esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 4 Submarine Amplifiers
### Pump Farming (SDM) Technology  
A straightforward approach to face increasing capacity needs is to increase the capacity per fiber by boosting the output power of submarine repeaters (over 20 dBm) and/or by using larger effective area (150 \(\upmu\)m\({}^{2}\)) fibers. However, this approach is constrained by current PFE (which can currently deliver power at around \(\sim\)18 kW) and thus may not be cost efficient. [PERSON] et al. in [23] introduced the optical and electrical formulas resulting that the PFE and OSNR are strongly linked. In addition, cost savings can be made on the resistivity of the conductor. In the upcoming SDM submarine era, to cope with future needs, new motivations especially in the field of optical amplification, are ready for implementation. Pump lasers or any other possible optical network component will have to be shared among high count of FPs in the submarine cable (i.e., SDM systems in Table 3) both for fail-over and economic reasons.  
Figure 20: \(\times\) Tb/s capacity at the cost of 4 fiber pairs (C Band only) on top, \(\times\) Tb/s capacity at the cost of 2 fiber pairs (C + L Band) band bottom part of the figure.  
A new innovation toward sharing submarine optical network equipment is repeater pump farming (RPF). The RPF structure (presented in Figure 21) is based on a group (called farm) of repeaters which are cross connected to each other. Both RPF and FPs groups support each other and therefore, a group of optical pumps are used to support a group of FPs. Moreover, RPF technology enhances the reliability of the submarine system even if more than one pump failures occur, as it will continue to pump the FPs. Therefore, it is a promising solution aimed at increasing the flexibility and reliability of a submarine cable system. RPF technology consists of sharing x repeater pumps (x > 2) with other y FPs (y > 1), cross-connected via two-stage optical fiber couplers. On the other hand, in terrestrial systems each pump is dedicated to one single FP.  
A variety of RPF schemes are proposed in [23], including:  
* 4 Pumps/2 Fiber Pairs, 4 Pumps/4 Fiber Pairs;
* 8 Pumps/4 Fiber Pairs, 8 Pumps/8 Fiber Pairs; and
* 16 Pumps/8 Fiber Pairs, 16 Pumps/16 Fiber Pairs.
## 4 Regulatory Mechanisms of Lily Floral Scent Components
### Transcriptional Regulation of Floral Scent Component Biosynthesis  
The transcriptional regulation of floral scent component biosynthesis involves multiple molecular mechanisms and regulatory factors. However, in studies on the transcriptional regulation of lily floral scent, only a limited number of transcription factors have been identified as having transcriptional regulatory effects on some key floral scent synthase genes (Table 4). In the floral volatiles of _Lilium_ 'Siberia', 1270 transcription factors (TFs) from 55 families, including bHLH, ERF, NAC, MYB, C2H2, MYB, and bZIP, have been predicted [21]. Thirty-one TFs have been identified as potentially positively regulating the production of monoterenoid volatiles in _Lilium_ 'Sorbonne', with many belonging to the bHLH, bZIP, and MYB families [52]. LibHLH22 and LibHLH63 significantly promote the expression of _LiDXK_ and _LiTPS2_, thereby enhancing floral scent release [53]. LiNAC100 has a novel function in regulating floral scent by directly controlling the linalool synthase gene _LiLIS_[54]. LiMYB1 forms transcriptional regulatory complexes with LiMYB308 and LiMYB330, jointly enhancing the activation of the _LiTPS2_ promoter, thereby regulating the synthesis of terpenoid floral scent compounds in _Lilium_ 'Siberia' [55]. LiMYB305 directly binds to and activates the _LiOCS_ promoter, increasing monoterpene synthesis [56]. The MYB transcription factor LiSRM1 from 'Siberia' petals negatively regulates the synthesis of major monoterenoid volatiles [57]. The R2R3-MYB transcription factor _LiDDO1_ is expressed only in the perianth, the main source of floral scent, with a circadian rhythm consistent with the expression rhythms of the shikimate pathway and _PAL_ genes, suggesting a key role in regulating the production of volatile benzenoids/phenylpropanoids in liliies [58].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transcription Factor** & **Target Gene** & **Regulatory Mode** & **Controlled VOCs** & **References** \\ \hline LibHLH22 & _LiDXK_, _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [53] \\ LibHLH63 & _LiDXK_, _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [53] \\ LiNAC100 & _LiLiS_ & Positive & Linalool & [54] \\ LiMYB1 & _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [55] \\ LiMYB305 & _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [55] \\ LiMYB330 & _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [55] \\ Complex of LiMYB1 and & _LiTPS2_ & Positive & Linalool, (_E_)-\(\beta\)-cómene & [55] \\ LiMYB308 & _LiOCS_ & Positive & (_E_)-\(\beta\)-Ocómene & [56] \\ LiSRM1 & _LiLiS_, _LiOCS_ & Negative & Linalool, (_E_)-\(\beta\)-cómene & [57] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The transcription factors and target genes related to ‘Siberia’ lily fragrance that have been validated through experiments.
