esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antenna Performance Measurements
### Diversity Performance Analysis  
Envelope correlation coefficient (ECC) is the first diversity and MIMO parameter to be analyzed. It gives a correlation between signals at the receiving end. In this work, ECC is calculated by using both S-parameter using Equation (5) and 3D radiation pattern using Equation (6) [14]. Simulated and measured ECC using S-parameters Equation (5) are shown in Figure 14.  
\[\rho_{e}=\frac{\left|S_{11}^{*}S_{12}+S_{21}^{*}S_{22}\right|^{2}}{(1-(\left|S_ {11}\right|^{2}+\left|S_{21}\right|^{2}))(1-(\left|S_{22}\right|^{2}+\left|S_{ 12}\right|^{2}))} \tag{5}\]  
\[\rho_{e}=\ \frac{\left|SS_{4\pi}F_{1}^{\rightarrow^{+}}(\theta,\varphi)\cdot F _{2}^{\rightarrow^{+}}(\theta,\varphi)d\Omega\right|^{2}}{SS_{4\pi}\left|F_{1} ^{\rightarrow^{+}}(\theta,\varphi)\right|^{2}d\Omega SS_{4\pi}\left|F_{2}^{ \rightarrow^{+}}(\theta,\varphi)\right|^{2}d\Omega} \tag{6}\]  
Figure 14: Measured and simulated ECC.  
Figure 13: 3-D Simulated Radiation Patterns. (**a**) Port 1 at 1.8 GHz; (**b**) Port 2 at 1.8 GHz; (**c**) Port 1 at 2.6 GHz; (**d**) Port 2 at 2.6 GHz.  
ECC using far field pattern Equation (6) is found to be 0.0218 and 0.0165 at 1.8 and 2.6 GHz, respectively and is shown as red dots in Figure 14. This figure also shows that ECC values are well below 0.5 in the desired frequency bands, which ensures good diversity performance [15].  
Diversity gain (DG) is another import parameter which assures good diversity and MIMO performance. In this work, DG is calculated using Equation (7) [8].  
\[DG=10e_{\rho} \tag{7}\]  
where  
\[e_{\rho}=\sqrt{\left(1-\left|0.99\rho_{e}\right|^{2}\right)} \tag{8}\]  
Figure 15 shows the measured and simulated diversity gain of the proposed dual-band MIMO antenna. The diversity gain in the both the frequency bands is almost 10 dB.  
Mean effective gain (MEG) is the last and very important parameter to be analyzed for diversity performance. It is defined as the ratio of the average power received at the antenna to the sum of the average power of the vertically and horizontally polarized waves received by isotropic antenna [16]. Using S-parameters MEG for each port, it can be measured using Equation (9) [17]. For similar power levels of each branch, power ratio k, which is equal to the difference in the magnitude of MEGs, is calculated using Equation (10) [18].  
\[MEG_{i}=0.5\eta_{i,rad}=0.5\left[1-\sum_{j=1}^{M}\left|S_{ij}\right|^{2}\right] \tag{9}\]  
\[k=\left|MEG_{1}-MEG_{2}\right|<3 dB \tag{10}\]  
Figures 16 and 17 shows the simulated and measured MEGs and both the figures clearly show that k is almost equal to 0 at both the desired frequency bands.  
Figure 16: Simulated Mean Effective Gain (MEG).  
Figure 15: Measured and simulated diversity gain.
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 3 Simulation and Investigation  
This section compares the performance of the FTD technique to the traditional ATD technique first. The BER performance was then presented and assesses after the adaptive power transfer system simulation occurs in the ideal scenario \(\rho=1\) of channel reciprocity without interference from background noise. Finally, to examine the impact of channel reciprocity on the adaptive power approach suggested in this research, the FSO system was simulated in the noisy state with various background noises. This simulation was conducted in MATLAB R2020b.
