esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Response Surface Methodology  
#### 3.2.1. Thrust Force  
The Design Expert software suggests that the relation between the analyzed factors (drill tip angle, tooth bite, and drill type) and thrust force could be best described by Equation (12), which is in coded form. The model is significant at 1% (Table 6). Therefore, one could observe that the trust force during the drilling of plywood is significantly influenced by the drill type (\(X_{3}\)). In addition, the drill tip angle (\(X_{1}\)) and tooth bite (\(X_{2}\)) influence the thrust force (\(Y_{1}\)). The tooth bite has a bigger influence than the drill tip angle (\(X_{2}>X_{1}\)) in the case of thrust force during the drilling of plywood. This result is the same as that obtained in our previous work wherein the drilling of prelaminated wood-particle boards was analyzed and optimized [16]. In addition, this result is correlated with the data reported in the literature for the plywood and prelaminated particle board panels, namely, it is stated that the feed rate affects the thrust force [3; 28]. Consequently, a low feed rate leads to a low thrust force, and a high feed rate implies a high thrust force. The tooth bite has a non-linear effect on the thrust force. This finding tells us that the optimum value of the tooth bite stands inside the analyzed range (0.1-0.7 mm). Moreover, the statistical analysis reveals the fact that there is an interaction effect of analyzed factors on thrust force (\(X_{2}X_{3}>X_{1}X_{2}>X_{1}X_{3}\)). The most significant interaction is between the tooth bite (\(X_{2}\)) and drill type (\(X_{3}\)). This result is the same with that obtained in the case of prelaminated wood-particle boards [16]. The influence of the drill tip angle (\(X_{1}\)) and tooth bite (\(X_{2}\)) on thrust force (\(Y_{1}\)) is plotted in Figure 8. Equations (13) and (14) could be used to predict the thrust force (\(Y_{1}\)) based on the drill tip angle (\(X_{1}\)) and tooth bite (\(X_{2}\)) during drilling of plywood. Equation (13) is for the flat drill and Equation (14) is for the helix drill. \[\hat{Y}_{1 coded}=192.55+18.51X_{1}+56.27X_{2}-120.64X_{3}+11.39X_{1}X_{2}+9.16X _{1}X_{3}-46.85X_{2}X_{3}+13.44X_{1}^{2}\]  
\[ \begin{array}{c}-21.31X_{2}^{2}-2.34X_{1}X_{2}X_{3}-24.53X_{1}^{2}X_{3}+27. 95X_{2}^{2}X_{3}\end{array} \]  
\[\hat{Y}_{1 flat}=208.52-3.01X_{1}+705.33X_{2}+1.016X_{1}X_{2}+0.018X_{1}^{2}-5 47.35X_{2}^{2}\]  
\[\hat{Y}_{1 helix}=14.34+1.16X_{1}-77.87X_{2}+0.67X_{1}X_{2}-0.0055X_{1}^{2}+73. 75X_{2}^{2}\]  
The ANOVA results are significant at 5% (\(\alpha=0.05\)) and are presented in Table 6.  
When the \(p\)-value was less than 0.05, the analyzed factors were significant at 95% confidence level. Therefore, it could be concluded that the next model terms are significant: \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{2}X_{3}\), \(X_{2}^{2}\), \(X_{1}^{2}X_{3}\), \(X_{2}^{2}X_{3}\).
# How to Optimize the Implementation of ITIL through a Process Ordering Algorithm
## 2 State of Art
### Static Sequences  
The reference [20; 30] are examples of static sequence where the authors establish a way to measure the relationship among the different processes. These processes, conveniently classified, determine a graph in which cyclical or dependent paths can be found. Thus, the more elements a path has, the more dependencies it contains, and so all the processes involved in that path are compromised: The cycle with the largest number of nodes (that is, processes) or processes is selected.  
In [31] the idea of fixing a static sequence is taken again: In this paper, ITIL implementation is organized from the definition of three models:  
* A first model of isolated ITIL processes.
* Another model that contains the dependencies between ITIL processes.
* A final model to relate the capacity level of the processes and the maturity level of the organization. One of the main characteristics of this proposal is the combination of concepts of the ITIL and Capablity Maturity Model Integration (CMMI) to propose a sequence.  
It could be thought that this proposal describes a dynamic sequence, but in fact the result is a fixed (or very close to be fixed) sequence. The reason is that the second model of the mentioned above, determines which processes should be implemented to implement the next one; so only slight differences in the initial processes can be found. Once these ones have been implemented, the rest of the sequence hardly vary from one organization to other.  
A fixed sequence of implementation is also shown in [32]: Although there is not an explicit justification, it shows a sequence that may be used as a reference. It means that companies can use the same sequence, as it is presented as company, geographically, and industry-independent.  
