esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design and Implementation
### Tests Performed during SLAM  
After testing on static measurements, we decided to use the proposed algorithm when performing SLAM. This was the last step before real-time testing. For the first test, 16 measurements were used with a sampling period averaging 1.2 s. During this measurement, the mapping device traveled approximately 3.3 m. A total of 15 objects were found during the identification of dynamic objects, 3 of which were falsely identified (walls), and 3 objects were not recognized, as they were assigned to a wall during segmentation and clustering. Dynamic objects are on the map in Figure 12 drawn in green, and blue indicates the trajectory of the measuring system. The total distance traveled by a dynamic object was approximately 11.7 m, while its average speed was 3.19 km/h. In Figure 13, identified objects are plotted. One of the other measurements that were made was if the object was moving at a low speed when the sampling frequency was very high, and there would be a problem of overlapping measurements. The result can be seen in Figures 14 and 15. A total of 25 measurements were used in this test. In this case, 29 objects were identified, of which 11 were falsely identified objects. There was a problem with the light reflectance as the sidewall in the corridor where the measurements took place was made of glass. In six images, the subject could not be recognized, due to the overlap of the subject area.  
Figure 11: Navigation map with applied navigation algorithm.  
Figure 12: Map with identified dynamic objects.
# Effect of Augmented Reality Support on Quality Inspection of Welded Structures
## 4 Results
### Error Rate  
The experiment not only investigated the execution time of the task but also focused on the evaluation of the error rate during this control process.  
Figure 9 provides a graphical overview of the mean error rate and standard errors, showing the error rate results for each group and how they compare between the AR and traditional methodologies. This graph shows the average number of errors, where a unit represents a single error, providing a visual representation of the frequency of errors recorded by participants during the research. The lowest average error rate was recorded in group G1 when using the AR method, indicating that this combination achieved the best results. The second lowest average is observed in group G2 when using the AR methodology. Group G1 using the traditional methodology shows a higher average error rate than the previous two groups, while group G2 using the traditional methodology has a relatively and significantly higher average.  
For the error rate, the Shapiro-Wilk test did not confirm the normal distribution of the data. Therefore, a non-parametric Friedman test was performed instead of a parametric ANOVA analysis. By calculating the Friedman test for the measured error rate data, a significance value of 3.09665 \(\times\) 10\({}^{-11}\) was obtained. This value reflects the significance of differences between groups. It indicates statistically significant differences between at least one pair of datasets.  
Given these results, post hoc tests were subsequently conducted to identify specific groups between which the differences were statistically significant. For this purpose, the Mann-Whitney U test was used, which is appropriate for comparing two groups. Using the Mann-Whitney U test, there were no statistically significant differences for any controlled pair of data groups (\(p>0.05\)). A possible explanation for this situation may be that the Friedman test is sensitive to any form of differences between groups, whereas the Mann-Whitney U test is primarily aimed at comparing two groups and may not be sensitive enough to small differences between groups. Statistically significant differences between pairs of groups may not be detected using the Mann-Whitney U test, but differences may still exist.
## 4 Case Study
### Reachability Analysis  
The reachability graphs, which represent the state space of CPN models, are analyzed in this section to verify various properties. For the transformed mTSP model into CPN, the property of interest is the liveness property. Under the liveness property, the absence of dead transitions signifies that all the functionality that is being modeled is used while the presence of dead markings signifies that the model is not stuck in an infinite sequence of steps and terminates after a finite sequence of steps.  
CPNTools provide support to automatically generate and analyze the reachability graph of the CPN model. Table 8 presents a part of the state space report generated by CPNTools for all the three transformed models of mTSP. It can be seen that, in all the three cases, there are no dead transitions and there are dead markings under the liveness property. This verifies that all the cities are visited by the salesmen and the tour lengths are finite.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization Objective** & \begin{tabular}{c} **Case A** \\ **(Time Units)** \\ \end{tabular} & \
\begin{tabular}{c} **Case B** \\ **(Time Units)** \\ \end{tabular} &
\begin{tabular}{c} **Case C** \\ **(Time Units)** \\ \end{tabular} \\ \hline miniSUM & 18 & 18 & NA \\ miniMAX & 8 & 7 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation Results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Liveness Properties**} \\ \hline \multicolumn{2}{c}{**Dead Transition Instances**} & \multicolumn{1}{c}{**Dead Markings**} \\ \hline
**Case One** & None & Present \\
**Case Two** & None & Present \\
**Case Three** & None & Present \\ \hline \hline \end{tabular}
\end{table}
Table 8: Part of State Space Report.
