esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Space Segment
There is also a clear trend toward higher frequencies as a means to address requests for always higher data rates, matching the evolution of the fast-growing terrestrial communication sector. In this section, we provide a review of the space segment, starting with a generic description of current satellite systems, including a particular focus on satellite constellations, followed by a discussion of antenna technologies currently used onboard advanced satellite systems and under development for future satellite systems.
## 4 Optimization Design Process and Results
### Optimization Algorithm  
The particle swarm optimization algorithm (PSO) [41,42] is a famous design optimization algorithm that has good global optimization performance. Figure 16 shows the basic flow of a modified PSO algorithm by the authors [43]: first, the position and velocity of particles in the initial particle swarm are obtained by initializing the particle swarm. After that, the nondominated rank of the population is sorted by particle fitness. Then, the crowding distance of each particle and niche count can be obtained. The better progeny populations could be obtained, and the population could be updated.  
Figure 14: Scatter plots of L/D versus x_flap (a): Scatter plots of L/D versus y_flap (b) Scatter plots of L/D versus angle_flap (c).  
Figure 15: Scatter plots of OASPL versus x_flap (a): Scatter plots of OASPL versus y_flap (b) Scatter plots of OASPL versus angle_flap (c).
## 5 Comparisons of the Control Performances
### Comparison with Other DVAs under Harmonic Excitation  
The mass ratio of each model takes \(\mu=0.1\). The inerter-mass ratio \(\beta=0.3\) is selected for the IN-Maxwell-type DVA and this paper's case 2c. The magnification ratio \(L=3.5\) is selected for the L-grounded-type DVA and this paper's case 1c. According to the optimal parameters formulas in Table 3, the normalized amplitude-frequency curves of each model under the optimal parameters are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Model of DVAs** & \(v_{opt}\) & \(\xi_{opt}\) & \(\alpha_{opt}\) \\ \hline Voigt-type [2] & \(\frac{1}{1+\beta}\) & \(\sqrt{\frac{3\mu}{8(1+\beta)}}\) & – \\ Grounded-type [4] & \(\frac{1}{\sqrt{1-\mu}}\) & \(\sqrt{\frac{3\mu}{8(1-0.5\mu)}}\) & – \\ IN-Maxwell-type [11] &  
The results show that the vibration reduction effect of proposed DVA is significantly better than the traditional Vogit-type and grounded-type DVAs under harmonic excitation. Compared with the IN-Maxwell-type and L-grounded-type DVAs, we can find that the lever and inerter have an effect of reducing the amplitude of the primary system and broadening the frequency band range. Moreover, the DVA control performance with positive grounded stiffness is better among these cases, especially when the stiffness coefficient of the viscoelastic Maxwell model and another grounded spring are positive simultaneously, while the performance drops slightly in the high frequency range. This performance degradation can be better explained by the change of natural frequencies, due to the addition of an inerter.  
Based on Equation (8), the mass ratio \(\mu=0.1\) and lever amplification ratio \(L=2\) are selected. The variation of two dimensionless natural frequencies with inerter-mass ratio \(\beta\) is shown in Figure 12. As the inerter-mass ratio increases, two natural frequencies move to the high frequency direction, which will lead to a slight decline in the vibration absorption performance of the proposed DVA with positive grounded stiffness in the high frequency direction.  
The harmonic force \(G(t)=F\text{cos}(\omega t)\) is applied to the primary system. The excitation amplitude is taken as \(F=1\) N, and the excitation frequency is taken as \(\omega=10.5\) rad/s. Figure 13 presents the normalized displacement response of the primary structure equipped with DVAs and without control. The displacement response of the primary structure  
Figure 11: Comparison of amplitude–frequency response curves with other DVAs: (**a**) Comparison of classical DVA with our results. (**b**) Comparison of IN-Maxwell-type, L-grounded-type DVA with our results.  
Figure 12: The variation of the two dimensionless natural frequencies with inerter–mass \(\beta\) under \(\mu=0.1\).  
without inerter or lever vibration absorbers exhibits obvious fluctuation. By using the proposed vibration absorber, the large vibration of the primary system is alleviated.
