esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study  
HSR [23; 24; 25] is an Ethernet redundancy protocol that provides fault-tolerant communication for HSR networks, based on the principle of sending duplicate frames over all available paths. The HSR protocol is usually used in ring topologies, including single rings and connected rings. An HSR single-ring network consists of only HSR terminal nodes, including a doubly-attached node for HSR (DANH) and redundancy box (RedBox) nodes, whereas quadruple port devices (QuadBoxes) are used to connect rings in an HSR connected-ring network. The standard HSR protocol works very well in single-ring networks; however, the HSR generates excessively redundant unicast traffic in connected-ring networks. This drawback causes the degradation of network performance.  
This section describes the DSP-based approach, which applies the DSP algorithm to HSR networks for providing fault-tolerant communication and significantly reducing the redundant unicast traffic in the HSR networks. The motivation of the case study is to demonstrate that the DSP-based approach not only provides fault-tolerant communication but also improves network traffic performance for wired and wireless networks.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Experiments
#### 5.4.2 Ablation Experiments  
In this section, we present the results of the MF-AMnet ablation experiment, presenting the role of each module in detail, including AM, MF, CAM, and RB, as shown in Table 2.  
The base CVNN was derived from the structure of SHPM-CR, which consists of three layers of C-Conv and two layers of C-FC. \"+\" means that we added or changed the module step by step and recorded the results. The last element is the complete, i.e., MF-AMnet. As can be seen from Table 2, MF was the module with the greatest performance improvement in the structure, indicating that multi-scale information extraction of feature maps was very important for DOA estimation. With only AM added, Acc5\({}^{\circ}\) showed a slight improvement, indicating that SHC still contained a lot of noisy information despite the removal of invalid information. The filtering effect of the mask makes the useful information in the SHC more explicit. The addition of CAM guided the network to pay  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Acc5\({}^{\circ}\) (\%)** & **Acc10\({}^{\circ}\) (\%)** & **Acc15\({}^{\circ}\) (\%)** & **MAE (\({}^{\circ}\))** \\ \hline DPD-MUSIC & 43.65 & 62.39 & 71.73 & 9.972 \\ SHPM-R & 81.34 & 97.13 & 99.31 & 3.467 \\ SHPM-CR & 82.31 & 97.27 & 99.40 & 3.352 \\ MS & 76.30 & 95.74 & 98.94 & 3.769 \\ CV-CNN & 87.45 & 98.19 & 99.77 & 2.819 \\ CVRAEN & 89.21 & 98.06 & 99.68 & 2.690 \\ SADOAnet & 85.97 & 97.55 & 99.49 & 3.821 \\ SDL & 86.71 & 97.69 & 99.49 & 3.809 \\ MF-AMnet & 94.07 & 99.54 & 99.95 & 2.029 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiments performance of MF-AMnet and other methods.  
attention to key information, and RB stabilized the network training, allowing the network to converge better.  
The trends of Acc5\({}^{\circ}\) and MAE are shown in Figure 4. The utilization of each module had a positive effect, with MF playing the largest role. The effective action and cooperation of all these modules greatly improved the performance and stability of MF-AMnet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Acc5\({}^{\circ}\) (\%)** & **Acc10\({}^{\circ}\) (\%)** & **Acc15\({}^{\circ}\) (\%)** & **MAE (\({}^{\circ}\))** \\ \hline Base CVNN & 82.31 & 97.27 & 99.40 & 3.352 \\ +AM & 84.63 & 96.81 & 99.44 & 3.187 \\ +MF & 91.76 & 99.35 & 99.91 & 2.44 \\ +CAM & 92.36 & 99.03 & 99.81 & 2.285 \\ +RB & 94.07 & 99.54 & 99.95 & 2.029 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation results of MF-AMnet.  
Figure 4: Trends in Acc5\({}^{\circ}\) and MAE.  
