esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Resource Allocation  
Resource allocation in mobile edge computing is the task of efficiently allocating available radio and computational resources to different slices based on their requirements and priority. Historically, resource allocation was a reactive step aimed at self-healing and fault tolerance. Proactive resource allocation with DL can reduce the effects of costly mistakes for under-provisioning slices that causes SLA violations, poor user experience, and customer churn. To reduce operational costs, deep learning techniques, borrowed from image processing, are used to anticipate network capacity based on metrics gathered in the mobile edge computing, such as signal quality, occupied resource blocks, and local  
Figure 5: Physical infrastructure and virtual resources (network, computation, storage) in service chaining for 5G slices. Each slice has a specific purpose and type of end device, but several slices may use the same types of virtual network functions (VNFs) to deliver services to their users. The VNFs for each slice are separated for privacy and security.  
computation loads [32]. Inter-slice resource allocation can be achieved by jointly optimizing according to slice priority (a slice providing remote healthcare could have priority over one for video streaming), under-stocking, fairness concerns [58], and QoE per slice [59].  
Resource allocation using DL is especially useful for predicting resources needed for tasks offloaded to the mobile edge network from smaller devices [60] and proactively assigning some of the limited available resources. Because mobile edge computing can exploit local views of wireless signal and service request patterns, resource allocation models operating with this information can be used to further minimize delays [61] and respond in real time to observed changes. DL models can also be applied beyond resource optimization for a single edge node by including both spatial and temporal connections among data dependencies between traffic nearby edge nodes to predict how these dependencies and dynamic regional traffic patterns will affect resource demands [26; 27].
## 3 Synthesis and Analysis of Planar SALT Trajectories
### Exterior Leg Backward Propagation  
Once a point of some \(L_{2}\) lunar gateway is selected, the exterior leg can be reconstructed directly in the BR4 BP by propagating the point backward in time until the trajectory re-enters the Earth-Moon RoP (or until the predefined maximum propagation time, set to 250 days in this work, is reached). The Sun's phase at the beginning of the propagation is to be specified.  
To demonstrate the abundance of SALT trajectories, we restrict ourselves to just a single contour line, the one shown in Figure 9. Propagating more than a thousand points along the contour line with 1500 different values of the Sun's phase in the \([0,2\pi]\) interval, we obtained almost 287 thousand potential exterior legs (about 14% of the propagated trajectories). Their phase states at crossing the Earth-Moon RoP boundary can be visualized on the \((x,\hat{x})\) plane (Figure 10). As earlier, the \(y\)-coordinate is readily retrieved from Equation (8), then \(\hat{y}\) can be recovered from the Jacobi integral value at the RoP re-entry, which is indicated by the color. The lower this value, the more pronounced was the solar gravity perturbation effect along the exterior leg. Meanwhile, we do not display the points with \(J>2.47\) because such exterior legs cannot be in principle patched with any departing leg: the corresponding launch energy C\({}_{3}\), according to Equation (A19) from Appendix A.2, appears to be less than \(-2.17\) km\({}^{2}/\)s\({}^{2}\), which is not enough for the spacecraft to reach the Moon and/or exit the Earth-Moon region of prevalence (after the TLI burn, the apogee distance would be less than 367,000 km).  
Figure 10: Earth–Moon RoP re-entry conditions for candidate exterior legs. The color reflects the Jacobi integral value at re-entry. The points with \(J>2.47\) are not displayed because it is unfeasible to patch the associated exterior legs with any departing leg.
## 2 RadCom System Model and Processing
### Waveform Parametrization  
To allow for the proper parametrization of a RadCom waveform, the following criteria should be respected for both communication and radar systems:  
* \(T_{cp}>T_{d}:\) where \(T_{d}\) is the maximal expected delay spread to avoid ISI,
* \(\frac{\ell_{max}}{\Delta f}\ll 1\): to avoid ICI
* \(T_{cp}*\Delta f\ll 1\): to achieve spectral efficiency.  
