esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TBM (Tunnel Boring Machine) Data Processing
### Data Preprocessing
#### 3.2.3 Data Denoising  
During TBM operations, machine vibrations consistently generate noise in the collected data. Noise in unprocessed data can significantly affect the prediction accuracy of elastic net regression models. Through proper preprocessing, the noise data is removed, which enhances the stability of the model, improves the extraction of the relationship between the cutterhead speed and other parameters, and reduces the overfitting phenomenon. To minimize the impact of noise on the model's solution speed, this study utilized a low-pass filter for denoising. Low-pass filters smooth the data by restricting high-frequency components while effectively preserving signal variation. Figure 5 compares torque data before and after filtering. The blue box indicates a magnified section, clearly showing that the filtered data is smoother and better suited for subsequent analysis.
## 3 Overview of FB Approach for MR
### Constellation Shape Features for MR  
Constellation shape, which refers to the total number of points and its locations, is used to investigate the geometry shape of the constellation diagram of modulating signals, such as PSK and QAM signals. Each location has distinct distance and phase with respect to the origin point and is used by Mobasseri [83] to transfer the phase-amplitude distribution to 1D distribution. In [84; 85], the authors adopted constellation features to identify M-QAM. However, the shortcomings of this method were its sensitivity to noise and high SNR requirement in identifying higher-order modulation. In [86], the authors improved the quality of M-QAM identification by combining the constellation WT characteristics of QAM signals. This feature was robust to AWGN. However, the SNR should be greater than four to achieve good performance.
## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.3 R and VR: A Comparison and Validation of the Modules
The results show that novice and expert students took relatively less time to merge data in VR compared to R (a 16 s difference between R and VR for experts and a 73 s difference between R and VR for novice students). In summary, IVEE was successful in reducing the time it took student participants to preform basic visual analytics tasks, especially novice students. An inspection of the results shows that, on average, novice students took less time to complete all types of tasks in the VR environment and managed to cut their total time spent on performing tasks in half. \begin{table}
\begin{tabular}{c c} \hline \hline
**Above Average/Expert Knowledge in R Programming** \\ \hline Average time to make a visualization in R & Average time to make a visualization in VR \\ \hline
22 s & 20 s \\ \hline No Knowledge/Basic/Average Knowledge in R Programming \\ \hline Average time to make a visualization in R & Average time to make a visualization in VR \\ \hline
1 min 12 s & 49 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average time to generate a visualization in R and VR: A comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**Above Average/Expert Knowledge in R Programming** \\ \hline Average time to subset data using R & Average time to subset data using VR \\ \hline
30 s & 34 s \\ \hline No Knowledge/Basic/Average Knowledge in R Programming \\ \hline Average time to subset data using R & Average time to subset data using VR \\ \hline
2 min 1 s & 1 min 2 s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average time to subset data in R and VR: A comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**Above Average/Expert Knowledge in R Programming (Expert)** \\ \hline Average Time to Complete Tasks in R & Average time to complete tasks in VR \\ \hline
7 min 51 s & 8 min 12 s \\ \hline No Knowledge/Basic/Average Knowledge in R Programming (Novice) \\ \hline Average time to complete tasks in R & Average time to complete tasks in VR \\ \hline
29 min 9 s & 15 min 9 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Tutorial average completion time in R and VR: A comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**Above Average/Expert Knowledge in R Programming** \\ \hline Average time to subset data using R & Average time to subset data using VR \\ \hline
30 s & 34 s \\ \hline No Knowledge/Basic/Average Knowledge in R Programming \\ \hline Average time to subset data using R & Average time to subset data using VR \\ \hline
2 min 1 s & 1 min 2 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average time to merge data in R and VR: A comparison.
## 2 Review of Tank Regulations and Codes  
### General Framework  
Following the Chile (1960) and Alaska (1964) earthquakes, numerous research investigations have been carried out about the seismic examination of tanks that store liquids. Within this particular context, the subsequent two facets were examined:  
1. In evaluating the seismic forces acting on tanks, great attention should be paid to the effects of sloshing of the liquid and the flexibility of the walls;
2. Tanks have lower ductility, absorb less energy, and have a more downward depreciable force than conventional buildings.  
