esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The concentration levels of natural (\({}^{40}\)K) and anthropogenic (\({}^{137}\)Cs) radionuclides in bovine meat samples from the Calabria region, Southern Italy, were evaluated through the High Purity Germanium (HPGe) gamma spectrometry. In particular, regarding \({}^{40}\)K, the variation range of its specific activity was found to be limited, since it is an essential nutrient; thus, its value does not exhibit a high variance for the same specimen type. With reference to \({}^{137}\)Cs, otherwise, the obtained values were found to be lower than the minimum detectable activity (MDA) in all cases, demonstrating the lack of residual contamination by anthropogenic radioactivity. Moreover, the results of \({}^{40}\)K activity concentration reported in the present study were compared with \({}^{40}\)K specific activity in bovine meat samples of other countries, revealing some differences. This may be due to different reasons, such as the physical properties of soil according to the geographical location, the characteristics of the growing grass, climatic condition during the growth of the grass, the race of grazing animals and their spending time on the pasture for grazing.  
Further, in order to assess any possible radiological risk for the population, the effective dose for bovine meat ingestion was evaluated. The calculated values were then compared with the total (external + internal) exposure to natural radioactivity for human beings, i.e., 2.4 mSv y\({}^{-1}\) and they were found to be about three orders of magnitude lower. It can therefore be concluded that, in our case, radionuclide intoxication with consequent adverse impacts for humans is not a concern.  
Data reported in this article thus confirm that the analyzed samples are safe for food purposes, in terms of natural (\({}^{40}\)K) and anthropogenic (\({}^{137}\)Cs) radionuclide content, and hence, no remedial actions are needed. Moreover, obtained results also represent a main reference for the investigated area and can be used as a baseline to extend this investigation to the whole region. Moreover, they will be implemented in the future with an increase in the sampling points and the number of samples analyzed, but, noteworthy, the approach reported in this paper could be applied, in principle, for the evaluation of any potential radiological health risk due to the presence of radioactive elements in a large variety of food samples, by constituting a guideline for investigations focused on the monitoring of food quality.  
Conceptualization, F.C. and V.V.; methodology, F.C. and G.P.; validation, D.M.; formal analysis, A.B., S.M. and M.D.; investigation, F.C. and V.V.; resources, F.C. and D.M.; data curation, F.C.; writing--original draft preparation, F.C.; supervision, D.M. and V.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Test Results  
The results of the three tests for samples A, B, and C are presented in Figures 6-8. The obtained peak strength for each confinement is presented in Table 4.  
As can be seen from the figures, the tests were always starting with zero confinement. This is, as stated above, not standard procedure for a triaxial test but it was carried out particularly in this research work in order to find the \"True-UCS\". The obtained \"True-UCS\" was then used to compare with the \"Estimated-UCS\" based on the subsequence triaxial steps (with different confinements) of the same sample. Thus, the \"True-UCS\" and the \"Estimated-UCS\" can be compared in the most consistent way--comparing the results from exactly the same sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confinement \(\mathbf{\sigma_{3}}\)**} & \multicolumn{3}{c}{**Peak Strength \(\mathbf{\sigma_{1}}\) (MPa)**} \\ \cline{2-4}  & **Sample A** & **Sample B** & **Sample C** \\ \hline
0 & 197.7 & 205.8 & 212.8 \\
2 & 228.1 & 241.5 & 243.7 \\
5 & 278.2 & 286.9 & 285.5 \\
10 & 338.3 & 348.8 & 344.1 \\
15 & 387.8 & 395.0 & 390.2 \\
20 & 426.3 & 432.5 & 430.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Peak strength at each confinement level for samples A, B, and C.  
Figure 6: Sample A—test record of radial strain, axial stress, and cell pressure versus axial strain. “A” and “Bi” are peak strengths at the corresponding confinement.  
Figure 7: Sample B—test record of radial strain, axial stress, and cell pressure versus axial strain. “A” and “Bi” are peak strengths at the corresponding confinement.  
With varying confinements, including zero confinement, the radial strain rate was larger than the axial strain rate at the near-peak period of every confinement step. This can be observed in the curves of the radial strain and axial stress versus the axial strain in Figures 6-8. The tests in this research were executed with an axial strain rate control only. The development of radial strain, i.e., the acceleration of radial strain, can be used as a guide to the operator as to when critical or peak strength is reached in order to change the test to the next confinement level. An experienced operator is required to be able to evaluate and to make such decisions.  
