esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 OCTA in Intermediate AMD  
AMD may present at different stages, and the \"intermediate AMD\" stage is clinically characterized by the presence of pigmentary abnormalities and/or large drusen [7]. Of note, eyes with subretinal fluid may be also characterized by the absence of neovascularization and thus classified as iAMD [32].  
Although the AMD pathogenesis is intricated and related to several systemic and lifestyle factors that may have a role in the development and progression of this disorder [33; 34], a growing body of evidence suggests that this disorder is ultimately characterized by damage of the unit comprised of photoreceptors, RPE, Bruch's membrane, and choriocapillaris (CC) complex [35; 36; 37]. Importantly, several pieces of evidence suggest that this may be considered as a tightly knit, integrated unit [38; 39; 40]. In AMD, this impairment causes the development of drusen and progressive photoreceptor, RPE, and CC degeneration [41; 42; 43; 44].  
Previous studies have fully characterized the CC perfusion in eyes with iAMD [45; 46; 47; 25; 48]. In detail, using both spectral domain and swept source technologies, [PERSON] and colleagues demonstrated that the CC is impaired (i.e., CC ischemia) in these eyes [45; 46]. Importantly, they provided a topographical analysis revealing that the CC impairment is mainly confined to the CC beneath and surrounding drusen [46]. Moreover, the CC perfusion was more affected in iAMD eyes with neovascular AMD in the fellow eyes [45], especially in those with type 3 MNV [47]. In two studies employing OCTA in iAMD eyes [49; 50], the authors showed that the CC perfusion is strictly correlated with macular function in these eyes, further highlighting the association between CC ischemia and outer retina dysfunction in iAMD.  
Although the CC is known to be the vascular plexus most affected in AMD, also retinal vessels were demonstrated to be impaired in these eyes [51; 52]. [PERSON] et al. demonstrated that eyes with iAMD are characterized by a lower retinal perfusion as compared with normal eyes [51]. Of note, iAMD eyes with OCT signs of nascent GA are featured by a greater reduction in retinal vessels perfusion [52].
## 4 Discussion
### Application and Implications  
A wide set of users have used Landsat data for mapping fire/burn severity of vegetation using classification and spectral indices. For example, the supervised classification for fire severity classes [16; 70], dNDVI for tree mortality and RdNBR for fire severity classes [71; 72], dNBR and CBI (composite burn index) for fire severity classes [73; 74], and dNBR for estimating live trees and forest floor biomass [75]. In the study done by [PERSON] et al. [15], the dNBR values from the Landsat data projected lower correlation compared to this study, which provided slightly stronger correlation with ground data when applied with MTMF. RdNBR detected a change in pre-fire low vegetation conditions in this study, similar to [PERSON] et al. [19]. The differences between the Landsat-derived RdNBR, dNBR, and hyperspectral NBR values were found minimal in studies done by [PERSON] et al. [76] and others [16; 63]. This would not justify the greater acquisition cost or even processing time of hyperspectral data versus Landsat data. Greater overall accuracy in using the MTMF method approach would be because unidentified background material that exists in every pixel post fire could be readily detected. Although tested in previous research for fire severity assessment [61; 76], multispectral data, and the partial unmixing approach are for the first time used in the present study to derive fire severity maps. In addition to Landsat data, some studies utilized SPOT [77; 78], AVIRIS [63], MODIS [67; 79] and hyperspectral data [57; 63] for assessing fire severity classes, and fractional components of post ground cover. Moreover, MODIS applications are useful in bushfire mapping and that uses a highly technical approach like deep learning on fire intensity using climatic data [80; 81]. Using spectral mixture techniques on Landsat data has been reported to generate higher accuracy, ranging from 77% to 85% Kappa values, in terms of post-fire detection [82]. As previous studies reported, this studies also obtained high correlation for scorched vegetation, dNBR values (0.75) and RdNBR values (0.76) resulting in high accuracy for moderately burn and high burn vegetation classes (Table 2). Therefore, the fire severity map of PVNP is useful for fire and land managers to look for high-severity sites for vulnerability to soil erosion and plant regeneration.  
The diverse group of wetland habitats in PVNP housed by bordered rivers and a row of limestone hills has faced forest fires intentionally set for the past years. Thus, the present approach has value for resource managers because it emphasizes the distinction between measures of severity after a fire and the resource impact of the fire. This study also generated the extent to which fire reflects potential ecosystem responses.
