esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus
### Subsystems Design
#### 5.2.5 Structure and Mechanism Subsystem (SMS)  
The structural parts, including the frame and hinge mechanisms, are made of aluminum 6061 alloy. The surfaces of these parts are anodized to prevent cold welding between the CubeSat deployers. Considering the payload integration, the frame design has a skeleton configuration with a high degree of freedom during the assembly process. Given the internal space, as depicted in Figure 14b, the avionics are assembled by functions; for instance, the stacked boards for CDHS and COMS are located on the +Y-axis, and the integrated ADCS module was adopted. By conducting a launch environment simulation with NX10.0 NASTRAN, the first mode frequency (f\({}_{0}\)) with the stowed configuration during the launch phase was analyzed above 80 Hz, above the recommended value to avoid resonance with a launch vehicle.  
Figure 19: Diagrams and configurations of the flight software architecture and on-board computer: (**a**) the primary on-board computer and the cFS-based flight software architecture. Each application is managed on the software message bus; (**b**) the back-up on-board computer and the flight software handover procedure. The primary OBC sends a wakeup to the EPS watchdog timer every 1 sec, and then, the response interrupts the backup OBC. Through CAN and I2C communications, two OBCs check status each other, and the recovery and isolation are conducted by handling power switches.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
###### Abstract  
In this paper, we propose a receiver designing principle for the multiple-input multiple-output (MIMO) visible light communication (VLC) systems with quadrichromatic light emitting diodes (QLEDs). To simultaneously transmit multiple data streams, the system consists of multiple QLEDs and multiple receivers; each includes four photodiodes (PDs). We optimize the Euclidean distance of the received signal over some constraints by considering the normal vectors of the receivers. The numerical results show that our proposed receiver design has a better bit error rate (BER) performance in comparison with the conventional receiver.  
Keywords:visible light communications; quadrichromatic light emitting diodes; multi-color crosstalk; receiver design +
Footnote †: journal: Electronics Electronics
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.2192\times 10^{-7}\) & 61.2836 \\ FOLD 2 & \(1.1617\times 10^{-7}\) & 62.7660 \\ FOLD 3 & \(1.2797\times 10^{-7}\) & 60.3519 \\ FOLD 4 & \(1.0192\times 10^{-7}\) & 63.1297 \\ FOLD 5 & \(1.2266\times 10^{-7}\) & 60.6625 \\ FOLD 6 & \(1.1618\times 10^{-7}\) & 62.2234 \\ FOLD 7 & \(1.2612\times 10^{-7}\) & 60.3512 \\ FOLD 8 & \(1.2467\times 10^{-7}\) & 60.4678 \\ FOLD 9 & \(1.160\times 10^{-7}\) & 62.3845 \\ FOLD 10 & \(1.1532\times 10^{-7}\) & 63.3267 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Loss and Accuracy of 1D-CNN+AllKNN+BO. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.2932\times 10^{-7}\) & 60.4554 \\ FOLD 2 & \(1.1550\times 10^{-7}\) & 63.9751 \\ FOLD 3 & \(1.2661\times 10^{-7}\) & 60.2484 \\ FOLD 4 & \(1.2093\times 10^{-7}\) & 61.9047 \\ FOLD 5 & \(1.2143\times 10^{-7}\) & 61.9047 \\ FOLD 6 & \(1.2710\times 10^{-7}\) & 60.0414 \\ FOLD 7 & \(1.2365\times 10^{-7}\) & 60.0414 \\ FOLD 8 & \(1.3500\times 10^{-7}\) & 56.9358 \\ FOLD 9 & \(1.3204\times 10^{-7}\) & 58.5921 \\ FOLD 10 & \(1.2167\times 10^{-7}\) & 60.2484 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Loss and Accuracy of 1D-CNN+AllKNN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.2426\times 10^{-7}\) & 60.4554 \\ FOLD 2 & \(1.2291\times 10^{-7}\) & 61.3871 \\ FOLD 3 & \(1.2550\times 10^{-7}\) & 60.4554 \\ FOLD 4 & \(1.2229\times 10^{-7}\) & 61.2836 \\ FOLD 5 & \(1.3167\times 10^{-7}\) & 58.5921 \\ FOLD 6 & \(1.2574\times 10^{-7}\) & 59.6273 \\ FOLD 7 & \(1.2315\times 10^{-7}\) & 61.3871 \\ FOLD 8 & \(1.2108\times 10^{-7}\) & 63.9026 \\ FOLD 9 & \(1.2678\times 10^{-7}\) & 62.2432 \\ FOLD 10 & \(1.2439\times 10^{-7}\) & 61.3943 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Loss and Accuracy of LSTM+AllKNN+BO. [PERSON] estimator in LSTM+AllkNN and 1D-CNN+AllkNN, respectively. After using Bayesian Optimization, there is a modest rise in the parameter. The pie chart of the overall Loss of LSTM, 1D-CNN, LSTM+AllkNN, 1D-CNN+AllkNN, LSTM+AllkNN+BO, 1D-CNN+AllkNN+BO is shown in Figure 5. 1D-CNN+AllkNN has a relatively low percentage loss compared to other models, indicating that the model outperforms the others. Furthermore, Figure 6 depicts the pie chart of the overall loss of LSTM, 1D-CNN, LSTM+AllkNN, 1D-CNN+AllkNN, LSTM+AllkNN+BO, and 1D-CNN+AllkNN+BO. The results show that 1D-CNN+AllkNN+BO has the lowest loss with 15.3% while LSTM and 1D-CNN have the highest loss with 18.3%. Figure 7 depicts the overall accuracy of LSTM, 1D-CNN, LSTM+AllkNN, 1D-CNN+AllkNN, LSTM+AllkNN+BO, and 1D-CNN+AllkNN+BO. The ranking is topped by 1D-CNN+AllkNN, which has an extremely high accuracy of 17.6 percent. This means that the model performs admirably in supply chain management.
