esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Network Architecture
Each fusion stage facilitates the interaction between high-level and low-level features through multi-scale iterative optimization, allowing the fused features to progressively transition from low resolution to high resolution. This process involves upsampling, implemented using the Pixel Shuffling technique, followed by PSTFR and the fusion of high-level and low-level features through the CrossFormer Block. PSTFR is the core component of the proposed BCSR-STF model. It comprises two primary modules: (1) Bidirectional Cross Fusion (BCF), which performs spatiotemporal feature fusion along both time and scale directions to address the limitations of single-direction fusion, and (2) Global Spectral Restoration and Feature Enhancement (GSRFE), which utilizes spectral correlations in coarse images for spectral recalibration and improved feature fusion. The overall process is outlined as follows:  
\[F_{st}^{(i)}=\text{BCF}(L_{0}^{(i)},L_{1}^{(i)},H_{0}^{(i)}) \tag{6}\]  
The fused spatiotemporal feature \(F_{st}^{(i)}\) is input along with the low-resolution fine features \(L_{1}^{(i)}\) into the GSRFE module. The goal of this module is to repair and enhance the spatiotemporal fusion features by utilizing spectral information from the low-resolution imagery, ensuring that details are preserved during the fusion process. \[F_{st_{\text{corr}}}^{(i)}=\text{GSRFE}(F_{st}^{(i)},L_{1}^{(i)}) \tag{7}\]  
After obtaining the repaired and enhanced spatiotemporal features \(F_{st_{\text{corr}}}^{(i)}\), they are further fused with the output features \(F_{st_{\text{final}}}^{(i+1)}\) from the previous stage at different levels to ensure effective complementarity between high- and low-level features and the preservation of details. \[F_{st_{\text{final}}}^{(i)}=E_{\theta_{3}}^{(i)}\Big{(}cat\Big{(}F_{st_{\text {corr}}}^{(i)},PS\Big{(}F_{st_{\text{final}}}^{(i+1)}\Big{)}\Big{)}\Big{)} \tag{8}\]  
Here, \(PS(\cdot)\) denotes the Pixel Shuffling upsampling technique, and \(cat(\cdot)\) represents concatenation. After the final fusion stage, the last fused feature, \(F_{st_{\text{final}}}^{(0)}\), is passed to the prediction head to reconstruct the fused image. This process begins with Pixel Shuffling to restore the feature map to its original dimensions, followed by two convolutional layers to produce the reconstructed fused image.
# Study on the Interference Process of Liquid Radial Reflux on the Stability of a Shaped Charge Jet
## 3 Simulation Analysis
### Simulation Results
Figure 11 depicts that the upper part of the liquid converged to contact with the jet surface, and the jet carried out the secondary impact on the reflux liquid. However, the crater diameter of liquid secondary reaming is small due to the decrease in the jet impact pressure and the existence of liquid motion inertia, which is ignored in the calculation. The position, pressure, speed, and other parameters of the gauge points were obtained by using the data export function of the software. The position-time relationship curves of gauge points 9, 10, and 11 were plotted to obtain the motion trajectory of the three gauge points, as shown in Figure 12. The follow-up gauge point in the liquid goes through the reaming and reflux processes and then continues to oscillate but only slightly. Moreover, the trajectories of the three points are similar. Figure 11: Propagation of the side-wall secondary reflection shock wave. Figure 10: Generation and propagation of the bottom plate reflection shock wave. In addition, the relation curve between velocity and time in the y-direction at gauge point 12 and the pressure stroke curve at gauge point 2 were drawn. Figure 13 shows that the liquid particles fell into low oscillation after reaming and radial reflux, and the reflux velocity of the liquid particles was approximately 500 m/s. The pressure-time curve at gauge point 2 in Figure 14 demonstrates the appearance of a pressure peak in approximately 48 s, which lasted for a period and then oscillated back down. Therefore, gauge point 2 on the jet surface is under the impact of liquid reflux. At this time, the pressure of jet particles at this position sharply increases under the action of liquid reflux, and the jet is prone to necking fracture. The simulation results demonstrate the generation and propagation of the initial shock wave, side-wall reflection wave, bottom plate reflection wave, side-wall secondary reflection wave, and reflux of liquid particles driven by the shock wave. The simulation results verify the previous theoretical analysis. Specifically, the interference of liquid in the jet includes the following three parts: side-wall reflection interference, bottom plate reflection interference, and side-wall secondary reflection interference. Figure 12: Trajectory of gauge points 9, 10 and 11. Figure 13: Y-velocity–time curve at gauge point 12.
