esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests
## 5 Conclusions  
This paper presents a lateral-directional control method for the high aspect ratio full-wing UAV without an aileron and rudder by using the differential thrust of propellers as the output. By doing research on flight stability, the yaw angle attitude control and trajectory tracking of the full-wing UAV through simulations and flight tests, the authors demonstrated the following key points:  
1. \(C_{Y\beta}\) was very important for the dutch roll mode of the high aspect ratio full-wing UAV. Large \(C_{Y\beta}\) can significantly improve the dutch roll mode damping.
2. It was feasible and effective to use the yaw angle instead of the roll angle as the controlled state for the high aspect ratio full-wing UAV. The differential propeller thrust had good control efficiency, which can replace an aileron and rudder to control the drone's lateral-direction.
3. The simulation and experiment results show that the lateral-directional control method for the high aspect ratio full-wing UAV designed in this paper had a good control effect, robustness, and disturbance rejection ability. At the same time, the controller had less calculation, fewer parameters to be adjusted, and a simple implementation process, which is very important for engineering applications.  
Conceptualization, X.Z. ([PERSON]). and Z.Z.; methodology, Z.M.; software, Z.M.; writing, original draft preparation, Z.M.; writing, review and editing, Z.M.; project administration, X.Z. ([PERSON]); data curation, X.Z. ([PERSON]).  
This research was funded by the Civil Aircraft Special Project (MJ-2015-F-009), the National Key R&D Program in Shaanxi Province (2018 ZDCXL-GY-03004), the Equipment Pre-research Project (No. 41411020401), and the Innovation Program of Research Institutions (No. TC2018 DYDS24).  
The authors declare no conflict of interest.
# Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
## 4 Case Study
### Spare-Part Distribution
The developed model of reactive-proactive mission planning (based on layers 1-4; see Figure 4) can be used to build a decision support system for dispatchers delegating specific work to service teams. Its multi-layered nature allows for support for decisions regarding general problem formulations (the solutions sought include all layers of the model) as well as the details for one of its layers. Figure 7: Forecasted weather conditions. Figure 8: Reactive plan for delivery of required equipment to 7 WTs: schedule (**a**), vessel route (**b**).
## 5 Test Comparison and Analysis
### Model Robustness Verification
Table 7 presents the model's average recognition accuracy and standard deviation distribution under varying SNR conditions. Analysis of Table 8 demonstrates that, when the SNR exceeds 30 dB, the classification performance of the condition recognition model remains unaffected. Conversely, as the SNR drops below 30 dB, although a slight decrease in accuracy is observed, the recognition rate still maintains a relatively high level. This indicates that the ConvNeXt model exhibits notable robustness against noise interference. Figure 19: Comparison of time–frequency spectra of vibration signals containing noise. \begin{table}
\begin{tabular}{c c} \hline \hline
**SNR** & **Average Recognition Accuracy** \\ \hline raw vibration signal & 97.9 \\
40 dB & 97.9 \\
30 dB & 97.9 \\
20 dB & 96.3 \\
10 dB & 95.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average recognition accuracy and sample difference under different signal-to-noise ratios. Figure 20: Boxplot of recognition accuracy of each measuring point including noise.
## 5 Engineering Validation  
The Daxialing Tunnel is located in the south of Ya'an City, Sichuan Province, which is the first longest tunnel under construction in Southwest China, with a maximum burial depth of 1648 m. In this study, eight sets of data were selected from the rockburst data of the Daxialing Tunnel [57] to assess the rockburst hazard and were predicted using the NGO-CNN-BiGRU-Attention rockburst intensity prediction model. Table 5 illustrates the findings of the predictions, and all rockburst cases were predicted correctly, indicating that the NGO-CNN-BiGRU-Attention model shows strong generalisation ability and engineering practicability, which corroborates the previous conclusions.  
In order to further prove the effectiveness of the NGO-CNN-BiGRU-Attention model, the prediction results of this model were compared with those of other models (see Table 6), and the results showed that the accuracy of the NGO-CNN-BiGRU-Attention model was the highest, thus indicating that the model is an effective rockburst prediction method.  
