esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 4 Discussion  
Based on the N-S equation, this study explores the flow field and wave surface of the opposite pipe for different internal solitary wave conditions. The analysis shows that the interaction of multiple internal solitary waves with the riser can be accurately estimated using numerical simulation, laying the foundation for further research. The main simulation results are as follows:  
1. Unlike a single internal solitary wave, after the two internal solitary waves collide at the riser, they gradually fuse. When they finally fuse into a single wave, their amplitude reaches the maximum and is slightly less than the sum of the amplitudes of the two waves. At that point, the velocity of the flow field around the riser reaches a minimum. When the two waves act together, the change in the wave surface tends to be gentle. When the amplitude difference between the two sides is less than a certain degree, the wave surface will not deform;  
Figure 17: Contrast diagram of wall shear force in x and y directions of the riser (\(t=30\) s). From left to right: working condition 1, working condition 3 and working condition 6.  
2. The force on the riser in the horizontal and vertical directions is more significant than that in the z direction, and the sectional force on the riser in the upper fluid area is greater. Overall, with the continuous reduction in the difference in amplitude, the stress direction, extreme value, and occurrence time of the extreme value in the horizontal and vertical directions will change. The overall force of the riser under the joint action of two waves is less than that under the condition of a single wave, and when the amplitude difference of waves on both sides is large, the riser will be subjected to an additional section of X negative force with small amplitude after being subjected to X positive force;
3. As the amplitude difference between the two sides decreases, the amplitude and time of the positive X force on the riser decreases; the X negative force first increases and then decreases, and the duration of the negative force increases. The horizontal force of the riser section changes before and after the collision and separation of two waves. At the same time, the force amplitude of the riser section is affected by the amplitude difference. When a stable single wave is generated in the flow field, the amplitude difference on both sides has a negative correlation with the horizontal force of the riser section; Before and after single wave fusion and separation, the amplitude difference between the two sides is positively correlated with the horizontal force of the riser section.
## 3 Results and Discussion
### Surface Modification of Substrate Nanoparticles  
The introduction of TAA groups onto the surface of the PVC nanoparticles was verified by FT-IR and XPS analyses. In the spectrum depicted in Figure S1 (ESI +), the unmodified PVC has features at 1208 cm\({}^{-1}\), corresponding to the C=S stretching vibration, and at 1415 and 1354 cm\({}^{-1}\), corresponding to the C-H bending vibration of the dithiocarbamate group [35]. Further, the two peaks at 760 and 1264 cm\({}^{-1}\) correspond to CH\({}_{2}\)-Cl asymmetric and symmetric stretching, respectively. Compared with PVC, PVC@TAA displays two new peaks at 1483 and 1377 cm\({}^{-1}\), corresponding to the C-N stretching vibration and the C-H bending vibration of the TAA group, respectively [36]. Compared with the results of the XPS measurements for PVC and PVC@TAA, a new peak component with a binding energy of 286 eV, attributable to the C-N+ species, appears in the C 1s core-level spectrum of PVC@TAA (Figure 2e), whereas a new peak at a binding energy of 402.3 eV, ascribed to the C-N+ species [37], arises from the quaternization reaction between the benzyl groups and TMA in the N1s spectrum (Figure 2f). In addition, the Cl 2p core-level spectra consist of the Cl 2p\({}_{3/2}\) and Cl 2p\({}_{1/2}\) doublet at binding energies of approximately 196.7 and 198.3 eV, respectively, assignable to the ionic Cl (Cl\({}^{-1}\)) species of PVC@TAA [38]. By comparing the intensities of the C-Cl species in Figure 2h, which shows high intensities for the anionic chloride species at 196.7 eV (Cl 2p\({}_{3/2}\)) and 198.3 eV (Cl 2p\({}_{1/2}\)), it can be concluded that most benzyl groups of the PVC have been modified. Thus, these results confirmed that TMA was successfully coupled to the PVC surface.  
