esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Electro-Optic Properties of Polymers
### Electro-Optic Properties of a Single Molecule  
Organic conjugated molecules and polymers show interesting and peculiar physical properties, given by electron delocalization of the \(\pi\)-bonds [1]. If we consider benzene (C\({}_{6}\)H\({}_{6}\)), it shows complete delocalization of \(\pi\)-electrons on the aromatic ring (Figure 1(a)). As we saw in the previous chapter, as benzene is a nonpolar molecule and it is centrosymmetric, it does not show any second order nonlinear contribution to the polarization term.  
If we substitute some hydrogen atoms with an electron acceptor A (for example NO\({}_{2}\)) or electron donor group D (for example NH\({}_{2}\)) (Figure 1(b,c)), the benzene ring allows the electrical charges to flow from the donor to the acceptor, generating a dipole moment along the conjugation direction. We can expect that non-centrosymmetric molecules, with large mobility of charges will show a large degree of polarization and nonlinear optical terms, especially of the second order.  
For a general molecule, using a description similar to that used in the previous paragraph for macroscopic nonlinear media, we can write down the expression of the induced dipole moment as:  
\[\mu_{i}=\mu_{0i}+\varepsilon_{0}\alpha_{y}F_{j}+\varepsilon_{0}\,\beta_{gi}F_{ j}F_{k}+\varepsilon_{0}\gamma_{ijkl}F_{j}F_{k}F_{l}+...\hskip 36.135 pti,j,k,l=1,2,3 \tag{5}\]  
where \(\mu_{0i}\) are the static electric dipole moment components, \(\alpha_{ij}\) are the polarizability tensor components, \(\beta_{ijk}\) and \(\gamma_{ijkl}\) are the components of the hyperpolarizabilities tensors of the first and second order, respectively, and finally \(F_{i}\) represent the \(i\) components of the local electric field \(\vec{F}\) that is related to the macroscopic electric field \(\vec{E}\) by the local field factor \(\mathscr{B}\) via the following relation:  
\[\vec{F}=f^{\,\omega}\vec{E} \tag{6}\]  
The local field factor in the range of the optical frequencies is given by the Lorenz-Lorentz formula:  
\[f^{\,\omega}=\frac{n^{\,2}(\omega)+2}{3} \tag{7}\]  
where \(n(\omega)\) is the refractive index at \(\omega\). For static fields, the field factor is given by the Onsager expression:  
\[f^{\,0}=\frac{\varepsilon_{r0}(\varepsilon_{r\omega}+2)}{\varepsilon_{r\omega }+2\varepsilon_{r0}} \tag{8}\]  
where \(\varepsilon_{r0}\) is the static relative dielectric constant and \(\varepsilon_{r\omega}\) is the relative dielectric constant in the range of optical frequencies [12,23].  
Figure 1: **(a)** Centrosymmetric benzene. **(b, c)** Non-centrosymmetric donor acceptor substituted benzenes.  
One of the best known electro-optic chromophore used in applications is the 4-[\(N\)-ethyl-\(N\)-(2-hydroxyethyl)]amino-4'-nitroazobenzene (Disperse Red 1; DR1), whose structure is shown in Figure 2(a).  
The main characteristics are reported in Table 1[12, 24, 25, 26, 27, 28, 29]. A comparison between electrostatic units (esu) and International System units (SI), is given in [30].
# An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures
## 2 Measurement of RT
### Implicit Measures in VR  
The interactions of the users with the virtual environment can be also studied by the analysis of their gaze movements, which have shown to be related to information processing in risky decisions [59] and problem solving [60]. The eye tracking (ET) measure can be integrated into a VR set-up, in order to record fixations and eye movements during an experience in a virtual environment. This technology has been applied in combination with VR for the study of the influence of contextual elements in human behavior, such as in street robbery [61], identifying if the presence of particular components of a physical space can influence in decision-making. Furthermore, ET has been employed to study whether if exists a relationship among gaze patterns and human behavior [62; 63], or even if these gaze patterns could contribute to predict humans' decisions [64].  
