esa-sceva/satcom-chunk-collection huggingface dataset:
# Phenolic Profiling of Five Different Australian Grown Apples
## 3 Results and Discussion
### Phenolic Compounds Profile by LC-MS/MS Analysis
#### 3.4.2. Flavonoids
The MS/MS analysis confirmed the compound **56** at product ions \(m/z\) 271 due to the corresponding loss of glucuronide (176 Da) and loss of glucuronide and \(m/z\) 253 due to the loss of H\({}_{2}\)O-CH\({}_{2}\)O (194 Da) from the precursor ion [65]. The presence of crissineol was confirmed by the product ions at \(m/z\) 330 [M+H-CH\({}_{3}\)], \(m/z\) 312 [M+H-CH\({}_{3}\)-H\({}_{2}\)O], \(m/z\) 297 [M+H-2 CH\({}_{3}\)-H\({}_{2}\)O] and \(m/z\) 284 [M+H-CH\({}_{3}\)-H\({}_{2}\)O-CO] [66]. According to previous reports, compounds have been characterised in several plants including Oximum species [66]. Compound **62** (Myricetin 3-O-galactosidaseside with ([M-H]\({}^{-}\)\(m/z\) at 479.081) identified in Red Delicious and compound **63** (Quercetin 3-O-glucosyl-xyloside with ([M-H]\({}^{-}\)\(m/z\) at 595.1291) identified in Pink Lady were only detected in the negative ionization mode, and identified according to the fragment peaks at \(m/z\) 317 [M-H-glucoside, loss of 162 Da] [67] and \(m/z\) 265 [M-H-glucose-xylos, loss of 330 Da] [51], respectively. Compound **65, 66** and **68** present in the negative mode of ionisation were identified as kaempferol 3-O-glucosyl-pharmacoside, kaempferol 3-O-(2\"-rhamosyl-galactosidaseside) 7-O-rhamoside and kaempferol 3,7-O-diglucoside according to the ([M-H]\({}^{-}\) at \(m/z\) 755.2068, \(m/z\) 739.2115 and \(m/z\) 609.1451, respectively Kaempferol 3-O-glucosyl-rhamosyl-galactosidaseside exhibited the product ions at \(m/z\) 285, corresponding to the loss of the sugar units from the precursor ion [68]. The presence of kaempferol 3-O-(2\"-rhamosyl-galactosidaseside) 7-O-rhamoside was confirmed by the product ions at \(m/z\) 593 [M-H-C\({}_{6}\)H\({}_{10}\)O\({}_{4}\)], \(m/z\) 447 [M-H-2C\({}_{6}\)H\({}_{10}\)O\({}_{4}\)], and \(m/z\) 285 [M-H-2C\({}_{6}\)H\({}_{10}\)O\({}_{4}\)-C\({}_{6}\)H\({}_{10}\)O\({}_{5}\)] [69]. Whereas, kaempferol 3,7-O-diglucoside exhibited the product ions at \(m/z\) 447 and \(m/z\) 285, corresponding to the loss of glucoside and consecutive loss of glucoside from the parent ion [70]. It worth noted that these compounds were first time detected in Australian grown apple samples to the best of our knowledge. Compound **73** and **75** detected in positive mode were identified as 6\"-O-Malonyldaidzin and violanone with precursor ion at \(m/z\) 503.1200 and \(m/z\) 317.1016, respectively. 6\"-O-Malonyldaidzin was confirmed by the product ion at \(m/z\) 255 [71], corresponding to the loss of malonyl-glucoside from precursor, while the compound violanone was confirmed by the intensive peaks at \(m/z\) 300 [M+H-CH\({}_{3}\), loss of 15 Da], \(m/z\) 285 [M+H-2 CH\({}_{3}\), loss of 30 Da] and \(m/z\) 135 [M+H-C\({}_{10}\)H\({}_{12}\)O\({}_{3}\)] [72]. Previously, several studies had discovered the existence of the above isoflavoids in fruits [73; 74; 75; 76; 71].
