esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
### Conclusions  
The problem of class imbalance in graph nodes is widely prevalent in real-world tasks such as fraud detection, fake user detection, malicious software detection, etc. This issue significantly affects the performance of classifiers on these minority classes, but there has been limited research in this area. Therefore, in this work, we investigate the task of imbalanced node classification and propose the ESA-GCN model to address the problem more effectively.  
Specifically, in this paper, we address the problem by employing ENN undersampling to balance the original dataset and reduce the interference of noisy samples on model training. Additionally, we enhance the model's performance and reduce the model parameters and computational complexity by incorporating attention mechanisms to adjust the importance of each node and regenerate edges. Through experiments conducted on three public datasets, we observe significant improvements in terms of AUC-ROC, F1-Macro metrics, and runtime speed compared to the traditional GraphSmote algorithm.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
###### Abstract  
Monitoring driving behaviour is important in controlling driving risk, fuel consumption, and CO\({}_{2}\) emissions. Recent advances in machine learning, which include several variants of convolutional neural networks (CNNs), and recurrent neural networks (RNNs), such as long short-term memory (LSTM) and gated recurrent unit (GRU) networks, could be valuable for the development of objective and efficient computational tools in this direction. The main idea in this work is to complement data-driven classification of driving behaviour with rules derived from domain knowledge. In this light, we present a hybrid representation approach, which employs NN-based time-series encoding and rule-guided event detection. Histograms derived from the output of these two components are concatenated, normalized, and used to train a standard support vector machine (SVM). For the NN-based component, CNN-based, LSTM-based, and GRU-based variants are investigated. The CNN-based variant uses image-like representations of sensor measurements, whereas the RNN-based variants (LSTM and GRU) directly process sensor measurements in the form of time-series. Experimental evaluation on three datasets leads to the conclusion that the proposed approach outperforms a state-of-the-art camera-based approaches in distinguishing between normal and aggressive driving behaviour without using data derived from a camera. Moreover, it is demonstrated that both NN-guided time-series encoding and rule-guided event detection contribute to overall classification accuracy.  
driving behaviour; convolutional neural networks; recurrent neural networks; telematics sensors +
Footnote †: journal: Applied Sciences  
1
# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 3 Test Verification  
### Data Preprocessing  
The experiment was performed on a QPZZ-II test rig, as depicted in Figure 4. The acoustic microphone and vibration sensor arrangement is shown in Figure 5. The bearings were mounted on the spindle. Two vibration sensors were placed vertically on the bearing housing to collect vibration data. Nine microphones were utilized for the measurement, with Microphone Number 0 positioned at the center to capture mono signals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Output Size** & **50-Layer** \\ \hline conv1 & \(112\times 112\) & \(7\times 7\), 64, stride2 \\ \hline \multirow{4}{*}{conv2\_x} & \multirow{4}{*}{\(56\times 56\)} & \(3\times 3\) max pool, stride2 \\ \cline{3-3}  & & \(\left[\begin{array}{c}1\times 1,64\\ 3\times 3,64\\ 1\times 1,256\\ fc_{c},[16,256]\end{array}\right]\times 3\) \\ \hline conv3\_x & \(28\times 28\) & \(\left[\begin{array}{c}1\times 1,128\\ 3\times 3,128\\ fc_{c},[32,512]\end{array}\right]\times 4\) \\ \hline conv4\_x & \(14\times 14\) & \(\left[\begin{array}{c}1\times 1,256\\ 3\times 3,256\\ 1\times 1,1024\\ fc_{c},[64,1024]\end{array}\right]\times 6\) \\ \hline conv5\_x & \(7\times 7\) & \(\left[\begin{array}{c}1\times 1,512\\ 3\times 3,512\\ 1\times 1,1048\\ fc_{c},[128,2048]\end{array}\right]\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: SE-ResNet50 network architecture.  
Figure 4: Test rig.  
