esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Numerical Efficiency
#### 5.3.3 Computing Time  
The third relevant criterion is the time required to achieve the inversion process. Again, this depends largely on the power of the computer, its clock frequency, the number of cores available, the ability of the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Successes** & **PDGF** & **PDHyTgF** & **PDLF** & **PDSF** \\ \hline Number & 309 & 382 & 355 & 224 \\ Proportion (\%) & 68.6 & 84.8 & 78.8 & 49.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number and proportion of successful model inversions.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Iterations** & **PDGF** & **PDHyTgF** & **PDLF** & **PDSF** \\ \hline
**Cases** & **s** & **u** & **s** & **u** & **s** & **u** & **s** & **u** \\ \hline Minimum & 2 & 1 & 1 & 1 & 1 & 1 & 2 & 1 \\
25 th percentile & 7 & 1 & 8 & 2 & 8 & 1 & 7 & 1 \\ Median & 10 & 2 & 12 & 2 & 12 & 3 & 9 & 2 \\ Mean & 20.8 & 3 & 18.6 & 7.2 & 18.7 & 7.2 & 10.8 & 4 \\
25 th percentile & 14 & 3 & 19 & 4 & 19.5 & 4 & 11 & 3 \\ Maximum & 71 & 8 & 53 & 27 & 53 & 27 & 25 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of iterations required, for successful (s) and unsuccessful (u) model inversions.  
software to take advantage of multithreading, etc. All results described in this paper were generated on the same computer, and the results are normalized by the fastest time achieved for each category (successful and unsuccessful), to provide a rating independent of the particular hardware and software available.  
Table 6 reports the average amount of time spent inverting each of the four models against the FAPAR data for each of the 25 locations in the target area and for each of the 18 seasons in the input data set. All four models can be inverted against the seasonal data in about the same amount of time when presented with an optimal data distribution. However, as the pattern of FAPAR data starts to diverge from expectations or to feature longer gaps of missing data, the inversion process takes more time to reach a solution. Interestingly, the PDLF and the PDGF models take less time to conclude in the worst cases than the PDSF and the PDHyTgF. On the other hand, inverting the PDHyTgF model is the fastest process to conclude with an unsuccessful fit, followed by the PDSF; the PDLF and especially the PDGF models appear to require more time before deciding to quit in unsuccessful cases.
## 5 Discussion  
The VMD-GWO-GRU model adopted in this paper integrates the GWO algorithm, VMD algorithm, and GRU neural network, which fully exploit the feature information hidden in the dissolved oxygen data and enhance the precision of water quality parametric prediction, but there are some aspects that need to be further discussed and improved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **RMSE** & **MAPE** \\ \hline GWO-GRU & 0.34507 & 2.8886\% \\ VMD-GWO-GRU & 0.20227 & 1.8811\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation indicators for the GWO–GRU and VMD–GWO-GRU models.  
Figure 14: (**a**) GWO-GRU and VMD–GWO-GRU prediction results, and (**b**) GWO-GRU and VMD–GWO-GRU prediction absolute errors.  
In this paper, we employed the VMD decomposition technique to further break down dissolved oxygen data, thereby fully capturing the water quality parameters' trends. The findings indicate that the VMD approach offers significant advantages in data decomposition, addressing the modal aliasing issue present in conventional signal decomposition algorithms, and enhancing the prediction accuracy of dissolved oxygen data. Additionally, we utilized the central frequency method to determine the number of decompositions, specifically eight. Future research can explore the use of intelligent optimization algorithms (such as PSO and GWO) to automatically adjust key parameters of the VMD algorithm, such as \(\alpha\) and K, overcoming the difficulties of manual parameter tuning. By selecting appropriate objective functions as the basis for optimization, signal loss due to insufficient decomposition can be avoided, thereby enhancing the robustness and efficiency of VMD in practical applications.  
Some hyperparameters of the GRU neural network were obtained by the grey wolf optimizer, and the experimental results showed that the GRU model established by applying the combination of these hyperparameters achieved a good predictive effect on the dissolved oxygen data. The number of experiments could be increased in future research to find better hyperparametric optimization intervals, reduce the number of iterations of GWO, and increase the speed of modeling. Meanwhile, the VMD-GWO-GRU model proposed in this paper currently focuses on predicting only one parameter, the DO level, with both model inputs and outputs being DO. Future research could explore the modeling prediction of more water quality parameters (including ammonia nitrogen level and pH) to improve the overall generalizability of the model. In addition, future studies should focus more on exploring the linkages between these water quality parameters, using more parameters as auxiliary variables for predicting a particular water quality parameter to improve the performance of the prediction model, and investigating the effectiveness of the model proposed in this paper in different regions and water body environments to provide a more reliable tool for water quality monitoring and treatment.
