esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Derivation of Models and Generalized Modeling for Rapid Prediction  
### Theoretical Derivation of Agent Model Based on PSO-SVR  
The input of the relationship model between infrared cage parameters and angular coefficients is infrared cage parameters, including spacing, height, small surface arc length, large surface arc length, small surface curvature, large surface curvature, circumferential offset, radial offset, and the output is angular coefficients, which belongs to the multivariate nonlinear fitting problem. The support vector regression (SVR) method achieves optimal linear fitting by calculating the distance from the sample points to the error band and adopts the kernel function to achieve the efficient conversion of dimensions, which is excellent in solving the multivariate nonlinear regression problem, so this paper adopts SVR for modeling. SVR parameters need to be determined before training SVR, and in this paper, a particle swarm optimization algorithm (PSO) is used to obtain them. The implementation flow of the improved PSO-SVR is shown in Figure 4. The process includes particle swarm optimization, SVR training, and SVR prediction.
## 4 Results and Analysis
### Response Characteristics and Parameter Influence Analysis
#### 4.2.1 Influence Analysis of NES Nonlinear Stiffness
Figure 8 shows that when the initial condition is (0, 0, 0, 0), the amplitude of \(x_{1}\) is approximately 0.0045, and that of \(Z\) is approximately 0.0045, which conforms to the lower branch curve at \(\sigma=-0.144\) of Figure 7. When the initial condition is (3, 3, 3, 3), the amplitude of \(x_{1}\) reaches 0.025, and the amplitude of \(Z\) reaches approximately 0.05, which conforms to the upper branch curve at \(\sigma=-0.144\) of Figure 7. Therefore, in the case of multiple solutions, different initial conditions will lead to different responses. The vibration amplitude of the pipe in the above three conditions was compared with the results without an NES, as shown in Figure 9.  
In Figure 9, different lines represent different nonlinear stiffness results, and the black solid lines represent the pipe without an NES. The results show that with the increase in nonlinear stiffness, the vibration suppression of an NES increases gradually, and when it increases to a certain extent, the saddle-node bifurcation and Hopf bifurcation appear, and the motion form is more complex, but the vibration amplitude of the pipe is largely suppressed. For example, when \(\kappa=367.38\) and \(\sigma=0\), the vibration amplitude of the pipe is approximately 20% of that without an NES.
# Development of a Novel Open Control System Implementation Method under Industrial IoT
###### Abstract  
The closed architecture of modern control systems impedes them from further development in the environment of the industrial IoT. The open control system is proposed to tackle this issue. Numerous open control prototypes have been proposed, but they do not reach high openness. According to the definition and criteria of open control systems, this paper suggests that the independence between control tasks and the independence between control tasks and infrastructures are the keys to the open control system under the industrial IoT. Through the control domain's formal description and control task virtualization to deal with the keys, this paper proposes a new method to implement open control systems under the industrial IoT. Specifically, given the hybrid characteristic of the control domain, a hierarchical semantic formal based on an extended finite state machine and a dependency network model with the time property is designed to describe the control domain. Considering the infrastructure's heterogeneity in the industrial IoT, a hybrid virtualization approach based on containers and Web Assembly is designed to virtualize control tasks. The proposed open control system implementation method is illustrated by constructing an open computer numerical control demonstration and compared to current open control prototypes.  
open control system; formal description; virtualization; container; Web Assembly Article
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Dataset Acquisition and Training  
As shown in Figure 5, to obtain the network training and testing dataset, 100 simulated damage points (indicated by blue circles) were randomly generated within the area defined by the lower-left coordinate (\(-100\), \(100\)) mm and the upper-right coordinate (\(100\), \(200\)) mm. The dark blue dot at (\(0\), \(50\)) mm represents the excitation signal position, while the other eight light brown dots are the signal reception positions. The training dataset was generated using Equation (11). To satisfy the conditions of the network training input data, the signals of each channel were initially dispersion-compensated, and the resolved signals were obtained using Hilbert transform. Then, a ToFC data block was generated for each simulated damage point in the imaging region, as the channel dimensions took into account both the real and imaginary parts of the signal, resulting in a channel dimension of 16 for the 8 receiver points. The imaging region is within the black dashed line in Figure 5. Since 100 random damage scenarios were simulated, the input data used for the network training consisted of 100 \(\times\) 8 = 800 data blocks, each with dimensions of 201 \(\times\) 101 \(\times\) 16. The target images for training were generated using the MVDR method. The network was implemented in Python 3.7 using Keras with a Tensorflow backend, running on a personal computer with an Nvidia GTX1660 graphics card. The training set and validation set data were split in a ratio of 8:2. During each training epoch, 30 data samples were fed in batches, and the entire training process consisted of 50 epochs.  
