esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Real Jamming Results Analysis
### High jamming Scenario
#### 6.2.2 Second Trajectory  
Similar to the first trajectory, the GPS DF, GPS TF, GPS TF + Galileo SF, and GPS TF + Galileo TF-based solutions fail to provide continuous positioning solutions during jamming intervals, as shown in Figures 29 and 30. The solutions that provide a continuous positioning solution during jamming intervals are both the GPS TF + Galileo TF + GLONASS SF and the GPS TF + Galileo TF + GLONASS DF-based solutions, due to the availability of more than 10 satellites during jamming, as shown in Figure 31. Additionally, the GPS TF + Galileo TF + GLONASS DF-based solution provides the best overall positioning performance during the jamming interval. It is clear from Table 9 that the GPS TF-based solution is worse than the GPS DF solution due to the lower availability of the GPS DF compared to the GPS TF counterpart. Likewise, the RMSE of the GPS TF + Galileo TF-based solution is worse than the GPS TF + Galileo SF counterpart, as the availability of the GPS TF + Galileo SF is less than the GPS TF + Galileo TF counterpart.  
The most accurate positioning solution is achieved through the GPS TF + Galileo TF + GLONASS DF combination. As shown in Table 9, the RMSE of the GPS TF + Galileo TF + GLONASS DF solution is improved by about 86% and 77%, compared to the GPS TF + Galileo TF counterpart.  
Figure 31: Number of visible satellites for different GNSS signal combinations [second trajectory].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Combination**} & \multicolumn{3}{c}{**Horizontal**} & \multicolumn{3}{c}{**Vertical**} \\ \cline{2-7}  & **RMSE** & **Mean** & **Max** & **RMSE** & **Mean** & **Max** \\ \hline GPS DF & 3.211 & 3.173 & 4.541 & 2.673 & 2.196 & 7.453 \\ \hline GPS TF & 12.559 & 3.638 & 356.038 & 10.902 & 2.940 & 187.952 \\ \hline GPS TF + Galileo SF & 12.272 & 4.201 & 138.110 & 11.368 & 3.195 & 124.674 \\ \hline GPS TF + Galileo TF & 13.161 & 6.455 & 58.532 & 18.938 & 8.636 & 122.068 \\ \hline GPS TF+ Galileo TF + GLONASS SF & 2.575 & 2.420 & 8.316 & 7.827 & 5.735 & 42.224 \\ \hline GPS TF + Galileo TF + GLONASS DF & 1.831 & 0.781 & 14.4477 & 4.296 & 2.343 & 23.135 \\ \hline \hline \end{tabular}
\end{table}
Table 9: RMS error, mean, and maximum (Max) positioning errors with high actual jamming [second trajectory].  
Figure 30: Vertical positioning error for different GNSS signal combinations [second trajectory].
# Musical Mix Clarity Prediction Using Decomposition and Perceptual Masking Thresholds
## 3 Test Stimuli
### Subjective Listening Test Results  
The listeners' results were screened based on the consistency of their repeat scores, as a lack of consistency between repeats showed the listener may not have had a consistent perception of mix clarity or may not have understood the task at hand. Repeat consistency was measured using the interclass correlation coefficient (ICC) between a given listener's repeats. Estimated ICCs and their 95% confidence intervals were calculated using SPSS [37]. The ICC was a mean-rating, absolute agreement, 2-way mixed effects model as recommended in [38]. Ratings from listeners whose repeat scores that had an ICC of below 0.75 were excluded from the final analysis, as it is suggested ratings with an ICC equal to or greater than 0.75 have a 'good' reliability [38]. In this case, the lower bound of the ICC estimates' 95% confidence intervals were taken as the value which needed to exceed the 0.75 threshold, as this ensured with 95% confidence the listeners had 'good' reliability. After post-screening, only ratings from the 15 most consistent listeners remained.  
The mean of each listener repeat rating was taken as the listener's subjective clarity score for a given stimulus. The median of all listeners' mean ratings for each stimulus was then taken as its median clarity score (MCS). Figure 2 shows these median subjective scores per stimuli, also indicating interquartile ranges, and outliers.  
The assumption of homogeneity of variances for ANOVA was not met, indicated by both [PERSON]'s and [PERSON]'s tests showing \(p<0.05\) for all stimuli. As such, a multiple pairwise Wilcoxon test was performed to identify significantly different MCSs. Holm correction was applied to account for multiple comparisons. The results affirm what is indicated by interquartile ranges shown in Figure 2, in that, stimuli whose interquartile ranges do not overlap were also shown as being significantly different (\(p<0.05\)) in the pairwise Wilcoxon analysis.  
