esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Moreover, the system stability proved its robustness because its SoC did not worsen during testing. Thus, this paper contributes the following:  
* We develop and propose a HESS using a fully active topology approach that will supply the vehicle's required cold cranking current for starting because it provides a higher degree of control freedom and efficiency. This development and proposal add an influence to studies in [5; 27] and [28], wherein [5], the study discusses the challenges that may face LIBs when used for SLI functions. Additionally, in [27], the authors propose potential methods for using hybrid ESSs for SLI functions. * We develop an integrated fuzzy-logic and triple-loop PI-based controller to effectively distribute the power between the ESSs. The integrated solution ensures that the batteries operate within their desired limits. A contribution that enhances the performance in [29], in which the study assessed the system in terms of improving vehicle range only. * We compare the performance of a single source battery for the HESS in terms of delivering the required cold cranking current. * We enhance the performance of LAB in terms of lifespan and storage capacity improvement. * We develop the HESS that has great longevity, less weight, and is reliable for use in the automotive sector. * We propose a battery life determination using a fatigue-cycle counting approach because it is well established and easy to implement. Thus, the paper breakdown is as follows: Unit 2 discusses the recent state-of-the-art literature within the subject area, and Section 3 describes the methods and materials adopted to achieve the results of the study. Section 4 presents the study results and elaborates the results significantly. Lastly, Section 5 provides the study concluding remarks and future area of study recommendations.
# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 5 Analysis of the Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Scenario** & **IDW p1** & **IDW p2** & **IDW p3** & **IDW p4** & **IDW p5** & **IDW p6** & **Kriging** & **NN** \\ \hline CEP\_13 & 12\% & 15\% & 35\% & 27\% & 27\% & 27\% & 39\% & 46\% \\ \hline OEP\_13 & 73\% & 70\% & 54\% & 54\% & 54\% & 54\% & 42\% & 31\% \\ \hline UEP\_13 & 15\% & 15\% & 11\% & 19\% & 19\% & 19\% & 19\% & 23\% \\ \hline CEP\_20 & 32\% & 26\% & 32\% & 37\% & 32\% & 42\% & 42\% & 53\% \\ \hline OEP\_20 & 47\% & 48\% & 42\% & 42\% & 42\% & 37\% & 32\% & 21\% \\ \hline UEP\_20 & 21\% & 26\% & 26\% & 21\% & 26\% & 21\% & 26\% & 26\% \\ \hline CEP\_26 & 46\% & 31\% & 31\% & 23\% & 31\% & 31\% & 62\% & 22\% \\ \hline OEP\_26 & 39\% & 46\% & 46\% & 46\% & 38\% & 31\% & 15\% & 39\% \\ \hline UEP\_26 & 15\% & 23\% & 23\% & 31\% & 31\% & 38\% & 23\% & 39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of the ratio of Correct Estimation Points (CEPs), Overestimation Points (OEPs), and Underestimation Points (UEPs) for selected interpolation methods and lowest RMSE tests. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Zone** & **IDW p1** & **IDW p2** & **IDW p3** & **IDW p4** & **IDW p5** & **IDW p6** & **Kriging** & **NN** \\ \hline
1 & 43\% & 30\% & 26\% & 21\% & 26\% & 30\% & 43\% & 35\% \\ \hline
2 & 23\% & 8\% & 31\% & 31\% & 31\% & 23\% & 23\% & 23\% \\ \hline
3 & 12\% & 35\% & 53\% & 47\% & 41\% & 47\% & 59\% & 59\% \\ \hline
4 & 0\% & 0\%The zonal approach to the REM analysis revealed that all the interpolation techniques perform worse in the zone close to the transmitter. A possible solution to this problem could be the development of a more complex algorithm for REM construction, i.e., identifying such zones and excluding them from the analysis. Another possible option could be the application of the hybrid method, like the one presented in [34].
