esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The ion \(m/z\,341\) is formed by the addition of two oxygen and the elimination of four hydrogen [27]. The removal of the double bond in the furan ring structure of AFB1 is evident in the product ions \(m/z\,303\), \(m/z\,341\), and \(m/z\,351\), while the modification of the lactone group and the elimination of the double bond in the furan ring were observed in AFB1 products \(m/z\,369\) and \(m/z\,321\). The fragment ion \(m/z\,189\), which was absent in the control plant extract, appeared only after treatment of AFB1 with plant extracts. Several studies have described the detoxification of aflat by herbal extracts and the molecular structures of aflatoxin degradation products [17; 19; 22; 27]. Modifications to the AFG1 lactone ring structure have been suggested as the detoxification mechanism of _T. ammi_ seed extract [17]. [PERSON] et al. [22], while studying the AFB1 detoxification effect of _Adhatoda vasica_ leaf extract, also reported the appearance of a product ion at \(m/z\,189.59\) and a loss of a molecular base ion at \(m/z\,313\) after incubation with _A. vasica_ leaf extract. Structural modifications, such as changes in the lactone group and the elimination of the double bond in the furan ring structure, have been observed in the products of AFB1 and AFB2 degradation after treatment with _T. ammi_ seed extract [27]. The continuous loss of carbon monoxide and methyl and methanol losses on the methoxy group present on the side chain of benzene were observed in the products of AFB1 degraded using _O. basilicum_ extract [19]. [PERSON] et al. [23] reported AFB1 detoxification using leaf extracts of _Centella asiatica, Eclipta prostrata_, and _Hybanthus enneaspermus_ and the formation of a fragment ion at \(m/z\,189\) due to two sequential losses of carbon monoxide. The furoturan ring and lactone ring of AFB1 are responsible for its cytotoxicity and mutagenic activity [47]. The disappearance of the ion peak at \(m/z\,313\) (unique to AFB1) and the generation of new
## 3 Feature Extraction
### Feature Reduction  
Feature reduction eliminates some features, reducing the computational complexity of the classifier and avoiding the possibility of redundancy. Initially, we obtained 24 features aimed at two class classification (STN-IN and STN-OUT). Many feature selection methods exist: Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), Independent Component Analysis (ICA), Butterfly, Firefly, Ant Colony, Harris Hawks optimizations methods, etc. In this work, the NCA (Neighborhood Component Analysis) selection method is used, which automatically selects the most meaningful features [34]. The steps of the NCA-based feature selector are given below. Thus, by having a threshold value (\(th=0.5\)), the complete set of features is entered and the final result of the algorithm provides the most relevant features. The NCA sorts the best features from the diagonal matrix that is constructed by maximizing the objective function that computes the average leave-one-out classification over the training set. The main steps in the NCA algorithm are the following:  
* Step 1. Using weight calculate the function to generate the weights \(W_{NCA}\) as defined in Equation (2). \(M_{feat}\) and \(T_{feat}\) are two feature observations randomly selected from the database. \[W_{NCA}=F\Big{(}M_{feat},T_{feat}\Big{)}\] (2) where \(F\Big{(}M_{feat},T_{feat}\Big{)}=\sqrt{\Big{(}M_{feat}-T_{feat}\Big{)}^{T}\;D (M_{feat}-T_{feat})}\) and \(D\) is a semi defined matrix [34].
* Step 2: Sort feature weights in descending order.
* Step 3: Select the most distinctive k features by using the highest positive weights according to the threshold values (greater and equal than 0.5).  
After completing the NCA process, a reduction in features of 45% was obtained, i.e., 11 out of 24 features were selected as relevant. It is important to note that 10 out of the 11 selected features belong to spike-independent features--Spike Firing Rate (SFR) for spike-dependent, with the rest are from spike-independent features: Cumulative bispectrum (Cb), Skewness (Sk), Bispectrum (Bs), 1D-Local Binary Pattern (LBP1), Variance (Va), Short-Term Fourier Transform (STFT), Standard deviation (SD), Root Mean Square (RMS), Teager Energy (Te), and Slope Sign change (SSC).  
