esa-sceva/satcom-chunk-collection huggingface dataset:
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 2 Materials and Methods
### Angular-Domain Recurrence-Plot Analysis
#### 2.1.3 Determination of Angle Delay  
The angle delay is also a critical parameter in phase-space reconstruction. If \(\delta\) is too small, the two trajectories in the phase space are too closed, which makes it impossible to unfold the attractor. If \(\delta\) is chosen largely, the co-ordinate components are statistically independent, and even the trajectory projection of chaotic attractors is without correlation.  
To obtain a proper \(\delta\), some approaches were proposed, including the average displacement autocorrelation function and mutual information [16] employed in this study. We considered nonlinear system (S,Q), which was composed of angular-domain discrete data series \(y(\theta)\) and delay vector \(y(\theta+\delta)\). Assume that S was measured. Mutual information \(I(Q,S)\) could be computed as in [17] by:  
\[I(Q,S)=\sum_{i}\sum_{j}P_{sq}(s_{i},q_{i})\log_{2}[\frac{P_{sq}(s_{i},q_{j})}{ P(s_{i})P(q_{j})}] \tag{5}\]  
where \(P(s_{i})\), \(P(q_{j})\) represent the probability of \(s_{i}\) in S and \(q_{j}\) in Q, respectively. \(P_{sq}(s_{i},q_{j})\) denotes the joint probability of \(s_{i}\) and \(q_{j}\). A suitable angle delay was selected at the value of the first minimum of the mutual-information function. Details about mutual information can be found in [16,17].
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Preprocessing & Post-Processing
#### 3.3.3 MCD19A2 Data Preparation  
The Moderate Resolution Imaging Spectroradiometer (MODIS) Land Aerosol Optical Depth (AOD) product is produced daily with hourly values at a 1 km pixel resolution to be utilized in this study, with its blue band (0.47 m) aerosol optical depth over the land product collected for our first scenario power plant. Google Earth Engine was used to obtain the power plants hourly MCD19A2 values in 2019. The image collection \"MODIS/006/MCD19A2_GRANULES\" was selected and filtered by date starting from 1 January 2019 to 1 January 2020. A filter bound was then applied, based on the coordinate, and the image collections \"Optical Depth 047\" band was selected.  
The extracted image was fed into a reducer, which produced an hourly value after computing the (weighted) arithmetic mean over the area of each band selected for the image. This generated a CSV file with a timestamp and an hourly value for \"Optical Depth 047\", which was then resampled to produce a daily average output for the entire year. The inclusion of this data source was an attempt to verify if any improvement could be applied to the machine learning models discussed in this paper.
## 3 Results and Discussion
### Seven-DOF Car Model  
To demonstrate this method applicable to complex structure systems, a car model with two groups of closely spaced modes is considered, as shown in Figure 11. The system is a seven-DOF system, including car body bounce, pitch, and roll. Each of the four wheels represents a DOF and has the dynamic behavior of bounce; related parameters are shown in Table 3. The mass matrix \(M\), stiffness matrix \(K\), and damping matrix \(C\) of the system are expressed as follows.  
Figure 11: Seven-DOF car model.  
Figure 10: Mode shapes of six-DOF chain model with heavy damping (the solid line is an analytic solution, the dotted line is ERA/DC identification result).  
and fifth modes are front-wheel bounce mode, the sixth and seventh modes are rear-wheel bounce mode, and the identification result is approximately coincident with theory.
# Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots
## 2 Biologically-Inspired Kalman Filter Based RBFNN Control
### Evolutionary KF-RBFNN Control
#### 2.4.2 GA-Based KF-RBFNN  
In the proposed KF-RBFNN, the accuracy and performance are influenced by the selections of the radial functions that are defined by a center vector \(B\), width vector \(C_{j}\), and weight vector \(W\). In other words, proper tuning of these parameters is an important part of the optimal KF-RBFNN designs. This study employs the modified GA to develop a parameter tuning process that optimizes the KF-RBFNN. When applying the GA to address this issue, a chromosome is defined by the RBFNN parameter sequence \(Chromosome=\left\{C_{j},B,W\right\}\) and the optimal RBFNN structure can be evolved via the GA process with Levy flight. The following fitness function (root mean square error, RMSE) for the \(N_{s}\) sample is used to evaluate the GA chromosomes.  
\[Fitness=\sqrt{\frac{1}{N_{s}}\sum_{k=1}^{N_{b}}\left({y_{p}}^{*}(k)-y_{p}(k) \right)^{2}} \tag{11}\]  
where \(y_{p}(k)\) is the output and \({y_{p}}^{*}(k)\) is the predicted output at the \(k^{\text{th}}\) sampling time. The following describes the GA process for KF-RBFNN optimization.  
