esa-sceva/satcom-chunk-collection huggingface dataset:
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Continuous Orchestration  
The first challenge facing 6G network automation and orchestration is the need for continuous end-to-end (E2E) orchestration starting from the end devices (commonly referred to as extreme edge) to the edge nodes all the way to the cloud. This means that there needs to be continuous orchestration and resource management at three levels, namely the extreme edge, edge, and the cloud. The challenge lies in the fact that most current management and orchestration (MANO) frameworks do not extend to the extreme edge. More specifically, the current orchestration solutions rely on ETSI's NFV MANO framework [81; 82]. This framework has been extended to handle edge nodes through dedicated/specialized virtual infrastructure managers (VIMs). However, these VIMs are still under the control of the centralized orchestrator. Therefore, current solutions are not yet suitable and applicable to the extreme edge.  
In addition to the MANO framework itself, other factors need to be considered when developing the continuous E2E orchestration for 6G networks. The first is the heterogeneous nature of the extreme-edge devices that will be orchestrated. More specifically, 6G networks are expected to handle personal (e.g., smartphones, laptops, wearables, etc.), commercial (e.g., smart home appliances, smart speakers, etc.), and industrial (e.g., manufacturing robots, drones, etc.) devices [91]. This means that any MANO framework needs to be able to cater to the capabilities of these devices. Additionally, these devices will use a variety of supporting technologies and will have different operating systems, underlying hardware, and various interfaces. As such, the orchestration framework needs to be aware of the resources available for these supporting technologies. Lastly, the extreme-edge devices may be located in uncontrolled environments. Hence, the corresponding resources in such environments may not always be available. This is challenging, particularly in terms of ensuring orchestration continuity given the global extreme coverage requirement that 6G networks are expected to meet.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 4 Adaptive GLAN Using Reinforcement Learning
### System Environment
#### 4.3.1 State Space  
The states used for learning are as follows:  
\[S_{AGLAN,t}=[S_{FRangle,t},S_{time,t}] \tag{4}\]  
The state consists of the FR angle and time that best represent the AGLAN network. First, the FR angle is the angle of the receiving zone. As the FR angle increases, the FR range widens, increasing the possibility that data will arrive at its destination reliably. Conversely, if it is smaller, the number of nodes to be covered is reduced and the network load can be reduced. Second, time refers to the time taken to travel from the starting point to the destination, or the time for which it does not arrive. As the FR range narrows, the arrival time to the destination is likely to be shortened and communication failures can be checked quickly, while the wider the FR range, the more stable it is. Since these two elements can represent various characteristics of the AGLAN network, they are suitable as components of the state for the learning model.
###### Abstract
The eXtreme Gradient Boosting (XGBoost) model was adopted in this study to assess the sensitivity. It excels in managing extensive datasets and intricate tasks due to its incorporation of both engineering enhancements and algorithmic improvements. XGBoost can also provide a comprehensive understanding of feature importance, shedding light on which features the model is particularly sensitive to. Additionally, the model leverages parallel processing capabilities, facilitating accelerated training processes, especially when deployed on multi-core processors [41]. Here, using machine learning, XGBoost was built to calculate the sensitivity of GPP to precipitation frequency based on data obtained from satellite observations from 1982 to 2015. The sensitivity was compared between different land cover types, precipitation intensity categories and seasons, in order to illustrate the detailed dependence of vegetation productivity on precipitation frequency.
