esa-sceva/satcom-chunk-collection huggingface dataset:
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 4 Electrospinning of PLA Fibers
### PLA Electrospinnability
#### 4.1.1 Solution Parameters  
Generally, the electrospinningnability of the PLA-based solutions, and the morphological features of the fabricated nanofibers, are specified by a set of factors relevant to the solution, such as viscosity, conductivity, polymer concentration, surface tension, and solvent system [35; 37].
## 2 Materials and Methods
### Capsules Image Analysis  
Capsules' size (area) and shape (circularity, major and minor axes) were measured using the free ImageJ software, version 1.41 (ImageJ, Rasband, WS, Bethesda, MD, USA, National Institutes of Health, Bethesda, MD, USA). For this purpose, optical images (\(2048\times 1536\) pixels) [7] taken with a stereoscopic microscope (2.5\(\times\), Nikon SMZ 1500, Tokyo, Japan,) equipped with a digital camera (Nikon DS-2 Mv, Tokyo, Japan) and a fluorescent ring light, adjusted to medium intensity (Universal driver model 13 plus, USA) to ensure the homogeneity of the light and reduce the shadows of images, was used. Images were acquired using NIS-Elements F 2.30 software (Nikon, Tokyo, Japan) in sRGB color, saved in TIFF format, and converted to grayscale (8-bit). The image analysis procedure was performed as follows: the capsule contour was selected using the \"wand (tracing)\" command. Then, the noise was removed using the \"clear outside\" tool. After that, the image was filled with the \"fill\" tool, and finally, the required parameters were defined. This analysis was performed in triplicate.
## 3 DMCAP System
### Multi-Channel Ensemble Model and Channel Attribute Classification Model  
As WUGSN needs multi-channel data transmission model, signaling parameters, and channel distortions are dynamically determined at each signaling intervals. The statistical channel analysis models and other conventional models analyze the channel qualities with nominal assumptions. In this case, the requirement of multi-channel ensemble classification model is more crucial. Algorithm 1 shows the procedure of multi-channel interference and energy model based on channel quality tuples, \(\mathrm{T}(\mathrm{S})\alpha^{\mathrm{i}}\) and \(\mathrm{T}(\mathrm{P})\alpha^{\mathrm{i}}\). Algorithm 1 initiates various classification procedures for analyzing the real-time quality of WUGSN channels. Most importantly, the multi-channel quality evaluation procedures are initialized in each sensor node deployed at either underground locations or surface points. Figure 1: Links and nodes. The subsets of Algorithm 1 lead to enable channel entropy classification practices, SNIR classification practices, attenuation/energy classification practices, and malicious event classifications on each wireless channel. In this deep channel quality analysis and learning model, lightweight ML and DL techniques are utilized for ensuring reliable data transmission conditions [34]. Figure 2 shows the basic signaling model. According to the basic model, the transmitted signal quality, receiving signal quality, channel distortions, and other adversarial factors are evaluated under uncertain mediums. In the first phase of channel quality assessment process, information entropy is determined and evaluated for multiple wireless channels. The information entropy model plays a major role in finding the actual liveliness of each wireless channel of WUGSN. In this regard, the proposed system implements conditional joint entropy functions with Ensemble Multi-Layer Perceptron (EMLP) classifier units in each sensor node. The proposed EMLP procedures are reactive against live channel parameters [35; 36]. The sensor nodes participating in underground communication and surface communication lead to significant information entropy. The entropy model is defined with joint conditional distribution function as provided in Equation (11). \[\mathrm{H}(t_{\mathrm{n_{ai}}}|t_{\mathrm{i}})=E_{t_{\mathrm{n_{ai}}}|r_{ \mathrm{n_{ai}}}}[-\log\mathrm{p}(t_{\mathrm{n_{ai}}}|r_{\mathrm{n_{ai}}})\,=- \sum_{t_{i}\in t_{\mathrm{n_{ai}}}}\mathrm{p}(t_{\mathrm{n_{ai}}}|r_{\mathrm{ n_{ai}}})\log\mathrm{p}(t_{\mathrm{n_{ai}}}|r_{\mathrm{n_{ai}}}) \tag{11}\]  
Let the Equation (11) as \(\mathrm{E}_{\mathrm{N}}^{i}\) be an entropy pair for active channel at time \(\tau\). The information entropy varies continuously for each data transmission. The proposed system implements EMLP network for classifying the continuous streaming of entropy determinations. The training phase of EMLP observes the determinations from Equation (11)  
Figure 2: Basic signal model. and the relationship model for entropy function is provided in Equations (12) and (13). Equation (13) illustrates mutual channel entropy value in the network [37].
