esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Oyster Packaging System
## 3 Experiments and Results
The Kalman filter in the oyster visual perception model can accurately predict the state information of the oyster. The tests have shown that only ten frames of iterative updating are needed to get accurate position information for oyster position information. The mechanical claw was chosen to allow for some deviation in the oyster grasping position so that the whole system had a high grasping success rate. The success rate of the system in grasping oysters at three conveyor speeds was tested in the range of 2-7 cm/s speeds, 25 times at each speed, and the results of the grasping experiments are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Actual** & **Localized** & **Transformed** & **Error** & **Absolute** \\  & **Coordinates** & **Coordinates** & **Coordinates** & **(mm)** & **Error** \\  & **(x, y)** & **(x, y)** & **(x, y)** & **(x, y)** & **(mm)** \\ \hline
1 & \((-0.1676,\) & \((301,246)\) & \((-0.1688,\) & \((1.2,\) & \(2.00\) \\  & \(-0.5029)\) & & \(-0.5013)\) & \(1.6)\) & 2.00 \\
2 & \((-0.2441,\) & & \((400,155)\) & \((-0.2460,\) & \((1.9,\) \\  & \(-0.5707)\) & & \(-0.5657)\) & 5.0) & 5.35 \\
3 & \((-0.2262,\) & & \((-0.2297,\) & \((3.5,\) \\  & \(-0.5501)\) & & \(-0.5559)\) & 5.8) & 6.77 \\
4 & \((-0.3095,\) & & \((-0.3148,\) & \((5.3,\) \\  & \(-0.3732)\) & & \((-0.3743)\) & \((1.1)\) & 5.41 \\
5 & \((-0.1705,\) & & \((312,407)\) & \((-0.1730,\) & \((2.5,\) \\  & \(-0.3867)\) & & \((312,\) & \(-0.3855)\) & 1.2) & 2.77 \\
6 & \((-0.2536,\) & & \((414,\) \(224)\) & \((-0.2548,\) & \((1.2,\) \\  & \(-0.5212)\) & & \((414,\) \(224)\) & \((-0.5160)\) & 5.2) & 5.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of oyster pose estimation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Total** & **Success** & **Success** & **Duration** \\  & & **Count** & **Rate (\%)** & **(s)** \\ \hline
1 & 6 & 6 & 100 & 46.3 \\
2 & 6 & 6 & 100 & 48.5 \\
3 & 6 & 5 & 83.33 & 46.1 \\
4 & 6 & 5 & 83.33 & 45.9 \\
5 & 6 & 6 & 100 & 47.2 \\
6 & 6 & 6 & 100 & 47.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The static grasping results of oysters. The grasping success rate of the system is more than 84% when the running speed of the conveyor belt is less than 5 cm/s. The success rate of oyster grasping decreased when the running speed was higher than 5 cm/s. This was due to the limited camera field of view, which led to abnormal trajectory planning when the oyster passed quickly, causing the prediction position of the oyster grasping by the system to exceed the workspace.
