esa-sceva/satcom-chunk-collection huggingface dataset:
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 2 Related Work  
### Crowd Simulation  
The dynamic formation control of the crowds based on PBD is influenced by various existing crowd simulation techniques. In this chapter, we review important prior research related to our work.  
Historically, crowd simulation has been focused on various aspects of collective behavior [10]. A typical technique of these is to use rules based on physical laws [11].  
The Social Force Model was a probabilistic model to predict pedestrian behavior proposed by [PERSON] and [PERSON] [12]. The idea behind the model is that pedestrian movements appear to be subject to social forces. In this model, pedestrians are influenced by several forces to reach their destination, including the force to move in the desired direction and the repulsive force when considering static obstacles.  
[PERSON] et al. proposed the Power-Law technique to control pedestrian interactions. It controlled crowds by measuring the energy of interaction between pedestrians [5]. It also described and reproduced crowd interactions from different situations, speeds, and densities of the crowd. When applied to the movement of a large number of pedestrians, it can predict not only the physical distance between pedestrians but also potential future collisions.  
Figure 1: Overview of controlling crowd formation using SRD. (**a**) There are two groups of agents, a red group and a blue group. Both groups are supposed to maintain a bird formation and are about to collide while moving to the opposite side. (**b**) The two groups are crossing and passing each other, losing their formation to avoid collisions. (**c**) After the two groups cross, the agents in each group restore the original formation automatically. (**d**) The blue group is moving to their destination with the formation restored.  
[PERSON] proposed a flocking technique in which flocks of animals, such as birds, schools of fish, etc., behave naturally based on local information about their dynamic environment [13]. In this algorithm, the flocks of animals behave in three states: separation, alignment, and aggregation. [PERSON] extended this work and proposed the Steering Behavior technique [14]. Steering Behavior expands the three states by adding additional states, such as tracking specific neighbors and collision avoidance to avoid obstacles. In Steering Behavior, the crowd decides on an action through three stages: action selection, steering, and movement. This technique allows the crowd to move back and forth between different states, creating natural crowd behavior.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 6 EKF, UKF, and CKF  
Suppose we have the estimated state \(\hat{\mathbf{x}}_{k-1|k-1}\) and associated covariance \(\mathbf{P}_{k-1|k-1}\) at time \(t_{k-1}\). Our goal is to obtain the estimated state \(\hat{\mathbf{x}}_{k|k}\) and associated covariance \(\mathbf{P}_{k|k}\) at time \(t_{k}\) after processing the measurement \(\mathbf{z}_{k}\) at time \(t_{k}\). Next, we describe the iteration of obtaining \(\hat{\mathbf{x}}_{k|k}\) and \(\mathbf{P}_{k|k}\) from \(\hat{\mathbf{x}}_{k-1|k-1}\) and \(\mathbf{P}_{k-1|k-1}\) using the nonlinear dynamic and measurement models. We assume that the process noise and measurement noise are zero-mean Gaussian. Consider the discrete-time nonlinear dynamic model in (1). For our problem, the dynamic models for NCV and NCT are given in (26) and (31), respectively. The nonlinear measurement model is given by (46)-(52). The iteration includes a prediction step (also called the time-update step) and a measurement update step.
## 5 Limitations and Future Works  
The research output may be limited due to potential social desirability bias, whereby respondents tried to portray themselves in a good light, which could have affected the results. Response bias, such as over-reporting positive behavior or under-reporting negative or undesirable behavior, will provide the tendency toward untruthful and biased results of the survey. The results are also limited to telecommunication companies, and, thus, the application of the findings to other industries should be carried out with caution. To further validate the results, we recommend this study be replicated in other industries where private and confidential information is vastly collected and retained, such as the financial industry and education and healthcare sectors.  
