esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discrimination Performance
To further verify the superiority of the proposed method, the performance curves of these four methods are given versus different signal-to-noise ratios (SNRs) or jamming-to-noise ratios (JNRs) in Figure 5. Although the SNR or JNR has effects on the detection of targets, just the influence of SNR/JNR on the measurement accuracy of range, angle and radial velocity are taken into account in the simulation. Because the fixed random error and bias error are usually small, only the SNR/JNR-dependent measurement errors are considered [36]. The range resolution, velocity resolution and beamwidth are assumed to be 150 m, 10 m/s and 1\({}^{\circ}\), respectively. SNR/JNR varies from 5 dB to 25 dB. The deception speed is set as 100 m/s. It can be seen from Figure 4(a) that the physical target discrimination probability of the proposed DVB method slightly deviates from the expected value of 0.99 mainly due to the high measurement error of radial velocity when the value of SNR/JNR is small and fluctuates in the vicinity of the expected value with the increase in SNR/JNR. The superiority of the proposed DVB method is more obvious in the discrimination of false targets, as shown in Figure 4(b). Due to the combination of LIA and DVB, the LIA-DVB method can improve the discrimination performance with small deception velocity cases when each of them has a decent discrimination ability. Figure 4: Discrimination performance of four methods versus different deception velocities. It is worth noting that comparing the performance of the VVA and DVB in Figures 4 and 5, their curves are closely approximated for both physical targets and active false targets. This is because the data used in both methods is the same, and the discrimination statistics of the physical targets obey the same distribution in both methods. However, as mentioned in Section 3.2, the DVB method is much more concise than the VVA method. The simulation results verify the feasibility and effectiveness of using the velocity information to identify the false target with range and velocity joint deception.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 2 Materials and Methods  
### Costs of a Photovoltaic Installation  
To obtain an accurate analysis, all system costs are kept up to date, as any variation can cause significant changes in the results. The total installed PV system cost includes the modules, inverter, support structure, electrical circuits and protections, the cables and structure anchor, as well as the engineering, the mechanical and electrical installation, and value-added tax (VAT).  
It is important to note that the depreciation values of components are not considered except for solar modules, hence the costs of all other components are fixed except for that of PV modules. For this reason, in our probabilistic calculation the cost of each component, except the cost of the PV panels, is considered by means of a constant probability density function (PDF), where it changes depending on the three sizes of installation considered in this study (500 kW, 50 kW and 5 kW).
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 5 Performance Assessment
### Queuing Delay Assessment  
To measure the queuing delay (\(\mathcal{D}\)) of the aperiodic messages transmitted in the CAP, i.e., the time spent by aperiodic messages in the queue while waiting for transmission, we run simulations using the OMNeT++ framework. The IEEE 802.15.4 physical layer model adopted is the one provided by the INETMANET libraries, while the MAC layer model was developed from scratch. \(\mathcal{D}\) was measured both with and without enabling the D-DSME extensions. The simulations were run with the parameters that are listed in Table 2.  
The simulated network includes: 25 aperiodic nodes (ANs) that transmit aperiodic messages of 59B during the CAP; \(n_{nd}\in[1,\ldots,34]\) real-times nodes (RTNs) that transmit two kinds of real-time messages (\(f_{1}\) and \(f_{2}\)), respectively, during the CFP; one _PAN-Coordinator_ that receives the messages transmitted by the end nodes. The aperiodic messages are generated by the ANs with a random period, following an exponential distribution with mean 31.2 ms. The periodic messages belonging to \(f_{1}\) and \(f_{2}\) flows are generated by the RTNs with periods equal to \(P_{f_{1}}=4T_{SF}\) and \(P_{f_{2}}=8T_{SF}\), respectively.  
Figure 4: Superframes required to accommodate all flows.  
