esa-sceva/satcom-chunk-collection huggingface dataset:
# Focus-Adjustable Head Mounted Display with Off-Axis System
## 2 Materials and Methods
If the object is at infinity, the image is formed at the focal point of the lens. If the object moves from infinity to a finite distance, the image is shifted by \(\Delta f\) in the direction of the optical axis. Therefore, it can be observed that the object distance changes with the image distance. Figure 4: The rotated transformed optical layout such that the axial rays of the off-axis optical system are straight. (**a**) depicts the original optical system. (**b**) demonstrates that the entire optical system is rotated so that the axial ray on the optical axis refracted from the first surface becomes a straight line. (**c**) indicates the result of similarly rotating the axial ray on the optical axis to a straight line from the second surface to the last surface. Figure 5: Image formation due to change in object distance. Equation (6) is a Gaussian imaging equation, when the image and the object are in the air. Here, \(l\) is the object distance, i.e., the distance from the object to the first principal point; \(l^{\prime}\) is image distance, i.e., the distance from the second principal to the image; and f is the focal length of the optical system [25; 41; 42]. Therefore, \(l\) and \(l^{\prime}\) are positive. \[\frac{1}{l}+\frac{1}{l^{\prime}}=\frac{1}{f} \tag{6}\]  
The movement of the image surface (\(\Delta f\)) can be derived from Equation (6); this is expressed by Equation (7) [25]:  
\[\Delta f=\frac{f^{2}}{l-f} \tag{7}\]  
Figure 6 reveals that the focus is adjusted by shifting the micro-display with respect to the optical system presented in Figure 1b. The figure also indicates that the object distance of the optical system is infinite at 0 D, and the axial rays (red) toward the object are collimated. Figure 6a displays the optical layout when the object distance is changed from infinity to 200 mm, which is a negative diopter. Figure 6b exhibits an optical layout when the object distance is +200 mm, a positive diopter [13]. In modern life, the images of many people are captured in front of the retina and their vision is corrected with a meniscus lens with negative refractive power [43; 44]. Therefore, products that are mounted on the eyes, such as HMDs, are designed with a negative average diopter value for an afocal system. \[EFL=-\frac{h_{0}}{u_{i}}=-\frac{n_{i}}{C},\hskip 5.690551 ptBFL=-\frac{h_{i}}{u_{i} }=-\frac{A\cdot n_{i}}{C} \tag{2}\]0 \(Cont\).
## 2 Theoretical Aspects of Lamb Waves
### Basic Equations of Lamb Waves  
For the functionally graded material plate shown in Figure 2, the propagation direction of the Lamb plate is along the positive x-axis, and the z-axis is along the thickness direction of the plate, with a plate thickness of h. The material parameters vary continuously in the thickness direction, Where \(c_{11}-c_{13}=2c_{44}\), the mass density \(\rho\) and elastic coefficients \(c_{11}\), \(c_{13}\), \(c_{44}\) are functions of the coordinate z.  
In this paper, the Lamb wave characteristics are described using the Rayleigh-Lamb wave equation [35]:  
\[\frac{\tan(qd)}{\tan(pd)}=-\frac{4k^{2}qp}{\left(q^{2}-k^{2}\right)^{2}} \tag{1}\]  
\[\frac{\tan(qd)}{\tan(pd)}=-\frac{\left(q^{2}-k^{2}\right)^{2}}{4k^{2}qp} \tag{2}\]  
In the equation, \(p^{2}=\omega^{2}/c_{\mathrm{L}}^{2}-k^{2},q^{2}=\omega^{2}/c_{\mathrm{T}}^{2}- k^{2},\omega\) represents the angular frequency, \(k=\omega/c_{\mathrm{P}}\) is the wave number where \(c_{\mathrm{P}}\) is the phase velocity of the plate. \(c_{\mathrm{L}}\) and \(c_{\mathrm{T}}\) are the longitudinal and transverse wave velocities, respectively, and \(2d\) is the thickness of the structure. Due to the nonlinear relationship between \(k\) and \(\omega\), \(c_{\mathrm{P}}\) and \(\omega\) are interconnected, leading to the occurrence of dispersion phenomena. This means that the propagation speed of different Lamb wave modes is correlated with their frequency. In practical scenarios, these modes propagate spatially with a group velocity \(c_{\mathrm{g}}\).