Figure 11: Schematic diagram of feature importance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**No.** & \(\sigma_{\theta}\) & \(\sigma_{c}\) & \(\sigma_{t}\) & \(\sigma_{\theta}\)/\(\sigma_{c}\) & \(\sigma_{c}\)/\(\sigma_{t}\) & \(W_{et}\) & **Actual** & **Prediction** \\ \hline
1 & 39.40 & 65.20 & 2.30 & 0.60 & 28.35 & 3.40 & III & III \\
2 & 38.20 & 71.40 & 3.40 & 0.54 & 21.00 & 3.60 & III & III \\
3 & 29.71 & 16.00 & 2.70 & 0.26 & 42.96 & 3.70 & II & II \\
4 & 29.10 & 94.00 & 2.60 & 0.31 & 36.15 & 3.20 & II & II \\
5 & 58.20 & 83.60 & 2.60 & 0.70 & 32.15 & 5.90 & IV & IV \\
6 & 57.20 & 80.60 & 2.50 & 0.71 & 32.24 & 5.50 & IV & IV \\
7 & 27.80 & 90.00 & 2.10 & 0.31 & 42.86 & 1.80 & I & I \\
8 & 25.70 & 59.70 & 1.30 & 0.43 & 45.92 & 1.70 & I & I \\ \hline \end{tabular}
\end{table}
Table 5: Prediction results of rock explosion cases in Daxialing Tunnel.  
The above results show that the NGO-CNN-BiGRU-Attention model has strong prediction performance and generalisation ability in the area of rockburst intensity prediction, but to some extent, the model still has certain deficiencies. Due to the different selections of the sample library, the generalisation ability of the trained model varies; the contribution of different predictors to the rockburst intensity level is different, and the performance of the model will be influenced to some degree by the different predictors selected, so it is necessary to further explore this effect in the research of rockburst prediction.
# How to Isolate Non-Public Networks in B5G: A Review
## 2 Deployments of NPNs
(**a**) Isolated NPN (**b**) Shared spectrum-only NPN (**c**) Shared RAN NPN (**d**) Shared RAN and CP NPN (**e**) Shared RAN and CN NPN (**f**) N3 LBO NPN (**g**) F1 LBO NPN. it is important to study isolation technologies to achieve the three kinds of NPN isolation; i.e., spectrum isolation, RAN isolation and CN isolation.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 6 Simulation Implementation
### Physical Layer (PHY)  
The physical layer (PHY) used was the one corresponding to the CC2420 chipset, with default values as implemented in Castalia. The chosen modulation type was \"Ideal\" with a signal-to-noise ratio threshold of 5 dB for successful reception. The transmit power was set to 0 dBm.
## 2 Soil Sample and ABAQUS Simulation of Torsional Shear Test
### Torsional Shear Test Modeling  
To obtain the G/Gmax and damping curves by simulation, a torsional shear (TS) test was modelled in ABAQUS. A cylindrical column was made with the same dimension as the TS test column in the laboratory, as shown in Figure 2a. The end of the column was fixed, and a torsional shear deformation was applied at the top-free end of the column as a torsional rotation with a sinusoidal form (Figure 2b). The torsional strain for a cylindrical column specimen could be defined based on the applied rotational angle (\(\theta\)), radius (\(r\)), and height (\(l\)) of the cylindrical column, as follows [14; 15]:  
\[\epsilon=\frac{r_{eq}\times\theta}{l} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Silica Sand** & **Mixture** & **Toyoura Sand** \\ \hline \multicolumn{4}{c}{NKH model (weighting factor w = 0.95)} \\ \hline C\({}_{1}\) & 652,591.60 & 1,725,682.99 & 2,808,911.52 \\ \(\gamma_{1}\) & 61,112.11 & 54,324.50 & 57,276.02 \\ C\({}_{2}\) & 107,118.11 & 361,551.47 & 261,815.97 \\ \(\gamma_{2}\) & 3819.71 & 5414.56 & 30,10.08 \\ C\({}_{3}\) & 0 & 67,624.35 & 37,380.88 \\ \(\gamma_{3}\) & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: G/Gmax and damping curve obtained from RCTs and the SNKH model for (**a**) silica sand; (**b**) mixture soil; and (**c**) Toyoura sand; and (**d**) MAE value for three soils. The arrows show threshold strain in (**a–c**), and the lowest lambda value in (**d**).  
In this study, the equivalent radius \(r_{eq}\) was taken to be 0.82 times greater than the radius of the cylindrical column [15]. The confining pressure was applied by surface pressure load and maintained during modelling to simulate a confining stress. The value of the confining pressure was chosen to be 200 kPa to produce a similar stress condition to the experimental RCT. The peak amplitude of the torsional shear deformation applied at the top-free end was changed in ten levels to obtain the shear strain of the column, which was defined from the maximum rocking angle, in the range of 0.0001% to \(\sim\)0.1%. The torsional shear stresses of elements located outside the column were captured, and the hysteretic loop of shear stress-shear strain could be obtained for each strain level. The G/Gmax and damping value could be easily defined from the measured hysteretic loops.
