esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 3 Methodologies
### PSO-GA Hybrid Algorithm  
The idea of PSO is introduced to improve the selection operator, crossover operator, and mutation operator of IGA.  
1. Selection operator: PSO is adopted to formulate a genetic selection strategy and establish the PSO population. The update operation of each particle selects global best, particle best, and particle position for the crossover operator.
2. Crossover operator: The ICO operator above is used for the crossover operation, and the idea of multi-agent inheritance is introduced. Three gene sequences are selected based on the selection operator, and the particle position is crossed with the global extreme and individual extreme, respectively, to generate four generations. Then, the four children are divided into two groups. The two children with a global best gene and the two children with a particle best gene cross over each other to generate eight children. The children with the largest evaluation value are selected from the eight children as the result of the crossover calculation to replace the particle position and complete the iteration. The specific operation is shown in Figure 4.
3. Mutation operator: In order to ensure the diversity of the population and avoid falling into the local optimal trap, the IMO above performs a mutation operation on the particle position. The mutation factor is set, and the particle position of the population is calculated according to the factor proportion according to the gene mutation operation.  
The PSO-GA only sets one parameter of mutation factor (MF), which is easy to operate and easy to implement.  
Figure 4: Selection operator and particle update operation.  
Figure 3: Process of IMO.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 5 Traffic Simulation Models
Calibration, prediction, and validation of the inputs and parameters are considered data-demanding and require efficient computation tools [55]. These simulation models also use a number of algorithms, such as the car following algorithm, the lane changing algorithm, and the gap acceptance algorithm. These algorithms are used to view the traffic status dynamically when increasing the speed of the vehicle or driving within multiple lanes. We describe these different algorithms in the traffic simulation models to comprehend how these models work realistically. Figure 4 shows a typical illustration of the car following, lane change, and gap acceptance algorithms used in traffic simulation models. Traffic simulation models are implemented in different traffic and transport planning software to show the traffic behavior in a graphical user interface, where the user can define the input parameters and view the output parameters [61]. According to [PERSON] et al. [7], the seven most widely known traffic simulation tools are SUMO, MATSim, AIMSU, CORSIM, Paramics, VISSIM, and TRANSIMS. We can consider traffic simulators to be dynamic visualization models that use statistical methods to examine traffic behavior and provide statistical reports for the simulated scenario. There are also some useful traffic simulators such as FreeSim [62], Traffsim [63], SUMMIT [64], and SifTraffic [65] that are designed for either microscopic or macroscopic traffic simulation. FreeSim traffic simulator is designed to conduct traffic simulations of freeways in real-time [62]. Traffsim simulator is widely known for modeling isolated traffic control strategies in different complex traffic environments [63]. In large traffic scenarios with massive and mixed traffic, SUMMIT traffic simulator provides useful features and functionalities to simulate vehicle driving, especially in urban scenarios [64]. SifTraffic is a traffic simulation tool that provides practical implications for different types of traffic applications [65].
## 4 Simulations and Analysis
### Simulation Results and Analysis  
#### 4.2.1 Muddy Road  
A muddy road is a typical medium adhesion track-ground condition with the largest adhesion coefficient around 0.4. Simulation results of these four strategies mentioned above conducted under a muddy road are shown in Figure 6, and the value of indicators are listed in Table 2. In Figure 6, VV, TV, HRT, MBT, and MRT are used as abbreviations of vehicle velocity, track velocity, hydraulic retarder torque, mechanical brake torque and motor regenerative torque respectively.  
As shown in Figure 6 and Table 2, taking any of these three strategies to prevent the locking of drive wheel in the process of emergency braking can enhance the performance greatly compared with full braking, which is the most popular way of emergency braking for tracked vehicles. The performance of SMSCR was the best among all these strategies, and it had an advantage of 0.12 s in stopping time and 1.29 m in stopping distance to the second best. As for the ability of controlling the slip ratio, the indicator D can be reduced by more than an order of magnitude with SMSCR compared with other methods. However, although SMSCM performed a little better than TABS in stopping time and controlling the slip ratio, its stopping distance was 0.79 m longer than the latter, which means that the traditional way of antilock braking performs effectively when the adhesion is not very low, and even if an advanced method is used to control the slip ratio, the performance may still not be improved significantly over the traditional way.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Vehicle mass, \(m\)(t) & 52 & Approach angle, \(\varphi_{A}\) (\({}^{\circ}\)) & 27.3 \\ Drive wheel radius, \(r\)(m) & 0.309 & Departure angle, \(\varphi_{D}\) (\({}^{\circ}\)) & 35.6 \\ Frontal area of the vehicle, \(A\)(m\({}^{2}\)) & 5.36 & Ratio of the wheel-side reducer, \(ir\) & 4.59 \\ Aerodynamic drag, \(C_{\mathrm{D}}\) & 1 & Ratio of the coupling mechanism, \(ic\) & 2.2 \\ Air density, \(\rho\)(Ns\({}^{2}\)/m\({}^{4}\)) & 1.22 & Rated power of the PMSM, \(P_{m}\) (kW) & 625 \\ Rotational inertia of drive wheel and power train, \(\left(\mathrm{kg\cdot m}^{2}\right)\) & 158.8 & Rated speed of the PMSM, \(n_{m}\) (rpm) & 3000 \\ Rotational inertia of idler, \(\left(\mathrm{kg\cdot m}^{2}\right)\) & 31.2 & Max speed of the PMSM, \(n_{mux}\) (rpm) & 9000 \\ Rotational inertia of road wheel, \(\left(\mathrm{kg\cdot m}^{2}\right)\) & 23.7 & Quantity of road wheels, half vehicle & 6 \\ Rotational inertia of support roller, \(\left(\mathrm{kg\cdot m}^{2}\right)\) & 13 & Quantity of support rollers, half vehicle & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the DDTV for simulation.
