esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The characteristics and performance of two linear isolators based on the parallel connection of an inerter and quasi-zero stiffness are evaluated and compared with those of other corresponding linear isolators. The absolute displacement transmissibility, the Shock Acceleration Ratio (SAR), the Shock Displacement Ratio (SDR) and the Relative Displacement Ratio (RDR) are used as evaluation indices for the harmonic base excitation and the impulse displacement excitation of the half-cycle sine, respectively.  
Figure 19: The RDR of the corresponding linear isolators with the damping ratio \(\zeta=0.1\) and some specific \(\alpha,\beta\) and \(\varepsilon\) values.  
Figure 20: Time responses of non-dimensional absolute displacement \(X\) under the base displacement excitation of a half-cycle sine pulse (severity factor \(\mu=4\)) for some isolators (shown in Figure 18).  
The isolators \((m,c,k_{p},k_{n},b)\) and \((m,c,k_{p},k_{n},b,c_{1})\) can achieve a very good isolation performance at ultra-low natural frequencies. They can provide comparative advantages in a very low frequency range over other corresponding linear isolators and enlarge the frequency band for effective vibration isolation. It is also found that, only in a certain low-frequency region to the right of the resonance frequency, the isolator \((m,c,k_{p},k_{n},b)\) provides slightly better vibration isolation than that of the isolator \((m,c,k_{p},k_{n},b,c_{1})\) with approximately the same resonance frequency; in other frequency ranges, especially in the high-frequency ranges, the isolator \((m,c,k_{p},k_{n},b,c_{1})\) has the best performance in terms of displacement transmissibility \(T_{d}\). In evaluating shock isolation performance under the impulse displacement excitation of the half-cycle sine, the isolator \((m,c,k_{p},k_{n},b,c_{1})\) can achieve better SAR, SDR and RDR indices in a very wide \(\mu\)-value region of the severity factor except for a small \(\mu\)-value region, compared with the isolator \((m,c,k_{p},k_{n},b)\). However, the isolator \((m,c,k_{p},b,c_{1})\) has the best SDR and RDR indices in a very wide \(\mu\)-value region, compared with other corresponding linear isolators discussed in this paper.  
Conceptualization, J.Z. and H.O.; Data curation, N.Y. and J.Y.; Formal analysis, N.Y., H.O., J.Z. and J.Y.; Investigation, Q.Y. and X.Z.; Methodology, N.Y. and J.Z.; Software, Q.Y. and X.Z.; Supervision, J.Z.; Validation, Q.Y. and X.Z.; Writing--original draft, N.Y.; Writing--review & editing, H.O. and J.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 2 Analysis Methodology
### Estimation of Nonlinear Response Due to Microstructural Defects  
In the previous section, a pulse-inversion technique is shown to nominally remove the linear response from the overall response, thus revealing the nonlinear response in the recorded signal. Using this technique, the nonlinear response \(x^{NL}(t)\) is generated \(n\) times with \(n\) different input amplitudes to estimate \(\beta\) in Equation (1). These \(n\) different time domain signals \(x^{NL}_{n}(t)\) are, then, subtracted from the baseline time domain signal \(x^{NL}_{0}(t)\) generated by the lowest input amplitude. The resulting difference signal \(y_{n}(t)\) is calculated as  
\[y_{n}(t)=x^{NL}_{0}(t)-x^{NL}_{n}(t). \tag{4}\]  
The \(n\)-th difference signal, \(y_{n}(t)\), is transformed into a two-dimensional contour map, using short time Fourier transform (STFT) [22]. STFT of a discrete time domain signal \(y_{n}(t)\) of length \(N\) is defined as  
\[Y_{n}(t,f)=\sum_{m=0}^{N-1}y_{n}(m)\omega(m-t)e^{-2\pi f/m/M}, \tag{5}\]  
where \(\omega(t)\) is a Hamming window function of length \(M<N\), and the frequency index \(f\)[22]. This study utilizes multiple frequency ultrasonic signals, and STFT is used to measure the nonlinear response of micro-cracks at each frequency. Analysis of difference signals in joint time and frequency domains using STFT can provide sufficient temporal and spectral information in the absence of a more detailed complex modal analysis.  
