esa-sceva/satcom-chunk-collection huggingface dataset:
# RF Energy Harvesting Wireless Communications:
## 3 RF-EHWC Hardware
### Design Principles of RF Energy Harvester
#### 3.1.2 Rectifier and Voltage Multiplier  
The alternating current (AC) output of a matching circuit cannot be directly used to charge an energy storage component (i.e., battery). The purpose of rectifier is to transform energy from AC to direct current (DC). The basic rectifier circuit consists of a few diodes to rectify the output to DC. Figure 9a shows a typical full-wave Graetz bridge rectifier.  
Due to the low power density of RF signal in the air, the output voltage of the rectifier may not be high enough to charge a battery or to drive a wireless communication module. In this situation, a voltage multiplier is implemented to boost the output voltage of the rectifier. In a real RF energy harvest circuit, the rectifier and voltage multiplier are usually implemented together in cascaded subnetworks consisting of capacitors and diodes [46], as shown in Figure 9b. In the figure, each subnetwork is referred to as a \"stage\"; each stage marks the output of the previous stage as a biasing reference. Using this approach, the subnetworks can pump up the output voltage stage-by-stage to reach the desired voltage.  
Schottky diodes and PN diodes are two options to build the rectifier and voltage multiplier. The main advantages of the Schottky diode are the low forward voltage (the forward voltage is the voltage drop across a diode if the voltage at the diode's anode is more positive than the voltage at its cathode) and small junction capacitance [47]. The Schottky diode can offer as small as 150 mV of forward voltage and 0.2 pF of junction capacitance, much lower than 700 mV of forward voltage and few picofarads of junction capacitance in a typical PN diode. For an RF energy harvester, the low forward voltage of the Schottky diode can improve the efficiency of power conversion, while the low junction capacitance can increase the maximum frequency of RF energy that a system can harvest.  
Despite its advantages, the Schottky diode is usually produced as discrete components and, therefore, integrating the Schottky diode into a standard CMOS process will significantly increase the fabricating costs. By contrast, the PN diode based rectifier and voltage multiplier can be incorporated into a single chip, which has apparent advantages on high consistency and low cost. Those features  
Figure 9: (**a**) Graetz bridge rectifier; and (**b**) three-stage Dickson voltage multiplier.  
make the PN diode attractive to RF-EHWC systems that have high demands on the portable size, low cost, and high reliability.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Data Fusion Level  
Even the level of fusion to be adopted is not a straightforward choice. The majority of studies employ low-level fusion, arguably because this is a more straightforward and computationally lighter approach [121]. Some studies seem to indicate that higher fusion levels tend to produce better results [30; 77; 80], arguing that the poorer results observed when lower level fusion is applied is likely the result of data redundancies arising from complementary information from distinct sensors [30]. Other studies have come to the opposite conclusion, with results favoring the low-level approach [113]. Because few studies have compared the different fusion levels, no definitive conclusions on the matter can be drawn. More comparative studies tackling different fusion levels are needed for a better understanding on how to treat data under different contexts and conditions.  
Low-level fusion attempts to directly combine different types of data directly. Since these have different value ranges and formats, some kind of normalization is almost always needed in order to make those different databases compatible [77]. If the only difference is the range of values, normalization tends to be relatively straightforward. On the other hand, if the types of data employed are of different natures (for example, images and meteorological data), the normalization process can be complex or even unfeasible, in which case, the use of higher data fusion levels may be inevitable.  
Mid-level fusion can be viewed as a two-step information extraction procedure, as data are first processed in order to generate meaningful features, which are then combined into the final answer. Although in most cases the features are extracted from different types of data (the features themselves are often also different), in some cases, different features are extracted from the same dataset [79]. Calling the combination of features originating from the same dataset \"data fusion\" is not appropriate in most cases, but sometimes those features represent such different aspects of the data, where the techniques used to combine them are akin those employed in actual data fusion.  
As mentioned before, high-level fusion tends to yield solid results, but this approach is the least used among the three levels of fusion. The explanation for this seems to be related to two main factors. First, high-level fusion can be computationally expensive, especially during training [121]. This is particularly true if one or more of the classifiers are based on deep neural networks. Second, choosing the right variables to feed a single model usually is challenging enough, so the process of selecting variables for multiple models may become somewhat impractical. Nevertheless, the use of high-level fusion tends to grow as the availability of computational resources becomes less of a problem, as the research on data fusion natures, and as the characteristics of each type of model/classifier become better understood.
