esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup and Materials  
### Experiment Platform  
The robotic grasping platform is depicted in Figure 5. The system comprises a UR5 robotic arm, a Realsense D435i camera, and a parallel gripper (Robotiq-2F85). The Realsense D435i is mounted at the end of the robotic arm, and the camera is connected to the computer. Additionally, the UR5 robotic arm is connected to the controller along with the parallel gripper (Robotiq-2F85), and they are connected to the PC via an Ethernet cable. Communication between the PC and the controller is achieved through sockets. We use the hand-eye calibration method [30] to obtain the transformation matrix and the camera's intrinsic parameters; the process is achievable within the Robot Operating System (ROS). PyTorch serves as the deep learning framework for both model training and prediction, which runs on an NVIDIA RTX3090 GPU (Nvidia, Santa Clara, CA, USA) with Ubuntu 18.04. For optimization, we employed the popular Adam optimizer with a learning rate set at 0.001. Additionally, we adopted a data split ratio of 9:1 for training and testing purposes.  
We randomly selected a variety of unknown objects for grasping tests, which included household items and adversarial objects. The household items encompass 16 different categories, such as mice, adhesive tapes, charging cables, screwdrivers, fruit models, medications, toiletries, beverages, etc. These items exhibited diverse colors, shapes, and sizes. To comprehensively validate the robot's grasping generalization capability, we randomly selected adversarial objects characterized by abstract geometric properties and uncertain surface features. The test objects are shown in Figure 5. In real-world grasping experiments, objects are randomly placed on a tabletop to create experimental conditions of single-object, multi-object, and cluttered scenes. The main challenge lies in the uncertainty in object shapes, sizes, placements, and quantities. Therefore, the model trained on the Jacquard dataset was used to infer grasp configurations in these scenes.  
Specifically, the robot moved to an initial position approximately 0.5 m away from the tabletop. The camera captured a depth image, which was then input into FAGD-Net. The model generated a best grasp configuration, which included grasp position, angle, and width. Then, the grasp configuration with the highest confidence was transformed into the robot coordinate system. Finally, the robot adjusted the gripper pose based on the predicted grasp position and angle, approached the target, and finally closed the gripper to grasp the object based on the predicted width. A successful grasp was recorded when the object was grasped and lifted at least 10 cm.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 7 Resource Allocation in the Smart Grid
### Reinforcement Learning  
We implement two RL algorithms: Q-learning and SARSA learning.  
Q-learning: Q-learning is a model-free RL algorithm [41]. The algorithm converges to an optimal policy for a finite number of states, and actions for a single agent. It is an off-policy temporal difference algorithm and therefore does not depend on the policy followed by the agent. The agent chooses an action in the environment based on the \(\varepsilon\)-greedy policy. The greedy policy allows the agent to explore all the states possible with probability \(\varepsilon\), and eventually, it will tune the Q-values by choosing the best actions with probability (1 minus \(\varepsilon\)) that maximize the discounted rewards. The total rewards Q  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actions** & **Previous State** & **Next State** & **Rewards** \\ \hline
**SCAN** & Hacked & Hacked & \(-200\) \\  & VULNERABLE & VULNERABLE & \(-200\) \\  & UNKNOWN & VULNERABLE & Importance \\  & UNKNOWN & HACKED & Importance \\  & UNKNOWN & PATCHED & \(0\) \\  & PATCHED & VULNERABLE & Importance \\  & PATCHED & HACKED & Importance \\  & PATCHED & PATCHED & \(-200\) \\
**PATCH** & Hacked & PATCHED & Importance \\  & VULNERABLE & PATCHED & Importance \\  & PATCHED & PATCHED & \(-200\) \\  & UNKNOWN & UNKNOWN & \(-200\) \\ - & ANY & TERMINAL 1 & \(500\) \\ - & ANY & TERMINAL 2 & \(-500\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: State transition and rewards.  
is computed according to (3) and updated according to (4). The optimal actions corresponding to a particular state are determined by Equation (5).  
