esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Electronic Nose, Electronic Tongue, and Electronic Eye in Sensory Food Sciences
### Electronic Tongue
Integrating multimodal information from E-nose, E-tongue, and E-eye provides a multilayered approach to sensory analysis to minimize cross-reactivity. E-nose sensors measure volatile compounds, while E-eye systems assess color and appearance, complementing the liquid phase measurement of the E-tongue. This modality fusion decreases cross-reactivity among chemically similar compounds and maximizes the specificity of sensory testing [55; 61]. The complexity of real-world samples is addressed through advanced data fusion techniques integrating information from multiple sensors. Hybrid systems combining electrochemical, optical, and spectrometric sensors provide a more comprehensive characterization of food matrices. Chemometric techniques such as PLS regression and SIMCA improve classification accuracy by identifying slight differences in highly complex samples [80; 86]. This facilitates the detection of food authenticity and adulteration with greater precision. Lastly, artificial intelligence (AI) and deep learning algorithms enhance the analysis and interpretation by identifying inherent patterns from large datasets. AI-based predictive modeling results in real-time food and beverage quality evaluation and improves the efficacy and reliability of sensory evaluation tests [31; 42]. By integrating multimodal sensor data and supervised learning approaches, predictive models continuously learn and improve their accuracy, improving stability and robustness to food analysis technologies [44; 45].
## 2 Materials and Methods
### Experimental and Theoretical Validations
#### 2.5.1 DSC Analysis  
DSC analysis was performed at ambient atmosphere using a DSC 131 supplied by Setaram (Caluire, France). Two series of experiments were performed, one with a 1 \({}^{\circ}\)C/min heating ramp for a preliminary estimation and the other one at a rate of 0.3 \({}^{\circ}\)C/min for more accurate transition detections. As for the studied compositions, 8 molar fractions of PA-SA (0/100, 15/85, 30/70, 40/60, 55/45, 70/30, 86/14, and 100/0 in molar percentages of PA/SA) were chosen to assess the best phase diagram possible in the least amount of time (knowing that this procedure can be really time-consuming when accurate results are required). The molar fraction was chosen to depict a coherent phase diagram in a minimal amount of data while providing insightful information regarding detected transitions using the IRT method and providing additional/complementary data in comparison with literature [15]. The phase transitions are generally related to the heat flux evolution as a function of temperature. A peak appears when a specific process, such as melting or crystallization, occurs. Transition temperatures on thermograms are identified according to the guidelines recommended in Reference [17]. The onset temperature is a better indicator of the transition process beginning when a transition peak starts and ends with a flat baseline. In such cases, the transition temperature hence corresponds to the onset temperature on the thermogram. However, multicomponent mixtures are often characterized by the presence of several transitions occurring either successively or almost simultaneously. A reliable onset temperature cannot always be extracted in those multipeak thermograms. Transition temperatures are therefore considered as the peak temperatures in those situations. Additionally, if a clear reading is still impossible, it is recommended, if available, to use complimentary techniques (differential thermal analysis, X-ray powder diffraction, etc.) to enhance the results interpretation.
## 4 Self-Adaptive Multi-Density Prediction Model
### Multi-Kernel Support Vector Machine Forecasting  
Many factors can influence time-series fluctuations; therefore, when predicting time series, the effects of the influencing factors should be fully considered, and a multi-kernel function is used to analyze the influencing factors. The multi-kernel function is a kernel function corresponding to different types of data construction and weights and combines according to the importance and the degree of each kernel function to form a multi-kernel function. As shown in Figure 5, multi-kernel functions can combine multiple types of influencing factors and adjust the weights to form an optimal function. Compared to traditional single-core functions, multi-kernel functions are more robust and accurate.  
Multi-kernel functions have two typical types: linear multi-kernel functions and nonlinear multi-kernel functions. Linear multi-kernel functions can be transformed into quadratic programming problems when solving decision functions. The parameters can be optimized according to the convexity of linear functions. For nonlinear multi-kernel functions, the quadratic programming problem does not have convexity, which makes it difficult to guarantee global stability.  
