esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiment Results and Discussion
#### 5.4.2 Comparison with GNN Baselines  
From Table 7, we can see that HertDroid outperforms these generic baselines and achieves the highest F1 value. The average F1 improvements of HertDroid is 7.94% compared to homogeneous baselines, such as GAT, GATv2, GCN, GraphSAGE, and 12.49% compared to heterogenous baselines like GAT-hete, Graph-hete, HAN, and Metapath2 vec.  
Furthermore, the number of parameters of HertDroid is smaller than most of the baselines except GCN and GAT. For instance, the number of parameters of GATv2 is approximately 10 times that of HertDroid.  
To access the recognition capabilities of each model for the malicious samples, we present the ROC (Receiver Operating Characteristic) curve of baselines and HertDroid in Figure 5. The figure reveals that HertDroid attains the highest area under the ROC curve (AUC) value, specifically 99.81%. Following closely, GAT achieves the next highest AUC value. Therefore, compared with generic baselines, HertDroid is more suitable for Android malware detection with the highest sensitivity and the lowest false positive rate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline HinDroid & 0.9420 & 0.9505 & 0.9536 & 0.9521 \\ DroidEvolver & 0.9060 & 0.9264 & 0.9172 & 0.9218 \\ HertDroid & 0.9700 & 0.9767 & 0.9735 & 0.9751 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experimental results of domain-specific baselines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** & **Params** \\ \hline GraphSAGE & 0.9293 & 0.9394 & 0.9616 & 0.9504 & 11.34 M \\ GAT & 0.9501 & 0.9504 & 0.9501 & 0.9502 & 5.61 M \\ GATv2 & 0.7082 & 0.7105 & 0.9854 & 0.8257 & 23.92 M \\ GCN & 0.9270 & 0.9292 & 0.9629 & 0.9458 & 5.67 M \\ GraphSAGE-hete & 0.9425 & 0.9683 & 0.9609 & 0.9645 & 24.93 M \\ GAT-hete & 0.9318 & 0.9316 & 0.9318 & 0.9317 & 21.21 M \\ HAN & 0.9306 & 0.9445 & 0.9672 & 0.9557 & 6.80 M \\ Metapath2 vec & 0.6284 & 0.8768 & 0.6018 & 0.7137 & 22.77 M \\ HertDroid & 0.9802 & 0.9841 & 0.9904 & 0.9873 & 10.23 M \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative experimental results of GNN baselines.  
In summary, our method demonstrates superior performance compared to all baselines, while also maintaining a lower parameter count compared to heterogeneous baselines. Importantly, our approach achieves this without the need for manual configuration of meta-paths, highlighting its efficiency and effectiveness in the domain of Android malware detection.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Resource Management
In [117], [PERSON] et al. proposed edge AI, an advanced AI technology that can handle the complex grid edge more efficiently. The connected physical devices near the customer are embedded with AI applications and are in charge of computation. Rather than completing the computation in the central data center, handling the data with the devices close to where it is produced can make the operation of edge devices more stable and reliable.
## 3 Results and Discussion  
### Characterization and Properties of CRMFoams  
#### 3.1.1 Optical and SEM Images  
Figure 1a,b shows the optical images of CRMFoam-5 and CRMFoam-2.5 at different magnifications. The images of both carbon foams reveal highly porous bodies with more or less elliptical and spherical pores with sizes ranging from 50 up to 500 \(\upmu\)m. It should be noted that the CRMFoam-25 sample, showed smaller wall thickness than that of CRMFoam-5 sample. A possible reason for this last fact is the higher employed temperature for the pyrolysis of CRMFoam-2.5 sample (1100 \({}^{\circ}\)C) than that for CRMFoam-5 (1000 \({}^{\circ}\)C) sample, which favors greater sintering of red-mud constituted oxides. The SEM images of the same carbon foams are depicted in Figure 2 as well the elemental color mapping at the microstructural level using EDS analysis. As shown in this figure, the carbon foams exhibit submicro-sized porous and a dense wall structure on which the various elements of red-mud are uniformly dispersed.  
