esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study
### Scalability Analysis  
Having gained insight into the algorithm performance, we apply the model in cases with more MGs in order to verify the scalability of the proposed method. The iterative process of the operation cost in Period 23 is shown in Figure 27. Table 9 shows the comparison of the total operation cost and the average iteration number between coalitions with different numbers of MGs.  
As shown in Figure 27 and Table 9, the algorithm with different numbers of MGs converges to a stable state in about 40 iterations. The result indicates that the model can also be applied to a larger cluster of MGs, and the algorithm has a better performance.  
Furthermore, we evaluate MGs of different sizes to demonstrate scalability. Take three MGs as an example: we have conducted simulation on three MGs of different sizes. Figure 28 shows the iterative process comparison of the operation cost in Period 23 between three MGs of different sizes. The total operation cost comparison between three MGs of different sizes is represented in Table 10.  
As shown in Figure 28 and Table 10, the algorithm can be applicable to scenarios with MGs of different sizes, which shows a good performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Number of MGs** & **Total Operation Cost (CNY)** & **Average Iteration Number** \\ \hline
3 MGs & 10,449.7 & 33.33 \\
6 MGs & 22,905.7 & 33.50 \\
9 MGs & 34,716.3 & 36.04 \\
12 MGs & 46,045.1 & 37.32 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The comparison of the total operation cost and average iteration number between coalitions with different numbers of MGs.  
Figure 27: The iterative process of the operation cost in Period 23 for coalitions with different numbers of MGs.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 7 Performance Assessments
### Assessment Metrics
#### 7.1.2 Resource Utilization  
Resource demands (RD): Calculate the actual bandwidth demands of each SBS based on real-time service demands.  
Available resources (AR): Determine the system's available spectrum resources allocated to each SBS, considering interference constraints that limit the amount of spectrum that can be used in a given area.  
Resources actually used (RAU): Calculate the volume of resources (e.g., bandwidth) actually used by each SBS based on real-time service demands and the resources allocated to users.  
Overall system resource usage (OSRU): Gather statistics on the usage of system resources across all SBSs to provide insights into resource utilization and potential areas for optimization.
# Optimized Implementation and Analysis of CHAM in Quantum Computing
## 2 Background
### Linear Layer Optimization
#### 2.3.1 PLU Factorization  
The PLU factorization allows us an easy way to implement a binary matrix. Given a square matrix \(M\); the algorithm funds a permutation matrix \(P\), a lower triangular matrix \(L\), and an upper triangular matrix \(U\); such that \(M=PLU\). The matrix \(L\) has a value of \(0\) except for the lower triangular matrix, and on the contrary, matrix \(U\) has \(0\) except for the upper triangular matrix.  
\[M=\begin{bmatrix}1&0&1&0\\ 1&0&1&0\\ 0&1&1&0\\ 0&1&1&0\end{bmatrix}=P\cdot L\cdot U=\begin{bmatrix}1&0&0&0\\ 0&0&1&0\\ 0&1&0&0\\ 0&0&0&1\end{bmatrix}\cdot\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 1&0&1&0\\ 0&1&0&1\end{bmatrix}\cdot\begin{bmatrix}1&0&1&1\\ 0&1&1&0\\ 0&0&0&0\\ 0&0&0&0\end{bmatrix} \tag{6}\]  
In Equation (6), the first matrix is arbitrary square matrix \(M\), the second one is matrix \(P\), the third one is matrix \(L\), and the last one is matrix \(U\). In our case, the PLU matrices were obtained utilizing SageMath 9.8 [[https://www.sagemath.org/](https://www.sagemath.org/)]([https://www.sagemath.org/](https://www.sagemath.org/)) (accessed on 10 April 2023), which is an open-source programming tool for multiple Python-based mathematics. PLU factorization can be used to find the inverse of a non-singular matrix or a solution to a row-linear equation. Applying this method to ciphers can reduce the number of XOR2s in a general circuit and CNOT gates in a quantum circuit.  
