esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Random Forest Model  
The random forest (RF) algorithm is an ensemble learning approach that combines multiple decision trees to enhance predictive performance and model stability [31; 32]. Predominantly applied to classification and regression problems, RF utilizes diverse sampling techniques to ensure that each tree in the ensemble generates distinct predictions, thereby increasing both accuracy and robustness [33]. Specifically, RF builds multiple decision trees on various bootstrap samples of the data, ensuring that each tree explores different patterns by selecting only a subset of features for each split. In classification tasks, RF finalizes predictions through majority voting, selecting the class with the most votes, whereas in regression, the predictions of all trees are averaged. Through this process, RF not only builds a reliable model less prone to overfitting but also offers feature importance insights, enabling the identification of key predictors that significantly influence outcomes [31; 32; 33]. In this study, we leverage RF to classify VHI based on a limited dataset, demonstrating the model's adaptability in smaller data environments while still providing accurate predictions and meaningful insights into feature contributions.
## 4 Demanding Applications of Blockchain Technology and the Motivation behind Them
### Protection of Copyright Through Blockchain Technology  
Several startups are using blockchain to protect their customers' intellectual property rights. Customers can protect their artwork on the platform from being used illegally without permission, once registered. Owners can also seek a legal injunction if their platforms are violated using the certificates provided. Companies such as Blockai and Copyrobo use blockchain technology and artificial intelligence to help artists protect their art online in seconds. Creating a timestamp or fingerprint on the blockchain will obtain a copyright certificate to prove their rights. It is through these platforms that copyright in fringement interoperability has been demonstrated in the U.S. Drug Supply Chain Security Act Interoperability Pilot programs are discouraged, and licensing is encouraged.  
A Blockchain-Based Solution for Tracking Drugs throughout Supply Chains and Securing Data in Healthcare  
In pharmaceutical supply chains, blockchain technology is utilized for tracking and tracing prescription drugs. Interoperability has been demonstrated in the U.S. Drug Supply Chain Security Act Interoperability Pilot program. This program can easily and quickly prevent and control the distribution of counterfeit drugs, as well as recall ineffective and harmful drugs.
## 3 Result and Discussion
### Verification of Significance of Vegetation Regeneration Area  
The research area is currently carrying out a restoration project to plant new trees, and it is expected that it will take more than three years to complete restoration. A follow-up investigation is necessary to monitor the vegetation regeneration area, but the forest fire in the Gangneung area is also a recent event, making monitoring difficult.  
Thus, in this study, we performed a spatial autocorrelation analysis based on Getis-Ord Gi* statistics for signature verification [46]. Spatial autocorrelation analysis derives the significance of the clustering results of pixels created based on coordinates. Getis-Ord Gi* statistics analyzes local clustering patterns to determine individual clustering tendencies with regions within a specific range. The results of NDVI show that the Z-score value was 5.83, the EVI value is 35.26, and the SAVI value is 5.83. All three indices are 99% significant. Spatial autocorrelation analysis can provide a good predictor of hotspot identification. If the \(p\)-value of each cluster is less than 0.05, this cluster indicates that vegetation is regenerating with statistical significance compared with other areas.  
The green areas in Figure 11 indicate hot spot areas where vegetation regeneration statistically significantly occurs. Therefore, these regions can be preserved without removing all trees and residues.  
Figure 11: Getis-Ord Gi* statistics analysis: the green areas indicate the Getis-Ord Gi* statistics results, and the brighter areas indicate the vegetation regeneration area extracted using three indices.  
