esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Methods and Results
### Analysis of the Power Spectrum by AR Model  
The AR model is one of the main methods for modern power spectrum estimation. In the AR model, it is a difficult problem to choose the proper order. The higher the order is, the more the characteristics of the time series are reflected. However, when the order is too high, the more parameters are required to be estimated, and the error rate will increase. Therefore, it is important to determine the appropriate order.  
In this paper, AIC was used to determine the order, which overcomes the artificiality of statistical hypothesis test, and the order obtained by AIC is the definite optimal estimate. Figure 3 shows the AIC value of the HIFU echo signal. It can be seen that the AIC value fluctuates greatly when the order is small, which is not suitable to be used as the order. As the order increases to 34, the AIC value tends to be stable. Therefore, the order of the HIFU echo signals is determined to be 34. Additionally, after analysis, the order of the noise signals is determined to be 23. In addition, the Yule-Walker method was used to solve the parameters of the AR model. After the order of AR model is solved, the parameters of AR model can be calculated by the Yule-Walker method.  
Figure 4 shows the power spectrum estimated by two methods of the signal shown in Figure 2a. The black part is the result of classic power spectrum estimation, and the red part is the result of AR model estimation. The calculation steps of classic power spectrum estimation are as follows: firstly, Fourier transform is directly applied to the signal, and then the square of its modulus is calculated and divided by the data points of the signal, and finally the power spectrum is obtained. It can be seen that the central frequency and the second harmonic can be well estimated by the AR model. Although the corresponding amplitude is somewhat reduced, it is helpful to calculate the spectral information entropy. Then power spectral information entropy is calculated.  
Figure 2: Time domain waveform of the sampled signals. (**a**) The HIFU echo signal; (**b**) the noise signal.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment
### System Function
#### 4.3.2 Recording Inspection Information  
The frequency of third-party inspections varies for different types of equipment, but it is clear that most equipment is inspected at least once a year. The completion of a third-party inspection must generate two records in the blockchain system. Special equipment users submit a third-party inspection application through the client, and then the inspection agency can accept the inspection task through their client, so the first record is written into the system. After that, the inspection agency completes the inspection according to the technical regulations, and initiates the second \"transaction\" in the blockchain system to record the inspection results. After obtaining the confirmation of the equipment user, this \"transaction\" is completed, and all the testing information has been recorded in the system. With this function, equipment users, primary supervisors, and supervisory agencies can obtain timely inspection results of the equipment and see its potential risks. If the inspection results show that there is a serious risk to the equipment, the supervisory agencies will immediately make a decision to stop using the equipment.
## 2 Materials and Methods
### Measurements of Plasma Emissions
#### 2.2.2 UV Effective Irradiance  
The effective irradiance was then calculated using Equation (1). The equation is a simplified version of the integral given in the work of [13], where each absolute spectral irradiance measurement is multiplied by the relative spectral effectiveness for the respective wavelength. This equation also factors in the measurement interval of the spectrometer.  
\[E_{eff}=\Sigma E_{\lambda}\cdot S(\lambda)\cdot\Delta\lambda \tag{1}\]  
where \(E_{eff}\) is the effective irradiance in \(\mu\)W/cm\({}^{2}\), \(E_{\lambda}\) is the absolute spectral irradiance from measurements in \(\mu\)W/cm\({}^{2}\)/nm, \(S(\lambda)\) is the relative spectral effectiveness, and \(\Delta\lambda\) is the bandwidth in nanometres of the measurement intervals in the spectrometer.  
Values of \(S(\lambda)\) for wavelengths in the 210-400 nm range are calculated using Equations (2)-(4) [16]:  
\[210\text{ nm}\leq\lambda\leq 270\text{ nm}\Rightarrow\] \[S(\lambda)=0.959^{(270-\lambda)} \tag{2}\]  
\[270\text{ nm}<\lambda\leq 300\text{ nm}\Rightarrow\] \[S(\lambda)=1-0.36\bigg{(}\frac{\lambda-270}{20}\bigg{)}^{1.64} \tag{3}\]  
\[300\text{ nm}<\lambda\leq 400\text{ nm}\Rightarrow\] \[S(\lambda)=0.3\times 0.736^{(\lambda-300)}+10^{(2-0.0163 \lambda)} \tag{4}\]  
The relative spectral effectiveness for wavelengths in the 180-400 nm range is given in the work of [17].
# Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
## 4 Experimental Setup and Result Analysis  
### Overall Design of AFP Surface Defect Detection Experimental Platform  
The experiment is based on the 8-tow automated fiber placement machine developed by Nanjing University of Aeronautics and Astronautics (NUAA), as shown in Figure 4. A complete defect detection system was integrated, including computer systems, industrial cameras, lighting sources, lenses, and other equipment. The automated fiber placement machine is responsible for automatic fiber placement of prepreg materials, while the computer control system operates and conducts defect detection. The industrial camera captures real-time surface images, the lighting source ensures image clarity and contrast, and the lens magnifies and focuses the images. By integrating these devices and technologies, the system can automatically detect defects on the laid surface, providing strong support for quality control in composite material production.  
During the AFP process, the industrial camera monitors the entire layup process and communicates with the computer. Once the industrial camera detects surface defects, the computer communicates with the programmable logic controller (PLC) of the AFP machine to trigger alarms or reduce the speed. In this study, Python programming was utilized to facilitate communication between the computer and the PLC of the AFP machine, with the Snap7 library serving as the communication tool, as illustrated in Figure 5.  
The defect detection algorithm analyzes images to promptly identify surface defects. When a defect is detected, the computer triggers a stop signal to the Siemens PLC. To achieve closed-loop control between the computer and PLC, a function named trigger_stop() was defined in Python to set the boolean variable of the stop address to True and send the stop signal to the PLC using the Snap7 library.  
After successful deployment, the computer acquires real-time image data through the industrial camera for defect detection and analysis. Once a defect is detected, the computer sends instructions to trigger the AFP machine to stop and perform necessary actions. This closed-loop control workflow not only enhances the efficiency and reliability of the AFP platform but also effectively improves its capability to handle surface defects.  
Figure 4: The 8-tow automated fiber placement machine developed by Nanjing University of Aeronautics and Astronautics (NUAA).  
Figure 5: Defect detection system’s detection process.
## 2 Materials and Methods
### Data Acquisition, Physio-Geographical and Climate Characteristics
The CLC 2018 dataset is essential for environmental monitoring, land management, biodiversity conservation, climate change research, and international cooperation. It allows for the monitoring of land cover changes, aids in informed decision making for land management and planning, supports biodiversity conservation efforts, helps study climate change impacts, and enables cross-border collaboration and harmonization of land cover data in Europe [44; 45]. In order to generate the average annual precipitation and average-annual-temperature-figure datasets regarding the rainfall and temperature, multiannual datasets were used from meteorological stations nearest to the three river basins. The average annual temperature and precipitation were calculated as 8.1 \({}^{\circ}\)C and 653 mm for Batin, 4.7 \({}^{\circ}\)C and 939 mm for Rasca, and 8.4 \({}^{\circ}\)C and 681 mm for Chinteni. The values are based on the meteorological station data from Cluj County, and the GIS climate figures developed (Figure 6). In terms of precipitation, the highest amounts occur in summer, 200-300 mm, and the lowest in winter, 70-120 mm. The torrential nature of the summer rains is particularly intense [36]. They activate torrential erosion and landslides. In the rainy years, the amount of precipitation almost doubles compared to the multiannual average, which leads to the exaggerated rise of the river levels (up to 5-8 m) and is the cause of catastrophic floods, especially because of the lack of forests and forest curtains, especially in the TAUs situated in the flatter areas with 4-6% degrees of forestation. This percentage is far below the limit of 16% aforestation (per physical-geographical unit), which is considered the limit below which the susceptibility to desertification is very high. For the hilly area of Cluj County, the critical period from the point of view of rain erosion is represented by the months of May, June and July.
