esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Database Content and Analysis Examples
### Data Analysis Example Using Standard Samples
#### 5.1.1 Particle and Crystallite Size Evolution  
The particle and crystallite size evolutions were evaluated using SAXS and XRD, as shown in Figure 20. These methods provide complementary insights into the structural changes in Pt- and Pt-Co-based catalysts under various treatment conditions.  
**Pt-Based Catalysts:** For Pt-based samples (e.g., TEC10 EA50E, TEC10V30E), SAXS revealed relatively small initial particle sizes (~2.7 \(\pm\) 0.1 nm in AsMade samples). After hydrogen reduction (H) and EC cycling, particles grew significantly (reaching ~3.3 \(\pm\) 0.9 nm by SAXS), and corresponding XRD analyses confirmed crystallite size increases. Notably, a correlation emerged between particle size and lattice strain; smaller initial particles tended to experience a reduction in lattice strain during post-treatment, as illustrated in Figure 21. This figure highlights how the lattice strain decreases more prominently for samples with smaller initial particle sizes, suggesting that structural relaxation accompanies particle growth and improved ordering.  
**Pt-Co Alloy Catalysts:** Pt-Co alloy samples (e.g., TEC35V31E, TEC36E52) started with larger initial sizes (~6.8 \(\pm\) 0.7 nm) and displayed relatively stable dimensions after treatments. Compared to Pt-only catalysts, the alloyed systems showed less pronounced growth under H or EC conditions and maintained more consistent lattice strain values, indicating that Co alloying contributes to structural stability and resistance to treatment-induced changes.  
Together, these findings emphasize that both the initial particle size and composition dictate the evolution of the catalyst nanostructure. In tandem, SAXS and XRD paint a nuanced picture of the size, strain, and stability, setting the stage for an integrated interpretation of how structural modifications are linked to electronic and catalytic properties.  
Figure 20: Correlation between particle sizes determined by SAXS and Scherer sizes determined by XRD, illustrating size differences in diameter. Distinct colors and markers represent samples listed in Table 5, with Pt-based samples in warm colors and Pt-Co alloys in cool colors. For AsMade samples, SAXS particle size distribution widths are indicated. Solid green and dashed gray arrows mark transitions to H and EC treatments, respectively. The diagonal dashed line represents ideal 1:1 correlation. SAXS and XRD sizes show strong correlation, with Pt-Co alloys and post-treatment samples exhibiting larger particle sizes.
###### Abstract  
Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or acoustic wave suffers high transmission loss and limited bandwidth. In this work, we present and demonstrate a rolling shutter (RS)-based underwater optical camera communication (UWOCC) system utilizing a long short-term memory neural network (LSTM-NN) with side glow optical fiber (SGOF). SGOF is made of poly-methyl methacrylate (PMMA) SGOF. It is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in an underwater environment to provide 360\({}^{\circ}\) \"omni-directional\" uniform radial light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance UWOCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER \(\leq\) 3.8 \(\times\) 10\({}^{-3}\)). We also found that thin fiber also allows performing spatial multiplexing to enhance transmission capacity.  
visible light communication (VLC); optical wireless communication (OWC); side glow optical fiber (SGOF); machine learning; long short-term memory neural network (LSTM-NN) +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]-[PERSON], [PERSON], [PERSON]; [PERSON] [PERSON]-W. Rolling Shutter-Based Underwater Optical Camera Communication (UWOCC) with Side Glow Optical Fiber (SGOF). _Appl. Sci._**2024**, _14_, 7840. https:////doi.org/10.3390/app14177840
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 3 Preliminaries
### PSL Model
#### 3.2.2 PSL Model  
To converge, the SGD-based LFA model requires several iterations, and the overall cost of time can be considerable. As a result, the convergence process must be accelerated. It can be shown that at each update point of the LFA model based on SGD, the instantaneous error between the actual value \(r_{ui}\) and the predicted value \(\hat{\tau}_{ui}\) will be measured and transmitted to the algorithm. Therefore, this procedure can be considered as a generalized discrete PID controller, with \(K_{I}=K_{D}=0\) omitting the integral and derivative terms. From this  
Figure 1: Flowchart of PID controller.  
