esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Generalized Procrustes Analysis
These two compounds could be assumed as marker of beers made with Solina wheat as confirmed by PLS-DA computed by [PERSON] et al. on the same beer samples [16]. As regards Vittorio wheat, several VOCs found in kernels were positively correlated to the first component, of which mainly aldehydes, alcohols, ketones, and alkanes, suggesting a more complex aromatic profile of this variety, whereas only Ethyl-9-decenoate distinguished Vittorio beers. Considering beer odor types, Solina was characterized by two molecules with floral notes (\(\beta\)-Linalool and 2-Phenethylacetate) while Vittorio was characterized by a molecule with a fruity note (Ethyl-9-decenoate). Within each wheat variety, a second classification may be observed on the basis of altitude of wheat cultivation, again along the first component. To better visualize the discrimination due to farm origin, a regression was performed using altitude as an independent variable (\(X\)) and the GPA scores on the first component as variable to predict (\(Y\)) for Solina and Vittorio wheat separately. After a first explorative linear regression, a second order polynomial equation was chosen as a mathematical model on the basis of the observation of the residuals distribution in the residuals plot. The regression results, shown  
Figure 4: GPA plot of centroid coordinates by using GC-MS datasets, along the first and second factors. Score nomenclature: (V) Vittorio wheat; (S) Solina wheat; F1: 70 m a.s.l. ; F2: 500 m a.s.l. ; F3: 1200 m a.s.l. The ellipses indicate the 95% confidence limit. in Figure 5, highlighted a good fit with a R\({}^{2}\) of 0.94 and 0.83 for Vittorio and Solina wheat, respectively. Vittorio scores were fitted better than Solina as confirmed by the CVRMSE (22% and 40%, respectively) and AIC (13.5 and 20.7, respectively) indexes. By observing the confidence intervals (Figure 5), it is possible to affirm that the regression model permitted to separate without any overlapping the scores of the first consensus component (between wheat and beers) on the basis of variety and altitude of wheat cultivation. This result suggests that differences in wheat volatiles induced by wheat origin (variety and cultivation site) are reflected in the beers' volatiles profiles.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 7 Conclusions  
In this paper, an approach for improving the alternating projection method by combining the alternating projections in two equivalent norms was proposed and justified. The approach in general form was considered for the case of projections to linear subspaces (Theorem 2). Then, this approach was applied to the problem of Hankel low-rank approximation, which is considered for the estimation of low-rank signals and is not linear. The theoretical results were obtained using the linearization (13). Numerical experiments confirmed that the linearization provides the correct conclusions for the convergence of the mixed Cadzow iterations (6). Using the theory of principal angles, it was shown why improving the estimation accuracy leads to increasing the computational cost of the conventional Cadzow iterations (Section 4.4). Using the features of the projector compositions, it was shown how the signal estimation can be improved by a combination of iterations with different norms (Theorem 6).  
Figure 4: RMSE depending on \(T_{1}+T_{2}\). The signal rank 2 and the target rank 4.  
Figure 5: RMSE depending on \(T_{1}+T_{2}\). The signal rank 6 and the target rank 6.  
As a result, the algorithm, which modifies the Cadzow weighted iterations by combining a good accuracy of slow iterations and a fast convergence of inaccurate iterations, was proposed.  
The numerical example in Section 5.2 demonstrates the effectiveness of the elaborated approach. The examples are implemented in R using the R-package rhlra, which can be found at [[https://github.com/furiousbean/rhlra](https://github.com/furiousbean/rhlra)]([https://github.com/furiousbean/rhlra](https://github.com/furiousbean/rhlra)) (accessed on 13 October 2022); see the R-code of the examples in Supplementary Materials.  
The R-code for producing the numerical results that are presented in the paper can be downloaded at [[https://www.mdpi.com/article/10.3390/a15120460/s1](https://www.mdpi.com/article/10.3390/a15120460/s1)]([https://www.mdpi.com/article/10.3390/a15120460/s1](https://www.mdpi.com/article/10.3390/a15120460/s1)).  
