esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Improvement by Angular Combining
In Figure 10, we plotted the combining improvement as a function of the distance between Tx and Rx. We can clearly see the higher coherent combining values but we cannot appreciate a clear trend of the combining improvement with respect to the distance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environment** & **Open** & **Wooded** & **Suburban** & **Urban** \\ \hline \multirow{2}{*}{GCC (dB)} & \(\mu\) & 0 & 1.5 & 1.2 & 2.2 \\  & \(\sigma\) & 0 & 2.8 & 2.7 & 2.5 \\ \multirow{2}{*}{G\({}_{\text{NCC}}\) (dB)} & \(\mu\) & 0 & 0.4 & 0.6 & 0.6 \\  & \(\sigma\) & 0 & 0.7 & 1.3 & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Angular combining improvement (in dB). GCC: coherent combining; G\({}_{\text{NCC}}\): non-coherent combining. Previous studies [15] of radiowave propagation at millimeter wave frequencies in city streets showed Rice fading with a K factor that decreased from a high value to 1 as the distance between the Rx and the Tx increased. Consequently, we expected that the combining improvement would increase with the distance, from near to 1 (0 dB) to the maximum value given by (9) and (10). Probably, a larger number of measurements and distances would be needed to observe such a trend.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 4 Results
### Performance Evaluation of the Proposed Feature Selection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Naïve** \\ **Bayes** \\ \end{tabular} & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & \
\begin{tabular}{c} **Multi-layer** \\ **Perceptron** \\ \end{tabular} &
\begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} \\ \hline Breast Cancer & & & & & \\ Wisconsin (Diagnostic) & 78.3 & 85.14 & 97.32 & 74.55 & 88.57 \\ Data Set & & & & & \\ US Congressional & 80.65 & 83.231 & 99.66 & 90.86 & 84.3 \\ Voting Records dataset & & & & & \\ Pima Indians & 64.84 & 72.72 & 80.5 & 66.06 & 75.36 \\ Diabetes dataset & & & & & \\ Polycystic ovary & & & & & \\ syndrome & 66.38 & 77.8 & 88.97 & 85.14 & 90.65 \\ (PCOS) dataset & & & & & \\ Parkinson Disease & 76.2 & 86.71 & 91.83 & 82.901 & 87.025 \\ Detection dataset & & & & & \\ Wine dataset & 80.97 & 79.7 & 93.34 & 87.26 & 82.36 \\ Zoo dataset & 67.55 & 72.53 & 89.36 & 84.321 & 79.015 \\ Connectionist Bench & & & & & \\ (Sonar, Mines vs. & 61.23 & 76.66 & 86.47 & 69.8 & 80.704 \\ Rocks) Data Set & & & & & \\ Lung Cancer Data Set & 70.4 & 80.5 & 88.63 & 76.04 & 83.9 \\ Climate Model & & & & & \\ Simulation Crashes & 72.84 & 80.97 & 92.41 & 86.32 & 89.01 \\ Data Set & & & & & \\ Scene dataset & 68.19 & 74.85 & 91.82 & 93.74 & 85.44 \\ Page Blocks & & & & & \\ Classification Data Set & & & & & \\ kc1 & 69.73 & 72.3 & 82.125 & 85.67 & 77.46 \\ pc1 & 74.96 & 76.72 & 93.1 & 87.58 & 90.11 \\ cm1 & 68.432 & 70.658 & 89.67 & 85.4 & 92.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracies obtained for different classification algorithms. Figure 5: Comparison of performance of different feature selection algorithms with proposed model on the basis of number of features selected. Table 4 shows the results according to the average accuracy of the Random Forest classifier and optimal subset of features selected. The suggested model's performance on classification tasks was evaluated using the Random Forest method, since it achieved the best classification results for the datasets under consideration. The results showed that the proposed model gave us good output results in identifying the optimal subset of features selected and showed improvements in classification accuracies for all datasets taken into consideration. Through the accuracy of classification, the suggested model obtained outstanding results while only selecting a relatively small percentage of the features in the dataset, thereby considerably reducing the amount of data to be processed.