Four distinct test conditions were examined: outer race fault, inner race fault, mixed inner and outer ring failure, and normal state. The physical diagrams depicting the inner ring failure and outer ring failure are comprehensively outlined in Figure 6. The model number of the bearing was HRB N205 EM. Specific parameters are detailed in Table 2.
## 3 Results
### MGWR Regression Results
#### 3.3.2 MGWR Visualization Results  
After obtaining the MGWR regression results, we filtered the available data and represented them in spatial coordinates. The magnitude of the regression coefficients is illustrated using hue variations, as shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **MGWR** \\ \hline Residual sum of squares & 33,239 \\ AIC & 161.880 \\ AICc & 165.299 \\ R\({}^{2}\) & 0.504 \\ Adjusted R\({}^{2}\) & 0.432 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regression results of the MGWR model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(p\)**-Value** & **Mean** & **STD** & **Min** & **Median** & **Max** \\ \hline X\({}_{1}\) (Slope) & \(<\)0.001 & 0.592 & 0.221 & 0.263 & 0.554 & 0.918 \\ X\({}_{2}\) (Elevation) & 0.077 & 0.318 & 0.032 & 0.271 & 0.312 & 0.384 \\ X\({}_{3}\) (Aspect) & 0.335 & 0.051 & 0.010 & 0.034 & 0.051 & 0.067 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics of the coefficients of the MGWR model.  
The X\({}_{1}\) slope factor exhibits a positive impact on the dependent variable (fractal dimension). However, its influence on the northwestern side of the settlement is not statistically significant. The distribution pattern of regression coefficients follows these principles:  
1. The regression coefficients are relatively lower in the northwestern part of the settlement and higher in the northeastern part.
2. The average value of regression coefficients is higher in steep mountainous regions.  
The X\({}_{2}\) elevation factor has a positive impact on the dependent variable, fractal dimension. However, in certain areas on the northern side of the settlement, its influence is not statistically significant. The distribution pattern of the regression coefficients is as follows:  
Figure 8: MGWR regression coefficient of X\({}_{1}\)-slope.  
Figure 9: MGWR regression coefficient of X2-elevation.  
From the village center to the periphery, the positive impact of elevation on the subspace fractal dimension becomes progressively stronger.  
Both higher and lower elevations exert a stronger positive impact on the subspace fractal dimension of the settlement.
## 2 Materials and Methods
### Chemical Analyses  
#### 2.3.1 German (DE) Laboratory  
DE sample analysis was conducted in the laboratory of the State Institute of Agricultural Engineering and Bioenergy at the University of Hohenheim. Dry matter (DM) and organic dry matter (ODM) concentrations for all samples were determined in accordance with DIN EN 12880 and DIN EN 12879 [25,26], by the volatile solids. The pH values of FL and DS were measured using a digital pH meter (pH 330, WTW, Weilheim, Germany). The acid concentration equivalent was determined by GC (GC2010 plus, Shimadzu Corp, Kyoto, Japan) with the capillary column WCOT Fused Silica (50 m length, 0.32 mm ID, coating CP-Wax 58 (FFAP)-CB) and an FID detector (280 \({}^{\circ}\)C) (both Varian, Palo Alto, CA, USA). Helium was used as the carrier gas. A detailed description of the method can be found in [PERSON] et al. (2019) [27].  
