esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Methodologies
#### 2.2.1 The Gravity Field Reflection Reference Surface Correction Method (GFRRSCM)
\]  
where:  
\[N_{\text{S}}=a/\sqrt{\left[1-\text{e}^{2}\sin^{2}(\text{B}_{\text{S}})\right]}, \tag{3}\]  
in Equation (2), the elevation correction \(\sigma_{\text{x}}\), \(\sigma_{\text{y}}\), and \(\sigma_{\text{z}}\) of \(S\) in the \(X\), \(Y\), and \(Z\) directions are \(H_{\text{S}}\text{cos}(B_{\text{S}})\text{cos}(L_{\text{S}})\), \(H_{\text{S}}\text{cos}(B_{\text{S}})\text{sin}(L_{\text{S}})\), \(H_{\text{S}}\text{sin}(B_{\text{S}})\), respectively, and \(a\) is the long radius of the WGS-84 ellipsoid. The latitude According to the triangle \(OSR\) and \(OST\), \(\gamma_{\rm T}\) and \(\gamma_{\rm R}\) are recalculated, respectively, which are recorded as \(\gamma_{\rm T}^{\prime}\) and \(\gamma_{\rm R}^{\prime}\). The mean of \(\gamma_{\rm T}\) is taken as \((\gamma_{\rm T}+\gamma_{\rm R}+\gamma_{\rm T}^{\prime}-\gamma_{\rm R}^{\prime} )/2\), and then \(\stackrel{{\rightarrow}}{{\mathbf{M}}}\), \(\stackrel{{\rightarrow}}{{\mathbf{S}}}\), \(\alpha_{\rm T}\) and \(\alpha_{\rm R}\) are recalculated according to the new \(\gamma_{\rm T}\). The above process is iterated, and the geoid modulation \(H_{\rm S}\) of \(S\) is introduced to correct in Equation (2) in each iteration until \(\alpha_{\rm T}=\alpha_{\rm R}\), at which \(S\) determines the accurate reflection geometric relationship on geoid, meaning that \(S\) has been corrected to geoid from the WGS-84 ellipsoid. Seven iterations on average can make the difference between \(\alpha_{\rm T}\) and \(\alpha_{\rm R}\) less than \(10^{-5}\) rad [1].
# A Pilot-Based Integration Method of Ranging and LS Channel Estimation for OFDM Systems
## 2 Signal Model
The method of LS channel estimation [37] is:  
\[Y[k]=\hat{H}_{LS}[k]X[k],\hskip 14.226378 ptk=0,1,2,\cdots,N-1 \tag{5}\]  
where \(\hat{H}_{LS}\) is the vector matrix of LS channel estimates, and \(\hat{H}_{LS}[k]\) represents the elements in \(\hat{H}_{LS}\) and represents the channel estimates of the \(k^{th}\) subchannel. \(X[k]\) and \(Y[k]\) represent the transmitted and received signals on the \(k^{th}\) subcarrier, respectively. Then, for the inserted ranging subcode, the LS channel estimate value of the corresponding subchannel response is  
\[\hat{H}_{Ls}[i]=\frac{\hat{P}[i]}{P[i]} \tag{6}\]  
where \(P[i]\) is the \(i\)th ranging code inserted at the transmitter, and \(\hat{P}[i]\) is the received ranging code signal after OFDM demodulation. Therefore, the channel response estimate of the sub-channel where the pilot is located can be obtained by Equation (6) at the receiver. The channel response of other sub-channels can be estimated by the interpolation function. In this paper, a linear interpolation function is utilized, assuming that the chan  
Figure 2: Comb pilot insertion in frequency domain. nel response of each sub-channel between adjacent pilots follows a linear variation rule. Therefore, the channel response of each sub-channel between pilots can be estimated by the estimated value of the channel response of adjacent pilots, and then the channel equalization of the transmitted signal will be carried out. Moreover, these extracted pilots have ranging information. In Section 3, these pilots containing ranging information will be utilized to complete the ranging by distance defuzzification algorithm when completing the channel estimation with the guarantee of ranging accuracy. This method innovatively changes the traditional way of using the pilot, which has solved the problem of low spectrum utilization caused by occupying band resources when using pilots for channel estimation.
