esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Coatings
#### 3.1.2 XRD of the Coatings  
EDS analysis revealed that the Cu and Ti coatings were partially oxidized; therefore, oxidation likely occurred. The phases formed during this process, along with the respective JCPDS number after the deposition of the coating, were determined via XRD, and the results are shown in Figure 4. No oxides were formed in the 85 Zn-15 Al coating, neither in the Zn (JCPDS:87-0713) nor in the Al (JCPDS:85-1327) phase. By comparison, a slight oxidation of the Cu coating occurred owing to the atomization of molten metal particles in the air [36], which was confirmed through EDS; therefore, it showed very small intensity peaks of Cu\({}_{2}\)O (JCPDS:77-0199) with the Cu (85-1326) phase. Our finding is corroborated by those of [PERSON] et al., who observed Cu\({}_{2}\)O along with the Cu phase [35] in a Cu coating deposited via an arc thermal spray process. The oxygen content in EDS was found to be 5.16%, which meant that the coating was partially, rather than completely, oxidized. The Ti coating was completely oxidized, owing to its high melting point; therefore, there was no Ti in the coating. This coating showed TiO (JCPDS:72-0020) and Ti\({}_{3}\)O (JCPDS:76-1644) phases. The oxygen content in EDS was very high, which corroborates the XRD results, according to which the Ti coating exhibited oxidation.  
The volume fraction (V\({}_{\text{i}}\)%) of each phase present in the coatings was determined using the JADE software built with XRD data. The results are listed in Table 2. Notably, 19.04% Cu\({}_{2}\)O was observed in the Cu coating, while no oxides of Zn or Al were found in the 85 Zn-15 Al coating. There is a possibility that native oxidation occurred, but due to the limitation of the XRD analysis, it was not observed. Most importantly, the content, that is, V\({}_{\text{i}}\)% of the Zn and Al phases, was almost identical to that observed in the EDS analysis. No Ti was observed owing to its oxidation during the coating process. Ti\({}_{3}\)O and TiO were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{5}{c}{**Elements (wt.\%)**} \\ \cline{2-6}  & **Cu** & **Ti** & **Zn** & **Al** & **O** \\ \hline Cu & 94.84 & - & - & - & 5.16 \\ \hline Ti & - & 80.15 & - & - & 19.85 \\ \hline
85 Zn-15 Al & - & - & 84.13 & 13.24 & 2.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDS analysis of the coatings.  
Figure 3: Cross-section SEM of (**a**) Cu, (**b**) Ti, and (**c**) 85 Zn-15 Al coatings at 500\(\times\).  
26.05% and 73.95%, respectively. Ti is oxidized either because of its high melting point or its free energy to form oxides with atmospheric oxygen [37; 38].
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 3 The Cybersecurity Aspects of Microgrid
### Vulnerabilities and Threats in Microgrid
#### 3.2.2 Threats against Microgrid  
A threat model commonly used against the microgrid is the one developed by the European Union Agency for Network and Information Security (ENISA) [44]. This model features cybersecurity threats to ICT and non-IT assets, which are physical assets of the main operations of the system. Based on this model, the potential threats to microgrids can be categorised into the following:  
* **Physical attacks occurring from intentional offensive actions.** These are targeted to perform distractions at the maximum level by gaining unauthorised access to assets of the microgrid and destroying them.
* **Eavesdropping.** This category of threats is realised by adjusting communication between parties without installing tools on the victim's side.
* **Nefarious Atrocious Activities.** This category is performed through cyberattacks or deliberate harmful activities which aims at system digital assets. Here, the attackers would use additional tools/software to attack the victim's software or IT infrastructure.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 3 State Estimation Model Based on Sample Migration
### State Estimation Model of Proposed Method  
Under the assumption that two distribution networks have similar topologies, common features can still be extracted from their measurements, even if there is a significant difference in observability. This enables the migration of data from high-observable distribution networks to low-observable distribution networks. The specific sample migration process is illustrated in Figure 1.  
