esa-sceva/satcom-chunk-collection huggingface dataset:
# Monolithic Si-Based AlGaN/GaN MIS-HEMTs Comparator and Its High Temperature Characteristics
## 5 Conclusions  
This article discussed the design, fabrication and stability testing of comparator circuits. These circuit schematics were systematically analyzed under a supply voltage of 10 V and reference voltages from 3 V to 8 V. Then, the comparator with a bootstrapped structure was chosen and fabricated based on AlGaN/GaN MIS-HEMT recessed gate technology. The GaN comparator showed good performance that matched the simulation results. The circuit was tested under both static and dynamic working conditions. The high-temperature test was applied, and the output showed reasonable variations up to 250 \({}^{\circ}\)C. The mechanism of such variations was explained based on the dynamic output waveform. Furthermore, a frequency test was carried out. The circuit suffered distortion at 1 MHz; future research can focus on improving the fabrication process and matching between D-mode and E-mode components in comparator circuits.  
Conceptualization, M.C. and F.L.; methodology, F.L. and Y.Z. (Yuhao Zhu); software: F.L. and A.L.; validation, F.L. and A.L.; formal analysis, F.L.; investigation, F.L.; resources, F.L. and Y.W.; data curation, C.D. and W.W.; writing--original draft preparation, F.L.; writing--review and editing, F.L. and W.L.; visualization, W.L. and F.L.; supervision, Y.Z. ([PERSON]), M.C. and W.L.; project administration, H.W. and W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Suzhou Science and Technology program under Grant SYG201923, SYG202113, in part by the Key Program Special Fund in Xi'an Jiaotong-Liverpool University (XJTLU) under Grant KSF-T-07 and in part by the XJTLU Research Development Fund under Grant RDF-16-02-11.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: The dynamic test of the comparator under different input frequencies. The input triangular waveforms and the output curves under 10 kHz, 100 kHz, 500 kHz and 1 MHz are presented from top to bottom, respectively.
## 3 Iterative Image Estimation  
Assuming perfect alignment between the data transmitting screen and camera, the images received through the D2C link can be represented as  
\[\mathbf{Y}_{t}[i]=\mathbf{H}_{t}[i]\ast\mathbf{D}_{t}[i]+\mathbf{N}_{t}[i], \tag{9}\]  
Figure 3: Pilot pixels allocation and virtual pilot selection in the high-frequency sub-band of a frequency domain image.  
where \(*\) denotes the convolution operation, \(\mathbf{Y}_{t}\) is the received data-embedded image, \(\mathbf{H}_{t}\) is the reference or channel image, and \(\mathbf{N}_{t}\) is the additive white Gaussian noise (AWGN) matrix. After receiving the images through the D2C link, they are converted into the frequency domain using the transformation matrices denoted by \(\mathbf{F}_{P}\) and \(\mathbf{F}_{Q}\). This can be mathematically represented as  
\[\begin{split}\mathbf{Y}_{F}[i]&=\mathbf{F}_{P} \mathbf{Y}_{t}[i]\mathbf{F}_{Q}\\ &=\mathbf{F}_{P}[\mathbf{H}_{t}[i]\mathbf{D}_{t}[i]]\mathbf{F}_{Q }+\left[\mathbf{F}_{P}\mathbf{N}_{t}[i]\mathbf{F}_{Q}\right]\\ &=\mathbf{H}_{F}[i]\mathbf{D}_{F}[i]+\mathbf{N}_{F}[i],\end{split} \tag{10}\]  
where \(\mathbf{Y}_{F}[i]\) denotes the frequency-domain image; \(\mathbf{H}_{F}[i]\) denotes the Fourier transform of \(\mathbf{H}_{t}[i]\); and \(\mathbf{D}_{F}[i]\) denotes the Fourier transform of the captured image, denoted by \(\mathbf{D}_{t}[i]\). The noise matrix in the frequency domain is denoted as \(\mathbf{N}_{F}[i]\).  
