esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Prediction Algorithm  
### Classical Extreme Learning Machine (ELM)  
The architecture of the ELM is shown in Figure 1, and the algorithm is presented as follows [17].  
The ELM is a single hidden-layer artificial neural network. It is assumed that the number of random hidden neurons in the ELM model is \(L\), and the number of different learning samples \((\mathbf{x},\mathbf{y})\) is \(N.\) It is also assumed that \(\mathbf{x}\in R^{d*N},\)\(\mathbf{y}\in R^{N},\)\(\mathbf{a}_{i}\in R^{1*d},\)\(b_{i}\in R,\) and \(\mathbf{a}_{i},\) and \(b_{i}\) are randomly generated matrices and vectors, respectively. Then, the output function can be rewritten as:  
\[f_{L}(x_{n})=\sum_{i=1}^{L}\beta_{i}G(\mathbf{a}_{i},b_{i},\mathbf{x}_{n})=y_{n} \tag{1}\]  
where \(\beta\) is the output weight vector, which connects the random hidden neurons with the output. \(G\) is the active function, which connects the \(i^{th}\) random hidden neuron with all of the input nodes, and can be any infinitely differentiable function, such as the Sigmoid function as shown below:  
\[G(\mathbf{a},b,\mathbf{x})=\frac{1}{1+\exp(-(\mathbf{a}\cdot\mathbf{x}+b))} \tag{2}\]  
Equation (1) can be written in the form of matrices, such as:  
\[\mathbf{H}\cdot\mathbf{\beta}=\mathbf{Y} \tag{3}\]  
where  
\[\mathbf{H}=\left[\begin{array}{cccc}G(\mathbf{a}_{1},b_{1},\mathbf{x}_{1})&...&G(\mathbf{a}_{L},b_{L},\mathbf{x}_{1})\\ \vdots&\ddots&\vdots\\ G(\mathbf{a}_{1},b_{1},\mathbf{x}_{N})&...&G(\mathbf{a}_{L},b_{L},\mathbf{x}_{ N})\end{array}\right]\]  
\[\mathbf{\beta}=\left[\begin{array}{cccc}\beta_{1}&\beta_{2}&...&\beta_{L} \end{array}\right]^{T}\mathbf{Y}=\left[\begin{array}{cccc}y_{1}&y_{2}&...&y_ {N}\end{array}\right]^{T}\]  
Then, the least-square solution of (1) is:  
\[\mathbf{\beta}=\mathbf{H}^{\dagger}\mathbf{Y}=\mathbf{H}^{T}(\mathbf{H} \mathbf{H}^{T})\mathbf{Y} \tag{4}\]  
where the matrix \(\mathbf{H}\) is the hidden layer output matrix, within which the \(i^{th}\) element (\(h_{i}\)) is the hidden layer output vector for input \(x_{i}\). \(\mathbf{Y}\) is the vector of the output of the training data. \(\mathbf{\beta}\) is the only parameter for the training process to determine. The upper bound of the required number of hidden nodes is the number of distinct training samples, in other words, the number of hidden nodes should be less than the number of training samples.  
To improve the stability and generalizability of the results, we added the regularization parameter \(C\) to (4) to transform it as [18]:  
\[\mathbf{\beta}=\mathbf{H}^{T}(\mathbf{H}\mathbf{H}^{T}+\frac{1}{C}\mathbf{E} )\mathbf{Y} \tag{5}\]  
Figure 1: The architecture of Extreme Learning Machine.
# Mdpj _sciences_
## 5 Pollutant Reduction Results
### VOC Reduction  
For SPRE tested in laboratory settings for VOC reduction, Treesubsumtor and Thiravetyan [57] identified _C. comosum_ and _S. trifisciata_ as the best species for biofilters, evaluating their performance under different light intensities, with an efficiency of 15-20%. Though not very high, this efficiency is functional for purifying the environment. In laboratory analyses, [PERSON] et al. [33] conducted three tests with increasing pollutant concentrations in the system, measuring output concentrations to determine the efficiency of the active green wall. After a single pass through the biofilter, the removal efficiency was 56.6 \(\pm\) 0.86%. Laboratory tests by [PERSON] et al. [27] showed an active green wall removal efficiency of 46 \(\pm\) 4.02%.  