standpoint, applying these two terms to the model will help it preform better and accelerate the convergence process. Based on the above assumptions, the PSL model is introduced. The main idea of the PSL model is to reconstruct the instantaneous error according to the PID principle, and then bring the reconstructed error into the SGD algorithm to accelerate the convergence of the LFA model. In the PSL model, according to the concept of PID controller, the integral term and the derivative term are used to extend the calculation of error, as seen in the formulas (11) and (12).  
\[\tau_{u,i}=r_{u,i}-\sum_{k=1}^{K}p_{u,k}q_{i,k}. \tag{11}\]  
\[\tau_{ui}^{t}=K_{P}\tau_{u,i}^{t}+K_{I}*\sum_{n=0}^{t}\tau_{ui}^{n}+K_{D}(\tau_ {ui}^{t}-\tau_{ui}^{t-1}). \tag{12}\]  
* \(\sum_{t=0}^{t}\tau_{ui}^{n}\) shows the sum of historical \(\tau_{ui}\)
* \(\tau_{ui}^{t}-\tau_{ui}^{t-1}\) shows the discrepancy between this time's error and last time's error The objective function (13) of the PSL model is deduced according to the above formulas.  
\[\min_{Q^{*},P^{*}}\sum_{(u,i)\in R}\tau_{ui}^{2}+\lambda(\|Q_{i}\|^{2}+\|P_{u }\|^{2}). \tag{13}\]  
By taking the derivative of the objective function, the following iterative formula (14) for \(P\) and \(Q\) is obtained.  
\[\left\{\begin{array}{l}P_{uk}^{t+1}=P_{uk}^{t+1}+\alpha(\tau_{ui}Q_{ki}- \lambda P_{uk}^{t}),\\ \\ Q_{ki}^{t+1}=Q_{ki}^{t+1}+\alpha(\tau_{ui}P_{uk}-\lambda Q_{ki}^{t}).\end{array}\right. \tag{14}\]
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 3 Results and Discussion
### Structural Characterization of the Samples
#### 3.2.2 Effect of pH on the Adsorption of Pb\({}^{2+}\) Ions  
The pH value is an important factor in controlling the adsorption process performance of Pb\({}^{2+}\) ions from aqueous solutions on the adsorbent [34] because its value influence not only the functional groups from the surface of the adsorbent, but also the chemical behaviour of the Pb\({}^{2+}\) ions in aqueous solution. Indeed, hydrolysis, complexation of organic and/or inorganic ligands, redox reactions, precipitation of metal ions are pH-dependent and the pH strongly influence the speciation and adsorption availability [35; 36]. Concentration of H\({}^{+}\) ions decreases with the increase of positive attraction forces between Pb\({}^{2+}\) ions and free spaces from the surface of the negatively charged material, resulting in an increase of adsorption capacity. At low pH values, there is a strong competition between hydrogen ions and metal ions. A series of adsorption experiments were performed in aqueous medium at different pH values for the nanocomposite MNPs35@SiO\({}_{2}\) and the results obtained are shown in Figure 8.  
The increase of the pH up to 6 leads to the increase of the adsorption capacity until 2.25 mg\(\cdot\)g\({}^{-1}\). The optimum range of pH determined by pZc of adsorption of the nanocomposite MNPs35@SiO\({}_{2}\) is  
Figure 8: The effect of pH on the adsorption of Pb\({}^{2+}\) ions on the magnetic nanocomposite MNPs35@SiO\({}_{2}\).  
Figure 7: The pH\({}_{\text{PZC}}\) of the magnetic nanocomposite MNPs35@SiO\({}_{2}\).  
4-6; however, at a pH \(>\) 6 lead can precipitate as Pb(OH)\({}_{2}\). Taking into account all factors is indicated to work at pH 4-6 [17; 37]. As expected, the adsorption of Pb\({}^{2+}\) ions is completely dependent on the pH: in acidic medium, the H\({}^{+}\) ions compete with the metal ions to reach the functional groups present on the surface of the nanocomposite, resulting a low adsorption capacity of Pb\({}^{2+}\), while with the increase of the pH the positively charged metal ions may be adsorbed at the negatively charged functional groups from the adsorbent surface due to the decrease of the competition between the H\({}^{+}\) ions and Pb\({}^{2+}\) ions.
