esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Microwave-Assisted Plasma Combustion Technology
### Industrial Experimental Setup  
The prototype burner, designed for this study, has been integrated with a microwave power generator (magnetron) and its auxiliary systems, as presented in Figure 2. The microwave energy generated by the magnetron at 915 MHz with an output power of 75 kW is transmitted to the burner through a series of rectangular waveguides, following its passage through a circulator and a three-stub tuner. Near the open end of a coaxial line located at the top of the burner, this microwave energy ionizes the air to generate plasma. The rectangular waveguide used is of type WR 975, with dimensions of 247.65 mm in width and 123.825 mm in height. A circulator is a device designed to control the direction of signal flow, ensuring that microwave energy propagates in the intended direction while preventing any reverse transmission. The three-stub tuner is installed between the burner and the circulator. To achieve maximum absorption of the supplied microwave power, the stubs of the tuner must be properly adjusted to minimize reflected power. Otherwise, a  
Figure 1: Plasma Flame, 2—Coaxial Line, 3—Tungsten Metal Rod, 4 and 5—Channel, 6—Igniter, 7—Camera and Pyrometer, 8—Damper, 9—Measurement Port, 10—Thermocouple, 11—Waveguide.  
portion of the microwave energy will be reflected rather than fully absorbed by the plasma and will subsequently be dissipated in the water load connected to the circulator.  
The cooling system includes four pumps operating at 6 bar pressure and 0.75 kW power. The water circulated by these pumps maintains the temperature of the magnetron, circulator, and power supply within the system, preventing overheating. The control system monitors and regulates the operating parameters of the magnetron, power supply, and cooling system to ensure stable plasma generation. The power supply cabinet provides electrical power to system components, primarily the magnetron. Protects the system equipment from electrical faults by disconnecting the power in case of an overload or short circuit.  
In the subsequent phase of the study, a newly designed pulverized coal storage system was integrated with the burner, comprising a coal bunker, a fan, and a pipe for transferring the coal-air mixture. The industrial-scale experimental setup incorporating this system is shown in Figure 2. The pulverized coal, transferred to the ejector with the help of a rotary feeder located below the bunker, is injected into the primary air stream at the fan outlet. This pulverized coal-air mixture is conveyed through a 300 mm diameter pipe to the burner. The feed rate of coal can reach up to a value of 3000 kg/h, which is the maximum output capacity of the electronically controlled rotary feeder. The flow rates of primary and secondary air can also be adjusted with the electronic control system connected to the fan. While the flow rate of secondary air can reach up to a maximum of 1000 Nm\({}^{3}\)/h, the flow rate of primary air can reach a maximum of 6000 Nm\({}^{3}\)/h.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 4 Field Data Example
**(a)** 10 Hz profile, **(b)** 20 Hz profile, **(c)** 40 Hz profile, and **(d)** 60 Hz profile. CLSSA uses 30 ms time window and iterates twice. Figure 7: _Cont_. From the above comparative analysis, it is clear that both the CLSSA method and the Greedy FISTA-based CLSSA method offer high stratigraphic resolution in frequency attribute maps, effectively highlighting the characteristics of the target layers. These methods mitigate the window tailing effect of STFT and the low time resolution of CWT at low frequencies. The CLSSA methods accurately emphasize thicker sedimentary layers at 10 Hz and thinner layers at 40 Hz, providing more precise estimates of layer thickness. In terms of computational efficiency, all four spectral decomposition methods were executed using a 6-CPU parallel pool (intel i7-12700). From Table 1, the computation times for the four methods are as follows: 80.2696 s for the STFT method, 75.8616 s for the CWT method, 275.6139 s for the CLSSA method (with two iterations), and 156.2003 s for the Greedy FISTA-based CLSSA method. Although the STFT and CWT methods offer higher computational efficiency, their spectral resolution is lower, especially at low frequencies where they fail to effectively display meaningful spectral characteristics. Compared to the traditional least squares constrained spectral analysis method, the Greedy FISTA-based CLSSA method significantly improves computational efficiency and enhances spectral resolution across different frequencies. This method provides a more accurate description of sedimentary layers, offering critical insights into predicting reservoir and caprock thickness.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 5 Discussion
### Implications and Future Work  
The findings of this review underscore the immense potential of FML models to significantly enhance cybersecurity defenses. Their ability to balance high accuracy with privacy preservation makes them particularly suitable for sensitive applications. To fully realize this potential, future work should prioritize: (1) developing standardized metrics and benchmarks for evaluating FML performance across diverse, real-world cybersecurity datasets; (2) designing advanced, inherently interpretable FML architectures to improve explainability and foster trust in high-stakes environments; (3) exploring novel communication optimization and resource management techniques to enhance scalability in large, heterogeneous networks; and (4) empirically validating the practical benefits and overcoming the implementation complexities of integrating FML with cutting-edge technologies like quantum computing and advanced privacy-enhancing mechanisms. Addressing these areas will be pivotal in translating the promise of FML into robust and widely adopted cybersecurity solutions.