The pilot signals \(\{Y_{F}^{p}[i]\}\) are extracted from the received frequency-domain image sequence \(\{Y_{F}[i]\}\) and the channel image \(\{H_{F}[i]\}\) is obtained from the information conveyed by the extracted pilot signals \(\{H_{F}^{p}[i]\}\). With the knowledge of the channel response \(\{H_{F}[i]\}\), the transmitted data samples \(\{D_{F}[i]\}\) can be recovered using the ZF receiver, given by  
\[\hat{\mathbf{D}}_{F}[i]=\frac{\mathbf{Y}_{F}[i]}{\hat{\mathbf{H}}_{F}[i]}, \tag{11}\]  
where \(\hat{\mathbf{H}}_{F}[i]\) is the estimate of a channel image. After demodulation, the reconstructed source binary information data pixels are obtained at the receiver output.
## 2 Materials and Methods  
### Plant Material  
Potatoes from three commercial cultivars, Spunta, Banba, and Sylvana (Figure 1), were grown in 2018 following the usual practices, in terms of soil cultivation, fertilization, irrigation, and plant protection, in neighboring plots in a production area under protected geographical indications (PGI) under the same weather conditions and by the same grower. Spunta tubers have an oval to long shape, white to yellow skin color, light to yellow flesh color, and smooth to intermediate skin texture. Banba tubers have oval to long shape, yellow skin color, light yellow flesh color, and intermediate skin smoothness, while Sylvana has oval shaped tubers with white to light beige skin color, medium yellow flesh color, and medium skin smoothness ([[https://www.europotato.org](https://www.europotato.org)]([https://www.europotato.org](https://www.europotato.org)); [[http://varieties.ahdb.org.uk](http://varieties.ahdb.org.uk)]([http://varieties.ahdb.org.uk](http://varieties.ahdb.org.uk))) (Accessed: 11 March 2021). Transplanting and harvesting dates were the same for all the cultivars. Plants were uprooted by hand in several sites within each plot and around 500 tubers from each cultivar were thoroughly examined by eye before selecting the 90 more representative, in terms of shape and size, free of visible defects. The samples were collected at the same day of harvest, early in the morning, were transferred to the laboratory within 90 min, were washed under tap water to remove dust and foreign particles from the surface, wiped with a paper towel, and were dried before finally choosing half of them (45 tubers) from each cultivar and storing them for 5 months at 5 \({}^{\circ}\)C in continuous darkness, in order to retard sprouting and maintain other various quality attributes [2]. The dimensions of the cold room were 4.2 \(\times\) 3.5 \(\times\) 2.1 m\({}^{3}\) (length \(\times\) width \(\times\) height) and the average temperature and relative humidity were 5.1 \(\pm\) 0.53 \({}^{\circ}\)C and 89.8 \(\pm\) 5.53%, as recorded by HOBO Pro v2 Temp/RH data loggers (ONSET, Bourne, MA, USA). Forty-five tubers were selected per cultivar, free of external disorders and were subjected to various measurements with optical and remote sensing technologies at 3 different storage periods (0, 2.5, and 5 months). The measurements were taken both on the external pericarm (outside) of each tuber, as well as in the flesh (inside) after halving the potatoes longitudinally.
## 3 Data and Methods
### Model Optimization
#### 3.4.2 Bayesian Optimization Algorithm for the Support Vector Regression Model  
The BOA [30; 31] involves testing each sample point according to prior knowledge and updating prior distributions to identify the global optimal value.  
Assume that a sample S is present, including t parameter groups and corresponding objective function value. Firstly, the posterior probability distribution \(D_{t}\) (namely, joint normal distribution) is calculated by the Gaussian process, and then a function \(a(x\,|\,D_{t})\) is selected to collect a new parameter value \(x_{t+1}\) so as to satisfy Equation (6):  
\[x=\text{argmax}E(\max\{0,f_{t+1}(x)-f_{best}|D_{t}\}) \tag{6}\]  
Thus, in the distribution, \(D_{t}\) is able to make the expected difference between the new estimate objective function value \(f_{t+1}(x)\) and current maximum objective function value \(f_{\text{best}}\) the largest, the new parameter is used to calculate the new actual objective function value and update the corresponding Gaussian process, and a new posterior distribution is obtained. The above process is repeated until the stop condition is satisfied.  