\[Q=\sum\
olimits^{n}\gamma^{t-1}\ r_{t}\ (s_{t},a_{t}) \tag{3}\]  
\[Q_{t+1}\ (s,a)=Q_{t}\ (s,a)+\alpha\ (r^{\prime}+\gamma\ max_{b}Q\ (s^{\prime},b)-Q_{t}\ (s,a)) \tag{4}\]  
\[a_{t}=argmax_{a}Q\ (s_{t},a) \tag{5}\]  
SARSA learning: SARSA is an on-policy model-free temporal difference algorithm. In contrast to Q-learning, it does not find the best action possible that maximizes the rewards; rather it continues to choose the next action using the same policy. The update rule for SARSA learning is according to Equations (6) and (7). \(\lambda\) refers to eligibility trace, and \(e_{t}\ (s,a)\) denotes the trace of the state-action pair. When \(\lambda=0\), there is a one-step backup return as in Q-learning. When \(\lambda=1\), there is a one episode backup return as in Monte Carlo. In our model for SARSA learning, \(\lambda=0.4\). \(\alpha\in[0,1]\): learning parameter, and \(\gamma\in[0,1]\): discount factor.  
\[Q_{t+1}\ (s,a)=Q_{t}\ (s,a)+\alpha\ (r^{\prime}+\gamma\ Q\ (s^{\prime},a^{ \prime})-Q_{t}\ (s,a))\ e_{t}\ (s,a) \tag{6}\]  
\[e_{t}\ (s,a)=\{\gamma\lambda\ e_{t-1}\ (s,a)+1;\ \text{if}\ (s=s_{t}\ \text{and}\ \text{a}=a_{t})\ \text{otherwise}\ \gamma\lambda\ e_{t-1}\ (s,a)\} \tag{7}\]
## 3 Thin-Film Materials and Their Applications
### Inorganic Thin-Film Materials
#### 3.1.1 Metal Oxide Thin-Films
The electronic devices made of the aforementioned materials and structures not only consume lower energy but also have higher computational efficiency. In the future, another important aspect that restricts social development is energy storage. The method of making a battery with small volume and large energy storage becomes the key to progression. Based on the study of binary oxides, Ni-Co, Mn-Fe, and Mn-Cu binary transition metal-oxides, it was shown that a combination of their mechanisms can produce supercapacitors [15; 98]. In addition, the development of transistors is also a primary focus in the field of metal oxide research. In recent years, the continuous improvement of transistor performance is largely accounted for due to the update and progress of conductive medium materials. With the improvement of transistor performance, the electronic products we use today have far lower power consumption than before, making it an outstanding contribution to environmental protection [101; 102].
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 5 Experimental Results and Analysis
### The Sensitivity of the Parameters  
The parameters \(u_{i},\beta_{i},p_{i},\gamma(i=1,2,\cdots,5)\) should be properly selected. We try each parameter tentatively until the iterated image is properly recovered. The ranges of each parameter are determined empirically as \(u_{i},\beta_{i}=0\)-\(20,p_{i},\gamma=0\)-\(1(i=1,2,\cdots,5)\). The parameters are adjusted by traversing these ranges with a step size of 0.01. When PSNR achieves a maximum value, the corresponding parameter values are selected as the optimal ones. Figure 3 depicts the effect of \(u_{i}\) on PSNR during the adjustment. Notably, when the PSNR reaches its maximum, the optimal selections of \(u_{i}(i=1,2,\cdots,5)\) vary, hence, it is effective to adjust these parameters separately.  
Figure 4 depicts the effect of variations in \(p_{i}\) on PSNR during the adjustment. When the \(p_{i}\) is too large, the image gradient sparsity is not sufficiently strong. However, if \(p_{i}\) is too small, the image gradients become too sparse. Thus, the value of \(p_{i}\) should be adjusted until the image recovery is satisfactory. As shown in Figure 4, the values of \(p_{i}\) are different when PSNR reaches the maximum value under the same experimental conditions. Therefore it is effective to separate the \(p_{i}\) of different gradients and directions of image.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Topology
#### 5.3.2 Distribution of Nodes in GTSP  
Because GTSP is not affected by the position of the reference node, this subsection extends the previous simulation of GTSP to different topologies. We do not include FTSP in this scenario because the convergence of FTSP depends on the hop distance from the reference node, as demonstrated in the previous evaluation. Figure 11b shows that the number of rounds required to synchronize the entire network differs according to the network topology. Even with the same topology, the number of rounds also varies in each execution. With each topology, the simulation is repeated 15 times with different seeds. Consequently, the initial clock drift and the time to broadcast the message of each node are different in every simulation. Therefore, the synchronization speed and the agreement clock rate also vary. However, Figure 11a shows that after achieving synchronized status, the synchronization error is similar among the various topologies.  
