esa-sceva/satcom-chunk-collection huggingface dataset:
Recent Progress in the Regeneration and Genetic Transformation System of Cucumber Transformation System of Cucumber  
[PERSON]  
1 Key Laboratory for Quality and Safety Control of Subtropical Fraits and Vegetables, Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, Ministry of Agriculture and Rural Affairs, College of Horticulture Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China; [EMAIL_ADDRESS] (J.F.); linil  
disease, and stress resistance have been mined successfully, and some progress has been made in the molecular mechanism analysis of cucumber plant sex and flower development, fruit development, and shoot branching [2].  
Genomic transformation is an important aspect of genetic engineering technologies. It is defined as an array of events including the selection of a desirable gene, delivery, integration into the plant cells, and expression, giving rise to a whole plant and final molecular identification (Figure 1). Among the variety of available DNA delivery systems, _Agrobacteriumium tunefaciens_-mediated transformation has been used primarily for optimization and trait improvement in cucumbers [4]. _A. tunefaciens_ achieves genetic improvement by transferring a plasmid fragment containing the gene of interest (called T-DNA) into host cells, and transgenic plants are then regenerated by cell and tissue culture techniques [5]. The variety cv. Poinsett 76 is used more successfully than most [1]. For example, transformation efficiencies have been up to 21% using cotyledons of cv. Poinsett 76 mediated by _Agrobacteriumium_ strain EHA105 [6]. The Xintai mici strain is also popularly used. Higher transformation rates have been achieved using its cotyledons and cotyledonary nodes mediated by _Agrobacteriumium_ strain GV3101 [7,8]. Leaf and hypocotyl tissues can also be transformed successfully when matched to the appropriate genotype [1,9].  
Although successful transformation was achieved decades ago in cucumber using _Agrobacteriumium_, progress in cucumber still lags [1]. High genotype dependence, requirement for exogenous hormones, selection of strain, infection method, and culture conditions before and after inoculation with _Agrobacteriumium_ are the major constraints for successful regeneration and transformation. Here, we review recent progress on the influencing factors of cucumber regeneration ability and genetic transformation conditions and summarize the achievements of transformational approaches in the study of molecular mechanisms underlying important agronomic traits.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Channel Characteristics of UIoT
#### 3.1.3 Environmental Condition  
Due to internal waves, mammals activity and other objects' behaviors lead to dynamic topology formation in UIoT networks [30]. The frequent changes of the UIoT network topology can cause rerouting, transmission loss and data accuracy issues [31]. Compared with the terrestrial IoT, in UioT networks, the nodes are sparsely deployed for data gathering and transmission. Furthermore, since the UioT nodes are mobile, localization, synchronization and secure communication are the other issues in UIoT networks.
## 5 Results and Discussions
Figure 5: The BER of the system when the single-frequency interference deviates from the signal carrier frequency. significantly lower than that of the long-code system. This observation demonstrates that the short-code system is highly sensitive to changes in the interference frequency offset. While the BER of the short-code system closely resembles that of the long-code system when the interference frequency offset is an integer multiple of the bit bandwidth, so that there are similar trends in the BER oscillations of the two systems, their BERs differ significantly when there is zero frequency offset or non-integer multiples of the bit bandwidth. Therefore, the BER of the long-code system cannot be directly applied to analyze the short-code system. Only when the frequency offset is not zero and is an integer multiple of the bit bandwidth can the theoretical BER of the long-code system approximate the BER of the short-code system. Figure 7 displays the variation curves of Bit Error Rate (BER) when there is a frequency offset in interfering signals. The short-code systems utilize spreading codes with the same length but different structures, and the simulation parameters can be found in Table 1. It is noticeable that the BER pattern changes with the interference frequency offset for these two short-code systems, except when the offset is a multiple of the bit bandwidth. This distinction arises due to the correlation despreader's behavior as a filter with specific frequency response characteristics. It consists of a spread spectrum code multiplier and a low-pass filter. As a result, the system's response differs when confronted with various frequency interferences. The BERs of both systems are practically identical when the interference frequency deviation is a multiple of the bit bandwidth. However, they reach their peak when the frequency deviation becomes equal to the bit bandwidth. These findings align with the analytical results discussed in Section 4.3. Figure 6: Comparison of anti-jamming performances of long-code and short-code systems under single-frequency interference with different frequency offsets. Figure 7: Anti-jamming performance of short-code systems employing spreading codes with the same length but different structures.
