esa-sceva/satcom-chunk-collection huggingface dataset:
# Sub-Diffraction Focusing Using Metamaterial-Based Terahertz Super-Oscillatory Lens
## 2 Materials and Methods
### Experimental Setup  
The schematic of the experimental setup is shown in Figure 7. A Keysight N5247A network analyzer was attached to a pair of N5256X10 WR-10 mm-wave extenders. This network analyzer for the 0.067 to 0.110 THz frequency range served as an emitter on one side and a detector on the other side. The dynamic range of this network analyzer is 88.5 dB. A circularly polarized incident beam was formed by a linear-to-circular polarizer or wave plate attached to the emitter extender, followed by a corrugated horn antenna to minimize the side lobe levels. A pair of conventional Teflon lenses, each with 150 mm diameter and 100 mm focal length, were used to collimate the antenna output onto the lens. A probe antenna with a 2.1 mm diameter aperture was attached to the detector extender through a second wave plate to capture the beam transmitted through the lens. To avoid the detection of reflected waves from the SOL, an absorber was placed at the tip of the probe antenna. Finally, the intensity distribution of the focused beam spot was measured by moving the detector using an XYZ translation stage. The bidirectional repeatability of the stage we used is \(\pm\)1.5 \(\upmu\)m.  
Figure 6: (**a**) Image of the fabricated super-oscillatory lens (SOL) with circular patterns; (**b**) schematic of the cross-section of the SOL; (**c**) microscopical image of the fabricated slit.
## 2 Preliminaries
### Consensus Tracking Problem  
The consensus tracking problem analyzed in this paper requires that spatially separative agents track a desired reference trajectory via cooperative control, and we take the ILC strategies for each agent. Additionally, we take into account a class of linear heterogeneous multiagent systems, and the dynamics of agent \(i\) are in the following form at the \(k\)th iteration.  
\[\left\{\begin{array}{l}\dot{x}_{i,k}(t)=A_{i}x_{i,k}(t)+B_{i}u_{i,k}(t)\\ y_{i,k}(t)=C_{i}x_{i,k}(t)\end{array}\right.,\;\;\;i=1,2,\cdots,n, \tag{2}\]  
where \(x_{i,k}(t)\in R^{p_{i}}\), \(u_{i,k}(t)\in R^{r_{i}}\), and \(y_{i,k}\left(t\right)\in R^{m}\) are the state and control input and output, respectively, of the agent \(i\), \(A_{i}\in R^{p_{i}\times p_{i}}\), \(B_{i}\in R^{p_{i}\times r_{i}}\), and \(C_{i}\in R^{m\times p_{i}}\) are matrices with proper dimension. The states of agents are different, because this paper studies the consensus problem of heterogeneous multiagent systems, which were mentioned in the article [15]. Since this paper deal with the output tracking problem, the output dimensions of all agents must be consistent with the reference trajectory in order to facilitate comparison with the reference trajectory.  
The reference trajectory \(y_{d}(t)\in R^{m}\) is defined on a time interval \(t\in[0,T]\), which is regarded as the leader's output trajectory. In the distributed coordination control framework, the leader's output trajectory \(y_{d}(t)\) is only accessed by a subset of the following agents, and the left agents reach the leader's trajectory indirectly. The control objective is to design a new ILC algorithm so that the output signals of all following agents converge to the reference trajectory \(y_{d}(t)\) asymptotically, i.e.,  
\[\underset{k\rightarrow\infty}{\lim}\left[y_{d}(t)-y_{i,k}(t)\right]=0,i\in V,t\in[0,T].\]  
**Remark 1**.: _The leader in this paper is a virtual leader, i.e., the reference trajectory is generated by the virtual leader, but the leader does not actually exist, so there will be no collision between the leader and the following agents. Moreover, we assume that there will be no collision between all followers, and we avoid collision by placing all followers in a reasonable position. For example, system (2) formulates the dynamics of a robot in industrial production lines, the output denotes the angle of the robot's joints, and the robots will not have collision problems due to their judicious distance._
###### Abstract  
As roll angle measurement is essential for two-dimensional course correction fuze (2-D CCF) technology, a real-time estimation of roll angle of spinning projectile by single-axis magnetometer is studied. Based on the measurement model, a second-order frequency-locked loop (FLL)-assisted third-order phase-locked loop (PLL) is designed to obtain rolling information from magnetic signals, which is less dependent on the amplitude and able to reduce effect from geomagnetic blind area. Method of parameters optimization of tracking loop is discussed in the circumstance of different speed and it is verified by six degrees of freedom (six degrees of freedom (DoF)) trajectory. Also, the measurement error is analyzed to improve the accuracy of designed system. At last, experiments on rotary table are carried out to validate the proposed method indicating the designed system is able to track both phase and speed accurately and stably. The standard deviation (SD) of phase error is no more than 3\({}^{\circ}\).  
