esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview and Taxonomy of Solar PV Monitoring System

Table 1: Classification of various electrical and environmental parameters monitored under large-scale and small-scale solar PV systems. Figure 4: Primary layers for the development of a solar-based monitoring system. Various state-of-the-art monitoring technologies for solar PV applications have been comprehensively studied based on the application of electronics modules required for data processing, data transmission protocols, and Artificial Intelligence (AI) techniques. Monitoring technology depending on various data processing boards has been explored. The advancements in solar PV monitoring systems in relation to BeagleBone [32], Arduino [33, 34] Raspberry Pi [35, 36], PLC [37], and microcontroller chips such as ATMEGA8 and ATMEGA16 [38] have been explained along with their limitations. Data transmission protocols such as ZigBee [39, 40], GSM [41], Wi-Fi [42], Bluetooth [43], and LoRa [44] have also been studied with regard to the various aspects such as range, parameters to be monitored, program language, sampling time etc. The review of the monitoring technologies based on two distinct classes is presented in Figure 5.
###### Abstract  
Currently, five new-generation BeiDou (BDS-3) experimental satellites are working in orbit and broadcast B1I, B3I, and other new signals. Precise satellite orbit determination of the BDS-3 is essential for the future global services of the BeiDou system. However, BDS-3 experimental satellites are mainly tracked by the international GNSS Monitoring and Assessment Service (iGMAS) network. Under the current constraints of the limited data sources and poor data quality of iGMAS, this study proposes an improved cycle-slip detection and repair algorithm, which is based on a polynomial prediction of ionospheric delays. The improved algorithm takes the correlation of ionospheric delays into consideration to accurately estimate and repair cycle slips in the iGMAS data. Moreover, two methods of BDS-3 experimental satellite orbit determination, namely, normal equation stacking (NES) and step-by-step (SS), are designed to strengthen orbit estimations and to make full use of the BeiDou observations in different tracking networks. In addition, a method to improve computational efficiency based on a matrix eigenvalue decomposition algorithm is derived in the NES. Then, one-year of BDS-3 experimental satellite precise orbit determinations were conducted based on iGMAS and Multi-GNSS Experiment (MGEX) networks. Furthermore, the orbit accuracies were analyzed from the discrepancy of overlapping arcs and satellite laser range (SLR) residuals. The results showed that the average three-dimensional root-mean-square error (3D RMS) of one-day overlapping arcs for BDS-3 experimental satellites (C31, C32, C33, and C34) acquired by NES and SS are 31.0, 36.0, 40.3, and 50.1 cm, and 34.6, 39.4, 43.4, and 55.5 cm, respectively, the RMS of SLR residuals are 55.1, 49.6, 61.5, and 70.9 cm and 60.5, 53.6, 65.8, and 73.9 cm, respectively. Finally, one month of observations were used in four schemes of BDS-3 experimental satellite orbit determination to further investigate the reliability and advantages of the improved methods. It was suggested that the scheme with improved cycle-slip detection and repair algorithm based on NES was optimal, which improved the accuracy of BDS-3 experimental satellite orbits by 34.07%, 41.05%, 72.29%, and 74.33%, respectively, compared with the widely-used strategy. Therefore, improved methods for the BDS-3 experimental satellites proposed in this study are very beneficial for the determination of new-generation BeiDou satellite precise orbits.  
BDS-3 experimental satellites; precise orbit determination; improved cycle-slip detection and repair algorithm; improved methods; normal equation stacking; step-by-step 201818402; doi:10.3390/s180514020
# An Experimental Study on the Reflection Characteristics of Laser Echo Light Waves
## 2 Basic Principles
### Light Intensity Flicker Probability Density Distribution Characteristics  
For atmospheric turbulence, probability distribution is the most basic method to describe its statistical characteristics. The probability models describing the received light intensity distribution include the log-normal model and the Gamma-Gamma model, which correspond to the probability density distribution of light intensity fluctuations in weak turbulence and medium-strong turbulence, respectively. In a weakly turbulent environment, the probability density function (PDF) of light intensity fluctuations obeys the log-normal distribution and can be expressed as [17]  
\[p_{\omega}(I)=\frac{1}{\sqrt{2\pi\sigma_{I}^{2}}}\frac{1}{I}\exp\left[-\frac{ \left(lnI+\sigma_{I}^{2}/2\right)^{2}}{2\sigma_{I}^{2}}\right] \tag{4}\]  
where \(I\) is the received light intensity, and \(\sigma_{I}^{2}\) is the logarithmic variance in the received light intensity.  
