esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Instruments and Reagents  
The morphology of prepared gold nanomaterials was observed on a transmission electron microscope (TEM, Tecnai G2 F30 S-Twin, FEI Company, Hillsboro, OR, The Netherlands) operating at an acceleration voltage of 300 kV. Dynamic light scattering (DLS) was also used to measure the size of the gold nanoparticles using a Nano ZS 90 Nanosizer (Malvern Instrument, Worcs, UK) equipped with a 628 nm laser source. The X-ray diffraction (XRD) patterns of Au nanomaterials were recorded on a PANalytical X'Pert PRO MRD X-Ray Diffraction System (PANalytical B. V., Almelo, The Netherlands) using a monochromatic Cu K\(\alpha\) source, \(\lambda=1.5406\) A. SERS spectra were measured on a DeltaNu 785 Raman spectrometer (DeltaNu Inc., Laramie, WY, USA). The laser power of the spectrometer is 120 mW with an excitation wavelength at 785 nm and a spectral range of 200-2000 cm\({}^{-1}\). The spectra were acquired with baseline off using NuSpec software (Copyright DeltaNu 2009) and analyzed using GRAMS/AI software (Ver 9.1, Thermo Fischer Scientific, Waltham, MA, USA).  
Chloroauric acid (HAuCl4-4H2O, 99%) was purchased from Shanghai Siyu Chemical Technology Limited Company (Shanghai, China). The 3,4-benzopyrene (98%), benzidine (\(\geq\)98%), and malachite green (AR) were supplied by Shanghai Macklin Biochemical Technology Limited Company (Shanghai, China). Chloroform (CHCl3, 99%), anhydrous methanol (CH3 OH, 99.5%), anhydrous ethyl alcohol (CH3 CH2 OH, 99.5%), and acetone (99%) were obtained from Hangzhou Shuanglin Chemical Reagent Limited Company (Hangzhou, China). Triphenylsilane (TPS, 99%), oleic acid (AR), oleylamine (85-90%), hydroxylamine hydrochloride (98.5%), and dimethyl sulfoxide (DMSO, 99%) were purchased from Shanghai Aladdin Bio-Chem Technology Co., Ltd. (Shanghai, China). All chemicals were used as received unless otherwise indicated.
# Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function
## 2 Weibull CDF Model  
The CDF of the Weibull distribution [15; 16] is expressed as:  
\[F(v)=1-e^{-(\frac{v}{C})^{k}} \tag{1}\]  
where \(F(v)\) represents the CDF of the wind speed series, \(k\) is the shape parameter and \(C\) is the scale parameter.  
Following the proposed analogy, the power generated by a WT, \(P\), as a function of the wind speed at hub height, \(v\), can be expressed as:  
\[P=P_{max}\cdot\left(1-e^{-(\frac{v}{C})^{k}}\right) \tag{2}\]  
where \(P_{max}\) is the maximum WT power.  
The parameters of the Weibull distribution can be obtained from the data given by the manufacturer, by interpreting the power values as frequencies. The procedure for wind speed series against frequencies can be read in [17] and is briefly explained in the following lines.  
With the double logarithmic action on both sides of Equation (2), it can be operated to be written as:  
\[\log(-\log(1-P))=k\cdot\log(v)-k\cdot\log(C) \tag{3}\]  
In order to simplify the process, the values of power, \(P\), can be normalized, i.e., the process is presented in per unit values (\(p.u.\)).  
The range of values of \(P\), \([P_{min},P_{max}]\), can be transformed into the interval \([0,1]\), according to Equation (4).  
\[p=\frac{P-P_{min}}{P_{max}-P_{min}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline \(F(v)\) & CDF of wind speed series \\ \(P\) & a function of wind speed \\ \(P_{max}\) & maximum WT power \\ \(P_{min}\) & minimum WT power \\ \(P_{l}\) & calculated power with proposed function \\ \(p_{i}^{s}\) & specified power (values given by the manufacturers) \\ \(k\) & shape parameter \\ \(C\) & scale parameter \\ \(v\) & hub height \\ \(C_{T}\) & the thrustcoefficient \\ \(K_{w}\) & the Waledecay constant \\ \(d\) & the distance between WTs \\ \(D\) & the rotor diameter of WTs \\ \(\xi\) & wake coefficient \\ \(\xi_{w}\) & the mean value of coefficient of mWTs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols used in the following sections.  