## 3 Methodology
### Loss Function
#### 3.3.3 Physical Knowledge Representation Loss  
The representation loss, \(L_{rep}\), is derived from physical knowledge and aims to enhance the accuracy and stability of the upgoing and downgoing wavefield separation by introducing physical rules. The representation loss measures the difference between the model-predicted upward wavefield, \(\widetilde{U}\), and downward wavefield, \(\widetilde{D}\). The knowledge representation wavefields, \(U_{rp}\) and \(D_{rp}\), are extracted based on the energy flux density vectors, specifically defined as  
\[L_{rep}=\left|\left|U_{rp}-\widetilde{U}\right|\right|_{h}+\left|D_{rp}- \widetilde{D}\right|\right|_{h}, \tag{16}\]where \(||\cdot||_{h}\) represents the error measurement using the Huber function. By introducing representation loss, the model not only focuses on the overall wavefield reconstruction but also emphasizes the consistency between the predicted wavefields and the knowledge representation of the upward and downward wavefields extracted through physical methods. This loss ensures that, during the separation of the upgoing and downgoing waves, the model can accurately capture the physical characteristics and propagation directions of the wavefield, thereby improving the separation accuracy. Furthermore, in complex geological structures, the representation loss provides additional constraints that prevent unreasonable wavefield reconstructions during separation, thereby enhancing the stability of the separation process.
## 2 Related Works
### Infrared and Visible Image Fusion
#### 2.1.2 Deep Learning-Based Fusion Methods  
In recent years, deep learning has been increasingly applied to image fusion, leading to various advancements. [PERSON] and [PERSON] employed a pre-trained VGG-19 network to extract high-frequency details [33], while [PERSON] et al. proposed an unsupervised framework using DenseNet's loss function for feature extraction and reconstruction [34]. [PERSON] et al. introduced FusionGAN [35] to preserve infrared thermal radiation and visible image gradients, though the results often lacked sharpness. [PERSON] et al. [36] utilized an autoencoder (AE) network, but its simple structure failed to capture deep features effectively, leading to information loss. Despite promising visual outcomes, deep learning-based fusion faces challenges in preserving weak textures and edges [29], suffers from poor interpretability, and heavily depends on training data, limiting generalization [3]. While these methods have improved fusion quality, they have yet to demonstrate clear superiority over traditional methods.
## 2 Materials and Methods  
Antiplane shear waves are considered, in the time-harmonic regime with radian frequency \(\omega\). At normal incidence the spatial distribution \(U(x)\) of the displacement component \(u(x,t)=U(x)e^{-i\omega t}\), along the \(z\)-axis perpendicular to the propagation direction \(x\), satisfies the Helmholtz-type equation of motion:  
\[\frac{d^{2}U(x)}{dx^{2}}+\frac{\omega^{2}}{c^{2}}U(x)=0,\qquad c=\sqrt{\frac{ \mu}{\rho}}, \tag{1}\]  
where \(\mu\) is the shear modulus of the linear elastic isotropic medium and \(\rho\) its density (note that the Helmholtz equation also describes low frequency waves in a rod. For longitudinal waves, \(U\) is the longitudinal displacement and \(c=\sqrt{E/\rho}\), with \(E\) being the Young's modulus; for torsional waves, \(U\) is the angle of twist and \(c=\sqrt{\mu/\rho}\)). The wave form can be expressed in terms of complex amplitudes \(A\) and \(B\) as:  
\[U(x)=Ae^{ikx}+Be^{-ikx},\quad k=\omega/c, \tag{2}\]  
where \(k\) is the wavenumber.  
