esa-sceva/satcom-chunk-collection huggingface dataset:
# A Separated-Flow Model for 2-D Viscous Flows around Bluff Bodies Using the Panel Method
## 2 Methods  
This work mainly deals with the flow around the bluff bodies. In general, the maximum operational speed of a train is no more than 350 km/h, which can be classified as a subsonic flow. Thus, the effect of compressibility is neglected here as the Mach number of the investigated problems in this paper is under 0.3. However, this is not the case for trains running in a tunnel, where the compressibility effect cannot be ignored [10]. The proposed model will be modified and improved dealing with this problem hereafter. In this paper, only the flow around the trains will be considered and thus the compressibility effect is not included.  
The calculation procedure of the methodology presented in the current paper is shown in Figure 1. The procedure begins with the coordinates of discretised geometry in panel form as an input. The inviscid flow field around this configuration is then simulated by the potential flow method. The integral boundary-layer method is then used to predict the separation locations for the bluff body based on inviscid velocities obtained from the potential flow calculation in order to model the separated flow. The calculated edge velocities are then used to correct the right-hand side of the potential flow simulation matrix by adding them into the streamwise freestream velocities. The calculated separation locations, where the vorticity shear layer leaves the surface of the bluff body, together with the calculated \"correct\" wake length map the separated-flow region of the bluff body. Eventually, the velocity and pressure fields are updated by a new potential flow calculation with the separated-flow model. With the updated velocity field, the boundary-layer calculation is made again to find the new separation positions. The iterative procedure continues until the predicted separation positions coincide with the last calculation. The predicted pressure field for the entire body in both attached and separated flow results from this procedure.  
Figure 1: Flow diagram of the calculation procedure.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Pdxi-mody (mody 4)  
The pancreatic and duodenal homeobox 1 (_PDXI_) gene is a homeodomain-containing transcriptional factor, which regulates insulin gene expression and pancreatic development [40; 56; 57]. _PDXI_-MODY is a rare type of MODY caused by heterozygous mutations in the _PDXI_ gene. It is important in the regulation of genes that code for glucagon, insulin, glucose transporter 2 (_GLUT2_), and glucokinase (_GCK_) enzymes [58]. It acts as the master switch for the pancreas' hormonal and enzymatic functions [59]. Heterozygous _PDXI_ mutations can result in defective insulin secretion, whereas homozygous mutations result in permanent neonatal diabetes (PND) and exocrine pancreatic insufficiency [56; 60; 61]. Patients with _PDXI_-MODY present with type 2 diabetes with early onset and no extra-pancreatic involvement. Metformin [62] and dipeptidyl peptidase-4 (DPP-4) inhibitors [63] have been shown to be effective in case reports. Diet, oral anti-diabetes drugs (OADs), and insulin are all options to treat individuals with MODY 4 [34; 51].
## 4 Results and Discussions
### Simulation Results with Length of Cyclic Prefix Varied  
Tables 9 and 10 discuss the BER and SNR performance analysis compared with conventional algorithms at different subcarriers K. The proposed approach provided the minimum BER value at different subcarriers compared to the conventional algorithm. The comparison of the proposed DFT_LS_WIENER approach and the conventional algorithm is shown in Figures 7-13.  
Figure 10 shows that when the cyclic prefix length is increased to \(1/4\)th of the number of subcarriers, the bit error rate is reduced compared to the previous cyclic prefix length (1/8 th of the number of subcarriers).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Bit Error Rate (BER)**} \\ \hline \multirow{2}{*}{**SNR (dB)**} & \multicolumn{1}{c}{\begin{tabular}{c} **No. of** \\ **Subcarrier** \\ \end{tabular} } & \multirow{2}{*}{**LS**} & \multirow{2}{*}{**LS-DFT**} & \multirow{2}{*}{**MMSE**} & \multirow{2}{*}{\
\begin{tabular}{c} **MMSE-** \\ **DFT** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **DFT-LS-** \\ **WEINER** \\ \end{tabular} } \\ \hline
15 & \(1/8\)th of K & 0.5667 & 0.4417 & 0.2333 & 0.21 & 0.02583 \\ \hline
15 & \(1/4\)th of K & 0.525 & 0.175 & 0.175 & 0.1583 & 0.02669 \\ \hline
15 & \(1/2\)th of K & 0.35 & 0.05833 & 0.05833 & 0.03 & 0.02683 \\ \hline \end{tabular}
\end{table}
Table 10: SNR vs. BER performance with the length of cyclic prefix varied.  