As the number \(n_{nd}\) of RTNs varies for each simulation, \(n_{SF}\) changes according to Equation (5) (and so, the MSF duration and the CAP frequency). Therefore, the queuing delay \(\mathcal{D}\) depends on \(n_{nd}\) and varies for each simulation. The measured average \(\mathcal{D}\) values are shown in Table 3. Every simulation was repeated multiple times, so as to obtain confidence intervals calculated at 95% in the order of tens of milliseconds.  
Table 3 shows that in some cases the queueing delay \(\mathcal{D}\) can be lower for D-DSME than for DSME. For instance, using a network with \(n_{nd}=13\) RTNs, the required SFs are \(n_{SF}=4\) for DSME and only \(n_{SF}=2\) for D-DSME. In such a case, D-DSME outperforms DSME (the measured \(\mathcal{D}\) values are respectively 0.79s and 1.60s). This happens because D-DSME allows using a shorter multi-superframe, thus increasing the CAP frequency.
## 6 Conclusions  
Within the represented research a comprehensive methodology for determining the components of a mass transfer coefficient evaluation was developed and verified. The datasets obtained for the consequent model verification during the experimental runs were conducted on the research set up, emulating the jet bioreactor circuit: frames were taken by a high-speed camera and utilized later as input data for the research algorithms. Both classical and new computer vision methods were developed and applied; benefits and drawbacks of each approach were analyzed. The individual bubble parameters were extracted; the total area of phase contact, known as a crucial value to calculate the volumetric mass transfer coefficient in light of its principal impact on the efficiency and performance of mass transfer apparatuses, bioreactors and fermenters, was determined. Accurate data was obtained on gas bubble sizes and their distribution as well as parameters of the interfacial surface representing the necessary conditions for precise fermentation control, development and optimization of bioreactor control systems. The demonstrated approach is valuable not only as an innovative complex methodology among non-invasive phase contact area measurements not involving ultrasound or potential measurements, but as the perspective to be applied in a literal or hybrid version for a wide range of high-velocity gas-saturated flow handling including reliable control of industrial fermentation machines. The current research is a part of a global research project devoted to studying the properties of jet bioreactors as the core of biotechnological solutions for single-cell protein production.  
Conceptualization, D.C. and I.N.; methodology, V.S., S.Y., A.Z. and A.N.; software, V.P., I.S. ([PERSON]), P.M., T.H., M.M. and S.V.; formal analysis, S.I. and S.K.; investigation, I.S. ([PERSON]), M.N., T.M. and A.K.; resources, F.B. and D.C.; data curation, T.M. and P.M; writing--original draft preparation, I.S. ([PERSON]), I.N., P.M., V.P., V.S., I.S. ([PERSON]) and D.C.; writing--review and editing, I.S. ([PERSON]), I.N., P.M., V.P. and D.C.; visualization, V.P., I.S. ([PERSON]) and P.M.; supervision, F.B. and D.C.; project administration, I.S. ([PERSON]) and D.C.; funding acquisition, I.S. ([PERSON]), F.B. and D.C. All authors have read and agreed to the published version of the manuscript.  
The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged.  
Not applicable.  
The authors declare no conflict of interest.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Effectiveness Evaluation and Analysis
#### 3.4.3 Simulation Data
Figure 10: The trajectory component plots for the centers of mass of the four walking wheels during Stage B: (**A**) unoptimized walking wheel trajectory; (**B**) optimized walking wheel trajectory: (**a**) x-values for the 4 walking wheels, (**b**) y-values for the 4 walking wheels, and (**c**) z-values for the 4 walking wheels.
## 5 Conclusions  
The preliminary study demonstrated that the accuracy of maintaining an USV carrying out hydrographic surveys in automatic mode using a popular Pixhawk autopilot and a multi-GNSS receiver supported by a Fluxgate magnetic compass enables the efficient performance of bathymetric measurements. The analysed solution allows a surface vessel to be maintained on sounding profiles with an accuracy of the transverse deviation from a course of 0.92-1.4 m (\(p\) = 0.68) and 2.01-2.39 m (\(p\) = 0.95). This means that it meets the IHO category 2 requirement [6] in terms of hydrographic system positioning accuracy. On this basis, it can be concluded that for the performance of selected hydrographic surveys, it is possible to use low-cost and widely available measuring equipment to successfully replace traditional solutions based on a professional autopilot. Moreover, the shallow draft of the vessel (20 cm) enables it to take bathymetric measurements of ultra-shallow waters in an automatic manner, which significantly increases their efficiency.  