## Appendix A

Table 3: Numeric results of spectral quality metrics of the Pan-sharpened images produced by select algorithms for urban test sites (blue: highest accuracy; red: lowest accuracy). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **CC** & **Zhou’s SP** & **SRMSE** & **SP ERGAS** \\ \hline \multirow{8}{*}{F2} & Ehlers & 0.923124 & 0.939587 & 0.027718 & 28.484374 \\  & GS & 0.985504 & 0.968697 & 0.008643 & 25.816466 \\  & GIHS & 0.986114 & 0.989954 & 0.012513 & 26.611553 \\  & HCS & 0.937058 & 0.949630 & 0.019802 & 21.018918 \\  & IHS & 0.913914 & 0.929653 & 0.027772 & 27.734556 \\  & CIELab & 0.996129 & 0.995455 & 7.17E-08 & 6.2709053 \\  & NNDiffuse & 0.960443 & 0.961942 & 0.012853 & 22.490210 \\ \hline \multirow{8}{*}{F4} & Ehlers & 0.820014 & 0.939655 & 0.017949 & 31.953533 \\  & GS & 0.989999 & 0.989842 & 0.009369 & 26.949381 \\ \cline{1-1}  & GIIHS & 0.992001 & 0.989953 & 0.012168 & 28.452035 \\ \cline{1-1}  & HCS & 0.775576 & 0.688826 & 0.018171 & 33.941348 \\ \cline{1-1}  & IHS & 0.919941 & 0.989666 & 0.018000 & 31.259808 \\ \cline{1-1}  & CIELab & 0.997280 & 0.996187 & 5.44E-08 & 5.8158174 \\ \cline{1-1}  & NNDiffuse & 0.914590 & 0.888001 & 0.012253 & 27.737860 \\ \hline \multirow{8}{*}{F9} & Ehlers & 0.924923 & 0.949622 & 0.024424 & 28.179705 \\ \cline{1-1}  & GS & 0.989237 & 0.982593 & 0.014115 & 26.132656 \\ \cline{1-1}  & GIHS & 0.978923 & 0.989960 & 0.015724 & 26.434713 \\ \cline{1-1}  & HCS & 0.860205 & 0.794738 & 0.023803 & 28.880067 \\ \cline{1-1}  & IHS & 0.912546 & 0.969662 & 0.024479 & 27.533331 \\ \cline{1-1}  & CIELab & 0.99848 & 0.998002 & 5.26E-08 & 4.4340045 \\ \cline{1-1}  & NNDiffuse & 0.929067 & 0.958606 & 0.021917 & 25.926934 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numeric results of spatial quality metrics of the Pan-sharpened images produced by select algorithms for urban test sites (blue: highest accuracy; red: lowest accuracy). Figure A8: Result and comparison of the proposed Pan sharpening method for the F3 test frame, zoomed areas, which are represented in a true color (RGB) combination. (**a**) MS; (**b**) Pan; (**c**) Lab; (**d**) GIHS; (**e**) GS; (**f**) HCS; (**g**) IHS; (**h**) NNDiffuse; (**i**) Ehlers. Figure A9: Result and comparison of the proposed Pan sharpening method for the F7 test frame, zoomed areas, which are represented in a true color (RGB) combination. (**a**) MS; (**b**) Pan; (**c**) Lab; (**d**) GIHS; (**e**) GS; (**f**) HCS; (**g**) IHS; (**h**) NNDiffuse; (**i**) Ehlers. Figure A10: Result and comparison of the proposed Pan sharpening method for the F8 test frame, zoomed areas, which are represented in a true color (RGB) combination. (**a**) MS; (**b**) Pan; (**c**) Lab; (**d**) GIHS; (**e**) GS; (**f**) HCS; (**g**) IHS; (**h**) NNDiffuse; (**i**) Ehlers.
