esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Bioactive Surfactant Levels in Selected Cereal Products
## 1 Introduction
An octanol-water partitioning coefficient (K\({}_{\text{ow}}\)) was adopted to assess the equilibrium distribution of non-ionic organic contaminants between plant roots and external media [35]. For perfluoroalkyl acids (PFASs), due to their hydrophobic and oleophobic nature and the presence of an ionospheric functional group that increases water solubility, the determined K\({}_{\text{ow}}\) coefficient is not appropriate for PFAS because it may be imprecise [32]. Nevertheless, log K\({}_{\text{ow}}\) of 1-4 may account for the increased uptake of PFAS [36]. A more comprehensive parameter used to compare different plant species with respect to their ability to accumulate PFAS could be the bioconcentration factor (BCF), which is commonly described as the ratio of analyte concentration in soil/substrate and a given plant tissue [37]. In general, as PFAS chain length increases, water solubility decreases [38]. Moreover, some PFASs with chain lengths C \(<\) 6 and C \(<\) 7 for PFSAs and PFCAS, respectively, are regarded as non-bioaccumulative and highly soluble, with reduced sorption tendency in solids which results in greater mobility in the environment [39; 40]. Some studies indicate that PFASs' affinity to phospholipids could play a significant role in plant tissue distribution [41]. The recent literature shows that there are wide variations in terms of PFASs' accumulation abilities and their distribution patterns in plant tissues. Nevertheless, with respect to edible parts, protein and lipid contents play a major role in these variations [42]. To the best of our knowledge, the existing literature provides little information on the determination of PFAS levels in cereal-based products. Thus, the aim of the study was to investigate the presence of 10 PFASs (the PFCAS were perfluorobutanoic acid (PFBA), perfluoroectonic acid (PFPeA), perfluorohexanoic acid (PFKA), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluoroanonic acid (PFNA) and perfluoroedanoic acid (PFDA); the PFSAs were perfluorobutane sulfonate (PFBS), perfluorohexane sulfonate (PFHoS) and perfluorooctane sulfonate (PFOS)) in various cereal products, such as flour, bran, bread and rolls, buckwheat, millet, rice, and noodle. The structures of the investigated analytes are shown in Figure S1 (Supplementary Materials). The broad scope of this study should help to indicate the range of PFAS contaminations, their levels,and the potential hazard to human health posed by a diet containing cereal-based products. Samples were treated by using a modified QuEChERS method with a dispersive solid-phase extraction step, after which they underwent chromatographic analysis (with a micro-HPLC-MS/MS system). The results of this research constitute part of a project grant which covers the analysis of levels of perfluoroalkyl substances in food groups that form a healthy eating pyramid. Based on current and previous results, a model daily food intake may be calculated.
## 4 Methodology
### Methods of Landslide Modeling
#### 4.4.4 Logistic Regression (LR)  
The logistic regression involves analysing a problem in which the outcome is estimated based on binary response of the dependent variable i.e., true or false and 0 or 1, specifically presence or absence of landslide event in this analysis which is predicted by one or a set of causative variables [65]. The relative contribution of a number (n) of independent variables (\(V_{1}\), \(V_{2}\),...,\(V_{n}\)) on a dependent (\(\Upsilon\)) is used to predict a logit transformation of probability based on the presence or absence of landslide. Though the LR model does not directly define the susceptibility, but the derived probability values could be helpful to draw an inference. The model was fitted using Equation (4).  
\[Y\ =\ \textit{Logit}\ (p)\ =\ \ln\left\{\frac{p}{1-p}\right\}\ =\ C_{0}+C_{1}V_{1}+C_{2}V_{2}+\ldots+C_{n}V_{n} \tag{4}\]where p represents the probability of landslide event Y (dependent variable), p/(1-p) is the likelihood ratio, C0 is intercept and C1, C2,...,Cn are the coefficients which were used to compute the contribution of the independent variables V1, V2,...,Vn.  
