esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Processing Method of Vibration Piezoelectric Signal
### Vibration Piezolectric Signal Analysis  
Waveform acquisition was conducted using SEGGER J-Scope software to monitor the output signals of the piezoelectric sensor during seed-impurity separation facilitated by the vibrating tray. The horizontal axis represents time in milliseconds (ms), while the vertical axis indicates voltage amplitude. The waveform sampling interval was set at 5 ms/div, and the target frame rate was maintained at 30 frames per second (FPS) for real-time visualization.  
Figure 7a-c display three waveform traces in red, blue, and green, corresponding to piezoelectric signals captured at different time intervals. These waveforms represent signal variations associated with the classification of seeds and impurities. (1) Waveform Features: The amplitude, frequency, and morphology of the waveforms reflect the physical interactions between the seed material and the tray during vibration. Differences in seed morphology--such as shape, structure, texture, and hardness--result in variations in contact mechanics, feedback force, and acoustic response, which manifest as differences in energy amplitude and waveform structure. (2) Buffer Utilization: The lower display panel shows the real-time status of the data buffer, including total capacity and the number of captured samples. This provides insight into the system's data-handling efficiency. (3) Vibration Intensity and Signal Characteristics: The amplitude of the waveform correlates with the intensity of tray vibration. Higher amplitudes indicate stronger vibration, which facilitates the improved separation of seeds and impurities. The shape of the waveform reflects the physical attributes of the contacting material, allowing differentiation between seed and impurity profiles. (4) Material-Specific Responses: Experimental results indicate that different materials produce distinct waveform patterns. Moreover, the specific vibration frequency significantly influences separation efficiency, highlighting the importance of frequency optimization for precise seed-impurity classification.  
Figure 7: _Cont._
## 4 Simulation Results  
In this section, we verify the effectiveness of the proposed algorithm through simulation experiments. The experimental scene is shown in Figure 3. Four identical sensors, \(\texttt{s}_{1}\texttt{-s}_{4}\), were evenly spaced in a straight line, and each sensor had a maximum detection distance of 150 m. The detection range was centered on itself, directly in front of a fan-shaped area with an angle of 45\({}^{\circ}\), and there were red, green, blue, and yellow contour lines inside. There were several overlapping areas between the detection ranges of the sensors. More than one sensor could detect the targets in the overlapping regions. Each sensor could only detect the targets within its detection range but could not obtain them outside the detection area. There were seven group targets in the scene. Each group target moved with a uniform linear motion, with the measurement rate and the extended profile remaining relatively unchanged. The state transfer equations that characterize the process are as follows:  
\[\mathbf{x}_{k+1} =\left[ \begin{array}{cc}\mathbf{I}_{2}&T_{s}\mathbf{I}_{2}\\ \mathbf{0}_{2}&\mathbf{I}_{2}\end{array} \right]\mathbf{x}_{k}+\mathbf{w}_{k}, \tag{47}\] \[\boldsymbol{\Sigma}_{k+1} =M(\mathbf{x}_{k})\boldsymbol{\Sigma}_{k}M(\mathbf{x}_{k})^{\text {T}},\] (48) \[\gamma_{k+1} =\gamma_{k}, \tag{49}\]  