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 3 Simulation  
### Construct a Simulation Signal  
The collected gearbox signal appears in the form of a mixture of the modulation signal of the gear mesh and the impact signal of the bearing rotation, and the signal contains noise in the working environment. As a result, this paper establishes a mathematical model containing a mixture of impact  
Figure 3: Optimized CEEMD method flow chart.  
signal, modulation signal, and noise signal for signal simulation. The mathematical model established is as follows:  
\[\left\{\begin{array}{l}x_{1}(t)=A_{m}\times\exp\Bigl{(}-\frac{g}{f_{m}}\Bigr{)} \sin(2\pi f_{c}t)\\ x_{2}(t)=(1+\cos(2\pi f_{n1}t))\sin(2\pi f_{z}t)\\ \mathit{noise}(t)=0.5\mathit{randn}(t)\\ X(t)=x_{1}(t)+x_{2}(t)+\mathit{noise}(t)\end{array}\right. \tag{13}\]  
where \(A_{m}=3.0\), \(g=0.01\), \(T_{m}=1/13\), \(f_{c}=130\) Hz, \(f_{n1}=10\) Hz, and \(f_{z}=50\) Hz. \(\mathit{randn}(t)\) is random noise. The number of sampling points is 1024 and the sampling frequency is 2048 Hz. Figure 4 is the time domain diagram and frequency domain diagram of the simulation signal, where the frequency domain diagram is obtained by performing FFT transformation on the time domain signal.  
Among them, \(X1\) is the impact signal of bearing rotation, \(X2\) is the modulation signal of gear mesh, and \(X(t)\) is the mixed signal of impact signal, modulation signal, and random noise signal. It can be observed from the figure that the \(X(t)\) signal contains a lot of noise signals.
# Numerical Analysis on the Performance of a Heat Pump Powered by Surface Water Freezing
## 2 The Heat Pump System and Its Numerical Model
### Condenser Simulation Model  
In the condenser, the heating water flows outside the tubes and obtains heat for heating, and the heat transfer process of refrigerant along the tubes can be divided into the superheated zone, two-phase zone, and subcooled zone. The relationships between the heats released by refrigerant \(Q_{\text{c,c}}\), \(Q_{\text{r,tp}}\), \(Q_{\text{r,sc}}\) and the heats absorbed by the high-temperature heat source water \(Q_{\text{g,c}}\), \(Q_{\text{g,tp}}\), \(Q_{\text{g,sc}}\) are shown as Equations (6), (7), and (8), respectively [27], when the heat loss is neglected.  
\[Q_{\text{r,c}}=m_{\text{r}}(h_{\text{c,in}}-h_{\text{c,out}})=Q_{g,\text{c}}=m _{\text{g}}c_{\text{p}}(t_{\text{c,out}}-t_{\text{c,in}})=K_{\text{c}}A_{\text {co}}l_{\text{c}}\Delta t_{\text{m},c} \tag{6}\]  
\[Q_{\text{r,tp}}=m_{\text{r}}(h_{\text{c,out}}-h_{\text{sc,in}})=Q_{g,\text{tp} }=m_{\text{g}}c_{\text{p}}(t_{\text{c,in}}-t_{\text{sc,out}})=K_{\text{tp}}A_ {\text{co}}l_{\text{tp}}\Delta t_{\text{m},\text{tp}} \tag{7}\]  
\[Q_{\text{r,sc}}=m_{\text{r}}(h_{\text{sc,in}}-h_{\text{sc,out}})=Q_{g,\text{ sc}}=m_{\text{g}}c_{\text{p}}(t_{\text{sc,out}}-t_{\text{sc,in}})=K_{\text{sc}}A_ {\text{co}}l_{\text{sc}}\Delta t_{\text{m},\text{sc}} \tag{8}\]  
In the simulation, the tube inner diameter of the condenser is 24 mm and the tube outer diameter is 25 mm, the tube total length is 120 m, the tube spacing is 50 mm, and the inlet water flow rate is 3 kg/s. By inputting the condensation temperature, condensation pressure, compressor discharge temperature, refrigerant mass flow rate and subcooled degree, inlet water temperature of the two-phase region, and inlet water temperature of the subcooled region into the model, the simulation calculation of the condenser is carried out. The results include the heat exchange amount and heat exchange tube length of the superheated region, inlet water temperature, heat exchange amount and heat exchange tube length of the subcooled region, and outlet water temperature.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
###### Abstract  
The communication module is crucial for control systems. Under thermal, electrical, and mechanical stresses, sensitive digital and analog components may degrade in performance or fail, compromising the module's long-term stability. Existing reliability-prediction methods, however, do not fully leverage multi-physics simulations to model stress-induced failure modes, leading to limited confidence in their predictions. This article proposes a systematic method to enhance the reliability prediction of mixed-signal electronic systems under complex operating conditions. First, we identify the key Complementary Metal-Oxide-Semiconductor (CMOS) chips and their associated failure mechanisms. Then, we use an I/O Buffer Information Specification-based (IBIS-based) topology to build a hybrid-precision simulation that models electrical stress. A Verilog-SPICE framework is employed to simulate component degradation and failure modes. Subsequently, by including mission profiles, we perform a simplified multi-physics coupling analysis to evaluate the effects of temperature and mechanical stress on failure mechanisms. Finally, the Physics of Failure models of components are integrated to derive the reliability curve of the module, and targeted optimization strategies are proposed. Compared to conventional methods, the method combines hybrid-precision simulation with multi-physics coupling modeling, improving the accuracy of critical failure modes. This method enhances the quantitative product reliability analysis and provides valuable support for optimized design.  