roll angle measurement; spinning projectile; 2-D CCF; magnetic sensor; FLL-assisted PLL
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 3 Control Scheme for Trajectory Tracking  
This section shows how to design the proposed control that allows trajectory tracking in three-dimensional space. It can be given in the following theorem.  
**Theorem 1**.: _Consider the underactuated underwater vehicle described by (1) and (6)-(13). The aim of the control is the stabilization of trajectory tracking errors, i.e., \(\lim_{t\to\infty}x_{e}=\rho_{x}\), \(\lim_{t\to\infty}y_{e}=\rho_{y}\), \(\lim_{t\to\infty}z_{e}=\rho_{z}\), \(\lim_{t\to\infty}\theta_{e}=\rho_{\theta}\), and \(\lim_{t\to\infty}\psi_{e}=\rho_{\psi}\), where \(\rho_{x},\rho_{y},\rho_{z},\rho_{\theta},\rho_{\psi}\) are some constants that define the neighborhood of zero. This means that it is possible to guarantee the convergence of tracking errors to this neighborhood. Thus, if Assumptions 1 \(\div 5\) are met, then the signals from the controller \(\tau_{u}\), \(\tau_{\xi_{4}}\) (including \(\tau_{u}\), \(\tau_{q}\)), and \(\tau_{r}\) enable the desired trajectory to be traced in three-dimensional space when parameter perturbations and external disturbances occur. The full control strategy includes the following:_  
_(a)_ _A kinematic algorithm for stabilizing kinematic errors;_  
_(b)_ _A dynamic algorithm consisting of_ \(\tau_{u}\)_,_ \(\tau_{\xi_{4}}\)_, and_ \(\tau_{r}\)_, which will guide the vehicle to the desired trajectory and provide uniformly ultimate boundedness._  
Proof of this theorem is given in Appendix B.
## 5 Experimentation and Analysis  
### Experiment Platform and Parameter Settings  
The computing equipment used in this study was as follows: a 10 th Gen Intel (R) Core (TM) i7-10870H CPU, an NVIDIA GeForce RTX 3070 laptop GPU, and the Windows 11 operating system. We also used Python version 3.9.7, PaddlPaddle [23] version 2.6.1, and PaddleSeg [24] version 2.8.0 software, and the unified computing device architecture selected CUDA 11.6, deep neural network acceleration library version is CUDNN v8.4. The dataset images were randomly divided into a training set (90%) and a validation set (10%) in a ratio of 9:1. The model is trained on the training set and tested on the validation set.  
Figure 7: Exemplary visual results from various models: row (**a**) illustrates the input images, (**b–f**) display the segmentation outcomes of UNet, ENet, BiSeNetv1, BiSeNetv2, and PP-LiteSeg, and (**g**) displays the results of our approach.  
