esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
## 4 Proposal Method According to the Issue
### Evaluation Method and Results of Proposed Method #1  
To compare the conventional method and proposed method #1, we evaluated the parameters as indicated in Table 2, where the communication method is the IEEE802.11g standard, frequency band is 2.4 GHz, transmission power is 0.2 mW, and drone speed is 5 km/h.  
Proposed method #1 uses the same simulation model as that shown in Figure 12, which is used in the evaluation of the previous method. The STA repeatedly handovers from AP1 to AP4, passes through the red-dotted path, and continuously uploads UDP data from the STA. Figure 16 compares the throughput characteristics of the conventional and proposed (#1) methods. The horizontal axis represents the time [s], and the vertical axis represents the throughput [Mbps]. In the conventional method, periods of low throughput exist because the handover is performed from AP2 to AP4 without connecting to AP3, as previously described. This is because, when the STA is connected to AP1 and AP2, it performs a handover before the connection link is disconnected due to the absence of other base stations with high reception strength in the vicinity. However, when the STA heads to AP3, the connection to AP2 is not made, the connection to AP3 is not made, and a handover is not performed until AP4, where AP2 is disconnected. This not only reduces the throughput, but also causes the connection to drop before 300 s. In contrast, proposed method #1 performs optimal AP selection by referring to the SINR threshold and MCS index, independent of whether a beacon is received. In other words, it performs a handover to AP3 even when the AP2 beacon is received. Compared with the conventional method, the proposed method achieves a maximum throughput of 20 Mbps or higher in the communication area of AP3, and the handover is performed seamlessly without loss of connection when switching to AP4. These results confirm the effectiveness of proposed method #1, SINR threshold, for an optimal handover.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conv. Method** & **Prop. \#1** & **Prop. \#2** \\ \hline Communication method & IEEE802.11g standard & \\ \hline Frequency band & & 2.4 GHz \\ \hline Transmission power & & 0.2 mW \\ \hline Flight speed & & 5 km/h \\ \hline Hand over method & Beacon counts & SINR threshold & SINR moving average \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for evaluation comparison.  
Figure 15: Flowchart of proposed method #1.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 3 Methodology
### UBD Estimation Model and Verification
The segmented linear regression model was used to calculate the statistics of the fitting results of the average building density of the 32 types of NTL intensity samples, as shown in Figure 8.  
Figure 7: Relation of UBD and the Luojia1-01 NTL value. The spatial analysis tool in ArcGIS10.3 software was used to spatially interpolate the UBD values of the 320 sampling points to obtain the overall distribution map of UBD in the main urban area of Hefei City (Figure 9); Table 1 gives the detailed UBD distribution data. The formula for the inverse distance weighted (IDW) method is as follows:  
\[\rho_{c}=\frac{\sum_{i=i}^{N}\rho_{c_{i}}/d_{c_{i}}^{2}}{\sum_{i=i}^{N}1/d_{c_{i }}^{2}} \tag{7}\]  
where \(\rho_{c}\) is the value of the UBD estimate for the \(c\)-type NTL intensity sample area; \(N\) is the number of samples; \(\rho_{c_{i}}\) is the average value of the UBD at the center point of the \(i\)th sample in the \(c\)-type NTL intensity sample area; and \(d_{c_{i}}\) is the distance between the center point of the \(i\)th sample and the \(c\)-point. Figure 8: Statistical chart of the average UBD fitting results of samples of various classes of NTL intensity. Figure 9: A sketch map of the overall UBD distribution in the study area. The actual UBD values of 20 test samples were obtained from _ZY3-02_ high-resolution images, and the final estimation results were tested. As shown in Table 2, the absolute UBD error of 11 in 20 test plots was less than 5%, and there are only 2 plots with an absolute error higher than 10%. From the average result, the absolute difference between the UBD of the sample area and the estimated UBD was 3.58%, which proves that the estimation result is highly reliable. For a single data, the estimated UBD for the high-density region is generally higher than the actual UBD because the high-density region has more interfering light sources and the blooming effect results in a larger estimated building area than the actual one. From the absolute error, the absolute error for the low-value region of the average luminance value is smaller than that for the high-value region. The reason for this is that the NTL intensity of the low-value zone is smaller, and a more realistic light index can be monitored at night.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 6 Results
### Synchronous Impulsive Noise  
This test is performed according to Section 5.1 with a flat response propagation channel with additional synchronous impulsive noise as described in Section 4.3.2.  