In the past, PLU factorization was used in various contexts. For instance, the \(32\times 32\) binary matrix of AES (the MixColumn matrix) [16, 20, 22, 31] or the implementation of an elliptic curve [32].
## 6 Simulation Results and Discussions
### Simulation Environment and Setting Parameters  
Interest in optimizing IoT network performance has grown in academia, leading to the evaluation of various modeling and experimental frameworks. This is driven by the challenges of real-world experiments, such as complexity, time, and cost. This research focuses on selecting an environment suitable for diverse smart service scenarios and smart city applications. Simulations were conducted in MATLAB 9.8.0.1323502 (R2020a) to model various IoT network situations, using a laptop with a 12 th Gen Intel Core i7-12700H processor, 16 GB RAM, and Windows 11 Pro. A rectangular sensing area with randomly spaced SNs and an external sink node was used, as shown in Figure 9. Simulation parameters, summarized in Table 9, align with comparative protocols to ensure fairness. The intra-DFG communication model involves SNs transmitting _R-bit_ data packets to PDFHs, which then communicate with the sink node or PR in the inter-DFG model. To address randomness from SN placement, each scenario was simulated 50 times, and the average results were recorded.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification of** & **Parameter Name** & **Value for the** & **Value for the** \\
**Parameters** & **Parameter Name** & **First Scenario** & **Second Scenario** \\ \hline \multirow{7}{*}{Network parameter} & Network sensing area (\(W\times W\)) & 100 \(\times\) 100 m\({}^{2}\) & 500 \(\times\) 500 m\({}^{2}\) \\ \cline{2-3}  & Number of SNs (\(N\)) & 100 nodes & 300 nodes \\ \cline{2-3}  & The sink node position (\(x\),\(y\)) & (0,75) & (0,275) \\ \cline{2-3}  & Number of DFGs (\(C\)) & Dynamic \\ \cline{2-3}  & Type of SNs deployment & Random \\ \hline \multirow{3}{*}{Packet parameters} & Data packet length (\(NL_{\text{D}}\)) & 4000 bits \\ \cline{2-3}  & Control packet length (\(NL_{\text{C}}\)) & 200 bits \\ \cline{2-3}  & Number of packets & Variable \\ \hline \multirow{7}{*}{Energy parameters} & Simulator & MATLAB R2020 \\ \cline{2-3}  & SN’s initial energy (\(E_{lo}\)) & 1 J \\ \cline{2-3}  & Electronic energy (\(E_{\text{\emph{e}lec}}\)) & 50 nJ/bit \\ \cline{2-3}  & Energy data aggregation (\(E_{\text{\emph{f}ission}}\)) & 5 nJ/bit \\ \cline{2-3}  & Energy consumed by the transmission amplifier (\(\varepsilon_{\text{\emph{f}s}}\)) & 10 pJ/bit/m\({}^{2}\) \\ \cline{2-3}  & Energy consumed by the transmission amplifier (\(\varepsilon_{\text{\emph{m}p}}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ \cline{2-3}  & The cutoff distance (\(d_{o}\)) & 87.7 m \\ \hline \multirow{3}{*}{Execution parameters} & Number of simulation rounds & Variable \\ \cline{2-3}  & Maximum number of rounds & 60,000 rounds \\ \cline{1-1} \cline{2-3}  & Number of simulations runs & 50 runs \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulation parameters of the proposed protocol.
