esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Experiment 2
#### 5.2.2 Performance Comparison and Discussion  
Figure 13 shows the recognition accuracy for different test sets for different CNN models. In Figure 13, it can be seen that the accuracy of the three models had hardly changed under the interference of a low-intensity noise. When the noise intensity increased to \(-10\) dB, the MDI-CNN still had a high recognition accuracy of 95.5%, but the accuracy of the other two models decreased, and it was up to about 50%. Thus, it can be concluded that the MDI-CNN model had great advantages regarding the recognition rate and robustness compared with the other two models.  
In order to explain the result clearly, the feature visualization of the sample was carried out. In Figure 14, the FFT samples and ESA samples, which were obtained by signal processing of the noisy samples, are presented. Compared with the original samples, the features of the noisy samples were more confusing; thus, the recognition rate of the three models for the noisy test set was reduced. Because of the good clustering of FFT samples and ESA samples, the accuracy of the MDI-CNN model was better than of the other two models. Therefore, it can be concluded that the MDI-CNN model has good robustness because of a good inhibitory effect of spectrum analysis on noise.  
Figure 14: Feature visualization of different samples. (**a**) Feature visualization of raw samples; (**b**) Feature visualization of noisy samples; (**c**) Feature visualization of FFT samples; (**d**) Feature visualization of ESA samples.  
Figure 13: Testing diagnosis results of different models under different noise intensity.  
In order to prove the performance advantage of the MDI-CNN model more accurately, a comparison was carried out between the proposed method and another model, using the raw data, the FFT data, and the ESA data as input data of the 2D-CNN and 1D-CNN, respectively. Table 5 shows the comparison results, where it can be seen that the proposed model had certain advantages, both regarding the recognition rate and the robustness.  
Hence, the following conclusions can be drawn: (1) The accuracy and robustness of the 2D-CNN and 1D-CNN models were improved by using the FFT data and ESA data as input data; (2) compared with the ESA data, the FFT data was less sensitive to the noise; and (3) the improvement effect of the ESA data on the recognition accuracy and robustness was less obvious than of the FFT data; in other words, differently processed data had different effects on model performance.  
The MDI-CNN achieves better stability and generalization performance by using multiple input layers to achieve effective fusion of multidimensional data.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 2 Seismic Response Analysis of Site-Embedded NPP Structural Systems
### Containment Structure
#### 2.2.2 Acceleration Response Spectrum  
Figure 20 presents the floor response spectra of the safety shell structure. It can be observed that in the 0-1 Hz frequency range, the floor spectra nearly coincide under different embedment depth conditions, indicating that the influence of embedment depth can be neglected in this range. However, the effects of varying embedment depths on the frequency range corresponding to the peak of the spectra are significant, with the peak frequency shifting toward lower frequencies. Similar to the factory structure, when frequencies exceed 10 Hz, the impact of different embedment depths tends to stabilize. Overall, the seismic response indicated by the floor spectra decreases as the embedment depth increases.  
Figure 19: Safety shell structure’s floor peak relative displacement.
## 6 Conclusions  
This study systematically investigated the impact of ON pitch scaling and channel curvature on the performance of 3D NAND flash memory. By focusing on reducing the spacer length (Ls) and gate length (Lg), we aimed to improve the memory characteristics through detailed TCAD simulations. Our findings indicate that Lg scaling significantly affects the programmed threshold voltage (Vth) due to increased short channel effects (SCEs) and reduced capacitive coupling, whereas Ls scaling has a comparatively smaller impact. Additionally, the analysis revealed that Z-interference characteristics deteriorate more with Lg scaling compared to Ls scaling primarily due to weaker gate controllability. We also examined the effects of concave and convex channel structures on memory performance. The results demonstrate that convex channels improve the program speed and mitigate Z-interference by confining trapped charges more effectively, although excessive curvature may lead to retention characteristic deterioration. Overall, this research provides important insights into optimizing the trade-offs between the memory window and retention characteristics. By systematically analyzing the effects of Ls and Lg scaling and channel curvature, we offer valuable guidelines for enhancing the reliability and performance of 3D NAND flash memory. These findings can inform future developments in memory technology, ensuring better performance and reliability in increasingly dense and complex 3D NAND flash based-storage systems.  