## 6 Application of the Method on Two Earthquakes in Greece
### Efpalio Earthquake  
#### 6.1.1 E-P-A Station in the Efpalio Area  
The Corinth Gulf in Greece is frequently disrupted by earthquakes. In order to detect them, a network of permanent and temporary seismic stations has been deployed across the area. The region also hosts one permanent GNSS station. The geographical distribution of the permanent observing stations is shown in Figure 2.  
On 18 January 2010, an earthquake occurred in the Efpalio area, with a magnitude of Mw 5.3. Its epicentre was close to the GNSS permanent station EYPA. A second moderate size earthquake (Mw 5.2) occurred on 22 January 2010; its epicentre was about 5 km from the EYPA station. These two most significant events were accompanied by a sequence of aftershocks that lasted for almost six months. Both M5+ shocks exhibited normal faulting along \(\sim\)E-W trending planes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Determined Shift**} & \multicolumn{2}{c}{**Co-Seismic Displacement from**} & \multicolumn{2}{c}{**RMSE of GNSS**} & \multicolumn{2}{c}{**Static Displacement from Seismic Model**} & \multicolumn{1}{c}{**Difference between the Averages**} \\  & \multicolumn{2}{c}{**Displacement from**} & \multicolumn{2}{c}{**GNSS (1 st Approach)**} & \multicolumn{2}{c}{**RMSE of GNSS**} & \multicolumn{1}{c}{**from Seismic Model**} & \multicolumn{1}{c}{**of Daily Results**} \\ \hline N-S & \(-0.13\) cm & \(0.02\) cm & \(0.48\) cm & \(-0.31\) cm & \(-0.22\) cm \\ E-W & \(1.22\) cm & \(0.07\) cm & \(0.76\) cm & \(0.78\) cm & \(1.15\) cm \\ Up & \(-4.10\) cm & \(0.10\) cm & \(1.38\) cm & \(-3.12\) cm & \(-4.04\) cm \\ \hline \end{tabular}
\end{table}
Table 1: Co-seismic displacement of the EYPA station position, as determined by GNSS observations (using the anharmonic spectral analysis) with RMSE from both approaches and the comparison with the static displacement calculated from the COULOMB 3.1 seismic model [13], with the difference between the averages of the daily GNSS results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Station**} & \multicolumn{2}{c}{**NS [cm]**} & \multicolumn{2}{c}{**EW [cm]**} & \multicolumn{2}{c}{**Up [cm]**} \\ \cline{2-10}  & **Offset** & **RMSE 1 st Approach** & **RMSE 2 nd Approach** & **Offset** & **RMSE 1 st Approach** & **RMSE 2 nd Approach** & **Offset** & **RMSE 1 st Approach** & **RMSE 2 nd Approach** \\ \hline STRF & \(-4.33\) & \(0.03\) & \(0.09\) & \(2.55\) & \(0.08\) & \(0.25\) & \(0.66\) & \(0.16\) & \(0.38\) \\ ZAKY & \(-3.89\) & \(0.20\) & \(0.45\) & \(-2.65\) & \(0.10\) & \(0.28\) & \(-0.70\) & \(0.30\) & \(0.75\) \\ PYRG & \(-0.78\) & \(0.08\) & \(0.21\) & \(-1.52\) & \(0.08\) & \(0.24\) & \(-0.35\) & \(0.22\) & \(0.56\) \\ \hline \end{tabular}
\end{table}
Table 2: Results of time series analysis before and after the earthquake in Zakynthos area with RMSE from the 1 st and 2 nd approaches.
# Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
## 5 Numerical Examples
### Structural-Acoustic Model
#### 5.1.2 Proposed Uncertainty Analysis Based on Surrogate Model  
An accurate response surface is obtained from a minimum number of design points in the given sample space. The design sample space is constructed using Latin hypercube sampling (LHS) [45]. After the design of experiments process, several combinations of input parameters and their corresponding responses are obtained. The material parameters (elasticity and density) and the thicknesses of the aluminum plates are taken the uncertain variables in this study as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Mean Value** & **Cov** & **Distribution Type** \\ \hline E (Pa) & \(2.1\times 10^{11}\) & 0.05 & Normal \\ \(\rho\) (kg/m\({}^{3}\)) & 7850 & 0.05 & Normal \\ \(t_{1}\) (mm) & 4 & 0.02 & Uniform \\ \(t_{2}\) (mm) & 2 & 0.02 & Uniform \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of random variables.  
Figure 5: 3D finite element model.  
Figure 6: Sound pressure level inside the acoustic cavity.  
