esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
\begin{table}
\begin{tabular}{l r r} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Transistor**} \\  & **IGBT (IKV30N60H3)** & **MOSFET (STW15 NB50)** \\ \hline V\({}_{\text{CE}}\) max & 600 V & 500 V \\ I (@ 25\({}^{\circ}\)) max & 60 A & 14.6 A \\ Turn-on delay time & 21 ns & 24 ns \\ Turn-off delay time & 207 ns & 15 ns \\ Rise time & 33 ns & 14 ns \\ Fall time & 22 ns & 25 ns \\ Switching frequency, min max & 20–100 kHz & 20 kHz–12 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 8: IGBT and MOSFET catalog datasheet. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Frequency, Hz**} & \multicolumn{2}{c}{**Harmonic Number, h**} & \multicolumn{1}{c}{**Percentage of Fundamental**} \\  & & **\%** \\ \hline
0 & DC & 0.23 \\
50 & Fnd & 100.00 \\
250 & 5 & 8.85 \\
350 & 7 & 3.28 \\
450 & 9 & 0.81 \\
550 & 11 & 1.26 \\
650 & 13 & 1.66 \\
7550 & 151 & 9.36 \\
7600 & 152 & 5.59 \\
7700 & 154 & 5.23 \\
7750 & 155 & 19.21 \\
7850 & 157 & 14.58 \\
7900 & 158 & 7.91 \\
8000 & 160 & 4.45 \\
8500 & 161 & 13.06 \\ \hline \hline \multicolumn{2}{c}{THD\%} & \multicolumn{1}{c}{57.49} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Harmonic distribution of MC input current. The implementation of the SAPF as the only filter interface between the MC input current and the supply grid resulted in a THD of 20.84% with h155 as 0.63% from FFT analysis. The SAPF could not achieve further compensation, as a large compensational current must be supplied by the SAPF. The hysteresis current controller maintained a constant switching frequency of 80 kHz for the inverter gating pulses. Figure 11c shows the THD of the MC input current in the FFT window. The introduction of the HAPF resulted in the overall increase in the RMS value of the fundamental current to 91.8 A. While the passive part of the HAPF attenuated the supraharmonics, the active part of the HAPF compensated the lower frequency harmonics. By this principle, better harmonic compensation was achieved. At the same sampling time and samples per cycle, the THD of the input current under the HAPF was successfully reduced to 2.31%, and the h155 harmonic level compensated to 0.16% of the fundamental 50 Hz. These are within the IEEE Std. 519-2022 standards. Figure 11d shows the FFT analysis of the MC input current when the HAPF was used.
## 2 Materials and Methods
### Data Augmentation  
To avoid overfitting, the researchers used different image data augmentation methods for increasing data comprising geometric transformation, and photometric shifting and primitive data augmentation methods. These methods include rotation, flipping, cropping, shearing, and translation in the geometric transformation.  
Flipping  
[PERSON] et al. 2018 [1] proposed the flipping method, which reflects an image around its horizontal or vertical axis or both. This method is helpful for maximizing the image number in the dataset without requiring other artificial processing techniques.  
Rotation  
[PERSON] and [PERSON] 2013 [2] proposed another geometric data augmentation method known as rotation, which rotates the images around the axis in the right or left direction by an angle between 1 to 359.  
Shearing  
[PERSON] et al. 2018 [1] proposed the shearing method, which changes the original image along the x and y directions. In this case, the existing object shape is changed to an image.  
Cropping  
Image cropping was utilized by [PERSON] and [PERSON] 2013 [2], which is also known as scaling or zooming. It magnifies the image using cropping.  
