esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Incident Angle  
The incident angle of copper atoms determines the arrival angle and the possible area of the trench on which these atoms deposit. By increasing the incident angle, it is more likely that the copper atoms can reach the sidewalls. Incident angle of the copper atoms can be controlled by bias during the deposition process. Incident angles of \(5^{\circ}\), \(10^{\circ}\), \(30^{\circ}\), \(50^{\circ}\), \(70^{\circ}\), and \(80^{\circ}\) were discussed in this study. To study the effect of incident energy, tantalum was selected as the substrate material, while the other parameters were set as follows: incident energy = 5 ev, deposition height = 5000 A, substrate temperature = 600 K, and deposition rate = 10 atoms/ps. The resulting copper seed layers under different incident angles are shown in Figure 17, and the overall distributions of the deposited copper atoms with respect to incident angles are illustrated in Figure 18. With more and more atoms arriving at the sidewall, the number of atoms deposited at the top sidewall increased significantly. This decreased the necking of the copper seed layer.  
At the mid and lower sidewalls, more and more atoms reached the sidewall by increasing the incident angle, However, this was not always the case for the mid sidewall and lower sidewall. As the incident angle exceeded \(30^{\circ}\), atoms were more likely to deposit at the top sidewall and mid sidewall rather than the lower sidewall, while as incident angle exceeded \(50^{\circ}\), atoms were more likely to deposit at the top sidewall rather than the mid sidewall. Thus, the deposited atoms at lower sidewall started to decrease after the incident angle of \(30^{\circ}\), while the deposited atoms at the mid sidewall started to decrease after the incident angle of \(50^{\circ}\). As for the bottom center and bottom side, atoms were less likely to reach the bottom of the trench with increases of the incident angle.  
Figure 17: Copper seed layer with different incident angles.  
Figure 18: Overall distribution of the deposited copper atoms with respect to incident angles.
## 2 Neumann Green's Function and Dipole Kernel for EM Flowmeters
We refer to Equation (12) as the _dipole form_ of the forward map, in contrast to Equation (9) where the flowmeter source term \(\
abla\cdot\big{(}\mathbf{v}\times\mathbf{B}\big{)}\) appears at a distribution of monopole sources in the domain \(D\). The dipole form allows us to gain an intuitive understanding of the spatial sensitivity of flowmeters, and the extreme, infinite sensitivity to flow near electrodes. As dipolar sources have magnitude proportional to \(1/r^{2}\), all of the \((\mathbf{v}\times\mathbf{B})\) near a measurement will contribute significantly more than the sources distant. Thus, any source within one eighth of the radius from an electrode will contribute four times more than a source half a radius away, and 16 times more than a source in the center of the domain, and so on. Whenis perpendicular to the direction to an electrode, that component of the flow contributes zero to the potential at the electrode. An application of separation of variables for harmonic functions in the domain \(D\) allows an easy route to determining the length scale in the axial direction required for the 2-dimensional approximation to be accurate [24]. For separated functions, the \(z\) solution is a decaying exponential  
\[\iint_{D}f(\mathbf{x})\ \text{d}\ V=\int_{\partial D}\psi(\mathbf{x})\ \text{d}S,\]0  
where \(\mu\) is set by the smallest positive zero of \(J_{0}^{{}^{\prime}}\). Thus, the slowest decaying exponential reaches the 5% mark at \(z\approx 0.79\). Therefore, the 2-dimensional flowmeter approximation is appropriate if the fluid velocity and magnetic field are approximately constant in \(z\) up to a distance equal to the diameter of the pipe away from the 2-dimensional plane being considered. For computational purposes, it is convenient to use the form of the forward map that is equivalent to Equation (11). Using vector identities again to shift the divergence off the term \(\mathbf{v}\times\mathbf{B}\), (2) becomes  
\[\iint_{D}f(\mathbf{x})\ \text{d}\ V=\int_{\partial D}\psi(\mathbf{x})\ \text{d}S,\]1  
For realistic fluids in a pipe we can also make the same assumption as earlier work of no shear at boundaries so that \(\mathbf{v}|_{\partial D}=0\), a feature of the boundary layer effect. This eliminates the second term in (14). When \(\mathbf{v}=(0,0,v)\), and \(\mathbf{B}=(B_{x},B_{y},0)\), this becomes:  
\[\iint_{D}f(\mathbf{x})\ \text{d}\ V=\int_{\partial D}\psi(\mathbf{x})\ \text{d}S,\]2  
For simplicity of examples we assume that the magnetic field is only in the \(y\)-direction, though the theory in this paper is easily extendable to include the \(x\)-component. We then define the function \(f=vB_{y}\) as a generalized \"flow\", that we use in the following section.