Conceptualization, J.K.P.; methodology, J.K.P.; software, H.Y.B.; validation, S.K.H.; formal analysis, H.Y.B. and S.K.H.; investigation, H.Y.B.; writing--original draft preparation, H.Y.B.; writing--review and editing, J.K.P.; visualization, H.Y.B.; supervision, J.K.P.; project administration, J.K.P.; funding acquisition, J.K.P. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Electron-trapped charge after program operations in (**a**) concave and (**b**) convex channel structures and hole-trapped charge after erase operations in (**c**) concave and (**d**) convex channel structures when Lg/Ls is 20 nm. (**e**) P/E window according to channel curvature change in both Lg and Ls scaling.  
Funding: This study was supported by the Research Program funded by SeoulTech (Seoul National University of Science and Technology).  
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method
### Orr-Rvfln
#### 2.5.1 \(N<k\)  
A small number of data were generated to construct the first model using Equation (16). Then, \(\mathbf{F_{0}}\) and \(\alpha_{0}\) were set as first hidden layer output and output weight, respectively. When the \(k\)th sample arrives, \(\mathbf{R_{0}}\) is initialized as the unit matrix.  
\[\alpha=\mathbf{F}_{m}^{T}\Bigg{(}\frac{1}{B}+\mathbf{R}_{m}\mathbf{F}_{m} \mathbf{F}_{m}^{T}\Bigg{)}^{-1}\mathbf{R}_{m}\mathbf{Z}_{m}, \tag{17}\]  
\[\mathbf{F}_{m}=\begin{bmatrix}\mathbf{F}_{m-1}\\ \rho\mathbf{F}_{m}\end{bmatrix}and\quad\mathbf{R}_{m}=\begin{pmatrix}\mathbf{ R}_{m}&0\\ 0&\rho\mathbf{R}_{m}\end{pmatrix} \tag{18}\]  
\(\rho\mathbf{F}_{m}\) is the hidden layer output. Moreover, \(\rho\mathbf{R}_{m}\) is the empirical loss weight.  
\[\begin{split}\mathbf{M}_{m}^{-1}&=\Bigg{(}\frac{1}{B}+ \mathbf{R}_{m}\mathbf{H}_{m}\mathbf{F}_{m}^{T}\Bigg{)}^{-1}\\ &=\begin{pmatrix}\mathbf{R}_{m-1}\mathbf{F}_{m-1}\mathbf{F}_{m-1}^{T}+ \frac{1}{B}&\mathbf{R}_{m-1}\mathbf{F}_{m-1}\rho\mathbf{F}_{m}^{T}\\ \rho\mathbf{R}_{m}\rho\mathbf{F}_{m}\mathbf{F}_{m-1}^{T}&\rho\mathbf{R}_{m} \rho\mathbf{F}_{m}\rho\mathbf{F}_{m}^{T}+\frac{1}{B}\end{pmatrix}^{-1}\end{split} \tag{19}\]  
Thanks to the tuning parameter \(B\), \(M_{m}\) can always be calculated by inversion.  
\[\mathbf{M}_{m}^{-1}=\begin{pmatrix}E&F\\ G&L\end{pmatrix} \tag{20}\]  
Equation (21) is obtained by using Equations (19) and (20).  
