esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conventional and Proposed Methods
### Comparison Between Conventional Methods  
The simulation parameters are listed in Table 1. This section compares the simulation results of the two conventional methods introduced earlier, in which the transmission power of the CSI-based proportional allocation method is allocated according to the CSI gain ratio. In this method, the CSI gain is adjusted and controlled to simulate both uniform and non-uniform CSI conditions and is compared with the equal power allocation method.  
Figure 3 shows the performance comparison between the CSI-based proportional allocation method and the equal power allocation method under a uniform channel environment. As can be seen from the figure, when the channel environment is relatively stable and uniform, there is no significant difference in BER between the CSI-based proportional allocation method and the equal power allocation method at various distances. This is because, in a uniform channel, there is little difference in CSI gains between the antennas, and the effect of power allocation based on the gain in the CSI-based proportional allocation method is not fully realized. As a result, the performance of both methods is almost identical, and no significant difference in BER is observed.  
Figure 4 shows the performance comparison between the CSI-based proportional allocation method and the equal power allocation method under a non-uniform channel environment. As clearly seen in the figure, when the channel environment is non-uniform, meaning that there are differences in gain between the channels, the performance of the equal power allocation method deteriorates compared to the uniform channel environment shown in Figure 3.  
On the other hand, the CSI-based proportional allocation method distributes the transmission power proportionally according to the gain ratio of each channel, which allows it to maintain relatively good performance even in such non-uniform channel environments. Specifically, since the CSI-based proportional allocation method distributes power according to the characteristics of each channel, it leads to an improvement in SNR, and a tendency to suppress BER is observed. As a result, in non-uniform channel environments, the CSI-based proportional allocation method exhibits better overall performance compared to the equal power allocation method.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Transmission Power & 5 W \\ Number of Antenna Elements & 16 \\ Transmission Gain & \(-\)8 dB \\ Modulation Method & 16 QAM \\ Carrier Frequency & 2.4 GHz \\ Number of Subcarriers & 48 \\ Doppler Frequency & 5 Hz \\ Number of Multipaths & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter settings.
## 4 Experiment
### Experimental Results Analysis
#### 4.5.3 Loss Balance Coefficient Experiment  
This study introduces contrastive learning loss into the original loss function of the CasRel model. The loss function employed by our model consists of two parts: the CasRel model's loss and the contrastive learning loss, as elaborated in Section 3.7. The complete loss function is formulated in Formula (10):  
\[\text{loss}=\lambda\text{loss1}+(1-\lambda)\text{loss}_{contrast} \tag{10}\]  
In this context, _loss_1 represents the CasRel model's loss function, which measures the discrepancy between the model's predicted outcomes and the actual labels. \(\text{loss}_{contrast}\) denotes the contrastive learning loss proposed in this paper, which provides additional  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** \\ \hline CasRel & 0.772 & 0.764 & 0.768 \\ CasRel+PY & 0.801 & 0.793 & 0.797 \\ CasRel+POS & 0.807 & 0.796 & 0.801 \\ CasRel+PY+POS & 0.816 & 0.817 & 0.817 \\ CJE-PCHF & 0.821 & 0.822 & 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation experiment results.  
constraints and adjustments to enhance the model's training process. The parameter \(\lambda\) signifies the loss balance coefficient. To validate the effectiveness of the proposed loss function, this section designs ablation experiments to explore the impact of varying the loss balance coefficient from 0 to 1 on the model's performance. The experimental results are presented in Table 6 and Figure 4.  
As shown in Table 6 and Figure 4, when \(\lambda\) = 0, it indicates the use of only the CasRel model's training loss, and when \(\lambda\) = 1, it signifies the exclusive use of the contrastive learning loss function proposed in this paper. According to the trend in Figure 4, the F1 score increases with \(\lambda\) in the range of (0, 0.3). When \(\lambda\) is between (0.3, 0.9), the model's performance maintains a high and stable F1 score, peaking at 82.2% when \(\lambda\) = 0.7. This suggests that, within this interval, the weight configuration of the model's self-training loss and the contrastive learning loss achieves a relatively balanced state for the current task and dataset. Subsequently, as \(\lambda\) increases, the F1 score begins to decline. Specifically, when \(\lambda\) is between (0.2, 0.9), the F1 score is higher than when \(\lambda\) = 0 or \(\lambda\) = 1, indicating that a linear combination of the two losses is more effective in enhancing model performance compared to using a single loss function.  