## 4 Conclusions  
In this paper, we propose a novel reversible data hiding scheme in encrypted 3D mesh models in cloud applications. Two efficient techniques, MGP and CPP, are proposed to improve the embedding rates. After preprocessing, the content owner first gathers vertices into groups and classifies them into reference vertices and embeddable vertices using MGP. Then, the CPP technique is used to predict embeddable vertices by the connected reference vertices. As more vertices are embeddable and the embeddable vertices can be accurately predicted, more spare room can be vacated for additional data embedding on the cloud server side. On the authorized third-party side, the embedded data can be totally extracted and the original 3D mesh model can be recovered exactly according to different keys, separately. Experimental results demonstrate that the proposed scheme achieves the best embedding rate and prominent improvement compared to state-of-the-art schemes. In the future, we will explore the efficient compression of vertices using deep learning techniques to further improve performance.  
**Author Contributions:** Methodology, X.W.; Software, X.W. and J.-C.L.; Validation, J.-C.L.; Formal analysis, J.-C.L.; Writing--original draft, X.W.; Writing--review & editing, X.W. and C.-C.C. ([PERSON]); Supervision, C.-C.C. ([PERSON]) and C.-C.C. ([PERSON]); Project administration, C.-C.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data can be shared up on request.  
**Conflicts of Interest:** The authors declared that they have no conflicts of interest with this work.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.3 Generative Capabilities for Data Augmentation  
DDPM's generative nature makes it well-suited for data augmentation in biomedical image segmentation tasks. The model can generate synthetic images that closely resemble real data, helping to address challenges associated with limited annotated datasets. Data augmentation enhances the diversity of training data, leading to more robust segmentation models [42; 44].
## 3 Results and Discussion
### Drying Behavior Analysis of Kurut Samples
The two-term model provided the best-fit for the K45 sample, and the relationship between the drying time of the samples and the moisture ratio is given in Figure 6c, and the fit-agreement between the experimental moisture ratio and the predicted moisture ratio is given in Figure 6d. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multirow{2}{*}{**Daying Technique**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy and**} \\  & & & **Parameter** & **Goodness-of-Fit and Measures** \\ \cline{3-5}  & & & **R\({}^{2}\)** & **RMSE** & \(\mathbf{x}^{2}\) \\ \hline Newton & KV35 & \(k=5.32\times 10^{-2}\) & 0.9689 & \(1.70\times 10^{-2}\) & \(3.63\times 10^{-4}\) \\ \(MR=exp(-kt)\) & KV45 & \(k=2.11\times 10^{-2}\) & 0.7615 & \(3.85\times 10^{-2}\) & \(1.85\times 10^{-3}\) \\ \(63\) & K35 & \(k=2.31\times 10^{-1}\) & 0.9718 & \(1.02\times 10^{-2}\) & \(1.30\times 10^{-4}\) \\  & K45 & \(k=3.05\times 10^{-1}\) & 0.9618 & \(1.71\times 10^{-2}\) & \(3.65\times 10^{-4}\) \\ \hline Page & KV35 & \(k=1.40\times 10^{-1}\), \(n=6.98\times 10^{-1}\) & 0.9945 & \(1.20\times 10^{-2}\) & \(2.41\times 