esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Model Training
#### 3.4.9 Attention Layers  
Attention mechanisms dynamically assign importance to different elements of a sequence, enhancing the performance in natural language processing (NLP), sequential modeling, and computer vision tasks [67]. Inspired by human cognition, they allow networks that concentrate attention on key segments of the input, boosting the learning efficiency and interpretability. Among the main attention networks are the following.  
1. Hard attention: Selects a single input position, making it non-differentiable [68]: \[\text{HardAttention}(Q,K)=\text{argmax}(QK^{T}),\] (30) where \(Q\) and \(K\) are the query and key matrices, respectively. Since backpropagation is infeasible, alternative approximations are preferred.
2. Soft attention: Assigns probabilistic weights to all input positions using the softmax function [69]: \[\text{SoftAttention}(Q,K,V)=\text{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}} \right)V.\] (31) where \(V\) is the value matrix and \(d_{k}\) is a scaling factor for numerical stability.
3. Self-attention: A special case of soft attention where queries, keys, and values are derived from the same input [70]. In Figure 10, a diagram of the self-attention mechanism is shown.  
Attention mechanisms are key in modern deep learning, enabling the handling of complex dependencies in large-scale data. In the proposed model, self-attention layers are placed after each BiLSTM block to dynamically weight temporal outputs and focus on the most informative segments of the EEG sequence.  
Figure 10: Block structure of the self-attention mechanism.  
Figure 9: Structure and connections of a BiLSTM layer.
# Towards Automatic Depression Detection:
## 3 Proposed Approach and Models
### Audio Features  
The commonly used voice descriptors in depression detection include the Mel Frequency Cepstrum Coefficient (MFCC), 79 COVAREP features [36] and Mel spectrograms.  
Compared with MFCC and COVAREPfeatures, Mel spectrograms produce better results in comparison experiments (see Section 6.3 for more details). Because Mel spectrograms can preserve detailed information that exhibits a better discrimination ability, it was finally adopted in our approach.  
Mel spectrograms are computed by multiplying short-time Fourier transform magnitude coefficients with the corresponding Mel filters [33]. Thus, it can be regarded as a non-linear transformation of the spectrogram, which maintains high level details in sound. The relationship between the normal spectrogram and Mel spectrograms is depicted in Equation (1), where \(f\) is the frequency of the spectrogram.  
\[mel(f)=2595\times log_{10}(1+f/700) \tag{1}\]  
Figure 1: Framework of the proposed model. Text features are trained using a BiLSTM model with an attention layer, and audio features are trained with a 1D CNN model. Outputs from both BiLSTM and 1D CNN models are concatenated and fed into two fully connected networks (FC), one of which generates labels indicating whether the interviewees are depressed or not, and the other predicts depression severity scores, respectively.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis  
### Experimental Setting  
To verify the effectiveness of the proposed method, a large number of experiments are performed on the TNO [28] and RoadScene [29] datasets. Meanwhile, nine state-of-the-artfusion methods are used to compare the proposed method, including VGG-19 and a multi-layer-fusion-based method (VggML) [30], ResNet and zero-phase component analysis (Resnet50) [31], Bayesian fusion (BayF) [32], algorithm unrolling image fusion (AUIF) [33], classification-saliency-based fusion (CSF) [34], dual-discriminator conditional generative adversarial network (DDcGAN) [35], semantic-aware real-time fusion network (SeAFusion) [36], visibility enhancement and hybrid multiscale decomposition (VEHMD) [17], and Y-shape dynamic transformer (YDTR) [37]. Six indicators are measured for each method as follows: edge-based metric (Q\({}_{\text{ab}t}\)) [38], structure-based metric (SSIM) [39], multiscale-feature-based metric (Q\({}_{\text{m}}\)) [40], phase-congruency-based metric (Q\({}_{\text{P}}\)) [41], mutual information for the wavelet (FMI\({}_{\text{w}}\)), and discrete cosine (FMI\({}_{\text{dct}}\)) features [42]. Higher values of these indicators represent better fusion results.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 2 Model Establishment and Simulation Test Design
### Establishment of Finite Element Model
#### 2.1.2 Mesh and Materials  
According to the actual size of the dimensional parameters of the hydrogen fuel cell city bus, the bus body frame is modeled in 3D in NX 10.0 software (Siemens PLM Software, Plano, TX, USA). The 3D model file is imported into HyperMesh 2017 (Altair, Troy, MI, USA,) for finite element modeling, and then the OptiStruct software 2018 (Altair, Troy, MI, USA) is used for simulation tests. Due to the numerous components and complex structure of the hydrogen fuel cell bus body frame, appropriate simplifications are required during the modeling process to improve modeling efficiency. Non-load-bearing components that do not affect the simulation results are simplified or omitted from the model.  