Figure 4: (a) Bispectrum plot of the mean of 10,000 randomly selected MER segments in STN-IN. (b) Bispectrum plot of the mean of 10,000 randomly selected MER segments from STN-OUT.  
In summary, the proposed classification procedure is depicted in Figure 5: the acquired MER signal is segmented in 1 s time window and preprocessed for artifact and noise removal; then, different features (spike- and non-spike-dependent) are extracted (24 in total). After that, the NCA feature reduction algorithm is used, obtaining 11 relevant features that will be used as the input to a machine learning classifier, which provides the two classifications (STN-IN and STN-OUT).
# A Ka-Band Cylindrical Paneled Reflectarray Antenna
## 5 Conclusions  
This work presents a variant of the reflectarray antennas derived from parabolic cylindrical reflectors. The proposed structure exploits the cylindrical symmetry and the reflectarray properties to increase the radiation aperture without modifying the focus location. As a result, the _f_/_D_ factor is reduced from 0.5 to 0.34, whereas in conventional reflectarrays this value is usually chosen to be at least 0.5. Moreover, the use of continuous strips of variable lengths instead of rectangular patches contributes to increase the radiation efficiency. The use of radiating elements along with the curvature of the reflecting surface allows the proposed antenna to have a radiation efficiency of 65%. The proposed configuration can be a valid solution in those applications where large-aperture low-profile antennas should be implemented.  
Figure 6: Normalized radiation pattern of the proposed configuration at 20 GHz. (**a**) E-plane; (**b**) H-plane (measurement).  
Figure 7: Measured and simulated antenna parameter: gain and matching. Simulations include the gaps shown in Figure 5.  
**Author Contributions:** Conceptualization, G.A. and L.B.; methodology, L.B., E.A.; software, F.G.; validation, F.G.; formal analysis, F.G. and L.B.; investigation, L.B.; resources, E.A.; data curation, G.A.; writing--original draft preparation, E.A.; writing--review and editing, L.B. and E.A.; visualization, F.G.; supervision, G.A.; project administration, G.A.; funding acquisition, G.A.  
**Funding:** This research was funded by TARANTO ECSEL project grant agreement ID 737454.  
**Acknowledgments:** The authors wish to thanks Belen Galocha, from the Polytechnic University of Madrid (Spain), for her support during the antenna radiation pattern measurements.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
###### Abstract  
The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.  
t 15 July 201810 1286 10.3390/rs10081286
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 2 Data and Methodology
### EDR Estimation
#### 2.3.2 The Second Estimation Method  
As the second method, the EDR estimation formula is shown in Equation (12):  
\[\varepsilon^{\frac{1}{3}}=\left(\frac{\gamma^{2}}{k_{h}-k_{l}+1}\sum_{k=k_{h }}^{k_{h}}\frac{S_{k}^{w}}{S_{k}^{model}}\right)^{\frac{1}{2}} \tag{12}\]  
where \(\varepsilon^{1/3}\) is the EDR to be estimated, \(\gamma\) is the correction factor, \(k_{h}\) and \(k_{l}\) are the coefficients \(k\) corresponding to the high frequency and low frequency, respectively, of the selected frequency interval, and \(\hat{S}_{k}^{w}\) and \(S_{k}^{model}\) are the measured energy spectrum functions and theoretical energy spectrum functions of turbulent flow, respectively.  
The specific estimation steps are as follows:  
(1) Estimate the true airspeed (TAS) by indicating airspeed and flight altitude.  
(2) Estimate the angle of attack (AOA). Firstly, the average angle of attack is estimated according to the left and right angles of attack of the aircraft. Then, the average angle of attack is corrected according to the correction value estimated by the length of the sensor arm, the pitch rate, and the true airspeed. Finally, the angle of attack based on the body axis is estimated by the angle transformation [34]. In this process, parameters such as the left and right angles of attack of the aircraft and the length of the sensor arm are not provided by the QAR data.  
(3) Estimate vertical wind, \(w\) based on the body axis, as shown in Equation (13):  
\[w=-TAS(\cos\theta\sin\alpha_{b}\cos\varnothing-\cos\alpha_{b}\sin\theta)-IVV \tag{13}\]  
where \(\theta\) is the pitch angle, \(\varnothing\) is the roll angle, \(\alpha_{b}\) is the aircraft angle of attack estimated in Step (2), and IVV is the inertial vertical speed of the aircraft.  