\[\begin{cases}E&=\mathbf{M}_{m-1}^{-1}+\mathbf{M}_{m-1}^{-1}\mathbf{R}_{m-1} \mathbf{F}_{m-1}\rho\mathbf{F}_{m}^{T}\mathbf{D}_{m}^{-1}\rho\mathbf{R}_{m} \rho\mathbf{F}_{m}\mathbf{F}_{m-1}^{T}\mathbf{M}_{m-1}^{-1}\\ F&=-\mathbf{M}_{m-1}^{-1}\mathbf{R}_{m-1}\mathbf{F}_{m-1}\rho\mathbf{F}_{m}^{ T}\mathbf{D}_{m}^{-1}\\ G&=-\mathbf{D}_{m}^{-1}\rho\mathbf{R}_{m}\rho\mathbf{F}_{m}\mathbf{F}_{m-1}^{T} \mathbf{M}_{m-1}^{-1}\\ L&=-\mathbf{D}_{m}^{-1}\end{cases}, \tag{21}\]  
\[\begin{split}\mathbf{D}_{m}&=\rho\mathbf{R}_{m}\rho\mathbf{F}_{m }\rho\mathbf{F}_{m}^{T}+\frac{1}{B}\\ &\quad-\rho\mathbf{R}_{m}\rho\mathbf{F}_{m}\mathbf{F}_{m-1}^{T} \mathbf{M}_{m-1}^{-1}\mathbf{R}_{m-1}\mathbf{F}_{m-1}\rho\mathbf{F}_{m}^{T} \end{split} \tag{22}\]  
Equation (23) is obtained by using Equations (17) and (22).  
\[\alpha=\mathbf{F}_{m}^{T}\mathbf{M}_{m}^{-1}\mathbf{R}_{m}\mathbf{Z}_{m} \tag{23}\]  
\(\mathbf{Z}_{m}=[\mathbf{Z}_{m-1},\rho\mathbf{Z}_{m}]\). When \(N<K\), between Equations (18) and (23), the update algorithm works when \(m\)th data arrive. Since \(\mathbf{F}_{m}\) contains consecutive examples, a problem arises in which the learning time increases with the accumulation of samples.
## 4 Results and Discussion
### Hardened Concrete Properties
#### 4.3.3 Compressive Strength  
The compressive strength values obtained for concrete samples with CWG are presented in Figure 12. The compressive strength as expected increased with age in conformity with the time-based hydration reaction. It is not clear from the present experimental data whether glass substitution affects the time to reaching peak strength as in all cases; as shown in Figure 12, there was a gradual increase in compression strength with curing age nearly plateauing at 28 days. The 28-days strength for the control mix was 31.07 MPa, which is consistent with the mix design. There is a gradual increase in peak strength with glass substitution peaking at 34 MPa for 15%. However, when reaching 30% of the fresh sand replacement, the compressive strength dropped to 31.5 MPa and continued to drop with increasing glass substitution reaching 25.81 MPa for 100% CWG sands. This behaviour could be a combination of factors. The faceted glass particles provided increased specific surface for adhesion and would account for increased strength for small substitutions. In addition, the finer glass particles may also provide increased compaction that fills gaps in the sand. However, the relatively smooth surface of the recycled glass aggregates and the sharp edges would amount to lower adhesion between the cement paste. The possibility of glass exhibiting pozzolanic activity would strengthen the concrete [30]; however, the ability of glass particles to act as effective pozzolans requires very high specific surface that can only be achieved in glass particles with an average diameter less than 140 \(\upmu\)m. The glass sands employed here with a particle size of 0.1-1.0 mm are less likely to offer much pozzolanic contribution.  
The fracture pattern of compression tests followed mostly ASTM type c, as shown in Figure 13 for all compositions. The possibility exists that the difference in density might have led to segregation and non-uniform distribution of glass sand that would cause a differentiation of fracture pattern for different levels of substitution. The similarity in fracture pattern suggests that the glass sand substituted was uniformly distributed for all proportions of glass sands.  
Figure 12: Effect of glass content on compressive strength concrete samples.
## 7 Results and Discussion
### Energy Consumed at a Constant Speed  
The energy consumed in one minute is shown in Figure 13, summarizing that the test was performed at different constant speeds and in different speeds gearbox. As can be seen, at low speeds, the energy consumption is similar. Still, the energy consumption is lower at high speeds, with the final ratio of 3.5 concerning the original final ratio of 4.3.
