esa-sceva/satcom-chunk-collection huggingface dataset:
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 2 System Model
### Radar Signal Model  
The DFRC signal transmitted via \(N_{t}\) antennas at the RSU can be expressed as  
\[\widetilde{\mathbf{s}}_{n}(t) =\mathbf{F}_{n}\mathbf{s}_{n}(t)\in\mathbb{C}^{N_{t}\times 1} \tag{4}\]  
where \(\mathbf{s}_{n}(t)=[s_{1,n}(t),...,s_{K,n}(t)]^{T}\in\mathbb{C}^{K\times 1}\) denotes the transmitted \(K\) downlink DFRC streams corresponding to \(K\) vehicles at the \(n\)-th slot, \(\mathbf{F}_{n}=[\mathbf{f}_{1,n},\mathbf{f}_{2,n},...,\mathbf{f}_{K,n}]\in \mathbb{C}^{N_{t}\times K}\) denotes transmit beamforming matrix, where each column represents a vehicle's beam forming vector. Therefore, the reflected signal echoes received at the RSU by vehicles can be formulated as [16]  
\[r_{n}(t)=\kappa\sum_{k=1}^{K}\sqrt{p_{k,n}}\beta_{k,n}e^{j2\pi\mu_{k,n}t}\mathbf{ v}(\theta_{k,n})\mathbf{u}^{H}(\theta_{k,n})\mathbf{s}_{n}(t-\tau_{k,n})+\mathbf{w}_{r}(t) \tag{5}\]  
where \(\kappa=\sqrt{N_{t}N_{r}}\) is the antenna array gain, \(p_{k,n}\) is the transmit power for the \(n\)-th slot to the \(k\)-th vehicle, \(\tau_{k,n}\), \(\mu_{k,n}\), \(\beta_{k,n}\) denote the time-delay, the Doppler frequency and the reflection coefficient for the \(k\)-th vehicle, respectively. \(\mathbf{w}_{r}\in\mathbb{C}^{N_{t}\times 1}\) denotes Gaussian noise with zero mean and variance of \(\sigma^{2}\). \(\mathbf{u}(\theta)\) and \(\mathbf{v}(\theta)\) are transmit and receive antenna array responses, which yield  
\[\mathbf{u}(\theta)=\sqrt{\frac{1}{N_{t}}}\Big{[}1,e^{-j\tau cos\theta},...,e^{ -j\pi(N_{t}-1)cos\theta}\Big{]}^{T} \tag{6}\]  
\[\mathbf{v}(\theta)=\sqrt{\frac{1}{N_{t}}}\Big{[}1,e^{-j\tau cos\theta},...,e^{ -j\pi(N_{t}-1)cos\theta}\Big{]}^{T} \tag{7}\]  
where we make the assumption that the antenna spacing of the ULA is half-wavelength. The beamforming vector is formulated based on the predicted angle, specifically, the \(k\)-th column \(\mathbf{f}_{k,n}\) of \(\mathbf{F}_{n}\) is:  
\[\mathbf{f}_{k,n}=\mathbf{u}\Big{(}\hat{\theta}_{k,n|n-1}\Big{)},k=1,2,...,K \tag{8}\]  
However, although RSU will receive echoes from multiple vehicles, the inter-beam interference can be neglected given the antenna array response are asymptotically orthogonal to each other under the massive MIMO system [19; 20]. Therefore, there is no interference between reflected echoes from different vehicles. The radar echo received by the RSU for the \(k\)-th vehicle at \(n\)-th slot is expressed as  
\[\mathbf{r}_{k,n}(t)=\kappa\sqrt{p_{k,n}}\beta_{k,n}e^{j2\pi\mu_{k,n}t}\mathbf{ v}(\theta_{k,n})\mathbf{u}^{H}(\theta_{k,n})\mathbf{f}_{k,n}\mathbf{s}_{k,n}(t- \tau_{k,n})+\mathbf{w}_{k,n}(t) \tag{9}\]
## 3 Results and Discussion
### LNC Estimation Based on Fluorescence Ratio  
Fluorescence ratios served as the input parameters to train the BPNN model with different training function and hidden layer sizes. The changes of \(R^{2}\) between the predicted and measured LNC with hidden layer size for different training functions are shown in Figure 4.  
Figure 4: Performance of BPNN for LNC estimation based on the published fluorescence indices varying with hidden layer sizes for different training function. The black error bar represents one standard deviation of mean values.  
