esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spectrum Correction Method Based on Optimizing Turbulence Intensity
## 2 Materials and Methods
### Power Spectrum Correction Method
Considering \(\text{df}=\frac{F_{max}-F_{min}}{N}\), if the magnitude of the frequency sample \(N\) is increased by an order (usually \(N\) is an exponential form with base 2), the simulation bias can indeed be reduced, but at the same time the computational memory will be doubled and the simulation speed will be seriously slowed down. Take the analog frequency range \([F_{\text{max}},\,F_{min}]=[10^{-5},12]\), and the other simulation conditions are consistent with the case of one-dimensional simulation in Section 3. The simulation experiment results are shown in Table 1.  
Table 1 and Figure 2 show that increasing the scale \(N\) causes the whole standard deviation bias of the turbulent wind field simulation to almost linearly reduce, or even when increased to \(N=2^{14}\), the overall standard deviation's bias of the turbulent wind field has reached the truncation error, but with a significant disadvantage of longer running time, and a great computing cost if more simulated points are set in the turbulent wind  
Figure 1: The longitudinal dimensionless velocity of the wind power density spectrum curve. field space. On the other hand, this result is not very ideal. When increasing the magnitude of \(N\), the simulated turbulence intensity of more simulation points falls outside the range of reference turbulence intensity and the defined turbulence intensity and needs to be removed, resulting in an unnecessary waste of computing resources. To sum up, the correction method should realize the minimum truncation deviation \(\varepsilon_{min}\) in theory and the minimum simulation deviation \(\hat{\varepsilon}_{min}\) generated in the actual simulation process, without adding more frequency sampling points to reduce the simulation efficiency. In addition, the method should generate as many effective simulation points as possible (effective simulation points are defined as the simulation points whose simulated turbulence intensity \(\hat{I}_{T}\) falls within the range of reference turbulence intensity \(I_{ref}\) and defined turbulence intensity \(I_{T}\)). Consider the error values of truncation bias \(\varepsilon\) and simulation bias \(\hat{\varepsilon}\):  
\[\Delta\varepsilon=\hat{\varepsilon}-\varepsilon \tag{16}\]  
Introduce compensation coefficient \(\beta\):  
\[\beta=(1+\Delta\varepsilon)^{2} \tag{17}\]  
If the variance \(\sigma^{2}\) in the PSD function is substituted into the compensation coefficient \(\beta\), then the original factor will be corrected \(\frac{\sigma^{2}}{\beta}\).
## 3 Methodology
### Implementation
#### 3.5.3 Baseline Method I: Window before  
The window before baselining methodology is implemented by using the last measurement before flexibility activation as a baseline for the flexibility activation period. Equation (3) describes the baselining procedure mathematically, in which \(t\) is the timestep, \(T_{act}\) is the list of activated timesteps, and \(P_{t}^{meas}\) the power measurement at timestep \(t\).  
This method is illustrated in Figure 3 and the following example: Assume a curtailment request starting at 14:00 and lasting three hours. In this example, the \(T_{act}=[14,15,16]\), and during this period the baseline is set equal to the previous timestep (in this example, timestep 13).  
\[P_{t}^{base}=\left\{\begin{array}{ll}P_{t-1}^{meas}&if\\ P_{t}^{meas}&otherwise\end{array}\right.\quad t\in T_{act} \tag{3}\]
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.7 Precise Calculation of Energy Consumption  
In FANETs, the energy consumption cost of UAVs depends on the power consumption for propulsion and communication to transmit and receive data from neighboring UAVs and GUs [41]. However, the propulsion power of UAVs consumes significantly more energy than the communication energy cost [9]. All of our reviewed routing protocols only consider the communication energy when calculating the energy cost. For a realistic performance, the energy cost should be obtained by considering both propulsion and communication power. Appropriate energy consumption defines the presence of UAVs in the aerial network and defines the accurate node density, which is directly related to communication performance. The propulsion power is proportional to the UAV trajectory.  
Thus, during a collaborative mission, the trajectory should be optimized and smooth, and all UAVs should travel approximately the same distance to execute the mission [44]. Additionally, the propulsion energy cost depends on the type of UAV deployed to execute the mission. A recent survey discussed the propulsion energy model according to the type of UAV [69].
