esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 3 Discussion
### Effect of Leak Hole Size
#### 3.2.2 Aeroacoustic Characteristics
Although \(Kn\) may seem slightly larger than the criterion to distinguish the continuous flow and rarefied flow, where \(Kn=0.1\), the computational results in this paper is still believed to be reliable. Because characteristic length has a significant influence on \(Kn\). For example, the diameter of the nozzle  
Figure 11: \(Kn\) contour with \(\lambda\) contour map of different leak hole diameter (The red line marked in the figure represents \(\lambda=0.6\) mm and the dotted line is an imaginary line to fix the discontinuity). (**a**) 2 mm; (**b**) 1 mm; (**c**) 0.5 mm. throat is often chosen as the characteristic length when computing the plume field generated by a Laval nozzle using the CFD-DSMC method. The leak hole in this paper is cylindrical, so the range of \(Kn\) can be appropriately relaxed. Due to the existence of a supersonic region, a silent zone is formed, and the reflected waves cannot propagate across the whole flow field, but propagate upstream clingy to the shear layer. Besides, it is not difficult to find that there is a proportional relationship between the \(Kn\) criterion and the leak hole diameter. So when only \(\lambda\) is considered, the criterion can be normalized to \(\lambda=0.6\,\mathrm{mm}\), as shown in Figure 11. Hence, the \(\lambda\) criterion seems to be more suitable than the \(Kn\) criterion to judge the boundary of the acoustic cavity. Although \(\lambda=0.6\,\mathrm{mm}\) may not define the cavity wall integrally because of the discontinuity of \(\lambda=0.6\,\mathrm{mm}\), it is apparent that the reflection is intense as long as \(\lambda=0.6\,\mathrm{mm}\) exists. The reflected waves within the \(\lambda\) criterion cause resonance phenomenon, partly forming a typical acoustic cavity resonance problem. Because of the vacuum environment, the sound wave energy cannot be transmitted to the far field. Part of the energy is transmitted to the plume, which is dissipated as the density decreases. Part of the energy is reflected upstream along the acoustic cavity wall (shear layer) and stimulates new instability waves near the leak hole lip area, and then generates multiple resonance modes. [PERSON]'s group [21; 22] conducted a leak-induced jet noise experiment on the ISS and found an obvious resonance phenomenon and harmonic characteristics in the spectrums, which still cannot be explained by any terrestrial jet noise theories. The appearance of the resonance phenomenon confirms that there is indeed a cavity-like structure in the vacuum jet. Furthermore, the limitation of the cavity makes more disturbance waves at different frequencies reflected upstream along the mixing layer. It is bound to excite multiple discrete tones at different frequencies, presenting harmonic characteristics in the spectrums. However, current work can only explain qualitatively why such spectrums under real leak conditions are formed. Further work is still needed to predict precisely when the resonance frequency occurs and how strong the resonance can be.
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
## 3 Results & Discussion
### Optimization Algorithm Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Optimization Method** & **Array Size** & **Failure Type** & **Reference SNR (dB)** & **Failed SNR (dB)** & **Optimized SNR (dB)** \\ \hline \multirow{8}{*}{Simulated Annealing (SA)} & Type 1 & 15 & 18 & 21 \\  & 8 \(\times\) 8 & Type 2 & 15 & 19 & 20 \\  & (64 elements) & Type 3 & 15 & 19 & 23 \\  & Type 4 & 15 & 19 & 25 \\  & Type 5 & 14 & 19 & 23 \\  & 6 \(\times\) 6 & Type 6 & 14 & 16 & 21 \\  & (36 elements) & Type 7 & 14 & 20 & 24 \\  & Type 8 & 14 & 20 & 21 \\ \hline \multirow{8}{*}{Genetic Algorithm (GA)} & Type 1 & 15 & 20 & 18 \\  & (64 elements) & Type 2 & 15 & 19 & 24 \\  & (64 elements) & Type 3 & 15 & 20 & 29 \\  & (64 elements) & Type 4 & 15 & 20 & 18 \\  & (64 elements) & Type 5 & 14 & 20 & 13 \\  & (64 elements) & Type 6 & 14 & 14 & 15 \\  & (36 elements) & Type 7 & 14 & 20 & 13 \\  & Type 8 & 14 & 13 & 15 \\ \hline \multirow{8}{*}{Particle Swarm Optimization (PSO)} & Type 1 & 15 & 19 & 15 \\  & (64 elements) & Type 3 & 15 & 24 & 15 \\  & Type 4 & 15 & 24 & 23 \\  & (750 elements) & Type 5 & 14 & 18 & 14 \\  & (36 elements) & Type 6 & 14 & 20 & 15 \\ \cline{1-1}  & Type 7 & 14 & 20 & 15 \\ \cline{1-1}  & Type 8 & 14 & 16 & 18 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of various types of failures for four different optimization methods of 8 \(\times\) 8 and 6 \(\times\) 6 planar arrays. explorations, PSO searches for the best solution space while GA searches via crossover [23]. This may lead to PSO providing the best solution for a problem. On the other hand, pattern search uses a directed move in a search space, while GA uses a sample search space and many generations. PS finds the best performance for a comparatively smaller search space and less computing time than GA [24]. In terms of time for the optimizations to converge, SA took 1439.593 s, PS took 1520.216 s, GA took 8120.882 s, and PSO took 9079.376 s on average. SA and GA took shorter time periods to converge, but the methods failed to meet the objective. On the other hand, the methods that provide expected results, PS and PSO, have a huge difference among them in terms of the time to converge. For real-time implementation, the optimization can be programmed in the 'C' language and processed in real-time on a Digital Signal Processor (DSP) embedded processor platform. This will make the process faster. The PSO method is suggested for better accuracy in matching the SNR value of the reference radiation pattern. This summarizes that the PSO method provides a better solution for failed planar array radiation pattern correction with the highest SNR reduction with a higher time to converge. It is also shown that the weights of the remaining working elements in a planar array can be optimized to produce a radiation pattern with a reduced SNR value to enhance the efficiency of a system with the presence of faulty elements in an array without compromising main lobe levels.
## 3 Method
### Calculation of the Calibration Constant  
For point target \(i\), the response energy calculated based on the C-SAR/01 image is \(\varepsilon_{p_{i}}\), RCS theoretical value \(\sigma_{ref_{i}}\) and corresponding local incidence angle \(\theta_{i}\) of the point target \(i\) are known, and the calibration constant of point target \(K_{i}\) is given as [17; 22]:  
\[K_{i}=\frac{\varepsilon_{p_{i}}}{\sigma_{ref_{i}}}sin\theta_{i} \tag{6}\]  
The average value of the calibration constants calculated for all point targets is usually used as the final calibration constants of data [17; 22]:  
\[K(dB)=10*lg\left(\frac{1}{N}\sum_{i=1}^{N}K_{i}\right) \tag{7}\]  
Figure 8: Schematic of point target response integration window.  
After obtaining the calibration constants \(K(dB)\), the Level-1A SLC data were converted into backscatter coefficient images, according to the calibration in Equation (8) of the GaoFen-3 series SAR satellite Level-1A data.  
\[\sigma_{0}(dB)=10*lg\Bigg{(}DN^{2}*\left(\frac{QV}{32767}\right)^{2}\Bigg{)}-K(dB) \tag{8}\]  
Here, \(\sigma_{0}(dB)\) is the value of the backscattering coefficient in dB, \(DN^{2}\) is the intensity value calculated from the Level-1A image of C-SAR/01, satisfying the equation \(DN^{2}=I^{2}+Q^{2}\), where \(I\) and \(Q\) represent the real part and the imaginary part, respectively, and \(QV\) is the image quantization maximum.
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 4 Transforming the Classical AES Algorithm into a Quantum-Gate-Based AES Algorithm
### Quantum Gate to Classical Logic Gate Association  
As analyzed in Section 4.1, the AND, OR, and XOR operations are performed during the AES encryption. We tried to implement the required classical logic gate as the quantum gate to utilize quantum computing. The transformation of each gate is as follows:
## 5 Energy-Harvesting Applications  
### Cognitive Radio for Wireless Sensor Networks  
Cognitive radio (CR) technology dynamically manages the usage of available spectra to improve the flexibility and efficiency of WSNs [91, 92]. This section discusses the fundamentals of cognitive radio, its application in WSNs, spectrum sensing and management, energy-efficient spectrum usage, and associated challenges.