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 5 Experimental Results
### Performance Comparison in Open Domain
Furthermore, the performance gap between Open DGML and the other algorithms was much wider in the open domain setting than in the non-open domain setting, indicating that Open DGML is better suited for open domain scenarios. The lower performance gap in the non-open domain setting suggests that the current mainstream algorithms can already achieve good performance, leaving little room for improvement. Open DGML, on the other hand, focuses on enhancing the model's generalization ability, especially in unknown domains, by incorporating meta-learning techniques and Mul-Mixup. To further validate the model's generalization ability in the target domain, we compared Open DGML with the FC-Net method using Frechet distance. The results, shown in Figure 3, indicate that Open DGML's Frechet distance is much smaller than that of FC-Net, indicating stronger generalization ability in the target domain. We also observed two rules from the results: (1) the Frechet distance is related to the domain itself, and (2) the Frechet distance is positively correlated with the model's recognition accuracy. In the four experiments using different datasets as the target domain, Open DGML achieved the \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**1 SCX2012**} & \multicolumn{2}{c}{**NDSec-1**} & \multicolumn{2}{c}{**CICIDS2017**} & \multicolumn{2}{c}{**CICIDS2018**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-11}  & **ACC** & **DR** & **ACC** & **DR** & **ACC** & **DR** & **ACC** & **DR** & **ACC** & **DR** \\ \hline HAST-IDS & 66.34 \% & 55.20\% & 41.82\% & 41.26\% & 53.67\% & 41.77\% & 37.13\% & 42.02\% & 49.47\% & 45.06\% \\ CLAIRE & 54.16\% & 52.02\% & 51.86\% & 53.60\% & 57.85\% & 47.53\% & 58.92\% & 48.47\% & 55.70\% & 50.41\% \\ FC-Net & 63.79\% & 51.16\% & 46.33\% & 44.46\% & 64.97\% & 57.74\% & 52.68\% & 46.33\% & 56.94\% & 49.92\% \\ \hline Open DGML & 75.69\% & 70.13\% & 68.50\% & 65.99\% & 67.45\% & 53.11\% & 63.29\% & 56.73\% & 68.73\% & 61.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of algorithm performance in open domain. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **ACC** \\ \hline HAST-IDS & 25.14\% \\ CLAIRE & 31.05\% \\ FC-Net & 28.21\% \\ Open DGML & 41.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of accuracy of algorithms for identifying unknown class. smallest Frechet distance on the ISCX2012 dataset, followed by NDSec-1 and CICIDS2017, and the largest on the CICIDS2018 dataset, which is consistent with the ranking of accuracy and detection rate when the model is applied to these datasets in the open domain setting.
## 3 Simulation Implementation
### Implementation of the Communication System
#### 3.2.4 Signal Demodulation  
To recover the modulated signal, the signal received from the decoupling circuit, which inherently includes also high frequency noise exiting the converters, is multiplied by the in-phase and in-quadrature carrier waves. This results in the separation of the PSK waves. Each wave passes through an integrator and low pass filter in order to avoid the high frequency components and then compared in the comparator to retrieve the odd and even bits. The obtained bits are then converted into integer numbers. Figure 8a shows the demodulation circuit implemented in SIMULINK.  
Figure 6: QPSK modulator circuit implemented in SIMULINK.  
Figure 7: Capacitive (**a**) coupling and (**b**) decoupling networks implemented in SIMULINK.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 3 Results and Analysis
### Model Input Selection
This can be explained by the fact that the main computational load of the MKELM model lies in calculating the kernel matrix and the pseudoinverse, with a time complexity of O(n\({}^{3}\)). When the sample size n is large, the computational load becomes significant. After dividing the dataset into k subsets, the reduced sample size in each submodel accelerates computation. However, when the sample size in each submodel becomes small, further increasing the number of subsets leads to less noticeable improvements in computation speed. From the perspective of model prediction accuracy, the accuracy mainly depends on the input sample features. For the same set of sample features, there are slight differences in prediction accuracy among the models. It was observed that the Kmeans-QPSO-MKELM-40 model had the lowest prediction accuracy when inputs were 1, 3, 4, 5, 6, 7, and 8, compared to the other models with the same inputs. This may be due to the excessive number of clusters in Kmeans, which leads to some classes having insufficient samples when training the submodels, resulting in inadequate training and poorer performance. For the remaining four models, the performance differences were relatively small. The QPSO-MKELM model performed slightly better than Kmeans-QPSO-MKELM-30, which in turn performed better than Kmeans-QPSO-MKELM-10, followed by Kmeans-QPSO-MKELM-20. Considering both the training time and prediction accuracy, the Kmeans-QPSO-MKELM-30 model was ultimately selected as the final model.