## 6 Simulation Results and Discussion
### Mean Acquisition Time Using Spiral Scan  
Figure 9a,b illustrate the mean acquisition time \(E[T_{\mathrm{s}}]\) and its relationship with the field of uncertainty (FOU) area and the satellite position's standard deviation, respectively, when the spiral scan method is employed. The acquisition time \(E[T_{\mathrm{s}}]\) increases with the size of the scan area and the error in the satellite's position. Specifically, \(E[T_{\mathrm{s}}]\) is approximately [0.09, 0.36, 1.45] seconds for \(\sigma=[10,20,40]\) \(\upmu\)rad, corresponding to 98% coverage of the uncertainty area, respectively. As expected, the time required to scan the uncertainty area increased as the receiver satellite moved farther from the center. This is because the spiral scan begins from the center, where the highest probability of locating the target satellite exists, and progressively moves toward the lower-probability regions near the edges. The relationship between the acquisition probability and the uncertainty area is described by the following equation:  
\[\theta_{\mathrm{area}}=2u=\sigma\sqrt{-2\ln(1-P_{c})}, \tag{81}\]  
where \(\sigma\) is the satellite position's standard deviation, and \(P_{c}\) is the desired acquisition probability, set to 0.98. It is evident that reducing the scan area size will decrease the acquisition time; however, this comes at the cost of lowering the acquisition probability, which could result in multiple scans of the uncertainty area.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Reaching 1 Gbps FSO Link from a Pico-Satellite  
In this subsection, we present the needed improvements to the FSO link in order to reach the 1 Gbps data-rate goal (from a pico-satellite to an FSO ground station). Reaching a 1 Mbps data rate from a nano-satellite using existing RF technology is very demanding and may require dedicated RF licensed frequencies. To further improve the FSO link, and in particular to allow a higher bit rate, the link budget should be improved. This can be achieved using the following improvements:  
1. Using high-efficiency InGaAs-biased detectors with a noise-equivalent power (NEP) as low as \(2\times 10^{-15}\) (\(Watt\) per \(Hz^{0.5}\)) at 1550 nm. Such detectors have a typical rise time of \(\sim\)100 picoseconds allowing up to 5 GHz detection (suitable for 1 Gbps data  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Units** & **Value** & **Remarks** \\ \hline Wavelength & nm & 850 & can be 532, 1550 \\ \hline Link Distance & km & 1000 & \\ \hline Tx Divergence & mrad & 1 & \\ \hline Elevation Angle & degree & 45 & \\ \hline Transmit Power (\(Tx_{power}\)) & dBm & 20 & \\ \hline Receiver Sensitivity (\(R_{s}\)) & dBm & \(-65\) & APD \\ \hline Receiver Area (\(R_{a}\)) & m\({}^{2}\) & 0.1 & 14′′ telescope \\ \hline System Loss (\(Rx_{loss}\)) & dB & 3 & \\ \hline Geometrical Loss (\(G_{loss}\)) & dB & 70 & 10\({}^{-7}\) \\ \hline Atmospheric Loss (\(Att_{loss}\)) & dB & \(<\)3 & clear night sky \\ \hline Turbulence Loss (\(Tur_{loss}\)) & dB & \(<\)1 & clear night sky \\ \hline Total Budget & dB & 85 & \\ \hline Total Loss & dB & \(<\)77 & \\ \hline
**Link Margin** & **dB** & \(>\)**8** & \\ \hline \end{tabular}
\end{table}
Table 2: Link-budget parameters.  
rate). Additional 5\(\times\) speedup bit rate may be obtained through improved quantum efficiency (i.e., less photons are required per bit).
2. A larger 30\({}^{\prime\prime}\)-lens telescope with a high-end narrow band-pass filter to minimize the noise. Using such a telescope may increase the SNR significantly, allowing an additional speedup of 4\(\times\)-5\(\times\) times.