In the case of wind power curves, \(P_{min}\) can be assumed to equal zero, because it is the minimum value for the wind, since dealing with negative values does not make any sense.  
Eventually, Equations (4) and (3) are further modified as shown in Equation (5).  
\[\log(-\log(1-p))=k\cdot\log(v)-k\cdot\log(C) \tag{5}\]  
Equation (5) can be represented in the form of a straight line, as illustrated in Figure 1:  
\[y=m\cdot x+b \tag{6}\]  
where \(y=\log(-\log(1-p))\), \(x=\log(v)\), \(m=k\) and \(b=-k\cdot\log(C)\).  
By solving Equation (5), the parameters \(k\) and \(C\) can be calculated. With the known values of \(k\) and \(C\), the relationship between \(p\) and \(v\) is given by Equation (7).  
\[p=1-e^{-(\frac{p}{C})^{k}} \tag{7}\]  
which is equal to Equation (1) with the only difference that \(F(v)\) has been changed to \(p\).  
Finally, the wind power values can be reconstructed within their actual range with the denormalization process shown in Equation (2), and this constitutes the final proposal for the power curve expression.
## Materials and Methods
### Cell Analytics
#### 2.6.2 RT-qPCR Analysis  
According to the manufacturer's instructions, RNAs were extracted from cell pellets or MCs covered with cells using the Nucleospin(r)RNA kit (Macherey-Nagel, Duren, Germany). The kit's purification procedure also includes an on-column digestion step using DNase I. RNA purity and quantity were assessed with the NanoDrop device (Thermo Fisher Scientific, Waltham, USA) total RNA integrity was periodically verified by conventional agarose gel electrophoresis. cDNA was obtained from 400 ng RNA using the GoScriptTM Reverse Transcription System (Promega, Madison, WI, USA), and the protocol can be found in Supplementary Material, Table S3. RT-qPCR of the SOX9, RUNX2, PPARG, PREF1, ZFP423, ZFP521, WISP2, and DKK1 genes was performed using the SsoAdvancedTMUniversal SYBR(r)Green Supermix kit (Biorad, Hercules, CA, USA) and detected using the CFX Connect system (Biorad, Hercules, CA, USA). RT-qPCR was performed for each gene using 20 ng of cDNA, and ACTB was used as an internal control. Each primer pair product was checked for proper amplification by agarose gel electrophoresis. Only primers that gave rise to single sharp bands of the expected size were used. Primer sequences, temperature, and cycle conditions can be found in Supplementary Material Tables S4 and S5. The data were analyzed using the CFX software to calculate the relative fold gene expression using the formula \(2^{-\Delta\text{ACt}}\). No statistical and significant tests were performed on gene expression.
###### Abstract  
This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time-frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.  
deep convolutional network; gearbox fault diagnosis; localized adaptive denoising
## 5 Numerical Simulation of Pressure Loads on Assembled Road Surfaces Underground
### Numerical Analysis of Displacement Under Dynamic Loads  
The dynamic load simulation is based on a three-dimensional solid model, where a constant pressure load is applied at a uniform velocity. The load duration is defined to correspond to the specified movement speed. The model's dimensions, material properties, and boundary conditions are consistent with those employed in the static analysis. In this dynamic load simulation, the applied pressure is set to 0.85 MPa, corresponding to the maximum load encountered in the roadway of the Liangshuijing coal mine. The load movement speed is set at 25 km/h, and the slab thickness is selected as 16 cm based on prior analysis. The load moves along a 6 m path, from 0 m to 6 m, with analyses performed at intervals of 1.5 m (i.e., at 0 m, 1.5 m, 3 m, 4.5 m, and 6 m). The corresponding displacement contour plots are presented in Figure 23.  
Figure 21: Vertical displacement diagram when the block  
As illustrated in Figure 23, when the load moves to positions 0 m, 1.5 m, 3 m, 4.5 m, and 6 m, the corresponding vertical displacements of the modular pavement are 0.76 mm, 0.78 mm, 0.76 mm, 0.80 mm, and 0.76 mm, respectively. During the entire dynamic loading process, the maximum vertical displacement reaches 0.80 mm, which remains below the design-allowable deflection limit of 0.83 mm.  