where \(T_{s}\) is the sampling time, \(\mathbf{I}_{2}\) denotes the identity matrix, and the kinematic state is \(\mathbf{x}_{k}=\left[\mathbf{p}_{k},\mathbf{v}_{k}\right]^{\text{T}}\in \mathbb{R}^{4}\). \(\mathbf{p}_{k}=\left[p_{x},p_{y}^{\text{T}}\right]\in\mathbb{R}^{2}\) is the position vector, and \(\mathbf{v}_{k}=\left[v_{x},v_{y}^{\text{T}}\right]\in\mathbb{R}^{2}\) is the velocity vector. \(\mathbf{w}_{k}\) is the process noise. The extent transformation function is \(M(\mathbf{x}_{k})distribution in this study. The measurement noise covariance matrix \(\mathbf{R}=\sigma_{v}^{2}\mathbf{I}_{2}\), where \(\sigma_{v}\) denotes the noise intensity. The simulations compared the proposed algorithm's fusion tracking performance when using WGA and WAA. When comparing the performance of different fusion algorithms, we considered the influence of the target measurement rate, target motion state, and extended target shape. Therefore, the following modified generalized optimal subpattern assignment (MGOSPA) distance was selected as a new evaluation metric:  
\[d_{p}^{(c,2)}(\mathbf{X},\mathbf{\hat{X}})= \left[\min_{\theta\in\Theta(|\mathbf{X}|,\mathbf{\hat{X}})}\sum_{( ij)\in\theta}d_{\text{MD}}^{(c)}(\xi^{i},\xi^{j})^{p}+\frac{c^{p}}{2}\left(| \mathbf{X}|-|\theta|+|\mathbf{\hat{X}}|-|\theta|\right)\right]^{\frac{1}{p}}, \tag{55}\]  
where \(c\) denotes the cutoff at base distance, and \(p\) denotes the severity of penalizing the outliers in the localization component. We set \(c=110\), \(p=1\). \(\Theta^{(|\mathbf{X}|,|\mathbf{\hat{X}}|)}\) denotes the set of all possible 2D assignment sets. \(\mathbf{X}\) and \(\mathbf{\hat{X}}\) are the true target set and tracking target set, respectively. \(d_{\text{MD}}^{c}(\xi^{i},\xi^{j})=\min(c;d_{\text{md}}(\xi^{i},\xi^{j}))\).
###### Abstract  
Object: Optimization of common iliac artery sonography images using an indigenous water phantom and [PERSON]'s analysis was successfully performed to improve the diagnostic accuracy in routine cardiac examination. **Methods:** A water phantom with two major compartments was developed, which satisfied [PERSON]'s unique criterion of optimization analysis. Two or three levels were assigned to five factors, namely, (A) the probe angle, (B) water depth, (C) sonography preset frame rate, (D) amplitude gain, and (E) imaging compression ratio. The resulting [PERSON]'s L\({}_{\text{1S}}\) orthogonal array contained 18 combinations of 5 factors, ensuring the same confidence level as a realm of 162 (\(2^{1}\times 3^{4}\)) combinations. The signal-to-noise ratio (S/N) was defined as the minimal difference between the practical survey and predicted areas of 50 mm\({}^{2}\) for the sonography imaging scans. The artifact was customized by creating stenosis with a diameter of 8 mm inside a silicon pipe with a diameter of 19 mm. **Results:** The derived optimal parameters included (A) a zero probe angle, (B) water depth of 6 cm, (C) frame rate of 45 Hz, (D) amplitude gain of 50%, and (E) compress ratio of 50% from 3 independent measurements in each group. Further ANOVA confirmed that the frame rate was a dominant factor, with \(s\) (sum of squared variances) of 56.6%, whereas the error and other terms were suppressed to 20.3% and 11.9%, respectively. The risks of the inappropriate setting of S/N were also discussed to avoid any misinterpretations. **Conclusions:** The quantified water phantom combined with [PERSON]'s approach proved to be instrumental in optimizing the sonography image scan quality in routine cardiac examination.  
s +
Footnote †: journal: Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Takun, Taichung 406, Taiwan 1
## 3 Results and Discussion
### Auxiliary Components Dynamics  
The prototype global performance can be also analyzed in terms of the effectiveness of auxiliary components, such as air-to-water and air-to-air heat exchangers. The dynamic effectiveness of these heat exchangers was calculated from the measured data using the equation (4). The plots of the dynamic effectiveness \(\epsilon_{w}(t)\) and \(\epsilon_{a}(t)\) are shown in Figure 11. As follows from Figure 11, there was no much difference in the dynamic effectiveness between the both tests. The effectiveness of the air-to-air heat exchanger \(\epsilon_{a}\) during the heat recovery operation reached 0.95 (see Figure 11, the curves with \"\(\Circ\)\" and \"\(\Box\)\" markers). This value fully corresponds to the nominal heat exchanger effectiveness at 150 m\({}^{3}\)/h of the air flow rate.  
