esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### DHT Does Not Regulate Cytokine Expression  
Previous studies have shown that DHT treatment inhibits the expression of the proinflammatory cytokine IL-6 and increases the expression of IL-10 [26; 27]. Therefore, we investigated changes in cytokine expression in DHT-treated SNU719 cells, NK-92 cells, and EBNA1 PBMCs using a cytokine array kit that detects 36 cytokines simultaneously. First, SNU719 cells treated with 100 nM DHT for 24 h showed a slight increase in MIF and IL-18/IL-1F4 (Figure 6A). Second, CXCL12/SDF-1 slightly decreased in NK cells treatedwith 100 nM DHT for 24 h (Figure 6B). Third, EBNA1 PBMCs treated with 100 nM DHT for 24 h showed a slight increase in MIF and IL-16 (Figure 6C). Overall, DHT treatment slightly increased or decreased some cytokines. To validate these changes, we performed RT-qPCR assays. Changes in _MIF_ and _IL-18_ transcripts in SNU719 cells treated with 100 nM DHT for 24 h were not statistically significant (Figure 6D). Additionally, _CXCL12_ transcripts in NK-92 cells treated with 100 nM DHT for 24 h were significantly increased compared to the control (Figure 6E). Finally, changes in _MIF_ and _IL-16_ transcripts in EBNA1 PBMCs treated with 100 nM DHT for 24 h were not statistically significant (Figure 6F). These results indicate that, contrary to our expectations and previous studies, DHT treatment did not strongly increase cytokine expression, suggesting that cytokines are not deeply involved in the DHT-induced increase in cytotoxicity of cellular immune cells.
## 5 Conclusions  
In the present work, efforts were directed toward tuning the stiffness and damping capacity of the NiTi WMD by heat treatment. Furthermore, the influence of heat treatment conditions on the NiTi WMD's vibration isolation performance is addressed. The main conclusions are as follows.  
1. Under dynamic loading conditions, the NiTi WMD is able to outperform its SS 304 counterpart in loss factor because the phase transformation damping mechanism of NiTi wire is combined with the existing dry friction damping mechanism of the elastic porous structure.
2. Under a sine sweep test condition, the NiTi WMD exhibits a conditional advantage over the SS 304 WMD regarding the vibration isolation performance. A lower transmissibility and a higher loss factor can be achieved by the as-fabricated NiTi WMD only when the system is subjected to input acceleration lower than 1.5 g. In the case of input acceleration higher than 1.5 g, the phase transformation damping capacity of the NiTi WMD is suppressed by the large strain within the highly compacted elastic porous structure.
3. The resonant frequency and damping capacity of the as-fabricated NiTi WMD can be properly adjusted by heat treatment. In general, a considerable improvement in the NiTi WMD's stiffness will be obtained at the expense of its loss factor. However, after being heat-treated at 200 for 30 min, the NiTi WMD exhibits improved load capacity without suffering from a large decline in loss factor.
4. The adjustable dynamic stiffness and loss factor of the heat-treated NiTi WMD can be attributed to the combinatorial effects of the rearrangement of the cold-work induced dislocation and the increasing residual stress during the heat treatment. As far as the tunable vibration isolation performance and the cost of NiTi wire are concerned, heat treatment is a more feasible approach, as compared to the mesh density adjustment when a specific combination of dynamic stiffness and loss factor is desired.  
