esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 4 Approach
### Vehicle Matching  
For the pure YOLOv5 smoke detector, false positives are inevitable since it may return all the smoke-like regions. These false positives are serious, especially on wet roads, shadow roads, cluttered backgrounds, etc. Worse, these false positives are difficult to remove through frame-wise smoke detection. Considering that each instance of vehicle smoke is emitted from a certain vehicle, we introduced a smoke-vehicle matching strategy to refine.  
Figure 7 depicts our smoke-vehicle matching strategy. Once a smoke region was detected, we resorted to using the YOLOv5n model pretrained on COCO data to detect vehicles. The vehicles included car, bus, and truck in the COCO dataset. We also tried to train a vehicle detector on our own vehicle data but found that it was less robust than the pretrained one. After vehicle detection, we obtained both the vehicle regions and smoke regions. As shown in Figure 7, there would be two cases that we needed to deal with  
Figure 5: Our re-designed YOLOv5 vehicle smoke detector, termed YOLOv5 tiny. It is modified from the YOLOv5 yet with fewer layers and GhostConv modules.  
Figure 6: Our light-weight YOLOv5 vehicle smoke detector.  
separately. For both cases, considering the smoke region usually comes from the back of vehicles, we only considered the front vehicles of a certain smoke region as possible smoky vehicles, i.e., the vehicle with its center above the smoke center. The first case is that the smoke region is overlapped with vehicle regions. For this case, our strategy regarded the largest overlapped vehicle as the smoky vehicle, see the green regions on the left of Figure 7. For the second case where there are no overlapped vehicles, we first computed the Euclidean distances between the bottom-middle points of the vehicles and the top-left (also top-middle and top-right) points of smoke regions, and then we set a threshold \(L_{dist}\) for the mean distance. A smoke-vehicle pair keeps the smallest mean distance that is less than \(L_{dist}\), see the green regions on the right of Figure 7.  
One could argue for the first case that it would make mistakes if there is a traffic jam. This is the truth in practice but still we have captured vehicle smoke, and maybe a further check from a human is needed. For the second case, the reason we did not just use the center points between vehicles and smoke regions for distance computation is that smoky vehicles are usually those large trucks whose center points can be far from smoke regions.
## 3 Simulations of Atmospheric-Based Disruption Including Turbulence and Clouds  
Together with the hardware representation of the atmospheric FSO channel, both atmospheric turbulence- and cloud-induced attenuation are simulated to be evaluated and provided the required test data for optical losses. Having their combined influence based on a joint probability density function (PDF), the full characterization of the FSO channel is considered.
## 2 Materials and Methods
### Antioxidant Activity Assays
#### 2.4.3 Ferric Reducing/Antioxidant Power (FRAP) Method  
The FRAP method consists in the reduction, in acidic medium, of the ferric 2,4,6-tripyrid-s triazine complex [Fe(III)-(TPTZ)2]3+ to the ferrous complex [Fe(II)-(TPTZ)2]2+ of intense blue color in the presence of the antioxidant species. Thus, the change in absorbance obtained at 593 nm for antioxidant samples is compared with the absorbance of solutions of a known concentration of ferrous ions.  
The FRAP method, although it only indirectly reflects the antioxidant activity, is a simple and cheap method for the determination of antioxidant activity [34] and was performed according to the method described by [PERSON], with some modifications [35].  
To determine the antioxidant activity using the FRAP method, methanol (Sigma-Aldrich, Taufkirchen, Germany) was used to dilute the extracts. The other reagents used were iron (III) chloride, sodium acetate, 2,4,6-tris(2-pyridyl)-1,3,5-triazine (Sigma-Aldrich, Germany) and hydrochloric acid (Sigma-Aldrich, Germany).  
First, 30 mL of FRAP reagent was prepared using a buffer solution of sodium acetate in water with pH 3.6, and then 0.3257 g of FeCl3 was dissolved in double-distilled water and brought to 100 mL. Next, 0.3124 g of 2,4,6-tripyrid-triazine (TPTZ) was dissolved under heating at approximately 50 \({}^{\circ}\)C in 40 \(\mu\)mol/mL HCl solution. Then, 25 mL of sodium acetate buffer solution was mixed with 2.5 mL of TPTZ solution and 2.5 mL of FeClsolution. The resulting solution was diluted with 2 volumes of double-distilled water and then incubated for 30 min at 37 \({}^{\circ}\)C.  
Then, 0.1 mL of extract sample (properly diluted with methanol) and 2.9 mL of FRAP reagent were mixed. The mixture was kept at room temperature for 2 h in the dark, and the absorbance was read at 593 nm.  