The ratio of volatile organic acids and total inorganic carbon (VOA/TIC) was obtained by determining acetic acid equivalents of the fermentation slurry through an automatic titration system (785 DMP Titrino-Metrohm, Filderstadt, Germany) after sample centrifugation in a refrigerated centrifuge at 5000 rpm (Z323K, Hermle-Labortechnik, Wehingen, Germany). The total ammonium nitrogen (TAN) was analyzed by using the automatic distillation system Gerhardt Vapodest\({}^{\circledr}\) 50s (Gerhard Analytical System, Konigswinter, Germany). The total nitrogen was detected by the method of [PERSON] (TKN) with Kjeltabs CX, THURBOTHERM and Vapodest\({}^{\circledr}\) 50s (all Gerhard Analytical System, Konigswinter, Germany) [24,28,29].  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Key indicators** & **DE1** & **DE2** & **DE3** & **CN1** & **CN2** & **CN3** \\ \hline Year of construction & 2011 & 2009 & 2013 & 2007 & 2008 & 2008 \\ Main technology & CSTR & CSTR & CSTR & USR & CSTR & USR \\ Mixing tank (m\({}^{3}\)) & & 201 & & 40 & 80 & 20 \\ Digester (m\({}^{3}\)) & & 1500 & 1880 & 847 & 1400 & 1800 & 320 \\ Secondary digester (m\({}^{3}\)) & - & 1880 & - & - & - & - \\ Digestate storage tank (m\({}^{3}\)) & 1500 & 13,539 & 1742 & 1500 & 1000 & 450 1  \\ Biogas compressor (kW) & - & - & - & 22 & 22 & 7.5 \\ CHP (kW) & 250 & 1550 & 75 & - & - & - \\ Substrates & CM, LM, GS & \
\begin{tabular}{l} CM, LM, MS, \\ GS, CL \\ \end{tabular} &
\begin{tabular}{l} CSM, CM, LM, \\ MS, GS \\ \end{tabular} & CSM & CSM, CM & PM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the selected German (DE) and Chinese (CN) biogas plants.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
###### Abstract
However, the system does not consider the speed of the dynamic target and the action space, and a relative position offset may cause volatile behaviors that cannot be precisely tracked by the PID controller. Moreover, additional features (quadrotor altitude, linear velocity, orientation, and angular velocity) must be included as a part of the decision-making layer input to achieve stable flight. In this paper, we propose a novel hierarchical control framework for UAVs' active tracking task. This framework uses the neural-based end-to-end perception and decision-making method for the high-level control module and the traditional control method for the low-level control module. Unlike the above methods that do not consider the UAV dynamics and target speed, all these factors are taken into account in this framework. The main contributions of this paper are summarized as follows:  
1. We develop a novel and interpretable neural architecture for the high-level controller to derive the spatial and temporal latent features of the dynamic target and output continuous tracking speed commands directly. This compact architecture reduces the number of parameters of the high-level controller. 2. End-to-end mapping from raw images to the high-level decision is trained via deep RL in the virtual environment based on V-REP [21]. We also leverage PyRep [22] to accelerate the simulation speed and run parallel environments for faster data collection. The simulation data are inexpensive, and no effort for ground-truth labeling is needed. 3. To further accelerate the training process, we adopt auxiliary segmentation and motion-in-depth losses, which can generate denser training signals. 4. Augmentation techniques are applied in the virtual environment to increase the robustness of the trained controller. In our experiment, the proposed hierarchical control framework and auxiliary losses can effectively decrease the training time. The quadrotor with a trained high-level control module and low-level PID control module can adapt to the dynamic target with random colors, paths, and speeds in the UAVs' active tracking task.
## 1 Introduction
### Extraction of Point Normal Vector  
In this section, a KD-Tree is constructed for the rapid retrieval of the k-nearest neighbors for each point \(P_{i}\) within the three-dimensional point cloud, denoted as the point set \(Q_{i}\). The normal vector of \(P_{i}\) is obtained through the PCA algorithm applied to the fitted plane of \(Q_{i}\)[50]. In the process of conducting Principal Component Analysis on \(Q_{i}\), it is essential to initially calculate the covariance matrix. Subsequently, eigenvalue decomposition is applied to acquire both the eigenvalues and eigenvectors. The eigenvector corresponding to the smallest eigenvalue signifies the direction with the minimum variance and is commonly selected as the normal vector for \(P_{i}\) (Figure 3).  
