esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Word Line Write Trip Voltage  
Word line write trip voltage (WWTV) is crucial in SRAM design as it influences the reliability and stability of write operations. A well-defined WWTV ensures that the correct voltage is applied to trigger the writing of data, reducing the risk of accidental data corruption. WWTV is defined as the voltage difference between VDD and the word line voltage at the intersection of the voltages at nodes Q and QB during a write operation. The proposed RHRSE-20T uses a total of three pairs of access transistors during the write operation, enhancing the SRAM's write performance. In Figure 10, the WWTV values of all comparison cells are presented. As a result, it achieves the best WWTV among the compared cells.
## 4 Methodology
### The Proposed LEACH-LMNN Protocol  
In this section, the proposed LEACH protocol with the LMNN approach is described. The flow-chart is shown in Figure 5. From Figure 5, it can be observed that the execution starts from the BS, where it generates the query for data from the established sensor network. The network checks whether it is the first round or not. If it is the first round of execution, the network will not calculate the energy for each sensor node, since all sensors have same level of energy. Thus, it directly jumps to the cluster head selection step, and the Levenberg-Marquardt neural network is embedded to select the CH node on the basis of remaining energy and concentration of nodes. Hence, in the first round only, the remaining energy parameter will not be considered for CH selection. After CH selection, the data aggregation process starts, where all N-CH nodes transfer their monitored data to the CH node. This aggregation of data is executed with specific time span with the help of TDMA. Therefore, all data are stored at CH nodes. In the steady stage, all CH nodes transfer their data to the BS via intermediate node or directly if they are in range of BS. In addition to this, information related to each node either CH node or N-CH node is calculated and compared with threshold energy. If a node energy exceeds the declared threshold energy, the node is alive; otherwise the node is dead. The network maintains a list of all nodes with information related to alive or dead states, and also maintains a record of CH nodes. Once a node is selected as the CH node, the next rounds will not give them priority to be selected as a CH node, in order to reduce the energy of the node to a greater extent. Further, in every round it is also checked whether the last node is dead or not. If the last node is dead, then the algorithm is stopped, since the network is dead. Specifically, last node dead (LND) checks whether the algorithm is completed or not. This is all the step by step description for the LEACH-LMNN approach proposed in this work. Results will be discussed in Section 4.  
Figure 5: A flow chart for the step-wise execution of our proposed LEACH-LMNN protocol.
## 4 Analysis and Discussion
Also for FinFET, _f(V\({}_{\textit{GS}}\))_ = (1 - \(V_{\textit{GS}}\)) was deliberately set to the fixed condition of \(V_{\textit{GS}}\) at 1.0 V, where the threshold voltage was set to \(-0.007\) V, as in Table 2. Also, _f(V\({}_{\textit{GS}}\))_ = \(V_{\textit{GS}}\) was deliberately set to the fixed condition of V\({}_{\textit{GS}}\) at 1.5 V, where the threshold voltage was set to 0.931 V, as in Table 3 [22]. In the fitting presented in Figure 7a,b, \(\mid\beta_{\text{eff}}\mid\) = 0.085 and \(\mid\beta_{\text{eff}}\mid\) = 0.1 correspond to \(p\) = 4.4 \(\times\) 10\({}^{23}\) (1/m\({}^{3}\)) and \(p\) = 1.6163 \(\times\) 10\({}^{22}\) (1/m\({}^{3}\)), which help to identify the thickness of the strongly inversed layer to be 182 angstrom at V\({}_{\textit{GS\_FinfFET}}\) = 1.0 V and 92 angstrom at V\({}_{\textit{GS\_MOSFET}}\) = 0.4 V, as shown in Figure 7c. Figure 7: _Cont._  
