esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling Methods
#### 2.3.1 Random Forest  
Random forest is a kind of integration algorithm. Based on the bagging integration built with decision tree as the learning machine, random attribute selection is further introduced into the training process of the decision tree. Random forest is simple, easy to implement and has low computational overhead, but it can show powerful performance in many practical tasks, and is considered as a method to represent the technical level of ensemble learning [30]. Due to the universality of random forest, we try to use random forest to establish the biomass model of rice. The diversity of basic learners in random forests not only comes from sample perturbations, but also from attribute perturbations. Therefore, the generalization of the final integration can be improved by increasing the differences among individual learners. The initial performance of random forest is usually poor, but with the increase in basic learners, random forest can gradually converge to a lower generalization error and achieve better results. An important parameter in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Correlation Coefficient with Plant Height** \\ \hline PW & 0.600954 \\ PH(V) & 0.727988 \\ PH & 0.896098 \\ SFD & 0.696987 \\ G\_g & 0.615098 \\ f1 & 0.559564 \\ LD2 & 0.521077 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation coefficient between rice characterization and plant height.  
Figure 6: Scatter diagram of SFD and plant height.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Correlation Coefficient with Dry Weight** \\ \hline PW & 0.644520 \\ SA & 0.925310 \\ SA/PH(V) \(\times\) PW & 0.525873 \\ IFD & 0.630591 \\ SFD & 0.893188 \\ G\_g & 0.782164 \\ LD1 & \(-\)0.577148 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation coefficient between characterization and dry weight of rice.  
model, N_ESTIMATORS, is the number of trees in the forest; that is, the number of base learners. The influence of this parameter on the accuracy of the random forest model is monotonous. The larger N_ESTIMATORS is, the better the effect of the model will be. However, any model has a decision boundary. After N_ESTIMATORS reach a certain level, the accuracy of the random forest usually stops rising or starts to fluctuate. Moreover, the larger N_ESTIMATORS is, the more computation and memory is required, and the longer training time will be.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 5 Experiments  
A list of comprehensive experiments was conducted to evaluate the performance of the AODV-EOCW routing protocol. The experimental results were compared with the vanilla AODV routing protocol and the AODV-UU [34; 35] routing protocol from the perspectives of the node movement speed, time variation, and node density. Finally, the authors also compared the end-to-end delay, packet delivery rate, and node survival rate.
## 5 Results and Discussion
### Effects of Using Different Numbers of EFEMs  
For the purpose of evaluating the effect of using different numbers of Edge Feature Extractor Modules (EFEMs), we conducted an experiment.  
As shown in Table 7, we could see that adjusting the numbers of EFEMs in our proposed PCHNet would affect the final segmentation accuracy. The accuracy of the model gradually increases and then slowly decreases, indicating that adding edge detection operators like Sobel, Laplacian, and Canny could improve the model's ability to extract edge features, and these edge features could increase the model's feature expression in top layers. However, using too many EFEMs would lead to redundant features. Thus, according to the experiment, we used only three EFEMs in our PCHNet.