## 2 Fisher-Snedecor \(\mathcal{F}\) Fading Channel Model
### Bivariate Fisher-Snedecor \(\mathcal{F}\) Distribution
#### 2.2.3 Joint CDF of the Instantaneous Output SNR  
To obtain an analytical expression of the joint CDF of the instantaneous output SNR in the bivariate Fisher-Snedecor \(\mathcal{F}\) model, we express Appell's function in (13) as the infinite series representations using ([43], Equation (9.180.2)). With the help of ([43], Equation (3.194.1)), along with some algebraic manipulations, the joint CDF of \(\gamma_{1}\) and \(\gamma_{2}\) can be deduced as:  
\[\begin{array}{ll}F_{\gamma_{1},\gamma_{2}}(\gamma_{1},\gamma_{2})&=\int \limits_{0}^{\gamma_{1}}{\int\limits_{0}^{\gamma_{2}}{f_{\gamma_{1},\gamma_{2} }(\gamma_{1},\gamma_{2})d\gamma_{1}d\gamma_{2}}}\\ &=\sum\limits_{k=0}^{\infty}{\sum\limits_{l=0}^{\infty}{\sum\limits_{l=0}^{ \infty}{\sum\limits_{j=0}^{\infty}{\frac{\rho_{\text{N}}^{k+l}\rho_{\text{G}}^{l }\phi\mathbf{\Theta}_{1}^{n_{1}+k}\mathbf{\Theta}_{2}^{m_{2}+k+l}}{k!\Pi!\Gamma( m_{l})\Gamma(n)\Gamma(n_{1})\Gamma(m_{2}+k+i)}}}}}\\ &\times\frac{(m_{2}-m_{1})(n_{2}-n_{1})\Gamma(\lambda_{1})\Gamma(\lambda_{2}+ i+j)}{(\Gamma_{2}+l+j)(m_{1}+k)(m_{2}+k+i)}\\ &\times_{2}F_{1}[\lambda_{1},m_{1}+k;m_{1}+k+1;-\alpha_{1}\gamma_{1}]\\ &\times_{2}F_{1}[\lambda_{2}+i+j,m_{2}+k+i;m_{2}+k+i+1;-\alpha_{2}\gamma_{2}].\end{array} \tag{15}\]  
Similarly, with the aid of the infinite series representations of \(F_{4}[\cdot]\) in [46], and invoking ([43], Equation (9.180.2)) and ([43], Equation (3.194.1/3)), the joint CDF for the identical shaping parameters case can be obtained as ([38], Equation (19)):  
\[\begin{array}{ll}F_{\gamma_{1},\gamma_{2}}(\gamma_{1},\gamma_{2})&=\sum \limits_{k=0}^{\infty}{\sum\limits_{l=0}^{\infty}{\frac{\rho_{\text{N}}^{k}\rho_{ \text{G}}^{l}\phi\mathbf{\Theta}_{1}^{n_{1}+k}\mathbf{\Theta}_{2}^{m_{2}+k} }{k!\Pi!\Gamma(m)\Gamma(n)B(l+n,k+m)}}}\\ &\times\prod\limits_{i=1}^{2}{\gamma_{i}}^{m+k}{{}_{2}F_{1}[\lambda,m+k;1+m+k,- \eta_{i}\gamma_{i}]}.\end{array} \tag{16}\]
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results
### Q Factor  
The \(Q\) factor of an antenna is defined in the same way as for resonant circuits, i.e., the ratio of energy stored to energy lost during one period of vibration. The difference is in the user's expectations, while for resonant circuits, a high \(Q\) factor is usually required, the opposite is true for antennas. By determining the \(Q\) factor, it is possible to easily estimate the bandwidth of an antenna. The larger \(BW\) is because of a reduction in the \(Q\) factor of the patch resonator, which is due to less energy being stored beneath the patch, and due to higher radiation. In order to determine the \(Q\) factor value in the first step, we analytically determine the bandwidth of the antenna based on the dimensions of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Component** & **Symbol** & **Dimensions (mm)** \\ \hline Ground plane width & \(W_{S}=W_{e}\) & 8.40 \\ Ground plane length & \(L_{S}=L_{e}\) & 6.20 \\ Patch width & \(W_{P}\) & 3.66 \\ Patch length & \(L_{P}\) & 2.14 \\ Copper thickness & \(C_{u}\) & 0.05 \\ Substrate thickness & h & 1.57 \\ Permitivity & \(E_{r}\) & 2.2 \\ Feed line width & \(W_{f}\) & 1.26 \\ Feed line length & \(L_{f}\) & 3.10 \\ Inset feed gap & \(Y_{0}\) & 0.50 \\ Width feed gap & \(X_{0}\) & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the optimized antenna model.  