Among various neural networks, during model training, deep neural networks (DNNs) suffer from parameter explosion, while recurrent neural networks (RNNs) are prone to gradient vanishing and explosion. In contrast, convolutional neural networks (CNNs) leverage techniques such as shared convolutional kernels and local feature extraction, which offer powerful capabilities for feature extraction and nonlinear representation, effectively capturing essential information from input data. This paper constructs a CNN model to capture the nonlinear relationship between distribution network measurements and state variables. The model is capable of performing state estimation for distribution networks under varying operating conditions and network topologies. The convolutional layers (CNN layers) extract feature information from the input data by applying sliding convolutional kernels. The fully connected layers (Dense layers), placed after the CNN layers, transform the extracted feature information into the target output. During sample migration, CNN layers are frozen and the structure and network parameters of CNN layers are saved to extract common features, while parameters of Dense lay  
Figure 1: The process of sample migration.  
a small number of samples from the new topology. The backpropagation algorithm is used to learn the parameters of each convolution unit [28]. Unlike other artificial neural networks (ANNs), CNN layers first process input blocks locally through convolution and then apply activation functions for further computation. The computational process of CNN can be described as:  
\[\mathbf{X}=\mathit{Lab},\mathit{P},\mathit{Q},\mathit{V} \tag{7}\]  
\[\mathbf{Z}=\mathbf{X}*\mathbf{K}+\mathbf{B} \tag{8}\]  
\[\mathbf{Y}=\mathit{AF}(\mathbf{Z}) \tag{9}\]  
where \(\mathbf{X}\) and \(\mathbf{Y}\) are the input and output of a CNN layer, respectively. \(\mathit{Lab}\) denotes the state of distribution networks, and \(\mathit{P}\), \(\mathit{Q}\), \(\mathit{V}\) signify measurement variables of distribution networks. \(\mathbf{Z}\) indicates the output of a convolution operation. \(\mathbf{K}\) and \(\mathbf{B}\) represent convolutional kernel and bias vector, respectively. \(\mathit{AF}\) denotes the activation function, the introduction of which endows CNN with powerful nonlinear representation capabilities.
## 2 System Topology  
The IPT system based on direct three-phase AC-AC matrix converter proposed in this paper is shown in Figure 1. The primary side consists of a three-phase AC-AC matrix converters and an LCCL resonant tank, and the secondary side consists of a single-phase full-bridge and an LCCL resonant tank. The proposed topology eliminates input inductor, DC link capacitor, and AC-DC-AC two-stage power conversion. Therefore, it achieves a direct conversion between low-frequency AC and high-frequency AC. The matrix converter consists of six bidirectional switches (\(S_{1}\)-\(S_{6}\)), each consisting of two back-to-back connected IGBT switches. In Figure 1, \(L_{1}\) and \(L_{2}\) respectively indicate the self-inductance of the primary side coil of the magnetic coupler and the self-inductance of the secondary side coil, and \(M\) is the mutual inductance between the primary and the secondary coils. The compensation network consists of a magnetic coupler and three other components: the series inductor \(L_{s}\) and \(L_{p}\), the shunt capacitor \(C_{s}\) and \(C_{p}\), and the series capacitor \(C_{1}\) and \(C_{2}\). In addition, \(v_{s}\) represents the voltage applied by the inverter to the input end of the resonant network, and \(v_{p}\) represents the voltage at the output end of the secondary side compensation network. \(i_{1}\), \(i_{2}\), \(i_{s}\), and \(i_{p}\) represent the currents flowing through the inductors \(L_{1}\), \(L_{2}\), \(L_{s}\), and \(L_{p}\) respectively.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 1 Introduction  
Considering the predetermined frequency allocation of typical wireless networks, there is only a limited amount of the wireless spectrum that is appropriate for broadband communications. As a result of this scarcity, the concept of Cognitive Radio (CR) communication was developed. CR communication comprises a number of mechanisms that enable licensed and unlicensed user systems to coexist on the same spectrum. The utilization of intelligent transceivers has become possible via developments in Software-Defined Radio (SDR), and digital signal processors, which allow for the dynamic and adaptive utilization of the available spectrum. Licensed and unlicensed wireless devices may share the same spectrum with the CR method, which was inspired by the concept of software-defined radio [1]. Figure 1 shows that numerous companies are making significant efforts to realize CR technology because of the wide variety of potential uses it could have, from TV White Spaces (TVWSs) [2] to satellite communications [3; 4; 5]. According to the CR network, principal (licensed) users (PUs) have spectrum priority. In contrast, Secondary (unlicensed) Users (SUs), also known as cognitive users, make use of this spectrum in a way that doesnot disrupt the typical operation of Licensed Primary Users (PUs). SUs are also known as cognitive users. In order for the SU to be able to take advantage of the unused sector of the spectrum, it is necessary for it to have CR capabilities. These CR capabilities allow the SU to collect information about its working conditions and to make autonomous adjustments to its radio parameters. Numerous studies have sought to define the \"CR\" and its various cognitive cycles since it started [6; 7]. Existing wireless systems can improve their spectral efficiency in two main ways:  
* Implementing a strategy for opportunistic spectrum access, commonly known as DSA.
