esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup  
### Road, Lane and Other Configuration  
The road network and traffic signals used for simulation were as shown in Figures 4 and 5 with time steps of 0.1 s, a lane width of 3.5 m, and three lanes in each direction (one shared lane for going straight and turning right, one straight lane, and one left-turn lane), and the length in each direction was 500 m. The traffic flow was mixed flow, including platooning vehicles (yellow vehicles are leaders and green vehicles are followers) and RVs (magenta vehicles). In the figures, there are also red vehicles in catch-up mode, blue vehicles in catch-up follower mode, and cyan vehicles that are CVs but moving similarly to RVs because they are not within any platoon range. The model only collects data on platooning vehicles during training. Data on RVs are not collected. Based on the data collected, the model selects phases to optimize the waiting time for both CVs and RVs.  
We gave several simulation scenarios to evaluate different aspects of the model. By varying the platoon size, we can model the traffic flow at sparse or dense levels. As mentioned in the introduction, the platoon size affected the model stability, and it is a trade-off between capacity and stability. Because urban intersections entail many constraints, the platoon was simulated with a size of 1, 2, and 3 vehicles.  
For the above reasons, we established a traffic flow with 50 platoons (with a size of 1, 2, and 3) and 80 RVs in each lane in one hour. Traffic generation is an important aspect that can affect model performance. In our model, the participating vehicles follow the Weibull distribution, with the characteristic that the traffic gradually increases to a peak in the middle of the episode and then gradually decreases towards the end. This should ensure that all vehicles enter with no equivalent between episodes.
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
###### Abstract  
Addressing the prevalent issues of low accuracy, low efficiency, and poor image quality in online diameter measurement of large-sized stepped shafts, this study introduces a novel method based on a symmetrical dual-telecentric optical path utilizing dual CCDs, specifically designed for step shafts with diameters ranging from 600 mm to 800 mm. By developing and optimizing an imaging system grounded in the object-image dual-telecentric optical path principle and employing Zemax software for comprehensive analysis and optimization, this research achieves significant findings. The system's Airy disk radius is calculated at 3.204 \(\upmu\)m; the modulation transfer function (MTF) remains above 0.6 across various fields of view at a spatial cutoff frequency of 71.4 lp/mm, with smooth MTF curves; the field curvature is confined within 0.1 \(\upmu\)m; and the distortion is maintained below 0.1%, fulfilling high-quality imaging requirements. Additionally, a tolerance analysis is conducted to ensure the system's stability and reliability. Applied to an experimental setup for measuring the diameter of large-sized step shafts, the system demonstrates an improved measurement precision of 0.02 mm. This research offers a robust technical solution for the high-precision online measurement of large stepped shaft diameters, presenting significant practical implications for enhancing productivity and product quality.  
Keywords:dual-telecentric principle; non-contact measurement; tolerance analysis +
Footnote †: journal: Applied
## 2 Materials and Methods
### Monitoring Arrangement  
The deformation monitoring data during the whole process of the excavation of the Huaxi Park Station pit were analyzed to understand the deformation temporal characteristics of mucky soil pit engineering in the construction process, so as to provide theoretical support for verifying the correctness of the 3D numerical model and for analyzing the deformation characteristics of mucky soil.  
The plan layout of the monitoring points is shown in Figure 2. Monitoring was conducted with a frequency of once per day during all excavation stages. Six main sections were set up along the longitudinal direction of the foundation pit. The top of the diaphragm wall and the deep horizontal displacement monitoring points were set up, and surface settlement monitoring points were set up in the range of 2 times the depth of the excavation of the foundation pit, and the surface settlement monitoring points were gradually thinned out from the foundation pit to the outside. Typical monitoring sections were selected to analyze the horizontal displacement of the diaphragm wall and the surface settlement outside the pit in the end well on one side and the standard section of the pit, respectively. The specific monitoring instruments include the following: (1) the surface settlement (DB1-DB16) was investigated using the Leica LS15 Laser Scanner; (2) the diaphragm wall's lateral  
Figure 1: Soil profile along the standard section of Huaxi Park Station.  
displacement (CX1-CX16) was monitored using inclinometers; and (3) the support axial force was investigated using a portable frequency reader. All of these instruments were examined during the excavation to reduce measurement errors.  