For the assumed geometric constraints, design of experiment (DOE) points are generated. Mathematical model is then generated by performing a Kriging prediction to obtain the maximum sound pressure level inside the cavity. The Kriging models are obtained using the Matlab toolbox package _Design and Analysis of Computer Experiments (DACE)_[51]. A second order polynomial global trend function and exponential correlation function are adopted. The estimator is constructed across different Latin hypercube sampling (LHS) points in order to observe their influence on the maximum sound pressure level. Additional test points are created to verify the precision of the substitution model. FEM are repeatedly performed by taking the training data (Table 1) as inputs. The accuracy of Kriging substitution model is determined by their MAE (Equation (23)), RME (Equation (24)) and RMSE (Equation (25)) as presented in Table 2 for 20 and 30 LHS points. A comparative study of the number of training points using LHS is illustrated by the goodness-of-fit graphs in Figures 7 and 8 for 20 and 30 LHS points, respectively.  
In Figures 7 and 8, the blue curve corresponds to the system response using FEM. However, the red points represent the dispersion of the response of the system calculated by the Kriging compared to the one calculated by the FEM at the LHS points. From Table 2 and cross-validation results Figures 7 and 8, it is clear that the Kriging predictor provides better approximation with 30 LHS points. Given the relevant results obtained using Kriging 30 LHS point, it will be used in the Monte Carlo simulations for the uncertainty analysis. Thus, a sampling set with the number of \(N=10^{6}\) for random variables (Table 1) is generated, then the MCS is performed using the Kriging model. The mean value and standard deviation of the sound pressure level (SPL) are presented in Figures 9 and 10, respectively.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 5 Performance Evaluation and Discussion  
In this section, a simulation is carried out to investigate the performance of training manners explained in Section 4 by considering both direct positioning and indirect positioning. To observe the impact of different parameters on the generalization in the IIoT scenario, simulations are performed in different scenarios including the clutter parameters, network synchronization error, and receiving timing error. In addition, performance in identical configuration between the training data set and test data set is also evaluated for reference.  
In the evolution, the metric is defined as the positioning error which is the distance between the coordinates derived based on the AI model and the ground truth. In addition, the positioning at the 90% percentile is also observed to check whether the precision requirement can be satisfied or not.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 5 Performance Evaluation
### The Impact of CTP, RPL, and LIBP on CPS Dependability
#### 5.4.1 Energy Profile  
A comparison of the average power consumption of each node for the various protocols is shown in Figure 8(a). The graph shows that RPL is on average significantly more power hungry than CTP and LIBP as most of its nodes were in the removed state with battery depleted after the simulation period. This could be due to the fact that the in RPL, sink node's is always on and that RPL is built upon a heavyweight communication protocol. The standard deviation of power consumption describes how well distributed the energy consumption within a topology is. It is a metric that gives insights to the running time of a network and how long it can run before its nodes' batteries are depleted. Low energy usage and low standard deviation shows that the protocol is energy efficient both in its distribution and implementation. Figure 8(b) shows that RPL-0 has the highest deviation for attacked nodes, while removed nodes have the highest deviation for RPL-ETX.
## 3 Real-Coding PBIL Algorithm for Multi-Satellite Scheduling  
The real-coding PBIL algorithm for multi-satellite scheduling is mainly based on the evolutionary framework, including the key part of encoding, population initialization, fitness function, value probability matrix, population update, etc.
# Random Access for Underwater Acoustic Cellular Systems
## 3 RAP Design
### Proposed RAP
#### 3.2.1 Proposed RAP 1
The Gauss sum expression, \(G(a\),\(N)\), in (7) is given by:  
\[G(a,N)=\Big{(}\frac{a}{N}\Big{)}\frac{1}{\sqrt{N}}\frac{1-j^{N}}{1-j} \tag{8}\]  
where (\(a\)/\(N\)) denotes the Legendre symbol with the value of \(\{-1,\,0,\,1\}\) and \(|G(a,N)|=1/\sqrt{N}\). Figure 7 shows the maximum value of the CAF of RAP 1. In Figure 7a, three pairs of root indices, (5, 3), (5, 200), and (10, 200), are used for the design of RAP 1 and the Doppler shift is changed from \(-50\) Hz to \(50\) Hz. As shown in this figure, a pair of odd and prime root indices, (3, 5), achieves smaller variation in magnitude. A pair of even root indices, (10, 200), produces higher variation in magnitude. For \(|f_{D}|\leq 50\) Hz, the ranges of variation are [0.027, 0.047], [0.01, 0.05], and [0.05, 0.075] when the pairs of root indices, (3, 5), (5, 200), and (10, 200) are used, respectively. However, the ranges of variation are smaller than 0.1 (10%). Figure 7b shows the distribution of the maximum magnitudes evaluated with RAPs by changing the value of the Doppler shift from \(-50\) Hz to \(50\) Hz with a step size of \(1\) Hz and the values of root indices of \(u_{c}\) and \(u_{d}\) for all possible combinations. As shown in Figure 7b, the maximum magnitude of CAF is concentrated around 0.07 (7%). Therefore, it is possible to use any root indices in RAP 1 with a cross-correlation level less than 0.1 even in high Doppler environments.
