esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### SINR under Different Bistatic Configurations  
Generally, the bistatic configuration can be categorized into two types, including the specular scattering case and the non-specular scattering case, as shown in Table 7[38]. Take Figure 1 for example. For the out-of-plane case, \(\sigma_{B}\) turns minimum when \(\varphi\) approaches 90\({}^{\circ}\), and it is almost same as that in the in-plane case when \(\varphi\) is near 0\({}^{\circ}\) or 180\({}^{\circ}\). \(\sigma_{B}\) achieves maximum in the case of specular scattering, which is a small probability event. While \(\varphi\)  
Figure 8: The relationship between pulse width and SINR of LEO SARs and GEO SARs images in the presence of scattered wave RFI (\(\sigma_{B}=\sigma_{0}\)); (**a**) the integration time is 0.5 s; (**b**) the integration time is 220 s.  
Figure 7: The relationship between bandwidth and SINR of LEO SARs and GEO SARs images in the presence of scattered wave RFI (\(\sigma_{B}=\sigma_{0}\)). The red line indicates that the LEO SAR has the same bandwidth as the GEO SAR, and the invalid value in the yellow box indicates the LEO SAR has the same FM rate as the GEO SAR; (**a**) the integration time is 0.5 s; (**b**) the integration time is 220 s.  
is \(180^{\circ}\), and \(\theta_{1}=\theta_{2}\), the backscatter case occurs; \(\sigma_{B}\) is larger than that in other cases but demonstrates specular scattering.  
In this paper, the bistatic scattering coefficient of bare soil in L band was given using an advanced integral equation model (AIEM) [41] based on the parameters shown in Table 2 (in order to overcome the thermal noise, the GEO SAR transmission power is 10 kw and the antenna diameter is 25 m in this case). Then, the corresponding SINR was obtained, which is shown in Figure 9.  
According to Figure 9a, obviously, the image quality is poor in the case of specular scattering (\(\varphi=0\)). Additionally, when \(\varphi\) approaches \(90^{\circ}\), the image quality is high due to the small value of \(\sigma_{B}\). The backscatter case resulted in a larger scattering coefficient; however, the impact can be still ignored. In addition, in the in-plane case, smaller differences between \(\theta_{1}\) and \(\theta_{2}\) will lead to poor image from Figure 9b. Finally, the impact of LEO-to-GEO RFI is more serious than that of GEO-to-LEO RFI in the same configuration.
# Changes in Collagen across Pork Tenderloin during Marination with Rosehip Nanocapsules
###### Abstract  
The objective of this study was to prepare zein-gum Arabic nanocapsules with roeship oil (NC-RH), apply them to pork tenderloin, and analyze the changes in collagen structure under different conditions (pH 6.5 and 4.0) and temperatures (25 \({}^{\circ}\)C and 4 \({}^{\circ}\)C). NC-RHs were prepared using the nanoprecipitation method. Nanocapsules had a particle size of 423 \(\pm\) 4.1 nm, a polydispersity index of 0.125 \(\pm\) 3.1, a zeta potential value of \(-\)20.1 \(\pm\) 0.41 mV, an encapsulation efficiency of 75.84 \(\pm\) 3.1%, and backscattering (\(\Delta\)B = 10%); the antioxidant capacity of DPPH was 1052 \(\pm\) 4.2 \(\mu\)M Eq Trolox and the radical scavenging capacity was 84 \(\pm\) 0.4%. The dispersions exhibited Newtonian behavior at 25 \({}^{\circ}\)C and 4 \({}^{\circ}\)C. Incorporating NC-RH into acid marination benefited the tenderness, water-holding capacity, and collagen swelling, and favored changes in myofibrillar proteins corroborated with histological tests. The conditions with the best changes in pork tenderloin were a pH of 4.0 at 4 \({}^{\circ}\)C with an NC-RH-administered 11.47 \(\pm\) 2.2% collagen area. Incorporating roeship nanocapsules modifies collagen fibers and can be applied in pork marinades to increase the shelf life of a functional product.  
