esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 3 Materials and Methods
### Channel Attention Enhancement Module  
The SSA operates on image features in the spatial dimension, whereas the CAE focuses on the channel dimension. This module evaluates the importance of different channels and assigns appropriate weights to enhance critical channels while suppressing irrelevant features, as shown in Figure 2. Feature \(f_{RQ}\) is processed through the CAE module to generate a refined feature map \(C_{f}\in R^{C\times H_{Q}\times W_{Q}}\). The module follows a compress-and-activate paradigm [32], which involves first compressing global information, then recalibrating feature responses along the channel dimension, and finally applying a sigmoid activation function to improve the network's representational capacity. The steps of this process are described in detail below.  
\[f_{avg} =F_{ca}(GA(f_{RQ})) \tag{8}\] \[f_{max} =F_{ca}(MAX(f_{RQ}))\] (9) \[C_{f} =f_{RQ}+sigmoid(f_{avg}+f_{max})\odot f_{RQ} \tag{10}\]  
Here, \(\odot\) represents element-wise multiplication, and the operations \(GA(*)\) and \(MAX(*)\) represent global average pooling and global maximum pooling, respectively. The \(F_{ca}(*)\) function encompasses post-pooling feature processing, which involves fully connected layers and ReLU activation functions. Global average pooling computes the average value of each channel, while global max pooling selects the maximum value of each channel. These two pooling methods are performed in parallel to capture different aspects of channel features. After pooling, weight coefficients for each channel are obtained; these reflect the importance of different channels in the object-counting task. These weight coefficients are then applied using a weighted product approach, where significant channel features are enhanced, and less relevant channel features are suppressed. To fully exploit the feature information, we integrate \(f_{ca}\) with \(f_{RQ}\) through element-wise addition, resulting in the final feature map \(C_{f}\). This approach ensures that both the salient channel features and the original spatial information are preserved and synergistically utilized for improved object-counting performance.  
Figure 2: Schematic diagram of the detailed structure of the channel attention enhancement module. Here, max() indicates the global maximum pooling, ga(0) represents the global average pooling, and sigmoid() represents the sigmoid function.
## 3 Results and Discussion
### Classification of Wines by Geographical Origin and Vintage Based on Oenological Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** \\ \hline Alcohol content (\%, \(v/v\)) & 12.92 \(\pm\) 1.04 b  & 13.36 \(\pm\) 1.16 ab  & 13.54 \(\pm\) 0.95 ab  & 13.93 \(\pm\) 0.56 a  & 13.87 \(\pm\) 0.95 a  & 13.93 \(\pm\) 0.73 a  \\ Titratable acidity (g/L, tartaric acid) & 5.78 \(\pm\) 0.76 a  & 5.65 \(\pm\) 0.50 a  & 5.81 \(\pm\) 0.88 a  & 5.74 \(\pm\) 0.44 a  & 5.85 \(\pm\) 0.72 a  & 6.01 \(\pm\) 0.70 a  \\ pH & 3.83 \(\pm\) 0.11 a  & 3.70 \(\pm\) 0.10 ab  & 3.65 \(\pm\) 0.11 b  & 3.71 \(\pm\) 0.16 ab  & 3.77 \(\pm\) 0.16 a  \\ Total phenols (g/L, gallic acid) & 2.52 \(\pm\) 0.29 a  & 2.79 \(\pm\) 0.61 a  & 2.84 \(\pm\) 0.62 a  & 2.83 \(\pm\) 0.58 a  & 2.84 \(\pm\) 0.63 a  & 2.94 \(\pm\) 0.61 a  \\ Total anthocyanins (g/L) & 0.05 \(\pm\) 0.02 c  & 0.08 \(\pm\) 0.03 c  & 0.13 \(\pm\) 0.09 bc  & 0.15 \(\pm\) 0.06 bc  & 0.21 \(\pm\) 0.10 ab  & 0.24 \(\pm\) 0.05 a  \\ Total tannin (g/L) & 3.14 \(\pm\) 0.29 a  & 3.16 \(\pm\) 0.98 a  & 3.03 \(\pm\) 0.58 a  & 3.34 \(\pm\) 0.98 a  & 3.29 \(\pm\) 0.70 a  & 3.30 \(\pm\) 0.40 a  \\ \hline \hline \end{tabular} Note: Different Latin letters indicate significant differences according to the HSD Tukey test (\(p<0.05\)).  