###### Abstract  
This paper proposes a hexagonal meander-shaped wideband electronically reconfigurable memunitarray (HMRTA) at Ku band for satellite communications and radar applications. The proposed memunitarray offers high gain, low profile, and wideband characteristics with beam-scanning and beam-forming features. The cascaded structure is a low-profile and compact transmitarray. The transmitter (T\({}_{\rm x}\)) layer has an angular hexagonal patch with a meandered shape and resonating parasitic patches to enhance the bandwidth. The receiver (R\({}_{\rm x}\)) layer comprises a two-part hexagonal receiver patch and a dual ring impedance-matching receiver layer. The current reversal phenomena have executed the 180\({}^{\circ}\) phase shift by integrating two diodes in opposite directions. The measured results of a unit cell achieve a minimum insertion loss of 0.86 dB and 0.92 dB for state I and state II. The maximum insertion loss is 2.58 dB from 14.12 GHz to 18.02 GHz and is about 24.83% at 16.5 GHz. The full-wave simulations of a 20 \(\times\) 20 space-fed reconfigurable memunitarray were performed. Good radiation patterns at all scanning angles of two principal planes are achieved, and the cross-polarization level remains less than \(-\)20 dB. The simulated 3 dB gain fluctuation bandwidth of the array is 15.85-18.35 GHz, and the wideband characteristics are verified. The simulation results show that the array can perform beam scanning \(\pm\)60\({}^{\circ}\) in the elevation (y-z) plane and obtain the beam-scanning characteristics for \(\pm\)60\({}^{\circ}\) in the Azimuth (x-z) plane.  
transmitarray; hexagonal; wideband; meander; reconfigurable; beam scanning; satellite communications 20231201957. 10.3990/electronics12091957. 23 March 2023  
10 April 2023  
17 April 2023  
Published: 22 April 2023
# Asynchronous Gathering in a Dangerous Ring +
## 3 Gathering When \(k\) Is Known and \(n\) Is Unknown
### In Oriented Rings  
Consider the following algorithm GoRight; agents are in two states: _explorer_ and _follower_. Initially, every agent is an _explorer_.  
**Algorithm**GoRight:  
1. An _explorer_ at node \(u\) moves to the \(right(u)\) using cautious walk. \((a)\) if it enters the black hole, it dies; \((b)\) if it enters a node visited by another agent, it becomes a _follower_,  
Figure 2: Example used in the Proof of Theorem 3.  
returns to \(u\), making the link safe; \((c)\) if it enters an unvisited node, it returns to \(u\) (maintaining the state _explorer_), it renders the right link safe and goes again to \(right(u)\) to continue the cautious walk.
2. A _follower_ at node \(u\): \((a)\) if the right link is safe, it moves to the \(right(u)\); \((b)\) if the link is active and there are at least \(k-1\) agents at \(u\), it terminates the execution of the algorithm; \((c)\) else, it waits until the condition changes.  
**Theorem 5**: \(RV(k-1)\) _can be always solved, and this can be achieved in a time of at most \(3(n-2)\), using at most \(nk-k^{2}/2+2n+o(nk)\) moves._  
Proof.: We first consider the correctness of the algorithm. The rightmost agent before the black hole will remain an _explorer_ and eventually it might enter the black hole. All the other agents eventually become _followers_. Since no follower will go past the last safe node explored by the sole remaining explorer, eventually all followers gather at one node.  
We now calculate the time complexity. Using cautious walk, the leftmost agent needs at most 3 time steps to traverse a link. Therefore, it will take it (and all other agents) at most \(3(n-2)\) time steps to reach the rightmost safe node and gather there.  
Finally, we consider the number of moves employed by the agents. All moves can be divided into _cautious walk_ and _right progress_. There are at most 2 cautious walk moves per edge, by all agents combined. An agent \(i\) will make at most \(s_{i}-1\) right progress moves, where \(s_{i}\) is the distance of the \(i\)'s starting location from the black hole, measured leftwards (the agent entering the black hole will make one more move). Since in each node, at most one agent starts, all \(s_{i}\)s are different. Summing together, we obtain the claimed bound.  