Moreover, the scope of research was restricted to the rational choice theory, with a focus on personal behavior and organizational factors. Future studies should, therefore, consider other factors such as management styles, industry, and work environment. In general, the finding of this study shows that job characteristics are not significant in relation to perceived security inconvenience. It is, therefore, proposed that more extensive research be done on mobile device security to explore this construct further in the future, in particular in relation to MDM. In this work, we attempt to gauge the implementation of MDM in organizations by analyzing the level of security monitoring employed. However, we did not collect much information on MDM in the study, and we did not measure the practice of MDM specifically. As such, there is insufficient evidence to conclude any findings on MDM from this paper. Other than the issue of security inconveniences, studies on MDM should further investigate behavioral implications over user privacy. Privacy concerns have always been found to suppress the expected benefits of BYOD programs, [70] and related compliance costs have been found to be a strong deterrent to compliance [71]. This is an area that remains to be addressed substantially.  
Conceptualization, Z.A., T.S.O., T.H.L. and M.N., methodology, Z.A. and T.S.O.; software: Z.A. and Y.W.G.; formal analysis: Z.A., Y.W.G. and T.S.O.; validation: T.H.L., M.N. and T.S.O.; data curation, Z.A. and Y.W.G.; writing--original draft preparation, Z.A.; writing--review and editing, T.S.O., Y.W.G., T.H.L. and M.N.; funding acquisition: Z.A. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS/2/2013/SS05/MMU/02/12).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Advantage of the PALSAR/Landsat Forest Map Compared to Other Data Forest Maps
This study suggests the PL forest map is suitable for forest mapping in fragmented areas like the Loess Plateau.
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 2 Methodology
### Zero-Phase Wavelet Estimation  
Figure 2 illustrates the estimation for the initial zero-phase wavelet. We first calculate the averaged seismic data (shown in Figure 2a) and its absolute amplitude spectrum (shown in Figure 2b). Then we smooth the absolute amplitude spectrum using a 7-point local cubic polynomial [24; 25]. The smoothed result is shown in Figure 2c. Finally, we perform inverse Fourier transform on the smoothed result to obtain the initial zero-phase wavelet (shown in Figure 2d). The whole process can be expressed by Equation (4) [10]:  
\[\mathbf{w}_{0}=\text{IFFT}\left(\text{smooth}^{3L}\left(\text{abs}\left(\text{ FFT}\left(\frac{1}{N_{tr}}\sum_{i=1}^{N_{tr}}\mathbf{y}_{i}\right)\right)\right) \right), \tag{4}\]  
where \(\mathbf{y}_{i}\) is \(i\)-th trace of the observed seismic data \(\mathbf{Y}_{\text{obs}}\), \(N_{tr}\) is the number of seismic traces, and \(\text{smooth}^{k}(\cdot)\) means applying \(k\) times smoothing. \(L\) is the number of time samples of seismic data. \(\mathbf{w}_{0}\) is the initial zero-phase wavelet.  
Figure 2: Illustration for the estimation of initial zero-phase wavelet. (**a**) Averaged seismic data waveform. (**b**) Averaged seismic data amplitude spectrum. (**c**) Smoothed seismic data amplitude spectrum. (**d**) Estimated initial zero-phase wavelet.  
After that, we integrate the initial zero-phase wavelet \(\mathbf{w}_{0}\) into the joint inversion of the the wavelet phase inversion network and the reflectivity inversion network. Then, we perform phase rotation on the initial zero-phase wavelet \(\mathbf{w}_{0}\) according to Equation (5):  
\[\mathbf{w}_{\text{pred}}\ =\text{Re}\Big{[}(\mathbf{w}_{0}+j^{*}\,\text{ hilbert}(\mathbf{w}_{0}))\cdot e^{-j\theta_{\text{pred}}}\,\Big{]}, \tag{5}\]  
where \(j=\sqrt{-1}\), \(\theta_{\text{pred}}\)  is the wavelet phase predicted by the inversion network, \(\text{hilbert}(\cdot)\) means performing the Hilbert transform on the signal, and \(\text{Re}[\cdot]\) means taking the real part of the complex signal.