Trolox was used as a reference standard antioxidant compound, and for the drawing of the calibration line, 5 standard solutions were prepared by dilution in methanol with concentrations in the range of 0.2-1 umol/mL, and the absorbance was measured for each standard solution against a blank of methanol.  
The antioxidant capacity of the studied samples or FRAP radical scavenging capacity was expressed in umol Trolox equivalent (TE)/100 mL.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 3 Results
### Prediction of DM, TPC, FRAP, and CUPRAC Using NIR Spectroscopy  
Table 3 summarizes the results obtained for the prediction of the four parameters of interest using NIR spectroscopy combined with PLS-R or SVR. As can be seen in this table, the pre-processing methods used had a significant impact on the quality of the PLS-R and SVR models.  
Using the PLSR algorithm, NIR spectroscopy showed good results for the prediction of dry matter content, with most of the R\({}^{2}\) values ranging between 0.67-0.79 and 0.27-0.74 (0.60-0.74 excluding the SG1 pre-processing) respectively for the calibration and prediction datasets, respectively. The relative error obtained varied between 0.40-0.51% and 0.38-0.48% for calibration and validation, respectively. The pre-processing method had a strong influence on the model quality, with the best model constructed using no pre-processing. In contrast, quite poor results were found using PLS regression for the prediction of TPC, FRAP, and CUPRAC contents, with RPD values <1.5 for all of these analytes.  
On the other hand, the application of the SVR algorithm showed satisfactory results for the prediction of the four analytes of interest. The best predictive results for TPC exhibited an R\({}^{2}_{\text{pred}}\) of 0.65 and an RMSEP of 464 mg/100 g, using the first derivative Savitsky-Golay pre-processing. For FRAP, the best model presented an R\({}^{2}_{\text{pred}}\) of 0.71 and an RMSEP of 70 mg/100 g. For CUPRAC, the best predictive model gave an R\({}^{2}_{\text{pred}}\) of 0.73 and RMSEP of 512 mg/100 g, while the best model for DM showed an R\({}^{2}_{\text{pred}}\) of 0.96 and RMSEP of 0.63% (Figure 3). Those models were calculated after applying the second derivative with the Savitsky-Golay algorithm to the spectral data (Table 3).  
As has been noted for PLSR, the application of spectral pre-processing had a significant effect on the prediction results obtained by SVR. Furthermore, the optimal pre-processing method differed depending on the targeted analyte.  
Figure 2: The MIR spectra of the chili samples are between 4000–400 cm\({}^{-1}\).
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on SST
#### 5.2.5 Attention Weight Analysis  
Figure 11 shows the attention weight of SP-CRN-1 (1,1,0) which is the best result in the SP-CRN-1 settings. The periodic pattern for all phases was clear, similar to the pattern between 2017 and 2018. Only the second peak on plus-1 of November-December was slightly different. The plus-1 and minus-1 had occasional importance according to some months, while the attention weight of self was smooth every month. The similar periodic pattern of two years indicated a similar amount of importance in the same periodicity phase.  
We then analyzed the attention weight of SP-CRN-2 (0,6,0) because it had the best performance compared to its settings. Figure 12 shows the attention weight of SP-CRN-2 (0,6,0) which showed the importance of periodic-depth from one to six years of a periodic cycle. The overall weights were about 0.1-0.2 for all months, which was about the average of six weights--0.167, excepts June and July for depth of five.  
SP-CRN-3 (0,0,2) performed the best in the PRD-3 setting, however, it could not be analyzed because it had a single representation.  
Figure 11: Attention weight of SP-CRN-1 (1,1,0) for SST prediction from the average of five runs. The attention weight of minus-1, self, and plus-1 are the minus one month, prediction month, and plus one month of one prior periodic cycle, respectively. The scale is from 0.2 to 0.6.
