esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aircraft Low Altitude Operation Scene
### Airworthiness Requirements of Avionics Systems for Aircraft Operating at Low-Altitude Design  
The existing system of general aviation low-altitude operation regulations and standards mainly includes three parts: first, low-altitude airspace air traffic management; second, low-altitude operation management; third, low-altitude service guarantee management [7]. Based on the basic requirements of general aviation low-altitude operation, low-altitude flight aircraft must meet the corresponding four types of airworthiness requirements. First, the general rules of operation and flight. Second, the airworthiness requirements of airborne equipment required for low-altitude flight, civil aviation products and parts certification provisions, and civil aviation materials, parts, and airborne equipment technical standards provisions. The third is the special regulations for civil aviation technical standards in low-altitude airspace, and the fourth is the revision and improvement of the training outline for the main professional technical qualified personnel such as flight, maintenance, air traffic control, and safety supervision in terms of professional license and qualification approval [12]; see Figure 2. In this paper, according to the design characteristics of avionics systems in low-altitude aircraft, the relevant airworthiness requirements of avionics system design are put forward.  
In the design of avionics systems, considering the characteristics of a low-altitude operation scene, as well as the design characteristics of aircraft in electromagnetic, air-ground data interaction, accurate navigation, and so on, it is suggested that some adaptability requirements should be added to the corresponding technical standards of airborne equipment.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design  
The correct operation of the charger/discharger requires a suitable controller to ensure both global stability and the desired speed correcting the voltage deviations caused by perturbations in the bus current. Therefore, this work proposes an SMC, where the switching function is designed based on the following conditions:  
* The error between the bus voltage \(v_{dc}\) and the desired value (reference) \(v_{r}\) must be corrected, thus the term \([K_{v}\cdot(v_{dc}-v_{r})]\) is introduced, where \(K_{v}\) is a parameter.  
* The perturbations introduced in the bus current \(i_{dc}\) could be quickly compensated by the magnetizing inductor current \(i_{m}\), thus the term \([K_{i}\cdot i_{m}-i_{dc}]\) is introduced, where \(K_{i}\) is a parameter.  
Based on the previous considerations, the following switching function \(\Psi\) and sliding-surface \(\Phi\) are defined:  
\[\Psi=K_{v}\cdot(v_{dc}-v_{r})+K_{i}\cdot i_{m}-i_{dc} \tag{19}\] \[\Phi=\{\Psi=0\} \tag{20}\]  
A stable SMC ensures the system operates within the sliding surface [37; 38]. Therefore, if the proposed SMC is stable, it will ensure the charger/discharger operation within the sliding surface (20), thus \(v_{dc}=v_{r}\) and \(K_{i}\cdot i_{m}=i_{dc}\). Since the bus current \(i_{dc}\) changes depending on the operation condition, the parameter \(K_{i}\) must be dynamically adapted; hence, the proposed controller is an adaptive SMC. The adaptive value of \(K_{i}\) is discussed afterward.  
The stability of an SMC is evaluated using three tests [37; 38]: transversality, reachability, and equivalent control. Those proofs are performed in the following subsections.
## 3 Tests Results and Analyses
### Sealing Effect
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**For Levels**} & \multicolumn{4}{c}{**Average \(SE\) (\%) for Factors**} \\ \cline{2-5}  & \multicolumn{2}{c}{**The Roughness of**} & \multicolumn{2}{c}{**Initial Water Flow**} & \multirow{2}{*}{**Gel Time (s) C**} & \multicolumn{2}{c}{**Fracture Opening**} \\  & \multicolumn{2}{c}{**Fracture (JRC) \(A\)**} & \multicolumn{2}{c}{**Rate (cm/s) \(B\)**} & & **(mm) \(D\)** \\ \hline \(SE_{1}\) & 43.870 & 67.668 & 66.340 & 67.463 \\ \(SE_{2}\) & 52.635 & 65.403 & 66.168 & 67.183 \\ \(SE_{3}\) & 56.375 & 52.833 & 50.058 & 54.000 \\ \(SE_{4}\) & 75.298 & 42.275 & 45.613 & 39.533 \\ \(Range\) (\(A\),\(B\),\(C\),\(D\)) & 31.428 & 25.393 & 20.727 & 27.930 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Range analysis of the influence of different levels on the sealing effect.