## 4 Results and Discussion
### Energy Management Results
#### 4.3.2 Comparison with Existing Energy Management Strategies
The proposed method increases the weighting factors of these two objectives and decreases the weighting factor of the SC SoC tracking. This ensures a smoother battery power output than the conventional MPC, and the power output is closer to the DP. Under high power demand, increasing the SC SoC weighting factor in advance can reduce the weight of battery capacity loss and battery power fluctuations. Thus, the battery will charge the supercapacitor in advance, which will increase the consumption of the battery but ensure the SoC level of the supercapacitor, consequently ensuring the efficiency of the energy supply. Compared to the conventional MPC method, the proposed method has advantages in terms of energy supply as well as mitigation of battery capacity loss. Figure 14: Comparison of battery power and SC power under high power demand (16-20 s). (**a**) Load power demand. (**b**) Battery power. (**c**) SC power. Figure 15: Battery capacity loss of different energy management strategies for the 0-30 s operation. The numerical battery capacity loss results of Figure 15 (end value) are listed in Table 5. The conventional MPC suffers from fixed weighting factors and has a similar final battery capacity loss value to the rule-based method; the battery capacity loss difference is only 1.2%. The proposed EMS improves the battery capacity loss by 12.7% compared with the conventional MPC. The offline DP provides the lowest result among the four methods. Compared with the offline DP, the battery capacity loss of the proposed EMS is only 6.8% higher thanks to the adaptation of the dynamic weighting factors to the diverse operation conditions. The proposed method adaptively changes the weighting factor to form an objective function suitable for complex operation conditions when the power demand is present, increasing the adaptability of energy management. Under high power demand, although battery energy is consumed when charging the supercapacitor, the high SC SoC can reduce the magnitude of the high power discharge of the battery in the future, which can protect the battery. Thus, the proposed method performs better than conventional MPC regarding battery capacity loss.
## 3 Results  
### Land Cover, NDVI, and EVI Distribution in Songkhla Province  
The land cover and vegetation composition of Songkhla is multifaceted, therefore it was deemed necessary in this study to map out and provide the spatial distribution of major local vegetation land cover. As a result, a land cover analysis map became appropriate to provide an insight into the Para Rubber (_Hovea brasiliensis_ (Muell. Arg.)) distribution across the study area. A portrait and a representation of the land cover categories are shown in Figure 3, while Figure 4 shows the summary of interpreted pixels based on maximum likelihood supervised classification technique consistent with the land development department system.  
As a result of correctly classifying 420 ground truth points, the overall accuracy is 68.81 percent and the Kappa coefficient is 0.63, as shown in Table 3. According to Google Earth's classification of Kappa coefficient value strength of agreement, the accuracy is good (0.61-0.80).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Land Cover**} & **User’s Accuracy** & **Producer’s Accuracy** & **Overall Accuracy** & **Kappa Coefficient** \\ \cline{2-6}  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Coefficient** \\ \hline \multirow{7}{*}{Classified data} & Natural Forest & 87.5 & 71.18 & & \\ \cline{2-6}  & Plantation & 80.51 & 68.88 & & \\ \cline{1-1} \cline{2-6}  & Agriculture & 75.29 & 49.61 & & \\ \cline{1-1} \cline{2-6}  & Built Up Area & 51.56 & 73.33 & & \\ \cline{1-1} \cline{2-6}  & Water Body & 91.37 & 92.98 & & \\ \cline{1-1} \cline{2-6}  & Bare Land & 35.41 & 89.47 & & \\ \cline{1-1} \cline{2-6}  & Miscellaneous Land & 45 & 85.71 & & \\ \cline{1-1} \cline{2-6}  & & & & 68.81 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy assessment results based on the confusion matrix computations.  
