esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Electromagnetic Clutch for Crankshaft Decoupling from a Belt Drive System
## 5 Experimental Validation  
The experimental tests were conducted using the belt test bench shown in Figure 6. The BDS is constituted by three pulleys: (1) belt starter-generator, (2) AC compressor, and (3) crankshaft. The clutch pulley device is installed on an e-motor that reproduces the crankshaft speed profile. When the clutch is open, the belt starter-generator electric machine drives the AC compressor. To identify the closing and opening phases, a microphone (4) measures the acoustic noise produced by these events.  
The clutch control unit (CCU) is based on a Texas Instruments F28379 processing unit together with a BOOSTXL-DRV8323 RS power module. The latter is a fully configurable 3-phase inverter with shunt current sensors incorporated. The power module is used as an H-bridge to supply the electromagnet with a modulated voltage and guarantee the required current profiles on the electromagnet. A proportional-integral (PI) controller is implemented to yield current tracking on the electromagnet. The CCU communicates via a controller area network (CAN) with the vehicle control unit. It receives the clutch commands and feeds back the clutch state. The software is deployed directly from the MATLAB environment using the embedded Coder support package for Texas Instruments C2000 Processors. The layout of the CCU is depicted in Figure 7.  
Figure 6: Testbed for experimental characterization of the clutch. (1) Belt starter–generator electric machine with belt tensioner on top, (2) AC compressor pulley, (3) crankshaft pulley with electromagnetic clutch installed, (4) microphone to measure the noise of the impact when the clutch changes its state, (5) clutch control unit (CCU).
## 1 Introduction
### Novelty and Contribution  
According to a review of past references, a complete model that considers the uncertainty of EV behaviors in the charging stations is not addressed. Most of the existing models have investigated the stochastic behavior of EVs in terms of arrival times, departure times, the amount of charging energy demand, the type of EVs and battery capacity, but there is no model to see all of these factors together. There is also no model for considering the uncertainty of type of EVs chargers. Actually, the EVs in a charging station do not use a specific charger type. Some use a fast charger level 3, and some use a charger level 2. This factor is considered in the proposed model as a probabilistic parameter. Therefore, this paper tries to propose a complete model for considering this uncertainty in the optimal energy management of a smart MG. In this proposed model, some uncertain effective factors on the EVs charging station load such as battery capacity, type of EVs, SoC, charging power level and response to energy price changes (price sensitivity) are considered and simulated by the MCS method. Therefore, the contribution of this paper is as follows:  
* A model for considering the uncertainty of the EVs charging station load is presented using MCS.  
The remainder of this paper is organized as follows. The system model is presented in Section 2. Then in Section 3, the proposed model for charging station load is presented. The scenarios generation and the model implementation are described in Sections 4 and 5 respectively. The optimization problem and the numerical results are provided in Sections 6 and 7, respectively. Finally, Section 8 states the concluding remarks.
## 3 The Radiative Transfer Equation
### RTE Numerical Solutions
#### 3.4.1 Discrete Ordinate Model
For DOM, there might be a few potential programming implementation issues that may lead to incorrect results. Here are some common issues that could cause inconsistencies in the DOM results:  
* Integration Scheme: The accuracy of the numerical integration can significantly impact the solution. Hence, ensure that the integration scheme, such as the choice of quadrature method or the number of discrete angles, is appropriate for the configuration scenario. * Convergence Criteria: The convergence criteria in the iterative solver may need adjustment. If the convergence tolerance is too strict or loose, it can affect the number of iterations required to reach a realistic solution. * Boundary Conditions: Check the code to see if it handles the boundary conditions properly. Incorrect boundary conditions can lead to deviations in the value range solution. * Absorption Coefficient: Verify that the absorption coefficient values used in the code are correct and consistent with the problem setup. * Initial Conditions: Ensure that the initial intensity distribution is appropriately set up for the problem. * Implementation Errors: Double check the implementation of the discrete ordinate method and the iterative solver for any logical errors or typos. Figure 10: DOM solutions.
## 2 Basic Principles
### Continuous-Wave Radar
An example can be seen in Figure 3.  