In this analysis, considering landslide event (training dataset) as dependent variable and sixteen causative parameters as independent variable, the spatial relationship was calculated using Equations (3) and (4). Similar to [PERSON] [66], in this study, using the probabilities, the possibility of occurrence of landslide was acquired using the coefficients from Equation (5).  
\[\begin{array}{ll}\text{LSI}&=-3.469-(\text{Altitude}\times B)+(\text{ Slope}\times B)+(\text{Rainfall}\times B)+(\text{TWI}\times B)\\ &+\text{Curvature}_{B}+(\text{Drainage}\times B)+(\text{Road}\times B)+\text{ Earthquake}_{B}+(\text{NDVI}\times B)\\ &+(\text{STI}\times B)+(\text{SPI}\times B)+\text{LULC}_{B}+\text{Geology}_{B}+ \text{Soil texture}_{B}+\text{Aspect}_{B}\\ &+(\text{Soil depth}\times B)\end{array} \tag{5}\]  
where \(B\) is the logistic regression coefficient value, while \(\text{Earthquake}_{B}\), \(\text{LULC}_{B}\), \(\text{Geology}_{B}\), Soil texture, and \(\text{Aspect}_{B}\) are the logistic regression coefficient values.
## 5 Role of IoT Technology in Education
### The Security Aspects  
#### 5.3.1 Privacy Preserving in IoT  
Privacy preservation in IoT is explored in this section, with a focus on a three-layer of the IoT stack, which is illustrated in Figure 4[12; 55; 56; 57].  
1. **Privacy Preservation in IoT Device Layer**. The IoT device layer is particularly vulnerable to attacks, so security measures such as access control, authentication, data encryption, and cryptography technology must be implemented. Selective RFID Jamming and a nonlinear key algorithm can be used for access control and data encryption, respectively. IPSec protocol and cryptography technology provide authentication, privacy protection, confidentiality, authenticity, and data integrity. Digital signatures and hash values are also used for secure communication protocols.
2. **Privacy Preservation in Platform/Infrastructure Layer**. This layer involves intelligent data processing and faces security challenges related to data confidentiality and integrity. Current network protocols are insufficient for machine-to-machine (M2M)  
Figure 4: IoT three-layer architecture.  
communication in resource-limited environments, which may hinder connections between machines. The heterogeneity of these networks creates security vulnerabilities that worsen network security, interoperability, and coordination. New security mechanisms specifically tailored for IoT environments are necessary, including end-to-end authentication and key agreement mechanisms, network virtualization, and IPv6 as standard network layer protocols to support inherited security measures.
3. **Privacy Preservation in Application Layer**. Different applications target different domains with unique data collection requirements that may require different security measures. Therefore, the application layer security considerations/requirements differ from the previous two layers in terms of nontechnical aspects such as privacy awareness and security management, as well as technical aspects such as cryptography and key agreements that incorporate symmetric and asymmetric cryptosystems and certification transfer technology.