Figure 2: Box plot of median subjective clarity scores collected in the subjective listening test.
# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 4 Experiments  
### Datasets  
MinImageNetis a widely used few-shot learning benchmark dataset, proposed in the literature [33] and derived from a subset of the ILSVRC-12 [34] (ImageNet Large Scale Visual Recognition Challenge 2012) dataset. MiniImageNet is designed for few-shot learning tasks and aims to evaluate the model's ability to recognize new categories given only a small number of training samples. This dataset contains 100 images of different categories; each category has 600 RGB color images, with a uniform size of 84 \(\times\) 84 pixels. For experimental needs, these data are usually divided into 64 categories for training, 16 categories for validation, and 20 categories for testing. This partitioning helps evaluate the performance of different few-shot learning methods under standard experimental settings.  
TieredImageNet is another dataset designed for few-shot learning tasks. It is similar to MiniImageNet but provides a larger number of categories and images, as well as a hierarchical category structure. TieredImageNet [35] is also derived from the ILSVRC-12 dataset [34] but contains 608 categories. The number of images in each category far exceeds MiniImageNet, with a total of more than 700,000 images. These categories are organized into a hierarchical structure, in which the 608 categories are divided into 34 higher-level categories (or \"supercategories\"). Each superclass contains 10 to 20 subcategories, which are further divided into a training set (20 categories, a total of 351 subcategories), a validation set (6 categories, a total of 97 subcategories), and a test set (8 categories, 160 subcategories in total). This hierarchical structure not only increases the diversity of data but also requires the model to understand the complex relationships between categories when learning. On average, each subdivision category contains approximately 1281 images, which provides more abundant and challenging training and testing resources for few-shot learning.  
The Tool dataset is a self-built dataset, covering hardware tools commonly used in life, such as hammers, screwdrivers, etc. The sources of the pictures are search engines such as Google and Bing. The image is in RGB color, and the size has been corrected to 84 \(\times\) 84 pixels. The dataset has a total of 60 different categories, and each category has 1000 images. The format of the dataset is divided into 36, 10, and 14, which correspond to the training set, verification set, and test set, respectively.
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 2 Trust Metric Model of Unmanned Aerial Vehicle Cluster Nodes Based on Hierarchical Blockchain
### A Dynamic Evaluation Method for Drone Node Trust Based on Task Perception
#### 2.2.3 Comprehensive Trust Calculation and Update
```
0: Number of nodes, heartbeat interval
0: the Latest fused trustworthiness of nodes
1: Initialize parameters \(\theta\)
2: Cluster head node \(C_{0}\) starts the first heartbeat
3: Ordinary nodes \(C=\{C_{1},C_{2},\ldots,C_{t}\}\) receive heartbeat from cluster head node and start computing \(T_{ij}^{d}\)
4:for each node \(C_{i}\) in \(C\)do
5: \(C_{i}\) sends a communication packet to \(C_{j}\)
6: \(C_{j}\) receives the packet and checks if it needs to be forwarded:
7:if forwarding is required then
8: Forward to the next relay node
9:else
10: Return response packet
11:endif
12: \(C_{i}\) receives the response packet from \(C_{j}\) and computes \(C_{j}\)'s \(PLR\), \(PFR\), and \(TOI\)
13: \(C_{i}\) calculates \(C_{j}\)'s \(T_{ij}^{d}\)
14:endfor
15:for each node \(C_{i}\) in \(C\)do
16: \(C_{i}\) sends a request packet for recommending trustworthiness of \(C_{j}\) to \(C_{n}\) and \(C_{m}\)
17: \(C_{n}\) and \(C_{m}\) receive the request packet and return \(T_{nj}^{d}\) and \(T_{mj}^{d}\) of \(C_{j}\)
18: \(C_{i}\) calculates \(C_{j}\)'s \(T_{ij}^{re}\) after receiving the response packets
19:endfor
20:for each node \(C_{i}\) in \(C\)do
21: Calculate the current round's \(FR\)
22: Calculate \(T_{ij}^{d}\) and \(T_{ij}^{re}\)
23: Calculate \(C_{j}\)'s \(T_{j}^{total}\)
24:endfor
25: Package all data and send to \(C_{0}\)
26: \(C_{0}\) collects all comprehensive trustworthiness result set \(T\)
27:for each node \(C_{i}\) in \(C\)do
28: Calculate \(C_{i}\)'s \(P(e_{j}|C_{j})\)
29:endfor
```  
**Algorithm 1** Trust Evaluation Algorithm
## 2 Description of the Classification Method
### Feature Extraction Process  
Figure 3 gives the graphical representation of the procedure for extracting the final features that contains several steps, i.e., feature calculation, feature fusion, and feature augment. In general, different features can capture different characteristics of the underwater acoustic signal. Therefore, multiple features can be combined to exploit the complementary information for further improvement of the feature design. At the stage of feature calculation and feature fusion, we extract the Log Mel (LM) as the first channel, the MFCC as the second channel, and the composition of Chroma, Contrast, Tonnetz, and Zero-cross  
Figure 1: The depiction of the classification method.  
