esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Electric Power System Description  
As the performance of the FLA was already extensively investigated in [11; 36], the aim of this paper is not duplicating the results, but rather emphasizing how one can establish a framework capable of a reliable and automated FLA execution in a real DN with LTE connectivity. In this Section, we first present the electric power system and the position of the installed measuring devices required to meet the prerequisites of the FLA, whereas a detailed description of the proposed edge-cloud computing infrastructure implementation is presented in the next Section.  
The considered distribution network is a 5.2 kV, 3-phase, low-impedance grounded network located in Spain and operated by Estabanell y Pahisa Energia S.A.U. (EyPESA). It consists of 16 buses (topology is presented in Figure 4) and is composed of both underground cables and overhead lines. The upstream grid has a short-circuit power of 54 MVA.  
In order to fulfil the requirements of the FLA, the network has been equipped with PMUs within the context of the European Union (EU) H2020 Horizon project RESOLVD. The locations of the deployed PMUs are marked as green dots in the figure and are set, according to the methodology, to a primary substation and secondary substation at the end of the feeder. Additionally, to satisfy the FLA requirement for pseudo-measurements, all the load busses of the considered network, denoted with blue dots, have been equipped with PQMs. The latter provide information about the load's consumed active and reactive powers, the root mean square (RMS) values of voltages and currents and the total harmonic distortion, whereas PMUs measure and report the time-tagged frequency, the rate-of-change-of-frequency (RoCoF) and the phasors of voltages and currents.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 5 Problem Formulation
### Effect of Mean Maximum Wait Time  
During a task cooperation in the D2D network, the requester typically wants the task to be done in a timely manner. For example, if a requester wishes to perform a neural network-based stock index prediction task [31] that predicts the price of a stock in 30 s, this requester will want to get the computation result within 30 s (could be 20 s, 25 s, etc., depending on the specific application logic and handling behind).  
As shown in Figure 6a, two heuristic cases started with a decrease in average runtime from mean maximum wait time changing from 15 s to 20 s. This may be due to the fact that when the mean maximum wait time is too low, the probability of finding a feasible solution in the cases with our heuristic algorithm is much lower. As mean maximum wait time continues to increase thereafter, the average task execution time increases in all four cases: the enlarged solution space is increasing the difficulty of our heuristic algorithm in finding the optimal solution; and in the greedy cases, the helpers cannot focus on helping fewer requesters since most requesters have similarly high mean maximum wait time. Particularly, the performance on the average task execution time of the greedy-credit case deteriorates approximately 36% from 14 s-19 s, while that for our heuristic case with our credit system deteriorates only 9% for the same change. This further proves the necessity of our heuristic algorithm. However, the normalized level of selfishness decreases with respect to the greedy-non credit case, with the two heuristic cases scaling down faster. Comparing the heuristic cases with and without our credit system, the sacrifice of around 10-15% of average task execution can bring us at least 40% less selfishness at a 50-s mean maximum wait time and almost 100% less at a 15-s mean maximum wait time.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 2 Materials and Methods
### Propeller/Rotor Efficiency
#### 2.3.1 Figure of Merit  
The Figure of Merit is a dimensionless factor used to calculate hover efficiency of rotor driven propulsion systems. The concept of a Figure of Merit was first proposed by [PERSON] (1903) and then [PERSON] (1935), but was first presented in its present form by [PERSON] of Kellett Aircraft. The Figure of Merit is defined in Equation (10).  
\[FoM=\frac{Ideal\, Power\, Required\, to\,\,lower}{Actual\, Power\, Required\, to\,\,lower}=\frac{C_{T}^{\frac{3}{2}}}{\sqrt{2C_{P}}} \tag{10}\]  
Equation (10) is derived from simple momentum theory, and thus assumes there is no viscous losses when calculating the ideal power required to hover. In the actual power required to hover measurement, viscous effects will result in both profile and induced loss contributions, and therefore for a real rotor, the Figure of Merit must be less than 1. Hover propulsion systems can be optimised to achieve FoM values of up to 0.9, however, achieving an optimal FoM value will result in a significantly lower propulsive efficiency value. Therefore, rotor propulsion systems are often designed to have FoM values of around 0.6, resulting in higher efficiencies at high speeds at the expense of lower efficiencies in hover conditions.  
