esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
## 3 Results and Discussion
### THz Spectral Properties of Coal-Rock and Coal-Rock Mixtures
#### 3.1.3 Application of THz-TDS in Coal-Rock Interface Identification
The first peak value is generated when the THz wave passes through the first surface of the sample, and the second peak value is generated when it reaches the second surface of the sample after passing through the interior of the sample. The time interval between the two peaks is consistent with the experimental results and the simulation results of coal seams with different thicknesses. In this study, the time-domain spectra of simulation results and experimental results have similar intervals between the two peaks when the thickness of coal seams were set to be same as the experimental coal slice. The proposed method is based on the high resolution and non-destructive properties of THz waves, and therefore, the thickness of the sample can be estimated by comparing the THz waveform obtained by the experiment and the simulation results. In this study, the feasibility of applying THz-TDS technology and CST simulations to estimate the thickness of the samples has been proved by comparing the time-domain spectra of 10 different thickness coal slices and the simulation results of six different thicknesses of coal slices (Table 4). The method still needs to be improved (for example, the thickness of the measured coal flake is small in this study). We hope to estimate any thickness of unknown coal slice based on the simulation and experimental results in the future.
## 3 Results and Discussion
### Editing the YOLOv2 Output Data into a Convenient Form
#### 3.4.1 Conversion of the True-Color RGB Image to a Grayscale Image  
The conversion of the true-color RGB orthomosaic to the grayscale image eliminated the hue and saturation information while retaining the luminance. The RGB values were converted to grayscale values by forming a weighted sum of the R, G, and B components: 0.2989 \(\times\) R + 0.5870 \(\times\) G + 0.1140 \(\times\) B (in compliance with broadcasting service (television) standard BT.601 for use in color video encoding to compute luminance from RGB signal) [34].
# Evaluating the Real-World NOx Emission from a China VI Heavy-Duty Diesel Vehicle
## 4 Conclusions and Outlook  
This study had presented and analyzed the data produced by four valid PEMS tests of a China VI (step B), N3 category heavy-duty diesel vehicle. The conclusions of the present study can be summarized to the following:  
* The highest tailpipe NOx emissions including the cumulative mass and the brake-specific emission were invariably found during urban operation, which was of great concern for urban air quality and human health. This was mainly because the SCR temperature of the catalytic converter was not high enough to ensure the urea solution injection in most of urban operation. Therefore, heating up SCR rapidly may be an effective means to reduce NOx emission in the urban section.
* For N3 category vehicle, the data producted during the urban section of a PEMS test was excluded from emissions analysis by MAW method due to the higher power threshold (20%) required by boundary conditions. Therefore, a lower power threshold should be used or power threshold boundary should be avoided.
* There was a great difference between the real NOx emission of the entire trip and 90 th cumulative percentile of valid MAWs NOx emission(g/kWh)whether the power threshold was set as 10% or 20%.
* The 90 th cumulative percentile of valid MAWs NOx emission just represents the emission characteristics of certain sections (rural and motorway for N3 category vehicles) of a PEMS test rather than the entire trip. So, average vehicle speed of the MAWs was used to categorize the MAWs into \"Urban MAWs\", \"Rural MAWs\" and \"Motorway MAWs\"; average engine coolant temperature of the MAWs was used to categorize the MAWs into \"Hot MAWs\" and \"Cold MAWs\". The evaluation results of the NOx emission of these two kinds of categorized MAWs were close to the real NOx emission, and the errors were all within \(\pm\)10%.  
In this work, we had pointed out the insufficient of the current evaluation method for heavy-duty vehicle real world NOx emission. Future studies should focus on the following aspects:  
* The control and evaluation for NOx emission of cold start (engine coolant temperature less than 70 \({}^{\circ}\)C) or low load operation conditions.
* More individualized boundary conditions or MAW rules for the real-world NOx emission of each category vehicle, such as the definition of power thresholds (PT), valid MAW, weights factors, etc.
* The real amount (g, g/kWh or g/km) of heavy-duty diesel vehicles' real world NOx emission, especially the urban section.  
Conceptualization, P.L. and L.L.; methodology, P.L. and L.L.; formal analysis, P.L.; investigation, P.L. and L.L.; resources, L.L.; data curation, P.L.; writing--original draft preparation, P.L.; writing--review and editing, L.L.; visualization, P.L.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number 51679176.  
