airfoil is put at incidence (\(\alpha\
eq 0\)), with the source now located at \((x_{s},y_{s})=(bcos(\alpha),-bsin(\alpha))\). For instance, considering again the top wall microphone, one gets (with, again, \(b=c/2\))  
\[r_{+}=\sqrt{\left[(c/2)(1-cos(\alpha))\right]^{2}+\left[h+(c/2)sin(\alpha) \right]^{2}}=(h/5)\sqrt{27-2 cos(\alpha)+10 sin(\alpha)} \tag{71}\]  
and  
\[r_{+}{}^{\prime\prime}=\sqrt{\left[(c/2)(1-cos(\alpha))\right]^{2}+\left[3h-( c/2)sin(\alpha)\right]^{2}}=(h/5)\sqrt{227-2 cos(\alpha)-30 sin(\alpha)} \tag{72}\]  
Considering again that \(c=h/2.5\), this leads to  
\[|\Delta r_{+}|=|r_{+}-r_{+}{}^{\prime\prime}|=(h/5)\left|\sqrt{27-2 cos(\alpha )+10 sin(\alpha)}-\sqrt{227-2 cos(\alpha)-30 sin(\alpha)}\right| \tag{73}\]  
As an illustration, in the high incidence case (\(\alpha=25^{\circ}\)), the difference in propagation paths associated with the top microphone now becomes \(|\Delta r|\cong 1.83h\). The RMS delta will thus oscillate over a cycle in frequencies such that \(|\Delta r/\lambda|=1\), i.e., \(\lambda=1.83h\)i.e., \(\ St=c/\lambda=1/(2.5\times 1.83)=0.218\), which coincides with the previous observations (\(\Delta S_{t}\sim 0.22\)). Similar analyses can be made for the other pair of microphones M2\({}^{\pm}\) and/or different incidences, which are not detailled here for the sake of conciseness. All the above observations further illustrate how the acoustic installation effects induced by the vein confinement may affect the TE noise signature, as well as its subsequent measurement using flush-mounted microphones. Additionally, they also exemplify how this bias can be assessed in a rather straightforward manner, so as to be prevented a priori or corrected a posteriori.
## 2 Preliminary Knowledge
### Multi-Threaded Control Based on C/S Architecture
The multi-threaded C/S architecture communication flow is shown in Figure 1. The server creates a listening socket and binds it to a specified port to enter the listening state; the client initiates a connection request and establishes a connection with the server; the server accepts the connection request and creates a new thread to process the client's request; the client sends a request for data; the server receives it and processes the request, and then returns a response; the client receives a response from the server and corresponding processing; after the communication ends, the client and server disconnect; the server continues to listen for new connection requests. This architecture allows the server to handle multiple client requests at the same time, which improves the concurrent processing capability and resource utilization of the system and ensures the real-time and reliability of communication.
## 4 Results  
### Dataset Description  
The dataset used in this study was acquired from the Volve oilfield located in the Theta anticline, encompassing data from eight wells: F-1, F-5, F-11-A, F-11-T2, F-14, F-15, F-15-B, and F-15-C. The data include Logging While Drilling (LWD) measurements and corresponding drilling engineering parameters for the Hugin Formation. Based on the lithological interpretation results, the lithology labels in the dataset are classified into six categories according to the Udden-Wentworth classification of detrial grain size: (1) Clay shale: Fine-grained sedimentary rock primarily composed of clay minerals with grain sizes smaller than 0.0039 mm. It typically exhibits foliation, is rich in organic matter, has low permeability, and is commonly deposited in hydrostatic environments. (2) Silt: Clastic sediments with particle sizes ranging from 0.0039 mm to 0.0625 mm, predominantly composed of quartz and feldspar. These sediments exhibit weak permeability and are often deposited through suspension under low- to medium-energy hydrodynamic conditions. (3) Fine sand: Terrigenous detrital particles with grain sizes between 0.125 mm and 0.25 mm. These are well sorted and sub-angular to sub-rounded, typically deposited in high-energy environments such as beach bars and delta fronts. (4) Coarse sand: Detrital material with grain sizes ranging from 0.5 mm to 1 mm, usually poorly to moderately sorted, often containing gravel and showing cross-bedding. This lithology indicates high-energy depositional settings, such as fluvial channels or eolian systems. (5) Fine sandstone: Transitional clastic rocks with grain sizes between 0.0625 mm and 0.25 mm, characterized by mixed grain-size distributions. These reflect sedimentary processes influenced by fluctuating hydrodynamic conditions. (6) Limestone: Chemical or biochemical sedimentary rocks composed primarily of calcite (CaCO\({}_{3}\)) exceeding 50%. To facilitate lithology identification, a total of 10 parameters are utilized in the dataset, including 6 LWD parameters: GR (natural gamma), DT (acoustic time difference), RD (deep resistivity), RS (shallow resistivity), NPHI (compensated neutron), and RHOB (density), and 4 drilling engineering parameters: ROP (Penetration rate), HKLA (average hook load), TQA (torque), and WOB (weight on bit).
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 2 Related Background
### RGB Color Space and Color Stacking  
#### 2.2.1 RGB Color Space  
The proposed VSS-QR is mainly designed by using RGB color space [29], so we review it briefly. Compared with CMY color space, RGB color space has the following features for VSS application.  
1. RGB color model has no color darkening while CMY color model has [30].
2. RGB model is suitable for screen displaying. Considering the trend in QR code usage nowadays that more and more QR code are displayed on screen (computer monitor, screen of a smart phone or a tablet), VSS-QR using RGB color space complies with this trend.  
RGB color space is an additive color system, which is commonly used when presenting colors electronically. As shown in Figure 2, when mixing any two colors of red, green and blue, a brighter secondary color can be obtained, such as cyan, magenta and yellow. When mixing the same amount of red, green and blue, the white color should be obtained.  
In RGB color space, red, green and blue colors are used as three basis colors. Any color in this space can be represented as linear combination of these three colors. Let \(X\) represents a given color in RGB space, then \(X\) can be expressed by  
\[X=r\times R+g\times G+b\times B, \tag{1}\]  
where \(R\), \(G\) and \(B\) represents the red, green and blue colors, respectively. Thus, any color in a RGB color space can be represented as a vector \([r,\ g,\ b]^{T}\). For convenience, we use boldface San-Serif font to  
Figure 2: Color mixing in RGB additive color space.  
denote some commonly used color vectors. The color vectors corresponding to red, green, blue, cyan, magenta, yellow, white and black are denoted as:  
\[\begin{split}&\mathsf{R}=[1\;0\;0]^{T},\;\mathsf{G}=[0\;1\;0]^{T}, \;\mathsf{B}=[0\;0\;1]^{T},\;\mathsf{C}=[0\;1\;1]^{T},\;\mathsf{M}=[1\;0\;1]^{T },\\ &\mathsf{Y}=[1\;1\;0]^{T},\;\mathsf{W}=[1\;1\;1]^{T},\;\mathsf{K} =[0\;0\;0]^{T}.\end{split} \tag{2}\]