The disc loading of a rotor is defined as the ratio of the thrust produced by the rotor to the rotor disk area. To make a meaningful comparison between the FoM values of multiple rotors, the rotors must have the same disc loading. The disc loading will increase the induced power relative to the profile power, resulting in a higher FoM which is a misleading value for comparison. Generally, rotors with high disc loadings will give higher figure of merit values than rotors with low disc loadings.
# SPME-GC/MS Analysis of Methanol in Biospecimen by Derivatization with Pyran Compound
## 2 Results and Discussion
### Extraction  
For the GC/MS analysis of the 2-methoxytetrahydrofuranropyyran from the reaction of methanol and DHP, extraction was performed using SPME (solid-phase micro extraction) at the headspace of the sample vial.  
Characteristics of commercial SPME fibers such as carboxen/polydimethylsiloxane (CAR/PDMS), polyacrylate (PA), PDMS, and carboxax/divinylbenzene (CW/DVB) were compared with one another for each sample containing methanol of 1000 \(\upmu\)g/mL and internal standard. The results are shown in Figure 8.  
When comparing the area ratio of the ion with _m/z_ 115 and that of _m/z_ 134, the fiber composed of CAR/PDMS showed better area response for the derivatized methanol than that of the derivatized internal standard and was used for this analysis (Figure 9).  
Figure 8: GC/MS chromatograms for the comparison of extraction characteristics of derivatized methanol and the internal standard by using commercial solid-phase micro extraction (SPME) fibers.  
Figure 9: Comparison of the area response for the ion of _m/z_ 115 (for the derivatized methanol) and the ion of _m/z_ 134 (for the derivatized internal standard) by using CAR/PDMS, PA, PDMS, and CW/DVB SPME fibers.
## 1 Introduction
The study begins by establishing the relevance of VLC as a communication resource for scenarios where ultra-high data rates are not paramount. Emphasizing the significance of diverse communication solutions, the paper positions VLC as a tailored and efficient option for specific applications within smart cities and industries. Subsequently, attention is directed towards the introduction of an open-source simulator designed to provide researchers with fundamental tools for designing and simulating VLC scenarios. The simulator serves as a versatile platform, enabling the modeling and analysis of VLC  
Figure 1: A few examples of related 6G VLC fields of research. communication systems in various urban and industrial environments. The paper then presents insightful results derived from simulations using innovative modulation schemes such as UFMC, a general and enhanced version of OFDM, showcasing the practicality and efficacy of integrating VLC in short-distance communications. Finally, a concluding section synthesizes the findings, emphasizing the role of VLC within the 6G landscape, and underscores the importance of continued exploration and innovation in optimizing communication resources for diverse applications in urban and industrial domains.
# The Spotting Distribution of Wildfires
## 1 Introduction  
Wildfires are capable of creating powerful updrafts, called convection columns, which launch burning plant materials--referred to as _firebrands_--into the atmosphere [1]. Generally one speaks of coupled fire-atmosphere interactions [2], in which heat and moisture exchanges occur between the fire, the convection column and the atmosphere, resulting in the birth of new fire-driven wind and convective updrafts. Firebrands are then transported by the ambient window, simultaneously combusting and decreasing in mass, until they reach the ground. Upon landing, depending on the local fuel and weather conditions at the landing site, a firebrand may ignite the local fuel and start a new fire. Such a fire is called a _spot fire_, and the process is called _spotting_ (see Figure 1a for a sketch of the spotting process). Spotting occurs in many ecosystems spanning the Earth, from the Americas to Europe, Africa and Australia.  
Spotting can play an important role in wildfire spread, and while many of the subprocesses outlined in Figure 1 have been studied in detail, many remain poorly understood. One thing is certain: where spotting is important, it is a very difficult spread mechanism to understand and therefore control.  
The model framework we present here has the potential to produce realistic spotting _distributions_: spatial maps describing the probability of spot fires downwind of an existing fire. We will show how it is possible to incorporate the vast, but disparate literature on spotting, with some new ideas, to create a very robust modelling framework. We hope to draw the interest of researchers, in particular those working in fire management and experimental or statistical modelling of spotting subprocesses, by highlighting the lesser known subprocesses and demonstrating some potential uses of our approach.  