Next, the standard deviation, \(\sigma_{n}(f)\), of the power intensity in \(Y_{n}(t,f)\). is calculated over the investigation time at different frequencies. By summing over all frequency components of \(\sigma_{n}(f)\) and normalizing the sum, the microstructural defect response (\(MDR\)) is obtained as  
\[MDR_{n}=\frac{\left[\sum\limits_{k=1}^{F}\sigma_{n}(f_{k})\right]}{F}, \tag{6}\]  
where \(F\) is the number of discretized frequencies. \(MDR\) is calculated \(n\) times and is then used to calculate actionable output (\(AO\)) which represents the proportionality of \(MDR\) to the square of the input amplitude. In the following sections, we will discuss how \(AO\) is calculated and how it is related to the density of microstructural defects (or, specifically the micro-crack density in the case of a steel beam sample examined in this study) in detail.
## 4 Experiments and Results
### Scenario 2: Estimating Path Loss in Underwater Environments  
To estimate the path loss in underwater environments, the second experiment was conducted in a 25 m wide and 50 m long swimming pool whose depth was from 3 to 16 ft. IRIS motes were put 1 ft under the water to estimate the path loss exponents and as well as overall path loss attenuation in underwater environments operating at 2.4 GHz. The conductivity of water in the experimented medium was 0.001 S/m. Therefore, the water is categorized as freshwater. To calculate the path loss underwater, a similar approach is followed as proposed in Section 4. The placement of nodes is similar to what has been illustrated in Section 4.1. The base station is connected with the PC to project the real-time results. The antenna orientation of the IRIS motes is kept 0\({}^{\circ}\) vertical in line with each other and with the base station antenna. To make sure that the motes are not damaged, IRIS motes are  
Figure 3: RSS values at different distances in indoor environments.  
kept in rectangular cubicles that are water proof. The boundaries of the rectangular cube are kept at a distance greater than \(\lambda/2\) of the highest frequency (upper frequency) of the selected channel of 2.4 GHz. This is important because, in LOS communication, if any object or obstacle comes under \(\lambda/2\), distance would make the small-scale fading factors significant enough to create large effects over the large-scale fading.  
Thus, the results obtained from the experiment are shown in Figure 4. Interestingly, when IRIS motes were put inside water, a drastic change was observed in signal transmission and reception due to the sudden change in medium from air to water. Due to severe attenuation in underwater medium, the reference distance is kept 0.1 m. The distance is increased with the step size of 0.1 m up to 0.5 m.  
The results shown in Figure 4 illustrate the overall path loss obtained from the RSS data collected in underwater environments, specifically in freshwater. The best possible approximation of linear regression is shown with the red graph. Mathematically, this linear approximation is given as:  
\[y=35\times x+12 \tag{10}\]  
The blue graph depicts the averaged RSS from 0.1 m to 0.5 m. From Equation (10), the value of the slope of the line, _i.e._, \(m=35\) is calculated. Thus, the path loss exponent will be \(n=3.5\). This verifies that the path loss exponent for an underwater environment is as mentioned in Table 2. Figure 5 is the bar graph of data illustrating the RSS measured at each distance.
## 3 Results and Discussion
### Freeze-thaw Damage Prediction Model Based on Grey Model by GM (1,N)
#### 3.2.2 Three-Variable Grey Model GM (1,3)  
To study the number of independent variables for the prediction accuracy of GM (1,N), the three-variable grey model GM (1,3) was also built according to the experimental data under 0-9 freeze-thaw cycles, to predict the damage characteristics under 12 and 15 freeze-thaw cycles for verification.  
