esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Post-transmission, the CPU load exhibits varying degrees of stability, reflecting the adaptability of the edge node; this adaptability is crucial for the efficient operation of vehicular communication networks, as it can ensure swift recovery and preparation for subsequent communication events. Furthermore, each distance scenario presents unique peaks in CPU usage during CAM transmission, indicating distance-specific computational demands. This highlights the need for tailored computational resources based on the communication range. Thus, adaptive algorithms capable of dynamically adjusting computational loads in response to distance-specific demands become imperative for optimizing edge node performance. These findings underscore the importance of optimizing edge computing solutions for varying communication distances. Future system design should prioritize adaptive algorithms capable of dynamically allocating computational resources based on real-time requirements. This adaptability can ensure efficient resource utilization and minimize the risk of performance bottlenecks, especially in scenarios with extended communication ranges. When integrated with previous findings on energy consumption patterns, the interplay between energy consumption and CPU load emphasizes the multidimensional nature of optimizing edge computing in vehicular communication networks. In conclusion, the analysis of CPU load provides a comprehensive view of the computational challenges and opportunities associated with varying communication distances, contributing to foundational knowledge for developing resilient and efficient edge computing systems tailored for real-world vehicular communication deployments.
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 6 Discussion
### Observed Trends  
The COVID-19 pandemic is affecting different regions around the World in different ways at different times. This is reflected in the different trends observed for the countries and continents presented in the previous Section.  
In some countries (e.g., Argentina and--not shown in the paper, but available in the Supplementary Materials--Venezuela, as well as many African countries), the epidemic is still at an initial stage of exponential growth. Other countries (e.g., India, Mexico, and Brazil) are currently facing the first wave, while other ones (e.g., Peru, Colombia, South Africa, and Egypt) are on the tail of the first wave. In many European countries (e.g., United Kingdom and Italy), as well as in China and Canada, the first wave has been completely overcome. In some countries (e.g., Spain and Russia), more or less evident signs of a second incipient wave can be observed. Other countries (e.g., Israel, Romania, United States, Morocco, Algeria, and Guam) are currently facing a second wave of the epidemic. Other ones (e.g., Japan, Serbia, and Australia) are already at the tail of the second wave. Lastly, in Iran, a third wave of the epidemic is currently going on.  
It should be observed that, in large federal nations, the trends of daily deaths observed at the national level are actually the sum of many local trends occurring in the single federated states. This could explain the plateau shapes observed in the daily deaths plots of Brazil and Mexico. Likewise,  
Figure 34: Actual and predicted trends in the World: (**a**) daily deaths, (**b**) total deaths.  
the second wave observed in the plot of the United States could correspond actually to a first wave of the epidemic affecting a different area of the country. For the above-mentioned countries, more accurate data fitting and extrapolations could be obtained by scaling down the model to consider the statistical data of single states and then accumulating the results at national level.  
In any case--except maybe for Iran, because of the incipient third wave-, the bimodal lognormal time distribution of daily deaths assumed by the model was able to fit with reasonable accuracy the observed data.