Figures 6-8 also indicate that after the zero-confinement step, sample A had undergone about 2300 micro-strains of radial strain and 3600 micro-strains of axial strain. Sample B had undergone about 2300 micro-strains of radial strain and 3500 micro-strains of axial strain. Sample C had undergone about 3200 micro-strains of radial strain and 3600 micro-strains of axial strain.
## 2 Results and Discussion
### Volatile Compounds in the Wines
#### 2.2.1 C6 Alcohols and Higher Alcohols  
C6 alcohols usually contribute \"vegetal\" and \"herbaceous\" note to wine, causing a negative effect on wine aroma profile. Three C6 alcohols, including 1-hexanol, (_E_)-3-hexen-1-ol and (_Z_)-3-hexen-1-ol were detected in this study, with only 1-hexanol exceeding threshold (Table S1, Figure 2). UFAs additions caused significant variation of C6 alcohol in wines, which were largely dependent on UFAs types and concentrations. Except for low concentrations of OA and LA (12 and 60 mg/L), other treatments resulted in increased contents of 1-hexanol and total C6 alcohols as compared to the control, especially ALA. For example, 1-hexanol in 240 mg/L ALA addition was 69.2%, 32.0%, and 15.9% higher than those of the control, OA_240 and AL_240, respectively, suggesting that ALA addition is more favorable for the biosynthesis of C6 alcohols when compared to other two UFAs. This could be ascribed to the fact that more C6 aldehydes and C6 alcohols can be generated from the degradation of ALA catalyzed by LOX/HPL pathway [24; 25].  
Higher alcohols are important yeast-derived volatile compounds in wine, and they are mainly derived from amino acids degradation via the Ehrlich pathway [5]. In this study, isoamyl alcohol (green and alcohol note) and 2-phenylethanol (rose and floral note) exceed their thresholds in all wines, isobutyl alcohol (sweet and alcohol note) was above the threshold only in ALA_60 and ALA_120 wines, while 2,3-butanediol (fruity, sweet and butter note) exceeded its threshold in OA_240, LA_240, ALA_120, and ALA_240 wines (Table S1, Figure 2). These data suggested that the formations of higher alcohols in response to exogenous UFAs were associated with compounds, UFAs type, and concentration. In general, increased UFAs content were beneficial for the formations of this group volatiles, which agreed with the previous literature results [15; 19]. It should be emphasized that the promotion extents varied with UFAs types. ALA presented highest stimulation as the highest content were found in ALA_240 wine, which was 2.8-, 1.6-, and 1.7-fold higher than that in the control, OA_240, and LA_240, respectively (Figure 3). The maximum amount of 2,3-butanediol was observed in ALA_120, which was 6.1-, 7.5-, and 2.6-fold higher than those of the control, OA_120, and LA_120. Interestingly, the highest contents of 2-phenylethanol was found in OA_12 wine. Higher alcohol exceeding 400 mg/L can cause an unpleasant flavor to wine. The concentrations of higher alcohols in OA_240 (427 mg/L), LA_240 (411 mg/L), ALA_120 (541 mg/L), and ALA_240 (689 mg/L) exceed this value, suggesting that it is crucial to control UFAs concentration in must to reduce the contents of higher alcohols in final wine.  
Figure 2: Colour active value (OAV) of main volatile compounds (OAV \(>\) 1) in nine with different treatments. Values of the same compound marked with the same lowercase letters indicates no significant difference (\(p\) < 0.05, Duncan’s test).
# Study on Single-Event Transient Hardness of Semi-Enclosed Gate NMOS
###### Abstract  
Based on the simulation software, single-event transient (SET) simulations were conducted on semi-enclosed gate NMOS devices. The simulation involved bombarding the semi-enclosed gate NMOS devices with heavy ions under specific conditions. A comparative analysis was conducted to evaluate the single-event transient tolerance of traditional NMOS and semi-enclosed gate NMOS. Simulation curves from transient to steady-state states under different Linear Energy Transfer (LET) values, as well as potential distribution and current density distribution maps following heavy ion bombardment, were analyzed. Furthermore, single-event transient simulations were carried out on inverters composed of both NMOS types, with subsequent analysis. The results ultimately demonstrate that the semi-enclosed gate NMOS exhibits superior single-event transient tolerance compared to conventional NMOS.  