## 3 Resequencing and Platooning Algorithm for Solving CSP
### Platoon Forming Algorithm  
In the framework of the DRP strategy, the crossing sequence of CAVs within the OZ undergoes reassessment every 2 s to ensure the timely sorting of newly entering CAVs. However, this mechanism may introduce challenges in certain scenarios. Figure 7 depicts a scenario where CAV 1, potentially colliding with CAV 2, exits the OZ. Subsequently, the updated crossing sequence may allow CAV 2 to enter the MZ before CAV 3, which shares the same organizing segment as CAV 1. These unnecessary updates could result in additional collision avoidance costs, leading to increased travel delays and impacting the overall efficiency of traffic flow.  
To mitigate this challenge, we have developed a PFA. This algorithm's essence lies in facilitating CAVs entering the MZ one after another within a single organizing segment to organize and sustain an orderly platoon. With this approach, even after the leading vehicle exits the OZ, the succeeding vehicles within the platoon can proceed into the MZ in a predetermined sequence, effectively minimizing the need for unnecessary updates to the crossing sequence. This strategic measure averts superfluous collision avoidance costs, resulting in a significant reduction in travel delays experienced by CAVs.  
Specifically, if the organizing segment identifier \(os_{i}\) in \(\textbf{sq}_{i}\) is the same as the organizing segment identifier \(os_{i-1}\) in \(\textbf{sq}_{i-1}\), then CAV \(sq_{i}\) will join the platoon \(P_{j}\) where CAV \(sq_{i-1}\) is located, forming the platoon vector \(\textbf{p}_{j}=\left[sq_{i},sq_{i-1},os_{i}\right]\). A platoon can accommodate multiple CAVs. If the organizing segment identifier \(os_{i+1}\) in \(\textbf{sq}_{i+1}\) is different from the organizing segment identifier \(os_{i}\) in \(\textbf{sq}_{i}\), then CAV \(sq_{i+1}\) will be unable to join platoon \(P_{j}\) and will instead form a new platoon \(P_{j+1}\).  
In Algorithm 2, \(m\) is the number of platoon vectors, and \(m\leq n\). The obtained set \(PQ=\{\mathbf{p}_{1},\ldots,\mathbf{p}_{\mathbf{m}}\}\) constitutes a crossing sequence of CAV platoons, achieving minimization of travel delays for CAVs. When the lead vehicle in platoon \(P_{j}\) exits the OZ, all CAVs in platoon \(P_{j}\) are removed from \(SQ\) and no longer participate in the PFA.  
```
1. Input: \(SQ=\{\mathbf{sq_{i}},\ldots,\mathbf{sq_{n}}\}\).
2. Generate the initial platoon \(P_{1}\), where the initial platoon vector \(\mathbf{p}_{1}=[sq_{1}]\), and the initial platoon label \(j=1\).
3. For\(i\) in range (2, \(n\))do If the identifier \(\mathbf{os}_{i}\) in \(\mathbf{sq_{i}}\) is the same as the identifier \(\mathbf{os}_{i-1}\) in \(\mathbf{sq_{i-1}}\) then add \(sq_{i}\) to platoon vector \(\mathbf{p}_{j}\). Else add \(\mathbf{os}_{i-1}\) to platoon vector \(\mathbf{p}_{j}\). add platoon vector \(\mathbf{p}_{j}\) to \(PQ\). \(j++\), generate platoon \(P_{j}\), add \(sq_{i}\) to platoon vector \(\mathbf{p}_{j}\). End if End for \(m=j\).
