esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 3 System Architecture, Design, and Implementation  
Building on the recognized need for enhanced safety measures and the limitations of current support policies, this study aims to develop a fast obstacle detection and warning system for electric wheelchairs. The proposed system incorporates multiple sensors and a front-facing camera to detect obstacles and provide timely warnings, thereby enhancing user safety in diverse environments. Furthermore, employing on-device processing minimizes reliance on external server communication, ensuring stable operation even in connectivity-limited scenarios.  
Figure 3 illustrates the architecture of a web application developed using the Spring Framework, incorporating key components such as Spring Boot, Spring Security, Spring Data JPA, Thymeleaf, Stomp, and REST API. Spring Boot provides the foundational structure, and Spring Security manages authentication and authorization. The Spring Controller processes user requests, and Thymeleaf and HTML5 render the user interface. Data management is performed via JPA and supported by technologies such as MariaDB, Hibernate, and QueryDSL. The Stomp protocol manages fast communication with devices such as cameras and PLCs, thereby enabling efficient data processing between web clients and the server and enhancing the system's scalability and maintainability.  
To improve the driving safety of personal mobility devices, we designed the algorithm depicted in Figure 4. The personal mobility device has a front camera and an ultrasonic sensor to recognize obstacles. A computing unit onboard the device processes the recognition and executes the algorithm. Although the user operates the device, the front camera first identifies distant obstacles, and the ultrasonic sensor recognizes nearby obstacles. The main computer relays recognition results to the user via TTS. Additionally, if the user does not stop despite obstacle detection, the system activates an automatic brake function to stop the device, ensuring safety.
## 1 Introduction
The main objective of this paper is to propose the vector expressions necessary to quantify the unbalanced powers and, as a consequence, the total apparent powers of an unbalanced three-phase linear system. These expressions are valid for perfectly analysing systems with balanced and unbalanced voltages y, and in both cases, it allows us to develop passive compensators for unbalance powers due to negative and zero-sequence currents [21; 22]. The modulus of the total apparent power obtained at any node in the system coincides with the apparent power of Buchholz. This value determines the maximum transferable power for each voltage waveform [23], including all efficient and non-efficient powers [24]. The components of these vectors are defined from terms of active and reactive power per phase; in this way, the same component of the vectors between two system buses or between loads connected in parallel can be arithmetically added to each other. From these expressions, it is possible to mainly analyze the unbalanced power flows in any electrical system. This allows, facing a change or variation in the system, analyzing the positive or negative contribution that this variation represents on the unbalance power and, as a consequence, its repercussion on the total apparent power on any bus of the system. Accordingly, in Section 2, based on the Buchholz power, the apparent power and unbalanced power of any system are analysed and studied for both balanced and unbalanced voltages. In Section 3, the new vector expressions of the apparent power are formulated. In Section 4, from the expressions deduced in the previous section, vector expressions of unbalanced power are formulated. In Section 5, the two typical cases to be considered in the use of the vectors of the apparent power and the vectors of the unbalanced power are studied: application to a bus with loads connected in parallel and application between two buses of the system united by a line. In Section 6, the formulation of the apparent power vector is extended to non-linear systems to determine the harmonic apparent power of the system. To help understand the use of these vector expressions, a practical case of a three-phase four-wire system with unbalanced loads and voltages is discussed in Section 7.
## 2 Experimental Setup  
### Test Samples  
The test specimens were epoxy/Al2O3 micro-composites with approximately 20 vol% Al2O3 micro-particles embedded in an epoxy matrix based on the manufacturing procedure of Figure 1. In case of epoxy composite, the properties of the composites would vary depending on the content ratio of the filler added [25; 26]. For example, a decreasing trend in the insulation BDS was observed in epoxy resins containing micro-sized Al2O3 fillers beyond a loading of 40 wt% [26]. In addition, Ref. [27] reported that the mechanical properties, such as tensile strength and bending strength, decreased when Al2O3 micro-fillers were added at amounts exceeding 40 wt%. In this research, a 20 vol% content ratio of Al2O3 filler was considered based on the fact that optimized performance improvement was obtained at 40 wt% in such studies. It is worth noting that a filler loading of 40 wt% approximately corresponds to 20 vol% considering the densities and weight ratios of epoxy resin, curing agent, and micro-sized Al2O3 fillers.  