As presented in Figure 8, \(\lambda\)(\(\rm{V_{A}}^{-1}\)) signifies the leakage current slope across the source and the drain, and shows suppression as (\(\rm{V_{GS}}-Vth\)) increases, which means that a more depleted region induces a leakage current reduction. All transistors are usually leakier if (\(\rm{V_{GS}}-Vth\)) is small because of the smaller depleted region. Somehow, FinFET_L2000 has more leakage current because the thickness of the gate oxide is designed to be around 20 A. Also, MOSFET_L90 has less leakage current owing to the halo implant or pocket implant taking effect. Furthermore, without kink effect adjustments, the fitting parameters for W120L2000 are presented in Table 5. In addition, three FinFET transistors, such as W120L100, W120L160, and W120L240, are also taken into account [16]. As noted, \(\rm{k_{N}}\) value is inversely proportional to the channel length and this is plotted in Figure 9, which is verified to be linear as expected. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gate Bias** & \(\rm{k_{N}}\) **(A/V\({}^{2}\))** & **Vth (V)** & \(\lambda\) & **x (V)** & **\$ (\mathbf{\delta}\) (A)** \\ \hline W120L100 & \(1.50\times 10^{-4}\) & 0.001 & 0.310 & NA & \(4.36\times 10^{-7}\) \\ \hline W120L160 & \(1.26\times 10^{-4}\) & \(-0.1065\) & 0.170 & NA & \(9.58\times 10^{-7}\) \\ \hline W120L240 & \(1.09\times 10^{-4}\) & 0.097 & 0.130 & NA & \(3.88\times 10^{-7}\) \\ \hline W120L2000 & \(7.07\times 10^{-5}\) & \(-0.007\) & 0.079 & NA & \(7.63\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Determined parameters of FinFET at \(\rm{V_{GS}}\) = 1.00 V without adjustment of kink effect. Figure 7: **(a)** Vth versus \(\rm{V_{GS}}\) for FinFET, **(b)** Vth versus \(\rm{V_{GS}}\) for FinFET, and **(c)** depletion region and strongly inversed layer in energy region presentation.
## 3 Results and Discussion
### Trending Topics: Overlay Visualization Analysis  
We used the overlay visualization feature of VOSviewer to generate a map of the newest vs. oldest topics. We proceeded by doing a text-mining analysis on all titles and abstract words analogous to the one carried out in the section on topic visualization, but with a threshold of 20 concurrencies for each term; then, for the 148 terms, a relevance score was calculated. Based on this score, only the top 60% of most relevant terms were selected (this is a standard feature of VOSviewer and was adopted in preceding scientific papers, so we adopted this value to conform to what appears to be a de facto standard), resulting in a total of 89 terms that are mapped in Figure 9. In order to identify trending terms, the terms were evaluated on the basis of the publication year of the papers from which they are mined. For every term, an average value of publication year was obtained, and this value is visualized with a blue color for older topics and a red color for trending topics. It must be noted that with this more selective choice of topics, the following three terms resulted as the more relevant: adaptive traffic signal control, smartphone and connected vehicle. Among the trending topics, the following terms were identified as the more trending: connected vehicle, autonomous vehicle, internet, accuracy, traffic system, travel time, experiment, VANET, emission and v2I.  
These words are just the outcome of a standard VOSviewer overlay visualization analysis and show a tendency of words to appear or disappear among titles and abstracts over time. The trending topics are trending among the selected papers, and the emerging terms  
Figure 8: Country visualization of database list “A” obtained by analyzing coauthorship.  
Figure 9: Trending topics (in red) versus old topics (in blue).  
such as internet and connected vehicles can be explained with the emerging connectivity that is a recent characteristic of new connected traffic signal systems.
## 6 Analysis of RAMS-ISAC Simulations
The DPR reflectivity assimilation is not able to correct the precipitation forecast in Southern Apulia, likely because the (only) DPR overpass is available 24 h before rainfall hits Southern Apulia. This  
Figure 17: (a) Relative humidity difference at the level 3171 m in the terrain following coordinate system of the ANL and CTRL on 16 November at 14:00 UTC. (b) DPR reflectivity at 3000 m on 16 November at 13:49 UTC. result is similar to other studies with radar data assimilation (see [PERSON] et al. (2018) and [PERSON] et al. (2018)), showing that the resilience of the impact of the assimilation is about 6 h.  
