esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method
## 3 Random Forest Assisted Deep Neural Network Model
### The Architecture of the Proposed Hybrid RFADNN Model  
To mitigate the disadvantages previously outlined for RF and to address the need for modeling long-term dependencies and complex patterns, a hybrid model is proposed that combines the feature extraction ability of deep learning with the high-accuracy estimation capabilities of RF from extracted features, as illustrated in Figure 1. This model introduces the acceleration signal, representing vertical force, as input-to-feature extraction modules derived from time series data. Information regarding tire pressure and rotational speed is integrated with the features extracted from these modules and then fed into the RF module. Utilizing both the extracted features and the pressure and speed information, the RF module outputs the estimated information for vertical force. This approach aims to leverage the strengths of both methodologies to achieve a more accurate and robust estimation model for vertical force estimation.
## 5 Conclusions  
AR Bispectrum analysis has been applied in many signal processing cases, but not previously in the analysis of arc faults in low-voltage AC circuits. Therefore, application of said analysis in this research would represent a first. Arc fault features are extracted to realize detection of series arc faults by AR bispectrum. The main conclusions in detail are listed as follows:  
(1) High frequency signals of circuits increase frequently when series arc faults occur in circuits, but they are usually mixed with much interference.  
(2) An AR model of arc fault is established to describe the coupling relationship of the mixed high frequency signals and reflect the dynamic characteristics of arc faults.  
(3) According to the AR bispectrum analysis on nine types of typical experimental loads which are mentioned in electrical standards, the signal phase information of arc fault is kept and the influence of noise such as Gaussian noise is restrained effectively. AR bispectrum analysis is more effective than power spectrum and time-domain analysis. When series arc faults occur, the numbers of spectrum peaks increase obviously; the distribution of spectrum peaks tends to diffuse and the bispectrum slices are also dispersed. To better describe series arc faults, bispectrum frequency features including distribution regularities of bispectrum peaks are extracted as support vectors.  
(4) Based on the above features of bispectrum, LSSVM is successfully used to discriminate arc faults from working states in different loads. The whole algorithm has been well run in the computer and has been verified through the arc fault experimental platform. The arc fault detection rate is over 97%. The result shows that the developed algorithm has good generalization ability in different loads' arc fault detection. For future research, in terms of algorithm improvement, arc fault detection rate will be further advanced. Furthermore, this algorithm may be applied in direct current (DC) arc fault detection, such as arc faults in photovoltaic systems and automotive power supply systems.
## 4 Implementation and Experimental Results
### Simulation Results
The figure consists of three curves representing input wavefront aberrations, wavefront residuals compensated by single DM architecture, and wavefront residuals compensated by FSM-DM cascaded architecture. The blue curve represents the wavefront value when the laser beam has not been compensated by any AO system, which is also the quality of optical communication without the AO system in FSO communications. It fluctuates from around \(\frac{1}{6}\) to \(\frac{4}{5}\) wavelength which in our context is 1.6 \(\upmu\)m to 6.4 \(\upmu\)m. By contrast, considering the red curve in Figure 13, it represents the compensation performance of traditional AO architecture which contains only one DM. The curve fluctuates around \(\frac{1}{6}\)\(\sim\)\(\frac{1}{10}\) wavelength with sharp glitches which occur because the model of DM cannot depict the overwhelmingly large tilts aberrations within the effective arguments range leading to a divergent result. This fact also demonstrates the advantage of FSM deployed in front of the DM with which the probability of divergence caused by parameter range limits are fully supressed. As shown in Figure 13, apart from the above curves, a green curve illustrates the stable tendency of wavefront residuals under \(\frac{1}{16}\) wavelength which is also displayed. Although, there are glitches, the reasons for their occurence lies in the coupling of tilts aberrations with high-order aberrations in the DM model. A calibration modeling strategy truthfully reflects the mapping relationship of spots offsets with voltages configured on the electrodes, however, tilts aberrations and high-order aberrations are coupled in the DM model. This demands the DM shoulder a part of the workload for tilts aberrations when conducting wavefront compensation. When generating random wavefront input using the DM model, the proportion of the tilt aberration of certain groups of inputs will be relatively high. As this part of the tilt aberrations are distributed to the workload of FSM, the glitch appears in the compensation of the remaining workload by the DM.