**Author Contributions:** Methodology, Y.S. and Y.W. ([PERSON]); validation, Y.S. and M.S.; formal analysis, Y.W. ([PERSON]); investigation, X.X.; resources, M.S. and J.W.; writing--original draft preparation, Y.S. and M.S.; writing--review and editing, Y.S. and J.W.; project administration, H.B.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China, grant numbers 12002088 and 12272094, and the Key Project of National Defence Innovation Zone of Science and Technology Commission of CMC.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 4 Numerical Examples
### Example 3--Deterministic Analysis: Bridge Topology Optimization  
The optimization procedure via BESO and SESO methods will be carried out for the examples of bridges shown in Figure 6a,b with a span of 120 m, a width of 10 m, and a height of 20 m. In Figure 6a, there is a bridge with a lower deck designed with a semicircular space in the x direction intended for traffic and will be considered a non-design domain. The lower left end of the board is fixed in all directions, while the right end allows movement in the x direction. The bridge illustrated in Figure 6b has an upper deck with the left end fixed in the x, y, and z directions, while the right end allows movement in the x direction. The load used in both designs is uniformly distributed with \(F=1\times 10^{5}\) N/m\({}^{2}\). The prescribed final volume is \(V_{f}=0.30\) and the filter radius is \(r=3\) m. The board is defined as a non-design domain region. In the evolutionary optimization procedure, the evolutionary ratio used in BESO was \(ER=0.03\), while in SESO, \(RR=0.01\) and \(ER=0.02\) were considered.  
Figure 4: Design domain and boundary conditions.  
Figure 5: Optimal topologies: (**a**) SESO and (**b**) BESO.  
In case (a), a bridge that has a hole in the x direction, the optimized structure resembles tied arch bridges, with concrete (blue color) shaping the arch above the deck, while steel is used in the tie rods and the deck (red), as seen in Figure 7a,b. Aesthetically, the cables take the form of catenaries due to the influence of gravity. In contrast, case (b), as seen in Figure 8a,b, presents a substantially different configuration, with the majority of the structure located below the bridge deck, as proposed by [31]. In this situation, an arch in the opposite direction to that in case (a) is formed, with the steel playing the main role by composing a brace that supports the main load of the bridge. Concrete, in turn, is used to build the deck and the small inclined columns that connect the deck to the belt. However, it is possible to observe that BESO presents a topology with the internal arc above the deck that is clearer than SESO, which presents a topology in the shape of two oblique cables.  
In case (b), the deterministic optimal topologies presented in Figure 8a,b, the presence of regions that transmit efforts (blue) and regions that are compressed (red) is evident. In this article, allowable stress values for tension and compression were incorporated, and, in the case of rods subjected to compression, it is essential to consider the possibility of failures due to buckling (s  
Figure 6: Design domain and boundary conditions: (**a**) Bridge with lower deck and semicircular opening in the x direction and (**b**) bridge with upper deck.  
Figure 7: Deterministic optimal topologies: (**a**) BESO and (**b**) SESO.
# Inhibited Channel Potential of 3D NAND Flash Memory String According to Transient Time
## 2 Structure of the Proposed 3D NAND Flash Memory  
Figure 1a shows the 3D NAND flash memory structure used [12]. This structure is composed of tungsten (W)/oxide (O)/nitride (N)/oxide (O)/polycrystalline silicon (poly-Si) and there are 16 WLs (WL0-WL15). Figure 1a also shows that the channel is vertical and not connected to the body. Figure 1b shows the simulation pattern according to the \(V_{th}\) of the adjacent cell. The values of the E, P1, P2, and P3 patterns indicate the \(V_{th}\) of the WL cell (E = \(-\)1 V, P1 = 1 V, P2 = 2 V, P3 = 3 V). In the case of P1 in Figure 1b, pattern E was set to erase WL7-WL9, and the remaining adjacent WLs were set to P1. Case P2 and, P3 also erased WL7-WL9. The adjacent WL was set to P2 in Case P2, and the adjacent WL was set to P3 in Case P3.  
