esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Identification Algorithms
The reflection \(x_{r}=(1+\alpha)x_{c}-\alpha x_{h}\) of \(x_{h}\) with respect to \(x_{c}\) with coefficient \(\alpha\) and the value of the function \(f_{r}=f(x_{r})\) at \(x_{r}\) is to be calculated. 5. Expansion. Then \(f_{r}\) is compared with the values \(f_{h},f_{g}\), and \(f_{l}\): If \(f_{r}<f_{l}\), the selected search direction is successful, and the search step can be increased using the expansion operation. A new point \(x_{c}=(1+\gamma)\)\(x_{c}-\gamma x_{r}\) and the function value \(f_{e}=\mathrm{f}(x_{e})\) are to be calculated. If \(f_{e}<f_{r}\), the simplex expands up to this point: set \(x_{h}\) equal to \(x_{e}\) and \(f_{h}\) equal to \(f_{e}\), and then finish the current iteration (go to Step 6). However, if \(f_{r}<f_{e}\), an expansion is too wide: set \(x_{h}\) equal to \(x_{r}\) and \(f_{h}\) equal to \(f_{r}\), and then finish the current iteration (go to Step 6). If \(f_{l}<f_{r}<f_{g}\), the point selection is acceptable; the new one is better than the two previous points. Set \(x_{h}\) equal to \(x_{r}\) and \(f_{h}\) equal to \(f_{r}\), and then finish the current iteration (go to Step 6). If \(f_{g}<f_{r}<f_{h}\) then set \(x_{h}\) equal to \(x_{r}\) and \(f_{h}\) equal to \(f_{r}\). 6. Contraction. Determine the point \(x_{s}=\beta x_{h}+(1-\beta)\)\(x_{c}\) and the function value at this point \(f_{s}=\mathrm{f}(x_{s})\). If \(f_{s}<f_{h}\) set \(x_{h}\) equal to \(x_{s}\) and \(f_{h}\) equal to \(f_{s}\), then go to Step 6. 7. Shrinking. If \(f_{s}>f_{h}\), that means that the initial points were more successful. Perform simplex global contraction (homothety) to the point with the least value of \(x_{i}=x_{l}+(x_{i}-x_{l})/2\), \(I\
eq 1\). The function values at these points are to be calculated. 8. The last step is the convergency check-up. It can be done by simplex vertices dispersion evaluation. The mutual proximity of the simplex vertices assumes their proximity to the searching objective function minimum. If the desired accuracy is not reached, the algorithm can be continued from Step 2.
## 3 Results and Discussion
### Exposure Testing of Photoresist Copolymer
#### 3.5.2 Lithographic Evaluation  
After a series of processing steps including pre-baking (100 \({}^{\circ}\)C, 1 min), mask exposure (the exposure light source is a 248 nm UV lamp and the exposure dose is 20 mJ/cm\({}^{2}\) and the line width of the exposed portions on the mask is 100 \(\upmu\)m), post-baking (100 \({}^{\circ}\)C, 1 min), and development (2.38% TMAH, 1 min), the photolithographic patterns of photoresists PTBMs and PBM were analyzed by SEM. The acid diffusion size (\(S\)), defined as the difference between the actual line width and the theoretical line width, was calculated. As depicted in Figure 10, the values of \(S_{\text{PTBM-0}}\), \(S_{\text{PTBM-1}}\), \(S_{\text{PTBM-2}}\), \(S_{\text{PTBM-3}}\), \(S_{\text{PTBM-4}}\), and \(S_{\text{PBM}}\) were 37 \(\upmu\)m, 25 \(\upmu\)m, 15 \(\upmu\)m, 12 \(\upmu\)m, 9 \(\upmu\)m, and 69 \(\upmu\)m, respectively. The acid diffusion ratio (\(s\)), which represents the ratio of \(S\) to the mask line width (L\({}_{\text{mask}}\)), the values of \(S_{\text{PTBM-0}}\), \(S_{\text{PTBM-1}}\), \(S_{\text{PTBM-2}}\), \(S_{\text{PTBM-3}}\), \(S_{\text{PTBM-4}}\), and \(S_{\text{PBM}}\) were 37%, 25%, 15%, 12%, 9%, and 69%, respectively.  