## 4 Agent Training with Markov Decision Process (MDP)
### DRL-Based Solution
#### 4.2.3 Efficient Training Mechanism  
To break the temporal correlation between data samples and improve sample utilization, we incorporate experience replay. Each interaction between the agent and the environment generates an experience tuple \((s,a,r,s^{\prime},d)\), which is stored in a fixed-size replay buffer \(\mathcal{D}\). Here, \(r\) denotes the immediate reward following the state transition from \(s\) to \(s^{\prime}\) via action \(a\). The buffer operates as a first-in-first-out (FIFO) queue with a maximum capacity of \(N_{\mathcal{D}}\). During training iterations, a mini-batch of \(N_{B}\) experience tuples is uniformly sampled at random from the replay buffer:  
\[B=\big{\{}(s_{i},a_{i},r_{i},s^{\prime}_{i},d_{i})\big{\}}_{i=1,2,\ldots,N_{b}} \tag{27}\]  
which is then used to compute the loss function and update the online network.  
To provide stable learning targets, we maintain a separate target network \(Q(s,a;\theta^{\prime})\). Unlike the online network \(Q(s,a;\theta)\), the target network's parameters \(\theta^{\prime}\) are not updatedvia gradient descent but instead through a soft update mechanism: after each update of the online network parameters \(\theta\), the target network parameters are adjusted as follows:  
\[\theta^{\prime}\leftarrow\tau\cdot\theta+(1-\tau)\cdot\theta^{\prime} \tag{28}\]  
where \(\tau\) is the update rate, controlling the speed at which the target network parameters converge toward those of the online network.  
To balance exploration of new actions and exploitation of current knowledge during training, we employ the \(\varepsilon-\text{greedy policy}\) as the behavior strategy for action selection. With probability \(\varepsilon\), an action is chosen uniformly at random from the action space \(A(t)\); with probability \(1-\varepsilon\), the agent selects the current optimal action corresponding to the highest Q-value output by the online network. The exploration rate \(\varepsilon\) decays over time according to:  
\[\varepsilon=\max(\varepsilon_{\text{min}},\lambda\varepsilon) \tag{29}\]  
where \(\varepsilon_{\text{min}}\) is the minimum exploration rate, \(\lambda\) is the decay rate, and \(\varepsilon\) is initialized to 1. This ensures that the policy prioritizes exploration in early training stages and gradually shifts toward exploitation as learning progresses.
## 2 Literature Review and Hypotheses Development
### Immersion and Presence  
Regarding immersion and presence, although frequently mentioned in virtual environments, they are still prone to confusion, often considered to convey the same meaning or used interchangeably. While some scholars classify both as psychological states [65; 68; 69], others define immersion either as a systemic objective attribute [70] or as a technological quality of the medium [71]. Presence, on the other hand, is delineated as a subjective psychological response to the system [66], occasionally characterized by illusions or hallucinations [30]. Ultimately, we summarize the concepts, immersion is typically associated with the technical attributes of virtual environments, reflecting the system's technical characteristics and quantified descriptions of technology, such as visual stimulation, interaction fluency, and richness of information feedback; presence is the subjective and dynamic result of immersion, describing users' subjective experiences in virtual environments, such as enjoyment and fulfillment.  
In various virtual environments, the interplay between immersion and presence has been substantiated to positively correlate with user engagement, enjoyment, satisfaction, memory retention, and spatial cognition. [PERSON] et al. delved into the realm of virtual gaming, elucidating the pivotal role of immersion in augmenting the perception of realism and thereby influencing players' spatial cognition [72]. [PERSON] et al. in the context of virtual classrooms, posited immersion as a fundamental aspect for enriching spatial awareness, encompassing facets like color perception, directional acuity, and size comprehension [73]. [PERSON] et al. through experimental research in virtual museums, demonstrated that participants experiencing a \"sense of presence\" derived greater enjoyment and exhibited higher user satisfaction during interactions with specific interfaces, corresponding to what is commonly referred to as presence [74]. [PERSON] et al. further corroborated this assertion by elucidating the correlation between presence and performance in spatial cognition taskswithin VR environments, underscoring the facilitative role of presence in spatial cognition assessment performance [75].  
