esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 3 Parameters to Be Considered for the Design and Analysis of a Flexible Antenna
### Reflection Coefficient  
One of the main parameters for the design of a flexible antenna is the reflection coefficient. The ratio of the amplitude of the reflected wave to the incident wave is known as an antenna's reflection coefficient. The reflection coefficient (\(S_{11}\)) represents how much power is reflected from the antenna. The acceptable value of \(S_{11}\) is \(-10\) dB so that if the antenna is designed with a low loss, most of the power given to the antenna is radiated.  
The resonance bandwidth and resonant frequency of the antenna can be used to describe the radiation properties of the flexible antenna. The resonance frequency range at the specified return loss of \(-10\) dB is known as the antenna's resonant bandwidth. Therefore, the reflection coefficient is a parameter that must be considered for the design of a flexible antenna so that any shift in frequencies due to bending, crumpling, temperature  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substrate** & **Dielectric Constant \(\varepsilon_{r}\)** & **Dielectric loss (\(tan\delta\))** & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & **Advantages** & **Disadvantages** \\ \hline Rogers (RO 3003) & 3 & 0.0013 & 1.52 & \begin{tabular}{c} Low moisture absorption, \\ good dimensional stability \\ \end{tabular} & Low flexibility \\ \hline PDMS & 2.7 & 0.013 & 1 & \begin{tabular}{c} Water-resistant, good \\ chemical stability \\ \end{tabular} & \begin{tabular}{c} Fabrication complexity, \\ high cost \\ \end{tabular} \\ \hline \begin{tabular}{c} Photo \\ Paper \\ \end{tabular} & 2.85 & 0.05 & 0.254 & \begin{tabular}{c} Low profile, easily \\ available \\ \end{tabular} & \begin{tabular}{c} Highly fragile, high \\ moisture absorption \\ \end{tabular} \\ \hline Kapton Polymide & 3.5 & 0.002 & 0.10 & \begin{tabular}{c} Good electrical stability, \\ Easy to fabricate \\ \end{tabular} & \begin{tabular}{c} High moisture \\ absorption \\ \end{tabular} \\ \hline Tac/Felt & 2.2 & 0.0009 & 1.52 & Low cost, easily available & \begin{tabular}{c} High moisture \\ absorption, less smooth \\ \end{tabular} \\ \hline LCP & 2.9 & 0.022 & 0.1 & \begin{tabular}{c} High heat resistance, good \\ mechanical properties \\ \end{tabular} & \begin{tabular}{c} Fabrication complexity, \\ High cost \\ \end{tabular} \\ \hline PEN & 2.6 & 0.005 & 0.1 & \
\begin{tabular}{c} Good electrical and \\ mechanical properties \\ \end{tabular} & Low thermal stability \\ \hline PET & 3.2 & 0.022 & 0.1 &
\begin{tabular}{c} Transparency, good \\ thermal and electrical \\ properties, good moisture \\ stability, and conformality \\ \end{tabular} & Low thermal stability \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the various substrate materials in flexible antenna design.  
difference, or interaction with the human body, should not affect the considerable return loss of the flexible antenna.
## 3 Results and Discussion
### Sorbent Characterization
#### 3.3.3 Measurement of Surface Area and Porosity  
It is well known that the CO\({}_{2}\) uptake capacity and stability of the sorbent reactivity during carbonation/decarbonation cycles are closely related to its micromorphology [12; 22]. The CO\({}_{2}\) uptake capacity of the sorbents is particularly dependent on the presence of micropores and mesopores in the surface topography. Porosity and pore size distribution for the selected sorbents were evaluated using the Micromeritics Autopore IV Model 9520, which measured a range of pore sizes (0.003-360 \(\upmu\)m) at pressures up to 413.6 MPa. Surface area measurements were performed for the selected samples using a Quantachrome Autosorb-1 automated system. Figure 7 clearly shows that the best CO\({}_{2}\) uptake groups for the samples before CO\({}_{2}\) uptake have higher porosity percentages, substantiating the previous observation from SEM images (see Figures 4 and 5) that high porosity results in higher CO\({}_{2}\) uptake. This observation is consistent with [PERSON] et al. (2012), who reported that sorbents with small grains and porous surfaces exhibit high CO\({}_{2}\) capture performance [20]. Moreover, the sorbent stability is closely linked with the pore structure, as discussed in Section 3.4. Similar results were reported by [PERSON] et al. (2009) [23]. The surface area measurement results shown in Figure 7 reveal no clear correlation between CO\({}_{2}\) uptake and large surface area in samples before CO\({}_{2}\) uptake. This is contrary to expectations, as a large surface area was anticipated to be the principal factor for higher CO\({}_{2}\) uptake capacity. Therefore, a large surface area is not an indicator that sorbents can have good stability over multiple carbonation and calcination cycles. These results are in accordance with [PERSON] et al.'s (2013) study [24].  
