esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Fundamental Frequencies in Axially Compressed Rotating Laminated Cylindrical Shells
## 4 Conclusions  
Based on the numerical results of this investigation, the following conclusions may be drawn:  
1. The influence of boundary conditions between FF ends and SS ends on the fundamental frequency of \(\left[\pm\theta/90/0\right]_{28}\) laminated cylindrical shells gradually decreased when the L/R ratio was large.
2. The optimal frequency \(\omega\) decreased with increasing L/R ratios and N/N\({}_{\rm cr}\) ratios. In addition, the influence of the L/R ratio on the optimal frequency \(\omega\) was more significant than that of the N/N\({}_{\rm cr}\) ratio.
3. With an increase in the L/R ratio, the fundamental vibration modes of the cylindrical shells displayed fewer waves in the circumferential direction. Additionally, when the L/R ratio was low, shells under higher axial compressive force exhibited fundamental vibration modes with fewer waves in the circumferential direction than those under lower axial compressive force.
4. The maximum fundamental frequency of the cylinder shells increased with increasing rotation speed. When rotating speed was increased, the fundamental vibration modes of the shells with high rotating speed had fewer waves in the circumferential direction.  
Figure 25: Vibrational modes of \(\left[\pm\theta/90_{2}/0\right]_{28}\) laminated cylindrical shells with two fixed ends under optimal fiber angle (R = 10 cm, L/R = 4, \(\Omega=20,000\) RPM).  
5. The maximum fundamental frequency of the shell increased with increasing d/R ratios. In addition, the fundamental vibration modes of the cylindrical shells tended to have more local modes around the cutout when the L/R ratio was small, the d/R ratio was large, and the N/N\({}_{\rm cr}\) ratio was large.
6. By understanding how the fiber orientation, L/R ratio, N/N\({}_{\rm cr}\) ratio, and rotation speed affect optimal fundamental frequency and vibration mode, engineers can tailor laminated cylindrical shells to achieve desired vibrational characteristics. This can minimize resonance issues and enhance stability in applications in aerospace engineering.  
Conceptualization, H.-T.H.; methodology, H.-T.H.; software, H.-T.H. and Y.-W.H.; validation, H.-T.H. and Y.-W.H.; formal analysis, Y.-W.H.; investigation, H.-T.H. and Y.-W.H.; resources, H.-T.H.; data curation, H.-T.H. and Y.-W.H.; writing-original draft preparation, H.-T.H.; writing-review and editing, H.-T.H.; visualization, H.-T.H. and Y.-W.H.; supervision, H.-T.H.; project administration, H.-T.H.; funding acquisition, H.-T.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data included in graphics and tables within the manuscript are available upon reasonable request to the authors.  
The authors declare no conflict of interest.  
References
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 3 Imaging Processing of AA-BiSAR with One-Moving Transmitter
### Correction of Range Cell Migration  
According to the analysis in Section 2, after the residual phase is corrected, the slant range is given by:  
\[R(\theta_{r})=\rho-R_{r}\cos\beta_{r}\cos(\theta_{r}-\theta_{n})+\sqrt{{R_{0} }^{2}+2R_{t}R_{n}(1-\cos(\theta_{n}-\theta_{t}))} \tag{17}\]To facilitate expression, let \(d_{t0}\) defined as follows:  
\[d_{t0}=\sqrt{{R_{0}}^{2}+2R_{t}R_{n}(1-\cos(\theta_{n}-\theta_{t}))} \tag{18}\]  
Substituting Equation (17) into Equation (16) to resolve the equation, the result is as follows:  
