esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model for Urban Area Air Quality Analysis Based on CFD Simulations
### Geometry Optimization  
The geometry of objects in CFD simulations is usually optimized, especially on models representing urban blocks. It is possible to construct models automatically or semi-automatically using geospatial data [32; 33]. The manual 3D modeling approach is also possible with consideration of the real object represented. In this study, the manual approach is used, and it has several constraints:  
1. buildings are modeled individually from a reliable source;
2. the ground shape of the buildings is extruded;
3. roof slopes are not modeled, because of complexity;
4. the smallest edge in the model is 30 cm;
5. vegetation is not modeled;
6. only areas that exceed 150 cm on the facade are modeled;
7. buildings are merged into one solid object for simulation optimization.  
In Figure 2, the real-world block and the block model are presented.  
Figure 1: Schematic workflow of the study. Step 1: Location data interpretation; Step2: CFD simulation; Step 3: Synthesis of the results; Step 4: Visualization of the results as color-coded 3D mesh and a graph.  
Figure 2: Example of real-world urban block modeling.  
The geometrical model was loaded into the CFD software for the analysis. It was also triangulated for the further analysis described in Section 3.4. The surface of the model was divided into sub surfaces no more than 3 m by 3 m, and then triangulated.
## 4 Results and Discussion  
In this study, 30 different simulations were performed, and three different flow regimes were simulated by varying the inlet phase velocities. Pressure data was collected at 81D distance from the inlet, as mentioned in Section 2.3. In addition, depending on superficial velocities, flow takes approximately 3 to 4 s to fully develop. That is why pressure signals used for post-processing were the signals obtained after the flow was fully developed. This study shows that fluctuation in pressure occurs in all flow regimes and the amplitude of fluctuation depends on flow regime and related turbulence.  
Flow morphology obtained from simulations were compared with experimental results from the literature, as shown in Figure 6. Stratified and slug flow regimes were compared against (KFigure 7 shows the void fraction readings of fully developed flow collected at a single location (81D from inlet), and it can be used as another level of data quality assurance to check that different flow regimes were developed successfully. Figure 7a shows stratified flow, in which the interface between gas and liquid is smooth and the gas phase stays on top and the liquid on the bottom due to the density difference, which is reflected consistently by the void fraction graph. Figure 7b, which looks like an electrocardiogram, shows that slug flow is characterized by the presence of Taylor bubbles and liquid slug [40]. In the void fraction graph, the presence of liquid slug is indicated when the pipe is filled with the liquid phase; in this study, a water void fraction value higher than 0.95 was considered the offset. Every time a slug passes by the measurement point, a peak in the void fraction value is recorded. Figure 7c shows annular flow, according to the definition [40] for this flow, liquid and gas flow as separate continuous phases, with gas phase flow in the core and the liquid phase forming a thin film along the pipe wall. The void fraction graph was drawn by collecting data on the line drawn from the bottom to the top of the pipe at a fixed location. In this case, 81D location was selected but this data can be collected at any other location as well. It can be clearly seen that at the bottom and top of pipe is filled with liquid phase (void fraction \(\approx 1\)), whereas in the core gas phase prevails.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Surface Salinity (OSS)
#### 2.4.2 Microwave Radiometer
It was observed that OSS values, obtained from these radiometers were relatively less accurate in near-polar regions due to decreased temperature and a less dense network of in situ instruments in high latitudes. Moreover, [PERSON] et al. (2017) compared OSS estimations from SMAP with SMOS and Aquarius in the Red Sea, which is an extreme case for OSS measurement due to the significant land contamination. It was observed that SMAP captured OSS in open ocean water with similar efficiency as the other two instruments. ML algorithms have also been employed along with microwave radiometer data for OSS estimation. For instance, [PERSON] (2020) estimated SMAP OSS in the Persian Gulf region using ANN, SVM, RF, and Gradient Boosting Method (GBM) algorithms. Feature importance analysis revealed the high importance of latitude in both RF and GBM algorithms [165].
