esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Efficient FFT Algorithms
### Fast Walsh-Hadamard-Fourier Transform  
[PERSON] _et al_. [76] proposed an efficient low computational complexity algorithm called Fast Walsh-Hadamard-Fourier Transform (FWFT) that combines the calculation of the Walsh-Hadamard transform (WHT) and the discrete Fourier transform (DFT). The FWFT algorithm can be used in Walsh-Hadamard spread orthogonal frequency division multiplexing systems (WHT-SOFDM) in order to increase the computational speed and to reduce the implementation cost. In conventional WHT-SOFDM systems the WHT and DFT are computed successively. However, the method is not efficient, because it requires the calculation of two transforms separately.  
In the proposed method, both the transforms are combined into a single transform using a unified butterfly, so that the number of arithmetic operations, delays and indexing are reduced. The proposed algorithm is based on the sparse matrices factorization method using the Kronecker product technique. The radix-4 FWFT algorithm is used for the fast calculation of a WHT-DFT sequence whose length equals to a power of four. By comparing the simulation results of the proposed algorithm with conventional radix-4 WHT-FFT algorithm, it was shown that radix-4 FWFT achieves a significant reduction in the number of arithmetic operations and computer run time.
# Determination and Risk Assessment of Flavor Components in Flavored Milk
## 3 Results and Discussion
### Composition Analysis of Seven Flavor Samples  
Strawberry flavor, milk flavor, chocolate flavor, wheat flavor, red date flavor, mango flavor, and yellow peach flavor were analyzed, as shown in Figure 2b-d. A total of 168 compounds were identified, esters being the main components among the seven flavor samples. The strawberry flavor had the highest proportion of esters, as the most important category [34], including propyl decalactones, ethyl 2-methylbutyrate, ethyl butyrate, methyl cinnamate, phyllyl acetate, and ethyl caproate. These compounds were also detected in previous studies [35]. The PCA (Figure 2c) showed that mango and strawberry had the most similar composition. Mango had a higher percentage of ethers, anhydrides, alcohols, and olefins [36; 37].  
The chocolate flavor had a complex composition of aldehydes, pyrazines, alcohols, esters, ketons, furans, acids, and phenols [38]. According to this study, chocolate had the most aldehydes, while other studies discovered pyrazines were the major volatile and key odor compounds in chocolate flavor. It is possible that pyrazines produced by the Maillard reaction were the most important compounds that contributed to the final chocolate flavor [39]. The PCA diagram revealed that chocolate and wheat flavors had a similar composition. Wheat flavor had the largest amount of pyrazines and alkanes. According to the heat map (Figure 2d), wheat flavor mostly consisted of pyrazines, thiazoles, and furan compounds. Milk flavor had the highest proportion of alcohols relative to other flavor samples and was mainly composed of alkanes, phenols, and acids. Yellow peach flavor was mainly made up of esters, alcohols, aldehydes, and ketones. Additionally, yellow peach flavor had a higher content of peach aldehyde (26.26%), benzyl acetate (17.71%), and hexyl acetate (16.64%) which was considered key odorants influencing the flavor quality of peach fruit [40].  
In the PCA analysis, the red date flavor had a unique flavor composition. The largest class of aroma-impact compounds was esters, including ethyl laurate, ethyl palmitate, methyl hydroxacetate, and isopentyl acetate. Another important class of odor-active chemicals was aldehydes, and three aroma-impact aldehyde compounds were found in the samples. 5-methylfuranal, furfural, and benzaldehyde were among them [41]. Flavor is a complex mixture of volatile compounds, and the composition was specific to the species and variety of fruits [42].
## 3 Experimental Validation
### Data Preprocessing  
A total of 250 samples are generated by Monte Carlo analysis for the above three CUTs in different faults, and the length of each sample is 1000. According to the ratio of 3:1:1, 250 samples are divided into 150 training samples, 50 verification samples, and 50 test samples to form a training set, a verification set, and a test set for the fault diagnosis of the above three CUTs, respectively. When the CUT is fault-free, the responses are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Code** & **Fault Type** & **Nominal** & **Lower Limit** & **Upper Limit** & **Fault Value** \\ \hline F0 & NF & - & - & - & - \\ F1 & R1 \(\downarrow\) & 10 K\(\Omega\) & 9.5 K\(\Omega\) & 10.5 K\(\Omega\) & 7 K\(\Omega\) \\ F2 & R1 \(\uparrow\) & 10 K\(\Omega\  
In this paper, the square wave is used as the stimulus of the CUT. Once the frequency of the square wave is determined, the frequency of its odd harmonic components will be determined accordingly. Responses vary from different fault types of the CUT, and therefore have different effects on the odd harmonic components of the response. Based on repeated experiments, when the number of the CUT response components is set to 5, the fault diagnosis efficiency is the highest. Therefore, this article sets the number of components decomposed by EWT to 5. Figure 10 shows the spectrum segmentation result of the fault-free Sallen-Key bandpass filter circuit response obtained by the improved EWT and the traditional EWT.  