Compared the SEM images of patterns of PTBM-4 and PBM (Figure 10E,F), it can be seen that the pattern of PTBM-4 exhibited a lower acid diffusion ratio because of the covalent bonding of photoacid units onto the copolymer backbone, which effectively reduced the diffusion effect of the acid and reduced the line width roughness. From Figure 10A-E, it can be seen that the addition of POSS units not only enhanced the thermal performance of the copolymer but also, due to the steric hindrance effect of the POSS structure, restricted the irregular movement of H\({}^{+}\), thereby reducing acid diffusion to a certain extent. It can be observed that there is a significant decrease in the acid diffusion size with the gradual increase of POSS content from PTBM-0 to PTBM-1 and from PTBM-1 to PTBM-2. However, the decrease in acid diffusion size is limited when continuing to  
Figure 9: FT-IR of PTBM-4 before and after exposure.  
increase the POSS content from PTBM-2 to PTBM-3 and PTBM-4, indicating that the impact of POSS on acid diffusion has reached an upper threshold limit.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 5 Future Aspects of IoSB
### Data Transmission and Localization  
For easier use and maintenance, each node in IoSB should provide accurate location information at any time and place. Therefore, a comprehensive solution for the third problem mentioned in the previous chapter should be proposed. In [102], a device tracking strategy in NB-IoT systems using Observed Time Difference of Arrival (OTDoA) measurements was presented, which can consume less power because of using NB-IoT [103]. In this strategy, the accuracy evaluation of mobile node in EPA and ETU channel models (corresponding to low and high delay spread environments respectively) were given and results showed that the maximum positioning error was about 300 m when moving at 10 km/h (close to cycling speed). More importantly, narrow band technology can bring massive capacity. The authors in [104] combine D2D link with NB-IoT for a routing extension and enable direct communication between nodes, which is expected to guarantee QoS in NB-IoT systems.  
Based on the above discussions, we give the future positioning and communication scenarios in IoSB in Figure 11. There are three parts in Figure 11 which are hybrid location, D2D-based NB-IoT communication and mixed power supply. In the NLoS condition, the bicycle node can be located by other nearby bicycle nodes. In addition, thanks to D2D communication, nodes can upload information to base stations in a multi-hop mode. Furthermore, multi-source power supply shown in Figure 11 can greatly prolong the life of a node. However, further study on device discovery, resource management, and mobility, security and privacy in D2D communication should be done beforehand [105].
## 4 Case Analysis
### Yangau Hydropower Station Flood Discharge Tunnel  
Yangau Hydropower Station is a large-scale hydropower hub project which mainly generates electricity. It started construction in December 2021 and is currently in the construction stage. The project is mainly composed of a river-blocking rockfill dam, a flood discharge tunnel on the left bank and a water diversion and power generation building on the right bank. Among these, the flood discharge tunnel is 467 m long and its permanent support adopts a C25 reinforced concrete circular lining with an inner radius of 5.3 m and a thickness of 0.5 m [28; 29]. With the calculation formula in Section 2, the maximum value of the internal force of the lining structure at half-water level, full-water level and pressurized water conveyance is calculated under the action of only internal water pressure, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Maximum Internal Force**} & \multicolumn{2}{c}{**Pressureless Water Conveyance Level**} & \multicolumn{2}{c}{**Pressurized Water Conveyance Pressure**} \\ \cline{2-5}  & **Half Water Level** & **Full Water Level** & **0.2 MPa** & **0.4 MPa** \\ \hline Bending moment (kN-m) & 16.8 & 62.8 & 487.6 & 913.8 \\ Axial force (kN) & 36.6 & 52.2 & 636.2 & 1220.2 \\ shearing force (kN) & 13.2 & 50.2 & 341.4 & 633.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The maximum internal force of water conveyance tunnel lining.  
According to the data in Table 2, due to the large cross-section size of the flood discharge tunnel, the internal force generated by the lining during pressurized water conveyance is large. Under the pressure of 0.2 MPa, the maximum axial tensile force of the lining reaches 1231.7 kN and the maximum axial tensile stress is 2.46 MPa, which is much higher than the tensile strength of C25 concrete. In addition, the maximum bending moment when discharging water under pressure is also very large. Therefore, the flood discharge tunnel should avoid generating water pressure inside.