Table 1 lists the device parameters used in the TCAD simulation. Table 1 shows gate length (WL, string select line (SSL), ground select line (GSL)), gate spacing, gate dielectrics (O/N/O), channel hole diameter, Poly-Si channel thickness, \(V_{CC}\) (bit line and SSL voltage), and doping (Arsenic/Boron). Figure 2 shows a time diagram of NLSB. In the diagram, the selected WL is WL8, and the unselected WL includes the rest of the WL, except WL8. NLSB is a phenomenon that occurs in the selected WL of the inhibited string. After the verification process is complete, the program process is started. When the program process starts, the channel becomes to floating state [13; 14]. If a program is started while this state is maintained, pass voltage (\(V_{PASS}\)) is applied to all the WLs, and the channel potential is increased by capacitive coupling [15]. At this time, because the cells on the selected WL are floating channels, they remain turned off and the channel potential increases according to \(V_{PASS}\). Subsequently, a program voltage (\(V_{PGM}\)) is applied to the selected WL [16; 17]. In the process of increasing the \(V_{PGM}\), electrons in the channel gather at the selected WL channel owing to the potential difference of the WL. Because of these electrons, the channel potential of the selected WL increases. This phenomenon of increasing the channel potential is called NLSB. NLSB has the effect of decreasing program disturbance. This is because unwanted programs are blocked owing to the selected WL channel potential increased in the inhibited string [18].  
Figure 1: (**a**) Structure of 3D NAND flash memory and (**b**) simulation pattern according to the \(V_{th}\) of the adjacent cell.
## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis
#### 4.3.3 Model Ablation Experiments
Notably, although we introduced the SE module to boost the network's representation capability, the model's number of parameters did not substantially increase. This indicates that we successfully optimized the performance of VGG16 while maintaining computational efficiency. This finding is significant for real-world applications, as it allows us to enhance the model's performance by introducing advanced network modules without sacrificing computational efficiency. In summary, the data in Table 12 confirms the significant effect of SE modules on the performance enhancement of the VGG16 network. It also shows that this enhancement is achieved while keeping the model parameters streamlined and ensuring efficient utilization of computational resources. This provides new ideas and directions for us to continue exploring and optimizing deep learning models in the future.
# Factorial Numbers and Their Practical Applications
## 3 Theory of Factorial Numbers
### Detecting Errors in Factorial Numbers  
Among the factorial numbers \(F\), erroneous numbers may appear during their transmission and storage. They are detected when restrictions on digit values are violated. Thus, the factorial number 2 3 2 1 0 is correct, but the number 2 3 2 2 0 is incorrect since the value of the first digit of this number is two, and it cannot be greater than one.  
The restrictions on the values of the digits in the factorial number are distributed unevenly. Thus, only the number 0 can appear in the zero digit. Any other number is a sign of an error. At the same time, in the highest \((n-1)\)-th digit, all \(n-1\) numbers are allowed, and therefore errors in it are not detected. Errors in other digits are detected but with different probabilities. Errors in digits of a factorial number can be corrected either by retransmitting them or by additional noise-resistant coding of these digits.
## 3 Experimental Validation of the Mechanical Amplifier
### Comparison of the Experimental and FEA Results
Figure 15: The displacement results obtained for the compression test using a roller (**a**) and a ball (**b**) terminal, where point 3 is the maximum displacement obtained for the applied force, while points 1 and 2 determine the required piezo stack stroke in order to obtain the maximum displacement. The errors obtained (for vertical displacements only) with the same applied force (i.e., 12 N), compared to the FEA results shown in Figure 16 under the same external conditions, are presented in Table 1.  