In most design codes, tanks under the second method are considered to have a higher earthquake coefficient than buildings. Among the codes that provide instructions for concrete tanks, the International Building Code (IBC) (1960) and the American Concrete Institute Standard (ACI) (1960) are the best known and are analyzed here. It should be noted that this study examines and compares their rules and regulations, similarities, differences, and respective limitations. Then, the design earthquake of the reservoir is evaluated along with the design forces of the buildings. These ratings help find out how the earthquake's intensity compares to the building with the seismic force and provide a better fit for the tanks.  
The regulations classify concrete underground water tanks as inflexible or limited in their ability to absorb energy. The International Building Code (IBC) (1960) indicates that the base shear coefficient ratio between the tank and the building equals or exceeds 4. This ratio holds even for prolonged periods of rotation due to the restricted spectrum range within the tanks. Despite the low energy absorption and flexibility, liquid storage tanks highly resist seismic forces. Therefore, it is no surprise to observe significant disparities between the various types of storage tank repositories described across different codes and regulations. A comparative analysis shows that tanks with limited flexibility exhibit a significantly higher impulsive base shear coefficient, approximately 6-7 times greater than that observed in more flexible structures such as buildings. By contrast, the value above exhibits a range of 3 to 4 across all codes when applied to a tank with heightened flexibility.
## 5 Discussion
### Model Evaluation by Forest Types and Measured AGB  
The mean errors of AGB prediction using the four models for different forest types and the measured values of AGB were also calculated to analyze the prediction accuracy of each forest type, and the data saturation in the Sentinel data was discussed (Figure 7). Generally, the estimated AGB values of the four forest types of 56 quadrats by the four models were all higher than the measured values (Figure 7a). Among that, the AGB estimation of the deciduous coniferous forests obtained the maximum error of 0.7 Mg \(\cdot\)ha\({}^{-1}\), and all models, excluding the GWR with ME values of \(-\)0.6 Mg \(\cdot\)ha\({}^{-1}\), performed the worst for deciduous coniferous forests compared to the other three forest types, with ME values ranging from 0.04 (SVR) to 1.8 (RF) Mg \(\cdot\)ha\({}^{-1}\). The AGB estimation of mixed broadleaf-conifer forests had the second-large error, with 0.6 Mg \(\cdot\)ha\({}^{-1}\), followed by that of broadleaved deciduous forests (0.2 Mg \(\cdot\)ha\({}^{-1}\)) and evergreen coniferous forests (0.02 Mg \(\cdot\)ha\({}^{-1}\)). As for the four models, the GWR performed best in the AGB estimation of mixed broadleaf-conifer forests, and the worst for evergreen coniferous forests. The ANN, SVR, and RF all gave the most accurate assessments for broadleaved deciduous forests. The ANN showed the least precise assessments for evergreen coniferous forests, but SVR and RF gave the worst for deciduous coniferous forests. In a word, the AGB estimation of broadleaved deciduous forests was the most accurate and stable in the study area using the four models based on Sentinel imagery. Among the five levels of AGB values, the last level with AGB above 160 Mg \(\cdot\)ha\({}^{-1}\) (from 160 to 320 Mg \(\cdot\)ha\({}^{-1}\)) had the least accuracy and the most fluctuated errors, based on Sentinel imagery, while AGB from 40 to 120 Mg \(\cdot\)ha\({}^{-1}\) obtained relatively higher accuracies (Figure 7b). The saturation level shown in this study was much higher than other studies (at around 60-70 Mg \(\cdot\)ha\({}^{-1}\)), using SAR C band data ([PERSON] et al., 2018). This could be attributed to the integration of abundant predictors from Sentinel-1 and Sentinel-2 in the study area with normal forest coverage, which was human-dominated zones with nearby towns and villages.