1. Initialize the GA computing with Levy flight.
2. Each GA chromosome in the population contains genes to represent the KF-RBFNN parameters, meaning that \(Chromosome=\left\{C_{j},B,W\right\}\).
3. Construct the KF-RBFNN using \(Chromosome=\left\{C_{j},B,W\right\}\) and evaluate the performance using the fitness function (11).
4. Perform GA crossover and mutation with the probabilities set by Levy flight.
5. Update the GA population.
6. Check the termination criterion. Go to Step 3 or output the optimized GA individual \(Chromosome^{*}=\left\{C_{j}^{*},B^{*},W^{*}\right\}\) for the proposed GA-KF-RBFNN.
## 3 Results and Discussion
### Impact Noise-Rubber Ball  
When using the impact ball as noise source, we have to consider that the excitation is different from the traditional tapping machine. As reported above, this methodology injects into the structures an impulse which is poor of middle-high frequencies (800-5000 Hz) and focuses its action in the range 100-630 Hz. The results of the standardized maximum impact sound pressure relate to bare and lined CLT floors are shown in Figure 13. As demonstrated above, bare CLT floors mostly present the same frequency trends when excited. Therefore, for the sake of brevity, only average bare floor trends are presented hereafter.  
From Figure 13, it is evident that configuration 2 acts efficiently in noise reduction especially at low (20-100 Hz) and high frequencies (1600-5000 Hz). The presence of the fibrous material within the ceiling air gap could influence the impact noise propagation. However, when the resonance frequency is overcome (over 80 Hz), its efficiency significantly decreases. Since the measured trend is composed by three different zones, showing three different behaviors, it is not worthy to infer a regressive equation.  
When only floating floor is considered (configuration 3), it is evident how the provided noise reduction is significantly lower. This is mainly due to the fact that the floating floor works at a different frequency range. It was previously demonstrated [38] that this technology reduces significantly the transmission on middle-high frequency ranges (1000-5000 Hz). When using the rubber ball, this range is not injected in the floor, as reported in Table 2. For this reason, a smaller reduction is found, compared to configuration 2. In this case, the trend presents a homogeneous behavior and therefore a regressive ap  
Figure 13: Impact sound pressure level for CLT floors (configurations 1, 2, 3, and 4). Source: rubber ball.  
proach may be used. The result is presented in Equation (13), with a regression coefficient of R\({}^{2}\) = 0.97:  
\[\Delta\mathrm{L}^{\prime}_{\mathrm{IA,Fmax,floating}}\ =35.1\log(\mathrm{f})-49.5\ ( \mathrm{dB}) \tag{13}\]  
It is interesting to note that, in comparison to tapping machine excitation, the equation is not linear anymore, but it follows a logarithmic trend, based on the exciting frequency.  
When considering suspended ceiling and floating floor together (configuration 4), a significant overall reduction is verified. Accordingly, in some frequencies, the background noise could have influenced the results, since they are comparable to it.  
In this case too, the trend presents a homogeneous behavior and therefore a regressive approach can be used. The result is presented in Equation (14), with a regression coefficient of R\({}^{2}\) = 0.74.  
\[\Delta\mathrm{L}^{\prime}_{\mathrm{IA,Fmax,floating}}\ =10\log(\mathrm{f})-38.1\ ( \mathrm{dB}) \tag{14}\]  
In this case, the frequency contribution is less significant than the configuration 3 represented by Equation (13) and a logarithmic trend is evidenced.
# Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
## 2 Theoretical Background  
The method proposed to analyze the vibration signal is derived from the information theory field of study and, therefore, the definition of entropy. The fundamental concept in the field of information entropy, or simply entropy, is that one unlikely event is more informative than a regular one [52]. To formalize the concept of quantity of information, the proposed expression must comply with several logical rules: regular events should have low to no information content, odd events should have higher information content, and independent events should have additive information (such as flipping a coin twice). Therefore, the self-information \(I\) of a random event \(r\) is defined as [53]  
\[I(r)=-\log_{2}\left(\mathbb{P}(r)\right), \tag{1}\]  
where \(\mathbb{P}(r)\) is the probability of the event. This definition is applicable for one discrete event, such as flipping a coin or tossing a dice. For a discrete variable \(R=\left\{r_{1},r_{2},\ldots,r_{n}\right\},\ n\in\mathbb{N}\), [PERSON] defined the entropy \(H(R)\) as [52]  
\[H(R)=-\sum_{i=1}^{n}\mathbb{P}(R=r_{i})\log_{2}\left(\mathbb{P}(R=r_{i})\right), \tag{2}\]  
where \(\mathbb{P}(R=r_{i})\) is the probability of the event \(R=r_{i}\), and \(n\) is the number of events. This concept can be used for time signals, where each value from the signal can be considered an event.  