## 2 Materials and Methods: Ten-Koh Mission Description
### Payload
The ADS1220, a 24-bit ADC, and COTS strain-gauges are used to measure strain in two orthogonal directions inside the samples. The payloads are controlled using an experiment control unit (ECU) PIC. The MM constantly measures the samples' properties and, therefore, implements a dedicated controller not to block or interrupt the CPD and DLP operations. The magnetometer was already described in the scope of the ADS subsystem because it serves a dual purpose of attitude determination and scientific data generation. Another secondary payload consist of a GNSS receiver, which was included to fulfill two purposes:  
1. To increase the spatial and temporal localization of other payload measurements. 2. To investigate the GNSS signal disruption, total or partial, due to ionospheric varying conditions. The FireFly GNSS receiver with an active antenna model TW2406 (for GPS/GLONASS L1 band) were selected. This GNSS receiver was selected due to its low size (17.0 by 22.4 mm) and low power consumption (150 mW). It is considered one of the world's smallest space-capable GNSS receivers according to the manufacturer's information ([[http://www.sensorcomm.co.jp/catalog/firefly_flyer.pdf](http://www.sensorcomm.co.jp/catalog/firefly_flyer.pdf)]([http://www.sensorcomm.co.jp/catalog/firefly_flyer.pdf](http://www.sensorcomm.co.jp/catalog/firefly_flyer.pdf))). This receiver has been used on-board small rockets launched by JAXA and other university satellites such as the Fireant Cubesat [21]. The receiver was included within the same PCB as the MM to optimize the space available in the top of the spacecraft, and to minimize the number of PIC microcontrollers. However, due to the lack of resources in the MM PIC microcontroller to handle both the MM experiment and the GNSS readings, and due to the low priority assigned to this secondary payload, the GNSS receiver has not been used. Therefore, it is not mentioned any further in this paper  
Figure 4: Representation of the DLP measuring system: (**a**) I–V (current–voltage) sweep locations around a spinning spacecraft; and (**b–d**) 1-D model representing the electrodes inside and outside of the plasma sheath. Figure 5: Representation of the DLP measuring system: (**a**) DLP electrode made of solid aluminum, coated with 10 \(\upmu\)m of gold in the spherical tip, ceramic material (in white color) in the shaft for isolation and FR4 plate (in gray) on the base for isolation purposes from the structure of the spacecraft; and (**b**) the DLP measuring circuit block diagram. The dashed line indicates the DLP measuring circuit physically implemented in a single PCB.
# A Review of Research on Dropwise Condensation Heat Transfer
## 4 Methods, Problems, and Solutions for Promoting Dropwise Condensation  
### Methods for Promoting Dropwise Condensation  
Surface modification techniques are the main methods of promoting dropwise condensation because it has bright industrial application prospects. It can be done in two ways: building low free energy surfaces and building micro-nano structure surfaces. According to the basis proposed by [PERSON] [34], it can determinate condensation form by the difference between the free energy of the surface and liquid, so the stable dropwise condensation can be realized by reducing the condensate surface free energy to improve \(\Delta\)y. At the same time, scholars have found that there are many natural hydrophobic surfaces, such as cicada wing, lotus leaf, and so on. By studying these natural surfaces, it is found that there are many micro-nano structures on the surface, on which droplets can be suspended to achieve dropwise condensation. Therefore, fabricating micro-nano structure surfaces is another main method of promoting dropwise condensation by surface modification technique.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 7 Current Situation and Development Trend
### Development Trend
#### 7.2.6 Wireless Security Assured by SI Algorithms  
At present, SI algorithms apply to intrusion detection in network security (e.g., attack source location and cluster analysis), to cryptography (e.g., block cipher), and to reduce computation in a cryptographic algorithm [12]. The characteristics of distributed SI and strong robustness have great development prospects in network security. In the future, it is suggested to improve the global optimum ability and convergence speed of the SI algorithms and reduce the alarming rate of intrusion detection.
## 5 Results
### Damage Estimation  
* Damage Estimation at the county scale  
Figure 10 shows the estimated damage map from Naive Bayes, SVM, and deep learning algorithms at the county scale. Based on the results of the three predictive algorithms, Napa and Sonoma counties had the most damage, and San Joaquin and San Mateo had the least damage. The results from both Naive Bayes and deep learning algorithms showed the \"violent\" damage class for San Francisco and \"strong\" damage class for Lake; while the results of the SVM algorithm predicted the \"strong\" earthquake damage class for San Francisco and \"violent\" and \"strong\" earthquake damage classes for Lake. Generally, according to Figure 10, with the distance from the earthquake center, the amount of damage was reduced, so because of the distance from the epicenter, people were less affected by the earthquake and published fewer earthquake-related messages.  
