esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### The Impact of Different Algorithms on System Performance  
In this subsection, \(I_{S}\) is set to 4, and the basic codes for BMST encoders are the same as those used in Section 4.1. The BER performance of the BMST-RSPC-PNC system using different algorithms is shown in Figure 8.  
For the BMST-RSPC-PNC system with \(m_{1}=m_{2}=5\), compared with the CD algorithm, the DC algorithm can obtain a coding gain of 2.9 dB at the BER of \(10^{-5}\). A similar phenomenon can be observed in the BMST-RSPC-PNC system with \(m_{1}=m_{2}=6\). This improvement arises because the DC algorithm performs computations after separate decoding, effectively avoiding the loss of useful information. In low-SNR regimes, the elevated BER is primarily attributed to error propagation during the iterative decoding process, where residual errors in the initial stages progressively accumulate and amplify across subsequent iterations. However, in the high-SNR region, the effect of error propagation is not observed. We can also find that the proposed system has comparable waterfall performance with different memories and that the error floor can be further lowered by increasing the memory. The proposed system maintains stable waterfall performance over different memory configurations while achieving progressively lower error floors with increased memory. For the DC algorithm, the BER performance of the system at 7 dB can be lowered from \(10^{-5}\) to \(10^{-6}\) by simply increasing the memory from 5 to 6. The experimental results demonstrate close alignment between all the performance metrics and their corresponding lower bounds in high-SNR conditions.
## 5 Solving Non-Ideal Issues during the Tests  
Note that the bridging resistor \(R_{\rm c1}\), being part of each of the presented summing solutions, intentionally limits the direct current (DC) gain of OA\({}_{1}\) to a finite value (\(\equiv\)200) in order to minimize the impacts of the saturation of the output because of nonzero DC offset caused by real asymmetry of the active elements. Moreover, effects of high-gain blocks, as well as remaining DC offsets, require manual compensations by DC voltage applied against real DC offset, as shown in Figure 11. These compensations are required in both paths of the studied topology. In our case, the input DC offsets reach values of tens of mV, approximately. However, it was sufficiently high to cause saturation (\(V_{\rm out}=V_{\rm DD}\) or \(V_{\rm SS}\)) of the OA output. Therefore, the compensation was provided really carefully. Note that the output DC shift above \(\pm\)50 mV causes a significant effect on the frequency response accuracy.  
Figure 10: The 3D plot of the studied ideal transfer response (22) in dependence on the value of \(\alpha\) variable: (**a**) magnitude vs order vs frequency dependence, (**b**) phase vs order vs frequency dependence.  
Figure 9: Practical solution of the summing response of fractional-order integrator and integer-order differentiator.  
measurements, enabled by this oscilloscope during the point-to-point FRA analysis, were necessary due to clear visibility of the correct setting of DC offset error compensation.  
We can calculate initial time constants of the OA-based integrator/differentiator directly from values shown in Figures 3, 5, 7 and 9: \(\tau_{1(\text{integer})}=100\) s (\(R_{1}\times C_{1}=1\) k\(\Omega\times 100\) nF), \(\tau_{2(\text{fractional}/\text{CPE})}=5.6\) ms (\(R_{2}\times C_{\alpha}=100\)\(\Omega\times 56\)\(\mu\)f/s\({}^{1/2}\)). The driving voltages \(V_{\text{set},\text{A1}}\) and \(V_{\text{set},\text{A2}}\) for the time constant adjustment by gains \(A_{1}\) and \(A_{2}\) are considered between 0.85 and 1.35 V (\(A_{1,2}\) between 0.5 and 5 because \(A_{1,2}=10^{(2\cdot(\text{Vset},\text{A1},2-1))}\)[30]) in order to observe a one-decade change of gain. However, increased gain requires compensation of instability as well as DC offset (VGA output offset depends on actual gain), as discussed above. Particular values of gains \(A_{1,2}\) are noted directly in presented graphs. Note that our design targets are on the low-frequency band of operation (application field between 100 Hz and 100 kHz) because of expected limits (DC offset impacts for high gain scale, instability) of active devices as well as simpler design for our exemplary purposes.