Data Availability Statement: Data sharing not applicable.
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## Appendix A Classification Results  
This appendix summarizes the classification performance of each under-sampling algorithm using 1 NN, J48, and SVM. Values in boldface highlight the best result for each database.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline  & **Original** & **RUS** & **CNN** & **NCL** & **TL** & **ENN** & **OSS** & **EUS** & **EE** & **BC** & **RUSBOOST** & **SBC** & **ClusterOSS** & **DBMIST-US** \\ \hline yeast-0-5-6-7.9\_vs\_4 & 61.2 & 73.5 & 50.7 & **83.0** & 69.0 & 78.9 & 72.2 & 78.4 & 73.2 & 73.5 & 76.7 & 77.1 & 58.9 & 75.4 \\ glass-0-1.6\_vs\_2 & 47.0 & 74.4 & 51.0 & 66.9 & 52.6 & 67.9 & 52.9 & 68.3 & 76
## 2 Materials and Methods
### Research Methodology
#### 2.3.2 Spectral-Spatial Residual Network Modeling  
The Spectral-Spatial Residual Network (SSRN) is an end-to-end deep learning network. The Spectral Residual block and Spatial Residual block in the network iteratively acquire discriminative features from the spectral and spatial characteristics of multispectral images. SSRN is a supervised deep learning framework designed to address the decline in accuracy of deep learning models. The residual blocks establish a connection with each of the other 3D convolutional layers by identity mapping, which enables the efficient propagation of gradients during backpropagation. Furthermore, [PERSON] et al. [33] incorporated batch normalization into every convolutional layer to standardize the learning process and enhance the classification performance of the trained model. This research utilizes a framework that enables the continuous extraction of spectral and spatial information for pixel-level multispectral classification, given that multispectrum is comprised of one spectrum and two spatial dimensions. Figure 2 illustrates the components of the SSRN, which include a spectral feature learning section, a spatial feature learning section, an average pooling layer, and a fully connected layer. The SSRN mitigates the phenomena of accuracy degradation by incorporating jump connections between each alternate layer, so creating a continuous block of residuals in the hierarchical feature representation layer.
# Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism
## 2 Semi-Analytical Model  
The analytical tools of celestial mechanics have been introduced  
The \(\sigma\) is the pure-eccentricity resonant argument which does not include inclination [15; 28; 68]. It can be checked that the contact transformation relationship holds. Then, we can write the Hamiltonian for the CRTBP in these new variables as  
\[\mathcal{H}_{MMR}=\mathcal{H}_{0}(\Gamma_{1},\Gamma_{4})+\epsilon\mathcal{H}_{ 1}(\Gamma_{1},\Gamma_{2},\Gamma_{3},\Gamma_{4},\sigma_{1},\sigma_{2},\sigma_{3 },\sigma_{4}), \tag{3}\]  
where \(\mathcal{H}_{0}\) is the Keplerian part, and \(\mathcal{H}_{1}\) is the disturbing term. The parameter \(\epsilon\) means \(\mathcal{H}_{1}\) is a very small quantity relative to \(\mathcal{H}_{0}\). It is a non-integrable system with four degrees of freedom (d.o.f). We can eliminate the d.o.f about \((\Gamma_{4},\sigma_{4})\) by averaging the \(\mathcal{H}_{MMR}\) over the fast angle \(\lambda^{\prime}\). Above all, following the technique developed by [PERSON] [82], the ascending node \(\Omega\) could be set to 0 [83]. The geometric symmetry of the CRTBP can understand this assumption well. Thus, the corresponding action-angle variable \((\Gamma_{3},\sigma_{3})\) can be eliminated from \(\mathcal{H}_{MMR}\). Then, the averaged Hamiltonian only depends on the critical angle \(\sigma\) and the argument of pericenter \(\omega\) and can be expressed as follows:  
\[\overline{\mathcal{H}_{MMR}}=\mathcal{H}_{0}+\epsilon\overline{\mathcal{H}_{ 1}}(\Gamma_{1},\Gamma_{2},\sigma_{1},\sigma_{2})=\mathcal{H}_{0}+\epsilon \overline{\mathcal{H}_{1}}(\Gamma_{1},\Gamma_{2},\sigma,\omega). \tag{4}\]  
Due to the separation of the timescale of \(\sigma\) and \(\omega\), we could transform the Hamiltonian into an integrable system by freezing the value of \(\omega\) (in our numerical integrations, the period of oscillation of \(\omega\) is of an order of 1500 yr (2/1 polar resonance) or 1000 yr (2/3 polar resonance), while the period of libration of \(\sigma\) is about 50 yr) [9; 15; 39; 84; 85]. Then, we can obtain the phase portraits in the \(a-\sigma\) plane at a different value of \(\omega\), and the resonant dynamics can be analyzed.
