esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dielectric Spectroscopy  
The dielectric properties of the ZnO composite material of this study have been studied for the frequency range 10 MHz-1 GHz, which fully covers the VHF (30-300 MHz) and partially covers the UHF (300 MHz-1 GHz) frequency spectrum.  
Both tan\(\delta\) and k' of composite samples were higher in low frequencies, namely in the range 1 to 10 MHz, and they decayed by increasing frequency, but in a different manner. Decay of tan\(\delta\) was exponential, while decay of k' was linear (Figure S3.1a,b and S3.2a,b Addendum), regardless of filler loading and AWE treatment interval. Decay of tan\(\delta\) is attributed to reduction in electrical conductivity [27]. The reason for k' decay is the inability of the dipoles to maintain polarization as the frequency of the applied field increased [27; 46]. The untreated and treated composites exhibited the same aforementioned behaviour as untreated C0 samples. Thus, dielectric properties of the composite were dominated by those of the epoxy material.  
As observed in Figure S3a Addendum, where the examined frequency range is 2 MHz-1 GHz, the most visually distinct dielectric relaxation phenomenon was observed at a frequency of 8 \(\times\) 10\({}^{7}\) Hz and, to a lesser extent, at frequencies of 1 \(\times\) 10\({}^{7}\) and 8 \(\times\) 10\({}^{8}\) Hz. Moreover, at the end of the AWE treatment at INT2880, these relaxations shifted to lower frequencies, namely by 10 MHz, 1 MHz and 100 MHz, respectively, due to the aging of epoxy resin (Figure S3a, Addendum). The main dielectric relaxation mechanisms in epoxy resin are defined as \(\alpha\), \(\beta\) and \(\gamma\) relaxations; however, their discrimination requires a specialized study of these properties under temperature variations [55]. While \(\alpha\) relaxation is prominent at lower frequencies, \(\beta\) and \(\gamma\) relaxations dominate at higher frequencies, mainly due to motions of dipolar groups like hydroxyl (-OH groups, tertiary amines), unreacted dipoles in resin (epoxy groups) and ZnO dipoles when present [31; 55]. All these functional groups affect polarization and, consequently, dielectric permittivity of the composite. Additionally, ZnO electronic polarization decreases as the frequency of the applied field increases, significantly influenced by crystal defects. ZnO particle size affects the k' values, which increase as the particle size increases [31].
# Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN
## 1 Introduction
In this method, not only the orthogonality but also the unit variance of the output in each layer are used [10]. The principal component analysis (PCA) net was proposed in 2014 by [PERSON] et al. In the cascaded principal component analysis stage of the model, the model obtains all overlapping image patches from a feature map and initializes the convolution kernel by calculating the principal components of image patches. However, the model has the disadvantage that the number of feature maps increases exponentially with the increase in layers, which limits the depth of cascaded PCA [11]. Many other scholars use an unsupervised pre-training method to initialize the convolution kernel. The main idea is to use sparse autoencoder (SAE) to initialize the convolution kernel of the first layer in the network and obtain the filter set that accords with the statistical characteristics of the dataset in order to solve the problem that the previous layers of the network cannot be fully trained [12; 13]. This paper proposed an improved method for the convolution kernel initialization method. Representative local patterns were extracted in typical sample images that were from a specific dataset. These representative local patterns were the initial value of the convolution kernels. The convolution kernels can extract the local patterns of the images better by matched filtering when the network started training, which made the network training locate near the convergence domain with high probability and obtain the global optimal solution. At the same time, the network got better discrimination accuracy. The training samples used the CIFAR-10. Typical sample images were selected in CIFAR-10. The Local Binary Patterns (LBP) algorithm was applied to these typical sample images, which got the texture information of these images. Then, this texture information was divided into several small blocks. These blocks were input into the sparse autoencoder for pre-training. After finishing the pre-training, a set of convolution kernel filters that accord with the statistical characteristics of the dataset was obtained. They were the initial value of the convolution kernel. The experimental result shows that the method can obviously accelerate the convergence speed of CNNs and improve the recognition accuracy of CNNs to an extent.
