esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Regression Neural Networks for Proportion Judgment
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset (Samples)** & **Model** & _MAE_ & _RMSE_ & \(\mathbf{R^{2}}\) \\ \hline \multirow{8}{*}{TOYds (10,000)} & vanilla CNN & 1.26 & 1.84 & 0.983 \\  & VGG-19 (scratch) & 3.26 & 4.83 & 0.971 \\  & VGG-19 (transfer) & 2.68 & 3.62 & 0.985 \\  & Xception (scratch) & 0.69 & 0.93 & 0.883 \\  & Xception (transfer) & **0.37** & **0.56** & **0.998** \\  & InceptionResNetV2 (scratch) & 0.90 & 1.29 & 0.997 \\  & InceptionResNetV2 (transfer) & 0.42 & 0.60 & 0.998 \\ \hline \multirow{8}{*}{TOYds (25,000)} & vanilla CNN & 0.23 & 0.29 & 0.998 \\  & VGG-19 (scratch) & 1.83 & 3.21 & 0.989 \\  & VGG-19 (transfer) & 1.25 & 2.88 & 0.991 \\  & Xception (scratch) & 0.45 & 1.69 & 0.998 \\  & Xception (transfer) & **0.21** & **0.27** & **0.999** \\  & InceptionResNetV2 (scratch) & 0.37 & 0.53 & 0.999 \\  & InceptionResNetV2 (transfer) & 0.17 & 0.25 & 0.998 \\ \hline \multirow{8}{*}{OFPds (1314)} & vanilla CNN & 6.95 & 10.87 & 0.817 \\  & VGG-19 (scratch) & 8.72 & 12.56 & 0.724 \\  & VGG-19 (transfer) & 5.66 & 8.44 & 0.892 \\  & Xception (scratch) & 7.85 & 8.74 & 0.875 \\  & Xception (transfer) & 5.43 & 8.54 & 0.890 \\  & InceptionResNetV2 (scratch) & 8.78 & 13.12 & 0.711 \\  & InceptionResNetV2 (transfer) & **5.38** & **8.34** & **0.956** \\ \hline \multirow{8}{*}{OFPds augmented (8509)} & vanilla CNN & 3.45 & 5.68 & 0.954 \\  & VGG-19 (scratch) & 3.90 & 6.95 & 0.927 \\ \cline{1-1}  & VGG-19 (transfer) & 3.56 & 5.61 & 0.952 \\ \cline{1-1}  & Xception (scratch) & 6.25 & 9.49 & 0.864 \\ \cline{1-1}  & Xception (transfer) & 3.28 & 5.64 & 0.952 \\ \cline{1-1}  & InceptionResNetV2 (scratch) & 4.03 & 6.62 & 0.933 \\ \cline{1-1}  & InceptionResNetV2 (transfer) & **2.90** & **4.44** & **0.970** \\ \hline \multirow{8}{*}{AILds (18,000)} & vanilla CNN & 2.13 & 3.98 & 0.939 \\ \cline{1-1}  & VGG-19 (scratch) & 2.27 & 4.46 & 0.923 \\ \cline{1-1}  & VGG-19 (transfer) & 1.77 & 3.39 & 0.956 \\ \cline{1-1}  & Xception (scratch) & 2.96 & 5.74 & 0.873 \\ \cline{1-1}  & Xception (transfer) & **1.69** & **3.13** & **0.962** \\ \cline{1-1}  & InceptionResNetV2 (scratch) & 2.50 & 5.06 & 0.901 \\ \cline{1-1}  & InceptionResNetV2 (transfer) & 1.75 & 3.37 & 0.956 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The prediction performance of the CNN-based models on the test datasets. However, further assessment is needed for whether the improvement in performance, which rarely exceeds 10%, justifies the high number of parameters and computational cost. For example, the basic Xception algorithm (modified for regression, in transfer mode) has 66% fewer parameters than the multichannel Xception * 3 model, or 55% shorter duration of each epoch during the learning phase.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
### Organization  
Section 2 provides details on the context and the corresponding narrowband signal model. In Section 3 the CRB derivation is presented for the delay, Doppler, amplitude and phase estimation of a band-limited signal in the dual source context. Section 4 introduces the CMLE and a sub-optimal estimator. These two estimators are used to validate the new CRBs. Section 5 provides results for two representative examples. Section 6.1 relates the presented results to the literature and Section 6.2 gives an overview of how the current study can branch out into specific studies. Finally a conclusion is drawn in Section 7.
