esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 2 Methodology
### Screening of Articles  
To ensure the relevance and quality of the included papers, we established a set of mandatory exclusion criteria designed to filter out studies misaligned with the review's objectives or lacking academic rigor. The screening process was conducted independently by two of the co-authors, using the available abstracts as the basis for screening. If reviewers were not sure whether any of the criteria applied, they used the full text to verify. Discrepancies were resolved through discussion until a consensus was reached. The exclusion criteria were applied sequentially, with any study not meeting a given condition being excluded immediately:  
* The paper was not written in English due to language constraints in the review team.
* The full text of the paper was not accessible for review, either due to restricted institutional access, paywall limitations, or the unavailability of a preprint or author-supplied version upon request.
* The paper did not focus on educational settings or learning processes as a primary context or objective.
* The paper did not use audio in its proposal or features that could be extracted from audio (e.g., text transcriptions).
* The paper focused exclusively on technical improvements to well-established audio processing methods (e.g., diarization or speech transcription) without applying them to educational data.
* During title/abstract screening, we required each manuscript to address at least one of our five core research questions. If a paper's title or abstract showed no substantive discussion of audio-feature methodology or feedback-oriented use of audio, it was excluded under \"Not answering enough research questions\". A total of 98 papers fell into this category. For instance, some studies mentioned the presence of audio but did not analyze or extract features from it, nor did they integrate it into the research objectives in a meaningful way.
* The paper was published in 2022 or earlier and had no citations on Google Scholar as of 4 December 2024. Given the volume and the goal of identifying impactful contributions, we applied this criterion as a secondary filtering mechanism to exclude papers that had not generated scholarly attention over time. This step affected only four papers.
## 5 Open Challenges and Research Perspectives
### Service Function Chaining  
Service Function Chaining (SFC) is essential for organizing and managing service delivery at the network edge. By enabling the dynamic sequencing of virtualized service functions in the network, this approach can optimize resource utilization and distribute the load, which is crucial in environments where computational resources are limited [78]. From a network performance optimization point of view, deploying service chains poses several challenges, including (i) strategically placing virtualized functions while designing efficient resource allocation mechanisms, and (ii) formulating routing policies to manage traffic flows across service nodes, particularly when multiple instances of the same network function exist.  
The integration of INC and ICN in the context of SFC presents numerous research opportunities [79]. Unlike traditional approaches, which direct traffic towards specific nodes, ICN can directly address service functions at the network layer. At each step of the service chain, a request can be fulfilled by any available node hosting the requested function, rather than a single pre-defined endpoint. This reduces reliance on static and predefined routes and inherently improves the flexibility and resilience of service delivery. However, intelligent algorithms are required to dynamically place and migrate named service functions across nodes to optimize latency, resource utilization, and service availability. In addition, new forwarding mechanisms that consider function availability, network congestion, and computational load are needed to allow efficient and robust traffic forwarding across service chains.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Healthcare Scenario: MC Children's Hospital
#### 4.1.2 Scenario Actors  
The MC is a children's hospital, and the actors involved in this scenario include  
* The child who needs treatment;
* Doctors who are responsible for providing care to the child;
* Nurses who are responsible for helping the doctors;
* Admin who represents the IT security administrator in the hospital;
* Social workers who are responsible for helping the children in case of trauma or abuse;
* Police officers who are responsible for investigating and establishing possible criminal charges and responsibilities in cases of trauma or abuse.  
Building upon the healthcare scenario previously explored in the literature [12; 35], the next sections will present the implementation of each access control model, RBAC, RiBAC, and ABAC, and their integration within this specific context.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 6 Experimental Evaluation
### Scheme Comparison  
This scheme is a quantum key distribution network based on actual commercial optical fiber, which realizes the unconditional security scheme of image encryption transmission. The comparison between the scheme and other schemes based on quantum keys for encrypting images is shown in Table 4. References [21; 22] used theoretically absolutely secure quantum keys, but the encryption method is only an XOR operation, which does not take into account the case that the key consumption is greater than the key generation for multiple consecutive images. The quantum keys used in References [25; 27; 28] have pseud randomness and are not secure quantum keys generated by a quantum key distribution. At the same time, the scheme in this paper is run in actual commercial fiber, while the image encryption in other references is run in experimental environments.  