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 5 Conclusions  
In this paper, we propose a novel multi-hop quantum network based on fusion to balance the network performance and consumption. Entanglement swapping can establish entanglement between nodes in multi-hop networks, while fusion can transform entanglement from Bell states to GHZ states. Purification can ensure high fidelity of the entanglement in the network. A purification algorithm is given to ensure the robustness of the communication system. In the algorithm, we set \(m_{1}=1\) and \(m_{2}=1\), which means applying a one-time purification after each entanglement swapping and a one-time purification after fusion. Simulation results indicate that our new scheme has better robustness against the noise than the Bell states scheme. It also consumes less entanglement than the GHZ states scheme. The calculation complexity is kept at a low level. Moreover, we give the circuit of a 5-node network and simulate it under bit-flip and depolarizing noise on the NISQ platform Cirq to verify our network. Finally, simulation results confirm the superiority of the proposed scheme in balancing network performance and consumption.  
Some problems still need to be solved. First, the influence of distance between nodes in the quantum network is not considered. Second, the performance of entanglement swapping can also be improved by developing a new technology. In the end, since purification needs huge amounts of entanglement and often fails, the number of nodes in the simulation is not enough because of the limited computer capacity.  
Conceptualization, [PERSON] and H.X.; methodology, J.X.; software, J.X.; validation, [PERSON], M.M. and P.W.; formal analysis, J.X.; investigation, J.X.; resources, J.X.; data curation, J.X.; writing--original draft preparation, J.X.; writing--review and editing, J.X.; visualization, J.X.; supervision, J.X.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
## 3 Experimental Results
### Performance Metrics  
The performances of these architectures are evaluated using five parameters, which include accuracy, recall, F1 score, precision, and training duration. The confusion matrix was also used and produced for each performance evaluation. The evaluation was in terms of accuracy, precision, recall, and F1 score [60]. Accuracy was used to evaluate overall correctness for a model. These factors that were used to evaluate performance are calculated using TP (true positive), FP (false positive), FN (false negative), and TN (true negative). Violence is a binary classification task where an event or human activity is determined as violent (positive class) or nonviolent (negative class) [61].  
Accuracy is a measure of how many predictions are correct out of the total predictions. It is calculated as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{17}\]  
Accuracy provides a general idea of the correctness of the trained model. Precision, commonly referred to as Positive Predictive Value, indicates the percentage of the model's positive predictions that turn out to be true. The formula to generate the precision is as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{18}\]  
Recall, which is a measure of how many actual positive cases the model accurately predicted, is sometimes referred to as Sensitivity or True Positive Rate. It is computed as follows:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{19}\]  
The harmonic mean of recall and precision is known as F1 score. It offers a fair assessment that considers both false positives and false negatives. The formula for calculating the F1 score is as follows:  
\[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{20}\]  
Recall and precision are weighed equally in the F1 score [60].  
In summary, accuracy gives an overall idea of how correct the model is during classification. Precision focuses on minimizing false positives. Recall focuses on minimizing false negatives. The F1 score balances precision and recall.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Correlation Coefficient Analysis
#### 3.1.2 Correlation Coefficient between Plaintext and Cipher Images  
The correlation coefficient between a plaintext image and its cipher image can be calculated as follows:  
\[CC_{P,C}=\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}(P_{(i,j)}-\overline{P})(C_{(i,j)}- \overline{C})}{\sqrt{\sum_{i=1}^{H}\sum_{j=1}^{W}(P_{(i,j)}-\overline{P})^{2} \sum_{i=1}^{H}\sum_{j=1}^{W}(C_{(i,j)}-\overline{C})^{2}}}, \tag{7}\]  
where \(P_{(i,j)}\) and \(C_{(i,j)}\) are the values of the pixel at index \(i,j\) of the plaintext image and the cipher image, respectively, and \(\overline{P}\) and \(\overline{C}\) represent the mean of the pixel values of the plaintext image and the cipher image, respectively.  
Figure 5: Most commonly used evaluation metrics.
