esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximation of Non-Linear Stress-Strain Curve for GFRP Tensile Specimens by Inverse Method
## 2 Experiment and FEA Setup
### Finite Element Analysis Setup  
Figure 4a-e shows the finite element models of the tensile specimen. In the models, the width (W, W\({}_{0}\)) of the tensile specimen was divided into two, four, six, eight, and ten elements, while the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbols** & **Units** & **wt 20\% GF** & **wt 30\% GF** & **wt 40\% GF** \\ \hline E & MPa & 4082.40 & 4833.49 & 5717.59 \\ \(\
u\) & - & 0.35 & 0.38 & 0.4 \\ \(\sigma_{y}\) & MPa & 7.25 & 8.08 & 10.62 \\ \(\varepsilon_{y}\) & - & 0.0038 & 0.0037 & 0.0039 \\ \(\rho\) & g/cm\({}^{3}\) & 1.05 & 1.11 & 1.21 \\ \(\sigma_{UTS}\) & MPa & 75.33 & 89.72 & 93.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of GFRP.  
Figure 3: Results of the tensile test: (**a**) engineering strain–stress curve of experimental; (**b**) offset yield stress of GFRP.  
aspect ratio of the elements ranged from 0.5 to 1.5. As the geometry of the specimen was not complex, two-dimensional (2D) triangle elements and three-dimensional (3D) tetra elements were not considered.  
As for the material properties used for the conformity evaluation of the elements, the experimental parameter (\(\sigma_{\text{e}}\)) of GFRP wt 20% is commonly applied. The finite element models were analyzed under the conditions shown in Figure 4f based on ASTM D638-14. The left section of the specimen model was set as a fixed section, while the right section was moved at a speed of 5 mm/min. When the FEA was conducted, the reactive force was obtained from the left node set of the specimen model, and the displacement of the gauge length was obtained using the specimen information.  
The implicit solver and explicit solver of LS-Dyna R910 were used as FEA tools. The analysis results are shown in Figure 5 according to the division of elements, solver type, and dimensions. Figure 5a shows that the division of elements and solver type did not significantly affect the accuracy of the analysis. However, they significantly affected the analysis time, as shown in Figure 5b.  
Figure 4: Finite element models for tensile test specimens: (**a**) element division 2 (average element size: 8.125 mm); (**b**) element division 4 (average element size: 4.0625 mm); (**c**) element division 6 (average element size: 2.7083 mm); (**d**) element division 8 (average element size: 2.0313 mm); (**e**) element division 10 (average element size: 1.625 mm); (**f**) finite element analysis model for tensile specimen.  
In general, models with implicit solvers, 3D elements, and a large number of divisions are selected to improve the accuracy of the analysis results. However, when estimating the mechanical properties, it was confirmed that selecting a model with explicit solvers, 2D elements, and a small number of divisions did not result in large differences. Therefore, in this study, models with explicit solvers, 2D elements, and a small number of divisions were used to reduce the analysis time.
## 2 Materials and Methods
### Methods of Analysis
#### 2.3.4 Particle Size and Surface Area Analysis  
The specific surface area of the PG was determined using the Brunner-Emmett-Taylor (BET) method, employing a Micromeritics ASAP 2020 PLUS HD88 analyzer (ASAP2460, Micromeritics, Norcross, GA, USA). The BET method is based on nitrogen adsorption, and the surface area is calculated from the amount of nitrogen gas adsorbed at different relative pressures (P/P\({}_{0}\) = 0.01 to 0.95). The BET analysis provides critical data on the surface area available for adsorption, which is a key factor in evaluating the efficiency of curing agents such as zeolite powder in immobilizing phosphorus and fluorine impurities. Additionally, the pore size distribution of the PG was obtained using the Barret-Joyner-Halenda (BJH) method, which is based on nitrogen desorption data from the isotherm. These data are critical for understanding how the porosity of PG influences the adsorption of curing agents and the immobilization of impurities. By analyzing both the surface area and pore size distribution, a more comprehensive understanding of the physical properties can be gained, informing the design of more efficient treatment strategies.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
###### Abstract  
Laser phase noise is a critical factor that limits the range and performance of coherent lidar systems, especially in high-resolution applications such as inverse synthetic aperture lidar (ISAL), which demands stringent coherence. The effective suppression of laser phase noise is essential to enable high-resolution imaging over long distances. This paper presents a phase noise compensation technique utilizing dual reference channels (DRCs) based on concatenated generated phase (CGP) principles. The proposed method uses two reference channels with different delay lengths: a long-delay channel for coarse phase noise compensation and a short-delay channel for fine adjustments. We performed ISAL imaging experiments on stationary and rotating targets using a seed laser with a 3.41 MHz linewidth, achieving round-trip distances exceeding 110 times the laser coherence length. Imaging quality closely matched a 100 Hz narrow linewidth laser, approaching theoretical resolution limits. Compared to prior methods based on residual error linear estimation, the DRC method enhances compensation speed tenfold while maintaining accuracy. These results highlight the efficacy of the proposed DRC method in mitigating laser phase noise, significantly improving ISAL imaging performance.  