## 3 Methods
### Wire Spacing Detection Method
#### 3.3.2 Contour Extraction  
As mentioned above, the imaging effect of the wire in the direction of the center of the camera is good, and the general manual inspection only observes the drop point and has been wound about 10 turns of the coil; the rest of the wire, due to the existence of tension, does not appear to change position after winding, so the method also narrowed the identification range of the camera to about 10 turns of the coil. It should be noted that the image already contains background information, which will change with the experimental environment. If we continue to use the threshold segmentation algorithm mentioned above for binarization, the selection of the threshold value is heavily influenced by the background, and the obtained binarized image is not suitable for subsequent processing, so we use the Otsu [34] binarization method to determine the binarization segmentation threshold, and the Otsu binarization method can be a segmentation algorithm based on the gray level, which can make threshold adjustments with environmental changes.  
After binarization, edge contour extraction is required. The Canny edge detection algorithm can identify the edge distribution of the image more precisely [35; 36], and the edge contour image in Figure 12c is obtained after the above operation is completed.
## 2 Methods  
### The Overall Architecture of MixerNet-SAGA  
MixerNet-SAGA represents an advanced deep learning model building upon the foundational strengths of the U-Net architecture and introducing pivotal modifications tailored for high-resolution remote sensing image road extraction. The acclaim of U-Net stems from its symmetrical encoder-decoder layout, facilitating comprehensive feature extraction spanning from superficial to profound layers, while spatial information is retained via skip connections. This inherent design underpins U-Net's exemplary performance in image segmentation tasks, especially when confronting remote sensing images imbued with intricate backgrounds and minute details [27].  
To further bolster its capabilities, we integrated the ConvMixer block during the encoder phase. This block marries the advantages of deep convolution with 1 \(\times\) 1 convolutions, enabling enhanced capture and amalgamation of multi-scalar features while preserving computational efficiency. Such an amalgamation augments the model's discriminative process, which is particularly essential in Tm [(insights)-250 0 1 1.  
cies and morphologies, especially in regions riddled with intricate intersections or areas intersecting with other terrain elements.  
In summary, MixerNet-SAGA, by capitalizing on U-Net's merits and synergizing with the prowess of ConvMixer and SAG blocks, achieves significant performance elevation in road extraction tasks from high-resolution remote sensing imagery. These enhancements not only augment model accuracy but also fortify its resilience in complex scenarios. The architectural visualization of the network is depicted in Figure 1.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 3 RMCSat Mission
### Performance Requirements and Constraints
#### 3.1.4 Orbit Parameters and Requirements
The grey beam represents the primary antenna main lobe. Figure 6: The proposed payload operations cycle for a noon/midnight SSO, shown with the Sun at zero declination. The grey zenith-pointing beam is the primary payload antenna, the green lower beam represents the secondary antenna for Earth flux sensing. The timing of measurement operations ensures that the Sun passes through the first null of the antenna beam at the beginning and end of each measurement. This example depicts a local noon descending node, however, the direction of noon equatorial crossing