Therefore, the 16 cm thick modular block pavement satisfies the design requirements for underground roadway surfaces at the Liangshuijing coal mine under dynamic loading conditions. Moreover, this design scheme may serve as a reference for underground roadway pavement design in other mining areas.  
Figure 23: Cont.  
Figure 23: Vertical displacement diagram under mobile load.
## 2 Model
### Auger Recombination Coefficient  
Following [40], the rate of Auger recombination of a localized hole and electron with the energy transfer to another electron is assumed to be equal to the rate of the electron energy loss via Coulombcollisions with other electrons. Therefore, sheet ARC: \(C_{sh}=\sigma vI/n_{sh}\), where \(\sigma\) is the one-dimensional cross-section of the electron capture by localized hole, \(v\) is the electron velocity, \(n_{sh}\) is the sheet electron density in InGaN QW, and \(I\) is the parameter proportional to the square of the overlap integral between the electron and hole wave functions. For two-dimensional electrons: \(\sigma=\pi a^{2}/v\tau_{e}\), where \(\tau_{e}=\frac{1}{2}m_{e}^{2}v^{2}/\pi^{2}\hbar E_{D}n_{sh}\) is the energy relaxation time [41], and \(E_{D}\) is the binding energy of a shallow donor in InGaN calculated within effective mass approximation. Estimating the overlap integral in a manner suggested in [40], \(\cong 2a\xi^{3}\big{(}E_{e}/E_{g}\big{)}\big{<}\varphi^{2}\big{>}/w_{e}\), and accounting for the fact that the electron kinetic energy, \(\frac{1}{2}m_{e}^{2}v^{2}\), should be equal to the energy transferred from one electron to another, i.e., \(E_{g}-E_{L}\), one can derive the following expression for the sheet ARC:  
\[C_{sh}=4\pi^{3}v_{C}a^{3}a_{g}^{2}\xi^{3}\big{(}E_{e}/E_{g}\big{)}\Big{<} \varphi^{2}\Big{>}/w_{e} \tag{5}\]  
Here, \(\
u_{C}=E_{D}/\hbar\) is the frequency of the Auger recombination typical for microscopic processes involving two electrons and a hole and \(a_{g}=\hbar/\left[2m_{e}\big{(}E_{g}-E_{L}\big{)}\right]^{1/2}\). Equations (4) and (5) show that the ratio \(B_{sh}/C_{sh}=8(v_{B}/\
u_{C})\big{(}E_{g}/E_{e}\big{)}/\pi^{2}a_{g}^{2}\) is practically independent of the hole localization radius at \(E_{L}\ll E_{g}\), like in the case of bulk InGaN alloys [31].  
As soon, as the sheet RRC \(B_{sh}\) and ARC \(C_{sh}\) are found from Equations (4) and (5), the corresponding three-dimensional recombination coefficients \(B\) and \(C\) can be calculated by using the following relationships: \(B=B_{sh}d\) and \(C=C_{sh}d^{2}\).  
The above theory considers the microscopic mechanism of Auger recombination involving a localized hole and two free electrons in InGaN QW. Generally, there is an alternative recombination channel, involving two localized holes and a free electron. This recombination mechanism seems, however, to be substantially suppressed. Indeed, if holes are localized on different sites inside the QW, the overlap of their wave functions is extremely small due to a small localization radius originating from the atomistic character of localization. In contrary, probability of the hole localization on the same site is very low because of the hole Coulomb repulsion. The predicted dominance of the microscopic process with two electrons and a hole, as compared to the alternative one, correlates with the observations reported in [42].
