esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Motion Dynamics Input Search Algorithm
### Search Model Creation
Figure 10: Discrete forward motion search model, depicting selective search patterns based on block positions in an image. Figure 12 is a snippet of MVs generated by the DS algorithm, in a case where the drone moved forward. This phenomenon consistently manifests whenever the drone advances, illustrating that MVs accurately adhere to the forward motion model outlined by MDIS. All the vectors point toward the center of the image. One very important observation, which was already discussed in the previous subsection, is that the further away the pixel is from the center, the more it will move towards the edge of the image. This is also considered in the search model. When searching for the most similar block, the search range is multiplied by the weighted probability function--the same probability function that was calculated earlier. This is a great benefit of the CSM in comparison with the DSM. Not only do the probabilities determine whether the search location should be considered at all, but they also dictate the search range. This can also be seen in Figure 12. Notice how blocks that are further away from the center have bigger MVs. Figure 13 shows the DSM for the left drone motion. The CSM was designed as well and is shown in Figure 14. Again, all the functions were designed with the intention of minimizing computational complexity, hence some functions have sharp transitions in probability values, even though there might be a better probability function available. The model for the right drone motion is analogous. The function _local_thresh_ sets all the values within a specified region to 0. The function _thresh_ assigns a value of 0 to all elements that fall below a certain threshold. The parameter \(A\) from function (2) is a two-dimensional area representing a part of an image, and the parameter \(T\) is a scalar value. \[local\_thresh(x)= \begin{cases}0&\text{if }x\in A\\ x&\text{else}\end{cases}  \tag{2}\]  
\[thresh(x)= \begin{cases}0&\text{if }x<T\\ x&\text{if }x\geq T\end{cases}  \tag{3}\]  
Figure 12: Visualization of motion vectors resulting from forward motion, showcasing directional pixel displacement. Figure 13: Discrete search model for leftward drone motion, illustrating search pattern adjustments based on an image block position.
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 4 Simulation Study
### Case I: Simulation for Single-Frequency Vibration Control  
A simulation for a single-frequency AVC with an output constraint has been accomplished at sample time \(T_{s}=0.001s\). The disturbance signal is chosen to be \(5\cdot\sin(2\pi\cdot 33\cdot nT_{s}+60)m/s^{2}\) with a white noise (RMS = 0.1 \(m/s^{2}\)). The simulation conditions are set as: (a) no control applied; (b) with constraint \(A_{u,\max}=10\) V, with 10 V being far bigger than the value that rejects \(e(n)\) to zero under the ideal conditions and representing no constraint here; (c) with constraint \(A_{u,\max}=~{}0.2\) V; and (d) with constraint \(A_{u,\max}=0.1\) V, respectively. The adaption step size is set as \(\mu=10^{-6}\). The simulation results using the proposed algorithm are shown in Figures 5-7.  
Figure 4: The bode diagram of the simulation models \(S(z)\) and \(\hat{S}(z)\).  
Figure 5: The time-domain curve of the drive \(u(n)\) under different conditions: (**a**) no control; (**b**) under the ideal conditions; (**c**) with a constraint \(A_{u,\max}=0.2\)\(V\); and (**d**) with a constraint \(A_{u,\max}=0.1\)\(V\).  
Figure 5 shows the simulation results of the output signal \(u(n)\)'s time curve under different conditions: in Figure 5a no control is applied, which means \(u(n)=0\); in Figure 5b, the amplitude \(A_{u}(n)\) of the output signal \(u(n)\) grows to about 0.297 V under the ideal conditions via the NFXLMS algorithm; Figure 5b,c depicts that the amplitude \(A_{u}(n)\) of the output signal \(u(n)\) has been accurately restricted under the constraints \(A_{u,\max}=\) [0.2 V, 0.1 V]. The relative errors of the control result are 1.5% and 1.35%, respectively. Figure 6 shows the time curve of the residual vibration \(e(n)\) under different conditions. Figure 6b depicts that the NFXLMS algorithm can reject the residual vibration \(e(n)\) to almost zero under ideal conditions while the constraints can maintain \(e(n)\) at a certain level in Figure 6c,d. The smoothness of the proposed algorithm triggering process can also be seen in Figure 6. The spectrum of the residual vibration \(e(n)\) at a steady state is shown in Figure 7. Figure 7c,d depicts that no significant harmonics are introduced to the control system.  