Figure 10: Comparison of the proposed method and NDVI with NBR: green indicates the extracted areas of the proposed method and red indicates the extracted areas of difference normalized burn ratio (dNBR) with NDVI.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 5 Experiments
### Experimental Results
However, the PU dataset exhibited higher sensitivity to noise. Under electrical interference noise, the PU dataset's performance dropped below 80%, and although it improved slightly under harmonic noise, it still remained below 85%. In contrast, random shock noise had a different impact, with the PU dataset achieving over 92% performance when processed with the MultiPatchTST model, demonstrating its robustness to aperiodic noise. Across all datasets and experimental conditions, the MultiPatchTST model consistently achieved the highest scores on accuracy, precision, recall, and F1 metrics. These findings emphasize that the inclusion of raw and noisy data enhances robustness against random shock noise compared to periodic noise. Moreover, the high sensitivity of the PU dataset to noise underscores its vulnerability in mixed-noise environments. The results suggest that aperiodic noise, such as random shock noise, can be effectively handled when mixed with original data, while periodic noise poses greater challenges. This experiment reaffirms the efficacy of patch-based learning, as implemented in the MultiPatchTST model, establishing it as a reliable methodology for bearing fault classification in noisy environments. The detailed experiments and results are shown in Appendix A. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{Random shock noise} & LSTM & 0.9114 & 0.9203 & 0.9158 & 0.918 \\ \cline{2-5}  & GRU & 0.9163 & 0.9245 & 0.9221 & 0.9232 \\ \cline{2-5}  & FCN & 0.9164 & 0.9242 & 0.9203 & 0.9222 \\ \cline{2-5}  & ResNet & 0.9108 & 0.9197 & 0.9152 & 0.9174 \\ \cline{2-5}  & InceptionTime & 0.9131 & 0.9229 & 0.9188 & 0.9208 \\ \cline{2-5}  & XceptionTime & 0.9145 & 0.9227 & 0.9189 & 0.9207 \\ \cline{2-5}  & XCM & 0.915 & 0.9231 & 0.9198 & 0.9214 \\ \cline{2-5}  & LSTMAttention & 0.9145 & 0.9231 & 0.9191 & 0.9211 \\ \cline{2-5}  & GRUAttention & 0.9149 & 0.9216 & 0.9186 & 0.9201 \\ \cline{2-5}  & TSSequencer & 0.9128 & 0.9209 & 0.9166 & 0.9188 \\ \cline{2-5}  & PatchTST & 0.9176 & 0.9263 & 0.9215 & 0.9239 \\ \hline
**MultiPatchTST** & **0.9204** & **0.9283** & **0.9249** & **0.9261** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 2 Literature Review
### Smart Building Multiple Sensor IoT Device Data Management Simulation
#### 2.5.1 The Cloud Computing Layer  
Also known as a data centre, the Cloud layer comprises powerful computational capability and almost limitless data storage capacity. Furthermore, due to the vast resources it makes available, service management and the processing of large volumes of data, including smart IoT device data, occurs in this layer. In addition, Fog computing applications and smart IoT device applications benefit from quality of service (QoS) management and the processing extensive data operations, including predictions, classifications, and preprocessing [34; 41]. Although Cloud computing possesses these vital characteristics, there are also associated weaknesses such as increased energy consumption, slow latency, and increased network bandwidth usage. Consequently, Fog computing was introduced to address the weaknesses inherent in Cloud computing by processing computational requests and temporarily storing data at the network's edge, closer to the IoT devices themselves.
## 2 Materials and Methods
### Bactericidal Effect Test for NW Preparations Stored for 126 Days  
After the 126-day storage period, the bactericidal effect of each test NW was examined, as shown below.  
The presence of _Streptococcus mutans_ (NBRC13955, _S. mutans_), a major causative bacterium of dental caries, was analyzed to examine the bactericidal effects of the test NW preparations. A bacterial suspension was obtained from single colony isolation on an agar plate and inoculated in overnight broth cultures at 37 \({}^{\circ}\)C. _S. mutans_ was cultured in brain-heart infusion broth (BHI; Becton, Dickinson and Company, Franklin Lakes, NJ, USA) under aerobic conditions. The bacterial cells were collected, washed twice with sterile phosphate-buffered saline (PBS; AS ONE, Osaka, Japan), and centrifuged at 3000 rpm for 15 min. After centrifugation, the bacterial pellet was resuspended in fresh sterile PBS to prepare a bacterial suspension with a concentration of approximately 2 \(\times\) 10\({}^{7}\) cells/mL, as measured by a bacterial counter. The bacterial suspension was then serially diluted, mixed with BHI agar medium (Becton, Dickinson and Company, Franklin Lakes, NJ, USA), and incubated aerobically at 37 \({}^{\circ}\)C for 48 h. The colony count on the medium indicated that the bacterial concentration in the suspension was approximately 1.6 \(\times\) 10\({}^{7}\) colony-forming units (CFU)/mL.  
