esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Temporal Properties of the Aftershock Sequence
### The Interevent Times Distributions for the Foreshock and Aftershock Sequences  
Furthermore, we study the temporal scaling properties of the foreshock and aftershock sequences by investigating the interevent times (or waiting times) distributions between the successive events. In this analysis, earthquakes are considered as a point process in time, marked by the magnitude of the event, with interevent times \(\tau\) between the successive events defined as \(\tau_{i}=t_{i+1}-t_{i}\), where \(t_{i}\) is the time of occurrence of the \(i\)th event \(\{i=1,2,\ldots,N-1\}\) and \(N\) the total number of events. First, we construct the cumulative distribution of the interevent times (\(M\geq M_{c}\)) for the foreshock and aftershock sequences, as well as for the NE and SW spatial clusters. We then model the observed distributions with the \(q\)-exponential function, derived in the framework of NESP [72; 82; 84; 86; 92; 93; 94; 95; 96]. It has been shown in various studies that the q-exponential function appropriately describes the distribution of interevent times in global, regional, and volcanic earthquake activity, as well as in aftershock sequences [91; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119  
distributions for the parameter values given in Figure 17 and Table 6. This is further confirmed by the expected linear dependence of corresponding \(q\)-logarithmic distributions \(\ln_{\text{q}}\)P(\(>\)\(\tau\)) with \(\tau\)[77], which are shown in the right panels of Figure 17. In all cases, the \(q\)-logarithmic function describes the observed distributions with high correlation coefficients, shown in the corresponding panels. The high values of \(q_{\tau}\) (Table 6) indicate long-range temporal correlations in the evolution of the earthquake activity and further confirm the high \(q_{\tau}\) values observed in aftershock sequences [84; 96; 97; 98].  
Figure 17: The cumulative distribution function \(P\)(\(>\)\(\tau\)) of the inter-event times \(\tau\) (in minutes) (left panels) and the corresponding \(q\)-logarithmic function (right panels), represented by circles, for the (**a**,**b**) foreshock sequence, (**c**,**d**) aftershock sequence, (**e**,**f**) NE aftershocks cluster, (**g**,**h**) SW aftershocks cluster. Fitting with the \(q\)-exponential function (Equation (10)) is shown with the solid lines, for the parameter values and the corresponding correlation coefficients shown in the down left corners.
# Security Risk Analysis of LoRaWAN and Future Directions
## 1 Introduction
The latest version of the WiMAX standard, namely 802.16m-2011, has introduced a theoretically more robust security protocol and included encryption for most of the control messages which in turn is expected to thwart most of the previously recognized attacks. As such, LoRaWAN should better be improved in the next releases to mitigate the security risks presented in this work. Our aim in this research was shedding light on the security vulnerabilities of LoRaWAN v1.1. While doing so we have used ETSI guidelines and also performed a thorough literature search to include every possible attack scenario against the presented protocol. Not only security weaknesses and vulnerabilities are identified, but also remedies and defense mechanisms are also presented for contributing future development of this technology. The structure of this article is as follows: Section 2 provides related work presented in the literature. This is followed by Section 3 containing an introduction to LoRaWAN 1.1 along with security changes and related assumptions. Then, in Section 4, newly introduced security related problems and vulnerabilities of LoRaWAN 1.1 are presented in an iterative way. Section 5.1 presents security risk assessment methods defined by ETSI. Section 5.2 summarizes the most imminent threats against LoRaWAN v1.1. In Section 5.3, all the risks resulting from the threats that are provided in the previous section are evaluated for LoRaWAN v1.1 by using the ETSI security risk assessment methodology. Section 6 provides suggestions to improve the security of LoRaWAN v1.1 based on the security concerns listed in Section 4 and also the security risk assessment presented in Section 5.3. Finally, the article ends with Section 7, which is comprised of conclusions and future work.