## 2 Materials and Methods
### Activated Sludge (MLSS) System Design  
The AS treatment system used in this study comprised a feed tank, an aerobic tank, and a secondary clarifier constructed using a transparent polyvinyl chloride sheet. The feed tank height was 28.3 cm with a diameter of 18 cm. The aerobic tank and secondary clarifier systems had the same height and diameter of 28.5 and 15 cm, respectively; however, the clarification tank had a cone with a height of 11 cm. The feed tank had a carrying capacity of 6 L, the aerobic tank had a carrying capacity of 4.2 L, and the secondary clarifier had a 5 L carrying capacity. The air was sparged using a Resun air pump (Resun Ac 9906, Shenzhen Xing Risheng Industrial Co. Ltd., Baolong, China) through silicon tubing connected to 3 air diffusers into the aerobic tank for efficient air supply. The air was pumped at a flow rate of 7 L/min. A 624 h old AS, which was used, was collected from a municipal wastewater treatment plant (WWTP) in Scottsdene near Cape Town, South Africa. The PSW was pumped into the system using a Gilson(r) Minipuls Evolution peristaltic pump (Gilson Inc., Middleton, WI, USA) at a flow rate of 3.4 mL/min at a hydraulic retention time (HRT) of 24 h. The AS overflow was recycled from the secondary clarifier back into the AS tank using the Gilson(r) Minipuls Evolution peristaltic pump at a 3.2 mL/min flow rate, and an AS retention time (SRT) of 10 days was maintained. This experiment was carried out for 240 h. The effluent from the aeration tank was pumped into the secondary clarifier at the same rate as the influent, and the HRT of the secondary clarifier was similar to that of the aerobic tank. The effluent from the secondary clarifier was pumped out at the same rate as the influent to the aeration tank to maintain a steady state of operation for the whole system. Figure 1 diagrammatically illustrates the activated sludge treatment system used in this study.  
Figure 1: Design of the bench-scale activated sludge treatment system.
# Weighted Similarity-Confidence Laplacian Synthesis for High-Resolution Art Painting Completion
## 3 Methodology
### Weighted Similarity-Confidence Laplacian Synthesis
To address this challenge, we seamlessly integrate patch-based propagation, employing a locally applied isophote-driven technique to synthesize the complicated details of art painting elements (Figure 1d). The core principle involves ensuring that the matched patches \(\Psi_{s}\) align along the boundary of a hole situated between two distinct colored regions. The optimal-matched patch from the source area is then replicated into the target area, taking into account both the confidence \(C(s)\) and data terms \(D(s)\). The highest priority is accorded to the best match \(P(s)\)([PERSON], 2018). \[P(s)=C(s)\cdot D(s) \tag{5}\]  
\[C(s)=\frac{\sum_{t\in\Psi_{s}\cap(I-T)}C(t)}{|\Psi_{s}|},D(s)=\frac{|\bigtriangledown I _{s}^{\perp}\cdot n_{s}|}{\gamma} \tag{6}\]  
where \(\Psi_{s}\) represents the current patch, while \(|\Psi_{s}|\) delineates the pixel region of \(\Psi_{s}\). The unit vector orthogonal to point \(s\) is denoted as \(n_{s}\), with the operator \(\perp\) signifying the perpendicular operation. The normalization factor, \(\gamma\), is set to a value of 255. The propagation unfolds systematically, guided by the priority \(P(s)\) across every pixel along the unknown boundary border, employing a clockwise filling approach. In the quest for optimal patches, we employ a fused approach that integrates the outcomes of Weighted Laplacian Synthesis and patch-based propagation through Pyramid blending [33]. Pyramid blending distinguishes itself among blending methods for its capacity to effectively handle scale differences between images, operating at multiple scales and considering both coarse and fine details. This characteristic ensures a comprehensive blending process that results in smoother transitions between images (Figure 1e,f). The efficacy of pyramid blending extends to its proficiency in managing abrupt changes or discontinuities within images. The hierarchical structure of pyramids facilitates a seamless transition across different levels, preventing artifacts or noticeable seams in the blended result. This proves advantageous, particularly when merging images with diverse content or structures. Moreover, pyramid blending demonstrates computational efficiency. By operating at various levels of resolution, the algorithm can focus on essential details without imposing excessive computational demands, making it suitable for real-time applications or large-scale image-blending tasks.