# Aromatic and Nutritional Composition of Edible Flowers of Garden Garlic and Wild Leek
## 4 Discussion
### Nutritional Compositions
Protein content is determined by the total nitrogen concentrations reached in the flowers and, therefore, by the cultivation conditions and the genetic capacity to absorb nitrogen and synthesize protein [43]. The protein concentrations of wild leek flowersdouble the concentrations of this nutrient present in garden garlic flowers. These protein values are higher than those indicated by other authors in ornamental edible flowers [36], and are also higher than those indicated for edible flowers of caulfilower and hibiscus [44], chive edible flowers [38] and other edible flowers [40], but they coincide with the protein concentrations (approximately 11-27%) that [PERSON] et al. [41] report for edible wild flowers from Mexico and with the values that [PERSON] et al. [45] indicate for the flowers of Diana rosa (_Lathyrus odoratus_) in their study of edible flowers used in Japan, or with the levels reported in the review by [PERSON] et al. [46] for broccoli flowers. The protein values of these flowers are at concentrations similar to those of foods of plant origin such as garlic and onion. Considering that the consumption of the flowers is mainly fresh, it is a protein that does not suffer degradation from thermal treatments. The fat in the studied flowers presents very low concentrations. It is the lowest macronutrient in the flowers. This reinforces that the flowers can be adequately used in the preparation of low-calorie and healthy diets. The fat values in the wild leek flowers double the concentrations of this nutrient in the garden garlic flowers, which indicates that the wild leek flower has greater potential to transmit flavors, due to the high relationship between fat and flavor [47]. The fat values of the studied flowers coincide with those referenced for some edible flowers, such as agave (_Agave salmiana_) and taro (_Coloasis esculenta_) [40] and with the fat concentrations that [PERSON] et al. [41] provide in wild and edible flowers from Mexico. Compared with the reported values (3.45 g 100 g\({}^{-1}\) dw) for the fat content of edible chive flowers [37], garden garlic and wild leek flowers have lower concentrations of fat. Fiber present in the edible part of the flowers includes polysaccharides, oligosaccharidesrides, lignin, resistant starch, inulin and substances of hydrocarbon chemical nature. Fiber is one of the most interesting nutrients in edible _Allium_ flowers. [PERSON] et al. [48] conclude that edible flowers belonging to the _Asteraceae_ family contain fiber levels that can be classified as a rich source of fiber, fulfilling a prebiotic function for intestinal bacteria. The fiber values of the present study are higher than those reported for chives flowers [37]. Carbohydrates are the most abundant macronutrient in edible flowers, reaching up to 90% of the dry weight [46; 49]. Carbohydrates are mainly concentrated in the nectar, being an important source of soluble sugars [50].
