esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### UAVs' Performance for Monitoring the Coast  
Figure 9 and Table 1 illustrates the digital surface models (DSMs) of the Matrice 300 RTK LiDAR, Matrice 300 RTK Camera, Phantom IV Advanced Multispectral, and Mavic II Pro. The data were corrected relative to the reference point, and a scatter plot was included. It is crucial to note that, given the Mavic II Pro's lack of an RTK positioning function, the DSM was manually adjusted to align the feature point in the image with the reference point.  
Figure 9: DSMs derived from each UAV monitoring stage (**left** panels) and comparisons of the extracted and measured topography data (**right** panels). The unit of RMSE and MAE is m. The dashed line represents the 1:1 line.  
As indicated by the results, the accuracy of the DSM generated by LiDAR was the highest, exhibiting the best fit and lowest errors for both sandy and muddy areas. Under the same conditions, the accuracy of the DSM generated using the SFM method was slightly lower in muddy areas compared to sandy areas. This discrepancy may be attributed to sunlight reflections on the water puddles covering the surface of the muddy beach, whereas SFM depends on ambient light and is weak in water reconstruction. Additionally, the lack of prominent ground objects in the intertidal zone's muddy beach for feature point extraction with the SFM method further contributed to speckle errors in the DSMs generated by the Matrice 300 RTK and Phantom IV Multispectral. The performance of the Mavic II Pro without an RTK system raised concerns, especially in scenes with high precision requirements. Our observations indicate that the DSM boundaries exhibited higher errors.  
Figure 10 shows the terrain model reconstructed by M300 RTK after acquiring the point cloud by LiDAR and assigning colors by the visible photos. Compared to SFM, LiDAR's active detection feature allows the model to have more accurate feature points, which is more advantageous in scenarios with high model accuracy requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RMSE (Sandy)** & **RMSE (Muddy)** & **MAE (Sandy)** & **MAE (Muddy)** & **COR (Sandy)** & **COR (Muddy)** \\ \hline Mavic II (SFM) & 6.3519 & 3.7001 & 5.2007 & 3.1133 & \(-\)0.18994 & \(-\)0.82301 \\ Matrice 300 & 0.49496 & 0.54614 & 0.41406 & 0.47336 & 0.88729 & \(-\)0.029335 \\ (SFM) & & & & & & \\ Phantom IV Multispectral & 0.20573 & 0.21945 & 0.16818 & 0.14261 & 0.98241 & 0.47184 \\ (SFM) & & & & & & \\ Matrice 300 & 0.2647 & 0.089003 & 0.10956 & 0.078173 & 0.92228 & 0.92774 \\ (LiDAR) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The effectiveness of each UAV in monitoring coastal topography, assessed through RMSE, MAE, and COR.  
Figure 10: **(A,B)**: different perspectives showcasing the detailed 3D model of the LGTF; **(C)** the topography of the LGTF generated through LiDAR data. The elevation is based on the mean sea level.
# CFD-Based Metamodeling of the Propagation Distribution of Styrene Spilled from a Ship
## 2 Mathematical Representations  
### Kriging Model  
The metamodel can be efficient and practical for the quick prediction of HNS propagation without further CFD simulations, contributing to a rapid response after an accident [14]. The present study simulated a total of 80 cases and used the well-known kriging model to develop the meta-model [14; 15]. According to the kriging model, we first set the unknown function \(y(x)\) consisting of a regression model and the stochastic process:  
\[y(x)=f(x)+Z(x) \tag{1}\]  
where \(f(x)\) represents the approximation function, and \(Z(x)\) represents the localized deviation interpolating the \(n\) sampled values by considering a Gaussian distribution. A correlation between \(Z(x_{i})\) and \(Z(x_{j})\) is given by a covariance matrix of the samples as follows:  
\[Cov\left[Z(x^{i}),Z(x^{j})\right]=\sigma^{2}R\left(\left[R(x^{i},x^{j})\right] \right), \tag{2}\]  
