esa-sceva/satcom-chunk-collection huggingface dataset:
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 2 The UV Process  
UV treatment kills bacteria by introducing energy to the surface of the food or into the liquid medium [9], which is capable of affecting microorganism DNA, altering metabolism and replication, and ultimately causing cell death [1]. When UV energy is incorporated into liquid media its germicidal energy is capable of penetrating liquids, however, the strength of UV light decreases as a result of attenuation and dissipation [9].  
The photochemical reactions of microorganism biomolecules primarily have a germicidal effect, inhibiting microbial growth or inactivating the cell. An anti-microbial impact of Ultraviolet radiation is caused by the cross-linkage in just the same target DNA seen between the foundation of neighboring dimer pyrimidine. All this leads to replication and transcription of nucleic acids, which is referred to as clonogenic death. In certain cases, depending on the species of the microorganism, the metabolic rate can cure DNA damage by photoreactivation or darkreactivation. However, due to broader damage, the healing becomes impossible at significant UV dosages [10].  
UV dose, which refers to UV or UV intensity stream and is characterized by the intensity and exposure duration. It is primarily responsible for the germicidal effects of UV radiation and is defined by the following equation:  
\[\text{D}=\text{I}\times\text{t} \tag{1}\]  
where;  
\[\text{D}\left(\text{J}/\text{m}^{2}\right)=\text{UV}\text{'dose'}\text{ or the sum of UV energy applied to a particular surface during a unit of time, I (W/m\({}^{2}\)) = UV intensity measured at the surface, and t (s) = exposure time [1]  
The distance between the UV light and the packaging affects the strength of the radiation. For example, in the case of yogurt filling, the studies have shown that 150-mm-depth cups can be disinfected during 4 s and sealed foils in 2 s under the same pressure. For this reason, the UV disinfection would be better in the packaging of acidic fresh milk products like yogurt and kefir that are held in the cool chain to extend their shelf life. This ensures the dairy industry would receive far fewer spoiled product complaints, saving time, efforts, and money in the process [24].  
UVC radiations can be used after disinfection with low-percentage hydrogen peroxide (1-3%) to effectively inactivate even the most UVC-resistant mold fungi. This process includes two mechanisms: UVC radiation inactivates bacteria that are resistant to hydrogen peroxide, such as _Bacillus subtilis_ spores, while hydrogen peroxide kills microorganismsthat need a high UVC dose, such as _Aspergillus niger_ spores. This results in broad and effective germicidal effect and the necessary microorganism reduction rate of 4 log, \"Ultraclean,\" [25]. The germ load reduction was reported at the level up to 99.99% [25].  
Another study conducted in the cheese processing industry showed that UV treatment resulted in a 5 log (99.999%) reduction of bacteriophages and a 3 log (99.9%) reduction of thermophilic bacteria [26].
## 3 Results
### Comparison of E-EFNet Performance with Baseline  
The effectiveness of the proposed model is assessed using accuracy, TPR, and FNR metrics, as per the evaluation performed by [28], for both FGG and YR. In FGG, the proposed method outperformed other state-of-the-art models, as depicted in Table 2. Table 2 presents a performance comparison of the proposed E-EFNet model with other state-of-the-art models on the FGG dataset. The evaluation metrics used in this comparison were false negative rate (FNR), accuracy, and false positive rate (FPR). The results show that the proposed E-EFNet model achieved a very low FNR of 0.22%, the second-best performance after the DFAN model with an FNR of 0.58%. E-EFNet also achieved the highest accuracy, 99.91%, among all the compared models, demonstrating its high effectiveness in accurately detecting fire in images.  
Figure 4: Visual results of the E-EFNet model over the YR dataset: (**a**) represents the fire images accurately classified by the proposed model, whereas (**b**) shows the normal images.  
Figure 3: Visual results of E-EFNet over FGG dataset: (**a**) images with fire (**b**) images without fire.  
Moreover, the FPR for E-EFNet was 0%, the lowest among all the compared models, indicating that the model has a shallow rate of falsely identifying non-fire objects as fire. Among the other models, several achieved high performance, including EMNFire, LWCNN, DFAN, and SE-CANet, with FNRs below 1% and accuracy above 95%. However, some models, such as CANetB0 and CNNFire, had relatively low accuracy and high FPRs, indicating that they may not be as effective in detecting fire in images. Overall, the results in Table 2 suggest that the proposed E-EFNet model outperforms most of the state-of-the-art models on the FGG dataset in terms of FNR, accuracy, and FPR and is highly effective in detecting fire.  