# Multi-Guide Set-Based Particle Swarm Optimization for Multi-Objective Portfolio Optimization
## 6 Results
### Hang Seng  
Table 5 shows that for the Hang Seng benchmark problem (the smallest benchmark problem), all of the algorithms performed similarly. SBPSO found solutions that were close to POF and diverse. Its multi-objective adaptation obtained a slightly higher GD score, but so did all of the multi-objective algorithms. On average, NSGA-II obtained portfolios with more return, but also more risk. The proposed MGSBPSO had the lowest average risk value, while MGPSO had a higher average risk value and worse return. Table 6 shows that SPEA2 was the highest-ranked algorithm, while MGSBPSO ranked last. However, the differences between the values obtained by the algorithms are small. Furthermore, Figure 3 shows that all algorithms were able to approximate the true POF.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Problem** & \(\rho_{c}\) & \(\iota_{c}\) & \(\rho_{m}\) & \(\iota_{m}\) \\ \hline Hang Seng & 0.5859 & 4.0 & 0.2578 & 5.0 \\ DAX 100 & 0.0078 & 33.0 & 0.5234 & 19.0 \\ FTSE 100 & 0.4843 & 41.0 & 0.546 & 44.0 \\ S\&P 100 & 0.4843 & 41.0 & 0.546 & 44.0 \\ Nikkei 225 & 0.4531 & 17.0 & 0.890 & 42.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal control parameter values for the strength pareto evolutionary Algorithm 2.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Problem** & \(\rho_{c}\) & \(\iota_{c}\) & \(\rho_{m}\) & \(\iota_{m}\) \\ \hline Hang Seng & 0.3984 & 1.0 & 0.8203 & 46.0 \\ DAX 100 & 0.4843 & 41.0 & 0.5468 & 44.0 \\ FTSE 100 & 0.3437 & 35.0 & 0.5937 & 32.0 \\ S\&P 100 & 0.3437 & 35.0 & 0.5937 & 32.0 \\ Nikkei 225 & 0.4531 & 17.0 & 0.8906 & 42.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal control parameter values for the non-dominated sorting genetic Algorithm 2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \(R\) & \(\tilde{\sigma}\) & **GD** & **IGD** & **HV** \\ \hline \multirow{2}{*}{SBPSO} & \(\tilde{\sigma}\) & 0.007607 & 0.001897 & 0.000218 & 0.000212 & 0.781949 \\  & \(\sigma\) & 0.000003 & 0.000000 & 0.000045 & 0.000002 & 0.026784 \\ \hline \multirow{2}{*}{MGPSO} & \(\tilde{\sigma}\) & 0.007261 & 0.001967 & 0.000723 & 0.000218 & 1.192743 \\  & \(\sigma\) & 0.000705 & 0.000407 & 0.000262 & 0.000025 & 0.002658 \\ \hline \multirow{2}{*}{NSGA-II} & \(\tilde{\sigma}\) & 0.007859 & 0.002171 & 0.000824 & 0.000200 & 1.195125 \\  & \(\sigma\) & 0.000103 & 0.000063 & 0.000064 & 0.000006 & 0.000737 \\ \hline \multirow{2}{*}{SPEA2} & \(\tilde{\sigma}\) & 0.007395 & 0.001921 & 0.000659 & 0.000173 & 1.194271 \\  & \(\sigma\) & 0.000099 & 0.000046 & 0.000042 & 0.000003 & 0.000345 \\ \hline \multirow{2}{*}{MGSBPSO} & \(\tilde{\sigma}\) & 0.007382 & 0.001814 & 0.000761 & 0.000252 & 1.190891 \\  & \(\sigma\) & 0.000552 & 0.000326 & 0.000077 & 0.000029 & 0.001930 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hang Seng results for each performance measure.
## 3 Case Study
### Comparison Results  
The above three cases give the corresponding parameters of the GRPEs. The maximum strain of the reinforcement was estimated using the present method, BS8006's (15) method, and EBGEO's (21) method, respectively. For the design method proposed in this paper, the vertical stress carried by the subsoil was obtained by substituting the parameters into Equations (18)-(21), and then, the maximum strain of the reinforcement was directly obtained from Figures 5-8. The results obtained were compared with the field test, which  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Type** & \(h\) **(m)** & \(\gamma\) **(kN/m\({}^{3}\))** & \(w\) **(\%)** & \(e_{0}\) & \(E_{8}\) **(MPa)** & \(c^{\prime}\) **(kPa)** & \(\varphi^{\prime}\) **(\({}^{\textbf{e}}\))** \\ \hline Silty clay & 2.5–3.0 & 19.1 & 27.2 & 0.806 & 5.30 & 0 & 24 \\ Mud & 2.0–3.9 & 17.3 & 59.9 & 1.687 & 1.47 & 0 & 20 \\ Mud clay & 15.0–16.5 & 17.1 & 47.0 & 1.318 & 2.07 & 0 & 22 \\ Sub-clay & 12.0–13.2 & 18.9 & 27.8 & 0.778 & 6.91 & 0 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical indexes of the soil layer.  
