esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Torsional Behavior Evaluation of Reinforced Concrete Beams Using Artificial Neural Network_  
**[PERSON] 1,2\({}^{\text{\textregistered}}\)**, [PERSON] 3\({}^{\text{\textregistered}}\), [PERSON] 1\({}^{\text{\textregistered}}\) and [PERSON] 4\({}^{\text{\textregistered},\text{\textregistered}}\)**-**(\({}^{\text{\textregistered}}\)  
Footnote 1: School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University,  
Seoul 03063, Korea; [EMAIL_ADDRESS] (M.H.); [EMAIL_ADDRESS] (D.S.)  
2 Civil Engineering Balochsian UET, Khuzdar 89100, Pakistan  
3 The MathWorks Inc., Natick, MA 01760, USA; [EMAIL_ADDRESS]  
4 Department of Architectural Engineering, Inha University, Inchen 22212, Korea  
\({}^{\text{\textregistered}}\)Correspondence: [EMAIL_ADDRESS]; Tel.: +82-32-860-7565  
**Abstract:** Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.  
**Keywords:** RC beam; torsion; artificial neural network; machine learning; PCA; autoencoder
## 1 Introduction
An international group of experts led by [PERSON] recently proposed a minimum of 250 lux (melanopic EDI) daytime level, 10 lux evening level and a 1 lux maximum as the night level for a hypothetical observer. Their results showed that a melanopic EDI below 4 lux results in minimal responses (<25% of maximum melatonin suppression), and a melanopic EDI above 300 lux strongly suppresses salivary melatonin (>75% of the maximum), depending on the exposure duration and experimental context [15]. These results have been corroborated by [PERSON] et al. [16]; even with longer durations (e.g., 270 min), a 75% suppression of melatonin can already occur at a melanopic EDI of 100 lux. Many studies have shown large individual variations in circadian photosensitivity across individuals, such as melatonin suppression, due to artificial light exposure, but recommendations for appropriate light exposure in real-life settings rarely consider such individual effects [8]. Individual differences in light sensitivity also occur at earlier developmental stages, and melatonin suppression by light is higher in children than in adults since they have large pupils and high spectral transmittance of the crystalline lens at short wavelengths; the nonvisual photoreception in 10-year-old children is twice that of 45-year-old adults [17]. Even healthy aging is associated with a progressive decrease in light transmission due to the clouding and yellowing of the natural crystalline lens, especially for short-wavelength light. When age-related cataracts develop, further worsening of ocular processes is found; specifically, they are associated with the disruption of circadian rhythms. However, when patients with cataracts undergo intraocular lens replacement with optimized spectral lens transmission, they may improve circadian photosensitivity, sleep, and cognitive function despite small pupils [8; 9; 18; 19]. As has been intended and described by other authors [11; 20; 21], providing a simple process for smart or integrative lighting design is important when the process is properly integrated into a traditional workflow. Its implementation requires lighting designers to understand the tedious terminology defined, evaluated and correlated, when possible, to elucidate strengths and limitations to progress in its knowledge. Many available calculators for computing nonvisual quantities are available as a recipe to promote circadian lighting projects in parallel to real designs and without real integration in the lighting design process. Recently, [PERSON] et al. [20] showed an implemented process to guide decisions prior to the transfer to the lighting built, and a posterior audit focused on no single solution promoting integrative points of view. In this paper, we first summarize potential key findings related to the aging of the crystalline lens on the spectral sensitivity of the melanopsin photopgment expressed in ipRGCs [22; 23; 24; 25]. In a second step, an easy and simplified method to calculate melanopic contribution depending on age is presented to be considered in lighting projects.
## 6 Applications of Actinobacteria in Biotechnology
### Antimicrobials  
Actinobacteria are best known as an agent that fight multi-drug resistant pathogenic organisms and antimicrobial compounds producers. The high increase in antibiotic resistance has urgently called for the screening and search for novel antibiotics with a new mechanism of action. Literature search has affirmed the genera _Streptomyces_ and _Micromospora_ as the major genera of actinobacteria responsible for almost 80% of antibiotic production [80, 81]. Some of the actinobacteria that possess antimicrobial abilities are _Streptomyces rimosus_[82] and _Streptomyces parvulus_[83]. Antibiotics produced from the bioactive compounds of actinobacteria can be classified into different classes. Example include aminoglycosides (kanamycin), anthracyclines (doxorubicin), macrolides (erythromycin), and ansamycins. Furthermore, some actinobacteria produce more than one antibiotic substance (_Streptomyces griesus_) and some antibiotic may be produced by different species of actinobacteria.
## 2 Methodology
### Grid Division and Independence  
In this study, the geometry of the V-shaped hole cleaning device meshed in a structured grid. Structured hexahedral grids were generated in the entire domain of the annulus, as shown in Figure 2.  