Figure 6: SNR vs. BER for DFT-LS-WIENER with 64 subcarriers.  
Figure 7: SNR vs. BER for DFT-LS-WIENER with 256 subcarriers.  
Figure 8: SNR vs. BER for DFT-LS-WIENER with channel taps = 6.  
Figure 10: SNR vs. BER for DFT-LS-WIENER with cyclic prefix length = 1/8 th of K.  
Figure 9: SNR vs. BER for DFT-LS-WIENER with channel taps = 10.  
Figure 11: SNR vs. BER for DFT-LS-WIENER with cyclic prefix length = 1/2 th of K.  
Figure 12: SNR vs. BER for DFT-LS-WIENER with pilot frequency = 16.  
Figure 13: SNR vs. BER for DFT-LS-WIENER with pilot frequency = 32.
# Testing Clean-Up Methods for the Quantification of Monosaccharides and Uronic Acids
## 1 Introduction
In particular, the fluoresce of phenols may interfere with saccharide detection when HPLC coupled with a fluorescence detector is used; in addition, the presence of compounds with hydroxyl groups may undergo derivatization reactions applied for analysis of the saccharide by Gas Chromatography-Mass Spectrometry (GC-MS) complicating the resulting chromatographic profiles. Saccharide purification can occur by physical or chromatographic separation as well as by chemical precipitation [26]. Chromatographic separation is the most common approach, with ion-exchange columns being the most widely used. The stationary phase of these columns is made up of resins classified as \"weak\" or \"strong\" exchangers. These terms are not related to the strength of ion binding, but to the extent that the ionization state of the functional groups of exchangers varies with pH. A \"weak\" exchanger is ionized over only a limited pH range, while a \"strong\" one shows no variation in ion exchange capacity with changes in pH [23]. In the literature, different applications of cationic exchange resins have been reported, such as Dowex(r) 50W X8 for removing interfering as inorganic ions in the analysis of monosaccharides deriving from glycoproteins and glycopeptides [27] and of saccharides in paint binders of ancient wall paintings [16]. Amberlyst(r) has been used for the purification of sugars hydrolysates from wheat bran [28]. Another cationic exchange resin, Amberlite 732, and the anion exchange resin, Amberlite FPA53, have been tested for the purification of the xylo-oligosaccharides originating from bamboo [29]. Ion exchange resins with an affinity for both anions and cations have also been frequently employed in the purification of mixtures of monosaccharides derived from the hydrolysis of polysaccharides, such as in the case of Zeolit DMF [30] and Amberlite MB 6113 [24] for the purification of saccharides in painting matrices. The aim of the present paper is to evaluate the best clean-up method, in terms of analyte recoveries, for the quantification of monosaccharides and uronic acids. Thus, two ion exchange resins and a Solid-Phase Extraction (SPE) have been tested. The purified extracts have been derivatized and then analyzed by gas chromatography (GC-MS) to determine the recovery of the purification steps. The derivatization reaction applied is widely used in the literature for the determination of monosaccharides in complex matrices [30] and involves the mercaptalation of the analytes on the carbonylic function, followed by two silanization steps involving all the hydroxylic groups. The presence of a high level of inorganic materials can severely affect the GC-MS analyses and interfere with the derivatization reaction, lowering its recovery and therefore affecting the reproducibility and sensitivity of the method [30]. To confirm the results obtained, the efficacy of the best purification method was validated by analyzing the preparation layer of a mural painting sample containing saccharides in the presence of inorganic material.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods
### Calculation of the Value of the Variables Involved in the Charging Process
#### 2.2.1 Selection of the Charger Capacitance and Its Initial Voltage
If a low number of steps is chosen, the final voltage that the accumulator will reach will be somewhat further away from its rated value, but the system will present a lower cost. Which decision must be taken depends on what the designer wishes to prioritize, as both are valid. The first value of the final voltage, U\({}_{\rm f0}\) (Figure 3), should be chosen so that the ratio C\({}_{\rm 1 max}\)/\(\Delta\)C is an integer. Constructively and industrially, it is easier to add or remove SC modules of the same capacitance. Identical modules are not only cheaper to manufacture, but their dimensions are also identical, and assembly is more practical and compact. It can be shown that if it is true that  
\[\frac{\rm U_{\rm 2N}-U_{\rm 02 min}}{\Delta V}=\rm n\ \rightarrow\ \frac{C_{\rm 1 max}}{\Delta C}=\rm n-1. \tag{26}\]  
For the relationship in (26) to be fulfilled, the first value of the final voltage, U\({}_{\rm f0}\), must be as follows:  
\[\rm U_{\rm f0}=\frac{n\cdot U_{\rm 2N}\cdot(U_{\rm 01}-U_{\rm 02 min})+U_{\rm 01 }\cdot U_{\rm 02 min}-U_{\rm 2N}\cdot U_{\rm 01}}{n\cdot(U_{\rm 01}-U_{\rm 02 min})-U_{\rm 2N}+U_{\rm 02 min}}, \tag{27}\]  
where n being an integer number that defines the steps that the voltage, U\({}_{\rm 02}\), will have from the value U\({}_{\rm 02 min}\) to U\({}_{\rm 2N}\). If U\({}_{\rm 02 min}\) = 0.5U\({}_{\rm 2N}\), the previous equation is transformed as follows:  
\[\rm U_{\rm f0}=\frac{(2n-1)\cdot U_{\rm 01}-n\cdot U_{\rm 2N}}{2n\cdot(\frac{U_{ \rm f01}}{U_{\rm 2N}})-n-1}. \tag{28}\]  
The voltage increase, \(\Delta\)V, used for the different steps, will be a function of the value of U\({}_{\rm f0}\) calculated in (29) as follows:  
\[\Delta\rm V=\frac{(U_{\rm 2N}-U_{\rm f0})\cdot(U_{\rm 01}-U_{\rm 02 min})}{U_{ \rm 01}-U_{\rm f0}}=\frac{(U_{\rm 2N}-U_{\rm f0})\cdot(U_{\rm 01}-0.5\cdot U_{ \rm 2N})}{U_{\rm 01}-U_{\rm f0}}=\frac{0.5\cdot U_{\rm 2N}}{n}. \tag{29}\]  
The charging station control system is very simple. Before connecting the vehicle, it is only necessary to measure the voltage of its accumulator, U\({}_{\rm 02}\), and, depending on this, then select the capacitance of the charger by adding or removing the necessary SC modules. Once the capacitance \(\Delta\)C of the blocks that make up the charger, and therefore the C\({}_{\rm 1}\) value, has been established, R\({}_{\rm 1}\) can be obtained; the properties of the cells that form the banks of SCs are known. It should be borne in mind that both C\({}_{\rm 1}\) and R\({}_{\rm 1}\), as well as C\({}_{\rm 2}\) and R\({}_{\rm 2}\), will change as the cells age. This will be analyzed when sizing the smoothing inductor so that the system will correctly operate throughout the lifetime of the charger and vehicle battery components. Figure 3 graphically shows the process described above.
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 6 Patents  
Using the method we proposed in this article, we published an invention patent. There is some information about our invention patent. More details can be searched for publication number (CN110427834A) from the official website of the State Intellectual Property Office of China.  
China Patent: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Behavior recognition system and method based on skeleton data [P]. CN110427834A,2019-11-08.  
**Author Contributions:** Conceptualization, J.D., Y.G. and H.J.L.; methodology, J.D. and B.H.; software, J.D. and H.Z.; validation, J.D., Y.Y. and H.Z.; formal analysis, B.H.; investigation, Y.G.; resources, Z.F.; data curation, J.D.; writing--original draft preparation, J.D.; writing--review and editing, H.J.L. and Y.G.; visualization, H.Z.; supervision, Y.G.; project administration, Z.F.; funding acquisition, Y.G. and Z.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported in part by the Youth Program of National Natural Science Foundation of China (Grand No.:61830108, 61772328). In part by the Chenguang Talented Program of Shanghai under Grand 17 Cg59. In part by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (GR2019R1D1A3A03103736).  
