esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 5 Discussion  
The classification performance usually drops and faces different failures in the presence of imbalanced datasets. However, imbalanced datasets exist in a broad range of real-life research. Hence, for imbalanced binary classification problems, samples are usually categorized into two classes, majority and minority. Generally, the minority class often illustrates the more significant and crucial samples and interests rather than the majority class samples. Nevertheless, compared to the minority class, the majority class has a larger amount of samples, and in some cases, the situation may be exceedingly serious. Therefore, handling these problems efficiently has become a crucial and significant topic in machine and deep learning methods. To overcome these challenges, we proposed two methods that are based on DNN and CNN algorithms. At first, several classical and well-known undersampling and oversampling methods, such as RUS, Tomek Links, OSS, Near Miss, ROS, and SMOTE, were used in the data preprocessing procedure. Furthermore, to achieve better performance, we normalized current datasets. Then, we considered the focal loss function in the process of training the desired models, which are widely implemented in neural network frameworks for class imbalance problems. Moreover, due to the limited amount of datasets samples which causes unstable classification results, we have trained and evaluated our models for 100 runs and 2000 epochs. In the end, we analyzed our proposed models concerning the accuracy, precision, recall, F1-score, G-Mean, specificity, AUC, and Kappa as evaluation metrics. Based on 24 imbalanced datasets, the average performance score of the evaluation metrics of the executed models is indicated in Table 12.  
According to Table 12, it can be founded that the mixed SMOTE + NORM + CNN model has the best performance with 99.08% accuracy, 99.09% precision, 99.08% sensitivity, 99.09% F1-score, 99.08% G-Mean, 99.03% specificity, 99.08% AUC, and 98.92% kappa. Furthermore, the comparison of performance metrics between our study and study [29] on the same imbalanced dataset is demonstrated in Table 13.  
According to Table 13, the results show the efficiency of our proposed model performance on 16 imbalances that 99.00% recall, 99.00% G-Mean, and 98.98% F1-score at \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Method** & **Rec (\%)** & **G-Mean (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} [PERSON] and \\ [PERSON], [29] \\ \end{tabular} } & DB\_HS + SVM & 95.80 & 88.30 & 81.40 \\  & DB\_HS + RF & 98.10 & 92.00 & 83.80 \\  & DB\_US + SVM & 92.70 & 88.50 & 81.50 \\  & DB\_US + RF & 95.60 & 93.80 & 87.90 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Current \\ Study \\ \end{tabular} } & SMOTE + NORM + CNN & 99.00 & 99.00 & 98.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The comparison of performance metrics on the same dataset.
# Pigment-Related Mutations Greatly Affect Berry Metabolome in San Marzano Tomatoes
###### Abstract  
The study describes the alterations in metabolomic profiles of four tomato fruit mutations introrogressed into _Solanum lycopersicum_ cv. San Marzano, a well-known Italian traditional variety. Three lines carrying variants affecting the content of all pigments, _high pigment-1_ (_hp-1_), _hp-2, pigment diluter_ (_ptd_), and a combination of _Anthlocumin fruit_ and _atroviolaccum_ (_Aft_atv_), were selected, and characterized. Biochemical analysis of 44 non-polar, 133 polar, and 65 volatile metabolites in ripe fruits revealed a wide range of differences between the variant lines and the recurrent parent San Marzano. Among non-polar compounds, many carotenoids, plastoquinones, and tocopherols increased in the fruit of high pigment lines, as well as in _Aft_atv_, whose \(\beta\)-carotene levels increased too. Interestingly, _pd_ displayed enriched levels of xanthophylls (all-trans-neoxanthin and luteoxanthin) but, simultaneously, decreased levels of \(\alpha\)-and \(\beta\)-/-/y-tocopherol. Looking at the metabolites in the polar fraction, a significant decrease in sugar profile was observed in _hp-1_, _pd_, and _Aft_atv_. Conversely, many vitamins and organic acids increased in the _hp-2_ and _Aft_atv_ lines, respectively. Overall, phenylpropanoids was the metabolic group with the highest extent of polar changes, with considerable increases of many compounds mainly in the case of _Aft_atv_, followed by the _pd_ and _hp-2_ lines. Finally, several flavor-related compounds were found to be modified in all mutants, mostly due to increased levels in many benzenoid, lipid, and phenylalanine derivative volatiles, which are associated with sweeter taste and better aroma. Construction of metabolic maps, interaction networks, and correlation matrices gave an integrated representation of the large effect of single variants on the tomato fruit metabolome. In conclusion, the identified differences in the mutated lines might contribute to generating novel phenotypes in the traditional San Marzano type, with increased desirable nutraceuticaltical and organoleptic properties.  