10^{-6}\) \\ \(MR=exp(-kt^{a})\) & KV45 & \(k=2.45\times 10^{-5}\), \(n=2.88\times 10^{-1}\) & 0.9694 & \(2.86\times 10^{-2}\) & \(1.36\times 10^{-3}\) \\ \(64\) & K35 & \(k=1.73\times 10^{-1}\), \(n=1.20\times 10^{-1}\) & 0.9785 & \(1.37\times 10^{-2}\) & \(3.15\times 10^{-4}\) \\  & K45 & \(k=5.30\times 10^{-1}\), \(n=5.95\times 10^{-1}\) & 0.9987 & \(1.02\times 10^{-3}\) & \(1.74\times 10^{-6}\) \\ \hline Modified Page & KV35 & \(k=5.32\times 10^{-2}\), \(n=1.00\times 10^{1}\) & 0.9892 & \(1.70\times 10^{-2}\) & \(4.84\times 10^{-4}\) \\ \(MR=exp(-(kt)^{a})\) & KV45 & \(k=2.11\times 10^{-2}\), \(n=1.00\times 10^{1}\) & 0.7615 & \(3.85\times 10^{-2}\) & \(2.48\times 10^{-3}\) \\ \(65\) & K35 & \(k=1.03\times 10^{-1}\), \(n=2.88\times 10^{-1}\) & 0.9732 & \(9.63\times 10^{-3}\) & \(1.55\times 10^{-4}\) \\  & K45 & \(k=3.05\times 10^{-1}\), \(n=1.00\times 10^{1}\) & 0.9618 & \(1.71\times 10^{-1}\) & \(4.87\times 10^{-4}\) \\ \hline Henderson and Pabis & KV35 & \(a=9.88\times 10^{-1}\),\(k=5.26\times 10^{-2}\) & 0.9632 & \(1.63\times 10^{-2}\) & \(4.41\times 10^{-4}\) \\ \(MR=exp(-kt)\) & KV45 & \(a=1.10\times 10^{-1}\), \(k=2.40\times 10^{-2}\) & 0.7863 & \(2.34\times 10^{-2}\) & \(9.15\times 10^{-4}\) \\ \(66\) & K35 & \(a=1.03\times 10^{-1}\), \(k=2.83\times 10^{-1}\) & 0.9732 & \(9.63\times 10^{-3}\) & \(1.55\times 10^{-4}\) \\  & K45 & \(a=9.66\times 10^{-1}\), \(k=2.94\times 10^{-1}\) & 0.9644 & \(1.39\times 10^{-2}\) & \(3.21\times 10^{-4}\) \\ \hline
**Wang and Singh** & KV35 & \(a=-4.50\times 10^{-2}\), \(n=6.00\times 10^{-4}\) & 0.9856 & \(6.07\times 10^{-3}\) & \(6.14\times 10^{-5}\) \\ \(MR=1+at+bt^{2}\) & KV45 & \(a=-1.73\times 10^{-5}\), \(n=3.78\times 10^{-4}\) & **0.9924** & \(5.24\times 10^{-3}\) & \(\mathbf{4
## 3 The Development of Detection Technology
### Advances in Flow Injection Analysis Combined Method  
Flow injection analysis is an automated analytical method developed in recent years and has been widely used in many fields. This method mainly automates sampling, reagent addition, reaction determination, and other experimental steps [80; 81]. Flow injection technology is generally combined with the electrode method, turbidimetric method, photometric method, and other technologies, which greatly increases the speed and convenience of Cl\({}^{-}\) detection.  
[PERSON] et al. [82] combined the flow injection method and the electrode method to determine Cl\({}^{-}\) in milk. [PERSON] et al. [83] combined the flow injection technology with mercury thiocyanate spectrophotometry for the detection of trace Cl\({}^{-}\) in industrial water. When the concentration flow of water sample was 1 mL/min and the concentration time was 20 min, the detected Cl\({}^{-}\) concentration had a good linear relationship within the range of 1.0-50.0 ug/L. [PERSON] et al. [84] combined the flow injection method with silver nitrate turbidimetry for the detection of sodium chloride content in food. Polyvinyl alcohol and gelatin were used as silver chloride precipitation protective agents, and the detection range was 50-600 mg/L. Flow injection analysis technology can realize remote monitoring and cleaning, and is one of the development directions of continuous Cl\({}^{-}\) monitoring in the future.
