esa-sceva/satcom-chunk-collection huggingface dataset:
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 5 Optical Methods
### Video Motion  
The general principle of ballistocardiography (BCG) has already been introduced in Section 4 of this review paper. While some groups including [PERSON] et al. [87] and [PERSON] et al. [158] have reported on BCG artifacts during PPG imaging before, [PERSON] and colleagues [95] were the first to systematically quantify BCG artifacts for PPGI of the face.  
However, treating the BCG component as an artifact in PPGI is not the only option in making use of this information. When heart rate estimation and not local PPG monitoring is of interest, the BCG artifact in image streams may actually be utilized as a separate signal source.  
With respect to using cameras as a BCG sensor, in 2013, [PERSON] and colleagues from the M.I.T. demonstrated that BCG can also be performed by video stream analysis [159]. In fact, these computer scientists demonstrated that proper face detection (OpenCV 2.4 Viola Jones algorithm [160]), subsequent OpenCV Lucas Kanade tracking of feature points and PCA decomposition allows for finding and magnifying the normally unnoticeable motions of the head in temporal image sequences, in that way similar to the color magnification performed by [PERSON] et al. [92].  
In 2015, [PERSON] and colleagues have shown that robustness and coverage of heart rate estimation can be increased by fusing PPG and BCG information extracted from visible video streams [161].  
Despite motion artifacts that can be expected during driving, an automotive application of camera-based BCG seems possible, but--according to our knowledge--has not been demonstrated thus far.
## 2 The Online Dynamic Balance System Design for Double-Face Correction  
### The Principle of Vector Synthesis  
There are many methods to exert a suitable correcting vector in a correcting face. According to the vector synthesis principle, the correcting vector in one face can be synthesized by the two nearest balance disks with suitable fixed unbalance. As an example, the synthetic principle of a correcting vector is explained as follows.  
Considering the structure characteristics of a balance disk, the fixed unbalance vector can be taken as a mass ball with a certain radius, and the centrifugal force comes into being when the mass ball rotates synchronously with the spindle. Because the axial positions of balance disks 1 and 2 are very close, their centrifugal force can be taken as in a face. The display of the vectors is shown in Figure 1.  
In Figure 1, \(\vec{U_{1}}=U_{1}\angle\alpha\) is assumed to be the fixed unbalance vector of balance disk 1, \(U_{1}\) and \(\alpha\) represent the amplitude and phase of the vector separately. \(\vec{U_{2}}=U_{2}\angle\beta\) is assumed as the fixed unbalance vector of balance disk 2, \(U_{2}\) and \(\beta\) represent the amplitude and phase of the vector separately. In practical application, \(\vec{U_{1}}\) and \(\vec{U_{2}}\) can be taken as the correcting vector of balance disks 1 and 2 in theory. \(\vec{Q_{1}}=Q_{1}\angle\phi_{1}\) is assumed to be the correcting vector in face 1, \(Q_{1}\) and \(\phi_{1}\) represent the amplitude and phase of the vector separately.  
Figure 1: The synthetic principle of the correcting vector in a face.  
Based on the vector geometrical relationship in Figure 1, Equation (1) can be obtained.  
\[\left\{\begin{array}{l}\frac{U_{2}}{\sin(\varphi_{1}-\alpha)}=\frac{U_{1}}{ \sin(\beta-\varphi_{1})}\\ Q_{1}^{2}=U_{1}^{2}+U_{2}^{2}-2U_{1}U_{2}\cos(180^{\circ}+\alpha-\beta)\end{array}\right. \tag{1}\]  
In Equation (1), the parameters \(U_{1}\), \(U_{2}\), \(Q_{1}\), and \(\varphi_{1}\) are known, and the amplitudes of the unbalance vectors are designed to be equal (\(U_{1}=U_{2}=U\)) in practice, so the radial positions of the balance disks 1 (\(\alpha\)) and 2 (\(\beta\)) can be calculated and expressed as follows.  
\[\left\{\begin{array}{l}\beta=\phi_{1}+\frac{1}{2}\arccos\frac{Q_{1}}{2U}\\ \alpha=\phi_{1}-\frac{1}{2}\arccos\frac{Q_{1}}{2U}\end{array}\right. \tag{2}\]  
According to the above analysis, the synthetic correction in one face can be synthesized and obtained easily by adjusting the radial positions of the two balance disks. Proper corrections of the two correcting faces can be synthesized by four balance disks.
