esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 2 Materials and Methods
### Strategy
#### 2.1.2 Hierarchical Organization at Millimeter Scale for the Control of Electromagnetic Propagation  
Periodic or gradient structuration of isotropic or pre-oriented nanocomposites has been achieved by stacking multiple layers of films (Figure 2a) alternating insulating dielectric polymer and conductive nanocomposite films. Stacks arranged in a gradient of conductivity are predicted to favor the penetration and progressive absorption of electromagnetic waves inside a nanocomposite material (Figure 2b), which has been confirmed experimentally [5]. A simple method based on chain matrix conversion [11] simulates the scattering parameters (S-parameters) and absorption index if the physical parameters of each layer are known. Figure 2b illustrates that a periodic stack alternating insulating polycarbonate (PC) layers and layers having graded increasing conductivity induces lower reflection and higher absorption than when using layers having uniform equivalent conductivity. The simulation of Figure 2 is conceptual rather than presenting real experimental results. It aims at demonstrating the superior performance of a generic gradient structure compared to a straightforward periodic one with the same overall loading of conductive filler. The permittivity values of the insulating layers correspond to PC, but the conductive layers are not meant to represent specific conductive composites. However, the corresponding conductivities can be experimentally obtained by PC-CNT nanocomposites with controlled CNT concentration in each layer.  
Figure 1: Strategy for controlling electromagnetic propagation: (**a**) nanocarbon support NCS (CNT or graphene) for controlling dielectric constant \(\varepsilon_{\mathrm{r}}\) and conductivity \(\sigma\); (**b**) magnetic nanoparticles (MNPs) for controlling permeability \(\mu_{\mathrm{r}}\) and (**c**) combination of NCS and MNPs for improving EM performances.  
Figure 2: (**a**) Stacks alternating insulating and conductive PC sheets according to the method described in [15] (**b**) simulation demonstrating that stacking into gradient of conductivity improves absorption in a wide frequency range: five layers having conductivity linearly rising from 3 to 15 S/m are stacked between pure polymer layers. The dashed curves correspond to uniform concentration from layer to layer.
## 3 Results and Discussion
### Influence of Environmental Factors on NH\({}_{3}\) Emission Rate  
The correlation coefficients for the NH\({}_{3}\) emission rate in summer against environmental factors are listed in Table 3. It was found that temperature, RH, and VR did not significantly correlate NH\({}_{3}\) emission rate in CC (\(p>0.05\)), which might attribute to the indoor temperature and RH were controlled well within narrow ranges, and NH\({}_{3}\) emission rates in CC during the day changed slightly (Figure 3(a3)). This agreed with the results of [PERSON] and [PERSON] [18]. Outdoor and indoor temperatures and VR had significantly positive correlations with the emissions in AV and NM (\(p<0.01\)). A significant and negative effect of outdoor and indoor RH on NH\({}_{3}\) emission rate was also revealed for AV (Outdoor RH: \(p<0.01\), indoor RH: \(p<0.05\)) and NM (\(p<0.01\)), which aligned with the findings of [PERSON] and [PERSON] [18]. While it was contrasted to the results reported by [PERSON] et al. [16], who found the RH had significant positive impact on NH\({}_{3}\) emissions. In [PERSON] et al. [16], the correlations between NH\({}_{3}\) emission rates and RH were analyzed using the data from 24 September 2007 to 31 December 2009, making no distinction between seasons. High RH would increase NH\({}_{3}\) emissions by promoting manure fermentation [37], especially in cold seasons with low VR, which might cause the difference between the two studies. Besides, the manure dropped directly into the manure pit, and was cleaned out annually to make the moisture in manure volatilized difficultly.  
Table 4 illustrated the average NH\({}_{3}\) emission rates on the first and second days of manure accumulation in CC, AV, and NM. On these two days of manure accumulation, the NH3 emission rate in NM was greatly higher than that in CC and AV (\(p<0.05\)). The RH of NM was the highest compared with that of CC and AV, which could cause higher moisture content in the manure of NM to improve microbial activity to produce more NH\({}_{3}\)[27; 37]. Besides, this discrepancy might also be caused by the different levels of manure residue on the belts. Additionally, the highest VR inside NM would also promote higher NH\({}_{3}\) emissions.
