esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 5 Results
### Applying the Methods to the Use Case  
Our fixed topology that represents the use case includes 12 ARs, as shown in Figure 1. These ARs represent the communication between the factory hall level and edge computing level, and communication between the edge computing level and cloud computing level. The ARs in Figure 1 represent AR categories but the real ARs in the definition files include specific end-points, for example, a certain type of sensors. We show in this section the generated EVN for a specific AR (AR11) in Figure 6. We also show the relevant part of our substrate topology defined in Alevin with the mapping results for AR11 in Figure 7.  
AR11 connects an application VNo (data gathering) from the edge computing level (location 2 domain 2) to an application VNo (data analysis) from the cloud computing level. AR11 defines redundancy and low latency requirements and connects different locations and domains. For these requirements, two redundant VNF requests are created with encryption, firewall, DPI, and monitoring VNFs. Furthermore, TAS VNF is added. The resulting FGs from the chain composition stage are similar to Figure 4. As mentioned before, TAS VNF is only added to the beginning of the FG and the chain embedding algorithm adds it to each server that hosts the FG. A load balancer is added and the target data analysis VNo is cloned, according to the calculated CPU demand and server capacity. The mapping results in Figure 7 show the mapping of the two redundant FGs over two completely disjoint paths (dotted line for FG1 and dashed line for FG2).
## A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks  
**[PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{1,3}\) and [PERSON] \({}^{1,4}\)**  
\({}^{1}\) Digital Image Processing, Telemedicine and Multimedia Laboratory, Faculty of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade 11000, Serbia;  
E-Mails: [EMAIL_ADDRESS] (I.R.); [EMAIL_ADDRESS] (B.R.)  
\({}^{2}\) ICT College for Vocational Studies, Zdravka Celara 16, Belgrade 11000, Serbia  
\({}^{3}\) Faculty of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade 11000, Serbia  
\({}^{4}\) Innovation Center of Faculty of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade 11000, Serbia  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +381-64-832-8910; Fax: +381-11-329-0650.  
_Received: 13 March 2012; in revised form: 28 May 2012 / Accepted: 29 May 2012 / Published: 7 June 2012_  
The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic--_i.e._, neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.  
keywords: artificial intelligence; Hopfield neural network; MANET; multicriteria optimization; routing protocol; wireless mesh network +
Footnote †: journal: Journal of Network Science
# Single-Stage Step-Down Power Factor Corrector without Full-Bridge Rectifier
## 2 System Configuration  
Figure 3 displays the system configuration of the proposed rectifier. As for the main power stage, it is constructed by the switches \(S_{1}\) and \(S_{2}\), the diodes \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\), the output inductor \(L\), the output capacitor \(C_{o}\), and the output resistor \(R\). As for the control stage, it is built up by one voltage divider, one analog-to-digital converter (ADC), one gate driver, and one FPGA, which is used to realize voltage-follower control. It is noted that only one gate driving signal is needed to drive the switches \(S_{1}\) and \(S_{2}\). Regarding the control concept, the output voltage is sensed by the voltage divider and sent to the ADC to obtain the corresponding digital signal, which is sent to the FPGA to create suitable gate driving signals to drive the switches \(S_{1}\) and \(S_{2}\), so as to attain the prescribed output voltage, along with a high PF, due to the proposed circuit being operated in DCM. In addition, there is a voltage controller constructed by a proportional-integral (PI) controller and a pulse-width-modulated (PWM) generator inside the FPGA.  
Figure 2: Single-stage step-down power factor corrector without full-bridge rectifier: (**a**) the proposed; (**b**) the existing [22].