## 2 Materials and Methods
### Materials
#### 2.1.3 Fibers  
Basalt fibers are a type of high-performance fiber material that is inorganic, environmentally friendly, and considered to be a green material. They are composed of various oxides, including silica, alumina, calcium oxide, magnesium oxide, iron oxide, and titanium dioxide. Basalt fibers possess several exceptional properties, such as high strength, excellent electrical insulation, corrosion resistance, and high-temperature resistance. The main  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Testing Standards** & **Results** \\ \hline Penetration (0.1 mm) at 25 \({}^{\circ}\)C & JTG E20-2011/T0604 & 75.2 \\ Softening point (\({}^{\circ}\)C) & JTG E20-2011/T0606 & 49.2 \\ Ductility (cm) at 5 \({}^{\circ}\)C & JTG E20-2011/T0605 & 35.5 \\ Viscosity (Pa-s) at 135 \({}^{\circ}\)C & JTG E20-2011/T0625 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Base asphalt’s physical features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Apparent** & **Water** & **Hydrophilic** & **Appearance** & \multicolumn{3}{c}{**Screening Test (\%)**} \\ \cline{3-6}
**Density g/cm\({}^{3}\)** & **Content (\%)** & **Coefficient** & **100** & **90–100** & **75–100** \\ \hline
2.792 & 0.33 & 1.0 & No agglomeration & 100 & 95.8 & 83.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the filler material.  
physical characteristics of the basalt fibers selected for this study are shown in Table 3. The bamboo powder used in this study was made from 5-6-year-old moso bamboo processed by mechanical crushing, and the physical properties are demonstrated in Table 4.  
To prevent fiber agglomeration in high-content asphalt mastics, the rheological performance of asphalt mastics was investigated at lower fiber content in this study. Two kinds of fibers, 6 mm basalt fibers and 200-mesh bamboo fibers, were utilized to create modified asphalt mastics with fiber concentrations of 3%, 6%, and 9%. The average length of 200-mesh bamboo fiber is around 1 mm, and the diameter of 6 mm basalt fiber should be around 6 um. These contents were chosen to ensure sufficient asphalt adsorption while preventing fiber agglomeration. Figure 1 illustrates the macroscopic looks of basalt and bamboo fibers.
## 2 Background and Related Work  
### Transmission Scheme in a Broadcasting Network  
One of the characteristics of the terrestrial broadcasting network is _fixed resources_. The method of transmitting a DASH segment file in a terrestrial broadcasting network with a fixed bandwidth can be expressed as the file broadcast scheme and live broadcast scheme as presented in Figure 1.  
The file broadcast scheme in Figure 1 transmits the next segment file after the first segment file is transmitted. The file broadcast scheme has mainly used the on-demand method in a communication network environment and is not unsuitable for use in a broadcasting network where an unspecified majority of people watch the same contents at the same time. For example, when a user (\(S\)) in Figure 1 accesses the file broadcast scheme, the segment File 2 cannot be displayed normally because part of the segment File 2 is not received. Therefore, the total delay time for the user (\(S\)) is greater than \(\gamma_{i}+2\rho\) because segment File 3 must be received and displayed. The bigger problem is that the user (\(E\)) in Figure 1 may not be able to watch the contents normally because the user cannot receive the segment file.  
The live broadcast scheme in Figure 1 transmits segment files periodically (\(\rho\)). Therefore, when the user (\(S\)) in Figure 1 approaches the live broadcast scheme, segment File 2 can be received and watched normally after the delay time \(\gamma_{i}\), and the total delay time is less than \(\gamma_{i}+2\rho\). In addition, the live broadcast scheme is suitable as a broadcasting network transmission scheme because users can watch the broadcasting contents regardless of when they access it.