## 2 Materials and Methods
### Support Vector Machine  
Support vector machine (SVM) is a supervised learning algorithm introduced by [PERSON] [23]. This algorithm aims to find a hyperplane that separates two classes of a multi-dimensional dataset with a maximum margin. For data to be classified by SVM, the dataset must be linearly separable. If the dataset does not fulfill this requirement, another kernel function must be used that will transform the data to a higher dimension to ensure they are linearly separable by a hyperplane that spans these dimensions. The choice of kernel depends on the type of data. The radial basis function (RBF) kernel is considered the best choice for practical applications [23]. When classifying spectral data collected from diseased and healthy eggs, the problem is the overlap of measurements from both classes. In this case, the use of RBF is preferred because it transforms the data to infinite  
Figure 1: Scheme of the laboratory setup for spectral data acquisition.  
dimensions in which the relationship between observations might be found. Then, the resulting value enables the creation of the decision boundary that enables the classification of multidimensional data [23; 24; 25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Variable** & **Wavelength (nm)** & **FWHM (nm)** \\ \hline A0 & 457 & 3 \\ A1 & 473 & 3 \\ A2 & 501 & 3 \\ A3 & 515 & 3 \\ A4 & 523 & 3 \\ A5 & 526 & 3 \\ A6 & 532 & 3 \\ A7 & 543 & 3 \\ A8 & 556 & 3 \\ A9 & 561 & 3 \\ A10 & 589 & 3 \\ A11 & 593 & 3 \\ A12 & 632 & 3 \\ A13 & 660 & 3 \\ A14 & 671 & 3 \\ A15 & 690 & 3 \\ A16 & 729 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation of input data variables and corresponding DPSS wavelengths and their full width at half maximum (FWHM).  
Figure 2: The visualization of an exemplary ten samples taken from each categorical subset: brown eggshell from (**a**) MS-infected and (**b**) healthy hens; white eggshell from (**c**) MS-infected and (**d**) healthy hens. Different colors determine different samples.
## 3 Results
The total dry matter yield of the system during the research period showed no interaction between the residual phosphate fertilization and inoculation, nor did it show a significant effect of phosphorus dose alone. However, for inoculation, higher productivity was observed in non-inoculated areas (Table 23). Plots with inoculated grasses in crop rotation had a 9.1% lower total DM than non-inoculated plots. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**DM**} \\ \cline{2-3}  & **kg ha\({}^{-1}\)** & \\ \hline
0 & & 16,193 \\
30 & & 18,222 \\
60 & & 17,245 \\
120 & & 17,204 \\
240 & & 18,143 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Mean values of dry matter (DM) production of _B. brizantha_ cv. Paiagua, _B. brizantha_ cv. Piata (first and second cuts), and sorghum (stem, panicle, and leaves) in a no-tillage system, considering the residual effect of phosphate fertilization and inoculation of preceding grasses with _A. brasilense._ Selviria/MS, 2021/2023. \begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**FM**} & **DM** \\ \cline{2-3}  & **kg ha\({}^{-1}\)** & \\ \hline \multicolumn{3}{c}{P\({}_{2}\)O\({}_{5}\) doses (kg ha\({}^{-1}\)) (P)} \\ \hline
0 & 15,962 & 5562 \\
30 & 17,156 & 6150 \\
60 & 15,553 & 5515 \\
120 & 13,928 & 5029 \\
240 & 15,020 & 5123 \\ \hline \hline \multicolumn{3}{c}{Inoculation (I)} \\ \hline Without & 14,806 & 5176 \\ With & 16,242 & 5776 \\ \hline Test—P & 0.441 ns & 0.388 ns \\ Test—I & 0.195 ns & 0.128 ns \\ Test—P \(\times\) I & 0.791 ns & 0.277 ns \\ \hline CV\% & 22.06 & 22.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Mean values of fresh matter (FM) and dry matter (DM) for _B. brizantha_ cv. Piata in fallow after grain sorghum harvest in an intercropping system under no-tillage, considering the residual effect of phosphate fertilization and the inoculation of preceding grasses with _A. brasilense._ Selviria/MS, 2023. In Table 24, carbon stock values for the 0.00-0.10 m layer are presented, showing that total organic carbon (TOC) increased significantly with higher residual phosphorus doses, following a quadratic trend. Among the carbon fractions, particulate organic carbon (POC) and mineral-associated organic carbon (MOC) did not vary significantly with fertilization. For the inoculation factor, TOC was significantly higher in inoculated plots (by 4.1%), suggesting that microbial activity may contribute to carbon stabilization in the soil. Although differences in POC and MOC were not statistically significant, the trend toward greater POC in inoculated areas suggests an early shift in carbon partitioning. These findings reinforce the potential of inoculation to enhance carbon sequestration, particularly when combined with adequate phosphorus management.