We conducted a mesh independent analysis of the V-shaped hole cleaning device with 522,100, 784,950, 1,036,950, and 1,396,200 hexahedral grids. The difference in pressure drops between 522,100 and 784,950 hexahedral grids was about 6.21%, and the difference in pressure drop between 784,950 and 1,036,950 hexahedral nodes was less than 0.5% (as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Properties** & **Values** & **Units** \\ \hline \(D_{1}\) & Hole diameter & 120 & mm \\ \(D_{2}\) & Pipe diameter & 73 & mm \\ \(E\) & Eccentricity & 0 & - \\ \(\rho_{1}\) & Density of fluid & 1000 & kg/m\({}^{3}\) \\ \(\rho_{8}\) & Density of cuttings & 2500 & kg/m\({}^{3}\) \\ \(K\) & Consistency index & 0.01/0.05/0.10/0.50 & Pa.s\({}^{n}\) \\ \(n\) & Flow behavior index & 0.7 & - \\ \(\omega\) & Rotational speed & 80/120/160/200 & rpm \\ \(d_{8}\) & Diameter of cuttings & 2 & mm \\ \(u_{in}\) & Fluid inlet velocity & 0.5 & m/s \\ \(h\) & Height of blade & 5.75 & mm \\ \(N\) & Number of blades & 4 & - \\ \(\alpha\) & Helix angle of blade & 0; 10; 20; 30 & degree \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters used in simulations.  
Figure 2: Grid representation of calculation region.  
shown in Table 2). The grids with 1,036,950 hexahedral grids produced grid-independent results as per the accuracy selected to balance computational cost and accuracy.  
Error was used to check the precision of the calculation method for different grid sizes:  
\[\text{error}=\left|\frac{\Delta p-\Delta p_{\text{finest mesh}}}{\Delta p_{ \text{finest mesh}}}\right|\times 100\% \tag{19}\]
# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 3 Data Annotation and Ground Truth Generation
### Dual N-Back Test  
N-Back-tests are memory demanding games requiring the resolution of tasks according to a stimulus presented N trials before. We used three variants of the n-back-tests to induce low, medium, and high mental workloads:  
1. Position 1-back for low workload. A square appears every few seconds in one of eight different positions on a regular grid over the screen. Players must press a keyboard key when the position of the square on the current screen is the same as the square of the previous grid.
2. Arithmetic 1-back for medium workload. An integer between 0 and 9 appears every few seconds on the screen while an audio message says an arithmetic operation (plus, minus, multiply, or divide). Players have to solve this operation using the current number and number that appeared prior.
3. Dual arithmetic 2-back for high workload. This test combines the two previous ones. An integer between 0 and 9 appears every few seconds in one of eight different positions on a regular grid. At the same time, for each number that appears on screen, an operator is presented with an audio message. As before, players have to solve this operation using the current number and the number that appeared two instances before. In addition, players have to press a key if the position of the current number is the same as the position of the number shown two screens before.  
The neurophysiological response of a subject against mentally demanding tasks depends on his baseline state, which is prone to vary across time. In order to account for differences in the baseline states of subjects, prior to the n-back-tests, participants watched a relaxing video for 10 min. For each experiment (1--low, 2--medium, and 3--high workload), we had a video watching stage, a baseline phase, and the n-back-test, the workload phase. Thus, we call BL1, BL2, and BL3 the baseline phases of the experiments; and WL1, WL2, and WL3 are devoted to the workload phases of the experiments.  
After the game, participants answered a TLX questionnaire to collect their subjective perceptions of game difficulty and workload. Results presented in [38] showed that the level of difficulty of the games was correlated to the performance of players and also to the subjective perception of WL computed using NASA-TLX questionnaire.  
A total of 20 subjects participated in the experiment. Subjects were adults between 20 and 60; all of them were healthy without any condition that might have caused an imbalance in the data recorded. The sequence of tasks was randomly assigned to subjects, and recording of each session was on different days and hours.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 4 Results  
In this section, in order to study the effect of satellite orbital parameters on the number of minimum PRN codes, we take the polar-orbiting, walker, and hybrid constellations as experimental objects and come to the conclusions.
## 2 Theory, Requirements and Synthesis
### Closed-Loop Control (CLC)  
#### 2.3.1 Limitations and Requirements  
We propose a concept that recycles the resonant energy, coupled into the otherwise unused drop port, to provide feedback to a CLC system. Namely, when the (un)desired sideband is most suppressed at the through port, most of its energy is coupled into the drop port. The task of the CLC system is to maintain the relative alignment of the laser-source's wavelength and the MRR's resonance such that one of the sidebands is accurately aligned with the resonance. We will now elucidate the closed-loop topology connected between the input and the drop port of the MRR, as marked by the underlying yellow polygon in Figure 2. The blue polygon marks the part of the topology constructing the RoF link, while the pink polygon denotes the feedback topology. At the input of the MRR the signal features a typical ODSB spectral profile, whereas at the through and drop ports the signals have one of their sidebands suppressed or isolated, respectively. Note, that a real, i.e., a lossy, MRR has a finite ER, which is one of the limitations to the performance of MRR-enabled OSSB RoF links.  
