esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Performance Evaluation in a Multiple-EHD Scenario
#### 5.2.1 Performance Evaluation in an Urban Environment
In particular, there is a substantial increase in the charging time with \(H_{\text{min}}\), which is much lower than the optimal altitude, as seen in Figure 6. Compared to the charging time performance when \(r_{\text{max}}=100\), as seen in Figure 4(a), the overall charging time increases  
Figure 5: Performance comparison of various methods when \(r_{\text{max}}=100\) m in an Urban environment. Figure 6: Average UAV altitude and its standard deviation via various methods when \(r_{\text{max}}=100\) m in an Urban environment. when \(r_{\max}=150\). For example, the charging time for the optimal design increases from \([14.2,52.4]\) to \([32.3,119]\). Also, it is shown that the conventional EB, i.e., W-sum EB, yields substantial performance degradation compared to the proposed EB. On the other hand, the performance in terms of execution time is similar for \(r_{\max}=150\) (Figure 6(b)) and \(r_{\max}=100\) (Figure 4(b)). As expected, proposed method #1 yields a much shorter execution time compared to the optimal design, and the shortest execution time for all methods is achieved using proposed method #2.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 3 Data-Partitioning Strategy Based on Grid Segmentation
### Preprocessing of File Data
#### 3.1.2 Pre-Scanning and Metadata Construction  
The storage method of grids and grid point coordinates in EnSight Gold data files differs significantly from other data types. It uses a separate set of global point coordinates for each part (representing a component of the geometric model), including XYZ three-dimensional coordinate values. Different types of grids share the ID of this set of coordinates, and in EnSight Gold, the IDs of points and grids start from 0 and increase by default in the order of storage. Since the number of parts is not positively correlated with the file size (a single part occupies 3 GB of storage space), a part cannot be directly assigned to each process. However, the number of grids in a part is positively correlated with file size. Therefore, this paper's strategy is to divide the grids in a part into sub-grids and assign them to different processes. To solve this problem, this paper designs a data structure called PartsInfoType to store key information about each part of the geometric model file. Before reading the geometric model file to build the grid, we need to scan the geo file first and store each part's basic information in PartsInfoType as metadata.
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 2 Numerical Model and Methods
### Initial Parameters and Boundary Conditions  
The flow field is initially filled with a mixture of propane oxygen and nitrogen at a temperature of 300K and a pressure of 0.128 MPa. Among them, the mass fraction of propane is 9.4% and the oxygen was 34%, the rest is nitrogen. The ignition zone is simplified as a semicircle with a diameter of 15 mm, whose temperature is 2500 K. The pressure-outlet is chosen as the exit boundary condition of calculation domain. The walls are adiabatic and no-slip boundary conditions [40].
## 5 Simulation Results
### Simulation Platform  
In this section, based on the IPMSM ITSCF model established in Section 3, a motor control simulation is constructed using a hybrid combination of Simulink and Simscape in MATLAB. The overall structure of the speed control system is illustrated in Figure 7. The outer loop of the speed control system, implemented in Simulink, calculates the speed error by comparing the reference speed with the feedback speed. The resulting speed error is then fed into a speed PI controller to generate the \(q\)-axis current command, while the \(d\)-axis current is set to 0A. The inner loop calculates the \(d\)-\(q\) axis current errors by subtracting the feedback \(d\)-\(q\) axis currents from their respective commands. These error signals are then fed into \(d\)-\(q\) axis current PI controllers to generate the corresponding \(d\)-\(q\) axis voltage commands. These voltage commands are transformed from the \(d\)-\(q\) reference frame to the stationary abc frame using Park and Clarke transformations, and then applied to the three-phase motor model implemented in Simscape. The motor model produces various outputs, including currents, back EMF, flux linkages, torque, and speed. Finally, the three-phase currents and speed are fed back to close the loop between the outer and inner control loops. The complete motor simulation parameters are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantity** & **Value** & **Units** \\ \hline Rated Voltage & 48 & V \\ Rated Current & 9 & A \\ Rated Power & 400 & W \\ Rated Torque & 600 & mNm \\ Max. Speed & 6000 & rpm \\ Poles (\(P\)) & 8 & pole \\ Stator Resistance (\(r_{s}\)) & 0.33 & \(\Omega\) \\ \(q\)-axis Inductance (\(l_{u}\)) & 1.2606 & mH \\ \(d\)-axis Inductance (\(l_{d}\)) & 1.0045 & mH \\ PM Flux Linkage (\(l_{m}\)) & 0.008625 & Wb \\ Rotor Inertia (\(l_{m}\)) & 0.0001 & kg\(\cdot\)m\({}^{2}\) \\ Damping (\(B_{m}\)) & 0.0000484 & N\(\cdot\)m\(\cdot\)s\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters for IPMSM.  