Figure 2: Framing the signal of the type of passengers into several frames s and s + 1 with no overlapping.  
ratio called CCTZ as the third channel. The first two channels both have the feature size of 60 \(\times\) 41, where 60 denotes the number of the bands and 41 denotes the number of frames. As for the third channel, the feature size of Chroma, Contrast, Tonnetz, and Zero-cross ratio is 24 \(\times\) 41, 6 \(\times\) 41, 6 \(\times\) 41, and 1 \(\times\) 41, respectively. To form a new feature set called CCTZ, the features are sequentially stacked with the remaining lines paddled by zero. Afterward, at the stage of feature augment, we apply the feature augment method of SpecAugment five times on the LM feature, leaving the other channels unchanged. SpecAugment originates from the data augmentation method for speech recognition and is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients) [20]. The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps [20]. The resulting three-dimensional feature matrix is composed of the three channels as mentioned.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 3 Preliminaries
### Certificate-Less Public Key Cryptography (CL-PKC)  
CL-PKC is a novel public key cryptographic system introduced by [PERSON] and [PERSON] in 2003. It was designed to address the shortcomings of traditional public key cryptography and identity-based public key cryptography (ID-PKC).  
In the CL-PKC system, a Key Generation Center (KGC) generates a partial private key for the user based on their identity information. The user then selects a random value and combines it with the partial private key to generate a complete private key. The user's complete private key consists of two independent secret components: one is the partial private key derived from the KGC based on the user's identity, and the other is the random key generated by the user. These two secret components are independent of each other, meaning that one cannot be used to compute the other. Consequently, the KGC cannot determine the user's complete private key, nor can the user deduce the partial private key generated by the KGC.  
The implementation of CL-PKC involves the following seven algorithms: System Initialization (Setup), Partial Private Key Extraction (Partial-Private-Key Extract), Secret Value Setting (Set-Secret-Value), Private Key Generation (Set-Private-Key), Public Key Generation (Set-Public-Key), encryption (Encrypt), and decryption (Decrypt). In practical applications, these algorithms work together to provide efficient and secure encryption, signing, and key-exchange functionalities.  
Compared to traditional Public Key Infrastructure (PKI), CL-PKC simplifies public key management, reduces reliance on centralized authorities, and enhances system flexibility and security. Therefore, CL-PKC is particularly well-suited for the scenarios we discussed.
# Erosion Rate Measurements for DART Spacecraft Ion Propulsion System +
## 2 Materials and Methods
In Figure 4, it corresponds to the flow condition designated as nominal (\(\eta\approx 0.900\), discharge flow rate = 41.812); the flow variations are with respect to this throttle condition. The nomenclature DTLXX-Y-Z designates a DART throttle condition, complete with flow rates: D indicates the throttle level is DART-specific, Y indicates the total discharge chamber flow rate (sum of discharge cathode and main), and Z designates neutralizer flow rate; A is the highest flow rate and O is the lowest. The propellant mass utilization, \(\eta\), is equal to \(\dot{m_{I}}/\dot{m_{T}}\), where \(\dot{m_{I}}\) is mass flow rate of ions from the thruster, and \(\dot{m_{T}}\) is the sum of ion and neutral rates (total discharge chamber mass flow rate). The neutralizer exit is a significant distance downstream from the grids and to the side; its flow adds little xenon density at the grid face, and it is excluded from the total discharge chamber mass flow rate and most of the analysis we will describe. The neutral flow fraction is given by \(1-\eta\) and, for fixed beam current (\(I_{b}\)) conditions as normally employed in the present study, \((1-\eta)/\eta\) is proportional to average xenon neutral density. To improve the accuracy of \(\eta\) figures used in the data plots, correction factors were estimated for both background gas ingestion by the thruster and Xe\({}^{2+}\)/Xe\({}^{+}\) fraction (about 4% in this case). The former correction assumed a Maxwell-Boltzmann velocity distribution and density as measured by the wall ion gauge above the thruster. The latter correction was based on available NEXT measurement data for the Xe\({}^{2+}\)/Xe\({}^{+}\) fraction. Both corrections were small.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G 5 th Generation  
BHO  Baseline Handover  
CART  Classification And Regression Tree  
CHO  Conditional Handover  
EPC  Evolved Packet Core  
GEO  Geostationary Earth Orbit  
GNSS  Global Navigations Satellite System  