The body frame of this particular bus model is primarily composed of thin-walled structural components, where the length and width of these components are much larger than their thickness. To model the thin-walled structure efficiently, mid-surface extraction is adopted, and the relevant mid-surfaces are then meshed. Due to the advantages of fast computation speed and stable solutions, the mesh type chosen is quadrilateral shell elements with a mesh size of 10 mm [31]. After dividing the bus body frame into meshes, the total number of shell elements is 1,028,691, among which triangular elements account for 0.33% of the total shell elements, with a count of 3294. This indicates that the mesh quality of the bus body frame is relatively good.  
When establishing the finite element model of the bus body frame, it is essential to consider both the connection relationships between components and their material properties. The body frame of this hydrogen fuel cell city bus is primarily assembled from different types of structural components, and welding is used as the connecting method. The material of the body frame studied in this paper is Q345 steel, and specific parameters for material assignment to various components of the body frame are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Density** & **Elasticity Modulus** & **Poisson’s Ration** & **Yield Strength** \\ \hline Q345 & 7850 kg/m\({}^{3}\) & 206,000 MPa & 0.3 & 345 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of Q345.  
Figure 1: Physical photo of the hydrogen fuel cell city bus.
## 2 Related Work
### Proposed Sensing Matrix Learning  
When the CS sensing matrix is applied to the images, the CS measurements which are much lesser than the total number of pixels in the image are created and the sensing matrix values are arbitrary and random. The sensing matrix proposed in this paper is a simple process that retains certain set of pixels and forces other pixels zero. The image reconstruction from the reduced set of pixels is a CS problem [34; 35; 36] and the retained pixels can be considered as CS measurements. This process can be modeled as array multiplication of image and a binary mask. The work [32] discussed in Section 2.1 also uses a binary mask that mimics SPC. Basically, in this work the CS masks for various MR are applied to the training images and the training network is made adaptable for multiple MR. The adaptation of the network for multiple MR is made possible by adding all MR representation of the dataset images in training phase. This also involves an image reconstruction process for both training and testing images that is done by inverse of single pixel camera process which is another matrix operation that involves a computation cost. The possibility of training the sensing matrix is discussed in this paper but not attempted. But another work [20] discusses on learning the sensing matrix jointly with CNN weights for classification, assuming SPC sensing process. This work also involves an image reconstruction process from the CS measurements for all the testing and training images. A 28 \(\times\) 28 MNIST handwritten dataset is used as the dataset. Our proposed work conceptually has the common goal as above [20; 32], but different in following aspects:  
1. [label=()]
2. Our sensing matrix is not based on single pixel camera principle but a simple pixel-retaining process using a binary mask.
3. We use an evolution algorithm for binary mask learning named Genetic Algorithm-based compressed domain learning (GACL). We evaluate our research outcomes purely by comparison of our own results with and without GACL.
4. We do not have an image reconstruction step both in testing and training phase.
5. We consider dataset with more natural images, for example cat and dog images.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### High-Order Tensor Model for ETC Gantry Data
#### 3.1.1 Multidimensional Tensor Construction for ETC Gantry Data Analysis  
We construct a three-dimensional tensor model that intricately integrates time, gantry space, and vehicle user dimensions. We measure the time dimension in seconds, setting the minimum counting unit at one second, and tailoring the parameters to reflect the dynamics of traffic flow accurately. The statistical duration determines the time dimension's granularity. The gantry space dimension captures the driving direction, gantry number, and functionality, providing a spatial context for our analysis. We define the vehicle user dimension through parameters such as license plate information, vehicle model, and transaction status, ensuring a comprehensive categorization. Our tensor model meticulously records vehicle passage times, offering granular insight into the temporal flow, while the spatial dimension is marked by specific gantry identifiers. However, the model's expansion across time, space, and vehicle diversity leads to increased computational complexity and tensor size--a challenge compounded by significant sparsity. Large periods and areas lacking vehicle transactions result in an unnecessarily inflated tensor, complicating data storage and computation. A three-dimensional tensor model of the ETC gantry data is shown in Figure 2.  