## 3 Numerical Examples
### Characterization of Longitudinal Thermoelastic Guided Wave
#### 3.2.1 Longitudinal Modes  
In this paper, we focus on the analysis of the dispersion characteristics of longitudinal modes when the circumferential order n = 0. Using the above equations, the longitudinal mode dispersion curves of the thermoelastic waveguide of the hollow cylinder of silicon nitride are solved, and the dispersion characteristics are discussed in comparison with those when there is no coupled temperature field.  
In Figure 4b, the phase velocity curves without the coupled temperature term are shown in red, and the phase velocity with the coupled temperature term is shown in black. It can be seen by comparison that for the L(0,1) mode, the difference between the two cases is very small. There is a slight difference at lower frequencies, and the difference between the two curves gradually decreases and finally tends to coincide with the increase in frequency. It shows that the effect on the L(0,1) mode is small. For the L(0,2) mode, the difference is small at the cutoff frequency, and the difference gradually increases with the increase in frequency. The difference is largest in the 4-6 MHz interval, while it can be seen that the convergence frequency is higher when the temperature is not added. For the other modes, there are generally differences near the cutoff frequency, and the convergence frequency decreases after the temperature field is added.
## 3 Results and Discussion
### Correlation between Phenolic Compounds and Antioxidant Potential
#### 3.4.3 Flavonoids and Isoflavonoids  
There were three flavonoids (compounds **9**, **10**, **11**), one flavone (compound **12**), one anthocyanin (compound **13**), and two isoflavonoids (compounds **14**, **15**) identified in this study. All flavonols, flavones, anthocyanins, and isoflavonoids were detected only in the fermented coffee beans, while the two isoflavonoids were only detected in unfermented Geisha coffee beans. Compound **9** was detected only in fermented coffee beans in the positive mode with [M+H]\({}^{+}\) ion \(m/z\) at 551.1028. Its identification was confirmed via the loss of malonyl conjugates (malonyl + glycosyl) 248 Da at \(m/z\) 303 from the precursor ions [54]. Quercetin 3-O-(6\({}^{\prime\prime}\)-malonylglucoside) was not previously reported to be detected in fermented coffee samples but was mostly found in lettuce, aquatic _Ranunculus_ species, _Gerbera flowers_, and _Clitoria termatea_ flowers [55]. Compound 10, prodelphalim dimer B3 (prodelphalirtion B3), was detected in fermented coffee beans in the positive mode with [M+H]\({}^{+}\) ion \(m/z\) at 611.1394. Prodelphalin B3 is a flavan-3-ol that was previously identified in colored barley grains and pomeramate peels and has antioxidant properties that are health promoting to humans [56; 57]. Quercetin 3-O-xylosyl-rutinoside was identified based on the precursor ion detected at positive mode [M+H]\({}^{+}\) ion with \(m/z\) at 743.2009. This compound was previously detected in the hops (_Humulus lupulus_ L.) [58].  
Compounds **12** and **13** were detected in fermented coffee beans giving [M+H]\({}^{+}\) at \(m/z\) 359.1148 and 580.1775. They were characterized as flavones gardenin B and anthocyanins pelargonidin 3-O-rutinoside, respectively. 6\({}^{\prime\prime}\)-O-Malonylgenistin and 6\({}^{\prime\prime}\)-O-Acetylglycitin were the two isoflavonoids detected only in unfermented Geisha coffee beans, with precursor [M+H]\({}^{+}\) ions at \(m/z\) 519.1118 and 489.1397, respectively. During the coffee bean fermentation, isoflaves could be transformed into a more bioavailable aglycones form via lactic acid bacteria (LAB) and, therefore, remain undetected in fermented coffee beans [59].