## 2 Materials and Methods  
### Preparation and Characterization of Adsorbent Material  
The raw materials used in the current study for the production of biochar were almond shell residues (AL), provided by a nut industry in North Greece. These were ground in a cutting mill, model Pulverisette 15 of Fritsch, sieved to a particle size lower than 500 \(\upmu\)m and homogenized by a Jones riffler.  
For biochar production, a stainless steel fixed bed reactor system was used. A detailed description of this system can be found elsewhere [21]. Each sample was devolatilized up to 700 \({}^{\circ}\)C, at a heating rate of 10 \({}^{\circ}\)C/min and a retention time at the final temperature of 30 min. Pyrolysis was carried out under nitrogen gas at a flow rate of 150 mL/min. Following the pyrolysis step, nitrogen was switched to steam by injecting distilled water at a flow rate of 0.5 mL/min via an automatic syringe pump. Water was passing through a 2 m pipe surrounding the reactor before contacting the sample, in order to achieve a uniform flow of steam over the bed. The activation step at 700 \({}^{\circ}\)C by steam lasted 1 h, after which the reactor was cooled by nitrogen.  
The characterization of raw and devolatilized almond shells, in terms of proximate and ultimate analysis, was conducted according to European standards CEN/TC335. Structural characterization was carried out using an analyzer model Autosorb 1Q-C-MP of Quantachrome (Quantachrome, Pittsburgh, PA, USA), applying the Brunauer, Emmett, Teller (BET) method. Prior to the measurements, the samples were out-gassed at 200 \({}^{\circ}\)C, for 1 h. From liquid nitrogen absorption data, the specific surface area and the pore volume and size were determined.  
The chemical functional groups of biochar samples before and after metals' adsorption were identified by a Fourier Transform Infrared Spectrophotometer (FTIR), model Spectrum 100 of Perkin Elmer. For measurements, pellets of sample/KBr at a ratio of 1/100 \(w\)/\(w\) were prepared. The range of wave numbers, where the spectra were recorded, was 400-4000 cm\({}^{-1}\) and the resolution was 4 cm\({}^{-1}\).  
Finally, in order to investigate the mineral phases on biochar surface before and after metals adsorption, an X-ray diffractometer (XRD), model S2 Ranger EPS of Bruker AXS, was employed. The scanning range used for the analysis was 2-70 20\({}^{\circ}\) with an increment of 0.02\({}^{\circ}\)/s. Database COD (Crystallography Open Database) and software v. 2016, DIFFRAC-Plus were used for spectra evaluation.
## 4 Results and Discussion
### Multilayer Perceptron Results  
Applying the Multilayer Perceptron (MLP) model, the values shown in Table 8 were obtained using different learning rates and momentum.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{_Decision Tree_} & \\ \hline
**Metrics/Ccriteria** & **Accuracy** & **Precision** & _Recall That_ & _F1 Score_ \\ \hline
**Gini index** & 71.58\% & 74.61\% & 0.8546 & 0.79670 \\ \hline
**Entropy** & **71.83\%** & **74.87\%** & **0.8544** & **0.79808** \\ \hline \end{tabular}
\end{table}
Table 7: Benchmark for Decision Tree.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{_Multilayer Perceptron_: MLP} & \\ \hline
**Metrics/Learning Rate and Momentum** & **Accuracy** & **Precision** & _Recall That_ & _F1 Score_ \\ \hline
**learning\_rate = constantmomentum = 0.1** & 69.86\% & 70.48\% & 0.70633 & 0.70556 \\ \hline
**learning\_rate = invscalingmomentum = 0.9** & 61.1\% & 65.23\% & 0.64333 & 0.64781 \\ \hline
**learning\_rate = adaptive momentum = 0.9** & **76.3\%** & **76.41\%** & **0.76466** & **0.76441** \\ \hline \end{tabular}
\end{table}
Table 8: Benchmark for Multilayer Perceptron (MLP).  