According to the foundation pit design requirements and the Technical Specification for Retaining and Protection of Building Foundation Excavations (JGJ120-2012), for station foundation pits with depths exceeding 15 m, the failure of the support structure or excessive soil deformation can severely impact the surrounding environment and the safety of the main structure construction. Therefore, the Huaxi Park Station foundation pit was classified as Grade I in terms of safety level.  
Considering the surrounding environmental conditions, the environmental protection level of the main foundation pit was classified as Grade II. Control measures were implemented based on the following criteria: the maximum surface settlement was 0.2%H, and the maximum allowable horizontal displacement of the retaining wall was 0.3%H, where H was the excavation depth.
# Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours
## 4 Smart-Median: A Real-Time Pitch Contour Smoother Algorithm
### Considerations  
The Smart-Median algorithm is based on the following considerations:  
1. Only the incorrectly estimated pitches need to be changed. Therefore, it is necessary to decide which jumps in a contour are incorrect.
2. To calculate the median, some of the estimated pitches around the incorrectly detected F0 should be selected. This represents the window length for calculating the median. Therefore, the decision on the number of estimated pitches before and/or after the erroneously estimated pitches provides the median window length. Thus, a delay is required in real-time scenarios to ensure sufficient successive pitch frequencies are available when correcting the current pitch frequency.
3. There is a minimum duration for which a human can sing.
4. There is a minimum duration for which a human can rest between singing two notes.
5. There is a maximum frequency that a human can sing
6. There is a maximum interval during which humans can move from one note to another when singing.
7. A large pitch interval in a very short time is impossible.  
The following section explains our decisions regarding each of the above considerations.
## 4 Results and Discussion
### Comparative Experiments of Various Networks
We show the classification accuracy of _D2_ for several SNR settings in which the proposed method performed significantly better than the other methods.
## 5 Simulation Results
### Runtime Analysis  
We conducted a further runtime analysis of our proposed PIDSM. We obtained the required 12 combinations from Table 4 and randomly generated 10 testing instances for each combination, with the target distribution density and observation time randomly selected. The proposed PIDSM was then run on each testing instance, and the running time was recorded.  
Figure 11 displays the statistical analysis results, indicating a significant increase in the running time of the PIDSM as the size of the testing instance increases, similar to the  
Figure 10: The reassignment results of the proposed PIDSM.  
trend of communication times shown in Figure 6b. This demonstrates the advantage of our algorithm in dealing with small and medium-sized problems, particularly for small-sized problems where we can obtain a good task assignment solution in only 0.04 s. There are also some differences between Figures 6b and 11. For example, in the {E \(\times\) 200} combination, there is a significant difference in running time compared to the {E \(\times\) 100} combination, while the difference in communication times is not significant. This is because our proposed algorithm enables satellites to communicate with each other about all tasks at once, so the increase in the number of tasks has a limited impact on communication times. However, the calculation of fitness value is more closely related to the number of tasks, and more tasks require a longer time to calculate the fitness value, ultimately resulting in an increase in the running time.
## 4 Case Study  
### Background  
#### 4.1.1 Description of Basic Situation  
A mobile phone recycling enterprise plans to set up a mobile phone recycling logistics network in Jilin Province. The province is divided into nine mobile phone recycling zones by cities, as shown in Figure 4. Through the analysis in the previous chapter, the enterprise takes the mobile phone sellers in each city as the recycling station, making each city own a recycling station, where the recycling scope can cover most areas of the city. The enterprise plans to set up testing and classification centers and recycling and processing centers from the nine recycling zones. In order to meet the demand for recycling, the number of testing and classification centers needs to be controlled between three and five, and the number of recycling and processing centers needs to be controlled between two and four.  
Figure 3: The flow chart of this proposed algorithm.  