Figure 6: EDX images of Ca6 Mg2 Ce1 sample.  
It was observed from Figure 8 that nitrogen adsorption started to increase at a relative pressure (p/p\({}_{0}\) > 0.8). These results suggest that most of the pores are in the macroporous range.
## 1 Introduction  
The ongoing fourth industrial revolution [1] is driven by the tremendous quantities of data associated with the internet of things [2], cloud computing [3], and big data analytics [4]. Fast and highly efficient tele- and data-communication systems are essential to support these data intensive technologies. Optical communication technology plays a key role in nearly every aspect of the modern communication links such as access networks, aggregation networks and core networks [5]. Silicon optical interconnect chips, especially silicon modulators and photodetectors, are at the heart of communication networks, thanks to their CMOS compatible fabrication process, cost- and energy-efficient properties [6; 7; 8].  
To meet the explosive demands of the data traffic, silicon optical interconnect chips need to exhibit extraordinary performance, underscored by the following key merits: bandwidths above 100 GHz, photodetector responsivities larger than 1 A/W, modulation depth larger than 3.5 dB and modulator power consumption less than 30 fJ/bit [9]. Pure silicon devices cannot meet these goals simultaneously owing to the drawbacks of its inherent material properties, such as its indirect bandgap of 1.14 eV and its carrier drift velocity of around 1000 cm\({}^{2}\)/(V-s) [10]. Thanks to the impressive optoelectronic properties of graphene, including its ultra-large heat conductivity [11], ultra-high carrier mobility [12], and ultra-wide light absorption spectrum [13], the hybrid integration of monolayer graphene and silicon photonic devices is a promising solution, with encouraging progress achieved in the last decade [14; 15; 16].  
In this review, we first analyze the current research status of silicon/graphene hybrid modulators, including thermal-optical modulators and electro-optical modulators. Next, we introduce high-performance silicon/graphene photodetectors, covering both the photodetection mechanisms and the state-of-the-art performances. Finally, we discuss the challenges and the future trend of the silicon/graphene hybrid devices.
## 2 Theory, Requirements and Synthesis
### Practical Implementations
#### 2.4.3 Voltage-Controlled Current Source (VCCS)  
The VCCS was designed as a single-supply device with a reference (zero) voltage biased to 6 V, i.e., half of the 12 V supply voltage. This is an H-bridge circuit with additional current-control circuitry. The voltage-to-current conversion slope was measured at approximately \(i_{\mathrm{in}}/u_{\mathrm{in}}\approx 86\) mA/V.  
Figure 9 shows the laboratory-built prototypes that were synthesized, designed, and manufactured to accommodate the experimental evaluation of the proposed concept. In the photograph, viewed from left to right, these are the VCCS, the DFB module, the PIN-FET module, and the CLC prototype.  
Figure 8: PIN-FET schematic (yellow) and a single-to-dual voltage source adapter.  
Figure 9: Photograph of the laboratory-built prototype devices.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 4 The Application of GMOs to Agriculture and Food Production
### Virus-Resistant Plants  
Viral infections are one of the most frequent reasons for smaller yields and substantial economic agriculture losses [100]. As viral infections in crops bring about 10-15% losses in worldwide yields, employing GE to create resistant plants to viral infections may be a solution to overcome this issue [15]. For example, viruses from the _Geminivitidae_ family infect a wide range of plants worldwide, which include bean, maize, tomato, cotton,  
Figure 6: Mechanism of action of _Bacillus thuringiensis_ toxin. Icons made by Ferepik and Smashicons from www.flaticon.com (accessed on 1 December 2021). Adapted from [99].  
