esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Element Contents and Nutritional Status of Leaves  
Levels for deficiencies, optimal supplies, and overdosages exist for various crops and elements (_e.g._, [3,27,28]). However, this information is lacking for the plant species that were analyzed in this study. Therefore, our results have been compared to general ranges and supply levels in other plants [27,28]. As only healthy plant individuals with no visible damages were analyzed, element contents falling below or exceeding the threshold of known ranges seem to have no negative effects on the plant and are only descriptive as a rough estimate.  
Plant growth is limited most often by the elements N and P. Consequently, the N:P ratio of plant tissue can be used for the prediction of limitation [29]. A ratio above 16, considered as an indication for P limitation, is found for _Anisophyllea quangensis_, _Canarium schweinfurthii_, and _Eugenia malangensis_ (Table 4) [29]. _Raphionacme madiensis_ and _Tristemma mauritianum_ are deficient in N (N:P < 14) (Table 4) [29]. Furthermore, as their N:S ratio is wide, they can be regarded as S deficient. Critical values for the N:S ratio vary from 9 to 17, depending on the specific plant species and developmental stage, indicating a potential lack of S in most of the analyzed plants in this study (Table 4) [30, 31, 32]. Only _A. quangensis_ and _E. malangensis_ show a narrow N:S ratio (1:1 and 2:1) and, thus, appear to be sufficiently supplied with S.  
All of the examined foliar contents are in the normal range for the macronutrients of Ca (3-30 g/kg), Mg (1-6 g/kg), and the majority also for K (5-20 g/kg) (Table 4) [27]. _A. quangensis_ (2.92 g/kg), _E. malangensis_ (2.03 g/kg), _L. lanceolata_ (4.54 g/kg), and _V. maliensis_ subsp. _maliensis_ (3.19 g/kg) are low in K. The content of the beneficial, but non-essential element Na is low for all of the studied plants (<1000 mg/kg), also indicating a low soil salinity [27].  
High foliar Fe contents were found for _R. madiensis_ (1086 mg/kg), _T. mauritianum_ (1307 mg/kg), and _V. madiensis_ subsp. _maliensis_ (1643 mg/kg) [27,28].  
In general, plant species with (heavy) metal contents above 1000 mg/kg in the dried leaves are called metal accumulators. Four of the studied plant samples (_A. quangensis_ 7884 mg/kg, _E. malangensis_ 13,989 mg/kg, _L. lanceolata_ 6809 mg/kg, _T. mauritianum_ 4674 mg/kg) show Al contents above 1000 mg/kg, and thus have to be considered as Alaccumulators (Table 4). Furthermore, the Mn contents of leaves from those Al accumulators growing on extremely acidic soils are high. As the respective foliar contents exceed critical concentrations (>200 mg/kg), their Mn content is known to cause toxicity in non-tolerant crop plants [27,28]. An accumulation of other elements, such as B, Cu or Zn could not be identified (Table 4).
## 2 Experimental Procedure
### Characterizations  
1H-NMR (Inova 600, Varian, Australia), FT-IR (FTS-175C, Bio-Rad Laboratories Inc., Hercules, CA, USA), and GC/MS (Clarus 600 series, PerkinElmer, Waltham, MA, USA) were used to confirm the aromatic and aliphatic N-heterocyclic chemical salts during the synthesis. The results we obtained by \({}^{1}\)H-NMR, FT-IR, and GC/MS for each synthe  
Figure 1: Schematic synthesis of the N-heterocyclic aromatic salts by SN2 reaction.  
