esa-sceva/satcom-chunk-collection huggingface dataset:
# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[210] & 2002 & SMNE & Symbiotic memetic neuro-evolution integrates implicit fitness sharing and particle swarm optimisation to maintain genetic diversity and enhance local search in neuro-controller development for nonlinear processes. & Ensemble \\ \hline
[184] & 2006 & DBSCAN & GA with density-based clustering to form subpopulations and fitness sharing within niches. & Ensemble \\ \hline
[2] & 2010 & ENA & Competing clearing and restricted tournament selection with information interchange. & Ensemble \\ \hline
[211] & 2014 & APSAGA & Advanced particle swarm-assisted genetic algorithm integrates particle swarm optimisation and genetic algorithm to handle constraints and maintain diversity in solution populations. & Ensemble \\ \hline
[159] & 2015 & KB-DNCA & Knowledge-based differential covariance matrix adaptation cooperative algorithm combines knowledge-based heuristics with differential covariance matrix adaptation to maintain multiple solutions and improve convergence in optimisation problems with binary and real-valued variables. & Ensemble \\ \hline
[180] & 2016 & SELFMMOGA & Multistrategy and coevolutionary genetic algorithm using clearing, sharing, clustering, restricted tournament selection, and crowding with competitive resource allocation. & Ensemble \\ \hline
[212] & 2021 & ECDENM & Ensemble crowding differential evolution with neighbourhood mutation combines ensemble strategies with crowding mechanisms to maintain diversity and avoid premature convergence in multimodal optimisation. & Ensemble \\ \hline
[213] & 2021 & CEN-DE & Co-evolutionary niching differential evolution dynamically evolves niching methods and integrates them into DE to preserve population diversity and enhance global optimisation. & Ensemble \\ \hline
[214] & 2022 & DHN-DE & Dynamically hybrid niching differential evolution combines various niching strategies dynamically throughout the optimisation process to maintain diversity and convergence, effectively addressing search spaces. & Ensemble \\ \hline
[181] & 2022 & ANSDE & Parallel population with adaptive probabilistic selection (DC, RTS, and CLR) of niching schemes using a fitness improvement and diversity quality measure. & Ensemble \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of niching algorithms: hybrid methods (ensemble). Table 12 provides a summary count of theoretical framework contributions in each, niching paradigm and Table 13 summarises their most essential components. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Frequency** \\ \hline Speciation & 75 \\ Embedded & 34 \\ Clustering & 30 \\ Crowding & 17 \\ Fitness sharing & 13 \\ Ensemble & 11 \\ Clearing & 7 \\ Multiobjectivisation & 6 \\ Hybrid & 5 \\ Sequential & 4 \\ RTS & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Frequency count of contribution per niching paradigm.
# Multimodal Tucker Decomposition for Gated RBM Inference
## 4 Tucker Decomposition  
Since tensors, specifically a three-way tensor, and its corresponding Tucker decomposition are at the core of this study, a brief overview of the subject of tensors is presented. First introduced by [PERSON] [19] and refined in subsequent articles by [PERSON] [20] and [PERSON] et al. [21], the Tucker decomposition is a form of higher-order Principal Component Analysis. Tucker decomposition factorizes a tensor into a (usually smaller) core tensor and a set of factor matrices. One factor matrix along each mode. Then, in the three-way case where \(\mathbf{\mathscr{W}}_{ijk}\in\mathbb{R}^{I\times I\times K}\), we have  
\[\mathbf{\mathscr{W}}_{ijk}\approx\mathbf{\mathscr{G}}_{pqr}\times_{I}\mathbf{A}_{ip} \times_{I}\mathbf{B}_{ji}\times_{K}\mathbf{C}_{kr} \tag{16}\]  
where the operator \(\times_{n}\) denotes the mode-\(n\) multiplication of a tensor by a matrix in mode \(n\). \(\mathbf{A}\in\mathbb{R}^{I\times P}\), \(\mathbf{B}\in\mathbb{R}^{I\times Q}\), and \(\mathbf{C}\in\mathbb{R}^{K\times R}\) are known as the factor matrices and can be thought of as the principal components in each mode. The tensor \(\mathbf{\mathscr{G}}\in\mathbb{R}^{P\times Q\times R}\) is called the core tensor, and its entries show the level of interaction between the different components. The Tucker decomposition of \(\mathbf{\mathscr{W}}_{ijk}\) is usually summarized as:  
\[\mathbf{\mathscr{W}}=[\mathbf{\mathscr{G}};\mathbf{A};\mathbf{B};\mathbf{C}] \tag{17}\]  
A comprehensive discussion on Tucker decomposition and tensor analysis is available in Kolda [22]. If \(\mathbf{\mathscr{G}}\) is the same size as \(\mathbf{\mathscr{W}}\), the Tucker decomposition is simply a change of  
Figure 5: A simplified gated RBM introducing factor layers.  
