esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithms Analysis
The confusion matrix gives the number or proportion of instances between the predicted and actual class. The selection of the elements in the matrix feeds the corresponding instances into the output signal. This way, one can observe which specific instances were misclassified and how. Figure 7 presents the confusion matrices of the neural network, tree, logistic regression, and naive Bayes models. The figure shows the confusion matrices for four different machine learning models: neural network, tree, logistic regression, and naive Bayes. In visual representations of confusion matrices, different colors are often used to make interpretation easier: Blue often used to highlight correct predictions (True Positive and True Negative); [PERSON] or Orange often used for incorrect predictions (False Positive and False Negative). Sometimes, the intensity of the color indicates the magnitude of the values, with darker shades representing higher numbers. Each confusion matrix illustrates the performance of these models in predicting categories labeled as \"Blade 1\", \"Blade 2\", and \"Blade 3\". Confusion Matrices Overview: Rows (Actual): Represent the true classes; Columns (Predicted): Represent the classes predicted by the model; and Cells: Show the percentage of correct or incorrect predictions for each class. Models Analysis  
Neural Network:  
Blade 1: 62.5% correctly classified, 10% misclassified as Blade 2, 0% as Blade 3. Blade 2: 90% correctly classified, some misclassification to Blade 1 and Blade 3. Blade 3: 66.7% correctly classified, 25% misclassified as Blade 1. The Neural Network shows strong performance in predicting Blade 2 and Blade 3 but slightly lower accuracy for Blade 1. Tree:  
Blade 1: 66.7% correctly classified. Blade 2: 90% correctly classified. Blade 3: 50% correctly classified. The decision tree model performs well with Blade 1 and Blade 2 but has some difficulty with Blade 3. Logistic Regression:  
Blade 1: 71.4% correctly classified. Blade 2: 76.9% correctly classified. Blade 3: 50% correctly classified. Logistic regression shows the best accuracy for Blade 1 but still struggles with Blade 3. Naive Bayes:  
Blade 1: 55.6% correctly classified. Blade 2: 100% correctly classified. Blade 3: 54.5% correctly classified. Naive Bayes has perfect accuracy for Blade 2 but lower performance for Blade 1 and Blade 3. Figure 7: Confusion matrix. Blade 2 All models perform quite well, with naive Bayes achieving 100% accuracy. Blade 1 and Blade 3 Generally, show lower performance across all models, with the neural network and logistic regression performing slightly better. Naive Bayes: Best at predicting Blade 2 but less accurate with Blade 1 and Blade 3. The results are shown in Figure 8, where the algorithm's selection is displayed, grouping images that highlight the damage to the wind turbine blades.
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 4 Experiment and Results
### Effect of Motion Direction  
In an open space, it is highly likely that during the movement a user changes her/his direction. Since the motion did not affect the positioning accuracy in the previous experiment, it indicates that the moving direction might not have a significant effect on the proposed method. Effect of the change of direction has been investigated in this experiment. Since different test fingerprint has different position and orientation, it is impractical to maintain common axis reference for angle calculation. However, since the database contains local coordinate, we used the origin point (\(XY\) = [0, 0]), UE coordinate, and other devices coordinate to make a triangle and calculate the angle of motion. In Figure 10 it can be seen that 351 test fingerprint (FP) took assistance from such devices which changed their course between 0\({}^{\circ}\) to 30\({}^{\circ}\) angular direction resulted in a mean 6.12 m error.  
307 test fingerprints communicated with other devices which were moving from 30\({}^{\circ}\) to 60\({}^{\circ}\) angle caused 5.59 m mean error. 403 test fingerprints communicated with other devices which were moving from 60\({}^{\circ}\) to 90\({}^{\circ}\) angle caused 5.42 m mean error. 276 test fingerprints communicated with other devices which were moving from 90\({}^{\circ}\) to 120\({}^{\circ}\) angle caused 5.28 m mean error. 223 test fingerprints communicated with other devices which were moving from 120\({}^{\circ}\) to 150\({}^{\circ}\) angle caused 5.84 m mean error. 290 test fingerprints communicated with other devices which were moving from 150\({}^{\circ}\) to 180\({}^{\circ}\) angle caused 5.37 m mean error. From all the segments in Figure 10 it can be seen that the mean positioning error is very close to the overall mean positioning error of the system, no particular characteristic is observable due to the change of movement direction. However, the highest difference in mean error is between the devices moving from 0\({}^{\circ}\) to 30\({}^{\circ}\) (6.12 m) and the devices moving from 90\({}^{\circ}\) to 120\({}^{\circ}\) (5.28 m).