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 2 Background Theories  
### Empirical Mode Decomposition  
[PERSON] proposed a systematic data processing algorithm, or EMD, that is specifically designed to decompose nonlinear and non-uniform time-domain signals [18]. Through the EMD process, every time-domain data can be divided in terms of zero-crossings, and fully reconstructed as a linear combination of multiple functions or IMFs. The general procedure for decomposing EMD into IMF is as follows:  
1. Find the local maxima and local minima of the signal \(x(t)\).
2. Connect the local maximum points as a spline to develop the upper envelope. Likewise, connect the local minimum points to obtain the lower envelope.
3. Obtain the average value \(m(t)\) of the upper envelope and lower envelope from Step 2.
4. Subtract the average value \(m(t)\) from the signal \(x(t)\) to obtain the value \(h_{1}(t)\).
5. The value \(h_{1}(t)\) becomes the first IMF of the signal, if it satisfies the following two conditions: (1) the difference between the number of maxima and zero crossings is zero or one; (2) The average of the upper and lower brackets is zero. If not, repeat Steps [1; 2; 3; 4; 5], until the \(h_{1}(t)\) satisfies both conditions.
6. Having finished \(k\) iterations, \(h_{1(k-1)}(t)-m_{1k}(t)=h_{1k}(t)\) or \(c_{1}(t)=h_{1k}(t)\). If \(c_{1}(t)\) satisfies the above conditions of IMF, then separate \(c_{1}(t)\) by subtracting it from the original signal \(x(t)\), i.e., \(r_{1}(t)=x(t)-c_{1}(t)\).
7. The above process is repeated, until the remaining \(r_{i}(t)\) has less than two extremes.  
Finally, when all of the \(n\)-th IMFs are obtained, the original signal \(x(t)\) will appear as shown in Equation (1) below, resulting in EMD:  
\[x(t)=\sum_{i=1}^{n}c_{i}(t)+r(t) \tag{1}\]
## 4 Basic PUMA Architecture
### Digital Item Selection  
Many different factors need to be taken into account for selecting, creating and modifying the workflows to suit a specific adaptation goal [1].  
* _User Preferences_ play a major role in selecting the content, as they tend to closer specify the user's interest in terms of topics or, in this case, actor, genre or director. However, such preferences can be used to also decide which content adaptation is more important for the viewer, e.g., picture size over bandwidth utilization, subsequently influencing the type of workflow which is selected.
* The user _device's capabilities_ help directly defining, among other things, the output stream's resolution, bit-depth, frames per second and encoding format. In one instance this is done by directly providing the properties of a device, like display size. Other than that, device's capabilities can be seen as e.g., a function of available processing power and bandwidth, for selecting a suitable codec.
* On the other side, _Transcoding Hints_ describe what adaptation the author of a content object deems as possible for still being within useful limits, e.g., scaling a scene down to a factor 1:4 would be within limits for the author, to get his point across, but smaller scaling or cropping definitely would not be.  
Figure 3: Example adaptation workflow for a mobile device.
## Appendix A Derivation of Surface Waves Uniformly Distributed on Grounded Anisotropic Slab  
For the grounded slab with infinite width as shown in Figure 1, pure TM or TE mode can be supported if the field distribution of surface wave along the transversal direction of the slab is assumed to be uniform (\(R_{y}=0\)), as analyzed in Section 2.1.  