* Spectrum sharing, which refers to the process of allowing primary user (PU) and secondary user (SU) systems to share the spectrum that is currently available.  
In addition, the most essential activities that are available in any of the hypothesized cognitive cycles are spectrum awareness, spectrum analysis, decision, and spectrum adaptation. These tasks need to be completed repeatedly until there is a full adaptation to the changed environmental state.  
The structure of this paper is as follows: Section 2 illustrates the spectrum sensing strategy. Section 3 highlights the main practical imperfections that may arise in a practical CR system cognitive radio network based on IoT. Then, Section 4 presents the cognitive capabilities operating in combination with IoT communication technologies. Section 5 concludes the paper.
## 2 System Model and Problem Formulation
### Problem Formulation  
In this paper, we aim to maximize the sum spectrum efficiency of the D2D pairs and CUs while guaranteeing a positive system gain and the rate requirement of CUs. Let \(\pi_{k,m}=1\) denote the D2D pair \(k\) reuse the subcarrier of CU \(m\), and \(\pi_{k,m}=0\) otherwise. Mathematically, the considered resource allocation problem can be formulated as  
\[\max_{\{p_{k,m}^{D},\pi_{k,m}\}}\sum_{m=1}^{M}\left(R_{m}^{C}+\sum_{k=1}^{K} \pi_{k,m}\cdot\Delta R_{k,m}^{D}(p_{k,m}^{D})\right) \tag{12}\]  
subject to  
\[\begin{array}{ll}\text{C1}:&\frac{p_{k,m}^{D}h_{k}^{DD}}{p_{m}^{C}h_{k,m}^{ C}+\sigma_{0}^{2}}\geq\frac{p_{k,m}^{D}h_{k,m}^{D}+\sigma_{0}^{2}}{\sigma_{0}^{ 2}},\forall k\in\mathcal{K},m\in\mathcal{M},\\ \text{C2}:&\log_{2}\left(1+\frac{p_{m}^{D}h_{m,B}^{C}}{\sum\limits_{k=1}^{K} \pi_{k,m}p_{k,m}^{D}h_{k,m}^{D}+\sigma_{0}^{2}}\right)\geq R_{m,\min}^{C}, \forall m\in\mathcal{M},\\ \text{C3}:&\sum\limits_{m=1}^{M}\pi_{k,m}p_{k,m}^{D}\leq p_{m}^{D},\forall k \in\mathcal{K},\\ \text{C4}:&p_{k,m}^{C}\geq 0,\forall k\in\mathcal{K},m\in\mathcal{M},\\ \text{C5}:&\sum\limits_{k=1}^{K}\pi_{k,m}\leq 1,\forall m\in\mathcal{M},\\ \text{C6}:&\pi_{k,m}\in\{0,1\},\forall k\in\mathcal{K},m\in\mathcal{M}.\end{array}\]  
where \(R_{m,\min}^{C}\) is the required rate of CUs. C1 and C2 guarantee the positive system gain and the rate requirements of the CUs, respectively. C2 can be transformed as follows:  
\[\sum_{k=1}^{K}\pi_{k,m}p_{k,m}^{D}h_{k,B}^{DB}\leq\frac{p_{m}^{C}h_{m,B}^{C}}{ 2^{R_{m,\min}^{C}}-1}-\sigma_{0}^{2},\forall k\in\mathcal{K}. \tag{13}\]  
C3 and C4 are the transmit power constraint of D2D pairs. C5 and C6 indicate that each CU's subcarrier resource can be reused by at most one D2D pair.