Nevertheless, weak effects of assimilating DPR can be seen in the comparison of the CTRL and ANL precipitation rate at 16:20 UTC (Figure 18), i.e., 2h and 20 minutes after the assimilation time, when an SSMIS overpass is available and captures the storm during its transition over the Ionian Sea. The precipitation rate field is compared to that obtained from the NASA GPROF-CLIM-SSMIS V05 product. The comparison shows important differences between SSMIS precipitation rates and both ANL and CTRL. This difference is caused by both forecast errors, and the low spatial resolution of the GPROF precipitation rate (SSMIS has spatial resolution three times lower than GMI and AMSR2). Nevertheless, the GPROF-CLIM-SSMIS rainfall pattern shows precipitation south of 36\({}^{\circ}\)N and west of 18\({}^{\circ}\)E, which is not simulated by CTRL forecast. ANL precipitation, however, extends more to the south compared to CTRL and, in particular, it extends to 35\({}^{\circ}\)N latitude and east of 18\({}^{\circ}\)E, in better agreement with GPROF-CLIM-SSMIS precipitation pattern. The southward shift of the ANL precipitation is confirmed by the comparison of the CTRL and ANL accumulated precipitation between 14:00 UTC and 18:00 UTC (Figure 18d). In this case differences up to 7 mm/4h are predicted south of 37\({}^{\circ}\)N. The southward extension of the precipitation forecast of ANL compared to CTRL is a consequence of DPR data assimilation, as shown in Figure 17a. Figure 18: (**a**) Rain rate simulated at 16:20 UTC by the CTRL forecast of RAMS-ISAC; (**b**) as in (**a**) for the simulation assimilating DPR reflectivity (ANL); (**c**) NASA GPROF-CLIM-SSMIS rainfall rate observed on 16 November at 16:20 UTC; and (**d**) difference between the rainfall accumulated on 16 November between 14:00 UTC and 18:00 UTC by ANL and CTRL. As shown above, the assimilation of the DPR reflectivity did not have a significant impact on the simulation of TLC Numa. However, the above results show the possibility to implement DPR reflectivity as an observation in data assimilation systems, leaving the question of its impact on the forecast open.
## 5 Conclusions  
Dynamic vortex fields shown frequently near island and reef channels can be dangerous to passing ships in terms of stability. In this study, a method for monitoring dynamic vortex fields was proposed based on the phase characteristics of vortex acoustic signals, which provides a new technical means to improve the safety of ship navigation. Using TRM technology, in this paper, the extraction of phase characteristics from dynamic vortex fields was achieved, and the impact of different moving vortex fields on the performance of the vortex acoustic signal characteristic extraction algorithm was defined, with the conclusions drawn below:  
1. By comparing the results of vertical-axis vortex acoustic signal measurement experiments with numerical simulations, it is verified that TRM technology can linearly amplify the phase jump caused by the dynamic eddy current field to a certain extent.
2. The amplification effect of the acoustic signal phase and the effectiveness of phase characteristic extraction in dynamic vortex are influenced by different factors, including the vortex field's movement speed, radius diffusion speed, and oscillatory movement amplitude. In the case that the time reversal count \(N\) is less than 6, for vortex fields in uniform motion, TRM can effectively extract the phase characteristics of the acoustic signal when the vortex center moving speed \(V_{up}\) is less than 2.2 \(\times\) 10\({}^{-3}\) m/s and the radius diffusion speed \(V_{r}\) is less than 2.5 \(\times\) 10\({}^{-3}\) m/s, disregarding the impact of the dynamic vortex field on the extraction results.
3. For vortex fields in oscillating motion, in the case that the time reversal count \(N\) is less than 6, when the oscillation amplitude \(L_{D}\) exceeds 0.15 m and the radius diffusion speed \(V_{r}\) exceeds 2.4 \(\times\) 10\({}^{-3}\) m/s, the influence of the oscillating vortex field on the vortex acoustic signal extraction algorithm must be considered.  
Conceptualization, H.L. and M.Y.; methodology, H.L. and M.Y.; software, H.L. and D.F.; validation, H.L. and D.F.; formal analysis, H.L., W.Z. and M.Y.; investigation, H.L.; resources, M.Y.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L., M.Y. and W.Z.; visualization, H.L.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 42052271349).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.