# Control and Position Tracking for UAVs
###### Abstract  
The proposed algorithm is based on a novel algorithm for finding the optimal path planning algorithm for the UAV's  
Searching for an optimal path between the starting and ending points through a complex environment can take time; therefore, many algorithms are used offline, and the results must be known at least by the moment of takeoff. The challenging case is to determine the optimal path in an unknown environment in real time. Another interesting issue to be solved and researched is an algorithm that allows the tracking of another object with completely different dynamics than UAVs, which moves through unknown paths. The optimization and planning of the flight paths of UAVs' swarm through a complex terrain with narrow corridors is also a major contribution to the research area related to this Special Issue, and it should be considered in future research.
## 5 Conclusions  
In this study, a new OMKHES-MS technique has been developed to accomplish security and privacy in the IoE environment. The OMKHES-MS model involves a series of processes namely SVD, COA based optimum pixel selection, MKHE based encryption, and PRO based key generation. Finally, the cipher image from the MKHE technique is embedded into the chosen pixel values of the cover image to generate stego image. The design of PRO algorithm helps in choosing optimum keys for the generation of cipher images. To investigate the enhanced performance of the OMKHES-MS model, a comprehensive simulation analysis is performed and the results are investigated under several aspects. The extensive comparative analysis reported the supremacy of the proposed model over the rennet approaches interms of different measures. Therefore, the OMKHES-MS model can be utilized as an effective tool to accomplish security in the IoE environment. In future, lightweight cryptographic techniques can be designed for IoE environment. Besides, detailed security analysis of the OMKHES-MS model will be made in our future work.  
Conceptualization, I.A. and A.M.; methodology, H.A.M.; software, I.Y.; validation, S.S.A., M.M.A. and A.S.Z.; formal analysis, M.A.H.; investigation, I.A.; resources, M.A.H.; data curation, I.Y.; writing--original draft preparation, H.A.M.; writing--review and editing, A.M.; visualization, A.M.; supervision, S.S.A.; project administration, M.A.H.; funding acquisition, H.A.M. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/45/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R114), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4210118 DSR05). The authors would like to thank Prince Sultan University for its support in paying the Article Processing Charges.  
Data sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
## 6 Experiments and Analysis
### Artificial Data Set
The results of dimensionality reduction by LLE algorithm are shown in Figure 4(a2),b(2),c(2), and Figure 4(a3),b(3),c(3) are the M-LLE embedded results in two dimensions. There is a large distortion in Figure 4(a2), and the residual error is 0.4377, which is larger than the residual error in Figure 4(a3). Besides, by comparing Figure 4b(2) with Figure 4b(3) we can find that M-LLE algorithm is beneficial to restore the manifold structure in the noisy environment, and the residual error is only 0.2085. Figure 4c(2),c(3) are the two-dimensional embedded results in noisy Punctured-sphere data set, which use LLE algorithm and M-LLE algorithm respectively. There are a large number of data points aliasing in the edge of Figure 4c(2), on the contrary, Figure 4c(3) can effectively separate the data points of different colors. What's more, the residual error in Figure 4c(3) is 0.8856, which is smaller than 0.9761 in Figure 4c(2). It means that M-LLE algorithm can get a better embedded result than LLE algorithm in noisy artificial data sets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Swiss-Roll** & **S-Curve** & **Punctured-Sphere** \\ \hline Dimensionality of data space (\(D\)) & 3 & 3 & 3 \\ Dimensionality of embedding space (\(d\)) & 2 & 2 & 2 \\ The size of nearest neighbor (\(k\)) & 25 & 25 & 25 \\ The number of points & 4000 & 4000 & 4000 \\ The number of noise points & 200 & 400 & 600 \\ The percentage of noise & 5\% & 10\% & 15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings of the LLE and M-LLE experiments reported in Figure 4.  