[PERSON] et al. [31] evaluated removal efficiency linked to the chemical properties of VOCs by placing an active green wall module in a test chamber and subjecting it to various harmful substances (e.g., acetone, ethanol, ethyl acetate). The average SPRE for each substance varied from 19.76% to 96.34%. In situ experiments on an active green wall by [PERSON] et al. [36] compared the efficiency of an active biofilter and a passive one, finding VOC concentrations of 300 \(\pm\) 3.04 ppb in a room with a passive green wall, reduced to 217 \(\pm\) 2.00 ppb in a room with an active green wall, demonstrating the higher efficiency of the system utilizing airflow through the technology.  
[PERSON] et al. [29] demonstrated the excellent performance of _S. trifisciata_, stating that total VOCs could be removed by about 40%. [PERSON] et al. [51] considered VOC removal by various plant species over time. The results showed that the best reduction in 24 h was achieved by _Davallia fejensis_, with an average of 1739.755 \(\upmu\)g m\({}^{-3}\), and in 48 h by _Platycurium bifurcation_, with an average of 2415.038 \(\upmu\)g m\({}^{-3}\). The species with the poorest mitigation performance after both 24 and 48 h was _Calathea lancifolia_, with averages of 66.138 \(\upmu\)g m\({}^{-3}\) and 94.766 \(\upmu\)g m\({}^{-3}\), respectively (Figure 10).  
Figure 10: Maximum percentage of VOCs that studies have succeeded in reducing [27; 29; 31; 33; 36; 57].
## 5 Results  
### Correlation Analysis  
Spearman correlation coefficients and \(p\)-values were calculated for the selected pairs of variables. The results indicate the following: (a) a moderate positive correlation between the event count and the sessions, suggesting higher user engagement with increased event count; (b) no significant correlation between the purchase revenue and the sessions was found; (c) a perfect positive correlation between the transactions and the conversions, indicating a direct relationship; (d) a weak negative correlation between the bounce rate and the purchase revenue, suggesting a higher bounce rate might slightly reduce the purchase revenue; and (e) a weak positive correlation between the event count and the conversions, indicating that increased event interactions might lead to slightly higher conversion rates (Table 8) [21].
## 3 Results
### Optimization of the Method  
#### 3.2.1 Effect of pH  
Alkaline or acidic condition of samples plays a crucial part for the preconcentration of heavy metals by solid adsorbents because it has a direct effect on Pb(II) and Cd(II) ion retention on the negative sites of EgLiCl-mGO. It may be possible for the cations to form coordination compounds with oxygen groups via electrostatic force. Recovery of the investigated ions on the EgLiCl-mGO was found Pb(II): 99.1 and Cd(II): 98.4 in pH of \(4\pm 0.5\). Quantitative recovery values were found in the pH range of 2-9. After pH-5, recovery values of the preconcentrated ions decreased because heavy metal ions were hydrolyzed with formation of metal hydroxides. Therefore, pH-4 was applied as the optimized pH value for further experiments. Additionally, in highly acidic pH, the negative active sites will be occupied by H+ sooner than the target cations.
# Enhancing Absorption Bandwidth through Vertically Oriented Metamaterials
## 2 Achieving Super Absorption with Vertical Metamaterials: Structure under Analysis  
Full wave simulations in Computer Simulation Technology (CST) Microwave Studio [43], Lumerical FDTD [44], and COMSOL Multiphysics [45] were performed to explore the coupling between VSRR and PSRR geometries. Since the results from the three software packages are in perfect agreement, only one set of data, obtained using Lumerical, is reported for brevity. A mesh convergence study was performed to ensure computational accuracy and solution stability.  