## 4 Simulations, Results, and Discussion
### Comparison with Other State-of-the-Art Systems  
In this paper, we compared and evaluated the differences between our proposed collaborative approach and other state-of-the-art methods for analysis. [PERSON] et al. proposed a blockchain-enabled system that achieves sustainable food security for the region of India [PERSON] et al. (2018). This system provided a solution for food supply chain trackability and privacy. The distributed application of the proposed system provides a secure platform for registering stakeholders over the public permissionless network. However, the collaborative nature of this system investigates the agriculture supply-chain, integrated MCDM, total interpretative structure, market trails, and evaluation used of fuzzy-cross impact matrix multiplication-applied classification. The system is secure and protects the ledger using blockchain-distributed technology, but the drawback is that it uses a predefined hash-encrypted strategy with a public blockchain network with defined consensus policies and a digital signature strategy.  
Figure 5: The simulation results of the frequency matrix of commodity price forecasting and classification: (**A**) the rate of delay between forecasting information using the linear-logistic regression (ML) method, (**B**) the rate of response between forecasting information, and (**C**) the rate of duty cycle between commodity classification and price forecast information.  
We examine, analyze, and evaluate various proposed methods; some of the well-known and related works are discussed as follows (shown in Table 3):  
Figure 6: Simulation results of the relationship between frequency of examination and analysis of forecast information transmission over permissioned blockchain private network. (**A**) The simulation of other non-blockchain and metaheuristic-blockchain-enabled solution (1), and (**B**) the simulation of other blockchain (Ethereum) and metaheuristic-blockchain-enabled solution (2).
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 5 Results
### Performance Analysis
#### 5.1.2 Time Analysis Based on Steps Involved in Protocol  
Table 3 provides a comprehensive overview of the duration required for critical protocol steps executed on the server, Core-i7 client, and the ESP-8266 embedded client. In Step 2 of the protocol, the server exclusively generates the symmetric key and the time period T for the symmetric key, while the client side involves certificate verification, key derivation, HMAC, and nonce generation. The server exhibits high efficiency, completing this step in 0.015 ms due to a reduced number of operations, whereas the Core-i7 client takes 2.767 ms. In contrast, the ESP-8266 experiences significantly greater latency at 41.143 ms, which can be attributed to hardware limitations.  
Step 3, which is executed solely on the server and entails the execution of all operations from Step 2 on the client side, requires 3.766 ms. In addition, Step 4, which primarily focuses on decryption and verification, is completed in 0.039 ms on the Core-i7 and 0.283 ms on the ESP-8266.  
The total execution time reflects the cumulative duration of all steps, with the server completing the entire process in 3.940 ms and the Core-i7 client achieving a time of 3.469 ms, indicating substantial efficiency. Although the ESP-8266 records a longer duration of 44.426 ms, it is noteworthy that this device remains capable of supporting the protocol, demonstrating its viability even under resource-constrained conditions.
## 5 Mathematical Modeling and the Control of Attitude Dynamics
### Controller Design
#### 5.4.1 Roll Attitude Controller  
From Figure 9, by including the torque from the three motors, and considering the value of the gyro gain as 0.86 volts/rad and the values of the moment of inertia of the selected propeller and the octocopter in the \(x\)-axis, the open-loop transfer function of the roll control loop without the controller is given as follows:  
\[\left(\frac{\phi}{\phi_{\text{desired}}}\right)_{\text{open\_loop}}=\frac{22.7 \times 10^{-8}}{s[(1.6\times 10^{-11})s^{2}+(1.1123\times 10^{-7})s+6.036\times 10^{-6 }]}. \tag{24}\]  
The open-loop poles of the roll control system are 0, \(-\)54.7, and \(-\)6897.2, and there is no zero from the transfer function. The dominant poles for the desired settling time of 0.1 s with 10% overshoot can be obtained from second-order under-damped relations and were found to be \(-\)40 \(\pm\) 54.6.  
The open-loop transfer function obtained from the roll attitude control system is a Type-1 system. As such, the steady-state error is always zero for a step input. Therefore, integral control is not required in the roll-control loop, and the roll-angle controller is a PD controller. The location of the PD controller's zero can be obtained using the angle criterion of the root locus for the desired dominant pole. In this study, it was found to be \(-\)177.8.  