Each test NW (9.0 mL) was added to 1.0 mL of the bacterial suspension by repetitive pipetting using a sterile glass pipette and vortexed for 3 min using a vortex mixer (Thermolyne Maxi Mix II Vortexer, Barnstead Thermolyne, Dubuque, IA, USA) to examine its bactericidal effect. After treatment, each test NW preparation mixed with the bacterial suspension was added to each BHI agar medium. Based on the results of the stepwise dilutions in the preliminary experiment, the volume added to the medium that facilitated colony counting was determined, and 0.1 mL for NW25 (SRT) only and 1.0 mL for all other conditions were mixed with 19 mL of the medium and incubated aerobically at 37 \({}^{\circ}\)C for 48 h. After incubation, the total number of surviving bacteria in the test NW (NW treated for 3 min against the bacterial suspension) was calculated from the CFU in the agar medium. The bacterial removal rates were calculated using the following equation:  
\[\text{Bacteria removal rate (\%)}=100\ (1-\text{N}_{\text{ surviving}}/\text{N}_{\text{initial}}) \tag{2}\]where N\({}_{\text{surviving}}\) is the number of surviving bacteria and N\({}_{\text{initial}}\) is the initial number of bacteria in contact with test NW (1.6 \(\times\) 10\({}^{7}\)).  
Bactericidal tests were repeated five times for each sample type (\(n\) = 5).
## 2 Materials and Methods
### Data  
#### 2.2.1 MODIS Images and Preprocessing  
The MODIS onboard the Terra and Aqua satellites were developed by the Earth observation system (EOS) and can capture images with a spatial resolution ranging from 250 to 1000 m and a revisiting period of 16 days [41]. The MOD13Q1 satellite data spatial resolution is 250 m, which provides 23 annual temporal images and contains two VI types, including the NDVI and EVI. The VI datasets were obtained from the Google Earth Engine (GEE) from 2003 to 2015 and 2020, with 323 images used for crop identification.
## 3 Results and Discussion
In Figure 6a, a reflection prism is set next to the LDB, which transforms the azimuthally polarized mode into a radially polarized mode by reflection. In Figure 6b, a reflection prism is set after the LDB, which transforms the radially polarized mode into an azimuthally polarized mode by reflection. Therefore, two outputs of radially polarized beam in +\(x\)-direction and two outputs of azimuthally polarized beam in +\(y\)-direction could be achieved. A continuous wave laser was used in the experiment for demonstration. Because of the symmetric structure, the two orthogonally polarized modes have the same optical path length (OPL). Having the same OPL implies that ideally, no temporal walk-off problem exists (polarization mode dispersion) between the orthogonally polarized modes in the design. Figure 4: Spatial distributions of Stokes vectors of S\({}_{0}\), S\({}_{1}\), S\({}_{2}\), and S\({}_{3}\): (**a**) radially polarized light and (**b**) azimuthally polarized light. Figure 3: Azimuthally polarized lights in the reflection output converted from a linearly polarized light: (**a**) donut-shaped beam profile and intensity distribution in the \(z\)-direction observed without an analyzer, and beam profiles observed with the transmitting axis of an analyzer at (**b**) 0\({}^{\circ}\); (**c**) 45\({}^{\circ}\); (**d**) 90\({}^{\circ}\); and (**e**) 135\({}^{\circ}\) with respect to the \(-z\)-axis. Accordingly, the device can operate with a broadband pulsed laser. In [13], an additional crystal-type spatial walk-off polarizer was required to compensate for the OPL difference between two orthogonal polarized modes. By contrast, the cost of the proposed device can be substantially reduced by applying the lateral displacement beamsplitters. However, lateral displacement beamsplitters are commonly constructed out of a cube and a prism. These separate components could slightly complicate the device. Figure 7 illustrates that a proper shift should occur between the cube and the prism of the LDB