According to the _Jilin Statistical Yearbook 2018_, by the end of 2017, the number of permanent residents in Jilin had reached 27.17 million, where the mobile phone users had reached 28.69 million, and the per capita number of mobile phones had reached more than one by the end of 2017, according to data from the Ministry of Industry and Information Technology. The frequency of mobile phone users replacing their mobile phones is affected by the upgrading of mobile phones and the contracts of mobile phone operators. Through market research and analysis, the mainstream mobile phone replacement strategy in the present mobile phone market is to replace every two years. Combined with data from the Ministry of Industry and Information Technology, this paper stipulates that each user owns 1.1 mobile phones on average.  
From the update speed of mobile phones, nearly half of China's mobile phones are facing elimination every year. However, because the market of mobile phone recycling is not sound yet, the recycling rate of mobile phones has been kept at a low level. According to investigation, the recovery rate of mobile phones in China is about 1-2%, which is a huge gap compared with the 30% recovery rate in developed countries. The big gap between the number of discarded mobile phones and the amount of recycling shows that there is a lot of room for improvement in China's mobile phone recycling market. It is believed that with the development of a mobile phone recycling market, the recycling rate of used mobile phones will eventually reach a satisfactory level. Taking the reverse logistics facility established in this paper as an example, its perfect recycling process will greatly improve the recycling rate of mobile phones, so the preset recycling rate of mobile phones in Jilin Province would be 20%. Through calculation, the recycling situation of mobile phones in each city of Jilin Province is obtained, as shown in Table 2.  
Figure 4: Regional map of Jilin Province.
## 2 Experimental Tests
### Experimental Process  
The equipment for the uniaxial compression test is depicted in Figure 2. It employs a self-sufficient assembly of instrumentation systems, which is segregated into three individual parts: an MTS 815 pressure loading system, an acoustic emission monitoring system, and an image monitoring system. The acoustic emission monitoring system consists of a PCI-E acoustic emission meter from PAC and a mic 30 probe. High-precision axial strain gauges with a \(\pm\)4 mm strain travel range and high-precision radial strain gauges with a strain travel range of +12.5, \(-\)2.5 mm are also included. Each system has a unique role in the loading process. The pressure loading system provides an axial load to load the specimen. The acoustic emission monitoring system collects the acoustic emission signals during the damage process, which are then analyzed to determine the energy release inside the specimen during loading. The image monitoring system is responsible for tracking the process of rupture expansion in real time on the surface of the specimen, which reflects the intuitive evolution of the rupture on the specimen surface. The three techniques were sequentially applied during the testing process. To facilitate the smooth progression of the test, the specimen was wrapped with cling film following the water immersion step. For the uniaxial compression test, both load-controlled and displacement-controlled pressurization were executed, with a loading rate of 0.1 KN/s, followed by a switch to displacement loading control of 0.01 mm/min after the preloading condition was reached.
## 3 Results
### Cavity of Length \(L=\lambda/4\)  
#### 3.1.1 Amplitudes in the Cavity  
In this section, we consider the free-walled resonator of length \(L=\lambda/4\) and a medium defined by \(N_{g}=5\times 10^{11}\) m\({}^{-3}\) and \(R_{0g}=2.5\times 10^{-6}\) m (\(f_{0}=1.35\) MHz). The void fraction \(v_{v}=3.2725\times 10^{-5}\). At the source we set \(f=200\) kHz (\(f_{0}/f=6.73\)) and \(p_{s}=20\) kPa. The distribution of amplitudes of the fundamental and harmonic components generated in the cavity is shown in Figure 1. The maximum amplitude is obtained at the source frequency \(f\). The maximum amplitude at the 2 nd harmonic is huge, \(88\%\) of \(p_{s}\), whereas the respective values at the 3 rd and 4 th harmonics are much lower, \(5.45\%\) and \(8.52\%\).