## 3 Results and Discussion
### Electrical Properties
The temperature dependencies of the Mott parameters \(R\) and \(W\) are shown in Figure 5.6  
One can see from Figure 8 that increasing the temperature leads to reductions in the hopping distance (Figure 8a) and to increase in the hopping energy (Figure 8b) for all investigated samples. Also, both \(R\) and \(W\) for the LMO sample are higher than for the LMO:Ga and LMO:Y samples at all the temperatures. This finding aligns with our earlier statement regarding the increase in the density of localized states at the Fermi level, \(N(E_{F})\), of the modified samples which contain rare earth ions (Ga and Y, respectively) due to the decrease in \(R\) and \(W\) in these samples compared to the \(R\) and \(W\) of the LMO sample, thus causing a decrease in the thermal activation energy of conduction, \(E_{A,cond}\), of Ga- and Y-doped LaMnO\({}_{3}\) relative to the \(E_{A,cond}\) of undoped LaMnO\({}_{3}\). After these encouraging outcomes for lanthanum manganite compounds, it can be affirmed that further investigations are warranted to ascertain their viability as candidates for energy conversion, such as metal-air batteries or fuel cell electrodes, due to their distinct physical and electronic attributes. The obtained results, namely that the electrical and structural properties of the synthesized Ga- or Y-doped LaMnO\({}_{3}\) ceramic powders can be changed by design and temperature variations, highlight the potential of these materials for utilization in thermo-electric devices and sensor applications.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## Conflicts of Interest  
The authors declare no conflict of interest. Abbreviations  
The following abbreviations are used in this manuscript:  
NASA National Aeronautics and Space Administration  
ESA European Space Agency  
JAXA Japan Aerospace Exploration Agency  
CSA Canadian Space Agency  
MPT Microwave power transmission  
LPT Laser power transmission  
TRL Technology readiness level  
WPT Wireless power transmission  
M-SSPS Microwave-based space solar power system  
FGS Fine guidance sensor  
VIS Visible  
NIR Near-infrared  
r.m.s Root mean square  
InGaAs Indium gallium arsenide  
ISE Institute for Solar Energy  
LLOs Low lunar orbits  
LRO Lunar Reconnaissance orbiter  
LADEE Lunar Atmosphere and Dust Environment Explorer  
LCRS Lunar Reconnaissance Orbiter Communication System  
CR3 BP Circular restricted three body problem  
ICs Initial conditions  
PSO Particle swarm optimization  
EPS Electrical power system  
SoC State of charge  
DRO Distant retrograde orbit  
RAAN Right ascension of the ascending node
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 3 TCS Design Proposal
### Heat Dispersion Design  
According to the typical heat flux of SSO and the location of the satellite separation device, the radiating surface is designated at the +Y panel near the OBDH location and \(-\)Y panel near the battery location, which are shown in Figure 5. The radiating region of +Y panel is 16,000 mm\({}^{2}\) in trapezoid and the radiating region of the \(-\)Y panel is a 40 mm \(\times\) 40 mm square. The coating of the internal surface of the radiating region is a heat conductivity membrane named HA95 and the external surface is a thermal control coating named F46.
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 3 Transformer-Based Cascading Reconstruction Network
### Incremental Details Reconstruction  
The incremental details reconstruction stage is designed as a dynamic fusion Transformer network to further improve the reconstruction quality. Figure 4 shows its network architecture. In order to better coordinate the two reconstruction stages, we design a dynamic feature fusion module to adaptively fuse the features of the two stages. The final reconstruction can be obtained by combining the incremental details and the overall structure reconstruction.  
Figure 4: The diagram of the dynamic fusion Transformer network for incremental details reconstruction.  
The dynamic feature fusion module integrates the Transformer features of two stages into three branches and performs adaptive fusion. The first branch calculates the initial reconstruction \({X_{lo}}^{\prime}\in\mathbb{R}^{n_{x}\times n_{y}\times B}\) from \(Y^{\prime}\) and then performs the operations of a convolution and two RWT modules on \({X_{lo}}^{\prime}\) to obtain the intermediate feature \(F_{query}\). The third branch accepts the deep feature \(x_{0}^{d}\) from the first stage. The second branch calculates an attention map \(M\) from \(F_{query}\) through the operations of two convolutions and one \(SoftMax\) and obtains a complementary feature \(f_{a}\) by gating between \({F_{1}}^{\prime}\) and the attention map \(M\), where \({F_{1}}^{\prime}\) is obtained from \(x_{0}^{d}\) through the operations of two convolution layers. The fusion weights of the three branches are learned adaptively by the network, which is helpful in promoting the two-stage network to reconstruct more incremental details. The core fusion operations can be formulated as  
\[F_{query}=f_{RWT,z}(conv({X_{lo}}^{\prime})), \tag{15}\]  
\[M=SoftMax(conv(conv(F_{query}))), \tag{16}\]  
\[f_{a}=M\odot conv(conv(x_{0}^{d})), \tag{17}\]  
\[\omega_{i}=SoftMax({\omega_{i}}^{\prime}),i=1,2,3, \tag{18}\]  
\[F_{fusion}=\omega_{1}F_{query}+\omega_{2}f_{a}+\omega_{3}x_{0}^{d}, \tag{19}\]  
where \(conv\) represents the convolution of \(3\times 3\), the function \(SoftMax\) in the module is to normalize \(F_{query}\) into a probability distribution map or normalize the parameter \({\omega_{i}}^{\prime}\). \(F_{fusion}\) is the fused feature.  