to concentrate the polarized beams at four quadrants from a long distance \(L\) to a short distance \(l\). When smaller components are used and glued together, a more compact and robust device can be achieved. Because of the application of bulk solid materials, the device has a high damage threshold of at least 50 W/cm\({}^{2}\). With the same principle, the device can be designed at an operation wavelength of 1064 nm. For high-power applications, zero-order half-wave plates should be used to eliminate the influence of the thermal effect. Figure 5: Spatial distributions of instantaneous electric vector field for **(a)** radially polarized mode; **(b)** azimuthally polarized mode; and **(c)** generalized cylindrical vector (CV) beams as a linear superposition of **(a)** and **(b)**. Figure 6: A reflection prism (RP) is applied to transform one mode into another: **(a)** two output radially polarized beams in the +\(x\)-direction, and **(b)** two output azimuthally polarized beams +\(y\)-direction.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 4 Proposed MAC for LLDN
### Proposed CSMA/CA Mechanism for Shared Slots
1:if\(SLOT_{S}\) and not \(SLOT_{O}\)then
2: locate backoff period boundary, and wait for \(R_{backoff}\) periods;
3:if\((SLOT_{duration}-R_{backoff})\leq(T_{CCA}+T_{L})\)then
4: resume backoff at the next \(SLOT_{S}\);
5:else
6: perform channel sensing as described in Algorithm 1;
7:if channel access success then
8: go to Step 14;
9:else
10: wait for next \(SLOT_{S}\);
11:endif
12:endif
13:else
14: start transmission
15:if transmission success then
16: stop;
17:else
18:if\((SLOT_{R})\) available then
19: start retransmission at the allotted \((SLOT_{R})\);
20:else
21: wait for next \(SLOT_{S}\);
22:endif
23:endif
24:endif
```  
**Algorithm 2** New channel access mechanism
###### Abstract  
The rise of deepfakes--synthetic media generated using artificial intelligence--threatens digital content authenticity, facilitating misinformation and manipulation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures--Xception, ResNet, and VGG16--for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, Xception achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These findings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deepfake threats.  
deepfakes; deep learning; Xception; ResNet; VGG; DFDC; FaceForensics++; adversarial robustness; detection models +
Footnote †: journal: Applied sciences
## 5 Simulation Results
### Sub-Swarm Formation for Distributed Estimation Consensus  
This experiment shows two cases of possible victims with different detection probability. The first case presents two victims in an open field with a high probability to be detected by the drone sensors as shown in Figure 11. Given the victim detection is easy for this case, the swarm is made up of 6 agents located in the vicinity of the victims. After sub-swarm creation, the consensus and control formation algorithm is performed as shown in Figure 11 as explained previously in this paper the consensus looks for to approach every drone to the victim, reducing the uncertainty of sensing factor over the possible victim. Additionally, the formation control redistributes the drones in a circle around the victim with the aim to provide better assistance to the victims and balance the sensor measurements. The second case presents a victim with the body partially covered and a sub-swarm of three agents. The number of agents for this case is lower than the first case given the difficulty of the victim detection. Once the victim is detected, the sub-swarm executes the processes of consensus and control formation as shown by Figure 12.  
Figure 10: Navigation and victims localization in environment with presence of obstacles.  
Figure 11: Formation control applied to 6 agents sub-swarm generated by localization of a victim.  
Figure 12: Formation control applied to the 3 agents sub-swarm generated by localization of a victim.