This publication proposed a number of modernisation solutions for a typical USV, previously controlled directly in a manual manner. It included all its basic components, such as the drive system, GNSS positioning, course control automatics, and the data transmission system. As a result, the vessel became able to take measurements in automatic mode and significantly increased its operational properties.  
Research results have shown that low-cost multi-GNSS receivers can be successfully used in USVs for applications related to, among others, hydrographic surveys [47], in supporting the navigation process [21], in underwater photogrammetry [48], or in geological works [49].  
Conceptualisation, M.S., C.S. and H.L.; Data curation, M.S. and P.C.; Formal analysis, M.S. and C.S.; Investigation, M.S., H.L. and P.C.; Methodology, C.S. and H.L.; Supervision, C.S. and H.L.; Visualisation, M.S. and P.C.; Writing--original draft, M.S. and C.S.; Writing--review & editing, H.L. and P.C.  
This research has been financed from the science budget for 2016-20, as a research project within the _\"Diamentowy Grant\"_ Programme No. DI2015 008545.  
The Authors declare no conflict of interest.
## 2 Materials and Methods  
### Mechanism of the Infrared Radiation Type Noncontact Blood Pressure Monitoring System  
The newly developed infrared radiation type noncontact blood pressure monitoring system of a transmitter and a receiver. The transmitter irradiates infrared rays to a person, and the receiver receives faint reflected infrared rays from the person's skin. When the reflected infrared light hits the receiver, the signal is processed by an amplifier and a filter circuit and is output as a detection signal. This signal varies based on the relative changes in blood flow. The algorithm for estimating blood pressure is based on a previous study [21]. In this previous study [21], a non-invasive monitoring device which can measure blood pressure, pulse rate, respiratory rate, and oxygen saturation continuously with a single sensor using the photoplethysmographic technique, was developed. The accuracy and precision of the device were measured in comparison with conventional monitoring methods used in intensive care units (ICUs). In this study, the detection algorithm for blood pressure measurement in this previous study [21], i.e., the photoelectric sphygmomanometer technique, was used. The method in this previous study [21] uses projected light to capture the displacement of the arterial surface due to the expansion and contraction of blood vessels. This makes it possible to receive photoelectric volumetric pulse waves. Based on this signal, blood pressure is calculated continuously. We have developed a steering-wheel blood pressure to monitor this algorithm [19]. As mentioned above, this system is useful in that it is noncontact and resistant to body motion. However, from a market launch perspective, its design needs to be reconsidered. Therefore, as a new blood pressure measurement system based on this algorithm, we developed a portable, noncontact blood pressure measurement system with infrared irradiation.  
The developed infrared radiation type portable noncontact blood pressure monitoring system is shown in Figure 1, and a block diagram of the developed infrared radiation type portable noncontact blood pressure monitoring system is shown in Figure 2. The external dimensions of the portable noncontact blood pressure monitoring system are approximately \(10\) [cm] \(\times\)\(5\) [cm] \(\times\)\(2\) [cm].  
Figure 1: The developed infrared radiation type noncontact blood pressure monitoring system.
## 6 Perspectives  
The GF-4 satellite can not only collect images with a large scope and at high temporal and spatial resolutions, but can also carry out \"staring observation\" in a specific region according to user instructions, which will play an important role in disaster reduction, meteorology, earthquake, forestry, and obtaining precise measurements in other fields. For example, GF-4 could practically observe a typhoon, since middle and low orbit satellites have high spatial resolution, but the retrogression time is relatively long, and the continuous observation of interesting areas is difficult. Compared with present high orbit continuous observation meteorological satellites, GF-4 could not only continuously provide information on the development of a typhoon, but could also take observations and measurements of details like typhoon textures.  