Figure 7: ROC curve—Decision Tree—entropy.  
Using MLP, with learning rate parameters equal to adaptive and momentum equal to 0.9, an accuracy of 76.3% was obtained. The precision was 76.41%, and the sensitivity was 0.76466.  
The F1 score reached the value of 0.76441, the general quality of the model. Following the ROC analysis, the best result was 0.84 (ROC score), with a hypothesis of an 84% correct classification in clinical evolution, using the Multilayer Perceptron--MLP model. Based on the hypothesis presented in this study, the results of the analysis of the behaviour of the MLP networks trained on the COVID-19 dataset proved to be entirely satisfactory, lacking in terms of time for model training.
## 4 Finite Element Analysis
### Finite Element Model Parameter Analysis  
Since only the axial pressure ratio of 0.1 is set in the test. In this section, five models of coal gauge concrete shear walls with axial pressure ratios of 0.1 to 0.5 are set up, numbered CGSWN-1 to CGSWN-5. The skeleton curve simulated by the finite element software ABAQUS on the five models is shown in Figure 19, and the resulting characteristic point load and displacement are shown in Table 11.  
It can be seen from Figure 19 that with the increase of the axial pressure ratio, the ultimate displacement of the specimen gradually decreases. In addition, the initial stiffness of the specimen will increase due to the increasement in the axial pressure ratio. It is mainly because the enhancement in axial compression ratio makes the vertical force restraint on the cracking and residual deformation of the specimen significantly enhanced, thus improving the overall stiffness of the specimen.  
It can be seen from Table 11 that the yield load and peak load of the specimen increase with the increasement of the axial pressure ratio. It is because the enhancement in the vertical force will increase the compressive stress on the section of the specimen, and offset the tensile stress generated by part of the tensile side, so the load is increased. In addition, due to the increase of the axial pressure ratio, the ductility coefficient of the specimen gradually decreases, indicating that the deformation ability decreases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Characteristic Point Bearing Capacity**} & \multicolumn{2}{c}{**Characteristic Point Displacement**} & \\ \cline{3-6}
**Specimen** & **Axial Pressure** & \multicolumn{2}{c}{**(kN)**} & \multicolumn{2}{c}{**(mm)**} & **Ductility Coefficient** \\ \cline{3-6}
**Number** & **Ratio** & **Yield** & **Peak** & **Yield** & **Ultimate** & **Coefficient** \\  & & **Load** & **Load** & **Displacement** & **Displacement** & \\ \hline CGSWN-1 & 0.1 & 101.837 & 113.099 & 3.76 & 22.36 & 5.95 \\ CGSWN-2 & 0.2 & 123.724 & 137.054 & 3.72 & 21.80 & 5.86 \\ CGSWN-3 & 0.3 & 137.069 & 152.586 & 3.69 & 21.38 & 5.79 \\ CGSWN-4 & 0.4 & 152.381 & 163.810 & 3.63 & 19.54 & 5.38 \\ CGSWN-5 & 0.5 & 165.397 & 170.159 & 3.61 & 19.11 & 5.29 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The characteristic point parameters of the specimen under different axial pressure ratios.  
Figure 19: The skeleton curve of the specimen under different axial pressure ratios.  
