esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 5 Conclusions  
This paper presented a PV MVDC grid-connected converter with LVRT capability. A novel isolated buck-boost topology and its modulation method was proposed as the module of the converter. The control system was simplified by only controlling the active semiconductors on the low-voltage side. The output voltage is dramatically increased by applying only diodes on the high-voltage side. The medium-voltage dc-dc module was developed based on this topology to increase the power density of the converter. A LVRT method based on the LC filter for MVDC converter was proposed. Compared with the schemes that require additional circuits to suppress overcurrent during LVRT, this scheme is a cost-effective solution. In order to solve the unstability problem caused by the resonance spike of the LC filter, an active damping control algorithm based on feedback output capacitor current was proposed. Without additional loss, the active damping method can make the control system stable. The complete control strategy is presented and the converter can operate on both normal and LVRT modes. The 5 kV/50 kW SiC-based dc-dc modules and \(\pm\)10 kV/200 kW PV MVDC converters have been developed. Experiments of the converter for the MVDC system in the normal and LVRT conditions are presented. High-efficiency and satisfied performance of the converter are achieved.  
Figure 29: Experimental result of the process of LVRT (Grid voltage drops 100%).  
**Author Contributions:** Conceptualization and methodology, H.W.; validation, H.W., Y.Z., X.H.; writing, H.W.; supervision, H.X.; project administration, H.W., Y.W.; funding acquisition, H.W., Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2018 YFB0904102 and \"Strategic Priority Research Project\" of the Chinese Academy of Sciences, grant number XDA21050301 and \"National Natural Science Foundation of China, grant number 51807187\".  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 3 Strategies
### Electrode Modification  
Researchers have been developing new technologies. A new approach was introduced in which the electrode material was modified to improve the output performance. For instance, [PERSON] et al. [115] fabricated a PVDF niobate-based nanocomposite thin film; in their work, they fabricated flexible nylon nanofiber-supported Ag nanowires (NWs) with a network film as a flexible electrode. [PERSON] et al. [20] developed a unique technique; they manipulated the electrode of a PENG. They introduced a unique design for a PENG based on a three-dimensional intercalation electrode. The intercalation-electrode-based nanogenerator, IENG, was fabricated using the spin-coating method. In the first step, a samarium-doped lead magnesium niobate nanowire (Sm-PMN-NW) composite was prepared via electrospinning. Second, a PDMS/Al electrode was fabricated. Third, the Sm-PMN-NW/PVDF composite was sandwiched between the bottom and top PDMS/Al electrodes. The PENG was constructed by stacking multiple layers of single PENG units formed as per the three steps described above. A boundary interface was created inside the piezoelectric material, thereby increasing the surface polarization charge. The IENG produced an output current of 320 \(\upmu\)A, which was 1.93 times higher than the previously recorded PENG value.
## 1 Introduction
The study also found that the AC of the extract increased even after 120 min, suggesting that DES could act as a protective agent for the extracted compounds. However, further studies are necessary to tune the properties of DESs and their water content for optimized extraction efficiency. Considering the previously mentioned articles, to the best of our knowledge, there seem to be a limited number of studies dedicated to the determination of the AC of the DES-based spice extracts. Therefore, the aim of the present work was to develop a green extraction process of bioactive compounds from six different spices: cinnamon, nutmeg, clove, ginger, cardamom, and coriander, using DESs as green and alternative extraction media to hazardous organic solvents. Three DESs composed of ChCl as HBA and urea (U), Gly, or propylene glycol (PG) as HBD, respectively, were prepared by a heating and mixing method. The adequate dilution of prepared solvents was performed to gain the ability to perform UAE. Spice extracts obtained using ethanol and a mixture of ethanol and water (70:30 \(v/v\)) were developed to compare the efficiency of extractions. The AC of all prepared extracts was determined by 2,2-diphenyl-l-picrylhydrazyl (DPPH), 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS), and ferric reducing antioxidant power (FRAP) assays, whereas the Folin-Ciocalteu (F-C) method was applied to measure TPC. Furthermore, chemometric methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to cluster the investigated spice extracts and analytical techniques, their characterization, and the detection of differences. In addition to the extraction of bioactive compounds from spices, this research can potentially contribute to the development of sustainable packaging materials. The obtained DES-based extracts will be explored in future studies as green active additives for packaging materials by enhancing antioxidant properties and acting as plasticizers.