n
## 4 Material Measurement Comparison  
### Parameter Extraction  
For all three instruments--T-Sweeper, TDS and FDS--the frequency-dependent (\(f\)) refractive index (\(n(f)\)) and absorption coefficient (\(\alpha(f)\)) of material under test can be calculated from the frequency-dependent field amplitude (\(E(f)\)) and phase (\(\Phi(f)\)) by using the equations [16; 17; 18]:  
\[n(f)=\frac{\Big{(}\Phi_{s}(f)-\Phi_{ref}(f)\Big{)}c}{2\pi fd} \tag{1}\]  
\[\alpha(f)=-\frac{2}{d}\cdot\ln\left[\frac{(n+1)^{2}}{4n}\frac{E_{s}(f)}{E_{ ref}(f)}\right] \tag{2}\]  
where \(c\) is the speed of light, \(d\) is the sample thickness, and the subscripts \(s\) and \(ref\) refer to the sample and reference data, respectively. In addition, the maximum measurable absorption is an important parameter in the interpretation for certain samples. The parameter is determined by the dynamic range of the device and represents absorption such that the transmitted amplitude is reduced to the level of the noise floor [23].  
\[\alpha_{max}=\frac{2}{d}\cdot\ln\left[DR\cdot\frac{4n}{(n+1)^{2}}\right] \tag{3}\]  
In the case of TDS, the field amplitude (\(E(f)\)) and phase (\(\Phi(f)\)) are obtained from the time-domain data by applying Fourier transform. The frequency resolution corresponds to the length of the time-domain scan length.  
In the case of the T-Sweeper, the manufacturer's software (Fraunhofer Heinrich Hertz Institute, Berlin, Germany) outputs the amplitude and phase spectrum. The phase spectrum is wrapped by \(-\pi\) to \(+\pi\); it is unwrapped using the function of the NumPy library for the python programming language. When interpreting the results of the unwrapping, it should be noted that phase unwrapping can produce errors, especially in combination with noise [18], which must be corrected. The signal data obtained from T-Sweeper is used to calculate the complex transmission function. The magnitude of this corresponds to the transmission in Equation (2) and the argument to the phase difference in Equation (1) [34]. Complex values are used because experience shows that the calculated values, especially the phase, are more accurate using this approach. The measured data of the T-Sweeper and the TeraScan were filtered with a forward and backward digital filter of the Butterworth type. The lowpass Butterworth filter had an order of 5 and a cut-off of 0.1 times the Nyquist frequency.
## 6 Bioavailability of _C. ternatea_ Components--Anthocyanins  
Globally, phytochemical components like polyphenols and anthocyanins from edible plants have been utilized as functional ingredients to fortify food and beverages due to their ability to prevent various diseases. The advantages of phenolic compounds are their accessibility, their specificity of response, and their tendency for low toxicity; however, their rapid metabolism and low availability are unexpected properties [26; 55].  
