esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 6 Conclusions  
This study has systematically explored the capabilities of physics-informed neural networks in modeling dynamic systems, marking a significant contribution to the field. Our research has highlighted the enhanced computational efficiency and accuracy of physics-informed neural networks, especially when compared to traditional deep neural networks. Physics-informed neural networks are very good at staying consistent with basic scientific laws because they use physical principles directly in the learning process. This is very important when working with complex dynamic systems. The novel contributions of this research lie in optimizing physics-informed neural networks for scenarios where the number of unknowns corresponds to the number of undetermined parameters. This approach has not only streamlined the process of dynamic system modeling but has also opened new avenues for research in this field. The application of our methodology to real-world scenarios, such as the COVID-19 pandemic model, further validates its practicality and relevance.  
Quantitative comparisons have been a cornerstone of this study, providing clear evidence of the superiority of physics-informed neural networks over deep neural networks. We have demonstrated this through various error metrics. Additionally, computational  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **I** & **R** \\ \hline MAE & 1.3053 \(\,\times\,10^{-3}\) & 1.2820 \(\,\times\,10^{-3}\) \\ MSE & 6.9586 \(\,\times\,10^{-6}\) & 6.0906 \(\,\times\,10^{-6}\) \\ RMSE & 2.6379 \(\,\times\,10^{-3}\) & 2.4679 \(\,\times\,10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: The error metrics of the SIR model.  
Figure 23: Absolute error plot between the COVID-19 data and the PINN solution of the SIR model.  
efficiency has been quantitatively assessed, revealing that physics-informed neural networks significantly improve processing speed, an aspect critical for real-time applications. Finally, our results show that physics-informed neural networks have a lot of potential as strong, fast, and accurate scientific modeling tools that can deal with the complexities of dynamic systems. This research opens up new possibilities for future studies. It shows how important it is to keep looking into and improving complex neural network designs for use in science. A key area for future work is to determine how to learn the time-dependent parameters of stiff dynamical systems.  
The code for the work can be found on github (accessed on 23 November 2023).  
All the authors have contributed equally to the conceptualization, software, validation, writing the original draft preparation, and visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Numerical Technique for Study of Noise Grating Dynamics in Holographic Photopolymers
## 2 Methods
### Holographic and Haze Readout  
Finally, the efficiency of the hologram is found by illuminating the material index. This index is taken to be the sum of the static (scattering) and dynamic (holographic photopolymer) index distributions. A Bragg matched reference beam incident on the surface z = 0 is propagated through the volume with FBPM. The diffracted intensity is integrated at the exit face in an angular range centered on the object beam. The readout reference beam at 405 or 660 nm is assumed to be sufficiently weak and/or outside the initiator absorption spectrum that it does not modify the index distribution. Next, a normally incident beam is used to measure the haze. Haze is calculated, per ASTM standard D1003, as the fraction of the power scattered by the sample out of a normally incident probe for all angles (measured from the normal) greater than 2.5\({}^{\circ}\) and less than 90\({}^{\circ}\). Typical angular spectra of the intensity exiting the material in both cases are illustrated in Figure 4.  
Figure 4: Intensity of holographic readout (**left**) and haze readout (**right**) for a transmission grating with 1 μm period written at 405 nm with both beams linearly polarized along the y direction. The material scatters due to embedded 2 μm diameter microspheres (**top**) or Rayleigh scattering (**bottom**). To illustrate the impact of reading wavelength, the top and bottom images use 405 and 660 nm, respectively. The diffracted light exiting the sample in both cases is rendered in the Fourier domain just inside the exit face of the material. The spatial frequencies corresponding to the angular limits of haze integration in air are indicated by circles. For the microspheres, scatter is observed in a small scatter cone around each beam. Conversely, Rayleigh scattering is uniform in the s or \(f_{x}\) plane and has \(\cos\left(\theta\right)\) distribution in the \(p\) or \(f_{y}\) plane.