Figure 4: **(a)** Diagram of the experimental setup; **(b)** Schematic of the plate, piezoelectric array, and damage.
## 4 Three-Dimensional Modelling by the SfM-MVS Approach
### Direct and Indirect Georeferencing
In photogrammetric applications, direct georeferencing offers several advantages over indirect georeferencing: (i) since it is no longer necessary to survey GCPs in the field, there is a significant advantage in terms of time and cost of surveying, (ii) concerning the efficiency of the flight mission, since a significant lateral overlap (sidelap) is not required, it is possible to reduce the number of flight lines per area, (iii) efficiency in integrated orientation and point matching during photogrammetric processing, and (iv) real-time mapping in emergency situations where an urgent response is required.
## 3 Method
### Models Description  
#### 3.6.1 Segmentation Models  
The proposed network includes the segmentation of lung and COVID-19 infection segmentation. Both models were trained separately. The standard U-Net was implemented using the keras library with the tensorflow backend, consisting of five blocks encoding path and a symmetric five blocks decoding path. At each level of the encoder, a convolution operation, a ReLU activation function, and a batch normalization operation were applied two times consecutively, followed by a max-pooling operation, to overcome the overfitting issue by minimizing the spatial size of the convolved features, before moving to the next level. The decoder recovers the original input size by applying the same sequence of operations by replacing the max-pooling operation with the transposed convolution as an up-sampling operation at every level. Additionally, the corresponding feature from the encoder is concatenated to the decoder's block input. A 1 \(\times\) 1 convolution, with a sigmoid activation function, was then finally added for the generation of the final binary prediction map. The network used thirty-two feature maps at its highest resolution and 512 at its lowest. The convolutions were applied with a kernel size of 3 \(\times\) 3, and the transposed convolutions were applied with a kernel size of 2 \(\times\) 2 with a stride of 2 \(\times\) 2. The network was trained, and the parameters were updated using the Adam optimizer with 0.0005 as a learning rate. To further optimize the training procedure, we used the cosine annealing scheduler, implemented as a custom callback, where learning rate starts at 0.0005 and then is dropped rapidly to 0.0001 before being increased again to the maximum. Both networks were trained with a batch size of thirty-two, and 80 and 16 epochs for lung and infection segmentations, respectively.  
Figure 5: A chest CT scan and its corresponding infection mask before and after cropping.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Storage
#### 6.3.2 Fusion Storage  
As shown in the previous subsections, after fusion of data acquisition and transmission, the data acquired by sensors are stored in the cloud and edge cloud for further analysis. However, storage resources are still limited on the server side. Therefore, fusion storage should be conducted on the data, i.e., a distributed storage system should be adopted to store high-quality heterogeneous sensor data in real time [52]. A distributed file system can effectively solve the problem of data storage and management. It extends a fixed file system to a multi-location multi-file system, in which many nodes form a file system network. Each node can be distributed in different locations, and communication and data transmission between the nodes can be conducted through the network. A distributed storage system is based on stream data access mode, and can process large-size data files. Furthermore, with this system, a one-time writing, a multiple-times reading, and processing of unstructured, semi-structured, and structured data can be realized. This method of data storage and management is very suitable for edge-cloud servers and cloud data centers distributed around the world; and, with the advent of 5G and the era of the Internet of everything, it is more suitable for future terminal requirements. Currently popular distributed storage systems include Google File System (GFS), Hadoop Distributed File System (HDFS), Lustre, Ceph, GridFS, mogileFS, Taobao FileSystem (TFS), and FastDFS. In the distributed storage system, the data cleaning and data integration technology can also be used to conduct further fusion on the data acquired at terminals to save storage resources and to improve the quality of the data.