# Design of K-Band Power Amplifier with 180-Degree Phase-Shift Function Using Low-Power CMOS Process
###### Abstract  
In this study, a K-band complementary metal oxide semiconductor (CMOS) power amplifier was designed using a low-power (LP) process to improve the integration of the beamforming system. In order to reduce the overall system size, a 180\({}^{\circ}\) phase-shift function was mounted. It was designed in a four-stage structure to secure sufficient gain. In addition, we propose a way to secure wideband characteristics by utilizing the gains of each of the four stages. The power amplifier was designed with a 40-nm LP CMOS process to verify the feasibility of the proposed technique. The measured \(\mathrm{P_{1 dB}}\) for 0\({}^{\circ}\) and 180\({}^{\circ}\) phase-shift modes were 15.25 dBm and 14.30 dBm, respectively, at the operating frequency of 25.0 GHz. The measured phase difference between the two modes was 217\({}^{\circ}\) at the 25.0 GHz.  
CMOS; LP process; phase shift; power amplifier; wideband +
Footnote †: journal:  
In terms of high frequency circuits, the LP process has a number of disadvantage factors in terms of power gain, output power, efficiency, and noise figure. However, in order to reduce the weight of the entire transceiver, research on high-frequency circuits, such as CMOS power amplifiers, variable gain amplifiers, attenuators, phase shifters, switches, and low-noise amplifiers using a LP process is essential.  
In this study, a K-band power amplifier was designed with a multi-stage structure using the LP CMOS process. In order to overcome the low \(g_{m}\) of the LP CMOS process and secure sufficient gain, the CMOS power amplifier was designed as a four-stage structure. In addition, to suppress the increase in the area of the entire transceiver due to the increased area of the CMOS power amplifier with the four-stage structure, we propose a structure capable of mounting the 180\({}^{\circ}\) phase-shift function of the phase shifter, which occupies a large chip area in the beamforming system, to the CMOS power amplifier. Although the area occupied by the 180\({}^{\circ}\) bit in the phase shifter varies depending on the structure, according to previous study, the 180\({}^{\circ}\) bit accounts for about 40% of the core size, as inferred from a chip photograph [8; 9]. However, the proposed CMOS power amplifier does not require an additional area for mounting the 180\({}^{\circ}\) phase-shift function.
## 4 Simulation and Comparison
### Simulation Results
Using the proposed NSPP, each robot reaches its target, and its trajectory is shown too. Figure 11: The path planning (green line) of robot-3 in the experiment. (**a**) Robot-1 has not yet become an obstacle to robot-3. (**b**) Robot-3 plans its path to avoid being blocked by robot-1 on its return trip. (**c**) Robot-1 continues to move back to its starting point, and then robot-3 changes its planned path to a shorter one that is behind robot-1. (**d**) Robot-1 gradually moves away from the area that affects robot-3. Robot-3’s path becomes similar to the planned path in (**a**). Figure 10: The path planning (green line) of robot-2 in the experiment. (**a**) Robot-1 has not yet become an obstacle to robot-2. (**b**) Robot-2 plans its path to avoid being blocked by robot-1. (**c**) Robot-1 continues to move toward the target point and then robot-2 changes its planned path to a shorter one, which passes behind robot-1. (**d**) Robot-1 gradually moves away from the area that affects robot-2.
# A VR Truck Docking Simulator Platform for Developing Personalized Driver Assistance
## 2 VISTA VR-Simulator Platform (VISTA-Sim)
### Path Planner and Path Tracking Controller
#### 2.2.1 Path Planner  
The path planner uses a lattice-based approach in combination with motion primitives, where the environment is divided into a set of discrete states which can be connected and which can create the complete solution for the path. In the first step, the operational environment is described in terms of free and restricted space using polygons which specify the location, shape and size of the obstacles in the sensed environment. The blue polygon in Figure 3a depicts the available space, whereas the red polygons represent the obstacles, whose positions are also consistent with the environmental models in the VR. The state of the vehicle combination is defined by the position of the centre of the semitrailer axle in the global coordinate system \(x_{2}\) and \(y_{2}\), the yaw angle of the semitrailer \(\theta_{2}\), and the articulation angle \(\gamma\). Thus, the path planning problem is reduced to finding the path between two discrete states in the path planning environment where the path segments, called motion primitives, are generated (see Figure 3b). This is achieved by solving an optimal control problem from one of the discrete states to the other, while using the kinematic equations of the articulated vehicle and differential constraints which limit the path curvature and steering angle rate and acceleration.  