Figure 1: Optical images of CRMFoam-5 (**a**) and CRMFoam-2.5 (**b**) samples at different magnifications.  
Figure 2: SEM images of CRMFoam-2.5 sample at different magnifications (**a**). Elemental color mapping at the microstructural level (**b**) and EDS spectrum (**c**) showing the distribution of elements and the composition of the foam.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Color Composites
Since all hydroxyl-containing minerals are strongly reflective in band 4, standard RGB band combinations are unable to distinguish between them individually. This means that the detail to which typical color composites can be applied is limited. Nonetheless, standard RGB combinations remain helpful when attempting more intricate and sophisticated mapping approaches first and one has to obtain a rough idea of the geological makeup of the study area.
###### Abstract  
Safety barriers (SBs) are important means of reducing failure risks of process systems. As barriers vary in type and function, their effectiveness needs to be evaluated in order to find a more reasonable configuration strategy. However, in practice, there is often a lack of accurate and complete data relating to SBs, which poses a significant challenge in quantitatively assessing their effectiveness. To address this issue, in this study, we propose a semi-quantitative approach for evaluating the effectiveness of both preventive and protective barriers in process systems by integrating expert elicitation, interval-valued intuitionistic fuzzy numbers (IVIFNs), and a bow-tie model. In this approach, the bow-tie model is first applied to describe the system failure scenarios and the action phases of the barriers, and then IVIFNs with expert judgment are introduced to obtain the failure probabilities of basic events and the effects of SBs. Subsequently, the effectiveness of each barrier is measured by comparing the relative change in failure risk due to the addition of the barrier. To verify the feasibility of this approach, a natural gas storage tank with some barriers was analyzed. The results show that the regular inspection of the deformation or damage of the storage tank has the highest effectiveness, followed by the installation and regular maintenance of safety electrical equipment. Furthermore, compared to a single barrier, multiple barriers can significantly reduce the system risk.  
risk assessment; safety barrier; bow-tie model; interval-valued intuitionistic fuzzy numbers; natural gas storage tank +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Dissolution Studies  
The dissolution profiles of the raw spiramycin particles and spiramycin microparticles are shown in Figure 9, the data of which was represented by spiramycin I. A higher dissolution rate was produced by the micronized spiramycin than the raw spiramycin particles. The concentration of micronized spiramycin at 120 min was taken as 100%, and the cumulative solubility of raw spiramycin  
Figure 8: Gas chromatograms of spiramycin microparticles, indicating the presence of residual DMSO (spiramycin microparticles are the micronized spiramycin by the homogenate-antisolvent precipitation under optimum conditions).  
Figure 7: Scanning electron micrographs of (**a**) unprocessed and (**b**) processed spiramycin (spiramycin microparticles are the micronized spiramycin by homogenate-antisolvent precipitation under optimum conditions), and particle size distribution of processed spiramycin under optimized experiment conditions (**c**).  
particles and micronized spiramycin after different times was expressed as a percentage of the concentration obtained in the concentration of micronized spiramycin at 120 min. As depicted in Figure 9, the process required 60 min for raw spiramycin particles to reach the maximum saturation concentration (approximately 18.57%). By contrast, at 60 min, the concentration of micronized spiramycin just reached saturation point with the maximum saturation concentration of about 95.90%. According to the Kelvin equation, the surface tension and the molar free energy of particles change when the particle becomes gradually smaller, thus leading to the increased solubility [44].
