esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Results of Parameter Extraction Based on the SDM  
For the SDM, the best solutions found by competitive algorithms with \(n=40\) and \(n=80\), including the PV parameters and objective function (RMSE) values, are summarized in Table 4. From the results, for the two tested population sizes, the best RMSE value of 9.860218778914 \(\times\) 10\({}^{-4}\) was obtained from the CO and ICO. For \(n=40\), SEDE and WSO (and for \(n=80\), WSO) gave the second-best solutions. It should be noted that the lower values of the RMSE indicate a higher accuracy of the estimation of the model parameters. The curves of the current and power in terms of voltage are illustrated in Figure 6a,b to verify the accuracy of the algorithm. Additionally, the values of IAEI and IAEP are drawn in Figure 6c,d over the voltage ranges. In all cases, the individual absolute error of current (IAEI) was less than 2.52 \(\times\) 10\({}^{-3}\), and the individual absolute error of power (AIEP) was less than 1.375 \(\times\) 10\({}^{-3}\), indicating that the CO and ICO were highly accurate in estimating the SDM parameters.
# Base Pounding Model and Response Analysis of Base-Isolated Structures under Earthquake Excitation
## 3 Engineering Case and Numerical Simulation
### Effects of Pounding
#### 3.2.5 Displacement  
Figures 20 and 21 show the displacement time-history curves of node 858 under different earthquake excitations. It can be inferred that there is little amplification of displacement, while the structure was undergoing bounding under the El Centro and the Taft earthquake excitations. Furthermore, the displacement decreased while the structure was undergoing bounding under the artificial earthquake. For model A (Table 8), 0.55 times growth was observed for the El Centro earthquake, 0.09 times growth for the Taft earthquake, and 0.19 times decrease for the Artificial earthquake). For model B (Table 9), 0.21 times growth was observed for the El Centro earthquake, 0.03 times growth for the Taft earthquake, and 0.52 times decrease for the Artificial earthquake).  
There is little effect of bounding on displacement due to the restriction of adjacent structures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multicolumn{3}{c}{**Maximum Velocity (mm/s)**} \\ \cline{2-4}  & **Without Pounding** & **With Pounding** & **Amplification** \\ \hline El Centro & 568.41 & 176.45 & 1.07 \\ Taft & 645.80 & 951.15 & 0.47 \\ Artificial & 460.34 & 602.81 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum velocity values for model A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multicolumn{3}{c}{**Maximum Velocity (mm/s)**} \\ \cline{2-4}  & **Without Pounding** & **With Pounding** & **Amplification** \\ \hline El Centro & 465.05 & 1398.14 & 2.00 \\ Taft & 501.82 & 1100.94 & 1.19 \\ Artificial & 553.26 & 690.54 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum velocity values for model B.  
Figure 20: Structural displacement under different earthquakes (model A). (a) El Centro earthquake; (b) Taft earthquake; and, (c) Artificial excitation.
## 3 Results and Discussion
### Nanophosphor Fingerprint Imaging Applications  
In this work, the experimenter pressed the finger on A4 paper (Figure 11a) and a door handle (Figure 11d), and it was difficult to see the fingerprint details with the naked eye in both cases. The nanophosphor obtained by the solvent heat treatment was applied to the pressed area, and the complete fingerprint image could be seen after turning on (Figure 11b,e) and off (Figure 11c,f) the UV light, and the clean ridges and edges could be observed by the naked eye, and some unique texture details (core, bifurcation, termination, pores, and islands) could be well observed after magnification of some areas of the fingerprint photo. It was demonstrated that the nanophosphor obtained in this work shows great potential for practical applications in the visualization and detection of fast fingerprint identification.