Micro-Doppler signatures are derived through time-dependent frequency-domain transformations. The first step is to transform the raw data of the beat signal to a time-dependent range distribution, referred to as the _time-range distribution_\(R(m,n)\) through the _fast Fourier transform_ (_FFT_), where \(m\) is the range index and \(n\) is the slow time index (time index along chirps). While the Fourier transformation is unable to calculate the time-dependent spectral distribution of the signal, the _short-time Fourier transform_ (_STFT_) is a widely used method for linear time-varying analysis that provides a joint time-frequency plane. In the time-discrete domain, the STFT is defined by the sum of the signal values multiplied by a window function, which is typically the Gaussian function, to provide the Gabor transform:  
\[X(m,f)=\sum_{n=-\infty}^{\infty}x[n]w[n-m]e^{-j2\pi/n} \tag{5}\]  
Applied to the time-range distribution matrix \(R(n,m)\), the time-discrete STFT can be computed by:  
\[\mathrm{STFT}(p,f)=\sum_{n=0}^{N-1}R[m,n]w[n-p]e^{-j2\pi f/n/N} \tag{6}\]  
The spectrogram, also referred to as _the Doppler-time (DT) spectrogram_, is derived from the squared magnitude of the STFT:  
\[\mathrm{spectrogram}\{x(t)\}(m,f)=|X(m,f)|^{2} \tag{7}\]  
Besides the STFT of the time-range distribution matrix, an FFT using a sliding window along the slow time dimension obtains time-specific transformations in the time-frequency domain, which is called _range-Doppler_ (_RD_) distributions. \[f_{\rm max}-f_{\rm min}=\frac{\mathrm{d}f}{\mathrm{d}t}\cdot T_{\rm r} \tag{1}\]0 Micro-Doppler signature (spectrogram) of a walking person (from [5]). A modification of the FMCW radar is the _Chirp Sequence Radar_[71]. It facilitates the unambiguous measurements of a range \(R\) and a relative velocity \(v_{\rm r}\) simultaneously, even in the presence of multiple targets. To achieve this, fast chirps of short durations are applied. The beat signals are processed in a two-dimensional FFT to provide measurements of both variables through frequency measurements in the time domain \(t\) and the short-time domain \(k\) instead of frequency and phase measurements, as is the case in regular FMCW radar. This method reduces the correlation between the range and relative velocity and improves the overall accuracy.
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
###### Abstract  
Resilience, the ability of a system to withstand disruptions and quickly return to a normal state, is essential for a low-Earth-orbit satellite communication system (LEO-SCS), as its large number of satellites, may encounter various disturbances. As a developing system with asymmetrical and insufficient data, it is with both aleatory and epistemic uncertainties, which renders existing resilience measures inapplicable. This paper utilizes uncertainty theory to define belief instantaneous availability, based on which a new resilience measure for the LEO-SCS is proposed. As the resilience is mainly determined by the dynamic and uncertain characteristics of an LEO-SCS, an uncertain satellite network evolution model is developed to describe its operating patterns, and an evaluation method is proposed to estimate its resilience. To illustrate our proposed uncertain theory-based resilience evaluation method, an LEO-SCS case with 48 satellites is studied, and its resilience values under different backup strategies are compared to assist in system design decisions.  
resilience analysis; instantaneous availability; LEO-SCS; uncertainty theory; uncertain satellite network evolution model +
Footnote †: journal:  
0000-0001-995-2231-1121][PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], X.