**Acknowledgments:** We thank [PERSON] from Jeonbuk National University for his great help. We also thank anonymous reviewers for their careful reading and insightful comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Cross-Subject (\%)** & **Cross-Setup (\%)** \\ \hline ST-LSTM(2016) [29] & 55.7 & 57.9 \\ Internal Feature Fusion(2017) [38] & 58.2 & 60.9 \\ GCA-LSTM(2017) [30] & 58.3 & 59.2 \\ Multi-Task Learning Network(2017) [39] & 58.4 & 57.9 \\ FSNet(2018) [40] & 59.9 & 62.4 \\ Skeleton Visualization (Single Stream)(2017) [41] & 60.3 & 63.2 \\ Two-Stream Attention LSTM(2018) [38] & 61.2 & 63.3 \\ Multi-Task CNN with RotClips(2018) [42] & 62.2 & 61.8 \\ Body Pose Evolution Map(2018) [43] & 64.6 & 66.9 \\ \hline
**ours** & **86.4** & **89.4** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of different methods, which are designed for 3D human activity analysis, using the cross-subject and cross-setup evaluation criteria on the NTU RGB+D 120 dataset.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 3 Results
### Width Evolution  
Deformable model fitting, as described in Section 2.2, allows for the estimation of fish width, defined at the thickest part of the fish silhouette, particularly when observed from a ventral perspective. When examining the frequency histogram in Figure 7a, it appears that the distribution shifts towards larger widths (A) from July 2020 to May 2021, i.e., suggesting an increase in fish width. However, given the presence of different cohorts, it is advisable to study width variations within groups of SFL. Additionally, the width increase is evident in the scatter plots of Figure 7b, where, in May 2021, the points reach higher values of A for the same SFL. It is notable that the point cloud is narrower in July 2020 and wider in May 2021, indicated greater variability in widths, implying that some fish experienced more substantial fattening than others.  
Given the presence of distinct fish cohorts, the study of width involves grouping specimens with similar SFLs. Thus, SFL was segmented into groups of 10 cm, ranging from 140 to 280 cm. The average width (\(\overline{\text{A}}\)) for each group was then calculated at different checkpoints spanning from July 2020 to May 2021. The results, presented in Table A3 in Appendix A and depicted in Figure 8, revealed an increase ranging from 3.0 to 8.0 cm (equivalent to 9% to 17%), depending on the fish's SFL.  
Figure 6: Comparison of cohorts identified in July 2020 using the automatic system and Von Bertalanffy growth curves for BFT [25; 26; 28; 29; 30; 31] from various studies, aimed at corroborating the correspondence of the cohorts identified in July 2020 with annual cohorts.
## 3 Simulation Tests Using Measurement Pool Simulator
In the remaining bands, such coded transmission with BER less than 0.001 is successful using m-sequence in conditions with a negative SNR (only for 5 kHz bandwidth positive SNR is required). For Kasami codes, the transmission is successful only in the 1 kHz bandwidth with a positive SNR, and for Gold codes, for the 5 kHz bandwidth when SNR is not less than 4 dB. In the case of 21 virtual sources, transmission with BER < 0.01 was achieved for 1 kHz bandwidth and SNR equal to 10 dB. The simulation experiment has shown that, regardless of the multipath propagation conditions (the number of virtual sources), the use of m-sequences produces a much lower BER than the use of Gold codes or Kasami codes. BER less than 0.001 can be achieved for very low SNR for signals transmitted in the bandwidth of 1 kHz or 2 kHz. As the bandwidth of the signal increases, the resulting BER also increases. It is clearly seen that, in the case of Kasami codes and Gold codes for some values of the bandwidth, it is not possible to improve BER to the value lower than 0.01 using BCH (\(7\),\(A\)) codes. If the number of virtual sources is greater than 3, this problem appears almost for every bandwidth value except 1 kHz.
## 2 Modelling of Microgrids
### Modeling of Different Energy Carriers
#### 2.2.1 Portable Resources Modeling  
A portable renewable energy generation system includes three parts: a small-scaled portable wind turbine, a set of PV panels, and an energy storage system.  