## 3 Results
### Mutation of the _aceF_ gene Increases Bacterial Tolerance to Hydrogen Peroxide  
The generation of reactive oxygen species (ROS) is a major bactericidal mechanism of neutrophils [39; 40]. _P. aeruginosa_ strains that are defective in the oxidative stress response have been shown to be attenuated in virulence [41; 42]. We thus examined bacterial survival following H\({}_{2}\)O\({}_{2}\) treatment. Compared to wild-type PA14, the _DaceF_ mutant displayed a higher survival rate (Figure 6A).  
Figure 5: Mutation of the _aceF_ gene affects bacterial pathogenesis in a murine acute pneumoniae model. (**A**,**B**) Each mouse was intranasally inoculated with 4 \(\times\) 10\({}^{6}\) CFU of bacteria. (**A**) At 12 hpi, the lung sections were observed with a 20\(\times\) objective lens following hematoxylin and eosin staining. (**B**) Bacterial numbers in the lungs from the infected mice at 12 hpi. (**C**) Each mouse was infected with 4 \(\times\) 10\({}^{8}\) CFU of _DexsA_ or _DexsA_DaceF_. At 12 hpi, the bacterial numbers in the lungs were determined. ns, not significant; \({}^{\star}\)_p_\(<\) 0.05 by ANOVA.  
Figure 6: RsmY and RsmZ increase bacterial tolerance to hydrogen peroxide in the \(\Delta\)_acoE_ mutant. (**A**) Survival rates of bacteria following treatment with 300 mM H\({}_{2}\)O\({}_{2}\) for 1 h. (**B**) The RNA levels of _oxyR_, _katA_, _katB_, _ahpB_ and _ahpC_ were determined by real-time PCR. (**C**,**D**) survival rates of bacteria following treatment with 300 mM H\({}_{2}\)O\({}_{2}\) for 1 h. Data shown represent mean \(\pm\) standard error of mean from three samples. * \(p<0.05\); ** \(p<0.01\); *** \(p<0.001\) by ANOVA.  
To understand the mechanism of the increased tolerance to H\({}_{2}\)O\({}_{2}\), we examined the expression of genes involved in bacterial oxidative stress response, including the regulator gene _oxyR_, catalase genes _katA and katB_ and alkyl hydroperoxide reductase genes _alhpB_ and _alhpC_. The mutation of the _aceF_ gene resulted in the upregulation of _alhpB_ and _katB_ and reduced the mRNA levels of _katA_ and _alhpC_ by 1.4- and 1.7-fold (Figure 6B). The deletion of _alhpB_ and _katB_ reduced the H\({}_{2}\)O\({}_{2}\) tolerance of the _\(\Delta\)aceF_ mutant, which reduced the differences in survival between wild-type P\(\Lambda\)14 and the _\(\Delta\)aceF_ mutant from 26.8-fold to 2- and 4.2-fold, respectively (Figure 6C). These results indicate that the upregulation of _alhpB_ and _katB_ contributes to the increased H\({}_{2}\)O\({}_{2}\) tolerance of the _\(\Delta\)aceF_ mutant.  
We next examined whether RsmY/Z are involved in the regulation of _alhpB_ and _katB_ in the \(\Delta\)aceF_ mutant. The deletion of _rsmY/Z_ reduced the expression levels of _alhpB_ and _katB_ (Figure 6B) as well as bacterial survival following H\({}_{2}\)O\({}_{2}\) treatment (Figure 6D). These results demonstrate that the upregulation of RsmY/Z increases the expression of _alhpB_ and _katB_ and subsequent H\({}_{2}\)O\({}_{2}\) tolerance in the _\(\Delta\)aceF_ mutant.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 4 Field Verification  
In order to better grasp the characteristics of wind-sand activities in this area, a full-element meteorological observation station was established at line K166 + 600, and the data of wind speed and wind direction in summer 2021 can be counted. The calculation of drift potential (DP) is listed in Equation (2) [39]. According to Equation (2), the sand-moving wind rose, and the sand-drift potential in this area can be obtained, as shown in Figure 14. In summer, the east-southeast (ESE) wind is dominant, and the resultant drift direction (RDD) is 298.27\({}^{\circ}\).  