basis. More often, we are interested in using a change of basis to compress \(\mathbf{\mathscr{W}}\). If \(P\), \(Q\), \(R\) are smaller than \(I\), \(J\), \(K\), the core tensor \(\mathbf{\mathscr{G}}\) can be thought of as compressed version of \(\mathbf{\mathscr{W}}\).  
For some computations presented in this document, it is important to be able to transform the indices of a tensor so that it can be represented as a matrix and vice versa. _Matricization_, also known as _unfolding_ or _flattening_, is the process of reordering the elements of a tensor (\(N\)-way array) into a matrix [23]. For instance, a \(3\times 4\times 5\) tensor can be rearranged as a \(12\times 5\) matrix or a \(3\times 20\) matrix, and so on.  
The matricized forms (one per mode) of (16) are:  
\[\mathbf{W}_{i} \approx\mathbf{AG}_{p}(\mathbf{C}\otimes\mathbf{B})^{T} \tag{18}\] \[\mathbf{W}_{j} \approx\mathbf{BG}_{q}(\mathbf{C}\otimes\mathbf{A})^{T}\] (19) \[\mathbf{W}_{k} \approx\mathbf{CG}_{r}(\mathbf{B}\otimes\mathbf{A})^{T} \tag{20}\]
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 2 Methodology and the Optimization Procedure
### Optimization Procedure of the Proposed Method  
To achieve ideal coil geometries, a design approach as depicted in Figure 3 was implemented, and the Ansys Electronics Desktop 2018.0 was employed to simulate and optimize \(Rx_{1}\), \(Rx_{2}\), and \(Tx\). These steps were followed in the design process:  
Step 1: The design restrictions are determined in accordance with the application. There must be a determination regarding the size and shape of the coils. Taking into account the form and size of the average human heart, the spherical receiver coils are selected with a maximum diameter of 3.4 mm (we selected two spherical receiver coils). As \(Tx\) lies on the chest, a planar circular shape is chosen to ensure comfort. The values \(Rx_{1}\), \(Rx_{2}\), and \(Tx\) are wound with copper, and the operational frequency is 13.56 MHz.  
Figure 2: The equivalent circuit diagram of the proposed method.  
Step 2: In this step, the parameters of \(Rx_{1}\), \(Rx_{2}\), and \(Tx\) are initialized. The parameters are the inner diameter of the coil (\(d_{1}\)), the width of the wire (\(w\)), the number of turns (\(n\)), the distance between turns (\(s\)), and the pitch (\(p\)), respectively.  
Step 3: The mutual inductance and \(Q\)-factor between the coils are determined by performing a parametric sweep with regard to the parameters mentioned in the preceding step.  
Step 4: The procedure for optimization is performed until both \(Q\) and \(M\) have increased to their maximum values. In order to determine the transmission coefficients, the capacitance is continually adjusted to 13.56 MHz.  
Step 5: At the fifth stage, a comparison is made between the power transmission efficiency of the coils when operating in air and when operating in a tissue medium.  
Step 6: Finally, the specific absorption rate, often known as the SAR, is calculated to ensure that the design complies with the requirements set forth by the Federal Communications Commission (FCC).
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 2 Materials and Methods  
In this section, we first introduce the proposed Siamese ConvNet architecture specialized for loop closure detection and then, explore three fusion strategies (early fusion, mid-level fusion, and late fusion) of combining RGB stream and depth stream for RGB-D loop closure detection. Our sole aim in this paper is to extract representative features to apply the loop closure detection tasks, and to learn better similarity metrics.
## 2 Materials and Methods
### The Size Characterization of SP-Loaded Microemulsions by Digital Light Scattering (DLS)  
DLS characterization of SP-incorporated microemulsions was carried out using the ALV-5000F goniometer system coupled with a diode-pumped solid-state laser to supply a polarized incident light. The system was also integrated with a digital correlator (ALV SP-86) with a sample range of 25 ns to 100 ms. DLS was performed at an angle of \(\theta=90^{\circ}\) to the incident ray by calibrating the intensity scale by toluene against ambient light scattering. Prior to the measurement, the sample solutions were directly filtered into the scattering cells using millipore millicx filters (0.22 \(\upmu\)m porosity) and were equilibrated for 10 min at the required temperature. In order to acquire a fitted correlation function, the sampling time was 5-10 min. All the experiments were carried out three times. The decay rate, \(\Gamma\), obtained by fitting a single exponential function to the autocorrelation function of samples, is related to the diffusion coefficient by using [28; 29; 30]:  
\[D=\frac{\Gamma}{q^{2}} \tag{1}\]  
where \(q\) is the scattering vector.  