\[\begin{array}{ll}S_{sf\_c}(f_{r},\theta_{r})=&A_{0}\omega_{a}\Big{(}\frac{ \theta_{r}-\theta_{n}}{\theta_{n}}\Big{)}{\rm W}_{r}(f_{r})\cdot\exp\Big{[}-j2 \pi(\frac{f_{r}+f_{c}}{c}\cdot(d_{t0}+\rho)\Big{]}\\ &\cdot\exp\Big{[}j2\pi\frac{f_{c}}{c}R_{r}\cos\beta_{r}\cos(\theta_{r}-\theta_{ n})\Big{]}\\ &\cdot\exp\Big{[}j2\pi\frac{f_{c}}{c}R_{r}\cos\beta_{r}\cos(\theta_{r}-\theta_{ n})\Big{]}\end{array} \tag{19}\]  
It can be seen from Equation (19) that the range frequency variable \(f_{r}\) and the azimuth angle variable \(\theta_{r}\) are coupled in the last term, which will cause severe range cell migration, and lead to the range envelope movement. In this paper, the KT method is proposed to correct the range cell migration by redefining a virtual azimuth variable \(\varphi\), and it is satisfied in the following relationship:  
\[\cos(\varphi-\theta_{n})=\frac{f_{c}+f_{r}}{f_{c}}\cos(\theta_{r}-\theta_{n}) \tag{20}\]  
After KT processing, the echo signal of the range frequency-azimuth time domain is expressed as:  
\[\begin{array}{ll}S_{sf\_k}(f_{r},\varphi)=&A_{0}\omega_{a}\Big{(}\frac{ \theta_{r}-\theta_{n}}{\theta_{n}}\Big{)}{\rm W}_{r}(f_{r})\\ &\cdot\exp\Big{[}-j2\pi\frac{f_{c}}{c}(d_{t0}+\rho-R_{r}\cos\beta_{r}\cos( \varphi-\theta_{n})\Big{]}\\ &\cdot\exp\Big{[}-j2\pi\frac{f_{c}}{c}(d_{t0}+\rho)\Big{]}\end{array} \tag{21}\]  
Then, perform range inverse fast Fourier transform on Equation (21), and obtain the range time-azimuth time domain expression of echo signal as follows:  
\[\begin{array}{ll}s_{r\_k}(t_{r},\varphi)&={\rm IFFT}\Big{[}S_{sf\_k}(f_{r}, \varphi)\Big{]}\\ &=A_{0}\omega_{a}\Big{(}\frac{\varphi-\theta_{n}}{\theta_{n}}\Big{)}\phi_{r} \Big{(}t_{r}-\frac{d_{t0}+\rho}{c}\Big{)}\\ &\cdot\exp\Big{[}-j2\pi\frac{f_{c}}{c}(d_{t0}+\rho)\Big{]}\cdot\exp\Big{[}j2 \pi\frac{f_{c}}{c}R_{r}\cos\beta_{r}\cos(\varphi-\theta_{n})\Big{]}\end{array} \tag{22}\]  
where IFFT \([\cdot]\) stands for the inverse fast Fourier transform operation, \(\phi_{r}[t_{r}-(d_{t0}+\rho/c)]\) is the inverse Fourier transform of \({\rm W}_{r}(f_{r})\). It can be seen from Equation (22), after KT processing, that the coupling between the range and azimuth is eliminated, and the echo envelopes of different range cells are corrected to the same range cell.
## 2 Modelling  
The geometrical configuration of the loop heat pipe used for the proposed mathematical model is based on Figure 1. It comprises a heat source (Node 8), a vapour transport line (Nodes 2 to 3), a liquid transport line (Nodes 5 to 6), a condenser (Nodes 3, 4 and 5), compensation chamber liquid (Nodes 6 to 7) and an evaporator (Node 1).  
In this model, the loop heat pipe is analysed as a system of nodes and control volumes, \(M_{i-j}\), as shown in Figures 2 and 3. Nodes are used to represent the solid elements, and control volumes \(M_{i-j}\) are assigned to represent a specific mass of liquid or vapour that exists on each section. For example, between Node 1 (evaporator entrance) and Node 2 (evaporator exit) there is control volume \(M_{1-2}\), which defines the contained mass in a vapour state within the evaporator vapour chamber. This logic is applied to the remaining nodes and control volumes.  
With these nodes and control volumes, we define a thermo-hydraulic model that can be represented as in Figures 2 and 3 using resistive, capacitive, and inductive elements.  
By defining the boundary conditions at the evaporator, compensation Chamber and external temperature nodes, i.e., \(Q_{evap}\), \(Q_{CC}\) and \(T_{ext}\), respectively, a 1D model represented by a set of Ordinary Differential Equations (ODEs) can be obtained. The results of these equations allow for a comprehensive understanding of the transient thermo-hydraulic behaviour of the loop heat pipe when operating as a heat switch.  
Figure 1: Loop heat pipe generic configuration.  
