esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**S/No**} & \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Scalability**} & \multicolumn{2}{c}{**Computing cost**} & \multirow{2}{*}{**Privacy**} & \multirow{2}{*}{**Latency**} & \multirow{2}{*}{**Deployment cost**} \\ \cline{3-4} \cline{6-7}  & & & **RSU** & & & & & \\ \hline
1 & Pseudonym _cum_ PKI & HIGH & HIGH & HIGH & MED & MED & HIGH \\
2 & Group Signatures & LOW & MED & HIGH & HIGH & HIGH & HIGH \\
3 & Trust-Based Schemes & LOW & HIGH & HIGH & LOW & HIGH & HIGH \\
4 & K-anonymity & LOW & MED & HIGH & HIGH & HIGH & MED \\ \hline
5 & Identity-Based Encryption & \multirow{2}{*}{HIGH} & \multirow{2}{*}{MED} & \multirow{2}{*}{HIGH} & \multirow{2}{*}{HIGH} & \multirow{2}{*}{HIGH} & \multirow{2}{*}{HIGH} & \multirow{2}{*}{HIGH} \\  & (IBE) with Pseudonyms & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for security schemes in VANETs.
## 3 Methodology
### Data Pre-Processing  
In the data collection stage, potential defects such as sample bias, selection bias and information bias may have a significant impact on the model training and testing stage, and then affect the prediction accuracy of the model. Therefore, the proper pre-processing of the original dataset is essential before building a model with the collected dataset and conducting a preliminary analysis.  
To mitigate these issues, we plan to process the data with the following strategies to reduce their potential impact on the model's predictive power.  
Resampling: Through the implementation of resampling technology, the aim is to improve the representativeness of the sample and reduce the sample bias.  
Data enhancement: The use of data enhancement techniques to balance the distribution of different categories and alleviate the problem of the accumulation of characteristic data.  
Sensitivity analysis: The sensitivity analysis of key feature parameters is carried out to assess their impact on the research results and provide theoretical bases for identifying and selecting feature factors with significant impacts [26].  
After implementing the above measures, we will verify the effectiveness of bias mitigation measures in the following ways.  
Cross-validation: Cross-validation is used to evaluate the stability and generalization ability of the model on different data subsets.  
External dataset comparison: Comparisons are made with external datasets to verify that the predictive power of the model has been substantially improved.  
In addition, we will use the test results to further adjust the model parameters to optimize the model performance. Through this series of comprehensive measures, we expect to be able to significantly improve the prediction accuracy of the model and ensure the reliability and validity of the research results.
## 3 Results and Discussion
### Influence of Atomizing Gas Type
#### 3.3.3 Minimum Oxygen Concentration  
In Figure 13, the minimum oxygen concentrations for nitrogen and air, as the atomizing gas, are compared. As shown in Figure 13a, the increases in the water and gas flow rates increase the minimum oxygen concentration. However, at a gas flow rate of 10 L/min, the minimum oxygen concentration decreased with an increase in water flow rate from 85 to 160 g/min. This is due to the fact that the water flow rate of 160 g/min is a slightly longer extinguishing time than that of 85 g/min, as shown in Figure 10a. Furthermore, the effect of the atomizing gas type on the minimum oxygen concentration was negligible. The trends for HRR of 5.23 kW are similar to those for HRR of 1.17 kW, as shown in Figure 13b. The minimum oxygen concentrations increased with increases in the water and gas flow rate conditions, and no significant differences between nitrogen and air were observed.  
In Figure 14, the relation between the extinguishing time and minimum oxygen concentration for nitrogen and air is shown. Increases in extinguishing times resulted in decreasing the minimum oxygen concentrations, which were similar to Figure 8. However, the relations in the nitrogen case were similar to those in the air case.
###### Abstract
Secondly, the unstable relative position and severe space environment can potentially degrade the link performance. Thirdly, satellites in LSNs have a limited bandwidth and memory and need to minimize the communication overhead as much as possible. Lastly, the scale of LSNs makes it relatively challenging to maintain traffic forwarding [10]. Therefore, routing algorithms suitable for LSNs need to prioritize latency performance and packet reception success rates [11]. Traditional networks have undergone extensive research with regard to routing optimization [12; 13; 14]. However, the algorithms developed for these networks cannot be directly used for LSNs due to their unique features. Therefore, this paper focuses on studying the routing optimization problem specifically in LSNs, taking into consideration their distinct features. The main contributions of this paper are as follows:  
1. The low-orbit satellite network model is defined as an unsplittable multi-commodity flow problem (UMCF) [15], which is then modeled as an MILP problem. 2. We propose a routing strategy called GAGR, which is based on GA assisted by ground access optimization; it incorporates GA to obtain an approximate optimal solution. 3. Before sending a flow from both the source and destination ground stations, we select three nearby satellites as candidate satellites, replacing the traditional strategy of selecting the nearest satellite. 4. We compare the performance of the GAGR routing strategy, virtual-topology-based shortest path (VTSR) routing strategy [16], and Floyd-Warshall (FW) [17] routing strategy under the UDP protocol and TCP protocol through experiments, and the experimental results demonstrate that our GAGR routing algorithm exhibits improved performance in terms of a reduced RTT, increased flow reception rate, and lower ISL occupancy rate. Given the extensive use of abbreviations in this paper, we provide Abbreviations section containing their definitions for reference. The rest of this article is organized as follows. Section 2 introduces the related work on satellite network routing. Section 3 introduces the network model and problem definition. In Section 4, we introduce the GA solution algorithm for UMCF and discuss the reasons for introducing three candidate satellites for ground stations. Section 5 describes the experiments and results. We summarize the paper in Section 6.
