esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Streamwise Vortex-Induced Vibration of a Flexible, Slender Cylinder
## 5 Conclusions  
Laboratory experiments on streamwise VIV of a flexible slender cylinder were performed in the towing tank facility at Tianjin University. The cylinder model was made of a coaxial composite tube with a length of 3.91 m and an outer diameter of 20.0 mm, resulting in a large aspect ratio of 195.5. The mass ratio of the cylinder model was 1.39 and the structural damping ratio was 0.0598. Strain gauges were used to measure structural strain responses at five points evenly distributed along the cylinder. The modal analysis method was applied to transform the measured strain signals to the response displacement.  
It was found that the RMS amplitudes in the streamwise direction reasonably agreed with the published data, which verified the fidelity of the experimental set-up and the post-processing approaches. For the streamwise VIV with 3D characteristics, the vibration amplitude in the transition region decreased with the increasing aspect ratio, and the present results provided supporting data for the conclusion. In the first excitation region, compared to the transverse response, the streamwise response showed an equivalent dominant vibration frequency but a much larger amplitude, resulting in a slightly inclined linear orbit in the \(x\)-\(y\) plane. However, in the second excitation region, the streamwise frequency was twice that in the transverse direction, and skewed figure-of-eight trajectories were observed. A pure spectral plot and a quasi-sinusoidal time history were observed when the vibration amplitude was large. A broad-peak spectral plot and an irregular and aperiodic time history were  
Figure 14: Maximum dimensionless streamwise displacements versus mass-damping parameters in the first (**a**) and second excitation regions (**b**). (**b**) [PERSON] and [PERSON] (2007), water; **a**) [PERSON] and [PERSON] (2010), water; \(\square\) [PERSON] et al. (2013), air; **c**) [PERSON] et al. (2010), water; **d**) [PERSON] et al. (2010), air; \(\circ\) present experimental data; – fitted curve).  
detected when the vibration amplitude was small. Finally, two least-square fitted curves were presented by compiling a large variety of investigations on streamwise VIV.  
The authors would like to acknowledge the financial support of National Natural Science Foundation of China (Grants Nos. 51479135, 51679167 and 51579175), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant no. 51621092).  
[PERSON] and [PERSON] carried out the experimental tests; [PERSON] analyzed the experimental data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Finite Element Analysis Procedure
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component No.** & **Component of** **Component of** **the Instrument** & **Material** & **Component** **Coordinates** & **Modulus of** **Elasticity** & **Poisson’ Ratio** & **Density** & **Crushing/Tensile Yield Strength** \\ \cline{4-7}  & **Component of** **the Instrument** & **Material** & **Component** **Elasticity** & **Ratio** & **Density** & **Crushing/Tensile Yield Strength** \\ \cline{4-7}  & & & **(GPa)** & **(-)** & **(kg m\({}^{-3}\))** & **(MPa)** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Soundboard} & \multirow{3}{*}{Spruce} & \multirow{3}{*}{(Orthotropic)} & \(\text{E}_{\text{L}}\) (X): 9.800 & _Vxy:_ 0.462 & \multirow{3}{*}{385} & \multirow{3}{*}{31.50} \\ \cline{4-7}  & & & \(\text{E}_{\text{T}}\) (Y): 0.580 & _Vyz:_ 0.422 & & & \\ \cline{4-7}  & & & \(\text{E}_{\text{R}}\) (Z): 1.250 & _Vxz:_ 0.255 & & & \\ \hline
2 & Soundbox & Mulberry & (Isotropic) & 9.32 & 0.33 & 690 & 48.20 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Neck} & \multirow{3}{*}{Beech} & \multirow{3}{*}{(Orthotropic)} & \(\text{E}_{\text{L}}\) (X): 13.900 & _Vxy:_ 0.24 & \multirow{3}{*}{710} & \multirow{3}{*}{57.00} \\ \cline{4-7}  & & & \(\text{E}_{\text{T}}\) (Y): 0.606 & _Vyz:_ 0.27 & & & \\ \cline{4-7}  & & & \(\text{E}_{\text{R}}\) (Z): 1.900 & _Vxz:_ 0.27 & & & \\ \hline