In summary, the results confirm the effectiveness of the contrastive learning loss. By tuning \(\lambda\), an optimal balance is achieved, and combining both losses improves performance in complex relation extraction tasks.
## 2 Materials and Methods
### MODIS EVI, FAP, and LAI Products  
The MODIS products of EVI, the fraction of photosynthetically active radiation (FPAR), and LAI were used in this study. There are close relationships among MODIS-derived indices, but these indices reflect plant photosynthesis in different ways, and different MODIS indices were used to model SIF for different aims. MODIS-derived NDVI has been widely employed in terrestrial ecosystem studies. However, due to the possible implications posed by the saturation effect [12; 21], we opted to use EVI as a means of gauging change in the vegetation cover in forest, cropland, and grassland areas. MODIS GPP was not considered in this study because there was no significant relationship between MODIS GPP and OCO-2 SIF in forest areas [12].  
LAI provides a measurement of the one-sided green leaf area present per unit ground area in broadleaf canopies and half the total needle surface area per unit ground area in coniferous canopies. The fraction of photosynthetically active radiation (FPAR) measure is the fraction of photosynthetically active radiation (400-700 nm) absorbed by green vegetation [37].  
The EVI was extracted from the 16-day composite MOD13A1 product, with a spatial resolution of 500 m. The LAI and FAPAR were extracted from the 8-day composite MOD15A2H product, with a spatial resolution of 500 m. These MODIS products were downloaded from the NASA Land Processes Distributed Active Archive Center (LP DAAC).
## 5 Numerical Simulations and Comparison
For this particular direction pointed at by every antenna, the distance to the source can be increased here by 8 cm, that is to say, larger than \(\lambda/3\), which is not negligible. Likewise, whereas in far-field conditions, the colatitude angle corresponding to that direction is by definition equal to 0\({}^{\circ}\) for every antenna, at a distance \(h=100\) m, it varies from one antenna to the other and can be as large as 2.3\({}^{\circ}\). As a consequence, these conditions are clearly not those of far-field imaging.
###### Abstract  
Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNR\({}_{\text{imp}}\)) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect.  
Keywords:threshold denoising; acoustic signal; empirical mode decomposition; improved fruit fly optimization algorithm; variation coefficient; disturbance coefficient; wavelet transform +
Footnote †: journal: Applied Soft. 2017
###### Abstract  
Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method.  
indoor positioning; RSSI fingerprinting; k-nearest neighbors; bluetooth low energy; particle filter +
Footnote †: journal:  
Article
## 3 System Model Design
### Calculating Mobile Similarity of Node and Narrowing the Node Range by a Single Threshold  
In our proposed algorithm, the attributes related to node movement are fully utilized and calculated from two aspects. The neighbor nodes in the opportunistic social network are relative to the transmission distance or the number of transmission hops. On the one hand, we can judge the similarity between two nodes by judging how many common neighbor nodes exist between nodes. According to the mobility of nodes, we judge the movement between nodes by the common neighbor nodes between nodes' similarity. On the other hand, we collect the position of the moving track of the node and its moving state at the time of information collection. The purpose is to judge the mobile similarity by calculating the similarity of the moving track during the information collection phase.
###### Abstract  
Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding.  
resistance spot welding; expulsion fault detection and diagnosis; wavelet decomposition; neural networks; sensor application Article]Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks
## 5 Discussion and Conclusions
### Conclusions  
This paper proposes a phenomenological model considering the base deflection of the gear fillet and the influence of the flexibility between the root circle and the base circle, the transmission path, and the meshing shock for planetary gears. The meshing stiffness and vibration response of a gear with different fault sizes were investigated. The conclusions of this paper are as follows:  
1. When calculating the mesh stiffness via the potential energy method, the flexibility between the root circle and the base circle should be considered.
