esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Xanthohumol Derivatives
### Naringenin-Chalcone Synthase (CHS)/Chalcone Isomerase (CHI)-Like Proteins  
Polyketide synthase III Chalcone synthase catalyzes the formation of preparylated chalcones from phenylpropanol precursors, such as xanthohumol. A total of 61 ESTs were found to be homologous to the lupulin gland-specific chalcone synthase, CHS H1 [118]. The enzyme catalyzes the two components (4-coumaroyl CoA and malonyl CoA) to form chcalonaringenin. CHS_H1 protein has a predicted molecular weight of 42.5 kDa, 26 amino acids were located at the conserved domains and residues with the same positions to the crystallography of alfalfa CHS (EC 2.3.1.74). CHS_H1 protein shows specific CHS activity for 4-coumaroyl-CoA. The structure model shows that CHS_H1 is significantly different from phlorisovalerophenone synthase. The peculiar expression of chs_H1 mRNA in glandular trichomes was detected by quantitative RT PCR during the ripening process of hop cones.  
In xanthohumol biosynthesis, several researchers previously indicated that the transcriptome data of CHI (EC 5.5.1.6) genes was the most abundant in the ESTs, and while the sampled hop glandular trichomes accumulate many amounts of chalcone [46,119]. Some CHI subfamilies (Type III and Type IV) account for this result for the CHI activity of type III and type IV CHIs to be lacking, so the type III and type IV CHIs were also named as CHI-like proteins (CHIL). Recently, the CHIL was regarded as having some special functions with fatty acid metabolism in planta [110], anthocyanin biosynthesis in Japanese morning glory (_Ipomea nil_) [120]. Two CHILs in _Humulus lupulus_ (HICHIL1 and HICHIL2) were characterized using engineered yeast containing the genes necessary for desmethylxanthohumol synthesis [60]. The results showed that CHIL2 is a component of an active desmethylxanthohumol biosynthetic metabolon in hop glandular trichomes, including a CHS and a membrane-bound prenyltransferase. In addition, type IV CHI-fold proteins from representative land plants have conserved functions to bind with CHS and enhance its activity. Desmethylxanthohumol synthesis rose 2.3-fold in yeast modified with HICHIL2.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 2 Experimental Programs  
### Specim Design and Material Properties  
A total of four short columns of 700 mm in length were tested under monotonic axial loads, including three SA-CS columns and one CFST column. The specimens were named SA-CS-230, SA-CS-280, SA-CS-330, and CFST-1. The corrugated steel plates and steel angles of the SA-CS columns had the same wall-thickness (\(t_{1}\) and \(t_{2}\)) of 2 mm, and the wave height (\(h\)) of the corrugated steel plates for all specimens was 20 mm. The experimental variables of the SA-CS columns were the width of the corrugated steel plate (\(B\)), whose values were 230 mm, 280 mm, and 330 mm. The dimensions of the steel angle were 30 mm \(\times\) 30 mm \(\times\) 2 mm. For CFST-1, the width (\(B_{0}\)) and thickness (\(t_{0}\)) of the steel tube were 230 mm and 2 mm, respectively. Figure 3 and Table 1 illustrate the detailed parameters of the specimens.  
Figure 2: Form of SA-CS column.  
Commercial concrete was used for the specimens, and six concrete blocks with dimensions of 150 mm \(\times\) 150 mm \(\times\) 150 mm, under the same curing conditions as the specimens, were tested under axial loading to obtain the compressive strength. The measured compressive strength (\(f_{\text{cu}}\)) was 35.4 Mpa [23]. The steel angles, corrugated steel plates, and steel tubes were all made of Q345 steel. Tensile tests of the steel were carried out; the measured yield strength (\(f_{\text{y}}\)) was 324 Mpa, the measured ultimate strength (\(f_{\text{u}}\)) was 363 Mpa, and the measured elastic modulus (\(E_{\text{s}}\)) was 202 Gpa. The corresponding yield strain was 1928 \(\mu\epsilon\)[24].  