## 2 Theoretical Background  
### Convolutional Neural Network  
A convolutional neural network (CNN) is a multi-layer feedforward neural network structure that mainly consists of input layers, convolutional layers, pooling layers, fully connected layers, and output layers [19]. The structure of a convolutional neural network is illustrated in Figure 1. The convolutional and pooling layers are arranged alternately, and the results are finally output through one or more fully connected layers. Additionally, loss functions, batch normalization, and other operations are added to the CNN structure to optimize its performance.  
The convolutional layer is the core of convolutional neural networks and mainly extracts autonomous feature information through convolutional kernels. By adjusting the size and stride of the convolutional kernels, the transformation from input to output can be controlled. The parameters and bias values of the convolutional kernels in convolutional neural networks are typically initialized randomly [20]. During the training process, these parameters and bias values are updated through backpropagation. In the convolutional layer, multiple convolutional kernels are used to convolve the input image data. After the addition of bias, a series of feature maps are obtained through an activation function [20]. The convolution process can be calculated as follows:  
\[x_{j}^{l}=f(\sum_{i\in M_{j}}x_{i}^{l-1}*k_{ij}^{l}+b_{j}^{l}) \tag{1}\]  
where the \(M_{j}\) represents the \(j-th\) convolutional region of the input feature map; \(x_{i}^{l-1}\) is the \(j-th\) element of the \(l-th\) layer in the network, and \(k\) is the matrix of the convolution kernel; \(f\) is a nonlinear activation function; \(b_{j}^{l}\) represents the bias of the output feature map.  
The pooling layer compresses the data volume through down-sampling, increasing the receptive field. Usually, two methods, mean pooling and maximum pooling, are used for pooling processing. After several alternating convolution and pooling layers, one or more fully connected layers are usually connected to achieve intelligent classification and recognition.  
Figure 1: Convolutional neural network framework.
## 4 Experimental Analysis of Link Quality
### Experimental Setup  
The sensor nodes used in the experiments is the MICA2 motes from Crossbow [34]. The MICA2 mote, designed specifically for deeply embedded sensor networks, is based on the Atmel ATmega128L microcontroller. Each sensor node contains 128 KB program flash memory, 512 KB measurement flash, and 4 KB configuration EEPROM. MICA2 uses the Chipcon CC1000 wireless transceiver, and supports multiple channels (868/916 MHz), hardware encoding (Manchester), frequency shift keying (FSK) modulation, and up to 38.4 kbps data rate. The RF power of MICA2 is programmable from -20 to +5 dBm, and the receive sensitivty is -98 dBm. The MICA2 51-pin expansion connector supports analog inputs, digital I/O, I2C, SPI and UART interfaces. These interfaces make it easy to connect to a wide variety of external peripherals. Any MICA2 Mote can function as a base station when it is connected to a standard PC (personal computer) interface or gateway board. A base station allows the aggregation of sensor network data onto a PC or other computer platform.  
MICA2 runs TinyOS, an open-source embedded operating system developed at UC Berkeley. It provides basic system services, such as communication and simple process scheduling, and access to hardware components such as sensors and actuators. The MAC layer implements a simple CSMA/CA protocol. A link-level acknowledgement can be sent by the receiver for each successful packet.  
The experiments are conducted on an open ground, as shown in Fig. 3. Nodes are placed equidistantly along a line with a spacing of 0.5m. One PC is connected with the base station using a MIB510 mote interface board. The PC is used to configure network parameters and collect  
Figure 3: Experimental deployment.  
experimental data via the base station. In the experiment, the first node, i.e. the more on the position marked as '1' in Fig. 3, transmits continuously a 13 byte data packet at a rate of 8 packets per second. The packet loss rates associated with the remaining nodes with different distances from the first node are measured.