The diffusion coefficient of nanoparticles or micelles can be characterized as \(R_{h}\) according to the Stokes-Einstein equation [28; 29; 30]:  
\[R_{h}=\frac{kT}{6\eta\pi D} \tag{2}\]  
where \(k\) and \(\eta\) denote the Boltzmann constant and water viscosity, respectively.  
Figure 1: Schematic of droplet microemulsions.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Discussion  
The improved performance of the proposed models, particularly those incorporating decision trees and random forests, suggests that the modifications such as hyperparameter tuning, feature selection, and data augmentation are effective in enhancing model accuracy and robustness. These findings are consistent with previous studies that have highlighted the importance of model optimization and feature engineering in improving machine learning model performance for intrusion detection [38; 39; 40].  
The marginal improvement observed with the logistic regression model suggests that linear models may have limitations in handling the complexity of intrusion detection tasks. This highlights the need for more sophisticated methods such as nonlinear models or ensemble techniques in order to better capture the intricacies of the data.  
In the broader context, these findings align with the growing body of research emphasizing the need for advanced machine learning techniques in cybersecurity contexts. As the landscape of cyber threats evolves, the need for robust and adaptable intrusion detection systems is becoming more critical. The success of the proposed models, particularly the random forest model, suggests that future research could explore further enhancements such as combining multiple models (ensemble learning), incorporating deep learning techniques, or applying semi-supervised learning to leverage both labeled and unlabeled data.  
Additionally, future research could focus on real-time implementation and testing of these models in dynamic environments to explore their scalability and resilience against adversarial attacks. Understanding the tradeoffs between model complexity, interpretability, and performance is crucial in developing practical and deployable solutions for intrusion detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.61529 & 0.61760 \\ Recall (Macro Avg) & 0.47534 & 0.58065 \\ F1-Score (Macro Avg) & 0.50116 & 0.58895 \\ Precision (Weighted Avg) & 0.81912 & 0.85338 \\ Recall (Weighted Avg) & 0.83543 & 0.85460 \\ F1-Score (Weighted Avg) & 0.81190 & 0.85353 \\ Accuracy & 0.83543 & 0.85460 \\ \hline \hline \end{tabular}
\end{table}
Table 24: Comparative results for random forest models: Split 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.63372 & 0.54920 \\ Recall (Macro Avg) & 0.48442 & 0.51740 \\ F1-Score (Macro Avg) & 0.51185 & 0.52878 \\ Precision (Weighted Avg) & 0.82432 & 0.83616 \\ Recall (Weighted Avg) & 0.84017 & 0.84141 \\ F1-Score (Weighted Avg) & 0.81829 & 0.83783 \\ Accuracy & 0.84017 & 0.84141 \\ \hline \hline \end{tabular}
\end{table}
Table 23: Comparative results for random forest models: Split 2.
## 5 Conclusions and Recommendations  
From the FE study that was carried out on single-bay RC frames strengthened with external pre-stressing tendons, the following conclusions can be drawn:  
* Most of the FE results are roughly in agreement with those of the laboratory tests in the case of simple beams.
* The results show that strengthening with external pre-stressing tendons significantly improves the ultimate loading capacity and mechanical behavior of RC beams and frames.
* External strengthening frames with pre-stressing tendons can delay the initial cracking load by about 100-104% compared with the unstrengthening frame.
* External strengthening frames with pre-stressing tendons can increase the load-carrying by about 400-460% for yielding and 16-28% for failure compared to the unstrengthened sample.
* The tendon profile and the number of inner deviators exert a good effect on loading capacity and a light effect on ductility. All the samples that were strengthened with external tendons in the positive bending zone of the frame-beam (i.e., straight-line tendon F\({}_{2}\) or U-shape tendons F\({}_{6}\)) increased loading capacity by about 11.5 and 19.1 %, respectively, compared to the samples that were strengthened without any tendons in the frame beam zone (i.e., straight-line tendon F\({}_{3}\) or F\({}_{4}\)). Therefore, the technique of strengthening with external pre-stressing U-shaped tendons is recommended in the case of single-bay frames.
