esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 JADEDO Histogram Analysis  
The histogram analysis for the JADEDO optimization algorithm across various test functions reveals distinct distributions of final fitness values, which reflect the performance and consistency of the algorithm, as shown in Figure 9. For F1, the majority of fitness values cluster tightly around 300, indicating high consistency with a few outliers. F2 displays a bimodal distribution with peaks around 400 and 410, suggesting variability in convergence. F3 demonstrates a pronounced skew toward the optimal fitness value of 600, with the majority of runs achieving near-optimal solutions. The distribution for F4 is more spread out, indicating variability in performance, with a concentration around the 830-850 range. F5 and F6 show similar patterns, where the algorithm frequently finds near-optimal solutions but with significant occurrences of less optimal outcomes, particularly in F6, which exhibits a wide spread of final fitness values. F7 and F9 highlight the algorithm's ability to converge to specific fitness ranges, with F7 showing a dominant peak around 2025, while F9 demonstrates a narrower spread around 2529. The histograms for F10 and F12 exhibit a broader range of final fitness values, with multiple peaks suggesting that the algorithm's performance is influenced by the initial conditions or the specific characteristics of these functions. Overall, the histograms indicate that while JADEDO can reliably converge to near-optimal solutions in many cases, its performance may vary depending on the function's landscape.  
Figure 9: Histogram analysis over CEC2022 (F1–F12).
## 2 Methodology
### Earthing Systems with Different Designs  
A good earthing system design greatly improves the efficiency and performance of a transmission line [37; 31]. Generally, the configuration of an earthing system is related to the footing shape. In a region of high soil resistivity, an additional earth electrode should be applied to the tower base. Within this section of the study, three earthing design arrangements, Designs A, B and C, were proposed to evaluate the performance of different earthing design arrangements. The models of the earthing designs were developed using SESCAD and they were implemented by the MALT module to compute the tower footing resistance. Figure 4 illustrates the default design with a 2 \(\times\) 60 m counterpoise, denoted as Design A. In this design, the base of the tower was 15 m \(\times\) 15 m, consisting of one vertically driven electrode at a depth of 9 m. The burial depth of the horizontal electrode was 0.5 m from the surface, and it was made of stranded copper with a radius of 6.35 mm (or 0.00635 m).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Spacing a (m)**} & \multicolumn{3}{c}{**Average Apparent Resistance (\(\Omega\))**} \\ \cline{2-4}  & **T42** & **T48** & **T50** \\ \hline
1 & 398.6 & 236.4 & 150.69 \\
1.5 & 233.12 & 95.92 & 89.15 \\
2 & 166.72 & 57.63 & 61.57 \\
3 & 115.61 & 35.97 & 34.25 \\
4.5 & 71.64 & 20.14 & 25.87 \\
6 & 42.77 & 16.92 & 19.06 \\
9 & 28.74 & 10.72 & 17.82 \\
13.5 & 14.25 & 9 & 8.41 \\
18 & 8.58 & 9.87 & 7.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Averages of apparent resistance (\(\Omega\)) values.  
Design B was a combination of a radial and a ring, which was extended from the default of Design A. The base of the tower was also 15 m \(\times\) 15 m, with 10 m stranded copper cable, extended horizontally from the tower leg, and 13 vertically driven electrodes. Similarly to Design A, the burial depth of the horizontal electrode was 0.5 m from the surface, and it was made of stranded copper with a radius of 6.35 mm (or 0.00635 m), as shown in Figure 5.  
Figure 3: Wenner measurement method.  
Figure 2: Top view: Apparent resistance measurements at the different location areas; (**a**) Tower T42; (**b**) Tower T48; (**c**) Tower T50.  
Lastly, Figure 6 illustrates the Design C, known as the diamond, with 4 \(\times\) 60 m counterpoises installed additionally to the vertical electrodes. For this particular design, there were 28 vertically driven electrodes installed at a depth of 3 m, on top of a 9 m electrode that was initially installed based on the default design.  
The analyses and discussions on the performance of the different earthing design arrangements (Design A, B and C) were considered and simulated using the CDEGS software approach.
# Mechanism of Rejuvenation in Aged SBS-Modified Asphalt by Density Functional Theory
## 4 Results and Discussion
### Dynamic Shear Rheological Test (DSR)  
As a viscoelastic material, the viscosity and elasticity of asphalt changed significantly after aging. Generally, DSR was used to study these changes.  
Temperature scanning tests were performed on asphalt samples with different aging levels. The test temperature was set to 52-76 \({}^{\circ}\)C. And the temperature gradient was set to 6 \({}^{\circ}\)C. The temperature equilibrium time was 9 s. The test strain was 1.25%. And the angular frequency was 10 rad/s. The test results are shown in Figure 7,b.  