In order to make the test results more reliable, this paper also compared the axial compression test results of the CFST column by [PERSON] et al. [25]. The length and cross-section dimensions of the specimen tested by [PERSON] were the same as those of CFST-1. The specimen was named CFST-2. Table 1 also lists the material properties of CFST-2.
## 2 Results and Discussion
### Polyphenol Content, Flavonoid Content, and Antioxidant Capacity  
Polyphenols are known to be heat-sensitive compounds [31], which makes them prone to degradation during the concentration and drying processes. The prolonged exposure to high temperatures during these stages, along with the introduction of oxygen during spray drying, likely contributes to the significant reduction in polyphenol content [32]. This oxidative environment can accelerate the breakdown of polyphenols, leading to a loss of their antioxidant properties. Figure 2 highlights that, despite these challenges, the microencapsulation process effectively preserved the polyphenol content, flavonoid levels, and antioxidant capacity in the jasmine instant tea. This is consistent with previous studies [33; 34], which suggest that microencapsulation is a reliable technique for safeguarding the bioactive compounds and functional properties of tea during processing. The encapsulation process helps protect these sensitive compounds from heat and oxygen exposure, thereby enhancing the stability and retention of key antioxidants.  
Figure 1: Encapsulation efficiency of various wall materials. Figure illustrates encapsulation efficiency observed for different wall materials, including gelatin, CMC, acacia gum, and maltodextrin. Data points are presented with error bars indicating standard deviations. Each measurement was replicated three times. CMC—carboxymethylcellulose.  
Figure 2: Effects of different encapsulation agents on antioxidant, polyphenol, and flavonoid contents of jasmine instant tea (CMC—carboxymethylcellulose). Different letters indicate statistically different at \(p\) < 0.05.  
Among the encapsulating agents evaluated, gelatin-based microcapsules showed the highest polyphenol content and antioxidant activity in the final product. Gelatin's superior performance can be attributed to its excellent properties as a microencapsulation material. It forms a stable, flexible matrix that effectively protects polyphenols from oxidative degradation and environmental stress during both processing and storage [35]. As a result, polyphenols encapsulated in gelatin remain more stable and bioavailable, which translates into enhanced antioxidant activity in the final product. In contrast, the maltodextrin-based microcapsules exhibited the lowest polyphenol content and antioxidant properties. This suggests that, compared to gelatin, maltodextrin may be less effective in protecting and retaining these bioactive compounds during the encapsulation process. The differing performances of the encapsulating agents underscore the importance of selecting appropriate wall materials to optimize the retention of polyphenols and the overall antioxidant capacity of jasmine instant tea.
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 5 BER Performance Verification Based on Hardware Platform  
For the proposed soft decision decoding algorithm and the decoder implementation architecture, we perform the performance verification and selection of the number of candidate codewords, \(C\), and the number of information sequences, \(I\), on the FPGAs. The FPGA emulation platform can effectively accelerate the evaluation of the error correction performance [56], and according to the BER performance, the two key parameters of decoders can be optimized. Several cyclic block codes are taken as examples, which are the (15, 5) BCH code, the (23, 12) Golay code, the (31, 21) BCH code, and the (63, 30) BCH code. All the decoders for these codes have been implemented on the FPGA (xc5 vlx110).
# An Analysis of the Altitude Impact on Roots Compressor Operation for a Fuel Cell System
## 2 Materials and Methods
### Simulation Conditions  
Throughout the simulations, the following conditions were maintained:  
* Ambient temperature and pressure conditions are calculated as a function of altitude according to the international standard atmosphere, in a range between sea level (0 km) and 5 km.
* The air filter is assumed to produce a maximum pressure drop of 2% for the highest operational air mass flow (0.3 kg/s). For lower air mass flows, a linear decay of this pressure drop is assumed.
* The compressor reduced speed is controlled to achieve the target air mass flow for a constant oxygen excess ratio of 2.
* The compressor electrical power is adjusted to control its pressure ratio, in a range between 1.05 and 2.8, with a step size of 0.025. The inlet pressure at the fuel cell is assumed to be equal to the compressor outlet pressure for both cathode and anode streams.
