esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Classification Performance
#### 5.1.2 Super-Resolution Videos  
Like for the bicubicly interpolated videos, the classification performance for super-resolution videos is provided in the confusion matrix in Figure 7. The confusion matrix reports the classification performance based on 10 videos per anomaly class. Table 4 provides the performance metrics for each class as well as the macro-averaged value for all classes.  
Figure 6: Confusion matrix for bicubic interpolation of videos.
# An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System
## 5 Experimental Results and Discussion
### Performance of the Precision-Recall Rates  
To assess the retrieval performance of the proposed technique, all of the database images are represented as the query image. Figure 11 shows the obtained average precision rate with respect to the different scales of recall rate. The results show that the proposed technique has surpassed the overall performance of the other competitive methods, producing an improvement of 19.43% and 26.78% compared to the ZMEG and ZMLDP precision/recall performance, respectively. It is also interesting to observe that the precision/recall performance of the proposed technique without the global descriptor, Hu moments, is better than that of ZMEG after the 30% recall rate. This indicates that, although the local descriptor with the proposed tree similarity algorithm has not been able to provide effective retrieval performance for multi-object images, it is still useful and worth combining with the global property feature. To clearly show the performance of each class, we further use the BES and retrieval ranking to compare the performance between our technique and the other two approaches in the next experiment.  
Figure 10: The comparison between the fuzzy inference system (FIS) and weighting assignment using precision and recall (P-R).
## 2 Materials and Methods
### Data  
#### 2.6.1 Multispectral Imagery Data Specification  
WorldView-2 is a high-resolution 8-band multispectral satellite operating at an altitude of 770 km. A corrected early season \"leaf-on\" image acquired in 2013 by this system was used for the study, minimizing radiometric variability manifested as differences in the coloration of image features (trees and openings) and radial displacement (or the apparent elongation or displacement of objects having height) [28]. The imagery provides 1.85 m multispectral resolution (source scale), and a swath width is 16.4 km at nadir with a demonstrated geolocation accuracy of less than 3.5 m without ground control.
## 5 Conclusions  
In this paper, the impact of EEPN on both BER performance and AIRs of Nyquist-spaced superchannel transmission system have been investigated based on the use of EDC and MC-DBP. Different modulation formats including DP-QPSK, DP-16 QAM, DP-64 QAM have been applied. Distortions from EEPN are more significant in the transmission system when the LO linewidth increases. This results in a reduction in both the AIRs and the AIR-distance products. It is shown that the FF-DBP is more susceptible to EEPN compared to the scenarios of EDC, single-channel, and partial-bandwidth DBP. For the investigated FF-DBP schemes, AIRs are reduced by \(\sim\)0.07 Tbit/s, \(\sim\)0.11 Tbit/s, and \(\sim\)0.57 Tbit/s in the DP-QPSK, the DP-16 QAM, and the DP-64 QAM systems, respectively, due to the EEPN with an LO laser linewidth of 1 MHz. Systems using higher-order modulation formats will improve the AIRs and spectral efficiencies, but will be more susceptible to EEPN. It is also found that the choice of a better-quality LO laser can significantly reduce the bandwidth requirement and the computational complexity in the MC-DBP. Our research provides a novel insight in the design of long-haul high-capacity optical communication systems using multi-channel nonlinearity compensation techniques.  
