esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
###### Abstract  
KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detection method based on correlation analysis and HMM. Correlation analysis is used to obtain the correlation between abnormal KPIs in the system, thereby reducing the false alarm rate of anomaly detection. The HMM (Hidden Markov Model) is used for anomaly detection by finding the close relationship between abnormal KPIs. In our correlation analysis of abnormal KPIs, firstly, the time series prediction model (1D-CNN-TCN) is proposed. The residual sequence is obtained by calculating the residual between the predicted value and the actual value. The residual sequence can highlight the abnormal segment in each data point and improve the accuracy of anomaly screening. According to the obtained residual sequence, these abnormal KPIs are preliminarily screened out from the historical data. Next, KPI correlation analysis is performed, and the correlation score is obtained by adding a sliding window onto the obtained anomaly index residual sequence. The correlation analysis based on the residual sequence can eliminate the interference of the original data fluctuation itself. Then, a correlation matrix of abnormal KPIs is constructed using the obtained correlation scores. In anomaly detection, the constructed correlation matrix is processed to obtain the adaptive parameters of the HMM model, and the trained HMM is used to quickly discover the abnormal KPI that may cause a KPI anomaly. Experiments on public data sets show that the method obtains good results.  
convolutional neural network (CNN); temporal convolutional network (TCN); anomaly detection of KPIs; hidden Markov model (HMM); correlation analysis +
Footnote †: journal: Applied sciences
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 2 The Concept of P2G
### Synthetic Natural Gas  
In the process of coal to natural gas, hydrogen plays an irreplaceable role. In addition to the most traditional process, methanation of coal-gas, there are steam gasification process and hygas process currently. Illustrated by the example of the hygas process, hydrogen as gasification agent and coal are converted into natural gas in the gasification unit. The simplification is expressed as follows:  
\[\mathrm{C}+2\mathrm{H}_{2}\rightarrow\mathrm{CH}_{4}\quad\mathrm{\Delta H}=-74.8\;\mathrm{kJ}\cdot\mathrm{mol}^{-1} \tag{2}\]  
The process is characterized by its by-products, aromatic compounds, such as benzene, produced by the reaction of coal and Hydrogen at high temperature, whose original aim is to generate methane. Here, hydrogen as the raw material of methanation reaction will be directly involved in the preparation of natural gas as follows:  
\[\mathrm{CO}+3\mathrm{H}_{2}\rightarrow\mathrm{CH}_{4}+\mathrm{H}_{2}\mathrm{O }\quad\mathrm{\Delta H}=-206.2\,\mathrm{kJ}/\mathrm{mol} \tag{3}\]  
\[2\mathrm{CO}+2\mathrm{H}_{2}\rightarrow\mathrm{CH}_{4}+\mathrm{CO}_{2}\quad \mathrm{\Delta H}=-247.4\,\mathrm{kJ}/\mathrm{mol} \tag{4}\]  
\[\mathrm{CO}_{2}+4\mathrm{H}_{2}\rightarrow\mathrm{CH}_{4}+2\mathrm{H}_{2} \mathrm{O}\quad\mathrm{\Delta H}=-165\,\mathrm{kJ}/\mathrm{mol} \tag{5}\]  
With the continuous development and promotion of biogas technology, the combination of hydrogen and biogas digesters will be a major way to achieve an energy conversion of power-hydrogen to gas, when the required hydrogen can be produced in large quantities.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies  
