esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Impact of external errors
### Impact of orbit selection
Sticking to classical repeat orbits that remain located within a +/- 1 km band from a nominal ground-track (to make easier the study of time-varying phenomena), we can then infer the frequency of maneuvers to maintain the orbit as it is required. The required rotation of the panels around their symmetric axis extends the surface drag of the satellite, accelerating the frequency of maneuvers and then reducing the satellite life-time or degrading the mass and cost budgets. In addition, the rotation of the interferometer antennas around the yaw axis further increases the drag. Figure 11 shows how the atmospheric drag can be decreased by approximately 80 % when selecting a sun-synchronous orbit. As a consequence, the frequency of maneuvers - which can be calculated from equation (14) (see Appendix 2) that gives the duration between 2 successive maneuver s- is decreased by a factor of 5. \[t\leq\sqrt{\frac{4m2\Delta trace_{\max}}{3R_{\gamma}\Omega_{\gamma}SC_{D}\rho_{0 }}\sqrt{\frac{a_{0}}{\mu}}} \tag{14}\]  
Figure 11: Evolution of the different contributions to the overall satellite surface drag. Figure 10: (a) Impact of the yaw steering onboard Jason-2 on the predicted observing time (b) Impact on the signal processing in terms of Doppler centroid. As far as the interferometric altimetry system is considered, flying a sun-synchronous orbit would eliminate many of the drawbacks of flying a non sun-synchronous one: (i) the observation geometry would be kept constant, (ii) the carrier platform could be made very stable and the sun-satellite relative geometry would be always optimal to provide energy to the satellite, avoiding negative impacts on the interferometric error budget. The overall impact of this change of orbit, and especially the problem of tidal aliasing will be considered in section 5.
###### Abstract  
This study investigated the proportions and clinicopathological presentations of oral candidal lesions in type 2 diabetes mellitus (DM) patients attending the Diabetic Clinic at Teaching Hospital Peradeniya; Sri Lanka. A descriptive cross-sectional study was conducted on 355 DM patients aged over 18 years who had been receiving treatment for at least one year. Clinical photographs and periodic acid-Schiff (PAS)-stained cytological specimens confirmed diagnoses. Oral candidal lesions were found in 17.6% of patients; with Denture Stomatitis (4.0%), Erythermatous Candidiasis (3.4%), Pseudomonas Candidiasis (3.1%), and Chronic Hyperplastic Candidiasis (2.8%) being the most common types. Notably; all lesions were identified as incidental findings. Erythermatous Candidiasis was more frequently noted among individuals older than 60 years (\(p\) = 0.041); while Denture Stomatitis was more common with higher glycemic levels (>140 mg/dL) (\(p\) = 0.045). Males were significantly more susceptible to oral candidal lesions (\(p\) = 0.002); except for Pseudomonas Candidiasis and Denture Stomatitis; which were more frequent in females. Smoking (\(p\) = 0.005) and betel quid chewing (\(p\) = 0.008) were also identified as significant risk factors. Binary logistic regression revealed that males (OR = 3.160) and denture weerrs (OR = 2.348) had a higher likelihood of developing oral candidal lesions. Despite the relatively low prevalence; routine oral examinations are recommended for early detection and management; ensuring better oral health in this at-risk population.  
diabetes mellitus; _Candidia_; denture stomatitis; erythemat candidiasis; pseudomembranous candidiasis; chronic hyperplastic candidiasis
## 4 Empirical Insights from Sectorial and Regional Studies
### AI Adoption and Digital Competencies in Spanish SMEs  
[PERSON] et al. [85] present a robust empirical investigation of the adoption of AI among Spanish SMEs, highlighting the decisive role of digital competencies and skill development. Based on a representative sample of 836 SMEs and a logistic regression analysis enhanced by Generative Adversarial Networks (GANs), the study identifies key enablers mapped to the organizational and technological dimensions of the TOE framework. Firms led by university-educated managers, staffed with information technology specialists and engaged in internal IT training initiatives showed significantly higher AI adoption rates.  
In addition, the adoption of ERP systems and marketing analytics tools--indicators of digital maturity--is positively correlated with the integration of AI, reinforcing the notion that cumulative digital capacity accelerates the adoption of innovation. In particular, SMEs collaborating with universities and research centers had a six-fold increase in the likelihood of adoption of AI, underscoring the environmental enablers emphasized in the TOE model. Quantitative indicators such as a 94.3% sensitivity and 85.7% overall model accuracy strengthen the reliability of these insights. This study offers both theoretical reinforcement and practical validation for our framework by illustrating how targeted capacity building and ecosystem collaboration can overcome the adoption inertia in resource-constrained contexts.