# Intelligent Neural Network Schemes for Multi-Class Classification
## 4 Experimental Results
### Discussions
Figure 9 shows the ACC of SL-Scheme with variation in the number of input dimensions selected by MI for some datasets. In this figure, three datasets, soybean, ecoli, and libras, are involved. The number on the top of a bar indicates the number of input dimensions selected by MI for the underlying dataset. For example, four cases with the number of input dimensions being 4, 5, 6, and 7, respectively, are presented for the ecoli dataset. The ACCs obtained for these four cases do not vary significantly. Figure 10 shows the ACC of SL-Scheme with variation in the number of input dimensions extracted by LDA for some datasets. In this figure, three datasets, yeast-sl, ecoli, and libra, are involved. The number on the top of a bar indicates the number of input dimensions extracted by LDA for the underlying dataset. For example, four cases with the number of input dimensions being 2, 3, 4, and 5, respectively, are presented for the libras dataset. The ACCs obtained for these four cases are almost identical. Our schemes can deal with a lot of data. For example, ML-Scheme work well with the genbase dataset, which has 1186 attributes and the mediamill dataset, which has 43,907 instances, as shown in Table 4. To show the scalability of SL-Scheme, we ran SL-Scheme on the pen-based dataset which collected samples from writers and has 10,992 instances. A testing accuracy of 0.9841 is obtained. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **ACC** & **Time(s)** & **Dataset** & **ACC** & **Time(s)** \\ \hline iris & 0.9667 & 7.0531 & soybean & 1.0000 & 3.4581 \\ glass & 0.6744 & 10.6245 & yeast-sl & 0.6027 & 66.0174 \\ ecoli & 0.8088 & 16.3213 & madelon & 0.5135 & 248.2281 \\ wine & 0.9722 & 9.1674 & sonar & 0.9286 & 12.2143 \\ libras & 0.9028 & 21.5882 & heart & 0.8197 & 14.5280 \\ breast & 0.9649 & 29.1769 & drivface & 0.9754 & 405.3066 \\ pd-speech & 0.8421 & 107.1434 & balance-scale & 0.9600 & 28.4199 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results for single-label datasets obtained by CNN. Figure 8: Performance of SL-Scheme with variation in the number of hidden neurons.
## Materials and Methods
### Quantification of Peanut Aroma Compounds Present in PE/PB-Based Films after Processing  
PA is characterized by its pyrazines and aldehydes content being 2,3-dimethylpyrazine (2,3-DMP), 2,6-dimethylpyrazine (2,6-DMP) and nonanal the main compounds present in the PA formulation. Both pyrazines and nonanal were quantified in PA by using a standard calibration curve. The calibration curve was carried out by incorporating different amounts (3.0-5.5 mg) of a diluted PA sample in _n_-hexane (1552.63 mg kg\({}^{-1}\)) on 20 mL vials. The compounds were determined by headspace-solid-phase microextraction (HS-SPME) followed by GC-MS analysis by using an Agilent 6890N GC System (Palo Alto, CA, USA) coupled to triple quadrupole mass spectrometry (Agilent 5973N, Palo Alto, CA, USA) equipment. The automated sample preparation procedure was performed with a Gerstel MultiPurpose autosampler (TDS-2, Gerstel GmbH, Mulheim an der Ruhr, Germany). Samples were heated at 75 \({}^{\circ}\)C for 25 min. Afterwards, the SPME fiber (DVB/CAR/PDMS SPME, 50/30 m, StableFlex, 1 cm long) fixed on the mechanical arm was exposed to headspace of the sample vial for extraction of the compounds. After 60 min of exposure, the fiber was withdrawn and introduced into the GC injector for desorption (3 min) and analysis. GC-MS injection port was set at 250 \({}^{\circ}\)C in the splitless mode. A DB-624 column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 1.4 \(\upmu\)m; Agilent, California, USA) was used which was programmed from 50 to 250 \({}^{\circ}\)C (hold 12 min) at 10 \({}^{\circ}\)C min\({}^{-1}\). Helium was used as carrier gas (1 mL min\({}^{-1}\)) and samples were analyzed in triplicate.  
To quantify the amount of aroma retained in the films after processing, a piece of each film (0.004 g) was placed in a 20 mL vial and sealed with an aluminum crimp cap provided with a polytetrafluoroethylene/silicone septum. The sample vial was placed in the sample trap and then it was transferred by a mechanical arm of the autosampler from the sample trap to the agitator with a temperature controller. The same HS-SPME procedure described above was employed to quantify the amount of 2,3-DMP, 2,6-DMP and nonanal in film's samples. The amount of aroma present in the films was calculated as the sum of the contents of 2,3-DMP, 2,6-DMP and nonanal. Each vial was analyzed five consecutive times to extract analytes quantitatively and the analysis was carried out in triplicate.  
