esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 TB-RPL Mechanism
### Presentation of TB-RPL Mechanism
While TO is still simply the originator of the DAOs set;
* When the receiver obtains this DAO including the weak flag, it (if the receiver is not a weak node) will read and store the content of TIO field in the provided order for producing a source routing header in the future and then performs the related functions like the LBR in the non-storing mode. For comparison, in the storing mode of standard RPL, when a node's memory is full and cannot store more routing entries, the new coming DAO will be just discarded. Then, the node will not send any consequent DAOs to its preferred parent, while, in our proposed work, the designed content in a designated order is contained in DAOs to solve that problem. TB-RPL DAOs set works on the above foundation. Figure 6 describes a complete process of TB-RPL to establish a downward route. In this case, node G is assumed to finish a downward routing establishment before node F. Currently, node C stores the downward route information of nodes G and D, then it runs out of memory space for route entries. So, node C does not save node F's downward route. When 2:DAO(F,C) arrives at node C, it turns into a weak node and outputs a DAO with the weak flag to its preferred parent node B. For a weak DAO, the order of element in \(Trg(T)\) is strictly designated, which will be used by node B to perform source routing in the future. This order-designated \(Trg(T)\) is stored by node B into an ordered tuple [C, E] as an atomic unit. At last, 4:DAO(F,A) is processed by node A, then the downward routes to node G and F have been established, respectively. Based on this downward route, node G and node F can send messages to each other. Figure 7 illustrates the data flows and related message control headers. Node B produces a source routing header, due to node C not storing the related route segment to node F. With the guide of source routing from node B, node C successfully forwards packets to node E. Then, the source routing finishes, and node E can find a route to F through its own route table. The other packets are transmitted as designed. P2P communications between node G and F are performed successfully. Figure 6: An establishment of downward route between node G and F and corresponding DAOs sets in TB-RPL. The pattern “#:DAO(X,Y)” (X, Y are placeholders) indicates that the logical order in the corresponding DAOs set is number #, while the originator of this downward routing is node X and the preferred parent of originator/current producer is node Y. Specifically, the route tables above only show the related items in this P2P communication between node G and F, while other non-related route entries are not shown.
# The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
## 4 Results and discussion
### Dependence on Liquid Viscosity  
As mentioned above, viscosity is one of the main physical properties of the liquid that influences the disperse characteristics of the formed liquid droplets. To analyze the effect of liquid viscosity on the disperse composition of the resulting aerosol, an aqueous solution with a volume content of glycerol of 20%; 30%; 40%; 50%; and 60% was used. The viscosities of the liquid were, respectively: 1.8; 2.5; 3.7; 6.0; and 10.8 mPa-s. The sound pressure level was 182 dB, and the liquid flow rate was 5 mL/s.  
Histograms of the droplet diameter distribution upon the spraying of liquids with different viscosities are presented in Figure 12.  
As can be seen from the histograms, with the increasing viscosity of the liquid, the droplet diameters tend to migrate to the large-diameter domain. The spread of drops relative to the median diameter increases likewise. For example, at a liquid viscosity of 1.8 mPa-s, the median diameter of the formed droplets is 34 \(\upmu\)m, and with increasing viscosity up to 10.8 mPa-s, the median diameter increases to 57 \(\upmu\)m.  
The dependence of the median diameter of the formed droplets on the viscosity of the liquid is presented in Figure 13. Each experimental point in Figure 13 was obtained by averaging 10 Sauter diameter measurements (determined using Malvern SprayTech). The experimental data satisfactorily correspond to polynomial regression. The error bars indicate standard deviation.  
Figure 11: Dependence of median diameters of formed droplets on sound pressure level. Error bars indicate standard deviation. Experimental data fit linear regression.  
The presented dependence shows that liquid viscosity had a poor effect on the diameter of the formed droplets. In the range of viscosities from 1 mPa-s (water) to 3.7 mPa-s (40% aqueous glycerol solution), a weak increase in the median diameter of the formed droplets (no more than 2 um) was observed. A further increase in the viscosity of the liquid resulted in an increase in the median diameter of the resulting droplets, up to 57 um. Apparently, this is explained by the fact that the forces of viscous friction prevent the development of Faraday waves and generation of smaller droplets. At the same time, the majority of droplets are formed by the mechanism of the structural breakdown of a liquid droplet (large droplets larger than 100 um are formed).