# A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure
## 2 Mathematical Model
### LEO Satellite Network Model
#### 2.3.2 Link Switch Model  
The satellite undergoes a series of works such as alignment before establishing the ISLL. It costs more and takes more time to establish new communications than to maintain the previous connection. We suppose that in any two adjacent \(\mathcal{G}(c-1)\) and \(\mathcal{G}(c),c=2,\cdots,N_{T}\), \(\mathcal{PO}^{c-1}\) and \(\mathcal{PO}^{c}\) are the optimal path sets, respectively, \(\mathcal{H}^{c-1}\) and \(\mathcal{H}^{c}\) are the corresponding optimal connection matrix, respectively. \(\mathcal{NS}_{c}\)=\([\)\(ns_{ij}^{c}]\in\mathbb{R}^{n\times n},ns_{ij}^{c}\in\{0,1\}\) is the switch matrix of \(\mathcal{G}(\[I_{j}=RP_{T}G_{T}G_{R}\eta_{T}\eta_{R}\left(\frac{\lambda}{4\pi d_{ij}}\right)^{2} \exp(-G_{T}{\theta_{ij}}^{2}) \tag{4}\]
## 3 Description, Implementation and Results of Experiments
### Application Results of GRA and ANOVA Methods
To validate the test result, we repeated the experiment for the optimal setting of the input factors \(a_{p}\) 4-_f_ 4-\(v_{c}\) 1. The results of the validation test were produced with repeatability 8 for the optimal level of input factors as shown in Table 8.  
From the confirmation experiment Table 8, GRG for several response parameters, such as flank wear and diameter deviation, was significantly improved (by 0.3858) by setting the optimal parameter combination. These results confirm the resulting response parameters, diameter deviation, and flank wear, performed under operating conditions on a CNC machine. Based on the above analysis, the input factors for the minimum values of flank wear and diameter deviation studied in the turning of steel C45 by GRA based on the Taguchi method are investigated. Some positive and negative experiences in the study of the application of a laser triangulation sensor to measure the diameter deviation after turning steel C45 standard:  
1. Ensure compliance with the certified deviations of some dynamic modules of CNC machines (e.g., spindle);
2. Limit the use of process media or provide suitable constructive protection of the sensor in the working area of the machine;
3. The results may be distorted by red light: this did not affect the results in the experiments;
4. Non-contact measurement method and speed of acquired data: high efficiency;
5. Can be used for static and dynamic measurements;
6. Since we always work with an electrical output signal of the sensor, it is necessary to solve the problem of interference of the sensor by electric or magnetic fields;
7. Program can process, analyze, filter the acquired data according to Figure 13 and decide about the product, whether it was manufactured according to the specified technical requirements. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Optimal Cutting Factors**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Prediction**} & \multicolumn{2}{c}{**Experiment**} \\ \hline Level & \(a_{p}\) 4-_f_ 1-\(v_{c}\) 4 & \(a_{p}\) 4-_f_ 4-\(v_{c}\) 1 & \(a_{p}\) 4-_f_ 4-\(v_{c}\) 1 \\ \hline \(VB_{B}\) & 0.1990 & & 0.0983 \\ \hline \(\Delta_{d}\) & 0.1148 & & 0.0058 \\ \hline \(GRG\) & 0.3615 & 0.8855 & 0.7473 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confirmation experiment (improvement in GRG = 0.3858). The defects on the machined surface documented according to Figure 14 were imaged using a Carl Zeiss Primotech D/A ESD microscope.
## 3 Characteristic of Picosecond Laser Measurements for Debris Targets
### Pulse-Bursts Picosecond Laser Ranging to Debris Targets  
DLR measurements were performed applying the above pulse-burst picosecond laser system to the existing DLR system of SHAO. Figure 20 shows the measurements of the Long March CZ-4 debris target (ID 20853, RCS 0.6 m\({}^{2}\)). The debris ranging precision is 16.4 cm, which gradually improves as the size of the debris decreases. Among the measurements, the longest ranging distance is 1726.8 km (with an RCS of 0.91 m\({}^{2}\), which is equivalent to an RCS of 0.1 m\({}^{2}\) at a distance of 1000 km). Figure 21 shows the good detection ability, with the x-axis is the RCS of targets and the y-axis is the measured range and precision of the targets. Employing the pulse-burst mode, the average power of the laser system can be increased by summing the number of laser pluses. This increase in power will allow the long-distance ranging of small debris in the future.  
Figure 20: Measurements of the debris target using the pulse-burst picosecond laser.
# Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings
## 3 Numerical Results
### Prediction Models  
The chosen prediction model utilizes an NN architecture. This architecture refers to the arrangement of neurons into layers, along with the connection patterns between layers. It depicts how a network transforms the received input into output. The NN model integrated in this project uses Keras, which is a high-level neural network API. The following characteristics of the model and its layers differ slightly among one-day and three-day predictions to account for the different levels of complexity. The input layer of the NN model represents the parameters used to help generate the output layer. The specific input parameters utilized in the model are ambient temperature, beam irradiance, diffuse irradiance, wind speed, and date and time. Both one- and three-day prediction models have densely connected input layers with seven neurons.  
The distinguishing factor between the models for one-day versus three-day simulation is the size of the hidden layer. A hidden layer is an intermediary layer between the input and the output. This is where the data received by the model are essentially studied for intricate patterns to produce accurate predictions. The hidden layer size for the one- and the three-day simulations is 7 and 14 neurons, respectively. There are more data to consider for three-day prediction, so having 14 neurons in the hidden layer allows the model to train more efficiently and handle the complexities and size of the data. The output layer for the one- and the three-day model is 24 and 72 neurons, respectively, which corresponds to the number of hours for which we want to obtain predictions. The adaptive moment estimation (Adam) optimizer is utilized during the compilation of each model. This ensures enhanced performance and the minimization of the model's loss function.  
The results from the comparison in Table 1 show that the NN model produced the most accurate results. It received the lowest values for each testing error metric compared with ARIMA and SVR. This high performance can be attributed to the model's architecture comprising hidden layers that utilize activation functions, ensuring that complex and non-linear patterns are captured. The results further highlight the advantages of a machine learning approach, as three different prediction models were evaluated, and accurate results were obtained (Table 4). While an analytical approach may provide predictions  
Figure 6: Input data for three-day simulation.  
through methodical calculations, it was not selected due to the added complexity. Selecting machine learning as a means to obtain predictions proved to be a more feasible alternative, as accurate results are obtainable and varying datasets can be used. This tradeoff allowed for the balance between simplicity and predictive accuracy, making the machine learning approach the more practical option for the project scope.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 3 Calculation and Simulation
### Simulation of the Impact of Key Parameters on the Performance of Flapping-Wing Aircraft
#### 3.2.2 Impact of Flapping Amplitude of Flapping Motion on Lift  
As can be seen from the lift calculation formula, under the same flapping frequency and mid-position angle conditions, the larger the flapping amplitude, the greater the lift  
Figure 5: The impact of the flapping quick return feature on lift.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Parameter**} & **Flapping Motion without Quick Return Feature** & **Flapping Motion with Quick Upward Strokes** & **Flapping Motion with Quick Downward Strokes** \\ \hline \(\phi_{max}\) & 2\(\pi\)/9 & 2\(\pi\)/9 & 2\(\pi\)/9 \\ \(T_{1}\) & 0.125 & 0.1 & 0.15 \\ \(T_{2}\) & 0.125 & 0.15 & 0.1 \\ \(\varphi_{1}\) & \(\pi\)/2 & \(\pi\)/2 & \(\pi\)/2 \\ \(\varphi_{2}\) & \(\pi\)/2 & 5\(\pi\)/6 & 0 \\ \(\phi_{0}\) & \(\pi\)/18 & \(\pi\)/18 & \(\pi\)/18 \\ \(T\) & 0.25 & 0.25 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 3: Control parameters of flapping motion with different quick return features.  
generated. Three groups of flapping functions are designed, simulated, and calculated to verify the impact of flapping amplitude on aerodynamic performance. The control parameters of the right-wing flapping function are shown in Table 4, and the left-wing flapping function is opposite to the right wing.  
The simulation time is set to 1 s, including four flapping cycles. The simulation data is imported into the software Origin, and the lift curves of the three flapping motions over time are drawn, as shown in Figure 6. It can be seen that the amplitude of flapping motion has a significant impact on aerodynamic performance. The average lift within a period was calculated (0.25 s-0.5 s). The average lift generated by the flapping motion with a flapping amplitude of 30\({}^{\circ}\) within a period is about 0.737 N. The average lift of the flapping motion with a flapping amplitude of 40\({}^{\circ}\) is about 0.782 N; the average lift of the flapping motion with a flapping amplitude of 50\({}^{\circ}\) is about 0.864 N. Therefore, it can be concluded that increasing the flapping amplitude can improve the aerodynamic performance of the flapping motion. This is consistent with the results of mathematical modeling analysis.
