esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Identification of Faulty Events
### Phase-A to Phase-B Fault  
A fault involving phase-A and phase-B (AB fault) was simulated as an incident at the sixth cycle. The currents recorded on the node 650 were processed using the algorithm and the results for a period of 0.2 s (12 cycles) are depicted in Figure 5. It can be seen from Figure 5a that the magnitude of the currents corresponding to phases-A & B increases after the AB fault, whereas the change in the current of phase-C is very small. Figure 5b depicts the WD-index corresponding to phases-A & B, which has a high magnitude during the incidence of a fault. The magnitude of this index corresponding to the phase-C is low after the incidence of a fault. Figure 5c illustrates the ALN-index which has a high magnitude corresponding to all the phases at the time of the fault incidence. Figure 5d illustrates the fault index. It can be inferred that the magnitude of the FI corresponding to faulty phases (phases-A & B) is high compared to the threshold, whereas this FI corresponding to the healthy phase (phase-C) has a low magnitude compared to the threshold. Hence, the algorithm proposed in this paper effectively detects the AB fault and also identifies the healthy and faulty phases.  
Figure 4: Identification of phase-A to a ground fault incident on node 671 of the test system (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.  
Figure 5: Identification of a phase-A to phase-B fault incident on node 671 of the test system (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.
## 3 Results and Discussion
### Effect of Initial Pressure on Flame Tip Velocity  
Flame tip velocity was defined, which had detailed discussion in previous work [42]. Flame tip velocity is obtained by the difference of flame front position between consecutive images. In the current work, 8000 images per second are collected by the image acquisition devices, namely the time internal of consecutive images is 0.125 ms. The uncertainty is no more than 0.18 mm on flame front location. As a result, the uncertainty is no more than 1.44 m/s on flame velocity. The flame tip velocities are obtained at 21.9 kg/m\({}^{3}\) filling density under different initial pressures in Figure 6.  
As shown in Figure 6, at the same filling density (21.9 kg/m\({}^{3}\)), the flame tip velocity in the cylinder filled with NSSs area is greater than that in the unfilled NSSs area. The degree of the burned gas expansion in the lower part of the cylinder filled with NSSs are greater than that in the upper part of the cylinder without filling NSSs. This is due to the flame was affected by skeleton structures of NSSs, the limitation of space and the expansion of burned products. In addition, due to the faster expansion of the burned products, as show in Figure 6, at the top position of the curve, the jet flame gains a faster velocity of 43.34 m/s (47.51 mm), 81.88 m/s (42.16 mm) and 46.64 m/s (28.44 mm) under different  
Figure 6: Flame tip velocity at initial pressures 0.2 MPa,0.3 MPa and 0.4 MPa.  
initial pressures (0.2 MPa, 0.3 MPa and 0.4 MPa). The velocity variation under different initial pressures was consistent with the flame passing through hole size of 2 mm and porosity of 12% orifice in doctoral thesis [43]. On the basis of the Bychkov theory [26], KH/RT instability with shear effect were triggered that played a vital role on increasing flame surface area and flame burning velocity. It is also further verified that flame velocity increases in the filled with NSSs area, and NSSs promote flame propagation.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 5 Proposed Classification
### Classification of RL-Based Routing Protocols Based on Learning Algorithm
#### 5.1.2 Deep Reinforcement Learning-Based Routing Method  
These routing protocols utilize deep reinforcement learning (DRL) in the routing process. This technique integrates deep learning (DL) with reinforcement learning (RL). This learning system is shown in Figure 12. These routing schemes can solve complex problems in FANETs. When the size of network is large, the value estimation and the optimal policy calculation are not simple. Therefore, a proper solution is to use a deep network to approximate these parameters. The deep network makes a high-intelligent agent and increases its ability to find the optimal policy. This routing protocol is a good choice for finding routing paths in large-scale networks because their learning speed is very high [49]. Note that, similar to the RL-based routing methods, the DRL-based routing approaches are also divided into two categories, single-agent and multi-agent, based on the learning agent. Additionally, these methods are categorized based on the learning model into two groups, model-based and free model, which were explained in Section 5.1.1.  
Figure 11: Free-model learning system.  
Figure 10: Model-based learning system.