Translation  
Using the translation method [1], the object is moved from one position to another position in the image. During this process, the image's black or white part is left after translation, which preserves the image data, or it includes the Gaussian noise or is randomized. The translation can also take place in X, Y, or both directions.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 1 Introduction
In summary, the ability of our approach to construct more complete CFG mainly depends on the following improvements: (1) the DGF technique is used to generate test cases that exercise indirect jumps instead of CGF, which can resolve more indirect jumps than undirected test cases generation used in existing approaches, and (2) an iterative feedback mechanism is proposed to continuously optimize the input generation, which alleviates the unsustainability of the existing approaches and further resolve more indirect jumps. On the basis of the techniques, we implement a prototype, dubbed by DGF-CFG constructor, for constructing CFGs of programs and evaluate it on eight benchmarks. In the experiments, we investigate the effectiveness of our DGF-based test case generation and the iterative feedback mechanism. The results demonstrate that our approach can resolve more indirect jump relations and construct more complete CFGs than existing approaches. As a preliminary step, we also evaluate the application of our techniques in real-world program analysis. The results show that our techniques is useful in discovering vulnerabilities deeply hidden in the code. The main contributions of this paper are summarized as follows:  
* We proposed a novel hybrid approach to construct more complete CFGs by utilizing the DGF technique in test case generation. * We designed an iterative feedback mechanism to make a virtuous cycle between CFG combination and test case generation. The mechanism further improves the completeness of constructed CFGs. * We implemented a prototype for constructing CGFs and evaluated the effectiveness of our techniques through comparing with the existing approaches on eight benchmarks. The remainder of this paper is organized as follows. Section 2 presents that indirect jumps is widely distributed in real-world programs, which brings challenges to the CFG construction. The overview of the proposed approach is described in Section 3. Section 4 describes the details of the four key steps in our method. The implementation details of our prototype are presented in Section 5. Section 6 evaluates our techniques. We summarize the related work in Section 7 and provide our conclusions in Section 8.
## 3 Results
### Comparative Analysis
#### 3.2.2 Descriptor Dimensionality Analysis  
Table 3 presents the matching performance under different descriptor dimensionalities. Five pairs of 1000 \(\times\) 1000-sized image patches were selected for evaluation. Although the 3 \(\times\) 6 \(\times\) 6 configuration achieved the highest number of correct matches (NCM), it also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**GF-3**} & \multicolumn{3}{c}{**Sentiel-1**} \\ \cline{2-7}  & **NCM** & **TCM** & **CMR** & **NCM** & **TCM** & **CMR** \\ \hline This paper & 288 & 459 & 62.75\% & 22 & 49 & 44.33\% \\ SAR-SIFT & 250 & 449 & 54.62\% & 10 & 25 & 40.72\% \\ I-SAR-SIFT & 259 & 442 & 55.68\% & 12 & 30 & 41.19\% \\ PSO-SIFT & 252 & 465 & 55.89\% & 19 & 44 & 42.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: CMR under different descriptors.  
Figure 6: Image distribution map.  
resulted in a significantly higher RMSE. In terms of efficiency, the descriptors with different dimensionalities exhibited no substantial differences. Therefore, the \(3\times 8\times 8\) configuration, which yielded the lowest RMSE, was selected as the final descriptor dimensionality.
###### Abstract  
High-pressure processing (HPP) is a promising method for preserving food, for example by inactivating pathogens and spoilage microbes. However, there is still a lack of knowledge about the optimal processing parameters for different food products. The aim of this study was to assess the effectiveness of different pressures to inactivate _Listeria monocytogenes_ in cold-smoked and warm-smoked rainbow trout (_Oncorhynchus mykiss_) ready-to-eat (RTE) fish products. _L. monocytogenes_ ATCC 7644 was inoculated into sliced cold-smoked rainbow trout fillets and whole warm-smoked rainbow trout fillets. The fish samples were pressure-treated at three different pressures, either at 200, 400, or 600 MPa, at \(4\pm 1\)\({}^{\circ}\)C for 3 min in each pressure. Bacterial enumeration of the samples and control samples were analysed 1, 14, and 28 days after the HPP treatment by using serial dilution and the spread plate technique. Based on the study results, the most effective pressure was 600 MPa and the number of _L. monocytogenes_ colonies, both in cold-smoked and in warm-smoked fish samples, was within the official limit of the Regulation (EC) No. 2073/2005 (100 cfu/g) even after 28 days of storage.  