2. The movement of the falling edge with the stiffness of the faulty gear pair caused by the fault shows a tendency to advance as the fault size increases. When the fault size is large (\(\geq\)1/2 of tooth height), the stiffness of the faulty gear pair may be 0, leading to the unstable state of the gear system.
3. Meshing impact is an important vibration excitation in the planetary gearbox. Compared with the traditional phenomenological model constructed by a series of cosine functions, the phenomenological model established in this paper considers the influence of the meshing impact and obtains a simulation signal that is more in line with the time-frequency domain characteristics of the actual signal.
4. Under healthy conditions, the frequency components at the meshing point of the gearbox are the meshing frequency \(f_{m}\) and its frequency doubling; the amplitude shows a gradual decreasing trend; and the sidebands appear at \(f_{m}\ \pm\ f_{c}\), \(f_{m}\ \pm\ 3f_{c}\). Under the sun gear broken tooth fault, the same frequency component and sidebands  
Figure 18: Comparison of measured and simulated signals at different rotational speeds under fault condition: (**a**) \(v=800\) rpm; (**b**) \(v=1000\) rpm; (**c**) \(v=1200\) rpm.  
appear at the meshing point as in the healthy case. In addition, there are also sidebands with the sun gear fault frequency \(f_{rs}\) as the interval near the meshing frequency under the fault condition, and a symmetrical sideband with \(f_{c}\) as the interval appears on both sides of \(f_{rs}\). Analyzing the vibration signal characteristics of the planetary gearbox under normal conditions and with sun gear broken tooth faults is helpful in the local fault diagnosis of the planetary gearbox.  
This paper was completed by the authors in cooperation. [PERSON] carried out theoretical research, data analysis, experiment analysis, and paper writing: [PERSON], [PERSON] and [PERSON] provided constructive suggestions; and [PERSON] revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No.62163020, No.62173168) and Kunming University of Science and Technology 'Double First-Class' Science and Technology Project (No. 202202 AG050002).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
With the continuous expansion of Internet usage, ensuring robust network security in distributed systems has become increasingly critical. Although numerous machine learning algorithms have been applied in previous research to improve the performance of intrusion detection systems (IDSs), achieving optimal results remains a challenge, particularly when dealing with high-dimensional data. IDSs still face vulnerabilities in classification, such as the risk of single algorithm failure in multiclass attack scenarios. To address these limitations, we propose a novel dimensionality reduction approach that integrates feature selection and feature extraction techniques to enhance the IDS dataset. Additionally, to mitigate issues related to multiclass attack detection, we introduce a new multi-stacking ensemble classifier, which we evaluate using two widely recognized intrusion detection benchmark datasets. Our methodology began with the LogitBoost algorithm to select optimal features from the datasets. Subsequently, we employed the ResNet algorithm to construct new features based on those selected by LogitBoost. Finally, we developed and evaluated a multi-stacking ensemble model.  
The experimental results demonstrated outstanding performance in multiclass classification for both datasets. Specifically, our model achieved high metrics on the UNSW-NB15 (_D1_) dataset: an accuracy of 90.29%, a recall of 90.29%, a precision of 90.45%, and an F1 score of 89.87%. For the CICIDS2017 (_D2_) dataset, our model achieved an exceptional multiclass classification accuracy of 99.69%, with the corresponding recall, precision, and F1 score also at 99.69%. These results show better performance for our model compared to state-of-the-art methods in the field of intrusion detection.  
Methodology, A.M.A.; software, A.M.A.; writing--original draft, A.M.A.; writing--review and editing, H.Z.; supervision, M.N.-B. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets utilized in this study are publicly available and can be accessed at UNSW-NB15 and CICIDS2017.  
The authors declare no conflicts of interest.