SNR value is the ratio of signal power (\(P_{signal}\)) to noise average power (\(P_{noise}\)). As described in Equation (1), the noise average power (\(P_{noise}\)) is calculated by weighting the base noise power (\(P_{base}\)) and the (\(P_{pulse}\)) through the total length of the noise. Equations (2) and (3) confirm that the pulse power and base power of the impulsive noise depend on the duration of the pulses. The aim of this test is to compare the impact of different temporal distributions of the same average noise power.  
Figure 11 shows the relation between SNR and FER as a function of the impulsive noise duration for DBPSK with convolutional encoder.  
Results for synchronous impulsive noise scenario have a turning point when the noise duration is of 500 \(\mu s\).  
For pulses of noise of a duration under 500 \(\mu s\), the shorter the pulse, the lower the FER for the same SNR (or the lower the SNR for the same FER). In these cases, the noise is considered impulsive as it is much shorter than a symbol, and the gain for impulsive noise of the OFDM technology is obtained.  
On the contrary, in Figure 11, for pulses of duration higher than 500 \(\mu\)s, the longer the pulse, the lower the FER for the same SNR. In this situation, the duration of the pulse begins to be comparable with the length of one OFDM symbol 2240 \(\mu\)s. The duration of the pulses may be high enough to corrupt a complete OFDM symbol and the interleaver (block interleaver of one OFDM symbol) will not avoid introducing a burst error to the Viterbi decoder, making the frame unrecoverable. Considering this, the duration is high enough to make the coding gain not useful and the pulse power (\(P_{pulse}\)) will be the limiting factor. Equation (2) demonstrates that synchronous impulsive noise with longer pulses (higher \(d\)) have lower pulse power (\(P_{power}\)) for the same SNR (same \(P_{signal}\) and \(P_{noise}\)). As the same energy is widespread in time, the probability of corrupting a whole OFDM symbol is reduced and, consequently, the FER.  
Additionally, comparing the results for 500 \(\mu\)s in Figure 11 with the results of \(DBPSK_{F}\) in Figure 10, it can be concluded that variations in amplitude of the noise of 20 dB in the form of impulsive noise affect to PRIME sensitivity up to 7.75 dB.  
Figure 11: Relation FER-SNR affected by synchronous impulsive noise with different duration.
# Missing Data Imputation in the Internet of Things Sensor Networks
###### Abstract  
The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted with electronics, sensors, and network connectivity. IoT sensor networks have become integral aspects of environmental monitoring systems. However, data collected from IoT sensor devices are usually incomplete due to various reasons such as sensor failures, drifts, network faults and various other operational issues. The presence of incomplete or missing values can substantially affect the calibration of on-field environmental sensors. The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors. To achieve this, we propose an efficient and robust imputation technique based on \(k\)-means clustering that is capable of selecting the best imputation technique for missing data imputation. We then evaluate the accuracy of our proposed technique against other techniques and test their effect on various calibration processes for data collected from on-field low-cost environmental sensors in urban air pollution monitoring stations. To test the efficiency of the imputation techniques, we simulated missing data rates at 10-40% and also considered missing values occurring over consecutive periods of time (1 day, 1 week and 1 month). Overall, our proposed BFMVI model recorded the best imputation accuracy (0.011758 RMSE for 10% missing data and 0.169418 RMSE at 40% missing data) compared to the other techniques (\(k\)Nearest-Neighbour (\(k\)NN), Regression Imputation (RI), Expectation Maximization (EM) and MissForest techniques) when evaluated using different performance indicators. Moreover, the results show a trade-off between imputation accuracy and computational complexity with benchmark techniques showing a low computational complexity at the expense of accuracy when compared with our proposed technique.  