## 4 Discussion
They found that the combination of a 5 mg/kg dose of DNJ and 25 mg/kg dose of morin effectively hindered the progression of diabetes mellitus type 2 (T2 DM) by 87.56%, which was achieved via  
Figure 5: A graphical summary of main results of this study. ARE is the abbreviation for anti-oxidative response elements. inhibition of suppressors of cytokine signaling 3 (SOCS3) and the promotion of PPARy as well as SOCS2 expression. The combination with morin reduced the dose of DNJ from 20 mg/kg to 5 mg/kg for the similar control level of T2 DM, thereby eliminating the potential adverse effect of high doses of DNJ [50]. [PERSON] et al. reported that the feeding of T2 DM model mice with DNJ containing water (10% DNJ, 200 mg/kg-day) significantly decreased the concentration of blood glucose and increased SOD activity which contributed to the ameliorated hepatic fibrosis [17]. [PERSON] et al. reported that food-grade mulberry powder enriched with DNJ suppressed the elevation of postprandial blood glucose in humans. They showed that single oral administration of 0.8 and 1.2 g of DNJ-enriched powder (corresponding to 12 mg and 18 mg of DNJ) significantly suppressed the elevation of postprandial blood glucose, indicating that a dietary supplement of DNJ-rich powder may be a potential way to prevent diabetes mellitus [51]. [PERSON] et al. reported that in mild dyslipidemia patients, daily uptake of three tablets of 280 mg of mulberry leaf powder (DNJ content was 0.367 mg/tablet) three times a day before meals for 12 weeks slightly increased the average erythrocyte glutathione peroxidase activity and significantly decreased the mean serum 8-isoprostane level of patients [52]. These previous clinical and animal studies indicated that DNJ and DNJ-containing mulberry extracts are promising in the treatment and prevention of diabetes or diabetes-associated diseases. Besides DNJ, phenolics and flavonoids are rich in mulberry and show various pharmacological effects [1]. More and more studies have confirmed that mulberry extracts are effective in anti-cancer, anti-inflammation, and the prevention of cardiometabolic risks [6; 53; 54]. The synergistic effects of DNJ with other mulberry natural products, the underlying mechanisms, and the optimal doses for specific medical aims need to be extensively studied.
## 4 Experimental Results and Discussions
### Comparison with Different Methods  
To validate the superiority of the proposed method, its performance was benchmarked against three prominent deep learning models: PointNet, PointNet++, and DGCNN. All models were evaluated on Dataset 1 using an identical input data format of [x, y, z, intensity]. The comprehensive results, which include both accuracy and computational runtime, are presented in Table 7.  
In terms of segmentation accuracy, the proposed method significantly outperforms the deep learning models across all key metrics, including precision, recall, and F1-score. This enhanced performance can be attributed to the method's ability to effectively capture subtle geometric features through neighborhood analysis and its robust handling of intensity variations, which are crucial for distinguishing leakage from the tunnel lining. Regarding computational efficiency, the proposed method demonstrates a pronounced advantage, particularly in the inference phase. As detailed in Table 6, the test time is orders of magnitude lower: only 0.03 min compared to the 9 to 45 min required by the deep learning architectures. It is also noteworthy that the total training time for the proposed method (2 h 9 m) encompasses both feature extraction and model training. The XGBoost model training itself was remarkably swift, requiring only 9.43 min, as shown in Table 5, with the remainder of the time dedicated to the one-time feature extraction process. This efficiency stems from avoiding the computationally intensive, end-to-end feature learning inherent in deep learning networks.  
These quantitative findings are further supported by the qualitative results illustrated in Figure 9. While all three deep learning models effectively detect leakages, they exhibit certain limitations; PointNet++ is prone to over-segmentation, and both DGCNN and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-score** & **Train Time** & **Test Time** & **Total Time** \\ \hline PointNet & 70.70 & 53.20 & 60.70 & 2 h 14 m & 45 m & 2 h 59 m \\ PointNet++ & 74.62 & 52.95 & 61.94 & 4 h 8 m & 42 m & 4 h 50 m \\ DGCNN & 54.78 & 76.11 & 63.71 & 3 h 50 m & 9 m & 3 h 59 m \\ Ours & 95.56 & 87.17 & 91.18 & 2 h 9 m & 0.03 m & 2 h 9.03 m \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with different methods (%). Note: h represents hour(s). m denotes minute(s).  
PointNet misclassify some tunnel lining points as leakage. In contrast, the proposed method successfully discriminates between leakages and tunnel lining, achieving a more precise segmentation that aligns closely with the ground truth shapes.  
Collectively, these results confirm that the proposed method provides a compelling balance of high accuracy and superior computational efficiency, making it a robust and practical solution for tunnel leakage detection.