The optimization flow chart of the BOA is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Penalty**} & \multirow{2}{*}{
\begin{tabular}{c} **Kernel** \\ **Function** \\ **Parameter** \(g\) \\ \end{tabular} } & \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Actual/\%**} & \multicolumn{2}{c}{**GS-SVR Prediction**} & \multirow{2}{*}{**Relevant**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MRE**} & \multirow{2}{*}{**Time/s**} \\  & & & & & & \(\prime\)\% & & \\ \hline \multirow{8}{*}{25.1} & \multirow{8}{*}{0.13} & 14 & 13.37 & 13.65 & 0.0209 & \multirow{8}{*}{25.1} & \multirow{8}{*}{0.013} \\  & & 16 & 19.34 & 20.60 & 0.0652 & & \\  & & 17 & 17.29 & 21.32 & 0.2331 & & \\  & & 20 & 22.31 & 22.57 & 0.0117 & \multirow{8}{*}{2.653} & \multirow{8}{*}{0.0846} & \multirow{8}{*}{44.8} \\  & & 26 & 20.19 & 20.19 & 0.0005 & & \\  & & 42 & 28.34 & 24.48 & 0.1362 & & \\  & & 43 & 29.76 & 25.04 & 0.1586 & & \\  & & 44 & 21.39 & 22.47 & 0.0505 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization results and model evaluation of GS method.  
Figure 4: BOA flow chart.  
From Table 3, it is evident that the MRE between the predicted cementing quality of BOA-SVR and the actual cementing quality is 8.00%, and the \(RMSE\) is 2.533, which suggests good optimization.
## 3 Inter-Satellite Laser Communication Model Based on Simulink
### System Simulation Model  
Building upon the previously analyzed principles of inter-satellite laser communication for space gravitational wave detection, a simulation model was developed using Simulink, as shown in Figure 11. The simulation model comprises the following key components: the DS/SS module, BPSK module, AWGN module, DPLL module and DLL module.  
To streamline the simulation process, the laser phase modulation and heterodyne interference processes are simplified and represented as BPSK modulation. Within the model, the DS/SS, BPSK and AWGN modules are primarily responsible for generating the beat note signal, which serves as the input for the DPLL module.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## Appendix A  
Let us consider a third-order system  
\[\begin{array}{l}\dot{x}_{1}=x_{2},\\ \dot{x}_{2}=x_{3},\\ \dot{x}_{3}=f_{0}(x_{1},\mathbf{r})+\mathbf{\theta}^{T}\mathbf{D}(\mathbf{x}+\mathbf{B}\mathbf{r})+\bm {g}^{T}\mathbf{x},\end{array} \tag{1}\]  
with the input \(\mathbf{r}\), output \(y=x_{1}\), state variables \(\mathbf{x}=\left[x_{1},x_{2},x_{3}\right]^{T}\), where \(\mathbf{\theta}\) are unknown, constant parameters and \(f_{0}(x,\mathbf{r}),\ \mathbf{D},\ \mathbf{B},\ \mathbf{g}^{T}\) are known, and let us define state transformation  
\[\mathbf{z}=\mathbf{T}\mathbf{x},\ \mathbf{T}=\left[\begin{array}{ccc}1&0&0\\ \alpha&1&0\\ \beta&\gamma&1\end{array}\right]. \tag{2}\]  
The inverse transformation is given by  
\[\mathbf{x}=\mathbf{T}^{-1}\mathbf{z},\ \mathbf{T}^{-1}=\left[\begin{array}{ccc}1&0&0\\ -\alpha&1&0\\ -\beta+\alpha\gamma&-\gamma&1\end{array}\right]. \tag{3}\]  