## 1 Introduction
The results obtained for NACA0015 foil for phase shift angle of 90 degrees between heaving and pitching motions show that non-sinusoidal trajectory considerably affects the energy extraction performance which depends on foil shape, location of the pitching axis, phase shift and the amplitudes of the participating motions. In some previous studies, see [PERSON] et al. [28] results for NACA0014 foil, it was concluded that a non-sinusoidal heaving and pitching trajectory enhances energy extraction coefficient by 17% and the energy extraction efficiency by 15%. Thereafter, as claimed by [PERSON] et al. [29], they found that comparatively to sinusoidal trajectory, a square-like pitching trajectory combined with a toothed-like heaving trajectory improves energy extraction coefficient by 87.5%. Heaving of the inverted wing in extreme ground effect was considered by [PERSON] and [PERSON] [30] who found that peak negative lift during the heaving cycle was greater than the same static values at the same ground clearances. [PERSON] et al. [31] studied aerodynamic effects of ceiling and ground vicinity on flapping wings, and concluded that the combined effect of the ceiling and the ground changes the aerodynamic forces through two effects, namely the narrow-channel effect and downwash-reducing effect. The analysis of the existing literature confirms an impressive growth of interest toward flapping wing propulsion albeit reveals lack of research covering wider range of aspect ratios and relative frequencies. The approach used in the present paper is guided by the concept of _extreme ground effect_ advocated in [PERSON] [32], where the method of matched asymptotic expansions is used to show that aerohydrodynamics of a lifting surface moving in immediate proximity to the ground is dominated by the channel flow under the wing. For vanishing ground clearances the corresponding 3D flow description becomes _almost_ two-dimensional in the plane parallel to the ground. Such a simplification of the flow problem opens a possibility to derive analytical solutions facilitating analysis of the behavior of the lifting and propulsive systems operating in close proximity to a solid boundary.
## 3 Results and Discussion
### Rapid Measurement System
#### 3.2.1 Effect of Speed on Accuracy  
Table 7 indicates that the radial direction error depends on the rotary speed. First, the measurement results must be converted to \(X_{0}\) relative offset values. The ring gauge is used for error analysis. When the speed changes, the total number of errors also changes. In the radial direction error, the maximum upper limit error is \(0.004\) mm, and the minimum lower limit error is \(-0.007\) mm (total of \(0.011\) mm) at a rotary table speed of \(2.5\) rpm. Thus, increasing the speed helps reduce the deviation in measurement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotary Table Speed (rpm)** & **1 Circle Time (s)** & **Total Number of Data** & **Total Number of Data in 1 s** \\ \hline
0.25 & 240.0 & 48709 & 203 \\
0.32 & 190 & 48700 & 257 \\
2.5 & 24.0 & 31817 & 1326 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data points estimated in \(1\) s under different rotary table speeds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rotary Table** & **Total Data** & **Upper Limit Error** & **Lower Limit Error** & **Total Error** \\
**Speed (rpm)** & **Number** & **(mm)** & **(mm)** & **(mm)** \\ \hline
0.25 & 48,709 & 0.027 & \(-0.038\) & 0.065 \\
0.32 & 48,700 & 0.020 & \(-0.012\) & 0.032 \\
2.5 & 31,817 & 0.004 & \(-0.007\) & 0.011 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Radial direction error depends on rotary speed.  
Due to the hardware limitations of the PLC, a total of 48,709 data points are captured when the prototype system is employed, but only 31,817 data points are captured when the rapid system is employed.  
Table 8 shows the determination of the arc length translated from the sampling interval, divide 360\({}^{\circ}\) by the total number of scanned data points to obtain the interval angle between each data point in a 360\({}^{\circ}\) rotation, and then calculate the arc length. In the prototype system, the sampling interval is 0.00739\({}^{\circ}\), and in the rapid system, the sampling interval is 0.01131\({}^{\circ}\). The total number of data points affects the sampling interval and precision. The most affected measurement is that of the tooth thickness, as Table 5 shown, the errors in the tooth thickness measurement when the measurement is performed by using an optical projector. Because the start and end positions of the toothed corners may not be measured correctly, the values obtained may be lower than the actual tooth-thickness values.  
The maximum sampling interval error is 0.038 mm (0.019 \(\times\) 2) when the rotary table speed was 2.5 rpm, which is within the tolerance level of 0.05 mm requested by customers; thus, we chose this rotating speed for final use.