Portable Wind Turbine Modeling  
The output power of a portable wind turbine system can be greatly influenced by the wind speed, blade area, and air density, and the mathematical expression of the wind turbine system can be expressed as:  
\[P_{WT,PORT}=\left\{\begin{array}{ll}0&v<v^{\prime}_{i}\text{ or }v>v^{\prime}_{0}\\ 0.5\cdot\rho\cdot A\cdot\eta_{w}\text{min}(v,v_{\text{nom}})^{3}&v^{\prime}_{i} \leq v\leq v^{\prime}_{0}\end{array}\right. \tag{1}\]  
Portable PV System Modeling  
The output power of a PV system can be directly affected by the ambient temperature and light intensity [25]; therefore, the output power of a portable PV system can be approximately equal to Equation (2):  
\[P_{PV,PORT}(t)=P_{PV,STC}\cdot\frac{GT(t)}{GT_{STC}}\cdot(1-\gamma\cdot(T_{b}( t)-T_{r})) \tag{2}\]  
where:  
\[T_{b}(t)=T_{amp}(t)+\frac{GT(t)}{GT_{STC}}\cdot(T_{NOC}-20) \tag{3}\]  
Portable Energy Storage System Modeling  
By participating in demand-response activities, local consumers can achieve the financial incentives granted by the government. Moreover, the operational cost of a portable energy storage system is relatively low compared with importing energy from the main grid; therefore, local users' total energy cost can be reduced significantly by installing a portable energy storage system. Equation (4) is the mathematical expression of the profit that consumers can obtain by participating in demand-response activities.  
\[B_{B,PORT}=\sum_{t=1}^{T}P_{B,PORT}(t)\cdot R_{PORT}\cdot\theta \tag{4}\]  
where:  
\[P_{B,PORT}(t)=(P_{WT,PORT}(t)+P_{PV,PORT}(t))\cdot\eta_{B,PORT} \tag{5}\]
# Impact of Lowering Speed Limit on Urban Transportation Network
## 2 Literature Review
### Review Studies on Speed Limit Reduction or Speed Management Measures on Transport Network Safety  
[PERSON] and [PERSON] performed a systematic search on the efficacy/effectiveness of speed control. It was found that speed cameras, engineering planning, intelligent speed adaptation (ISA), speed limits and zones, vehicle activation signs, and integration strategies were the most common strategies reported in the literature. Different strategies had different effects on the average speed of the vehicle in the range of 1.6 to 10 km/h. It has also been reported that the use of different strategies reduced human casualties and fatalities by 8-65% and 11-71%, respectively [3].  
[PERSON] et al. conducted a review based on the recognition that speed management is a pivotal factor in achieving a safer road and traffic system. The study found that speed management, speed limit setting, and infrastructure treatment were effective to enhance safety [4].  
[PERSON] presented a meta-analysis of 33 studies that have evaluated the effects on road safety of area-wide urban traffic calming schemes. The meta-analysis showed that area-wide urban traffic calming schemes on the average reduced the number of injury accidents by about 15%. The largest reduction in the number of accidents was found for residential streets (about 25%) and similar reductions were found in the number of property damage-only accidents [5].  
[PERSON] and [PERSON] reviewed empirical studies regarding driving speed and crashes. It was found that crash rate increased faster with an increase in speed on minor roads than on major roads. Also, speed dispersion was concluded to be an important factor in determining crash rate. Larger differences in speed between vehicles were related to a higher crash rate [6].  
[PERSON] et al. conducted a simulation study of speed limit signs and speed markings. The effectiveness of speed management devices was based on the speed reduction according to the speed limit and the comparison of means test analysis. The results of the analysis showed that the most effective speed management device is the speed limit sign by reducing the speed about 4 km/h (7%) [7].
## 2 Message Replication in OppNets for Sensed Data Collection
### Existing Message Replication Techniques  
Due to freedom of user movement, short-lived encounters and device inconsistencies, it is often required to send multiple copies of messages through different routes in order to guarantee their delivery. At the same time, the number of copies needs to be kept minimal due to resource constraints on portable handheld user devices. The absence of supporting infrastructure, end-to-end connectivity and central administration imposes many challenges in achieving this balance. Consequently, network designers resort to inferring knowledge from locally available information through best effort heuristics, without guarantees about their convergence properties towards the desired global outcome. This section provides an overview of the existing message replication techniques for OppNets according to the taxonomy in Figure 2.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 5 Conclusions  
This review consolidates recent advances in understanding the genetic underpinnings and imaging characteristics of BAV with a focus on how well-established genetic determinants such as NOTCH1, GATA4, GATA5, GATA6, and SMAD6 can be integrated with imaging data to better understand disease progression and clinical outcomes [37; 38; 39; 40].  
The integration of genetic data with imaging techniques provides a comprehensive approach to diagnosing and managing BAV. Wall Shear Stress (WSS) has emerged as a valuable imaging biomarker, offering insights into the hemodynamic forces that contribute to aortopathy in BAV patients. Studies have demonstrated the potential of WSS to guide clinical decisions, such as the timing of surgical interventions and the strategies for postoperative care.