esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Agent-Based Modeling  
Agent-based modeling (ABM) is a type of computational model that explores systems of multiple interacting agents which are spatially situated and can evolve over time. ABMs are very effective in exhibiting how complex patterns emerge from micro-level rules over a time period. In contrast to equation-based models (EBMs), such as statistical models and partial differential equations [45] that are rooted in deductive inference, ABMs can not only work as an inductive inference technique where a conclusion is drawn from a series of observations, but also as a pure form of abductive inference by which the best explanation for the phenomena under the study is captured by simulation. ABMs have become a significant modeling trend in a large number of domains ranging from epidemics studies [46] and bio-warfare threat [47] to formation of social norms [48], optimization of supply chain [49], and cooperation in project teams [50].  
Unlike EBMs that basically focus on the relationship among macro-variables of a system in a top-down manner, using a bottom-up approach, ABMs try to model how local and non-trivial interactions among micro-level elements of a system are able to generate a complex macro-level behavior of that system [51]. ABM methodology is grounded on complexity theory and network science. In terms of complexity theory, ABMs are developed to explain how simple rules generate complex emergence, and in terms of network science, ABMs are applied to analyze the pattern that arises from agents' interactions over time [52]. ABMs involve three building blocks of (1) agents, (2) environment, and (3) interactions [53; 54; 52]. Agents as the first building block of ABMs are the basic computational units of agent-based models. They are characterized by two main aspects of (1) properties and (2) behaviors (or rules of action). The agent's properties are internal or external states that can be changed by its behaviors, and the agent's behavior is a set of rules based on which it decides to act. As the second building block of ABMs, the environment entails all conditions all around the agents as they interact within the model. In other words, the environment is where an artificial social life unfolds [54]. As the third building block of ABMs, interactions refer to rules of behaviors for both agents and the environment [54]. These rules actually enable agents to interact with both themselves and others. All building blocks of ABM of this study will be discussed in the following.
# Volatiles in Berries: Biosynthesis, Composition, Bioavailability, and Health Benefits
## 2 Biosynthesis of Volatiles in Plants
### Biosynthesis of Terpenes  
All terpenes are synthesized from two universal precursors (C5), isopentenyl diphosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) [32]. A series of condensation reactions of IPP and DMAPP produce prenyl diphosphates, which are precursors of terpenes. Condensation of IPP and DMAPP produces geranyl diphosphate (GPP, C10), the precursor of monoterpenes, by catalyzation of geranyl diphosphate synthase (GDS). Two IPPs and a DMAPP are condensed to the precursor of sesquiterpenes, farnesyl diphosphate (FPP, C15), by farnesyl diphosphate synthase (FDS) [33]. Three IPPs and a DMAPP also produce geranylgeranyl pyrophosphate (GGPP, C20), the precursor of diterpenes. Geranyl-farnesyl diphosphate (GFPP, C25) is the recently discovered precursor of sesterterpenes [33]. These prenyl precursors of terpenes are converted to terpen (iso-, mono-, sesquiterpenes, and so on) by terpene synthases (TPS). Monoterpenes (C10) are one of the major groups of terpenes in essential oils and berries [34]. During the conversion of prenyl precursors to monoterpenes, many different catalytic reactions, including hydroxylation, oxidation, reduction, acetylation, methylation, glycosylation, isomerization, conjugation, and others, occur to modify the structure of monoterpenes and produce many different compounds of terpenes [35; 36; 30].
# An Integration Strategy for Acoustic Metamaterials to Achieve Absorption by Design
## 3 Impedance, Absorption and Green Function
### Impedance and Green Function  
When the lateral sample dimension is much smaller than the wavelength so that the diffraction effect can be neglected, impedance may be alternatively defined as  
\[Z=p/\dot{\delta} \tag{3}\]  
where \(p\) denotes pressure modulation, and \(\dot{\delta}\) is the displacement velocity. The Green function is defined as  
\[G=\delta/p \tag{4}\]  
Hence, for harmonic waves we have a simple relationship between the impedance and the Green function:  
\[Z=\frac{i}{\omega G} \tag{5}\]  
i.e., the two are essentially the inverse of each other, with the real and imaginary parts switched. The advantage of expressing the impedance in this manner is that the Green function can be explicitly written down through the eigenfunction expansion approach. For the acoustic metamaterials, the eigenfunctions are the resonance modes, which must have the Lorentzian form. To illustrate how the resonance can tune the impedance, let us consider the case of a system having two discrete resonances with resonance frequencies \(\Omega_{1}<\Omega_{2}\):  
\[Z(\omega)=\frac{i\rho_{0}d}{\omega}\left(\frac{\alpha_{1}}{\Omega_{1}^{2}- \omega^{2}}+\frac{\alpha_{2}}{\Omega_{2}^{2}-\omega^{2}}\right)^{-1} \tag{6}\]  
where \(\rho_{0}\) denotes the density of air, and \(\alpha_{1(2)}\) is the oscillator strength, to be made explicit later. Here, we treat the dissipation to be negligibly small. It is clear that at \(\omega=\Omega_{1(2)}\) the impedance is zero, and there is always a frequency \(\Omega_{1}<\widetilde{\omega}<\Omega_{2}\) where the sum of the two terms vanishes in the parenthesis, so that the magnitude of the impedance diverges. This is denoted the antiresonace point [11]. Hence, with resonances one can tune the impedance of a resonant metamaterial structure at will by varying the frequency. Since there is a one-to-one correspondence between the impedance and absorption, it follows that one might be able to obtain absorption by design, by properly integrating together multiple resonances. Below we show precisely how this proper integration can be carried out in accordance with the desired target absorption spectrum, and how the causality equality can be used to fix a crucial design parameter so as to achieve consistency with the goal of attaining absorption by design with the minimum sample thickness.