# Analysis of the Influence of Segmented Rollers on Slab Bulge Deformation
## 2 3D Solidification Model Description
### 3D Solidification Finite Element Model
In this study, the equivalent specific heat method was used to solve the latent heat of solidification and \(q\) is calculated as:  
\[q=-\rho_{ls}\frac{\Delta H_{f}}{T_{L}-T_{S}}\frac{\partial T}{\partial t} \tag{5}\]  
where  
\(\Delta H_{f}\) is latent heat of solidification under the action of various metals (\(kJ\cdot kg^{-1}\)),\(T_{L}\) is the liquidus temperature (\(K\)), and  
\(T_{S}\) is the solidus temperature (\(K\)). Equation (5) was introduced in the solidification heat transfer differential equation of the two-phase zone and Equation (1) was rearranged as:  
\[\rho_{ls}c_{eff}\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\bigg{(} \lambda_{ls}\frac{\partial T}{\partial x}\bigg{)}+\frac{\partial}{\partial y} \bigg{(}\lambda_{ls}\frac{\partial T}{\partial y}\bigg{)}+\frac{\partial}{ \partial z}\bigg{(}\lambda_{ls}\frac{\partial T}{\partial z}\bigg{)} \tag{6}\]  
where \(c_{eff}\) is the equivalent heat capacity (\(J\cdot(kg\cdot K)^{-1}\)) and is calculated as:  
\[c_{eff}=c_{p}+\frac{\Delta H_{f}}{T_{L}-T_{S}} \tag{7}\]  
\(\lambda_{ls}\) is the thermal conductivity of the two-phase zone (\(W\cdot(m\cdot K)^{-1}\)), and is calculated as:  
\[\lambda_{ls}=\lambda_{s}+\frac{\lambda_{L}-\lambda_{S}}{T_{L}-T_{S}}(T-T_{S}) \tag{8}\]  
\(\rho_{ls}\) is the density of the two-phase zone (\(kg\cdot m^{-3}\)). A half three-dimensional solidification finite element model was established due to the symmetry of the slab, and had dimensions of 1200 mm \(\times\) 2000 mm \(\times\) 250 mm. DC3 DB is an eight-node linear heat transfer hexahedral element in the ABAQUS software (6.14, Dassault Systemes Simulia Corp., Providence, RI, USA) and was used to mesh the solidification model to perform thermal simulation analysis. The size of the element was 25 mm (length) \(\times\) 25 mm (width) \(\times\) 10 mm (height), and 46,080 heat transfer elements and 51,597 nodes were included in the model. The bulge deformation at the end of the foot roller section was investigated because the bulge deformation of the slab is serious when the slab leaves the foot roller section. Therefore, segmented rollers have been widely adopted to control this deformation. Then, the temperature field at the end of the foot roller section was studied for the analysis. The location of the segmented rollers and the final solidification point are listed in Figure 1.  
Figure 1: Slab caster roller map.