## 2 Experimental
### Methodology  
#### 2.2.1 Submerged Membrane Adsorption Hybrid System (SMAHS)  
Figure 1 is a schematic diagram of the SMAHS. The experiments were carried out in a dynamic system which can be readily applied in a real treatment process. The volume of water in the reactor was maintained at 4 L. The adsorbent was added to the water in the reactor and the membrane was submerged into the reactor. The adsorbent performance was evaluated in terms of volume of treated water and the amount of nitrate adsorbed. A peristaltic pump (Extech Equipment Pty Ltd., Boronia, VIC, Australia) was used to feed the influent containing 20 mg N/L to the reactor and periodically remove effluent from the tank at a controlled flux. The effect of filtration flux was studied by conducting experiments at a flux of 2.5, 5, 10 and 15 L/m\({}^{2}\) h. 10 g adsorbent was added to the reactor and the treatment was continued until the concentration of nitrate in the effluent reached the WHO prescribed nitrate concentration limit of 11.3 mg N/L [16]. Air was pumped at a rate of 3.5 L/min to keep the adsorbent particles in suspension. The membrane was cleaned using 0.4% NaOCl (Sigma Aldrich, St. Louis, MO, USA) before commencing each experiment. Experiments were conducted with each of the four adsorbents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adsorbent** & **Particle Size (\(\upmu\)m)** & \begin{tabular}{c} **Zero Point of** \\ **Charge pH \({}^{**}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Langmuir Adsorption** \\ **Capacity (mg N/g)** \\ \end{tabular} &
\begin{tabular}{c} **Column Adsorption** \\ **Capacity \({}^{*}\)(mg N/g)** \\ \end{tabular} \\ \hline Dowex-Fe & 300-1200 & \textgreater{}8 & 75.3 & 31.4 \\ Dowex & 300-1200 & \textgreater{}8 & 27.6 & 18.6 \\ Amine grafted coconut copra & 300-600 & \textgreater{}8.5 & 50.2 & 18.6 \\ Amine grafted com cob & 300-600 & \textgreater{}8.5 & 49.9 & 15.3 \\ \hline \hline \multicolumn{4}{c}{* Nitrate initial concentration 20 mg N/L; Flow velocity 5 m/h, Initial dry bed height 15 cm. Adsorption capacity} \\ \multicolumn{4}{c}{calculated at column saturation (complete breakthrough of nitrate); \({}^{**}\) Zero point of charge pH is the pH at which net surface charge on the adsorbents was zero.} \\ \end{tabular}
\end{table}
Table 1: Characteristics of the adsorbents [14,15].  
Figure 1: Submerged membrane adsorption hybrid system.
## 1 Related Work  
Performance evaluation is applied in communications system development and assessment. In recent years, most of the works focused on systems that were very efficient, and able to handle numerous incoming requests. The topic of stream preparation was omitted.  
Authors model systems in different ways [1, 2]. On the one hand, there are formal models used to analyze performance [3, 4]. On the other hand, to describe systems, they use simulation models: QNs [5, 6], CPN [5, 7] or QPN [8, 9, 10]. In recent years, most works focused on web systems [4, 6, 11, 12], which are very efficient, and able to handle numerous incoming requests. Moreover, QPNs are useful models in regard to specifying and verifying, e.g., protocol behaviors [13].  
Usually some kind of workload generator is applied to analyze the behavior of the modeled system. A survey of such generators for general purpose web-based systems is presented in [14]. Specialized traffic models may be considered for different systems, e.g., for industrial networks [15] or streaming video data, both for input [16] and output streams [17]. In such models, an input stream generator is usually a part of the workload generator. Such an arrival stream generator produces a stream of random input data according to the expected distribution. Petri net models of the system typically are built based on predefined structures-patterns [18, 19]; in such an approach, a stream generator is one of the integral components.  
We created a stream generator (SG) client model using queueing Petri nets (QPNs), which guarantee performance analysis [12]. QPNs provide greater modeling power than queueing net (QN) and Petri net (PN) models. We used two types of formal models that were exploited in one model. Queueing Petri nets have the advantages of queueing nets (e.g., the network efficiency and evaluation of the system performance) and Petri nets (e.g., logical assessment of the system correctness). To the best of our knowledge, none of the existing techniques presented in the literature enable the preparation of such a stream.  
[PERSON] et al. in [20] describe these kinds of Petri nets, named QPNs. [PERSON] et al. [21] provide an introduction to QPNs.  
The modeling approach presented in this paper differs from that of previous works [9], in regard to performance analysis, because we modeled different stream behaviors; they are similar to [7], but we prepared the SG based on another formalism. This approach could be treated as an extension of the paper [7].  
In [7], colored Petri nets (CPNs) were used to model the SG. Two different ways were considered to impose restrictions on mapping between users and resources--transition guards or arc expressions. In this paper, QPNs are used instead of CPNs. Appropriate mapping in the output stream was achieved by means of transition modes and firing weights.