## 3 Shift Strategy Modification Based on Vehicle Load and Road Slope
### Modification of Shift Strategy Based on Vehicle Load  
The self-weight coefficient of heavy-duty mining trucks is much lower than that of ordinary passenger vehicles, and even under 0.7 (\(\frac{M_{0}}{M_{\text{max}}-M_{0}}\leq\) 0.7). The unloaded mass of the truck studied in this paper is \(M_{0}\) = 30,000, and the full-load mass is \(M_{\text{max}}\) = 72,000. This huge load capacity will have a non-negligible impact on the shift strategy. Assuming that other conditions are constant, according to the aforementioned method, the vehicle speed and throttle opening of the shift point under no-load and full-load conditions can be obtained, respectively. In order to simplify the calculation, this paper chooses the linear interpolation method to obtain the best economy shift point corresponding to any vehicle load.  
\[f(M)=\frac{M-M_{\text{max}}}{M_{0}-M_{\text{max}}}f(M_{0})+\frac{M-M_{0}}{M_{ \text{max}}-M_{0}}f(M_{\text{max}}). \tag{15}\]  
Figure 4: The relationship between fuel consumption per hour and truck speed at different gears.  
Figure 5: The traditional two-parameter economical shift strategy.  
Let \(\sigma_{M}=\frac{M-M_{\max}}{M_{0}-M_{\max}}\), then Equation (15) can be transformed into  
\[v_{v,M}=\frac{M-M_{\max}}{M_{0}-M_{\max}}v_{v,0}+\frac{M-M_{0}}{M_{\max}-M_{0}}v _{v,\max}=\sigma_{M}v_{v,0}+(1-\sigma_{M})v_{v,\max}. \tag{16}\]  
Taking the shift line of the 3-4 upshift as an example, the shift schedule based on vehicle load correction is shown in Figure 6a. From the shift lines corresponding to the three different vehicle loads in the figure, it can be seen that the vehicle load has a great impact on the shift point. At the same throttle opening, the shifting speed increases as the vehicle load increases.
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 3 ELM1 Based on the Adaptation of FRF
### Equivalent Inertance  
As can be seen from Figure 4, the vibration of the structure in one cycle can be divided into four stages.  
From 0 to \(t_{1}\). The structure begins to move after given an initial displacement \(x(0)=x_{0}\), and the CID starts working as an IMD until the structure reaches its peak velocity at time \(t_{1}\). In this stage, the structure vibrates at the frequency of \(\omega_{IMD}\) for a quarter of cycle. Thus, \(t_{1}\) is given by  
\[t_{1}=\frac{\pi}{2\omega_{IMD}}=\frac{\pi}{2}\sqrt{\frac{m+b}{k}} \tag{26}\]From \(t_{1}\) to \(t_{2}\). At time \(t_{1}\), the structure reaches the peak velocity, the acceleration is decreased to zero in the meantime, and its direction reverses at the next moment. The speed of the structure begins to decrease and the CID does not work for the structure because of the clutch effect. During this stage, the structure vibrates at the frequency of \(\omega\) for a quarter of cycle. Thus, the time elapsed in this stage is  
\[t_{2}-t_{1}=\frac{\pi}{2\omega}=\frac{\pi}{2}\sqrt{\frac{m}{k}} \tag{27}\]  
From \(t_{2}\) to \(t_{3}\). At time \(t_{2}\), the structure reaches the maximum displacement in the opposite direction to that at the initial time. Then, the other CID comes into use until time \(t_{3}\). During this stage, the structure vibrates at the frequency of \(\omega_{IMD}\) for a quarter of cycle. Thus, the time elapsed in this stage is  
\[t_{3}-t_{2}=\frac{\pi}{2\omega_{IMD}}=\frac{\pi}{2}\sqrt{\frac{m+b}{k}} \tag{28}\]  
From \(t_{3}\) to \(t_{4}\). At time, the structure reaches the maximum velocity in the opposite direction to that at time \(t_{1}\). Then, the other CID is divorced from structure. In this stage, the structure vibrates at the frequency of \(\omega\) for the last quarter of cycle. Thus, the time elapsed in this stage is  
\[t_{4}-t_{3}=\frac{\pi}{2\omega}=\frac{\pi}{2}\sqrt{\frac{m}{k}} \tag{29}\]  
The total time during one circle can be determined by summing the four time stages above:  
\[T_{CID}=\frac{\pi}{\omega}+\frac{\pi}{\omega_{IMD}}=\pi\sqrt{\frac{m}{k}} \Big{(}1+\sqrt{1+\mu}\Big{)} \tag{30}\]  
From Equation (12), the period of the ELS can be obtained:  
\[T_{ELS}=2\pi\sqrt{\frac{m+b_{eq}}{k}} \tag{31}\]  
Therefore, the key is that the period of the CID system is equal to that of the ELS.  
\[T_{CID}=T_{ELS} \tag{32}\]  
Figure 4: Responses of free vibration of SDOF system with CID (\(\mu=1\)).  
