esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 2 Air Amplifier Setup
### Mass Flow Measurement Procedure  
On the evaluation of the entrained flow, \(Q_{i}\), which ultimately defines the amplification factor of the device, several methodologies can be used. Measurement of internal or volumetric mass flows is normally carried out by industrial devices, via mass flow meters of different kinds. However, empirically determining the flow entrained by the air amplifier carries additional complications. In circular-section ducts, an appropriate acquisition of pressure data under stationary conditions may be used to accurately calculate the mass flow going through the duct. With this aim, a contraction on the duct is installed down/upstream from possible perturbations, forming a so-called \"Venturi-tube\". The Venturi-tube is formed by a conic-convergent section, followed by a straight cylindrical part, and finally connected to a conic-different section. The dimensions and measurements of these sections are not arbitrary. In particular, in this work, the Spanish UNE-EN ISO 5167 standard [25] has been followed. This standard establishes the general principles for the measurement procedures and mass flow calculations, defining the requirements, dimensions, and installation of a Venturi-tube for measurement, together with the limits for the flow conditions and tube dimensions.  
The contraction of the Venturi-tube is preceded by a long constant section pipe, along which static pressure taps are strategically placed in a cross pattern (see Section 3.3). The pipe connects to a diametrical section contraction in which the flow is accelerated and the static pressure is measured at its lowest value, allowing for the pressure drop generated by the contraction to be obtained. The flow and pipe characteristics, together with the corresponding pressure drop values, can be used to determine the mass flow that passes through the contraction following the UNE-En ISO 5167 standard.  
Combining the mass flow measurements at the Venturi tube contraction with instrumental data from the high-pressure line of the air amplifier, the amplification factor of the device can be characterized and evaluated.  
Finally, for repeatability and a better comparison of numerical and experimental results, we clarify the units used for the volumetric flow rate in this work. Standard liter per minute (SLPM) is a unit of volumetric flow rate of a gas at standard conditions for temperature and pressure (STP), which is most commonly practiced in the United States, whereas European practice revolves around normal liter per minute (NLPM) units. The conversions between each volume flow metric are calculated using the following relations:  
\[1\text{LPM}=1\text{ NLPM}\frac{T_{gas}}{293.15\text{ K}}\frac{0.101325\text{ MPa}}{P_{gas}}=1\text{ SLPM}\frac{T_{gas}}{273.15\text{ K}}\frac{0.1\text{ MPa}}{P_{gas}}. \tag{6}\]  
Equation (6) can be reformulated to be based on the flow density, as follows:  
\[1\text{LPM}=1\text{ NLPM}\frac{1.2043\text{ MPa}}{\rho_{gas}}=1\text{ SLPM}\frac{1.2756\text{ MPa}}{\rho_{gas}}. \tag{7}\]
## 6 Result and Discussions
### Evaluation Metrics
#### 6.3.4 Character Error Rate  
A metric, character error rate (CER), is used to assess how well the language model performs at the character level. A lower CER indicates we have fewer incorrectly predicted characters. We have obtained a CER of 0.14, shown in Table 6, which indicates the model performs well with predicted characters.
Molecules_**2011**, _16_, 5833-5839; doi:10.3390/molecules16075833  
_molecules_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
_Article_  
**Two New Withanolide Lactones from Flos Daturae**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Key Laboratory of Chinese Materia Medica, Heilongjiang University of Chinese Medicine,  
Ministry of Education, Harbin 150040, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86 45182193001; Fax: +86 45182110803.  
_Received: 1 June 2011; in revised form: 29 June 2011 / Accepted: 5 July 2011 / Published: 11 July 2011_  
**Abstract:** Chemical investigation of the 50% ethanol eluate fraction from a macroporous resin of flowers of _Datura metel_ L. collected in the Jiangsu Province of China resulted in the isolation of two new withanolides, baimantuoluoline G **(1)** and baimantuoluoside H **(2)**. Their structures were elucidated as (12\(\beta\),6\(\beta\),22\(R\))-1,10-seco-6,12,27-trihydroxy-26-oxo-witha-3,5,24-trienolide-1-oic acid-\(\varepsilon\)-lactone **(1)** and (5\(\beta\),6\(\alpha\),12\(\beta\),22\(R\))-5,6,12,27-tetra-hydroxy-1,26-dioxo-witha-2,24-dienolide-27-O-\(\beta\)-glucopyranoside **(2)** on the basis of extensive spectroscopic analysis (1D, 2D-NMR and HRESIMS) and chemical studies.  
