esa-sceva/satcom-chunk-collection huggingface dataset:
### Electromotive Force of Magnetic Induction Coil  
The calculation diagram of electromotive force is shown in Figure 2. The permanent magnet reciprocates in the vertical direction. The coil cuts the horizontal magnetic induction line. Therefore, the electromotive force is independent of the vertical magnetic field component \(B_{z}\). In the xoz plane, it is only related to the horizontal component \(B_{x}\). The magnetic field and coil are axisymmetric, so the magnetic force on a single coil can be calculated.  
Figure 1: The system diagram.  
The electromotive force generated by a single-turn coil is expressed by the following equation:  
\[\varepsilon_{ij}=\oint_{L}\left(\overset{\rightarrow}{v}\times\overset{ \rightarrow}{B}\right)\cdot d\overset{\rightarrow}{l}=v(t)B_{x}\left(z_{i},x_{j} \right)\cdot 2\pi r_{j} \tag{1}\]  
where \(v(t)\) is the vertical vibration velocity of the magnet, \(B_{x}\) is the radial component of the field intensity at any point in space, \(r_{j}\) is the radius of the \(j\)th coil from inside to outside, \(r_{j}=r_{1}+(d/2)+(j-1)d\), \(r_{1}\) is the radius of the innermost coil, and \(d\) is the conductor diameter. \(z_{i}\) is the distance from the \(i\)th layer coil to the bottom of the permanent magnet, and for \(z_{i}=z_{c}+(d/2)+(i-1)d\), \(z_{c}\) is the vertical distance from the coil top to the permanent magnet bottom. \(x_{j}\) is the coordinates in the horizontal direction of the central water of the \(j\)th coil, and \(x_{j}=r_{j}\). The total induced electromotive force on the coil is as follows:  
\[\varepsilon=\sum_{i=1}^{m}\sum_{j=1}^{m}\varepsilon_{ij}=v(t)\cdot\sum_{i=1}^{ m}\sum_{j=1}^{m}B_{x}\left(z_{i},x_{j}\right)\cdot 2\pi r_{j} \tag{2}\]  
where \(m\) is the number of vertical layers of coil, \(m=(h/d)\), \(h\) is the total coil thickness, \(n\) is the number of horizontal layers of coil, \(n=(r_{2}-r_{1})/d\), and \(r_{2}\) is the outer diameter of the coil.  
Let \(k_{u}=\sum_{i=1}^{m}\sum_{j=1}^{n}B_{x}\left(z_{i},x_{j}\right)\cdot 2\pi r_{j}\). The value of \(k_{u}\) is only related to the magnetic field distribution and the relative space position between the coil and the magnet. \(k_{u}\) is considered to be the electromechanical coupling coefficient reflecting the relationship between the motion speed of the magnet and the induced electromotive force of the coil. The Equation (2) can be written as  
\[\varepsilon=k_{u}v(t) \tag{3}\]
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 2 Overview of Open Questions, Guidelines, Recommendations, and Standards  
In the last three decades, the number of sources of non-ionizing radiation has significantly increased. Most of them belong to radio frequency (RF) sources. A concern for human health has emerged because of radiation sources increasing in residential areas. Heat absorption due to exposure to RF-EMF is expressed in the SAR (specific absorption rate). The SAR is the rate of absorbed energy by a human body exposed to RF-EMF. Experiments on animals show the effects of their behavior due to radiation exposure with a SAR value of approximately 4 \(W\)/kg (1 \({}^{\circ}\)C tissue heating) or higher. The accepted SAR limit is 4 \(W\)/kg for the occupational population and 0.08 \(W\)/kg for the general population. With the emergence of new technologies in the urban environment, people are constantly exposed to low levels of RF radiation. These RF levels are unable to cause thermal effects. However, due to the continuing increase in the number of wireless devices, the issue of non-thermal effects is gaining importance. In 2011, the World Health Organization (WHO) and the IARC classified RF radiation as \"group 2B\"--possibly carcinogenic to humans [4]. This classification means that the number of health studies is insufficient to draw reliable conclusions and that scientific research should continue. This classification is still valid today. So far, many studies have been conducted, but no study has provided a satisfactory methodology [5].
## 7 RF-EH Circuit
### Topologies of the Impedance Matching Network (IMN) and the Input RF Filter  
#### 7.3.1 RF Filter Designs  
The rectifying circuit (rectenna) filters the input RF signals to prevent harmonics from being generated by the rectifying circuit's diode. Input RF filters are used in a stacked setup to allow required frequencies to pass through a broadband or multiband rectenna. Few studies have examined the input RF filters used in the rectenna design, and those  
Figure 4: Greinacher rectifier configuration [144].  
that have done so have been published. A low-pass filter with two additional band-stop filters added to it was found to be effective in preventing second-order harmonics of both frequencies [146].