Figure 6: The time-domain curve of the residual vibration \(e(n)\) under different conditions: (**a**) no control; (**b**) under the ideal conditions; (**c**) with a constraint \(A_{u,\max}=0.2\ V\); and (**d**) with a constraint \(A_{u,\max}=0.1\ V\).  
Figure 7: The spectrum of the residual vibration \(e(n)\) under different conditions: (**a**) no control; (**b**) under the ideal conditions; (**c**) with a constraint \(A_{u,\max}=0.2\ V\); and (**d**) with a constraint \(A_{u,\max}=0.1\ V\).
# MinerGuard: A Solution to Detect Browser-Based Cryptocurrency Mining through Machine Learning
## 3 Related Work
### Machine Learning Mechanism  
Based on our study, Trend Micro published Trend Micro Locality Sensitive Hashing (TLSH) [21] in 2018, the first application of Machine Learning techniques that can quickly compare and judge similarities of mining behaviors. Given a string with a minimum length of 50 bytes, TLSH can generate a Hash for comparing similarities. Objects with a higher similarity will have higher Hash similarity to each other. Trend Micro generated the Hash from the collected virtual currency mining virus through TLSH technology and clustered similar samples. Furthermore, these clusters of samples were analyzed to identify the features of these virtual currency mining viruses, and more files with these features could be identified.  
[PERSON] et al. [31] likewise suggested an algorithm using Machine Learning techniques for cryptojacking discovery. The writers provide a browser-based crypto-mining detection technique by assessing the vibrant opcode. The presented model can differentiate between crypto-mining sites and benign weaponized websites, such as safe areas in which the crypto-mining code has been infused, the deweaponized crypto-mining websites such as crypto-mining sites to which the begin() call has been removed, and real-world benign areas. [PERSON] et al. [37] suggested an approach to spot the browser's harmful mining behavior. They designed a device to extract asynchronously and immediately classify the lot picture and stack features using Recurrent Neural Networks (RNNs). They conducted an experiment in which 1159 destructive examples were analyzed, and the outcomes reveal that the precision of the recognition of the original mining examples is 98% if the pieces are not encrypted and 93% if they are. Reference [38] suggested CapJack, a maker learning-based discovery mechanism able to detect in-browser dangerous cryptocurrency mining tasks. They created a machine-learning algorithm named CapsNet that simulates biological neural organization. CapJack uses system features such as the CPU, Memory, Disk, and network usage, applying a host-based option with a discovery rate of 87%. [PERSON] et al. [39] suggested Crypto-Aegis that cannot just find mining habits yet likewise determines actual cryptojacking tasks with the machine learning modern technology. However, it is designed as a network-based system that spots and recognizes cryptographic operations by analyzing the accumulated network website traffic that stands out from other device learning-based devices. It carries out a detection accuracy of 96%. Nonetheless, the effectiveness of discovery might be affected by numerous cryptocurrencies and VPN items.
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 2 Materials and Methods
### Reference Data and Measurements  
Multi-GNSS 1 Hz measurements from IGS station WROC and from permanent stations of the national ASG-EUPOS system ([[http://www.asgeupos.pl](http://www.asgeupos.pl)]([http://www.asgeupos.pl](http://www.asgeupos.pl))) were used. Trimble R10 geodetic-grade receiver and antenna were used for performing measurements in the static mode (2 h-long sessions) on all control points and, then, the position data were processed through the automatic post-processing service POZGEO of ASG-EUPOS. Reference coordinates were provided in the Polish PL-2000 coordinate system, which was a Gauss-Kruger projection of coordinates determined in the European Terrestrial Reference System (ETRS) 89.