reliability prediction; failure mechanism; multi-physics; Physics of Failure; mixed-signal Article
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 7 Related Work
While FHE schemes remain impractical currently, they, however, offer interesting constructions, such as reusable garbled circuits [33] and verifiable computing capabilities [34]. In our proposed system, we enable the client to efficiently verify whether a cloud server has actually evaluated the garbled circuit, without depending on any FHE scheme. [PERSON] _et al._[35] present an implementation of a \"somewhat\" homomorphic encryption scheme, which is capable of computing simple statistics such as mean, standard deviation and regression, which require only addition operations, and a very few multiplication operations. Another recent work (P4P [36]) provides privacy preservation for computations involving summations only. In comparison, our scheme, which is based on [PERSON]'s garbled circuits, is not limited to computation of simple statistics or summations only; _i.e._, our scheme is more generic, and can perform any arbitrary computation. [PERSON] _et al._[37] have proposed a secure two party computation system, which however has three participants: [PERSON], [PERSON] and a Proxy. In their work, [PERSON] is a mobile device that delegates the task of evaluating the garbled circuits to the Proxy, which is a cloud server, and [PERSON] is a webserver that creates garbled circuits. We note that our computation and adversary models are very different from that of [PERSON] _et al._'s work [37]. First, in their work, both [PERSON] and [PERSON] provide private inputs for the secure two party computation that they wish to perform jointly; however, in our work, only one party, i.e., the mobile client, provides inputs and obtains the result of the computation in garbled form. Second, [PERSON] _et al._'s [37] scheme requires that neither [PERSON] nor [PERSON] can collude with the Proxy; in a sharp contrast, even if the evaluating server colludes with all but one of the cloud servers that participated in the creation of the garbled circuit, our method can preserve the privacy of the client's data.
## 5 Experiment and Analysis
### Robustness and Generalizability  
The method proposed in this study could effectively screen the correct segmentation points and accurately identify the tangent points and inflection points. In particular, for the parts with different placement angles that had the same detection effect, the detection method had a certain degree of robustness. The experimental results are shown in Figure 23.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & \begin{tabular}{c} **Average** \\ **Radius (Pixel)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Length (Pixel)** \\ \end{tabular} & \begin{tabular}{c} **Design Tilt** \\ **(°)** \\ \end{tabular} & \
\begin{tabular}{c} **Clamping Angle** \\ **(°)** \\ \end{tabular} &
\begin{tabular}{c} **Fitted** \\ **Value(Pixel/°)** \\ \end{tabular} & **Relative Error** \\ \hline \(l_{1}\) & 364.896 & & & 362.35 & 0.703\% \\ \(l_{2}\) & & & 10 & 9.91 & 0.90\% \\ \(l_{3}\) & 91.224 & & & 92.48 & 1.35\% \\ \(l_{4}\) & & 100 & & 100.61 & 0.61\% \\ \(l_{4}\) & & 181.5925 & & & 190.24 & 4.54\% \\ \(l_{5}\) & & 33.845 & & & 32.31 & 4.75\% \\ \(l_{6}\) & & 33.845 & & & 35.38 & 4.33\% \\ \(l_{7}\) & & & 10 & & 10.04 & 0.4\% \\ \(l_{7}\) & & 181.5925 & & & 182.01 & 0.22\% \\ \(l_{8}\) & 91.224 & & & & 92.38 & 1.25\% \\ \(l_{9}\) & 91.224 & & & & 91.66 & 0.47\% \\ \(l_{10}\) & & & 100 & & 99.57 & 0.43\% \\ \(l_{10}\) & & 363.185 & & & 354.12 & 2.55\% \\ \(l_{11}\) & 91.224 & & & & 92.26 & 1.12\% \\ \(l_{12}\) & 182.448 & & & & 181.11 & 0.73\% \\ \(l_{13}\) & & & 10 & & 10.13 & 1.3\% \\ \(l_{13}\) & & 281.125 & & & 284.42 & 1.15\% \\ \(l_{14}\) & & & 281.125 & & & 277.83 & 1.18\% \\ \(l_{14}\) & & & & 165 & 165.22 & 0.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relative errors of fitting results.  
