esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Preprocessing with ICEEMDAN and Feature Extraction  
The vibration-based monitoring technique is one of the most effective approaches for fault diagnosis of rolling bearings. However, vibration signals of rolling bearings are highly nonstationary and nonlinear, which complicates the process of fault symptom extraction. To address the signal nonstationarity, the recently developed ICEEMDAN is properly introduced to preprocess the acquired vibrational accelerator signals, so that each monitoring signal \(\mathbf{s}\) is decomposed as described in Section 2.1 and a set of IMFs \(\mathbf{d}_{k}\) (\(k=1,2,\ldots,K\)) is thus obtained. Before the decomposition, some parameters need to be determined. Firstly, to control the signal-to-noise ratio (SNR) between the added noise and the residue to which the noise is added, the constants \(\beta_{k}\) (\(k=0,1,\ldots,K\)) should be chosen. To simulate the same increasing SNR as that in the EEMD, it was suggested in [13] that \(\beta_{0}=0.2\times std(\mathbf{s})/std(E_{1}(\mathbf{w}^{(i)}))\) and \(\beta_{k}=0.2\times std(\mathbf{r}_{k})\) (\(k\geq 1\)), where \(std(\cdot)\) is the standard deviation operator. This strategy is also considered in this work. For the ensemble realizations \(I\) of the final averaging, a number of 100 usually leads to satisfying analysis results for real applications. To further address the nonlinearity of the vibration signals, the IMFs are processed to extract nonlinear dynamic features of entropy as rolling bearing fault features in this work. Specifically, dispersion entropy (DisEn), permutation entropy (PerEn), as well as sample entropy (SampEn) are considered for their effectiveness and stability. The DisEn features for all the IMFs are obtained as detailed in Section 2.2, while PerEn and SampEn for each IMF are calculated following the procedures in Table 1.  
In entropy-based analyzing methods of time series, as in other types of nonlinear dynamics analysis, appropriate choice of the related parameters is an important issue. For DisEn, there are three parameters to be determined, i.e., the number of classes \(c\), embedding dimension \(m\), and time delay \(\tau\). Theoretically, two amplitude values that are far from each other are more likely to be assigned to the same class if \(c\) is too small while a very small disturbance (such as noise) may change the original classes with a too large \(c\). Additionally, the computational burden increases with the number of class \(c\). To make a tradeoff between useful information detection and computational efficiency, it is recommended to choose \(c\) from 4-8 for practical uses of DisEn. For the embedding dimension \(m\), if it is too small, the dynamic changes of the time series might not be detected. In contrast, if \(m\) is chosen too
## 4 Experiment
### Evaluation of Feature Matching Accuracy
#### 4.2.3 Results  
This experiment utilizes the Matching Mean Accuracy (MMA) metric to quantitatively evaluate the feature matching performance of the proposed method under various challenging conditions, within an error threshold range of 1-10 pixels. As illustrated in Figure 5, the proposed approach consistently outperforms mainstream state-of-the-art methods across all test scenarios, including comparisons with LoFTR and SuperPoint+LightGlue.  
In illumination variation scenes, our method achieves the highest matching accuracy at all pixel thresholds, demonstrating strong robustness to lighting changes. Notably, under smaller thresholds (1-3 pixels), it significantly outperforms conventional methods, indicating superior performance in fine-grained feature matching tasks. In viewpoint variation scenarios, the method also exhibits exceptional adaptability, effectively handling geometric transformations caused by perspective changes, with matching accuracy markedly surpassing that of baseline approaches.  
Comprehensive evaluation results show that the proposed algorithm maintains top performance across the full range of error thresholds, highlighting its overall robustness in dealing with complex environmental changes. Particularly under low-error conditions, it demonstrates higher matching accuracy than existing methods, further underscoring its practical applicability and potential for real-world deployment.  
Overall, the proposed matching framework outperforms existing algorithms in multiple challenging scenarios in terms of accuracy and stability. By effectively combining the strengths of detector-free and detector-based methods, it leverages their complementary advantages in feature extraction and matching. Additionally, the introduction of a coarse-to-fine inlier filtering mechanism enhances both the accuracy and robustness of the matching results, significantly improving the algorithm's generalization capability across diverse environments.
## 4 Experimental Results  
### Data Quality  
The imaging data and their statistics are first examined by eye for quality control. Some image pairs of patients have extensive misregistration artifacts. This misregistration is often due to motion of the shoulders and the legs of the patient. The misregistration is also due to differences in the fields of view between the two images of a patient, particularly in the front-to-back, or sagittal, axis. In the cases where the images of a patient suffer from this artifact and are misregistered, the images are excluded from further analysis.  