Subcarrier spacing is limited by the maximal Doppler shift, hence the speed of the target, while the cyclic prefix is limited by the delay, hence the target's distance. These two parameters add additional constraints for the radar. Maximal unambiguous distance is determined by subcarrier spacing \(\Delta f\) as follows:  
\[d_{un}=\frac{c}{2\Delta f}, \tag{14}\]  
which also determines the distance resolution:  
\[\Delta d=\frac{c}{2N_{c}\Delta f} \tag{15}\]  
\(N_{c}\Delta f\) is bandwidth. Furthermore, maximal unambiguous velocity is determined by the symbol time (denoted \(T_{mcm}\) to account for both OFDM and UFMC):  
\[v_{un}=\frac{c}{2f_{c}T_{mcm}}, \tag{16}\]  
which also determines the distance resolution:  
\[\Delta v=\frac{c}{2f_{c}N_{symb}T_{mcm}} \tag{17}\]  
\(N_{symb}T_{mcm}\) is observation time.
# Fast Performance Modeling across Different Database Versions Using Partitioned Co-Kriging
## 5 Experimental Evaluation
### Trade-Off on Choosing \(K\)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Cluster Size** & **Sample Size** & **1n** & **2n** & **3n** & **4n** & **5n** & **6n** & **7n** & **8n** & **9n** & **10n** & **11n** & **12n** & **13n** & **14n** & **15n** \\ \hline
1 & 15.044 & 12.459 & 12.017 & 12.191 & 12.138 & 12.042 & 12.119 & 12.192 & 12.166 & 12.237 & 12.285 & 12.187 & 12.199 & 12.303 & 12.246 \\
2 & 14.722 & 14.492 & 12.353 & 10.992 & 11.229 & 10.969 & 10.314 & 10.285 & 10.454 & 10.515 & 10.429 & 10.445 & 10.364 & 10.357 & 10.337 \\
3 & **12.399** & **12.177** & **10.222** & **10.734** & **10.747** & 10.922 & **9.618** & **9.268** & **9.455** & **9.273** & **9.303** & **9.2304** & **9.183** & **9.280** & **9.197** \\
4 & 14.247 & 14.326 & 11.447 & 11.927 & 11.372 & **10.819** & 10.606 & 10.524 & 10.207 & 10.735 & 10.251 & 10.239 & 10.019 & 10.100 & 9.981 \\
5 & 16.045 & 13.674 & 12.458 & 11.943 & 12.435 & 11.119 & 10.381 & 10.569 & 11.027 & 10.347 & 10.097 & 10.010 & 10.084 & 10.027 & 9.931 \\
6 & 16.070 & 13.497 & 12.611 & 11.596 & 11.941 & 11.295 & 11.491 & 11.129 & 11.732 & 10.668 & 10.181 & 10.776 & 10.166 & 10.695 & 10.298 \\
7 & 15.287 & 15.504 & 13.382 & 13.005 & 12.271 & 11.748 & 11.547 & 12.520 & 11.918 & 11.731 & 11.631 & 11.924 & 11.408 & 11.821 & 11.858 \\
8 & 15.300 & 14.245 & 13.270 & 11.910 & 12.629 & 11.490 & 11.636 & 11.928 & 11.258 & 11.040 & 11.284 & 10.901 & 10.944 & 10.785 & 10.732 \\
9 & 15.464 & 14.066 & 12.943 & 12.756 & 12.254 & 12.332 & 11.842 & 11.601 & 11.929 & 10.984 & 10.949 & 10.999 & 11.042 & 10.920 & 10.892 \\
10 & 17.349 & 14.897 & 13.553 & 13.476 & 12.945 & 12.484 & 11.818 & 11.894 & 11.514 & 12.064 & 10.908 & 11.383 & 11.270 & 10.762 & 10.811 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: MRE comparison with different \(K\) for MySQL.
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 4 Numerical Experiments
### Discussion
#### 4.3.1 The Proposed Model Enhances Toll Station Performance with Computational Efficiency  
Based on Table 3, it is evident that the current allocation of ETC lanes is excessive, leading to the concentration of MTC-HV vehicles on a limited number of MTC lanes. The total system throughput time at the toll station is influenced not only by the service rate of individual lanes but also by the overall lane distribution and resource scheduling. The overloading of MTC lanes results in decreased system efficiency.  
After optimizing the lane allocation scheme using our model, the total travel time of the toll station was reduced by 90.44% compared to the current situation and proportional allocation method. This demonstrates that our model is effective in significantly improving toll station performance under heterogeneous traffic flow conditions, maximizing the potential of ETC lanes to reduce congestion.  