## 4 Experiments and Result Analysis
### Hyperparameter Optimization  
The sklearn RF classifier's tunable hyperparameters include the number of decision trees (n_estimators), the maximum depth of the decision tree (max_depth), the split criteria (criterion), the minimum number of samples for internal node splitting (min_samples_split), the minimum number of samples for leaf nodes (min_samples_leaf), whether bootstrap sampling is used (bootstrap), and the maximum number of features (max_features). In this paper, the hyperparameters are optimized using an improved grid search method.  
As shown in Section 4.4, the order of parameter optimization is max_depth, min_samples_leaf, min_samples_split, criterion, n_estimators, bootstrap, and max_features. The roc_auc score is the area under the roc curve, and it is used as the RF classification model's evaluation standard in this experiment. The sklearn RF classifier is used as the experiment's base classifier, and it has been optimized. The default parameters of the base classifier are shown in Table 5.  
min_samples_leaf increases, the model complexity decreases, and the roc_auc score also decreases, so min_samples_leaf is set to 1.  
(3) Choose min_samples_split  
As is illustrated in Figure 8, the best min_samples_split is the base classifier's default value of 2, which has the highest complexity of the model. As min_samples_split increases, the model's complexity and roc_auc score decreases, so min_samples_split is set to 2.  
Figure 8: Performance of min_samples_split.  
Figure 6: Performance of max_depth.  
Figure 7: Performance of min_samples_leaf.  
choose criterion  
When the gini coefficient is used as the splitting criterion, the roc_auc score is 0.98909. If the splitting criterion used is entropy, the roc_auc score is 0.98901. When the gini coefficient is used as the splitting criterion, the score is higher, as shown in Figure 9. As a result, the gini coefficient is chosen as the best splitting criterion.  
Choose n_estimators  
This parameter is primarily used to lower the overall model's variance and improve its fitting ability. The experimental results are shown in Figure 10. The accuracy of the model will improve as the number of decision trees grows. The roc_auc score reaches a maximum of 0.9898 when the number of decision trees is 240, and it no longer increases significantly as the number of decision trees increases, as shown in Figure 10. As a result, a total of 240 decision trees are chosen.  
Choose bootstrap  
The experimental results show that the roc_auc score is the same when sampling with bootstrap and using the entire training set as a sample. As a result, bootstrap sampling uses the base classifier's default value. As with the base classifier, bootstrapping is used for sampling.  
Choose max_features  
Increasing the number of features in a model can improve performance because the model has more options on each node to consider, but it reduces the diversity of a single tree, which is the unique advantage of random forests, so it's important to pick the right  
Figure 10: Performance of n_estimators.  
Figure 9: Performance of criterion.
# Biodiesel and Biolubricant Production from Waste Cooking Oil: Transesterification Reactor Modeling
## 1 Introduction
Even though some studies have attempted the total or partial design of this kind of plant (especially concerning the reactor design for biolubricant production [37]), works devoted to the design of chemical plants for the implementation of this technology at an industrial scale are not commonly found in the literature, presenting a great opportunity to spread knowledge about this field, where multiple disciplines (such as chemical engineering or chemistry) play important roles. It is especially interesting because of the multiple variables affecting biolubricant production, where the nature of the raw material and, especially, the selected alcohol play important roles in kinetics and conversion. Therefore, each case presents particularities that require a thorough study of each process, with the subsequent relevance of studies that can complete and extend the knowledge about the design of plants based on such heterogeneous raw materials. Considering the above, the aim of this work was to produce biodiesel and a biolubricant from waste cooking oil. Thus, the main operating conditions at a laboratory scale were covered, with the subsequent design of a plant devoted to biolubricant production. Specifically, the objectives were the following:  
* Fatty acid methyl ester (biodiesel) production using a first transesterification with methanol and waste cooking oil, including its characterization according to the UNE-EN 14214 standard;
* Biolubricant production through double transesterification of fatty acid methyl esters with 2-ethyl-2-hydroxymethyl-1,3-propanediol, with a study about the effects of the temperature, pressure, and catalyst concentration on the conversion. The viscosity and oxidation stability, among others, were determined;
* Improvement of the oxidation stability of the biolubricant by antioxidant addition;
* Kinetic study of the second transesterification of FAMEs;
* Preliminary design and economic feasibility study of an industrial plant for biodiesel and biolubricant production, with a special focus on the reactor design.