* Strengthening frames with external tendons in the beam-column connection zone is not an appropriate technique for single-bay frames because the tension in this region increases the stress on the columns and the effect of pre-stressing does not extend to the frame beam with adequate value.
* Further investigations need to be carried out to devise a convenient method of RC frames strengthening with external pre-stressing tendons based on the experimental study.  
**Author Contributions:** Supervision, visualization, writing--original draft preparation: G.A.M.; validation, formal analysis, writing--review and editing: A.S.E.; investigation, software, resources: M.H.E.-F.; writing--review and editing, resources: P.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Slovak Research and Development Agency under the contract no. APVV-20-0281.  
**Acknowledgments:** This work was supported by the Slovak Research and Development Agency under the contract no. APVV-20-0281 and VEGA 1/0363/21.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a number of challenges due to the sheer volume of traffic data that needs to be processed in real-time. Thus, it is not scalable and lacks fault tolerance and data privacy. This study designs and evaluates a scalable distributed machine learning-based scheme to predict highway traffic flows in real-time. The proposed system is segment-based where the vehicles in each segment form a cluster. We train and validate a local Random Forest Regression (RFR) model for each vehicle's cluster (highway-segment) using six different hyper parameters. Due to the variance of traffic flow patterns between segments, we build a global Distributed Machine Learning Random Forest (DMLRF) regression model to improve the system performance for abnormal traffic flows. Kappa Architecture is utilized to enable real-time prediction. The proposed model is evaluated and compared to other base-line models, Linear Regression (LR), Logistic Regression (LogR), and K Nearest Neighbor (KNN) regression in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R\({}^{2}\)), and Adjusted R-Squared (AR\({}^{2}\)). The proposed scheme demonstrates high accuracy in predicting abnormal traffic flows while maintaining scalability and data privacy.  
inter of vehicles; real-time traffic flow prediction; intelligent transportation systems; big data processing; distributed machine learning; distributed architecture; Apache Spark
# Subspace Detours Meet Gromov-Wasserstein
## 2 Background
### Classical Optimal Transport
#### 2.1.1 Kantorovitch Problem  
There exists several types of coupling between probability measures for which a non-exhaustive list can be found in [18] (Chapter 1). Among them, the so called optimal coupling is the minimizer of the following Kantorovitch problem:  
\[\inf_{\gamma\in\Pi(\mu,\
u)}\ \int c(x,y)\mathrm{d}\gamma(x,y) \tag{1}\]  
with \(c\) being some cost function. The Kantorovitch problem (1) is known to admit a solution when \(c\) is non-negative and lower semi-continuous [19] (Theorem 1.7). When \(c(x,y)=\|x-y\|_{2}^{2}\), it defines the so-called Wasserstein distance:  
\[W_{2}^{2}(\mu,\
u)=\inf_{\gamma\in\Pi(\mu,\
u)}\ \int\|x-y\|_{2}^{2}\ \mathrm{d} \gamma(x,y). \tag{2}\]  
When the optimal coupling is of the form \(\gamma=(Id,T)_{\#}\mu\) with \(T\), some deterministic map such that \(T_{\#}\mu=\
u\), \(T\) is called the Monge map.  
In one dimension, with \(\mu\) atomless, the solution to (2) is a deterministic coupling of the form [19] (Theorem 2.5):  
\[T=F_{\
u}^{-1}\circ F_{\mu} \tag{3}\]  
where \(F_{\mu}\) is the cumulative distribution function of \(\mu\), and \(F_{\
u}^{-1}\) is the quantile function of \(\
u\). This map is also known as the increasing rearrangement map.
## 3 Results and Discussion
### Design of the Conventional TCED Process with PG as New Proposed Extractive-Solvent
#### 3.1.3 Effect of the Feed Tray and Reflux Ratio on the Performance of the ED Column  
Given the foregoing results, our subsequent simulations assessed the effect of NF2 in the range of 16-24. It can be seen from Figure 7 that the water concentration in the IPA product and the reboiler duty decreases while lowering the position of NF2 toward the column's bottom. The optimal solution is to feed the azeotropic flow on stage 20. For this column configuration, an IPA distillate product with 0.077 mol.% water and 99.92 mol.% IPA were obtained. In addition, the reboiler duty for the column configuration was 5921 kW, as shown in Figure 7.  
