esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Technical Methods
#### 3.2.3 Time Series Analysis  
The premise of these methods is based on assuming that past trends of unrest events will continue into the future, and then extrapolating future event developments based on historical trend patterns. Hidden Markov models are often used for the analysis of time series processes. They describe a process where a hidden Markov chain generates an unobservable state sequence (\(\mathbf{z}\)), which in turn generates an observed random sequence (\(\mathbf{x}\)) for each state. [PERSON] et al. [16] divided riot events into 3-5 states, and, given input sequence data, used hidden Markov models to search for the most likely hidden state sequence that generates the sequence data as the event prediction result of future time series, conducting experiments on Iraq and Afghanistan. Similarly, point process models are commonly used for event prediction, aiming to calculate the intensity function \(\lambda(t|\mathbf{X})\) of an event's occurrence using historical data and then predicting the next event. The determination of the intensity function \(\lambda(t|\mathbf{X})\) is closely related to the probability distribution of event occurrences. [PERSON] et al. [33] simulated the intensity function of volcanic eruption events using the Weibull distribution and predicted volcanic eruption events. Additionally, when there is no information about a specified distribution, non-parametric methods can be used to learn complex distributions from data. For example, [PERSON] and [PERSON] [34] used an RNN to learn highly nonlinear functions of \(\lambda(t|\mathbf{X})\). Furthermore, the most typical model for time series analysis is the Autoregressive Moving Average (ARMA) model and its variants. These models divide the sequence into deterministic (observed states) and uncertain parts (unobserved states). By introducing a random variable \(\varepsilon\), they build mathematical models linking the state \(\mathbf{x}_{t+1}\) at the next time point with previous states \(\{\mathbf{x}_{0},\ldots,\mathbf{x}_{t}|\mathbf{x}\in\mathbf{X}\}\), thereby predicting the state at the next time point. [PERSON] et al. [35], for riots in Afghanistan, utilized a variant of the ARMA model called the ARFIMA model for prediction and provided the degree of riot occurrence. Tables 2 and 3 summarize representative research from recent years on social security event prediction, focusing on data sources, feature signals, main methods, and prediction forms.
## 2 Basic Generator Topologies
### The Inductive Energy Discharge Pulse Generators  
The third group of pulse generators is based on inductive energy discharge circuits, which transfer energy stored in the magnetic fields of coils into well-defined voltage pulses. The circuit concept is shown in Figure 10.  
The generator uses an AC power supply, a low voltage capacitor bank C for a primary electric energy storage and an inductor for secondary magnetic energy storage. When the switch SW1 is closed, the electric energy is discharged from the capacitor bank C into the inductor L. Hence, during the first quarter period of the sinusoidal AC-current, the magnetic energy stored in the inductor increases and the electric energy stored in the capacitor bank decreases. At the peak of sinusoidal current, the magnetic energy stored in the inductor reaches its maximum. At this point, the opening switch SW2 is activated and the current is abruptly interrupted influencing high rate of change of the magnetic flux (dB/dt). Such a transient process induces voltage in the inductor, which is further discharged by a spark gap through the load [67].  
The inductive energy discharge pulse generator has many drawbacks and is not used for electroporation research. Instead, improved circuits are applied (i.e., the diode opening switch (DOS)  
Figure 10: The conceptual circuit of an inductive energy discharge pulse generator.  
Figure 9: The conceptual circuit of a Blumlein pulse generator.  
topology). This is a commonly used concept for generation of high voltage (several kV) pulses in the nanosecond range [29]. The diode opening switch circuit is presented in Figure 11.  
The diode opening switch circuit is based on the DC voltage power supply V, which charges the capacitor bank C1 through the resistor R. When the capacitor is fully charged, the switch SW is closed and the energy in the capacitor C1 starts circulating in the resonant network (C1, C2, L1 and L2). After completing half of the period, the resonant network starts pumping the current through the diodes in the reverse direction. Ideally, the diode abruptly stops conducting and commutates the L2 current into the load. Therefore, this type of circuit is based on the saved energy transfer from inductor L2 to the load with a good repeatability [29].  