Above all, the axial pressure ratio specifically impacts the ductility and seismic performance of the coal gangue concrete shear wall. The axial pressure ratio should be avoided from a high value in the actual project, to ensure that the ductility and seismic performance of the wall are not significantly affected. The horizontal bearing capacity of the gangue concrete shear wall with the axial pressure ratio between 0.2 and 0.3 is high, and the ductility is also guaranteed. Hence, the seismic performance is the best.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 6 Conclusions  
In this paper, we have discussed the three categories of data-collection frameworks in WSNs based on sink type. The different types of sinks under study are static sink, ground mobile sink, and aerial mobile sink. The main objective of this study is to find out the suitable application scenario by discussing and finding the relative performance of the data collection frameworks in WSNs depending on the sink types. In order to do so, we have discussed and compared ten different data-gathering frameworks divided into three categories. The frameworks have been qualitatively analyzed in terms of their working principles, advantages, and limitations. Two tabular comparisons along with an elaborate analysis present the in-depth overview of the investigated protocols. The tables have been produced based on the main ideas, optimization criteria, and performance evaluation parameters. In addition, we have selected three representative frameworks from the investigated frameworks for quantitative comparison, where their performance has been evaluated in terms of energy, the number of dead nodes, delays, packet drop ratios, and the number of exchanged control packets. The lessons learned and recommendations have been provided as the outcome of our comparative study, which will assist future researchers in producing more suitable and versatile WSN data-gathering frameworks. After all the analysis, we can conclude that all the sink types have their own advantages and disadvantages based on the application areas. For a delay-constrained application scenario, a static sink is more suitable option. A ground robot sink is applicable to the accessible terrain, and the WSN's lifetime can be elongated as well. However, for a hard-to-reach and infrastructure-less environment, an aerial sink is the only solution. Lastly, it can be inferred that a more robust solution is possible in terms of QoS and fault tolerance by introducing different types of sinks in a single solution and there is a gap in such research in the literature.  
The individual contributions of the authors are as follows: R.A.N. reviewed, analyzed, and summarized the data-gathering frameworks for WSNs. S.M. directed the research and contributed to the review and analysis of the frameworks. The paper was drafted by R.A.N. and subsequently revised and approved by S.M. All authors have read and agreed to the published version of the manuscript. This work was supported in part by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1F1A1060501). The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper. The authors declare no conflict of interest.
## 3 Results
### On the Best Value for the Ideality Factor
In order to compare both types of fit, the values of the ideality factor that gave the lowest values of x for the analytical fittings, \(a_{bf-a}\), were plotted against the values obtained from the numerical fittings, \(a_{bf-a}\), in Figure 6. It can be seen that the correlation between the fits is high, with the exception of only two devices: the Azur Space 3G30C solar cell, and the Selex Galileo SPVS X5 solar panel (which are both based on the same photovoltaic technology, as shown in Table 1). However, this correlation does not allow us to identify any rule that would suggest a reasonable value for the ideality factor. We, therefore, plotted the average values of the non-dimensional RMSE, \(\xi_{av}\), obtained from the analytical fits versus the ideality factor, \(a\). It can be seen from Figure 6 that the minimum value of \(\xi_{av}\) was obtained at \(a=0.98\). As a consequence, the value suggested for all devices is \(a=1\). Figure 7 shows a comparison of the non-dimensional RMSE values obtained from the numerical fit, the analytical fit, and the analytical fit for \(a=1\) (see Table 4) for all of the photovoltaic devices. Although the differences may appear high, it should be taken into account that photovoltaic systems are normally designed to operate at the MPP, which is exactly reproduced by all of the analytical approximations for solving the parameter extraction of the 1-D/2-R equivalent circuit model. In addition, we reviewed the results from several works published over the last five years on parameter extraction for 1-D/2-R equivalent circuit models, based on the same experimental data from the RTC France solar cell and the Photowatt PWP 201 solar panel. The values obtained in these works for the non-dimensional RMSE for the fits were lower than the results from our method, with average values of \(\xi=1.20\cdot 10^{-03}\) (RTC France solar cell) and \(\xi=4.03\cdot 10^{-03}\) (Photowatt PWP 201 solar panel). Although these more accurate values were based on numerical rather than analytical procedures, it should be pointed out that numerical approaches do not always reach a more accurate solution [92]. Figure 6: **(a)** Ideality factor corresponding to the best analytical fit to the 1-D/2-R equivalent circuit model, \(a_{bf-a}\), for the studied _I-V_ curves, versus ideality factors obtained from the numerical fittings, \(a_{bf-a}\). The line corresponding to perfect correlation between both factors is shown as a reference. **(b)** Averaged non-dimensional RMSE for all analytical fittings, \(\xi_{av}\), versus the ideality factor, \(a\), used in the fits.