Figure 4 demonstrates that the rough and uneven nature of the rock mass surface (caused by vegetation, among other reasons) may lead to opposite directions of the computed normal vectors for points belonging to the same discontinuity, potentially impacting the accuracy of point cloud classification. To address this issue, Equation (1) is employed to adjust the normal vector direction for each point:  
\[d=\frac{\overrightarrow{u}\times\overrightarrow{v}}{|\overrightarrow{v}|} \tag{1}\]  
where \(\overrightarrow{v}\) represents the selected direction, and \(d\) is the projection of the normal vector \(\overrightarrow{u}\) onto \(\overrightarrow{v}\). The direction of the normal vector \(\overrightarrow{u}\) remains unchanged when \(d\geq 0\), while the direction of the normal vector \(\overrightarrow{u}\) is reversed when \(d<0\).  
Figure 5 depicts the color mapping of the point normal vector in the RGB color system applied to the point cloud. It can be seen that points on the same discontinuity set have similar colors.  
Figure 4: Varying normal vectors for points on the same discontinuity.  
Figure 3: Schematic of point normal vector computation. \(u_{1}\), \(u_{2}\), and \(u_{3}\) represent the three eigenvectors of the covariance matrix of \(Q_{i}\), respectively. \(u_{3}\) is the eigenvector corresponding to the minimum eigenvalue. The red dots represent nearest neighbors, and the gray dots represent non-nearest neighbors.
## 2 Methodology
### Lightweight Super-Resolution Network Optimization Based on Channel Attention Mechanism
#### 2.4.3 Loss Function  
By using this composite loss function, the network effectively suppresses the artifact of the metal lattice structure and restores high-quality CT images by improving the image's detail representation in the spatial domain while also accounting for the consistency of physical information in the Radon projection domain. The reconstruction error in the spatial domain and the physical consistency error in the Radon projection domain make up the two components of the composite loss function established in this research, which efficiently combines the physical limitations of the Radon transform.  
\[L=\lambda_{1}L_{pixel}+\lambda_{2}L_{Radon} \tag{13}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are weight coefficients, which are used to balance the reconstruction error in the spatial domain and the consistency error in the projection domain. \(L_{pixel}\) is the pixel reconstruction loss, and \(L_{Radon}\) is the physical consistency loss in the Radon domain.  
Pixel reconstruction loss (Pixel Loss) uses mean square error (MSE) to quantify the difference between the high-resolution real picture \(Y\) and the network output image \(\hat{Y}\):  
\[L_{pixel}=\frac{1}{N}\sum_{i=1}^{N}\parallel\hat{Y}_{i}-Y_{i}\parallel_{2}^{2} \tag{14}\]  
In order to guarantee that the network's output image in the Radon transform domain is consistent with the projection of the actual image, as well as to encourage artifact suppression and structure restoration, the physical consistency loss in the Radon domain (Radon Loss) is utilized:  
\[L_{Radon}=\frac{1}{N}\sum_{i=1}^{N}\parallel\mathcal{R}(\hat{Y}_{i})-\mathcal{ R}(Y_{i})\parallel_{2}^{2} \tag{15}\]  
where \(R(\bullet)\) represents the Radon transformation operation and \(N\) is the number of training samples.  
During the training process of the network, while the network calculates the pixel-level loss of the sinogram, it also calculates the loss of CT slices obtained by the inverse Radon transform. This both ensures pixel-level features and takes into account the physical consistency of the Radon domain.
