esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices
## 3 Preliminaries
### Reducing Model Complexity
#### 3.3.1 Proper Orthogonal Decomposition
Replacing the velocity \(u\) with \(u_{r}\) in the incompressible Navier-Stokes equations, as seen in [46], and applying a Galerkin projection on the POD basis functions, the following POD-Galerkin ROM is obtained:  
\[\frac{da_{j}(t)}{dt}=v\sum_{i=1}^{N_{r}}B_{ji}a_{i}(t)-\sum_{k=1}^{N_{r}}\sum_ {i=1}^{N_{r}}C_{jki}a_{k}(t)a_{i}(t),\quad j=1,\ldots,N_{r}, \tag{29}\]  
where  
\[B_{ji} =\left\langle\
abla\varphi_{j},\
abla\varphi_{i}\right\rangle_{L^{2}} \tag{30}\] \[C_{jki} =\left\langle\varphi_{j},\left(\varphi_{k}\cdot\
abla\right) \varphi_{i}\right\rangle_{L^{2}}\] \[a_{j}(0) =\left\langle\varphi_{j},u_{1}(x)\right\rangle_{L^{2}}.\]  
One can obtain the ROM by a projection of the state vector \(x\) using the POD basis functions  
\[x(t)=V\hat{x}(t), \tag{31}\]  
where \(\hat{x}(t)\) is an \(m\)-dimensional vector in the reduced space, containing the time-dependent amplitudes of \(m\) basis vectors, contained in the columns of the projection matrix \(V\). After applying the projection to the nonlinear system, the resulting ROM is of the form  
\[ \begin{split}\dot{\hat{x}}(t)&=V^{T}f(V\hat{x}(t), u(t))\\ \hat{y}(t)&=h(V\hat{x}(t)).\end{split}  \tag{32}\]  
As for the time-domain discretization, a second-order backward Euler time discretization scheme is used in order to preserve the stability of the discretized system:  
\[\dot{\hat{x}}(t)\approx\frac{3\hat{x}^{n}-4\hat{x}^{n-1}+\hat{x}^{n-2}}{2 \Delta t}=V^{T}f(V\hat{x}^{n},u^{n}), \tag{33}\]  
where \(\Delta t\) denotes the time step and the superscript \(n\) denotes the time index of the current solution. Subtracting the right side from Equation (33), the nonlinear system of equations simplifies to  
\[F(\hat{x}^{n})=3\hat{x}^{n}-4\hat{x}^{n-1}+\hat{x}^{n-2}-2\Delta tV^{T}f(V\hat {x}^{n},u^{n})=0, \tag{34}\]  
for \(\hat{x}^{n}\), which is typically performed using Newton iteration. It is important to note that the Jacobian needs to be evaluated at every iteration at a given time step to obtain the flux term \(f(\cdot)\). This implies that the simulation of ROM could be no more efficient than the original system, since the flux term evaluation takes most of the computation time. This issue will be addressed in the next section. For an error assessment on the accuracy of the POD reduction method, refer to [44].
