esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results  
In this section, we investigate the impact of using the diagonal and stair matrices in the performance and the computational complexity of a detector based on the NI, NS, SOR, GS, JA, and RI methods. It will be presented in the bit-error-rate (BER), the signal-to-noise ratio (SNR), and number of multiplications. In simulations, independent and identically distributed (i.i.d) Gaussian channels are considered, the MIMO sizes is \(32\times 256\), the modulation scheme is 64 QAM, and several iterations are conducted.  
Table 2 shows that the convergence condition in Equation (7) is satisfied for all random \(10^{4}\)\(\mathbf{S}\) and \(\mathbf{D}\). However, statistical values are smaller in the case of \(\mathbf{S}\) than the values in the case of \(\mathbf{D}\). For instance, the mean values of the convergence condition are 0.7027 and 0.7255 when using \(\mathbf{S}\) and \(\mathbf{D}\), respectively.  
Figure 2 presents the performance of the NI, the NS, the SOR, the GS, the JA, and the RI based detector using both \(\mathbf{S}\) and \(\mathbf{D}\) matrices. Detectors initialized by \(\mathbf{S}\) outperform detectors initialized by \(\mathbf{D}\) in each iteration. For example, at \(n=1\), the \(\text{BER}=10^{-2}\) is obtained at \(\text{SNR}=11\) dB and \(\text{SNR}=13\) dB for the GS based detector using \(\mathbf{S}\) and \(\mathbf{D}\), respectively. In the SOR based detector, the target performance is achieved at \(n=1\) and \(n=2\) using \(\mathbf{S}\) and \(\mathbf{D}\), respectively. The worst performance is occurred when a detector based on the RI and the NS is initialized by \(\mathbf{D}\). In other words, all detectors can achieve the target performance when the number of iterations is large (i.e., \(n\geq 5\)), but the best detector achieves the target performance with the smallest number of iterations (lowest complexity).  
Figure 3 illustrates the relationship between the performance and the computational complexity to obtain \(\text{BER}=10^{-3}\). The lowest computational complexity is achieved by a detector based on the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Matrix** & **Mean** & **Median** & **Standard Deviation** & **Prob. Quadratic Convergence** \\ \hline Stair (\(\mathbf{S}\)) & 0.7027 & 0.7007 & 0.2776 & 1 \\ \hline Diagonal (\(\mathbf{D}\)) & 0.7255 & 0.7233 & 0.2868 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of \(\|\mathbf{I}-\mathbf{A}\mathbf{A}_{(0)}^{-1}\|\) in Equation (7) for \(10^{4}\) channel realizations for \(32\times 256\) M-MIMO system.  
Figure 2: Performance of a detector based on iterative matrix inversion methods with \(\mathbf{D}\) and \(\mathbf{S}\) for \(32\times 256\) M-MIMO system.  
GS and initialized by \(\mathbf{S}\) when \(\text{SNR}=14.5\) dB. In contrast, a detector based on the NS method and initialized by \(\mathbf{D}\) requires the highest computational complexity to achieve the target BER performance.
# Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia
## 7 Conclusions  
In this paper, we have investigated the impact of rainfall in urban and highway scenarios in 5G communication networks at mmWave frequencies. The rainfall attenuation, LOS and NLOS probability, path loss, and link margin are estimated based on the indoor and outdoor probability models. Our simulation results show that the path loss increases and the link margin decreases as the path difference and rainfall rate increase. The impact of rainfall on path loss at higher frequencies is significantly higher than at lower frequencies. The optimal path difference for urban and highway scenarios with and without rainfall is estimated and compared. The urban scenario without rainfall provides a higher optimal distance compared to the highway scenario with and without rainfall attenuation, and the urban scenario with rainfall attenuation achieves high data rates of 1 Gbps.  