## 6 Conclusions  
In this paper, we introduce a deep learning-based spatio-temporal data prediction model that enables inbound and outbound passenger flow prediction for urban rail transit networks. Firstly, the spatio-temporal data is divided into three timescales: closeness, periodicity, and trend. Then the attention mechanisms are introduced between the residual units and the convolutional networks to process spatio-temporal passenger flow information. Moreover, external factors including holiday and meteorological data are fused to achieve more accurate predictions.  
We evaluated our model using real data from several Beijing subway lines. Comparison with five baseline models using multiple evaluation metrics shows that the proposed ST-RANet model shows the best results. The ablation experiments demonstrate that taking external factors into account can cope with real data variations, and the model incorporating the attention mechanism can focus more on the parts that are helpful for the prediction goal, improving the model's modeling ability for time-series data. The experimental results show that our model has a prediction accuracy of more than 99%, which is highly applicable and significantly better than the other five baseline models.  
In addition, there are certain limitations to our model. Firstly, it does not integrate other important factors that affect changes in passenger flow, such as major events. Secondly, the interpretability of the model is poor, as it is unable to show the entire process of data changes in the model. In the future, we will attempt to address these limitations and improve the applicability of the prediction model to fully meet the needs of actual operating work. Overall, passenger flow prediction technology has extensive application prospects and research significance, and our research will continue to focus on this technology to promote its better service to urban development and public welfare.  
The authors confirm contribution to the paper as follows: study conception and design, [PERSON] and X.D.; data collection: H.Y.; analysis and interpretation of results: [PERSON] and J.C.; draft manuscript preparation: X.D. and X.H.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (grant No. L201015), and the National Special Project of Science and Technology Basic Resources Survey (grant No. 2022 FY101400).  
Not applicable.  
Readers can access the data used in this study by following this link: [[https://github.com/DXueRu/RANet_dataset.git](https://github.com/DXueRu/RANet_dataset.git)]([https://github.com/DXueRu/RANet_dataset.git](https://github.com/DXueRu/RANet_dataset.git)), accessed on 11 September 2023.  
We would sincerely want to thank the people who supported this work and the reviewing committee for their estimable feedback.  
The authors declare no conflict of interest.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results
### UIAI-Lab Dataset
#### 5.2.2 Acoustic Emission Load-Variant UIAI-Lab Datasets  
To validate the reliability, stability, and robustness, we investigated the fault identification capabilities of the SVM + FOR-ACO, the SVM + FOR-SPIO, and the SVM + FOR-PIO on the AE load-variant datasets provided by UIAI-Lab when the cracks remain fixed. The ACA values are shown in the following table (Table 14). Concerning this table, the average accuracy of the identification of the degradation for the SVM + FOR-ACO, which was used to validate the robustness, is about 97.65%. For the SVM + FOR-SPIO method, it is about 94.45%, and for