esa-sceva/satcom-chunk-collection huggingface dataset:
# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
## 4 Discussion
It initially reaches maximum value against torque 300 N.m then follows the decreasing trend and reaches its minimum value as the torque reaches its lowest value. The torque and current levels of Mamdani-FLC are smaller as compared to the P-I controller response as shown in \"Figures 12 and 13\" respectively. The result indicates that the P-I controller cannot effectively track the desired speed of the fuzzy MFLC controller. This shows the efficient handling of EV parameters as energy can be saved and effectively supplied for a longer duration. The robustness is observed in fuzzy MFLC controllers by introducing disturbance (transition speed) as a step function. The fuzzy controller can swiftly reach the desired value after externally introducing disturbance. An efficient speed controller must reject the disturbance within minimum time and deviation from desired speed. The proposed fuzzy MFLC speed controller tracking operation was compared with P-I, FLC, fuzzy FOPID (ACO), fuzzy FOPID (GA), and fuzzy FOPID (PSO) controllers. The operational characteristics of all controllers are compared at the same speed of 40 km/h. Table 5 gives the time-domain parameters of the EV model using the aforementioned controllers. It is observed from the results that the fuzzy MFLC controller is superior to the aforementioned controllers in terms of settling time and overshoot percentage. The increase in increase time provides a smooth curve towards the desired speed without overshoot. Furthermore, the fuzzy MFLC speed controller is taking a step-change in speed level as compared to the ramp curve applied for the aforementioned controllers which guarantee the reliable performance of a speed controller under dynamic driving situations. Irrespective of certain advantages of the speed controller model, it has some design limitations: transforming the rule base for Mamdani fuzzy logic controller (MFLC) to meet predefined speed transitions takes time as it demands prerequisite experience and expertise. The model is trained for three speed modes 40, 60, and 80. The model works on fixed speed and achieves the desired speed based on pre-defined speed mode. The model is not valid for driverless EVs. \begin{table}
\begin{tabular}{c c c c c} \hline
**Speed Controller Type** & **Rise Time (\(T_{r}\))** & **Settling Time (\(T_{s}\))** & **Peak Time** & **Overshoot Percentage** \\ \hline P-I controller & 0.18 s & 1.2 s & 0.45 s & 6 \\ Mamdani FLC & 0.55 s & 1.1 s & 1.1 s & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between P-I controller and Mamdani fuzzy logic controller (high-speed transition). \begin{table}
\begin{tabular}{c c c c c} \hline
**Speed Controller Type** & **Rise Time (\(T_{r}\))** & **Settling Time (\(T_{s}\))** & **Peak Time** & **Overshoot Percentage** \\ \hline P-I controller & 0.2 s & 1.3 s & 0.5 s & 7 \\ Mamdani FLC & 0.5 s & 0.7 s & 0.7 s & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between P-I controller and Mamdani fuzzy logic controller (medium-speed transition).
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 7 Discussion and Evaluation
### Security Analysis  
The proposed SSI framework uses the Veramo (uPort) SDK and integration with a banking card. To evaluate the security of Veramo, we applied the threat model from [54]. SSBI does not rely on the official uPort mobile app; however, we utilize the same SDK functions:  
1. The private key in uPort is stored on the mobile secure enclave, where the SSBI stores the key on the banking card, which in turn cannot be extracted because the threat score is reduced significantly.
2. To counter the potential threat of stealing or gaining unauthorized access to the smartphone, SSBI isolates the private key store on external devices (the banking card), which prevents attacker identity operations.
3. uPort does not include an additional layer of protection such as a PIN or biometrics. We implement access control to identity the app using a PIN or by presenting the smart card.
4. Recovery mode in uPort is available with a social mechanism; however, we applied a different mechanism by involving the trusted bank to store the keys (HSM), while the user is responsible for the passphrase. The key cannot be regenerated without having the two components.