## 4 Discussion  
This study examined the effect of upper and lower-body compression garments on the body kinematics and shooting accuracy of basketball free-throws. Our study found that upper-body (top) or full-body (top + bottom) compression garments significantly improved the performance of basketball free-throws; however, there was no significant improvement in the consistency of performance. Overall, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Range of Motion, Mean (Standard Deviation)**} & \multicolumn{3}{c}{**ANOVA Repeated Measure**} \\ \hline  & **Control-Pre** & **Top** & **Bottom** & **Full** & **Effect Size** & \(p\)**-Value** \\ \hline L hip FL/EX & 23.56 (6.8) & 22.58 (6.65) & 22.34 (7.8) & 21.81 (7.19) & 0.076  & 0.22 \\ R hip FI/EX & 26.47 (4.49) & 25.14 (6.02) & 26.19 (6.26) & 25.93 (5.85) & 0.065 & 0.274 \\ L hip AB/AD & 5.69 (1.73) & 6.46 (2.38) & 5.57 (1.64) & 6.28 (2.36) & 0.069 & 0.251 \\ R hip AB/AD & 6.95 (2.6) & 7.22 (2.72) & 6.31 (2.42) & 6.97 (2.62) & 0.069 & 0.248 \\ L hip rotation & 9.99 (3.57) & 10.03 (3.52) & 9.31 (3.04) & 9.45 (2.53) & 0.043  & 0.445 \\ R hip rotation & 12.62 (3.89) & 12.83 (4.19) & 12.43 (4.17) & 12.30 (4.61) & 0.013 & 0.861 \\ L knee FL/EX & 51.86 (8.63) & 51.30 (8.15) & 49.25 (10.08)  & 48.95 (3.73)  & 0.212 & 0.003  \\ R knee FL/EX & 53.77 (7.33) & 52.90 (7.88) & 53.61 (6.61) & 52.94 (6.67) & 0.036 & 0.549 \\ L knee rotation & 10.64 (4.39) & 11.35 (5.59) & 10.26 (3.99) & 10.35 (4.21) & 0.051  & 0.37 \\ R knee rotation & 14.96 (4.52) & 14.72 (6.12) & 15.68 (5.86) & 15.43 (5.01) & 0.031 & 0.61 \\ L knee AB/AD & 7.15 (4.52) & 7.92 (4.46) & 6.81 (2.89) & 7.00 (3.28) & 0.029  & 0.587 \\ R knee AB/AD & 8.32 (3.72) & 7.67 (3.84) & 9.10 (4.32) & 8.48 (3.58) & 0.048 & 0.418 \\ L ankle PL/DO & 61.22 (16.82) & 64.36 (8.64) & 61.71 (8.01) & 61.83 (9.08) & 0.049  & 0.362 \\ R ankle PL/DO & 60.93 (11.16) & 61.97 (6.1) & 61.07 (7.49) & 61.82 (8.21) & 0.019  & 0.682 \\ L ankle EV/V & 25.13 (4.166) & 22.84 (13.08) & 24.29 (11.37) & 23.38 (12.54) & 0.041 & 0.49 \\ R ankle EV/V & 26.49 (13.19) & 23.04 (11.28) & 22.95 (3.83) & 21.27 (10.34) & 0.123 & 0.056 \\ L ankle AB/AD & 15.28 (4.08) & 15.47 (4.5) & 15.52 (4.76) & 16.12 (5.18) & 0.016  & 0.732 \\ R ankle AB/AD & 13.98 (3.73) & 14.69 (4.46) & 15.17 (4.9) & 14.43 (4.14) & 0.077 & 0.204 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics and one-way ANOVA repeated measures outcome of the range of motion of the lower limb in different compression garment conditions.