[PERSON] and [PERSON] proposed the concept and initiated the study. [PERSON], [PERSON], and [PERSON] carried out numerical simulations. [PERSON], [PERSON], and [PERSON] conducted analytical calculations. [PERSON], [PERSON], and [PERSON] supervised the studies. The paper was written by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 4 Case Studies of Multi-Channel and Multi-Hop QoS
### Single-Channel, Point-to-Point Network  
As the simplest case, a single-channel point-to-point network provides a starting point for developing the QoS model. This case, as depicted in Figure 3, is similar to the baseline example presented in [4]. Since there is only one channel and one point-to-point connection, there is only one set of QoS characteristics. For the single-channel point-to-point network, those arbitrary characteristics are also shown in Figure 3, complete with the calculated Probability of Confidentiality and Probability of Integrity. This first case is intended to be somewhat representative of a simple, yet realistic, communication network and its characteristics. As such, this network is presented without encryption or unusual encoding. This network is assumed to be wireless, and will be assigned a probability of interception of 0.5 and, because noise is a natural phenomena, the probability of noise will be assigned as 0.25. Finally, because it is wireless, the network is also susceptible to jamming and, to a much lesser degree, spoofing. With there being only one channel, that implies the number of listeners and disruptors is at most one each. These QoS values will also serve as a baseline comparison for the multi-hop and multi-channel networks.
Another current application of lead-acid batteries is in electric two-wheeler vehicles (E2 Ws), which are equipped with electric motors rated for continuous power between 0.25 and 4 kW, have a top speed of 45 km/h, and require approximately 8 h to charge. These vehicles are overwhelming sold in Asian markets, particularly in China and Vietnam [13]. E2 Ws are regarded as significantly more energy efficient than their petrol-based counterparts, consuming three to five times less energy than gasoline-powered two-wheelers [14]. For lower-income countries, E2 Ws present an attractive alternative, as they are more affordable compared to gasoline cars. With prices starting from around EUR 100 [15], E2 Ws provide an economical and sustainable transportation option, making them especially appealing in regions where cost is a critical factor in purchasing decisions. There have always been complaints about batteries failing without prior notice to the driver. Battery degradation is natural and expected, but the lack of information provided by the monitoring system can make this event a major issue for the driver [16]. This work'smain objective is to propose a go/no-go test for the SOH of lead-acid batteries. Our hypothesis is that batteries with deteriorated SOH exhibit a decay in cross-correlation with batteries in good SOH when analyzed across multiple time scales. This diagnostic method, based on the Detrended Cross-Correlation Analysis Coefficient (DCCAC), aims to identify this correlation decay, providing a clear indication of whether a battery remains in good condition or is nearing the end of its useful life. This approach helps drivers avoid unexpected failures and plan timely replacements.
## 4 Results
### Combinations
A combination of an electric thruster with a tether could be conceived of for the following reasons:  
1. The tether in drag mode is able to generate power [40] that can be potentially used by the electric thruster;
2. The two systems can potentially be integrated, sharing some components like the cathode and the power management system, thus providing a synergistic effect;
3. A combined off-the-shelves system could be used at any orbital inclination with near-optimal performance. This is particularly interesting for constellations of satellites deployed in different orbital inclinations, where choosing a single combined commercial unit (instead of different ones, tether vs. electric, optimized for different inclinations) is advisable for mass production and integration.
# Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP
## 4 Experiments
(**a**) The original unwrapped phase distribution of low frequency; (**b**) the phase map of (**a**) after position and phase variation compensation; (**c**) the obtained correct unwrapped phase map of high frequency. The RMS (root mean square) error is employed to evaluate the accuracy of the proposed method. The true value of the object is obtained by the eight-step traditional PSP algorithm with the static object. With the proposed method shown in Figure 7a,b, the RMS error is 0.073 mms and 6.775 mm for the traditional PSP in Figure 7c,d. The error source of the proposed method may come from the following aspects. (1) Movement tracking: As the proposed method utilizes the movement information to compensate the motion-induced error, the accuracy of the movement tracking is important for the reconstruction accuracy. This paper employs the SIFT algorithm to track the movement. The SIFT algorithm can achieve a subpixel level tracking accuracy. Other movement tracking methods can be used to pursue higher reconstruction accuracy. (2) Movement mismatch: When the object movement is tracked, the corresponding points on the object before movement and after movement can be obtained. However, affected by the tracking accuracy and difference in the view of the camera, the movement mismatch will still exist. Improving the tracking accuracy and minimizing the difference of the view before movement and after movement will be helpful for reducing the movement mismatch. Please note that, as the proposed method introduces the phase shift by motion, the object can have movement in any direction except parallel to the fringes. When the movement direction is parallel to the fringes, there is no phase shift generated. The details can be found in [17]. From the derivation of the proposed method, there is no limitation on the selection of the frequency. However, as two frequencies are employed to unwrap the phase value, the frequency of the fringe pattern should be designed according to the rules of the dual-frequency phase unwrapping method. The proposed method requires capturing the moving object without blur. Therefore, the camera capture speed should be  
Figure 7: The reconstructed results. (**a,b**) The result obtained by the proposed method; (**c,d**) the reconstructed result with the traditional PSP. increased when the object's movement speed is fast. There are also no specific limitations on the object size. However, the object is required to stay within the scene of the camera, which is also a requirement of traditional PSP.
# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 4 RotJoint-Based Pipeline
### How to Use RotJoints in Static Pose Comparison?  
The simplest and most intuitive method for measuring pose similarity is to quantify the position changes between the given and ground truth (GT) poses. However, current pose estimation metrics, such as MPJPE for skeleton-based methods, only consider joint position differences or angular differences, while metrics like MPVPE for mesh recovery mainly focus on vertex position differences, neglecting rotational information and both local and global pose differences. We separate the body parts physiologically. The joints in a skeleton are grouped into five categories: torso (including the head), arms, and legs. The structural representation is then calculated for each body part. Inspired by the IQM approach, the joint angles are normalized using the perceptually relevant mean-subtracted contrast normalization (MSCN) procedure. The MSCN coefficients of the \(j\)th joint in each \(p\)th body part can be obtained as  
\[\bar{q}_{j}^{p}=\frac{q_{j}^{p}-\mu_{j}^{p}}{\sigma_{j}^{p}} \tag{4}\]  
where \(\bar{q}_{j}^{p}\) is the normalized rotation angle, \(q_{j}^{p}\) is the original angle from \(\hat{Q}_{body}\), and \(\mu_{j}^{p}\) and \(\sigma_{j}^{p}\) are the mean and standard deviation. The global structure is represented by a rotation angle vector \(Q_{R}=(q_{1},q_{2},...,q_{j})\), where \(q_{j}\) is the rotation of the \(j\)th joint. Local and global similarities are defined as  
\[\text{local}(X_{R},X_{T})=\frac{2(\hat{Q}_{R}\cdot\bar{Q}_{T})+C}{\hat{Q}_{R}^ {2}+\hat{Q}_{T}^{2}+C},\quad\text{global}(X_{R},X_{T})=\frac{2(\hat{Q}_{R} \cdot\hat{Q}_{T})+C}{\hat{Q}_{R}^{2}+\hat{Q}_{T}^{2}+\hat{C}} \tag{5}\]  
The overall similarity combines both:  
\[\text{similarity}(X_{R},X_{T})=\alpha\sum_{i=1}^{5}\text{local}_{\hat{P}_{i}} (X_{R},X_{T})+\beta\cdot\text{global}(X_{R},X_{T}) \tag{6}\]  
where \(\alpha\) and \(\beta\) weight local and global contributions, providing a comprehensive pose similarity measure while taking into account both fine-grained joint rotations and overall structural consistency. A higher \(\alpha\) emphasizes local differences, making the metric more sensitive to variations in individual limb rotations. A higher \(\beta\) prioritizes global structural alignment, reducing sensitivity to minor limb rotations while maintaining overall pose similarity.
## 2 Method  
The proposed network in this paper aims to automatically segment the LV in cardiac MR images, thus reducing the tedious manual segmentation and improving the disease diagnosis efficiency. The new medical image segmentation framework proposed in this work is shown in Figure 3, and the backbone part uses a combined architecture of UNet 3+ and Transformer. The network takes the same cardiac MR images as input and predicts both pixel probability maps and SDM. A loss function composed of two main components is designed to train the segmentation network. One is for computing the pixel probability map and the other one is for computing the SDM.  