The morphological structures of the PVC and PVC@TAA nanoparticles were characterized using SEM and BET. As shown in Figure 3, a smooth surface for the spherical PVC particles was observed in the SEM image (Figure 3a). However, it was noticeably different from the PVC@TAA surface (Figure 3b), because the latter demonstrated a rough surface with a substantial number of short holes, leading to a higher specific surface area (119.63 m\({}^{2}\cdot\)g\({}^{-1}\)) than that of PVC (34.87 m\({}^{2}\cdot\)g\({}^{-1}\)) (Table S1, ESI +). Porous nanoparticles with a high specific surface area can enhance the attachment via bio-affinity, enabling higher protein loading. Finally, the content of IAA groups was measured using the precipitation titration method and found to be 4.47 \(\pm\) 1.38 umol per gram of PVC@TAA.  
Figure 2: (a) Wide scan, (b) C 1s, (c) N1s, (g) Cl 2p, and (i) S 2p XPS results for PVC nanoparticles and (d) wide scan, (e) C 1s, (f) N1s, and (h) Cl 2p XPS results for PVC@TAA nanoparticles.
# Generalized Frequency Division Multiplexing-Based Low-Power Underwater Acoustic Image Transceiver
## 4 Conclusions  
This paper proposes a transceiver scheme for UWA image communication that integrates advanced transmission technologies such as GFDM-based modulation, LDPC code, adaptive 4-QAM and 16-QAM, and a power assignment mechanism to transmit image sensor signals over UW channels. Simulation results show that the GFDM-based UWA image transceiver with the PCE can have the same transmission BER with lower SNR than the transceiver with CEE of 20%. For a BER of \(2.73\times 10^{-5}\), 4-QAM outperformed 16-QAM, with an SNR increase of 12.04 dB. Compared with 16-QAM, 4-QAM showed better PSNR performance of the received UW image signals using GFDM-based UWA image transceiver with the PCE, the CEEs of 5%, 10%, and 20%, and with SNR gains of approximately 7.08 dB, 7.22 dB, 7.99 dB, and 12.13 dB, respectively, when BER was \(10^{-4}\). As the CEE increased, the SNR gains with 4-QAM were better than those with 16-QAM.  
Figure 9: PSR performance of the GFDM-based UWA image transceiver for the \(k\)-th user with a BER of \(10^{-4}\). (line: 4-QAM, dotted line: 16-QAM, \(\alpha\): perfect, +: CEE of 5%, \(\square\): CEE of 10%, and *: CEE of 20%).  
When using 4-QAM and 16-QAM strategies, with CEE of 10%, the PSR of 90% outperformed the PSR of 40%, with an AWGNk increase of 0.0290 and 0.00451, respectively. The 4-QAM outperformed 16-QAM, under a PSR of 60%, with the AWGNk decreasing by 0.0201. The PCE outperformed the CEEs of 20%, under 4-QAM and a PSR of 60%, with the AWGNk decreasing by 0.0111. The PCE outperformed the CEEs of 20%, under 16-QAM and a PSR of 60%, with the AWGNk decreasing by 0.0051. As the CEE increased, the AWGNk fading decreased.  
The PSNR of the received UW image signal 1, 2, and 3, using 4-QAM, with a CEE of 10%, transmission BERs of \(10^{-4}\), were 62.39 dB, 44.46 dB, and 45.64 dB, respectively. The PSNR of the received UW image signal 1, 2, and 3, using 16-QAM with a CEE of 10%, transmission BERs of \(10^{-4}\), were 71.63 dB, 48.79 dB, and 49.87 dB, respectively. The received UW images have high PSNR values and high image resolutions. The proposed GFDM-based UWA transceiver could meet the transmission BER quality-of-service requirements for UW image sensor applications by maximizing the transmission throughput or minimizing power consumption.  
