esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Encapsulation Efficiency  
Encapsulation efficiency (EE) is one of the crucial indicators of the efficiency of the microencapsulation process in properly encapsulating the core material. It represents the percentage of compounds successfully encapsulated in the capsules. The effects of different plant parts and extraction solvents on encapsulation efficiency are shown in Figure 1. As can be seen in Figure 1, significant differences were observed between the encapsulation efficiencies of the okra extracts (\(p<0.05\)). The EE ranged from 73.84 \(\pm\) 0.10% (ethanolic leaf extract) to 90.6 \(\pm\) 0.14% (ethanolic seed extract). Other previous works studying spray drying have also shown similar encapsulation efficiencies [43; 44].  
The average EE for all phenolic compounds analyzed was 82.01 \(\pm\) 1.48%. Similarly, [PERSON] et al. [45] reported an EE of 82% for a spray dried commercial grape seed polyphenol extract using MD as a coating material. However, a lower EE of 55 to 79% was found for encapsulated phenolic extracts from white and red wine [46]. Other authors have shown that the encapsulation efficiency is influenced by several factors, such as the technique used, the solubility of the matrix in organic solvents and the solubility of the organic solvent in water [47]. In addition, the type of coating material used and the ratio between the core material and the coating material has a strong influence on the EE. Maltodextrin has been reported to improve encapsulation efficiency due to its high solubility in water and low viscosity values even at high concentrations [21]. In addition, pectin is well suited as both a coating and gelling material [48]. Our results are in conformity with those of [PERSON] et al. [49] that proved the efficiency of use of a 10:1 maltodextrin/pectin weight ratio (11% \(w/v\)) matrix to encapsulate polyphenols. The MD/P mixture is capable to mask the disagreeable smell of the extracts. Homogeneity with this mixture facilitates product solubility in water. The results confirmed that spray drying could be a feasible technique to improve the quality of phenolic compounds sensitive to high-temperature [50].
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
## 3 Results and Discussion
### Predictions for the NASA Dataset  
To verify the prediction accuracy of the method proposed in this paper for other battery types, we conducted tests using batteries numbered B0005, B0007, and B0018 from the NASA dataset. It should be noted that the NASA dataset and the University of Maryland dataset employed different types of batteries for aging tests. First, we extracted the same health features in accordance with the method proposed in this paper, using the data of battery B0005 as the training set of the model and the data of batteries B0007 and B0018 as the test sets for the SOH estimation. The estimation results of the two batteries are presented in Figure 9 and Table 5. Compared with the University of Maryland dataset, the NASA dataset has a shorter battery cycle life and a faster battery capacity degradation rate, which directly results in a relatively insufficient amount of effective information extracted from
## 5 Constellation of Formations
### Dedicated Navigation  
It is well-known that the relative navigation based on Global Navigation Satellite System (GNSS) receivers can be improved by using interferometry in the L-band using the carrier phase (see [33]), which is known as Real-Time Kinematic (RTK). Other sensors can also be used to improve the accuracy of relative navigation, which confirms the benefit of implementing specific feedback for the formation control separated from the absolute control. On the other hand, the maintenance of the absolute orbit within the constellation must use absolute information, which can be implemented by using Precise Point Positioning (PPP) as proposed in [34].  
The Relative Orbit Elements for each of these objectives are computed using the Mean Orbit Elements based on the Ustinov parameters and the analytic formulas as shown in [31] and the works of literature. However, this could not be enough to attain the high accuracy needed for autonomous orbit control, even using the PPP and RTK methods. To this end, a nonlinear filter with finite time memory can directly smooth the control error given by the Relative Orbital Elements, which is compatible with low thrusts, as shown in [34]. This filter can be applied by storing all the implemented control accelerations and measured Relative Orbital Elements, during a certain time horizon, for instance, the last (moving) orbit period. The resulting smoothed control error has enough accuracy to enable autonomous orbit control with a feasible propellant consumption (i.e., the navigation noise is not translated into a permanent actuation and waste of propellant).