Each influencing factor sample is represented in the corresponding coordinate system, and the distribution characteristics of the factor are judged according to the sample distribution. According to the characteristics of each factor, we choose the multi-kernel function \(\text{K}_{\text{MKL}(x_{i},x_{j})}\) corresponding to the optimal kernel function \(\text{K}_{(x_{i},x_{j})}\):  
\[K_{MKL}(x_{i},x_{j})=\sum_{n=1}^{m}\lambda_{n}K_{n}(x_{i},x_{j}) \tag{2}\]  
Above, \(\text{K}_{\text{MKL}(x_{i},x_{j})}\) represents the single kernel function, \(\lambda_{n}\) represents the kernel function weight, \(x_{i}\) represents the time point, \(x_{j}\) is the j-th influencing factor's sample value at the time point, and m is the number of influencing factors.
###### Abstract  
Maintaining nitrate-N and orthophosphate (dissolved reactive phosphorus) concentrations in a water sample over time is critical for water quality research. This study investigated the ability of current preservation methods to maintain nitrate-N and orthophosphate concentrations in environmental water samples over a period of up to 14 days. The United States Environmental Protection Agency (US EPA) currently recommends adding sulfuric acid for nitrate-N and filtering the water sample at the time of collection for orthophosphate. This study compared these recommended methods to baseline control concentrations in subsurface tile drainage water, pond water, and stream water. In addition, sodium omadine was tested as a potential alternative preservative. Across the three sources of water tested, sulfuric acid was the most reliable preservation method for both nitrate-N and orthophosphate, with percent changes from the baseline concentrations on Day 14 ranging from 2.30-3.27% for nitrate-N and 1.94-8.48% for orthophosphate. The control samples also maintained the baseline concentrations quite well, with percent changes on Day 14 ranging from 0.09-6.68% for nitrate-N and 2.63-11.87% for orthophosphate. When sodium omadine was added to the water samples, the nitrate-N concentrations had percent changes of 0.13-6.10% from the baseline concentration on Day 14. In contrast, sodium omadine was less effective for maintaining orthophosphate concentrations, with percent changes of 8.86-13.06% from the baseline concentration on Day 14. However, there were no significantly different orthophosphate concentrations when sodium omadine was added to stream water, showing a promising alternative for this scenario. Depending on the objective, researchers may need to consider the source water and length of storage of water samples to determine what, if any, preservative is necessary.  
preservation; tile drainage water; pond water; stream water; field samples; sulfuric acid; sodium omadine +
Footnote †: journal: Applied Soft. 2019
## 4 Lempel-Ziv Compression on a Parallel System
### Parallel Decompression
If \(s_{1},...,s_{m}\) are the partial sums, the target of \(q_{i}\) is the substring over the positions \(s_{i-1}+1\cdots s_{i}\) of the output string. For each \(q_{i}\) which does not correspond to an alphabet character, define \(first(i)=s_{q_{i}-\alpha-1}+1\) and \(last(i)=s_{q_{i}-\alpha}+1\). Since the target of the pointer \(q_{i}\) is the concatenation of the target of the pointer in position \(q_{i}-\alpha\) with the first character of the target of the pointer in position \(q_{i}-\alpha+1\), link the positions \(s_{i-1}+1\cdots s_{i}\) to the positions \(s_{first(i)}\cdots s_{last(i)}\), respectively. As in the sliding dictionary case, if the target of \(q_{i}\) is an alphabet character the corresponding position in the output string is the root of a tree in a forest and all the nodes in a tree correspond to positions of the decoded string where the character is the root. Since the number of children for each node is at most \(\alpha\), in \(\text{O}(k)\) time and \(\text{O}(n/k)\) processors we can store the forest in a doubly linked structure and decode by means of the Euler tour technique [9]. Las Vegas work-optimal parallel decoders for LZ1 and LZ2 compression were presented in [30,31] with a computational complexity independent from the dictionary size and the match length.
