esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 4 Simulation Results and Analysis
### Position Switching Performance Analysis
\begin{table}
\begin{tabular}{c c c c} \hline  & **RPID** & **LADRC** & **DLADRC** \\ \hline \hline Overshoot (\%) & 0 & 2.4 & 4 \\ Settling time (5\%, in s) & 11.9 & 5.12 & 5.26 \\ Settling time (2\%, in s) & 17.5 & 8.4 & 10.8 \\ \hline Mean (V = 6 m/s) & 0.0042 & 0.0007 & 0.0006 \\ Max. (V = 6 m/s) & 0.0385 & 0.0185 & 0.0153 \\ Median (V = 6 m/s) & 0.0006 & 0.0000 & 0.0000 \\ RMS (V = 6 m/s) & 0.0092 & 0.0032 & 0.0025 \\ Response time & 100 & 44 & 44 \\ \hline Mean (V = 3 m/s) & 0.0013 & 0.0002 & 0.0001 \\ Max. (V = 3 m/s) & 0.0102 & 0.0047 & 0.0039 \\ Median (V = 3 m/s) & 0.0004 & 0.0000 & 0.0000 \\ RMS (V = 3 m/s) & 0.0025 & 0.0008 & **0.0006** \\ \hline \end{tabular}
\end{table} 
Table 3: Switching performance indicators and tracking errors for wind disturbances of V = 6 m/s and 3 m/s.
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 1 Introduction
The isolator studied in this work effectively overcomes the environmental adaptability shortcomings of rubber-based materials and addresses issues such as the nonlinear stiffness characteristics of metal-rubber isolators. This novel design offers a promising alternative to conventional vibration isolators. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Three-Directional** & **Advantages** & \begin{tabular}{c} **Comparative Limitations** \\ **(vs. Metal Spring)** \\ \end{tabular} & \begin{tabular}{c} **Typical Applications** \\ \end{tabular} \\ \hline \begin{tabular}{c} Metal Spring \\ Three-Directional Isolator \\ \end{tabular} & \begin{tabular}{c} High load bearing capacity \\ Effective low \\ frequency isolation \\ Predictable linear stiffness \\ \end{tabular} & N/A (Reference standard) & \begin{tabular}{c} Heavy machinery \\ Power generation \\ systems \\ Rail transport \\ \end{tabular} \\ \hline \begin{tabular}{c} Air Spring \\ Three-Directional Isolator \\ \end{tabular} & \begin{tabular}{c} Adjustable stiffness \\ Ultra-low frequency isolation \\ Excellent high \\ frequency attenuation \\ \end{tabular} & \
\begin{tabular}{c} Higher maintenance costs \\ Limited environmental \\ adaptability \\ \end{tabular} &
\begin{tabular}{c} Precision instrumentation \\ Optical tables \\ Luxury vehicle \\ suspensions \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between metal spring and contemporary three-directional vibration isolators. The present study analyzes the influence of Coulomb friction on the dynamic performance of the isolator, providing theoretical insights into the optimization of three-directional vibration isolation systems in future engineering applications. The findings presented herein contribute to the advancement of efficient and durable vibration isolation solutions, paving the way for the enhanced performance of precision systems operating under multi-directional dynamic loads.
# Machine Learning Based Energy-Efficient Design Approach for Interconnects in Circuits and Systems
## 4 Analysis Results
### Stripline with Differential Signal
#### 4.2.2 ANN Model and Optimal Structure in Frequency Domain

Table 5: ANN model prediction performance in stripline structure. used as a criteria to determine low loss and crosstalk design. Finally, the design procedure proposes the optimal structure of \(Z_{0}\) of 96.85 \(\Omega\) at 10 Ghz with W = 85 \(\upmu\)m and \(K_{S}\) = 4. The S-parameter comparison of HFSS and ANN is shown in Figure 10.  