# Research on Fleet Size of Demand Response Shuttle Bus Based on Minimum Cost Method
## 4 Numerical Simulation
### Impact of Different Fleet Sizes on Operation
#### 4.3.2 Average Waiting Time  
The simulation results of fleet size and demand density on average waiting time are shown in Table 7. From the table, it can be seen that for a given fleet size, when travel demand increases, it means that the probability of a vehicle serving a newly requested stop increases, the route deviation distance increases, and the passenger waiting time increases. For a given level of demand, increasing fleet size results in an increase in the probability that a passenger with a new request for service will be accepted and a decrease in waiting time. When the fleet size is two to three vehicles/hour, an increase in demand density of 10 persons/hour increases the average waiting time by about 10 min, while when the vehicles exceed four vehicles/hour, the average waiting time increases by less  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**B**} & \multicolumn{4}{c}{**Q**} \\ \cline{2-6}  & **10** & **20** & **30** & **40** & **50** \\ \hline
1 & 30.34 & 39.92 & 48.97 & 57.87 & 66.71 \\
2 & 24.51 & 30.34 & 35.26 & 39.92 & 44.47 \\
3 & 21.81 & 26.67 & 30.34 & 33.67 & 36.84 \\
4 & 20.00 & 24.51 & 27.64 & 30.34 & 32.85 \\
5 & 18.63 & 22.99 & 25.84 & 28.21 & 30.34 \\
6 & 17.52 & 21.81 & 24.51 & 26.67 & 28.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical simulation results of DRC vehicle travel distance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Q**} & \multicolumn{2}{c}{**k = 10**} & \multicolumn{2}{c}{**k = 20**} & \multicolumn{2}{c}{**k = 30**} & \multicolumn{2}{c}{**k = 40**} \\ \cline{2-9}  & **B** & **minC** & **B** & **minC** & **B** & **minC** & **B** & **minC** \\ \hline
10 & 0.96 & 408.68 & 0.89 & 409.18 & 0.84 & 410.39 & 0.80 & 411.98 \\
20 & 1.92 & 616.42 & 1.79 & 637.76 & 1.69 & 658.73 & 1.60 & 678.00 \\
30 & 2.88 & 1220.98 & 2.69 & 1277.04 & 2.53 & 1330.93 & 2.39 & 1379.64 \\
40 & 3.83 & 1522.29 & 3.58 & 1526.96 & 3.37 & 1626.92 & 3.19 & 1716.86 \\
50 & 4.79 & 2020.34 & 4.47 & 2087.51 & 4.21 & 2146.70 & 3.99 & 2189.65 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key influencing factors results analysis table.  
than 3 min, suggesting that the right fleet size improves the operational efficiency, while continuing to increase the size reduces the waiting time insignificantly, but increases the overall operational cost.
###### Abstract  
Traffic management in a smart city mainly relies on video feeds from various sources such as street cameras, car dash cams, traffic signal cameras, and so on. Ensuring the confidentiality of these video feeds during transmission is necessary. However, due to these devices' poor processing power and memory capacity, the applicability of traditional encryption algorithms is not feasible. Therefore, a selective encryption system based on singular value decomposition (SVD) and chaotic maps is presented in this study. The proposed cryptosystem can be used in smart traffic management. We apply SVD to identify the most significant parts of each frame of the video feed for encryption. Chaotic systems were deployed to achieve high diffusion and confusion properties in the resulted cipher. Our results suggest that the computational overhead is significantly less than that of the traditional approaches with no compromise on the strength of the encryption.  
SVD; chaotic maps; selective encryption Article Using Singular Value Decomposition and Chaotic Maps for Selective Encryption of Video Feeds in Smart Traffic Management
# Hydrodynamics of an Airlift Column for Aeration in Molten Sulfur
## 1 Introduction  
The Claus process is widely used in the treatment of hydrogen sulfide found in raw natural gas and by-product gases derived from oil refineries [1]. However, one of the main challenges in elemental sulfur production is the dissolution of hydrogen sulfide into molten sulfur, which can escape into the surrounding environment gradually in the later stage [2]. Currently, aeration or agitation is the most commonly used method for the removal of hydrogen sulfide from molten sulfur. Air stripping and injected oxygen components contribute to the separation of dissolved hydrogen sulfide from molten sulfur. A suitable flow field of molten sulfur facilitates the gas-liquid mass transfer. However, agitation involves rotating equipment with a complex structure, which are not suitable for a long-term operation. Therefore, a passive mixture column has been proposed.  
An aeration airlift column exhibits liquid circulation and a gas upflow pattern, which provide good mass transfer and mixing performances. The airlift column consists of an upward zone and a downcomer. Pressurized air in the form of dispersed bubbles enters the upward zone through the gas distribution device located at the lower part of the airlift zone. Since the gas holdup in the riser is higher than in the downcomer, there is a gas holdup difference between the riser and the downcomer, forming a liquid circulation driving force.  