## 3 Results
Figure 3 shows that the experimental data fit linearly to the grain SCM model for OXMN009 and OXMN009P for all gases studied. The calculated reaction orders of OXMN009 and OXMN009P with CH\({}_{4}\) (0.7; 0.7), H\({}_{2}\) (1.11; 1.09), and CO (1.5; 1.0), respectively, as shown in Table 8, are within the ranges reported for synthetic Mn with CH\({}_{4}\) (1.0) and O\({}_{2}\) (0.65), and within the ranges reported for synthetic Cu-based minerals with CH\({}_{4}\) (0.4), H\({}_{2}\) (0.6), CO (0.8), and O\({}_{2}\) (1.0); Fe with CH\({}_{4}\) (1.3), H\({}_{2}\) (0.8), CO (1.0), and O\({}_{2}\) (1.0); Nor with CH\({}_{4}\) (0.4), H\({}_{2}\) (0.6), CO (0.8), and O\({}_{2}\) (1.0) [28]. In general, the reaction order also depends on the fuel used. Regarding CH\({}_{4}\), the reaction order obtained presents the same order of magnitude based on what was previously reported by [PERSON] and collaborators [23]. The results indicate that the reaction rate for the transporters studied follows first-order kinetics on average. The complete \begin{table}
\begin{tabular}{l c c} \hline \multicolumn{3}{c}{**RI (\%/min)**} \\ \hline
**Material** & **CO** & **H\({}_{2}\)** \\ \hline MnBR [2] & 8.2 (3.0) & 20.5 (12.5) \\ Mineral de Mn [2] & - & 19.8 b  \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{1}{c}{**Material**} & **Reactive** & **n** & **r\({}^{2}\)** \\ \hline \multirow{4}{*}{OXMN009} & H\({}_{2}\) & 1.1 & 0.9574 \\  & CH\({}_{4}\) & 0.7 & 0.9999 \\  & CO & 1.5 & 0.9999 \\  & H\({}_{2}\) & 1.0 & 0.9472 \\  & CH\({}_{4}\) & 0.7 & 0.9174 \\  & CO & 1.0 & 0.9990 \\ \hline \end{tabular} The reaction order results are similar to [14].  
\end{table} 
Table 7: Reaction order. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{1}{c}{**Sample**} & **Fuel** & _Ea (kJ/mol)_ & \begin{tabular}{c} **K m/s** \\ **(mol/m\({}^{3}\))\({}^{\rm n}\)** \\ \end{tabular} & **Order (n)** & \begin{tabular}{c} **Temperature** \\ \end{tabular} & \
\begin{tabular}{c} **Fuel** \\ **Coentration** \\ **\% **(r/v)** \\ \end{tabular} &
\begin{tabular}{c} **Oxygen** \\ **Concentration** \\ **\% **(v/v)** \\ \end{tabular} \\ \hline \multirow{3}{*}{OXMN009} & CH\({}_{4}\) & 59.38 & 2.26 \(\times\) 10 & 0.7 & 650–950 & 15–30 & 21 \\  & H\({}_{2}\) & 18.74 & 5.29 \(\times\) 10\({}^{-1}\) & 1.11 & 650–950 & 15–30 & 21 \\  & CO & 23.97 & 1.38 \(\times\) 10\({}^{-1}\) & 1.53 & 650–900 & 15–30 & 21 \\ \hline \multirow{3}{*}{OXMN009P} & CH\({}_{4}\) & 40.49 & 1.44 \(\times\) 10 & 0.7 & 650–950 & 15–30 & 21 \\  & H\({}_{2}\) & 12.09 & 4.61 \(\times\) 10\({}^{-1}\) & 0.99 & 650–950 & 15–30 & 21 \\ \cline{1-1}  & CO & 11.07 & 7.96 \(\times\) 10\({}^{-2}\) & 0.97 & 650–950 & 15–30 & 21 \\ \hline \end{tabular}
\end{table} 
Table 8: Kinetics of materials 0 XMN009 and OXMN009P. conversion times \(\tau\) found are shown in Table 8. The correlation coefficients obtained from the linear regression X vs. time range of 0.9594 to 0.9999 reflect that the conversion varies linearly with time and fits the SCM model proposed following what was stated by [PERSON] (2016) [16].