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 4 Simulation and Experiments
### Performance Comparison of Different Algorithms
On the 30 \(\times\) 30 grid map, the improved BSGA* algorithm is 93.74%, 5.45%, and 80.87% lower than the ACO algorithm, 74.34%, 92.64%, and 5.30% lower than the GA, and 86.82%, 5.30%, and 79.23% lower than the D* line algorithm in these three aspects, respectively. From the above data, it can be seen that the improved BSGA* algorithm has a superior performance compared to the ACO algorithm, GA, and the D* line algorithm. Therefore, the paths generated by the improved BSGA* algorithm are more suitable for DDMRs. Figures 14 and 15 are the column graphs and error bars drawn according to the above comparison experiments. Since both the D* line algorithm and improved BSGA* algorithm are heuristic algorithms, the optimal paths generated in each execution are the same, so the standard deviations of path length and total turning angle are 0 in 50 executions. The paths generated by the ACO algorithm and the GA have randomness. As can be seen from Figures 14 and 15, after enough iterations, although the path generated by the ACO algorithm and GA is close to the improved BSGA* algorithm in length, the standard deviation of total turning angle, however, is larger due to the randomness of path point \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Map Size**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Average Calculation**} & \multirow{2}{*}{**Total Angle/\({}^{\circ}\)**} & \multirow{2}{*}{**Path Length**} \\  & & & & \\ \hline \multirow{5}{*}{\(15\times 15\)} & ACO & 1214.73 \(\pm\) 89.46 & 262.80 \(\pm\) 78.65 & 21.68 \(\pm\) 0.23 \\  & GA & 1211.47 \(\pm\) 156.62 & 491.40 \(\pm\) 21.75 & 21.77 \(\pm\) 0.45 \\ \cline{1-1}  & D* line & 3473.67 \(\pm\) 69.46 & 270.00 & 21.55 \\ \cline{1-1}  & Improved BSGA* & 366.31 \(\pm\) 83.5 & 36.87 & 20.42 \\ \cline{1-1}  & ACO & 15,190.43 \(\pm\) 376.30 & 342.00 \(\pm\) 106.49 & 44.62 \(\pm\) 0.27 \\ \cline{1-1}  & GA & 3/50.64 \(\pm\) 338.03 & 888.30 \(\pm\) 141.11 & 45.77 \(\pm\) 0.77 \\ \cline{1-1}  & D* line & 7217.43 \(\pm\) 84.26 & 315.00 & 44.55 \\ \cline{1-1}  & Improved BSGA* & 950.80 \(\pm\) 14.47 & 65.42 & 42.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of paths generated by ACO, DA* line, and improved BSGA* algorithms under 15 \(\times\) 15 and 30 \(\times\) 30 grid maps; the values of average calculation time are reported as mean \(\pm\) standard deviation for 50 executions. Figure 13: Comparison of paths generated by ACO, GA, D* line, and improved BSGA* algorithms under different maps: (a) Map size is 15 \(\times\) 15; (b) Map size is 30 \(\times\) 30.  
selection. This means that both algorithms are not stable enough in terms of path smoothing. In contrast, the paths generated by the improved BSGA* algorithm are not only smoother, but also the optimal path generated is unique and performs more stably when the grid map, starting point, and target point are determined.
## 3 Experiment and Results
### Ablation Experiment  
We conducted ablation experiments on the CFGN network itself using Dataset1 to demonstrate the effectiveness of the CFG module and the Bottom Transformer Layer. In our study, the baseline network consists of a simple model composed of two \(3\times 3\) convolutional layers with a stride of 2, eight Residual-6 Blocks, a pooling layer, and a fully connected layer. We gradually added different modules to the baseline to verify their effectiveness. The evaluation method for this section is consistent with the previous experiments.  
Table 7 presents the results of model optimization. The baseline model achieved an \(ACC0.5\) of \(67.4\%\) and an \(ACC1\) of \(90.1\%\). After adding the CFG module, \(ACC0.5\) improved to \(68.8\%\), and \(ACC1\) increased to \(92.2\%\). When the bottom Transformer layer was added on top of the CFGN, \(ACC0.5\) further improved to \(69.6\%\), and \(ACC1\) increased to \(92.5\%\). These results demonstrate the effectiveness of both the CFG module and the Transformer layer in improving model performance.
###### Abstract  
The X-ray spectra of L-shell transitions in Neon-like Xenon ion (Xe\({}^{44+}\)) have been precisely measured at the Shanghai Electron-Beam Ion Trap using a high-resolution crystal spectrometer. Focusing on the line-intensity ratio of the 3F [2p\({}^{6}\)-(2p\({}^{5}\)\({}_{1}\)/2s\({}_{1}\)/2)\({}_{1}\)] and 3D [2p\({}^{6}\)-(2p\({}^{5}\)\({}_{3}\)/2d\({}_{5}\)/2)\({}_{1}\)] lines (3F/3D), our measurements have achieved remarkable precision improvements over the previous studies. These spectra have been simulated using the collisional-radiative model (CRM) within the Flexible Atomic Code, showing good agreement with the measurements. The previously reported discrepancies, approximately ranging from 10% to 20%, have been significantly reduced in this work to below 1.4% for electron-beam energies exceeding 6 keV and to around 7% for lower energies. Furthermore, our analysis of population fluxes of the involved levels reveals a very high sensitivity of the 3F line to radiation cascades. This suggests that the current CRM, which conventionally excludes interionic population transfer processes, may underestimate the population of the upper level of the 3F line and the cascade-related higher levels, thus explaining the remaining discrepancies. These findings provide a solid foundation for further minimizing these discrepancies and are crucial for understanding the atomic structure and plasma model of these ions.  