While fully realistic distributions are still some time away, a major advantage of our approach is that as subprocesses become better understood, our spotting distributions will become more accurate.
# Light Transmission in Fog: The Influence of Wavelength on the Extinction Coefficient
## 4 Extinction Coefficient Results
### Estimation of the Extinction Coefficient
This is possible using the relationship:  
\[Ratio(\lambda,D,V)=e^{-k(\lambda,V).D} \tag{13}\]  
Therefore, to calculate the extinction coefficient \(k\), it is sufficient to calculate the slope \(a\) of the following function by orthogonal linear regression with a vanishing intercept coefficient:  
\[D\to ln(Ratio(\lambda,D,V))=a.D \tag{14}\]  
Then,  
\[k(\lambda,V)=-a. \tag{15}\]  
The extinction coefficient depends on the MOR \(V\) and the wavelength \(\lambda\). This operation must therefore be performed for each type of fog, each wavelength, and each MOR. An example of regression is given in Figure 8.  
In Figure 8, the two curves give the same value of the extinction coefficient \(k=0.11\) m\({}^{-1}\) (resp. \(k=0.10\) m\({}^{-1}\)) for radiation fog (resp. advection fog). It is also possible to calculate the reference extinction coefficient \(k_{ref}\) from the MOR in meters, measured by the transmissometer, which was considered the reference sensor. This was true only for wavelength \(\lambda=550\) nm, used by the transmissometer for the measurement, with the following relationship:  
\[k_{ref}=-\frac{ln(0.05)}{V} \tag{16}\]  
The numerical application with \(V=25\) m gave \(k_{ref}=0.12\) m\({}^{-1}\). In the case of Figure 8, the reference value of \(k_{ref}\) therefore corresponded approximately to the values measured by the spectroradiometer. However, the data for the two types of fog did not perfectly follow the theoretical  
Figure 8: \(Ratio(\lambda,D,V)\) as a function of \(D\) for \(\lambda=550\) nm and \(V=25\) m for the two types of fog generated in the PAVIN BP facility. model. The correlation coefficient \(R\), between zero and one, made it possible to make a synthetic measurement of the reliability of the relationship between \(D\) and \(ln(Ratio(\lambda,D,V))\). The closer \(R\) was to one, the better the estimation of \(k\). Table 2 gives the correlation coefficient as a function of MOR. As shown in Table 2, the correlation coefficient globally decreased as the MOR increased. This was due to the fact that there was more instability when the MOR increased because fog dissipation was faster for a higher MOR (see Table 1). The correlation coefficient was also unavailable (NA values) for \(\lambda=360\) nm and 2400 nm and \(V=15\) m: this was due to the spectroradiometer measurement range, which did not have enough light signals. These wavelengths were at the limit of the spectroradiometer, and the sensitivity was therefore lower for these. When the correlation coefficient was lower than 0.9 (\(R\leq 0.9\)), the measurements were labeled as uncertain data. The value of 0.9 was arbitrarily chosen, but makes Figure 9 easier to read. The extinction coefficient \(k\) was calculated for each wavelength: it is now possible to examine the influence of wavelength on this coefficient.
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 3 Damage Identification Methodology and Their Fundamentals
### Continuous Wavelet Transform  
One of the most utilized wavelet transforms (WT) is the continuous wavelet transform (CWT), which applies a variable-sized window method into a time-domain signal in order to convert it to a time-frequency diagram (see Figure 7).  
First, a mother wavelet (MW) is selected, and this base signal is used to continuously create a wavelets' family by means of the translation and dilation of the MW itself along the entire signal to be analyzed. Then, this process is implemented sequentially, and, in this way, it is possible to assess the similarity between the MW chosen and each segment of the complete signal to be examined throughout the entire time. As a consequence of this process, the discontinuities contained in the analyzed signal will generate high CWT coefficient magnitudes, which could be related to damage [62].  
It is important to mention that there is a wide range of types of MWs, and the correct selection depends on the application in which it is required to be used, following a trial-and-error process to find the most convenient one.  