Figure 2 visually represents the three-dimensional tensor model for ETC gantry data. In this model, the \(x\)-axis represents time, segmented into units of 4 s each, capturing the temporal aspect of traffic flow. The \(y\)-axis corresponds to the gantry ID, delineating the specific location of each gantry along the highway. The \(z\)-axis indicates the presence or absence of vehicles in different lanes at any given time. Distinct colors represent different lanes for ease of interpretation: orange for the first lane, yellow for the second, and green for the third. This color-coding aids in visualizing the distribution and movement of traffic across various lanes. The diagram exemplifies the model's capacity to encapsulate detailed information about vehicle flow, including temporal and spatial dynamics. However, it also  
Figure 2: Diagram of the three-dimensional tensor model of ETC gantry data.  
highlights the challenge of tensor inflation due to large periods and areas lacking vehicle transactions, which can complicate data storage and computational processes.  
To address these issues, our analysis employs innovative techniques to compress the tensor effectively. These methods enable us to discern significant patterns within the sparse dataset, preserving the fidelity of the temporal and spatial details. Our approach ensures that the model remains both manageable and reflective of the intricate patterns within traffic data.
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 3 Results
### Analysis of Tail Vortex Structure
Comparing vorticity cloud maps under different wind speeds, it becomes evident that the vortex core position gradually rises under wind conditions of \(-1\) m/s and \(0\) m/s, and the overall vortex structure remains relatively consistent. However, under \(-1\) m/s wind conditions, there is a faster vorticity decay. At the exact cross-sectional location, the core spacing is smaller, and the complete separation of the two vortices is achieved at an earlier position. Subsequently, under forward wind speeds of \(1\) m/s, \(2\) m/s, and \(3\) m/s, the vortex structure undergoes noticeable deformation. This is primarily due to the disruptive effects of wind disturbances and turbulence within the wind field, which disturb the internal balance of the vortex. As a result, the vortex shape changes, with distortions such as twisting or stretching, leading to an overall 'flattened' appearance. The most pronounced deformation occurs under \(1\) m/s wind conditions, and vorticity and core spacing decrease  
Figure 10: Full lifecycle diagram of the vortex under calm air conditions. faster than still air conditions. Additionally, the vertical displacement of the vortex core increases with higher wind speeds. Figure 11: _Cont._  
Notably, under 3 m/s wind conditions, a secondary vortex separates from the primary vortex and attaches to its outer side. With increasing distance of backward movement, the secondary vortex rapidly decays and gradually dissolves into the primary maelstrom. Turbulence, characterized by irregular, rotating motion within the airflow, destabilizes the structure of the primary vortex to a certain extent. Intense turbulence can trigger the generation of secondary vortices.
## 2 Results and Discussion
### Self-Aggregation of FYNF-NH\({}_{2}\) Investigations  
#### 2.4.1 Fourier-Transform Infrared Spectroscopy (FTIR)  
The investigation into peptide self-assembly and its correlation with secondary structure, particularly the formation of \(\beta\)-sheet structures, involved the use of FTIR spectroscopy analysis. To elucidate the secondary structure of the FYNF-NH\({}_{2}\) peptide, the examination focused on the amide bands, specifically, the amide I band. Figure S4 in the Supplementary Materials illustrates the broad shape of the amide I band, attributed to overlapping bands from distinct secondary structures, primarily consisting of peptide backbone C=O stretching vibrations [35]. Additionally, the amide II region (Figure S4) mainly reflects N-H and C-N bending modes [36], with the absorption band at 1511 cm\({}^{-1}\) potentially linked to the tyrosine residue's aromatic ring, according to [PERSON] et al., which suggests its occurrence at higher wavenumbers in free amino acids [37].  
For a detailed assessment of the FYNF-NH\({}_{2}\) peptide's secondary structure, quantitative Gaussian peak fitting was applied to the FTIR data (Figure 4), utilizing OriginPro 85.0 SR1 software (OriginLab Corporation, Northhampton, MA, USA). Following a linear baseline subtraction for the amide I band (Figure S4), the second derivative was calculated to identify the position and number of absorption bands essential for peak fitting, employing a 20-point Savitzky-Golay smoothing function [38]. Subsequently, Gaussian peaks were introduced into the amide I band based on positions indicated by the second derivative spectrum, and the corresponding structures were identified referencing literature [35; 36; 37; 38], as shown in Figure 4.  