## 4 Challenges and Future Directions
### Cost  
The deployment of communication sensors in the underwater environment is a challenging issue. In addition, installation, managing, and maintaining those communication devices are much more complicated processes when compared with terrestrial networks. Moreover, underwater sensors can be easily damaged or lost or become non-functional during the digging process or due to the harsh underwater environments. Therefore, efficient installation of sensors is required, which can reduce the work of re-installation and maintenance. For instance, a communication sensor that demands more energy should be deployed near the surface or near the power source for the ease of management, and also to reduce the cost of deployment. In addition, to avoid the frequent battery failures of the sensors, efficient power-saving protocols can be used for communication.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 4 Active Barriers
### Antioxidants  
For many years, sulphites have been added to wines as a protection for polyphenols during its aging in barrels [48]. sulphites readily react with oxygen to form stable sulphates, inhibiting propagation in the radical chain of oxidative degradation in the wine. This technique is self-explanatory and has been translated to some packaging constructs [134]. sulphites, but also ascorbic acid, \(\alpha\)-tocopherol (vitamins C and E) and phosphites can be applied as part of the packaging material or in the form of a pouch or sticker [15; 135; 136; 137].  
Within this category, vitamin C is the most popular scavenger since adding a food component to the packaging immediately implies food contact compliance. Even though the degradation phenomena involving ascorbic acid are relatively complicated, much research on this subject has been performed [138; 139; 140]. A schematic representation is shown in Figure 9. It is important to note that these reactions are described as they occur in juices. Oxidation as a packaging additive might occur slightly different.  
The degradation of vitamin C can occur both aerobic and anaerobic, but the aerobic oxidation reaction has faster kinetics. It can react with reactive oxygen species (photo-initiated or heat-initiated in packaging processing) via two one-electron exchanges to dehydroroscorbic acid (DHA) with mono-dehydroascorbic acid as the intermediate in an easily reversible reaction. Depending on the radical count in the matrix, the flexibility of the stabilized intermediate radical structure can provide either an antioxidative, quenching other radicals by forming DHA, or a prooxidative nature, abstracting hydrogen from non-radical organics and forming L-AA [141; 142; 143].  
Instead of reverting back to L-AA, DHA can also undergo a hydrolysis reaction, forming 2,3-diketogulonic acid through ring cleavage. The resulting acid exhibits further degradation towards the brown-colored 2-furoic acid and 3-hydroxy-2-pyrone [144; 145]. Under anaerobic and acid conditions, L-AA may also slowly hydrolyze in a ring-cleavage, degrading to furfural, which also incites a brown coloration [138; 139]. In order to accelerate the scavenging, sometimes a transition metal Fenton-catalyst, as explained in Section 2.1, is added to vitamin-containing compositions to increase the oxidation rate. However, this is usually not an effective barrier in comparison to other  
Figure 9: Degradation mechanism of L-Ascorbic Acid in fruit juice [15].  
active barriers [146; 147]. This technology is therefore not often employed and will also not achieve the requirements for the emerging application fields.
## 5 Results and Discussion
### Results for the 33-Node AC MG Operating in Islanded Mode  
Table 7 presents the results obtained after optimizing the three objective functions in the 33-node MG during islanded operation through the coordinated dispatch of active and reactive WT power. The table includes the best solutions achieved by each methodology, along with their average values, standard deviations, and average processing times.
# Superconducting Electronic-Photonic Platform for HEB-Based Terahertz Spectrometers
## 3 Results and Discussion
### Cleanroom Process  
Driven by the results of EM simulations and prototyping, we choose a silicon-on-insulator (SOI) substrate with the following layered structure. The topmost layer is presented by a 27 \(\upmu\)m thick high-resistivity Si. It is implemented on a 1 \(\upmu\)m thick SiO\({}_{2}\) acting as an etch-stopper upon processing of the topmost layer and 300 \(\upmu\)m thick Si handle-wafer.  
The cleanroom fabrication process includes:  
* Deposition and patterning of an NbN microbridge integrated with Ti/Au bias lines on the topmost layer of SOI substrate;
* Fabrication of a hexagonal lattice of openings in the topmost layer by the Bosch process;
* Rear-side removal of the Si handle-wafer and SiO\({}_{2}\).  