In some cases, the objective is not to define explicitly a sequence but, rather, address a starting point, which means defining the first process to implement. This is the case in [9; 17; 19]--where the proposal has empirical basis supported by market analysis. Once more, the first process to implement the proposed is Incident Management, followed by Service Level Management, and Service Catalog Management.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 2 Ground Tests and on-Orbit Validation
### On-Orbit Validation  
The designed life of Zhurong was 90 Mars days. Within the design life, Zhurong operated in a highly efficient detection mode, and traveled a total of 899 m with 57 movements [26,27], 15.7 m on average each time, and the longest planning distance for a single time was 26.8 m, which posed a challenge to the visual positioning accuracy of the Mars rover. To solve this problem, [PERSON] would have a short stay between adjacent stations for imaging, and its imaging area overlapped with the two adjacent stations, so as to obtain precise positioning, the specific process was as follows:  
1. Step 1: Station \(N\). When [PERSON] arrived at Station \(N\) (as shown in Figure 3), a 3D reconstruction of the environment and the visual positioning was performed. Selecting the middle point \(X\) and the target point Station \(N\)+1 based on the environment information and generating the Station \(N\)-\(X\) path planning strategy.
2. Step 2: Station \(X\). [PERSON] drove to the Station \(N\)-\(X\) according to the instructions, which were called the blind movement mode. It performed a visual positioning at point \(X\) to obtain the relative position of point \(X\) to Station \(N\).
3. Step 3: Station \(N\)+1. Then, [PERSON] drove to the \(X\)-Station \(N\)+1 section using the autonomous obstacle avoidance movement mode. When it arrived at Station \(N\)+1, step one was repeated.  
From the above described process, when [PERSON] drove using blind movement mode in step two, we knew its planning path and the actual end point of arrival. It was available for the on-orbit slip validation. The wheel of the Zhurong Mars rover is printed with the Chinese word \"\(\#\)\". When driving on the surface of Mars, the distance and position of two adjacent words in the rut mark (as shown in Figure 4) can be used to assist in judging whether the rover has slipped.  
Figure 2: The indoor test site of ground tests.
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 1 Introduction  
The application of stainless steels to structural members has been receiving increasing attention from researchers and engineers owing to their attractive material characteristics compared with conventional carbon steel [1; 2; 3]. They possess not only high ductility, which enhances the fire performance of a steel structure, but also excellent corrosion resistance, used advantageously in marine structures. However, the irreconcilable conflict between initial material cost and performance has impeded more widespread utilization of stainless steel in the structures.  
Nowadays, a novel lean duplex stainless steel, which contains a lower amount of nickel than the austenitic counterpart, has been developed to take economic advantages. In addition, European design code [4] has provided design rules for the lean duplex stainless steel from the 2015 edition, although the current American cold-formed stainless steel standard [5] and Australasian cold-formed steel standard [6] have still not covered it.  
Block shear capacity of the steel connections has been assumed as the combination of tensile resistance and shear resistance in the current design codes. In the design for block shear of welded connection, consideration should be given to both the constraint (stress triaxiality) effect and the extent of shear strain hardening. The former is a result of the presence of weldment that hinders necking of the plate material in the direction perpendicular to the loading, while the latter is associated with material ductility.  
In fact, the material ductility effects on shear strain hardening capacity have been studied well with experimental and numerical data of bolted connections over a wide range of steel materials by [PERSON] [7], [PERSON] et al. [8], [PERSON] et al. [9], [PERSON] et al. [10], and [PERSON] et al. [11].  
However, the constraint effect has not yet been demonstrated. [PERSON] [12], [PERSON] [13], and [PERSON] et al. [14] have pointed out that the block shear mechanism in welded connection under the lateral constriction promoted by the weldment is significantly different from the bolted case. The current design specifications have still not provided separate block shear equations for welded connection.  
The present paper thus investigates two issues related to the block shear capacity of welded connection: (i) the constraint effect on the tensile stress and (ii) the shear strain hardening capacity of welded connection in lean duplex stainless steel sheet. A total of 17 test results of welded connection specimens failing in block shear were presented. In order to examine the block shear response, a finite element model coupled with material softening was developed in the present paper. In this way, initiation and propagation of fracture can be simulated. In addition, the stress and strain response over the whole loading history up to the completed fracture can be captured.