Combining Equation (31), Equation (32), and Equation (33), the equivalent inertance is given by  
\[b_{eq}=\left(\frac{\left(1+\sqrt{1+\mu}\right)^{2}}{4}-1\right)m \tag{33}\]  
Finally, the equivalent inertance-mass ratio is easy to obtain, as in [PERSON] et al. [16]:  
\[\mu_{eq}=\frac{b_{eq}}{m}=\frac{\left(1+\sqrt{1+\mu}\right)^{2}}{4}-1 \tag{34}\]
## 3 Theory/Calculation
### Model Formation
Such requirements for the angular displacement \(\beta\) apply also to the displacement of AM \(l_{d}\), instead of _sign_\(\beta_{D}\) the requirements contain _sign_\(l_{dD}\):  
if _sign_\(l_{dD}=+\), then \(p=p_{AM2}\)_sign_\(l_{dD}>0\) whereas _sign_\(l_{d}=+\) and \(p_{AM1}=\text{const}>0\),  
if _sign_\(l_{dD}=-\), then \(p=p_{AM2}\)_sign_\(l_{dD}<0\) whereas _sign_\(l_{d}=-\) and \(p_{AM2}=\text{const}>0\). Figure 5: Static properties of PAMs in antagonistic connection at variable filling pressure. The following shall apply to the total angular displacement of the actuator pulley shaft:  
\[\beta=\frac{l_{d}}{r_{kl}}=\frac{abs(l_{d})\cdot sign(l_{d})}{r_{kl}}, \tag{10}\]  
\[\beta=abs(\beta)\cdot sign(\beta), \tag{11}\]  
whereas the following applies to the displacement and the total length of the respective AM of the actuator:  
\[l_{d}=abs(l_{d})\cdot sign(l_{d}), \tag{12}\]  
\[l=l_{0}+l_{d}=l_{0}+abs(l_{d})\cdot sign(l_{d}) \tag{13}\]  
additionally, the following applies to the magnitude of the actuator AM contraction \(k\):  
\[k=l_{max}-l=l_{max}-l_{0}-abs(l_{d})\cdot sign(l_{d}), \tag{14}\]  
Displacement \(l_{d}\), as compared to the reference point E, will correspond to the intersection of relevant AM properties. For example, in Figure 5 they are points 1-4. The figure depicts a situation in which AM1 has a constant air pressure and the pressure of AM2 changes from the maximum to the zero value. At that moment, the actuator arm reaches the position of point C, i.e., the maximum positive displacement from the reference point E. The same procedure can also be applied in the case of opposite distribution of air pressures in the AM. Then, the actuator arm reaches the position of point D, i.e., the maximum negative displacement. With changing pressure in individual muscles, the working point of the actuator position will move from the initial reference point E along the relevant property with pressure pm either to point C or to point D.
## 2 Materials and Methods
### Dataset Creation and Preparation
Following the slice selection procedure, a region of interest (ROI) measuring 150 \(\times\) 150 pixels was extracted from each image, with the adrenal lesions positioned at the center. For the lesion slices, this extraction was performed deterministically using bounding boxes provided by medical expert annotations. The coordinates of the extracted ROI were also used to crop the non-lesion slices. This process could be automated using adrenal or kidney detection models; such automation falls outside the scope of this work. To ensure effective machine learning training and evaluation, the final dataset was divided into three subsets: train, validation, and test. In this split, it was ensured that images from each patient were included in only one dataset to prevent data leakage and that an even distribution of lesion sizes across all datasets was maintained. These datasets with the ROIs were used to train and evaluate the lesion detection DL model. For the classification model, we used the same dataset split, but focused solely on lesion patches (cropped bounding boxes from the medical expert annotation) excluding all non-lesion slices, as our primary interest was the classification of the lesions. Both the ROIs used for detection and the lesion patches used for classification were normalized using the min-max algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Slice Index** & **Axial Slice** & **Included** \\ \hline
1 & Non-Lesion & No \\
2 & Non-Lesion & Yes \\
3 & Non-Lesion & Yes \\
4 & Non-Lesion & Yes \\ \hline
5 & Non-Lesion & No \\
6 & Non-Lesion & No \\ \hline
7 & Lesion & Yes \\
8 & Lesion & Yes \\
9 & Lesion & Yes \\
10 & Lesion & Yes \\ \hline
11 & Non-Lesion & No \\
12 & Non-Lesion & No \\ \hline
13 & Non-Lesion & Yes \\
14 & Non-Lesion & Yes \\
15 & Non-Lesion & Yes \\ \hline \hline \end{tabular} Note: The color in the table is used to highlight the inclusion of slices in the algorithm.  