## 3 Results
### Ablation Experiments  
To prove the effectiveness of the proposed MCNN-BLSTM model, we designed the following two models for ablation experiments:  
1. 1 BCNN-BLSTM: The model has the same structure as MCNN-BLSTM, but can only input a single branch ECG signal. When the length \(L\) of the segment is longer than the original ECG recording, zero padding is performed for the segment length. When the length of the segment is shorter than the original ECG recording, the ECG segment with the front length \(L\) of the original ECG recording is intercepted and used as the input of the model. 2. MCNN-ULSTM: The model has the same structure as MCNN-BLSTM, but BLSTM was modified to ULSTM to verify the influence of the BLSTM module on atrial fibrillation detection. In this study, we used short single-lead ECG recordings for atrial fibrillation detection. The MCNN module can set branch numbers according to the input ECG recordings and segment length. Here, the segment length \(l\) is set to 6 s (11-branch CNN), 10 s (7-branch CNN), 15 s (5-branch CNN), 20 s (4-branch CNN), 25 s (3-branch CNN), or 30 s (3-branch CNN), and the input length of 61s was carried on 1 BCNN-BLSTM model. Table 4 shows the experimental results of the three models under different input lengths. As shown in Table 4, in the model of MCNN-BLSTM, when the segment length \(l=25\) s, the average score of F1 is the highest, which is 84.56%. In the model of MCNN-ULSTM, when the segment length \(l=25\) s, the average score of F1 is the highest, which is 83.59%. In the model of 1 BCNN-BSLTM, when the segment length \(l=61\) s, the average score of F1 is the highest, which is 82.08%. To compare the performance of the three above-mentioned models more intuitively, we use the precision-recall curve (PR curve) to analyze the results. First, when comparing the three models of the same input, the PR curve close to the upper right corner represents the most accurate test. Second, by calculating the area under curve (AUC) of PR for each test, we found that the higher the accuracy, the higher the AUC value. As shown in Figure 2, MCNN-BLSTM consistently maintains higher performance than the other two models. By \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{Reference} & Positive & \(\mathit{TP}\) & \(\mathit{FN}\) \\  & Negative & \(\mathit{FP}\) & \(\mathit{TN}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix. calculating the AUC area under each PR curve, we found that the AUC value of MCNN-BLSTM was always higher than that of the other two models. When the input segment was 25 s, the AUC value of the MCNN-BLSTM model reached the highest, followed by MCNN-ULSTM, and the effect of 1 BCNN-BLSTM was the worst. This also more fully proves the validity of our proposed model.
## 2 Materials and Methods  
The starting materials were Al (99.7 wt %), B (99.5 wt %), Ce (99.9 wt %), Fe (99.99 wt %), Si (99.999 wt %), and Ni (99.99 wt %). An argon-arc furnace, equipped with a water-cooled copper crucible and a non-consumable tungsten electrode, was used to prepare the samples. After samples were melted three times to ensure homogeneity, they were sealed in an evacuated quartz tube and annealed at 900 \({}^{\circ}\)C for 21 days. These annealing temperature and time were chosen so as to grow high volume percentage of the Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B solid solution in each sample. Hitachi S-3400N Scanning Electron Microscopy (SEM) (Hitachi, Tokyo, Japan), combined with wavelength-dispersive X-ray spectroscopy (WDS), was used to analyze compositions, morphologies, and homogeneity ranges of the constituent phases observed in the alloys. X-ray diffraction (XRD) patterns were obtained using a PANanalytical Xpert Pro powder X-ray diffractometer (PANAnalytical, Almelo, The Netherland) with Cu K\(\alpha\) radiation at 45 kV and 40 mA from 20 to 90\({}^{\circ}\) 2\(\theta\) with a 0.02\({}^{\circ}\) step size. XRD study of the alloys was carried out using X'Pert HighScore Plus Rietveld analysis software (PANAnalytical, Almelo, The Netherland). Pearson's crystal structure database [33] was used to export the crystallographic data so as to identify the known phases in the samples. Saturation magnetizations (\(M_{\mathrm{S}}\)) and anisotropy fields (\(H_{\mathrm{A}}\)) were measured using a Quantum Design physical property measurement system (PPMS-9T). The demagnetization data were taken on bulk samples in external fields of up to 50 kOe at 25 \({}^{\circ}\)C. \(H_{A}\) was determined by the singular point detection (SPD) method, using second derivative of magnetization (d\({}^{2}\)M/dH\({}^{2}\)) [34; 35; 36; 37]. A Perkin-Elmer 7 Series thermogravimetric analyzer (TGA) was used to determine the Curie temperatures (\(T_{\mathrm{C}}\)) of the materials by observing the temperature dependence of the force exerted on the sample in a small magnetic field gradient provided by a permanent magnet placed close to the sample. A nickel metal standard was used to give a transition point reference, and \(T_{\mathrm{C}}\) was taken as the point where the magnetization due to the Ce\({}_{2}\)(Fe\({}_{14-x}\)T\({}_{x}\))B vanishes [17; 24].
