esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Object Description
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Item** & **Unit** & **Values in Experiment** & **Setting Values** \\ \hline Jet mixture CH\({}_{4}\)/air & vol\% & 25/75 \\ \hline Pilot mixture composition, mass fraction & \% & See [42] \\ \hline N\({}_{2}\) & & 73.42 \\ \hline O\({}_{2}\) & & 5.40 \\ \hline O\({}\) & & \(7.47\times 10^{-2}\) \\ \hline H\({}_{2}\) & & \(1.29\times 10^{-2}\) \\ \hline H\({}_{2}\)O & & 9.42 \\ \hline CO & & 0.407 \\ \hline CO\({}_{2}\) & & 10.98 \\ \hline OH & & 0.28 \\ \hline NO & & \(4.8\times 10^{-4}\) \\ \hline Main jet inner diameter, \(d\) & mm & 7.2 \\ \hline Pilot annulus inner diameter, \(D\) & mm & 7.7 \\ \hline Pilot annulus outer diameter, \(d_{\rm p}\) & mm & 18.2 \\ \hline Burner outer diameter, \(D_{\rm p}\) & mm & 18.9 \\ \hline Jet bulk velocity & m/s & \(49.6\pm 2\) & Profile; see [42] \\ \hline Jet inlet turbulent kinetic energy & m\({}^{2}\)/s\({}^{2}\) & Profile; see [43] \\ \hline Jet inlet turbulent dissipation rate & m\({}^{2}\)/s\({}^{3}\) & - & Profile; estimated with Equation (1) \\ \hline Jet inlet specific dissipation rate & 1/s & - \\ \hline Pilot bulk velocity & m/s & 11.4 \(\pm\) 0.5 & Profile; see [42] \\ \hline Pilot inlet turbulent kinetic energy & m\({}^{2}\)/s\({}^{2}\) & Profile; see [43] \\ \hline Pilot inlet turbulent dissipation rate & m\({}^{2}\)/s\({}^{3}\) & - & Profile; estimated with Equation (1) \\ \hline Pilot inlet specific dissipation rate & 1/s & - & Profile; estimated with Equation (2) \\ \hline Air co-flow velocity & m/s & 0.9 \(\pm\) 0.05 & Profile; see [42] \\ \hline Air co-flow inlet turbulent kinetic energy & m\({}^{2}\)/s\({}^{2}\) & - & Profile; see [43] \\ \hline Air co-flow inlet turbulent dissipation rate & m\({}^{2}\)/s\({}^{3}\) & - & Profile; estimated with Equation (1) \\ \hline Air co-flow inlet specific dissipation rate & 1/s & - & Profile; estimated with Equation (2) \\ \hline Jet temperature & K & 294 \\ \hline Pilot temperature & K & 1880 \(\pm\) 50 & 1880 \\ \hline Co-flow temperature & K & 291 \\ \hline Reynolds number, Re\({}_{\rm jet}\) & - & 22,400 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Burner geometry data and boundary conditions for the Sandia Flame D.  
The turbulent dissipation rate, \(\varepsilon\), at the inlets can be estimated as:  
\[\varepsilon=\frac{k^{3/2}}{\ell} \tag{1}\]  
where \(k\) is turbulent kinetic energy and \(\ell\) is the turbulence length scale. The specific dissipation rate, \(\omega\), used for the turbulence models, can be estimated as:  
\[\omega=\frac{k^{1/2}}{C_{\mu}\ell} \tag{2}\]  
where the value of \(C_{\mu}\) is 0.09.