The effectiveness of the air-to-water heat exchanger \(\epsilon_{w}\) is directly correlated with the water storage stratification state. It is clear, that the greater is the temperature difference between the inlet and outlet of this heat exchanger (see Figure 9), the higher is the effectiveness \(\epsilon_{w}\) (see Figure 11, the curves with \"\(\triangle\)\" and \"\(\Diamond\)\" markers). The maximum air-to-water heat exchanger effectiveness was 0.96 for the fixed bed (test 1), while for the moving bed (test 2) it was 0.9, before the temperature in the bottom part of the water tank was going to increase.  
The positive effect of the heat recovery on the effectiveness \(\epsilon_{w}\) can be shortly observed in Figure 11 (\"\(\triangle\)\" and \"\(\Diamond\)\" curves) as a small plateau between 28 min and 30 min. For the fixed packed bed, the value of \(\epsilon_{w}(t)\) continued to diminish gradually from 0.88 to 0.48 that occurred within the same time as the sorption phenomenon was extinct and the overall temperature of the water tank tended to uniformization. On the contrary, the effectiveness \(\epsilon_{w}\) for the moving bed test was kept almost constant at the level of 0.78 during 24 min (see Figure 11), that clearly demonstrates the strong side of the moving bed reactor.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology
### Grid-Based Map Segmentation  
When constructing a tensor, many researchers are using one tensor to model the spatiotemporal dataset. In this paper, the goal was to extract many different traffic flow patterns to capture more diverse patterns that are different for many parts of the city. The grid-based map segmentation approach is used to divide the city into many smaller cells. Then, for every cell, all transitions were extracted. The cell's size was fixed to 500 \(\times\) 500 m. According to [37], this cell size is sufficient to capture the most important traffic patterns. Transitions were further filtered by discarding every road segment with a speed limit smaller than 50 kmph. This filter was used to avoid any possible false anomaly detection regarding the observed road segments' low speed or parking lots. There are many different approaches for map segmentation. This approach is used because this paper aims to find and analyze the anomalies on a city-wide scale and give an overview of the city's most dangerous road segments and possible problems with inadequate traffic signalization.
## Abbreviations  
The following abbreviations are used in this manuscript: \begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**0-1500 ms**} & \multicolumn{2}{c}{**250-500 ms**} & \multicolumn{2}{c}{**500-1000 ms**} & \multirow{2}{*}{**N**} \\ \cline{3-3} \cline{5-8}  & & \(\mathbf{M}\) & \(\mathbf{SD}\) & \(\mathbf{M}\) & \(\mathbf{SD}\) & \(\mathbf{M}\) & \(\mathbf{SD}\) & \\ \hline \multirow{3}{*}{Dependency} & BERT & 0.00213 & 0.00102 & 0.00256 & 0.00098 & 0.00194 & 0.00083 & 14 \\  & GPT-2 & 0.00301 & 0.00121 & 0.00231 & 0.00126 & 0.00304 & 0.00133 & 14 \\  & GloVE & 0.00270 & 0.00163 & 0.00226 & 0.00134 & 0.00299 & 0.00147 & 14 \\ \hline \multirow{3}{*}{Entity} & BERT & 0.00266 & 0.00124 & 0.00201 & 0.00141 & 0.00295 & 0.00078 & 14 \\  & GPT-2 & 0.00237 & 0.00128 & 0.00244 & 0.00178 & 0.00228 & 0.00095 & 14 \\  & GloVE & 