* The temperature of cathode and anode gas streams, as well as the internal fuel cell stack temperature, is set at 73 \({}^{\circ}\)C in order to reach the maximum proton conductivity of the membrane.
* Relative humidity at both cathode and anode gas streams is set at 70%.
* The hydrogen excess ratio defined at the anode inlet is set at 1.5, with the hydrogen excess being used to calculate power consumption from the recirculation pump.
* The voltage in the fuel cell stack is limited to a minimum value of 0.6 V/cell in order to operate in the range covered during the validation phase and ensure that the stack  
Figure 3: Compressor performance maps: (**A**) isentropic efficiency map and (**B**) compressor speed map.  
does not reach oxygen-limited operation that could eventually lead to its degradation in real conditions.
## 5 Simulation Experiments and Analysis
### Comparative Analysis of Algorithm Performance  
#### 5.3.1 Algorithm Convergence Analysis  
Reinforcement learning is model-free learning, in the process of training, because the agent does not have any experience accumulation in the early stage of learning, it takes a lot of time to find the path, and the agent may encounter different obstacles in the process of searching. However, as learning progresses, the agent continues to accumulate experience and knowledge, so as the number of training rounds increases, the number of steps the agent needs in each round will also decrease.  
The changes in the number of steps in the training and learning process of the SARSA algorithm, Q-learning algorithm, speedy Q-learning algorithm, and OWS Q-learning algorithm in different scenarios are compared and analyzed. The steps of each UGV in the four algorithms in three scenarios are shown in Figure 10. The three different scenarios are recorded as scenarios 1, 2, and 3 in turn, among them, columns 1, 2, and 3 are the steps convergence result graphs of scenario1, scenario2, and scenario3, and rows 1, 2, 3, and 4 are the steps changes in UGV under SARSA algorithm, Q-learning algorithm, Speedy Q-learning algorithm, and OWS Q-learning algorithm.  
Figure 10: Steps convergence of each UGV of the four algorithms in three scenarios.  
Figure 10 results show that in the three experimental scenarios, the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm have no convergence trend under 1800 iterations of learning. In scenario1, after about 850 episodes of learning, the three UGVs in the OWS Q-learning algorithm can find their respective target points in the same round, and the training effect is relatively stable. In scenario2, after about 800 episodes of learning 3 UGVs in the OWS Q-learning algorithm, the 3 UGVs can find their respective target points in the same episode after that, and the results are relatively stable. In scenario3, after about 1000 episodes of learning 3 UGVs in the OWS Q-learning algorithm, the 3 UGVs can find their respective target points in the same episode after that, and the training effect is relatively stable. It can be seen that the OWS Q-learning algorithm proposed in this paper is effective for solving multiple UGV path planning problems.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 4 Grounding Configurations and Optimization Techniques
### Deep-Driven Rods and Vertical Electrodes  
Deep-driven rods perform effectively in soils with high resistivity due to increased contact with lower-resistivity layers. Figure 3 shows the ground electrodes used in vertical  
Figure 2: Conventional grounding system arrangements; (a) single road, (b) 2-parallel rods, (c) 3-parallel rods, (d) triangular rod grid, (e) 4-parallel rods, and (f) rectangular rod grid.  