## 3 Modeling of Electro-Thermal Coupling  
### Electric-Thermal Relationship  
In the EPR process, it is not only the use of the Joule heating effect that is important, but also how to fully exploit the non-Joule heating effect, which is to achieve the low-temperature or even room-temperature EPR key effect. However, the test conditions subject to the electric field are very difficult. At present, the electric field distribution can only be tested by the temperature field conditions induced by Joule heat. Therefore, the electro-thermal relation is the major method to test the electric field distribution, which can feed back the distribution characteristics of the current density, which is conducive to better control of the electric field distribution and the maximization of the actual electroplastic  
Figure 1: The uneven field measurement device: (**a**) the tensile device with wrap angle; and (**b**) electron flow diagram.  
Figure 2: The Mg strip thermal imaging of the tensile device with wrap angle.  
effect of the wide Mg strip. Suppose that the temperature rises of Mg strip caused by an electric pulse under ideal conditions is represented as  
\[\frac{\mathrm{d}T_{m}}{\mathrm{d}t}=\frac{R_{m}}{c_{m}\rho_{m}}\bigg{(}\frac{I_{ A}}{S_{m}}\bigg{)}^{2}=\frac{R_{m}I_{e}^{2}}{c_{m}\rho_{m}} \tag{1}\]  
where \(T_{m}\) is the temperature, \(t\) is the time, \(\rho_{m}\) is the density, \(c_{m}\) is the specific heat capacity, \(R_{m}\) is the resistivity, \(S_{m}\) is the cross-sectional area of Mg strip, \(I_{A}\) is the root mean square current, \(I_{e}\) is the root mean square current density. According to Equation (1), Joule heat is linearly related to the square of current density, and the temperature rise of Mg strip can be calculated by the current. In this way, the characteristics of the electric field distribution can be obtained by regression based on the distribution law of the temperature field, and feedback control of the temperature and electric pulse parameters can be achieved. It is clear that an accurate measurement of the distribution of the current density field of a Mg foil under different conditions is significant for the realization of a high-precision electric rolling process for wide Mg foils.
## 2 Materials and Methods
### Head and Neck (H&N)  
#### 3.2.1 Nasopharynx  
Critical Structure Evaluation  
A total of 90 nasopharynx cancer patients were included in the study; contrast agent's effect was evaluated for 20 critical structures. In terms of mean HU, normality test indicated that for most critical structures, HU was not normally distributed. The structures that demonstrated normal distributions were parotid glands in both NCCT and CCT images, brain stem, mandible, and right brachial plexus in NCCT images, and larynx in CCT images. The differences in mean HU between CCT and NCCT were statistically significant for 16 critical structures. The other four that showed insignificant mean HU differences were left cochlea, right cochlea, left lens, and esophagus.  
In terms of relative dose distribution differences, normality test revealed that most of the 20 critical structures did not exhibit a normal distribution, except brain stem, brain, oral cavity, esophagus, larynx, and spinal cord. Wilcoxon signed rank test and _t_-test indicated that dose differences were found to be significant only for brain stem, brain, optic nerves, esophagus, optic chiasm, right brachial plexus, and spinal cord. The number of patients with a maximum relative dose difference of more than 2% for each critical structure is summarized in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dose Metric**} & \multicolumn{2}{c}{**D\({}_{2\%}\)**} & \multicolumn{2}{c}{**D\({}_{98\%}\)**} \\ \cline{2-5}  & **NCCA** & **CCA** & **NCCA** & **CCA** \\ \hline Minimum & 99.24 & 98.84 & 84.06 & 83.66 \\ Maximum & 109.35 & 110.77 & 104.10 & 104.08 \\ Mean & 105.40 & 105.57 & 97.54 & 97.36 \\ STD & 1.70 & 1.82 & 3.01 & 3.17 \\ Median & 105.80 & 105.93 & 97.85 & 97.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical analysis results for all brain plans revealed significant dosimetrical differences between NCCT and CCT plans, despite that these differences are within \(\pm\)1%.  
