esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resonant Converter Families
### Structure of Resonant Power Converter
#### 3.1.1 Control Switching Network (_Csn_)  
The _CSN_ is a setup that facilitates the conversion of _DC_ power into _AC_ power. The most familiar switching networks are full- and half-bridge as their utilization is dependent on the required power. The full-bridge inverter is mainly used in high-power applications, while half-bridge inverters can offer only 50% of the active switch input voltage. As the latter has low levels of voltage transition, it is ideal for high input voltage applications [82]. Most times, resonant power converters and conventional _DC-DC_ converters seem alike in terms of achieving soft switching and the chance of working at a high switching frequency. This is due to the possibility of _DC-DC_ power conversion from _DC_ to _AC_ using inverters. The _AC_ can either be stepped up or down using an electromagnetic component, which then goes through a rectifier network to be supplied as _DC_ power to the load. Typically, resonant converters are employed with full- or half-bridge inverters, together with each full-bridge or center-stapled rectifier [83, 84]. The _CSN_, as depicted in Figure 18, can generate a square waveform voltage \(V_{S}(t)\) (Volt) of the switching frequency _Fs_ (\(\omega_{S}=2\pi\) fs), as represented by the Fourier series in Equation (3). Considering the response of the resonant tank that has been noted to overpower the basic component fs of the voltage waveform \(V_{S}(t)\), the infinitesimal response exhibits harmonic frequencies _nfs_, \(n\) = 3, 5, 7,... Therefore, the power that corresponds to the basic voltage waveform \(V_{S}(t)\) component is propagated to the resonant tank, as shown in Equation (4). This basic component is a sinusoidal waveform of peak amplitude equal to (4/\(\pi\)) times the _DC_ source voltage. The basic component is in the same phase as the initial waveform.  
Turning ON _S1_ produces a positive sinusoidal switched current (_t_), while the negative version is produced by turning OFF _S2_ since the two switches work at the same time and its peak _Is_1 amplitude with phase is equivalent to _g_s. In the meantime, _DC_ to _CSN_ input current is obtained by dividing the sinusoidal switched current over half the switching duration, as expressed in Equation (5) [9,85,86].  
\[V_{s}(t)=\frac{4V_{S}}{\pi}\sum_{n=1,3,5,...n}\frac{1}{n}sin(n\omega_{s}t) \tag{3}\]  
\[V_{s1}(t)=\frac{4V_{S}}{\pi}sin(n\omega_{s}t) \tag{4}\]  
\[i_{s}(t)=I_{s1}sin(\omega_{s}t-\varphi_{s}) \tag{5}\]  
\[I_{input}=\frac{2}{T_{s}}\int_{0}^{\frac{T_{s}}{2}}i_{s}(t)dt=\frac{2}{\pi}I_ {s1}cos(\varphi_{s}) \tag{6}\]  
Figure 18: Equivalent circuit of _CSN_[16].
## 3 An Implementation of the Proposed Architecture
### In-Campus Facial Recognition Service Integration  
With the proposed technology integration model, applications can be easily integrated with smart classrooms. For example, a door access control application based on facial recognition technology can be easily implemented by integrating existing facial recognition cloud service and school information system. Figure 14 shows the face registration screen of an in-campus facial recognition cloud service made for smart meeting rooms. With the facial recognition cloud service, meeting attendance registration in the smart meeting rooms can be done with face instead of RFID solutions. For smart classrooms, a facial recognition client application can be implemented and deployed to the information boards next to the front doors of smart classrooms.  
Figure 14: The face registration screen of an in-campus facial recognition cloud service. Portion A is the title of the screen which means ‘Smart Meeting Room’. This screen is for users to register their face and the face registration screen of an in-campus facial recognition cloud service. Portion B is the title of the screen which means ‘Smart Meeting Room’. This screen is for users to register their face and the face registration screen of an in-campus facial recognition cloud service. Portion C is the title of the screen which means ‘Smart  
for facial recognition applications. In portion B, users need to enter user information, such as user type, employee id, name, gender, and agree the 'Personal Data Collection Agreement'. Then users press button C to start to take photos. The program will take 5 photos in different angle. Then users press button F to retake photos or press button E to finish the registration process.  