Due to the diversity of the construction resources, harsh environment, and complex nature of construction projects, the data acquired by each RS technology is missing or erroneous. Furthermore, each RS technology has its capabilities and drawbacks that limit its application. In fact, depending solely on a single technology to acquire data on-site in an accurate and timely manner is unreliable. In this way, the integration of various RS technologies together can improve the data acquisition process. Plus, it can help to overcome the limitations of an individual technology and makes it possible to provide the required information even if one of these data sources is not fully available [16; 25]. This integration could happen in different scenarios. In a typical scenario, the integration includes the separate employment of various RS technologies in different stages of construction activity. Examples of this type of integration are available in [25], in which integrated barcode-RFID and photogrammetry-laser scanning systems are used for resource tracking and quantity measurement on site. In another scenario, the data acquired by each technology could enhance the data acquired by other technologies which result in having more accurate and robust information. An example of the latter integration approach is the data fusion of various RS technologies together or a multi sensor data fusion (MSDF) approach. In fact, data fusion refers to the combining of information from multiple sources in order to improve the quality of information obtained separately from each source. It is utilized to make inference decisions about the state of a construction project based on the data that are acquired from different sources [16]. Therefore, informed decisions and objective assessments of the activities progress on site are not only based on a single data source. In fact, a number of data sources are combined together in order to provide sufficient details about various activities on site. It should be noted that the level of data fusion is an important factor in order to have a more robust and accurate approach [28]. Recent studies in this field are focused on the integration of various RS technologies in order to benefit from identification, localization, and visualization capability of each of these technologies with applications in tracking objects and activities on site. Table 3 shows some of these integrated systems along with their capabilities over standalone usage of each technology and their drawbacks for implementation on site.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 3 Mutual Information
To demonstrate the robustness of MI scores compared to \(R^{2}\), Figure 3 shows four scatterplots representing four types of relationships: perfect linear (a), linear with noise (b), linear with outliers (c), and nonlinear (d). Figure 3b shows that \(R^{2}\) is more sensitive to noise than MI. The drastic reduction in \(R^{2}\) with a small number of outliers is apparent in Figure 3c. The MI score in the nonlinear case (Figure 3d) remains high, indicating strong dependence between the two quantities. As \(R^{2}\) measures only linear associations, it does not detect the nonlinear, yet strong, dependence depicted in Figure 3d.
## 2 Theoretical Framework
### Modified RFM Model and Customer Engagement Index (CEI)
Here, \(i\) represents a transaction, \(j\) represents a subsequent transaction, and \(n\) reflects the total number of transactions for each customer or the frequency of purchases during a specific period. \(X_{j}\) and \(X_{i}\) denote the transaction dates. Note that \(\Delta t_{ij}\) is incremented by 1 to include the current transaction date. To calculate the sum of \(MLE\), \(\Delta t_{ij}\) is the duration between two transactions or between the last transaction and a dummy research date. \(W\)MLE is the weighted approximation obtained by summing the product of \(\Delta t_{ij}\)and\(W_{i}\). A \(CEI\) value greater than 0 (\(CEI\) > 0) indicates high customer engagement or an active customer, while a \(CEI\) value less than 0 (\(CEI\) < 0) signifies low customer engagement or an inactive customer. However, for this research, we assume that the highest \(CEI\) value is normalized to 1, and the lowest value is normalized to 0. Figure 1 mentions the schematic diagram for calculating customer engagement activity, meaning that the more transactions completed by the customer, the higher the \(CEI\). This also happens if the duration between pair of transactions is small enough. This denotes a frequent customer. Figure 1: Schematic Diagram of Customer Engagement Activity.
###### Abstract  
Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.  