## 6 Conclusions
#### SWOT Analysis of the Proposed Model  
To strategically position this work within the research landscape, we summarize the key strengths, weaknesses, opportunities, and threats (SWOT) of the HetMECNet framework:  
* Comprehensive analysis framework incorporating heterogeneous server characteristics (transmission power, computing capability, CPU cores, waiting queue length).  
Figure 8: Experimental results of different waiting queue length. (**a**) \(P_{t}\) of HetMECNet as a function of \(B_{2}\) for different waiting queue length. (**b**) \(P_{b}\) of HetMECNet as a function of \(B_{2}\) for different waiting queue length.  
Quantitative evaluation of critical metrics: average delay, immediate service probability, and blocking probability.  
* Simplified assumptions (exponential service time distribution, homogeneous PPP spatial model) may limit real-world applicability.  
* Extension to energy/mobility-aware models via hybrid queuing approaches.
* Validation in operational testbeds.  
* Rapid technological evolution in edge computing may require dynamic model adaptation.
* Complexity of real-world deployments (e.g., non-uniform geographical server distributions).  
While the proposed model provides actionable insights for HetMECNet optimization, its simplified stochastic assumptions--particularly the exponential service time distribution and homogeneous PPP-based spatial modeling--may limit applicability in scenarios involving complex multi-resource tasks or geographically constrained deployments. To address these constraints, future work could enhance fidelity through task-aware probability distributions and GIS-informed spatial pattern integration. To bridge the gap between simulation and real-world applicability, future work will validate the proposed framework in operational edge-cloud testbeds, such as AWS Wavelength, Google Edge TPU, or 5G network slicing infrastructure.  
Conceptualization, W.L.; methodology, W.L.; software, W.L.; validation, W.L. and H.Z.; formal analysis, W.L.; writing--original draft preparation, W.L.; writing--review and editing, W.L. and H.Z.; visualization, W.L. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used in this study can be found at [[https://github.com/24-50546671/Data.git](https://github.com/24-50546671/Data.git)]([https://github.com/24-50546671/Data.git](https://github.com/24-50546671/Data.git)) (accessed on 9 April 2025).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} MEC & Mobile Edge Computing \\ QoS & Quality of Service \\ CC & Cloud Computing \\ EC & Edge Computing \\ PPP & Poisson Point Process \\ APs & Access Points \\ BSs & Base Stations \\ HetMECNets & Heterogeneous Mobile Edge Computing Networks \\ FCFS & First Come First Served \\ \end{tabular}
## Materials and Methods
### Theory of Dynamic System  
A spring-damper-type suspension expresses its characteristics through the spring constant of the spring and damping coefficient of the damper; the reaction force of the suspension can be expressed as Equation (1). However, because a hydraulic-pneumatic suspension can perform the role of a spring and damper only by combining a cylinder, a proportional control valve, and an accumulator, it is not possible to perform characteristic tests by separating each component in the manner of a spring-damper suspension. Thus, in this study, the test was divided into two conditions to allow the spring constant and damping coefficient to be measured separately in the hydro-pneumatic suspension in which the cylinder, proportional control valve, and accumulator were assembled.  
\[F=kx+c\dot{x} \tag{1}\]  
where:  
\(F\): reaction force, N;  
\(k\): spring constant, N/m;  
\(c\): damping coefficient, Ns/m;  
\(x\): displacement, m;  
\(\dot{x}\): velocity, m/s.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Operating pressure & 210 bar \\ Working load range & 150–300 kg \\ Type of fluid & SAE 80W-90 \\ Kinematic viscosity at 40 \({}^{\circ}\)C & 55.75 mm\({}^{2}\)/s \\ Kinematic viscosity at 100 \({}^{\circ}\)C & 9.506 mm\({}^{2}\)/s \\ Supply voltage & 12 VDC \\ Maximum current & 1.8 A, 100\% duty cycle at max 50 \({}^{\circ}\)C \\ Proportional valve type & Normal closed \\ Piston outer diameter & 30 mm \\ Piston inner diameter & 18 mm \\ Accumulator volume & 160 cm\({}^{3}\) \\ Accumulator pre-charge pressure & 40 bar \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of hydro-pneumatic semi-active suspension.  
