esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Comparison with Existing Methods  
The proposed model is compared with several classical defocus image deblurring methods on the DPDD and RealDOF datasets, with the experimental results shown in Tables 1 and 2. Additionally, Figure 1 presents a scatter plot comparing the model's parameters and FLOPs against other methods, highlighting its superior efficiency in termsof computational resource utilization. In addition to the methods mentioned in [4; 8; 9; 11], [PERSON]. [15] proposed an approach that estimates spatially varying defocus blur kernels by reblurring edge features and gradient magnitude, which are then extended to the entire image using a filter. [PERSON]. [16] utilized sparse representation and image decomposition to directly estimate blur intensity using sparse edge estimation. [PERSON]. [17] introduced a method for synthesizing dual-pixel data and designed a recursive convolutional network, MDPNet, to enhance the deblurring effect. [PERSON]. [36] proposed a realistic dual-pixel defocus model and a recursive network to address the defocus deblurring problem. On the DPDD dataset, the proposed model achieves PSNR, SSIM, and MAE scores of 25.54 dB, 0.778, and 0.039, respectively. As shown in Table 1, compared to the end-to-end convolutional neural network DPDNet-S, the proposed algorithm shows significant improvements in both PSNR and SSIM, with the indoor and outdoor comprehensive test results increasing by 1.2 dB and 0.032, respectively. The model's parameter count is reduced from 34.52 M to 9.74 M. This demonstrates that the proposed method, which combines parallel feature extraction through the CNN branch for local feature information and the Mamba branch for global information, effectively improves image quality. In the comparison, although the Uformer-B model slightly outperforms in certain metrics, such as in the outdoor scenes, the proposed model exhibits more balanced performance across several key aspects. In terms of model parameters, the proposed model has only 9.74 M parameters, representing an 80.9% reduction compared to Uformer-B. Additionally, the FLOPs decrease from 89.46 G to 39.39 G, significantly reducing the computational complexity and resource consumption. These improvements make the model more efficient and suitable for a wider range of applications. In addition, in indoor scenes, the model performs well in PSNR (28.37 dB) and MAE (0.025), which indicates that the model has a good defocusing ability of indoor images. In the combined scenario, the model has a PSNR of 25.54 dB, which is slightly lower than the 25.65 dB of the Uformer-B. However, the signif
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 4 Discussion
### Recommendations for Future Studies
#### 4.4.3 Ultrasonographic Imaging  
Ultrasonographic evaluations are frequently used in the clinic because of their easy access and use. Although this is not in itself a novel technology, some its novel features can be taken into consideration in the future to understand the soft tissue properties in PFP through elastographic, architectural, and echo-intensity feature assessments of muscles [82; 83; 84]. In addition, with the introduction of portable systems and transducer fixation devices, dynamic skeletal muscle evaluation has become possible during different motor tasks [85].
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 3 An Interpretable Time-Shift Invariant Harmonic Phase Model
### NRD Estimation Example Based on the Sawtooth Wave
Figure 17 represents the results when SNR = 30 dB and FM = 2.5 Hz. Figure 15: Overlay representation of magnitude spectra (**top**), and unwrapped NRD vectors (**bottom**) of a sawtooth signal that is FM modulated (0.25 Hz deviation around the mean) and whose SNR is 30 dB. It can be seen that, in this case, the blurred aspect of higher-order harmonics in the magnitude spectrum is a natural consequence of the fact that the frequency deviation is proportional to the harmonic order. In particular, for \(\ell=19\), the maximum frequency deviation around the mean is 50 Hz, which represents \(1/3\) of the average fundamental frequency. It can be confirmed that despite the frequency modulation, the dispersion on the unwrapped NRD vectors is quite small and quite comparable with the one that is observed in Figure 15, in which the frequency modulation is negligible. This relevant experimental outcome unequivocally confirms that the NRD phase-related feature is intrinsically independent of the fundamental frequency. In other words, the stability of the estimated unwrapped NRD vectors reflects mainly the fact that the waveform shape of the periodic signal that is observed in each frame of the signal does not change appreciably. When FM = 2.5 Hz and SNR = 10 dB, new results are obtained that are represented in Figure 18.  