## 3 Results
### Statistical Analysis of Chemical Compounds

Table 2: [PERSON]’s test statistics of the chemical grass composition of control (Ctr) plants at each sampling on a fresh matter (FM) basis in relation to glyphosate (Gly) and drought (Dry) treatment. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DAA** & **Treatment** & **Chla** & **Chlb** & **Chlb** & **Car** & **PP** \\ \hline \multicolumn{8}{c}{(mg/g DM)} \\ \hline
**0** & \multicolumn{2}{c}{Ctr} & 5.95 a  & 1.29 a  & 7.25 a  & 2.18 a  & 3.93 a  \\  & \multicolumn{2}{c}{Gly} & 5.91 a  & 1.29 a  & 7.72 a  & 2.18 a  & 3.93 a  \\  & \multicolumn{2}{c}{Gly} & 5.91 a  & 1.29 a  & 7.21 a  & 4.37 a  \\  & \multicolumn{2}{c}{Dry} & 5.88 a  & 1.29 a  & 7.17 a  & 2.18 a  & 3.93 a  \\  & \multicolumn{2}{c}{Gly} & 5.91 a  & 1.29 a  & 7.72 a  & 2.21 a  & 4.37 a  \\  & \multicolumn{2}{c}{Dry} & 5.88 a  & 1.29 a  & 7.17 a  & 2.18 a  & 3.93 a  \\  & \multicolumn{2}{c}{Gly} & 5.91 a  & 1.29 a [FOOTNOTE:a]Footnote a: Days after application, Chla = chlorophyll \(a\), Chlb = chlorophyll _b_Trends are even stronger when the statistics are calculated on a dry matter basis. Photosynthetic pigments, both chlorophyll \(a\) and \(b\) as well as total chlorophyll, were reduced under glyphosate treatment already two days after application. Polyphenols were strongly related to the dry matter content and behave different when analysed on a dry matter basis (Figure 4D); while polyphenols increased with glyphosate application on fresh matter basis (Table 2, Figure 4B), a decrease was observed on dry matter basis which was significant 6 days after glyphosate application (Table 3). Therefore, it is important to record the development of the dry matter content in experiments where plants were exposed to stress. This different trend between chlorophyll and polyphenols is also clear when comparing the development of the chlorophyll and polyphenol content over the experimental time on fresh and dry matter basis (Figure 4A-D). In case of chlorophyll, the same trend is visible on fresh and dry matter basis, which is even more pronounced when looking at the dry matter data. A different result was found for the polyphenols: a clear increase in the polyphenols was found under stress conditions with time when the results are shown on fresh matter basis while no specific trend can be recorded from the data on dry matter basis. The polyphenol content is highly correlated with the dry matter content (r\({}^{2}\) = 91%) and is therefore mainly reflecting the loss of water with drought stress and glyphosate treatment.
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
###### Abstract  
In view of the stability of the unmanned aerial vehicle (UAV) power system, this paper found that the voltage spike generated by IGBT mainly occurs when the IGBT is turned off in the study of the power system interior and IGBT drive and overvoltage protection, and an excessive voltage spike will lead to IGBT damage. To eliminate this serious threat to the safe operation of the circuit, an IGBT overvoltage protection circuit combining dynamic voltage feedback and active clamping is proposed. In this method, active clamping and dynamic voltage feedback circuits are operated alternately, and the drive circuit is controlled by the feedback of capacitor's the dynamic voltage rise rate. The gate current output of the IGBT is directly compensated to control the signal delay of the gate, combined with improving the lifting effect of the active clamp circuit on the gate voltage and suppressing the peak of the turn-off voltage. According to the IGBT turn off process, combined with SABER simulation, this paper finally builds IGBT turn off experimental circuit to prove that the proposed method suppresses the size of the turn off voltage spike, further analyzes the effect of key circuit parameters on the suppression of the turn off voltage spike, thereby ensuring the safe operation of the IGBT, and improving the stability of the UAV power system.  
IGBT; active clamping; turn-off voltage spike; dynamic voltage feedback; SABER simulation; unmanned aerial vehicles +
Footnote †: journal: Applied Sciences  
feedback voltage rise rate, and the turn-off voltage spike is fed back to the gate through the active clamping circuit to recharge the gate, thus suppressing voltage spikes.  
In this paper, through an analysis of the IGBT turn-off process, combined with SABER simulation, the influence of important parameters in the dynamic voltage feedback circuit on the turn-off process is studied, and the suppression effects of different parameters on the turn-off voltage spike are proposed. Finally, the corresponding circuit verification is built to verify the suppression effect of different parameters.
## 3 Modeling and Steady Computation Using CFD  
### Modeling  
The three-pass perforated tube silencer of a car that is considered in this paper is presented in Figure 7. The silencer is divided into three chambers by two baffles. The inlet tube and the middle buffer tube are respectively perforated by 40 and 48 orifices with 4 mm diameter, and the porosity of the tubes are 2.7% and 5.8%, respectively. There is no perforation in the outlet tube. Additionally, the axes of the three tubes are not on the same plane. Consequently, it is difficult to build a fluid model directly inside the silencer because of its complicated structure. Therefore, firstly CATIA (version V5R20, Dassault Systemes, Paris, France, 2010) software was used to build its structural model, and then it was filled using ANSYS Workbench (version 14.5, ANSYS Inc., Canonsburg, PA, USA, 2012) software to obtain the fluid model. Following this, the fluid model was split into several parts to generate mesh individually. Tetrahedral mesh was applied to the perforation area and the transition tube, and hexahedral mesh was applied to the remaining parts.  