Note that there are situations in which the \(3(n-2)\) time bound is indeed achieved: Consider a scenario where there are agents in the two sites neighboring the black hole; the leftmost agent wakes up first and all other agents join the execution only when an agent arrives to their node. Clearly, the leftmost agent must wake up all other agents, and every edge must be traversed using cautious walk.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Predictive Coordination Between NTN and RIS
#### 4.2.3 Doppler-Aware RIS Phase Alignment  
To maintain coherent combining of direct and reflected signals, the RIS phase shift for each element is adjusted to compensate for the Doppler-induced phase drift:  
\[\phi_{m}(t{+}1)=-\arg(h_{d}(t{+}1))+\arg(g_{m}^{*}(t{+}1)h_{m}(t{+}1))-2\pi(f_{ D,g_{m}}+f_{D,h_{m}})(t{+}1)\]  
This ensures that the reflected signals remain phase-aligned with the Doppler-rotated direct signal. The Doppler shifts are estimated via differential phase tracking across successive CSI samples:  
\[\hat{f}_{D,x}=\frac{1}{2\pi\Delta t}\arg\left(\frac{h_{x}(t)}{h_{x}(t{-}1)}\right)\]  
where \(x\in\{d,h_{m},g_{m}\}\), and \(\Delta t\) is the channel sampling interval.
## 3 Methodology
### Validation and Deployment
#### 3.4.2 Continuous Learning  
During the simulation of the algorithm deployment, the model continues learning from the data stream, aiding in the validation of the inputs and ensuring that the data are still relevant. The simulator clusters the items after certain intervals of shoppers, which shows the effect of clustering in relation to shopping efficiency. Store layout changes cannot be avoided, thus the machine learning model identifies changes in the proximity calculations and makes the appropriate changes in the proximity matrix. Scenarios such as when items are moved, replaced, or removed altogether are accounted for by resetting the proximity values for affected pairs after a certain number of runs.
## Numerical Propagation Process
### Numerical Simulation  
The typical model of a round bar with diameter \(D_{0}\) and length \(L\) that contains a surface crack in its median cross section has been used in many experimental tests and numerical simulations. [PERSON] (1996) presented the experimental results of fatigue crack growth for a straight-fronted edge crack in an elastic bar under axial loading with a diameter of 12 mm, a length of 90 mm, and carbon steel S45 as the material. Table 1 lists material parameters for steel S45. [PERSON] (1996, 2000) calculated the surface cracks in round bars with 50 mm diameters through finite-element analysis. Since the propagation of crack shape is defined by the crack configuration for a given loading type [PERSON] (1998), in the present paper, the models are established for different values of these initial parameters to compare the fatigue crack propagation with the experimental and simulation results from [PERSON] (1996) and [PERSON] (1996, 2000).  
The finite element analysis software AbaqusTM (France) is used to simulate the scenario. Since the bar geometry and applied loads present two planes of symmetry, 3D finite element analysis was performed by modeling a quarter of the round bar, as shown in Figure 3. About 350,000-380,000 quadratic hexahedral elements have been employed in each model. The 1/4-node displacement method and fine meshing with a 0.02 mm mesh size has been used around the crack front to model the stress field singularity and improve the accuracy of the contour integral calculation.  
For each crack configuration defined by parameters \(b/D_{0}\) and \(b/a\), the stress-intensity factor \(K_{j}(j=O,A,B,C,D)\) along the crack front is obtained through the above described finite-element  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Monotonic Tensile** & **Nominal Ultimate** & **True Ultimate** & **Young’s** & **Poisson’s** & **Crack Growth** \\
**Yield Strength \(\sigma_{0}\)** & **Tensile Strength \(\sigma_{m}\)** & **Tensile Strength \(\sigma_{f}\)** & **Modulus \(E\)** & **Ratio \(v\)** & **Parameter \(m\)** \\ \hline
635.07 MPa & 775.65 MPa & 2101.65 MPa & 2.06 \(\times\) 10\({}^{5}\) MPa & 0.33 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Material parameters for steel S45.  