Figure 10: Cont.  
* Damage Estimation at the city scale  
Figure 11 shows the estimated damage map from Naive Bayes, SVM, and deep learning at the city scale. Based on Figure 11, the estimated damage map for all three predictive algorithms showed most of the earthquake losses were in cities located in the counties of Napa, San Francisco, Contra Costa, Almeda, Santa Clara, and Solano. In addition, the results of the SVM classification model showed that little damage was estimated for the cities located in San Joaquin, whereas the results from the Naive and deep learning models have predicted a great deal of damage for these cities.  
Figure 11: Cont.  
Figure 10: The estimated damage map from (**a**) Naive Bayes, (**b**) SVM, and (**c**) deep learning algorithms at the county scale.  
* Damage Estimation at the 10 \(\times\) 10 km grids scale  
Figure 12 shows the estimated damage map from Naive Bayes, SVM, and deep learning algorithms at the 10 \(\times\) 10 km grids scale. The results of earthquake damage estimation by three predictive algorithms showed that with increasing distance from the center of the earthquake, the intensity of earthquake damage was reduced.  
Figure 11: The estimated damage map from (**a**) Naive Bayes, (**b**) SVM, and (**c**) deep learning algorithms at the city scale.  
Figure 12: _Cont._
# Concurrent Validity of the Inertial Measurement Unit _Vmaxpro_ in Vertical Jump Estimation
## 2 Materials and Methods
### Experimental Procedure  
The procedure was carried out in three sessions separated by seven days each in the sports science laboratory of the University of Alicante, as shown in Figure 1. All participants performed the tests at the same time of day to avoid possible effects associated with circadian rhythms. In the first session, participants were familiarized with the experimental protocols and anthropometric measurements were carried out (Table 1). In the second and third sessions, participants performed a standardized warm-up consisting of five minutes of continuous running followed by three minutes of dynamic range-of-motion exercises. Next, they executed two minutes of familiarization jumps in which the subjects were instructed in the initial and final positions of VJH. The warm-up was followed by four minutes of rest during which the inertial device was placed, and the jumping protocols were reviewed. Finally, subjects performed 10 CMJ jumps with two minutes between each attempt to avoid the effects of fatigue [30].  
To avoid displacements in the transverse and frontal plane, jumps were performed on a 29.2 cm \(\times\) 42 cm bounded surface so that both the take-off and landing phases were executed completely within the boundaries. CMJs were performed with a rapid descent from a self-selected depth by each participant [31; 32] followed by a quick ascent to achieve take-off. All tests were performed with the hands placed on the iliac crests in the Akimbo position [33] to avoid the variability generated by the action of the arms.  
The participants were instructed to jump as high as possible on each attempt and to land on the tips of their toes, imitating the position adopted by the ankle joint in the take-off phase to minimize the error produced by variations in the angle of ankle flexion in the landing phase [13]. Jumps were always supervised by an instructor to avoid any errorsin execution. Attempts were considered invalid if the subjects did not land within the established limits, did not land on their toes, or separated their hands from the iliac crests in any phase of the jump. All records were collected simultaneously by both measuring instruments, IMU _Vmaxpro_ and MOCAP _Optitrack system_.