where \(R\) is the correlation matrix, which is an \((n\times n)\) diagonal-symmetric matrix, and \(\sigma^{2}\) is the process variance. \(R(x_{i}x_{j})\) represents the Gaussian correlation function, which is expressed as:  
\[R\left(x^{i}_{k},x^{j}_{k}\right)=\exp\left[-\sum\
olimits_{i,j=1}^{n}\theta _{k}\left|x^{j}_{k}-x^{j}_{k}\right|^{2}\right]. \tag{3}\]The predictor can be described at an arbitrary point \(x\) using:  
\[y=\beta+r^{T}(x)R^{-1}\big{(}y-f\beta\big{)} \tag{4}\]  
where \(y\) is the CFD simulation results (a column vector of length \(n\)), and \(f\) is an \(n\times 1\) unit column vector. \(r^{T}(x)\) represents a column vector \(\{x_{1}\),..., \(x_{n}\}\) of arbitrary points \(x\). This column vector is expressed as follows:  
\[r^{T}(x)=\big{[}R\big{(}x_{k},x_{k}^{1}\big{)},R\big{(}x_{k},x_{k}^{2}\big{)}, \cdots,R\big{(}x_{k},x_{k}^{n}\big{)}\big{]}^{T} \tag{5}\]  
In Equation (4), \(\beta\) is mathematically given by \(\big{(}f^{T}R^{-1}f\big{)}^{-1}f^{T}R^{-1}y\). The correlation parameter \(\theta_{k}\) in Equation (3) is also determined using maximum likelihood estimation as follows:  
\[\max\limits_{\theta_{k}>0}(\theta_{k})=-\bigg{[}n\ln\bigg{(}\widehat{\sigma} ^{2}\bigg{)}+\ln\lvert R\rvert\bigg{]}/2, \tag{6}\]  
where \(\sigma^{2}\) is estimated using:  
\[\sigma^{2}=\bigg{[}\big{(}y-f\beta\big{)}^{T}R^{-1}\big{(}y-f\beta\big{)} \bigg{]}/n. \tag{7}\]
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 4 Backpropagation Neural Network
### Results of the BPNN  
According to the discussion in Section 4.2, the 10-dimensional features are selected as the input. Moreover, the BPNN structure with two hidden layers and four nodes in each layer is selected. The schematic diagram of the feature extraction and network structure is illustrated in Figure 9. In the hidden layer of the BPNN, the nodes adopted the ReLU function as the activation function and Bayesian regularization algorithm as the training algorithm [47, 48]. The learning algorithm is gradient descent with momentum [49], the loss function is MAE, the learning rate is 0.01, and the maximum training epochs is set to 500. The program adopts the EarlyStopping function [50], which can stop the training and output of the previous optimal model if the loss of validation cannot be improved in 40 epochs. The loss history of training and validation are shown in Figure 10.  
Figure 8: Optimization of the BPNN structure (10 feature values): (**a**) R\({}^{2}\) of validation set (one hidden layer); (**b**) MAE of validation set (one hidden layer); (**c**) R\({}^{2}\) of validation set (two hidden layers); (**d**) MAE of validation set (two hidden layers).  
The results show that in the early stage of training, the loss of training decreases with the increase of epoch. The loss of validation reaches the minimum (MAE = 0.1475) when the epoch is 13, and the program stopped training at the end of the 53 rd epoch.  
Finally, the test set is input into the optimal model, and the prediction results are shown in Figure 11. The statistical indexes of prediction errors are shown in Table 3. Evidently, the prediction results of the BPNN based on the STFT features are significantly better than those based on the FFT features. Nevertheless, both prediction results are poor. This is because the BPNN model is not capable of capturing the strong nonlinearity of the studied scenario. Therefore, a model based on the convolutional neural network is discussed in the subsequent section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction Method** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline STFT & 0.1499 & 0.0813 & 0.2852 & 0.6784 \\ FFT & 0.2436 & 0.0938 & 0.3063 & 0.4277 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical indexes of prediction results. STFT, short-time Fourier transform; FFT, fast Fourier transform.  
Figure 10: Loss (MAE) history of the BPNN.  
Figure 9: Feature extraction and network architecture.