color mutations; landrace; San Marzano tomato fruits; biochemical analysis +
Footnote †: journal: _Horticulturae_
## 2 Materials and Methods
### Data  
The MultiSpectral Imager (MSI) onboard of Sentinel-2 acquires data in 13 spectral bands, from 442.7 nm to 2202.4 nm, at 3 different spatial resolutions: 10 m (bands 2, 3, 4, 8), 20 m (bands 5, 6, 7, 8a, 11, 12) and 60 m (bands 1, 9, 10). It is a push-broom sensor with 290 km swath and a revisit time of 5 days. For lava flow mapping, we used the spectral bands at the higher spatial resolution, namely 492.4 nm (Band 2--Blue), 559.8 nm (Band 3--Green), 664.6 nm (Band 4--Red) and 832.8 nm (Band 8--NIR). We downloaded SENTINEL-2 MSI level 1 free-access data from [[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)).  
Since our goal is to discriminate the most recent lava flow fields from older ones, information on the imaged area before and after the eruption has to be considered. Sentinel-2 MSI data is available twice a day, but images may be inadequate to the task due to cloud or snow covering, or other forms of corruption. Additionally, the post-eruptive images should be taken as close as possible to the end of the eruption, a constraint that may be relaxed for the pre-eruptive data. Indeed, for the post-eruptive data, rather than a single perfectly clear image taken much later, it may be better to use multiple images with partial covering or corruption, but taken closer to the end of the eruption, so that defective pixels in one image may be compensated by clear pixels in the other. Ultimately, in the best situation, the candidate post- and pre-eruptive images are recorded just a few days after and a few days before the eruption date; however, in the worst case they may have been recorded even months apart.  
By only using multispectral images, misclassification errors may occur more frequently, i.e., some pixels classified as covered by fresh lava flows actually do not belong to the active flows. One significant source of error is due to the steep slopes of the volcano. Using the DEM to mask out parts of the image where the slope is very large allows us to significantly mitigate this problem. For all case studies, we utilize the 2-m resolution DEM of Etna in shaded relief form considering the solar angle at the Sentinel 2 acquisition time in order to take into account terrain shadows, thus allowing to discriminate them with respect to recent lavas. DEM was generated by very high-resolution tri-stereo optical imagery acquired by the Pleiades-1 satellite constellation on 18 July 2016 [76].  
Finally, for each effusive eruption, we considered two post-eruptive Sentinel-2 images, one pre-eruptive Sentinel-2 image, and the DEM updated to 2016 to build-up the input matrix for the machine learning algorithm. In particular, each pixel of the imaged area (having a size of NxM) is considered as made of 13 components, i.e., 4 values (2, 3, 4, 8 bands) for each of the two post-eruptive images and a pre-eruptive image, plus the DEM information. Thus, the input matrix for the algorithm has size (N \(\times\) M) \(\times\) 13.