Figure 16: Overlay representation of magnitude spectra (**top**), and unwrapped NRD vectors (**bottom**) of a sawtooth signal that is FM modulated (0.25 Hz deviation around the mean) and whose SNR is 10 dB. Figure 17: Overlay representation of magnitude spectra (**top**), and unwrapped NRD vectors (**bottom**) of a sawtooth signal that is FM modulated (2.5 Hz deviation around the mean) and whose SNR is 30 dB. It is easy to anticipate from the spectral magnitude representation that the strong noise influence, when it is allied to the significant frequency deviation of high-order harmonics, paves the way for serious difficulties in phase estimation, which is reflected in the substantial deviation of the unwrapped NRD vectors relative to the ideal ground-truth model. In particular, phase estimation errors cause the phase unwrapping algorithm to introduce sudden jumps of 0.5 in certain unwrapped NRD vectors. However, mitigating strategies could be easily adopted that could detect and avoid such jumps. The Matlab code that generates signals and creates the overlay representations, as illustrated in previous figures, is available (_geraway_v4_.m_). This facilitates obtaining results for other simulation parameters.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 3 Results  
### Isothermal Drying Process  
Drying of concrete is a nonlinear moisture diffusion process [23]. When subjected to the isothermal drying condition at 105 \({}^{\circ}\)C, internal water continuously escapes under the temperature gradient and humidity gradient, thus the moisture content of specimens decreases gradually. With the progress of drying, the remaining moisture is being lost with increasing difficulty and much slower [24]. The water-losing rate of cement paste, mortar, and concrete during the drying process, defined as the water mass escaped from unit surface area of specimen per unit time (Unit: kg/(m\({}^{2}\cdot\)h)), was plotted in Figure 3.  
It is evident from Figure 3 that the water-losing rate of cement paste reached the maximum value rapidly and then gradually decreased, while that of mortar and concrete showed a trend of rising first and then falling, which is similar to the observation of [PERSON] et al. [25]. This isothermal drying process can be divided into three stages: rising-rate drying stage, falling-rate drying stage, and slow drying stage. When the water-losing rate is less than 0.01 kg/(m\({}^{2}\cdot\)h), it could be considered that the slow drying stage begins, and when it is less than 0.001 kg/(m\({}^{2}\cdot\)h), the specimen is considered to be completely dry. In rising-rate drying stage, a moisture gradient and temperature gradient develops easily within the specimen. Internal water continuously migrates outward through interconnected microcracks and pores intrinsic in cement-based materials, resulting in the rise of the water-losing rate. The duration of this stage of cement paste, mortar, and concrete is \(\leq\)1 h, 2 h, and 4 h respectively, shown in Figure 3a-c, and the water loss accounts for about 5.9%\(\sim\)9.1%, 12.2%\(\sim\)15.1%, and 31.7%\(\sim\)-35.1% of the total water loss. In falling-rate drying stage, there are less temperature gradients inside the specimen due to continuous heating for long time. When the water diffusion inside the specimen is not sufficient to supply the water evaporation on the specimen's outside surface, drying surface would spread to the internal section of the specimen. The duration of this stage is \(\sim\)48 h, 40 h, and 30 h, and the water loss accounts for about 84.7%-86.3%, 74.8%-77.3%, and 50.7%-53.5% of the total water loss, respectively, for cement paste, mortar, and concrete. In the slow drying stage, drying takes place entirely in the internal of the specimen. Water removal becomes more and more difficult because of the very low moisture gradient and the hindrance of hydration products and aggregates. This stage lasts longest but the water loss of cement paste, mortar, and concrete only accounts for 6.5%\(\sim\)12.4% of the total water loss. Table 5 suggests that the time required for completely dry state, final water loss, and maximum water-losing rate all increase with increasing the mass fraction of paste.
_Algorithms_**2015**, \(8\), 951-964; doi:10.3390/a8040951  
_article_  
**A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 3,* ***  
1 School of Digital Creation and Animation, Shenzhen Polytechnic, Shenzhen 518055, China; E-Mail: [EMAIL_ADDRESS]  
2 School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China; E-Mail: [EMAIL_ADDRESS]  
3 School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-138-5200-6092.  
Academic Editor: [PERSON]  
_Received: 4 August 2015 / Accepted: 15 October 2015 / Published: 22 October 2015_  
**Abstract:** As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.  
**Keywords:** clustering; krill herd; elitism strategy; swarm intelligence
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Predictive Model for Multi-Level Selective Maintenance Using ANN and SVR
#### 4.2.2 The General Concept of SVR Model  
The Support Vector Regression (SVR) model is a Kernel-based nonlinear learning technique used for regression as well as classification. It consists of defining or creating hyperplanes to discriminate predictions from training data. SVR approximates predictions using a function \(f(\cdot)\) that has a fixed deviation threshold from the actual targets [40], which is formulated by Equation (13).  