T +
Footnote †: journal: Applied sciences
## 3 Experimental Results  
Figure 12 shows the die photograph of the proposed resistive-bridge readout IC. The IC is fabricated using a TSMC 180 nm CMOS process, resulting in the RFC CFIA block size of 293 \(\upmu\)m \(\times\) 183 \(\upmu\)m. The printed circuit board (PCB) was designed to evaluate the performance of the proposed IC, the test board and the measurement environment are shown in Figure 13.  
Figure 11: Monte Carlo simulation results of the (**a**) CMRR and (**b**) PSRR.  
Figure 12: Die photograph of the proposed resistive-bridge readout IC.  
Figure 14 shows the voltage output of the IC, which varies along the pressure applied to the resistive strain gauge force sensor, which has 10 kOhm flat resistance; the resistance varies from 14 k\(\Omega\) to 26 k\(\Omega\)[18]. Figure 15a shows the measured transient response when the input source is a 5 mV\({}_{\text{PP}}\) differential sine wave input. Figure 15b shows the measured transfer function of the CFIA when the gain setting is maximum (<_DIN_> = 0000), resulting in 46.12 dB DC gain and 580 kHz unit gain bandwidth (UGBW).  
The measured input-referred noise is 86.6 nV/\(\surd\)Hz at 1 Hz and 32.6 nV/\(\surd\)Hz at 200 Hz, as shown in Figure 16. The minimum current consumption of the IC is 3 \(\upmu\)A (where only IA and bias block are used and if the LPF, buffer, ADC, and timing generator are fully utilized) the power consumption is 7.954 \(\upmu\)A. The noise-efficiency-factor (NEF) is widely used to compare the power-noise trade-offs [19]. The NEF can be calculated by (9).  
Figure 14: Measured voltage output response, due to pressure changes of the force sensor.  
Figure 13: Measurement environment of the proposed IC with test board.  
Figure 15: (**a**) Measured transient response (**b**) Transfer function of the proposed CFIA at the maximum gain.  
The NEF value is proportional to the input reference noise of the IA and the current consumption, which can be used to determine whether IA with low NEF has performance advantages. The NEF of the proposed IA is calculated to be 4.94. The performance comparisons of the proposed circuit, with the previous works regarding resistive-bridge ROIC, are summarized in Table 3.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 5 Analysis of Image Encryption and Decryption  
The analysis of the proposed encryption algorithm is carried out with the help of several important indicators that ensures the sensitivity of the key, image quality, randomness of the encrypted image and similarity of the decrypted image with that of the input plain image.  
There are several test images that are commonly employed; this article, for the analysis of the algorithm, considers a standard peppers image with a size of \(256\times 256\times 3\) as an objective image. The main focus of our study involves understanding the influence of watermarking on secret images at different levels after decomposition. The results based on the algorithm for different levels of watermarking are presented in this section in the form of simulations of the numerical values. The numerical values are then compared with different algorithms proposed in the literature. An investigation is then performed with statistical analysis comprising histogram analysis, pixel correlation, \(\chi^{2}\) test and robustness analysis supported by noise attacks. Finally, key sensitivity analysis under different key variations is performed. The embedded secret images, results of image encryption and decryption and and recovered secrete images in binary form are presented in Figures 8, 9 and 10, respectively.  
Figure 8: Secret images embedded in the sub-band of level 2 ((**a**) Lena) and the sub-band of level 1 ((**b**) Fingerprint) in an objective image of size \(256\times 256\) with level 2 decomposition.
## 3 Results
### Evaluation of CP and NDF Reference Data  
Table 1 shows the dates of micro NIR sensor measurements and pasture sample collection, number of samples, the mean and SD of pasture quality parameters (NDF and CP, in % of dry matter, DM). Mean reference value of NDF was 45.7% (range between 33.0 and 58.6%), which indicates the great variability between fields in the Mediterranean region, although all the pasture fields are in the phase of peak production (between the end of March and beginning of April). In regards to CP, the mean was 12.9% DM (range between 8.2% and 20.9% DM), which clearly highlights the great spatial variability of the pasture quality in the Montado ecosystem.  
Figure 3: Thermoluviometric graphic for the districts of Beja, Evora and Portalegre between July 2020 and June 2021 (source: Portuguese Institute of Sea and Atmosphere). (**a**) Monthly mean temperatures (**b**) monthly rainfall.