# An Integrated Cyber Security Risk Management Approach for a Cyber-Physical System
## 4 An Integrated Risk Management Approach
### Risk Management Process
#### 4.2.1 Activity 1: Risk Management Context
It defines the degree or extent to which the asset is readily usable along with the necessary IT and management procedures, tools, and technologies required to enable, manage, and continue to make it available. * Integrity (I): Integrity refers to the ability of critical infrastructure organizations assets to perform their required functions effectively and efficiently without any disruption or loss of service. It includes the critical aspect of any asset which stores, processes, and retrieves data, its design, implementation, and usage. Integrity ensures that the data managed by systems and messages communicated over the network altered by unauthorized users. * Resiliency (R): This KPI allows for the CPS to be able to work on an acceptable level of efficiency even when external or internal disturbances occur. * Reputation (RE): Reputation is the trust and confidence the organization has gained by the public or given to the public. * Authenticity (AUT): This KPI improves the identification and verification technology of an authorized user in order to provide security, ease of use, and administration. It has the capacity to identify an authorized user to its specific appropriate information and service type. * Nonrepudiation (NR): This KPI provides certifiable evidence of a message being delivered to both communication endpoints in order to ensure that either the sender or the receiver does not deny sending and/or receiving the message. * Maintainability (M): Maintainability is associated with the mean time to repair (MTTR) an asset and get it to work perfectly within a specified period of time. The time could be categorized as less than a day, several days, one week, several weeks, month(s), or even a year. Step 3: Risk acceptance level  
The risk acceptance level gives an organization a guideline with which risk needs to be controlled based on management decision linking with residual risks. With a proper risk management process, risk can be eliminated, but not to a zero level, therefore, the remaining risk is referred to as the residual risk and should be accepted to a certain level with reference to Table 1 below. Accepting risk to a certain level is really important for a critical infrastructure and organization and its surrounding context. There are no risk-free systems; therefore, the need to understand which level of acceptance of risk after control is important for an organization. A well secured CPS can resist any form of disturbances either internally or externally and is able to continue working on an acceptable efficiency level [34]. Based on the probability of occurrence and impact, the risk level will be categorized into five different risk levels. Therefore, the risk management approach decides what level of risk can be accepted for the organization.
## 3 Formulation of Mathematical Models
### Portz, Kuang, and Nagy (PKN) Model  
In this section, we briefly review the PKN model. For a more detailed explanation of this model, the reader is referred to [13]. The PKN model assumes constant death rates for cancer cells \((d_{1},d_{2})\). CS and CR cells have androgen cell quota \(Q_{1},Q_{2}\) respectively. \(A\) denotes the serum androgen concentration, which is interpolated and used in the model:  
\[\frac{dx_{1}}{dt} = \underbrace{\mu_{m}(1-\frac{q_{1}}{Q_{1}})x_{1}}_{\text{growth}} -\underbrace{d_{1}x_{1}}_{\text{death}}-\underbrace{\lambda_{1}(Q_{1})x_{1}} _{\text{CS to CR}}+\underbrace{\lambda_{2}(Q_{2})x_{2}}_{\text{CR to CS}}, \tag{11}\] \[\frac{dx_{2}}{dt} = \underbrace{\mu_{m}(1-\frac{q_{2}}{Q_{2}})x_{2}}_{\text{growth}} -\underbrace{d_{2}x_{2}}_{\text{death}}-\underbrace{\lambda_{2}(Q_{2})x_{2}} _{\text{CR to CS}}+\underbrace{\lambda_{1}(Q_{1})x_{1}}_{\text{CS to CR}},\] (12) \[\frac{dQ_{1}}{dt} = \underbrace{\