Figure 3: Number of patients with relative dose difference between NCCT and CCT greater than 2% for critical structures in nasopharynx treatment plans.  
PTV Evaluation  
Mean HU values in PTV showed normal distribution for both NCCT and CCT image sets based on normality test, and there was a statistically significant HU difference between NCCT and CCT based on \(t\)-test.  
Mean relative dose difference values for D2% and D98% were within 1% of each other for both NCCT- and CCT-based plans (see Table 7), even though there were statistically significant differences in D2% and D98% values between NCCT- and CCT-based plans.
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 3 Results
### Seasonal Patterns of SIF and GPP and Their Relationship
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{**Temporal Resolution**} & \multicolumn{2}{c}{**Half-Hourly**} & \multicolumn{2}{c}{**Daily**} \\ \hline \multicolumn{2}{c}{**Mathematical Function Model**} & \multicolumn{1}{c}{**Linear**} & \multicolumn{1}{c}{**Nonlinear**} & \multicolumn{1}{c}{**Linear**} & \multicolumn{1}{c}{**Nonlinear**} \\ \hline \multirow{3}{*}{\(\text{SIF}_{\text{Red}}\)} & Formula & \(y=57.23x\) & \(y=\frac{43.69x+0.40}{x+0.45}\) & \(y=62.52x\) & \(y=\frac{79.24x+3.09}{x+1.25}\) \\  & R\({}^{2}\) & 0.80 & 0.83 & 0.80 & 0.81 \\  & RMSE & 3.80 & 2.89 & 2.76 & 2.21 \\ \hline \multirow{3}{*}{\(\text{SIF}_{\text{Far-red}}\)} & Formula & \(y=31.84x\) & \(y=\frac{29.99x+0.37}{x+0.36}\) & \(y=35.97x\) & \(y=\frac{33.66x+1.05}{x+0.55}\) \\  & R\({}^{2}\) & 0.70 & 0.79 & 0.79 & 0.82 \\ \cline{1-1}  & RMSE & 5.15 & 3.23 & 3.48 & 2.11 \\ \hline \end{tabular}
\end{table} 
Table 1: Statistical results of linear and nonlinear fitting for SIF\({}_{\text{Red}}\) and SIF\({}_{\text{Far-red}}\) with GPP. Figure 3: The relationship between SIF\({}_{\text{Red}}\) (**a**), SIF\({}_{\text{Far-red}}\) (**b**), and GPP at the seasonal scale at the AR site, large green dots represent daily averaged data, small gray dots represent half-hourly averaged data, the dashed lines represent linear fit lines, the solid lines represent non-linear fit lines, and the green and gray lines represent the daily averaged data fit lines and the half-hourly averaged data fit lines of SIF and GPP, respectively.
## 4 Simulation Results
In the presence of a sensing error, it is an expected result that the system becomes vulnerable to eavesdropping. The change in the secrecy rate according to \(P_{f}\) is shown in Figure 9. In this simulation, it is assumed that \(N=8\). As the probability of a false alarm (\(P_{f}\)) increases, the performance of the system decreases. This decrease exhibits a linear behavior according to Equation (25). An increment in \(P_{\text{j}}\) increases \(\beta_{1}\) and shortens \(\beta_{2}\). The nonsymbiotic scenario is a typical wireless powered communication system with an energy-harvesting-then-transmitting protocol, with the silent BS-active HT scenario accounting for most of the number of transmitted bits. At this point, decreasing the \(\beta_{2}\) time reduces the number of bits transmitted to the IR. Figure 7: Secrecy rate variation according to the power reflection coefficient. This causes a decrease in system performance. For this reason, in the symbiotic scenario, increasing \(P_{i}\) under \(P_{f}\) change improves the system performance, while in the nonsymbiotic scenario, increasing \(P_{i}\) under \(P_{f}\) change reduces system performance. At \(P_{f}\) = 0.3, the \(R_{\text{sec}}\) value for the four scenarios in Figure 9 is approximately equal. Although the symbiotic scenario performed better until \(P_{f}\) = 0.3, the nonsymbiotic scenario gave the best performance after this critical point. This result can be explained as follows: since the mutualistic relationship between BS-HT in a symbiotic scenario is severely dependent on the possibility of false alarm, increasing \(P_{f}\) damages the symbiotic relationship. As  
Figure 8: Secrecy rate change according to the number of eavesdroppers in the system model. Figure 9: Secrecy rate change under different \(P_{f}\) values. expressed in Figure 6, bit transmission to the IR is more advantageous for the idle scenario of a nonsymbiotic relationship than for the symbiotic scenario. As a result, it is not a good idea to design the system model symbiotically for high \(P_{f}\) values. In Table 3, the secrecy rate change according to spectrum sensing time is calculated for \(N=8\) and \(P_{\text{j}}=300\) W, considering four different scenarios. Increasing the spectrum sensing time narrows the bit transmission times of HT and BS in a period in the dynamic time allocation scheme. This situation causes decreases in system performance. There is no solution to the optimization problem, since the minimum number of bits per Hz that must be sent to IR to ensure QoS after a certain value of \(\beta_{0}\) cannot be achieved. For example, while there is no solution for symbiotic/nonsymbiotic scenario with sensing errors after \(\beta_{0}=0.41\) s, there is no solution for symbiotic scenario without sensing errors and nonsymbiotic scenario without sensing errors after \(\beta_{0}=0.78\) s and \(\beta_{0}=0.74\) s, respectively.