pepper, and potato, among others. These viruses bring about considerable agricultural damage, which results in lower yields. Mosaic, leaf curling, mottle, vein yellowing, leaf yellowing, crumpling, and rugosity are just some of the signs of the diseases that gemiviruses cause [101]. As climate change globally affects insect vector distribution, with high levels of recombinations, rearrangements, and mutations of viral genomes, both the prevalence and severity of geminivirus-related diseases have significantly increased in the past 20 years [101; 102; 103; 104]. These viruses will pose a significant threat to agriculture worldwide. The use of GE tools has emerged as an efficient possibility to achieve resistance against geminiviruses [15]. For instance, recent studies have shown the efficacy of the CRISPR-Cas system in developing resistant _Nicotiana benthamiana_[105]. Such findings indicate that this approach can be followed to create increasingly resistant plants to viral infections with time [15; 101]. These methods are being extensively investigated to develop resistance to wide-ranging plant viruses. One example of such is GE tools, which have been employed with cucumbers to enhance resistance to two potyviruses and to three other viruses that cause severe cucumber damage. The _Potyvirus_ genus accounts for a high percentage of vegetable viruses and is responsible for decreasing the marketability of many crops. The CRISPR-Cas system can limit pathogenicity and improve its tolerance against potyviruses, while no significant differences have been observed in dry weight, flowering, and growth between transgenic and wild-type plants, although the durability of this resistance should be further tested [15; 106].
## 5 Case Studies
### Case Study One: Khan Academy
#### 5.1.3 Insights and Implications from Khan Academy  
Integrating the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) into Khan Academy provides valuable insights into the future of personalized education. The successful application of DFDLOF highlights the profound impact of machine learning in enhancing the adaptability and effectiveness of online learning platforms. One of the key insights is the significant improvement in learner engagement and performance. Providing a more tailored learning experience that adapts to individual learning styles and paces makes students more likely to remain engaged and achieve better outcomes.  
Another critical insight is the role of real-time feedback in enhancing the learning process. The DFDLOF's capability to provide immediate and personalized feedback based on learner interactions has been instrumental in reinforcing learning and correcting misconceptions. This approach accelerates the learning process and ensures a deeper understanding and retention of knowledge.  
The implementation of DFDLOF into Khan Academy also underscores the importance of data in personalizing education. Using student performance data to tailor educational content and assess learning needs demonstrates the potential of data-driven approaches in revolutionizing educational methodologies.  
Furthermore, the case study reveals the scalability of such frameworks. With its vast user base, Khan Academy demonstrates that advanced machine learning frameworks like DFDLOF can be effectively scaled to benefit many learners, transcending geographical and socio-economic barriers.
## 3 Simulation
### Simulation Results  
The experimental results are shown in Table 2. The red laser simulation results are shown in Figure 6. The red LED simulation results are shown in Figure 7. The blue LED simulation results are shown in Figure 8. The red dot is the radiation pattern of the entire illumination plane, the red dot is the position of the light source on the light source plane, and the graph is the cross-sectional view corresponding to the radiation pattern \(Y=0\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Value** \\ \hline The maximum number of iterations & 500 \\ \hline Laser population size & 10 \\ Red LED population size & 20 \\ Blue LED population size & 10 \\ \hline Coefficient \(c_{1}\) & 1.4 \\ \hline Coefficient \(c_{2}\) & 1.4 \\ \hline Laser diffuser enlargement factor \(\tau\) & 10 \\ \hline Illumination plane size & 50 cm \(\times\) 50 cm \\ \hline Distance between laser and LED light source plane and light plane & 40 cm \\ \hline Irradiance D at 40 cm on the LED optical axis & 1 W/m\({}^{2}\) \\ Irradiance at 40 cm on the laser optical axis \(D\) & 15 W/m\({}^{2}\) \\ \hline Laser and LED light intensity and irradiance conversion constant \(K_{\lambda}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameters of particle swarm algorithm.  
It can be seen from Table 2 that, as the mean value of irradiance decreases, the lower the energy utilization rate, the higher the uniformity of the irradiation plane. The high-uniformity irradiation system can play a good contrast effect in the cultivation experiment. However, in the actual work of plant factories, energy consumption must be fully considered. However, if the energy efficiency is only high, plant growth on the entire planting plane becomes uneven. Combined with plant light saturation and light inhibition, uniformity is poor. The lighting system can lengthen the growth cycle of the entire planting plane and reduce the light energy utilization rate of the plant, which is very unfavorable for  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Light Source** & **Parameter _d_** & **Energy Utilization** & **Evenness** \\ \hline \multirow{3}{*}{Laser} & \(d=10\) & 100\% & 40\% \\  & \(d=1\) & 85\% & 80\% \\  & \(d=0.3\) & 82\% & 86\% \\ \hline \multirow{3}{*}{Blue LED} & \(d=10\) & 90\% & 70\% \\  & \(d=1\) & 84\% & 74\% \\  & \(d=0.3\) & 77\% & 84\% \\ \hline \multirow{3}{*}{Red LED} & \(d=10\) & 90\% & 70\% \\  & \(d=1\) & 85\% & 78\% \\  & \(d=0.3\) & 77\% & 84\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results.  
