esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### What Are the Main Technological Uses of Audio Features in Educational Research? (RQ1)  
Before presenting our classification, we clarify what we mean by the technological use of audio features. In this review, a use refers not to the pedagogical goal per se but to the function that audio-derived data fulfills within the study's analytical design. This includes tasks such as identifying question types, detecting emotional cues, inferring group behavior, or producing automated feedback. The key contribution lies in how audio features drive analytical understanding and facilitate automated processes.  
This perspective allows us to frame audio usage as a continuum of increasing abstraction. At one end are studies that focus on low-level classification tasks (e.g., detecting specific interventions); at the other end are those that synthesize audio-derived insights into feedback intended for stakeholders.  
Our classification scheme thus aims to make sense of the current research landscape by analyzing what problems audio features are being applied to solve. While many studies touch on multiple goals, we assigned each to a single primary category to support consistent comparison. The distribution of studies across these categories is shown in Figure 5.  
The most common applications fall into two categories: feedback provision and behavioral/emotional analysis, which together account for more than half of the reviewed studies.  
A smaller but still significant portion of the literature (18 out of 82 studies) concentrates on classroom dynamics, particularly in characterizing classroom climate and identifying teaching strategies. Finally, intervention classification emerges as the least frequent category in our review. These studies focus on assigning teacher or student interventions to predefined categories established during the research process.  
The four categories follow a conceptual hierarchy based on the level of abstraction of the information extracted. At the lowest level, studies on intervention classification focus on isolated speech events; these feed into analyses of emotions and behaviors, which in turn contribute to understanding broader classroom dynamics. The highest level of abstraction is found in feedback-oriented studies, which synthesize insights from the previous categories to inform educational decision-making.
## 2 Methods
### Northern Goshawk Optimisation Algorithm (NGO)  
The Northern Goshawk Algorithm (NGO) is a population-based optimisation algorithm with the advantages of strong global search capability, fast convergence, good robustness and flexibility. The NGO algorithm simulates the behaviour of the Northern Goshawk throughout the process of the hunting process, including prey recognition and attack, chase and escape [40], and establishes mathematical models based on the different phases of the hunt, as shown in Figure 5.  
1. Prey identification phase. The northern goshawk will randomly select a prey and then quickly attack it. Its mathematical expression is shown below: \[P_{i}=X_{k},i=1,2,\ldots,N,\ k=1,2,\ldots,i-1,i+1,\ldots,N\] (19)  
Figure 5: Schematic diagram of the optimisation algorithm for the Northern Eagle.  
\[x_{i,j}^{new,P1}=\begin{cases}x_{i,j}+q(p_{i,j}-Ex_{i,j}),&F_{P_{i}}<F_{i}\\ x_{i,j}+q(x_{i,j}-Ep_{i,j}),&F_{P_{i}}\geq F_{i}\end{cases} \tag{20}\]  
\[X_{i}=\begin{cases}X_{i}^{new,P_{1}},&F_{i}^{new,P_{1}}<F_{i}\\ X_{i},&F_{i}^{new,P_{1}}\geq F_{i}\end{cases} \tag{21}\]  
where \(P_{i}\) represents the prey position corresponding to the \(i\)th hawk, \(N\) stands for the sample length, \(k\) is a random integer that falls within the range \([1,N]\) and not equal to \(i\); \(F_{P_{i}}\) is the value of the objective function, \(F_{i}\) is the value of the objective function corresponding to the \(i\)th solution; \(X_{i}\) is the initial solution of the \(i\)th hawk, \(x_{i,j}\)is the value of the \(i\)th hawk in the \(j\)th dimension and \(x_{i,j}^{new,P1}\) denotes the value of the \(i\)th northern hawk in the \(j\)th dimension in the 1 st hunting phase; \(P1\) is the 1 st hunting phase; \(F_{i}^{new,P1}\) is the value of the objective function corresponding to the new solution; and the parameter \(q\) and the parameter \(E\) are random parameters, where \(q\) is a random number belonging to the intervals between the \([0,1]\), and \(E\) has the value 1 or 2.