Figure 10: Spectrum segmentation result of the fault-free Sallen–Key bandpass filter circuit response: (**a**) spectrum segmentation based on the improved EWT; and (**b**) spectrum segmentation based on the traditional EWT.  
The stimulus of the Sallen-Key bandpass filter circuit is a square wave of 10 kHz. The main purpose of the EWT method is to accurately extract the components with 10 kHz and odd times of 10 kHz as the centre frequency to better analyse the influence of different types of faults on circuit performance. As shown in Figure 10b, the traditional EWT can extract the first four components with the highest amplitude in the spectrum of response signals of the Sallen-Key low-pass filter circuit. The centre frequency of the second component is 10 kHz, and the centre frequency of the fourth component is 30 kHz. However, the fifth component confused the 50 kHz and 70 kHz components with the residual component, and the first and third components were invalid components. As shown in Figure 10a, for the five components obtained by the improved EWT proposed in this paper, the centre frequencies of the first four component signals are 10 kHz, 30 kHz, 50 kHz, and 70 kHz, and the fifth component is the residual component. It can be concluded that the improved EWT can accurately extract the component signal centred at an odd multiple of the stimulus frequency, which is helpful for improving the efficiency of fault diagnosis. Figure 11 shows the AM-FM components of the responses obtained by the improved EWT when there is no fault in the CUT.
## 2 Proposed Electronically Tunable VM OTA-C Biquad  
OTA offers a high linear electronic controllability and its transconductance gain has a wide tunable range [19]. The electrical symbol of the OTA is shown in Figure 1. According to Figure 1, the single-ended output OTA consists of an input differential pair, an output current mirror, and a \(\mathrm{g_{m}}\) control unit. The output current of a single-ended OTA is characterized by \(\mathrm{I_{O}}=\mathrm{g_{m}}(\mathrm{V+}-\mathrm{V-})\), where \(\mathrm{V+}\) and \(\mathrm{V-}\) are the input signals applied to the non-inverting and inverting terminals, respectively [19]. The \(\mathrm{g_{m}}\) value is electrically controllable by an external DC bias current \(\mathrm{I_{B}}\), which leads electronic controllability to the design biquad or oscillator parameters. The DO-OTA has two identical current outputs, which can be described by \(\mathrm{I_{O}}=\mathrm{I_{Q}}=\mathrm{g_{m}}(\mathrm{V+}-\mathrm{V-})\). Figure 2 shows the proposed biquad \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & [25] & [26] & [27] & [28] & [29] & [30] & **Proposed** \\ \hline Active and passive & 6 OTAs, & 6 OTAs, 2 & 5 OTAs, & 5 OTAs, & 5 OTAs, & 1 DO-OTA, 3 & 1 DO-OTA, 3 \\ elements & and 2 C & Rmos, and 2 C & and 2 C & and 2 C & and 2 C & OTAs, and 2 C & OTAs, and 2 C \\ Resistorless structure & yes & no & yes & yes & yes & yes & yes \\ Component-matching & & & yes & no & no & no & no \\ condition (R = 1/g\({}_{\mathrm{m}}\)) & & & & & & & \\ Using grounded passive & & yes & yes & yes & yes & yes & yes \\ elements & & & & & & & \\ No inverting-type input & & yes & yes & yes & yes & yes & yes \\ signal & & yes & yes & yes & yes & yes & yes \\ High-input impedances & yes & yes & yes & yes & yes & yes & yes \\ Orthogonal control of \(\mathrm{\omega_{0}}\) & & yes & yes & yes & yes & yes & yes \\ and Q & & & & & & & \\ Versatile input/output & & no & no & yes & yes & yes & yes \\ functions & & & & & & & \\ Simultaneously realizing & & no & no & yes & yes & yes & yes \\ three filtering functions & & & & & & & \\ Offering five standard & & & yes & yes & yes & yes & yes \\ filters & & yes & yes & yes & yes & yes & yes \\ Easily transformed into a & & & & & & & \\ QO with output current & & no & no & no & no & yes \\ amplitude & & & & & & & \\ Easily realizing oscillator & & & & & & & & \\ with AM/ASK signals & no & no & no & no & no & yes \\ \hline \hline \end{tabular} Note: QO: quadrature oscillator; AM: amplitude modulation; ASK: amplitude shift keying.  