The probability density function of the Gamma-Gamma distribution model can be expressed as [18]  
\[f_{I}(I)=\frac{2(\alpha\beta)^{\frac{\alpha+\gamma}{2}}}{\Gamma(\alpha)\Gamma (\beta)}I^{\frac{\alpha+\beta}{2}-1}K_{\alpha-\beta}\left(2\sqrt{\alpha\beta I }\right) \tag{5}\]  
where \(\Gamma(\cdot)\) is the Gamma function; \(K\)\(n(\cdot)\) is the modified Bessel function of the second order; and \(\alpha\) and \(\beta\) are parameters for measuring the characteristics of large-scale and small-scale eddies in atmospheric turbulence, respectively, which can be calculated by the following formula in general  
\[\left\{\begin{array}{l}\alpha=\left(exp\left(\frac{0.49\sigma_{R}^{2}}{\left( 1+1.11\sigma_{R}^{12/5}\right)^{7/6}}\right)-1\right)^{-1}\\ \beta=\left(exp\left(\frac{0.51\sigma_{R}^{2}}{\left(1+0.6\sigma_{R}^{12/5} \right)^{3/6}}\right)-1\right)^{-1}\end{array}\right. \tag{6}\]  
where \(\sigma_{R}^{2}\) is the Rytov variance.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 3 Transient Finite Element Simulations
### Two Contact Wires
Figure 9: The phase velocity dispersion spectra of two wires from nodal accelerations with an equal components triangular pulse force along the \(x\), \(y\), and \(z\) directions: (**a**) the active wire and (**b**) the passive wire. The theoretical phase velocity dispersion curves obtained in Figure 1a are represented by the black dotted lines. wire surfaces with a narrow bandwidth excitation signal loaded on the active wire end face were transformed into 2D FFT spectra, as shown in Figure 10. Dispersion nephograms in the spectra of both the active and passive wire agree with the theoretical phase velocity dispersion curve of the single L(0,1) mode below 500 kHz. The directions of the excitation loads determine the generation of guided wave mode propagation in the structure. As a result of the contact stresses between the two wires, the CAN phenomenon of the active wire was observed. The additional frequency component of 120 kHz that doubles the excitation frequency of 60 kHz appeared in the spectrum. The frequency components in the passive wire covered a bandwidth range of 60-120 kHz. This may have occurred because the dispersion of guided waves in the passive wire transferred by contact stresses was more severe than that generated by direct excitation. From the perspective of energy transfer, the guided wave energy distribution in the active and passive wires fit the results of the energy-based model described in Section 2.3, as shown in Figure 4.
## 4 Conclusions  
The intelligent classification of microseismic events is of great significance for coal mine disaster detection and early warning, and the classification method based on machine learning principles has been paid more and more attention by researchers because of its advantages in modeling. In this paper, the application of an FCN-LSTM network in the intelligent classification of microseismic events in coal mines is studied.  
The available training set data are obtained by increasing the number of samples and balancing the sample distribution through bandpass filtering, upsampling, window slicing, and window warping. The FCN-LSTM hybrid network structure is built, and five convolutional layers are added in front of the LSTM structure. Then, 70%, 20%, and 10% of the enhanced data set are randomly selected as the training set, validation set, and test set,  
Figure 11: The training process of the FCN-LSTM network with 5 convolutional layers. (**a**) is the accuracy of the training set and validation set, (**b**) is loss of the training set and validation set.  
respectively. Through training, the accuracy of the model validation set is 93.41%, and the accuracy of the validation set is 91.18%. The trained FCN-LSTM model is highly suitable. The first kind of microseismic event is related to underground water movement. The number and proportion of water inrush disasters markedly increase during its occurrence. Attention should be given to the number and proportion of these kinds of events during microseismic monitoring.  