The circuit elements presented in Figures 2 and 3, the thermal and hydraulic network systems, respectively, are based on equivalent electrical circuit elements such as Resistive \(R\), Capacitive \(C\) and Inductive \(L\). For clarity and consistency within this article, symbol \(G\) is used to represent thermal conductance, deviating from conventional symbol \(R_{th}\) used for thermal resistance, given that \(G=\frac{1}{R_{th}}\). This decision was made to avoid confusion with the use of \(R_{fl}\) in the hydraulic model, as previously described.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Mobility in Three Dimension (3D)  
UAVs typically fly at high speeds above the BS antenna height in 3D space. 3D mobility changes the UAV's altitude which consequently influences the propagation channel characteristics. Thus, 3D coverage that can adapt to changing UAV elevations is required, and speed limitations must be maintained.
# Optimal Trajectories of Diffractive Sail to Highly Inclined Heliocentric Orbits
## 2 Mission Description and Models Used
The analysis of the behaviour of optimal trajectories of reflective sails inspired the two-phase strategy used in the study of diffractive sails' transfers, which will be illustrated in the following section. The model used in the description of spacecraft dynamics uses modified equinoctial orbital elements, whose time derivatives as a function of \(\mathbf{a}_{\text{p}}\) are given in Equations (8)-(20) of Ref. [19]. The minimum-time trajectories for transferring the spacecraft to the target orbit have been calculated by solving an optimal orbit-to-orbit transfer problem using the control law described in Ref. [19], in which the only control variable is the clock angle. More precisely, the angular position of the spacecraft on the initial and final orbit is left free to vary (ephemeris-free case), and the exact values of the spacecraft true anomaly at the beginning and end of the transfer are considered as the outputs of the optimal problem solution.
## 5 Implementation
### Operation  
The basic workflow and the transaction process scenario considering assumptions are shown in Figure 6a, which is a diagram expressing the workflow for the management of fine dust at a construction site using this system, and it has a simple and linear structure. However, because IoT sensor data are periodically generated, \"End\" means that the construction has been completed. Figure 6b shows the flow of data and transaction flow in each work process, and it is a diagram reconfigured according to the assumption based on Figure 2.  
Figure 6: (**a**) Workflow; (**b**) transaction scenario.  
First, the government inspection agency (RegulationID:3-1) sets the fine dust concentration regulation at the construction site as 300 \(\upmu\)g/m\({}^{3}\) through the DApp. Then, the IoT sensor (IoTID:1-1) transmits the fine dust concentration value. Since the fine dust concentration value is 320 \(\upmu\)g/m\({}^{3}\) at 18:01 and exceeded 300 \(\upmu\)g/m\({}^{3}\), it automatically alarms the construction company (GCID:2-1) through the Reduction Action Management Function. The contractor implements fine dust reduction measures, and when a picture is uploaded, the hash value of the picture is stored in the blockchain. In this process, the information generated by the IoT, contractor, inspection agency, and system is recorded on the blockchain and can be viewed through various tools.
## 3 Results
### Match Rate  
Different combination icons had a significant effect on the match rate cognition of all subjects (F (3, 248) = 1687.105, _p_! 0.05, \(\eta^{2}\) = 0.953). The recognition of the matching of combination icon types was ranked as follows: Ci + Ct, Ci + At, Ai + Ct and Ai + At. The match rate of Ci + Ct was the highest, followed by Ci + At, Ai + Ct and Ai + At (_p_! 0.05). The match rate of Ci + At was significantly lower than that of Ci + Ct (_p_! 0.05), but it was significantly higher than that of Ai + Ct and Ai + At (_p_! 0.05); the match rate of Ai + Ct  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source of Variance**} & \multicolumn{3}{c}{**Response Time**} & \multicolumn{3}{c}{**Match Rate**} \\ \cline{2-7}  & **F** & **_p_ & **\(\mathbf{\eta^{2}}\)** & **F** & **_p_ & \(\mathbf{\eta^{2}}\)** \\ \hline Combination design type & 332.055 & 0.000 & 0.801 & 1687.105 & 0.000 & 0.953 \\ Construal level & 46.740 & 0.000 & 0.159 & 430.062 & 0.000 & 0.634 \\ Design type \(\times\) construal level & 9.305 & 0.000 & 0.101 & 57.68 & 0.000 & 0.411 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA results for combination design type and construal level.  
Figure 4: Response time for four types of combination design icons in users of two types of construal level.  
was significantly lower than that of Ci + At (\(p<0.05\)), but significantly higher than Ai + At (\(p<0.05\)). The match rate of Ai + At was significantly lower than that of Ci + Ct, Ci + At and Ai + Ct (\(p<0.05\)).  