TAKE photo FROM the camera on the information board  
CALL the facial recognition cloud service by sending the captured photo  
IF the face is recognized as a teacher OR a student  
GET personal class schedule FROM the school information system  
GET door status of H204  
IF the person has a class in H204 (now OR in next 10 minutes) AND the door is locked  
THEN unlock the door AND turn on 50% of lights  
GET the ON/OFF status of all devices in H204  
FOR each device  
IF the device is OFF  
THEN turn on the device  
The client application captures faces with the camera on the information board, send the picture to the backend cloud service, get the facial recognition results, then send L3 combo commands to control smart classroom depending on the predefined rules. Figure 15 shows the procedure of the integrated facial recognition application in smart classrooms. Teachers and students, who have registered their face before and have class in this classroom, can unlock classroom door using their face instead of RFID identity cards.  
With the proposed technology integration model, low-level hardware control operations in smart classrooms are defined as standard functions. High-level applications can access smart classroom functions with web application interface calls. As a result, smart classroom functions can be easily extended by linking different kinds of services.
## 1 Introduction
The results showed that the PCF (Penetrating Cone Fracture) principle could be used to analyze the high-pressure CO\({}_{2}\) fracturing performances. In other words, it was found that tension stress fields were formed when high pressure was applied at the bottom of boreholes. Meanwhile, annular fractures were formed due to the stress concentrations in rock masses, which had then tended to develop toward the free surfaces. Finally, conical fractures had formed in the rock, which had achieved the purpose of fracturing the rock masses [14; 17; 19; 20]. In recent years, the technology has been gradually popularized in developed countries, and is now widely used in cement, electric power, and other industries, such as pipeline blockage removal, caking removal, ice breaking, and so on [17; 21]; however, there are still a series of problems: 1, Its engineering practices and scientific research have mainly focused on gas-type coal mining; 2, there are few application studies in surface rock excavation; 3, research on the impact of cracking effect is lack; 4, there is little research on the impact of adjacent buildings. Therefore, in the current study, based on the analysis of the principle of liquid CO\({}_{2}\) phase-transition fracturing, field tests and vibration monitoring were carried out in a foundation pit excavation project located in Fangshan District, Beijing, China. The fracturing effects, granular distributions, particle velocities, and vibration energy levels following fracturing excavations were scientifically analyzed. The feasibility of liquid CO\({}_{2}\) phase-transition fracturing technology in the foundation pit excavations had been successfully demonstrated in this study, which will potentially facilitate its popularization and future applications.
## 4 Measurement of the 8-Channel T/R Module with 7-Bit TTD
### Noise Figure  
NF was measured using the Agilent Noise Figure Analyzer N8975A. As shown in Figure 8, the measured results of NF are below 3.65 dB within the bandwidth of 1.2 GHz, which meets the theoretical value of 3.6 dB.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Environmental Applications
#### 2.3.2 Air Monitoring  
Air is a vital element for human lives and nowadays the air pollution of the atmosphere is a result of many modern human activities. WSNs can be utilized for air quality monitoring in occupied regions in order to prevent dangerous diseases and contaminations or risk the health of people.  
A characteristic example of an application of this kind is presented in [20], where an air quality monitoring application assisted by WSNs, called WSN-AQMS, is proposed. The specific system combines gas sensors along with Libelium waspnotes [21] to measure air quality parameters of gases such as ozone, CO, and NO\({}_{2}\). The waspnotes monitor in real time the air quality and utilize the Zigbee protocol for data communication. The authors further introduced the Clustering Protocol for Air Sensor network (CPAS) in order to support the operation of this system
# Gaussian Process Modeling of Specular Multipath Components
## 3 Gaussian Process Regression  
GP regression usually consists of two steps: analyzing data obtained from measurements and predicting data at other positions that have not been measured.