For TM surface wave, substituting \(B_{\text{I}}=B_{\text{II}}=0\) into Equations (7)-(14) and applying continuity conditions of all the tangential fields across the interface lead to:  
\[k_{z}\tan(k_{z}t)=\varepsilon_{x}k_{oz}. \tag{1}\]  
By solving [PERSON]'s equation, the dispersion relations in Regions I and II can be derived as:  
Region I  
\[\beta_{x}^{2}+\frac{\varepsilon_{z}}{\varepsilon_{x}}k_{z}^{2}=\varepsilon_{z} u_{y}k_{0}^{2}. \tag{2}\]  
Region II  
\[\beta_{x}^{2}-k_{0z}^{2}=k_{0}^{2}. \tag{3}\]  
For TE surface wave, \(A_{\text{I}}=A_{\text{II}}=0\). Similarly, the boundary conditions and the dispersion relations in the anisotropic medium and air regions lead to:  
\[k_{z}\cot(k_{z}t)=-\mu_{x}k_{oz}, \tag{4}\]  
\[\beta_{x}^{2}+\frac{\mu_{z}}{\mu_{x}}k_{z}^{2}=\varepsilon_{y}\mu_{z}k_{0}^{2}, \tag{5}\]  
\[\beta_{x}^{2}-k_{0z}^{2}=k_{0}^{2}. \tag{6}\]
## 2 Overview of the Research Site  
### Geotechnical Conditions and Seismic Monitoring  
The KZ Coal Mine, situated in Xuzhou City, Jiangsu Province, China, predominantly mines the No. 7 and No. 8 coal seams [24]. The No. 7 coal seam has an average thickness of 5 m, predominantly comprising magma rock gangue. Positioned beneath the No. 7 seam, the No. 8 coal seam exhibits an average thickness of 4 m, with its distance from the overlying No. 7 seam ranging between 4.17 and 40.18 m. Characterized by a monoclinic structure inclined towards the northwest, the mine's field encompasses a fully covered coalfield, exhibiting gentle, wave-like undulations. Within the coalfield, 49 faults have been identified, each exhibiting a displacement exceeding 20 m out of a total of 277 faults, of which 98.9% are normal faults and 1.1% are reverse faults. The predominant fault orientation is northeast, with fault dips mainly grouping into northwest and southeast directions. In the mine area, the faults exhibit a northeast strike, forming a step block pattern with northwest ascending and southeast descending features.  
The Seismic Observation System (SOS), designed by the Central Mining Institute of Poland, is used in the KZ Coal Mine for activity seismic monitoring [7]. This system is capable of capturing vibration signals with energies exceeding 100 J, frequencies ranging from 1 to 600 Hz, a sampling rate of 500 Hz, and an anti-interference capability of less than 30 DB. The geophone, a vertically oriented detection unit, can transmit data at rates up to 1 MB/s, offering 16-bit A/D conversion, and is typically secured to floor bolts using a special protective device. As depicted in Figure 1, geophones are classified into two categories: fixed and moved. Fixed geophones, specifically numbers 1, 2, 3, 4, 5, 13, 14, and 15, are permanently installed at predefined locations within the mine. In contrast, moved geophones, numbers 11 and 12, are relocated as necessary to improve monitoring quality in tandem with changes in the extraction area.  
Figure 1: Layout and geophone distribution of the LW7434 case study. The blue and green circles represent fixed and moved geophones, respectively. The black arrow shows the mining direction.