We considered indoor LOS probability models and estimated the path loss and link margin. The concept and estimated results are applicable to any location experiencing comparable weather conditions. In the future, indoor NLOS probability models could be used to estimate the path loss, link margin, and optimal path difference by designing a small indoor region. Notably, the impact of walls, curtains, ceilings, etc., in indoor regions remains to be addressed in future research.  
Conceptualization, C.S.; methodology, C.S.; software, C.S.; validation, M.R. and L.L.C.; writing--original draft preparation, C.S. and M.R.; reviewing, editing, and supervision, M.R., A.W., A.F.O. and M.H.J.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript. This work was supported and funded by the Fundamental Research Grant Scheme FRGS/1/2021/ICT09/MMU/02/1 of the Ministry of Higher Education, Malaysia.  
Not applicable.  
The authors declare that there are no conflict of interest arising from this paper.
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 2 Background
### Combinatorial Optimization Problem
#### 2.1.5 Bin Packing Problem  
The bin packing problem (BPP) is one of the most classical COPs, where a series of items with different sizes are required to be packed into a series of bins with positive integer capacities such that the number of bins used is minimized. Generally, there are two types of BPPs: online BPP, and offline BPP. The online BPP considers a situation in which the items keep appearing in a given order and they need to be placed inside the bins one by one, while the latter one concerns modifying the given list of items. BPP has many variations, such as 2D packing [161], 3D packing [162], linear packing [163], packing by weight [164], packing by cost [165], and so on. Such problems have arisen in resource allocation with an increasing frequency over the past years, such as edge computing [166], cloud storage [167], parcel delivery [168], aircraft maintenance task allocation [169] and product transportation [170]. For the online version, there are a diverse set of online algorithms designed for BPP. Single-class algorithms consist of next fit [171], next-k-fit [172], first-fit [173], best-fit [174] and worst-fit [175]. Refined algorithms include harmonic-k [176] and refined-harmonic [177]. On the other side, the offline algorithm is able to observe all the items before beginning to place them into bins. Multiplicative approximation is the simplest approach utilized by the offline algorithm. The members in this family are first-fit-decreasing [178], next-fit-decreasing [179] and modified first-fit-decreasing [178]. Additive approximation [180] and exact algorithms [181] are also widely mentioned in solving BPPs.
## 1 Introduction
However, the majority of existing studies are experimental in nature, investigating the effects of specific colors and surface coatings on laser intensity solely through direct measurements. Currently, no study in the literature predicts how LIDAR intensity values vary across surfaces with different reflectivity coefficients using modeling and prediction methods. This study fills this gap by developing a statistical and machine learning-based model for laser intensity prediction, integrating the technical specifications of various LIDAR models and the optical properties of vehicle surfaces. The proposed modeling approach extends beyond traditional experimental analyses by providing a generalizable framework for modeling and predicting the impact of surface reflectivity variations on LIDAR laser intensity. This study aims to model the effects of the technical characteristics of different LIDAR models and vehicle surface pains on laser intensity measurements and to predict the relationships between these effects. The primary objective is to analyze how technical differences among LIDAR sensors--particularly transmitter power variations depending on the number of channels--impact laser intensity performance. In this context, laser intensity data obtained from the experiments conducted by [PERSON] et al., along with variables such as vehicle color, angle, and distance, were utilized in the training and testing processes of various machine learning algorithms. Statistical analyses were conducted on Gaussian Process Regression (GPR), Polynomial Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting algorithms, and their performance in predicting laser intensity values was compared. To enhance the accuracy of the selected model, hyperparameter optimization was conducted using the Bayesian optimization algorithm. The evaluation of this study was conducted using a Coventry University Public road dataset for Automated Cars (CUPAC) dataset recorded by Coventry University in Coventry, UK, in 2020 [16]. This dataset was utilized to assess the impact of different vehicle surface pains on laser intensity measurements under real-world conditions. In this study, LIDAR data processing, model training with machine learning algorithms, and curve fitting procedures were carried out using MATLAB R2021b (version 9.11.0.2358333). This study presents a systematic framework for laser intensity estimation by comparatively analyzing the effects of vehicle surface pains on LIDAR performance across different LIDAR models, particularly in relation to the number of channels. The proposed method establishes a foundational approach for predicting and enhancing laser-based sensing performance in autonomous driving systems.
# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 4 Simulation Results
### Case 2: Facial Expression Recognition
#### 4.2.2 Comparison and Discussion  
In order to verify the performance of the proposed GA-BEL in terms of recognition accuracy and execution speed, we compare GA-BEL with SVM [29], LS-SVM [30] and the original BEL [18] on JAFFE and Cohn-Kanade. In this study, the Gaussian kernel was used for SVM. The ten-fold cross-validation strategy was employed to perform the comparisons. The detailed results including the average recognition accuracies and the computing time per image are shown in Table 8.  
As shown in Table 8, GA-BEL achieved the best performance in JAFFE and Cohn-Kanade. For effectiveness, the average recognition accuracies are 2.22%, 1.01% and 1.54% higher than SVM, LS-SVM and the original BEL on the JAFFE dataset and 1.36%, 1.05% and 2.49% higher than SVM, LS-SVM and the original BEL on the Cohn-Kanade dataset, respectively. For efficiency, the computational cost of the BEL method is significantly less. Especially, GA-BEL takes only 0.2736 s and 0.2958 s to process one image in JAFFE and Cohn-Kanade, respectively, which is much faster than SVM and LS-SVM.  
For further comparison, we list the results of the previous methods that investigated the facial expression recognition, as shown in Table 9. The results indicate that GA-BEL can obtain better classification accuracy than other traditional methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Emotion**} & \multicolumn{6}{c}{**Recognition Accuracy (\%)**} \\ \cline{2-7}  & **Happy** & **Angry** & **Sadness** & **Disgust** & **Fear** & **Surprise** \\ \hline Happy & **98.63** & 0.0 & 0.0 & 0.0 & 0.11 & 1.26 \\ Angry & 0.0 & **96.79** & 1.43 & 1.78 & 0.0 & 0.0 \\ Sadness & 0.0 & 2.01 & **94.21** & 1.43 & 2.35 & 0.0 \\ Disgust & 0.0 & 0.46 & 1.34 & **95.85** & 2.35 & 0.0 \\ Fear & 0.0 & 0.0 & 2.06 & 2.40 & **94.37** & 1.17 \\ Surprise & 1.32 & 0.0 & 0.0 & 0.0 & 1.53 & **97.15** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix for the Cohn–Kanade database.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Databases** & **Classifiers** & **Time (s)** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{JAFFE} & SVM & 2.3481 & 93.35 \\  & LS-SVM & 1.2872 & 94.56 \\  & BEL & 0.3156 & 94.03 \\  & **GA-BEL** & **0.2736** & **95.57** \\ \hline \multirow{4}{*}{ Cohn–Kanade} & SVM & 3.6738 & 94.81 \\  & LS-SVM & 2.7506 & 95.12 \\ \cline{1-1}  & BEL & 0.5591 & 93.68 \\ \cline{1-1}  & **GA-BEL** & **0.2958** & **96.17** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The performance comparisons of different algorithms.
## 2 Surface Reconstruction from Point Cloud
### Non-Uniform Rational B-Spline (NURBS)  
The NURBS are mathematical representations of 3D geometry that accurately define a generic geometric entity, such as simple or more complex shapes. The NURBS curve is mathematically defined by the following equation:  
\[C(u)=\frac{\sum_{i=0}^{n}N_{i,p}(u)\;w_{i}\;P_{i}}{\sum_{i=0}^{n}N_{i,p}(u)\;w_ {i}} \tag{1}\]  
where the \(w_{i}\) are the weights, the \(P_{i}\) are the control points, and the \(N_{i,p}(u)\) are the normalized B-Spline basis functions of degree \(p\) recursively as [17; 18]:  
\[\begin{array}{l}N_{i,0}(u)=\left\{\begin{array}{cc}1&if\;u_{i}\leq u\leq u _{i+1}\\ 0&otherwise\end{array}\right.\\ N_{i,p}(u)=\frac{u-u_{i}}{u_{i+p}-u_{i}}N_{i,p-1}(u)+\frac{u_{i+p+1}-u}{u_{i+ p+1}-u_{i+1}}N_{i+1,p-1}(u)\end{array} \tag{2}\]  
where \(u_{i}\) are the knots forming the knot vector \(U=\left\{u_{0};\;u_{0};\dots;\;u_{n+p+1}\right\}\).  
