esa-sceva/satcom-chunk-collection huggingface dataset:
# The Infestation of Olive Fruits by _Bactrocera oleae_ ([PERSON])
## 1 Introduction
The key enzymes acting on olive phenolic glucosides are \(\beta\)-glucosidase that hydrolyze these glucosides [18], forming secoiridoid aglucones containing the phenolic alcohols tyrosol (\(p\)-HPEA) and hydroxytyrosol (3,4-DHPEA), and the oxidoreductase, mainly polyphenol oxidase (PPO), which catalyzes the oxidative degradation of phenolic compounds and reduces the final phenolic content of VOO [19]. The characteristic phenolic profile of VOO comprises of five major groups of compounds: tyrosol and hydroxytyrosol containing compounds, lignans, flavonoids, and simple phenolic acids. Tyrosol and hydroxytyrosol derivatives, having a secoiridoid chemical structure, are the most abundant phenolic components in most olive oils. Among them, the aplucones of oleuropein (3,4-DHPEA-EA), ligstroside (\(p\)-HPEA-EA), decarboxymethyloleuropein (3,4-DHPEA-EDA, oleacein), and decarboxymethylligstroside (\(p\)-HPEA-EDA, oleocanthal) are those having the most prominent biological activities. As in the case of volatile compounds formed through the LOX pathway, some secoiridoid compounds act as defense chemicals against herbivores and pathogens. Similarly, the main enzymes involved in shaping the phenolic profile of the VOO, such as \(\beta\)-glucosidase and PPO, seem also to be associated with the plant's natural defense mechanisms [20; 21]. The two main biotic stressors of the olive fruit are the olive fruit fly, _Bactrocera oleae_, and the _Verticillium dahliae_ fungus [22; 23]. _B. oleae_ is a dipteran of the _Teplritidae_ family whose larvae feed on the pulp of the olive fruit causing multiple detrimental effects, such as the premature fall of the fruits, the decrease in oil yield, and/or negative alterations in the organoleptic properties of VOO [23]. The differences in susceptibility/tolerance of the different olive cultivars to the _B. oleae_ pest may rely on mechanical factors (e.g., cuticle waves), chemical factors (e.g., phenolic or volatile compounds), morphology of the fruits (e.g., size of the fruit), or a combination of them, so it is clear that a number of genes may be affected as a consequence of the infestation [24]. We have previously found that infection of Picual olive trees by the defoliating pathotype of _V. dahliae_, apart from reducing olive fruit yield, altered the balance between C6 and C5 volatile compounds in oil aroma while reducing the synthesis of the main phenolic compounds [25]. There is very little data on the impact of _B. oleae_ infestation on the volatile profile of VOO and the few studies carried out on the effect of this pest on the phenolic composition of oils reported very different and sometimes contradictory results. The aim of the present study was to analyze the changes induced by _B. oleae_ infestation in the biosynthesis of volatile and phenolic compounds in the olive fruit (cvs. Picul, Manzanilla and Hoijblanca), and evaluate the effect of those changes on the deduced organoleptic and functional quality of VOO.
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 2 Proposed Model Layout
The experimental work of the proposed model is not available in laboratory due to high cost of devices; however, the analytical approach of the proposed model is investigated with realistic values of the modules and data transmission link, using Optisystem simulation model, which is considered as powerful tool for optical fiber transmission systems analysis. Optisystem provides a large set of optical and RF components library to form a complete downlink and uplink transmission system. It also provides a high level of integration with MATLAB for signal processing and implementation of custom based equalizers for the receiver side. Table 1 lists the names of elements with their notations and magnitudes, which are applied for analyzing the WDM-RoF model and minimizing the impact of PAPR, NLIs and LDs. The values used in this setup have been taken from the recent field trials and experimental work to present a realistic analysis.