# Analysis of Compound Stained Cervical Cell Images Using Multi-Spectral Imaging
## 3 Experiments and Results
### The Performance of Cervical Cell Screening
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analysis Method** & **Cell Types** & **Quantity** & **Mean of DI** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **CV** \\ \hline \multirow{4}{*}{Feulgen staining method} & Diploid cells & 3517 & 1.034 & 0.092 & 8.918 \\  & Tetraploid cells & 243 & 2.032 & 0.128 & 6.276 \\  & Aneuploid cells & 86 & 1.353 & 0.134 & 9.934 \\  & High ploidy cells & 32 & 3.424 & 0.429 & 12.544 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Compound staining method of this paper \\ \end{tabular} } & Diploid cells & 3780 & 1.026 & 0.094 & 9.111 \\  & Tetraploid cells & 210 & 2.039 & 0.141 & 6.921 \\ \cline{1-1}  & Aneuploid cells & 90 & 1.377 & 0.152 & 11.063 \\ \cline{1-1}  & High ploidy cells & 30 & 3.440 & 0.463 & 13.462 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contrast data of positive case. Figure 9: The analysis results of positive case; (**a**) the results of Feulgen-staining method, (**b**) the results of compound-staining method. cells with a higher gray level of the nucleus. However, the mean values of DNA indices of diploid cells and tetraploid cells calculated by both methods are basically consistent, and cells with a DNA index greater than 2.5 can be detected by both methods. Therefore, this multi-spectral imaging system still provides high accuracy and reliability in screening cervical cells.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 1 Introduction  
### Motivation  
With the aggravation of greenhouse effect and the consumption of fossil energy, automobile companies are developing pure electric vehicles (EVs) vigorously to reduce emissions and improve fuel economy [1]. The efficiency of electric drive system (EDS) is a key factor affecting the energy consumption of the whole vehicle and EVs with high EDS efficiency can bring good driving experience to the driver [2], so it is essential to further improve the EDS efficiency.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Confocal Microscopy
A large number of different confocal microscopes are reported in the literature and are based on different scanning methods to image different sections of the body organs, which are described later in the paper [20,21]. In case of confocal endoscopy, the illumination and collection system are aligned on a single focal plane. The laser light is transported to the distal lens using an optical fiber bundle focused at the tissue sample. The reflected light is refocused through the same lens with a pinhole, which excludes the out-of-focus scattered light, increasing the resolution. Usually, the scanning is made possible by placing the scanning mirrors at the proximal end of the fiber bundle. The systematic design of a confocal endoscope is shown in Figure 3.  
Cell-viZio developed by Mauna Kea Technologies is one of the most widely used technologies for clinical applications that uses a variety of confocal miniprobes to provide high-quality in vivo tissue imaging [22]. The newly developed Cell-viZio device provides confocal fluorescence microscopy imaging using optical fibers. This technology is named fibered confocal fluorescence microscopy (FCFM). [PERSON] et al. tested three different FCFM probes for microvascular observations and compared the results with intravital fluorescence imaging proving the flexibility, micro-invasiveness, and in vivo imaging of the deep areas over the conventional microscopy. The probe with the smallest cross-sectional area has a diameter of 650 \(\upmu\)m, and provides images with lateral and axial resolutions of 5 and 15 \(\upmu\)m, respectively, with a frame rate of 12 Hz [23]. Recently, [PERSON] et al. developed an ultra-small sized confocal endomicroscope. The 2.4-mm-diameter probe (compatible with the 2.8-mm working channels of clinical endoscopes) used by [PERSON] et al. to image the colon of a mouse provides images with a lateral and axial resolution of 1.5 and 12 \(\upmu\)m, respectively. The rigid length of the head of an imaging probe is one of the key parameters that dictates the body part that can be imaged. The optical path of the light beam at the fiber tip is folded on-axis using reflecting mirrors to reduce its distal rigid length. The large field of view of 350 \(\upmu\)m \(\times\) 350 \(\upmu\)m, the small working distance of 50 \(\upmu\)m, and the small diameter make this device compatible in future clinical applications [24]. Figure 4a shows an in vivo confocal reflectance image of the tissue phantom containing cervical cells and collagen [20],  
Figure 3: Schematic diagram of a confocal endoscope. and Figure 4b,c show confocal images of an adenoma [24], and the normal mucosal surface of the colon of a mouse in fluorescence [24].
# Tunable Effect of the Calcination of the Silanol Groups of KIT-6 and SBA-15 Mesoporous Materials
## 3 Results and Discussion  
### Effect of Calcination Temperature on the Quantity of Polymer  
The effect of calcination on the amount of polymer in the mesoporous material was assessed by measuring the amount of carbon remaining in the material by elemental analysis. The results are shown in Table 2.  