## 6 Models Development Priorities
### Trait-Based Modeling  
The current PFT approach to modeling vegetation is limited to feedback that results from changes in species distribution since most models assume that attributes within a PFT do not change with climate [140]. To capture the adaptation and evolution of vegetation, the concept of trait-basedmodeling was introduced by [PERSON] and [PERSON] [141]. Trait-based modeling links plant traits that act as plant responses to the environment with plant traits that represent the effect of plants on the ecosystem in order to capture the plant assemblage of a region. A trait can be defined as a feature that describes the morphology, physiology, or phenology characteristics of an individual, which can vary across an environmental gradient [142]. Trait based modeling allows dynamic vegetation to move beyond simple climate based temperature controls on vegetation existence and establishment. Therefore, species distribution would be a response to N and drought in the ecosystem. This approach has been used to improve two dynamic vegetation models (aDGVM, [128]; JeDi-DGVM, [129]). The Jedi-DGVM outperformed other leading dynamic vegetation models for Leaf Area Index (LAI), NPP, CO\({}_{2}\) seasonality, C fluxes, and, in some regions, C stocks [129]. Incorporating plant traits in the CSIRO Atmospheric Biosphere Land Exchange (CABLE) model improved the biogeographical distribution of major forests that have multiple dominant PFTs [130]. Using plant traits to capture whole-plant hydraulics has even been suggested to improve C and water use responses to drought [143]. The benefit of trait-based modeling is that the environment acts as a filter for trait composition, analogous to evolutional selection processes [140], and is not limited by climate and geography. This is particularly important considering that [PERSON] et al. [144] found that species' foliar N responses to N deposition were dependent on two main plant traits, leaf mass area and shade tolerance. [PERSON] et al. [136] also suggested several plant traits that could influence the availability of water to a plant that span across leaves, stems, and roots. The challenge is finding data to support the choice of traits and how different traits co-vary.
# Intra Prediction-Based Hologram Phase Component Coding Using Modified Phase Unwrapping
## 2 Phase Information of Full-Complex Hologram
### Effect of Phase  
As described above, a full-complex hologram is composed of complex numbers of real and imaginary parts. It can also be expressed in terms of amplitude and phase. We show the full-complex hologram in Figure 1. Figure 1a,b are real and imaginary components, respectively, and Figure 1c,d are amplitude and phase components. Figure 1e,f are the amplitude and phase components of the hologram diffraction result. In the case of compression, it is true that the real and imaginary components show better efficiency. However, when the real and imaginary components are independently compressed, the phase information of the hologram may not be considered, and the quality of the reconstruction result of the hologram may not be proportional to the quality of the hologram. The hologram, Horse in Figure 1, was optically captured using the phase-shifting method [18], and is published as an experimental data set in JPEG Pleno.  
Figure 1: Components of full-complex hologram (Horse [18]): (**a**) real, (**b**) imaginary, (**c**) amplitude, and (**d**) phase of a hologram, (**e**) amplitude, and (**f**) phase of reconstruction.  
We tried to infer the characteristics of the phase component of the hologram by compressing the real and imaginary components in different ways. Reconstruction quality in four cases was considered according to the quality of hologram and phase as shown in Table 1. What we can note in this experiment is that even though the PSNR (31.54 dB) of the hologram is high, the quality of the reconstruction (25.81 dB) is not high when the PSNR (16.68 dB) of the phase is low. In comparison, the PSNR (29.31 dB) of the hologram is lower, and the PSNR (26.43 dB) of the reconstruction is higher when the PSNR of the phase is higher (17.11 dB). Through this experiment, we can confirm that not only the quality of the hologram itself, but also the phase information of the complex hologram are very important to the reconstruction efficiency. The phase information can be proportional to the consistency of the relationship between real and imaginary components. Compressing the phase information itself would be a very good way to maintain the phase relationship well. However, the phase information is very difficult to compress due to its randomness. Therefore, we try to efficiently compress phase information that is very difficult to compress.