## 4 Challenges and Opportunities in Marine Biotechnology  
The global market for microalgae-derived bioactive compounds is experiencing rapid growth, driven by rising demand for sustainable, functional ingredients in the health, cosmetic, and food sectors. Recent reports estimate that the global microalgae market surpassed USD 1.5 billion in 2023 and is projected to reach USD 2.9 billion by 2030, growing at a compound annual growth rate of approximately 9-10% [126]. Key growth segments include astaxanthin, phycocyanin, PUFAs (especially DHA and EPA), and polysaccharides, with applications expanding across dietary supplements, fortified foods, anti-ageing skincare, and pharmaceuticals. Spirulina [127] and _Chlorella_[128] dominate current commercial production, while species like _Haematococcus_, _Nannochloropsis_, and _Porphyridium_ are gaining traction for their high-value pigments and therapeutic polysaccharides. For instance, _Haematococcus pluvialis_ commands a premium market value due to its capacity to produce astaxanthin at concentrations up to 3.8% of dry biomass, while the global demand for Spirulina-derived phycocyanin as a natural colourant is increasing, with applications in beverages, confectionery, and personal care. The increasing focus on clean-label products, plant-based nutrition, and the blue bioeconomy is expected to further accelerate investment and innovation in this space [126].  
Marine biotechnology is rapidly emerging as a key driver in the sustainable utilisation of marine microalgae for the production of high-value bioactive compounds. However, the commercial translation of these bioproducts into scalable applications for health, cosme, and functional foods remains constrained by several technical, economic, and regulatory challenges. Concurrently, this field presents unique opportunities driven by advances in synthetic biology, systems biology, bioprocess optimisation, and policy support for the blue bioeconomy [129; 130; 131].
## 1 Introduction
[PERSON] [23] et al. simulated the rubbing of the rotor experimentally and a serious rub chaos was observed. [PERSON] [24] et al. found that different rubbing forms and transient forces may exist under different operation conditions. [PERSON] [25] et al. researched the influence of different parameters on the rubbing condition and utilized the proposed algorithm to detect rubbing faults. [PERSON] [26] et al. also proposed a method based on harmonic components to detect rubbing fault. [PERSON] [27] et al. analyzed the characteristics of backward rub both experimentally and numerically. The work done by [PERSON] [28] et al. showed that high-order harmonic terms may appear in the response of rotors subjected to rub-impact. However, few studies have been done investigating the sensitivity of the response of rotors subjected to rubbing forces. [PERSON] [29] et al. investigated the sensitivity of a Jeffcott rotor with rub-impact to different parameters, but his research was mainly concentrated on transient sensitivity in the time domain, which is not so practical since the periodic stable vibration is the conventional focus of engineering applications. In this paper, the sensitivities of steady-state response of rotors subjected to nonlinear rubbing forces with respect to parameters of the rubbing interfaces are investigated. Based on the multi-harmonic response obtained in frequency domain, analytical expressions for sensitivity coefficients are derived by differentiating the maximum displacements with respect to the investigated parameters, such as normal stiffness and initial gap, etc. Considering the effect of buckling and bifurcation in nonlinear systems, the pseudo arc-length continuation method is applied to trace the equilibrium path of nonlinear equations. In this section, the research background and status about sensitivity analysis and response of rotors with rub-impact are elaborated. In Sections 2.1 and 2.2, basic theories for the adopted methods are introduced, including the harmonic balance method (HBM) and the pseudo are-length continuation method. The analytical expressions for sensitivity coefficients are derived in Section 2.3 for the purpose of accuracy and performance. Then in Section 3.1, the accuracy of the adopted algorithm is illustrated utilizing two classical models. Sections 3.1 and 3.2 shows the applications of the proposed methods to two different rotor models, a lumped Jeffcott rotor and a finite element model. The summary of results and discussions are finally presented in Section 4.
## 2 Experimental Equipment and Data Processing
### Data Processing  
In the experiment, the pressure around the cooling surface, partial pressure of water vapor in cavity, temperature of air, and the temperature of cooling surface are recorded every 5 s. The frost layer image is collected every 1 min. The frost image taken by the CCD camera is binarized to obtain the average frost thickness. After the experiment, the cooling surface is heated with a heater until all the frost melts into water. The frost accumulation is obtained by weighing the melted frost, which is absorbed by the dust-free paper. In this paper, the frost density is calculated indirectly using the frost accumulation and the average frost thickness, which is shown in Equation (1):  
\[\rho_{\rm f}=m/(y_{t}\cdot A) \tag{1}\]  
The uncertainties of the frost thickness and frost accumulation are calculated using the method of [PERSON] et al. (2014). The uncertainty of the frost density is calculated indirectly by the uncertainties of frost thickness and frost accumulation. The accuracy of the apparatus used and calculated variables in this test are shown in Table 1.  