## 5 Simulations and Performance Evaluations
### Different Numbers of MTs  
As shown in Figure 4, we measured the total time consumption by the system in completing all tasks under the 3 strategies with different numbers of MTs. In simulations, we increase \(M\) from 30 to 100, while other settings are listed in Table 1, except fixed values \(R=6\), \(K=5\), \(\phi_{k}=2.4\times 10^{6}\) MBPS, \(s_{k}^{\text{CS2 NBS}}=80\) MB/s, \(s_{k}^{\text{NBS2C}}=30\) MB/s, \(s_{k}^{\text{BS2 NBS}}=s_{k}^{\text{NBS2B8}}=30\) MB/s, \(\forall k\in\mathcal{K}\).  
When \(M\) increases, the total time for the system to complete the tasks increases correspondingly, these 3 strategies are in accordance with the law. At the same time, the total time consumed by OMEC and OCMEC is always lower than NMEC, OCMEC is always lower than OMEC. So, computing offloading and data caching can reduce the system delay.  
With the increase of \(M\), the total load of the system increases. When \(M\in\{30,40,50\}\), because of the low task load, the gain effect of OCMEC on reducing the delay is relatively  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Value** & **Value** \\ \hline OCMEC & Our offloading and caching strategy in MEC environment \\ \hline OMEC & Only offloading strategy in MEC environment \\ \hline NMEC & No MEC environment \\ \hline \hline \end{tabular}
\end{table}
Table 2: The three schemes in MATLAB simulation.  
Figure 4: \(f\) gained by the 3 schemes with the increase of \(M\).  
low; when \(M\in\{50,60,70,80\}\), the task load increases continuously, and the total time-consuming gap between OCMEC and NMEC is increasing, and the gain effect of reducing the delay is getting better and better. When \(M\in\{80,90,100\}\), the total load exceeds the capacity of MEC-BS and MEC-NBS, and they cannot fully meet the calculation requirements of the tasks, so the total time gap gradually narrows, and the gain effect for reducing the delay gradually decreases.
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## 5 Conclusions  
In this paper, we initially present a novel tractable path gain model. This model was extracted from experimental results by employing the trust region method for the signal attenuation and a stochastic process for the pointing errors. Based on this model, we provide novel closed-form expressions for the SNR in transdermal OWCs, which take into account the channel particularities, the characteristics of the optical transceivers and the TX-RX pointing errors. Our findings reveal that a transmission window exists for wavelengths between 700 and 1300 nm, while the optimal transmission wavelength is 1100 nm. Moreover, TOLs are proven to outperform the corresponding RF links, in terms of received signal quality with lesser power demands. In addition, TOLs operate in a non-standardized frequency region, where an extremely large amount of unexploited bandwidth exists. Thus, there is no interference from other devices. Finally, we quantified the detrimental effect of pointing errors, which can degrade the TOL's performance more than 10%. Next steps of TOLs should include development of novel architectures utilizing transdermal OWCs, as well as extensive performance analysis of the achievable spectral resolution, link quality and power efficiency with regard to the safety of the human body.  
[PERSON] and [PERSON] constructed the system model. [PERSON] and [PERSON] performed the theoretical analysis. [PERSON] performed the simulations and produced the results. [PERSON], [PERSON] and [PERSON] validated the theoretical analysis and the simulation results and conceived the concept. All authors analyzed the results, and contributed to writing and reviewing the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Initial Conditions  
Table 2 presents the FoS values of soils when their saturation degrees varied between 40% and 60%, representing the initial conditions before the rainfalls. The parameter \(P_{0}\) (see Table 1) used in FLAC addresses the air entry value (AEV), and AEV is known to represent the critical suction state of soils where the air can enter into the soil and desaturation starts. The AEV is known as one of the most important indicators of the suction capacity. [PERSON] et al. (2018) studied the rainfall intensity and duration threshold in terms of unsaturated parameters by using hypothetical soils. Among the parameters in their study, the AEV was found to be the most critical. Thus, when the \(P_{0}\) values of the soils were compared where the other variables were neglected, a higher AEV resulted in a higher FoS value.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 2 Formation Mechanisms of Benzene and PAHs
### Formation Mechanisms of PAHs
#### 2.2.2 Hydrogen Abstraction and Vinylacetylene Addition Mechanism  
[PERSON] et al. [46] investigated the formation of tars and emphasized the significance of vinyl acetylene as an intermediate species. They demonstrated that the presence of vinyl acetylene influences the reactions leading to PAH formation. It was established that PAH formation primarily occurs through vinyl acetylene addition, followed by hydrogen abstraction. In various combustion flame types, the abundance of vinyl acetylene increases the probability of PAH formation via the HAVA mechanism, eliminating the requirement for subsequent benzene ring formation [47; 48; 49].  