## 2 Modeling of The Compliant Buoy-Ballast Sensing System
### Dynamics Analysis through Differential Equations  
Taking into account small angles, i.e., tan \(\theta_{i}\approx\sin\theta_{i}\approx\theta_{i}\), the dynamics equations of motion of the buoys in the left-right horizontal direction are  
\[m_{buoy}\frac{dv_{B1}}{dt} = -F_{d}^{buoy,1}+T_{1}+W_{buoy}.\theta_{1}-\frac{C_{s}}{\ell} \Big{[}2(\frac{\pi}{2}-\theta_{1})+(\frac{\pi}{2}+\theta_{2})\Big{]} \tag{18}\] \[m_{buoy}\frac{dv_{B2}}{dt} = -F_{d}^{buoy,2}+T_{2}+W_{buoy}.\theta_{2}+\frac{C_{s}}{\ell} \Big{[}2(\frac{\pi}{2}+\theta_{2})+(\frac{\pi}{2}-\theta_{1})\Big{]} \tag{19}\]  
Then, distance \(d\) is expressed as  
\[\frac{d(d)}{dt} = \frac{d(r_{B_{1}B_{2}})}{dt}=v_{B2}-v_{B1} \tag{20}\]  
The subtraction of Equation (19) from (18) yields  
\[m_{buoy}\frac{d^{2}(d)}{dt^{2}} = F_{d}^{buoy\,1}-F_{d}^{buoy,2}+W_{buoy}(\theta_{2}-\theta_{1}) +T_{12}+3.\frac{C_{s}}{\ell}(\theta_{2}-\theta_{1}+\pi) \tag{21}\]  
with \(T_{12}=T_{2}-T_{1}\). Since \(d\approx\ell(\theta_{1}-\theta_{2})\),  
\[m_{buoy}\frac{d^{2}(d)}{dt^{2}} = F_{d}^{buoy,1}-F_{d}^{buoy,2}-\left(W_{buoy}+3\frac{C_{s}}{\ell }\right)\cdot\frac{d}{\ell}+T_{12}+3\pi.\frac{C_{s}}{\ell} \tag{22}\]  
In addition,  
\[F_{d}^{buoy,1}-F_{d}^{buoy,2} = D.(v_{B1}-v_{B2})=-D.\frac{d(d)}{dt} \tag{23}\]  
where\(D=k.\mu\) for a laminar water flow.
* \(D=D(v_{B1},v_{b2})=\frac{1}{2}.C_{D}.A.\rho_{w}.(v_{B1}+v_{B2})\) for a turbulent water flow. Noting that \(E=W_{buoy}+3\frac{C_{s}}{\ell}\), the differential equation relative to \(d\) is the \[m_{buoy}\frac{d^{2}(d)}{dt^{2}}+D.\frac{d(d)}{dt}+\frac{E}{\ell}.d = T_{12}+3\pi.\frac{C_{s}}{\ell}\] (24)  
The undamped pulsation is \(\omega_{0}=\sqrt{\frac{E}{\Gamma m_{buoy}}}\). The following transfer function of the linearized system can be written as  
\[H(j\omega) = \frac{d}{T_{12}+3\pi.\frac{C_{s}}{\ell}}=\frac{\ell/E}{1+(j\omega )\frac{D,\ell}{E}+(j\omega)^{2}.\frac{\ell.m_{buoy}}{E}} \tag{25}\]  
In the case of a laminar flow, the buoy-ballast system can be seen as a second-order low-pass filter for small angles with the damping coefficient \(\xi=\frac{1}{2}.k.\mu.\sqrt{\frac{\ell}{E.m_{buoy}}}\).  
In the case of a turbulent flow, if we consider the sum of velocities \((v_{B1}+v_{B2})\) to be above a certain threshold and to vary around an average velocity \(v_{s}\), then the buoy-ballast system can be seen as a second-order low-pass filter for small angles, with the damping coefficient \(\xi=\frac{1}{2}.\frac{1}{2}.C_{D}.A.\rho_{w}.v_{s}.\sqrt{\frac{\ell}{E.m_{buoy }}}\). The setting of the damping coefficient to a few units above 1, namely \(\xi_{threshold}\), helps with adjusting parameters \(\ell\) and \(V_{buoy}\) in minimizing \(v_{s}\) as much as possible, taking into account the other constraints (value of \(d_{rest}\) and \(\ell\) not too high with regard to the size of the ROV):  
\[\xi > \xi_{threshold} \tag{26}\] \[v_{s} > \frac{4}{C_{D}.A.\rho_{w}}.\sqrt{\frac{E.m_{buoy}}{\ell}}.\xi_{ threshold} \tag{27}\]
# Multimode Guided Wave Detection for Various Composite Damage Types
## 4 Detection of Various Damage Types Using ABT-ABT on a 3-mm Quasi-Isotropic Composite Plate
### Pure Mode Generation  
The tuning angle of the 3-mm quasi-isotropic CFRP composite plate was calculated using the SAFE method for pure-mode guided wave excitation.  
