esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
###### Abstract  
To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail. In response to the segmentation issues encountered in point clouds of small industrial components, a novel Point Transformer network incorporating a multiscale feature fusion strategy is proposed. This network extends the original Point Transformer architecture by integrating multiple global feature extraction modules and employing an upsampling module for contextual information fusion, thereby enhancing its modeling capabilities for intricate point cloud structures. The network is trained and tested on the self-constructed gear dataset, yielding promising results. Comparative analysis with the baseline Point Transformer network indicates a notable improvement of 1.1% in mean Intersection over Union (mIoU), substantiating the efficacy of the proposed approach. To further assess the method's effectiveness, several ablation experiments are designed, demonstrating that the introduced modules contribute to varying degrees of segmentation accuracy enhancement. Additionally, a comparative evaluation is conducted against various state-of-the-art point cloud segmentation networks, revealing the superior performance of the proposed methodology. This research not only aids in quality control, structural detection, and optimization of precision industrial components but also provides a scalable network architecture design paradigm for related point cloud processing tasks.  
point cloud segmentation; point cloud dataset; global feature; multi-scale fusion; micro-gear Article
## 5 Simulation Results and Discussions
### Tracking Control without Acoustic Localization  
In order to demonstrate the role of acoustic localization for the AUV tracking control, we also run a set of simulations without acoustic localization. The system states used for MPC feedback are purely provided by the dead-reckoning module. This is the case when the AUV is not equipped with any positioning devices in an underwater environment. In this case, the AUV can never even reach the destination. Therefore, we set the simulation time the same as in the case with acoustic localization, and the tracking results are shown in Figure 10. It can be observed that the trajectory deviates far away from the reference path. The tracking task fails which means the tracking control algorithm with purely dead-reckoning is not acceptable in practical applications. Compared with Figure 5, Figure 10 further demonstrates the effectiveness of the proposed integrated acoustic localization and tracking control approach. Note that since no acoustic localization feedback is used, there is no measured trajectory and the estimated trajectory coincides with the dead-reckoning trajectory. Moreover, since now the controller is purely based on the dead-reckoning states which are subject to no disturbances, the dead-reckoning trajectory can follow the reference path well.  
Figure 10: AUV tracking control trajectories without acoustic localization: real-\(\mathbf{x}(t)\), estimated-\(\mathbf{\xi}(t)\), and dead-reckoning-\(\mathbf{\xi}(t)\).  
Figure 9: AUV tracking control trajectories with prediction biased errors: real-\(\mathbf{x}(t)\), estimated-\(\mathbf{\xi}(t)\), dead-reckoning-\(\mathbf{\xi}(t)\), and measured-\(\mathbf{y}(t)\).
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 2 Materials and Methods
### Calculation of the Average Brightness Level of Images  
The region of the OD has the maximum intensity of retinal images, and thus it is time-consuming to look over dark areas. For this reason, the average brightness level was calculated in order to reduce calculation time. The average brightness value was considered as a threshold and the pixels below the threshold value were eliminated from the image. The threshold value and the number of eliminated pixels are specific for each fundus image.  
The calculation of the average brightness level is given by the following equation:  
\[\text{Threshold Level}=\frac{1}{MxN}\sum_{i=0}^{M}\sum_{j=0}^{N}Img(i,j) \tag{1}\]  
M and N represent the number of rows and columns of an image, respectively. In order to calculate the average color value, the grayscale color values, which take a range of possible values from 0 to 255, of all pixels are added and divided by the total number of pixels in the image (M \(\times\) N). A block diagram for this calculation is given in Figure 5.  
Elimination of the pixels below the specified threshold value from the retinal image was applied with the formula:  
\[\text{Img(i,j)}=\left\{\begin{array}{rl}255,if(\text{Img(}i,j)<\text{ Threshold Level}\\ \text{Img(}i,j),\text{ otherwise}\end{array}\right. \tag{2}\]  
Figure 4: (**a**) Original healthy retinal image from the DRIVE dataset; (**b**) Enhancement of image (**a**) using CLAHE; (**c**) Original diseased retinal image from the DIARETDB0 dataset; (**d**) Enhancement of image (**c**) using CLAHE.  