Figure 7,b displays the impact of temperature on complex shear modulus (G*) and phase angle (\(\delta\)). The conclusion can be summarized that with the increase of asphalt aging level, the \(G^{*}\) value increased and the \(\delta\) value decreased. When temperature reached 76 \({}^{\circ}\)C, the G* of virgin SBS-modified asphalt was 1.1 kPa, while those of short-term and long-term aged SBS-modified asphalt were 1.62 kPa and 2.89 kPa, respectively. And \(\delta\) of virgin SBS-modified asphalt was 91.5\({}^{\circ}\), while those of short-term and long-term aged SBS-modified asphalt were 87.7\({}^{\circ}\) and 78.7\({}^{\circ}\), respectively. Obviously, the aging severity can affect asphalt properties. This is because SBS-modified asphalt is a viscoelastic material. The light components have higher viscosity, while the heavy components have higher elasticity. After the temperature was raised, some light components volatilized, the other light components transformed to heavy components due to oxidation, which cause the proportion of viscous components to decrease while the elastic components increased. So, rejuvenators were added to mitigate this phenomenon.  
According to Figure 8,b, it can be observed that as the aging level of asphalt becomes more severe, both the rutting factor and fatigue factor increase. This is reflected in the fact that during normal service life, SBS-modified asphalt pavements undergo aging due to environmental and load impact, which ensure high-temperature resistance and rutting performance of the pavement. However, the energy loss under the impact of loads increases, making the pavement susceptible to cracking and damage. After rejuvenator was added, the aged asphalt was provided with sufficient light components. Viscoelastic characteristics of aged asphalt are restored, and mechanical properties are enhanced.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & \(\mathbf{Q_{transfer}}\)**/**e** \\ \hline Aromatic -SBS & 0.1270 \\ Aromatic–SBS molecular aging product 1 & 0.1367 \\ Aromatic–SBS molecular aging product 2 & 0.0878 \\ Saturate—SBS & \(-\)0.1263 \\ Saturate—SBS molecular aging product 1 & \(-\)0.1709 \\ Saturate—SBS molecular aging product 2 & \(-\)0.2066 \\ \hline \hline \end{tabular}
\end{table}
Table 13: \(Q_{transfer}\) between light components in rejuvenator and SBS molecule with its aging products.  
Figure 7: (**a**) Effect of temperature on \(G^{*}\); (**b**) effect of temperature on \(\delta\).
## 4 Simulation Results and Analysis
### Profit vs. number of AI services  
As shown in Figure 5(a), DQN increases by an average of 31.07% profit more than the benchmark, and the average error between the DRL-based algorithm and MILP is 30.41% in a large network. This is reasonable because the benchmark randomly assigns the AI-distributed model training tasks to the edge data center and consumes more computational resources with less profit compared with the DRL-based algorithm.
## 5 Outage Probability Analysis  
In this paper, the outage probability is considered to be a performance metric to evaluate the end-to-end performance of the MRS with the PPS using local CSI and investigate the impact of the SNR correlation coefficient \(\eta_{k,l_{k}}\) on the end-to-end performance. It is noted that the SNR correlation coefficient may also similarly affect other performance metrics such as bit/symbol error rate and ergodic capacity that are dependent upon a minimum SNR of hops for each path, where a minimum SNR for each path means \(q_{k}\) in this paper. Let the outage probability be defined as the probability that the end-to-end data rate falls below the target data rate \(R_{t}\), where the end-to-end data rate for path \(k\) is given by \(\frac{1}{N_{k}}\log_{2}\left(1+q_{k}\right)\) as in [32]. Then, the outage probability of the MRS with the path selection is expressed as  
\[P_{out}=\sum_{k=1}^{K}\Pr\left\{\frac{1}{N_{\zeta}}\log_{2}\left(1+q_{\zeta} \right)<R_{t},\zeta=k\right\}, \tag{14}\]  