Conceptualization, C.-F.L. and S.-H.C.; methodology, C.-F.L. and S.-H.C.; software, C.-F.L., C.-F.W. and C.-L.H.; formal analysis, C.-F.L. and S.-H.C.; investigation, C.-F.L. and S.-H.C.; writing--original draft preparation, C.-F.L. and S.-H.C.; writing--review and editing, C.-F.L., S.-H.C., I.A.P. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the grant from The Ministry of Science and Technology of Taiwan, under contract Nos. MOST 108-2221-E-992-026 and MOST 105-2923-E-022-001-MY3.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Result
### Result and Comparison
#### 4.3.2 Train and Test with Random Spatial datasets  
In the previous experiment, the performance of each model was investigated with public datasets and the YU dataset. However, in order to clarify the weakness of the 3D-CNN model, random spatial information was tested. The training results of the Indian Pines dataset to which the random spatial is applied are in the Table 10. All models except the proposed model deteriorated, and the 3D-CNN models especially were severely degraded. Table 11 is the training result of the Salinas dataset to which random space is applied. It did not degrade the performance compared to random space Indian Pines, but it still shows a decrease. The Hu model and the proposed model, which are 1D-CNN, did not show any performance degradation. In the case of the Pavia University dataset to which the random space was applied, like the Salinas dataset, the performance of the 3D-CNN models fell, and the 1D-CNN did not drop significantly. Table 12 shows the results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{5}{c}{**[PERSON] Model**} & \multicolumn{2}{c}{**[PERSON] Model**} & \multicolumn{2}{c}{**[PERSON] Model**} \\ \hline \# & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.000 & 0\% & 0.942 & 99.33\% & 0.997 & 99.89\% \\ \hline
2 & 0.000 & 0\% & 0.697 & 83.69\% & 0.985 & 99.61\% \\ \hline
3 & 0.000 & 0\% & 0.936 & 99.20\% & 0.997 & 99.91\% \\ \hline
4 & 0.090 & 4.71\% & 0.858 & 83.27\% & 0.955 & 97.71\% \\ \hline
5 & 0.000 & 0\% & 0.887 & 97.35\% & 0.983 & 98.73\% \\ \hline
6 & 0.000 & 0\% & 0.293 & 17.23\% & 0.888 & 81.79\% \\ \hline OA(\%) & 4.694\% & & 68.58\% & & 98.25\% \\ \hline AA(\%) & 0.79\% & & 80.01\% & & 96.27\% \\ \hline Kappa & 0.000 & & 0.7010 & & 0.9748 \\ \hline \multicolumn{5}{c}{**Chen Model**} & \multicolumn{2}{c}{**Hu Model**} & \multicolumn{2}{c}{**Proposed Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.514 & 37.36\% & 0.052 & 95.34\% & 0.996 & 99.44\% \\ \hline
2 & 0.000 & 0\% & 0.799 & 99.29\% & 0.998 & 99.90\% \\ \hline
3 & 0.000 & 0\% & 0.451 & 29.23\% & 0.990 & 99.63\% \\ \hline
4 & 0.090 & 0\% & 0.815 & 99.87\% & 0.989 & 97.46\% \\ \hline
5 & 0.000 & 0\% & 0.806 & 67.53\% & 0.990 & 98.08\% \\ \hline
6 & 0.000 & 0\% & 0.343 & 20.69\% & 0.980 & 81.49\% \\ \hline OA(\%) & 35.54\% & & 50.136\% & & 99.47\% \\ \hline AA(\%) & 6.23\% & & 68.66\% & & 98.67\% \\ \hline Kappa & 0.016 & & 0.386 & & 0.992 \\ \hline \end{tabular}
\end{table} 
Table 9: Classification results of the YU Paint dataset to which random spatial information is not applied.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Visual Analysis  
This experimental section ends with a visual analysis of the results for both blue and green areas of size 3.7 km \(\times\) 3.6 km (465 pixels \(\times\) 450 pixels) displayed in Figure 5. The analysis is performed for RF and TempCNN. The original temporal sampling is used for RF, whereas the regular temporal sampling at two days is used to train TempCNN. Both models are trained on the datasets with three spectral bands. The full maps are available in our online repository at [[https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)]([https://github.com/charlotte-pel/temporalCNN](https://github.com/charlotte-pel/temporalCNN)).  