Conceptualization, N.Z. and N.G.; methodology, N.G.; software, N.Z.; validation, N.Z. and N.G.; formal analysis, N.Z. and N.G.; investigation, N.Z. and N.G.; resources, N.G.; writing--original draft preparation, N.Z. and N.G.; writing--review and editing, N.Z. and N.G.; visualization, N.Z.; supervision, N.G.; project administration, N.G.; funding acquisition, N.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by RFBR, project number 20-01-00067.  
Not applicable.  
We thank the two anonymous reviewers whose comments and suggestions helped to improve and clarify this manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SLRA & Structured Low-Rank Approximation \\ (G)LRR & (Generalized) Linear Recurrence Relation \\ RMSE & Root Mean Square Error \\ \end{tabular}
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 2 Comparative Study of BPv7 and IPv6 Functionalities
### Feature Analysis
#### 2.3.7 Store and Forward  
IPv6 lacks an inherent store-and-forward capability at the network layer, limiting its performance in scenarios with intermittent connectivity. In contrast, BPv7 integrates store and forward as a core feature, enabling nodes to store data until transmission is feasible. To adapt IPv6 for DTN scenarios, developers must consider embedding store-and-forward functions directly at the network layer, allowing dynamic re-routing and queuing similar to BPv7's approach. This, in turn, requires the addition of storage capacity as another resource to manage in store-and-forward nodes. Still, long-term storage space is not a new issue, as current intermittently connected wireless sensor and LEO networks already deal with it in vendor-proprietary and mission-specific ways. Moreover, a storage management strategy must be designed based on factors such as data priority, expiration time, etc. This will improve the utilization rate of storage resources.
## 4 The Global SAC and Grid Systems  
From the discussion above, the SACs are defined only in a portion of the sphere (inside the reference triangle). In other words, they are local coordinates. In this section, we introduce the extension of the SACs to the entire sphere. The first step is to partition the globe into several cells (the initial global grid), each consisting of the same regular spherical triangle. Further, the cells constitute a complete tiling of the globe, exhaustively covering the globe without overlapping. This criterion ensures that the sum of cell areas equals the surface area of the globe and that the coordinates of all shared cell edges are identical. Since cells of the initial global grid are spherical triangles, we can treat them as reference triangles and establish local SACs for each cell. When putting these patches together, we finally create a global coordinate system over the sphere. We expect that the initial global grid is regular. Since we require that the grid cells be a triangular cell, we now have only three options: the spherical tetrahedron, octahedron, and icosahedron versions. After the global SAC system has been developed, the coordinate grid naturally spans the globe.
## 2 Material and Methods
### Methodology of the Proposed Prediction Model
#### 2.2.2 Feature Selection Based on IDPSO
To address the problem of finding the local solution, we incorporated dynamic control parameters in our proposed IDPSO algorithm. Specifically, we updated the setting parameters such as _PM_, \(c_{1},c_{2}\) and \(c_{3}\) after a certain number of iterations, as described in Equation (11). \[If\;rand\leq p_{mwc}:V_{ij}^{(t+1)}=wv_{ij}^{(t)}+c_{1}r_{1}\times(y_{ij}(t)-x_ {ij}(t))+c_{2}r_{2}\times(\dot{g}_{j}(t)-x_{ij}(t))+c_{3}r_{3}(x_{ij}(t)- \widetilde{Y}_{i}(t)) \tag{10}\]  
Figure 2: The movements of particles in PSO. where \(\tilde{Y}_{i}(t)\) denotes the position of particle the particle with the worst fitness function in time \(t\) and \(p_{move}\) is a small value accounting for the probability of moving particles toward the worst particle. \[ \begin{cases}if\;iter<specific_{iter}:PM,c_{1},c_{2(base)}\times\frac{(iter\_ {max}+iter)}{iter\_{max}}\\ if\;iter>specific_{iter}:PM,c_{1},c_{2(base)}\times\frac{(iter\_{max}-iter)}{ iter\_{max}}\end{cases}  \tag{11}\]  
where PM is the mutation parameter. \(PM_{base}\) is the initial value for PM. \(iter\_{max}\) is the maximum iteration number and \(iter\) is the variable that shows the number of algorithm iterations. Considering Equation (11), at the first iteration the value of PM is increasing which means that more random particles are generated to search sufficiency among all problem search spaces. After specific iterations, the random particle movements can be limited by decreasing the value of PM in each step. As mentioned above, each particle represents a possible solution to the problem which can be a set of selected features. Only features with the corresponding value of one are selected for the classification. For instance, in the sample particle shown in Table 2, the optimal feature subset is composed of \(\{f_{1},f_{3},f_{4},f_{n-1}\}\). The main goal of using IDPSO is to reduce the minimum number of features to achieve the highest accuracy simultaneously. So, regarding the fitness function, we considered the above factors in Equation (12). \[ff_{i}=\min[k_{1}(1-acc_{i})+k_{2}(n_{features}-n_{s-features})] \tag{12}\]  
where \(ff_{i}\) and \(acc_{i}\) are the fitness function and accuracy calculated after the classification step for particle \(i\). The parameter \(n_{features}\) is the number of total features and \(n_{s-features}\) is the number of selected features for particle \(i\). Moreover, \(k_{1}\) and \(k_{2}\) are the parameters used to adjust the operation of IDPSO. Figure 3 shows the pipeline of the proposed IDPSO.
