esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result Analysis
### Effect of the Groove-Diameter Ratio on Sealing Performance  
When \(\lambda_{c}=48\), the average vapor phase volume fraction in Figure 6a first increases and then decreases with an increase in the groove-diameter ratio. The maximum value is reached when the groove-diameter ratio is 0.5. According to Figure 6b, with an increase in the groove-diameter ratio, the opening force first decreases and then increases. The minimum value is obtained when the groove-diameter ratio is 0.7. Based on Figure 6c, the leakage rate increases continuously with an increase in the groove-diameter ratio. When the groove-diameter ratio is relatively small, the working surface area on the leeward side of the spiral groove decreases, the sudden change area of the pressure is smaller, and the low-pressure area is not obvious in the entire liquid film. Furthermore, the degree of phase change decreases, the average vapor phase volume fraction decreases, and the opening force increases. With a gradual increase in the groove diameter ratio, the area of the working face on the leeward side of the spiral groove, the range of the low-pressure area, and the degree of phase change all increase. However, the area of the working surface on the windward side of the spiral groove also increases, thus making the dynamic pressure effect more obvious. When generating a groove-diameter ratio of 0.5, the dynamic pressure effect on the inhibition of the spiral groove area exceeds the low-pressure phase transition region. Therefore, the occurrence of the phase transition is effectively suppressed, thus resulting in a significant reduction in the average volume fraction of the vapor phase. Similarly, when the groove-diameter ratio is 0.7, the effect of dynamic pressure on the opening force of the end face is more evident than the effect of reducing it in the low-pressure area. Therefore, the opening force increases with an increase in the groove-diameter ratio. The increase in the groove-diameter ratio leads to a continuous increase in the spiral groove area and a continuous enhancement in pumping capacity. Consequently, the leakage rate presents an increasing trend.  
When \(\lambda_{c}=0.1\), the average vapor phase volume fraction, opening force, and leakage rate are almost equal to the groove-diameter ratio. However, the average vapor phase volume fraction is significantly affected by \(\lambda_{c}\), and the average vapor phase volume fraction at 0.1 is insignificant. Finally, the opening force and the leakage rate vary within the same interval.  
Figure 5: Effect of helix angle on sealing performance. (**a**) Effect on the average vapor phase volume fraction. (**b**) Effect on the opening force. (**c**) Effect on the leakage rate.  
Figure 6: Effect of groove-diameter ratio on sealing performance. (**a**) Effect on the average vapor phase volume fraction. (**b**) Effect on the opening force. (**c**) Effect on leakage rate.
## 4 Experimental Results and Observations  
The test results are presented and discussed in this section. Selected test photos are shown in Figures 10 and 11, and the cage deformation profiles are illustrated in Figures 14-18 along with simulation results, which will be discussed in the next section. The maximum measured deflection of the test specimens is listed in Table 4, which also includes the external concentrated load values for the corresponding test scenarios. Cage #1 and Cage #2 had internal stiffening elements, and the deflection profiles are presented in Figures 14 and 15, respectively. While these two cages showed similar behavior, Cage #2 had slightly less deflection than Cage #1 due to the presence of U-bolts connectors. For both cages, internal stiffening elements reduced cage deflection compared with specimens with no internal stiffening elements, as shown in Table 4. This was significantly more observable in test scenario 3, where the cages were lifted by both ends; the maximum deflection of specimens with internal stiffening elements was limited to 2% of their length. Conversely, specimens without internal stiffening elements showed deflection values ranging from 8.32% to 13.09%. Comparing C2-S1 with C2-S2 and C2-S3 with C2-S4, applying an external concentrated load slightly increased the specimen's deflection as expected but did not jeopardize the integrity of the cage. It is worth mentioning that, in scenario 7, for all specimens, a considerable part of the specimens crossed out of the DIC cameras' fields of view (see Figure 11d); consequently, the deformation of the specimens was not quantitively measured. However, the specimens neither failed nor experienced extreme deflection. In Cage #3 and