## 5 Experimental Results and Analysis
### Hardware Platform  
MATLAB (R2018a) was utilized for all software-based simulations and computation, while Intel (R) Core(TM) i5-4210U CPU @ 1.70 GHz with 64-bit Windows 10 was used as the physical device. The device enclosed an 8 GB RAM and a 2.40 GHz CPU. The leave one subject out (LOSO) cross-validation method was applied to evaluate the established method's accuracy. The new conclusions over the crowd-based dataset and tentative outcomes are discussed in the results segment.
# Algorithms for Various Trigonometric Power Sums
## 5 Twisted Trigonometric Power Sums  
As indicated in the introduction, a twisted trigonometric power sum is one whose trigonometric power is multiplied by an extra cosine factor. Originally, this name was applied to the \(l=1\) case of (10) with \(\cos(2 rk\pi/m)\) inserted in the sum, and \(r\) was any integer from 1 to \(m-1\)[6; 7; 12]. In actual fact, \(r\) can be much greater \(m\), but then it must be replaced by the modulo value with respect to \(m\), viz., \(\text{mod}(r,m)\). This issue will be discussed in more detail in Section 6. Moreover, since the trigonometric power sum in (10) has \(lm\) in the denominator, we shall consider twisted trigonometric power sums where the cosine factor appears as \(\cos(2 rk\pi/lm)\) as in (9). Hence, we will be able to study both the Dowker and Gardner-Fisher cases of (9). Note, however, the upper limit is now \(lm-1\), whereas in the previous sections, we considered \(m-1\) as the upper limit in both cases. Obviously, this has no effect in the Dowker case, but in the Gardner-Fisher case, it will play a role. For \(l=2\), we shall be required to express the sums in (9) in terms of those appearing in previous sections. It should also be mentioned that the case of the cosine factor accompanying the basic trigonometric power has already been studied in [25]. When one considers twisted trigonometric power sums, one frequently ends up having to study other trigonometric power sums, denoted here by \(V_{v,j,l}^{\pm}(m)\), and defined as  
\[V_{v,j,l}^{\pm}(m)\doteq\sum_{k=1}^{lm-1}(\pm 1)^{k}\csc^{2r}\Big{(}\frac{k \pi}{lm}\Big{)}\cos^{2j}\Big{(}\frac{k\pi}{lm}\Big{)}, \tag{65}\]  
where \(j\) ranges from 0 to \(r\). These sums arise when the factor of \(\cos(2 rk\pi/lm)\) in a twisted trigonometric power sum is expressed as a sum in powers of \(\cos^{2}(k\pi/lm)\), as will be seen later. Nevertheless, these sums are not only interesting in their own right, but are also formidable. By expressing the cosine powers in terms of power of sines via the binomial theorem, one finds that  
\[V_{v,j,l}^{\pm}(m)=\sum_{i=0}^{j}(-1)^{i}\binom{j}{i}\sum_{k=1}^{lm-1}(\pm 1) ^{k}\sin^{2i-2\wp}\Big{(}\frac{k\pi}{lm}\Big{)}. \tag{66}\]  
For the case of \(j<v\), the inner sum becomes a pure cosecant power sum. Consequently, it will yield polynomials in powers of \(m^{2}\) with number-theoretical coefficients or coefficients expressible in terms of the Riemann zeta function. However, when \(j>v\), there will also be sums involving basic trigonometric power sums. In particular, when \(i\geq v\) and the summand does not alternate, the sum over \(k\) simply becomes \(S(i-v,m)\) given by (36). On the other hand, for the alternating case, it can be expressed in terms of \(S_{0,l}^{-}(i-v,m)\) in Lemma 1. As we have seen in the previous section, such sums are combinatorial in nature. Hence the second case is more complicated than the first case. Nevertheless, both cases can be considered together in one lemma as follows.
# Food Risk Entropy Model Based on Federated Learning
## 3 The Quantitative Evaluation Method Based on Risk Entropy
### Multi-Departments Data Fusion  
The federated learning server can transmit the risk entropy algorithm to clients in different departments. These clients use their own data to calculate risk entropy and aggregate it in the server. Based on protecting the privacy and security of sensitive data, it enables data fusion and efficient utilization in different departments.  