4. Output: \(PQ=\{\mathbf{p}_{1},\ldots,\mathbf{p}_{\mathbf{m}}\}\), \(m\leq n\).
```  
**Algorithm 2**Platoon Forming Algorithm
## 1 Introduction  
The space industry is experiencing a surge in new satellite constellations, some of which are much larger than those originally conceived in the 1990s, creating a new category commonly referred to as \"mega-constellations\" [1]. Despite advancements in satellite technology through the decades, the process of deploying these constellations still relies on traditional methods, such as orbital transfer vehicles or significant propulsion systems onboard each satellite, which still pose the same challenges for constellation operators, largely revolving around dispersing their satellites in orbit [2, 3]. Momentum exchange tethers (METs), a non-traditional propulsion method, could be a solution to alleviate some of the difficulties associated with satellite deployment and dispersion. MET-equipped deployers or orbital transfer vehicles (OTVs) could be made to rotate payloads on tethers and release them, providing rapid \(\Delta\)V changes without necessarily requiring propellants based systems. Early research insights have determined that they could simplify and expedite constellation deployment, reducing deployment times from years to weeks.  
For this new concept blending the practices of satellite constellations and METs, this article will explain some of the basic concepts and considerations involved, which could be used to support potential future constellations. While not an exhaustive explanation of all factors to consider, it nonetheless provides a collection of building blocks to help start designing such a constellation. This article includes insights specifically on tether-deployed constellation orbit design and a conceptual deployment walkthrough. After introducing the existing technology, challenges, and significance of this research, five different questions for orbit design are discussed, the answers to which help narrow down options that can be selected for different constellation architectures. One of these options is then selected for a conceptual constellation of 12 satellites, deployed using one of the simplest approaches described here.
# Synthesis and Application in Cell Imaging of
## 2 Materials and Methods
### Synthesis and Identification of MeAcd12C and MeAcd12P  
Similar synthesis procedure for MeAcd12C and MeAcd12P incorporating 1-bromododecane and 1,12-dibromododecane was adopted. A total of 5.13 mmol of acridone, 5.14 mmol of potassium iodide, 9.09 mmol of potassium hydroxide, and 20 mL of THF were mixed in a pot and heated to 40 \({}^{\circ}\)C for 1 h. 10-(12-bromododecyl) acridone was extracted by the mixture of DCM and NaOH.  
NMR spectroscopy was used to identify the compounds and intermediates present during the synthesis procedure (Figure S1a). Details of the \({}^{1}\)H-NMR data of MeAcd12P are as follows: \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)): \(\delta\) 8.87 (d, \(J\) = 6.5 Hz, 4H), 8.70 (d, \(J\) = 2.2 Hz, 2H), 8.28-8.24 (m, 6H), 8.22 (d, \(J\) = 10.9 Hz, 2H), 8.00 (d, \(J\) = 9.2 Hz, 2H), 7.62 (d, \(J\) = 16.3 Hz, 2H), 4.58 (s, 2H), 4.26 (s, 6H), 3.27 (dd, \(J\) = 11.2, 2.1 Hz, 4H), 2.96 (s, 3H), 1.83 (s, 2H), 1.78-1.70 (m, 4H), 1.63 (s, 2H), 1.58-1.44 (m, 4H),1.38 (s, 2H), 1.28 (s, 14H). MS(ESL,m/z): 652.4; found, 654.6; Anal. Calcd % for C44H52N4O: C, 80.94; H, 8.03; N, 8.58; O, 2.45. Data for MeAcd12C (Figure S1b): 1H NMR (400 MHz, DMSO-\(d_{6}\)): 8 8.89-8.84 (m, 4H), 8.68 (d, \(J\) = 2.2 Hz, 2H), 8.25 (d, \(J\) = 1.5 Hz, 4H), 8.22 (d, \(J\) = 16.9 Hz, 2H), 8.00 (d, \(J\) = 9.3 Hz, 2H), 7.61 (d, \(J\) = 16.3 Hz, 2H), 4.58 (t, \(J\) = 7.7 Hz, 2H), 4.25 (s, 6H), 1.81 (d, \(J\) = 7.3 Hz, 2H), 1.51 (d, \(J\) = 7.9 Hz, 2H), 1.35 (d, \(J\) = 8.5 Hz, 2H), 1.24 (d, \(J\) = 10.3 Hz, 14H), 0.85-0.79 (m, 3H). MS(ESI,m/z): 569.3; found, 569.5; Anal. Calcd % for C39H43N3O: C, 82.21; H, 7.61; N, 7.37; O, 2.81.