Figure 6: Flowchart of discrete wavelet analysis.  
Although Simulink can also be used to build an IPMSM model, it is based on standard three-phase balanced motor equations. Consequently, it cannot replicate the three-phase imbalance characteristics that arise when modeling the motor's ITSCF. Consequently, to accurately simulate the IPMSM model under ITSCF conditions, a Simscape-based implementation is necessary. As illustrated in Figure 8, the three-phase voltages generated by the Simulink control loop are applied to the individual phase windings of the motor model constructed in Simscape. After passing through the inductance and resistance models, three-phase current responses are generated. The magnetomotive force and magnetic flux linkage produced by the currents are used to calculate the motor torque. Finally, the calculated torque is applied to the mechanical model of the motor rotor to obtain the rotor angle and speed responses, which are then fed back to the field-oriented control and speed control loops.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 4 Experiments
### Experimental Results  
We first used 304 images in our dataset to train the meter dial location network and then used the remaining images (i.e., 76 images) to test our location network. The AP metric and running time are employed to evaluate the detection performance of different methods. Table 1 reports the quantitative results of our method and three widely used deep detection networks, i.e., SSD [57], RetinaNet [58], and YOLOv4 [59]. From this table, it can observed that all detection methods achieve satisfying performance, resulting in very close AP values. This means that meter dial detection is not a very challenging task for existing state-of-the-art generic object detection models. Meanwhile, although YOLOv4 has a slightly higher AP value than our method, the proposed method obtains approximately 3 times the computational efficiency.  
We further compare the performance of the proposed reference point segmentation model with three representative deep segmentation models, i.e., FCN [32], SegNet [35], and U-Net [33]. The comparison results in terms of mIoU and running time are listed in Table 2. It can be easily found that the proposed segmentation model outperforms the other competing models, while it has a slight degeneration in efficiency. More importantly, a slightly increased computational cost results in a significant gain in the accuracy of dial number reading. Meanwhile, the relative error of the recognized pointer value from our method is approximately 0.011. In most practical applications, this small relative error indicates a high reading accuracy. Specifically, it means that the proposed method can achieve an accuracy of 98.9%. For U-Net, FCN, and SegNet, the relative errors are 0.057, 0.094, and 0.116, respectively. The proposed segmentation method is significantly better  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **AP** & **Time (ms)** \\ \hline Faster R-CNN & 94.1 & 429 \\ SSD & 94.7 & 267 \\ RetinaNet & 95.3 & 302 \\ YOLOv4 & 95.9 & 181 \\ Our Method & 95.1 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of different detection methods for meter dial location.  
than the competing segmentation methods. In addition, Figure 6 shows six meter images and their reading results obtained by our method. It can also be found that the proposed method accurately locates the meter dials and recognizes the values of dial pointers. These results clearly verify the effectiveness of our method.
## 3 Methodology
### Loss Function  
To calculate the loss for each pixel, COD tasks typically use binary cross entropy loss (LBCE). However, when the background pixels are far more than the current scene pixels, the model tends to ignore the foreground. [PERSON] [37] used a weighted binary cross entropy loss (\(L_{w}BCE\)) that assigned different weights to pixels based on the difference between them and their neighborhoods. In addition, they also introduced weighted _IOU_ loss (\(L_{w}BCE\)), which could achieve global constraints, and was proven effective in salient object detection.  