## 3 Results
### Synthesis of Variables  
Knee pain was assessed by combining data from the Visual Analogue Scale for pain (VAS pain) [58; 63; 65; 66; 67; 68; 69] and the Numeric Pain Rating Scale for pain (NPRS pain) [64]. Knee function or knee disability was assessed with data from the Western Ontario and McMaster Universities Arthritis Index (WOMAC) [62; 63; 65; 66], the Lower Extremity Functional Scale (LEFS) [64], and the Hospital for Special Surgery knee score (HSS) [61; 67]. Dynamic balance has been assessed by integrating data from the Timed Up and Go-Test (TUGT) [60; 61; 62; 68; 69] and the Five Times Sit-to-Stand Test (FTSST) [59]. The knee flexion ROM was measured using goniometry [63; 64; 66; 68]. The strength of the quadriceps muscle was assessed with dynamometry [65; 68; 69]. Gait speed was assessed by calculating the time spent walking a distance in m/s or km/h [58; 59; 69]. Aerobic capacity was assessed with data from the 6-Minute Walk Test (6 MWT) [62] and the 2-Minute Walk Test (2 MWT) [64]. Functional independence was assessed by combining data from the Functional Independence Measure (FIM) [65] and the Modified Barthel Index (MBI) [69]. Table 2 shows the variable assessed in each RCT included, the measurement tool or test employed, and the qualitative findings.
## 4 Enhanced P&O Algorithm  
### The Conventional P&O Technique  
Among the existing MPPT techniques, the most popular and frequently used is the P&O technique due its simplicity [8]. The operation principle is based on periodical perturbation on the terminal voltage of the PV module and comparison between the current power value of the PV output and the power at the previous perturbation. The operating point is continuously moving in the same direction if the PV module power rises with increasing operating voltage; otherwise, the operating point direction will be reversed. Figure 4 explains the MPP tracking procedure of the P&O technique.  
In general, regarding the issues facing the P&O, the first main disadvantage is its oscillations around the MPP, which can impact the tracking speed. Another drawback is that under fast changes in radiation, the P&O may lose the correct direction of tracking and, consequently, a considerable energy loss would be accrued [35]. Furthermore, the classical form of P&O is not efficient under partial shading conditions. In this work, the classical P&O algorithm is enhanced to con  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Irradiance (W/m\({}^{2}\))** & \begin{tabular}{c} **V\({}_{\text{mpp}}\)** \\ **(V)** \\ \end{tabular} & \begin{tabular}{c} **I\({}_{\text{mpp}}\)** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **P\({}_{\text{mpp}}\)** \\ **(W)** \\ \end{tabular} & \begin{tabular}{c} **R\({}_{\text{mpp}}\)** \\ **(f)** \\ \end{tabular} & \begin{tabular}{c} **D\({}_{\text{mpp}}\)** \\ **(V)** \\ \end{tabular} & \begin{tabular}{c} **V\({}_{\text{out}}\)** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **I\({}_{\text{out}}\)** \\ **(f)** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}_{\text{L}}\)** \\ **(mH)** \\ \end{tabular} &
\begin{tabular}{c} **C** \\ **(uF)** \\ \end{tabular} \\ \hline
**1000** (Higher) & 70.4 & 9.1 & 640.6 & 7.74 & 0.61 & 179 & 3.6 & 50 & 1.6 & 500 \\
**200** (Lower) & 63.57 & 1.793 & 114 & 35.5 & 0.16 & 75.7 & 1.5 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: PV array and boost converter characteristics under the lower and higher solar irradiances.  
Figure 4: Flowchart of the conventional Perturb and Observe (P&O) algorithm.