A general device geometry is illustrated in Figure 1. In each simulation, the unit cell was considered infinitely periodic along the X and Y directions. The incident field was perpendicular to the plane of the supporting membrane, and the electric field was polarized parallel to the SRR gap. In this paper, we focus on spectral absorption as the figure of merit for comparing device behavior within the unit cell's parameter space. Absorption is defined as \(A=1-R-T\), where \(R\) and \(T\) represent reflectance and transmittance, respectively. This investigation considered three cases: a unit cell containing a single VSRR, a unit cell containing a single PSRR, and a unit cell containing a VSRR and PSRR. The first of these cases is illustrated in Figure 1.  
An individual SRR is defined by its height (\(l_{s}\)), width (\(w_{s}\)), thickness (\(t_{s}\)), arm width (\(w_{a}\)), and gap width (\(w_{g}\)). Additionally, rotation of the SRR about the \(y\)-axis could also be considered, but is not shown in Figure 1. The unit cell is defined by its periodicity along X and Y directions (\(\Lambda_{x}\) and \(\Lambda_{y}\), respectively), wall height (\(h_{w}\)), total wall thickness (\(t_{w}\)), membrane thickness (\(t_{m}\)), and thickness of the bottom gold layer (\(t_{au}\)). The SRR material is gold, and the walls and membrane are silicon. Due to the bottom gold layer, \(T=0\) and the absorption is simply \(A=1-R\). Dispersive optical constants for all the materials modeled were acquired using variable angle spectroscopic ellipsometry. The unit cell geometry is the same in each configuration, and is defined as \(\Lambda_{x}\) = 2.30 \(\mu\)m, \(\Lambda_{y}\) = 2.30 \(\mu\)m, \(h_{w}\) = 2.90 \(\mu\)m, \(t_{w}\) = 0.16 \(\mu\)m, \(t_{m}\) = 1.00 \(\mu\)m, and \(t_{au}\) = 0.50 \(\mu\)m.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 1 Introduction
Although existing studies have preliminarily explored the application of multimodal interaction technologies in remote education (e.g., speech recognition, affective computing), the following research gaps remain unresolved:  
* Real-time multimodal data fusion and latency mitigation in authentic classroom scenarios require more robust solutions;
* There is a lack of systematic research on the cross-cultural and multilingual adaptability of multimodal systems;* Most empirical validations are conducted in controlled laboratory environments, with limited large-scale data from real-world educational settings. This paper adopts the literature review method to review the representative research papers on educational multimodal interaction published in recent years and identify the technological advances, applications and challenges in this field.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
###### Abstract  
This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as delays in steering system execution. The proposed solution is a hierarchical control method, grounded in mass estimation and model predictive control(MPC). Initially, to counter the variation in model parameters, a mass estimator is developed. This estimator utilizes the recursive least squares method with a forgetting factor, coupled with M-estimation, thereby enhancing the robustness of the estimation and achieving model correction. Subsequently, an upper-level MPC controller is constructed based on the error model, thereby augmenting the precision of tracking control. To address the delay in the steering system execution common in autonomous commercial vehicles, a lower-level steering angle compensator is designed to expedite the response speed of the execution. The feasibility of the vehicle's front wheel angle is constrained via the rollover index, thereby enhancing vehicle stability during operation. The efficacy of the proposed control strategy is demonstrated with joint simulations using TruckSim/Simulink and real vehicle tests. The results indicate that this strategy can effectively manage the model mismatch caused by load changes in commercial vehicles and the delay in steering system execution, thereby exhibiting commendable tracking accuracy, adaptability, and driving stability.  
