esa-sceva/satcom-chunk-collection huggingface dataset:
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 4 The Rogue Node Detection Scheme
### The K-Means Algorithm  
A K-means algorithm is an unsupervised clustering algorithm. The basic idea of K-means is to divide the samples into k classes according to the distance between samples for a given sample set so that the distance between samples in the same class is close to each other and the distance between two different samples in different classes is as large as possible [21,30]. In this paper, the speed values of most mobile vehicles are relatively close, and some abnormal vehicles' speeds are much lower or much higher than most vehicles' speeds. Therefore, the collected average speed of vehicles is divided into three categories, which is in line with the actual traffic situation. The specific steps of speed classification are as follows. First, the velocity samples are arranged in ascending order. The lowest speed value, the fastest speed value and the medium speed value are chosen as the initial  
Figure 6: The basic idea of the scheme.  
centroids of the three classes in the first round, respectively. According to the relative distance between each sample and the three centroids, all samples are grouped into three categories. In the second round, the new central point is calculated and is then selected as the new barycenter in each category. The above classification method is repeated until the classification is relatively stable. Figure 7 shows the process of the K-means algorithm for sample classification.
# Circularly Polarized High-Gain Fabry-Perot Cavity Antenna with High Sidelobe Suppression
## 3 Simulation and Experiment Results
Characteristics of the fabricated antenna such as radiation power pattern, gain, directivity, co-and cross-polarization levels, axial ratio, and sidelobe levels are measured in the anechoic chamber, which consists of the antenna under test (AUT), RF transmitter system, reference antenna, receiver system, and position system. To cancel all the electromagnetic reflections within the chamber, radar-absorbing material (RAM) is installed on each side. The overall performance of the CP-FPCA is summarized by listing important parameters in Table 1. As described, impedance bandwidth, realized gain, aperture efficiency, axial ratio, cross-polarization level, and sidelobe suppression, which are promising characteristics for practical applications are well attained. The CP-FPCA's performance is compared with other recently published related works in Table 2 and is found to perform better in terms of realized gain, aperture efficiency, AR, and SLS. The authors conclude that the CP-FPCA is highly effective in achieving minimum AR with high realized gain in the broadside direction. \begin{table}
\begin{tabular}{c c c} \hline
**Analysis Parameters** & **Simulated** & **Measured** \\ \hline Total efficiency & 92.9\% & 91.9\% \\ Impedance bandwidth 1  & 418 MHz & 970 MHz \\ Fractional bandwidth & 4.18\% & 9.7\% \\ Directivity & 15.75 dBi & 14.77 dBi \\ Realized gain & 15.40 dBi & 14.42 dBi \\
3 dB peak gain bandwidth & 503 MHz & 630 MHz \\ Aperture efficiency & 25\% & 20\% \\ Bandwidth efficiency 2 & 120\% & 65\% \\ Axial ratio (AR) & 0.12 dB & 0.65 dB \\
3 dB axial ratio bandwidth & 233 MHz & 303 MHz \\ X-polarization level (dB) 3 & \(-\)42 & \(-\)25 \\ SLS (\(xz\)-plane) & 23.8 dB & 18.92 dB \\ SLS (\(yz\)-plane) & 25 dB & 23.63 dB \\ \hline \end{tabular}
\end{table} 
Table 1: Analysis parameters of the CP-FPCA at 10 GHz. Figure 11: **(a)** The fabricated CP-FPCA operates at \(f_{0}\) = 10 GHz. Here, 8 plastic M3 spacers are used to keep the PRS as flat as possible; **(b)** anechoic chamber measurement setup.
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
###### Abstract  
In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.  
full-order state observer; active suspension; particle swarm; genetic; LQR
# An Automatic Approach for Individual HU-Based Characterization of Lungs in COVID-19 Patients
## Materials and Methods
### Threshold Definition Methods
The thresholds were given by the following relations:  
\[\text{th1} = b1+\sigma 1\] \[\text{th2} = b3-\sigma 3\]  
where:  
* \(\text{th1}\) is the value of HU that separates the aerated component from the intermediate one;
* \(\text{th2}\) is the value of HU that separates the intermediate component from the consolidated one;
* \(b1\) and \(b3\) are the mean values of the Gaussians and \(\sigma 1\) and \(\sigma 3\) are the relative standard deviations. All fits were realized with a Matlab script using the cftool for the curve fitting. The fitting method used was the non-linear least squares with the least absolute residual method. The second method was the maximum gradient \"on the fit\". With this approach the proper inflection points of the curve were found by looking at the points where the second derivative of the curves was 0. The third method used was similar to the previous one, without considering the Gaussian fit but directly using the original data. This method was identified as maximum gradient \"on the data\". The frequency values of the individual HU densities were jagged, and thus had many inflection points. Therefore, in order to apply the maximum gradient  
Figure 2: (**a-d**) Examples of HU-density histogram fit. Each plot shows the overall fit and the three Gaussians that compose it. method, it was necessary to post-process the distributions of the HU densities. First, a rebinning of the data to 25 HU was performed; the values obtained were then interpolated, and a smooth function applied (see Figure 3). At this point it was possible to apply the maximum gradient method, as in the case of the fit function, and find the threshold values. For each of the three methods, the individual thresholds for each lung and the median values over the whole population were extracted. The threshold values individually obtained with the three methods were then compared by means of Kuskall-Wallis tests. Summary statistics of the different lung components and their relative ratios were also reported focusing on the third method, which was found to be the most stable in defining th2 (including visual inspection by two expert radiologists of 30 sample patients).