## 4 Verification and Validation
### Validation of the Stability Improvement
#### 4.2.4 Density of the Flexible Structure  
The inertial force is also called the d'Alembert force and significantly affects the amplitude of the vortex-induced vibration. This force is commonly discretized using a finite difference method, such as Equation (14). However, the stability can be maintained only for low-mass cases, and the critical mass is much smaller than the required mass to induce the vortex vibration. Thus, a penalty method is proposed to mitigate this problem, where a user-defined coefficient is required [14]. The proper value for this coefficient is much more difficult to determine than the fastening force. Thus, a filament in a uniform flow is simulated to study the effect of the weighted iterative method on the direct difference discretization of the inertial force.  
A filament is placed horizontally in a rectangular domain with dimensions \(L\times W=800\times 400\). The filament is 100 units long and is discretized with \(N_{b}=301\) points. The filament upstream point is fixed to (100, 200). The fluid flows from left to right, the inlet velocity is 0.01, and the fluid viscosity is set to \(\
u=0.0056\). All quantities are expressed in lattice units.  
To make the filament almost inextensible and maintain a sufficiently small displacement of the upstream end, we select \(\overline{K}_{s}=2.0\times 10^{3}\) and \(\overline{K}_{f}=2.0\times 10^{3}\), by which the filament vibration is no longer sensitive to the bending rigidity [9]. The dimensionless filament mass is defined as \(S=m_{s}/\rho L\), where \(m_{s}\), \(\rho\), and \(L\) are the filament linear density, fluid density, and filament length, respectively. As was well documented in [4; 9], in a uniform flow field, a low-mass filament tends to be more stable than a high-mass filament, and a massless filament is always stable regardless of the magnitude of the initial perturbation. The computed critical mass using the proposed method is \(S=0.28\), which is consistent with that reported in [23]. The vorticity field obtained with \(S=0.28\) is shown in Figure 14. When other parameters are fixed and both explicit and weighted iterative methods are used, the computed results show the following: the explicit method will cause instability even without the filament mass, whereas the proposed method can obtain the maximal  
Figure 12: Streamlines after the flow field settles at \(\overline{K}_{b}=1.75\times 10^{-1}\).  
Figure 13: Vorticity field after the flow field settles at \(\overline{K}_{b}=1.75\times 10^{-1}\).  
filament mass up to \(S=0.8\). Compared to the original IB-LB coupling scheme using the penalty approach, the AWI approach avoids introducing the artificially fastening coefficient to impose the inertial force, which is relatively more accurate and efficient.
## 1 Introduction
Most of the existing iterative linear detectors are utilizing the diagonal matrix (\(\mathbf{D}\)) in estimating the initial vector because the equalization matrix is diagonally dominant. In some cases, the diagonal matrix has a slow convergence, or it may not converge. In [18], a stair matrix (\(\mathbf{S}\)) is proposed as an alternative solution to replace \(\mathbf{D}\) in initializing iterative linear detectors based on the NS and JA methods. This paper aims to study the impact of \(\mathbf{S}\) and \(\mathbf{D}\) in initialization of iterative linear M-MIMO UL detectors where iterative methods are utilized such as the SOR, GS, JA, and RI methods. In addition, a comparison between the performance-complexity profiles of \(\mathbf{S}\) and \(\mathbf{D}\) will be presented. Therefore, thousands of random channel matrices have been generated to examine the convergence rate using both \(\mathbf{S}\) and \(\mathbf{D}\). This paper is organized as follows: Section 2 presents the system model and the definition of a stair matrix and a diagonal matrix. Section 3 presents the NS, NI, SOR, GS, JA, and RI methods with the initialization using stair and diagonal matrices. Section 4 provides a comprehensive analysis of the computational complexity required by each detector. In Section 5, results and discussion are presented. Section 6 concludes the paper.