## 1 Introduction
Unfortunately, mobile devices are not base stations that use redundant processing, storage, and backup generators. Figure 1 below succinctly captures a typical OLSRv2 mobile scenario comprising four mobile nodes gradually drifting apart. Broadcasts from node A are heard by node B. However, node B's broadcasts are unable to reach node A. Thus, the link between nodes A and B is asymmetric. Nodes B and D are two-hop neighbors because node C has a symmetric link with both. Node C is equally a one-hop neighbor of both nodes B and D. Although the nodes are drifting apart, OLSRv2 expects each node to broadcast HELLO messages at a HELLO_INTERVAL(s) of 2 s, which will continually trigger table updates and dissipate energy, even when nodes are outside each other's broadcast range. Figure 1: Group of OLSRv2 nodes (Letters A-D represent examples of nodes). A careful analysis of the opposing arguments in [30] revealed that OLSRv2 and OLSR excessively flood the network with messages. Therefore, a logical solution to this problem is to modify OLSRv2 by reducing the need for continuous network flooding while maintaining optimum link quality and energy efficiency. The significance of routing techniques in D2D communication systems has pushed many researchers to optimize the routing protocol for efficient and reliable data routing in D2D communication. However, none of the related work presents a structured and energy-efficient link sensing technique, database maintenance technique and multi-criteria mobility, battery, node degree and connection to the base station for MPR selection based on node status, which is the goal of this paper. The main contributions of the paper are as follows:  
* The introduction of a non-forwarding HI and Emergency Hello (EH) messages as a response to HELLO messages and for priority Hello broadcast, respectively. * A novel message sequence that replaces OLSR's HELLO, HELLO, and TC message sequence with a new sequence that interweaves HELLO and TC messages with three database (DB) chores and LISTEN states for improved link and energy efficiency. The new message sequence is _DB_SELF_, _LISTEN_, HELLO, _DB_NEIGHBORS_RCVD_, HI, _DB_NEIGHBORS_BOTH_, TC, and _DB_ROUTES_ sequence. * The implementation of the modified algorithm into OLSRv2 protocol that selects the best node as an MPR considering the four (4) parameters, namely node battery level (BL), mobility speed (MB), node degree (ND), and connection to base station (CBS) for optimum relay selection, network lifetime, and energy efficiency.
## 5 Conclusions  
In this study, the BEPS-derived NPP and MODIS-derived NDVI anomaly for 2003-2018 were used to examine the response of NPP to drought events. We affirmed that NPP and NDVI anomalies are good indicators of drought and its impacts on vegetation productivity. The following provides a summary of the main findings of this study:1. The study indicated that the BEPS model could predict terrestrial NPP in Mongolia. The growing season NPP was increased in 56.37% of the vegetated lands, whereas 43.63% of areas showed a decreasing trend from 2003 to 2018. Overall, the variation of NPP exhibited a slightly increasing trend over the entire study period.
2. Whilst the NPP trend was partly influenced by various climatic variables, drought events played a key role in NPP variations in the Mongolian rangeland. A 10-50% drop in NPP was observed for mild and moderately drought-affected areas of the county, while the NPP in severe drought areas, mainly in the eastern regions, declined by approximately 10-75% during the six major drought events.
3. The response of the NPP to drought conditions for different vegetation types was diverse due to variation in their drought resistance ability. The forests were generally more drought resistant than shrubland, while sparse vegetation exhibited extreme sensitivity to drought followed by wetland and grassland.
4. The model and data used in this study identified the major drought events in Mongolia and highlighted vegetation types that would most likely be sensitive to future drought events. Therefore, our results suggest that this methodology could be applied to assess the impacts of drought on NPP for arid and semiarid regions, which is the basis for drought preparedness strategies.  
Conceptualization and funding acquisition: J.Z.; data curation, writing--original draft preparation, software, methodology, validation, and formal analysis: L.N.; writing--reviewing and editing: B.T., S.Y., S.R., F.A.P., and T.P.P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CAS Strategic Priority Research Program (No. XDA19030402), the National Natural Science Foundation of China (No. 41871253 and 31671585), and the Taishan Scholar Project of Shandong Province.  
Not applicable.  
The authors would like to acknowledge the valuable comments on the manuscript provided by the chief editor and anonymous reviewers.  
The authors declare no conflict of interest.
# The Impacts of Climate and Human Activities on Grassland Productivity Variation in China
## 3 Results
### The Effects of Climate Factors on Grassland GPP  
PRE, TEM, and RAD, as the main climatic factors, had significant effects on grassland change. The Sen-MK trend detection results showed that the temporal trends of PRE, TEM, and RAD in the whole region had no significant trend during 2000-2018. Spatially, over 9.04% of the study area presented an obvious increasing trend of 9.38 mm yr\({}^{-1}\), mostly distributed in the middle, northeast, and southwest of the study area (Figure 5a). In contrast, approximately 7.53% of the area showed the opposite trend (\(-\)11.48 mm yr\({}^{-1}\)), and these areas were concentrated in the northwest of the study area. Similar to PRE, nearly 9.71% and 7.61% of the study area had significant uptends and downtends, respectively (Figure 5c). Moreover, in the southwest of the study area, the central QH and SC experienced significant increases. Compared with PRE and RAD, only 9.48% of the study area had a significant downtrend, and most had an increasing trend (Figure 5e). Individual provinces (autonomous regions), including SX (5.73 mm yr\({}^{-1}\)), SAX (8.20 mm yr\({}^{-1}\)), GS (3.40 mm yr\({}^{-1}\)), SC (0.04 \({}^{\circ}\)C yr\({}^{-1}\)), and QH (0.04 \({}^{\circ}\)C yr\({}^{-1}\)), showed significant trends for PRE and TEM.  