It can be concluded that the physically developed amplification mechanism is consistent with the 3D modeled and FEA version, despite a maximum absolute error of 7.52%. A potential source of error between the two systems is the sensitivity of the load cell. Given the measuring range of 0 to 5 kN for the lower load cell, the measured value of 12 N may have an error of \(\pm\)0.5 N, which falls within the 0.01% tolerance of the amplifier. Another cause for this error could be the manufacturing limitations regarding the right edge areas and surface parallelism that may influence the mechanism trajectory. Despite this, we can conclude that the experimental test confirms the good correlation between the models and supports moving forward with the product development. One must point out that we have chosen experimental tests instead of simulations using COMSOL Multiphysics platform (COMSOL, Inc., Burlington, MA, USA), for example, because for simulations, it depends a lot on how the contact area in COMSOL is declared between the V-shaped cap of the piezo stack and the mechanical amplifier. The sliding between the hemisphere and the V-shaped cap must be modeled; the prestressing of the stack must be considered as well. This is the reason why, for such ascertainments, a compression (experimental) testing method is safer in order to obtain error-free results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test No.** & **Measured Points** & **FEA Displacement [mm]** & \multicolumn{2}{c}{**Absolute Experimental Displacement [mm]**} & **Error [\%]** & **Pass/Fail** \\ \hline \multirow{2}{*}{1} & Point 1 (\(Z\) axis) & 0.0065 & 0.007 & \(-7.52\%\) & Pass \\  & Point 2 (\(Z\) axis) & 0.0931 & 0.091 & 0.15\% & Pass \\ \hline \multirow{2}{*}{2} & Point 1 (\(Z\) axis) & 0.0672 & 0.048 & 22.78\% & Fail \\  & Point 2 (\(Z\) axis) & 0.0562 & 0.035 & \(-32.35\%\) & Fail \\  & Point 3 (\(Z\) axis) & 0.1731 & 0.193 & 11.49\% & Fail \\ \hline \multirow{2}{*}{3} & Point 1 (\(Z\) axis) & 0.0672 & 0.063 & 6.25\% & Pass \\  & Point 2 (\(Z\) axis) & 0.0562 & 0.053 & 5.69\% & Pass \\ \cline{1-1}  & Point 3 (\(Z\) axis) & 0.1731 & 0.163 & 5.83\% & Pass \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison between the values of the vertical displacement errors obtained using FEA vs. experiments. Figure 16: The FEA analysis for the entire amplification mechanism, considering a compression test carried out with an 11 N applied force.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 3 CRC Computational Process
### The Sarwate's Algorithm and Slicing by an N Algorithm  
To speed up the process, instead of processing one bit at a time, [PERSON] et al. [12] proposed an algorithm that can process eight bits (one byte) at a time. In this algorithm,  
Figure 3: Partial structure of the linear shift register for G(x).  
a lookup table with a 256-entry is precomputed and stored in memory. As mentioned previously, a lookup table must fit into the cache memory to make the process practical. The index of the lookup table is computed by XORing eight bits of current CRC code of that iteration and the current data byte. The new CRC is obtained by shifting current CRC by eight bits to the right then XORing it with the output of the lookup table. Then, it moves to the next byte and continues until the end of all the data bytes of the input message. Pseudocode of [PERSON]'s algorithm is shown in Algorithm 2.  
```
1:Input: Byte Array Data
2:Crc = 0 XFFFFFFFF
3:While index < size of data do
4: CRC = (CRC >> 8) XOR TABLE[(CRC AND 0 xFF) XOR data[index]]
5: index + 1
6:end while
7:Return CRC XORFFFFFFFF
```  
**Algorithm 2** Sarwate Algorithm.  
Using one single lookup table does not grant much benefit anymore. In order to keep memory requirements reasonably small, a \"Slicing-by-N\" algorithm uses a multiple 256 entries lookup table which can be derived from the lookup table proposed in [PERSON]'s algorithm. More details about the method can be found in Ref. [13]. By performing multiple lookups at once, a modern CPU can speed-up the process by fine-grained parallel processing. As reported in Ref. [14], the speed-up factor can be varied from 2x to 3x depending on the number of tables. Algorithm 3 shows Slicing-by-4 pseudocode, but it can be extended to a general case that uses N tables.  