**Keywords:** Flos Daturae; _Datura metel_ L.; withanolide lactones  
**1. Introduction**  
Flos Daturae is the dry flower of _Datura metel_ L. (Solanaceae), which widely distributed in China [1]. Flos Daturae, known as \"baimantuoluo\" or \"yangjinhua\" in China, has been used for centuries in Traditional Chinese Medicine for the treatment of asthma, convulsions, pain, and rheumat [2]. Flos Daturae has been found to be rich in tropane alkaloids [2]. Besides, a number of withanolides have also been isolated from Flos Daturae [3-8]. Recently Flos Daturae has been used clinically for the treatment of psoriasis in China [9]. The effective part for psoriasis, namely the non-alkaloid water-soluble fraction of Flos Daturae, has been demonstrated to have anti-inflammatory, anti- skin titillation and anti-anaphylaxis actions by detailed pharmacological experiments [9]. However, its active constituents and pharmacological effects related to the treatment of psoriasis were not fully elucidated. As a part of a continuing project to study the active constituents of Flos Daturae for psoriasis [6-8], we investigatedthe 50% ethanol eluate fraction from a macroporous resin of the flowers of _D. metel_. Our extraction and separation method can greatly enrich fractions in withanolide compounds so trace withanolides can be isolated. In this paper, we present the isolation and structural characterization of the two new withanolide lactones (Figure 1) on the basis of the interpretation of spectral data, including 1D, 2D NMR and HRESIMS data. A 1,10-seco withanolide is reported from only the third time in herb plants.
## 3 Proposed Methodology
### Detection of Malicious Nodes  
Initially, a network is set up by selecting the IDS set with enough energy to broadcast the status packets in the network. There are two types of nodes that are present in the network, legitimate nodes and malicious nodes. Initially, the blacklist (malicious node list) is empty, but after the first transmission, the IDS node predicts which nodes are malicious nodes and adds them to the blacklist. Before sending the data packets towards the destination node, it is essential to ensure the route from the source to the destination has no malicious nodes, in order to gain a high throughput. To find out which nodes are the malicious nodes in the network, the IDS nodes broadcast a status packet and wait for it to be acknowledged with a reply. All of the legitimate nodes receive the status packet and reply to all of the questions genuinely without any ambiguity. After some time, if there is no reply to the  
\begin{table}
\begin{tabular}{l} Q.1 The Intrusion detection system (IDS) node asks the normal node: What is the sequence number? \\ Q.2 The Intrusion detection system (IDS) node asks the normal node How many packets have been received. \\ Q.3 The IDS node asks: How many packets have gone forward? \\ Q.4 The IDS node asks: How many packets have been dropped and why? \\ \end{tabular}
\end{table}
Table 2: Intrusion detection system (IDS) analysis through questions in status packet.  
status packet, then it is assumed that there must be some malicious activity found in the network. For malicious nodes, there will be two conditions; it either sends false information to the IDS node to hide its identity or it does not send any reply to the IDS node and simply drops the status packet. Because the malicious node is a fabricator node, it sends a fabricated reply and never shows its real identity to the IDS node. After receiving replies from all of the nodes, the IDS node checks which node is not responding properly and why. After some time, if any node is not responding, i.e., not answering the questions or sending bogus replies and failing to satisfy the pre-defined questions without any reason for the link failure, energy, or queue size, then the IDS node declares that node as a malicious node. The IDS node broadcasts the block message to notify all nodes in the network to block that malicious node. Immediately, all of the legitimate nodes will include the malicious node's ID in their blacklists.
# Analysis of the Guide Vane Jet-Vortex Flow and the Induced Noise in a Prototype Pump-Turbine
###### Abstract  
The start-up process of a pump-turbine in pump mode is found with obvious noise, especially at the small guide vane opening angle. The turbulent-flow-induced noise is an important part and must be reduced by flow control. Therefore, the computational fluid dynamics (CFD) method is used in this study to predict the internal flow in a high head prototype pump-turbine (the specific speed \(n_{q}\) is 31.5) under an extremely off-design condition (\(C_{\varphi}\) = 0.015 and \(C_{\alpha}\) = 0.096). The acoustic analogy method is also used to predict the near-field noise based on the turbulence field. Special undesirable flow structures including the flow ring between the runner trailing-edge and the guide vane, guide vane jet, twin-vortexes adjacent to guide vane jet, inter stay vane vortex, stay vane jet, and volute vortex-ring are found in a pump-turbine. These complex jet-vortex flow structures induce local high turbulence kinetic energy and an eddy dissipation rate, which is the reason why noise is generated at small guide vane opening angle. Three dominating frequencies are found on the turbulence kinetic energy pulsation. They are the runner blade frequency \(f_{b}\) = 64.5 Hz, the dominate frequency in the guide vane and the stay vane \(f_{g\text{s}v}\) = 9.6 Hz, and the dominate frequency in volute \(f_{\text{s}v}\) = 3.2 Hz. The flow pulsation tracing topology gives a good visualization of frequency propagation. The dominating regions of the three specific frequencies are clearly visualized. Results show that different flow structures may induce different frequencies, and the induced specific frequencies will propagate to adjacent sites. This study helps us to understand the off-design flow regime in this prototype pump-turbine and provides guidance when encountering the noise and stability problems during pump mode's start-up.  