###### Abstract
Moreover, normalization is an essential preprocessing step for machine learning applications, as highlighted by the systematic review conducted by [PERSON] et al. in July 2022 [16]. Indeed, especially when features in the dataset have varying scales and ranges, the normalization ensures that different features contribute equally to model training. As a corrective step, the normalization ensures that the parameter is independent from any confounding factor that can influence the ECG pattern (such as risk factors) and that is standardized, allowing for consistent comparisons across various studies and populations. Without the proper adjustments provided by normalization, variations due to confounding factors can lead to misinterpretation of results. This approach to normalization is essential for maintaining the integrity and comparability of ECG data analysis. While there has been significant work on ECG normalization as a preprocessing step, there is a lack of in-depth understanding of why normalization is applied to ECG-derived measures and how it affects their marker role, i.e., their accuracy in assessing the cardiac risk. In order to fill this gap, a scoping review was conducted. The aim of this review is to systematically map the research on this topic and provide a comprehensive collection of open-access published research that examines normalized ECG-derived parameters used as markers of cardiac anomalies or instabilities, where normalization is applied as a corrective step to compensate for potential biasing factors. More specifically, the objectives of this review are to provide answers to the following questions:  
* Which ECG-derived features used as cardiac risk indices have been normalized in the literature? * How was normalization performed? * Why was normalization performed?
# Dense-FG: A Fusion GAN Model by Using Densely Connected Blocks to Fuse Infrared and Visible Images
## 4 Experiments and Results
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **SF** & **EN** & **SCD** & **AG** & **SD** & **SSIM** & **PSNR** & \(Q_{F}^{A/B}\) & **VIF** & **MI** \\ \hline AVG & 6.522 & 6.084 & 1.5361 & 3.4586 & 21.7940 & **0.7872** & **19.1160** & 0.3672 & 0.3590 & 2.0310 \\ \hline LP & **10.985** & 6.2214 & 1.5774 & **5.8541** & 23.7810 & 0.7496 & 18.9590 & **0.5113** & 0.3950 & 1.7672 \\ \hline FSDP & 9.247 & 6.1346 & 1.4970 & 4.9874 & 22.6290 & 0.7513 & 18.9800 & **0.5045** & 0.4420 & 1.8655 \\ \hline DWT & 10.604 & 6.1636 & 1.5565 & 5.7666 & 22.8310 & **0.7530** & **19.0090** & 0.4435 & 0.3360 & 1.7352 \\ \hline Fusion-GAN & 6.592 & 6.2567 & 1.4009 & 3.4929 & 25.6690 & 0.6519 & 16.0430 & 0.2893 & 0.2580 & 2.0750 \\ \hline Dense-Fuse & 9.729 & 6.6278 & **1.7955** & 5.2541 & **33.5800** & 0.7150 & 17.4820 & 0.4465 & 0.4230 & **2.1868** \\ \hline RFN-Nest & 7.033 & **6.7754** & 1.7646 & 3.6195 & **34.6520** & 0.6808 & 17.021 & 0.3951 & **0.4490** & 1.9810 \\ \hline Dense-FG & **11.276** & **6.6750** & **1.8053** & **6.1461** & 32.5990 & 0.7227 & 18.2110 & 0.4627 & **0.4970** & **2.2712** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean values of image metrics for different fusion algorithms. (PSNR), visual information fidelity (VIF), and mutual information (MI) index values for the fusion results of the seven algorithms mentioned above, is shown in Figure 6. The proposed method Dense-FG has the best fusion performance.
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
###### Abstract  
Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.  
wavelet transform; non stationary; time series; time-frequency; decomposition; applied sciences +
Footnote †: journal: Applied Sciences
# An Ensemble-Based Machine Learning Approach for Cyber-Attacks Detection in Wireless Sensor Networks
## 4 Simulation Results
* We used three classical ensemble classifiers: Bagging, Boosting, and Stacking. The Bagging-based model was built based on a decision tree algorithm, the Boosting-based model was built with the AdaBoost algorithm, and the Stacking-based model was implemented using three different classification methods for level 0, KNN, RF, NB, and LR for the level 1 classifier. * The proposed model, WSS, achieved comparable results to other classical ensemble classifiers in terms of accuracy, probability of false alarm, probability of detection, probability of misdetection, positive predictive rate, processing time, and average prediction time per sample. Further analysis and testing are required to prove its effectiveness for other attack types in energy-constrained networks. * Ensemble techniques functioned well when detecting cyber-attacks targeting WSN for most of the results and achieved similar values, except for the Stacking algorithm. Bagging has the best processing time and average prediction time per sample, while Stacking performed the worst in terms of processing time and prediction time per sample, making it a poor choice to detect DoS attacks on a WSN. * A comprehensive analysis of the results indicates that WSS, along with the other ensemble techniques, performs well when detecting most DoS attacks in terms of accuracy, probability of false alarm, probability of detection, and probability of misdetection with the default hyperparameter tuning settings. WSS, followed by Bagging, are the best candidates for detecting WSN attacks using the labeled datasets extracted from WSN-DS since both achieve reasonable results regarding the evaluation metrics used in this study. Further investigation and analysis using other datasets are required considering other types of attacks targeting WSNs. * WSNs have unique properties caused by constrained power, storage, and bandwidth; therefore, the proposed approach, WSS, achieved promising results for identifying cyber-attacks that target WSNs. The proposed approach is simpler, diverse in terms of selecting base classifiers, and flexible in adopting any number of ML models compared to Bagging, Boosting, and Stacking ensemble techniques. The proposed approach selects the appropriate ML model dynamically, which could significantly improve detection performance.