An image resulting from the elimination of the pixels below the threshold from the retinal image is shown in Figure 6.
# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 1 Introduction
Based on the aforementioned related works, several attempts have been made to address the problem of NFT image plagiarism using artificial intelligence methods. However, many of these methods have depended on traditional deep learning architectures such as convolutional neural networks (CNN). They have yet to include more recent advancements in computer vision, such as EfficientNet. The proposed work in this paper overcomes this limitation using an EfficientNet-based model that has demonstrated state-of-the-art performance to detect plagiarized images in the NFT system. In addition, training the model with the triple semi-hard loss function has enhanced the model's ability to distinguish between images with similar features. The proposed work offers a novel method for detecting NFT image plagiarism that incorporates recent computer vision and deep learning developments. While further testing and evaluation are needed, the proposed method has the potential to outperform existing methods and enhance the security and integrity of NFT-based art. This study proposes an EfficientNet-based approach using the EfficientNet-B0 architecture to detect plagiarism in NFT images. Our specific contributions include the following:  
* Preprocessing the NFT dataset and using it to generate augmented images. * Detecting plagiarized NFT images based on EfficientNet-B0-based deep neural network (EfficientNet-B0-DNN) with triplet semi-hard loss. * Developing a network suitable for actual NFT ecosystems and providing high accuracy and reliable performance. * Comparing several modern deep learning methods and pointing out the EfficientNet-B0-DNN method outperforms other CNN methods such as ResNet50, DenseNet, and MobileNetV2 in terms of loss and accuracy. The remainder of the study is organized as follows: in Section 2, we introduce the concept of NFTs, and the proposed solution scheme, detail the augmentation process for the dataset, and describe the NFT image dataset used in the research. Section 3 explainsResNet50, DenseNet, MobileNetV2, and EfficientNet-B0-DNN techniques that we compared. Section 4 presents the hyperparameter settings we used, the Euclidean distance and triplet semi-hard loss as performance criteria, results, and a comparison of our proposed method with other models. Finally, in Section 5, we summarize the key findings, draw conclusions, and suggest areas for future research.
## 2 Results and Discussion
### Volatile Compounds in Goji Juice
These compounds smell fatty and grassy, and they lead to the production of a rancid odor as their concentrations increase [25]. In the present study, the odor was not enhanced in the goji juice after fermentation. The benzaldehyde, benzeneacetaldehyde, (\(E\))-2-octanal, (\(E\))-2-nonenal, and 3,5-dimethyl-benzaldehyde contents were increased after fermentation. Benzaldehyde provides a cherry aroma as its concentration increases [19]. Among the fermented samples, the (\(E\),\(E\))-2,6-nonadienal content only increased significantly in the juice fermented with SLV, and it was not detected in the juices fermented with other mixtures. This compound may be metabolized by \(L\). _reuteri_ or decomposed or transformed by \(L\). _plantarum_ or \(B\). _licheniformis_. The concentration of 2-pentyl-furan, a fatty acid oxidation product [26], increased by 8.72, 6.44, and 11.63 times after being fermented with SLV, SZP, and SZVP, respectively. [PERSON] et al. [27] found that the enzymes produced by _Aspergillus_ in fermentation of tea increased the content of 2-pentyl-furan, thereby enhancing the floral and grassy odors to improve the sensory quality. Therefore, it is proposed that the metabolites produced by the fermentation strains may increase the 2-pentyl-furan content, where the metabolites generated by \(B\). _velezensis_ may be important. The concentration of 2-methoxy-4-vinylphenol increased by 6.29, 5.42, and 6.90 times after fermentation with SLV, SZP, and SZVP, respectively. The odor threshold is low for 2-methoxy-4-vinylphenol and, thus, it made significant contributions to the aroma of fermented products, such as red wine [28]. Overall, the volatile compounds had positive effects on the aroma, and their contents were higher in the goji juice samples fermented with SZVP and SLV. The higher alcohol and ketone contents in the fermented goji juice may be responsible for the strongly floral and fruity aroma. In addition to the volatile compounds comprising alcohols, acids, aldehydes, and ketones, [PERSON] et al. [29] found that some amino acids can contribute to the aroma.