## 3 Results and Discussion
Considering the temperature effect (Figure 4a), a typical decrease in both ohmic resistance (R\({}_{\rm o}\)) and R\({}_{\rm p}\) is observed, which is in line with the thermo-activated nature of ongoing processes. However, when the bias is changed from OCV conditions to PCFC load, the following interesting tendencies occur: R\({}_{\rm o}\) attains almost constant values, but the R\({}_{\rm p}\) level grows more noticeably, implying a deterioration in the electrochemical behaviour. The latter agrees with a number of studies [36; 37; 38], which report similar R\({}_{\rm p}\) growth with decreasing cell bias. The observed R\({}_{\rm p}\) deterioration might either be associated with a decrease in the p-type electronic conductivity of the proton-conducting electrolyte, thus providing a lower current distribution at the EL/CFL interface [39; 40], or with an increase in the concentration type of R\({}_{\rm p}\) taking place at high current densities [41; 42]. Figure 5 depicts the absolute values of R\({}_{\rm o}\), R\({}_{\rm p}\), R\({}_{\rm t}\) along with their ratio. As can be seen, the overall PCFC resistance is equal to 0.86 \(\Omega\) cm\({}^{2}\) at 550 \({}^{\circ}\)C and 0.43 \(\Omega\) cm\({}^{2}\) at 700 \({}^{\circ}\)C for the OCV conditions. At the same time, the R\({}_{\rm p}\) contribution is lower than 50% for all measured temperatures; this indicates that the parameters of the electrolyte (resistance and, especially, thickness) are dominant factors affecting the performance of the PCFC. When the cell bias corresponds to the P\({}_{\rm max}\) conditions, the overall PCFC resistance rises to 0.97 \(\Omega\) cm\({}^{2}\) at 550 \({}^{\circ}\)C and 0.48 \(\Omega\) cm\({}^{2}\) at 700 \({}^{\circ}\)C due to an increase in R\({}_{\rm p}\) (its contribution attains \(\sim\)51% at 550 \({}^{\circ}\)C and 16% at 700 \({}^{\circ}\)C). According to the obtained data, further research efforts should be aimed at obtaining an improvement in electrode behaviour under load rather than the conventional OCV conditions. Figure 4: Typical impedance spectra of the fabricated PCFC at different temperatures under conditions corresponding to open circuit voltage (OCV) (**a**) and P\({}_{\rm max}\) (**b**). To illustrate the prospect of the tape calendering method as a means of fabricating PCFCs via a one-step procedure, a comparative analysis was performed (Table 1). As can be seen, the rational selection of the functional materials, as well as the utilisation of convenient laboratory technology, allow PCFC fabrication with promising performance across the intermediate temperature range. It should be noted that the effect of ohmic resistance is excluded from consideration in order to provide a clear relationship between the polarisation resistance and power density. From this viewpoint, the achieved electrode activity competes with the electrochemical activity of electrodes as prepared by other methods, although the number of temperature regimes is reduced to only one.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 4 Discussion
### Elevation and Mass Changes of the Entire SPI
The advance of Pio XI Glacier has been described in previous studies and attributed to a surge or possibly triggered by geothermal activity [58; 59]. However, our results do not show a clear source area from where the mass would have been transported down-glacier in a surge causing the advance and thickening of the tongue. Such a pattern was observed at many other surging glaciers e.g., in the Karakoram or western Himalaya [21; 22]. In contrast, the potential source area in the accumulation zone shows positive elevation change rates as do many other glaciers on the west coast of the SPI. [PERSON] and others [56] already proposed that the geometry and pinning points of the glacier tongue may also be relevant to explain the observed advance. This agrees with the interpretation by [PERSON] stating that his class B glaciers have long and narrow tongues that are nourished by a large high elevation accumulation area [17]. It may hence be speculated that continuous positive surface mass balances together with the particular glacier geometry may be responsible for the observed dh/dt pattern of Pio XI rather than surging or geothermal activity in the accumulation area (e.g., reported by [60]).