4 & Neck head & & (Isotropic) & 13.90 & 0.24 & & & \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Tuning keys/Pegs} & \multirow{3}{*}{Rosewood} & \multirow{3}{*}{(Isotropic)} & \multirow{3}{*}{13.93} & \multirow{3}{*}{0.37} & \multirow{3}{*}{835} & \multirow{3}{*}{67.20} \\ \cline{4-7}  & & & & & & & \\ \hline
6 & \
\begin{tabular}{c} String tie \\ (String holder) \\ \end{tabular} &
\begin{tabular}{c} Maple \\ (Isotropic) \\ \end{tabular} & 12.62 & 0.35 & 705 & 54.00 \\ \hline
8 & Nut & Hornbeam & (Isotropic) & 12.10 & 0.38 & 735 & 50.50 \\ \hline
\begin{tabular}{c} ASTM A228 \\ (UNS K08500) \\ \end{tabular} & & & & & 2860–3165 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Components and corresponding material properties assigned in the FEA study.
## 3 E2E Approach
### Hybrid CNN-LSTM Architectures  
While CNNs typically outperform LSTMs in PD classification tasks, some studies indicate that hybrid CNN-LSTM architectures can also perform effectively. [PERSON] et al. [32] use variational mode decomposition to denoise speech signals and obtain the Mel-spectrograms. Next, pre-trained ResNets models (ResNet-18, ResNet-50 and ResNet-101) are used to extract deep features from the Mel-spectrograms, which are then given as input to the LSTM model. The best result is obtained when combining the ResNet-101 and LSTM, leading to an accuracy of 98.61%.  
[PERSON] et al. [47] use four different types of speech task stimuli: subjects describing images shown to them on a computer screen in their native language (IMAG), sustained phonemes (PHON), monosyllabic targets (DIDK), and spontaneous speech (SPON). The authors apply stimuli-specific and pooled models to each of these stimuli. The stimuli-specific models are trained and tested on one specific speech task stimulus (e.g., train on IMAG and test on IMAG), whereas the pooled models are trained on all types of speech tasks stimuli and tested on each stimulus individually. For both the stimuli-specific and pooled model, the highest accuracies (96.20% and 98.27%) are achieved when using SPON, followed by IMAG, DIDK, and PHON. For PHON, the accuracy scores are considerably lower than for the other speech stimuli, ranging from 59.45% to 73.57% and 70.25% to 82.18% for the stimuli-specific and pooled model, respectively. It can be observed that the pooled model outperforms the stimuli-specific model in all cases. This suggests that the hybrid CNN-LSTM benefits from receiving various types of speech tasks as input during training. In another study performed by these authors [48], they again employ a hybrid CNN-LSTM architecture and are able to reach an accuracy of 90.98%. Initially, this model is trained to predict three classes, Amyotrophic Lateral Sclerosis (ALS), PD, and HC, and is then fine-tuned to perform binary classification between PD and HC only.
## 2 Theory of Full Waveform Inversion
### Forward Modelling for Ultrasonic Wave Propagation  
Reliable forward modelling for wave propagation is a primary prerequisite for ultrasonic FWI tomography. Ultrasonic wave propagation in solids is typically described by an elastic wave equation [22]. To boost the whole efficiency of FWI, the scalar acoustic wave equation is usually used to approximate the ultrasonic wave propagation [17]. The formalism of a 2D acoustic wave equation in a frequency-domain is expressed as:  
\[(\
abla^{2}+\omega^{2}m^{2})\psi(r,\omega)=-s(r,\omega) \tag{1}\]  
where \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}+\frac{\partial^{2}}{\partial z ^{2}}=\partial_{x}^{2}+\partial_{z}^{2}\) is the Laplace operator, \(\omega\) is the angular frequency, \(m=1/c\) is the slowness, \(c\) is the wave speed, \(r=(x,z)\) is the spatial position in the Cartesian coordinate system and \(\psi(r,\omega)\) is the displacement wavefield. \(s(r,\omega)=s(\omega)\delta(r-r_{s})\) is the excitation signal where \(s(\omega)\) refers to the Fourier transform of the source, \(\delta(r-r_{s})\) denotes the Dirac function and \(r_{s}\) is the excitation position.  
