esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works
Moreover, an additional block was integrated to classify the states of the traffic lights (red, amber, or green). The model demonstrated excellent performance when evaluated on the DriveU [43] traffic light dataset. [PERSON] et al. [44] introduced an advanced driver assistance system (ADAS) designed for real-time detection of traffic signs, vehicles, and pedestrians. The system is portable and image-based, and uses the YOLO v5 algorithm to achieve high detection speed and accuracy. The training process involved a fusion of the German Traffic Sign Recognition Benchmark (GTSRB) dataset and a dataset tailored for the study, intentionally designed to assess system performance under diverse lighting and weather conditions. The model underwent training on a Tesla P100 graphics processing unit (GPU) using nearly 2500 images over a period of 8 h. To evaluate the performance of the model, various implementation metrics such as F1 score, precision (P), recall (R), and precision-recall (PR) curves were computed. Furthermore, the real-time performance of the model was systematically compared across different low-power, high-performance embedded platforms and on a test computer. The results indicated that the Jetson Xavier AGX platform achieved the highest real-time detection speed, recording 43.59 frames per second. [PERSON] et al. [45] introduced a traffic sign recognition application that achieves a good trade-off between speed and accuracy, rendering it well-suited for embedded implementations. The proposed approach for traffic sign recognition rested on a tailored CNN featuring three convolution layers, a non-linear activation layer, batch normalization layer, and max-pooling layer to compress feature maps. The CNN was scaled to balance different dimension parameters, resulting in a compact model size suitable for embedded implementation. The technique proved efficient, as the proposed CNN achieved an impressive accuracy of 99.3% upon evaluation using the European traffic sign recognition dataset; the real-time processing capability was demonstrated at 250 frames per second (FPS) when implemented on an NVIDIA GTX960 GPU. For further exploration of related research in these domains, interested readers are encouraged to consult the following sources: Refs. [46; 47] for traffic sign detection, Refs. [48; 49] for traffic light detection, and Ref. [50] for road surface marking detection.
# Real-Time Filtering Non-Intentional Bid Request on Demand-Side Platform
## 4 Research Methodology
### Building Features
#### 4.2.3 The Interval Time  
Since our dataset is a 1% sample of the full dataset, it does not accurately reflect the access behavior regarding the interval time of fraudsters; therefore, the time interval has not seemed to be helpful in detecting abnormal behaviors of audiencelds and (IP, UA) when the average interval time is not short enough as well as when this interval time does not follow any regularity. However, in the full dataset, this is an important rule used to detect abnormal behaviors.  
Figure 2: The CDF graph of the number of bid requests of audienceld.  
Because interval times are not effective in detecting abnormal behaviors, we can discover another method based on time to observe. Based to ordinary observations, legitimate audiences do not click on the link more than once or twice within one second. As shown in Figure 4, most audiences have only one or two requests within one second. For this reason, a high number of bid requests in two seconds is also considered as abnormal behavior. As a result, two simple heuristics of normal and abnormal access behavior of audienceld and (IP, UA) based on the number of bid requests in one second might be summarized as below:  
* General trend: an audience has one or two bid requests within one second;
* Abnormal behavior: having more than two bid requests within one second.
## 3 Model Inversion and Discussion
### Influence of Noise on the Inversion Results  
In the objective function, noise suppression was realized by the data misfit term. In most cases, the norm value of the data objective function \(q\) equals 2. We add Gaussian white noise with the mean value of 0 and the standard deviation of the percentage of the abnormal amplitude. The forward modeling data of Model 3 are superimposed with different degrees of noise, and the data after adding noise is shown in Figure 11. The set maximum number of iterations is 100D. The inversion results are shown in Figure 12. With the increase of noise, the abnormal shape deviates from the real model, and false anomalies appear in the deep. In Figure 13, the observed data and predicted inversion results with different noises are compared. The inversion results can reflect the original shape of the observed data.  
Figure 10: The data reconstruction results of different p values. (**a**) \(p=1\), (**b**) \(p=1.1\), (**c**) \(p=1.3\), (**d**) \(p=1.5\), (**e**) \(p=1.7\), (**f**) \(p=2\).  
Figure 11: The abnormal magnetic data of Model 3 with different noise level.  
Figure 12: The inversion results with different noise level. **(a)** 1% noise, **(b)** 5% noise, **(c)** 10% noise.
## 7 Concluding Remarks and Future Work  
We investigated the MCMRS problem for a DSN. We required full coverage of a set of targets in a predetermined region. To conserve energy, we determined an arrangement of cover sectors such that the number of covered targets was maximized but the number of active sensors was minimized. Because the MCMRS problem is NP-complete, it will take a long computational time to determine the optimal schedule of active sensors. We employed two nondeterministic algorithms, the DPSO algorithm and the DPSO_GA algorithm, which is a hybrid DPSO scheme with GA operators, to manage this complexity and produce solutions in a relatively short computation time. Our proposed algorithms quickly determined high-quality sensor schedules and could be applied in situations with more numerous targets and sensors. The hybrid DPSO_GA algorithm produced solutions that were consistently superior to those produced by both the GA and the DPSO algorithms.  
