esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This paper proposes a system for drowsiness detection based on medical signals. This system comprises feature extraction and classification tasks. The feature extraction task is carried out using several trigonometric transformations including DWT, FFT, DCT, and DST. The classification task is performed using machine learning and deep learning algorithms. Figure 17 shows the extracted features and their importance for the output. The accomplished system comprises FFT with DWT for feature extraction and LR for classification. In this section, we discuss the performance of the proposed system in different cases including the performance of the LR classifier on the signals in the time domain and different transformations in addition to its performance with the added DWT denoising technique. Figure 18 shows a comparison of the proposed system in different scenarios including binary-class and multi-class scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **MCC** \\ \hline \multirow{8}{*}{**Multiclass**} & SVM & 87 & 87 & 86 & 86 & 80 \\  & RF & 89 & 89 & 89 & 89 & 83 \\  & DT & 73 & 73 & 73 & 73 & 59 \\  & KNN & 90 & 85 & 85 & 84 & 79 \\  & QDA & 88 & 87 & 87 & 87 & 75 \\  & MLP & 87 & 87 & 87 & 87 & 81 \\  & L.R. & 90 & 90 & 90 & 86 \\  & CNN & 79 & 79 & 78 & 78 & 73 \\  & C-CNN & 47 & 48 & 47 & 48 & 42 \\ \hline \multirow{8}{*}{**Binary-Classes**} & SVM & 97 & 96 & 96 & 96 & 93 \\  & RF & 96 & 96 & 96 & 96 & 92 \\  & DT & 82 & 82 & 82 & 83 & 65 \\  & KNN & 92 & 91 & 91 & 91 & 83 \\  & QDA & 92 & 89 & 90 & 90 & 81 \\  & MLP & 93 & 93 & 93 & 93 & 87 \\  & L.R. & 96 & 96 & 96 & 94 \\  & CNN & 91 & 89 & 90 & 90 & 82 \\  & C-CNN & 77 & 51 & 64 & 64 & 59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Brief Comparison among The Proposed Machine Learning Models.  
Figure 17: Plot of The Importance of The Input Features.  
Moreover, the proposed models are compared with the efforts in the literature. This comparison comprises both feature extraction and classifications utility for each research work. Table 8 illustrates this comparison prior to accuracy of detection. [PERSON] et al. [40] proposes a system based on multimodal analysis for feature extraction and ANN for classification. They achieved 83% accuracy of detection. Another work was proposed by [PERSON] et al. [41] which used FFT for feature extraction and ANN for classification. They deployed their system on EEG signals from a wireless device, like Neurosky. They achieved an accuracy of detection of 90%. It can be observed that the employment of the DWT denoising technique and different classifiers leads to a considerable enhancement of the accuracy. [PERSON] et al. proposed their system based on FE and SVM for feature extraction and classification tasks, respectively. They achieved an accuracy of detection of 95%. This comparison reveals that the proposed system presents a superior performance than the works in the literature. Therefore, it can be considered as an efficient fatigue detection system for real-life applications.