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 5 Results & Analysis
### Aspect Term Extraction Result
#### 5.1.2 Ablation Study  
To study the effects of different components in the ATE model, we remove the components to be studied and study the effects of the remaining structures on both datasets. The results in Table 3 show that removing the convolutional neural network reduces the remaining model structure by 0.93% and 0.9% on the Laptop and Restaurant datasets, respectively, and removing the Bi-LSTM reduces the remaining model structure by 1.35% and 1.05% on the Laptop and Restaurant datasets, respectively. Convolutional neural networks extract contextual features at the local level and BiLSTM relates contextual information at the global level, both of which further enrich the word vector information. The model does not make further use of external information such as syntactic information and lexical information, and only uses the basic BERT model to obtain contextual information, so the model has much room for improvement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & **Domain** & **Laptop** & **Restaurant** \\ \cline{2-4}  & **Model** & **F1** & **F1** \\ \hline \multirow{2}{*}{RNN} & BiLSTM & 73.72 & 81.42 \\  & MNA & 78.63 & 85.71 \\ \hline \multirow{2}{*}{CNN} & DTBCSNN & 75.66 & 83.97 \\  & DE-CNN & 81.59 & 74.37 \\  & STC & 82.34 & - \\ \hline \multirow{2}{*}{BERT} & BERT-AE & 73.92 & 82.56 \\  & CSAE & 77.65 & **86.65** \\ \hline \multirow{2}{*}{Our} & Ours-model-ATE-Conv & 81.84 & 85.17 \\  & Ours-model-ATE-BiLSTM & 81.42 & 85.02 \\ \cline{1-1}  & Ours-model-ATE & **82.77** & **86.07** \\ \hline \hline \end{tabular}  
* Note: The best result in each dataset is highlighted in bold. ‘\(\cdot\)’ means no result.  
\end{table}
Table 3: Comparing our ATE model with other advanced methods based on F1 scores (%).
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 4 Full-Coverage Path Planning
### Dead Zone Escape Strategy  
When a robot moves to a certain grid position, if the neighboring grids are obstacle or covered grids and some uncovered grids remain in the map, the robot may fall into a dead zone position. At this point, the robot needs to find the shortest possible path to escape the dead zone.  
To address this situation, we developed a strategy to escape from dead zones. First, based on the wildfire algorithm, the properties of the eight adjacent grids are searched based on the grid where the robot is located. If an uncovered grid is found, it is marked. The grid around range is continually expanded; in other words, the attributes of 16 grid are searched, and any uncovered grid is marked. Then, the search scope is gradually expanded until the boundary of the grid map is searched, and the distance between the marked grids and dead zone grid is calculated. The grid with the shortest distance is the initial target point.  
After finding the initial target point, a path to move from the dead zone position to the initial target point must be planned. We used the direction function to plan the path, which can effectively solve this problem. The direction function is defined as follows.  
\[F=\min\{f_{i}=h_{i}+k_{i},i=1,2,3,...,8\} \tag{3}\]  
\[h_{i}=\frac{d_{n}}{d_{i}} \tag{4}\]  
\[d_{n}=\sqrt{\left(x_{n}-x_{m}\right)^{2}+\left(y_{n}-y_{m}\right)^{2}} \tag{5}\]  
\[d_{i}=\sqrt{\left(x_{i}-x_{m}\right)^{2}+\left(y_{i}-y_{m}\right)^{2}} \tag{6}\]  
\[k_{i}=a\frac{\theta_{k}}{\pi} \tag{7}\]  
where \(h_{i}\) represents the distance function; \(k_{i}\) is the steering function, \(d_{n}\) and \(d_{i}\) represent the distance from the next-move grid to target point grid and the distance from the current grid to target point grid, respectively; \((x_{n},y_{n})\), \((x_{m},y_{m})\), and \((x_{i},y_{i})\) denote the next-move grid coordinates, the target grid coordinates, and the current grid coordinates, respectively; \(i\) represents the number of robot-adjacent grids. Because the next-move grid is the domain  
Figure 9: U-shaped obstacle avoidance strategy.  
grid of the current grid, the steering angles \(\theta_{k}\) are 0, \(\pi/4\), \(\pi/2\), \(3\pi/4\), and \(\pi\). The value of \(a\) is 0.2, which represents the influence of the steering angle on the robot's selection of grid position for the next move.  
