esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Extraction Methods and Indicator Development
### Multi-Feature Extraction Method in Frequency Domain  
The fast Fourier transform method was employed to analyze the monitoring data acquired in normal state and interlayer debonding state to observe the changes in amplitude and frequency. This method was also used to extract the ground vibration caused by high-speed trains and analyze the vibration characteristics of ballastless track on bridges [44; 45]. The mathematical expressions are presented in this section. A comparative analysis between the baseline track segment without interlayer debonding and the track segment with interlayer debonding was performed, and the performance of the frequency-domain features is assessed in Section 4.3.  
The Fourier transform of a time-domain signal \(x(t)\) can be expressed as  
\[X(\omega)=\int_{-\infty}^{+\infty}x(t)e^{-j\omega t}dt \tag{1}\]  
by which the continuous spectrum of the signal \(x(t)\) was calculated. However, in the actual monitoring system, discrete samples of continuous signals were obtained. The Discrete Fourier Transform (DFT) of a finite length discrete signal \(x(n)\), \(n=0\), \(1\),..., \(N-1\), is defined as  
\[X(k)=\sum_{n=0}^{N-1}x(n)W_{N}^{kn}(k=0,\;1,\;\ldots,\;N-1) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Expression** & **Description** \\ \hline Peak acceleration & \(X_{\text{max}}=Max(x(t))\) & Max value of the amplitude of signals. \\ \hline \hline \multirow{7}{*}{Shape factor} & \multirow{7}{*}{\(K=\frac{X_{\text{max}}}{|X|}\)} & Shape factor refers to a value that is affected by the shape of waveforms. In this equation, mean value \(\overline{X}=\frac{1}{N}\sum_{n=1}^{N}x_{n}\) and root mean square (RMS) \\  & & \(X_{rms}=\sqrt{\frac{1}{N}\sum_{n=1}^{N}x_{n}^{2}}\) \\ \hline \multirow{2}{*}{Crest factor} & \multirow{2}{*}{\(C=\frac{X_{\text{max}}}{X_{rms}}\)} & Crest factor is the measure of a waveform to show the ratio of the peak value to RMS \\ \hline \multirow{2}{*}{Impulse factor} & \multirow{2}{*}{\(I=\frac{X_{\text{max}}}{|X|}\)} & Impulse factor is the measure of a waveform to show the ratio of the peak value to the mean value. \\ \hline \multirow{2}{*}{Clearance factor} & \multirow{2}{*}{\(L=\frac{X_{\text{max}}}{\left(\frac{1}{N}\sum_{n=1}^{N}\sqrt{|x_{n}|} \right)^{2}}\)} & Clearance factor is the measure of a waveform to show the ratio of the peak value to the \(\left(\frac{1}{N}\sum_{n=1}^{N}\sqrt{|x_{n}|}\right)^{2}\). \\ \hline \multirow{2}{*}{Kurtosis factor} & \multirow{2}{*}{\(K_{\text{p}}=\frac{\sum_{n=1}^{N}\left(x_{n}-\overline{X}\right)^{4}/N}{X_{\text{ min}}^{2}}\)} & Kurtosis factor is the ratio of kurtosis \\  & & \(\left(\sum_{n=1}^{N}\left(x_{n}-\overline{X}\right)^{4}/N\right)\) to the fourth power of RMS. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of time-domain features.  
where \(W_{N}=e^{-j\frac{2\pi}{N}}\). Using the symmetry and periodicity of \(W_{N}\), the DFT of \(N\) points is decomposed into two DFT with \(N/2\) points, and the computational cost is reduced to half of the original.
## 3 Test Results
### Chloride Ion Penetration Resistance  
Figure 6 shows the amount of charge passed during chloride ion permeation tests on the HFLMC mixtures. The solid line corresponds to the permeability criteria specified by the ASTM C1202. Fiber-reinforced mixtures had less charge passed compared to plain mixtures, and those containing hybrid fibers were less permeable than those containing single-type fibers. The single-type fiber system passed about 1500-2000 coulombs, corresponding to \"low\" permeability according to ASTM C1202. In contrast, the hybrid fiber system passed only 1000 coulombs or less, corresponding to \"very low\" permeability. After 28 days, the permeabilities of mixtures containing PVA, jute, nylon, jute-nylon, PVA+jute, and PVA+nylon fibers decreased compared to those of plain mixtures by 20.4%, 22.9%, 37.5%, 50.2%, 49.8%, and 56.0%, respectively.  