## 4 Design of Proposed Algorithm
### Algorithm Design
#### 4.3.2 MTD3 CO
Initialize \(\phi^{\prime}\leftarrow\phi\), \(\theta_{1}^{i}\leftarrow\theta_{1}\), \(\theta_{2}^{i}\leftarrow\theta_{2}\) and \(\phi^{\prime}\leftarrow\phi\), \(\theta_{1}^{\prime}\leftarrow\theta_{1}\), \(\theta_{2}^{\prime}\leftarrow\theta_{2}\)
7. Initialize experience pool \(D_{i}\)
8. Follow \(a\sim\pi_{\phi^{\prime}}+\epsilon\), \(\epsilon\sim\mathcal{N}(0,\sigma)\), Sampling the task \(T_{i}\) and storing the trajectory in \(D_{i}\)
9. for iterations \(i\ =\ \{1,2,\dots,k\}\)do
10. Sampling N trajectories from the experience pool \(D_{i}\)
11. According to the objective function \(f_{critic}^{TD3}\), Update parameters using mini-batch gradient \(\theta_{1}^{i}\), \(\theta_{2}^{i}\)
12. if i mod \(d\)then
13. According to the objective function \(f_{actor}^{TD3}\), Update parameters using mini-batch gradient \(\phi^{i}\)
14. Update target network parameters
15. \(\phi^{\prime^{\prime}}\leftarrow\tau\phi^{i}+(1-\tau)\phi^{\prime^{\prime}}\)
16. \(\theta_{1}^{i^{\prime}}\leftarrow\tau\theta_{1}^{i}+(1-\tau)\theta_{1}^{i^{ \prime}}\)
17. \(\theta_{2}^{i^{\prime}}\leftarrow\tau\theta_{2}^{i^{\prime}}+(1-\tau)\theta_{2}^{i ^{\prime}}\)
18. end if
19. Calculate \(grad_{actor}^{\text{MTDSCO}}\), \(grad_{critic}^{\text{MTDSCO}}\)
20. end for
21. Update meta-parameters \(\phi\ =\ \phi+\beta\
abla_{\phi}f_{actor}^{\text{MTDSCO}}(\phi)\)
22. Update meta-parameters \(\theta_{1,2}\ =\ \theta_{1,2}+\beta\
abla_{\theta_{1,2}}f_{critic}^{\text{MTDSCO}}( \theta_{1,2})\)
23. end for
24. end for
```  
**Algorithm 1**Meta TD3 computation offloading  
The parameters of the meta-strategy and value network in the algorithm are \(1\) and \(2\), respectively, and the algorithm is a training process for the meta-parameters, which is mainly divided into inner-loop learning and outer-loop learning, where the learning task is first sampled once, followed by performing inner-loop training for each sampling task. When all inner-loop training is completed, we update the meta-parameters using the formula in lines 21 and 22 of Algorithm 1, and then proceed to the next inner- and outer-loop training.
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 6 Simulation and Performance Evaluation  
In this section, with the help of the NS2 simulator and Aquasim [33] in NS2, we evaluate the performance of our proposed protocol by comparing it with DBR, VBF and DVOR, which were proposed in [7; 22; 34], respectively. In order to compare the overall transmission performance, we have equipped these routing protocols with corresponding MAC protocols. We also simulated the protocol using a Monte Carlo method. All numerical simulation results were obtained after 100 calculations.
## 4 Simulations
### Performance Comparison
#### 4.3.3 Comparison with the Other Differential Beamforming Methods  
In this section, to demonstrate the performance and advantages of the proposed method, we compare the performance of the proposed method with three other existing broadside differential beamforming methods: (i) the DD method [7], which combines the differential and delay-and-sum patterns, (ii) the MN method [26], which utilizes the null position for the target beampattern to design the beamformer, and (iii) the MNA method [26], which uses the null position and the angle corresponding half-power bandwidth for the target beampattern to design the beamformer. In the simulations, the regularization parameter of the proposed method is set to 0.01 across the whole evaluated frequencies.  