## 4 Validation of MD Calculation Scheme for Lifter Operation
### MD Calculation Using Frequency Components Around Peak Frequencies  
The vibration characteristics of the lifter were carefully determined. Figure 10 shows in detail the configuration of the target lifter. The lower left of panel Figure 10a shows a pair of assembled lifters, only one of which was operated and targeted in this study. The lifter has a carriage (Figure 10c) for loading products. The lifter has a belt to move the carriage up and down and a pulley to operate the belt. The pulleys are located at the bottom and top of the lifter, shown in Figure 10b,d, respectively. To monitor the vibration of the lifter, two accelerometers, R1 and R2 (PCB, 603C01, upper limit frequency: 10 kHz within a variance of \(\pm 3\) dB), were set at the positions shown in Figure 11. As will be described later, the receiver at R1 was set to determine the mechanical conditions of the belt and guide wheel, whereas that at R2 was set to determine that of the bearings. The reason for the location of these sensors are as follows: Firstly, the vibration caused by the contact between the pulley and belt shown in the right figure of Figure 11a can be sensitively captured at the receiver R1. In addition, the vibration caused by the contact between the guide wheel on the carriage (Figure 10c) and the aluminum column easily transmits via the aluminum column and can be sensitively captured at receiver R1 as well. On the other hand, since the rotational vibration caused by the bearing is relatively weak compared to the vibration caused by the above devices that needs capturing on the surface of the bearing shown in the right figure of Figure 11b, each was attached magnetically to a steel plate. Receiver R1 was set on the steel plate surrounding the pulley, while R2 was set on the steel plate near the bearings used when the pulley at the top of the lifter is rotated. Each of the lifter parts--the belt (Figure 12A), the guide wheel (Figure 12B), and the bearings (Figure 12C)--was artificially damaged. For the belt, the green coating material on the side meshing with the pulley was sanded to make the meshing of the pulley and the belt unstable. For the guide wheel, its rubber-coated surface was melted, making the carriage operation unstable. Finally, a vertical force totaling \(1\) kN was applied to each of the bearings to destabilize the pulley rotation.  
Figure 11: Details of the setting of the vibration receivers on the surface of the lifter. In these pictures, the upper one indicates (**a**) receiving point of R1, while the lower one indicates (**b**) that of R2.  
Figure 10: Detailed configuration of the target lifter. In this figure, (**a**) target lifter of the right side, details of the (**b**) upper pulley, (**c**) carriage, and (**d**) lower pulley are indicated.
## 3 Results
### STZ Induces T1D
#### 3.3.2 Effect of STZ on Insulin Levels  
The insulin levels were similar among the groups of rats on week 3 before the STZ injection was provided to the G2 rats. After STZ treatment, the levels in most of the subgroups in G2 and G3 were significantly decreased (_p_ < 0.05) when compared with those in G1 (Figure 2). However, the levels in the 1% OLE and 3% OLE subgroups in G2 were not significantly different from those in the 3% OLE and Met. subgroups in G1. Furthermore, the insulin levels in the 1% OLE and 3% OLE subgroups from G2 were significantly higher  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Experimental** & **Initial Body** & **Final Body** & **Weight** \\  & **Groups** & **Weight (g)** & **Weight (g)** & **Changes (g)** \\ \hline \multirow{4}{*}{G1} & D.W G1 & 199.28 \(\pm\) 15.39 & 255 \(\pm\) 37.74 & 55.71 \(\pm\) 27.14  \\  & 1\% OLE G1 & 258.33 \(\pm\) 18.34 & 319.16 \(\pm\) 10  \\  & 3\% OLE G1 & 220 \(\pm\) 18.70 & 291 \(\pm\) 28.80  & 71 \(\pm\) 16.73  \\  & Met. G1 & 244.5 \(\pm\) 30.97 & 280.83 \(\pm\) 35.55 & 36.33 \(\pm\) 10.80  \\  & D.W G2 & 254 \(\pm\) 51.64 & 252 \(\pm\) 77.91 & \(-\)2 \(\pm\) 50.81 \\  & 1\% OLE G2 & 265 \(\pm\) 14.71 & 287.5 \(\pm\) 45.73 & 22.5 \(\pm\) 55.45 \\  & 3\% OLE G2 & 283 \(\pm\) 19.55 & 279 \(\pm\) 63.38 & \(-\)4 \(\pm\) 56.94 \\  & Met. G2 & 268.75 \(\pm\) 11.81 & 285 \(\pm\) 26.77 & 16.25 \(\pm\) 18.90 \\  & D.W G3 & 254 \(\pm\) 51.64 & 252 \(\pm\) 77.91 & \(-\)2 \(\pm\) 50.81 \\  & 1\% OLE G3 & 245 \(\pm\) 14.71 & 196 \(\pm\) 26.31 & \(-\)49 \(\pm\) 25.83  \\  & 3\% OLE G3 & 226.66 \(\pm\) 36.56 & 235 \(\pm\) 40.49  \\  & Met. G3 & 258 \(\pm\) 43.09 & 246 \(\pm\) 32.86 & \(-\)12 \(\pm\) 59.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of various treatments on the body weights of the rats.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **\% of Diabetic Rats** & **\% of Nondiabetic Rats** \\ \hline G2 & 35\%  \\ Subgroups (1\% OLE, 3\% OLE) & Five rats were not protected & Nine rats were protected from \\ n = 14 & from STZ toxicity & STZ toxicity \\ G3 & 57\%  \\ Subgroups (1\% OLE, 3\% OLE) & Eight rats were not protected & Six rats were protected from \\ n = 14 & from STZ toxicity & STZ toxicity \\ \hline \hline \end{tabular}
\end{table}
Table 3: Streptozotocin toxicity.  