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 2 Materials and Methods
### Model Selection  
Once the H2 OAutoML model is trained, we obtain the performance metrics on test data of all the models--in our case, 15 models--built by H2 OAutoML and sorted by their performance in terms of AUC. The Gradient Boosting Machine (GBM) classification model achieved an AUC of 0.8256, marginally lower than the Generalized Linear Modeling (GLM) model's AUC of 0.826. However, the decision to use the GBM model was driven by the need for more nuanced insights from variable importance analysis, which the GBM facilitates through its ensemble of decision trees. While GLMs are advantageous due to their simplicity and interpretability, they can sometimes fall short in capturing complex nonlinear relationships in the data that GBM models can uncover. The GBM model's ability to identify these intricate patterns was deemed valuable for our predictive analysis, potentially providing a richer understanding of the factors influencing the outcomes. The GBM model for classification that was generated by H2O for this research study consists of 50 trees with a depth range of 5. The model has a relatively small size, occupying approximately 22.7 KB in memory. The GBM algorithm leverages an ensemble of decision trees and boosting methods to iteratively optimize the model's predictive functioning by decreasing the loss function [36]. Each tree in the ensemble is built to adjust the errors made by the preceding trees, with a greater emphasis placed on the samples with higher residuals. This iterative process allows the model to capture intricate interactions and nonlinear relationships in the data, leading to enhanced predictive accuracy.  
The GBM model's summary provides valuable insights into its structure and characteristics. The depth of the trees indicates the complexity of the learned relationships, with a maximum depth of 5 indicating that the model can capture moderately complex patterns [37]. The number of leaves in each tree indicates the model's granularity, with an average of 28.5 leaves per tree. This level of granularity allows the model to make fine-grained predictions by considering a variety of conditions within the input features. The summary also reports the number of internal trees, which refers to the number of trees excluding the initial tree in the boosting process. This information helps assess the computational complexity of the model and the number of iterations required for convergence. The GBM model, with its ensemble of decision trees and boosting techniques, offers a powerful tool for predictive modeling in various domains. It has been commonly utilizedin applications like fraud detection, customer churn prediction, and disease diagnosis [38]. The model's ability to capture complex relationships and handle large-scale datasets makes it suitable for a wide range of research and practical applications. Researchers and practitioners can leverage the GBM model's capabilities to uncover important insights from their data and make accurate predictions.
# Silicon Ribbon-Based Dual-Beam Optical Phased Array with Low Crosstalk and Large FoV
###### Abstract  
In this paper, a silicon ribbon (SR)-based microstructure is developed and added to a 32-channel optical phased array (OPA) to reduce the crosstalk between the antennas of grating waveguides. The spacing between the chirped grating antennas can be as close as 600 nm to effectively improve the field of view (FoV) of the OPA in the horizontal direction to 95 degrees. This SR-based approach substantially reduces the side lobe by 10 dB, effectively suppressing the noise and increasing the main lobe by 6 dB and considerably expanding the grating length with linear energy decay. The full width at the half maximum of the light spot reaches about 0.24 degrees. The antenna sites can simultaneously be scanned vertically by bi-directional inputs, effectively increasing the FoV to 30 degrees in the vertical direction.  