## 6 Conclusions  
In this study, the effects of vertical motion on the uplift of underground structures induced by soil liquefaction were explored via a numerical simulation approach using the FLAC program with the PM4 Sand model. The PM4 Sand model is modified from the bounding surface plasticity model to model behaviors of liquefiable soils for geotechnical earthquake engineering applications. The numerical model was first verified and calibrated using centrifuge tests from [PERSON] and [PERSON] [1] for soil layers with Dr = 30%, 50%, and 80%. Then, the effects of vertical motion were studied using 48 ground motion time histories from 5 earthquake events. The simulation results showed that the vertical motion could cause an increase, decrease, or negligible change in the uplift. Additional simulations of TCU071\(-\)N and HWA027\(-\)N cases showed that the variation in the peak acceleration amplitude causes an increase or a decrease in the uplift depending on frequency contents between the horizontal and vertical motions. This observation effectively explains the contrary results of [PERSON] and [PERSON] [2] and [PERSON] et al. [6]. The effects of the vertical motion on the uplift are summarized as follows:  
1. Most simulation cases with vertical input motion have uplift ratios between 0.9 and 1.1 (a 10% difference in the uplift). Exceptions are cases where the vertical motion lowers the uplift by 20% to 50%. These exceptions occur in cases with higher peak acceleration, Arias intensity, and CAV. These cases come from the Chi\(-\)Chi earthquake event, which possessed a higher earthquake magnitude.
2. If the amplitude of FFT of the vertical motion is higher than the amplitude of FFT of the horizontal motion at a frequency range higher (twice as high) than the natural frequency of soil, then, the vertical motion decreases the uplift. Conversely, if the amplitude of FFT of the vertical motion is higher than the amplitude of FFT of the horizontal motion in a frequency range close to the natural frequency of soil, then the vertical motion increases the uplift.  
For a preliminary calculation without a numerical simulation, the uplift with the vertical motion can be estimated conservatively using 1.1 times the uplift without the vertical motion. However, the effects of the vertical motion on the uplift in this study were based on the numerical simulation results only. These observations still need to be verified through a model test (the centrifuge test or the shaking table test).  
Figure 15: Comparisons of amplitude of FFT for additional HWA027\(-\)N cases.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** I like to thank the National Center for Research on Earthquake Engineering (NCREE) for providing the catalog of earthquake motions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Effect of Tree Quantity and Distribution on the Electric Field under Transmission Lines
## 4 Effect of Tree Quantity and Distribution on Power Frequency Electric Field
### Effect of Tree Quantity  
It is assumed that the model and all parameters of trees are the same. On one side, the coordinates of the first tree are \((0,7.5)\), and the wheelbase between the center points of adjacent trees is 3 m. On the other side, the coordinates of the first tree are \((0,-7.5)\), and the axial spacing between the center points of adjacent trees is 3 m. Meanwhile, the layout of trees is symmetrical about x = 0.  
According to the conclusion obtained from transverse measurements, the maximum electric field intensity of the AC transmission line is below the side conductor, so this paper investigates the distribution of the industrial frequency electric field by changing the number of trees below the side conductor.  
In this paper, assuming that the green belt formed by trees is symmetrical along the lateral conductor, the number of trees is set to 9, 19, 29, 54, and 133, respectively. So we can obtain the field strength distribution at 1.5 m above the ground for different numbers of trees, as shown in Figure 9. Then, the measurement path was selected as L1, and the variation curves of the electric field intensity under the lateral conductor with longitudinal distance were plotted for different numbers of trees, as shown in Figure 10. The maximum field strength values at the longitudinal measurement points for different numbers of trees are shown in Table 3.  
Figure 8: Equivalent model of 500 kV transmission lines and Xing’an larch.  