Figure 3: The performance of different inputs at different SNRs and RT60s. (**a**) MSE of methods under different SNRs; (**b**) RMSE of methods under different RT60s.
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 2 Materials and Methods
### Data
#### 2.2.2 Oil Spill Data  
Oil spill data obtained from the Nigerian Oil Spill Monitor website [[https://oilspillmonitor.ng/](https://oilspillmonitor.ng/)]([https://oilspillmonitor.ng/](https://oilspillmonitor.ng/)) (Accessed on 11 December 2017) were used to analyse the oil spill incidences recorded in Nigeria from 2006 to 2018. The data were compiled by the National Oil Spill Detection and Response Agency (NOSDRA), which relies on the voluntary support of oil companies to provide information on oil spills, such as their geographic coordinates, date and estimated spill volumes. The information on oil spills, which encompasses the dates, times, causes, magnitude, and GPS coordinates of oil spill locations, is determined through a process called Joint Investigation Visit (JIV), which includes the representatives of regulatory agencies, the oil company, the affected community, and the security forces [48,49]. The oil spill information is constantly validated and updated on the database as soon as an oil spill incident occurs [48]. The Nigerian Oil Spill Monitor website has been a source of data for research that utilises oil spill data in the Niger Delta [48,50,51,52]. However, no fieldwork was undertaken to collect ground data to validate the oil spill site or to collect soil samples. The Niger Delta is a challenging environment for field-based investigations due to the physical inaccessibility and security threats which makes it impossible to assess the spatial extent of oil spill impacts using traditional survey and sampling techniques [51], and it would have been capital-intensive and time-consuming, considering the huge area involved [10].  
Figure 2 shows the distribution of oil spill occurrences within the study area that were used to determine the impact of oil spills on vegetation. In order to determine the vegetation-specific effects, the oil spills were split into categories according to the type of vegetation cover on which they occurred (Figure 2a). Additionally, a separate corresponding sample of sites not affected by oil spills was selected for each vegetation type for use as controls against which to compare the health of vegetation exposed to oil (Figure 2b).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & \multicolumn{3}{c}{**NDVI Acquisition Dates**} \\ \hline
2006 & 9 December 2006 & \\
2008 & 14 February 2008 & 7 December 2008 & 30 December 2008 \\
2010 & 2 January 2010 & 18 January 2010 & \\
2011 & 11 January 2011 & 14 January 2011 & 21 January 2011 \\
2012 & 17 January 2012 & 9 February 2012 & \\
2013 & 10 January 2013 & 21 December 2013 & \\
2014 & 13 January 2014 & \\
2015 & 9 January 2015 & 16 January 2015 & 27 December 2015 \\
2016 & 3 January 2016 & 29 December 2016 & \\
2017 & 5 January 2017 & \\
2018 & 1 January 2018 & 8 January 2018 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image acquisition dates and number of images acquired per year.
# Review of Photogrammetric and Lidar Applications of UAV
## 5 Selected UAV Sensors and Their Applications
The resulting point cloud had an average point density of 547.01 pts/m\({}^{2}\). The study [63] describes the scanning of trees in an area of 1500 m\({}^{2}\) using a multi-rotor UAV Droidworx Skyjib Oktokopter with a Canon 550D digital SLR camera, which captures images with a resolution of 5184 \(\times\) 3456 pixels. The flight height of 30 m was set at the front at 95% with side overlaps at 66%. In total, 425 images were acquired. Data processing was performed in Agisoft Photoscan Professional v.1.0.0 software with the result of 85 million points with an average density of 5652 pts/m\({}^{2}\). In the area of interest, 14 ground control points were used. As a result, in block alignment, a horizontal RMSE of 0.40 m and a vertical RMSE of 0.14 m were obtained. The authors also placed an Ibeo LUX laser scanner on the Droidworx Skyjib Oktokopter multi-rotor and scanned the same area of interest from the same flight height. Average density in the point cloud using this device s was 174 pts/m\({}^{2}\). However, the vertical and horizontal RMSE error on 10 GCP points have almost the same value as when using the photogrammetric approach, in which there was a horizontal RMSE value of 0.42 m and a vertical RMSE = 0.17 m  
The tropical savanna forest in the Litchfield Savanna SuperSite, located in the Litchfield National Park in Australia, was investigated using lidar and two UAV flights by the authors in [65]. UAV data were collected using a RIEGL RiCOPTER with a VUX-1 UAV scanner. A high-resolution flight over an area of 98,000 m\({}^{2}\) at a flight speed of 3 m/s had an average point density of 1800 points/m\({}^{2}\) and was conducted at 55 m AGL. In this case, the RMSE was 0.75 m.  