## 1 Introduction
[PERSON] & [PERSON] (2016) proposed a method that combines the ARIMA forecasting technique and a multi-objective GA based on the Pareto optimal to predict the next optimum. Their method is based on historical optimums and is used to optimize \(AR(p)\) and \(MA(q)\) to find a non-dominated Pareto front solution with an infinite number of points. They found that their method improved the prediction accuracy. However, these authors assumed that the data were accurate and used the Pareto front solution to select a proper forecasting. In addition, they did not use any forecasting error rate to evaluate the forecasting results. The aim of this study has been to develop a novel two-level multi-objective GA to optimize time series forecasting in order to forecast cost data for fans used in road tunnels. The first level of the GA is responsible for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. This level gives possibilities of finding the optimal forecasting solution. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the approach of using an ARIMA model only. We argue that a multi-objective GA decreases the complexity, increases the flexibility, and is very effective when selecting an approximate solution interval for forecasting. The remainder of the paper is organized as follows. The next section presents the materials and methods, which include data collection, the ARIMA model, a two-level multi-objective GA based on the ARIMA model, a two-level multi-objective GA based on the dynamic regression model and a model evaluation method. Section 3 describes the results and decisions for each method presented in the previous section. Section 4 offers the concluding remarks.
# Projection Pursuit Multivariate Sampling of Parameter Uncertainty
## 1 Introduction
Due to the maximization of the distance between any pair, the realizations of the sample tend to spread out across the sampling space, resulting in a better multidimensional uniformity. The latter approach expands the univariate uniformity obtained by CLHS to the multivariate context. The algorithm first generates more realizations than are required, and the realizations that are close to each other (or redundant realizations) are sequentially eliminated in the multidimensional space. The post-processing of the realizations is then carried out to enforce the uniformity in the high-dimensional space. Considering both maximin LHS and LHSMDU, it is important to note two things: (1) the realizations generated by both techniques are still based on CLHS, and (2) both techniques initially require a large number of sampling designs to be generated, which significantly increases the central processing unit (CPU) run time. A better approach for enforcing the sparsity in the sampling designs, however, would be to use projection pursuit [22; 23] iterations. The idea of projection pursuit is that the projected data is expected to have a univariate Gaussian distribution if the original data is multivariate Gaussian. The original data is, therefore, generally first transformed to normal scores and then sphered so that the projection index only measures the deviation of the distribution of any projected data from the standard Gaussian distribution \(N(0,1)\). The projection pursuit multivariate transform (PPMT) proposed by [24] makes use of this idea and applies a normal score transformation along a projection vector in an iterative fashion so that the original data will be eventually transformed to the uncorrelated and multivariate Gaussian scores. In fact, this amounts to saying that the multidimensional uniformity of the sampling design can be ensured through this technique. We demonstrate the applicability of PPMT as an efficient sampling technique in two synthetic case studies using the low (bivariate) and high (five-variate) dimensional sampling spaces. The results of the simulation studies indicate that considering the various sample sizes, PPMT yields much fewer sampling errors and exhibits better space-filling characteristics than the other sampling techniques.
## 4 Results and Discussion
### Evaluation of the Phenological Characteristics of MOD17A2H  
According to Figure 9, MOD17A2H does express the phenological characteristics of GPP in the Arctic, with cross-sites R and RMSE of 0.62 and 8.9 days, respectively. In particular, as shown in Figure 9c, the peak GPP timing DOY of grasslands was the most consistent with that extracted from in situ data (R = 0.86, RMSE = 4 days). By contrast, the discrepancy between MOD17A2H and in situ peak GPP timing over forest sites was the highest (Figure 9b), with R and RMSE of 0.52 and 11.9 days, respectively, which demonstrates that the characteristics of the forests (mostly evergreen needleleaf) GPP, derived from MOD17A2H, are difficult to capture compared with the characteristics of other land cover types. This might be due to two reasons: (1) forests are composed of evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, and mixed forests; therefore, the diversity of forests makes phenological characteristics difficult to capture by satellite observations; and (2) evergreen needleleaf forest is one of the major forest types in the Arctic, which is less sensitive to climate changes according to [46], thus the phenological characteristics of evergreen needleleaf forests are hard to capture.