EBIT; fusion plasma diagnostics; Neon-like Xenon ions; X-ray spectrum +
Footnote †: journal: Appl. Sci. 2024, 14, 4381. [[https://doi.org/10.3390/app1414381](https://doi.org/10.3390/app1414381)]([https://doi.org/10.3390/app1414381](https://doi.org/10.3390/app1414381))
## 2 Materials and Methods
### Study Area and Data Acquisition  
The study was conducted in two real-world parking areas located in the Alparslan neighborhood of Kayseri, Turkiye. These selected parking areas do not have predefined parking lines. The geometric properties of the parking areas, as well as the positions and dimensions of the vehicles within them, were prepared in shapefile format for analysis. A sample dataset was developed based on an initial vehicle arrangement, in which the vehicles were randomly placed, aiming to maximize the number of additional vehicles placed in the remaining empty spaces. This approach realistically reflects the complex spatial arrangement challenges commonly encountered in unmarked parking areas due to irregular geometries and varying vehicle sizes.  
The selected parking areas and the occupied vehicle zones are illustrated in Figure 1.  
Figure 1: Parking areas and occupied spaces (Source: author’s own elaboration).  
The first parking area is a roadside section designated for parallel parking, measuring approximately 93.25 m in length and 3.00 m in width, with a total area of approximately 279.74 m\({}^{2}\). The second parking area is a designated section within a parking lot intended for perpendicular parking. This area measures approximately 5.75 m in width and 39.70 m in length, with a total area of approximately 227.01 m\({}^{2}\).
## 6 Discussion
#### 6.1.1 Gas Mutation States  
Figure 6 illustrates the reconstruction performances under gas mutation states when different numbers of measuring points were used (\(M=0.1\)\(N\)\(\sim\)\(N\)), during which the signal was reconstructed using the TVS-MH algorithm. The initial parameters were set below.  
The first penalty factor was set as: \(\mu^{0}=2^{8}\).  
The error for stopping the algorithm was set as: \(tol=10^{-6}\).  
The maximum iteration number was set as 300.  
It can be observed that when \(M\geq 0.3\)\(N\), the gas signal can be accurately reconstructed using the TVS-MH algorithm. In a measurement curve, the reconstructed signal can well reflect the variation of the original signal in both gas mutation states and steady states. In particular, the reconstructed signal can track the mutation segments in original signals well, suggesting TVS-MH algorithm's favorable reconstruction performances for different types of signals.  
Table 1 presents the relations of \(M\) with the reconstruction error and the interaction number (i.e., the computation complexity) during the reconstruction of gas mutation data using TVS-MH algorithm. It can be observed that, when the compression ratio is greater than or equal to 0.3, the reconstruction error is smaller than 4%, and the iteration number is smaller than 100, suggesting that the TVS-MH algorithm was relatively simple in computation and is thus convenient for applications in practical engineering.  
Figure 6: Compression perception and the reconstruction error of gas mutation data (TVS-MH).
## 5 Conclusions  
In this paper, we proposed a frequency-dependent stereophonic image enhancement method that could be applied to two closely spaced omni-directional microphones available for portable audio recording devices. First, the A-F plane was obtained from the spectral magnitudes of stereo audio signals. Next, the direction at each frequency bin was estimated as the azimuth at which the A-F plane was minimized. Finally, a frequency-dependent amplitude panning technique was also proposed to enhance the stereophonic image from the stereophonic law of sines. The performance of the proposed method was evaluated in three different recording environments: anechoic, convolutive, and real reverberant. First, the directivity pattern of the proposed method was compared to that of a conventional method based on a DS beamformer. Second, the directional accuracy of the stereo audio processed by the proposed method was compared to that processed by a conventional method by the measurement of listeners' perceived directions. Finally, a DMOS assessment test was carried out to evaluate quality degradation after the proposed method had been applied. Consequently, it was revealed that the proposed method gave better directivity, higher directional accuracy, and less quality degradation than the conventional method. It was argued here that, compared to the conventional method, the proposed method could improve performance with the help of frequency-dependent processing.  