stabilty; tenderness; marination; infiltration; characterization Article
# Dynamic Characteristic Master Curve and Parameters of Different Asphalt Mixtures
###### Abstract  
Using an AMPT tester and based on laboratory tests, this paper performed a comparative study on the dynamic characteristics of different asphalt mixtures, analyzed the influence of different asphalt binders on the characteristic parameters of the dynamic modulus master curve and the phase angle master curve of asphalt mixture, and expounds the evaluation function of the phase angle master curve for mixture relaxation characteristics. The results show that the modulus master curve parameters of the asphalt mixture are closely related to voids in the mineral aggregate, mixture density, and asphalt content of the asphalt mixture. For the same kind of asphalt mixture, because the gradation of mineral aggregate is fixed and the volume parameters are almost the same, the ultimate modulus of the mixture at different temperatures is unique; when the temperature changes or the asphalt changes, the shape parameter \(\beta\) of the modulus master curve changes regularly, which brings different dynamic responses, and the lower \(\beta\) will show the characteristics of a higher modulus. Asphal is the source of the viscoelasticity of the asphalt mixture. Although the influence of particle gradation of the mixture will bring about the change of modulus, the phase angle of the mixture depends on the viscoelastic properties of asphalt, and the initial phase angle in the main curve is positively correlated with asphalt penetration and negatively correlated with the softening point and viscosity, while the peak phase angle A is negatively correlated with penetration, and the softening point viscosity is positively correlated. The viscoelastic interval, represented by \(\omega\), is negatively correlated with penetration but positively correlated with the softening point and viscosity. The peak position, parameter \(\omega\)c, of the phase angle master curve can evaluate the relaxation characteristics of the mixture, and the crack resistance of different mixtures can be compared without complex model calculation. In the comparison of the relaxation time of asphalt mixture, the relaxation time of foam cold-recycled mixture is the largest, which is significantly higher than that of other forms of cement mixture; the emulsified asphalt cold-recycled mixture is equivalent to AC20 and LSPM30 mixtures; the SBS-modified asphalt mixture has the best relaxation characteristics.  
Keywords:asphal mixture; master curve; characteristic parameter; relaxation characteristics; cold recycling; foamed asphalt; emulsified asphalt +
Footnote †: journal: Applied Sciences
## 7 Results on SM Problems  
We measured the performance of our algorithms in terms of the number of search steps. For these tests, we generated 100 SM problem instances for each of the following sizes: 100, 200, 300, 400 and 500. In the following, we show only the results of our best algorithm, which is SML2. We studied how fast SML2 converges to a stable marriage, by measuring the ratio between the number of blocking pairs and the size of the problem during the execution. Figure 2(a) shows that SML2 has a very simple scaling behavior. Let us denote by \(\langle b\rangle\) the average number of blocking pairs of the marriage found by SML2 for SM problem instances of size \(n\) after \(t\) steps. Then, the experimental results shown in Figure 2(a) have a very good fit with the function \(\langle b\rangle=an^{2}2^{-bt/n}\), where \(a\) and \(b\) are constants computedempirically (\(a\approx 0.25\) and \(b\approx 5.7\)). Figure 2(a) shows that the analytical function \(\langle b\rangle\) has practically the same curve as the experimental data. The figure shows also that the average number of blocking pairs, normalized by dividing it by \(n\), decreases during the search process in a way that is independent of the size of the problem.  
We can use function \(\langle b\rangle\) to conjecture the runtime behavior of our local search method. Consider the median number of steps, \(t_{med}\), taken by SML2. Assume this occurs when half the problems have one blocking pair left and the other half have zero blocking pairs. Thus, \(\langle b\rangle=\frac{1}{2}\). Substituting this value in the equation for \(\langle b\rangle\), taking logs, solving for \(t_{med}\) and grouping constant terms, we get \(t_{med}=cn(d+2\log_{2}(n))\), where \(c\) and \(d\) are constants. Hence, we can conclude that \(t_{med}\) grows as \(O(n\log(n))\).  
We then fitted this equation for \(t_{med}\) to the experimental data (using \(c\approx 0.26\) and \(d\approx-5.7\)). The result is shown in Figure 2(b), where we see that the experimental data have the same curve as function \(t_{med}\). This suggests that we can use such an equation to predict the number of steps our algorithms needs to solve a given SM instance.