LiDAR; waveguide; optical phased array +
Footnote †: journal: Applied sciences
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 4 Biomedical Applications
### Magnetic Separation (MS)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Application** & **Magnetic Nanoparticles** & **Diameter** & **Synthesis Method** & **Aim** & **Findings** & **References** \\ \hline \multirow{4}{*}{Magnetection} & Polyethylneimine & \multirow{2}{*}{10–15} & Chemical & Intracellular gene & Therapeutic efficiency through & \multirow{4}{*}{[189]} \\  & MNFs loaded & & co-precipitation and & electrostatic & silencing and & apoptosis and autophagy & \\  & with siRNA & & adsorption & imaging & induction in glioblastoma cells & \\ \cline{2-6}  & Polyethylneimine iron & \multirow{2}{*}{10} & Co-precipitation and & Gene expression & Anti-tumor effect based on & \multirow{4}{*}{[190]} \\  & oxide NPs loaded with & & electrostatic & mediation & apoptosis on oral squamous cell & \\  & gene vectors & & adsorption & & carcinoma; high & carcinoma; high \\  & (pACTER-EGFP) & & & & transfection efficiency & \\ \cline{2-6}  & Polyethylneimine iron & \multirow{2}{*}{100–156} & Thermal & \multirow{2}{*}{RNA delivery} & Efficient protein knockdown in & \multirow{2}{*}{[191]} \\  & oxide NPs loaded with & & decomposition & & breast cancer & \\ \cline{2-6}  & Iron oxide & \multirow{2}{*}{6} & Non-toxic metal & Reproductive & Sertoli cells damage in blood & \multirow{2}{*}{[192]} \\  & salts-based protocol & & system evaluation & testicular barrier, organ & \\  & & & in vivo & accumulation and cytotoxicity & \\ \hline \multirow{4}{*}{Magnetic resonance imaging} & Iron oxide & \multirow{2}{*}{1–3} & Modified thermal & Testing tissue & \multirow{2}{*}{Effective brain imaging} & \multirow{2}{*}{[193]} \\  & imaging & & method & & & \\ \cline{2-6}  & Anti-mesothelin & & & & & \\ \cline{2-6}  & antibody conjugated & \multirow{2}{*}{110–130} & \multirow{2}{*}{Co-precipitation and & \multirow{2}{*}{In vivo}} & Pancreatic cancer & \multirow{2}{*}{Efficient targeting of SW1990 cells; } \\  & with silica-coated iron & & incubation with & targeting and & & high stability and nontoxicity both & \multirow{2}{*}{[194]} \\  & oxide NPs & & antibody & imaging & & in vitro and in vivo & \\ \hline \multirow{4}{*}{Magnetic particle imaging} & Iron oxide NPs & Unknown & \multirow{2}{*}{Unknown} & Preclinical & \multirow{2}{*}{Highly iron oxide NPs} & \multirow{2}{*}{[182]} \\  & & & evaluation of & & & \\ \cline{2-6}  & Iron oxide NPs & Unknown & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \cline{2-6}  & & & & & & \\ \hline \multirow{4}{*}{Magnetic particle imaging} & \multirow{4}{*}{SPIONs} & \multirow{4}{*}{200} & Thermal & \multirow{4}{*}{Investigation of \(
# An SQP Algorithm for Structural Topology Optimization Based on Majorization-Minimization Method
###### Abstract  
When applying the sequential quadratic programming (SQP) algorithm to topology optimization, using the quasi-Newton methods or calculating the Hessian matrix directly will result in a considerable amount of calculation, making it computationally infeasible when the number of optimization variables is large. To solve the above problems, this paper creatively proposes a method for calculating the approximate Hessian matrix for structural topology optimization with minimum compliance problems. Then, the second-order Taylor expansion transforms the original problem into a series of separable and easy-to-solve convex quadratic programming (QP) subproblems. Finally, the quadratic programming optimality criteria (QPOC) method and the QP solver of MATLAB are used to solve the subproblems. Compared with other sequential quadratic programming methods, the advantage of the proposed method is that the Hessian matrix is diagonally positive definite and its calculation is simple. Numerical experiments on an MBB beam and cantilever beam verify the feasibility and efficiency of the proposed method.  
topology optimization; majorization-minimization; Hessian matrix; sequential quadratic programming; compliance +
Footnote †: journal: Applied  
doi:10.3390/app12136304  
Footnote †: journal: Applied
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 4 Algorithm Simulation and Analysis
### Load Balancing Across Cluster Members  
Load balancing is critical in wireless sensor networks to prevent early node death due to uneven communication burden. We define load imbalance as the standard deviation of data transmission counts among nodes. Lower values indicate more equitable task distribution across the network. Figure 7 presents the load imbalance over time for all three protocols. LEAD-RP exhibits the lowest load imbalance due to its dual-factor (energy and distance) cluster head selection and dynamic cluster radius adjustment. LEACH, on the other hand, shows increasing imbalance as nodes die prematurely due to poor head placement, while HEED offers moderate improvement but lacks real-time adaptivity.  
As shown in Table 5, LEAD-RP consistently outperforms LEACH and HEED across both node scales. It achieves lower energy consumption, a longer network lifetime, and higher data throughput. Moreover, it offers superior stability in cluster head distribution and significantly better load balancing. These results indicate that the proposed LEAD-RP protocol is both scalable and robust under different network densities. Paired \(t\)-tests confirm that all LEAD-RP improvements over HEED are statistically significant (\(p<0.01\)) with [PERSON]'s \(d>0.8\). Though EDsHEED outperforms LEACH by 18% in terms of network lifetime, its static clustering radius causes 23% higher energy imbalance than LEAD-RP in dense networks (\(N=300\)).