## 5 Assessment of Leak Detection Methods  
The effectiveness of leak detection methods can be evaluated using five criteria:  
* Leak sensitivity--the ability to detect small leaks. * Leak location--the capability to pinpoint the leak. * Real-time monitoring--the possibility to continuously monitor the WDS. * False alarm--the frequency of generating false alarms when leaks do not exist. * Cost. Table 4 outlines the performance characteristics of the water leak detection methods detailed in this study. Non-acoustic methods, such as visual surveys and gas injection, provide high sensitivity and an accurate leak location but lack real-time monitoring, with associated low costs and occasional false alarms. Acoustic methods, such as leak noise correlation and loggers, offer real-time monitoring with medium sensitivity and infrequent false alarms. Inline technologies, including Sahara, SmartBall, and fiber optic, showcase high sensitivity and accurate leak location but do not provide real-time monitoring and have high costs. Software-based methods, such as water balance, hydraulic modeling, and artificial intelligence, vary in sensitivity, leak location capability, and real-time monitoring, with associated costs ranging from low to high. Table 5 highlights the limitations and the potential improvements for various water leak detection methods. In non-acoustic methods, visual surveys are constrained to detecting visible surface water leaks only, suggesting the need for complementary technologies to assess leaks comprehensively. Gas injection accuracy is affected by environmental factors, urging the refinement of the process and the selection of suitable tracer gases. Thermography faces challenges in detecting minor temperature fluctuations, indicating the  
necessity for sensitivity improvements. Ground-penetrating radar encounters difficulty in differentiating between water pipes and other buried objects, prompting adaptation to diverse soil types. Negative-pressure waves' accuracy is impacted by pipe characteristics, suggesting the integration of data analysis techniques. Acoustic methods, such as manual listening sticks, are less efficient in large systems, warranting the development of noise-filtering technologies. Leak noise correlation is sensitive to pipeline parameters, emphasizing the need for integration with calibration methods. Inline technologies, such as SmartBall, face challenges in finding the exact leak location, necessitating the integration of data-processing algorithms. Fiber optic accuracy is affected by pipe materials, indicating the need for optimization. Software-based methods, such as water balance, may show inaccuracies, suggesting a combination with other techniques for improved data accuracy. Hydraulic modeling requires effective calibration techniques, and artificial intelligence performance can be enhanced by diversifying data and parameters. These identified limitations provide clear directions for future research to refine and advance water leak detection technologies.
## 3 Results
### Expression Mode Analysis of DcTPSb1 in Different Tissues from D. chrysotoxum  
The expression patterns of genes across various tissues are essential for understanding their regulatory mechanisms. In higher plants, related TPS genes have tissue specificity, occurring in tissues such as flowers, leaves, and roots, similar to the temporal and spatial specificity of volatile terpene release. Therefore, samples from different tissues of _Dendrobium chrysotoxum_ were collected to investigate the expression differences in DcTPSb1 across four common plant tissues and to explore its expression profile. Specifically, flower tissue samples were collected at three different periods, spanning from the budding to half opening period to the blooming period. Subsequently, a quantitative PCR experiment (QPCR) was employed to quantify the expression level differences in DcTPSb1 across these different tissues and developmental stages. The findings revealed that _DcTPSb1_ displayed extremely low expression levels in stems and leaves, lower expression in roots (Figure 4a), and the highest expression in flowers (Figure 4b). Moreover, as the flowers progressed through their developmental stages, the expression levels of _DcTPSb1_ mRNA exhibited fluctuations. Specifically, _DcTPSb1_ was minimally expressed during the bud stage, experienced a significant increase in expression during the semi-open flowers stage, and reached its peak expression level in fully open flowers (Figure 4c). These outcomes indicate that the floral terpen regulated by _DcTPSb1_ are predominantly emitted during the flowering stage of _D. chrysotoxum_, with a notable release occurring during the fully open flower stage.  