where \(\zeta\in\{k^{G},k^{L}\}\). In this section, we derive the outage probability only for the PPS using local CSI (i.e., \(k^{L}\)) since the outage probability for the BPS using global CSI (i.e., \(k^{G}\)) is easily obtained by replacing \(l_{k}\) with \(N_{k}\). From Equation (14), the outage probability for the local CSI-based PPS is derived as  
\[ \begin{array}{rcl}P_{out}&=&\sum_{k=1}^{K}\Pr\left\{q_{k}<z_{k},q_{k,l_{k}}> q_{1,l_{1}},\cdots,q_{k,l_{k}}>q_{k-1,l_{k-1}},q_{k,l_{k}}>q_{k+1,l_{k+1}}, \cdots,q_{k,l_{k}}>q_{K,l_{K}}\right\}\\ &=&\sum_{k=1}^{K}\left[\Pr\left\{q_{k,l_{k}}>q_{1,l_{1}},\cdots,q_{k,l_{k}}>q_ {k-1,l_{k-1}},q_{k,l_{k}}>q_{k+1,l_{k+1}},\cdots,q_{k,l_{k}}>q_{K,l_{K}}\right\} \right.\\ &&\left.\quad-\Pr\left\{q_{k}>z_{k},q_{k,l_{k}}>q_{1,l_{1}},\cdots,q_{k,l_{k}}> q_{k-1,l_{k-1}},q_{k,l_{k}}>q_{k+1,l_{k+1}},\cdots,q_{k,l_{k}}>q_{K,l_{K}}\right\} \right]\\ &=&1-\sum_{k=1}^{K}\left[\prod_{j=l_{k}+1}^{N_{k}}\Pr\left\{\gamma_{k,j}>z_{ k}\right\}\right]\\ &&\quad\times\Pr\left\{q_{k,l_{k}}>z_{k},q_{k,l_{k}}>q_{1,l_{1}},\cdots,q_{k,l_{ k}}>q_{k-1,l_{k-1}},q_{k,l_{k}}>q_{k+1,l_{k+1}},\cdots,q_{k,l_{k}}>q_{K,l_{K}}\right\} \\ &=&1-\sum_{k=1}^{K}\left\{\prod_{j=l_{k}+1}^{N_{k}}\left(1-F_{\gamma_{k}}(z_ {k})\right)\right\}\\ &&\quad\times\int_{z_{k}}^{\infty}\int_{0}^{y_{k}}\cdots\int_{0}^{y_{k}}f_{q_{1,l_{1}}}(y_{1})\cdots f_{q_{K,l_{K}}}(y_{K})dy_{1}\cdots dy_{k-1}dy_{k+1} \cdots dy_{K}dy_{k},\end{array}  \tag{15}\]  
where \(z_{\zeta}=2^{N_{\zeta}R_{t}}-1\). In Equation (15), using Equation (2), we first obtain  
\[\prod_{j=l_{k}+1}^{N_{k}}\left(1-F_{\gamma_{k,j}}(z_{k})\right)=\left\{\prod _{j=l_{k}+1}^{N_{k}}\left(e^{-\frac{z_{k}}{\tilde{\tau}_{k,j}}}\sum_{i=0}^{m_{ k,j}-1}\frac{1}{\Gamma(i+1)}\left(\frac{z_{k}}{\rho_{k,j}}\right)^{i}\right) \right\}. \tag{16}\]  
Then, using Equation (4), we derive the integral term in Equation (15) as follows:  
\[
## 3 Results and Discussion
### Results for Time-Frequency-Domain
#### 3.1.4 Influence of Finite Porosity Layer Thickness  
The influence of the thickness of the finite porosity layer on the dispersion is investigated here. Assuming that the thickness is 0.025, 0.05, 0.10 m, respectively, and the porosity is 0.1, the calculated dispersion curves of the \(\alpha\), L(0,1)\({}_{E}\) and L(0,1)\({}_{P}\) are plotted in Figure 5. When the thickness of the finite porosity layer increases from 0.025 m to 0.10 m, the phase velocity curve of the L(0,1)\({}_{P}\) corresponding to the thickness moves toward the low-frequency domain, indicating that the curves of the L(0,1)\({}_{P}\) vary significantly at low frequencies. The larger the thickness of the porous medium layer, the steeper the drop in the dispersion curve, while all curves approach the same shear wave velocity in porous medium at high frequencies (2871 m/s). However, there is almost no change in the \(\alpha\) and L(0,1)\({}_{E}\) mode curves compared to the surrounding infinite porous medium. This is because an increase in the surrounding medium thickness is accompanied by an increase in the number of guided waves, and the change in complexity is best illustrated in the phase velocity diagram shown in Figure 5.
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control
### FPGA-Based Feedback Controller  
To achieve better performance, we developed a control hardware system using an ADC, a DAC, and an FPGA. Figure 3 presents the block diagram of the closed-loop feedback control system for reducing the fluctuation in the amplitude of the RF signal applied to ion traps using this method. The RF signal is converted by the ADC (Texas Instruments ADS8698 EVM-PDK, Dallas, TX, USA) to digital data. Then, the closed-loop control input is calculated by the FPGA (Digilent ARTY S7, Pullman, WA, USA). The calculated data are then converted by the DAC (Texas Instruments DAC8734, Dallas, TX, USA) to an analog signal. The FPGA-based system allows changing the control method as well as the feedback configuration. In this paper, we added a voltage adder circuit to the output of the DAC and a subtractor circuit to the input of the ADC, which was very effective in increasing the resolution and hence in regulating the secular frequency. This would not be possible in the commercial servo method.