In this paper, the model training is done using the pre-training weights of ImageNet, which is an image classification dataset containing 1.35 million images, including 1000 categories. The input image scaling is \(512\times 512\) when training the model, the initial learning rate is \(5\times 10^{-3}\), the batch size is set to 4, the maximum number of iterations is 40,000, the optimizer is stochastic gradient descent (SGD) [25], the momentum (momentum) is 0.9, the weight decay (weight decay) is 0.00005, and the learning rate decay strategy is polynomial decay (polynomial decay). The specific parameters are shown in Table 2. The loss function is OhemCrossEntropyLoss [26] with the following expression:  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}\left\{\begin{array}{c}\log(p_{t\arg ct}),y_ {t\arg ct}=1\\ \log(1-p_{t\arg ct}),y_{t\arg ct}=0\\ 0,otherwise\end{array}\right. \tag{7}\]
###### Abstract  
Autonomous mobile vehicles need advanced systems to determine their exact position in a certain coordinate system. For this purpose, the GPS and the vision system are the most often used. These systems have some disadvantages, for example, the GPS signal is unavailable in rooms and may be inaccurate, while the vision system is strongly dependent on the intensity of the recorded light. This paper assumes that the primary system for determining the position of the vehicle is wheel odometry joined with an IMU (Internal Measurement Unit) sensor, which task is to calculate all changes in the robot orientations, such as yaw rate. However, using only the results coming from the wheels system provides additive measurement error, which is most often the result of the wheels slippage and the IMU sensor drift. In the presented work, this error is reduced by using a vision system that constantly measures vehicle distances to markers located in its space. Additionally, the paper describes the fusion of signals from the vision system and the wheels odometry. Studies related to the positioning accuracy of the vehicle with both the vision system turned on and off are presented. The laboratory averaged positioning accuracy result was reduced from 0.32 m to 0.13 m, with ensuring that the vehicle wheels did not experience slippage. The paper also describes the performance of the system during a real track driven, where the assumption was not to use the GPS geolocation system. In this case, the vision system assisted in the vehicle positioning and an accuracy of 0.2 m was achieved at the control points.  
Keywords:signal fusion; odometry; wheels sensors; autonomous driving +
Footnote †: This paper is an extended version of our paper published in the 27 th International Conference \"Mixed Design of Integrated Circuits and Systems\", Lodz, Poland, 25–27 June 2020.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 5 Results  
We performed several experiments with different channel impairments and algorithms to evaluate both AC RL approaches. Table 1 provides the theoretical data rates as a point of comparison for our experiments. The evaluation focuses on analyzing the impact of the channel and chosen RL algorithm.  
The algorithm is trained from scratch each time using a uniform weight initialization and applied on an IEEE 802.11p flowgraph with a bandwidth of 10 MHz. Based on the observed state \(S\) of the previous transmission, the agent produces a probability distribution over the entire action space, which will be referred to as _the recommendation_. After sampling from the recommendation, the chosen MCS is applied to a simulated transmission in the GNU Radio environment. The agent then receives the reward \(R\) as described in Section 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MCS** & **CDR in Mbps** & **Modulation** & **CR** & **CBR in Mbps** \\ \hline
0 & 3 & BPSK & 1/2 & 6 \\
1 & 4.5 & BPSK & 3/4 & 6 \\
2 & 6 & QPSK & 1/2 & 12 \\
3 & 9 & QPSK & 3/4 & 12 \\
4 & 12 & 16 QAM & 1/2 & 24 \\
5 & 18 & 16 QAM & 3/4 & 24 \\
6 & 24 & 64 QAM & 2/3 & 36 \\
7 & 27 & 64 QAM & 3/4 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MCS values with associated coded data rate (CDR), modulation, code rate (CR), and coded bit rate (CBR) for the IEEE 802.11p OFDM PHY layer at a bandwidth of 10 MHz, which represents the used action space for each of the following experiments [20].  
The reward graph displays the effective data rate in Mbps and is used as the main metric for the comparisons between the different approaches. The effective data rate is calculated as the PSR multiplied with the encoded bit rate. Therefore, only correctable packets and detected packets are considered, while non-correctable packets and non-detected ones count as a loss. In addition, the action selection process is shown on the right-hand side of each plot. It covers the absolute number of steps per episode in which an action \(A\) was chosen. At the beginning of each experiment, the recommended actions are approximately equally distributed within an episode, indicating the agent is guessing and cannot rely on any experience. After a few episodes, the agent begins to recognize more promising MCSs and adapts its recommendations accordingly until the count of some actions increase while others decrease noticeably. This metric provides further insights on how the effective data rate is achieved.  
The training procedure iterates for 500 training episodes with 50 steps each, during which the agent's performance typically converges. After each episode, an episodic reward can be determined, which contributes to the smoothed running reward.