In a homogeneous layer of thickness \(d\), located between the initial coordinate \(x_{0}\) and the final coordinate \(x_{1}=x_{0}+d\), displacements satisfying Equation (1) and corresponding tractions \(\tau(x)=\mu\,\partial U/\partial x\) at points \(x_{0}\) and \(x_{1}\) are related by means of the unimodular transfer matrix \(\mathbf{M}=[m_{ij}]\). Collecting displacement and traction in the generalised displacement vector \(\mathbf{v}(x)=[U(x)\,\,\,\tau(x)]^{T}\), within a unit cell:  
\[\mathbf{v}(x_{1})=\mathbf{M}\,\mathbf{v}(x_{0}), \tag{3}\]  
or, explicitly:  
\[\left(\begin{array}{c}U(x_{1})\\ \\ \tau(x_{1})\end{array}\right)=\left[\begin{array}{cc}m_{11}&m_{12}\\ \\ m_{21}&m_{22}\end{array}\right]\left(\begin{array}{c}U(x_{0})\\ \\ \tau(x_{0})\end{array}\right). \tag{4}\]  
In the following, we compute the explicit form of the transfer matrix for different interface configurations. First, we show the transfer matrix for a single layer or unit of continuous or discrete nature; then, we couple the transfer matrices of the single layers to obtain the global matrix for systems composed of units arranged in series or in parallel.
## 3 Discussion  
The techniques of confocal microscopy and image scanning microscopy (ISM) have been reviewed. Confocal microscopy is an established technique, which is widely used both in biomedical imaging and in industrial and material examinations. ISM is a comparatively new technique. When proposed, suitable detectors for its implementation were unavailable, so it took two decades before it was experimentally demonstrated. Basically, it can be performed simply by replacing the pinhole and detector of a confocal microscope with a detector array. It seems likely that, in many cases, it will replace the existing form of the confocal microscope. The most straightforward way to process the image is using pixel reassignment, where the signal from a detector element is assigned to the most likely origin in the sample. There are two main ways this can be implemented: by optically reassigning the data and recording a reconstructed image directly or by storing all the image data and digitally processing. Both approaches have their merits: the first is faster, but the second allows more flexibility and more advanced digital processing. For the second approach, as a detector array with only a small number of elements is required, multiplexing by multi-point illumination is also available. We expect further improvements in image reconstruction algorithms in the future based on developments in deconvolution on the one hand and using the statistical properties of the imaging data on the other.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **Resolution Improvement Factor** \\ \hline Conventional & 1 \\
2-beam (a) SIM & 1.40 \\
2-beam (b) SIM & 2.26 \\
3-beam SIM & 1.71 \\ Confocal, ideal & 1.39 \\ Confocal, ideal, with Bessel beam & 1.72 \\ ISM, 2 AU array & 1.53 \\ ISM 0.836 AU array, with Bessel beam & 1.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resolution improvement for different systems.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: They author acknowledges the support and useful discussions with the staff of Istituto Italiano di Tecnologia, Genova, Italy, especially [PERSON], [PERSON], [PERSON], and [PERSON].  
**Conflicts of Interest**: The author declares no conflict of interest.
## 3 Characterization of the Compounds Presented in Balsamic Vinegas
### Furanic Compounds  
The presence of furanic compounds in balsamic vinegars is associated with the heating of glucose solutions. Furans are generally formed by Maillard reactions between reducing sugars and amino acids, but they can also come from oak barrels, or from the addition of caramel as a frequently used food colorant. The importance of these analytes lies in identifying the organoleptic properties of the products and the possible commercial fraud [10; 20; 39]. The major furans in balsamic vinegars include hydroxymethylfurral (HMF), furfural, and 5-acetoxymethylfurral, with some studies reporting dominance of HMF (Figure 5) and others 5-acetoxymethylfurral. Other frequently identified furans are 5-methylfurral and 2-acetylfuran [10; 38; 39].  
[PERSON] et al. [54] focused their work on the analysis of HMF in various types of vinegar. This compound is a good indicator of age, heat treatment, deterioration, and food adulteration. The HPLC method coupled with the diode array detector (DAD) was used for analysis. The authors concluded that balsamic vinegars, and especially traditional balsamic vinegars, contain the highest concentration of HMF compared to other types of vinegar. The same conclusion was reached by [PERSON] et al. [20].  