Therefore, as  
\[\mathbf{T}\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right]=\left[\begin{array}{c}0\\ 0\\ 1\end{array}\right], \tag{4}\]we have  
\[\dot{z}=T\left[\begin{array}{cc}\left[\begin{array}{cc}0&1&0\\ 0&0&1\\ \theta^{T}D+g^{T}\end{array}\right]\end{array}\right]T^{-1}z+\left[\begin{array}[ ]{c}0\\ 0\\ f_{0}(z_{1},r)\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ \theta^{T}DBr\end{array}\right]. \tag{10}\]  
Simple matrix manipulations provide that the last columns of the state matrix in (10) are  
\[\mathbf{T}\left[\begin{array}{cc}\left[\begin{array}{cc}0&1&0\\ 0&0&1\\ \theta^{T}D+g^{T}\end{array}\right]\end{array}\right]T^{-1}=\left[\begin{array} []{cc}*&1&0\\ *&\alpha-\gamma&1\\ *&\beta-\gamma^{2}+\left(\theta^{T}D+g^{T}\right)\left[\begin{array}{c}0\\ 1\\ -\gamma\end{array}\right]&\gamma+\left(\theta^{T}D+g^{T}\right)\left[\begin{array} []{c}0\\ 0\\ 1\end{array}\right]\end{array}\right], \tag{11}\]  
Therefore, the system (10) is transformed into the ONP form if  
\[q_{1}=\alpha-\gamma,\] \[q_{2}=\beta-\gamma^{2}+\left(\theta^{T}D+g^{T}\right)\left[ \begin{array}{c}0\\ 1\\ -\gamma\end{array}\right], \tag{12}\] \[q_{3}=\gamma+\left(\theta^{T}D+g^{T}\right)\left[\begin{array}[] {c}0\\ 0\\ 1\end{array}\right]\]  
do not depend on \(\theta\).  
For Arneodo system (13)  
\[\theta=\left[\begin{array}{c}a\\ b\end{array}\right],\;\;\mathbf{D}=\left[\begin{array}{ccc}1&0&0\\ 0&-1&0\end{array}\right],\;\;\mathbf{B}=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right],\;\;g^{T}=\left[\begin{array}{cc}0&0&-1\end{array}\right]. \tag{13}\]  
Therefore,  
\[\theta^{T}D+g^{T}=\left[\begin{array}{cc}a&-b&-1\end{array}\right]. \tag{14}\]  
As \(q_{2}=\beta-\gamma^{2}-b+\gamma\), selecting \(\beta=b\) and any \(\alpha,\;\gamma\) that do not depend on unknown parameters constitutes a family of transformations into the ONP form. One of them is \(\alpha=1,\;\beta=b,\;\gamma=1\), applied in (14).  
Therefore, the Arneodo system is linearly transformable into the ONP form. Additionally, several other popular chaotic systems such as Arneodo-Coullet [38], Genesio-Tesi [39], and Lur'e [35] belong to this class.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 4 Experimental Section
### Completion Comparison Experiments under Different Missing Rates  
Experiments were conducted to complete the same buildings with different missing rates of 10%, 15%, 20%, and 25%, respectively, to evaluate the model's capacity to restore missing buildings at varying rates of absence. The precise quantitative outcomes are displayed in Table 3, illustrating the performance of Model [14], VRC-Net, and our model on the building test dataset. The \(d_{CD}\) score is multiplied by \(10^{3}\), and lower \(d_{CD}\) scores indicate better performance.  
In Figure 12, the visualization shows the variation of \(d_{CD}\) values for three models: VRC-Net, Model [14] and Our.  