Figure 8: Blue LED simulation diagram.  
Figure 6: Laser simulation diagram.  
Figure 7: Red LED simulation diagram.  
the efficiency of the plant factory. Therefore, it is necessary to comprehensively consider the problems of high energy utilization and uniformity.
## 1 Introduction
### Research Aim  
The development of new LBAs contributes to the sustainable management of natural resources and to the improvement of the properties of cement composites. Therefore, the importance of a study that gathers existing data in the literature and manages to demonstrate the importance of LBA for chemical engineers, materials engineers, agronomists, civil engineers, and other professionals, is remarkable. Through a scientometric analysis and a qualitative discussion, this study aims to highlight the current state of LBAs research applied to cement-based composites and critically review the existing works. The proposed review strategy allowed the authors (1) to identify publication trends, most frequently studied terms and keywords, most relevant publication sources, and most influential and collaborative scholars and countries in LBAs research; (2) to analyze the studies found in the literature and discuss the main characteristics of new LBAs incorporated in cement mixtures; (3) to identify current knowledge gaps and propose new possibilities for near-future research on LBAs applied to cement-based composites.  
As far as we know, no work exists in the literature that presents a scientometric approach and a qualitative discussion on the subject that covers so many articles, proving the novelty of the present work. Previous studies mentioned the application of LBAs in cement-based composites [44; 45]. However, the reviews published so far focused on manufacturing routes and chemical characteristics of lignin-based materials, not emphasizing the applicability of LBAs in cement mixtures. In this sense, the proposed scientometric review quantitatively addresses the main parameters of LBAs research, while the qualitative discussion presents the current research scenario as a reference point to early-stage scholars. This holistic review will help the specialized scientific community to better understand the evolution of the theme over time, the existing weaknesses so far, future expectations, and existing bottlenecks for the application of LBAs on a larger scale. In addition, this study highlights LBAs as an alternative to producing more efficient and sustainable cement composites.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 3 Energy-Efficient Subcarrier Pairing and Power Allocation Algorithm
However, for another extreme case where \(\mathbf{\Psi}(t)\) approaches infinity, no transmission is the best policy to maximize the objective function in (17). We first discuss the optimal solution for a given parameter \(\mathbf{\Psi}(t)\). For simplicity, we use \(\mathbf{\Psi}\) instead of \(\mathbf{\Psi}(t)\) in the remaining parts of this paper. Since the problem (P.3) is a mixed-binary integer non-linear programming problem, the computational complexity is very high for finding the globally optimal solution. For a fixed subcarrier pairing in (17), the problem (P.3) becomes a standard convex optimization problem, and the optimal solution can be determined by applying the dual decomposition method. The Lagrangian function of (17) can be given by  
\[\mathcal{L}\left(\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{\rho},\boldsymbol{\Omega},\lambda,\boldsymbol{\mu},\boldsymbol{\
u}\right) =\frac{1}{2}\sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j}\left(\frac{ \alpha_{i}^{d}}{\ln 2}\hat{\Omega}_{i}+\beta_{i}^{d}\right)-\sum_{j=1}^{K}\frac{ \alpha_{j}^{e}}{\ln 2}\left(\hat{P}_{R,j}+\ln\left(\left|h_{RE,j}\right|^{2} \right)-\ln\left(\sigma_{E,j}^{2}\right)\right)-\beta_{j}^{e}\] \[-\mathbf{\Psi}\left(\sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j}\left(e ^{\hat{P}_{S,i}}+e^{\hat{P}_{R,j}}\right)+P_{\text{SC}}\right)-\lambda\left( \sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j}\left(e^{\hat{P}_{S,i}}+e^{\hat{P}_{R,j}} \right)-P_{max}\right)\] \[\log_{2}\left(1+e^{\Omega_{i}}\right) \geq\frac{\alpha_{i}^{d}}{\ln 2}\hat{\Omega}_{i}+\beta_{i}^{d},\ \forall i \tag{13}\]0  