Figure 3: The finite element models of a surface-cracked round bar.  
analysis. The fatigue growth for the initial defects with \(b/D_{0}=0.05,\ 0.08,\ 0.1\) and \(b/a=0,\ 1\) is considered in this paper.
# Evaluation of the Radiological and Chemical Risk for Public Health from Flour Sample Investigation
## 2 Materials and Methods
### ICP-MS and ICP-OES Analysis  
For the analysis, approximately 0.5-1.0 g of sample, together with 5 mL of ultrapure (for trace analysis) HNO\({}_{3}\) (67-69%) and 1 mL of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), as well as 5 mL of ultrapure water, were directly introduced into a 100 mL TFM vessel. Acid digestion was performed using a Milestone microwave system, Ethos 1, in four steps: 5 min at 1000 W and 85 \({}^{\circ}\)C, 10 min at 1000 W and 145 \({}^{\circ}\)C, 4 min at 1000 W and 180 \({}^{\circ}\)C, 15 min at 1000 W, and cooling [41]. The mixture was filtered and filled up to 100 mL with distilled H\({}_{2}\)O.  
ICP-MS calibration solutions for the analytes measured at 1, 5, 10, 100, and 200 ppb for As, Be, Cd, Cu, Pb, and Zn, as well as at 0.2, 0.5, 1.0, 2.0, and 3.0 ppb for Hg, were prepared by opportune dilutions of two different reference materials (o2 si Multi-element aqueous RM 100 mL 20 \(\mu\)g mL\({}^{-1}\), and 02 si Single-element Mercury Hg aqueous RM 100 mL 1007 \(\mu\)g mL\({}^{-1}\)) in 0.5 % (\(v/v\)) HNO\({}_{3}\) and 0.5% (\(v/v\)) HCl [42]. Internal standardization was applied, with an internal standard at 20 ppb, added on-line via a T-piece before the nebulizer. For the ICP-MS measurements a Thermo Scientific iCAP Qc ICP-MS was used [43] (see Figure 3).  
The sample introduction system consisted of a cooled (3 \({}^{\circ}\)C) Peltier, a baffled cyclonic spray chamber, a PFA nebulizer, and a quartz torch with a 2.5 mm i.d. removable quartz injector. The instrument operated in a single collision cell mode, with kinetic energy discrimination (KED), using pure He as collision gas. All samples were presented for analysis using a Cetac ASX-520. The iCAP Qc ICP-MS operated in a single KED mode using 1550 W forward power, 0.98 L min\({}^{-1}\) nebulizer gas, 0.8 L min\({}^{-1}\) auxiliary gas, 14.0 L min\({}^{-1}\) cool gas flow, 4.5 mL min\({}^{-1}\) He collision cell gas, 45 s each for sample uptake/wash time, optimized dwell times per analyte (0.1 s, except 0.5 s for As), one point per peak, and three repeats per sample.  
For the ICP-OES measurements, a Perkin Elmer Optima 2100 DV was employed [44] (see Figure 4).  
The operating conditions were 1450 W RF power, 15 L min\({}^{-1}\) plasma gas flow rate, 0.2 L min\({}^{-1}\) auxiliary gas flow rate, 0.8 L min\({}^{-1}\) nebulizer gas flow rate, 1.5 L min\({}^{-1}\) sample flow rate, 50 s read delay, CCD detector, and nitrogen purge gas, with 589.592 nm (Na), 285.213 nm (Mg), 317.933 nm (Ca) wavelengths.
# Diet and Asthma: A Narrative Review
## 4 Diet and Asthma
### Fiber  
Some of the protective effects observed with fruit and vegetable consumption may also be attributed to the fiber content of these food groups [47]. Whole grains, beans, fruits, and vegetables are all sources of dietary fiber [63]. SCFAs are produced by gut bacteria following the fermentation of soluble fiber and are the principal fuel of colonocytes [64; 65]. Butyrate has been demonstrated to activate the peroxisome proliferator-activated receptor alpha, (PPAR) which inhibits the NF-kB activity, a pro-inflammatory transcription factor [66]. [PERSON] et al. observed that oral administration of SCFAs to mice significantly reduced the severity of allergic airway inflammation [67]. Dietary fiber and SCFAs have also shown anti-inflammatory effects by activating free fatty acid receptors such as G protein-coupled receptors 41 and 43 (GPR41 and GPR43) in animal models [64; 65]. GPR43 and GPR41 activation can influence the functions of immune cells, such as neutrophils and dendritic cells. This regulation can lead to a decrease in the release of pro-inflammatory mediators. GPR43 and GPR41 activate the Gi/o proteins that reduce the levels of the secondary messenger cAMP, thereby counteracting the pro-inflammatory effects of the cAMP-mediated signaling pathways [64; 65].  