## 3 Data and Methods  
### Data  
Sentinel-1 is an Earth monitoring satellite with C-band SAR launched by the European Space Agency (ESA) to provide all-time and all-weather images ([[https://www.esa.int/](https://www.esa.int/)]([https://www.esa.int/](https://www.esa.int/)) (accessed on 9 January 2023)). The Sentinel-1 satellite mostly acquires large-scale and wide-range SAR data in TOPS imaging mode. It is widely used in the extraction and analysis of large-scale glacier movements due to its short revisit period, wide coverage area, and free and easy access to data [20]. In this paper, we use ascending track SAR data acquired from the Sentinel-1A satellite from 12 September 2021 to 5 January 2023 with a spatial resolution of 2.33 m \(\times\) 13.97 m and a revisit period of 12 d.  
LT-1 consists of two advanced full-polarization L-band radar satellites, LT-1 A and LT-1 B. Satellite A was launched on 26 January 2022, and satellite B was launched on 27 February of the same year. The satellite constellation supports two imaging modes. The bistatic InSAR mode is implemented to produce a digital elevation model (DEM) with high accuracy and spatial resolution. The monostatic mode is implemented by having the satellites follow each other in flight with a 180\({}^{\circ}\) orbital phasing difference to shorten the revisit time to 4 days, allowing the use of D-InSAR technology to measure terrain changes at large scales with millimeter accuracy [21; 22; 23]. The LT-1 SAR makes full use of the characteristics of  
Figure 1: Study area and coverage of satellite SAR images. (**a**) A sketch of the tectonic background of the Qinghai–Tibetan Plateau and adjacent areas; (**b**) regional tectonic background of the 5 September 2022 M\({}_{\text{S}}\) 6.8 Luding earthquake. The gray polygon indicates the area of Mt. Gongga extracted from the Second Glacier Inventory of China version 1 [19]. The blue polygon shows the coverage area of the cropped Sentinel-1A images. The magenta polygon shows the coverage area of the cropped LT-1 SAR images.  
the long L-band radar's wavelength and penetrating ability in vegetated areas, which can provide longwave SAR data with wide coverage, a high density of measurement points, and a high frequency of repeated observations for early identification of hazards. In this paper, preseismic and postseismic interferometric image pairs acquired by the LT-1 SAR satellite on 4 September and 12 September 2022 are used to study the influence of earthquakes on the evolution of glacier motion on Mt. Gongga. The detailed acquisition parameters for these two SAR data stacks are listed in Table 1, with their coverages shown in the inset map in Figure 1.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Attestation
The authors implemented MTRA on TPM-enabled devices (i.e., Odroid-XU4) and non-TPM devices (i.e., Raspberry Pi), and the performance evaluation proved that MTRA is more suitable for lightweight devices than existing remote attestation protocols. PASTA [132] is a new attestation protocol for autonomous embedded systems, especially low-end embedded devices. It is fully decentralized, as each prover initiates a new token generation periodically to ensure freshness, which makes network disruptions or arbitrary device outages tolerable. The authors implemented the proposed protocol and conducted measurements in a simulated network. The results proved that PASTA is practical in large networks with millions of devices and is also able to robustly detect physical attacks. [PERSON] et al. [126] presented an efficient attestation scheme against physical attacks (EAPA) for IoT devices. EAPA uses a distributed attestation mode to attest devices by their direct neighbors, which cuts the total run-time to O(1). In addition, the authors introduced an accusation mechanism to report compromised devices and designed a unique key update method, which makes the proposed scheme more efficient and secure. Compared to DARPA [153], SCAPI [154] and US-AID [23], EAPA has the lowest energy cost and run-time consumption in a large-scale network.