Figure 2: Schematic synthesis of the N-heterocyclic aliphatic salts by SN2 reaction.  
sized chemical salt are as follows: [MPy]\({}^{+}\)[I]\({}^{-}\).\({}^{1}\)H-NMR \(\delta\) = 4,77 (s), 8.08 (t), 8.48 (t), and 9.36 (d) ppm; [DI]\({}^{+}\)[Br]\({}^{-}\).\({}^{1}\)H-NMR \(\delta\) = 0.90 (t), 1.19 (m), 1.78 (m), 4.18 (t), 7.89 (d), 7.89 (d), and 9.51 (s) ppm. MS:M\({}^{+}\) = 124; [BMTA]\({}^{+}\)[BF4]\({}^{-}\).\({}^{1}\)H-NMR \(\delta\) = 0.90 (t), 1.18 (m), 1.75 (m), 4.18 (t), 7.91 (d), 7.91 (d), and 9.45 (s) ppm; [DMpy]\({}^{+}\)[I]\({}^{-}\).\({}^{1}\)H NMR \(\delta\) = 2,07 (t), 3.17 (s), and 3.42 (t) ppm. IR: = 2945 (C-H), 1460 (C-N) cm\({}^{-1}\); [DMP]\({}^{+}\)[I]\({}^{-}\).\({}^{1}\)H-NMR \(\delta\) = 1,06 (m), 2.08 (m), 4.98 (s), and 4.98 (t) ppm. IR: = 2945 (C-H), 1460 (C-N) cm\({}^{-1}\), [E-TEDA]\({}^{+}\)[Br]\({}^{-}\).\({}^{1}\)H-NMR \(\delta\) = 1,5 (t), 2.98 (t), 3.21 (t), and 3.21 (t) ppm. MS: M\({}^{+}\) = 141. As a result, it was confirmed that the aromatic and aliphatic N-heterocyclic chemical salts were synthesized well.  
Cyclic voltammograms (CVs) for V(acac)\({}_{3}\) in acetonitrile were obtained using a Versa-STAT 3 Potentiostat Galvanostat (AMETEK, Inc., Berwyn, PA, USA) by three electrodes: an Ag/AgNO\({}_{3}\) (0.1 M tetrabutylammonium hexafluorophosphate acetonitrile) reference electrode, a platinum wire counter electrode, and composite-coated glassy carbon (GC; diameter, 3.0 mm) working electrode.  
The charge-discharge cycles were performed using a single-cell flow device with two half-cells separated by the membrane for anion exchange (Neosepta-AFN). The measurements of the charge-discharge cycles were performed by an automatic battery cycler (WBCS3000, WonATech, Seoul, Korea) under acetonitrile and 0.01 M V(acac)\({}_{3}\) with 0.1 M synthesized N-heterocyclic chemical salts.  
In this system, the negative electrolytes, such as [PF\({}_{6}\)]\({}^{-}\) and [BF\({}_{4}\)]\({}^{-}\), were transported through the anion exchange membrane during the charge-discharge process. However, the positive electrolytes and vanadium cations (V(acac)\({}_{3}\)\({}^{+}\)) prohibited the permeability of the membrane due to the Donnan effect [16], as shown in Figure 3.
## 5 Experimental Measurement and Analysis
### Comparison of Frequency-Dependent Capacitance for Transformers with Normal Insulation Humidity
Figure 13: Insulation condition analysis for transformers T5, T6, T7, and T4 based on capacitance ratios at two distinct frequencies (with trend line for transformer T6). Applying the capacitance ratio method using frequencies such as \(C_{\rm 2 Hz}/C_{\rm 50 Hz}\), \(C_{\rm 0.1 Hz}/C_{\rm 50 Hz}\), and \(C_{\rm 0.01 Hz}/C_{\rm 50 Hz}\) requires selecting capacitance values at frequencies 0.01, 0.1, 2, and 50 Hz from the measured curves. The resulting capacitance ratios are summarized clearly in Figure 16.  