\end{table} 
Table 2: Axial slices inclusion automated procedure (direction: caudal-to-cranial).
## 3 Experiments and Discussion
### Wrist Motion Signal Detection  
The overall performance of the wrist sensor in the actual application was tested by using the testing system in Figure 4. In order to reduce the random error and evaluate the sensor quickly, an adult male and an adult female were selected as two subjects. The sensor was worn on the wrists of the subjects, which is shown in Figure 10. During the test, the subjects were asked to keep their forearms horizontal and perform different gestures, each of which should be repeated five times. The original data collected by the digital oscilloscope and the same filtering processing was carried out as above.  
Figure 11 presents measured response waveforms of the sensor during different hand movements. The waveform collected when the subject made a fist is shown in Figure 11a. It can be clearly seen that there are five fist periods and two peaks in each period. Point A is the first peak corresponding to making a fist, while point B is the second peak corresponding to the return to the initial state. Figure 11b shows the waveform of the thumb-bending of the subject. There are three thumb-bending periods, and one peak Point C in each period at which the thumb is bending. Figure 11c shows the waveform of wrist stretching and bending. There are four periods and two peaks in each period consisting of Point D and Point E, respectively. Point D and Point E demonstrated the wrist in stretching and bending, respectively. The waveform of the wrist waving motion is shown in Figure 11d. There are four periods and two peaks consisting of Point F and Point G in each period at which the hand is at two ends of waving, respectively.  
Figure 8: Frequency-amplitude characteristic curve of the sensor.  
Figure 9: (**a**) Schematic of the measuring circuit; (**b**) Admittance circles.  
Figure 11: Measured response waveforms of the sensor during different hand movements including (**a**) making a fist; (**b**) thumb bending; (**c**) wrist stretching and bending; and (**d**) wrist waving.  
Figure 10: Schematic of the sensor position and initial state of the hand.  
As clearly seen in Figure 11, the greater the motion amplitude, the greater the output response amplitude, and the sharper the waveform. As shown in Figure 11b, the output response amplitude is small, and the waveform is relatively flat, because of the small amplitude of thumb bending. As for the test results shown in Figure 11a,c,d, there is little difference in the amplitude between the three sets of different movements, but there is a distinct difference in the shape of the waveform. Making a fist is the most intense action, and the changes in the corresponding waveform are the greatest, while the other two sets are more placid. In summary, there is a significant difference in the four sets of signal waveforms that were acquired from different movements.
## 4 Hazards Analysis
### Desertification Hazard Assessment
#### 4.4.1 The Soil Quality Index (SQI)  
The Soil Quality Index (SQI) evaluates how suitable soils are for supporting vegetation and resisting degradation. It is calculated as the geometric mean of six key factors: granulo  
Figure 13: Distribution of historical wildfire events across hazard categories according to the Wildfire Hazard Map. Most events fall within the high- to very-high-hazard zones, supporting the spatial reliability of the susceptibility model.  
metric composition, parent material, coarse material content, soil depth, slope inclination, and hydromorphy. The formula used is:  
\[\text{SQI}=(\text{Granulometric Composition}\times\text{Slope Inclination}\times\text{Parent Rock}\times\text{Soil Depth}\times\text{Coarse Material Content}\times\text{Hydromorphy})(1/6)\]  
Each of these parameters reflects a critical characteristic of soil performance. Using the geometric mean allows for balanced integration of the components, reducing the influence of any single extreme value on the final result.  
Based on the calculated SQI, land is classified into three categories:  
* High quality: SQI \(<\) 1.13;
* Medium quality: SQI between 1.13 and 1.45;
* Low quality: SQI \(>\) 1.45.  
For Lefkas Island, the soil conditions vary across the parameters (Table 4, Figure 14). The granulometric composition is generally favorable, with most areas receiving the best possible score (value: 1), indicating textures that support plant growth and resist erosion. The slopes on the island are predominantly steep (28-35%) to very steep (\(>\)35%), corresponding to values of 1.5 and 2, which negatively affect soil stability.  