HAP  High-Altitude Platform  
HOF  Handover Failure  
HOM  Handover Margin  
ISL  Inter Satellite Link  
LEO  Low Earth Orbit  
MEO  Medium Earth Orbit  
NB-IoT  Narrowband-Internet of Things  
NGSO  Non-Geostationary Orbit  
NTN  Non-Terrestrial Network  
O-RAN  Open Radio Access Network  
PCell  Primary Cell  
PP  Ping Pong  
PSCell  Primary-Secondary Cell  
RACH  Random Access Channel  
RRC  Radio Resource Control  
SCell  Secondary Cell  
SRI  Satellite Radio Interface  
TN  Terrestrial Networks  
TTT  Time To Trigger  
UAV  Unmanned Aerial Vehicle  
UE  User Equipment  
XGBOOST  Extreme Gradient Boosting  
eMTC  Enhanced Machine-Type Communication  
gNB  Next Generation Node B  
MIMO  Multiple Input Multiple Output
## Methods
### Equipment Setup and Signal Recording  
In this study, the ADS1299 EEG-FE kit of Texas Instruments (Dallas, Texas, United States) was used as the biopotential signal collection device and was connected to an Arduino microcontroller. The ADS1299 EEG-FE demonstration kit has obvious advantages in the collection of biopotential signals such as eight channels of low-noise synchronous EEG recording (which can collect multiple signals simultaneously); a 24-bit high resolution analog to digital converter (ADC), \(-110\) dB common-mode rejection ratio, and very low internal noise (which ensures the accuracy of biopotential signal detection); a 1-24 times adjustable programmable gain amplifier (PGA), which meets the needs of different occasions; a single channel 16 kHz sampling rate (which ensures that the biopotential signals bandwidth is sufficient); and an integrated driven-right-leg circuit (which further reduces environmental interference) [43]. In addition, its flexible connection with the Arduino microcontroller (Arduino is an electronic product development platform based on the microcontroller systems) ensures the development and integration of hardware and software components [44].  
As shown in Figure 1, the three previously described biopotential signals (ECG, EOG, and EMG) were collected by the aid of electrodes attached to the subject's 1  skin through the shielded cables into the ADS1299 EEG-FE hardware 3, then the recorded signals went through the Arduino microcontroller 4  via a USB interface to the laptop 5 ), and finally the waveforms and spectra components of the collected signals were analyzed using the MATLAB programming tool. Additionally, shielded cables were used as electrode leads, so as to protect the signal in the core from extraneous interferences.  
The recorded signals were sampled at the rate of 2000 Hz and subsequently amplified 24 times. To compare the effects of the hardware-based shielded drive technology with the software-based method, this study also designed a 3-order Butterworth software notch filter with a bandwidth of 6 Hz and an attenuation of 120 dB at the 50 Hz center frequency. The software filter was applied to the raw data of Method 1 (No-Shield), and the performance was evaluated by comparing the time waveforms and spectra of the biopotential signals with different hardware-shielded methods.  
Figure 1: Schematic diagram of the data acquisition system, 1� the subject; 2� different shielded drive methods proposed in this study; 3� ADS1299 EEG-FE kit, where EEG_FE refers to analog front-end designed for electroencephalography, GND is the ground of the power supply, and CH means the input channel index of the kit; 4� Arduino Microcontroller Unit (MCU); 5� the laptop.
# Interested Keyframe Extraction of Commodity Video Based on Adaptive Clustering Annotation
## 6 Conclusions  
In order to solve the problem that different users have different definitions of keyframes in commodity video, this paper proposes a keyframe recognition algorithm of commodity video that integrates transfer learning and interest information, and extracts the keyframes of different users' preferences. First, the differential frames were extracted from commercial videos by frame-to-frame difference; then, the ACBD algorithm was used to cluster these  
Figure 12: Final result of algorithm. (**a–d**) correspond to the first, second, third, and fourth categories, respectively.  
frames to simplify the user annotation process. The data annotated by the user were used to train the Mobilenet-V2 network, and finally, the keyframes for individuals were extracted. The experimental results on the commodity video dataset constructed in this paper show that the proposed algorithm is effective and extracts the keyframes accurately. However, user preferences tend to change over time, which is not considered in this paper, so how to cater to the changing interests of users is the focus of our next research.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12031502/s1](https://www.mdpi.com/article/10.3390/app12031502/s1)]([https://www.mdpi.com/article/10.3390/app12031502/s1](https://www.mdpi.com/article/10.3390/app12031502/s1)), Video S1: Demonstration.  
