esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 2 VLC System Model Applied to Underground Mining Environments
### Channel DC Gain
Based on the mathematical expression of the typical Lambertian channel model, the DC gain of the scattering optical wireless channel produced by scattering on the optical path LED \(i\)-\(S_{n}\)-PD \(j\), which corresponds to the light beam that travels from the LED \(i\), interacts with the local scatterer \(S_{n}\) and reaches the PD \(j\), which can be written as [21]  
\[H_{sca,i,j}=\lim_{N\rightarrow\infty}\sum_{n=1}^{N}\frac{A_{p}(m+1)G_{n}(\mu) }{2\pi D_{I-n-j}^{2}}\cos^{m}(\phi_{i-S_{n}})\cos(\theta_{S_{n}-j})rect\bigg{(} \frac{\theta_{S_{n}-j}}{\Theta}\bigg{)}, \tag{11}\]where \(G_{n}(\mu)\) is a coefficient introduced by each scatterer \(S_{n}\), \(\theta_{S_{n}-j}\) represents the angle between the vector from the \(n^{th}\) scatterer of a set of \(N\) scatterers to PD \(j\) and \(\pi_{j}^{tilt}\), \(\phi_{i-S_{n}}\) is the radiance angle measured between \(\pi_{i}^{tilt}\) and the vector from LED \(i\) to PD \(j\), and the path length \(D_{i-n-j}\) represents the total distance that light travels from the LED \(i\) via \(S_{n}\) to the PD \(j\). This path length can be expressed as \(D_{i-n-j}=d_{i-S_{n}}+r_{n}\), where \(d_{i-S_{n}}\) is the distance of the transmitted optical link between the LED \(i\) and \(S_{n}\) and \(r_{n}\) is the distance from the \(n^{th}\) local scatterer to PD \(j\). Finally, the overall DC channel gain for UM-VLC systems is the sum of the LoS, non-LoS and scattering components, namely  
\[H_{miner,i,j}=H_{LoS,i,j}+H_{NLoS,i,j}^{(1)}+H_{sca,i,j}. \tag{12}\]
###### Abstract  
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.  
automated visual inspection; industrial applications; deep learning; computer vision; convolutional neural network; vision transformer +
Footnote †: journal:  
00204  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open-Access Papers
## 2 Materials and Methods  
### GNSS Rinex Data  
We used GNSS data from 13 IGS stations [14] in different regions. Figure 1 shows the location of the stations. There are two stations at the northern high latitudes and two stations at the southern high latitudes; four stations are located in the equatorial region, and five stations are at mid-latitudes. We tried to choose stations in different longitudinal sectors: 4 are in the American sector, 5 are in the European--African sector, and 5 are in the Asian sector.  
The first data set included data from 2000 to 2020. However, we preferred to exclude the year 2000 to eliminate data when selective availability was turned on. Figure 2 provides detailed statistics for each station for each year. There were from \(\sim\)2900 to \(\sim\)7200 daily files for each station.  
Figure 1: Map of GNSS stations. Labels show station ID and coordinates. Lines show the geomantic equator and 60 deg geomagnetic parallels.  
Our preliminary analysis showed that our estimations could be biased if we used different periods for different models. It is due to differences in solar and geomagnetic activity and differences in GNSS equipment and signal quality. The bottom panel in Figure 2 shows when data were available for all the models simultaneously. We had 1500-2100 daily files for each station. The data covers the end of 2014 and the entire 2015-2020 because NeQuickG and BDGIM coefficients started to be broadcast after 2014 and 2010, respectively. We used all available data, including those collected during perturbed periods. The analysis of the model's performance dependence on geomagnetic and solar flux indices is given later in this section.  