Despite the established correlations between immersion, presence, and various user metrics across different virtual environments, there remains limited research attempting to measure these factors within the context of OVM and their associations with potential influencing factors. Our objective is to empirically examine the relationships between immersion, presence, and spatial cognition within the OVM environment, thereby contributing to a deeper understanding of user experiences in virtual museum settings. Therefore, the following hypotheses are proposed:  
**H1:**: _Immersion (IM) has a positive effect on spatial cognition (SC);_
**H2:**: _Presence (PR) has a positive effect on spatial cognition (SC);_
**H3:**: _Immersion (IM) has a positive effect on presence (PR)._
## 2 Second configuration: The Improved ADRC (IADRC)
### Results of the Decoupled ADRC Control Scheme [23]
The tracking output response for the IADRC was better than in the CADRC, specifically during the transient period, where both configurations entirely attenuated the effect of the exogenous disturbances \(w_{1}\) and \(w_{2}\), the state couplings for each subsystem, and the time-varying input gains \(g_{1,1}\), \(g_{1,2}\), \(g_{2,1}\), and \(g_{2,\ 2}\) on the output response of the two channels. \(\bullet\) **Case (2): Input and State Decoupling**  
Figure 7: The output response of Equation (42) using IADRC, (**a**) output \(y_{1}\), (**b**) output \(y_{2}\), (**c**) control signals \(u_{1}\) and \(u_{2}\), and (**d**) estimated generalized disturbances \(\hat{\xi}_{1,3}\) and \(\hat{\xi}_{2,3}\)[23]. Two reference inputs \(r_{1}\) and \(r_{2}\) sequentially applied to the nonlinear system (Equation (42)) to check the disassociation of the input and state couplings between the two subsystems. The transient response of the outputs \(y_{1}\) and \(y_{2}\) in response to reference inputs \(r_{1}\) and \(r_{2}\) are illustrated in Figures 8 and 9. As can be noticed from these figures, the decoupling requirement, stated in the problem statement, was very well satisfied, with a smooth response on each output channel. The proposed
Sensors_**2013**, _13_, 3358-3393; doi:10.3390/s130303358  
Sensors**ISSN 1424-8220  
www.mdpi.com/journal/sensors  
In-Network Processing of Joins in Wireless Sensor Networks  
Hyunchul Kang  
School of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Korea;  
E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-2-826-5306; Fax: +82-2-826-0853.  
Received: 7 December 2012; in revised form: 26 February 2013 / Accepted: 6 March 2013 /  
_Published: 11 March 2013_  
**Abstract:** The join or correlated filtering of sensor readings is one of the fundamental query operations in wireless sensor networks (WSNs). Although the join in centralized or distributed databases is a well-researched problem, join processing in WSNs has quite different characteristics and is much more difficult to perform due to the lack of statistics on sensor readings and the resource constraints of sensor nodes. Since data transmission is orders of magnitude more costly than processing at a sensor node, in-network processing of joins is essential. In this paper, the state-of-the-art techniques for join implementation in WSNs are surveyed. The requirements and challenges, join types, and components of join implementation are described. The open issues for further research are identified.  
**Keywords:** join query; wireless sensor network; query processing; sensor network database
## 3 Experimental Results
### Overall Experimental Structure  
Figure 9 illustrates the overall experimental structure. First, we used a scanner to convert a receipt image into a high-resolution image. Next, the receipt image was preprocessed through adaptive binarization, trademark removal, and receipt character classification (i.e., printed or handwritten characters). The handwritten and printed receipt characters were recognized using the methods described in the preceding section. The recognition results were uploaded to a database, and a web interface is provided for users to review and correct errors. Finally, the results were exported in the form of tax filing documents to complete the tax filing process.  
Figure 9: Overall experimental structure.  
The receipt information required for tax filing in Taiwan mainly consists of eight items (Figure 10): receipt number, receipt date, seller ID, buyer ID, total amount, tax-free sales amount, sales amount, and tax amount. Therefore, the proposed ARRS can primarily recognize these eight items.