Therefore, a NURBS curve is defined by four characteristics: the degree, the control vertices, the knot vectors and the weights. The degree of the NURBS (a positive integer) defines mathematically the piecewise polynomial blending function. The higher the degree of the polynomial, the more flexible the curve and surface. The control vertices are a row of points at least equal to (degree + 1). The knot vectors define how the polynomial pieces are blended together with the proper smoothness. Generally, there are two kinds of knot vector definition: uniform (i.e., with constant spacing between the knots) and non-uniform (i.e., with varying spacing between knot vectors).  
A weight is associated with each control point (i.e., its ability to attract the curve). Excluding some exceptions, weights are positive numbers. When the control points of a curve all have the same weight (usually equal to 1), the curve is called \"non-rational\" and the NURBS curve is reduced to a B-spline curve; otherwise, it is called \"rational.\" For this reason, the letter R of the acronym NURBS stands for \"rational\" and indicates that a NURBS curve can be rational.
# DSP-Based 40 GB/s Lane Rate Next-Generation Access Networks +
## 4 Discussion  
The choice of modulation format and DSP configuration depends on the fiber distance. For short-reach SMFs of up to 10 km, optical Duobinary without any DSP offers the best trade-off between the link power budget. For the typical standardized transmission distance of 20 km SMF, DSP-free PAM-4 can be considered if the minimization of the receiver complexity is the priority, while electrical Duobinary with 4-state MLSE is preferred if a link with link budget as large as possible is preferred. For 30 km and 40 km, electrical Duobinary is preferred from both the link power budget and the DSP complexity points of view. Note that electrical Duobinary has the best performance with MLSE, but it requires a larger transmitter bandwidth, as indicated in Section 3.1, which may require  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DSP Algorithm** & **Modulation** & **Multiplications** & **Sums** \\ \hline Linear FFE–DFE (\(M=7\), & Opt./Electr. Duobinary, & \(M+N\) & \(M+N-1\) \\ \(N=5\)) & PAM-4 & 12 & 11 \\ \hline (Non)linear FFE–DFE (\(M=7\), \(N=5\)) & Opt./Electr. Duobinary, & \(\frac{M^{2}+3M+N^{2}+N-2}{2}+12\) & \(\frac{M^{2}+M+N^{2}-N}{2}+11\) \\ (\(M=7\), \(N=5\)) & PAM-4 & 61 & 49 \\ \hline (Non)linear & & & \\ FFE–DFE–MLSE (\(K=2\), & Opt./Electr. Duobinary & \((L+1)\cdot K^{L+1}+61\) & \((L+2)\cdot K^{L+1}+49\) \\ \(L=2\)) & & & \\ \hline (Non)linear & & & \\ FFE–DFE–MLSE (\(K=4\), & PAM-4 & \((L+1)\cdot K^{L+1}+61\) & \((L+2)\cdot K^{L+1}+49\) \\ \(L=1\)) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity (number of multiplications and sums per symbol) of various DSP algorithms.  
simple pre-equalization if a lower bandwidth transmitter is used in practice. As indicated in [25], pre-equalization, together with receiver FFE/DFE, do reduce the transceiver bandwidth requirement. In addition, PAM-4 halves the baud rate compared to electrical/optical Duobinary, which must be considered as a higher priority when the system DAC/ADC sampling rate and signal processing speed are limited. The complexity of both nonlinear FFE/DFE and MLSE can be reduced by optimizing implementation, indicating that the proposed DSP is feasible for access networks.  
