esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimization Methods
### Combined Local and Global Optimization Method  
In the combined optimization algorithm, we used the output of the local optimization method to define the search space for the global optimization technique to speed up and reach the global solution faster. Since the local optimization has been constrained by smoothing terms (i.e., second terms of Equations (2) and (3)), the combined optimization algorithm is linearly constrained by applying the output of the local optimization algorithm to define the lower and upper bounds of the search space. Fundamentally, the search space is determined by modifying the range (minimum and maximum) of the model parameters obtained from the output of the local optimization algorithm. Depending on the quality of the output from the local optimization algorithm, scaling the model parameters up and down by 10 to 30% is recommended to obtain a good result. The variability (10-30%) of the model parameters was selected based on the expected variation in the modeled geophysical properties, such as velocity and resistivity, in Saudi Arabia. This process creates adequate diversity in the initial population for the global optimization algorithm. A flowchart illustrating the combined local and global optimization algorithm is shown in Figure 2. The summary of all terms we used to describe the combined optimization algorithms is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S/N** & **Inversion Type** & **Abbreviation** & **Description** \\ \hline
1 & Local optimization method & LOM & Optimization involves the derivatives of the objectives, e.g., Gauss–Newton. \\ \hline
2 & Global optimization method & GOM & Non-derivation optimization, e.g., genetic algorithm. \\ \hline \end{tabular}
\end{table}
Table 1: A summary of all the types of inversion applied in this study, their description, and abbreviations are presented below.  
Figure 2: Flowcharts of the proposed combined local and global optimization algorithm.
## 5 Conclusions  
In order to address the issue of high-frequency instability encountered when employing the TMM for long-span hydraulic pipeline systems, this paper first analyzes the instability mechanism from a mathematical perspective. Subsequently, a mixed variable approach is introduced to enhance the expression form of the transfer matrix, leading to the construction of a mixed variable transfer matrix specifically for pipeline units. By integrating this with the matrix representation of boundary conditions and utilizing a recursive algorithm for the mixed variable transfer matrix, an optimization technique known as the MVTMM is proposed. The stability and accuracy of this method are validated using data from Dubee pipelines alongside the measured vibration data from a real hydraulic pipeline system. The main conclusions drawn from this study are as follows:  
1. The MVTMM effectively mitigates high-frequency instability associated with the TMM, maintaining a global coefficient matrix dimension consistently at 28 \(\times\) 28. This ensures sustained efficiency, even when handling long-span hydraulic pipelines. 2. The MVTMM can accurately predict frequency domain vibration characteristics of hydraulic pipelines, demonstrating maximum errors of 3.03% when compared to Dubee pipeline data and 4.35% relative to experimental data based on fluid excitation within real hydraulic pipeline systems. 3. Data obtained from the fluid excitation-based pipeline experiment indicate that FSI significantly influences frequency domain vibration characteristics in pipelines; thus, it should not be overlooked during predictive analyses. The theoretical framework presented herein shows promise for engineering applications following validation and can be utilized for precise predictions regarding frequency domain vibration characteristics in ship-based long-span hydraulic pipeline systems. Future work will aim to extend this methodology into ship pipeline scenarios while considering complex constraints. Conceptualization, F.Z. and J.L. ; methodology, F.Z. and J.L. ; writing--original draft, F.Z. ; writing--review and editing, Z.C. ; visualization, X.T. supervision, L.H. and J.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the fund general projects of the National Key Laboratory on Ship Vibration and Noise (Grant No. JCKY2023207 CI03). Figure 13: A comparison of the FSI model and the classical water hammer model. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 6 Conclusions
### High-Frequency Phenomena  
In the winding, the skin and proximity effects increase with frequency, affecting both the resistance and the inductance values of the coils. To model those effects correctly, a detailed geometry of the stator slot must be considered, and the location of the conductors inside the slot must be also roughly described. The skin effect depends on the wire diameter and frequency, so it can be eliminated using conductors smaller than the skin depth in parallel strands. However, a high number of strands is counterproductive after a point due to the proximity effect, being also more difficult to manufacture. Litz wires are used to reduce skin and proximity effects, with an especial transposition to reduce the proximity effect, but above a frequency, the proximity effect is not softened.  