## 3 Proposed Framework
### Cloud-Level Feedback and Adaptive Weight Adjustment  
To further enhance the effectiveness of the aggregation process, the central server provides adaptive feedback to the edge nodes. This feedback includes updated weights and guidance on adjusting local aggregation intervals or the weighting of specific devices. For example, if the cloud server identifies an edge node with low participation frequency, it may recommend increasing the aggregation interval to collect more updates, therefore enhancing the model's robustness. Similarly, the central server may provide updated device weights to help edge nodes balance contributions from heterogeneous devices more effectively.  
The adaptive weight adjustment at the edge nodes follows a simple rule based on the feedback from the central server. Let \(w_{i}\) be the local model update from IoT device \(i\), and let \(v_{i}^{(t)}\) be the updated weight provided by the central server at time \(t\). The edge-level aggregation at time \(t\) can be expressed as:  
\[w_{e}^{(t+1)}=\frac{1}{\sum_{i\in A_{t}}v_{i}^{(t)}}\sum_{i\in A_{t}}v_{i}^{(t )}w_{i} \tag{5}\]  
This adaptive mechanism ensures that each device's contribution is proportional to its performance, therefore optimizing the local aggregation process and aligning it with the global learning objectives.
## 4 Discussion  
Few studies [2; 10; 11] have investigated the reliability/accuracy of LII taken from digital models, but no one, to our knowledge, has evaluated these features in association with different degrees of dental crowding. Among these studies, only [PERSON] et al. [2] has evaluated the LII measurements in both the maxillary and mandibular arches while other studies [3; 11; 12] have assessed the index only on one dental arch. In this contest, two other studies [11; 13] investigated the accuracy of LII respectively on both arches and on the upper arch, although using only manual measurements of the dental casts. Regarding the accuracy of the digital LII measurements, our study disagreed with the findings reported by [PERSON] et al. [2], which demonstrated no significant difference between the digital and analogic methods and that accurate and precise measurements of LII could be taken also digitally. In fact, findings from our study demonstrated that the differences between the analogic and digital measurements were significant, especially for maxillary arches with a mean difference of 0.43 mm (\(p\leq 0.01\)), whereas differences were lower in the mandibular arch (0.24 mm) (\(p\leq 0.05\)). In this contest, our findings were in accordance with the study of [PERSON] et al. [12], which showed a difference of 0.41 mm between the mean values obtained from digital and plaster casts but concluded that LII measured on digital models could be considered reliable and, consequently, could be employed in clinical practice, although a little overestimation for the digital measurements was present. In this contest, the differences found in the present investigation were of a very small entity and could not affect the accuracy of the LII measurements, especially when they were carried out on the mandibular arch, considered the guidance arch to plan a correct orthodontic treatment [14; 15; 16]. Regarding the reliability of the LII digital and analogical measurements, our study corroborates findings reported by [PERSON] et al. [11], which demonstrated an improvement of LII reliability by using a digital measurement method. In fact, our findings demonstrate an excellent intra- and inter-operator reliability obtained in the two record sessions, for both the digital and plaster models, and this is coherent with other studies [2; 11; 12; 15; 17; 18; 19; 20]. In this study, we evaluated LII measurements in both the lower and upper arches and for the first time we have also considered the correlation between the accuracy of the LII digital measurements, with respect to the analogic ones, and the dental crowding by selecting five crowding groups according to the ABO Discrepancy Index. Our findings demonstrated that there was no correlation between the accuracy of the digital LII measurements and the different degrees of dental crowding (\(p\geq 0.05\)) (Table 4).
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
###### Abstract  
This paper investigates the asymmetric dual-hop multiple input multiple output (MIMO) mixed radio frequency (RF)/free space optical (FSO) decode-and-forward (DF) relaying system. This kind of system can utilize two different fading characteristic channels to reduce the possibility of the system falling into deep fading. In addition, each link of the system adopts MIMO technology to mitigate the disadvantages of fading. In this paper, the closed form expressions of the outage probability, bit error rate (BER) and average ergodic capacity are derived. The approximate expression of the system outage probability considering the pointing error is also derived. Additionally, asymptotic performance for diversity order and diversity-multiplexing tradeoff (DMT) of the system is analyzed and discussed, which provides direct theoretical basis for practical engineering design.  