## 6 Model
It is essentially composed of a body with rotational symmetry that rotates around the axis of such symmetry. It informs if there was any movement on the device's own axis. The node takes a measurement every 5 min if there is no change in the sensor and sends a value. If a change occurs before the measurement is taken, the device wakes up to carry out a transmission. 6. Environmental sensor: This node presents humidity and temperature sensors allowing knowledge of the temperature and humidity in a certain space. This node takes a sensor measurement every hour due to the little change that occurs in that period of time. 7. Gateway: it is the device that acts as a connection interface between devices and allows sharing resources between two or more computers. The gateway used contains the LoRa and WiFi protocols. This gateway obtains all the transmitted data through the LoRa protocol, these data are then transmitted through WiFi allowing them to be found on the ThethingsNetwork platform. In this platform it is possible to display the data separately from each node. Knowing when it made its last transmission, the frequency of transmission of each device and its measurements. This device obtains all the data transmitted by the ZigBee coordinator, all the data transmitted by the LoRa nodes and the data transmitted by the BLE concentrator. 8. BLE Nodes: It is an industrial specification for Wireless Personal Area Networks (WPAN), which operates on the frequency of 2.4 Ghz as well as ZigBee. There are four BLE nodes that work as beacons, it is a low-consumption device that emits a broadcast signal, i.e., they are devices that are constantly sending data. In this case they have a light sensor and they are constantly sending the value of light. In addition, there is a fifth device which works as beacon scanner, the function of this device is to receive all the data of the other nodes, decode them and send them through LoRa using another module that contains that protocol. 9. In Figure 3, a representative scheme of the sensors according to the wireless technology and their respective application is presented. Coordinating nodes are observed that will serve as a bridge between three of the studied technologies: WiFi, ZigBee, and Bluetooth Low Energy (BLE). In Figure 4, a satellite map of the university campus of the Universidad Panamericana in Guadalajara, Mexico is shown. We have an outline of the approximate positions of the main sensors, so that it serves as a basis for taking measurements. This map is useful to get an idea of the green areas, buildings, or constructions, pedestrian and vehicular routes and the approximate total area where the sensors are located. Figure 4: Network sensors position along the university campus. Figure 3: Network sensors under each technology. The coexistence between Bluetooth and BLE in the same device is ensured by the common MAC layer. This layer also performs channel quality measurements (such as Received Signal Strength Indication (RSSI), Quality of service, and packet loss rate for Bluetooth) to update the channel map with \"good\" channels and eliminate those channels marked as \"bad\".
## 4 Results
### On-Board PRF Optimization for Real-Time Operation  
The proposed method enables the on-board PRF optimization to be realized in a real time. In general, the SAR operational parameters are updated based on the altitude that is predicted at the ground segment. The updated table is then uploaded via the S-band uplink channel to the satellite prior to the image acquisition. The SAR controller uses the uploaded table for the image acquisition as preparing and managing the instrument timing for every pulse. In a conventional operation, the SAR operational parameters generated on-ground has HEX units. The SAR controller needs to convert the received parameters  
Figure 17: SAR Performances: (**a**) NESZ, (**b**) RAR, (**c**) AAR for PRF optimized with the second method while using the incidence angle as the reference one; (**d**) NESZ, (**e**) RAR, (**f**) AAR for PRF optimized with the proposed method while using the look angle as the reference one.  
from HEX values to real ones and extract the real-time operational parameters based on mathematical equations stored in EEPROM. When the real-time operation is required due to urgent requests, such parameter update causes overload for the SAR controller because the time required for the update is relatively long. The update time, however, can be significantly reduced when PRF is optimized using the proposed method. This feature realizes the real-time operation. Figure 18 shows the procedure for the real-time operation. The SAR controller calculates the SAR operational parameters using the actual altitude that is transferred from the platform. Simultaneously, the SAR controller optimizes PRF using Equation (15). PRF is always located at the center of the clear zone even if the actual altitude is changed during the image acquisition or deviated from the predicted altitude. The parameter preparation procedure show in Figure 18 corresponds to the third procedure in Figure 10, SAR operational parameter optimization. For the parameter preparation time, the SAR controller just needs to implement the sub-routine that calculates and controls several operational parameters. Those parameters are optimized for the altitude variation. As a result, the proposed method does not overload the SAR controller even for real-time operation.