0.00228 & 0.00124 & 0.00184 & 0.00142 & 0.00269 & 0.00095 & 14 \\ \hline \multirow{3}{*}{Pos} & BERT & 0.00253 & 0.00091 & 0.00223 & 0.00108 & 0.00262 & 0.00113 & 14 \\  & GPT-2 & 0.00262 & 0.00109 & 0.00246 & 0.00160 & 0.00275 & 0.00114 & 14 \\  & GloVE & 0.00271 & 0.00117 & 0.00283 & 0.00218 & 0.00210 & 0.00125 & 14 \\ \hline \multirow{3}{*}{TF-IDF} & BERT & 0.00271 & 0.00137 & 0.00250 & 0.00125 & 0.00309 & 0.00119 & 14 \\  & GPT-2 & 0.00270 & 0.00095 & 0.00263 & 0.00126 & 0.00256 & 0.00073 & 14 \\ \cline{1-1}  & GloVE & 0.00302 & 0.00099 & 0.00252 & 0.00189 & 0.00288 & 0.00121 & 14 \\ \hline \hline \end{tabular} \begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**0-1500 ms**} & \multicolumn{2}{c}{**250-500 ms**} & \multicolumn{2}{c}{**500-1000 ms**} & \multirow{2}{*}{**N**} \\ \cline{3-3} \cline{5-8}  & & \(\mathbf{M}\) & \(\mathbf{SD}\) & \(\mathbf{M}\) & \(\mathbf{SD}\) & \(\mathbf{M}\) & \(\mathbf{SD}\) & \\ \hline \multirow{3}{*}{Dependency} & BERT & 0.00048 & 0.00311 & 0.00276 & 0.00142 & 0.00208 & 0.00243 & 14 \\  & GPT-2 & 0.00182 & 0.00279 & 0.00339 & 0.00253 & 0.00258 & 0.00197 & 14 \\  & GloVE & 0.00093 & 0.00334 & 0.00361 & 0.00211 & 0.00247 & 0.00261 & 14 \\ \hline \multirow{3}{*}{Entity} & BERT & 0.00139 & 0.00189 & 0.00214 & 0.00173 & 0.00174 & 0.00260 & 14 \\  & GPT-2 & 0.00224 & 0.00250 & 0.00206 & 0.00144 & 0.00165 & 0.00254 & 14 \\  & GloVE & 0.00435 & 0.00252 & 0.00297 & 0.00116 & 0.00401 & 0.00439 & 14 \\ \hline \multirow{3}{*}{Pos} & BERT & 0.00157 & 0.00322 & 0.00163 & 0.00135 & 0.00154 & 0.00188 & 14 \\  & GPT-2 & 0.00336 & 0.00255 & 0.00276 & 0.00116 & 0.00271 & 0.00324 & 14 \\ \cline{1-1}  & GloVE & 0.00058 & 0.00307 & 0.00317 & 0.00134 & 0.00150 & 0.00416 & 14 \\ \hline \multirow{3}{*}{TF-IDF} & BERT & 0.00047 & 0.00207 & 0.00234 & 0.00209 & 0.00164 & 0.00269 & 14 \\ \cline{1-1}  & GPT-2 & 0.00148 & 0.00355 & 0.00233 & 0.00184 & 0.00159 & 0.00280 & 14 \\ \cline{1-1}  & GloVE & 0.00259 & 0.00386 & 0.00235 & 0.00130 & 0.00278 & 0.00346 & 14 \\ \hline \hline \end{tabular}
## 4 Discussion  
According to the different method types presented in Section 2.5, as can be seen from Figure 5, the root-mean-square error (RMSE) of the sorting results obtained using the method proposed in this paper is the smallest, all of which are less than 1. In comparison with the other three recommendation methods, the RMSE corresponding to the recommendation results of the electricity sale package recommendation method based on the multi-attribute decision-making approach is the largest, indicating a poorer recommendation effect. Furthermore, if the differences in attributes of the electricity sale packages are not considered, Figure 5 demonstrates that the mean RMSE for various customers is 1.214, 1.471, 1.318, and 1.521, respectively. This suggests that the recommendation effect is significantly inferior to that of the method proposed in this paper, which takes into account attribute differences and incomplete weight information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Electricity Sale Packages**} & **The Recommended Ranking in This Paper** & **Simulated Actual Ranking** \\ \hline C1 & 2 & 3 \\ C2 & 4 & 4 \\ C3 & 1 & 1 \\ C4 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recommended ranking results and simulated actual ranking results.  