## 3 Results
### Effect of Pilates on the Pelvic Stabilization Muscle Strength  
In this study, we confirmed pre-test and post-test HF, HA, and HS. When changes in HF, HA, and HS were analyzed, they were found to have increased significantly in the PE group when compared to the CON group (HF: \(z=-3.037\), \(p=0.002\), HA: \(z=-3.344\), \(p=0.001\), HS: \(z=-2.595\), \(p=0.009\); Figure 1A-C). Moreover, in terms of within-group differences, all measurements increased significantly post-test compared to the pre-test measurements in the PE group (HF: \(z=-2.371\), \(p=0.018\); HA: \(z=-2.670\), \(p=0.008\); HS: \(z=-2.192\), \(p=0.028\)), whereas only HA decreased significantly post-test in the CON group (\(z=-2.375\), \(p=0.018\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Pilates (\(n=9\))**} & \multicolumn{2}{c}{**CON (\(n=7\))**} & \multicolumn{2}{c}{**Diff (Post-Pre)**} \\ \cline{2-7}  & **Pre** & **Post** & **Pre** & **Post** & \(z\) & \(p\) & **Cohen’s \(r\)** \\ \hline BW (kg) & \(57.98\pm 7.91\) & \(64.01\pm 7.76\) ** & \(53.67\pm 5.70\) & \(58.37\pm 5.65\) * & \(-1.590\) & \(0.112\) & \(0.398\) \\ TBW (L) * & \(28.97\pm 3.33\) & \(31.59\pm 3.50\) ** & \(27.63\pm 2.45\) & \(29.26\pm 2.84\) * & \(-2.286\) & \(0.022\) & \(0.572\) \\ ICW (L) ** & \(17.71\pm 2.06\) & \(19.54\pm 2.12\) ** & \(16.90\pm 1.54\) & \(17.99\pm 1.66\) * & \(-2.818\) & \(0.005\) & \(0.705\) \\ ECW (L) & \(11.26\pm 1.28\) & \(12.04\pm 1.39\) ** & \(10.73\pm 0.92\) & \(11.27\pm 1.18\) * & \(-1.232\) & \(0.218\) & \(0.308\) \\ ECW/ICW & \(0.64\pm 0.01\) & \(0.62\pm 0.01\) * & \(0.64\pm 0.01\) & \(0.63\pm 0.01\) & \(-2.170\) & \(0.030\) & \(0.543\) \\ BFM (kg) & \(18.40\pm 4.92\) & \(20.70\pm 5.04\) ** & \(15.86\pm 3.06\) & \(18.30\pm 3.06\) * & \(-0.106\) & \(0.916\) & \(0.027\) \\ PBF (\%) & \(22.50\pm 3.15\) & \(24.84\pm 3.02\) ** & \(20.59\pm 1.64\) & \(22.41\pm 1.67\) * & \(-1.643\) & \(0.100\) & \(0.411\) \\ SMM (kg) ** & \(21.10\pm 2.66\) & \(23.49\pm 2.76\) ** & \(20.09\pm 2.00\) & \(21.41\pm 2.18\) * & \(-2.811\) & \(0.005\) & \(0.703\) \\ \hline \hline \end{tabular}
\begin{tabular}{c} BW: body weight, TBW: total body water, ICW: intracellular water, ECW: extracellular water, ECW/ICW: ECW to ICW ratio, BFM: body fat mass, PBF: percentage of body fat, SMM: skeletal muscle mass. Diff (post – pre): difference change from pre to post between groups. * \(p<0.05\), ** \(p<0.01\)** from pre and post. * \(p<0.05\), ** \(p<0.01\)** change (post – pre) between groups. Values are presented as mean \(\pm\) SD. \\ \end{tabular}
\end{table} 
Table 2: Body composition. Figure 1: Effect of Pilates exercise on back pain-related muscle strength, (**A**) Hip flexion, (**B**) Hip abduction, and (**C**) Hip extension following 12 weeks of Pilates exercise. Bars represent mean \(\pm\) SD (PE: n = 9, CON: 7, per group). * \(p<0.05\) from Pre to Post. PE: Pilates exercise group, CON: control group.
## 3 Research Results  
### Analysis of Power Supply and Demand at Each Facility  
As mentioned above, the load pattern can be divided into operation-plan line and always-in-operation line, and the power generation pattern can be divided into waste and solar power generation. Figures 6-9 show the change in power demand during regular operation on a specific day. The operation plan line clearly shows the power load during non-operation and operation. On the other hand, the always in-operation line requires a constant power load throughout the day. In addition, waste power generation is stable throughout the day, while the amount of power generated by solar power generation fluctuates greatly depending on the weather and time of day. Therefore, in this paper, the load pattern and power generation pattern are roughly divided into operation plan line, always in operation line, waste power generation, and solar power generation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Processing Facility**} & \multicolumn{4}{c}{**Priority in the Processing Process**} & \multirow{2}{*}{**Remarks**} \\ \cline{2-2} \cline{4-6}  & **Carry-in** & & & **A-SY** & **Carry-Out** & **Remarks** \\  & **Schedule** & & & & **Schedule** & \\ \hline Crushing facility A & \(\times\) & \(\times\) & \(\times\) & & \(\bigcirc\) & - Adjusted to meet the fuel demand of the power plant \\ Manufacturing & & & & & & - Can be operated for half a day \\ facility A & \(\bigcirc\) & \(\bigtriangleup\) & \(\times\) & & \(\bigcirc\) & - Crosider the destination schedule \\  & & & & & - Acceptance is required \\ Recycling facility A & \(\bigcirc\) & \(\bigcirc\) & \(\times\) & \(\times\) & & it is filled fully \\  & & & & & & - No storage capacity on the carry-out side \\ Manufacturing & & & & & - Even if the amount of delivery is not enough, it will be processed as soon as feedstock is received \\ facility B & \(\bigcirc\) & \(\bigtriangleup\) & \(\times\) & \(\times\) & \(\times\) & - It is necessary to take measures at the time of \\ Solidification facility & \(\bigtriangleup\) & \(\bigtriangleup\) & \(\bigtriangleup\) & \(\times\) & & acceptance \\  & & & & & - No effect on operation even if the facility is stopped \\ Recycling facility B & \(\bigcirc\) & \(\times\) & \(\times\) & \(\times\) & - Annual delivery schedule can be recognized here \\ Crushing facility B & \(\times\) & \(\times\) & \(\times\) & \(\times\) & - Feedstocks are processed immediately \\ \hline \hline \end{tabular}
\end{table}
Table 2: Priorities in the treatment process.  