## 2 Measurement Approach
### Parameter Definition to Quantify the Laminar Flow Reduction (LFR)  
To evaluate the influence of premature flow transitions, the absolute distance between the natural transition \(y_{\text{tr,nat},i}\) and the actual transition \(y_{\text{tr,act},i}\) in each image column \(i\) is calculated, see Figure 4. Divided by the absolute distance between the leading edge \(y_{\text{LE},\,i}\) and the natural transition of the respective column, this yields the parameter  
\[\text{LFR}_{i}=\frac{|y_{\text{tr,act},i}-y_{\text{tr,nat},i}|}{|y_{\text{LE}, i}-y_{\text{tr,nat},i}|}\ \ \text{in}\ \ [0;1], \tag{1}\]  
which quantifies the reduction of the laminar flow in the column \(i\) relative to the case of no premature flow transition. If the position of the actual flow transition equals the position of the natural flow transition, which means no premature transitions occur, \(\text{LFR}_{i}\) equals 0. For a premature flow transition occurring near the leading edge, \(\text{LFR}_{i}\) converges to 1.  
Figure 3: Sketch of the rotation motions of the thermographic image and the evaluation line (black line) parallel to the \(y^{\prime}\)-axis. The rotation angles \(\alpha_{1}\) and \(\alpha_{2}\) align one group of premature transition line segments perpendicular to the evaluation line, respectively. The angles between the evaluation line and the flow transition line (black dashed line) are almost perpendicular, increasing the sensitivity of the local gradient maxima localizations.  
To quantify the average LFR along the rotor blade or the image field of view, respectively, the average value of the laminar flow reduction over \(I\) image columns is considered:  
\[\overline{\text{LFR}}=\frac{1}{I}\sum_{i=1}^{I}\text{LFR}_{i}. \tag{2}\]
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 4 Proposed System Model  
The main idea of the proposed system is to use a combination of (FFT/DWT)-OFDM with the proposed FRF interleaving mechanism. The FRF interleaver can be a potential candidate for practical OFDM systems due to its low computational complexity and good BER performance. The block diagram of the proposed (FFT/DWT)-OFDM with helical interleaving is shown in Figure 6. The conventional OFDM block is modified by adding an interleaving stage. Both the in-phase and quadrature fields of the OFDM signal (the output of IFFT/IDWT) are interleaved. \(S\) is supposed to be known by the receiver.  
Figure 5: Helical and FRF interleaving of an \(8\times 8\) matrix. (**a**) The \(8\times 8\) matrix. (**b**) Data with error bursts after helical interleaving. (**c**) Data with error bursts after FRF interleaving. (**d**) Effect of error bursts after helical de-interleaving. (**e**) Effect of error bursts after FRF de-interleaving.
# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 3 A Robotic Edge Intelligence Architecture
### State Transition Diagram of Tasks  
As mentioned above, an accurate scheduling model is important to apply an REI-based multi-robot system, where the design of the model depends on the states of the tasks and robots. The state transitions can help to instruct the REI-based multi-robot system, allowingit to assign tasks to appropriate robots. The state transition diagrams should be universal and applicable to all kinds of multi-robot systems, where each state can be effectively detected by the edge robots. Then, the REI architecture can perceive state information and optimize the scheduling schemes based on the state transition information, thus realizing collision-free multi-robot aggregation.  
There are two primary state transition diagrams: the task state transition diagram and the robot state transition diagram.  