## 3 Materials and Methods  
### RED/BLACK Concept of Communication in Aviation  
The RED/BLACK concept represents a fundamental framework in secure communications, underpinning the design of mission-critical systems. This concept delineates sensitive classified RED domains from non-sensitive unclassified BLACK domains, enabling the simultaneous transmission and processing of both open and restricted information flows. In aviation systems, for example, the RED designation refers specifically to communication networks, equipment, protocols, and data authorized to handle confidential, secret, or top-secret information. This encompasses systems that transmit encrypted communications vital for military, law enforcement, or other operations pertinent to national security. Conversely, the BLACK designation refers to standard commercial systems for public aviation communication and air traffic control operations involving routine non-sensitive information. The segregation between RED and BLACK ensures that sensitive information remains isolated and uncompromised while allowing regular aviation communication to occur freely across the BLACK domain. This bifurcation is physically manifested through separate infrastructure for both domains including distinct radio systems, antennas, networks, storage systems, and facilities restricting access only to personnel with appropriate security clearances for the RED domain. The segregation also encompasses stringent security protocols enveloping all aspects of RED communications, ensuring confidentiality and integrity. These include encryption mechanisms, access control and monitoring systems, robust physical security measures, and extensive personnel vetting procedures aligned to the level of information sensitivity. The main applications of the RED/BLACK concept are shown in Table 1.
## 3 Results  
### Characteristic of HIFU System  
Figure 6 exhibits the output waveforms created by the HIFU system at the various output voltages. The maximum output voltage the system could generate was \(\pm\)21 V while the minimum was \(\pm\)6 V. All the output waveforms had the center frequency of 2 MHz and the rising and falling time was equal to 30 ns. There was a moderate overshoot (20%) at the moment the waveforms changed from positive to negative and vice versa. The output signals with the symmetric positive and negative side square waveforms had been set at a 50% duty cycle before applying to the HIFU transducer. The acoustic pressure generated from the transducer was measured by the hydrophone at the focal point under the \(\pm\)9 V voltage level. The peak-peak amplitude of the hydrophone signal was measured to 211 mV and the acoustic pressure was 105 mV in an electrical unit, which was equal to 2.3 MPa due to the 45 mV/MPa sensitivity. Using Equation (2) and several physical constants as described above, the acoustic pressure in this circumstance was 358 W/cm\({}^{2}\). It is estimated that with the \(\pm\)21 V output waveforms, the attainable acoustic pressure could be 5.4 MPa, and thus, a maximum acoustic intensity was possibly 1949 W/cm\({}^{2}\)[40].  
The thermal ability of the HIFU system in the experiment with the chicken breast tissue was conducted to discover the maximum temperature at various output voltages. The temperature distribution at the surface of the tissue is shown in Figure 7a functioning by HIFU transducer at the biggest output voltage of \(\pm\)21 V. The transducer beam formed a circular affected region on the surface of the tissue as the temperature rose with the time of exposure. There was a difference in the energy transmission between metal and tissue along with the thermocouple. Therefore, the circular treated region surrounding the thermal wire tip is slightly distorted. The tissue was heated to the highest  
Figure 6: The output waveforms from HIFU circuit measured at various voltage amplitudes at 50% duty cycle: (**a**) \(\pm\)6 V (**b**) \(\pm\)9 V (**c**) \(\pm\)12 V (**d**) \(\pm\)15 V (**e**) \(\pm\)18 V (**f**) \(\pm\)21 V.  
temperature at 65 \({}^{\circ}\)C after 50 s of the treatment. The temperature data recorded by the thermocouple were presented in Figure 7b also confirmed this peak. It could be noticed that the temperature at the focused beam sharply increased in the first 20 s of the exposure time from 20.1 \({}^{\circ}\)C to 47.8 \({}^{\circ}\)C, then enhanced to 64.9 \({}^{\circ}\)C for the rest of the treatment. There is an insignificant deviation between the two ways of measuring then the data recorded from the thermocouple were rather reliable for the next experiment.