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## 3 Results and Discussion
### Experimental Campaign 1: Effect of Concrete Type on Cracking through Oxygen Availability
The percentual improvement calculated as per (1) has been summarized in Table 5 in order to evaluate the improvements of using HPC, UHPFRC, and UHPFRC-n compared to SQC. The results show that UHPFRCs in cracked states (LS and HS) have an improvement in the Id.O\({}_{2}\) parameter of 50% when compared to uncracked SQC (that is, oxygen availability was reduced). There was also an improvement by using HPC, even in cracked state, as compared to SQC in the uncracked conditions, with a 23% of improvement. Similarly, when comparing UHPFRC and UHPFRC-n with HPC in uncracked state as per (2), the results indicate that UHPFRCs produce an improvement in in the Id.O\({}_{2}\) parameter of 21% for UHPFRC and 25% in UHPFRC-n (Table 5). \[\Delta\%\textit{vs}\textit{SQC}\_Ref=\frac{\textit{Id.O}_{2\textit{SQC}\_Ref}- \textit{Id.O}_{2\textit{i}\_j}}{\textit{Id.O}_{2\textit{SQC}\_Ref}} \tag{1}\]  
\[\Delta\%\textit{vs}\textit{HPC}\_Ref=\frac{\textit{Id.O}_{2\textit{HPC}\_Ref}- \textit{Id.O}_{2\textit{i}\_j}}{\textit{Id.O}_{2\textit{HPC}\_Ref}} \tag{2}\]  
where \(i\) is the concrete type and \(j\) the cracking condition.
# Influence of Medium Frequency Light/Dark Cycles on the Cultivation of _Auxenochlorella pyrenoidosa_
## 4 Discussion
### Light Regime  
As light attenuation induces light zones (well-illuminated area) and dark zones (insufficient illuminated area), the microalgae cells do not experience continuous illumination, but a pattern of light and darkness as they move through the light and dark volumes of the broth. This illumination of cells is called intermittent light, which has the same irradiance as the continuous light as per the light regime in Figure 2A. In general, this intermittent light ranges from seconds to minutes, and is also known as medium frequency light/dark cycle in practical microalgae cultivation systems. The results obviously indicate that the light/dark cycle caused by light attenuation in microalgae culture systems leads to a decrease in biomass productivity, as shown in Figure 4A,B. These also prove that the previous hypothesis in this study was correct.  
In addition, the results of Figure 4C or Figure 5 also indicate that improving the light intensity with an optimal value of the light/dark ratio (L/D) would be helpful in enhancing the biomass productivity in medium frequency light/dark cycle. Furthermore, the product of the light intensity and the light/dark ratio is ultimately reflected in the total energy absorbed by the microalgae cells. Therefore, the results of Figures 4 and 5 obviously indicate that biomass productivity strongly depends on the light intensity and the light/dark ratio in intermittent light. The same results have been reported for continuous light, and some parameters such as average light intensity and volume average light intensity have been used to describe the relationship between the growth rate.  
In conclusion, these results show a new method to improve the biomass productivity in practical microalgae cultivation systems with artificial light. Based on Lambert-Beer's law, the light intensity decreases rapidly with the increase in light path and cell concentration. Therefore, as the concentration of microalgae increases during the cultivation process, the light intensity could be appropriately increased. Moreover, the increases in light cycle time in the light/dark cycle could enhance the biomass productivity in practical microalgae cultivation systems.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results
#### 5.2.2 Performance Trend Analysis with the Number of Users  
Figure 6 shows the performance comparison of different algorithms under varying user scales using bar charts with error bars. Each bar represents the average hop count of an algorithm for a specific number of users, ranging from 1000 to 6000. The error bars indicate the standard deviation of 10 independent runs, reflecting the degree of variability in the performance of the algorithms. Smaller error bars suggest that the algorithms perform more consistently under different initial populations, while larger error bars indicate higher sensitivity to initial conditions.  
Figure 4: Average hop count of various deployment algorithms.  
Figure 5: Time consumption of each algorithm.  
The number of users presents a more rigorous test of algorithm performance. In this experiment, we evaluate different algorithms as the number of users increases from 1000 to 6000, with the results shown in Figures 6 and 7.  
As illustrated in Figure 6, the average hop count varies across different user scales, with each algorithm's performance represented by a distinct bar. QSA consistently achieves the lowest average hop count across all user scales, demonstrating its superior performance in minimizing delay. Furthermore, the error bars for QSA are notably smaller than those of other algorithms, indicating that QSA's performance is more stable and less sensitive to variations in initial conditions. This stability suggests that QSA can effectively achieve optimization goals across different user scales, highlighting its strong scalability.  