## 5 Simulation and Experiment
### Experiment and Analysis  
In order to evaluate the performance of the ACRA, we designed a test-bed in our laboratory of which the environment is similar to office or home environments. The terminal node is shown in Figure 15. The node basically has four parts: serial communication part, control core, power line communication core, and coupling circuit part. The serial communication part supports downloading, debugging the program, and connecting with PC. The control core has a Mega8 processor which controls the operative mode of the PLC core. The PLC core has a ST7538 which supports for multi-carrier modulation. The coupling circuit part is responsible for coupling the carrier signal to 220 V power line.  
The topology of test-bed is shown in Figure 16. It contains 10 terminal nodes. Among them, node 1 is the BS, which connects with PC via serial communication part to record the data from the other terminal nodes. The test-bed also contains computers, air-conditioner, motor and some other devices which are commonly used in the laboratory (e.g., lamps, scanner, printer, _etc._) as noise sources. The ACRA is compared with CSP. The accuracy rate of receiving data is used as performance measure.  
Figure 14: Simulation results of channel status. (**a**) Simulation results of channel conflict; (**b**) Simulation results of channel utilisation.  
Figure 15: PLC terminal node.  
In order to calculate and observe the experimental results conveniently, we set that node2-node10 send character data \"a\" to \"n\", or hex 0x61 to 0x6E to BS. The PC connected with BS is responsible for displaying and recording the data from these nodes. In ACRA, noe2-node10 constitutes a single artificial web network, and the node6 is the central node. In the process of experimental, ACRA and CSP continuous work 5 hours before and after the adding of the same noise sources respectively. We record the data and do some statistical analysis. The results of statistical analysis are shown in Figure 17. When there are no noise sources in the network, the accuracy rate of CSP and ACRA is 89.87% and 99.83%, respectively. When there are similar noise sources in the network, the accuracy rate of CSP and ACRA is 97.67% and 49.61%, respectively. Subject to the effects of noise, the accuracy rate of CSP and ACRA are a certain degree of reduction, but the ACRA is less affected. These indicate that because of the terminal nodes of CSP send data disorderly, the utilization and conflict of channel are undesirability which result in the high bit error rate and low accuracy rate, low anti-interference ability and low reliability of PLC. The terminal nodes of ACRA send data to the central node according to the timing in Figure 7a and Figure 14a, which avoids the conflict, improves the utilization rate of channel, reduces the bit error rate, ensures the accuracy rate of data. Because of choosing the proper relay node (node 6), ACRA reduce the influence of noise on PLC effectively, improve the reliability of PLC.
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
## 3 Digital Processing of Spatially Distributed Sensor Array  
The signal acquisition and data processing module of the AOA detection system using a spatially distributed sensor array are shown in Figure 7. A spatially distributed sensor array outputs five currents independently, which are converted into voltage signals. After amplification, they are sent to the A/D converter (ADC) and converted into digital signals. The microcontroller reads values from five channels and transmits them to the computer via a USB port, where they are processed using a multi-sensor data fusion algorithm to calculate the AOA. In future research, we plan to use FPGA boards for AOA calculation to improve the output bandwidth of the spatially distributed sensor array.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 4 Methodology
### System Design  
The proposed model considers multiple mobile sensor devices within an IoT environment, where communication is facilitated between the sensors or network structure. The proposed scheme comprises two prominent actors, mobile sensors and access points, as well as node-to-node communication systems, node-to-structure communication systems, and gateway nodes. The various forms of communication among the actors presented in the proposed model are exhibited in Figure 2.  