The methodology employed in this paper for recommending electricity sale packages integrates BLAP clustering, Prospect Theory, the SIR method, and the Choquet integral, thereby demonstrating enhanced comprehensiveness. On the one hand, it appropriately accounts for the subjective factor of customers' risk psychological preferences. On the other hand, it also addresses issues related to attribute correlation and the determination of weights. The section devoted to case analysis further validates the applicability and effectiveness of the proposed methodology.
## 3 Results
### Comparison with Different Multivariate Models  
In this study, we take the PM2.5 concentration at its own site and the PM2.5 concentration at 11 other sites as the input variables of the multivariate model. The prediction effects of the univariate TF-BiLSTM-attention and multivariate TF-BiLSTM-attention models are shown in Figure 8. Figure 8 illustrates that the multivariate model provides significantly better prediction performance compared to the univariate model. This suggests that incorporating the PM2.5 concentration data from the remaining 11 sites as input variables enhances the predictive capability of the model.  
For this study, we select four basic models, LSTM, BiLSTM, Gate Recurrent Unit (GRU), and Bidirectional Gate Recurrent Unit (BiGRU), and four hybrid models, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, to compare with our proposed models. Also, two models, TF- BiLSTM and TF-CNN-BiLSTM, are constructed to compare with our proposed models. The prediction performances of different multivariate models are shown in Tables 3 and 4. Tables 3 and 4 provide a clear depiction, illustrating that the mean value of RMSE, the mean value of MAE, and the mean value of MAPE are the smallest, and the mean value of R\({}^{2}\) is the largest for the 12 sites of our proposed model. This indicates that the TF-BiLSTM-attention model has the best prediction performance among these multivariate models.  
Figure 8: Performance comparison of univariate TF-BiLSTM-attention model and multivariate TF-BiLSTM-attention model.  
Figure 6: Comparison of mean values of RMSE and MAE for 12 sites with different univariate models.  
Figure 7: Comparison of mean values of MAPE and R\({}^{2}\) for 12 sites with different univariate models.  
From a single site, the values of the evaluation metrics of the multivariate LSTM, BiLSTM, GRU, and BiGRU models do not differ much from each other, which suggests that under the same conditions, the prediction effect of these four models is about the same. Meanwhile, the difference in the values of the evaluation metrics between the hybrid model with the CNN network added and the base model is also not significant, which indicates that adding a layer of 1D-CNN to a single base model in this study does not significantly improve the prediction effect of the model.  
From the overall effect of the 12 sites, when the three multivariate models, TF-BiLSTM, TF-CNN-BiLSTM, and TF-BiLSTM-attention, are compared with each other, the TF-BiLSTM-attention model has the best prediction effect, the TF- BiLSTM model is the second best, and the TF-CNN-BiLSTM model is the worst. This indicates that after adding the frequency domain information in the model, the feature information cannot be better extracted using the CNN network. Instead, adding the attention mechanism in the model can play a role in improving the prediction effect of the model. Meanwhile, compared with the univariate model, the multivariate model with the addition of the attention mechanism has a higher degree of improvement in the prediction effect than that of the univariate model.
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 3 Results and Discussion
### Emission Levels of the GTE  
A gas analyzer determines the percentage composition of exhaust gas emissions (CO, NOx, CO2, etc. ), differential flue temperature, and ambient carbon monoxide compositions. The influence of the load variation impacts the MGT engine fuel combustion rate under operation with an integrated hybrid plasma-rich fuel mixture. The exhaust released for all loading conditions is displayed in Table 8.  