[PERSON] [42] demonstrated that convergence speed depends on the distribution property of a network. In detail, it depends on the number of neighbors (links) of each node. Hence, increasing the density and node connections in a network will increase the convergence speed of GTSP. A simple method to increase the convergence speed is to increase the transmission range of the nodes, as shown in Figure 12. In this scenario, the transmission range of nodes in a random topology (50 nodes) is increased gradually. As a result, the number of links is increased, and the convergence time is reduced. Although this is not always possible in WSNs because the transmission range is limited by hardware. However, it can provide insights into the acceleration of convergence time by exploiting the number of links in GTSP [43].  
Figure 10: (**a**) Number or rounds required to synchronize entire network with different hop distances between reference node and farthest node. (**b**) Synchronization error of FTSP with different hop distances between reference node and farthest node.
## Materials and Methods  
### Heliostat Control System  
The HCS controls the heliostat in order to reflect the solar radiation on the desired target. It is a mechatronic system, which consists of mechanical, electronic, and communication units. The block diagram of the HCS is shown in Figure 1. The HCS proposed consists of a supervisory unit with a GUI, a mobile app, a wireless communication network with radio frequency (RF) XBee modules and a Bluetooth radio module, and an MCU-based RTU connected to the mechanical structure of the heliostat.
###### Abstract  
An efficient management of production plants has to consider several external and internal factors, such as potential interruptions of the ongoing processes. Automated guided vehicles (AGVs) are becoming a widespread technology that offers many advantages. These AGVs can perform complex tasks in an autonomous way. However, an inefficient schedule of the tasks assigned to an AGV can suffer from unwanted interruptions and idle times, which in turn will affect the total time required by the AGV to complete its assigned tasks. In order to avoid these issues, this paper proposes a heuristic-based approach that: (i) makes use of a delay matrix to estimate circuit delays for different daily times; (ii) employs these estimates to define an initial itinerary of tasks for an AGV; and (iii) dynamically adjusts the initial agenda as new information on actual delays is obtained by the system. The objective is to minimize the total time required for the AGV to complete all the assigned tasks, taking into account situations that generate unexpected disruptions along the circuits that the AGV follows. In order to test and validate the proposed approach, a series of computational experiments utilizing real-life data are carried out. These experiments allow us to measure the improvement gap with respect to the former policy used by the system managers.  
automated guided vehicles; dynamic task assignment; internal logistics; heuristic optimization; disruption management Article
## 3 Results
### CNN Validation  
The validation of the proposed CNN model was performed using 40 reserved wafer images, 10 from each defect class, and the result is shown in Table 4. It shows that 38 out of the 40 images were classified correctly. Two images, one in each of the center and local defects, appears to have been misclassified. A closer examination of these two misclassified images shows that they both exhibit multiple types of defect, yet only one type was labeled, as the example in Figure 8 shows. The wafer image in Figure 8 was labeled as a local defect, but both local and scrape-defect types can be observed. Since this study only included the four main types of defects and did not include mixed types, the two images were misclassified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defect Type** & **Sample Size** & **Correct** \\ \hline Center & 10 & 90\% \\ Local & 10 & 90\% \\ Random & 10 & 100\% \\ Scrape & 10 & 100\% \\ Total & 40 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: CNN validation results.  
