esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modelling Description
### Description of CFD Simulations  
CFD simulations emerge as a vital complementary technique to traditional thermal experimental testing, offering significant advantages in terms of cost and time efficiency for thermal management strategies. The computational domain for these simulations is illustrated in Figure 3. To accurately follow the true geometric conditions of the model while conserving computational resources, periodic boundary conditions are applied to the side planes of the domain. The model uses both fluid and solid meshes created in Ansys ICEM CFD, specifically designed to address the conjugate heat transfer problem. This includes a refined proximity mesh around the unit to particularly model heat dissipation. The mesh's total size is approximately one million cells, a figure determined after verifying that the simulation results are mesh-independent. The defined computational domain employs a velocity inlet and a pressure outlet, facilitating the process of air-cooling simulations and allowing for a comprehensive analysis of thermal management within the system.  
Figure 2: Local cylindrical coordinates for the end windings and Cartesian coordinates for the mid windings.  
The CFD simulations are conducted in a commercial fluid simulation software ANSYS Fluent 2020, using an iterative solver with a convergence criterion of \(1\times 10^{-6}\) for both energy and momentum equations. The simulations are considered converged when the residuals for all equations drop below the specified criterion and remain stable. The k-epsilon turbulence model with the scalable wall function is used to provide an accurate solution to the turbulence modelling. Moreover, a wall function is needed because the standard k-epsilon turbulence model often fails to predict flow separations. Here, the scalable wall function is selected due to the local y+ of the boundary layer. For grids designed with a y+ < 11.225, this wall function virtually displaces the local y+ of the boundary layer to approximately 11.225, and for those with a y+ > 11.225, the scalable wall function provides identical results to the standard wall function. The y+ of the current case ranges from 10 to 15, hence the scalable wall function is an appropriate wall function to apply to the turbulence model. The unit of temperature is set to Kelvin, denoted as K.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 5 Performance Evaluation
### Simulation Results
#### 5.2.3 Complexity Analysis  
So far, the proposed technique outperforms the SCF with SVM technique in terms of classification accuracy. However, the computational complexity is another important parameter that needs to be explored in order to make the optimal choice of features.  
By applying the FFT accumulation method described in Section 2, the computational complexity of SCF estimation is given by \(\mathcal{O}(2N[4+2\log_{2}(N^{\prime})+4N+2N^{\prime}+N^{\prime}\log_{2}( \frac{4N}{N^{\prime}})])\), where \(N\) is the signal length [20]. By keeping the highest order terms of the Big-O notation, the overall time complexity is \(\mathcal{O}(N^{2})\). Therefore, the SCF combined with SVM technique is computationally expensive although it has a relatively good classification performance.  
On the other hand, the extraction of IF features involves empirical mode decomposition, Hilbert transform and singular value decomposition. The computational complexity of the three algorithms is \(\mathcal{O}(KN)\)[40], \(\mathcal{O}(N\log_{2}(N))\)[41] and \(\mathcal{O}(K^{2}N)\)[42], respectively. Therefore, the overall time complexity of the proposed technique is as low as \(\mathcal{O}(N\log_{2}(N))\). Moreover, the dataset size of 4500 \(\times\) 4 significantly decreases the training processing time of the classifier.  
Figure 5: Classification accuracy with respect to SNR.
## 4 Results
### Reference Parametric Optimization of the Inverse Kinematic Matrix (from [17])  
The parametric optimization presented in [17] was focused on calibrating the value of the parameters of the APR-02 mobile robot and the computation of the inverse kinematic matrix of the robot from these parameters. Nine specific mobile robot parameters were calibrated in [17]: the radii of the wheels \((r_{a},r_{b},r_{c})\), the distance between the center of the robot and each wheel \((R_{a},R_{b},R_{c})\), and the angular orientation of each wheel \((\delta_{a},\delta_{b},\delta_{c})\). The computation of this parametric optimization required 903 s in a high-performance workstation.  