Figure 5: Image of operation shift.  
Figure 8: Amount of power generated by waste power generation facilities.  
Figure 6: Operation planning line (crushing facility A) load pattern.  
Figure 7: Load pattern of continuous operation line (a new water treatment facility).  
Figure 9: Amount of solar power generation by weather.
# Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis
## 3 Results  
### Optimum Value Estimation  
Since the contingency in the interpolation results of AOD always exists, 10 experiments were repeated. Figure 4 shows the average relative error of the generated AOD obtained from the 10 experiments with R from 10 km to 500 km. The analysis indicates that the relative error is the lowest when R is 20 km, and when R is between 20 km and 110 km, the relative error is below 15%. Due to the spatial resolution of used AOD being \(10\times 10\) km, we could get that the error of generated AOD increases with R. Since AOD data are always missing because of the thick clouds and the spatial resolution of the AOD used to infer Vis is \(1^{\circ}\times 1^{\circ}\), this paper set R to 110 km to get more AOD data. Since the meteorological elements have a direct influence on sea fog and precipitation conditions and these two weather phenomena are the main factors affecting Vis, it's reasonable to ignore the effect of aerosols on Vis when inferring Vis, because large aerosols will only act as CCN (cloud condensation nuclei), leading to fog and cloud formation. So, if there is no AOD data within 110 km around the research location because of the thick clouds, which block the satellite's access to aerosol data, the effect of AOD on Vis is not considered in the analysis. However, if the clouds are high, then aerosols will play an important role in Vis.  
Figure 4: The average relative error of the generated aerosol optical depth (AOD) obtained from the 10 experiments with R values from 10 km to 500 km.  
Vis is usually classified differently in application to navigation activities; this classification of Vis data in ICOADS is given at 10 levels (Table 1) [23]. When Vis mentioned in this study, it means a \"visibility level\" (see Table 1).  
Since the transit time of the satellites monitoring AOD are 10:30 and 13:30 at local time, respectively, it is necessary to screen out data at this time from the ICOADS to make the data from ICOADS consistent with AOD in time. This paper assumed that AOD from 10:00 to 11:00 at local time is the same as AOD in 10:30 at local time, and AOD from 13:00 to 14:00 at local time is the same as AOD in 13:30 at local time, which is to get more data containing meteorological parameters and AOD. The sample data set to train the model for both fog and precipitation is shown in Table 2.
## 2 Antenna Design and Configuration
The area of the slot is inversely proportionate to the effective capacitance and directly proportionate to the effective inductance. The effective capacitance will reduce due to the decrement of the slot area, and, as a result, the resonance frequency and the effective inductance will increase due to the increase of the slot area. As such, the results will be at the lower cutoff frequency. The aforementioned relationships and DGS circuit diagrams are discussed in paper [29].
## 2 Materials and Methods
### Data Collection  
Observations. A set of predefined observation points, including situations where participants interacted with specific robot functions or exhibited emotional responses toward the robots, were established. By systematically examining and analyzing these key points, detailed data on the interactions were collected. Data were gathered through both manual observation and video recording for later review, allowing for in-depth analysis of participant-robot interactions.  