IoT; low cost sensor; missing data; imputation Article
## 2 Preliminary Knowledge
### Pinhole Imaging Opposition-Based Learning  
To help the algorithm get rid of the local optima, some scholars have tried to combine opposition-based learning (OBL) [31,32] with intelligent optimization algorithms to expand the search range by calculating the reverse solution of the current feasible solution, and thus find out the candidate solutions at more optimal positions. Based on this idea, [PERSON] et al. [33] successfully used the pinhole imaging opposition-based learning to improve the convergence accuracy and speed of the modified Whale Optimization Algorithm. In this paper, we attempt to introduce this strategy to increase the possibility of the IHSAO jumping out of the local optima. The basic schematic of PIOBL is shown in Figure 3.  
In the figure, the upper and lower boundaries of the coordinate axes are \(a,b\). There is a small aperture screen placed at the base point \(O\). \(X_{best}\) (the current global optimal solution) represents the projection of the light source \(P\) whose height is \(h\) on the \(x\)-axis, when the light source through the small aperture will get an inverted image \(p^{*}\) of height \(h^{*}\) at the imaging  
Figure 3: Principle of pinhole imaging opposition-based learning.  
Figure 2: Distribution histogram of logistic chaotic map and tent chaotic map. (**a**) Logistic chaotic map. (**b**)Tent chaotic map.  
screen, at which time the projection of \(p^{\prime}\) on the \(x\)-axis is \(X^{*}_{best}\) (the newly generated inverse solution). According to the geometric relationship of the line segments in the figure, it can be derived that:  
\[\frac{(a+b)/2-X_{best}}{X^{*}_{best}-(a+b)/2}=\frac{h}{h^{*}} \tag{22}\]  
Let \(h/h^{*}=K\) be substituted into the above equation, and the variation yields the expression for \(X^{*}_{best}\):  
\[X^{*}_{best}=\frac{(a+b)}{2}+\frac{(a+b)}{2K}-\frac{X_{best}}{K} \tag{23}\]  
When the algorithm is solving a high-dimensional complex function, the small-aperture inverse learning solution can be computed by the following equation:  
\[X^{*}_{best,j}=(a_{j}+b_{j})/2+(a_{j}+b_{j})/2K-X_{best,j}/K \tag{24}\]  
where \(X_{best,j}\) is the optimal solution in the \(j\)th dimension, \(X^{*}_{best,j}\) denotes the inverse solution of \(X_{best,j}\), respectively, and \(a_{j}\) and \(b_{j}\) are the minimum and maximum values in the \(j\)th dimension on the search space.  
When \(K=1\), Equation (25) can be simplified as follows:  
\[X^{*}_{best}=a+b-X_{best} \tag{25}\]  
It can be seen that when \(K=1\) the PIOBL is the common OBL strategy. The candidate solutions obtained by the common OBL strategy are generally fixed, but a wider range of inversion positions can be obtained by changing the distance between the imaging screen and the pinhole plate to adjust the scale factor \(K\) in the PIOBL strategy.
# Control and Analysis of Layered Soil Structure to Ensure Railway Roadbed Stability
###### Abstract  
This article discusses a method for analyzing the layered structure of soil using the phase-metric method of geoelectric monitoring to ensure the reliability of a railway track. The importance of monitoring soil layers for timely detection of changes that may affect the stability and safety of railway tracks is emphasized. The use of geophysical monitoring methods, such as phase monitoring of the geoelectric signals, allows us to optimize measures to strengthen the roadway and increase its durability. The present article describes laboratory experiments in which a specialized setup was created to simulate the process of drilling through various soil layers. Geoelectric methods involving the registration of phase characteristics of the electromagnetic field were used in an experimental setup. The experiments demonstrated the effectiveness of the phase-metric method for determining the characteristics of the layered structure of the soil. The results showed that the change in the phase of the signal recorded at the receiving electrodes can be used to identify different soil layers with different electrical characteristics, such as moisture and density. The method of modeling the physical and geological environment using equivalent circuits of elements in the form of a dielectric made it possible to more accurately analyze the electrical properties of the soil. Based on the obtained data, an automatic monitoring system was developed using recurrent neural networks (RNNs), in particular long short-term memory (LSTM) networks, for automatic detection of bends and transitions in signal time series. Evaluation of the model's effectiveness showed high accuracy in identifying layers, which contributes to increasing the reliability and efficiency of monitoring the condition of the railway track.  