Compared to the frequency-dependent dissipation factor of the tested high-voltage transformer, the relative differences between temperatures for the capacitance ratio are lower. The traditional capacitance ratio (\(C_{\rm 2 Hz}/C_{\rm 50 Hz}\)) inaccurately indicated relatively dry insulation across all measured temperatures, with ratios not exceeding 1.2 (Figure 16). However, by lowering the frequency ratio to \(C_{\rm 0.1 Hz}/C_{\rm 50 Hz}\), ratios above 1.5 become noticeable. Further reducing the frequency to \(C_{\rm 0.01 Hz}\) and \(C_{\rm 0.001 Hz}\) clearly shows the highest ratio at 23 \({}^{\circ}\)C (2.39 and 4.72 in Figure 16). However, a significant advantage of the \(C_{\rm 2 Hz}/C_{\rm 50 Hz}\) ratio is its temperature independence, as the differences for different temperatures are minimal (between 0 and 23 \({}^{\circ}\)C, it is only a 9.6% difference). At the lowest temperatures (0 and 4 \({}^{\circ}\)C), the transformer presented the lowest capacitance ratios, misleadingly indicating superior insulation conditions. Thus, for reliable  
Figure 14: Frequency-dependent capacitance curves of the tested transformer measured at different temperatures. Figure 15: Frequency-dependent dissipation factor of the tested transformer measured at different temperatures. insulation assessment, it is recommended to perform evaluations at higher temperatures using capacitance ratios at lower frequencies, specifically \(\text{C}_{0.01\text{Hz}}/\text{C}_{50\text{Hz}}\) and \(\text{C}_{0.001\text{Hz}}/\text{C}_{50\text{Hz}}\).
## 5 Numerical Results and Discussion  
### Simulation Parameters  
Consider one transmitter-receiver pair and jammers working in a frequency band of 20 MHz. Both users' and jammer's signal were raised cosine waveforms with roll-off factor \(\eta=0.6\) and performed full-band detection every \(t_{s}=1\) ms with \(\Delta f=100\) kHz. The bandwidth of the jammer and transmitter was 4 MHz. So, the number of actions set \(\left\|\mathcal{A}_{j}\right\|=5\). The jamming power was 30 dBm. The signal power was 0 dBm. Demodulation threshold \(\beta_{th}\) was 10 dB. Five kinds of jamming modes were considered against DRL-based user:1. Sweeping jamming (sweeping speed is 0.8 GHz/s);
2. Comb jamming (jamming at 2 MHz, 10 MHz, and 18 MHz simultaneously);
3. Dynamic jamming (change the sweeping and comb jamming patterns periodically for every 100 ms);
4. Jamming based on statistics (select top three the most frequently used channels by users in 100 ms);
5. RL-based jamming method (jamming three bands).  
Figure 3(a) shows the spectrum waterfall of the first three jamming modes where the horizontal axis represents frequency and the longitudinal axis represents time. The simulation results of them combating with a DRL-based anti-jamming method can be seen in [24]. The patterns of jamming based on statistics and RL were dynamically changing according to user's signal, which can be seen in Figure 3(b).  
Three kinds of users are taken into consideration against the RL-based jammer:  
1. Frequency-fixed user;
2. Frequency-hopping user with frequency hopping table shown in Table 1;
3. DRL-based user [24].  
According to paper [40], the floating-point operations per second (FLOPS) of DRL in our simulation test was \(10^{9}\), and processing speed magnitude of normal CPU was \(10^{11}\) FLOPS. By now, the embedded neural-network processing unit (NPU) can reach \(1.6\times 10^{13}\) FLOPS. It is likely to run DRL algorithm in many scenarios, such as [41; 42], underwater, unmanned aerial vehicle (UAV) systems in future.
## 6 Conclusions  
In this paper, five features are selected to predict intrinsically disordered proteins, including Shannon entropy, topological entropy, permutation entropy and two amino acid preferences. Among them, permutation entropy is the first application in this field. In the data preprocessing stage, we used a sliding window to connect adjacent residues in the sequence. At the same time, we used the SMOTE oversampling scheme and two integrated learning algorithms to solve the imbalance of positive and negative samples in the original data. These schemes greatly improved Forecast accuracy. By comparing some existing schemes, our scheme has better F1 value and MCC value. The results show that the LightGBM-PE scheme can reach the highest MCC value of 0.526. Our solution uses only five features, has lower computational complexity, shorter training time, lower memory usage, and can adapt to training with a large number of data samples.  