3. Finally, using a narrower (0.5 milliradian) and stronger (say, 1 Watt) laser beam, an additional 5\(\times\) speedup may be achieved.  
Combined, an approximate \(\sim\)100\(\times\) time speedup can be achieved. Naturally, this goal requires a massive engineering effort, which is not expected to be completed anytime soon. Yet, all the improvements mentioned above are based on existing components. Moreover, NASA was able to demonstrate a \(\sim\)5 Gbps laser link [42]. Finally, the improved link budget can also be used to allow low-speed FSO communication in non-perfect conditions, such as light pollution and long-range links.
## 1 Introduction
The germane findings can be summarised as follows: For P\({}_{\text{i}}\) adsorption,it was found that the HSAB principle governs the adsorption mechanism, in contrast to As\({}_{\text{i}}\) which chemisorbs via ligand exchange (As-O-Al bridging) [18]. The identified knowledge gap in this study was focused on the adsorption properties of the TMCs with arsenate and orthophosphate in binary aqueous solutions in an effort to gain insight into the binding mechanism onto the TMC adsorbents with variable metal centers. We posit that TMCs with variable types of metal ions are anticipated to yield variable arsenate selectivity. To this end, three types of TMC materials were prepared and characterized based on three metal systems (Al-TMC, Fe-TMC, and Cu-TMC). In particular, the IR spectra, PZC measurements, and adsorption equilibrium profiles of these materials with phosphate and arsenate under controlled pH and conditions were investigated to reveal the different metal coordination onto the biopolymer framework. Ion chromatography (IC) was performed to investigate the role of different metal centers on P\({}_{\text{i}}\) and As\({}_{\text{i}}\) selectivity and to provide further insight into the overall adsorption mechanisms of the various TMCs. This work contributes to the field of adsorption science and technology since the biocomposites reported herein with variable metal centers lend to the development of adsorbent technology with improved arsenate selectivity over orthophosphate in aqueous media, which is highly relevant to aquatic environmental samples.
###### Abstract  
This article investigates vibration rejection for a continuous smart structure using piezoelectric bimorph patches. Such a structure has inherent nonlinearities, such as hysteresis and creep, and the whole system may experience unexpected disturbances, uncertainties, and noise from external sources. Thus, it is very important to design the active control scheme carefully with adaptive filtering systems to deal with these conditions. An advanced adaptive filtering algorithm was developed based on the conventional least mean squares (LMS) method and sliding mode control for the active vibration rejection system. The sliding mode controller is applied to the standard LMS algorithm to overcome problems with misadjustment and excess error in an optimal manner. A numerical analysis and laboratory experiment show that the technique can significantly attenuate the vibration of the smart structure at different levels and broadband frequency spectra. In addition, unidentified impedance is chosen to change the distribution of the mass, and the robustness and the adaptivity of the proposed approach are verified. The experimental results show that the method can isolate impulse-type vibrations of at least 2.8 dB, even with the adjusted mass arrangement.  
active vibration rejection; adaptive filtering system; filtered-X LMS; sliding mode control; least mean squares algorithm; smart structures +
Footnote †: journal: Applied Sciences
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
## 4 Application Examples
### Characteristics of Bridge Dynamics Under Different Traffic Densities  
The probability distribution functions for vehicle type, weight, speed, and distance, which are derived from the statistical analysis of traffic flow data, are utilized to develop a Monte Carlo random sampling program using the MATLAB platform. The specific procedures for generating stochastic traffic flow are illustrated in Figure 8. The implementation steps of this method for generating stochastic traffic flow are as follows: (1) Construct probability distribution models for vehicle type, lane, weight, speed, and distance based on empirical data. (2) Use MATLAB's random number generation functions to perform independent sampling from each probability distribution, ensuring that the parameters of the generated stochastic traffic flow conform to statistical characteristics. (3) Generate a sequence of stochastic traffic flow samples through continuous sampling, thus creating a stochastic traffic flow that is consistent with actual traffic patterns.  
All results presented in Figures 9-13 are derived from numerical analyses conducted by the authors using the proposed method. These analyses comprehensively evaluate the dynamic response of the bridge under various traffic densities and wind load conditions, thereby providing a detailed understanding of the structural behavior under complex loading scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** &
\begin{tabular}{c} **Elastic Modulus** \\ **(GPA)** \\ \end{tabular} & **Poisson Ratio** & **Density (kg\(\cdot\) m\({}^{-3}\))** \\ \hline Main girder & 206 & 0.30 & 7850 \\ Cables & 201 & 0.30 & 7800 \\ Pier & 31.0 & 0.20 & 2400 \\ Pylon & 34.5 & 0.23 & 2500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material parameters of the bridge components.  