After performing the operations of one RWT module and two convolutions upon \(F_{fusion}\), we can obtain the incremental reconstruction \(X_{re2}\in\mathbb{R}^{n_{x}\times n_{y}\times B}\). Then, the incremental reconstruction is summed up with the overall structure reconstruction \(X_{re1}\).  
After four 3D convolution layers, the final reconstruction result \(\hat{X}_{k}\) is calculated as  
\[\hat{X}_{k}=conv3d_{\times 4}(X_{re1}+X_{re2}). \tag{20}\]
## 1 Introduction  
Wireless Body Area Network (WBAN) consists of several tiny, heterogeneous sensor nodes attached to the human body. The greatest use of WBAN applications is in the healthcare sector, where the body's several vital signs are measured and sent to the medical server to receive treatment appropriately. For that, an effective routing algorithm along with a suitable sink node placement always plays a significant role in the overall performance of such a network [1]. The nodes in WBAN are free to move due to the body movement; hence network topology becomes unpredictable and hard to manage. Thus, there is always a demand to choose routing protocol efficiently to deal with various uncertainties of WBAN [2]. The node's movement patterns of WBAN are characterized by various mobility models, and for data transfer, different routing protocols exhibit different behavior for such mobility models. In order to design an effective and efficient network model, it is very important to choose a particular routing protocol that deals with the frequently changing topologies of WBAN. This work presents the best-suited routing protocol for WBAN with respect to scalable wireless networks [3, 4]. However, there are many surveys conducted on the behavior of routing on mobility models [5, 6], but they were carried out only for random waypoint mobility models, and WBAN has not been evaluated yet for all routing protocols. Many wireless networks have already been evaluated for such scenarios, viz. Vehicular Ad Hoc Network (VANET), Flying Ad Hoc Network (FANET), and Mobile Ad Hoc Network (MANET) [7, 8]. Work performed in this paper focuses on analyzing the performance of various routing algorithms of WBAN for different mobility models under different scenarios and network parameters.
# Multi-Topology Routing Algorithms in SDN-Based Space Information Networks
## 5 Design and Experiment Results
### TBA Algorithm Simulation  
In the experiment, the hardware environment is: Intel Core i7-8750 CPU, 8 G memory, 1 TB hard disk space. The software environment includesSTK9.0 and MATLAB R2014a. Walker constellation is implemented in STK. The constellation parameters of Walker constellation are shown in the Table 2.  
To verify the TBA algorithm, we only select two LEO satellites in the Walker constellation, the total bandwidth of the link is 1000 KBps. Packet loss rate is randomly selected between 0.5% and 1%. First, the data of Task1 and Task2 are transmitted at 600 KBps and 300 KBps respectively. Ten seconds later, Task3 data is sent at a rate of 600 KBps, with the sending rate of the three tasks as its request bandwidth. The minimum required bandwidth of Task1, Task2, and Task3 are 400 KBps, 100 KBps and 300 KBps respectively. The bandwidth allocation factors of Task1, Task2 and Task3 are 0.9, 0.1 and 0.67, respectively. Here, we define a new variable, Bandwidth Satisfaction Ratio (BSR), to allocate the ratio of the actual bandwidth \(b_{r}{}^{task}\) to the requested bandwidth \(ab_{r}{}^{task}\). The BSR expression for the r-hop in the physical link is as follows.  
\[BSR=\frac{b_{r}{}^{task}}{ab_{r}{}^{task}} \tag{19}\]  
For Task1, Task2, and Task3, the simulation results without TBA algorithm and using TBA algorithm are shown Figures 9-11 respectively.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Number of** & **Orbital** & **Orbital** & **Orbital** & **Orbital** & **Link State** & **Minimum** \\  & **Satellites** & **Altitude/km** & **Inclination** & **Period/s** & **Number** & **Link State** & **Elevation** \\ \hline MEO & 12 & 10,355 & 55.0 & 21,600 & 3 & Permanent & 22 \\ LEO & 48 & 1400 & 52.0 & 6840 & 8 & Permanent & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Walker constellation parameter.  
