esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The thermal conductivity of expanded graphite plate (EGP) and/or multi-wall carbon nanotube (MWCNT)-filled, shape-stabilized, phase change material (SSPCM), based on paraffin, high-density polyethylene (HDPE), and styrene-butadiene-styrene copolymer (SBS), was investigated. The results demonstrated that both EGP and MWCNT increased the thermal conductivity of the SSPCM. EGP showed a greater thermal conductivity improvement than MWCNT. The conductivity of EGP-filled SSPCM reached 0.574 W/mK at 9 wt %, while that of MWCNT was just 0.372 W/mK at the same loading. Furthermore a series of EGP/MWCNT hybrid fillers were prepared and introduced into the SSPCM, and a synergistic effect was observed between the two fillers. When the EGP/MWCNT ratio was 8:2, the most significant thermal conductivity enhancement to the SSPCM was obtained. The thermal conductivity was 0.674 W/mK, 288% that of the SSPCM and 117% that of 9 wt % EGP-filled SSPCM. The SEM photos showed that a bridging of two-dimensional (2D) planar EGP by flexible one-dimensional (1D) MWCNT was constructed. The so-formed EGP-MWCNT network favored heat transfer along it and led to a decreased thermal interface resistance due to the increased EGP-MWCNT junctions.  
shape stabilized phase change material (SSPCM); thermal conductivity; expanded graphite plate (EGP); multi-wall carbon nanotube (MWCNT); synergistic effect +
Footnote †: journal: Applied Physics Letters
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results
### Influential Parameters on the Axial Behaviour of FGRBSs
#### 3.3.1 Effect of EL on the Axial Behaviour of FGRBSs  
Several studies have explored the optimal EL in FGRBSs. [PERSON], [PERSON] [66] suggested that EL should be less than four times the bolt diameter to achieve a uniform distribution of shearing stress along the bolt. They also suggested that the axial bearing capacity increased by increasing EL from 110.6 mm to 316 mm. After that, [PERSON], [PERSON] [67] investigated the performance of FGRBSs with embedment lengths of 200 mm and 300 mm, while [PERSON], [PERSON] [43] considered both short and long ELs in their models. The aim of considering these different ELs was to minimise free-end displacement and achieve uniform stress distribution. The length of the grouted area can also influence the behaviour of the FGRBSs under axial load conditions. Research by [PERSON], [PERSON] [68] and [PERSON], [PERSON] [69] suggested that longer embedment lengths result in a five-stage load-displacement curve. When the embedment length is relatively long, the load-displacement curve shows a five-stage behaviour: (I) elastic; (II) elastic-softening, (III) elastic-softening-debonding, (IV) softening-debonding, and (V) debonding (see Figure 8). In contrast, shorter ELs may not exhibit a fully developed softening stage [70] (see Figure 9). [PERSON], [PERSON] [71] carried out a series of pull-out tests and found that the W/G ratio can affect the critical EL. They quantified the critical EL as 250 mm, 320 mm, and 360 mm for W/G ratios of 40%, 46%, and 50%, respectively (Figure 10). A linear relationship between EL and W/G ratios was proposed. Additionally, it was observed that the BS is dependent on EL, exhibiting a linear proportionality to the UCS of the grout. To evaluate the influence of EL on BS, [PERSON], [PERSON] [72] performed a series of pull-out tests on FGRBSs with ELs ranging from 250 mm to 1600 mm. Their results indicate a positive correlation between EL and BS, with higher ELs leading to enhanced BS and consequently higher axial bearing capacity. The tests also revealed distinct behavioural changes based on EL. Rock bolts exhibited elastic behaviour for ELs less than 850 mm. When the EL reached 860 mm, a yielding stage was observed for the first time. Notably, with an EL of 1500 mm, the rock bolts displayed a complete stress-strain response, encompassing elastic ascent, yielding, and hardening stages without rupture. Finally, necking, a localized reduction in bolt diameter, was observed in samples with the longest EL of 1600 mm. [PERSON], [PERSON] [73] suggested that the critical EL could be determined based on the sum of three following parameters: (a) the elastic deformation length; (b) a plastic deformation length; and (c) a completely deboned length.
# Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel
## 4 Simulations and Experiments
### Practical Sea Trial  
The final practical sea trial is located in a bay of Yangjiang City, Guangdong Province, as shown in Figure 14, which is a satellite map showing the sea trial site. We take many experiments to verify the effectiveness of proposed algorithm. In this successful sea trial, a typical step in the total automatic docking trajectory is described in Figure 15. Figure 15 are shot by high-definition camera equipped on the Unmanned Aerial Vehicle, where the USV is tracking the mobile dockyard. Moreover, the final scene of the USV returning to the dockyard successfully is illustrated in Figure 16, which indicates that the automatic docking process has been completed. For clarity, the actual automatic docking trajectory on actual map is described in Figure 17. The red curve denotes the USV's trajectory, and the blue curve denotes the dockyard's trajectory. The trajectory result clearly shows that the USV and dockyard finally reached the same point. It is obvious that the USV has planned an optimal trajectory according to the Dubins curves, and successfully reached the dockyard. To summarize, the above experimental results verify the feasibility of this method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{\(V_{min}\)} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline ADTA-ACO & 2176.65 & 2235.283 & 2361.63 & 2395.10 \\ ADTA-TRD & 1900.28 & 2053.65 & 2193.95 & 2249.20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The influence of minimal speed of the USV on voyage time.  
Figure 13: Automatic docking trajectories with water-influenced mobile dockyard.  
Figure 14: Sea trial site.  
Figure 15: Automatic docking process.
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Comparison with the State-of-the-Arts

Table 8: Comparison with eight existing IQA algorithms on IVC dataset (20 Gaussian blur images). We take Spearman’s rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) as evaluation indexes. In each column, the best performance value is marked in boldface. \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **SROCC (\(p\)-Values)** & **PLCC (\(p\)-Values)** & **RMSE (\(p\)-Values)** \\ \hline PSNR & 0.8406 (0.000027) & 0.9609 (0.000003) & 0.3448 (0.000203) \\ SSIM [1] & 0.8646 (0.012710) & 0.8737 (0.001305) & 0.6071 (0.000518) \\ BIQI [14] & 0.8065 (0.010284) & 0.8352 (0.000208) & 1.0273 (0.009078) \\ BRISQUE [4] & 0.8505 (0.001421) & 0.8630 (0.000932) & 0.6287 (0.000455) \\ TIP [2] & 0.8531 (0.002316) & 0.8352 (0.005613) & 1.5324 (0.000062) \\ NIQE [17] & 0.8325 (0.009727) & **0.8639** (0.000686) & **0.6267** (0.000188) \\ BLIND-II [3] & 0.8555 (0.008715) & 0.8577 (0.000384) & 0.6415 (0.000512) \\ NR-CSR [18] & 0.8520 (-) & 0.8339 (-) & 0.6476 (-) \\
**NSSIM** & **0.8995** & 0.7357 & 1.0471 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison with eight existing IQA algorithms on TID2013 dataset (125 Gaussian blur images). We take Spearman’s rank order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) as evaluation indexes. In each column, the best performance value is marked in boldface. collected. It can be seen from Table 10 that our NSSIM metric achieves the highest mean SROCC and the third best of mean PLCC and RMSE. Meanwhile the _t_-test results shown in Table 10 led to the acceptance of the null hypothesis at the 5% significance level, indicating that the differences of average performance between NSSIM and the other metrics are not statistically significant on various datasets. This is understandable since our NSSIM cannot achieve significant improvement on every dataset in terms of all indexes. However, considering the differences of image complexity in different datasets, such as image size, contrast and diversity, the experimental results validate that the proposed NSSIM can be adapted to different image categories. Noticing that [3, 4, 17] all need prior training procedure, our NSSIM performs the best to maintain the balance of IQA and time-efficiency. These test results also validate that NSSIM is a demonstration of the relationship between quantified image naturalness and perceptual image quality. NSSIM establishes a simple method to identify image quality without reference or prior training on human judgments of blurred images. Besides, compared with up-to-date NR IQA metrics NR-CSR [18], MCNN [19] and SFA [20], NSSIM is less time costly since the needless of training or learning procedure.
# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 3 State-of-the-Art Approaches for MAC-Protocols Optimization  
The literature has concentrated on the optimization of MAC protocols in the networks of wireless sensors for critical applications in areas such as emergency alerting and monitoring as well as health care [27; 28]. Those subcategories are general approaches, game theory-based approaches, heuristic-based approaches, meta-heuristic-based approaches, machine learning-based approaches, and MAC scheduling in cross-layer approaches.
