esa-sceva/satcom-chunk-collection huggingface dataset:
# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 5 Experiments and Discussion
### Experimental Results and Analysis
#### 5.4.1 Random Online Data Augmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **mAP** & \(\mathbf{AP^{S}}\) & \(\mathbf{AP^{M}}\) & \(\mathbf{AP^{L}}\) \\ \hline \multirow{4}{*}{Faster R-CNN} & ResNet-50 & 0.485 & 0.458 & 0.439 & 0.499 \\ \cline{2-6}  & ResNet-50 & 0.522 & 0.413 & 0.499 & 0.529 \\  & (RDA) & (+0.037) & (\(-\)0.045) & (+0.060) & (+0.030) \\ \cline{2-6}  & ConvNeXt-T & 0.521 & 0.414 & 0.475 & 0.526 \\ \cline{2-6}  & ConvNeXt-T & 0.542 & 0.501 & 0.494 & 0.589 \\  & (RDA) & (+0.021) & (+0.087) & (+0.019) & (+0.063) \\ \cline{2-6}  & Swin-T & 0.542 & 0.533 & 0.463 & 0.533 \\ \cline{2-6}  & Swin-T & 0.566 & 0.488 & 0.490 & 0.534 \\ \cline{2-6}  & (RDA) & (+0.024) & (\(-\)0.045) & (+0.027) & (+0.001) \\ \cline{2-6}  & Res2 Net-101 & 0.465 & 0.441 & 0.406 & 0.457 \\ \cline{2-6}  & Res2 Net-101 & 0.509 & 0.430 & 0.464 & 0.477 \\  & (RDA) & (+0.044) & (\(-\)0.011) & (+0.058) & (+0.020) \\ \cline{2-6}  & ResNeXt-101 & 0.497 & 0.345 & 0.428 & 0.520 \\ \cline{2-6}  & ResNeXt-101 & 0.534 & 0.428 & 0.494 & 0.540 \\ \cline{2-6}  & (RDA) & (+0.031) & (+0.083) & (+0.066) & (+0.020) \\ \cline{2-6}  & PVTv2-B2 & 0.549 & 0.537 & 0.527 & 0.537 \\ \cline{2-6}  & PVTv2-B2 & 0.583 & 0.566 & 0.548 & 0.592 \\ \cline{2-6}  & (RDA) & (+0.034) & (+0.029) & (+0.021) & (+0.055) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of mAP for Random Online Data Augmentation, where \(AP^{S}\) represents the AP for small objects with area \(<32^{2}\), \(AP^{M}\) represents the AP for medium objects with \(32^{2}<\text{area}<96^{2}\), and \(AP^{L}\) represents the AP for large objects with area \(>96^{2}\). All the results are the best of 5 runs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **mAP** & \(\mathbf{AP^{S}}\) & \(\mathbf{AP^{M}}\) & \(\mathbf{AP^{L}}\) \\ \hline \multirow{4}{*}{Faster R-CNN} & ResNet-50 & 0.485 & 0.458 & 0.439 & 0.499 \\ \cline{2-5}  & ResNet-50(RDA) & 0.522 & 0.413 & 0.499 & 0.529 \\ \cline{1-1} \cline{2-5}  & ResNet-50(RDA) & 0.527 & 0.434 & 0.508 & 0.535 \\ \cline{1-1} \cline{2-5}  & & 0.516 & 0.481 & 0.509 & 0.524 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the accuracy of small objects with RDA. where \(AP^{S}\) represents the AP for small objects with area \(<32^{2}\), \(AP^{M}\) represents the AP for medium objects with \(32^{2}<\text{area}<96^{2}\), and \(AP^{L}\) represents the AP for large objects with area \(>96^{2}\).