smooth orthogonal decomposition; permutation entropy; weighting matrix; fault classification; rotary machinery +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Spectral Observation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wave** & **Primary** & \multicolumn{2}{c}{**PCA**} & \multicolumn{1}{c}{**Functional**} & \multicolumn{1}{c}{**Vibrational**} & \multicolumn{1}{c}{**Band**} \\
**Number** & **Spectral** & \multicolumn{1}{c}{**Score Plot ***} & \multicolumn{1}{c}{**Group**} & \multicolumn{1}{c}{**Mode ***} & \multicolumn{1}{c}{**Assignment**} \\ \cline{4-7}
**(cm\({}^{-1}\))** & **Difference*** & **PC1** & **PC2** & & & \\ \hline
2923 & \(\blacktriangle\) & (\(\cdot\)) & (\(\cdot\)) & \(-\)(CH\({}_{2}\))\({}_{n}\)\({}_{-}\) & \(\
u_{\rm ss}\)(CH\({}_{2}\)) & Lipid \\
2852 & \(\blacktriangle\) & (\(\cdot\)) & (\(\cdot\)) & \(-\)(CH\({}_{2}\))\({}_{n}\)\({}_{-}\) & \(\
u_{\rm ss}\)(CH\({}_{2}\)) & Lipid \\
1743 & \(\blacktriangle\) & & (\(\cdot\)) & \(-\)CH\({}_{2}\)\({}_{-}\)COOR & \(\
u\)(C=O) & Phospholipid esters \\
1712 & \(\blacktriangle\) & & & C=O & \(\
u\)(C=O) & Nucleic acid \\
1650 & \(\blacktriangle\) & + & + & & 80\% \% (\
u\)(C=O)/20\% & Amide I peptide \\
1639 & \(\blacktriangledown\) & + & + & O=C\(-\)N\({}^{-}\)H & \(\
u\)(CN) & Amide I peptide \\
1624 & \(\blacktriangledown\) & (\(\cdot\)) & & \(\
u\)(CN) & \\
1552 & \(\blacktriangle\) & & + & O=C\(-\)N\({}^{-}\)H & 60\% \%(\gamma\)(NH)/ & Amide II peptide \\
1544 & \(\blacktriangledown\) & + & + & O=C\(-\)N\({}^{-}\)H & 30\%(\gamma\)(CN)/10\% \% (\
u\)(CC) & CH in phenyl ring \\
1512 & \(\blacktriangledown\) & & - & 8 CH & CH in phenyl ring \\
1467 & \(\blacktriangle\) & & + & \(-\)(CH\({}_{2}\))\({}_{n}\)\({}_{-}\) & \(\delta\)CH\({}_{2}\) & Lipid \\
1380 & \(\blacktriangle\) & & & C\(-\)CH\({}_{3}\) & \(\gamma_{\rm ss}\)(CH\({}_{3}\)) & Lipid \\
1236 & \(\blacktriangledown\) & & RO\(-\)PO\({}_{2}\)\({}^{-}\)\({}^{-}\)\({}^{}\)\({}^{}\)\({}^{}\)\({}^{}\)\({}^{}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\(}\) & \(\gamma_{\rm ss}\)(PO\({}_{2}\)\({}^{-}\)) & DNA/RNA, phospholipid \\
1170 & \(\blacktriangle\) & & R\(-\)COO\({}^{-}\)\({}^{}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\(}\)\({}^{\)\({}^{\)\(}\)\({}^
## 4 Interference Monitoring
### Power Spectral Density  
Frequency characteristic is the most direct reflection of signal characteristics. Through observing the frequency characteristic of GNSS signals, a variety of information can be obtained, including signal bandwidth, power spectral density, in-band stray, and interference performance. Therefore, the analysis of frequency characteristic is firstly carried out in interference monitoring. Power Spectral Density (PSD) reflects the signal power distribution within the signal bandwidth. With the measurement of the PSD, whether the power of every frequency is well-distributed or there is abnormal power can be seen. PSD is also an effective means to monitor the GNSS signal interference.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
###### Abstract  
In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise.  