## 4 Thermal Storage Model
### Thermal Storage Control Structures
#### 4.1.1 Control Valve at the Thermal Storage System Inlet (TS MCVi)  
The control valve at the thermal storage system inlet includes two tasks, depending on the operation mode of the thermal storage system, as shown in Figure 10. The first task is applied during charge mode, where this valve allows the surplus of thermal oil with a temperature of 393 \({}^{\circ}\)C to pass through it into the heat exchangers in order to transmit the thermal power from the HTF to the molten salt. Thereafter, the HTF leaves the TSS with a temperature of 293 \({}^{\circ}\)C through the control valve at the thermal storage system outlet (TS MCV\({}_{\text{o}}\)). Subsequently, the HTF that exited the thermal storage system will be mixed with the HTF, which comes from the PB and enters the SF again. The second task is activated during the cloudy periods and the evening hours; in the compensation periods, the direction of the HTF flow will be reversed, where the HTF flows directly from the PB to the TSS through the redirection control valve (RDCV), and then through TS MCV\({}_{\text{o}}\). Therefore, TS MCV\({}_{\text{o}}\) and TS MCV\({}_{\text{i}}\) are considered to be the inlet and outlet of the TSS, respectively. Afterwards, the heated HTF flows to the PB to replenish the rest of the nominal mass flow (600 kg/s).  
The HTF flow direction is reversed based on two conditions (T\({}_{\rm HTF}\) = 393 \({}^{\circ}\)C, and m = 600 kg/s) that are applied to the selector: Firstly, this control valve remains open when the HTF flow is directed from the SF to the TSS (SF-TS direction) during charge mode, as long as the design temperature of the thermal oil at the outlet of the SF and the nominal HTF mass flow are achieved. Secondly, when the one of the mentioned conditions (393 \({}^{\circ}\)C and 600 kg/s) is not accomplished, the discharge mode starts, and the HTF flow direction is changed from the SF-TS to the TS-PB direction.
## 4 Results
### Altered Landscape Pattern
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LU/LC**} & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**Trend**} & \multicolumn{2}{c}{**Market-based**} & \multicolumn{2}{c}{**Conservation**} \\ \cline{2-11}  & **Area (\%)** & **Mean Elev** & **Area (\%)** & **Mean Elev** & **Area (\%)** & **Mean Elev** & **Area (\%)** & **Mean Elev** & **Area (\%)** & **Mean Elev** \\ \hline \multirow{2}{*}{Paddy} & 0.1 & 445 & 0.1 & 456 & 0.2 & 476 & 0.2 & 457 & 0.1 & 472 \\   Maize & 12.2 & 992 & 13.5 & 834 & 17.7 & 657 & 18.7 & 735 & 12.3 & 915 \\   Cash crops & 0.8 & 632 & 0.4 & 734 & 1.2 & 1185 & 0.9 & 832 & 0.2 & 1446 \\   Perennial trees & 0.0 & 253 & 1.9 & 620 & 9.9 & 943 & 1.9 & 626 & 2.1 & 654 \\   Everygreen forest & 11.2 & 1029 & 10.7 & 1026 & 9.3 & 1063 & 10.3 & 1149 & 11.5 & 1019 \\   Dedetuous forest & 71.2 & 442 & 64.5 & 1041 & 53.4 & 972 & 61.2 & 992 & 69.4 & 1032 \\ Forest plantation & 0.2 & 589 & 0.3 & 541 & 0.4 & 555 & 0.3 & 502 & 0.3 & 704 \\   Settlement & 0.2 & 477 & 0.2 & 606 & 0.6 & 793 & 1.0 & 706 & 0.4 & 836 \\   Others & 0.1 & 390 & 0.0 & 400 & 0.0 & 302 & 0.0 & 299 & 0.0 & 322 \\   Water & 0.2 & 843 & 0.2 & 400 & 0.3 & 409 & 0.3 & 386 & 0.3 & 407 \\   Shifting cultivation & 3.8 & 350 & 8.0 & 956 & 6.9 & 1088 & 5.2 & 982 & 3.4 & 1,111 \\ \hline \multicolumn{1}{c}{Total} & 100.00 & 100.0 & 100.0 & 100.0 & 100.0 & & & & \\ \hline \hline \end{tabular} Note: Total area of watershed classes1 and 2 covers879,832 ha (approximately 72% of Nan Province).  