## 2 CFD Model
### Grid Model  
In Figure 4, the grid model utilized in this research is displayed, with trimming grids filling the computational domain. To optimize computational efficiency and accuracy, the domain is divided into different regions for grid discretization. Region 1, representing the flow field around the train, undergoes extremely rapid development and is filled with fine grids. Region 2, corresponding to the flow field inside the tunnel, also experiences rapid development and is filled with medium grids. Region 3, representing the far-end atmospheric domain, is filled with coarse grids (Figure 4a). On the surfaces of the train and bogie, an 8-layer boundary layer with an expansion ratio of 1.5 is established. This boundary layer is filled with prismatic grids, and through calculations, the value of \(y\)+ falls between 10 and 20, aligning well with previous research results [36]. It is essential to note that, as depicted in Figure 4b, the surface of the falling debris is rough and irregular. Directly dropping the debris would lead to non-convergence. To address this, a sphere (Region 4) with a radius of \(0.02L\) is positioned outside the falling debris and dropped together with it. The finest grid is utilized to fill the inside of the sphere. The possible flight area (Region 1) of the falling debris is also filled with fine grids, with a longitudinal length of \(0.2L\), spanning the entire tunnel transversely. Through the aforementioned approach, the computational domain is meshed, and the total number of grids amounts to approximately 55 million.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 2 V2X Technology
### State-Of-The-Art V2X Communications  
This section presents state-of-the-art and future technologies in V2X communications. We begin with state-of-the-art V2X technologies.  
Figure 2: Different types of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) scenarios. (**a**) Direct (802.11p/PC5) (**b**) Full network assisted and (**c**) Hybrid (5G+PC5/802.11p).
## 4 Simulation Results and Complexity Analysis
### Simulation Results and Discussion
Figure 14: BER performance vs. number of iterations with \(N_{R}\times N_{T}=128\times 16\) and SNR = 34 dB for (**a**) OFDM and (**b**) UFMC. different schemes to the number of antennas is also worth observing and discussing. For instance, Chebyshev-MAOR exhibits strong dependence on the number of antennas. Its performance degrades considerably under low antenna ratio MIMO configurations, such as \(\beta=4\), and only shows good results when the number of receiving antennas is high enough, such as \(\beta=16\). On the other hand, for Chebyshev-RI the performance improvement remains marginal even when the number of antennas increases. In contrast, the proposed CHSOR method demonstrates robust and consistent performance across various antenna settings. CHSOR remains stable and effective regardless of whether the antenna ratio is low or high, highlighting its superior adaptability and reliability in M-MIMO systems. Figure 15: BER performance vs. number of iterations with \(N_{R}\times N_{T}=192\times 16\) and SNR = 32 dB for (**a**) OFDM and (**b**) UFMC. Figure 17: BER vs. \(\beta\) for different detection schemes when the number of iterations is 1 and the SNR is 30 dB for (**a**) OFDM and (**b**) UFMC. Figure 18: _Cont._
# A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding
## 1 Introduction
It employs the mean shift clustering algorithm to determine the optimal solution for the density of candidate circle center samples. By using a fast Gaussian kernel with an adaptive bandwidth, the algorithm addresses the issue of sample point weighting.
## 6 Simulation
### Half of Deadlines with Concurrent Traffic  
This scenario considers \(50\%\) of minimum deadlines (\(d_{a}=d_{\textit{min}}/2\)) with concurrent traffic. We use a \(128\)-node network (we do not consider more nodes due to NS limitations), and vary the percentage of nodes generating data traffic (\(16\%\), \(20\%\), \(25\%\), and \(33\%\) of \(128\) nodes) and \(|\mathbf{V}|\) (\(256\), \(512\), \(1024\), and \(2048\)). The objective of this scenario is to identify the best strategy in critical applications when the network traffic gradually increases.  
In Figure 11, \(d_{a}\) is met only by complex formulation. The reason is that the \(\mathbf{\Psi}\)-reduction, in complex formulation, is gradually performed during the data routing, and fewer data is delivered. Particularly, when the percentage of nodes generating data is \(33\%\), nearly \(6.5\%\) of data is delivered by the complex formulation, while \(10\%\) is delivered by the simplified formulation. Thus, considering the deadline achievement, the complex formulation is more appropriate in this scenario.  