The finite difference technique is the most common method to solve partial differential equations [23; 24] and is applied in forward modelling here. In order to simplify the denotation, Equation (1) can be expressed by the matrix equation \(\mathbf{F}\mathbf{\psi}=\mathbf{s}\), where \(\mathbf{F}=\
abla^{2}+\omega^{2}m^{2}\) is a \(l\times l\) complex-valued impedance matrix, which is related to the frequency, material properties, discrete format and boundary conditions. \(\mathbf{\psi}\) and \(\mathbf{s}\) are \(l\times 1\) column vectors at the nodal points of the discrete grid.  
To reduce the computation cost, the LU factorization is utilized to solve the wavefield \(\mathbf{\psi}\):  
\[\mathbf{LU}\mathbf{\psi}=\mathbf{s} \tag{2}\]  
where \(\mathbf{L}\) and \(\mathbf{U}\) denote the lower and upper triangular matrices from the LU factorization. As the solution is reusable for different sources to solve the forward problem, multiple source problems can be handled efficiently [25]. To alleviate the computation burden and avoid reflection interference, a perfectly matched layer (PML) boundary condition is applied around the computational domain to absorb outgoing waves in a similar configuration as done in [26].
## 5 Experiments
### Multi-Antenna Frequency-Selective Channel Case  
We now consider the prediction performance for multi-antenna frequency-selective channels as a function of the number of training samples \(L^{\text{new}}\) in Figures 8 and 9, as well as versus the number of frames \(F\) in Figure 10. For meta-learning, we set \(L^{\text{tr}}=L^{\text{new}}\) in order to avoid discrepancies between meta-training and meta-testing [29]. Figure 8 and Figure 9 shows that meta-learning and transfer learning, which utilize \(F=500\) previous frames, can significantly outperform conventional learning in terms of number of required pilots \(L^{\text{new}}\). This key observation motivates the use of transfer and meta-learning in the presence of limited training data. Furthermore, confirming the analysis in Section 3.3 and Section 4.6, meta-learning can outperform all other schemes as long as one selects a naive parametrization for slowly varying environments, and the LSTD parametrization for fast-varying environments. For sufficiently large \(L^{\text{new}}\), transfer learning can, however, improve over meta-learning on fast-varying environments, as seen in Figure 8. This stems from the split of training and testing set applied by meta-learning, which can lead to a performance loss as \(L^{\text{new}}\) increases.  
Figure 8: Multi-antenna frequency-selective channel prediction performance as a function of the number of training samples \(L^{\text{new}}\), under 19-clustered, two taps (\(W=2\)), multi-antenna (\(N_{T}=4\), \(N_{R}=2\)) 3 GPP SCM channel model for a fast-varying environment with total number of features \(K=2\) unless determined by Section 5.2.  
Figure 9: Multi-antenna frequency-selective channel prediction performance as a function of the number of training samples \(L^{\text{new}}\), under 19-clustered, two taps (\(W=2\)), multi-antenna (\(N_{T}=4\), \(N_{R}=2\)) 3 GPP SCM channel model for a slow-varying environment.  