Latex creates a thick impermeable layer within the concrete, resulting in decreased permeability [29]. This explains the excellent permeability resistance shown by all mixtures. In addition, fiber reinforcement further reduced permeability by suppressing the occurrence and growth of microcracks within the concrete [28; 29]. Microcracks likely occur in rapid-set concrete because of the high heat of hydration that occurs during the early stage of curing [1; 2]. This is largely eliminated by incorporating both latex and fiber reinforcements. Of the various mixtures evaluated, the PVA+nylon hybrid fiber-reinforced concrete passed the smallest amount of charge, corresponding to \"very low\" permeability.  
Figure 6: Permeability of HFLMC mixtures.
## 3 Preliminaries
### System Model  
As illustrated in Figure 1, the proposed design comprises four components: the implementation of a smart contract \((SC)\) on a blockchain platform, a fog server \((FS)\), a data generator \((dg_{i})\), and a data requester \((dr)\).  
* Data generators \((dg_{i})\) are conceptualized as entities that interact with sensors to produce environmental sensing data. To generate environmental data ciphertexts and \(k\) ciphertexts, \(dg_{i}\) encrypts various types of environmental data using distinct keys and their associated \(k\). Subsequently, the FS receives these ciphertexts to share environmental information with \(dr\). Additionally, the keyword-encrypted data are added to the blockchain for future processing.
* Vehicle insurance companies are among the entities requesting access to environmental data on automobiles. While maintaining the confidentiality of the search term, the requester transmits search requests to the data guardian, along with a token and supporting evidence. The data guardians are provided with proof to verify that the \(k\) is part of the approved \(k\) set. Once the proof is successfully verified, the data guardians transmit permission to the blockchain. To conduct searches for the desired terms, the requester can utilize the blockchain to access a search trapdoor. Additionally, the requester can confirm the accuracy of the decrypted environmental data and the precision of the encrypted environmental data retrieved from the file system.
* The responsibility of maintaining encrypted environmental data and the corresponding \(k\) ciphertexts transmitted by \(dg_{i}\) lies with the fog server (FS). Additionally, it is accountable for providing search services.
* Smart contracts are implemented using blockchain technology. The Aggregate Smart Contract (ASC) refers to the blockchain \(SC\). The ASC performs three primary functions: creating a search trapdoor for approved \(dr\)s, combining the various keys from \(dg_{i}\)s, and collecting keys from environmental data.  
Figure 1: System model.
## 3 Evaluation of Melt Test Parameters and Analysis of DSL Service Provision  
After execution of the MELT test procedure, a set of test parameters is obtained and can be evaluated according to defined ranges. After this evaluation, the analysis that can help technicians to determine the cause of failures occurred on the tested homogeneous symmetric line is realized. On beginning, it is necessary to define the ranges of parameters that must be evaluated.  
The first parameter is represented by foreign voltages (AC and DC). Foreign voltages may occur differentially between Tip-Ring (TR), between Tip/Ring-Ground (TRG) in a common mode or between Tip-Ground (TG) and between Ring-Ground (RG) in a single-ended mode. The second parameter presents the Capacity (C) determined in relation to three sections (Figure 1):  
* Tip-Ground (\(\text{C}_{\text{TG}}\)),
* Tip-Ring (\(\text{C}_{\text{TR}}\)),
* Ring-Ground (\(\text{C}_{\text{RG}}\)).  
Figure 1: Scheme with defined sections RG, TR and TG for the Capacity parameter.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 4 Simulation's Results
### PLL Testbench  
Once all the blocks that compose the PLL have been designed, a testbench has been built. Here, the netlist generated by the layout parasitic extraction has been used for all the blocks (CP, FFD, VCO, Loop Filter, and FD), while the connections between the blocks are still ideal. On this testbench, a Transient simulation has been performed to analyze the locking behavior and the Phase Noise. As for the simulations presented in Section 3.1.3, the Transient Noise Analysis Option has been used for the Phase Noise evaluation.