Figures 12 and 13 show the error evaluation considering a \(128\)-node network, varying the percentage of nodes that generate data traffic, and keeping \(|\mathbf{V}|=256\). As we can see in Figure 12, results show that in all cases we have \(\mathbf{\Upsilon}\leq 30\%\). In both formulations, \(\mathbf{\Psi}_{\mathrm{random}}\) has a smaller \(\mathbf{\Upsilon}\)-error. However, in the complex formulation, \(\mathbf{\Psi}_{\mathrm{central}}\) presents a smaller \(\mathbf{\Upsilon}\)-error when we have fewer data traffic (\(16\%\) and \(20\%\)). The reason is that the complex formulation executes fewer consecutive \(\mathbf{\Psi}\)-reductions. Figure 13 shows that, in all cases, we have \(\mathbf{\Phi}\approx 20\%\), note the increase in \(\mathbf{\Phi}\)-error, compared to previous scenarios (\(\mathbf{\Phi}\approx 5\%\)). The reason is that in both formulations there more consecutive \(\mathbf{\Psi}\)-reductions affecting data quality. Therefore, considering the confidence interval, both \(\mathbf{\Psi}\)-reductions have the same behavior. Again, when the percentage of nodes generating data is high, the simplified formulation has a smaller \(\mathbf{\Phi}\)-error.  
Figure 11: Delay considering the half of deadlines with concurrent traffic.  
The data error evaluation suggests that the simplified formulation is slightly better than the complex one. However, the partial conclusion, considering this critical and realistic real-time scenario, is that the complex formulation is more appropriate, because deadlines are met and data representativeness is kept. Considering the sampling algorithms, the behavior is kept in both \(\boldsymbol{\Psi}\)-reduction strategies.
# Bidirectional Representations for Low-Resource Spoken Language Understanding
## 3 Experiments
### Evaluation  
We choose specific metrics to assess the performance of our models, primarily focusing on accuracy and F1 score. Accuracy measures the proportion of correctly predicted instances, encompassing both intents and slots. The F1 score, which is computed as the harmonic mean of precision and recall, provides a more holistic evaluation. While we consider the F1 score to offer a more comprehensive assessment, we also examine accuracy to facilitate comparisons with established models.  
In our investigation into the model's performance within a low-resource setting, we deliberately limit the number of available training examples. This allows us to assess its adaptability and effectiveness under conditions of data scarcity.  
Furthermore, to gain deeper insights into the representations generated by our model, we conduct a qualitative analysis. We achieve this by visualizing the representations using t-SNE [29], a technique that helps identify clusters of examples, shedding light on the structure and organization of the learned features. We also visualize the attention weights to understand how the model makes certain predictions.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 2 Materials and Methods
### Methods
Since the last layer predicts perceptual quality, it contains one node. body plus regression head structure was trained using the Adam [65] optimizer with the default parameters of the Keras [54] deep learning library (\(\beta_{1}=0.9,\beta_{2}=0.999\)) and a batch size of 16. First, a model is trained through 40 epochs with a learning rate of \(1\times 10^{-4}\). During training, the PLCC computed between the predicted and ground-truth quality scores on the validation set is monitored and after 40 epochs the best performing option is restored. Second, this restored model is trained through 20 epochs with a learning rate of \(5\times 10^{-5}\). Similarly to the previous step, PLCC on the validation set is constantly monitored during the training process, and after 20 epochs, the best performing one is reloaded. The quality predictions of the six branches are fused together to obtain an overall quality score for the input image. Specifically, average and median pooling were applied in this work. A parameter study was conducted to ascertain which is the better option (presented in Section 3.1).