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
###### Abstract  
This work studies the buckling and free vibration behavior of Shape Memory Alloy Hybrid Composite (SMAHC) sandwich beams under a thermal environment. The sandwich beams consist of layers reinforced with SMAs and a FGM core, and they are simply supported at both ends. The higher order theory is combined with the Minimum Potential Energy principle or Hamilton principle to derive the governing equations of the thermal buckling and thermal vibration problems, respectively. The material properties of the beam are assumed as temperature-independent (TID) or temperature-dependent (TD). In the last case, two different types of thermal distribution are considered, namely a uniform and a linear distribution. The results based on the proposed formulation are verified against the reference literature, with a very good matching. A parametric study checks for the influence of different effective parameters such as thickness-to-length ratios, volume fraction powers, initial strain, volume fraction of SMA wires, and temperature distribution on the overall mechanical response of the selected structural member, with useful insights from a design standpoint.  
high order theory; SMAHC sandwich beam; thermal buckling; thermal vibration; uniform and linear temperature distribution +
Footnote †: journal: Applied Sciences
Then, using the Gaussianity of the sum rate for multiple users, the achievable scheduling gain is derived as a closed form. According to our analysis for the case of perfect CSI, as the number of antennas increases and tends towards infinity, the scheduling gain of the MF receiver increases and converges to a constant value, while that of the ZF receiver decreases to zero. Thereafter, our analysis is extended to the case of imperfect CSI at the BS. It is shown that when there is insufficient CSI available at the BS, the variance of the sum rate for the ZF receiver increases as the number of antennas increases, similar to the MF receiver. This is because the multiuser interference cannot be completely removed, owing to the imperfectness of the CSI. However, the scheduling gain of the ZF receiver still decreases to zero with the increasing number of antennas when the user selection is performed based on the imperfect CSI. Thus, user scheduling for massive MIMO systems is still beneficial for the MF receiver, regardless of the imperfectness of CSI; however, the benefit of user scheduling is negligible for the ZF receiver. The remainder of this paper is organized as follows. Section 2 presents an uplink massive MIMO system model with linear receivers. In Section 3, the scaling laws and feasibility of user scheduling according to the number of antennas are investigated under the assumption of perfect CSI at the BS and these results are extended to the case of imperfect CSI in Section 4. Section 5 presents the simulation results to verify our analyses, and Section 6 concludes the paper.
## 2 Conventional Face Gear Pair
### Tooth Surface of Face Gears  
The tooth surface of a face gear is defined based on the gear shaping process of a involute gear, as shown in Figure 1. In this case, the angle between the shaft of the face gear and the involute gear is \(90^{\circ}\), and the angle of the involute gear \(\varphi_{\text{c}}\) and the angle of the face gear \(\varphi_{\text{f}}\) satisfy the relationship shown in Equation (6), where \(N_{\text{f}}\) is the number of teeth of the face gear.  
\[\frac{\varphi_{\text{c}}}{\varphi_{\text{f}}}=\frac{N_{\text{f}}}{N_{\text{c}}} \tag{4}\]Hence, the position vector \(R_{\text{f}}\) and the normal vector \(n_{\text{f}}\) of the face gear surface \(\Sigma_{\text{f}}\) are obtained through the coordinate transformation of the following equation. The coordinate systems \(S_{\text{c}}\) and \(S_{\text{d}}\) are established on the gear cutter, where \(S_{\text{c}}\) is fixed in space and \(S_{\text{d}}\) rotates with the cutter; \(S_{\text{e}}\) and \(S_{\text{f}}\) are on the face gear coordinate systems, where \(S_{\text{e}}\) is fixed and \(S_{\text{f}}\) rotates with the face gear; and \(r_{\text{f}}\) and \(r_{\text{p}}\) are the pitch radius of the face gear and the cylindrical gear.  
