esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Verification
### Field Test of Cutting Piles  
In order to observe and study the process of shield machine cutting reinforced concrete piles, a field test of cutting two piles was conducted earlier in Suzhou, China. Shield operational parameters, such as the total thrust force, the total torque, the cutter head rotation and the advance rate, were collected in the process of cutting. The data were processed and compared with the results from the calculation model to judge the accuracy of the calculation model. Considering the feasibility, safety and economy of the test, two test piles were cast in front of the tunnel portal as in Figure 8. The layout of the test piles is shown in Figure 9. The pile A is arranged at the centerline of the portal and the pile B pile is 1.8 m away from the axis of the tunnel. The longitudinal offset between the two piles was 0.3 m. Twenty main bars of \(\varphi\) 22 and HRB 335 were installed in each pile. The concrete strength is C30 and the thickness of concrete protective layer of piles is 50 mm. The top and bottom of the test piles were fixed by means of welding and planting bars, which can prevent the pile from moving back and forth during the cutting.  
Figure 8: Photo of the field test site.  
Figure 7: Cutting force and penetration force under different penetrations.  
The cutting process is divided into five stages according to different cutting conditions and advancing parameters, which are shown in Figure 10. The advance distance is 150 mm, 150 mm, 180 mm, 230 mm and 300 mm, respectively. Shield operation parameters in different stages are listed in Table 3.
## 4 Tracking
### Estimation  
Estimation means that the desired state is obtained based on measurements undergoing the effects of noise, disturbance and, uncertainty. For a navigation system, many methods are used to estimate the state, including the Kalman filter, EKF, UKF, CKF, and PF. We introduce these methods in order, detail the relationship between them and their characteristics, and compare the performance of their algorithm. Owing to length considerations, the specific steps of the algorithms are listed in Appendix A Tables A1-A3.  
Even readers already familiar with these estimation methods will benefit from the information in this section; for newcomers to robot navigation research, this information should prove to be significant. Our focus is on the algorithm itself, and we succinctly explain the origin of the algorithm, while avoiding complex algorithmic deductions; the goal is to enable algorithmic engineers to quickly learn and use algorithmic processes.
# Autonomous Navigation Based on the Earth-Shadow Observation near the Sun-Earth L2 Point
## 5 Conclusions  
Aiming at the small-scale orbit near the Sun-Earth L2 point, this paper studied the navigation method based on the Earth-shadow measurement. The paper also considered effects such as the atmosphere and oblateness of the Earth and the Sun in spacecraft autonomous navigation. This method takes the proportion of the Sun blocked by the Earth as the measurement function and calculates the position and velocity of the spacecraft through the continuous change in the measurement function \(H\). For the 1 th orbital period, the position error along the X direction is about 4.8581 km and the velocity error is 0.002 m/s. The position error along the Y direction is about 0.1518 km and the velocity error is 0.0004 m/s. For the 2 th orbital period, the position error along the X direction is about 8.7276 km and the velocity error is 0.003 m/s. The position error along the Y direction is about 0.1877 km and the velocity error is 0.001 m/s. The above simulation results indicate that the characteristics of convergence and observability along the Y axis are good. Furthermore, this method makes some contribution to the difficulty that the Sun-Earth L2 point has few observations available for navigation and improves the accuracy for autonomous navigation. The autonomous navigation method using satellite-borne equipment has wide application prospects in gravitational wave detection, asteroid exploration, and other deep space exploration fields. Based on this work, the method of autonomous navigation and filtering algorithm could be further conducted in the future.  
Conceptualization, Y.W.; methodology, Q.L.; software, T.Q. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 12202048; Grant No. 12102441), and the Youth Innovation Promotion Association CAS (Grant No. 2020295). This research was also funded by The National Key RD Program of China (Grant number 2020 YFC2201200).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Structure Prototype and Finite Element Model
### 2.2 220 kV Transmission Line Loading  
Without proper tower loading, towers are worthless. The most critical aspect of tower design is the loading on the tower. The transmission tower design in this study considers the loading calculations on the tower, which are made in accordance with the ASCE standard [34]. The loadings considered are dead load, wind load, and seismic load (time history analysis). Figure 5 describes the structural loading terms and their directional conventions.  