## 2 Description of the Cracked Rotor System
We have \(\mathbf{D}=\mathbf{C}+\omega\mathbf{G}\), for which the damping matrix is \(\mathbf{C}\) taken as a classical Rayleigh damping for the shaft (i.e., \(\mathbf{C}=\alpha\mathbf{M_{s}}+\beta\mathbf{K_{s}}\), where \(\mathbf{M_{s}}\) and \(\mathbf{K_{s}}\) are the mass and stiffness matrices for the rotor shaft, and \((\alpha,\beta)\) constants of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Notation** & **Value** \\ \hline Radius of the shaft & \(R\) & 0.005 m \\ Length of the shaft & \(L\) & 0.5 m \\ Outer radius of discs 1 and 2 & \(R_{1}\) and \(R_{2}\) & 0.025 m and 0.01 m \\ Thickness of discs 1 and 2 & \(t_{1}\) and \(t_{2}\) & 0.015 m \\ Mass unbalance & \(m_{u}\) & 0.001 kg \\ Eccentricity of the mass unbalance & \(e_{u}\) & 0.01 m \\ Left vertical support stiffness & \(k_{x,1}\) & \(5\times 10^{5}\) N/m \\ Left horizontal support stiffness & \(k_{y,1}\) & \(7.5\times 10^{5}\) N/m \\ Right vertical support stiffness & \(k_{x,2}\) & \(5\times 10^{5}\) N/m \\ Right horizontal support stiffness & \(k_{y,2}\) & \(5\times 10^{5}\) N/m \\ Density of the shaft rotor & \(\rho\) & 7800 kg/m\({}^{3}\) \\ Young’s modulus of elasticity for the shaft rotor & \(E\) & \(2\times 10^{11}\) N/m\({}^{2}\) \\ Poisson ratio & \(\
u\) & 0.3 \\ Density of discs 1 and 2 & \(\rho_{1}\) and \(\rho_{2}\) & 7800 kg/m\({}^{3}\) \\ First Rayleigh damping coefficient & \(\alpha\) & 2.6 \\ Second Rayleigh damping coefficient & \(\beta\) & \(5\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanical and geometrical properties of the rotor system. proportionality). For the rest of the study, the values of these two proportional damping coefficients \((\alpha,\beta)\) are estimated by considering that the two reference vibration modes with a damping ratio of 0.5% (for the healthy rotor at rest) are associated with the first and second forward modes. It should be noted that the global stiffness matrix \(\mathbf{K}_{c}\) of the rotor system due to the presence of the crack, situated at the \(i\)th beam location on the rotor shaft, is given by  
\[ \begin{array}{ccccccccc}\text{diag}(\mathbf{K}_{\text{c}})=&(&\mathbf{0}& \cdots&\mathbf{0}&\mathbf{K}_{crack}&\mathbf{0}&\cdots&\mathbf{0}&)\\ &&&&\uparrow&&&&\\ &&&&i\text{th element}&&\end{array}  \tag{4}\]  
where \(\mathbf{K}_{crack}\) defines the stiffness matrix of the crack element and \(\mathbf{0}\) defines the \(8\times 8\) null matrix.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 3 Classification of Multi-Objective Evolutionary Algorithms from the View of Search Strategy
### Dominant Relation-Based MOEA Algorithms  
#### 3.2.1 Vector Evaluation Genetic Algorithm  
To process the target vector, [PERSON] proposed the VEGA in 1985 by extending the simple genetic algorithm (SGA). He invented the multi-objective optimization genetic algorithm and provided a Pareto non-inferior solution to the problem utilizing a parallel approach, which resolved the challenging issue of multi-objective optimization that had puzzled the operations research theoretical community [77]. It took the MOGA until the middle and late 1990s to establish a research field and gain success in numerous engineering applications due to computer hardware limitations.  
Vector assessment of the genetic algorithm is considered the improved version of the single objective genetic algorithm.It contains some original SGA operators using a proportional selection mechanism. The objective function for each child is produced corresponding to child groups, i.e., it is the objective function [78]. As we observe from Figure 3, the \(k\) multi-objective problems need to be assigned into random \(k\) groups, and each group size is \(N/k\), where \(N\) scales for the whole group. Each sub-objective function evaluates and selects independently in its corresponding sub-groups, and forms a new group to perform crossover and mutation operations. In this way, the process of \"segmentation, parallel, evaluation, selection, and merger\" proceeds in a cycle, resulting in a non-inferior solution to the problem.
# A Construction Method for the Random Factor-Based G Function
## 3 Analysis of the Principles and Limitations of Commonly Used G Functions
### G Function Based on M Sequence  
The G function based on m sequence is expressed as follows [27]:  
\[F_{n}=G(F_{n-1},X_{n},m) \tag{4}\]  
where m represents the m sequence. The current hopping frequency is not only related to the current information data and the previous hopping frequency, but also to the selected m sequence.  
The frequency-hopping points are divided into b subsets, and each log-b bit in the m sequence is used to select a subset. Specifically, when b = 4, each two bits of the m sequence (00, 01, 10, 11) correspond to frequency subsets (P1, P2, P3, P4), respectively. The frequency-hopping sequence generated by this method uses subset transformation to randomly jump to different subsets, thereby increasing the frequency point interval and improving the randomness and uniformity of the frequency-hopping sequence.  