## 3 Results and Discussion
### Contact Angles for Membrane Wettability  
The hydrophilicity of the membrane was identified by studying water contact angle (WCA) and oil contact angle (OCA) [27]. The dynamic water contact angle of the water droplet on the membrane surface was assisted within milliseconds. As represented in Figure 7, the water contact angle (WCA) of the TNF/CNF membrane is 0\({}^{\circ}\), which implies that the membrane is superhydrophilic. Contrariwise, as represented Figure 7, the underwater oil contact angle (OCA) is 161 \(\pm\) 1.5\({}^{\circ}\), which indicates that the membrane is superoleophobic underwater [28]. While oil and organic substances can cause fouling on a membrane surface, the superhydrophilicity and the underwater superoleophobicity can significantly reduce the fouling of the TNF/CNF membrane. The surface free energy of the CNF/TNF membrane was investigated by using the contact angle [29]. The surface free energy of the membrane was determined to be 72.4 mN/m. The value of surface free energy is high relatively, indicating high surface polarity [29].
## 2 Vehicle Model Analysis and Simplification
### Quadratic Static Equation  
Based on the above discussion, \(P_{bat}\) can finally determine \(m_{f}\) with knowing \(w_{0}\) and \(P_{0}\). In addition, according to (13)-(15), the battery SOC can be calculated based on \(P_{bat}\). Thus, \(P_{bat}\) can be treated as the control variable and the connection bridge to realize the power split between the engine and the battery. Due to the complex structure and coupling characteristics, the fuel rate can be simplified and can be herein considered as a series of quadratic equations with respect to battery power \(P_{bat}\), as  
\[m_{f}=\left\{\begin{array}{cc}a_{2}(w_{0},P_{0})\cdot P_{bat}^{2}+a_{1}(w_{0},P_{0})\cdot P_{bat}+a_{0}(w_{0},P_{0})&P_{0}\geq P_{eng\_threshold}\\ 0&P_{0}<P_{eng\_threshold}\end{array}\right. \tag{16}\]  
where \(a_{2}(w_{0},P_{0})\), \(a_{1}(w_{0},P_{0})\) and \(a_{0}(w_{0},P_{0})\) are fitting coefficients, which can be determined by \(w_{0}\) and \(P_{0}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Parameter** \\ \hline Battery type & Lithium-ion battery \\ Parallel number & 1 \\ Serial number & 72 \\ Minimum SOC & 0.2 \\ Maximum SOC & 1 \\ Initial SOC & 0.9 \\ Termination SOC & 0.3 \\ Capacity & 37 Ah \\ Nominal voltage & 259.2 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Battery parameters. SOC: state of charge.  
Figure 4: Simplified model of the battery pack.  
Figure 5 compares the engine fuel rate calculated by the quadratic equations with that looked up in the engine map, proving the proposed method can accurately describe the fuel rate variation. Hence, the fuel rate can be alternatively described by the quadratic equations efficiently. In terms of knowing the driving cycle, the vehicle speed and driveline power demand can be determined. Thus from (16), we can find that two control variables, i.e., \(P_{bat}\) and \(e_{on}\), need to be calculated to achieve the energy management. As shown in Figure 6, \(a_{2}(w_{0},P_{0})\) is always more than zero. Then, the proposed method can be translated into a typical convex problem when the engine is on, which can be solved via the interior point method. Additionally, as shown in (16), the engine fuel rate can be also influenced by the engine on/off command. Here, an engine on/off threshold \(P_{eng\_threshold}\) stated in (3) needs to be determined properly to generate the proper engine on/off command and the simulated annealing algorithm is employed to estimate it.  
Next step, the interior point method and simulated annealing method are applied together to find the effective solutions for the battery power and engine ON/OFF commands, respectively.