Figure 4: The temporal trend of GPP during 2000–2018 in 10 provinces (autonomous regions) (Significance levels are denoted by asterisks: ** for \(p\) < 0.05).  
Based on the partial correlation analysis, the relationships between GPP and PRE, TEM, and RAD during 2000-2018 were identified (Figure 5b,d,f). Overall, among the three main climate factors, PRE played a major role in affecting GPP dynamics, with a positive correlation greater than 0.59, and these areas occupied nearly 13.79% of the study area, mainly in the middle and northeast regions. Moreover, the spatial distribution of the positive and negative effects of TEM and RAD on GPP was relatively consistent; an obvious positive correlation was found in the middle of the study area, that is, at the junction of QH, TB, and SC, and the negative effect was most significant in the northeast of the study area. The partial correlation analysis results of each province showed that, except for SC, TB, and QH, the GPP of grassland in seven provinces showed a significant positive correlation with PRE. However, only three provinces (autonomous regions), including SC, GS, and QH, had a significant positive correlation with TEM. In addition, there was no significant partial correlation between RAD and GPP at the provincial (autonomous regions) level.  
Figure 5: Spatial variation of TEM, PRE, and RAD (**a,c,e**) and correlation between GPP (**b,d,f**) during 2000–2018 (Significance level is \(p<0.05\)).
# Aqueous-Organic Phase Transfer of Gold and Silver Nanoparticles Using Thiol-Modified Oleic Acid
## 3 Results
### Synthesis of Gold and Silver Nanoparticles  
Gold nanoparticles (GNP) resulted in spherical morphology, evaluated by scanning transmission electron microscopy (STEM) (Figure 4a). GNP presented good dispersion in an aqueous solution and displayed a small particle size of 13 \(\pm\) 2 nm when assessed by the particle size distribution histogram presented in Figure 4b. The solid line in that figure is a Gaussian fit to the data. The reaction temperature from 100 \({}^{\circ}\)C left a smaller particle size distribution and uniform morphologies [1]. [PERSON] and [PERSON] reported that uniform spherical gold nanoparticles of around 10, 25, and 50 nm were produced through the same method [1].  
Silver nanoparticles (SNP) presented mostly spherical morphology, but with the presence of few rods, as could be seen in the STEM micrograph shown in Figure 4c. SNP presented good dispersion in aqueous solution and small particle sizes of 57 \(\pm\) 7 nm when evaluated by the particle size distribution histogram shown in Figure 4d. The solid line in the figure is a Gaussian fit to the data. Similar nanoparticle sizes were reported by [PERSON] et al. [19], who used the same method. These silver particles can be used as a nucleation center for additional growth by adding more silver nitrate and hydroxylamine hydrochloride. A direct increase in the absorbance at the 400 nm peak, related with the surface plasmon resonance of silver nanoparticles, by increasing the silver nitrate concentration, is reported in the literature [19].  
Figure 3: (**a**) \({}^{1}\)H NMR spectrum of OAS in CDCl\({}_{3}\); (**b**) \({}^{13}\)C NMR spectrum of OAS in CDCl\({}_{3}\).
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods  
This section first introduces two data decomposition methods, ensemble patch transform (EPT) and variational modal decomposition (VMD). Then, a data sequence complexity analysis method, FE, is introduced. finally, the temporal convolutional network (TCN) and temporal pattern attention (TPA) mechanisms are introduced.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Genetic Algorithm Setting
#### 3.5.3 Initial Population  
The initial population is created by generating random-valued chromosomes that satisfy all bounds and linear constraints. This can be performed by creating many individuals on the boundaries of the constraint region and then creating a well-dispersed population.