## 3 DMPs-DPF Approach for Obstacle Avoidance
### Description of the DPF Coupling Term
Figure 4: Motion analysis diagram of the system and obstacle. In summary, overall considering the velocity, angle, and relative distance, the coupling term can be constituted by multiplying Equations (14), (15), and coefficient \(\gamma\) used to directly adjust the amplitude of force field. The basic form of the coupling term is  
\[p(o,x,v)=\gamma Rv\psi. \tag{16}\]  
It is worth noting that Equation (16) is only suitable for a single and small obstacle, for it treats the obstacle as a point with no size and shape. An obstacle with non-negligible shape and size  
When encountering an obstacle with non-negligible shape and size, which is shown in Figure 5 for illustration, we choose the nearest point on the obstacle to approximate the obstacle's boundary. In this situation, the coupling term can be defined as  
\[p(o,x,v)=\gamma_{p}Rv\psi_{p}+\gamma_{o}Rv\psi_{o}+\gamma_{d}Rv\exp(-kd). \tag{17}\]  
In Equation (17), the first term \(\gamma_{p}Rv\psi_{p}\) is generated by the point \(P\) which represents the nearest point on the obstacle to the system, and the selection of point \(P\) changes in real time with the relative location of the system and obstacle; the second term \(\gamma_{o}Rv\psi_{o}\) is generated by the centroid of the obstacle, which is fixed during the whole process; the third term \(\gamma_{d}Rv\exp(-kd)\) just relies on the relative distance with its purpose to guarantee the coupling term can generate enough force to avoid the obstacle, even if the first two terms are close to 0 when the system is moving towards the obstacle. Equation (17) is the final form of the DPF-based negative feedback coupling term. It can timely feedback the complex obstacle information into the DMPs model in a simple enough form, so as to achieve a stable and smooth obstacle avoidance behavior. Additionally, the learning and generalization process based on DMPs-DPF is similar to the whole process based on DMPs. We only have to combine the transformation system with the corresponding coupling term, the robot can follow similar steps to learn demonstrated tasks and generalize them to a new environment--even existing obstacles on its motion path.
# Track Line Recognition Based on Morphological Thinning Algorithm +
## 5 Discussion  
Beginning with the extraction of medium- and low-speed freight railway track lines, this paper proposes an improved ZS thinning algorithm combined with a denoising algorithm for track line detection. Our algorithm can realize the detection function under complex road conditions, different weather and other scene conditions, and has a higher feasibility and better effectiveness than several others. However, due to uneven illumination, shooting angle, heavy snow and other severe weather conditions, the shallow layer feature information of the track in the image is insufficient, which undoubtedly has adverse effects on the effect of the algorithm. In addition, the time taken to process a single frame of image exceeds 150 ms, which does not meet the video-processing requirement of 10 frames per second on a fast railroad. The next work direction of the authors is to achieve wider adaptability of the optimization algorithm and reduce its processing time.  
Conceptualization, W.N. and Y.Z.; methodology, W.N. and Z.C.; software, W.N. and Z.C.; validation, W.N., X.S. and Y.Z.; formal analysis, W.N.; investigation, X.L.; resources, W.N.; data curation, W.N.; writing--original draft preparation, W.N.; writing--review and editing, Y.Z.; visualization, X.S.; supervision, X.S.; project administration, W.N.; funding acquisition, W.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jiangsu Province (Grant number BK20210710).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Video 1**} & \multicolumn{2}{c}{**Video 2**} & \multicolumn{2}{c}{**Video 3**} & \multicolumn{2}{c}{**Video 4**} \\ \hline
**Scenes** & straight (Uphhill, Downhill) & straights, Curves & straight (Different Light) & straight, turnouts \\ \hline
**TNF** & 498 & & 486 & & 524 & 516 \\ \hline
**GNF** & good frames & accuracy rate & good frames & accuracy rate & good frames & accuracy rate & good frames & accuracy rate \\ \hline
**Result** & 464 & 93.17\% & 443 & 91.15\% & 492 & 93.89\% & 465 & 90.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the comparative experiment under subway tunnel conditions scenario.  
Figure 19: Track region extraction in different subway tunnel conditions (including uphill and downhill straight track line, curved track line, multiple turnouts).