Figure 22: The effect of interpolation processing.  
The effects of contour defects on the segmentation points could be effectively removed by adjusting the fitting tolerance threshold (\(T\)) when NMS algorithm selection was adopted. Rectangular parts with more severe boundary contour defects were used to verify the noise immunity capability of this detection method. The results are shown in Figure 24.  
In this study, we processed the following two parts using a laser cutting machine (Figures 25 and 26). After image preprocessing, front and back chord tilt complementation, NMS algorithm selection, and fitting with constraints, the detection effect of the proposed method is shown in Table 6. Four sizes were selected for comparison.  
Figure 23: Segmentation points selected from different placement angles.  
Figure 24: Noise suppression effect of NMS segmentation point selection.  
As can be seen in Table 6, there was no significant difference between the visual inspection results in this study and the value of part size in practice, and the relative error was less than 0.6%.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Proposed Algorithmic Process  
The following presents the algorithmic procedure of Algorithm 1.  
Figure 7: Proposed beam hopping working procedure.  
```
1:Initialization:\(\mathcal{A}=\emptyset\), \(\Phi_{1}=\Phi_{2}=\ldots=\Phi_{k}=\ldots=\Phi_{K}=\emptyset\), \(\mathcal{R}=\emptyset\), \(\phi_{1},\phi_{2},\ldots,\phi_{K}\)
2:Input:\(Traffic^{t}_{beam,i}\), \(K=4\)
3:Step 1: Choose non-hotspot cells to form \(\mathcal{A}\)
4:for\(Traffic^{t}_{large,i}=1,2,\ldots,M\)do
5:if\(Traffic^{t}_{large,i}\leq Traffic^{t}_{threshold}\)then
6: Add \(cell_{i}\) into \(\mathcal{A}\)
7:Step 2: Partition non-hotspot cells into K clusters by \(\phi_{k}\), \(\forall k=1,2,\ldots,K\)
8:for each element \(e_{i}\) in \(\mathcal{A}\)do
9:if\(e_{i}\in\phi_{k}\), \(\forall k=1,2,\ldots,K\)then
10: Add \(e_{i}\) into the \(\Phi_{k}\)
11: Assign K consecutive time slots to serve the above K clusters, with one time slot for each cluster.
12:repeat
13:if there are speared beams then
14:Step 3: Choose medium and small cells to the clusters served in current time slots
15:for\(Traffic^{t}_{middle,i}=1,2,\ldots,N\)do
16:if\(cell_{i}\) has no interference with chosen cells and \(Traffic^{t}_{middle,i}>\) threshold then
17:\(cell_{i}\) will be served as a medium cell in current time slot.
18:if there are speared beams then
19:for\(Traffic^{t}_{small,i}=1,2,\ldots,N\)do
20:if\(cell_{i}\) has no interference with chosen cells then
21:\(cell_{i}\) will be served as a small cell in current time slot.
22:until end of simulation
```  
**Algorithm 1** This algorithm takes the virtual traffic of each cell as input and generates a BHTP based on the templates prepared during the initialization phase
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 4 Charging Type
### Wireless Charging: Charging Type  
Wireless charging is a plausible solution with which to tackle the security and privacy issues of the conductive charging of EVs [79, 80], as it does not involve any physical connection for EV charging. It utilizes electromagnetic fields to charge EVs wirelessly through the battery without the need to establish any connection cable. Inductive coupling or inductive power transfer is the main principle that facilitates the transfer of energy to EVs without any cable connection [22, 77]. For example, [PERSON] et al. [81] discussed wireless charging in relation to the pickup system that is embedded within the EV, which accumulates energy transferred by the power supply system wirelessly installed at the CS. These wireless charging types are as follows:  
* Stationary wireless charging After conductive charging, stationary wireless charging is one of the technologies that facilitate the charging of EVs at a static location. It signifies that EVs can only be charged in public parking spaces or garages. It means a community of multiple residents can also adopt stationary wireless charging infrastructure to provide static charging. However, EVs are not operable during this period, as the stationary wireless charging infrastructure does not facilitate this. Many National Laboratories have worked on stationary wireless charging technology to provide commuter satisfaction. For example, Oak Ridge National Laboratory collaborated with Toyota Research Institute and Hyundai American Technical Center to develop a prototype for stationary wireless charging systems [82]. Stationary wireless charging is beneficial for users who want to charge their EVs in a safe and convenient environment. There is no physical connection between EVs and the power grid for charging. Despite these advantages, stationary wireless charging does not provide an efficient solution for EV charging in terms of CS scheduling, efficiency, charging time, power supply, etc. [83]. To tackle the aforementioned issues of stationary wireless EV charging, many researchers have conducted experimental studies to provide a stationary wireless charger for EVs. Some of the research works are as follows: the authors in [84] proposed an optimized procedure for a stationary wireless EV charger equipped with an LCC-series resonant network. The simulation analysis achieved a 94.8% efficiency with low power loss due to the incorporated series of resonant networks. Then, [PERSON] et al. [85] designed a 200 kW stationary wireless charger for Light-Duty EV to ensure a safer environment for electromagnetic emission by handling the misalignment issues. The simulation performance reduced field emissions by 26.8%. Similar to the authors in [84], [PERSON] et al. [86] also proposed high efficiency wireless charging for stationary