In addition, GF-4 will further accelerate the development of advanced models supported by remote sensing data. Currently, models in disparate fields such as ecology, hydrology, geochemistry, and meteorology are driven by remote sensing data, but are limited by the temporal and spatial resolution of available remote sensing data, and face issues of fulfilling simultaneous goals such as having \"short time\", \"high accuracy\", and \"large space\" [53; 54; 55]. Most models driven by remote sensing are on time scales of days, months, or years. Even if a part of these models could realize hourly time scales from meteorological satellites such as those available from NOAA, the spatial resolution of their simulation results is over 1 km, which could not accurately estimate the spatial diversity of observation pixels [56; 57; 58]. The comprehensive advantages of GF-4 of increased temporal and spatial resolution and large widths would facilitate the appearance of various kinds of remote sensing models on fine time scales at minutes or even seconds.  
Conceptualization, G.L.; methodology, G.L.; software, X.C.; validation, X.C.; writing--original draft preparation, G.L.; writing--review and editing, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Fund of China, grant number: 41601598, the Talent Project of Ludong University, grant number: LY2014020, and an NERC Industrial Innovation Fellowship awarded to AA (grant number: NE/R013489/1).  
**Acknowledgments:** Thanks to the help of [PERSON] and [PERSON] of Seoul National University. Without their data from Korea Flux Monitoring Network (KoFlux), we would not have been able to complete the verification of ET.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 4 Evaluation
### Evaluation Measures  
The model's efficiency in binary classification was evaluated through the metrics of precision, accuracy, recall, F1-score, and the ROC curve and the AUC. The ROC curve is an essential performance measurement for classification problems. ROC is a probability curve and the AUC, which is the area under the curve, represents the degree or measure of separability. The higher the AUC value, the higher the classification accuracy of the model. Additionally, a confusion matrix gives a summary of the estimation results in a classification problem and they are frequently used in the literature. Usage of the confusion matrix enables one to obtain an idea of the errors made with the classifier. The confusion matrix also gives an idea of the types of errors made by the model. For binary classification models, the confusion matrix appears as in Table 4 [57].  
\[TPR=\frac{TP}{TP+FN} \tag{7}\]  
\[FPR=\frac{FP}{FP+TN} \tag{8}\]  
Our model was tested with the CNN, GRU, and CNN + GRU algorithms, which are among the deep learning architectures. In Table 5, the parameter values of these three algorithms are presented. The heuristic parameter estimation method was used for the selection of hyperparameters in the study. The hyperparameters were estimated by using our foreknowledge of the issue. The model was designed according to these hyperparameters and the results were observed. In light of the results, new hyperparameter estimations to increase the performance of the model heuristically were performed followed by regeneration and retraining of the model, and finally the results were observed again. This operation continued until the groups of parameters that were suitable enough to render the intended performance were found.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Positive Real State** & **Negative Real State** \\ \hline Positive Prediction & TP & FP \\ Negative Prediction & FN & TN \\ \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline
**Message** & **Group** & **Group Text** \\ \hline
125 lira & 2 & Normal \\ Baskanin aksam toplantsi fenaymis & 2 & Normal \\ BiP ile mesajalirimi aninda, daha gelenceli gonderiyorum! & 1 & Spam \\ Sen de hemen kullan! & & \\ \hline \end{tabular}
\end{table}
Table 2: Examples from the Turkish SMS Spam Collection dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Message** & **Group Text** \\ \hline Ok lar...loking wif u oni... [PERSON] \\ U dun say so early hor...U c already then say... [PERSON] \\ Did you hear about the new “Divorce Barbie”? It comes with all of [PERSON]’s stuff! & Spam \\ \hline \end{tabular}
\end{table}
Table 3: Examples from the UCI SMS Spam dataset.