In the future, our proposed algorithms could be applied for targets with different coverage requirements (i.e., different coverage weights or priorities). It is also worth focusing on combining other metaheuristic methods to solve MCMRS problem. Moreover, energy is required to rotate the sensors in practical applications; this cost of reorientation could be investigated in future research.  
Conceptualization, C.-K.L.; software, Y.-A.F.; investigation, Y.-A.F.; methodology, C.-K.L.; validation, C.-K.L.; writing--original draft preparation, Y.-A.F. and C.-K.L.; and writing--review and editing, C.-K.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 7 Conclusions  
A new fusion-based method has been developed for removing the speckle noise in SAR images using amalgamating of the anisotropic diffusion, discrete wavelet transform, and method noise. The proposed methodology employs log and exponential transformation to handle multiplicative noise cases. The efficiency and performance of the developeddespeckling method have been computed using quality metrics such as SNR, PSNR, SSIM, and UIQI. The proposed method achieved notable improvements in various metrics, such as a PSNR of 29.37 dB and a Structural SSIM of 0.6538, outperforming traditional filters. This demonstrates the method's ability to reduce speckle noise while preserving critical image details like edges and textures.  
However, during the application of the proposed methodology, several limitations were encountered. The first challenge was computational complexity, especially when processing larger SAR datasets, which increased the processing time. Although the method achieves superior noise reduction, this complexity could limit its use in real-time applications. Another limitation was that, at higher noise variances, the method sometimes struggled to fully preserve fine textures, leading to slight blurring in more detailed areas of the image. Additionally, the method's performance was less robust in highly heterogeneous areas where the intensity variations were extreme, which occasionally resulted in incomplete noise suppression. To overcome these challenges, future work will focus on improving the computational efficiency of the algorithm by exploring parallel processing and optimizing the implementation for faster performance. Furthermore, to address the limitations in texture preservation at high noise levels, incorporating machine learning models, such as convolutional neural networks (CNNs), could improve the method's adaptability to different noise patterns and further enhance fine-detail preservation. We also plan to expand the dataset used for validation to include more diverse SAR images, including real-world scenarios, to ensure the robustness of the approach across various conditions. Lastly, future research will explore integrating this technique with adaptive filters and hybrid models to better manage intensity variations in highly heterogeneous areas, improving both noise reduction and structural preservation.  
Conceptualization, A.K.S. and R.S.; methodology, A.K.S.; software, A.K.S.; validation, A.K.S., R.S. and J.N.; formal analysis, R.S. and J.N.; investigation, A.K.S. and J.N.; resources, R.S. and J.N.; data curation, A.K.S.; writing--original draft preparation, A.K.S. and J.N.; writing--review and editing, R.S. and J.N.; visualization, A.K.S.; supervision, R.S. and J.N.; project administration, R.S.; funding acquisition, A.K.S. and J.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 6 Conclusions  
In this paper, the task offloading problem of mobile edge computing in MEC scenarios was studied in depth, and a task offloading scheme based on the near-end strategy optimization algorithm was proposed. This method improves task scheduling and resource allocation through collaborative computing between edge devices, thereby increasing the efficiency of computing resources. Moreover, utilizing service caching technology can avoid redundant data transmission, reduce communication latency, and network load. Next, we constructed the task offloading and resource allocation problem as a Markov Decision Process (MDP). The problem was solved using the near-end policy optimization (PPO) reinforcement learning algorithm, which can dynamically and adaptively adjust. In scenarios where the simulated network environment undergoes significant changes, the algorithm comprehensively optimizes latency, energy consumption, and computational efficiency. However, the current service caching strategy is based on static variables. In the future, we will consider incorporating cache update strategies and more intelligent algorithms to better adapt to dynamic network environments and user needs, thereby making task offloading more flexible and adaptable.  
Conceptualization, [PERSON] and J.T.; methodology, Y.M. and J.T.; software, Y.M.; validation, Y.M.; formal analysis, Y.M. and J.T.; investigation, Y.M. and J.T.; resources, Y.M. and J.T.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, J.T.; visualization, Y.M.; supervision, J.T.; project administration, Y.M.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Graph of reward values and convergence under different numbers of edge node devices.  
**Funding:** This research was funded by the Funds of the Central Government for Local Science and Technology Development (236Z0701G).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare that this study was conducted without any commercial or financial relationships that could be considered potential conflicts of interest.
