esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Receptive Fields Convolutional Network for Action Recognition
## 4 Experiments
### Visualization  
Compared with the baseline, MSRFNet has shown a significant improvement in recognition accuracy. In this section, we analyze specific action classes of UCF101. Table 9 lists the action classes that improved the most after applying the method of multi-scale receptive fields. We then selected six action classes (\"Pommel Horse\", \"Nunchucks\", \"Long Jump\", \"Cricket Shot\", \"Walking with Dog\", and \"Shaving Beard\") from the table and drew the corresponding activation maps to visualize the model's focus, as shown in Figure 10. It can be observed from the figure that the method of multi-scale receptive fields not only helps to handle small moving objects, but also improves the recognition of large moving objects. For instance, before our method was applied, the attention of the model for the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Split**} & \multicolumn{2}{c}{**UCF101**} & \multicolumn{2}{c}{**HMDB51**} \\ \cline{2-5}  & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline Split-1 & 90.6\% & 97.9\% & 60.3\% & 87.5\% \\ Split-2 & 91.5\% & 98.1\% & 59.5\% & 86.5\% \\ Split-3 & 90.9\% & 98.0\% & 59.8\% & 87.7\% \\ Average & 91.0\% & 98.0\% & 59.9\% & 87.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance on three splits of UCF101 and HMDB51 after pretraining on Kinetics-400.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **UCF101** & **HMDB51** \\ \hline IDT [4] & 85.9\% & 57.2\% \\ scLSTM [29] & 84.0\% & 55.1\% \\ Two-Stream [5] & 88.0\% & 59.4\% \\ TSN-RGB [14] & 85.7\% & 51.0\% \\ C3D1 net) [7] & 82.3\% & - \\ C3D(3 nets) [7] & 85.2\% & - \\ I3D-RGB [8] & 95.1\% & 74.8\% \\
3D ResNet-50 [15] & 89.3\% & 61.0\% \\ P3D [16] & 88.6\% & - \\ MiCT-Net [33] & 88.9\% & 63.8\% \\ P3D ResNet-50 & 87.8\% & 54.1\% \\
**MSFNet** & **91.0\%** & **59.9\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with state-of-the-art on UCF101 and HMDB51 datasets after pretraining on Kinetics-400, averaged over three splits.  
\"Walking with Dog\" action was on the ground. However, after applying the method of multi-scale receptive fields, the model's attention was focused on the moving object. A similar situation occurred in the \"Long Jump\" action where the multi-scale receptive fields method made the model's attention close to the position of the moving object. In the \"Shaving Beard\" action, our method also made the model more focused on the hand region, resulting in better action representations. For other actions, such as \"Nunchucks\" and \"Cricket Shot\", the model focused on the more important parts of the moving object under the influence of the multi-scale method. These results demonstrate that the model can handle moving objects of different sizes.
## 3 Results
### Model Validation and GPP Simulation Stability  
The model performance validation against GPP-EC indicated that the estimated GPP outputs were significantly correlated with GPP-EC (the verifier) with higher R\({}^{2}\) values (\(p<0.05\)), lesser RMSE and RE values and tolerable BIAS values according to [PERSON] [56]. This assessment indicated a strong correlation (R\({}^{2}\) = 0.88) between GPP-EC and GPP-CASA, followed by VPM Canopy model with the correction of R\({}^{2}\) = 0.85 between GPP-EC and GPP-VPM Canopy. GPP-VPMChl and MOD17A2H values exhibited optimal and fair correlations with GPP-EC at R\({}^{2}\) = 0.72 and R\({}^{2}\) = 0.59, respectively. The CASA model indicated the lowest simulation error values among all the other employed LUE models, which indicates the indispensability of the CASA model to simulate GPP in the GLR-SSA and other tropical-related ecosystems. The model validation results and LUE models' stability assessment results are illustrated in Figure 5.  
Figure 4: The spatial and patterns distribution of mean inter-annual GPP for different LUE models (GPP-CASA, GPP-VPMChl and GPP-VPMCanopy) and the observed GPP (MOD17A2H) during 2001–2020.
## 2 Materials and Methods
### Experimental Setup  
The available data for testing our fault detection approach are provided by three grid-connected photovoltaic system of 99.84 KWp on central Greece. Each system consists of 8 inverters and 416 PV panels on the park, mounted in a fixed south facing position with 25 degrees' tilt angle. The technical characteristics of the equipment are presented in Appendix A. All systems have the same layout and equipment, something that is important for the comparative analysis that follows. The available measurements are irradiance, back panel temperature, ambient temperature, DC voltage and AC power from inverter inlet. The dataset covers a period of 4 years (1 January 2013-31 December 2016) of operation. The monitored performance parameters of the PV installation are recorded in a data logger at 15 min intervals. The main technical characteristics of the PV system are shown in Appendix A. An important detail in the system's design that should be mentioned here, is the lack of a cleaning system for the panels' surfaces.