20252025593
###### Abstract  
To address the key challenges of poor real-time interaction, insufficient integration of operating rules, and limited virtual-physical synergy in current carrier-based aircraft scheduling simulations, this study proposes an immersive digital twin platform that integrates a two-layer genetic algorithm (GA) with hardware-in-the-loop (HIL) semi-physical validation. The platform architecture combines high-fidelity 3D visualization-based modeling (of aircraft, carrier deck, and auxiliary equipment) with real-time data exchange via TCP/IP, establishing a collaborative virtual-physical simulation environment. Three key innovations are presented: (1) a two-tier genetic algorithm (GA)-based scheduling model is proposed to coordinate global planning and dynamic execution optimization for carrier-based aircraft operations; (2) a systematic constraint integration framework incorporating aircraft taxing dynamics, deck spatial constraints, and safety clearance requirements into the scheduling system, significantly enhancing tactical feasibility compared to conventional approaches that oversimplify multidimensional operational rules; (3) an integrated virtual-physical simulation architecture merging virtual reality interaction with HIL verification, establishing a collaborative digital twin-physical device platform for immersive visualization of full-process operations and dynamic spatiotemporal evolution characterization. Experimental results indicate that this work bridges the gap between theoretical scheduling algorithms and practical naval aviation requirements, offering a standardized testing platform for intelligent carrier-based aircraft operations.  
carrier aircraft job scheduling; simulation platform; scene generation; combination of virtuality and reality; real-time presentation Article
## 1 Introduction  
Although a number of computer programs such as Abaqus, OpenSees, Ansys, and MSC.Marc are readily available for the structural analysis of frames, approaches to obtain the exact solutions in closed form are helpful in many situations. The exact analytical or numerical solutions are useful for ready use by engineers and researchers, for understanding the basic structural concepts, and for computational efficiency, for example, conducting parametric analysis conveniently based on an exact solution. In addition, the exact solutions may be used for verifying new structural analysis methods.  
Among the various structural analysis methods [1; 2; 3; 4; 5; 6], the matrix structural analysis [7; 8; 9; 10; 11; 12; 13; 14; 15] is found to be an efficient method ideally suitable for obtaining the exact solutions and for convenient computer implementations. The matrix structural analysis approach is essentially a matrix form of the displacement (stiffness) method [16; 17] in basic structural mechanics and has been widely used in the traditional analysis associated with the flexural deformations of beam-column elements. This approach analyzes each member as an element and focuses on the element-end deformations and forces. The element-end deformation-force relationship is governed by the element stiffness matrix, which can be assembled to be the structural stiffness matrix for analysis of the entire structure. Therefore, formulating the element stiffness matrix is essential for the matrix analysis approach.  
In the matrix structural analysis of the solutions for the buckling and second-order effect, the second-order effect (i.e., geometric nonlinearity) of beam-columns can be considered by using the exact element stability stiffness matrix. This second-order matrix structural analysis considering geometric nonlinearity has been verified in many classical publications as well as new applications (e.g., [7; 8; 11; 13; 14; 15; 18]).  
This paper undertakes the formulation of the element second-order stiffness matrix (or stability stiffness matrix) of axial-loaded Timoshenko beam-columns considering geometric nonlinearity and the application of the stiffness matrix for stability analyses. An equilibrium analysis of an axial-loaded Timoshenko beam-column is conducted, and the element flexural deformations (translation and rotation angle) and corresponding forces (shear force and bending moment) are analyzed. The element stability stiffness matrix is derived to show the relationship between the element-end flexural deformations and the corresponding forces. A matrix structural analysis approach for the elastic buckling analysis of Timoshenko beam-columns is then established, and classical application examples are further presented. The errors of a previous simplified expression of the stability stiffness matrix are discussed by comparison with the exact expression.
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 5 Open Problems
### Overlapping Reversed LZ Factorization  
[PERSON] et al. (2014) ([Section 5]) gave a variation of \(\mathsf{LPnrf}\) that supports overlaps, and called the resulting array the longest previous reverse factor table \(\mathsf{LPrF}\), where \(\mathsf{LPrF}[i]\) is the maximum length \(\ell\) such that \(T[i\,..\,i+\ell-1]=T[j\,..\,j+\ell-1]^{\text{R}}\) for a \(j<i\). The respective factorization, called the overlapping reversed LZ factorization, was proposed by [PERSON] et al. (2015) ([Definition 4]): A factorization \(F_{1}\cdots F_{z}=T\) is called the overlapping reversed LZ factorization of \(T\) if each factor \(F_{x}\) is either the leftmost occurrence of a character or the longest prefix of \(F_{x}\cdots F_{z}\) that has at least one reversed occurrence in \(F_{1}\cdots F_{x}\) starting before \(F_{x}\), for \(x\in[1\,..\,z]\). We can compute the overlapping reversed LZ factorization with \(\mathsf{LPrF}\) analogously to computing the (non-overlapping) reversed LZ factorization with \(\mathsf{LPnrf}\). As an example, the overlapping reversed LZ factorization of \(T=\mathsf{abbabbab}\) is \(\mathsf{a}\cdot\mathsf{bbabba}\cdot\mathsf{bab}\). Table 4 gives an example for \(\mathsf{LPrF}\).  