## 4 Simulation Experiment Results and Discussion
### Directional Variable Polarization Modulation with MP-WFRFT  
To address the limitations of the current satellite communication systems, such as inadequate resistance to scanning, low secrecy rates, and weak decryption resistance, this paper proposes an enhanced approach by integrating the MP-WFRFT and DM algorithms based on the VPAPM scheme. The simulation parameters are configured as detailed in Table 4.  
Figure 13: BER curves of \(\cos\gamma\) (H) and \(\sin\gamma e^{i\varphi}\) (V) directions for VPAPM scheme.  
The simulation results are presented in Figure 14. Notably, an azimuth of \(54^{\circ}\) represents the desired receiver position. Meanwhile, the correct transformation orders of the MP-WFRFT \(\cos\gamma\) and \(\sin\gamma e^{j\varphi}\) directions are respectively \(\alpha=0.5\) and \(\beta=0.5\). From the analyses in Figure 14, it is evident that the fusion algorithm of MP-WFRFT and DM significantly enhances the anti-interception performance compared to using a single secure transmission technique. Specifically, the BER is improved by at least an order of magnitude when the signal-to-noise ratio exceeds 10 dB, which demonstrates the robustness and effectiveness against interception.  
When the azimuth angle in DM is set to a specific value, the BER curves for both the horizontal transformation order \(\alpha\) (\(\alpha=0.5,0.6,0.7,0.8\)) and the vertical transformation order \(\beta\) (\(\beta=0.5,0.6,0.7,0.8\)) over the same scanning interval are observed. Notably, alterations in the \(\cos\gamma\) direction result in a marked improvement in the BER performance of the satellite communication system. In contrast, the BER curves for the \(\sin\gamma e^{j\varphi}\) direction exhibit a phenomenon characterized by clustering, which does not contribute to significant enhancement in the anti-interception performance. Furthermore, it is evident that random changes in the parameters of the MP-WFRFT algorithm, coupled with a substantial deviation of the DM azimuth from the ideal direction, can have a profound impact on the BER performance of the VPAPM modulation-based scheme.
## 7 Results
### Attestation Time
If the ML-EAT token uses string-encoded claim keys, the encryption overhead for model subclaims averages around 6.39 ms for architectures with fewer than 10 layers, while for models with more than 10 layers, the average overhead increases to 19.71 ms. In comparison, for integer-encoded claim keys, the encryption overhead is lower, with an average difference of 7.42 ms for models with fewer than 10 layers and 10.45 ms for models with more than 10 layers. Integer-encoded tokens are generally more efficient, especially for larger models, in terms of both overall time and the added encryption overhead. On average, the attestation time for an integer-encoded token with encrypted model subclaims is 2.6 ms higher than that of a string-encoded token with plain data. By standardizing the ML-EAT token to use integer-encoded claim keys, token generation time could be significantly reduced while maintaining the encryption of model subclaims. This approach would lead to more efficient processing and lower overhead, particularly for larger models. The baseline of 27.8 ms corresponds to the attestation generation time when the ML-EAT does not include the model architecture subclaims, and the model's hash is precomputed during the device's provisioning phase. Figure 8: Performance of the dual attestation mechanism for the evaluated models. Tokens generation time and model hash overhead.
## 3 Test Results and Discussion
### Sample Deformation  
Figure 11 shows the deformation of the coarse-grained soil after the triaxial tests under different end-restraint conditions. The end restraint restricted the radial deformation of the ends of the specimens, and the radial deformation of the middle portion of the specimen with the end restraint was significantly larger than that of the end-free specimen (microfriction), showing bulging around mid-height. The end-free specimen (microfriction) was relatively uniformly deformed up and down.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Program**} & \multicolumn{4}{c}{**Shear Strength Index**} & \multicolumn{4}{c}{**E–B(\(\upmu\)) Model Parameters**} \\ \cline{2-13}  & \(c\)**(kPa)** & \(\varphi\) & \(\varphi_{0}\) & \(\Delta\varphi\) & \(K\) & \(n\) & \(R_{f}\) & \(K_{b}\) & \(m\) & \(G\) & \(F\) & \(D\) \\ \hline End restraint & 82.5 & 39.2 & 47.6 & 7.1 & 614 & 0.23 & 0.83 & 279 & 0.12 & 0.379 & 0.337 & 5.50 \\ \hline End free (microfriction) & 77.1 & 38.8 & 46.0 & 6.0 & 521 & 0.21 & 0.77 & 218 & 0.10 & 0.383 & 0.113 & 1.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Duncan–Chang E–B(\(\upmu\)) model parameters of coarse-grained soil.