## Materials and Methods
### Data Acquisition and Processing  
Changes in prefrontal cortex haemoglobin responses were monitored by using a Wearable Optical Topography (WOT, Hitachi High-technologies Corporation, Tokyo, Japan) fNIRS system on 6 freely moving healthy adults. The fNIRS system integrates 6 light sources (705 nm and 830 nm) and 6 detectors arranged in an alternating geometry and creating 16 measurements channels (Figure 1b), with an inter-optode separation of 3 cm. The WOT system operates at a sampling frequency of 5 Hz. The headset covers the dorsolateral and rostral prefrontal cortex (Figure 1b). The changes in heart rate and breathing rate were simultaneously recorded using a wearable monitoring belt (Bioharness, Zephyr Technology, Annapolis, MD, USA). Heart rate and breathing rate were sampled at 250 Hz and 25 Hz respectively, and internally averaged by the device to provide output data at 1 Hz. Three cameras were used to record the entire experimental session and to recover the start and end of each condition. All the recordings were synchronised.  
Relative changes in HbO\({}_{2}\) and HHb from an arbitrary zero baseline at the beginning of the measurement period were calculated by the fNIRS system processing unit [32]. Using the modified Beer-Lambert law the concentration values were calculated and expressed in molar concentrations (mmol/L) multiplied by the path length (mm) as they are not corrected for the optical path length. The processing included down-sampling to 1 Hz, and motion artifacts identification and correction was performed through a wavelet-based method [33]. Physiological noise reduction was achieved by using a band-pass filter (3 rd order Butterworth band-pass filter, 0.008-0.2 Hz) to remove slow trends and physiological noise (e.g., respiration). The general pipeline is shown in Figure 2.  
Figure 1: (**a**) Examples of the baseline, ongoing, social prospective memory, non-social prospective memory, and ongoing contaminated conditions during the experiment; (**b**) functional near infrared spectroscopy (fNIRS) channels distribution.
## 3 Methodology for Human Navigation Path Simulation via Inverse Reinforcement Learning
### EEG Signal Classification via WNFG-SSGCNet-ADMM  
In the proposed ERIL framework, we employ a Weighted Normalized Feature Graph Screening-Sparse Spatial Graph Convolutional Network with Alternating Direction Method of Multipliers (WNFG-SSGCNet-ADMM) for EEG signal classification.  
The WNFG-SSGCNet-ADMM method combines spectral graph convolution with sparse optimization. The key innovation lies in the Weighted Neighborhood Field Graph (WNFG) construction, reducing graph complexity from \(O(n^{2})\) to \(O(Kn)\) (\(K\ll n\)) through frequency-domain sparsification. We further enhance this framework by integrating ADMM-based pruning [8], which iteratively removes redundant parameters while preserving classification accuracy. Specifically, the ADMM optimization solves  
\[\min_{W,Z}\|W\|_{1}+\lambda\|Z\|_{F}^{2}\quad\text{s.t.}\quad W=Z, \tag{1}\]  
where \(W\) represents the network weights, and \(Z\) is an auxiliary variable. This approach achieves a 10\(\times\) parameter reduction without degrading performance, as validated in Section 5.1. For detailed implementation, we refer readers to the original SSGCNet work [8].  
This method substantially reduces model parameters through a parameter sparsification strategy, making it particularly suitable for deployment on resource-constrained embedded devices. Regarding classification tasks, binary classification aims to achieve dichotomous discrimination of EEG signals (e.g., pathological vs. physiological EEG discrimination in seizure detection), while multi-class classification (quadratic or quintuple classification) addresses mutually exclusive category recognition (such as limb movement differentiation in motor imagery classification or discrete emotion discrimination in affective computing). It should be emphasized that the sample annotation mechanism for multi-class tasks fundamentally differs from binary classification: the former adopts a single-label annotation system where each sample corresponds exclusively to one class label, whereas the latter may involve multi-label annotations in specific application scenarios.  