# Control for Ship Course-Keeping Using Optimized Support Vector Machines
## 1 Introduction
Globally optimal solution can also be guaranteed in SVM since convex quadratic programming is performed. Additionally, by applying the kernel trick, the curse of dimensionality can be avoided in SVM, which is usually inevitable in NN. SVM has found wide applications in the field of control engineering and in the past years, there have been some SVM applications to ship motion control. For instance, [PERSON] et al. applied fuzzy SVM to the course-keeping [26]. [PERSON] et al. proposed SVM based general predictive control (GPC) for ship course-keeping [27]. [PERSON] et al. presented a robust SVM controller for the ship course-keeping [28]. This paper presents a robust controller for the ship course-keeping by using SVM identification and L2-gain design. The investigated plant is composed of the ship dynamics and the dynamics of rudder actuator. Uncertainties are taken account of in the cascaded system, which refer to modeling errors and external disturbance. In the controller design, the modeling errors are identified by SVM while the external disturbance is suppressed by using L2-gain design. To guarantee the robust stability of the controller, a Lyapunov recursive function method is adopted. The main novelty of our approach is the determination of the Lagrangian factors in SVM. Rather than calculate the Lagrangian factors through a matrix equation, one can obtain these factors by solving differential equations proposed. To improve the control accuracy, the parameters in SVM are optimized by incorporating particle swarm optimization (PSO). The rest of the paper is organized as follows. In Section 2, the mathematical model of ship steering, the methodologies of SVM and PSO are described; in Section 3, the details of the controller design and the stability analysis are given; in Section 4, a simulation example is presented to illustrate the validity of the controller designed; and the final section is the concluding remarks.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 3 Material and Methods
### Simulation Implemented in this Research
#### 3.3.2 Software for Simulation and Justification for Selection  
NetSim is developed by an Indian organization called TETCOS and was initially developed by BOSON. The connection is made possible by a NetSim cloud server that remotely connects client nodes. Our workstation connects through the server's VPN. NetSim enables users to virtually create a network along with its components, such as devices, links, and applications, etc. One other feature NetSim provides is a graphical editor interface that is used to build models for different network entities [17].  
In this study, vehicles were represented by mobile wireless LAN workstations that move randomly. The software is very efficient in the study of application-based protocols. Its potential to generate several traffic types adds another advantage. Its C source code enables us to modify the protocol code by adding features that are unique to this research and thus extend knowledge in the study of sinkhole attacks. The resulting dashboard is easy to read and analyze with graphs and tables. Our target within the tool is its ability to redefine the source code of the protocol for improved performance and security.
# Study on a Quality Evaluation Method for College English Classroom Teaching
## 4 The Evaluation Index System of College English Classroom Teaching Quality
### Evaluation Index Construction Principle of College English Classroom Teaching Quality  
The evaluation indexes of college English classroom teaching quality are constructed according to the following principles:  
(1) Scientific principle  
Science refers to the correct and standard degree of knowledge in college English classroom teaching. The degree of mastering and confirming knowledge mainly depends on the degree of imparting and explaining knowledge in college English classroom teaching. Therefore, teachers should adhere to the scientific attitude of seeking truth from facts. In addition, the taught knowledge should be scientific and standard knowledge in order to ensure that the taught knowledge accepted by students is objective and standard knowledge.  
(2) Technical principle Technique refers to the teaching skill to creatively accomplish teaching tasks based on combining students with the actual classroom situation in the teaching process. The specific contents of the technology include language expression ability, organization and management ability, keen observation ability, and the flexible handling of unexpected events.  
(3) Effectiveness principle  
Effectiveness refers to the mastering degree of the taught contents for students in the teaching process. It directly reflects the acceptance and internalization degree of the teaching content for students. It includes the mastering degree of knowledge and skills, and a relevant knowledge application ability.  
(4) Creativity principle  
Creativity refers to creatively teaching the teaching content, duly teaching the learning actuality of the teaching content, and introducing or guiding their research. Creativity requires novelty and the integrity of the teaching content. At the same time, it should pay attention to whether the teaching method is suitable for the characteristics of the students, and whether it will help the students to cultivate their creative ability.  
The four principles restrict each other and together affect teaching quality. If the classroom teaching quality can be objectively evaluated, the four principles must be evaluated. Advantages and disadvantages are put forward in the evaluation process in order to more effectively avoid weaknesses, release early and iterate, and quickly improve teaching ability. The goal is to ensure the high reliability and efficiency of a college English teaching quality evaluation.
## 2 Materials and Methods
### Surface Characterization
#### 2.3.4 Hydrophobicity  
Surface hydrophobicity was assessed through contact angle measurements, and subsequently estimated using the Van Oss approach [51].  
The sessile drop technique was employed for contact angle measurements using a contact angle meter (Databases OCA 15 Plus, Filderstadt, Germany) at ambient temperature (23 \(\pm\) 2 \({}^{\circ}\)C). Three different liquids (water, formamide, and \(\alpha\)-bromonaphthalene; Sigma-Aldrich Co., St. Louis, MO, USA) were used as references. Each liquid and surface combination underwent a minimum of five measurements across three independent trials (\(n\) = 15).  