Interviews. Semi-structured interviews were conducted with participants to capture their reflections and experiences interacting with the robots. To maintain consistency and relevance, a semi-structured interview guide was developed, grounded in the study's purpose, research questions, and theoretical framework. Questions were designed to encourage open-ended responses, such as the following: \"What was your overall experience with each robot?\", \"How did meeting these two different robots--one with and one without a human face--differ?\", \"Do you see potential uses for these types of robots in companies or workplaces?\", \"To what extent do you think these robots could replace a human in similar scenarios?\", \"Was there anything specific about the robots' behavior that stood out to you?\", and \"How did you perceive the robots' ability to convey human emotions, like when the Pepper robot appeared happy or sad?\"  
The interview guide also included questions about participants' views on the future of robots, such as the following: \"How do you envision the future use of robots? Do you think they will become more common, and if so, why?\", \"Can you provide examples of areas where you think robots will be used?\", \"What challenges do you foresee in the integration of robots? Can you give specific examples?\", and \"How do you personally feel about a future where robots are more prevalent?\"  
All interviews were conducted face-to-face, allowing participants to engage directly with researchers. Each interview lasted approximately 15-20 min, and all sessions were audio recorded and transcribed verbatim.
## 2 Materials and Methods
### ANN-GA Model  
Experimental data have been used to train the hybrid ANN-GA model, and parametric analysis has been performed by varying the following factors:  
Population size: 25, 50, 75, and 100; Number of hidden neurons: 5 and 10.  
The ANN model follows the sigmoid function described in Equation (2). As for the GA algorithm, the set to optimize the ANN is constructed following the main components: the chromosome encoding, the fitness function, selection, recombination, and the evolution scheme.
## 3 Results and Discussion
### Sensory Property
For the grapes harvested at 20 and 22 WAFB, linalool and its derivative seemed to develop the floral flavor significantly. An appreciable difference in the flavor scores between grapes obtained after 20 and 22 WAFB and those obtained after 16 and 18 WAFB strongly supports this result (Table 2). However, there were no significant differences in sourness scores among the samples, which is consistent with the trend of instrumental TA values. Although the instrumental measures of PTC and firmness of the berries harvested at 16 WAFB were significantly higher than those of the others, the sensory astringency and firmness scores showed no significant differences among the samples. Small differences in the instrumental measures of tannin content and mechanical texture properties of ripened Shine Muscat grape berries rarely affect their sensory astringent sensation and firmness. Regarding consumer acceptance, the correlation coefficients presented in Figure 4b indicate that sweetness and flavor were highly positively correlated with overall acceptability (\(p<0.01\)). In contrast, the sourness was negatively correlated with the overall acceptability (\(p<0.05\)). Astringency and firmness were not significantly correlated (\(p>0.05\)). It was obvious that the sweetness and flavor attributes strongly affected the overall acceptability compared with the other attributes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**1 WAFB** & **Sweetness** & **Sourness** & **Astringency** & **Firmness** & **Flavor** &
\begin{tabular}{c} **Overall** \\ **Acceptance** \\ \end{tabular} \\ \hline
16 & 3.19 \(\pm\) 0.8 b & 3.68 \(\pm\) 0.8 a & 3.97 \(\pm\) 1.1 a & 3.66 \(\pm\) 1.1 a & 3.00 \(\pm\) 1.0 b & 3.48 \(\pm\) 1.0 b \\
18 & 3.53 \(\pm\) 1.2 ab & 3.76 \(\pm\) 1.2 a & 3.83 \(\pm\) 1.2 a & 3.63 \(\pm\) 1.1 a & 2.97 \(\pm\) 1.1 b & 3.39 \(\pm\) 0.8 b \\
20 & 4.26 \(\pm\) 1.1 a & 3.76 \(\pm\) 0.9 a & 4.00 \(\pm\) 1.2 a & 3.52 \(\pm\) 1.0 a & 4.13 \(\pm\) 1.5 a & 4.20 \(\pm\) 1.1 a \\
22 & 4.23 \(\pm\) 1.1 a & 3.59 \(\pm\) 1.5 a & 3.96 \(\pm\) 0.5 a & 3.43 \(\pm\) 0.7 a & 4.04 \(\pm\) 1.0 a & 4.16 \(\pm\) 0.9 a \\ \hline \hline \end{tabular}  
* WAFB: Weeks after full bloom. Different letters (a, b) within the same column indicate significant differences at \(p<0.05\).  