\[L=L_{w}BCE+L_{w}IOU. \tag{10}\]  
\(L_{w}BCE\) is the weighted binary cross-entropy loss. Binary cross-entropy loss is commonly used in binary classification problems to measure the difference between the probability distribution predicted by the model and the actual labels. \"Weighted\" implies that each sample can have a different level of importance in the calculation of the loss. \(L_{w}IOU\) is the weighted Intersection over Union (IoU) loss. The Intersection over Union is a commonly used metric to evaluate the performance of object detection models, especially in terms of bounding box predictions. It calculates the overlap area between the predicted bounding boxes and the actual bounding boxes. The weighted version means that different predictions can have varying weights in the loss calculation. Overall, this formula combinesbinary cross-entropy loss and Intersection over Union loss, which we use for camouflaged object detection tasks where it is necessary to consider both classification accuracy and spatial location accuracy. Through this combination, we can simultaneously optimize our model's ability to classify and locate camouflaged objects.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 4 Use Cases
### Network Densification
For this use case, a BBU, providing vPHYs as a service to MNVOs/verticals/operators, would have the proposed hypervisor architecture multiplexing the signal of several vPHY at downlink side while a channelizer would be deployed at uplink side in order to provide each one of the vPHYs with a signal corresponding to its allocated bandwidth, just as depicted in Figure 4. This use case is aligned with standardization efforts made by 3 GPP that consider a BS serving both LTE and NB-IoT users (mobile devices and sensors as shown in Figure 7) [40]. This application of the proposed hypervisor allows a single BS to flexibly serve LTE devices and NB-IoT devices (e.g., sensors), decreasing costs and increasing the spectral efficiency by densification. Figure 7: Application of the proposed hypervisor where a single BS serves both LTE and NB-IoT devices/sensors.
# I/Q Imbalance and Imperfect SIC on Two-Way Relay NOMA Systems
## 3 Performance Analysis
### Outage Probability Analysis
}e^{-\bar{b}_{2}\beta_{m}\left|A\right|^{2}};\quad\beta_{m}= \frac{\gamma_{thm}}{\rho_{u}a_{2}\left(\left|\left|\mu_{r}\right|^{2}-\gamma_{ thm}\left|\
u_{r}\right|^{2}\right)},\) with \(\left|\mu_{r}\right|^{2}>\gamma_{thm}\left|\
u_{r}\right|^{2};\)\(\Theta_{3}=\tilde{b}_{2}^{m_{1}-1}\tilde{b}_{1}^{j}\beta_{m_{1}}^{m_{1}}a_{1}^{n_{1}} \rho_{u}^{n_{1}}\left(\left|\
u_{r}\right|^{2}+\varepsilon\left|\mu_{r}\right| ^{2}\right)^{n_{1}}\left(\left|A\right|^{2}\right)^{m_{1}-n_{1}};\)\(\beta_{0}=\max\left(\beta_{1},\beta_{2}\right);\)\(\beta_{2}=\frac{\gamma_{thm}}{\left|\mu_{r}\right|^{2}\rho_{r}\left(\left|\mu_{l}\right|^{2}b_{2}- \gamma_{thm}\left(\left|\varepsilon\right|\mu_{l}\right|^{2}b_{1}+\left|\
u_{r }^{\ast}\right|^{2}\right)\right)}\), with \(\varepsilon=\left[0,1\right]\), \(b_{2}>\frac{\gamma_{thm}\left(\left|\mu_{l}\right|^{2}b_{1}+\left|\
u_{r}^{ \ast}\right|^{2}\right)}{\left|\mu_{l}\right|^{2}}\) and \(\beta_{1}=\frac{\gamma_{thf}}{\left|\mu_{r}\right|^{2}\rho_{r}\left(\left|\mu_ {l}\right|^{2}b_{1}-\gamma_{thf}\left(\left|\
u_{r}^{\ast}\right|^{2}+\left| \mu_{l}\right|^{2}b_{2}\right)\right)}\), with \(b_{1}>\frac{\gamma_{thf}\left(\left|\
u_{r}^{\ast}\right|^{2}+\left|\mu_{l} \right|^{2}b_{2}\right)}{\left|\mu_{l}\right|^{2}}\)._  
Proof. : Please see Appendix B.
## 3 Materials and Methods
### Geometric Correspondence
#### 3.5.1 Feature Extraction  
The contour features of the image are obtained by employing an edge detection algorithm [29]. Commonly employed edge detection operators are the Sobel operator, which is a first-order differential operator, the DoG operator, which is a second-order operator, and the Canny operator.  
The Sobel operator approach is known for its computational simplicity and speed. On the other hand, the DoG operator is not only straightforward to implement and rapid in calculation, but it also demonstrates robustness to scale changes and rotation, resulting in the extraction of detailed local feature information. The Canny operator has a high rate of detecting edges and precise localization of the edge points. However, it is susceptible to noise and may result in the loss of finer features of the image's edges during the detection process.The edge detection results are shown in Figure 6.  
In comparison with the DoG operator and Canny operator, the Sobel operator detection yields detection outcomes that preserve the entire outline of the target and also demonstrate anisotropy. Hence, in this study, we opt for the Sobel operator for the purpose of detecting edges in images.