## 4 Performance
### Variation of Number of Sending Nodes
#### 4.2.4 Energy Consumption Comparison  
Figure 13 presents the energy consumption comparison. With RAIT, the total amount of energy consumption was higher than that with MintRoute and SRI, because all the packets were transferred to sink nodes with RAIT, whereas some packets were lost during transmission with MintRoute and SRI. When more packets are delivered to the sink nodes, more energy is consumed and less energy is wasted. With RAIT, no packet was lost during transmission, no energy was wasted. Therefore, although the total amount of consumed energy is increased with RAIT, the energy efficiency is increased. On the other hand, with MintRoute and SRI, many packets were lost during transmission and thus the total amount of consumed energy was less than RAIT, however, wasted energy was higher than RAIT due to the packet drop. In summary, we could say that RAIT guarantees the reliable transfer of images with high energy efficiency and without energy waste.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 1 Introduction
Particularly in the case of ancient masonry, where mechanical properties and boundary conditions are often poorly understood, AE offers a non-destructive method to assess structural behaviour in situ. Moreover, the integration of AE-based observations with stiffness-based modelling strategies, such as the stiffness separation method, has been recently proposed for structural diagnostics of complex systems, especially in large-scale truss structures [18; 19]. These approaches enable efficient damage localisation without the need for complete global modelling, suggesting promising pathways for simplifying the interpretation of AE data and its integration with reduced or partial numerical models. This study applies AE monitoring to the historic masonry dome of Santa Maria delle Grazie in Barano d'Isclia. The objective is to evaluate the potential of AE for identifying micro-cracking and damage propagation in a highly heterogeneous material that cannot be well replicated under laboratory conditions. The experimental design is integrated with advanced numerical modelling and traditional structural assessments to validate the AE findings and develop a robust framework for monitoring similar historic structures.
## 3 Results  
### Normal Distribution and Variance Homogeneity  
Only the variables body height (_p_ = 0.882), body fat (_p_ = 0.603), and CMJ (_p_ = 0.812) were normally distributed. One parameter (body mass: \(p\) = 0.012) did not have variance homogeneity. Therefore, we calculated the median and arithmetic means in order to allow for a comparison of the presented results as means \(\pm\) standard deviations (Tables 2-4) with other studies. Moreover, nonparametric tests (the Kruskal-Wallis H test and the Mann-Whitney U test) were used to evaluate parameter differences depending on age.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Body Height (m)** & **Body Mass (kg)** & **BMI (kg/m\({}^{2}\))** & **Body Fat (\%)** \\ \cline{2-5}  & **Median** & **Median** & **Median** & **Median** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\  & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** \\ \hline
**U10** (_n_ = 12) & 1.45 & 45.0 & 21.8 & 26.6 \\
**U10** (_n_ = 12) & 1.44 \(\pm\) 0.06 & 44.7 \(\pm\) 8.2 & 21.3 \(\pm\) 3.1 & 24.6 \(\pm\) 7.2 \\  & (1.39–1.50) & (34.0–55.4) & (17.4–25.2) & (20.1–29.2) \\  & 1.44 & 44.0 & 22.8 & 26.5 \\
**U11** (_n_ = 11) & 1.46 \(\pm\) 0.10 & 49.2 \(\pm\) 16.8 & 22.7 \(\pm\) 5.7 & 28.7 \(\pm\) 9.1 \\  & (1.40–1.51) & (38.0–6.04) & (18.7–26.8) & (24.0–33.5) \\  & 1.57 & 71.0 & 27.0 & 22.6 \\
**U12** (_n_ = 12) & 1.57 \(\pm\) 0.11 & 71.6 \(\pm\) 25.3 & 28.8 \(\pm\) 9.4 & 23.5 \(\pm\) 6.6 \\  & (1.52–1.62) & (60.8–82.3) & (25.0–32.7) & (19.0–28.0) \\ Kruskal–Wallis (_p_) & \(p\) = **0.008** & \(p\) = **0.007** & \(p\) = 0.070 & \(p\) = 0.387 \\ Significant Differences between Adjacent Age Groups (_p_) & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Demographic and anthropometric characteristics in relation to age groups. Bold font denotes \(p\) < 0.05.