## 3 Results and Discussion  
### Optimization of UE  
#### 3.1.1 Effect of the Extraction Solvent  
Effective extraction of the determinants depended on the efficiency of the solvent ability to penetrate the solids in order to sufficiently break down and obtain enough surface area to extract from. In order to sufficiently extract the target analyte, extraction time needed to be controlled so as to prevent excessive exposure to irradiation, which may degrade the contaminants in the sample and reduce the extraction amount to be recovered. Therefore, when choosing the correct extraction solvent, extraction time was of high importance and the effect of the extraction solvent was investigated using different solvent mixtures with a 1:1 ratio: (i) nhexane (nH) + acetone (ACT), (ii) nhexane + water (H\({}_{2}\)O), (iii) nhexane + ethyl acetate (EA) and (iv) n-hexane. The n-hexane + acetone mixture gave the highest recoveries, with percentages between 93.7-121%, while nhexane + ethyl acetate gave low recoveries ranging between 69.8-99.8% (Figure 2). This could be attributed to the small dielectric constant compared to acetone and ethyl acetate, thus leading to their inadequacy in extracting the target analyte. The recoveries in Table S1 were not significantly different for nH + ACT to nH + EA (\(p\) = 0.18) and nH + H\({}_{2}\)O (\(p\) = 0.06),while a significant difference was observed for nH (_p_ = 0.007). Additionally, no significant difference in recoveries was observed for nH + EA to nH + H\({}_{2}\)O (_p_ = 0.12) and nH to nH + H\({}_{2}\)O (_p_ = 0.20), while the opposite is true for nH + EA to nH with a \(p\) value of 0.01. These results were obtained when the extraction time was at 60 min. The high recoveries obtained from the use of the nhexane + acetone showed that it is an effective extraction solvent, and the order of the extraction efficiencies for most of the PAHs was as follows:hexane + acetone > nhexane + H\({}_{2}\)O >hexane + ethyl acetate >hexane. Its efficiency could be attributed to the high polarity of acetone in comparison to water and ethyl acetate, which led to a high penetration power of the solvent, thus breaking down the solid samples, increasing their surface area [7]. In turn, this aided hexane (non-polar) to sufficiently dissolve the analyte through contact, which ultimately led to a successful extraction [23; 24]. The n-hexane-acetone solvent mixture has been used in other studies to extract quantitative amounts of PAHs in matrices such as soil, sediment and plant material [25].
# Fault Diagnosis and Prognosis of Satellites and Unmanned Aerial Vehicles: A Review
## 6 Conclusions and Future Work  
In this literature review, we have explored FDP within space and aerial vehicles, primarily focusing on satellites and Unmanned Aerial Vehicles. The insights from this study pave the way for future recommendations and research directions in this critical field of study. Tables 5-7 delineate a comprehensive overview of each study indicating the approaches and applications that have already been addressed in the literature.
###### Abstract  
(\(\bullet\)) Orthognathic surgery is a necessary procedure for the correction of severe skeletal discrepancies, among which are skeletal Class III malocclusions. Currently, both conventional fixed braces and clear aligners can be used in orthognathic surgery. However, the use of clear aligners remains a little-chosen option. The present study aimed to evaluate the skeletal and aesthetic improvements in adults with Class III malocclusion after surgical treatment and compare the results achieved by fixed appliances versus clear aligners. The study sample included four patients (three males and one female, aged 18 to 34 years) with skeletal Class III malocclusion, three of whom underwent a bimaxillary surgery and one of whom underwent only a bilateral sagittal split osteotomy. Two patients were treated with fixed appliances and two with clear aligners. The pre- and post-surgical hard and soft tissue cephalometric measurements were performed and compared for each patient and between fixed appliances and clear aligners. One year after surgery, all patients showed an essential modification of the face's middle and lower third with an increase in the convexity of the profile and the Wits index and a reduction in the FH'NB angle. No differences were noted between fixed appliances and aligners. Therefore, thanks to the 3D-assisted surgery associated with orthodontics, every participant achieved proper occlusal function and an improved facial aesthetics. In addition, the clear aligners can be considered a valid alternative for pre- and post-surgical orthodontic treatment.  
orthognathic treatment; surgical treatment; class III malocclusion; osteotomy; clear aligners; computer-assisted surgery Article
## 4 Pricing Algorithm
### Approximate Algorithm
#### 4.2.1 Define  
This section proposes an approximate algorithm. Our goal is to obtain the least expensive provenance of a query result. The approximate algorithm pursues the individual local optimum value rather than the global optimum value.  
The approximate algorithm is presented in Algorithm 2. The \(w^{min}_{i}\) of \(t_{i}\) is selected by Line 3 of the algorithm. Line 5 selects the \(w^{min}_{i}\) with the largest size. Finally, combine all \(w^{min}_{i}\), and figure out the approximate price through the last two lines.  