Figure 1: Hydro-pneumatic semi-active suspension: (**a**) Figuration and (**b**) hydraulic circuit diagram.
## 5 Experimental Evaluation and Analysis
### Experimental Results
#### 5.2.3 Impact of Node Initial Energy  
The delivery ratio, delivery delay, and network life are greatly related to the initial energy. The following experiment in Figure 12 represent the network performance of the two protocols with different initial energies. As shown in Figure 12(a), the proposed protocol almost achieves the upper bound for delivery ratio compared to the Flooding routing. However, both of the protocols actually depend on the initial energy of the nodes to transmit more data packets to the base station. In Figure 12(b), we can find that the delivery time of packets in SFRRP is less than that of Flooding routing. The reason is that the traffic load of the network in the Flooding routing is more than that of the SFRRP protocol. The delay times of the transmitted packets goes up for both protocols when increasing the initial energy of nodes, because many more packets are transmitted to the base station with the increasing initial energy of the nodes. However, the changes of delay times in the Flooding routing are very high compared to the proposed SFRRP protocol. Figure 12(c) shows that network life in SFRRP is higher than the Flooding  
Figure 11: Impact of buffer size, (**a**) average delivery rate; (**b**) average delay; and (**c**) network life.  
routing protocol. However, the difference on their performance is very noticeable when initial energy of sensor nodes is low.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Environmental Factors
#### 3.4.2 Humidity  
Humidity, or the moisture content in the surrounding environment, is another influential factor in sticking behavior. High humidity can facilitate the absorption of moisture by MXene flakes, causing them to swell or become sticky. This increased stickiness promotes the agglomeration of flakes, leading to sticking. Conversely, low humidity conditions can reduce flake moisture absorption and mitigate sticking. It is important to note that the surface chemistry of MXene flakes, such as the presence of hydrophilic or hydrophobic functional groups, can affect their affinity for moisture absorption and thus impact sticking behavior in humid environments.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 4 End-Edge Computation Offloading Based on Lyapunov-Guided DRL
### Lyapunov-Guided A3C Algorithm  
With the formulated MDP model, we further propose the LyA3C algorithm based on the A3C algorithm. The structure of our proposed algorithm is depicted in Figure 2. There are a global network and several worker agents, where the globe network and the worker agents are set in the same actor-critic structure.  
Taking one of the worker agents as an example, it includes two modules: the actor module and the critic module. The two modules accept the state and output the state value as well as the corresponding policy. Specifically, \(a(t)=\pi(s(t)|\theta)\) denotes the policy learned from the current state, where \(\pi(s(t)|\theta)\) is the explored offloading policy. The network parameter of actor network is denoted as \(\theta\). The gradient of the expected cumulative discounted reward is  
\[\
abla\theta\mathbb{E}_{\pi}\left[\sum_{t=0}^{\infty}\gamma^{t}r(t)\right]= \mathbb{E}_{\pi}[\
abla_{\theta}\log\pi(s(t)|\theta)A(s,a)], \tag{25}\]  
where \(A(s,a)\) is the advantage function. \(A(s,a)\) is used to measure the dominance of the state \(s(t)\) when the agent selects action \(a(t)\) and executes the policy \(\pi\) compared to executing an arbitrary action. According to the current strategy, the value of \(A(s,a)\) is obtained from the value \(V(s(t))\) and is calculated as  
\[A(s,a)=r(t)+\gamma V(s(t+1))-V(s(t)), \tag{26}\]  
where \(\gamma\) is discount factor.  
The parameter \(\theta\) is updated based on  
\[\theta=\theta+\alpha\sum_{t}\
abla_{\theta}\log\pi(s(t)|\theta)A(s,a), \tag{27}\]  
where \(\alpha\) is the learning rate of the actor network.  
In contrast, the parameter of the critic network \(\varphi\) is updated as  
\[\varphi=\varphi+\lambda\!\!\sum_{t}\
abla_{\theta}(r(t)+\gamma V(s(t+1))-V(s( t)))^{2}, \tag{28}\]  
where \(\chi\) is the learning rate of the critic network.  