\end{table} 
Table 1: Comparison of the proposed biquad and the biquads previously reported in the literature [25; 26; 27; 28; 29; 30].
## 5 Error Analysis and Correction
### Correction of 3D Reconstruction
#### 5.2.3 Results Verification  
The verification method is as follows: fixing the calibration plate and verifying the validity of parameters of correction function at different water surface height. Figure 15 shows the comparison of errors before and after correction of the adjacent camera and we can see that the error is greatly reduced.  
It can be concluded that before calibration, the maximum error is 8.45 mm and the average error is 2.06 mm and after correction, the maximum and average error are, respectively, 2.37 mm and 0.53 mm. The error distributions are shown in the Figure 16 and It is obvious that the error is significantly reduced after correction.  
Figure 14: Results of fitting error of combinations of adjacent cameras in \(Z\)-axis.  
Figure 16: Errors distribution, (**a**) Errors distribution before correction (**b**) Errors distribution after correction.  
Figure 15: Error of each camera combination before and after correction.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm
### Routing Selection Strategy
From the existence of different reliability coefficients for each route evaluation parameter, the parameter itself, as the deciding factor for selecting the next hop, has some indeterminacy \(\Omega\), which is derived from the suitability of the evaluation parameter as one of the routing decision factors. Therefore, to characterize it, the fuzzy contribution degree of parameter \(k\) about indeterminacy \(\Omega\) is defined as follows:  
\[m_{\Omega}(k)=1-\sum_{j=1}^{|FN(i)|}m_{j}(k) \tag{24}\]  
By Equations (23) and (24), the parameter \(k\) contribution degree \(m_{j}(k)\) of forward neighbor node \(j\) and the fuzzy contribution degree \(m_{\Omega}(k)\) of indeterminacy \(\Omega\) to \(k\) can be respectively calculated. Concurrently, although there are uncertainties in the routing evaluation parameters, both \(m_{j}(k)\) and \(m_{\Omega}(k)\) are direct manifestations of the network state of the forward neighbor area \(FNA(i)\) on a parameter \(k\); therefore, any simple human abne negation of them may lose important information (e.g., threshold value method), and the choice of the next-hop node is flawed. Therefore, to more clearly explain and implement the routing optimization strategy in this paper, the following calculation method of fusion contribution degree \(m_{j}(V)\) is defined. Here, considering that not all routing evaluation parameters are in a state of indeterminacy, it should be ensured that the calculation of the contribution degree under each scenario combination includes at least one parameter contribution degree. Thus, a one-dimensional set \(\theta=\left\{\theta_{1},\theta_{2},\cdots,\theta_{\eta},\cdots,\theta_{T}\right\}\) is defined and \(\theta_{\eta}\) is a random binary sequence of length \(K\) except for all zeros, representing a scene combination. By traversing all the \(T=2^{K}-1\) binary sequence combinations, the fusion contribution degree \(m_{j}(V)\) of the forward neighbor can be obtained, and the formula can be expressed as follows:  
\[m_{j}(V)=\sum_{\eta=1}^{T}\left\{\prod_{k=1}^{K}\left[\left[m_{j}(k)\right]^{ \theta_{\eta}^{k}}\cdot\left[m_{\Omega}(k)\right]^{1-\theta_{\eta}^{k}}\right]\right\} \tag{25}\]  
where \(\theta_{\eta}^{k}\) is the \(k^{th}\) value of the binary sequence \(\theta_{\eta}\), corresponding to the parameter \(k\). When \(\theta_{\eta}^{k}\) it is equal to 1, \(k\) takes the parameter contribution degree. When \(\theta_{\eta}^{k}\) it is equal to 0, \(k\) takes the fuzzy contribution degree. Finally, in \(FNA(i)\), the current node \(i\) selects the forward neighbor with the maximum fusion contribution degree as the next hop \(g\), that is,  
\[g=\max_{j}\left[m_{j}(V)\right],j\in FN(i) \tag{26}\]
## 3 Membrane Performance Measurements
### Uncertainty of Measurements
#### 3.6.2 Measurement and Calculation Uncertainties  
The two main errors in the calculation of the performance factors are measurement errors and errors arising from assumptions made in the calculations. As the variables included in the equations were measured by sensors, the sensor uncertainty values were considered. The sample specifications and errors of the instruments used in the experiment are listed in Table 2.  