## 3 Experimental Investigation
### Quasi-Static Cyclic Tests  
After performing the shaking table dynamic tests, the SS-TTDs installed in the upper storey were subjected to cyclic quasi-static loading until failure in order to quantify their ultimate energy dissipation capacity. The specimens were disassembled from the RC structure and tested on a SAXEWAY T1000 universal testing machine with a maximum load capacity of 1000 kN, as shown in Figure 13. Both the inner and outer tube were welded to a 25-mm base plate that was rigidly bolted to the base of the testing machine and to the actuator, respectively. All the tests were carried out under displacement control with the same cyclic rate of 0.02 Hz. Two displacement transducers (LVDTs), connecting the inner and outer tubes in the direction of the applied load, controlled and measured relative displacements between the tubes. Three different loading histories consisting of cyclic forced displacements were applied to the SS-TTDs. Specim SS-TTD4 was subjected to an initial displacement of 10 mm in the positive direction followed by cycles of constant amplitude equal to 5 times the yield displacement \(\delta_{yST}\). Specimens SS-TTD5 and SS-TTD6 were subjected to sequential sets of cycles with incremental amplitude, following the protocol proposed by ATC [18]. In both cases the increment of displacement amplitude from one set of cycles to the next, \(\Delta\delta\), was \(\Delta\delta\)/\(\delta_{Yst}=1\); the difference was the number of cycles \(n_{c}\) applied within each set. For specimen SS-TTD5 \(n_{c}=10\), while for specimen SS-TTD6 \(n_{c}=4\). The SS-TTD damper was assumed to fail when the force under increasing displacements dropped below 20% of the maximum force attained in previous cycles of deformation.  
The entire axial force-displacement, \(Q\)-\(\delta\), hysteretic curves obtained from the quasi-static cyclic tests are plotted in dotted lines in Figure 14. The solid line represents the hysteretic behaviour before failure, with the dotted lines showing the \(Q\)-\(\delta\) curves after failure.  
Figure 13: Experimental setup for static cyclic tests: (**a**) identification of components, (**b**) photograph.
## 4 FWA Design Considerations, Challenges, and Solutions
### RF Technology Selection  
The real-world product solution selection is another imperative thing to be considered. There is a significant need for supporting sub-6 GHz as well as centimeter wave/mm-wave 5G FWA systems. This will enable the FWA to support advanced 5G RAN technologies that are based on cm-wave and mm-wave spectra. Furthermore, this will not only enable efficient support for eMBB, URLLC, and mMTC 5G applications, but also for verticals such as Smart cities, Automotive, Industry 4.0, and Healthcare.  
In addition, it is highly essential to have integrated transmit and receive front-end modules for the FWA system. Besides, it is very important to meet the requirements for passive-cooling of the infrastructure at high temperatures [264]. For instance, it is highly imperative for the active antenna array to be very robust and power-efficient. This will enable all-outdoor tower top electronics to be passive-cooled. Moreover, the BSs should  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Equipment Class** & **Power (EIRP)** & **Reference** \\ \hline Base Station & 75 dBm/100 MHz & [264; 321; 322; 323; 324] \\ Mobile Station & 43 dBm & [12; 264; 321; 322; 323; 324; 325] \\ Transportable Station & 55 dBm & [12; 264; 321; 322; 324; 325; 326] \\ \hline \hline \end{tabular}
\end{table}
Table 10: FCC power limits for 28 GHz and 39 GHz bands.  
Figure 18: HBF architecture for mm-wave MIMO system.  
be sufficiently compact and lightweight to be mounted in different places such as on the traditional cell towers and/or on the street lamp poles [12].
## 2 Theory of the LRSC Algorithm
### Sparse Solution of the Regression Coefficient  
The general sparse solution mainly recognizes which class one sample belongs to on the basis of the NN or nearest subspace (NS) method. Since spectral data are typically in the form of a small number of samples of high dimensions, this linear system can be regarded as an overdetermined equation that can produce a unique but inaccurate solution, which mainly calculates the solution by the minimum \(l^{2}\)-norm:  
\[\left(l^{2}\right):\hat{\beta}_{2}=\text{argmin}\|\beta\|_{2}\text{\ subject to \ }X\beta=y \tag{4}\]  
where \(\|\cdot\|_{2}\) denotes the second-order norm of this vector. In case of the second-order norm, the solution of \(\hat{\beta}_{2}\) is not very sparse, which does not have a positive effect on classification recognition. To determine the sparse solution, according to the definition, the zero-order norm of the space will be minimized by solving the linear Equation (5)  
\[\left(l^{0}\right):\hat{\beta}_{0}=\text{argmin}\|\beta\|_{0}\text{\ subject to \ }X\beta=y \tag{5}\]  
where \(\|\cdot\|_{0}\) denotes the zero-order norm of this vector. However, there is no effective method to directly determine this zero-order sparse solution [17]. With the development of knowledge about sparse representation and compressed sensing, the minimization of the \(l^{1}\)-norm can be used to minimize the \(l^{0}\)-norm [18]. As shown in Equation (6), this problem can be transformed to a linear programming problem.  