## 2 Related Works
As we have seen in this short review of the state of the art, most of the work focused on AIM is based on communication between VAs and IMs but does not consider the latency required for communication between them. There is a small set of works that have taken it into account [23; 24; 25; 26]. Of note is that of [PERSON] et al. [24], where they presented a delay-tolerant intersection management protocol that considered the network delay for vehicular control. Their findings revealed that the presented method outperformed traditional traffic signal systems when traffic was low (<360 veh/h) or asymmetric (the flow of the north-south branches was three times greater than that of the east-west branches). However, when traffic was medium-high, or when the traffic was symmetrical, the result shown by their proposal did not surpass that obtained with traffic lights. Another interesting work that accounts for Round-Trip communication Delay (RTD) was presented by [PERSON] et al. in [25]. In this case, RTD was estimated by message exchange and followed a similar approach to FCFS. The results showed that they could improve the flow of a single intersection compared to FCFS by 26%. An improvement of the previous work was presented by [PERSON] et al. in [26]. In this case, they improved the synchronization process between IM and VAs by message exchange and enhanced the decision-making algorithm. They used FCFS as the method for passing-priority decision-making. The results obtained showed a reduction in the positioning error by more than 15%, increasing the throughput by 36% compared to other AIM techniques, as well as a complete reduction in crashes. However, both AIMs were based on fixed mathematical control rules that do not allow for learning an advanced and adaptive control policy. Moreover, the proposed system was only tested in scenarios where the traffic density was extremely low (<300 veh/h). It is clear that the works on AIM so far have focused on improving vehicular flow as well as reducing lost time, but very few have considered the influence of latency inherent in wireless communication systems. As a result, we believe that there is still a dearth of work addressing the study of latency in AIMs, particularly using advanced approaches such as multi-agent DRL, in which the system can learn autonomously without the need for any control rules and obtain advanced control techniques. By doing so, it could offer higher security and the possibility to learn from the environment under control to perfectly adapt its behavior to the conditions and properties.
# Suppression of Instabilities in a Plasma Voltage Stabilizer
## 1 Introduction  
One of the key ways to foster the development of industrial facilities and social infrastructure in hard-to-reach regions all over the world is to design small-scale and high-power nuclear reactors that are environmentally friendly [1]. In such designs, it is necessary to use electronic components for current control in reactor circuits. Such components (devices) should work reliably at high radiation levels and ambient temperatures exceeding 1000 K [2]. Semiconductor electronic devices are unable by their nature to meet these requirements [3]. This problem can be solved by means of gas-discharge devices based on nonequilibrium plasma with nonlocal properties [4].  
An example of such plasma is a low-voltage beam discharge (LVBD) in inert gases [5]. This type of plasma contains groups of electrons of different energies [6], which can be controlled by additional electrodes, providing more flexibility for modification of plasma properties in desirable ways. This is due to the fact that different energetic groups of electrons are responsible for different processes, and their density modifications yield control over corresponding plasma processes to optimize the energy parameters of plasma devices [7].  
This technology can be used in a number of high-tech industries and products, including plasma chemistry, plasma processes applied to different coatings [8], light sources [9], gas analyzers [10, 11], and plasma-based devices [12].  
Attempts to improve the energy parameters of plasma-energy devices cause plasma instabilities [13, 14, 15] related to the falling volt-ampere characteristic of the discharge [14, 16]. Oscillations and instabilities of this type can be used for practical purposes, but they also have a destructive effect on the energy and electrokinetic parameters of plasma sources and stabilizers. It is obvious that the fight against instabilities should involve controlling the electron velocity distribution function (EVDF) in the operating modes of plasma devices [17, 18, 19].  
Paper [20] studied the electrokinetic parameters of a helium LVBD in a three-electrode device with a control electrode (CE) located outside the interelectrode gap. Such a design made it possible to control groups of electrons of different energies and to identify modes that help to solve the problem of voltage stabilization in the range up to 110 V.  