Figure 8 shows the WNG and DI of the proposed method compared with the methods DD, MN, and MNA. In Figure 8a, the DD method has the lowest WNG among the four methods below 2 kHz, indicating the worst anti-perturbation ability at low frequencies. The MN method has the maximum WNG above 1 kHz. The proposed method has the highest WNG below 500 Hz and has the same WNG as the MNA method in the frequency range of 1-2.5 kHz. Although the WNG of the proposed method is the lowest above 2.5 kHz, the value is still greater than 5 dB, which is considered as a proper level of robustness in practical applications. Figure 8b shows the DI of the DD and MN methods increases with frequency. The DI of the MNA method maintains almost the same below 2 kHz but increases with frequency above 2 kHz. The DI of the proposed method can maintain a constant above 1 kHz. Below 1 kHz, due to the trade-off between the WNG and DI, the DI of the proposed method exhibits slight fluctuations.  
The wideband beampattern can intuitively illustrate the broadband frequency-invariant characteristics of the beamformer. We present the synthesized beampattern of these four methods in Figure 9. The main lobes of the DD and MN methods become narrower as the frequency increases. Below 2 kHz, the main lobe of the MNA method remained almost the same. However, grating lobes appear above 2 kHz. In contrast, the proposed method  
Figure 7: The averaged simulated and ideal beampatterns of the sixth-order broadside differential beamformers synthesized by MM-JAD and the proposed method with the perturbation added to the frequency response of the loudspeakers at different frequencies. (**a**) 250 Hz, (**b**) 500 Hz, (**c**) 750 Hz, and (**d**) 1 kHz.  
maintained the frequency-invariant pattern in the frequency range of 750-4k Hz. Although the proposed method cannot hold the frequency-invariant beampattern below 750 Hz, the beampattern shown in Figure 9d reflects the actual beampattern radiated by the line array due to the increased WNG.  
Figure 8: Performance of generating the sixth-order broadside differential beampattern regarding (**a**) WNG and (**b**) DI.  
Figure 9: Synthesized beampatterns of (**a**) DD, (**b**) MN, (**c**) MNA, and (**d**) proposed method.
# Quantum Approximation for Wireless Scheduling
## 3 Wireless Scheduling Modeling Using Maximum Weight Independent Set (MWIS)  
Suppose a wireless network consists of a set of one-hop links under very high transmission power using millimeter wave [14]. Due to the high data transmission rate, the queue backlog should always be cleared; otherwise, the signal decoding always fails because of extremely high interference. For the scheduling, a conflict graph is organized where the set of vertices is (the links) and two vertices are connected by an edge if the corresponding links suffer from interference. The conflict graph can be formulated by its adjacency matrix, whose \(\mathcal{E}_{(i,j)}\) are defined as follows:  
\[\mathcal{E}_{(i,j)}=\left\{\begin{array}{ll}1,&\text{if $l_{i}$ interferes with $l_{j}$ where}\\ &l_{i}\in\mathcal{L},l_{j}\in\mathcal{L},\text{and $i\
eq j$,}\\ 0,&\text{otherwise.}\end{array}\right. \tag{13}\]  
For wireless network scheduling, the objective is for finding the set of links (i.e., nodes of the conflict graph) where two adjacent links connected via edges cannot be simultaneously selected because the two adjacent connected links are interfering with each other. This is equivalent to the case which maximizes the summation of weights of all possible independent sets in a given conflict graph. Thus, it is obvious that wireless network scheduling can be formulated with MWIS as follows:  
\[\max: \sum_{\forall l_{k}\in\mathcal{L}}w_{k}\mathcal{I}_{k},\] (14) s.t. \[\mathcal{I}_{i}+\mathcal{I}_{j}+\mathcal{E}_{(i,j)}\leq 2, \forall l_{i}\in\mathcal{L},\forall l_{j}\in\mathcal{L}, \tag{15}\] \[\mathcal{I}_{i}\in\{0,1\},\forall l_{i}\in\mathcal{L},\] (16) \[\text{where} \tag{17}\]  
Here, \(w_{k}\) is a positive integer weight at \(\forall l_{k}\in\mathcal{L}\). The above formulation ensures that conflicting links are not scheduled simultaneously: if \(\mathcal{E}_{(i,j)}=0\) (no edge between \(l_{i}\) and \(l_{j}\)), then \(\mathcal{I}_{i}+\mathcal{I}_{j}\leq 2\), i.e., both indicator functions can be \(1\). In contrast, if \(\mathcal{E}_{(i,j)}=1\), \(\mathcal{I}_{i}+\mathcal{I}_{j}\leq 1\), i.e., at most, one of the two indicators can be \(1\). In wireless communication research, the \(w_{k}\) where \(\forall l_{k}\in\mathcal{L}\) is usually considered as the transmission queue backlog which should be processed when the link is scheduled. More details are can be found in [14].