In the field of RT, ET has been employed as a reliable indicator of information processing patterns in risky decisions. On the first hand, greater number of fixations, longer fixations and larger quantity of available information fixed have been related to deeper prediction processes, which lead to risk aversion [65; 66; 67; 68]. On the other hand, in a study with construction workers, lower dwell time was connected with a higher risk perception [69]. The authors interpreted this result as follows: participants with higher risk perception identified the hazards rapidly, so they could spend their time searching other possible hazards present in the situation.  
In addition to behavioral measures, physiological measures have been proposed as implicit measures of human behavior [42]. Galvanic skin response (GSR) has been successfully used as a measure of implicit processes such emotional arousal [70], which plays a decisive role in the decision-making process. GSR has been employed in combination with VR to evaluate the stress generated by changes in contextual aspects, such as architectural stimuli [71], as predictor of anxiety level [72] and as a measure to discriminate between Autism Spectrum Disorder and typical development populations [73], among others.  
In the field of RT, high physiological arousal acts as a \"warning signal\" in risky situations and tends to lead to safe decisions [1]. This relationship has been demonstrated to be mediated by emotional intelligence, such a way that, low emotional intelligence may lead to maladaptive decision-making, due to an impaired interpretation of physiological arousal [74]. Additionally, situational factors, such as time pressure, have an influence on the relationship between GSR and RT. In an experiment with two kind of decisions (time pressure and time delay), the relationship between GSR and RT was positive in situations under time pressure, and negative in situations under time delay [75].  
Despite these measures having been widely adopted in VR-based experiments, to our knowledge, ET and GSR have not been employed in combination with VR to evaluate RT.
# Estimation of Stability Parameters for Wide Body Aircraft Using Computational Techniques
## 3 Xflr
### Analysis  
XFLR gives the liberty of aerodynamic and stability analysis according to the user's choice. It is recommended to run the aerodynamic analysis first to obtain insights into the performance of the aircraft. The analysis techniques are lifting line theory (LLT), the Horseshoe method, and VLM. The choice of viscous or inviscid is also present. Once the aerodynamic analyses are run, a series of graphs are generated to display the aircraft's performance through CL, CD, and Cm. After aerodynamic analysis, stability analysis is defined and run for already defined flight conditions to generate stability derivatives, and results are obtained in the form of root locus/time response. Both longitudinal and lateral modes are catered for in XFLR with a detailed review of Phugoid, short period, roll, and Dutch-roll modes of flying aircraft. Animations of all the modes are also available within wide ranges of speeds and amplitudes. The derivatives of stability are stored in a text file  
Figure 11: Airfoil analysis; Variations of lift, drag, and moment coefficients with angle of attack obtained using XFLR.  
with the name of log file. This file also covers eigenvalues and eigenvectors of the aircraft at given conditions.
# Forest Fire Detection Using New Routing Protocol
## 5 Methodology of LAR-Based Reliable Routing Protocol (LARRR)  
Several protocols have been presented based on mobile sensor networks with the purpose of improving the network performance. The routing protocols differ in the manner of finding routes. The LAR protocol works efficiently with respect to consuming energy, where the process of discovering routes is initiated when there is a need for sending a packet. The routes that need to be created are performed by transmitting control packets over the network. As we mentioned previously, the LAR protocol defines and determines the request zone, where the nodes within this zone are authorized only to forward the control packets. Hence, the routing overhead will be reduced. According to the advantages of the LAR protocol, we select this protocol to be a base for our work. The major goal of this work is to develop the LAR protocol and make it perform efficiently and reliably in forest fire detection. The new routing protocol is named the LAR-Based Reliable Routing Protocol (LARRR). The LARRR protocol is adapted to route the packets between the mobile sensor networks for forest fire monitoring. Furthermore, every node of the LARRR can sense the temperature continuously. More information about finding the best routes is explained in the next subsection.