## 6 Evaluation and Results
FPGA hardware was \(\approx\)20% faster than mobile GPU and \(\approx\)69% faster than the PC-system. Consequently, it provided highest fps and consumed the least power as compared to both mobile GPU and PC-system. From these results, it can be concluded that the presented FPGA design was a suitable solution for the targeted mobile application. \begin{table}
\begin{tabular}{l l} \hline \hline
**Platform** & **Specifications** \\ \hline PC-system & Corsair 200R desktop, Windows 10, Intel core i7-7700K CPU, 512 GB SSD and 16 GB RAM. \\ Mobile GPU & NVIDIA® Jetson Xavier \(\text{NX}^{\text{TM}}\), CUDA cores = 384,  Max. Memory = 8 GB LPDDR4x @51.2 GBits/s. \\ FPGA MPSoC & Xilinx Zynq UltraScale + ZCU102 MPSoC contains \\ LUTs = 274,080, FFs = 548,160 and BRAMs = 1824. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PC-system, mobile GPU and FPGA specifications. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Optimization**} & **Execution** & **Gain** & **Max.Latency** & **BPR in** & **Abs.Rel** & \multicolumn{3}{c}{**Utilization Vivado**} \\ \cline{3-10}  & **Time in ms** & **in ms** & **in clocks** & **\%** & **Error** & **LUTs** & **FFs** & **BRAMs** & **DSPs** \\ \hline \(\text{BS}_{1}^{\text{PP}}\),\(\text{BS}_{2}^{\text{PP}}\), \(\text{BS}_{1}^{\text{ct}}\) & 1121.25 & & \(3.64\times 10^{9}\) & 18.36 & 0.146 & 15,628 & 19,623 & 10 & 16 \\ \(\text{BS}_{2}^{\text{bc}}\), Temporal & 726.96 & 394.28 & \(1.34\times 10^{9}\) & 18.36 & 0.146 & 16,986 & 20,520 & 10 & 16 \\ \(\text{BS}_{2}^{\text{bc}}\), Temporal & 604.06 & 122.89 & \(1.85\times 10^{9}\) & 18.36 & 0.146 & 14,845 & 19,094 & 10 & 15 \\ \(\text{BS}_{2}^{\text{bc}}\), Spatial & 600.33 & 3.73 & \(1.85\times 10^{9}\) & 18.36 & 0.146 & 18,291 & 21,619 & 10 & 40 \\ \(\text{BS}_{2}^{\text{bc}}\), Spatial & 562.62 & 37.71 & \(2.08\times 10^{8}\) & 18.54 & 0.145 & 18,896 & 21,811 & 5 & 16 \\ Combination & 334.41 & 228.21 & \(1.41\times 10^{7}\) & 18.36 & 0.146 & 20,900 & 25,150 & 5 & 13 \\ Hamm\_Distance & 311.11 & 23.3 & \(1.41\times 10^{7}\) & 18.37 & 0.146 & 21,446 & 25,085 & 10 & 267 \\ Memory\_Opt. & 27.18 & 283.92 & \(6.72\times 10^{6}\) & 18.45 & 0.147 & 29,412 & 28,769 & 1797 & 272 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Actual execution time, gain, utilization and error comparison
# Advances in Random Fiber Lasers and Their Sensing Application
## 2 RFLs Based on Various Optical Fibers
### PMF-Based RFLs  
Standard SMF would have random birefringence, due to external mechanical stress in the practical application [90; 91]. Thus, the polarization of light varies irregularly as it travels through fibers. However, for PMF, the strong birefringence and the short beat result in a high-quality polarization state of the transmitted light. A linearly polarized Raman RFL based on 1 km PMF was demonstrated in 2015 [92]. The result showed the pump quantum efficiency of third-order Stokes light exceeds 80%. The scheme of all-PMF Raman RFL is shown in Figure 8. The pump is linearly polarized with a wavelength of 1050 nm, which propagates in the PMF through a 1050/1100 nm PM filtered WDM (FWDM). A PMF loop mirror formed by a fused PMF is spliced with a coupler (1050 nm) with a splitting ratio of 50/50. In the same year, 500 m PMF and PMF optical ring mirrors were utilized to realize a linearly polarized RFL, in which the polarization extinction ratio of output is up to 25 dB [93]. In 2017, a Brillouin RFL based on PMF obtained a high-efficiency output, of which the polarization extinction ratio is higher than 25 dB [94; 95]. Thus, the polarization extinction ratio and the conversion of optical quantum efficiency of the output emission can be enhanced by applying PMF into the RFL system.  
Moreover, these mentioned optical fibers, research on other fiber-based RFLs is investigated widely. For example, the RFL formed by MMF [96] could cut the spectrum of output [97], the RFL based on TOF can emit high power lasing [51], and TWF-based RFL can generate controllable double wavelength random lasing [98]. It means that we can fulfill different applications according to different RFLs formed by fitting fiber.  
Figure 6: The schematic setup of the RFL is based on dispersion compensation fiber (DCF). OPM, optical power meter.  