\[F_{optimal}=\text{max}\left[\frac{1}{N}\sum_{i=1}^{N}(10^{\frac{S_{\text{2H}}} {2\mu}}-10^{\frac{S_{\text{4H}}}{2\mu}})\right] \tag{9}\]  
where:  
\(S_{\text{21}}\): The insertion loss of the differential pair  
\(S_{\text{41}}\): The FEXT caused by adjacent differential pair  
\(N\): The number of frequency points
## 5 Conclusions  
Monitoring breathing parameters is fundamental for the prevention or early diagnosis of cardiovascular diseases; thus, accurate and continuous detection of respiratory data can aid medical staff in following the patient's condition during the rehabilitation phase. Wearable devices represent a powerful tool for detecting biophysical parameters, thereby providing a complete and accurate overview of the user's status in a local and non-obtrusive manner. This paper proposes a novel chest band for monitoring the respiratory parameters (i.e., RR, inhalation and exhalation time, IER, flow rate). This design uses a custom strain sensor realized with a piezoresistive textile (EeonTex LTT-SLPA-20K) and a low-power electronic conditioning and acquisition section. The microcontroller unit (SAMD21G18A) acquires, filters, and processes the gathered data to extract the breathing parameters. Moreover, the MPU-6050 inertial sensor data support the extraction of breathing parameters, and the removal of signal components due to body movements that are extraneous to breathing. The onboard BLE module transmits the collected breathing parameters to a custom mobile application, allowing remote data monitoring. Test campaigns were carried out in different operative conditions (seated, standing, and walking) to establish the performance of the developed chest band. Correlation analysis of RR measurements obtained by the chest band, compared to those from counting breaths, demonstrated the proper operation of the wearable system, which achieved a Pearson correlation coefficient of 0.958 when the user was seated. Bland-Altman analysis indicated an MD of 0.8 BrPM, and an LoA of +3.47 and \(-\)1.87 BrPM, when the user was seated.  
Furthermore, preliminary tests were carried out to establish the performance of the developed device in measuring the other breathing parameters (TI, TE, IER, V) using the data provided by a spirometer as a reference. The obtained results confirm that the developed chest band accurately estimates these parameters, demonstrating a measurement error below 8% for all parameters.  
However, a more in-depth analysis will be performed in the future to provide a more complete and extended characterization of the device's performance.  
Conceptualization, R.D.F. and P.V.; methodology, R.D.F., P.V. and M.D.V.; software, R.D.F.; validation, R.D.F. and P.V.; data curation, R.D.F., M.D.V. and P.V.; writing--original draft preparation, R.D.F. and P.V.; writing--review and editing, P.V., R.D.F. and M.D.V.; supervision, M.D.V. and R.D.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data of our study are available upon request. Conflicts of Interest: The authors declare no conflict of interest.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 2 Background
### Comparisons
#### 2.3.4 Stability  
**Details:** The stability is mainly related with the quality of bicycle and the robustness of the renting system, which also affects the maintenance strongly.  
**Ranking:** HZBike (3) > Mobike (2) > OFO (1)  
**Reasons:** First of all, the dockless model adds great difficulty to manual maintenance, even if they are equipped with GPS positioning equipment. By comparison, the traditional public bike is much better, and the excellent operation of the HZBike has made it one of the most popular public bike systems in the world [40]. In terms of body materials, limited by manufacturing costs (see Table 6), OFO and HZBike are made of common iron, which is more vulnerable to corrosion and the performance of components in bicycle will decrease, even broken.
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 3 Results and Discussion
### Performance of Different Frequency Bands  
To explore the influence of widths and types of frequency band on the measurement of coal MC, comparative experiments based on multiple frequency bands combined with the aforementioned machine learning algorithms were also carried out. As mentioned in Section 2.2, each original microwave spectrum curve contained 133 frequency points in the range of 8.05 GHz-12.01 GHz. Therefore, 133 frequency points were divided into seven narrow bandwidth signal bands that were independent of each other, and each band contained 19 frequency points for testing the correlation with the coal MC. Each band was analyzed using the six-fold NCV method, and the results are shown in Table 6.  
