esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation of the Problem
### Method of Solution  
The spline method is used because it uses a series of lower-order approximations rather than global higher-order approximations, affording fast convergence and high accuracy.  
The differential equations consist of second-order derivatives in \(U(R)\), \(V(R)\), \(W(R)\), \(\Psi_{r}(R)\) and \(\Psi_{\theta}\ (R)\). Therefore, these functions are approximated using cubic splines as:  
\[\begin{array}{rcl}U^{*}\ (R)&=&\sum\limits_{i\ =\ 0}^{2}\ a_{i}\ R^{i}\ +\ \sum\limits_{j\ =\ 0}^{N-1}b_{j}\ (\ R-R_{j}\ )^{3}\ H\ (\ R\ -\ R_{j}\ )\\ V^{*}\ (R)&=&\sum\limits_{i\ =\ 0}^{2}\ c_{i}\ R^{i}\ +\ \sum\limits_{j\ =\ 0}^{N-1}d_{j}\ (\ R-R_{j}\ )^{3}\ H\ (\ R\ -\ R_{j}\ )\\ W^{*}\ (R)&=&\sum\limits_{i\ =\ 0}^{2}\ e_{i}\ R^{i}\ +\ \ \sum\limits_{j\ =\ 0}^{N-1}f_{j}\ (\ R-R_{j}\ )^{3}\ H\ (\ R\ -\ R_{j}\ )\\ \Psi_{r}\ ^{*}\ (R)&=&\sum\limits_{i\ =\ 0}^{2}\ g_{i}\ R^{i}\ +\ \sum\limits_{j\ =\ 0}^{N-1}p_{j}\ (\ R-R_{j}\ )^{3}\ H\ (\ R\ -\ R_{j}\ )\\ \Psi_{\theta}^{*}(R)&=&\sum\limits_{i\ =\ 0}^{2}\ l_{i}\ R^{i}\ +\ \sum\limits_{j\ =\ 0}^{N-1}q_{j}\ (\ R-R_{j}\ )^{3}\ H\ (\ R\ -\ R_{j}\ )\\ \end{array} \tag{10}\]  
in which \(H\ (\ R\ -\ R_{j}\ )\) is the Heaviside function and \(a_{i}\), \(c_{i}\), \(e_{i}\), \(g_{i}\), \(l_{i}\), \(b_{j}\), \(d_{j}\), \(f_{j}\), \(p_{j}\) and \(q_{j}\) are unknown coefficients (i.e., spline coefficients).  
Let us assume that the interval \(R\ \varepsilon\ [\ 0\,\ \ 1\ ]\) is divided into \(N\) equal subintervals. The knots are at \(R\ =\ R_{s}\ =\ \frac{s}{N}\ ;\ s\ =\ 0\,\ 1\,\ \ldots\,\ N\). We have the system of \((\ 5\ N\ +\ 5\ )\) homogeneous equations in \((\ 5\ N\ +\ 15\ )\) spline coefficients.  
The simply supported (S-S) boundary condition is considered.  
Each of these cases gives 10 more equations, thus making a total of \((5N+15)\) equations, in the same number of unknowns. The resulting field and boundary condition equations may be written in the form  
\[[\ M\ ]\ \ \{\ q\ \}\ =\ \lambda^{2}\ \ [\ P\ ]\ \ \ \{\ q\ \} \tag{11}\]where \([\ M\ ]\) and \([\ P\ ]\) are square matrices and \(\{\ q\}\) is a column matrix. This is treated as a generalized eigenvalue problem in the eigenparameter \(\lambda\) and the eigenvector \(\{\ q\}\), whose elements are the spline coefficients.
## 1 Introduction  
In this paper, we focus on the development of a path primitive and the so-called extend procedure (i.e., a local planning algorithm generating a path connecting two robot configurations), which is crucial for many path-planning algorithms utilized in the navigation of mobile robots. Despite a large body of work concerning path planning for mobile robots and autonomous vehicles (e.g., References [1; 2]), this problem remains a challenge, especially in the presence of various constraints arising in practical scenarios. On one hand, mechanical limitations of the robot, such as limited steering and articulation angles in wheeled robots, or aerodynamic constraints in unmanned aerial vehicles, result in path curvature limits. On the other hand, the presence of a bounded-motion environment with obstacles and forbidden areas leads to state constraints imposed on the robot, which reduces the set of feasible paths. It is also important to maintain a high degree of path continuity to achieve smooth control of the robot and increase the comfort of passengers or the safety of the payload. To account for all these constraints, we built upon our approach introduced in Reference [3], where we proposed the extend procedure generating \(\mathbb{G}^{3}\)-continuous planar paths (that is, paths with continuous curvature derivative with respect to curve arc length) taking into account a limited curvature of motion in cluttered environments. In contrast to Reference [3], we present the new extend procedure for the carlike kinematics taking into account vehicle-body dimensions in planning collision-free paths, admitting the nonconvex polygonal obstacles present in the operational space. As a consequence, the motion-planning strategy presented in the current paper inherits beneficial properties of the original approach presented in Reference [3], but extends its potential applications to more practical path-planning scenarios.