A task state transition diagram for REI-based multi-robot systems is shown in Figure 2. There are five task states and five state transition processes (corresponding to the ellipses and arrows in the figure, respectively). The ellipses indicate the state of tasks, while the text on the arrows determines the action to trigger the task state transition. The five states are defined as follows, which can be detected and implemented by servers or robots:  
* New--This is the initial state of a task. If a task is detected to demand robots, it will be initialized to a new state in the REI system before an assignment.
* Assignment--This is the second state of a task, which involves a new task being assigned to an idle robot. A task can be assigned and scheduled to suitable robots before task processing. The robotic edge intelligence is designed to optimize the assignment scheme in a multi-robot system.
* Processing--If a task is scheduled according to the optimized assignment and corresponding robots are allocated, then the task can be processed by the assigned robots.
* Waiting--During the task processing on a robot, an interruption (i.e., collisions resulting in collaborative robots not reaching the gathering point in time) will break off it. Then, the interrupted task will be in a waiting state. If the interruption is completed, all waiting tasks need to be returned to the assignment state, in order to further check whether the original assignment is available and whether it needs to be reassigned.
* End--If a task is completed, it will be eliminated from the REI system and, at the same time, the assigned robot is considered to be idle again.
## 3 Experiments and Results
To demonstrate the effect of parallax, two letters in different depth planes are depicted, which is shown in Figure 7. A letter \"A\" at the virtual distance \(d_{v}=50\,\mathrm{mm}\) and a letter \"B\" at \(d_{v}=150\,\mathrm{mm}\). For the used LA with a focal length of \(f=41.90\,\mathrm{mm}\), this results in a vertical distance between the LCD and the LA of \(d=22.80\,\mathrm{mm}\) and \(d=32.75\,\mathrm{mm}\), respectively. The alignment of the LCD and the LA is kept the same. The camera, placed in a vertical distance of \(750\,\mathrm{mm}\) above the LA, was horizontally moved between capturing three images along the x-axis to change the viewing angle while the camera's objective kept being focused on the depth plane of the \"A\". Figure 7a was captured while the camera was horizontally shifted by \(40\,\mathrm{mm}\) from the optical axis in the positive \(x\)-direction (to the right), resulting in a change of the viewing angle by \(3.1^{\circ}\). In Figure 7b, the camera is placed on the optical axis. Figure 7c shows the case where the camera was moved by \(-40\,\mathrm{mm}\) in the other direction (to the left), which changed the viewing angle by \(-3.1^{\circ}\). As expected, the letter closer to the viewer (\"A\") seems to move horizontally relatively to the letter further in the background (\"B\"). If the camera is placed on the right side of the optical axis, the \"A\" appears to be laterally closer to the \"B\" and if the camera is placed on the left side, the \"A\" is laterally farther away from the \"B\". This experiment clearly shows the inherent effect of parallax that the system is able to provide as a crucial depth cue. The Video S2 in the Supplementary Materials also shows parallax and focus blur of the display.
## 4 Methods
### Evaluation  
This study mainly used five evaluation indicators: Explained Variance Score, R-squared score, adjusted-R-squared score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).  
The description and calculation formulas of the performance indicators for each are as follows:  
\[R^{2}=1-\frac{SSR}{SST}\]  
\[SSR=\sum\limits_{i=1}^{n}\big{(}y_{i}-y_{p}\big{)}^{2}\text{, }SST=\sum\limits_{i=1}^{n}(y_{i}-\overline{y})^{2}\text{, }y_{i}\text{ is the actual value, }y_{p}\text{ is the predicted value, and }\overline{y}\text{ is the average of the actual values.  
\[Adj.\text{ }R^{2}=1-\frac{\frac{SSR}{R-k-1}}{\frac{SST}{R-k-1}}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**XGBoost**} \\ \hline colsample\_bylevel & 0.88 \\ \hline Gamma & 0.5 \\ \hline colsample\_bytree & 1 \\ \hline learning\_rate & 0.04 \\ \hline max\_depth & 6 \\ \hline min\_child\_weight & 3 \\ \hline n\_estimators & 1000 \\ \hline reg\_alpha & 0.7 \\ \hline Subsample & 0.89 \\ \hline \multicolumn{2}{c}{**CatBoost**} \\ \hline Depth & 4 \\ \hline Iterations & 800 \\ \hline learning\_rate & 0.05 \\ \hline Objective & MAE \\ \hline \multicolumn{2}{c}{**Ridge regression**} \\ \hline model\_alpha & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters for XGBoost, CatBoost, and Ridge Regression.  
where \(n\) is the number of samples and \(k\) is the number of explanatory variables.  
