esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of HS-SPME
#### 3.2.6 The Effect of Desorption Temperature  
The complete desorption of VOC pre-adsorption on the SPME coating is the key to GC-MS analysis. The desorption temperature and time were optimized, and the results are shown in Figure 6. Increasing the desorption temperature led to shorter desorption time of the VOCs from the SPME. Considering the high volatility of the compounds of the leachate of landfill, a fiber desorption temperature between 220 and 280 \({}^{\circ}\)C was tested, with a constant desorption time of 5 min. Since most components are completely desorbed at 250 \({}^{\circ}\)C, it was selected as the optimum desorption temperature (higher temperatures are not recommended so as to avoid siloxane compounds shedding from SPME).
## 2 Materials and Methods
### Shrinkage  
As listed in Table 2, the influences of the _MP_ and _ST_, as well as the quadratic effect of sample thickness, were significant (\(p\) < 0.0001) on the shrinkage of the dried samples. The interaction effect of the _MP-ST_ was not significant. Based on Figure 9, the highest shrinkage (33.39%) was recorded in the untreated samples with 6 mm thickness dried at the _MP_ of 300 W, while the ultrasonicated samples with a thickness of 4 mm dried at the _MP_ of 100 W showed the lowest shrinkage (21.29%); as expected, a rise in the _MP_ increased the shrinkage due to the contraction stress at higher powers, which led to the cellular collapse (deformation of the product and its cracking), and these results are in line with previous works [11; 66]. An increase in the _ST_ from 2 to 4 mm decremented the shrinkage followed by an increment in the shrinkage by further thickness enhancement from 4 to 6 mm. The highest shrinkage was generally observed in the thickest samples (6 mm) for all treatments. Drying will result in deformation and volume reduction, hence the shrinkage of the product. Heating and water removal from the matrix of the food products will  
Figure 8: Relationships between microwave power and thickness with color-dependent variable: (**a**) control, (**b**) ultrasonicated, and (**c**) blanched samples.  
lead to cellular stress, decrementing the dimensions of the product and contraction of its capillary structure. At higher thicknesses, these changes are more profound [67]. These findings coincided with the reports by other researchers on drying apple [68] and Persian shallot [69] of different thicknesses.  
According to Figure 9, the use of an ultrasonic pretreatment and blanching led to a lower shrinkage compared to the untreated samples. The carrot slices pre-treated with ultrasound waves showed the lowest shrinkage at all \(MP_{\text{s}}\) and \(ST\) followed by the blanched samples and controls, respectively. Tissue hydration and damage during pretreatment increased the \(MC\) of the blanched samples which led to more shrinkage in the blanched samples than in sonicated samples [70].
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 6 Experimental Results and Analyses
### Network Reliability  
Based on Figures 17 and 18, which show the one-hop MFRs of nodes for RICC and NCCC, the theoretical analyses match the simulation results well. In the near-sink area, the one-hop MFR is positively correlated with the distance to the sink node; however, when the distance exceeds the transmission radius \(r\), the one-hop MFR becomes horizontal because the transmission distance stays at \(r\). The one-hop MFR for RICC is significantly lower than that for NCCC, which fully illustrates the effectiveness of RICC in improving the reliability. The one-hop MFR in RICC is lower than the one-hop MFR in NCCC. This indicates that the network reliability in the RICC scheme is better.  
The simulation results of weighted E2E MFR are presented in Figure 19, and they are consistent with the theoretical analysis shown in Figure 12. We also present the average reduced weighted end-to-end MFR in Figure 20, which shows that the weighted E2E MFR is improved by at least 50%; moreover, the larger the network is, the less of the reduced percentage.  
Figure 17: One-hop MFR of nodes with NCCC.  
Figure 18: One-hop MFR of nodes with RICC.
## 1 Introduction
The aims of this methodological study were (i) to assess the suitability of four parametric double S-shaped functions (the Gaussian, Hyperbolic Tangent, or Logistic and Sine) to describe the phenology of savanna vegetation in the Lowveld, and (ii) to evaluate the capability of these data and models to account for the Gross Primary Productivity (GPP) of the vegetation around the Skukuza flux tower in South Africa's Kruger National Park. This is of particular interest insofar as GPP is one of the contributing factors to estimate biomass accumulation or yield (in an agricultural context).