Lastly, we investigate the effect of the number of previous frames \(F\) for transfer and meta-learning. As a general result, as demonstrated by Figure 10, an increase in the number \(F\) of previous frames results in better performance for both transfer and meta-learning. Furthermore, in a slow-varying environment with a small value of \(F\), transfer learning can outperform meta-learning due to the limited need for adaptation, whereas meta-learning with the correctly select type of parametrization, outperforms transfer learning otherwise.
# A Low-Profile Dual-Polarized Transmitarray with Enhanced Gain and Beam Steering at Ku Band
## 2 UC Design
The CST Studio suite software (2023 version) is employed to simulate the magnitude (T\({}_{\rm xx}\) and T\({}_{\rm yy}\)), and the phase (\(\angle T_{\rm xx}\) and \(\angle T_{\rm yy}\)), of the transmission coefficient for co-polarized signal components xx or yy, respectively, for each of the eight UCs. These transmission coefficients correspond to the scattering parameter magnitude \(|S_{2,1}|\) and phase \(\angle S_{2,1}\) between ports 1 and 2, respectively. Moreover, due to symmetry, the x- and y-polarized transmission coefficients are identical. UC boundary conditions are applied in Floquet port configuration, along the x and y directions, as illustrated in Figure 2, to simulate an infinite array. The open boundaries are imposed along the z direction. The Floquet ports along the z-axis are used for exciting plane waves. The obtained results for the different UC\({}_{\rm i}\) are plotted in Figure 3a. The highest transmission loss, obtained at the frequency f\({}_{0}\) = 12 GHz, is \(\alpha_{\rm T}\) = 1.23 dB, corresponding to UC\({}_{6}\). From Figure 3b, it can be observed that, at the frequency f\({}_{0}\), eight UCs cover the 3-bit phases required for co-polarized components. Moreover, in the TA design, it is important to investigate the performance of the UC for different incidence angles \(\theta\). For this purpose, the transmission magnitude and phase for the co-polarized components are simulated for different incidence angles \(\theta\). Figure 4a shows the transmission loss \(\alpha_{\rm T}\) for co-polarized components is less than 1.9 dB at for an incidence angle \(\theta\) = 30\({}^{\circ}\). Figure 4b illustrates the transmission phases \(\angle\)T\({}_{\rm xx}\) and \(\angle\)T\({}_{\rm yy}\) of co-polarized components at f\({}_{0}\) = 12 GHz. For \(\theta\) = 30\({}^{\circ}\), the transmission phase is close to the value obtained at \(\theta\) = 0\({}^{\circ}\) with a maximum simulated displacement of about \(\triangle\)P = 20\({}^{\circ}\). In all cases, the UCs will transmit the incidence ray in the proper direction with low transmission loss. The behavior of these UCs for cross-polarized components xy, yx is also investigated. The transmission magnitude T\({}_{\rm xy}\) and T\({}_{\rm yx}\) as a function of the frequency f for the cross-polarized components of these UCs are plotted in Figure 5. The highest transmission magnitude value among all these cross-polarized components is T\({}_{\rm max}\) = \(-\) 51 dB which is very low. Figure 2: UC simulation by assigning Floquet ports. Figure 3: (**a**) Transmission magnitude \(\left|\text{S}_{2,1}\right|\) as a function of the frequency f for co-polarized components; (**b**) Transmission phase \(\angle\text{S}_{2,1}\) as a function of the frequency f for co-polarized components.
# Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors
## 3 Results  
### Resource Requirements for Different Repeater Deployment Strategies  
This subsection analyzes the resource demands of each of the four proposed strategies for radio station deployment along the UAV flight corridor. The key metric considered is the total number of radio repeater units required to ensure uninterrupted coverage, depending on the corridor length and coverage radius of each repeater. The analysis is based on the mathematical expressions derived earlier for each strategy in Section 2.1.  
Figure 11 illustrates the relationship between the corridor length and the number of radio stations required for each strategy, assuming a fixed coverage radius of \(R=5\) km.  