```
Input:\(I\), \(Q\) Output:\(a\_p_{I}(Q)\)
1 for\(\forall\ t_{i}\in Q(I)\)do
2 obtain \(W^{i}\) defined in [11];
3 select \(w^{min}_{i}\) that min \(\|\{s_{I}(t_{j})\}\|_{p}\) (\(t_{j}\in w^{min}_{i}\));
4 ifthe number of \(w^{min}_{i}\) is greater than \(1\)then
5 select \(\max w^{min}_{i}\);
6
7 end if
8
9 end for
10\(a\_M(Q,I)=\bigcup_{i}w^{min}_{i}\);
11\(a\_p_{I}(Q)=\|a\_M(Q,I)\|_{p}\);
```  
**Algorithm 2**Approximate pricing.  
Suppose the query result contains \(n\) result tuples, each of which contains \(m\) why-provenances. Based on this assumption, the complexity of selecting \(w_{i}^{min}\) of each \(t_{i}\) is \(O(m)\). Then, the total complexity of selecting all \(w_{i}^{min}\) is \(O(mn)\). Thus, the total complexity of calculating the approximate price of the query is \(O(mn)\).  
**Example 6**.: _Suppose \(Q(I)=\{t_{1},t_{2},t_{3}\}\), \(W^{1}=\{\{x_{1},x_{4}\}\), \(\{x_{1},x_{2},x_{3}\}\}\), \(W^{2}=\{\{x_{1},x_{2}\},\{x_{1},x_{4}\}\}\), \(W^{3}=\{\{x_{2},x_{3}\}\}\). The prices of the base tuples are as follows: \(s_{I}(x_{1})\) = 3, \(s_{I}(x_{2})\) = 4, \(s_{I}(x_{3})\) = 5, \(s_{I}(x_{4})\) =6. Here, set \(p\) = 1 for the \(p\)-norm._  
The algorithm chooses \(\{x_{1},x_{4}\}\) for \(t_{1}\), since the price of \(\{x_{1},x_{4}\}\), namely nine, is lower than the price of \(\{x_{1},x_{2},x_{3}\}\), namely 12. In the same manner, the algorithm chooses \(\{x_{1},x_{2}\}\) for \(t_{2}\) and chooses \(\{x_{2},x_{3}\}\) for \(t_{3}\). Finally, the algorithm combines all the chosen minimal why-provenances, as \(\{x_{1},x_{2},x_{3},x_{4}\}\). The prices of the base tuples are the following: \(s_{I}(x_{1})\) = 3, \(s_{I}(x_{2})\) = 4, \(s_{I}(x_{3})\) = 5, \(s_{I}(x_{4})\) =6. Thus, the price of the query \(Q\) is \(3+4+5+6\), namely 18.