Biological pathways of anthocyanins, such as metabolism [56], gastrointestinal absorption [57; 58], and tissue accumulation [59], are influenced significantly by the types of anthocyanin ingested, as demonstrated by previous studies. It was discovered that pelargonidin-3-O-\(\beta\)-D-glucopyranoside has high bioavailability (>13%), whereas other anthocyanins,such as cyanidin-3-\(O\)-\(\beta\)-D-glucopyranoside and delphinidin-3-\(O\)-\(\beta\)-D-glucopyranoside, have low bioavailability (<1%) [56; 58; 60]. The low absorption profile of anthocyanins in the gastrointestinal tract may be partly caused by the instability of cyanidin-3-\(O\)-\(\beta\)-D-glucopyranoside and delphinidin-3-\(O\)-\(\beta\)-D-glucopyranoside in intestinal condition, which are weakly acidic to alkaline.  
Furthermore, acylated anthocyanins are comparatively stable due to their intramolecular hydrophobicity, stacking between the aromatic ring on the acyl moiety and the anthocyanin (aglycone of anthocyanin) [61]. An acylated anthocyanin is protected by its side chains of acyl and sugar groups, which boosts the anthocyanin's molecular stability and biological activity [62]. The metabolic pathways of these molecules in vivo have been well elucidated by studies on the absorption of acylated anthocyanins with a molecular weight range of 817-1185 from different plant components [63; 64; 65; 66]. Acylated anthocyanins are absorbed to a similar degree as non-acylated anthocyanins, despite having a higher molecular weight [64; 65].  
[PERSON] et al. [67] conducted an examination of the gastrointestinal absorption of peratnins, the polyacylated anthocyanins that contain two or more aromatic acyl groups that are found in \(C\). _ternatea_ flowers. Gastrointestinal absorption of peratnins in rats was examined after oral administration of the \(C\). _ternatea_ flowers extract. The results indicated that preternatin A3 and nine other peratnins were detected in the blood plasma of rats 15 min after oral dosing. Similar to other acylated and non-acylated anthocyanins, 10 peratnin analogues produced from \(C\). _ternatea_ flowers are absorbed in their original acylated forms. This indicates that peratnins derived from \(C\). _ternatea_ flowers may benefit the health-promoting effects in vivo in their polyacylated forms.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
## 3 Measurement Results and Discussion
The antenna prototype has been fixed at a height of 1.5 m and aligned with a compact CP UHF antenna with gain \(G_{tag}\approx 4\) dBic connected to the tag device. The transmitted power \(P_{tx}\) was opportunely regulated for each frequency to let \(P_{EIRP}=36\) dBm (cable loss effects were also compensated for), and experimental read ranges for the proposed antenna and the antenna in [28] (\(G_{reader}\approx 9\) dBic) are depicted in Figure 10. As deduced by Equation (1) and demonstrated in Figure 10, enhanced gain \(G_{reader}\) is desirable for achieving enhanced read range capabilities.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Radio Resource Management  
There are limited radio resources (e.g., bandwidth, power, storage, etc) for UAVs to operate efficiently when the number of users increases. It results in a need for radio resource  
Figure 15: Coverage of control plane of gNBs to support a FANET.  
management and optimization to efficiently utilize available resources and provide services to the network's edge [161]. In addition, when UAVs are used to create flying hotspots, the radio resources should be carefully divided and allocated for communications between UAVs and ground users as well as communications between UAVs. This resource allocation to links between UAVs has to be considered with the routing of FANET itself and traffic from/to ground users in each UAV.  
Furthermore, since the source of power in UAVs is mostly limited, i.e., UAVs are mainly powered by batteries, power efficiency through efficient resource management is especially important when UAVs are utilized in public safety applications [43]. Hence, having application-based resource optimization scenarios is a crucial research direction in future FANETs. This may be done according to different criteria to decrease the mission completion time, decrease total cost, increase scalability and survivability of the network.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Flavonoids  
Flavonoids constitute a vast class of plant-derived polyphenic compounds characterized by a C6-C3-C6 skeleton, consisting of two aromatic rings linked by a three-carbon bridge forming a heterocyclic ring [110; 111]. Their structure determines physicochemical properties such as solubility, polarity, biological activity, and bioavailability [7]. The hydrophilicity of flavonoids largely depends on the number and positioning of hydroxyl groups (-OH) as well as the presence of sugar moieties (glycosylation). The incorporation of glucose, rhamn, or other sugars increases their water solubility and improves their pharmacokinetic properties, positively influencing absorption and distribution in the body [112; 113; 114; 115; 116]. Hydroxyl groups play a key role in forming hydrogen bonds with water molecules, and their location affects not only solubility but also the capacity to interact with biological receptors [110; 111; 117].  