Figure 8a,b illustrate the CFD mesh model and the acoustic mesh model of the fluid model, which will be used for CFD steady computation and acoustic computation, respectively. There is a different node density between the two models. The CFD mesh is refined in the perforation area in order to obtain more accurate results. However, the accuracy of the acoustic computation is determined by the overall mesh. Local mesh refinement can not improve its accuracy, and thus the size of the acoustic mesh element should be kept as uniform as possible.  
Figure 5: Comparison of the predicted and measured _TL_ for Pattern 1 (\(M\) = 0.2, \(T\) = 288 K).  
Figure 6: Comparison of the calculated and measured _TL_ for the cross-flow silencer (\(v\) = 17 m/s, \(T\) = 374 K).  
The steady flow computation is carried out using the CFD mesh model. The governing equations for pressure velocity coupling--based on a finite volume method--are solved by the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm. Turbulence is examined using the standard k-epsilon model [23]. The fluid material is air--with the density conforming to the ideal gas law--and is considered as incompressible. The boundary conditions are concretely set as follows: (1) the velocity inlet boundary condition is defined at the inlet of the silencer. According to the information provided by the manufacturer, when the engine runs at 5500 r/min, the velocity and temperature of the inlet are 55 m/s and 760 K, respectively; (2) the pressure outlet boundary condition is defined at the outlet of the silencer, and the gauge pressure is 0 Pa--which is relative to one standard atmospheric pressure; and, (3) the walls are assumed to be stationary--with no slip condition--and adiabatic.
## 3 Data
### MODIS Data and In Situ Water Vapor Content Data  
#### 3.3.1 MODIS Data  
MODIS, which is on board the Terra and Aqua satellites, has 36 spectral channels ranging from 0.405 to 14.385 \(\upmu\)m. The MODIS data products used in this study are given in Table 1 and, are downloaded from \"Reverb\", a next generation metadata and service discovery tool [31]. It is worth noting that the MOD05 product is affected by the MODIS band 5 stripe noise. In this study, the noise is removed using median filtering with a 9 \(\times\) 9 pixel filter kernel. Moreover, the International Geosphere-Biosphere Program (IGBP) global vegetation classification from the land cover product MCD12Q1 is used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product Name** & **Spatial Resolution** &
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Name of Datasets and Usage** \\ \hline MOD03 & 1 km & Daily & Geolocation/Input \\ MOD05 & 1 km & Daily & WVC/Input \\ MOD11A1 & 1 km & Daily & LST/Validation \\ MOD13Q1 & 250 m & 16 days & NDVI/Input \\ MCD12Q1 & 500 m & Yearly & Land cover types/Input \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the MODIS products used in this study.  
Figure 2: Location of the study area and distribution of the ground sites (including _in situ_ LST measurements sites and WVC sites) used in this study. The location of the HiWATER-MUSOEXE area is indicated with the small red quadrangle (in the left image) and a solid black quadrangle (in the right image). The black square in the left image is the Heihe Matrix study area, which will be fully analyzed in the LST spatial distribution analyses.
## 3 System Model  
In this section, we introduce and derive a system model and an optimization problem for computation offloading and resource allocation in the context of an Industrial Internet multiedge-terminal MEC. Within this MEC system, the energy consumption comprises the combined energy consumed by all terminal nodes for computation, as well as the energy consumed during transmission to the edge servers.  
As illustrated in Figure 1, we propose a multiedge-terminal collaborative MEC system in an intelligent factory. It is assumed that the system consists of \(M\) mobile smart industrial terminal devices and \(N\) small base stations with edge servers. The index of terminal devices is \(i\in\{1,2,\ldots,M\}\), and the index of edge server is \(j\in\{1,2,\ldots,N\}\). The terminal devices are located on the same production line in the intelligent factory. Each device has a task chain, and the total data volume of the task chain is \(I_{i}\). Several independent tasks make up the task chain. Each epoch \(t\) handles one task, \(t\in\{1,2,\ldots,\mathcal{T}\}\); thus, there are \(\mathcal{T}\) epochs in total. \(I_{i,t}^{a}\) represents the size of the data for the task to be computed by the terminal devices \(i\) in the present epoch \(t\),  
\[I_{i}=\sum_{t=1}^{\mathcal{T}}I_{i,t}^{a}. \tag{1}\]  
Figure 1: MEC systems for an intelligent factory.  