Figure 4: The optimized antenna model view, front and back side with dimensions.  
proposed antenna model (dimensions of the radiating element and the thickness of the substrate), using the following dependencies:  
\[\begin{split} BW=3.771\cdot\left[\frac{\epsilon_{z}-1}{\left( \epsilon_{r}\right)^{2}}\right]\cdot\frac{h}{\lambda_{0}}\cdot\left(\frac{W}{L} \right)\\ =3.771\times\left[\frac{2.2-1}{2.2^{2}}\right]\times\frac{1.57}{1 0.7}\times\left(\frac{3.66}{2.14}\right)=3.771\left[\frac{1.2}{4.84}\right] \times 0.146728\times 1.71028=23\%\end{split} \tag{13}\]  
The theoretical bandwidth for the antenna, calculated from mathematical relationships, is about 23%. The \(BW\) of the microstrip antenna is inversely proportional to its \(Q\) factor and is given by [32] the following:  
\[BW=\frac{(VSWR-1)}{Q\sqrt{VSWR}} \tag{14}\]  
Having determined the theoretical bandwidth \(BW\) and taking \(VSWR=2\) as the limit value for the bandwidth, after transforming the above Relation (14) the \(Q\) factor of the antenna was determined as:  
\[Q=\frac{(VSWR-1)}{BW\sqrt{VSWR}}=\frac{(2-1)}{0.23\sqrt{2}}=\frac{1}{0.3252}=3. 07. \tag{15}\]  
The analytically low value of the antenna's \(Q\) factor confirms its ability to operate in a wide frequency range.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 4 Experiments
### 3D Point-of-Regard Estimation Experiment in Free Space  
In this experiment, we used a box to test the effect of our method. The actual scene during experiment is shown as the left image in Figure 10.  
We randomly placed a box anywhere from 0.6 m to 1.5 m away from the system, while ensuring that the box could be viewed through the system in comfort. Then a person was invited to look at the six visible corners of the box and get the observation data. We compared the experimental result with the real spatial condition of the corner of the box. This experiment proves that the gaze tracking system proposed in this paper can be applied to common scenes. The result of this experiment is shown in Figure 11.
## 3 Method
### Site Restoration  
Frameworks for the online MOT problem generally require one or more powerful appearance models. The appearance model aims to determine that a tracklet (an object being tracked) and a detected object are the same object based on their visual information. The appearance model is mainly used when the motion model cannot predict due to the complicated movement of the object, or when re-identification is needed based on the visual data of the object due to the failure of the detection. However, unpredictable noise such as motion blur caused by the movement of an object has a negative effect on the data association because of difficulties in the identification of the appearance model. We thus discriminate whether the detected objects are blurry images or sharp images before the image is used in the appearance model, and then restore the detection regions of those discriminated as blurry images. Figure 3 represents our site restoration method.  
To classify whether each element of \(D^{complete}\) is blurry or sharp, we use the Laplacian kernel, inspired by [40]. Laplacian kernels are generally used to detect edges of an image, but can be used to quantify the blur of an image as well. The usability is based on the fact that sharp images show a large number of edge detections. On the contrary, blurry images  
Figure 3: Schematic representation of the proposed site restoration method.  
show a small number of edge detections. The variance value of the convolution operation using a Laplacian kernel is called Laplacian variance, which means the quantified blur. Therefore, we can discriminate that the Laplacian variance value is blurry at when low or and sharp at when high.  
We define the function to find Laplacian variance of the detection set \(D\) as:  
\[L=\{l_{i}\mid i=1,2,\ldots,detectsNum\}\gets Laplacian Variance(GrayScale(x),D) \tag{7}\]  
If the Laplacian variance is lower than the blur threshold \(\tau^{blur}\), \(d\) is determined to be the blurry image.  
\[D^{blurry}=\left\{d\mid l\leq\tau^{blur},d\in D^{complete}\right\} \tag{8}\]  
Finally, the image restoration is conducted on the blurry image using the _Restoration Module_. Consequently, in order for the reconstructed image to be used in the appearance model, the damaged areas of the original image are replaced with the reconstructed image.