According to the theory of phase change kinetics, the phase change is related to the partial pressure of water vapor. Therefore, the partial pressure of water vapor is also used as a control variable. Furthermore, since the overall pressure of humid air is much larger than the partial pressure of water vapor, the working pressure around the cooling surface is approximated as the overall pressure in the cavity. For safety and operability reasons, the upper limit of high pressure in this experiment is set to 2.0 \(\times\) 10\({}^{5}\) Pa. Within the range of working pressure around the cooling surface from 0.25 \(\times\) 10\({}^{5}\) Pa to 2.0 \(\times\) 10\({}^{5}\) Pa, a series of experimental frosting studies have been conducted. The temperatures of air and cooling surface and partial pressure of water vapor are adjusted to study their impacts on frost formation under different working pressures around the cooling surface. The experimental conditions are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Apparatus (Variables)** & **Accuracy (Uncertainty)** \\ \hline Pressure transmitter & \(\pm\)0.3\% \\  & \(\pm\)0.1 \({}^{\circ}\)C (Temperature) \\ Temperature and humidity sensor & \(\pm\)1.5\% (Relative humidity) \\ Thermocouples & \(\pm\)0.5 \({}^{\circ}\)C \\ Frost thickness & \(\pm\)4.04\% \\ Frost accumulation & \(\pm\)4.16\% \\ Frost density & \(\pm\)8.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uncertainty of measured and calculated variables.  
Figure 2: Schematic diagram of test cavity with adjustable pressure.
## 6 Results and Discussions
### Machining Deviations  
As shown in Figure 7, the variation trends of contact pressure on Model 1, 4 and 5 with the axial-directional nodes are similar. For the same axial node of each component, there exist the following features: the contact pressure of Model 5 has the largest value, model 1 has the second largest, and Model 4 has the smallest. The maximum difference of contact pressure is within 25 MPa in Figure 7a. Thus, the interference magnitude is inversely proportional to the contact pressure on mating surfaces. Machining deviation within a reasonable range has a small impact on the contact pressure of mating surfaces.
## 1 Introduction
### Related Works
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & **Year** & **Objective** & **Technologies** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{[PERSON] et al. [18]} & \multirow{3}{*}{2010} & Security services with a central alarm station and dispatch operations & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{N} & Y & Y & N & N \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [19]} & \multirow{2}{*}{2020} & Blockchain-based access control system & \multirow{2}{*}{Blockchain, IoT} & Y & N & Y & N & N \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & \multirow{2}{*}{2018} & Blockchain-based access control to access control for industry 4.0 & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Y} & N & Y & Y \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{2020} & Blockchain-based access control to controls the smart home devices & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Y} & N & Y & N & N \\ \hline \multirow{2}{*}{**[PERSON] et al. [22]**} & \multirow{2}{*}{2020} & Smart campus with the Internet of Things and blockchain & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Y} & N & Y & N & N \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{3}{*}{**[PERSON] et al. [23]**} & \multirow{3}{*}{2019} & Accessing private smart home with smart devices through an & \multirow{3}{*}{Smart home, IFTTT, IoT} & N & N & Y & N & Y \\ \cline{1-1}  & & IFTTT Gateway & & & & & \\ \hline \multirow{3}{*}{**[PERSON] et al. [24]**} & \multirow{3}{*}{2020} & Remote anonymous authentication for smart home & \multirow{3}{*}{Smart home, IoT} & N & N & Y & Y & Y \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}  
Notes: (1) Focus on a blockchain, (2) security services by a third-party, (3) proposing an architecture or framework, (4) method classification, (5) security analysis, (Y) yes, (N) no.  
\end{table} 
Table 2: The related works survey. To summarize the shortcomings of the current research, there is no literature proposing complete security services by a third-party; for example, private security guards, and community security companies. Properly storing the content of the incident is also important for trust between the services provider and client, so it is also important to store complete records of the event processing to achieve traceability, integrity, non-repudiation, etc. Therefore, we propose smart contracts that enable chaining the event's record on the blockchain center. The remaining sections of this paper are organized as follows: Section 2 introduces the techniques that are used in our proposed scheme. Section 3 presents our proposed scheme. The security analysis and comparison discussion are given in Sections 4 and 5, respectively. Finally, Section 6 concludes this paper.