\end{table} 
Table 2: Oenological parameters of red wine from the six vintages (2013–2018) in the Ningxia region. (\(p<0.05\)).
## 3 Experiments and Results
### Scenario 4: Demographics, Pre-College, and Academic Features  
The last scenario includes all students data, that is to say, demographics, pre-college, and academic features. Performance of the obtained prediction models is reported in Table 9. The best performance by the end of term 1, is the LR method with the term performance represented _by courses_, whether single or cumulative, with 62.5%. When the prediction is performed later by the end of the terms 3, 4, 5, and 6, we observe similar results with all the past scenarios, that is, the best performance is mainly obtained with the LR method, with term the performance represented _by cumulative GPA_. Looking at Figure 8b,d, we observe that the performance of the cumulative models are improving from 62.5% (term 1) to 93.5% (term 6) with decreasing prediction window size. Models using only one past term reach a performance pick mostly when the prediction is performed by the end of term 4 or term 6 (see Figure 8a,c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **ML Algorithm** & **1 st Term** & **2 nd Term** & **3 rd Term** & **4 th Term** & **5 th Term** & **6 th Term** \\ \hline \multirow{4}{*}{**Single**} & \multirow{4}{*}{**Course**} & LR & **0.626** & 0.691 & 0.762 & 0.818 & 0.819 & 0.826 \\ \cline{3-8}  & & RFC & 0.617 & 0.659 & 0.723 & 0.808 & 0.791 & 0.800 \\ \cline{3-8}  & & GNB & 0.560 & 0.632 & 0.764 & 0.813 & 0.796 & 0.809 \\ \cline{2-8}  & & LR & 0.616 & 0.679 & 0.757 & 0.830 & 0.821 & 0.836 \\ \cline{3-8}  & & RFC & 0.591 & 0.665 & 0.725 & 0.819 & 0.801 & 0.807 \\ \cline{3-8}  & & GNB & 0.565 & 0.651 & 0.732 & 0.804 & 0.798 & 0.802 \\ \hline \multirow{4}{*}{**Cumulative**} & \multirow{4}{*}{**Course**} & LR & **0.625** & **0.706** & 0.779 & 0.861 & 0.912 & **0.929** \\ \cline{3-8}  & & RFC & 0.617 & 0.675 & 0.758 & 0.842 & 0.892 & 0.907 \\ \cline{2-8}  & & GNB & 0.560 & 0.657 & 0.735 & 0.794 & 0.853 & 0.887 \\ \cline{2-8}  & & LR & 0.616 & 0.697 & **0.794** & **0.873** & **0.921** & **0.930** \\ \cline{2-8}  & & RFC & 0.596 & 0.693 & 0.773 & 0.862 & 0.903 & 0.918 \\ \cline{2-8}  & & GNB & 0.565 & 0.687 & 0.751 & 0.825 & 0.887 & 0.919 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy results of the ML Models with the academic, demographic, and pre-college features.  
Figure 7: Accuracy plots along the terms of the ML Models with the academic and pre-college features.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## Vehicle Model and Its Coupling with Road and Environment
### MR Damper Model  
The Tanh model of the MR damper was selected while taking into account a compromise between limited model complexity and its accuracy. The MR damper model was implemented and simulated in the following form, which is also presented in details in [15]:  
\[F_{mr}=-(\alpha_{0}+\alpha_{1}\sqrt{i_{mr}})\tanh(\beta\cdot z_{us})-(c_{0}+c_{ 1}\sqrt{i_{mr}})\cdot z_{us}, \tag{18}\]  
where \(i_{mr}\) is a control current supplying the MR damper and \(z_{us}\) denotes the relative velocity of the damper piston and simultaneously the relative velocity of vehicle suspension. The parameters of the model were estimated based on identification experiments performed for an MR damper subjected to sinusoidal displacement excitation of amplitude equal to 25 mm and frequency of 1.5 Hz, as was presented in [52]. The following values of parameters are used: \(\alpha_{0}\) = 62.42 N, \(\alpha_{1}\) = 1340 N\(\sqrt{A^{-1}}\), \(\beta\) = 39.96 sm\({}^{-1}\), \(c_{0}\) = 802.8 Nsm\({}^{-1}\), and \(c_{1}\) = 488.5 Nsm\({}^{-1}\sqrt{A^{-1}}\). Figure 5 presents the sample force-velocity characteristics of the model.  