## 4 Defining the Optimization Objectives
### The Classic Objective Functions  
#### 4.2.1 Minimizing the L-Index  
In the following equation, \(i\) is the index related to G generator buses and j is the index of the load buses. The matrix is calculated using the following equations [15].  
\[L_{max}=max\left(L_{j}\right),\ L_{j}=\left|1-\sum_{i=1}^{NG}\left(\frac{V_{i}} {V_{j}}\right)\ \right| \tag{21}\]  
\[minf_{5}=L_{max}\times\left(1+\alpha P_{QG}+\beta P_{V}+\lambda P_{tap}+ \delta P_{Cap}\right) \tag{22}\]  
\[\left[\begin{array}{c}I_{G}\\ I_{L}\end{array}\right]=\left[\begin{array}{cc}Y_{GG}&Y_{GL}\\ Y_{LG}&Y_{LL}\end{array}\right]\left[\begin{array}{c}V_{G}\\ V_{L}\end{array}\right] \tag{23}\]  
In the above equations \(V_{G}\), \(I_{G}\), \(V_{L}\), \(I_{L}\) are the current and voltage vectors of the generator buses and the load. By sorting the above equations, Equation (22) is obtained:  
\[\left[\begin{array}{c}V_{L}\\ I_{G}\end{array}\right]=\left[\begin{array}{cc}Z_{LL}&F_{LG}\\ K_{GL}&Y_{GG}\end{array}\right]\left[\begin{array}{c}I_{L}\\ V_{G}\end{array}\right] \tag{24}\]  
Finally:  
\[[F_{LG}]=-[Y_{LL}]^{-1}[Y_{LG}] \tag{25}\]To improve voltage stability, the L-index for each load bus should not exceed its upper limit, which is one. In other words, the L-index describes the system stability and the maximum value among \(L_{j}\) load buses. This index is a numerical quantity that estimates the real distance of the system from its stability boundary.
### Authors' Contribution  
Since OppNets rely on users to contribute with the resources on their devices to routing, their willingness to participate is crucial for the success of sensed data collection. The issue of OppNet routing protocols subjecting most of the routing duties to only few nodes in the network threatens user willingness to participate and thereby motivates us to address the problem of routing unfairness. This paper contributes in the following aspects:  
1. Supported with simulation experiments, we investigate the impact of spatial locality inherent to user movement in sensed data collection scenarios on OppNet routing protocols and also identify drawbacks of existing burden detection approaches. Then, from our findings, we suggest design guidelines that need to be considered when addressing fair OppNet routing for collecting sensed data in real-world scenarios.
2. Next, we follow our design guidelines to propose the Fair Locality Aware Routing (FLARoute) technique, which, to the best of our knowledge, presents the first routing approach that considers the impact of spatial locality inherent to user movement in urban environments. FLARoute implements a fair forwarding utility and distributed mechanisms, namely Relative Burden Detection (ReBurD) and Maximum Burden Estimation (MaxBE). Fair forwarding decisions are made with the Fair Locality Aware Forwarding (FLAFord) algorithm, which introduces a new burden balancing mechanism. Through simulation experiments, we verify that FLARoute can be incorporated into relevant routing protocols to improve fairness and throughput under conditions that compromise the performance of existing fair routing solutions.
## 8 Challenges and Open Issues
However, focusing on multi-objective routing protocols is a research subject that can be considered by researchers in the future. For example, designing the reward function based on several objectives such as delay and link quality. * **Designing a predictive RL-based routing protocol:** This is a serious challenge, which must consider in the future. These protocols should be able to predict the latter positions of vehicles on the network to make routing decisions more accurately. * **Testbed:** Most researchers use simulation tools such as network simulator version 2 (NS2), NS3, and MATLAB to analyze the routing approaches. However, these tools are not accurate and cannot evaluate the performance of these methods correctly. They should be simulated in real environments to evaluate their performance carefully. However, this is extremely expensive. * **Scalability:** RL-based routing approaches should be suitable for different sizes of networks. However, when the network is large, it experiences a long delay in the data transfer operation. This weakens the performance of routing protocols. Therefore, designing scalable RL-based routing methods is a serious challenge that should review by researchers.
# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 3 Unsupervised Learning Techniques
### K-Means Clustering Algorithm  
K-means is a well known unsupervised machine learning algorithm that is used for clustering [25]. The objective of K-means is basically to group similar data points together and discover underlying patterns. It consists on determining a set of \(k\) points in a dataset, called centroids (every centroid is associated to a cluster). Subsequently, each data point is allocated to each cluster minimizing the distance \(d_{ij}\) from each object within the dataset to its closest centroid [16]. To summarize, K-means algorithm identifies the k number of centroids and then allocates every data point to the nearest cluster, keeping the centroids as small as possible. Figure 1 illustrates the K-means clustering for five clusters within a three-dimensional space. It is worth mentioning that the number of clusters in the proposed application is limited by the number of setting groups available in commercial DOCRs.  