Figure 8: Task2 best route diagram.  
beginning of transmission. After 10 s, the data transmission rate of Task2 decreases to about 200 KBps without TBA algorithm because of the start of Task3 data transmission, when using TBA algorithm, Task2 can only share the bandwidth of 60 KBps, so it cannot effectively transmit information and terminate its transmission. For Task3, the transmission rate is 350 KBps without TBA algorithm, and 427 KBps with TBA algorithm.  
The simulation results show that the bandwidth satisfactions of Task1, Task2 and Task3 are 75%, 66.7% and 58.3% respectively without TBA algorithm. With the proposed TBA algorithm, Task1 and Task3 are more important and have higher bandwidth allocation factor. Their bandwidth satisfactions are 95.5% and 71.2%, which are increased by 20.5% and 12.9% respectively. Therefore, the TBA algorithm proposed in this paper is conducive to ensuring the timely transmission of important task data for special users, and can terminate the transmission of users who do not meet the transmission conditions, thus avoiding the waste of resources.
## 5 Conclusions  
In this paper, we propose to use SVM-based soft decision for consecutive-symbol-expanded 4-D constellation in bandwidth-limited nonlinear UVLC system. To reduce the computational complexity, bit-based binary SVM multi-class strategy and data preprocessing method are employed. Key parameters of the proposed method are discussed. Experimental results prove that the performance can be significantly improved. When LMS-based linear equalizer is used for post-equalization, the available baud rate is increased from 400 MBaud to 412.5 MBaud, corresponding to the data rate improvement of 50 Mbps. With the joint effort of nonlinear equalizer and the proposed scheme, the BER can still satisfy the 7% FEC threshold at 425 MBaud.  
Figure 9: BER versus Baud rate with hard decision and SVM-based soft decision.  
**Author Contributions:** Conceptualization, W.N. and N.C.; methodology, W.N. and N.C.; software, W.N.; validation, W.N.; formal analysis, W.N., J.C. and Z.L.; data curation, W.N.; writing--original draft preparation, W.N., J.C. and Z.L.; writing--review and editing, J.S. and N.C.; visualization, W.N.; supervision, N.C.; project administration, J.S. and N.C.; funding acquisition, J.S. and N.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Key Research and Development Program of China: 2022 YFB2802800; National Natural Science Foundation of China: 61925104; National Natural Science Foundation of China: 62031011; Major Key Project of PCL: N/A.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 6 Conclusions and Future Work  
This paper presents PADP, a surface gateway deployment scheme designed to overcome the unique challenges posed by underwater sensor networks, especially sensor node mobility. The implementation combines the Interacting Multiple Model, Branch-and-cut, and Disjoint-set data structure to ensure a high level of sensor node coverage by surface gateways, while simultaneously satisfying budget constraints, enhancing connectivity, and lowering average end-to-end delay as well as power consumption during redeployment. Thus, PADP mitigates the previously unaddressed issues of long propagation delay and energy consumption by effectively positioning gateways to relay information to a control station.  
The critical advantage of PADP is in that it considers both current and predicted positions of mobile sensor nodes for gateway redeployment decision. Therefore, gateways cover more sensor nodes for longer times compared with the previous static deployment algorithm. Ensuring coverage  
Figure 12: Impact of mobility pattern on coverage.  
Figure 13: Impact of Mobility pattern on end-to-end delay.  
for more extended periods will reduce the frequency and hence the cost required to readjust the buoys. Moreover, the network architecture is optimized as in [42; 43] which improves the computing power and allow the network to adapt to more applications.  