## Abbreviations  
The following abbreviations are used in this manuscript:  
GNSSGlobal Navigation Satellite Systems  
C/N0 Carrier-to-noise ratio  
OSF-RCNNOriented Squeeze-and-Excitation-based Faster Region-based  
Faster R-CNNFaster Region-based Convolutional Neural Network  
RPNRegion Proposal Network  
SESqueeze-and-Excitation  
FPNFeature Pyramid Network  
GPSGlobal Positioning System  
GLONASSGlobal Navigation Satellite System  
DOADirection of Arrival  
TDOATime Difference of Arrival  
RSSReceived Signal Strength  
COTSCommercial off-the-shelf  
MIITMinistry of Industry and Information Technology  
J911 Jamming 911  
IONNInstitute of Navigation  
SVMSupport Vector Machine  
RCNNRegion-based Convolutional Neural Network  
YOLOYou Only Look Once  
ResNetResidual Network  
RoIsRegions of Interest  
FCFully connected  
RoAlignRotated Region of Interest Align  
SE-ResNet50  
MLPMultilayer perceptron  
IoUIntersection over Union  
VGGVisual Geometry Group  
RMSERoot Mean Square Error
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 2 System Design and Detection Principle  
### System Design Scheme  
The general schematic diagram of the high-voltage switchgear online partial discharge monitoring system is shown in Figure 1, which mainly consists of an external UHF sensor, a wave detection circuit, a data collector, a 4G module and so on. When there is insulation degradation inside the switchgear cabinet, the UHF sensor can be coupled to the UHF signal with a frequency of 300 MHz\(\sim\)1.5 GHz, which is amplified and detected to meet the sampling requirements of the data collector. The data collector is responsible for analog-to-digital conversion and data processing and sends the processed data to the server through the 4G module.  
In order to realize intelligent and standardized monitoring of high-voltage switchgear, the software design adopts B/S architecture. The various monitoring nodes of the high-voltage switchgear mainly realize the data collection of the insulation condition and use the 4G network to send the data to the server side at irregular intervals. On the server side, information such as switchgear number and geographic location can be judged based on the received data, and the monitored data can be stored in the database. The monitored data can be viewed intuitively on the PC website, so that the operation status of the high-voltage switchgear can be monitored anytime and anywhere.
## 3 Methodology
### Active Network Implications  
To identify and evaluate the implications of these findings on an active network, we introduce the testbed depicted in Figure 3. This testbed employs a process-level network utilizing serial Modbus communication for a pump, linear actuator, and temperature sensor. These devices are connected to a Programmable Logic Controller (PLC), which converts Modbus TCP communications into Modbus RTU messages to control the physical processes.  
The PLC subsequently communicates with a protocol translation gateway between Modbus TCP and DNP3. This gateway facilitates bi-directional communication, allowing values from the DNP3 network to be mapped to the Modbus network. For this study, the Modbus network reads register values to determine the required  
Figure 3: Physical testbed hierarchical diagram.  
Figure 2: Server crash connection establishment.  
linear actuator in the serial network. The gateway is the only master device in the DNP3 network and uses only solicited data, polled at regular intervals.  
To evaluate the resistance of each device under threat conditions, we conducted targeted tests on the PLC, gateway, and DNP3 outstation. This included SYN-flood, RST, and PSH+ACK floods increased incrementally at rates of 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 packets per second. The tests were introduced with and without the activation of various mitigation strategies to assess their effectiveness in reducing or preventing disruptions under increasing network stress.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results  
The system presented in this study comprises three models: a BERT-based Line-level weakness analysis model, a configuration for integrating analysis results of multiple static analysis tools model, and an alarm classification model. Figure 1 shows the structural diagram of the system presented in this study. The BERT-based Line-level Weakness Analysis (BWA) model tokenizes and embeds the input C/C++ source code and then trainsand analyzes the weakness pattern. The configuration for integrating analysis results of multiple static analysis tools uses several static analysis tools to analyze the weaknesses within the source code. The Alarm Classification Model (ACM) is a model for classifying alarms (true and false alarms) based on the decision tree model, which enters the evaluation results of the analysis of multiple static analysis tools and line-level weaknesses analysis through the BWA model. As a result, the ACM reclassifies whether each static analysis result is a true or a false alarm.  
The dataset used in this study is the Juliet test suite [38], developed by the Center for Assured Software of the U.S. American National Security Agency (NSA), and was first published in December 2010. It consists of relatively short codes characterized by control flow, data flow, or usage data structure and type. The Juliet test suite used is the 1.3 version and it contains a total of 118 weakness classes. C/C++ officially contains 64,099 test cases, including 53,476 C source codes and 46,276 C++ source codes, including 4422 C/C++ header files, for a total of 104,174 files.