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.7 Accelerometer  
Accelerometers [21] are sensors that can measure changes in movement and orientation, including acceleration forces. These sensors have a wide range of applications in healthcare, where they are used for monitoring physical activity levels and detecting abnormal movement patterns. They are valuable in fall detection systems, especially in the elder care context, where sudden changes in acceleration can signal a fall and allow for timely assistance. Moreover, accelerometers play a crucial role in assessing motor function and gait abnormalities in individuals with neurological disorders. Examples include the Fitbit Charge 4 Fitness Tracker, Garmin Vivasmart 4 Activity Tracker, Xiaomi Mi Band 6 Fitness Tracker, etc.
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 3 Parameter Identification Method for the Hyper-Viscoelastic Model
### Results and Discussions
#### 3.5.3 Verification of the Identified Results  
In order to verify the accuracy of the estimated parameters, one out of the four groups of the identified parameters in Tables 3 and 4 are employed in ANSYS to predict the corresponding responses of the hyper-viscoelastic materials. Comparisons of the results calculated by ANSYS and the data in Figure 5 are plotted in Figure 6.  
In Figure 6, the solid line represents the original data and the scatter points represent the results from ANSYS by using the identified parameters. It is obvious that errors in every experiment are very small, which means the proposed analytical model for hyper-viscoelastic materials and the identification method by using PSA are reliable and efficient.
## 5 Conclusions  
Key aspects of power domain NOMA (PD-NOMA) and cooperative relaying PD-NOMA are successfully presented in this study. An FIS-based system is used to precisely address the power optimization problem, which is a fundamental aspect of PD-NOMA. The proposed system evaluates the performance of PD-NOMA in a scenario where mmWave signals are degraded around the gNodeB. Overall, the proposed system achieves user-fairness, and system performance is considerably improved thanks to FIS-based power optimization and cooperative relaying. Future studies on the proposed technique can include multiuser clustering systems with multiple cooperation stations, allowing multiple cell-center users to cooperate with the cell-edge users for performance improvement in denser PD-NOMA networks.  
Figure 14: Performance analysis for two users in the proposed FIS based system where U\({}_{1}\) is assigned a maximum power factor and U\({}_{2}\) a minimum power factor.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Users** & **d** & **i/p SNR** & **d\({}_{f}\)** & **Power Factor** & **Power Factor** & **SNR Level** & **SNR Level** \\  & & & **(DBPA)** & **(FIS)** & **(DBPA)** & **(FIS)** \\ \hline UE\({}_{1}\) & 60 m & 14 dB & 10 m & 0.23 & 0.34 & 10 dB & 8.8 dB \\ \hline UE\({}_{2}\) & 180 m & 10 dB & 28 m & 0.77 & 0.66 & 5 dB & 6 dB (with D\&F), 7 dB (No D\&F) \\ \hline UE\({}_{1}\) & 40 m & 15 dB & 5 m & 0.32 & 0.44 & 9 dB & 8 dB \\ \hline UE\({}_{2}\) & 80 m & 10 dB & 10 m & 0.68 & 0.56 & 5.4 dB & 6.3 dB (with D\&F), 6.6 dB (No D\&F) \\ \hline UE\({}_{1}\) & 90 m & 15 dB & 25 m & – & 0.64 & – & 6 dB \\ \hline UE\({}_{2}\) & 110 m & 25 dB & 8 m & – & 0.36 & – & 8 dB (No D\&F) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance analysis of two users in PD-NOMA and Cooperative PD-NOMA with PA through DBPA and the FIS-based strategies.  
**Author Contributions:** Conceptualization, A.M., M.M. and M.Z.; formal analysis, M.M. and M.M.N.; data curation, A.M. and M.A.E.; investigation, A.M. and M.M.; methodology, A.M. and M.Z.; project administration, M.M.; supervision, M.M., M.Z., M.M.N. and M.A.E.; validation, A.M.; writing--original draft, A.M.; writing--review and editing, M.M., M.M.N. and M.A.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Article Processing Charges (APC) was funded by Prince Sultan University.  