However, the periodicity and specific correlation of the m sequence can lead to uneven frequency subset selection. This means that some subsets may be selected multiple times in a specific period, while the frequency points of other subsets are selected less frequently, resulting in uneven probabilities from a certain frequency-hopping point to other frequency-hopping points outside the subset. Additionally, if the number of frequency points in a subset is much larger than the number of frequency points actually used (known as the fan-out coefficient), the probability of going from a certain frequency-hopping point to other frequency-hopping points in the subset is also unequal.  
Therefore, while the frequency-hopping sequence generated by the G function based on the m sequence does increase the randomness and anti-interference ability of the frequency-hopping sequence to a certain extent, it may also affect the two-dimensional continuity performance of the sequence due to the unequal probability distribution.
## 2 Materials and Methods
### Experimental Setup
#### 2.2.3 Measurement System  
The core of the measurement system is a data logger. The logging system collects the sensor signal with an acquisition frequency of 1 kHz. To monitor pressures and temperatures during the tests, several gauges and thermocouples (type K) are installed along the fluidic line and on the hybrid motor. The quantities collected are upstream and downstream \(N_{2}\) pressure regulator (PR) pressures, oxidizer tank pressure and temperature, CV upstream pressure, catalyst bed inlet pressure and temperature, catalyst bed outlet pressure and temperature, and combustion chamber pressure. Due to their importance for the motor performance analysis, the combustion chamber pressure sensors are redundant. The oxidizer mass flow to the combustion chamber can be measured directly thanks to a Coriolis mass flow meter, which is positioned near the outlet of the oxidizer tank. The motor thrust is measured by means of a load-cell. The sensor list is reported in Table 1.  
The measurement system also includes the instruments necessary to measure the physical dimensions of the motor's consumables. A scale, with an uncertainty of 1 g, is used to weigh each component of the combustion chamber both before and after the test. Additionally, a dimensional control of the components is performed with a vernier caliper (uncertainty of 0.05 mm) to ensure that the dimensions meet the tolerances specified in the technical drawings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **Measured Variable** & **Range** & **Accuracy** \\ \hline Upstream PR & Pressure & 0-250 bar & \(\pm\)1.25 bar \\ Downstream PR & Pressure & 0-160 bar & \(\pm\)0.8 bar \\ Oxidizer tank & Pressure & 0-250 bar & \(\pm\)1.25 bar \\ Upstream CV & Pressure & 0-100 bar & \(\pm\)0.5 bar \\ Inlet catalyst bed & Pressure & 0-100 bar & \(\pm\)0.5 bar \\ Outlet catalyst bed & Pressure & 0-60 bar & \(\pm\)0.3 bar \\ Combustion chamber & Pressure & 0-100 bar & \(\pm\)0.5 bar \\ Combustion chamber & Pressure & 0-60 bar & \(\pm\)0.3 bar \\ Oxidizer tank & Temperature & 0-1100 \({}^{\circ}\)C & \(\pm\)2.2 \({}^{\circ}\)C or \(\pm\)0.75\({}^{\circ}\)\({}^{1}\) \\ Inlet catalyst bed & Temperature & 0-1100 \({}^{\circ}\)C & \(\pm\)2.2 \({}^{\circ}\)C or \(\pm\)0.75\({}^{\circ}\)\({}^{1}\) \\ Outlet catalyst bed & Temperature & 0-1100 \({}^{\circ}\)C & \(\pm\)2.2 \({}^{\circ}\)C or \(\pm\)0.75\({}^{\circ}\)\({}^{1}\) \\ Oxidizer mass flow & Mass flow & 0-5 kg/s & \(\pm\)0.75\% \\ Motor thrust & Thrust & 0-1000 kg & \(\leq\)0.03\% \\ \hline \hline \end{tabular} \({}^{\dagger}\)Thermocouple true accuracy is the lower of the two values, according to the measurement.  
\end{table}
Table 1: List of the gauges used to monitor the pressures, temperatures, oxidizer mass flow and motor thrust. These data are collected to be analyzed and post processed after the experimental test.  
Figure 6: Cross section of the lab-scale hybrid motor.