## 3 Simulation and Validation of the Proposed Algorithm
### Validation of the Proposed Algorithm
Frequency-modulated signal (multi-order signal)  
A frequency-modulated signal with three orders is applied to the two ANC systems again. Figure 9a illustrates the time history of signal 2 controlled by the conventional method. The controller cannot sufficiently reduce the order signals without divergence. Figure 9b illustrates the time history of signal 2 controlled by the OW-LMS algorithm. Two weight vectors were used in the controller. The controller with a long weight vector (\(L=300\)) converged after 1 s, whereas the controller with a short weight vector (\(L=30\)) converged after 2 s. From Equation (15), the optimal filter length is 18,000/RPM since \(\Delta k\) is 2 and the sampling frequency is 3000 Hz. Therefore, the length of the weight vector should be 300 since the minimum rotational speed is 600 rpm (10 Hz) for this signal. As the rotational speed increases, the length of weight vector can be decreased for stable control. In this simulation, the controller with a long weight vector (\(L=300\)) converged at low rotational speed, but the controller with a short weight vector (\(L=30\)) converged at high rotational speed. Figure 9c,d show the short-time Fourier transform (STFT) for the signals controlled by the OW-LMS algorithm. Expectedly, at a low rotational speed of less than 1 s, the controller with a short weight vector still converges as shown in Figure 9c, whereas the controller with a long weight vector has converged, as shown in Figure 9d. Figure 9e shows the STFT for the error signal controlled by the conventional algorithm. The order error signal was not completely attenuated by the controller with the conventional algorithm. Figure 9f shows a comparison of PSD functions of error signals obtained by the three controllers. From these results, we determined that the OW-LMS algorithm is superior for the active control of a frequency modulated signal compared to the conventional method. Figure 8: Time history and PSD of signal 1 controlled by the leaky LMS-algorithm-based adaptive notch filter (\(\overline{\mu}=0.5\), \(\gamma=0.5\)) and the OW-LMS algorithm (\(\overline{\mu}=1\)). (**a**) Comparison between the time history of the conventional method and OW-LMS (\(L=30\)); (**b**) Comparison between the time history of non-control and OW-LMS (\(L=300\)); (**c**) Comparison between the PSD of conventional method and OW-LMS (\(L=30\)); (**d**) Comparison between the PSD of non-control and OW-LMS (\(L=300\)); (**e**) Image plot of spectrum of weight vector associated with rpm (\(L=30\)); (**f**) Image plot of spectrum of weight vector associated with rpm (\(L=300\)); (**f**) Spectrum of weight vector (\(L=30\)) at 4200 rpm (**h**) Spectrum of weight vector (\(L=300\)) at 4200 rpm.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 3 Results and Discussions
### Analysis of Variance (ANOVA)
Equations (2)-(4) show the prediction model developed for tensile strength (\(TS\)), impact toughness (\(IT\)), and hardness (\(HD\)) of understudy CFRP. The equations adequacy was analyzed by a lack-of-fit test. The coefficient of determination, i.e., R-square (R-sq), adjusted R-square (R-sq(adj)), and predicted R-square (R-sq(pred)), are summarized in Tables 3-5. The lack-of-fit test shows the adequacy of the relationship between modeled curing parameters and responses/properties. The \(p\)-value of lack-of-fit is an indicator of the regression model adequacy. As the \(p\)-values of lack-of-fit for \(TS\), \(IT\), and \(HD\) are greater than 0.05, it shows that lack-of-fit is insignificant. In other words, prediction models are properly formulated, and the important parameters and their interaction terms are included. The R-sq, R-sq(adj), and R-sq(pred) of \(TS\), \(IT\), and \(HD\) equations show that the regression model describes the experimental values with good approximation. The validation of the regression models is appended in Appendix A to this article. Validation was carried out by selecting curing parameters other than those used in Table 2, and the regression models output was compared with that of experimental results [47]. \[TS=2970-19.58T_{c}-32.05t_{c}+182P_{au}+0.2979T_{c}t_{c}-1.266T_{c}P_{au} \tag{2}\]  