## 3 Wireless Sensor Network Model for the Internet of Things  
### The Role of the Internet of Things in Wireless Sensor Networks  
The IoT framework design of the remote sensor arrangement is shown in Figure 1, comprising four parts: the underlying network distribution, an intelligence convergence gateway, Internet convergence, and the terminal user application. As shown in Figure 1, a large number of IoT basic frameworks are specifically disseminated within a physical space, and the network distribution is shaped by comparing strategies relevant to its particular characteristics. The underlying network collects the exchanged data through RFID (Radio Frequency Identification), WSNs, wireless LAN, and other network technologies and transmits it to the intelligence convergence gateway. It accesses the network integration system through the intelligence convergence gateway.  
One of the important characteristics of the Internet of Things is to enable information exchange between sensors and networks. Each sensor is an object. Therefore, the IoT underlying network responsible for sensing and recording data must be able to reflect the characteristics of each object. The engineering of remote sensor arrangements based on the Internet of Things is shown in Figure 2. A large number of sensor hubs are connected within the checking region within the framework of an irregular spread or manual situation, and the organization is built through self-organization. The data in the area monitored by the sensor node finally arrive at the sink node through the multihop routing transmission of the nodes in the network. The data may be fused and compressed by multiple nodes during the transmission process, and they finally reach the management node of the terminal through a satellite, Internet, or wireless access server. Users can perform feedback operations such as configuration management, task publishing, and security control on WSNs through the management node.  
Figure 1: The Internet of Things system architecture of a wireless sensor network.
# Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking
## 5 Nonlinear Optimal Control Allocation
### Tyre and Wheel Load Model  
The Magic Formula tyre model [25] is used to map the combined lateral and longitudinal tyre forces \(\left[F_{x,i}\right.\left.\left.F_{y,i}\right]=f(\alpha_{i},\kappa_{i},F_{z,i})\) at each wheel. Besides the tyre sideslip angle \(\alpha_{i}\) and the longitudinal tyre slip \(\kappa_{i}\), [29], the wheel load \(F_{z,i}\) is input to the tyre model. The wheel loads are calculated based on the longitudinal and lateral accelerations of the CoG of the vehicle, \(a_{x}\) and \(a_{y}\), respectively,  
\[F_{z,1} =\frac{m}{2(l_{f}+l_{r})}(bg-ha_{x})\left(1-\frac{h_{f}}{w_{f}g} a_{y}\right), \tag{10a}\] \[F_{z,2} =\frac{m}{2(l_{f}+l_{r})}(bg-ha_{x})\left(1+\frac{h_{f}}{w_{f}g} a_{y}\right),\] (10b) \[F_{z,3} =\frac{m}{2(l_{f}+l_{r})}(ag+ha_{x})\left(1-\frac{h_{r}}{w_{r}g} a_{y}\right),\] (10c) \[F_{z,4} =\frac{m}{2(l_{f}+l_{r})}(ag+ha_{x})\left(1+\frac{h_{r}}{w_{r}g} a_{y}\right), \tag{10d}\]  
where \(h\) is the height of the CoG relative to the road plane, \(h_{f}\) and \(h_{r}\) capture the influence of vehicle roll on the load shift for the front and rear axle, and \(g\) is the acceleration of gravity. The tyre characteristics employed in this study are shown in Figure 5. In the subsequent CA, the tyre parameters are assumed to be known. Further research is required to assess the impact of parameter and model errors on the model following performance; however, a simpler tyre model with fewer parameters could be sufficient, as realized in the ESC [30].