Figure 11 displays the produced land cover maps. The first row displays the results for the blue area, whereas the second row displays the one for the green area. The first column displays the Formosat-2 image in false color for 14 July 2006 (zoom of Figure 6). The second and third columns give the results for the RF and the TempCNN algorithms, respectively. Images in the last column display the disagreements between both classifiers in red. Legend of land cover maps can be found in Table 1.  
Although the results look visually similar, the disagreement images between both classifiers highlight some strong differences on the delineations between several land cover, but also at the object-level (e.g., crop, urban areas, forest). Regarding the delineation disagreements, we found that RF spreads out the majority class, i.e., urban areas, leading to an over-detection of this class, especially for mixing pixels. Regarding object disagreements, one can observe that RF mistakenly classifies an area as 'urban' (light pink) while it is a sunflower crop (purple). That crop, which was part of the test set, is highlighted with a green outline in the top-row results. Finally, this visual analysis shows that both classification algorithms exhibit some salt and pepper noise, that could be potentially removed by a post-processing procedure or by incorporating some spatial information into the classification framework.  
\begin{table}
\begin{tabular}{r r r} \hline \hline
**Batch Size** & **Training Time** & **OA** \\ \hline
**8** & 3 h 45 min & 93.54 \(\pm\) 0.67 \\
**16** & 1 h 56 min & 93.65 \(\pm\) 0.73 \\
**32** & 1 h 06 min & 93.59 \(\pm\) 0.74 \\
**64** & 34 min & 93.43 \(\pm\) 0.71 \\
**128** & 19 min & 93.45 \(\pm\) 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training time and averaged Overall Accuracy (OA) over five folds for the same model learned for five batch sizes.
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 3 Theory of Nano-TES and JES Bolometers
In JES-mode, the device has to be biased at \(T_{e}(I)\), i.e., at \(R=0\), and the role of \(R_{S}\) is to limit the current flow through the sensing element below the retrapping current (\(I_{R}\)) [46], that is the switching current at which a diffusive superconducting wire switches into the dissipationless state from the normal-state during a current down-sweep. This happens for \(R_{S}\leq R_{N}I_{R}/I_{bias}\) and brings \(A\) quickly back to the superconducting state after radiation absorption. Therefore, the sensor always operates in the superconducting state. Finally, in both configurations the variations of the current circulating in the inductor (\(L\)), as a consequence of radiation absorption, can be measured via a conventional SQUID amplifier. In the next sections, we will show all the theoretical relations describing the sensing properties of the nano-TES and the JES active regions when continuously irradiated (bolometer). Therefore, we will neglect all the noise contributions arising from outside the sensors, such as the noise coming from the electronic circuitry [44].
## 2 Measurement Method and Experimental Setup Design
### M-DLS Measurement Cycle
If the M-DLS operates once per sol within several hours, the output data will contain up to 28 spectrum packets and numerous data packets from the SSU. The total volume of data received within one sol will not exceed 500 Kb. Power consumption does not exceed 15 W, 12 W in heating mode, and 10 W in spectra acquisition mode. Important technical details of the M-DLS are presented in Table 1.
