esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 3 Numerical Analysis
### Parameters for CAVs and HVs  
In general, parameters need to be calibrated using real data. However, due to the current situation, it is not possible to calibrate the parameters required for experiments based on actual traffic conditions. Consequently, several parameters are derived from previous studies to simulate the real vehicle situation. The dynamic performances of CAVs and HVs are assumed to be the same in this study, and the main difference lies in the reaction time. The study assumes that the CAVs are equipped with 100% fully connected communication devices, allowing seamless communication between vehicles. This enables the CAVs to cruise with constant headways, ensuring string stability. Additionally, the study assumes ideal communication conditions, without any packet drops or communication delays. Furthermore, the analysis considers a level terrain and excludes the presence of heavy vehicles. All vehicles are assumed to maintain the same size, with a length of 4.5 m. The parameters used in the numerical analysis are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**References**} &
\begin{tabular}{c} **Type of** \\ **Vehicle** \\ \end{tabular} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(\mathbf{\tau_{HH}}\) **(s)**} & \multirow{2}{*}{\(\mathbf{\tau_{HC}}\) **(s)**} & \multirow{2}{*}{\(\mathbf{\tau_{CH}}\) **(s)**} & \multirow{2}{*}{\(\mathbf{\tau_{CC}}\) **(s)**} \\ \hline
[7] & CAV &
\begin{tabular}{c} Simulation with \\ PTV Vissim 11 \\ Simulation with \\ simulation model \\ MIXIC 1.3 \\ \end{tabular} & 0.9 & 0.9 & 2.0/2.5 & 0.6 \\
[21] & CACC & simulation model & 1.4 & 1.4 & 1.4 & 0.5 \\
[35] & CACC/ACC &
\begin{tabular}{c} Simulation with \\ PTV Vissim 5.3 \\ analysis \\ \end{tabular} & / & / & 1.4 & 0.5 \\
[27] & CAV &
\begin{tabular}{c} Numerical \\ simulations \\ \end{tabular} & 0.8–2.2 & 0.8–2.2 & 0.7–1.5 & 0.6–1.1 \\
[38] & CACC/ACC &
\begin{tabular}{c} Numerical \\ simulations \\ \end{tabular} & / & / & 0.8–2.2 & / \\
[39] & AV &
\begin{tabular}{c} Simulations with \\ Aimsun 20.0.1 \\ \end{tabular} & 1.8 & 1.8 & 1.2 & 0.9 \\
[40] & AV &
\begin{tabular}{c} Simulations with \\ SUMO 1.0.0 \\ \end{tabular} & 0.9 & 0.9 & 0.6 & 0.6 \\
[41] & AV &
\begin{tabular}{c} Simulations with \\ simulator \\ \end{tabular} & 1.69 & 1.69 & 0.1–3.0 & 0.1–3.0 \\
[42] & CAV &
\begin{tabular}{c} Simulations with \\ PTV Vissim 11 \\ \end{tabular} & 1.2 & 1.2 & 0.9 & 0.9 \\
[43] & CACC &
\begin{tabular}{c} Simulations with \\ ITS Modeler \\ Qualitative \\ questionnaire \\ \end{tabular} & / & / & 0.9–1.5 & 0.9–1.5 \\
[45] & ACC &
\begin{tabular}{c} Simulations with \\ ITS Modeler \\ Qualitative \\ questionnaire \\ \end{tabular} & / & / & / & 0.3–1.4 \\
[46] & CACC/ACC &
\begin{tabular}{c} Field test \\ Simulations with \\ MATLAB R2014a \\ \end{tabular} & / & / & 1.0–2.6 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reaction time selection of different studies.