\[IT=180.6-2.098t_{c}-1.575T_{c}-23.98P_{au}+0.01843T_{c}t_{c}+0.18445T_{c}P_{au }+0.2615P_{au}t_{c}-0.00199T_{c}P_{au}t_{c} \tag{3}\]  
\[HD=-6.27+0.3263T_{c}+0.1236t_{c}+0.494P_{au} \tag{4}\]Figure 8: Residuals of properties vs. fitted value for (**a**) tensile strength, (**b**) impact toughness, and (**c**) hardness data. The uneven distribution indicates the constant variance of the data points. Figure 7: Normal probability plot of residuals for (**a**) tensile strength, (**b**) impact toughness, and (**c**) hardness data. _p_-values > 0.05 indicate that residuals are normally distributed.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 3: Analyze Threats to Base Energy Security  
#### 4.3.1 Step 3.1: Reference Baseline Threats  
In this case study, we use the baseline threats outlined in Step 3.1 in the Methodology (Section 3) above. We also include cyber attack because the fictionalized case study assumes that Naval Support Activity Monterey has extensive SCADA systems and automation (many military bases have not yet upgraded to SCADA systems and automated or remote-operated controls) which can be vulnerable to cyber attack [66; 67; 68], severe storms due to the installation's proximity to the coastline, and plane crash due to proximity of runways and flight paths to critical loads. As opposed to civilian microgrids and nanogrids, militaries are often specifically concerned with plane crashes due to a variety of causes (training accident, intentional attack, equipment malfunction, etc.), and with the potential for much worse outcomes due to munitions and fuel loads [69]. We omit additional threats beyond these for brevity. However, a full analysis of a real military installation should ensure any additional location-specific threats are accounted for. The following threats are analyzed in the case study: loss of all generators, loss of all PV, insider threat, fire, explosion, fuel delivery disruption, cyber attack, severe storms, and plane crash.  
Figure 5: Microgrid and Nanogrid Design. This one-line diagram represents the overview of the chosen nanogrid architecture highlighting the amount of loads, DERs, and controller architecture.
## 4 Conclusions  
A dual-functional electrochemical system based on the HABDD and FNi electrodes was used simultaneously for wastewater degradation and hydrogen generation. The HABDD film electrode was custom-made and prepared on the patterned template using the MWCNT method. It was characterized by SEM and Raman, which showed the details of the HABDD microstructures. The HABDD was used as the anode for fast degradation of wastewater and generation of H\({}^{+}\) ions for the hydrogen evolution on the FNi cathode. A fast liquid-solid convection with relative stirring was formed during the degradation process, making the flow state change from stagnation to turbulence near the electrode surface. Different kinds of wastewater were chosen to test the electrochemical performance of this system. The relationship between wastewater degradation and hydrogen evolution was evaluated in terms of the COD decline and the hydrogen yields. The electrochemical efficiency was discussed, and the advantages of this electrochemical system were mainly caused by the diffusion path structure created by the arranged holes of the HABDD. The HABDD had a faster effective mass transfer in comparison to the flat BDD, which promoted the COD removal rate due to the holed structure. The hydrogen production efficiency was evaluated in correlation to the COD decline. This electrochemical system based on the HABDD and FNi electrodes showed its advantages in environmental protection and energy generation. Future development should focus on efficiently coupling this system with regenerative energy to reduce energy consumption.  
[PERSON] proposed the conceptualization, performed the electrochemical experiments and wrote the article. [PERSON] collected the wastewater samples and performed the characterization. [PERSON] performed data collection and computation. All authors have read and agreed to the published version of the manuscript.  
Fundamental Research Funds for the Central Universities, and Science & Technology Department of Sichuan Province (2022 NSFSC0314, 23 NSFSC1174).  