## 3 Results  
The mean \(\pm\) 95% confidence interval total running time was 7 min and 39 s \(\pm\) 46 s at a treadmill speed of 7.3 \(\pm\) 0.4 km/h and an inclination of 1.7 \(\pm\) 0.2%. The mean treadmill speed and inclination at the end of each run were 11.1 \(\pm\) 0.8 km/h and 3.6 \(\pm\) 0.4%, respectively. The blood lactate concentration was different among the three conditions at post-run 5 and 10 min (condition \(\times\) time interaction: F\({}_{10,238}\) = 3.04, \(p\) = 0.001; condition effect: F\({}_{2,238}\) = 12.38, \(p\) \(<\) 0.0001; Table 1). Specifically, the IBP condition (6.7 mmol/L, 23%, \(p\) = 0.001, ES = 0.82) and the active condition (6.9 mmol/L, 20%, \(p\) = 0.01, ES = 0.75) showed lower blood lactate values than the passive condition (8.6 mmol/L) at post-run 5 min. At post-run 10 min, the active condition showed lower blood lactate values than the passive condition (4.4 vs. 6.0 mmol/L, 27%, \(p\) = 0.02, ES = 0.90). Regardless of the condition (time effect: F\({}_{5,238}\) = 314.95, \(p\) \(<\) 0.0001), the blood lactate values decreased gradually after running and they did not return to the pre-run level (1.3 mmol/L) at the end of the experiment (post-run 30-min: 3.1 mmol/L, \(p\) \(<\) 0.0001, ES = 2.41; Figure 3A). The heart rate values differed between the conditions at each time point (condition \(\times\) time interaction: F\({}_{10,238}\) = 8.39, \(p\)\(<\) 0.0001; condition effect: F\({}_{2,238}\) = 17.61, \(p\)\(<\) 0.0001; time effect: F\({}_{5,238}\) = 1243.89, \(p\)\(<\) 0.0001; Table 1). The IBP condition (96 bpm, 22%, \(p\)\(<\) 0.0001, ES = 2.52) and the passive condition (103 bpm, 17%, \(p\) = 0.002, ES = 1.92) resulted in lower heart rate values than the active condition (123 bpm) at post-run 5 min. Energy expenditure among the three cool-down conditions at each time point did not differ (no condition \(\times\) time interaction: F\({}_{10,238}\) = 1.65, \(p\) = 0.09; time effect: F\({}_{5,238}\) = 224.40, \(p\)\(<\) 0.0001; Table 1). Figure 3: **(A)** Changes in blood lactate and fatigue perception regardless of conditions. Error bars are upper and lower limits of 95% confidence intervals. \(\dagger\) Different from other time points in blood lactate (\(p<\) 0.0001). \(\ddagger\) Different from post-run 0, 5, and 10 min in fatigue perception (\(p<\) 0.0001). **(B)** Correlation between blood lactate and fatigue perception. There is a high correlation (r = 0.75) between blood lactate and fatigue perception. However, the total energy expenditure between the conditions was different (condition effect: F2,238 = 4.81, \(p\) = 0.01) in that subjects in the active condition had a 14% higher total energy expenditure compared to the IBP condition (219 vs. 189 kcal, \(p\) = 0.01, ES = 0.48; Figure 4).
## 2 Climbing Scaffold Status Monitoring Method
### Climbing Scaffold Status Warning Model
#### 2.3.1 CUSUM-Based Anomaly Detection Model  
The CUSUM algorithm follows a recursive formulation to track cumulative deviations in displacement data. For the coordinate sequence of GNSS monitoring \(X(t),t=1,2,\ldots,n\), it is assumed to be mutually independent and follows a normal distribution \(N(\mu_{0},\sigma)\), where \(\mu_{0}\) represents the mean of the sample sequence and \(\sigma\) denotes the standard deviation. If the monitored object undergoes deformation, the coordinate sequence transitions to an approximate distribution of \(X(t)\sim N(\mu_{0}+\delta\sigma,\sigma)\), with a mean shift of \(\delta\sigma\). The null hypothesis \(H_{0}\) states that the coordinate sequence remains unchanged, meaning its mean does not vary. The alternative hypothesis \(H_{1}\) posits that an abrupt change occurs at time \(s(s<n)\), indicating a deformation at time \(s\). It is assumed that, from time \(s\) onward, the mean of the monitoring data undergoes a significant change. The probability density functions before and after time \(s\) can be expressed as follows:  
\[\begin{cases}f_{0}(x)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\frac{(x-\mu_{0}) ^{2}}{2\sigma^{2}}\right],&H_{0}\text{established}\\ f_{1}(x)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left[-\frac{(x-\mu_{1})^{2}}{2\sigma ^{2}}\right],&H_{1}\text{established}\end{cases}. \tag{25}\]  
Construct the log-likelihood ratio statistic:  
\[\lambda_{n}=\sum\limits_{i=1}^{n}\ln\frac{f_{1}(x_{i})}{f_{0}(x_{i})}=\sum \limits_{i=1}^{n}\frac{1}{2\sigma^{2}}\Big{[}(x_{i}-\mu_{0})^{2}-(x_{i}-\mu_{ 1})^{2}\Big{]} \tag{26}\]  
Let the mean shift be \(\mu_{1}-\mu_{0}=\delta\sigma=\Delta\), \(k=\Delta/2\). Then, we have \(\lambda_{n}=\sum\limits_{i=1}^{n}\frac{\Delta}{\sigma^{2}}(x_{i}-\mu_{0}-k)\).  