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
###### Abstract  
Electrospray ionisation (ESI) is renowned for its ability to ionise intact proteins for sensitive detection by mass spectrometry (MS). However, the use of a conventional direct current ESI voltage can result in the formation of relatively large initial droplet sizes, which can limit efficient ion desolvation and sensitivity. Here, pulsed nanoESI (nESI) MS using nanoscale emitters with inner diameters of \(\sim\)250 nm is reported. In this approach, the nESI voltage is rapidly pulsed from 0 to \(\sim\)1.5 kV with sub-nanosecond rise times, duty cycles from 10 to 90%, and repetition rates of 10 to 350 kHz. Using pulsed nESI, the performance of MS for the detection of intact proteins can be improved in terms of increased ion abundances and decreased noise. The absolute ion abundances and signal-to-noise levels of protonated ubiquitin, cytochrome C, myoglobin, and carbonic anhydrase II formed from standard denaturing solutions can be increased by up to 82% and 154% using an optimal repetition rate of \(\sim\)200 kHz compared to conventional nESI-MS. Applying pulsed nESI-MS to a mixture of four proteins resulted in the signal for each protein increasing by up to 184% compared to the more conventional nESI-MS. For smaller ions (\(\leq\)1032 \(m/z\)), the signal can also be increased by the use of high repetition rates (200-250 kHz), which is consistent with the enhanced performance depending more on general factors associated with the ESI process (e.g., smaller initial droplet sizes and reduced Coulombic repulsion in the spray plume) rather than analyte-specific effects (e.g., electrophoretic mobility). The enhanced sensitivity of pulsed nESI is anticipated to be beneficial for many different types of tandem mass spectrometry measurements.  
electrospray ionisation; nanoelectrospray; proteins; alternating current; top-down +
Footnote †: journal: Applied Sciences
## 2 WSNs Data Acquisition Model
### Traditional Hierarchical Routing Method  
As pointed out in the previous section, a compressive sensing model can be used in most single-hop WSNs [1,2] or centralized environment [5,22]. In what follows, we present a method for gathering data for a multi-hop WSN in a distributed environment.  
To avoid confusion, it is important to note that in this section interpretation of all the variables is involved. Suppose \(N\) sensors, denoted as \(x_{1},x_{2},...,x_{N}\,\), form a multi-hop route to the sink and the original signal vector is denoted as \(\,X\,\)\(\in\,R^{N}\,\). Let \(\,d_{j}\,\) denote the readings obtained by node \(\,x_{j}\,\). There are two simple data collection schemes: the baseline and compressive methods in a multi-hop route, referred to as Equations (3) and (4), respectively [3]. To avoid excessive overloading in single-tier WSNs, a clustering method has been used in some routing approaches [23,24]. The hierarchical routing method utilizes randomized rotation of local cluster-head nodes to distribute the energy load evenly among the sensors in the network [25]:  
\[x_{1}:d_{1}\to x_{2},d_{1}+d_{2}\to x_{3},...,\sum_{i}^{N-1}d_{i}\to x_{N}, \sum_{i}^{N}d_{i}\to\,\text{sink} \tag{3}\]  
\[x_{1}:\sum_{j}^{M}\phi_{j1}d_{1}\to x_{2},\sum_{j}^{M}\phi_{j1}d_{1}+\sum_{j}^ {M}\phi_{j2}d_{2}\to x_{3},...,\sum_{i}^{N}\sum_{j}^{M}\phi_{ji}d_{i}\to\, \text{sink} \tag{4}\]
## 2 Basic Theory
### VMD Decomposition Principle
#### 2.1.2 Optimizing Parameters--AO-VMD  
In 2021, [PERSON] et al. introduced the Aquila Optimizer algorithm, a new intelligent meta-heuristic optimization algorithm [20]. The AO algorithm is a novel meta-heuristic optimization algorithm inspired by the hunting behaviors of the Aquila (eagle) in nature. This optimization method is designed to effectively navigate the search space for optimal solutions, mimicking Aquila's strategic phases in catching prey [21].  
In the optimization of VMD parameters, the AO is employed to navigate the parameter space, with the fitness function defined as the ratio of kurtosis to envelope entropy. The AO algorithm is initialized with a predefined population size, dimensionality for the optimization, and the maximum number of iterations. This approach ensures that the VMD is configured to yield the most accurate signal representation by leveraging the exploratory and exploitative capabilities of AO. The flowchart of the AO-VMD algorithm is shown in Figure 1.  
In this study, the AO primarily optimizes two critical parameters of VMD: the number of decomposition levels \(K\) and the penalty factor \(\alpha\).
