esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Equations of Key Circuit Components in the Presented LED Driver
### Design of Input Low-Pass Filter  
The input low-pass filter is composed of an inductor \(L_{f}\) and a capacitor \(C_{f}\), and the cut-off frequency of the input low-pass filter is given by:  
\[f_{cut-off}=\frac{1}{2\pi\sqrt{L_{f}C_{f}}}. \tag{11}\]  
The design consideration of the cut-off frequency in the input low-pass filter is determined to be one-tenth of the switching frequency (which is 5 kHz) in order to filter the high-frequency switching noises. The design equation of the inductor \(L_{f}\) is represented by:  
\[L_{f}=\frac{1}{4\pi^{2}f_{cut-off}^{2}C_{f}}. \tag{12}\]  
On choosing a capacitor \(C_{f}\) of 2 \(\mu\)F (two capacitors of 1 \(\mu\)F in parallel connection), the inductor \(L_{f}\) is given by:  
\[L_{f}=\frac{1}{4\pi^{2}f_{cut-off}^{2}C_{f}}=\frac{1}{4\pi^{2}(5 kHz)^{2}2\mu F }=2.5 mH\]
## 1 Introduction
At high temperatures, uniform IL-coated silica columns can experience film disruption leading to a decrease in the analyte retention times and efficiency. In this regard, polymerized ionic liquids (PILs) can provide the required answers, maintaining the excellent thermal stability of the columns, and combining the main features of an ionic liquid and the typical polymer properties such as improved mechanical stability and processability [39; 40; 41; 42]. Furthermore, in recent years, [PERSON] et al. reported the development of cross-linked polymeric ionic liquid stationary phases for GC with higher thermal stability (up to 380 \({}^{\circ}\)C) [43] than the columns coated with the monocationic or dication ILs with similar structures [44]. More recently, this research group developed different crosslinked PILs in order to better understand the effects of the crosslinker on the efficiency, selectivity, and thermal stability of PILs-based stationary phases [45]. These studies have focused on the use of dicationic ILs as the crosslinker agent. However, the use of a simple, cheap, and commercially available crosslinker like divinylbenzene (DVB) can provide significant advantages, as the use of functional crosslinkers can be of lower efficiency, considering the reduced accessibility associated with functional moieties in the crosslinked regions. Thus, the use of the DVB crosslinker can be used as a simple design element to tune the morphological properties of the resulting polymeric matrices. As far as we know, the use of crosslinked PILs-based stationary phases for GC using DVB as a crosslinker agent has not been studied. In this work, a series of silica-supported polymeric ionic liquid (PIL)-based stationary phases have been prepared using a vinylic \(L\)-valine ionic liquid derivative (AAIL) as a monomer and divinylbenzene (DVB) as a crosslinking agent in order to provide an homogeneous coating of the silica surface and improve the column efficiency. The thermal stabilities of these stationary phases based on AAIL-DVB were evaluated, and the Abraham model was used in order to determine their solvation parameters. Furthermore, the effect of the amount of the AAIL and the crosslinker was analyzed and carefully optimized to improve the retention, selectivity, and resolution of the columns using the Grob test.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 5 Non-Oxide CMCs
### Tungsten Carbide CMCs
An increasing number of research papers focused on Ni-base cemented carbides have been published in recent years [173, 174]. Both cobalt and nickel exhibit good wettability with WC, and fully dense hard metals without high porosity can be produced [175]. The major difference between them is the higher stacking fault energy of Ni which allows for lower hardening rates [176]. That is why the hardness and strength of WC-Co grades tend to be greater than those exhibited by WC-Ni ones. Although, an increase in the work-hardening rates of the Ni binder may be achieved by adding other elements, such as chromium or silicon [177]. Furthermore, Cr additions result in a large increase in the corrosion resistance of WC-Ni hard metals [178]. Research approaches have proven that nickel-based tungsten carbide, with a composition of WC up to 60 wt%, can be produced with laser cladding and reveal zero cracks. From this research, it was also obtained that on top of laser cladding parameters, a small percentage of dissolution of the tungsten carbide particles occurs, allowing for finely dispersed mixed carbides, as well as the creation of a metal phase around the primary carbides [179]. Moreover, WC-NiCr premixed powder was tested on low-carbon steel by laser cladding. The results showed that the selection pore-free layers had minimal dilution and good metallurgical bonding without cracks, while the composition of Cr was about 8 wt%. In addition, the variety of tungsten carbide-nickel bonded matrices has a low composition of Cr and Ni, with the greatest number of percentages to be close to 10 wt%, to achieve higher toughness and better wear resistance [180].