```
1:Input: Byte Array Data
2:Crc = 0 XFFFFFFFF
3:While index < size of data do
4: CRC = CRC _XOR natal[index+3]_
5: XOR TABLE1[(CRC AND 0 xFF)]
6: XOR TABLE2[((CRC >> 8) AND 0 xFF)]
7: XOR TABLE3[((CRC >> 16) AND 0 xFF)]
8: XOR TABLE4[((CRC >> 24) AND 0 xFF)]
9: index + 4
10:end while
11:Return CRC XOR 0 XFFFFFFFF
```  
**Algorithm 3** Slicing by 4 Algorithm.
## 4 Emerging Lightweight Security Approaches
### Symmetric Encryption--Lightweight AES Approaches
#### 4.1.2 Low-Power AES Data Encryption Architecture (LPADA)
This request message is not encrypted, but it has an MIC that is calculated using these three values, plus the frame header and a DevNonce value, which is a random number. The Join Accept message is encrypted by the Join Server using the AES-128 ECB decrypt operation. It is also a Mac-then- Encrypt (MtE) message. The Join Server uses the decrypt operation because the LoRa mote supports AES encryption but not decryption. This keeps the mote simpler. When the Join Accept message is encrypted at the LoRa mote, the session keys NwkSKey and AppSKey are generated at the device. The Join Server also generates these two keys and sends them to the Network and Application Servers. This process repeats when a LoRa device joins a new network or loses the session key information. However, in the LPADA architecture, a new key updating procedure is proposed in which a new key update request is sent from the mote to the Join Server. This encrypted message uses the old AppSKey as the plaintext, with some other values. Then, the key update answer message is also encrypted by the Join Server using AES-128. However, this new LPADA key update method has a disadvantage. It will require that the LoRa mote supports AES decryption operations, so that it can decrypt the key update answer message. Although they are the same transformations of AES as in Figure 7A, they are called in reverse order and require more complex logic at the firmware level.
## 3 Inherent Optical Properties (IOPs)
### Relationship between Effective Radius and Mode Radius
#### 3.4.3 Cloud IOPs  
Clouds consist of liquid water droplets or ice (frozen water) particles. While liquid water droplets can be assumed to have spherical shape, ice crystals can have a variety of non-spherical shapes. If we assume for simplicity that all cloud particles consist of spherical water droplets or spherical ice particles, (For ice crystals, a spherical model may be unrealistic. For a good introduction to this topic, see the textbook by [PERSON] and [PERSON] [40].) we can use a Mie code to compute their IOPs because their refractive index is known. Hence, we may use Equations (58)-(60) to compute \(\alpha_{\rm p}(\lambda)\), \(\beta_{\rm p}(\lambda)\), and \(p_{\rm p}(\lambda,\Theta)\).  
The real part of the refractive index of pure water needed in the Mie computations may be taken from [41], while the imaginary part \(\tilde{m}_{i,w}\) is calculated from the absorption coefficient (\(\alpha_{\rm w}(\lambda)=4\pi\tilde{m}_{i,w}/\lambda\)) obtained from published data [42; 43; 44] for wavelengths between 340 and 700 nm, and from another source [45] for wavelengths between 720 and 900 nm.  
It is customary to introduce the liquid water content (LWC) defined as  
\[\text{LWC}\equiv\rho_{\rm w}\int_{r_{\rm min}}^{r_{\rm max}}\left(\frac{4\pi} {3}\right)r^{3}n(r)dr\equiv\rho_{\rm w}\ f_{\rm V}\qquad[\text{kg}\cdot\text{m} ^{-3}] \tag{81}\]  