Cage #4, the internal stiffening elements were removed, and the U-bolt connectors provided stability to the cage with contributions from tie-wire connections. The trend in the behavior of these two cages was similar, but Cage #3 had less deflection than Cage #4, as plotted in Figures 16 and 17. The difference between the stiffnesses of Cage #3 and Cage #4 could be attributed to the type of transverse reinforcement used, as can be seen in Table 1. In scenarios 1 and 2, the deflections were insignificant (0.54% and 0.66% of the cage length in Cage #3 and 1.25% and 1.45% of the cage length in Cage #4). On the other hand, in extreme pick-up conditions (i.e., picked up by two ends in scenarios 3 and 4), the cage deflections were 8.32% and 9.10% for Cage #3 and 11.79% and 11.91% for Cage #4. Despite large deformations (about 10% of the cage length), the specimens did not collapse, and no failure was observed in the cages. It can be concluded that U-bolts effectively maintained the cage integrity since a tie-wired cage without internal stiffening elements would collapse in similar loading conditions, as will be shown in Section 5.3. In C3-S7 and C4-S7, similar to C2-S7, the cages were picked up and tilted to about 40 degrees. The specimens maintained their integrity without noticeable distortion under this loading condition. Figure 10: Deformed shape of Cage #1 during different test scenarios: (**a**) scenario 1; (**b**) scenario 6.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Read Stability Comparison  
During a read operation, the node storing '0' is connected to the bit line pre-charged to VDD, and the voltage generated at the '0' storage node due to the read current can flip the stored data [6]. In conventional SRAM design metrics, read stability is evaluated through the read static noise margin (RSNM) [27]. The RSNM can be simulated through the circuit presented in Figure 5a. To measure SNM during the read operation, the bit lines BL and BLB are pre-charged to the supply voltage VDD. Subsequently, noise is applied to each node of the SRAM cell to simulate the linear DC characteristics of each node. By overlapping the two voltage transfer curves, the butterfly curve can be obtained. As shown in Figure 5b, the RSNM is calculated as the diagonal length of the largest square that can fit within the butterfly curve. Figure 5b shows the RSNM curves for various cells at VDD = 1 V, and Figure 6 presents the RSNM values for all cells at different VDD voltages.  
Generally, a higher CR results in a higher RSNM because it generates a lower voltage at the '0' storage node. RHBD10T, having the highest CR except for RHHS16T, shows a higher RSNM than all other comparison cells except RHHS16T. Cells like WEQUATRO, QUCCE12T, RHBD14T, and RHPD14T, which have both the storage node and internal node accessed by the bit line through two access transistors, are more vulnerable to read upsets compared to cells such as RHD12T, RSP14T, and RHBD10T, where only the storage node is accessed by the bit line through a single access transistor. Due to the highest CR (3.3) and the read isolated characteristic of the proposed RHHS16T, the proposed cell shows the highest RSNM among all comparison cells, as shown in Figure 6. Since only the internal node of RHHS16T is accessed by the bit line during the read operation, the storage node of RHHS16T is not directly affected by noise during a read operation. Moreover, the highest CR of RHHS16T generates the lowest voltage at the internal node S0, which stores '0'. Therefore, the proposed RHHS16T cell demonstrates the highest read stability among all comparison cells.  
Figure 4: Read access time (**a**) measuring method (**b**) simulation result for different comparison cells with supply voltage variation.  
Figure 5: RSNM (**a**) simulation circuit diagram for conventional SRAM cell; (**b**) butterfly curves for various cells at VDD = 1 V.
## 2 Modeling and Controller Design
### PV Side Converter Control  
As the power of PV array changes with PV voltage, the PV side converter adjusts the DC link voltage for extracting the optimum solar energy under solar radiation and temperature change. In the steady-state, the following equation represents the inner current dynamics in the dq frame for the grid side converter (VSC-PV) as shown in Figure 1[46].  
\[L\frac{di_{gd}}{dt}+Ri_{gd}=L\omega_{0}i_{gq}+V_{d}-V_{gd} \tag{1}\]  
\[L\frac{di_{gq}}{dt}+Ri_{gq}=-L\omega_{0}i_{gd}+V_{q}-V_{gq} \tag{2}\]  
The following equation controls the dynamics of the DC link voltage of grid side converter (VSC-PV).  
\[C\frac{dV_{dc}}{dt}=I_{dc}-i_{gd} \tag{3}\]  
In this work, an enhanced incremental conductance method is employed to track the maximum power point (MPP) of PV.  