Future research will examine the impact of the deployment scheme on the design of network protocols, including MAC and routing, by evaluating performance measures such as end-to-end delay, throughput, and energy consumption. According to the existing research, we infer that the accuracy of prediction is proportional to the tracking accuracy. The accuracy of the IMM framework will decrease with the increase of the prediction intervals and the maximum speed of nodes. At the same time, we expect that the cycle time of gateway adjustment and update will be shorter. The tracking accuracy is a dynamic value in a dynamic network and is affected by the parameters of the networks, such as mobility model of nodes in network. We will further explore incorporating AUVs with controlled mobility into our scenario.  
Conceptualization, J.L., W.G. and J.-H.C.; Methodology, J.L., W.G. and M.A.-B.; Writing--Original Draft Preparation, J.L., G.H. and L.F.; Writing--Review & Editing, W.G. and G.H.; Supervision, J.-H.C., M.A.-B. and L.F.  
This work was supported by the National Natural Science Foundation of China (Grants No. 61631008), and the Fundamental Research Funds for the Central Universities 2017 TD-18, and the State Key Laboratory of Robotics No. 2015-O06. Here we also want to thank the National Natural Science Foundation of China under Grant No. 61572172 and No. 61872124, the Open fund of State Key Laboratory of Acoustics under Grant SKLA201901.  
The authors declare no conflict of interest.
## 4 Experimental Verification  
This section presents the experimental verification of the proposed fault diagnosis algorithm. A 12 kW three-phase IM from RENAULT TWIZY, used for EV applications, is described in Table 5. The motor parameters and fault resistance were measured using the impedance analyzer. The phase resistance of the IM was 11.5 m\(\Omega\), and a fault resistance of 3.6 m\(\Omega\) was considered. Figure 7 shows the manufactured IM and terminal box used to implement the winding connections and simulate fault conditions. The windings of the IM were separated, as shown in Figure 8, and connected through the terminal box. The experimental setup for ITF diagnosis is shown in Figure 9. The test motor was coupled to a dynamometer system, and both Y and \(\Delta\) connections were implemented through the terminal box. A three-phase inverter was used to apply the space voltage vector to the IM. Fault diagnosis was conducted with both Y and \(\Delta\) connections, as illustrated in Figure 10. The fault resistance was connected in parallel with the faulty phase. The voltage and current were measured using voltage and current sensors, such as the HAH1 DR series from LEM, and the impedance was calculated for each space voltage vector. The measured voltage and current were converted to digital values using an analog-to-digital converter in a digital signal processor (DSP), specifically the TMS320F28069, to enable impedance calculation. Moreover, the calculated impedance, the phase of the space voltage vector, and the diagnostic status were converted back to analog signals via the DSP's digital-to-analog converter and displayed on an oscilloscope. The experimental results for the Y and \(\Delta\) connections are shown in Figures 11 and 12, respectively. As shown in Figure 11, when an ITF occurs in phase A, the impedance reaches its maximum at a phase angle of 90\({}^{\circ}\) and its minimum at 0\({}^{\circ}\), corresponding to the specific phase angles of the space voltage vector in the three-phase inverter. When the fault occurs in phase B, the maximum impedance is observed at 30\({}^{\circ}\) and the minimum at 120\({}^{\circ}\). Similarly, for a fault in phase C, the impedance is maximized at 150\({}^{\circ}\) and minimized at 60\({}^{\  
Figure 8: Winding configuration for validation of inter-turn fault algorithm: (**a**) phase A; (**b**) phase B; (**c**) phase C.  
Figure 10: Connections for experiment: (**a**) Y connection; (**b**) \(\Delta\) connection.  
Figure 7: Manufacture and experimental environment: (**a**) stator; (**b**) rotor; (**c**) terminal box.  
Figure 9: Experimental environment for inter-turn fault diagnosis: (**a**) motor and terminal box; (**b**) fault situation; (**c**) inverter for fault diagnosis.