## 3 Results
### Correlation between Baring Capacity and Input Parameters
\begin{table}
\begin{tabular}{c c c c c c c c c c}  & & \(X2\) & \(X3\) & \(X4\) & \(X5\) & \(X6\) & \(X7\) & \(X8\) & \(Y\) \\ \hline \multirow{3}{*}{\(X2\)} & Pearson & 1 & \(-0.370\) ** & \(-0.234\) * & \(-0.221\) * & 0.086 & 0.088 & \(-0.229\) * & \(-0.229\) * \\  & Correlation & & 0.000 & 0.019 & 0.027 & 0.396 & 0.707 & 0.022 & 0.022 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X3\)} & Pearson & \(-0.370\) ** & 1 & 0.463 ** & 0.011 & 0.206 * & 0.259 ** & \(-0.063\) & 0.099 \\  & Correlation & \(0.000\) & & 0.000 & 0.916 & 0.040 & 0.029 & 0.531 & 0.326 \\  & Significance & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X4\)} & Pearson & \(-0.234\) * & 0.463 ** & 1 & 0.270 ** & 0.124 & 0.051 & \(-0.433\) ** & \(-0.138\) \\  & Correlation & & 0.019 & 0.000 & & 0.007 & 0.218 & 0.612 & 0.000 & 0.172 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X5\)} & Pearson & \(-0.221\) * & 0.011 & 0.270 ** & 1 & \(-0.489\) ** & \(-0.555\) ** & \(-0.189\) & \(-0.142\) \\  & Correlation & & 0.027 & 0.916 & 0.007 & & 0.000 & 0.000 & 0.059 & 0.159 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X6\)} & Pearson & \(0.086\) & 0.206 * & 0.124 & \(-0.489\) ** & 1 & 0.876 ** & 0.335 ** & 0.449 ** \\  & Significance & 0.396 & 0.040 & 0.218 & 0.000 & & 0.000 & 0.001 & 0.000 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X7\)} & Pearson & \(0.088\) & 0.259 ** & 0.051 & \(-0.555\) ** & 0.876 ** & 1 & 0.446 ** & 0.563 ** \\  & Significance & 0.070 & 0.009 & 0.612 & 0.000 & & 0.000 & 0.000 & 0.000 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(X8\)} & Pearson & \(-0.229\) * & \(-0.063\) & \(-0.433\) ** & \(-0.189\) & 0.335 ** & 0.446 ** & 1 & 0.866 ** \\  & Significance & 0.022 & 0.531 & 0.000 & 0.059 & 0.001 & 0.000 & 0.000 & 0.000 \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{\(Y\)} & Pearson & \(-0.229\) * & 0.099 & \(-0.138\) & \(-0.142\) & 0.449 ** & 0.563 ** & 0.866 ** & 1 \\  & Significance & 0.022 & 0.326 & 0.172 & 0.199 & 0.000 & 0.000 & 0.000 & \\  & N & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \end{tabular}
\end{table} 
Table 5: Matrix of the correlation between the gotechnical parameters (**: Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level). On the other hand, we were generally interested in the correlation between inputs if the multicollinearity phenomenon existed. This could appear between certain independent variables with a high \(R\), causing problems when fitting the model and interpreting the results, and reducing the statistical power of the regression model. However, the correlation coefficient presented in Table 5 indicates a moderate \(R\) between inputs, indicating a moderate multicollinearity, but it is not severe enough to require corrective measures. For this reason, there was no interest in the correlation coefficient between input variables.
## 5 Kinematic Reliability Analysis  
One goal of kinematic reliability analysis is to obtain the probability that the displacement, velocity and acceleration of the end-effector of any one mass-produced robot/mechanism according to a design blueprint fall into the allowable accuracy range during the design process. Based on kinematic reliability and its sensitivity analysis, this paper aims to find out the crucial indicator for evaluating the performance of mechanisms to improve the kinematic reliability of the robot.  
1. Assume that the geometric parameters errors, the clearance errors of revolute joints, and the driving errors (at any time) are independent of each other and that each error has a normal distribution. From Equation (18), the position errors of the robot end-effector obey the normal distribution in the \(x\), \(y\), and \(z\) directions at any time. The mean and standard deviation are shown as Equations (19) and (20).  
\[\mu_{s}=\sum_{i=1}^{n}Kq_{si}\mu_{si} \tag{19}\]  
\[\sigma_{s}=\sqrt{\sum_{i=1}^{n}Kq_{si}^{2}\sigma_{si}^{2}} \tag{20}\]  
where: \(\mu_{si},\sigma_{si}\) are the mean and standard deviation of each original input error respectively.  
Referring to the definition of kinematic accuracy reliability as used in traditional mechanisms, the kinematic reliability of the parallel mechanism is defined as follows:  
\[R=P(\varepsilon_{1}<\Delta q<\varepsilon_{2})=\Phi(\frac{\varepsilon_{2}-\mu_ {s}}{\sigma_{s}})-\Phi(\frac{\varepsilon_{1}-\mu_{s}}{\sigma_{s}}) \tag{21}\]  
where: \(\Phi(\bullet)\) is the standard normal distribution function; and \(\varepsilon_{2},\;\varepsilon_{1}\) are upper and lower limits of the allowable limit errors.  