Therefore, the liquid in the downcomer exhibits a continuous downward trend. The liquid flows upward in the upward zone and downward in the downcomer to form a liquid circulation pattern in the entire column.  
The aeration airlift column is suitable for the removal of dissolved hydrogen sulfide from molten sulfur. Compared with the prior art, the airlift column significantly changes the fluid flow state in the column and enhances the mixing effect due to the existence of the draft tube. The hydrodynamics of the airlift column affect the oxidation and removal of hydrogen sulfide. However, there are few studies on the hydrodynamics of aeration airlift columns with the industrial-scale physical properties of molten sulfur. This work focuses on the effects of superficial gas velocity and height on the internal flow field of the airlift column. Three main parameters of airlift column were investigated: Gas holdup, liquid circulation velocity, and bubble diameter. Some empirical correlations are optimized by considering the influence of bubble diameter, and through a comparison with the Chisti correlation and simulation results. The schematic of the airlift column is shown in Figure 1.  
The outer column of the airlift column had an inner diameter of 1600 mm, and different heights of the column were considered. The inner diameter of the concentric draft tube was 1150 mm. Elliptical heads with a short axis length of 400 mm were placed at both ends of the outer column. Nine gas injection pipes were inserted into the riser. The detailed parameters of the airlift column are shown in Table 1.
## 5 Results and Analysis
### Results under Typical Conditions
Figure 5 shows the operation cost (\(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\)) of each system component. The operation cost (\(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\)) reveals the improvement order of system components from the exergoeconomic perspective. It is clear that large \(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\) mainly occurs in the compressor and the turbine, which have the greatest impacts on the system exergoeconomic performance, with a total proportion of 84.3%. The compressor has the highest \(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\), which reaches USD 508.76/h, accounting for 58.5% of the total system operation cost. And it also has a high \(f_{\rm k}\) (88.68%) for great capital investment, which means decreasing the investment cost of the compressor at the expense of its isentropic efficiency, which could lower its operation cost effectively. After the compressor, the turbine has a non-negligible \(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\) (25.7%), and its investment cost accounts for 27.92% of its operation cost. It implies that the exergy destruction of the turbine has a bigger impact on its operation cost, and improving its isentropic efficiency at the cost of its investment cost should be selected. The third largest operation cost appears in the TES3 (USD 51.41/h); thus, the heat transfer efficiency should be improved to lower its operation cost at the cost of higher investment. Although the cooler has a large value of \(r_{\rm k}\), it has the little influence on the system exergoeconomic performance for its low operation cost. The TES3, compressor, and turbine have both high \(r_{\rm k}\) and \(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\), which means that lowering their \(\dot{Z}_{\rm k}+\dot{C}_{\rm D,k}\) could improve the system exergoeconomic performance notably. Figure 3: Temperature profiles in all TES heat exchangers. (**a**) Temperature trace in the TES1; (**b**) temperature trace in the TES2; (**c**) temperature trace in the TES3; (**d**) temperature trace in the TES4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Point** & **Fluid** & \(\mathbf{P}\) & \(\mathbf{T}\) & \(\boldsymbol{h}\) & \(\boldsymbol{s}\) & \(\dot{\boldsymbol{E}}\) & \(\dot{\boldsymbol{C}}\) & \(\boldsymbol{c}\) \\  & **MPa** & \({}^{\circ}\mathbf{C}\) & **kJ/kg** & **kJ/\((\mathbf{kg}\cdot\mathbf{K})\)** & **kW** & **USD/h** & **USD/GJ** \\ \hline  
ater & 0.1 & 25.00 & 104.92 & 0.37 & 0.00 & 0.00 & 0.00 \\
22 & Water & 0.1 & 30.00 & 125.82 & 0.44 & 24.82 & 56.17 & 628.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Thermodynamic values of system state points under the design conditions. Figure 4: \(\dot{E}_{\text{D}}\) ratio of system components. Table 8 shows the system calculation results of main performances under typical conditions. It is found that the input power needs to reach 14.08 MW for a 10.00 MW system, and the energy storage efficiency reaches 71.04%. Meanwhile, the system total exergy efficiency achieves 71.21%, and the system cost per unit product is USD 40.03-GJ\({}^{-1}\).