## 3 Numerical Simulation
### Mesh Independence Testing  
When using the CFD method for research, mesh independence testing is necessary first. This testing ensures that the simulation results will not change with further mesh refinement. It is common to perform at least three simulations to evaluate changes in the results of interest and to obtain the minimum number of meshes and mesh size settings that guarantee reliable results. Five meshes with different mesh numbers are used in this work to perform mesh independence testing. The entire computational domain has a total of \(N_{r}\times N_{z}\) mesh points along the radial and axial directions. There are \(n_{r}\times n_{z}\) mesh points in the central possible flame region (0 \(\leq r\leq\) 10 mm, 0 \(\leq z\leq\) 22 mm), and the mesh points corresponding to different mesh numbers are shown in Table 1. The local heat flux (\(q\)) distribution is used to verify mesh independence since heat transfer in the PMIFJ is the main focus. Figure 4 shows the \(q\) distribution of the stagnation point along the \(+r\) direction. The \(q\) distribution within the wall jet region remains almost consistent. There are obvious differences in the \(q\) distribution at the stagnation and the jet-turning regions, and the \(q\) distribution decreases as the mesh number increases. When the mesh number increases to 78,634 and 103,434, the heat flux distributions from the stagnation point to the peak almost overlap, indicating that the two mesh numbers have fairly good mesh independence. In other words, when the number of meshes is higher than 78,634, the effect of the meshes on the heat transfer performance is negligible. Therefore, we finally selected the mesh number 78,634 as the best mesh number for CFD simulation in this study, which not only produced the smallest numerical error but also reduced the computational cost and time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Mesh Number** & \(N_{r}\) & \(N_{z}\) & \(n_{r}\) & \(n_{z}\) \\ \hline
20,856 & 163 & 130 & 36 & 75 \\
36,946 & 198 & 188 & 51 & 111 \\
42,536 & 229 & 188 & 68 & 111 \\
78,634 & 423 & 188 & 201 & 111 \\
103,434 & 423 & 247 & 201 & 148 \\ \hline \end{tabular}
\end{table}
Table 1: Meshes for independence testing.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2.1 Network Lifetime  
Figure 10a-c shows the stability period of AURP, AEERP, AEDG and AEDG without GN rotation. In AURP, the GN is selected on the basis of the minimum distance and is equipped with extra energy. As the network evolves, the GN is rotated on the basis of the minimum distance. The stability period of the AURP decreases due to the unbalanced energy consumption. The next GN is selected when the first one dies, which leads to the decreased stability period. In AEERP, the GNs are selected on the basis of the RSSI value of hello packets transmitted by the AUV. As the network evolves, the nodes that exist in the direct communication range of the AUV are selected as the GNs on the basis of residual energy. The residual energy threshold is defined to balance the energy consumption. In this way, the energy consumption is balanced throughout the network lifetime. Hence, the AEERP has a greater stability period compared to the AURP. However, if a large number of member nodes are associated with the GN, its energy is quickly depleted, which leads to the creation of an energy hole (the rest of the network is disconnected). In AURP, the GNs, which are extensively involved in the data relaying, are fixed (not rotated over time). Thus, their batteries are drained earlier than the other nodes, as is evident from the jump in Figure 10a-c. In AEDG, the sub-optimal number of member nodes are associated with the GNs. In other words, the member nodes associated with the GNs are limited in number, which causes less energy consumption of the GN, and hence, the maximum number of nodes are alive for a long duration. Moreover, the residual energy threshold mechanism balances the energy consumption, which ultimately increases the stability period, and the effect of the GNs rotation is also clearly visible.
###### Abstract  
In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residential load profiles helps energy providers and utility companies develop an optimal generation schedule to address the demand. Initially, a comprehensive multi-criteria analysis of different machine learning approaches used in energy consumption predictions was carried out. Later, a predictive micro-grid model was formulated to synthetically generate the stochastic load profiles considering occupancy rate as the critical input. Finally, the synthetically generated data were used to train the proposed eight-layer deep neural network-based model and evaluated using root mean square error and coefficient of determination as metrics. Observations from the results indicated that the proposed energy prediction algorithm yielded a coefficient of determination of 97.5% and a significantly low root mean square error of 111 Watts, thereby outperforming the other baseline approaches, such as extreme gradient boost, multiple linear regression, and simple/shallow artificial neural network.  
deep learning; energy management systems; load forecasting; machine learning and microgrids +
Footnote †: journal: Medical Imaging  
0000-0001-9993-3300-0000][PERSON]  
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; [EMAIL_ADDRESS]  
[EMAIL_ADDRESS]  
[EMAIL_ADDRESS]
# A Fluid-Structure Coupling Analysis of a Far-Field Flat Mirror for AliCPT-1 Telescope Calibration
## 6 Conclusions  
The far-field flat mirror in a simulated environment of AliCPT-1 observatory was analyzed using fluid-structure coupling FEA. The following conclusions can be drawn:  
(1) The FFF structure was designed to ensure safety and stability at a low cost. Furthermore, the installation and precise adjusting plan are straightforward and convenient. When in a static state, the mirror demonstrates a high level of flatness. Even under extreme wind loads, the mirror's deformation is small. The overall impact on flatness is below \(\lambda/20\), which satisfies the requirement of flatness.  