Even as the number of input points gradually decreased from 10% to 25% compared to the true point cloud data, our model remained ahead in terms of building point cloud completion compared to Model [14] and VRC-Net. Nevertheless, when the missing rate of building data surpassed 20%, the substantial loss of building point cloud data resulted in a  
Figure 12: Line plot of point cloud completion results at different missing rates: (**a**) G\(\rightarrow\)R refers to the variation in \(d_{CD}\) values when pointing from generation to real point clouds; (**b**) R\(\rightarrow\)G refers to the variation in \(d_{CD}\) values when pointing from real to generated point clouds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Missing Ratio**} & \multicolumn{2}{c}{**VRC-Net**} & \multicolumn{2}{c}{**Model [14]**} & \multicolumn{2}{c}{**Our**} \\ \cline{2-7}  & **G\(\rightarrow\)R** & **R\(\rightarrow\)G** & **G\(\rightarrow\)R** & **R\(\rightarrow\)G** & **G\(\rightarrow\)R** & **R\(\rightarrow\)G** \\ \hline
10\% & 2.431 & 2.095 & 2.619 & 2.283 & 1.835 & 1.606 \\
15\% & 2.879 & 2.607 & 3.011 & 2.718 & 2.051 & 1.811 \\
20\% & 5.154 & 4.942 & 4.914 & 4.868 & 3.586 & 2.963 \\
25\% & 8.672 & 8.261 & 8.226 & 8.052 & 7.902 & 7.515 \\ \hline \hline \end{tabular}
\end{table}
Table 3: On the BuildingNet dataset, we present the comparative results of \(d_{CD}\) for point cloud completion between our method and Model [14], as well as VRC-Net, across four different missing rates: 10%, 15%, 20%, and 25%. For each model, the paired numerical values represent \(d_{CD}\) for G\(\rightarrow\)R and R\(\rightarrow\)G. Lower values indicate better performance.  
notable absence of contour structure information for the building. This makes it difficult for us to construct contour constrained polygons that fit the original building, leading to a noticeable decrease in the point cloud completion performance of our model for buildings. Nonetheless, even in the case of high data loss, our approach can effectively fill in the missing parts of the point cloud data. Compared to other models, it still demonstrates notably impressive building point cloud completion capabilities.
## 3 Results and Discussion
### Effect of the Duration of the Heat Treatment: Simulations 6 and 7  
The following simulations attempt to evaluate the capability of the model to capture the effect of two key parameters for heat treatment of the additively manufactured part: the duration of the stress relief heat treatment and the temperature of the heat treatment. First, the duration is investigated.  
In simulation 6, the dwell time of the heat treatment is reduced to one hour, while simulation 7 uses a 3-h holding time. These two simulations are compared to the original stress relief heat treatment, which has a two-hour holding time.  
The short heat treatment has identical deflections and stresses compared to the benchmark, for both process sequences. This shows that the majority of the stress relaxation takes place in a span of one hour. The model predicts that a heat treatment of one hour is sufficient to relax all the stresses in the part.  
For both the beam subjected to sequence A and sequence B, the deflection of the tip of the beam for simulation 7 is larger than the same deflection for simulation 3. Focussing on sequence A, the change in the deflection is found to be less than ten percent, and the change in the stress is similarly low. To find the reason for this difference, the stress at the centre of the top surface of the cantilever is plotted in Figure 13 for both simulations 1 and 7. It is clear from this plot that the stresses are reduced during the high-temperature part of the stress relaxation. On the other hand, the stress is restored upon cooling back down to room temperature, although they are more homogeneous, which can be seen by comparing Figure 11 with Figure 8A. The small difference in the deformation results from the small change in stress, which results from the reduction that follows the stress relaxation. The results also indicate that the stress relaxation is calculated from the beginning of the heat treatment and instantly applied.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulation Number** & **1** & **2** & **3** & **4** & **5** \\ \hline Element size (m) & \(5\times 10^{-4}\) & \(6\times 10^{-4}\) & \(7.5\times 10^{-4}\) & \(8\times 10^{-4}\) & \(1\times 10^{-3}\) \\ Longitudinal normal stress (MPa) & 841.6 & 840.5 & 835.1 & 832.8 & 825.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The values of the maximal longitudinal normal stress after the entire process chain (sequence A).