Figure 4: _Cont._  
Through the above simulation, we can find that M-LLE algorithm can effectively preserve the intrinsic morphology of manifold in different artificial data sets. In addition, regardless of the degree of noise interference, M-LLE algorithm can get the perfect local structure of manifolds. Therefore, M-LLE algorithm has strong robustness.
# Investigation of Railway Network Capacity by Means of Dynamic Flows
## 3 Optimization Model for Determining a Network-Wide Capacity
### Mathematical Formulation of the Model
#### 3.3.2 Objective Function  
This paper's approach to calculate the capacity of the investigated core network is to maximize the sum over all \(y\)--variables.  
\[\max\ \ z=\sum_{k\in K}y^{k} \tag{3}\]  
The optimization model therefore attempts to schedule as many of the defined trains as possible, more precisely all trains in \(K_{sch}\) (ensured by appropriate constraints) and as many trains as possible in \(K_{pot}\).
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 4 Torque Control Based on the Neural Network
### Multi-Layer Perceptron (MLP) Design  
In this study, we designed an MLP neural network aiming to achieve precise reflection of dynamic characteristics. The neural network architecture consists of one input layer, two hidden layers, and one output layer. The data set \(X\) undergoes processing in the input layer as follows.  
\[Z^{[1]}=W^{[1]}\cdot X+b^{[1]} \tag{2}\]  
where \(Z\in\mathbb{R}^{n}\) is processed data, \(W\in\mathbb{R}^{n\times x}\) represents the weight matrix, and \(b\in\mathbb{R}^{n}\) is a bias vector. And \(x\in\mathbb{N}\) is the number of input variables, and \(n\in\mathbb{N}\) is the number of neurons in the layer. The ReLU activation function is employed in the hidden layer, where the data undergoes the following transformation.  
\[\begin{split} Z^{[i]}&=W^{[i]}.A^{[i-1]}+b^{[i]}\\ A^{[i]}&=\text{ReLU}\Big{(}Z^{[i]}\Big{)}\end{split} \tag{3}\]  
where \(i\) represents the order of the layers (\(i=\{1,\,2,\dots,m\}\)) and \(m\) is the number of the layers. Additionally, a dropout layer is incorporated into the network to prevent overfitting. The Mean Squared Error (MSE) is employed as the loss function to enhance the accuracy of the predicted values, and it is calculated as follows.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\Big{(}\hat{y}^{(i)}-y^{(i)}\Big{)}^{2} \tag{4}\]  
where \(\hat{y}\) is the predicted value, and \(y\) is the actual value. And \(n\in\mathbb{N}\) denotes the number of data points.  
Through linear regression, we can predict joint torque even for trajectories that are not directly part of the learning process. Figure 6 illustrates the structure of the designed MLP neural network. After successful learning based on the data set \(X\) from the training trajectory, we can predict joint torque \(\tau_{ref}\) by inputting a data set for the reference trajectory \(X_{ref}\).  
Figure 6: MLP neural network.