Regarding the capacitance couplings, the properties of dielectric insulation materials must be precisely known. Otherwise, the computation of the capacitances might differ from real values. It is remarkable than when manufacturing the electric machine, some conductors may vary their position from the theoretical one, making the capacitance calculation differ from the measures too.  
Moreover, the end-winding must be also considered, including the skin and proximity effects in that region. Concerning the capacitive coupling, even if the end winding represents less than the 1% of winding-to-stator capacitance, it is up to 40% of the total winding-to-rotor capacitance and it influences the phase-to-phase capacitance, so it must be taken into consideration.  
With respect to the magnetic core, Eddy currents are significant in high frequency. Not only due to the iron losses they create, but also due to the shielding effect they produce. Due to this shielding effect, the magnetic flux is pushed out from the magnetic core, decreasing the effective ferromagnetic material area and leading to a decrease in the value of the winding inductance. The frequency when the shielding effect starts depends on the thickness of the sheet and its resistivity. The lamination of the stator prevents the flux being totally pushed out of the core, leading to a higher winding inductance compared to a bulk stator. The effect of lamination also leads to a higher resistance of the winding compared to a bulk core because the surface area where Eddy currents flow is larger and the proximity effect between sheets increases the Eddy currents in the core.
# SDN-Based Survivability Analysis for V2I Communications
## 5 Verification and Analysis of V2I Networks Survivability Model  
### V2I Networks Survivability Model Verification  
In this subsection, we use probabilistic model checker PRISM 4.6 for the model checking and validation. During this process, the survivability model is first designed by the PRISM programming language through the probabilistic model detection platform, and then the model is validated. During the verification process, the model checking system will automatically search all the state space and also automatically carry out state statistics, state transfer statistics, node statistics in the MTBDD, terminal node statistics, model establishment time calculation, iterative algebraic calculation in the source state reaching the target state and its iterative time calculation, and so forth. The validation of the survivability model is shown in Table 2. All the results are obtained for the given parameters as \(\lambda_{1}=0.1\), \(\lambda_{2}=0.1\), \(\mu_{1}=0.1\), \(\mu_{2}=0.1\), and \(T=50\), respectively. In Table 2, as the \(N\) of the maximum number of vehicles increases, the number of model states and state transitions will correspondingly increase. Meanwhile, the number of nodes in the Multi-Terminal Binary Decision Diagram (MTBDD) [38] and the number of terminal nodes also increase accordingly. The time of model building, number of iterations, and iteration time increase with the increased states. The model checking results verify the correctness of our proposed survivability model.  
Figure 5: The survivability model of SDN-based V2I networks.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 2 EH Circuits
### FBR Circuit  
When the FBR circuit is connected in parallel with the PD, it charges the load capacitor in 4 modes of operation. A flow chart summarising the modes of operation of FBR circuit in both positive and negative half cycles is shown in Figure 7. The operations in different modes are briefly outlined in Table 1. Further details and explanations are available in the authors' recent publication [32].  