## 1 Introduction
The antipodal linearly tapered slot antenna (ALTSA), developed from the Vivaldi aerial [23], has numerous excellent qualities, such as good radiation orientation, low cost, being lightweight, easy fabrication, and easy integration, etc. The combination of the ALTSA and wide-beam antenna has the potential to achieve broader applications. However, research on this subject is scarce. In [24; 25], artificial material units were loaded on the surface of an antipodal tapered slot antenna (ATSA) to improve directivity. Nevertheless, these methods further narrowed the beamwidth and required the printing of a large number of units, which increased the overall size. In [26], a smart configuration was proposed. Two antipodal tapered patches were designed as a fan-like shape for widening the beam, and good results were realized. However, the HPBW amplification was limited, and the design was also complex. It can be observed that none of the above designs focus on widening the beams uniformly in all cut-planes without sacrificing antenna size and complexity, especially for end-fire antennas. This limits the applications of wide-beam antennas. In this paper, a beam-widening design based on an ALTSA is first proposed. By loading two rows of subwavelength metallic strips on the blank area between the two tapers of the basic ALTSA, the HPBW can be widened by about 200% in entire cut-planes that include the boresight at the central frequency of 15 GHz, and the difference between each HPBW amplification is small. Thus, a quasi-hemispherical pattern can be acquired. In this way, there is almost no increase in overall size and structural complexity. Along with the above characteristics, the proposed design also has the advantages of being low cost, lightweight, and having strong reliability, which makes it a good candidate for a wide-beam antenna.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Global Navigation Satellite Systems  
From all the GNSS measurements [37] available, in this paper, we use the pseudorange and pseudorange rate (Doppler). The carrier phase and the accumulated delta range are left for future work.
## 5 Epigenetic Regulation of ALS
### Studies Using Drosophila Models
#### 5.2.2 Histone Modifications
Immunoprecipitation assay combined with Western blotting revealed that acetylated lysine residues of BRP increased in _HDAC6_ null mutants and decreased in flies overexpressing _HDAC6_[111]. In addition, in vitro assay revealed that HDAC6 deacetylates BRP [116]. Thus, HDAC6 is both necessary and sufficient for BRP deacetylation [116]. In mutant flies for _TBPH_, a _Drosophila_ homologue of _TDP-43_, _HDAC6_ mRNA levels were reduced, whereas in flies expressing human wild-type TDP-43 or TDP-43 carrying pathogenic mutations, _HDAC6_ mRNA was upregulated [116,117]. Decreased BRP acetylation, larger vesicle-tethering sites, and increased neurotransmission were also observed in flies expressing the mutant form of TDP-43 [116]. Of note, these defects were similar to those observed with the expression of _HDAC6_ and opposite to _HDAC6_ null mutants. Consequently, reduced levels of HDAC6 or increased levels of ELP3, which acetylates BRP, rescued the presynaptic density deficits in TDP-43-expressing flies and the locomotive defects in adults [116]. These studies suggest a link between HDAC6 and ALS pathogenesis.