One may see that most notable absorption bands are located at 1633 cm\({}^{-1}\) and 1672 cm\({}^{-1}\), corresponding to \(\beta\)-sheet and \(\beta\)-turn structures, respectively. This observation is supported by the data presented in Table S1 from the Supplementary Materials, where it is revealed that the peaks associated with \(\beta\)-sheet/turn structures contribute significantly to the overall area of the amide I band. However, while the self-assembly of FEYNF-NH\({}_{2}\) peptide is primarily driven by the formation of \(\beta\)-sheet structures, it is noteworthy that FTIR spectrum may also exhibit signals indicative of \(\alpha\)-helix and side chain structures.  
Figure 3: MS spectrum of the eluted fraction at 9.09 min confirming \(m/z\) corresponding to FEYNF-NH\({}_{2}\) with two different matrices: (**a**) DHB and (**b**) HCCA; (**c**) assignment of \(m/z\) signals resulting from MALDI-ToF MS/MS tandem mass spectrometry analysis; fragmentation of the molecular ion [FYNF + H]\({}^{+}\) (\(m/z\) 718.55) was performed by collision-induced dissociation (CID).  
structures. Nevertheless, as indicated by FTIR analysis, the primary structural pattern observed in the self-assembly of peptides is linked to the formation of \(\beta\)-sheets. As a result, further analysis will be employed to deepen and understand the intricacies of FEYNF-NH\({}_{2}\) peptide self-assembly.
## 3 Problem Modeling
### Destroy Operators  
A certain number of the customers are removed with this kind of operator. Denote the number of customers planned to be removed as \(n_{r}\), and the set of currently removed customers as \(S\). The customer removal operators adopted in this paper are listed as follows.  
RR operator (Random Removal). This operator chooses \(n_{r}\) customers randomly, and then removes them from the current solution.  
WR operator (Worst Removal). With this operator, the difference between the evaluation values with and without each customer is calculated first, denoted as \(\Delta f_{j}\) for customer \(j\). Then, iteratively remove the customer with the \(\left\lfloor|S|\cdot e^{\lambda_{WR}}\right\rfloor^{th}\) largest \(\Delta f_{j}\) (remove the largest one if \(|S|=0\)). The parameter \(\varepsilon\) is randomly generated in \([0,1)\). To further randomize the removal, the parameter \(\lambda_{WR}\) is considered. The iteration is repeated \(n_{r}\) times until customers have been removed.  
WER operator (Worst Energy Removal). It performs just like the WR operator, except that the worst one is evaluated by the difference between the energy consumption values, instead of evaluation values. Similarly, there is a parameter \(\lambda_{WE}\) for this operator.  
WDR operator (Worst Distance Removal). This operator is similar to the WR and WER operator, with the worst one being evaluated by the difference between the travel distance. The similar parameter \(\lambda_{WD}\) is associated.  
SR operator (Shaw Removal). This operator aims to remove similar customers. First, we randomly chose a customer \(i\) and removed it. Then, the value of relativeness \(R(i,j)\) between customer \(i\) and customer \(j\) is calculated as follows.  
\[R(i,j)=\lambda_{1}\frac{d_{ij}}{\underset{(l,m)\in A}{\max}(d_{lm})}+\lambda_ {2}\alpha_{ij}+\lambda_{3}\frac{|q_{i}-q_{j}|}{\max(q)-\min(q)}+\lambda_{4} \frac{e_{ij}}{\underset{(l,m)\in A}{\max}(e_{lm})} \tag{44}\]  
where \(\alpha_{ij}=-1\) means node \(i\) and node j are on the same route, and otherwise, 1. \(e_{ij}\) indicates the energy consumption from node \(i\) to \(j\), and \(\lambda_{1},\ \lambda_{2},\ \lambda_{3},\ \lambda_{4}\) are the weights. Similar to the previous operators, a parameter \(\lambda_{Shaw}\) is used.  
RRR operator (Random Route Removal). This operator randomly selects a route and removes all the customers on it.  
RCwPS operator (Remove Customer with Preceding Station). This operator removes the customers along with the preceding RSs.  