(2) Under the maximum wind load, the safety factors of the mirror, mirror support frame structure, and support rods exceed 8. The torque impact on the mount EL axis caused by the wind load remains within the motor reducers' capacity. The analysis results demonstrate the safety and reliability of the FFF system under the influence of both gravity and wind pressure loads.  
(3) The convergence of the grid was verified, and validation was performed for actual operating conditions. Grid convergence conducted on the internal and external flow domains demonstrated a good consistency between grid quality and the number of grids in the flow field.  
(4) The application of the fluid-structure interaction (FSI) method proves to be an effective approach for simulating the wind-induced deformation of the FFF. The results demonstrate that, under a wind speed of 17.5 m/s, the maximum deformation of the FFF reflective mirror, occurring at the center, is 0.0587 mm. The overall analysis suggests that the FFF reflective mirror can maintain normal functionality under wind loading conditions.  
(5) Finite element analysis has shown that the FFF reflective mirror maintains structural stability under extreme wind loading conditions. This structural design approach can be applied to the design of large-scale, slender-supported reflective mirror structures in the future.  
Conceptualization, J.C.; methodology, J.C.; software, J.C.; writing--original draft preparation, J.C.; supervision. A.Z.; writing--review and editing, X.L. ([PERSON]), C.L., Y.L., Z.X., Z.L. and X.L. ([PERSON]); project administration, A.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (2020 YFC2201604).  
Figure 19: Stress contour plot in displacement load of 0.44 mm.
## 3 Results and Discussion
### Quantitive Determination of Citric Acid and Ethanol in SDexF  
The quantitative determination of citric acid and ethanol was performed to assess the efficiency of the washing process of SDexF with ethyl alcohol and subsequent drying. The citric acid and ethanol content in SDexF are presented in Table 4. Average residues of citric acid and ethanol in SDexF were 0.006 \(\pm\) 0.003 g/100 g and 4.40 \(\pm\) 0.24 g/100 g, respectively. The amount of residual citric acid in the samples was very low and not significantly different, which indicates the repeatability of the applied dextrinization conditions. Furthermore, it confirms the effectiveness of washing SDexF with ethyl alcohol to remove possible residues of citric acid. European law allows the use of citric acid (E330) as a food additive in many food products at quantum satis levels [58]. SDexF is intended to enrich vegetable and fruit mousses designed for consumption by children. Therefore, it is important that the product does not contain ethanol. With the assumed addition of SDexF to the mousse (5 g), the alcohol content will be less than 0.5%, which is below the permitted amount for non-alcoholic drinks [59].  
\begin{table}
\begin{tabular}{c c} \hline
**Sample** & **Solubility in Water at 20 \({}^{\circ}\)C [\%]** \\ \hline Native potato starch & 0.40 \(\pm\) 0.34 \({}^{\mathrm{a}}\) \\ SDexF1 & 95.80 \(\pm\) 0.85 \({}^{\mathrm{b}}\) \\ SDexF2 & 96.40 \(\pm\) 0.14 \({}^{\mathrm{b}}\) \\ SDexF3 & 99.15 \(\pm\) 0.85 \({}^{\mathrm{c}}\) \\ SDexF4 & 100.20 \(\pm\) 0.49 \({}^{\mathrm{c}}\) \\ \hline \end{tabular} \({}^{\mathrm{a,b,c}}\) Distinct letter superscripts within identical columns denote statistical significance (\(p<0.05\)).  
\end{table}
Table 2: Solubility in water of SDexF1-SDexF4 batches in comparison to native potato starch.  
\begin{table}
\begin{tabular}{c c} \hline
**Sample** & **Devrose Equivalent (DE)** \\ \hline Native potato starch & 0.20 \(\pm\) 0.10 \({}^{\mathrm{a}}\) \\ SDexF1 & 4.48 \(\pm\) 0.09 \({}^{\mathrm{b}}\) \\ SDexF2 & 4.45 \(\pm\) 0.05 \({}^{\mathrm{b}}\) \\ SDexF3 & 4.54 \(\pm\) 0.08 \({}^{\mathrm{b}}\) \\ SDexF4 & 4.57 \(\pm\) 0.09 \({}^{\mathrm{b}}\) \\ \hline \end{tabular} \({}^{\mathrm{a,b}}\) Distinct letter superscripts within identical columns denote statistical significance (\(p<0.05\)).  
\end{table}
Table 3: Dextrose equivalent (DE) of native potato starch and SDexF.