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
###### Abstract  
User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PFG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.  
biometric identification; ECG; PPG; deep learning; transfer learning; VGG16; long-term short-term (LSTM) model +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Master Curve Parameters

Table 6: Master curve parameters for the Tensile Creep Test (TCT). Analysis of the master curve and shift factor models suggests that more reliable results should be obtained for the TCT, regardless of the model used. Some difficulties occurred for the \"branching\" modification of the Richards model, but further work should eliminate this problem. In the case of the BBCT, reliable results should be obtained using only Arrhenius shift factor model. The WLF model can influence analyses conducted at higher temperatures.
## 4 Numerical Calculation Results and Analysis
### Discussion of Seismic Time-History Response Results  
According to the finite element model and the input seismic waves, this section mainly analyzes the deformation of the tank, the distribution of stress along the height of the tank wall, as well as the liquid sloshing, the slippage, and uplift response of the tank bottom. The summation of the maximum seismic response of the tank under the four cases are listed in Table 3. In order to see more intuitively the effect of the input direction on the response of the tank body, set the result of the X excitation as 1, and list the response comparison under the El Centro ground motion, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Earthquake Excitation** & **Peak Response Quantities** & **X** & **X + Z** & **X + Y** & **X + Y + Z** \\ \hline \multirow{8}{*}{Imperial Valley, 1940} & Horizontal displacement/mm & 6.97 & 7.94 & 7.41 & 8.23 \\  & Hoop stress/MPa & 103.92 & 123.10 & 102.85 & 119.95 \\  & Axial stress/MPa & 24.26 & 27.66 & 31.72 & 34.11 \\  & Mises stress/MPa & 100.74 & 119.97 & 99.58 & 116.39 \\  & Sloshing/m & 0.515 & 0.501 & 1.078 & 1.076 \\  & Base shear/kN & 4766.66 & 5966.91 & 4685.81 & 5583.32 \\  & Base moment/(10\({}^{3}\) kN m) & 35.75 & 30.65 & 23.92 & 33.80 \\  & Slip/mm & 11.00 & 12.06 & 36.51 & 37.27 \\  & Uplift/mm & 4.05 & 4.45 & 7.69 & 7.75 \\ \hline \multirow{8}{*}{Tangshan, 1976} & Horizontal displacement/mm & 6.94 & 7.35 & 7.19 & 7.37 \\  & Hoop stress/MPa & 103.22 & 115.44 & 104.68 & 115.71 \\  & Axial stress/MPa & 23.04 & 23.79 & 24.38 & 25.18 \\  & Mises stress/MPa & 100.47 & 109.57 & 101.85 & 110.13 \\  & Sloshing/m & 0.171 & 0.174 & 0.175 & 0.177 \\  & Base shear/kN & 4739.40 & 5079.25 & 4699.27 & 5100.14 \\  & Base moment/(10\({}^{3}\) kN m) & 22.11 & 23.34 & 22.51 & 22.77 \\  & Slip/mm & 3.62 & 4.79 & 4.88 & 5.66 \\  & Uplift/mm & 3.23 & 3.63 & 3.42 & 4.00 \\ \hline \multirow{8}{*}{Northridge, 1994} & Horizontal displacement/mm & 7.01 & 7.14 & 6.92 & 7.29 \\  & Hoop stress/MPa & 102.07 & 115.51 & 100.98 & 115.03 \\  & Axial stress/MPa & 22.91 & 22.04 & 22.16 & 22.29 \\  & Mises stress/MPa & 98.99 & 111.77 & 99.50 & 110.65 \\  & Sloshing/m & 0.0335 & 0.0372 & 0.0338 & 0.0393 \\  & Base shear/kN & 4709.30 & 4822.63 & 4034.51 & 4184.47 \\  & Base moment/(10\({}^{3}\) kN m) & 22.08 & 21.89 & 20.65 & 21.36 \\  & Slip/mm & 3.39 & 3.41 & 6.74 & 6.68 \\  & Uplift/mm & 2.73 & 2.85 & 4.09 & 4.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the peak response quantities under the four cases.  
Figure 5: Selected feature points for analysis (plan).
## 4 Numerical Simulation
### Gaussian Noise Together with Shot Noise  
The measurement noise satisfies the Gaussian distribution with zero mean, and the measurement covariance matrix changes suddenly to 30 times the original at \(t=10\) min, \(R_{k}^{10}=30R_{k}\). Considering that when \(\sigma=5\), the filtering performance in RSRCD-MCCKF is good regardless of Gaussian noise or non-Gaussian noise; consequently, we still choose \(\sigma=5\) here. The RMSE\({}_{\text{pos}}\) and RMSE\({}_{\text{vel}}\) of each algorithm are shown in Figure 4.  