The power extracted from the FBR circuit (P\({}_{\text{o}}\) (t)) varies with the rectified voltage (V\({}_{\text{dc}}\)) [34]:  
\[\text{P}_{\text{o}}\left(\text{t}\right)=\ \frac{2\,\text{V}_{\text{dc}}}{\pi} \big{(}\text{I}_{\text{P}}-\text{V}_{\text{dc}}\ \omega\text{C}_{\text{P}}\big{)} \tag{3}\]  
With a load capacitor of low capacitance (C\({}_{\text{load}}\)), most of the charges from the harvester will flow to the output, but the output voltage is expected to be low and unstable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Circuit**} & \multicolumn{3}{c}{**Positive Half Cycle**} & \multicolumn{3}{c}{**Negative Half Cycle**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Mode 1**} & \multicolumn{2}{c}{**Mode 2**} & \multicolumn{2}{c}{**Mode 3**} & \multicolumn{2}{c}{**Mode 4**} \\ \hline \multirow{4}{*}{H-Bridge} & When t\({}_{\text{0}}\) \(<\) t \(\leq\) t\({}_{\text{1}}\) & When t\({}_{\text{1}}\)\(<\) t \(\leq\) t\({}_{\text{2}}\) & When t\({}_{\text{2}}\)\(<\) t \(\leq\) t\({}_{\text{3}}\) & When t\({}_{\text{3}}\)\(<\) t \(\leq\) t\({}_{\text{4}}\) \\  & \(\bullet\) & Interval 1 begins & \(\bullet\) & Interval 2 begins & \(\bullet\) & Interval 1 begins & \(\bullet\) & Interval 2 begins \\  & \(\bullet\) & H-Bridge: OFF & \(\bullet\) & MOSFET 1, 4: ON & \(\bullet\) & H-Bridge: OFF & \(\bullet\) & MOSFET 2, 3: ON \\  & \(\bullet\) & Load capacitor: Not & \(\bullet\) & Load capacitor: & \(\bullet\) & Load capacitor: Not & \(\bullet\) & Load capacitor: \\  & \(\bullet\) & charging & & Charging & charging & & Charging \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four modes of operation of H-Bridge, FBR circuit in positive and negative half cycles.  
Figure 7: A flow chart summarising the modes of operation of full bridge rectifier (FBR) circuit in positive and negative half cycles.
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 3 Architecture
### Exponential Partial Unit (EPU)
\[k\sim\mathcal{U}(0,1) \tag{8}\]  
Figure 1 illustrates the comparison between the ReLU and EPU functions, highlighting their respective behaviors across the specified range. One of the major advantages of the EPU is its ability to introduce non-linearity while maintaining smooth gradient flow, which enhances optimization and helps prevent \"vanishing gradients\". Another advantage is that its first derivative can be easily calculated during training. This property simplifies the backpropagation process, as the gradient of the activation function can be efficiently computed, ensuring smooth and stable updates to the learnable parameters. The derivative of the exponential function is straightforward, making it computationally efficient for optimization tasks, and it helps maintain the stability of the training process. The 3D plot is shown in Figure 2, providing a visual representation of the function's behavior. \[\frac{d}{dx}(e^{kx})=ke^{kx} \tag{9}\]  
We acknowledge the extensive development of ReLU variants, each designed to address specific limitations of the original ReLU function. While these variants generally introduce only minor modifications to the core ReLU concept, they nonetheless offer measurable improvements in aspects like mitigating the vanishing gradient problem, accelerating convergence, or enhancing the performance of certain tasks. In contrast, our proposed activation function presents a novel approach that distinguishes itself from the traditional ReLU family. By uniquely integrating the exponential function in a way that fundamentally redefines activation behavior, our method offers an innovative alternative that not only addresses existing limitations, but also introduces distinct performance benefits and enhanced stability. This new concept goes beyond incremental modifications, representing a significant and original contribution to the design of activation functions. In modern deep learning frameworks, neural networks rely heavily on automatic differentiation to efficiently compute gradients during training. This capability is particularly important when employing novel activation functions like our Exponential Partial Unit (EPU). Thanks to the straightforward derivative of the exponential function our EPU enables faster, and more efficient gradient computation compared to more complex activation functions. This computational efficiency not only accelerates the automatic differentiation process, but also contributes to faster overall training and enhanced stability during backpropagation. Furthermore, as noted in [22; 23], the existing literature does not provide a detailed analysis of activation functions that employ such an exponential integration approach. This highlights the novelty of our method, which extends beyond traditional modifications of ReLU and offers a unique contribution to activation function design.