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
## 2 Related Research  
There are numerous related research studies in the literature, both experimental and numerical, regarding the inverse dependence of the temperature of monocrystalline and polycrystalline PV panels with their efficiency, and consequently the necessity of cooling solutions for removing this shortcoming [6; 8; 9; 11; 19; 20].  
Related research works on PV panels' cooling by using air are presented in the literature, and a large number of technologies and solutions to improve their efficiency are presented [9; 21; 22; 23; 24; 25; 26]. Recent experimental and numerical studies regarding the passive air cooling of PV panels by using heat sinks [8; 27; 28] tend to confirm noticeable and promising results [6; 8; 22; 23]. The two main materials used for manufacturing the heat sinks are aluminum and copper [23]. The cooling effect is analyzed through experimental tests under different conditions and the conclusion is that copper has superior results [23]. Also, numerical and experimental analysis showed that the presence of perforations on the fins of the heat sinks can improve the efficiency of the PV panels by reducing their operating temperature [6; 23]. The predicted temperature drop, due to the use of these solutions, can reach values between 7 \({}^{\circ}\)C and 10 \({}^{\circ}\)C [23].  
Numerical simulations regarding the wind direction towards the back, front and side of heat sinks attached to PV panels showed a potential of operating temperature reduction towards 10 \({}^{\circ}\)C below the basic case temperature (when no cooling is applied) [8; 11; 15; 29; 30]. The improvement of the PV panels' performance was determining a rise of normalized power production up to 0.90 of the standard test conditions one [31]. As an average, the normalized power generation of photovoltaic panels can reach about 0.70-0.83 compared to the STC rating [18].
## Results
### Variations in ET and WUE during the Water Depletion (WD) Periods  
There was an overall 9.3% increase in ET during the WD period (August-November) as compared to the whole year (Figures 2 and 4). In contrast, WUE was 8.2% lower during the WD period as compared to annual values (Figures 2 and 4). Within the WD period, we found a 4%-18% rate of decrease in ET, except in 2005 when a negligible decline was observed (Figure 4). In contrast, WUE  
Figure 4: Seasonal variations (monthly means) in (**a**) ecosystem gross primary production (GPP), (**b**) ecosystem evapotranspiration (ET), and (**c**) ecosystem water use efficiency (WUE) based on daily smoothened values for each year from 2004–2014. Different colors were assigned for each year. Gray-shaded regions are the confidence intervals and the colored line in the grayscale line is the smooth curve. The two vertical broken lines in each panel represent the average day of the year for the beginning and end of the water depletion (WD) period. GPP data are from [25].  
increased by 1%-17%, except in 2005 and 2014, when a slight decrease was observed during the WD period (Figure 4).  
During WD periods, the combined effect of Rg and REW drove 48% of the variability in ET (Table 2); yet, Rg was the primary controlling factor. There was no effect of Ts on ET during the WD periods (Table 2). There was a positive relationship between Rg-normalized ET and SWSI for each year (Figure 5). These linear relationships differed among years; the slopes were much higher in the years 2004, 2008 and 2012-2014, and these slopes were all statistically different from those for 2005 and 2006 (Tables A1 and A2).  
Variations in WUE during WD periods were also driven by Rg, with a slight influence of Ts (Table 2). REW had no control over WUE. The relationship between Rg-normalized WUE and SWSI differed among years (Figure 6). We observed no relationships between the two variables in 2008, 2011,  
Figure 5: Linear relationship between radiation-normalized evapotranspiration (ET) and soil water stress index (SWSI) during the water depletion period (WD). Gray-shaded regions are the confidence intervals and the black line in between is the linear trend. Each dot corresponds to one day. The greater the SWSI, the lower the soil water stress.  
and 2014, whereas the relationships were negative in 2004, 2006, 2007, 2009, 2010, 2012 and 2013, and positive in 2005.