The exact value of the inverse kinematic matrix \((M_{PARAMETRIC}{}^{-1})\) obtained from the parametric calibration presented in [17] is:  
\[M_{PARAMETRIC}{}^{-1}=\begin{bmatrix}-0.0892930568372762&-0.0005606566978203&0.08 67466159313470\\ 0.0520955668635434&-0.1010213675626970&0.0533117820461363\\ 0.2364093797047320&0.2341358647406650&0.2353792947863630\end{bmatrix} \tag{8}\]  
This parametric inverse kinematic matrix represents the following errors in the determination of the parameters of the mobile robot: 4.7% in the size of the radius of the wheel \(a\) (\(r_{a}\)), 2.4% in \(r_{b}\), 3.2% in \(r_{c}\), 12% in the distance from the wheel \(a\) to the center of rotation of the robot (\(R_{a}\)), 10% in \(R_{b}\), 11% in \(R_{c}\), 0.4% in the angular orientation of the wheel \(a\) (\(\delta_{a}\)), 0.2% in \(\delta_{b}\), and 0.5% in \(\delta_{c}\). These huge errors obtained in the parametric calibration, higher than 10% in some cases, were too high to be justified as being caused by assembling or manufacturing errors. Therefore, the drawback of the parametric calibration was that the results could not be interpreted as an improved description of the physical parameters of the mobile robot: the radii of the wheels \((r_{a},\ r_{b},\ r_{c})\), the distances from the wheels to the center of rotation of the mobile robot (\(R_{a},\ R_{b},\ R_{c}\)), and the angular orientations of the wheels \((\delta_{a},\ \delta_{b},\ \delta_{c})\). Consequently, these results suggested the development of a comparative non-parametric calibration of the inverse kinematic matrix of the mobile robot.
## 3 Results and Discussion
### Physical Characterization of the Eutectic  
Water content of the AOT + PEG-600 eutectic was 479 ppm. Density and viscosity are among the most relevant properties of fluid systems for process design. Both properties were measured for the eutectic at temperatures from 288.15 K to 348.15 K, every 10 K, at atmospheric pressure. Experimental data are shown in Figure 3. Numerical data can be seen in Table S2 of the Supplementary Materials.  
Equation (10) was obtained as a mathematical model to express the linear relationship between the density and temperature:  
\[\rho=1.3619-7.8551\times 10^{-4}\times T \tag{10}\]  
where \(\rho\) is the density in g/cm\({}^{3}\) and \(T\) is the absolute temperature, expressed in K. The standard deviation of the fit was \(7\times 10^{-5}\) g/cm\({}^{3}\).  
Figure 2: Dynamic TGA thermograms of the eutectic mixture (black, dotted line), AOT (red, dashed line), and PEG-600 (solid, blue line).  
The variation of the viscosity (\(\eta\)) with temperature was correlated using the Vogel-Fulcher-Tammann (VFT) equation, in particular the modified version by [PERSON] and [PERSON] [40]:  
\[\eta=2.4944\times 10^{-2}\times T^{0.5}\times\exp\biggl{(}\frac{668.17}{T-200.42} \biggr{)} \tag{11}\]  
where \(\eta\) is the viscosity in mPa \(\cdot\)s and \(T\) is the absolute temperature, expressed in K. The relative standard deviation of the fit was 1.9 \(\times\) 10\({}^{-2}\).  
Results of the correlations are also shown in Figure 3. As can be seen, the correlation equations describe very well the trends exhibited by the experimental data points.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Calcium Oxide Nanoparticles  
Calcium is an important biogenic trace element. Most of the calcium in the human body (99%) is in the skeleton, where it is bound to the HAp molecule and also serves as a reservoir for maintaining serum calcium levels [153]. It is a key mediator in a number of metabolic and regulatory processes in the human body [154]. Therefore, it is understandable that it is widely used and researched in the development of materials for bone regeneration. Calcium oxide shows antibacterial activity against _Escherichia coli_, _Salmonella typhimurium_[155], _Staphylococcus aureus_, and _Bacillus subtilis_[156].  
Calcium oxide nanoparticles (CaO-NPs) are biocompatible and low-toxic agents with outstanding antimicrobial properties, making CaO-NPs an excellent material for medical use. Besides antibacterial activity, CaO-NPs are known for their antifungal properties. Calcium oxide contained in heated scallop-shell powder can be used to treat dermatopthytosis caused by _Trichoptyton_[157].  