As expected, the number of stations increases linearly with the corridor length for all strategies. However, the rate of increase varies significantly. Strategy 1 consistently requires the fewest stations. Strategy 2 approximately doubles the requirement due to full overlap. Strategy 3 falls between Strategies 2 and 4, offering a balance of redundancy and resource use. Strategy 4 results in the highest demand, doubling the number of physical units at each location.  
This comparison highlights the trade-off between reliability and infrastructure cost, where more fault-tolerant strategies demand greater physical deployment.  
Figure 12 presents the relationship between the coverage radius and the number of stations required, assuming a fixed corridor length of \(D=100\) km.  
Figure 11: Effect of corridor length on resource requirements.  
Figure 12: Effect of coverage radius on resource requirements.  
As the coverage radius increases, the number of required stations decreases sharply across all strategies. The trend is particularly steep between \(R=1\) km and \(R=5\) km, after which the reduction becomes more gradual. Strategy 1 benefits most from an increased radius, requiring the fewest units overall. In contrast, Strategy 4 remains the most resource-intensive, even as \(R\) increases.  
These results emphasize the importance of optimizing repeater coverage radius during corridor design, as it significantly influences resource efficiency across all deployment strategies.
## 7 Summary of \(V_{oc}\) Losses  
Figure 8 summarizes the \(V_{oc}\) at each step. This solar cell has a very high \(V_{oc}\) potential when assuming an SRV of 0 cm/s. Each subsequent processing step, such as non-ideal passivation of the front and rear surfaces, laser doping on the front surface, metallization on both front and rear surfaces and edge isolation, introduces an increase of recombination, and thus an increase in \(J_{0}\). Calculation results and measurement results on either FZ test structures or the UTSi solar cells on steel are coincident with each other. The best achieved \(V_{oc}\) was of 642.3 mV. The primary \(V_{oc}\) loss is due to the recombination at the SiO\({}_{2}\) passivated p\({}^{+}\) surface. Thus the greatest opportunity to improve \(V_{oc}\) lies in the improvement of the rear surface passivation. The second dominant \(V_{oc}\) loss that can be further reduced is the 10 mV loss due to edge isolation. Once the cell size increases from 2 \(\times\) 2 cm\({}^{2}\) to 10 \(\times\) 10 cm\({}^{2}\), edge recombination is less critical and zero \(V_{oc}\) loss is possible. The third largest \(V_{oc}\) loss is caused by the relatively high front SRV. If the SRV decreases from 13 to 5 cm/s, there will be another 15 mV increase. Improvements in these three aspects can lead to a \(V_{oc}\) increase of more than 40 mV, hence a \(V_{oc}\) of more than 680 mV can be achieved in the UTSi solar cells.  
\begin{table}
\begin{tabular}{c c c c c} \hline Steps & Component & \(J_{\theta}\) (A/cm\({}^{2}\)) & \(V_{oc}\) (mV) & Steps \\ \hline
**4** & \(J_{Obs}+J_{Des}+J_{0e-pc}\) & \(2.02\times 10^{-13}\) & 670.0 & Rear contact \\
**–** & \(J_{Obs}\) & \(2.14\times 10^{-13}\) & **–** & **–** \\
**5** & \(J_{Obs}+J_{Des}+J_{0e-pc}+J_{Obs-id}\) & \(4.16\times 10^{-13}\) & 650.0 & Laser doping/metallization \\ \hline \end{tabular}
\end{table}
Table 8: \(J_{0}\) and \(V_{oc}\) of the thin silicon solar cell after the front surface laser doping and metallization. \(J_{Obs-id}\) is the reverse saturation current density introduced by the laser doping and metallization on the front surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline Steps & Component & \(J_{\theta}\) (mA/cm\({}^{2}\)) & \(V_{oc}\) (mV) & Steps \\ \hline
**5** & \(J_{Obs}+J_{Obs}+J_{Obs}+J_{Obs}+J_{Obs-id}\) & \(4.16\times 10^{-13}\) & 650.0 & Laser doping/metallization \\
**–** & \(J_{Obs}\) & \(1.93\times 10^{-13}\) & **–** & **–** \\
**6** & \(J_{Obs}+J_{Obs}+J_{Obs}+J_{Obs-id}+J_{Obs-edge}\) & \(6.09\times 10^{-13}\) & 640.0 & Edge isolation \\ \hline \end{tabular}
\end{table}
Table 9: \(J_{0}\) and \(V_{oc}\) of the UTSi solar cell after edge isolation. \(J_{Obs-edge}\) is the reverse saturation current density introduced by edge isolation.