## 5 Approached Methodology
Running the simulations for the TE mode in COMSOL Multiphysics allowed the resonant frequency of the respective unit cell to be found individually for each geometry. All simulations were carried out for the same microwave frequency range. However, for each unit cell geometry, the resonant frequency found differed. Figure 6 shows example plots from the COMSOL Multiphysics 5.4 environment, where plotting of the S\({}_{11}\) parameter and resonant frequencies is shown. The resonant frequency information of each unit cell should be adapted to a version that is accessible and understandable for input to the multilayer perceptron. For this purpose, all 100 resonant frequencies were stored in a data vector with a length of 100 words. The description of the unit cell's geometry was achieved using a matrix. The conductive part of the unit cell and the dielectric part of the unit cell were determined in binary. Considering the division of unit cells into blocks, two states of the block can be distinguished: 1, denoting the conductive part, or 0, denoting the dielectric part. An example of a binary separation of blocks into two states is shown in Figure 7. Such a separation was written in a matrix using functions and libraries available in the Pycharm Community 2024 IDE (Integrated Development Environment) [65]. Data preparation was performed in the Python 3.12.0 version programming language (Python programming was applied in Pycharm Community 2024-dedicated Python Integrated Development Environment). In the same environment, a multilayer perceptron was designed and used for testing. For the input of the multilayer perceptron, 100 matrices specifying the unit cell geometry and a vector containing 100 words assigned to each unit cell geometry and its resonant frequency derived from the simulation were used. The target action of the neural network was to  
Figure 6: (**a**–**c**) Examples of different resonant frequency curves depending on the shape of the unit cell geometry. provide our desired resonant frequency, and at its output, to generate a unit cell geometry adapted to this frequency. The multilayer perceptron was trained on 100 different links; 80% of the dataset was used for training and 20% for testing. The model was trained using the Adam optimizer with a learning rate automatically set by the optimizer. For the loss function, Binary Cross-Entropy was used to compare the predicted binary matrix against the target matrix since the output consisted of a matrix with binary values (0 and 1). The architecture of the MLP used in the proposed work consisted of one neuron on the input layer, three hidden layers, and one output layer. The first hidden layer had 64 neurons, the second hidden layer had 128 neurons, and the third hidden layer had 64 neurons. All of the hidden layers used a ReLU activation layer. The output layer consisted of 36 neurons with a sigmoid activation function.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.6 Indoor Mesh Test  
This test was performed inside Torre Archimede. One node was placed on the underground floor, two nodes on the third floor, and two others on the 7 th floor. Figure 15 shows this.  
The node used a spreading factor of 7 for this test. Table 10 contains the data gathered from this test.  
This experiment, along with the previous indoor test, shows how LoRa and the solution we have outlined in this document could be used to implement a smart managing system for large buildings. It could be used to move data gathered from temperature and presence detection sensors to improve the overall efficiency of the building, lowering energy consumption and reducing lighting, heating, and pol  
Figure 15: Positioning of nodes in the indoor mesh test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 87\% \\ RSSI (db) & [\(-\)104; \(-\)99] \\ SNR & [1; 3] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results obtained from the underground garage test with spreading factor 12.  
network can make sure that, even in case a node dies, data can still be delivered to the right place.
## 1 Introduction  
Extremely and very low frequency (ELF/VLF) signals (<30 kHz) have been widely employed over several decades for radio waves, submarine communications, geophysical prospecting, and upper atmospheric remote sensing [1, 2, 3, 4, 5, 6]. The primary reason is that ELF/VLF signals can propagate long distances and penetrate through conductive barriers [7]. In previous works, ELF/VLF magnetic fields have been demonstrated to be a viable source for imaging conductive objects that are obscured by metal shields [7, 8]. However, the relative sensitivity of through-shield imaging on both material and geometric properties of hidden objects remains an unexplored area of investigation. As such, we parametrically investigate the sensitivity of nondestructive ELF/VLF testing using simulations of objects with different material properties and geometries.  