## 4 Discussion
However, in our study the universal primer set NRPS-1F--NRPS-1R for the amplification of adenylation (A) domain was used, thus targeting a much broader range of NRPS biosynthetic clusters. PKS gene clusters were detected in _B. safensis_, _Bacillus_ sp., and _S. colmii_ strains. Type I PKSs are large multifunctional enzymes, implicated in pharmacologically important metabolites, most often detected within prokaryotes [93; 98]. PKs were detected in _B. subtilis_, _B. amyloliquefaciens_, _B. methyldotribicus_, _B. laterosporus_, and _Paenibacillus_ sp. [93]. Within the _B. subtilis_ group, three types of antimicrobial PKs are produced, including bacilliene, difficidin, and macrolactin [98]. Moreover, [PERSON] et al. [99], reported the partial isolation of a gene cluster from _Staphylococcus lentus_ encoding a PK exerting broad-spectrum activity against fungi and bacteria. To the best of our knowledge, this is the first time that PKS encoding genes have been detected in bacteria isolated from honey exerting antimicrobial potential. Interestingly, 13 isolates in this study contained both NRPS and PKS gene clusters. Often NRPSs and PKSs are synthesized as hybrid products by NRPS and PKS gene clusters [31; 33; 100], such as bacilliene, compactin, fusarin C, or salinosporamide [98]. Twelve NRPS amplicons and 11 PKS amplicons were sequenced, in order to validate our detection approach. Three out of the 12 NRPS amplicons were implicated in the synthesis of surfactin. Surfactin is a cyclic lipoheptapeptide produced by various strains of _Bacillus subtilis_, which alters membrane integrity and permeability acting against several microorganisms [51; 92; 101]. Furthermore, it has been reported that _Paenibacillus_ strains, isolated from various environments, produce a wide range of antibiotics [64; 92]. Three _Paenibacillus_ species were detected in honey, _P. alvei_, _P. polymyaxa_, and _P. larvae_[3; 28; 92; 102]. Polymyaxins produced by P. polymyna_ have been used as a last-resort treatment of multidrug-resistant Gram-negative bacteria infections. They also exhibit strong antifungal activity [93]. Finally, our sequencing results regarding the PKS amplicon from _Bacillus subtilis_ CTA9 strain suggest the presence of a PKS implicated in bacillaeene biosynthesis. Bacillaeen was firstly isolated from _B. subtilis_ strains, but since then has been reported to be produced by several other _Bacillus_ spp. It is a hybrid PKS synthesized by a PKS-I and NRPS operon and it has demonstrated a strong activity against various bacteria and fungi [92; 93; 98]. Taking together all these data, it is evident that most of the bacterial isolates in this study could be potent producers of secondary metabolites including antibiotics, bacteriocins, and enzymes that might lead to future biotechnological applications.
## 3 Defocus Effect Calibration and Compensation
### Defocus Position Determination and Defocus Effect Compensation
It shows favorable monotonicity in defocusing direction determination. To sum up, a high-sensitivity determination algorithm combined using the above three defocus evaluation indicators is proposed. The working process of the proposed algorithm is as follows. Firstly, the frequency domain evaluation values \(F\), maximum radii \(R\), and energy density \(NM\) are calculated from a BFP image. Since the \(F\)-\(x_{i}\) curve is divided into a high-sensitivity part and a less-sensitive part by the threshold value \(Fp\). When \(F\) is less than \(Fp\), the maximum radius method is used to calculate the defocus position based on the \(R_{max}\)-\(x_{i}\) curve; otherwise, the frequency domain method is adopted. Two solutions exist: \(x_{1}\) and \(x_{2}\). The nonmonotonicity is then determined by comparing \(NM_{1}\) and \(NM_{2}\). The algorithm flow chart is shown in Figure 5. The threshold \(Fp\) used to determine the defocus position calculation can be determined according to the position where the gradient of the frequency standard curve drops sharply, as shown in Figure 4a. Determining the position \(x_{i}\) of the defocus BFP image according to the above process provides a key link for the defocus BFP image compensation and a basis for subsequent defocus measurement. A complete BFP image undistortion and ellipsometric error compensation example is shown in Figure 6a using the proposed method. Firstly, the original biased ellipsometric parameters corresponding to each pixel radius were calculated, as shown in Figure 6b, using Equations (3) and (4). Then, based on a calculated defocus position, the incident angle calibration coefficient \(f^{\prime}\) and the ellipsometric compensation amounts \(\tan^{2}\delta_{\Psi}\) and \(2\delta_{\Delta}\) were determined from the previous calibration results, using which the \(R\)-\(\theta\) correction and ellipsometric parameter compensation were completed, as shown in Figure 6c. Finally, the film thickness was further calculated using LM fitting. Figure 5: Flow chart of the proposed defocus position determination algorithm.