Figure 4 shows the changes of \(R^{2}\) with hidden size for different training functions by the BPNN model based on fluorescence ratios. By comparing the \(R^{2}\) and SD values, the \"trianrp,\" \"trainbr,\" \"trainlm,\" and \"trainbfg\" served as the training function in the BPNN model and exhibited high \(R^{2}\) and low SD. In addition, the hidden layer size will influence the stability of the BPNN model for LNC estimation. For fluorescence ratios serving as input parameter to train the BPNN model, the optimal train function \"trainlm\" and hidden layer size of 4 are selected. \(R^{2}\) and SD are 0.807 and 0.026, respectively.
## 2 V2P System Architecture
### Safety Messages  
A typical safety message may contain speed, location, and direction of the respective vehicle or VRU. This information may then be used for the _detection, tracking and trajectory prediction_ phases by the recipients of the safety message. Vehicles may transmit 10 safety messages per second (i.e., at fixed 10 Hz frequency). VRUs may transmit safety messages with varying frequency. This frequency may depend on various parameters, such as, their location and speed.
# Three-Dimensional Film Image Classification Using an Optimal Width of Histogram
## 3 Proposed Algorithm
### Cropping Pattern Images on Film Using Fast Fourier Transform Algorithm  
To inspect the pattern in the 3D film of Figure 1, the image must be cut for each pattern. We use the fast Fourier transform to cut the image by pattern in the printed 3D film [27].  
Let \(I(x,y)\) be \(N\times N\) binarized image in the spatial domain, which is sampled from the discrete Fourier transform and is defined as \(F(n,m)\) as shown in Equation (1).  
\[F(n,m)=-\underset{x=0}{N-1}-\underset{y=0}{N-1}I(x,y)e^{-2\pi i(\frac{m}{N}+ \frac{m}{N})}, \tag{1}\]  
where the exponential term is a fundamental function representing sine waves and cosine waves with increasing frequencies corresponding to each \(F(n,m)\) in Fourier space. For the Fourier image \(F(n,m)\), we convert it to spatial domain \(I(x,y)\), as shown in Equation (2).  
\[I(k,l)=\frac{1}{N^{2}}-\underset{n=0}{N-1}-\underset{m=0}{N-1}F(n,m)e^{2\pi i (\frac{m}{N}+\frac{m}{N})}, \tag{2}\]  
Because the Fourier transform is separable, it can be decomposed into two equations. Equation (3) represents the first transformation of the spatial image to an intermediate image using \(N\) one-dimensional Fourier transformations. Equation (4) represents the transformation of the intermediate image to the final image in a series of \(2N\) one-dimensional transformations.  
\[F(n,m)=\frac{1}{N}-\underset{l=0}{N-1}\ T(n,l)e^{-2\pi i(\frac{m}{N})}, \tag{3}\]  
\[T(n,l)=\frac{1}{N}-\underset{k=0}{N-1}\ T(k,l)e^{-2\pi i(\frac{m}{N})}, \tag{4}\]  
From Equation (4), we use the real part of the processed image because the imaginary part is almost always negligible, and images are real-valued functions. We determine the index of the white pixel values of \(I(k,l)\) to obtain the horizontal \(I_{i}(i,j)\) and vertical \(I_{j}(i,j)\), as shown in Equations (5) and (6).  
\[I_{i}(i,j)=\begin{cases}255,&T(n,l)>0\\ 0,&elsewhere\end{cases}, \tag{5}\]  
\[I_{j}(i,j)=\begin{cases}255,&T(n,l)>0\\ 0,&elsewhere\end{cases}. \tag{6}\]  
From the result in Equations (5) and (6), the pixel coordinates are obtained by the intersection. After that, the intersection information can be used to cut the pattern images of the 3D film.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 2 An Overview on Multi-Objective Optimization and Multi-Objective Evolutionary Computation  
The multi-objective optimization problem is a pervasive challenge in many areas of practice. It may not be possible to fully achieve all objectives, so each objective must be assigned a relative weight in a scoring method [52]. Determining these weights is a primary issue in this research. Meanwhile, the genetic algorithm inspired by the principles of biological evolution has also gained significant attention [53]. Combining these two approaches allows the global search capabilities of the genetic algorithm to be exploited while avoiding the risk of conventional multi-objective optimization methods becoming stuck in local optima and maintaining population solution diversity. As a result, a genetic algorithm-based multi-objective optimization strategy has been applied across various fields.  
