esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System
## 4 Case Study  
Most aging systems that include bearings, seals, glands, shafts, and couplings are more likely to suffer from several degradation processes due to harsh operating constraints such as high temperatures, vibration, and dynamic load, and likely the deficiency of the maintenance plan as well. In this section, the ITCA methodology is deployed on simulated data that reproduce the degradation of a turbofan engine proposed by NASA. It is known as the PHM08 challenge dataset. The dataset is generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) simulator based on MATLAB(r) and Simulink(r) [29]. The simulator uses the combination of three specific operation variables to generate different degradation profiles. The high-pressure compressor (HPC) degradation fault mode is selected as an illustrative example.  
Based on the C-MAPSS user guide, as shown in Figure 7A, the engine consists of several interconnected subsystems (inlet, bypass nozzle, fan, low-pressure compressor (LPC), high-pressure compressor (HPC), combustor, high-pressure turbine (HPT), low-pressure turbine (LPT), and core nozzle). The fuel valve controls the fuel flow into the combustor that turns the HPT. The HPT rotates the HPC, LPT, LPC and the inlet fan. The turbofan engine has two state variables: the fan speed and the core speed [30]. Based on the thermodynamic cycle, the air is compressed and combusted by the engine to produce propelling. Figure 7B describes the ambient airflow to the engine. First, the air enters the engine through the inlet and the fan. Then, it is divided by the splitter into two portions. One portion passes through the compressor and then the burner to mix with fuel and produces combustion. The hot exhaust passes through the core and fan turbines to the nozzle, while the other portion is bypassed to the back of the engine. The airflow is controlled by the bypass ratio, which is the ratio of the bypassed mass airflow to the mass airflow that goes through an engine core [31]. The HPC's main functionality drives the airflow to higher pressure and temperature states to prepare it for combustion by using its spinning blades. Therefore, the change in the bypass ratio is the main control element for controlling the HPC outlet air pressure and its temperature for the burning phase.  
The challenge addressed by the ITCA methodology is to model the HPC fault causality structure in a dynamic manner so that the model can demonstrate the effect of the root cause changes over time on the main HPC degradation curve.  
Figure 7: The simulated turbofan engine based on C-MAPSS [32] (images courtesy of NASA). (**A**) Simplified diagram of the turbofan engine; (**B**) Turbofan engine modules layout and connections.
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 5 Simulation Results  
This paper considers a uniform linear transmit array consisting of \(M_{T}=10\) antenna elements spaced one-half wavelength apart. The purpose of this array is to maximize the directivity of the transmitted signal and minimize interference at the same time. In addition to the radar operation within the main beam, it is assumed that a communication message of two FSK symbols is added cooperatively during each radar pulse. This is performed to develop a composite signal containing four bits of information transmitted through the channel. The baseband signals with FSK modulation are generated using the frequencies \(f_{1}=100\) Hz and \(f_{2}=200\) Hz in our simulations. The main beam is fixed in a specified direction. All simulations are performed using Matlab 2021\(a\), on a system with an Intel microprocessor _Corei5_, 11 th generation, 8 GB RAM and 2 GB graphics memory.  
In the following sections, we will present the simulation results with different illustrative examples for clarity and manageable acquaintance.  
Figure 4: Detailed demodulator design in terms of matched filtering after the bandpass filter at the communication receiver.
# Modeling a Dual-Mode Controller Design
## 5 Conclusions  
A dual-mode control scheme for the QRF converter was used in this study. The dual-mode control scheme involved a valley-switching detection technique to satisfy the zero-voltage switching requirement. A constant frequency mechanism was proposed to reduce the conduction loss of the QRF converter when it was operated in the CCM and when a heavy load was applied. A 100 W QRF converter prototype with dual-mode control was proposed to verify the applicability of theoretical analysis. Moreover, an AC small-signal model for a QRF converter was proposed. An adaptive controller with a detection circuit that can operate under light- or heavy-load conditions was designed and used in the study. The simulation and experimental results of the proposed control scheme for the fly-back converter were demonstrated. Moreover, the accuracy of an AC small-signal model was compared and verified. An adaptive Type II compensation for a QRF converter that can operate under a heavy-load and a light-load was designed. The function and performance of a QRF circuit were evaluated to verify the practicality of the proposed dual-mode control approach.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the prototype circuit; [PERSON] and [PERSON] performed simulations and experiments, and analyzed data obtained by [PERSON], [PERSON], [PERSON] and [PERSON]; [PERSON] contributed equipment, materials, and analysis tools. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
This research was funding by the National Science Council of Taiwan under grant number MOST 107-2622-E-224-009-CC3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline _Control Mode_ & _Vin (V)_ & _Iin (A)_ & _Pin (W)_ & _Vo (V)_ & _Io (A)_ & _Po (W)_ & \(\eta\) (\%)_ \\ \hline QR Control & 127 & 0.842 & 106.68 & 18.73 & 5 & 93.65 & 87.70 \\ QR Control & 380 & 0.281 & 106.95 & 18.81 & 5 & 94.05 & 87.90 \\ DM Control & 127 & 0.833 & 105.41 & 18.85 & 5 & 94.25 & 89.20 \\ DM Control & 380 & 0.277 & 105.26 & 18.90 & 5 & 94.50 & 89.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with existing control mode.  