The analysis revealed natural forest, plantation, agriculture, built-up area, waterbody, bare land, and miscellaneous land (Land with highly dynamic land uses, may include soil pits, landfills, literite, and ornamental sites) to be the land cover types in Songkhla province. The trend across the classified land cover classes indicates that plantations occupy as much as 33% of the total land cover, while only 1% of the total land cover is bare land.  
In this work, the vegetation indices were used as a reinforcement to the vegetation status, which could be a reflection of the vegetation distribution throughout the province. Figures 5 and 6 are indications of the NDVI and EVI status, respectively. From these figures,  
Figure 4: Analysis of land cover classes in Songkhla province.  
Figure 3: Land cover distribution in Songkhla province based on supervised classification.  
NDVI and EVI mean distribution across the province are 0.4 and 0.6 with corresponding standard deviations of 0.18 and 0.24, respectively.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion
### Display of Various Index Results of Different Noise Reduction Methods  
#### 3.2.1 Index Analysis of Wavelet Soft Threshold Denoising  
When performing wavelet soft threshold denoising, it is necessary to set multiple parameters, including threshold selection criteria, wavelet basis functions, and wavelet levels of decomposition. Among the wavelet basis functions of Haar, Daubechies, Coiflets and other wavelet basis functions of the wavelet basis family, the wavelet basis of the Daubechies family has orthogonality, biorthogonality, compact support, and approximate symmetry, and can perform discrete wavelet transform. Therefore, wavelet transformation is performed using this wavelet basis function. At the same time, wavelet bases with different vanishing moments in the Daubechies family are used to process the denoised signal, and the processing results are compared. In practice, the basic wavelet is often not only required to meet the admissible condition, but also to impose the so-called vanishing moment condition, so that as many wavelet coefficients as possible are zero or as few non-zero wavelet coefficients as possible are generated, which is conducive to data compression and noise elimination. The larger the vanishing moment, the more wavelet coefficients are zeroed out. But in general, the higher the vanishing moment is, the longer the support length is, and the smoother the wavelet is. Therefore, in terms of support length and vanishing moment, we must make a compromise. Considering the general situation, the selected level of decomposition is 4. Change the vanishing moments of the Daubechies wavelet family, and take db3, db6, and db9 three vanishing moments for noise reduction, and analyze the influence of different vanishing moments on noise reduction.  
Tables 1 and 2 show the index analysis results of the first fault signal of type A and type B after wavelet soft threshold denoising.
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 2. Accident Description and Prediction Method
### 2.1. Accident Description
At this stage, the network emergency communication vehicle uses three 4G cellular networks, each with three frequency point configurations: FDD1800M, TDD's D1 frequency point, and TDD's D2 frequency point. The direction carrier bandwidth of each cellular capacity is 60M, which can meet the normal internet access and traffic needs of 1200 people.
# A Study on the Conductivity and Infrared Spectroscopy of Elastic Polymer Composite Materials
## 4 Discussion
### Application of Elastic Polymer Composite Materials Adsorption of Carbon Powder  
Carbon elastic polymer composite materials exhibit a broad range of conductivities due to the diverse types of carbon materials available, allowing them to be utilized in electronic devices. This study leverages the high-surface-area properties of carbon materials to fabricate electronic components from carbon powder. This technology holds promise for widespread use in circuit components and the development of new electronic technologies. Additionally, the extremely low inductance of carbon elastic polymer composites makes them highly suitable for high-frequency circuits [45; 46].  
Carbon black (CB) is a preferred filler in large-scale industrial processes, primarily due to its cost-effectiveness. When added to elastic polymer composite materials, CB can impart electrical conductivity [47]. These systems exhibit percolation-type conductive behavior [48; 49; 50], and the introduction of fillers also significantly impacts the mechanical properties, making the material much tougher [51; 52]. By selecting different fillers and adjusting the required conductivity levels, unique materials can be designed for various applications, including electromagnetic shielding, thermal resistors, automotive boards, power cable shielding, chemical vapor sensors, and pipe applications [53; 54].  