###### Abstract  
A liquid crystal on silicon spatial light modulator (LCoS SLM) with large phase modulation has been thoroughly characterized to operate optimally with several linear phase modulation ranges (\(\pi\), \(2\pi\), \(3\pi\), \(4\pi\), \(6\pi\), and \(8\pi\)) for an intermediate wavelength of the visible spectrum (\(\lambda_{\text{G}}\) = 530 nm). For each range, the device response was also measured for two additional wavelengths at the blue and red extremes of the visible spectrum (\(\lambda_{\text{B}}\) = 476 nm and \(\lambda_{\text{R}}\) = 647 nm). Multiorder diffractive optical elements, displayed on the LCoS SLM with the appropriate phase modulation range, allowed us to deal with some widely known encoding issues of conventional first-order diffractive lenses such as undersampling and longitudinal chromatic aberration. We designed an achromatic multiorder lens and implemented it experimentally on the SLM. As a result, the residual chromatic aberration reduces to one-third that of the chromatic aberration of a conventional first-order diffractive lens.  
Keywords:liquid crystal spatial light modulator; liquid crystal on silicon device; phase characterization; phase modulation; diffractive optical element; multiorder diffractive lens; harmonic lens; chromatic aberration; aberration compensation; achromatic lens +
Footnote †: journal: Applied Physics Letters
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.7 Implementing the Mobile Application  
The smartwatch application was developed using Kotlin as the main programming language, alongside Wear OS to ensure seamless integration with smartwatch operating systems. For designing modern and user-friendly interfaces, Jetpack Compose for Wear OS was utilized. After examining each component that contributed to the system's development, it became evident that the implementation phase went beyond creating individual parts in isolation. A comprehensive, integrated approach was essential to ensure all functionalities worked together cohesively within the system. This interconnected design was critical in achieving the functional requirements established during the analysis phase. We utilized both the emulator provided by the Android Studio development environment and a physical Samsung Galaxy Watch 5, compatible with the mobile application developed for the Wear OS 4.0 operating system.
## 1 Introduction
The IDHIL simulator consists of the dSPACE Automotive Simulation Models (ASM), which run on a dSPACE HIL simulator Mid-Size [15], for the vehicle models, dSPACE ASM MotionDesk, ModelDesk, and ControlDesk for the driving and traffic simulators [16], and QualNet for the network simulator [17]. To develop an ECU for the cooperative eco-driving function of a connected hybrid electric vehicle (CHEV), a dSPACE MicroAutoBox is attached to the integrated simulator [18]. Furthermore, Cohda Wireless MK5 devices are attached to the integrated simulator for HIL testing of the V2X communication device by utilizing a network emulation function of the V2X communication packet [19]. In the evaluation, the hybrid control algorithms based on V2X communication and cooperative eco-driving speed guidance application are used to demonstrate the use of proposed IDHIL simulator for developing and testing a cooperative eco-driving system. The remainder of this paper is organized as follows. The background of the integrated HIL simulator is described in Section 2. The architecture of the proposed integrated simulator is presented in Section 3. Section 4 describes the simulation environment and evaluation results of the application of the cooperative eco-driving system. Finally, some concluding remarks are given in Section 5.