Several simulations were carried out to investigate the effect of RR2 on the water and PG concentrations in the distillate and the reboiler duty. The simulations were performed for a reflux ratio from 0.7 to 1.5. Figure 8 shows that when the reflux ratio increases, a decrease in the water and PG concentrations in the distillate product is observed. Unfortunately, the increase in RR2 requires higher energy consumption. Based on the data shown in Figure 8, which are also in agreement with the EDC targets, it can be concluded that the most adequate RR2 was 1.5. An IPA distillate product with 0.0713 mol.% water and a maximum of 2 ppm PG was attained for this reflux ratio, but the duty of the EDC reboiler was high at 6639 kW. In the bottom product of the EDC (subsequently labeled as B2), only 0.0615 kmol/h IPA was lost from the total of 109.5 kmol/h in the PC feed.  
Figure 6: The influence of the NT2 effect on the water concentrations in the IPA product and EDC reboiler’s duty (E/D1 = 3.4/1, NF2 = 16, NFE = 5, RR2 = 0.9).  
The final best design variables for the EDC were E = 570 kmol/h, E/D1 = 3.4/1, NT2 = 30, NFE = 5, NF2 = 20, and RR2 = 1.5.
## 2 Theoretical Analysis  
### SBS Process Simulating  
The PRBS phase-modulated monolithic laser system we considered is shown in Figure 1. The PRBS signal was amplified by an RF amplifier and driven by an electro-optic phase modulator (EOPM), and the modulated lightwave entered a passive fiber with a length of L.  
We used the transient SBS three-wave coupling equation to simulate the time course and obtained the output waveforms of the laser and Stokes light. The equations can be written as follows [20,21]:  
\[\frac{c}{n}\frac{\partial A_{L}}{\partial z}+\frac{\partial A_{L}}{\partial t}+ \left[\frac{c}{n}\frac{l_{s}}{2}+\frac{i\omega_{s}n_{2}}{nA_{eff}}\Big{(}|A_{s }|^{2}+2|A_{b}|^{2}\Big{)}\right]A_{L}=-\frac{\gamma_{c}\omega}{2\rho_{0}n^{2}} A_{S}Q \tag{1}\]  
\[\frac{c}{n}\frac{\partial A_{S}}{\partial z}+\frac{\partial A_{S}}{\partial t} +\left[\frac{c}{n}\frac{l_{s}}{2}+\frac{i\omega_{s}n_{2}}{nA_{eff}}\Big{(}|A_ {b}|^{2}+2|A_{s}|^{2}\Big{)}\right]A_{S}=-\frac{\gamma_{c}\omega}{2\rho_{0}n^{ 2}}A_{L}Q^{*} \tag{2}\]  
\[\frac{\partial Q}{\partial t}+\left(\frac{1}{2}\Gamma_{B}-j(\Omega_{B}-\Omega )\right)Q=-\frac{i\gamma_{c}\omega}{2c^{2}\
u_{A}A_{\omega}}A_{L}A_{S}{}^{*}+f \tag{3}\]  
Here, \(A_{L}\), \(A_{S}\), and \(Q\) denote the amplitudes of the laser, Stokes wave, and acoustic waves, respectively. \(c\) represents the speed of light in a vacuum, while n is the fiber's refractive index. The nonlinear refractive index is denoted by n\({}_{2}\), and \(l_{s}\) indicates the fiber's  
Figure 1: Schematic diagram of signal phase modulation and passive fiber structure (we studied time-domain characteristics of output light and Stokes light in passive fiber after phase modulation).  
background loss. The angular frequencies are \(\omega\) (\(\omega=\omega_{\rm L}\simeq\omega_{\rm S}\)) for the laser and \(\Omega_{B}\) for the acoustic wave, and \(\Omega\) is the Brillouin scattering shift. The electrostrictive constant \(\gamma_{e}\) connects electrostrictive pressure to the electric field, and \(\rho_{0}\) is the fiber medium's background density. Additionally, \(A_{eff}\) is the effective mode field area, \(v_{A}\) is the velocity of the acoustic fundamental mode in the fiber core, and \(A_{ao}\) is the interaction area between the acoustic wave and the signal laser. The sound wave's damping rate is \(\Gamma_{B}=2\pi\times w_{BGS}\), where \(w_{BGS}\) is the Brillouin gain spectrum, and \(f\) represents Langevin noise. Table 1 summarizes the key parameters and their values used in our simulations.  
In passive fibers, the SBS setup time constant at the fiber inlet can be expressed as follows [22]:  
\[\tau_{\rm C}=\frac{2 nA_{eff}}{gPc} \tag{4}\]  
where P is the laser power, and \(g\) is the SBS gain coefficient.