As mentioned above, flavonoids can occur as free aglycones or in glycosylated forms. Glycosides display higher solubility in aqueous environments, which enhances their biological availability [112; 114; 115]. In contrast, aglycones are less polar, which may favor their penetration through lipid membranes but simultaneously reduce their solubility in plasma. According to Journal of Immunology Research, highly hydrophilic flavonoids tend to localize near the surface of cellular membranes, where they form a hydrophilic protective layer. This localization aids in protecting membranes against oxidative stress and free radical-induced damage [116].
# Uniaxial Testing of Soil-Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
## 4 Laboratory Test Results and Discussion of the Obtained Results
### Results of Unloading/Reloading Procedures Derive Material Stiffness in Cycling Loading  
The results presented above are focused on the elastic modulus of soil-cement composites in the first cycle of loading. Some samples were unloaded and reloaded once or twice. A significant increase in stiffness may be observed in the subsequent stages of unloading/reloading. The graph given below presents the results in the composite strength domain and in the composite density domain. As it was presented above, a higher density (better compaction of the composite) is positively correlated with composite compressive strength and, consequently, with the stiffness measured by means of elastic modulus.  
Figure 13 presents all the results of maintained compressive strength tests, meaning the continuous tests without unloading (till rupture of the specimen), and tests with a single unloading, and tests with two unloading cycles. All values were consequently derived according to the procedure described in Section 3.5. It may be observed that despite the large variability of the results, a clear increasing trend may be identified for every additional load cycle.  
A similar image may be obtained concerning the elastic modules dependence on material density in Figure 14. Despite the fact that strength is usually positively correlated with density, there is no clear trend concerning the density-modulus relationship.  
The presented results look slightly paradoxical, but one should bear in mind that concerning well-mixed soil-cement composites, a higher amount of cement grout may result in a higher porosity and a lower amount of natural mineral aggregate, and finally, it may cause a decrease in the material density. Such a phenomenon was observed in the former authors' experiments. The need to control composite's density was also expressed in a piece of work [36] concerning the stabilization of organic soils.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Number of** \\ **the Sample** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **g/cm3** \\ \end{tabular} & \begin{tabular}{c} **Compressive Strength** \\ **kPa** \\ \end{tabular} & \
\begin{tabular}{c} **Critical Deformation** \\ **\end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **MPa** \\ \end{tabular} \\ \hline
10 & 1.536 & 802.55 & 2.40\% & 87.131 \\
11 & 1.424 & 317.04 & 3.60\% & 10.958 \\
12 & 4.742 & 3006.62 & **+60\%** & **2145.93** \\
13 & 1.474 & 1315.14
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Lower Arch**} & **IOS** & **Alginate Impression** & \multirow{2}{*}{**Repeated Measure**} \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **ANOVA * \\  & **(Min-Max)** & **(Min-Max)** & **(\(p\)-Value)** \\  & **(Micron)** & **(Micron)** & \\ \hline \multirow{2}{*}{Without brackets} & 94.90 \(\pm\) 4.09 & 92.40 \(\pm\) 4.55 & \multirow{2}{*}{1.000} \\  & (85–100) & (87–99) & \\ \hline \multirow{2}{*}{With brackets} & 96.80 \(\pm\) 4.69 & 95.30 \(\pm\) 1.95 & \multirow{2}{*}{0.210} \\  & (94–104) & (94–100) & \\ \hline Repeated measure & \multirow{2}{*}{1.000} & \multirow{2}{*}{0.803} & \multirow{2}{*}{\({}^{*}\  
As shown in our study, some areas of maxillary and mandibular posterior teeth were measured with lower accuracy. In this study, the overall deviations between IOS measurements in models with and without orthodontic brackets were within 0.10 mm. [PERSON] et al. [24] reported that overall deviations between IOS measurements with and without orthodontic brackets were within 0.30 mm. Moreover, the accuracy of intraoral scans of the model is clinically acceptable even with the brackets. Few errors are likely to have occurred due to the high accuracy IOS system and alginate impression protocol. Although [PERSON] and [PERSON] [25] mentioned that a measurement error of <0.20 mm between the actual model and the scanned images can be permitted, a study by [PERSON] et al. [26] found that an orthodontic model can still be clinically useful if its accuracy