Figure 11: The quantum key consumption of the user.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **30 s** & **60 s** & **90 s** & **120 s** & **150 s** & **180 s** & **210 s** & **240 s** & **270 s** & **300 s** \\ \hline User1 & 948.17 & 2129.58 & 3345.83 & 4345.2 & 4969.9 & 5479.06 & 5998.53 & 6517.91 & 7026.44 & 7548.63 \\ User2 & 1064 & 2219.31 & 3414.83 & 4379.33 & 4980.24 & 5468.71 & 5966.79 & 6464.92 & 6952.69 & 7453.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The quantum key consumption in different time periods.  
In this paper, in order to verify the image encryption and decryption speed, the Lena images were encrypted and decrypted by using an industrial computer with 8G memory and an i5-4200U CPU, a Windows 10 operating system, and MatlabR2016a environment. The experimental results are shown in Table 5. The average encryption and decryption time of Reference [27] is 0.0815 s, and that of Reference [25] is 0.335 s. We adopted a multi-modal hybrid encryption architecture of \"one-time encryption\" + AES. When the quantum key was sufficient, the one-time encryption algorithm was adopted, and the average encryption and decryption time was 0.0509 s, which is shorter than that in References [25,27]. However, when the quantum key was insufficient, the AES algorithm was adopted, and the average encryption and decryption times were more than 1.5 s. Therefore, in order to run the system quickly, it is necessary to ensure that the quantum key capacity is large enough.
## 2 Materials and Methods
### Characterization  
The crystal phase of the Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) and Y\({}_{2}\)O\({}_{3}\):Eu@MF was identified by X-ray diffraction (XRD, DMAX 2500, Rigaku, Wilmington, MA, USA) measurement. Particle size  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Solvent**} & \multicolumn{2}{c}{**Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) Loaded Amount**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**Stirring**} & \multirow{2}{*}{**Calcination**} \\  & & & **Based on MF or TMF (wt\%)** & & & **Temperature (\({}^{\circ}\)C)** \\ \hline \multirow{6}{*}{\(\mathrm{Y_{2}O_{3}}\):Eu@MF} & \multirow{6}{*}{Ethanol} & 7 & \multirow{6}{*}{65} & \multirow{6}{*}{65} \\ \cline{3-3} \cline{5-6}  & & & 8 & & \\ \cline{3-3} \cline{5-6}  & & & 7 & & \\ \cline{3-3} \cline{5-6}  & & & 5 & & \\ \cline{3-3} \cline{5-6}  & & 20 & & & \\ \cline{3-3} \cline{5-6}  & & & 8 & & \\ \cline{3-3} \cline{5-6}  & & 10 & & & 75 \\ \cline{3-3} \cline{5-6}  & & 10 & & & 750 \\ \cline{3-3} \cline{5-6}  & & & & & 850 \\ \hline \multirow{2}{*}{\(\mathrm{Y_{2}O_{3}}\):Eu@TMF} & \multirow{2}{*}{20} & \multirow{2}{*}{85} & \multirow{2}{*}{650} \\ \cline{3-3} \cline{5-6}  & & & 85 & & \\ \cline{3-3} \cline{5-6}  & & 20 & & & 650 \\ \cline{3-3} \cline{5-6}  & & 30 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coating conditions of Y\({}_{2}\)O\({}_{3}\):Eu@MF and Y\({}_{2}\)O\({}_{3}\):Eu@TMF red phosphors.  
distribution of Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) nanosol was observed by a particle size analyzer (PSA, ELS-Z, Otsuka Electronics, Tokushima, Japan). The zeta potential of Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\), MF and TMF was analyzed by ELS-Z. To improve reliability of measurements, the average value was obtained by measuring 4 times. Field emission scanning electron microscopy (FE-SEM, JSM-6700F, JEOL, Akishima, Japan) was used to analyze the coating image and coverage of Y\({}_{2}\)O\({}_{3}\):Eu\({}^{3+}\) particles coated on MF or TMF surface. The emission or excitation spectra of all the prepared samples were measured by spectrophotometer (LS 50, PerkinElmer, Waltham, MA, USA). The gloss properties of these films prepared with MF, TMF, Y\({}_{2}\)O\({}_{3}\):Eu@MF, and Y\({}_{2}\)O\({}_{3}\):Eu@TMF were measured using a micro-TRI-gloss meter ([PERSON], Wesel, Germany). In this case, a glass plate with a refractive index of 1.567 was used as the standard sample. Gloss was measured by projecting a beam of light at 20\({}^{\circ}\), 60\({}^{\circ}\), and 85\({}^{\circ}\) angles onto the film surface.