Each task in the task chain has three choices to be computed. Firstly, tasks can be partially or fully computed locally on the terminal device. Secondly, the tasks can be partly or fully offloaded to any single edge server for computing. Thirdly, the tasks can be partly or fully offloaded to multiple different edge servers to be computed simultaneously. Because of the powerful processing capabilities of edge servers, they can simultaneously process tasks offloaded from multiple terminal devices in parallel.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Load Branch Correlation Matrix Method  
This study aims to evaluate whether the reconfiguration plan adheres to the specified constraints by establishing a load branch matrix for the power system network [24]. The load branch adjacency matrix method, an enhancement of the extended matrix method, is a commonly employed analytical tool in power system studies. It serves to characterize the influence of loads within the power system, adequately accounting for the availability of backup pathways. This approach facilitates the description of interconnections among various components, as well as enables fault node localization, state estimation, and steady-state analysis. The formation of this matrix is determined by the topology of the power system and the electrical parameters of the components within the network.
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 1 Introduction
This algorithm prioritizes routes that traverse the chosen anchor domain while considering parameters like bandwidth capacity and the number of domains involved in the data transfer path. The remainder of this paper is organized as follows: Section 2 summarizes the issues with existing mobility management methods. Section 3 gives a formal description of the addressed problem. Section 4 presents our method in more detail. Section 5 explains the simulation setup and the results, and Section 6 gives the conclusions of our study.
## 1 Introduction
The current research trends include using a contactless energy harvester to power the condition of monitoring sensors in a smart grid to harvest more energy [15]. A major issue experienced with using WPT to charge electric vehicles was stated in various publications as stray magnetic fields. The existence of stray magnetic fields within the WPT charging field results in the charging magnetic field becoming weak [16]. Studies have been conducted on measuring the stray magnetic fields for improving the charging apparatus so as to limit stray magnetic fields [17; 18; 19; 20]. Radiofrequency (RF) energy harvesting is an emerging technology and an intriguing research area that promises to produce enough power to run miniaturized wireless devices and supply power to wireless sensor networks (WSNs). The RF energy harvesting system aimed to convert the received ambient RF energy into usable DC power. The evolution of RF energy scavenging in free space originated in the late 1950s with a microwave-powered helicopter [21]. The existence of electromagnetic radiation started with the research conducted by [PERSON], who proved the presence of electromagnetic radiation and radio waves in experiments from 1886 to 1888 [22]. Popular energy harvesters used today include solar cells, wind, waves from the ocean, thermal energy, and vibration [23]. Although these are extensively used, energy harvesters have limited capabilities, such as high installation costs, and solar cells will not work efficiently in cloudy weather. The types of devices that produce RF signals from which energy can be harvested according to operating distances are Bluetooth (2.40 to 2.48 GHz), WIFI (2.40 GHz to 5.8 GHz), and cellular technology (700 MHz to 2.7 GHz) [24]. The MRC offers freedom from the annoying wires during charging, and when combined with the RF energy harvesting as an alternate power source [25], it becomes an efficient method for charging the batteries of mobile devices. There are various studies on WPT techniques, MRC, and related components, but limited research has been conducted on the wireless transfer of RF harvested power using the MRC method. This research contributes to filling this literature void of the dearth of studies on the application of MRC for the wireless transfer of RF harvested power to charge the batteries of mobile devices.
## 3 Case Study
### Geological Conditions and Steel Support Layout  
The foundation soil layers in the station area, from top to bottom, consist of filling layers (plain filling and miscellaneous filling), sandy silt layers, clay layers (mucky clay, silty clay, and silty clay containing sand), silty layers, gravelly medium sand layers, rounded  
Figure 5: Schematic diagram of the project’s surrounding environment.  
gravel layers, and mud siltstone layers (ranging from completely weathered to moderately weathered). Specifically, the base slab of Sanbao Station (Zones 1 to 5) is situated within the silty clay layer, while the walls are founded in the moderately weathered mud siltstone stratum. The detailed geological profile is shown in Figure 6.  
The standard section width of Zones 1 to 5 at Sanbao Station is 23.3 m, while the end shaft width is 27 m. Each steel strut weighs approximately 11.5 tons. The installation process for the steel struts involves pre-assembly on the ground, followed by segmented hoisting into the foundation pit using a 20-ton gantry crane, where the assembly is completed. In certain areas, manual chain hoists are used to assist with assembly adjustments. For the transfer nodes, steel struts are segmented and hoisted into the foundation pit using a 50-ton  
Figure 6: Typical geological profile of zones 1 to 5 at Sanbao Station.  
crawler crane, where they are subsequently assembled. The layout of the second layer of steel struts within the foundation pit is illustrated in Figure 7.