Table 3 presents a performance comparison of the proposed E-EFNet model with other state-of-the-art models on the YR dataset. The evaluation metrics used in this comparison are F1 score, precision, recall, and accuracy. The results show that E-EFNet achieved better performance compared to other models, including ResNetFire [25], LW [3], EFDNet [22], and DFAN [2], in terms of all evaluation metrics. Specifically, our model obtains a precision of 98.82%, recall of 98.70%, F1 score of 98.74%, and accuracy of 98.75%. These results show the higher effectiveness of the proposed model in accurately detecting fire in images, which is a critical requirement for fire safety and prevention. Compared to other models, EFDNet obtained the second-best performance, with precision, recall, F1 score, and accuracy of 94.11%, 96.00%, 95.00%, and 95.00%, respectively. LW and DFAN models achieved comparable results but slightly lower accuracy and F1 scores than EFDNet. The ResNetFire obtained the most inadequate performance, with precision, recall, F1 score, and accuracy of 88.00%, 86.00%, 86.00%, and 86.67%, respectively. Overall, the results in Table 3 suggest that the proposed E-EFNet model is highly effective in detecting fire and outperforms the existing models on the YR dataset.
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 3 Experiments and Analysis
### Comparative Simulation Test of Calculation Efficiency of Different PnP Methods  
The above simulation tests demonstrated that the solution accuracy error of the Hidden PnP algorithm was smaller than that of existing excellent PnP algorithms in Ordinary 3D, Planar Case, and Quasi-Singular cases. The Hidden PnP outperformed in anti-noise ability, which, however, was not enough. As mentioned in the introduction, an excellent PnP algorithm should perform not only with stability, accuracy, and universality, etc., but also with high computational efficiency. This section compares the computational efficiency of algorithms, including LHM, EPnP+GN, RPnP, DLS, OPnP, SRPnP, RDLT, and ASPnP, with Hidden PnP. In this simulation experiment, the pixel value \(\delta\) of zero-mean Gaussian noise was fixed to be 2, and the number of spatial reference points n in the PnP algorithms involved increased from 4 to 500. Each PnP algorithm was independently tested 1000 times with different numbers of spatial reference points, and the average running time calculated. As shown in Figure 9 and Table 2, the simulation results here were the same as those mentioned in the introduction. The main factor affecting the efficiency of the LHM algorithm was the iterative solution, the duration of which accelerated with increase of spatial redundant point quantity. The DLS algorithm transformed the pose estimation problem into an unconstrained least square minimization problem through the construction of a cost function, which failed to avoid increase in its computational time with more spatial redundant points. The computation time of the ASPnP and OPnP algorithms did not increase much with the climbing spatial reference points. However, the Grobner technique required the construction of a large elimination template matrix, which was the main burden that affected the operational efficiency of these algorithms. It is worth noting that the phased solution of the RPnP algorithm had the fastest speed among all algorithms when the spatial reference point was n \(\leq\) 200. Nevertheless, with the increase in the number of points, the computational time gradually increased. The SRPnP, as an improved version, also had the same problem. The RDLT algorithm adopted the traditional linear solution, considering the constraints between points, the efficiency of which was better than that of the ASPnP algorithm when the spatial reference point was n \(\leq\) 100. To sum up, the runtime simulation results from the various algorithms showed that, at 500 spatial reference points, the hidden variable-based PnP algorithm had a computational efficiency 1.5 to 7 times higher than those of the other algorithms that are considered excellent. Figure 9: Comparison of the average running time of the PnP algorithm when the number of spatial reference points n ranged from 4 to 500.
# Utilizing Energy Storage for Operational Adequacy of Wind-Integrated Bulk Power Systems
## 4 Application of the Proposed Operational Adequacy Framework
### Operational Adequacy Assessment of Wind and Energy Storage Integrated Systems  
The operating adequacy of the three system configurations measured using the load-point and system LOLP indices are shown in Figure 8. Since the risks associated with the N-1 criterion have been widely accepted in conventional system operation, the values obtained for Configuration 1 can be used as acceptable benchmarks. The system LOLP of 0.00133 shown by the red horizontal line in Figure 8 is, therefore, used as a benchmark in this study to compare against other system configurations. It can be seen that the risk is not uniform among load points for a system configuration. The LOLP at Bus 5 is higher than at other buses. This can be attributed to its higher dependence upon the transmission lines compared to the rest of the load points that are located close to generating sources.  