Figure 2 shows the implementation scenario of the proposed study model with the aid of multiple actors: (i) The first actor of this model is the _mobile sensor_ (MS), which is considered to be possessed by the user within a definitive IoT environment. The proposed scheme assumes that a specific application related to the smart city runs on the user's handheld device, which is also equipped with a sensor. The purpose of this mobile sensing device is to facilitate communication by acting as a source, destination, or relay node during the transmission process. (ii) The second actor in this model is the _access point_  
Figure 2: Communication system among the actors.  
(AP), which collects essential data from various mobile sensors and other hotspots situated within a smart city environment. The primary purpose of the AP is to enable the MSs to communicate with each other and the surrounding network structure. (iii) The third actor of this model is a _node-to-node_ (N2N) communication system, where one MS can exchange data with one of multiple MSs within an IoT environment. (iv) The fourth actor is _node-to-structure_ (N2S) communication, in which one MS can communicate with the current network structure. It should be noted that both N2N and N2S are facilitated by AP, which initially identifies a specific MS and then authenticates its genuine identity and privileges to participate in data exchange within an IoT environment. (v) The fifth actor is the _gateway node_ (GN), which connects all the APs residing in one smart city to communicate with APs residing in different smart cities. This significantly assists in increasing the transmission coverage. The proposed scheme introduces the Ethereum blockchain to validate the identity of the MS for both the communication processes (N2N and N2S) when a specific mechanism of enrolling the identity of the MS is followed by a validation framework to safeguard the transmission process among MSs. Table 2 highlights the notations used in the manuscript.  
The scheme also considers the rate of data transmission as well as the temporal attribute association, which is computed based on the current location of the MS. Furthermore, the model introduces cost modeling for its resources based on the saturation state of the server. All of these operational modules are integrated to perform user cost management securely. The essential operations are discussed as follows:
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 3 Results and Discussion
### Temperature Effect on EDFA Gain  
The EDFA gain is displayed, in Figure 3, versus the input signal wavelength at \(-\)40 \({}^{\circ}\)C and +80 \({}^{\circ}\)C for the two different pumping wavelengths. The fiber length is chosen to be 5 m. The input pump and signal power are 200 mW, and 10 \(\upmu\)W, respectively. As expected, in both parts (a) and (b), the maximum gain is achieved at \(\sim\)1531 nm, which corresponds to the wavelength of the maximum emission cross-section.  
The effect of \(\beta\) and its variation with temperature is also considered in Figure 3, for both pumping cases. Figure 3a shows that \(\beta\) has no effect in the case of 980-nm pumping. However, in the case of 1480-nm pumping, it has a noticeable effect, as depicted from Figure 3b. This agrees with the results obtained by [PERSON] et al. [14]. Noting that, neglecting the \(\beta\) effect leads to a false increase in the gain value by around 4 dB at a signal wavelength of 1531 nm for both temperature values. It is also noted that the effect of \(\beta\) is neglected in the case of 1480-nm pumping at longer signal wavelengths, \(\sim\)1600 nm, which is out of the scope of our study.  
It is worthy to study the significant impact of temperature on the EDFA gain. Below a certain wavelength, the increase in temperature has a positive influence on the gain. On the other hand, after this wavelength, the effect is negative, and a decrease in gain is observed. This wavelength is 1542 nm in the case of the 980-nm pumping schema, while its value is 1535 nm in the 1480-nm pumping case.