\[f(x)=w\cdot\varnothing(x)+b \tag{13}\]  
where \(\varnothing(\cdot)\) is the transfer function, \(x\) is an input data point and the parameters \(w\) and \(b\) are estimated by solving the following optimization problem stated by [PERSON] and [PERSON] [41].  
\[min\Big{(}\frac{1}{2}\cdot\|w\|^{2}+C\sum_{i=1}^{n}\psi_{i}+\psi_{ i}^{*}\Big{)}\] s.t. \[y_{i}-\varnothing(x_{i})-b\ \leq\ \psi_{i}+\varepsilon\] \[\varnothing(x_{i})+b-y_{i}\ \leq\ \varepsilon+\psi_{i}^{*}\] \[\psi_{i},\ \psi_{i}^{*}\ \geq\ 0\ i=1,2,\ldots,n\]  
where \(\|w\|^{2}\) is the regulation, \(y_{i}\) is the \(i^{th}\) target, \(n\) is the size of the training data, \(\varepsilon\) is the deviation threshold of the function \(f(\cdot)\), \(C\) is the penalty parameter and \(\psi\) is the relaxed error that guarantees the existence of a solution under these constraints.  
Note that Equation (13) is useful for solving linear problems, but for nonlinear problems a transformation to a high-dimensional space is necessary. For this, a Kernel function is used which maps the original input data to a higher nonlinear feature space. The Lagrangian form equivalent is given by  
\[f(x)=\sum_{j=1}^{n}(\alpha_{i}-\alpha_{i}^{*})\cdot K\big{(}x_{i},x_{j}\big{)}+b \tag{14}\]where \(K\big{(}x_{i},x_{j}\big{)}\) is the Kernel function and \(\alpha_{i}\), \(\alpha_{i}^{*}\) are the Lagrangian multiplier. Four usual Kernel functions are generally used which are linear function, polynomial function, Radial Basis Function (RBF) and Hyperbolic Tangent Function (HTF). In this paper, we will use SVR with linear Kernel function and SVR with RBF Kernel in which their explicit functions are defined by Equation (15) and Equation (16), respectively.  
\[K\big{(}x_{i},x_{j}\big{)}=x_{i}^{T}\cdot x_{j}+c \tag{15}\]  
\[K\big{(}x_{i},x_{j}\big{)}=exp\frac{\|x_{i}-x_{j}\|}{\gamma} \tag{16}\]  
where \(c\) and \(\gamma\) are adjustable parameters.
## 2 Materials and Methods
### GNSS Dataset  
A total of 27 Global Positioning System (GPS) permanent stations belonging to the Continuously Operating Reference Stations (CORS) network, managed by the National Oceanic and Atmospheric Administration (NOAA)/National Geodetic Survey (NGS), were used in this study. Figure 1 displays the station's location (blue triangles for the tomographic grid). GPS observations in RINEX format are available online at [[https://geodesy.noaa.gov/CORS/](https://geodesy.noaa.gov/CORS/)]([https://geodesy.noaa.gov/CORS/](https://geodesy.noaa.gov/CORS/)) (accessed on 13 January 2024). We used the GAMIT/GLOBK (GG) package to process and analyze GPS data [59], available at [[https://geoweb.mit.edu/gg/](https://geoweb.mit.edu/gg/)]([https://geoweb.mit.edu/gg/](https://geoweb.mit.edu/gg/)) (accessed on 15 December 2023). The specific processing strategies are summarized in Table 1.  
The GG output includes total, dry, and wet delays, as well as equivalent PWV values in the zenith direction and north-south (\(G_{NS}\)) and east-west (\(G_{EW}\)) gradients at a 10-degree elevation. The next step involves mapping the PWV to Slant Integrated Water Vapor (SIWV), considering the azimuth and elevation of each satellite and the effects of azimuthal asymmetry in the atmospheric delay. The contribution of the azimuthal asymmetry delay (\(L_{az}\)) can be calculated by  
\[L_{az}(\varepsilon,\alpha)=G_{NS}\cdot m_{az}(\varepsilon)\cdot\cos(\alpha)+G_{ EW}\cdot m_{az}(\varepsilon)\cdot\sin(\alpha) \tag{8}\]  
where \(\varepsilon\) and \(\alpha\) are the satellite elevation and azimuth angle, respectively, and \(m_{az}\) is the gradient mapping function [60] that depends only on \(\varepsilon\), given by  
\[m_{az}(\varepsilon)=\frac{1}{\sin(\varepsilon)\cdot\tan(\varepsilon)+0.0007} \tag{9}\]  
Finally, the SIWV is computed as  
\[\text{SIWV}=PWV\cdot m_{w}+L_{az} \tag{10}\]  
where \(m_{w}\) is the wet mapping function (in our case, the VMF3).  