## 4 Discussion
### Uncertainties of the \(\mathrm{f_{esc\_GPR\_SR}}\) Model  
Although the \(\mathrm{f_{esc\_GPR\_SR}}\) model has been shown to be superior, there are still uncertainties in the modeling process. Firstly, in addition to the influence of soil background reflectance, the assumption that \(\omega_{N}\) was 1 also contributed to the underestimation of by the NIRv/FAPAR model, because \(\omega_{N}\) is actually less than 1. However, considering that \(\omega_{N}\) is the sum of leaf reflectance and transmittance in the near-infrared band and the absorption effect of leaves in this band is very weak, numerous studies have shown and acknowledged that \(\omega_{N}\) is relatively stable and close to 1 [15; 27; 28; 29]. Therefore, we did not pay attention to the uncertainty caused by the assumption that \(\omega_{N}\) was 1 but focused on the effect of soil reflectance on \(\mathrm{f_{esc}}\) estimation. In the future, improving the precision of \(\mathrm{f_{esc}}\) can be considered by calculating \(\omega_{N}\) accurately. Secondly, in this study, the correction factor was modeled using a combination of soil reflectance and the vegetation index NDVI, which represents vegetation coverage. While this combination was selected after evaluating the models' performance using four common vegetation indices, the correction factor could be further optimized in the future by testing more than two multi-factor combinations. Thirdly, despite the computational advantages of machine learning methods, they are still black-box models and heavily dependent on training datasets, which reduces their adaptability in special conditions. In the future, we plan to improve the model by establishing a new semi-empirical analytical model that considers the influence of soil reflectance on \(\mathrm{f_{esc}}\) estimation in two parts (one is the influence on the calculation of pure vegetation reflectance and the other is the influence of single scattering between soil and canopy), based on the basic model (\(\mathrm{f_{esc}}=\mathrm{Ref_{NIR}}/(i_{0}\cdot\omega_{N})\)) proposed by [PERSON] et al. [27]. Additionally, there are still uncertainties that cannot be clearly analyzed and quantified, including the retrieval error of SIF. In a word, there are still many problems to be addressed in future SIF-downscaling research.
## 3 Temperature Prediction Model for Subgrade
### Modeling Subgrade Temperature Prediction Based on NARX Network  
Figure 7 outlines the specific process for predicting subgrade temperatures in seasonally frozen regions using the NARX network.  
Given the high sensitivity of subgrade water content and frost heave to temperature changes, these two variables were selected as inputs for the network. The actual measured subgrade temperature served as the network output for training. The dataset consists of 10,000 consecutive data points, with 60% allocated for training the neural network model parameters, while the remaining 40% was applied as a test set to validate the model's generalization capability and accuracy. To reduce the magnitude disparity between samples  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Functions** & **Define** & **Range** \\ \hline \(f_{0}\) (Linear) & \(x\) & Real number domain (R) \\ \(f_{h}\) (Tansig) & \(\frac{\text{e}^{2\pi}-1}{\text{e}^{2\pi}+1}\) & [\(-\)1,1] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Activation functions for both layers of hidden and output.  
Figure 7: Flowchart of the NARX network prediction model.  
and enhance the model's convergence speed, the sample data underwent normalization within the range of \([-1,1]\).
## 5 Evaluation Tasks and Metrics
### Evaluation Metrics
#### 5.2.2 Stability and Robustness Metrics  
Stability metrics assess the consistency and reliability of the solutions and the optimization process itself.  