## 1 Introduction
[17] took the pump control system of a cogeneration plant as an example and, to minimize energy consumption, analyzed the optimal number of pumps to be operated and the synchronous speed when the water level changes. The results show that taking a small number of pumps to run is not necessarily the most energy-efficient in certain cases. Based on the above research findings, this study focuses on a certain pumping station project, with its head and flow as constraints and its minimum input power as the objective function. A genetic-algorithm-based optimization method is constructed for the operation combination and flow distribution between pumping station units. By optimizing the combination of units, the optimal control plan of the pumping station system is determined under the premise of a certain total water intake to enable efficient operations, and create maximum economic and social benefits.
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 3 Experimental Results
### Evaluation of Different TopK Matching Approaches  
In the experiment evaluating the effectiveness of the large model, this study first tested the performance difference between two different TopK matching approaches designed in this study. Approach 1 involved matching the test set based on the average highest matching rate, while Approach 2 involved matching multiple images based on a matching rate threshold.  
In the performance evaluation focused solely on the TopK matching approach, this study found that although matching multiple test set instances to a single image as the ground truth label improved the accuracy of obtaining correct labels, it also limited the use of only one image feature for counting. Therefore, matching multiple images based on a pre-defined threshold achieved better precision compared to using a single image. This study defined the matching accuracy as mean absolute error (MAE).  
The final results of the evaluation experiment are shown in Table 1:
# The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen
###### Abstract  
This research aims to produce ozone using a dielectric barrier discharge to extend the shelf life and preserve the quality of mangosteen. The optimum condition of this system was a minimum breakdown voltage of about 6 kV\({}_{\text{P-P}}\), a resonance frequency of 224 kHz, and an oxygen flow rate of 2 L/min. The findings demonstrate that the maximal ethylene concentration value of treated fruit with ozone from oxygen flow rates 2 L/min lower than untreated fruit was approximately 11-fold. The L\({}^{*}\), \(a^{*}\), \(b^{*}\), weight loss percentage, pericarp firmness value, and sensory evaluation were used to assess the quality of mangosteen. Compared to control fruit at 15 \({}^{\circ}\)C, fruit treated with ozone can have a prolonged shelf life of 9 days. Furthermore, assessing the quality and sensory score indicates that ozonation preserves the sensory quality of mangosteen. The weight loss percentage and pericarp firmness of fruit-treated ozone compared with the control were decreased by 3.34% and increased by 11.18 N, respectively. The sensory score of the fruit treated with ozone was higher than the control fruit, significantly different (\(p<0.05\)).  
plasma ozonizer system; ozone; mangosteen; shelf life extend; quality preserved Article  
The Plasma Ozonizer System for Mangosteen Storage Container to Preserve the Quality of Mangosteen  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Wideband Versatile Receiver for CubeSat Microwave Front-Ends
## 5 Conclusions  
In this contribution, a microwave receiver is proposed as the wideband front-end for CubeSat applications. After a preliminary simulation phase devoted to the system design, the entire receiver was accurately tested and tuned. COTS components were employed with the task of designing a cost-effective system, which is one of the main requirements of CubeSat projects. The minimum spurious contribution was confirmed by an accurate experimental characterization, allowing an easy system integration for the new frontier of CubeSat technologies. In detail, the spurious level was maintained below the minimum detectable signal by demonstrating at least a \(-45\) dBc suppression. Future research might be directed towards the development of compact-size receivers with low measured energy consumption to be finally tested on board in a real space environment.  
**Author Contributions:** Conceptualization, E.C., R.C. and P.V.; methodology, E.C. and R.C.; software, E.C.; validation, E.C., R.C., P.V. and E.C.; formal analysis, E.C.; investigation, E.C., R.C. and P.V.; resources, E.C. and P.V.; data curation, E.C.; writing--original draft preparation, E.C.; writing--review and editing, E.C.; project administration, P.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.