\end{table} 
Table 4: Land use in watershed classes 1 and 2 in Nan Province and mean elevation for each LU/LC.
## 2 Materials and Experiments
### Raw Materials and Sample Preparation
#### 2.1.3 GF Particles Heat- and Humidity-Resistance Check and Surface Treatment  
According to current Chinese specification [25], under conditions of 210 \({}^{\circ}\)C and 1 h, the weight damage rate of the sample must be not more than 6% and the sample should not exhibit combustion. GF particles were placed in a beaker and heated in an oven at 210 \({}^{\circ}\)C for 1 h to measure the weight damage rate. With a mean damage rate of 3.2% from two measurements and no combustion, it was determined that the GF was resistant to high temperature and met the relative requirement for asphalt modification. For a humidity-resistance check, GF particles were prepared in a beaker with moderate deionized water and placed in a curing box at about 20 \({}^{\circ}\)C and 90% relative humidity to observe its shape and color changes and record quality change after 5 days. With no obvious change of color, no dispersed trend of the fiber clump, and a 1% (less than 5%) water absorption rate, it was determined that the GF was resistant to humidity and met the relative requirement for asphalt modification [25].  
The GF particles surface treatment process was as follows. Firstly, the GF particles were dried at 230 \({}^{\circ}\)C for 1 h and after cooling they were then soaked in acetone for 1 h to remove the surface impurities. Three types of silane coupling agents (UP152, KH550, and KH792) were chosen for comparison. Silane coupling agent, ethanol, and water (at a volume ratio of 5:85:10) were stirred fully and the blend was placed for 10 min until  
Figure 1: Raw GFRP composites from wind turbine blade at end-of-life: (**a**) wind turbine blade at end-of-life; (**b**) recycled GFRP.  
Figure 2: Three types of GF particles obtained after grading of GFRP pieces: (**a**) <0.075 mm; (**b**) 0.15-0.075 mm; (**c**) 0.3–0.15 mm.  
hydrolysis. The GF particles were immersed in quantitative coupling agent solution for 1 h and was then taken out to be dried and ready for use.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 3 Performance Analysis for Downlink WPCN
### Average Number of Active Node  
Due to the cells in multi-tier heterogeneous WPCNs form a weighted Poisson Voronoi tessellation, the probability density function (PDF) of the size of the normalized Voronoi cell is approximated by a two-parameter gamma function [34]  
\[f_{\mathrm{s}}\left(x\right) = \frac{3.5^{3.5}}{\Gamma\left(3.5\right)}x^{3.5}e^{-3.5x}. \tag{11}\]  
For a given cell size, the number of users associated with a BS follows a Poisson distribution with parameter \(\frac{A_{k}\lambda_{u}}{\lambda_{k}}\). The probability mass function (PMF) of the number of users associated with a BS in tier \(k\) can be derived from (11) as  
\[\mathbb{P}\left(N_{k}=n\right) = \int_{0}^{\infty}\frac{\left(\frac{A_{k}\lambda_{u}x}{\lambda_{k} }\right)^{n}}{n!}e^{-\frac{A_{k}\lambda_{u}x}{\lambda_{k}}}f_{S}\left(x\right)dx\] \[= \int_{0}^{\infty}\frac{\left(\frac{A_{k}\lambda_{u}x}{\lambda_{k }}\right)^{n}}{n!}e^{-\frac{A_{k}\lambda_{u}x}{\lambda_{k}}}\frac{3.5^{3.5}}{ \Gamma\left(3.5\right)}x^{3.5}e^{-3.5x}dx\] \[= \frac{3.5^{3.5}\Gamma\left(n+3.5\right)\left(A_{k}\lambda_{u}/ \lambda_{k}\right)^{n}}{n!\Gamma\left(3.5\right)\left(A_{k}\lambda_{u}/ \lambda_{k}+3.5\right)^{n+3.5}}.\]  