Figure 6: Variation of average hop count with the number of users.  
Figure 7: Time consumption trend with increasing number of users.  
Figure 7 further reveals that as the number of users increases, the computational time required by all algorithms also increases. This is because each user must be processed individually to compute the hop count. These results highlight QSA's ability to scale efficiently while maintaining its effectiveness in solving edge deployment problems across various user densities.
# Assessment of Bioavailability Parameters of Mono- and Bistriazole Derivatives of Propynoylbetulin
###### Abstract  
Bioavailability describes the properties that determine the passage of a compound through biological barriers. In many cases, bioavailability depends on the lipophilicity of the compound. In this study, the lipophilicity as well as other bioavailability properties of the mono- and bistriazole derivatives of betulin are presented. The lipophilicity was determined using RP-TLC and theoretical methods. The experimental lipophilicity of mono- and bistriazole derivatives is in the range from 4.39 to 7.85 and from 3.75 to 8.83, respectively. The lipophilicity of mono- and bistriazoles is similar, and the logP\({}_{\mathrm{TLC}}\) depends on the type of substituent at the triazole ring. The introduction of a substituent with oxygen and nitrogen atoms decreases lipophilicity. Comparing the experimental and theoretical lipophilicity shows that the milogP and XLOGP3 programs best reproduce the experimental values. The in silico-determined pharmacokinetic parameters show that monitorazole derivatives could be used as oral drugs while bistriazoles show low availability after oral administration. Triazoles could be used as transdermal drugs. The analysis of in silico bioavailability parameters shows that the type of substituent at the triazole ring influences the pharmacokinetic properties, while the number of triazole rings slightly affects the bioavailability properties of the compound.  
b 2024 14 1695 1
# Surface Tracking of MgO/Epoxy Nanocomposites: Effect of Surface Hydrophobicity
###### Abstract  
Surface tracking has been one of the challenges for outdoor organic insulations, in electronic and electrical devices. In this paper, surface tracking behavior of nano-MgO/epoxy composite samples were measured according to the standard IEC 60112. Improved tracking resistance was obtained in nanocomposites with an 18.75% uplift in the comparative tracking index, and a decrease of 58.20% in the surface ablation area at a fixed 425 V. It was observed that the tracking resistance and surface hydrophobicity shared the same tendency--both, the comparative tracking index and surface contact angle increased with an increase of the nanofiller content. Samples with better hydrophobicity exhibited a higher tracking resistance. It could be the case that the conductive pathway of contamination was harder to form, as a result there were fewer discharging processes. With the development of surface tracking, the surface contact angle abruptly decreased, at first, and tended to be constant, which was also accomplished with the failure of samples. In addition, reduced surface resistivity was also found in the nanocomposites, which was beneficial for releasing surface charges and inhibiting distortions in the electric fields.  
surface tracking; epoxy; nanocomposite; hydrophobicity +
Footnote †: journal: Applied Sciences
# Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study
## 5 Discussion
As the business process of splitting parcels is well defined and tracked thanks to BPMN, the consequence of any event is logged and can be queried. * What are the consequences of changing the category of parcel A (from agricultural to urban) to building X? Following the BPMN process model, any event has an input and a defined output. * What are the consequences of the application of a new law concerning the rules for the classification of land and buildings in a city? All BPMN events transformed in Petri net, that are involved and executed will be logged with their corresponding consequences in the PostgreSQL/PostGIS tables. * Why is the topological relation between building A and parcel B modified? The log file for \"Manipulate topological relations\" is the consequence of \"update spatial\" related to one of the land reform BPMN events (split, merge, etc.). * Why is the spatiality of parcel A modified (in a specific period)? The log file for \"update spatial\" is the consequence of one of the land reform BPMN events (split, merge, etc.). Indeed, this paper bridges the gap between conceptual modeling and data management processes. It extends beyond geographical data organization and exposes the analysis of the characteristics of specific spatiotemporal objects that may undergo multiple events over time. These contributions collectively aim to streamline and enhance the utilization of geographical data within organizations, promoting efficiency, adaptability, and a unified understanding.