The K-means algorithm features the following procedure of five steps: (1) \(k\) points are selected as the number of groups desired to create initial centroids, (2) every data point is associated with the closest centroid to create temporal groups from the distance \(d_{ij}\), (3) new centroids are calculated for each temporary group, (4) each data point is reassigned to the group with the closest centroid from the distance \(d_{ij}\), and (5) the procedure is repeated until convergence is achieved. For calculating \(d_{ij}\), the Euclidean distance is commonly used, as shown in Equation (10), where \(x_{i}\) is the object, \(c_{j}\) is the center of the group, and \(m\) is the number of objects in the group \(g_{j}\).  
\[d_{ij}=\sqrt{\sum_{i=1}^{m}(x_{i}-c_{i})^{2}} \tag{10}\]
## 3 Distributed Residual Current Protection Utilizing Closed-Section Methods
### Blocking and Tripping of Protection  
The surplus residual current within the closed section may not always stem from a ground fault within that specific section. Abnormalities in the residual current CT at the external contact point of the closed section or malfunctions in the protection device can lead to anomalous data measurements, consequently resulting in excess residual current within the section. To mitigate the risk of protection system malfunctions, a protective blocking matrix denoted as \(B\) is defined, with the corresponding expression outlined as follows.  
\[B=\left[\begin{array}{ccc}B_{11}&\ldots&0\\ \vdots&\ddots&\vdots\\ 0&\ldots&B_{MM}\end{array}\right] \tag{21}\]  
The protection blocking matrix \(B\) is an M-dimensional diagonal matrix, and its diagonal elements \(B_{ii}\) (\(i=1,\,2,\ldots,M\)) have two states.  
\[\left\{\begin{array}{c}B_{ii}=1\\ B_{ii}=0\end{array}\right. \tag{22}\]  
\(B_{ii}=1\) means that all external contact point devices in the closed section operate normally; \(B_{ii}=0\) means that the device on one or more external contact points in the closed section is detected as abnormal, and the protection should be blocked.  
Define the protection trip matrix as \(T\), and the expression is as follows.  
\[T=\left[T_{1}\cdots T_{i}\cdots T_{M}\right] \tag{23}\]  
\(T_{i}\) (\(i=1,\,2,\ldots,M\)) in the matrix has two states according to whether the protection acts or not.  
\[\left\{\begin{array}{c}T_{i}=1\\ T_{i}=0\end{array}\right. \tag{24}\]  
\(T_{i}=1\) means that in the corresponding closed-section protection, the switch trips; \(T_{i}=0\) means that the switch does not trip. The relationship between \(T\), \(D\), and \(B\) is as follows:  
\[T=DB \tag{25}\]
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 2 Structural Diversity and Roles of Rice Secondary Metabolites
### Other Metabolites  
Other secondary metabolites in rice include anthracenes (**263-265**), tocopherols (**269-272**), and tocotrienols (**273-276**). Their names and biological activities are listed in Table 9. Their structures are shown in Figure 9.  
(_E,E_)-2,4-Heptadienal (**261**) is a JA-responsive volatile component in rice plants. (_E,E_)-2,4-Heptadienal (**261**) has both antibacterial and antifungal activities against _Xoo_ and _M. oryzae_. In addition, it is also toxic to rice plants. (_E,E_)-2,4-Heptadienal (**261**) is essential for rice survival against pathogen attacks [120].  