where \(\lambda\geq 0\) is a Lagrangian dual variable associated with the transmit power constraint. The vectors \(\boldsymbol{\mu}=\left[\mu_{1},\mu_{2},\cdots,\mu_{K}\right]^{T}\) and \(\boldsymbol{\
u}=\left[\
u_{1},\
u_{2},\cdots,\
u_{K}\right]^{T}\) are the dual variables corresponding to the constraints (C.5) and (C.6), respectively. Then, the dual Lagrangian function can be written as  
\[\log_{2}\left(1+e^{\Omega_{i}}\right) \geq\frac{\alpha_{i}^{d}}{\ln 2}\hat{\Omega}_{i}+\beta_{i}^{d},\ \forall i \tag{13}\]1and the dual optimization problem is  
\[\log_{2}\left(1+e^{\Omega_{i}}\right) \geq\frac{\alpha_{i}^{d}}{\ln 2}\hat{\Omega}_{i}+\beta_{i}^{d},\ \forall i \tag{13}\]2  
This dual problem is divided into a master problem and a subproblem, and an iterative approach is adopted to solve it. In the subproblem, the \(\mathbf{P}_{S},\mathbf{P}_{R}\), and \(\rho\) are obtained for fixed Lagrangian multipliers, whereas by resolving the master problem, we update \(\lambda\), \(\mu\), and \(\
u\).
## 4 Maneuvering Policy Generation Algorithm Designing in 1-vs-1 WVR Air Combat Based on MADDPG
### Target Position Prediction  
The air combat strategy needs to quickly occupy a dominant position and attack the target if required in order to enable the learned air combat strategy of a UCAV to acquire the intent of the target UCAV and perform predictive maneuvers. In this section, we will discuss the method of target position prediction. As we already explored the algorithm framework for predicting the target position in Section 3.2, in this section, we will mainly focus on the calculation of perturbation.  
We assume that the air combat strategy of UCAVs on both sides of the battlefield is completely rational. Subsequently, the maneuvering action of one UCAV will minimize the \(Q\) function value of the other UCAV. For clarity, we assume UCAV\({}_{1}\) to be our UCAV and UCAV\({}_{2}\) to be the target UCAV. When UCAV\({}_{1}\) takes action \(a_{1}\), then UCAV\({}_{2}\)'s action in the next moment, \(a_{2}^{*}\), can be described by Equation (23).  
\[a_{2}^{*}=\operatorname*{arg\,min}_{a_{2}}Q^{\mu_{1}}(s,a_{1},a_{2}) \tag{23}\]  
Likewise, from the viewpoint of UCAV\({}_{2}\), Equation (24) can be used to predict the action of UCAV\({}_{1}\).  
\[a_{1}^{*}=\operatorname*{arg\,min}_{a_{1}}Q^{\mu_{2}}(s,a_{1},a_{2}) \tag{24}\]  
The critical challenge in our proposed method of action prediction of the opponent UCAV is the minimization expressed in Equations (23) and (24). The direct optimization of minimization is difficult owing to the non-linearity of the continuous action space and the \(Q\) function. A simple approximate solution can be found using an inner-loop gradient descent while performing an update step in Equation (23) or Equation (24); however, this is too computationally expensive for practical use. To simplify the calculation, a simple linear local optimal solution is herein presented. The main concept behind the solution can be summarized in two steps: (1) approximate the non-linear \(Q\) function using a locally linear function; (2) replace the inner-loop minimization with a 1-step gradient descent. Note that the core idea is the locally linearized \(Q\) function adapted from recent adversarial training techniques originally developed forsupervised learning. We use this function to derive an approximation \(\widehat{\mathbf{\epsilon}}\) for the worst-case perturbation by taking  
\[\widehat{e}_{a_{2}} =-\alpha\
abla_{a_{2}}Q^{\mu_{a_{1}}}(s,a_{1},a_{2}) \tag{25}\] \[\widehat{e}_{a_{1}} =-\alpha\
abla_{a_{1}}Q^{\mu_{a_{2}}}(s,a_{1},a_{2}) \tag{26}\]  
where \(\alpha\) is the adjustable coefficient representing the perturbation rate. This can also be explained as the step size of the gradient descent. When \(\alpha\) is small, the local approximation error is also small, but, because the perturbation is extremely small, it can be assumed that the target is merely following the current maneuver. When \(\alpha\) is large, the approximation error may disturb the entire learning process, and the agent may not be able to learn a good policy.