## 3 Reliability Evaluation Method
### Monte Carlo Simulation  
Monte Carlo is also known as the statistical test method, stochastic simulation method, and random sampling method, which is one method for studying distribution characteristics by repeatedly generating time series to calculate the estimated parameters and statistics [19; 20].  
The frequency of events or arithmetic mean of random variable \(X\) is regarded as the solution of the problem by the Monte Carlo simulation method. Suppose that the samples of the random variable \(X\) are as follows: \(X_{1}\), \(X_{2}\),..., \(X_{N}\), the arithmetic mean of the random variable \(X\) is shown in Equation (25).  
\[\overline{X}=\frac{1}{N}{\sum_{i=1}^{N}X_{i}} \tag{25}\]  
Equation (26) can be gained by the strong law of large number, in which the \(\varepsilon\) is any small positive. From Equation (26), when \(N\) is big enough, \(\overline{X}\) converges to \(E(X)\), which is the convergence guarantee of the Monte Carlo simulation method [21; 22].  
\[\lim_{N\rightarrow\infty}P\big{\{}\big{|}\overline{X}-E(X)\big{|}<\varepsilon \big{\}}=1 \tag{26}\]  
Equation (27) can be gained by the central limit theorem, in which the \(\mu_{a}\) is any value.  
\[P\bigg{\{}\big{|}\overline{X}-E(X)\big{|}<\frac{\mu_{a}\sigma}{\sqrt{N}}\bigg{\}} \approx\frac{2}{\sqrt{2\pi}}{\int_{0}^{\mu_{a}}\exp\Big{(}t^{2}/2\Big{)}dt}=1-\alpha \tag{27}\]  
where  
\(\alpha\) is the significant level,  
\(\sigma\) is the standard deviation of the random variable \(X\), and  
\(\mu_{a}\) is the quantile of the standard normal distribution.  
It is necessary to construct one probability statistics model, define the random variable, sample based on the known distribution model, generate random data, and calculate when using Monte Carlo method. The accuracy of the Monte Carlo method is related to the number of samples--the larger the number of samples, the smaller the error [23].
## 3 Methodology
### PCA Dimensionality Reduction  
Finding a lower-dimensional representation of the original data while maintaining the greatest variance is the goal of PCA's popular linear dimensionality reduction approach [40]. The fundamental principle of PCA is finding the best transformation matrix \(W^{*}\) to project the original feature space \(X\) (spectral or fractal characteristics) onto a lower-dimensional subspace. The PCA transformation matrix \(W^{*}\) may be calculated mathematically by working through the following formula:  
\[W^{*}=argmax\;_{W^{T}W=I}\;Tr\left(W^{T}\;Cov\;(X)\;W\right) \tag{1}\]  
where:  
* _Tr_ stands for the trace operation, which is the sum of a matrix's diagonal members.
* _Cov_ (\(X\)) is a representation of the original data \(X\)'s covariance matrix.  
The objective function in Equation (1) seeks to find the transformation matrix \(W^{*}\) that maximizes the trace of the transformed covariance matrix \(W^{T}\;Cov\;(X)\;W\). Accordingly, PCA aims to identify the directions in the original feature space that maximize the variance of  
Figure 1: The suggested SF-ICNN classification method’s flowchart. The proposed method consists of several key steps: spectral–fractal feature generation, CNN classification, and iterative integration of PMs into the feature data cube to improve the classification accuracy.  
the data. The main components, or the orthogonal directions in the original feature space that account for the most variation in the data, are found via PCA by solving this equation. Applications for the transformed data in the lower-dimensional subspace produced by PCA include feature extraction, dimensionality reduction, and data visualization.
## 2 Materials and Methods
### Soil Sampling and Laboratory Measurements
#### 2.2.2 Laboratory Measurements  
All samples were air-dried, grounded, and filtered through 2 mm sieves [34] to remove water, large debris, stones, and stubble. Each sample of dataset 1 was split in half: one for physicochemical analysis and the other for spectral analysis. But the sample of dataset 2 was only for pH and EC measurement. In this study, pH (1:5 mixture of soil and water) [35] and EC (1:5 mixture of soil and water) [36] were measured by using a pH meter (Leici PHS)-5, Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China) and conductivity meter (Leici DDS-307, Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China). CO\({}_{3}\)\({}^{2-}\) and HCO\({}_{3}\)\({}^{-}\) were evaluated by using hydrochloric acid. Similarly, these two ions were measured in an extracted solution at a 1:5 mixture of soil and water. Moreover, total organic carbon (TOC) of soil was measured with a TOC analyzer (Analytik Jena multi N/C 2100S, Analytik Jena AG, Jena, Germany).  