# Experimental Study on the Impact of CO2 Treatment on Different Lithofacies in Shale Oil Reservoirs
## 3 Results and Discussion
### Mineral Composition Analysis of Different Lithofacial Types of Shale during CO2 Treatment  
In order to explore the effect of CO2 treatment on the mineral compositions of different lithofacial types of shale, XRD analysis is carried out before and after CO2 treatment, and the results are shown in Table 2. Although there are significant differences in mineral composition among these three main types of shale samples, the change rules of mineral composition are similar between them. Compared with the samples without CO2 treatment, the proportion of feldspar and carbonate minerals of the treated samples decreases by 4.4-7.2% and 2.8-9.1%, respectively. In combination with SEM analysis, it is revealed that happens because of the dissolution of feldspar and carbonate minerals in the CO2 treatment. Quartz does not react with CO2 and the increase in its content is caused by the decrease in the overall quality of the shale samples. Meanwhile, as shown in Formulas 1 and 5, albite  
Figure 7: SEM analysis results before and after CO2 treatment: (**a**) foliaceous shale before treatment; (**b**) laminated feisic shale before treatment; (**c**) laminated diamictic shale before treatment; (**d**) foliaceous shale after treatment; (**e**) laminated feisic shale after treatment and (**f**) laminated diamictic shale after treatment.  
and chlorite react with CO2 to create quartz. As a result, the proportion of quartz increases obviously after CO2 treatment.
# Hybrid Renewable Energy Systems--A Review of Optimization Approaches and Future Challenges
## 4 HRES Optimization
### Optimization Criteria  
Furthermore, the reviewed studies also emphasize a wide array of the most used broad-spectrum criteria in HRES optimization (see Table 1), categorized into economic, environmental, energy, and social dimensions. Economically, Total Net Present Cost (TNPC) [28; 38; 42] and Levelized Cost of Energy (LCOE) [10; 20; 63; 66] appear as dominant measures, complemented by Operation and Maintenance Cost (OMC) [15; 58; 67]. Environmentally, CO\({}_{2}\) emissions [35; 50] and Renewable Fraction (RF) [32; 53] are recurrent metrics, reflecting sustainability concerns. In terms of energy performance, key metrics include Loss of Power Supply Probability (LPSP) [19; 48; 64; 68] and Energy Not Supplied (ENS) [57; 58; 68], which evaluate system reliability and efficiency. On the social front, criteria like Job Creation Potential (JOBC) [9; 10] are used. These widely adopted criteria underscore a comprehensive approach to balancing economic feasibility, environmental sustainability, operational reliability, and social benefits in HRES optimization.  
To conclude, here are some observed relationships between criteria and optimization methods across the reviewed articles:* _Economic indicators on multi-objective approaches._ Economic optimization criteria like TNPC and LCOE are most frequently paired with MOPSO [31; 43; 59; 62]. This technique is used in studies focusing on cost optimization and energy management.
* _Environmental criteria on evolutionary algorithms._ CO\({}_{2}\) emissions [21; 39; 50] and Renewable Fraction (RF) [32; 53] as a part of environmental indicators are often used together with nature-inspired algorithms like PSO, CSA and Strength Pareto Evolutionary Algorithm 2 (SPEA2). These methods provide robust solutions to highly non-linear, multi-modal optimization problems where ecological considerations are crucial.
* _Reliability criteria on simulation-driven techniques._ Reliability measures like Loss of Power Supply Probability (LPSP) [37; 48; 59] and ENS [58], are predominantly optimized through a combination of simulation tools such as HOMER Pro 3.12-3.16 and MATLAB 9.1-9.13. These criteria are addressed within multi-criteria decision-making solutions like TOPSIS and Evaluation Based on Distance from Average Solution (EDAS), highlighting their adaptability.