(\(p<0.05\)) than those in the D.W and Met. subgroups in the same group. Similarly, significant increases (\(p<0.05\)) in insulin levels were observed in the 1% OLE and 3% OLE subgroups in G2 when compared with all untreated subgroups in G3 (Figure 2).
## 2 Diversity of Marine Carotenoids
### Synthesis of Some Important Marine Carotenoids and Enzymes  
\(\beta\)-Carotene as well as xanthophylls astaxanthin, zeaxanthin, lutein, and fucoxanthin are some representative marine carotenoids due to their abundance in marine organisms and their inherent antioxidant properties. \(\beta\)-Carotene is synthesized from the cyclization of lycopene, a key step in generating carotenoid diversity by lycopene cyclases, which can also lead to \(\omega/\gamma\)-carotene formation (Figure 1). The \(\beta\)-cyclase catalyzes the symmetrical formation of two identical \(\beta\)-ionone rings of \(\beta\)-carotene. On the other hand, \(\alpha\)-carotene contains two different ring structures (\(\varepsilon\) and \(\beta\)) formed by the action of additional \(\varepsilon\)-cyclase with \(\beta\)-cyclase. Four distinct families of lycopene cyclases, CrtY-type \(\beta\)-cyclases in proteobacteria, CrtL \(\beta/\varepsilon\)-cyclases in some cyanobacteria, the heterodimeric cyclases in some Gram-positive bacteria and FixC dehydrogenase superfamily lycopene cyclases in _Chlorobium tepidum_ and _Synechococcus sp_. PCC 7002, have been identified to date [62]. Further decorations occur via a variety of ketolation (oxidation), hydroxylation (Figure 4), which are the major causes for the diversity among carotenoids [29]. \(\beta\)-Carotene ketolase (CrtW or CrtO) adds the keto groups at the 4,4'-position of the ring and \(\beta\)-carotene hydroxylase (CrtZ) adds the hydroxyl group at the  
Figure 3: Diverse modifications of carotenoids. The structure shows the modification of a half carotenoid backbone. The stereo configurations are not shown in the structures. Glycosl moieties of fucose and glucose are represented by Fuc and Glu, respectively.  
3,3'-position [63]. Both enzymes are responsible for the formation of astaxanthin via zeaxanthin or canhaxanthin routes in some cyanobacteria and algae (Figure 3). Lutein formation is ascribed to the hydroxylation of \(\alpha\)-carotene by cytochrome P450 enzymes in _Arabidopsis thaliana_[64], while the pathway and enzymes remain to be elucidated from marine organisms. Fucoxanthin with a unique allelic and epoxide structure is derived from zeaxanthin in brown seaweeds, diatoms and dinoflagellates. Genome analysis indicates that zeaxanthin epoxidases epoxidize zeaxanthin to form antheraxanthin (H\({}_{\text{a2}}\)H\({}_{\text{a5}}\), Figure 3) and violaxanthin (H\({}_{\text{a5}}\)H\({}_{\text{a5}}\), Figure 3) [65]. Two possible routes have been proposed for the synthesis of fucoxanthin from violaxanthin via neoxanthin (H\({}_{\text{e1}}\)H\({}_{\text{a5}}\), Figure 3) or diadinoxanthin [66]. Very recently, a cytochrome P450-type caroten hydroxylase (PuCHY1) has been isolated from red alga _Porphyra umbilicalis_. The compensatory expression of PuCHY1 results in the formation of violaxanthin, neoxanthin, and lutein in _A. thaliana_ by the \(\beta\)/e-hydroxylation activities [67]. Some of the carotenogenic enzymes characterized from marine organisms have been summarized in the literature [28].