## 3 Results and Discussion
### Sensitivity and Reproducibility of the Electrochemical Biosensor  
Under the optimal experimental conditions, the DPV responses of the prepared electrochemical biosensor at different _E. coli_ concentrations are shown in Figure 8. It is found that the DPV peak current decreases with the increasing concentration of _E. coli_ from 5.0 \(\times\) 10\({}^{2}\) CFU/mL to 5.0 \(\times\) 10\({}^{7}\) CFU/mL. The prepared electrochemical biosensor has a good linear relationship between the peak current (ip) value and the logarithm of _E. coli_ concentration (n) in the range from 5.0 \(\times\) 10\({}^{2}\) CFU/mL to 5.0 \(\times\) 10\({}^{7}\) CFU/mL (Figure 9). The resulting linear equation is ip (\(\mathrm{\SIUnitSymbolMicro a}\)) = \(-\)0.368n + 4.29, with a correlation coefficient of 0.996. In addition, the limit of detection (LOD) is 80 CFU/mL at a signal-to-noise ratio of 3, which is lower than other methods reported previously for the detection of _E. coli_ (Table 1) [6; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. Figure 6: The relationship between the ST-AP concentration and the peak current under the reaction with 5.0 \(\times\) 10\({}^{2}\) CFU/mL _E. coli_. Figure 7: The relationship between the \(\alpha\)-NP concentration and the peak current under reaction with 5.0 \(\times\) 10\({}^{2}\) CFU/mL _E. coli_. Moreover, the measurement time of this method is 150 min, which is shorter than that of PCR, ELISA, immunomagnetic bead sandwich assay and electrochemical immunoassay, not to mention the plate counting method (72 h). It is worth mentioning that the linear range is also wide, spanning 5 orders of magnitude. Figure 8: DPV responses in licorice extract of 4% solid content: (a) 5.0 \(\times\) 10\({}^{2}\) CFU/mL, (b) 5.0 \(\times\) 10\({}^{3}\) CFU/mL, (c) 5.0 \(\times\) 10\({}^{4}\) CFU/mL, (d) 5.0 \(\times\) 10\({}^{5}\) CFU/mL, (e) 5.0 \(\times\) 10\({}^{6}\) CFU/mL and (f) 5.0 \(\times\) 10\({}^{7}\) CFU/mL _E. coli_. Figure 9: Calibration plot of peak currents versus concentration of _E. coli_.
# Precision Farming in Hilly Areas: The Use of Network RTK in GNSS Technology
## 5 Conclusions  
Based on the results obtained with a RDP in the Lazio Region 2007/2013 measure 1.2.4, this paper reveals the actual possibilities of utilizing satellite guidance systems for agricultural vehicles in hilly areas, which are certainly less optimal than areas with more regular orography. The results obtained showed suitable possibilities of using these systems even in hilly environments. Therefore, the research topic proposed in this paper gives innovative insights, especially for researchers and even for farm producers. Considerable benefits have been achieved in terms of uniformity of machining, potential reduction of (chemical) products used, and operator safety during the working time.  
**Acknowledgments:** Research carried out under the RDP Project Measure 124 entitled \"Introduction of Sustainable Processes and Methods using RTK Satellite Technologies and Corporate GIS for the Development of Precision Agriculture in Tuscia\" financed by the Lazio Region with a measure of concession of aids n. 31/124/10 of December 12 th, 2014; Public Notice DGR n.76/2014 and s.m.i.  
**Author Contributions:** [PERSON] performed the experiments, analyzed the data, and contributed materials and tools; [PERSON] designed the experiments; [PERSON] and [PERSON] wrote the paper; [PERSON] was involved in the critical review of the results obtained.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Modeling and Fault Tolerance Analysis of ZigBee Protocol in IoT Networks
## 4 Analysis of the Simulation Results Concerning the Fault Tolerance Issues in ZigBee Protocol
### The Model and Topology of the Analyzed Network  
The research carried out here was based on ZigBee technology based on IEEE 802.15.4 specification. ZigBee is a wireless ad-hoc network with low power consumption, low data rate (250 kbps), and short-range. For this reason, it is well suited for use in wireless sensor networks and more broadly in the Internet of Things. A ZigBee device can be divided into three categories: coordinator, router, and terminal device. ZigBee device symbols in the Riverbed Modeler simulator are demonstrated in Figure 3.  