The entropy indicators attempt to quantify the complexity of a signal (continuous or discrete) by measuring the uncertainty or irregularity of the system described by the signal [54]. Although several types of entropy can be found in the literature [36; 37; 55], permutation and spectral entropy are used, as they reflect the fundamental idea of entropy indicators. Additionally, both types of entropy have fast and robust implementations.
## 3 Model and Algorithm Design
### Model Optimization Strategies
#### 3.2.3 Adaptive Learning Rate and Optimization Algorithm  
For datasets with varying complexity, using a static learning rate often leads to slow convergence or premature trapping in local optima. To address this issue, a learning rate scheduler can be introduced to adaptively adjust the learning rate based on the changes in loss during the training process [32]. Common learning rate scheduling methods include exponential decay and step decay. The formulas for these methods are as follows:  
The calculation method for exponential decay is as follows:  
\[\alpha_{t}=\alpha_{0}\cdot e^{-\lambda t}\]  
where \(\alpha_{t}\) is the learning rate at the \(t\)th iteration, \(\alpha_{0}\) is the initial learning rate, and \(\lambda\) is the decay rate.  
The calculation method for step decay is as follows:  
\[\alpha_{t}=\alpha_{0}\cdot\gamma^{\{\frac{t}{2}\}}\]  
where \(\gamma\) is the decay coefficient, and \(T\) is the period of learning rate decay.  
Due to the complexity of the CNN-LSTM model, it is often challenging to directly interpret the decision-making process of the model in practical applications. Therefore, introducing interpretability methods is crucial. Techniques, such as Class Activation Mapping (CAM), can visually demonstrate which physiological signal features have a significant impact on the final decision [33; 34]. Its formula is as follows:  
\[CAM(x,y)=\sum_{k}\omega_{k}f_{k}(x) \tag{23}\]where \(\omega_{k}\) is the weight of the class activation mapping, and \(f_{k}(x)\) is the output of the input signal at the \(k\)th convolutional kernel.  
By comprehensively optimizing the above strategies, not only can the model's accuracy and robustness be improved, but it can also become more reliable and transparent in practical applications.
## 2 Materials and Methods
### Data  
#### 2.2.1 Remotely Sensed Crop Information  
The remotely sensed crop information we used in this study includes maize distribution maps and vegetation indices (VIs) data. We used annual maize cultivation areas over the study region in the period 2010 to 2015, which were estimated by [PERSON], et al. [37] and have users' accuracies greater than 80%. The remotely sensed VIs were required in the RS crop model to simulate the actual yield. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and LAI in this study. NDVI and EVI data with 1 km and 16-day resolution, retrieved from MOD13A2 and MYD13A2 products (available through [[https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)]([https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)), accessed in 3 May 2020), were used. As the study area is large enough and the typical maize planted region is usually larger than 1 km\({}^{2}\), the used images with 1 km resolution could capture the main spatial characteristics of maize in the study area. The retrieved data were preprocessed in terms of the following procedures before use to remove unreliable data and noise:  
* Quality control: Pixel values with snow or cloud cover in the retrieved data were replaced by linearly interpolating the nearest available pixels in time.
* Time-series filter: TIMESAT3.3 was used to filter the retrieved VI data, with the required parameters were set, as shown in Section 2.3.5.
* Temporal interpolation: Eight-day VI data were linearly interpolated to daily data.  
The LAI of maize was not directly retrieved from the MODIS product as crop LAI was reported to be significantly underestimated by the MODIS product. In this study, maize LAI was calculated using empirical equations, calibrated by [PERSON], et al. ([PERSON] et al., 2018), in terms of EVI. This method was calibrated with samples collected from the US, Europe, and China. Thus,  
\[\text{LAI}=\left\{\begin{array}{ll}24.805\text{EVI}^{2}-15.444\text{EVI}+2.382& \text{ before EVI peaking}\\ 9.249\text{EVI}-1.236&\text{ after EVI peaking}\end{array}\right. \tag{1}\]  
where LAI was calculated in two ways during one growing season, and we used a quadratic equation before EVI peaking and a linear equation after it.  