The predictive values of _VA_, _STS_, and _ITSM_ compared with the experimental results are presented in Figure 9 based on the three-variable grey model GM (1,3) of _VA_, _STS_, and _ITSM_ tests. It is apparent that the relative errors for _VA_ of AM and BFAM are within 3%, the relative errors for _STS_ of AM and BFAM are within 6%, and the relative errors of _ITSM_ of AM and BFAM are around 8%. Compared with two-variable GM (1,2) in Section 3.2.1, the results revealed that the relative error of three-variable GM (1,3) is smaller than that of GM (1,2) for the aspect of prediction accuracy. Thus, to some degree, it could be indicated that GM (1,3) has the better prediction accuracy than GM (1,2) under the conditions of the same data.  
Through overall consideration, the three-variable grey model GM (1,3) based on _VA_, _STS_, and _ITSM_ tests is an effective prediction model for asphalt mixtures under the action of freeze-thaw cycles, which can reflect the evolution of freeze-thaw damage for asphalt mixtures more accurately.  
Figure 8: Comparison between predictive and experimental values for AM and BFAM based on GM (1,2) of _STS_ and _ITSM_ tests: (**a**) Damage degree of _STS_ for AM; (**b**) Damage degree of _STS_ for BFAM; (**c**) Damage degree of _ITSM_ for AM; (**d**) Damage degree of _ITSM_ for BFAM.
## 3 Materials and Methods
### Fusion Module  
As shown in Figure 3, to combine both extracted text and image modalities, a late fusion module approach was utilized, enabling each modality to be handled individually. This approach is particularly effective for memes, which inherently include both text and image components [43]. The late fusion module operates by integrating the results from the independent classifications of the extracted text and image data. This integration ensures that both text and image analyses are considered in decision-making processes. The decision-making process, as detailed in Algorithm 1, outlines the steps for determining the final classification by integrating the results of the extracted text and image analyses, thus ensuring accurate handling of multi-modal data.  
```
1:Begin
2:iftext_class is not None and image_class is not None then
3:iftext_class == image_class then
4:iftext_class == 0 then
5:fusion_message \(\leftarrow\) \"Input does not contain any cyberbullying.\"
6:else
7:fusion_message \(\leftarrow\) \"Input contains this class {text_class_} of cyberbullying.\"
8:endif
9:else
10:fusion_message \(\leftarrow\) \"Input contains cyberbullying. Text label is: {text_label_number} and Image label is: {image_label_number}\"
11:endif
12:else
13:fusion_message \(\leftarrow\) \"This is not Multi-Modal Data!\"
14:endif
15:End
```  
**Algorithm 1** Fusion logic for decision making on multi-modal data.  
The decision-making process, as detailed in Algorithm 1, first determines whether both extracted text and image classifications are available, signifying the presence of multi-modal data. If both analyses agree that there is \"no cyberbullying\", the system outputs a message indicating the absence of cyberbullying. Conversely, if both analyses classify the input as the \"same type of cyberbullying\", the system assigns a specific class to the input. In cases where the text and image classifications disagree, the module acknowledges the presence of cyberbullying but notes that the classifications differ. When either classification is missing, the system identifies the input as non-multi-modal data and provides an appropriate  
Figure 17: Model architecture of hybrid (RoBERTa+CNN) model for multi-class cyberbullying classification of multi-modal data.  
Figure 18: Model architecture of hybrid (RoBERTa+ViT) model for multi-class cyberbullying classification of multi-modal data.  
message. Ultimately, the process concludes by synthesizing the final output message. This cohesive response is effectively communicated to the user, providing clear and concise results of the cyberbullying classification process.
## 2 Problem Formulation
### Optimal Location  
At present, determining the optimal location of DGs is a crucial issue in terms of the power produced by DGs being delivered reliably and efficiently to consumers. For this reason, monitoring and controlling the power produced by DGs, managing the power flow, ensuring system reliability, and maintaining the balance between supply and demand are necessary. Generally, DGs owners determine the optimal location of DGs according to the maximum profit that can be achieved, without paying attention to the aforementioned. However, a distribution company (DISCO) which distributes electrical energy under license determines the optimal DG location to meet consumer demand with maximum profit and create the best investment plan for the network. In some regions, the name distribution system operator (DSO) is used interchangeably with DISCO, but DSO is responsible specifically for managing and optimizing the distribution system. In some cases, DSO or DISCO may be an independent entity. This entity is called the independent distribution system operator (IDSO), and it aims to increase transparency and efficiency in the electricity distribution sector. Also, distribution system planning and determining the location of DGs can be carried out optimally with IDSO. In this study, it is assumed that the optimal locations of DGs are determined through the IDSO; bear in mind that the definitions between DISCO, DSO, and IDSO may vary between different regions and countries and may not be universally applicable in every electricity distribution system [31].  