Condition on the typical node associated with a BS in tier \(k\), the PMF of the number of other users associated with the BS can be obtained similarly to (12)  
\[\mathbb{P}\left({N^{\prime}}_{k}=n\right) = \frac{3.5^{3.5}\Gamma\left(n+4.5\right)\left(A_{k}\lambda_{u}/ \lambda_{k}\right)^{n}}{n!\Gamma\left(3.5\right)\left(A_{k}\lambda_{u}/ \lambda_{k}+3.5\right)^{n+4.5}}. \tag{13}\]  
The average bandwidth allocated to the typical user associated with BS in tier \(k\) is  
\[\mathbb{E}\left(\frac{W}{N}\right) = W\cdot\mathbb{E}\left(\frac{1}{{N^{\prime}}_{k}+1}\right)\] \[= W\cdot\sum_{n=0}^{\infty}\frac{1}{n+1}\mathbb{P}\left({N^{\prime }}_{k}=n\right)\] \[= W\cdot\sum_{n=0}^{\infty}\frac{1}{n+1}\frac{3.5^{3.5}\Gamma \left(n+4.5\right)\left(A_{k}\lambda_{u}/\lambda_{k}\right)^{n}}{n!\Gamma \left(3.5\right)\left(A_{k}\lambda_{u}/\lambda_{k}+3.5\right)^{n+4.5}}\] \[= \frac{W\lambda_{k}}{A_{k}\lambda_{u}}\sum_{n=1}^{\infty}\frac{3. 5^{3.5}\Gamma\left(n+3.5\right)\left(A_{k}\lambda_{u}/\lambda_{k}\right)^{n}} {n!\Gamma\left(3.5\right)\left(A_{k}\lambda_{u}/\lambda_{k}+3.5\right)^{n+3.5}}\] \[\stackrel{{(f)}}{{=}} \frac{W\lambda_{k}}{A_{k}\lambda_{u}}\left[1-P\left(N_{k}=0\right)\right]\] \[= \frac{W\lambda_{k}}{A_{k}\lambda_{u}}\left[1-\frac{3.5^{3.5}}{ \left(A_{k}\lambda_{u}/\lambda_{k}+3.5\right)^{3.5}}\right]\] \[\stackrel{{(g)}}{{\approx}} \frac{W\lambda_{k}}{A_{k}\lambda_{u}},\]  
where (f) is obtained from the PMF of the number of users described in (12), and (g) is obtained due to the assumption \(\lambda_{u}\gg\lambda_{k}\).
## 1 Introduction
On the other hand, the typical values of the control signal period are much shorter than the thermal time constants for classic power transistors [4; 25]. Therefore, in the computer analyses of switching circuits with these transistors, ripples of the junction temperature are typically neglected at the steady state [4; 26]. Now, more and more popular power semiconductor devices are made of wide bandgap semiconductor materials [27; 28; 29; 30]. In this group the devices made of silicon carbide (SiC) play an important role. Such devices are characterized by a high value of the maximum allowable voltage and high switching frequency [28]. In the papers [27; 28; 29; 30] much information is given regarding the possibility of replacing silicon power transistors with such devices made of silicon carbide. In this paper, a fast computation method of ripples in the junction temperature of discrete power SiC-MOSFETs at the steady state is proposed. This method is applied based on the concept described in [19] but is possible to be implemented by the method of separated iterations with the SPICE software, without the need to use other programs. The elaborated method is described and the method of its practical implementation in the SPICE software is presented on the example of a power SiC-MOSFET. The usefulness of the proposed method is shown for two circuits: a transistor switch and a boost converter. Some results illustrating the influence of the cooling conditions of the tested transistor, switching frequency and load resistance on the waveforms of the power dissipated in this transistor and its junction temperature are shown and discussed. The investigations were performed for three different cooling systems of the tested transistor. Section 2 presents the proposed calculation method and the manner of its implementation in SPICE. Section 3 contains a description of the analyzed circuits and the considered cooling conditions of the tested transistors. Section 4 presents and discusses the obtained results of the calculations.