\[R_{\text{f}}(u_{s},\theta_{s},\varphi_{\text{f}}) =M_{\text{fd}}(\varphi_{\text{f}})R_{\text{c}}(u_{s},\theta_{s}) \tag{5}\] \[n_{\text{f}}(u_{s},\theta_{s},\varphi_{\text{f}}) =M_{\text{fd}}(\varphi_{\text{f}})n_{\text{c}}(u_{s},\theta_{s})\] (6) \[M_{\text{fd}} =M_{\text{fe}}M_{\text{ed}}M_{\text{cd}} \tag{7}\]  
\[M_{\text{cd}}=\begin{bmatrix}\cos\varphi_{\text{c}}&\sin\varphi_{\text{c}}&0&0 \\ -\sin\varphi_{\text{c}}&\cos\varphi_{\text{c}}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}M_{\text{cc}}=\begin{bmatrix}1&0&0&0\\ 0&\cos\gamma&-\sin\gamma&-r_{\text{f}}\\ 0&\sin\gamma&\cos\gamma&r_{\text{p}}\\ 0&0&0&1\end{bmatrix}M_{\text{fe}}=\begin{bmatrix}\cos\varphi_{\text{f}}&-\sin \varphi_{\text{f}}&0&0\\ \sin\varphi_{\text{f}}&\cos\varphi_{\text{f}}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}\]  
The cutter and face gear satisfy the following meshing equation, the theoretical face gear tooth surface can be successfully obtained.  
\[f_{2}(u_{s},\theta_{s},\varphi_{\text{f}})=n_{\text{c}}\cdot\frac{\partial R _{\text{f}}(u_{s},\theta_{s},\varphi_{\text{f}})}{\partial\varphi_{\text{f}}}=0 \tag{8}\]
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Analysis of Influence of Reinforced Layer on Dynamic Characteristics of Gravel Soil
#### 3.1.2 Effect of the Number of Reinforced Layers on Resilient Modulus  
The variation of resilient modulus (Ed) with number of cycles of the samples is compared in Figure 8. The figure shows that in the first 100 loading cycles, the resilient moduli of the samples remain almost constant, with very slight variation. Overall, the samples with reinforcement are higher in resilient modulus comparing to the non-reinforced sample. The resilient modulus then increases with the number of loading cycles at nearly logarithm scale. For the unreinforced sample, the resilient modulus tends to stabilize after about 8000 cycles, and the resilient modulus increment slows down after that. For the sample with one layer of reinforcement, the resilient modulus increment rate decreases after 8000 cycles. For the other two samples, resilient modulus still increases at nearly logarithm scale after 10,000 cycles. At the end of the tests, the more layers of reinforcement, the higher the resilient modulus. The percentage of increment is higher in the unreinforced sample and the sample with three layers of reinforcement. If we disregard the variation of the resilient modulus of the samples during the first 100 cycles, and normalize resilient moduli of the samples with the values at the 100 th loading cycle, (Ed100), as shown in Figure 8b, we can see that the resilient moduli of all the samples increased about 20% at the end of the tests compared to the values at the end of the 100 th loading cycle. This may not be used as a conclusion as at the end of the tests, the resilient moduli of the samples still increase with the loading cycle at different rates as shown in the figure.  
Based on the analysis of Figures 7 and 8, it can be seen that as the number of loading cycles increases, the overall growth trend of the axial cumulative strain is consistent with the modulus of resilience, and can be divided into a rapid growth phase, a transition phase, and a stable phase. This is due to the gradual increase in the axial cumulative strain of the sample under cyclic loading, and relative displacement between soil particles, and then the soil becomes denser, which results in the soil elastic modulus gradually increasing. With the continuous increase in the cyclic load, the compaction state of the soil tends to be stable, and the changes in the axial cumulative strain and the modulus of resilience also decrease.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of Reinforcement Layers**} & \multicolumn{4}{c}{**Fitting Parameters**} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} \\ \cline{2-2} \cline{4-5}  & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) \\ \hline
0 & \(-5.379\) & 0.814 & 7.930 & 0.998 \\
1 & \(-4.713\) & 1.786 & 7.685 & 0.999 \\
2 & \(-6.266\) & 2.394 & 9.291 & 0.996 \\
3 & \(-6.748\) & 2.469 & 11.460 & 0.999 \\
# Theoretical Design of a Novel Vibration Energy Absorbing Mechanism for Cables
## 3 Theoretical Modeling of the VEAM
### Hydraulic Viscous Damper Model  
When \(F_{\rm{f}}\) is less than \(F_{\rm{m}}\), the PM pushes the piston and the hydraulic viscous damper begins to work. The damping due to the hydraulic cylinder is much greater than that due to the structural damping of the coil springs. Therefore, the structural damping is neglected in the theoretical model. The VEAM sub-system can be simplified as an SDOF model, as shown in Figure 5c. The figure graphically shows that the damping of the hydraulic cylinder (\(c_{\rm{h}}\)) is connected in parallel with the stiffness of the two coil springs (\(k_{\rm{s}}=k_{\rm{S1}}+k_{\rm{S2}}\)). Thus, the equation of motion of the VEAM sub-system can be expressed as:  