The intensity statistics of the whole image is the sum of the statistics from the images of the various coils. In this study, the statistics are the intensity co-occurrences. In some cases, the intensity range corresponding to noise in one of the coil images overlaps with the intensity range of the signal in another one of the coil images. In these cases, the noise intensity range and hence noise image region cannot be discriminated from the signal intensity range in the statistics of the whole image. The image sets of a patient that suffer from such extreme acquisition artifact are excluded from the automated image analysis.  
Overall, six images suffer from misregistration artifacts and two images suffer from the signal acquisition artifact. Thus, out of a total of 39 images, there are 31 valid images that are considered in this study.
## 5 Discussion  
As can be seen from the experimental results in the fourth part, the GAFM proposed in this paper has the dual advantages of prediction accuracy and speed on different data sets. Specifically, we have the following findings:  
1. The influence of parameter drop on the accuracy and speed of model prediction is positive, but it will fluctuate to different degrees due to the size of data set, and the influence effect is not significant compared with that of parameter g, but as a component of GAFM, it can play a role in adjusting the structure of model effect.  
2. The influence of parameter g on the accuracy and speed of the model is significant. With the increase of g, the prediction accuracy of the model increases while the running speed decreases, which can be used to select the best value of g. In this paper, g = 0.91 is selected, this will be explained later in this paper.  
3. According to the experimental results, when the value of parameter g is low, it does not have a good effect in terms of accuracy. Even though it is fast in speed, it should be abandoned in practical application. Only when g is greater than a threshold value the comparison of accuracy can have practical significance.  
4. GAFM model on the data sets of different size is different, the significance of a comparison of three data sets, the greater the size of the data set and adjust the parameter g for the more significant the influence of running speed, and time factors on small data sets is very small, so the GAFM is suitable for the large data feature engineering scenarios recommendation system, can significantly optimize the accuracy and speed.  
In this paper, the three models compared with GAFM are Wide & Deep, PNN and AFM. Except for the low accuracy when g value is too low, GAFM is better than Wide & Deep model in both accuracy and speed, while the accuracy is better than PNN when g value is high and the speed is similar. When g value is low, the speed is obviously better than AFM and the accuracy is similar. Therefore, in the actual application scenario, it is necessary to adjust the values of parameters g and drop repeatedly to reach an optimal solution suitable for this scenario, which is crucial for the overall efficiency of the model, scenarios in this experiment, comprehensive consider three data sets, because the parameter g for accuracy and running speed, especially with high sensitivity, selection and parameter value too low will lead to sharply lower accuracy, through the test, the drop = 0.84 selecting parameters, parameter g = 0.91, can achieve the optimal effect on the three data sets, and it's a nice improvement over other models. Overall, GAFM improves both prediction accuracy and operating speed.
## 2 Materials and Methods
### Steps 2 and 3: Conceptualization and Computational Modeling
#### 2.2.1 Operational Availability and Intelligent Maintenance
Since the MTTR values of the scheduled maintenance activities are average values (assuming normal distribution), the triangular distribution is used to incorporate some variance in the data. The main purpose of modeling the scheduled maintenance at both system and component levels is to address all planned maintenance activities that are causing production stops. These stops are decisive to address as they can be used as opportunistic maintenance intervals in which PdM can be leveraged in terms of intelligent maintenance. Hence, if the intelligent maintenance system enables detecting and predicting the future deterioration propagation of a failure, it can allocate the future required maintenance activity to a coming opportunistic maintenance interval, as long as this interval appears prior to the component fault.
# Experimental Analysis of the Behavior of Mirror-like Objects in LiDAR-Based Robot Navigation
## 7 Conclusions  
A potential fully autonomous robot was implemented using Turtlebot along with the ROS ecosystem to perform experiments in an environment with mirror objects. When a mirror was in the range of the LiDAR, a negative space or undetected area was seen in the range between the LiDAR and mirror due to complete reflection of all the laser scans. While traveling alongside a mirror, detection was seen behind the surface of the mirror when diffuse surfaces were in the range of the LiDAR. The robot detected and mapped the mirror reflection of the opposite wall to the mirror in test cases 2-4. The robot was also observed to detect itself in the mirror at all points where it was perpendicular to the mirror. This suggests that the mirror acts as a diffuse object in this situation. Additionally, it was noticed that several times, in the case of two parallel mirrors, the robot was detected only in one mirror. This infers that those inconsistencies in light reflection on the mirror may affect laser scans as well. This phenomenon was seen in test cases 3 and 4.  