is not important for data collection operations. A different SSO with a mean local time (MLT) other than noon-midnight is possible but requires an adjustment to the pointing regime of RMCSat to ensure that the Sun passes through the antenna boresight. A latitude shift of 15\({}^{\circ}\) per hour means that for each hour of MLT earlier or later than local noon, the satellite must be rotated about its velocity axis by 15\({}^{\circ}\) to align the peak gain of the payload antenna with the Sun. This tilt also affects any secondary antenna, changing the footprint of the Earth's brightness temperature sensed and its ability to replicate the terrestrial thermal emission seen by the primary antenna. Figure 7 shows the offset of both the primary and secondary antenna beams. The amount of tilt required for an offset SSO remains fixed; so, if known in advance, the secondary antenna could be mounted at an angle to maintain nadir and compensate for the tilt. A non-SSO results in a different MLT for each orbit and the payload antenna requires a different tilt offset applied on each successive pass. If effective shielding against radio interference is possible, such an orbit may result in usable observations, particularly if the orbital inclination is low. As orbital inclination increases, however, greater tilt offsets are required to align with solar declination, meaning some passes do not generate useful data due to Earth interference. Ideally, a repeating ground track orbit, where the satellite revisits the same geographic point after a set period of time, is used in conjunction with a GPS receiver to help compensate for terrestrial contributions to RF emissions. Over time, a pattern of radio interference from the surface brightness temperature and radar interference may be established and used for post-processing of the primary antenna data as a backup for the secondary antenna. However, it is unlikely a repeating ground track orbit is achievable for RMCSat given the limited launches for such a specialized orbit. The primary implications of the mission orbit on subsystems relate to the time available for solar power generation, the pointing accuracy of the ADCS potential tilting nanoeuvers, and the flexibility for a tilted mounting for the secondary antenna.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 4 Algorithm Performance Analysis and Comparison
### Evaluations on Fault Diagnose Time Complexity  
Processing speed is also an important consideration in identifying faults so that mitigation can be implemented quickly and operational disruption minimized. In this regard, the prediction time for classifying given unknown data into three fault modes is used to evaluate the processing time, which can provide a relative measure of the time requirements for using machine learning algorithms in fault detection. This is based on first training each machine learning algorithm-based detector using historical data offline, and then implementing each algorithm for fault detection and localization. As illustrated in Figure 16, the processing time for the three classifiers increases with more input testing data awaiting to be classified. For the three algorithms trained with S\({}_{11}\) and S\({}_{21}\), the orders of time complexity are the same. SVM has the longest processing time and LR has the shortest processing time. Compared with the LR classifier, GBDT improves the classification accuracy but requires more processing time. This is because of the preliminary building and judging of pre-defined numbers of trees in the ensemble algorithm, which consumes more time than a single algorithm and increases the overall processing time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **S\({}_{11}\)** & **S\({}_{21}\)** \\ \hline SVM & 99.84\% & 94.30\% \\ \hline LR & 99.74\% & 98.65\% \\ \hline GBDT & 99.72\% & 99.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: AUC for three algorithms.  