\[DP=V^{2}(V-V_{\rm t})t \tag{2}\]  
where \(DP\) is the drift potential, \(V\) is the average wind velocity at 10 meter height, \(V_{\rm t}\) is the impact threshold wind velocity (\(V_{\rm t}\) = 5.75 m/s [40]), and \(t\) is the time wind blew, expressed as a percentage in a wind summary.  
Figure 12: Sand cloud map of the convex windward embankment (t = 60 s).  
Figure 13: Sand cloud map of the concave windward embankment (t = 60 s).  
In order to verify the accuracy of the numerical simulation in actual engineering, the three-dimensional aerial survey of unmanned aerial vehicle was used to photograph and process the sand distribution around the embankment at K166 + 100 in April and August 2021 (Figure 15).  
It can be seen from Figure 15 that the angle between the sand-moving wind direction and the line direction is about 50\({}^{\circ}\). There was basically no sand accumulation on either side of the line in April 2021. In August 2021, it can be seen that there was a small amount of sand at the foot of the windward side slope. There is a lot of sand accumulation at the foot of the slope on the leeward side, and the sand accumulation on the lower side of the line was far more than the sand accumulation on the upper side. Comparing with Figure 5(b), it can be found that the two have good consistency.  
Figure 16 is a map of sand accumulation around the embankment with a curve radius of R = 5500 m. The tangent of the curve is 1048 m. In this paper, only a circular curve with a tangent length of 300 m from the center of the circle was selected, and the line direction was at a certain angle to wind direction. It can be seen that there was some sand accumulation on both sides of the embankment at the intersection of the curve, while there was almost no sand accumulation on both sides of the embankment at the starting position of the curve. Considering the influence of wind direction and the angle of the line on the sand accumulation, it can also be seen that the concave curve had a gathering effect on the sand particles, resulting in sand accumulation in the middle of the line. This phenomenon can better verify the sand cloud map of the concave windward embankment.  
Figure 14: The sand-moving wind rose and drift potential at K166 + 600 of Wuhan–Maqin highway.  
Figure 15: Distribution of on-site sand accumulation at K166 + 100 of Wuma highway.
## 4 Training Result and Comparison
### Evaluated Model  
SoUNet can output both the crack segmentation image and the crack edge line image. The optimal model was extracted to predict the test set, and the segmentation and edge detection images were output. An input image corresponds to one segmentation image and six edge line images. One segmented image was linearly fused with the other six edge line images to optimize the segmentation results. The fusion images and fusion results are presented in Figure 11. Table 3 lists the MIoU, mean pixel accuracy, ODS-F, and OIS-F measured using different methods. SoUNet-Output-1 is the output of the semantic segmentation network in SoUNet, which is the image of Output 1. SoUNet-Fusion-jj is the linear fusion of outputs i and j. The numerical value shows that the linear fusion of the semantic segmentation results and edge line detection results can effectively improve the crack segmentation accuracy. The MIoU value increased by 2.47%, and the MPA value increased by 9.58%. SoUNet-Fusion-13 has high MIoU and MPA values and is the most stable under various accuracies from the result of the comprehensive comparison. The results are compared with those of other semantic segmentation models in Section 4.3.  