## 2 Results and Discussion
### Methyl-\(\beta\)-Cyclodextrin/AEDTA Complex Characterization
The signals due to the more external protons, H\({}_{1}\) and the protons of the methoxy groups (Figure 5), underwent a minor shift, further indicating that the guest interacted with the internal cavity. The NH\({}_{2}\) signal of AEDTA could not be seen in the NMR spectra because the amino group had exchangeable protons that broadened the signals. Thus, no direct interaction between these protons and the host was detectable by NMR. Nonetheless, the change in the amino group scissoring vibration, observed in the FTIR spectra, supported the hypothesis of an interaction between AEDTA and methyl-\(\beta\)-cyclodextrintrim involving the NH\({}_{2}\) group and the cyclodextrin internal protons, while the thiadiazole ring and ethyl group of AEDTA remained external to the cavity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methyl-\(\beta\)-Cyclodextrin** & \(\mathbf{H_{1}}\) & \(\mathbf{H_{1 us}}\) & \(\mathbf{H_{3s}}\) & \(\mathbf{H_{3 us}}\) & \(\mathbf{H_{5}}\), \(\mathbf{H_{6}}\) & \(\mathbf{H_{4}}\) & \(\mathbf{H_{2 us}}\) & \(\mathbf{H_{CH3}}\) & \(\mathbf{H_{2s}}\) \\ \hline \(\delta\) (ppm) & 5.313 & 5.131 & 4.636 & 4.401 & 3.926 & 3.712 & 3.664 & 3.624 & 3.445 \\ \(\Delta\delta\) free-complex (ppm) & \(-0.007\) & \(-0.002\) & 0.558 & 0.346 & 0.006 & 0.006 & \(-0.006\) & 0 & \(-0.007\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical shifts (\(\delta\)) of methyl-\(\beta\)-cyclodextrintrim and chemical shifts displacements (\(\Delta\delta\)) in the complex (1:1 molar ratio). Figure 4: \({}^{1}\)H-NMR spectra (600 MHz, D\({}_{2}\)O) of AEDTA, methyl-\(\beta\)-cyclodextrintrim, and their complex. It is worthy of note that no new peaks were observed in the NMR spectrum of the complex, suggesting that the inclusion of AEDTA in methyl-\(\beta\)-cyclodextrintrim occurred with an exchange process in the NMR timescale [12].
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 4 Results, Analysis, and Discussion
### Results and Analysis
#### 4.1.2 Quantitative Analysis  
The size and shape of slope units can be used to quantitatively evaluate the extracted results.  
The unit size is crucial for mapping landslide susceptibility, as various indicators describing geological and environmental factors are extracted on the basis of slope units.  
Figure 6: Slope units extracted via the TFPF-SU and hydrological methods: (**a**) Case 1—the TFPF-SU method is used to divide the slope units; (**b**) Case 1—the hydrological method is used to divide the slope units; and (**d**) Case 2—the hydrological method is used to divide the slope units.  
To make the environmental factors of each unit comparable, the area covered by each unit should be similar. In this study, size uniformity can be reflected by the distribution of the unit area. Figure 7a,b shows the area distributions of the slope units extracted via the two methods. The TFPF-SU method divides the region into slope units with areas generally between 100,000 and 500,000 m\({}^{2}\), with 69.93% of the units falling within this range. The hydrological method divides the region into slope units with areas generally between 20,000 and 1,000,000 m\({}^{2}\), with 38.78% of the units falling within this range. In addition, the average, standard deviation, minimum, and maximum values of the slope unit area obtained with the TFPF-SU method are 422,774, 286,190, 1819, and 4,802,357, respectively, and those for the hydrological method are 1,600,211, 1,381,013, 21,829, and 13,206,480, respectively. Overall, the TFPF-SU method more uniformly extracts slope elements.  
The shape of a slope unit can be described by the ratio of the square of the perimeter to the area (R). The minimum ratio of circles is 12, whereas the ratios of squares and equilateral  
Figure 7: The size and shape of the slope elements were evaluated via the TFPF-SU method and the hydrological method.  
triangles are moderately higher, with values of 16 and 20, respectively. If a unit is too flat or elongated, the ratio greatly increases. When the value of R reaches 30, the aspect ratio of the slope unit exceeds 5:1. Among all slope units extracted via the TFPF-SU method, 70% of the units have R index values ranging from 16 to 28, and 16.79% of the R index values exceed 30. Among all the slope units extracted via the hydrological method, approximately 32.03% of the units have R index values between 16 and 28, and 57.94% have R index values exceeding 30. It can be seen that the slope units extracted by the TFPF-SU method are more uniform than those extracted by the hydrological method.  
Therefore, the TFPF-SU method is more suitable than the hydrological method for dividing landslide susceptibility mapping units.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 4 Multi-Channel Routing in WSNs
### Complexity of the Multi-Channel Allocation Scheme  
For the proposed multi-channel operation, each sensor node is assigned a specific receiver channel, which is the channel in which it can receive. Nodes remain tuned to their respective receiver channels by default, and temporarily switch to that of the receiver channel of their parent for transmission. We first show that our multi-channel assignment problem is similar to the coalition formation game described in [48; 49].  