## 6 Simulations Results and Discussions
### Number of Alive Sensor Nodes over the Rounds  
In Figure 7, the number of alive SNs over the number of rounds is shown, with all two hundred SNs initially in an active state, meaning that they have energy. The graphs display a decreasing trend as the number of rounds increases, indicating the gradual decrease in the number of SNs due to energy depletion. The proposed protocol, EEUCH, outperforms the benchmark routing protocols, as it results in a larger number of alive SNs for a longer time (i.e., number of rounds). This improvement is attributed to the shorter transmission distance between the ground SNs and UAV, as well as the elimination of additional energy consumption by CH SN. This leads to a decrease in the overall energy consumption, which in turn, and consequently, leads to longer lifetimes of the SNs.
## 5 Finite Element Analysis  
### Variation of Drawing Force  
The reaction force of the drawing die was extracted as the drawing force [31; 32], and a comparison diagram of the drawing force was drawn, as shown in Figure 11. As the drawing speed increases, the variation in the drawing force at different angles shows different trends. When the friction coefficient is 0.05, the speed is below 0.6667 m/s and the drawing force remains almost unchanged with increasing speed; however, when the speed exceeds 0.6667 m/s, the change in drawing force varies depending on the outer die angle. At low drawing speeds, the sensitivity of the drawing force to the drawing speed is relatively low. The maximum drawing force of 67.1 kN occurs when the outer die taper angle is 16\({}^{\circ}\), and the minimum value of 65.2 kN occurs when the taper angle is 15\({}^{\circ}\). At high drawing speeds, the drawing force is more sensitive to speed. The maximum drawing force of 68.7 kN occurs when the outer die taper angle is 16\({}^{\circ}\), and the minimum value of 63.8 kN occurs when the taper angle is 17\({}^{\circ}\), as shown in Figure 11a. As shown in Figure 11b, When the drawing speed is 1.3333 m/s, the friction coefficient increases and the drawing force increases continuously across different outer die taper angles. The minimum and maximum drawing forces are 50.7 kN and 77.3 kN, respectively. The drawing force is significantly affected by the friction coefficient, showing a nearly linear relationship between drawing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drawing Speed (m/s)** & **Outer Die Taper Angle (\({}^{\circ}\))** & **Friction Coefficient** \\ \hline
0.3333 & 13 & 0.0125 \\
0.6667 & 14 & 0.025 \\
1.3333 & 15 & 0.05 \\
2.6667 & 16 & 0.06 \\
5.3333 & 17 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.  
Figure 10: (**a**) Tensile specimen. (**b**) Real stress–strain curve at room temperature.  
force and friction coefficient, while the outer die taper angle has a less pronounced effect on the drawing force.
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 4 Definition of Popularity  
Popularity is a value that is associated with a message which develops over time and is influenced by the attitudes of the users. For example, some videos are more popular on YouTube than others. It is straightforward to compute the popularity looking at historical data, but predicting it in advance is very hard. Moreover, the popularity of data changes over time and tends to be different for different geographic regions or social groups (see Figure 2).  
To the best of our knowledge, we are the first to define and leverage it in a data dissemination protocol by predicting it based on (few) already seen users' reactions. More precisely, if we assume that users react to a specific data item with an ordered set of possible reactions (e.g., _delete, like, save_), we define _popularity_ as the percentage of all people in the network who will _save_ the data item (will react with the maximum reaction to it) [6]. A reaction to a specific message may differ from user to user based on their views and interests and, therefore, no data item will ever reach a global popularity of 100%, even if that is possible in smaller social groups.  
It is important to note that popularity must be understood as a time-dependent snapshot for the complete network. It is a value that is typically not available at individual nodes, but only from a global perspective. Therefore, local protocols can only try to compute a localised estimation of it.