## 4 Results
### No Feature Selection and Equal Width Transformation  
When data transformation was applied with equal width, the Random Forest predicted with an accuracy of 85.5%, as shown in Table 5. From the analysis, it was found that the equal width data transformation technique can be further investigated with feature selection, as the result showed slight improvements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Measure** \\ \hline Random Forest & 0.850 & 0.911 & 0.873 & 0.911 \\ Log. Reg. & 0.846 & 0.992 & 0.845 & 0.913 \\ kNN & 0.837 & 0.939 & 0.870 & 0.903 \\ Tree & 0.837 & 0.936 & 0.872 & 0.903 \\ CN2 Rule Inducer & 0.837 & 0.938 & 0.871 & 0.903 \\ SVM & 0.820 & 0.960 & 0.841 & 0.896 \\ Neural Network & 0.811 & 1.00 & 0.811 & 0.896 \\ Naive Bayes & 0.804 & 0.931 & 0.844 & 0.885 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data transformation (equal frequency), feature selection (none).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Measure** \\ \hline CN2 Rule Inducer & 0.851 & 0.960 & 0.870 & 0.913 \\ Random Forest & 0.851 & 0.952 & 0.875 & 0.912 \\ Log. Reg. & 0.847 & 0.992 & 0.846 & 0.913 \\ Tree & 0.843 & 0.939 & 0.876 & 0.907 \\ kNN & 0.841 & 0.939 & 0.874 & 0.905 \\ Naive Bayes & 0.821 & 0.957 & 0.844 & 0.897 \\ Neural Network & 0.811 & 1.00 & 0.811 & 0.896 \\ SVM & 0.785 & 0.927 & 0.829 & 0.875 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data transformation (none), feature selection (none).
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 4 Performance Evaluation
### Evaluation
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Test\_Sequence** & **WS-PSNR Y Channel** & **WS-PSNR U Channel** & **WS-PSNR V Channel** \\ \hline DrivingInCity\_300 frames\_30 fps\_1920\(\times\) 1080 & 39.36 & 45.14 & 44.52 \\ DrivingInCity\_300 frames\_30 fps\_3840\(\times\) 1920 & 38.68 & 45.33 & 44.63 \\ GasLamp\_300 frames\_30 fps\_1920\(\times\) 1080 & 41.47 & 46.74 & 46.08 \\ GasLamp\_300 frames\_30 fps\_4096\(\times\) 2048 & 42.19 & 47.34 & 46.61 \\ Harbor\_300 frames\_30 fps\_1920\(\times\) 1080 & 40.92 & 47.36 & 47.52 \\ Harbor\_300 frames\_30 fps\_4096\(\times\) 2048 & 41.24 & 48.43 & 48.25 \\ KiteFlite\_300 frames\_30 fps\_1920\(\times\) 1080 & 38.03 & 44.22 & 45.49 \\ KiteFlite\_300 frames\_30 fps\_4096\(\times\) 2048 & 38.94 & 46.05 & 46.83 \\ Trolley\_300 frames\_30 fps\_1920\(\times\) 1080 & 39.46 & 45.43 & 46.81 \\ Trolley\_300 frames\_30 fps\_4096\(\times\) 2048 & 39.78 & 47.05 & 47.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: WS-PSNR (Peak Signal-to-Noise Ratio) comparison. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Test\_Sequence** & **ARIA Total Time** & **ARIA Time** & **AES Total Time** & **AES Time** \\ \hline ParkScene\_240 frames\_25 fps & 5.596 s (42.88 fps) & 3.16\% & 5.07 s (47.33 fps) & 2.82\% \\ Kimono\_240 frames\_25 fps & 5.61 s (42.7 fps) & 2.53\% & 5.39 s (44.5 fps) & 2.07\% \\ Basketball\_500 frames\_25 fps & 11.89 s (42.05 fps) & 2.28\% & 11.55 s (43.29 fps) & 1.14\% \\ Cactus\_500 frames\_25 fps & 11.92 s (41.94 fps) & 2.19\% & 11.28 s (44.32 fps) & 0.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Secure algorithm benchmark on proposed system using Crypto++. Figure 17: Hevcrowser parsing: **(a)** unencrypted HEVC bitstream; **(b)** encrypted HEVC bitstream.
###### Abstract  
Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research.  
l 2025  
25 June 2025  
26 July 2025  
28 July 2025  
Subject:3 August 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The First Step of AI in LEO SOPs: DRL-Driven Epoch Credibility Evaluation to Enhance Opportunistic Positioning Accuracy. _Remote Sens._**2025**, 17, 2692. [[https://doi.org/10.3390/rs17152692](https://doi.org/10.3390/rs17152692)]([https://doi.org/10.3390/rs17152692](https://doi.org/10.3390/rs17152692))  
**Copyright:** 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 4 Multi-Objective Modeling and Solving
### Optimization Model
#### 4.2.1 Non-Dominated Sorting Differential Evolution Algorithm  
The NSGA-II is a popular optimization method used to solve MOOP [37]. In 1994, NSGA was proposed by [PERSON] and [PERSON] [38]. Based on the shortcomings of NSGA, [PERSON] et al. introduced NSGA-II in 2000 [39], which has gradually become the mainstream algorithm used for solving MOOP. It uses non-dominated sorting and shared variable methods to effectively maintain the diversity of Pareto frontiers. However, NSGA-II does not perform well in processing MOOP with a complex Pareto front. Given these shortcomings of NSGA-II, we designed an improved algorithm, which we have called the non-dominated sorting differential evolution (NSDE) algorithm. NSDE introduces a differential evolution operator to replace the crossover operation in NSGA-II and adaptively adds the global optimal solution information in the new generations to improve the local search accuracy and diversity. Considering the characteristics of the model, we combined NSGA-II with DE, and designed the following solving algorithm.  