## 2 Assessment of the SPWM Angles
### Numerical Evaluation of the Angles  
As can be seen in Figures 4 and 5, the values of \(t_{1}\) and \(t_{1f}\) are within the range \(n_{t}t_{p}<t_{1}<t_{p}\Big{(}n_{t}+\frac{1}{2}\Big{)}\). Therefore, it is possible to define a sampling range named \(dm\).  
\[dm=t_{p}\bigg{(}n_{t}+\frac{1}{2}\bigg{)}-n_{t}t_{p}=\frac{t_{p}}{2} \tag{22}\]  
The solution of Equations (18) and (21) will always be in this sampling range. To find the solution, the sampling range is divided into \(N\)s samples, and Equations (18) and (21) are evaluated for each sample. The value closest to zero will be considered the equation's solution. The greater the samples' \(N\)s, the more accurate the solution to the equation will be. The precision is sufficient for this application with \(N\)s = 10, but it is possible to use greater values.  
The sampling vector for the positive slope is  
\[\mathbf{M}=\bigg{\{}n_{t}t_{p},\ n_{t}t_{p}+\frac{dm}{N_{s}},\ n_{t}t_{p}+ \frac{2 dm}{N_{s}},\ \ldots,n_{t}t_{p}+\frac{N_{s}dm}{N_{s}}\bigg{\}} \tag{23}\]  
The sampling vector for the negative slope is  
\[\mathbf{M^{\prime}}=\bigg{\{}t_{p}\bigg{(}n_{t}+\frac{1}{2}\bigg{)},\ t_{p} \bigg{(}n_{t}+\frac{1}{2}\bigg{)}+\frac{dm}{N_{s}},\ t_{p}\bigg{(}n_{t}+\frac{1}{2 }\bigg{)}+\frac{2 dm}{N_{s}},\ \ldots,t_{p}\bigg{(}n_{t}+\frac{1}{2}\bigg{)}+ \frac{N_{s}dm}{N_{s}}\bigg{\}} \tag{24}\]  
Once the times \(t_{1}\) and \(t_{1f}\) are obtained for the first pulse, the angles can be calculated with the following expressions:  
\[\beta_{1}=\omega t_{1} \tag{25}\]  
\[\beta_{1f}=\omega t_{1f} \tag{26}\]  
\[\alpha_{1}=\beta_{1f}-\beta_{1} \tag{27}\]  
where \(\omega\) is the angular frequency and \(\alpha_{1}\) is the width of the first pulse, which, as seen in the equation, is the subtraction of \(\beta_{1f}\) minus \(\beta_{1}\). However, this process must be repeated for each triangular pulse signal.  
The complete algorithm for angle calculation is detailed in the flowchart of Figure 6.  
Figure 5: Comparison for downstream angle (\(\beta_{1f}\)).  
In this flowchart, mu represents one of the vector's \(\mathbf{M}\) and \(\mathbf{M^{\prime}}\) components. The process is as follows: first, the specifications are defined, and the main variables are calculated after the first angle of the negative slope line is evaluated (\(\beta_{1}\)). For this, the times (\(t_{n-}\)) for each component of vector \(\mathbf{M^{\prime}}\) are obtained, and Equation (18) is an assessment for each one, generating the error vector (\(\mathbf{e_{n-}}\)). Then, the minimum value of vector \(\mathbf{e_{n-}}\) is obtained (\(e_{min}\)) after the value of time (\(t_{1-}\)) corresponding to this \(e_{min}\) is obtained with the corresponding angle value \(\beta_{1}\). Subsequently, the same process is undertaken to assess the final angle of the first pulse (\(\beta_{1f}\)), resolving Equation (21) using the solutions vector \(\mathbf{M}\). The process continues for each pulse until the pulse \(Na\). The algorithm assesses the pulse for only one quarter of the wave, since the rest of the angles are evaluated for symmetry.