Phase-velocity dispersion curves of the 3-mm quasi-isotropic CFRP composite plate in the 0\({}^{\circ}\) direction are shown in Figure 17a. The ABT tuning angle was calculated with Equation (1), as shown in Figure 17b. At 500 kHz, SH0 mode could be excited, and the tuning angle was around 48\({}^{\circ}\). However, the S0 mode could not be generated at 500 kHz, since its phase velocity was smaller than the wedge velocity of 2720 m/s, as shown in Figure 17a. Therefore, the excitation frequency had to be reduced in order to generate pure S0 mode in the 3-mm quasi-isotropic composite plate. In this study, the excitation frequency of the pure S0 mode was set to 300 kHz, and it was found that S0 mode was nondispersive at this frequency. In addition, a broadband angle beam transducer with a central frequency of 500 kHz was used in the experiment, and the 300-kHz excitation frequency of the S0 mode was close to the central frequency. At 300 kHz, the tuning angle of the S0 mode was about 26\({}^{\circ}\). Therefore, the incident angles of the ABT-ABT were set to 26\({}^{\circ}\) and 48\({}^{\circ}\), respectively, to excite pure S0 mode at 300 kHz and SH0 mode at 500 kHz in the 3-mm quasi-isotropic CFRP composite plate.  
Figure 16: Ultrasonic C-scan results: (a) the delamination simulated by the Teflon insert; (b) impact damage.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 3 Mathematical Formulation
### Electrolyzer Unit  
The function of electrolyzer, is decomposition of water into hydrogen and oxygen by a simple electrolysis process. In this way, the direct current of electricity passes through the path between two electrodes that immersed in water, causing water to decompose into oxygen and hydrogen. Oxygen is produced in the cathode and hydrogen at the anode side. In the proposed model, this paper is used to reduce the energy consumption of a compressor-less design, due to the usage of a polymer electrolyte membrane (PEM) fuel cell. In view of the fact that in this type of pressure fluid, the pressure required by hydrogen is 1.2 bar, the developed is very flexible and can be easily added to the compressor model. The electrochemical interactions in the electrolysis apparatus of water are  
\[\mathrm{H_{2}O}\rightarrow\frac{1}{2}\mathrm{O_{2}}+2\mathrm{H^{+}}+2\mathrm{ e^{-}} \tag{4}\]  
To model the electrolyzer, its efficiency is used as an input parameter. The thermal value of hydrogen is 3.4 kV per cubic meter, which, taking into account the efficiency of 90% for an electrolyzer to produce a kilogram of hydrogen, consumes energy at 41.99 kWh/kg as (5).  
\[Consumed\_Power=\frac{\frac{3.4\mathrm{(kwh/m^{3})}}{0.09\mathrm{(kg/m^{3})}} }{90}\times 100=41.97\mathrm{kwh/kg} \tag{5}\]  
The hydrogen weight produced by the excess of energy produced from the system to the electrolyzeris obtained at 41.97 kWh/kg.  
\[Generated\_H_{2}=\frac{P_{\mathrm{ electrolyzer}}(\mathrm{kwh})}{41.97\mathrm{(kwh/kg)}} \tag{6}\]
## 3 Targeting Bacterial Virulence
### Phagosomal Regulation/Hindering Intracellular Survival  
#### 3.3.1 MptpB Inhibitors  
The _M. tuberculosis_ protein-tyrosine-phosphatase B (MptpB) is another putative target for anti-virulence compounds (Figure 1). This kinase is secreted into the cytoplasm of host macrophages allowing for inhibition outside the thick and difficult to overcome mycobacterial cell wall [111]. The function of MptpB is not fully described yet, but the protein has been reported to be necessary for bacterial survival in guinea pigs [112]. So far, it was shown that MptpB dephosphoryllytes host phosphotyrosine substrates, phosphoserine/threonine substrates and phosphoinosititides, with the latter being essential for host macrophage maturation [113].  
Several isoxazole-based molecules were created to block the primary and secondary phosphate-binding pockets of MptpB followed by phenotypic testing for activity. In these ex vivo assays, the compounds led to a reduction of mycobacterial burden in macrophages (J774 and THP-1) and in a guinea pig model, without affecting extracellular growth in broth. Additionally, attenuated growth of MDR strains of _M. tuberculosis_ in the presence of compound 13 was shown in macrophages. In addition, this inhibitor caused increased sensitivity of a BCG strain to rifampicin and isoniazid in an ex vivo macrophage infection experiment [114]. A similar effect has not been published for _M. tuberculosis_ yet.