To identify and remove inconsistencies in the generated maps caused by mirrors, the symmetrical property of the mirror can be used. This approach requires a reference object, which can be the robot itself. By moving the robot, the mirror symmetry between the robot and its image can be utilized to locate the mirror. Self-detection of the robot can simplify this process. The second solution is using supervised machine learning to differentiate reflective and nonreflective objects. This method requires numerous training samples, locating the robot at different positions and orientations and collecting LiDAR data. This method has some drawbacks. Preparing training sets is a tedious and time-consuming task. Additionally, in the case of a changing working environment or the LiDAR, the whole data collection process should be repeated. Therefore, we propose the DBSCAN clustering algorithm to cluster diffuse and reflective surfaces according to the range and intensity information of LiDAR. A postprocessing step is needed to remove the reflective surfaces from the created map.  
Figure 23: Proposed Postprocessing Algorithm.  
**Author Contributions:** All authors contributed equally. All authors have read and agreed to the published version of the manuscript[PERSON]  
**Funding:** Natural Sciences and Engineering Research Council (NSERC) of Canada discovery grants program and Canada Foundation for Innovation (CFI) JELF grant.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data related to this paper are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multiband Frequency Tuneable Antennas for Selection Combining Strategy in White Space Applications
## 6 Conclusions  
This paper presented a reconfigurable antenna that operates at the white-space frequency band. By integrating the DTC components, the proposed structure could sweep the resonant frequency from 470 to 700 MHz. The S-parameter results show good agreement between the simulation and measurement. According to Table 4, the proposed structure has compact dimensions and is suitable for a portable device with a small clearance zone. With a high-loss, low-cost, and common material, this design is easy to use in real appliances. Furthermore, the relative bandwidth of 39.32% is comparable to that of related works. In addition, the antenna radiation properties were discussed with the realized gain of \(-2.2\) dB at 470 MHz and 1.87 dB at 700 MHz. Due to its performance, the antenna could be a good candidate for a white-space application antenna.  
**Author Contributions:** Conceptualization, L.-H.T.; Data curation, L.-H.T.; Methodology, L.-H.T.; Software, L.-H.T. and F.F.; Supervision, F.F.; Validation, L.-H.T.; Writing--original draft, L.-H.T.; Writing--review & editing, L.-H.T. and F.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number C2021-26-05.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Antenna** & **Substrate** & **Frequency Band/** \\  & **Occupancy Size * & **tan\(\delta\)** & **Relative Bandwidth** \\ \hline
[18] & 0.18\(\lambda_{g}\)\(\times\) 0.080\(\lambda_{g}\)\(\times\) 0.0020\(\lambda_{g}\) & 0.0036 & 754–1034 MHz/31.32\% \\
[20] & 0.15\(\lambda_{g}\)\(\times\) 0.030\(\lambda_{g}\)\(\times\) 0.0200\(\lambda_{g}\) & 0.0250 & 60–950 MHz/45.16\% \\
[21] & 0.25\(\lambda_{g}\)\(\times\) 0.350\(\lambda_{g}\)\(\times\) 0.0003\(\lambda_{g}\) & 0.0022 & n/a \\ This work & 0.06\(\lambda_{g}\)\(\times\) 0.016\(\lambda_{g}\)\(\times\) 0.0160\(\lambda_{g}\) & 0.0250 & 470–700 MHz/39.32\% \\ \hline \hline \end{tabular}  
* \(\lambda_{g}\) is the wavelength at the central frequency.  
\end{table}
Table 4: Performance comparison with related works.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency (MHz)** & **ECC Value** \\ \hline
470 & 0.475 \\
700 & 0.0481 \\ \hline \hline \end{tabular}  
\end{table}
Table 3: Envelope correlation coefficient.
The main goal of this paper is to deliver for the first time a more in-depth PTR-MS study that provides not only highly accurate \(m\)/z values of the product ions but also information on the product ion distributions over a range of \(E\)/\(N\) values (approximately 60-210 Td). In addition to providing details on the product ions resulting from the reactions of H\({}_{3}\)O\({}^{+}\) with propofol, we have significantly extended the original [PERSON] et al. study by using a proton transfer reaction-time-of-flight mass spectrometer (PTR-ToF-MS) in so-called selected reagent ion (SRI) mode, which is referred to as SRI-ToF-MS. This has allowed us to investigate the reactions of two other commonly used reagent ions in soft chemical ionisation mass spectrometry, namely NO\({}^{+}\) and O\({}_{2}\)\({}^{+\bullet}\), and with their associated product ion distributions being also provided as a function of \(E\)/\(N\). Propofol is efficiently metabolised in the liver. Although the main metabolite of propofol, 2,6 disopropyl-1,4-benzoquinone, is tentatively identified in the mass spectra of breath samples analysed by [PERSON] et al., there has been no independent study of the metabolite to confirm this. We have therefore included in this investigation a PTR/SRI-ToF-MS study of that compound for completeness. This paper provides much-needed fundamental knowledge on the ion-molecule chemistry occurring in the drift tube of the SRI-ToF-MS for potential analytical chemistry applications to the real-time monitoring of propofol and its major metabolite in exhaled breath. This is important not only for its potential use to determine plasma concentrations continuously during surgery (as mentioned above), which could be used to provide feedback-controlled anaesthesia [16; 17], but also for investigations of the metabolic processes resulting in the production of the metabolite and the elimination of propofol from the human body post-surgery. Post-surgery measurements could be used, for example, to provide information on abnormal metabolic stresses [6]. Given the potential breath analysis applications, we have undertaken many of our measurements under what we have described as _normal_ and _humid_ drift tube conditions [18]. The _humid_ drift tube conditions were applied in order to mimic the operational conditions that will occur for direct breath sampling, i.e., for which the water content in the exhaled breath has not been removed prior to the sample entering the drift tube. Such investigations provide much-needed information on potential analytical problems, such as whether secondary product ions occur that result from the reactions of the primary product ions with the H\({}_{2}\)O present in the drift tube. This can lead to a reduction in analytical sensitivity for the detection of a compound, as has been found to occur for the detection of a number of compounds [19; 20], and in the context of this study relating to anaesthetics, a number of volatile inhalation anaesthetics, namely isoflurane, enflurane, sevoflurane and desflurane [18; 21; 22; 23].