The MOP is usually transformed into single-objective problems by weighting and pre-processing, after which they can be solved by mathematical programming methods [47]. However, this method can only yield one optimal solution for a single set of weights per return. Additionally, there is a probability that the objective and constraint functions of problems are discontinuous and nonlinear and cannot be solved by conventional mathematical programming methods. Conventional mathematical programming is ineffective and susceptible to the weights or rankings provided by the objective [54]. A multi-objective optimization solution is often composed through a series of equilibrium solutions, i.e., a collection of optimal solutions organized into many sets of Pareto optimal solutions, with each component being referred to as a Pareto optimal solution [55]. It aims to simultaneously achieve several objectives in each region as efficiently as possible. In multi-objective optimization, since multiple sub-objectives are optimized simultaneously, and these sub-objectives are often in conflict with each other, over-optimization of one sub-objective will inevitably lead to less favorable optimization results for at least one of the other sub-objectives. When dealing with the MOP, we do not try to find the single best solution [43]. In the multi-objective optimization algorithm, there are different optimization objectives for different sub-objective functions. Generally, there are three cases, as follows:  
* Minimize all sub-objective functions.
* Maximize all sub-objective functions.
* Minimize the sub-objective function and maximize other sub-objective functions.
## 3 Results and Discussion
### Analysis of an Optimized Arrangement of Helmholtz Resonators  
#### 3.3.1 Optimized Solutions for Variable Cavities  
In a previous study, [PERSON] et al. [33; 34; 35] analyzed high-speed trains meeting in partially expanded sections of tunnels of different lengths and measured the pressure changes generated during the process. They concluded that the presence of partially expanded sections can effectively mitigate changes in the wall pressure of tunnel. Hence, a new type of tunnel with varying chambers, 72 and 64, was proposed to address the phenomenon of increasing wall pressure in the aforementioned tunnel.  
The core of the new tunneling solution is based on the creation of multiple artificially locally expanded sections inside the tunnel by regularly reducing the Helmholtz resonators to sacrifice a small part of the micro-pressure wave mitigation effect in exchange for the tunnel walls, i.e., pressure fluctuations should be within reasonable limits. The specific arrangements and effects are presented in Table 1 and Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **M-1** & **M-2** & **M-3** & **Wall Pressure in the Tunnel** \\ \hline Conventional tunnel & 1021.85 & 641.26 & 292.22 & 6149.85 \\
94-Cavity New & 703.87 & 436.91 & 195.12 & 7176.44 \\ Tunnel & \(-31.12\%\) & \(-31.87\%\) & \(-33.23\%\) & +16.69\% \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of results of different solutions.  
Figure 12: Initial compressional wave gradient comparison chart.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 1 Introduction
In this paper, an interferometer model with unknown parameters, including the laser beam angle and emitting point position, is proposed to avoid inaccurate modeling so that the measurement result is entirely unaffected by installation errors. A solution of the interferometer model without any linearization is proposed to avoid an inaccurate solution. For the problem arising in the model solving that the singular Jacobi matrix leads to no unique solution of the equations, the theory of solution component uniqueness is proposed to obtain a unique and accurate solution of the displacement in the model. This paper is organized as follows. In Section 2, the mathematical model of the laser interferometer is proposed, which shows the relation between interferometer readings and multi-DOF displacements. In Section 3, the displacement computation principle is proposed, and the focus problem that the solution of a nonlinear equation system is not unique due to the singular Jacobi matrix is discussed. In Section 4, the interferometer measurement and the displacement computation are simulated. The simulation result verifies the effectiveness of the proposed interferometer. Section 5 is the conclusion of the paper.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 7 Performance Evaluation  
### Simulation Setting  
Simulations are conducted to verify the effectiveness of the double-scale adaptive transmission mechanism in terms of channel prediction and communication performance. The proposed scheme and contrast schemes are evaluated through simulation under the same parameters setting.  
The channel state sequence is generated with large-scale and small-scale dynamics. In each slot, the sender transmits one block for 2 s, and the block has 1000 symbols. Unless otherwise specified, the simulation parameters are shown in Table 1.  