Figure 8 shows a large increase in operating adequacy risks in Configuration 2 due to the replacement of a conventional unit by a wind farm. The system LOLP of 0.00828 is more than 5 times the acceptable risk of the benchmark value. The integration of wind resources is expected to bring environmental and economic benefits by offsetting the conventional fuel-based generation, thereby reducing the carbon emissions and operational costs. However, it needs to be ensured that the system risk does not exceed the acceptable limits. The operating adequacy risk can, however, be reduced by either raising the operating reserve requirement or by limiting the wind generation in the unit commitment. Both solutions, however, contradict the prime objectives of integrating wind resources into the power system. An alternate and promising solution is to use ESS to absolve the uncertainty and reduce the risks associated with wind generation.  
Figure 8 shows that the operating adequacy risks are significantly lowered with the use of ESS in Configuration 3. The system LOLP of 0.00120 is about 10% lower than the benchmark value of 0.00133. The improvement in operational adequacy results from the energy storage support the wind generation as well as its ability to mitigate the loss of load events arising from the generating unit failures. This demonstrates the value of energy storage for wind-integrated power systems as they can serve power systems to achieve the objective of reliable, affordable, and clean power delivery to customers. It must be noted that the contribution of ESS depends on its operating strategy, and they could be subjected to different operating strategies depending on the objectives of their utilization.
# In Vitro Gene Expression Responses of Bovine Rumen Epithelial Cells to Different pH Stresses
## 4 Discussion
### Integrated Analysis of the Transcriptome and Metabolome  
The mechanisms underlying metabolite changes in rumen acidosis and the associated genetic mechanisms provide insight into the association between genes and the regulation of the expression of important metabolites [62; 63]. In this study, further correlation analysis revealed that several metabolic pathways were specifically enriched in the SARA and ARA groups, illustrating the changes in metabolism associated with the pH stress response of BRECs. In control vs. SARA, FMO3 (flavin containing dimethylaniline monooxygenase 3) was significantly downregulated and MAOA (monoamine oxidase A) was upregulated in drug metabolism-cytochrome P450. FMO3 activity is similar to that of the CYP family and is an important process for the oxidation of metabolites in many cases [64]. For choline metabolism in the cancer pathway, SOS Ras/Rho guanine nucleotide exchange Factor 2 (SOS2) was significantly upregulated, and receptor-induced membrane recruitment of the cytoplasmic protein SOS led to Ras/MAPK cascade activation [65], which may be one of the ways that BRECs activate the MAPK pathway in the face of low-pH stress. In addition, pH = 5.5 also causes changes in D-glutamate metabolism in rumen epithelial cells. Glutamate is essential for cellular physiology. The catabolism and anabolism of other amino acids are both related to glutamate metabolism [66], and the TCA cycle metabolite \(\alpha\)-ketoglutarate is produced when glutamate is deaminated. This is an essential anaperiodic pathway [67].  
In the control vs. ARA comparison, the correlation analysis results showed that CYP1A1 was positively correlated with most differential metabolites and negatively correlated with differential genes, which suggests that there is a regulatory relationship among genes and metabolites. It has been reported that downregulation of CYP1A1 mRNA can alleviate the inflammatory damage of epithelial cells, accompanied by the NF-\(\kappa\)B signaling pathway activation [68], which is consistent with the results of this experimental study. However, its specific regulatory mechanism needs to be further studied. DEGs and differential metabolites were significantly enriched in lipid and carbohydrate metabolic pathways in the control vs. ARA comparison. In particular, pH 4.5 caused significant changes in lipid metabolism and alpha-linolenic acid (ALA) was significantly lower than that of the control group. It is worth noting that FADS mRNA decreased significantly, and was enriched in alpha-linolenic acid metabolism and the PPAR signaling pathway. FADS2 is a key enzyme in the polyunsaturated fatty acid (PUFA) metabolism of alpha-linolenic acid (ALA) into eicosapentaenoic acid (EPA) [69]. It has been reported that FADS2 is also significantly correlated with inflammation, which needs further research [70].
###### Abstract  
This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.  
Keywords:computer visions; human action recognition; view-invariant feature descriptor; classification; support vector machines +
Footnote †: journal: Applied
# Mobile Robot Path Planning Algorithm Based on NSGA-II
## 2 Global Path Planning
### Selection Operators  
Selection operations are used to determine how individuals are chosen from the parent population according to a certain method, followed by crossover operations to generate new individuals. When designing selection operators, it is necessary to ensure that excellent individuals are retained, while also maintaining diversity and randomness among individuals. Therefore, this paper proposes a probability-based selection strategy. Initially, the probability of each individual being chosen is calculated based on its non-dominated rank and crowding distance. A higher probability of being chosen indicates a greater likelihood of the individual being an optimal solution, whereas a lower probability of being chosen suggests a smaller likelihood of the individual being an optimal solution.  