The incorporation of CaO-NPs in different types of polymers can improve their mechanical properties and provide antimicrobial effects. [PERSON] et al. studied mechanical and biocidal characteristics of polyethylene composites with incorporated CaO-NPs with a diameter of 55 nm and 25 nm. It was shown that polymer modified with CaO-NPs has a higher Young's modulus than pure polymer. Nanomaterial modified with CaO-NPs caused the death of more than 99.99% of the bacteria. It was emphasized that size reduction of CaO-NPs leads to increasing antibacterial effects. One of the important advantages of CaO-NPs is that these nanoparticles do not require any external stimulus, such as any kind of irradiation, to obtain antibacterial activity [158].  
[PERSON] et al. tested PCL and PCL/gelatin electrospun matrices loaded with CaO-NPs. Mechanical characteristics, morphology, and mouse-cAlvaria-derived osteoprecursor cells (MC3T3-EI) behavior in the matrix were studied. This study demonstrated increased cell viability, adhesion, osteogenic differentiation, and proliferation in the electrospun matrices loaded with CaO-NPs. However, it was found that a high concentration of CaO is connected with decreasing mechanical properties [159].
# 0.5-V High-Order Universal Filter for Bio-Signal Processing Applications
## 3 Simulation Results
Figure 16 shows the equivalent output noise of the second-order LPF with \(I_{set}\) = 625 pA. The integrated output noise from 0.01 to 11 Hz was calculated to be 67 \(\mu\)V.  
The transient responses of the second-order LPF with applied input sine wave \(V_{in-amp}\) = (25, 50, 100) mV@10 Hz for \(I_{set1-3}\) = 5 nA are shown in Figure 9:0 The total harmonic distortion (THD) was below 2%. Figure 9:1 Output noise of the second-order LPF. Figure 9:2 Frequency characteristics of gains with \(I_{set}\) = 625 pA for the LPF: 200 runs MC (**a**), process corners (**b**), voltage supply corners (**c**), and temperature corners (**d**). To test the biomedical signal processing filter, a noisy ECG signal, containing 2 mV@400 Hz noise, was applied to the LPF input with \(I_{set}\) = 5 nA. Figure 18 shows the transient characteristics of the noisy ECG used and the signal at the output of the filter where the noise is filtered out. Table 5 summarizes a comparison of this work with recently published similar circuits. The second-order LPF for ECG signal processing in [12], the second-order universal filter with ultra-low power consumption for biological signal processing in [13], the nth-order universal filter in [32], and the high-order universal filter with ultra-low power consumption in [38] were selected for comparison. As can be seen from Table 5, the proposed filter has the capability to generate ten filter responses, namely non-inverting and inverting LPF, HPF, BPF, and APF, without the requirement of an inverted input signal. Compared to [32], the proposed filter has the capability of electronic tuning of natural frequency and has a much lower voltage supply. Compared to [12; 13], the proposed filter offers higher DR with lower power consumption. Finally, the proposed filter confirms its suitability for processing biological signals of extremely low frequencies (in the Hertz region). Figure 9:3 The transient response of the second-order LPF with different amplitudes of input signal at 10 Hz. Figure 9:4 Transient response of the second-order LPF when a noisy ECG signal is applied to its input and a filtered signal that appears at its output. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Proposed** & [12] **2024** & [13] **2024** & [32] **2022** & [38] **2023** \\ \hline Realization & 180 nm & 180 nm & 180 nm & 180 nm & 180 nm \\ \hline Order & 2 & 2 & 2 & 3 & 2 \\ \hline Number of active devices & 3-OTA & 3-OTA & 3-OTA & 3-DDCC & 3-OTA \\ \hline Number of passive devices & 2 C & 2-C & 2-C & 2 C, 3-R & 2 C \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of this work with recently published ones.
## 3 Fecal VOC in Gastrointestinal DISEASES
### Other Gastrointestinal Diseases
#### 3.5.2 Non-Alcoholic Fatty Liver Disease  
Obesity prevalence is dramatically rising in Western society, and is accompanied by different comorbidities, including nonalcoholic fatty liver disease (NAFLD). Prevalence of NAFLD in affluent societies varies within 20-30% [57]. Alterations in microbiome composition are one of the prevailing assumptions on development and maintenance of obesity and NAFLD. Some bacterial strains, such as _Escherichia coli_, produce a significant amount of ethanol, which may reach the liver via the bloodstream in a continuous fashion and promote steatosis and liver injury [58]. Several studies assessed microbiome composition and fecal VOC composition in obesity related NAFLD [58; 59; 60].  