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 5 Mode Enhancement and Defect Detection Simulation
### Mode Enhancement Simulation Results  
The simulation process of mode enhancement is the same as Section 3, only the excitation method is changed to transducer array. The simulation results are compared with the frequency wavenumber dispersion curves as shown in Figure 11.  
From Figure 11 we can see, the result of 2D-FFT coincides well with the frequency wavenumber curve of the corresponding mode and there is no interference mode. The wavenumber \(\xi\) and frequency  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Phase Velocity Value (m/s)** & **\(\Delta\)L (mm)** \\ \hline
7 & 2737.6 & 78 \\
3 & 2286.1 & 65 \\
1 & 1983.8 & 57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transducer intervals.  
Figure 11: Frequency wavenumber curves. (**a**) 2D-FFT result of mode 7; (**b**) 2D-FFT result of mode 3; (**c**) 2D-FFT result of mode 1.  
\(f\) are substituted into Equation (10). The calculation results of simulated phase velocities are shown in Table 4, and compared with theoretical values. It can be seen that the maximum error is 0.68%.  
The above discussion is about phase velocity, then the process of the group velocity calculation is described as following. The simulation data at 1 m and 2.2 m from the excitation nodes are extracted and the simulation results of mode 7, mode 3, and mode 1 are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & **Simulated Phase** & **Theoretical Phase** & **Phase Velocity Error** \\  & **Velocity Value (m/s)** & **Velocity Value (m/s)** & **Phase Velocity Error** \\ \hline
7 & 2719.0 & 2737.6 & 0.68\% \\
3 & 2281.5 & 2286.1 & 0.20\% \\
1 & 1982.0 & 1983.8 & 0.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Phase velocities of modes.  
Figure 12: _Cont._  
The simulation group velocity can be calculated by calculating the peak time difference between 1 m and 2.2 m and the results are shown in Table 5.  
As can be seen from Table 5, the group velocity error of each mode is relatively small. The results show that the method of array excitation can enhance the expected mode and suppress the interference mode, which indicates a relatively single mode is successfully excited.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 4 Experimental Validation
### Model Validation
Thus, any plane which has OA is perpendicular to \(\triangle\)CDF, which means \(\triangle\)OAC and \(\triangle\)OAD are both perpendicular to \(\triangle\)CDF. Therefore \(\angle\)CFD is the wind direction angle \(\alpha_{\rm WD}\). In \(\triangle\)CDF, CD\(\,\perp\)DF because CD\(\,\perp\triangle\)OAD. Thus, the wind direction is given as below. \[\alpha_{\rm WD}=\tan^{-1}\left(\frac{\mid\text{CD}\mid}{\mid\text{DF}\mid} \right)=\tan^{-1}\left(\frac{\tan\alpha_{\rm Rev2\#}}{\sin\alpha_{\rm Rev1\#}}\right) \tag{39}\]  
Figure 18: Yaw angle and wind direction calculation based on front and side plates. (**a**) Front plate and side plate rotation; (**b**) geometry analytical model. Hence, if rotated in both REV1# and REV2#, respectively, in front and side protractors, the practical stick yaw angle and wind direction are given in Equations (38) and (39), respectively. In this way, when the camera is placed in front as in Figure 17 4 or at bottom as in Figure 17 5, pure imaging cases, which only consider the yaw angle variation, can be simulated by changing REV1# as side view imaging cases, and REV2# as front view cases when the camera is placed in front. Accordingly, the pure bottom view case is simulated by changing REV1# when the camera is placed at bottom. Those cases will be included in Section 4.3.1--validation of pure imaging views. If REV1# and REV2# are coordinated at the same time to a certain amount of magnitude, the detected yaw angle and wind direction will be compared with theoretical ones in coupled bottom view and coupled side-front view.