high-pressure processing; _Listeria monocytogenes_; rainbow trout; _Oncorhynchus mykiss_; ready-to-eat +
Footnote †: journal: Applied Sciences
## 3 Methodology
#### 3.1.2 Model Verification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Incident Azimuth** & **Incident Pitch Angle** & **The X Coordinate from Which the Formula Emerges** & **The X Coordinate from Which Zemax Emerges** & **The Y Coordinate from Which Zemax Emerges** & **The Difference Between the X Coordinate from the X Coordinate from the Formula and the Y Coordinate from Zemax & **The Difference Between the Y Coordinate from Zemax** & **The Difference Between the Y Coordinate from the Formula and the Y Coordinate from Zemax & **The Difference Between the Y Coordinate from Zemax** & **The Difference Between the Y Coordinate from the Formula and the Y Coordinate from Zemax & **The Difference Between the Y Coordinate from the Zemax** & **Coordinate from the Formula and the Y Coordinate from Zemax** & **Coordinate from Zemax** \\ \hline \(0.1^{\circ}\) & \(0^{\circ}\) & \(1.78143214\) & \(1.7453503\) & \(0.00800332\) & \(0.00164635\) & \(-0.03608184\) & \(-0.00635697\) \\ \hline \(0.1^{\circ}\) & \(20^{\circ}\) & \(1.78143214\) & \(1.7453505\) & \(-363.96229\) & \(-363.97422\) & \(-0.03608164\) & \(-0.01193\) \\ \hline \(0.1^{\circ}\) & \(40^{\circ}\) & \(1.78143214\) & \(1.7453517\) & \(-839.09051\) & \(-839.109\) & \(-0.03608044\) & \(-0.01849\) \\ \hline \(0.1^{\circ}\) & \(60^{\circ}\) & \(1.78143214\) & \(1.7453527\) & \(-1732.0375\) & \(-1732.0699\) & \(-0.03607944\) & \(-0.0324\) \\ \hline \(0.1^{\circ}\) & \(80^{\circ}\) & \(1.78143214\) & \(1.7453545\) & \(-5671.2447\) & \(-5671.345\) & \(-0.03607764\) & \(-0.1003\) \\ \hline \(0.1^{\circ}\) & \(100^{\circ}\) & \(1.78143214\) & \(1.7453549\) & \(5671.24473\) & \(5671.345\) & \(-0.03608224\) & \(0.10027\) \\ \hline \(0.1^{\circ}\) & \(120^{\circ}\) & \(1.78143214\) & \(1.7453516\) & \(1732.03755\) & \(1732.070\) & \(-0.03608054\) & \(0.03245\) \\ \hline \(0.1^{\circ}\) & \(140^{\circ}\) & \(1.78143214\) & \(1.7453506\) & \(839.090514\) & \(839.10881\) & \(-0.03608154\) & \(0.018296\) \\ \hline \(0.1^{\circ}\) & \(160^{\circ}\) & \(1.78143214\) & \(1.7453502\) & \(363.962295\) & \(363.97422\) & \(-0.03608194\) & \(0.011925\) \\ \hline \(0.1^{\circ}\) & \(180^{\circ}\) & \(1.78143214\) & \(1.7453506\) & \(-0.0080033\) & \(-0.0016464\) & \(-0.03608154\) & \(0.0063569\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data table for incident azimuth angle \(0.1^{\circ}\) and incident pitch angle (\(0^{\circ}\)-\(180^{\circ}\)). The calculated results demonstrated minimal discrepancies between our formula-derived target coordinates (\(X,\ Y\)) and ZEMAX simulations, primarily due to mechanical and systemic errors. Although we could not simulate all possible values, we validated our approach by changing multiple sets of off-axis displacements and rotating dual grating rotation angles. Therefore, the usability of the proposed formula can be verified. Design and Simulation of the Fuzzy PID Control System for the Rotating Dual-Grating Two-Dimensional Scanning Model Based on Inertial Navigation Feedforward Technology
## 3 Empirical Results and Analysis  
### Econometric Test of the Benchmark Model  
#### 3.1.1 Regression Results of the Benchmark DID Model  
Based on the regression Models (1) and (2), this paper adopts the Panel OLS-DID estimation and the fixed effect model FE to carry on the regression analysis to the full sample, in order to study the impact of the APCP Action Plan policy on _GTFP_[34]. The regression results of the benchmark model are shown in Table 3.  
The estimated regression coefficients for the variable Policy in Table 3 are significantly positive in Columns (1)-(4). For example, the 95% confidence interval for the coefficient in Column (1) is [0.0020.0.0274]. These results indicate that the implementation of the APCP Action Plan improved the average provincial _GTFP_.  