We also considered the error caused by ideal gas constant assumptions and changes in the volume within the cell that were added during the experimental and calculation steps. In the case of the volume, a change of \(\sim\)1 mm over the 10 cm height of the cell was measured, giving an error of 1%. During the experimental design, all the air that passed through the membrane was assumed to be pure nitrogen, and the content of oxygen and other gases was not considered. The mole mass of nitrogen and oxygen was calculated by including an error of about 3.2%. When analyzed according to the experimental conditions established earlier with a confidence interval of 95%, the error in the permeance of H\({}_{2}\)O and N\({}_{2}\) and selectivity is calculated as follows [18].  
\[\sigma_{\text{H}_{2}\text{O}}=\left(\sigma_{RH}^{2}+\sigma_{m}^{2}+\sigma_{p_{ s}}^{2}+\sigma_{C}^{2}\right)^{0.5} \tag{10}\]  
\[\sigma_{\text{N}_{2}}=\left(\sigma_{RH}^{2}+\sigma_{T}^{2}+2\sigma_{p}^{2}+ \sigma_{p_{s}}^{2}+\sigma_{V}^{2}+\sigma_{R}^{2}+\sigma_{C}^{2}\right)^{0.5} \tag{11}\]  
\[\sigma_{\text{n}}=\left(\sigma_{\text{H}_{2}\text{O}}^{2}+\sigma_{\text{N}_{2 }}^{2}\right)^{0.5} \tag{12}\]  
From the uncertainty analysis, the uncertainties in the H\({}_{2}\)O permeance, N\({}_{2}\) permeance, and selectivity were 6.47%, 7.48% and 8.54%, respectively. Although the uncertainties showed relatively larger than the cases of strict experiments, it is acceptable for characterizing the trends in the membrane performance with simple test configurations.
# Calculation of Intracoronary Pressure-Based Indexes with JLabChart
## 2 Material and Methods
### Instantaneous Wave-Free Ratio (iFR)  
Maximal hyperemia is required to perform an FFR measurement with the use of adenosine as a pharmacological agent. To reduce the use of drugs, the instantaneous wave-free Ratio (iFR) index is used to evaluate coronary artery stenosis [18]. iFR is a pressure-only index that is measured without vasodilators [41]. It is based on intracoronary pressure sampling during the diastolic wave-free period. The iFR measurement is based on diastolic intracoronary pressure values, both proximal and distal to the stenosis, during the diastolic interval in which the resistors are spontaneously minimum (that is during the wave-free period). Generally, the measurement of the \(iFR\) requires an average over 5 heart beats, but sometimes it can be performed by using a single heart beat. Wave-intensity analysis of the coronary flow curve is performed by means of a guide wire, equipped with pressure and Doppler sensors. The \(iFR\) isolates the wave-free period in the diastole and evaluates the ratio of the distal coronary pressure (\(P_{d}^{pure-free\ period}\)) to the proximal aortic pressure (\(P_{a}^{pure-free\ period}\)) over this period, as shown in Figure 5.  
The onset of the diastole is identified by the dicrotic notch, while the end of the diastole is defined as the point of the lowest pressure leading into the next cardiac cycle. Therefore, the \(iFR\) can be calculated by Equation (7):  
\[iFR=\frac{P_{d}^{pure-free\ period}}{P_{a}^{pure-free\ period}}. \tag{7}\]  
An iFR value for an healthy subject is equal to 1.0. A value lower than 0.9 usually suggests a flow limitation, while a value higher than 0.9 indicates a negligible stenosis.  