\[\left(l^{1}\right):\hat{\beta}_{1}=\text{argmin}\|\beta\|_{1}\text{\ subject to \ }X\beta=y \tag{6}\]  
Considering the noise of this linear system, a noise term can be added to Equation (7), transforming it to:  
\[y=X\beta_{0}+e \tag{7}\]  
where \(e\) denotes the noise of the predicted spectral data. Equation (8) tries to solve the following optimization problem for a solution of Equation (7):  
\[\left(l_{s}^{1}\right):\hat{\beta}_{1}=\text{argmin}\|\beta\|_{1}\text{\ subject to \ }\|X\beta-y\|_{2}\leq\varepsilon \tag{8}\]  
where \(\varepsilon\) is a value used to represent the upper limit of fault tolerance in this linear system [18], which determines the regression of the training samples to the prediction samples under noise tolerance. As discussed above, for such highly dimensional spectral data, the linear system itself is over-determined and can have a unique solution. To determine the sparse solution to this problem, we can solve this equation by regarding it as an underdetermined equation [19], which can be solved by methods such as homotopy and proximal gradient. Here, homotopy was used to solve this optimization problem. As the noise was considered, the basis pursuit de-noising (BPDN) model was used, as shown in the following Equation (9):  
\[\min_{\beta}\frac{1}{2}.\|y-X\beta\|_{2}^{2}+\lambda\|\beta\|_{1} \tag{9}\]  
where \(\lambda\) denotes the normalized parameter, the value of which ranges from 0 to 1. This optimization problem can be transformed into a quadratic programming problem. We omit the details of the solution, which can be found in Reference [17].
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 5 Outage Analysis in the Presence of NLOS, Noise and Blockage  
In the previous sections, we derived the CF of the aggregated interference power with different antenna array configurations, and characterized the distribution of the aggregated interference power. Moving toward the modeling of practical mmW and sub-THz systems, in this section, it is shown how the NLOS propagation, noise power, and blockage can be integrated into the model.
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System  
### System Configuration  
In this paper, we propose a novel method to improve the recognition rate of the deep learning models trained with a general database for vehicle license plate recognition using CCTV images. Figure 1 shows the flowchart of the proposed license plate recognition system. First, a commonly used deep learning model YOLO v2 [9] is applied to the input image. This model is used to acquire an image, including a license plate area, which is the area of interest [9]. From the detected ROI image, a high-resolution license plate image is obtained using SRGAN. The license plate area is extracted, and the boundary pixels of the license plate are obtained by using the image segmentation technique. A linear approximation method is applied to the boundary pixels, and a separate linear equation is used for each side of the license plate. The intersection points of all straight lines are calculated by using the above-applied set of linear equations. The homography between the feature points is calculated, and the perspective distortion is corrected. Finally, the YOLO v2 based character recognition technique is applied to the corrected image for recognizing the license plate character.
## 4 Conclusions  
This study showed that breeding lines differ greatly based on their individual volatile compositions (VOC contents and types), sugar contents, and color. BL-30 and BL-20 showed the highest average VOC content and the most different VOCs. Other important lines to be considered for future projects, based on their antimicrobial and health beneficial properties, are BL-12, BL-4, BL-7, and BL-9. In this study, we found one new compound- nervl acetone, which, to the best our knowledge, has not been previously reported in melons, and will be further evaluated for its sensory and antibacterial properties in our future projects. The three main volatiles found in abundance based on their average concentrations were benzaldehyde, geranylacetone, and \(\beta\)-ionone. D-limonene was present in all the breeding lines and has potential antimicrobial efficacy on foodborne pathogens. Moreover, 32 of the compounds identified here have antimicrobial properties with major compounds such as hexanal, octanoic acid, nonanoic acid, carveol, citral, cinnamaldehyde, \(\alpha\)-calacorene, benzothiazole, \(\beta\)-ionone and benzyl alcohol -calacorene, benzothiazole, and benzyl alcohol. Our future studies include determining the invitro antimicrobial activities and the plants extract from cantaloupe against foodborne pathogens from these breeding lines. Our findings will be valuable for the development of healthier melons with high sugar contents and high levels of antimicrobial compounds.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/plants10102166/s1](https://www.mdpi.com/article/10.3390/plants10102166/s1)]([https://www.mdpi.com/article/10.3390/plants10102166/s1](https://www.mdpi.com/article/10.3390/plants10102166/s1)), Table S1: Number of volatiles present and total content of volatiles present in each BL; Table S2: No of BLs present in each volatile compound; Table S3: Relative contents (ug/kg FW) of each type of compound class in 28 Breeding lines; Table S4: Total content (ug/kg) of each type of volatiles in 28 melon breeding lines; Table S5: Total % of each type of volatiles in 28 melon breeding lines.  