The reason for this may be that the APCP Action Plan can effectively promote regional innovation and development of green technology, effectively limit the high pollution and high emission industries, promote the green transformation and optimization of the industrial structure, and reduce the concentration of air pollutants such as sulfur dioxide and \(PM_{2.5}\)[11], thus increasing the _GTFP_. This also confirms Hypothesis 1. For this reason, in their efforts to achieve the goal of carbon neutrality and high-quality development, countries can reasonably use policy tools to promote green technological innovation [35] in high-pollution and high-emission industries. Moreover, it can reduce pollution emissions from this type of enterprise in the production process. Finally, it is important to improve  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **OLS (1)** & **FE (2)** & **OLS (3)** & **OLS (4)** \\ \hline  & _InGTFP_ & _InGTFP_ & _InGTFP_ & _InGTFP_ \\ \hline Policy & 0.0151 ** & 0.0151 * & 0.0152 * & 0.0443 *** \\  & (2.44) & (1.96) & (1.74) & (3.89) \\ UL & 0.0303 & 0.0303 *** & 0.0215 *** & 0.0183  
energy efficiency [36], enhance overall air quality [12], and promote people's happiness in life.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 3 System Design  
The general architecture of deploying NDN network over edge computing infrastructure has two separated parts: cloud and edge. The Cloud side is responsible for edge nodes management, NDN containerized network functions deployment over managed edge nodes. The edge side contains all the edge nodes located around disaster areas. Each edge node is responsible for running one containerized NDN router. Among them, nodes which provide Wi-Fi access point are border edge nodes. They act as NDN gateway for responders to connect and exchange information through the NDN network.  
When deploying this architecture in disaster scenarios, a suitable edge platform that can work well in disaster conditions is required. Based on the characteristics that we discussed in the previous part, we choose KubeEdge as the platform to deploy the NDN network over.  
Figure 2: KubeEdge Cloud-Edge transmission reliability design.
## 5 Performance Evaluation
### Result Analysis and Discussion  
In our work, the analysis was conducted using several routing protocols and varying network densities and packet sizes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Network Area & 1000 \(\times\) 1000 sqm. \\ Node Variation & 10, 50, 100, 150, 200, 400, 600 800, 1000 \\ Transmission Range & 80 m \\ Data Size & 40 Bytes \\ Frequency/Node & 1/60 s/node \\ Buffer Length & 20 packets \\ Sink Speed & 0, 3, 6, 9, 12, 15 km/h \\ Mobility Model & Controlled Model \\ Transmission Energy & 50 nJ/bit \\ Reception Energy & 20 nJ/bit \\ \(E_{elc}\) & 20 nJ/bit \\ \(E_{DA}\) & 5 nJ/bit/frame \\ \(\epsilon_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(\epsilon_{mp}\) & 0.0013 pJ/bit/m\({}^{2}\) \\ Data Rate & 25 Kbps \\ Routing Protocol & LBDD, MSRP, Mob M-LEACH, Ring Routing, Modified Ring \\ Routing & Routing \\ Simulation Time & 60 min \\ MAC Protocol & 5-MAC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
## 4 Discussion
### Alternating Thickness (AT) Design  
Table 8 and Figure 18 summarize the results on the performance metrics for the cellular structure with a SWT design configuration. To understand the meaning of these findings, Figures 19-21 will be used.  
From Figure 19, it is evident that there are no interaction effects between these design factors. This finding allows us to safely interpret the _mean plots_ for each response based on the design factors as shown in Figure 20. From Figure 20, it is evident that (i) the peak and mean forces increase as the thickness \(T_{1}\) and \(T_{2}\) increases. However, these forces are not affected by the length of the structure. The same applies for the specific energy absorption (\(E_{SEA}\)) and (ii) the energy absorption is affected by all three factors, including length. This is obvious since there is more deformation distance before densification is reached. From these performance metrics, the mean force and \(E_{SEA}\) are most crucial to be looked at. Hence, it can be concluded that the length of the structure is not a crucial factor to be considered. This will have an implication on the design space and manufacturing requirements. Figure 21 depicts a typical force displacement graph along with the deformation mode for these structures. It can be seen that the deformation mode and response are also stable indicating good crash absorber characteristics. However, unlike the UT and SWT designs, from Figure 21, it is evident that the deformation modes not only are in a progressive manner, there is also a slight global buckling taking place in the structures with lengths 100 and 150 mm, respectively. This indicates that the design variables \(T_{1}\) and \(T_{2}\) are producing a structure with less stiffness when compared to UT and SWT.  
Figure 17: Mean effect plots for each performance metric with respect to the design factors.  