Figure 16: _Cont._  
Figure 15: ROC curves for different algorithms trained on (a) S\({}_{11}\), (b) S\({}_{21}\).
## 3 Results
### Stereo Camera Calibration
Concerning the G band, the error was relatively low at 800 mm (18.36%) but increased at longer distances, peaking at 23.22% at 1000 mm, and then decreasing to more moderate values (18.88% and 21.32% at 1300 mm and 1370 mm, respectively). Although this behavior was replicable for all the other band configurations, the RE band showed the best stability in terms of error, with a maximum of 1000 mm error of 10.41%. Also in this case, the effect of the Y-axis reprojection error emerged, which for the RE band showed a more stable and linear behavior. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{6}{c}{**Depth Estimation (Z) Error, Band G vs. R**} & \multicolumn{6}{c}{**Depth Estimation (Z) Error, Band B vs. R**} \\ \hline dist\_mm & nPoints & Mean \(\pm\) std & Err\% & dist\_mm & nPoints & Mean \(\pm\) std & Err\% \\ \hline
800 & \(1.49\times 10^{5}\) & 653.11 \(\pm\) 2.86 \(\times 10^{-5}\) & 18.36 & 800 & 1.26 \(\times 10^{5}\) & 750.43 \(\pm\) 3.13 \(\times 10^{-5}\) & 6.20 \\
1000 & \(1.05\times 10^{5}\) & 767.77 \(\pm\) 2.58 \(\times 10^{-5}\) & 23.22 & 1000 & 91,071 & 884.05 \(\pm\) 3.19 \(\times 10^{-5}\) & 11.60 \\
1300 & 70,961 & 1054.60 \(\pm\) 5.35 \(\times 10^{-5}\) & 18.88 & 1300 & 55,859 & 1207.90 \(\pm\) 4.45 \(\times 10^{-5}\) & 7.08 \\
1370 & 55,048 & 1077.90 \(\pm\) 1.02 \(\times 10^{-4}\) & 21.32 & 1370 & 45,625 & 1238.10 \(\pm\) 8.46 \(\times 10^{-5}\) & 9.63 \\ \hline \multicolumn{6}{c}{**Depth Estimation (Z) Error, Band NR vs. R**} & \multicolumn{6}{c}{**Depth Estimation (Z) Error, Band RE vs. R**} \\ \hline dist\_mm & nPoints & Mean \(\pm\) std & Err\% & dist\_mm & nPoints & Mean \(\pm\) std & Err\% \\
800 & \(1.37\times 10^{5}\) & 729.23 \(\pm\) 2.50 \(\times 10^{-5}\) & 8.85 & 800 & 99,057 & 762.22 \(\pm\) 3.23 \(\times 10^{-5}\) & 4.72 \\
1000 & 99,340 & 856.87 \(\pm\) 2.85 \(\times 10^{-5}\) & 14.31 & 1000 & 82,246 & 895.92 \(\pm\) 3.38 \(\times 10^{-5}\) & 10.41 \\
1300 & 52,389 & 1173.40 \(\pm\) 4.53 \(\times 10^{-5}\) & 9.74 & 1300 & 48,286 & 1227.02 \(\pm\) 4.19 \(\times 10^{-5}\) & 5.95 \\
1370 & 49,960 & 1195.80 \(\pm\) 6.28 \(\times 10^{-5}\) & 12.72 & 1370 & 55,965 & 1258.60 \(\pm\) 8.57 \(\times 10^{-5}\) & 8.13 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of the depth estimation (Z) in mm for stereo pairs (G vs. R, B vs. R, NR vs. R, and RE vs. R) at fixed distances (800 mm, 1000 mm, 1300 mm, and 1370 mm). The number of points used (nPoints), excluding NaN values and outliers (\(a<\) 0.05), the mean value \(\pm\) standard error, and relative error (%), are reported. Figure 3: Reprojection errors expressed as the average values along the X and Y components for a single distance, relative to the stereo pairs used in the calibration process.
## 2 Problem Formulation
### Optimal Economic Environmental Dispatch of Power System
#### 2.1.2 Constraints  
1. Power Output Limits \[P_{Gi}^{\min}\leq P_{Gi}(t)\leq P_{Gi}^{\max}\] (4) where \(P_{Gi}^{\min}\) and \(P_{Gi}^{\max}\) represent the minimum power output and maximum power output of unit \(i\), respectively.
2. Ramp Rate Limits \[\left\{\begin{array}{cc}P_{Gi}(t)\geq\max\Big{\{}P_{Gi}^{\min},P_{Gi}(t-1)- \Delta P_{Gi}^{down}\Big{\}},&P_{Gi}(t)\leq P_{Gi}(t-1)\\ P_{Gi}(t)\leq\min\Big{\{}P_{Gi}^{\max},P_{Gi}(t-1)+\Delta P_{Gi}^{up}\Big{\}},&P_{Gi}(t)\geq P_{Gi}(t-1)\end{array}\right.\] (5) where \(\Delta P_{Gi}^{up}\) and \(\Delta P_{Gi}^{down}\) represent the ramp up rate and the ramp down rate of unit \(i\), respectively.