## 4 Plasma Technology for Crop Sustainability and Food Processing
In plasma-mediated food processing, cold atmospheric or low pressure plasma can sterilize the food without compromising its flavor, odor, color, and prolonged shelf life. These plasma attributes have attracted the attention of food industry researchers. A comparison of different beverage processing techniques found that the plasma method retained the contents of ascorbic, chlorogenic, sinapic, and gallic acids in a tomato beverage, whereas pasteurization and other non-thermal methods resulted in reduced levels of these same acids [113]. Gas-phase plasma maintained the levels of phenolic compounds and hydroxycinnamic acids and reduced the anthocyanin content (23%), suggesting that anthocyanin is susceptible to the reactive oxygen species produced by cold atmospheric pressure plasma in juice [114]. The color and texture of food are very important to consumers, and studies have shown that plasma treatment has only minor (or no) effects on the color and texture of fruits. [PERSON] et al. [115] used a DBD cold plasma source to process packaged strawberries and observed no changes in the firmness or color, but there was a significant reduction in the microbial load. Similarly, the qualitative attributes of fresh-cut fruit (apple and melon) were not affected by plasma treatment under storage conditions [97,116]. Other food qualities, such as pH, acidity, antioxidants, and the contents of soluble sugar and vitamins, have also been investigated after plasma treatment. About pH, the pH changed due to the reactive oxygen species generated on food after plasma treatment. On the other hand, the pH did not change after plasma treatment because of the buffering capacity of the liquid in living tissue and the physiological activity of removing the acid from the surface [117]. The soluble sugar content is important for the taste of fruit and their juices cold atmospheric pressure plasma reduced the fructose and glucose contents and increased the sucrose content in fruit juices [118,119]. This is because the reactive species generated by plasma promotes ozonolysis reactions, which breaks the glycosidic bond of oligosaccharides and reduces the sugars via oxidation [119,120]. The vitamin contents of fruit and their juices are stable under plasma treatment [116], while the antioxidant activities of fresh food are variably influenced by cold atmospheric pressure plasma treatments, depending on the food products, plasma sources, treatment conditions, and doses [117].
# Accuracy of Guided Endodontics in Posterior Teeth
## 4 Discussion
The planning effort has surely increased with the application of GE; however, the effective treatment time for the patient is probably reduced, as the tedious search for PCC is omitted. Therefore, GE may possibly improve the patient's comfort, even though additional treatment steps such as intraoral scanning are necessary. In addition, numerous case reports and studies regarding GE have already been published, focusing mainly on anterior teeth [20]. CBCT has become an important tool for preoperative planning in dentistry. In endodontics, CBCT aids in identifying resorptions, calcifications, root canal morphologies and apical pathologies [21]. However, CBCT results in higher radiation doses than conventional radiography techniques. Therefore, the indication for CBCT must be carefully evaluated. According to a joint statement of the American Association of Endodontists and the American Academy of Oral and Maxillofacial Radiology, the use of limited field-of-view (FOV) is indicated to help identify and locate calcified canals and depict complex canal morphology [22]. A limitation of this ex vivo study is related to the fact that none of the treated teeth were severely calcified. Severe calcification could have negative implications for virtual access cavity planning and may result in lower overall accuracy. However, it is difficult to find extracted teeth that are severely calcified, yet caries- and restoration-free, at the same time. In recent years, 3D-printed teeth have also been used in the field of endodontic teaching and research. The major advantage of these teeth over extracted teeth is the possibility to digitally create root canal calcification as needed. However, studies have also shown that various types of 3D-printed teeth cannot fully reproduce the optical and haptic properties of human dentin [23]. In GE, additional time is required to perform CBCT and intraoral scanning, to complete the virtual planning and to fabricate the guide template. Further studies are needed to address aspects such as the differences between conventional freehand and GE access cavity preparation in posterior teeth with regard to time, operator experience, possible additional costs and the amount of tooth substance loss that is associated with the techniques.
## 4 Evaluation
### The Accuracy of LACE  
To study the estimation accuracy of LACE, we use the link correlation calculated with actual data traces as the ground truth: If the two links receive/lose a packet at the same time, the instant correlation is 1. Otherwise, the link correlation is 0. We use LACE to measure the link correlation before each packet transmission and study its accuracy compared to the above ground truth. For example, if the estimated link correlation is 0.8 and the packet is received by both links, the accuracy is 0.8; if the estimated link correlation is 0.8 and the packet is not received by both links (at least one receiver loses the packet), the accuracy is 1 \(-\) 0.8 = 0.2.  
We conduct the experiment 100 times and then calculate the average accuracy for all packet transmissions. Figure 4a depicts the estimation accuracy of LACE. We can see that for low and high link correlation, the LACE accuracy is high, while for intermediate link correlation links, the LACE accuracy is relatively low. This is due to the conditional probability-based link correlation metric having a PRR (packet reception ratio) bias: when the two links have both low/high PRR, the probability that they receive the packet at the same time will also be low/high. As a result, when links are high or low, the estimated link correlation is more accurate. For intermediate links (PRR around 0.5), the estimated \(ci\) will be around 0.25. However, the actual link correlation with two 0.5 links can be any value from 0\(\sim\)1. The error is more likely to be large.  