Figure 9: Power frequency electric field distribution for different numbers of trees.  
In Figure 9, the cloud diagram of electric field distribution at a height of 1.5 m above the ground shows that the distribution of electric field intensity gradually weakens with the increase in the number of trees. It can be seen more intuitively in Figure 10 that, as the number of trees increases, the field strength below the side conductor decreases gradually. When the number of trees increased to 54, the field strength below the side conductor was reduced to below 4 kV/m. When the number of trees increased to 133, the green belt formed by the trees spread all over the transmission line below the side conductor, and the electric field strength at the longitudinal measurement point was 1.19 kV/m, which is 78.75% lower than that when there are no trees.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 9 Future Research Directions  
This work provides a proof of concept for integrating MC within multicast transmissions, but several practical questions remain open for further research. For instance, we assumed that the users are static for the entire duration of the multicast transmission. The impact of user mobility on the proposed algorithms remains to be studied. Allowing for mobility will imply that the sets of users served under a certain allocation keep changing as a function of time. Therefore, new resource allocation algorithms need to be developed that can take this into consideration. Since the problem of resource allocation in MC multicasting is shown to be NP-hard, machine learning-based algorithms can also be developed for optimizing the allocation decisions. Another interesting research direction would be to consider a system where a number of different multicast streams can be simultaneously broadcast in a multicast region.  
**Author Contributions:** Formal analysis, S.u.Z. and P.C.; software, S.u.Z.; supervision, P.C., A.K. and H.V.P.; visualization, S.u.Z.; writing--original draft, S.u.Z.; writing--review and editing, S.u.Z., P.C., A.K. and H.V.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The video traces used for the simulations in Section 7 are part of the Arizona State University Video Trace Library ([[http://trace.eas.asu.edu/](http://trace.eas.asu.edu/)]([http://trace.eas.asu.edu/](http://trace.eas.asu.edu/)), (accessed on 20 March 2023)) [11].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Price Predictions Using Deep Learning Models
### Time-Series Forecasting Models
#### 3.2.2 Simple Recurrent Neural Networks (Simple RNNs)  
Simple recurrent neural networks (RNNs) have emerged as powerful tools for time-series forecasting due to their ability to process sequential data and capture inherent temporal dependencies. Unlike traditional feedforward neural networks, RNNs incorporate recurrent connections that enable information to persist across processing steps. This internal \"memory\" allows RNNs to learn evolving patterns and long-term dependencies within the data, making them well suited for tasks like predicting future values in a time series.  
However, simple RNNs suffer from significant limitations when applied to large and complex time-series datasets. The major challenge lies in the vanishing and exploding gradient problems. Vanishing gradients occur when information from earlier data points gradually fades during backpropagation, making it difficult for the network to learn long-term dependencies. Conversely, exploding gradients can amplify information, leading to unstable training and unreliable predictions.
## 2 Overview
### Quantum Cryptography
#### 2.1.1 Quantum Key Distribution  
Quantum Key Distribution (QKD) refers to the process of establishing cryptographic keys via a quantum channel. QKD protocols can be categorized based on the type of quantum particles they use. The first category-for example, the BB84 [9] and B92 [10] protocols-uses independent single qubits. The second category-for example, the E91 [11] protocol-operates on pairs of qubits with entangled states, which means that the measurement of one qubit instantaneously determines the state of the other [12].  
The security of QKD relies on two fundamental principles of quantum mechanics: [PERSON]'s uncertainty principle and the no-cloning theorem. The uncertainty principle [13] states that certain pairs of related physical properties cannot be simultaneously measured with arbitrary precision. In other words, this means that any attempt to measure a quantum state inevitably changes it, which may help detect that an adversary tries to intercept the communication [6; 14]. The no-cloning theorem states that unknown quantum states cannot be copied [15]. This principle is crucial because it makes passive eavesdropping on the quantum key virtually impossible. Although an attacker can access the quantum channel, they cannot simply observe the qubits without altering their state. As a result, any eavesdropping attempt becomes detectable, limiting the attacker to actions such as slowing down or intercepting the communication rather than remaining undetected.