u_{m}\frac{q_{m}-Q_{1}}{q_{m}-q_{1}}\frac{A}{A+v_{h} }}_{\text{Androgen influx to CS cells}}-\underbrace{\mu(Q_{1}-q_{1})}_{\text{uptake}}- \underbrace{bQ_{1}}_{\text{degradation}}, \tag{13}\]\[\frac{dQ_{2}}{dt} = \underbrace{v_{m}\frac{q_{m}-Q_{2}}{q_{m}-q_{2}}\frac{A}{A+v_{h}}}_{ \text{Androgen influx to CR cells}}-\underbrace{\mu(Q_{2}-q_{2})}_{\text{uptake}}- \underbrace{bQ_{2}}_{\text{degradation}}, \tag{14}\] \[\frac{dP}{dt} = \underbrace{\sigma_{0}(x_{1}+x_{2})}_{\text{baseline production}}+ \underbrace{\sigma_{1}x_{1}\frac{Q_{1}^{m}}{Q_{1}^{m}+\rho_{1}^{m}}}_{\text{tumor production}}+\underbrace{\sigma_{2}x_{2}\frac{Q_{2}^{m}}{Q_{2}^{m}+\rho_{2}^{m}}}_{\text{tumor production}}-\underbrace{\delta P_{\text{c}anceance}}. \tag{15}\]
## 5 Potential 6G Applications in the Maritime Sector
### Just-in-Time Port Arrivals and Smart Port Synchronization  
Congestion at ports leads to delays, wasted fuel, and disrupted supply chains. Sixth-generation-enabled Just-in-Time (JIT) arrival systems use predictive analytics and ultra-reliable communication to coordinate ship arrivals, berth allocation, cargo handling, and customs processing [81]. Ships adjust their arrival times based on real-time port data, improving throughput and environmental sustainability [82]. Optionally, RIS-aided links, terahertz relays, and MEC platforms at sea BSs or buoys enable high-bandwidth, low-latency connections even several nautical miles from port. The scenario depicted in Figure 9 (fleet route optimization) may be considered for JIT arrival by assuming that routing decisions target to accelerate or delay the ships' movement so as to ensure on-time arrival at port.
## 4 Method Validation
### A Non-Uniform AFG Beam on an Inhomogeneous Viscoelastic Foundation
They are given by  
\[\text{Sinusoidal:}\quad q(x,t) =q_{0}\sin(2\pi\omega t) \tag{52a}\] \[\text{Pulse:}\quad q(x,t) =q_{0}\frac{1}{\sqrt{2\pi\sigma}}e^{\frac{\bar{k}}{L}\frac{(-t_{ 1})^{2}}{2\sigma^{2}}}\] (52b) \[\text{Moving:}\quad q(x,t) =q_{1}\delta(x-v_{\text{load}}t) \tag{52c}\]  
where \(q_{0}=1\) kN/m, \(q_{1}=1\) kN, \(\omega=60\) Hz, \(t_{1}=5\) ms, \(\sigma=0.5\), \(\delta(\cdot)\) is the Dirac delta function, and \(v_{\text{load}}\) is the moving velocity of the load. The left end of the beam is simply supported and the right end is clamped. Figure 5 shows the displacement responses of the beam at three points, i.e., \(x=L/4\), \(x=L/2\), and \(x=3L/4\), under sinusoidal and pulse loads. A total of 100 Timoshenko beam elements are used in the FEM, and 16 polynomials are used in the Chebyshev spectral method. In the case of the moving load, the number of finite elements increases from 100 to 250, while the number of polynomials of the Chebyshev spectral method remains unchanged. The results of the moving load case are shown in Figure 6, where two speeds of the moving load and two values of the foundation damping coefficient are considered. By comparing the calculation results, it can be seen that the calculation results of the Chebyshev spectral method agree very well with those of the finite element method for all types of varying cross-sections, material properties, foundation parameters, and loads. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**B.C. **} & \multicolumn{2}{c}{\(\tilde{\omega}_{n1}\)} & \multicolumn{2}{c}{\(\tilde{\omega}_{n2}\)} & \multicolumn{2}{c}{\(\tilde{\omega}_{n3}\)} & \multicolumn{2}{c}{\(\tilde{\delta}_{n1}\)} & \multicolumn{2}{c}{\(\tilde{\delta}_{n2}\)} & \multicolumn{2}{c}{\(\tilde{\delta}_{n3}\)} \\ \cline{2-13}  & **Present** & **FEM** & **Present** & **FEM** & **Present** & **FEM** & **Present** & **FEM** & **Present** & **FEM** & **Present** & **FEM** \\ \hline PP & 3.7578 & 3.7578 & 5.8360 & 5.8361 & 8.5965 & 8.5967 & 1.2124 &
## 3 Current Signal Processing and Feature Extraction
### Characteristic Processing of High-Frequency Current Signal
#### 3.1.5 Total Harmonic Distortion Rate  
After FFT transformation of the time domain signal, it is found that the current waveform has different degrees of distortion in the fault state. The total harmonic distortion rate \(e_{THD}\) is used to characterize the distortion degree of the fault signal, and the calculation formula is as follows:  
\[e_{THD}=\sqrt{\sum\limits_{k=2}^{H}{(\frac{I_{k}}{I_{1}})^{2}}}=\sqrt{\sum \limits_{k=2}^{H}{(\frac{D_{k}}{D_{1}})^{2}}} \tag{5}\]  
In the formula, \(I_{1}\) is the effective value of the fundamental wave of the current signal, \(I_{k}\) is the effective value of the \(k\)-th harmonic of the current signal, \(D_{1}\) is the amplitude of the fundamental wave, \(D_{k}\) is the amplitude of the \(k\)-th harmonic, and \(H\) is the specified order.