Not applicable.  
Data Availability Statement: The data that support the findings of this study are available from the authors upon reasonable request.  
[PERSON] for SEM characteriazation.  
The authors declare no conflicts of interest.
## 2 Methodology
### Events Management  
#### 2.5.1 The Need for an Adaptive Step Size  
To effectively simulate the dynamic model, the inclusion of an adaptive step size is imperative for several reasons, as follows:  
* As elucidated in the subsequent subsection, certain events transpire during the simulation that necessitate detection in order to modify the model's configuration. Occasionally, it becomes essential to recompute a simulation point at a prior moment during the simulation. Should a fixed step size be employed for efficiency, it must be exceedingly small, thereby prolonging the computation time of the simulation. Hence, an adaptive time step is favored in this context;
* The simulation must be halted once the sizing criteria have been established. Initially, the steady state of the model must be identified. Subsequently, another operating period must be conducted in a steady state to ascertain the sizing criteria;
* The FFT computation, detailed in the following subsection, necessitates a time-domain simulation recorded at fixed sampling intervals. Consequently, the adaptive time step will be adjusted to capture simulation points that adhere to the sampling period.
## 3 Results and Discussion
### HLD Equation of AAE(6 EO4 PO)  
Concerning Figure 2, the slopes (\(-\)c\({}_{\rm T}\)/b) and the intercepts ((K\({}_{\rm n}\)/b)EACN \(-\) (\(\beta\)/b)) of the graph plotted between the optimal salt concentration and the temperature were used to determine the constants (i.e., b, K\({}_{\rm n}\), c\({}_{\rm T}\), and \(\beta\)) of each model oil and each temperature with Excel Solver. According to the solver assumption, Figure 5 shows the optimal salt concentrations from the HLD prediction and the actual experiment. Linear regression was fitted with R\({}^{2}\) = 0.99, meaning that the HLD equation with the constants resulting from the solver process could effectively predict the optimal salinity of the single AAE(6 EO4 PO) system.  
The average values of the results obtained from all the model oils and all the conducted temperatures showed that b = 0.12 \(\pm\) 0.00 (g/100 mL)\({}^{-1}\), K\({}_{\rm n}\) = 0.15 \(\pm\) 0.01, c\({}_{\rm T}\) = 0.02 \(\pm\) 0.00 \({}^{\circ}\)C\({}^{-1}\), and \(\beta\) = \(-\)0.46 \(\pm\) 0.18. The electrolyte co-efficiency constant (b) was close to the typical value, which is 0.13 for NaCl [27; 29; 30; 65; 66]. Moreover, this finding is also supported by the work of [PERSON] et al. (2016), who found that the b value for NaCl of the nonionic surfactant system was 0.10-0.12 \(\pm\) 0.01 (g/100 mL)\({}^{-1}\)[56]. The hydrophilicity of the surfactant (K\({}_{\rm n}\)) depended on the hydrophilic part (the head) of the surfactant structure. The K\({}_{\rm n}\) of this study showed the same result as the K\({}_{\rm n}\) value for the alkyl ethoxylated alcohol, which was 0.15 [29]. The similarity could be due to the six-ethylene oxides in the AAE(6 EO4 PO) molecule, which affected the K\({}_{\rm n}\) value. For the temperature factor (c\({}_{\rm T}\)), this work showed a lower value than the previous report value of c\({}_{\rm T}\) (\(\sim\)0.06 \({}^{\circ}\)C\({}^{-1}\) for the nonionic surfactants) [42]. From the controlled condition in this work, the \(\beta\) value of AAE(6 EO4 PO) was negative, which indicated that AAE(6 EO4 PO) was a hydrophilic surfactant. The HLD of AAE(6 EO4 PO) is presented in Equation (8):  
\[\rm HLD_{AAE(6 EO4 PO)}=(0.12)S\ -\ (0.15)(EACN)+(0.02)(T-T_{ref})-0.46 \tag{8}\]