[PERSON] and [PERSON] [53] performed the analysis of selected furfurrals (HMF, 5-methylfurral, furfural) and patulin in food products, including balsamic vinegar, using the HPLC-DAD method. Furfural and patulin are good markers of food quality. Regarding patulin, this is the first study to specifically target this mycotoxin in balsamic vinegar. The balsamic vinegar contained all three selected furfurals but did not show signs of patulin. The authors recommend the method for controlling food quality.  
[PERSON] et al. [66] in their study reported changes in some furanic compounds (HMF, furoic acid, furfural) during the concentration of grape must be used to produce balsamic vinegar. They used the HPLC-DAD method to analyze these compounds. The results of the study show that the furanic content increases during the thermal concentration of must, with the highest increase for HMF.  
The determination of furanic compounds was studied by [PERSON] et al. [16]. The aim of this work was to create a simple and fast method for the determination of selected furanic compounds in TBVM. They used the method of ion exchange chromatography (IEC) with DAD detection. The presence of HMF, furfural, and 2-fluoro acid was detected  
Figure 5: Analysis of HMF in TBV by HPLC-DAD method. Conditions: Luna Omega 3 \(\upmu\)m Polar C18 100 column, mobile phase acetonitrile/water gradient ([PERSON], 2022, unpublished data).  
in all samples. In contrast, no sample contained 4-hydroxy-2,5-dimethyl-3-(2H)-furanone (DHMF), 2-acetylfuran, or furfuryl alcohol.  
Several other works have dealt with the LC method and furanic compounds [13; 51; 52; 55; 67; 91; 92]. However, their list is beyond the scope of this paper.
## 2 Materials and Methods
### Fracture Toughness  
To measure fracture toughness (FT), the notched specimens (\(n\) = 6 per group) were subjected to a three-point flexural strength test in a universal testing machine (Zwick Roell Z005, Ulm, Germany) with a traverse speed of 1 mm/min. A metal mold was used to prepare rectangular samples (2 mm \(\times\) 4 mm \(\times\) 20 mm). A sharp central notch was created by inserting a blade into a carefully manufactured slot positioned at the midpoint of a mold.  
The FT was calculated from Formula (1):  
\[\text{FT}=\left[\frac{\text{P}\cdot\text{S}}{\text{B}\cdot\text{W}^{3/2}} \right]\cdot\text{f}\left(\frac{\text{a}}{\text{W}}\right) \tag{1}\]  
Here, P is the peak load at fracture, S is the span (14 mm), B is the specimen thickness, W is the specimen width, and \(\text{f}\left(\frac{\text{a}}{\text{W}}\right)\) is the calibration function for a given geometry \(\text{f}\left(\frac{\text{a}}{\text{W}}\right)=2.9\left[\frac{\text{a}}{ \text{W}}\right]^{\frac{1}{2}}-4.6\left[\frac{\text{a}}{\text{W}}\right]^{ \frac{3}{2}}+21.8\left[\frac{\text{a}}{\text{W}}\right]^{\frac{2}{2}}-37.6 \left[\frac{\text{a}}{\text{W}}\right]^{\frac{7}{2}}+38.7\left[\frac{\text{a }}{\text{W}}\right]^{\frac{9}{2}}\)[13].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Manufacturer** & **Type** & **Matrix** & **Filler** & **Filler Content** \\ \hline Resin F & & Unfilled resin & None & 0 \\ Flow-Art & Arkona & Flowable composite & bis-GMA, TEGDMA, & Al-Ba-B-Si glass, & 62 \\ Arkon & (Niemce, Poland) & Universal composite & UDMA, bis-EMA & Ba-Al-B-F-Si glass, & 78 \\ \hline  & bis-GMA—Bisphenol A glycerolic dimethacrylate; TEGDMA—trethylene glycol & dimethacrylate; UDMA—urethane dimethacrylate; bis-EMA—Bisphenol A ethoxylate dimethacrylate. & & \\ \end{tabular}
\end{table}