Three anthracene derivatives, i.e., orizaanthracenol (1-methoxyanthracen-2-ol, **263**), 1-hydroxy-7-((25,3_R,AR_,55)-2\({}^{\prime\prime}\),3\({}^{\prime\prime}\),4\({}^{\prime\prime}\)-trihydroxy-5\({}^{\prime\prime}\)-(hydroxymethyl)tetrahydrofuranydro-2\(H\)-pyran-1-yloxy) anthrac2-yl 3\({}^{\prime}\),7\({}^{\prime}\)-dimethyloctanoate (**264**), and 1-hydroxy-7-((25,3_R,AR_,55)-2\({}^{\prime\prime}\),3\({}^{\prime\prime}\),4\({}^{\prime\prime}\)-trihydroxy-5\({}^{\prime\prime}\) -(hydroxymethyl)tetrahydrofuranydro-2\(H\)-pyran-1-yloxy) anthrac2-yl 3\({}^{\prime}\),7\({}^{\prime}\),11\({}^{\prime}\),15\({}^{\prime}\),19\({}^{\prime}\)-pentamethyltricosaoate (**265**), have been isolated from the rice hulls of _O. sativa_. Among the three compounds, orizaanthracenol (**263**) exhibited the highest inhibitory activity with respect to the germination of radish (_Raphanus sativus_) seeds, at 40 ug/mL [121].  
Figure 8: Structures of the alkaloids isolated from rice.  
(Z)-3-Hexen-1-ol (**262**) and other volatiles are released from elicitors (CuCl\({}_{2}\), JA, UV, Met, and chitosan oligosaccharidesacharide)-treated and rice blast fungus-infested rice leaves [66].  
(55)-5-(Acetyloxy)-3-(1-methylmethyl)-2-cyclohex-1-one (also named 3-isopropyl-5-acetoxycyclohexene-2-one-1 (**266**) is released from rice seedlings. It inhibited the growth of weeds _E. crus-galli_ and _Cyperus difformis_[55].  
_cis_-12-oxo-Phytodienoic acid (**267**) stimulated rice defense response to the brown planthopper (_Nilaparvata lungens_), a piercing-sucking insect pest of rice. This compound also stimulated the resistance ofadish (_R. sativus_) seedlings to green peach aphid _Myzus persicai_ which indicates the potential application of _cis_-12-oxo-phytodienoic acid (**267**) to stimulate plant defense responses to piercing-sucking insect pests in agriculture [122].  
Figure 9: Structures of the other compounds isolated from rice.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Rice Part Used for Isolation** & **Biological Activity and Function** & **Ref.** \\ \hline \hline \multicolumn{4}{c}{(_E.P_)-2A+Hepbatical (**261**)} & Whole plants & Antibacterial and antifungal activities, toxic to rice plants & [120] \\ \multicolumn{4}{c}{(_G_)-2-Hexen-1-ol (**262**)} & Leaves & - & [66] \\ Orizaanthrac
# Electrical Coupling of Monolithic 3D Inverters (M3 INVs): MOSFET and Junctionless FET
###### Abstract  
In this paper, we investigated the electrical coupling between the top and bottom transistors in a monolithic 3-dimensional (3D) inverter (M3 INV) stacked vertically with junctionless field-effect transistor (JLFET), which is one of candidates to replace metal-oxide-semiconductor field-effect transistors (MOSFET). Currents, transdonutances, and gate capacitances of the top N-type transistor at the different gate voltages of the bottom P-type transistor as a function of thickness of inter-layer dielectric (\(T_{LD}\)) and gate channel length (\(L_{g}\)) are simulated using technology computer-aided-design (TCAD). In M3 INV stacked vertically with MOSFET (M3 INV-MOS) and JLFET (M3 INV-JL), the variations of threshold voltage, transconductance, and capacitance increase as \(T_{LD}\) decreases and they increase as \(L_{g}\) increases, and thus there is a strong coupling in M3 INV at the range of \(T_{LD}\leq 30\) nm. In M3 INV, the coupling between stacked JLFETs in M3 INV-JL is larger than that between MOSFETs in M3 INV-MOS at the same \(T_{ILD}\) and \(L_{g}\). The switching threshold voltage (\(V_{m}\)) and noise margins (\(N\)Ms) of M3 INV are calculated from the voltage transfer characteristics (VTC) simulated with TCAD mixed-mode. As the gate lengths of M3 INV-MOS and M3 INV-JL increase, the \(V_{m}\) variations increase and decrease, respectively. The smaller the gate lengths of M3 INV-NOS and M3 INV-JL, the larger and smaller the variation of \(V_{m}\), respectively. The noise margin of M3 INV-MOS is larger and better for inverter characteristics than one of M3 INV-JL. M3 INV-MOS has less electrical coupling than M3 INV-JL.  
3D monolithic inverter; MOSFET; junctionless FET; electrical coupling