We selected 30 crack pictures from the test set and measured the width of the initial position, middle position, and end position of the crack. The measurement direction is perpendicular to the crack trend, as shown in Figure 12a. The same method is used to measure the crack width in the label image, SoUNet result image, and U-NET result image, respectively. Taking the crack width measured in the label image as the actual width, the width error statistical charts of SoUNet and U-NET are obtained. As shown in Figure 12b, S1-3 in the figure shows the SoUNet initial position, middle position, and end position of the crack, respectively. U1-3 indicates the U-Net initial position, middle position, and  
Figure 10: Identification results under the interference of different environmental conditions: (**a–c**) are single crack, multiple cracks, and net-shaped crack in water interference, respectively; (**d–f**) are single crack, multiple cracks, and net-shaped crack in shadow interference, respectively.  
end position of the crack, respectively. It can be seen from Figure 12b that the width error measured by SoUNet is about 2 pixels, and the measurement error is less than that of U-Net.
## 3 Test Results and Analysis
### Influence Analysis of Fiber Content  
The variation rule between fiber content and stress peak strength is shown in Figure 3.  
Figure 2: The stress–strain curves of basalt fiber reinforced concrete (BFRC) for different fiber contents and temperatures when the impact velocity is 8.8 m/s. (**a**) Temperature: 20 \({}^{\circ}\)C; (**b**) temperature: 200 \({}^{\circ}\)C; (**c**) temperature: 400 \({}^{\circ}\)C; (**d**) temperature: 600 \({}^{\circ}\)C; and (**e**) temperature: 800 \({}^{\circ}\)C.  
Figures 2 and 3 show that the optimum fiber content is different at different temperatures. For example, at 20 \({}^{\circ}\)C, when the fiber content is 0.1%, the specimen has high strength and peak strain. At 200 \({}^{\circ}\)C, the dynamic peak stress is basically same under different fiber content. When the fiber content is 0.2%, the specimen has a higher peak strain. At 400 \({}^{\circ}\)C, the specimen has a high peak strain when the fiber content is 0.3% and high dynamic strength when the fiber content is 0.2%. At 600 \({}^{\circ}\)C, the specimen has high dynamic strength and peak strain simultaneously when the fiber content is 0.2%. At 800 \({}^{\circ}\)C, when the fiber content is 0.2%, the specimen has a higher dynamic strength, and when the fiber content is 0.3%, the specimen has a higher peak strain. To sum up, when the fiber content is between 0.1% and 0.3%, BFRC at different temperatures has higher dynamic strength and ductility. When the fiber content is low, the fiber crack resistance cannot be fully exerted, while when the fiber content is high, it is easy for the concrete to be stirred into a group during the concrete mixing process. In addition, when the temperature is between 400 \({}^{\circ}\)C and 800 \({}^{\circ}\)C, the dynamic peak stress increases firstly and then decreases with the change of fiber content.
# Condensation Flow of Refrigerants Inside Mini and Microchannels: A Review
## 3 Condensation Flow of Refrigerants Inside Mini/Micro-Channels
### Numerical Studies Focused on Mini/Microchannel Condensation of Refrigerants  
Numerical simulations offer a valuable avenue for comprehending microchannel condensation in the absence of dedicated experimental data or correlations. In an effort to mitigate experimental costs and efforts, there is a growing emphasis on developing numerical models that are independent of particular geometries and fluid properties. Existing literature features several numerical investigations concentrated on elucidating the fluid flow and heat transfer mechanisms associated with refrigerant condensation within mini/microchannels. In numerical simulation studies of condensation flow in microgeometries, researchers first addressed the dynamics of liquid film formation and the behaviour of the flow regimes that occur during condensation. Following this, in light of experimental findings, simulation studies have begun to be carried out on the effects of parameters such as steam quality, mass flux, and hydraulic diameter on the flow regime and heat transfer mechanisms in order to clarify local flow phenomena. [PERSON] and [PERSON] [59] proposed a transient numerical investigation for analysing condensation heat transfer and flow characteristics. Their research delved into simulating the condensation flow of R32 within a circular tube of 0.1 mm diameter. They systematically investigated four distinct flow patterns (annular, injection flow, slug flow, and bubbly flow) within a two-dimensional computational domain. [PERSON] and [PERSON] [60] conducted simulations of R134a condensation within a circular minichannel with a 1 mm inner di