Thus, if a MW is considered [63]  
\[\psi(t)\in L^{2}(\mathbb{R}), \tag{2}\]  
its two fundamental properties are zero mean and its normalized nature. These properties are mathematically represented as follows:  
\[\int_{-\infty}^{\infty}\psi(t)dt=0 \tag{3}\]  
\[\parallel\psi(t)\parallel^{2}=\int_{-\infty}^{\infty}\psi(t)\psi^{*}(t)dt=1. \tag{4}\]  
Then, considering that it can be translated and dilated, the MW can create a basis set, as indicated below:  
\[\left\{\psi_{s,u}(t)=\frac{1}{\sqrt{s}}\psi\left(\frac{t-u}{s}\right)\right\} \biggr{|}_{u\in\mathbb{R},s\in\mathbb{R}+}. \tag{5}\]  
where \(u\) is the translataion parameter and \(s\) is the scale parameter. Thus, the CWT is the coefficient of the base \(\psi_{s,u}(t)\), that is  
\[Wf(s,u)=\left\langle f(t),\psi_{s,u}\right\rangle \tag{6}\]  
\[Wf(s,u)=\int_{-\infty}^{\infty}f(t)\psi^{*}_{s,u}(t)dt \tag{7}\]  
\[Wf(s,u)=\int_{-\infty}^{\infty}f(t)\frac{1}{\sqrt{s}}\psi^{*}\left(\frac{t-u}{ s}\right)dt \tag{8}\]  
Figure 7: Representation of the CWT.  
In this way, by using this transform, a three-dimensional colored CWT diagram can be obtained with good resolution for all the parameters (time, scale, and coefficients) from a one-dimensional time signal; however, obtaining these diagrams with a high resolution and with wide ranges of scale and time will require a high computational consumption time, even more for long bridges and for vehicles crossing with low speed.
## 4 Materials and Methods
### Effect of Photodynamic Therapy on the Viability of the _S. aureus Biofilm_  
The evaluation of the effect of photodynamic inactivation on the viability of _S. aureus_ biofilm cells was based on two procedures that are detailed below.  
Procedure 1. After biofilm formation on the surface of glass/steel discs (see the protocol described above), MB, MB+AuBNsP, or MB+AuChNPs were dropped on the surface of each disc, and these samples were incubated in the dark at 37 \({}^{\circ}\)C for 30 min. Then all discs were exposed to diode laser lamp light (the peak power wavelength \(\lambda\) = 665 nm; output power 40 mW; power density of 105 mW cm\({}^{-2}\)) for 30 min. This study was carried out with 4 experimental groups: laser light alone (L+, n = 3); control probe; biofilm without photosensitizer), MB and laser light (L+MB, n = 3), MB+gold nanoparticles and laser light (L+MB+AuBNPs, n = 3), (L+MB+AuChNPs, n = 3). This procedure was performed for MBat concentrations of 31.25, 62.5, and 125 mg L\({}^{-1}\). The viability of cells was estimated using a colony counting assay according to the method described in Supplementary Information (The dark cytotoxicity of Methylene Blue and gold nanoparticles).  
Procedure 2. The inactivation of the _S. aureus_ biofilm formed on the surface of the discs by aPDT was carried out according to the procedure described above with some modifications. In the first step, the biofilms (after dark incubation with MB at a concentration of 31.25 mg L\({}^{-2}\)) were treated with laser light for 10 min (energy fluence 63 J cm\({}^{-2}\)). After that, the biofilms were washed with sterile deionized water to remove the damaged cells from the surface of the biofilm, and fresh photosensitizer (MB) was added. All discs were again exposed to diode laser lamp light for 10 min (energy fluence of 63 J cm\({}^{-2}\)). This procedure was repeated three times according to the following scheme: 1. irradiation for 10 min/biofilm washing+adding a fresh MB; 2. irradiation for 10 min/biofilm washing+adding a fresh MB and 3. biofilm irradiation for 10 min/biofilm washing and estimate of bacteria viability. The viability of cells was estimated using a colony counting assay according to the method described in Supplementary Information (The dark cytotoxicity of Methylene Blue and gold nanoparticles). Deionized water, which was used to wash the biofilm culture, was seeded on an agar medium, and the number of viable cells was determined by a serial dilution method and administered as CFU mL\({}^{-2}\).
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 4 Case Study: FDI System for Oil Pipeline  
### Brief Description of the Oil Pipeline  
The oil pipeline (plant) under study in this paper is located in Peru. It transports oil for delivery to customers. Figure 4 shows a view of this oil pipeline.  