Coalitional game theory mainly deals with the formation of groups, i.e., coalitions, that allow each player to strengthen their positions in a given game. Players may prefer to collaborate to form coalitions for maximum gains. We use the framework of coalitional game theory to determine the _stable_ coalition structure, i.e., a set of coalitions whose members do not have incentives to break away. Essentially, a coalition game consists of three main components: a player set, a set of disjoint coalitions, and a _value_ for each coalition. The outcome of this game should be an optimal coalition structure generation such that possible gains are fairly distributed among the players.  
Our _multi-channel allocation_ problem is identical to the coalition structure generation problem by assuming the sensor nodes as agent set \(\mathcal{N}\) and the assignment of the channels to the sensor nodes as a coalition structure. Thus the problem boils down to find out the optimal allocation of channels to the sensor nodes, to maximize the social utility. As the optimal coalition structure generation problem is NP-hard, our multi-channel assignment problem is NP-hard too. This is because of the fact that the number of possible coalition structures is given by the Bell number, which exponentially grows with \(|\mathcal{N}|\)[48].
## 4 Discussion
#### Limitations and Practical Implications  
The current study has some limitations that future research should address. This study only focused on the effects of VAR on the matches played in the Turkish Super League. Therefore, there is a need to analyze matches played in many other football leagues for more generalizable results. This research only analyzed match performance indicators that are related to referee decisions. However, several factors may have influenced the findings, including home advantage, crowd noise, referee experience, team ability, tactical evolution, general adaptation of player behavior, and changes in refereeing at the international level. It is therefore recommended that future research should consider these factors when analyzing VAR. Finally, future research could assess the effects of VAR on players, coaches, and referees using qualitative methods alongside quantitative data; investigate how VAR affects referees' decision-making processes by conducting interview-based analysis; and investigate players' behavior and team performances in the minutes after VAR decisions by conducting time-stamped intervention analysis.  
The results of this study provide several practical implications for stakeholders in professional football. Coaches and players should be aware that the consistent use of VAR leads to a measurable decrease in fouls, offsides, and yellow cards, suggesting that more disciplined and strategic behaviors are increasingly rewarded under VAR-regulated conditions. Consequently, training sessions may incorporate situational simulations involving video review scenarios to prepare athletes for real-time decision-making under scrutiny. Referees and officiating committees may also use these findings to design targeted educational programs that improve the interpretation and implementation of VAR protocols. Finally, sports governing bodies could consider these results when evaluating the broader impact of VAR on game fairness, flow, and perception by fans. These behavioral shifts align with earlier findings indicating that the introduction of VAR modifies incentives and reduces unsporting conduct among players due to greater oversight [31].
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Example 1  
Figure 2 illustrates the synthesis of antenna weights for varying lobe widths and array sizes, demonstrating how these factors influence the radiation pattern. By adjusting the antenna weights, it is possible to control the main lobe's width, affecting both angular resolution and directivity. Additionally, increasing the array size sharpens the beam, reduces side lobes, and enhances gain in the primary direction. This optimization process aims to shape the radiation pattern to minimize interference and maximize system performance.  
In Example 1, the radiation patterns of antenna arrays are generated and analyzed using the [PERSON] method for different array sizes (4, 8, 16, and 32 elements). The taylor1p function is used to calculate the weights of the array, considering a side-lobe level of 20 dB and a half-wavelength element spacing. The gain1d function then computes the array gain at 720 angles. The radiation patterns (gain versus angle) are plotted for each array configuration, with thickened curves for better visibility using the LineWidth property. Additionally, the weights are converted into magnitude and phase values, which are displayed in degrees. The results are presented both numerically (magnitude and phase) and graphically in dB, showcasing how increasing the number of array elements reduces sidelobes and narrows the main lobe. This demonstrates the impact of [PERSON]'s array synthesis method on antenna performance.