# Method of Multi-Label Visual Emotion Recognition Fusing Fore-Background Features
## 2 Related Work
### Multi-Label Emotion Classification  
Multi-label classification methods can be categorized into three strategies: first order, second order, and high order [12]. The first-order strategy decomposes multi-label classification into multiple independent binary classification tasks while neglecting correlations between labels. The second-order strategy considers pairwise label relationships and distinguishes between related and unrelated labels. However, in real-world scenarios, label dependencies often exceed the limitations of second-order correlations. The high-order strategy addresses the associations between multiple labels and the influence of each label on all others, thus providing a more robust framework for modeling label correlations. Traditional high-order methods include ML-KNN [13], ML-RBF [14], BPMLL [25], and MLC-ARM [26]. ML-KNN [13] generates predictions by considering the relevance of local samples, but it fails to capture the global correlations across all labels. ML-RBF [14] employs a kernel function in feature space to account for label dependencies, while BPMLL [25] introduces a ranking loss function to minimize the dissimilarity between labels. However, as data correlations exceed the second-order level, the effectiveness of both ML-RBF and BPMLL becomes constrained. MLC-ARM [26] leverages association rules mined from sample correlations to perform multi-label classification, but its efficiency is hampered by the high computational costs involved.  
In recent years, several approaches adapted modified Transformer architectures to learn correlations between different labels. For instance, methods like Q2L [15], ML-Decoder [16], and SAML [27] utilize enhanced Transformer models and the self-attention mechanism to capture inter-label correlations. However, these methods are better suited for capturing correlations in long sequence data and may not perform optimally when modeling label correlations in image data. Additionally, they demand significant computational resources and storage capacity.  
In the field of object detection, several studies [28; 29; 30; 31] constructed simple graph structures to represent the relationships between different objects within a single image, where an edge with a value of 0 denotes that two labels are unrelated, and an edge with a value of 1 indicates that they are related. Graph convolutional networks (GCNs) are then employed to learn local correlations across different images. However, these methods fail to account for the varying strengths of label correlations and overlook the global relationships between labels, and the application of GCNs for modeling correlations has seldom been explored in the domain of emotion recognition.
## 2 Methodology
### Strategy of SRP Modeling  
The SRP modeling employed L-band observations collected at 80 stations and ISL observations from 240 links. The time span is from day-of-year (DOY) 086 to 366 of 2020. The different SRP modeling strategies are grouped in Table 2. In terms of G1/G2 POD, only the L-band code and carrier measurements collected by ground stations were used. For the J1/J2 POD, the ISL data were used combined with the L-band observations. In addition, a control experiment marked as G2/J2 was designed to model the effect of the SAR antenna. A modified version of the Positioning And Navigation Data Analyst (PANDA) software package capable of handling ISL data was used [27]. The detailed SRP modeling strategy is listed in Table 3.  