The dead load is considered as vertical load and consists of the structural weight members, ground wire, and conductor. The dead load of conductor and ground wire acts on the tower cross arm, \((V_{w})\) and the top of the tower.  
The following equation provides the vertical load of the wire:  
\[V_{w}=wt\;of\;bare\;wire\;(kN/m)+1.24(d+I)I\times L\times\gamma \tag{1}\]  
where,  
\(d\) referred to diameter of wire in (m)  
\(I\) denoted to ice thickness (m)  
\(L\), designed weight span (m)  
Figure 5: Convention of vertical and transversal structural loading direction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Symbol** & **Value** \\ \hline
**Modulus of Elasticity** & E & 199,948 MPa \\
**Shear Modulus** & G & 76,903 MPa \\
**Poisson’s Ratio** & v & 0.3 \\
**Tensile Yield Strength** & Fy & 345 MPa \\
**Tensile Ultimate Strength** & Fu & 448 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material Properties of ASTM A 992/A 992M.  
\(\gamma\) is the vertical load factor, and equal to 1.50.  
The transverse loads are caused by wind pressure on wires and structures, and the transverse component of the line tension at angles. The transverse load due to wind on the wire is given by the following equations:  
\[W_{h}=p\times\frac{d}{12}\times Horizontal\ Span\times OCF \tag{2}\]  
\[P=0.6(0.00256)KzKztKdV^{2} \tag{3}\]  
where,  
\(W_{h}\) is the transverse wind load on wire in (kN).  
\(p\) is the wind pressure in (kN/m\({}^{2}\)).  
\(d\) is the diameter on wire in (m).  
\(OCF\) are the Overload Capacity Factor equals to 2.50.  
\(V\) is the basic wind speed, 120 mph, and exposure B.  
\(Kz\) is velocity pressure coefficient, 0.85.  
\(Kzt\) is the topographic factor, 1.0.  
\(Kd\) is the wind directionality factor, 0.55.  
When a line changes direction, the overall transverse load on the structure is equivalent to the sum of the transverse wind load and the transverse component of the wire tension in the new direction. When calculating the total load, the wind direction should be chosen so that the largest resultant load is obtained while considering the impacts on the wires and structure.  
The following equation is used to calculate the transverse component of wire tension acting on the structure:  
\[H=2T\sin\theta/2 \tag{4}\]  
where,  
\(H\) represents the transverse load caused by wire tension in kN  
\(T\) is the wire tension in kN.  
\(\theta\) is the angle of the line in degrees.  
Table 4 describes the basic utilization limits for spotting on the 220 kV transmission line:
## 4 Interpretation and Discussion
### Surveying Small-Scale Morphologies
Laser scanning provides a rapid measurement of a large number of points, even for large areas, and even without the need for additional illumination of underground spaces. However, its use may be technically, logistically, and financially more demanding, and, in the case of larger areas, the size of the final data output may be excessively large. Digital photogrammetry is financially more affordable and technically less demanding, but its use for large and complex areas would be much more laborious. On the one hand, it also provides textural data on the imaged surface; however, on the other hand, its use is significantly influenced by the illumination in the given areas. Another important consideration can be done regarding the amount of time and effort it takes to implement these methods. Generally, digital photogrammetry can be less time and effort demanding in terrain survey, since the only terrain process encompasses photogrammetric imaging (including possible illuminating, referencing/scaling). However, the subsequent image processing can be quite demanding, especially in terms of computer's computational power. Furthermore, the surveyor cannot be sure of the quality of the results until the computer image processing is complete. On the other hand, terrestrial laser scanning takes more time and effort in the field (regarding the manipulation with instruments, individual set-ups at survey stations, scanning time, etc.). However, the surveyor can get, and initially quality-check, the point cloud directly after the scanning. A general comparison of the advantages and disadvantages of both methods is given in Table 2.  