All the computations involving gridded MODIS data were carried out using the GDAL (the Geospatial Data Abstraction Library) package under the Python2.7 environment.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 3 Encapsulation Methods
### Banana Starch  
[PERSON] et al. reported the development and characterization of composite films made from starch and aloe vera gel incorporated with curcumin-loaded starch nanoparticles from native and acetylated banana starch using a nanoprecipitation method. Starch was dispersed in a curcumin-acetone solution under magnetic stirring and then water was added dropwise with constant stirring. The resulting suspension was stirred at room temperature until the acetone was completely vaporized. The obtained SNPs were washed several times with ethanol to remove any excess of curcumin, to be used in the preparation of composite films [18].
# Ion Mobility-Mass Spectrometry Approach for the Comparison of Sheep and Goat Milk Lipidomes
## 1 Introduction
An instrumental setup such as ion mobility has already been used for the study of lipids present in complex matrices such as milk. By applying liquid chromatography-ion mobility-mass spectrometry, [PERSON] et al., in 2020, characterized the triacyl glyceride profile of human milk and annotated 98 new TAGs [18]. The aim of this work was to develop a mass spectrometric workflow to study and compare the comprehensive lipidomes of goat and sheep milk samples. This workflow combines an iterative MS/MS method with a drift-tube ion mobility-LC-QTOF-MS/MS analytical platform to study complex milk lipids.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 3 Proposed Method
### Parametric Measures  
The performance of the classification model can be measured in several ways. In our experiments, accuracy and F1-score are used to evaluate the performance for classifying the steel surface defects. The accuracy is one of the most common evaluation measures, which denotes the ratio of the number of properly classified samples to one of the entire testing samples. The accuracy metric can be defined as follows.  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{8}\]  
where TP, TN, FP, and FN are the abbreviations for true positive, true negative, false positive and false negative, respectively. Those components can be calculated with a confusion matrix that indicates a table of classified results between actual and predicted classes. Besides, F1-score is adopted in this study to further investigate the performance when the testing data set is unbalanced. Unless the testing data set is completely divided in a way of stratified sampling, it would not be enough to investigate only the accuracy metric. It is a harmonic mean value between _precision_ and _recall_ metric, which can be determined as follows.  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{9}\]  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Layer** & **Operation** & **Channel** & **Kernel Size** & **Stride** & **Layer** & **Operation** & **Channel** & **Kernel Size** & **Stride** \\ \hline
1 & Convolution & 16 & \(3\times 3\) & 1 & 10 & Convolution & 128 & \(3\times 3\) & 1 \\
2 & Convolution & 16 & \(3\times 3\) & 1 & 11 & Convolution & 128 & \(3\times 3\) & 1 \\
3 & Max pooling & - & \(2\times 2\) & 2 & 12 & Max pooling & - & \(2\times 2\) & 2 \\
4 & Convolution & 32 & \(3\times 3\) & 1 & 13 & Convolution & 256 & \(3\times 3\) & 1 \\
5 & Convolution & 32 & \(3\times 3\) & 1 & 14 & Convolution & 256 & \(3\times 3\) & 1 \\
6 & Max pooling & - & \(2\times 2\) & 2 & 15 & Global average pooling & 256 & - & - \\
7 & Convolution & 64 & \(3\times 3\) & 1 & 16 & Dense & 10 & - & - \\
8 & Convolution & 64 & \(3\times 3\) & 1 & 17 & Dense & 6 & - & - \\
9 & Max pooling & - & \(2\times 2\) & 2 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed architecture of CNN with CAM.  