the SVM + FOR-PIO plan, it is about 81.6%. Additionally, the SVM + FOR-ACO outperforms the SVM + FOR-SPIO, yielding, on average, the accuracy of identification improved by about 3.4% and 3% for 3 and 6 mm crack sizes, respectively. Moreover, the SVM + FOR-ACO also outperforms the SVM + FOR-PIO, yielding, on average, the accuracy of identification improved by about 16.9% and 15.2% for 3 and 6 mm crack sizes, respectively. Regarding the power of the SVM+FOR-ACO for multi-type fault identification of a bearing for crack-variant and load-variant tests, the SVM+FOR-ACO is more persuasive than the other two schemes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **SVM + FOR-ACO** & **SVM + FOR-SPIO** & **SVM + FOR-PIO** \\ \hline Crack Sizes (mm) & 3 & 6 & 3 & 6 & 3 & 6 \\ NRM & 100\% & 100\% & 100\% & 100\% & 88\% & 88\% \\ IRF & 97\% & 98\% & 95\% & 95\% & 80\% & 80\% \\ ORF & 97\% & 99\% & 96\% & 94\% & 78\% & 80\% \\ BLF & 98\% & 98\% & 97\% & 96\% & 81\% & 83\% \\ IRF-BLF & 98\% & 99\% & 94\% & 94\% & 82\% & 84\% \\ ORF-BLF & 98\% & 97\% & 95\% & 95\% & 85\% & 84\% \\ IRF-ORF & 97\% & 98\% & 94\% & 96\% & 80\% & 82\% \\ IRF-ORF-BLF & 97\% & 97\% & 95\% & 96\% & 81\% & 81\% \\ ACA\% & 97.8 & 98.3 & 95.8 & 95.6 & 81.9 & 82.8 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The Accuracy of Multi-type Fault Identification When the Torque Speed is 500 RPM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Techniques** & **SVM + FOR-ACO** & **SVM + FOR-SPIO** & **SVM + FOR-PIO** \\ \hline Crack Sizes (mm) & 3 & 6 & 3 & 6 & 3 & 6 \\ NRM & 100\% & 100\% & 100\% & 100\% & 86\% & 86\% \\ IRF & 98\% & 96\% & 90\% & 90\% & 80\% & 80\% \\ ORF & 96\% & 97\% & 92\% & 94\% & 75\% & 78\% \\ BLF & 96\% & 99\% & 94\% & 95\% & 80\% & 81\% \\ IRF-BLF & 97\% & 98\% & 93\% & 96\% & 80\% & 86\% \\ ORF-BLF & 98\% & 98\% & 96\% & 94\% & 84\% & 85\% \\ IRF-ORF & 98\% & 97\% & 94\% & 94\% & 80\% & 82\% \\ IRF-ORF-BLF & 97\% & 97\% & 94\% & 95\% & 80\% & 83\% \\ ACA\% & 97.5 & 97.8 & 94.1 & 94.8 & 80.6 & 82.6 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The Accuracy of Multi-Type Fault Identification to Test the Motor Speed-Variant Dataset.
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 3 Cooperative Control of Variable-Sweep Wing UAV via MADDPG
### Structure and Training Process of the MADDPG-VSC Algorithm
#### 3.4.2 Algorithmic Performance Evaluation
Similarly, in Table 7, MADDPG-VSC's average deviation is 2.55 m, outperforming MADDPG's 3.91 m and SAC's 3.15 m, indicating that MADDPG-VSC offers  
Figure 8: UAV trajectories using MADDPG, MADDPG–VSC, and SAC algorithms. (**a**) MADDPG algorithm; (**b**) MADDPG–VSC algorithm; (**c**) SAC algorithm. more precise trajectory control, reducing deviations and keeping UAVs on their intended paths in complex tasks. Additionally, the adaptive multi-objective reward system dynamically balances task priorities and optimizes UAV coordination in real-time, ensuring global optimization and preventing local optima, which is essential for maintaining stability across complex missions. In summary, MADDPG-VSC demonstrates clear advantages in regards to stability and trajectory accuracy when compared with both MADDPG and SAC. Although SAC occasionally performs similarly to MADDPG-VSC in simpler scenarios, it generally falls short in precision and stability in more complex, dynamic environments. These attributes make MADDPG-VSC more reliable for multi-UAV cooperative tasks, particularly in achieving precise navigation and formation control in challenging environments.
# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 3 Proposed Architecture
### Multi-Scale Multi-Band Dilated Time-Frequency DenseLSTM  
The proposed architecture, MMDilDenseLSTM, has the advantage of having a wide receptive field in the time axis as well as the frequency axis of the spectrogram by combining the dilated block designed in the previous study [33] with the MMDenseLSTM introduced in [31]. In addition, the model used in our previous study, based on a CNN architecture, had limitations in utilizing time series information in spectrograms. MMDenseLSTM extracts CNN features from dense blocks and then extracts features considering time series information from LSTM blocks. We can extract a CNN feature considering a wider range by adding a dilated block to the dense block, and then the LSTM block takes over the influence by using the CNN feature as the input. It is an architecture in which the spectrogram is divided into three bands and MDilDenseLSTM is arranged in parallel in each band and the full band, as shown in Figure 4. Since we used the audio sampled at 16 kHz as our dataset, we used a spectrogram with a frequency range of 8 kHz as input. Therefore, the frequency band was divided by the boundary of 2 kHz and 4 kHz, and the ratio was equal to that of MMDenseLSTM [31]. Moreover, 0-2 kHz is a low band, which is a frequency band where the speech signals mainly exist, 2-4 kHz is called the middle band, and 4-8 kHz is called the high band. The output of each MDilDenseLSTM is combined into one tensor, and a mask of the same size as the input is outputted through the dilated dense block, the compression block, and the last \(3\times 3\) convolution. Figure 4: Proposed architecture for speech enhancement. Figure 3: The architecture of LSTM block. For each MDilDenseLSTM, the growth rate and the number of composite functions in the dilated dense block, and the number of LSTM blocks were applied differently. Table 1 shows the detailed architecture of MMDilDenseLSTM. The growth rate, the number of composite functions, and the scale of each band, which are hyper-parameters of MMDilDenseLSTM, were referred to MMDenseLSTM [31]. Since the low frequency band is important due to the characteristics of the acoustic signal, the growth rate \(k\) and the number \(L\) of the composite function in the low band and full band, which include low frequency band, were applied more than other bands. In addition, the LSTM block in the low and full band was applied before the up-sampling, which makes scale \(1\) and the up-sampling located at the smallest scale. However, the LSTM block in the high and middle band was applied only before the up-sampling located at the smallest scale. In full band, since the input size is more than twice that of other bands, the receptive field must be large; therefore, the scale is lower than that of other bands. In order to combine the output feature maps along the frequency axis from each MDilDenseLSTM except for the full band, the number of output feature maps must be the same; therefore, the number of output feature maps is made the same by adjusting the compression rate applied to each band.
## 4 Hardware Implementation
### State Control Level
#### 4.1.2 Trajectory Mode  
The process in trajectory mode is significantly more complicated than in manual mode. A simple parallel simulation of the trajectory run is performed in the microcontroller to generate the target values for position and orientation. The drive speed is changed in a specified trapezoidal or triangular profile. This speed is integrated into the target position. The desired speed profile is mathematically  
\[\omega_{(t)}=\begin{array}{cc}\dot{\omega}\cdot t&t\leq t_{AD}\\ \dot{\omega}\cdot t_{AD}&t_{AD}<t\leq t_{SD}\\ \dot{\omega}\cdot(t_{SD}-t)&t>t_{SD}\end{array} \tag{54}\]  
The target position is the integration of the target speed  
\[\phi_{(t)}=\int_{\tau=0}^{t}\omega_{(\tau)}d\tau. \tag{55}\]  
If the simulation has reached the target, the position value is frozen. Suppose that the robot's position only deviates by a maximum error when the position value is frozen; in this case, the trajectory is considered to have been completed successfully, and the robot reports this to the mini-computer, which then transmits new target coordinates if necessary.
## 2 Background and Related Works  
### Radiation Effects and Soft Errors  
The effects of radiation on semiconductors can be classified into cumulative effects and single event effects (SEEs). The first ones, as their name implies, are due to the prolonged exposure of materials to radiation, and can usually cause changes in the electrical properties of the materials of electronic components [3]. Moreover, SEEs originate from the impact of high-energy particles on semiconductor materials, generating deposition and transportation of charges. The effects of SEEs can be permanent or transient. The latter are known as'soft errors' [2]. If the impact of a high energy particle causes the generation of an electron-hole pair, a bit flip may occur. When the bit flip occurs in a storage element, such as a memory cell or a flip-flop, the effect is known as single event upset (SEU). When the change of the bit occurs in a combinational circuit, the effect is called single event transient--SET [3].
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 2 Design and Optimization of the Power Amplifier  
Herein, we present the optimization technique and the design process of a high-gain differential PA with the capacitive neutralization method in detail. In this study, a 65 nm RFCMOS is selected to design a differential three-stage common-source PA with high gain. The target frequency bandwidth is between 26.5-29.5 GHz. with a supply voltage of 1 V. Further, a neutralized capacitor \(C_{nu}\) is used to improve the stability of the high-gain PA, and the capacitance required depends on the transistor size [6, 7, 8].  