Figure 8: The prediction result of IAPP based on our system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sens** & **Spec** & **F1** & **MCC** \\ \hline GBDT-PE & 0.774 & 0.791 & 0.566 & 0.471 \\ LightGBM-PE & 0.781 & 0.862 & **0.621** & **0.526** \\ DISOPRED2 & **0.972** & 0.405 & 0.482 & 0.470 \\ BVDEA & 0.817 & 0.728 & 0.568 & 0.451 \\ DisPSSMP & 0.767 & 0.848 & 0.605 & 0.463 \\ RONN & 0.603 & **0.878** & 0.498 & 0.395 \\ IsUnstruct & 0.911 & 0.688 & 0.601 & 0.518 \\ FoldIndex & 0.488 & 0.811 & 0.342 & 0.224 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction performance comparison based on test set R80.  
Figure 9: The prediction result of IAPP based on DisPort.  
**Author Contributions**: Conceptualization, H.L.; project administration, H.L.; supervision, H.L.; validation, H.L. and X.Z.; formal analysis, X.Z.; investigation, X.Z.; methodology, X.Z.; software, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z.; resources, H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The author(s) received no specific funding for this study.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found on the website: [[https://disprot.org](https://disprot.org)]([https://disprot.org](https://disprot.org)) (accessed on 6 October 2021).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Design and Treatments  
The study was based on three factors: three levels of drought stress (100%, 60% and 30% field capacity), various concentrations of Brassica water extract (control, water spray, 0.5%, 1.0%, 1.5%, 2.0%, 2.5% and 3.0%) and two contrasting wheat cultivars, drought-tolerant Ihsan-2016 and the drought-sensitive Galaxy-2013. The sowing of wheat was performed under uniform soil moisture conditions and the foliar application of Brassica water extract and drought stress were imposed at 45 DAS. After treatment implementation, soil moisture was determined on a daily basis with the help of a soil moisture meter (TZS-W, Zhejiang, China), and water losses were compensated by adding water to achieve the described level of field capacity in the respective treatment. Leaf samples from each pot were collected after 15 days of stress imposition for the determination of the biochemical and physiological attributes. The concentration of Brassica water extract that revealed significantly higher morphological growth attributes was compared with the control in terms of photosynthetic pigments and oxidative metabolism in the wheat seedlings. The study was conducted in a glasshouse where the treatments were arranged under a complete randomized design with a factorial arrangement and each treatment was repeated three times.
## 2 Results and Discussion
### Profiling of Volatile Compounds in Sweet Potato
#### 2.4.2 Aldehydes  
Thirteen aldehydes were identified as the common compounds in the four cultivars, among which hexanal and (E)-2-heptenal were described as grass and soap flavor, respectively, showing similar levels in the four cultivars. All the other aldehyde compounds were significantly different. The content of benzeneacetaldehyde, characterized by an attractively sweet and honey odor, was abundant in Ayamurasaki. Benzeneacetaldehyde is the most abundant compound detected in sweet potato, which was one of the most potent odorants in baked 'Jewel' sweet potatoes with the highest flavor dilution (FD) value [38]. This is produced from the aromatic amino acid phenylalanine (Phe) degradation [40; 41]. From the same origination, benzaldehyde, with its odor linked to pleasant sweet and caramel odor, also showed a higher level in Ayamurasaki than in the other cultivars. 2,4-Dimethylbenzaldehyde, a cherry and vanilla flower flavor contributor [42], was produced from benzaldehyde and was only detected in Anna. Thus, we may conclude that in this study, aldehydes derived from the Phe contributed to the fragrant and sweet flavor of sweet potato, especially in Ayamurasaki. (E,E)-2,4-Decadienal and (E)-2-ocrenal were also predominant components among the aldehydes, which were considered as aroma contributors to the baked sweet potato [9; 10; 24]. (E,E)-2,4-Decadienal is the second most abundant compound in Jieshu95-16 and Shuangzai, significantly higher than that in Ayamurasaki. A high amount of (E,E)-2,4-decadienal gave an unpleasant flavor with fat, greasy, and rancid notes since it presented a very low odor threshold (0.07 ppb) [43]. Thus, the unpleasant heavy oily flavor produced by (E,E)-2,4-decadienal might have a greater effect