## 3 Results and Discussion
### Wake Flows
In Figure 18a, it can be observed that the differences in vortex core height \(h\) computed by different models are small, while height \(h_{0}\) of vortex \(V_{c1}\)\({}^{\prime}\) and distance \(w\) between the two vortex cores are significantly different. The height \(h\) obtained with the SST\(-\)IDDES model is 10% greater than that obtained with the SA\(-\)IDDES model, and the shortest and longest distances \(w\) of the two vortex cores are obtained with the SST\(-\)IDDES and SA\(-\)IDDES, respectively, with a difference of 10.3%. Figure 16: Iso-surface of Q (Q = 20,000 s\({}^{-2}\)), coloured according to the resultant velocity \(U\), under different turbulence model methods at (**a**) \(t_{1}\) and (**b**) \(t_{3}\). Figure 17b presents the streamlines at position \(t_{3}\). As the distance from the tail car increases, the vortex size increases and the interaction between vortices \(V_{c1}\) and \(V_{c1}\)\({}^{\prime}\) becomes stronger, leading to a greater change in their shape. The height and width of the vortices are found to be significantly larger at position \(t_{3}\) compared to position \(t_{2}\). Vortices \(V_{c2}\) and \(V_{c2}\)\({}^{\prime}\) on the non-intersection side with the different turbulence models are found to be rather similar. Nevertheless, the \(h_{0}\), \(h\), and \(w\) values are significantly different, as illustrated in Figure 18b. The highest and lowest values of \(h_{0}\) were obtained with SST k-\(\omega\) and SA-IDDES, respectively, with a difference of 11.8%, and the highest and lowest values of \(h\) were obtained with SST-IDDES and SA-IDDES, respectively, with a difference of 17%. Furthermore, the highest and lowest values of \(w\) were obtained with SST-IDDES and SST k-\(\omega\), respectively, with a difference of 30.9%.
## 5 Results
### User Feedback  
User feedback (detailed in the appendix; see Table A1) provides a deeper understanding of the system's strengths and weaknesses and highlights key areas for improvement. Four main themes are identified: Graphics and Immersion, Interaction and Customization, Navigation and Controls, and Functional Suggestions.  
* **Graphics and Immersion**: Render quality and immersiveness were among the most appreciated aspects, with users emphasizing the high level of detail contributing to the experience. One participant remarked, _\"The graphics exceeded my expectations... it is an excellent way to customize and imagine your boat before purchasing it\"_ Many users felt they were truly aboard the yacht, thanks to the high-quality visuals of objects and materials. One participant noted, _\"The integrated details were so well done, it felt like I  
Figure 10: Distribution of individual SUS scores, indicating the mean SUS (orange line), the “Acceptable threshold” of 68 (blue line), and the range covered by the scores (min = 52.2, max = 100).  
was actually inside a yacht...\"_. However, some users suggested improving material reflections and expanding the environment to make it more engaging (e.g., adding a nearby island).
* **Interaction and Customization**: The ability to interact with, customize, and move objects in real time was widely praised for being intuitive, making the experience practical and independent. Another participant stated, _\"1 particularly liked interacting with the objects and making changes on my own, without needing someone else's help.\"_ However, users desired a wider variety of materials, colors, and textures with more realistic and practical options.
* **Navigation and Controls**: Although the controls were generally intuitive, seven out of thirty participants expressed a desire for greater freedom of movement and alternatives to teleportation, which was often inaccurate or challenging in tight spaces. As reported by one user, _\"...1 was disappointed by the teleportation method which occasionally failed to recognize the correct floor to move on.\"_ Additionally, selecting smaller objects remotely with ray casting proved to be challenging. Furthermore, some right-handed users found the placement of the configuration controls on the left controller uncomfortable.
* **Functional Suggestions**: Other suggestions for improvement included a more precise locomotion system, clearer training, and haptic feedback to indicate an avatar-object collision. Two out of thirty participants also reported physical fatigue and dizziness caused by the headset weight and occasional lag.