3. Line Capacity Limit \[S_{l}(t)\leq S_{l}^{\max}\] (6) where \(S_{l}^{\max}\) is the maximum capacity of line \(l\).
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 3 LED to Ambient Light Ratio Optimization
### A Top-Down Explanation on How L2A Ratio and LED Density Determine Bit Detection Accuracy
#### 3.3.2 Effect of Extinction Ratio and Interference on Bvar and Wvar  
Bvar and Wvar of the On and Off LED features are determined by the characteristics of LEDs in the received image. In VOCC, these imaging LED characteristic includes LED extinction ratio and LED interference as illustrated in Figure 5.  
Obviously, the Bvar is larger when the extracted features of On LEDs are more different compared to that of Off LEDs. After all, the On LEDs are differentiated to Off LEDs in the image only because of the difference in their greyscale. Therefore, the more difference between On and Off LED greyscale, the larger Bvar is.  
In the filed of image processing, the greyscale difference between On and Off LEDs can be expressed by the term contrast. However, in optical communication, the difference between two received optical powers that represent the logic levels 1 and 0 is usually expressed by the term extinction ratio. In VOCC, the logic level 1 is conveyed by the On LEDs while the logic level 0 is conveyed by the Off LEDs in the captured image. Therefore, in the VOCC system, the difference in greyscales of the On and Off LEDs in the received LED panel image is expressed by the term extinction ratio:  
\[r_{e}=\frac{G_{On}}{G_{Off}}, \tag{8}\]  
where \(r_{e}\) is the LED extinction ratio, \(G_{On}\) and \(G_{Off}\) are the greyscales of the On and Off LEDs, respectively.  
As shown in Figure 5, a higher extinction ratio creates a significant difference in the greyscale of On and Off LEDs in the image, and thus a larger Bvar, between the On and Off LED features.  
Figure 5: Extinction ratio and interference of LED panel images. (**a**) Low extinction ratio. (**b**) High extinction ratio. (**c**) Low interference. (**d**) High interference.  
Besides LED extinction ratio, LED interference is another important characteristic of LEDs in the captured image that has direct influence on the characteristic of extracted LED features. LED interference is defined as the interference between adjacent LEDs in the captured image. Because of the interference, the boundary of On and Off LEDs are not well-defined as illustrated in Figure 6. Consequently, the larger LED interference results in the larger variance of the extracted features of On and Off LEDs. As Wvar indicates the homogeneity of the On and Off features, a higher interference creates a larger Wvar.  
The impacts of LED extinction ratio and LED interference on Bvar and Wvar can be described in Figure 6.
## 4 Conclusions  
In this study, we numerically examined the impact of the RGV of a stage in a two-stage pump on its indicators, including operating parameters such as head, losses and hydraulic coefficient of efficiency.  
Three RGVs with different Outlet angles were studied: \(\alpha_{6}\)--original RGV with \(\alpha_{6}=90\) deg, RGV1 with \(\alpha_{6}=110\) deg and RGV2 with \(\alpha_{6}=128\) deg.  
Based on the simulations conducted, the following key conclusions can be made:  
* When operating with RGV2, the pump stage ensures nearly 20% more head than when operating with the original RGV;
* When operating with the three types of RGV, the head loss and hydraulic coefficient of efficiency obtained are practically the same;
* When operating with the three types of vanes, losses in the diffuser dominate over those in the impeller;
* Based on the entropy production analysis, it can be concluded that in all considered operating cases, losses due to velocity fluctuation dominate, and losses from viscous diffusion are insignificant. Losses resulting from wall friction are similar in value to those obtained due to velocity fluctuation;
* The impact of the larger angle \(\alpha_{6}\) on the head of the pump stage is expressed in the creation of a negative circulation relative to the axis of rotation in front of the impeller inlet, leading to its increase. In this sense, the original RGV creates a positive circulation, while RGV1 and RGV2 create a negative circulation, which is valid in all studied modes of pump stage operation.  