As seen above, risk entropy can quantitatively evaluate the safety of a product as a way to identify potential risks. In addition, risk entropy is a cumulative macroscopic broad metric. In the federated learning server, data fusion is performed based on the risk entropy matrix \(E\) uploaded by departments. The formula is as follows:  
\[L_{i}=\sum_{j=1}^{p}w_{ij}e_{ij} \tag{7}\]  
where \(w_{ij}\in[0,1]\) is the weight and \(\sum_{j=1}^{p}w_{ij}=1\). The weight \(w\) is taken by the entropy weight method.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 6 Conclusions  
In this study, the types and characteristics of GIS-based engineering geology maps were introduced, and various analytical methods and mapping cases for mine subsidence were reviewed. The typical procedure for mapping the mine subsidence susceptibility was presented, and various influential factors and analytic techniques considered in the evaluation, e.g., verification methods, grid cell sizes, and countries, were carefully investigated. Moreover, the method of mapping the subsidence risk was introduced, and the elements exposed to the risk and the type of final result were summarized. GIS technology has been proven to be effectively used in the process of processing and analyzing the mine subsidence from the geospatial perspective.  
However, numerous studies on subsidence susceptibility mapping, which show the zonation of the relative spatial probability of future subsidence events, are incorrectly described as the subsidence hazard mapping in their title, which indicates the annual probability of a subsidence event. As such, researchers must pay attention to their choice of terminology when formulating their final map or title. In addition, as various deep learning techniques have recently been developed, it will be necessary to apply these techniques in mining subsidence prediction and to identify why a specific model is the most appropriate for subsidence prediction. Moreover, the published literature studies investigated in this paper do not include all studies on mining subsidence GIS. For example, numerous published literature sources where the GIS technique was applied to the mining subsidence were excluded because they are not included in the three map types according to the classification of engineering geological maps. In future research, it would be interesting to extend the scope of mining subsidence GIS research from various perspectives.  
Although the subsidence susceptibility values from the various types of geospatial predictive models are not absolute and represent only a relative degree of hazard, they provide a measure of subsidence initiation localities and assist in the design of effective subsidence management systems. Furthermore, a subsidence risk map enables decision makers to select appropriate decisions and to take suitable measures from an economic perspective considering profit and loss. In this sense, these types of studies are helpful for both engineers and planners involved in the design and implementation of risk mitigation and management strategies in mining areas.  
This research received no external funding.  
The author declares no conflict of interest.
## 4 Surface-Enhanced Infrared Absorption (SEIRA) Spectroscopy
### Classical Applications of SEIRA  
SEIRA is a technique similar to SERS, which is a powerful tool for structural characterization of ultrathin films and well-ordered monolayers on metal surfaces. Thin films at interfaces are prepared with different procedures and developed for various applications. The fabrication and characterization of ultrathin films is an exciting area of research in which some of the most interesting subjects are: (1) mono- and bilayers at a liquid-liquid interface; (2) adsorption monolayers at an air-water interface; (3) films and monolayers at a liquid-solid interface; (4) deposit films at an air-solid interface. In the literature one can find studies about the molecular organization of monolayers of porphyrin derivatives, of azamacrocycles, and their metallic derivatives [73].  
In addition, a modest enhancement factor (10-100) makes IR spectroscopy an attractive and powerful analytical tool. The analytical application of SEIRA as a surface-sensitive spectroscopy requires a thorough examination of metal-molecule interactions and polarization effects that may give rise to a distinct vibrational spectrum, in many cases, quite different from that of the parent molecule. Peak positions and relative intensities in the enhanced spectra may be different from those of normal spectra of the same molecules, and the deviation is larger for strongly chemisorbed analytes. This means that the databases of normal IR spectra cannot be used directly for automatic identification of compounds through their SEIRA spectra and that new database must be built. The enhancement itself is short range, being most effective for molecules on or near the metal surface, and therefore, the band intensity in SEIRA is not a linear function of the amount of molecules.  