Figure 8: Stochastic traffic flow simulation flowchart.  
Figure 10: The short-term test result of the vertical dynamic response of sparse traffic.  
Figure 9: The long-term test result of the vertical dynamic response of sparse traffic.  
Figure 11: The long-term test result of the vertical dynamic response of dense traffic.  
Figure 12: The short-term test result of the vertical dynamic response of dense traffic.  
Traffic density influences bridge vibration through both the vehicle load effect and inter-vehicle coupling vibration effect. In sparse traffic conditions, vehicle spacing is larger, leading to load dispersion and a smaller vibration response. Conversely, in dense traffic, vehicle interactions are enhanced, resulting in a significant increase in both vibration amplitude and frequency. The Monte Carlo method is employed for stochastic traffic flow simulation, with the annual average daily traffic volume serving as the simulation sample capacity based on the vehicle movement parameters [33]. As shown in Table 2, the number of vehicles in the eight two-way lanes is derived from the 24 h traffic load survey data of the bridge.
## 7 Experimental Results
### Bi-Objective vs. Three-Objective Approaches  
Comparing the results obtained for the bi-objective and three-objective approaches in the two previous subsections, we verify that MO-FA could be recommended as a general solving method for the two approaches studied. Focusing on instance size, MO-FA provides robust performance in small and large instances, while MO-VNS provides robust performance in medium ones. This conclusion is supported by Figure 1, which shows the average performance for each metaheuristic according to the set of instances addressed. This figure was generated by combining hypervolume and set coverage values through a 1-2 standardization for the case studied. Thus, Figure 1a-d show the average performance of the metaheuristics for small, medium, large, and all instances.  
The difference in performance between these two metaheuristics, as well as with the others, could be due to the movement operator used in MO-FA. This operator successfully fits the RNPP, defining a way of producing new solutions by moving the RNs in an individual according to promising solutions, resulting in that the population evolves towards better solutions. This is the reason why MO-FA provides a robust performance independently of the instance size.  
For the case of MO-VNS, the algorithm defines a successfully way of generating new solutions based on an incremental local search, which is a search in the vicinity. This local search fits the RNPP, defining a way of improving a previous solution by boundedly moving the RNs. However, the performance of MO-VNS is mainly due to the application of this local search. Thus, MO-VNS is less competitive to other algorithms in large instances because the search space is large and the algorithm fails in exploration. For the case of small instances, MO-VNS is also less competitive because the search space is reduced and then, it is relatively simple for a metaheuristic to find good solutions to the problem. As a result, MO-VNS provides good performance in medium instances.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Instance size**} \\ \cline{2-6}  & **50\(\times\)50** & **100\(\times\)100** & **200\(\times\)200** & **300\(\times\)300** & **All** \\ \hline
**NSGA-II** & 21.00\% & 23.95\% & 39.52\% & 22.80\% & 28.92\% \\
**SPEA2** & 21.24\% & 28.12\% & 40.54\% & 37.38\% & 35.38\% \\
**MO-VNS** & 55.83\% & 72.36\% & 48.77\% & 40.80\% & 50.80\% \\
**MO-VNS*** & 67.86\% & **73.19\%** & **49.37\%** & 33.51\% & 49.61\% \\
**MO-ABC** & 40.48\% & 43.20\% & 28.75\% & 63.12\% & 44.94\% \\
**MO-FA** & 72.00\% & 65.68\% & 88.71\% & 51.12\% & 67.52\% \\
**MO-GSA** & 81.78\% & 12.74\% & 10.68\% & 30.43\% & 24.70\% \\
**MOEA/D** & 38.69\% & 12.15\% & 20.43\% & 18.66\% & 19.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average set coverage for a metaheuristic compared to any other solving the three-objective RNPP.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
###### Abstract  
Aiming at the problem that the surface defects of bIAister tablets are difficult to detect correctly, this paper proposes a detection method based on the improved U2 Net. First, the features extracted from the RSU module of U2 Net are enhanced and adjusted using the large kernel attention mechanism, so that the U2 Net model strengthens its ability to extract defective features. Second, a loss function combining the Gaussian Laplace operator and the cross-entropy function is designed to make the model strengthen its ability to detect edge defects on the surface of blister tablets. Finally, thresholds are adaptively determined using the local mean and OTSU(an adaptive threshold segmentation method) method to improve accuracy. The experimental results show that the method proposed in this paper can reach an average accuracy of 99% and an average precision rate of 96.3%; the model test only takes 50 ms per image, which can meet the rapid detection requirements. Minor surface defects can also be accurately detected, which is better than other algorithmic models of the same type, proving the effectiveness of this method.  