Figure 5 shows that there is an interaction between the match rate of the combined icon types and construal levels (F (3, 248) = 57.68, \(p<0.05\), \(\eta^{2}\) = 0.411). Compared with other icon types, in the two groups of subjects with different construal levels, the Ci + Ct match rate was the highest (\(p<0.05\)), but there was little difference in matching cognition between the two groups of subjects (\(p=0.422\)). There is a significant difference between the two groups of subjects' visual cognition match rates between Ci + At, Ai + Ct and Ai + At (\(p<0.05\)). In detail, the visual cognition match rate was better for Ci + At than Ai + Ct, while that for Ai + Ct was better than that for Ai + At (\(p<0.05\)).
## 3 Results  
### Influence of the Refractive Index Matching on the Raman Signal  
Figure 6 show the results of the set of experiments, each as a function of the refractive index differences between the inner water and oil phase and the outer water and oil phase, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Set of Experiment**} \\  & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** \\ \hline Filling degree/w-\% & 8.17 & 10.21 & 15.31 & 20.40 & 30.61 \\ Ammonium nitrate concentration/w-\% & 61.25 & 48.91 & 32.46 & 24.50 & 16.33 \\ Amount W\({}_{1}\)/g & 36.731 & 24.482 & 18.366 & 12.257 & 9.811 \\ Amount ammonium nitrate/g & 6.01 & 6.00 & 5.96 & 6.00 & 6.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Survey of the test series with corresponding weights and concentrations.  
Figure 5: Scheme of the capillary system including the measuring point.  
In Figure 6A, each column of data points represents one ammonium nitrate concentration. Thereby, the ammonium nitrate concentration decreases from left (61%) to right (16%). The only exemption is the column at \(\Delta\)N = 0.0173 (diamonds instead of circles). The ammonium nitrate concentration of this column is 61%. For this column, the signals are plotted over the absolute refractive index difference to illustrate the linear dependency between the signal and the refractive index difference. The linear trend lines for each glycerol concentration have Pearson coefficients of more than R\({}^{2}\) = 0.95.  
The emulsions with 49% ammonium nitrate, respectively the smallest refractive index difference, show the highest signals within a constant glycerol concentration. When the refractive index difference increases, in both positive and negative directions, the signal gets weaker. Within one column, the signal increases with increasing glycerol concentration.  
Figure 6: Results of the experiments presented as a function of refractive index differences between the oil phase and the inner water phase (**A**) and the outer water phase (**B**).  
In Figure 6B, each column of data points represents one glycerol concentration, and the concentration decreases from left (70%) to right (0%). The signals decrease with decreasing glycerol concentration. Within one column, the signal increases with increasing ammonium nitrate concentration. The exception is the ammonium nitrate concentration of 61% (green dots). It is past the point of best matching and the values are below the maximum. The maximum is at 49% ammonium nitrate (orange dots) and is the best matching.  
Furthermore, the measured values show a linear dependence on the glycerol concentration. The Pearson coefficients of the linear trend lines of all glycerol concentrations are all above R\({}^{2}\) = 0.99.
## 4 Discussion
### Conceptual Model
The details of the water quality controls inherent to the conceptual model include:  
* High-level deposit maximum paleo-lake extent influence marks the transition between low-TDS groundwater suitable for water supply at higher ground and high-TDS, brackish groundwater in areas overlain by lacustrine terrace/high-level deposits closer to the current lake;
* Low-TDS groundwater closer to the lake is limited to mid-to-low reach influent leakage of low-TDS rivers incising terrace deposits;
* Progression and, or mixing of water types is observed with young, freshly recharged Ca-HCO\({}_{3}\)-type waters in upland weathered (or fractured) basement rocks and higher elevation colluvium mixing with, or evolving to waters increasingly influenced by longer residence times and rock interactions leading to Ca-Na-HCO\({}_{3}\)-type waters and higher-TDS salinity;
* Increased TDS and sodium from evaporitic enrichment/ion exchange may be expected from contact with finer-grained clay lithologies frequent in the lacustrine sediments, either from lateral groundwater flows intermingling with clayey horizons, or else infiltration and leakage through high-level or lake terrace deposits driven by downward gradients that may be expected over river mid-reach to near-lake approach;
* Worst-quality, highest TDS and Na groundwater occurs around the medium to higher lake terraces deposited prior to, or approaching the time of paleo-lake enclosure likely influenced by repeat seasonal sediment wetting, drying and evaporation;
* Some elevated iron concentrations occur and are more common towards the east of the area in boreholes near to, but either side of the high-level deposit boundary;
* Anthropogenic influences on surface water and groundwater quality expected to be increasingly influential arising from agricultural and sanitation facility sources in particular and urbanisation in time. Figure 15: Conceptual model of the Lake Chilwa Basin–Palombe District groundwater system illustrating the geohydrological control of the paleo-lake environments and recommendations for Integrated Water Resource Management shown with labelled locations [a] to [f] of example application (approximate line of section is shown in Figure 2). Flow regime and water quality control detail inherent to the conceptualisation are summarized in the text bullet points.