# Collaborative Multi-Robot Formation Control and Global Path Optimization
## 4 Global Path Planning for Multi-Robot Formations
### Formation Control Based on Consistency Method  
The formation structure in this study is a distributed control structure in a navigator-follower formation, and the formation control is achieved through the combination of graph theory and consistency algorithms. The first-order continuous system model of the formation system is as Equation (22).  
\[\dot{x}=u_{i} \tag{22}\]  
\(x_{i}\), \(u_{i}\) denote the state quantity and the input quantity of the node, respectively, \(x_{i}\), \(u_{i}\)\(\in R^{n}\), and \(n\) denotes the dimension of the state quantity. Ideally, the control inputs are as Equation (23):  
\[u_{i}=\sum_{j\in N_{i}}a_{ij}(x_{j}-x_{i}) \tag{23}\]  
where \(a_{ij}\) is the adjacency matrix element of the formation and \(N_{i}\) is the set of neighbors of member \(i\). After introducing Equations (22) and (23) into the global state vector \(x=[x_{1},x_{2},\ldots,x_{n}]^{T}\in R^{n}\), the global dynamic relations are obtained as follows:  
\[\dot{x}=-Dx+Ax=-Lx \tag{24}\]  
Equation (24) shows that the closed-loop dynamic properties of the formation depend on the matrix \(L\). The -\(L\) eigenvalues are all located in the left half-plane of the complex plane when and only when the topological graph \(G\) of the formation has a spanning tree, so that Equation (23) ensures that the system achieves consistency and the final state values are as follows:  
\[c=\sum_{i=1}^{N}p_{i}x_{i} \tag{25}\]  
Equation (22) can be discretized as:  
\[x_{i}(k+1)=x_{i}(k)+u_{i}(k) \tag{26}\]  
In this study, assuming that the navigator leads the other robots from the starting point towards the target point, ideally the algorithm for the followers in the formation takes the following form:  
\[x_{i}(k)=\epsilon\sum a_{ij}(x_{j}(k)-x_{i}(k)-r_{ij}(k)) \tag{27}\]  
where \(\epsilon>0\), \(r_{ij}\) denotes the relative positions of robot \(i\) and robot \(j\). The control equation for leader \(N\) is:  
\[u_{N}(k)=m+KD(k)+\sum_{i\in N_{i}}a_{N_{i}}r_{N_{i}}(k) \tag{28}\]  
\(m\) and \(k\) are constants, and \(D(K)\) is the distance between the leader and the target point at moment \(k\). The velocity is affected by the neighboring nodes and the distance from the target point.
# Architectural Design for Digital Twin Networks
## 3 Digital Twin Network
### Reference Architecture
#### 3.2.2 Digital Twin Layer  
The heart of the DTN is composed of three closely interconnected subsystems. The first is the Data Repository Subsystem, which is tasked with collecting and storing data from the real network. This subsystem ensures that accurate and up-to-date information is readily available to create precise models. The second subsystem, the Service Mapping Models Subsystem, focuses on developing and managing various data models that represent the network's components and functions. These models are essential for activities like analysis, diagnosis, and optimization, as they mirror the state and behavior of the actual network. The third subsystem is the Digital Twin Network Management Subsystem, which oversees the lifecycle, performance, and resource management of the twin. It is responsible for monitoring, visualizing, and controlling various elements of the twin, including the network topology and security, to ensure continuous synchronization with the real network. Designed with flexibility in mind, these subsystems can operate within a single domain or across multiple domains. This capability allows the DTN to deliver efficient and effective services, regardless of the complexity or geographical spread of the underlying network.