Figure 15: Mesh models created by Poisson Surface Reconstruction with Octree depth 12; left—laser scanning, right—photogrammetry.
## 4 Conclusions and Future Research  
Wormhole attacks are one of the most severe threats to MANET routing and are difficult to detect as they can be launched in several modes, with each enforcing its own distinct requirements on the detection mechanism. This paper has presented a new robust wormhole detection algorithm based on Traversal Time and Hop Count Analysis (TTHCA) for the _Ad hoc_ On-Demand Distance Vector (AODV) routing protocol. Simulation results, including a statistical significance test have proven TTHCA consistently provides superior wormhole detection performance allied with low false positive rates for all known wormhole types in a range of MANET scenarios, without incurring either significant computational or network cost. While the detection process is dependent on accurate packet processing time measurements on intermediate nodes, TTHCA has proven to be able to tolerate measurement errors well within acceptable bounds of existing wireless hardware.  
In terms of future research, one important aim for the TTHCA algorithm is to ensure the integrity and accuracy of the packet processing time measurement (\(\Delta T\)) on MANET devices and the prevention of possible measurement tampering. It is feasible that a participation mode wormhole node will deliberately give false measurement information concerning \(\Delta T\), so potentially compromising the wormhole detection mechanism. Research is focused upon evaluating the impact of malicious node measurement tampering for each wormhole type and investigating how TTHCA can be refined to ensure secure measurement and delivery of \(\Delta T\). One strategy being explored is replacing \(\Delta T_{TOT}\) in the route reply packet with a \(\Delta T\) vector which includes the individual \(\Delta T\) values from intermediate node, to afford the possibility of identifying suspicious \(\Delta T\) measurements.
## 3 Classifier Training and Results  
The classifiers were trained according to the procedures described in Section 2.1. The main aim was to compare the performance of different classifiers for parametrized signals and raw signal samples recorded from the inertial sensors.  
The 10-fold cross validation method was used to evaluate the classifiers' performance in the head gesture classification task. The training sets (for both the \(SP\) and \(TDR\) training procedure) were therefore randomly divided into 10 different subsets of equal cardinality. Each subset was sequentially used as the validation set while the other nine subsets served as a training set for the classifiers. Each classifier was cross validated on the same subsets, i.e., the division of the training and validation sets was made first and then each classifier was trained on identical data subsets. Tables 1 and 2 report the results of the 10-fold cross-validation for the \(SP\) and \(TDR\) training procedures, respectively.  
For the classifiers trained on the parametrized signals (training procedure SP), the random forests and the SVM with a third degree polynomial kernel excelled and achieved accuracies over 98%. The poorest classification results were obtained for the \(k\)-NN classifier, but even this classifier achieved accuracies exceeding 95%.  
For the classifiers trained directly on signal samples (training procedure \(TDR\)), all classifiers but the SVM achieved accuracies better than 97%. Although, the SVM classifier yielded results no worse than 92% correct head gesture recognitions.  
We have evaluated the performance of the classifiers and compared whether the accuracies they yield are statistically different. Because we consider more than two classifiers and our data do not have a normal distribution, the Friedman test was used for this purpose [37]. For both the training procedures, i.e., the \(SP\) (with parametrization of IMU signals that generate 42 dimensional training vectors) and the \(TDR\) (raw IMU signal samples that generate six dimensional training vectors), we have rejected the null-hypothesis, i.e., we concluded that there are statistically significant differences (\(p<0.05\)) between the classifiers. We also compared the chosen seven classifiers (Tables 1 and 2) by conducting the paired Wilcoxon signed rank test. For the \(SP\) training procedure for all classifiers we have also rejected the null-hypothesis. In the case of the \(TDR\) training procedure, there were no significant differences between the random forest and the \(k\)-NN for \(k=19\) (the null-hypothesis accepted). For other classifiers for the \(TDR\) procedure we have rejected the null-hypothesis, i.e., the classifiers' performances are statistically equal.