(4) Estimate the vertical wind of the trend wind and windowing function. The vertical wind is transformed based on the estimated matrix.  
(5) The vertical wind obtained in Step (4) is subjected to discrete Fourier transform to obtain the turbulent measured energy spectrum, and the theoretical energy spectrum of the turbulent flow is obtained by looking up the \"Table 1 Parameters used in the estimation of DEVG\" on Page 31 of [28].  
In summary, both estimation methods involve parameters that are not provided in the QAR data. It is impossible to estimate the EDR by directly applying the two methods. Therefore, this paper provides a method to approximate the EDR estimation by concerning both methods.
# EEG-Based Attention Classification for Enhanced Learning Experience
## 4 Discussion
### Limitations and Future Work  
This study's generalizability is constrained by its participant pool of 19 right-handed, beginner-level Chinese learners. Future research should include participants from more diverse linguistic, cultural, and cognitive backgrounds to validate broader applicability.  
While gamma-band features demonstrated strong classification power, multiband models and cross-frequency coupling (e.g., gamma-theta interactions) could enhance accuracy and robustness. These relationships may provide a richer representation of attention and cognitive load. Furthermore, exploring advanced architectures like convolutional neural networks (CNNs) or graph neural networks (GNNs) may improve the model's ability to capture spatiotemporal patterns.  
The current study assessed short-term effects within a single experimental session. Longitudinal studies are needed to investigate long-term benefits such as retention, transfer of learning, and user adaptation over time. Additionally, usability testing, learner trust, and motivational aspects should be evaluated to guide deployment in real-world educational platforms.  
Finally, system scalability and compatibility with wearable EEG headsets should be explored for broader classroom integration and mobile learning applications.
## 3 Experimental Setup
### Data-to-Image Conversion  
This study aims to verify the feasibility of CNN-based fault diagnosis using a single-axis (Id-axis) current by comparing the performance of a CNN diagnosis based on three-phase current image conversion with that based on a Id current. To this end, three widely used methods--three-phase RGB mapping, DWT, and STFT--were each applied using the single-phase (Id) current and compared through image conversion.
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
###### Abstract  
As a revolutionary new technique, laser-engineered net shaping (LENS) is a layer additive manufacturing process that enables accurate, rapid and automatic repair of industrial component damage. In the laser repair (LR) process or in service, surface cracks can appear, which have a detrimental effect on the repair quality and the mechanical performance; therefore, the surface crack detection of repaired components has attracted much attention. Laser spot thermography is an important nondestructive testing method with the advantages of non-contact, full-field and high precision, which shows great potential in the crack detection of repaired components. The selection of thermographic process parameters and the optimization of thermal image processing algorithms are key to the success of the nondestructive detection. In this paper, the influence of material properties and thermographic process parameters on the surface temperature gradient is studied based on the simulation of laser spot thermal excitation, and the selection windows of thermographic process parameters for iron-based and nickel-based alloys are obtained, which is applied to the surface crack detection of repaired components. To improve the computational efficiency of thermal images, the Prewitt edge detection algorithm is used in the thermal image processing, which realized fast extraction of cracks with a high signal-to-noise ratio (SNR), and the detection sensitivity of crack width can reach 10 \(\upmu\)m. To further study the influence of surface roughness on the thermographic detection, repair layers with and without polishing process are characterized, which show that the Prewitt edge detection algorithm is well applicable to crack detection on surfaces with different roughness level.  