It can be noticed that MR dampers exhibit nonlinear behaviour, including force saturation indicated for higher piston velocities. The maximum damper force that is reached for piston velocity equal to 0.5 ms\({}^{-1}\) is close to \(-\)1500 N. It can be also highlighted based on the characteristics and mathematical model (18) that the dependence of damper force on control current is also nonlinear (dependent on root square), while a greater variation of force with respect to control current can be obtained for lower currents.
## 4 Simulation Analysis  
To verify the effectiveness of the LCP-based MPC algorithm proposed in this article, a joint simulation was conducted on the automotive simulation platform Carsim2019/Simulink2019 to verify the path tracking effect. The operating system is Windows 10. It connects modules in Simulink, and the MPC vehicle lateral controller written by Matlab2019 calculates the front wheel angle. After sending it to Carsim, it outputs the current status of the vehicle body, including longitudinal position, lateral position, yaw angle, vehicle speed, and front wheel angle.  
In order to assess the stable control of the proposed algorithm, at a speed of 15 m/s, we compare the performance of two solvers, LCP and QP, as well as the classical method, PID, under two different road friction coefficients, \(\mu_{s}=1\) and \(\mu_{s}=0.4\). The vehicle system parameters are presented in Table 1. We evaluate the performance of the algorithm using the following parameters. The controller parameters for this study are as follows: \(T_{s}=0.02\), \(Q=diag\{50,50,80,80,100,100\}\), \(R=50\), \(N_{p}=15\), \(N_{c}=5\), and \(\kappa=1000\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Definition** & **Value** \\ \hline \(m\) & Vehicle mass & 1370 kg \\ \(a\) & Horizontal distance from center of gravity to front tire & 1.22 m \\ \(b\) & Horizontal distance from center of gravity to rear tire & 1.21 m \\ \(l_{z}\) & Yaw inertia & 2125 kg-m\({}^{2}\) \\ \(C\)/\(0\) & Front tire turning stiffness & 62,108 Nm/rad \\ \(C\)/\(0\) & Rear tire turning stiffness & 46,505 Nm/rad \\ \(I_{fw}\) & Moment of inertia of the front wheels & 2.8 kg m\({}^{2}\) \\ \(B_{fw}\) & Viscous friction of front wheels & 13 Nm/rad \\ \(I_{fw}\) & Moment of inertia of motor & 0.0025 kg-m\({}^{2}\) \\ \(B_{m}\) & Motor damping coefficient & 0.032 Nm-s/rad \\ \(I_{c}\) & Moment of inertia of lower steering column & 0.03 kg-m\({}^{2}\) \\ \(B_{c}\) & Lower steering column damping coefficient & 0.06 Nm/s/rad \\ \(B_{r}\) & Rack damping coefficient & 7.5N-s/m \\ \(m_{r}\) & Rack quality & 1.820 kg \\ \(r_{p}\) & Pinion radius & 0.0065 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle system parameters.  
Figure 4: The control flowchart.
# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 6 Conclusions and Future Work
### Future Work  
The assessment of our methodology presented in this paper is based on a small number of WBAN nodes (i.e., 3), with no need for routing. Nonetheless, our many-multi-optimization engine could be applied to a case with more nodes, further body positions, and additional design criteria. In future work, we, therefore, plan to apply our methodology in a more complex case with a number of nodes, involving multi-hop topologies and routing algorithms. In particular, we will consider energy efficiency, which is an area of great research interest, as one of the improvement criteria. However, whereas most of the many studies in the literature concentrate on the routing algorithms themselves, we will attempt to formulate and solve the node placement optimization problem using one of the existing efficient routing algorithms. Increasing the number of nodes will require greater computational effort since a larger number of scattering matrix elements \(\mathrm{s_{ij}}\) will need to be evaluated. The routing simulation will be performed for the placement of each candidate node during the optimization process.  
A further focus of our future investigations will be the optimization of wireless networks operating in the ISM 5.8 GHz band as well as in the 3.6 GHz band allocated for the 5G (Fifth Generation) of wireless communication systems. For this purpose, we will analyze the performance of the network with different wearable antennas instead of simple dipoles.  
Conceptualization: L.J.; Methodology: L.J.; P.D.B., S.H.; Software: P.D.B., L.J., S.H.; Formal Analysis: L.J.; Investigation: L.J.; Writing-Original Draft Preparation: L.J., P.D.B.; Writing-Review & Editing: S.H.; Visualization: L.J.  
This research received no external funding.  
The authors declare no conflict of interest.
# Coupling Efficiency of a Partially Coherent Radially Polarized Vortex Beam into a Single-Mode Fiber
###### Abstract  
We study the problem of coupling partially coherent radially polarized (PCRP) vortex beams into a single-mode optical fiber. Using the well-known concept of the cross-spectral density (CSD) matrix, we derive a general expression for the coupling efficiency of the partially coherent beam into a single-mode fiber. We adopt PCRP vortex beams for incident beams and use our general results to discuss the effects of the coherence, topological charge, and wavelength on the coupling efficiency of an optical beam focused onto a single-mode fiber with a lens. Our results should be useful for any application that requires coupling of partially coherent beams into optical fibers.  
coupling efficiency; partially coherent; vortex beam; single-mode fiber +
Footnote †: journal: Applied Physics Letters
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 3 Methods
### State-Exchanging Long Short-Term Memory (SE-LSTM)  
As discussed in Section 2, LSTMs are excellent at processing sequential data. Unlike other RNNs, LSTMs consist of internal gates, which can adjust the flow of information. These gates can help manage which data in a sequence are saved or discarded. Because of these advantages, LSTM is special among training models and successfully uses backpropagation to solve the vanishing gradient problem. Moreover, each cell inside the layer contains a cell state, which controls and stores previous information.  
In handling sequential data to make predictions, especially image data, the LSTM cell state is doing a vital job. Therefore, during this study, we focused on updating and resetting cell state information outside of the standard cell state update process. The first step of this process extracts the previous time step's (\(C_{t-1}\)) cell state from both streams' LSTM units. Then we averaged the extracted cell state information and updated the cell state of the next time step (\(C_{t}\)). Figure 1 gives the general idea of our proposed state-exchanging process.  
Since no such connection physically exists between cell states, we had to implement the proposed model; we define this process as \"State-exchanging.\" Only within two-stream networks, we can exchange information between the cell states of both LSTM streams during their adjacent time steps. This process was done by extracting the LSTM's cell state of both streams and averaging and updating the cell states of the next time step. One of our major contributions, the state-exchanging process, is discussed in the following section.
## 2 Literature Review
### Specialized Detection Models  
[PERSON] et al. (2023) [21] investigated the detection of COVID-19 fake news using transformer-based models, noting the surge of misinformation during the pandemic as a significant public health concern. The paper evaluated various machine learning algorithms and the effectiveness of fine-tuning pre-trained transformer models, such as BERT and COVID-Twitter-BERT (CT-BERT), for this purpose. By integrating downstream neural network structures, including CNN and BiGRU layers, with either frozen or unfrozen parameters, the authors conducted experiments on a real-world COVID-19 fake news dataset. Their findings reveal that augmenting CT-BERT with a BiGRU layer yields a state-of-the-art F1 score of 98%, underscoring the promise of advanced machine learning techniques in combating misinformation.  