The slope at the MPP point is \(\frac{dP_{pv}}{dV_{pv}}=0\) or \(\frac{d(V_{pv}I_{pv})}{dV_{pv}}=0\)  
\[I_{pv}+V_{pv}\frac{dI_{pv}}{dV_{pv}}=0\text{ or }\frac{dI_{pv}}{dV_{pv}}=- \frac{I_{pv}}{V_{pv}} \tag{4}\]  
Figure 1: PV-Battery connected MMC-HVDC system.  
However, under rapidly changing environmental conditions, Equation (4) results in a division by zero. After multiplying both sides of Equation (4) by \(dV_{pv}\), we get  
\[dP_{pv}=I_{pv}dV_{pv}+V_{pv}dl_{pv} \tag{5}\]  
If the voltage remains unchanged in the subsequent sample, then Equation (5) becomes  
\[dP_{pv}=V_{pv}dl_{pv} \tag{6}\]  
Figure 2a illustrates the control block diagram of DC link voltage regulation that is formed based on Equations (1)-(3). The DC link voltage setpoint comes from the MPPT algorithm. Equations (4) and (6) form the basis of modified incremental conductance (MIC) algorithm where reference DC link voltage is produced. Converter dq voltage produces the three phase modulating signal (Ma,b,c) for the VSC-PV.
# Development of a Datalogger for Submarine Glider: Integration of Fault-Tolerant Software Layers
## 2 Datalogger Design
### Data Writing
In case the potential power loss failure occurs, a record of the last write address is kept stored in the flash memory chip of the microcontroller and from this address, writing is started each time the datalogger is used. When the microcontroller turns on, the first thing that is done is to read a pointer with the last writing address of the NAND flash memory chip. This block address is assigned to a location in the flash memory of the microcontroller. Each time a complete block is written, the flash memory location updates the last block written. The writing operation begins when the datalogger receives a write command via serial bus. The writing process is visualized in the state diagram of Figure 11. The write operation begins by reading the pointer that contains the last physical address written to NAND flash memory. Then, the \(DMA1_{0}\) state which activates channel 1 of the DMA to receive 223 bytes of message via serial UART. Then, it goes to the INI state and remains there as long as channel 1 of the DMA is active. Then, it goes to the \(DMA1_{1}\) state that activates channel 1 of the DMA again and immediately goes to the COD state which encodes the \(223\times 8\)-bit vector that was acquired by the DMA, at this time, both the encoding and the data reception are executed in parallel. To avoid overwriting information due a power failure, a pointer indicator of the last page and last block written is updated in the Pointer state. In this way, in case of power loss, the previously stored information is not corrupted. Then, it returns to the INI state which again waits for channel 1 of the DMA to be inactive. The iterations continue until the last cycle is reached, in which the last bytes are received, which, once the information is encoded, fills a page of NAND Flash memory chip. After the NAND flash memory is written, the last written address pointer is updated. To continue writing in the NAND flash memory the WRT signal is needed. In the case of continuing writing, it re-activates channel 1 of the DMA to receive, encode, and finally write to the NAND Flash memory. In the case that the period of time in the falling and rising edge of R/B# could be fast, the microcontroller could cycle waiting for the falling edge of R/B#. Therefore, a step is not put to wait for that fallinf edge. Instead, the result of the 0x70 command is awaited; thus, the microcontroller does not remain cycled. In the case of not continuing to write, then it goes directly to the COD state to encode, then to the WR_NF state to write in NAND Flash and the writing process is finished.
# Large-Scale 3D Multi-Story Enterprise Layout Design in a New Type of Industrial Park in China
## 6 Case Study
### Computational Results in Different Scenarios
#### 6.3.2 Consider Current Layout  
One of the problems that must be considered when determining a layout is that several enterprises in the park have already determined the advanced location. These enterprises will also affect the overall risk value of the park; therefore, the current layout is needed to consider. Six risk enterprises (I1, I2, I14, I15, I22, and I23) from three different risk enterprise categories were taken to determine the locations in advance. In this case, the location layout of the remaining 29 enterprises was determined.  
Figure 6: Trace plot considering only the rental profit.  
Figure 7: Layout results considering only rental profit.  