5. The action time stamp exists and can cause false revocation. This is possibly mitigated by forcing the SSBI app to fetch a governance timeserver.
6. Denial identity actions due to stealing or losing the mobile device can be mitigated via the recovery mechanism.
7. uPort app deletion means erasing the private keys. In contrast to the SSBI app, the deleted key is still safe inside the user's banking card.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Inter-Module Collaboration Mechanism  
The proposed tracking framework achieves efficient and robust multi-object tracking through the tight collaboration of three core modules, each with distinct and complementary responsibilities. The Cross-Frame Appearance Matching (CFAM) module extracts and associates appearance features across frames, generating an appearance similarity matrix \(S_{\text{app}}\). The Dynamic Motion Model (DMM) module predicts target positions based on motion states, outputting a motion similarity matrix \(S_{\text{mot}}\). The comprehensive matching decision method integrates these two modalities by dynamically weighting and employing a hierarchical decision strategy to finalize target association.  
The system's key strength lies in the complementary enhancement of multi-modal information. Under normal tracking conditions, appearance and motion information work synergistically to validate each other. In challenging scenarios, when one modality becomes unreliable, the other compensates effectively as follows:  
* When significant appearance changes occur (e.g., lighting variations or partial occlusion), DMM's motion prediction provides a stable positional reference.
* When abrupt motion changes occur (e.g., sudden stops or turns), CFAM's appearance matching capability ensures target continuity.
* During short-term occlusion, CFAM's memory recall mechanism and DMM's predicted positions jointly maintain trajectory continuity.
* For long-term occlusion, DMM's multi-hypothesis trajectory backtracking and CFAM's memory recall mechanism work in tandem to maximize trajectory recovery probability.  
Notably, CFAM's memory recall mechanism and DMM's multi-hypothesis trajectory backtracking complement each other across temporal scales as follows: the former enhances appearance features under short-term occlusion (1-3 frames), while the latter provides multi-path prediction strategies for mid- to long-term occlusion (more than 3 frames). This multi-scale, multi-modal collaboration significantly enhances the system's robustness in complex scenarios, particularly excelling in handling challenges such as occlusion, dense targets, and non-linear motion.
# Mechanical and Energy Evolution Characteristics of Sandstone under True Triaxial Cyclic Loading
## 3 Test Results and Analysis
### Analysis of Deformation and Failure Characteristics of Specimens
#### 3.1.2 Analysis of Sandstone Failure Characteristics under Different Stress Paths  
The macroscopic failure characteristics of sandstone under different stress paths are shown in Figure 5. In this study, the results of the uniaxial compression test, true triaxial compression test, and true triaxial cyclic test under the same conditions were adopted as examples to further describe the different macroscopic failure characteristics of sandstone under different stress paths.  
The diagram indicates that the failure state, crack development degree, and failure mode of sandstone differed under the three stress paths. Under uniaxial compression, the reaction of sandstone failure was severe, and the inverted V-shaped shear crack was completely penetrated to cause instability failure. The crack development was sufficient, showing obvious brittle failure characteristics. Under true triaxial compression, sandstone was mainly dominated by tension-shear composite failure, and tensile cracks were observed near the minimum principal stress surface. The cracks were fully developed. Under true triaxial cyclic loading, many incompletely developed non-interpenetrated cracks far from the minimum principal stress plane were found in sandstone. Near the minimum principal stress plane, several tensile cracks that were nearly perpendicular to the maximum principal stress plane were formed and finally split into rock plates. Under the  
Figure 4: Axial stress–strain curves under different stress paths.  
different stress paths, the development patterns of the cracks differed when sandstone was damaged. Uniaxial compression was beneficial to the formation of shear cracks; true triaxial compression was beneficial to the formation of tensile-shear cracks, and the true triaxial cycle was beneficial to the formation of tensile cracks, resulting in differences in macroscopic failure characteristics.