Figure 19: Interaction plots between design parameters and performance metrics for the AT design.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Type**} & **T1** & **T2** & **L** & **FP** & **F\(\mu\)** & **EA** & _ESEA_ \\ \cline{3-8}  & & **[mm]** & **[mm]** & **[mm]** &  
Figure 21: Example of force-displacement and deformation modes for (**a**) \(L=50\) mm, (**b**) \(L=100\) mm, (**c**) \(L=150\) mm. The shaded area indicates the energy absorbed before densification.  
Figure 20: Mean effect plots for each performance metric with respect to the design factors.
###### Abstract  
Numerous studies have been done to determine the hydrocarbon potential of Malaysia's formations and basins due to the need to identify more conventional or unconventional hydrocarbon resources. Due to the fact that none of these investigations were carried out with any prior knowledge in the relevant regions with hydrocarbon potential, some of them did not, however, yield the expected results. This study aims to provide researchers with all the necessary information about potential hydrocarbon-producing areas in Malaysia and the various lithologies connected to them by analyzing all earlier studies carried out in Malaysia. This was accomplished by determining patterns in the distribution of organic matter and characteristics of the formations in Malaysia. Total Organic Carbon (TOC), Generic Potential (GP), Vitrinite Reflectance (R\({}_{\text{o}}\)), and Hydrocarbon Yield (S2) were the most important hydrocarbon generation potential indicators discussed. A heat map was created using a statistical weight ranking with a weight of 45% for the TOC value and 30%, 15%, and 10% for the GP, S2, and R\({}_{\text{o}}\) values, respectively. According to the data, the Bintulu, Pinangah, Begrih, Liang, and Tanjong formations have the highest potential to generate hydrocarbons in Malaysia, while the Kroh, Setap, Kalabakan, Temburong, and Belaga have the least potential. Majority of formations with high hydrocarbon potential are tertiary in age and consist primarily of coal and carbonaceous shale formations. The most promising formations are mostly immature, with type II or type III kerogen quality. It is recommended that critical exploration activities be focused on the tertiary-aged formations, particularly those in East Malaysia, in order to increase Malaysia's hydrocarbon production.  
Keywords:Total Organic Carbon (TOC); Rock-Eval Pyrolysis; shale; coal; source rock evaluation; organic matter +
Footnote †: journal:
SMS is a wireless, bi-directional, low data rate transmission technique employed by all 2G (second generation), 3G (third generation), and 4G (fourth generation) mobile communication systems,  
Figure 1: Connection between the Smart Grid (SG) Control Center (CC) and the DFIG-based wind turbine by the mobile network. which has been considered for use in different power systems applications. For instance, in [33], an SMS-based control system for home appliances using GSM technology, without accessing the local network, was presented. In [34], authors proposed the use of the GSM network as an option for SG communication infrastructure, in order to control thousands of mobile Plug-in Electric Vehicles (PEV) chargers, using a simple SMS interface. At certain times of the day, charging large numbers of PEV may result in overloading sections of the electric power grid. However, the SG can use SMS messages to delay charging or regulate charging rates, allowing the existing power system to handle the increased load without having to increase the peak power capacity of feeders or add generation. In [35], a Smart Load Management (SLM) system is presented, to be effectively utilized to meet emergency customer demands (light and fan) when power generation is not sufficient for the current demand. The connectivity is provided by a GSM-based mobile network. The SLM device can configure and control loads by SMS, without changing hardware or software. In the proposed system, a 2G GSM network infrastructure was chosen to send the control information to the wind generator via SMS, due to its simplicity of development, wide coverage, and low operation cost (in the USA, unlimited SMS packages can cost around a few dollars per month). This GSM infrastructure was developed using a Software Defined Radio (SDR) transceiver connected to an Open Base Transceiver Station (OpenBTS) platform. The control information can be directly inserted into the OpenBTS server (located at the CC), or sent by a wireless device (e.g., mobile phone) connected to the GSM network, and then transmitted to the wireless receiver at the wind turbine. The receiver on the turbine side was developed in a microcontroller board with a GSM card, and the DFIG controller was implemented in a Digital Signal Processor (DSP) board. To the best of our knowledge, this is the first work proposing this strategy to transmit power control references to wind generators. In addition to this introductory section, this work is organized as follows: Section 2 briefly describes the fundamental equations for the DFIG and the adopted control strategy. Section 3 discusses the mobile communication infrastructure employed in the experimental tests. The practical implementation of the proposed system and corresponding evaluation tests are shown and discussed in Section 4. Finally, the main conclusions are presented in Section 5.