In this article, a device of this type is used to solve the problem of suppressing plasma instabilities by controlling the sign of differential plasma conductivity using an external control electrode.
## 7 Conclusions  
The paper presented ZERO a probabilistic routing protocol designed as a generalization of the Collection Tree Protocol. ZERO load balances the traffic of the network by means of oriented but probabilistic path selection. Two metrics for path selection have been presented. A proportional distance metric assigns weights to candidate path as a proportion of their quality with respect to the quality of the possible paths. A triangular distribution based metric assigns path probability according to a triangular distribution function. Both techniques introduce randomization to path selection augmenting the number of paths to the sink. The choice for both techniques is biased to the best candidate in order to do not compromise the reliability of the overall network. A series of studies through simulation and real deployment have been performed. The objective was to validate the suitability of randomized path selection compared to restrictive path selection of CTP that suffers from the hot spot problem. Our studies showed that ZERO outperforms pure CTP in terms of delivery ratio as CTP under congestion suffers from high amount of packet collisions. Besides, the experiments showed that ZERO makes use of more relaying nodes but these nodes in average transmit much less packets, leading then to a more balanced energy consumption. The paper did not compare both metrics presented which we let as a future work, but however some directions have been already shown. The distance metric would require certain tuning depending on the network, but once tuned it would outperform triangular distribution metric which is very simple to implement and provides a very interesting load balancing.
# Smart Grid Security: A PUF-Based Authentication and Key Agreement Protocol
###### Abstract
From a professional perspective, one recent benchmark study from IANS and Artico cited in VentureBeat found that utilities spent an average of 8% of their IT budgets on cybersecurity in 2022, highlighting the importance of this critical domain [7]. From an academic perspective, in recent years, extensive research has been conducted to secure the infrastructure of smart grids. In a review of cyber attacks and defense mechanisms for improving security in smart grid energy systems, [PERSON] et al. point out the expanding use of multiple sensors, controllers, meters, and wireless networks to control and transmit data, suggesting that the issues caused by cyber attacks on these heterogeneous types of infrastructures should receive more attention. They also call for researchers to keep pace with different methods of detecting cyber attacks and develop up-to-date countermeasures--among which they suggest updating or creating new protocols to prevent the access of attackers to the grid [8]. Similarly, [PERSON] et al. conducted a review on cyber-physical and cyber-security systems in smart grid. The authors focus on the increased complexity of managing the security aspects of the grids due to the challenging combination of communication technology, standards, protocols, and applications. To reduce security threats and increase the system's reliability, they also propose to look at security requirements from a defense life cycle (pre-attack, under attack, and post-attack) where, at each phase, different techniques can be used [9]. To address these concerns, the community of researchers proposes various cyber-security protocols. However, many existing designs suffer from security vulnerabilities. In this paper, we propose a new protocol for smart metering systems to address issues found in existing protocols and enhance the efficiency of smart grids.