on Jieshu95-16 and Shuangzai than Ayamurasaki. (E)-2-Octenal, which contributed an unpleasant green and fatty flavor note to samples, was more abundant in Shuangzai. Both (E,E)-2,4-decadienal and (E)-2-ocrenal mainly originated from the autoxidation of oleic acid, which is involved in the process of fatty acid oxygenation [44]. Fatty acid degradation products associated with green leaf or fat flavor like (E)-2-hexenal, nonanal, (E,E)-2,4-nonadienal [45], and some other derivatives provided fruity or fat odor notes such as (E)-2-pentenal, heptanal, (E,Z)-2,4-decadienal, and (E,E)-2,4-heptdiagnal. Among these compounds, nonanal (fat, citrus, green) and (E,E)-2,4-nonadienal (fat, wax) have been reported to be active contributors of flavor in baked sweet potato and other compounds were not reported in sweet potato. In the present study, the relative contents of 2-hexenal (green), (E,Z)-2,4-decadienal (fried, wax), and (E,E)-2,4-heptdiagnal (nut, fat) in Jieshu95-16 were significant higher than that in Anna and Ayamurasaki. Thus, most aldehydes produced by fatty acid oxidation in this study were generally related with unpleasant green, fatty and wax odor. The content of these compounds in Ayamurasaki and Anna were generally lower than that in Jieshu95-16
## 4 Results and Discussion  
With the using of the LOS and 3D deformation data sets, we aim to demonstrate the effect of using the 3 Doseismic deformation fields on fault slip inversion. For the 2017 Iraq Mw7.3 earthquake, based on the LOS and 3D deformation data sets, respectively, we first assumed simple uniform-slip fault and used a nonlinear optimization approach to find the optimum model parameters. Subsequently, we compared our results with the published parameters [1; 7] to confirm the main fault. Finally, we took the obtained fault geometry and inverted the fault variable slip using a linear approach.  
Figure 4: The 3 Doseismic displacements maps. (**a–c**) represent the deformation maps in up–down, east–west, and north–south directions. The red star represents the epicenter of the mainshock. The red solid squares represent the city Halabja and Sarpol-e-Zahab. The black line AB is the deformation bound in Figure 2. The arrows in (**a**) are the horizontal motion vectors including EW and N–S displacements vectors.
## 3 Results
### The Relationship between Meteorological Drought Indices and Vegetation GPP
Figure 5: Spatial distribution of the correlation coefficients (\(R\)) between gross primary productivity (GPP) and standardized precipitation evapotranspiration index (SPEI) across China, under (**a**) annual and seasonal temporal scales including (**b**) spring, (**c**) summer, (**d**) autumn, and (**e**) winter from 2001 to 2020, and (**f**) annual and (**g**) seasonal frequency distribution of correlation coefficients (\(R\)) between GPP and SPEI. Figure 6: Frequency distribution of the areas with positive correlation (\(R>0\)) between gross primary productivity (GPP) and standardized precipitation evapotranspiration index (SPEI) for different vegetation types, including (a) evergreen forests, (b) deciduous forests, (c) mixed forests, (d) woody savannas, (e) savannas, (f) grasslands, and (g) croplands under different temporal scales (annual and seasonal) across China from 2001 to 2020. Figure 7: Spatial distribution of the correlation coefficients (\(R\)) between gross primary productivity (GPP) and vapor pressure deficit (VPD) across China under (**a**) annual and seasonal temporal scales including (**b**) spring, (**c**) summer, (**d**) autumn, and (**e**) winter from 2001 to 2020, and (**f**) annual and (**g**) seasonal frequency distribution of correlation coefficients (\(R\)) between GPP and VPD. Figure 8 shows the frequency distribution (%) of the areas with a negative correlation (\(R<0\)) between GPP and VPD for different seasons and vegetation types in the study area. At the annual scale, water deficit had the greatest negative impact (\(R<0\)) on GPP in savannas (73.0%) and the least impact on croplands (56.3%). The sensitivity of vegetation to water deficit varied in different seasons. Except for grasslands, all vegetation types showed the strongest sensitivity in autumn. In spring, savannas were the most sensitive to water deficit. Grasslands were the most sensitive to water deficit in both summer and winter. In autumn, croplands were the most sensitive to water deficit. This result is consistent with the relationship between GPP and SPEI.