As shown in Figure 2, the samples were manufactured in a disc shape with a diameter of 120 mm and an average thickness of 1.05 mm, making them suitable for measuring dielectric BDS through the insulation material. To comprehensively characterize the properties of the epoxy/Al2O3 micro-composites, the relative permittivity and dissipation factor, which are related to insulation performance, were measured prior to the AC-BDS test. The Fully Automatic Capacitance and Loss Factor tan\(\delta\) Precision Measuring Bridge from Tettex, shown in Figure 3, was used as the measurement equipment. During this process, a test voltage of 1 kV and a frequency of 60 Hz were applied to the upper electrode of the Type 2914 using AC dielectric tester. The capacitance (\(C_{DUT}\), unit: pF) and dissipation factor of the epoxy/Al2O3 micro-composites specimens were recorded through the output of the Type 2818A.  
Figure 1: Manufacturing process of epoxy/Al2O3 micro-composites.  
To investigate the changes in the relative permittivity and dissipation factor with temperature, the temperature of the epoxy/Al2O3 micro-composites was varied using Type 2966A equipment. The temperature points were set at 30 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, and the temperature was applied through the electrodes of the Type 2914. The entire procedure was conducted in accordance with the IEC 62631-2-1 standard [28], and the measured values for 10 specimens were averaged at each temperature point. The relative permittivity was calculated from the capacitance of the epoxy/Al2O3 micro-composites measured with Type 2818A as  
\[\epsilon_{DUT}=\frac{C_{DUT}}{\epsilon_{0}}\times\frac{t_{DUT}}{S_{Electrode}} \tag{1}\]  
where, \(C_{DUT}\) is the capacitance of the specimens, \(t_{DUT}\) is the thickness of the epoxy/Al2O3 micro-composites specimen, \(S_{Electrode}\) is the surface area of the measuring electrode, and \(\epsilon_{0}\) is the permittivity of vacuum (\(8.854\times 10^{-12}\) F/m).
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
###### Abstract  
In practical engineering, due to the lack of information, it is impossible to accurately determine the distribution of all variables. Therefore, time-variant reliability problems with both random and interval variables may be encountered. However, this kind of problem usually involves a complex multilevel nested optimization problem, which leads to a substantial computational burden, and it is difficult to meet the requirements of complex engineering problem analysis. This study proposes a decoupling strategy to efficiently analyze the time-variant reliability based on the mixed uncertainty model. The interval variables are treated with independent random variables that are uniformly distributed in their respective intervals. Then the time-variant reliability-equivalent model, containing only random variables, is established, to avoid multi-layer nesting optimization. The stochastic process is first discretized to obtain several static limit state functions at different times. The time-variant reliability problem is changed into the conventional time-invariant system reliability problem. First order reliability analysis method (FORM) is used to analyze the reliability of each time. Thus, an efficient and robust convergence hybrid time-variant reliability calculation algorithm is proposed based on the equivalent model. Finally, numerical examples shows the effectiveness of the proposed method.  
interval variables; random variables; time-variant reliability analysis; hybrid uncertain model +
Footnote †: journal:  
0021  
An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 4 Conclusions  
Partial substitution of soy flour for urea-formaldehyde resin has the potential to decrease carcinogenic formaldehyde emission in plywood panels. Shear strength was also improved as soy flour content increased. However, this had a negative effect on the water absorption and thickness swelling of plywood panels. The addition of micron-sized wollastonite mitigated the undesirable increased hydrophilicity in panels caused by soy flour. It was concluded that 10% soy flour and 5% wollastonite provide the lowest formaldehyde emission and the most optimum physical and mechanical properties.  
Methodology, H.R.T. and S.G.; validation, H.R.T., S.G., M.G., and S.B.H.; investigation, H.R.T. and S.B.H.; writing--original draft preparation, H.R.T., and A.N.P.; writing--review and editing, H.R.T., S.B.H., and A.N.P.; visualization, H.R.T. and S.B.H.; supervision, H.R.T. and A.N.P. All authors read and agreed to the published version of the manuscript. This research received no external funding.  