## 2 Methodology
### Partition Matching Using Shi_Tomasi and SIFT
#### 2.4.2 Feature Description Using the SIFT Descriptor  
The SIFT descriptor was employed for feature description of the feature points detected in this work. The SIFT descriptor proposed by [PERSON] in 2004 is a local feature descriptor based on the gradient distribution in the detected regions. By assigning one or more consistent orientations to each feature point based on the gradient directions of a local image, the feature descriptor of the feature point can be represented relative to the orientations and therefore achieve invariance to image rotation. For each feature point (x, y) of the scale image \(L\), the gradient magnitude \(m\) (\(x\), \(y\)) and orientation \(\theta\) (\(x\), \(y\)) can be calculated using Equations (9) and (10), respectively.  
\[m(x,y)=\left[(L(x+1,y)-L(x-1,y))^{2}+(L(x,y+1)-L(x,y-1))^{2}\right]^{\frac{1}{2 }}, \tag{9}\]  
\[\theta(x,y)=arctan(\frac{L(x,y+1)-L(x,y-1)}{L(x+1,y)-L(x-1,y)}), \tag{10}\]  
An orientation histogram is formed from the gradient orientations of the sample points within a region around the feature point. It has 36 bins covering the 360\({}^{\circ}\) range of orientations. Each bin covers 10 degrees. As shown in Figure 5, the peak in the histogram, indicating the dominant orientation of the local gradient image, is assigned as the orientation of the feature point.  
After determining the orientation of a feature point, the SIFT descriptor of the feature point is calculated using three steps, as illustrated in Figure 6.  
Figure 4: Schematic of the [PERSON]_[PERSON] algorithm.  
Figure 5: The orientation histogram of a feature point.  
First, the coordinates of the descriptor and the gradient orientations are rotated relative to the orientation of the feature point to achieve orientation invariance. Then, the orientation histograms over 4\(\times\)4 sample regions centered with the feature point are created. There are eight directions for each orientation histogram, in which the length of each arrow represents the size of that histogram entry. Therefore, [PERSON] suggested using a \(4\times 4\times 8=128\) element feature vector for each feature point. Finally, the feature vector is normalized to unit length to reduce the effects of illumination change.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 4 Design and Simulation
### Quad-Band Circuit Design
#### 4.2.1 Scenario 1: Quad-Band Circuit with a Single Receiving Antenna  
In this scenario, the four-matched single-band voltage multipliers had a single receiving antenna at the input; the output of the four circuits was connected directly via the load resistor [52; 74] as illustrated in Figure 6. A band-pass filter (BPF) of the elliptic design was inserted after the receiving antenna for each circuit of the four voltage multipliers. The parameters of BPFs are depicted on the schematic circuit in Figure 6.  
Figure 6: The proposed quad-band circuit with a single receiving antenna.
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 3 Multiple Preprocessing Hybrid Level Set Model
### Multiple Preprocessing
#### 3.2.4 Initial-Value Detection  
According to the shape-based energy functional (Equation (10)), the initial value significantly influences the segmentation results in HLSM. Furthermore, a suitable initial value can reduce the number of iterations and speed up the convergence of the contour. In this paper, the adaptive threshold-based ellipse fitting is proposed to obtain the initial value. Figure 16 shows the flowchart of initial value detection. Figure 17 shows the result of each process.  
Figure 16: The flowchart of initial-value detection ((**a**–**f**) correspond to the images in Figure 17).  
Figure 14: Frequency domain image after each process.  
Figure 15: After blood-vessel and noise removal, bright noise (parts indicated by blue arrows) is generated, rendering the OD boundaries not obvious.  
The value channel (Figure 17a) in the HSV color space is processed to obtain the initial value. First, the binary image (Figure 17b) is received by using the adaptive threshold (Equation (13)). \(I\) is the original image, and \(I_{binary}\) is the obtained binary image. \(w,h\) are the width and height of the batch, respectively. In this paper, _wandh_ are the half width and height of ROI, respectively. \((x,y)\) is each point in the image, while \((x^{{}^{\prime}},y^{{}^{\prime}})\) is each point in the batch. \(C\) is the offset that was set as 0 in this paper. Then, the small links between each connected area are disconnected with morphological opening (Figure 17c). Next, the maximal 4-connected area is selected (Figure 17d), and its boundary is detected (Figure 17e). Lastly, the initial value (Figure 17f) is generated with ellipse fitting from this boundary. OD is oval-shaped in fundus images, and an ellipse-shaped initial value can render the segmentation result roughly elliptical; therefore, the segmentation result is not affected by bright noise. As shown in Figure 18, there are some PPA regions in Figure 18a, the Figure 18b and Figure 18c are the binary image after adaptive thresholding and the image of maximal 4-connected area, respectively. These PPA regions are removed in initial-value detection, as the detected initial value excludes PPA regions. Under the action of a shape-based term, segmentation results are not affected by PPA regions.  