Through the above process, each worker agent will send the accumulated updates to the global network, then the global network asynchronously updates \(\theta\) and \(\varphi\).  
To sum up, the overall flowchart of the LyA3C algorithm is shown in Figure 3.  
Figure 2: Algorithm structure.  
The algorithm is summarized as Algorithm 1.  
```
1: Initialize number of industrial terminals \(N\); number of industrial base stations \(M\); Lyapunov drift-plus-penalty parameter \(V\);
2: Initialize the A3C network with actor \(\pi(s)\) and critic \(V(s)\);
3:repeat
4: Collect a set of trajectories \(\{s(t)\), \(a(t)\), \(r(t)\), \(s(t+1)\}\);
5:for each time slot \(t=1\) to \(K\)do
6: Actor step sample action \(a(t)\sim\pi(s(t))\) ;
7: Critic step compute value estimate \(V(s(t))\) ;
8: Compute advantage function using Equation (25);
9: Each worker agent update \(\theta\) and \(\varphi\) using Equations (27) and (28);
10: Global network update \(\theta\) and \(\varphi\);
11:endfor
```  
**Algorithm 1** The LyA3C Algorithm
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 2 Methods  
### Model Description and Numerical Case Design  
#### 2.1.1 Cabin Ventilation Systems  
The geometrical model used in this study is a twin-aisle wide-body aircraft cabin with seat distribution of 3-3-3. There are 6 rows with 54 seats in the cabin. The study involves three traditional ventilation systems and a personalized ventilation system. In the personalized ventilation system, a series of personalized exhaust diffusers are added on the back of passenger seats based on the sidewall mixing ventilation system. The schematic diagram of the specific ventilation system is shown in Figure 1.  
The parameters of air supply and exhaust diffusers of various ventilation systems are shown in Table 1.  
According to Reference [23], the passenger's mouth as the inlet is 1.2 cm\({}^{2}\) when breathing, and the inlet is simplified as a circle with a diameter of 1.24 cm, as shown in Figure 2. In order to assess the infection risk of passengers, a spherical breathing area with a radius of 20 cm is established around the face. Droplets inside the breathing area are regarded as particles that threaten passengers. In addition, the seat arrangement is marked as follows.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ventilation System** & **Air Supply Diffusers** & **Air Exhaust Diffusers** \\ \hline CMVS & 0.03 m \(\times\) 5.04 m & 0.05 m \(\times\) 5.04 m \\ SMVS & 0.03 m \(\times\) 5.04 m & 0.05 m \(\times\) 5.04 m \\  & & Air exhaust diffusers on both sides: \\  & & 0.05 m \(\times\) 5.04 m \\ DVS & 0.10 m \(\times\) 5.04 m & Center air exhaust diffusers: \\  & & 0.1 m \(\times\) 5.04 m \\  & & Main air exhaust diffusers: \\ PVS & 0.03 m \(\times\) 5.04 m & 0.05 m \(\times\) 5.04 m \\  & & Personalized air exhaust diffusers: \\  & & 0.02 m \(\times\) 0.44 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of ventilation systems.  
Figure 1: Schematic of ventilation systems. (**a**) Ceiling mixing ventilation system (CMVS). (**b**) Sidewall mixing ventilation system (SMVS). (**c**) Displacement ventilation system (DVS). (**d**) Personalized ventilation system (PVS).