## 5 Proposed Software-in-Loop System  
One of the greatest advantages of _software-in-loop_ over _hardware-in-loop_ is that there is no need for actually building all the nodes to simulate complex networks whilst maintaining the capacity to test critical software. Moreover, testing real embedded software instead of carrying out system-level or traditional network simulations allows a faster and more efficient debugging.  
In a _hardware-in-loop_ simulation, all the nodes have to be physically built. A _hardware-in-loop_ simulator takes the modulated signal generated by each node as inputs and generates a real-time combined signal for each node as output. That means that the border line between implementation and simulator is located in the electrical domain, between modem and radio. The modem's output signal, in this case, is not connected to an actual radio emitter, but to the simulation/emulation platform. On the contrary, in a _software-in-loop_ simulation there is no need to build any node since the simulated interface is in the digital domain, and it is located between the CPU's data interfaces and the modem. Under this scheme, the _software-in-loop_ simulator receives and processes the bits that should be received by the system's modem, instead of electrical signals. In this case, channel propagation and transceiver behavior are simulated, since the emphasis is put on how the software handles information.  
For the _software-in-loop_ simulation proposed in this paper, an _ad hoc_ simulator has been developed. Usually, channel simulators are event-driven and it is not feasible to integrate external developed-and-compiled communication protocols. As STNAG 5066 is implemented and built on its own executable ELF binary, it is not possible to integrate STNAG 5066 stack (nor its events) in an event-driven simulator, as the implemented stacks have a significant amount of temporized events which highly rely on Operating System (OS) calls and are managed internally by the executable. It is obvious that any event-driven simulator must have knowledge about the temporized events in any software that runs in the complete simulation environment since it has to be able to queue them into its internal event list. This does not happen with compiled software that manages its temporized events with OS resources.
## 5 Experimental Evaluations
### Results  
Using a different group of users (100-1000), we show the average latency, energy consumption, and resource utilization results. We considered the vehicular user movements from the LuST data set [10]. The performance comparisons show that the COME-UP outperforms the others (DMPO and TAOM) with a 32% reduction in latency, 16% energy savings, and increasing the resource (CPU) utilization by 21%.  
The latency values illustrated in Figure 5 are consistent with the acceptable delay requirements for the 5G network, which have been established in other studies [49]. The average latency of the proposed framework is noticeably lower than the other techniques in all cases of user distributions.  
The higher number of mobile users has resulted in a significant increase in the energy consumed by edge servers. The illustration of the proposed model's energy usage in comparison to that of the other models is shown in Figure 6. The CPUs draw the critical portion of the energy in edge servers [38]. We use the CPU utilization model for both energy estimation and server utilization. Figure 7 shows the server utilization with different server distributions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline
**Macrocell radius** & 200 m \\
**No of cell sites** & 144 \\ Moving velocity of the user & 20–80 km/h \\ Server CPU frequency and instruction set & 3 GHz \& CISC \\ Mobile device CPU frequency and instruction set & 3 GHz \& RISC \\ Input Data size & 1\(\sim\)30 MB \\ No of users & 100–1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation settings and values.  
We used 50 sequences of LSTM as test data of user mobility patterns. The highest count of the error term is near to zero in this case, indicating the model's acceptable error range. The observed error range is less than 0.5 during complete training, as shown in Figure A3.