# HAIPO: Hybrid AI Algorithm-Based Post-Fabrication Optimization for Modern 3D NAND Flash Memory
## 3 Overview of NAND Post-Fabrication Optimization  
### Example of Post-Fabrication Optimization: Program Operation  
Post-fabrication optimization is a key step of the NAND flash manufacturing process to meet various requirements on reliability and performance while maximizing the yield. After fabricating a NAND flash chip, manufacturers should carefully optimize various internal _device parameters_ that determine the target voltage levels and timing values closely related to NAND read/program/erase operations. Each target device parameter value would be stored inside the NAND flash chip using an electrical fuse (eFuse) described in Section 2, and the NAND flash chip performs read/program/erase operations referring to optimized device parameters.  
Figure 5 shows how the device parameters for program operation are configured during post-fabrication optimization, which significantly affects the NAND flash chip's reliability and performance. Modern NAND flash memory commonly adopts the _incremental step pulse programming (ISPP)_ scheme [14] to secure sufficient \(V_{th}\) margins (shown in Figure 2) between adjacent threshold voltage (\(V_{th}\)) states (i.e., narrowing the width of each state's \(V_{th}\) distribution). To maximize \(V_{th}\) margin for flash reliability, the ISPP scheme performs multiple programming steps, gradually increasing the program voltage of each  
Figure 4: Overview of NAND eFuse operation.  
step from V\({}_{\text{PGM1}}\) by a certain amount of step voltage \(\Delta\)V\({}_{\text{PGM}}\). At the end of each programming step, the ISPP scheme performs _verify operations_ to determine whether target flash cells are programmed sufficiently (i.e., check each cell's current \(V_{th}\) level). If a cell's \(V_{th}\) level has sufficiently increased to be higher than the target verify voltage V\({}_{\text{VFFYi}}\), the ISPP scheme excludes the cell from the next programming steps. Therefore, for optimizing the program operation in NAND flash memory, we need to configure various voltage and timing parameters such as V\({}_{\text{PGM1}}\), \(\Delta\)V\({}_{\text{PGM}}\), and V\({}_{\text{VFFYi}}\).
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 2 System Model and Problem Formation
### Problem Formulation  
The main objective of this paper is to propose a sensorless control law for the boost converter. It is assumed that the induc current \(i\) is not measured. For the given \(v_{\star}\), the control law with the following features is designed.  
1. The estimate of the state \(i\) can converge to its real value with the FTC. That is, \[\hat{i}(t)=i(t),\ \ \forall t>t_{c}\] (3)
2. \((i_{\star},v_{\star})\) is an exponentially stable equilibrium of the closed-loop system. Namely, for all initial conditions, the following claim is achieved with the estimate \(\hat{i}\). \[\lim_{\infty}v(t)=v_{\star},\] (4) with all signals bounded.
## 4 The Proposed Key Distribution Framework
### A Multipath-Multi-Key Distribution Algorithm Complexity Analysis  
The complexity of the proposed spatiotemporal diversification-based multipath-multi-key distribution framework is analyzed in its core components: path selection, routing, and security evaluation mechanisms.  
1. The _path selection process_ uses an improved KSP algorithm, specifically an optimized version of [PERSON]'s algorithm, to identify secure and disjoint paths for key distribution. The computational complexity of [PERSON]'s algorithm for finding \(K\) shortest paths in a graph G(V,E) with \(m=|V|\) nodes and \(n=|E|\) edges is \(O(K\cdot(n+m\log m))\)[23]. In addition, a correlation-based path optimization step is introduced to minimize redundancy and reduce attack surfaces, adding an overhead of \(O(K^{2})\). Therefore, the overall complexity of the path selection phase is \(O(K\cdot(n+m\log m)+K^{2})\).
2. The _routing procedure_ employs an adaptive security-aware ExDijkstra algorithm, which evaluates paths based on a multiplicative security probability metric instead of an additive shortest-path criterion. The traditional Dijkstra algorithm has a complexity of \(O(m\log m+n)\), but with additional security computations, the proposed model introduces an overhead \(P\) for security evaluations, leading to a worst-case complexity of \(O(m\log m+n+P)\). Furthermore, the inspection module dynamically evaluates the trustworthiness of the nodes and enforces penalties for compromised relays. This phase consists of compromised node detection (\(O(K\cdot M)\), where \(M\) is the number of monitored relays). Combining these components, the total complexity of the proposed model is given by \(O(K\cdot(n+m\log m)+K^{2}+m\log m+n+K\cdot M)\). Since the number of selected paths (\(K\)) and monitored nodes (\(M\)) is significantly smaller than the total number of network nodes (\(m\)) and edges (\(n\)), the proposed algorithm remains computationally efficient and scalable.