formulation [19]. This grounding system has been employed and tested in Cyberjaya, Malaysia. The reduction in resistance with increasing depth is as follows:  
\[R_{d}=\frac{\rho}{2\pi L}\left[ln\left(\frac{4l}{d}\right)-1\right] \tag{14}\]  
Table 4 shows that as the rod length increases, the resistance decreases. In formation V1 (1.8 m rod), the resistance is highest at 35 \(\Omega\), indicating poor conductivity with the shorter rod. In formation V2 (3.6 m rod), resistance drops to 17 \(\Omega\), improving grounding efficiency. This trend continues in formation V3 (5.4 m rod) with a resistance of 15 \(\Omega\), and formation V4 (7.2 m rod) shows a further slight decrease to 13.5 \(\Omega\). While longer rods provide better grounding, the reduction in resistance diminishes as rod length increases, suggesting a point of diminishing returns. The error range was typically \(\pm\)0.2 to \(\pm\)0.5 \(\Omega\), depending on soil moisture at the time of testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formulation** & **Rod Length (m)** & **Measured Resistance (\(\Omega\))** & **Measurement Error (\(\pm\Omega\))** \\ \hline a & 1.8 & 35.0 & \(\pm\)0.5 \\ \hline b & 3.6 & 17.0 & \(\pm\)0.4 \\ \hline c & 5.4 & 15.0 & \(\pm\)0.4 \\ \hline d & 7.2 & 13.5 & \(\pm\)0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of rod depth on resistance.  
Figure 3: Ground electrodes used; (**a**) formation V1, (**b**) formation V2, (**c**) formation V3, and (**d**) formation V4.
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
## 1 Introduction
One major SMI-based micro particle characterization research aspect is particle size distribution estimation by analyzing the signal frequency power spectrum subjected to the dynamic scattering light from the particle population [20, 21, 22, 23]. Another perspective is developing the device or data processing algorithm for particle flowing velocity spatial distribution profile via Doppler frequency spectrometer [24, 25, 26, 27, 28]. Neither focus on the individual particle properties, and to date only a few works have reported the characteristics of a single object. [PERSON] et al. designed a single particle detection system based on SMI technique for the first time [29]. More recently, [PERSON] et al. have developed a numerical simulation model for an SMI-based particle-sensing technique [30]. In 2019, we employed Hilbert transform in SMI detection to measure the micro particle size, by counting the interferometric fringe number, where we observed that a 300 nm resolution can be approached [31]. So far, no relevant work has been reported in actual human living cells for clinical diagnosis or detection. In this paper, for the first time, we propose an SMI-based optical flow cytometry system for single cell classification without fluorescence labeling. Based on the known self-mixing theory framework, we propose a model to describe the SMI burst signal properties that occurs while the cell passes through the laser beam. A custom-made optofluidic cytometer system was designed, and all the components--including the laser sensor, hydrodynamic focusing platform and signal processing--are described in detail. In order to investigate the reliability of our detection system, a series of experimental validations with both artificial beads and human living cells were performed and the results of a large number of individual particle/cell measurements are analyzed with a statistical approach.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Comparative Analysis Methodology  
The comparative analysis with sources [33; 34; 35; 36; 37; 38; 39; 40] employed a hybrid approach within our standardized simulation environment. Where algorithmic specifications were publicly available, we directly implemented core features while eliminating implementation-specific optimizations. For proprietary or insufficiently detailed systems, we utilized parametric modeling based on performance characteristics from the original papers. Model validity was established through sensitivity analysis and cross-verification with publicly available implementations where possible. All systems were tested under equivalent conditions, with standardized metrics collected across multiple runs, ensuring that performance variances reflected algorithmic efficiency rather than environmental aspects. Key metrics derived from each reference system included throughput (transactions per second), latency, and security measures, including encryption success rates and tamper-resistance factors.  
The validation test consisted of identical test cases for all system configurations, including graduated load testing up to 100 TPS, 24-h sustained load testing, and stress testing with simulated network disruptions. Each test was performed five times to ensure statistical significance, with results analyzed using one-way ANOVA.
## 3 Results and Discussion
### Nanoparticle Characterization
#### 3.2.5 SEM Imaging  
The size of the SeNPs produced by _U. lactuca_ is depicted in Figure 6. The SEM images of the SeNPs illustrated that they had a smooth surface, were oval to spherical in shape, and ranged in size from 24.32 to 30.56 nm. The size-distribution analysis was conducted using the dynamic light scattering approach, which correlates with SEM analysis.