## 3 Optical Error Analysis
### Simulation Experiment Results
#### 4.2.2 Mirror Tilt Error of FTIS  
According to the analysis in Section 3.2.2, the effect of fixed tilt error is to convolve \(I_{0}(\Delta)\) with the optical path error function \(g_{FTIR}(\Delta)\). For the interferogram \(I_{2}(\Delta)\), this effect can be considered as each frequency-shifted component \(a_{m}I_{0}(\Delta\pm\Delta_{FPI})\) being convolved with \(g_{FTIR}(\Delta)\). Therefore, \(I_{1}(\Delta)\) and \(I_{2}(\Delta)\) still maintain a linear relationship with \(I_{0}(\Delta)*g(\Delta)\). Using Hitran spectral data as the input spectrum, we simulated and analyzed the MJI-HI spectral super-resolution results for cases where the FWHM of \(g_{FTIR}(\Delta)\) is \(\lambda_{\min}/4\) (0.125 \(\upmu\)m) and \(\lambda_{\min}/2\) (0.25 \(\upmu\)m), as shown in Figure 16. Here, \(I_{0}\) represents the FTIS interferogram without fixed tilt error, \(I_{0-tilt}\) represents the FTIS interferogram with fixed tilt error, and \(I_{sup}\) is the MJI-HI interferogram reconstruction result in the presence of fixed tilt error. The simulation results indicate that since the linear relationship is preserved, the reconstruction results remain highly accurate and conform to the mathematical model of the inversion algorithm. Converting \(I_{0-tilt}\) and \(I_{sup}\) into spectra and computing their errors relative to \(B_{0}\) using Equation (36), we find that when \(FWHM_{tilt}=0.125\)\(\upmu\)m, the errors for \(I_{0-tilt}\) and \(I_{sup}\) are \(-17.85\) dB and \(-17.69\) dB, respectively. When \(FWHM_{tilt}=0.25\)\(\upmu\)m, the errors are \(-12.10\) dB and \(-11.91\) dB, respectively. The decrease in accuracy is due to the reduction in interferogram intensity, leading to spectral  
Figure 15: **(a1)** Interferogram reconstruction result when the \(FWHM_{\text{D}}\) of \(Defect_{1}\) is \(\lambda_{\min}/4\); **(a2–a4)** are its local enlargements; **(b1)** interferogram reconstruction result when the \(FWHM_{\text{D}}\) of \(Defect_{1}\) is \(\lambda_{\min}/2\); **(b2–b4)** are its local enlargements; **(c1)** interferogram reconstruction result when the \(FWHM_{\text{D}}\) of \(Defect_{1}\) is \(\lambda_{\min}\); **(c2–c4)** are its local enlargements. Here, \(I_{0}\) is the ideal interferogram, \(I_{sup}\) is the MJI-HI interferogram reconstruction result without using the improved method, and \(I_{sup-imp}\) is the MJI-HI interferogram reconstruction result using the improved method.  
energy loss, which requires spectral correction processing. From the interferogram and spectral error comparison between \(I_{sup}\) and \(I_{0\text{-}tilt}\), it can be observed that both are closer to each other than to \(I_{0}\), indicating that the inversion algorithm produces results equivalent to \(I_{0}(\Delta)*\text{g}(\Delta)\). Therefore, the fixed tilt error requirement for MJI-HI is the same as that for FTIS with the same aperture size, i.e., \(\beta_{FTIS}<1/(14\text{v}_{\text{max}}\text{r}_{0})\).
## 2 Materials and Methods
### Grid Strategy and Grid Independence Verification  
In the numerical simulation of droplet motion in the airflow, the surface mesh fitness will directly affect the accuracy of droplet simulation. Therefore, the mesh of the tilted surface of the windshield and the droplet motion region has high requirements. Thus, a polyhedral mesh is used for spatial discretization of the whole computational domain, and a smooth transition scheme is adopted between different mesh sizes. In this study, the development of the flow field in the inclined area of the 3D model is considered, and the main encrypted area is the inclined area of the model. A total of eight encrypted areas are established in the whole computational domain to ensure the grid size of the inclined area meets the needs of analyzing the droplet motion. In addition, to accurately obtain the flow inside the boundary layer and to satisfy the principle that the Y+ value is established in the logarithmic law layer, a fine prismatic layer mesh is used to delineate the surface of the window, with the first layer mesh set at 0.1 mm and the growth ratio at 1.1, totaling five layers. The encrypted region and boundary layer division of the whole computational domain are shown in Figure 5.  