In this paper, we propose a real-time intelligent classification method for microseismic events based on data augmentation theory and machine learning theory. This method can obtain a highly suitable network model in the case of limited and unbalanced distribution of microseismic monitoring data, complete intelligent classification of microseismic events in real time, and have a high accuracy.  
**Author Contributions:** Formal analysis, L.Z.; Resources, H.L.; Writing--original draft, G.S.; Writing--review & editing, G.S., D.L. and G.Q.; Supervision, L.L. and X.L.; Funding acquisition, D.L. and X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was by the National Natural Science Foundation of China (52174218, 52204257) and the China Postdoctoral Science Foundation (2022M713371).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by both the China University of Mining and Technology and Shenzhen Urban Public Safety Technology Research Institute Co., Ltd., authors [PERSON] and [PERSON] were employed by the company Shenzhen Urban Public Safety Technology Research Institute Co., Ltd., author [PERSON] was employed by the Dagang Oilfield Foreign Cooperation Projects Department of PetroChina. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Detection of On-Chip Generated Weak Microwave Radiation Using Superconducting Normal-Metal SET
## 3 Experimental Setup and Results
### Effect of Emitted Phonons  
To study possible effects of the substrate-mediated phonons on the detector output, an electromagnetically uncoupled reference source was used, positioned at the same distance from the detector as the coupled one. We measured the detector response for both sources operating in the subgap and above gap regimes. Figure 9a shows the detector response for an electromagnetically coupled (orange curve) source and an uncoupled (blue curve) source in subgap voltages, \(V_{\mathrm{J}}<2\Delta/e\). The detector response to the uncoupled source signal, scaled by the square of the critical current ratio, is substantially weaker (almost up to an order of magnitude) compared to the coupled source, and we found it to be gradually vanishing in the same manner as observed for the coupled source (see Figure 7), when sweeping the magnetic flux from 0 to \(\Phi_{0}/2\) (the curve not shown). On the contrary, the detector current responding to the quasiparticle current at \(V_{\mathrm{J}}>2\Delta/e\) in either of the Josephson sources increases with a similar linear slope (see Figure 9b) indicating a comparable rise in the counting rate for the same increase of the quasiparticle generation (and recombination) rate both in the coupled and in the uncoupled sources. We attribute this behavior to the contribution of the recombination phonons [36; 37] propagating via the substrate and achieving the detector after traveling over similar distances from both sources.  
Figure 9: (**a**) The detector signal for the coupled (orange curve) and the uncoupled source (blue curve) operating in the subgap voltage region. The signal of the uncoupled source was scaled down by a factor \((I_{\mathrm{C,Coupled}}/I_{\mathrm{C,Uncoupled}})^{2}=0.28\) for the sake of adequate comparison to account for the difference in the radiated microwave power. The coupled and uncoupled source critical currents are different due to technological reasons; (**b**) The detector signals for the same two sources biased in the quasiparticle branch \(V_{\mathrm{J}}>2\Delta/e\). The signal increases with the same slope for both sources (black dashed lines).
###### Abstract  
The improvement for hybrid radio frequency-free space optical (RF-FSO) communication system in wireless optical communications has acquired growing interests in recent years, but rarely improvement is based on hybrid modulation. Therefore, we conduct a research on end-to-end mixed RF-FSO system with the hybrid pulse position modulation-binary phase shift keying-subcarrier intensity modulation (PPM-BPSK-SIM) scheme. The RF link obeys Rayleigh distribution and the FSO link experiences Gamma-Gamma distribution. The average bit error rate (BER) for various PPM-BPSK-SIM schemes has been derived with consideration of atmospheric turbulence influence and pointing error condition. The outage probability and the average channel capacity of the system are discussed as well. Simulation results indicate that the pointing error aggravates the influence of atmospheric turbulence on the channel capacity, and the RF-FSO systematic performance is improved obviously while adopting PPM-BPSK-SIM under strong turbulence and severe pointing error conditions, especially, when the system average symbol length is greater than eight.  
