esa-sceva/satcom-chunk-collection huggingface dataset:
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 4 Comparison of the Routing Protocols
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Routing Metric** & **Major Features** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{CTEB 1[40]} & \multirow{4}{*}{Average packet delivery ratio, network lifetime} & \multirow{4}{*}{*} & Forwarding nodes run out of energy exactly simultaneously; & \multirow{4}{*}{*} & Combination of two types of games leads to achieving network-wide balance; & \\  & & Forwarding regions are set based on the network density; & & \\  & & & network density; & & \\  & & Combination of classical game theory and evolutionary game theory for routing & & & \\  & & & number of nodes within a & \\  & & & protocol design. & & contention domain. \\ \hline \multirow{4}{*}{EEREC 2[41]} & \multirow{4}{*}{Energy, network lifetime} & \multirow{4}{*}{*} & Optimizes cluster size using optimal cluster size algorithm; & \multirow{4}{*}{*} & Handles hotspot problem & \\  & & Terminates mechanism during the process of & & in WSNs; & \\  & & CH selective; & & Routing scheme is both centralized and distributed. & \\  & & & energy burden of the nodes near the & & \\  & & sink node. & & \\ \hline \multirow{4}{*}{CEEC 3[42]} & \multirow{4}{*}{Energy, network lifetime} & \multirow{4}{*}{*} & Adoption of evolutionary game theory to achieve exhausted equilibrium; & \multirow{4}{*}{*} & \multirow{4}{*}{} & \\  & & Stationary deployment of location & & Reduced energy dissipator; & \\  & & aware nodes; & & Location awareness. & \\  & & Random and distributed deployment of nodes. & & & \\ \hline \multirow{4}{*}{ERG 4[45]} & \multirow{4}{*}{No routing metric} & \multirow{4}{*}{*} & Evolutionary game-based routing ensuring evolutionary stable strategy that cannot be invaded by any other greedy strategy; & \multirow{4}{*}{*} & Can be deployed in a distributed manner; & \multirow{4}{*}{*} & No comparative analysis with the other protocols in \\  & & & The fairness of the proposed equilibrium & & strategy choices to evolutionary & \\  & & solution under selfish node behavior & & stable strategy under & \\  & & is analyzed. & & dynamic conditions. & \\ \hline \multirow{4}{*}{GBRA 5[43]} & \multirow{4}{*}{Average throughput, network lifetime} & \multirow{4}{*}{*} & Node’s reputation depends on its behavior; & \multirow{4}{*}{} & \\  & & Routing path from sender to destination is & & \\ \cline{1-1}  & &designed as a hop-by-hop game; & & & \\ \cline{1-1}  & & The pay-off function of a sender node is & & \\ \cline{1-1}  & & related to its opponent’s reputation, & & \\ \cline{1-1}  & & remaining energy, and the distance between & & \\ \cline{1-1}  & & its opponent and the destination. & & \\ \hline \hline \end{tabular}  
* 1 Game Theoretic Energy Balanced Routing 2 Energy-Efficient Routing Protocol based on Evolutionary Game; 3 Game-Theory-Based Energy-Efficient Clustering; 4 Evolutionary Routing Game; 5 Game-Theory-Based Routing Algorithm.  
\end{table} 
Table 9: Comparison of routing protocols based on evolutionary and reputation-based games.
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 3 Automotive Security Models
### Security Modeling Methods for Automotive Industry
#### 3.1.3 Sina  
A methodology named Security In Networked Automotive (SINA) is presented in [97] to analyze and identify security issues for connected vehicle systems. The car2X scenarios are distinguished according to the communication participants in order to classify the different security threats for chosen problems. Similar to the method of STRIDE, SINA also applied the data flow diagrams to analyze the target system. The potential threats are described with a keyword-based threat classes. Beyond that, SINA defined an entity named communication zone as boundaries in the communication networks and the threats are categorized into seven classes. Besides the threats like tampering, denial of services, and information disclosure listed in STRIDE, SINA employed other specific threat types like \"creation of additional data on a communication channel\", \"modification of transient information as it is exchanged in a data flow\", \"eavesdropping on a communication channel\", and \"blocking of a data flow\". A model based approach is used to enumerate the threats based on the DFD. In order to improve the coverage, attack trees are built for the most severe threats to reveal the risks. The method is designed in alignment with an automotive safety development process so that the potential security threats can be identified at the design phase. The risks of the probable effects are evaluated according to the safety severity. Other impacts like privacy and finance are not considered in SINA. The general process of SINA methodology is shown in Figure 5.