When selecting individuals based on probability, it is necessary to calculate the probability of each individual being chosen. Let us denote the probability of each individual being selected as \(P_{j}\), where \(j=1,2,\ldots,M\) and \(M\) represents the maximum number of individuals in the population. The probability of each individual being chosen consists of two parts: one is calculated based on the crowding distance, denoted \(P_{d}\), and the other is calculated based on the non-dominated rank, denoted \(P_{r}\).  
When computing the probability based on the crowding distance, the crowding distance values of individuals are normalized to obtain the probability \(P_{d}\) calculated based on crowding distance, as follows:  
\[P_{d}(j)=\frac{d(j)}{\sum_{k=1}^{M}d(k)},j=1,2,\ldots,M \tag{7}\]  
where \(d(j)\) represents the crowding distance value of an individual \(j\).  
The probability \(P_{r}\) calculated based on non-dominated rank is  
\[P_{r}(j)=\frac{\lambda^{r_{j}-1}}{(r_{j}-1)!}e^{-\lambda},j=1,2,\ldots,M \tag{8}\]  
where \(r_{j}\) is the non-dominated rank of an individual \(j\), and \(\lambda\) is a calculation parameter.  
Therefore, the probability of an individual being chosen is given by  
\[P_{j}=P_{d}(j)\cdot P_{r}(j), \tag{9}\]  
Based on the probabilities of individual selection, the entire population is divided into two sets: one suitable for entering the offspring population, and the other not suitable for entering the offspring population. Proportionally, individuals are selected from both sets. Randomly selecting a few individuals with lower selection probabilities can enhance the population diversity. When the algorithm is trapped in local optima, this provides  
Figure 2: Path angle.  
opportunities for the algorithm to escape local optima and find better solutions, thus improving the algorithm's ability to escape local optima.
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.4 Propagation Model  
Researchers should not consider the propagation model as a free space in VANET because this network includes buildings, trees, and other vehicles that can play the role of obstacles. Also, researchers must regard the interference of wireless connections related to other vehicles or personal access points when selecting the propagation model in VANET [30; 34].
# Two-Channel OTDM System for Data-Center Interconnects: A Review
## 2 Two-Channel OTDM System with Constant-Phase Pulse  
### System Architecture  
An optical pulse with a repetition rate of \(B\) is necessary to generate a two-channel OTDM signal with a baud rate of \(2B\); the pulse width need not be narrow. A sinusoidally modulated optical pulse with a repetition rate of \(B\) can be generated using a Mach-Zehnder modulator (MZM) (biased at the quadrature point) with an electrical clock at a frequency of \(B\), as illustrated in Figure 1. The pulse is divided into two parts using an optical coupler, with each part modulated by an electrical PAM signal to generate a return-to-zero (RZ) PAM signal. One signal is delayed by \(1/(2B)\) to realize time-multiplexing in the optical domain, and the other signal is passed through a domain shifter (DS) with a quarter-wave plate or a phase shifter to avoid beat components during multiplexing. The generated OTDM signal with a baud rate of \(2B\) is amplified by a semiconductor optical amplifier (SOA), detected using a single PD, demultiplexed in the electrical domain, and equalized using a \(2\times 2\) MIMO equalizer.
## 2 Sample Preparation Methods
### Solid-Phase Extraction
#### 2.1.1 Dispersive Solid-Phase Extraction  
Dispersive SPE (DSPE) is based on the SPE methodology, but the sorbent is directly added to the sample without conditioning the clean-up, being easily carried out by shaking and centrifugation. After that, the sorbent with the analytes is washed with an appropriate solvent to recover analytes [111]. Because the contact surface between the sorbent and sample is higher, the extraction equilibrium is more quickly achieved, reducing the extraction time. The adsorbents do not need to be packed into the SPE cartridges and the problems of column blocking and high pressure often encountered in SPE are overcome. Furthermore, it is a more environmentally friendly method than standard solid-phase extraction, as the amount of sorbent and volume of solvent are lower. However, the crucial centrifugation/filtering step is difficult to automatize and become a critical hindrance in environmental analysis due to the high number of samples [78; 112]. A noteworthy improvement of DSPE arises with magnetic solid-phase extraction (MSPE), where the centrifugation step is not necessary, in order to overcome this drawback.  
The DSPE was applied to the analysis of antibiotics in mineral waters [113] and wastewaters [111], and natural hormones in river waters [111]. In the last study, a new organic adsorbent was prepared by the electrospinning method with polyacrylonitrile (PAN) and activated carbon. The activated carbon decorated PAN nanofibers showed LOQs that were between 0.53-2.17 \(\upmu\)g/L. These sorbents can reusable, with satisfactory recoveries for more than ten uses.