# A Siphon Drainage Method for Consolidation of Soft Soil Foundation
## 4 Field Test of Siphon Drainage in Soft Soil Consolidation
### Changes of Groundwater Level during the Siphon Drainage
#### 4.3.1 Changes of Groundwater Level in Siphon Drainage Holes  
As shown in Figure 13, the groundwater level drawdown in drainage holes reaches more than 9 m, which is close to the limit lift of the siphon. The fluctuation range of the drawdown is within 0.6 m, indicating that the siphon drainage process is stable.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layers**} & **Moisture Content (\%)** & **Wet Density (g/cm\({}^{3}\))** & **Dry Density (g/cm\({}^{3}\))** & \multirow{2}{*}{**Pore Ratio**} & & **Vertical** & **Horizontal** & \multirow{2}{*}{**Compressibility Coefficient (cm/s)**} & \multirow{2}{*}{**Ap-1 (MPa\({}^{-1}\))**} \\  & **Moisture Content (\%)** & **Wet Density (g/cm\({}^{3}\))** & **Dry Density (g/cm\({}^{3}\))** & & & **Perseability Coefficient (cm/s)** & & & **Ap-1 (MPa\({}^{-1}\))** \\ \hline
1 & 46.1 & 1.74 & 1.19 & 1.284 & 14.0 & & & 0.83 \\
2 & 41.7 & 1.78 & 1.26 & 1.165 & 13.6 & 4.21 \(\times 10^{-7}\) & \(5.78\times 10^{-7}\) & 0.95 \\
3 & 43.6 & 1.77 & 1.23 & 1.215 & 14.7 & & 0.97 \\
4 & 50.5 & 1.70 & 1.13 & 1.417 & 14.1 & & \(3.90\times 10^{-7}\) & \(6.30\times 10^{-7}\) & 1.21 \\
5 & 48.3 & 1.71 & 1.15 & 1.368 & 15.5 & & & 1.06 \\
6 & 43.8 & 1.77 & 1.23 & 1.210 & 13.9 & & & 0.73 \\
7 & 54.1 & 1.66 & 1.08 & 1.544 & 18.2 & \(1.69\times 10^{-7}\) & \(2.54\times 10^{-7}\) & 0.13 \\
8 & 48.2 & 1.72 & 1.16 & 1.361 & 17.5 & & & 1.18 \\
9 & 48.1 & 1.72 & 1.16 & 1.359 & 17.6 & \(2.50\times 10^{-7}\) & \(3.93\times 10^{-7}\) & 0.82 \\
10 & 46.4 & 1.74 & 1.19 & 1.314 & 20.3 & & & 1.06 \\
11 & 57.8 & 1.63 & 1.03 & 1.653 & 17.4 & \(1.71\times 10^{-7}\) & \(2.49\times 10^{-7}\) & 1.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil physical properties of numerical model.  
Figure 12: Mesh schematic of the numerical model.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
###### Abstract  
Continuous Global Positioning System (GPS) observations have been logged at the Finnish Antarctic research station (Aboa) since February 2003. The station is located in Dronning Maud Land, East Antarctica. Almost 5000 daily observation files have been archived based on yearly scientific expeditions. These files have not been fully analysed until now. This study reports for the first time on the consistent and homogeneous data processing and analysis of the 15-year long time series. Daily coordinates are obtained using Precise Point Positioning (PPP) processing based on two approaches. The first approach is based on the Kalman filter and uses the RTKLIB open source library to produce daily solutions by unconventionally running the filter in the forward and backward direction. The second approach uses APPS web service and is based on GIPSY scientific processing engine. The two approaches show an excellent agreement with less than 3 mm rms error horizontally and 6 mm rms error vertically. The derived position time series is analysed in terms of trend, periodicity and noise characteristics. The noise of the time series was found to be power-law noise model with spectral index closer to flicker noise. In addition, several periodic signals were found at 5, 14, 183 and 362 days. Furthermore, most of the horizontal movement was found to be in the North direction at a rate of 11.23 \(\pm\) 0.09 mm/y, whereas the rate in the East direction was estimated to be 1.46 \(\pm\) 0.05 mm/y. Lastly, the 15-year long time series revealed a movement upwards at a rate of 0.79 \(\pm\) 0.35 mm/y. Despite being an unattended station, Aboa provides one of the most continuous and longest GPS time series in Antarctica. Therefore, we believe that this research increases the awareness of local geophysical phenomena in a less reported area of the Antarctic continent.  