The position error in the combined direction is  
\[f(S)=\sqrt{\Delta x^{2}+\Delta y^{2}+\Delta z^{2}} \tag{22}\]  
The mean and variance of the total position errors are approximated by the moment method from Equations (23) and (24), and the distribution of the total position errors can be determined by the Monte Carlo method [35; 36].  
\[E(\Delta)=f(\mu_{s1},\mu_{s2},\cdots,\mu_{sn})+\frac{1}{2}\sum_{i=1}^{n}\frac {\partial^{2}f(s)}{\partial s_{i}^{2}}|_{s=u}\cdot\sigma_{si}^{2} \tag{23}\]\[D(\Delta)=\sum_{i=1}^{n}\left(\frac{\partial f(s)}{\partial s_{i}}\big{|}_{s=\mu} \right)^{2}\cdot\sigma_{si}^{2} \tag{24}\]  
where: \(s_{i}\) is the independent random variable of each original input error.
2. It is assumed that the discrete distribution data (or the distribution law) of each original input error is known. According to the Monte Carlo method, assign a value for random variable in the error source. Then a sampled value of \(\Delta q\) or \(f(S)\) can be obtained from Equation (18) or Equation (22). The multiple sampled values then used to create a frequency histogram. Furthermore, parameter estimation and hypothesis testing of the distribution type of \(\Delta q\) or \(f(S)\) are now completed. As such, the kinematic reliability of the mechanism in the \(x,y,z\) directions or in the combined direction based on the distribution function can now be calculated.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 4 Baseline Algorithms for RF Fingerprinting Method  
In this study, for performance comparison, three other baseline methods were carefully designed and implemented based on algorithms from the literature [4; 5; 7; 8].  
Before describing the details, we note that the signal preprocessing steps, such as preamble extraction [5] and signal difference calculation after signal decoding [7], are not covered in this study. The goal of this study was to identify the emitter ID in the physical layer of the FHSS network. Therefore, we focused on analog SFs that can be obtained from the physical layer of the system. To this end, all baseline SFs were set to RT, SS, and FT, and the feature extraction and classification processes were designed to reflect the approaches in the literature.
## 6 Results and Discussion
### Experimental Results
#### 6.2.1 Experimental Scenarios  
1. HyCAD Architecture  
The proposed system architecture presented in Section 5 is applied to the dataset described in Section 4. Norm-VGG16 integrating kernel regularizer is trained on the dataset from scratch aiming at higher performance metrics. It is used for RoI generation and feature extraction. A set of 512 features are extracted from the global average pooling layer of the CNN network. The hand-crafted feature extraction methods, namely HOG and DAISY, when applied on the segmented RoI generated  
Figure 12: Confusion matrix.  
8500 features. At the fusion stage of our HyCAD system, all the extracted features are fused and fed into a three sequential dense layer neural network for a classification decision.  
2. Norm-VGG16 (kernel regularized) Deep Learning Architecture  
In this scenario, the modified Norm-VGG16 Deep Learning architecture is applied on the OCT dataset. The performed classification is based solely on the CNN-based features. This scenario is presented to elucidate the merit of incorporating human articulated features on the performance of the system.  
3. ResNet-50 Net (kernel regularized) Deep Learning Architecture  
ResNets [32; 44] can be considered one of the most applied deep learning architectures for image recognition and classification. ResNets include residual blocks which allow deeper network architecture to avoid information loss during training. In addition, it was shown to achieve lower validation loss compared to VGG16 on the ImageNet dataset [44]. Therefore, ResNet-50 architecture (shown in Appendix A) is chosen as one of our experimental scenarios, to compare its performance to the proposed HyCAD system. The ResNet-50 best model is modified by adding kernel regularization in the final dense layer.  
In addition to these implemented experimental scenarios, the results of HyCAD are compared with the work of [PERSON] et al. [4] and [PERSON] et al. [20]. These were chosen as their results were reported on the same dataset using deep learning approaches and attained high results.
## 2 Materials and Methods
### Characterization of Nanoparticles  
Transmission electron microscopy (TEM) was used to determine the structure and dimensions of nanoparticles in the mass dispersion. The TEM specimens were generated by drop coating of the main suspension on carbon-coated copper grids. Several aspects of the particle, namely, shape, size, size distribution and tendency of aggregation, were characterized. A Zeiss EM 900 transmission electron microscope ([PERSON], Oberkochen, Germany) was used in the Department of Electron Microscopy at the Biocenter of the University of Wurzburg. The zeta potential of the NPs in the media (DMEM-EM and human plasma) at pH 7.4 was investigated by a ZetaSizer 3000 HSA (Malvern Instruments Ltd.) at the Fraunhofer Institute for Silicate Research ISC in Wuerzburg. Dissolved Zn\({}^{2+}\) ions in the culture medium (DMEM-EM and human plasma) were determined by atomic absorption spectrometry (SpectraAA 55 B, Varian GmbH, Darmstadt, Germany).