The parent material is of medium quality, composed mainly of Tertiary and Quaternary calcareous pebble deposits (assigned value: 1.7). In contrast, soil depth is classified as very shallow, which limits root development and increases vulnerability to degradation (value: 4).  
Regarding coarse material content, the soils are notably rocky, which improves drainage and structural resilience (value: 1). Hydromorphic conditions are also favorable, as the soils are generally well drained, reducing the risk of waterlogging and further erosion.
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 4 Results and Discussion
### Temperature Measurement for the Driver Board of the LED Lamp  
The temperature data of the LED lamps were measured and recorded by the thermocouples and DR230 data recorder. The temperature variations of the points on the printed circuited circuit board assembly (PCBA) are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Result** & & & & & & \\ \hline Area Removed & 0\% & \textless{}5\% & 5–15\% & 15–30\% & 35–65\% & \textgreater{}65\% \\ \hline Rating & 5B & 4B & 3B & 2B & 1B & 0B \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test patterns for the ASTM D3359 standard.  
Figure 10: Test specimen preparation for the resistance measurements.  
As shown in Figure 6, there were different types of LED lamps for the test, the original lamp, the fin-removed lamp, and the NGC lamp. The variations of temperature are shown in Figure 11a for the original lamp, Figure 11b for the fin-removed lamp, and Figure 11c for the NGC lamp, respectively. It was observed that the time for the steady temperature was about 45 min for the original lamp, 60 min for the fin-removed lamp, and 25 min for the NGC lamp. The steady temperatures are listed in Table 7 for comparison. Points 5-8 are the points for the LED chips, which are electronic elements of high power. At these points, the temperatures were higher than those of the other four points 1-4. In particular, the highest temperature was found at point 7, which was close to the live wire terminal. The temperature at point 7 was 75.7 \({}^{\circ}\)C for the original lamp, 85.9 \({}^{\circ}\)C for the fin-removed lamp, and 70.5 \({}^{\circ}\)C for the NGC lamp. Taking the temperature at point 7 of the fin-removed lamp as the reference temperature, the temperature drop ratio for the original lamp was about 11.9% (10.2 divided by 85.9)  
Figure 11: Variation of temperature for the different LED lamps. (**a**) Variation of temperature for points 1–8 of the original lamp. (**b**) Variation of temperature of points 1–8 for the fin-removed lamp. (**c**) Variation of temperature for points 1–8 of the NGC lamp.  
and represents the fin effect. Similarly, the temperature drop ratio for the NGC lamp was about 17.9% (15.4 divided by 85.9) and represents the NDG coating effect. The result shows that the NDG coating had a better temperature drop ratio than that of the original lamp with cooling fins.
## 5 Analysis and Discussion
### Real-Time Deployment and Field Testing
#### 5.2.2 Field Testing  
To validate the effectiveness and user acceptance of the SER system in real educational settings, we propose conducting field tests in collaboration with educational institutions offering distance learning programs. The field tests will involve the following steps:  
Participant Recruitment: Educators and learners from diverse backgrounds and cultural contexts will be invited to participate in the field tests. Informed consent will be obtained, and participants will be briefed on the purpose and procedures of the study.  
Integration with Distance Learning Platforms: The SER system will be integrated into the existing distance learning platforms used by the participating institutions. This integration will ensure a seamless user experience and allow for the collection of real-time emotional data during online learning sessions.  
Data Collection: During the field tests, the SER system will be used to analyze the emotional content of speech data from both educators and learners. The system will record the predicted emotion labels along with relevant metadata, such as timestamps and user identifiers (anonymized to protect privacy).  
User Feedback and Evaluation: Participants will be asked to provide feedback on their experience with the SER system through surveys and interviews. The feedback will cover aspects such as the system's usability, the perceived accuracy of the system, and the impact of emotion recognition on the learning experience.  
Data Analysis and Refinement: The collected data and user feedback will be analyzed to assess the system's performance in real educational settings. This analysis will focus on metrics such as accuracy, user satisfaction, and the system's influence on teaching and learning outcomes. Based on the findings, the SER system will be refined and optimized to better meet the needs of educators and learners.  
These field tests in real educational settings will provide valuable insights into the practical effectiveness of the SER system and its acceptance by users. The feedback and data collected during these tests will inform future iterations of the system, ensuring its continued improvement and alignment with the requirements of distance learning environments.  
Figure 19: SER deployment using the Flask web interface.