# Design and Optimization of Vertical Axis Wind Turbines Using QBlade
## 4 Power Generation Calculations  
Power generated by the wind turbine is influenced at a particular time mostly by air mass elevated of the rotor blades. An increase in tip speed ratio leads to a decrease in the mass being lifted and will affect the power output. The maximum power that the turbine could capture depends on turbine design including blade airfoil, and pitch angle. Figure 2 shows the details of the developed approach used in this research paper to analyze and optimize the vertical axial wind turbine.  
The produced power by the wind turbine is determined based on the power coefficient (\(C_{p}\)) including the tip speed ratio (\(\lambda\)) and pitch angle (\(\beta\)) parameters. The tip speed ratio \(\lambda\) will often adjust over time because it is determined by the turbine rotor's angular velocity. The feedback from the turbine rotor to the ratio of tip speed can be created in a closed loop.  
The available kinetic energy in the wind that can be converted to mechanical energy is not exceeding 59% of the total value (theoretically), as specified in the law of Betz, where the power extracted practically is typically less than 45% of the available energy. By considering the field swept by VAWTs, the area \(A\) = \(rl\), velocity of air = \(v\), length of the VAWT blade = \(l\), density of the air = \(\rho\), and theoretical Betz limit \(Cp\) = 0.59 of the output power. Then the maximum power (\(P\)) that is available can be calculated as follows:  
\[P_{available\ max}\ =\ \frac{1}{2}\rho Av^{3}Cp \tag{7}\]  
where \(A\) = swept area of the rotating blades, \(\rho\) = air density, \(V\) = air velocity and \(Cp\) = Power coefficient.  
Rather than using an alternator or generator that is readily available, a permanent magnet generator is built according to requirement. The generator is equipped with a multi-stage and is used to maximize the output of the generator with the same size of the rotor. Each stage is composed of 12 coils and 24 magnets.
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.3.2 Complexity Analysis  
To objectively measure the complexity of the proposed model, the parameters (Params) and floating-point operations (FLOPs) were computed in this study [25; 26; 27]. Additionally, a comparative analysis was conducted with other CNN network models for reference. The GFLOPs were calculated based on a 224 \(\times\) 224 input scale, and the experimental results are presented in Table 3.  
Generally, a model's computational complexity is directly proportional to its parameter count and FLOPs. Larger models typically entail more parameters and computational requirements. Choosing an appropriate model depends on the specific application requirements and the availability of computational resources.  
The data from Table 3 reveal that the proposed methodology in this study has a parameter count of 27.6 million and a computation workload, measured in FLOPs (Floating Point Operations), of 4.2 G. In comparison, the VGG16 network possesses a parameter count  
Figure 8: Training set loss values.  
Figure 9: Accuracy curves.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Parameters** & **Floating-Point Operations (FLOPs)** \\ \hline VGG16 & 138 M & 32.2 \\ Densenet121 & 8.1 M & 5.9 \\ MFS-GC & 27.6 M & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different model.  
of 138 million and a FLOP value of 32.2 G. Hence, it is asserted that the model presented in this paper demonstrates superior performance. Subsequently, a comparative analysis between the MFS-GC model and the DenseNet121 network reveals that while DenseNet121 has a relatively lower parameter count, it exhibits higher FLOPs. Consequently, after carefully weighing computational resources against model performance, the MFS-GC model was selected due to its provision of enhanced feature representation. Notably, this model achieves a significant reduction in parameter count and computational workload while concurrently improving performance.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Server: Retransmission Count  
Table 5 lists the five scenarios of packet drop probabilities and the corresponding retransmission count. We observe that the retransmission increases with increasing packet drop probabilities. For instance, 1092 TCP segments were retransmitted (highest number) by the FTP server at \(p_{drop}=10\%\).
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 4 Proposed Parallel Phase Demodulation Using Dual Frequencies
### Parallel Phase Demodulation Method
In summary, the proposed parallel phase demodulation method can reduce both the total time for one distance measurement and the noise effects.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Operational Considerations  
This sub-section details alternative mechanisms that may be easier to enact than BVLOS operations to achieve similar effects, as well as common operational considerations that need to be addressed for BVLOS operations in forest environments.