Notably, the proposed framework demonstrates excellent modularity, allowing researchers to substitute the classification algorithm with better-performing alternatives according to specific requirements.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 4 Experimental Outcomes and Discussion
### Objective Evaluation
Concurrently, the Retinex preprocessing method combined with refined transmittance estimation significantly boosts structural detail recovery, reflected in consistently higher average gradient ratios. Crucially, our approach excels in preserving color fidelity, achieving near-zero saturation pixel ratios in most test images, and effectively mitigating color distortion. This comprehensive superiority over both conventional (MSR, DCP, Meng, Tarel) and deep learning (Grid_Net) benchmarks underscores the efficacy of integrating Retinex theory with the enhanced dark channel prior framework. The robust quantitative results across diverse datasets (self-collected, NH-HAZE, RESIDE) and scene types (with/without sky) confirm the algorithm's reliability and generalizability in producing high-quality, haze-free images.
## 5 Discussion
### Improvements of EFSDAF Compared with FSDAF  
The fusion results of EFSDAF were better than FSDAF due to the following reasons. Firstly, EFSDAF considered the mixed pixels phenomenon of the fine resolution image (e.g., Landsat), which was the most significant improvement of EFSDAF. In this study, the SVD model was introduced to perform the spectral unmixing by using a fully constrained least squares method. Moreover, the abundances of MODIS pixels were calculated by averaging the abundances of all Landsat pixels in one MODIS pixel, and the final changes of Landsat images were solved by a linear mixture that combined the abundances of endmembers and the variations of endmembers for Landsat images from \(T_{B}\) to \(T_{p}\). Hence, the proposed EFSDAF considered the intra-class variability, and it can reserve more spatial details than FSDAF, which can be seen in the yellow elliptical circles of Figures 4 and 7. Secondly, the spatial unmixing of the coarse resolution image (e.g., MODIS) was performed in a sliding window, which was different from the spatial unmixing of FSDAF using the whole MODIS image. It was more in line with the actual situation by using sliding windows for spatial unmixing due to the heterogeneity of land surface. The size of the sliding window for spatial unmixing was recommended to \(7\times 7\) MODIS pixels because the fusion accuracy is the best. Furthermore, selecting k purest MODIS pixels of a sliding window for spatial unmixing ensured the minimal impacts of collinearity [32]. Thirdly, the differences between the Landsat and MODIS images were considered, which included the differences of the sensors and the pre-processing errors. In this study, we used a linear model to adjust the differences between the Landsat and MODIS images, but FSDAF did not consider the differences. Finally, a new residual index (_RI_) was introduced to guide the residual distribution instead of the homogeneity index (_HI_) of FSDAF. The _HI_ will not be suitable to guide the residual distribution when there are land cover changes or misclassification [33] because it was calculated from the classification map at \(T_{B}\). The proposed _RI_ adequately considered the source of the errors, and it was based on the differences of the interpolator results between the MODIS image at \(T_{B}\) and the MODIS image at \(T_{P}\). Greater differences represent more significant land cover change and will have a larger _RI_ value. The _RI_ was calculated in a sliding window, and the size of the window should not be too large because the result will not be accurate for the edge of different land cover types. The recommended size was \(51\times 51\) Landsat pixels according to Section 2.2.5 in this study. More information of land cover change can be monitored by EFSDAF from the yellow elliptical circles in Figure 8e,f. In addition, EFSDAF is an automated fusion method compared to FSDAF because additional input parameters were not required.  
Figure 9: Scatter plots of the true values and the predicted values for NIR band in dataset 2. From left to right, (**a–d**) the scatter plots for STARFM, STRUM, FSDAF and EFSDAF, respectively.
## 3 Measurement Results and Discussion
### Experiments under Cloudy Weather Conditions
#### 3.3.2 Measured Images with Visible, Near-Infrared, and Far-Infrared Cameras  
Figure 13 shows the images of the first measurement at the Imperial Place in cloudy weather. Compared to the summer image in Section 3.1, the far-infrared wavelength image looked very different. In the summer image, the ground was white, and the sky was black, whereas, in this winter image, the ground and sky had a similar appearance in the far-infrared wavelengths. The heat load on the human body is discussed in the next section, as well as the summer data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Location** & **Date** & **Temperature** & **Humidity** & **Barometric Pressure** & **Starting Time of** \\  & & [\({}^{\circ}\)**C**] & **[\%]** & **[\%]** & **[\({}^{\circ}\)**HPa]** & **Measurement** \\ \hline The Imperial Palace & 19 October 2021 & 14.3 & 68 & 1013.2 & 12:00 noon \\ \hline The Imperial Palace & 25 October 2021 & 16.6 & 59 & 1021.4 & 12:00 noon \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environmental conditions of measurement location in cloudy weather.  
Figure 12: Calculated radiation energy to human body in winter experiment at the Imperial Palace.