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 4 Formation Control with Virtual Leader
### Discussion
As illustrated in Figure 8(b), the new obstacle is inserted between \(t=13\) s to \(t=19\) s. The experimental results primarily focus on the formation maneuvering \(t=0\) s-40 s and are obtained as follows. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Speed (s)** & **Track Accuracy (\%)** \\ \hline Affine-based maneuvering & 40 & 67.34 \\ The proposed method & 450 & 89.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation tests (mean value). Figure 10: _Cont._  
It has been shown that the proposed method improves the maneuvering ability of the formation. From Figure 10b, it is evident that the trajectory error is reduced by the proposed method. Furthermore, the velocity of the X axis in the classic method undergoes more drastic changes compared to the proposed method, as observed in Figure 10c,e. Similarly, Figure 10d,f demonstrate that the velocity of the Y axis in the proposed method changes smoothly.
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 2 Materials and Methods
### Experimental System  
To verify the feasibility of the proposed method, an experimental system, as shown in Figure 10a, was built, which mainly consisted of an imaging module that included three sets of binocular cameras, a marker projection module composed of five lasers, a data processing module of a high-performance computer, and a bracket for equipment installation. Model QJY0816 industrial cameras with an image size of 2/3 inch were used in the experimental system, each equipped with an 8 mm fixed-focus lens and the field angle of 56.5 \(\times\) 43.9\({}^{\circ}\). According to the conclusion of the above simulations, it is a prerequisite for achieving high accuracy in diameter measurements to keep the central angle larger than 168\({}^{\circ}\). Therefore, considering the field angle of typical industrial cameras and final-cost control in our study, three sets of binocular cameras were selected to form the experimental system, and the central angle corresponding to the measurement point was set at about 180\({}^{\circ}\). The main computer components responsible for processing digital images were the Intel Xeon Gold 5218 and RTX3090.  
In addition, to obtain high-precision measurement results as reference, we used the Hexagon brand Inspector Performance series Coordinate Measuring Machine (CMM) to measure the pipe. Figure 10b shows the site where the CMM was used to measure the diameter of the pipe. The accuracy of this type of CMM reaches 0.001 mm.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods
### Signal Detection
To alleviate the sensitivity of the \(l_{1}\) loss to different predicted bounding box sizes, the bounding box loss is a weighted sum of the \(l_{1}\) loss and the generalized intersection over union (GIoU) loss [48] function. The bounding box loss is defined as  
\[L_{box}\Big{(}s_{i},\hat{s}_{\sigma(i)}\Big{)}=\lambda_{iou}L_{iou}\Big{(}s_{ i},\hat{s}_{\sigma(i)}\Big{)}+\lambda_{L1}\Big{\|}s_{i},\hat{s}_{\sigma(i)} \Big{\|}_{1} \tag{11}\]  
where \(\lambda_{iou}\) and \(\lambda_{L1}\in R\) are the weights for the GIoU loss function and \(l_{1}\) loss function, respectively. \(s_{i}\) represents the true labels of the bounding boxes, and \(\hat{s}_{\sigma(i)}\) corresponds to the predicted box results under optimal matching conditions. The bounding box loss \(L_{iou}\) using the GIoU loss function is defined as  
\[L_{iou}\Big{(}s_{i},\hat{s}_{\sigma(i)}\Big{)}=1-\left(\frac{\Big{|}\hat{A}_{ \sigma(i)}\cap A_{i}\Big{|}}{\Big{|}\hat{A}_{\sigma(i)}\cup A_{i}\Big{|}}- \frac{\Big{|}B\Big{(}s_{i},\hat{s}_{\sigma(i)}\Big{)}\backslash\hat{A}_{ \sigma(i)}\cup A_{i}\Big{|}}{\Big{|}B\Big{(}s_{i},\hat{s}_{\sigma(i)}\Big{)} \Big{|}}\right) \tag{12}\]  
where \(\hat{A}_{\sigma(i)}\) and \(A_{i}\) represent the boundaries of the predicted and true bounding boxes, respectively, and \(B(\cdot,\cdot)\) denotes the area of the minimum bounding rectangle of the two rectangles. The smaller the overlap area of the two bounding boxes, the larger the GIoU loss. The set results predicted by TATR need to undergo a reverse mapping operation to restore the parameters of the TT&C signal for detection. The mapping from the FFN prediction results to signal parameters is given by  
\[ \begin{split}& f_{s}=\frac{\mathcal{O}^{x}}{\mathcal{O}_{\max}^{x}- \mathcal{O}_{\min}^{x}}(f_{\max}-f_{\min})\\ & B_{s}=\frac{w}{\mathcal{O}_{\max}^{x}-\mathcal{O}_{\min}^{x}}(f _{\max}-f_{\min})\end{split}  \tag{13}\]  
where \(f_{s}\) and \(B_{s}\) represent the center frequency and bandwidth of the satellite signal to be detected, \(x\) and \(w\) are the normalized center abscissa and width from the FFN-predicted detection box results. Different from previous detection methods, using bipartite graph matching allows us to train the entire network end-to-end without the need for pre-setting anchors for non-maximum suppression, simplifying the signal detection process.