## 3 Research Methodology  
### Data Processing and Analysis  
In this study, we conducted a research on multiple platforms (Figure 2). The steps are as follows: (1) On the GEE platform, NDVI and NPP image data of the growing season (April-October) in Anhui Province from 2000 to 2020 are screened for cloud filtering, water masking, and cropping. Then, the FVC is calculated using the dimidiate pixel model, and the EQI of the study area for the corresponding time period is calculated based on the FVC and NPP. (2) In ArcGIS 10.6, trend analysis is used to study the spatial and temporal changes of EQI in Anhui Province from 2000 to 2020, and the distribution characteristics of EQI at different elevations and slopes are counted using the raster statistics function. (3) To study the effect of average temperature and precipitation on EQI partial correlation analysis is implemented in R. (4) Then the spatial correlation between human activities and EQI with bivariate Moran index is studied using the GeoDa software. (5) In MGWR2.2.1, spatial regression models between climate, topography, human activity factors and EQI are developed to analyze the spatial heterogeneity of their EQI influencing factors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Spatial Resolution** & **Time Resolution** & **Website/GEE Dataset** \\ \hline NDVI & 500 m & 1 year & MODIS/061/MOD13A \\ NPP & 500 m & 8 day & MODIS/006/MOD17A3 HG \\ precipitation & 5566 m & 1 day & UCSB-CHCHHRS/DAILY \\ Average temperature & 11,132 m & 1 month & ECMWF/ERA5\_LAND/MONTHLY \\ DEM & 30 m & N/A & projects/sat-io/open-datasets/ASTER/GDEM \\ Human Footprint & 1000 m & 1 year & [[https://doi.org/10.6084/m9.figshare.16571064](https://doi.org/10.6084/m9.figshare.16571064)]([https://doi.org/10.6084/m9.figshare.16571064](https://doi.org/10.6084/m9.figshare.16571064)) \\  & & & accessed on 15 December 2022. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data sources.  
Figure 2: Flow chart of data processing.
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 7 Conclusions  
In this paper, the control of a GF-inverter with an L-type filter is studied and the idea of estimating the output filter current was proposed. In the proposed idea, the output filter currents were estimated with an observer-based controller, which eliminates the need to use the grid-side current sensors. Using a lower number of sensors, the total cost of the system is reduced, and the reliability and fault-tolerant capability of the GF inverter are increased. To develop the analytical formulation of the proposed idea, modeling of the GF inverter with a focus on the DC-link voltage dynamic model is presented. It is shown that the output filter currents are observable and can be estimated by measuring the DC-link voltage and employing a linear observer. In the design of the observer matrix, the linearized model of the non-linear DC-link voltage dynamic is used, which provides a simple constant observer with proper accuracy. To increase the performance in estimating the real system dynamics, the nonlinear model of DC-link voltage is employed in the observer implementation. Moreover, the stability of overall system is evaluated by using  
Figure 8: Dynamic response of the system in the presence of uncertainty in output filter inductance, (**a–c**) output active and reactive power and DC-link voltage of the GFL inverter in presence of +20% uncertainty in \(L_{f}\), and (**d–f**) output active and reactive power and DC-link voltage of the GFL inverter in presence of \(-20\%\) change in \(L_{f}\).  
the system state-space equations in different operating points through eigenvalue analysis. Various time domain simulation in MATLAB/SIMULINK and case studies verify the proper suitable tracking, estimation functionality, dynamic performance, and stability margin of the proposed control system in all operating pints even under presence of uncertainty in output filter inductance.  
Conceptualization, M.A.G. and S.F.Z.; methodology, M.A.G. and S.F.Z.; software, M.A.G., Z.S.; validation, Z.S., S.P.; formal analysis, M.A.G. and S.F.Z.; investigation, S.P., F.B.; resources, M.A.G.; data curation, M.A.G.; writing--original draft preparation, M.A.G.; writing--review and editing, S.F.Z.; visualization, S.F.Z.; supervision, S.P.; project M.A.G.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion  
Both the qualitative results obtained through the measurement of corrosion potential and the quantitative results deduced from the corrosion rates (Figures 3 and 4) show that for chloride ion percentages of 0.4%, the bars are in a passive state but with higher corrosion rates in test specimens with LFS than in the standard test specimens. For test specimens with chloride ion percentages above 0.4%, the bars demonstrate very high states of corrosion both in standard test specimens and in those with LFS, being slightly larger in the former.  