The coordinator serves as the starting node to which other devices can connect. There can be only one coordinator in a given network. It usually also plays the role of a data collector. A router is responsible for forwarding packets, allowing multiple hops. The terminal device is the last element of the network, which is responsible for collecting data and forwarding it to the router. Because the distance between two nodes in end-to-end communication is restricted, the type of unit and the route node is required to transmit the information. The above-mentioned three kinds of nodes are the idea of the network layer, and their arrangement determines the topology of the ZigBee network.  
This research aimed to test the resilience of the network against data communication threats such as node failures. For this purpose, a network topology was created that  
Figure 3: ZigBee device symbols in the Riverbed Modeler simulator.  
included one coordinator, several routers, and end devices. Several scenarios were then simulated in which different devices were intentionally damaged. The first scenario with all devices working served as a baseline for the other three. The devices have a limited range, so they were deployed in such a way that they could communicate with each other without hindrance. The second scenario assumes the failure of a few devices, which should not disrupt the network to a significant degree. The failed nodes are marked with a green X. In the third scenario, more devices fail, which should translate into less efficient network operation. The failed nodes were marked with a blue X. The fourth scenario assumes that many devices fail, paralyzing the network operation. Both end devices and routers fail. This should prevent communication between devices, especially with the coordinator, and consequently paralyze the network. The failed nodes are marked with a red X. The topology of the analyzed network and scenarios along with the designation of the damaged nodes is shown in Figure 4 and described in Table 1.  
Figure 4: Network topology (green cross—nodes damaged in scenario II, blue cross—nodes damaged in scenario III, red cross—nodes damaged in scenario IV).  
The devices have a limited range, so they were placed in such a way that they could communicate with each other without interference. A few devices were selected and their statistics were checked after each simulation, as well as the global network statistics.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 5 Performance Evaluation
### Analysis of Energy Consumption  
The presented APSA is compared with LEACH-AP, UCR-H and EDDUCA which are all centralized routing protocols. For each protocol, 50 different samples of the network model are generated to execute the protocol and the result is based on the average value of repeated simulations. In Figure 5, the x-axis represents the number of rounds the network runs and the y-axis represents the total energy consumption of the network. It obviously shows that with rounds going, the total energy consumption of the presented APSA increases more slowly compared to the other three algorithms. In about the  
Figure 4: Cluster result of APSA (100 sensors).  
Figure 3: Cluster result of affinity propagation-based self-adaptive (APSA) algorithm (50 sensors).  
1000 th round, APSA achieves about 33.33%, 52.5% and 54.21% performance gain compared to UCR-H, LEACH-AP and EDDUCA respectively.
# Photocatalytic Activity of Monosized AuZnO Composite Nanoparticles
###### Abstract  
Photocatalytic activity of monosized AuZnO composite nanoparticles with different compositions were synthesized by the one-pot polyol procedure, using the triblock copolymer poly(ethylene glycol)-block-poly(propylene glycol)-blockpoly(ethylene glycol) (PEO-PPO-PEO) as the surfactant. The structure and morphology of the composite nanoparticles were analyzed by X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), selected area electron diffraction (SAED), a transmission electron microscope (TEM) and high resolution transmission electron microscopy (HRTEM). The characterization showed that the AuZnO composite nanoparticles were spherical, with narrow particle size distribution and high crystallinity. The Fourier transform infrared spectroscopy (FTIR) study confirms the PEO-PPO-PEO molecules on the surface of the composite nanoparticles. The investigations by ultraviolet-visible light absorbance spectrometer (UV-Vis) and photoluminescence spectrophotometer (PL) demonstrate well the dispersibility and excellent optical performance of the AuZnO composite nanoparticles. Photocatalytic activity and reusability of the AuZnO nanoparticles in UV and visible light regions was evaluated by the photocatalytic degradation of Rhodamine B (RhB). The experimental results show that the AuZnO composite nanoparticles with a suitable amount of Au loading have stability and improved photocatalytic activity. AuZnO composite nanoparticles are effective and stable for the degradation of organic pollutants in aqueous solution.  