Figure 1: Cyclic causation diagram showing the inherent and unwanted instability of the MRR-enabled power-fading mitigation concept. As “problem #1” is treated by the “solution” brought by the MRR’s OTF (dotted green line) the original ODSB signal (dash-dotted pink line) is modified into the OSSB signal (solid purple line). This yields the “result”-ing signal with a reduced power ripple in the power-penalty function (solid blue line) compared to the non-treated result (dashed turquoise line). Due to MRR’s temperature dependence (“problem #2”) its OTF (solid green line) detunes over time (dash-dot-dotted orange line) degrading the carrier-to-sideband ratio (CSR).
## 7 Evaluation
The WRFH technique significantly improves the performance of the RFH, but is still far from the performance level of the other hopping techniques. Among the other hopping techniques, it cannot be said that a specific technique is much better than the others, and the same happens with the quality metrics. For the considered scenario, the combination of UBAFH technique and 95% quantile metric results to be the best solution with a 9% of overall PER, but different interfering scenarios might result in other quality metrics and hopping techniques being the best. Configuration parameters other than the ones presented in Table 4 could also yield a different solution. Nevertheless, the main objective of the method here presented is not to determine a quality metric and a hopping technique that best fits most of the possible scenarios, but to automatically find the best solution for any specific scenario after analysing all the possible solutions. Figure 22: Packet Error Rate for all possible node connections for different quality metrics and hopping techniques. Each element contains the Packet Error Rate (PER) value (percentage) for the specified connection. \(1>G\), for instance, indicates the connection from node 1 to the gateway. Figure 23: Inferred topologies with \(w_{\mathit{PER}}=1\) and \(w_{\mathit{DIST}}=0\). Minimizing the overall PER results in direct connections between nodes and gateway for all the hopping techniques and quality metrics. Figure 24: Inferred topologies with \(w_{PER}=0\) and \(w_{DIST}=1\). Minimizing the transmission distances results in multi-hop connections for all the hopping techniques and quality metrics. The overall PER is considerably increased, except for the HGFH technique, which remains the same as with single-hop topology. Figure 25: Inferred topologies with \(w_{PEE}=0.5\) and \(w_{DIST}=0.5\). Different hopping techniques and different quality metrics result in different topologies.
# Simultaneous Analysis of Heavy Metal Concentration in Soil Samples
## 4 Conclusions  
The application of FT-IR enables the simultaneous determination of several harmful metals in soil samples, which impacts the cost, speed, and effectiveness of environmental monitoring. The accuracy achieved with this method was good, but lower than that realized using conventional laboratory analyses. Such results indicated that FT-IR can be considered as a supplementary method to other traditionally used instrumental methods for determining metal content, such as ICP-OES. The PLS models constructed from the FT-IR spectra for the studied dataset were useful in the estimation of soil contamination by heavy metals. The results obtained indicated that both spectral techniques, namely, attenuated total reflectance (ATR) and diffuse reflectance (DRIFT), and spectra collected to each spectral region, namely far-IR, mid-IR and near-IR, enabled the construction of proper calibration models for a large number of samples for a single region for following metals: Cr, Zn, Ba, Hg, Ni, and Pb. Application of spectra for the far-IR slightly improved the prediction possibilities of the model in comparison to the mid-IR region. The models based on NIR-spectra were characterized by slightly better linearity than other ranges and the lowest RMS values. Attempts to create good models failed for several metals: Mo, Sn, As, Se, and Cu. Taking into account calibration and prediction errors, FT-IR spectroscopy could be applied in the monitoring of changes over time regarding heavy metal concentrations or the mapping of contamination levels across large areas.  
Conceptualization, A.S. and A.S.; methodology, A.S.; validation, A.S. and P.K.; formal analysis, A.S. and A.S.; investigation, A.S. and A.S. data curation, A.S.; writing--original draft preparation, A.S. and P.K.; writing--review and editing, A.S.; visualization, A.S. and A.S.; supervision, A.S.  
**Funding:** The research work was supported by the Ministry of Science and Higher Education within the Research Project no 111101117-321.  