BDS-3 MEO satellites follow a yaw-steering attitude mode [36; 37]. Theoretically, the solar panels are always perpendicular to the sunlight and only the +X and \(\pm\)Z panels of the satellite body are exposed to the Sun. We use the AWB model to construct an a priori model of the satellite body. The optical parameters of +X and \(\pm\)Z panels are adjusted by POD, respectively. The ECOM1 model was then utilized to describe the constant acceleration of the solar panel as well as other unmodeled SRP accelerations. The refinement process of the a priori SRP model for the BDS-3 MEO body using L-band observation and ISL observations can be seen in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **Observation** & **SAR Antenna** \\ \hline G1 & L-band & Unconsidered \\ J1 & L-band, Ka-band & Unconsidered \\ G2 & L-band & Considered \\ J2 & L-band, Ka-band & Considered \\ \hline \hline \end{tabular}
\end{table}
Table 2: SRP modeling strategy for BDS-3 MEO satellite.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Models** \\ \hline Tide displacement & Solid Earth tide, pole tide, and ocean tide loading [32] \\ Relativity effect & Schwarzschild and Lense-Thirring \\ Geopotential & EGM2008 up to 12 \(\times\) 12 degrees and orders [33] \\ N-body gravitation & Jet Propulsion Laboratory (JPL) DE405 ephemeris \\ Earth radiation pressure & Applied [34] \\ Antenna thrust & 310 W for CAST satellite and 280 W for SECM satellite [35] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dynamical model for SRP modeling.  
To reduce parameter correlation, an a priori constraint of 0.1 was applied to the parameters of the AWB model. The station-related coordinates, tropospheric delays, and clock offsets were estimated by precise point positioning and fixed throughout the POD processing. The WUM precise orbits were set as the initial orbit to minimize the influence of inaccurate initial orbits on parameter estimation. The constraint of \(\alpha+\rho+\delta=1\) is not applied, as the optical parameters can absorb a portion of the unmodeled effects.
## 3 Estimation Methods and Techniques
### Statistical Application
\begin{table}
\begin{tabular}{c c c} \hline
**Rain Gauge** & **Description, Advantages, and Disadvantages** & **Implementation in Indonesia** \\
**Interpolation Method** & **This method divides the area into polygons, each containing one rain gauge, and each point in the polygon is represented by data from the rain gauge. If the rain gauge distribution is tight, this method can provide a fairly good estimate because each polygon covers a specific area. However, this method does not consider topographic variations [92].** & **Applied in Pontianak City by dividing the area into polygons based on the location of the rain stations. The rainfall classification is only divided into two classes, and this area is classified as having high rainfall [94].** & **Applied in Pontianak City by dividing the area into polygons based on the location of the rain stations. The rainfall classification is only divided into two classes, and this area is classified as having high rainfall [94].** & **divided into two classes, and this area is classified as having high rainfall [94].** \\  & **Unfortunately, there is no explicit mention of statistical metrics and validation results.** & **Matulated metrics and validation results.** \\ \hline \multirow{4}{*}{Inverse Distance Weighting} & This method gives greater weight to closer points [99]. Even though it produces smoother interpolations, IDW is still susceptible to bias if the rain gauge is uneven, especially in complex mountainous areas, where rainfall variations can be high. This method is more appropriate to use in locations with dense and even rain gauge points. & **Successfully applied in East Java Province for interpolation of monthly rainfall with dense points [100], with an RMSE value of 100.435 mm** \\  & Using mathematical functions to minimize surface curvature, it forms a spline to estimate areal rainfall. It can produce a continuous interpolation function, but is very dependent on the distribution and density of data points [94], and therefore it is not suitable for application in areas with significant topographic diversity. & **Produced smooth and continuous rainfall maps in Pontianak City. However, it is less effective if there is a significant value difference at a very short distance between measurement points [94]. Unfortunately, there is no explicit mention of statistical metrics or validation results.** \\ \hline \multirow{4}{*}{Kriging} & Geostatistical interpolation considers the distance and degree of variation between known data points when estimating values in an unknown area [101]. Suitable for areas with uneven distribution of rain gauge stations. & **Applied in the Bali Region. With a daily time scale, the results help understand the spatial pattern of rainfall in the study area [102]. The RMSE value varies, mostly below 20 mm-day\({}^{-1}\)** \\ \hline \end{tabular}
\end{table} 
Table 2: Several rain gauge interpolation methods, descriptions, and advantages and disadvantages, which are applied in Indonesia.