Keywords:sparse matching; outlier detection; genetic algorithm; epipolar geometry; evolutionary search; guided sampling; adaptive thresholding +
Footnote †: journal:
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 7 Illustrative Examples
### Fractional-Order LQR Control for Viscoelastic Damper System
Judging from Equations (62) and (63), the eigenvalues of the matrix \(\mathbf{A}_{so}\) are more stable than those of the matrix \(\mathbf{A}_{cl}\). This proves that state estimation by the state observer can be designed to be faster than state convergence by the regulator. Consequently, the state observer gain matrix \(\mathbf{H}\) is obtained as follows:  
\[\mathbf{H}=10^{3}\times\big{[}\ 0.0340\ \ \ 0.4310\ \ \ \ 2.4130\ \ \ 4.9957\ \ \big{]}. \tag{66}\]  
In practice, in the simulation, the initial conditions for the states and those for the estimated values are assumed to be as follows:  
\[\mathbf{x}(0)=\big{[}\ 0\ \ \ 0\ \ \ \ 1\ \ \ 0\ \ \big{]}^{T}, \tag{67}\]  
\[\hat{\mathbf{x}}(0)=\big{[}\ 0\ \ \ 0\ \ \ 1.2\ \ \ 0\ \ \big{]}^{T}. \tag{68}\]  
In addition, the parameter in the numerical calculation method is set as \(\Delta t=0.0005\). Using the above-mentioned values, whether the fractional-order state observer can estimate all of the states is first investigated under the free vibration condition. Figure 5 shows each estimation error, and Figure 6 compares each actual state and each estimated state. As shown in Figure 5, each estimation error converges to zero, although the estimation error about \(x_{4}\) converges more slowly than other errors. Figure 6 demonstrates that each state can be estimated successfully by the fractional-order state observer. Figure 5: Estimation errors for states (\(e_{1}\): blue line, \(e_{2}\): green line, \(e_{3}\): red line, and \(e_{4}\): light blue line). Next, fractional-order LQR control is conducted. In comparing the states with control and those without control, the gaps between the actual states and the estimated values are shown in Figure 7, and the state evolution is illustrated in Figure 8. The results with control are obtained with output feedback control using a fractional-order LQR. Figure 7 confirms that the estimation of the states by the fractional-order state observer is performed successively. Judging from the results in Figure 8, compared with the no-control case, good damping effects are confirmed in the case that fractional-order LQR control is applied. Consequently, using the feedback gains obtained with the iteration-based method and the fractional-order state observer, it is proven that fractional-order LQR control can be carried out for a controlled object that includes fractional-order derivative states in its state equation. Figure 6: Results for actual (blue lines) and estimated (red broken lines) states. \[\lambda_{so}=-10.0,\ -9.0,\ -8.0,\ -7.0. \tag{63}\]0 _Cont._  
\[\lambda_{so}=-10.0,\ -9.0,\ -8.0,\ -7.0. \tag{63}\]1 Difference between actual (blue lines) and estimated (red broken lines) states.
## 3 Introduction of Basic Principles
### Dilated Convolution Model  
To further extract the temporal-spatial correlation in sequence data, this paper uses the Conv1d convolution method to extract and fuse features of data with different dimensions.  
In convolution, the Kernel is a weight array that weights the input data and is the core of computation. As the Kernel size increases, the model can capture larger features, but it increases the number of parameters and computational complexity, and may lead to overfitting. When the Kernel size is reduced, the model can capture more detailed features, but it may lose some important information. Therefore, selecting an appropriate Kernel size is crucial for the performance of the model.  
In this paper, the dilated convolution method is used to extract features, and the specific structure is shown in Figure 4(b). Differently from the traditional convolution method, the dilated convolution introduces hyperparameters of the differentiation rate.  
Figure 5: Chart of the basic unit model structure.  
It increases the distance between the Kernel's data processing, so as to achieve a higher Receptive field.  