Figure 4: Proposed architecture of the convolutional neural network with class activation map.  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TN}{TN+FP} \tag{11}\]
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Alternatives** & **DMU 8** & **DMU 1** & **DMU 2** & **DMU 3** & **DMU 4** & **DMU 5** & **DMU 6** & **DMU 7** & **Weight** \\ \hline DMU 8 & (1,1) & (2,3) & (3,4) & (1,2,3) & (2,3) & (2,3) & (2,3) & (1,1) & (2,3) & (4) & 0.2425 \\ DMU 1 & (1/41/3,1/2) & (1,1,1) & (4,5,6) & (1,2,3) & (2,3) & (3,4) & (1,2,3) & (5,6) & 0.2306 \\ DMU 2 & (1/51/41/3) & (1/61/5,1/4) & (1,1) & (1/51/41/3) & (1/31/2) & (1/41/3,1/2) & (1/31/2) & (1/31/21) & 0.0361 \\ DMU 3 & (1/3,1/2,1) & (1/3,1/2,1) & (3,45) & (1,1,1) & (2,3,4) & (3,4,5) & (1,2,3) & (1,1) & 0.1478 \\ DMU 4 & (1/41/3,1/2) & (1/41/3,1/2) & (1,2) & (1/41/3,1/2) & (1,1,1) & (1/41/3,1/2) & (1/51/41/3) & 0.0500 \\ DMU 5 & (1/41/3,1/2) & (1/51/41/3) & (2,3) & (1/51/41/3) & (2,3) & (1,1) & (1/41/3,1/2) & (1/31/21) & 0.0670 \\ DMU 6 & (1,1,1) & (1/3,1/2,1) & (2,3) & (1/31/2,1) & (1,2,3) & (2,3) & (1,1,1) & (1,2,3) & 0.1316 \\ DMU 7 & (1/41/3,1/2) & (1/7,1/6,1/5) & (1,2,3) & (1,1,1) & (3,4,5) & (1,2,3) & (1/31/2,1) & (1,1,1) & 0.0944 \\ \hline \multicolumn{11}{c}{Total} & \multicolumn{11}{c}{\(CR=0.08413\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison matrix for SWO based on alternatives. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Alternatives** & **DMU 8** & **DMU 1** & **DMU 2** & **DMU 3** & **DMU 4** & **DMU 5** & **DMU 6** & **DMU 7** & **Weight** \\ \hline DMU 8 & (1,1,1) & (1,2,3) & (3,4,5) & (2,3,4) & (4,5,6) & (1/4,1/3,1/2) & (1/4,1/3,1/2) & (1,2,3) & 0.1594 \\ DMU 1 & (1/3,1/2,1) & (1,1,1) & (3,4,5) & (1,2,3) & (3,4,5) & (1/3,1/2,1) & (1,1,1) & (2,3,4) & 0.1488 \\ DMU 2 & (1/5,1/4,1/3) & (1,1,1) & (1/4,1/3,1/2) & (1/3,1/2,1) & (1/5,1/4,1/3) & (1/6,1/5,1/4) & (1/3,1/2,1) & 0.0346 \\ DMU 3 & (1/4,1/3,1/2) & (1,3,1/2) & (2,3,4) & (1,1,1) & (1/4,1/3,1/2) & (1/5,1/4,1/3) & (1/3,1/2,1) & (1/4,1/3,1/2) & 0.0565 \\ DMU 4 & (1/6,1/5,1/4) & (1/5,1/4,1/3) & (1,2,3) & (2,3,4) & (1,1,1) & (1/3,1/2,1) & (1/4,1/3,1/2) & (1/3,1/2,1) & 0.0684 \\ DMU 5 & (2,3,4) & (1,2,3) & (3,4,5) & (3,4,5) & (1,2,3) & (1,1,1) & (1,2,3) & (2,3,4) & 0.2449 \\ DMU 6 & (2,3,4) & (1,1,1) & (4,5,6) & (1,2,3) & (2,3,4) & (1/3,1/2,1) & (1,1,1) & (4,5,6) & 0.2078 \\ DMU 7 & (1/3,1/2,1) & (1/4,1/3,1/2) & (1,2,3) & (2,3,4) & (1,2,3) & (1/4,1/3,1/2) & (1/6,1/5,1/4) & (1,1,1) & 0.0796 \\ \hline \multicolumn{11}{c}{Total} & \multicolumn{11}{c}{\(CR=0.0905\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison matrix for TEM based on alternatives.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 3 CRC Computational Process
### Hybrid Method  
To fully leverage parallel computation of such platforms, a hybrid algorithm combining both slicing-by-N algorithm and matrix multiplication has been introduced [18]. From a system perspective, slicing-by-N is fine-grained parallelism while a matrix transformation method is coarse-grained parallelism. For a given input message \(M\), it is split into N blocks and each block is processed simultaneously with a slicing-by-N algorithm. Finally, all partial CRCs of each block are multiplied by their corresponding modular matrices to align their positions and recombine by XORing all of them to obtain the final CRC of the whole message. The system is shown in Figure 5.  
The system can be implemented on a multi-core CPU; however, as mentioned earlier and emphasized here, it will never achieve the theoretical speed-up factor due to thread synchronization management overhead. In the following, we describe the development of a parallel CRC computation system based on the hybrid method to take advantage of the parallelism offered by the FPGAs.  
Figure 4: Superposition property of the CRC generation method.