The design process described below is summarized as follows. First, the size of the transistor to be used in the PA was determined through load-pull simulation. Parasitic components were extracted based on the determined transistor. After that, the optimum value of \(C_{neu}\) was derived using the proposed equivalent circuit of the power cell of the differential structure including \(C_{neu}\), and the equations derived from the equivalent circuit. In this case, the power gain and k-factor of the PA were considered. Finally, the value of \(C_{neu}\) was corrected based on the EM simulation result considering the layout effect.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 2 Principle behind FOS-LW  
The FOS-LW senses the deformation of the soil and rock mass through a change of light energy intensity in the fiber optic sensor cable. When the displacement resulted from deformation of the slope increases, the system identifies movements in the soil and rock from the change of signal energy intensity caused by localized micro bending in the cable. Figure 1 demonstrates the configuration of the FOS-LW system showing the signal generator sending out light into the sensing fiber. The light is modulated in the energy regulator, in which the light intensity is \(I_{p}\). Through the displacement (event) sensors, a light signal is detected, which has an intensity of \(I_{o}\). In the electro-optic modulator, the intensity change between the input light and output light is calculated as  
\[\Delta I=I_{p}-I_{o} \tag{1}\]  
where \(\Delta I\) can be transmitted to the threshold judgment module. In the threshold judgment module, the data are compared with a pre-defined threshold, if the measured result is bigger than the threshold then the controller will send out a message to trigger the alarm; otherwise, the controller sends a message to the controller, which then commands the signal generator to send out the next round of light. The data can also be transferred to an analyzer for processing and analysis via Ethernet.  
The key to the early warning system lies in establishing the relationship between the deformation of the landslide and the light intensity. Therefore, the event sensor is the key coupling of these two variables and a special linkage device is developed within the event sensor to enable the FOS-LW to detect the deformation of the landslide accurately.
# Wideband Eight-Antenna Array Designs for 5G Smartphone Applications
## 5 Battery Component Simulation  
The smartphone battery is one of the largest components in the device. Therefore, an actual lithium battery from the Xiaomi 8 Pro (model BM3F) with dimensions of 76.9 mm \(\times\) 64.2 mm \(\times\) 3.7 mm is applied and integrated into the antenna array simulation. The battery cell is made of aluminum alloy and covered with a 0.3 mm thick plastic film. Figure 19 shows the corresponding position of the battery on the ground plane of the smartphone. Figure 20a displays the simulated reflection coefficient of the proposed antenna affected by the battery. The battery's position, being near the feed locations of the four middle antennas (Ant.2, Ant.3, Ant.6, and Ant.7), causes changes in the operating frequency bands of these antennas, especially at the 4 GHz resonance frequency. Nevertheless, all eight antennas meet the bandwidth requirements of 3300-5925 MHz. Figure 20b shows the simulated total efficiency of the proposed antenna affected by the battery. The four middle antennas are significantly affected, with their efficiency slightly reduced to 60% (from 62%) in the low band, due to the influence of the feeding lines by the battery, while the efficiency of the other antennas remains nearly unchanged.  
Figure 19: Battery component integration in the simulation.  
Figure 20: Proposed antenna with battery component (**a**) refection coefficients and (**b**) total efficiency.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 6 Conclusions
### Future Research Directions  
While the proposed Hybrid (XGBoost + LTC) model has demonstrated improved forecasting accuracy and economic benefits, several opportunities remain open for future research to further enhance wind power prediction and its practical applications.  
First, expanding the dataset to include multi-year as well as multi-location wind data could improve model generalizability and robustness across diverse geographical and climatic conditions. This would also allow for a more accurate analysis of seasonal influences on wind power generation.  
Second, future studies should investigate methods to reduce errors in the predicted meteorological elements, as these significantly impact the training and performance of forecasting models. The use of localized meteorological observation data from wind farm areas could refine input data quality and improve model accuracy.  
Finally, hybridization strategies could be further refined by combining deep learning architectures (e.g., Transformers or Graph Neural Networks) with statistical models to capture both temporal dependencies and structural patterns in wind behavior. Additionally, ensemble learning approaches that dynamically weight model contributions based on recent performance could offer further accuracy gains.  
Conceptualization, D.N. and I.L.-B.; methodology, J.K. and A.M.; formal analysis, J.K. and M.M.; investigation, J.K. and A.M.; resources, D.N.; data curation, J.K.; writing--original draft preparation, D.N. and I.L.-B.; writing--review and editing, D.N., I.L.-B. and M.M.; visualization, J.K. and A.M.; supervision, D.N. and I.L.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.