## 4 Discussion
Track spacing calculations shown in Equation (3) were used to estimate the track spacing during these experiments, as well as the dose required to induce DPP dependence in the MOS_kin_. The calculations provided in this study consider the dose delivered within asingle bunch, as it is the shortest structure of the PEER beam, at 100 ps in length. However, the initial recombination is said to occur over timescales as low as 10 ps or less. The assumption is made that all of the charge is delivered in the transverse plane to facilitate these calculations. At 100 ps, of course this cannot be true in reality. This leads to the suggestion that the MOS_kin_ can actually experience a higher DPB than the estimated 133 Gy while retaining DPP independence. However, the calculations produce an average track spacing, by considering a homogeneous distribution of incident tracks and infinite dose rate. The track structure could be investigated further with the use of Monte Carlo simulations, but the possibility that a track directly follows the existing path of another cannot be completely ignored. Hole transport in MOS devices is in the order of seconds, so it could be assumed that delivering large total doses over extremely small timescales may actually reduce this phenomena, compared to clinical deliveries occurring over minutes. While the Geminate model assumes that sufficient separation renders e-h pairs isolated, of course, some charge overlap may occur prior to the required separation of 16 nm used in the calculations; these values are calculated based upon assumptions and simplifications that are unlikely to truly represent the physical system. To increase the dose high enough to test these predictions will require ultra-short bunches and likely the use of focused beams to achieve the orders-of-magnitude-higher dose required. Table 1 shows the typical parameters defining the pulse structure of Varian Truebeam accelerators as well as PEER. It is clearly evident that these are vastly different irradiation fields. This, as well as the large changes in energy between clinical and VHEE regimes, has created the challenge for dosimetry and diagnostics within UHDR VHEE investigations. Many advances have been made toward devices capable of characterising these beams, with great success. However, no existing device suitable for UHDR VHEE dosimetry is also utilised clinically. The MOS_kin_ has proven to be the first device capable of providing accurate measurements for quality assurance within both clinical X-ray/electron, and UHDR VHEE environments, with no changes to either the detector or the readout system. This is a substantial leap toward meeting the critical requirements for eventual pre-clinical, and clinical trials of VHEE radiotherapy.
###### Abstract  
This study addresses the persistent issue of urban waterlogging in Wujin District, Changzhou City, Jiangsu Province, using a comprehensive approach integrating an optimized drainage network and low-impact development (LID) measures. Utilizing the Storm Water Management Model (SWMM), calibrated with extensive hydrological and hydraulic data, the model was refined through genetic algorithm-based optimization to enhance drainage efficiency. Key results indicate a substantial reduction in the average duration of waterlogging from 7.43 h to 3.12 h and a decrease in average floodwater depth from 21.27 cm to 8.65 cm. Improvements in the drainage network layout, such as the construction of new stormwater mains, branch drains, and rainwater storage facilities, combined with LID interventions like permeable pavements and rain gardens, have led to a 56.82% increase in drainage efficiency and a 63.88% reduction in system failure rates. The implementation effectively minimized peak flood flow by 25.38%, reduced runoff, and improved groundwater recharge and rainwater utilization. The proposed solutions offer a replicable, sustainable framework for mitigating flooding in urban environments, enhancing ecological resilience, and ensuring the safety and quality of urban life in densely populated areas.  
urban flooding; storm water management model (SWMM); genetic algorithm (GA); low-impact development (LID); model optimization Article
## 4 Experimental Evaluation
### Experimental Setup  
#### 4.1.1 Experimental Environment  
The simulation experiments were developed in PyCharm 17.0.10, with Python 3.9 as the programming language and PyTorch 2.3 as the deep learning framework. The hardware configuration was as follows: Intel(R) Core(TM) i7-7820 HQ CPU @ 2.90 GHz, 32 GB RAM, NVIDIA Quadro P5000 GPU with 16 GB video memory, and 2 TB HDD.  
The simulation of the Space-Air-Ground Integrated Network system included 10 terminal nodes, 3 edge nodes, and 1 central server. Among these, 4 terminal nodes were located in the ground layer, directly connected to edge devices; 3 terminal nodes were in the aerial layer; and 3 terminal nodes communicated with edge nodes via satellite. Appendix A shows the code structure of the simulation experiment.  
Table 1 shows the dynamic topology parameters. The simulation dynamically adjusts these parameters throughout the training process to evaluate the robustness of our proposed framework under various network conditions. Particularly, we programmed specific topology change events, such as periodic LEO satellite connectivity interruptions, UAV flight path variations, and ground device mobility, to test the framework's resilience to SAGINs' dynamic nature.