\[I_{binary}(x,y)=\left\{\begin{array}{c}1,\text{ if }\frac{\sum_{x^{{}^{ \prime}}=-x-\lfloor\frac{y}{2}\rfloor}^{x^{\prime}+\lfloor\frac{y}{2}\rfloor} \sum_{y^{{}^{\prime}}=-\lfloor\frac{y}{2}\rfloor}^{y^{\prime}+\lfloor\frac{y} {2}\rfloor}I^{(x^{{}^{\prime}},y^{{}^{\prime}})}}{w\times h}-C\leq I(x,y)\\ 0,\text{ else}\end{array}\right. \tag{13}\]  
Figure 17: The results of initial-value detection.
## 4 Capability on Obstacle Avoidance
### Summary of This Section  
In a comparison of the capabilities of FWS and RWS on obstacle avoidance, it was observed that RWS has the behavior of the rear end being pushed out at the first timing and causes a delay of lateral displacement. Using dynamic simulation, it was shown to be especially difficult for the driver to steer to compensate for the delay and avoid the obstacle in RWS. Therefore, it can be judged that FWS is superior to RWS in obstacle avoidance performance.  
PMVs with active tilting mechanisms have obstacle avoidance ability equal to or higher than that of passenger cars, because they have a much smaller roll moment of inertia than passenger cars and responsiveness equivalent to passenger cars. Thus, sufficient social acceptance of the capability of obstacle avoidance of PMVs with active tilting mechanisms could be confirmed also from their dynamic mechanism.
## 3 Results and Discussion
### Results of 3D CFD Modeling of Liquid-Gas Ejector
#### 3.1.3 Recirculation and Vortex  
The CFD simulation can predict the internal flow regimes in the liquid-gas ejector, as shown in Figure 7a. According to this Figure, air is entrained by a water jet into the mixing chamber. The gas and liquid are in contact with each other in mixing tube and then liquid-gas mixture is discharged through the diffuser. The magnified interface around the mixing chamber (Figure 7b) shows that a recirculation zone occurs and the vortex is generated in this section that immediately disappears in the diffuser [44]. The main reason that causes the creation of recirculation zone is the influence of mainstream on circular jet. With mixing the mainstream and circular jet bends, a fan-shaped flow trajectory is made. The mentioned created flow acts like an umbrella to become an aerodynamic barrier in the current field, behind which a negative pressure zone is created, and due to this phenomenon, a recirculation zone is made that is shown in Figure 8.  
Figure 6: Variation of gas phase hold-up with time at a constant liquid flow rate of 2.17 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s for the air–water system (nozzle diameter = 6 mm).  
Figure 7: **(a)** The pathlines of air velocity [m/s], **(b)** the vector of air velocity.  
3.1.4. Ejector Performance and the Effect of Liquid Flow Rate on Negative Pressure  
Figure 9 shows the pressure and velocity contour inside the liquid-gas ejector. It can be seen from these contours that the maximum flow velocity takes place at the exit of the nozzle. Additionally, momentum exchange and mixing with the secondary fluid stream decrease the velocity. Another noteworthy finding is that the effects of boundary layer give rise to a velocity gradient from the wall to the center line flow of the ejector. At the nozzle's exit, the view depicts the transformation of pressure energy to kinetic energy. There is also pressure energy generated in the diffuser section from the remaining kinetic energy. Negative pressure is a criterion for evaluation of the performance of ejector. The negative pressure formed when the fluid passes through the narrow throat enables the self-entrainment of ambient air into the channel. In this regard, we investigated the relationship between negative static pressure and liquid flow rate. Figure 10 illustrates that at higher liquid flow rate (2.49 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s to 2.64 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s), more negative pressures are observed in broader area, which is in agreement well with observation in literature reported by [PERSON] et al. [46]. By enhancing the mass flow rate of liquid, the outlet velocity of the ejector increases. This increment has a direct impact on the creation of negative pressure zone in this position. Under other conditions, the greater the speed, the greater the negative pressure.  