The designed transfer function of the PD controller is therefore K(s + 177.8), where K is the gain corresponding to a 10% overshoot. From the root locus of the PD-compensated system for the roll control shown in Figure 12, the value of K in this study was 12.6. By comparing the obtained transfer function with the standard form of the PD controller, the derivative gain KD = 12.6, and the proportional gain KP = 2240.28. The octocopter response with the roll controller for the desired roll-attitude command was obtained using MATLAB,  
Figure 11: Yaw rate control loop.  
Figure 10: Pitch attitude control loop.  
as shown in Figure 13. From the MATLAB result, it can be seen that the setting time is less than 0.1 s, with an overshoot of approximately 10%.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.3.4 Communication Protocols  
In the majority of the source material studied, real-time data exchange between IoT devices, AI systems, and VR interfaces is achieved through communication protocols such as MQTT (Message Queuing Telemetry Transport), WebRTC (Web Real-Time Communication), and SIP (Session Initiation Protocol) alongside HTTPS (Hypertext Transfer Protocol Secure). MQTT is favored for its lightweight messaging capabilities and low bandwidth requirements, making it ideal for IoT applications. WebRTC enables peer-to-peer communication, ensuring low latency and high-quality audio and video streams essential for immersive VR experiences. SIP supports the establishment, modification, and termination of communication sessions, providing a robust framework for integrating multiple systems. These protocols ensure low latency and reliable data transmission, which is critical for time-sensitive applications.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 2 Materials and Methods
### Multibody Model and Solver Settings  
The gear pair geometry was exported from Siemens NX and imported into MSC Adams View 2024.2 as a rigid flexible multibody model, as shown in Figure 1. Shafts were coupled through flexible joints, while the rolling element bearings were represented by four-parameter viscoelastic bushings.  
Dynamic equations were solved with the Hilber-Hughes-Taylor (HHT-\(\alpha\)) implicit time-integration scheme using \(\alpha\) = -0.05. A relative error tolerance of 1 \(\times\) 10\({}^{-6}\) and an absolute error tolerance of 1 \(\times\) 10\({}^{-8}\) were applied. The maximum time step was limited to 5 \(\times\) 10\({}^{-5}\) s, which guarantees at least 50 solution points per mesh period at 1000 r min\({}^{-1}\). Automatic step-size control was activated with a Courant factor of 0.6. These baseline settings follow the recommendations of Hexagon's official Gear AT tutorial and were found to deliver numerically stable results without excessive CPU time [16].  
The main geometric and mass properties of the 23/81 helical gear pair are listed in Table 1.
# Design of Novel Fiber Optical Flexible Routing System
## 2 Configuration and Operating Principle of the Proposed Optical Routing System
Similarly, the signal input by input port 1 of AWG1, the odd channels will come out from port 8 on the right side of AWG2, then reach the OC1 by the cross status of SW1, and then reach the output port 1 through the bar status of SW2. That is, when SW1 is in the cross status and SW2 and SW3 are in the bar statuses, the even channels will be switched to output port 2 and the odd channels will be switched to output port 1. In addition, when the SW1 was set to the bar status and SW2 and SW3 were set to the cross statuses, the proposed router will realize the ROADM functionality to drop or add certain signal wavelengths. For example, in this status setting, the odd channel \(\lambda_{1}\) will appear from the port 1 on the right side of the AWG4 through SW1, OC3, SW3, OC4 and OC6 after input from the input port 1. Because we are dropping the wavelength \(\lambda_{1}\), SW8 is set to the cross status. Therefore, the wavelength \(\lambda_{1}\) comes out of port 1 on the right side of AWG4, then returns to port 2 on the left side, and then comes out from port 8 on the right side. Finally, wavelength \(\lambda_{1}\) reaches drop port 1 via OC8. When \(\lambda_{1}\) is dropped, we can reuse the \(\lambda_{1}\) carrier to add another signal from add port 1. Since the optical switch SW3 is in the cross status, when \(\lambda_{1}\) is added from OC4, it passes through SW3 and OC3 and then returns to SW3, and then to the output port 2. Similarly, we can drop the even-channel from drop port 2 and add it from add port 2. As mentioned above, the proposed all-optical router can perform the functions of an interleaver through the routing rules of the AWGs. The functions of OXC and ROADM can also be achieved by AWGs and optical switches. Therefore, this designed router can be directly applied to the DWDM network system.