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Basic Statistics
#### 4.3.2 Daily Data
 \\ \hline average temperature (\({}^{\circ}\)C) & \(-19.1\) & \(26\) & \(7.4\) & \(8.6\) & \(-1.3\) & \(16.3\) \\ minimum temperature (\({}^{\circ}\)C) & \(-26.7\) & \(23.3\) & \(2.7\) & \(3\) & \(-5.7\) & \(12.1\) \\ maximum temperature (\({}^{\circ}\)C) & \(-15.3\) & \(32.6\) & \(12.2\) & \(13.8\) & \(2.9\) & \(21\) \\ amount of water precipitation (mm) & \(0\) & \(8.5\) & \(0.1\) & \(0\) & \(0\) & \(0\) \\ humidity (\%) & \(20\) & \(100\) & \(72.5\) & \(75.5\) & \(60.6\) & \(85.7\) \\ vapor pressure (hPa) & \(0.5\) & \(26.1\) & \(9.4\) & \(7.6\) & \(3.7\) & \(14.5\) \\ dew point temperature (\({}^{\circ}\)C) & \(-29.7\) & \(21.8\) & \(2.1\) & \(2.8\) & \(-7.1\) & \(12.4\) \\ atmospheric pressure (hPa) & \(900\) & \(940.6\) & \(926.1\) & \(926.2\) & \(922.5\) & \(930\) \\ sea-level pressure (hPa) & \(985.4\) & \(1038.1\) & \(1016.2\) & \(1016.5\) & \(1009.8\) & \(1022.4\) \\ hours of sunshine (hr) & \(0\) & \(0.6\) & \(0.3\) & \(0.3\) & \(0.1\) & \(0.4\) \\ solar radiation quantity (MJ/m2) & \(0\) & \(1.3\) & \(0.6\) & \(0.5\) & \(0.4\) & \(0.8\) \\ amount of snowfall (cm) & \(0\) & \(83.4\) & \(2.4\) & \(0\) & \(0\) & \(0\) \\ total cloud cover (decile) & \(0\) & \(10\) & \(3.1\) & \(2.2\) & \(0\) & \(5.8\) \\ middle and low level cloud cover & \(0\) & \(9.8\) & \(2.2\) & \(0.9\) & \(0\) & \(4.3\) \\ ground-surface temperature (\({}^{\circ}\)C) & \(-14.6\) & \(32.2\) & \(9.5\) & \(9.6\) & \(-0.2\) & \(19.2\) \\ wind speed, x-component (m/s) & \(-4.5\) & \(5\) & \(0.1\) & \(0.1\) & \(-0.1\) & \(0.4\) \\ wind speed, y-component (m/s) & \(-14.9\) & \(7.4\) & \(-1.8\) \begin{table}
\begin{tabular}{r r r r r r r r r r} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{6}{c}{**Holurly input**} \\ \hline \multicolumn{1}{c}{**Model**} & \multicolumn{3}{c}{**MLP**} & \multicolumn{3}{c}{**LSTM**} & \multicolumn{3}{c}{**CNN**} \\ \hline
**Temperature** & **average** & **minimum** & **maximum** & **average** & **minimum** & **maximum** & **average** & **minimum** & **maximum** \\ \hline
1 day & **1.541 (0.026)** & **1.937 (0.054)** & **2.418 (0.032)** & **1.630 (0.185)** & 1.960 (0.587) & 2.722 (0.256) & **1.481 (0.019)** & 1.807 (0.129) & **2.367 (0.051)** \\
3 days & 1.644 (0.027) & 2.026 (0.030) & 2.539 (0.046) & 1.729 (0.506) & 1.971 (0.590) & 2.899 (0.660) & 1.518 (0.046) & **1.700 (0.049)** & 2.461 (0.073) \\
1 length & 5 days & 1.758 (0.033) & 2.118 (0.041) & 2.738 (0.086) & 1.748 (0.549) & **1.957 (0.194)** & **2.715 (0.068)** & 1.551 (0.030) & 1.771 (0.072) & 2.487 (0.086) \\
10 days & 2.058 (0.095) & 2.281 (0.054) & 3.072 (0.095) & 1.717 (0.489) & 2.075 (0.494) & 2.944 (0.911) & 1.597 (0.031) & 1.829 (0.073) & 2.593 (0.072) \\
30 days & 2.524 (0.070) & 2.716 (0.055) & 3.152 (0.044) & 2.067 (0.925) & 2.539 (0.646) & 2.986 (0.534) & 1.686 (0.080) & 1.961 (0.079) & 2.714 (0.163) \\ \hline \end{tabular}
\end{table} 
Table 16: _Cont._
## 3 Full-Scale Model Test
### Test Results
#### 3.2.2 Weight-Falling Test  
Figure 8 shows an example of the time-history waveform of the vertical acceleration on the ladder sleepers and the acceleration in the ballast when the weight was fallen. The sleeper acceleration lasted about 0.1 s, while the ballast acceleration converged in about 0.05 s, which was nearly the same as the vibration caused by the impulse hammer. The vibration characteristics of the ladder sleepers and the PC sleepers were compared by the response magnifications of the sleeper acceleration and the acceleration in the ballast as shown in Figure 9.  