As shown in Figure 10, if the robot moves to position 1, it falls into a dead zone. At this time, the nearest uncovered grid position searched by the wildfire algorithm is 3, which is set as the initial target point. The path from the dead zone position 1 to the initial target point position 3 calculated by the direction function is indicated by the blue line. The first uncovered grid position in this path is 2, which is the expected target point. As the robot moves from 1 to the expected target point 2, it escapes the dead zone.
## 1 Introduction
Defect localisation and characterisation are vital steps in NDT application, with their own parameters that affect maintaining sustainable aviation operation and airworthiness. That structural recognition and comprehensive knowledge of possible defects will ease maintenance operations, followed by sustainable aviation, is not in doubt. In this article, the fusion approaches already used in NDT studies have been investigated in terms of application potential for aerospace structures. The related algorithms and their evaluation are presented and discussed. It should be emphasised that NDT fusion is still a fresh research topic, and utilizing image fusion strategies for NDT defect detection application when applying the individual inspection methods is not satisfactory [15]. This comprehensive review study, which stems from a current PhD study exploring advanced NDT fusion of an ultrasonic phased array and pulsed thermography for inspection applications in composite structures, contributes the following:  
* It introduces the current studies on NDT image fusion and evaluation strategies;
* It provides a reference source for the NDT-based fusion attempts;
* It reports the limited amount of work existing in fusing NDT techniques (particularly for composite structures);
* It fills the research gap of comprehensively reviewing image fusion for NDT inspection (particularly towards composite structures). The rest of this paper is structured as follows. A brief discussion of the primary NDT techniques is given in Section 2. The basic principles of image fusion are discussed in Section 3. Section 4 includes fusion strategies and rules for various NDT techniques discussing principles and examples. Section 5 discusses the evaluation of fusion, focusing on performance metrics. Section 6 provides insights from the survey into NDT fusion and future prospects. Conclusions are drawn in Section 7.
## 3 Results
### Experimental Results and Analyses
#### 3.2.2 Ablation Experiment  
In this section, we analyze the performance metrics of different models after training with different datasets to illustrate the effectiveness and importance of the BN layer.  
The comparison of accuracy curves in Figure 6a shows that the model with the BN layer converged faster around the fourth epoch, and its recognition accuracy was significantly higher than that of the model without the BN layer. Figure 6b demonstrates that at the sixth epoch, the model with the BN layer had a license plate symbol recognition accuracy  
Figure 5: Performance of models with different datasets. (**a**) Accuracy on the test set during model training using different datasets; (**b**) comparison of PFC values on the test set at the beginning of training for models trained using different datasets.  
of more than 90% and a probability of recognizing the correct license plate number of about 56%, which is much higher than the model without the BN layer, which had a recognition accuracy and a PFC value of about 80% versus 20%. These results clearly show that the BN layer makes deep network training more efficient by reducing the phenomena of gradient vanishing and gradient explosion [39], thereby effectively improving the overall performance of the model, greatly improving the model's recognition accuracy in areas with high sand and dust and large illumination variations.  
Observing Figure 7, we can see that while the difference between the predicted and true values of the different models in the training set gradually decreased with the increase in training epochs, their performance on the test set varied significantly. Without the BN layer, the error on the test set initially decreased with the increase in training epochs but then began to rise. This indicates that, in the later training sessions, the model's predictions of the results on the test set became more different from the true results, and the model overlearned the image features in the training set. Although the model's recognition accuracy remained almost unchanged, the increase in the loss function value revealed the presence of overfitting. In contrast, the model with the BN layer did not exhibit overfitting. This phenomenon indicates that the BN layer, by normalizing the input of each layer, reduces the impact of internal covariate shift and introduces a certain level of noise [40], thereby effectively serving a regularization function [41]. This regularization helps to mitigate the risk of overfitting on the training set and allows the model to maintain better recognition performance when encountering unseen data.  
Figure 6: Performance of models with different structures. (**a**) Changes in accuracy on the test set during training of different models using the same dataset; (**b**) comparison of accuracy and PFC for different models at almost convergence.  