Figure 2: Data sets. (**a**) shows the whole data set, and (**b**) shows the data set when every model allows TEC calculations.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 5 Conclusions  
This paper describes the principles, design, manufacture, and measurement results of a multi-band ARC working in the L-, C-, X- and Ka-bands. Multi-band and multi-functional antennas, for example,  
Figure 14: Linearity error for the full dynamic range: (**a**) L-band; (**b**) C-band; (**c**) X-band; (**d**) Ka-band.  
the L/C-band common aperture and the dual-polarized C-band, were used in the ARC for the first time. A multi-band gain calibration circuit with a thermal stability of better than 0.1 dB over the temperature range of \(-20\) to \(-50\)\({}^{\circ}\)C was designed to ensure a gain stability of about 0.2 dB for a significant period of time for all four operational frequency bands.  
We manufactured three multi-band ARCs, and the gain stability, system transfer function, gain flatness, and linearity of ARC receiver were measured in the laboratory in order to characterize the multi-band ARC at its operational frequency bands. The results agreed well with the theoretical predictions and proved that the present multi-band ARC has excellent performance. For example, the gain stability is better than \(\pm 0.2\) dB for the L-band, \(\pm 0.22\) dB for the C-band, \(\pm 0.21\) dB for the X-band, and about \(\pm 0.25\) dB for the Ka-band for a significant period of time--about two weeks. These three multi-band ARCs will be used as the prime calibration standards during a validation and calibration campaign of the Shenzhen-1 SAR, Luojia-2 SAR, and other SARs after the launch of the satellites.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] the paper.  
This research was funded by the National Natural Science Foundation of China grant number 61571417; The authors thank the anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflict of interest.
## 5 Studies on the Effects of _M. oleifera_ Leaf Extracts on Male Reproductive Function
### Therapy and Medications: Highly Active Antiretroviral Therapy  
The increment in global access to highly active antiretroviral therapy (HAART) has significantly improved the management of HIV/AIDS [108]. HAART positively impacts AIDS patients' survival with a considerable decline in morbidity and a reduction in new HIV cases. HAART involves a combination of two or more antiretroviral drugs to treat HIV infection. The drugs mainly combined are nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) and protease inhibitors (P.Is) or non-nucleoside reverse transcriptase inhibitors (NNRTIs). The first line of HIV-1 defence contains a combination of two NRTIs (tenofovir and emtricitabine) and NNRTIs (efavirenz) [109]. Despite its high success rate, chronic use of NRTIs is followed by many adverse effects. Clinical practice and experimental studies have shown the negative consequence of the long-term use of HAART on male fertility [110]. The use of HAART leads to extensive apoptotic degeneration of germinal cells and metabolic disruption of testicular cells. HAART is also extensively involved in generating free radicals leading to oxidative stress damage. Studies on the animal model showed HAART administration reduced sperm parameters by distortion of testicular cell architecture and induction of DNA fragmentation of the testicular tissue. Hence, disturbing spermatogenesis and steroid synthesis [111]. It has also been indicated that exposure to HAART causes degeneration of the seminiferous tubules, spermatids necrosis and deformation of spermatocytes [112].  
Long-term exposure to HAART in patients with HIV-1 impairs sperm parameters by causing mitochondrial DNA depletion [109]. NRTIs prevent the replication of the virus; however, they also cause the depletion of mitochondrial DNA by inhibiting DNA polymerase, which leads to a decrease in the production of proteins involved in the electron transport chain, such as cytochrome c oxidase [113]. Hence, this causes damage to sperm motility and progressiveness [114]. Additionally, in HIV-1-infected men, HAART results in lower ejaculate volume and abnormal morphology of spermatozoa [114]. Impairment of sperm parameters may also be due to increased testicular oxidative stress induced by HAART [112].  
_M. Oleifera_ increased the levels of FSH, LH and testosterone hormone in male Wistar rats exposed to HAART therapy. _M. Oleifera_ also reduced the percentage of sperm with abnormal morphology and increased semen quality, characterised by fast sperm progressivity, increased sperm volume, increased motility of sperm and increased sperm count and viability [90]. Additionally, _M. Oleifera_ (100 and 300 mg/kg) increased the testicular weight and restored the microarchitecture of testicular morphology altered during HAART treatment [90]. Therefore, _M. Oleifera_ shows predominant effects in alleviating the side effects of HAART therapy.