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 4 Results  
### Experimental Evaluation of Hidden Message Distortion  
In Figures 2-6, the results of our experiments on BER estimation are shown for different methods of generating chip codes of length \(n=1024\). We changed the number \(k\) of hidden information bits and the coefficient \(P\) for amplifying the power of the spreading signals in (9).  
The obtained results of experimental studies correspond to the points in the given diagrams. Each point corresponds to a value averaged over 5000 tests. For better perception, these and all subsequent figures also show trend lines.  
Figure 2: \(BER(k)\) dependencies for different values of \(P\) rule for generating chip codes \(N1\).  
Figure 4: \(BER(k)\) dependencies for different values of \(P\) rule for generating chip codes \(N3\).  
Figure 3: \(BER(k)\) dependencies for different values of \(P\) rule for generating chip codes \(N2\).  
Figure 5: \(BER(k)\) dependencies for different values of \(P\) rule for generating chip codes \(N4\).  
Figure 6: \(BER(k)\) dependencies for different values of \(P\) rule for generating chip codes \(N5\).  
The analysis of the results obtained (see Figures 2-6) enables us to make the following observations.  
First, an increase in the coefficient \(P\) makes it possible to achieve low BER values for any of the considered classes of chip codes. At the same time, chip codes with generation rules N1, 2 and 3, 4 give similar BER values, i.e., for this indicator, such spreading sequences are equivalent to each other.  
Walsh-Hadamard sequences give significantly better results in terms of error rates. Even at low values of \(P\), these chip codes provide low BER.  
The second observation is that the BER increases as the number of bits in the hidden message increases. However, different chip codes give different results. For example, spreading sequences N1 and N2 at \(k=10\) provide \(BER<10\%\) only at \(P>8\). For chip codes N3 and N4, the same is provided with a lower power gain, for example, for \(P>4\). The Walsh-Hadamard sequences even with \(P=1\) and \(k=20\) provide \(BER<1\%\).  
In order to compare the BER dependences corresponding to different methods of forming chip codes, Figure 7 shows diagrams \(BER(P)\). These are averaged over 5000 test results for \(k=20\) bits.  
The analysis of the results shown in Figure 7 demonstrates that the techniques for generating chip codes N1 and N2, as well as N3 and N4, do indeed give  
Walsh-Hadamard sequences show significantly better BER results. For this family of chip codes, the error rate, other things being equal, is one to two orders of magnitude lower. At the same time, BER can be significantly reduced only for large \(P\) values, and this will inevitably lead to container distortion.