The symptoms produced by corrosion show as growing rust stains appearing over time and, therefore, with the increase in current in the standard test specimens, appearing first in the test specimens with 2% chloride ions. The stains began to coincide with the bar through which the external current passed, only to extend gradually along the edge and the lower face of the test specimens, but without cracking 608 days after the start of the experiment and having undergone 283 days of accelerated corrosion.  
In the test specimens with LFS, the rust stains began to form later than in the standard test specimens, on the upper face of the test specimen, coinciding with its central bar. In the test specimens with 1.2% chloride ions, 174 days after the commencement of accelerated corrosion, cracks appeared on the lower face and on the edge of the test specimens; these increased in width and length over time until 282 days, when the upper face cracked. In the test specimens with 2% chloride ions by weight of cement, the cracks began 132 days after the commencement of the accelerated corrosion on both the upper and lower faces and on the edge of the test specimens, coinciding with the central bar of the test specimen. The passage of time and the increase in current increased the cracks, with a mesh of cracks appearing in the lower part of the test specimen after 250 days.  
Breaking of the test specimens enabled observation of the penetration of corrosive products on the mortar pores in the test specimens with 2% chloride ions; this showed corrosion in the form of pitting produced by the presence of chlorides, beginning with the loss of corrugation in the bar and continuing with the loss of sections in a localized manner (Figure 8).  
Figure 9 shows the corrosion rates in standard test specimens and those with LFS for different chloride ion percentages within 546 days after the experiment onset. The results show that in test specimens with chloride ion percentages of 0.4%, the corrosion rates were slightly higher in the slag test specimens than in the standard ones. By contrast, for chloride ion percentages higher than the EHE Instruction Limit, the corrosion rates were similar and even higher in standard test specimens.
## 1 Introduction  
The detection of emerging contaminants in the environment and in waste and supply water is increasing due to the ever-growing role of chemistry in industrial production and to advancements in analytical technology [1; 2; 3]. These, commonly addressed as a group under the term of 'contaminants of emerging concern' (CECs), are diverse and ubiquitous, frequently lumped into categories that describe their purpose, use, or other characteristic (Table 1) [4]. These contaminants could negatively affect water uses, human health, and ecosystem integrity, and cause the spread of antibiotic-resistant bacteria due to low persistent doses of residual pharmacological principles in human and animal exceta [5]. It was estimated that, just in Germany, about 16,000 tons/year of discarded pharmaceutical and personal care products (PPCPs) are flushed down toilets, or disposed of in household waste, eventually finding their way into natural waters [6]. Online measurement of these pollutants in the environment, and especially in water supply systems, is a very sensitive issue, which has not yet been satisfactorily addressed [7]. The increasing tendency towards wastewater reuse, both for non-potable and drinking uses [8], is prompting the development of more robust water and wastewater treatment through increasingly sophisticated multiple barrier treatment (MBT) schemes [9]. However, even in current state-of-the-art wastewater treatment plants (WWTPs), these contaminants may be removed but not degraded to satisfactory levels: Many current advanced processes, for example, adsorption, ion exchange, reverse osmosis, and membrane nano/ultra-filtration, only concentrate contaminants, without degrading or ultimately eliminating them, leaving this step to a subsequent phase. Even with the use of advanced degradation technologies, these compounds may only be partly transformed, possibly into hazardous byproducts or intermediates. Furthermore, their transformation pathways may often still be undetermined [10]. Several studies have shown that many compounds subject to biological, chemical, or surface processes are not completely degraded (mineralized) but may end up as transformation byproducts in the effluent, or accumulated within the residual solid phase (i.e., excess sludge) [11; 12; 13]. These residuals, then, require additional processing to avoid byproducts' environmental dispersion. Incomplete removal (even in ug/L-ng/L concentration ranges) of these contaminants has been related to potential long-term adverse impacts on the environment and human health.
# Intuition and Symmetries in Electromagnetism: Eigenstates of Four Antennas
## 1 Introduction  
Various commercial software packages are interested in studying symmetry problems, such as HFSS [1], CST [2], Cadence [3], Keysight [4], Comsol [5], Optiwave [6]...etc. In addition, symmetry analysis can be employed successfully in many aspects of electromagnetic theory. It can be implemented in the differential, integral, variational and matrix description of electromagnetic phenomenons. Therefore, [PERSON]'s equations have a very high symmetry. So, all exact solutions of the wave equations in Cartesian, cylindrical and spherical coordinates are derived from the symmetry of these differential equations [7]. In addition, several bibliographies have focused on the applications of symmetry in physics and in particular in electromagnetism. The properties of an antenna array are related to the type of symmetry that prevails in it. Group point theory is a systematic [8, 9], but not always convenient, tool to exploit this symmetry, in particular when it comes to finding the eigenvectors and eigenvalues of an operator [10, 11, 12].  