The paper [66] aim was to obtain spatially dense and accurate measurements for forest inventory applications and create point clouds using UAV-borne lidar. The authors used an OktoCopter Droidworx/Microcopter AD-8 multi-rotor VTOL UAV with an Ibeo LUX laser scanner. The Ibeo LUX laser scanner has a maximal range of 200 m and a maximal scanning range of 110\({}^{\circ}\), scans in four parallel layers, and has a beam divergence that reaches 0.8\({}^{\circ}\), a scanning frequency set to 12.5 Hz, and an angular resolution of 0.25\({}^{\circ}\). An area of 100 \(\times\) 100 m at the University of Tasmania was surveyed during four flights at an average flight height of 50 m with an approximate speed of 3.3 m/s. The resulting point cloud has a point density of approximately 50 points/m\({}^{2}\). The horizontal RMSE on all 130 GCPs is 0.34 m and the vertical RMSE = 0.15 m.
## 3 Mathematical Foundations
### Pure Pursuit Algorithm  
As is well-known [9], from a set of waypoints, the current robot position, and the look ahead distance \(L^{ref}\), the Pure Pursuit path-tracking algorithm provides the next point to be followed by the robot. This target point is reached at a desired speed, known as the reference linear velocity \(V^{ref}\). Let us name \(D\) as the distance from the current robot position to the target point. \(D\) can be decomposed into Cartesian components \(d_{x}\) and \(d_{y}\), as depicted in Figure 4.  
In the present work, a modified version of the algorithm is proposed. Since the mobile platform is able to laterally and longitudinally move, \(V^{ref}\) can also be decomposed into Cartesian components, i.e., lateral (\(V_{x}^{ref}\)) and longitudinal (\(V_{y}^{ref}\)) reference linear velocities.  
Additionally, the set of waypoints is composed of a subset of so-called via-points (see, e.g., [39]). The main goal of these via-points is to improve the approach to the target point in curved sections of the path. The procedure needs to define a minimum distance \(\epsilon\). Then, the algorithm searches for the next target point. If this point is not a via-point, the algorithm proceeds as usual. If the point is a via-point, it will be retained as the current target point while the distance \(D\) is greater than \(\epsilon\).  
Finally, the algorithm computes the reference linear velocities \(V_{x}^{ref}\) and \(V_{y}^{ref}\). They are parameterized by a variable gain \(K_{V}\in[0,1]\) that depends on \(D\) in such a way that \(K_{V}=\frac{D}{L^{ref}}\). If the target point is not a via-point, \(D=L^{ref}\); hence, \(K_{V}=1\). Note that for a via-point, the lower \(D\) is, the lower the velocities will be. The modified Pure Pursuit path-tracking algorithm is detailed in Algorithm 1.  
Figure 4: Pure Pursuit.  