## 3 Related Works
### Limitations of the State-of-the-Works and Our Contributions  
As the hand-crafted features-based methods [46; 47; 48; 49; 50; 51; 52] require explicit contact by the researchers, it is always a tedious task to find out meaningful features from the skeleton sequences. It is also very hard to design a powerful deep learning model to process the raw skeleton joints to capture spatial and temporal information [53; 54; 55; 56; 57; 58; 59; 60]. Above all, the deep learning-based human action recognition methods using raw skeleton dataset show comparatively worse performance than the deep learning-based methods using spatio-temporal representation of skeleton sequence. Sometimes, the spatio-temporal encoding cannot maintain local information as well as the global information significantly from the skeleton joints. The methods described [61; 62; 63; 64; 65; 66; 67; 68] suffer from spatio-temporal design complexities such as view dependency, lack of motion, and deficiency of spatial and temporal information. These disadvantages lead to the low performance in skeleton-based human action recognition.  
Thus, we figure out a simple yet robust, effective, and efficient way to represent the 3D skeleton joint coordinates into image format called STIF that defends both spatial and temporal variation of human behaviors specific movements. The STIF mapping facilitates to train the fine-tuned deep learning models that can automatically generate the meaningful features and performs the classification of human activity.  
The major contributions of this paper are summarized as follows:  
1. We idealize a novel technique to map 3D skeleton joints into spatio-temporal image. Our spatio-temporal image provides more discriminable features.
2. We adopt several fusion strategies (element-wise average, multiplication, and maximization) to expose the performance variations. Element-wise maximization shows better performance than average and multiplication.  
We justify the robustness of our approach using both light weight and heavy weight deep learning models. We consider MobileNetV2 as the light weight and DenseNet121 and ResNet18 as the heavy weight models.  
We compare the experimental results with prior works to show the effectiveness of our proposed method.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 5 Experimental Results and Discussion  
In our simulation, we consider four gray-level images (Lena, Jet, Peppers, and Sailboat) sized 512 \(\times\) 512 as test images, as shown in Figure 8[24], for both joint and separable systems.
## 5 Results
### Spatio-Temporal Evolution of NPP in Northeast China
#### 5.1.3 Spatial Evolution of NPP, from 2001 to 2019, in Northeast China, in Different Periods  
Based on the GLO-PEM, this study firstly estimated the NPP of Northeast China from 2001 to 2019, and then analyzed its spatio-temporal evaluation in different stages, namely the phases 2001-2005, 2006-2010, 2011-2015 and 2016-2019 (Figure 8). The dynamic evolution of NPP in the study area showed a clear spatial heterogeneity, with a decreasing trend from southeast to northwest. Overall, the average NPP of the study area was 778.54 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\) and the change of NPP in the high-value areas was from 700 to 1300 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), with a small fluctuation every five years. The regions with high NPP were mainly located in the east of Changbai Mountain and the south of Zhangguangcai Mountain, which were mainly covered with deciduous broad-leaved forest, mixed forest of temperate coniferous and deciduous broad-leaved, as well as temperate deciduous shrub. Because this region has favorable hydrothermal conditions affected by the monsoon climate and it is located in the humid zone of the mid-temperate zone, thus resulting in better vegetation growth. For the early stage, the median-area NPP (700-900 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)) increased by 10.36% from the first stage (2001-2005) to the second stage (2006-2010), mainly in the northwest of Bin County in Zhangguangcai Mountain, the west of Wuchang City, Acheng City, Ningan City, Jilin City and the west of Shulan City. The reason is likely that the lower-value area (0-500 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)) gradually moved to the middle value area over time, only accounting for 9.05% of the study area during the period of 2006-2010. Compared with other different time stages, the high-value NPP (700-1300 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\)) in Changbai Mountain from 2017 to 2019 occupied a larger area. For instance, the NPP had an increasing rate of 6.92% compared to the stage of 2011-2015. This indicated that the ecological environment in Changbai Mountains had recovered quickly due to the implementation of natural forest protection project in recent years.  
Figure 8: Spatial distribution of NPP in Northeast China, during different periods.  
Figure 7: The inter-annual variation of different forest types in Northeast China, during the period of 2001–2019.