Figure 8: Wafer image exhibiting both scrape and local defect types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **LR** & **RF** & **SVE** & **CNN** \\ \hline \multirow{4}{*}{**Precision**} & Center & 89.88 & 94.22 & 92.54 & **98.89** \\  & Local & 70.49 & 85.75 & 83.91 & **96.62** \\  & Random & 86.25 & 95.18 & 95.78 & **98.83** \\  & Scratch & 75 & 89.66 & 81.36 & **96.19** \\ \hline \multirow{4}{*}{**Recall**} & Center & 77.85 & 82.70 & 87.31 & **98.43** \\  & Local & 31.0 & 57.37 & 55.78 & **97.68** \\  & Random & 77.53 & 88.76 & 89.33 & **98.32** \\  & Scratch & 16.12 & 32.23 & 39.67 & **96.65** \\ \hline \multirow{4}{*}{**F-Measure**} & Center & 83.44 & 88.08 & 89.85 & **98.66** \\  & Local & 43.13 & 68.74 & 67.01 & **97.15** \\  & Random & 81.66 & 91.86 & 92.44 & **98.57** \\  & Scratch & 26.53 & 47.41 & 53.33 & **96.42** \\ \hline \multirow{4}{*}{**AUC**} & Center & 99.45 & 99.57 & 99.80 & **99.85** \\  & Local & 93.08 & 98.36 & 98.75 & **99.68** \\ \cline{1-1}  & Random & 99.95 & 99.97 & **99.98** & 99.79 \\ \cline{1-1}  & Scratch & 91.21 & 96.90 & 97.90 & **99.75** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results of various methods.
# Plasmonic and Dielectric Metasurfaces: Design, Fabrication and Applications
## 4 Discussion
There are several impressive works reported recently focusing on tunable and reconfigurable metasurfaces: (1) by combining the Fano resonant metal nanoparticles with a single layer of graphene, obviously electrically-controlled damping is observed in the Fano resonances in the near-infrared regime [137]; (2) by positioning a thin graphene sheet over asymmetric silicon nanobars, the transmission spectrum of the proposed device can be efficiently adjusted at the near-infrared frequencies [138]; (3) a metal nanomechanical metasurface is fabricated on the nanoscale thickness dielectric membrane, and the displacement of the metamolecules can be controlled by light illumination [139]; (4) using differential thermal expansion and Lorentz force, electrothermal tuning and magnetic modulation are achieved in a reconfigurable metal metasurface [140]; (5) using a vanadium dioxide metasurface lens, the amplitude spectrum and the focal intensity can be adjusted by changing the temperature in the terahertz regime [141]; (6) utilizing a heating-induced vanadium dioxide planar metamaterial, the device is able to be switched between capacitive and inductive responses [142]; (7) by combing the metal nanoantenna array with indium tin oxide, the phase and amplitude of the reflected light can be controlled at near-infrared wavelengths [143]. In spite of facing many challenges, metasurfaces are still one of the devices with the most potential applied in beam-steering, spatial light modulator, nanoscale-resolution imaging, sensing and novel quantum optics devices. Besides, the increasing demand for the large capacity of data transmission and fast signal processing promotes eliminating the bottleneck between optical fiber communication networks and electronic data handling. Achieving this goal will require strong and fast nonlinearities for switching light with light and much improved manipulation of the electromagnetic fields with external stimuli, such as electric signals in a compact space. Metasurface-based devices can considerably enhance these functionalities by exploiting the characteristics of metasurfaces [136]. In the future, metasurface-based devices would be developed as tunable, reconfigurable, nonlinear, switchable, gain and quantum metasurfaces, which might be achieved by adopting new materials (such as superconductors or graphene) and hybridizing plasmonics metal nanostructures with other functional materials, such as nanocarbon, organics, nanosemiconductors, organic polymers and phase-change media [130].
## 3 Optimal Feature Set Acquisition
### Feature Selection Based on RF-RFECV  
Among various feature selection models, RF-RFECV has emerged as the preferred method for feature selection when dealing with rare earth element datasets owing to its unique advantages. Notably, the dataset in this study possesses a high-dimensional and complex feature space, making manual selection of the number of features exceedingly challenging. RF-RFECV excels in automatically determining the optimal number of features, eliminating the subjectivity associated with manual selection, and ensuring the stability and reliability of the chosen feature subset. Additionally, the feature selection outcomes of RF-RFECV remain unaffected by the randomness and noise in the data, making it particularly well-suited for situations such as rare earth element datasets, which may be susceptible to noise and instability.  
Second, RF-RFECV integrates the characteristics of random forests, recursive feature elimination, and cross-validation, effectively enhancing model performance and efficiency. By estimating feature importance using random forests, RF-RFECV identifies the most informative features, thereby reducing the introduction of noise and complexity associated with unnecessary features. The incorporation of cross-validation comprehensively considers the randomness and noise in the data, significantly reducing the instability of feature selection results. This aspect is particularly crucial for datasets involving rare earth elements, where highly reliable and stable feature selection results are essential for predicting the content of rare earth elements accurately.  