## 3 Results
### Percentage Distributions of the Likert-Scale Items
#### 3.3.2 Classroom, Methodology, and Resources
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Grade** & **Group A** & **Group B** & **Group C** & **Total** \\  & **of Satisfaction** & **(_n = 28_)** & **(_n = 28_)** & **(_n = 21_)** & **(_N = 77_)** \\ \hline Q6 & Unsatisfactory 1 & 7.1 & 0.0 & 4.8 & 3.9 \\  & Neutral & 7.1 & 3.6 & 9.5 & 6.5 \\  & Satisfactory 2 & 85.7 & 96.4 & 85.7 & 89.6 \\ Q7 & Unsatisfactory 1 & 3.6 & 32.1 & 9.5 & 15.6 \\  & Neutral & 21.4 & 21.4 & 4.8 & 16.9 \\  & Satisfactory 2 & 75.0 & 46.4 & 85.7 & 67.5 \\ Q8 & Unsatisfactory 1 & 3.6 & 3.6 & 4.8 & 3.9 \\  & Neutral & 7.1 & 10.7 & 0.0 & 6.5 \\  & Satisfactory 2 & 89.3 & 85.7 & 95.2 & 89.6 \\ Q9 & Unsatisfactory 1 & 3.6 & 0.0 & 4.8 & 2.6 \\  & Neutral & 0.0 & 3.6 & 0.0 & 1.3 \\  & Satisfactory 2 & 96.4 & 96.4 & 95.2 & 96.1 \\ Q10 & Unsatisfactory 1 & 3.6 & 0.0 & 4.8 & 2.6 \\  & Neutral & 14.3 & 14.3 & 4.8 & 11.7 \\  & Satisfactory 2 & 82.1 & 85.7 & 90.5 & 85.7 \\ Q11 & Unsatisfactory 1 & 0.0 & 0.0 & 4.8 & 1.3 \\  & Neutral & 7.1 & 3.6 & 0.0 & 3.9 \\  & Satisfactory 2 & 92.9 & 96.4 & 95.2 & 94.8 \\ Q12 & Unsatisfactory 1 & 0.0 & 0.0 & 4.8 & 1.3 \\  & Neutral & 3.6 & 10.7 & 4.8 & 6.5 \\  & Satisfactory 2 & 96.4 & 89.3 & 90.5 & 92.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentages (%) of pre-service teachers’ ratings on classroom, methodology, and resources.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 4 Results and Experimentation
### Cluster Lifetime  
Cluster lifespan is defined as the time that has transpired between the establishment of a cluster and the point at which the cluster formation has ended. Several factors influence this, including the relative mobility of nodes, the pace at which nodes use energy, and  
Figure 6: Clustering building time.  
the overall number of clusters in the network. When the level of mobility is high, nodes may be able to alter their cluster assignments more quickly than usual. Similarly, when high energy consumption rates, a chosen CH may quickly become invalid, necessitating a re-clustering effort. Due to the shorter lifetime of the cluster, it is necessary to recall the clustering factor regularly. Therefore, there is a rise in the need for computational power. Figure 7 depicts the cluster lifetimes between the proposed model and the LEACH, LEACH-C, PSO-NSA, and SEED models. The suggested model outperforms all other models by a wide margin in this metric. When considering the size of the cluster, it is too large to incorporate all of the clusters inside the transmission coverage of the CHs. The results demonstrate that increasing the number of nodes in cluster results in a decrease in the cluster's lifetime. Due to the mobility nature of UAVs, which causes topology to alter regularly, this might be the case.
## 2 Methodology  
### Field Ground Measurements  
#### 2.1.1 Observation System  
In this study, we used an improved AutoSIF (Bergsun Inc., Beijing, China) to observe the SIF and PAR of a maize canopy automatically. The observation system includes a control computer, two spectrometers, an optical multiplexer, and a photon meter (Figure 1). The two spectrometers include a QE65 pro spectrometer (Ocean Optics Inc., Dunedin, FL, USA) and an HR4000 spectrometer (Ocean Optics Inc., Dunedin, FL, USA). The QE65 pro spectrometer has a spectral range of approximately 640 nm to 800 nm, a high spectral resolution (SR) of approximately 0.3 nm, and a spectral sampling interval (SSI) of approximately 0.15 nm. Its signal-to-noise ratio (SNR) can reach approximately 1000:1. The HR spectrometer has a spectral range of 198 nm to 982 nm and a SR of approximately 1 nm. To reduce dark current and noise, the two spectrometers were contained within a refrigeration system chamber that maintains a temperature below 25 \({}^{\circ}\)C. Since the QEpro spectrometer had a higher RS and SNR, we used only the data collected by the QEpro spectrometer to retrieve SIF in this study. The optical multiplexer (MPM-2000, Ocean Optics Inc., Dunedin, FL, USA) had 16 different observation channels, and the 2 spectrometers were connected to 8 channels. Two of the channels were used to measure the incident irradiance for the two spectrometers, two channels to measure the dark current, and the other channels to measure the upwelling radiance of the different targets. The observation sequence of the AutoSIF was to first measure the target with a QE spectrometer and then immediately measure the same target with an HR spectrometer. In one measurement section of a target, a spectrometer performed the following steps: (1) integration time optimization of the \(E\), (2) measurement of the \(E\), (3) measurement of the dark current, (4) integration time optimization of the \(L\), (5) measurement of the \(L\), and (6) measurement of the dark current. An up looking fiber with a CC-3 cosine-corrector probe (Ocean Insights Inc., Dunedin, FL, USA) was used to measure \(E\). A down looking bare optical fiber with a FOV of 25\({}^{\circ}\) was used to measure the \(L\) of the canopy. The PAR data were collected using a quantum sensor (ML-020P, EKO Instruments Co., Ltd., Tokyo, Japan). The quantum sensor was set to sample every 250 milliseconds. Approximately two to four PARs could be recorded in a second due to data loss. Since the spectrometer data and the photon meter data were collected by the same computer, the timing of the two data was matched.