## 3 Materials and Methods
### Proposed SkinSwinViT Architecture
A multi-head self-attention mechanism fuses global information, followed by residual connections and layer normalization for feature representation learning. Subsequently, an MLP layer, comprising two linear layers and a nonlinear activation function (e.g., GELU), integrates feature information from different locations to generate a comprehensive global feature representation. This block enables global contextual comprehension through self-attention calculations on all feature vectors. The algorithm for the global attention mechanism is articulated as follows:  
\[Att(Q,K,V)=Softmax\bigg{(}\frac{QK^{T}}{\sqrt{d}}\bigg{)}V \tag{6}\]  
\[\widetilde{x}=W(G_{Con}Att_{i}(z,z,z))+b \tag{7}\]  
\[\hat{x}=G_{GlobalAtt}(z)=MLP\big{(}LN\bigg{(}\widetilde{x}+z\bigg{)}\bigg{)} \tag{8}\]  
Here, \(Att(Q,K,V)\) represents the multi-head self-attention mechanism, \(Q,K,\text{ and }V\) are the query, key, and value matrices, respectively. \(\sqrt{d}\) is the dimensionality of the key vectors and \(\sqrt{d}\) is used to scale the dot product to prevent gradient vanishing or exploding. The Softmax function converts the dot product results into a probability distribution. \(G_{con}\) signifies the concatenation of multi-head attention mechanisms. \(W\) is a weight matrix, and \(b\) is a bias term. \(G_{GlobalAtt}\) denotes the transformation function of the Global Attention Block. \(\widehat{x}+z\) denotes the residual connection. \(LN\) stands for Layer Normalization, which is used to stabilize the training process. 3. Classifier Block: Incorporated within the model decoder, the Classifier Block commences with layer normalization to stabilize data distribution and expedite training. Following normalization, the feature vector undergoes dimensionality reduction via adaptive average pooling to compress feature vector length from sequence length to a fixed length 1. This compression retains essential features, facilitating efficient data representation. The data then progresses to a linear transformation layer to learn input-output linear relationships. Ultimately, the linear layer output is processed through a Softmax function, yielding a probability distribution for final classification. This distribution reflects model confidence or likelihood for each class, enabling classification based on the highest probability class. Figure 3:0  
4. Loss Function: The model's classification loss function utilizes cross-entropy loss, quantifying disparities between model-predicted probability distributions and true label distributions. The loss function is expressed as follows:  
Figure 3:1  
Here, \(L\) denotes the loss value, \(y^{\prime}_{j}\) represents category \(j\) value in the true label (utilizing one-hot encoding). \(P_{y^{\prime}_{j}}\) symbolizes model-predicted probability value for category \(j\), \(N\) represents category count, and \(M\) represents sample count. The summation symbol \(\sum\) accumulates all categories.
## 1 Introduction
This approach combines the non-destructive acquisition of natural frequencies and curvature mode shapes with advanced analytical techniques (including deep learning, information entropy, and finite element simulation) to enable both global damage localization and quantitative severity assessment. While initially developed for beam-like structures [21; 22; 23; 24; 25; 26], the validity was verified by a cantilever and simply supported beam models, showing that the change in curvature mode shape is more significant locally in the damage region, from which the location and extent of damage can be determined [27], and the methodology has been successfully extended to various engineering applications. For high-pile wharf foundations, researchers developed a curvature mode difference (CMD) method incorporating a Mode Deletion Model, achieving precise damage localization through a comparative analysis of pre- and post-damage curvature modes [28]. In the building envelope structure, a two-stage damage detection technique employing Boundary Modal Curvature (BMC) was specifically developed for structural sealants. The method first identifies damaged edges using the Assurance Criteria of BMC (ACBMC), then performs detailed comparative BMC analysis between affected and intact regions for precise localization [29]. For railway systems, crack propagation models quantitatively relate crack parameters to curvature variations, as experimentally validated [30]. A modal curvature-based damage identification method is developed for a 4000-tonne hull-ginder simplified ship structure. Finite element simulations with varying stiffness reductions validate its effectiveness in detecting both single and