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 4 Discussion
### Bumblebee Pollination Improved Tomato Flavor
Although tomato fruits under different pollination treatments were from flowers opened at the same day and the fruits were collected at the same time, bumblebee pollination might accelerate the fruit development of tomato, resulting in higher contents of fructose and glucose, and lower amount of sucrose and acids than tomatoes from PGR or vibrator treatment. However, we only measured the sugar and acid content of mature fruits and didn't focus on the content dynamic in different fruit development stage. Further studies are needed to indicate whether pollination method would influence the changes of sugar and acid content during tomato fruit development. In our study, we found bumblebee-pollinated tomato fruits had a different volatile profile compared to PGR or vibrator treated tomatoes. It was known that bumblebee pollinated tomato fruit had more jelly and fewer cavities than PGR-treated tomato [24] and VOCs were different in different fruit structures [55]. We assumed that the volatiles differences between bumblebee-pollinated tomato and non-bee-pollinated tomato were mediated through the tissue proportion in tomato fruits. In our study, only the volatiles of whole fruits from different pollination treatments were analyzed. Further work is required to analyze the effects of bumblebee pollination on tissue composition and the volatiles of different tissues in tomato. The ripeness of fruits from different pollination methods might also affect fruit volatiles. Like sugar and acid contents, the volatiles also change during fruit developmental stage and ripeness [56]. It is essential to analyze whether pollination methods affect the volatiles emitting dynamic during tomato fruit development. When explaining the reason for the better flavor of bumblebee pollinated tomato, it is essential to analyze the composition of consumer liking related volatile compounds. In our study, we identified 21 compounds positively related with consumer liking and 11 compounds negatively with consumer liking. For the compounds which make positive contributions to consumer liking, bumblebee pollinated tomato fruits usually contained higher contents than PGR or vibrator treated tomatoes. However, we found that bumblebee pollinated tomatoes also contained more compounds which negatively related with consumer liking. Studies showed that many of the consumer liking related volatiles were metabolically linked [28]. The pollination method does influence the fertilization process and the initial fruit set of plants [54], but little is known about how these processes would affect the metabolic synthesis process of fruit. Further studies are needed to test whether and how the pollination methods affect the metabolic synthesis of consumer liking related volatile compounds.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 5 Results and Discussion
### Performance Evaluation of Prediction Models  
Using the same set of training and testing samples, the prediction performance of RIME-LSSVM, PSO-LSSVM, LSSVM, and Kuz-Ram models was compared with that of the GA-LSSVM model. RMSE, MAE, and r were selected as evaluation metrics for prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Initial Parameters** & \(n_{V}\) & \(n_{P}\) & \(n_{R}\) & \(n_{G}\) & \(c_{1}\) & \(c_{2}\) & \(w_{d}\) & \(\omega\) \\ \hline value & 4 & 200 & 100 & 7 & 1 & 2 & 0.99 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MOPSO optimization model parameter settings.  
The calculated evaluation metrics for the prediction performance on both the training and test samples are shown in Table 4.  
Figure 7 visually displays the evaluation metric values for the five models on the training and test sets. It is evident that the models based on intelligent algorithms (GA-LSSVM, RIME-LSSVM, PSO-LSSVM, and LSSVM) show lower prediction errors and better fit compared to the Kuz-Ram empirical model. Additionally, the hybrid models (GA-LSSVM, RIME-LSSVM, PSO-LSSVM) exhibit better prediction performance than the single model (LSSVM). Using the prediction metrics of LSSVM as a reference, it can be observed that LSSVM optimized by GA outperforms LSSVM optimized by RIME and PSO. These data demonstrate that GA-LSSVM has the best prediction performance among the compared models.  
Figure 8 illustrates the predicted values and their fitting curves for both the training and testing sets, as well as the degree of fit between the fitted curve and the reference curve (Y = T). It can be observed that the predicted values from the intelligent models GA-LSSVM, RIME-LSSVM, PSO-LSSVM, and LSSVM exhibit a high degree of fitting with the actual values, while the Kuz-Ram empirical model performs poorly. It is noteworthy that the predicted values from GA-LSSVM are closer to the actual values, indicating a better-fitting degree. The r values for the training set and the test set are 0.925 and 0.962, respectively, demonstrating the superior predictive performance of this model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Training Datasets**} & \multicolumn{3}{c}{**Test Datasets**} \\ \cline{2-7}  & **RMSE** & **MAE** & **r** & **RMSE** & **MAE** & **r** \\ \hline GA-LSSVM & 1.805 & 2.088 & 0.925 & 1.947 & 1.688 & 0.962 \\ RIME-LSSVM & 2.067 & 1.639 & 0.904 & 2.967 & 2.344 & 0.958 \\ PSO-LSSVM & 3.008 & 2.113 & 0.889 & 3.870 & 2.564 & 0.959 \\ LSSVM & 3.324 & 2.656 & 0.876 & 3.869 & 2.584 & 0.921 \\ Kuz–Ram & 4.240 & 3.646 & 0.751 & 4.800 & 3.871 & 0.833 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation results of prediction performance metrics.  