# Modeling Transient Flows in Heterogeneous Layered Porous Media Using the Space-Time Trefftz Method
## 4 Numerical Examples
### Transient Flows in Two-Layered Porous Media
There exists 7200 boundary points for domain, as illustrated in \[h_{1}(r,\theta,t=0)=\sinh\mathrm{h}(r\sin\theta)r\cos\theta,(r,\ \theta,\ t=0)\in\Omega_{1} \tag{25}\]7 For modeling transient flows in two-layered porous media, the DDM was utilized [35]. The domain boundary can then be divided into several subdomains. The Dirichlet boundary conditions are prescribed. Based on Equation (11), the sub-boundaries at the interface, including \(\Gamma_{12}\) and \(\Gamma_{22}\), must satisfy the continuity of flows and the flux conservation. The profiles of total head at the final time were selected to express the approximations, as shown in \[h_{1}(r,\theta,t=0)=\sinh\mathrm{h}(r\sin\theta)r\cos\theta,(r,\ \theta,\ t=0)\in\Omega_{1} \tag{25}\]8 The approximations at the final time from our approach were further compared with the steady-state solution. It is found that the approximations utilizing our approach may agree well with the steady-state solutions. The absolute difference at the final time is depicted in \[h_{1}(r,\theta,t=0)=\sinh\mathrm{h}(r\sin\theta)r\cos\theta,(r,\ \theta,\ t=0)\in\Omega_{1} \tag{25}\]9 The absolute difference for case I and case II is in the order of \(10^{-6}\) and \(10^{-4}\), as given in \[h_{2}(r,\theta,t=0)=\sinh\mathrm{h}(\sqrt{\frac{D_{1}}{D_{2}}}r\sin\theta)r \cos\theta,(r,\ \theta,\ t=0)\in\Omega_{2} \tag{26}\]0 Results demonstrate that our approach may obtain reasonable results for modeling transient flows in two-layered porous media.
# Multi-Objective, Reliability-Based Design Optimization of a Steering Linkage
## 1 Introduction
The deviation distance of the instantaneous center of an actual steering mechanism and its theoretical value is a steering error [33], as shown in Figure 3.  
From the figure, the trigonometric relation is used to formulate the steering error as  
\[x_{O}=\frac{W_{b}/W_{b}}{\tan\theta_{O}}=\frac{1}{\tan\theta_{O}} \tag{3}\]  
Figure 2: Ackermann principle in the form of an angle distance (AD) (reproduced from [17]). (4)  
Then, the Euclidean distance of a steering error is defined as  
\[\delta s=x_{O}-x_{I}=\frac{1}{\tan\theta_{O}}-\frac{1}{\tan\theta_{I}}-\frac{W_{t }}{W_{b}} \tag{5}\]  
where \(x_{O}\) and \(x_{I}\) are dimensionless. This equation shows the steering error as a function of \(\theta_{O}\), \(\theta_{I}\), and a car geometric ratio \(W_{t}/W_{b}\). Notice that the last term is smaller than \(1\) due to it being dependent on the geometry of a car. In the previous work, a proper value of a scaling factor (\(k\)) to minimize the steering error was proposed as [33]  
\[\delta s=x_{O}-x_{I}=\frac{1}{\tan\theta_{O}}-\frac{1}{\tan\theta_{I}}-k(\frac {W_{t}}{W_{b}}) \tag{6}\]  
The numerical experiment showed that common four-bar linkage cannot meet the design requirements of a steering linkage. Consequently, it leads to the use of the six-bar linkage [20; 22; 23; 24; 25]. The six-bar steering linkage is proposed to be the steering mechanism, which has an expectation to satisfy the Ackermann principle while it is better than the previous mechanism [26; 27; 28; 29; 30; 31; 32]. Nevertheless, it can fulfill the Ackermann principal for only some angles. The McPherson suspension is therefore combined with the steering linkage to achieve a high precision design [28; 30; 34]. A probability-based reliability technique increased the reliability and precision of the steering linkage design, as proposed in [1]. Furthermore, there are three coupling systems, including hub motor driving, differential assisted steering, and semi-active suspension systems of an electric vehicle used to study the performance enhancement of the integrated chassis system [33]. From the literature, the best objective function, constraint handling, and optimizer from the previous study in [33] are combined with a multi-objective optimization technique with the worst-case scenario and fuzzy uncertainties. The expectation is reduced complexity in the reliability-based steering linkage design and increased reliability of the design result. The remainder of this paper starts with Section 2, preparing kinematic analysis, the steering errors, and the brief detail of the optimizer. Next, the multi-objective reliability-based design optimization technique (MRBDO) is proposed in Section 3. The simple test problem for validation of the proposed  
Figure 2.0 Ackermann principle in the form of Euclidean distance (reproduced from [17]). MRBDO is presented in Section 4, followed by a numerical experiment of reliability analysis of steering linkage, which is given in Section 5. The design results, and conclusions and discussions are in Sections 6 and 7, respectively.