Spectral reflectance of soil samples was acquired by using a field portable spectrometer (SVC HR-1024i, Spectra Vista Corporation, Poughkeepsie, NY, USA). The spectrometer has a wavelength ranging from 350 nm to 1000 nm and the highest spectral resolution of 3.5 nm, matching the bandwidth of the hyperspectral image used in this study. In order to reduce spectral noises from soil water, roughness and debris, the spectra were measured using the air-dried, grounded, and sieved soil samples in a dish of 7.5 cm diameter and 2 cm depth. The measurements were collected with natural sunlight at 10:00 a.m. to 14:00 p.m. in clear days. Spectral signatures were collected at a height of 10 cm above soil surface at a nadir position, giving a 25\({}^{\circ}\) field of view (FOV). Before each measurement, the reflectance of white reference panel was recorded. Five reflectance spectra of each sample were averaged, smoothed, and resampled into the bandwidth of the hyperspectral image used in the present study.
## Nomenclature  
\begin{tabular}{l l l l} ANN & Artificial Neural Network & RH & Relative Humidity \\ LSTM & Long Short-Term Memory & KGE & Kling-Gupta Efficiency \\ TR & Typical Reference & TMY & Typical Meteorological Year \\ FS & Finkelstein Schafer & FT & Fourier Transfer \\ MSE & Mean Square Error & RMSE & Root Mean Square Error \\ MAE & Mean Absolute Error & StD & standard deviation of the errors \\ \(\left(\rho\mathcal{C}_{p}\right)_{eff}\) & effective volumetric heat capacity at constant pressure & \(\rho\) & moisture air density [kg/ m\({}^{3}\)] \\ \(C_{p}\) & heat capacity [J/(kg\(\cdot\)K)] & \(u\) & moisture air velocity [m/s] \\ \(T\) & temperature [K] & \(k_{eff}\) & effective thermal conductivity [W/(m\(\cdot\)K)] \\ \(L_{V}\) & latent heat of evaporation [J/kg] & \(\delta_{p}\) & vapor permeability [s] \\ \(\phi\) & relative humidity & \(\omega_{v}\) & vapor mass fraction \\ \(p_{sat}\) & vapor saturation pressure [Pa] & \(Q\) & heat source [W/ m\({}^{3}\)\(\cdot\)s] \\ \(\xi\) & moisture storage capacity [kg/ m\({}^{3}\)] & \(D_{W}\) & moisture diffusivity [m\({}^{2}\)/s] \\ \(G\) & moisture source [kg/m\({}^{3}\)\(\cdot\)s] & \(p\) & fluid pressure [N/m\({}^{2}\)]. \\ \(K\) & viscous force [N/m\({}^{2}\)] & \(F\) & external forces applied to the fluid [N/m\({}^{2}\)]. \\ \(X(k)\) & FT pairs in time domain & \(x(j)\) & FT pairs in frequency domain \\ \(f_{t}\) & forget factor & \(\sigma\) & sigmoid activation functions \\ \(tanh\) & hyperbolic tangent activation functions & \(h_{t}\) & recurrent information \\ \(i_{t}\) & input factor & \(c_{t}\) & cell state \\ \(o_{t}\) & output factor & \(b\) & bias \\ \(W\) & weights & \(x_{t},y_{t}\) & input and output. \\ \end{tabular}
## 3 Results
### Doping of HA Nanoparticles
#### 3.3.8 Copper-Doped Nano-Hydroxapatite (Cu-Nano-HA)  
Copper (Cu), an essential trace element in the human body, plays a critical role in various biological processes, including hematopoiesis, neurological function, and skeletal development. Its role in the skeletal system is particularly significant, as copper acts as a cofactor for lysyl oxidase, an enzyme crucial for the cross-linking of collagen fibers to form stable bone matrices [59]. Copper deficiency can result in severe conditions such as anemia, pancytopenia, neurodegeneration, and impaired bone formation, highlighting its importance for overall health and bone regeneration.  
The incorporation of copper ions (Cu\({}^{2+}\)) into hydroxyapatite (HA) creates a multifunctional material with enhanced biological and antibacterial properties [66]. Cu-HA has been shown to promote mesenchymal stem cell differentiation into osteoblasts, stimulate endothelial cell proliferation during wound healing, and enhance the delivery of vascular endothelial growth factor (VEGF), which supports angiogenesis [62; 67]. These pro-angiogenic effects improve the viability of bone-forming cells within implants, further aiding in bone regeneration.  