## 3 Results and Discussion  
### Effect of Reaction Time on Grafting and Wettability  
The AM-NaA copolymer hydrogel was grafted onto the plasma-treated PVDF membrane surface via free radical polymerization, initiated by the thermal decomposition of an azo compound. The grafting yield for a typical plasma-induced free radical polymerization depends on reaction time. Figure 2a shows that the grafting yield increases steadily from 17.234% at 60 min to 19.068% at 210 min. It was found that increased reaction time alters the wettability of the membrane surface (Figure 2b). The contact angle (CA) was measured immediately after droplet contact with the AM-NaA/PVDF membrane. At a reaction time of 60 min, the water contact angle (WCA) was 46.603\({}^{\circ}\), indicating good water absorption;however, this absorption decreases as the grafting yield increases, with the WCA rising to 56.76\({}^{\circ}\) at 210 min. This shift may result from changes in surface chemistry or morphology induced by prolonged reaction times.
## 3 Offline Payment Function after Types of Currency
### Offline Payment Function with CBDC Proposed by [PERSON]
#### 3.3.5 Security Enhancement of Devices through Cryptographic Technology  
When executing the offline payment protocol, the process of transaction execution by obtaining a public key and a certificate becomes enabled, based on the public-key infrastructure (PKI). The PKI consists of certificate authorities (CA) that manage the validity of certificates, registration authorities (RA) that verify the identity of users, and directory services that store the certificates. The transactions between users A and B through digital certificates [102] are processed as follows:  
**Stage 1** User A requests a certificate from RA, and RA sends a certificate issued by the CA to [PERSON] through communication with the CA;  
**Stage 2** CA publishes [PERSON]'s certificate through the directory service, and [PERSON] verifies [PERSON]'s published certificate;  
**Stage 3** After completing [PERSON]'s certificate verification, the transaction between [PERSON] and B proceeds.  
[PERSON] et al. [95] used the two-tier hierarchical infrastructure method based on the PKI. Transactions are processed by issuing certificates from the presented device. The process of issuing a certificate starts with the TA registration protocol mentioned above. Details are as follows:  
**Stage 1** User A creates a signing key pair--\((T_{A}.vk,\ T_{A}.sk)\);  
**Stage 2** Send information related to \(T_{A}.vk\) to the server to issue a certificate--\([cert]\);  
**Stage 3** Save \((vk_{A},\ cert.vk)\) in \(S.Registry\), which is the server's storage.  
In this case, \(vk\) and \(sk\) represent a public key and a private key. Information encrypted with the public key can be decrypted only with the paired private key. When an offline payment occurs in [PERSON]'s device through this method, the counterpart can confirm that [PERSON] is not a malicious user through [PERSON]'s certificate. Consequently, they can proceed with secure transactions.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 1 Introduction
From a medical viewpoint, ulcers and wounds are thought of as different; an internal problem can be caused by an ulcer, while an external problem can be caused by a wound. Deep learning (DL), which is a novel concept in computer intelligence, is significant to researchers as it shows a superior capability to classical methods [23]. We have witnessed huge progress in different CV and pattern recognition tasks due to the utilization of DL models [24; 25; 26; 27]. Deep convolutional neural network models (DCNNs) have been employed to classify DFU against normal skin [28; 29]. Although these methods have performed well, they still need to be optimized further These methods have also utilized small private DFU datasets, while deep learning models demand a significant amount of training data to perform well. Additionally, collecting DFU images is challenging due to both the acquisition time and the need for an expert to label them. Furthermore, to the best of our knowledge, there is only one small diabetic foot ulcer (DUF) dataset that is available online [28]. The DUF classification task has a serious suffer from lack of data to train deep learning models and it is the same situation with most of medical imaging classification tasks. Therefore, the lack of training needs to be addressed in case the performance is to be improved. One of the best solutions of the lack of training data is transfer learning (TL). TL is a technique that stores knowledge obtained while solving one task and applying it to a different task. Most medical imaging classification tasks that have utilized TL employed it from models that trained on the ImageNet (consists of natural images such as pen, cars, animals) dataset. In this case, this is an unrelated learning task to medical tasks. In order to boost the performance, TL should be from a related task. For example, the knowledge obtained while learning to classify lung diseases could apply when trying to recognize COVID19 in the lung. In this paper, we employ TL to solve the lack of training data for DCNNs model then we investigate the benefit of using the same and different sources of TL. This paper is organized as follows: Section 2 reviews convolutional neural networks (CNNs) in image classification and the state-of-the-art DCNNs models. Section 3 describes the challenges and research problem. Section 4 lists the aims and the contributions of our work. Section 5 explains the methodology. Section 6 presents the experimental results. Finally, the conclusion of the work is drawn in Section 7.