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 5 Conclusions  
In this paper, a high-accuracy calibration method based on linearity adjustment is proposed for ECDSs. The sensitivity and linearity of the output curve of the sensor are adjusted based on the idea of partition, and, in addition, the \(\upmu\)SVM method is employed to avoid the over-fitting problem. Results show that the linearity of the adjusted output characteristic curve is significantly increased by more than 1.9%, which reaches over 99.9%. In accuracy and precision experiments, EDE tests the deviation of the relative displacement between the measured value and the actual value, whose error range can reach 0.27-0.52 \(\upmu\)m when the relative displacement is 0.25 mm for instance in this paper. In addition, ADE tests the deviation of the absolute displacement, which can reach 1-10 \(\upmu\)m at the range of 6 mm. Then, the 95% confidence interval is calculated from repeated tests, and the repetition precision is less than \(\pm\)5 \(\upmu\)m. The improvement of the ECDSs\({}^{\prime}\) performance by the proposed method provides a reliable guarantee for high accuracy displacement measurement in the quality control of the industrial process.  
**Author Contributions:** All authors contributed to this work. [PERSON] and [PERSON] set up the calibration system; [PERSON] instructed and amended the construction of the system; [PERSON] and [PERSON] conceived and designed the calibration method and experiments; [PERSON], [PERSON] and [PERSON] performed the method implementation and experiments, analyzed the data, and drafted the manuscript; [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the method design and the discussion of the results; and all the authors contributed to the writing and discussion of the paper.  
Figure 13: 95% confidence interval of ADE for each sensor.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Minimum**} & \multicolumn{2}{c}{**Maximum**} & \multicolumn{2}{c}{**Median**} & \multicolumn{2}{c}{**Error Fluctuation Range**} \\ \cline{2-9}  & **Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline
**Sensor 1** & \(-\)1.5738 & 0.0151 & 11.7100 & 3.6448 & 8.0314 & 2.2914 & 13.2838 & 3.6297 \\
**Sensor 2** & 2.6729 & \(-\)0.1753 & 17.5170 & 0.9299 & 11.9520 & 0.3472 & 14.8441 & 1.1052 \\
**Sensor 3** & \(-\)2.3386 & 0.3509 & 15.1110 & 2.5863 & 8.4100 & 2.0165 & 17.4496 & 2.2354 \\
**Sensor 4** & 0.3548 & 1.4598 & 24.7550 & 9.0554 & 17.6790 & 6.7096 & 24.4002 & 7.5956 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accumulated displacement errors (ADEs) (\(\upmu\)m) before and after adjustment.  
**Acknowledgments:** This research was co-supported by the National Natural Science Foundation of China (Nos. 51622501, 51621064), the high-level personnel innovation support program of Dalian (No. 2017 RJ04).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# CenterInst: Center-Based Real-Time Instance Segmentation
## 4 Experimental Results
### Experimental Results  
In Table 1, we compare the performance of our method (CenterInst) with that of some state-of-the-art methods on the COCO test-dev dataset, focusing on accuracy and inference speed, as CenterInst is primarily designed for real-time instance segmentation tasks. To objectively measure the complexity of the proposed model, floating point operations (FLOPs) were computed in this study [30; 31; 32]. The evaluation was conducted on COCO test-dev. We provided two variations of CenterInst with different backbones and compared them with other state-of-the-art methods using the same backbone. The results indicated that CenterInst achieved higher accuracy while maintaining speed. Specifically, with the ResNet50 [33] backbone, our proposed CenterInst model achieved a 0.6 AP improvement over the current leading real-time instance segmentation model, FastInst, with fewer training epochs and less inference time. Additionally, we presented CenterInst-large, employing a dual-path inference structure like FastInst. It achieved a 0.9 AP improvement over FastInst but with a slight increase in inference time. Furthermore, using the ResNet-50-d-DCN [34; 35] as its backbone, our model achieved a 49.2 FPS and a 41.0 AP, demonstrating higher efficiency and accuracy than FastInst, validating the effectiveness of our model.
# Investigation of the Opposite-Electrode Effect on the Planar Solid-State Pulse-Forming Line
###### Abstract  
The planar solid-state pulse-forming line (planar solid-state PFL) is an important solid-state device used in compact pulse power systems. Moreover, pulsed power systems constitute a crucial element within electroporation systems. In this paper, we present theoretical and simulation analyses of the influence of the ground electrode structure of the planar solid-state PFL on the edge electric field and thermal distribution of high-voltage electrodes and the design of a novel improved solid-state PFL (opposite-electrode PFL) that differs from the classic planar solid-state PFL (full-electrode PFL) in which the ground electrode covers the entire plane. The ground electrode of the opposite-electrode PFL is structured to be consistent with the high-voltage electrode and positioned directly opposite to enhance the withstand voltage capacity of the planar solid-state PFL. The simulation results show that when the ground electrode width is the same as the high-voltage electrode, the electric field strength at the edge of the electrodes is smaller. In the electrostatic field simulation, the edge electric field strength of the high-voltage electrode in the opposite-electrode PFL is smaller than that of the full-electrode PFL, which indicates that the opposite-electrode PFL may have a higher withstand voltage. The experimental results show that the opposite-electrode PFL has a higher withstand voltage than the full-electrode PFL, which verifies the correctness of the theoretical and simulation analyses. Furthermore, the opposite-electrode PFL surface temperature rise showed a better performance after running the same test repeatedly. The findings of this study are conducive to enhancing the maximum output voltage or compactness of pulsed power systems and highlight the additional potential for the utilization of solid-state pulse generators in electroporation systems.  