RCwSS operator (Remove Customer with Succeeding Station). This operator removes the customers along with the succeeding RSs.  
WURR operator (Worst-charge Usage RS Removal). Removing RSs from the solution may decrease the total cost. The WURR operator aims at making full use of the battery of each EV as much as possible. The RSs are sorted descending to the SoC of all EVs when they visit them, and the first \(\varphi\) RSs are removed from the solution.
## 3 Methodologies
### Model Description
#### 3.2.5 K-Nearest Neighbor (KNN)  
KNN predicts the output for a given input by averaging the outputs of its k-nearest neighbors in the feature space. It can model complex relationships in the data without assuming any specific underlying distribution. In this study, the number of neighbors (n_neighbors), the distance metric (p), and the weight function are the main influences on model performance. For instance, the n_neighbors influence the smoothness of the prediction, and p determines how the distance between data points is calculated. Note that two types of weight functions were utilized to improve model performance, i.e., uniform and distance. Obviously, optimizing these hyperparameters is essential to enhance the accuracy and robustness of the model, ensuring that KNN can effectively capture the underlying patterns in the data and provide reliable predictions. The overall objective function for KNN in regression tasks can be formulated as:  
\[Obj(y,\partial,w)=\min\left[\frac{1}{T}\sum\limits_{t}^{T}\left(y_{t,true}- \frac{\sum\limits_{b\in\partial_{t}}w_{tb}y_{t}}{\sum\limits_{b\in\partial_{t }}w_{tb}}\right)^{2}\right] \tag{5}\]  
where \(y_{t,true}\) is the actual value of the \(t\)-th data point, \(\partial_{t}\) is the set of neighbors of the \(t\)-th data point, \(w_{tb}\) is the weight assigned to the neighbor \(b\) of the \(t\)-th data point, and \(T\) represents the total number of data points.
# Experimental Observations of Heat-Assisted Boycott Effect in Trapezoidal Enclosures
## 3 Results
### Temperature of the Walls  
Figure 19 depicts the temporal evolution of wall temperature \(T_{w}\) relative to ambient temperature \(T_{a}\) (\(\Delta T=T_{w}-T_{a}\)). The formation of temperature stratification is observed between the upper and lower sections of the cell, and this phenomenon is evident in both initial concentration scenarios, 1500 ppm and 3000 ppm, as well as in the power input conditions of 4.5 W and 9 W. As anticipated, the initial concentration of particles has a negligible impact on the temperature distribution. It should be noted that, while stratification occurs, the temperature of the lower wall does not exhibit a substantial increase. At the outset of the experiment, a temperature disparity between the vertical and inclined walls is discernible, particularly in the upper region of the cell. This discrepancy gradually diminishes as the system approaches a state of thermal equilibrium. Furthermore, an estimate of the ratio of particle buoyancy to viscous forces can be obtained by computing the Archimedes number, defined as \(\mathrm{Ar}=\frac{g(\rho_{p}-\rho_{f})\,d^{3}\,\rho_{f}}{\mu_{f}^{2}}\), where \(\rho_{f}\) and \(\rho_{p}\) are the fluid and particle densities, respectively, \(d\) is the particle diameter, \(\mu_{f}\) is the dynamic viscosity of the fluid, and \(g\) is the gravitational acceleration. The present experimental data yield values on the order of \(\mathcal{O}(10^{-2})\), confirming the predominance of viscous forces.  
Figure 18: Effect of heat injection on particle concentration for the case of an initial concentration of 3000 ppm: for the analyzed rows \(F_{1}\), \(F_{2}\), \(F_{3}\), and \(F_{4}\), for three analyzed times from left to right (1, 10, and 35 min).
## 4 Results and Discussion
### Optimization of the RIN
This is a further 10 dB improvement in the millihertz range of 1 mHz\(\sim\)1 Hz compared to Figure 10. However, except for frequencies below 3 mHz, it is well within the requirement of LISA. Compared to the out-of-loop RIN in [18], our result is lower than \(-70.5\) dBc/Hz (3.0 \(\times\) 10\({}^{-4}\)/Hz\({}^{-1/2}\)) at 1 mHz, but higher than \(-114\) dBc/Hz (2.0 \(\times\) 10\({}^{-6}\)/Hz\({}^{-1/2}\)) at 1 Hz.