Figure 7: Output power as a function of pump power [88].
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 4 MPRM Module
### Euclidean and Probability Distance  
In the traditional PRM algorithm, the distance between two points is usually defined by the Euclidean distance between two points. The PRM algorithm adopted in this paper ensures that the shortest feasible path can be found, and it tries to ensure the similarity with the original trajectory. Therefore, this paper redefined the distance between two points as the weighted sum of the Euclidean distance and the probability distance, that is:  
\[d_{ep}=(1-\lambda)d_{euler}+\lambda d_{pdf} \tag{14}\]  
where \(d_{euler}\) represents the Euclidean distance between two points. \(\lambda\) is the coefficient with the value range of \([0,1]\), used to balance the length of the planned path and the similarity with the task path, that is the larger \(\lambda\) is, the greater the similarity with the task path; the smaller \(\lambda\) is, the shorter the length of the planned path; \(d_{pdf}\) represents the distance generated by the probability density.  
The higher the probability density of the point is, the closer to the task path. Therefore, the probability distance \(d_{pdf}\) is defined as the following form:  
\[d_{pdf}=\frac{1}{p} \tag{15}\]where \(p(x;\mu,\Sigma)=\frac{1}{\sqrt{2\pi^{D}|\Sigma|}}e^{-\frac{1}{2}}\Big{(}(x-\mu)^{T }\Sigma_{k}^{-1}(x-\mu)\Big{)}\), \(D\) is the dimension of the variable \(x\), \(\mu\) is the mean value, and \(\Sigma\) is the covariance matrix.  
Finally, according to the weighted distance \(d_{ep}\), construct a locally weighted roadmap \(G_{k}=\{E_{k},V_{k}\}\) for each collision path, where \(E_{k}\) is the \(k\)-th edge and \(V_{k}\) is the \(k\)-th vertex.
###### Abstract  
With the rapid growth of IoT devices, ensuring the security of embedded firmware has become a critical concern. Despite advances in existing vulnerability discovery methods, previous research has been limited to vulnerabilities occurring in binary programs. Although an increasing number of vendors are utilizing Lua scripting language in firmware development, no automated method is currently available to discover vulnerabilities in Lua-based programs. To fill this gap, in this paper, we propose FLuaScan, a novel progressive static analysis approach specifically designed to detect taint-style vulnerabilities in Lua applications in IoT firmware. FLuaScan first heuristically locates the code that handles user input, then divides the code into different segments to conduct a progressive taint analysis. Finally, a graph-based search method is applied to identify vulnerable code that satisfies the conditions of taint propagation. To comprehensively compare FLuaScan with state-of-the-art tool Tscancode, we conducted various experiments on a dataset consisting of 13 real-world firmware samples from different vendors. The results demonstrate the superior performance of FLuaScan in terms of accuracy (increased TP rate from 0% to 42.50%), effectiveness (discovered 21 vulnerabilities, of which 7 are unknown), and practicality (acceptable time overhead and visual output to assist in manual analysis).  