In trace analysis, SEIRA has been used as a detector for flow-injection systems and applied in the analysis of environmentally hazardous chemicals in waste water [78]. Silver islands on zinc selenite as SEIRA-active surfaces have been successfully employed for coupling to liquid chromatography (LC) and gas chromatography (GC) [73]. Detection limits with then times lower in comparison with GC-FTIR have been reported for chemisorbed molecules.  
Two fungicides thiram and ziram being known to be hazardous have been analyzed [79] and also several bio analytical applications including immunoassays have been described in the literature [80]. Biosensors use antibodies or enzymes, immobilized on a platform, to interact selectively with antigens or substrates, and then one of several possible transduction mechanisms to detect this interaction. Applications describing the sensitive detection of _Salmonella_ and staphylococcal protein A have been described [73]. Several different applications applying SEIRA have been described in the review by [PERSON] including a description upon the analysis down to the ppb level in different matrices such as water and soil [81].
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 4 AI/ML Technology in 6G Network  
According to recent studies, the network architecture for all 6G network technologies must include AI and ML. The 6G networking industry has paid close attention to artificial intelligence. A lot of training data and strong processing capacity are needed to adopt AI/ML in 5G networks. However, AI/ML have become essential to 6G networks. Different frames of 6G's security defense and protection are secured using AI and ML. Security systems have improved in autonomy, accuracy, and predictiveness due to the use of AI and ML.  
This subsection discusses a few of the issues with AI/ML in the 6G system [64; 65]:  
1. _Reliability_: AI manages network security and the accuracy of machine learning models and components.  
2. _Visibility:_ Real-time AI and ML-based security functions are monitored to assure credibility and control.
3. _Ethical and Legal Considerations_: AI-based optimization approaches can potentially exclude specific clients or applications. Whether or not AI-powered security solutions protect all users, AI controls who oversees security services that fail.
4. _Versatility and adaptability:_ Protected data transfers are essential to protecting federated learners' privacy. AI/ML struggle with the scalability of the necessary computation, storage resources, and communication.
5. _Managed security duties_: There could be a lot of overhead when AI/ML security solutions are linked to huge data activities. The learning and inference phases should be safe and secure for the model's flexibility. The anticipated intelligent 6G system would leverage advanced AI methods and methodologies to satisfy the demands of new use cases, high service needs, and necessary capabilities. Figure 1 shows the AI/ML-based 6G secured architecture; the following sentences characterize it.
# Efficient Video Frame Interpolation Using Generative Adversarial Networks
## 4 Experimental Results and Analyses
### Subjective Results
Figure 8: Histogram comparison between the ground truth (black line), the output without refinement (blue line), and the output with refinement (green line) on cross-domain action categories from the UCF101 dataset. Each diagram corresponds to a same-label-group in Figure 7. The correlation coefficient between the histograms of two pairs of frames, which are the ground truth-the output without refinement and the ground truth-the output with refinement, are (**a**) 0.6855 and 0.8886, (**b**) 0.8731 and 0.9609, (**c**) 0.5298 and 0.8933 and (**d**) 0.7996 and 0.9767. Figure 7: Experimental results on cross-domain action categories from the UCF101 dataset. For each group, the ground truth is shown in the first row, and the generated output of the proposed method is shown in the second row, in which the left one is the output without refinement, and the right one is the refinement output. Quality metrics for the generated output: (**a**) w/o refinement: PSNR = 30.56, SSIM = 0.834; with refinement: PSNR = 30.16, SSIM = 0.782; (**b**) w/o refinement: PSNR = 27.85, SSIM = 0.856; with refinement: PSNR = 28.51, SSIM = 0.852; (**c**) w/o refinement: PSNR = 30.68, SSIM = 0.849; with refinement: PSNR = 32.78, SSIM = 0.839; (**d**) w/o refinement: PSNR = 34.03, SSIM = 0.896; with refinement: PSNR = 34.39, SSIM = 0.845. Figure 10: Comparison of various combination of loss functions on the proposed frame interpolation network and the UCF101 dataset. The proposed loss function output is preferable output in human vision. The figures are demonstrated in four columns. From left, they are (**a**) the ground truth, (**b**) the combination of the adversarial loss, L1 loss, and GDL, (**c**) the combination of the adversarial loss, L2 loss, and GDL, and (**d**) the proposed combination of loss function. Figure 9: Comparison of SepConv and the proposed method on the generated output for the UCF101 dataset. The proposed framework keeps more structural details. Quality metrics for the generated output: (**a**) SepConv, PSNR = 30.35, SSIM = 0.796; (**b**) the proposed method (w/o refinement), PSNR = 29.66, SSIM = 0.839; (**c**) the proposed method (with refinement), PSNR = 30.71, SSIM = 0.807.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Data Preparation
#### 3.1.3 Post-Segmentation Preprocessing Stage  
Using the points extracted from the segmentation process, the signal portion representing the activity is cropped and manipulated using the same processing procedure described in the \"Before Segmentation Processing\" section.  