Beside the application of diodes, it is also common that the inductive energy discharge pulse generator can be improved with the step-up pulse transformer. In this case the amplitude of the voltage pulse did not affect the opening switch [39]. A simplified transformer-based pulse forming circuit is presented in Figure 12.  
Despite the advantages, the transformer-based pulse generator also has several issues, like core saturation, reset time after pulse delivery and distorted pulse shape because of the parasitic circuit elements. All these issues must be considered during the design of the pulse forming circuit [39]. On the other hand, the transformer based topology can be combined with the modular approach discussed before providing additional flexibility and pulse control [68].
# Present and Future Marine Microwave Satellite Missions in China
## 4 History of Progress in China's Marine Surveillance Satellite Remote Sensing Technology
### Marine Target Recognition Technology  
Since the early 1990s, in accordance with the research results of various international research institutions and colleges regarding SAR target detection and recognition methods combined with airborne SAR data, China's researchers have carried out studies regarding the detection and recognition of large strategic targets such as airports, ports, oil depots, and thermal power stations [76; 77].  
In terms of marine target monitoring, the current mainstream SAR ship targeting detection methods can be divided into the following two categories according to the most important grey features in SAR images: (1). Ship target detections based on background clutter statistical distributions; and (2). Ship target detections based on polarimetric decomposition. [PERSON] et al. [78] determined the spatial distributions of targets using a kernel density estimator, and successfully eliminated the false detection results of targets caused by azimuth ambiguities and the scattering intensities of clutter pixels similar to ships. It was found that when other parameters remained unchanged, the systems with longer wavelengths had a lower probability of ambiguity in the oblique direction. The reason for those results was found to be related to the fact that the ambiguity had exceeded the target area in the oblique direction. Therefore, in the SAR image ship detections, [PERSON] et al. [79] focused on the removal of azimuth ambiguity and proposed corresponding solutions. For example, according to the characteristics of the high-resolution SAR data, [PERSON] et al. [80] calculated the three characteristics of the kernel density estimations (KDE), length-width ratios, and pixel numbers, and then determined the weight proportion of the characteristics using an AHP. Subsequently, the confidence degree of the preliminarily detected target belonging to the ship was obtained through the optimization of multiple characteristics for the discrimination threshold of the ship characteristics, finally achieving the optimized ship detection results. [PERSON] [81] proposed a signal-to-noise ratio enhancement method. It was considered that such a method would significantly improve the signal-to-noise ratio of the SAR images. Then, based on the that method, small targets on the sea surface were monitored, and strong results were obtained.  
[PERSON] [82] detected ships using SAR image data, analysed the characteristics of the ships, and then determined the detailed information of various ships, extracted ship target slices, and established a test sample library based on the matching information provided by AIS (automatic identification system). The extracted ship features were then used for classification and recognition of ship types. Using deep learning technology, ship classification in high resolution SAR has also made some progress. The accuracy of classification is more than 95% [83].
## 3 Edge Artificial Intelligence  
The workload on edge devices varies dynamically with time and location. Therefore, EC must be optimized for computational offloading, resource allocation, latency improvement, energy consumption, and overall user experience. Integrating AI with EC enables the storage and processing of large amounts of IoT data, providing application services in a decentralized, real-time mode with intelligent decision-making capabilities [104]. [PERSON] et al. outline the motivation for combining AI and EC, highlighting the mutual benefits of this integration [105; 106].  
* AI-driven solutions address challenges such as task scheduling, resource management, delay reduction, energy efficiency, and privacy and security concerns encountered during the development of EC.
* Cloud-based AI training and inference can lead to considerable delays and raise concerns regarding data privacy and security. By hosting AI tasks closer to the edge nodes, EC enhances stability, reliability, and user experience, effectively addressing these challenges.  
The complexity, scale, and precision of AI models vary significantly, and even efficient algorithms may not always be sufficient. Edge devices host a diverse range of AI algorithms, such as regression and classification models, clustering techniques, and natural language processing (NLP) algorithms, which are driven by recent innovations and real-world applications. Despite the promising advantages of Edge AI, its deployment and usability face several roadblocks, as discussed below:  
* The management of large volumes of data in Edge AI presents a major challenge, as edge devices frequently collect real-time data that may be incomplete or noisy, leading to inaccurate predictions and reduced performance.