* Define the parameters of the algorithm. Define population size, iterations, and the crossover probability parameter, and the counter is set to 0.
* Initialize the population. The initial population is randomly generated and the individual encoding structure is outlined in Section 3.3.
* Sort the population. According to the objective function and constraints, evaluate the individuals. Use non-dominated sorting on the initial population to obtain the Pareto solution set. Then individuals are given ranks and crowding distance values. The binary tournament selection operation is implemented on the population.
* Mutation and crossover. The mutation operator based on DE was used to mutate the population. Simultaneously, a new population \(Q\) is obtained by using crossover operation on each of the individuals with a certain probability.  
* Evaluate the temporary population. The temporary population is composed of the present population \(P\) and the offspring population \(Q\), and non-dominated sorting of the temporary population is performed by comparing the rank and crowding distance of the individuals.
* Generate a new population. Select the best individuals from the temporary population to generate the new population.
* Evolution and iteration. If the number of iterations is reached, the Pareto optimal solution is output. Otherwise, the counter increases, and we continue back to Step 3.
## 5 Experiments and Analysis
### Performance Analysis  
To comprehensively evaluate the performance differences between the MATD3-TORA task offloading algorithm and other benchmark methods, this section conducted a detailed analysis under varying total task volumes and transmission bandwidths. Notably, the Random algorithm was excluded from the analysis due to its failure to achieve convergence. Consequently, the comparison focused on four algorithms: MATD3-TORA, MADDPG, Local-Only, and Edge-Only. This approach ensured a rigorous and meaningful evaluation of the proposed algorithm's effectiveness in diverse operational scenarios.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 5 Conclusions  
In this study, a data-driven approach based on engine acoustic signals for classification was proposed for ICE fault diagnosis. Three faulty conditions, namely engine misfire, ignition timing variation and AFR variation, were used to train and test the applicability of the approach. Three models were studied, i.e., the standard classification approach (Model 1), the Bayesian optimized model (Model 2) and the use of PCA alongside Bayesian optimization (Model 3). In all cases, WPT was used to decompose the acoustic signals, and the statistical features were extracted from the wavelet coefficients, which were then used as the input of the three classification models. The aim of the study was not only to achieve the highest accuracy, but also to reduce the computational time by eliminating redundant features. In doing so, the feature sets with reduced dimension would be more suitable for the future online implementation in real-time engine fault diagnosis applications. Both Model 2 and Model 3 achieved better accuracy and the other performance metrics than the standard model for all three faults. With similar accuracy level compared to Model 2, Model 3 achieved \(\sim\)20% less combined training and testing evaluation time and 8-19% less time for testing evaluations for the fault cases. This indicates that the WPT with PCA and Bayesian optimization technique could be a promising tool for real-time acoustic engine fault diagnosis application.  
In reality, there is a trade-off between the fault classification accuracy and the processing time saving, and it will depend on the industrial application and their specific requirement. For example, if the application requires high accuracy in the fault diagnosis, as a false alarm can result in excessive downtime costs, the user may prefer to select Model 2 at the expense of additional processing time. On the other hand, if, for example, some combustion faults need to be identified within a short time duration, before they lead to catastrophic component damage, then the user may consider that implementing Model 3 on fast computing electronics will be essentially beneficial for this application.  
The authors contributed equally to the conception of the idea, the design of experiments and the analysis and interpretation of results. [PERSON] wrote the manuscript. [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors thank [PERSON], [PERSON] and [PERSON] for their support in the experimental work.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Calibration Curve  
Prior to HPLC analysis of the samples, a calibration curve was constructed using standard solutions prepared according to the protocol described in Section 2.6 and HPLC analysis performed according to the previously described protocol (Section 2.3). The analysis time was 8 min and retention time for lutein was 4.2 min. The constructed calibration curve is shown in Figure 1 and obtained R\({}^{2}=0.99989\).  
The time of analysis was shortened from 20 min, as recorded in previous research [14; 20; 32], to 8 min. The retention time of lutein was also reduced to 4.2 min. Shortening analysis time and shorter retention time represent significant savings of time and chemicals.