## 2 Related Work and the Proposed Algorithm
### Related Work
Atmospheric conditions that affect the optical images at different acquisition times could be a potential source of error and they should be taken into consideration to avoid missed detection or false-offs. The use of active devices directed toward the target to be investigated, like scanners [37] or lasers [38; 39] or sonars [5; 40] the use of depth sensor like the Kinect [41], allows overcoming these drawbacks particularly when real-time performance is desired. In the other hand, when the configuration of the different sensors is less complex, the geometric modelisation (calibration) of the whole system becomes possible and more accurate. This allows us to define the properties of the epipolar geometry (calibration parameters) that could be used afterwards to enhance the different image processing, particularly to remove the outliers [5; 6] facilitate the matching process using epipolar lines [5; 36] and obviously for 3D triangulation [5; 6]. Furthermore, methods such as SURF detector [6] or Harris corner detector [5] or Sobel edge detector [2] were used to detect most interesting points in the image. These methods, despite their good detection rate, necessitate big computation time since they use complex calculations (convolution with a Gaussian, gradient vectors, descriptors, etc.). Therefore, some authors propose different alternatives that permit to substitute global methods with a combination of standard processing methods such as those presented in [13; 36; 40] which permits to ensure good accuracy and run time.
## 2 Physiological and Biochemical Impacts of Oxidative Stress towards Cropped Plants  
The two primary ways abiotic stressors cause oxidative stress towards plants is either by creating imbalance in the metabolic pathways or a reduction in the efficiency of antioxidant enzymes [39; 40; 41].  
Abiotic stressors can reduce growth and metabolism by increasing unmoderated production of free-radicals, such as reactive oxygen species (ROS) [3; 4; 42]. Free radicals that form after exposure to these abiotic stressors, such as OH\({}^{-}\), are capable of damaging the heterocyclic bases and the sugar-phosphate backbone of DNA. Other ROS, including H- free radicals and free electrons, react with the double bonds in DNA [43]. Free radicals can damage lipids and other cellular components by oxidizing them (via attack of paired electrons), and are directly linked to a reduction in cellular viability leading to oxidative stress [2; 44]. On a farming scale, the impacts of oxidative stress on crops can potentially result in large losses of biomass, yield, reduced seed vigor, reduced germination efficiency, improper plant development, loss of plant viability and ultimately, reduced economic benefit to the grower [1; 45; 46].  
Most abiotic stressors inflict oxidative damage towards cropped plants by inducing the overproduction of free radicals as part of essential metabolic processes, including photosynthesis. These abiotic stressors can include shifts in diurnal cycling, where a cropped plant is exposed to fluctuating light intensities [47], and drought conditions in soils where osmotic stress impacts the crop [48].  
During photosynthesis, when exposed to high levels of light, the electron transport chain becomes over-reduced and harmful ROS species are produced [49]. Similarly, fluctuations in temperature lead to stress development in plants. Low temperature stress reduces seed vigor, growth, and metabolic activity [50], whilst high temperature exposure in plants results in leaf membrane degradation and lipid peroxidase damage linked to increased ROS activity, as observed in _Jatropha curcas_ (Nettlespurge) [51]. Variances in water availability lead to osmotic stress (the reduction in turgor and water potential of plants), sufficient to disrupt normal metabolism and induce desiccation of plant tissues [52; 53]. In _Brassica napus_ L. (canola) leaves, growth reducing oxidative damage caused by reduced water availability has been observed to occur at around \(-1.5\) MPa. This is due to increased lipid peroxidation, reduced phospholipid and galactolipid content of leaf cells, and altered leaf structure [54].  
Trace element availability has varying effects on oxidative stress in cropped plants and is the abiotic stressor that has the highest influence on antioxidant enzyme activity. The bioavailability of trace elements for cropped plants are heavily influenced by soil factors including: soil pH, soil organic matter (SOM) content, and Mn/Fe oxyhydroxide presence [55], indicating that irrigation and soil properties are ultimately the drivers of trace element induced oxidative stress towards cropped plants.