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 4 Impact on the Smart Home Network
### Physical Impact  
* Delayed actuation: the research of [PERSON] et al. [54] demonstrated the smart home ZigBee sinkhole attack, as well as how it advertises itself as a favorable route via a rogue node to the ZigBee controller. This results in delayed actuation wherein a rogue node alters or drops the data carried forward from Zigbee sensors.
* Breach of physical privacy: the privacy of an individual in a household for a given point in time or for long and extended periods can be invaded during the transmission of data via an eavesdropping attack [55]. Veracode, a security solutions provider, described the attack as seizing the wink relay touch controller to switch on the microphone to record background audio covertly. Using audio as a means of breaching physical privacy, a privacy violation that takes advantage of a software vulnerability found in the android debug bridge (ADB), which was subsequently patched in an update [56], is demonstrated. Many of the smart home devices are shipped with poor security solutions in place from their manufacturers. The lack of security exposes many areas of exploitation for violation of physical privacy. On the web are search engines that allow users to search for vulnerable devices connected to the internet, such as shodan.io. An attacker using these search engines may locate an insecure open port and record the header or banner information of any device. The data may include the type and model of the device used, its manufacturer, and the installed software version. According to [PERSON] et al. [2], searching on shodan.io using a search query such as \"has_screenshot: true port:554\" resulted in an exhaustive list of camera devices along with their IP addresses, the screenshots captured by them, and their exact geographic location. With the help of such search engines, an attacker can gain access to surveillance cameras installed both in a home and outside, giving them complete visibility and knowledge of the victim's daily  
Figure 5: Impact of the smart home system.  
routine in a smart home environment. There have even been concerns of an attacker gaining access to baby monitoring systems and covertly spying on children [57; 58; 59].
* Prevented actuation was suggested by disabling the vacation mode on devices [60]. Users use this mode when they are away for an extended period to give any criminal the impression that the home is occupied. The user's smartphone can interfere with this mode and disable the actuation expected by the users when they are away from home.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 4 Discussion
### Real-Time Head-Wave Extraction  
To improve the operation efficiency and reduce the extraction time, Equation (2) can be rewritten into Equation (6). In this way,\(N_{L}^{2}/N_{S}^{2}\) can be set as a constant, which is then directly stored into the register, and \(x(i)^{2}\) and \(x(i-1)\cdot x(i+1)\) can also be calculated in parallel. The algorithm can be decomposed into multiple steps and accelerated by a pipeline mode.  
\[Z(n)=\frac{N_{L}^{2}}{N_{S}^{2}}\frac{\left(\sum_{i=n-N_{S}+1}^{i=n}x(i)-\sum_{ i=n-N_{S}+1}^{i=n}x(i-1)x(i+1)\right)^{2}}{\left(\sum_{i=n-N_{L}+1}^{i=n}x(i)- \sum_{i=n-N_{L}+1}^{i=n}x(i-1)x(i+1)\right)^{2}} \tag{6}\]  
Figure 8 demonstrates the program structure of head-wave extraction, which primarily comprises the shift register group, multipliers, accumulators, dividers, counters, comparators, and other modules. Each module cooperates with one another and processes data in a pipeline mode to extract the arrival time and amplitude of the head wave in real time. After the filtered 16-bit digital echo signal enters the FPGA's shift register, the FPGA starts to receive data and counts. When counting to the end of transducer excitation, the data-processing module starts operating.  
Figure 8: Program structure of head-wave extraction.  