A typical methodology for testing conductive objects using magnetic fields utilizes an array of several inductive coils that surrounds the objects [9, 10, 11]. The inductive coils generate magnetic fields and are applied at frequencies in the range of 1-30 MHz [9, 11, 12]. One of the coils is excited, while the other coils are treated as receivers. The applied electromagnetic fields induce Eddy currents on conductive objects. Eddy currents lead to scattering magnetic fields with a depletion region [13]. Based on the receiver measurements, image reconstruction can be performed with various inversion algorithms such as LASSO, Ridge regression, elastic-net, and the Gauss-Newton method [14, 15, 16]. Although magnetic induction-based inversion has been employed by several researchers, few previous works have considered nondestructive testing of metallic objects that are surrounded by a highly conductive shield (\(10^{6}\) S/m \(<\sigma<10^{8}\) S/m) [8]. In real materials, nickel (1.28 \(\times\)\(10^{6}\) S/m), aluminum (3.5 \(\times\)\(10^{7}\) S/m), copper (5.8 \(\times\)\(10^{7}\) S/m), and silver (6.3 \(\times\)\(10^{7}\) S/m) are in the range of high conductivity [17]. The purpose of this work is to extend the body of literature and consider the viability of ELF/VLF magnetic fields for the nondestructive evaluation of shielded objects with realistic material and geometric properties. Section 2 discusses analytical approximations for understanding basic properties of ELF/VLF field distortions due to conductive objects inside shielded enclosures. Section 3 utilizes numerical simulations to investigate the impact of material properties and geometry. Section 4 provides a conclusion and discussion of the results with implications for future work.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Radiation Effects: Takeaways  
The field of radiation effects in silicon photonics is, as we have shown, still quite young. A summary of the current work in this field showing some of the devices' vulnerabilities and tolerances to radiation is presented in Figure 14. TID and DD effects seem to pose fewer concerns for silicon photonics in space. SETs, on the other hand, have shown to  
Figure 13: Resultant optical transients (right) caused by electrical transients generated within the MZM driver (left) for a voltage transient that is (**a**) smaller than, (**b**) equal to, and (**c**) larger than the modulator’s V\({}_{\pi}\) (after [87]).  
quite problematic. It is difficult to say with certainty whether an SET/OSET will present serious concerns, since they have to be looked at from the system level and analyzed with various modulation schemes and other variables. The black lines in Figure 14 signify that there is currently no research performed in these areas. Overall, interaction mechanisms and design trade-offs with photonic components and systems still need to be addressed and quantified.  
One of the reasons this field has been slower to mature is that radiation facilities around the world do not generally have the testing infrastructure to handle photonics during the radiation test procedure. In some cases, clearances are too small to fit fiber fixtures under a beam. In other situations, there is no proper equipment (e.g., optical tables), to carefully align fibers to on-chip couplers. While these challenges have impeded the progress of this field, there is little doubt that clever techniques will be developed to circumvent the shortcomings.
# Prediction of Grain Porosity Based on WOA-BPNN and Grain Compression Experiment
## 2 Porosity Experiment Determination Method
### Experimental Principles  
Porosity was determined using the water injection method [31], where the absorption of water by the grain particles can be ignored due to the short injection time (water injected within 1 min).  
The equation for the initial grain porosity is:  
\[n_{0}=\left(1-\frac{\rho_{0}}{\rho_{s}}\right)\times 100\% \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grain Type** & **Initial Moisture Content MC\({}_{0}\)/\%** &
\begin{tabular}{c} **Actual Moisture Content** \\ **after Formulation MC/\%** \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{Wheat} & \multirow{2}{*}{10.70} & 10.70 \\ \cline{2-3}  & & 11.26 \\ \cline{2-3}  & & 12.89 \\ \hline \hline \multirow{2}{*}{Corn} & \multirow{2}{*}{11.58} & 11.58 \\ \cline{2-3}  & & 12.34 \\ \cline{2-3}  & & 13.47 \\ \hline \hline \multirow{2}{*}{Soybean} & \multirow{2}{*}{10.14} & 8.58 \\ \cline{2-3}  & & 10.14 \\ \cline{2-3}  & & 13.43 \\ \hline \hline \multirow{2}{*}{Paddy} & \multirow{2}{*}{12.93} & 11.69 \\ \cline{2-3}  & & 12.93 \\ \cline{2-3}  & & 14.52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Moisture content of various grain samples.  
Figure 1: Grain specimen compression experiment: (**a**) Front view; (**b**) Side view.  
where \(n_{0}\) is the initial porosity of the grain, \(\rho_{0}\) is the initial density of the grain specimen in kg/m\({}^{3}\), and \(\rho_{s}\) is the density of the grain particles in kg/m\({}^{3}\).  