vertical electrical resistivity tomography; phase-metric method; borehole observations; borehole drilling; drilling optimization; data analysis; time series; railway Article
## 4 Analysis of Test Results
### Strength Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group Number** & **Program** & **Contact Area/cm2** & **Peak Force/KN** &
\begin{tabular}{c} **Peak Compressive** \\ **Strength/MPa** \\ \end{tabular} & **Deformation/mm** \\ \hline \multirow{3}{*}{Group 1} & Programme 1 & 30 & 2.64 & 0.88 & 3.24 \\  & Programme 2 & 30 & 2.04 & 0.68 & 2.01 \\  & Programme 3 & 34 & 3.14 & 0.92 & 1.91 \\ \hline \multirow{3}{*}{Group 2} & Programme 4 & 42 & 3.01 & 0.71 & 2.50 \\  & Programme 5 & 32 & 2.78 & 0.87 & 2.11 \\  & Programme 6 & 30 & 1.32 & 0.44 & 1.21 \\ \hline \multirow{3}{*}{Group 3} & Programme 7 & 36 & 3.66 & 1.01 & 2.34 \\  & Programme 8 & 30 & 2.64 & 0.88 & 2.70 \\  & Programme 9 & 30 & 1.13 & 0.38 & 1.32 \\ \hline \multirow{3}{*}{Group 4} & Programme10 & 30 & 1.23 & 0.41 & 1.72 \\  & Programme11 & 30 & 1.54 & 0.51 & 1.44 \\ \cline{1-1}  & Programme12 & 40 & 3.60 & 0.90 & 3.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specimen strength test data. Figure 13: Intensity distribution histogram of samples. the Jingxin Coal Industry, the 3122 comprehensive discharge face abandoned roadway filling material requires a strength of not less than 0.57 MPa, the best choice is the water/cement ratio of program 4, program 5, and program 8 of 1:1.5, with foaming multiples of 2-3 times, and a strength of 0.7 to 0.9 MPa--basically the characteristics of being low-cost and high-strength are the best choice for the abandoned roadway filling material. When preparing the material on site, it can be prepared according to the material proportion of program V, i.e., water:material A:material B:foaming agent = 1:1.5:0.12:0.1, a foaming time of 8-10 min, and an initial setting time of 15-25 min.
## 2 Materials and Methods
### Determination of Bioactive Compounds  
#### 2.4.1 Quantification of Total Phenolic Compounds  
Phenols were quantified according to the Folin-Ciocalteu method described by [PERSON] et al. [20], with some modifications: a dilution of propolis extract (1:1000 \(w\)/\(v\)) was made using 85% ethanol, it was homogenized until complete solubility using a Vortex, then the extracts were centrifuged at 18,510\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C (Hermle, Z 36 HK, Gosheim, Germany). For this assay, 0.3 mL of the supernatant were taken in a test tube and mixed with 1.5 mL of Folin-Ciocalteu 0.2 N reagent, incubated for 8 min at room temperature and in the absence of light. Then 1.2 mL of 0.7 M Na\({}_{2}\)CO\({}_{3}\) were added, they were homogenized and incubated at room temperature for 120 min in the absence of light. Absorbance was measured at 765 nm using a spectrophotometer (Jenway 6715, Ware, UK), against a water blank. The total phenolic content was determined from a gallic acid standard curve (0 to 100 mg/L). The amount of total phenols was expressed as mg equivalents of gallic acid (mg GAE/100 g propolis extract), according to the following equation. All samples were analyzed in triplicate.  