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Jumping Performance (cm)** & **Sprinting Performance (s)** \\ \cline{2-4}  & **CMJ** & **SJ** & **10 m** & **15 m** \\ \cline{2-4}  & **Median** & **Median** & **Median** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\  & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** & **(95\% CD)** \\ \hline
**U10** (_n_ = 12) & 21.9 \(\pm\) 3.03 & 16.6 \(\pm\) 3.58 & 2.59 \(\pm\) 0.54 & 3.63 \(\pm\) 0.53 \\  & (20.0–23.1) & (14.3–18.9) & (2.30–2.88) & (4.44–7.05) \\
**U11** (_n_ = 11) & 21.8 \(\pm\) 2.92 & 18.3 \(\pm\) 4.43 & 2.35 \(\pm\) 0.42 & 3.43 \(\pm\) 0.46 \\  & (20.2–23.5) & (15.9–20.7) & (2.05–2.66) & (3.76–5.60) \\  & 22.3 & 18.5 & 3.00 & 3.99 \\
**U12** (_n_ = 12) & 22.6 \(\pm\) 2.05 & 19.2 \(\pm\) 3.73 & 2.77 \(\pm\) 0.52 & 3.82 \(\pm\) 0.46 \\  & (21.0–24.2) & (16.9–21.5) & (2.48–3.07) & (3.76–5.60) \\ Kruskal–Wallis (_p_) & \(p\) = 0.586 & \(p\) = 0.377 & \(p\) = 0.211 & \(p\) = 0.194 \\ Significant Differences between Adjacent Age Groups (_p_) & & & \\ \hline \end{tabular}
\end{table}
Table 3: Jumping and sprinting performance in relation to age groups.
###### Abstract  
This study investigated the antioxidant status of roots, leaves, and fruit upon microbial inoculation (AMF+PGFR, arbuscular mycorrhizal fungi, and plant growth-promoting rhizobacteria, respectively) of young organically farmed apple trees over two growing seasons. Three cultivars--'Topaz', 'Chopin', and 'Odra'--were selected to test the relationship between inoculation and enzymatic and non-enzymatic antioxidant components. The antioxidant metabolism was highly dependent on tissue type and growing season. The greatest effect on antioxidant status following application of the inoculum was found in roots, then leaves, but it was almost negligible in fruit. Roots were influenced most by application of the inoculum in the first growing season, while leaves were influenced most in the second season. Considerable differences between the inoculated and control plants were found for root glutathione reductase (GR) and catalase (CAT) activity, as well as glutathione and ascorbate contents; root phenolics were not influenced by inoculation. In the case of leaves, effect of microbial inoculation on GR activity was revealed in the first growing season, while for global phenolics in the second season, and only the concentration of glutathione was significantly higher in the leaves of inoculated trees in both growing seasons. Leaf ascorbate content and CAT activity were not influenced by the microbial inoculation. The control and inoculated trees expressed a similar total antioxidant capacity, irrespective of the tissue type tested. Furthermore, the response of the cultivars to inoculation varied and also changed in consecutive growing seasons. Based on this study, it is likely that the effect of microbial inoculum as a tool for enhancing health-promoting properties in the fruit of perennial plants is weaker than that described for vegetables where different plant organs are edible.  
apple; arbuscular mycorrhizal fungi (AMF); plant growth-promoting rhizobacteria (PGFR) ascorbate; glutathione; glutathione reductase; phenolics +
Footnote †: journal: Applied Sciences
## 1 Introduction  
The complete elimination of microorganisms from root canal space is mandatory to increase the success rate of endodontic treatment [1]. Among different bacteria found in the root canal, _Enterococcus faecalis_ (_E. faecalis_) has significant implication in secondary endodontic infections, due to its tolerance of harsh conditions [2]. Bacterial reduction within the root canal system is commonly attempted through the use of mechanical preparation and chemical irrigants [3] and adjunctive use of high and low power laser irradiance may be employed to enhance the results [4]. Among low power methods, antimicrobial photodynamic therapy (aPDT) is based on the application of a photosensitizer (PS) which, when irradiated by an appropriate wavelength result in the generation of reactive oxygen species (ROS) and other free radicals which are toxic for microorganisms which finally cause cell death [5; 6].  
Different PSs are used for aPDT in endodontic therapy including methylene blue (MB), toluidine blue (TBO) and indocyanine green (ICG), all of which can be activated by a commercially available diode laser of specific complementary wavelength [7; 8]. [PERSON] et al. evaluated biofilm formation gene expression (_esp_) in _E. faecalis_ treated using MB, TBO and ICG accompanied by 660, 635 and 810 nm diode laser irradiation wavelengths, respectively. They concluded that ICG demonstrated higher efficacy in the reduction of _esp_ expression and can be considered as the best PS in treating endodontic infection [9]. The application of a natural product for treatment and management of disease has gained special attention [10].  