## 4 Conclusions  
The polarity of the solvent influences the extraction yield and antioxidant activity both qualitatively and quantitatively. Studies on horseradish have shown that ethanol/water extracts show a better yield than the aqueous extract [39]. In addition, a study conducted on grape pomace showed that in ethanol  
Figure 5: Percentage of vitality of HS68 fibroblast cells, determined by MTT (each data is presented as mean \(\pm\) SD; n = 12). Cells treated with different extracts of _Calendula_ (ethanol 80%, SFE and SFE CS) for 24 h to concentrations 2.4, 12, and 24 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (0.1% \(<\) ethanol). Co = control (no treatment); CM: _C. maritima_; CF: C. _suffruticosa subsp. fulgida_; CI: C. _hybrid_ (_maritima \(\times\) fulgida_); CA: C. _arrensis_. Figure 6: Effect of _Calendula_ extract on fibroblast cells exposed to oxidative stress, induced by H\({}_{2}\)O\({}_{2}\) Co = control cells; HP = cells treated only with HP hydrogen peroxide, without extracts; NAC = cells pretreated with the synthetic antioxidant NAC and then exposed to HP; CM = cells pretreated with different extracts of _C. maritima_ and then exposed to HP; CF = cells pretreated with different extracts of _C. suffruticosa subsp. fulgida_ and then exposed to HP; CI = cells pretreated with different extracts of _C. hybrid_ (_maritima \(\times\) fulgida_)and then exposed to HP; CA = cells pretreated with different extracts of _C. arensis_ and then exposed to HP. Lowercase letters indicate significant differences (a, b, c: \(p<0.05\)) according to the analysis of variance (ANOVA). extracts the concentration values of polyphenols were about twice in respect to those found in water; furthermore, ethanol extracts showed the highest DPPH radical scavenging activity [32]. In our work, it has been observed that the various species of Calendula show a high antioxidant activity due to their content in polyphenols and that the use of hydroalcoholic solvents, such as ethanol 80%, allows to obtain a better yield and a more efficient extraction of polyphenols. The results of the DPPH test and the reducing power indicated that the hydroalcoholic extract has a greater antioxidant power than the extracts obtained with solvents of different polarities and with SFE, due to the content of polyphenols. Moreover, among the various species analysed, CF and CA are those that show a greater antioxidant activity. The in vitro tests confirm the antioxidant properties and the protective effect of Calendula (CF, CI, and CA) extracts with ethanol 80%. Although the best yields have been observed with the ethanol 80% extract, the SFE extract shows antioxidant activity similar to the extract obtained in ethanol 80%, so given its eco-friendly characteristics and absence of use of solvents, it seems to be a promising technique for the sustainable production of Calendula extracts to be directed to local niche productions.
# Zinc Oxide-Based Rotational-Linear Triboelectric Nanogenerator
## 3 Results and Discussion
### Application for Harvesting Wind Energy  
The RL-TENG was adapted to be a wind-harvesting device as shown in Figure 10a. The wind-harvesting device is composed of (a) a case that encloses the RL-TENG for mechanical support, providing a way for the vertical shaft to spin freely using deep-groove ball bearings, (b) the vertical shaft where the cam is mounted using an aluminum adapter and (c) three cups attached to the vertical shaft. As the wind blows, the cups create rotational torque, spinning the vertical shaft and as consequence the RL-TENG, as shown in Figure 10b (Video S1). For measuring the air velocity, a hand-held anemometer was utilized (UT363 BT, UNI-T\({}^{\copyright}\), Dongguan, China) that provided real-time and data logging capabilities via Bluetooth\({}^{\copyright}\), displayed at a smartphone app (iENV2.0, UNI-T\({}^{\copyright}\)).  
Figure 9: (**a**) Part of a more robust triboelectric generator and (**b**) output voltage as a function of time at 350 rpm.  
Figure 11 shows the power generated by each triboelectric pair as a function of the wind velocity. We see that the different triboelectric pairs do not have the same performance. This is normal since the different pairs were prepared, to some extent, manually and not with a standardized manufacturing and assembly process. Moreover, the rotational speed of the device as a function of the wind velocity is presented in Figure 11b. We see that there is a very good match between the triboelectric pairs for which the measurements were not performed simultaneously.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework
### The Proposed Approach
#### 2.2.4 Relationship Learning
* Feed \(\overline{c}_{new}^{r}\) into the next component named relationship learning as new input feature vector. A supervised pre-training scheme is performed layer by layer to train the \(\overline{c}_{new}^{r}\) to regress to the labelled stiffness elements \(\overline{\sigma}^{r}\). * Finally, perform fine-tuning for relationship learning component by jointly optimizing all layers to achieve an improved accuracy of the proposed framework. * Testing: after training, the weights are fixed for the proposed framework. Testing datasets are feed into the fixed framework and the performance is evaluated in terms of the MSE and the R-value. Numerical studies will be presented in the next section.