This guarantees that the created path segments are kinematically viable for the vehicle and are still negotiable by the human driver given human physiological limitations in actuation of the steering wheel. The detailed description of the optimal control problem with a desired cost function to optimize some parameters, the kinematic constraints and its solutions, can be found in [27; 28].  
Given a library of motion primitives, an algorithm is required to find an optimal combination of motion primitives to traverse from one point to another, using the free space within the operational environment and avoiding the obstacles. For this purpose, the graph search algorithm A star algorithm is used with a collision detection module, which checks for collisions while planning. The customized A star algorithm is also extensively explained in [27].
## 4 Experimental Results
### Subrate  
This subsection presents the impact of different F\({}_{\text{K}}\) subrate used in the proposed JD-TV on the F\({}_{\text{NK}}\) reconstruction. A GoP size of 8, as indicated from the former experimentation (balance point) instead of 16/32 is opted. The evaluation is carried on two arrangements. Figure 5: Average SSIM evaluation at GoP sizes of 3, 5, and 8 for different video sequences at different subrates for independent BCS-TV-AL3 and proposed JD. * First setup, encoding of both \(\rm F_{K}\) and \(\rm F_{NK}\) at the similar subrate i.e., \(\rm M_{K}=\rm M_{NK}\) (0.05, 0.1, 0.15, 0.2, 0.25, 0.3). * Second setup, the encoding of \(\rm F_{K}\) at a static subrate of MR = 0.5 and \(\rm F_{NK}\) at various subrates i.e., \(\rm M_{NK}\) (0.05, 0.1, 0.15, 0.2, 0.25, 0.3) with 0.05 interval among the subrate. The evaluation result shown in Table 4 indicates the importance of subrate on the reconstruction quality of the frames. In other words, \(\rm F_{K}\) subrate has a substantial effect on the \(\rm F_{NK}\) reconstruction quality. In the first setup, where subrate \(\rm M_{K}=\rm M_{NK}\) the reconstruction quality, when compared with traditional scheme, improves on average from 2 dB-3.5 dB. However, when the subrate is \(\rm M_{K}>\rm M_{NK}\), it is noted that the gain increases significantly (3.5 dB-5 dB) when compared with JD-TV (\(\rm M_{K}=\rm M_{NK}\)) and BCS-TV. The reason that the reconstruction quality at subrate \(\rm M_{K}>\rm M_{NK}\) is better than at subrate \(\rm M_{K}=\rm M_{NK}\) is because, when encoding of both \(\rm F_{K}\) and \(\rm F_{NK}\) is performed at the same subrate, the generated \(\rm F_{K}\) does not comprehend enough material that could considerably assist the reconstruction of \(\rm F_{NK}\).
## 3 Results and Discussion  
### Modulus of Elasticity  
The graph below, Figure 1, presents the MOE ofiferous and deciduous plywood with alternative fillers. The highest MOE values were recorded for hardwood plywood with a coconut flour filler, where the filler constituted 20 pbw--17,228 N mm\({}^{-2}\), and for plywood with lupine flour, where the filler constituted 15 pbw--16,387 N mm\({}^{-2}\). In contrast, the lowest values in coniferous plywood were recorded for plywood with spirulina--8440 N mm\({}^{-2}\) and bamboo flour--8167 N mm\({}^{-2}\), with a filler content of 15 pbw. Analyzing the lowest MOEs for deciduous plywood, the worst results were obtained for spirulina--14,569 N mm\({}^{-2}\), where the filler mass proportion was 10 pbw, for bamboo flour--14,709 N mm\({}^{-2}\), with 15 pbw of filler, and for coconut flour--13,568 N mm\({}^{-2}\), also with 15 pbw of filler. The only statistically significant differences for birch plywood were found between 15 pbw spirulina and 15 pbw coconut plywood. For pine plywood, such differences have been found for 10 and 20 pbw coconut plywood.  