GPS; Antarctica; time series; coordinates; velocity; trend; periodicity; noise; uncertainty 2018/10, 1937; doi:10.3390/rs10121937
## 4 Experiments and Results
### Experiment Setup
#### 4.1.3 Evaluation Metric  
Generally, stock index trend prediction can be considered as a classification problem. In order to evaluate the quality of predictions, we use Accuracy as the evaluation metric. Accuracy represents the proportion of samples that be correctly predicted to the total number of samples. The higher the Accuracy, the better the predictive performance of the model. Accuracy is calculated as follows:  
\[Accuracy=\frac{N_{correct}}{N_{all}}\times 100\% \tag{10}\]  
where \(N_{correct}\) represents the samples with the same predicted trend as the actual trend, and \(N_{all}\) represents the total number of samples.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 6 Experimental Results  
### Impact of Selfishness on Energy Consumption  
Altruistic (normal) nodes in MANETs behave cooperatively without considering the remaining energy in the storage, while selfish nodes unfold their selfishness whenever it lacks energy during network operations. In order to investigate how much energy could saved by selfish behaviors, the following comparison experiments are conducted.  
In the experiments, there are sender, receiver, and 10 intermediate nodes deployed. The simulation time is set as 100 s. Two sets of simulations are executed. In the first set, all intermediate nodes are set as normal nodes, while in the second one, the nodes are set as selfish ones. With considering a fair comparison, we compare the same nodes from each simulation because they have the same settings except for the selfishness. The residual energy levels during the simulation procedure are plotted in Figure 4.  
Obviously, when nodes are selfish, the accumulation of residual energy is much larger than altruistic energy. Quantitatively speaking, the accumulated residual energies of the selfish and altruistic nodes are 3.474881833 (J) and 2.128937979 (J), respectively. The node saved about 1.345943854 (J) energy in 100 s by behaving selfishly. This experiment quantitatively explored the motivation of node selfishness.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Nominal Capacity & 0.05 J \\ Shutdown Capacity & 0 J \\ Start Capacity & 0.025 J \\ Initial Capacity & 0 J \(\sim\) 0.05 J \\ Power Generation & 1 mW \\ Off P.C. & 0 mW \\ Sleep P.C. & 1 mW \\ Switching P.C. & 1 mW \\ Receiver Idle P.C. & 2 mW \\ Receiver Busy P.C. & 5 mW \\ Receiver Receiving P.C. & 10 mW \\ Transmitter Idle P.C. & 2 mW \\ Transmitter Transmitting P.C. & 100 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy consumption parameters. (P.C. stands for power consumption).
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 8 Underwater SLAM Systems Evaluation and Comparison
### Localization Evaluation
#### 8.2.2 DL-Based SLAM Metrics
These segment-based metrics highlight how well a DL-based SLAM system maintains consistency over longer traverses, a critical aspect in feature-poor underwater environments. **Scale Drift:** Scale drift refers to the gradual scaling inconsistency that accumulates in monocular or learning-based SLAM systems. While some DL methods attempt to learn scale from stereo or depth data, purely monocular systems might suffer from drifting scales over time. Evaluating the percentage of scale error over extended trajectories or comparing learned scale estimates against ground truth depths can help quantify how effectively the DL-based SLAM maintains metric consistency [160]. **Feature-based Accuracy:** For some DL-based methods that learn feature extraction end-to-end, additional metrics can include feature repeatability and matching precision. Although more common in feature evaluation than full SLAM systems, these metrics can indirectly inform how the learned front-end influences overall SLAM accuracy [161]. Improved feature repeatability and robustness to underwater image degradation can lead to reduced trajectory error downstream. DL-based SLAM metrics often combine odometry losses (positional and angular) to assess the quality of learned pose estimation directly, segment-based drift measures to understand long-term consistency and cumulative errors, scale drift assessments to ensure metric correctness, especially in monocular settings, or feature-based evaluations to examine the quality of learned visual representations. In summary, evaluating underwater SLAM involves assessing both mapping and localization performance. Mapping metrics, such as SSIM and IoU, measure how accurately the SLAM-generated map reflects the true environment. Localization metrics such as APE, RPE, RMSE, and ATE gauge how closely the estimated trajectory matches the true trajectory. Additional factors like sensor sampling rate, environmental conditions, and sensor capabilities influence these metrics. Accurate localization is vital for underwater SLAM, as errors in position estimation can lead to significant deviations in the generated map. Therefore, this section focuses on localization evaluation and comparison.