Following the original definition reported in the ADVISE study, we developed an independent algorithm for calculating iFR, that we described in the FORECAST study and in the FORECAST-LM study. The calculation of iFR with JLabChart is based on that algorithm.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 2 Materials and Methods
### Theoretical Background
In this case, the threshold value corresponding to sub-band \(j\) will be:  
\[\lambda_{j}\ =\ \frac{\sqrt{2}\sigma^{2}}{\sigma_{x,j}} \tag{3}\]  
in the case of the other detail sub-bands (corresponding to wavelet decomposition levels 2 and 1 and represented by the colors green and white in Figure 1), the significance map is drawn up based on the absolute value of the parent coefficient corresponding to each coefficient belonging to these sub-bands (in Figure 1, the relation descending coefficient--parent coefficient is also represented); thus, if the value of the parent coefficient is higher than a threshold value \(T\), the coefficient considered is classified as significant, otherwise it is classified as insignificant; the two classes of coefficients, having different informational significance, have different statistical properties, therefore they are processed differently:  
\[\sigma_{x,sig}^{2}\ =\ max(0,\sigma_{t,s}^{2}-\sigma^{2}) \tag{4}\]  
where:  
\[\sigma_{t,s}^{2}\ =\ \frac{1}{M_{s}}\sum_{i\in sig}w^{2}(i) \tag{5}\]  
_sig_ representing the significance map, and \(M_{s}\) the number of coefficients in this map. insignificant classified coefficients have small values and represent smooth regions; for each such coefficient the dispersion is estimated using a window of dimensions \(5\times 5\), but from which the significant coefficients are eliminated; if \(\hat{\sigma}_{i}^{2}\) represents the thus estimated dispersion of the signal, then the a posteriori maximum probability estimator will be:  
\[\hat{w}(i)\ =\ \frac{\hat{\sigma}_{i}^{2}}{\hat{\sigma}_{i}^{2}+\sigma^{2}}w(i) \tag{6}\]  
As it can be seen, the algorithm, just like other algorithms that determine the contours, introduces as a parameter a T-threshold value.
## 4 Discussion
### Performance Comparison  
Based on the time series modeling ability of LSTM, this paper combines it with CNN for damage identification, obtaining promising results. However, it is essential to compare the performance of the CNN-LSTM model against the CNN model to further verify the merit of this research. The architecture of the corresponding CNN model is presented in Figure 12, and the detailed parameters of such a model are tabulated in Table 6. The training set, validation set, and test set of these two models come from the database established in Section 3.2.  
Figure 11: Influence of environmental noise on the proposed method.  
Figure 13 shows a comparison of the damage recognition results between the CNN-LSTM model and the CNN model.  
As shown in Figure 13, compared to the CNN-based damage identification method, the overall recognition accuracy of the damage localization based on the CNN-LSTM model increases by 8.13%, and the ARIE of severity prediction decreases by 5.20%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer** & \multicolumn{2}{c}{**Size/No.**} & **Stride** & **Padding** & **Input/Output** & **Activation** \\ \hline \multirow{3}{*}{Convolutional block 1} & Conv1 & \(64\times 1/32\) & 2 & SAME & \(31\times 1024/32\times 512\) & ReLU \\  & BN layer & - & - & - & - & - \\  & Pool1 & \(3\times 1/32\) & 2 & SAME & \(32\times 512/32\times 256\) & - \\ \hline \multirow{3}{*}{Convolutional block 2} & Conv2 & \(3\times 1/64\) & 2 & SAME & \(32\times 256/64\times 128\) & ReLU \\  & BN layer & - & - & - & - & - \\  & BN layer & - & - & - & - \\  & Pool3 & \(3\times 1/28\) & 2 & SAME & \(64\times 128/64\times 64\) & - \\ \hline \multirow{3}{*}{Convolutional block 3} & Conv3 & \(3\times 1/128\) & 2 & SAME & \(64\times 64/128\times 32\) & ReLU \\  & BN layer & - & - & - & - & - \\  & Pool3 & \(3\times 1/128\) & 2 & SAME & \(128\times 32/128\times 16\) & - \\ \hline \multirow{3}{*}{Convolutional block 4} & Conv4 & \(3\times 1/256\) & 2 & SAME & \(128\times 16/256\times 8\) & ReLU \\  & BN layer & - & - & - & - & - \\  & Pool4 & \(3\times 1/256\) & 2 & SAME & \(256\times 8/256\times 4\) & - \\ \hline \multirow{3}{*}{Fully connected block 1} & Fc1 & - & - & - & \(1024/1024\) & - \\  & BN layer & - & - & - & \(256/256\) & ReLU \\ \hline Fully connected block 2 & Fc2 & - & - & - & \(256/31(62)\) & - \\ \hline Fully connected block 3 & Fc3 & - & - & - & \(31(62)\) & Sigmoid (-) \\ \hline \hline \end{tabular} Note. The size of the parameter used for damage localization is in brackets, and the size of the parameter used for damage severity prediction is outside the brackets.  
\end{table}
Table 6: Parameters of the corresponding CNN model.  
Figure 12: Architecture of the corresponding CNN model.