Photoactive+ is a mixture of chlorophylllin-phycocyanin that is effective against biofilms of Gram-positive and Gram-negative bacteria, when activated by red (620-660 nm) and blue (400-450 nm) photonic energy wavelengths [11]. chlorophylllin is used as a coloring agent in food industry with several characteristics including antimicrobial and antimuoral effects [12] and phycocyanin extracted from Spirulina as an FDA-approved agent for coloring food is a water-soluble nontoxic agent with anticancer, antioxidant, antimicrobial and anti-inflammatory effects [13]. These natural products possess pharmacological activity with no toxic side effects [14]. The combined application of these two natural products with photo-activation using separate light sources may enhance the results.  
The aim of this study was to evaluate the effect of aPDT with chlorophylllin-phycocyanin mixture (Photoactive+) as PS on _E. faecalis_ and irradiation using different laser wavelengths (635 nm and 660 nm) and light-emitting diode (LED) source (420-480 nm) and the combination of red laser and LED, to improve the efficacy of aPDT.
# _microorganisms_
###### Abstract  
Cyanobacteria can exist in water resources and produce odorants. 2-Methylisoborneol (2-MB) and geosmin are the main odorant compounds affecting the drinking water quality in reservoirs. In this study, encoding genes 2-MIB (_mic_, _monoterpene cyclase_) and geosmin (_geo_, _putative geosmin synthase_) were investigated using newly developed primers for quantitative PCR (qPCR). Gene copy numbers were compared to 2-MIB/geosmin concentrations and cyanobacterial cell abundance. Samples were collected between July and October 2020, from four drinking water sites in South Korea. The results showed similar trends in three parameters, although the changes in the 2-MIB/geosmin concentrations followed the changes in the _mic_/_geo_ copy numbers more closely than the cyanobacterial cell abundances. The number of odorant gene copies decreased from upstream to downstream. Regression analysis revealed a strong positive linear correlation between gene copy number and odorant concentration for _mic_ (\(R^{2}=0.8478\)) and _geo_ (\(R^{2}=0.601\)). In the analysis of several environmental parameters, only water temperature was positively correlated with both _mic_ and _geo_. Our results demonstrated the feasibility of monitoring 2-MIB/geosmin occurrence using qPCR of their respective synthase genes. odorant-producing, gene-based qPCR monitoring studies may contribute to improving drinking water quality management.  
geosmin; 2-MIB; _mic_ gene; _geo_ gene; drinking water reservoirs; cyanobacteria; quantitative PCR (qPCR)
## 3 Results and Discussion
### Foam Stability  
Figure 4a-d presents the evolution of transmission and backscattering spectra and the Turbiscan stability index (TSI) values of liquid foams. There can be recognized the following phenomena: the foam drainage process identified by a peak in the backscattering profile and the foam collapse recognized as a peak in the transmission profile. The increase in transmission indicates that the samples became transparent confirming the occurrence of destabilization processes. Drainage and coalescence cause an increase in the liquid phase related to the foam, therefore, there is an increase in transmission and a decrease in backscattering. Disproportionation associated with a smaller number of larger bubbles causes an increase in transmission [37]. The most stable was the WPI/10% sucrose foam (Figure 4c), which is reflected in the scan distribution. It is also noticeable that the foam collapse decreased remarkably for this sample. On the other hand, the samples WPI/0% (Figure 4a) and WPI/15% (Figure 4d) demonstrated a completely different dynamics of scans, confirming the smallest stabilities of the obtained foams. One of the mechanisms of decreased stability is disproportionation, i.e., an increase in the air bubble diameter. According to [PERSON] et al. [37], the increase in the air bubbles diameter in time might result from the air diffusion through the protein film caused by the pressure gradient between the air bubbles (Oswald ripening). The scan distribution for the WPI/10% sucrose sample proves that this phenomenon can be reduced by the sugar addition. [PERSON] and [PERSON] [20] analyzed the effect of sugar addition on the microstructure of WPI foams. Their observations confirmed that sucrose improved stability of foams due to the retarded drainage rate. [PERSON] et al. [38] reported that the smaller surface tension values of the protein solutions promoted the formation of smaller bubbles in the foam, which corresponded to their improved stability. Moreover, the viscosity of the pre-foam solutions is related to the increased stability of foams, which was also observed by [PERSON] and [PERSON] [39].  