# A New Method to Verify the Measurement Speed and Accuracy of Frequency Modulated Interferometers
## 3 Experiments and Results
From Equation (5), when \(k=0.8\), \(\lambda=635\) nm, and \(f=20\) MHz, the maximum measurable speed of 2.54 m/s can be achieved. However, due to the limitation of the hardware caused by the low bandwidth of the high-voltage amplifier (HVA) and LIAs, we could not perform a virtual displacement of several m/s. We plan to use a higher bandwidth HVA and LIAs to improve the measurement speed. Another disadvantage of the proposed method is that the use of the EOM in one arm of the interferometer can cause the polarization mixing effect, and, hence, it may induce some noise of the interference signal. To improve the interference signal, some polarization optics can be used in the next experiment. Figure 3: Measurement: (**a**) virtual displacement measured by the interferometer (solid line) compared with the calculated displacement function \(F=A+B\times\sin(2\pi\times 20\ \mathrm{kHz}\times t)\) (dot line; see Equation (11), where \(A=-0.152\) μm and \(B=-0.076\) μm); (**b**) difference between the result obtained by the interferometer and F; and (**c**) displacement noise floor analyzed from (**b**) by Fourier transform.
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.3 Land Cover Data  
A 30 m land cover map was used to analyze the composition of land cover types on the footprint of field GPP in our study. This map was classified by using Chinese HJ-1 data [39]. Studies have shown that this land cover map has high accuracy (the overall classification accuracy of the map is 92.19%) [40; 41].
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 1 Introduction
This quasi-uniform quantizer can efficiently increase the dynamic range of messages and improve the BER performance under the same quantization precision without additional complexity in symbol detection. In other words, the quasi-uniform quantizer can achieve the same BER performance as the uniform one with less quantization bits. The main contributions of this paper are summarized as follows:  
* The Sub-log MPA is considered in order to accelerate the convergence of conventional Max-log MPA by splitting the factor graph into two parts and utilizing the latest updating messages at the current iteration. * Instead of the additive white Gaussian noise (AWGN) channel considered in [45; 46; 47], we investigate a more general Rayleigh fading channel to describe the standard uniform quantizer. * To improve the BER performance of SCMA systems, a new quasi-uniform quantizer is formulated without increasing the decoding complexity compared to the traditional one. To the best of our knowledge, this is the first attempt to apply the non-uniform quantizer to the SCMA system. * Simulation results confirm the validity of our proposed quasi-uniform quantizer, which can significantly reduce the computational complexity to achieve the same BER as the standard one; in other words, it can improve the BER performance as compared to the uniform quantizer by using identical quantization bits. The rest of the paper is organized as follows. Section 2 introduces the system model for SCMA systems and the conventional log-domain MPA detector. In Section 3, we propose a quasi-uniform quantizer to overcome the limitations of the traditional uniform one. Numerical results are provided in Section 4, followed by conclusions in Section 5.
## 6 Conclusions  
In this study, the start-up characteristics of an air conditioner were analyzed according to the refrigerant charge amount under various operating conditions. In addition, dynamic models for the start-up characteristics were developed with r-SVM and used to create a prediction method for the refrigerant charge amount. The following important results were obtained:  
1. The changes in the dynamic characteristics according to the refrigerant charge amount during the start-up of an air conditioner can be monitored.