\end{table} 
Table 4: Changes in the sensory attribute scores of Shine Muscat grapes during ripening. Figure 3: Biplot of principal component analysis (PCA) for active odorants in Shine Muscat grape during ripening from 12 to 22 weeks after full bloom (WAFB): Red dots represent grape samples harvested at different periods.
## 4 ZnO-Based Nanomaterials for Antimicrobial Application in Water Treatment
### Strategies for Enhancing ZnO-Based Nanomaterials Antibacterial Activity
#### 4.4.2 Transition Metals Doping into ZnO  
The addition of transition metal ions can generate electronic states in the intermediate bandgap region to change charge separation and recombination kinetics, which is beneficial to enhancing the ability of photocatalytic antibacterial [190]. Numerous common transition metals, such as Co, Cu, Ni, Fe, and Mn, are often doped into ZnO to enhance its photocatalytic antibacterial ability [191].  
Fe is the most used metal for enhancing the photocatalytic antibacterial ability of ZnO. Iron has two oxidation states with ionic radii of 0.61 A and 0.55 A, which are used as dopants for zinc lattice sites, respectively, since their ionic radii are smaller than those of zinc +2 (0.74 A) radius. Hence, the doping can be alternative or interstitial to increase the conductivity of the product. [PERSON] et al. [192] doped Fe into ZnO and investigated the antibacterial properties of the composites. As shown in Figure 16, TEM images of Figure 16a pure, Figure 16b Fe doped, and Figure 16c capped ZnO NPs are observed. For undoped and doped ZnO NPs, they are more spherical with dimensions of 17-19 nm. Simultaneously, the agglomeration phenomenon occurs when ZnO is doped with Fe, which is due to the greater surface area and energy. Furthermore, the antibacterial activity against _E. coli_ showed that the capped ZnO NPs were less toxic to the organism than ZnO NPs. As shown in Figure 17, the XRD patterns associated with various different ZnO-based nanomaterials are displayed. Iron doping of ZnO reduces the grain size, resulting in a further increase in the grain size of glucose-terminated ZnO nanoparticles, which is consistent with previous reports [193]. Therefore, Fe-ZnO nanoparticles have good antibacterial activity.  
Figure 15: (**a–c**) Diameter of inhibition zones of Zn\(1-\)xMgxO against bacteria pathogen. Reprinted/adapted with permission from Ref. [188]. Copyright © 2022, Elsevier Ltd.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 2 Background Study
### Nvis  
The use of NVIS communications dates back to the Vietnam War in the late 1960s. However, it is known that this type of communication had already been in use since 1939 at the advent of World War II, although not under this name. These communications were high-powered and were aimed primarily for voice transmission. In these systems, reference was already made to the use of antennas as horizontal dipoles to cover bands from 2 to 8 MHz with the objective of covering 24 h of operation due to the bouncing characteristics of the ionosphere using short waves [30]. In the overview of [31], a brief review of the history of NVIS is provided which starts with the discussion of the existence of the Hevyside layer by [32] as well as the Appleton and Builder experiment using the ionosphere as a refraction medium [33]. The reason for the use of these communications boils down to their rapid deployment and widespread supply.  
The HF band has always been used for long distance communications with antennas at a low angle to the horizon. In this way, a coverage area of 250 km around the transmitter is produced. However, the link distance depends on the angle: the smaller the angle, the lower the Maximum Usable Frequency (MUF), mainly between 3 and 10 MHz [7]. Even so, the biggest disadvantage of this type of communication is the availability and behavior of the ionosphere due to the ionization of its own layers and solar activity. This implies that these communications will not be available during certain times at the same frequency. The use of multiband antennas can assure communication for an entire day. As an advantage, these communications are easy to install due to their antennas, they usually feature low-cost platforms, and they do not require the transmitter and receiver to have direct line of sight, allowing them to overcome large mountains, which makes them ideal for remote areas.  
The antennas are usually wired and dipole types in order to optimize performance [7]. The horizontal dipole and the inverted Vee are the most frequently used antennas for NVIS, the latter being more interesting due to the need for a single mast [34].  
To reduce the size of the antennas and to power the sensors with batteries, an efficient physical layer is required that can work with the lowest possible transmit powers and simple equalization.