## 8 Conclusions  
Performance verification of GNSS receivers under signal jamming attacks is never up to researchers' or users' satisfaction. The more experiments are conducted, the more knowledge is gained about the issue. This paper presented fresh experiments performed with state-of-the-art GNSS equipment and simulation tools to assess the effect of multi-frequency, multi-constellation GNSS signals on improving the GNSS receiver performance under different signal jamming conditions. First, the contribution of using GPS multi-frequency measurements on the receiver performance was assessed by enabling a dual-frequency (DF) signal at a time, then enabling GPS triple-frequency (TF) signals. The benefit of multi-constellation GNSS signal inclusion to the GNSS receiver performance was evaluated by enabling GPS TF signals and Galileo single-frequency (SF) signals at a time, then enabling GPS TF signals and Galileo TF signals all at once. After that, the GNSS receiver was assessed by enabling GPS TF, Galileo TF, and GLONASS SF signals at a time and then enabling GPS TF signals, Galileo TF signals, and GLONASS DF signals all at once. Additionally, the effect of both simulated and real jamming signals on GNSS signals was examined at different jamming power levels. To further assure the accuracy of the experiments, two airborne trajectories were created with different dynamics. The simulation was completed using the powerful Orolia GSG-8 GNSS signal simulator. The simulated jamming signals were also inserted using the same tool. On the other hand, the real jamming signals were generated using the NavWar Electronic Attack Trainer (NEAT) jammer by NovAtel. The research concluded that using a multi-frequency GPS-only receiver can maintain, to a good extent, a reliable positioning solution. Moreover, a multi-constellation GNSS receiver can maintain a continuous, robust positioning solution even under high power jamming conditions. There were always more than a dozen visible satellites, regardless of the type/level of applied jamming signal, when enabling the latter receiver feature. This allowed for open-sky-like positioning accuracy.  
Methodology, A.E., M.K. and H.E.; Validation, A.E.; Formal analysis, A.E., M.K. and H.E.; Investigation, A.E. and H.E.; Resources, A.E.; Data curation, A.E.; Writing--original draft, A.E. and H.E.; Writing--review & editing, A.E., M.K. and A.N.; Visualization, A.E.; Supervision, A.N.; Funding acquisition, A.N. All authors have read and agreed to the published version of the manuscript. This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant numbers: RGPIN-2020-03900 and ALLRP-560898-20.  
The authors declare no conflict of interest.
# Evaluation of Measurement Strategy for Track Side Monitoring of Railway Wheels
###### Abstract  
Wheelsets form an indispensable part of the railway rolling stock and need to be periodically inspected to ensure stable, safe, reliable, and sustainable rail operation. Wheel profiles are usually inspected and measured in a workshop environment using handheld equipment or by utilizing wayside measuring equipment. A common practice for both methods is to measure the wheel profile at one position along the circumference of the wheel, resulting in a one-slice measurement strategy, based on the assumption that the wheel profile has the same shape independent of the measurement position along the wheel. In this article, the representability of a one-slice measurement strategy with respect to the wheel profile parameters is investigated using handheld measurement equipment. The calculated range of standard deviation of the parameters estimated such as flange height, flange width, flange slope, and hollow wear from the measurements shows a spread in the parameter value along the circumference of the wheel. As an initial validation of the results, measurements from the wayside monitoring systems were also investigated to see if a similar spread was visible. The spread was significantly higher for flange height, flange width, and flange slope estimated from wayside measurement equipment than for the same parameters estimated using the handheld measurement equipment.  
railway wheel; wheel profile; wheel parameters; wayside monitoring; condition monitoring