# Diet and Asthma: A Narrative Review
## 2 Asthma Phenotypes and Endotypes
Adhering to a western diet has been shown to increase the risk of having current asthma and doctor-diagnosed asthma in school-aged children [33]. Another hypothesis implicates gut microbiota changes in the development of many diseases, including asthma and obesity. Changes in the microbiota during childhood are thought to play a role in the development of asthma and obesity [14]. Obesity has been linked to a decrease in the diversity of gut bacteria in humans [34; 35], and there is evidence that a diet-induced weight loss promotes a Firmicutes to Bacteroidetes ratio decrease [36]. Fiber can modulate the gut microbiota, leading to the production of short-chain fatty acids (SCFAs), including butyrate, which has immunomodulatory and anti-inflammatory effects [37]. A high-fat diet modifies the composition of the gut microbiota, more specifically causes the reduction of protective bacteria, the expansion and colonization of invasive bacteria, and the reduction of SCFA concentrations, implying a potential role in inflammation and the immune response [3]. These changes in microbiota could play an additional role in obesity-related asthma through changes in the production of bacterial-derived or modified metabolites, e.g., lowering the production of SCFAs that can induce anti-inflammatory effects [38]. Additionally, it has been documented that individuals with obesity may present higher circulating concentrations of many inflammatory markers as a consequence of the disease [39], and GINA guidelines recommend weight loss as part of the strategy of asthma management in obese patients [2]. A systematic review found that, for obese asthmatic adults, the more effective dietary intervention is energy restriction [40].
## 4 Results and Discussion
### Limitations and Suggestions  
There are limitations when the narrowband bio-indicators and empirical calibration models are used for routine mapping of ecosystem C fluxes. The primary limitation derives from the semi-empirical feature of this method, which requires the availability of EC tower data for calibration of narrowband bio-indicators. Semi-empirical models may lack robustness when applied to different biomes and need to be validated. Hyperspectral data are still not available in all regions where the EC towers are operational.  
The other limitation is the lack of accurate validation approaches. Although the spatial pattern inversed in the study was consistent with the results of other models, the maps of C fluxes still could not be validated quantitatively due to the lack of observational data at spatial and temporal scales.  
Despite these limitations, the strong correlations between bio-indicators and EC flux tower data indicate that narrowband bio-indicators are a valid approach for mapping C fluxes over large spatial scales. This approach will be improved following the completion of a new global spectral network, SPECNET, being built in China. The SPECNET will be composed of multi-angles spectrometer and eddy covariance systems. It will link hyperspectral spectrum measurements with mass and energy flux sampling across biome spatial scales. This network will enhance the linkage of EC tower fluxes and hyperspectral data across different terrestrial ecosystems and spatial scales, improving the application of narrowband bio-indicators in China.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion  
To evaluate the accuracy of the proposed methods, the following studies are carried out:  
* First, simulated values of \(P_{E}(k)\) and \(P_{C}(k)\) with no noise and a good sampling frequency are considered. The average power delivered to the load is calculated using the proposed methods and compared to the results obtained with the reference procedure [17].
* Then, the sensitivity to the noise is analysed, for which a random noise is added to the simulated of \(P_{E}(k)\) and \(P_{C}(k)\);
* Afterwards, the sensitivity to the measurement frequency is analysed, for which the sampling frequency of \(P_{E}(k)\) and \(P_{C}(k)\) is reduced;
* Finally, real measurements of \(P_{E}(k)\) and \(P_{C}(k)\) are used to conduct the analysis.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DOA & direction of arrival \\ T-F & time-frequency \\ STFDs & spatial time-frequency distributions \\ SPTFDs & spatial polarimetric time-frequency distributions \\ MUSIC & multiple signal classification \\ ESPRIT & estimation of signal parameters using rotational invariance technique \\ TFDs & time-frequency distributions \\ SAR & synthetic aperture radar \\ PTF-MUSIC & polarimetric time-frequency MUSIC \\ TEM & transverse electromagnetic \\ WVD & Wigner-Ville distribution \\ JAD & joint antidiagonalization \\ TF-MUSIC & time-frequency MUSIC \\ SNR & signal-to-noise ratio \\ JSR & jamming-to-signal ratio \\ RMSEs & root-mean-square errors \\ \end{tabular}