Figure 4: The RMSE comparison of each algorithm under abnormal measurement. (**a**) RMSE\({}_{\text{pos}}\) of each algorithm under abnormal measurement; (**b**) RMSE\({}_{\text{vel}}\) of each algorithm under abnormal measurement.  
Figure 3: The RMSE comparison of each algorithm under non-Gaussian noise. (**a**) RMSE\({}_{\text{pos}}\) of each algorithm under non-Gaussian noise; (**b**) RMSE\({}_{\text{vel}}\) of each algorithm under non-Gaussian noise.  
As shown in Figure 4, when the measurement is abnormal, the error of the SRCD-CKF algorithm increases abruptly, which is not robust. While the other algorithms effectively resist the abnormal measurement. Among them, the VBSRCD-CKF algorithm shows better robustness, and it has the most obvious effect on abnormal measurement suppression; the RSRCD-MCCKF algorithm is second only to the VBSRCD-CKF algorithm and shows good robustness with high accuracy, while the suppression effect of the HRSRCD-CKF algorithm and the MRSRCD-CKF algorithm is slightly worse, indicating that the RSRCD-MCCKF algorithm and the VBSRCD-MCCKF algorithm are more effective in dealing with abnormal measurement problems.
## 3 Results and Discussions
### Microorganisms Related to Verticillium Wilt of Horseradish Roots  
2.1 Quantitative Analysis of Microorganisms Colonizing Horseradish Roots Affected by Verticillium Wilt  
The number of isolates obtained from internally infected horseradish roots varied according to the growing seasons. In the first year of the study (2015), the most intensive  
Figure 1: Infection of horseradishids roots by a fungi complex causing Verticillium wilt. Disease severity index (DSI) [%] (homogeneous groups, [PERSON]’s HSD test, \(\alpha=0.05\)). Values marked with the same letters do not differ statistically significantly, \(p<0.05\). Control—Without protection; ChP—Chemical protection; RChP—Reduced chemical protection with the participation of biopreparations and biostimulants; BPPo—Biological protection (root cuttings treatment before planting with _Pythium oligandrum_); BPPs—Biological protection (root cuttings treatment before planting with _Bacillus subtilis_); BPEm—Biological protection (root cuttings treatment before planting with _Ecklonia maxima_). development of Verticillium wilt of horseradish roots (Figure 1) was represented by the smallest number of isolated microorganisms (Figure 2). In the following years (which were wetter), with lesser disease severity, the number increased. As the material collected for microbiological analysis was carefully weighed and cut into small pieces, it can be concluded that the number of isolates obtained corresponded to the number of colony-forming units (CFU) per 5 g of a diseased root. In this approach, on average, the highest amount of CFU in 5 g was found in infected roots from the 2017 growing season and the lowest in those from 2015. It was shown that in the plots where seedlings were treated with PGP (_E. maxima_) and BCA (_P. oligandrum_, _B. subtilis_), more CFUs were isolated from 5 g of roots that were showing the symptoms of the disease than in those treated with the ChP, RChP, or in the control group. At the same time, a significant correlation was found between CFU and the intensity of Verticillium wilt of horseradish roots from the plots where the seedlings were treated with _P. oligandrum_, _B.subtilis_, and thiophanate methyl before planting (Table 3). The data do not support the conclusion that there is a correlation between CFU and DSI in the plots where seedlings were treated with _E. maxima_ extract and the control plot.