A test of grid independence is required before the numerical simulation calculation. When using the CLSVOF method for the droplet simulation, the minimum mesh size for displaying the droplet morphology needs to be considered. In the grid independence test, the minimum grid size is set to 0.05 mm, 0.1 mm, 0.15 mm, and 0.2 mm. The total number of grids drawn with the same grid strategy are 3,663,255, 2,388,324, 1,446,903, and 1,023,322, respectively. We take a 3 mm diameter droplet with an initial velocity of 0.5 m/s impinging on the inclined plane as an example and compare the droplet spreading factor (the ratio of droplet spreading length to droplet diameter). The comparison results are shown in Figure 6. The results show that a reasonable grid scale is a prerequisite for capturing the phase interface accurately. According to the graphical results, the model with the minimum grid size set to 0.1 mm, which corresponds to the total grid number of 2,388,324, is selected.  
Figure 5: Schematic diagram of the numerical model of liquid droplets.
## 4 Experimental Results
### Single Flying Height
\begin{table}
\begin{tabular}{c|c c c c c c} \hline \hline
**System Parameters** & \(\mathbf{\Delta X}\) & \(\mathbf{\Delta Y}\) & \(\mathbf{\Delta Z}\) & \(\mathbf{\Delta\omega}\) & \(\mathbf{\Delta\phi}\) & \(\mathbf{\Delta\kappa}\) \\ \hline \(\Delta X\) & 1.0000 & 0.1258 & 0.0000 & 0.1303 & **0.6647** & 0.0231 \\ \(\Delta Y\) & 0.1258 & 1.0000 & 0.0000 & **0.9905** & 0.0922 & 0.0296 \\ \(\Delta Z\) & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \(\Delta\omega\) & 0.1303 & **0.9905** & 0.0000 & 1.0000 & 0.0916 & 0.0263 \\ \(\Delta\phi\) & **0.6647** & 0.0922 & 0.0000 & 0.0916 & 1.0000 & 0.0351 \\ \(\Delta\kappa\) & 0.0231 & 0.0296 & 0.0000 & 0.0263 & 0.0351 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation matrix of mounting parameter estimates from calibration test 1 (the potential high correlation values are shown in red). in the correlation between \(\Delta Y\) and \(\Delta\omega\) from 0.9905 to 0.9032 and the correlation between \(\Delta X\) and \(\Delta\phi\) is also reduced from 0.6647 to 0.1025. These reductions can be attributed to the variation in \(z^{\prime}\) and \(x^{\prime}\)-coordinates, as derived in the theoretical bias impact analysis. The correlation between all the other parameters has also decreased, thus proving the improvement in calibration results. The average accuracy after calibration as quantified by the square root of the a-posteriori variance factor (\(\hat{\sigma}_{o}\)) is 2.22 cm in this case, which is also less than the previous case of 3.12 cm.
# High-Power Broadband Frequency Chirped Intensity-Modulated Single-Frequency 1064-nm Laser
## 5 Experimental Results and Discussions
### Modulation Depth  
In order to compare the modulation depth before and after amplification. The modulation curves measured in the time domain with an oscilloscope are presented in Figure 8. When the modulation frequency was fixed at 1 GHz, the modulation depth decreased from 0.88 to 0.78 after amplification. The modulation waveform was smoother after amplification, as the higher order harmonics of the modulation frequency were suppressed. The decrease of the modulation depth may be due to a high spontaneous emission in the fiber amplifier. When the modulation frequency increases from 10 MHz to 1 GHz, the modulation depth decreases from 1 to 0.78.  
Figure 6: Stability of the spectra of modulation (**a**) before and (**b**) after amplification.
## 2 Rf Model: Simulation Results vs. Experimental Measurements
### Eigenmode Results  
Eigenmode simulations have been performed by using CST Microwave Studio. To properly accelerate particles, the cavity operates in the fundamental TEM mode of the coaxial cavity, whose vector plot along the structure median plane is visible in Figure 4. As expected, the maximum amplitude corresponds with the contours of the Dee. Since the plot is a snapshot taken at a particular (and arbitrary) fixed phase, the electric field arrows in one side of the Dee (beam entrance) have opposite directions with respect to the other side of the Dee (beam exit). Actually, if the magnetic field is properly tuned, the RF field phase will be synchronized to the revolution period of the particle; in this latter case, the electric field arrows direction will be inverted in the time the particle travel from one side to the other, again providing acceleration to the beam both at the entrance and at the exit side.  