## Materials and Methods  
### Plant Material, Animals and Chemicals  
Date palm pit variety Majdool (KSA) was obtained from a commercial store in Cairo (Egypt) and subjected to roasting (RS) as described in Section 2, whereas roasted date commercial pit products Nawat-Altamr Coffee \"Almasmak Trade(r)\" (P1) and (P2) were purchased from Riyadh (Saudi Arabia). A commercial coffee powder \"Hintz(r)\", Germany\" (C) was used for comparison. The SPME holder and fiber coated with 50 \(\upmu\)m/30 \(\upmu\)m DVB-CAR-PDMS were supplied by Supelco (Oakville, ON, Canada). Normal saline was purchased from El Nasr Pharmaceutical Chemicals Company (Cairo, Egypt). Phenobarbital sodium and all other chemicals and volatile standards were provided from Sigma Aldrich (St. Louis, MO, USA).  
For the biological study, adult male Swiss mice (22 \(\pm\) 4 g) were purchased from the animal house colony of National Research Centre (NRC), Giza, Egypt. The animals were housed in stainless steel wire-meshed plastic cages under standard conditions of humidity, temperature (25 \(\pm\) 2 \({}^{\circ}\)C), and light/dark cycles (12/12 h). Rodent chow diet and water were allowed ad libitum. All experiments were carried out in accordance with the research protocols established by Research Ethics Committee in the Faculty of Pharmacy, Cairo University and by Medical Research Ethics Committee (MREC) in NRC, which follow the recommendations of the National Institutes of Health Guide for the Care and Use of Laboratory Animals (Ethical Approval Certificate number MP (1589)).  
The Preparations of Roasted Date Pits, Commercial Pit Product and Coffee Product Extracts for Chemical and Biological Analyses  
For the preparation of home-roasted pit (RS) for subsequent chemical analysis, date palm pits (100 g) were oven roasted at 120 \({}^{\circ}\)C for 3 h, with stirring using a magnetic stirrer; roasted pits were then ground into fine powder.  
For biological studies, commercial products, viz., roasted date pits (P1) and coffee (C), 150 g of each were extracted with 100% methanol by cold maceration until complete exhaustion. The methanol extract was evaporated under reduced pressure at a temperature not exceeding 40 \({}^{\circ}\)C until dryness, and the solvent was further evaporated under reduced pressure to yield the corresponding extracts (4.6 and 106.5 g) respectively.
## 4 Metabolic Pathway and Host Engineering for Advanced Biofuels Production
### Fatty Acid-Based Biofuels
For example, NADPH-dependent fatty aldehyde reductase from _Marinobacter aquaeolei_ VT8 exhibited reducing activity for both acyl-CoAs and subsequently produced aldehydes [55], which suggests that expression of this enzyme possibly reduces accumulation of toxic aldehyde intermediates [49]. Lastly, the methyl ketone pathway has been engineered in an _E. coli_ strain with _fadA_ gene deletion by overexpression of \(\beta\)-ketoacyl-ACP synthase II (FadB), an acyl-CoA oxidase from _Microococcus luteus_, and a thioesterase (FadM) to produce \(\beta\)-keto fatty acids followed by hydrolysis and decarboxylation to ketones [29, 56]. Since fatty acids are the primary precursors, many engineering approaches have focused on increasing available fatty acids [39]. Those approaches include overexpression of thioesterases, blocking fatty acids degradation via \(\beta\)-oxidation, genomic modification to increase metabolic flux to malonyl-CoAs and balancing FA pathway intermediates by sensor-regulator and FA synthase subunits. In addition, novel and synthetic pathways for fatty acid production were proposed. Most significantly, fatty acids were over-produced by reversing \(\beta\)-oxidation while avoiding ATP consumption and tight regulation related to acetyl-CoA carboxylase, which produces the FA synthesis precursor, malonyl-CoA [57]. Using this synthetic pathway, fatty acids were produced up to \(\sim\)7 g/L (up to 80% of its theoretical maximum yield) [57]. In another study, the carbon chain lengths of fatty-acid-derived fuels were extended in the range from C\({}_{6}\) to C\({}_{1}\)s in _E. coli_ by introducing alternative carboxylic acids reductase from _Mycobacterium marinum_[58]. Furthermore, it was reported that the composition of fatty acid-derived biofuels could mimic that of diesel or aviation fuel when free fatty acid pools were modified to have iso-branched fatty acids and alkanes in _E. coli_[59]. Composition of iso-branched fatty acids could be further increased to 20% of the total fatty acids by expression of biosynthesis genes for threonine and isoleucine, although the total titer of fatty acids was decreased [60].