A scanning electron microscope (SEM) image of the fabricated HEB mixer is provided in Figure 7. The mixer is based on a 4 nm thick NbN film, which has a critical temperature of 8.7 K with a transition width of 0.7 K and critical current density of 2.5 MA/cm\({}^{2}\). Sheet resistance of the film equals 830 \(\Omega\) at room temperature. The sensitive element of the mixer is presented by an NbN bridge with nominal linear dimensions of 0.2 \(\upmu\)m \(\times\) 2.0 \(\upmu\)m (length \(\times\) width). We observe a mean value of room temperature resistance of 73 \(\Omega\) for the mixers produced. This suggests that the bridge area slightly deviates due to imperfections in e-beam-assisted patterning of NbN film and Ti/Au contacts.  
For the implemented geometry of Si photonic crystals, we measure \(D=18.27\pm 0.47\)\(\upmu\)m. The mean value differs from the nominal of 18 \(\upmu\)m only by 1.5%. The dispersion is most likely due to the rather thick photoresist mask required for selective protection of the topmost layer of SOI substrate in the Bosch process. Specific attention is paid to the exposure parameters to ensure verticality of generatrices in the lattice of openings. The achieved tilt angle is 88-89\({}^{\circ}\). The lattice constants along with the contours of MMIPCs and PCWG integrated with HEB-loaded TSL are almost identical to those defined by EM model of the mixer. We also observe no notable distortion of the developed geometry of DRA arrays upon fabrication.  
Our findings confirm robustness of the developed fabrication process. Thus, we plan to fabricate a series of THz HEB mixers with the proposed design to conduct detailed performance tests in the near future.  
Figure 7: SEM image of a fabricated, nearly all-dielectric 2.7 THz HEB mixer.
## 4 Incorporation of Synthetic Data
### Data Fitting with One Parameter
#### 4.1.2 \(N=2\)  
In Figure 10, we estimate \(b\) from data generated with \(b=0.25\), \(\eta=5\), \(a=1\) in the case of two interacting species. In this case, the algorithm does not converge to the parameter the data were generated with, but converges to a parameter value that has a lower log-likelihood value. The parameter \(b\) is slightly overestimated, which leads to a more aggregated estimated territory in Figure 10c than the genuine territory depicted in Figure 10b. Additionally, the estimated territory has a higher maximum value and is less symmetric than the true territory.  
Figure 8: Iterations of SGD minimizing the negative log-likelihood function to estimate \(\eta\) using data generated with \(M=300\), \(N=1\), \(b=0.25\), \(a=1\), and \(\eta=5\), and the equilibrium solutions to System (2) using the estimated parameter and the parameter used to generate the data. (**a**) Iterations of SGD and corresponding estimated \(\eta\) (**top**) and negative log-likelihood value (**bottom**). (**b**) PDF used to generate data. (**c**) PDF from estimated parameters.  
Figure 9: The histogram displays estimated \(\eta\) values from 60 SGD trials for different \(M\) values, using data generated with \(b=0.25\), \(a=1\), and \(\eta=5\). The average values across simulations are 4.7295, 4.6357, and 4.6667, with corresponding variances of 0.1171, 0.0772, and 0.0462 for each case.  
In Figure 11, we similarly estimate \(\eta\) from data generated with \(\eta=5\), \(b=0.25\), \(a=1\), and \(N=2\). The algorithm underestimates \(\eta\) and converges to a lower log-likelihood value than that of the true parameter value. This underestimation of \(\eta\) is in line with the pattern we saw in Figure 9. It is again worth comparing the territory used to generate the data with the predicted territory. Figure 11c shows the equilibrium solution found using the parameters predicted from SGD, and Figure 11b shows the equilibrium solution used to generate the data. The territory boundaries are similar, with the true territory being more symmetric, more spread out, and having a slightly lower maximum value.