In general, symmetry properties are used to simplify the solution of electromagnetic phased array problems by reducing the domain analysis, where each system obeying these (symmetry) characteristics undergoes several transformations that facilitate their rewriting [13, 14, 12]. In this context, other special cases are the result of the symmetry intuition (these are rare cases in electromagnetism, as explained in [15]). This is why, in this paper, we propose a special case of eigenstates of four antennas (extendable to a subarrays symmetry problem) given as the result of a superposition of symmetry states that are established by combinations of axial symmetries of a perfect electric conductor (PEC) wall and a perfect magnetic conductor (PMC) wall [13, 14, 16]. A technique of phase shift between two sources based on the calculation of S-parameters is used to determine the different combinations of symmetries [17]. Note that a superposition of symmetry states equivalent to the sources is possible with the S parameters. This approach can be applied to several numerical methods in electromagnetics [13, 14], including the method of moments simplified by equivalent circuits (MoM GEC) [18].  
This article is divided into four parts. First, we start by formulating the problem by explaining how to construct the symmetries and how to use them to establish the superposition theorem to produce an eigenstate of four antennas (compared to the symmetry group point theory). Next, a phase shift technique between these sources is used to reveal these symmetries. Then, results are presented based on the MoM-GEC and HFSS to clarify these symmetries. An advantage of reducing the analysis domain of the problem is discussed. Finally, a perspective is provided and conclusions are drawn.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 3 BER Analytical Expression
### Non-Cooperative PD-NOMA  
Since OOK modulation is considered, the signal constellation of \(U_{k}\) is represented as \(\sum_{i=1}^{2M-k}\sum_{j=k+1}^{M}P_{j}V_{ij}\), where the matrix \(V\) exhibits the possible position of the symbols in the constellation based on the transmitted OOK message \(S_{k}\). Figure 5 shows a signal constellation example for three users.  
\[V=\left[\begin{array}{ccccc}V_{1\;k}&V_{1\;k+1}&\ldots&V_{1\;M-1}&V_{1\;M} \\ V_{2\;k}&V_{2\;k+1}&\ldots&V_{2\;M-1}&V_{2\;M}\\ V_{3\;k}&V_{3\;k+1}&\ldots&V_{3\;M-1}&V_{3\;M}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ V_{2^{M-k}\;k}&V_{2^{M-k}\;k+1}&\ldots&V_{2^{M-k}\;M-1}&V_{2^{M-k}\;M}\\ \end{array}\right]=\left[\begin{array}{ccccc}0&0&\ldots&0&0\\ 0&0&\ldots&0&1\\ 0&0&\ldots&1&0\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&1&\ldots&1&1\\ \end{array}\right]. \tag{24}\]  
Probability of error in decoding the \(k\)-th signal is expressed as:  
\[P_{\rho_{k}}=1-\left[\frac{1}{2^{M-k}}\sum_{i=1}^{2^{M-k}}1-\mathbf{Q}\left(\frac{ \sqrt{2}R_{p}h_{k}(Th_{k}-\sum_{j=k+1}^{M}P_{j}V_{ij})}{\sqrt{\sigma_{n_{k}}^{ 2}}}\right)\right] \tag{25}\]  
where \(Th_{k}\) is the maximum likelihood (ML) detection threshold of \(U_{k}\), which is expressed as:  
\[Th_{k}=\frac{\sum_{m=k}^{M}P_{m}}{2}. \tag{26}\]The Gaussian \(\mathbf{Q}\) function is given by:  
\[\mathbf{Q}(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}\textit{exp}\left(\frac{-\text{y }^{2}}{2}\right)dy. \tag{27}\]
## 2 Computational Tools  
The current study utilises the SCALE 6.2 code system as the main tool for the reactor physics and criticality calculations. The SCALE 6.2 package is developed and supported by Oak Ridge National Laboratory (ORNL), USA, and distributed by the OECD/NEA databank [13]. The 2D lattice physics calculations were performed using the Polaris module of SCALE, which employs the method of characteristics as the transport solver [24; 25]. The self-shielding effect is calculated using embedded self-shielding method (ESSM). Polaris is optimised for light water reactor analysis and can be used with 252 and 56 multigroup ENDF/B-VII.1 based cross-section libraries. In this research, Polaris was utilised for the homogenised two-group cross-section preparation for use in nodal analysis with DYN3D code [26] and estimation of the spent fuel composition of BWR fuel assemblies for further criticality analysis.  