Figure 4: Transmission and backscattering of liquid foams. (a) VPI/0% sucrose; (b) WPI/5% sucrose; (c) WPI/10% sucrose; (d) WPI/15% sucrose and corresponding Turbiscan stability index (TSI) values.  
TSI varies from 0 to 100 and this is a parameter to monitor the destabilization kinetics of the foams. It is also noticeable that according to the TSI scale, the TSI values above 10 are reserved for the largely unstable and visible systems like foams. The closer to 100, the more unstable the foam is. The WPI/0% sucrose sample was the least stable during the analysis. The increase in sucrose up to 10% results in the TSI decrease. The foam obtained from WPI/10% sucrose was the most stable, which was proved by having the smallest TSI value. The Turbiscan measurements confirmed the observations made earlier when the rheological properties of the foams and as well as their relation to overrun and air phase fraction were determined.
# Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars
## 2 GPR System and Data Processing  
Ground-penetrating radar (GPR) refers to electromagnetic (EM) techniques that use radar pulses to image the subsurface. The GPR is based on the propagation of high-frequency electromagnetic waves that radiate into the media from a transmitting antenna. The wave frequency usually varies from 200 MHz to 2.6 GHz for most civil engineering applications. The dielectric constant and electrical conductivity are two physical properties of the GPR that control electromagnetic wave propagation. The GPR, an EM energy, is subjected to attenuation (i.e., loss of radar energy) as it propagates into a material. If the material is resistive (i.e., low conductivity), such as dry sand, ice, or dry concrete, the signal stays immaculate longer and is thus able to penetrate a considerable depth into the material. In contrast, the GPR energy will be absorbed before going very far in the conductive materials (e.g., saltwater, and wet concrete). As a result, the GPR techniques are suitable for inspecting construction materials such as concrete, sand, wood, and asphalt.  
The material's dielectric constant indicates the speed of the radar energy traveling through the material. The GPR transmits EM waves into concrete, and it can measure travel time to receive the reflected waves from the embedded objects. As the energy speed is known, multiplying the two-way travel time and wave speed provides the objects' depths. The GPR energy propagates in the air at almost the speed of light while it propagates in the water at about one-ninth speed, corresponding to the dielectric values ranging from 1 (air) to 81 (water). For construction materials, the dielectric values typically vary from 3 to 12, corresponding to radar wave velocities from 0.178 to 0.089 m per nanosecond [51]. The GPR signal amplitude is stored in a two-dimensional matrix where column and row represent horizontal location and depth, respectively. It is directly exhibited in a two-dimensional unreconstructed image by a data interpretation proprietary software that comes with the GPR equipment.  
When the radar energy encounters a subsurface/embedded object or a boundary between materials having different electrical conductivity and dielectric values, such as rebars, voids, the boundary between two different materials, or other inhomogeneous materials, it is reflected to the surface and picked up by the receiving antenna. The more significant the difference in these values, the stronger the reflections will be. For example, embedded rebars in concrete slabs show powerful reflection because they are conductive materials.  
A GPR antenna consists of a pair of a transmitter (T) and a receiver (R); one transmits the signal, and the other receives the reflected signal. In this study, 1.6 GHz antenna with 58 mm T-R offset (distance between transmitter and receiver) were selected for the GPR system. It should be noted that a common GPR signal processing technique called \"background removal\" has been used to remove noises of the surface reflection (i.e., direct coupling) pulses.
## 4 Experimental Evaluation
### Scenario 1: Antenna Direction
#### 4.2.1 Experimental Environment of Scenario 1  
S1 examines the impact of the antenna direction on communication performance. As shown in Figure 2, the distance between the STA and AP is 50 cm, and the four sides of the antenna are investigated. Side A/A means that Side A of the STA's antenna faces Side A of the AP's antenna. The traffic flow is from the STA to the AP.