As shown in Table 6, although the machine learning algorithms used were different, the broad bandwidth microwave signals can significantly improve the accuracy of coal MC measurement compared to the narrow bandwidth signals. From the analysis results based on the DW-kNN algorithm, it can be seen that the \(R^{2}\) values obtained using the narrow bandwidth signals were all lower than 0.9; the best \(R^{2}\) result was 0.8985, and it was achieved for the signal band of 10.9 GHz-11.44 GHz, but it was still less than the best result of 0.9106 obtained using the broad bandwidth microwave signals. This indicates that appropriately increasing the number of frequency points can not only provide more features related to the coal MC but can also improve the accuracy of regression analysis. Similarly, the MAE and RMSE values obtained using the broad bandwidth microwave signals were better than those of the narrow bandwidth signals, which showed that using broader bandwidth microwave signals to predict the coal MC had smaller errors, and  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency Bands**} & \multicolumn{6}{c}{\(R^{2}\)} & \multicolumn{6}{c}{**MAE**} & **RMSE** \\ \cline{2-11}  & **RF** & **SVR** & **PLS** & **AdaBoost** & **XGBoost** & **DT** & **kNN** & **DW-kNN** & **(DW-kNN)** & **(DW-kNN)** \\ \hline
8.05–8.59 GHz & 0.8751 & 0.8644 & 0.8647 & 0.8766 & 0.8707 & 0.8376 & 0.8701 & 0.8869 & 0.2610 & 0.3265  
the prediction results were closer to the actual values. Thus, using broader bandwidth microwave signals to predict coal MC can ensure greater advantages.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 6 Results and Discussion
### Summary of Main Findings  
In general, there is a significant advantage of HTTP/3 compared to either HTTP/2 or HTTP/1.1 in many network environments especially in large RTT satellite links. The results of our investigation can be summarized in the following.  
* Most of the performance gain of HTTP/2 and HTTP/3 compared to HTTP/1.1 comes from stream multiplexing of complex web pages. However, this comes at the cost of much larger protocol overhead due to mandatory TLS connection handshake and encryption. We can conclude that the stream multiplexing provides the highest gain when downloading multi-object pages over large RTT links such as satellite links.
* The protocol overhead can dominate the performance of both HTTP/2 and HTTP/3 especially in low RTT links. HTTP/1.1 performs similar to HTTP/2 and HTTP/3 over very low RTT even when it fetches the web page sequentially, because it does not suffer from the overhead of connection handshake and encryption as in HTTP/2 and HTTP/3.
* HTTP/3 further benefits from reducing the initial TLS connection handshake and the non-blocking feature of QUIC. The gains here are large enough to offset the protocol overhead when operating over large RTT links.
* The effect of stream multiplexing is clearly visible when using complex web pages of multiple objects with varying sizes. From our investigation, we found that the perceived visual rendering of web pages will be better in HTTP/2 and HTTP/3 depending on the position of the most significant object within the HTML web page. This is especially notable in HTTP/3 since it does not suffer from HoL blocking at the transport layer.
* BBR congestion control outperforms Cubic regardless of the type of HTTP protocol because it is able to quickly fill the pipe achieving higher utilization of the satellite link. Cubic fails to utilize the satellite link because it only updates its transmission rate every RTT, implying slower growth in these large RTT links.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis
### Results and Analysis
#### 4.2.2 Comparison between ECL-INS-LMOA and Comparative Algorithms  
In this section, Congo (K) is the region to be imaged, and the imaging task planning results obtained by ECL-INS-LMOA are compared with NSGA-II, LMOCSO, and LMEA.  