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 6 Numerical Study
### Sensitivity Analysis  
To understand the influence of different minimum turnaround times, four additional scenarios are introduced, namely, \(-10\), \(-5\), \(+5\), and \(+10\), representing changes from the original minimum turnaround time of C-type aircraft. The time of aircraft in each scenario is shown in Table 9.  
Figure 12 illustrates the results across different scenarios. As the turnaround time for C-type aircraft increases from 35 to 55 min, taxi fuel consumption initially decreases before rising again. This trend occurs because some flights using Runway 15 are allocated stands slightly farther from the runway than remote stands. Consequently, at a turnaround time of  
Figure 11: Gantt chart of ground support vehicles. (**a**) Ferry vehicles; (**b**) Fuel trucks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Instances** & \(-\)**10** & \(-\)**15** & **Base Case** & **+5** & **+10** \\ \hline C-type aircraft & 35 & 40 & 45 & 50 & 55 \\ D-type aircraft & 60 & 60 & 60 & 60 & 60 \\ E-type aircraft & 75 & 75 & 75 & 75 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Testing instances for different scenarios of C-type aircraft.  
30 min, fuel consumption experiences a slight increase. The driving time of ferry vehicles increases with longer turnaround times, as more flights are allocated to remote stands. After the turnaround time reaches 45 min, more D-type and E-type aircraft, which require additional ferry vehicles, are allocated to remote stands. Idle time shows minimal variation across scenarios and trends opposite to the other two objectives. Shorter turnaround times lead to longer intervals between flights, while longer turnaround times result in only necessary safety intervals. These findings suggest that changes in turnaround time mainly impact the allocation of aircraft to remote stands, thereby influencing taxi fuel consumption and parking stand idle times.
## 3 Methods
### EEG-TCNTransformer  
The concept of EEG-TCNTransformer is inspired by EEG-TCNet and EEG-Conformer. EEG-TCNet performs well in learning the local features in EEG signals because of the EEGNet and TCN modules. Besides that, EEG-Conformer solves the problem of global feature dependencies, which improves the learning of long sequence inputs. EEG-TCNTransformer has a convolution module, self-attention module, and classifier module.  
1. [label=()]
2. Convolution module: The sequence of a 2D convolution layer, depthwise convolution layer, separable convolution layer, and average pooling layer extracts the temporal and spatial information in EEG signals. After that, a sequence of TCN blocks continues to learn large receptive fields efficiently with small parameters because of dilated causal convolution and a residual block structure. In this study, the different numbers of filters \(k\) in a TCN block and the numbers of a TCN block \(T\) are validated to select the best hyperparameter for EEG-TCNTransformer. Then, the average pooling layer smooths the features and rearranges the features to the shape of channels of each temporal point.
3. Self-attention module: Six self-attention blocks with a multi-head attention architecture learn the global temporal dependencies in features from the convolution module. The self-attention module calculates as below: \[Attention(Q,K,V)=Softmax(\frac{QK^{T}}{\sqrt{k}})V,\] (8) where \(Q\) is a matrix of stacked temporal queries, \(K\) and \(V\) are temporal matrices that denote key and value, and \(k\) is the length of tokens. Multi-head attention (\(MHA\)) is applied to strengthen representation performance. The tokens are separated into the same shape \(h\) segments, described as follows: \[MHA(Q,K,V)=[head_{0};\ldots;head_{h-1}],\] (9) \[head_{l}=Attention(Q_{l},K_{l},V_{l}),\ l=1,2,\ldots,h\] (10) where MHA is the multi-head attention \(Q_{l},K_{l},V_{l}\in\mathbb{R}^{m\times k/h}\), indicating the query, key, and value acquired by a linear transformation of a segment token in the \(l\)th head, respectively.
4. Classifier module: Two fully connected layers transform the features into M labels that calculate probability distribution by the Softmax function. The loss function is the cross-entropy loss, as follows: \[\mathcal{L}=-\frac{1}{N_{b}}\sum_{i=1}^{N_{b}}\sum_{c=1}^{M}y\ \log{(\hat{y})}\] (11) where \(N_{b}\) is the batch size, \(M\) is the number of target labels (\(M=4\)), \(y\) is the ground truth, and \(\hat{y}\) is the prediction.  