Using the Van Oss approach [51], the total surface free energy (\(\gamma^{TOT}\)) of a pure substance is calculated by summing the apolar Lifshitz-van der Waals component (\(\gamma^{LW}\)) with the polar Lewis acid-base component (\(\gamma^{AB}\)):  
\[\gamma^{TOT}=\gamma^{LW}+\gamma^{AB} \tag{1}\]  
The polar component (\(\gamma^{AB}\)) comprises the electron acceptor (\(\gamma^{+}\)) and electron donor (\(\gamma^{-}\)) parameters, and is calculated as follows:  
\[\gamma^{AB}=2\sqrt{\gamma^{+}\gamma^{-}} \tag{2}\]  
To determine the surface free energy components of a solid surface (\(s\)), contact angles (\(\theta\)) of three different liquids (\(l\)) with known surface tension components are measured [52]. This is followed by solving three simultaneous equations in the following form:  
\[(1+\cos\theta)\gamma_{l}=2\bigg{(}\sqrt{\gamma_{\text{s}}^{LW}\gamma_{l}^{LW }}+\sqrt{\gamma_{\text{s}}^{+}\gamma_{l}^{-}}+\sqrt{\gamma_{\text{s}}^{+} \gamma_{l}^{-}}\bigg{)} \tag{3}\]  
The hydrophobicity of a surface is quantified by the free energy of interaction (\(\Delta G\), mJ\(\cdot\)m\({}^{-2}\)) between two entities of that surface when submerged in a polar liquid, typically water (\(w\)). Consequently, \(\Delta G\) is determined by  
\[\Delta G=-2\bigg{(}\sqrt{\gamma_{\text{s}}^{LW}}-\sqrt{\gamma_{\text{w}}^{LW }}\bigg{)}^{2}-4\bigg{(}\sqrt{\gamma_{\text{s}}^{+}\gamma_{w}^{-}}+\sqrt{ \gamma_{\text{s}}^{-}\gamma_{w}^{+}}-\sqrt{\gamma_{\text{s}}^{+}\gamma_{\text {s}}^{-}}-\sqrt{\gamma_{\text{w}}^{+}\gamma_{w}^{-}}\bigg{)} \tag{4}\]  
When examining a solid surface, this approach suggests that the material's hydrophobicity or hydrophilicity is determined by the strength of interactions between entities and water. If \(\Delta G\) < 0 mJ\(\cdot\)m\({}^{-2}\), this suggests that the interaction between two entities is stronger than their individual interactions with water, and the material is classified as hydrophobic. Conversely, if \(\Delta G\) > 0 mJ\(\cdot\)m\({}^{-2}\), the material is deemed hydrophilic, signifying a repulsive free energy of interaction [51].
## 5 Conclusions  
In this paper, we have proposed a UAV-assisted CCS by using NOMA for multiple users in hydropower IoT systems, where the UAV can provide NOMA services to two users at the same time. One user is selected as the receiver of the covert messages, and the other acts as the jammer used to interfere with the detection of the covert communications. Besides the hydropower IoT network, the proposed UAV-assisted communication scheme can be deployed in other complex IoT networks with large coverage areas. To improve the performance of covert communications, the UAV is used as the transmitter of covert messages due to its mobility and the LOS link between the UAV and the served users. To maximize the covert rate, we formulated a jointly optimized problem to optimize the flying altitude of the UAV, the transmit power, and the block length of covert communication channels. It is first demonstrated that the covert rate can be maximized when all available channels are utilized by setting the maximum finite block length as its maximum value. To solve the proposed optimization problem, we first convert the optimization problem to an SP one and then develop an SGPA-based algorithm solved by the problem. Simulation results show that the proposed CCS can achieve a positive covert rate, and the developed algorithm can fast convergence in comparison with traditional optimization algorithms.  
This work serves as a first foray into the design of UAV-assisted covert communication with NOMA. Many interesting directions follow this work and deserve further investigation. First, covert wireless communication aided by UAVs can serve multiple users. How to extend the traditional covert wireless communication model to that with more receivers of covert messages and more wardens remains open. Second, since multiple antennas are equipped in the base station, how to select more than one antenna from all antennas to transmit covert messages deserves further investigation.  
Conceptualization, Y.W. and Y.S.; methodology, Z.L.; software, Q.X.; validation, Z.L., Q.X. and Y.Q.; formal analysis, Z.L.; investigation, Y.Q.; resources, G.H.; data curation, Q.X.; writing--original draft preparation, Q.X.; writing--review and editing, Z.L.; visualization, W.P.; supervision, G.H.; project administration, Y.S.; funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by State Grid Electric Power Company Science and Technology Project: No. 5700-202140381A-0-00.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & 5 th Generation Mobile Communication Network \\ DEP & Detection Error Probability \\ GP & Geometric Program \\ LoS & Line of Sight \\ MDP & Miss Detection Probability \\ NOMA & Non-orthogonal Multiple Access \\ SCA & Successive Convex Approximation \\ SGPA & Successive Geometric Programming Approximation \\ \end{tabular}