Multiple sets of channel state series with large-scale and small-scale fluctuations are generated by the superposition of multiple signal sources with the MATLAB toolbox. The generated channel state series combines certain regularity and randomness, which conforms to the channel state fluctuation characteristics described in the literature. For example, if the component of large-scale fluctuation is \(f_{t}\), and that of small-scale fluctuation is \(g_{t}\), the generated channel state series for simulation can be calculated as  
\[h_{t}=f_{t}\ +\ g_{t}+\ C, \tag{67}\]  
where \(C\) is the coefficient to adjust the mean value of the series so that the channel state approximately satisfies the attenuation and distance relationship of the underwater acoustic channel.
## 3 Experimental Confirmation of SCN's Universal Approximation Property  
In this section, we validate the universal approximation property of the SCN through experiments. The common predictive methods employed in existing models for power load data processing include Support Vector Regression (SVR) [41], Random Vector Functional Link Networks (RVFLs) [29], and Fully Connected Layers (FCs) [27]. In addition, we include Linear Regression (LR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression Tree (GBRT) models for comparison. [PERSON] et al. [30] have demonstrated the universal approximation property of the SCN in their studies on three regression datasets: Stock, Concrete, and Compactiv. These three datasets are all from the KEEL (KEEL. Available online: [[http://www.keel.es/](http://www.keel.es/)]([http://www.keel.es/](http://www.keel.es/)) (accessed on 22 June 2024)) database. In this study, we validate the universal approximation property of the SCN on the Stock and Concrete regression datasets, and compare the performance of various models on these datasets. Specifically, we randomly selected 80% of the samples in each dataset as the training set, and the remaining 20% as the test set.
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 3 Results and Discussion
### Objective Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Methods**} \\ \cline{2-9}  & **ASR** & **CVT** & **DTCWT** & **MSVD** & **CSMCA** & **NSST** & **Proposed Method** \\ \hline EI & 67.026 & 79.944 (2) & 75.086 & 64.169 & 70.435 & 75.318 & **80.087 (1)** \\ \hline MI & 4.279 & 3.904 & 4.030 & 4.227 & **4.346 (1)** & 4.116 & 4.339 (2) \\ \hline VIFF & 0.272 & 0.254 & 0.249 & 0.286 & 0.297 (2) & 0.241 & **0.356 (1)** \\ \hline \(Q_{p}^{AB/F}\) & 0.472 & 0.421 & 0.435 & 0.392 & **0.481 (1)** & 0.421 & 0.480 (2) \\ \hline SSIM & 0.593 & 0.276 & 0.413 & 0.301 & 0.537 & **0.600 (1)** & 0.599 (2) \\ \hline AG & 6.662 & 7.887 (2) & 7.421 & 6.812 & 6.877 & 7.471 & **7.980 (1)** \\ \hline RMSE & 0.029 & 0.029 & 0.029 & 0.028 & 0.029 & 0.024 (2) & **0.021 (1)** \\ \hline PSNR & 16.857 & 17.171 & 17.720 & 15.804 & **17.892 (1)** & 13.981 & 17.794 (2) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis of the various fusion methods for MRI-CT (set-11). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Methods**} \\ \cline{2-9}  & **ASR** & **CVT** & **DTCWT** & **MSVD** & **CSMCA** & **NSST** & **Proposed Method** \\ \hline EI & 51.347 & 63.877 & 58.355 & 49.732 & 51.899 & 65.474 (2) & **65.802 (1)** \\ \hline MI & 4.186 & 3.878 & 3.995 & 4.090 & 4.284 (2) & 4.214 & **4.549 (1)** \\ \hline VIFF & 0.356 & 0.362 & 0.365 & 0.348 & 0.412 (2) & 0.340 & **0.484 (1)** \\ \hline \(Q_{p}^{AB/F}\) & 0.465 (2) & 0.418 & 0.431 & 0.380 & 0.461 & 0.446 & **0.478 (1)** \\ \hline SSIM & 0.674 (2) & 0.338 & 0.507 & 0.417 & 0.663 & 0.590 & **0.694 (1)** \\ \hline AG & 5.065 & 6.231 & 5.719 & 5.197 & 5.045 & **6.349 (1)** & 6.326 (2) \\ \hline RMSE & 0.028 & 0.029 & 0.029 & 0.026 & 0.028 & 0.022 (2) & **0.018 (1)** \\ \hline PSNR & 17.396 & 17.268 & 17.649 & 16.392 & **18.644 (1)** & 14.096 & 18.024 (2) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis of the state-of-the-art methods for MRI-CT (set-15) dataset. method occupied the first position by overperforming other fusion algorithms when average values are considered in fusion metrics. In general, the consistent performance of any image fusion algorithm in quantitative results is mainly due to the good visual quality of fused images, fusion gain, and less fusion loss and fusion artifacts. We have already seen from the visual result analysis that the proposed method can transfer the source image information into the fused image with less fusion loss and artifacts compared to the other fusion algorithms. It is also evident from the fusion metrics that our method is giving a stable performance. Hence, we can conclude that the proposed method is promising, stable, and efficient from qualitative and quantitative comparative analysis.