Figure 3: The proposed LV segmentation model is based on an encoder–decoder architecture. The network outputs pixel probability maps and SDM.
## 6 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Methods** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Measure** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **ROC-AUC** \\ **(\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [41] & \(\chi^{2}\)-DNN & 94 & 93 & - & - & - \\ \hline [PERSON] et al. [42] & Decision tree Plus PCA & 99.00 & 97.00 & 98.00 & - & - \\ \hline [PERSON] et al. [43] & MLP & 97.95 & 98.00 & 98.00 & - & - \\ \hline [PERSON] et al. [44] & Linear SVC + DNN & 98.56 & 99.35 & 97.84 & - & - \\ \hline  & **RF** & **100** & **100** & **100** & **100** & **100** \\ \cline{2-6}  & **DT** & **98.80** & **99** & **98** & **100** & 99 \\ \cline{2-6}  & **GB** & **97.60** & **98** & **98** & **98** & **100** \\ \cline{2-6}  & **KNN** & **90.80** & **84** & 97 & **90** & 97 \\ \cline{2-6}  & **AB** & **88.00** & **91** & **86** & **89** & **94** \\ \cline{2-6}  & **LR** & **84.80** & **89** & **82** & **86** & **91** \\ \cline{2-6}  & **NN** & **84.80** & **92** & **81** & **86** & **91** \\ \cline{2-6}  & **QDA** & **83.60** & **85** & **83** & **84** & **91** \\ \cline{2-6}  & **Nu SVC** & **83.20** & **91** & **79** & **85** & **90** \\ \cline{2-6}  & **LDA** & **82.80** & **87** & **80** & **84** & **91** \\ \cline{2-6}  & **NB** & **82.40** & **86** & **81** & **83** & **88** \\ \cline{2-6}  & **SVM** & **68.80** & **69** & **69** & **69** & **73** \\ \hline  & **RF** & 99.41 & 100 & 98.89 & 99.40 & 99.97 \\ \cline{2-6}  & **DT** & 99.60 & 99.23 & 100 & 99.61 & 99.62 \\ \cline{2-6}  & **GB** & 99.41 & 99.50 & 99.45 & 99.53 & 100 \\ \cline{2-6}  & **KNN** & 86.06 & 86.19 & 86.25 & 86.04 & 94.86 \\ \cline{2-6}  & **AB** & 90.93 & 89.99 & 92.24 & 91.05 & 97.57 \\ \cline{2-6}  & **LR** & 84.59 & 89.98 & 82.31 & 85.59 & 91.88 \\ \cline{2-6}  & **NN** & 95.03 & 95.64 & 94.76 & 95.19 & 98.54 \\ \cline{2-6}  & **QDA** & 94.16 & 94.49 & 94.27 & 94.33 & 98.74 \\ \cline{2-6}  & **Nu SVC** & 94.83 & 95.45 & 94.60 & 94.97 & 98.54 \\ \cline{2-6}  & **LDA** & 93.56 & 93.54 & 93.93 & 93.71 & 98.60 \\ \cline{2-6}  & **NB** & 82.15 & 85.94 & 80.63 & 83.12 & 90.76 \\ \cline{2-6}  & **SVM** & 92.19 & 93.36 & 91.68 & 92.49 & 97.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative study of the proposed method with existing method.
## 3 Results
### SBS in ER Mode  
Table 4 and Figure 3 present the enamel SBS values in the ER mode under TC. Three-way ANOVA showed that all the factors significantly influenced the values and that all interactions were significant (\(p<0.001\)).  