\[m\ddot{y}+c_{\rm{h}}\dot{y}+k_{\rm{s}}y=-m\ddot{y}_{\rm{c}} \tag{21}\]  
The derivation process to get the displacement transmissibility is similar to that described in Section 3.2. Thus, the equation is obtained, as follows:  
\[T=\sqrt{\frac{Y^{2}}{Y_{\rm{c}}^{2}}+1+\frac{2Y}{Y_{\rm{c}}}\left(\frac{-\rm{ mY}\omega^{2}+k_{\rm{s}}Y}{\rm{mY}_{\rm{c}}\omega^{2}}\right)} \tag{22}\]
## 4 Solution Method for Optimization Model
### Nsga-Ii  
NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is a multi-objective evolutionary algorithm based on elitist strategy. Owing to non-dominated sorting, crossover, and mutation, the diversity of the population is maintained, improving the convergency and searching efficiency. The application process of NSGA-II in solving the proposed model is illustrated in Figure 2. The procedures are listed as follows:  
1. Initialization: Each individual (solution) is encoded as a real-valued vector, including daytime/night reactive power output mode of PV, \(p\) and \(Q\) set points of FID ports, and the active power curtailment ratio of PV. Randomly generate an initial population, ensuring that all solutions satisfy constraints. Set the parameters of population size as 100, maximum iterations as 200, crossover probability as 0.9, and the mutation probability as the reciprocal of the variable quantity.
2. Fitness evaluation: Make power flow calculations for each solution and further calculate the objective function. Use a penalty function to remove solutions that exceed constraints.
3. Non-dominated sorting: Sort the population into multiple Pareto fronts based on objective function values.
4. Crowding distance calculation: Calculate the crowding distance in the objective space to measure the distribution density of the solutions. Select solutions with larger crowding distances to maintain population diversity.
5. Selection, crossover, and mutation: Select parent individuals through binary tournament selection. Use simulated binary crossover (SBX) with a crossover distribution index of 20. Use polynomial mutation with a mutation distribution index of 20, ensuring variables remain within feasible bounds.
6. Iteration and termination: Combine parent and offspring populations. Perform non-dominated sorting and crowding distance calculation and retain the best N individuals. When the maximum number of iterations is reached or the convergence conditions are achieved, the iterations terminate.  
Figure 2: Flow chart of solving proposed model by NSGA-II.
## 5 Methods and Algorithms
### Infill Strategies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Rectangle**} & \multicolumn{3}{c}{**Triangle**} & \multicolumn{3}{c}{**U**} \\ \hline
**Infill Strategy** & \(k_{tot}\) & \(k_{area}\) & \(k_{vol}\) & \(k_{tot}\) & \(k_{area}\) & \(k_{vol}\) & \(k_{tot}\) & \(k_{area}\) & \(k_{vol}\) \\ \hline Standard-zigzag & 0.96 & 0.98 & 1.05 & 0.86 & 0.95 & 1.14 & 0.80 & 0.81 & 0.93 \\ \hline Raster & 0.99 & 1.08 & 1.37 & 0.56 & 0.64 & 0.86 & 0.73 & 0.79 & 1.06 \\ \hline Enclosed-zigzag & 0.96 & 0.97 & 1.06 & 0.93 & 0.97 & 1.41 & 0.91 & 0.94 & 1.72 \\ \hline Defined-end-zigzag & 0.95 & 0.97 & 1.15 & 0.82 & 0.90 & 1.17 & 0.85 & 0.88 & 1.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(k_{tot}\), \(k_{area}\) and \(k_{vol}\) for all infill strategies and test slices for \(d_{path}=10\,\mathrm{mm}\). Figure 12: Two path segments (**left**); a path interruption (**middle**); and coverage calculation grid (**right**). Figure 13: Coverage analysis of exemplary infill strategies of a 80 \(\times\) 120 mm rectangle: path width, 10 mm; grid spacing, 0.2 mm; light blue, cavity; pink, material outside of the intended slice; green, one material layer; yellow, two material layers; red, three material layers; purple, four or more material layers. The standard-zigzag pattern provides good coverage abilities as long as the width of the polygon is significantly larger than the path width. For such polygons, the enclosed-zigzag is better suited, although it causes a significant increase in volume surplus. The defined-end-zigzag covers slices worse than the standard-zigzag does. However, the raster shows the worst coverage abilities of all analyzed infill strategies.