These findings align with prior studies that have highlighted the critical role of lane allocation in minimizing travel time at toll stations [8; 12; 22]. Additionally, our model's approach differs from traditional models by incorporating bi-level programming and Lagrangian relaxation, allowing for a more accurate representation of heterogeneous traffic flow. Compared to simpler proportional allocation methods, our allocation strategy achieves greater reductions in system throughput time. Moreover, by deriving the relaxed analytical solution, the computational convenience of this allocation strategy is significantly enhanced.
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q2. What Is the Importance of Resource Allocation in 5G?
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Algorithm/Scheme/Strategy** & **Problem Addressed** & **Improvements/Achievements** & **Limitations/Weakness** \\ \hline \multirow{4}{*}{[97]} & & & \(\bullet\) & Manages spectral resources & \\  & & Lacking intelligence & & efficiently. & \\  & Multitier H-CRAN & perspective using & \(\bullet\) & Enhances control. & \(\bullet\) & Necessary to \\  & Architecture & existing C-RAN & \(\bullet\) & Enhances control. & & improve bits of \\  & & methods. & \(\bullet\) & End-to-end optimization. & intelligence. \\  & & & \(\bullet\) & Ensures QoS by 15\%. & \\ \hline \multirow{4}{*}{[98]} & & & \(\bullet\) & Enhances resource utilization. & \(\bullet\) & Focused on the \\  & & algorithm & inaccessibility of & \(\bullet\) & Ensures the fairness of & \(\bullet\) & Limited to specific \\  & & wireless slices. & \(\bullet\) & allocation. & & parameters and \\ \hline \multirow{4}{*}{[99]} & & & \(\bullet\) & Beneficial resource & \\  & & & utilization. & & \\  & & Enhancing service & \(\bullet\) & Low computational & \\  & & demands like low & & complexity. & \\  & & latency, enormous & \(\bullet\) & Supports dynamic IoT. & \\  & & maximum data rate. & \(\bullet\) & slicing architecture. & \\  & & & \(\bullet\) & Improves efficiency and & \\  & & & flexibility. & & \\ \hline \multirow{4}{*}{[100]} & & & \(\bullet\) & The gain in end-to-end & \(\bullet\) & Limited \\  & & & \(\bullet\) & delay. & & parameters. \\ \hline \multirow{4}{*}{[101]} & & & \(\bullet\) & Attains the concave & & \\  & Hybrid DF-AF scheme & incorporating various & envelope of the maximum & & \\  & & wireless networks to & between AF rate and & & \\  & & deliver higher & DF rate. & & parameters are \\  & & data rates. & \(\bullet\) & Substantial gains for & \\  & & & RFDRC. & & \\ \hline \multirow{4}{*}{[102]} & & & \(\bullet\) & Decreases transmission & \(\bullet\) & Only for URLLC \\  & Cooperative resource & Scheduling and & & collision probability. & traffic. \\  & allocation and scheduling & resource allocation & \(\bullet\) & Enhances the reliability of & \\  & approach & problems. & \(\bullet\) & upcoming 5G. & \(\bullet\) & Considers limited \\  & & & \(\bullet\) & Enhances & \\  & & & vehicle-to-everything & & \\  & & & (ev2x) communications. & & \\ \hline \multirow{4}{*}{[103]} & & & \(\bullet\) & Maximizes energy & \(\bullet\) & Limited to \\  & & & efficiency. & & downlink. \\ \cline{1-1}  & & & \(\bullet\) & Effective capacity. & \(\bullet\) & Considers limited \\ \cline{1-1}  & & & & & metrics. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 2 Background
Linear regression was found to be overfitting. The research concluded that ensemble learning, especially weighted average, was a recommended method for forecasting [33]. Another previous study examined data mining's role in predicting retail sales for Walmart's outlets using supervised machine learning techniques. By analyzing factors like past sales, promotions, holidays, and economic indicators, the research helps businesses optimize sales forecasts and marketing strategies. The results suggest that simple regression techniques might not be optimal for short-term sales prediction with limited historical data. Ensemble learning techniques, involving the averaging of results from multiple decision trees, show better accuracy. Thus, for such scenarios, business owners are advised to opt for ensemble learning models [34]. [PERSON] et al. (2022) proposed a demand forecasting methodology for the sports retail industry using ensemble learning. The methodology includes a cluster-based demand prediction using the time-series forecasting methods LSTM and prophet and majority voting and BMA as ensemble learning techniques. The aim was to improve the accuracy of future daily demand forecasting by combining different models and assigning higher weights to better-performing models. The results show that the clustered-ensembled approach improves prediction accuracy compared to using single models, leading to minimum values of MAPE, MAE, and RMSE. Their proposed framework had a considerable increase in prediction accuracy in various seasonal and monthly cases [18]. [PERSON] et al. (2022) introduced a Spatial-Temporal Graph Attentional LSTM (STGA-LSTM) neural network for predicting short-term bike sharing demand utilizing various data sources. This model outperforms baseline approaches, leveraging deep learning to capture spatiotemporal patterns in bike sharing systems [35]. Table 1 provides a summary of selected previous works, focusing on the application domain, ensemble algorithms applied, and features used.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 3 Proposed Method
### Efficient Competition Learning Particle Update Strategy
#### 3.1.4 Winner Selection Based on SDE  
In ECLUS, there are two types of relationships between two individuals in a competitive particle pair. The first is that when two individuals have a dominant relationship, the winner and the loser can be determined through non-dominant sorting. The second is that when two individuals are non-dominated with each other, the winner is selected by the shift-based density estimation (SDE) [45] instead of the crowding distance in NSGA-II.  