## 2 Materials and Methods
### Data Acquisition and Data Fusion  
#### 2.2.1 Data Acquisition  
The data acquisition experiment was divided into constant and variable temperature experiments. Constant temperature data acquisition experiment: the Red Face strawberry was selected to be the experiment subject in this paper. The experiment used fresh strawberry fruits at about 4.5 kg as test samples, which had more than 90% maturity, equal fruit size, and no mechanical injuries or pests. All of the strawberries were divided into three groups, and each group was about 1.5 kg. The sensor node was placed in a container of 45 \(\times\) 30 \(\times\) 35 cm together with fresh strawberry samples. They were placed and well-sealed in a High and Low Temperature Alternating Humidity Test Chamber (HSLHP-225) with temperature control of 0, 5, and 20 \({}^{\circ}\)C in turn. The environmental parameters were monitored continuously for seven days, with a sensor acquisition frequency of 30 s/time. The data gathered by the thermostatic data acquisition experiment would be used for correlation analysis with quality parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Functions** & **Results** \\ \hline Input and output function & logsig \\ Training function & trainrp \\ Activation function & purelin \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant functions configuration results.  
Figure 4: The shelf-life prediction model based on BP neural network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Results** \\ \hline Epochs & 1000 \\ lr & 0.01 \\ Goal & 0.001 \\ mc & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relevant parameter configuration results.  
Variable temperature data acquisition experiment: The experiment simulated the fluctuant temperature condition (in the range of 0-4 \({}^{\circ}\)C) of strawberry CCT in a refrigerated truck by the temperature controller of HSLHP-225. Meanwhile, about 1.5 kg of fresh strawberries was selected and placed in the experimental box together with the sensor node (using the same text sample and container above). Data monitoring lasted seven consecutive days, and the sensor acquisition frequency was 30 s/time. The collected data in the variable temperature data acquisition experiment would be used to train and test the shelf-life prediction model.  
The sensor node and sink node accomplished data acquisition together in this paper. The sink node sent data acquisition order to the sensor node at a settled frequency. After receiving the data acquisition order, the sensor node collected environmental parameters such as temperature, humidity, O\({}_{2}\), and CO\({}_{2}\) in the storage environment. The collected environmental parameters were uploaded through the wireless communication network composed of the LoRa module. The environmental data were transmitted to the host computer or server through a 4G module after receiving by the sink node.
# Research of Adaptive Extended Kalman Filter-Based SOC Estimator for Frequency Regulation ESS
## 4 Conclusions  
In this paper, the FR ESS algorithm was designed and the FR test pattern of the ESS was analyzed to verify the SOC estimation performance of the EKF and AEKF in the test pattern. The frequency on the date on which the ESS usage was close to the annual average, and the frequency on the date when the ESS usage was higher than the annual average, were both obtained from the power grid data. The frequencies for these two dates were input to the FR ESS algorithm to generate a normal pattern and severe patterns, and the operating characteristics of the two patterns were analyzed. The SOC estimation of the frequency adjustment test pattern was performed using the EKF algorithm, and the SOC estimation performance of the EKF was compared with the Coulomb counting method, which is widely used as an SOC estimation technique. Significant noise was also added to the FR test pattern to show the accuracy with which the EKF estimates the SOC for the data that contain noise. In the case of the Coulomb counting method, the SOC estimation performance was greatly degraded owing to the noise of the test pattern, while the EKF algorithm showed an estimation error within 3%. An AEKF which updates the noise covariance according to the incoming input data was designed for comparing and analyzing the SOC and voltage estimation performances compared with the conventional EKF about severe noise data. The AEKF exhibited a better estimation performance than the conventional EKF for the data with severe noise. The AEKF continuously updated the information about noise in the algorithm, and adjusted the severe noise generated in the measured value to the error covariance in the algorithm. By doing this, the estimated value was calculated by placing more weight on the predicted value than the measured data, and it was confirmed that the estimated performance of the AEKF had a better noise elimination than the conventional EKF.  