1. **The distribution of solutions in the objective space**  
Figure 10 shows the distribution of non-dominated solutions obtained by four algorithms for Congo (K). According to the imaging satellite task planning model in Section 2.1, in Figure 10, the vertical axis is the objective function 1, representing the difference between 1 and the regional coverage rate. The smaller the value, the greater the regional imaging  
Figure 9: Strategy comparison between BCSO and BPSO. (**a**) The changing HV with evolution; (**b**) the changing maximum coverage with evolution; and (**c**) the number of imaging strips corresponding to the maximum coverage per generation evolution.  
coverage rate. And the horizontal axis is the objective function 2, representing the ratio of the participating imaging strips to the total candidate strips. The smaller the value, the more efficient the utilization of the satellite. In Figure 10, the red, blue, pink, and green curves represent the distribution of non-dominated solutions obtained by ECL-INS-LMOA, LMEA, LMOCSO, and NSGA-II, respectively. Table 5 shows the values of the optimal coverage solution for each algorithm in Congo (K), as well as the corresponding coverage rate and the number of strips participating in the imaging. The gray shadow marks the maximum imaging coverage rate and the minimum number of strips participating in imaging.  
From this experiment, three conclusions can be drawn: Firstly, although NSGA-II is not an algorithm designed for large-scale optimization problems, it still achieves good results in planning for Congo (K), with 135 decision variables. The regional coverage rate of NSGA-II reaches 98.88%, surpassing LMEA. But NSGA-II requires more imaging strips than other algorithms, which means that satellite resources have not been effectively saved in NSGA-II. Secondly, each algorithm can achieve good results in terms of coverage with little performance difference, not exceeding 3%. However, there is a significant difference in the required imaging strips. ECL-INS-LMOA has 29 fewer imaging strips than NSGA-II, accounting for approximately 21.5% of the candidate strips. Thirdly, ECL-INS-LMOA is significantly superior to comparison algorithms, always utilizing fewer imaging strips to achieve a higher regional coverage. ECL-INS-LMOA only uses 64 imaging strips to achieve almost full coverage of the imaging region, achieving the goal of a high regional coverage while saving satellite resources. The satellite resources are saved by 52.6%.
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 5 Spoofing Detection Performance
### Results of Data Set 1 (Suburban Environment)
This is because the Kalman filter puts more weights on normally high-quality GPS measurements than on the high drift MEMS IMU measurements. Nevertheless, the RIO solutions significantly deviate from the spoofed GPS and GPS/RIO trajectories, confirming spoofing detection, an important warning to the user. Figure 9 shows the RIO errors for different update intervals (\(t_{u}\)) in the spoofing case. The errors are the differences between GPS and RIO solutions. During the first 80 s, the receiver was not spoofed, hence spoofing detection metric outputs (error values) are below the threshold. However, once the spoofing attack begins, the errors significantly rise. As expected the longer the update interval, the larger the errors, hence better correct spoofing detection. Detection performance is a function of the relative authentic and spoofed trajectories and consequently the horizontal error values shown in the figure vary as a function of time. For instance, during the time interval 240-260 s, the horizontal error for \(t_{u}\) = 5 s is below the detection threshold and the attack during this period cannot be detected. This is because the spoofing trajectory matches the authentic one during this period. In general, a spoofing attack using this approach cannot be detected during periods when GPS and RIO trajectories match. Figure 8: Horizontal trajectories for spoofed GPS, GPS/RIO, RIO with 20 s correction updates for MEMS (cell phone) IMU.
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network  
### Underwater Sensor Network and a Terrestrial Sensor Network  
The underwater and terrestrial sensor networks are very different from one another. Below is an illustration of how terrestrial and underwater sensor networks compare.  
Underwater sensor networks will use acoustic rather than radio signals since they operate at incredibly low frequencies and cannot travel very far underwater, rendering them unusable for terrestrial sensor networks [33].  
**Power:** Compared to terrestrial sensor networks, underwater sensor networks require greater power because the signal will travel through water. Other factors include a high sensor-to-sensor distance and a complicated environment [34].  
**Memory:** Terrestrial sensors have a certain amount of storage space. However, it may be necessary for underwater sensors to be able to perform some data caching. Therefore, they need additional memory.  