One study assessed the acute effects of a single meal rich in soluble fiber (175 g yogurt with 3.5 g inulin and probiotics) compared with a simple carbohydrate meal (200 g of mashed potato) on asthmatic airway inflammation, including changes in free fatty acid receptor gene expression. The studied outcomes were analyzed 4 h after the meal intake. It was observed that airway inflammation biomarkers, such as sputum total cell count, macrophages, neutrophils, sputum IL-8, lymphocytes, and eNO were significantly lower in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fruits and** & & & \\
**Vegetables** & & & \\ \hline
**Author, Year,** & & & \\
**Study Design** & **Sample, Age** & & \\  
the soluble fiber group and this corresponded to increased GPR41 and GPR43 sputum gene expression and improved lung function [37].  
The consumption of whole grain products, which are a source of dietary fiber, was found to be inversely associated with asthma in a study assessing 598 Dutch children [68]. For current asthma, the adjusted OR was 0.46 (95% CI 0.19; 1.10), whereas for atopic asthma with bronchial hyperresponsiveness was 0.28 (95% CI 0.08 to 0.99) [68]. Additionally, a high consumption of whole grains was associated with lower odds of asthma (OR = 0.52, 95% CI 0.33; 0.82) [69]. However, as previously observed, other nutrients found in fiber-rich foods, such as fruits and vegetables, cereals, and legumes, may also be contributing to the inverse association found between dietary fiber consumption and airway inflammation. Many fiber-rich foods contain antioxidants and other micronutrients, such as polyphenolic compounds and flavonoids, which may help to reduce the oxidative stress in asthma patients [48].  
Table 2 summarizes the results of research on fiber and asthma.
## 4 The Coalition Formulation Game Model
### Distributed Coalition Formation Algorithm  
Next, we propose a distributed time slot coalition formation algorithm based on the join-and-leave rule. The algorithm has three phases: discovery of near UAVs, adaptive coalition formation, and joint transmission. Algorithm 1 summarizes the distributed coalition formation algorithm.  
Phase 1: Each UAV discovers neighboring UAVs in the range of \(d_{C}+d_{I}\) in order to share the time slots in the coalition for no collision transmission. The network discovery and information collection can be based on a fixed control time slot allocated to every UAV.  
Phase 2: Following the discovery phase, adaptive coalition formation begins, and UAVs mutually intend to form a time slot coalition. Then, an iteration of sequential join-and-leave runs depending on the utility improvement. The adaptive coalition formation terminates until there is no any UAV obtaining utility improvement between two iterations.  
Phase 3: Once a coalition structure is formed, the UAVs in a coalition start transmitting packets at the sharing time slots simultaneously.