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion
### RS Data Used in Wetland Studies
#### 3.4.4 Data Type Resolution  
The RS data types employed in wetland studies can be classified into three categories based on their spatial resolution: high-resolution (5 m), medium-resolution (5 to 30 m), and coarse-resolution (>30 m). Figure 13 illustrates how frequently each of these data types is used in review papers. Medium-resolution images hold the largest share (with 66%) by far out of all data types. This is followed by high-resolution images, which take up 30% of the share, and coarse-resolution images, which take up 4%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite** & **Life Span** & **Polarization** & \
\begin{tabular}{c} **Wavelength** \\ **(cm)/Band** \\ \end{tabular} &
\begin{tabular}{c} **Repeat Cycle** \\ **(Days)** \\ \end{tabular} & \#Studies** & **Ref.** \\ \hline Sentinel-1A, & 2014-Present & Single/Dual & 5.54/C-band & 6 & **45** & [21,82,82,89,96,97,115,122] \\ \hline RADARSAT-2 & 2007–Present & Single/Dual/Quad & 5.55/C-band & 24 & **31** & [41,45,78,82,106,123,124] \\ \hline ALOS PALSAR & 2006–2011 & Quad & 23.6/L-band & 46 & **25** & [110,124,125,126,127,128,129] \\ \hline TerraSAR-X & 2007–Present & Quad & 3.11/X-band & 11 & **9** & [41,124,130] \\ \hline ENVISAT ASAR & 2002–2012 & Dual & 5.63/C-band & 35 & **6** & [110,131] \\ \hline Gaofen-3 & 2016–Present & Single/Dual/Quad & 5.4/C-band & 29 & **3** & [69] \\ \hline Huang Jing-1C & 2012–Present & Single-V & 3.13/S-band & 31 & **2** & [132] \\ \hline ERS-1 & 1991–2000 & Single-V & 5.66/C-band & 35, 3, 168 & **2** & [126] \\ \hline ERS-2 & 1995–2011 & Single-V & 5.66/C-band & 35 & **2** & [126] \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of the commonly used SAR satellites wetland studies, ranked by the number of times they have been utilized in the papers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
# Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation
## 5 Conclusions  
In this paper, we first used the noise-resistant mathematical morphology edge detection algorithm to coarsely locate the edges of an O-ring image, then we used cubic spline interpolation to precisely locate the O-ring edges at the sub-pixel level, and finally performed circle fitting by least squares to calculate the line diameter and diameter of the O-ring. The experimental data show that the algorithm has a mean square error of 0.00480 mm for the outer diameter and 0.00018 mm for the wire diameter. The outer diameter error is kept within \(\pm 0.1\) mm and the wire diameter error can be kept within \(\pm 0.015\) mm. By comparing with the whole-pixel edge detection algorithms (Sobel and Canny), the algorithm in this paper has a higher accuracy and good stability in the dimensional measurement of O-rings. This meets the design requirements of the algorithm.  
The following are available online at [[https://www.mdpi.com/article/10_3390/app11083716/s1](https://www.mdpi.com/article/10_3390/app11083716/s1)]([https://www.mdpi.com/article/10_3390/app11083716/s1](https://www.mdpi.com/article/10_3390/app11083716/s1)). The supplementary file contains mainly algorithms written in MATLAB and experimental data. Algorithms: Algorithmic Program, Pictures used in the experiment: Experimental Pictures, Experimental results: Experimental Data.  
Conceptualization, X.Z. and H.H.; methodology, X.Z.; software, X.Z.; validation, X.Z., H.H. and J.Y.; formal analysis, X.Z.; investigation, X.Z.; resources, H.H.; data curation, Y.W.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; visualization, X.Z.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Key Research and Development Program of China (2018 YFB2003800).  
Figure 12: Error analysis chart. (**a**) Comparison chart of outside diameter measurement error; (**b**) comparison chart of wire diameter measurement error.