```
Require:\((\widehat{X},\widehat{Y})_{k}^{T}\wedge(X^{ref},Y^{ref})\wedge(L^{ref},V^{ref})\wedge\epsilon\) Global\(i=1\); // \(i\) represents the vector index position of the current reference position \(\left(X_{i}^{ref},Y_{i}^{ref}\right)\) \(d_{x}=0\); \(d_{y}=0\); \(D=0\); \(V_{x}^{ref}=0\); \(V_{y}^{ref}=0\); \(end\_loop=1\); while\(i\leq length\left(X^{ref},Y^{ref}\right)\wedge end\_loop\)do \(d_{x}=X_{i}^{ref}-\widehat{X}\); \(d_{y}=Y_{i}^{ref}-\widehat{Y}\); \(D=\sqrt{d_{x}^{2}+d_{y}^{2}}\); ifis_via_point\(\left(X_{i}^{ref},Y_{i}^{ref}\right)\)then if\(D\leq\epsilon\)then \(i=i+1\); else end_loop = 0; endif endwhile if\(D<L^{ref}\)then \(i=i+1\); else end_loop = 0; endif endwhile if\(i\leq length\left(X^{ref},Y^{ref}\right)\)then \(K_{V}=sat\left(\frac{D}{L^{ref}},0,1\right)\); \(V_{x}^{ref}=V^{ref}\cdot K_{V}\cdot cos\left(atan2\left(\frac{dy/D}{d\pi/D} \right)\right)\); \(V_{y}^{ref}=V^{ref}\cdot K_{V}\cdot sin\left(atan2\left(\frac{dy/D}{d\pi/D} \right)\right)\); endif
```  
**Algorithm 1**Calculate \((V_{x}^{ref},V_{y}^{ref})_{k}^{T}\)
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### BExo Impact on PPE in Different Tasks  
The analysis of perceived physical exertion across different tasks, such as walking, lifting, and carrying a box, revealed significant variations, as shown by the ANOVA results with an F-ratio of 9.44 and a \(p\)-value of 0.0003 (Table 3). This indicates that the type of task substantially affects the level of physical exertion experienced by individuals, emphasizing the importance of task characteristics in evaluating the effectiveness of exoskeleton usage. The significant differences across tasks suggest that exoskeletons may provide varying degrees of support depending on the specific activities performed.  
[PERSON]'s HSD test was performed to compare the PPE scores across different tasks (walking, carrying a box, and lifting a box) with and without exoskeleton support. For the walking task, the mean difference in the PPE scores between the exoskeleton and no exoskeleton conditions was 3.8 (\(p\)-value of 0.0553), indicating no statistically significant difference (95% CI: \(-\)0.07 to 7.67). For the lifting task, the test showed a significant mean difference of 8.15 in the PPE scores between using or not the BExo, with a \(p\)-value of 0.0420 (95% CI: 0.31 to 15.99), confirming that the exoskeleton significantly reduces physical exertion in lifting tasks. For the carrying task, the mean difference in the PPE scores was 6.55, with a \(p\)-value of 0.0814, suggesting no statistically significant difference (95% CI: \(-\)0.78 to 13.88).  
Figure 4: Participants’ average sum of PPE with and without BExo per body part.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **DF** & **Sum of Square** & **Mean Square** & **F-Ratio *** \\ \hline Task & 2 & 1812.1333 & 906.067 & 9.4402 \\ Error & 57 & 5470.8500 & 95.980 & \\ C. Total & 59 & 7282.9833 & & \\ \hline \hline \end{tabular}  
* Corresponding \(p\)-value is 0.0003 at \(\alpha=0.05\) level.  
\end{table}
Table 3: ANOVA of PPE by task.  
These results highlight the effectiveness of the exoskeleton in significantly reducing physical exertion during lifting tasks, while its impact on walking and carrying tasks is less pronounced. These results are visually confirmed in Figure 5 and provide compelling argument for the targeted use of exoskeletons in particularly strenuous tasks. This could potentially guide ergonomic interventions more effectively in workplace settings.