Total phenolic compounds (mg GAE/100 g) = (GAC \(\times\) V \(\times\) DF \(\times\) 100)/(weight of the sample (g))  
where:  
GAC = Gallic acid concentration from the standard curve (mg/mL);  
V = Volume of sample (mL);  
DF = Sample dilution factor.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 10 Conclusions  
We have proposed a Dynamic Grouping and Virtual Pseudonym (DGVP) changing scheme to protect location privacy in the case of vehicular communication networks. Road context information, such as vehicle speed, position, and the number of neighboring vehicles, is used to form a dynamic grouping of vehicles. The pseudonym update process takes place to change the pseudonyms of vehicles in a grouped manner. In the case of a lower number of vehicles within transmission range, a virtual pseudonym-changing procedure is used. In the virtual pseudonym change method, some randomized version of the pseudonym is created to anonymize the vehicles in the group. We use the position-mixing method to hide the vehicle's position and identity while communicating with LBS. The DGVP scheme is formally modeled and specified using HLPN. The formal model shows the correctness of the proposed method. The proposed scheme is validated with the help of simulation results with improved anonymity, entropy, reduced location traceability, lower computation cost, and impact on VANET applications. In the future, we  
Figure 27: Communication latency at a different number of vehicles.  
are planning to do more experiments on the diverse nature of the road network to explore other parameters for a vehicle's location privacy.  
Conceptualization, I.U. and M.A.S.; methodology, A.K.; software, I.U.; validation, A.W., M.A.S. and A.K.; formal analysis, I.U. and A.W.; investigation, C.M.; resources, C.M.; data curation, A.K.; writing--original draft preparation, I.U.; writing--review and editing, M.A.S. and A.W.; visualization, I.U. and A.W.; supervision, M.A.S.; project administration, A.K. and C.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
Carsen Maple gratefully acknowledges the support of the Engineering and Physical Sciences Research Council (EPSRC) through the Academic Center of Excellence in Cyber Security Re-search-University of Warwick under Grant EP/R007195/1, The Alan Turing Institute under Grant EP/N510129/1, Autotrust under Grant EP/R029563/1, and the National Centre of Excellence for the IoT Systems Cybersecurity, PETRAS under Grant EP/S035362/1.  
Not applicable.  
Thanks to Higher Education Commission, Pakistan for supporting academic studies.  
The authors declare no conflict of interest.
# An Overview of Key Technologies in Physical Layer Security
## 4 Challenges and Promising PLS Solutions
### Promising PLS Solutions  
#### 4.2.1 Simultaneous Wireless Information and Power Transfer (SWIPT)  
The phenomenon of simultaneous wireless information and power transfer was derived from the idea that RF signals can carry energy that is used for transmitting the information [91]. SWIPT has been identified as a sustainable proposition for harvesting of energy from the radio frequency (RF) signals, which in turn is supplied to finite-powered wireless communication devices including wireless sensors and electronic gadgets [92; 93]. As it can be seen in Figure 17, the transmitter transmits the information signal to the information receivers and also transfers the power to the energy receivers responsible for harvesting the energy [94]. However, it has been shown that in some cases the energy-harvesting receivers might have a better channel for receiving the information aimed at the information receivers, and they might jeopardize the security of the transmitted information. Additionally, efficient methods are also required for improving the efficiency of energy-harvesting receivers to enhance the amount of energy that can be harvested. In order to address these challenges, PLS has been employed on SWIPT in many different studies [92; 93; 94; 95; 96; 97; 98; 99]. The authors of [94] investigated SWIPT for MISO secrecy channel and considered transmit beamforming for two cases, with AN and without AN, with the aim of maximizing the secrecy rate as well as the harvested energy. [PERSON] et al. in [93] proposed a robust resource allocation scheme jointly responsible for time allocation and power control, taking into consideration uncertainty regarding the CSI. In [100], the authors consider a secure beamforming design for SWIPT in heterogeneous cellular networks which are formed by a single macrocell consisting of multiple macrocell users and a single femtocell comprising a single information receiver and multiple eavesdroppers. They use semi-definite programming and proposed an iterative algorithm to maximize the secrecy rate at information receivers while guaranteeing the SINR requirement recorded at each macrocell user.  
Figure 17: A multiple-input multiple-output (MIMO) system for Simultaneous Wireless Information and Power Transfer (SWIPT).