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
## 3 Design of Online Dynamic Correction Strategy for Interceptor
### The Description of THPEA
: _The THPEA is an ellipsoid area with a trajectory expectation as the center and the \(3\sigma\) variance of the independent distribution of the target position as a three axial radius. As shown in Figure 5._  
The area size is related to the variance \(\sigma\) of the target coordinate distribution. After the interceptor is launched, the early warning detection system improves its target tracking accuracy over time. As a result, the distribution variance \(\sigma\) of the target's position will inevitably decrease. The change in \(\sigma\) is related to time, at the next instant, \(t_{1}>t\),  
\[ \begin{cases}\sigma_{x}(t_{1})<\sigma_{x}(t)\\ \sigma_{y}(t_{1})<\sigma_{y}(t)\\ \sigma_{z}(t_{1})<\sigma_{z}(t)\end{cases}  \tag{22}\]  
as time goes on, the variance \(\sigma\) of the target position distribution tends towards 0, and the THPEA of the target will gradually converge towards the real PIP. Figure 5 shows the change process of the THPEA. It can be seen from the above analysis that the dynamic updating of the THPEA is mainly reflected in the variances \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\) of the target position distribution on the \(x\), \(y\), and \(z\) axes. In practice, the variance \(\sigma\) of the target's position distribution is related to the target maneuvering mode, trajectory prediction algorithm, and other factors, and its change rule is only applicable for specific targets. Figure 5: The dynamic change process of the PIP and THPEA of high-speed gliding target.
## 5 Rapid Calculation Model of Dual-Chamber Air Spring Stiffness and Damping
### Test of Calculation Model  
In statistics, regression analysis is performed using the variables. The coefficient of determination. \(R^{2}\) is the ratio between the regression sum of squares and sum of squares for total. \(R^{2}\) is between 0 and 1. The closer it is to 1, the better the fit of the regression. A goodness of fit of more than 0.8 is typically considered acceptable.  
In multiple regression analysis, \(R^{2}\) expression:  
\[R^{2}=1-\frac{SSE}{SST} \tag{17}\]  
where \(SST\) is the sum of squares for total; and \(SSE\) is the sum of squares for error.  
\[SST=\sum_{i=1}^{N}\left(f_{1i}-\overline{f}_{1i}\right)^{2} \tag{18}\]  
\[SSE=\sum_{i=1}^{N}\left(f_{1i}-\hat{f}_{1i}\right)^{2} \tag{19}\]  
The coefficient of determination \(R^{2}\) is related to the number of independent variables. In order to accurately test the accuracy of the model, it is necessary to take into account the degrees of freedom and make adjustments of \(R^{2}\) expression according to the size of the formula.  
\[R_{a}^{2}=1-\frac{SSE/(n-p-1)}{SST/(n-p)}=1-\left(1-R^{2}\right)\cdot\left( \frac{n-1}{n-p-1}\right) \tag{20}\]  
\(R_{a}\)\({}^{2}\) is denoted as the adjusted coefficient of determination; \(n\) is the total number of experiments, and \(p\) is the number of variables. The adjusted coefficient of determination more accurately reflects the degree of fit between the model and experimental data.  
The experiments performed here were representative in the parameter range, and the polynomial prediction model is appropriate for these values. The application of the polynomial prediction model is as follows: upper and lower air chamber volume ratio, \(\xi_{1}\in[0.5-2.0]\), orifice ratio, \(\xi_{2}\in[0-0.6]\), loading amplitude ratio, \(\xi_{3}\in[0.01-0.07]\), and loading frequency \(f\in[0.497-5.474]\).
## 6 Conclusions  
In summary, the simulation results showed that after applying the proposed solution, the SVM and RF methods obtained the highest positioning accuracy, at 8.6 cm and 10.2 cm, respectively. The SVM  
Figure 24: Estimated route before noise reduction and area division.  