## 3 Description of the Method
### Definition of the Keel Curve  
The keel curve of the planing hull is shown in Figure 5. This curve can be divided into three segments based on different longitudinal features, with \(C^{1}\) continuity maintained at the joint points \(\textbf{P}_{k1}\) and \(\textbf{P}_{k3}\). The first segment (i.e., the bottom straight section) is located near the stern of the hull. Some boat designs employ a certain longitudinal incline in this segment to facilitate the arrangement of propulsion devices, which can be constructed using straight-line segments based on the endpoints. The second segment (i.e., bow curved lift section) of the keel curve is located in the bow's planing surface lift section. It starts at point \(\textbf{P}_{k1}\), passes through point \(\textbf{P}_{k2}\), and ends at point \(\textbf{P}_{k3}\), exhibiting relatively high curvature. The final segment (i.e., the bow outward section) is positioned at the stem of the ship and shows the tangential continuity at \(\textbf{P}_{k3}\) with the second segment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Name** & **Description** \\ \hline \multirow{6}{*}{Length} & \(L\) & Hull length \\  & \(L_{c}\) & Length along the chine curve \\  & \(L_{0}\) & Length of the straight line of the keel \\  & \(L_{sm}\) & Abscissa of the maximum width along the \\  & \(x_{Ac}\) & Theer curve \\  & \(x_{Ac}\) & Centroid abscissa of the horizontal projection \\  & & of the planing surface \\ \hline \multirow{6}{*}{Height} & \(h_{t}\) & Height of the keel curve at the stern \\  & \(H_{c}\) & Height of the chine curve \\  & \(H_{f}\) & Height of the bow \\  & \(H_{t}\) & Height of the stern \\  & \(B_{t}\) & Width of the sheer curve at the stern \\  & \(B_{sm}\) & Maximum width of the sheer curve \\  & \(B_{c0}\) & Width of the inner chine at the stern \\  & \(B_{cm}\) & Maximum width of the sheer curve \\ \hline \multirow{6}{*}{Angle} & \(\alpha_{k}\) & Angle at the foremost point of the keel in \\  & \(\beta_{s}\) & Angle at the foremost point of the sheer in \\  & \(\beta_{c}\) & Angle at the foremost point of the inner chine \\  & \(\beta_{c}\) & in plan view \\ \hline Area & \(A\) & Area of planing surface in plan view \\ \hline \multirow{3}{*}{Shape Parameters} & \(\lambda_{k1}\), \(\lambda_{k2}\) & Shape coefficients of keel curve in lateral view \\  & \(\lambda_{s1}\), \(\lambda_{s2}\) & Shape coefficients of sheer curve in lateral view \\  & \(\lambda_{c}\), \(\lambda_{f}\) & Shape coefficients of upper surface stations \\ \hline \multirow{6}{*}{Longitudinal Function} & \(\theta(x)\) & Longitudinal function of deadrise angle \\  & \(\gamma_{k}(x)\), \(\gamma_{c}(x)\) & Longitudinal function of tangent angle at the \\  & \(\gamma_{k}(x)\), \(\gamma_{c}(x)\) & endpoints of station curves of the planing \\ \cline{1-1}  & \(k_{s}(x)\), \(k_{m}(x)\) & Longitudinal function of shape coefficients of \\ \cline{1-1}  & \(W_{r}(x)\) & Longitudinal function of the width of the spray rail \\ \hline \end{tabular}
\end{table}
Table 2: Design parameters.
## 3 Results
### Health Indices of Blackcurrant, Cranberry and Strawberry Seed Oils  
Taking into account results obtained for cranberry, black currant and strawberry seed oils concerning health indices, such as the atherogenicity index (IA), thrombogenicity index (IT) and hypocholesterolem/hypercholesterol ratio (HH) (Table 1), it can be stated that the studied oils are of favorable nutritional and health values. The lowest atherogenic index was observed for strawberry seed oil (IA = 0.057), whereas the highest value of IA was observed for black currant seed oil (IA = 0.098). Considering the thrombogenicity index, the lowest value was found for strawberry seed oil and the highest value for black currant seed oil. In the case of strawberry and cranberry seed oils, IA and IT values were close, indicating a low incidence of coronary heart disease and beneficial health effects. Strawberry seed oil was also characterized by the highest value of HH index. Black currant seed oil showed a higher IA and IT compared to other studied oils and the lowest value of HH index, which can result in increased risk of cardiovascular disease. Importantly, both IA and IT are proven valuable and well-known indices that can be successfully applied to assess the potential impact of the fatty acid profile of fat on the risk of incidence of cardiovascular disease and the occurrence of blood cloths, atheroma and thrombus. Oils with lower IA and IT values are thought to possess health benefits and minimize the risk of cardiovascular diseases [30; 31]. Fats with IA values below 1.0 and IT values below 0.5 are particularly recommended for human consumption. In general, owing to the low IA value, all categories of nut oils have beneficial health effects. In the nut and seed product category, the highest atherogenic index was found for cocoa butter (IA = 0.67). Despite having polyphenic compounds, cocoa butter lipid contains approximately 60% SFA, 35% MUFA, and a very low amount of PUFA (below 5%) (IA = 0.67, IT = 3.09), showing an increased risk of cardiovascular diseases [32].  