This oil pipeline consists of three sections with a pumping station located at the beginning of each section with a total length of 450 km. Detailed data of dimensions and relative heights of each section are shown in Table 1, where \(L_{i}\) is the length, \(D_{i}\) is the outer diameter of the pipes, \(d_{i}\) is the inner diameter of the pipes, \(\delta_{i}\) is the wall thickness of the pipes, and \(z_{0},z_{L_{i}}\) are the relative heights at the beginning and the end of each \(i\) section, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**No.** & **L, km** & **D, mm** & \(\delta\), mm** & \(z_{0}\), m & \(z_{L}\), m \\ \hline
1. & 150 & 720 & 8 & 50 & 60 \\
2. & 180 & 720 & 8 & 60 & 70 \\
3. & 120 & 720 & 8 & 70 & 180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oil transportation plant characteristics.  
Figure 5 shows a diagram with the structure of the oil pipeline under study, identifying its corresponding sub-systems detailed ahead. \(Q_{i}\) and \(h_{ui}\) are the oil flow rate (measured with flow sensor) and the head (measured with pressure sensor) before the oil-pumping station of each i section of the OPS, respectively. Applying Definition 1, \(\Sigma\) is the OPS and using Definition 3, \(\Sigma_{i}\) is the i section of the OPS.  
The head discharge \((Q-H)\) characteristics of the pumping station operating in stationary regimes are often approximated by the following equation:  
\[\Delta H=F(Q)=a-b\cdot Q^{2} \tag{1}\]  
where \(\Delta H\) is the differential head, \(Q\) is the flow rate, and \(a\) and \(b\) are the approximation factors of the \((Q-H)\) characteristic.  
The characteristics of pumping stations are detailed in Table 2, including the pump types, the head-discharge \((Q-H)\) characteristics, and the positive suction heads (P.s.h.).  
The temperature of the pumping oil is 30 \({}^{\circ}\)C, the maximum admissible pressure in the pipeline is 11.1 MPa, the density of the transported oil is 850 kg/m\({}^{3}\), and the efficiency of the system is 1500 m\({}^{3}\)/h.
## 3 Verification of NREL 5 MW OWTG FAST Model under Load Cases of OC3 Phase I
### Load Case 5.3  
The environmental effect is included in Load Case 5.3. The wind conditions and wave conditions are given according to OC3 Phase I standards. Partial analysis results under Load Case 5.3 are shown in Figures 4 and 5. Figure 4 is the variation of base shear force under stochastic wind flow. Figure 5 is the variation of base overturning moment under drastic wave conditions. The analysis results by individual OC3 research teams are similar in the frequency interval of 0-1.5 Hz. However, for frequency beyond 1.5 Hz, significant analysis errors between individual teams have been observed due to the difference of simulation settings and applied analysis methods. Since the variations of turbulent wind and irregular wave are given randomly, the simulation results of individual teams can hardly be identical. That is why there are analysis errors between NREL and INER in Figures 4 and 5, though the OWTG model and analysis software of the two teams are the same.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Load** & **Enabled DOF** & **Wind Conditions** & **Wave Conditions** & **Analysis Type** \\
**Case** & **(Degrees of Freedom)** & & & & \\ \hline \multirow{2}{*}{1.2} & Substructure, tower, & & & & Eigenanalysis with gravity \\  & drivetrain, blades & & & & and structural damping \\  & Substructure, tower, & & & & Time-series statistics, DELs \\  & drivetrain, blades & & \(\sigma_{1}\) = 2.674 m/s, & & \\  & & & & & (Damage Equivalent Loads), \\  & & & & & power spectra \\ \hline \end{tabular} \(V_{\text{half.}}\): Hub height wind speed; \(\sigma_{1}\): Longitudinal wind speed standard deviation; \(H_{s}\): Significant wave height; \(T_{p}\): Peak spectral period; Pierson-Moskowitz wave spectrum is used for irregular sea state.  
\end{table}
Table 1: Load Cases Definitions of OC3 (Offshore Code Comparison Collaboration) Phase I.  
Figure 3: Full System Natural Frequencies under Load Case 1.2 of OC3 Phase I.