When Kernel size = 3 and dilated = 4, the same convolution Kernel can capture data information with a distance of \(3*4=12\). That is to say, in the case of the same feature map, dilated convolution can obtain a larger receptive field and obtain more dense data on the premise of retaining data characteristics.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Performance Evaluation
#### 4.2.5 Packet Delivery Ratio (Pdr)  
PDR is the ratio of the total number of packets successfully delivered in the destination nodes (RSUs) compared to the gross packet transmitted from the source node during the communication on the network. In this sense, the higher number of vehicle packet data reaching the RSU, the better performance of the system. In our scenario, PDR represents the blockchain packet broadcasted by vehicle to the RSUs. PDR was computed by calculating the ratio of the total number of received packets (\(P_{receive}\)) to the total number of sent packets (\(P_{sent}\)) as shown in the following equation.  
\[PDR=\frac{\sum P_{receive}}{\sum P_{sent}}*100 \tag{4}\]  
Figure 9 shows the PDR ratio of our proposed protocol over 10 RSUs on different distances. In the beginning, the PDR value is low due to an increasing number of hops between vehicles and RSUs, while the protocol needs to complete a route-finding process before packets can be successfully transferred. The stable PDR results are obtained between RSU-1 and RSU-5, but a consecutive slump in the packet delivery between RSU-6 and RSU-10. As a result, we can see that RSU-1 has the highest PDR due to its nearest distance in this scenario. Besides, RSU-10 as the farthest distance has the lowest PDR caused by the fading channel's effect.
## 3 Method
### Multi-Task YOLOv5 with Transformer
#### 3.1.1 Shadow Region Detection  
As an input shadow image given, firstly we utilize a transYL to produce feature maps (denoted as \(EF_{1}\), \(EF_{2}\), \(EF_{3}\), \(EF_{4}\), and \(EF_{5}\)) at different scales [41].  
These layers contain complementary information for shadow detection. Shallow layers capture both shadow and non-shadow detail feature information, while deep layers focus on shadows but also overlook some regions. Hence, we adopt short connections [42] to integrate the last 4 feature maps, producing new feature maps (denoted as \(DF_{2}\), \(DF_{3}\), \(DF_{4}\), and \(DF_{5}\)). Specifically, the merged feature map \(DF_{k}\) at layer \(k\) (\(k=2,\ldots,5\)) is obtained by:  
\[DF_{k}=Conv(Concat(EF_{k},\ldots,EF_{5})).\]  
The short connections integrate low-level spatial details with high-level semantics. This allows the network to leverage features across scales rather than just the deepest layer. By fusing coarse shadow regions and fine boundary cues, the model can comprehensively represent shadows. The multi-scale refinement compensates for lost spatial or contextual information to improve detection robustness.  
The shallowest features (\(EF_{1}\)) are merged with the deepest features (\(EF_{5}\)) to produce a new feature map(\(EF_{1}\)) for predicting the shadow edge map. To integrate the shadow edge with region information, we refine the \([EF_{k},k=2,\ldots,5]\) maps by:  
1. Up-sampling \(EF_{k}\) to match the spatial resolution \(DF_{1}\)
2. Element-wise addition of \(DF_{1}\).  
This produces refined feature maps \(\{RF_{k},k=2,\ldots,5\}\) defined as:  
\[RF_{k}=up(DF_{k})+DF_{1}\]  
The up-sampling and addition injects low-level spatial details into the semantic features. This allows precise localization guided by the high-level shadow cues. By combining complementary edge and region information, the model can holistically represent and segment shadows. The multi-scale integration also enables shadows to be detected across sizes and resolutions.  
Finally, from the multi-scale features \(DF_{2}\), \(DF_{3}\), \(DF_{4}\) and \(DF_{5}\), we predict four shadow region maps. We also generate an integrated map (denoted as \(S_{\beta}\)) element-wise by adding the refined feature maps:  
\[S_{f}=Pred(\sum_{k=2}^{5}RF_{k})\]  
The prediction \(Pred(\cdot)\) is implemented by applying three \(3\times 3\) convolutional layers and a \(1\times 1\)convolutional layer with another layer of sigmoid activation [43] to the features.  
In summary, shadow regions are detected at multiple scales from the merged encoder features to capture shadows across sizes. The refined maps integrate complementary boundary information to localize shadows precisely. Fusing predictions provides rich, context-aware representations of scene illumination. This comprehensive multi-scale, multi-task approach allows robust detection of subtle to large shadows in complex images.