The retention capacity (%) of the peanut aroma in the polymer matrix was calculated as the comparison of the amount of the aroma found in the packaging material after processing (mf; g aroma g\({}^{-1}\) polymer matrix) and the theoretical content of the aroma initially added to the material (m\({}_{0}\); g aroma g\({}^{-1}\) polymer matrix) [21; 28] following this equation:  
\[\text{Retention capacity (\%)}=(\text{m}_{\text{f}}/\text{m}_{0})\times 100 \tag{3}\]
## 2 Wireless Corrosion Monitoring Sensor and Network  
Compared with the traditional corrosion monitoring approaches, wireless technology-based monitoring is very efficient in terms of cost, convenience, and human-operation overhead, and thus has attracted much attention in the past few years. In this work, we design a WSN to monitor the corrosion of RC structures. The advantages of our WSN-based test-beds are as follows. First, the designed corrosion-detecting device can be built using commercial off-the-shelf wireless motes, such as the Crossbow Mica mote and the Berkeley Telosb mote, so the hardware costs are very low, and the network is easily extended. Second, once the corrosion-detecting device is attached to the wireless mote, we can control the sampling rate by dynamically setting its parameters in an over-the-air fashion.
## 5 Conclusions  
In this paper, a physical model with a cavity in a two-dimensional tube was developed. The governing Galbrun equations and Navier-Stokes (NS) equations were depicted, and a method combining CAA and CFD was proposed in order to calculate the frequencies of the trapped mode with the presence of flow; the PML technique was used in order to simulate the unbounded domain. The trapped modes in the cavity-duct waveguide were researched, different types of mode around the cavity were displayed, and the trapped modes of a different order were also captured. Furthermore, the impacts of the Mach number and the size of the cavity on trapped modes were also studied, and the trend of trapped-mode frequency was revealed.  
Conceptualization, X.A. and C.M.; methodology, X.A. and C.M.; validation, C.M. and X.A.; formal analysis, C.M., P.N. and X.A.; investigation, C.M. and P.N.; data curation, C.M.; writing--original draft, C.M.; writing--review and editing, C.M. and X.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Talent Introduction Research Fund grant number 11105092921.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAA & Computational aeroacoustics \\ NS & Navier-Stokes \\ CFD & Computational fluid dynamics \\ PML & Perfectly matched layer \\ FEM & Finite-element method \\ PDEs & Partial difference equations \\ LEE & Linearized Euler equation \\ APE & Acoustic perturbation equations \\ \end{tabular}  
Figure 6: The evolution of the frequency of the first trapped mode with the Mach number.
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 4 Conclusions and Future Work  
We proposed a new detection method based on knowledge distillation in this paper to address the problem that defective samples are difficult to obtain in the damage detections of the mineral-transportation conveyor belt. Firstly, the DCGAN was used to generate conveyor belt scratch samples, and a data enhancement method combining GAN and copy-pasting strategies was proposed to effectively solve the problem of insufficient samples to train the neural network model. In addition, to address the requirements of model accuracy and speed in production environments, this paper pruned the model of YOLOv5n, investigated the model performance under different pruning ratios, and finally generated a smaller miniature network, before adopting a knowledge distillation method based on fine-grained feature simulation for the lightweight network YOLOv5n and the miniature network YOLOv5n-slim for knowledge distillation. For the task of detecting conveyor damage, the [EMAIL_ADDRESS] can reach 97.33% after knowledge distillation for the lightweight network YOLOv5n and 94.83% for the micro network YOLOv5n-slim, both of which meet the requirements of detection accuracy and speed in industrial applications.  
Compared with other research, our study innovatively introduced model pruning and knowledge distillation to the task of conveyor-belt-damage detection. The model-pruning and knowledge distillation method designed in this paper significantly reduces the size of the model and improves the detection accuracy of the model without increasing the complexity of the model, enabling it to approach the detection accuracy of medium and large models with a smaller size and a faster inference speed. This allows our detection model to be deployed on lower-performance devices.  
In following research, we will continue to work on the lightweighting of the model and the improvement of the detection accuracy. Additionally, we will study the influence of environmental factors, such as light and dust, on the detection results.  
Conceptualization, methodology, software and writing--original draft preparation Q.Y.; validation and formal analysis, F.L.; investigation, H.L. and Z.W.; resources, S.X.; writing--review and editing, H.T.; visualization, Q.F.; supervision, C.L.; data curation and funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Performance comparison of the models.  