Polaris is internally combined with the ORIGEN module to conduct depletion and decay calculations on fuel assembly level [13]. This calculation sequence is referred to as the Polaris/ORIGEN sequence in this study when referring to fuel isotopic predictions due to depletion/decay processes. The OPUS module, which parses results from the ORIGEN binary concentration files, is utilised here for spent fuel composition extraction after lattice physics calculations in Polaris. KENO V.a, another module within the SCALE package, is used to perform criticality estimations for the storage cask filled with BWR spent fuel assemblies. KENO is a 3D Monte Carlo module within the SCALE package which can estimate effective multiplication factor (k\({}_{\text{eff}}\)), flux densities and other parameters of interest in 3D systems [13]. It can perform criticality analysis based on multigroup or continuous nuclear data libraries. KENO V.a is an earlier version of KENO VI module and has less flexible geometry capabilities but has a significantly lower computation burden and was therefore employed in this study.  
The reactor core analysis was performed using DYN3D nodal core simulator [26] developed by Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany. Two-group macroscopic cross-section library was used to provide fully coupled neutronic/thermal-hydraulic analysis based on the nodal approach. DYN3D produced axial burnup profiles of the discharged FAs and axial coolant density distributions for FAs during the reactor operation used in further depletion calculations.
# Maximization of Average Achievable Rate for NOMA-UAV Dual-User Communication System Assisted by RIS
## 2 System Model
To ensure successful decoding, when the signal-to-noise ratio (SNR) of the received signal exceeds the decoding threshold, the receiver can recover the target signal. The residual interference in subsequent signal processing can be significantly reduced by reconstructing and eliminating the interference component. This expresses it as:  
\[SNR_{target}\geq SNR_{threshold}, \tag{11}\]  
where \(SNR_{target}\) is the target SNR of the current signal, and \(SNR_{threshold}\) is the minimum SNR required for successful decoding. To minimize the influence of the signal on subsequent processing after decoding, the interference power must be minimized. This can be quantified by the interference-to-noise ratio (INR) of each signal, we can get:  
\[INR=\frac{P_{interference}}{P_{noise}}\to min, \tag{12}\]  
Next, we sort the signal intensities and determine the decoding order. After decoding the interference signal, perfect SIC can be achieved by minimizing the interference power. In the NOMA system, ground users employ SIC to decode their signals. To streamline the analysis, we posit the sequence of decoding [26]. We first regard the signal of User 2 as noise to decode the signal of User 1, and then remove it from the superimposed signal. Then you need to meet the following constraints. \[H_{2}[k]\geq H_{1}[k], \tag{13}\]  
\[p_{2}[k]\leq p_{1}[k], \tag{14}\]  
\(p_{1}[k]\) and \(p_{2}[k]\) are the power allocated by the UAV to User 1 and User 2 for transmission in \(k\) time slots, respectively. \(H_{1}[k]=\left\|h_{ug_{1}}[k]+h_{g_{1}}^{H}\Theta_{1}[k]h_{ur}[k]\right\|^{2}\) and \(H_{2}[k]=\left\|h_{ug_{2}}[k]+h_{g_{2}}^{H}\Theta_{2}[k]h_{ur}[k]\right\|^{2}\) represent the joint channel vectors of UAV to ground User 1 and ground User 2 at the \(k\)th time slot, respectively. Then the average achievable rates of User 1 and User 2 take the form of  
\[R_{1}[k]=\log_{2}\biggl{(}1+\frac{p_{1}[k]H_{1}[k]}{p_{2}[k]H_{1}[k]+\xi^{2} }\biggr{)}. \tag{15}\]  
\[R_{2}[k]=\log_{2}\biggl{(}1+\frac{p_{2}[k]H_{2}[k]}{\xi^{2}}\biggr{)}. \tag{16}\]  
Then we can obtain the average achievable rate  
\[h_{rg_{1}}^{\text{LOS}}=\Bigg{\{}1,...,e^{-j\frac{2\pi}{\lambda}d_{rg_{1}}^{ \text{(m,n)}}[k]},...,e^{-j\frac{2\pi}{\lambda}d_{rg_{1}}^{\text{(M_{R}-1)}[ k]}}\Bigg{\}}^{T}, \tag{6}\]0