* Compatibility challenges arise due to the diverse nature of hardware, software, and communication protocols. Edge devices have distinct specifications, architectures, and interfaces, making seamless integration with other devices and systems complex and non-trivial.
* The integration of Edge AI with other systems introduces data security threats. Edge devices often handle sensitive information, such as personal health records, financial data, and biometric details, making them prime targets for data breaches, cyberattacks, and privacy violations.
* Edge AI systems frequently encounter scalability challenges that affect performance, reliability, and flexibility. Techniques such as load balancing, parallel processing, and distributed computing help mitigate these issues while enhancing system efficiency.
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 3 Model and Analysis  
In this paper, we build a neural network model, which belongs to a classical regression model. The three sets of data sets are trained and predicted in the same model, and the dropout mechanism is added to the last hidden layer to enhance the generalization of the neural network, prevent overfitting phenomenon, and greatly improve the work efficiency and prediction performance. The results obtained by the neural network model belong to a specific type of substrate, the number of input and output layers are set by themselves according to the experimental needs, the arch height and span of the gold wire bonding and the microstrip line width have a great influence on the microwave transmission characteristics of the S-parameter, so the number of input layers is set to be 6, and the output layer is set to be 123, and the hidden layer needs to be constantly fine-tuned to optimize the neural network model, and the activation function has a certain influence on the performance of the neural network model, and the number of neurons needs to be repeatedly experimented to determine the neuron counts. Although the number of neuron layers and the activation function may not be the optimal choice, the results obtained from its constructed neural network model are in line with the expected results.
## 2 Methodology
### Simulation Steps and Related Software
#### 2.4.6 Surface to Surface (S2S) Heat Radiation Equation  
\(\
abla q_{r}\) in the energy equation is the divergence of the radiative heat flux, which was described by the surface to surface (S2S) model:  
\[q_{r,m}=\varepsilon_{m}\sigma T_{m}^{4}-(1-\varepsilon_{m})\sum_{n=1}^{N}F_{ m,n}q_{r,n} \tag{16}\]  
where \(q_{r,m}\) is the energy flux leaving the surface \(m\), \(\varepsilon_{m}\) is the emissivity, \(\sigma\) is the Stefan-Boltzmann constant, \(q_{r,n}\) is the energy flux leaving the surface \(n\), and \(F_{m,n}\) is the view factor between surface \(n\) and surface \(m\), described as follows:  
\[F_{m,n}=\frac{1}{A_{m}}\iint\frac{cos\theta_{m}cos\theta_{n}}{\pi r^{2}} \delta_{mn}dA_{m}dA_{n} \tag{17}\]  
where \(\delta_{mn}\) is determined by the visibility of \(dA_{n}\) to \(d_{m}\). \(\delta_{mn}=1\) if \(dA_{m}\) is visible to \(dA_{n}\) and 0 otherwise.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Cyber-Physical System Resilience  
Cyberphysical system resilience is a critical concern in the evolution of the IoV in smart cities. Research attempts in this area include the study of ways to maintain the stability and functionality of IoV systems against cyberattacks and technical or communication breakdowns. Such efforts require the development of advanced mechanisms that can detect and respond to cyber-physical threats in real time. Cities can bring the benefits of connected mobility without worrying about potential disruptions if the interoperability of IoV systems is enhanced in terms of their resiliency.
# Development of a Mobile Buoy with Controllable Wings: Design, Dynamics Analysis and Experiments
## 5 Experimental Verification  
To verify the feasibility of the mobile buoy design concept, a scaled-down mobile buoy prototype was fabricated and fundamental experiments were conducted in a laboratory tank. A high-resolution digital camera was used to record the relevant photograph sequences of the mobile buoy in Argo mode and Glider mode.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction
### Machine Learning  
In the solutions that use machine learning, the NS performs the learning and delivers the results to the end nodes to predict some metric being monitored, for instance network congestion, using centralised machine learning. This approach centralises the training data on one machine, the NS. This can create a bottleneck in collecting training data. Future work may include using distributed learning of the nodes rather than centralised learning. Distributed machine learning enables end nodes to collaboratively learn the prediction model whilst keeping all the training data on the end node and reducing learning error. Future work could also include the use of different optimization methods for predicting the network metric under scrutiny.