defect detection; U2 Net; large kernel attention mechanism; Gaussian Laplace operator; blister tablets +
Footnote †: journal: 2024, 17, 429. [[https://doi.org/10.3390/a17100429](https://doi.org/10.3390/a17100429)]([https://doi.org/10.3390/a17100429](https://doi.org/10.3390/a17100429))
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 2 Hardware Description
### Power Supply  
The use of two bipolar power supplies to separately power the input and output stages of the IA allows its operation within its specifications, reaching the maximum input range. A block diagram of the power supply module is shown in Figure 3.  
Figure 2: Simplified block diagram of ISO122P requirements.  
As Figures 1 and 3 show, the isolation amplifier has two selectable primary energy sources: the mains and a lithium polymer battery. In the first case, the direct connection to the mains requires a single 230 V/15 V transformer and rectification and filtering modules for stabilization. This is achieved with a 1N4007 diode bridge that allows bipolar rectification and filtering with a 3000 \(\upmu\)F capacitor, that provides a low-ripple 20 V voltage.  
The power supply in the high voltage input of the ISO122 has been implemented with an isolated Traco Power TDN 1-2423 WI DC/DC converter. The input of this converter can be connected to both primary energy sources: that provided by the mains, and the batteries when no electric network is available. Therefore, the required \(\pm\)15 V for the input IA stage are isolated from its output stage.  
To obtain stable biases for the output stage of the isolation amplifier, both the transformed and filtered mains voltage and the battery voltage are stabilized using two low-dropout regulators (LDO regulators). In this work, a 7815 and a 7915 LDO have been selected, providing stable output voltages of +15 V and -15 V, respectively.  
The conditioning and filtering electronics of the proposed probe are managed by a microcontroller (Figure 1), that adjusts the circuit behaviour according to the input requirements. Both the electronics and microcontroller are powered at 3.3 V provided by an additional TPS7B8833Q linear voltage regulator whose input voltage is provided by the 7815 LDO (Figure 3).
## 3 Results
### Temperature Variations and Karsi Groundwater Quality Indicator Responses
#### 3.2.1 Changes in pH of Groundwater at Various Depths under Different Temperature Conditions
This substantial fluctuation indicates that the groundwater in this karst surface zone experiences severe TEC exceedance, signifying serious microbial pollution. (ii) As demonstrated in Figure 9, throughout the entire soil surface karst zone soil layer, at lower temperatures, the general trend shows a decrease in groundwater TEC values with increasing soil depth, although the range of this change is relatively minor. At higher \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Depth/(m)**} & \multicolumn{3}{c}{**Temperature**} \\ \cline{2-4}  & **10 \({}^{\circ}\)C** & **20 \({}^{\circ}\)C** & **30 \({}^{\circ}\)C** \\ \hline
0.1 & 38,102 & 41,825 & 290,667 \\
0.2 & 46,483 & 51,089 & 293,333 \\
0.3 & 38,900 & 47,019 & 340,000 \\
0.4 & 42,792 & 45,521 & 278,667 \\
0.5 & 26,900 (_min_) & 28,900 & 386,667 (_max_) \\ Average value E & 38,635 & 42,871 & 317,867 \\ Range R & 19,583 & 22,189 & 108,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The TBC of different depths of groundwater under different temperature conditions. Figure 8: The TBC curve for different depths of groundwater under different temperature conditions. temperatures, groundwater TEC values initially decrease and then tend to stabilize as soil depth increases. (iii) Under different temperature scenarios, with the summer temperature rise, karst groundwater TEC values increase, demonstrating larger fluctuations and more instability. When temperatures are at their lowest, groundwater TEC values reach their minimum, with the least fluctuation, indicating greater stability.