# Investigations of the Energy Transfer
## 2 Materials and Methods
### Time-Resolved Spectroscopy  
The samples were pumped with 580 nm femtosecond (fs) pulses obtained using an optical parametric amplifier (Orpheus, Light Conversion) from the 1030 nm pulse emitted by an Yb:KGW laser (Pharos, Light Conversion) of 170 fs laser pulse duration and 80 kHz repetition frequency. For the transient absorption (TA) experiments (Harpia, Light Conversion), a supercontinuum probe pulse was obtained by focusing 10% of the 1030 nm pulse in a sapphire crystal. The generated white light (WL) extends from 480 to 780 nm. After traveling through an optical delay line, the WL transmitted through the sample is detected using a spectrograph with 300 L/mm grating combined with an array detector. Absorption changes (differential absorption) are measured as the difference in optical density between the pumped and unpumped sample. The polarization between the pump and probe pulses was set at the 54.7\({}^{\circ}\) magic angle. The samples were measured in the same quartz cuvettes of 1 mm optical path length that were used for recording the UV-VIS absorption spectra. The fluorescence kinetics was investigated using the time-correlated single photon counting (TCSPC) setup (Chimera, Light Conversion). The samples were excited with vertically polarized light, and the fluorescence emission was collected through a long-pass filter suitable for each sample emission and an emission polarizer set at the magic angle (54.7\({}^{\circ}\) relative to the polarization of the pump pulse). The fluorescence signal was focused on the entrance slit of a double monochromator and detected using a single photon sensitive photomultiplier (Becker&Hickl PMC-100-1 standard). The FWHM of the instrument response function (IRF) was 340 ps. For the TCSPC experiments, the average power of the pump beam was 2 mW, while for the TA experiments, laser pulses of 3-4 mW were used.
## 3 Experimental Setup
### Experimental Procedure  
THW-L2 device (Thermtest Inc., Hanwell, NB, Canada) based on the transient hot-wire method is especially designed to measure thermal conductivity of fluids in agreement with ASTM D7896-14 in the range 0.01 to 2 W m\({}^{-1}\) K\({}^{-1}\). Figure 1 depicts the layout of the commercial experimental arrangement. An EchoTerm Dry Bath (2) is coupled with the THW-L2 device (1), which allows one to maintain the studied fluid at the test temperature. In this study, tests were performed in the temperature range 283.15 to 333.15 K.  
The sensor where the wire is welded (5) is the main element of the above-mentioned device. Its structure is based on an alumel wire of 60 mm in length and 0.1 mm diameter connected to the THW-L2 instrument by an isolated cable. This apparatus includes a sample container (3) and a sample cell (4), where the tested fluid is introduced to perform the measurements. The sensor is inserted within the sample cell, and both of them are introduced in the dry bath.  
The device was operated using the Beta version of the Thermtest THW software. Before starting any measurement, this software allows one to perform a power test in order to adjust the applicable heating power for each fluid in such a way that leads to a certain temperature rise, depending on the viscosity of the fluid. In this work, tests were performed in a temperature rise range of 2.0 to 2.8 K. The software records the temperature history during the transient heating of the sample from the change of the electrical resistivity of the wire. Then, as usual in THW technique, the thermal conductivity is obtained from the slope of the temperature rise over the logarithm of the time in the linear region, as indicated in Equation (6).  
Hereunder, three independent experiments comprised of sets of six repetitions at each studied temperature in the range 283.15 to 333.15 K were carried out to ensure the repeatability of the thermal conductivity values, with the duration of each measurement being 1 s. Temperature stabilization of the sample was allowed between each test.
## 3 Results  
### Experimental Results  
The average force deflection curves obtained during the pin bending tests (for 4 pins) and the interface tests (four torque levels, 4 pins, 4 clamp assemblies) show a slight deflection which may be caused by slippage between the interfaces (Figure 14). This is deduced as the point of deflection increases with the increase in the tightening load.  
The average force deflection curves for the system tests showed slight changes between each configuration (Figure 15). Distance from clamp to bone surface was 50 mm in all 6 tests while the pin placement differed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Material Type** & **Material Properties Used** \\ \hline Clamps & AISI 1215 Steel & E = 210 GPa, G = 80 GPa, \(\
u\) = 0.3 \\ Shaft & AISI 1215 Steel & E = 210 GPa, G = 80 GPa, \(\
u\) = 0.33 \\ Pins & AISI 316L Stainless Steel & E = 200 GPa, G = 79.05 GPa, \(\
u\) = 0.265 \\ Bone analogous & Homopolymer Acetal (Delrin \(\copy\)) & E = 3.1 GPa, G = 2.9 GPa, \(\
u\) = 0.32 \\ Pin + Clamp simplification (for & Defined bi-linear isotropic hardening metal & E1 = 96.88 GPa, Yield Strength = 134 MPa, E2 = 52 GPa, \(\
u\) = 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of each component, used for simulations and calculations.  
Figure 14: Results of the pin bending (Blue) and interface tests. Tightening load 6 Nm (Green), 8 Nm (Red), 10 Nm (Dark Blue), and 12 Nm (Black).