MIMO; mixed RF/FSO; decode-and-forward +
Footnote †: journal: Applied Sciences  
1
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion
### TEM Investigation of IONPs  
Iron oxide nanoparticles were investigated using TEM analysis for the determination of the shape, size and particle size distribution of the phytosynthesized IONPs. In this context, the biogenic IONPs were observed to be embedded within a matrix-like structure, which could be assigned to the biomolecules of _L. subtilis_ leaf extract utilized in the biosynthesis procedure, as shown in Figure 6[58]. The biogenic iron oxide nanoparticles exhibited a spherical morphology, with an average particle size of 46.3 nm (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Functional Groups of the Aqueous Leaf Extract of _L. nobilis_**} \\ \hline \multirow{2}{*}{**No.**} & **Absorption** & \multirow{2}{*}{**Appearance**} & \multirow{2}{*}{**Functional Groups**} & **Molecular Motion** \\ \cline{3-4}  & & & & \\ \hline
1 & 3427.08 & Strong, broad & Phenols & O-H stretching \\
2 & 2927.22 & Medium & Alkanes & C-H stretching \\
3 & 2847.61 & Medium & Alkanes & C-H stretching \\
4 & 1632.01 & Medium & Amines & N-H bending \\
5 & 1030.37 & Medium & Carbonyl groups & C-O stretching \\
6 & 540.36 & Weak, broad & Alkyl halides & C-Br stretching \\ \hline \multicolumn{5}{c}{**Functional groups of the biogenic IONPs**} \\ \hline
1 & 3434.74 & Strong, broad & Phenols & O-H stretching \\
2 & 2921.47 & Medium & Alkanes & C-H stretching \\
3 & 1621.34 & Medium & Amines & N-H bending \\
4 & 1444.05 & Medium & Aromatic compounds & C-C stretching \\
5 & 1352.94 & Medium & Phenols & O-H bending \\
6 & 1176.47 & Medium & Amines & C-N stretching \\
7 & 1075.45 & Medium & Primary alcohols & C-O stretching \\
8 & 574.83 & Weak, broad & Alkyl halides & C-Br stretching \\ \hline \hline \end{tabular}
\end{table}
Table 1: Functional groups of the _L. nobilis_ leaf extract and the biogenic IONPs.  
Figure 5: FTIR spectrum of _L. nobilis_ leaf extract and the biogenic IONPs (A: FTIR spectrum of _L. nobilis_ extract; B: FTIR spectrum of the biogenic IONPs).  
Figure 6: TEM image of the phytosynthesized Fe\({}_{2}\)O\({}_{3}\) nanoparticles.  
Figure 7: Particle size distribution of the phytosynthesized IONPs.
# Biodeterioration of Salon de Reinos, Museo Nacional del Prado, Madrid, Spain
## 3 Results and Discussion
### Ecology of Fungal Isolates  
The deterioration of wall paintings by fungi is a subject that has occupied the attention of restorers and microbiologists, even more than the biodeterioration induced by bacteria. For this reason, there is abundant literature on the topic, which reflects the harmful effects of the colonization of frescoes and wall paintings in churches, monasteries, tombs, and catacombs [6; 7; 9; 10; 11; 34; 35; 36; 37].  
_Penicillium chrysogenum_ is one of the fungi most frequently isolated in wall paintings, and obvious signs of biodeterioration have been found on the surface of paintings [9; 41; 42; 43]. Therefore, their abundance in the _Salon de Reinos_ paintings is not surprising.  
The cosmopolitan genus _Alternaria_ is made up of multiple saprophytic and pathogenic plant species. [PERSON] et al. [44] considered _Alternaria angustiovoidea_ to be a synonym for _Alternaria alternata_, based on the study of the genome and comparison of the transcriptome and molecular phylogenies. _Alternaria alternata_ is widespread on wall paintings [36; 41; 43].  
The remaining fungi from Table 4 (_Cladosporium xylophilum_, _C. macrocarpum_, _Penicillium fuscogliaucum_, _Mucor racemosus_, _Stagonosporopsis lupini_) were commonly isolated from plants and foods [45; 46; 47; 48; 49], and _Botryotrichum domesticum_ from a house [50]. As far as we know, no reports on their presence on wall paintings have been published, other than for _C. macrocarpum_[41; 51].