The red arrows (high intensity) show that the electric field has a dominant accelerating component in-plane. However, corresponding to the center of the Dee, an orthogonal component is present at a lower intensity (blue arrows) which pushes the beam up and down transversely to the median plane and never acts as an accelerating component. The TEM mode resonance frequency of the cavity can be tuned by moving the top and bottom sliding shorts. Further fine tuning of the operating frequency (in the KHz range) can be performed by using the movable trimmer. The cavity resonant frequencies obtained in simulation at different position of the sliding shorts result in excellent agreement with the measured frequencies (see Figure 5) obtained by connecting the coupler port of the coaxial cavity to a Vector Network Analyzer (VNA).  
Figure 4: Electric field vector-plot of the fundamental TEM mode plotted along the cavity median plane; the green arrow represents the beam direction of motion. Note: arbitrary electric field normalization has been applied.  
Figure 5: Sliding short distance from the median plane vs. TEM mode frequency. Eigenmode simulations performed with CST (red dots) are compared with experimental measurements (blue dots).
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 5 Influence of Structure Parameters on Torque Density
### Thickness of Permanent Magnet  
From Equation (8), the maximum value of a modulated field excited by an armature current will be improved when the thickness of permanent magnet is decreased, which brings a positive influence on the torque density. From Equation (11), the amplitude of imaginary current of a permanent magnet will be reduced when the thickness of the permanent magnet is decreased, which brings a negative influence on the torque density. When both positive and negative influences are considered, there is a reasonable range of the thickness of permanent magnet.  
The thickness of permanent magnet \(h_{\mathrm{m}}\) is analyzed as the unique variable by 3D FEM, and the results are shown in Figure 12. From Figure 12: (1) the average torque reaches the maximum value when the thickness is 1.5 mm; and (2) the torque ripple reaches the minimum value when the thickness is 1.25 mm.  
In addition, the overload capability of the motor is influenced by the thickness. If the permanent magnet is thin, the core will become more saturated when the motor works under overload conditions, as the equivalent air-gap length is small. Moreover, the permanent magnet is more liable to be irreversibly demagnetized, which is dangerous for the motor. Thus, a larger thickness should be chosen when the overload capability is considered.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 3 Framework  
The overall network architecture of CarrDet, as depicted in Figure 2, consists of four main steps: the feature extractor, carrier feature block attention module, detection head, and symmetry-based defective carrier detection module. From the input image, the feature extractor learns feature representation. Then, we strengthen the attention on the edge of the carrier through the channel attention and spatial attention mechanism of the carrier feature block attention module. With these features as input, the detection head generates a series of carrier candidate frames. Following that, the symmetry-based defective carrier detection module eliminates defective carrier candidate frames by pairwise similarity evaluation of corresponding positions. Finally, the refined candidate frames are concentrated to form the final output. The detailed implementations of each module are illustrated as follows.
## 3 Experiments
### Evaluation Performance Metrics  
Three evaluation performance metrics are employed to evaluate the effectiveness of proposed EnGS-DGR model, including the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE).  
1. _RMSE_ values reflect the deviation between observed value and true value, which is described as: \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{\left({{{\gamma}_{i}}-{{\hat{Y}}_{t}}} \right)^{2}}}.\] (14)
2. _MAE_ values reflect the actual situation of the forecast value error, which is given by: \[MAE=\frac{1}{n}\sum_{i=1}^{n}{\left|{{{\gamma}_{i}}-{{\hat{Y}}_{t}}}\right|}.\] (15)
3. _MAPE_ values measure the accuracy of forecasting, which is described as: \[MAPE=\frac{100}{n}\sum_{i=1}^{n}{\left|{{{\frac{{{Y}_{t}}-{{\hat{Y}}_{t}}}{{{ Y}_{t}}}}}}\right|}{{{\%}_{\text{e}}}}.\] (16)
## 3 Results
### Experimental Results in Urban Canyon 1  
#### 3.2.1 Results of GNSS-RTK Availability Assessment  
To evaluate the proposed GNSS-RTK availability assessment method, the ATE of the u-blox measurement and the corresponding mean elevation angle mask of the surrounding local map is depicted in Figure 6a with three representative places annotated. The mean elevation angle mask is defined as that of a fixed-size local map with 50 m (Euclidean distance) both forward and backward of the rover. Thus, u-blox epochs within 50 m from the start and the end have no corresponding mean elevation angle masks. Figure 6b displays streetscapes in these three places. Figure 6c shows the elevation angle mask at each subregion of the local point cloud map 360 degrees around the place. It is worth mentioning that the direction from 0\({}^{\circ}\) to 360\({}^{\circ}\) on the outermost circle, namely the azimuth angle defined by Equation (2), refers to the position of the 3D LiDAR sensor, thus depending on the scanning pattern of the LiDAR at the correspondent epoch. As for Velodyne HDL-32E, the 0\({}^{\circ}\) direction at different epochs changes continuously for all-around detection. It is not equal to the forward direction of the vehicle. For consistency, the directions on each elevation angle mask are labeled clockwise with the 0\({}^{\circ}\) direction upward. The elevation angles (\({}^{\circ}\)) are depicted by concentric circles increasing from the outside to the inside.  