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 6 Conclusions  
In this article, we present a hybrid ant colony optimization (ACO&CM) for solving the QoS multicast routing problem. The primary innovation is to combine the solution generation process of the ACO algorithm with the CM. Since a high pheromone trail on some edges may lead to fast convergence and fall into local optimum, within the framework structure of the ACO, we embed a cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone concentration on edges. To avoid the possibility of loop formation in the process of constructing a feasible multicast path, we devise a memory detection search (MDS) strategy, that is, the current list (\(CL\)) and the path list (\(PL\)), and integrate it into the path construction. The simulation results have demonstrated that our ACO&CM algorithm was able to attain better or competitive performance. The main limitations in this study mainly lie in that the test instances are not large enough, and the self-adaptive adjustment of principal parameters is not considered. These key issues are to be solved in future research work. Therefore, in the future, we are planning to further improve the performance of the ACO&CM algorithm, especially in large-scale instances. We will study the comparison between this algorithm and other algorithms, such as cuckoo search, particle swarm optimization, etc. Besides, according to the search characteristics of ACO&CM, our future work includes designing a new self-adaptive adjustment strategy of principal parameters to improve the adaptability of the algorithm to different practical conditions.  
[PERSON] designed the algorithms and wrote the paper; [PERSON] performed the experiments; [PERSON] was responsible for the reviews and discussed the results.  
This work is supported by National Science Foundation of Liaoning (Grant No. 20170540471) and Foundation of Liaoning Educational Committee (Grant No. L2015265).  
The authors declare no conflicts of interest.
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 4 Conclusions  
The objective of this research was to investigate the performance of different optimization methods for selecting the most suitable techniques for recovering far-field radiation. The findings indicate that the SA and GA methods are not suitable for failed planar array radiation pattern correction. The PSO method provided the highest SNR reduction (1 to 10 dB) among all the methods. The PS method also provided an ideal solution for this case where it managed to reduce 2 to 5 dB of failed SNR values. The PSO and PS methods took 9079.376 s and 1520.216 s, respectively, to converge. Next, users can compare cost and time factors upon applying these optimization methods in real life to correct failed planar array correction.  
Both PSO and PS methods successfully find optimum weights of the remaining working element to provide a beam pattern similar to the beam pattern without any failure in the array. The optimized weights with failed elements can successfully provide a beam pattern that gives reduced SNR compared to failed patterns that closely match the reference radiation pattern.  
Conceptualization, N.B., A.K.R. and F.N.; methodology, N.B., A.K.R. and F.N.; software, N.B. and F.N.; validation, N.B., F.N. and A.K.R.; formal analysis, N.B.; investigation, N.B.; resources, A.K.R.; data curation, N.B.; writing--original draft preparation, N.B.; writing--review and editing, N.B., A.A.A., A.K.R. and F.N.; visualization, N.B. and A.A.A.; supervision, A.K.R. and F.N.; project administration, A.K.R.; funding acquisition, A.K.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UNITEN BOLD, grant number RJO10517844/065. The APC was funded by Universiti Tenaga National (UNITEN) BOLD Publication Fund and UNITEN BOLD, grant number RJO10517844/065.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion
### Limitations  
While this study provides valuable insights into the effects of squat and ballistic jump exercises on judo-specific performance, several limitations should be noted. First, although the sample size was estimated using a priori calculations, the small sample size (\(n=10\)) limits the generalizability of the findings to a broader population of young judokas. A larger sample size would enhance the statistical power and improve the reliability of the results. Second, the study only included male participants, which restricts the applicability of the findings to female judokas. Future research should explore whether similar results are observed in female athletes or across different age groups and competition levels. Third, we used squats and ballistic jumps as conditioning activities and a recovery time of 8 min. Of note, previous studies [5; 31] have suggested that PAPE could be moderated by the type of conditioning activities, exercise type, loading, recovery interval, performance parameters, etc. Therefore, future research should focus on various moderating factors that could potentially affect PAPE in judo. Fourth, the focus was on immediate performance effects rather than the long-term impact of repeated conditioning activities. While the study assessed acute performance improvements, it remains unclear how consistent use of these exercises in training over time would affect performance, fatigue, and injury risk. Future studies could explore the chronic effects of incorporating these conditioning exercises into regular training programs. Fifth, the study measured handgrip strength but did not assess other important physical parameters, such as lower body power or muscle fatigue, which could provide a more comprehensive understanding of how these exercises influence judo-specific performance. Incorporating a broader range of performance and physiological measures in future research would help clarify the full scope of the impact of conditioning activities. Lastly, although a randomized crossover study design was adopted to minimize the confounding effects of extraneous variables on the results, controlled laboratory conditions (e.g., temperature) could be an appropriate setting.