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
## 2 System Model
### Asynchronous Transmission Model
\(x_{g1}^{(d_{j})}\) and \(x_{g2}^{(d_{j})}\) mean the codeword sent by user \(d_{j}\) and \(w_{k}\) is the additive white Gaussian noise (AWGN), \(w_{k}\sim\mathcal{CN}(0,\sigma^{2})\).
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 3 Results and Discussion
### Variable Importance  
To evaluate the influence of variables on RF regression, the variable importance scores were obtained. In particular, the variable importance scores were evaluated for each site, and the importance of each variable was averaged by all classes, as shown in Table 4. In terms of the importance of an individual variable, regardless of the site, the intensity of the SAR image contributed the most, followed by the mean of window sizes \(3\times 3,5\times 5,7\times 7\), and \(9\times 9\). In the case of the Gabor filter and standard deviation, the contribution was approximately 4-6%, which was relatively insignificant. On the other hand, in terms of the variable type, the contributions of intensity, Gabor filter, mean, and standard deviation were approximately 13-16%, 25-30%, 36-37%, and 17-20%, respectively; thus, it is confirmed that all variables are properly influenced.  
However, it should be noted that the variable importance scores are relative; therefore, they depend on the number of variables included. In other words, the importance scores can be changed by removing or replacing the predictors, as different inter-correlated variables could act as substitutes.  
Figure 11: Scatter plots for each class: (**a**) Site 1, (**b**) Site 2 (**c**) Site 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Class 1** & **Class 2** & **Class 3** & **Class 4** & **Class 5** & **Class 6** \\ \hline Site 1 & 0.9856 & 0.9209 & 0.9171 & 0.9745 & 0.8698 & 0.8771 \\ Site 2 & 0.9594 & 0.8775 & 0.8791 & 0.9606 & 0.8584 & 0.8792 \\ Site 3 & 0.9450 & 0.8917 & 0.8692 & 0.9591 & 0.8593 & 0.8717 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(R^{2}\) Values of the predictive models for each class.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 2 Data and Methods
Thus, the linear fit serves a reference of each individual measurement profile, and the derived \(\Delta hTEC\) is used for the \(N_{e}\) retrieval. The algorithm assumes that the \(F\)-region contributions to the \(hTEC\) in the \(D\)/\(E\)-region vary linearly over a narrow \(h_{t}\) range, and this linear variation can be estimated and removed using the \(hTEC\) measurements below the \(D\)-region. As a result, the new algorithm is perhaps more sensitive to residual ionospheric errors (RIES) and phase scintillations that are uncorrectable with the dual-frequency GNSS RO receivers, but these residual errors are relatively smaller than the systematic error from the spherical homogeneity assumption about the \(F\)-region. Fourth, the bottom-up retrieval should not be used to replace the Abel inversion for the whole ionospheric \(N_{e}\). Because the linear function used to remove \(F\)-region effects is a fit to the lower-ionospheric data, its uncertainty tends to increase with height and causes a large error in the \(N_{e}\) retrieval in the upper ionosphere. Thus, the Abel inversionfrom _hTEC_, which requires absolute calibration of the \(\phi_{ex}\) profile, still works better for the whole-ionospheric \(N_{e}\) retrieval.