The reason for the above bias is the use of the average or the lack of a statistical distribution for link correlation. As discussed in Section 2.2 and in [21], the link correlation variation depends on the background noise and interference patterns. Under different environments, the interference patterns will be different. Thus, we can infer that the link correlation distributions will also be different with different environments. We expect that with a given distribution, the estimation accuracy can be further improved. We would like to study the model and distribution of link correlation in our future works.  
Figure 4: Accuracy of link correlation estimation. (**a**) Average estimation accuracy; (**b**) Variability of estimation errors.  
LACE is more accurate than beacon-based link correlation estimation (BCM). The reason is that LACE extracts the link correlation directly from the data packet receptions. Due to the difference of data and beacon packets, the beacon-based methods suffer from large estimation errors.  
Figure 4b shows the cumulative distribution function (cdf) of the estimation accuracy of LACE and BCM. We can see that when using LACE, about 60% of the estimations have errors under 0.2. While using BCM, only about 25% of the estimations have errors under 0.2. The reason is two-fold: (1) LACE directly uses data packet receptions/losses, which are more likely to capture the noise and interference patterns experienced by periodic packet transmissions; and (2) LACE uses short-term PHY layer information for calibration.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 2 Histogram-Based Image Enhancement Arts
### Adaptive Histogram Equalization (AHE)  
The function of an AHE is based on the computation of different image histograms to use them in the reallocation of the intensities for an image contrast enhancement. It is therefore a more viable procedure than a standard HE in regional contrast improvement and sharpness of image edges. However, it has a great tendency toward noise amplification in the image homogeneous parts.
## 4 Discussions  
The test results were consistent with the simulation, and the energy consumption in the proposed MP/DD acquisition method was only 21.5% of the conventional DD/MAX when the carrier to noise ratio was 23 dB/Hz, which shows its great advantages for energy-constrained applications.  
From Figures 3b and 7, for GPS weak signals, the decrease ratio in the mean acquisition computation overhead and mean acquisition energy of MP/DD compared with DD/MAX was close: 60% vs. 64% for CN0 of 22 dB/Hz and 74% vs. 78.5% for CN0 of 23 dB/Hz. Since AMCO takes correlation into consideration while the VLSI architecture includes units such as matched filter, Doppler mixing, and SRAMs, the ratio has little difference. For signals whose CN0 are higher than 23 dB/Hz, the detection probabilities of MP/DD and DD/MAX are both high, and therefore mean acquisition computation overhead and mean acquisition energy of these two methods are almost equal to each other.
## 3 Results and Analysis
### MIP Analysis of Test Results
Both the pore volume distribution curves and cumulative pore volume curves are significantly lower. Analysis of mechanism: from the known hydration mechanism of ordinary silicates, the mechanism occurs in hydrolysis and hydration reactions of water with tricalcium silicate (3 CaO-SiO\({}_{2}\)), dicalcium silicate (2 CaO-SiO\({}_{2}\)), tricalcium aluminate (3 CaO-Al\({}_{2}\)O\({}_{3}\)), tetracalccium aluminioferrite (4 CaO-Al\({}_{2}\)O\({}_{3}\)-Fe\({}_{2}\)O\({}_{3}\)), calcium sulfate (CaSO\({}_{4}\)). The strength source of cement-based materials mainly comes from generating compounds, including aluminosilicate gel, calcium hydroxide, hydrous calcium silicate, hydrous calcium aluminate, and hydrous calcium ferrite through the above reactions. However, cement and its hydration products occur in serial physicochemical reactions with HA and FA in cement soil, affecting its reinforcing effect. Incorporating UFC to form a compound curing agent can regulate the hydration rate and degree of cement, generating more hydration products to a certain extent, weakening the influence of HA, and then achieving the purpose of the pore structure of denser cement soil. As shown in the above test results, incorporating UFC increases the proportion of fine particles in the compound curing agent, the curing agent's Blaine area, the hydration reaction rate, the hydration reaction extent, and the hydration products' quantity. These hydration products continuously cement particles and fill the interior pores, changing the connection mode of mineral aggregates and the pore structure of the cement soil. The macropores provide a larger space for the hydration reaction. They are pre-filled with hydration products as the UFC proportion increases. The macropores then transform into smaller medium and small pores, gradually increasing the percentage of medium and small pores' volume. This improvement is very beneficial for the strength and durability of cement soil. The significant reduction in the percentage of pore volume means that the distribution of pores is concentrated and uniform, there is a low probability of pore connectivity, and there is no longer an apparent connectivity of the pores. Finally, cement soil presents a denser and better-integrated microstructure.