# Risk Assessment of Bisphenol A in the Korean General Population
## 4 Risk Characterization of BPA
In the case of the third POD, epidemiological causality has not been established, so further research is needed to determine the causality between BPA exposure and thyroid cancer. The MOE approach uses a reference point, and judgment of the MOE is important for risk managers. For non-genotoxic substances, an MOE of >100 is routinely applied to the POD in animal studies [64]. When compared with PODs from epidemiological evidence, an MOE of >10 would be of low concern from a public health point of view, and it might be considered a low priority for risk management actions. The MOE for current levels of exposure to bisphenol A was calculated by dividing each POD value by the age-specific exposure level of the general population presented in Table 3. Table 4 shows the MOE calculated for each age group based on the POD values and exposure levels. The current BPA exposure level in the general population above 3 years of age in Republic of Korea is a sufficient margin of exposure when compared to animal toxicity levels. However, when compared to the 95 th percentile exposures to the toxicological dose based on newly discovered health effects, immunotoxicity, and an epidemiological association with thyroid cancer, the MOE was calculated to be under 10 in all age groups. However, direct causality between exposure to BPA and health effects, such as immunological or thyroid dysfunction and thyroid cancer, has not been confirmed. In this study, the potential for these risks was evaluated by the MOE method, so a more accurate evaluation of causality with BPA will be required in the future. Contrary to the results of domestic bisphenol risk assessments so far, our result, which was evaluated with the MOE approach, shows that exposure management of domestic bisphenol A is necessary. Specifically, efforts to reduce exposure to BPA are needed to obtain a sufficient margin of exposure for the potential risk of health effects in the high-exposure group. Diet is known to be the main source of BPA exposure, but in most food, BPA is either undetectable or at a very low level. In the biomonitoring-based exposure assessment, the high-exposure group had a relatively low contribution of food. Therefore, it will be necessary to not only continuously monitor exposure in the body, but to also to investigate in detail the sources of further exposure in high-exposure groups. Figure 2: Distribution of urinary bisphenol A level (ug/g creatinine) in the general population from the 2009–2020 National Survey of Korea (upper figure) and the urinary BPA levels for the case-control study population (lower table). The percentile of urinary BPA levels in the general population is expressed as a range from cycle 1 to cycle 4 of the survey [63]. The red lines in the upper figure represent the 25 th and 75 th percentiles of BPA levels in the case-control study population, respectively.
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 2 Radiation Transport Model and Surrogate Formulations
### Model Geometry  
To provide an example of an urban area, we selected a 250 m \(\times\) 180 m block in downtown Washington, D.C., located at approximately 38\({}^{\circ}\)54'48\" N by 77\({}^{\circ}\)1'60\" W (Johnson Avenue NW) to serve as our domain. Buildings in this area are primarily brick and concrete residential housing and are generally 1-5 stories in height. Using data from the OpenStreetMaps database ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/))), we constructed a 2-D representation of the area to serve as the test geometry. Our implementation treats the buildings as a set of disjoint polygons \(P_{j},\ j=1,2,...,N_{g}\), each of which is assigned a corresponding macroscopic cross-section \(\Sigma_{t}\). A satellite photo of the area with an overlay of the constructed representation is provided in Figure 1.  
Approximate calculations indicate that wood and concrete buildings correspond to an optical thickness of around 3 mean free paths (MFPs), where the mean free path denotes the mean distance traveled by the photons between collisions with atoms of the building. Consequently, we randomly selected cross-sections for each building so that their optical thickness is between 1 and 5 MFPs. The random sampling was also weighted according to the volume of each building, so that smaller buildings were biased towards smaller optical thicknesses and vice versa. The regions between buildings were treated as dry air at standard temperature and pressure, with cross-sections taken from the NIST XCOM database ([[http://www.nist.gov/pml/data/xcom/](http://www.nist.gov/pml/data/xcom/)]([http://www.nist.gov/pml/data/xcom/](http://www.nist.gov/pml/data/xcom/))).  
For this geometry, the admissible parameter space is  
\[\mathcal{X}=[0,250]\times[0,180]\times[5\cdot 10^{8},5\cdot 10^{10}]. \tag{4}\]  
The first two dimensions define the spatial location representing the simulated 250 \(\times\) 180 m urban block. The third dimension restricts the source intensity to vary between \(5\times 10^{8}\) and \(5\times 10^{10}\) Bq.