## 1 Introduction
To address time constraints, a straightforward approach is to require all UAV paths to have the same number of waypoints and synchronized arrival times [40]. Therefore, this paper assumes that the waypoint arrival time and timestamp increments are identical for each UAV. The primary contributions of this paper are as follows: (1) A bionic social learning strategy PIO (BSLSPIO) is developed. Unlike other PIO variants, this improved method retains the core operations of the standard PIO while introducing only two additional parameters. Comparative experiments demonstrate its distinct and competitive advantages. (2) Multi-UAV path-planning models with various constraints to simulate real-world environments are proposed, and optimized executable paths are generated. The remainder of this paper is organized as follows. Section 2 reviews the standard PIO and presents the principles of the BLSPIO. Then, the stability and complexity are proved. Section 3 demonstrates the efficiency of the proposed methods through comparative experiments. The influence of additional parameters of the BLSPIO is also discussed. Section 4 introduces the cooperative path planning scenario. Then, multi-UAV path-planning models and cost functions are designed. Finally, the path-planning simulations satisfying some constraints are conducted. Conclusions and future work are presented in Section 5.
# Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations
## 5 Conclusions  
A quantum dual signature scheme based on CPCOs and coherent states is presented in this paper, which can be used to guarantee the communication security between customer, merchant, and bank. The customer [PERSON] has an order message and payment message which are separately sent to the merchant [PERSON] and the bank Charlie. For preventing eavesdropping and attacking, [PERSON] signs her two messages based on the CPCO encryption which can defend attacker's forgery effectively. To ensure that the payment corresponds to the order, [PERSON] then aggregates the two signatures to generate a quantum dual signature. The receiver [PERSON] verifies the order message's signature to confirm the identity of [PERSON] and the information of order. When the signature of order message is valid, [PERSON] transmits the payment message's signature which is blind to him and [PERSON]. Then [PERSON] verifies whether the signature of payment message is authentic and sends the result to [PERSON]. Security analysis shows that our scheme satisfies the security criteria of quantum signature. In other words, impossibility of forging, disavowing or denying for the signature protocol are observed in our scheme. In addition, simulation experiments are performed on the Strawberry Fields platform. The experimental results show that the proposed CPCOs are feasible and expected coherent states are obtained with high fidelity, which demonstrates that the scheme could be implemented on real quantum devices.  
Conceptualization, J.S. and S.C.; methodology, S.C. and J.L.; software, S.C. and Y.F.; validation, J.S., S.C. and J.L.; formal analysis, J.S., S.C. and J.L.; investigation, J.S., J.L. and Y.F.; resources, S.C. and J.L.; data curation, J.L. and F.L.; writing--original draft preparation, S.C. and J.L.; writing--review and editing, J.S., S.C., and F.L.; visualization, S.C. and F.L.; supervision, F.L. and R.S.; project administration, J.S. and S.C.; funding acquisition, J.S., J.L., F.L., and R.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972418, 61872390, 61977062), the Natural Science Foundation of Hunan Province (2017J)3415), the Training Program for Excellent Young Innovators of Changsha (Grant Nos. kq1905058).  
The authors declare no conflict of interest.  