The principle of SDE is as follows: For individuals in the same dominant layer, when the SDE of the individual \(p\) is calculated, the objective function values of the individual \(p\) and other individuals \(q\) are sequentially compared in each objective function dimension \(i\). If the convergence of individual \(q\) is better than that of individual \(p\), then individual \(q\) will be moved to the position of individual \(p\) in this objective dimension \(i\). Then, the distance between individual \(p\) and the moved individual \(q^{\prime}\) is calculated. Finally, the minimum distance between all \(q^{\prime}\) and the individual \(p\) is the SDE of the individual \(p\), as shown in Figure 2(a).  
The equation used for calculating the SDE of the individual \(p\) is as follows:  
\[density(p)=\min_{q\in P\setminus\{p\}}\sqrt{\sum_{i=1}^{M}\left(\max\{0,f_{i} (q)-f_{i}(p)\}\right)^{2}} \tag{24}\]  
where \(density(p)\) is the SDE value of individual \(p\), \(P\) is the set of all individuals that are non-dominated with \(p\), \(f_{i}(q)\) and \(f_{i}(p)\) are the \(i\)-th objective function values of individuals \(q\) and \(p\), respectively, and a smaller value represents better convergence. \(M\) is the number of objective functions.  
As shown in Figure 2(b), according to the crowding distance, individual A with a poor convergence but good diversity was retained, while individual B with good convergence was eliminated. However, for SDE, it is inverse. This is because the crowding distance only considers population diversity, while SDE considers both population diversity and convergence simultaneously. Therefore, the winner selection strategy based on SDE can better assist in large-scale optimization algorithms.  
Similarly, in addition to the winner selection in ECLUS, SDE is also used in NSGA-II, which is run in this paper. We call it NSGA-II-SDE.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
###### Abstract  
With the development of satellite cluster technology, the earth observation capability of satellite clusters has been greatly enhanced, along with the improvement of satellite earth observation and inter-satellite data transmission ability. Nevertheless, it is difficult to coordinate satellite observation, inter-satellite data transmission, and satellite-ground data download to satisfy the constraints of satellite multi-subsystems. In this article, the multi-satellite observation-relay transmission-downloading coupling scheduling problem is described. Based on the conventional tabu search algorithm for multi-satellite earth observation, the data transmission path planning algorithm is integrated to carry out the entire coupling process of multi-satellite observation, inter-satellite data transmission, and satellite-ground data downloading. Referring to the idea of the artificial potential field method, the satellite cluster profit-state evaluation function is introduced to enhance the local search process within the tabu search framework. Moreover, in the data transmission planning algorithm, the rule-based Dijkstra data transmission path planning method is proposed based on two data transmission path planning strategies and the satellite cluster state-strategy selection rules. The simulation results show that the proposed method can realize the entire process of scheduling satellite cluster observation, relay transmission, and downloading and enhance the ability of the satellite cluster to obtain observation data. The improved Dijkstra method enhances the adaptability of the data transmission path planning method to the multi-subsystem coupled problem, and the improved local search in the tabu search method elevates the searching capability of the algorithm.  