We have only experimented with a single source throughout this study, so we are planning to examine what happens when multiple sources are available. One possible approach will be to detect multiple directions from the A-F analysis and propose an appropriate panning method that can treat multiple angles.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the government of Korea (MSIP) (No. 2015R1A2A1A05001687), and by the ICT R&D program of MSIP/IITP (R01261510340002003, Development of hybrid audio contents production and representation technology for supporting channel and object based audio).  
All authors discussed the contents of the manuscript. [PERSON] contributed to the research idea and the framework of this study. [PERSON] performed the experimental work.  
The authors declare no conflict of interest.
# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
## 3 Effects of Process Parameters on Ablation Results
### Effects of Process Parameters on Ablation Width  
Figure 6 shows the effect of laser power and scan speed on W for a pulse frequency of 50 kHz. It can be observed that W decreases with an increase in scan speed at a constant laser power. However, W does not decrease indefinitely: the lower limit of W is approximately the diameter of the laser focus spot.  
Figure 7 shows the effect of laser power and pulse frequency on W for a scan speed of 150 mm/s. It can be observed that W increases with an increase in pulse frequency, and this relationship is approximately linear.  
Figures 6 and 7 show that W increases significantly with laser power. Table 6 shows the ANOVA results for W. Laser power has the highest degree of influence, at 60.9%, followed by pulse frequency and scan speed, at 30.6% and 8.5%, respectively.  
Figure 5: Effect of laser power and pulse frequency on D.  
Figure 6: Effect of laser power and scan speed on W.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Process Parameter**} & **Degree of** & **Sum of** & **Mean** & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} & **Influence** \\  & **Freedom** & **Square** & **Square** & & & **Level** \\ \hline Laser Power & 2 & 571.197 & 285.598 & 30.092 & 0.000 & 52.5\% \\ \hline Pulse Frequency & 3 & 303.424 & 101.141 & 10.657 & 0.000 & 18.6\% \\ \hline Scan Speed & 3 & 472.076 & 157.359 & 16.580 & 0.000 & 28.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA results of D.
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 5 Conclusions  
In conclusion, the results obtained in this paper allow us to assert that the use of quantum algorithms, particularly the HHL algorithm, is effective for solving the two-dimensional Poisson equation. Traditional methods for solving the two-dimensional Poisson equation often require significant computational resources and encounter issues when dealing with grid size expansion. A new approach based on quantum computers uses the properties of quantum mechanics, such as the superposition of states and parallel operations, to effectively solve these problems.  
This article considers in detail the problems associated with solving the Poisson equation on a grid using classical methods, emphasizing the computational complexity and resource intensity of these approaches. It discusses the limitations of analytical solutions and the necessity of numerical methods in practical scenarios. The utilization of the HHL algorithm to assess the suggested approach showcases the capability of quantum algorithms to effectively solve the Poisson equation on a 2D grid. The modified version of the HHL algorithm presented in this article, which uses CRZ rotations, offers a reduction in the number of qubits and gates required in the circuit.  
The error estimation, expressed in qubits, yields an approximate value of 0.508, which can be deemed acceptable given the constraints of quantum computing. When running on a local simulator, the quantum portion of the algorithm takes roughly 0.66 s to execute, while the classical part only requires approximately \(10^{-3}\) s. These execution times were observed when working with a small matrix size. To achieve favorable outcomes, it is essential to have the capability to utilize quantum computers and conduct tests on them.  
In the future, there are plans to develop a quantum computing algorithm for the problem of fluid flow in the near-wellbore zone of the formation.  
Conceptualization, T.I.; Software, A.M.; Supervision, B.D. All authors have read and agreed to the published version of the manuscript.  
The research is funded by a grant from the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan under the project number AP09260564.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Comparison and Discussion Between KANs and MLPs
### Prediction Errors and Discussion
#### 5.2.1 KAN-A vs. MLP-A  
In terms of training set performance, KAN-A's MSE (0.0019) was approximately 92% lower than that of MLP-A (0.0262), with a 97% lower MSE on the test set. The R\({}^{2}\) metrics showed KAN-A achieved near-ideal values 1 (0.9949 training, 0.9983 test), outperforming MLP-A (0.9319 and 0.9304).  