The optimal placements of DGs and/or CBs are pre-identified with LSF. First, the LSF values of all buses in the distribution system are calculated and ordered from largest to smallest. Then, the buses at the top of the list are chosen as candidate buses for the optimal location of DG and/or CB. Finally, DG and/or CB are installed on the candidate buses in the optimization algorithm, and the placement of the bus that reduces the most power losses is determined as the optimal location. The determination of the optimal location of DGs and/or CBs is expressed in Figure 1.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 1 Introduction
* We present the challenges of QKD implementation in 5G networks. The remainder of this paper is organized as follows. Section 2 discusses the security challenges in 5G. The importance of QKD is demonstrated in Section 3, while Section 4 presents the QKD classifications. Lastly, the conclusion and future works are discussed in Section 5.
## 3 Preliminaries
### The MOL Scheme  
In the PMRC structure, the cluster heads closer to the sink node are burdened with heavy relay traffic and tend to die early, which leads to energy holes. The MOL scheme [18] is proposed to mitigate this problem and overcome the limitation imposed by the fixed layer boundary and overlap range of the OL scheme [17].  
We assume that a node is eligible to be elected as a cluster head only if its residual energy is higher than some pre-defined threshold. As described in Section 3.1, when the residual energy of one cluster head falls below the threshold, a new round of network formation will be triggered. In network reformation, each node will receive the BP sent from nodes located in its inner layer and select the new cluster head. Its layer no. will be set according to the layer no. of its cluster head. As a result, some nodes may have their layer no. changed. Consequently, throughout the network lifetime, the boundary between neighbor layers is moving towards the sink as needed and the cluster size is dynamically changed.  
Figure 3 illustrates the moving of boundary between neighbor layers in the MOL scheme. Figure 3(a) shows the initial topology with node 2 serving as the cluster head for the cluster composed of nodes 3, 4, 5 and 6 at layer 2. After the residue energy of node 2 drops below the threshold, the network reformation is performed and node 1 is selected as the new head by nodes 3 and 5, as shown in Figure 3(b). Node 4, which is out of the transmission range of node 1, selects node 9 as its new cluster head. Node 6, which is originally within the transmission range of node 2, is \"pushed\" to layer 3 as it is out of the transmission range reachable by any node at layer 1. The result is that the boundary between layer 2 and layer 3 is moving towards the sink. Note that nodes initially belonging to layer 1 (e.g., node 2 in Figure 3) will not be pushed to layer 2 because they can still reach the sink in one hop.  
Figure 2: Network operation phases.  
The MOL scheme [18] has the following properties.: first, unlike from the OL scheme, the required minimum overlap between neighbor layers is gradually increased on demand during network lifetime. Second, the MOL scheme overcomes the limitation caused by static network topology control. As such, the MOL scheme can adapt to any randomly deployed network as long as the initial topology is connected. Third, the MOL scheme inherently helps balance the energy consumption among cluster heads within the same layer.