During the HAVA mechanism, the formation of PAH radicals leads to the generation of a new ring in two steps: the initial addition of vinyl acetylene, followed by cyclization. Experimental studies on the formation of naphthalene and phenanthrene at elevated temperatures provided insight into this mechanism. Kinetic simulations further demonstrated that the presence of double and triple bonds enhances the activity of the carbon atom, facilitating their attack on carbon atoms in PAH molecules [48; 50]. This mechanism has been observed in various environments, including the interstellar medium, where PAH formation occurs both at high temperatures during chemical vapor deposition processes and in cold molecular clouds. In these conditions, the formation of naphthalene has been observed via a barrierless route of the HAVA mechanism, suggesting it is an alternative pathway to HACA [21; 51; 52].  
Further, [PERSON] et al. [53] proposed the formation of higher and more complex PAH via the HAVA reaction pathway, which involves the photodissociation of phenanthrene and anthracene. These fragments then react with vinylacetylene to form six-ring PAHs. Additionally, [PERSON] et al. [52] found that benzene ring formation was favored at the zigzag edge surface site rather than the free edge during the formation of phenanthrene through the reaction of PAH radicals and vinylacetylene. Similar pathways were observed for the formation of naphthalene and phenanthrene through the reaction of phenyl radicals with vinylacetylene and naphthyl radicals with vinylacetylene, respectively. Overall, the HACA mechanism exhibits versatility in different reaction environments and conditions for PAH formation [54; 55; 56; 57], as illustrated in Figure 6.
## 4 Method
### Loss Function  
The loss function of LGAR-Net2 comprises the auxiliary LGCA loss and losses for the regression of the graspable region's confidence, angle, and grasp width.  
1. LGCA auxiliary losses: The function \(loos_{\text{s}}=SmoothL1(\hat{y}_{i}^{g},y_{i}^{g})\) is defined, where \(\hat{y}_{i}^{g}\) represents the predicted value image of the network's grabber region and \(y_{i}^{g}\) denotes the label for the grabber region.
2. Regression loss estimation for the confidence, angle, and width of a crawlable region: The definitions of \(loos_{\text{cos}},loos_{\text{sin}},loos_{\omega}\), and \(loos_{\omega}\) are equivalent to \(SmoothL1\cos,\sin,uv(\hat{y}_{i}^{g},y_{i}^{g})\), where \(y_{i}^{g}\in R^{H\times W}\) represents the ground truth image of the graspable region mapping for the i-th sample, while \(\hat{y}_{i}^{g}\in R^{H\times W}\) denotes the predicted value image of the graspable region for the i-th sample.  
The definition of the total network loss is as follows:  
\[loos=\lambda_{1}(\frac{1}{n}{\sum_{i=1}^{n}loos_{uv}})+\lambda_{2}(\frac{1}{n }{\sum_{i=1}^{n}loos_{\text{cos}}}+\frac{1}{n}{\sum_{i=1}^{n}loos_{\text{sin} }})+\lambda_{3}(\frac{1}{n}{\sum_{i=1}^{n}loos_{\omega}})+\lambda_{4}(\frac{1 }{n}{\sum_{i=1}^{n}loos_{\text{s}}}) \tag{11}\]  
where \(n\) represents the number of training samples, and, respectively, denotes the true value and estimated value for both the confidence in identifying a graspable region and the attitude information.  
The definition of the \(Smooth\)\(L1\) loss is as follows:  
\[Smooth_{L1}(x)=\left\{\begin{array}{l}0.5x^{2},if|x|<1\\ |x|-0.5,\text{otherwise}\end{array}\right. \tag{12}\]  
Given the substantial influence of grasp position and angle on grasping success, the relative importance of local feature range, as indicated by claw opening width, is considered to be less significant compared to these factors.