The IEEE and ITU-T standardization ends up with 25 Gb/s and 50 Gb/s per wavelength of next generation high-speed PON systems. For the 50 Gb/s case, the general performance trend, as well as the performance difference between each modulation format would be similar with the 40 Gb/s case. The difference would be the transceiver bandwidth requirement and the achievable power budget.
# The Impact of GoLo Technology on the Quality Properties of Dealcoholised Wines
###### Abstract  
Winemakers are increasingly adopting partial or total dealcoholisation of wines and alcoholic beverages in response to market trends and the impact of climate change on wine alcohol content. In this study, the patented very low-temperature vacuum wine distillation technology known as GoLo was used to dealcoholise various types of red, white, and rose wines from different Spanish designations of origin (DOs) in order to examine changes in oenological parameters as pH, sulphites, total acidity, volatile acidity, and sugars and the perceptible differences among a set of wines through sensory analysis and the chemical analysis related, such as turbidity, total phenolic content, and a profile of volatile compounds. The results indicate that there is an increase in the overall polyphenol content in the range of 8 to 12%, turbidity increased in the range of 13 to 70%, and sulphites decreasing in the range of 20 to 40%. The aroma profile also reveals that the most volatile and less soluble compounds--such as esters (reduction between 60% and 96%), terpen (reduction between 45% and 80%), and aldehydes (reduction between 86% and 95%)--are lost in varying percentages during the dealcoholisation process, depending on the type of wine. Meanwhile, other more soluble compounds like phenols and acids undergo transformations during GoLo's dealcoholisation process, leading to an increase in their concentrations up to more than 90% in the case of phenols in white wines or 35% for acids in red wines.  
dealcoholisation; GoLo; polyphenols; quality; volatile compounds; wine +
Footnote †: journal: Applied sciences  
0000-0001-5002-3867-2277][PERSON]  
1 Department of Agricultural Chemistry, Faculty of Chemistry, Regional Campus of International Excellence \"Campus Marc Nostrum\", University of Murcia, Campus of Espinardo, 30100 Murcia, Spain; 1 jclm@umse. (J.C.-M.); [EMAIL_ADDRESS] (M.V.-M.-B.); [EMAIL_ADDRESS] (J.O.)  
2 Catedra de Quimica Agricola, E.T.S. de Ingenieria Agronomica y de Montes y Biotecnologia, Universidad de Castilla-La Mancha, Avda. de Espana s/n, 02071 Albacete, Spain; 2 [EMAIL_ADDRESS] (C.C.-T.); [EMAIL_ADDRESS] (R.S.-G.)  
28 February 2025
# Research on Image Denoising in Edge Detection Based on Wavelet Transform
###### Abstract  
Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The _PSNR_ and _MSE_ of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the _PSNR_ and _MSE_ of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.  
edge detection; wavelet transform; wavelet function; canny; Pratt quality factor 2023  
1130166848.0
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
###### Abstract  
The paper presents the developed integrated GNSS/IMU gyrocompass which, unlike the existing systems, contains a single-axis rotating platform with two antennas installed on it and an inertial measurement unit with tactical grade fiber-optic gyros. It is shown that the proposed design provides attitude solution by observing the signals of only one navigation satellite. The structure of the integrated GNSS/IMU gyrocompass, its specific features and prototype model used in the tests are described. The given test results in urban conditions confirmed heading determination accurate to \(\pm 1.5^{\circ}\) (\(3\sigma\)).  
fiber-optic gyro; two-antennas tightly-coupled system; GNSS/IMU-gyrocompass; single difference of phase measurements; multi-GNSS (GPS/GLONASS); common clock; single-frequency receivers +
Footnote †: journal: remote sensing