## 3 Results
### Assessment of NDZ and Comparison of Existing Islanding Detection Strategies  
NDZ is the main criterion that reflects the effectiveness of the islanding recognizing method. It is an area where protection techniques are unable to recognize islanding conditions. For active power NDZ, is determined by [47]:  
\[\Delta\mathrm{P}\ =\ -3\mathrm{V}\ \times\Delta\mathrm{V}\ \times\ \mathrm{I}\ \times\ \cos\ \varphi \tag{7}\]  
where \(\Delta\mathrm{P}\) is the active power mismatch, \(\mathrm{V}\) and \(\mathrm{I}\) are the rated voltage and current respectively, \(\Delta\mathrm{V}\) is the voltage deviation and \(\cos\varphi\) is the power factor. The allowable voltage variations in the distribution network under study are 0.9 pu and 1.1 pu. Considering these voltage levels, deviation \(\Delta\mathrm{V}\) is \(-0.1\) and \(1.1\), respectively. The NDZ region of active power mismatch \(\Delta\mathrm{P}\) studied for this system is +0.20 MW and \(-0.20\) MW. For reactive power NDZ, is determined by [47]:  
\[\Delta\mathrm{Q}\ =\ \frac{3\mathrm{V}^{2}}{\omega_{\mathrm{n}}\mathrm{L}}\ \left(1-\frac{ \mathrm{f_{n}}^{2}}{\left(\mathrm{f_{n}}\pm\Delta\mathrm{f}\right)^{2}}\right) \tag{8}\]  
where V is the rated voltage, fn in the nominal frequency, \(\Delta\mathrm{f}\) is the frequency deviation and \(\omega_{\mathrm{n}}\) = \(2\times\pi\times\mathrm{f}\). Figure 11: Magnitude of voltage phase angle during fault switching conditions (**a**) three-phase to ground (ABCG) fault (**b**) three-phase (ABC) fault (**c**) two-phase to ground (ABG) fault (**d**) AG one phase to ground (AG) fault (**e**) two-phase (AB) fault. The allowable range for frequency variation \(\Delta\)f lies between \(-0.5\) Hz to \(0.5\) Hz. Hence, NDZ of the study system with respect to reactive power mismatch is 0.2128 MW and \(-0.2192\) MW. Simulation results also reveal that when power mismatch is 0.001 MW and 0.001 MVar, the proposed scheme working efficiently. Compared to convention techniques of OF/UF (over frequency/under frequency) and OV/UV (over voltage/under voltage), the proposed technique enhances the accuracy and insignificant the NDZ as shown in Figure 12.  
The analysis of passive indices presented in Section 2 reveals that voltage phase angle exhibits the best performance in terms of sensitivity and islanding detection time. Islanding operation only recognizes when the magnitude of voltage phase angle becomes less than the predefined threshold. Non-islanding events magnitudes remain greater than the threshold in comparison to islanding events. A brief overview of some existing islanding detection methods including the proposed strategies is compared based on detection time, non-detection zone, method type, power quality, algorithm complexity and concept used are presented in Table 4.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
###### Abstract  
Under demanding operational conditions such as traffic surges, coverage issues, and low latency requirements, terrestrial networks may become inadequate to provide the expected service levels to users and applications. Moreover, when natural disasters or physical calamities occur, the existing network infrastructure may collapse, leading to formidable challenges for emergency communications in the area served. In order to provide wireless connectivity as well as facilitate a capacity boost under transient high service load situations, a substitute or auxiliary fast-deployable network is needed. Unmanned Aerial Vehicle (UAV) networks are well suited for such needs thanks to their high mobility and flexibility. In this work, we consider an edge network consisting of UAVs equipped with wireless access points. These software-defined network nodes serve a latency-sensitive workload of mobile users in an edge-to-cloud continuum setting. We investigate prioritization-based task offloading to support prioritized services in this on-demand aerial network. To serve this end, we construct an offloading management optimization model to minimize the overall penalty due to priority-weighted delay against task deadlines. Since the defined assignment problem is NP-hard, we also propose three heuristic algorithms as well as a branch and bound style quasi-optimal task offloading algorithm and investigate how the system performs under different operating conditions by conducting simulation-based experiments. Moreover, we made an open-source contribution to Mininet-WiFi to have independent Wi-Fi mediums, which were compulsory for simultaneous packet transfers on different Wi-Fi mediums.  