Table 1: Detailed information about tested materials.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aging Protocol** & **Description** \\ \hline Control & 24 h, 37 \({}^{\circ}\)C, distilled water \\
7 d NaOH & 7 days, 60 \({}^{\circ}\)C, 0.1 M NaOH \\ thermocycling + NaOH & 7500 cycles, 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, water and 7 days, 60 \({}^{\circ}\)C, 0.1 M NaOH \\
5 d water + NaOH & 5 days, 55 \({}^{\circ}\)C, water and 7 days, 60 \({}^{\circ}\)C, 0.1 M NaOH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the selected aging protocols.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Time-Correlated Single Photon Counting (TCSPC) Technique
#### 3.2.2 Single- and Dual-Channel TD-NIRS Systems Based on the TCSPC Technique  
In 1999, [PERSON] et al. developed a single-channel TD-NIRS system based on the TCSPC technique (TRS-10, Hamamatsu Photonics K.K., Japan), and in 2009 it was extended to a dual-channel system (TRS-20, Hamamatsu Photonics K.K., Japan) [43], both for research use. Figure 8 shows a photograph and block diagram of the TRS-20 consisting of three pulsed laser diodes with wavelengths of 760 nm, 800 nm, and 830 nm, a TCSPC unit with a CFD/TAC, A/D converter, histogram memory, etc.  
From the TR reflectances measured by the TRS-10 and TRS-20, \(\mu_{a}\) and \(\mu_{s}\)' of the media are estimated under the assumption that the media are homogeneous and semi-infinite. The TR reflectance  
Figure 8: Photograph and block diagram of the TRS-20 system. The picosecond light pulser emit lights with wavelengths of 760, 800, and 830 nm; the photo detector unit consists of a variable optical attenuator, the photomultiplier tube (PMT), and a fast amplifier; the TCSPC unit consists of a CFD/TAC, an A/D converter, a histogram memory, etc.  
from a homogeneous semi-infinite medium is given by the analytical solution of the TD-DE as Equation (15). For the zero-boundary condition, Equation (15) is slightly simplified as the following,  
\[R(\rho,t)==\frac{z_{0}}{(4\pi Dc)^{3/2}t^{5/2}}\exp(-\mu_{a}ct)\exp(-\frac{\rho^{ 2}+z_{0}^{2}}{4 Dct}), \tag{28}\]  
here, the wavelength dependences are not explicitly written. Equation (28), convoluted by the instrumental response function (IRF), is fitted to the measured TD reflectance to estimate \(\mu_{a}\) and \(\mu_{s}{{}^{\prime}}\) of the medium using a non-linear least-squared technique based on the Levenberg-Marquardt method. Applying Equation (25) to the case of the two components of oxy-Hb and deoxy-Hb with background absorption gives the following equation with the wavelength dependence written explicitly,  
\[\mu_{a}(\lambda)=\varepsilon_{oxy-Hb}(\lambda)C_{oxy-Hb}+\varepsilon_{deoxy -Hb}(\lambda)C_{deoxy-Hb}+\mu_{a,BC}(\lambda) \tag{29}\]  
where \(\mu_{a,BC}\) is the absorption coefficient of the background medium. Solving the simultaneous equations of Equation (29) for the three wavelengths used in the instrument provides \(C_{oxy-Hb}\) and \(C_{deoxy-Hb}\), and \(C_{total-Hb}\), and \(S_{\text{O2}}=C_{oxy-Hb}/C_{total-Hb}\) is calculated.  
For verification of the quantities obtained by the TRS-10, experiments were performed using a blood phantom consisting of an Intralipid suspension and blood with the controlled S\({}_{\text{O2}}\) from 0% to 100% [44]. The values of \(C_{oxy-Hb}\), \(C_{deoxy-Hb}\), and \(C_{total-Hb}\) measured by the TRS-10 agreed well with the values calculated from the measurements of pH, \(P_{\text{O2}}\), and \(P_{\text{CO2}}\) in the phantom by conventional methods.