**Cost:** While terrestrial sensors are less expensive than underwater ones, the latter requires an additional hardware protection system. Underwater sensor networks  
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Author** & **Method** & **Description** & **Environmental Parameters** & **Protocols** & **Advantages** \\ \hline
[27] & Data driven & Low target location error & Sound speed, noise removal, high depth, and reflection loss & Co-UWSN NC HAMA ECOCA & Computational Limitation. Low packet loss \\ \hline
[28] & Software and hardware data analysis & AUV operation with acoustic modem telemetry & Ocean prototypical acoustic prototypical & CSRP LLIPR RSTP VAQS & High-level communication. Increases the lifetime of sensor node \\ \hline
[29] & Localization of RSSI & Sensor activity and target tracking & Sensor node analysis, horizontal and vertical node deployment & HCRP LDR V-SDEDA T-DDMAMAC & Understand the sensor extract position. Region of network detection. \\ \hline
[30] & Deployment of UWSN & Node deployment extraction for node filtering algorithm & Doppler node classification, protocol-based routing, flush time, delay of time & D-TAN RIP RIPv2 IGRP BGP & Easy to develop AUV \\ \hline
[31] & Two-dimensional architecture & Different deployment strategies & The latter is more appropriate for identifying and observing occurrences that cannot be adequately noticed when monitoring the ocean floor &
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
[32] & The theoretical framework for target tracking localization & Attacker’s location and the timing & Attacker’s location adversary models, privacy evaluation models, security analysis & LLDP EHCRP CS-RT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related paper study.  
are often more sparsely distributed, whereas terrestrial sensor networks are frequently widely installed.  
**Spatial Correlation:** Due to the more significant separation between sensors in underwater networks, spatial correlation is less likely to be observed than in signals from terrestrial sensors [35].
# Double-Track One-Direction Operation Mode for Overnight Trains on High-Speed Railway
## 4 Case Study
### Case Introduction  
The Xuzhou-Lanzhou high-speed railway is an important route through central China with a length of 1434 km, of which the Xuzhou to Baoji section is 1033 km long with a designed speed of 350 km/h and the Baoji to Lanzhou section is 401 km long with a designed speed of 250 km/h. Currently, the section from Xuzhou to Xi'an runs at a maximum speed of 310 km/h, while the section from Xi'an to Lanzhou runs at 250 km/h. The Yangtze River Delta, the Central Plains, and the Guanzhong regions are interconnected by the Xuzhou-Lanzhou high-speed railway. It connects the economically developed eastern regions with the labor exporting regions of the central and western regions. Using this line as a case has practical value. It has four main node stations connected with other lines, including Xuzhoudong, Zhengzhoudong, Xi'anbei, and Lanzhouxi. Figure 10 shows the diagram of the Xuzhou-Lanzhou high-speed railway and other lines connected with the Xuzhou-Lanzhou high-speed railway.  
The speed was calculated using uniform values to simplify the calculation. Differences in train performance were not considered. The average speed of trains without stopping was 235 km/h with a maximum speed of 250 km/h and 330 km/h when the maximum speed was 350 km/h. The time to each station according to the speed was calculated. Mileage, pass-through time, and number of arrival and departure tracks of each station are shown in Table 1.  
Figure 10: Diagram of the Xuzhou-Lanzhou high-speed railway.  
Mileage and pass-through time calculated between the Xuzhoudong station and the current station.  
Because Xi'an is closer to the boundary of the high-speed railway network, the passenger flow is unbalanced between the two sides, and there is no need to run so many overnight trains on the west side. Zhengzhou as the center is more reasonable.  
In this section, \(t^{ss}\) = 4, and the numerical range of variables in the algorithm is as follows: \(d_{i}^{p}\in[0,30]\), \(d_{i}^{q}\in[0,30]\), \(P\in[10,35]\), other parameter values: \(\eta=0.2\), \(\theta=0.4\), \(D_{MAX}=360\), \(t_{start}=23:30\), \(t_{end}=05:30\).