## 1 Introduction
[17] established a method for directly extracting initial sticking probabilities/coefficient from thickness profiles recorded in constructions with a high aspect ratio. They demonstrated that at high-aspect-ratio  
Figure 1: MD film growth deposition method [8]. structures, the leading front of the thickness profile provides direct information on the reactant sticking probabilities. The front slope of the thickness was utilized to calculate the sticking probability of Al(CH3)3 and H2O during Al2O3 ALD. The initial sticking coefficient (SCo) was giving (0.5-2) \(\times\) 10\({}^{-3}\) for Al(CH3)3 and (0.8-2) \(\times\) 10\({}^{-4}\) for H2O at Tset = 275 \({}^{\circ}\)C (Tsub-220 \({}^{\circ}\)C). The initial sticking coefficient value of H2O was shown to be temperature-dependent, decreasing to (1.5-2.3) \(\times\) 10\({}^{-5}\) at 150 \({}^{\circ}\)C. The film thickness determines the sticking coefficient--the film's thickness profiles slope increases as the deposition temperature increases. The experimental profiles show that the SC increases with increasing deposition temperature since steeper slopes correspond to higher SCs [13]. While lateral-high-aspect-ratio trenches were utilized for their study, the aforementioned method can also be used for other 3D features [17]. [PERSON] et al. [18] investigated the sticking process of methyl radicals on carbon dangling bonds using molecular dynamics simulations. They discovered that the proximity of unsaturated carbon atom sites significantly impacts the chemisorption of a CH3 radical onto a dangling bond. The structure and dynamics of water molecules and the hydrogen bonding network, formed when water molecules interact with a solid crystalline substrate, were examined by [PERSON] et al. using MD [19]. At room temperature, [PERSON] et al. [20] used molecular dynamics simulations to investigate the dynamic characteristics of water at the silica-liquid interface. They found that the anisotropic reorientation of water molecules is greatly influenced by the relative orientation of interfacial water molecules and their interactions with surface hydroxyl groups. The wide range of methodologies used to calculate the sticking coefficient like to use various experimental methods, such as Auger Electron Spectroscopy and QCM measurements, and theoretical approaches such as density functional theory (DFT) calculations or Monte Carlo modeling. More recently, sum-frequency generation has been reported in the literature [21]. This study aims to better understand the structure of precursor sticking coefficient during a high temperature of 573 K and high-pressure ALD process at a molecular level. The study focuses on the ALD deposition process of aluminum oxide, Al2O3, using trimethylaluminum (TMA--(Al(CH3)3)) and water (H2O) as precursors. The SC has been determined from the thickness profiles generated from the MD approach of the ALD process. While lateral-high-aspect-ratio trenches were utilized in [PERSON] et al. 's [17] study, we aim to prove that the same method could be used for other surface structure as it was used for our study.
# Volatile Organic Compound from _Trichoderma asperelloides_ TSUI: Impact on Plant Pathogenic Fungi
## 3 Results
### Effect of 2-PE on Fungal Growth  
We found 2-PE to be a major peak by SPME/GC/MS analysis (Figure 5), and it was reported as an antifungal compound [44]. 2-Phenylethanol is a primary alcohol that is ethanol-substituted by a phenyl group at position 2. It has a role as a fragrance, a _Saccharomyces cerevisiae_ metabolite, a plant metabolite, an _Aspergillus metabolite_, and a plant growth retardant. It is a primary alcohol and a benzene, and is an antimicrobial, antiseptic, and disinfectant that is also used as an aromatic essence and preservative in pharmaceutics and perfumery. In this study, commercial 2-PE was diluted at concentrations of \(10^{-1}\), \(10^{-2}\), and \(10^{-3}\) and subjected to the sealed plate method to test its antifungal activity against plant fungal pathogens. The results show that the fungal colonies of the tested plates were smaller than that of the control, similar to the result of VOCs from _T. asperelloides_ TSU1 (Figure 6). The high-percentage inhibition was related to the high concentration. At \(10^{-1}\) dilution, the commercial 2-PE inhibited fungal growth with a percentage inhibition ranging from 48.33% to 77.51% (Figure 7). The percentage inhibition of 2-PE against _C. cassicola_, _F. incarnatum_, _N. clavispora_, _N. cubana_, and _S. rofsii_ was 48.33%, 64.28%, 67.91%, 63.49%, and 77.51%, respectively (Figure 7).  
Figure 6: Sealed plate method for the analysis of the effects of commercial 2-PE at \(10^{-1}\) dilution against fungal pathogens _Corynespora cassicola_, _Fusarium incarnatum_, _Neopestalotiopsis clavispora_, _N. cubana_, and _Sclerotium rofsii_.  
Figure 7: Percentage inhibition of commercial 2-PE at different dilution levels (\(10^{-1}\), \(10^{-2}\), \(10^{-3}\)) against fungal pathogens by sealed plate method. Different letters indicate statistically significant differences among treatments (\(p<0.05\)) using [PERSON]’s test.