## 2 Results and Discussion
### Analysis of the Volatile Fraction
#### 2.1.4. Chiral GC-MS Analysis of EOs  
The EOs from the nine commercial varieties were subjected to chiral GC-MS analysis to determine the enantiomeric distribution of \(\alpha\)-pinene, \(\beta\)-pinene, limone, linalool, (\(E\))-caryophyllene and caryophyllene oxide. This analysis could be helpful to detect the origin of the EO samples based on the enantiomeric ratio shown by the main constituents. In this regard, Table 3 reports the ratios within the enantiomeric pairs of these chiral terpenes. Both the enantiomers of \(\alpha\)-pinene and limoneene were detected in all the varieties. For \(\alpha\)-pinene, the predominance of the (+)-form was observed, while the (\(-\))-enantiomeric form was the prevalent one in the case of limone. No significant differences in the enantiomeric ratios were obtained for these two compounds among the nine studied varieties. On the other hand, \(\beta\)-pinene and linalool were characterized by higher variability in the enantiomeric ratios. In fact, the (+)-enantiomer of \(\beta\)-pinene was generally predominant, although the presence of only the (\(-\))-form was found in EOs from two varieties, namely, 24 K and Gorilla Glue. Moreover, the prevalence of the (+)-enantiomer of linalool was detected, and it was the only recognized form in Fresh Mountain, Gorilla Glue and Venom OG EOs. It is worth noting that the elution order of the enantiomeric forms of \(\alpha\)-pinene and \(\beta\)-pinene was compliant with that found in one of our previous works, where the same column was employed [8]. Interestingly, (+)-\(\alpha\)-pinene and (+)-\(\beta\)-pinene, as the main enantiomers in the nine EOs, were reported to be endowed with antibacterial and antifungal properties [47]. Concerning sesquiterpenes, (\(E\))-caryophyllene and caryophyllene oxide were present exclusively in the (\(-\))-enantiomeric form (Table 3), as confirmed by another study on hemp EOs [8]. From the findings provided, it is possible to conclude that the enantiomeric ratio of the main volatile terpenes does not allow us to differentiate most of the varieties analyzed, apart from 24 K and Gorilla Glue, which showed the predominance of the (\(-\))-\(\beta\)-pinene.  
Figure 1: Chemical structures of the main sesquiterpenes found in the 9 EOs.
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 2 Molecularly Imprinted Polymer (MIP)
### Advantages and Limitations of MIP  
The advantage of using MIP in the analysis process is the increased selectivity of the analytical method. MIP can extract analytes from samples with greater efficiency as it involves using molds suitable for the analyte. The molding process produces an active polymer site in a cavity that remains following a particular conformation. Affinity can also be maintained in the presence of hydrogen bonds. This causes the analyte to be easily captured at the active site of the MIP. MIP is flexible so that it can be used for various analytical purposes. MIP can also maintain stability and is study in a broad pH and pressure range [44].  
MIP's main drawback is template leakage [21; 29; 35; 45; 46; 47; 48; 49; 50; 51; 52]. This happens when not all templates are released during the template removal process, some are still left in the MIP cavity. To overcome this limitation, the utilization of dummy templates or templates analogous, can be chosen. Dummy template can be in the form of derivative compounds, those in the same group to analyte or the use of deuterated molecules or their isotope analogs. The use of this dummy template has proven to be able to overcome the shortcomings of MIP. The use of dummy templates can also overcome another drawback of MIP related to the availability of templates, where some templates are challenging to obtain due to price issues [45; 48; 49; 50; 51; 52].
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Preprocessing
#### 3.3.1 The Preprocessor Module  
The actions of the preprocessor module include:  
* Construction of the _Height Matrix(H)_ data structure.
* Calculation of the environment parameters required by the region-filling algorithm.  
Height Matrix Data Structure  
The _Height Matrix (H)_ data structure stores the normalized altitudes of different locations in the disaster affected area. The altitude data are obtained as a JSON file (_Coordinate: Altitude_ format). A normalization factor is calculated by finding the altitude of the  
Figure 2: An outline of the process.  
lowest-lying point in the terrain. The normalized altitudes are obtained by subtracting the normalization factor from each altitude, then populated to the Height Matrix data structure. Environmental Parameters  
We define the various environmental parameters specific to the drones and the terrain in Table 1. We note the average altitude of the terrain from \(H\), then add a threshold value based on the terrain elevation profile. The sum of the average normalized altitudes and threshold value forms \(H_{min}\).