## 3 Controller Design
### Sliding Mode Controller Design (SMC)
Figure 6 illustrates the block diagram of the sliding mode control implementation for the manipulator, showcasing that the input is the desired trajectory of the manipulator involving both translation and orientation motions. The controller employs the inverse kinematic Equations (5) and (6) to transform the desired position and orientation of the manipulator into the necessary actuator angles. These angles are subsequently utilized as set-points for the six local controllers. The controller then executes the required control actions on the hybrid stepper motor drivers, enabling the accurate rotations of each input link and achieving the desired angles.
## 5 Experimental Results
### Results without Pathologies
#### 5.2.4 Results for Decision Tree with Classical Feature Selection (DT-RFECV)  
For this case, RFECV has selected a single variable, age, achieving an average accuracy rate of 0.6821 and a maximum of 0.7186, as shown in Table 3. Classical Feature Selection algorithms have solutions with very few variables; in our case, we are looking for models that explain more extensively the causes of overweight and obesity, so we explore other solutions such as those based on evolutionary algorithms that allow us to obtain models with a greater number of variables and similar accuracy. Looking at Figure 3, the accuracy reaches its maximum value with only one variable, but here we have to consider two facts:  
1. This value is not very far from the case of using all the variables;
2. From a medical point of view, the use of a single variable model does not provide any help to clinical practice.  
Figure 3 shows the variation of the cross-validation score (inside RFECV strategy) for the different algorithms for both the pathology and non-pathology cases. This value is of little interest if we look at the small difference in the values on the horizontal axis and at the fact that we are comparing two different datasets. What really interests us is to see the differences in the number of selected features (peaks marked with a red dot) and how these values vary depending on the algorithms and the datasets.  
Figure 9: Impact of variables with SHAP, Gradient Boosting with _Stud_ selection, without pathologies.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 3 Materials and Methods
### Volume Estimation for Length Control  
With reference to Equation (1), the function \(sin(\theta)/\theta\), also known as the \(sinc\) function in the field of signal processing, is bijective within the range \(0\leq\theta\leq\pi/2\). Hence, for a given desired actuator length, \(L(\theta)\), a single corresponding \(\theta\) value can be found. Unfortunately, Equation (1) cannot be arranged to solve for \(\theta\) directly because the function \(sinc(\theta)\) has no simple inverse. Instead, lookup tables and linear interpolation were used to estimate \(\theta\). Using the resulting value of \(\theta\), the volume of the chamber required to reach \(L(\theta)\) can be found using Equation (3).
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### Special Platoon Lane Change Procedure  
The platoon leader can automatically detect the behavior of platoons on other lanes through V2V communications, and then adjust the velocity of its platoon for the sake of safety. On the freeway, several platoons in a certain area of the road segment can be depicted in Figure 2, which shows the Current Lane (CU) platoon, the Front Left Lane (FL)platoon, and the Rear Left Lane (RL) platoon. We denoted the velocity and the position of platoon \(P_{CU}\) as \(v_{CU}\) and \(x_{CU}\), respectively.  
Since the platoon leader is the command vehicle, the platoon leader is responsible for the entire traffic condition detection and lane-changing decision. Platoon members only follow the leader's commands. In this work, we assumed that the \(l+1\) lane, the left lane as shown in Figure 2, is the overtaking lane. The platoon leader constantly evaluates the safety distance of the \(P_{RL}\) and \(P_{FL}\) through V2V communications and decides when to change the entire platoon to the left lane.  
We assumed that at least one vehicle (\(|P_{n}|\geq 1\)) is driving in the lane in each platoon. For example, when platoon \(P_{CU}\) executes a lane change operation, it should not cause \(P_{RL}\) to emergency brake and there should not be a collision. In other words, \(P_{CU}\) is completely in front of \(P_{RL}\), and \(P_{CU}\) is completely behind \(P_{FL}\). Accordingly, the safety distance between them is \(safe_{CU,RL}\) and \(safe_{CU,FL}\). The platoon leader calculates the safety condition using the following equations:  
\[\begin{cases}x_{CU}-x_{RL}\geq safe_{CU,RL}\\ safe_{CU,RL}=L_{CU}+(v_{RL}-v_{CU})\cdot T+d_{pgap}\\ L_{CU}=|P_{CU}|\cdot(L_{j}+d_{pgap})-d_{pgap}\end{cases} \tag{12}\]  
\[\begin{cases}x_{FL}-x_{CU}\geq safe_{CU,FL}\\ safe_{CU,FL}=L_{FL}-(v_{FL}-v_{CU})\cdot T+d_{pgap}\\ L_{FL}=|P_{FL}|\cdot(L_{j}+d_{pgap})-d_{pgap}\end{cases} \tag{13}\]  
where \(x_{CU}\), \(x_{RL}\), and \(x_{FL}\) are the position of the platoon (platoon leader), \(L_{CU}\) and \(L_{FL}\) are the platoon length, \(L_{j}\) is the vehicle length, \(v_{RL}\), \(v_{CU}\), and \(v_{FL}\) are the velocity of the platoon, \(T\) is a time gap, and \(d_{pgap}\) is the platoon inter-distance.  