## 2 Reliability Analysis Method
### ISSA-Kriging-MCS Model
#### 2.2.2 Issa  
Four improvement strategies (Sine chaotic initialization, dynamic adaptive weights, integration of Cauchy mutation, and opposition-based learning) are adopted to optimize the SSA, as detailed in references [26; 27]. The sparrow population is initialized by Sine chaotic mapping to improve the global search ability. The adaptive weight mechanism is added to achieve an effective balance between local exploration and global development capabilities. The optimal solution position is perturbed and adjusted by combining Cauchy mutation and opposition-based learning to enhance resistance to local optimal problems. Then, the ISSA is used to optimize the relevant parameters \(\theta\) of the Kriging surrogate model, automatically searching for the optimal parameter combination, avoiding the tedious and erroneous manual adjustment of parameters, and improving the overall calculation accuracy and efficiency of the model.
## 3 Results and Discussion
### Support Vector Regression  
SVR methods were also studied in terms of predicting the compressive strength of LWAC. For this purpose, we used linear, polynomial, RBF and sigmoid kernels, and different values of \(C\), \(\epsilon\) and \(\gamma\) were explored. The regularisation parameter \(C\) was varied between \(0.1\) and \(100\), and \(\epsilon\) was drawn from the set \(\{0.00001,0.0001,0.001,0.002,0.003,0.004,0.005,\)  
Figure 19: Results for XGBoost models with varying \(\lambda\); learning rate \(=0.05\), number of estimators \(=130\), maximum depth \(=3\), \(p=0.75\), minimum sum of instance weight needed in a child node \(=1\), dropout rate \(=0\), \(\alpha=0\). (**a**) RMSE, training dataset; (**b**) RMSE, test dataset; (**c**) RMSE, all datasets.  
Figure 20: Results for XGBoost models with varying dropout rate; learning rate \(=0.05\), number of estimators \(=130\), maximum depth \(=3\), \(p=0.75\), minimum sum of instance weight needed in a child node \(=1\), \(\alpha=0\), \(\lambda=0.001\). (**a**) RMSE, training dataset; (**b**) RMSE, test dataset; (**c**) RMSE, all datasets.  
\(0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.3,0.4\)). The values for \(\gamma\) were \(\gamma=\frac{1}{d}\) and \(\gamma=\frac{1}{d\operatorname{Var}(X)}\), where \(d\) is the number of input variables and \(\operatorname{Var}(X)\) is the variance of the input dataset. The use of linear, polynomial and sigmoid kernels failed to improve the performance of the RBF kernel, giving models with RMSE values greater than four. We, therefore, focus here on an analysis of the SVR models with RBF kernel. Figure 21 illustrates the behaviour of the SVR models with RBF kernel for various values of \(\epsilon\) for the epsilon tube. The best results were obtained with \(\epsilon\leq 0.05\) for both choices of \(\gamma\). For \(\epsilon>0.1\), the models were underfitted.  
The influence of the parameter \(C\) is illustrated in Figure 22 for good values of \(\epsilon\). Figure 22a,b shows the behaviour of the RMSE for \(\gamma=\frac{1}{d}\), and Figure 22c,d shows the results for \(\gamma=\frac{1}{d\operatorname{Var}(X)}\). Different behaviour was observed depending on the value of \(\gamma\). For \(\gamma=\frac{1}{d}\), the best results were obtained with a value for \(C\) of between two and nine; however, for \(\gamma=\frac{1}{d\operatorname{Var}(X)}\), a lower value of \(C\) was needed to avoid overfitting. In this case, the optimal values of \(C\) were between 0.3 and 0.7, and this model outperformed the one based on \(\gamma=\frac{1}{d}\). Table 12 displays several metrics, with \(C\) in this range, for this value of \(\gamma\).  
The performance of these models was similar to that of the RF models, with a mean RMSE of between 3.8543 and 3.8783 for the test sets and between 3.6591 and 3.7637 for the overall dataset.