Figure 11: The pile layout of the test section.  
Figure 10: Schematic diagram of piled embankment profile.  
is detailed in Figure 12. It shows that EBGEO's [21] method roughly underestimated the maximum strain of the reinforcement. The method of BS8006 [15] yielded a roughly larger maximum strain of the reinforcement as it excludes the contribution of subsoil. It can also be noted that the results of the proposed method were in better agreement with the results of field test when compared with the results yielded by EBGEO [21] and BS8006 [15]. The maximum deviation was less than 18%.
## 1 Introduction
Improvement in image quality was noted in the resulting image of the thyroid region. During imaging of the thyroid region in the neck, the spatial resolution (the ratio of the lateral resolution before and after compensation) was 0.60 and the SNR improved by 3 dB as compared to the SNR in the Region of Interest (ROI) without compensation. Nevertheless, the clinical feasibility of the proposed method, and especially its use in abdominal imaging, needs to be studied further.
# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 4 Discussion
It is therefore important to know the computational requirements of each model so that it can be used, in accordance with the resources available, to increase the data in the IRDs and, in turn, use it to train the classifiers. Based on this review, DCGAN and StyleGAN2 were the most widely used architectures, reflecting their popularity in generating synthetic data. The metrics most used to evaluate the architectures include FID, ROC/AUC, ACC, SSIM, and other relevant metrics. However, the results show significant variability between the studies, indicating that the choice of the best architecture depends on the specific context of the application. Considering the cases analyzed and choosing the most used, StyleGAN2 stands out as the most recommended option, especially in scenarios with a reduced number of training data, due to its ability to generate highly realistic samples. However, DCGAN remains a robust alternative due to its simplicity and stability in training, which makes it a viable choice in applications that require less computational complexity. Thus, the selection of the most suitable architecture must take into account the volume of data available and the specific requirements of the study. This study has limitations. One of the limitations identified in this systematic review is the small number of studies dealing with generative methods for data augmentation in IRDs. This scarcity makes it difficult to explore the potential of generative models for data augmentation in these diseases, which has a direct impact on the development of robust and reliable classifiers. The lack of studies involving these techniques for data augmentation contributes to keeping datasets small, with unbalanced classes that compromise the efficiency of classifiers and their ability to generalize. As future work, continuing research in this area has great potential for advances, not only in generative methods, but also in IRDs, in the development of methods and tools to support professionals in detecting and classifying these diseases efficiently and reliably. The information identified in the literature on other retinal diseases could be applied to IRDs in future work, thus boosting progress and development in this area. In addition, future studies could include standardization of architecture and metrics that allow a more direct comparison between the results obtained and thus promote consistent advances in the area.
## 3 Materials and Methods
### Algorithms and Frameworks  
In this paper, several algorithms were examined for goodness of fit.  
Random forest (rf) is an aggregation technique suggested in [51] and considered one of the most precise general purpose tools. It consists of the creation of several decision trees over samples of a data set generated by random sampling with replacement. Its basic principle consists of injecting randomness to the construction of each individual tree to improve accuracy of the aggregated model.  
Linear Discriminant Analysis (lda) [52] is a linear transformation technique used to reduce dimensionality. lda determines the directions (linear discriminants) representing the axes that maximize the separation between multiple classes.  
Logistic Model Trees (Imt) combine model trees and logistic regression functions at the leaves. A stagewise fitting process is used to construct the logistic regression models that can select relevant attributes in the data [53].  
Generalized Linear Model (glm) is a generalization of classic linear regression. Generalized linear models were presented [54] in 1998 as a way of unifying several other statistical models including linear regression, logistic regression, and Poisson regression under one theoretical framework. This allowed them to develop a general algorithm to estimate maximum likelihood in all these models.  