p 28 March 2019; 9, 1971; doi:10.3390/app9101971
## 2 Methodology
### Grid Independence Study and Model Validation  
In order to balance the computational time and the accuracy, a grid independence study is needed. The results of grid independence study of the determined model described above at \(\varphi\) = 0.05, Re = 25 is summarized in Table 3. The temperature and velocity of the outlet, and the average Nusselt number are chosen to evaluate the grid independence study. As the grid nodes changing form 800 \(\times\) 40 to 1000 \(\times\) 50, the relative differences of temperature, velocity and the average Nusselt number are 0.006%, \(-\)0.384%, and \(-\)0.088%, respectively, which indicates that the results hardly change when increasing the grid number. Therefore, the grid nodes of 800 \(\times\) 40 are adopted in the following analysis.  
For the purpose of validating the numerical method used in this paper, a comparison between the present numerical method and that from [PERSON] et al. (2018) is made and the results are illustrated in Figure 2. Wherein, inlet fluid temperature 293 K and middle wall temperature 303 K, consistent with boundary conditions reported in the literature, are used and the working substance is water. The inlet velocity is dependent on the Reynolds number. The aspect ratio (length/height) of the two-dimensional channel is 100. A good agreement can be observed in Figure 2, which validates the numerical method employed by this paper.
## 3 Results
### Characterization of EPSs Produced by B. velezensis VTX20  
Moisture indicators such as water solubility index and water-holding capacity of B. velezensis levan were determined. EPS was completely soluble in distilled water at 25 \({}^{\circ}\)C. High EPS concentrations resulted in appearance change from clear to turbid. EPS exhibited a high water solubility index (81.9 \(\pm\) 3.4%) with water capacity (100.2 \(\pm\) 3.4). The water molecules might be retained by the hydrophilic fructose residues of levan, leading to gelling and water retention capacities [39].  
The structure of EPS was elucidated by NMR experiments. The \({}^{1}\)H NMR spectrum revealed the peaks at \(\delta_{\rm{H}}\) 4.09 (d, \(J\) = 8.5 Hz, H-3), 4.00 (t, \(J\) = 8.0 Hz, H-4), 3.85 (m, H-5), 3.80 (m, H-6a), 3.68 (d, \(J\) = 12.0 Hz, H-1a), 3.56 (m, H-1b), and 3.45 (t, \(J\) = 9.0 Hz, H-6b) (Figure S1). The \({}^{13}\)C HMBC spectrum showed six signals at \(\delta_{\rm{C}}\) 104.3 (C-2), 80.2 (C-5), 76.6 (C-3), 75.4 (C-4), 63.4 (C-6), and 59.9 (C-1) (Figure S2), which are almost identical with those of \(\beta\)-(2\(\rightarrow\) 6)-fructofuranoside [40]. In support of this result, chromatographic analysis revealed that two peaks subjected to fructose and glucose were detected, in which fructose is the predominant sugar (data not shown). The high content of fructose in the EPS corresponded to the occurrence of levan, and an extremly low amount of glucose identified is attributed to dextran, a glucose polymer. Our results confirmed that levan occupied predominantly, followed by an extremely low level of dextran polymer. The monosaccharide composition of EPS from Bacillus strains varies dramatically depending on strains and cultivation parameters. The constitution of EPS from _B. velezensis_ VTX20 is not comparable with EPS from _B. mojavensis_ MT012152 (Table 1). _B. amyloliquefacis_ C-1 produced an EPS composed of glucose, mannose, galactose, arabinose [25], while _B. tequilensis_ GM was reported to produce only fructose [41].  
Figure 5: The EPS (**A**) and levan biosynthetic gene cluster (**B**) identified in _B. velezensis_ VTX20.