earth observation satellite; mission scheduling; inter-satellite data transmission; satellite-ground data downloading; multi-subsystems; tabu search; Dijkstra algorithm; artificial potential field +
Footnote †: journal: Remote Sensing  
15639
# Effect of IoT Capabilities and Energy Consumption behavior on Green Supply Chain Integration
## 3 Methodology
### Measurement Model  
Structural equation modeling is based on two types of models; first, a measurement model and, second, a structural model. In the measurement model, the validity and reliability of an instrument is analyzed [87]. Here, validity is measured through content validity, convergent validity, and discriminant validity [78; 88]. Whereas, reliability is measured through Cronbach's Alpha and composite reliability. Reliability can be measured through Cronbach's Alpha value. It typically checks the consistency or internal consistency of items. In this study, the reliability of items is tested, and the values of Cronbach's Alpha test are mentioned in Table 3. The minimum acceptable value for Cronbach's Alpha test is 0.6 [89]. In this study, the Cronbach's Alpha values of each variable have a value greater than 0.6. Therefore, all variables are reliable for further analysis. Figure 2 shows the Cronbach's Alpha values for the reliabilities of all variables. Composite reliability is the consistency of all items of the specific construct [90]. It can be obtained through the PLS-SEM method. The results of composite reliability are mentioned in Table 3. The composite reliability is measured when items are reflective, while if the items have formative measures, then the variance inflation factor (VIF) can be measured [78; 91]. In this study, reflective measures are used to measure both reliability and composite reliability. The minimum acceptable value for composite reliability is 0.6 [92]. In this study, the composite reliability values of each variable have a value greater than 0.6. All the constructs have a more than acceptable composite reliability value and that is why they are composite reliable. Figure 3 shows the composite reliabilities of all variables. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Mean** & **S.D.** & **CI** & **ECB** & **GSCP** & **GT** & **IoT** & **SI** \\ \hline
**CI** & 3.40 & 0.491 & 0.727 & & & & & \\
**ECB** & 3.41 & 0.510 & 0.89 & 0.76 & & & & \\
**GSCP** & 3.50 & 0.501 & 0.736 & 0.737 & 0.688 & & & \\
**GT** & 3.38 & 0.498 & 0.78 & 0.776 & 0.753 & 0.75 & & \\
**IoT** & 3.36 & 0.540 & 0.78 & 0.775 & 0.87 & 0.774 & 0.691 & \\
**SI** & 3.40 & 0.489 & 0.781 & 0.784 & 0.77 & 0.988 & 0.788 & 0.737 \\ \hline \end{tabular}
\end{table} 
Table 2: Mean, standard deviation, and Cornell and Larcker criterion method for discriminant validity. Figure 2: Cronbach’s Alpha values of research variables.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 1 Introduction
To achieve that, this paper proposes an enhanced LDM (En-LDM) solution that supports the multi-service transmission, which enables two service streams with different SNR ranges to be simultaneously transmitted in the same frequency band, while the SNR threshold for the fixed service is higher than that for mobile services. In the proposed scheme, part of the data extracted from the original EL fixed service through puncturing is transmitted through the CL of the LDM. The SNR of this part of the fixed service data is higher than that of the remaining fixed service data of the EL, which is more beneficial for decoding correctly. At the receiving end, after the data retrieved by the puncturing is restored in accordance with the original puncturing rules, the overall demodulation performance of the EL fixed service is improved, and the transmission rate of the fixed service is also increased accordingly. Among them, the selection of the puncturing method is based on the low-density parity-check-encoded (LDPC-encoded) bit puncturing method, which is more flexible, so that the modulation constellation of the fixed service data puncturing part can be selected according to the actual channel conditions. The technical comparison among the proposed En-LDM scheme and several typical LDM schemes is illustrated in Table 1.  
The rest of this paper is organized as follows. The traditional LDM scheme is briefly introduced in Section 2. The En-LDM scheme mechanism will be proposed in Section 3, along with the theoretical analysis and numerical simulation on the channel capacity of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Transmitter** & **Receiver** & **Achieved Capacity** & **Complexity** \\ \hline LDM & LDM & SIC & low & low \\ LTDM [22] & LDM, TDM & SIC & medium & medium \\ En-LDM & LDM, FDM, Punctured LDPC & SIC & high & high \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison among several layered division multiplexing (LDM) schemes. each service. In Section 4, the system performance of the En-LDM scheme will be presented and evaluated. Finally, conclusions are drawn in Section 5.