**Acknowledgments:** The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) for this publication.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 3 Results
### Evaluation of Synchronization Algorithms  
The results of evaluating two PLL-based synchronization techniques using the proposed evaluation tool and method are presented as examples of use. The synchronization algorithms were (a) SRF-PLL and (b) DSOGI-PLL with LPF. Figure 14 shows the operation diagrams of the synchronization algorithms implemented based on [18; 21]. The algorithms were programmed using the Arduino Due board and configured according to [21]. The ADCs of the digital controller are 12-bit and work with 96 samples per cycle of the fundamental frequency. The parameters of the synchronization algorithms are \(f_{grid}=50\) Hz, \(\zeta=1/\sqrt{2}\), \(t_{s}=40\) ms, and a maximum grid amplitude of 1.5 p.u. Table A1 in Appendix A shows the parameters used in the digital controller.
## 4 Wavelet Transform
### Wavelet Transform Method
#### Continuous Wavelet Transform  
The continuous wavelet basis functions are generated by applying dilation and translation operations to a single mother wavelet function \(\psi\) (t), as expressed in the following Equation (24):  
\[\psi_{a,b}=\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t-b}{a}\bigg{)}, \tag{24}\]  
In the formula, \(a\) represents the scale parameter and \(b\) represents the translation parameter. As \(a\) increases, the mother wavelet is stretched more, while a smaller \(a\) compresses the mother wavelet. Smaller values of \(b\) are suitable when the mother wavelet is narrow, as they reduce the displacement distance.
## 6 Simulation Results and Analysis
### Energy Consumption of Nodes under Different Kinds of Network Partition
It is worth noting that in Table 6, the energy consumption balance at \(k=3\) is slightly worse than that at \(k=4\) or \(k=5\). The reason is that when the network is divided into three annuli, according to Equation (17), the area of RCCHs in the second and the third annulus are almost the same. Due to the uniform distribution of nodes, the number of nodes in these RCCHs are almost the same. However, the load on the nodes in the second annulus is heavier than those in the third annulus, thus, there is a certain difference in their energy consumption.
## 6 Example Analysis
### Analysis of SOP and Energy Storage Optimization Configuration Results  
Once the SOP and energy storage site selection have been completed, the SOP and energy storage optimization configuration model should be constructed. The system operating parameters are as follows. The maximum allowable node voltage of the system is 1.05 p.u., the minimum allowable node voltage of the system is 0.95 p.u., and the maximum allowable branch current is 0.4 kA. The relevant parameters of the SOP are as follows. The service life of the SOP is 20 years. The investment cost per unit capacity of the SOP is CNY 1000/kVA. The cost coefficient of SOP operation and maintenance is 0.01. The loss coefficient of SOP is 0.02, and the discount rate of SOP is 0.08. The parameters related to energy storage are as follows. The maximum and minimum values of the energy storage state of charge are 0.9 and 0.1, respectively. The initial value of the energy storage state of charge is 0.5. The energy storage charging efficiency is 0.9, and the energy storage discharge efficiency is 0.9. The maximum energy storage charging and discharging rate is 0.2. The service life of energy storage is 20 years. The unit rated power cost of energy storage is CNY 1000/kW, and the unit capacity cost of energy storage is CNY 1200/kW. The operation and maintenance cost of the energy storage unit's rated power is CNY 0.5/kW, and the discount rate of energy storage is 0.08.  
In this paper, the SOP and energy storage optimization configuration models are solved through programming using the YALMIP optimization toolbox on the MATLAB R2023a platform and the IBM ILOG CPLEX 12.6 algorithmic package.  
The SOP and energy storage access schemes can be classified into three distinct categories. Scheme 1 is designed to optimize the grid economy by configuring SOP and energy storage. Scheme 2, as proposed in this article, aims to balance the grid economy and load balancing by configuring SOP and energy storage. Finally, scheme 3 is intended to minimize load balancing through the configuration of SOP and energy storage. A comparison of the various SOP and energy storage configuration schemes is presented in Table 2.  
Figure 6: Voltage sensitivity of non-power nodes of the snowflake-shaped grid.