## 4 Evaluation Results and Discussion
### Performance Analysis
#### 4.2.1 Wii Classification of Experimental Scenarios
The path of each blue graph line is essentially dependent on how great or small the \(w_{1},w_{2},w_{3},w_{4}\) values are, with regard to the adopted Wii combination. For instance, in plot a., _Influence of \(PJ\)_, of Figure 7, the blue graph line of \(PJ\) deviates in a smoother way from the green area chart of QUAKE, in contrast to the respective plot a. of Figure 8, where the line of \(PJ\) dips sharply. This is an expected effect, since the Wii combination 3 favors a better jitter profile than the Wii combination 10, whereas the blue graph line depicted in plot d., _Influence of \(RCR^{norm}\)_, of Figure 8, exhibits smoother deviations as the green area chart of QUAKE progresses, in contrast to its plot d. counterpart of Figure 7. This differentiation occurs because \(VNR_{2}\) dictates more robust computational capabilities than the requirements of \(VNR_{1}\). Figures 7 and 8 show that it is possible to verify the correct and desired operation of the proposed path ranking methodology, directly from the extracted results, without prior knowledge about the specialized features and functionality of the implemented framework. Indeed, the emergency communications application over a UAV-assisted network (\(VNR_{1}\)) requires a strong effect of \(PJ\) and \(L\) on the QUAKE values, while the XR-based activities over an industrial environment (\(VNR_{2}\)) need a more even influence upon the values of the composited metric. By these plots, the weighting scheme of the performance-aware QUAKE framework is bidirectionally affirmed, not only by design but also by the output data. Figure 7: QUAKE’s reaction to the Wii #3-based influence of (**a**). PJ, (**b**). L, (**c**). \(RNR^{norm}\), and (**d**). \(RCR^{norm}\), for \(VNR_{1}\).
## 2 Methods
### Experimental Measurements
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Throat diameter (d\({}_{\text{PC}}\)) & 5 mm \\ Number of Orifices (n) & 6 \\ Orifice diameter (d\({}_{\text{ord}}\)) & 1.15 mm \\ Pre-Chamber Volume (V\({}_{\text{PC}}\)) & 1911 mm\({}^{3}\) \\ Volume ratio \(\left(\frac{V_{\text{PC}}}{V_{\text{dr}}}\right)\) & 4.7\% \\ Orifice area-to-volume ratio \(\left(\frac{d_{\text{dr}}^{\text{T}}\text{T}}{4}\cdot\text{n}\right)/V_{\text{PC}}\) & 0.033 1/cm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pre-chamber geometry. In Figure 1b, a load sweep is shown where the load is varied by changing the air dilution level from \(\lambda=1.0\) to \(\lambda=2.2\) (lean limit). The blue curve represents the operating points with the highest indicated efficiency, while the green curve represents the operating points that satisfy the \(\text{NO}_{\text{X}}\) regulation limit by retarding the spark timing. By looking at the operating points that satisfy the \(\text{NO}_{\text{X}}\) regulation limit, it can be concluded that the operating range for low NOx emissions is between the loads of IMEP = 4 bar and IMEP = 5.5 bar. These operating points will be used as the first set of reference results, to see how much the lean limit can be extended, both with PCSI combustion at increased compression ratio and with PC-HCCI combustion, and how comparable the obtained performance would be. The boundary conditions and operating parameters of this set are listed in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Compression** \\ **Ratio** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Reference** \\ **Point ID** \\ \end{tabular} } & \multirow{2}{*}{**IMEP [bar]**} & \multicolumn{2}{c}{**Intake** \\ \cline{3-8}  & & **Temperature** & **\(\mathbf{\lambda_{\text{global}}}\) [-]** & **ST [\({}^{\circ}\)CA** \\  & & **[\({}^{\circ}\)C]** \\ \hline \multirow{4}{*}{12.8} & R1 & 3.90 & 34.1 & 2.20 & 22 & 0.30 & 123.9 \\ \cline{2-8}  & R2 & 4.52 & 33.9 & 2.00 & 20 & 0.30 & 131.3 \\ \cline{2-8}  & R3 & 5.23 & 34.6 & 1.79 & 16 & 0.30 & 140.6 \\ \cline{2-8}  & R4 & 5.71 & 33.6 & 1.62 & 4 & 0.30 & 150.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PCSI reference points taken from [6]. Figure 1: An example of performed analysis. (**a**) Optimization of combustion phasing of a single operating point; (**b**) optimal operating points at different engine loads.