Figure 5: Experimental results. The subfigures (**a–d**) represent the output of the first mode, the second mode, the third mode and the forth mode, respectively. The vertical axis indicates the probability that \(x\) or \(p\) takes a value on the horizontal axis. Besides, \(x\) and \(p\) can take the corresponding value under the maximum probability to form a coherent state.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 3 Calibration Method Based on Linearity Adjustment Model
### Overall Adjustment Model  
One-loop adjustment is completed as described above. Then, the one-loop adjustment model \(c_{1}:u\to U\) can be expressed as \(c_{1}=f_{z}\circ g_{m}\circ g_{z}\circ g_{f}\) and the residual is \(\Delta=U-u\circ c_{1}\). The residual is optimized cyclically until \(\Delta\), and then the final adjustment function \(L\) of the output characteristic curve can be expressed as follows:  
\[\begin{split}& L=c_{1}\circ c_{2}\cdots c_{n},\\ & s.t.\quad\Delta=U-u\circ L\to 0,\end{split} \tag{19}\]  
where \(c_{i}(i=1,2,\cdots,n)\) is the \(i\)th loop adjustment model.  
Consequently, the overall adjustment error results of Sensor 1 are shown in Figure 9. Through seven loops, the output voltage deviation is decreased from over 0.4 V to less than 0.1 V, which makes the sensitivity of Sensor 1 more than 99.9%, increasing 3.2% than the original linearity (96.77%). Table 3 shows the sensitivity and linearity of each sensor. The sensitivity after overall adjustment (about 7-15 loops) reaches 1.6644-1.6841 V/mm, and the linearity reaches 99.90-99.92%, which is increased by 1.9-5.0% over the original (95.13-98.00%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensor 1** & **Sensor 2** & **Sensor 3** & **Sensor 4** \\ \hline
**Number of Loops** & 7 & 15 & 12 & 8 \\
**Output Range (V)** & 0.0286–10.0916 & \(-\)0.0073–9.9788 & \(-\)0.0133–9.9855 & \(-\)0.0209–10.0837 \\
**Sensitivity (V/mm)** & 1.6772 & 1.6644 & 1.6665 & 1.6841 \\
**Linearity (before adjustment)** & 96.77\% & 95.13\% & 98.00\% & 97.16\% \\
**Linearity (after adjustment)** & 99.91\% & 99.90\% & 99.91\% & 99.92\% \\
**Linearity increase** & 3.2\% & 5.0\% & 1.9\% & 2.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of adjusted sensitivity and linearity.  
Figure 9: Overall adjustment results.
## 3 Field Test Analysis and Delay Adjustment Under Vibration Control
### Engineering Background
This paper primarily investigates the influence of the spatiotemporal relationship between monitoring points and blast sources on wave arrival times, as well as the associated vibration control issues. The reconstruction of single-hole vibration waves and the  
Figure 6: Schematic of blast vibration monitoring points. Figure 7: Time of arrival of the vibration waves from each hole at monitoring point B1. (**a**) Hole-5–84. (**b**) Hole-85–168. (**c**) Hole-169–252. (**d**) Hole-253–338. prediction of multi-hole vibrations serve as validation steps for vibration control effectiveness. Taking monitoring point B1 as an example, Figure 8a shows the measured single-hole vibration curve, with a dominant frequency of 9.94 Hz obtained through Fourier transform, corresponding to a half-period (\(1/2T\)) of approximately 50 ms. Using this as the baseline curve and following the methodology outlined in Section 2, the predicted multi-hole vibration curve is generated. A comparison between the predicted and measured vibration curves is presented in Figure 8b, where the initial 830 ms segment corresponds to the single-hole vibration curve of the test blast hole and is not displayed. The high degree of agreement between the two curves demonstrates that the waveform prediction accuracy based on the superposition method meets the requirements for engineering applications. The core of the vibration control method proposed in this paper is (1) single-hole vibration curve reconstruction based on measured data and (2) wave propagation time calculation based on the spatial relationship. In the reconstruction of the single-hole vibration curve, this method adopts the measured waveform as input data, which effectively avoids the limitations of the theoretical assumptions; in the calculation of the vibration wave propagation time, we accurately determine the actual coordinates of the blast source and the monitoring point based on the technology of GPS positioning, which provides the basic parameters for the calculation of wave propagation time. Under different geological conditions, researchers can realize effective vibration control by adjusting the delay parameters after obtaining the above basic data through experiments.