2. The dynamic models for the condensation temperature and degree of subcooling can predict the distinct start-up characteristics that depend on the refrigerant charge amount. The estimated RMSEs of the condensation temperature and degree of subcooling of the test data are 0.53 and 0.84 \({}^{\circ}\)C, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & \multicolumn{2}{c}{**Refrigerant Charge Amount (g)**} & \multicolumn{2}{c}{**Error (\%)**} & \multicolumn{1}{c}{**Average**} \\ \cline{2-5}
**Variable** & **Actual** & **Detected** & & **Error (\%)** \\ \hline \multirow{4}{*}{T\({}_{\text{c,sat}}\)} & 600 & 625/632/645 & 5.7 & \multirow{4}{*}{3.32} \\  & 700 & 703/724/726 & 2.5 & \\  & 800 & 794/827/831 & 2.7 & \\  & 900 & 875/881/935 & 2.9 & \\ \hline \multirow{4}{*}{\(\Delta\text{T}_{\text{sc}}\)} & 600 & 569/622/638 & 4.9 & \multirow{4}{*}{2.79} \\  & 700 & 702/706/707 & 0.7 & \\  & 800 & 824/833/837 & 3.9 & \\  & 900 & 894/920/921 & 1.8 & \\ \hline \multirow{4}{*}{W} & 600 & 662/669/696 & 13.3 & \multirow{4}{*}{7.3} \\  & 700 & 717/719/756 & 4.4 & \\ \cline{1-1}  & 800 & 781/882/950 & 10.2 & \\ \cline{1-1}  & 900 & 850/924/950 & 4.6 & \\ \hline \multirow{4}{*}{T\({}_{\text{c,sat}}\) and \(\Delta\text{T}_{\text{sc}}\)} & 600 & 586/629/635 & 3.9 & \multirow{4}{*}{2.54} \\ \cline{1-1}  & 700 & 701/711/712 & 1.1 & \\ \cline{1-1} \cline{2-5}  & 800 & 815/832/834 & 3.5 & \\ \cline{1-1} \cline{2-5}  & 900 & 889/909/924 & 1.7 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Refrigerant charge detection accuracy.  
3. The refrigerant charge is one of predictors that define the dynamic characteristics of response variables. The refrigerant charge can be estimated by minimizing the RMSEs of the predicted values of the dynamic model and experimental data.
4. The proposed method, which uses the dynamic model during start-up operation, is an effective technique for predicting the refrigerant charge amount. The average prediction error for the test data is 2.54%.  
Experiment, analysis, and writing--original draft preparation, Y.Y.; supervision, writing--review and editing, Y.S.C. All authors have read and agreed to the published version of the manuscript.  
This research was conducted with the funding of the government (Ministry of Science and ICT) and with the support of the National Research Foundation of Korea (NRF-2019R1F1A1062125).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 1 Introduction
On the other hand, GitHub takes a more modular approach, allowing developers to integrate third-party CI/CD tools alongside GitHub Actions. Its emphasis on community-driven development, ease of collaboration, and an intuitive user interface has made it the preferred choice for open-source contributors and agile development teams. Choosing between Azure DevOps and GitHub depends on several key factors, including the project's complexity, the size of the development team, the level of automation required, security considerations, and budget constraints. Enterprises that require strict governance, advanced security features, and deep integration with Microsoft tools may find Azure DevOps to be the ideal solution. Conversely, teams prioritizing flexibility, open-source collaboration, and Git-native automation may benefit more from GitHub's streamlined approach. The cost structure of each platform also plays a crucial role in decision-making, as organizations must consider licensing models, subscription plans, and the total cost of ownership when selecting a CI/CD tool [5]. This paper compares Azure DevOps and GitHub by evaluating their features, scalability, security, pricing, and overall usability. By analyzing real-world case studies, industry best practices, and adoption trends, this study offers valuable insights to software engineers, DevOps professionals, and IT decision-makers evaluating these platforms for their development pipelines. Understanding the strengths and limitations of each platform is essential for making an informed decision, as the choice between Azure DevOps and GitHub can have long-term implications for a development team's efficiency, security posture, and ability to scale operations. Through an in-depth exploration of their CI/CD capabilities, automation workflows, security features, cost implications, and enterprise readiness, this paper will provide actionable guidance on when to choose one platform over the other. Whether an organization seeks a fully managed DevOps suite or a flexible, Git-based CI/CD solution, this study will help decision-makers navigate the complexities of modern software development and select the best tool for their specific needs.