The concentrations across all measured GHG emissions from the fabricated GTE under normal and integrated plasma technology demonstrated the lowest value with the biodiesel fuels. This is due to low values of unburnt hydrocarbon and nitrogen per million liters of applied fuel in the combustion chamber of the GTE. The significant emissions of the MGT are oxygen (O\({}_{2}\)), carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), nitrogen monoxide (NO), and nitrogen dioxide (NO\({}_{2}\)). The released O\({}_{2}\) emissions under plasma-assisted combustion technology do not depend on the fuel type and engine loading. CO is mainly formed through incomplete combustion of fuel within the engine combustion chamber due to insufficient available oxygen. There is variation in CO emissions from different fuels under varying MGT engine loading conditions, with B75 recording 0.07% and 2.8% content values for CO and CO\({}_{2}\), respectively, as displayed in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fuel Types**} & \multicolumn{6}{c}{**Loading Operating Condition (kgf)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Normal Operating Scenario**} & \multicolumn{3}{c}{**External Integrated Plasma-Rich Fuel**} \\  & \multicolumn{2}{c}{**Mixture Scenario**} \\ \cline{2-7}  & **Low** & **Medium** & **High** & **Low** & **Medium** & **High** \\ \hline Diesel & 1.7 & 2.2 & 4.1 & 1.8 & 2.45 & 4.3 \\ \hline Kerosene & 1.81 & 2.3 & 4.1 & 1.9 & 2.5 & 4.4 \\ \hline B20 & 1.7 & 2.1 & 4.2 & 1.82 & 2.3 & 4.3 \\ \hline B50 & 1.65 & 2.1 & 4.2 & 1.38 & 2.3 & 4.3 \\ \hline B75 & 1.65 & 2.1 & 4.1 & 1.74 & 2.3 & 4.35 \\ \hline
100 & 1.65 & 2.2 & 4 & 1.8 & 2.4 & 4.2 \\ \hline \hline \end{tabular} In the Table 7, green highlights indicate the loading comparison result under regular and integrated plasma-rich fuel mixtures.  
\end{table} 
Table 7: Thrust value per loading function. The oxides of nitrogen in the exhaust emissions contain nitrogen monoxide (NO) and nitrogen dioxide (NO\({}_{2}\)), which are influenced by the fuel's cylinder pressure, temperature, and oxygen content. The adoption of plasma technology in the fabricated GTE reduced the  
Figure 10: CO and CO\({}_{2}\) emission contents comparison plot. \begin{table}
## 3 Stability Analysis Based on Lyapunov Exponential Method
### Parameterization of Dynamical Models  
In the simulation experiment, the dynamic equation of the tethered UAV is first wrlin the MATLAB environment. After obtaining the equation, the flight conditions under different flight stages are obtained through the flight principle of the tethered UAV, and the corresponding Lyapunov exponential spectrum is simulated in MATLAB R2016b. The analysis shows that to establish a dynamic model of the quadrotor, it is necessary to obtain the following parameters: \(m\), \(g\), \(L\), \(\mathrm{C_{T}}\), \(\mathrm{C_{Q}}\), \(R\), \(K\), \(I_{x}\), \(y_{y}\), \(I_{z}\). A tethered UAV test prototype platform is built to obtain the above parameters, which mainly included three parts: the UAV platform, tethered cable, and ground power supply box, as shown in Figure 7.  
Figure 6: Flow of Lyapunov exponent calculation.  
The parameters of the tethered UAV platform can be determined by either a direct or indirect measurement method. Among them, the weight of the UAV platform \(m\) = 1.365 kg, the arm length \(L\) = 0.225 m, and the blade radius \(R\) = 0.127 m that can be directly measured. The local gravitational acceleration \(g\) = 0.98 m/s\({}^{2}\) can be obtained by looking up the local reference book. Air density \(\rho_{a}\) = 1.293 kg/m\({}^{3}\). The moment of inertia \(I_{x}\) and \(I_{y}\) can be measured indirectly by the two-wire suspension method, and the moment of inertia \(I_{z}\) can be measured by the four-wire suspension method. The lift coefficient \(C_{T}\) and the reaction torque coefficient of the motor tensile experiment can be used to measure \(C_{Q}\). Finally, the tethered UAV's structural parameters are shown in Table 1.