## 4 Numerical Analysis
### Analysis of Travel Time Characteristics
(3) High EV penetration (B\({}_{\rm EV}\) = 0.8 to 1.0)  
When B\({}_{\rm EV}\) is high (i.e., B\({}_{\rm EV}\) = 0.8 to 1.0), EVs account for almost all the vehicles on the road, which means that almost all vehicles need to use charging piles. Therefore, the burden on the charging pile increases, and the queuing phenomenon at recharge nodes becomes common, resulting in a significant extension of the recharge time. When Q exceeds 500, the recharge time tends to stabilize because the charging piles approach their maximum service capacity. Although the traffic volume continues to increase, due to the limited number of charging piles and queuing space, the replenishment time will not increase indefinitely,  
Figure 4: Time spent at a recharge node tq. reaching a saturated state and plateauing. Due to the saturated charging demand, the charging piles become the bottlenecks of the system, and the efficiency of the overall traffic system is reduced. In this case, even if the traffic volume increases, the replenishment time will not be significantly extended because the number of charging piles and queuing space can no longer accommodate additional charging needs. However, the lack of power for EVs can cause vehicles to slow or even stop, causing traffic paralysis. 2. The total time spent by a vehicle at a recharge station \(\mathrm{T_{W}}\)  
T\({}_{W}\) represents the total time spent at a recharge node, which is calculated according to Formula (8), and the corresponding results for 10 kinds of EVs are calculated, as shown in the figure below. Figure 5 indicates that the magnitude of \(\mathrm{T_{W}}\) is closely related to that of \(\mathrm{t_{q}}\). Similarly, BEV has a significant impact on \(\mathrm{T_{W}}\). At low electric vehicle penetration rates (\(\mathrm{B_{EV}}\) = 0 to 0.3), \(\mathrm{T_{W}}\) increases slowly with increasing traffic volume, the number of charging stations is sufficient, and the waiting time is short. At medium penetration rates (\(\mathrm{B_{EV}}\) = 0.4 to 0.7), \(\mathrm{T_{W}}\) increases significantly as the demand for charging increases and charging stations approach saturation. At high penetration rates (\(\mathrm{B_{EV}}\) = 0.8 to 1.0), almost all vehicles require charging, the burden on charging stations is high, \(\mathrm{T_{W}}\) increases and tends to stabilize, and charging stations become the bottleneck of the system, leading to a decline in overall traffic efficiency.
## 2 Feature Extraction Method for Axis Motion Trajectories
### Axis Trajectory Signal Feature Extraction  
This paper applies the distance sequence representation method to extract features from axis trajectory signals. Using the adaptive feature point selection method, the axis trajectory pattern is traversed to obtain a series of feature coordinates. The distances between each feature coordinate and the unique fixed point in the two-dimensional plane are then sequentially calculated, resulting in a distance data sequence that characterizes the geometric features of the pattern. The main process includes two parts: feature point selection and distance data sequence calculation. The specific principles and process are as follows.
## 2 Literature Review
These backbones were trained on diverse datasets, including REFUGE, Drishti-GS, and RIM-ONE, and were evaluated individually. Their approach achieved better results than individual models in multiple datasets, demonstrating its superior performance. [PERSON] et al. [15] employed U-Net with attention and ResNet50 for segmentation, along with a modified Inception V3 for classification, achieving high accuracy on the RIM-ONE dataset. They utilized gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ techniques for enhanced interpretability, producing heatmaps to assist in automated glaucoma detection. The study reported high accuracy, sensitivity, and specificity for glaucoma detection. The above methods offered a solution to glaucoma detection, but with some limitations. While these deep learning techniques have shown promising results in glaucoma detection, there are still areas for improvement, particularly in terms of segmentation results, generalizability to other datasets, and potential issues with data augmentation strategies. Despite these limitations, this work makes significant contributions to the field of automated glaucoma detection using DL methods. We proposed an approach that combines the U-Net architecture with pretrained ResNet34 for segmentation tasks and a pretrained EfficientNetB0 model for classification tasks. It segments the OD and OC regions in retinal images, crucial areas for glaucoma detection, then uses these regions for classification by the EfficientNetB0 model. The project involves data preprocessing, OD and OC segmentation, region of interest (ROI) extraction, segmented image postprocessing, and glaucoma presence classification. Utilizing the pretrained EfficientNetB0 model in classification aims to enhance precision and efficiency, potentially accelerating glaucoma detection for early intervention and treatment.