* Success Rate: The ratio of successful evaluations (resulting in finite, non-NaN scores) to the total number of evaluator calls.
* Coefficient of Variation (CV): Defined as the ratio of the standard deviation of scores to the mean score (\(\sigma/\mu\)). It provides a normalized measure of score dispersion.
* Stability Score: A normalized metric, typically derived from multiple runs of the same solution, often calculated as \(1/(1+\text{CV})\), where CV is the Coefficient of Variation. It reflects the reproducibility of a solution's score.
* Score Range: The difference between the maximum and minimum valid scores observed for a particular solution or across a set of solutions.
# Chemical Analysis of Gunpowder and Gunshot Residues
## 3 Analysis of Gunpowder and Gunshot Residue  
Chemical analysis has been used to identify GSR particles on suspects, cloths, objects, and surfaces. However, over the last decade, research has been focusing on other applications, such as the estimation of firing or time since discharge [18; 52; 61] as well as the determination of chemical profiles with discriminative potential to identify manufacturers and even models of ammunition [5; 15; 18].
## 3 Results
### Response Mechanism of GPP to Extreme and Normal Climate Conditions
#### 3.3.2 Response Mechanism of GPP to Climate Change  
To further elucidate the response mechanisms of GPP to climatic change in the desert steppes of the NFYS from 2001 to 2023, this study utilized a random forest model. The model identified normal climatic factors and extreme climate indices that collectively explain 76.82% of the variance in GPP, with the overall results passing the 1% significance level test. Among these, maximum temperature (T\({}_{\text{max}}\)), minimum temperature (T\({}_{\text{min}}\)), and the index ID0 were found to be significant at the 99% confidence level (\(p<0.01\)), while the T\({}_{390P}\) and T\({}_{\text{N90P}}\) were significant at the 95% confidence level (\(p<0.05\)), as depicted in Figure 3a. Building on this foundation, a Partial Least Squares Structural Equation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indices** & **Lag Time** & **0** & **1** & **2** & **3** \\ \hline R10 & 0.87 *** & 0.94 *** & 0.88 *** & 0.82 *** \\ R20 & 0.65 *** & 0.78 *** & 0.90 *** & 0.73 *** \\ R25 &  
Modeling (PLS-SEM) approach was employed to develop a GPP estimation structural equation model based on normal climatic factors and Extreme Climate Indices (ECI). The model was constructed to account for both the original state and lag effects, yielding goodness-of-fit values of 0.76 and 0.66, respectively, as illustrated in Figure 3b.  
The absolute values of the loadings for the observed variables (e.g., \(\mathrm{T_{max}}\), \(\mathrm{T_{N90p}}\), ID0) associated with each latent variable (normal climate conditions (ATM), extreme temperature conditions (ETI), and extreme precipitation conditions (EPI)) were relatively high, indicating robust explanatory power of the observed variables for their corresponding latent variables. In both the structural equation models (without considering lag effects and with lag effects of individual indices on GPP), the ATM, composed of \(\mathrm{T_{max}}\), \(\mathrm{T_{min}}\), and PP, exhibited a significant positive impact on GPP. Conversely, the ETI and EPI demonstrated a notable negative impact on GPP. In the model incorporating lag effects, the positive  
Figure 3: Model results of GPP response mechanisms to climate change. (**a**) Random forest contribution ranking, where ns stands for no significant and (**b**) GPP simulation models with and without lag effects.  