## 3 Realization Process
### Experimental Results  
The simulation and measurement results obtained using the proposed BPF based on T-shaped and fold-type SIRs are depicted in Figure 4. From the figure, the experimental results are discussed the insertion loss (\(l_{L}\)), return loss (\(R_{L}\)), and bandwidth (\(\Delta\)) as given by Equations (4)-(7) [30].  
\[I_{L}=-20 log|T|=-10 log_{10}\bigg{(}\frac{P_{out}}{P_{in}}\bigg{)}\ [dB] \tag{4}\]  
\[R_{L}=-20 log|\Gamma|=-10 log_{10}\bigg{(}\frac{P_{out}}{P_{in}}\bigg{)}\ [dB] \tag{5}\]  
\[\Delta=\bigg{(}\frac{\omega_{1}-\omega_{2}}{\omega_{0}}\bigg{)}\ \bigg{(}\frac{f_{1}-f_{2}}{f_{0}}\bigg{)}\times 100\% \tag{6}\]  
\[\Delta=\bigg{(}\frac{\omega_{1}-\omega_{2}}{\omega_{0}}\bigg{)}\ \bigg{(}\frac{f_{1}-f_{2}}{f_{0}}\bigg{)}\times 100\% \tag{7}\]  
where the \(T\) and \(\Gamma\) are coefficients of transmission and reflection, and the \(P_{in}\) and \(P_{out}\) are the power of incident and reflection, respectively. In addition, the \(\omega_{1}\) and \(\omega_{2}\) are angular frequencies of the lower and upper bands. Then, the \(\omega_{0}\) is the center angular frequency.  
The \(f_{1}\) and \(f_{2}\) are the frequency of the lower and upper bands. Then, the \(f_{0}\) is the center frequency.  
The simulation results of the insertion and return losses for the first frequency pass band are 0.09 dB and 24.8 dB with a bandwidth of 120% at the center frequency of 2.45 GHz (0.94-3.88 GHz) and the same for the second frequency pass band is 0.10 dB and 27.3 dB with a bandwidth of 105% at the center frequency of 7.50 GHz (6.19-8.82 GHz) using the proposed BPF. In addition, the measurement result of the insertion and return losses for the first frequency pass band is 0.102 dB and 19.13 dB with a bandwidth of 120% at a center frequency of 2.43 GHz (0.85-3.85 GHz) and the same for the second frequency pass band  
Figure 4: Experimental results obtained using the proposed BPF.  
are 0.103 dB and 19.96 dB with a bandwidth of 105% at a center frequency of 7.49 GHz (5.96-8.62 GHz) using the proposed BPF.
## 2 Interactive BM Segmentation Based on Supervised Learning Models
### Segmentation Refinement  
In our study, we conceived automated segmentation as an intermediate task within a typical workflow in radiotherapy planning. The active decision support of the machine learning procedure reduces user-interaction that is limited to two phases: a preliminary \"detection\" phase with which to specify the VoI, as illustrated in Section 2.1, and a post-processing phase aimed to refine the identified segments. Refinement is made necessary by commission and omission errors that are most likely to occur when lesions are of small size, and the tumour area presents inhomogeneity due to necrosis and active parts. Our strategy provides both for computer-aided manual editing as illustrated in Section 4 and an automated procedure based on the use of Morphological Operators to refine the segmented masks in an attempt to reduce omission and commission errors and making the segmented tumour area more compact. For each selected slice, Opening and Closing Operators with spherical shape apply consequently. The Opening Operator removes from the binary input image all the connected components that have a lower number of pixels than a set value and outputs a new binary image. The Closing Operator closes holes present in the image and returns the closed binary image. We performed three different tests, using for all a disk-shaped structuring element, aimed to tune parameters values of the Morphological Operators and decide the order of application. In the first test, only the opening morphological operator (Open) was applied by varying the radius; in the second test, only the morphological closing operator (Close) was applied, varying the radius; in the third test, we applied both operators in a different sequence. The best result was obtained by applying first the opening morphological operator with a radius of 5 and then the closing operator with a radius equal to 10.  
Figures 3 and 4 illustrate the refinement of automated segmentation results performed manually by the user and by the Morphological Operators, respectively.  
Figure 3: From left to right: crop of a source T1c MRI slice, superimposition of the automated segmentation mask, superimposition of the mask manually refined by the user.