is approximately 0.30 mm. This is supported by another study by [PERSON] et al. [24], which showed that errors in all 3D images in the presence or absence of brackets were within 0.30 mm. These conclusions can be used clinically in 3D images in orthodontics. A limitation of this study was that the situation during data acquisition was not the same as it might be in a patient's oral cavity. There was no saliva, metal restorations, or mini-implant anchorages that might interrupt the impression or operator's skills and therefore affect the accuracy of STL files. Moreover, crowding or angulation of teeth, cheeks, small mouth openings, gag reflex, and tongue movement can affect the accuracy of STL files. Future studies could try to include different models that have crowding or angled teeth, which have been found to distort the alginate impression upon removal. The accuracy of the intraoral scanner and the alginate impression in this study were not statistically different in models with and without orthodontic brackets. Thus, we can conclude that both TRIOS3(r) intraoral scanners and alginate impressions can be used for the acquisition of STL files from upper and lower models. The accuracy was around 100 microns in both groups. Further in vivo studies are required to confirm these results.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 4 Study on Synchronization and Desynchronization Time  
The second study on the performance of Pecora-Carroll analog-discrete and discrete-analog synchronization is synchronization and desynchronization time. This study assesses the time required for the synchronization and desynchronization at different noise levels. The following subsections elaborate more on the study details and analyze the acquired results.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods
### Methodological Approach  
The study involves five major aspects related to big data visualization and analysis, namely: (1) extraction and downscaling of daily climate variables to a local station; (2) simulation of climate change impacts on hydrological processes in the Upper Bernam River Basin (UBRB); (3) computation of available discharges for the main water conveyance system from the Bernam River Headwork (BRH) to the Tengi River Headwork (TRH) and at the key points in the main canal; (4) assessment of storage reservoir inflow and release patterns with the need for irrigation water demand and available water for supply under future climate change; and (5) development of a climate-smart agro-hydrological model. Climate variables for baseline (1976-2005) and future (2010-2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway (RCP) scenarios (RCP4.5, RCP6.0, and RCP8.5). The data was downscaled using a developed climate-smart decision support system (CSDSS). The Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) hydrological model simulated climate change impacts on hydrological processes in the Upper Bernam River Basin (UBRB) and the Hydrologic Engineering Center River Analysis System (HEC-RAS) hydraulic model was used to compute available discharges for the main water conveyance system from the Bernam River Headwork to Tengi River and at the key points in the main canal. The spatial data required by the models were processed using the GIS extension of the ArcGIS software. Based on design parameters, the inflow and release patterns for a newly built reservoir were assessed with the need for irrigation water demand and available water for supply under future climate change. A climate-smart agro-hydrological model was finally developed using Excel-based Visual Basic for Applications (VBA) to visualize and analyze climate and hydrological knowledge for wise adaptive water management practices under new climate change realities. The systematic approach of the study is depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Cropping** & \multicolumn{4}{c}{**Dry Season**} & \multicolumn{4}{c}{**Wet Season**} \\
**Activities** & & & & & & & & \\ \hline  & **ISA I** & **ISA II** & **ISA III** & **ISA IV** & **ISA I** & **ISA II** & **ISA III** & **ISA IV** \\ \hline Pre-saturation & 1-January & 1-February & 1-March & 1-April & 1-July & 1-August & 1-September & 1-October \\ Sowing starts & 15-January & 15-February & 15-March & 15-April & 15-July & 15-August & 15-September & 15-October \\ Normal irrigation & 1-February & 1-March & 1-April & 1-May & 1-August & 1-September & 1-October & 1-November \\ Irrigation ends & 10-April & 10-May & 10-June & 10-July & 10-October & 10-November & 10-December & 10-January \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current cropping calendar and distribution of irrigation supplies for the dry and wet irrigation seasons in the Tanjung Karang Rice Irrigation Scheme.