Figure 4: Melting curves of cranberry (blue), black currant (black) and strawberry (red) seed oils.
## 1 Introduction
### Mobile Networks, Unmanned Aerial Vehicles and Their Synergies  
As far as mobile network technologies are concerned, the need for solutions oriented towards acquiring data that can be used by the owners of the facilities and equipment used to offer mobile communications is of major importance. Data are required to have a clear view of how powerful network signals (3G, 4G and others) are in a specific area, location or even large structure (for example, a commercial aircraft). Depending on signal power figures, it might be required to redesign the future deployment of the communication infrastructure. Typically, measurements are taken by a team of trained staff (technicians, engineers) tasked with obtaining ground data regarding signal strength in different coordinates in an area. This team is deployed in the location of interest and, by using their equipment, they can obtain the measurements that are being sought. While such a procedure ensures that some information will be retrieved, it is flawed due to several reasons: (a) the area that can be covered by a team of people is restricted in terms of size and time; (b) the costs of such a procedure tend to be rather large; (c) there are multiple zones that, due to the nature of the equipment used, cannot be covered or are done so in a minimal way. Overall, although making use of trained staff can provide a certain quantity of measures, in the end, it cannot offer the same level of data quantity and quality that a free flight throughout a three-dimensional volume could offer, as the team deployed for such a purpose is unlikely to reach every point of a 3D space, or if they manage to do so, it will require a vast amount of resources in terms of time and budget. Our proposal puts forward an improvement, which is explained in the very next section.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Boundary Segmentation
#### 2.2.2 Region-Based Methods  
In order to improve the flexibility of source image segmentation, the region-based image fusion method came into being. The zone-based approach is similar to the block-based approach. The main difference is that the activity level is measured in each irregularly sized segmented area rather than a block. [PERSON] et al. [48] initially proposed a region-based  
Figure 7: Multi-focus image fusion framework based on adaptive region segmentation [19]. Reprinted/adapted with permission from Ref. [19]. 2019, WSPC.  
multi-focus image fusion method. [PERSON] et al. [21] proposed a multi-focus image fusion method based on content adaptive fuzzy (CAB), in which the absolute difference between the original image and the CAB blurred image was used to generate the initial segmentation graph, and morphological operators and graph cutting techniques were used to improve the segmentation accuracy. [PERSON] et al. [49] proposed an adaptive initialization method for image depth estimation. The image depth was approximated by the iterative solution of the partial differential equation. The target image was adaptively divided into three regions: clear region, fuzzy region, and transition region. Finally, the multi-focus image fusion was realized by extracting the pixels of the clear region and fusing the pixels of the transition region.
# Selective Control by _Pistacia vera_ L. and Its Carotenoid Zaxanthin on SARS-CoV-2 Virus
## 2 Results
### Inhibition of SARS-CoV-2 Variants by Zexanathin  
Based on previous data obtained in our laboratory demonstrating that zexanathin, a component found in both NRRE and RURE, exhibited antiviral activity against HSV-1 by blocking its entry [19], we investigated whether zexanathin could exert a similar inhibitory effect on SARS-CoV-2 by preventing its binding to host cells. To this end, first, we tested zexanathin cytotoxicity on A549 cells to determine the CC\({}_{50}\) value (Figure S3, Table S1). Then, we pretreated pseudotype variants with zexanathin for 1 h at 4 \({}^{\circ}\)C, a temperature that typically prevents fusion/entry but is not binding. After pretreatment, we transduced A549 cells with both pseudotype variants for 2 h. This procedure is graphically illustrated in Figure 2a. The data obtained revealed that zexanathin significantly inhibited the binding of the o-SARS-CoV-2 but not the \(\alpha\)-variant (Figure 2, panel b vs. c). This different inhibition highlights the specificity of zexanathin's interaction with SARS-CoV-2 variants and may be attributed to structural differences in the receptor-binding domain (RBD) of these variants. In silico analyses were carried out to elucidate the mechanism underlying this different activity (Figure 3).