## 2 Materials and Methods
### AIS Data Scope and Sources  
In order to identify suspicious vessels, data from the automatic identification system (AIS) were analyzed. The data were collected by the Maritime University of Szczecin (Poland) using its own AIS receivers, as well as land-based systems such as AIS PL (Poland) and AIS DK (Denmark). The messages transmitted via the AIS are encoded according to the NMEA standard (National Marine Electronics Association), a protocol used for communication between electronic navigation devices. An example of raw data in NMEA format is presented in Figure 2.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 3 CF of the Aggregated Interference in LOS
### Numerical Validation on Aggregated Interference
Figure 8: Average number of users served vs. antenna ratio \(\log_{2}(N_{c}/N_{v})\), within \(R=100\) m, while keeping constant the total number of antennas \((N_{c}N_{v}=256)\) for different array height \(h=\{5,10,15,20\}\) m, \(R_{max}=400\) m, SIR threshold \([T]_{dB}=5\), normalized amplitude\(|\beta_{0}|=1\), \(\lambda=5\times 10^{-2}\) m\({}^{-2}\): Solid lines correspond to \(2b=2\) and dashed lines corresponding to \(2b=3.6\). Figure 7: Average number of users served vs. antenna ratio \(\log_{2}(N_{c}/N_{v})\), within \(\bar{R}=50\) m, while keeping constant the total number of antennas \((N_{c}N_{v}=256)\) for different SIR thresholds \([T]_{dB}=\{0,5,10\}\), AP height \(h=5\) m, \(R_{max}=400\) m, normalized amplitude \(|\beta_{0}|=1\), \(\lambda=5\times 10^{-2}\) m\({}^{-2}\): solid lines correspond to \(2b=2\) and dashed lines corresponding to \(2b=3.6\). A pragmatic conclusion from these evaluations is that for most real-life scenarios where UE densities are not too high, it is beneficial to invest in UCA arrangement rather than UcylA. There are two reasons supporting this result: (i) the most powerful interferers are in the vicinity of the AP, while the minimum separable angle by the ULA is \(\Delta\theta_{min}\approx\frac{\lambda}{N_{e}d_{obs}(\theta_{0})}\), where \(d_{v}\) is the vertical inter-element spacing, and \(\theta_{0}\) is the target elevation; (ii) although the resolution was not angle dependent, decomposing the whole elevation plane into small portions of the same width, provides emphasis in the same as near and to far-away UEs, while the most powerful UEs are closer ones. In the rest of the numerical examples in the paper, we focus on the usage of UCA, since the target threshold used is set to \([T]_{dB}=0\) and the interferer density used is not extremely high, which justifies the usage of a UCA over UcylA. Note that although the leftmost parts of the curves (i.e., corresponding to a ULA or a very tall UcylA) are shown in the figures, in practice, they are not feasible to deploy. Therefore, one might consider the range \(N_{c}>N_{v}/8\) as practical solution.
## 3 Results
### Linear Density of Fibers  
The linear density values of okra best fibers are presented in Table 2 and Figure 3. As can be seen, the results show a clear, decreasing trend from the bottom \(>\) middle \(>\) top (\(p\)-value \(3.69\times 10^{-11}\)). This trend is valid for both water-retted and dew-retted fibers. Similar findings were reported by [PERSON] et al. [14] for okra best fibers and by [PERSON] et al. [16] for nettle fibers. These findings suggest that okra best fibers, which occur throughout the okra stem, have a tapered shape. However, it should also be noted that the measured fibers are not single fibers; they are a bunch of fibers in basic form. So, the efficiency of the fiber separation and of the removal of extra-cellulosic substances also play an important role in the final linear density of the fibers. The dew-retted fibers generally exhibit higher linear density values compared to the water-retted fibers (\(p\)-value \(7.29\times 10^{-11}\)). Dew-retting for longer durations could be tested to determine if it leads to lower linear density values. Histograms of different samples are shown in Figure 4. The broadest diameter distribution belongs to fibers extracted from the bottom portion of the okra stems, for both water- and dew-retting.