# A New Framework for Visual Classification of Multi-Channel Malware Based on Transfer Learning
## 3 Methodology
### Interpolation Algorithms
#### 3.2.1 Nearest Neighbor Interpolation Algorithm  
The nearest neighbor interpolation algorithm is simple in operation and requires less computation. Generally, the gray value of the pixel closest to the location of the sampling point to be measured is selected as the gray value of the sampling point. The generated image is shown in Figure 3a. Let point \(A(x,y)\) be the sampling point to be measured and \((x_{0},y_{0})\), \((x_{0},y_{1})\), \((x_{1},\,y_{0})\), and \((x_{1},\,y_{1})\) be the pixel coordinate points of the original image. Because the \((x_{0},\,y_{1})\) coordinate point is closest to the sampling point to be measured, so the interpolation result is \(A=f(x,y)=f(x_{0},y_{1})\). Although the nearest neighbor interpolation algorithm is very fast, the effect is not good and will produce obvious sawtooth and mosaic phenomena.
## 4 Experiment
### Model Training and Evaluation
* Loss function and optimisation: The Adam optimiser was utilised to minimise the loss, with MSE serving as the loss function. The ReLU activation function was employed in the neural networks, aiding the ability of the model to learn intricate patterns, maintain gradients, and reduce the vanishing gradient issue. This approach is computationally inexpensive, facilitating quicker training and more efficient learning. * Regularisation: A regularisation approach, such as dropout, was applied in the PNN to address overfitting issues by randomly disabling a subset of neurons during training. This implies that a portion of neurons is temporarily eliminated from the network, including their connections, to compel the network to develop more robust features from the data that are not dependent on any specific group of neurons. This method enhances the ability of the model to generalise to new data by simplifying its complexity and avoiding over-reliance on certain features or patterns from the training set. **Post-training analysis**:
* Model comparison: To determine which models performed the best, all models were compared after training. The models stacked with PyTorch increased the accuracy of the lowest-performing models.
# Recovery Behavior of Fe-Based Shape Memory Alloys under Different Restraints
## 4 Result and Discussion
### Fe-SMA Behavior under Free Restraint  
Figure 6 and Table 5 present the comparison of temperature-recovery strain curves under free restraint and the activation test results of the Fe-SMA specimens with 0.04 pre-strain and 200 \({}^{\circ}\)C of heating temperature under free restraint. The residual strain for the FSMA-A specimen was 0.03033 before heating. The strain decreased to 0.02406 after being heated to 200 \({}^{\circ}\)C and subsequently dropped to 0.02077 when the FSMA-A specimen was cooled back to an ambient temperature. Thus, the recovery strain for the FSMA-A specimen was 0.00956. For the FSMA-B specimen, the residual strain was 0.03214 under a 0.04 pre-strain condition. In the heating stage, the strain was gradually reduced and reached 0.02103 at 200 \({}^{\circ}\)C because of SME. When the specimen was cooled back to an ambient temperature, the strain was 0.01769. Therefore, the recovery strain for the FSMA-B specimen was 0.01455 which is 51% higher than the FSMA-A specimen. Before heating, the residual strain for the FSMA-C specimen with a 0.04 pre-strain was 0.03234. The strain decreased to 0.01703 after being heated to 200 \({}^{\circ}\)C and subsequently dropped to 0.01257 when the FSMA-C specimen was cooled back to an ambient temperature. Thus, the recovery strain for the FSMA-C specimen was 0.01977. For the specimen that includes extra Ti composition, while the mechanical properties were enhanced, the recovery strain was lower. This is thought to be because the Ti added in the SMAs affects the mechanical properties and recovery strain. Even though the FSMA-B and FSMA-C have the same chemical composition, the recovery strain in the FSMA-C was 36.8% higher than the one from the FSMA-B. The growth of the recovery strain in the FSMA-C specimen was suspected to be caused by Carbon added in the FSMA-C specimen that encouraged it to have higher recovery strain [19; 21]. Thus, the heat treatment in manufacturing Fe-SMA has been shown to be beneficial in imparting higher recovery strain.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of SMA** & **Yield Strain** & **Yield Stress** & **Ultimate** & **Ultimate** & **Elastic Modulus** \\  & & **(MPa)** & **Strain** & **Stress (MPa)** & **(GPA)** \\ \hline FSMA-A & 0.00666 & 599 & 0.22556 & 1140 & 125 \\ \hline FSMA-B & 0.00496 & 380 & 0.39369 & 1105 & 126 \\ \hline FSMA-C & 0.00530 & 410 & 0.39510 & 1080 & 123 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of direct tensile test result.  
Figure 5: Comparison of stress-strain curves by direct tensile test.