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 5 Conclusions  
This paper presents a novel structured light projection method for three-dimensional shape measurements using MEMS to realize the scanning of phase-shifted fringe projection. This developed system achieved 8 Hz 3D face shape measurement limited by the used camera. The experiments demonstrate how 3D measurement could be performed using a MEMS infrared scanning device as a projector. The measurement results of the standard plane and ball samples show that the accuracy is better than 0.2 mm. Face mask experiment demonstrated the success of this proposed method and have been compared with the data obtained by an expensive DLP projector. Real human face measurements also prove that it can be applied in the field of face recognition, where rapid 3D shape measurements are required. We expect that this work can help improve the field of 3D scanning application by developing a low cost and simple solution. For the future work, the calibration of the MEMS to obtain an equal length of the scanning fringes will be investigated.  
Conceptualization, J.X. and Q.Z.; Data curation, M.W. and Y.H.; Investigation, J.X., C.L. and W.L.; Software, J.X. and W.L.; Writing--original draft, J.X. and Q.Z.; Writing--review & editing, J.X., Q.Z., C.L. and M.W.  
This research was funded by the National Key Scientific Instrument and Equipment Development Projects of China, grant number 2013 YQ490879; and was funded by the Open fund for Artificial Intelligence Key Laboratory of Sichuan Province of China, grant number 2018 RYJ07.  
The authors would like to thank [PERSON] for helpful valuable discussions and as a face model in experiment. The authors also would like to thank [PERSON] and [PERSON] in the Optics and Metrology group of NSLS-II at Brookhaven National Laboratory in USA for helpful language revision.  
The authors declare no conflict of interest.
## 9 Electronics
### Control Board  
The control board of the laser beacon is responsible for receiving remote commands and interpreting them to control both the laser head and the optical attenuator. Additionally, it generates all the necessary logic signals for their operation. The control board also manages the power board's operating modes, monitors battery status, and reads the container's temperature. At the heart of this board is the 16F model 886 family PIC microcontroller. These microcontrollers belong to the RISC architecture [24].  
The control board comprises four distinct subsystems, each briefly described below:  
Block A: This segment incorporates the PIC microcontroller and an external oscillator essential for the proper functioning of the PIC. The oscillator utilizes a 4 MHz quartz crystal.  
Block B: Consisting of a MAX232 integrated circuit, this block handles the adaptation of RS232 communication voltage levels from external sources. It ensures compatibility with the PIC's voltage requirements.  
Block D: Responsible for temperature measurement within the container, this block employs a linear sensor and two amplifiers. The first amplifier operates in follower mode to match the impedance, whereas the second is configured in non-inverting mode to amplify the temperature sensor's output voltage. To finely adjust the gain, a potentiometer is employed in lieu of a standard resistor.
## 5 Results and Discussions
### Average End-to-End Delay (ETE)  
End-to-End delay (ETE) is measured since a UAV starts a remote sensing mission until it completes data collection and returns to the base station. This metric is used to study the performance of route planning algorithms to forward UAVs through short and loop-free paths. This approach offers a number of benefits--mainly enhanced data freshness.  
As Figure 4 shows, Q-learning outperforms IEMF if the number of SPs is increased. This stems from the exploration-exploitation capacity of Q-learning that allows environment interaction aiming to learn the best-fitted actions and find a greater number of SPs (maximize the reward). Hence, Q-learning reduces ETE as compared to IEMF if SP density is increased and a greater number of SPs is visited during exploration flights. However, IEMF works according to a heuristic and greedy approach, through which SPs are visited one-by-one based on their distance to the current UAV's location. This increases ETE when the number of SPs is increased.  
IEMF outperforms Q-learning (and TBID) when SPs are rare. This is because the UAV wrongly finishes the remote sensing mission and returns to the base station, as it is unable to find any close SP to the current location. Indeed, IEMF forwards UAV into the base station with a minimized latency, as this suffers a lack of environment interaction to discover the farm fully. Figure 5 supports this and shows how IEMF addresses longer ETE for each visited SP if SPs are rare (e.g., 64).  
TBID outperforms Q-learning and IEMF UAV route planning as this establishes a spanning tree in advance to move UAV through. By this, the UAV needs to compute no route while flying over the farm for remote sensing. This results in reduced ETE.  
Figure 4: The average ETE (based on synthesized data of the model generated for this study).