## 1 Introduction
It obtained an optimal path planning that enabled mobile robots to stabilize, avoided obstacles in time, and accurately moved to the destination [16]. The multi-objective intelligent water drop algorithm (MO-IWD) was based on the coefficient of variation (CV). This method had good diversity at the same time [17]. The non-dominated sorting genetic algorithm II (NSGA-II) enabled mobile robots to determine the best route for multi-objective problems with minimal cost [18]. The above-mentioned multi-destination global route planning research has verified that multi-destination route planning was more efficient than multiple single-destination route planning. However, the multi-destination path planning algorithm is more likely to fall into the local optimum. In the global path planning, these problems are low utilization rate of environmental information, complicated calculation structure, low sorting efficiency, large amount of calculation, and slow operation speed. In view of the above problems, this article takes wheeled mobile robots as an example to propose the global multi-destination path planning method. The global multi-destination path planning method is based on the goal of passing through the fewest obstacles. It includes the reconstruction of environmental maps based on the rules of the Go chess game, the sorting of simulated annealing strategy and the path planning of Q-learning optimization algorithm. Its structure is shown in Figure 1. The sections of this article are arranged as follows. In the second part, the grid map is built to simulate the real environment. Based on the rules of go, the grid map is optimized and reconstructed to obtain the grid environment and obstacle values between the two destinations. The obstacle values between the two destinations are used to optimize the arrival sequence of multi-destination robots by the simulated annealing strategy. The optimal mobile node for robot path planning is obtained. In the third part, the article optimizes the Q-learning algorithm to obtain the mathematical model of the mobile robot path planning. The mathematical model can obtain the optimal path. The optimal path for multiple destinations has the fewest obstacles. In the fourth part, the multi-destination path planning simulation of mobile robots is implemented by using MATLAB software to verify the rationality and effectiveness of the proposed method.
###### Abstract  
This study presents the design and implementation of a vehicular visible light communication (VLC) system that establishes an expandable VLC-based chain network within tunnel environments to facilitate the exchange of driving dynamics data, such as target speed and acceleration, between consecutive vehicles. The primary aim of the proposed system is to improve road safety by reducing the risk of chain collisions and hard braking events, particularly in tunnels, where limited visibility and the absence of global positioning system signals hinder drivers' ability to accurately assess road conditions. A key feature of the proposed system is its adaptive beam alignment mechanism, which dynamically adjusts the orientation of the light-emitting diode (LED) module on the transmitting vehicle based on rhw wheel angle data estimated by the inertial measurement unit sensor. This adjustment ensures a continuous and reliable communication link with surrounding vehicles, even when navigating curves within the tunnel. Additionally, the proposed system can be integrated into actual vehicles with minimal modification by utilizing a built-in lighting system (i.e., LED taillights), offering a cost-effective and scalable solution to achieve the objective.  
vehicle to vehicle communication; VLC; adaptive beam alignment; system implementation +
Footnote †: journal: Applied
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
###### Abstract  
The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN's deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.  
open RAN; O-RAN; open source +
Footnote †: journal: Sensors  
0000-0001-4000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-00000-00000-00000-000000-000000-000000-000000-000000-0000  
explained in [4]. The O-RAN Alliance architecture, summarized in [5], does not include the bigger picture of the Open RAN movement. O-RAN Alliance architecture is also discussed in [6; 7], including its advantages and limitations that could be addressed in future research. However, these references could not serve the complete picture of Open RAN's history, present condition, and what opportunities Open RAN can open up in the future.  
This paper explains Open RAN technology in detail, emphasizing its further evolution from vRAN, as mentioned earlier. We first provide an overview of the larger Open RAN landscape, including the most current landscape of Open RAN in terms of projects and activities. Next, this paper describes the components of Open RAN and their implementation. The study focuses on O-RAN Alliance standards and architecture as a reference. This paper also explains the challenges faced in Open RAN and future research possibilities for next-generation technology. In addition, this paper will suggest some solutions to Open RAN challenges based on open-source approaches.  
The rest of this survey paper is structured as follows: First, we explain why we need Open RAN in Section 2. Section 3 provides the background of the Open RAN movement and the technologies related to the Open RAN field. Section 4 reviews the projects, activities, and standards related to Open RAN. Section 5 discusses the O-RAN Alliance architecture. Section 6 describes the challenges and future research directions of the Open RAN field and the RAN technology in general. Finally, Section 7 addresses some of the challenges from an open-source perspective.
## 5 Performance Evaluation  
This section presents the performance evaluation results of the proposed scheme. To demonstrate an application process of the proposed MIMO mode switching algorithm, we describe the special case of \(M=2\) and \(N=2\). In addition, the numerical results of the exact link level capacity are presented to justify the benefit of MN MIMO mode switching on the link level capacity.