Note that the vehicles are equipped with a safe movement control model (e.g., ACC model and CACC model) and V2V communications. This feature will allow the vehicles to obtain necessary information, such as the position and velocity change in neighboring vehicles, to dynamically determine road conditions. However, if the situation of performing lane-changing is unsafe, the platoon \(P_{CU}\) will stay in the original lane in order to wait for the vehicles of \(P_{RL}\) or \(P_{FL}\) to pass through, and then initiate the lane-changing procedure again to seek the safety condition that allows for lane-changing.
## 4 Real-Time Implementation and HIL Method  
The real-time implementation of the proposed system is presented in Figure 3. The reference signal and the 2 DOF manipulator robot model were realized in Matlab. The neural identifier based on the RHONN architecture, the EKF algorithm for the training, and the controller based on the discrete time sliding modes were implemented in a hardware-in-loop architecture using in the Evaluation Board Virtex 7 FPGA with an internal data acquisition device.  
The testing of the system is performed with a virtual stimulus provided in HIL architecture so it gives an extraordinary opportunity to perform a real test case with a virtual stimulus, allowing the system to process the signals in real time and consequently obtain results in real time.  
The nonlinear 2 DOF manipulator robot is modeled in Matlab using the discrete-time presented in Section 2. The RHONN neural network is trained online using the EKF algorithm, where the associated state and measurement noise covariance matrices are composed by the coupled variance between the plant states. The implementation of the RHONN as the neural identifier was realized in the XSG design and the hardware-in-loop approach with a Virtex 7 FPGA. The results of the identification process and tracking control architecture was validated in a Matlab/Simulink environment and with the FPGA results. Figure 4 shows an image of the hardware-in-loop architecture.
## 4 Support Vector Machine Models
### Performance Evaluation  
It is necessary to know the performance of the SVM model. To evaluate the performance of forecasting solar radiation data, statistical indicators were used [67]. According to the literature, there are five popular indicators that could determine the accuracy of the predicted data [68; 69]:  
* Mean absolute percentage error (MAPE): It is used to express the absolute error of the predicted and observed variables in percentage [70]: \[MAPE=\frac{1}{m}\sum_{i=1}^{m}\left|\frac{y_{i}-\hat{y_{i}}}{y_{i}}\right|\] (12)
* Root mean square error (\(RMSE\)) sizes the goodness of the fit related to forecast with high errors [71]:  
Figure 4: Mapping of the kernel function.  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y_{i}})^{2}} \tag{13}\]
* Mean bias error (\(MBE\)) indicates the deviation of predicted data from the observed data to provide the performance information of short and long-term use of the model [72]: \[MBE=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y_{i}})\] (14)
* Mean absolute error (\(MAE\)) indicator gives a perspective of the performance of the prediction model by viewing how close the predicted variables are to observed variables [73]: \[MAE=\frac{1}{m}\sum_{i=1}^{m}|y_{i}-\hat{y_{i}}|\] (15)
* Relative root mean square error (\(RRMSE\)) quantifies the relative spread in the error [74; 75] \[RRMSE=\frac{1}{g}\sqrt{\sum_{i=1}^{m}\frac{(y_{i}-\hat{y_{i}})^{2}}{N}}\] (16) where \(y_{i}\) is the global solar radiation measured, \(\hat{y_{i}}\) is the predicted global solar radiation, \(\hat{y}\) is the mean global solar radiation, \(m\) is the number of forecast data points and \(N\) is the number of validation data. These indicators make it possible to know the efficiency of the SVM models: If the statistical indicator value is zero in the ideal case and presents a good performance if is closer to zero [8; 57; 67; 76].