Keywords:one-pot polyol synthesis; AuZnO composite nanoparticles; optical performance; photocatalytic activity +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
### Introduction to the Real Fluid RFM Model  
The simulation of single-phase (liquid or gas) compressible flows has its own set of challenges, mainly due to the possible appearance of shock waves. This, in addition to the presence or appearance of a second phase (cavitation, boiling, etc.), can be quite challenging for the numerical solvers due to the sharp variations of the thermodynamic properties across the subcritical interface as well as across the Widom line, which is generally considered as the transcritical interface, as explained in [15; 16]. These numerical challenges of non-linearity of thermodynamic properties due to phase change in subcritical and transcritical regimes make such studies difficult and require employing accurate real fluid equations of state. Indeed, the work of [PERSON] [16] has demonstrated that the ideal gas equation (EoS) is no longer valid when the conditions are close to the transcritical conditions. Thus, to accurately resolve the sharp gradients of density, pressure and other thermodynamic values, we need a precise real fluid EoS for simultaneously studying two-phase flows with shock formation, evaporation and condensation. Consequently, to be able to simulate such complex phenomena, this work proposes a real fluid model, referred to in this work as RFM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Inlet Total Pressure** & **Inlet Total Temperature** & **Subsonic Outlet Pressure** \\  & \(P_{inlet}=P_{t}\) **(MPa)** & \(T_{inlet}=T_{t}\) **(K)** & \(P_{exit}\) **(MPa)** \\ \hline
1 & 5.896 & 314.67 & 1.973 \\
2 & 6.535 & 311.99 & 2.281 \\
3 & 7.353 & 313.60 & 2.437 \\
4 & 7.999 & 313.94 & 2.729 \\
5 & 8.474 & 313.88 & 2.805 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inlet and outlet conditions of the nozzle from experiments carried out by [PERSON] et al. [11].
## 3 Proposed System and Methodology  
The proposed approach for protocol identification consists of two main phases: (1) _Signal Denoising_: Reconstructing the unlicensed signals by removing the noise and the signal degradation effects using the DL model. (2) _Protocol Identification_: Identifying the unlicensed bands for the corrupted signals based on denoising DL weights.  
To study the proposed system's performance, first, a simulation environment is developed for LTE and WLAN 802.11 standards. Second, the data are generated and collected under different SNR values and channel propagation models. Third, data preprocessing is performed to prepare the DL model's data by mapping the signals to the corresponding spectrograms. Fourth, the signal denoising is performed for each dataset to reconstruct the corrupted received signal spectrogram for each protocol using Convolutional Denoising Autoencoders (CDAEs). Finally, a signal classification is performed to identify the received protocol using CNN classifier model taking into input the CDAE representation. Figure 3 illustrates the proposed system for using DL.  
Figure 3: Proposed system for unlicensed protocol identification under noise and different channel propagation models.  
Figure 2: Spectrograms for different IEEE 802.11 protocols and unlicensed LTE signals: (**a**) the transmitted spectrograms, the received spectrograms for (**b**) _Scenario 2_ and SNR 20 dB, (**c**) _Scenario 1_ and SNR 5 dB, and (**d**) _Scenario 1_ and SNR 0 dB.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines  
As electricity generation from wind energy is intermittent, the increasing integration of wind turbines into existing electricity grids poses a challenge for the flexibility, security and stability of electricity systems. Predicting power generation is becoming increasingly important in this context, and artificial intelligence is recognized as an essential component in the operational phase of wind farms. In addition, the integration of AI technologies into the management processes of wind farms leads to improved efficiency, and AI in this context includes the following:  
* Improved accuracy of electricity generation and consumption forecasting
* Dynamic optimization of wind turbine performance
* Efficient remote monitoring and control of wind turbines
* Efficient bird collision avoidance system with wind turbine blades
* Integration of wind turbines with smart grids
* Improved predictive maintenance capabilities of wind turbines