The first author appreciates the constant scientific support of [PERSON] (retired, Horticulture and Forestry Science, Queensland Department of Agriculture and Fisheries, Australia), as well as Alexander von Humboldt Stiftung (Bonn, Germany).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Binder** & **pH** & **Gel Time (s)** & **Viscosity (6 rpm) (cP)** & **Viscosity (10 rpm) (cP)** \\ \hline UF 100\% & 7.4 & 75 & 449.9 & 455.9 \\ UF 95\% + SF 2 5\% & 5.4 & 105 & 564.9 & 566.9 \\ UF 90\% + SF 10\% & 5.2 & 128 & 824.8 & 794.8 \\ UF 90\% + SF 10\% + W 3 5\% & 5.6 & 214 & 689.9 & 656.9 \\ UF 90\% + SF 10\% + W 10\% & 5.2 & 230 & 569.9 & 551.9 \\ UF 85\% + SF 15\% & 5.6 & 146 & 1300 & 1302 \\ UF 85\% + SF 15\% + W 5\% & 5.5 & 172 & 1120 & 1086 \\ UF 85\% + SF 15\% + W 10\% & 5.1 & 158 & 949.8 & 902.8 \\ UF 80\% + SF 20\% & 5.6 & 172 & 2000 & 1911 \\ \hline \hline \end{tabular}  
* UF = urea-formaldehyde resin; 2 SF = soy flour content; 3 W = wollastonite content  
\end{table}
Table 4: Properties of the 12 resin mixtures used to manufacture three-layer plywood.  
Figure 11: Cluster analysis of seven different three-layer plywood panels produced with urea-formaldehyde resin, and 10% and 15% soy flour substitution for UF resin, plus addition of wollastonite at 5% and 10% (Soy% = soy flour content; W% = wollastonite content).
# A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information
## 5 Evaluation Experiments
### Verification of Proposed Control Method
#### 5.2.2 Experimental Result  
Figure 14 shows the assisted walking sequence on the treadmill. The horizontal axis shows the gait percentage of the right leg. The red box marker means the pressurized PARM on the right leg. The TF and TB PARMs are pressurized before the initial contact (gait percentage 0%) between the foot and the ground to absorb impact force and stabilize the stance leg posture. The CF PARMs are pressurized from the initial swing phase to the middle stance phase for raising up the trainee's toe. The CB PARMs are the pressurized closed chambers, and their shrinking force is given to the trainees' leg through the whole gait cycle. The total 100 walking steps were measured, and the success rate of the phase detection was counted in each case. The RCF PARM pressure trajectories of the 90 and 140 BPM walking are shown in Figure 15. The horizontal axis is time (s), and the vertical axis is pressure (kPa). The solid and dashed lines are the measured pressure and the reference pressure. Both methods achieved 100% success rate in the case of 90 BPM. On the other hand, in the case of 140 BPM, the proposed control method achieved 100% detection, and the success rate of the conventional control method was 72%.
## 2 Results  
### Audible Sound Affects the Growth of S. cerevisiae during Liquid Fermentation  
The most commonly reported effect of audible sound on microorganisms is increased growth rate compared to cultures in silence [10; 11; 12; 28; 29; 30; 31]. Therefore, we measured the growth rate of _S. cerevisiae_ strain CLIB382 on malt extract broth exposed to two sound stimuli (low-frequency at 100 Hz and high-frequency at 10 kHz) and compared this growth profile to fermentations without sound (silence). All fermentations were conducted in series in our Sound Isolation Chamber (See Section 4).  
Biomass was measured using OD\({}_{600}\) at several time points throughout the fermentations and was compared between sound conditions. Following the end of the lag phase (16 h), there was significantly greater biomass in fermentations exposed to both sound treatments compared to those treated with silence (Figure 1; \(p\) < 0.001). Furthermore, there was a significantly higher OD\({}_{600}\) observed in fermentations exposed to low-frequencysound (0.893 \(\pm\) 0.07, 100 Hz) compared to high-frequency (0.683 \(\pm\) 0.02, 10 kHz, \(p\)! 0.01, Figure 1). However, the biomass at the end of the fermentations (33 h) was not significantly different between cultures exposed to both sound treatments and silence (\(p\)! 0.05). We compared the maximum growth rates, calculated from maximum change in biomass, in the fermentations during the exponential growth phase. The maximum growth rate of cultures grown without sound stimulus was 0.137 h\({}^{-1}\)\(\pm\) 0.005, which was lower than the maximum growth rates of the cultures exposed to low- and high-frequency sound (0.155 h\({}^{-1}\)\(\pm\) 0.008 and 0.177 h\({}^{-1}\)\(\pm\) 0.011, respectively). However, only the maximum growth rate of cultures exposed to high-frequency sound showed statistical significance when compared to the control cultures exposed to silence (\(p\)! 0.05). Nonetheless, the length of the exponential growth phase was reduced to 26.5 h under both sound treatments compared to 34.0 h in the silence control.
# Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots
## 3 Kinetics of Tracked Robot during Steady-State Turning
### Simulation Analysis for Lateral Relative Offset  
The parameters of the three roads are listed in Table 2. In order to explore the corresponding relationship between the structural parameters, ground parameters, relative steering radius of the tracked robot and the relative deviation of the robot's steering, the value range of the structural parameter of the robot is determined as \(\lambda\)= 1.2 \(\sim\) 1.8. The relative steering radius is \(\rho=R/B=0.5\)\(\sim\) 20.  
In the case of a hard road surface, the corresponding relationship between the changes in \(\lambda\), \(\rho\), \(a_{R}\), and \(a_{L}\) was determined. It can be seen from Figure 3 that the lateral relative offset \(a_{R}\) of the right track of the robot has a large range of variation under the condition of a certain hard road, whereas the \(a_{L}\) of the left track is small. The lateral relative offset increases with an increase in the relative steering radius \(\rho\), whereas \(a_{L}\) decreases with it; however, both \(a_{R}\) and \(a_{L}\) increase with the increase in \(\lambda\).  
In addition, when the steering angular velocity of the robot is constant, the larger the \(\lambda\), the more severely the robot will skid. The energy of the robot is lost to the work consumed by the robot's track skidding, which reduces its energy utilization rate. \(\rho\) = 0.5\(\sim\)15, \(a_{R}\) = 0.33\(\sim\)0.51 and \(a_{L}\) = 0.11\(\sim\)0.21. To achieve a good turning effect, the structural parameter of the robot should be as small as possible. In this study, the structural parameter of the robot was set as \(\lambda\) = 1.21 mm, which meets the requirements.  
When the robot drove on a hard road, the ground deformation coefficient and steering resistance coefficient of the road surface were determined, and the changes in the \(a_{H}\) and \(a_{L}\) of the robot under different road conditions were calculated. The road parameters were substituted into Equation (11).  
As shown in Figure 4, when \(\rho\) is small, the lateral relative offsets of the left and right tracks of the tracked robot do not differ significantly but are slightly different. \(a_{R}\) will increase as \(\xi\) and \(\rho\) increase, and \(a_{L}\) will decrease \(\xi\) and \(\rho\). The difference between \(a_{R}\) and \(a_{L}\) increased as \(\rho\) increased.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \(f\) & \(\mu_{\text{max}}\) & \(\varepsilon\), \(\beta\)/\(\mu_{\text{max}}\) \\ \hline Cement road & 0.0445 & 0.68 & 0.066 \\ Asphalt road & 0.0382 & 0.49 & 0.078 \\ Dirt road & 0.0490 & 0.55 & 0.089 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical road parameters.  
Figure 3: The relationship between \(\rho\), \(a_{R}\), \(a_{L}\) and \(\lambda\).