To avoid day-boundary discontinuities, we adopt a multi-day approach. It consists of starting the run 6 h before (18 h of the previous day) and stopping 36 h later. Lastly, 12 h of processing are discarded (6 at the beginning and 6 h at the end), resulting in a seamless time series without discontinuities.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 3 Channel Characterization Metrics
### Symbol Error Rate  
The SER is estimated according to the model for a CSK transmission shown in Equation (21). In this expression, \(s_{i}\) represents the values of luminous flux at each color LED for the i-th symbol, \(r_{i}\) is the value of the three currents at the photodetectors for the i-th symbol, \(H\) is the cross-talk interference matrix, and \(n_{i}\) is the noise.  
\[r_{i}=\mathbf{H}\cdot s_{i}+n_{i} \tag{21}\]  
Following Equation (21), the model under consideration represents a noise communication channel concerning the optical-to-electrical conversion in each channel of the color detector. The thermal and shot noises are included in the received symbols. According to [24], these noises can be modeled by a normal distribution, with the standard deviation of the total noise expressed by Equation (22):  
\[\sigma=\sqrt{\mathbf{G}\cdot(\sigma_{th}^{2}+\sigma_{sh}^{2})} \tag{22}\]  
where \(\mathbf{G}\) represents the gain of the transimpedance amplifier commonly used to obtain a voltage signal from the photocurrent, \(\sigma_{th}\) is the standard deviation of the thermal noise, and \(\sigma_{sh}\) is the standard deviation of the shot noise. The variance of the thermal noise depends on the temperature \(T\), the bandwidth \(B\), and the scaling factor \(\mathbf{G}\), defined as follows:  
\[\sigma_{th}^{2}=\frac{4k_{B}TB}{\mathbf{G}} \tag{23}\]  
where \(k_{B}\) is Boltzmann's constant. The variance of the shot noise \(\sigma_{sh}^{2}\) depends on the corresponding color channel current \(i_{n}\) and the dark current \(i_{d}\) as follows:  
\[\sigma_{sh}^{2}=2q_{e}(i_{n}+i_{d})B. \tag{24}\]
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Clustering
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Vehicular Applications** & **Issues** & **Techniques Used** \\ \hline \hline  & Specific Field: safety, traffic, infotainment & \\  & - & Anticipating and notifying users about accidents, forecasting driver conduct, and detecting movement patterns & RF [25], CNN [126], PCA [128], SVM [128], NB [129], DT \\ Safety and Traffic & - & Mitigate traffic bottlenecks following a collision RL [131], GRU [132], CNN [133], LSTM [133], ACO [134], ANN [135], KNN [136] \\  & - & Transmission of audio and visual information, ensuring high-quality video and supporting QoS for run-time applications. & CNN [141], DAE [142], PSO [143] \\ \hline \hline  & Specific Field: physical, network, application, and cloud layers & KNN [148], GRU [149] ANN [151], CNN [152] SVM [154], RL [155] \\  & - & Attack detection and prevention & GRU [150], PSO [156], LSTM [153] \\ \hline \hline  & Specific Field: continuous communication for optimal routing & \\ Routing & - & Factors affecting the velocity of vehicles & ACO [107], SVM [159], DT [48], NV [129], LSTM [160], PSO [162] \\  & - & Decision of signal-relaying stations & \\ \hline  & Specific Field: resource management, access methods & \\ Resource & - & Postponement for susceptibility & \\ Management & - & The resilience of vehicle apps is an essential element of RSUs and OBU & DRL [165; 166], SVM [167] \\  & - & Comprises various tools, i.e., 4G LTE, 5G, and 6G & \\ \hline  & Specific Field: fast mobility; driving patterns, traffic & \\ Mobility & - & Modelling of mobility Transmission in highly mobile scenarios GRU [132] & CNN [169], ANN [170], KNN [172], GRU [132] \\  & - & Interruption of connection & \\ \hline \hline  & \multicolumn{1}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{} & \\ \end{tabular}
\end{table} 
Table 9: Use of AI and ML approaches in vehicular network to solve issues.