## 3 Results
### In Situ Gas Analysis during Process Monitoring  
In the following analysis, the long-term trends of the concentrations of detectable reactive species in the process with and without oxygen were investigated. During the process, the concentrations of the main products HCN, NH\({}_{3}\), C\({}_{2}\)H\({}_{2}\), and CH\({}_{4}\) were monitored with an average time resolution of \(\Delta t\) = 20 min. Figure 3 presents the results of this long-term measurement, both for the process with a 6% oxygen admixture (red line) and without (black line).  
As a result of the O\({}_{2}\) addition, the concentrations of the carbon-containing species (HCN, C\({}_{2}\)H\({}_{2}\), CH\({}_{4}\)) were reduced significantly while the concentration of NH\({}_{3}\) was increased. With progressing treatment time, the concentrations of HCN and C\({}_{2}\)H\({}_{2}\) slightly decreased, while NH\({}_{3}\) and CH\({}_{4}\) were almost unaffected. This trend can be explained by the temperature balance and the resulting AS power in the ASPN reactor. Since the reactor is water-cooled, there is balance between the energy supplied by the AS and the energy dissipated at the cooled wall. This equilibrium, however, is approached over time and was not initially established. Thus, at the beginning of the process, the AS power was higher and decreased over time. Since the production rates of the species generated at the AS show a power dependency, the concentrations of the species changed with time and decreased steadily for HCN and C\({}_{2}\)H\({}_{2}\). Nevertheless, the conditions during the material treatment are relatively stable in terms of the concentrations of the detected gas species.
## 3 Case Study
### Data Source
#### 3.1.2 SMA Measurement System  
To ensure that the EPB can excavate in accordance with the DTA, it is necessary to use a laser navigation system to measure the SMA in real-time. As represented in Figure 5, the system is composed of a total station, laser targets, rear-view prisms, industrial computers, and other components. The total station measures the spatial coordinates of the laser target by the emitted laser at regular intervals. It then measures the position of the shield head and tail center relative to the laser target according to the zero position, thereby calculating the geodetic coordinates of the EPB. Finally, the industrial computer calculates the SMA parameters relative to the DTA and displays the results in the cockpit.  
In the tunneling process, the deviation of the shield head and shield tail from DTA directly determines the future trajectory of EPB machine and further affects the quality of the segment assembly [17]. Therefore, horizontal deviation of the shield head (HDSH), vertical deviation of the shield head (VDSH), horizontal deviation of the shield tail (HDST), and vertical deviation of the shield tail (VDST) are used as the SMA prediction parameters of the EPB in Chengdu Metro Line 19. Figure 6 shows the distribution of the SMA parameters. By continuously monitoring these four parameters, the machine operators achieve manual control of the shield in the excavation process. However, owing to the inevitable hysteresis of manual control, it is particularly necessary to advance the control process and issue a warning timely when the SMA is about to deviate from DTA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Specification** & **Unit** \\ \hline Shield type & EPB & \textbackslash{} \\ Excavation diameter & \(\Phi\)8640 & mm \\ Rotation speed & 0\(-\)2.76 & rpm \\ Maximum penetration speed & 80 & mm/min \\ Maximum cutterhead thrust & 81,895 & kN \\ Rated cutterhead torque & 25,401 & kN\(\cdot\)m \\ Escape cutterhead torque & 30,482 & kN\(\cdot\)m \\ Horizontal turning radius & 350 & m \\ Maximum propulsion stroke & 2600 & mm \\ Data acquisition frequency & 1 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main technical parameters of shield machine.  
Figure 5: Workflow of the laser navigation system.
# Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019
## 3 Results
### Geographical Detection Model for NDVI Drivers
#### 3.4.4 Differences in Influence for Drivers on the NDVI  
The ecological detector can reflect significant differences in the effect of factors on the spatial distribution of NDVI. This can further verify the dominant influencing factors and evaluate the variability of their mechanisms of action. The results of the ecological probe (Figure 12) show that meteorological factors, land use type, elevation, and slope all had significantly different effects, indicating that although these factors play dominant roles in the NDVI, they have different mechanisms for their effects on vegetation growth. Specifically, precipitation and soil type exhibit significant differences in their effects on the NDVI, wherein the effect of soil type on vegetation growth is significant in areas where precipitation is the main driver. Other factors, such as temperature and soil type, do not show significant differences in their effect on vegetation, because while increased temperature enhances photosynthesis, it also leads to faster transpiration by leaves and increased evapotranspiration, thereby reducing soil moisture and inhibiting vegetation growth [75; 77]. The NDVI values within the different landscape types did not vary significantly according to landscape changes, and the elevation and slope were not the main factors contributing to the spatial variation in NDVI.  
Figure 11: Interaction detector results between factors and mean NDVI in Inner Mongolia from 2000 to 2019. Note: positions marked with red stars in the figure represent a bi-variable enhancement of the interaction between two different interaction factors, and non-marked positions represent a non-linear enhancement of the interaction between two different interaction factors).
# Method for Measuring Surface Charge on Insulating Materials Based on the Vibrating Capacitor Method
## 2 Methods and Simulation  
### Simulation Model  
Based on the working principle of the vibrating capacitor method and the structural characteristics of the probe, this study establishes a simulation model using Comsol software. Two models are adopted for the charged region shape. In the contact-separation frictional charging process, a circular charged region with uniform charge distribution is selected. For the rotational friction process, a ring-shaped charged region with varying charge density is chosen.  
According to the characteristics of the two charged region shapes mentioned above, we respectively establish two-dimensional axisymmetric models and three-dimensional models. The established two-dimensional axisymmetric model represents the circular charged region and mainly includes two geometric entities: the probe and the tested dielectric material. As shown in Figure 1a, the probe is a cylinder with a height of 1 mm and a radius of 2 mm. The tested dielectric material is cylindrical with a radius of 145 mm and a height of 5 mm. These two entities are coaxially arranged, and the lower surface of the probe is kept 2 mm away from the upper surface of the dielectric material. The three-dimensional model is designed for the ring-shaped charged region. To simulate the charge distribution, the ring is divided into 12 subregions, each with a uniform charge distribution. Similarly, there are two parts: the probe and the tested dielectric material, as shown in Figure 1b. The geometric characteristics of the probe are consistent with the above two-dimensional model; namely, a cylinder with a height of 1 mm and a radius of 2 mm. The tested dielectric material is cylindrical with a height of 5 mm and a bottom radius of 15 mm. The probe is located directly above the ring with an outer diameter of 11 mm and an inner diameter of 9 mm, maintaining a distance of 2 mm from the upper surface of the tested dielectric material. In both models, an external computational domain surrounds a region with a thickness of 10 mm, which serves as an infinite element domain. The infinite element domain approximates an infinite domain by using grids with extremely low densities to replace grids at far distances.  
Under the combined effect of bias voltage and surface charge of the sample, the probe generates induced charges on its surface. By performing steady-state electrostatic field simulations at the upper and lower limit positions of the probe's vibration, the surface charge quantities at these two extreme positions are obtained. The difference between them represents the change in surface charge. When the change in surface charge is zero, the voltage applied to the probe corresponds to the surface potential measurement value.  
Figure 1: Simulation geometric model: (a) 2D axisymmetric model; (b) 3D model.
## 4 Bayesian Analysis of Weibull Mixture Distribution Using NG-RJMCMC Algorithm
### Change the Weibull Distribution into a Member of the Location-Scale Family
#### 4.1.2 Finite EV-I Mixture Distribution  
Based on the explanation provided in the previous subsection, \(T\sim\mathrm{Weibull}(\eta,\lambda)\) as a member-scale family can be transformed into a member of the location-scale family \(Y\sim\mathrm{EV}-\mathrm{I}\Big{(}\mu=\ln\lambda,\sigma=\frac{1}{\eta}\big{)}\), where \(Y=\ln T\). It is well-known that \(T\sim\mathrm{[PERSON]}(\eta,\lambda)\) and \(Y\sim\mathrm{EV}-\mathrm{I}\Big{(}\mu=\ln\lambda,\sigma=\frac{1}{\eta}\Big{)}\) are equivalent models [68]. As \(Y\sim\mathrm{EV}-\mathrm{I}\Big{(}\mu=\ln\lambda,\sigma=\frac{1}{\eta}\Big{)}\) belongs to the location-scale family, it is sometimes easier to work with \(Y\sim\mathrm{EV}-\mathrm{I}\)\(\Big{(}\mu=\ln\lambda,\sigma=\frac{1}{\eta}\Big{)}\) rather than \(T\sim\mathrm{Weibull}(\eta,\lambda)\)[68], especially in the analysis of the mixture model. Consequently, the next analysis will use the variable \(Y\). The EV-I mixture distribution with \(k\) components is defined as in Equation (15):  
\[f(y|\Theta)=\sum_{j=1}^{k}w_{j}f\big{(}y|\mu_{j},\sigma_{j}\big{)}, \tag{15}\]  
where \(\Theta=(\theta,w)\) refers to the complete set of parameters to be estimated, where \(\theta=(\mu_{1},\sigma_{1};\mu_{2},\sigma_{2};\cdots;\mu_{k},\sigma_{k})\).