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### Radiation Imaging, Detector Operation Modes  
Radiation imaging data are acquired by the pixel detector (see Section 2.2) in two modes: energy mode and imaging mode (see Section 3.1). The detector data stored include  
Figure 5: Detection efficiency of X rays and gamma rays according photon energy. Values shown for selected configurations of semiconductor sensor material and thickness (500 \(\upmu\)m Si, and 1000 and 2250 \(\upmu\)m CdTe). Data computed using NIST attenutation values (physics.nist.gov, accessed on 28 February 2022) and nominal device specifications. For more information, visit www.amptek.com. Adapted from [27].  
information on the number of single pixels hit by radiation or also in the form of an energy histogram (deposited energy spectra). Additionally developed SW onboard enables partial processing of the measured data in orbit (single-photon filtering, image projection profiles or image reduction to lower resolution).  
The entire radiation field is measured during the detector operation exposure time which is suitably set typically in the range from ms up to seconds and longer. During this period a detector frame is registered and subsequently readout with a dead time which is overall at the level of few tens of ms during which no further radiation is registered.
# Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
## 2 Materials and Methods
### Table Tennis Test Protocol  
Participants were asked to perform four different stroke conditions (labeled \"exercises\" in the following sections) in front of the measurement systems. Players had to hit the ball as they would in a competitive match. The ball must be hit cross-court to aim at a defined target on either the left or right side of the table. In this current study, precision indicators were not used and reported.  
To counteract the order effect, the players were instructed to perform the exercise condition in a counterbalanced order between participants. For each individual, the order was kept identical for the series. Exercises were identically standardized for all participants; a ball launcher, Amicus expert from Butterfly, was used to throw a series of thirty balls for each stroke condition. Players had 10-20 warm-up balls to adjust their positioning before the test. A 2-min pause was given to the players between each stroke condition. The overall test took 10-15 min per player. The strokes asked to the players to perform were a forehand topspin on topspin ball (Exercise 3), a backhand topspin on topspin ball (Exercise 4), a forehand topspin on backspin ball (Exercise 1), and a backhand topspin on backspin ball (Exercise 2). The incoming ball kinetic parameters were measured and are displayed in Table 1.  
Players had to perform 6 series per exercise on different dates and spaced unevenly for each player. A total of 720 balls were hit by each player (4 exercises \(\times\) 6 series \(\times\) 30 balls per series). A total of 216 series were recorded (9 players \(\times\) 4 exercise conditions \(\times\) 6 series), but 13 were removed due to a high number of faults on the series (at least 50% of balls were faulted), corresponding to 6% of missing series. In total, 6480 balls were hit by the players. For each player series (6 in total), we averaged ball linear speed and spin to get one data point for each series, like Equations (4) and (5):  
\[\overline{v_{E,P,j}}=\frac{1}{k}\sum_{i=1}^{k\leq N}v_{i,E,P,j} \tag{4}\]  
\[\overline{\omega_{E,P,j}}=\frac{1}{k}\sum_{i=1}^{k\leq N}\omega_{i,E,P,j} \tag{5}\]  
with \(v\) the linear speed of the ball in m.s\({}^{-1}\), \(\omega\) the rotation speed (RPS), \(i\) the ball index, \(k\) the number of hit balls, \(P\) the player, \(E\) the exercise (from 1 to 4), \(j\) the indices of the played series (from 1 to 6), and \(N\) the number of balls in the series (\(N=30\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stroke Type** & **Incoming Effect** & **Exercise Number** & **Incoming Speed (m.s\({}^{-1}\))** & **Incoming Rotation (RPS)** \\ \hline Forehand & Backspin & 1 & 6.5 & 25 \\ \hline Backhand & Backspin & 2 & 5.9 & 35 \\ \hline Forehand & Topspin & 3 & 5.9 & 54 \\ \hline Backhand & Topspin & 4 & 6.2 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exercises overview.
## 3 Results and Discussion
### Clilling Injury Detection Based on Spectral Features
The detailed classification results of LDA model based on the optimal number of spectral features for different combinations are presented in Table 2. For the spectral resolution of 1.25 nm, misclassification errors could both be observed between the healthy and slightly chilling classes, and slightly and severely chilling classes at two speeds. The accuracy of each class was improved using the spectral resolution of 5.03 nm, which was better than that of 1.25 nm. Although the classification accuracies for the damaged samples at 20 mm/s with the spectral resolution of 5.03 nm were slightly lower than those of 8 mm/s, the data size of hyperspectral images at 20 mm/s reduced about 2.5 times as compared with that of 8 mm/s and the scanning efficiency was improved, which was preferred in designing a multispectral imaging system for jujube chilling injury identification.