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 2 Proposed Fish Counting Algorithm  
This section details the fish count algorithm used in the paper. The algorithm employs the echo-integration technique to count fish in a cage. The algorithm was designed under the assumption that the cage under investigation contains fish of the same species only, but potentially of variable length, which is generally the case in fish farming cages. The basic idea of the algorithm is to estimate the fish count based on the population density (per m\({}^{2}\)) of a cage, which is calculated by scaling \(s_{a}\) (measured by echosounder) with the average target strength (TS) (measured by sampling) of fish in the cage. Therefore, the more fish are uniformly distributed in a cage, the higher the accuracy of the algorithm. The algorithm assumes low fish density and sufficiently random distribution; thus, the shadowing effect is not considered.
## 5 Performance Evaluation
### Performance Metrics
* _Energy consumption per packet:_ This is the ratio of the total energy consumption of all the nodes to the total number of packets successfully delivered.
## 7 Conclusions  
This paper presents the study on solving three-dimensional free surface flow problems in arbitrary geometries using the moving boundary-type meshless method based on the MFS. The proposed method is verified and application examples are performed. The significance of the research is addressed as follows:  
1. The study on solving three-dimensional free surface flow problems is still limited to the conventional mesh-based method. This study presents a pioneering work using a novel moving boundary-type meshless method based on the MFS capable of solving three-dimensional free surface flow problems over arbitrary geometries. Compared to conventional mesh-based methods, the proposed method is relatively simple because the points are collocated only on the problem boundary.
2. With the advantage of the boundary-type meshless method, only the collocation points on the moving surface have to be renewed during iteration for the computation of the location of the three-dimensional nonlinear free surface. It avoids the most difficult task for handling the three-dimensional geometric complexity.
3. The validation examples demonstrate that the MAE from the computed results can achieve the accuracy with the order of \(10^{-12}\). It is significant that our method may yield highly accurate results. The effectiveness and ease of use for solving three-dimensional nonlinear free surface flows are also revealed.
4. The appearance of layered soils is often found in free surface flow problems such as the zoned embankment type dam. The anisotropic nature of layered soils is usually difficult to solve using the MFS. Further research is recommended to solve free surface flow problems in layered heterogeneous soils. It is suggested that the domain decomposition method may be an alternative to integrate with the MFS to deal with these problems in layered heterogeneous soils.
5. Furthermore, the transient free surface flow problem may be a great challenge for the MFS. Several studies have been found to solve the transient heat equation using the MFS. Since the governing equation of the transient free surface flow and the transient heat conduction problems are identical, it is recommended that further research may be considered to apply the MFS for solving transient free surface flow problems.  
Conceptualization, C.-Y.K.; Data curation, C.-Y.L.; Methodology, J.-E.X.; Validation, J.-E.X.; Writing--original draft, C.-Y.K.; Writing--review and editing, C.-Y.K., and J.-E.X.  
This study was funded by Ministry of Science and Technology of the Republic of China under grant MOST 108-2119-M-019-001.  
The authors thank the Ministry of Science and Technology for the generous financial support.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.
# Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System
## 1 Introduction
The memory saving of image reconstruction is important to such a system for its intended point-of-care applications, such as a disaster rescue or in an ambulance, where only a normal portable computer, e.g., a laptop, is available for computation. This paper consists of four main sections. Following Section 1--Introduction, it is Section 2--Theory where the signal equation, image reconstruction, and the characteristics of a non-linear SEM supplied by a PMA are detailed. In Section 3, the approaches to improve image quality by increasing the density of signal space for a PMA-based MRI system are investigated. In this section, the effects of an increase in the density of the signal space on image quality improvement, image reconstruction time, and memory consumption are examined. A dense signal space is preferred for an improvement in image quality while on the other hand, it results in a considerable memory consumption. An approach for a memory-saving reconstruction is used and the effectiveness is presented numerically in Section 4. Lastly, this paper is summarized in Section 5.