Figure 12: Experiment efficiencies of the dual-mode QRF converter.  
**Acknowledgments:** This research was supported by the National Science Council of Taiwan under grant number MOST 107-2622-E-224-009-CC3. The equipment used for experiments was provided by the Basic Research Program, Renewable Energy Research Center, National Yunlin University of Science and Technology.  
**Conflicts of Interest:** The authors have no conflict of interest to declare.
## 4 Results and Discussion
### RFI Classification Results  
The performance of the developed MLP approach in [21] is considered for RFI classification. Therefore, a two-layer MLP is used with 10 and 4 neurons in the input and output layers, respectively. Moreover, the logarithmic sigmoid and linear functions are used as the hidden and output layer activation functions [21]. Table 3 presents the details of the designed MLP using different Hidden Layer Neurons (HLN) and Batch Sizes (BS). The training process of this study is performed by the MLP using a 10-fold cross-validation.  
As the results show, by increasing the HLN the accuracy can slightly improve. Moreover, due to high variety of classes in the dataset the online learning mode performs better in which the network's key parameters are updated based on each sample one by one. Therefore, the most accurate result has been achieved using \(HLN=30\) and online learning mode (BS = 1), with a precision of 99.58%. Table 4 illustrates the results of applying the proposed MLP + GA approach for RFI classification, using the different Numbers of Features (NoF) with \(HLN=30\). According to the results, the highest accuracy, 99.97%, is obtained with \(BS=1\) and \(NoF=8\). Also, the classification precision is increased by dealing with more features. Table 5 demonstrates the result of deploying PCA + MLP for the first classification phase using a various number of features and learning modes such as online learning (\(BS=1\)), mini-batch, and Full-Batch. As can be seen, the classification accuracy reached to 97.05% with only five features. Table 5 demonstrates the result of deploying MLP + PCA for the first classification phase. As can be seen, a precise classification was achieved with only five features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SoI Power** & **CWI Power** & **MCWI Power** & **CI Power** \\ \hline \(-40\) dBm & \(-33\) dBm & \(-35\) dBm & \(-32\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured power of received signals.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**HLN** & **BS = 1** & **BS = 100** & **BS = 500** & **BS = 1500** & **Full-Batch** \\ \hline
10 & 98.58\% & 91.56\% & 56.88\% & 45.18\% & 39.29\% \\
20 & 98.50\% & 92.75\% & 61.46\% & 49.16\% & 37.83\% \\
30 & 99.58\% & 92.88\% & 59.86\% & 49.16\% & 40.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: RFI Classification results based on MLP at different HLN and BS.
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 2 Computational Model and Governing Equations  
### Computational Model  
Figure 1 presents a computational model for solving the seismic effects of an arbitrary-shaped canyon site under P-wave incidence. As shown in Figure 1, \(\Omega\) is the half-space region that is a uniform, isotropic, and fluid-saturated porous elastic medium. \(\lambda\) and \(\mu\) are [PERSON]'s constants, and \(\rho\) is the total density of the fluid-saturated medium. There is a plane P-wave with incident angle \(\gamma_{1}\) (the angle between the propagation direction and the horizontal direction) and the circular frequency \(\omega\), which causes the canyon response. The width of the canyon is \(2a\). The cartesian coordinate system \(x\)-\(o\)-\(y\) and the polar coordinate system \(r\)-\(o\)-\(\theta\) are introduced, where \(o\) is the midpoint of the canyon. There exists any point on the boundary where \(\hat{n}\) is the normal line passing through the point, and \(\alpha\) is the angle between the normal line and the polar diameter \(r\) passing through the point. According to the definition in [21], when \(r\) turns clockwise to \(\hat{n}\), \(\alpha\) is negative; when \(r\) turns counterclockwise to \(\hat{n}\), \(\alpha\) is positive.
# Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
## 2 Preliminaries
### Problem Formulation  
It should be noted that signal sources may be distributed in a wide area such that one usually requires the robot to search for signal sources from one region to another region. For example, the localization task can be described as \"The whole area is partitioned into four regions. The robot should orderly search for the signal sources from one region to the other region infinitely often\". In order to model the above localization task with temporal requirements, the linear temporal logic (LTL) formula can be used [14].  