Figure 8: Schematic of a circular transverse jet in cross flow [45].  
Figure 9: (**a**) The pressure contour [Pag], (**b**) the velocity contour [m/s].
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
###### Abstract  
Spectral efficiency is crucial for implementing 5G cellular networks and beyond. Non-orthogonal multiple access (NOMA) is a promising scheme to enhance efficiency. This paper introduces two improvements that will further enhance the channel capacity using the NOMA algorithm. We first introduce a novel algorithm, the User Sub-Channel Fair Matching Algorithm (USFMA), by applying a new sub-channel sorting and compensations scheme and then benefiting from the well-known Hungarian algorithm to allocate users to each sub-channel in a way that guarantees an optimum overall system performance. Then, for per sub-channel power allocation, we convert the non-convex objective function into a convex sub-problem using the concave-convex procedure (CCP) by converting the objective function into convex sub-problems and using the successive convex approximation to solve the convex sub-problems to find effective sub-optimal solutions. We have built a MATLAB simulation cellular environment to evaluate and compare the system performance with other known schemes. The results are promising and showed significant improvements compared to the other capacity and energy efficiency schemes.  
non-orthogonal multiple access; user allocation algorithm; Hungarian algorithm; user fairness; non-convex problem; DC programming +
Footnote †: journal:  
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## 3 Results and Discussion
### Classification Performance of the Proposed Model
#### 3.1.3 Network Parameter Optimization  
In order to optimize the network parameters, we have conducted comparative experiments to compare and optimize the parameters of batch size, regularization options and loss function. Based on the experimental results of the time-frequency features extraction section, the window function is fixed to Hann, and the window size is fixed to 256. In this part, iterations generations are also 100 and the evaluation approach is still five-fold cross-validation.  
As shown in Table 2, batch sizes are 16, 32, 64, respectively, loss functions are Cross entropy loss (abbreviated as C) [40] and multi-class Hinge loss (abbreviated as H) [43]. In the column where L2 Regularization is located, 'Yes' means 'with regularizations' and 'No' means 'without regularizations'. The experimental results show that the test accuracy rate of method with regularization term is over 99.5% regardless of the size of the batch size and the loss function. It is worth mentioning that the accuracy is 99.92% when the batch size is 32 and the loss function is Cross entropy loss. Therefore, we believe that the regularization item is necessary. In terms of Loss function, the cross entropy loss performs better than the multi-class Hinge loss as a whole.  
After the parameter optimization, the trained model parameters can be saved. When the model is later applied to a new set of data, the model can predict the tea classification with original tea sample as input.  
Figure 8: (**a**) Classification accuracy for Hann window with different window size; (**b**) classification accuracy for Hamming window with different window size; (**c**) classification accuracy for Gaussian window with different window size.
## 4 GNC Subsystem Architecture
### Attitude Determination and Control  
The attitude determination and control (ADCS) subsystem includes a fine sun sensor, photodiodes (coarse sun sensors), magnetometers, and gyro sensors, which estimate the attitude states as the quaternion and angular velocity using the extended Kalman filter (EKF) algorithm [38]. The deputy satellite, which requires precise attitude and orbit control, has two actuators: the reaction wheel assembly and the magnetorquer. The primary actuator is a reaction wheel that controls attitude pointing for charging, communication, and thrust firing. Because the reaction wheels generate torque through momentum conservation, the magnetorquer reduces the exceeded angular speed of the wheels. The requirements of the ADC subsystem are presented in Table 5.