## 3 Results and Discussion
### Contour Plots (2D) and Response Surface Plots (3D)
#### 3.4.1 Turbidity Removal  
The interaction between pH and the dosage of PSE significantly influences turbidity removal. As shown in Figure 5a,b, increasing the coagulant dosage while maintaining a lower pH (acidic conditions) results in a reduction in residual turbidity in wastewater, indicating an improvement in turbidity removal efficiency. The improvement in turbidity removal at higher doses of PSE is mainly due to the increased concentration of active compounds [42], which are more effective in acidic conditions (pH = 4). These compounds interact with suspended particles, facilitating floc formation. This effect can be attributed to the positive charges on the protein molecules present in PSE. At pH levels below 4, most proteins carry a positive charge, allowing them to function effectively as cationic coagulant agents [43,44]. This enhances their ability to neutralize negatively charged particles, promoting coagulation and improving wastewater treatment efficiency.  
Figure 5: _Cont._Similarly, Figure 5c,d illustrate the interaction between pH and the chemical coagulant FeCl3. Turbidity removal improves as the FeCl3 dosage increases while maintaining a pH of up to 5. This can be explained by the strong influence of pH on the coagulation activity of chemical coagulants, as pH affects charge neutralization and floc formation. Additionally, Figure 5e,f highlight the combined effect of PSE and FeCl3 on turbidity removal. The data indicate that increasing the dosage of both natural and chemical coagulants leads to a further decrease in residual turbidity. This suggests that both coagulants play a crucial role in destabilizing and attracting suspended solids, thereby enhancing the overall coagulation process.
## 3 Analysis of Simulation Result
### Streamer Development Process
In the first and second stages, when the streamer gradually moves away from the needle electrode, the field intensity gradually decreases because the development of the streamer consumes part of the energy and weakens the ionization degree of the stream head. After the streamer transits from the upper surface to the right surface, the electric field intensity gradually increases. This is due to the fact that when the streamer develops on the right surface of the insulator, the electric potential gradient is very large and the background electric field is very strong, resulting in an intensified collision reaction of electrons and the generation of more electrons. At this time, the streamer is in a state of \"accelerated motion\". Figure 6 shows the variation of photon flux in the streamer head during discharge. During the development of the streamer, the photon flux first decreases and then increases. When the streamer reaches the cathode, the photon flux increases from \(2.18\times 10^{9}\) m\({}^{-3}\)s\({}^{-1}\) to \(3.83\times 10^{9}\) m\({}^{-3}\)s\({}^{-1}\). By comparing Figures 4 and 5, it can be found that the variation law of photon flux is comparable to that of electric field intensity, and similar phenomena have also been observed in [29]. According to the data in Figure 6a,c, the maximum photon flux can be expressed as follows:  
\[I=2.138\times 10^{10}e^{-1899d_{1}} \tag{8}\]  
\[I=5.999\times 10^{8}e^{367.2d_{2}}+0.6474e^{5838d_{2}} \tag{9}\]  
where \(I\) is the photon flux, Equation (8) is the expression of stage 1, \(d_{1}\) is the distance from the streamer head to the anode, Equation (9) is the expression of stage 3, and \(d_{2}\) is the distance from the streamer head to the cathode.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
###### Abstract  
Remote calibration is an advanced methodology that leverages electricity meters, intelligent detection, and computing technologies to enhance calibration efficiency and precision significantly. However, current research predominantly focuses on isolated calibration architectures tailored to single-laboratory environments. In contrast, distributed remote calibration systems that integrate multiple nodes remain in their early developmental stages, despite their considerable potential for improving scalability and operational efficiency. The purpose of this paper is to propose a multi-point collaborative distributed remote calibration model that improves scalability and operational efficiency for remote sensing devices. It addresses the challenge of resource allocation for synchronous calibration across distributed nodes by introducing a hybrid genetic algorithm that optimises scheduling and resource management. Experimental results reveal that the proposed algorithm surpasses other comparable methods in its category, highlighting its capability to improve resource efficiency in distributed remote calibration systems. Additionally, the hybrid genetic algorithm offers profound insights and effective solutions to the intricate challenges of task scheduling in dual-container synchronisation, enhancing both scheduling performance and system dependability.  