# Statistical Analysis on Random Matrices of Echo State Network in PEMFC System's Lifetime Prediction
## 2 Mathematical Backgrounds
### Particle Swarm Optimization  
The hyperparameters of the ESN need to be carefully tuned, including the leaking rate \(\alpha\), spectral radius \(\rho\), regularization coefficient \(\gamma\), reservoir neuron number \(N\), reservoir connectivity \(c\), etc. Some straightforward hyperparameter setting methods such as trial-and-error [18], grid-search [15], and stochastic gradient descent [10] are computationally complex. The effectiveness of some metaheuristic methods such as PSO [30], GA [31], and big bang-big crunch (BB-BC) [20] have been proven in different tasks. Optimizing all hyperparameters is time-consuming, so three key hyperparameters of the ESN (\(\alpha,\rho\), and \(\gamma\)), which have the biggest effects on the prediction results, are selected to be optimized [16].  
The PSO is a satisfactory global search method that is easier to be implemented, has fewer parameters, is computationally less expensive, and can converge more quickly [32]. A population of particles is used for searching, and each particle indicates a candidate solution to the problem. The velocity \(v_{i}\) and position \(x_{i}\) updating of the particle at time step \(k\) are shown as  
\[\left\{\begin{array}{l}v_{i}^{k+1}=wv_{i}^{k}+c_{1}r_{1}(p_{ib}-x_{i}^{k})+c _{2}r_{2}(g_{ib}-x_{i}^{k})\\ x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1}\end{array}\right. \tag{6}\]  
where \(p_{ib}\) and \(g_{ib}\) are the best personal and global positions, \(w\) is the inertial weight, \(c_{1}\) and \(c_{2}\) are the learning factors, and \(r_{1}\) and \(r_{2}\) are the random variables from 0 to 1. The objective function is to minimize the RMSE between the predicted value \(y^{pre}(n)\) and target value \(y^{tar}(n)\), as shown in Equation (4). In this case study, the swarm size is 30, the maximum number of iterations is 600, the function tolerance is 1 \(\times\)10\({}^{-6}\), both learning factors are 1.49, and the inertial weight is 0.8.  
Figure 1: The probability density function of uniform distribution and Gaussian distribution.
## 6 Analysis and Discussion
#### 6.1.2 Influence of Reinforcement Ratio  
To investigate the influence of the reinforcement ratio on the bearing capacity of the secondary lining, a secondary lining with a thickness of 1.35 m for Grade V surrounding rock and a reinforcement ratio of 0.6% was selected. The \(P_{0.3}\) and \(P_{\text{max}}\) values were calculated under different reinforcement ratios, as shown in Figure 14.  
Figure 14: Influence curve of reinforcement ratio on secondary liner \(P_{0.3}\) and \(P_{\text{max}}\).  
Figure 13: Influence of surrounding rock grade on secondary lining \(P_{0.3}\) and \(P_{\text{max}}\).  
It was observed that both \(P_{0.3}\) and \(P_{\rm max}\) increased nearly linearly with the increase in reinforcement ratio. As the reinforcement ratio increased from 0.2% to 1.0%, \(P_{0.3}\) increased by 12.13%, 23.92%, 33.98%, and 43.20%, respectively, with an average increase of about 5.40% for each 0.1% increase in reinforcement ratio. \(P_{\rm max}\) increased by 4.83%, 5.49%, 9.31%, and 10.60%, respectively, with an average increase of about 1.32% for each 0.1% increase in reinforcement ratio. After the reinforcement ratio increased to 0.8%, further increases to 1.0% resulted in only a small increase in \(P_{\rm max}\), specifically by 1.28%. These results indicate that while the reinforcement ratio significantly improves the \(P_{0.3}\) value, its effect on \(P_{\rm max}\) is relatively minor. This suggests that increasing the reinforcement ratio primarily enhances the structural capacity of the secondary lining under normal service conditions while it has a smaller effect on the ultimate load-bearing capacity.
# Numerical Study of Heat Transfer Enhancement for Laminar Nanofluids Flow
## 3 Results & Discussion
### Case 3: Turbine Oil-Based Nanofluid in Straight Tube with Constant Heat Flux Using SPH  
Case 3 considers turbine oil-based nanofluids with the same parameters used in previous two cases. The effect of base fluid was investigated using three different base fluids viz. water, ethylene glycol and turbine oil with the same nanoparticle (Al\({}_{2}\)O\({}_{3}\), \(\phi_{b}\) = 4%) as shown in Figure 7.  