free space optical (FSO); pulse position modulation-binary phase shift keying-subcarrier intensity modulation (PPM-BPSK-SIM); bit error rate (BER); pointing error; average symbol length +
Footnote †: journal: Applied Soft. 2019, 9, 3724; doi:10.3390/app9183724
## 5 Conclusions  
In this study, we investigated how the design parameters resulting from the electrical/mechanical performance of knitted meshes for satellite antennas affect the optical properties that are directly related to the thermal behavior of the mesh. For this purpose, the optical properties of eight types of specimens with different knitting patterns, OPI, numbers of twists, and tensile directions were measured. From the measurement results,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{\multirow{2}{*}{**w/o Solar Panels**}} & \multicolumn{1}{c}{\multirow{2}{*}{**3 EA**}} & \multicolumn{1}{c}{\multirow{2}{*}{**4 EA**}} & \multicolumn{1}{c}{\multirow{2}{*}{**6 EA**}} & \multicolumn{1}{c}{\multirow{2}{*}{**7 EA**}} & \multicolumn{1}{c}{\multirow{2}{*}{**8.5 m Area per Panel**}} & \multicolumn{1}{c}{\multirow{2}{*}{**(2.2 m \(\times\) 5.5 m Area per Panel)**}} & \multicolumn{1}{c}{\multirow{2}{*}{**(2.2 m \(\times\) 3.5 m Area per Panel)**}} \\ \cline{3-3} \cline{5-1  
we experimentally demonstrated that the optical properties of the mesh did not depend on a single design parameter. Additionally, depending on the pore characteristics of the mesh, the transmittance was measured to be over 80% for all specimens. Thus, it was shown through orbital thermal analysis that the transmission characteristics must be reflected to increase the reliability of the mesh antenna reflector thermal model. Then, thermal behavior and sensitivity analyses of the reflector were performed through antenna thermal analysis using the optical properties of the specimen. Furthermore, the effect of the deployed solar panels on the thermal gradient of the reflector was investigated. The results indicated that reducing the solar panel length while increasing the number of panels is one way to minimize the increase in the thermal gradient.  
Conceptualization, M.-YS. and H.-U.O.; methodology, M.-YS., B.-G.C. and H.-U.O.; validation, B.-G.C. and H.-U.O.; formal analysis, M.-YS.; investigation, H.-M.S.; data curation, H.-M.S.; writing--original draft preparation, B.-G.C.; writing--review and editing, B.-G.C. and H.-U.O.; visualization, B.-G.C. and H.-U.O.; supervision, H.-U.O.; project administration, B.-G.C. and H.-U.O.; funding acquisition, H.-U.O. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Institute of Civil-Military Technology Cooperation (ICMTC), Republic of Korea.  
The data presented in this study are available on request from the corresponding author.  
This research was financially supported by the Institutional of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government under grant No. 22-CM-EC-32.  
Author [PERSON] was employed by the company STEP Lab. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Classification of Harmonic Emissions Profiles
### Implementation of Support Vector Machines  
As a last step after definition of the five classes for the emission profiles and selection of features for their separation, the actual classifiers had to be implemented. Therefore, support vector machines (SVM) with a radial basis function kernel (RBF kernel) were proven as a suitable choice [22]. The SVM as a classifier is robust against typical problems like overfitting, which results in higher classification rates [34]. The computational effort is usually lower in comparison to other methods like Bayes classifications [35].  
The basic idea of SVMs is to construct a hyperplane within the feature space in order to divide the emission profiles according to their known classes. The hyperplane aims to separate two classes with a clear margin that is as wide as possible. The class of new emission profiles, which are mapped into the same feature space, are predicted based on which side of the hyperplane they are allocated. The regularisation parameter, \(C\), that strongly affects the resulting hyperplane is user specified [36].
# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 3 Results
### Results of Sharpness and Resolution Measurements
#### 3.1.3 Influence of Applied Sharpening of Dermoscopy Image in Melanoma Diagnosis  
The image sharpening process refers to any enhancement technique which concentrates on the edges and the fine details of an image. Sharpening operations are manifest through the SFR. With high sharpening, image noise and image sampling artefacts can be amplified along with the signal content. From the results in Table 2, it is evident that the tested cameras have image sharpening algorithms with different levels of sharpness.  
Two cropped images of the slanted edge presented in Figure 7e,f were captured with a Canon EOS M10 camera. In Figure 7e, the image is captured with a low sharpening level and a gradual transition is visible from the bright area to the dark area. The high level of the sharpness was set by capturing the image on Figure 7f, where the transition from the bright area to the dark area is more contrasting, and the artefacts and noise are visible. We wondered how these artefacts influence the assessment of dermatologists when observing dermoscopy images.  