## 4 Simulation and Experimental Results  
### Simulation and Analysis  
The trajectory planning method proposed in this paper was simulated from the starting point coordinates (\(-152.5\) mm, \(0\), \(-800\) mm) to end point coordinates (\(152.5\) mm, \(0\), \(-800\) mm) using \(h=50\) mm. Assume that the prescribed geometrical constraint was \(6\)mm, \(V_{B}=1.2\) m/s, \(V_{N}=0.8\) m/s, \(V_{\max}=2.4\) m/s. As a comparison method, the trajectory planning based on motion superposition [35] was also simulated. In the comparison method, the maximum velocity in the vertical direction was set to \(1.2\) m/s, and the maximum velocity in the horizontal direction was set to \(2.4\) m/s, which were the same as the proposed method.  
Figure 4 illustrates the path of the mentioned two approaches in base coordinate systems for Delta robot. The deviation between the right angle and PH curve in the proposed method is \(6\) mm, while the deviation in the comparison method is \(3.7\) mm. The deviation in the comparison method is fixed and cannot be changed, while the deviation in the proposed method can be set by specifying the prescribed geometrical constraint.
## 2 Materials and Methods
### Assumptions and Limitations  
Several considerations were assumed for the simplification of the analysis of impacts. The most significant are listed below:  
* The fixed CO2 by the growth of biomass (either maize crops or wood) was excluded from calculations. The principal reason was the difficulty of obtaining reliable data, and the consequent deviations generated in the comparative LCA.
* The physical and chemical properties of cellulose were used for the simulation of the starch hydrolysis process, given the similarities between the two polymers.
* Potential uses for the lignin streams have not been included in the scope of this work, as well as the impacts derived from wastewater treatment processes.
* Secondary products and allocation methods were not considered. This involved both systems that were evaluated under the worst environmental scenario since the impacts are not shared with the by-products obtained in the processes. At this point, it must be taken into account that the Ecoinvent 2.2 database includes an allocation method for the maize starch production process. Specifically, the applied economic allocation assigns 83% of the environmental impacts on starch. Despite this fact slightly affects the absolute values of the mid-point indicators obtained for the maize system and comparative LCA results are barely influenced, it has been taking into account for calculations. Thus, mid-point indicators of MS-bp were obtained, excluding the allocation method considered within the Ecoinvent 2.2 database. For comparison purposes, values of mid-point indicators of MS-bp showed in the supporting information are presented in both ways (with and without allocation).
* Procedures downstream of the hydrolysis stage, such as the purification of the glucose, were not included in the scope of this analysis. Purification main impacts arise from the evaporation of the exceeding process water to reach a suitable product concentration for commercial applications [2]. As identical starting and end concentrations of the glucose solutions in both analyzed processes would lead to identical results, this stage has not been considered.
* Other auxiliary processes, such as wastewater treatment, heat integration, or solid combustion, were not taken into account.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### Password Change  
When a user \(U_{i}\) wants to update his password, he invokes the optional password change procedure. The steps of this procedure are described below:  
1. \(ID_{i}\) and \(PW_{i}\) are input to its \(OBU_{i}\) by \(U_{i}\).
2. First, \(OBU_{i}\) verifies \(ID_{i}\) checks whether \(B_{i}=h(PW_{i})\oplus C_{i}\). If so, \(U_{i}\) will be requested to input his new password \(PW_{i}^{*}\). \(OBU_{i}\) computes \(C_{i}^{*}=C_{i}\oplus h(PW_{i})\bigoplus h(PW_{i}^{*})\) and replaces \(C_{i}\) with \(C_{i}^{*}\). Otherwise, the request will be rejected.