## 2 Materials and Methods  
As is known, the efficiency of PV arrays is greatly affected by irradiance and PSC. To demonstrate this situation, a simulation example made by giving different irradiance to a string consisting of 4 PV panels is given in Figure 1. In this simulation, a comparison was made first with full irradiance (1000 W/m\({}^{2}\)) on the panels and then with 1000-800-800-600 W/m\({}^{2}\) irradiance under PSC. The PV panel parameters used both in this simulation and in the later stages of the article are Isc = 3.74 A, Voc = 23 V, Pmax = 65 W. To simulate the PV panel model, a 5-parameter PV module equivalent model was designed and used in Matlab program.  
As seen from the simulation results, the MPP in the simulation decreases dramatically depending on the irradiance values that form the partial shade. Bypass diodes parallel to the panels were used to prevent the hotspot effect on the panels, and it is inevitable that local maximum points will occur on the P-V curve in PSC. This situation is clearly seen in Figure 1. The motivation of this study is to obtain electrical characteristics with a higher MPP by reconfiguring the connections of predetermined panels in the PV array under current irradiance conditions with a developed GA-based method.  
By creating a PV array by connecting PV panels in series and parallel among themselves, higher current-voltage values can be obtained. Depending on the connection types, there are array structures such as serial and parallel (S&P), total cross tied (TCT), bridge link and honey comb. The most preferred S&P and TCT series structures are shown in Figure 2. In this study, the TCT array circuit model, which has the most suitable structure for the reconfiguration method, was selected.  
Figure 1: A Matlab simulation showing the partial shading effect.  
In all reconfiguration methods automatically performed in PV array structures such as S&P, honey comb, etc., the panels with changing connections require a series connection within the array, necessitating a significantly higher number of electronic switching elements and a more complex connection structure. However, in PV arrays with a TCT (total cross tied) structure, panels with automatically changing connections are connected in parallel, requiring minimal electronic hardware. This advantage also translates into lower computational costs for the control algorithm. Therefore, similar to other commonly used reconfiguration methods, this study has also adopted the TCT structure for the PV array.
## 3 Experimental Setup: Methods and Procedures
### Using Neural Networks for Hardness Determination  
#### 3.2.1 Convolutional Neural Network  
For the classification of samples with different hardness based on intrinsic magnetic field measurements, a CNN was used. CNNs are unique artificial neural networks designed to efficiently process data with a grid structure, such as images. CNNs are very effective in image and video processing tasks but are also used in other areas, such as speech recognition, time series analysis, and natural language processing. Their primary function is convolution, which passes through the input data and extracts the important features of these inputs. The whole process is performed in several layers of the CNN: an input layer, convolution layer, activation function, pooling, fully connected layers, and output layer. For our purposes, a modified version of the convolutional network called Google NET (144 layers) was used, a type of artificial neural network developed by Google for various tasks in machine learning and artificial intelligence. These neural networks are the basis for many of Google's applications and technologies, including search, speech recognition, translation, image classification, and machine vision. GoogleNet was chosen for its robustness, which makes it a reliable solution for various tasks, and its adaptability, which also enhances its reliability. Our positive previous experiences with it in biological signal processing tasks further reinforce its reliability. Positive results were consistently delivered, demonstrating GoogleNet's capacity to handle intricate datasets, extract meaningful patterns, and generalize effectively across diverse problem spaces.