[PERSON] and colleagues conducted a study to characterize and compare fecal microbiota and bacteria derived fecal VOCs in obese NAFLD and healthy subjects by GC-MS [59]. Concentration of two different VOCs varied widely between obese NAFLD patients and healthy controls. Additional information on the study could not be outlined, since only a conference abstract is available. In 2013, [PERSON] et al. analyzed fecal microbiota and bacteria-derived VOC metabolites of 30 obese patients with NAFLD (BMI > 30 kg/m\({}^{2}\)) and 30 controls (BMI < 25 kg/m\({}^{2}\)), by means of GC-MS [58]. It was found that 12 fecal VOCs were significantly less, and 18 VOCs more abundant in NAFLD subjects (Table 3). Recently, [PERSON] and colleagues aimed to assess the structural and functional role of gut microbiota in onset and progression of pediatric NAFLD by analysis of fecal microbiota composition and bacterial derived fecal VOCs, by means of GC-MS-SPME [60]. In total, 61 children were included, comprising of 27 (NAFLD), 26 nonalcoholic steatohepatitis (NASH), 8 obese subjects without steatosis and 54 controls. They found that 26 VOCs were up-regulated in NASH, NAFLD and obese subjects. A combination of high levels 2-butanone and low abundance of _Oscillospira_ seems to be specific for NAFLD subjects compared to healthy controls.  
The results of previous discussed studies indicate that the found VOC profiles might be associated with a compositional shift in the microbiome of obese NAFLD patients, which is hypothesized to be part of the etiology of NAFLD in obese patients. In addition, these studies underline the potential of fecal VOC analysis in further unraveling of the role of microbiota in development of NAFLD in obese patients and application of VOC analysis as novel non-invasive biomarkers for diagnosing and evaluating NAFLD.
# Current Context and Research Trends in Linear DC-DC Converters
## 6 LDO's Recent Research Trends
### Improving the Compactness  
An output capacitor that connects to the LDO is very useful in an intrinsic manner for reduced noise level, smoothing voltage spikes, handling fast load current variations, and improving the PSRR [40]. The dominant pole of LDO is simply created by a gigantic off-chip capacitor placed at the output of the LDO [41]. Normally, output capacitors are bulky, and due to the requirement of output capacitor, the LDO chip needs to accommodate additional pins so that the pin count increases. However, with state-of-the-art chip designs, bulky output capacitors compromise the compactness aspects of the design. Therefore, researchers are currently investigating designing capacitorless LDOs [42; 43]. The challenge is to develop a capacitorless LDO without compromising the positive effects of the output capacitor in the LDO [44]. [PERSON] et al. [44] propose a push-pull buffer for driving the pass transistor in the LDO. As an example, Texas Instruments TPS744 xx series is capable of stable operation using much smaller ceramic capacitors [19].
# Ultrafast Optical Signal Processing with Bragg Structures
## 3 Optical Signal Processing in Resonantly Absorbing Bragg Reflector (RABR)
### Experimental Work on RABR  
Many methods, such as photo lithography, e-beam, and self-assembling, can be used to fabricate 1D photonic structures. Due to the extremely thin layers filled by dopant atoms, the molecular-beam epitaxy technique is particularly suitable for fabricating RABRs. In Reference [51], InGaAs/GaAs multi-quantum-well structures, which may be seen as typical RABRs, have been made by using this technique. In a 200 layer InGaAs/GaAs multi-quantum-well pattern, ultrafast switching based on the ac Stark has been observed [52].  
For the purpose of buffering the optical pulse, the number of layers should be increased to about 1000 [43]. In this case, the sample will be very easy to peel off from the substrate with the help of demoulding. Normally, for measuring the nonlinear effect of the InGaAs/GaAs multi-quantum-well, the experiment should be performed at a low temperature (4 K-10 K) [48; 52]. These strict experimental conditions limit the application of InGaAs/GaAs multi-quantum-wells in optical signal processing.