In 1992, [PERSON] [55] presented support vector machines (svm), a specific class of algorithms characterized by the usage of kernels, absence of local minima, and control over the number of supporting vectors. Support vector machines can be applied both to classification and regression problems [56].  
[PERSON] [57; 58] presented the Stochastic Gradient Boosting (gbm) algorithm. This algorithm employs several models that aggregate and result in a final model with better predictive accuracy than that of the models used individually.  
The eXtreme Gradient Boosting (XGBoost) software library is an open source implementation of a supervised learning algorithm that attempts to predict in an appropriate way a destination variable by combining estimations of a group of simpler and weaker models. It offers several advanced features for model tuning, computing environments, and algorithm enhancement [59]. XGBoost is suitable for performing the three main forms of gradient boosting, and it is robust enough to support fine-tuning and inclusion of regularization parameters. XGBoost has proven to work quite well in automatic learning competitions.  
Nowadays, several frameworks are used to work with predictive models such as CORElearn [60], mlr [61], or Scikit-learn [62]. In the present work, we used the caret package (Classification and Regression Training) [63]. Caret is an interface that unifies under just one framework several machine learning packages, making data preprocessing, training, optimization, and validation of predictive models easier, and with native support for parallel calculations [43].
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Scenario Setup and Performance Metrics
On the other hand, for the data packet transmission scenario shown in Figure 4b, two intermediate nodes (A and B) at the same hop level relay the data packet, thereby resulting in 3 redundant data packets being forwarded to node C. Although assuming that node C discards the redundant data packets, it still leads to a total of 9 data packets being forwarded by the intermediate relay nodes, resulting in a PRC value of 0.5. A greater PRC value implies formation of more multiple relay nodes at single hop level referring to the inefficient unicast operation of the routing protocol. Figure 4: Illustration of data packet transmission scenario: (**a**) perfect unicast transmission and (**b**) data packet transmission with multiple relay nodes at single hop level.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Description of Testing Tools
#### 2.2.1 Karlos  
Karlos is a tool designed for analyzing and verifying C-ITS communication. In this article, we focus on analyzing CAM and DENM. The tool processes input PCAP files that contain these messages and generates three main output files from the analysis:  
* Summary--This file contains selected parameters from CAMs/DENMs and compares their values with the relevant ranges defined in the respective standards. Users have the option to specify in advance which parameters will be included in this summary. This approach allows for targeted analysis, such as focusing only on speed values (speedValue) and their deviations from expected values. An example of such a summary is shown in Figure 3. Here, you can see an example of a list of parameters and their evaluation based on the number of messages. The number in brackets indicates the number of messages corresponding to each state (OK, error, warning).  
Figure 3: Summary of parameters and their state evaluation for CAM messages in Karlos. The number in brackets represents the count of messages per status: OK (green), error (red), and warning (yellow).  
* Complete List--This file provides detailed information about each individual CAM/ DENM, enabling users to trace specific messages and conduct in-depth analysis. This list is crucial for potential retrospective tracking of anomalies or erroneous messages.
* Map Visualization--This feature allows CAMs/DENMs to be displayed on a map. Each CAM is shown based on the speed indicated in the message, with different speeds represented by different colors. Each message can be clicked on the map to view detailed information about it. An example of this map visualization is shown in Figure 4.  
Karlos also allows users to specify how the analysis should be conducted, enabling filtering of messages based on specific parameters, such as stationID, allowing for a more focused examination of the selected data.  
Karlos is developed in Python and utilizes the Wireshark tool, specifically its component tshark, for preprocessing PCAP files.  
The output values in the files are categorized into three types:  
* Correct Result--The analyzed parameter value lies within the permissible range according to the specification. If possible, an interpretation of the value is also provided.
* Result with Warning--This type of result indicates that the parameter value is permissible according to the specification, but its actual significance is limited. This typically occurs in situations where the parameter suggests that the actual value is unavailable or where an optional parameter is missing in the message.
* Incorrect Result--This result indicates situations where the parameter value lies outside the defined range, or where the parameter indicates too high a measurement uncertainty, rendering the measurement invalid. An incorrect result also occurs when a mandatory parameter is missing in the message.