## 3 Analysis of Test Results
### Relationship between Pile End Resistance and Pile Top Load  
In this load test, the bottom measured section was closer to the pile end, and the pile end resistance was approximated by the axial force calculated from the measured section. The variation of pile end resistance with top pile load is shown in Figure 9. During the loading process, as the pile top load gradually increases, the pile end resistance also increases. When the top pile load was less than 2500 kN, the pile end resistance of both test piles increased linearly and accounted for no more than 30% of the pile top load. With an increase in the pile top load, the pile end resistance of PHC\(-\)2 firstly entered into a non-linear phase and accounted for an increasing proportion of the pile top load, gradually  
Figure 8: Distribution of side friction resistance under different loads of PHC\(-\)1 and PHC\(-\)2: (**a**) Distribution of side friction resistance of PHC\(-\)1; (**b**) distribution of side friction resistance of PHC\(-\)2.  
showing obvious characteristics of end-bearing piles. Under the maximum load, the pile end resistance of the two test piles accounted for 43.5% and 45.7%, respectively, showing that PHC\(-\)2 had more characteristics of end-bearing piles.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 2 Background
### TID on Flash-Based FPGAs  
Prolonged exposure of electronics devices to ionizing radiation (particle radiation and high-energy electromagnetic radiation) can cause cumulative effects known as total ionizing dose effects. The ionization dose is deposited by particles passing through the materials changing their electrical properties. From the TID point of view, FPGAs are very complex circuits because they not only include  
Figure 1: Jitter sampling principle.  
Figure 2: High-frequency oscillators. (**a**) RO scheme with enable. (**b**) Self-Timed Ring structure.  
programmable logic and memory to implement designs, but also several peripheral blocks intended for programming and testing functions.  
In flash-based FPGAs, the TID effects at device level are focused on the floating gate and CMOS transistors. The floating gate can be affected by three different radiation-induced phenomena which reduce its threshold voltage: holes injected into the floating gate, holes trapped into the oxides and electrons emitted over the polysilicon/oxide barriers [9]. Regarding the CMOS transistors, it is necessary to distinguish between NMOS and PMOS transistors. For NMOS, trapped holes tend to decrease the threshold while interface states tend to increase it. On the other hand, in PMOS transistors, trapped holes and the interface states tend to increase (in absolute value) the threshold voltage [10]. In low voltage devices with thin oxide, TID effects in CMOS transistors are negligible.  
All in all, these radiation-induced deteriorations of the electrical characteristics will result in a degradation of the propagation delay and an increase of the core power supply current and threshold voltage (\(V_{t}\)) of the FPGA switches.
## 4 Statistical Optimization and Model Validation
### Speeding Up the Forward Propagation of Uncertainty Via a Surrogate Model  
A classical Bayesian analysis, discussed in the previous section, requires propagation of the parametric uncertainty through the given computational model. This task is also known as the _uncertainty propagation_ (UP). Typically, a MCMC sampling method requires a significant number of model evaluations in order to yield statistical convergence. In practice, however, the computational complexity of the underlying numerical model, as well as the total available computational budget severely restrict the number of evaluations that one can actually carry out. In such situations, the estimates produced by the Bayesian analysis lack sufficient trust, as the limited number of model evaluations can yield additional uncertainty.  
The most common approach when dealing with an expensive model, as it is the case in this study, is to replace it with an easy-to-evaluate surrogate. Simply put, one evaluatesthe model on a set of design points and then strives to establish an accurate relationship between the response surface and the design points. Then, the original computational model can be substituted by its surrogate in the Bayesian analysis to obtain \"surrogate\" posterior distribution of uncertain parameters. This distribution could be evaluated orders of magnitude faster than the \"direct\" posterior using the computational forward model. The idea of surrogate-based Bayesian inverse problems with MCMC methods has been exploited in many applications including groundwater flow modelling [54].  
In this study, we employ polynomial chaos expansions (PCE). PCE is one of the most rigorous approach to UP, thanks to its strong mathematical basis and ability to provide functional representations of stochastic quantities. The goal of this section is to highlight how a surrogate model using a PCE can be constructed to accelerate the Bayesian inference in the task of optimization for computationally intensive models with as few simulations as the computational budget allows. It is evident that replacing the original model with its surrogate introduces another source of uncertainty to the analysis. To remedy this problem, we exploit a Bayesian Sparse learning extension to PCE to account for the surrogate's prediction errors. A detailed description of PCE is provided in Appendix A.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 2 Evolution from Traditional RAN to vRAN  
This section will summarize the evolution history of RAN. The real-world implementation of the RAN is better known as the Base Station (BS) [1]. Two major units of a BS are Radio Unit (RU) and Baseband Unit (BBU). RU is responsible for transmission and reception. Meanwhile, BBU is responsible for radio management, resource utilization, and other operations.