First, considering the area constraint, the locations of these enterprises (see Figure 8) were assumed, and constraints were added to the solution algorithm. The model was then solved, and the results are shown in Figures 9 and 10. The overall association risk value of the park was 476.3, which was greater than the original layout result (i.e., 350.6). This also reminds us that, when optimizing the layout considering risks, try to conduct unified planning for all enterprises, which is more likely to obtain better results. The rental profit of 10 non/low-risk enterprises was 438,772 yuan, an increase. As can be seen from the layout results in Figure 10, most non/low-risk enterprises were located on the first floor, which may lead to higher rental profits. In the proposed model, rental profit is significantly affected by the upper-level layout. The advanced location of some risk enterprises may also positively impact rental profits.  
From the above scenario simulation, we can see that the proposed model can implement the function of dynamic layout. By changing the input parameters and adding corresponding constraints, the layout of the park at any specific moment can be conducted. Because the lease terms signed by enterprises and the park are generally longer and the frequency of enterprise changes is low, this study did not consider time variables. However, as time goes on and the number of enterprises increases, the total association risk value may change significantly. In addition, from the layout results, it is better to lay out multiple enterprises at once than multiple times. In this sense, it is advisable to re-plan the layout of all enterprises when the total associated risk value exceeds the acceptable level  
Figure 8: Current layout to consider.  
Figure 9: Trace plots of the improved GA solving considering current layout: (**a**) Trace plot of upper-level layout; (**b**) Trace plot of lower-level layout.  
of the industrial park. Achieving enterprise reallocation is a complex issue that requires multi-party coordination. Consequently, this model is more suitable for solving the layout problem when a large number of enterprises settle in the early stages of the park.
## 3 Materials and Methods
### Modelling Process
#### 3.2.3 Model Evaluation  
Once the optimum hyperparameters have been obtained for each model, their performance is assessed by measuring errors of mean square error (\(MSE\)), root mean square error (\(RMSE\)), and mean absolute error (\(MAE\)) on test dataset. \(MSE\) is the average of the squared difference between the actual and the predicted value. It is the most commonly applied loss function for regression models. \(MSE\) is calculated using Equation (3).  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}, \tag{3}\]  
\(RMSE\) is just the square root of the \(MSE\). It has the same unit as the target variable. Irregularly, \(RMSE\) is preferable than \(MSE\) because understanding the error values of \(MSE\) is tricky due to the squaring effect, particularly if the target variable describes quantities in a unit of measurements. The formula of \(RMSE\) is described by Equation (4).  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
The \(MAE\), also known as the absolute loss, is the mean of the absolute errors (the deviation between the actual and the predicted value). Similar to \(RMSE\), \(MAE\) is measured in the same units as the target variable. It is mathematically denoted by Equation (5).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{5}\]  
Coefficient of determination, \(R^{2}\), is another valuable quantitative measure of goodness of a prediction for a regression model. It is the fraction of response variance that is achieved by the model, and it can be described as a standardized version of the \(MSE\), for greater interpretability of the model's performance. The value of \(R^{2}\) can be computed by the mathematical relation expressed by Equation (6). The value of \(R^{2}\) is bounded between 0  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties to Be Examined** & **Models** & **Number of Features** & **Feature Details** \\ \hline \multirow{4}{*}{Compaction} & OMC-EM & 7 & All the variables under the category of amended soils (soil, cement, and lime), Afterberg limits (LL, PL, and PI), and soil classification. \\  & MDD-EM & 7 & All the variables under the category of amended soils (soil, cement, and lime), Afterberg limits (LL, PL, and PI), soil classification, and the compaction properties (OMC and MDD). \\  & UCS-EM\({}^{-}\) & 7 & All the variables under the category of amended soils (soil, cement, and lime), Afterberg limits (LL, PL, and PI), and soil classification. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of features considered in each model.  
and 1 in case of model training, but it can become negative in case of testing. If \(R^{2}=1\), the model fits the data perfectly with a corresponding \(MSE=0\).  