* _Social criteria on emerging frameworks._ During the analysis 2 social indicators were found: the JOBC [9] and the Composite Sustainability Index (CSI) [10] are addressed within multi-criteria decision-making solutions like TOPSIS and EDAS, highlighting their adaptability.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Summary of Wildfire Manager Needs
#### 2.2.1 Fast and Consistent Data Delivery  
Daily wildfire management activities follow planning cycles which depend on the scale of management occurring. For example, an incident command team responsible for planning and carrying out wildfire operations on a large wildfire may have different needs for the frequency and timeliness of information than those planning strategic response at a regional, provincial, or national scale. Generally speaking, in order for intelligence to be incorporated into daily planning activities data must reflect the current situation (i.e., low/short latency), but it is also important to receive the information at a consistent time of day to facilitate routine integration.
## 3 Results
### Optical Parameters  
The ABW model can adjust the satellite optical properties by introducing observations. However, there are high correlations among the parameters since the model contains nine parameters. Therefore, special attention should be paid to the correlation between the model parameters when applying this model for SRP modeling. Figure 3 shows the correlation between the parameters of the C20 satellite (\(\beta\approx 51.03^{\circ}\)) and the C29 satellite (\(\beta\approx 4.09^{\circ}\)) of the solution of DOY 100 in 2020. As seen in Figure 3, the most obvious change after adding the ISL observations is the decrease in the correlation between satellite state parameters of positions and velocities. Moreover, there are some changes in the correlation  
Figure 2: Flowchart for the refinement of SRP models of BDS-3 MEO satellite.  
between the SRP parameters. The correlations related to \(Y_{0}\) are decreased to some extent. It is also noted that the correlations between some of the parameters slightly increased, e.g., +\(ZAD\) vs. \(-ZAD\) and +\(XAD\) vs. +\(X\). Additionally, the additional introduced hard delay parameters (\(SD\) and \(DD\)) of ISL equipment have no significant correlation with respect to other parameters. The above analysis shows that the correlation between the estimated parameters is satisfactory. The introduction of ISL observations can reduce the correlation between them, which is helpful for the refinement of the SRP modeling.  
To update optical parameters, we performed a statistical analysis based on satellite type. The weighted averages were calculated using only valid estimated orbit arcs, as certain satellite orbit arcs were inaccurately estimated in the modeling period. The optical parameters resulting from the different modeling strategies are presented in Figure 4. It reveals that parameter estimates in the +X direction exhibit better performance, with the sum of parameters (\(\alpha+\rho+\delta\)) approaching 1. Conversely, parameters in the \(\pm Z\) direction demonstrate higher correlations, leading to large differences among estimates from different strategies. The parameters estimated using ISL data are closer to the \(\alpha+\rho+\delta=1\) condition.  
Figure 3: Correlation between the parameters for CAST (C20) satellite and SECM (C29) satellite obtained by the G1 and J1.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 3 Analysis Tools
### Finite Element Methods (FEM)
#### 3.1.1 Analysis of Eddy Currents and Iron Losses  
As is well known, the iron losses depend on the frequency, so that the higher the frequency is the higher both the hysteresis and Eddy currents are.  
According to some authors, to model those losses accurately, the nonlinear permeability of the magnetic core must be considered [52, 61]. In [52], the nonlinear permeability is  
Figure 4: Parasitic Capacitances. (**a**) Motor view. (**b**) Winding turns.  
considered applying different permeability values to different regions depending on the flux density vector, and the mesh element size is set bellow the wavelength of the PWM excitation frequency. In this way, the non-linearity of the magnetic material BH curve is considered. On the other hand, a linear approximation of the BH curve is proposed in [58], obtaining accurate results.  
Even if the high-frequency motor models found in the literature do not mention explicitly how hysteresis and excess losses are calculated, in [82] an improved core loss model is proposed for transformers and machines considering high-frequency and non-sinusoidal supply, which is validated up to 1.6 kHz.  
With respect to the model geometry, if working with 2-D models, there are two effects that are not considered, the lamination of the magnetic core and the end effect. Thus, 2-D models tend to overestimate the shield effect in the sheets, resulting in lower iron losses, lower coil inductance and higher resistance [55; 66].  
In [62] the lamination effect is considered in 2-D models with an equivalent conductivity-D laminated and bulk core simulations are used first. The conductivity of the bulk core is adjusted so that it has equal Eddy losses than the laminated one. The effect of lamination on the magnetic flux distribution can be seen in Figure 5a.  