[PERSON]-[PERSON] and [PERSON] conceived and designed the experiment; [PERSON], [PERSON] and [PERSON] performed the experiment; visualization; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the theory; [PERSON]-[PERSON] wrote the manuscript; [PERSON] and [PERSON] participated in research plan development and revised the manuscript; supervision [PERSON]; All authors contributed to the manuscript.  
Figure 15: Comparison of Kalman gain for EKF and AEKF during voltage estimation.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF), grant funded by the Space Core Technology Development Project (No. NRF-2017M1A3A3A03016056) and was supported by the projects of the Korea Electric Power Corporation (R17 TA08).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Results and Discussion  
In this section, the implementation condition, dataset, and inputs are explained. After that, the results will be discussed in detail. All of the algorithms and methods are implemented using MATLAB software (version R2017b). Furthermore, the following configurations and settings are considered in our study of the algorithms. For the FSHS-GMDH algorithm and the original GMDH, each maximum number of the layers and neurons is set to 4 and 10, respectively. In addition, the number of neurons in the hidden layer is set at 8 (4-8-1) for the multilayer perceptron (MLP) model. Furthermore, R\({}^{2}\), MSE, and Root Mean Squared Error (RMSE) are calculated, using Equations (20)-(22) to evaluate and compare the proposed hybrid algorithm and other methods. We used only two parameters, including density (RHOB) and V\({}_{\text{P}}\) in this study to estimate the shear wave velocity. Figure 3 shows the inputs data and original shear wave velocity. Equations (20)-(22) are as follows:  
\[\text{R}^{2}=\sqrt{\frac{\text{N}\times\sum\limits_{\text{t=1}}^{\text{N}}{( \text{X}_{\text{t}}\times\text{Y}_{\text{t}})}-\sum\limits_{\text{t=1}}^{\text {N}}{(\text{X}_{\text{t}})}\times\sum\limits_{\text{t=1}}^{\text{N}}{(\text{Y }_{\text{t}})}}{\sqrt{\left[\text{N}\times\sum\limits_{\text{t=1}}^{\text{N}}{( \text{X}_{\text{t}}^{2})}-\left(\sum\limits_{\text{t=1}}^{\text{N}}{(\text{X} _{\text{t}})}^{2}\right)\times\left[\text{N}\times\sum\limits_{\text{t=1}}^{ \text{N}}{(\text{Y}_{\text{t}}^{2})}-\left(\sum\limits_{\text{t=1}}^{\text{N}}{ (\text{Y}_{\text{t}})}^{2}\right]\right]}}} \tag{20}\]  
\[\text{MSE}=\frac{\sum\limits_{\text{t=1}}^{\text{N}}{(\text{Y}_{\text{t}}- \text{Y}_{\text{t}})}^{2}}{\text{N}} \tag{21}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\times\sum\limits_{\text{t=1}}^{\text{N}}{ (\text{Y}_{\text{t}}-\text{Y}_{\text{t}})}^{2}} \tag{22}\]  
In our research, the used shear wave velocity is original and measured by the dipole sonic log in the well-logging operation. The correlation between the mentioned parameters and the shear wave velocity must be evaluated in the first step. Figure 4 illustrates that the correlation between the V\({}_{\text{S}}\) and two selected input data are 0.71 and 0.86 for RHOB and V\({}_{\text{P}}\), respectively. Therefore, they can be reliable as the input data.  
Figure 2: A schematic view of the FSHS-GMDH algorithm.
## 4 Model Construction
### Layout Aesthetics and Visual Cognitive Mapping Model
#### 4.2.3 Improved Gray H-Convex Correlation  
This section calculates the similarity between two lines by calculating the H-convexity between them, that is, calculating the correlation between layout aesthetics lines and visual cognitive lines.  