## 5 Discussions and Numerical Results  
In this part of the article, a comparative evaluation of the outcomes that have been obtained in this work and those of the previous literature will be included to validate the proposed model. For this reason, the influence of several parameters on the behavior of non-dimensional displacement \(u\), temperature change \(\theta\), axial heat stress \(\sigma_{xx}\), and deflection \(w\) will be discussed.  
The characteristics of the material are described as follows [44]:  
\[\begin{array}{c}K=315\;\text{Wm}^{-1}\text{K}^{-1},\;E=180\;\text{Gpa},\; \rho=1930\;\text{Kgm}^{-3},\;\upsilon=0.44\\ \alpha_{t}=2.59\times 10^{-6}\;\text{K}^{-1},\;C_{E}=130\;\text{J}/\text{kgK},\;T_{0}=300 \;\text{K}\\ J=732\;\text{J}/\text{m}^{2},\;R=0.93,\;\delta=15.3\;\text{nm},\;t_{p}=2\;\text{ ps}\end{array}\]  
In addition to the above physical values, the microscopic beam is assumed to have a length-to-thickness ratio of \(L/h=5\), where h is the beam thickness. Also, in the numerical calculations, the parameters of the microbial beam dimensions are taken into account, taking the values \(L=1\), \(b/h=0.5\), and \(z=h/3\). The results will be presented tabularly and graphically in Figures 3-10 in the direction of the axis of the beam between the values of 0 and 1.0. The numerical results indicate that the distributions of different physical quantities are influenced not only by time and spatial coordinates but also by different effective parameters such as laser pulses and beam foundations. Both the graphs and numerical calculations will be organized into four different groups.
# A Unified PUF and Crypto Core Exploiting the Metastability in Latches
## 6 Conclusions  
In this paper, we present a unified PUF crypto core implementation, exploiting the metastability of the latches in an FPGA. The implemented crypto core contains the ChaCha20 cipher using a 4-QR module. The PUF is implemented in one of the stages of the ChaCha20 core using unbalanced latches. The PUF challenge is introduced to select each output bit between two branches of latches. The PUF-ChaCha20 implementation occupies 2416 LUTs and 1026 FFs, resulting in a 3.1% resource overhead. The PUF application is tested in three XC7A100 TCSG324-1 Xilinx FPGAs, resulting in responses with a uniformity of 49.15%, a uniqueness of 49.85%, and a reliability of 99.25%, on average. Finally, the unified implementation can be used in OTK generation for ChaCha20-Poly1305 AEAD in TLS 1.3, generating an additional secure approach in end-to-end computer networks.  
**Author Contributions:** Supervision, C.-K.P., C.D. and T.-T.H.; methodology, R.S., M.S. and T.-K.D.; investigation, R.S.; writing--original draft preparation, R.S.; writing--review and editing, R.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the New Energy and Industrial Technology Development Organization (NEDO) project JPNP16007.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analysed in this study. Data sharing is not applicable to this article.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Slices** & \multicolumn{2}{c}{**Performance** [**Cycles/Byte**]} \\ \hline
**Module** & **LUT** & **FF** & **Total** & **Standalone** \\ \hline
**This work** & 2416 & 1026 & 3442 & 0.343 \\ \hline
[31] & 2288 & 1058 & 3338 & 0.340 \\ \hline
[41] & 2369 & 2152 & 4521 & 0.530 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance summary and comparison in the FPGA.  
Figure 12: Inter- and intra-PUF Hamming distances in nominal conditions.  
**Acknowledgments:** This paper is based on results obtained from project JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).