pulsed power device; solid-state pulse-forming line; opposite-electrode PFL; thermal characteristic; withstand voltage +
Footnote †: journal: Applied Sciences
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 4 Experiments and Evaluations
### BER Performance in an Extended Range of SNR  
We also evaluated the BER performance of each method over an extended range of SNR, which corresponded to the data-recovery capabilities. Figures 11 and 12 compare the BER performance for each method, where our proposed method achieved the best performance. The performance of BER depends on the accuracy of the channel-matrix prediction on all data symbols. We can see from the figure that the BER of the proposed algorithm was always lower than that of LS, DNN, and ChannelNet. For example, compared to LS, it improved by about 555.9% at SNR = 30 dB and by about 498.8% at SNR = 20 dB. Similarly, compared to the KAU1 environment, the gap of several deep-learning methods for the NOF1 environment was smaller, and the KAU1 gap for more complex features more obvious.
## 3 Permute-Scatter-Gather Implementation
### Overview and Design Goals  
The idea of the Permute-SG is basically to unlink the index of a multiplier from its memory location, thereby making it infeasible to figure out the multiplier used during the exponentiation. For this, the proposed method obfuscates the memory locations of the multipliers through a pseudo-random permutation. Specifically, given an index idx and a pseudo-random permutation P, the location of the multiplier is determined by the permuted index idx' = P(idx). In this way, all the 32 multipliers are rearranged in the table according to their permuted indices. By mounting cache attacks, an adversary might get the trace of P(idx). However, he/she cannot infer which multipliers are actually used from the obtained trace.  
We construct the Permute-SG technique with consideration of achieving the following design goals:  
* _Resistance against fine-grained cache attacks._ No information about the actually accessed multiplier should be revealed to adversaries who can observe memory accesses with bank-level granularity.
* _Computational efficiency._ Performance degradation in modular exponentiation due to applying this method should be minimized.
* _Adaptability._ It should be easily integrated into the existing implementation (e.g., OpenSSL library) without significant modification of source codes.
## 3 Results and Analyses
### Damage Process of Specimens
Phase IV: Significantly intensified AE activity occurred in both specimens, featuring prolific high-energy events and substantial count rates. These findings indicate that differing eccentricities induced divergent AE signatures during initial damage progression. However, convergence in parametric trends emerged during advanced failure stages. The traditional \(b\)-value comes from the Gutenberg Richter formula in seismology, which has been proven effective for AE analysis. Researchers have proposed an improved b-value called the \(lb\) value, which is calculated as follows:  
\[lb=\frac{log_{10}N(\mu-\alpha_{1}\sigma)-log_{10}N(\mu-\alpha_{2}\sigma)}{( \alpha_{1}-\alpha_{2})\sigma} \tag{2}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are coefficients, here taking 1; \(N\) is the cumulative ring count within the selected time period; and \(\mu\) and \(\sigma\) are the mean and standard deviation of amplitudes. A high \(lb\)-value characterizes fracture processes dominated by microcracking, whereas a low \(lb\)-value indicates macro-fracture dominance. Figure 9 illustrates the evolution of the \(lb\)-value (derived from amplitude distributions) across loading stages. Both specimens exhibited elevated \(lb\)-values during initial loading, signifying micro-crack predominance. As loading progressed, \(lb\)-values decreased into a lower range and displayed oscillatory behavior, reflecting concurrent development of macro- and micro-cracks during Phase II. With further load increase, sustained low \(lb\)-values coincided with Phase III macro-crack propagation in the tension zone. However, prior to the conclusion of this phase, a transient \(lb\)-value peak occurred due to micro-cracking initiation in the compression zone concurrent with tension reinforcement yielding. Ultimately, \(lb\)-values stabilized at low magnitudes as macro-scale vertical cracking developed in the compression concrete. This observation confirms that \(lb\)-value analysis provides a reliable framework for delineating damage stages in eccentrically compressed RC columns. Figure 9: Lifetime dependency of the \(lb\)-values.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.3 Influence of Buffer Size
Figure 12: Energy for varying buffer size. From the above, we know that CBCAIS can achieve a higher message delivery ratio with lower overhead ratio compared to SMART, Multicent, and GTA, although it has a slightly larger average delay and energy consumption.