multiple damages [31]. A method leveraging Gaussian process regression and Bayesian recursive partitioning is introducedto identify damage in glass fiber-reinforced polymer (GFRP) materials. This method analyses both numerical and experimental modal shapes, with modal analysis being central to damage identification [32]. Similarly, the Tree Gaussian Process (TGP) method is also based on modal analysis, which provides critical data support. The TGP method compares results across different crack sizes in experimental and finite element model modal shapes to identify manufacturing defects in extruded GFRP thin-walled profiles, highlighting the importance of modal analysis in both approaches [33]. Based on previous research, this investigation proposes a quantitative analysis method for structural sealant damage based on curvature modal. The curvature mode demonstrates significant advantages in detecting local damage by effectively capturing structural stiffness variations. As damage occurs, the local stiffness reduction directly perturbs curvature modes in the vicinity of the damaged location. Consequently, the curvature mode proves to be an effective tool for detecting local damage.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 5 Numerical Evaluations and Discussion  
In this section, simulation results are provided to validate the analytical results in Sections 2-4. The top view of the scenario is shown in Figure 2, and unless otherwise specified, all parameters are set to the values in Table 2 by default. The simulation results show the correctness of the theoretical analysis and reveal that the distance-dependent Rician factor would transform the channel from LoS to NLoS when the transmission distance is increasing and vice versa; the outage probability would also be affected by different distance-dependent Rician factors; and although the DS cannot be removed when the direct link exists, it can be aligned to the same value. Thus, the Doppler spread can be eliminated. Besides, all results are obtained by using a personal computer with a 3.2 GHz AMD Ryzen 7 6800H CPU and 16 GB RAM. The software environment used is MATLAB R2020a.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline BS position (\(D_{\mathrm{BS}}\)) & \([0,20,50]^{\mathrm{T}}\) meter (m) \\ MR position (\(D_{\mathrm{MR}}(t)\)) & \([-250+vt,2,20]^{\mathrm{T}}\) m \\ RIS height (\(r\)) & 15 m \\ Speed of the vehicle (\(v\)) & 180 km/h \\ Transmitter power (\(P_{t}\)) & 20 dBm \\ Additive noise (\(\sigma^{2}\)) & \(-80\) dBm \\ Carrier frequency (\(f_{c}\)) & 2.4 GHz \\ Element size (\(dx\), \(dy\)) & \(\lambda/2\) m, \(\lambda/2\) m \\ Number of elements (\(M\)) & \(64^{2}\) \\ Environmental parameters (\(\varrho\), \(i\)) & 13, 0.03 \\ SNR threshold (\(\gamma_{\mathrm{th}}\)) & 10 dB \\ Realization number & \(5\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters [12,20].
# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 4 Proposed Approach
### YOLOv5 Network Model  
Currently, the latest version of YOLOv5 is v6.1, with mAP5095 at 49% on the COCO dataset. YOLOv5 contains five architectures, ordered by size from smallest to largest as YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The YOLOv5s architecture is adopted considering accuracy, real-time, and lightweight of models deployed on FPTLIR. Figure 9 shows the main structure of the YOLOv5 model, including input, backbone, neck, and head.  
Figure 8: Example of a prior synthetic dataset. (**a**) Normal image; (**b**) backgrounded-free image; (**c**) annotation.  
The input includes self-adaptive image scaling, Mosaic data enhancement, and self-adaptive anchor box calculation. Different images are scaled to fixed size by self-adaptive image scaling with normalization. The Mosaic data enhancement uses four images and stitches them together in a randomly scaled, cropped, and matched manner. This enhancement method can combine several images into one, enriching the dataset and improving the training speed of the model. Meanwhile, it could reduce the memory requirement of the model.  
The backbone benchmark network is usually some high-performance classifier to extract general features. The CSPDarknet53 structure is introduced into the backbone, and the focus structure is used as the backbone to crop the input image by slice operation and stitch in the channel dimension, reducing the params and FLOPs. The focus is replaced with \(6\times 6\) sized convolutional layers in YOLOv5 v6.0, which are equivalent. However, the convolutional layers are more efficient for GPU devices.  
The neck is located in the middle of the backbone and head to further improve the features' diversity and output the enhanced effective feature.  