## 3 Outage Performance Analysis
### Outage Probability of \(\mathrm{D}_{2}\)
\begin{split} B_{23}&=\sum\limits_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\frac{I\Omega_{SR}}{I\Omega_{SR}+\Omega_{SP} \partial k}\exp\left(-\frac{I\Omega_{SR}+\Omega_{SP}\gamma k}{P\Omega_{SP} \Omega_{SR}}\right)\\ &-\frac{1}{\Omega_{SP}}\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\int_{\frac{\omega}{T}}^{\infty}\left(2\sqrt{ \frac{k}{\eta\Omega_{RP}\Omega_{SR}}}yK_{1}\left(2\sqrt{\frac{k}{\eta\Omega_ {RP}\Omega_{SR}}}y\right)\exp\left(-\left(\frac{I\Omega_{SR}+\Omega_{SP}\gamma k }{I\Omega_{SP}\Omega_{SR}}\right)y\right)\right.\\ &+2\sqrt{\frac{\gamma k}{\eta I\Omega_{RD_{2}}\Omega_{SR}}}yK_{1} \left(2\sqrt{\frac{\gamma ky}{\eta I\Omega_{SR}\Omega_{RD_{2}}}y}\right)\exp \left(-\left(\frac{I\Omega_{SR}+\Omega_{SP}\gamma k}{I\Omega_{SP}\Omega_{SR}}y \right)\right)\right.\\ &-2\sqrt{\frac{\left(\gamma\Omega_{RP}+I\Omega_{RD_{2}}\right)ky}{\eta I \Omega_{RD_{2}}\Omega_{RP}}}K_{1}\left(2\sqrt{\frac{\left(\gamma\Omega_{RP}+ I\Omega_{RD_{2}}\right)ky}{\eta I\Omega_{SR}\Omega_{RD_{2}}\Omega_{RP}}}\right)\exp \left(-\left(\frac{I\Omega_{SR}+\Omega_{SP}\gamma k}{I\Omega_{SP}\Omega_{SR}} \right)y\right)\right)dy\end{split}  \tag{33}\]  
We then compute \(A_{24}\) as  
\[ \begin{split} B_{24}&=\Pr\left\{\frac{\left|h_{SR_{ k}}\right|^{2}}{\left|h_{SP}\right|^{2}}>\delta,\left|h_{SP}\right|^{2}>\frac{I}{P}, \left|h_{R_{k}D_{2}}\right|^{2}<\frac{\delta\left|h_{RP}\right|^{2}}{I},\left| h_{RP}\right|^{2}>\frac{I}{\eta\left(\frac{\left|h_{SR_{k}}\right|^{2}I}{\left|h_{SP} \right|^{2}}-\delta\right)}\right\}\\ &=\int\limits_{\frac{1}{P}}^{\infty}f_{\left|h_{SP}\right|^{2}} \left(y\right)\int\limits_{\frac{\omega}{T}}^{\infty}f_{\left|h_{SR_{k}} \right|^{2}}\left(x\right)\int\limits_{\frac{1}{\eta\left(\frac{1}{y}-\delta \right)}}^{\infty}f_{\left|h_{RP}\right|^{2}}\left(z\right)F_{\left|h_{R_{k}D_{ 2}}\right|^{2}}\left(\frac{\delta z}{T}\right)dzdxdy\\ &=\frac{2}{\Omega_{SP}}\sum\limits_{k=1}^{K}\left(\begin{array}{c }K\\ k\end{array}\right)(-1)^{k-1}\int\limits_{\frac{1}{P}}^{\infty}\left(\sqrt{ \frac{ky}{\Omega_{RP}\Omega_{SR}\eta}}K_{1}\left(2\sqrt{\frac{ky}{\Omega_{RP} \Omega_{SR}\eta}}\right)e^{-\left(\frac{\Omega_{RP}+\Omega_{SP}\mu}{H_{SR}\mu_{ SP}}\right)y}\right.\\ &-\left.\frac{I\Omega_{RD_{2}}}{\Omega_{RP}\delta+I\Omega_{RD_{2}}}\sqrt{ \frac{\left(\Omega_{RP}\delta+I\Omega_{RD_{2}}\right)ky}{\Omega_{RD_{2}}\Omega_ {RP}\Omega_{SR}\eta I}}K_{1}\left(2\sqrt{\frac{\left(\Omega_{RP}\delta+I\Omega _{RD_{2}}\right)ky}{\Omega_{RD_{2}}\Omega_{RP}\Omega_{SR}\eta I}}\right)e^{- \left(\frac{\Omega_{RP}+\Omega_{SP}\mu}{H_{SR}\mu_{SP}}\right)y}\right)dy \end{split}  \tag{34}\]  
We continue to use the fact that \(\Pr\left\{\left|h_{SP}\right|^{2}<\frac{I}{P}\right\}=1-\exp\left(-\frac{I}{ \Omega_{SP}}\right)\); then, \(B_{2}\) is given as  
\[B_{2}=\left(B_{21}+B_{22}\right)\left(1-\exp\left(-\frac{I}{P\Omega_{SP}}\right) \right)+B_{23}+B_{24}. \tag{35}\]