Conceptualization, G.M. and X.S.; methodology, G.M.; software, G.M.; validation, G.M. and X.S.; writing--original draft preparation, G.M.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grants No. 61876184.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviation is used in this manuscript:  
ACBD Adaptive Cluster Based on Distance
# Implications of Metamaterial on Ultra-Wide Band Microstrip Antenna Performance
## 3 Metamaterials Design
### Circular SRR (C-SRR) and Square SRR (S-SRR) Design  
Figure 10 shows the schematic view of circular SRR and its resonance frequency is expressed as:  
\[\omega_{0}=\sqrt{\frac{2}{\left(C_{0}+C_{\mathrm{g}}\right)\mathrm{L}}} \tag{1}\]  
where C0 is the distributed capacitance, Cg is the gap capacitance and L is the self-inductance of the rings.  
The resonant frequency of square SRR, demonstrated in Figure 11, is given by [41; 42]:  
\[\mathrm{f_{0}}=\frac{1}{2\pi}\sqrt{\frac{1}{L_{T}C_{eq}}} \tag{2}\]  
Figure 11: Schematic view of a square split-ring resonator.  
Figure 10: Schematic view of a circular split-ring resonator.  
Figure 9: SRR equivalent circuit model.  
L\({}_{\rm T}\) is the total inductance and C\({}_{\rm eq}\) is the total equivalent capacitance. For detailed C-SRR and S-SRR design equations, refer to references [29; 34; 35].
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### CRP Update  
To ensure freshness, the CRPs of vehicles and RSUs need be updated after a certain time. Here we use the vehicle as an example to describe how to update, and the CRPs of RSUs are updated in the same way. The detailed update steps are as follows.  
1. The vehicle generates a new CRP \((C_{v}^{n},R_{v}^{n})\) by PUF chips employed on the vehicle, where \(R_{v}^{n}=PUF(C_{v}^{n})\). Calculate \((P_{Fv}^{n},R_{v}^{n})=Gen(R_{v}^{n})\). Next, the vehicle calculates \(R_{Fv}=Rep(PUF(C_{v}),P_{Fv}),\overline{C}_{v}^{n}=C_{v}^{n}\oplus R_{Fv}\) and \(\overline{R}_{Fv}^{n}=R_{Fv}^{n}\oplus R_{Fv}\). And the vehicle generates a timestamp \(t_{4}\) and \(\phi_{4}=H(AID_{v}||R_{Fv}||t_{4})\). Finally, the vehicle sends \(M_{4}:\{AID_{v},\overline{C}_{v}^{n},\overline{R}_{Fv}^{n},t_{4},\phi_{4}\}\) to CA;
2. On receiving \(M_{4}\), CA verifies \(\phi_{4}\) and \(t_{4}\). If both are correct, CA calculates \(C_{v}^{n}=\overline{C}_{v}^{n}\oplus R_{Fv},R_{Fv}^{n}=\overline{R}_{Fv}^{n} \oplus R_{Fv}\). Then, CA deletes the old CRP \((C_{v},R_{Fv})\) from its database and updates it with a new CRP \((C_{v}^{n},R_{Fv}^{n})\).
## 3 Uncertainty Modeling Techniques
### Probabilistic Methods
#### 3.1.1 Numerical and Sampling Methods  
Numerical and sampling methods are extensively applied in uncertainty modeling to manage and analyze the variability in systems and predictions. Numerical methods involve algorithms for solving mathematical problems through approximate solutions and are especially useful when exact solutions are unattainable. Monte Carlo simulation is particularly effective for modeling uncertainty, relying on repeated random sampling to assess risks in complex scenarios with multiple uncertain variables. Another advanced sampling technique, Latin Hypercube Sampling (LHS), improves upon Monte Carlo simulation by dividing the input distribution into equally probable intervals, ensuring comprehensive sampling across all segments. This method increases efficiency and accuracy, especially in multidimensional power system models, making both Monte Carlo and LHS valuable tools for uncertainty quantification and risk management in power grid operations.