## 3 Theory and Method  
### The TB from the Sun Arriving at the Antenna  
Based on the theory of microwave radiation transfer in the atmosphere, the observed TB by radiometer can be estimated by the radiative transfer equation. When the antenna is pointed directly at the sun, the TB received by the antenna can be determined as follows [16; 17]:  
\[T_{sun}^{\prime}(\varphi,\theta)=T_{bg}e^{-\tau(\theta)}+T_{m}\Big{[}1-e^{- \tau(\theta)}\Big{]}+\frac{\Omega_{S}T_{sun}}{\Omega_{A}}e^{-\tau(\theta)}, \tag{3}\]  
where \(\varphi,\theta\) are the antenna azimuth and elevation angle, respectively, \(T_{m}\) and \(T_{bg}\) are the atmospheric mean radiating temperature and cosmic background TB (\(T_{bg}\)= 2.75 K), respectively, \(T_{sun}\) is the average TB of the sun, \(\Omega_{S}\) and \(\Omega_{A}\) are the solid angle of the sun and antenna, respectively. \(\tau(\theta)\) is the atmospheric opacity of each direction and it can be calculated by the Tipping calibration method [17; 18; 19].  
When the antenna scans the sky and the sun is not in the beam, the atmospheric TB can be calculated as follows:  
\[T_{sky}^{\prime}(\varphi,\theta)=T_{bg}e^{-\tau(\theta)}+T_{m}\Big{[}1-e^{- \tau(\theta)}\Big{]}. \tag{4}\]  
The TB increment because of the solar radiation arriving at the antenna without atmospheric attenuation can be obtained by subtracting (4) from (3):  
\[\Delta T_{sun}(\varphi,\theta)=\frac{\Omega_{S}T_{sun}}{\Omega_{A}}, \tag{5}\]  
where \(\Delta T_{sun}\) is the TB increment because of the solar radiance, and it is the received solar power as a function of the angle radius from the center of the antenna beam. The TB increment received by the antenna is proportional to the ratio of the solar solid angle to the antenna solid angle [16].
## 4 Data Preprocessing
### Hybrid Feature Selection  
In order to improve the model's accuracy, this paper proposes a new hybrid feature selection method, which combines the Pearson correlation test and the RF-OOB algorithm to effectively eliminate irrelevant and redundant features. The method is divided into two parts, as shown in Figure 2: in the first part, the Pearson correlation coefficient is first utilized to measure the correlation between each feature and the target variable, and the \(k\) features whose absolute value of the correlation with the target variable is greater than or equal to 0.3 are screened out from the sample training set \(D(X_{m}^{u},Y_{m})\). In the second part, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Texture\_Mean** & **Perimeter\_Mean** & **Area\_Mean** & **Smoothness\_Mean** & **Symmetry\_Mean** \\ \hline
10.38 & 122.8 & 1001 & 0.1184 & 0.2419 \\
17.77 & 132.9 & 1326 & 0.08474 & 0.1812 \\
21.25 & 130 & 1203 & 0.1096 & 0.2069 \\
20.38 & 77.58 & 386.1 & 0.1425 & 0.2597 \\
14.34 & 135.1 & 1297 & 0.1003 & 0.1809 \\
15.7 & 82.57 & 477.1 & 0.1278 & 0.2087 \\
19.98 & 119.6 & 1040 & 0.09463 & 0.1794 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partial sample characteristic data.  
out-of-bag estimation random forest algorithm is used to calculate the feature importance of the remaining \(k\) features. Simultaneously, feature combinations are performed according to the feature scores from high to low. Lastly, the feature combinations with the highest score are iteratively filtered to obtain \(k^{\prime}\) features to realize redundant feature removal.  
Using Equation (5), the Pearson correlation coefficients between each feature and the target variables (benign and malignant) are calculated. The feature variables with an absolute value of correlation coefficient greater than or equal to 0.3 are filtered out. Finally, the filtered feature variables are used as candidate features. In order to avoid redundancy among the candidate subsets, it is necessary to carry out the secondary feature screening for RF-OOB on the candidate subsets, and the specific steps are shown in Figure 3: (1) firstly, RF-OOB is applied to calculate the feature importance of each feature, and the features are ranked according to feature importance. The subset of features with the highest feature importance is used as the initial feature combination, and RF-OOB is applied to calculate the model score. (2) Add the subset of features with the second highest feature importance as a new feature combination to be input into the RF-OOB algorithm and calculate the new model score. (3) Add a subset of features one by one according to their importance as a new combination of features and compute a new model classification score. This is iterated until all the feature subsets are traversed. Finally, the feature combination with the highest model classification score is selected as the optimal feature.