\[R^{2}=1-\frac{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\frac{1}{n}\sum _{i=1}^{n}(y_{i}-\overline{y})^{2}}=1-\frac{MSE}{Var\;(y)} \tag{6}\]  
where \(n\) is the number of observations, \(y_{i}\) is the actual target value, \(\hat{y}_{i}\) is the predicted output value, \(\overline{y}\) the mean value of the actual target, and \(Var\) is the variance of the target variable.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Proposed System's Performance  
Compared with reference systems, the proposed system improves throughput and reduces latency at an innovative level. The throughput is elevated to 2500 TPS with State Channels and is even higher at 3500 TPS with Rollups, surpassing the best observed 3400 TPS in reference systems. One of the system's key strengths was latency, which was recorded at 5 s at baseline. At the same time, State Channels introduce a latency of 2 s, whereas Rollups provide a latency of 1.5 s. Both are significantly higher than the reference systems, where the minimum latency recorded was 5 s, and for Rollups, it was 2.3 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References No.** & **Scenario** & **Throughput (TPS)** & **Latency (s)** \\ \hline \multirow{4}{*}{Our System} & Baseline & 800 & 5 \\ \cline{2-4}  & State Channels & 2500 & 2 \\ \cline{2-4}  & Rollups & 3500 & 1.5 \\ \hline \multirow{4}{*}{[33]} & Baseline & 750 & 6 \\ \cline{2-4}  & State Channels & 2300 & 3 \\ \cline{2-4}  & Rollups & 3300 & 2 \\ \hline \multirow{4}{*}{[34]} & Baseline & 800 & 5 \\ \cline{2-4}  & State Channels & 2400 & 2.5 \\ \cline{2-4}  & Rollups & 3400 & 1.8 \\ \hline \multirow{4}{*}{[35]} & Baseline & 700 & 5.5 \\ \cline{2-4}  & State Channels & 2200 & 3.2 \\ \cline{2-4}  & Rollups & 3100 & 2.2 \\ \hline \multirow{4}{*}{[36]} & Baseline & 770 & 6.2 \\ \cline{2-4}  & State Channels & 2350 & 3.1 \\ \cline{2-4}  & Rollups & 3250 & 2.3 \\ \hline \multirow{4}{*}{[37]} & Baseline & 800 & 5.2 \\ \cline{2-4}  & State Channels & 2450 & 2.7 \\ \cline{2-4}  & Rollups & 3400 & 1.9 \\ \hline \multirow{4}{*}{[38]} & Baseline & 790 & 5.4 \\ \cline{2-4}  & State Channels & 2400 & 3 \\ \cline{2-4}  & Rollups & 3350 & 2 \\ \hline \multirow{4}{*}{[39]} & Baseline & 780 & 5.6 \\ \cline{2-4}  & State Channels & 2500 & 2.8 \\ \cline{2-4}  & Rollups & 3250 & 2.1 \\ \hline \multirow{4}{*}{[40]} & Baseline & 760 & 6.1 \\ \cline{2-4}  & State Channels & 2300 & 3.3 \\ \cline{2-4}  & Rollups & 3100 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative metrics using throughput (TPS) and latency (s).
## 3 Parametric Study
### The Effect of Distance between the Radiating Patch and the Edge of the Substrate (\(d\))  
Figure 16 illustrates the simulated RLBW and ARBW under variable distance between the radiating patch and the edge of the substrate (\(d\)): 14, 19, and 24 mm. As \(d\) increased, the reflection coefficient (\(\left|\mathrm{S}_{11}\right|\)) deteriorated while AR became smaller. The optimal \(d\) was thus 19 mm.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)
### Mechanical Model
#### 2.1.2 Model of DDIS  
As depicted in Figure 3, the proposed DDIS consists of an inerter, a DDE, and a spring, where \(k_{t}\) denotes the stiffness coefficient of the spring. In the DDIS, the inerter and DDE are mounted in parallel and are deployed in series with the spring. The supplemental vibratory system consisting of the spring and inerter is tuned to the primary structural frequency, resulting in enhanced energy absorption. Unlike the conventional VDIS in which the viscous damping element is used only for energy dissipation, the DDE of the DDIS provides both energy dissipation and additional stiffness, which further reduces the structural response. The mass enhancement effect [1] of the inerter ensures efficient energy absorption in the DDE without implying an additional weight. And the implements of the spring and inerter constitute the damping enhancement mechanism to dissipate more energy since the DDE deformation of DDIS is amplified and larger than the displacement of controlled structure (Figure 3). The detailed explanation of the intrinsic benefit of the DDIS is given in Sections 3.2 and 4.1.  
Figure 3: Schematic of the proposed displacement-dependent damping inerter system (DDIS) and its damping-enhancement effect.  
Figure 2: Hysteresis curve of the displacement-dependent damping element (DDE).