**Funding:** The authors sincerely thank for the support by the Key Laboratory of Modern Measurement and Control Technology, Beijing University of Information Science and Technology, Ministry of Education (OMTIKF2021001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data in this study are available on request from the corresponding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization of Chrysin-Loaded Microemulsion  
#### 3.2.1 Macroscopic Appearance, Chrysin Content, and Microemulsion Encapsulation Efficiency  
The CS-ME obtained appeared as an optically clear, homogeneous, transparent, and slightly yellow colored system, without any precipitate formation or phase separation, whose chrysin content and mean encapsulation efficiency (\(EE\)) were 1 mg/mL and 97.7%, respectively. Compared to chrysin solubility in water (0.005 mg/mL), the microemulsion allowed for an approximately 200-fold increase in concentration in the aqueous dispersion. Furthermore, since \(EE\) values \(>\)80% are considered satisfactory for a NE system, the incorporation of chrysin into CS-ME prior to its complete formation has proven to be a very efficient method of NE preparation.
## 8 Discussion  
### Delay Analysis  
From Figure 9, the histogram displays a unimodal distribution with a prominent peak. The data are skewed towards the right, as evidenced by the tail extending towards longer time delays. The mode value of 460 ms is in proximity to the median value of 470 ms, suggesting that the most common time delay is close to the middle of the distribution.The mean value of 593.28 ms is higher than both the median and mode. This disparity is possibly due to a natural asymmetry in the delays, rather than extreme values. The continued presence of higher delays after exclusion of outliers indicates issues with the imaging system that must be addressed to decrease the frequency of such high delays, which can be directly linked to the SD speed rate.  
Figure 15: Two histograms are presented, each in log scale, representing the distribution of pixel intensities from the minimum to maximum pixel values in the images: (**a**) a 16-bit image from subpayload 1, and (**b**) an 8-bit image from subpayload 2, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **PCO Panda 4.2** & **IDS UI-3370 CP-M-GL** \\ \hline Analysis Period (min) & 92 & 92 \\ Total Images & 13,477 & 21,817 \\ Exposure Time (ms) & 100 & 100 \\ Resolution (pixels \(\times\) pixels) & 2048 \(\times\) 2048, 1024 \(\times\) 1024 & 2048 \(\times\) 2048 \\ FOV (deg \(\times\) deg) & 29.6 \(\times\) 29.6 & 41 \(\times\) 41 \\ Unique RSOs Detected & 245 & 11 \\ Total RSO Detections & 30,855 & 669 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between the PCO camera and IDS camera on a variety of metrics.
## 2 Tooth Profile Characteristics of Harmonic Reducer  
The representative tooth profiles used in harmonic reducers are the involute tooth profile and the modified involute tooth profile. In the case of harmonic drive systems, these profiles are categorized as the CS type, which uses a standard involute profile, and the IH type, which adopts a modified involute profile. Figure 1 illustrates the IH tooth profile developed and applied by harmonic drive systems. This profile is generated by combining convex and concave curves: the addendum surface is formed from a convex curve, the addendum surface from a concave curve, and the transition between the two is a straight line [23].  
The application of the IH tooth profile has led to improvements in fatigue strength and torsional stiffness compared to conventional involute profiles. In particular, the R-type profile is reported to enhance torsional stiffness in low-torque regions. This profile brings the flexspline and circular spline into contact from the initial stage of meshing, increasing the total number of meshing teeth by approximately 10-30% compared to the standard involute profile. Additionally, the use of a wide, circular dedendum reduces stress concentration, allowing for a thinner and shorter flexspline. Since input torque is proportional to the deformation of the flexspline, a larger deformation requires an increased inner diameter of the flexspline and, hence, greater deformation of the inner bearing. This deformation is closely linked to the offset coefficient, which in the case of harmonic drive, is approximately 1. A comparative summary of the tooth profile curve, meshing ratio, efficiency, and transmission error for various profiles is presented in Table 1. As shown, both the cycloidal and composite IH tooth profiles exhibit higher contact ratios than the involute profile. However, this increased meshing can result in slightly lower efficiency. Despite this, the IH profile offers improved torque transmission and reduced root stress due to its geometry [24].