calibration systems; distributed systems; resource management; evolutionary algorithms; task scheduling +
Footnote †: journal: Applied sciences
# Wind Source Localization System Based on a Palm-Sized Quadcopter
## 3 Development of a Wind Source Localization Quadcopter System
### Design of the Anemotaxis Algorithm  
We propose an anemotaxis algorithm that localizes to the wind source based on wind direction and speed data obtained from the wind direction sensor. Figure 3a shows the flowchart of the proposed algorithm. The algorithm is accomplished by repeating the following steps: (1) observation of the environmental wind, (2) determination of the direction of movement, and (3) movement. During the environmental wind observation phase, the quadcopter measures the environmental wind while hovering because the wind is generated as the quadcopter moves. Since the sampling rate of the wind sensor is 1 s, the environmental wind is observed by hovering for 10 s. After observing the wind direction information, the mode value is calculated to determine the wind source direction. If the wind speed is not fast, no environmental wind is likely to occur, and therefore, it is not included in the mode value calculation. In this study, we set the wind speed threshold  
Figure 2: Determination of the height of the wind sensor arrangement. (**a**) Schematic diagram of the sensor setup attached to the quadcopter. (**b**) Comparison of measured wind direction values at different mounting heights. (**c**) Investigation of wind direction measurements caused by the wake.  
to 0.27 m/s empirically. If the number of data points not included in the mode calculation increases, the quadcopter selects the random search state because it is close to a windless environment. After the direction of the natural wind is estimated by the mode value calculation, the direction of movement is determined. The four directions of movement are the front, back, left, and right. Therefore, the obtained wind direction information was divided into four parts, as shown in Figure 3b, to determine the direction of quadcopter movement. The quadcopter moves 0.2 m per movement, and wind source localization is achieved by repeating this process.
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 6 Simulation Results
At a lower SNR, the outage probability \(P_{out}=10^{0}\) varies slowly until a sharp decline at \(\gamma=5\) dB. On the other hand, the outage probability is essentially indistinguishable with the growth of \(\gamma_{1}\) when compared with \(\gamma_{2}\) from the SNR \(\gamma=25\) dB. In comparison with Equation (22), Equation (23) provides more accurate approximate results at a higher SNR via the handling function \(K_{1}(x)\). The outage probability numerical results are shown in Figure 7. It shows almost the same curve trend as that shown in Figure 6, except there is a subtle  
Figure 5: Outage probability vs. different outage threshold \(\gamma_{th}\) for \(\gamma=8\) and \(16\), \(A^{2}=1/h_{1}^{2}\), and \(A^{2}=1/(h_{1}^{2}+N_{0})\). Figure 6: A comparison of the outage probability of AF relay-assisted vehicular networks based on Equation (22). difference in the case of \(\gamma_{1}=\gamma_{2}\), \(2\gamma_{2}\), and \(3\gamma_{2}\). At a lower SNR, the outage probability \(P_{out}=10^{0}\) varies slowly until it sharply declines at \(\gamma=3\) dB. Figure 8 shows the outage probability of AF relay-assisted vehicular networks based on the Taylor expansion \(e^{x}\). The result of Equation (24) greatly reduces the complexity of the outage probability \(P_{out}\). In Figure 8, the outage probability \(P_{out}\) declines with the growth of SNR \(\gamma\). Similarly, in Figure 6 and Figure 7, when \(\gamma_{1}=2\gamma_{2}\) and \(\gamma_{3}\), the outage probability results are essentially indistinguishable. It is easier to achieve an in-vehicle device in practice; however, the outage probability deteriorates from \(10^{0}\) to \(10^{0.7}\) at \(\gamma=2\) dB. However, this deterioration is stopped at a high SNR where \(\gamma=35\) dB. In spite of simplifying the gradually outage probability expression from Equation (22) and Equation (23) to Equation (24), there is an excellent match expression among the analytical results. Figure 8: A comparison of the outage probability of AF relay-assisted vehicular networks based on Equation (24). Figure 7: A comparison of the outage probability of AF relay-assisted vehicular networks based on Equation (23). Figure 9 compares the performance of the outage probability versus the outage threshold \(\gamma_{th}\), while the overall SNR \(\gamma=8\). With regard to the achieved trend related to \(\gamma_{1}=\gamma_{2},2\gamma_{2}\), and \(3\gamma_{2}\), it can be observed that the three outage probability type curves are essentially indistinguishable between the three subgraphs. The final expression could describe the performance of system. However, the computational complexity is greatly reduced.