All the nanofluids (Al\({}_{2}\)O\({}_{3}\)-water, Al\({}_{2}\)O\({}_{3}\)-EG and Al\({}_{2}\)O\({}_{3}\)-turbine oil) significantly enhance the value of Nu as compared to the any base fluid alone. It may be noted that the Nu enhanced by 16% for Al\({}_{2}\)O\({}_{3}\)-water nanofluid as compared to the water, 12% for the Al\({}_{2}\)O\({}_{3}\)-EG nanofluid as compared to ethylene glycol and 8% for the Al\({}_{2}\)O\({}_{3}\)-turbine oil when compared to turbine oil. The Nu enhancement is due to similar reasons as previous two cases.  
The heat transfer enhancement using nanofluids may be affected by several factors such as Brownian motion, Brownian diffusion, dispersion and friction force between the fluid and nanoparticles. Because of this several factors, the heat characteristics of nanofluids are dependent on the properties of the base liquid, the dispersed phases and particle concentration. Therefore, the general form of the Nu yields:  
\[Nu = f(Re,Pr,\frac{k_{p}}{k_{bf}},\frac{\left(\rho C_{p}\right)_{p}}{ \left(\rho C_{p}\right)_{bf}},\phi_{b}) \tag{12}\]  
In the present study a new correlation for Nu (Equation (13)) has been developed covering a wide range of the nanoparticle concentration (0% \(\leq\phi_{b}\leq\) 10%) and Prandtl number (6.0-500) under the constant heat flow and laminar flow conditions (25 < Re < 1500).  
Figure 8 shows the comparison between the present computed Nu results, the experimental results and the computed data from the literature with the present proposed correlation (Equation (13)). It may be observed from Figure 8 that the present results of Nu are in good agreement with the experimental results developed by [PERSON] [20], [PERSON] et al. [23], [PERSON] et al. [46], [PERSON] et al. [49] and with the computational results from [PERSON] et al. [21].  
\[Nu = 0.4381 Re^{0.36}Pr^{0.42} \tag{13}\]  
Figure 7: Comparison of Nu for Al\({}_{2}\)O\({}_{3}\)-water, Al\({}_{2}\)O\({}_{3}\)-EG and Al\({}_{2}\)O\({}_{3}\)-turbine oil at \(\phi_{b}\) = 4%.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 3 D-Q Transformation
### D-Q Synchronous Reference Frame  
The rotating reference frame can be obtained from this stationary reference frame by applying the following equation with a random angular velocity:  
\[\left[\begin{array}{c}I_{d}^{e}\\ I_{q}^{e}\end{array}\right]=\left[\begin{array}{cc}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{array}\right]\left[\begin{array}{c}I_{d}^{s}\\ I_{q}^{s}\end{array}\right] \tag{8}\]  
In Equation (8), \(I_{d}^{e}\) is the \(d\)-axis current of the rotating reference frame and \(I_{q}^{e}\) is the \(q\)-axis current. This transformation is called Park's Transformation.  
The angle \(\theta\) between the axis of the stationary reference frame and the axis of the rotating reference frame rotating at the angular velocity of \(\omega\) changes over time, and can be obtained by integrating the rotational angular velocity of \(\omega\) as follows:  
\[\theta=\int_{0}^{t}\omega(\tau)d\tau+\theta(0) \tag{9}\]  
where \(\theta(0)\) is the initial angle at t = 0, and it is generally set as \(\theta(0)=0\). This synchronous reference frame rotates in synchronization with the rotor system.  
Fault diagnosis using the method of identifying the distortion of the circle by expressing it as a Lissajous pattern (because the phase of \(I_{d}^{e}\) and \(I_{q}^{e}\) rotates together with the rotor system by a difference of 90 degrees) is called PVA. When this is observed as a movement over time, it is expressed as a DC value because the stator and rotor rotate in the same direction in the ideal case, and the size is the maximum value of the \(I_{d}^{e}\) and \(I_{q}^{e}\) AC values.  
In a synchronous reference frame over time, the size on the \(d\)-axis is represented by the direction only and the components of the magnetic flux are moved to the \(d\)-axis. In this case, the \(q\)-axis always outputs a value of zero.  