The MaxAE revealed KAN-A's test set maximum absolute error (0.3350) was 46% lower than MLP-A's (0.6283). Notably, KAN-A's test performance comprehensively exceeded its training performance, whereas MLP-A exhibited typical training-test performance degradation.  
MLP-A showed striking time efficiency, with a runtime of \(\sim\)1% of KAN-A's. This discrepancy stems from KAN-A's optimization of continuous spline basis function parameters, versus MLP-A's discrete parameter space with inherently faster computation. However, the comparison is inequitable: after one-time training, KANs provide explicit mathematical relationships for direct reuse in future modeling, eliminating retraining costs. In contrast, MLPs' runtime grows exponentially with increased depth and width. KAN is more cost-effective for long-term applications.
## 2 Experiment Setup and Measurement of PD Data  
### PD Test System  
PD experiments were conducted at the high-voltage laboratory of the National Taiwan University of Science and Technology. The pulse current method specified in the IEC 60270 standard was followed to conduct the PD measurements.  
The PD measurement setup consisted of a Hipotronics 700-DI series (Brewster, NY, USA) AC Dielectric test set model with a high-voltage source, Haefely 9230 model (Basel, Switzerland) high-voltage capacitive divider, PD test subjects representing the epoxy resin test samples with artificially fabricated voids at different positions that were immersedin a test oil tank, and a height-adjustable copper electrode on the top that could lock the high-voltage lead to connect to the high-voltage end of the test subject. The bottom was a screw connected to the low-voltage end of the test object, which was simultaneously connected to a Haefely AKV 9310 series PD analyzer that was optimized for use with the Haefely DDX 9121b series PD detector. The experimental setup of the epoxy resin with a void at the bottom is shown in Figure 1.  
An AC voltage source operating at a frequency of 60 Hz was used to supply high voltage to the test subjects. A test oil tank with an epoxy resin sample and void defect was connected to a PD analyzer. This constituted a wideband quadrupole system for PD measurements with a lower-limit frequency range of 34-7 kHz and an upper-limit frequency of >8 MHz. The bandwidth and center frequencies were set to 400 and 230 kHz, respectively. These parameters determined the frequency range over which the PD signals could be effectively captured and analyzed.  
The capacitive voltage divider had the primary function of stepping down the high voltage across the test subjects to a manageable level for measurement. This voltage reduction allowed for safe and accurate measurements without overwhelming the measurement equipment.  
PD signals were acquired from the PD analyzer, whereas voltage signals were obtained from the capacitive voltage divider. The PD and voltage signals at the corresponding voltage levels were captured simultaneously using a PD detector and stored in a notebook for further analysis.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
### Paper Organization  
The remainder of this paper is organized as follows: Section 2 discusses the related literature and presents the chief objective for disaster-monitoring applications based on wireless sensor networks. In Section 3, we describe the design requirements for the collaborative disaster-monitoring system and explain its architecture. We then analyze how to acquire seismic data in a highly energy-efficient manner. Section 4 presents the proposedHT-MAC scheme and analyzes the clustering, data transmission, and abnormity-handling operations. In Section 5, based on the embedded Markov chain method, the formulas for the mean queue length, mean cycle time, and mean upper bound of the frame delay are derived. Section 6 presents the simulations and performance tests of the HT-MAC scheme, and the results are analyzed. Finally, Section 7 presents the conclusions and future work of this study.
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 5 Performance Evaluation
### Performance Analysis
#### 5.2.3 Storage and Memory Footprint  
Finally, from the storage point of view, at the CDS, the amount of RAM is the most limiting aspect, compared with permanent storage, which is usually an order of magnitude larger. Table 12 shows the comparative memory footprint between the full HidraR software and just the connectivity stack, where text is the program size, data is the initialized RAM size, and bss is the uninitialized pre-zeroed RAM (e.g., buffers).  
Assuming that the sizes are dependent on the programming style, measurements of the increment on the storage occupancy are 18,544 bytes, and the memory footprint is 1450 bytes.  
Therefore, although dependent on the specific implementation, the impact is considered acceptable considering the available 128 kB and 8 kB of flash and RAM memory in such constrained devices, i.e., C0 CDSs, such as those used in the implementation [37]. One additional remarkable result is the considerable influence of the activated debugging and measuring instructions on the final footprint measurements.