# Automatic Tortuosity Estimation of Nerve Fibers and Retinal Vessels in Ophthalmic Images
###### Abstract  
The tortuosity changes of curvilinear anatomical organs such as nerve fibers or vessels have a close relationship with a number of diseases. Therefore, the automatic estimation and representation of the tortuosity is desired in medical image for such organs. In this paper, an automated framework for tortuosity estimation is proposed for corneal nerve and retinal vessel images. First, the weighted local phase tensor-based enhancement method is employed and the curvilinear structure is extracted from raw image. For each curvilinear structure with a different position and orientation, the curvature is measured by the exponential curvature estimation in the 3D space. Then, the tortuosity of an image is calculated as the weighted average of all the curvilinear structures. Our proposed framework has been evaluated on two corneal nerve fiber datasets and one retinal vessel dataset. Experiments on three curvilinear organ datasets demonstrate that our proposed tortuosity estimation method achieves a promising performance compared with other state-of-the-art methods in terms of accuracy and generality. In our nerve fiber dataset, the method achieved overall accurary of 0.820, and 0.734, 0.881 for sensitivity and specificity, respectively. The proposed method also achieved Spearman correlation scores 0.945 and 0.868 correlated with tortuosity grading ground truth for arteries and veins in the retinal vessel dataset. Furthermore, the manual labeled 403 corneal nerve fiber images with different levels of tortuosity, and all of them are also released for public access for further research.  
Keywords: tortuosity; retinal vessel; nerve fiber; ophthalmic image +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Differential Metabolites  
The flavour compounds with \(p<0.05\) and an FC value greater than 2 were selected as differential metabolites [40; 41]. The flavour metabolites of SJ changed markedly after fermentation, with seven significantly upregulated and two downregulated VOCs (Figure 4B). Specifically, 3-octanone (c3), phenylethyl alcohol (al17), 2,3-butanediol (al8), tridecane (alk5), octanoic acid ethyl ester (e19), 6-methyl-5-hepten-2-one (c5) and acetoin (O8) showed an obvious increasing trend, while benzoic acid (ac10) and tridecane (alk3) obviously decreased. A total of 10 VOCs, including 2-methyl-propanol (al2), 3-ethoxy-1-propanol (al5), 1-heptanol (al6) and benzyl alcohol (al16), were newly generated in SW, along with significantly differential metabolites, and had log\({}_{2}\)FC values of infinity (Inf). 2,3-Butanediol imparts a sweet scent, and gave the wine a mellow and natural flavour. [PERSON] et al. [42] also reported that 2,3-butanediol was a differential metabolite of ale and lager beers. 2-Methyl-propanol and benzyl alcohol, with unique alcohol/wine-like and floral scents, respectively, were produced in large quantities during fermentation, and they were also important to the overall flavour profile of fermented and distilled wines [23].  
In total, 51 significantly upregulated VOCs were found in SW after distillation (Figure 4C), including 27 esters, eight carbonyl group compounds, four alcohols, three acids, two alkanes and seven other compounds. Linalool (al7), isobutyl isovalerate (e6), ethyl esters of octanoic acid (e18) and nonanoic acid (e23) showed obvious increasing trends. Linalool is naturally present in _Prunus dulcis_[43], tomato [44], tea [45] and kimchi [46], and not only gives food a lily fragrance, but also has antibacterial effects [47]. Overall, 14 downregulated VOCs were found in SW after distillation, including four alcohols (1-hexanol (al4), 2-furanmethanol (al13), benzyl alcohol (al6), phenylethyl alcohol (al7)), four esters (pentanoic acid ethyl ester (e1), butanoic acid, 1-methylpropyl ester (e3), hexanoic acid 1-methyl ethyl ester (e9), 3-methyl-butanoic acid ethyl ester (e15)), two carbonyl group compounds (6-methyl-5-hepten-2-one (c5), 3-octanone (c3)), acetic acid (ac1), tridecane (alk3) and acetoin (O8). The decrease in specific acids and alcohols is related to the formation of corresponding esters. For example, the decrease in acetic acid and phenylethylanol is due to the esterification reaction of these two substances to further form phenylethyl acetate [48; 49].