# Groundwater Prediction Using Machine-Learning Tools
## 3 Techniques Used
### Support Vector Machine and Support Vector Regression  
SVM is a powerful machine-learning technique that has the capability to perform structural risk minimization (SRM), which enables it to avoid overfitting by minimizing the bound on the generalization error [52]. SVMs may be extended to apply to estimation and regression problems: this extension is known as support vector regression (SVR) [53]. SVR maps the input data into a higher-dimensional feature space via nonlinear kernel functions [54]. The objective is to choose a vector of regression coefficients with a small norm, while minimizing the sum of the distances between the data points and the regression hyperplane in the higher-dimensional space [55].
# An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
## 5 Experiments and Results
### Performance Metrics  
The performance metrics involved in this work are described as follows. (a) The throughput is defined as \(S=\frac{L_{data}}{D_{total}}\), where \(L_{data}\) is the total length of user data received, and \(D_{total}\) is the total delay, the duration of an SDDA's round, which is formulated as \(D_{total}=D_{w\_RE}+D_{w\_DATA}+td_{ACK}\), where \(td_{ACK}\) is the transmission delay of an ACK packet. In throughput simulations, the number and the size of data packets to be sent by each terminal node are the same, denoted as \(m\) and \(L\), respectively. Then \(L_{data}=mnL\), and \(D_{w\_RE}+D_{w\_DATA}\) is up to \(m\) and \(n\). Consequently, the throughput \(S\) is related to \(n\) and \(m\). (b) The channel utilization is measured as the ratio of the data delay to the total delay during the given transmission period, i.e., \(\frac{D_{data}}{D_{total}}\). (c) The end-to-end delay is measured as the average delay of the packets received from generation to reception, i.e., \(\frac{\sum_{k=1}^{n}(t_{kA}-t_{cA})}{n_{k}}\).  
(d) The packet delivery ratio is measured as the ratio of the number of packets delivered successfully to the number of packets sent by all nodes, i.e., \(\frac{n_{d}}{n_{s}}\).
# Rifampicin-Loaded PLGA/Alginate-Grafted pNVCL-Based Nanoparticles for Wound Healing
## 2 Materials and Methods
### In Vitro Release Study of Formulations  
The in vitro release studies of RF from lyophilized nanoparticles were conducted using the dialysis bag diffusion technique. The dried samples were loaded into the dialysis bag (cellulose membrane, molecular weight of 12,000 to 14,000 Da), and hermetically sealed via standard closure (Repligen, Waltham, MA, USA). The dialysis tube was immersed into 100 mL phosphate buffer (pH = 7) at 37 \({}^{\circ}\)C with a magnetic stirrer stirring at 300 rpm. At predetermined time intervals, 3 mL of the dissolution medium was withdrawn from the receptor compartment and replaced with an equal volume of fresh medium. The sample solution was assayed via HPLC for drug content. All the experiments were carried out in triplicate.  
The release data obtained via the dialysis bag diffusion technique were fitted using the Korsmeyer-Peppas mathematical model (Equation (1)) [20].  
\[\frac{M_{\mathrm{t}}}{M_{\infty}}=k_{\mathrm{r}}\cdot t^{n_{\mathrm{r}}} \tag{1}\]  
where \(M_{\mathrm{t}}\) is the drug mass released at time t; \(M_{\infty}\) is the total drug mass released at time t and at equilibrium; \(k_{\mathrm{r}}\) is a rate constant dependent on the characteristics of the drug loaded system; and \(n_{r}\) is the release exponent which defines the release mechanism. The value of the release exponent, \(n_{r}\), suggests the nature of the release mechanism. If \(n=0.5\), we have a Fickian diffusion mechanism of the drug from the matrix, whereas if \(n>0.5\) an anomalous transport or non-Fickian behavior, and \(n<0.5\) a quasi-Fickian diffusion occurs.