## 4 Testing of the Solution
### Discussion and Comparison of the Performance Results
When all is said and done, the tests that have been done so far show no significant sign of unforeseen events that may jeopardize the system in the way that has been conceived. More importantly, they seem to be able to withstand the significant number of requests that has been used without jeopardizing data delivery or performance results. This is revealing in the sense that the development works that have been done so far can continue in the same direction and there will be little need in modifying extensive parts of the code that has already been created.
## 4 Discussion
### Structure Instabilities  
The analysis of the MTInSAR time series was also carried out on two structures subject to already known instability phenomena, located in the Montecaglioso village.  
In the case of the structurally damaged buildings (Figure 5), the cause of the displacements is due to the swelling phenomenon of the sub-Apennine clays [42], which outcrop in this area. The slope of the hill was reshaped and excavated for the building construction. The lightening of the underlying clay, also exposed to rainwater, has caused the swelling phenomenon with structural consequences on the building, such as differential settlements and consequent cracking (Figure 5d). Swelling, which is a very slow phenomenon that  
Figure 11: (**a**) Enlargement of the mean LOS S1 ascending PS displacement map of the Pomario NE side area (same area as Figure 9a); (**b–e**) ground pictures of (**b**) cracks on the retaining wall and on the road upstream, (**c**) cracks on the retaining wall and on the road downstream, (**d**) cracks on the road, (**e**) damaged sidewalk.  
completely exhausts over many years, continued to cause small and constant displacements, as shown by the evolution of the crack pattern (Figure 5e) and displacement time series of the target labeled P1 in Figure 5b. Furthermore, the phenomenon affects the whole area around the hill, where the time series show trends consistent with an uplift, as shown by the trend of the target labeled P2 (Figure 5c). This analysis highlights the high sensitivity of the methodology, already observed in other sites subject to the swelling phenomenon, although in profoundly different contexts [43].  
In the case of the road section in Figure 6, the analysis of the MTInSAR time series has detected continuous surface displacements, which have caused landslides (Figure 12a). These require continuous maintenance interventions (Figure 12b), as the potential evolution of the instability phenomenon represents both a danger for users and a problem for land management. Certainly, the study of MTInSAR time series is valuable for monitoring the evolution of instability phenomena along the road corridors. Furthermore, the combination of these analyses with vulnerability maps, such as those presented in [44], may provide support to regional planning by identifying the priorities for maintenance interventions. This approach becomes essential in areas characterized by distant urban centers and few connecting roads in a hilly environment, which is common in many regions.
## 1 Introduction
The key contributions of the current paper are listed below:  
1. **Comprehensive analysis of ACN architecture components**: We provide a detailed analysis of the fundamental components of a typical ACN architecture, elucidating the role and functionality of each device. This analysis provides a clear understanding of the ACN system's operation and the interdependencies between its components. 2. **Security evaluation of ACN architectures using ISO/SAE 21434**: We employ ISO/SAE 21434, a cybersecurity standard designed for automotive systems, to conduct a comprehensive security assessment of ACN architectures. This structured approach categorizes threats based on their severity and identifies potential vulnerabilities that could be exploited. 3. **In-depth analysis and countermeasures for high-risk ACN threats**: We explore the root causes and possible impacts of high-risk threats to ACN systems, enabling the development of effective countermeasures. This detailed examination strengthens the security posture of ACN systems by mitigating the most critical threats. To establish a clear and coherent manuscript, the document is structured into nine distinct sections:  
* The second section delves into related work analogous to the current paper within the context of ACN and threat analysis in the automotive domain. * The third section introduces the research methodology employed, highlighting the objective of the present work and the research question it seeks to address. * The fourth section provides an overview of the features essential for the realization of a typical ACN architecture, accompanied by an analysis of selected architectures. * The fifth section elaborates on the TARA methodology utilized for the security evaluation of the considered architectures. * The sixth section presents a comparative analysis of the considered system architectures. * The seventh section meticulously outlines a comprehensive threat analysis of both systems. * The eighth section aims to engage in a discussion on potential security enhancements to the architectures under consideration. The impetus for this discussion stems from the necessity of providing a practical and tangible response to the threats identified in the seventh section. * The ninth section concludes the research findings.