Figure 25: Estimated route after noise reduction and area division.  
algorithm had the best improvement in execution time (approximately 78%). Our results indicated that if practical applications need a low positioning error, SVM is the optimal option. For applications that need fast execution time but moderate accuracy, we suggest kNN, which demonstrated a positioning accuracy of 13 cm and an average computational time of 5.6 ms.  
In this article, we proposed an enhanced ML-based indoor positioning solution using LED lights. Our solution not only improved positioning accuracy, but also reduced computational time. In addition to using signal pre-processing to achieve more accurate positioning, the novel adoption of dual-function ML was employed. The classification function saved execution time and provided a slight improvement in positioning accuracy, while the regression function helped determine the exact location of the object in the final step. In particular, this study compared four popular ML algorithms: SVM, DT, RF, and kNN. The results from this comparison gave us a comprehensive view of the advantages and disadvantages of each algorithm in positioning applications using VLC.  
Because developing VLC-based indoor mobile robot applications is our main pursuit, the optimization of ML algorithms to improve positioning accuracy and execution time in a real environment is our highest priority. In our future work, we continue to optimize positioning accuracy using deep machine learning.  
[PERSON] proposed the idea, designed the proposed algorithm, performed the simulation, and wrote the manuscript. As the corresponding author, [PERSON] supervised the research, provided the guidance for data analysis, and revised the article.  
This work was supported by Korea Hydro & Nuclear Power company through the project \"Nuclear Innovation Center for Haeoleum Alliance\".  
The authors declare no conflict of interest.
## 4 System Architecture
### Starting Point of OFDM Frame Detection  
[PERSON] et al. suggested a time-frequency joint synchronization approach that uses CP to calculate a maximum likelihood function to predict the time and frequency deviation [17,18]. By utilizing the properties of cyclic-prefix, the start of OFDM frame may also be detected. In [9], we proposed the Van de Beek algorithm to detect the frame of RS-OFDM symbol in real-time for the OCC system. However, as we mentioned above, the mobility channel has a lot of effects on the OCC system (blur effect and SNR reduction, among others), so the conventional algorithm does not provide good performance for the RS-OFDM system. In this paper, we proposed deep learning for detecting the start of OFDM frame, as shown in Figure 3. We used a basic deep learning neural network model with two hidden layers to avoid overfitting the model. Following preamble detection, we can accurately detect the start of a frame of OFDM signals, improving the OCC system performance compared with matched filter technology in a mobility environment. In the case of overfitting, the accuracy of the test dataset is undermined when there are five or more hidden layers. The dataset was collected from several cases (different distances and velocities) with rolling shutter camera. The results of our proposed approach compared with the conventional scheme in [9] are shown in Section 5.
## 4 Results and Discussion  
### Experimental Dataset and Setup  
#### 4.1.1 Experimental Dataset  
The experiment used 30 UCI public datasets ([[http://archive.ics.uci.edu/ml/datasets.php](http://archive.ics.uci.edu/ml/datasets.php)]([http://archive.ics.uci.edu/ml/datasets.php](http://archive.ics.uci.edu/ml/datasets.php))) as test datasets to verify the performance of the algorithm. The information regarding the samples, attributes, and categories of each dataset is shown in Table 2.  
The computational complexity of the WELAD algorithm is composed of two factors: the decision tree algorithm and the feature selection required to generate the base classifier with diversity. The computational complexity of the decision tree algorithm is \(O\left(N^{2}F\right)\). The computational complexity of the feature selection required to generate the base classifier with diversity is \(O\left(2^{F}\right)\), where \(F\) is the number of features in the dataset. Hence, the overall computational complexity of the WELAD algorithm for each dataset is \(O\left(N^{2}F2^{F}\right)\).