## 4 Experimental Results  
In this section, we present the results of our many experiments to present the performance of the proposed audio watermarking algorithm. First, there were parameter optimization experiments, followed by a comparison of the results with some state-of-the-art audio watermarking algorithms. We also show experiment results on 5.1 channel and stereo audio. Finally, synchronization is discussed.  
Figure 6: Watermark extraction procedure.  
Figure 7: Feedback process.  
Like other watermarking algorithms, we used bit error rate (BER) as a metric to evaluate the robustness of the watermarking algorithm. It is defined as:  
\[BER=\frac{Number\ of\ incorrect\ bits}{Number\ of\ total\ bits}\ \times 100\% \tag{10}\]  
The objective difference grade (ODG) was utilized to measure imperceptibility, as it is one of the output values acquired according to the Perceptual Evaluation of Audio Quality (PEAQ) measurement technique prescribed by the ITU-R BS.1387 standard [25]. The ODG values were between 0.0 and \(-4.0\) (imperceptible to very annoying), and they are shown in Table 1.  
In the rest of this part of the paper, if there is no specific notice of parameters, experiments were performed with following default parameters: Frame length \(T_{f}=500\) samples (96 bps), delay \(\tau=45\), integration time \(T=500/2-45=205\) samples, \(l_{a}=10\) samples, \(\Delta\)k = 0.1, \(\alpha=0.005\), and passband frequency \([F_{lc},F_{uc}]=[2500\ \sim\) 5500 Hz]. CSNR was calculated using 24 kbps HE-AAC v1 compression. We terminated the feedback process of a frame if it was conducted more than five times and it resulted in average of 2.13 feedbacks per frame.  
The ODG was measured by basic PEAQ model, which was implemented from the Telecommunications & Signal Processing Laboratory of McGill University [26]. The \"fdkaac.exe\" software [27], which was developed by Fraunhofer, was used to conduct HE-AAC compression. The \"ffmpeg.exe\" [28] was used to conduct MP3 compression and decode the compressed audio.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Sr. No.** & **ML Algorithms** & **Training** & **Inference** \\ \hline
1 & Linear Regression (OLS) & \(O(nm^{2}+m^{3})\) & \(O(m)\) \\
2 & Linear Regression (SGD) & \(O\left(n_{epoch}nm\right)\) & \(O(m)\) \\
3 & Logistic Regression (Binary) & \(O\left(n_{epoch}nm\right)\) & \(O(m)\) \\
4 & Logistic Regression (Multiclass OvR) & \(O\left(n_{epoch}nmc\right)\) & \(O(mc)\) \\
5 & Decision Tree & \(O(n\text{log}(n).\ m)\) & \(O(d_{tree})\) \\
6 & Random Forest Classifier & \(O(d_{trees}n\text{log}(n).\ m)\) & \(O(d_{trees}d_{tree})\) \\
7 & Support Vector Machine & \(O\left(n^{2}m+n^{3}\right)\) & \(O(mn_{sv})\) \\
8 & k-Nearest Neighbours & - & \(O(nm)\) \\
9 & Naive Bayes & \(O(nm)\) & \(O(mc)\) \\
10 & Principal Component Analysis (PCA) & \(O(nm^{2}+m^{3})\) & - \\
11 & t-SNE & \(O(n^{2}m)\) & - \\
12 & k-Means Clustering & \(O(iknm)\) & - \\ \hline \end{tabular} Source: Daily dose of data science [12].  
\end{table} 
Table 1: ML model’s time complexity. applications [14]. In the threat intelligence sphere, DL models are employed to analyse large amounts of data from various repositories to derive more accurate predictions of imminent cyber threats [15]. A comprehensive analysis is essential to uncovering ML and DL's role in cybersecurity. It will help enhance the security system's efficiency, accuracy, and scalability. The holistic view of these advanced techniques is crucial for understanding the mechanism of automatic threat detection, reducing false positives and improving response times. ML and DL establish the future of cybersecurity, possessing the ability to assimilate data presented and respond to previously concealed challenges. They offer proactive and intelligent defence against dynamic, evolving cyber threats.