## 2 Materials and Methods
### In Vivo Tumor Regression Study Post Intravenous Delivery of CA Loaded siRNA Complex  
All the in vivo work was done upon the approval by Monash University Animal Ethics Committee (MARP/2016/126). Female Balb/c mice (6-8 weeks old) of 15-20 g of body weight (obtained from School of Medicine and Health Science Animal Facility, Monash University) were maintained in 12:12 light:dark condition and provided with ab libitum and water. During the treatment period, the body weight and size of the tumor were measured at regular intervals. The maximum duration for which the experiments were run was 4 weeks. During this period there was no significant tumor burden for the mice to develop pain/distress since the tumor volume would not exceed 1 cm\({}^{3}\) within the 4 weeks. Furthermore, there was no drop in the body weight of the mice during the experimenting period. Additionally, there was no labored or irregular breathing observed. To reduce the pain during injections, as a local anaesthetic, Emla cream was applied topically on the injection site before proceeding for intravenous injections. At the end, all the animals were humanely euthanized by cervical dislocation.  
Subcutaneous injection of around 1 \(\times\) 10\({}^{5}\) 4T1 cells dissolved in 100 \(\mu\)L PBS the mammary pad was done. When the tumor reached at palpable stage, mice were randomly arranged, 5 per group. The tumor area was shaved with Veet shaving cream, to locate the tumor and accurately measure the volume. Complexes loaded with CA nanoparticles were formed with 8 \(\mu\)L of CaCl\({}_{2}\) mixed in 200 \(\mu\)L of DMEM supplied with bicarbonate (44 mM) and selected single integrin siRNAs. Incubation at 37 \({}^{\circ}\)C for 30 min was done for the variables and complex generated was immediately put on ice to stop formation of particles and slow the aggregation. Intravenous injection of 100 \(\mu\)L treatment complexes at right/left caudal vein was done. Second or subsequent doses were administered two days later. Tumor volume and tumor weight was monitored frequently. Tumors were collected and weighed at the end of study and images of isolated tumors were taken. The following formula was used to calculated tumor volume:  
\[\text{Tumor volume (in mm}^{3})=\text{Length }\times\text{(width)}^{2}/2\]
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 6 Security Considerations
### Toward the Higher Order SCA-Resistant Schemes  
The schemes in Figures 10 and 11 are secure against the first-order SCAs. To resist the higher order SCA, we need more random freshnesses in general. Assume that, in addition to \(r\), the randomnesses \(r_{2},r_{3},\cdots\) are randomly generated in the encryption device to compute \(n_{2},n_{3},\cdots\), which mask the input and the output of BC, and sent to the decryption device as elements of \((r,r_{2},r_{3},\cdots,c)\). It seems to lead the higher order SCA-resistant scheme. However, it is insecure because the message recovery attack, by reusing \((r,r_{2},r_{3},\cdots)\), is possible.  
Let us consider another scheme in Figure 12 and Algorithms 5 and 6. The idea to achieve the higher order SCA resistance is to generate a fresh randomness for a pair \((m,r)\). In the SCA, the adversary queries \(m\) or \((r,c)\) as the encryption query or the decryption query, respectively. By letting the mask depend on the pair, the masks for the input and the output of BC should be always fresh for each query. This prevents the higher order SCA.  
The scheme in Figure 12 is complicated, unlike the previous two schemes, which shows the trade-off between the security and efficiency. Another construction, where the session key also depends on both \((r,m)\), can be considered.  
```
Input: master secret key \((mk_{1},mk_{2},mk_{3})\) and plaintext \(m\) Output: ciphertext \((r,c)\)
```  
1. Choose a randomness \(r\)
2. Compute \(tk=g_{1}(mk_{1},r)\)
3. Compute \(n_{2}=g_{2}(mk_{2},r)\)
4. Compute \(c=\text{BC}(tk,m\oplus n_{2})\oplus g_{3}(mk_{3},r)\)
5. Compute \(r_{2}=r\oplus g_{4}(mk_{4},c)\)
6. Return \((r_{2},c)\) ```  
**Algorithm 5** Higher order SCA-resistant re-keying encryption scheme: \(\text{enc}_{\text{HO}-\text{RK}}\)  
Figure 12: Higher order SCA-resistant re-keying scheme.