It is worth mentioning that removing the outliers, converting the signal to a stationary signal, and smoothing the signal can be straightforward. However, determining the best number of principal components is considered challenging. In the pre-segmentation processing stage, we determined the number of principal components by looking at the variance and at the number of signals that fall within the fourth case of the segmentation process that is described in the \"segmentation\" section. Using the percentage of segmentation failure is not appropriate since we are working with post-segmentation signals. To determine the number of principal components to use, we relied on the following two indicators: First, we calculated the variance available in each of the principal components in each communication stream. The variance results are shown in Figure 14. These results show that most of the data are contained in the first six components. In particular, the first six components contain 99.6% of the data as shown in Figure 14.  
The second indicator we used to determine the number of components to use is by trying different principal components ranging from 4 to 9 components. We calculated the system's accuracy for each set of components when a different number of features is used. The results are shown in Figure 15. The results clearly show that using six principal components yields better results, in terms of accuracy, than using any other number of principal components. Based on the results shown in Figures 14 and 15, we are using six principal components in the experiments hereafter.  
Figure 16 shows the different transformations that occur on the extracted segment. The signal plotted in Figure 16 is the signal's segment of a subject picking up a pen from the ground. Figure 16a shows the segment before applying any of the three-stage preprocessing procedure. The first stage will be removing the outliers using the Hampel filter as shown in Figure 16b. Once the outliers have been removed, the signal will be transformed into a stationary signal using the differencing procedure. The ADF test for this signal before differencing is equal to 0.5218, which was reduced to 0.001 after applying the differencing algorithm indicating that it is now stationary. Using the obtained stationary signal, several principal components will be extracted. Figure 16d shows the second principal component extracted from the signal. Finally, the Gaussian filter is applied to smooth the signal as shown in Figure 16e.  
Figure 14: Variance in principal components post segmentation.  
Figure 15: Effect of the number of PCA components used on the system’s accuracy.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### Which Studies Provide Feedback for Participants Derived from Obtained Results? (RQ5)
#### 3.5.3 When Is Feedback Delivered?  
Feedback timing varies considerably across studies, revealing trade-offs between immediate interventions and reflective practice.  
Real-Time Feedback  
A small subset of studies implement real-time feedback, offering live insights during classroom sessions. In these scenarios, both teachers and students make immediate adjustments: one system updates a display of talk-time balances every 20 min, leading to a quick rebalancing of classroom discourse [58]. Another approach visualizes student interest levels in real time, allowing instructors to quickly pivot if engagement appears to wane [27].  
Post-Session Feedback  
More commonly, feedback is delivered after a session or across multiple sessions. Teachers might receive summaries of their questioning techniques, discourse moves, or engagement metrics after each class [71,100,102]. This setup favors reflective practice, allowing educators to review and adapt without the pressure of real-time classroom management. Longitudinal feedback, where data is collected and returned across weeks or months, has also been explored to track instructional change over time [41,101].  
Single Exposure or Irregular Delivery  
A few studies deliver feedback only once or at irregular intervals, often within pilot trials or prototype demonstrations. For instance, [83] uses vignette-based dashboards to gauge educators' trust in feedback systems but does not implement sustained use. These instances serve more as proof-of-concept explorations than fully integrated classroom tools.