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample Source** & **Sample Code** \\ \hline Water-retted bottom okra best fiber & BBW \\ Water-retted middle okra best fiber & MBW \\ Water-retted upper okra best fiber & UBW \\ Dew-retted bottom okra best fiber & BBD \\ Dew-retted middle okra best fiber & MBD \\ Dew-retted upper okra best fiber & UBD \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample codes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Okra Bast Fiber** & **Fiber Yield (\%)** & \multicolumn{2}{c}{**Linear Density (tex)**} & \multicolumn{2}{c}{**Breaking Strength (cN/tex)**} & \multicolumn{2}{c}{**Elongation at Break (\%)**} \\ \cline{3-8}  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline BBW & 14.47 & 11.90 & 4.30 & 42.01 & 17.40 & 3.33 & 1.07 \\ MBW & 14.33 & 10.25 & 2.66 & 45.76 & 16.29 & 3.40 & 0.92 \\ UBW & 12.65 & 9.45 & 2.79 & 51.80 & 18.30 & 3.48 & 1.30 \\ BBD & 14.73 & 17.09 & 6.95 & 33.93 & 18.99 & 2.75 & 0.98 \\ MBD & 10.85 & 13.13 & 3.68 & 32.50 & 14.09 & 2.83 & 0.87 \\ UBD & 14.25 & 11.17 & 3.07 & 37.12 & 16.71 & 2.90 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The effect of extracted plant part and retting method on the physical and mechanical properties of okra best fibers.  
Figure 4: _Cont._  
Figure 3: Effect of plant section used for fiber extraction and the retting method on the fiber linear density (tex) (Error bars represent standard errors).  
The linear density values of the obtained okra best fibers are given in Figure 3.
## 2 Material and Methods
### Models Design and Evaluation
#### 2.2.5 Models Processing and Performance Evaluation  
The processing steps of the proposed models were implemented through five stages as presented in Figure 2. MATLAB software was used to design and evaluate all models. Herein, eight variables are used as inputs for modeling S and CS of SCC. First, the whole data was normalized using Equation (17) for modeling, and output of models was inversed to original data for evaluation. Then, the data was divided into three categories, the training phase for designing the proposed models, testing phase for evaluating the models, and validation phase for qualify the models. The data was divided into 70% and 30% for the training and testing stages, respectively; whereas, the whole data was used in the validation phase. After that, the models were designed and evaluated in the training stage and compared in the testing stage. The models were evaluated and compared using a large number of data in the validation stage. Here, four statistical criteria, Equations (18)-(21), were applied to assess the performance of the proposed models; these are \(r\) (Equation (18)), \(RMSE\) (Equation (19)), mean absolute error (\(MAE\)) (Equation (20)), and percentage of \(RMSE\) (\(PE\)) (Equation (21)).  
\[d_{n}=(D-D_{min})/(D_{max}-D_{min}) \tag{17}\]  
where, \(d_{n}\) is the normalized data; \(D\), \(D_{max}\), and \(D_{min}\) are the data value, maximum and minimum data used, respectively.  
\[r=\frac{\sum_{i=1}^{N}(S_{o}-S_{o})\Big{(}S_{p}-S_{p}\Big{)}}{\sqrt{\sum_{i=1 }^{N}(S_{o}-S_{o})^{2}\sum_{i=1}^{N}\big{(}S_{p}-S_{p}\Big{)}^{2}}}\times 100 \tag{18}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\big{(}S_{p}-S_{o}\big{)}^{2}}{N}} \tag{19}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\big{|}S_{p}-S_{o}\big{|} \tag{20}\]  
\[PE=\frac{RMSE}{S_{o\_mx}-S_{o\_mi}}\times 100 \tag{21}\]  
where \(S_{p}\)_and_\(S_{o}\) are the estimated and measured values of S or CS, respectively; N is the number of samples; \(S_{p}\)_and_\(S_{p}\) are the mean values of the estimated and measured values of S or CS, respectively, and \(S_{o\_mx}\)_and_\(S_{o\_mi}\) are maximum and minimum values of measured S or CS, respectively.