## 5 Performance Comparison Before and After Optimization
### Load Simulation Analysis  
In load analysis, average torque and torque ripple are important performance parameters of PMA-SynRM. The load simulation conditions are carried out at rated RMS current (2 A) and rated speed (1500 rpm). The average torque of the basic and optimized models at different current phase angles is given in Figure 14. In Figure 14, it can be seen that the peak torque is reached for both the basic and optimized models at a current phase angle of 30 degrees. Figure 15 shows the cloud of flux density for peak torque at rated current for the proposed and basic models. From Figure 15, it can be noticed that the flux density at the bypass magnetic bridges of the optimized model is higher than that of the basic model. This indicates that the bypass magnetic bridges can effectively shunt the magnetic flux, thereby reducing the pressure on the permanent magnets and reducing the risk of demagnetization of the permanent magnets. The torque waveforms of the basic and optimized models are shown in Figure 16. From Figure 16, it can be shown that the torque fluctuation of the optimized model is less. Table 3 demonstrates the torque performance parameters of the basic and optimized models. From Table 3, the average torque of the optimized model decreased by 2.37% compared to the basic model. The authors of [29] summarized some studies on the error of simulation and experiment on output torque,  
Figure 12: Comparison of no-load flux density between the basic and optimized models. (**a**) Waveform diagram of flux density. (**b**) Harmonic decomposition of flux density.  
Figure 13: Comparison of the no-load Back-EMF of the basic model and the optimized model. (**a**) Back-EMF waveform diagram. (**b**) Harmonic decomposition of Back-EMF.  
and the average error is 2.93%. The average torque decrease rate of the optimized model is lower than the average error, so the output torque of the optimized model is hardly affected. The torque ripple of the optimized model is reduced by 44.14% as compared to the basic model. The optimized model has less torque ripple and better stability.  
Figure 14: Average torque of the basic and optimized models for different current phase angles.  
Figure 15: Flux density cloud at peak torque for the two models. (**a**) Basic model. (**b**) Optimized model.
# Comparison of Modern Deep Learning Models for Speaker Verification
## 4 Models Overview
### Ecapa  
The Ecapa-TDNN model improves the existing x-vector speaker representation extraction architecture with multiple enhancements inspired by the recent advancements in face verification. The authors introduce the Squeeze-and-Excitation blocks into the 1-D Res2 Net modules to explicitly model channel interdependencies. An SE block expands the temporal context of the frame layer by rescaling the channels according to the global properties of the recording [42]. Authors also aggregate and propagate features of different hierarchical levels to leverage the ability of the neural networks to learn these hierarchical features. They also improve the statistics pooling module with channel-dependent frame attention, which enables the network to focus on different subsets of frames while estimating statistics. The proposed architecture outperforms state-of-the-art TDNN-based systems on the VoxCeleb test datasets [28]. The Ecapa-TDNN model is trained on the VoxCeleb2 dataset, which includes approximately 5.9K unique speakers. The authors also apply augmentations to the training data audio files, and, as a result, generate six samples per utterance. The aggregations include babble, noise, reverberation, increased and decreased tempo and the effects of using different Opus codec compression settings. The output speaker representation has a fixed size of 192 elements.
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 2 Collaborative Channel Perception Algorithm  
This section first briefly introduces the collaborative channel state perception model and the key issues that need to be solved, namely the reasonable allocation of node weights. Then, it analyzes the typical characteristic parameters of node devices that can be used to describe traffic pulse statistical capabilities. Finally, a new collaborative channel state perception algorithm is proposed.