# Two-Phase Deep Learning-Based EDoS Detection System
## 4 System Design
### Internal Modules
#### 4.3.2 Feature Extractor  
This module loads the pcap file from pcap file storage and extracts the data information to take out the appropriate features, as shown in Table 2. These attributes are then normalized before fitting them to the ANN or LSTM model. These features are key, and are selected by applying the correlation-based feature selection process proposed in [25]. The purpose of feature selection is to increase the detection accuracy of the model and decrease the computing resources. Using irrelevant or redundant features will decrease the accuracy and waste computing resources. As proposed in [25], a feature set S will be evaluated using the metric \(M_{S}\):  
\[M_{S}=\frac{k\overline{r_{cf}}}{\sqrt{k+k(k-1)\overline{r_{ff}}}} \tag{10}\]  
where \(k\) is the number of features in the feature set S, \(\overline{r_{cf}}\) is the mean correlation value between features and class label \(f\) in S, \(\overline{r_{ff}}\) is the average inter-correlation between two features in S. In addition, \(\overline{r_{cf}}\) and \(\overline{r_{ff}}\) are calculated by the information gain (IG), which measures the correlation between two random variables \(X\) and \(Y\):  
\[IG=H(X)-H(X|Y)=H(X)+H(Y)-H(X,Y) \tag{11}\]  
where H(X), H(Y), H(X,Y) are calculated as:  
\[H(X)=\sum_{x\in X}p(x)\log_{2}p(x) \tag{12}\]  
\[H(Y)=\sum_{y\in Y}p(y)\log_{2}p(y) \tag{13}\]  
We calculate \(M_{S}\) for each random subset of features and chose the subset \(S\) that has the largest value \(M_{S}\).  
The module runs a script that takes advantage of Apache Spark framework [26], which is an open-source unified analytics for large-scale data processing to speed up the calculation time. After being extracted, these features need to be normalized because they do not have similar ranges of values, and their formula can be expressed as follows:  
\[z=\frac{x-\mu}{\sigma} \tag{14}\]  
where \(x\) is the standardized value, \(\mu\) is the mean of the distribution, and \(\sigma\) is the standard deviation of the distribution.  
This module is called again to extract and calculate features from five pcap files of a 1-s time period to make sequential data for the LSTM model when the ANN model detects that there is an attack during a 5-s time period.
## 6 Evaluation Results
### Comprehensive Results for Comparative Evaluation  
Comprehensive results on the four main properties discussed above were selected and are listed in Table 8. The table displays the results of the average mass of the oil leaked per samples (average of \(l\)), followed by the duration during which the samples leaked oil (average of \(t_{\rm I}\)). Next, based on the filler content measurement of \(r\) and \(h\) for each sample, followed by their respective ratios to the original sample filler content (\(f_{\rm a}\) and \(f_{\rm i}\)), a discrepancy was derived to comparatively evaluate the filler content settlement stability of the samples.  
With the results from Table 8, a radar chart with different scales was drafted. The areas of the four-sided figures drawn over the four difference axes provide a general guideline on the expected performance of the evaluated products on the overall oil leakage stability. Larger surface areas indicate lower filler content settlement stability. For the section evaluating the discrepancy between the average and IL filler content differences, values that went into the positive (+) were not displayed in the radar chart.  
In terms of comparative performance, it was shown that Samples A and B with the largest surface area displayed the lowest stability among the samples that leaked oil. Samples C and G with the smallest surface area displayed higher stability. Having shown no signs of leakage, Samples F and H were determined to be the most stable SPRG out of the tested samples in this demonstration. Refer to Figure 6 for details.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Average Filter** & **IL Day Filter** & \
\begin{tabular}{c} **Average Filter Content \%** \\ **Difference (\(f_{\rm a}\)) (Leaked** \\ **Oil/Normal Sample) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Average Filter Content \%** \\ **Difference (\(f_{\rm j}\)) (IL Day** \\ **Sample/Normal Sample) (\%)** \\ \end{tabular} \\ \hline A & 13.2 & 15.2 & 51.1 & 58.9 \\ B & 13.6 & 16.1 & 59.9 & 70.9 \\ C & 12.1 & 11.5 & 47.5 & 45.1 \\ D & 10.1 & _9.7_ & 36.9 & 35.4 \\ E & 12.7 & 13.9 & 49.2 & 53.8 \\ F & No Leak & No Leak & No Leak & No Leak \\ G & 11.6 & 11.8 & 42.8 & 43.5 \\ H & No Leak & No Leak & No Leak & No Leak \\ \hline \hline \end{tabular}
\end{table}
Table 7: Emulsion stability estimation through comparing overall average filler content change (\(f_{\rm a}\)) and filler content at IL day (\(f_{\rm i}\)).