If \(\Delta>0\), the above equation is equivalent to \(C_{n}^{+}=\max\sum\limits_{i=1}^{n}(x_{n}-\mu_{0}-k)\), where \(C_{n}^{+}\) represents the likelihood ratio statistic, which can be further expressed as:  
\[C_{n}^{+}=C_{n-1}^{+}+(x_{n}-\mu_{0}-k
## 4 Results and Discussion
### Influence of the Soil Strengths on the BC/BC\({}_{\rm NV}\) of the Footing-Cavity System
#### 4.2.2 Influence of the Cohesive Soil Materials  
The analysis of BC/BC\({}_{\rm NV}\) for the footing-cavity system is conducted in cases involving cohesive soils. Figure 6a shows the variation of BC/BC\({}_{\rm NV}\) with \(r\)/\(D\) for a cohesive soil material (\(c\) = 10 kPa). The results exhibit a single line, visible only for the scenario of a shallow cavity within the range \(r\)/\(D\) = 4 to \(r\)/\(D\) = 10. It is noteworthy that the value of BC/BC\({}_{\rm NV}\) barely reaches unity along the horizontal axis. This is attributed to the dominance of shear stress induced by the soil unit weight around the cavity over cohesive resistant forces. In particular, the range between \(r\)/\(D\) = 0 and \(r\)/\(D\) = 4 is an initially unstable area and is out of the target of this study. Figure 6b-d shows the failure mechanism for a cavity at the points K, L, and M, respectively. The strain distributions reveal a general shear failure at the footing base and roof failure at the cavity with an extension of relatively small strain between the footing and the cavity. In addition, the closer the footing and cavity are, the stronger the interaction inducing the void and bearing failure (Figure 6b,c).  
Figure 5: Illustration of the obtention of parameters \(R\) and \(H\) on the RPFEM deformed model of the footing on the soil without cavity.  
Figure 7 illustrates the distribution of normalized BC/BC\({}_{\rm NV}\) within the plane defined by normalized distances X and Y when soil cohesion \(c\) is set at 50 kPa. This representative result corresponds to a footing size of \(B\) = 1 m and a cavity size of \(D\) = 1 m. The findings reveal that BC/BC\({}_{\rm NV}\) increases with the augmentation of both \(X\) and \(Y\). The BC/BC\({}_{\rm NV}\) varies within the range of 0.80 to 0.99 (approximately 1). It is also observed that the influence of the cavity in this type of soil is 3 times more emphasized in the vertical direction than in the horizontal direction. This is because, for the cohesive soils, the slip surface beneath the footing is not expanding widely in the horizontal direction. The passive earth pressure at the rigid wedge directly beneath the footing is decreased due to the presence of the void.  
Figure 6: Behavior of the BC/BC\({}_{\rm NV}\) variations along \(r/D\) and the failure modes for cohesive soils with small cohesion (\(c\) = 10 kPa). (**a**) Variation of BC/BC\({}_{\rm NV}\) along \(r/D\); (**b**) Point K (\(r/D\) = 5); (**c**) Point L (\(r/D\) = 7); (**d**) Point M (\(r/D\) = 10).