vulnerability detection; IoT security; firmware; taint analysis; embedded devices Article  
1 School of Cyber Science and Engineering, Information Engineering University, Zhengzhou 450000, China 1  
State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450000, China 1
## 4 Sensitivity Analysis  
### Technology  
In order to conduct a technology sensitivity analysis, each technology infuses the optimized design variables to determine how the design variables change. In general, a technology sensitivity analysis enables one to observe how a target value varies when the technology is infused in the design space. In this study, technology sensitivity analyses were performed with the eight selected technologies. The deviations for each of the target responses are depicted in Figures 9-13. As a result, the following observations were made. (i) The advanced composite material (T8) technology has the greatest impact on the MTOW and reduces it by 3.4%. (ii) The flow circulation flap (T1) and advanced ducted fan (T5) technologies have the lowest impacts on the MTOW, reducing it by 1.2%. (iii) The piezoelectric active skin (T4) technology increases the MTOW by 0.6%. Both the wing span and wing root chord show identical trends with regard to the MTOW. (iv) The flow circulation flap (T1) technology has the greatest impact on the P/W ratio, reducing it by 8.7%. (v) The advanced ducted fan (T5) technology has the lowest impact on the P/W, reducing it by 3%. (vi) The liquid metal (T6) and advanced composite material (T8) technologies increase the P/W by 0.4% and 0.5%, respectively. (vii) Regarding the fuel efficiency, all technologies increase the fuel efficiency. For instance, the nanocoating (T3) technology increases the fuel efficiency by 8.4%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Design** & **MTOW (lb)** & **Wing** & **Wing Root** & **Wing Loading** & **Power-to-Weight** & **Fuel** \\
**Parameters** & **Span (ft)** & **Chord (ft)** & **(W/S) (lb/ft2)** & **(P/W) (hp/lb)** & **Efficiency** \\ \hline Target & \(\leq\)2500 & \(\leq\)40 & \(\leq\)5.3 & \(\leq\)17 & \(\leq\)0.07 & Maximization \\ Optimal & 2291 & 33.0 & 5.7 & 16 & 0.06 & 14.18 \\ baseline & 2036 & 31.1 & 5.3 & 16  
Figure 11: Technology sensitivity analysis: deviation from optimal wing root chord target value.  
Figure 12: Technology sensitivity analysis: deviation from optimal power-to-weight target value.  
Figure 10: Technology sensitivity analysis: deviation from optimal wing span target value.
## 4 Numerical Experiments, Results and Discussions
### Compared with Other Two ICA-Based Algorithms  
The results obtained by the DICA1 are compared with that obtained by other two ICA-based algorithms for solving TSP. One is proposed in literature [34] (abbreviated as OICA in the following) and another is proposed in literature [36], combined with tabu search (abbreviated as ICATS in the following). The comparison is arranged in Table 3, in where the column \"Best.Err\" and \"Ave.Err\" represent the percentage deviation of the best result and the average of the results over the known optimal solution, respectively, calculated as the formula 8, the \"NA\" represents that the data is not given in the corresponding literature. The bold data in the table are best.  
\[Err=\textit{(the result}-\textit{opt)}/\textit{opt}\times 100\% \tag{8}\]  
From Table 3, it can be seen that the performance of the OICA is the worst in the three algorithms. On every instance, it cannot obtain the known optimal solution. The performance of the ICATS is centered in the three algorithms, though it can obtain the known optimal solution for every instance, but the average of the results obtained by it for every instance except pr76 is worse than that obtained by the DICA1. The performance of the DICA1 is the best in the three algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{2}{c}{**DICA1**} & \multicolumn{2}{c}{**ICATS**} & \multicolumn{2}{c}{**OICA**} \\ \cline{2-7}  & **Best.Err (\%)** & **Ave.Err (\%)** & **Best.Err (\%)** & **Ave.Err (\%)** & **Best.Err (\%)** & **Ave.Err (\%)** \\ \hline eil51 & **0** & **0.2934** & **0** & 2.5822 & 1.39 & NA \\ berlin52 & **0** & **0** & NA & NA & 0.09 & NA \\ st70 & **0** & **0.2519** & NA & NA & 0.44 & NA \\ eil76 & **0** & **0.5112** & NA & NA & 0.99 & NA \\ pr76 & **0** & 0.1771 & **0** & **0.1072** & NA & NA \\ kroA100 & **0** & **0.1151** & **0** & 0.5451 & 0.10 & NA \\ kroB100 & **0** & **0.2414** & **0** & 0.8356 & 0.38 & NA \\ eil101 & **0** & **1.01** & **0** & 7.9491 & NA & NA \\ kroA150 & **0** & **0.5033** & **0** & 0.7917 & NA & NA \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compared with two other ICA-based algorithms.