## 2 Materials and Methods
### Data Analysis  
Values in the text are expressed as mean \(\pm\) standard error (SE) if not otherwise specified. The 95% confident limits of the mean LMS values (expressed as minutes of NRT) were assessed against their corresponding background assessment criteria (BAC) and environmental assessment criteria (EAC) developed for the technique [44]. Statistical analyses were carried out using the software packages IBM(r) SPSS Statistics v.17 and Stragraphics Centurion XVII. All data were preliminarily checked for normality (Shapiro-Wilk test) and homoscedasticity (Levene's test); when necessary, they were transform using Arcsine\(\sqrt{x/100}\) or Box-Cox transformation (lambda was found using the most likely estimate (MLE) approach) to meet the required assumptions [45]. Differences in NRT, %LMS, and %CI among treatments were identified using multifactorial ANOVA (2-way ANOVA). The factors were temperature with 3 levels (10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C and 27.5 \({}^{\circ}\)C) and salinity with 3 levels (27-28, 32-33, and 37-38). To obtain specific LMS information for all treatments and for each temperature and salinity condition, a pairwise comparison ([PERSON]'s HSD post hoc, \(p\) < 0.05) of 9 treatments was carried out: (i) the 3 ranges of salinity levels for each temperature and (ii) the 3 temperature levels for each salinity. Additionally, a pairwise comparison was performed to determine the differences between the CIs of the reference organisms in the three samplings (March, May, and June--on day 0) with the CI values of each treatment (after 21 days of exposure).  
Finally, [PERSON]'s correlation coefficient was used to study the correlations between temperature, salinity, LMS, and %CI.
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 3 Field Experiment  
### Experimental Scenario  
On 12 May 2022, an experiment was conducted at the Dongshahe Station of the Beijing Longshan Administration, located at coordinates \(40^{\circ}11^{\prime}25.59^{\prime\prime}\mathrm{N}\) and \(116^{\circ}15^{\prime}11.55^{\prime\prime}\mathrm{E}\). Figure 4 shows the equipment installation location during the experiment, where no obstructions were present above the site and the water surface was calm. To mitigate the reception of reflected signals from the grass and surrounding buildings at the experimental site, we impose an azimuth limitation for the satellites considered in the calculation, restricting them to the range of \(45^{\circ}\) to \(250^{\circ}\). The receiver equipment was housed in a cabinet, and a solar power system was closely connected to the cabinet. The field experiment data were transmitted to a monitoring platform using 4G technology.  
Figure 4: GNSS-R experiment setup at Dongshahe station.  
The signal emitted by GNSS satellites is a RHCP signal. After reflecting off the water surface, it changes to a signal dominated by left-hand circular polarization. The higher the elevation angle of the satellite, the larger its left-hand circular polarization ratio. The left-hand circularly polarized antenna is suitable for receiving reflected signals with an elevation angle greater than 20\({}^{\circ}\)[44]. Therefore, in our experiment, we set the satellite cut-off elevation angle to 20\({}^{\circ}\). Equation (13) contains three unknown parameters. Therefore, to solve these unknowns, at least four or more satellites must be observed simultaneously. Figure 5 displays a 24 h GNSS sky plot observed at the experimental site on 10 July 2022. As shown in Figure 5, the number of available reflections with elevation angles above 20\({}^{\circ}\) and azimuth angles within the range of 45\({}^{\circ}\) to 250\({}^{\circ}\) is sufficient to meet the calculation requirements.  
To determine the height of the antenna connecting rod above the water surface, the steel tape measure was used ten times, and the average height was found to be 3.03 m. Figure 6 illustrates the location of the water gauge at the experimental site, which confirmed the water depth at this location to be 1.08 m. The final height of the antenna connecting rod above the water level reference point (the zero point at the bottom of the water gauge) was calculated to be 4.11 m. To compare and validate our experimental results, water level data obtained by the site staff through the water gauge were collected at 07:30, 15:30, and 18:30 daily.  
Figure 5: Sky plot of GNSS for 24 h on 10 July 2022.  
Figure 6: The water level at the experimental site was obtained using a water gauge.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods  
The proposed architecture is a radio-system component that we have considered as an ML(Machine learning)/DL(Deep Learning) co-processor that allows its use in an SDR scheme, see Figure 2. The processor receives the flow of the data packets; evaluates the conditions; and measures, extracts, and sends the features to the ML/DL co-processor. Four hardware architectures to build the co-processor based on an FPGA in this article are examined, designed, implemented, and compared with other works based on hardware and software implementations. Our co-processor allows us to evaluate the features sent by the processor to determine if the channel quality is low or high. The components can use this output to modify their hardware error correction modules, filters, operating channel processes, converters to change their precision and accuracy, improving QoS. In addition, the digital hardware component can select better fault tolerance algorithms (software), modulation schemes, and transforms that adapt best among other algorithms or functions. By considering these conditions, we focus on having an SDR that can modify its hardware and software in most aspects. The advantages of the proposed architecture in Figure 2 are changing between algorithms that can consume less energy to others that consume more energy but provide:  
* Tolerant communication schemes;
* Capability to notify in a context of the change of channel;
* Provide better conditions of communication bandwidth or less noise.  