Moreover, in Figure 5b the end effect can be seen, where the end-winding leakage flux causes a local flux density increase in the edge of the tooth-shoe region, generating extra core losses [78].  
The stator is a CM current path at high frequency, so its impedance must be calculated. In [83] the impedance of the electrical sheets is calculated for high-frequency transformers, including the two axes. The one parallel to the winding, and the perpendicular one.
## 3 Results
### Residue Contents in Austrian Must and Wine Samples
Reduction of Pesticide Residues by Wine Treatment with Different Fining Agents: The Overall Effect on the Pesticides Examined  
Figure 3 shows the recoveries of the pesticide residue groups botryticides, fungicides (without botryticides), herbicides and insecticides as well as total residues after fining with the various agents for white and red wine. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Residues**} & \multicolumn{1}{c}{ \begin{tabular}{c} **Fining Product** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } & \multicolumn{1}{c}{ \begin{tabular}{c} **Granucol** \\ \end{tabular}  } \\ \hline \multirow{2}{*}{\ \begin{tabular}{c} Napropamide \\ (\(\mu\)g/L) \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} WW \\ RW \\ \end{tabular}  } & 46.5  & 35.3  & 39.8  & 32.0  & 38.5  & 24.0  & 25.0  & 11.5  & 17.7  \\  & & 50.9  & 39.2  & 43.8  & 35.0  & 42.6  & 27.7  & 12.7  & 21.7  \\ \hline Chlorpyrifos & WW & 38.0 [FOOT  
The greatest reduction was found in the total insecticides studied, where more than 50% of the insecticide content was reduced (recovery less than 50%) in all products except bentonite. The reduction of botryticides, fungicides (excluding botryticides) and one herbicide were about the same. In the case of the fining preparations based on yeast cell wall preparations, the reduction tended to be greater for the botryticides compared to the other residues, while in the case of the other fining products, the herbicide studied was reduced more. The reduction for the carbon products was between 50 and 80% (recovery 50-20%), that for the plant fiber between 40-60% (recovery 60-40%), and that for the two mixed products Purity D and CarboTec GE between 30-60% (recovery 70-40%). Purity D performed better, the two fining agents based on yeast wall products led to a reduction between 15-60% (recovery 85-40%) and bentonite between 10-15% (recovery 90-85%). A similar result was obtained for red wine (Figure 4). The charcoal products reduced the pesticide residues most effectively, followed by the plant fiber Flowpure, the mixed products and the yeast wall products. The reduction was lowest for NaClit. The insecticides studied were reduced more than the other groups.
## 6 Other Flow-Rate Estimation Methods for Comparison
### Flow-Rate Estimation by Differentiating Load Cell Data  
Flow rate was estimated by differentiating the weight of the outflow liquid measured by the load cell. However, because the load cell data included a significant level of noise, it was processed by a low-pass filter. This flow-rate estimation method could be described as follows:  
\[\frac{dW_{lf}}{dt} = -\omega_{lf}W_{lf}+\omega_{lf}W_{L}, \tag{39}\] \[q_{lf} = -\frac{\omega_{lf}}{\rho}W_{lf}+\frac{\omega_{lf}}{\rho}W_{L}, \tag{40}\]  
where \(W_{lf}\) is the weight of the outflow liquid processed by the low-pass filter, \(\omega_{lf}\) is the cut-off frequency of the low-pass filter, \(\rho\) kg/m\({}^{3}\) is the liquid density, and \(q_{lf}\) m\({}^{3}\)/s is the estimated flow rate. In this study, the cut-off frequency is given as \(\omega_{lf}\) = 1.5 rad/s for noise reduction. In the experimental implementation, the method described in Equations (39) and (40) could be represented by the discrete time equations as follows:  
\[W_{lfn+1} = (1-\omega_{lf}T_{s})W_{lfn}+\omega_{lf}T_{s}W_{Ln}, \tag{41}\] \[q_{lfn} = -\frac{\omega_{lf}}{\rho}W_{lfn}+\frac{\omega_{lf}}{\rho}W_{Ln}, \tag{42}\]  
where \(T_{s}\) s is the sampling interval. In this study, it is given as \(T_{s}=0.020\) s.