In clinical evaluation, high sharpening of dermoscopy images emphasises artefacts (pieces of the dead epidermis, hair follicles, dust particles, air bubbles...), as well as colour and structural changes (Figure 7a,b), especially on additional magnification in partial image evaluation (Figures 7c and 8d). The changes that result from image sharpening can, therefore, reduce the capability to recognise characteristic signs for melanoma, especially on larger magnification and in cases when the dermoscopy procedure is performed by inexperienced dermatologists.  
Figure 7: Influence of sharpening in a mole dermoscopy image captured with a Canon EOS M10 camera with a DermLite 4 DL dermoscope (**a**) with a low sharpening level (**b**) with a high sharpening level, (**c**) a low sharpening level in partial image evaluation, (**d**) a high sharpening level in partial image evaluation, (**e**) cropped image of slanted edge in low sharpening level, and (**f**) cropped image of slanted edge in high sharpening level.  
Figure 8: The colour difference between captured and reference colours from the test chart is measured and derived as the CIELAB \(\Delta\)E colour distance. Diagrams for illuminance light source of 3200 K and 5500 K colour temperatures are presented, and for the Canon EOS 800D with the flashlight in the last row. Diagrams of captured colours are shown for the following devices: iPhone 5s (**a**), iPhone 7 (**b**), iPhone XR (**c**), Samsung S10 (**d**), Huawei P10 (**e**), Canon EOS M10 (**f**) and Canon EOS 800D (**g**).
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 4 3 GPP Release 17: Expansion of 5G Horizons  
3 GPP Release 17 further expanded 5G horizons by advancing existing features while introducing innovations tailored to emerging verticals and use cases.
## 5 Conclusions  
An SPR-based optical fiber sensor with pure high-order modes produced by an MSC is proposed and simulated. This designed SPR sensor containing an MSC can enhance the strength of the interaction between the propagation mode and the silver film. Thus, the sensitivity of the sensor using the LP\({}_{11}\) mode (the LP\({}_{21}\) mode) increases by approximately 330% (360%), which can be explained by theoretical calculations. The structural parameters of MSC are optimized to produce pure higher-order modes. In the sensing area of this sensor, the intensity modulation is adopted to calculate the variation of detected light intensity to measure RI. The RI resolution of our sensor, using the LP\({}_{11}\) mode (LP\({}_{21}\) mode), is \(2.6\times 10^{-4}\) RIU (\(2.4\times 10^{-4}\) RIU). Therefore, the sensor presented in this study providesan optimum device for the measurement of RI. Related experiments will be carried out in the future to further verify our results.  
Conceptualization, C.Y. and J.Z.; methodology, C.Y., H.Y. and D.J.; software, C.Y. and J.Z.; validation, C.Y., B.Y., Q.W. and J.Z.; formal analysis, C.Y. and B.Y.; investigation, C.Y.; writing--original draft preparation, C.Y.; writing--review and editing, C.Y., B.Y., Q.W., J.Z. and D.J.; visualization, C.Y.; supervision, H.Z., H.Y., H.F. and D.J.; project administration, D.J.; funding acquisition, D.J. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the National Natural Science Foundation of China (grant number: U1813207, 61875152) and the Key Projects in the Tianjin Science and Technology Pillar Program, grant number 20 YFZCSY00390, and in part by the Foundation of Wenzhou Safety (Emergency) Institute of Tianjin University under grant TJUWYY2022029.  
Not applicable.  
The data underlying this article will be shared upon reasonable request to the corresponding author.  
The authors declare no conflict of interest.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Fluid-Injection Scheme  
#### 4.4.1 Influence of Injection Flow Rate Amplitude  
When injecting fracturing fluid using a sinusoidal waveform, the mean injection flow rate of the fracturing fluid is kept constant, while the amplitude varies at 30%, 50%, and 80% of the mean value. The calculation results are presented in Figure 13. The simulation demonstrates that when the mean injection flow rate remains constant, the greater the amplitude variation in the injection flow rate, the more significant the change in downhole pressure, and the better the actual fracturing effect.