## Appendix A SEM Analysis of Microalgae for Chemical Extraction Methods  
As shown in Figure 11, ultrasonication resulted in superior lipid extraction compared to other extraction processes. Structural analysis using SEM was performed here to study the extraction methods in depth. The structure of dried microalgae used in this study before extraction can be observed in Figure 11. For _Chlorella_ sp., the cells have a spherical shape with an uneven surface of the phospholipid membrane, and appear to be clotted. From the EDX analysis, _Chlorella_ sp. consisted of 40.2% carbon, 35.7% oxygen, and 23.4% nitrogen for the area analyzed. On the other hand, _Spirulina_ sp. contained 43.7% carbon, 30.4% oxygen, and 25.2% nitrogen (25.2%) for the region analyzed.  
Figures A2 and A3 show the structure of dried _Chlorella_ sp. and _Spirulina_ sp. after the Soxhlet extraction processes. The cells were shrunk compared to the original ones, some cells were broken, and the surfaces of those were distorted. Although Soxhlet extraction affected the structure of some microalgal cells, others were still intact, and the membrane cell did not show any damage. This explains the reason for the lower oil yield for Soxhlet extraction using hexane as the solvent. Hexane is the most popular and economical solvent commonly used in extraction studies. However, hexane was less effective in lipid extraction from _Chlorella_ sp. compared to _Spirulina_ sp., as seen in Figure A1. Comparing the 1000\(\times\) magnification images from Figures A2 and A3, it is visible that _Spirulina_ sp. cells were more affected by the process than those of _Chlorella_ sp., thereby resulting in less yield. Since solvent extraction produced an even worse percentage, the oil extraction sample was not further analyzed. Consequently, a solvent mixture of multiple components was used further to maximize oil production using the Soxhlet extraction process.
# Reliability of a New Portable Dynamometer for Assessing Hip and Lower Limb Strength
## 3 Results  
### Intra-Session Reliability  
The peak torque values of all bilateral tests had very high intra-session reliability (Table 2). Specifically, no systematic bias, very high to excellent test-retest reliability (ICC\({}_{2,k}\) > 0.85), and low within-individual variation (CV \(<\) 9%) was shown for bilateral HADD (ICC\({}_{2,k}\) = 0.94; CV = 3.5-3.6%), HABD (ICC\({}_{2,k}\) = 0.86-0.87; CV = 7.8-8.6%), HIR (ICC\({}_{2,k}\) = 0.86-0.88; CV = 5.0-6.6%), HER (ICC\({}_{2,k}\) = 0.94-0.95; CV = 2.1-2.2%), and NHE (ICC\({}_{2,k}\) = 0.87-0.91; CV = 2.6-3.6%).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**S1 Mean (SD)**} & \multirow{2}{*}{**S1 95\% CI**} & \multirow{2}{*}{**S2 Mean (SD)**} & \multirow{2}{*}{**S2 95\% CI**} & \multirow{2}{*}{**\%CV  
For unilateral strength tests, the peak torque values for HADD and HABD had high intra-session reliability, with no systematic bias, high test-retest reliability (ICC2,k = 0.77-0.90), and low within-individual variation (CV = 5.4-8.5%). For the remaining unilateral strength tests, the intra-session reliability of peak torques was moderate to good (Table 2). Specifically, no statistically significant systematic bias, moderate to good test-retest reliability, and low (CV \(<\) 9%) within-individual variation was shown for HEXT (ICC2,k = 0.69-0.81; CV = 3.9-6.4%), HFLX (ICC2,k = 0.60-0.69; CV = 7.3-8.2%), KEXT (ICC2,k = 0.67-0.84; CV = 5.6-10.0%). The exceptions were: peak torque of left leg during PFLX (ICC2,k = 0.43-0.44; CV = 6.3-6.4%) and peak torque of left leg during HFLX, which displayed statistically significant systematic bias (\(F\) = 7.56, \(p\) = 0.01; \(d\) = 0.46). Moderate to very high test-retest reliability (ICC2,k = 0.54-0.86) and low to moderate within-individual variation (CV = 6.2-18.7%) was shown for HIR and HER, with one unilateral measurement of HIR showing statistically significant systematic bias (\(F\) = 8.53, \(p\) = 0.01; \(d\) = 0.39).  