Figure 3: Graph of the result of an experiment on subcellular localization of DcTPSb1 from _D. chrysotoxum_. Red represents chloroplast autofluorescence; green represents GFP display fluorescence; yellow represents fluorescence of the fusion protein connecting GFP and DcTPSb1, i.e., indicating cellular localization of the DcTPSb1 protein.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 4 Results
### The Performance of DOA Estimation for a Single Signal Source  
Initially, we examined the method's performance under the condition of only a direct signal with different signal-to-noise ratios, array sizes, and incident-angle conditions.  
Figure 8 presents the RMSE results for different input signals, \(C/N_{0}\), and array sizes, with \(C/N_{0}\) increasing from \(30\) dBHz to \(50\) dBHz. Figure 8a-d illustrate the results for scenarios \(N=4,8,12,16\), respectively. The results show a significant improvement in DOA estimation accuracy with increasing \(C/N_{0}\). When \(C/N_{0}\) is low, the performance of the OMP method is slightly better than that of the proposed method, and both of them are significantly better than MUSIC and Capon. As \(C/N_{0}\) increases, the DCB method can achieve a similar performance to OMP. Moreover, higher array sizes lead to greater estimation accuracy. The proposed method consistently outperforms the other methods, validating its effectiveness. Even under low-signal-to-noise-ratio conditions, satisfactory estimation results can still be achieved.  
Figure 7: Block diagram of the proposed method.  
In Figure 9, the DOA estimation results are presented for varying incident angles within the range of \([-60,\,60]\). Figure 9a,b show the estimation errors when the number of array elements is \(N=8\) and \(N=12\), respectively. It can be seen from Figure 9 that the estimation error increases with the increase in the incident angle, and the greater the number of array elements, the smaller the estimation error. The performance of the proposed method and the OMP method is better than that of the MUSIC method and the Capon method.  
Figure 8: Statistical results of DOA estimation under different \(C/N_{0}\) ratios: (**a**) \(N=4\); (**b**) \(N=8\); (**c**) \(N=12\); (**d**) \(N=16\).  
Figure 9: Statistical results of performance estimation for different incident angles: (**a**) \(N=8\); (**b**) \(N=12\).
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
###### Abstract  
Photovoltaic (PV) energy is one of the most promising renewable energies in the world due to its ubiquity and sustainability. However, installation of solar panels on the ground can cause some problems, especially in countries where there is not enough space for installation. As an alternative, floating PV, with advantages in terms of efficiency and environment, has attracted attention, particularly with regard to installing large-scale floating PV for dam lakes and reservoirs in Korea. In this study, the potentiality of floating PV is evaluated, and the power production is estimated for 3401 reservoirs. To select a suitable reservoir for floating PV installation, we constructed and analyzed the water depth database using OpenAPI. We also used the typical meteorological year (TMY) data and topographical information to predict the irradiance distribution. As a result, the annual power production by all possible reservoirs was estimated to be 2932 GWh, and the annual GHG reduction amount was approximately 1,294,450 tons. In particular, Jeollanam-do has many reservoirs and was evaluated as suitable for floating PV installation because of its high solar irradiance. The results can be used to estimate priorities and potentiality as a preliminary analysis for floating PV installation.  
floating photovoltaic system; renewable energy; solar energy; photovoltaic energy; greenhouse gas Article
## 4 Experimental Results and Analysis
### Experiments on PASCAL VOC  
We compared the improved algorithm with some other typical object detection algorithms in detection accuracy and detection speed. The methods of comparison are Faster-RCNN, YOLO, SSD, and DSSD. The test results are shown in Table 1. The main considerations are _mAP_ and FPS.  
It can be seen from Table 1 that the improved algorithm in this paper has obtained relatively good test results on the VOC dataset. The _mAP_ of the SSD algorithm is 77.5%, and our algorithm is 3.2% higher in accuracy than the SSD algorithm, which is improved by 17.3% compared with the YOLO algorithm. Compared with the YOLOv4 algorithm, the accuracy is 3.6% different, and as for the YOLOv5 algorithm, the accuracy is 1.2% different. Compared with the DSSD algorithm, the accuracy is improved by 2.1%, and as for the FSSD algorithm, the accuracy is improved by 1.9%.