Highly conductive elastic polymer composite materials can be utilized to create both flexible printed circuit boards and traditional printed circuit boards. As illustrated in Figure 15, the process involves dissolving the material in an organic solvent and then using screen printing to form the conductive circuit. This method eliminates the need for metal materials like copper and avoids waste liquid from corrosion, thus reducing costs and benefiting the environment. These composite materials are also heat-resistant and adaptable to extreme climates, making them suitable for a wide range of applications.  
Figure 14: Resistivity and temperature characteristics of the elastic polymer composite materials doped with 45 M concentration of activated carbon.
# Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
## 3 Research Results and Analysis
### X-Ray Test Results  
Computed tomography (CT) and digital radiography (DR) are the two main systems used for X-ray testing. X-ray testing has become a basic tool for obtaining three-dimensional images [45] and is widely used for detecting internal flaws in vehicle parts [26]. Since DR is more cost-effective and efficient than CT, it is preferred for detecting internal faults in wheel rims.  
To identify the causes of atypical cycles of vibration spectra in the frequency range of wheel rim sound (NF of HOOP [Sound of Hoop]), they were subject to evaluation of the quality of welded joints with the VT and X-ray methods [45]. This experimental section complements and validates the results obtained in Section A, serving as confirmation of the damage identified in the rims selected for testing.  
X-ray tests were conducted in accordance with the EN ISO 19232-1 standard [46], and the quality evaluation of the welded joints was conducted based on the EN-ISO 5817 standard [47]. Analysis of the collected images revealed anomalies in the welded joints, specifically longitudinal ruptures over the welded joint of the wheel rims (Figure 8). These ruptures were identified in all welded connections both using vibration and acoustic analysis.  
It is important to note that no surface rupture was identified during the VT tests conducted in compliance with ISO 6520 [48] and ISO 5817 [47] standards.  
The detected ruptures varied in size, ranging from a few centimetres to the entire width of the wheel rim, depending on the specific wheel rim. Since the damage was consistently located at the border of the wheel rim, it is likely that these defects emerged during the calibration and profile forming stages of wheel rim production. However, no further studies have been conducted to determine the exact causes of these ruptures.  
Figure 8: View of a rupture of the butt joint of the 9 \(\times\) 15.3 wheel rim (vibration spectrum shown in Figure 6). 1—rupture of the butt joint of the wheel rim; 2—mark of an X-ray of the wheel rim disc.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Security of IEEE 802.15.6 Protocols  
The IEEE 802.15.6 standard provides three security levels: Unsecured Communication Level, Authentication Level, and Authentication and Encryption.  
* Unsecured Communication Level: This is the least-secure level, where data are transmitted through unsecured frames. This lacks mechanisms for data authentication, integrity, confidentiality, and privacy protection.
* Authentication Level: An intermediate security level where data are transmitted through secured authentication without encryption. This level does not support confidentiality or privacy.
* Authentication and Encryption Level: The highest security level involves authenticated and encrypted frame transmission. It addresses security concerns not covered by the previous lower security levels.  
During the association process of the master unit and sensor node, one of the security levels is chosen. Unicast secured communication activates a Master Key (MK), which can be pre-shared or established through unauthenticated association. A Pairwise Temporal Key (PTK) is then created for a single session. For multicast secured communication, a Group Temporal Key (GTK) is shared with the corresponding group via the unicast method.  
It is important to note that, given the sensitivity of the data gathered by WBANs, using unsecured communication and authentication levels is impractical. The focus should solely be on the authentication and encryption levels. However, vulnerabilities have been identified in this approach, primarily from the pre-sharing or establishment of the MK through unauthenticated association [11]. The use of unauthenticated pre-shared or established MKs comes from the limitations of certificate-based authentication, which, although effective in traditional networks, proves unsuitable for WBANs. These limitationsarise from the resource constraints nature of WBAN devices, particularly their limited computational power and memory capacity. In this work, different authentication methods are explored.