Figure 3 shows the changes in current caused by the D-Q transformation. Here, (a) represents the 3-phase input current and (b) represents the D-Q stationary reference frame changed from 3-phase to 2-phase. Furthermore, the result of applying the rotor velocity to Equation (9) is the D-Q synchronous reference frame over time in (c). As can be seen from (c), the rotor reference frame is expressed by the DC component in the ideal case.  
If an imbalance occurs between two waveforms when the current waveforms of two phases \(I_{d}\) and \(I_{q}\) are expressed as a circular pattern, the circle is distorted.  
On the other hand, when an imbalance occurs in the synchronous reference frame, which was synchronized with the rotor system by Equation (9), only incorrect changes in the flux or current can be extracted within the range of normal angular velocity \(\omega\). Furthermore, a pulsating phenomenon will occur for the DC component of \(I_{d}^{\omega}\). Therefore, the D-Q synchronous reference frame over time was used for the diagnosis of ITSC in this study.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
### Measurement Uncertainty of Coupling Capacitance \(C_{1}\)  
In order to estimate the value of the coupling capacitance \(C_{1}\), scaling measurements are performed on the basis of which the value of the coupling capacitance module is determined:  
\[C_{1}=\left|\frac{U_{2}C_{2}}{U_{1}-U_{2}}+j\frac{U_{2}}{\omega R_{s}(U_{1}-U _{2})}\right| \tag{20}\]  
Standard complex uncertainty takes the form of the equation:\[u(C_{1})=\left(\frac{\partial C_{1}}{\partial U_{1}}\right)^{2}u^{2}(U_{1})+ \left(\frac{\partial C_{1}}{\partial C_{2}}\right)^{2}u^{2}(C_{2})+\left(\frac{ \partial C_{1}}{\partial\omega}\right)^{2}u^{2}(\omega)+\left(\frac{\partial C _{1}}{\partial R_{s}}\right)^{2}u^{2}(R_{s})+\left(\frac{\partial C_{1}}{ \partial U_{2}}\right)^{2}u^{2}(U_{2}) \tag{21}\]  
and individual uncertainties should be determined as described above.  
The uncertainty calculations for various variants of the probe positioning showed that it is in the range of 1% to 5%, which is an acceptable level for the online PD monitoring system.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 3 Methods
### DySample Upsampling Operator  
In YOLOv8's neck network, nearest neighbor interpolation assigns the value of the nearest known pixel to the target pixel during upsampling. However, with this method, since only the value of the nearest pixel is considered, the interpolated image may have a jagged effect and the edges may not look smooth and continuous, especially when high magnification is performed; the nearest neighbor interpolation method will appear to be inaccurate; and the detail and sharpness of the image may be significantly degraded.  
To solve this problem, this paper replaces the default upsampling operator in YOLOv8 with a new upsampling operator, DySample [21]. Dysample is a data-driven upsampling method based on the core idea of determining the optimal sampling pattern through learning. Unlike traditional interpolation methods, DySample does not simply apply a fixed formula for interpolation but allows the network to autonomously learn how to recover high-quality, high-resolution images from low-resolution feature maps. This approach effectively preserves image details, reduces artifacts, and improves the performance of downstream tasks such as target detection.  
The execution flow of DySample, shown in Figure 3, consists of two main steps: generating the sampling set and performing grid sampling. The specific flow is as follows:  
An initial offset O is first generated from the input feature map X. Given an upsampling scale factor s, we use a linear layer with the number of input and output channels C and 2s\({}^{2}\), respectively, and the size of the generated offset O is 2s\({}^{2}\times\mathrm{H}\times\mathrm{W}\). Then, its size is changed by pixel shuffling operation as follows:  
\[\mathrm{O}=\mathrm{shuffling}(\mathrm{linear}(\mathrm{x})) \tag{1}\]  
To increase the flexibility of the bias, DySample further introduces a dynamic range factor, as shown in Figure 4. The dynamic range factor is generated by performing two linear transformations on the input feature map X and using the Sigmoid activation function. The dynamic range factor adjusts the range of the offset to make it more adaptable to join the feature map content. The new offset formula is as follows:  
\[\mathrm{O}=0.5\times\mathrm{sigmoid}(\mathrm{linear}_{1}(\mathrm{X}))\times \mathrm{linear}_{2}(\mathrm{X}) \tag{2}\]  
Figure 2: RG-C2f Structure.  