## 2 Silymarin Extraction Methods: From Conventional to Novel
### Ultrasound-Assisted Extraction  
Ultrasound-Assisted Extraction (UAE) involves the application of ultrasonic waves to the plant material, showcasing superior performance in mass transfer, cell disruption, solvent penetration and capillary effect [40]. Notably, this technique preserves temperature-sensitive botanical materials while being both straightforward and cost-effective [41]. Furthermore, it is the simplest and most economical technique, and can be easily scaled up for industrial production. The waves create cavitation, leading to the mechanical disruption of cell walls and increased mass transfer. As a result, UAE is recognized as a green extraction technique due to its lower energy consumption and reduced solvent use compared to conventional methods.  
In terms of yield, UAE is capable of producing high yields under optimized conditions, particularly when using polar solvents like ethanol, which are effective at solubilizing silymarin's polar components, such as silychristin and silydianin. However, the yield can be influenced by several factors, including ultrasound power, solvent concentration, and extraction time, necessitating careful optimization to balance yield with purity. The stability of silymarin compounds during UAE is another critical consideration. The high-energy ultrasound waves generate localized heat and free radicals, which can potentially degrade sensitive flavonolignans like silybin. Therefore, controlling the ultrasound parameters (e.g., intensity, duration) is essential to minimize thermal degradation and preserve the bioactivity of the extracted compounds [42].  
A study compared the efficiency of traditional ethanol/water maceration with maceration using glycerol/water mixtures, enhanced by ultrasonication, for the extraction of silymarin from Silybum marianum [43]. While glycerol/water maceration initially demonstrated slightly lower efficiency than the ethanol/water method, ultrasonication significantly enhanced the extraction process. Specifically, during a 60-min UAE at 80 \({}^{\circ}\)C using a 40% (_w/w_) glycerol solution, the amount of silymarin extracted reached 107.19 \(\upmu\)g/mL, which was comparable to the 116.17 \(\upmu\)g/mL obtained via ethanol/water maceration. In terms of extraction yield, the UAE process yielded approximately 10.93 mg/g of silymarin from the glycerol-based solvent, compared to 11.82 mg/g from the ethanol-based maceration. This minor difference underscores the efficiency of glycerol as a solvent when assisted by ultrasonication. Furthermore, the purity of the silymarin extract was maintained at high levels, with silybin A and B being the most abundant components, reflecting the effective extraction of key flavonolignans. Additionally, the antioxidant activity of the extracts, as indicated by radical scavenging activity (RSA), was closely correlated with the silymarin content, reinforcing the viability of glycerol as a green solvent alternative. This method not only reduces solvent toxicity but also enhances the applicability of the extracts in cosmetic formulations.
## 4 Results and Discussion  
### Prediction Capability  
Taking MS as the prediction target, the performance of three proposed AI methods, namely PSOANFIS, GAANFIS and SVM was investigated with both training (Figure 6a,c,e) and testing datasets (Figure 6b,d,f). As regards to the training part, the PSOANFIS technique had the closest fitted line to the diagonal, confirmed by the highest value of R (i.e., 0.9266 compared to 0.9111 and 0.9110 using the GAANFIS and SVM, respectively). With respect to RMSE and MAE, the PSOANFIS appeared the best predictor of the MS as the corresponding values were smallest (Table 5). This is a good indication that RMSE and MAE were in excellent agreement with R, demonstrating that the PSOANFIS performed better than other techniques in term of training dataset.  
As regard to the testing part, the SVM technique has the closest fitted line to the diagonal one, proved by the highest value of R (i.e., 0.8711 compared with 0.8692 and 0.8181 while using the PSOANFIS, GAANFIS, respectively). Besides, the SVM algorithm was a very strong candidate with the smallest values of RMSE and MAE (i.e., RMSE = 0.5978 and MAE = 0.3804). All the values are summarized in Table 5.  
The training part of an AI algorithm is used for the construction of the model, whereas the testing one reflects its prediction capability [39]. With the main focus on the performance of the AI algorithms to predict the MS, the results on the testing parts are the focused in this study. Besides, a ratio of 70/30 was kept constant for the training/testing data, as recommended by [PERSON] et al. [53]. The effect of random sampling of both training and testing datasets will be analyzed in the next section. It is noteworthy noticed that the results presented in the present section refer to one random combination of data indexes. It has been reported elsewhere that the prediction capability was greatly affected by the choice of sample index in the training/testing parts [39]. Therefore, the robustness of the three proposed AI models needs to be analyzed.