In addition to its osteogenic benefits, Cu-HA exhibits strong antibacterial properties, effectively reducing the risk of implant-related infections. Studies by [PERSON] et al. demonstrated that Cu-HA not only promotes osseointegration between bone and implant surfaces but also inhibits bacterial biofilm formation, addressing two critical challenges in orthopedic and dental implantology [62]. However, the release of excessive copper ions poses a risk of cytotoxicity, emphasizing the need for controlled doping to balance biological efficacy and safety.  
Copper doping in HA also influences the material's physicochemical properties, making it suitable for metallic implants designed for repairing severely damaged or fractured bone tissue. The effectiveness of Cu-HA in bone healing can be monitored using imaging techniques, such as digital panoramic radiography, by calculating parameters like fractal dimension (FD), lacunarity, and Feret diameter (FeD), which assess changes in bone density and structure over time [68].  
By combining osteogenic, angiogenic, and antibacterial properties, Cu-nano-HA offers a versatile and effective solution for bone tissue engineering. Its potential to enhance both biological functionality and implant integration underscores its value in advanced regenerative medicine applications.
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 1 Introduction
Other researchers have tried to agglutinate different acoustic features with the optimism of boosting accuracy and precision rates of speech emotion recognition [25; 26]. This technique has shown some improvement, nevertheless it has yielded low accuracy rates for the fear emotion in comparison with other emotions [25; 26]. [PERSON] et al. [26] fused some acoustic features such as MFCC, energy, zero crossing rate (ZCR) and fundamental frequency that gave an accuracy of 77.00% for the fear emotion. Similarly, [PERSON] et al. [27] used a deep neural network (DNN) to extract bottleneck features that achieved an accuracy of 62.50% for recognizing the fear emotion. The overarching objective of this study was to construct a set of hybrid acoustic features (HAFs) to improve the recognition of the fear emotion and other emotions from speech signal with high precision. This study has contributed to the understanding of the topical theme of speech emotion recognition in the following unique ways. * The application of a specialized software to extract highly discriminating speech emotion feature representations from multiple sources such as prosodic and spectral to achieve an improved precision in emotion recognition. * The agglutination of the extracted features using the specialized software to form a set of hybrid acoustic features that can recognize the fear emotion and other emotions from speech signal better than the state-of-the-art unified features. * The comparison of the proposed set of hybrid acoustic features with other prominent features of the literature using popular machine learning algorithms to demonstrate through experiments, the effectiveness of our proposal over the others. The content of this paper is succinctly organized as follows. Section 1 provides the introductory message, including the objective and contributions of the study. Section 2 discusses the related studies in chronological order. Section 3 designates the experimental databases and the study methods. The details of the results and concluding statements are given in Sections 4 and 5 respectively.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 5 Results and Discussion
### Computational Complexity Analysis  
In this study, it was also crucial to perform computational complexity analysis to assess the efficiency of WECALM relative to other algorithms in terms of the time required to detect the total number of protein complexes in standard complexes. To compare the computational complexity of each algorithm for simplicity we ran each program with its default settings. We then compared the time taken to detect the total number of detected protein complexes and matrices including the F-measure, CR, MMR, Sep, and ACC. In this analysis, we used reference Human and Yeast reference complexes (see Table 2).  
Figure 6: The effect \(\eta\) on the performance of WECALM based on Recall, Precision, F-measure, CR, and MMR matrices. \(\eta\) is a predefined local modularity threshold. (**A**): performance on BioGRID based on NewMIPS. (**B**): performance on DIP based on NewMIPS. (**C**): performance on BioGRID based on CYC2008. (**D**): performance on DIP based on CYC2008. The precision and F-measure is maximum when \(\eta=0.8\) and \(\eta=0.4\) respectively on both the NewMIPS and CYC2008.  
To compare the computational complexity of each algorithm we set the parameters of the other eight algorithms based on the authors' recommendations while for our proposed WECALM we set \(\omega\), \(\pi\), and \(\eta\) at the default values obtained from the experimental results given in Section 5.2. We discovered in Table 3 that WECALM and EWCA had low computational complexity, indicating good efficiency in detecting the total amount of protein complexes in Human standard complexes. Furthermore, when compared to other algorithms, WECALM had the highest MMR, Sep, and ACC scores, demonstrating a balance between accuracy and efficiency. On standard yeast complexes, a similar performance trend was seen, with WECALM and EWCA having the lowest time computational complexity. However, we found that WECALM detected more protein complexes with a better performance efficiency than other algorithms, making it the overall best-performing algorithm for the detection of protein complexes on both Human and Yeast standard complexes.