## 3 Results and Discussion
### Characterization of the Silicon-Carbon Nanocomposite  
The detailed morphology, structure, and composition of the silicon-carbon nanocomposite prepared under the optimal conditions in this work were characterized. Figure 5 shows the SEM images of the different products prepared by the DC magnetoelectric plasma system. Figure 5a shows the spherical pure Si nanoparticles (size is in the range of 50-100 nm, under the ratio of hydrogen/silane of 1:1), the size distribution of the Si nanoparticles (inset) was obtained by analyzing the SEM image using SigmaScan software. Figure 5b shows the pure tremelliform carbon nanosheets (with a size of 200-400 nm, ratio of hydrogen/methane of 1:3). Figure 5c,d show the SEM images of the silicon-carbon nanocomposite prepared under the optimal condition (ratio of hydrogen/silane/methane of 1:1:3). It is obvious that the spherical Si nanoparticles are evenly dispersed among the tremelliform carbon nanosheets., which can effectively alleviate the agglomeration and volumetric change of Si nanoparticles during the cycles.  
Figure 4: SEM images of the silicon-carbon nanocomposite prepared with different hydrogen/methane ratios. (**a**) No hydrogen, (**b**) 1:20, (**c**) 1:10, (**d**) 1:5, (**e**) 1:3, (**f**) 1:2.  
Figure 5: SEM images of the pure Si nanoparticles (**a**), pure tremelliform carbon nanosheets (**b**), silicon-carbon nanocomposite (**c**,**d**) prepared by DC magnetoelectric plasma under optimal conditions. Inset: size distribution of the Si nanoparticles.  
The XRD spectra of the silicon-carbon nanocomposite are shown in Figure 6a. The diffraction peaks at 28.4\({}^{\circ}\), 47.3\({}^{\circ}\), and 56.1\({}^{\circ}\) correspond to the (110), (220), and (311) crystal planes of Si nanoparticles, respectively [34; 35; 36; 37]. Graphite (002) and (100) crystal planes were also found at 26.4\({}^{\circ}\) and 42.2\({}^{\circ}\), which indicates that a part of carbon in the silicon-carbon nanocomposite was graphitized [38]. The diffraction peaks of SiC were not found in the spectra, indicating that the composite prepared by plasma was composed of only Si nanoparticles and tremelliform carbon nanosheets, and the silicon and carbon are physically bound together, which is completely consistent with the SEM image of the silicon-carbon nanocomposite (Figure 4). Figure 6b shows the Raman spectra of the silicon-carbon nanocomposite. A well-defined D band (1343 cm\({}^{-1}\)) is attributed to the amorphous structure of carbon, and the G band (1573 cm\({}^{-1}\)) associates with sp\({}^{2}\)-bonded corresponding to the graphitized carbon in the silicon-carbon nanocomposite [39]. The characteristic peak at 504 cm\({}^{-1}\) corresponds to the crystalline Si. However, there is no typical peak of SiO\({}_{2}\) (SiO\({}_{2}\) has a asymmetric peak at 460 cm\({}^{-1}\), a sharp peak at 490 cm\({}^{-1}\), and two broad features peaks at 800 and 1050 cm\({}^{-1}\)) [40], further indicating that the silicon-carbon nanocomposite prepared by the plasma method is mainly composed of tremelliform carbon nanosheets and Si nanoparticles.
## 8 Sensitivity Analysis  
In this section, we investigate the sensitivity of the proposed approach against parameter variations. To that end, we will assume that all system parameters in are uncertain within a \(\pm 10\%\) bound; we check the robustness of the ETC mechanism in this case by using the MATLAB Robust Control Toolbox. The analysis results show that the obtained closed-loop stability can tolerate up to \(320\%\) of the modeled uncertainty, which indicates that the designed controller exhibits high robustness against parameter variations. The nominal parameter values and the minimum stabilizing values are presented in Table 3.  