# Mining Road Traffic Rules with Signal Temporal Logic and Grammar-Based Genetic Programming
## 6 Experimental Evaluation
### Results
#### 6.3.2 RQ2: Specifications that are Readable and Interpretable for a Human
To summarize, the mined specifications closely mimic what a car stuck in dense traffic would do and point to the effectiveness of our approach. We believe the reason for such readability to be the parsimony of the mined STL formulas. Intuitively, parsimony is directly linked to interpretability. In fact, as reported in Table 1, median solution size \(|\varphi|\) is 52.5, which is a reasonable size for an STL formula.
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 4 Performance Evaluation
### Simulation Results
#### 4.2.5 Total Data Transfer  
Figure 13 shows the total data transfer (SN-to-SN and SN-to-C traffic) in both systems. Unlike the Pols scheme, the proposed scheme needs less total data transfer, owing to the fact that the proposed system reported lower SN-to-SN and SN-to-C traffic transfer. For instance, for case 1, the application of the proposed scheme requires 1.6 \(\times\) 10\({}^{6}\) Mbytes at time 25, while the application of the Pols scheme for the same case requires 1.9 \(\times\) 10\({}^{5}\) Mbytes at time 25. For case 2, the use of the proposed scheme requires 1.8 \(\times\) 10\({}^{6}\) Mbytes, while the use of the Pols scheme for the same case requires 1.9 \(\times\) 10\({}^{6}\) Mbytes at time 25.  
The relationship between the total data transfer and the utility of the photos is displayed in Figure 14. As shown clearly, the proposed scheme attained superior utility of the photos with a similar amount of total data transfer as opposed to the Pols scheme.  
Figure 15 highlights the reduction ratio of the SN-to-SN and SN-to-C traffic of the proposed system as opposed to the Pols scheme. After 5 h, the proposed system had a reduction ratio of 35% as opposed to the Pols scheme. Then, after 15 h, the reduction ratio was 25%. After 25 h, case 1 showed a reduction ratio of 15%, while case 2 had a  
Figure 14: Relation between total data transfer and photos utility.  
Figure 13: Total data transfer.  
reduction ratio of 10% compared to the Pols scheme. Case 1 has a higher reduction ratio when compared to case 2 because case 2 has a higher number of sub-regions that requires the additional overhead of photos exchange.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Planning experiments in right-angle turn scenarios  
The real underground environment has complex intersecting lanes, and the lanes often intersect each other in the form of close to a right-angle. So, a right-angle turn scenario planning experiment was designed for testing. The experimental scene is shown in Figure 23, and the target point is set at about 4 m past the right-angle turn, and the \(X\)-axis and \(Y\)-axis relative to the initial position of the robot are 6 m and \(-6\) m, respectively.  
The experimental results of the MINCO trajectory method are shown in Figure 24. Figure 24 shows the first planning result given by the algorithm, and Figure 24b shows the result of replanning when the robot enters the bend. Figure 24c shows the positional attitude of the robot as it enters the turn. Figure 24d shows the positional attitude of the robot when it exits the turn.  
Figure 23: Right-angle turn scenario test.  
Figure 22: Comparison of multiple obstacle trajectories between MINCO trajectory method and Fast-Planner algorithm.  
The experimental results of the Fast-Planner planning algorithm are shown in Figure 25. In the right-angle turn scenario, the Fast-Planner algorithm cannot plan a reasonable trajectory.  
The robot motion trajectory of the MINCO trajectory method is shown in Figure 26. In this scenario, a total of six plannings were carried out during the robot motion, with a total planning time of 0.428 s. The length of the robot motion trajectory was 11.63 m. The Fast-Planner algorithm fails to plan in this scenario. Since there is no environmental input to the front-end search of the Fast-Planner method, the algorithm relies mainly on the continuous replanning and optimization of the back-end for trajectory planning. However, the back-end adjustment is based on the front-end search results. The distance from the path point to the passable place of this front-end search result is much larger than the previous scenarios, which leads to the algorithm's inability to find a safe trajectory, and it ultimately fails to complete the planning.