Conceptualization, K.K. and J.G.; methodology, G.P.; software, K.K. and J.G.; validation, B.K. and S.I.; formal analysis, J.G., K.K., G.P., B. K. and S.I.; investigation, S.I. and B. K.; resources, J.G. and K.K.; data curation, K.K., G.P. and B.K.; writing--original draft preparation, K.K., J.G. and G.P.; writing--review and editing, G.P.; visualization, K.K., B.K. and J.G.; supervision, K.K. and G.P.; project administration, S.I.; funding acquisition, J.G. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No BG-RRP-2.013-0001.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Total Least Squares Parameter Estimation  
The principle of least squares parameter estimation is to obtain the parameter estimation value of the linear model \(y=a_{1}x+b_{1}\) by minimizing the sum of squared residuals, i.e., \(min\sum\limits_{i=1}^{p}\left[y_{i}-(a_{1}x_{i}+b_{1})\right]^{2}\). At this point, the estimated values of the regression coefficients \(\hat{a}_{1}\) and \(\hat{b}_{1}\) are calculated according to Equation (6) [19].  
\[\left\{\begin{array}{l}\hat{a}_{1}=\frac{\sum\limits_{i=1}^{p}\left(x_{i}- \overline{x}\right)\left(y_{i}-\overline{y}\right)}{\sum\limits_{i=1}^{p} \left(x_{i}-\overline{x}\right)^{2}}\\ \hat{b}_{1}=\overline{y}-\hat{a}_{1}\overline{x}\end{array}\right. \tag{6}\]  
where \(\overline{x}=\sum\limits_{i=1}^{p}x_{i}/p\), \(\overline{y}=\sum\limits_{i=1}^{p}y_{i}/p\), and \(p\) is the number of sample points used for fitting.  
The total least squares method [20] is used to obtain the parameter estimation value of the linear model \(y=a_{2}x+b_{2}\) by minimizing the sum of squared vertical residuals, i.e., \(min\frac{1}{1+a^{2}}\sum\limits_{i=1}^{p}\left[y_{i}-(a_{2}x_{i}+b_{2}) \right]^{2}\). The estimated values of the regression coefficients \(\hat{a}_{2}\) and \(\hat{b}_{2}\) are calculated according to Equation (7).  
\[\left\{\begin{array}{l}\hat{a}_{2}=-c\pm\sqrt{1+c^{2}}\\ \hat{b}_{2}=\overline{y}-\hat{a}_{2}\overline{x}\end{array}\right. \tag{7}\]  
where \(c=\frac{\sum\limits_{i=1}^{p}\left(x_{i}-\overline{x}\right)^{2}-\sum\limits _{i=1}^{p}\left(y_{i}-\overline{y}\right)^{2}}{\sum\limits_{i=1}^{p}\left(x_ {i}-\overline{x}\right)\left(y_{i}-\overline{y}\right)}\); \(``\pm\"\) in \(\hat{a}_{2}\) depends on the distribution of \((x_{i},y_{i})(i=1,2,\cdots,p)\). If the sample covariance is greater than 0, we take \"\(+\"\); if it is less than 0, we take \"\(+\"\).  
The common reliability distribution function in engineering is the Weibull distribution, \(F(t)=1-\exp\left[-(t/\eta)^{\delta}\right]\), where \(x=\ln t\), \(y=\ln[\ln(1/(1-F(t)))]\) can be transformed into a linear model, \(y=ax+b\)[21]. In terms of practical significance, it can be seen that \(x_{i}\) and \(y_{i}\) are positively correlated, and the covariance is greater than 0. Therefore, in Formula (7), \(\hat{a}_{2}=-c+\sqrt{1+c^{2}}\). At this point, the Weibull distribution parameter estimations are as follows.  
\[\left\{\begin{array}{l}\hat{\eta}=\exp\left(-\hat{b}_{2}/\hat{a}_{2}\right) \\ \widehat{\beta}=\hat{a}_{2}=-c+\sqrt{1+c^{2}}\end{array}\right. \tag{8}\]  
where \(\widehat{\beta}\) is the Weibull distribution shape parameter, and \(\hat{\eta}\) is the Weibull distribution scale parameter.