Figure 8: Example of the time-history waveform of the vertical acceleration on the ladder sleepers and in the ballast.  
Figure 7: Identification results of natural frequency and vibration mode shape of ladder sleepers.  
From Figure 9, it was found that the response magnification of the ladder sleepers was smaller than that of the PC sleepers in the frequency bands other than 100 Hz, 580 Hz, and 810 Hz, which was almost the same as the case of the vibration caused by the impulse hammer. The response magnification was determined by dividing the acceleration in the ballast by the acceleration on the sleepers.  
From these experimental results, it was confirmed that the ladder sleeper has the effect of reducing vibration transmission in a wider frequency band than the PC sleepers.
## 2 Computational Details  
### Quantum-Chemical Calculations  
The optimized structures of MAY, LUT, and ACA were calculated with the hybrid meta-GGA M06 density functional [33; 34] combined with the 6-31G(d) basis set [35]. The solvation method of the conductor-like polarizable continuum model (CPCM) [36] was used for calculations in the aqueous phase. All calculations were performed in Gaussian09 [37]. The charge distribution of the amino acids and the new alternatives of treatments were obtained with the population analysis of Hirshfeld [38].  
Density functional methodology was used to obtain the frontier molecular orbitals: the Highest Occupied Molecular Orbital (HOMO) and the Lower Unoccupied Molecular Orbital (LUMO), of each ligand (ACA, MAY, and LUT). This methodology provides an excellent framework to define a set of known chemical concepts, in particular, ionization potential (IP) [39; 40] and electron affinity (EA) [39; 40], which were calculated by the energy difference E(\({}_{N}\))-E(\({}_{N-1}\)) and E(\({}_{N}\))-E(\({}_{N+1}\)), respectively, where N indicates the neutral molecule and \(N-1\) and \(N+1\) correspond to the cation and anion radicals generated after electron transfer. Once the IP and EA were obtained, additional reactivity parameters were calculated, such as chemical hardness (\(\eta\)) [41; 42], electronegativity (x) [40; 41], electrophilicity (\(\omega\)) [43], and chemical potential (\(\mu\)) [44]. These reactivity parameters are defined as:  
\[\eta=\frac{1}{2}(IP-EA) \tag{1}\]  
\[\chi=-\mu=\frac{1}{2}(IP+EA) \tag{2}\]  
\[\omega=\frac{\mu^{2}}{2\eta} \tag{3}\]  
The overall interactions between the ACA, MAY, and LUT with the amino acids involved in the active site of maltase-glucoseamylase (NtMGAM) were quantified through the charge transfer between the chemical species. This parameter determines the behavior of different molecular systems as an electron donor or acceptor. In this case, it is referred to as the electron transfer from the ligands to the amino acids of the active site of NtMGAM or vice versa. The global interactions between ligands and protein were estimated using the charge transfer parameter (\(\Delta\)N) [45; 46], which is given by:  
\[\Delta N=\frac{\mu_{B}-\mu_{A}}{2\left(\eta_{B}-\eta_{A}\right)} \tag{4}\]  
The molecular polar surface area (PSA) and octanol-water partition coefficient (logP) were obtained through Molinspiration, a free software readily available on the Web [47]. SMILES (Simplified Molecular Input Line System) was used for the PSA and logP prediction in the ACA, MAY, and LUT. This software is a chemical notation system designed for modern chemical information processing [48].