Figure 7: Change in the value of the loss function.
## 4 Modified Functional Observer with Unknown Input
### Existence Conditions
\begin{array}{c}\psi\\ \Delta\end{array} \right]=\text{rank}(\Delta),\]  
meaning that  
\[\text{rank}\left[ \begin{array}{cc}F\overline{A}_{\omega}E_{1}&F\\ \overline{C}_{\omega}\overline{A}_{\omega}E_{1}&\overline{C}_{\omega}\\ \overline{C}_{\omega}E_{1}&0\\ \overline{E}\overline{A}_{\omega}E_{1}&\overline{E}_{\omega}\end{array} \right] =\text{rank}\left[ \begin{array}{cc}\overline{C}_{\omega}\overline{A}_{\omega}E _{1}&\overline{C}_{\omega}\\ \overline{C}_{\omega}E_{1}&0\\ \overline{E}\overline{A}_{\omega}E_{1}&\overline{E}_{\omega}\end{array} \right] \tag{31}\]  
It can be proved that Condition 1 of Theorem 2 and Equation (31) are equivalent (see Appendix A.1) by post-multiplying both sides of Equation (23) by the full-row rank matrix \(\left[ \begin{array}{cc}H_{1}&E_{1}&0\\ 0&0&I_{n+m}\end{array} \right]\). Therefore, upon satisfaction of Condition 1 of Theorem 2 and using the generalized matrix inverse approach [21], a solution to Equation (29) always exists and is given by  
\[\left[ \begin{array}{cc}Q&T&\mathcal{L}\end{array} \right]=\psi\Delta^{+}+Z(I_ {2p+n+m}-\Delta\Delta^{+})\enspace, \tag{32}\]  
where \(\Delta^{+}\) is the generalized inverse of \(\Delta\), and \(Z\in\Re^{\times(2p+n+m)}\) is an arbitrary matrix, which will be further utilized to establish the stability of matrix \(N\). Then, substituting Equation (32) into Equation (27) results in  
\[N=\Phi-Z\Omega, \tag{33}\]  
where \(\Phi\in\Re^{\times\times\times}\) and \(\Omega\in\Re^{(2p+n+m)\times\times}\) are known matrices, which can be defined as  
\[\Phi=(F\overline{A}_{\omega}H_{1}-\psi\Delta^{+}\overline{\Theta}),\ \Omega=(I_{2p+n+m}-\Delta\Delta^{+})\overline{\Theta}, \tag{34}\]  
in which \(\overline{\Theta}=\left[ \begin{array}{c}\overline{C}_{\omega}\overline{A}_{ \omega}H_{1}\\ \overline{C}_{\omega}H_{1}\\ \overline{E}\overline{A}_{\omega}H_{1}\end{array} \right]\). In Equation (33), the matrix \(N\) is a Hurwitz function for matrix \(Z\) only if the pair \((\Omega,\ \Phi)\) is detectable, i.e.,  
\[\text{rank}\left[ \begin{array}{c}sl_{x}-\Phi\\ \Omega\end{array} \right]=\kappa,\ \forall s\in\text{C},\ \Re(s)\geq 0. \tag{35}\]Now, it can be shown that Condition 2 of Theorem 2 is equivalent to Equation (35) by post-multiplying both sides of Equation (24) by the full-row matrix \(\left[ \begin{array}{ccc}H_{1}&E_{1}&0\\ 0&0&I_{n+m}\end{array} \right]\) (see Appendix A.2), therefore ensuring that matrix \(N\) is a Hurwitz function. **Remark 1**. : _As in [4], the modified functional observer proposed here is only applicable for proper systems and not for strictly proper systems, because if \(D=0_{pcom}\), i.e., \(\overline{C}_{o}=\left[ \begin{array}{cc}C&0\end{array} \right]\), the column rank of the existing condition (Equation (23)) drops under the assumption that the system is square and \(F\)_is of full-row rank_ (\(rank(F)=\kappa\))_. An alternative method for strictly proper systems can be found in [14], however it does not apply to proper systems. This remark can be proven, as shown in [4]._