###### Abstract  
The convergence of IT and OT networks has gained significant attention in recent years, facilitated by the increase in distributed computing capabilities, the widespread deployment of Internet of Things devices, and the adoption of Industrial Internet of Things. This convergence has led to a drastic increase in external access capabilities to previously air-gapped industrial systems for process control and monitoring. To meet the need for remote access to system information, protocols designed for the OT space were extended to allow IT networked communications. However, OT protocols often lack the rigor of cybersecurity capabilities that have become a critical characteristic of IT protocols. Furthermore, OT protocol implementations on individual devices can vary in performance, requiring the comprehensive evaluation of a device's reliability and capabilities before installation into a critical infrastructure production network. In this paper, the authors define a framework for identifying vulnerabilities within these protocols and their on-device implementations, utilizing formal modeling, hardware in the loop-driven network emulation, and fully virtual network scenario simulation. Initially, protocol specifications are modeled to identify any vulnerable states within the protocol, leveraging the Construction and Analysis of Distributed Processes (CADP) software (version 2022-d \"Kista\", which was created by Inria, the French Institute for Research in Computer Science and Automation, in France). Device characteristics are then extracted through automated real-time network emulation tests built on the OMNET++ framework, and all measured device characteristics are then used as a virtual device representation for network simulation tests within the OMNET++ software (version 6.0.1, a public-source, open-architecture software, initially developed by OpenSim Limited in Budapest, Hungary), to verify the presence of any potential vulnerabilities identified in the formal modeling stage. With this framework, the authors have thus defined an end-to-end process to identify and verify the presence and impact of potential vulnerabilities within a protocol, as shown by the presented results. Furthermore, this framework can test protocol compliance, performance, and security in a controlled environment before deploying devices in live production networks and addressing cybersecurity concerns.  
Iiot; vulnerability analysis; formal verification; construction and analysis of distributed processes; CADP; cyber-physical systems; operational technology +
Footnote †: journal:  
_Article_
## 2 Microlensing Parallax

Table 1: Microlensing parameter definitions obtained from a photometric curve. Here, \(\epsilon=\dfrac{R}{AU}\) and we use \(\lambda=\dfrac{\pi}{6}\) in our simulations. The parallax vector \(\pi_{E}=(\pi_{E}cos\theta,\pi_{E}sin\theta)\), where \(\theta\) is the lens source trajectory angle. To obtain the shifts due to parallax, we obtain:  
\[\delta_{\tau} = \pi_{E}\cdot\mathbf{o} \tag{10}\] \[= \pi_{E}cos\theta_{\parallel}cos\Omega+\pi_{E}sin\theta_{ \parallel}sin\lambda sin\Omega\] (11) \[\delta_{\beta} = \pi_{E}\times\mathbf{o}\] (12) \[= \pi_{E}cos\theta_{\parallel}sin\lambda sin\Omega-\pi_{E}sin \theta_{\parallel}cos\Omega \tag{13}\]  
where \(\Omega=\dfrac{2\pi}{P}(t-t_{0})+\Phi\), P is the orbital period, and \(\Phi\) is the orbital phase relative to \(t_{0}\)[8]. We use \(\theta=\dfrac{\pi}{4}\) in our modelling. From Equation (6), we can write  
\[u(t)=\left[u_{0}^{2}+\tau^{2}\right]^{1/2} \tag{14}\]  
where \(\tau=\left(\dfrac{t-t_{0}}{t_{E}}\right)\). We can define \(\tilde{u}(t)\) as the microlensing equations due to parallax [8]:  
\[\tilde{u}(t)=\sqrt{(u_{0}+\delta_{\beta})^{2}+(\tau+\delta_{\tau})^{2}} \tag{15}\]  
In this manner, we can define the new amplification equation as  
\[\tilde{A}(t)=\dfrac{\tilde{u}(t)^{2}+2}{\tilde{u}(t)\sqrt{\tilde{u}(t)^{2}+4}} \tag{16}\]  
Thus, from a photometric curve with a microlens parallax, we can obtain \(u_{0}+\delta_{\beta}\) and \(\tau+\delta_{\tau}\).
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 1 Introduction
In response to these problems, this study proposes a lightweight model based on the improved GhostNetV2 to detect the severity of wheat yellow rust. The contributions of this study are as follows:  
1. A channel shuffling operation is added to the Ghost module to strengthen the information flow between group feature maps and enhance the model learning ability. 2. The shallow layer of the model is replaced with the Fused-MBConv module, the training time of the model is reduced by 37.49%, and the training efficiency of the model is improved. 3. The SE of the model is replaced with the ECA to further improve the focus of the model on the disease and reduce the parameters of the model.