The LTL formula \(\phi\) consists of a finite set of atomic propositions \(O\), temporal operators, e.g., \(G\) (always), \(\Rightarrow\) (implication), \(U\) (until), and the standard Boolean operators, e.g., \(\wedge\) (conjunction), \(\vee\) (disjunction), and \(\
eg\) (negation). Based on the LTL formula, the aforementioned search task can be described by  
\[\phi=G((A\Rightarrow AUB)\wedge(B\Rightarrow BUC)\wedge(C\Rightarrow CUD) \wedge(D\Rightarrow DUA)) \tag{4}\]  
Figure 2: CC2530 radio frequency module.  
where \(A,B,C,D\) denote four searching regions; \(AUB\) means that \(A\) has to hold at least until \(B\) is true; and \(G\Psi\) means that \(\Psi\) is true at all positions of the infinite sequence. For example, there are two infinite sequences:  
\[\mathbf{v}_{1} = AABBBBCCCDDDDA...\] \[\mathbf{v}_{2} = AAABBBCBCCCDDA...\]  
It is forward to see that sequence \(\mathbf{v}_{1}\) meets formula \(\phi\). However, sequence \(\mathbf{v}_{2}\) does not satisfy formula \(\phi\). Hence, this paper mainly deals with the following problem.  
For a mobile robot (1) and an LTL formula \(\phi\) that are used to model the search task with temporal requirements, how to design an RHC controller \(u(k)\) that can enable the robot to locate signal sources and avoid obstacles while ensuring that the generated trajectory meets \(\phi\).
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 3 Fundamental Characteristics of the Suggested MIMO Antenna
In Figure 11, the efficiency characteristics of the MIMO design are represented. Figure 11a illustrates the high radiation efficiency of each antenna element, surpassing 80%. Additionally, it can be observed that these elements exhibit total efficiencies exceeding 70% over most of the operational frequency range. The achieved efficiencies are deemed quite satisfactory for smartphone operation, indicating that the introduced MIMO antenna design performs efficiently in practical scenarios. High radiation efficiency ensures that a significant portion of the input power is converted into electromagnetic waves, leading to effective signal transmission and reception. Moreover, the total efficiency considers  
Figure 9: (a) \(S_{\mathrm{m}}\) and (b) \(S_{\mathrm{m}n}\) results of the antenna elements. all losses and mismatches in the system, reflecting the effectiveness of the antenna in real-world usage [51; 52]. Figure 10: Three-dimensional radiation patterns at (**a**) 3.5 GHz and (**b**) 5.5 GHz.
## 2 Experimental Setup and Theoretical Analysis
### Integration of Interferometer and Optical Trap  
Figure 3 corresponds to the setup resulting from the integration of the optical trap with the Mach-Zehnder interferometer, previously explained. Based on the optical trap setup, the blue laser replaces the white light source, while the condenser objective is replaced by the long working distance objective. Before the blue laser enters this objective, the expander and the first beam splitter are placed, and finally, the reference beam is redirected to the splitter in front of the camera; it should be noted that the low numerical aperture of the long working distance objective, together with the low power of the blue laser, does not allow the generation of a significant gradient force that would disturb the optical trap. The rest of the optical trap system remains unchanged; so, both configurations share the large numerical aperture microscope objective (MO2) and, consequently, the same immersion oil.  
In this way, the interaction of the optical trap with the structures under study can be quantitatively analyzed using the integrated Mach-Zehnder interferometer. Yet, as mentioned above, if the power of either laser source changes, the interferometric measurements will have an additive phase effect that must be compensated for.