The 1 place has skyscrapers almost on three sides with more than half part of the sky mask reaching up to 50\({}^{\circ}\) shown in Figure 6b1,c1, respectively. As displayed in Figure 6a, the calculated mean elevation angle mask and the ATE of the u-blox are consequently large, which implies the GNSS-RTK solution is unavailable. What is more, at the 2 place as shown in Figure 6b2, there is an open crossroad. Consistent with the scenario, the estimated mean elevation angle mask illustrated in the bottom panel of Figure 6a,c2, and the ATE of the u-blox illustrated in the top panel of Figure 6a, are notably small meaning that the GNSS-RTK solution tends to be reliable. However, at the 3 place shown in Figure 6b3, it is open near Victoria Harbor. The u-blox measurement is significantly accurate, seen in the top panel of Figure 6a. Unexpectedly, the elevation angle mask is large, with more than one-third reaching up to 40\({}^{\circ}\), as shown in Figure 6c3, due to the tall trees detected in the point cloud map.  
Figure 6: (**a**). The top panel shows the ATE of the u-blox GNSS-RTK solutions calculated by RTKLIB [7] collected in urban-1. The bottom panel shows the mean elevation angle mask of the local point cloud map around the correspondent u-blox epoch. 1, 2, and 3 denote representative cases. (**b**). (**b1–b3**) are streetscapes of the three places denoted in (**a**) respectively. (**c**). (**c1–c3**) are the sky masks of the three places denoted in (**a**) respectively.
# Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
## 2 Materials and Methods
### Other Features Extraction
#### 2.3.2 HRV Features  
For ECG signal, the R-peak is detected to obtain the R-R interval (RRI) sequence. In this study, 7 HRV features were calculated using mean value (\(MNN\)), standard deviation (\(SDNN\)), root mean square of continuous difference (\(RMSSD\)), \(NN50\), \(PNN50\), _skewness_, and _kurtosis_ of RRI sequence: all are shown in Table 3[10; 11].
## 4 Example Application  
### Road Network Properties  
The road network consists of 5 nodes and 7 road sections, node 1 is the origin of the road network, and the destinations are node 4 and node 5. \(q_{14}=10\), \(q_{15}=15\). The topology of the road network is shown in Figure 2. The road network attributes are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Road Section**} & \multicolumn{2}{c}{**Free Flow Travel**} & \multicolumn{1}{c}{**Capacity**} & \multirow{2}{*}{**Probability**} \\  & **Time (min)** & & **(pcu/h)** & \\ \hline
1 & 4 & 25 & 1 \\ \hline
2 & 5.2 & 25 & 0.8 \\ \hline
3 & 1 & 15 & 0.9 \\ \hline
4 & 5 & 15 & 0.7 \\ \hline
5 & 5 & 15 & 1 \\ \hline
6 & 4 & 15 & 0.6 \\ \hline
7 & 4 & 7.5 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 1: Road network attribute table.  
Figure 2: Example road network diagram.  
The capacity of road fluctuates with probability. There are 16 road network states in total, which are arranged from large to small according to different state probabilities, as shown in Table 2. The BPR (Bureau of Public Road) function is selected as the link impedance function, where let \(\alpha=0.15\) and \(\beta=4\). In order to simplify the analysis, the threshold value of travel time and travel demand is selected as the same value: \(\tau=1.05\), \(\mu=1.20\), and the service level correction coefficient is taken as 1. The parameters \(\theta=0.05\) which denote traveler's proficiency in route selection in the SUE model.