Both SBA-15 and KIT-6 lose most of their organic templates at 300 \({}^{\circ}\)C in the first two heat treatment methods. After treatment for 5 h, 0.32% and 0.42% of the carbon remains for SBA-15 and KIT-6, respectively. Upon increasing the treatment time to 10 h, the amount of carbon remaining drops to 0.19% for SBA-15 and 0.30% for KIT-6, indicating that the polymer remains in the mesoporous materials. Previous studies [51; 55; 69] have shown that, at 200 \({}^{\circ}\)C, the polymer decomposes and that, by calcining for 4 h at 300 \({}^{\circ}\)C or for 4 h at 350 \({}^{\circ}\)C, the polymer is removed; however, to the best of our knowledge, no work has quantified the residual polymer content. When comparing the results obtained at a higher temperature (500 \({}^{\circ}\)C) with those obtained at an intermediate temperature (400 \({}^{\circ}\)C), the difference in the amount of organic material removed is too small. Another factor is the time at higher temperatures; calcining for 10 h does not result in substantial benefits in terms of the removal of organic material, since the values of % C remaining are identical at 400 \({}^{\circ}\)C and are almost equal at 500 \({}^{\circ}\)C for the two  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Temperature \({}^{1}\)(\({}^{\circ}\)C)** & **Time (h)** & **SBA-15 Mass (mg)** & **KIT-6 Mass (mg)** \\ \hline
1 & 300 & 5 & 514 & 519 \\
2 & 300 & 10 & 515 & 516 \\
3 & 400 & 5 & 512 & 556 \\
4 & 400 & 10 & 515 & 516 \\
5 & 500 & 5 & 508 & 514 \\
6 & 500 & 10 & 502 & 503 \\ \hline \hline \end{tabular} \({}^{1}\) The heating ramp of 2\({}^{\circ}\) min\({}^{-1}\).  
\end{table}
Table 1: Methodologies used for the calcination of SBA-15 and KIT-6 samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Residual Carbon Content (\%)** & **Samples** & **Residual Carbon Content (\%)** \\ \hline SM0 & 25.33 & KM0 & 28.86 \\ SM1 & 0.32 & KM1 & 0.42 \\ SM2 & 0.19 & KM2 & 0.30 \\ SM3 & 0.08 & KM3 & 0.09 \\ SM4 & 0.08 & KM4 & 0.09 \\ SM5 & 0.03 & KM5 & 0.09 \\ SM6 & 0.01 & KM6 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Residual carbon content in the SBA-15 and KIT-6 samples.  
mesoporous materials. Nonetheless, at 300 \({}^{\circ}\)C, there is a large difference between calcination at 5 and 10 h. The results of this analysis are shown in Figure 2.
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 5 Experimental Results
### Verifying the Deep Learning-Based Localization Switching Method to Solve Corridor Effect
#### 5.3.2 Verification of the Localization Switching Method in Practice  
On the experimental corridor, we first manually move the mobile robot along the ground truth to record the trajectory of SLAM localization. Simultaneously, the deep learning-based classifier also is used to detect the long corridor regions. As the experimental results (shown in Figures 17 and 18 and Table 5), it is impossible to complete the trajectory tracking and localization task if using SLAM only. Otherwise, by switching between SLAM and odometry localization system using our proposed method, the AGV can complete the trajectory tracking even in sparse LiDAR feature environment. Our experimental results proved the effectiveness of the deep learning-based localization switching method that involve improved Pure Pursuit robustness and feasibility.  
the corridor effect. The practical experimental results verified that the maximum error of the modified Pure Pursuit is within 45 mm, with a 77% improvement rate compared to the original Pure Pursuit. The improved Pure Pursuit algorithm also increased the speed by more than 5.6%. Moreover, the proposed localization switching method using deep learning helps to increase 36.25% of completion rate higher than that only using SLAM localization, prove the robust effectiveness of the proposed method in practice.  
[PERSON] is responsible for revising papers, design deep-learning based localization switching method, verify experiments; [PERSON] is responsible for designing SLAM and Odometry localization, design deep-learning based localization switching method; [PERSON]-[PERSON] is responsible for path planning part and improve PurePursuit trajectory tracking method; [PERSON]-H.K. is the advisor who orients research direction for the paper, gives comments and advices to do this research. All authors have read and agreed to the published version of the manuscript.  
Ministry of Science and Technology, Taiwan under Grant MOST 109-2221-E-011-112-MY3 and the \"Center for Cyber-physical System Innovation\" from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.  