# Probabilistic Retry and Threshold Multirate Loss Models for Impatient Calls
## 5 Conclusions  
We propose multirate loss models for a single link that services different service-classes whose calls follow a Poisson process. New calls have an initial and a reduced bandwidth requirement. In the probabilistic retry loss model, calls that cannot be accepted in the link with their initial bandwidth requirement are allowed to retry with their reduced bandwidth requirement at the cost of an increased service time. The latter may lead some impatient calls to decide not entering the system. To this end, it is assumed that blocked calls retry with a predetermined probability. In the probabilistic threshold loss model, new calls may reduce their bandwidth requirement (before blocking takes place) based on the occupied link bandwidth. The proposed multirate loss models do not have a PFS. However, we show that recursive formulas do exist for the computation of CBP. The accuracy of the proposed formulas is quite satisfactory compared to simulation. As a future work, we will extend these models in order to incorporate the interesting case of elastic and adaptive multirate traffic, where the bandwidth given to in-service calls is not constant during their service time [47; 48; 49; 50; 51; 52; 53; 54].
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
## 3 Results
### Theoretical Analysis of the Maximum Error of the 3D Medical Images DWT
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**BPC** & \(PSNR\), **dB** & \(db1\) & \(db2\) & \(db3\) & \(db4\) & \(db5\) & \(db6\) & \(db7\) & \(db8\) & \(db9\) & \(db10\) \\ \hline \multirow{2}{*}{8} & 40 & 11 & 11 & 12 & 12 & 12 & 13 & 13 & 13 & 13 & 14 \\  & \(\infty\) & 13 & 13 & 14 & 14 & 14 & 15 & 15 & 15 & 15 & 15 \\ \hline \multirow{2}{*}{12} & 60 & 14 & 14 & 15 & 16 & 16 & 16 & 17 & 17 & 17 \\  & \(\infty\) & 16 & 17 & 17 & 18 & 19 & 19 & 19 & 19 & 19 & 20 \\ \hline \multirow{2}{*}{16} & 80 & 17 & 18 & 18 & 19 & 19 & 19 & 20 & 20 & 20 \\  & \(\infty\) & 20 & 21 & 22 & 22 & 23 & 22 & 23 & 23 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Minimum values of \(r\), at which the result of 3D medical images DWT with Daubechies wavelets reaches high and maximum quality. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**BPC** & \(PSNR\), **dB** & \(sym1\) & \(sym2\) & \(sym3\) & \(sym4\) & \(sym5\) & \(sym6\) & \(sym8\) & \(sym9\) & \(sym10\) \\ \hline \multirow{2}{*}{8} & 40 & 11 & 11 & 12 & 12 & 13 & 13 & 13 & 13 \\  & \(\infty\) & 13 & 13 & 14 & 14 & 15 & 15 & 15 & 15 & 15 \\ \hline \multirow{2}{*}{12} & 60 & 14 & 14 & 15 & 15 & 16 & 16 & 17 & 17 & 17 \\  & \(\infty\) & 16 & 17 & 17 & 18 & 19 & 19 & 19 & 19 & 19 \\ \hline \multirow{2}{*}{16} & 80 & 17 & 18 & 18 & 19 & 19 & 20 & 20 & 20 \\  & \(\infty\) & 20 & 21 & 22 & 22 & 23 & 23 & 23 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Minimum values of \(r\), at which the result of 3D medical images DWT with symlets reaches high and maximum quality. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**BPC** & \(PSNR\), **dB** & \(coif1\) & \(coif2\) & \(coif3\) & \(coif4\) & \(coif5\) \\ \hline \multirow{2}{*}{8} & 40 & 12 & 13 & 13 & 14 & 14 \\  & \(\infty\) & 14 & 15 & 15 & 16 & 16 \\ \hline \multirow{2}{*}{12} & 60 & 15 & 16 & 17 & 17 & 17 \\  & \(\infty\) & 18 & 19 & 19 & 20 & 20 \\ \hline \multirow{2}{*}{16} & 80 & 18 & 19 & 20 & 20 & 21 \\  & \(\infty\) & 22 & 22 & 23 & 24 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Minimum values of \(r\), at which the result of 3D medical images DWT with coiflets reaches high and maximum quality. Minimum bit-width \(r\) of wavelet filters coefficients at which the result of 3D medical images DWT does not contain distortions \((PSNR=\infty)\) can be determined by a formula  
\[r=5+B+\left\lfloor\sqrt{\frac{k}{2}-1}\right\rfloor, \tag{9}\]  
where \(B\) is the image BPC. Formulas (6)-(9) are an approximate since the values \(r\) obtained at their use are sometimes redundant, that is, exceed values presented in Tables 10-12. However, they allow one to accurately calculate the non-redundant bit-width of the quantized wavelet filters coefficients in most cases. These formulas are applicable to both grayscale and color images.