###### Abstract  
A novel electromyography (EMG)-based wheelchair interface was developed that uses contractions from the temporalis muscle to control a wheelchair. To aid in the training process for users of this interface, a serious training game, _Limbitless Journey_, was developed to support patients. Amyotrophic Lateral Sclerosis (ALS) is a condition that causes progressive motor function loss, and while many people with ALS use wheelchairs as mobility devices, a traditional joystick-based wheelchair interface may become inaccessible as the condition progresses. _Limbitless Journey_ simulates the wheelchair interface by utilizing the same temporalis muscle contractions for control of in-game movements, but in a low-stress learning environment. A usability study was conducted to evaluate the serious-game-based training platform. A major outcome of this study was qualitative data gathered through a concurrent think-aloud methodology. Three cohorts of five participants participated in the study. Audio recordings of participants using _Limbitless Journey_ were transcribed, and a sentiment analysis was performed to evaluate user perspectives. The goal of the study was twofold: first, to perform a think-aloud usability study on the game; second, to determine whether accessible controls could be as effective as manual controls. The user comments were coded into the following categories: game environment, user interface interactions, and controller usability. The game environment category had the most positive comments, while the most negative comments were primarily related to usability challenges with the flexion-based controller. Interactions with the user interface were the main topic of feedback for improvement in future game versions. This game will be utilized in subsequent trials conducted at the facility to test its efficacy as a novel training system for the ALS population. The feedback collected will be implemented in future versions of the game to improve the training process.  
electromyography; amyotrophic lateral sclerosis; serious games; usability; eye tracking 2025, 15, 5268
# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 4 Deep-Learning (DL) Methods for AE Testing
### Convolutional Neural Network (CNN) Solutions
These features have been used for the classification of cracks by exploiting the CNN. Three types of acoustic emission waves were classified using synchronized wavelet transformation plots in various time-frequency scales. To speed up the training procedure, the authors applied transfer learning, whereas Bayesian optimization was applied to optimize the hyperparameters. [PERSON] et al. [83] identified damage in carbon-fiber-reinforced composites using AEs and CNNs. The authors collected AE signals from tensile tests artificially causing fiber breakage, matrix breakage, and delamination. The data obtained in the form of time series were directly sent to a CNN-Inception Time obtaining a very high classification accuracy. [PERSON] et al. [84] applied CNNs to effectively classify AE signals for bearing failure diagnosis. The authors extracted envelope spectra (ES) from raw AE signals, and since these features demodulate the signals by returning information on the frequency of failures and variations at unstable speeds, CNNs have learned to extract distinctive features to effectively diagnose the defects of the bearings. [PERSON] et al. [85] studied the incipient failures of a bearing through the analysis of the AE signals and the application of CNNs. The authors used acoustic spectral imaging (ASI) of acoustic emission (AE) signals by training a CNN with transfer learning. [PERSON] et al. [86] identified failures of rotating machinery with CNNs and AE signals: temporal and spatial information of raw data from multiple AE sensors is considered. In Table 3, a summary of the described methods is shown. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Input** & **Features Extracted** & **Accuracy** \\ \hline [PERSON] et al. [76] & AE from powder & & \\ [PERSON] et al. [76] & bed additive & & \\  & manufacturing process & & \\  & AE from simulated & & \\ [PERSON] et al. [77] & seismic event on a & & \\  & physical scale model of & & \\  & a foundation on stills & & \\  & AE from an excited & & \\  & aluminum plate with & & \\ [PERSON] et al. [78] & pencil-lead break and & & \\  & two steel balls of & & \\  & different diameters & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CNN-based methods for AE testing.
## 2 Related Work and Theory
### Theoretical Analysis
#### 2.3.3 Combined Advantages of Wavelet Transform and Attention Mechanisms  
Combining the wavelet transform with the attention mechanism can make the rain removal effect significantly improved, mainly in the following aspects:  
1. Fine feature extraction and rain streak attention: the wavelet transform captures multi-scale details and high-frequency features of the image, while the attention mechanism enhances the focus on these high-frequency rain streaks. This combination allows the model to effectively remove rain streaks while preserving background details, avoiding excessive smoothing.
2. Rain removal and detail recovery: in the process of removing rain streaks, especially in the high-frequency part, the background details may be affected. Wavelet transform provides an efficient decomposition, while the attention mechanism can help the model to recognize and recover the details lost in the rain removal process. For example, the spatial attention mechanism enables better retention of background information, while the channel attention mechanism helps to remove rain streaks without losing important image details.
3. Improved generalization and inter-domain adaptability: the attention mechanism's adaptive weighting, combined with the wavelet transform, boosts the model's performance across various complex rainy scenes. It also addresses inter-domain differences between synthetic and real data, improving the model's robustness and stability in real-world conditions.