The mean SBS values in the SE mode ranged from 38.4 to 48.3 MPa in CS, 38.0 to 51.6 MPa in GU, and 30.9 to 44.9 MPa in OX. All the adhesives showed higher SBS values in Group II than in Groups I and III, regardless of the storage period (Table 4). In particular, the SBS values of all three adhesives at TC 10,000 in Group II were significantly higher than at the other storage periods in Groups I and III, except for GU at TC 10,000 and TC 30,000 in Group I. In Group I, although no significant differences in SBS values were observed among the storage periods in the case of CS, GU and OX presented with significantly higher values after TC than at 24 h (Table 4). In Group II, no significant differences among the storage periods were observed in the cases of CS and GU. Similarly, no significant differences among the storage periods were observed in the case of CS in Group III; however, GU and OX presented with significantly lower SBS values at TC 30,000 than at the other storage periods. The SBS values in OX were significantly lower than in the other adhesives during most of the storage periods, regardless of the application group (Table 4). Furthermore, GU and OX showed significantly higher SBS values in the ER mode at 24 h than in the SE mode. However, no significant differences in the SBS values of the three adhesives were observed between the SE and ER modes at TC 30,000 in Group III.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Group I**} & \multicolumn{3}{c}{**Group II**} & \multicolumn{3}{c}{**Group III**} \\ \cline{2-10}  & **24 h** & **TC 10,000** & **TC 30,000** & **24 h** & **TC 10,000** & **TC 30,000** & **24 h** & **TC 10,000** & **TC 30,000** \\ \hline CS & 32.8 (3.2) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 33.9 (4.5) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 29.9 (1.8) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 41.2 (2.5) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 43.7 (3.2) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 43.8 (1.7) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 39.6 (3.9) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 35.9 (4.9) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) & 36.0 (4.5) \({}^{\text{(}}\))\({}^{\text{(}}\))\({}^{\text{(}}\)) \\ \hline
## 6 Conclusions  
This study comprehensively investigates the critical role of SiC particle size, specifically nano and submicron-sized particles, on the compressibility and density distribution of Mg-SiC composites under Cold Isostatic Pressing. The research unveils valuable insights that pave the way for optimized processing techniques and superior composite properties by leveraging an advanced elastoplastic-modified Drucker-Prager Cap constitutive model. The findings underscore three key aspects:  
1. Particle morphology plays a pivotal role in material cohesion and friction angle. The presence of fine and equiaxed particles in M10 Sn decreases compressibility and densification compared to M10S\(\upmu\), which exhibits flake-like and irregular-shaped particles.
2. The smaller grain size in M10 Sn, relative to M10S\(\upmu\), introduces more grain boundaries, contributing to increased resistance to plastic deformation. Consequently, M10 Sn exhibits lower cap eccentricity and faces more significant densification challenges.
3. The distribution of reinforcement particles profoundly influences pressure and density distribution. Nano-sized SiC particles in M10 Sn foster a more extensive contact surface area with the Mg matrix, leading to heterogeneous pressure distribution and density distribution compared to the M10S\(\upmu\) composite.  
Furthermore, this study underscores the utility of employing a modified Drucker-Prager-Cap (DPC) model in simulating the Cold Isostatic Pressing (CIP) process. This approach offers a practical and dependable method for predicting the compaction behavior of Magnesium Silicon Carbide nanocomposite powders, potentially informing and guiding future research in this area.  
Figure 6: Morphology of milled powders (**a**) M10 Sn and (**b**) M10S\(\upmu\). The Backscattered electron image of (**c**) M10 Sn and (**d**) M10S\(\upmu\) displays the dispersion of SiC particles (white) in the magnesium matrix (light grey) homogeneously [33].  
**Author Contributions:** Conceptualization, F.R.M., S.K., C.F. and M.S.; methodology, F.R.M. and M.S.; software, F.R.M. and M.S.; validation, F.R.M. and M.S.; formal analysis, F.R.M.; investigation, F.R.M.; resources, F.R.M., S.K., C.F. and M.S.; data curation, F.R.M. and M.S.; writing--original draft preparation, F.R.M. and M.S.; writing--review and editing, F.R.M., S.K., C.F. and M.S.; visualization, F.R.M. and M.S.; supervision, C.F. and M.S.; project administration, F.R.M., C.F. and M.S.; funding acquisition, S.K., C.F. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their gratitude to the North-German Supercomputing Alliance (HLRN) for providing the necessary computation facilities for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.