###### Abstract  
In the realm of anesthetic management during surgical procedures, the reliable estimation of mean arterial pressure (MAP) is critical for ensuring patient safety and optimizing drug administration. This paper investigates the determination of the optimal Long Short-Term Memory (LSTM) architectures aimed at enhancing the estimation of MAP. Using data from a trial involving 70 patients undergoing Total Intravenous Anesthesia (TIVA) provides the effect-site concentrations of Propofol and Remifentanil as key input variables for LSTM models. Our solution categorizes the selection strategies into three distinct methodologies: (i) a population-based method applying a single model across all patients, (ii) a patient-specific method tailoring models to individual physiological responses, and (iii) a novel category-specific method that groups patients based on the correlation between input variables, the effect-site concentrations of Propofol and Remifentanil, and MAP output. The novelty of this paper lies in the proposed method to identify the optimal architecture, evaluating 288 models to fine-tune the best model for each patient and category. Our findings suggest that the patient-specific model outperforms others, highlighting the benefits of personalized model architectures in medical artificial intelligence (AI) applications. The category-specific models provide a pragmatic solution, with reasonable accuracy and enhanced computational efficiency. By contrast, the population-based models, while efficient, have a lower estimation accuracy. This study confirms the significance of sophisticated LSTM architectures in medical AI, providing insights into their potential for advancing patient-specific anesthetic care by accurately online estimating MAP.  
artificial intelligence; anesthesia; LSTM; data-driven LSTM architecture; online estimation; hemodynamics Article
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
## 5 _Acrocomia aculeata_ Oil Extraction Using Supercritical Fluids
#### Oil Extraction Conditions from Acrocomia aculeata Pulp  
The extraction of _A. aculeata_ oil from the fruit pulp has been investigated using different techniques and solvents, Table 3. In recent studies, supercritical carbon dioxide extraction was compared with conventional organic solvent extraction, such as n-hexane and ethanol. The results demonstrate that in SFE, operational conditions of temperature and pressure significantly affect the yield and composition of the obtained oil [14]. The authors verified that pressure variations at constant temperatures generate an increase in solvent density, which in turn increases the extraction yield due to the higher loads of solute in the solvent. However, at higher temperatures, the highest yield was found at lower pressures and, consequently, higher densities. This indicates that the increase in the solute vapor pressure had more influence on the increase in the extraction yield than the decrease in the solvent density. This phenomenon can be identified in the literature on several plant matrices [14]. Regarding Soxhlet and low-temperature extraction techniques, higher yields of macaque oil occurred compared to SFC experiments. However, due to the high selectivity of CO\({}_{2}\), the supercritical extracts obtained for all experimental conditions showed higher levels of fatty acids, especially oleic acid (C(18:1)), compared to conventional extractions. Another study compared the oil yields in extraction under supercritical conditions using propane as a solvent and conventional extraction via Soxhlet using n-hexane and dichloromethane as solvents [15]. The authors show an increase in oil yield using propane compared to the other cited studies that used supercritical carbon dioxide as a solvent. In addition, the use of supercritical propane provided 86% of the yield obtained by conventional extraction, but the extraction time was only 30 min, while Soxhlet requires 480 min to reach approximately 26% yield. It is important to note that in supercritical conditions, it is widely used as a solvent due to its cost, selectivity, and inertness, among other advantages. However, because it has low solubility to triacylglycerides, it requires high pressures and long extraction times to achieve a satisfactory yield. In this context, propane, which has a critical point under milder conditions (43 bar), has an advantage over carbon dioxide and may present higher oil yields in a shorter extraction time using a smaller volume of solvent [14; 15; 25]. Many authors point to advantages in the use of propane in relation to the extraction of phytosterols, tocopherols, and carotenoids when compared to supercritical carbon dioxide [26; 27]. The literature is still scarce and presents many opportunities for studies involving supercritical technology for obtaining bioactive compounds and high-quality oil.
# Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
###### Abstract  
In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDPSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram).  
bearing early fault; feature extraction; adaptive empirical wavelet transform; Laplace wavelet dictionary; feature sign search