The equation for calculating the porosity of grain specimens during loading is:  
\[n_{i}=\frac{\Delta V_{i}}{A(H_{0}-\Delta H_{i})}\times 100\% \tag{2}\]  
where \(n_{i}\) is the porosity of the grain specimen after loading at level \(i\); \(\Delta V_{i}\) is the total volume change of water in the volumetric cylinder after loading at level \(i\), in cm\({}^{3}\); \(A\) is the bottom area of the specimen loading chamber, in cm\({}^{2}\); \(H_{0}\) is the initial height of the grain specimen, in cm; and \(\Delta H_{i}\) is the total compression of the grain specimen after loading at level \(i\), in cm.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 3 Evaluation Criteria
### Tracking Performance  
The Cramer-Rao Lower Bound (CRLB) is the generic theorem used to assess the theoretical accuracy of time-delay estimation. It defines the ultimate accuracy and sets the physical limit of the signal in white noise with given bandwidth for any estimate of a non-random parameter [29; 30]. The CRLB is given as:  
\[CRLB=\frac{B_{L}}{C/N_{0}\left(2\pi\right)^{2}\int_{-B_{r}/2}^{B_{r}/2}f^{2}G \left(f\right)df} \tag{8}\]  
where, \(B_{L}\) refers to the single-sided equivalent bandwidth of the code tracking loop, \(C/N_{0}\) is the ratio of carrier power to noise density, \(G(f)\) is the normalized PSD of the signal over the front-end bandwidth \(B_{r}\). For convenience, the Gabor bandwidth, defined as the root of the second moment of the signal PSD, is employed to be an alternative interpretation of CRLB. Its expression is:  
\[f_{Gabor}=\sqrt{\int_{-B_{r}/2}^{B_{r}/2}f^{2}G\left(f\right)df} \tag{9}\]  
Therefore, the greater Gabor bandwidth, the better the potential code-tracking accuracy will be.
## 2 Methods
### Extraction Method  
Based on this, we propose a simple method, performed by COMSOL Multiphysics, to rapidly extract accurate COM parameters. It is divided into two steps: stationary analysis and modal analysis.  
At first, stationary analysis is adopted to extract the \(C\), which employs the FEM/PML model in this paper. Figure 2 shows a single wavelength primitive cell of the infinitely periodic IDTs on the piezoelectric substrate. A perfectly matched layer (PML) is placed at the bottom of the substrate to avoid reflections from the bottom [20]. An air layer is taken into account due to the charge on the IDT-air interfaces [21]. The solid mechanical field and electrostatic field of COMSOL are coupled. The detailed boundary conditions are listed in Table 1. The stationary analysis is performed with a voltage drop \(\Delta V\) loaded between the left and right electrode. When the analysis is done, the normalized static capacitance \(C_{n}\) can be deduced from the electrostatic field energy \(W_{e}\), namely:  
\[C_{n}=\frac{2W_{e}}{(\Delta V)^{2}W}. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Boundary**} & **Mechanical** & **Electrical** \\  & **Boundary Conditions** & **Boundary Conditions** \\ \hline \(\Gamma_{T}\) (top of air) & Free & Zero charge \\ \(\Gamma_{A}\) (air-solid interfaces) & Free & Continuity \\ \(\Gamma_{+}\), \(\Gamma_{-}\) (\(\pm\)electrode-solid interfaces) & Free & \(V_{\Gamma+}-V_{\Gamma-}=\Delta V\) \\ \(\Gamma_{B}\) (bottom of substrate) & Fixed & Zero charge \\ \(\Gamma_{L}\), \(\Gamma_{R}\) (left and right boundaries) & Periodic continuity boundary condition \\ \(\Gamma_{F}\), \(\Gamma_{E}\) (front and back boundaries) & Periodic continuity boundary condition \\ \hline \hline \end{tabular}
\end{table}
Table 1: The boundary conditions of the stationary analysis.  
Thus, the static capacitance per unite \(C\) can be extracted.  
Then, modal analysis is adopted to extract the remaining COM parameters. That is, model analysis is performed to solve the eigenvalues under SC and OC conditions. These eigenvalues correspond to \(f_{\text{SC}-},f_{\text{SC}+},f_{\text{OC}-},f_{\text{OC}+}\), which are the key values to determine the remaining COM parameters. The SC and OC conditions are realized by setting zero voltage and charge, respectively, at the electrode-solid interfaces (\(\Gamma_{+}\), \(\Gamma_{-}\)). The detailed boundary conditions for the modal analysis are given in Table 2. Finally, meshing the primitive cell. Mesh division determines the solution mode of the model and directly affects the calculation time and accuracy. In this work, the boundary surface is firstly meshed, and then the whole model is meshed along the thickness direction.