## 1 Introduction
Tunnels and subways will be the first structures to experience damages when any movements happen, before the forces caused by faults reach the ground surface. Hence, the stability of tunnel structures should be given more consideration by engineers. Millions of people use underground public transportation every day, and hence, fault ruptures could pose a major hazard to their lives. Restoration of structures is another challenge because it can be very costly and is sometimes impossible. Thus, more research in this area is needed to reduce the damages to tunnels and buildings. Besides fault characteristics, soil properties also impose major influences and need particular attention. Several studies have previously been conducted in fault ruptures, in which none have considered soil properties as one of the major aspects of tunnel displacement. The simulation of normal and reverse faults can be shown by lifting the bottom of the physical model upward or downward, for simulating reverse and normal faults, respectively. Researchers have evaluated the limitations of displacements, and different data have been found, showing that \(\frac{h}{H}\%\) (h stands for movements of the hanging wall and H stands for the depth of the sand box) can produce the range of 4% to 10%. The displacement will cause some changes to ground surfaces, which are different based on soil properties [27; 28; 29]. In this study, the ranges were chosen between 2.7% to 8%. In previous studies, the effect of displacements was only measured on the ground surfaces; while in this study, the displacements also have been measured on an embedded tunnel. In this paper, an experimental investigation was conducted to explore the effects of various soil properties, such as cohesion, water content, particle size, and friction angle, and various fault properties, such as fault types, angles, and tunnel depth, by using a 1g model. Furthermore, differences and similarities of normal and reverse faults, the effects of various fault angles, and tunnel depths on tunnels have also been investigated. Plane strain and axial symmetry (PLAXIS) software is a useful tool for analyzing two- and three-dimensional models, and has been used for different circumstances, such as modeling rock mass parameters, tunnels, foundations, and many more. In this paper, PLAXIS software was used for simulating the effect of faults on soft soil and rock, and also the influence of foundation on tunnel displacements. Furthermore, unlike other studies that only investigated free fields, this research focuses on a field with a tunnel. The outcome of this study is believed to help engineers in understanding the level of damage that might be imposed on tunnels at the area of a fault, with various properties of soil and faults.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## 4 Results  
The results in terms of final flow rate show how the RM led to a balanced situation within tolerances: the terminal with the greatest deviation was terminal C1, which had a flow rate difference of 8.8% from the nominal value. As far as the progressive flow method is concerned, its operation did not involve the measurement of flow rate at individual terminals other than in terms of the increase in the overall flow rate at the time of its activation. In any case, the method proceeded to sequentially activate the terminals only when the updated total flow rate was at the desired value, so the value at the end of the procedure could be precisely adjusted to the nominal value (2600 l/h), within the accuracy limits of the rotameter. After considering this, the equivalence of the two different procedures was assessed by comparing, in addition to the total flow rate just discussed, (i) the final positions of the balancing devices and (ii) the final pump speed. Useful values for comparison referring to both methods are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Branch**} & \multirow{2}{*}{**Terminal Unit**} & **H/C Capacity** & **User \(\Delta\)P** & \multicolumn{2}{c}{**Flow Rate**} & \multicolumn{2}{c}{**TU Valve Position**} \\ \cline{3-7}  & & **[W]** & **[kPa]** & **l/h** & **l/s** & **Reference** \\ \hline \multirow{2}{*}{A} & A1 & 1149 & 29.21 & 200 & 0.06 & 0.6 \\  & A2 & 1724 & 5.01 & 300 & 0.08 & 3 \\ \hline \multirow{2}{*}{B} & B1 & 1724 & 5.01 & 300 & 0.08 & 3 \\  & B2 & 2011 & 6.82 & 350 & 0.10 & 3 \\ \hline \multirow{2}{*}{C} & C1 & 1437 & 6.51 & 250 & 0.07 & 2.5 \\  & C2 & 2299 & 5.54 & 400 & 0.11 & 3.5 \\ \hline \multirow{2}{*}{D} & D1 & 1724 & 5.01 & 300 & 0.08 & 3 \\  & D2 & 2874 & 5.95 & 500 & 0.14 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design flow rates and drop simulation valves setup.  
Regarding the final valve configurations and referring to the values reported in Table 3, a radar graph (Figure 13) has been provided to compare the positions reached after the application of each procedure.  
The chart highlights some local differences; it may be noticed that the B2 and C2 terminals differed significantly (respectively, 56% to 70% for B2 and 75% vs. 92% for C2).  
Application of the PFM did not require direct measurements at the B2 and C2 terminals and so, direct comparison in terms of flow rate values was not possible.
# Quality Assessment of Organic Kefirs Made with Kefir Grains and Freeze-Dried Starter Cultures
###### Abstract  
Organic dairy products, including fermented milk, are gaining popularity among consumers. Traditionally, for kefir production, kefir grains are used; however, in the modern industry, freeze-dried cultures are commonly employed. This study aimed to analyze the quality of kefirs produced from organic cow milk with two fermentation times (12 and 24 h) with the use of kefir grains or freeze-dried culture. During a 3-week storage period, physicochemical properties, color, syneresis index, texture, sensory, and microbiological quality were evaluated. The results proved that organic cow milk was suitable for kefir production both with the use of kefir grains and freeze-dried culture. Using freeze-dried culture and a 24 h fermentation period resulted in kefir with the lowest syneresis tendency (3.65-9.62%), along with the best textural properties (the highest cohesiveness and viscosity index), and desired sensory characteristics. Kefir grains had a better acidification ability, and the resulting products had a higher count of yeasts, lactobacilli, and lactococc compared with kefirs obtained with freeze-dried culture. However, both the type of starter culture and the fermentation time influenced the product's quality. The longer fermentation time of 24 h was more appropriate for the kefir production. Kefir grains, as a traditional form of kefir culture, may be preferred in organic kefir production.  
kefir; kefir grains; freeze-dried kefir culture; organic milk +
Footnote †: journal: Applied sciences  
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