The Explained Variance Score is 1--((Sum of Squared Residuals--Mean Error)/Total Variance). The only difference between the Explained Variance Score (EVS) and the R-squared score is that the R-squared score subtracts the mean error from the Sum of Squared Residual (SSR). If the mean error is not close to zero, and a negative or positive value is obtained, the error is biased to one side and, thus, the model is biased. In other words, if the R-squared and the explanatory variance score are different, the error is biased, and there is a high possibility of incorrect fitting.  
\[\text{MAE}=\frac{\big{|}\big{(}y_{i}-y_{p}\big{)}\big{|}}{n}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\big{(}y_{i}-y_{p}\big{)}^{2}}{n}}\]  
Compared to MAE, RMSE has the advantage of giving a sizable penalty for a significant error value difference and is strong.
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 2 Methodologies  
### Convolutional Neural Network (CNN)  
In the context of multi-source data features from three distinct data sources, this paper proposes the utilisation of convolutional neural networks (CNNs) for the extraction of local features within the data. The primary objective of this approach is to facilitate the subsequent TBM rock grade prediction model in discerning the inherent patterns present within the data. CNNs constitute a category of feed-forward neural networks. As illustrated in Figure 2, the model's primary structure encompasses an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer [29]. The convolution layer performs the convolution operation on the input information by means of a convolution kernel, so as to extract the features and generate a new feature map. The process of convolution is described as follows:  
\[y_{i,j}(n)=w_{i-1,j}\cdot x_{i,j}(n)+b_{i-1,j} \tag{1}\]  
In this equation, \(w_{i-1,j}\) and \(x_{i,j}(n)\) are the weights and bias terms of the \(j\)th convolution kernel in the \(i-1\)th layer, respectively, while \(y_{i,j}(n)\) represents the output of the \(n\)th feature map and the output resulting from the convolution of the \(j\)th convolution kernel in the \(i\)th layer.  
A batch normalisation layer is incorporated after the convolutional layer with the objective of accelerating the training process of the neural network and enhancing the generalisation ability of the model. Additionally, an activation function layer is included to preserve the effect of each layer following the convolutional operation and to acquire nonlinear features. The nonlinear activation function selected in this paper is the ReLU function, which has been shown to be computationally faster than other nonlinear functions and to perform better in practical applications [30]. The expression for the ReLU function is as follows:  
\[a_{i,j}(n)=\delta(y_{i,j}(n))=max\{0,y_{i,j}(n)\} \tag{2}\]  
Figure 1: Overall flowchart.  
where \(a_{i,j}(n)\) represents the value after nonlinear activation, and \(\delta\) denotes the activation function.  
Redundant information is ultimately eliminated through the pooling layer, thus reducing the size of the feature map and preventing overfitting. The following procedure is employed to achieve this objective:  
\[p_{i,j}(n)=pool(a_{i,j}(n)) \tag{3}\]  
where \(p_{i,j}(n)\) signifies the value corresponding to the feature map in layer i after the pooling operation, and \(pool\) is the pooling function.