## 4 Analysis and Discussion
### Business Layer  
All network values are monitored and compared with typical values, such as bandwidth, latency (delay), bandwidth--delay product throughput, and jitter. At the end of this stage, a thorough report is built and sent to users and administrators.
## 3 Results
### Test Quality Parameters of the Prediction Methods
#### 3.2.3 Discriminant Analysis  
A 5-fold cross-validated discriminant analysis was calculated and repeated five times with various partitions. The corresponding results were averaged and provided another way to make talent predictions. Because there were only two service groups (binary), only one discriminant function was required here. The ability to separate the two groups can be seen in the averaged eigenvalue (this was \(EV\) = 0.286, canonical correlation: \(r\) = 0.471). Thus, the groups could be separated satisfactorily based on their group centroids. In addition, the discriminant function should now show the best possible separation. In this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Tennis Recommendation Score**} & \multicolumn{3}{c}{**Predicted/Recommended**} \\ \cline{2-5}  & & **Low Performer** & **Top** & **Percentage** \\ \hline \multirow{3}{*}{Observed/Existing} & Top Performer & 4 & 12 & 75\% \\  & Low Performer & 131 & 27 & 82.9\% \\ \cline{1-1}  & Percentage (total) & _77.6\%_ & 22.4\% & 82.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity and specificity of the tennis recommendation score at \(z_{TRS}\) = 1.3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**(Binary) Logistic Regression 1**} & \multicolumn{3}{c}{**Predicted/Recommended**} \\ \cline{2-5}  & & **Low Performer** & **Top** & **Percentage** \\  & & **Performance** & **Correct** \\ \hline \multirow{3}{*}{Observed/Existing} & Top Performer & 10 & 6 & 37.5\% \\  & Low Performer 2 & 144 & 6 & 96\% \\ \cline{1-1}  & Percentage (total) & 92.8\% & 7.2\% & 90.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sensitivity and specificity of the (binary) logistic regression analysis.  
analysis, [PERSON]'s lambda was 0.775 (chi-square(11) = 31.81, \(p\) < 0.01). The Area Under ROC Curve was 0.800 (standard error = 0.069; 95% confidence interval: min = 0.663, max = 0.936). Consequently, significant differences between the two groups could be detected via the discriminant function, but the model had only a limited selectivity due to the high Wilks lambda. Nevertheless, the cross-validation classification showed good results (Figure 2).  
Using this method, 11 of the 16 later top performers could be correctly identified (Figure 2). This corresponds to a sensitivity of 68.8%. Additionally, 121 of the later 150 low performers could be correctly classified (specificity of 80.7%). The overall prediction quality was thus 79.5% (132/166), and the sum of the two parameters was 149.5%. A random prediction with the same classification rate (24.1%) would result in a sensitivity of 24.1% and a specificity of 75.9%, with an overall prognostic quality of 70.9%. While the discriminant analysis differed only slightly from the random sample in specificity and total result, in sensitivity it showed a clear advantage, with 40% more true-positive hits. This means that, as compared with a random prediction, seven additional top performers could be identified via the discriminant analysis.
# Impact of Silicon Substrate with Low Resistivity on Vertical Leakage Current in AlGaN/GaN HEMTs
###### Abstract  
The role of low-resistivity substrate on vertical leakage current (VLC) of AlGaN/GaN-on-Si epitaxial layers has been investigated. AlGaN/GaN high-electron-mobility transistors (HEMTs) grown on both p-type and n-type Si substrates with low resistivity are applied to analyze the vertical leakage mechanisms. The activation energy (E\({}_{\text{a}}\)) for p-type case is higher than that for n-type at 0-600 V obtained by temperature-dependent current-voltage measurements. An additional depletion region in the region of 0-400 V forms at the AlN/p-Si interface but not for AlN/n-Si. That depletion region leads to a decrease of electron injection and hence effectively reduces the VLC. While in the region of 400-600 V, the electron injection from p-Si substrate increases quickly compared to n-Si substrate, due to the occurrence of impact ionization in the p-Si substrate depletion region. The comparative results indicate that the doping type of low-resistivity substrate plays a key role for VLC.  
p-type Si substrate; n-type Si substrate; vertical leakage current; electron injection; impact ionization +
Footnote †: journal: Applied Physics Letters  
Article