# Secure and Efficient High Throughput Medium Access Control for Vehicular Ad-Hoc Network
## 5 State-of-the-Art Technology Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**PECA/**} & \multicolumn{1}{c}{**ENCCMA**} & \multicolumn{1}{c}{**MS \(-\) ALOHA**} & \multicolumn{1}{c}{**SLOP**} & \multicolumn{1}{c}{**EDF \(-\) CSMA**} \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{\(\mathbf{S}\)\(-\) ENCCMA & & & & \\ \hline Environment & C.H.R & Flowing vehicles freely & Highway and Urban & driver & \\ Algorithm & NSCA/SCA & \((\)NCC \(-\) FDMA \(-\) TDMA & MS \(-\) ALOHA & Wave \(-\) Slotted aloha & EDF \(-\) CSMA \\ Vehicle varied Density & Yes & No & No & No & No \\ Simulator used & SIMITS & SIMITS & VISSIM & YES \((\)NA\()\) & NS \(-\) 3 \\ MAC USED & 802.11p MAC & 802.11p MAC & 802.11p MAC & 802.11p MAC & 802.11p MAC \\ Mobility & Yes & Yes & Yes & Yes & Yes \\ Channel sharing available & Yes & Yes & No & No & No \\ Reference & (Ours) & [37] & [53] & [54] & [17] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: State of-art-techniques comparison.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 7 Efficiency and Security Analysis  
### Cost Analysis  
#### 7.1.1 Communication Cost  
We present a breakdown of the communication cost for two \(\text{Sillcom}\) schemes in Table 3, where \(\text{Sillcom}_{trd}\) utilizes a two-layer structure. As our initial goal, both schemes effectively compress the online communication rounds and online communication volume. In particular, \(\Pi_{SED}\) and \(\Pi_{SC}\) exhibit very low communication, both online and offline. With the communication optimization, \(\text{Sillcom}_{3R}\) requires only 6 ml + 2 ml, bits of data to be sent by the three computation servers during the online phase. In contrast, FAPRIL, which also uses a constant-round design, employs garbled circuits to compute top-k with just one round of communication. However, the large security parameter \(\lambda\) leads to substantial online communication volume, making it a bottleneck that severely affects the throughput of their scheme. The large \(\lambda\) also impacts the size of the DDCF key, which constitutes the main part of the offline communication in our design. As \(m\) grows larger, the communication volume caused by transmitting \(O(m^{2})\) DDCFs during the offline phase also affects the throughput of our scheme during the preprocessing stage. It can be observed that \(\text{Sillcom}_{trd}\), by sacrificing an additional three rounds of communication and a small increase in online communication volume, significantly reduces the time spent in preparing the DDCF keys.
# Fault Diagnosis of Induction Motor Using Convolutional Neural Network
## 5 Conclusions  
In this paper, an induction motor fault diagnosis system based on a CNN is proposed. In the proposed method, fault diagnosis of an induction motor is performed by inputting the data obtained from a simulator into the CNN. As a result, we could confirm that the motor state was diagnosed accurately from the motor data obtained using the simulator and that fault diagnosis can be performed without frequency domain transformation. In this study, fault diagnosis of an induction motor was performed in three states, namely, normal, rotor fault, and bearing fault. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors. In addition, a GUI for fault diagnosis was developed and applied to a real environment. In the real environment, the accuracy of the fault diagnosis system was 98%, 98%, and 100% for the normal, rotor fault, and bearing fault states of induction motors.  
The proposed induction motor fault diagnosis system is expected to contribute to the maintenance of facilities and reduce costs by preliminarily estimating the existence of a fault in a real plant. The performance of the system was verified through experiments on real data obtained using a simulator. It is expected that the fault diagnosis technology developed in this study will be applicable to similar systems and devices. In the future, studies will be conducted to apply the proposed method to industrial equipment.  