Head is the classifier and regressor of YOLOv5 for predicting the location and classification of targets. The enhanced effective feature layers obtained by the backbone and neck are processed by \(1\times 1\) convolution in the head to output three feature layers. As shown in Figure 10, the size of the feature layer matrix is \(\mathsf{S}\times\mathsf{S}\times(\mathsf{B}\times(5+\mathsf{C}))\). The feature matrix is divided into \(\mathsf{S}\times\mathsf{S}\) grid cells; each grid cell has \(\mathsf{B}\) anchor boxes; each box includes center coordinates (\(\mathsf{x}\) and \(\mathsf{y}\)), width (\(\mathsf{w}\)), height (\(\mathsf{h}\)), confidence, and probability of \(\mathsf{C}\) classes. The prediction box is obtained by decoding the anchor box. Equation (4) shows the total loss \(L_{\text{total}}\) of the interaction between the prediction box output results and the real box.  
\[L_{\text{total}}=\sum_{i}^{N}(\lambda_{1}L_{\text{box}}+\lambda_{2}L_{\text{ obj}}+\lambda_{3}L_{\text{cls}})=\sum_{i}^{N}(\lambda_{1}\sum_{j}^{B_{i}}L_{ CloU_{j}}+\lambda_{2}\sum_{j}^{S_{i}\times S_{i}}L_{obj_{j}}+\lambda_{3}\sum_{j}^{B_{i} }L_{cls_{j}}) \tag{4}\]  
where \(N\) is the number of detection layers, \(B_{i}\) is the number of annotations assigned to the anchor box, \(\mathsf{
## 5 Discussion
### Pros and Cons of CSWGAN
Furthermore, there can be large resolution differences between certain datasets, for example, at least an 8\(\times\) resolution difference between Landsat TM/ETM+ and MODIS, and a 30\(\times\) resolution difference between Sentinel-2 and Sentinel-3. In these cases, it is challenging to accurately handle the temporal, spatial, and spectral degradation relationships among multiple data using SR methods. Therefore, the use of spatiotemporal data fusion algorithms is vital to address the constraints of spatiotemporal resolution [54; 55; 56; 57]. Based on our proposed model, a feasible improvement is to provide one or two pairs of high spatial resolution data sourced from low temporal resolution, and high temporal resolution data with low spatial resolution from prior dates as inputs to the model. This approach aims to predict one or more high temporal resolution data with low spatial resolution on prediction dates. The effectiveness of this plan will be discussed in the future.
## 3 Results and Discussion
### MM/PBSA Binding Free Energy Calculation: Calibration  
RMDS analyses, after MD simulations for the 11 known inhibitors, suggested that both ligands and the protein are stable (Figure S4). Finally, the MM/PBSA methodology was applied to determine the complex's binding free energy. Table 2 provides the binding free energies obtained via the MM/PBSA calculations. These calculations involve evaluating various energy components, including van der Waals interactions, electrostatic forces, calculations from the Poisson-Boltzmann equation, and non-polar solvation energies. Together, these components contribute to determining overall binding free energies, providing essential insights into the intricate nature of protein-ligand complexes.  
The scatter plot in Figure 5 shows the relationship between two key parameters: IC\({}_{50}\) values, representing the inhibitory potency of a set of compounds, and \(\Delta G_{\text{MM/PBSA}}\) (\(\Delta G_{\text{bind}}\) in Table 2), which indicate the calculated binding free energy of these compounds with YTHDC1 from MM/PBSA analysis. The Spearman's rank correlation coefficient between the two parameters is as low as 0.14, indicating that ranking ligands based on their \(\Delta G_{\text{MM/PBSA}}\) is not reliable. However, the Pearson correlation coefficient between experimental IC\({}_{50}\) values and \(\Delta G_{\text{MM/PBSA}}\) is encouraging (0.36), suggesting that a compound can be judged with satisfying confidence to be a strong or weak binder if its \(\Delta G_{\text{MM/PBSA}}\) is low enough. Note that 5 out of 7 compounds with \(\Delta G_{\text{MM/PBSA}}\) lower than \(-40\) kJ/mol are strong binders with IC\({}_{50}\) values lower than 1000, while 3 out of 4 compounds with \(\Delta G_{\text{MM/PBSA}}\) higher than \(-40\) kJ/mol are weak binders with IC\({}_{50}\) values remarkably larger than 1000.