The sampling set S is formed by combining the original grid G with the generated offsets O, i.e.  
\[\text{S}=\text{G}+\text{O} \tag{3}\]  
Finally, the dynamic sampling set \(S\) resamples the input feature map X using the grid sample function to generate the final upsampled map, as shown in Equation (3).  
\[\text{X}^{\prime}=\text{grid sample}(\text{X},\text{S}) \tag{4}\]
## 3 Results and Discussion  
### Phenolic Composition of the Plant Material  
Plant material was characterized in terms of low molecular weight phenolics, which are considered most suitable for dermatological purposes [24; 25]. A three-step extraction process was employed to exhaustively isolate the components (Figure S1) and assess the total content in defatted seeds. Chromatographic analysis (Figure 2) revealed the presence of two predominant phenolic constituents in the extract with retention times of approximately 18 and 26.3 min. Both components exhibited strong absorbance in the region of 200-400 nm. Based on mass spectrometry data (Figure 3) and comparison with standards, the identified components were determined to be ellagic acid and kaempfer 3-O-coumaroyl-glucose (tiliroside).  
Additionally procyanidin, kaempfer glucose, quercetin glucoside, pelargonidin-3-(malonyl)-glucose, catechin, and catechin hexoside, along with derivatives of ellagic acid, including rhamnoside and methylellagic acid hexoside, were identified in the extracts. Mass spectrometry (MS) data and the quantification results of the identified metabolites in the extracts are presented in Table 1.  
Figure 3: Mass and UV-Vis spectra of tiliroside (**upper panels**) and ellagic acid (**lower panels**).  
Figure 2: Chromatograms of the extract from defatted seeds including base ion chromatogram (green), and chromatograms at wavelength of 254 nm (blue) and 350 nm (red).  
The determined content of the main identified compounds in defatted strawberry seeds were in accordance with the values documented in existing literature. For instance, [PERSON] et al., who investigated material derived from three seasons of cultivation, reported free ellagic acid content ranging from 43.8 to 58.2 mg/100 g dry matter, anthocyanins from 9.8 to 15.2 mg/100 g, and kaempfer glycosides from 24.4 to 28 mg/100 g. The predominant flavonoid, tilioside, was found to be in the range of 139.7 to 184.0 mg/100 g [16]. Tilioside is a valuable plant metabolite with diverse biological activity commonly found in the plant kingdom [26]. Literature data have evidenced that this polyphenol exhibits anti-inflammatory, hepatotoxicity, anti-hyperglycemic, and antidiabetic properties [27]. The richest source of tilioside is the flowers of _Tilia cordata_, or the aerial parts of _Potentilla_ spp. where its amount reaches above 1 mg/g [20,28]. Although the amount of tilioside in defatted seeds is lower, it is worthy to note that it is relatively high compared to other plant [29].
## 2 Materials and Methods  
Nineteen (19) different samples provided by different Italian and European electric steelworks were analyzed. Chemical composition and mineralogical characterization were performed by means of ED-XRF and XRD, respectively. The results were reported in previous papers [7,8,26].  
Standard leaching tests were performed according to EN 12457-2 (24 h in 10 l/kg deionized water stirred by a rotatory mixer at 10 rpm) both on granulated slag of 4 mm and on slag powder (obtained by ring-mill grinding). The leached concentration of Cr was measured by ICP-MS (Induced Coupled Plasma-Mass Spectroscopy, detection limit 10 \(\upmu\)g/L). Chemical composition, basicity index (BI\({}_{4}=\frac{\text{CaO}+\text{MgO}}{\text{SiO}_{2}+\text{Al}_{2}\text{O}_{3}}\)), phase fraction, and Cr leached concentration are reported in Table 1. In all the investigated samples, Cr was mainly bound in spinels, and in three samples (8, 13 and 18) also in Ca-Cr compounds. In a few samples, Cr was also detected in brownmillerite [8,26,27], which is suspected of contributing to Cr leaching, but in this work its effects were not taken into consideration. Brownmillerite was excluded because the samples featured by this phase belong to the same group, thus making a non-significant comparison.  
The local chemical composition of Cr-bearing phases and wustite was measured by SEM-EDS on slag molded in araldite-based resin, before being ground and polished. Ten (10) measurements for each phase were performed on each sample. This data was correlated with slag basicity, spinel fraction and leached Cr.  
Figure 1: Relation between Cr leached by bulk slag (standard conditions) and by slag powder (marked samples represent remarkable behavior slag).