Similarly, [PERSON] et al. (2023) [22] addressed concerns surrounding news authenticity in the context of socio-political influences and biased news dissemination. They proposed a novel evaluation framework for Bengali language news that integrates blockchain technology, smart contracts, and incremental machine learning. This framework combined machine classification with human expert opinion on a decentralized platform to assess news credibility. The NLP model undergoes continuous training, achieving initial accuracies of 84.94% for training and 84.99% for testing, which improve to 93.75% and 93.80%after nine rounds of incremental training. Their simulation on the Ethereum test network demonstrates the successful implementation of this innovative system, highlighting the potential of leveraging blockchain for enhancing news verification processes.
## 4 Experiments and Analysis
### Ablation Experiment
#### 4.5.2 Effect of Different Encoding Networks
In particular, the fusion method using concatenation outperforms the additive fusion method, which is likely due to the fact that additive fusion directly sums features, resulting in conflicts between features from different sources, while concatenation preserves the features by listing them together, thus better maintaining the independence of each feature, indicating that using feature concatenation in the fusion process is a more appropriate choice. Furthermore, our fusion method outperforms the method in reference [1] in terms of IoU, Precision, OA, and F1-Score, as it incorporates the fusion features of the previous layer during the fusion process, which enhances the expression of the features, thus improving the model's performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Encoding Network** & **IoU** & **Recall** & **Precision** & **OA** & **F1-Score** & **Parameter (M)** & **Flops (G)** \\ \hline \multirow{8}{*}{Jiangsu Province} & MobileNetv3 & 67.97 & 77.98 & 84.11 & 93.51 & 80.93 & 26.59 & 26.02 \\  & Xception & 68.46 & 81.96 & 80.61 & 93.34 & 81.28 & 33.11 & 21.28 \\  & ResNet50-ResNet101 & 71.49 & 86.59 & 80.38 & 94.09 & 83.37 & 207.91 & 126.62 \\  & VGG13-VGG16 & 68.26 & 79.87 & 82.43 & 93.65 & 81.13 & 46.73 & 40.19 \\  & Sum-Fusion & 70.33 & 81.92 & 83.25 & 93.89 & 82.58 & 41.75 & 36.45 \\  & Reference [1] & 70.49 & 82.25 & 83.15 & 94.12 & 82.69 & 43.29 & 38.71 \\  & Ours & 71.38 & 82.23 & 84.39 & 94.18 & 82.02 & 46.51 & 41.43 \\ \hline \multirow{8}{*}{Anhui Province} & MobileNetv3 & 69.41 & 79.99 & 84.01 & 95.93 & 81.94 & 26.59 & 26.02 \\  & Xception & 71.85 & 81.56 & 85.79 & 96.31 & 83.62 & 33.11 & 21.28 \\  & ResNet50-ResNet101 & 72.58 & 84.79 & 83.44 & 96.11 & 84.11 & 207.91 & 126.62 \\ \cline{1-1}  & VGG13-VGG16 & 71.64 & 78.98 & 88.49 & 96.19 & 83.47 & 46.73 & 40.19 \\ \cline{1-1}  & Sum-Fusion & 73.24 & 83.29 & 85.84 & 96.49 & 84.55 & 41.75 & 36.45 \\ \cline{1-1}  & Reference [1] & 73.81 & 84.54 & 85.32 & 96.35 & 84.92 & 43.29 & 38.71 \\ \cline{1-1}  & Ours & 74.31 & 84.52 & 86.01 & 96.63 & 85.26 & 46.51 & 41.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The evaluation metrics obtained using different encoding networks (%).