## 3 Illustrative Simulation and Analysis
Figure 7: Estimation output of four models compared with real values (January–April–July–October). Figure 10 and Table 1 present how much each climate factor impacts upon the local solar irradiance depending on the season, as determined using two ANN algorithms (MLNN and LSTM). Amount of total cloud cover (Tcc) mainly dominated local solar irradiance. Usually amount of Tcc includes amount of middle and low-layer cloud cover (MLcc) as well, therefore, MLcc is not an independent element and the element information, amount of Tcc could be enough to deduct local solar irradiance. For example, if there is same amount of Tcc but only changed amount of MLcc, the impact is relatively small, because amount of Tcc mainly dominates the local solar irradiance. Both temperature and amount of Tcc significantly dominate local solar irradiance in the summer season. Temperature and total cloud cover have relatively less impact on the GHI in other seasons. Changes in the humidity ratio impact the solar irradiance, because it also represents seasonal changes. However, during the summer season, the humidity ratio influences the solar irradiance inversely. It is estimated that a high humidity ratio in the summer season is highly related to the amount of total cloud cover and precipitation, because Seoul is located in a hot and humid climate; hence, it experiences heavy rains in the summer season. Other climate factors (such as wind velocity and direction, and air pressure) can be neglected when estimating solar irradiance, because their impact factors are relatively small compared to the major impact sources (temperature, total cloud cover, middle and low-layer cover, and humidity ratio). The major context of this research is the application of ANNs to solar irradiance estimation based on weather parameters which could prove several advantages, e.g., ability to work with insufficient data/ knowledge and higher performance in a broad range of applications over classical techniques like linear regression and principal component analysis (PSA). Figure 10: Impact factors of input climate elements determined by ANN models in four seasons. The results presented suggest that local solar irradiance in urban or rural areas could be estimable by artificial neural networks based only on weather reports without the need for actual solar irradiance sensors. However, this study is limited, and more factors should be explored via further research. Solar irradiance can also be affected by variable factors such as geomagnetic storm activity with sunspot numbers increased, ozone, aerosols, and pollutants. Future studies should consider additional environmental factors to increase the accuracy of prediction results. Additionally, more regular training with recently updated weather data can efficiently response from future climatic changes unprecedently. Future studies should consider additional environmental factors to increase the accuracy of prediction results with unprecedent climatic data.
## 2 Methodology
### Multiple Converter Configuration Model (C-CM)
#### 2.6.2 UC Electrical Model  
The UC is represented by an RC circuit as shown in Figure 6:  
The elements that make up a capacitor electrical model are equivalent resistors and capacitors. An internal resistance of the UC (load) and the self-discharge resistance represent the chosen model. The voltage equation at the ultracapacitor terminals \(V_{uco}\) is:  
\[V_{uco}=V_{uc}-R_{ucs}I_{uc} \tag{4}\]  
where \(C_{uc}\) is the UC capacitance, \(R_{ucs}\) is the equivalent internal resistance, \(I_{uc}\) is the current that flows through the ultracapacitor, and \(V_{uco}\) is the voltage found in the ultracapacitor terminals. In this work the resistance \(R_{ucp}\) is considered with high resistance values in such a way that it is represented by an open circuit. The voltage dynamics in the capacitor \(C_{uc}\) are given by the following differential equation:  
\[C_{uc}\frac{dV_{uc}}{dt}=-I_{uc} \tag{5}\]  
The state of charge of the UC can be determined as follows:  
\[SOC_{uc}= (V_{uco}/V_{uc\_max})^{2} \tag{6}\]  
The above equation determines the UC state of charge depending on its voltage given by \(V_{uco}\) and the maximum UC voltage (\(V_{uc\_max}\)).