Since \(\mathsf{LPrF}[i]\geq\mathsf{LPnrf}[i]\) by definition, the overlapping factorization seems more likely to have fewer factors. Unfortunately, this factorization cannot be expressed in a compact coding like Section 3.1 that stores enough information to restore the original text. To see this, take a palindrome \(P\), and compute the overlapping reversed LZ factorization of \(\mathsf{a}P\mathsf{a}\). The factorization creates the two factors \(\mathsf{a}\) and \(P\mathsf{a}\). The second factor is \(P\mathsf{a}\) since \((P\mathsf{a})^{\text{R}}=\mathsf{a}P\). However, a coding of the second factor needs to store additional information about \(P\) to support restoring the characters of this factor. It seems that we need to store the entire left arm of \(P\), including the middle character for odd palindromes.  
Besides searching for an efficient coding for the overlapping reversed LZ factorization, we would like to improve the working space bounds needed for its computation. All algorithms we are aware of [5,14] embrace [PERSON]'s algorithm [42,43] to find the maximal palindromes of each text position. To run in linear time, [PERSON] stores the arm lengths of these palindromes in a plain array of \(n\lg n\) bits. Unfortunately, we are unaware of any time/space trade-offs regarding this array.
## 4 Optimization of the Damping Layer's Position and Thickness
 \tag{31}\]  
The two functions above are employed to simulate the manufacturing tolerances that arise in the optimized function \(h_{dpa}(x)\), presented in Equation (30). The impact of manufacturing tolerances in the damping layer thickness on the ABH effect is illustrated in Figure 14.  
Figure 12: The graph of functions \(h_{dpa}(x)\) and \(h_{d\tau}(x)\) given in Equations (27) and (30), respectively. The areas enclosed by the two function curves and the x\(-\)axis are the same. Figure 13: The comparison of the impact of damping layer thicknesses set according to the functions \(h_{dpa}(x)\) and \(h_{d\tau}(x)\), as defined in Equations (27) and (30), on the ABH effect. As can be seen from Figure 14, when the optimized shape of the thickness does not undergo significant changes, minor manufacturing tolerances in the damping layer thickness only cause minor variations in the system's vibration reduction performance. When the damping layer thickness is slightly increased, the system's vibration reduction performance is also slightly enhanced, and vice versa. This indicates that optimizing the thickness profile of the damping layer is feasible in practical applications. To achieve more precise control over the thickness of the damping layer in actual manufacturing, the following strategies and methods can be considered [42; 43]: Firstly, the thickness of damping materials can be controlled through optimized design. For instance, the surface of the structure can be divided into multiple zones, with the thickness of the damping material in each zone treated as a design variable for optimization. This approach allows the damping layer to meet specific requirements for vibration reduction. Secondly, the performance of damping materials can be enhanced through methods such as blending and copolymerization. These techniques help minimize tolerance issues arising from material inhomogeneity. Additionally, a multi-layer structural design can be employed, with the overall thickness uniformity controlled by adjusting the thickness and number of layers. Moreover, to maximize the effectiveness of the damping layer, the bonding area of the damping material should be as extensive as possible. Considering the entire construction process, a bonding area exceeding 80% can serve as a process indicator to ensure the quality of damping material installation. Figure 14: The impact of manufacturing tolerances in the damping layer thickness on the ABH effect: (**a**) the impact of the increased thickness; (**b**) the impact of thickness reduction.