Considering the unique nature and requirements of rare earth element datasets, RF-RFECV stands out as the feature selection method of choice in this study. Its inherent qualities, including automation, robustness, and resilience, align well with the stringent standards and reliability requirements of feature selection, consequently greatly enhancing the performance and interpretability of the rare earth element content prediction model. Hence, we have chosen to employ RF-RFECV as the feature selection model in this study. The steps for obtaining the optimal feature subset based on the RF-RFECV method are depicted in Figure 2.  
Figure 2: Flow chart showing the process of obtaining the optimal feature set.  
Initially, a dataset of rare earth image data is procured. Subsequently, machine vision technology is employed to extract the RGB color features of the image. The color space conversion formula is then utilized to convert these into HSV and YUV color features. Following this, the gray level co-occurrence matrix is used to extract the spatial texture features of the image. Finally, the RF-RFECV algorithm is applied to screen the color features, spatial texture features, and key features, ultimately yielding a mixed feature set.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## Appendix B  
From (5), we define \(\mathbf{Y_{T}}=(\mathbf{\Omega}\circ\mathbf{B})\mathbf{PCS}\) as a compact matrix form of transmitted signal. The received response in the time domain is  
\[\mathbf{Y_{R}}=\mathbf{\ddot{B}}\mathbf{G}(\mathbf{\theta})\mathbf{Y_{T}} \tag{11}\]  
where \(\mathbf{\ddot{B}}\) is the demodulation matrix, \(\mathbf{\ddot{B}}\mathbf{B}=\mathbf{I}_{N_{sc}\times N_{sc}}\). Considering that the channel coefficients are \(h_{i}(\theta),i=1,\cdots,N_{sc}\), a guard interval (or cyclic prefix) with length \(N_{sc}\) is employed. Note that the signal is transformed into the frequency domain after modulation. To complete the convolution through matrix multiplication, \(\mathbf{G}(\mathbf{\theta})\) is defined as a circulant matrix that is given by the following \(N_{sc}\) by \(N_{sc}\) matrix [66],  
\[\mathbf{G}(\mathbf{\theta})=\begin{pmatrix}h_{1}(\theta)&h_{2}(\theta)&\cdots&h_ {N_{sc}}(\theta)\\ h_{N_{sc}}(\theta)&h_{1}(\theta)&\cdots&h_{N_{sc}-1}(\theta)\\ \cdots&\cdots&\cdots&\cdots\\ h_{2}(\theta)&h_{3}(\theta)&\cdots&h_{1}(\theta)\end{pmatrix} \tag{12}\]  
The circulant matrix \(\mathbf{G}(\mathbf{\theta})\) can be diagonalized by eigenvalue decomposition. In the OFDM case, \(\mathbf{G}(\mathbf{\theta})\) can be written as follows:  
\[\mathbf{G}(\mathbf{\theta})=\mathbf{B}^{H}\mathbf{H}(\mathbf{\theta})\mathbf{B}. \tag{13}\]  
where \(\mathbf{H}(\mathbf{\theta})=\text{diag}\{h_{1}(\theta),h_{2}(\theta)\cdots,h_{N_{ sc}}(\theta)\}\) is a diagonal eigenvalue matrix. Matrix \(\mathbf{B}\) is symmetric and unitary for the OFDM signal, \(\mathbf{\ddot{B}}=\mathbf{B}^{H}=\mathbf{B}\). Then, the received signal is given by the following:  
\[\begin{split}\mathbf{Y_{R}}&=\mathbf{\ddot{B}}\mathbf{G}( \mathbf{\theta})\mathbf{Y_{T}}\\ &=\mathbf{B}^{H}\mathbf{B}^{H}\mathbf{H}(\mathbf{\theta})\mathbf{B} \mathbf{B}\circ\mathbf{\Omega}\mathbf{PCS}\\ &=\mathbf{H}(\mathbf{\theta})(\mathbf{\Omega}\circ\mathbf{I})\mathbf{PCS }\end{split} \tag{14}\]