## 5 Performance Evaluation
### Simulation Results
#### 5.2.11 Performance of Individual Protocols  
According to the definition of the OR protocol, MAC and routing layers operate simultaneously. Hence, the packet filtering method for the MAC layer requires the routing layer information. The MAC layer packet-filtering performance of the implemented protocols is shown in Figure 17. The MAC layer packet classification of the four protocols, including the number of packets received and sent from the routing and radio layers and the number of ACK packets sent, is shown in Figure 17a-d.Based on the analysis, in a high-density network, the number of packets received from the radio layer of FCM-OR is less than those of ORW and ORR. This implies that fewer transmissions of redundant packets exist in the proposed protocol. This is because FCM clustering was used, which drove packets through a low-density area. However, the \"sent data\" or sender waiting time was reduced in FCM-OR, as this protocol limited the number of forwarders.  
It is found in Figure 17 that the sender's waiting time (as indicated by the \"sent data\" metric) rises in FCM-OR when comparing ORW with FCM-ORW and ORR with FCM-ORR. This suggests that even though there are eligible nodes in high-density areas, the sender may encounter longer delays in FCM-OR while waiting for a suitable forwarder from a low-density region. Although this design decision effectively reduces redundant transmissions, latency is introduced. Investigating a trade-off mechanism that strikes a balance between the advantages of low-density forwarding and the requirement for timely data delivery is, therefore, crucial.
## 4 Experiments and Discussions
### Image Quality  
Figure 9 shows the visual results of proposed method. The target models for the visual experiments are ResNet (based on CIFAR-10) and MobileNet (based on ImageNet). In the ImageNet experiment, only three pixels were perturbed, resulting in generated AE and RAE with visually indistinguishable effects. The restored image is identical to the original image, hence the intermediate process is not shown in the figure. The high image quality of RAEs can also be observed from the visual results on both the CIFAR-10 and ImageNet.  
As shown in Table 2, the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) are calculated between the original images and their corresponding RAEs, as well as between the original image and the restored image. The experimental setup involves attacking the MobileNet on the ImageNet dataset, with the number of perturbed pixels set to 3, 5, and 10, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **RGB** & **HSV** & **RGB + HSV** & **Proposed** & **RGB + HSV * \\ \hline Successful Rate & 88.42\% & 91.16\% & 97.66\% & **98.95\%** & 0 \\ \hline \hline \end{tabular}  
* Use a \(3\times 3\) median filter to replace the Laplacian operator. The best results are shown in bold. The same applies to the tables that follow.  
\end{table}
Table 1: Comparison of the different perturbation pixel detection approach.  
Figure 8: Visual results of the difference matrices generated by different approaches. (**a–d**) correspond to RGB, HSV, RGB + HSV, and Proposed, respectively.  
When only three pixels are allowed to be perturbed, the RAE generated by the proposed method achieves a PSNR of 48.32 dB and an SSIM of 0.9986 when compared to the original image. This indicates that the generated RAEs possess high image quality. Moreover, even with 10 perturbed pixels, a PSNR of 43.97 and an SSIM of 0.9959 can still be obtained. The RAEs generated by the proposed method can be restored perfectly to the original image, resulting in a PSNR of \(\infty\) and an SSIM of 1 when the restored image is compared to the original image. This indicates that the proposed method is a reversible algorithm exhibiting a zero-bit error.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Methodology  
To verify the effectiveness of our proposed DSAH method, seven state-of-the-art cross-modal hashing methods are compared. Among them, CMFH [32] and FSH [33] are unsupervised cross-modal hashing methods, and SCM-Seq [34], SePH-km [35], DCH [36], LCMFH [37], and SRLCH [42] are supervised ones. We have briefly introduced the compared baselines in Section 2. For fair comparison, the experimental results with citations are copied from the corresponding works.  
To evaluate our proposed DSAH, we conducted two cross-modal retrieval tasks: (1) \"Image2 Text\" using an image query to retrieve texts; (2) \"Text2 Image\" using a text query to retrieve images. In this paper, three widely-used evaluation measures are used to evaluate the retrieval performance, i.e., mean average precision (mAP), precision-recall curves (PR) and precisions w.r.t top-\(k\) returned image (P@\(k\)).