## 4 Conclusions  
An FTIR spectrometer mockup for recording IR absorption spectra in the wavelength range from 1.0 to 1.7 \(\upmu\)m with a spectral resolution of \(\delta v=10\) cm\({}^{-1}\) is described. The CO\({}_{2}\) absorption line with a central wavelength \(\lambda\) = 1.60 \(\upmu\)m, the absorption line of CH\({}_{4}\) with \(\lambda\) = 1.66 \(\upmu\)m and the absorption line of O\({}_{2}\) with \(\lambda\) = 1.27 \(\upmu\)m were recorded from the reflected solar radiation. The atmospheric transmittance was monitored throughout the day in urban conditions. The obtained data allowed monitoring of the integral and volumetric concentrations of CO\({}_{2}\) and CH\({}_{4}\). It was shown that the time dependence of CO\({}_{2}\) and CH\({}_{4}\) concentrations well reflects the degree of traffic congestion on that day. We developed an optical scheme (Figure 3) and design (Table 2) of a compact low-resolution FTIR spectrometer. Small spacecraft like CubeSat 16U can use the spectrometer for remote sensing of multiple greenhouse gases. The instantaneous size of the ground object is about 5.75 km at an orbit altitude of 575 km, the spectral resolution is 2 cm\({}^{-1}\), the spectral ranges are 0.75-0.80 \(\upmu\)m and 2.0-2.4 \(\upmu\)m and the mass is less than 10 kg. The estimated SNR for spectral band at 2.0-2.4 \(\upmu\)m is about 450. The detection limit for the described spectrometer is about 1% of the nominal values of CO\({}_{2}\) and CH\({}_{4}\) concentrations in the atmosphere. The precision and accuracy of gas detection and concentration evaluation are determined by the spectral resolution at a level of 2 cm\({}^{-1}\). To calculate CO\({}_{2}\) and CH\({}_{4}\) concentrations, we use normalization with a 0.75-0.8 \(\upmu\)m O\({}_{2}\) A-band. The FTIR spectrometer allows for simultaneous concentration measurements of several greenhouse gases and is designed for CubeSat spacecraft. Figure 10: Registered carbon dioxide and methane concentrations throughout a day. **Author Contributions:** Conceptualization, D.R., S.T., A.M. and V.M. ([PERSON]); spacecraft design, D.R., S.T., A.M., V.M. ([PERSON]) and N.L. ; FTIR spectrometer design, A.M., I.G. ([PERSON]) and V.S. ; data analysis, I.G. ([PERSON]) and I.F. ; project administration, V.M. ([PERSON]) and I.F. ; software development I.G. ([PERSON]), D.A. and I.V. ; funding acquisition, A.M. and D.R. ; writing V.M. ([PERSON]) and I.F. All authors have read and agreed to the published version of the manuscript. **Funding:** The project was carried out within the framework of the <<BaumanGoGreen>> strategic project of the <<Priority-2030>> program of The Ministry of Science and Higher Education of the Russian Federation. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript:
## 5 Parameter Estimation  
In order to perform realistic model simulations, we need to obtain reasonable parameter values and their ranges. We start by estimating the realistic ranges for each of them. Parameters \(\mu,\ d_{1},d_{2}\) are taken from [33], where they assess the growth and death rates of prostate cells under different concentrations of androgen. In [12], it was shown that, under continuous treatment, the fastest resistance rate is \(c\approx 0.0001\). The approximate levels at which sensitive and resistant cells proliferate was studied in [34], from which we approximated \(q,q_{1}\), and \(q_{2}\).  
In patients with no prostate cancer, PSA levels are usually less than \(5\ \frac{\mu\mu}{C}\), accounting for benign tumor hyperplasia [8]. This implies that when tumor volume is near zero, the steady state of PSA given by: \(\frac{bQ}{\epsilon}\) shall be approximately \(5\ \frac{\mu\mu}{C}\). Prostate tumor volumes are normally bounded by \(80\) mm in length and, on average, they are about \(13.4\) mm [31]. Since all of our patients have advanced prostate cancer, we assumed a maximum length of \(40\) mm, and we compute the corresponding tumor volume assuming that tumors are spherical. Under complete androgen independence, tumor volume should not exceed \(700\) (mm\({}^{3}\)). Thus, \(\frac{\mu}{S}\approx\frac{\mu}{Q_{2}}+\frac{\mu}{Q_{2}}\approx 700\) (mm\({}^{3}\)). Parameter \(Q_{m}\) is patient specific and is taken from the maximum androgen serum concentration of each patient during the first \(1.5\) cycles of treatment. Parameter \(\gamma_{1}\) is held constant among every patient and \(\gamma_{2}\) has a range of \(0\)-\(0.01\ \frac{nmol}{Lday}\). The half-saturation variables \(K,R,R1,\) and \(R2\) are estimated from [28]. Table 1 shows definitions, ranges, units, and sources for each of the parameters in our models.  
Figure 2: Bifurcation diagram displaying \(x_{1}\) cell population vs. parameters \(\mu\) and \(\gamma\) (**left**) and \(\gamma\) and \(d_{1}\) (**right**). This figure depicts the regions in which \(x_{1}\) can go extinct. This happens when androgen levels \(\gamma\) are very low, or cancer cells’ proliferation rate \(\mu\) is very low, or cancer cells’ death rate \(d_{1}\) is very high.