Not applicable.  
The authors declare no conflict of interest.
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
## 4 Parameter Study of the Rib Design
### Comparison between the Original and Modification Model  
The results of our parameter studies showed that increasing the length or height was more effective than increasing the width of the rib. For the same width of 4 mm, the efficiency of dividing the stiffness increase by the mass of the ribs by varying the length and height was compared. Finally, the most efficient rib shape parameters with a length of 20 mm and height of 25 mm were selected for the modification model. Figure 12 shows the final modified design considering the shape and manufacturability of the housing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**L, mm** & **H, mm** & **Rib Mass, g** & **Freq, Hz** & **Stiff. Line, N/mm** & **Stiff. Increase, \%** & **Efficiency, \%/g** \\ \hline \multirow{6}{*}{10} & 15 & 5.5 & 1437.9 & \(3.14\times 10^{2}\) & 11.5 & 2.08 \\ \cline{2-7}  & 20 & 7.4 & 1446.9 & \(3.24\times 10^{2}\) & 14.8 & 1.99 \\ \cline{2-7}  & 25 & 9.4 & 1453.4 & \(3.31\times 10^{2}\) & 17.2 & 1.83 \\ \cline{2-7}  & 30 & 11.3 & 1457.4 & \(3.35\times 10^{2}\) & 18.9 & 1.67 \\ \cline{2-7}  & 35 & 13.3 & 1460.2 & \(3.39\times 10^{2}\) & 20.2 & 1.52 \\ \hline \multirow{6}{*}{15} & 15 & 8.4 & 1459.2 & \(3.34\times 10^{2}\) & 18.5 & 2.21 \\ \cline{2-7}  & 20 & 11.3 & 1476.7 & \(3.54\times 10^{2}\) & 25.5 & 2.26 \\ \cline{2-7}  & 25 & 14.2 & 1489.1 & \(3.69\times 10^{2}\) & 30.9 & 2.18 \\ \cline{2-7}  & 30 & 17.1 & 1498.3 & \(3.81\times 10^{2}\) & 35.2 & 2.06 \\ \cline{2-7}  & 35 & 20.1 & 1504.6 & \(3.91\times 10^{2}\) & 38.5 & 1.91 \\ \hline \multirow{6}{*}{20} & 15 & 11.3 & 1474.5 & \(3.49\times 10^{2}\) & 23.9 & 2.11 \\ \cline{2-7}  & 20 & 15.2 & 1499.6 & \(3.79\times 10^{2}\) & 34.5 & 2.27 \\ \cline{2-7}  & 25 & 19.0 & 1519.1 & \(4.06\times 10^{2}\) & 43.8 & 2.31 \\ \cline{2-7}  & 30 & 22.9 & 1533.9 & \(4.27\times 10^{2}\) & 51.6 & 2.25 \\ \cline{2-7}  & 35 & 26.8 & 1544.8 In the modification model, the local stiffness of the stiffness weak point (part 2) was also evaluated. The driving point FRF was compared with the original model in the frequency range of interest (Figure 13). According to the results, the maximum amplitude of acceleration decreased by about 1.87 dB, and the local stiffness increased by about 43.3% compared with the original model.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis
### Modal Analysis  
The first step in performing a dynamic analysis is determining the structure's natural frequencies and mode shapes, taking zero damping into account. The results of this analysis define the structure's dynamic behavior and can be used to predict how the structure will respond to dynamic loads. The modal analysis of the combination of the separation mechanism and the MF satellite is performed to determine the natural frequency, verify whether the natural frequency meets the requirements of the launch vehicle, and avoid resonance with the launch vehicle during the launch process. The Lanczos algorithm is used to extract the first three modes of the combination of the separation mechanism and the MF satellite, as illustrated in Figure 9, and their natural frequencies are listed in the Table 4. The combination's natural frequency is significantly higher than the required frequency of the launch vehicle to meet design requirements. The first three modes of the separation mechanism are shown in Figure 10, and the natural frequencies are shown in Table 5. The separation mechanism has a high fundamental frequency and will not resonate with the launch vehicle.  
Figure 8: Quasi-static acceleration analysis results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Frequency (Hz)** & **Mode Shape** \\ \hline
1 & 77.7 & \(X\)-Direction \\
2 & 70.8 & \(Y\)-Direction \\
3 & 82.6 & \(Z\)-Direction \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modal analysis results of the combination.  
Figure 9: First three-order natural frequencies of combination.