# Cross-Pol Transponder with Frequency Shifter for
## 3 Results
### Field Test
Figure 17 shows one of the interferograms obtained with a couple of calibrated bistatic images. Figure 9:1 Bistatic block scheme. In order to quantify this phase stability, we selected the point marked with A both in monostatic and bistatic interferograms, and we detected the \"apparent\" displacement (\(\Delta R\)) calculated as follows:  
Figure 9:2  
where \(\lambda\) is the wavelength at the central frequency and (\(\Delta\varphi\)) is the detected phase in the interferogram. As the point marked with A is presumably a stable point, any displacement fluctuation is due to the radar system, atmospheric fluctuation, and environmental temperature changes. Figure 18 shows the time series of the three measured displacements of the point A: monostatic, bistatic without calibration, and bistatic with calibration. The monostatic displacements had mean value of \(-0.1238\) mm and standard deviation of \(0.2753\) mm. These values are in agreement with other measurement campaigns with similar  
Figure 9:3 Interferogram between two calibrated bistatic radar images. Figure 9:4 Time series of the three measured displacements: monostatic, bistatic without calibration, and bistatic with calibration. equipment [4; 11; 12]. The calibrated bistatic displacements gave mean value of \(-0.1361\) mm and standard deviation of \(0.3378\) mm, in perfect agreement with monostatic measurements. This means that the up and down frequency converters did not introduce uncontrolled phase shift. It is important to note that the same values could not be obtained without the calibration path provided by the third antenna of the transponder. Indeed, the bistatic radar image was acceptable even without calibration of the transponder, but the mean value of the displacement of point A resulted in mean value of \(2.840\) mm with a standard deviation of \(4.247\) mm, as shown in Figure 9:5 These values prevented any possible use of the interferograms for detecting displacements. The reason for this phase instability is related to the reference coaxial cable that connects the radar head to the transponder. When the radar head moves along the mechanical guide, the movement of the cable gives uncontrolled phase changes. We verified this experimentally by intentionally moving the cable.
# Analysis of Grinding Flow Field under Minimum Quantity Lubrication Condition
## 5 Conclusions  
In this study, a three-dimensional simulation model of the grinding wheel flow field under MQL grinding conditions is established. The simulation results show that the grinding wheel linear speed and the minimum clearance between the grinding wheel and the workpiece affect the distribution of the grinding wheel flow field. The grinding fluid flow rate, the grinding wheel linear velocity and the compressed gas pressure are key factors affecting the effective flow rate of grinding. The laws of these influencing factors on the grinding temperature were experimentally verified. The conclusions are as follows.  
1. There is a clear relationship between the grinding temperature and the grinding fluid flow rate. The grinding temperature decreases as the grinding fluid flow rate increases. When the flow rate of the grinding fluid is lower than 45 mL/h, the grinding temperature is inclined to reduce more with the increase in the flow rate of the grinding fluid. When the flow rate of the grinding fluid is larger than 45 mL/h, the tendency of grinding temperature decreasing with the increase in grinding fluid flow rate slows down.
2. The change in speed of the grinding wheel affects the flow rate of the grinding fluid, which in turn affects the grinding temperature. As the linear speed of the grinding wheel improves, the effective flow rate of the grinding fluid first improves and then reduces. Accordingly, the grinding temperature first reduces and then improves. When the linear speed is less than 30 m/s, with the increase in linear speed, the effective flow rate increases and the grinding temperature decreases. When the linear speed is greater than 30 m/s, with the increase in linear speed, the effective flow of grinding fluid decreases and the grinding temperature increases.
3. The compressed gas pressure affects the effective flow rate of grinding fluid and the grinding temperature. As the compressed gas pressure increases, the effective flow rate of grinding fluid grows first and then reduces. Accordingly, the grinding temperature decreases first and then increases. The temperature curve inflection point is different for different grinding wheel linear speed.  
In the paper, only one type of grinding fluid was used for research. In future research, different types of grinding fluid can be considered for MQL grinding research to analyze the influence of grinding fluid parameters on the cooling effect of MQL.  
Conceptualization, Z.D. and W.N.; methodology, W.N.; software, J.S.; validation, W.N. and J.S.; investigation, W.N.; writing--original draft, W.N.; writing--review and editing, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 51705323.  
Not applicable.  
The authors declare no conflict of interest.