## 3 Design and Implementation of the Proposed CBC Method  
The proposed CBC method is based on cluster classification of normal and abnormal traffic in the network. This technique aims to detect and classify unknown malicious traffic flow as an attack or benign traffic for a two-class classification. The enhanced CBC approach for intrusion detection involves oversampling unbalanced clusters, developing detection models, and selecting optimal models for intrusion detection and classification across eight clusters. To address the issue of unbalanced data, we employed the Synthetic Minority Over-sampling Technique (SMOTE). This method results in synthetic samples for the minority class to improve model performance and balance the dataset. The models also trained to detect eight types of classes, namely Mirai, Benign, Web, Brute force, DoS, Recon, DDoS, and Spoofing. The model is also trained to detect their subtypes. The clustering approach helps detect novel attacks because it calculates the similarity between new and malicious packets, then determines whether the new packet is virus-free or not. The features selected by SelectKBest are loaded from the dataset. They are further scaled and normalized. The dataset is divided into training and testing sets. Since the model is designed for both two-class and eight-class classification, eight distinct classes are mapped into these two classification categories. The distribution of data for the two-class and eight-class classifications is provided in the research about the benchmark dataset [20]. K-mean clustering is applied using the NumClusters algorithm. For two-class classification, the data are mapped to eight clusters. For eight-class classification, the data are mapped to two clusters. After the dataset is mapped to clusters, the sampling method is applied for data balancing. Afterwards,the machine learning classifiers are applied to the balanced data. First, the performance of the individual classifier is evaluated. Then, the performance metrics are aggregated, and overall result evaluation is carried out using Equation (1). Algorithm 1 shows the pseudo-code of the proposed CBC approach for a two-class classification problem. It starts with the loading of the dataset and the application of the oversampling method to balance the data to be used for the classification models. Similarly, Figure 1 depicts a flow chart of the proposed cluster-based classification (CBC) method. \[Overall\ performance=\frac{1}{NumClusters}\sum_{i=1}^{NumClusters}OverallMetrics \tag{1}\]  
The Algorithm 1 also highlights the pseudo-code of the proposed CBC approach for eight-class classification. To visualize the clusters, PCA (principal component analysis) is applied to the dataset for dimensionality reduction. Figure 2 shows the two-class cluster representation of attack and benign classifications using eight clusters. Figure 3 presents the eight considered classes, namely Mirai, Benign, Web, Brute force, DoS, Recon, DDoS, and Spoofing, using a two-class cluster representation. Here, we consider the IoTCIC2023 dataset [20]. Figure 1: Flow diagram of the proposed CBC approach.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 4 Simulation Results and Discussion
### Roll Controlled by SJA  
When the morphing wing of the aircraft is not considered, i.e., \(\Delta L=0\), one can find that the synthetic jet-based control for fixed-wing aircraft can effectively track the command signal of roll motion at a high AoA, as shown in Figures 12 and 13, wherein the reference angle signal is set as \(x_{d}=\sin 4t\). Furthermore, one can conclude that the controller, which takes approximately 0.5 s to reach stabilization, meets the performance requirement and eliminates the influence of the disturbance induced by SJA.  
Figure 11: Angle and speed tracking response.  
Figure 9: The control input due to morphing motion.  
It has been found that an aircraft's altitude is only able to be controlled well by SJA; in other words, the system is over-actuated with redundant control when additional control by the morphing span is added, which means that control due to the morphing wing can be treated as an auxiliary tool that can be properly allocated [37]. On the other hand, the morphing span can also be considered the controlled object, i.e., the roll motion of aircraft with a preplanned morphing strategy (e.g., Equation (23) and Figure 14) can be controlled via the SJA, which belongs to the first control approach for large-scale morphing aircraft.  
\[\delta_{\text{morphing}}=\left\{\begin{array}{ll}1.4,&\delta_{\text{morphing }}\geq 1.4\\ 1.6|\sin t|,&\delta_{\text{morphing}}<1.4\end{array}\right. \tag{23}\]  
Figure 14: The actual control input due to the morphing motion.  
Figure 12: The intermediate control input due to SJA.  
Figure 13: Angle and speed tracking response.  
The tracking responses obtained via SJA and the morphing wing are shown in Figure 13. One can see from Figure 13 that the reference signal can be tracked perfectly. Especially when compared with the case controlled by SJA alone, the redundant control input due to morphing can make the tracking response converge to the target more quickly. In addition, the span extends asymmetrically and the curve of morphing in Figure 14 is quite smooth, such that the flight stability during the morphing motion can be guaranteed [5; 38].