where \(n(r)\) is the cloud droplet size distribution [\(\text{m}^{-3}\cdot\text{m}^{-1}\)] and \(\rho_{\rm w}\) is the liquid water mass density [\(\text{kg}\cdot\text{m}^{-3}\)] and \(f_{\rm V}\) stands for the dimensionless liquid (cloud) particle volume fraction defined in a similar manner as AMC in Equation (80), i.e., \(f_{\rm V}=\text{LWC}/\rho_{\rm w}\). For a liquid water cloud, a typical value for LWC is about 0.5 \(\text{g}\cdot\text{m}^{-3}\), implying that \(f_{\rm V}=5\times 10^{-7}\) for \(\rho_{\rm w}=10^{3}\ \text{kg}\cdot\text{m}^{-3}\). In Equation (70) for the effective radius, the numerator is proportional to the concentration or LWC, while the denominator is related to the scattering coefficient:  
\[\beta_{c}=\int_{0}^{\infty}dr(\pi r^{2})Q_{\beta}(r)\ n(r)dr\qquad[\text{m}^{ -1}].\]  
If the size of the droplet is large compared to the wavelength \(\lambda\), then \(Q_{\beta}(r)\to 2\). Therefore, in the visible spectral range where \(2\pi r/\lambda\gg 1\), we find:  
\[\beta_{c}\approx\frac{3}{2}\frac{1}{\rho_{\rm w}}\frac{\text{LWC}}{r_{\rm eff} }=\frac{3}{2}\frac{f_{\rm V}}{r_{\rm eff}}\qquad[\text{m}^{-1}]. \tag{82}\]  
For ice cloud particles assumed to be spherical in shape a similar expression for the scattering coefficient is obtained with \(f_{\rm V}\) being the ice particle volume fraction. For a liquid water cloud with \(f_{\rm V}=5\times 10^{-7}\) and \(r_{\rm eff}=5\times 10^{-6}\) m, we get \(\beta_{c}=\frac{3}{2}\frac{f_{\rm V}}{r_{\rm eff}}=0.15\) m\({}^{-1}\), and hence an optical thickness of 15 for a 100 m thick cloud layer.
## 3 Governing Equations and Numerical Implementations
### Governing Equations
\[\left[\left(\frac{\partial A_{\flat}}{\partial\xi}-\frac{\partial^{2}A_{ \flat,\xi}}{\partial\xi^{2}}\right)\Delta\hat{Q}^{n+1,k}+\frac{\partial^{2}}{ \partial\xi^{2}}\Big{(}A_{\flat,\xi}\Delta\hat{Q}^{n+1,k}\Big{)}\right]=\frac {\partial^{2}A_{\flat,\xi}}{\partial\xi^{2}}\Delta\hat{Q}^{n+1,k} \tag{14}\]  
Similar processes can be applied to the viscous flux terms in the \(\eta\)- and \(\zeta\)-directions. The final form of the governing equation can then be expressed as follows  
\[ \begin{split}\left[\left(\frac{3\Gamma}{2\Delta t}+\frac{\Gamma} {\Delta\tau}+\frac{\partial A}{\partial\xi}+\frac{\partial B}{\partial\eta}+ \frac{\partial C}{\partial\xi}-\frac{\partial^{2}A_{\flat,\xi}}{\partial\xi ^{2}}-\frac{\partial^{2}B_{\flat,\eta}}{\partial\eta^{2}}-\frac{\partial^{2} C_{\flat,\xi}}{\partial\xi^{2}}\right)\Delta\hat{Q}^{n+1,k}\right]\\ =-\left[\begin{array}{c}\Gamma_{e}\left(\frac{3\hat{Q}^{n+1,k} -\frac{\Delta\hat{Q}^{n+1,k}}{2\Delta t}}{2\Delta t}\right)-\hat{S}^{n}\\ +\frac{\partial\left(\hat{E}^{n+1,k}-\frac{\hat{E}^{n+1,k}}{2\Delta t}\right)} {\partial\xi}+\frac{\partial\left(\hat{E}^{n+1,k}-\hat{E}^{n+1,k}_{\eta} \right)}{\partial\eta}+\frac{\partial\left(\hat{E}^{n+1,k}-\hat{E}^{n+1,k}_{ \eta}\right)}{\partial\xi}\end{array}\right]\end{split}  \tag{15}\]  
Equation (15) is solved numerically implicitly, using the alternating direction implicit algorithm. The temporal derivative terms of the physical and pseudo times are discretized using the 2 nd and 1 st order backward differences, respectively. The monotonic upstream-centered scheme for conservation laws (MUSCL), and the 2 nd order central difference schemes, are applied to the convective and viscous flux terms, respectively. For more detailed descriptions of the numerical methodologies, except for the additional linearization terms of the viscous flux that are the main subject of this study, the studies conducted by [PERSON] and [PERSON] [12] and [PERSON] et al. [16] can be referred to.