**Acknowledgments:** We would like to thank the team of the Laboratory of Solid Waste Analyses of the Central Mining Institute and especially [PERSON] for advice, performing analyses and providing samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 L-MMCF Manufacturing Process
### Fibre Spinning  
The dope is forced through a heated tube to reduce viscosity and improve rheological properties for fibre formation [2; 47]. The spinneret is located at the end of the heated tube, which consists of a fitting with small holes, varying in size and quantity depending on end application. The resulting fibres are then subjected to air over a specified distance (i.e., air gap spinning). The length of the exposure to air directly correlates with the desired crystallinity of the formed fibre. Shorter distances result in more amorphous regions being formed, while longer distances result in high degrees of crystallinity and fibril alignment. The fibres then enter a coagulation bath, which contains a dilute solution of NMMO. The fibres reach their final state in this bath. The temperature of the bath is kept at room temperature. After fibre formation is complete, the fibres are washed in a demineralised water bath to remove the residual NMMO from the fibre surface. The fibres are then dried and wound for further processing. Figure 7 illustrates the L-MMCF wet jet-dry spinning process [20]. The NMMO from all stages in production is recovered for reuse in pulp dissolution with a recovery rate of 99% or more [11; 21; 22; 43].  
Figure 6: Phase diagram cellulose-NMMO-water (reproduced from [20] with permission from Elsevier).  
Fibre properties can be modified at each stage of the spinning process, depending on end use specifications [20]. Additionally, environmental conditions, such as temperature and humidity, can affect the rate of crystallisation, degree of crystallisation, and fibre orientation. Rheological properties, specifically the viscosity of the dope at various temperatures (i.e., viscous response and elastic response), rate of spinning (e.g., extrusion pressure, frictional forces within the capillary), spinneret geometry, drawing rate, and winder torque also affect these parameters, as well as the potential for fibrillation to occur along the length of the formed fibre [8; 22; 119]. For example, drawing the fibres harder will align fibrils in the axial direction, significantly affecting fibre orientation and denier The distance of the air gap is critical for determining fibre orientation, and whether the crystallites remain oriented or relax upon entering the coagulation bath. An air gap of 10 to 20 cm has been found to promote fibre orientation and crystallinity without excessive relaxation or promotion of fibrillation [8].
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 2 Reduced-Order Modeling  
### Kolmogorov-Arnold Networks  
Kolmogorov-Arnold Networks (KANs) are a novel neural network architecture based on the Kolmogorov-Arnold representation theorem, which provides a theoretical foundation for expressing multivariate continuous functions through compositions of univariatefunctions. This enables the decomposition of complex functions into simpler and more interpretable components, making KANs highly effective for function approximation. Figure 1 illustrates the architecture of Kolmogorov-Arnold Networks. According to the Kolmogorov-Arnold theorem, any continuous multivariate function can be expressed as a combination of finite univariate functions, as shown by the following equation:  
\[f(\mathbf{x})=f(x_{1},\ldots,x_{n})=\sum_{q=1}^{2n+1}\Phi_{q}\left(\sum_{p=1}^ {n}\phi_{q,p}(x_{p})\right) \tag{1}\]  
where \(\phi_{q,p}(x_{p})\) represents a univariate function defined on each input variable \(x_{p}\), while \(\Phi_{q}\) is responsible for combining these univariate functions into the final approximation.  
KANs leverage this theoretical property by parameterizing a set of functions, replacing conventional linear weights in neural networks with univariate functions represented by spline curves. In contrast to traditional multilayer perceptrons (MLPs), which utilize predefined activation functions at their nodes, KANs introduce adaptive activation functions on the connections between nodes. These activations, modeled using B-spline parameterizations, are capable of adjusting dynamically throughout training to more accurately model the underlying data characteristics. Formally, a KAN layer with \(n_{\text{in}}\)-dimensional inputs and \(n_{\text{out}}\)-dimensional outputs can be expressed as an array of one-dimensional functions \(\mathbf{\Phi}=\{\phi_{q,p}\}\), indexed by \(p=1,2,\ldots,n_{\text{in}}\) and \(q=1,2,\ldots,n_{\text{out}}\). Each function \(\phi_{q,p}\) is characterized by parameters that are optimized during the training process. Extending this concept to practical implementations, KAN architectures typically adopt a multilayer structure, where each layer sequentially applies transformations inspired by the Kolmogorov-Arnold representation. Consequently, the overall mapping from input to output in a multilayer KAN model can be succinctly expressed through function composition as  
\[\text{KAN}(\mathbf{x})=(\mathbf{\Phi}_{L-1}\circ\mathbf{\Phi}_{L-2}\circ\cdots \circ\mathbf{\Phi}_{1}\circ\mathbf{\Phi}_{0})\mathbf{x} \tag{2}\]  
where each \(\mathbf{\Phi}_{l}\) corresponds to a distinct KAN layer. This layered architecture not only retains the hierarchical structure characteristic of conventional neural networks but also offers exceptional nonlinear approximation capabilities, making KAN particularly effective for complex data modeling and analytical tasks.