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 3 Results and Discussion
### Pitching Airfoil Simulations  
#### 3.2.1 Baseline (No Flow Control)  
The baseline case of no flow control is simulated for the OA209 airfoil with the pitch motion of \(\alpha=13\pm 7^{\circ}\). Since the peak angle \(\alpha=20^{\circ}\) is 6 degrees higher than the static stall angle \(\alpha_{stall}=14^{\circ}\), this pitch motion yields deep dynamic stall as shown in Figure 13. Both the moment and lift stall occur beyond the static stall angle. The OA209 airfoil encounters the moment stall around \(\alpha=16^{\circ}\) due to the convection of DSV toward the trailing edge. The aerodynamic lift continuously increase up to \(C_{l,max}\approx 2.0\) in the simulation, and the lift stall occurs around \(\alpha=17^{\circ}\) due to the detachment of DSV from the airfoil. The pitching OA209 airfoil experiences oscillatory loads for the range of \(18^{\circ}\leq\alpha\leq 20^{\circ}\). The drag peak coincides with the lift peak in the upstroke due to the presence of DSV on the suction side. Further details for the deep dynamic stall are described in the introduction with Figure 1.  
The current RANS simulation is able to capture major features observed in the wind tunnel experiment of [PERSON] et al. [38] (see Figure 13). The shaded area indicates the range of the experimental data points [38]. This clearly shows that the dynamic stall in the experiment is not same every pitch cycle. It can be conjectured that the nature of the flow separation associated with the stall and, as a consequence, massively separated flow is highly sensitive to environment and detailed experimental conditions. Nonetheless, the overall aspect of the deep dynamic stall is acceptably reproduced in the current simulation. The moment stall around \(\alpha=16^{\circ}\) and the lift stall around \(\alpha=17^{\circ}\) are captured in the simulation. The magnitude of the peak moment and the peak lift are slightly over-estimated in the simulation. In the upstroke, the current simulation predicts higher \(C_{l}\), compared to the test, which is presumably related to the deviation of the experimental data from the theoretical lift slope for the static airfoil (see Figure 3). No drag data is reported from the wind tunnel experiment.  
Figure 12: Comparison of two grids for the tangential jet actuation.  
Similar to the static baseline, a supersonic pocket is observed in this pitching conditions (see Figure 14). The supersonic region already forms at the mean angle, continuously grows till \(\alpha\simeq 15^{\circ}\). Once the DSV separates from the airfoil, the supersonic pocket disappears.
## 5 Discussion
### Integration of QR Codes  
Incorporating QR codes on work tools can significantly enhance the efficiency and accuracy of a robotic system in industrial applications. Computer vision algorithms are essential for performing key tasks such as detecting tool types, determining their orientation, and pinpointing their coordinates on the workbench. By integrating QR code detection into these algorithms, the ability to more quickly and reliably estimate the position, rotation, and identification of tools might be achieved, even in challenging environments with poor lighting conditions. QR codes offer a major advantage by encoding specific information that allows for immediate object recognition, reducing the dependence on visual features alone. This is particularly beneficial for distinguishing between tools that may appear similar to traditional object detection models. Each tool can be assigned a unique QR code, simplifying the recognition process. Moreover, QR code scanning is computationally less intensive than using deep learning models for object detection, which eases the load on the robot's control system. Python's extensive libraries, such as OpenCV, Pyzbar, and ZBar, are available to facilitate the efficient scanning of QR codes from RGB images. In theory, QR codes could serve as a secondary input channel, cross-validating the resultsof the object detection algorithm and thereby enhancing the overall system's reliability and precision. However, one limitation is that QR codes are susceptible to damage from physical wear, oils, dirt, or frequent handling, which could hinder their functionality in industrial environments. This makes it crucial to balance the benefits of QR code integration with the practical challenges of ensuring their durability in demanding conditions.
## 5 Key Enabling Technologies of Healthcare 4.0
### Device-to-Device (D2D) Communications  
D2D communication allows devices within range of each other to make direct contact. D2D communication can increase network spectrum efficiency, energy economy, transmission latency, traffic offloading, and core network congestion relief [99]. It can assist Healthcare 4.0 by enabling direct communication between nearby devices without the need for central network infrastructure.