**Definition 1**.: _As shown in Figure 5, there is an arbitrary line \(DEF\), where segment \(DF\) represents the span and the perpendicular \(HE\) from vertex \(E\) to span \(DF\) represents the arch height._  
**Definition 2**.: _As shown in Figure 5, the ratio of the arch height \(HE\) to span \(DF\) is the H-convexity of the folding lines \(DEF\), represented by \(d_{H-convex}\ =\ \frac{HE}{DF}\)._  
(1) Calculate H-convexity  
As shown in Figure 6, based on \(x(k)\), \(x(k+1)\), and \(x(k+2)\) in the standardized criteria series \(X\) plotting the fold, \(DEF\) spans \(DF\ =\ \sqrt{(k+2-k)^{2}+\big{[}x(k+2)-x(k)\big{]}^{2}}\ =\ \sqrt{4+\big{[}x(k+2)-x(k)\big{]}^{2}}\). For easy calculation of values, the value of \(GE\) is used instead of the arch height \(HE\). \(GE\ =\ x(k+1)-\frac{x(k)+x(k+2)}{2}\), then the H-convexity value of the sequence \(X\) at \(t\ =\ k+1\) is recorded as \(\hat{d}_{H}(X,k+1)\):  
\[\hat{d}_{H}(X,k+1)\ =\ \frac{x(k+1)-\frac{x(k)+x(k+2)}{2}}{\sqrt{4+\big{[}x(k+2)- x(k)\big{]}^{2}}} \tag{18}\]  
(2) Calculate the gray H-convex correlation coefficient  
Figure 5: Folding lines for different spans and arch heights.  
In this step, the aesthetic indicator weights obtained earlier are brought into the indicator data series. Let the aesthetic evaluation indicator be \(M\) and the object be \(N\). \(x_{m}^{n}\) is the evaluation value for the \(m\)th \((m~{}=~{}a,b,\cdots,M)\) indicator and the \(m\)th\((n~{}=~{}1,2,\cdots,N)\) object. The weighted standardized data series of the \(m\)th indicator can be represented as \(X_{a}~{}=~{}\omega_{a}\big{(}x_{a}^{1},x_{a}^{2},\cdots,x_{a}^{N}\big{)}\), \(X_{b}~{}=~{}\omega_{b}\big{(}x_{b}^{1},x_{b}^{2},\cdots,x_{b}^{N}\big{)},\cdots,X_{M}~{}=~{}\omega_{M}\big{(}x_{M}^{1},x_{M}^{2},\cdots,x_{M}^{N}\big{)}\). Then, the gray H-convexity coefficients of \(X_{a}\) and \(X_{m}\) at \(t~{}=~{}k\) are:  
\[\hat{\gamma}_{H(X_{a},X_{m})}(k)~{}=~{}\text{sgn}\Big{(}d_{H}(X_{a},k),\hat{d} _{H}(X_{m},k)\Big{)}\frac{1}{1+\Big{|}\Big{|}\hat{d}_{H}(X_{a},k)\Big{|}-\Big{|} \hat{d}_{H}(X_{m},k)\Big{|}\Big{|}} \tag{19}\]  
where \(\hat{d}_{H}(X_{a},k)\), \(\hat{d}_{H}(X_{m},k)\) is the H-convexity of the sequence \(X_{a}\) and \(X_{m}\) at \(t~{}=~{}k\), \(k~{}=~{}2,\cdots,n-1\), and \(\text{sgn}(\cdot)\) represents the sign function.  
\[\text{sgn}(x)~{}=~{}\begin{cases}1,&x>0\\ 0,&x~{}=~{}0\\ -1,&x<0\end{cases} \tag{20}\]  
(3) Calculate the gray H-convex correlation degree.  
The gray H-convex correlation degree between sequences \(X_{a}\) and \(X_{m}\) is denoted by \(\hat{R}_{H}(X_{a},X_{m})\), abbreviated as \(\hat{R}_{H}(a,m)\). The calculation method is:  
\[\hat{R}_{H}(X_{a},X_{m})~{}=~{}\frac{1}{n-2}\sum_{k~{}=~{}2}^{n-1}\hat{\gamma} _{H(X_{a},X_{m})}(k) \tag{21}\]