## 3 Calibration and Results
After calibrating the magnetometer, we measured the 99% pure magnetite microparticles, which had been previously purchased. This sample was previously measured using a scanning magnetic microscope, which has a magnetic moment sensitivity of \(1.3\times 10^{-11}\) Am\({}^{2}\)[16]. To obtain a magnetization curve using this equipment, scanning magnetic maps for each applied magnetic field are necessary. Figure 6 represents magnetic maps of induced magnetic field, we can obtain the magnetic moment, using the point technique developed by [PERSON] and his collaborators [17]. This method consists of obtaining two points in each scanning magnetic map: One with the minimum intensity of the sample-induced magnetic field and another with the maximum intensity. Using a moment that considers the cylindrical cavity of the sample holder, we can obtain the magnetic moment. Afterward, by dividing by the sample mass, we can obtain the magnetization curve (See Figure 6). The scanning magnetic microscope is expensive and has a high cost to be built in most research laboratories because it requires some commercial equipment [16]. In Figure 7a, we can observe the comparison curve between the measurement obtained by our homemade instrument and that obtained in a scanning magnetic microscope. Comparing the two devices (Scanning Magnetic Microscope and PUC-Rio), the average relative error of the most distant point, in relation to the same magnetic field applied, was approximately 39%, whereas in the saturation region, the largest error was only 29%. We believe that these values could be improved if an Arduino \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Applied Field (T)** & 0.33 & 0.32 & 0.30 & 0.29 \\ \hline
**PUC-Rio (Am\({}^{2}\)/kg)** & 45.7 & 44.6 & 49.2 & 49.9 \\ \hline
**SQUID (Am\({}^{2}\)/kg)** & 53.9 & 53.8 & 53.5 & 53.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Magnetization values in Am\({}^{2}\)/kg for each applied magnetic field. Figure 6: Magnetic maps obtained through a scanning magnetic microscope, field from 231 mT to 1 mT, and back to 274 mT. board or an acquisition board had been used, such as a National Instruments Board, model NI USB-6008 (See Figure 7b). Thus, more points could be acquired. The existence of high-resolution magnetometers, which are often expensive equipment, prevents the application of this study in basic physics laboratories. The constituted magnetometer, as well as the theoretical model of a simple dipole, can be a fundamental step for the application of this type of equipment in the teaching of Physics, mainly in the teaching laboratory, since most of the components that make up this equipment are accessible. Feeding in this way, the study of magnetism of materials such as magnetic nanoparticles, microparticles, and even bulk material.
## 1 Introduction
Without using zero vectors to reduce the circulating current, this modulation synchronizes the output voltage of the converter by carrier phase shifting; however, this is so complex that it is not easy to realize. After analyzing the production principle of ZSCC, [PERSON] et al. proposed a method to suppress it using a proportional-integral (PI) controller to regulate the zero-vector action time in a space vector pulse width modulation (SVPWM) controller [20]. This control scheme is simple to implement, but the PI technique is susceptible to different operating conditions [21]. [PERSON] et al. used a deadbeat controller instead of a PI controller, which further verified the correctness of the zero-vector action time adjustment method [22]. [PERSON] et al. improved the traditional PI control method by adding zero-vector feedforward to SVPWM control [23]. [PERSON] et al. divided the ZSCC into a small number of high-frequency components and a large number of low-frequency components. The high-frequency components were suppressed by a dual-carrier SVPWM, and a variety of low-frequency harmonics were controlled by a PR controller [24]. In view of the above analysis, this paper examines the ZSCC of two paralleled three-phase full-bridge voltage-source PWM converters with common DC bus and proposes a strategy to suppress ZSCC based on proportional-integral quasi-resonant (PIQR) control and feedforward compensation control. Firstly, for a single converter, the dual-loop decoupled control of inner current loop and outer voltage loop was used to generate the reference voltage, and the switching signal was generated by an SVPWM controller. Then, by establishing an equivalent physical model of ZSCC, the causes were analyzed. To reduce the amplitude and main harmonic components of the ZSCC, a zero-vector duty ratio adjusting factor was initially generated by the PIQR controller. Finally, for the purpose of eliminating the effect of the difference of the zero-sequence duty ratio on the system and improving the dynamic response of the zero-sequence current loop, a feedforward compensation controller was added to correct the adjusting factor. The simulation results showed that the proposed method could effectively reduce ZSCC, and its performance was better than the traditional PI control. The rest of this paper is organized as follows. In Section 2, the voltage-current dual-loop decoupled control strategy of a single PWM converter is presented. Section 3 establishes the mathematical and physical models of paralleled PWM converters and analyzes the generation of ZSCC. The proposed control strategy in parallel systems is introduced in Section 4. Section 5 illustrates the simulation process and presents the results in detail. Section 6 concludes the paper.