## 2 Optical Uplink Channel Model  
Considering PPM modulation, after beam propagating from ground station to deep space satellite, received optical power of a PPM slot is expressed as  
\[P_{r}=hP_{t}+P_{b}, \tag{1}\]  
where \(h\), \(P_{t}\), and \(P_{b}\) are the channel fading coefficient, transmitted optical power, and power of background noise, respectively. In a high speed optical communication scenario [42], \(h\) is presumed to be constant over a PPM symbol duration. Various crucial factors impair optical power. We denote the power fading coefficient \(h\) as  
\[h=h_{t}h_{s}h_{p}h_{a}h_{c}, \tag{2}\]  
where \(h_{t}\) and \(h_{s}\) are caused by atmospheric turbulence and coronal turbulence-induced beam scintillation, respectively. Moreover, \(h_{p}\), \(h_{a}\), and \(h_{c}\) denote the fading coefficients that result from pointing errors, AOA fluctuations, and link attenuation, respectively.  
In the following, the statistical properties of the above channel fading factors are first introduced. Then, a closed-form expression of the probability density function (PDF) of \(h\) is derived.
# Maximizing the Capacity of Edge Networks with Multicasting
## 2 Related Work
### Gap in the Existing Literature  
The aforementioned works employ multicasting to improve the performance of edge networks. However, to the best of our knowledge, few works investigate the relationship between multicasting and the system capacity in edge networks. Furthermore, because of the dynamics in user sessions and content popularity, it is difficult to compute the content transmission distance. The work [27] inspired us to study the joint influence of multicast and caching on the system capacity, where the content is popular and requested by many users. This provides the basis for deploying multicast sessions in edge caching networks.
## 4 Analysis of Calculation Results
### Span Mid-No-Rope Zone  
The calculation results are shown in Figure 8, and meanings of \(M\)/\(\sum\)\(M\)(%) and \(\gamma\)/\(\sum\)\(\gamma\)(%) in the figure are the same as above. The effects of changing the length of the span-free zone on the moment of the main girder and moment of the tower differed. The moment in the span of the main girder and moment at the root of the main girder tower increased with increasing \(l_{1}\)/\(l_{0}\); the moment in the span increased slowly, while the moment at the root of the tower slightly decreased with increasing \(l_{1}\)/\(l_{0}\). Deflection of the main girder and top deflection of the tower increased with increasing \(l_{1}\)/\(l_{0}\). This outcome was because the stiffening effects of the stayed cable on the main girder were weakened as the span of the cable-free zone became longer, and the overall stiffness of the bridge was weakened. Therefore, the bending moment and deformation of the main girder increased, and the main tower was less affected by the cable-free zone.  
Figure 6: Comparisons of the structural effects caused by changes in stayed cable stiffness. (**a**) midspan bending moment; (**b**) moment at the root of the main girder tower; (**c**) midspan deflection. (**d**) tower top deflection.  
Figure 8: Influence of the length of the cable-free zone in the span on internal force and deformation of the structure. (**a**) moment; (**b**) deflection. Note: \(M\)/\(\Sigma\)M(%), \(\gamma\)/\(\Sigma\)\(\gamma\)(%) have the same meaning as above.  
Figure 7: Influence of the length of the cable-free zone at the bottom of the tower on internal force and deformation of the structure. (**a**) moment; (**b**) deflection. Note: \(M\)/\(\Sigma\)M(%), \(\gamma\)/\(\Sigma\)\(\gamma\)(%) have the same meaning as above.  
Figure 9 shows comparisons of internal forces and deformations of each main span and main tower that had been affected by change of the non-cable zone in the span, which is consistent among the three main spans and among the four cable towers. The bending moment in the span and bending moment at the root of the main girder tower increased with increasing \(l_{1}\)/\(l_{0}\), while the bending moment at the root of the bridge tower decreased with increasing \(l_{1}\)/\(l_{0}\). The changes in bending moments in the span and at the root of the tower were opposite, mainly because the vertical load borne by the longer stayed cable in the cable-free zone was reduced, and more vertical load was borne by the main girder. Midspan deflection of the three spans and deflection of the top of the four towers increased with increasing \(l\)/\(l_{10}\). When \(l_{1}\)/\(l_{0}\) > 0.22, deflection of the main span slowed, while after increasing \(l_{1}\)/\(l_{0}\) to a certain degree, deflection of the top of the tower did not change significantly. The increase in deformation was because the overall stiffness of the bridge decreased, and deformation resistance was weakened.