## 2 Gas Turbine
### Evolution of Combustion Technology in Modern Gas Turbine
#### 2.1.2 Trapped Vortex and Mild Combustion  
The first strategy to overcome the trade-off is optimizing the fuel turbulence to produce stable combustion in the second generation. The system includes trapped vortex combustion (TVC) and mild combustion (MILD) or flameless combustion. TVC's working principle is based on cavity stabilization. The cavity inside the TVC combustor provides a recirculation zone to trap the vortex pilot flame and produces a constant source of ignition at a high rate [12; 13]. The continuous ignition or pilot flame offers better mixing inside the cavity that helps to increase the efficiency of the combustion process and reduce greenhouse gas emissions [14]. As reported by [PERSON] in [15], the technology can achieve from 10 to 40% of NOx emission reduction due to the high inlet velocity for the vortex, which reduces the combustion temperature and improves flame stabilization [16]. The trapped turbulent vortex also provides some significant pressure drop reduction, as reported by [PERSON] in [17]. The TVC combustor with the most flexibility in fuel operation and application using biofuel is investigated in [18]. Despite the promising performances, the major drawback of the technology is the dependency on the geometric design, especially the cavity area, to attain efficiency. Only proper design and adjustment produce a stable vortex and combustion. Moreover, the continuous firing in the area leads to severe cavitation, further complicating the material selection process [19].  
Therefore, the cavitation problem is addressed in the MILD burner. The cavitation area is converted into a constant hot stream of gas to preheat the air before entering the combustor. Then, the hot product gases and the oxygen-rich mixture are formed from the process [20; 21]. Therefore, the controlled oxygen-rich mixture oxidizes the fuel in the combustor, producing flameless uniform burning. The biggest advantages of this system are the high combustion flame stability [22; 23] and about 90% of NOx emission reduction [24]. Additionally, the system offers high fuel flexibility and low acoustic oscillation for better operation [25]. This technology, however, portrays challenges such as high-pressure loss from the injection nozzle [26], the economic disadvantage of preheating and a relatively high concentration of COx gas from the mild combustion flame. Then, the combustion technology is progressed into the third generation to address previous constraints.
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
## 1 Introduction
The simulation results show that the scheme can increase the number of connections of D2D users and increase the total system capacity under the premise of ensuring the communication service quality of cellular users. The following content of this paper is arranged as follows: Section 2 gives the channel allocation problem discussed in this paper according to the system model of D2D communication. Section 3 introduces the QoS-based clustering channel allocation algorithm in a D2D communication system in detail. Section 4 gives the simulation results and analysis. Section 5 provides the discussion of the comparative analysis, while Section 6 is the conclusion.
## 7 Device Data Processing
### Use Case Model  
In order to simplify the workflow on the proposed use case model, a hierarchical simulation was performed on the vertical axis, and performance was monitored on the horizontal axis as shown in Figure 4. A three-layer model was proposed on both the horizontal and vertical axes to manage devices based on specific services.  
Every use case is a result of the analytics done on the datasets taking into consideration the position of the use case compared to the horizontal and vertical axis. The definition of a use case is not standardized by any organization. However, in the proposed model for device analytics, we can see that, for three-layer horizontal and vertical axes, we deduced nine use cases.  
Let \(V_{L}\) be the number of vertical layers and \(H_{L}\) the number of horizontal layers. The number of use cases that we can build from the system, \(U\), is provided by a simple equation:  
\[U=V_{L}\cdot H_{L}.\]  
* Horizontal Layers: The horizontal layers aim to provide performance indicators. The concept of the global index (GI) is invoked to address different classes of users. Key parameter indicators are aggregated to form a key quality index.  
Figure 4: Three-layer horizontal and three-layer vertical analytics model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Extra Parameters** & **Description** \\ \hline TAC & Type allocation code, eight digits of the IMEI, identifying the manufacturer and model \\ \hline Manufacturer & Manufacturing company \\ \hline Model & Model of the device as provided by the manufacturer \\ \hline Class & Device class (smartphone, dongle, route, or any other class available in the file) \\ \hline OS & Operating system of the device \\ \hline LTE & Indication of whether the device supported an LTE network. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Enrichment information parameters.  
* Vertical Layers: The vertical layers categorize the device and provide a drill-down mechanism from manufacturing to models. In this case, network operators can identify manufacturers or devices with poor performance.