Figure 7: Architecture of EEG-Conformer [26].  
Figure 8 visualises the architecture of an EEG-TCNTransformer architecture. Instead of a shallow convolution module in EEG-Conformer, the temporal convolution, depthwise convolution, and separable convolution are used to extract the temporal information, frequency-temporal information, and temporal summary from feature maps. The TCN module exploits the temporal features from feature maps before the global temporal dependencies are learned by the self-attention module. Finally, the classification is produced by the classifier module.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 4 Discussion
### Accuracy Aspect
Furthermore, the results of the two polarizations VV and VH showed no obvious difference in response to heterogeneity. We sorted and counted the heterogeneity of the RGB and NDVI bands for sites with significantly better accuracy in VH than VV and sites with significantly better accuracy in VV than VH. The heterogeneity values of these two types of sites were very scattered, which means that the FT state at sites with a certain degree of (high/low) heterogeneity was not always better estimated in a certain polarization. In general, there is no direct indication that the difference in performance between the two polarizations was directly affected by the heterogeneity of the environment around the site. Figure 8: Environmental heterogeneity at each site of RGB and NDVI signals. When observing the accuracy of each soil layer at each site according to the land-cover grouping, the fluctuation of accuracy did not have direct directivity either. By using the gamma 3 \(\times\) 3 filter (Figure 9), the results of the Infra group were overall lower than the others, and the results of the N-nVege group were relatively high, but this phenomenon was not repeated in the results using a 5 \(\times\) 5 window filter. In general, according to the available results alone, there was no support for drawing strong conclusions about whether the land-cover type used by the algorithm in this study could perform better and how the land cover contributes to the detection process. By further comparing the results of VV and VH considering the land cover, it can be seen that the difference between the results of VV and VH varied with the land cover (Figure 10). The N-Mix group showed the largest differences (VV vs. VH) with a mean of \(-\)0.09, the N-Vege and N-nVege groups had similar average differences of \(-\)0.05 and \(-\)0.04, respectively, and the Infra group had the only average difference greater than zero (0.01). VH was expected to show superiority over VV, especially in areas with dense vegetation coverage. The result is not entirely in line with the expectation. One regrettable point is that, because the analysis was based on sparsely distributed sites over large regions with very diverse surrounding environments, it could not meet the finer divisions, and the land cover could only be roughly classified to ensure the sample size of each type. Figure 9: Accuracy of different test soil depths, based on datasets from two preprocessing methods, clustered by four land-cover categories.
###### Abstract  
This study investigated how older Asian adults (aged 50+) accept fitness apps, focusing on experiential factors derived from the technology acceptance model (TAM) and the Diffusion of Innovations (DOI). Between December 2023 and March 2024, a convenience sampling method was used to recruit older adults with three months of experience using fitness apps from social media platforms and community groups in multiple Asian countries. Of the 700 initial respondents, 587 valid questionnaires were retained (an 83.8% validity rate). Structural equation modeling (SEM) assessed relationships among relative advantages, compatibility, trialability, gamification, observability, perceived ease of use (PEU), perceived usefulness (PU), behavioral intention, and actual usage. Trialability, relative advantages, and compatibility significantly enhanced PEU (\(p<0.01\)), while gamification and observability did not. By contrast, gamification and observability positively influenced PU (\(p<0.05\)). Both PEU and PU in turn predicted behavioral intention, which explained actual app usage (\(p<0.01\)). These findings underscore the importance of designing fitness apps that accommodate older adults' skills and preferences, while also incorporating engaging features that reinforce perceived usefulness. Healthcare professionals and developers may leverage these insights to tailor digital health interventions, potentially improving exercise habits and well-being among aging populations in Asia.  
technology acceptance model (TAM); diffusion of innovations (DOI); acceptance behavior; digital health; user experience; aging research Article