Similar studies used chestnut flour as a filler in three-layer plywood technology [47]. The filler content tested in these studies was 1, 5, 10, and 20 pbw. It was observed that the MOE increased with the increasing filler content, with the highest value recorded for the sample where the filler constituted 20 pbw, and the MOE value was 17,753 N mm\({}^{-2}\).  
Another example of the application of alternative food materials is ground banana peel, which has also been utilized as a filler in three-layer plywood technology [48]. The studies employed inner, outer, and mixed banana peels. The results indicated that the highest MOE value was achieved with the filler derived from the inner banana peel, where the MOE measured 9288 N mm\({}^{-2}\) with 5 pbw of the filler. Conversely, the lowest MOE value (7187 N mm\({}^{-2}\)) was observed in plywood containing 20 pbw of filler from the whole  
Figure 1: Modulus of elasticity values for different filler types.  
banana peel. Similarly, low values were obtained in the studies conducted on spirulina with a filler content of 15 pbw in the plywood.  
Scientists have attempted to use coconut flour as a filler in plywood, where the flour content constituted 15 pbw in the adhesive mixture [1]. Its inclusion lowered the strength parameters, including MOE, yet it still met the standard requirements. Similar results were obtained in our studies, but we also demonstrated that changing the proportion of coconut flour in the adhesive composition can benefit parameters such as MOE.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 3 The Proposed VSS-QR
### VSS-QR Decoder  
After receiving all the three shares, the decoder stacks them to get the reconstructed color QR code **T** (Figure 3). Fortunately, this color QR code is directly recognizable by a standard QR code decoder. A QR code decoder first pre-process **T** to generate a binary QR code \(\textbf{T}^{\prime}\), which includes color to grayscale conversion and binarization. Even though these pre-processing steps are assembled into a QR code decoder, we describe them here since they are essential for understanding the perfect reconstruction feature of VSS-QR.
## 2 RNAi in the Western Corn Rootworm
### Systemic Spread  
In organisms highly susceptible to RNAi, systemic spread of the silencing signal is an essential step for sufficient silencing. During a viral infection, cells initially exposed to dsRNA reverberate the signal to distal tissues, priming uninfected cells against the pathogen [106; 107]. In _C. elegans_, RNA-directed RNA polymerases (RdRp) use primary siRNA and mRNA targets to form secondary siRNAs, amplifying the signal [108; 109; 110], while SID transmembrane proteins allow dsRNA to move from cell to cell [57; 111]. However, putative homologs of SID proteins (SILs) in insects are not required for a systemic RNAi response and most insects lack RdRps entirely [65; 112]. As previously stated, at least two homologs of _sid_ genes can be found in the WCR genome; however, involvement in the RNAi mechanism is unclear [63; 64]. In addition, no RdRp homolog is found in the WCR genome and small RNA sequencing after exposure to dsRNA found no evidence of secondary siRNA production in WCR, which suggests all siRNAs derive from the original dsRNA molecules [113]. However, this sequencing method would not have identified putative 5\({}^{\prime}\) triphosphate modified siRNAs, small ncRNAs shown to be amplified in an AGO2-dependent manner from viral DNA in _D. melanogaster_ hemocytes [106]. Whether this RdRp-independent amplification step is present in WCR or if it can be triggered through non-viral DNA is unknown and requires further examination.  
Despite a lack of demonstrated causative elements, spread of the RNAi response in WCR has been documented, evidenced by transcript silencing in tissues distal to the exposure site [113; 114; 56; 115]. Recent work in _T. castaneum_ and _L. decmelineata_ suggests that after the initial dsRNA uptake, siRNAs or dsRNAs travel cell to cell via extracellular vesicles [116; 117]. Further work is required to illuminate the pathway of systemic spread in WCR.
## 4 PSO Based on the PSO-BP Model
### Results and Analysis
#### 4.5.2 PSO-BP Model Correlation Coefficient Evaluation  
As shown in Figure 14, the correlation coefficients between predicted outputs and target values were calculated for the training set, validation set, test set, and full dataset: coefficient of determination (R) for training set = 0.97/82, validation set = 0.98134, test set = 0.99447, full dataset = 0.96626. Results indicate all R-values approach 1, demonstrating a highly linear relationship between neural network outputs and expected values. This statistical feature confirms the effective construction of the PSO-BP model and its robust predictive performance, proving its suitability for subsequent experimental analysis.