**Author Contributions:** Methodology, J.-H.L. and I.-S.L.; Investigation, J.-H.L., J.-H.P. and I.-S.L.; Experiments, J.-H.L. and J.-H.P.; Data analysis, J.-H.L. and I.-S.L.; Writing-original draft, J.-H.L.; Writing-review & editing, I.-S.L.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019 RI11A3A01058319). Additionally, this study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 3 System Model and Framework Overview
### System Model  
This paper proposes a personalized strategy for user data privacy protection during the process of mobile data collection, which builds upon the dynamic division of plane space and the construction of a private space decomposition index proposed by previous researchers. Considering the diverse privacy protection needs of different workers, we introduce varying Laplace noises into leaf grid nodes while ensuring that tasks are allocated in the most cost-effective manner. In the simulation experiment, this paper adds noise to the node, queries the summarized data result within a specific range, compares it with the actual result and then assesses the level of privacy protection capability. The task allocation framework for users with different protection levels is evaluated using parameters such as task propagation time, completion percentage and forwarding distance. Experimental data demonstrate that the personalized privacy protection strategy proposed in this algorithm is more effective in the data collection process and performs better in task allocation results.
## 2 Methodology
### Seismic Source Characterization and PGA Distribution Modeling
#### 2.3.2 Computation of PGA Probability Distributions from Artificial Earthquakes  
Using Frank copula sampling, 10,000 sets of \(f_{c}\) and \(M_{0}\) were extracted. Equation (1), with the parameters \(\kappa_{0}\) and \(Q(f)\) from Section 2.2.1, was then applied in the Monte Carlo Simulation (MCS) to generate artificial earthquakes as follows:  
\[A(M_{0i},R,f)=(-2\pi f)\left(\frac{CM_{0i}}{1+(f/f_{ci})^{2}}\right)R^{n}\text {exp}\left\{\frac{-\pi fR}{\beta 163f^{0.745}}\right\}A^{\bullet}(f)\text{exp}\left\{-0.014 \pi f\right\} \tag{22}\]  
where \(M_{0i}\) denotes the \(i\)-th sample of the seismic moment, and \(f_{ci}\) represents the \(i\)-th sample of the corner frequency, as both \(M_{0i}\) and \(f_{ci}\) are paired values obtained through copula sampling. The MCS allows the exploration of a broad range of scenarios by incorporating variability in the source parameters. Statistical analysis was then conducted on the PGAs of the 10,000 generated artificial earthquakes.  
The procedure of the proposed methodology to estimate probability distributions of PGA is as follows:  
1. [label=(0)]
2. Obtain recorded source parameters from the earthquake source in the vicinity of the site of interest.
3. Determine the Probability Density Function (PDF) of source parameters using Equations (20) and (21).
4. Generate sample pairs of \(f_{c}\) and \(M_{0}\) by Frank copula sampling for use in MCS.
5. Define the seismological and geological parameters needed to compute the FAS.
6. Use the MCS to generate artificial earthquakes with Equation (22) and transform them into the time domain as described in Section 2.2.2.
7. Determine PGAs and calculate their probability distribution.
## 3 Treatment of Curved Boundaries
### High-Order Re-Mesh
Applying the RBF interpolation method, the displacement \(\mathbf{s}(\mathbf{x})\) of the point \(\mathbf{x}\) in the computational domain can be expressed as  
\[\mathbf{s}(\mathbf{x})=\sum_{j=1}^{n_{b}}\mathbf{a}_{j}\psi\Big{(}\|\mathbf{x}-\mathbf{x}_{bj}\|\Big{)}, \tag{22}\]  
where \(\mathbf{x}_{bj},j=1,2,\ldots,n_{b}\) are the positions of the boundary control points, \(\psi\Big{(}\|\mathbf{x}-\mathbf{x}_{bj}\|\Big{)}\) is the \(j_{th}\) radius basis function for the displacement field \(\mathbf{s}\), and \(\mathbf{a}_{j}\) is the corresponding unknown coefficient in that basis. With the given boundary displacements, \(\mathbf{s}(\mathbf{x}_{bi}),i=1,2,\ldots,n_{b}\), the unknown coefficients \(\mathbf{a}_{j}\) can be determined by solving the following system of \(n_{b}\) equations:  
\[\mathbf{s}(\mathbf{x}_{bi})=\sum_{j=1}^{n_{b}}\mathbf{a}_{j}\psi\Big{(}\|\mathbf{x}_{bi}-\mathbf{x} _{bj}\|\Big{)}. \tag{23}\]  
Once the coefficients \(\mathbf{a}_{j}\) are determined, the interior displacement of any point in the domain can be calculated by Equation (22). Concerning further details of the RBF interpolation, we refer to the work of Reference [31].