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 3 Methodology
### Seed Sampling Strategy
\begin{array}{ll}\text{Place a seed point}&\text{if }\varphi_{i}- \varphi_{i}\cap S=\text{None}\\ \text{Scan }\varphi_{i}\text{ by }S&\text{if }\varphi_{i}-\varphi_{i}\cap S \
eq\text{None}\end{array} \right.. \tag{4}\]  
If \(\varphi_{i}\) is completely covered by \(S\), a seed point is placed at the centroid of \(\varphi_{i}\); otherwise, \(\varphi_{i}\) is scanned with a sliding window \(S\), as shown in Figure 7. If the length or width of \(\varphi_{i}\) covered by \(S\), \((\varphi_{i}\cap S)\), is more than half of the length or width of the search interval, then a seed point is placed in the centroid of the covered area. The sliding window \(S\) moves to the next position and determines whether the placement condition is met until it covers the entire region. Figure 6: The jointly homogeneous map combined by orthogonal initial homogeneous maps. (**a**) the compact initial vertical scan map \(\phi^{\prime}{}_{v}\) after merging adjacent initial regions in Figure 4c; (**b**) the compact initial horizontal scan map \(\phi^{\prime}{}_{h}\); (**c**) the jointly homogeneous map \(\psi\). After placing seed points for each region \(\varphi_{i}\in\psi\), an initial set of seed points that can adapt to the image structure is obtained. As a summary, Algorithm 1 gives the main steps of our adaptive seed sampling strategy. ```
Input: An image \(I\) and its binary edge map \(e\); similarity metric threshold \(M_{T}\) and seed search interval \(S\). Output: Initial seed set of the image \(I\) /* Initialization */  Set the initial value of rays in direction \(\theta_{v}\), \(\pi+\theta_{v}\), \(\theta_{h}\) and \(\pi+\theta_{h}\) to 0 /* Initial homogeneous map \(\psi^{*}\)/ for each direction \(\theta_{i}\) of rays do  scan \(e\) by rays in direction \(\theta_{i}\) if current \(p_{i}=-1\) and previous \(p_{i-1}\
eq-1\)then  the value of ray \(r(p_{i})=r(p_{i-1})+1\) else: \(r(p_{i})=r(p_{i-1})\) If\(p_{i}\
eq-1\)then  Label \(l_{p_{i}}=r(p_{i})\) to form initial scan map in direction \(\theta_{i}\) endif endfor  Construct \(\phi_{v}\) by combining scan maps in direction \(\theta_{v}\) and \(\pi+\theta_{v}\)  Construct \(\phi_{h}\) by combining scan maps in direction \(\theta_{h}\) and \(\pi+\theta_{h}\) For \(\phi_{v}\) and \(\phi_{h}\)do  merging adjacent regions based on \(M_{T}\) to obtain \(\phi^{\prime}_{v}\) and \(\phi^{\prime}_{h}\) endfor  construct \(\psi\) by combining \(\phi^{\prime}_{v}\) and \(\phi^{\prime}_{h}\) /* Seed points placement */ for each region \(\varphi_{i}\in\psi\)do if\(\varphi_{i}-\varphi_{i}\cap S=\phi\)then  place a seed point inside \(\varphi_{i}\) else:  scan \(\varphi_{i}\) and place seed if Equation (4) is met endif endfor
```  
**Algorithm 1**Adaptive seed sampling strategy.
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 4 Discussion
### Additional Applications of Rapid-DEM  
Rapid-DEM could be developed outside of GEE using other open-source cloud-based geoprocessing solutions such as GRASS GIS's Actinia ([[https://actinia.mundialis.de/](https://actinia.mundialis.de/)]([https://actinia.mundialis.de/](https://actinia.mundialis.de/)), accessed on 6 December 2021) [65] or GeoTrellis ([[https://geotrellis.io/](https://geotrellis.io/)]([https://geotrellis.io/](https://geotrellis.io/)), accessed on 6 December 2021). In future work Rapid-DEM will be developed into an open-source application to provide an accessible method to update DEMs. The complete application could be configured to a specific location as a semi-automated service that continuously updates the priority queue and context for a given time interval (e.g., monthly, quarterly). As priority areas are updated with UAS-based data fusion the priority queue is updated moving the next highest priority location into its place. Over time a time series of location priority and context will produce a robust database of land change phases that may provide additional insights into local development trends. The fusion method [18] is also under development as a standalone plugin for WebODM [24].