## 3 Results and Discussion
### Adsorption Isotherms  
At higher initial dye concentrations, the mass transfer resistance between the solid surface and the solution can be more easily overcome. With an increasing concentration of dye, empty active centers at the adsorbent surface are rapidly filled. Thus, as the initial concentration increased, the adsorption capacity increased [60].  
For the optimization of an adsorption process, it is important to establish the most appropriate correlation for the equilibrium curve. Two widely known isotherm models, namely the Langmuir and Freundlich models [56; 61], were used in this study to describe the adsorption process of MB and MG onto 4 AZW.  
The Langmuir model assumes that only one solute species will occupy one active site on the homogeneous surface of the adsorbent, and the corresponding nonlinear equation is:  
\[\mathrm{q_{e}}=\frac{\mathrm{k_{L}}.\mathrm{C_{e}}.\mathrm{q_{max}}}{1+\mathrm{ k_{L}}.\mathrm{C_{e}}} \tag{12}\]  
where \(\mathrm{C_{e}}\) is the equilibrium concentration of the adsorbate (mg/L), \(\mathrm{C_{0}}\)is the initial concentration of the adsorbate (mg/L), and qmax (mg/g) and \(\mathrm{K_{L}}\)(L/mg) are the maximum adsorption capacity and a constant related to the energy of adsorption, respectively. One can also introduce a separation factor:  
\[\mathrm{R_{L}}=\frac{1}{1+\mathrm{k_{L}}.\mathrm{C_{0}}} \tag{13}\]  
This is a useful dimensionless constant: The isotherm is either unfavorable (\(\mathrm{R_{L}}\)\(>\) 1), linear (\(\mathrm{R_{L}}\) = 1), favorable (0 \(<\) R\({}_{L}\) < 1), or irreversible (\(\mathrm{R_{L}}\) = 0) [56]. For the adsorption of MB and MG onto 4 AZW, all the \(\mathrm{R_{L}}\) values are in the range of 0.008 to 0.043 and 0.015 to 0.064, respectively. Then, the adsorption process is favorable.  
The Freundlich isotherm is an empirical model assuming a process on heterogeneous adsorption surfaces, and is not restricted to monolayer formation. The corresponding nonlinear equation is:  
\[\mathrm{q_{e}}=\mathrm{k_{F}}.\mathrm{C_{e}^{\frac{1}{n}}} \tag{14}\]  
where \(\mathrm{k_{F}}\) (mg/g)/(mg/L)\({}^{1/n}\) and n (dimensionless) are constants representing the adsorption capacity and the adsorption intensity, respectively.  
The Langmuir and Freundlich models were compared for our experimental results (Figure 8). On the basis of \(\mathrm{R_{2}}\) determination coefficients values, the Langmuir model gave high values for both dyes (0.993 and 0.994; see Table 3), while those obtained by the Freundlich model were lower (0.949 and 0.985, detailed data not shown). The Langmuir maximum adsorption capacity (\(\mathrm{q_{max}}\)) for MB and MG onto 4 AZW at 25 \({}^{\circ}\)C is determined as 9.95 mg/g (0.031 mmol/g) and 45.64 mg/g (0.125 mmol/g), respectively (Table 3).
# Non-Classic Atmospheric Optical Turbulence: Review
## 2 Measurements and Synthesis
### Deep Turbulence Effects  
While performing wave-optics simulation in the strong focusing regime of turbulence, [PERSON] and [PERSON] [47] discovered a significant mismatch between numerically estimated and theoretically predicted values of the optical wave scintillation index (normalized variance of fluctuating intensity). The authors showed in their analysis that the reason for this disagreement is due to the irregular appearance of giant irradiance spikes with the amplitudes several times exceeding the diffraction-limited intensity. These spikes would emerge spontaneously due to random formation of focusing lenses extended along the propagation path. Such lenses \"trap\" irradiance speckles of a suitable size; hence, the spikes can propagate over distances of several kilometers. The statistical analysis of probability of giant spikes appearance provides the distance ranges where these spikes are most likely to be observed. It was also shown that, if compared to the long-exposure (mean) intensity, the spikes whose amplitudes exceed the mean value by a factor of several tens are observed, with the probability being nearly independent from the propagation distance. The probability of spikes' occurrence obtained in the numerical simulations were compared to the theoretical estimations based on the log-normal probability distribution. According to the wave-optics simulations, the giant spikes exceeding the diffraction-limited intensity value by a factor of five or more emerge 10- to 20-fold more frequently than the theory predicts.