laser repair; surface cracks; NDT; Prewitt edge detection; laser scanning thermography Article
## 3 Experiment
### Experimental Results

Table 3: Evaluation indicators of the four algorithms for the homogeneous single-tumor phantom (I) imaging results. using the four algorithms. The MICF-DMAS algorithm has an SCR of 13.94 dB and a CR of 20.32. The SCR of the image obtained using the MICF-DMAS algorithm is about 6 dB higher than that of the images obtained using the CF-DMAS algorithm. In addition, the CR values also indicate better performance, with a higher percentage of intensity values focused on the tumor and less clutter. We used phantom (III) for imaging to validate the imaging performance of the MICF-DMAS algorithm for multiple targets. The tumors had a diameter of 10 mm each, with tumor 1 located at (17 mm, \(-\)23 mm) and tumor 2 located at (\(-\)25 mm, 20 mm). As shown in Figure 13, the presence of multiple targets led to multiple internal reflections, resulting in more clutter in the images obtained by the four algorithms [30]. The DMAS and CF-DAS algorithms produced images with a significant amount of high-intensity clutter. More clutter is also present in the images obtained by the CF-DMAS algorithm. More high-intensity clutter may be mistaken for tumors, which may have certain effects on tumor localization. The intensity and instances of clutter are significantly improved upon in the images obtained by the MICF-DMAS algorithm. Table 5 presents the results, indicating that the MICF-DMAS algorithm achieved the highest SCR and CR. Since there were two \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **D (mm)** & \(X\) (mm)** & **SCR (dB)** & **CR** \\ \hline MICF-DMAS & 8.5 & (21,\(-\)20) & 13.64 & 21.32 \\ CF-DAS & 10.9 & (22,\(-\)20) & 5.03 & 9.41 \\ DMAS & 11.4 & (21,\(-\)21) & 5.36 & 10.39 \\ CF-DMAS & 10.2 & (21,\(-\)20) & 7.93 & 15.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation indicators of the four algorithms for the heterogeneous single-tumor phantom (II) imaging results. Figure 12: Imaging results of the four algorithms for the heterogeneous single-tumor phantom (II) (Z = 45 mm). The portion circled by the red circle indicates high intensity clutter. tumors in phantom (III), X took the coordinates of the two points with the highest intensity of the focal points, and \(D\) also took the portion of high intensity around these two points. Similar coordinates of highest intensity for the two points were obtained within the images using the four methods, with the smallest \(D\) obtained by the MICF-DMAS algorithm and a larger \(D\) obtained using the CF-DAS and DMAS algorithms.
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 2 Materials and Methods
### The Self-Gated Attention Block
#### 2.4.1 Channel Attention Unit  
Firstly, we define the input feature map as \(F\). For the purpose of extracting global features, a Global Max Pooling operation (\(GMP\)) is used to calculate a global feature C1 with a size of \(1\times 1\times C\) (where \(C\) represents the number of channels). Then, C1 is input into a Gate Convolution Filter (\(GCF\)) to generate a weight map \(D1\). A detailed description of the \(GCF\) is shown in Section 4. In addition, a Global Average Pooling operation (\(GAP\)) is used to calculate a global feature C2 with a size of \(1\times 1\times C\). Additionally, C2 is input into another Gate Convolution Filter (\(GCF\)) to generate a weight map \(D2\). The purpose of the \(GCF\) is to control feature information flowing into the following layers. After that, \(C1\) and \(C2\) are added together to generate a concatenated channel attention weight map \(E1\). Finally, the input feature map \(F\) is convolved by \(E1\) to generate the final output \(G1\). Through convolving with \(E1\), the weight of the channel feature related to cracks would be increased and the weight of the channel feature unrelated to cracks would be decreased. The whole process of the Channel Attention Unit can be calculated as:  
\[Z(F)=F\otimes[GCF(GAP(F))\oplus GCF(GMP(F))] \tag{1}\]  
where \(Z(F)\) refers to the process of Channel Attention Unit, \(F\) refers to the input feature map, \(GCF\) refers to the Gate Convolution Filter, \(GAP\) refers to the Global Average Pooling operation, \(GMP\) refers to the Global Max Pooling operation, \(\otimes\) refers to the convolution operation, and \(\oplus\) refers to the add operation.
## 3 Methodology
### Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM)  
The three network components, namely, LSTM, GCN, and graph attention mechanism, are combined to establish the proposed neural network model structure STGA-LSTM.  
The input data structure is a three-dimensional tensor that contains the features of each node at each time step. The exogenous variables can be concatenated at the dimension of features to demonstrate the information for each station.  