Calculated agonist:antagonist ratios had in general lower intra-session reliability than peak torques (Table 3). In bilateral strength tests, the calculated agonist:antagonist ratios had no systematic bias, good to very good test-retest reliability (ICC2,k = 0.66-0.83), and acceptable within-individual variation (CV = 8.1-11.9%). In contrast, the calculated agonist:antagonist ratios for unilateral strength tests had lower intra-session reliability. Significant systematic bias was observed for unilateral HIR:HER and HFLX:HEXT ratios (\(d\) = 0.49 and 0.40, respectively), with ICC coefficients ranging from low (0.39 for the right HIR:HER ratio) to high (0.8 for the right FLX:EXT ratio) and CVs ranging from low (7.5% for the right FLX:EXT ratio) to high (22.5% for the right HIR:HER ratio).
## 4 Implementation
### User Study
#### 4.3.4 Study 3  
The third study evaluated the impact of the DFF device in a VR application. We tested the impact of the DFF on immersivity within a virtual environment when interacting with VR objects. A VR game was designed wherein the player was required to catch free-falling objects of three different sizes. The participants' task was simply to catch the objects. The study comprised two sections. Section 1 entailed catching a free-falling ball without the DFF, and Section 2 entailed catching the ball with the DFF. The objective of the study was to compare the virtual experience of the two section. We used E2I questionnaires [23] and our own to measure the presence, immersion, and enjoyment of the virtual environment. The participants were asked to complete the questionnaires after each section. The overall evaluation of the study was well reflected in the experimental results (Figure 12). A significant difference was noted between the sections. The DFF improved  
Figure 11: The correct answer rate of the directional sensation experiment: (**a**) forward; (**b**) upward; (**c**) backward; (**d**) downward..  
the VR presence and immersion. Compared to Section 1, participants in Section 2 reported higher scores of presence (mean Section 1 = 3.17; mean Section 2 = 5.17; \(t(11)=-3.25\); \(p=0.00\)) and immersion (mean Section 1 = 3.92; mean Section 2 = 5.17; \(t(11)=-2.32\); \(p=0.04\)). However, the enjoyment questionnaire did not show a significant difference. Based on the responses of participants during the interview, this was caused by the simplicity of the VR game.
## 7 Advantages of SCA over MFO and EP  
A balance in the exploration and exploitation processes is required to obtain an optimum solution for a damping controller. Various metaheuristic algorithms for optimisation have used stochastic methods that have deficiencies in solution convergence. Several proposed methods provide good exploration in a search space but are limited in exploitation, or vice versa.  
Metaheuristic optimisation techniques are used to find the maxima or minima. These techniques are either swarm-based or evolution-based algorithms. Swarm-based algorithms include PSO, firefly algorithm, ant colony algorithm, gravitational search algorithm (GSA) and immune algorithm (IA). Evolution-based algorithms include evolutionary programming differential algorithm, EP, and GA. GA and PSO have the limitation of premature convergence due to the local minimum stagnation problem. GSA has the limitation in solving complex optimisation problems. IA works better during the exploration process than in the exploitation process to find optimal solutions for a damping controller. EP works better in the exploitation process than in the exploration process. Some other optimisation techniques, such as TS and SA optimisation techniques, have slow convergence rates and are incapable of solving multimodal problems [4].  
The advantage of the SCA over other algorithms is that it allows exploration and exploitation in a search space, that is, for the first and second halves of the iterations, respectively. The main advantage of the SCA optimisation technique is that it shows better results in the exploration and exploitation processes to find the maxima or minima in eigenvalue analysis. The proposed SCA was validated with MFO and EP techniques.