influence of normal climate conditions on GPP was smaller compared to the model without lag effects, while the negative influence of extreme climate conditions on GPP was more pronounced. This suggests that extreme climate conditions exert a cumulative and amplified influence on vegetation over time.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Feature Extraction and Dimensionality Reduction
We found that the highest accuracy was achieved with 25% upsampling of the minor class. After balancing the data set, 2141 min of training data were obtained for each of the SS, TR, and SB activities. In addition, 1057 min of data were obtained for each of NS, EAS, and MS experiments during the SS; 407 min of data were obtained for each of NS, EAS, and MS experiments during SB exercise; and 296 min of data were obtained for NS, EAS, and MS experiments during TR exercise. The final data set sizes of the balanced class sizes after upsampling and downsampling are reported in Table 1 (Column 5). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Biosignal** & **Feature** & **Number of Features** \\ \hline ACC-X & Statistical Features (SD, mean, median, quarterlies,...) & **ACC: 225** \\ ACC-Y & Mathematical Features (Slope, derivatives related, max-min, & BVP-Raw: 148 \\ ACC-Z & max/min, absolute summation, absolute inverse cosine, zero-cross, & BVP-Pr.: 148 \\ \({}^{\star}\)ACC & difference/ratio of 1 st and 3 rd quarterlies, mean/SD, mean/median, & HR: 98 \\ BVP-Raw & autoregressive all-pole model parameters, logarithm related features, & ST: 111 \\ BVP-Pr. & wavelet decomposition coefficients, signal-to-noise ratio (SNR),...) & GSR: 136 \\ HR & Moment Features (Central) (different orders), kurtosis, skewness & \\ ST & Frequency Domain (Amplitude and location of peak frequency, & \\ GSR & mean normalized frequency, power, magnitude, FFT features,...) & \\ Correlations between other biosignals (Coefficients of & \\  & (ACC-X, GSR),...), (ACC-X), ACC-Y), (ST, HR), (ACC-X, GSR),...) & \\ Ratio of some fundamental features (SD ACC-X / SD ACC-Z),... & Total: 866 \\ \hline BVP & Heart Rate Variability Features (low frequency, high frequency, & \\  & very low frequency, ultra low frequency, LF/HF, number of peaks,...) & \\ \hline GSR & Data Specific Features (mean energy, variance of local peaks, & \\ ST & total energy response, mean amplitude of local peaks, & \\  & low frequency, high frequency, very low frequency, LF/HF) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of extracted primary features (BVP-Pr: Processed BVP signal). (*ACC: Magnitude of ACC-X, ACC-Y, and ACC-Z; **ACC: Total number of extracted features from ACC-X, ACC-Y, ACC-Z, and ACC). Figure 7: Example feature extraction for galvanic skin response (GSR) (illustration of common statistical and mathematical features such as minimum, quartiles, mean, and slope).
## 2 A Behavior-Based Self-Deployment and -Repair Algorithm
### Algorithm Implementation
The force in charge of collision avoidance \(f_{r1}\) is the same as in the previous case. However, there are different repulsion forces for deployment in this case. L1 nodes tend to keep away from all of the other nodes, including the sink. However, repulsion from the sink node is stronger, meaning that they are not affected by far L0/L1 nodes. L0 nodes are not repelled by the sink, only by L1 nodes and other L0 nodes. Repulsion from L1 nodes is stronger in this case, meaning that L0 nodes registered to the same L1 node can conform small clusters, but still aim to deploy themselves as spread out as possible. In this case, there are only clustering forces between L1 nodes and the sink and between L0 nodes and L1 nodes. This forces are also in charge of self-healing after role swapping: if a given L1 node fails, one of the nearby L0 nodes will become L1 (Figure 1). At this point, it will start to become strongly attracted to the sink node and, hence, reach a location where it is directly connected to the sink. Besides, it will start to attract L0 nodes. Orphaned nodes or any other L0 node closer to the new L1 node than to its own will try to register to it, and hence, one-hop connectivity to the sink will be restored. Figure 4a shows an example of an MWSN deployed with all clustering forces in Table 1. If the clustering force between L1 nodes and the sink node is removed, L1 nodes tend to travel further from the sink node (Figure 4b), sometimes even further than their own L0 nodes. If the clustering force between L0 and L1 nodes is removed, deployment is less homogeneous, and the distances from L0 to L1 nodes are more random (Figure 4c). Figure 3: MWSN with geographic routing: (**a**) without clustering force; (**b**) with clustering force (figures are scaled 1:10 m).