Figure 7: Evaluation metric results: (**a**) training sets; (**b**) testing sets.
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 2 Materials and Methods
### Attention U\({}^{2}\)-Net Model for Mangrove Detection
#### 2.3.2 Model Performance Evaluation Method  
The performance of the attention U\({}^{2}\)-Net model is evaluated through the overall accuracy (\(OA\)), precision (\(P\)), and \(F1\)-score (\(F1\)). The expression of these indices can be found in Formulas (3) to (5). \(OA\) is the percentage of all correct classifications in the total test data samples. \(P\) is the ability of the model to classify all positive classes. These metrics are commonly used to evaluate the quality of the model output, as they measure how well the model can correctly identify and classify the mangroves from the background. The  
Figure 4: The structure of the attention gate: g is the gating signal, x is the input feature, and \(\alpha\) is the attention coefficient.  
\(F1\)-Score is a metric that combines precision and recall, which are two indicators of how well the network can correctly identify and classify the mangroves from the background.  
\[OA=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{1}\]  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[F1=\frac{P\times R}{P+R} \tag{3}\]  
where \(TP\) is the number of positive classes that are classified correctly, \(TN\) is the number of negative classes classified as negative, \(FP\) is the number of positive classes classified as negative, and \(FN\) is the number of negative classes classified as negative. \(R\) is recall, which can be expressed by  
\[R=\frac{TP}{TP+FN} \tag{4}\]  
For the binary classification task in this study, the confusion matrix could be represented as shown in Table 2. The confusion matrix is an analysis table that summarizes the prediction results of the classification model.
## 4 Evaluation of Prototypes with Edge Damping  
The following sections introduce the real prototypes used to evaluate the influence of edge damping on the surface acceleration and sound field radiation. Furthermore, the measurement methods used employed for the acoustic sound field and the mechanical displacement measurements are described and the surface acceleration and sound field radiation are evaluated for all three variants. The improvements in reduced off-axis radiation and a more homogeneous directivity response become visible when using additional damping. The mean value and standard deviation of the directivity response as well as the sensitivity are calculated to evaluate the differences of all three variants.
## 9 Concluding Remarks  
This paper discusses a complete design procedure and provides implementation results for a QC-RL-LDPC coding system, specifically tailored for IoT uplink transmission. Our focus is on creating an energy-efficient encoding procedure for IoT nodes built with microcontroller devices, without strict throughput requirements, but where the energy efficiency is an important factor. To achieve this, we implemented a short-block-length Raptor-like LDPC scheme that is efficiently encodable. We showed that LDPC coding, commonly used in high-throughput communication systems and advanced protocols, is also applicable to such computationally constrained devices. Our experimental results showed that Raptor-like QC-RL-LDPC coding can be beneficial in terms of energy efficiency, in comparison with fixed-rate LDPC coding, and not worse that state-of-the-art 5G coding. Then, the developed design procedure, including QC-RL-LDPC code design, encoder implementation and the proposed decoding scheme for Raptor-like decoding, can be used in a rate-adaptive uplink transmission, with flexible parameter selection: block length, code rate and submatrix size.  
The entire proposed system was implemented and validated for several designed binary QC-RL-LDPC parity check matrices, as well as compared with a similar design based on 5G codes. The comparison shows that the designed codes have at least similar efficiency to the 5G codes, while in some configurations, the flexible submatrix size choice can provide a slight energy efficiency improvement, even over 5G codes. The coding system can still evolve, and future work will be concentrated on the very promising extension of the proposed concepts to the nonbinary (NB) case, that is, the development of NB QC-RL-LDPC codes and the respective codec implementations.  
Conceptualization, J.H. and W.S.; Methodology, W.S.; Software, J.H.; Validation, J.H.; Investigation, J.H.; Supervision, W.S. All authors have read and agreed to the published version of the manuscript. This research was co-financed by the Ministry of Education and Science of Poland under grant No. DWD/3/7/2019 as well as research subsidy for Silesian University of Technology, No. 02/160/BK_23/0009. The authors declare no conflict of interest.