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Contact Mechanisms  
RF MEMS switches typically rely on two main contact mechanisms: capacitive (i.e., metal-insulator-metal contact) and ohmic (i.e., metal-to-metal contact). Capacitive switches utilize changes in capacitance between metals and insulators to control signals, making them ideal for high-performance signal processing applications such as precision filtering and frequency selection [44]. Ohmic switches, on the other hand, which operate through direct contact between metals, are particularly suitable for handling high-power and fast-switching applications [45]. When selecting RF MEMS switch types, decisions should be based on the specific requirements of the application, considering factors like frequency range, power handling capability, switching speed, isolation, and insertion loss, among other key parameters. Scattering parameters of RF MEMS switches are typically used to assess their RF performance, and they can be theoretically calculated using Equation (1) [46]. When an RF MEMS switch is on, RF signals are attenuated due to factors like series resistance and impedance mismatch, and S\({}_{21}\) represents the insertion loss, which measures signal transmission efficiency. When the switch is off, RF signals are grounded through the off-state capacitance, and S\({}_{21}\) indicates isolation, which is the ability to block signals.  
\[S_{21}=\frac{2}{2+\omega CZ_{0}} \tag{1}\]  
where \(\omega=2\pi f_{0}=(k/m)^{1/2}\), and the resonance frequency \(f_{0}=1/2\pi\sqrt{LC}\). Here, \(k\), \(m\), and \(L\) represent the spring constant, effective mass [47], and equivalent inductance of the beam membrane, respectively; \(C\) equals \(C_{u}\) or \(C_{d}\), representing the up-state or down-state capacitance, respectively; and \(Z_{0}\) is the switch's characteristic impedance.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 4 Experimental Results and Analysis
### Data Processing and Evaluation Index
#### 4.1.2 Metrics of Performance Evaluation  
The coefficient of determination (R2) and the mean absolute percentage error (MAPE) were used to evaluate the accuracy of the model. R2 quantifies the proportion of variance in the dependent variable explained by the independent variables in a regression model, as defined in Equation (15). R2 values range from 0 to 1; a higher R2 indicates a greater proportion of explained variance, reflecting better model fit and more accurate predictions. MAPE is a metric used to evaluate the accuracy of forecasting models, as defined in Equation (16). A smaller MAPE value indicates higher predictive accuracy of the model. Additionally, the normalized mean prediction interval width (NMPIW) and prediction interval coverage probability (PICP) were used to assess the reliability of model predictions, as defined in Equations (17) and (18), respectively. The PICP helps researchers understand a model's predictive capability and stability by reflecting the probability of actual values falling within the prediction interval. The NMPIW is used to evaluate the width of the prediction interval, reflecting its compactness; a smaller NMPIW value indicates a narrower prediction interval, implying lower uncertainty. PICP values range from 0 to 1; a higher PICP indicates a greater proportion of actual observations within the confidence interval, signifying more accurate prediction intervals.  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_{ i}-\overline{y})^{2}} \tag{15}\]  
\[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}|\frac{y_{i}-\hat{y}_{i}}{y_{i}}|\times 1 00\% \tag{16}\]  
\[\text{NMPIW}=\frac{\frac{1}{N}\sum_{i=1}^{N}upper_{i}-lower_{i}}{S} \tag{17}\]  
\[\text{PICP}=\frac{1}{N}\sum_{i=1}^{N}c_{i} \tag{18}\]  
Here, \(y_{i}\) and \(\hat{y}_{i}\) represent the true and predicted values of the test samples, respectively; \(\overline{y}\) is the average of the target values in all test samples; \(N\) represents the number of test samples; \(c_{i}=\left\{\begin{aligned} & 1,\lower_{i}<y_{i}<upper_{i} \,\lower_{i}\ \text{and}\ upper_{i}\ \text{represent}\ \text{represent}\ \text{the lower and upper}\ \text{limits}\\ & 0,else\end{aligned}\right.\) limits of the prediction confidence interval of the \(i\)-th sample; and \(S\) is the difference between the maximum and minimum predictions in all test samples.