Figure 7: Calculation flow of digital filtering.  
The parallel computation processes of short- and long-window energies are shown above and below the dotted line in Figure 6, respectively. Multiplier 11 and multiplier 21 are used to calculate, and multiplier 12 and multiplier 22 are used to calculate at the same time. The results of the above multipliers enter the corresponding accumulators and participate in the accumulation and summation operation. When the count reaches the window length, both short- and long-window subtractors perform subtraction operations on the corresponding accumulator output. Multiplier 13 and multiplier 23 perform square calculation on the output of the subtractor. A hardware divider is designed at the outlet of these two multipliers, which operates through data shift, comparison, and other steps for calculating the energy ratio between the short and the long window.  
When the calculation result of the divider \(R(n)\) is greater than the threshold \(\alpha\), the current counter value corresponds to the arrival time of the head wave, which is then cached and output by the head-wave arrival register. At that time, the digital comparator is set to output a flag to enable the echo amplitude detection process. The echo amplitude comparator screens the peaks and troughs of the received waveform and then feeds the calculation result into the echo amplitude register.
# Life Cycle Assessment for the Production Phase of Nano-Silica-Modified Asphalt Mixtures
## 3 Methodology  
LCA methodology was used (as standardized by the ISO in 2006) to assess the environmental impact of nano-silica-modified asphalt mixtures. There are numerous nanomaterials whose effect on asphalt binder and mixtures have previously been evaluated. However, based on the extensive literature review, the common nanomaterials which have been experimentally shown to have a greater impact on asphalt concrete performance include nanoclay and nano-silica. Consequently, nano-silica was used in this study. However, any other nanomaterial (especially nanoclay) which uses a similar production process could give similar results when modified with asphalt binder and materials. Also, the analysis of this study focused on only material extraction and production phases and does not include the operational or the disposal phase. The inclusion of the operational phase in the LCA analysis could change the inference about the conformity of nano-silica-modified asphalt mixtures.  
The structure of LCA studies adopted includes goal and scope definition, inventory analysis, impact assessment, and improvement assessment or interpretation stages.
## 5 Research Gaps  
Although the FAO-56 approach is the most widely used and reliable method for estimating crop water requirements, studies need to adopt and use one of the most direct and defensible micro-meteorological methods. The FAO-56 approach utilizes published crop coefficients (crop factors) that may not be representative of the different microclimates (locations) of the field orchards. Therefore, there is a need to use a more direct method of estimating the actual ET, such as the eddy covariance system. In this review, only one study used the eddy covariance system, yet it has great potential to determine the accurate ETc value. It has been widely used and performed well for other agricultural crops; thus, there is a critical need for studies to adopt the use of EC systems to estimate ETc in plum orchards. Therefore, the estimated plum ETc can also be used to calibrate and validate other methods and technologies. The EC system is mathematically complex and requires a lot of expertise to install and process data, but it is imperative to use.  
Of the retrieved articles, no studies utilized ground-based plant measurements to estimate the water requirements of plum cultivars. When estimating crop water requirements, it is essential to understand the plant-water relations. Plant measurement methods such as the stem water potential (SWP) aid in monitoring plant water status and tracing the onset of water stress. Future studies should integrate soil, plant and atmospheric measurements to estimate accurate water requirements of various plum cultivars.
## 5 The INS-Aided Acquisition Experiments with Different Grade INS
### INS-Aided Acquisition Experiments and Results  
Due to the fact that the clock error of the software receiver has been compensated before the INS-aided acquisition experiments and the satellite ephemeris is true, the residual frequency shift is the Doppler shift caused by the INS position and velocity errors. In this case, the performance of the INS-aided acquisition can be fully verified. In Table 7, the search parameters with different grades of INS-aided and no INS-aid acquisition is illustrated. Due to the decrease of frequency and code phase range, the acquisition time of the INS-aided case is shorter than no INS-aid case. Besides, if the frequency search space is smaller, the accuracy of the Doppler shift estimation is higher.  
The selected MEMS-grade INS has the same search parameters as the selected higher grade INS. In other words, the selected MEMS-grade INS can satisfy the demands of fast acquisition under highly dynamic conditions. Considering the price, the weight and the size, the selected MEMS-grade INS is the best choice for the deeply coupled integration.  
After the signal interruption, the navigation information provided by INS can not only be used to save acquisition time, but also it can keep tracking in a short time. If the LOS error estimated by the local clock and INS position is less than half a chip, instantaneous acquisition can be achieved in the code phase direction. Similarly, the instantaneous acquisition of carrier frequency can be realized if the frequency error estimated by SINS is less than the equivalent bandwidth of the tracking loop. This offers great advantages in applications when the signal is frequently interrupted but the duration of the interruption is short, such as urban vehicle navigation.