Suppose the STGA-LSTM model contains several layers \(0,1,\ldots,m\) from input to output. Every vertex in the graph at layer \(l\) has a feature vector with a length of \(C^{l}\), where \(l=0,1,\ldots,m\). For each layer \(l\), the STGA-LSTM model propagates as follows:  
\[H^{l}=\sigma\left(Z^{l-1}W^{l}_{T}\right)=\sigma\left(\hat{A}H^{l-1}\Theta^{l -1}W^{l}_{T}\right) \tag{16}\]  
where \(H^{l}\) is the vector of the hidden state after implementing the temporal attention mechanism; \(H^{l}=\left[h^{\prime l}_{1},h^{\prime l}_{2},\ldots h^{\prime l}_{i}\right]\), \(Z^{l-1}\) represents the convolved kernel at the \((l-1)\)th layer; \(W^{l}_{T}\in\mathbb{R}^{F^{l-1}\times C^{l}}\) represents a weight parameter matrix for the \(l\)th layer; and \(\sigma\) is an activation function. In this paper, Rectified Linear Activation Unit (ReLU) is applied as the activation function, as shown in the following Formula (17). The ReLU function will retain a positive value and convert a negative value into zero; it allows faster and effective training of deep neural network architectures on large datasets [47]. \(H^{l}\in\mathbb{R}^{T\times N\times C^{l}}\) is the output of the \(l\)th layer and the input of the \((l+1)\)th layer, and \(H^{0}=X\), \(T\) is the number of time steps.  
\[ReLU(x)=max(0,x) \tag{17}\]  
The product of \(\Theta^{l-1}\in\mathbb{R}^{C^{l-1}\times C^{l-1}}\) and \(W^{l}_{T}\in\mathbb{R}^{F^{l-1}\times C^{l}}\) can be replaced by one matrix \(W^{l}\in\mathbb{R}^{C^{l-1}\times C^{l}}\) during the training process. Therefore, Equation (16) can be simplified as follows:  
\[H^{l}=\sigma\left(\hat{A}H^{l-1}W^{l}\right) \tag{18}\]where \(W^{l}\in\mathbb{R}^{C^{l-1}\times C^{l}}\) is the weight parameter matrix to be learned, and \(H^{l}\in\mathbb{R}^{T\times N\times C^{l}}\) is the prediction sequence; e.g., the prediction of rental and return demand of the shared bikes in \(N\) stations for the next hour. Figure 3 shows the whole process from input to output of the STGA-LSTM model. It visualizes the details of the connections between the different modules in the model. After the calculation by the input and hidden layer, the sequence \(H^{l}\) is generated. It mines features from the input data. Then the sequence is fed into the output layer to obtain the final prediction; the last element in the sequence will be regarded as the final prediction in general. The output is the rental and return demand at each bike-sharing station. This proposed model auto-learns the spatial and temporal information among stations due to the combination of the graph convolution and LSTM structure.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks  
[PERSON]  
1 Department of Electrical and Electronics Engineering, Ozyegin University, Istanbul 34794, Turkey 1  
[PERSON]  
2 Engineering Division, New York University Abu Dhabi (NYUAD), Abu Dhabi P.O. Box 129188, United Arab Emirates 23 Center of Excellence in Optical Wireless Communication Technologies, Ozyegin University, Istanbul 34794, Turkey 3
## 5 Comparative Analysis  
### Comparison with Formula  
The reliability and accuracy of the QPSO-RBF-SVM model for predicting cable force were comparatively analyzed by comparing it with the formulae derived from the energy method and the magnetic flux method [26]. The basic parameters corresponding to the samples in the test set were substituted into the formula to calculate the value of cable force, and the results are presented in Figure 8.  
According to Figure 8, it can be observed that the results of the different methods in the calculation of the cable force are significantly different, among which the magnetic flux method calculates the largest error in the value of the cable force, which may be due to the external interference in the magnetic field resulting in the deviation between the measured and actual values of the magnetic flux. Compared with the magnetic flux method, the accuracy of the energy method is improved, but the prediction result still has a high error with the actual value. In contrast, the QPSO-RBF-SVM model fully considers the nonlinear characteristics of the variation of the cable force, and through the combination of the optimization algorithm and the machine learning technique, it shows higher accuracy  
Figure 8: Comparison of the measured and formulae values of the cable tension on the test set.  
Figure 7: Sling rope tension measured and predicted relative error.  
in predicting the value of the cable force and its prediction results are closer to the actual value of the cable force.