## 3 Results
### Analysis of the Depth of Influence  
#### 3.5.1 Analysis Basis  
Evaluation of geocell-reinforced aeolian sands on the roadbed is dependent on the effect of vehicle vibrations on the roadbed [54]. The vertical vibration velocity attenuation research is a prerequisite for the influence depth study. Analysis of the attenuation of vibrations from the perspective of the principal frequency amplitude alone is relatively discrete. Variation of moving load road vibrations cannot be quantified in this manner. Compare exhaustively the attenuation in different structural layers of energy generated by vehicle load. Therefore, the equivalent amplitude \(A_{d}\) is defined to represent the amplitude in the entire frequency domain and is calculated as follows:  
\[A_{d}=\Sigma(A_{i}\times f_{i}) \tag{2}\]  
where \(A_{i}\) is the individual amplitude in the one-third octave analysis, \(f_{i}\) is the frequency corresponding to \(A_{i}\). The frequency \(f_{d}\) of \(A_{d}\) satisfies:  
\[\Sigma(A_{i}\times f_{i})=\Sigma(A_{i+d}\times(f_{i}+d)),i=1,2\ldots d. \tag{3}\]  
In terms of energy decay, the entire frequency domain can be represented by a simple harmonic function \(f(t)\) with amplitude \(A_{d}\) and frequency \(f_{d}\). \(f(t)\) at this point is a function of vibration velocity against time. Its integration against time can be obtained as a function of vibration displacement about time \(g(t)\) that is:  
\[\int f(t)dt=g(t) \tag{4}\]  
Since there is no initial vibration displacement at 0, the integration constant is also 0. At the same time, \(g(t)\) is also a simple harmonic function, and its amplitude is \(A_{d}\). Therefore, the energy generated by the vibration is:  
\[E=\frac{1}{2}kA_{d}^{2} \tag{5}\]  
where \(k\) is the modulus of elasticity of the material.  
The equivalent amplitude can be used to measure the vibrational energy. The rate of vibration energy dissipation can be determined by comparing the decay rate of equivalent amplitude. In addition, the amplitude decay ratio can quantify the attenuation performance of the structural layer to the vibration velocity along the depth direction, which is calculated as:  
\[\varphi=\frac{A_{s}-A_{x}}{A_{s}h}\times 100\% \tag{6}\]  
where \(A_{s}\) is the vibration amplitude of the upper boundary of the structure in mm/s, \(A_{x}\) is the vibration amplitude of the lower boundary of the structure in mm/s, and \(h\) is the thickness of the structural layer in cm.
# Analysis and Optimization of Output Low-Pass Filter
## 4 Design Trade-Offs  
Based on the analyses, the baseline filter values were determined as \(C_{n}=0.1\) pu, \(f_{c}=500\) Hz, and \(Q=4\). This filter configuration was more advantageous than the other three configurations as it was within the limits of the specified standards [10; 11]. In this section, the response of the current THD, power factor, and damping resistance power loss to the variation in the baseline filter parameters are analyzed in detail to show how effective the filter configuration is. The analysis was conducted utilizing PSIM (9.1) power electronics simulation software. Figure 16 illustrates the simulation circuit. The simulation results were analyzed using MATLAB/Simulink (R2023a) software.  
In this inverter design, the photovoltaic PV array, combined with a DC link inductor, functions as a constant current source. The MOSFET switch serves as a waveform shaper, operating as a PWM current chopper, generating a waveform with a fundamental component resembling a full-wave rectified sine wave. Subsequently, the H-bridge inverter unfolds this full-wave rectified sine wave. Finally, the low-pass CL filter effectively eliminates the high-frequency components introduced by PWM to create a sinusoidal output.
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 1 Introduction
The topology control scheme maintains robust connectivity that provides high-quality alternate routes within the pipe and thus improves route stability. 5. The proposed Pipe and TC-Pipe routing schemes provide superior routing performance (measured by the packet delivery ratio (PDR), the number of route breaks, and the route up time). 6. The TC-Pipe routing scheme further improves the routing performance over the Pipe routing with a minimum impact on the coverage performance (measured by coverage and coverage fairness), even with multiple flows/targets. Paper Organization: We first review several existing routing and topology control schemes that focus on providing robust routes in Section 2. The overview of the pheromone-based UAV mobility model used in our work is discussed in Section 3. Next, we discuss our proposed pipe routing and topology control schemes in Sections 4 and 5, respectively, followed by their control overhead and computational complexity in Section 6. The relay -based routing scheme is discussed in Section 7, which is used as a benchmark scheme. We evaluate the performance of our routing schemes in Section 8; this is followed by the conclusions and future work in Section 9.