Figure 16 shows the state response when subject to \(\pm 50\%\) parameter uncertainties. We see that all the system states converge to the origin, which supports the obtained robustness results.  
\begin{table}
\begin{tabular}{c c c|c c c} \hline \multirow{2}{*}{**Parameter**} & **Nominal** & **Min Stabilizing** & \multirow{2}{*}{**Parameter**} & **Nominal** & **Min Stabilizing** \\  & **Value** & **Value** & & **Value** & **Value** \\ \hline \(K_{ig}\) & 0.272 & 0.0406 & \(T_{p1}\) & 0.6 & 1.0912 \\ \(K_{tp}\) & 0.0014 & \(5.7271\times 10^{-4}\) & \(
# Depth Retrieval Procedures in Pulsed Thermography: Remarks in Time and Frequency Domain Analyses
## 4 Evaluation of Analysis Parameters for Depth Prediction
### Assessment of Correlation Factor C in Frequency Domain Analysis  
As presented in the Introduction, depth quantifications using phase data require knowledge of the correlation factor \(C\), which range from 1.5 to 2, with the value of 1.82 typically adapted in experimental studies. Nevertheless, when investigating anisotropic materials such as the present CFRP panel, the above value may be different, creating uncertainties on the acquired depth results.  
Figure 14: Phase contrast profiles for defect C4 after selecting three different sound areas from the background and the overall surface.  
Figure 13: (**a**) Thermal image of the inspected surface showing the regions from which sound areas 1,2, and 3 were selected to calculate the slope of the thermal contrast for Defect A1; and (**b**) temporal evolution of the correspondent thermal contrast slope curves.  
In this direction, the selection of the correlation factor was assessed in a graphical manner, plotting the nominal depth \(z\) of the insertions as a function of the measured diffusion length \(\mu\) and retrieving the slope of the produced linear function, after linearly fitting these experimental data. Figure 15 presents the correlation results acquired from the two quantification procedures either selecting a sound area from the background (left column plot) or selecting the entire surface as a sound area (right column plot). As stated above, the experimental data were fitted by a least square linear fitting function resulting to high correlation coefficients (0.98). Regardless of the procedure used to estimate the depth, the constant \(C\) was measured to be 1.69 in the former case and 1.71 in the latter one (between the range discussed above and very close to the value used in this study). From the graphical assessment of the correlation factor, it can be seen that the value used in this study had a low impact on the acquired results. However, if these graphically retrieved slopes were used for depth quantifications, the results presented on the tables would provide a greater accuracy than this derived using the value of 1.82.
###### Abstract  
Adopting mobile robots for high voltage (HV) live-line operations can mitigate personnel casualties and enhance operational efficiency. However, conventional mechanical arms cannot inspect concealed spaces in the power cable tunnel because their joint integrates metallic motors or hydraulic serial-drive mechanisms, which limit the arm's length and insulation performance. Therefore, this study proposes a 7-degree-of-freedom (7-DOF) bionic mechanical arm with rigid-flexible coupling, mimicking human arm joints (shoulder, elbow, and wrist) designed for HV live-line operations in concealed cable tunnels. The arm employs a tendon-driven mechanism to remotely actuate joints, analogous to human musculoskeletal dynamics, thereby physically isolating conductive components (e.g., motors) from the mechanical arm. The arm's structure utilizes dielectric materials and insulation-optimized geometries to reduce peak electric field intensity and increase creepage distance, achieving intrinsic self-insulation. Furthermore, the mechanical design addresses challenges posed by concealed spaces (e.g., shield tunnels and multi-circuit cable layouts) through the analysis of joint kinematics, drive mechanisms, and dielectric performance. The workspace of the proposed arm is an oblate ellipsoid with minor and major axes measuring 1.25 m and 1.65 m, respectively, covering the concealed space in the cable tunnel, while the arm's quality is 4.7 kg. The maximum electric field intensity is 74.3 kV/m under 220 kV operating voltage. The field value is less than the air breakdown threshold. The proposed mechanical arm design significantly improves spatial adaptability, operational efficiency, and reliability in HV live-line inspection, offering theoretical and practical advancements for intelligent maintenance in cable tunnel environments.  
cable tunnel; bionic mechanical arm; live-line operation; self-insulation Article