###### Abstract  
Ocean surface radial current velocities can be derived from synthetic aperture radar (SAR) Doppler shift observations using the Doppler centroid technique and a recently developed Doppler velocity model. However, comprehensive evaluations of the accuracy and reliability of these retrievals remain limited. To address this gap, we analyzed 6341 Sentinel-1 SAR scenes acquired over the South China Sea (SCS) between December 2017 and October 2023, in conjunction with drifting buoy observations, to systematically validate the retrieved radial current velocities. A linear fitting method and the dual co-polarization Doppler velocity (DPDop) model were applied to correct for the influence of non-geophysical factors and sea state effects. The validation against the drifter data yielded a bias of 0.01 m/s, a root mean square error (RMSE) of 0.18 m/s, and a mean absolute error (MAE) of 0.16 m/s. Further comparisons with the Surface and Merged Ocean Currents (SMOC) dataset revealed bias, RMSE, and MAE values of 0.07 m/s, 0.14 m/s, and 0.12 m/s in the Beibu Gulf, and \(-\)0.06 m/s, 0.23 m/s, and 0.19 m/s in the Kuroshio intrusion area. These results demonstrate that SAR Doppler measurements have a strong potential to complement existing ocean observations in the SCS by providing high-resolution (1 km) ocean surface current maps.  
s 2025  
7 June 2025  
2025 19 May 2025  
7 June 2025  
9 June 2025  
10 June 2025  
1 Satellite Oceanography Laboratory, Russian State Hydrometeorological University,  
St. Petersburg 195196, Russia; [EMAIL_ADDRESS] (S.F.); [EMAIL_ADDRESS] (V.K.)  
[PERSON], [PERSON] Comprehensive Assessment of Ocean Surface Current Retrievals Using SAR Doppler Shift and Drifting Buoy Observations.
schemes have been adopted by Reference [14] for solving a stochastic frequency-based assignment considering pre-trip/en-route path choice behaviour. Similarly, Reference [15], which extended the approach proposed in Reference [16], compared different MSA-based resolution techniques in the case of a stochastic assignment with multi-vehicle types (i.e., connected, automated, and autonomous vehicles, as well as traditional ones).  
A generalisation of the averaging procedure, not limited to the traffic assignment problem, was provided by Reference [17]; while, Reference [18] proposed a refresh memory method for solving the combined assignment-control problem.  
With the computing capabilities of modern PCs, the solution time of the traffic assignment problem is fairly short, also for large-size networks, if the problem has to be solved only once or a few times. Network Design Problems (NDPs) are usually solved by adopting procedures that use assignment algorithms as subroutines (bi-level approach); some reviews on NDPs can be found in References [19; 20; 21; 22]. Indeed, for evaluating the objective function corresponding to a solution to the NDP, it is necessary to estimate traffic flows on the network. In this case, in most approaches, the assignment problem has to be solved a number of times equal to the solutions examined by the NDP algorithm; hence, several thousands of times in real-scale applications. Therefore, reducing the computing time of assignment algorithms is an important target since it may lead to a significant reduction in computing times for solving NDPs. In this context, Reference [23] proposed an MSA-based algorithm acting on the traffic flows adopted in the initialisation phases for reducing calculation times of NDPs when the topology of the network configuration remains unchanged.  
With respect to the previous literature, in this paper we propose a new method that adopts a variant to the MSA algorithm: It is based on a parameter that can be set for each network to minimise the computing time. Moreover, in order to show its promising performance, it has been compared to other methods proposed in the literature. Finally, after the setting phase, the algorithm can be included within the NDP algorithm, minimising the whole computing time for solving the problem.  
The remaining part of the paper is organised as follows: Section 2 introduces the model formulation; the proposed algorithm is analysed in Section 3, and Section 4 summarises the numerical results; Section 5 concludes the paper and outlines the research prospects. In Appendix A the convergence of the proposed algorithm is stated, and Appendix B presents a comparison with some MSA-based algorithms is provide. In Appendices C and D, the Network Design Problem and the algorithm used for its solution in the numerical test are illustrated.