In the following sections, we describe the co-processor development. We will explain how we selected the machine learning model that determines if a channel has a high or low quality based on a trade-off analysis. The learning-model implementation is based on an FPGA hardware architecture, which is part of an SDR.
## 5 Experimental Validation of Quadrotors  
In this section, the validation results of the proposed UT-CLAT method are presented. Validation was performed using hardware platforms that included three quadrotors tracking a ground robot. As shown in Figure 9, the system consists of three Intel Aero RTF quadrotors, referred to as quad1-3, and one TurtleBot ground robot. Each quadrotor is equipped with UWB transmitters and a downward monochrome camera. The UWB sensors measure the relative distance and transmit information when two quadrotors are within the functional range of the UWB sensors. A camera is rigidly connected to the body frame of each quadrotor. A target is detected by the camera when the target is within the field-of-view (FOV). Furthermore, quad1 is assumed to have access to its position through the onboard navigation system. In addition to the above onboard devices, UWB ground anchors are used to record the ground truth states of both robots and the target.  
Figure 8: Target distributed estimation PRMSEs of the UT-CLAT vs. the EKF-CLAT for robots 1–4.  
Figure 9: Intel Aero RTF quadrotors (equipped with an onboard navigation system, ultra-wide bandwidth (UWB) transmitters, and downward cameras) and the TurtleBot ground robot.
## 4 Analysis of Factors Influencing Segment Fracture Width
### Effect of Thrust Magnitude on the Fracture Width of a Segment  
In shield tunneling, when the attitudes of the shield machine and segment are inconsistent, the thrust direction and normal direction of the segment torus are not identical, and a tangential shear force will be generated on the segment ring, resulting in different degrees of fracture in the segment. The actual inclination angle between the two is generally between 0\({}^{\circ}\) and 2.5\({}^{\circ}\). This section quantitatively describes the relationship between the inclination angle and fracture width when the inclination angle is 1\({}^{\circ}\), 2\({}^{\circ}\) and 3\({}^{\circ}\). The simulation results are listed in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline
**Thrust (kN)** & \(\sigma_{se}\) **(MPa)** & \(L_{\text{max}}\) **(mm)** & **Fracture Width (mm)** \\ \hline
20,000 & 0.69 & 2.3 \(L_{1}\) & 0.13 \\
21,000 & 1.59 & 2.3 \(L_{1}\) & 0.15 \\
22,000 & 2.17 & 2.4 \(L_{1}\) & 0.17 \\
23,000 & 2.71 & 2.5 \(L_{1}\) & 0.19 \\
24,000 & 3.95 & 2.5 \(L_{1}\) & 0.22 \\ \hline \end{tabular}
\end{table} 
Table 4: Calculation and simulation results of fracture width of segment with total thrust of 2000–24,000 kN. As shown in Table 5, when the inclination angle between the gripper and the normal direction of the segment torus is 1\({}^{\circ}\), the thrust reaches 20,000 kN. The fracture width is still controllable. When the inclination angle between gripper and the normal direction of the segment torus is 2\({}^{\circ}\), the segment fracture width will exceed the limit (0.23 mm) when the total thrust reaches 18,000 kN. When the inclination angle between gripper and the normal direction of the segment torus is 3\({}^{\circ}\), the segment fracture width will exceed the limit (0.27 mm) when the total thrust reaches 16,000 kN. Therefore, the orientation of the gripper should be strictly controlled in shield tunneling to prevent too large a deflection angle between the thrust direction and segment normal direction. The simulated figure of fracture widths at different inclination angles are shown in Figure 17. When the inclination is 3\({}^{\circ}\) and the thrust is 16,000 kN, the crack width of the segment has exceeded the limit of 0.27 mm. When the inclination is 2\({}^{\circ}\) and the thrust is 18,000 kN, the crack width of the segment has exceeded the limit of 0.23 mm.