U
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 5 Performance Evaluation of OFDM Navigation Signals
### Tracking Accuracy  
The tracking accuracy of the code loop directly reflects the tracking performance of the navigation signal and is the most important indicator for the measurement of the navigation signal [28]. In this section, the theoretical tracking lower bound of the code loop is first analyzed, which determines the highest tracking ability of the code loop and provides a theoretical basis for the optimization of the receiver code loop. Then, the non-coherent delay-lag loop (NELP) is simulated. The tracking performance of the incoherent delay-lag loop is not affected by the jump of the navigation message, therefore the integration time of the navigation signal can be increased, and the gain of the navigation signal can be improved. The incoherent lead-lag code loop tracking error is the mean square root of the code tracking loop error. In this section, the bandwidth of the receiver is set to 36 MHz, noise bandwidth of the code loop is 0.1 Hz, integration time is 20 ms, interval between the leading branch and lagging branch is 20 ns, and variation range of the carrier-to-noise ratio CN0 is 20-40. An analysis of Figure 10 shows that with the improvement of the carrier-to-noise ratio of the navigation signal, the code loop tracking lower bound and the NELP tracking error both significantly decrease. The tracking performances of the proposed navigation signal OFDM(128) and OFDM(8) are significantly better than BPSK-R(10) and BOC(1,1); the tracking performance of the navigation signal OFDM(128) and OFDM(8) is slightly lower than that of BOC(10,5), but there is an improvement to the carrier-to-noise ratio, and the tracking performance is similar. When the carrier-to-noise ratio CN0 is 20 dB, the theoretical tracking accuracy of the OFDM(8) code loop is improved by 56% compared with BOC(1,1), and the tracking accuracy of OFDM(128) is improved by 46% compared with BOC(1,1). The code loop tracking accuracy is significantly improved. By comparing the code loop tracking accuracy lower bound with the NELP tracking error, it can be found that the NELP tracking error is slightly larger than the code loop theoretical tracking's lower bound, but close in value. Using this set of code loop parameters, a better tracking accuracy can be obtained. When the carrier-to-noise ratio CN0 is 40 dB, the accuracy of the OFDM-modulated navigation signal is about 0.04 m.
## 4 Potential Research Directions of RSMA-Assisted ISAC Systems
### Millimeter Wave and Terahertz Technologies  
MmWave and THz bands have been considered as core spectrum for 6G wireless networks. To fully leverage the abundant frequency resource in mmWave and THz bands, beamforming techniques based on massive MIMO antenna arrays have been popularly used to compensate for the severe path loss in higher frequency bands. One viable technique using these bands is cell-free massive MIMO (CF-mMIMO) systems that can significantly improve the data transmission range and performance by deploying access points (APs) distributed in a wide area to collectively serve user equipment (UE) [69]. The combination of RSMA and CF-mMIMO provides a broad coverage area via CF-mMIMO and enhances interference management through RSMA, leading to improved energy efficiency [70]. This can address the high propagation loss in mmWave and THz communication and the cost issues associated with many RF chains.  
In addition, due to the need for wider bandwidth, research on hybrid ISAC in the 30-300 GHz spectrum is being investigated to enhance both communication performance and sensing accuracy [57]. In [71], the use of wider bandwidth in THz bands enables high-resolution sensing. Thus, THz-based ISAC systems can enhance data transmission rate and high-resolution sensing capabilities, improving both spectral and energy efficiency. However, due to the high directivity and substantial atmospheric absorption in the THz band, an advanced interference management technique based on highly directional and adaptive beamforming is required to perform the sensing in the THz band. Note that the synergistic design of THz bands and ISAC systems can be applied in various fields such as smart cities and intelligent factories [72]. Recently, the integration of radar and massive MIMO communication has been studied to fully exploit vast spatial degrees of freedom and  
Figure 6: RSMA-assisted RIS-ISAC systems.  
abundant bandwidth resources in ISAC systems [73]. To this end, accurate channel training is essential for effective target sensing functionality, and CV technology can be applied to analyze sensing information, enabling precise understanding of wireless environments [74]. The advantages of RSMA in multi-antenna environments have been demonstrated for addressing multi-user interference in mmWave communication [75]. In [17], it is shown that RSMA-assisted mmWave-ISAC (see Figure 7), using hybrid beamforming algorithms, outperforms both NOMA-ISAC and SDMA-ISAC.