## 5 Results and Discussion
### Simulation Settings  
This technique uses a flat network model with nodes distributed at random across a 100 \(\times\) 100 m\({}^{2}\) region. The suggested approaches are contrasted with the following algorithms: SEP, DEEC, Z-SEP, and PSO-ECSM. All algorithms use the same set of input setup settings for the network. In addition, the base station (BS) is positioned in the network's upper-center, guaranteeing that every sensor node has at least one neighbor. Each sensor node has the ability to send data packets in a single hop to the BS. Furthermore, the sensor nodes exhibit heterogeneity and possess the same communication range. Further details regarding the system can be found in Table 1.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Localization of Mobile Nodes  
A mobile underwater node typically employs an inertial navigation system (INS) for orientation and directional control while navigating aquatic environments. The node can ascertain its travel angles, denoted as \(\alpha\) and \(\beta\), relative to the depth axis (\(z\)-axis) and the horizontal plane (\(x\)-\(y\) plane) within its own reference frame. Beginning at the position \((x_{1},y_{1},z_{1})\), the node's new position \((x_{2},y_{2},z_{2})\) after traveling a distance \(d\) can be computed using the following formula:  
\[z_{2}=z_{1}+d.cos\alpha \tag{32}\]  
\[x_{2}=x_{1}+d.(\sin\alpha).(\cos\beta) \tag{33}\]  
\[y_{2}=y_{1}+d.(\sin\alpha).(\sin\beta) \tag{34}\]  
Since the resultant shape depicts all the possible node positions based on that transmission, for a mobile mode the motion trajectory needs to be factored in. If the node travels with the above parameter and receives a transmission, the 3D geometric shape of the possible positions can be remapped through vector transformations. To simplify this action, as shown in Figure 6, we can move the centers of the circles from \((x_{r1},y_{r1},z_{r1})\) to \((x_{r2},y_{r2},z_{r2})\) with the equations below:  
\[z_{r2}=z_{r1}-d.cos\alpha \tag{35}\]  
\[x_{r2}=x_{r1}-d.(\sin\alpha).(\cos\beta) \tag{36}\]  
\[y_{r2}=y_{r1}-d.(\sin\alpha).(\sin\beta) \tag{37}\]  
Figure 6: Location projection due to the underwater node mobility.  
This transformation essentially relocates the 3D contour back in time and essentially guarantees overlapping with the first contour. We can apply this to all subsequent transmissions received by the mobile node, and the overlapping contours will follow the same principle as described for the stationary node above (see Sections 4.3 and 4.4).
# A Critical Review of Supersonic Flow Control for High-Speed Applications
## 4 Active Control Methods
### Experimental Investigation
They extended their work for Mach 2.5 and 3.0, and cross-sectional areas of 2.56, 3.24, 4.84, and 6.25; the L/D ratio of the duct was 10 to 1, and the NPR used were from 3 and 11 [95]. To control the drag, the researchers [96] worked with base drag and experimentally studied the aerosikes behind the base of bluff bodies. A 1 mm thick plate with two spikes at 11.5 mm was mounted between the nozzle as a passive device. The efficacy of the continuous grooved cavity was tested experimentally to control the base flows with specific parameters [97]; in the transonic system, the aerosikes effectively control the base pressure [98] without disrupting the main flow, respectively. Figure 7: Experimental setup [93]. Reprinted under the Creative Commons (CC) License (CC BY 4.0). In summary, [PERSON] et al., investigated the active control of the base pressure [1; 15; 82; 83; 84; 85; 86; 87; 88; 89] in which they considered the differed Mach number at various supersonic flow ranges, such as Mach numbers 1.25, 1.30, 1.48, 1.6, 1.8 and 2.0 in [84; 85]; 1.87, 2.2 and 2.58 in [15]; and 2.0, 2.5 and 3.0 in [82], respectively. The test area ratio was from 2.56, 3.24, 4.84, and 6.25; the NPR was from 3 to 9; and the duct length for L = 10D until 1D. More attention was given in the recent study to wall pressure distribution for different inertia levels, relief to the flow, NPR, and the L/D ratio [93; 100; 101; 102; 103; 104; 105; 106; 107]. After monitoring the flow from the active microjet control, it is also necessary to know whether the flow changes in the duct or not. Hence, recent findings have also shown that several studies are conducted with equivalent or variable Mach number and area ratios with the same NPR and L/D ratios. The majority of wall pressure flows were considered with varying the area ratio, such as 2.56 [93; 100], 3.24 [101; 102; 108; 109], 4.84 [103; 104; 105; 101], and varying the Mach number for the same area ratio 2.56 with Mach 2 and 3 [106], 1.3. 1.9, and 2.4 [111] and for the area ratio 6.25 with Mach 1.1 and 1.5 [112] and 2.1 and 2.8 [113].