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
## 5 Simulation and Experimental Results  
### Simulation Results  
To obtain the comparative results and prove the effectiveness of the proposed method, the GPC+ESO and PI controller are compared in simulations. The reference speed is chosen as 200 r/min and 600 r/min, respectively. Simulation was performed on the MTALAB/Simulink platform and the simulation results are presented as follows. The parameters of PMSM for simulation and experiment are shown in Table 1.  
The speed response curves of two control methods are shown in Figure 2. As shown in the figures, in different given reference speed, compared with PI method, the GPC+ESO control method has a better speed tracking and no overshoot. It can also be seen that the GPC+ESO method takes less time to reach steady state.  
Figure 3 show the simulation curves of the speed response when a load torque \(\tau_{L}=2\) N\(\cdot\)m is added at \(t=0.5\) s and it is removed at \(t=1\) s. The results clearly show that the GPC+ESO control method has a smaller speed fluctuation and shorter adjust time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Description** & **Value** & **Unit** \\ \hline rated speed & 1000 & r/min \\ rated torque & 14.5 & N\(\cdot\)m \\ stator resistance & 1.84 & \(\Omega\) \\ \(d\)-axis inductance & 6.65 & mH \\ \(q\)-axis inductance & 6.65 & mH \\ rotor flux & 0.32 & Wb \\ moment of inertia & 0.0027 & Kg\(\cdot\)m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of PMSM.  
Figure 2: Simulation curves of speed response. (**a**) \(n=200\) r/min; (**b**) \(n=600\) r/min.  
The estimated load torque of the ESO is shown in Figure 4. Given different values of parameter \(\rho\), the tracking speed of load disturbance are also different. In addition, the parameter value should be selected in a certain range for the reason that an excessively big parameter value can also cause overshoot while improving the tracking performance of the ESO.
# Adhesive Single-Lap Joint Evaluation Using Ultrasound Guided Waves
## 2 Theory
### Ultrasound Guided Wave Propagation in a Single Plate
For a plate in vacuum oriented in the X direction and with the thickness in Z direction, coincident with the wave propagation direction, the in-plane (\(u_{xs}\)) and out-of-plane (\(u_{zs}\)) displacements of symmetric modes are given by [53; 54]  
\[u_{xs}=\frac{q^{2}-k^{2}}{2p}\frac{\sin(qh)}{\sin(ph)}\cos(pz)-qcos(qz), \tag{4}\]  
\[u_{zs}=\frac{q^{2}-k^{2}}{2k}\frac{\sin(qh)}{\sin(ph)}\sin(pz)+ksin(qz), \tag{5}\]  
and the displacements for the anti-symmetric modes (\(u_{xn}\)) and (\(u_{zn}\)) by  
\[u_{xa}=-\frac{q^{2}-k^{2}}{2p}\frac{\cos(qh)}{\cos(ph)}\sin(pz)+qsin(qz), \tag{6}\]  
\[u_{za}=\frac{q^{2}-k^{2}}{2k}\frac{\cos(qh)}{\cos(ph)}\cos(pz)+kcos(qz). \tag{7}\]  
For the case of a water immersed plate, Equations (4)-(7) become complex, which can be interpreted physically as a small-angle rotation of the ellipse displacement axes from the reference coordinate system [59]. Nevertheless, the real part of the displacements practically coincides with the displacements obtained from Equations (4)-(7) [54]. So, as an approximation, these equations can be used to represent the displacements as a function of plate thickness in immersion cases. In analogy with optical phenomena, when an ultrasound wave reaches a boundary between two different media obliquely, refracted waves can occur with different transmission angles than the incident one. The incident (\(\theta_{1}\)) and transmitted (\(\theta_{2}\)) angles related to the normal plane are correlated by the well-known Snell's law, given by  
\[C_{1}sin(\theta_{2})=C_{2}\sin(\theta_{1}), \tag{8}\]where \(C_{1}\) and \(C_{2}\) are the propagation velocities in the different media. In practice, if one intends exciting a specific propagation mode in a plate, the phase velocity \(c_{p}\) (obtained from the dispersion curves) coincides with \(C_{2}\), and in this case, \(\theta_{2}=90^{\circ}\), since the propagation develops along the plate. Then, knowing the \(C_{1}\) value, which corresponds to the propagation velocity in the fluid (\(c_{0}\)), the angle of incidence \(\theta_{1}\) can be easily obtained, to generate the desired propagation mode:  
\[\theta_{1}=\arcsin\biggl{(}\frac{c_{0}}{c_{p}}\biggr{)}. \tag{9}\]  
For each mode, there is a re-emission angle, that by reciprocity is equal to the angle of incidence.