## 3 Results  
The Influence of Site Structure and Role of Seasonal Fruits in the Expansion of Medfly Population in the Absence of Any Control Measures  
The contribution of seasonal hosts to population buildup is presented in Table 2, showing a cascade of rapidly increasing numbers of eggs laid, larvae, pupae and adult medfly females emerging in the plots containing subsequent seasonal fruits. As a result, even a very small cohort of a few females, if it arrives in an area with continuous and abundant seasonal availability of fruit and is left uncontrolled, can lead to a population \"explosion\" towards the end of the season (Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Site A** & **Site B** & **Site C** \\ \hline  & \multicolumn{3}{c}{**Eggs**} \\ VE apricot & 543 & 371 & 471 \\ M peach & 29,049 & 20,603 & 20,262 \\ L apple & 945,083 & 120,421 & 95,796 \\ TOTAL & 974,675 & 141,395 & 116,529 \\ \hline  & \multicolumn{3}{c}{**Larvae**} \\ VE apricot & 434 & 294 & 365 \\ M peach & 24,584 & 17,432 & 17,210 \\ L apple & 771,713 & 98,477 & 78,384 \\ TOTAL & 796,731 & 116,203 & 95,959 \\ \hline  & \multicolumn{3}{c}{**Pupae**} \\ VE apricot & 192 & 141 & 172 \\ M peach & 9649 & 7022 & 6917 \\ L apple & 279,335 & 36,088 & 28,478 \\ TOTAL & 289,176 & 43,251 & 35,567 \\ \hline  & \multicolumn{3}{c}{**Adult females**} \\ VE apricot & 133 & 75 & 115 \\ M peach & 4293 & 3045 & 3066 \\ L apple & 52,278 & 7976 & 5945 \\ TOTAL & 56,704 & 11,096 & 9126 \\ \hline \end{tabular}
\end{table}
Table 2: The influence of site structure and role of seasonal fruits in the expansion of medfly population (number of medfly stages developing in the absence of any control measures). (VE, M and L denote very early, mid-season and late fruit, respectively).  
Interestingly, our data revealed a significant effect of site structure on the potential for medfly development. The mere rearrangement of plots with existing fruits of different seasonality (change of site A to site B) resulted in a more than five-fold reduction in the peak annual population of flies, from 39,087 to 6,582 females in sites A and B, respectively (Figure 2). This effect can be largely attributed to the significantly fewer females that reached and laid eggs on late apple fruits at site B (Table 2). Further modification of the reduced area covered by very early and late fruits (change from site B to site C) resulted in additional reduction in medfly development (Table 2 and Figure 2). In all three sites, even without any control measures, the infestation of the very early and mid-term fruits was negligible, below 0.4%. However, the infestation of the late fruit varied substantially, from 15.2%, to 3.3% and 7.3% on sites A, B, and C, respectively. Interestingly, although the medfly population at site B was slightly larger, the degree of infestation of late fruit was more than two times lower than at site C, where the share of late fruit was smaller and therefore the medfly pressure at the end of the season was relatively higher.
## 2 Motivation and Problem Statement  
This work in this area is motivated by fundamental concepts and knowledge gaps. Traditional policy-based access control systems have limitations in a complex and dynamic environment where user and resource characteristics change. First, system administrators create complicated rules with numerous conditions and permissions. Secondly, they create increasingly fine-grained regulations, resulting in policy conflicts, rule explosions and a slowdown in policy decision-making when expanding the scope of policies in conventional access control systems degrades their performance. In addition, there is another challenge related to privacy concerns when the policy decision point (PDP) directly communicates with the policy administration point (PAP) to generate access decision-making in the same access process, as there is a possibility of vulnerability and penetration. Therefore, direct communication between PDP and PAP should be avoided.  
The current access control PDP is a centralised, multi-value logic system. When increasing the number of entities or expanding the scope of the policies, efficiency decreases. Consequently, the access control system's performance will be impeded, as will the core business system's normal functioning. Due to these flaws, the current access control system is insufficient, resulting in security issues such as attacks, expensive administration, policy conflicts, decision-making delays and poor performance. Therefore, an access control system should be adaptable enough to address complex and dynamic access control states and make appropriate decisions regarding unexpected access control requests. Implementing access decision-making distributed to multiple nodes is necessary, which reduces cost and is not limited by the number of policies and requests. In addition, it achieves a high level of privacy and delivers high-performance decisions promptly when distributing them.
###### Abstract  
This paper investigates the optimization of cylindrical metasurface meta-covers designed to enable beamforming capabilities in single linear antennas. This study focuses on the development and application of advanced optimization tools to tailor the electromagnetic response of these metasurfaces, enabling precise control over the radiation patterns of the antenna. In particular, we develop a genetic algorithm-based optimization tool, which achieves precise manipulation of the main beam direction and null placement in the radiation pattern. The results further expand the applications of metasurface-based meta-covers in enhancing the functionality of dipole antennas in various communication and sensing systems.  
metasurface; beamforming; null-steering; genetic algorithm 2020 IEEEexample:BSTcontrol