**Author Contributions:** [PERSON] carried out all the experiments, analyzed data under supervision of [PERSON], [PERSON] helped in executing the experiments, GC-MS analysis and critical revision of the manuscript. [PERSON] designed the experiment, supervised the project, and contributed to the critical revision of the manuscript. [PERSON] helped edit and contributed to the critical revision of the manuscript. [PERSON] provided the melon fruits and contributed to the critical revision of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by USDA-NIFA- SCRI- 2017-51181-26834 through the National Center of Excellence for Melon at the Vegetable and Fruit Improvement Center of Texas A&M University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors of this paper declare no conflict of interest of any sort.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method  
The SEMS method consists of the following steps.  
Step 1: Collect information on disaster depots via V2X.  
Step 2: Evaluate the scenario coefficient based on the damage.  
Step 3: Build the fairness model based on the scenario coefficient.  
Step 4: Establish the optimal model for emergency material scheduling with the objectives of maximum fairness and minimum time.  
Step 5: Design the SEMS algorithm to obtain an optimized solution.  
The SEMS method consists of the following main modules: (1) evaluate the SEMS scenario coefficient; (2) calculate the transportation time; (3) identify the fairness of emergency material scheduling; (4) optimize the emergency material schedule. These are detailed below.
# Tire Bubble Defect Detection Using Incremental Learning
## 3 Experimental Results
### Comparision of the Different Incremental YOLO Architectures  
Table 9 lists the comparison results of the above five architectures in conjunction with the incremental learning. The proposed improved YOLOv3 SPP method achieves the highest sensitivity among the results obtained using the incremental learning. The proposed model's detection rate is 6% higher than the modified YOLOv3 SPP method without the incremental YOLO architecture (see Table 8), significantly reducing the number of false negatives in defect detection. The proposed method's detection time is shorter than all other methods except the Tiny YOLO architecture. Nevertheless, despite the Tiny YOLO architecture's simple, easy-to-use network structure and low computing resource requirements, it is unsuitable for the detection task due to its poor detection rates. Hence, our proposed method provides a semi-automated defect detection approach for tire manufacturers, effectively reducing labour costs and improving inspection accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Result**} & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} \\ \cline{2-5}  & **Tread** & **Sidewall** & **Tread** & **Sidewall** \\ \hline TP & 758 & 459 & 801 & 495 \\ TN & 490 & 487 & 481 & 432 \\ FP & 10 & 13 & 19 & 68 \\ FN & 52 & 46 & 9 & 10 \\ \hline Sensitivity & \multicolumn{2}{c}{92.54\%} & \multicolumn{2}{c}{98.56\%} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results Obtained by (a) the Modified YOLOv3 SPP Architecture, (b) the Incremental YOLOv3 SPP Architecture.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Result**} & \multicolumn{4}{c}{**Tread**} & \multicolumn{4}{c}{**Sidewall**} & \multirow{2}{*}{**Sensitivity**} \\ \cline{2-2} \cline{4-9}  & **TP** & **TN** & **FP** & **FN** & **TP** & **TN** & **FP** & **FN** \\ \hline (a) & 638 & 496 & 4 & 172 & 411 & 486 & 14 & 94 & 79.77\% \\ (b) & 650 & 499 & 1 & 160 & 419 & 496 & 4 & 86 & 81.29\% \\ (c) & 714 & 497 & 3 & 96 & 431 & 492 & 8 & 74 & 87.07\% \\ (d) & 668 & 500 & 0 & 142 & 442 & 490 & 10 & 63 & 84.81\% \\ (e) & **758** & **490** & **10** & **52** & **459** & **487** & **13** & **46** & **92.54\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results Obtained by Different Architecture (a) Tiny YOLO (b) YOLOv3 (c) YOLOv3 SPP (d) Modified YOLOv3 (e) Proposed modified YOLOv3 SPP Model.