2. Pursuit and escape phase. After a northern goshawk has captured its prey, the prey will often try to escape. During the chase, given the high speeds typically exhibited by northern goshawks, they have the ability to capture prey in a variety of situations. Assuming that the hunt is at an attack position at radius \(R\) at this point, the mathematical expression is shown below: \[x_{i,j}^{new,P2}=x_{i,j}+R(2r-1)x_{i,j}\] (22) \[R=0.02\bigg{(}1-\frac{t}{T}\bigg{)}\] (23) \[X_{i}=\begin{cases}X_{i}^{new,p2},&F_{i}^{new,p2}<F_{i}\\ X_{i},&F_{i}^{new,p2}\geq F_{i}\end{cases}\] (24) where \(x_{i,j}^{new,P2}\) denotes the value of the \(j\)th dimension of the \(i\)th northern goshawk in the 2 nd hunting stage, and \(P2\) represents the 2 nd hunting stage; \(t\) denotes the current number of iterations, \(T\) denotes the maximum number of iterations, respectively; \(R\) stands for the radius of the attack; and \(F_{i}^{new,p2}\) denotes the value of the objective function for the stage.
## 3 System Development  
### Problem Overview  
The SDA system is depicted in Figure 1, and it demonstrates how the blocks of the system interact. An RF frontend composed of low-noise amplifiers and bandpass filters is connected to a hypothetical 16-element antenna array. This block is responsible for amplifying the signal and eliminating the noise outside the band of interest. This is different to conventional systems, which perform analog down-conversion before sampling the signal, and therefore requiring less components overall.  
The SDA controller oversees signal acquisition via integrated ADCs and subsequent down conversion. The baseband signals are then fed to the beamforming controller, where crucial digital beamforming operations, such as complex weight multiplication and summation of sampled signals, take place. This involves combining array channel samples in a manner that promotes constructive interference for signals at specific directions, while inducing destructive interference for others and, therefore, forming beams directed at the intended GNSS satellites. The GNSS receiver provides the controller with the satellite positions to which the beams should point after signal decoding.  
Following these operations, the computed signal undergoes conversion back to its analog form at its carrier frequency, facilitated by a DAC. The analog signal is then transmitted to a GNSS receiver for position calculation.
## 3 D Numerical Model of Buried Corroded Concrete Pipelines
### Model Description  
Compared with DEFORM software which is good at analyzing metal forming and LS-DYNA which is suitable for nonlinear dynamic analysis such as impact and contact [24; 25; 26]. ABAQUS is an advanced universal finite element system and one of the most powerful finite element software for analyzing complex solid mechanics and structural mechanics systems. Thus, a three-dimensional model of a buried corroded gasketed bell-spigot reinforced concrete drainage pipeline with two inspection chambers was developed using ABAQUS finite element software (Figure 8a). The length, width, and height of the model were 23, 10, and 8 m, respectively. A sand bedding with thickness of 0.15 m was set at 120\({}^{\circ}\) below the pipeline. An inspection chamber was placed at each end of the pipe. The length, width, and height of the inspection chambers were 2.5, 2.5, and 3.0 m, respectively. The size of the pipe in the numerical simulation model is consistent with that in the full-scale test. The corrosion that reduced pipe wall thickness was simulated by excavating the pipe wall at the crown (Figure 8b); \(d_{i}\) is the corrosion depth and \(\alpha_{i}\) is the corrosion width, expressed as the cross-sectional angle at the springline subtended by the arc of the corrosion damage (in degrees).  
A Mohr-Coulomb constitutive model was used for the soil and the sand bedding. The concrete damage plasticity constitutive model developed by [PERSON] and [PERSON] [27] was used for the pipeline. The gasket, which was treated as a hyperelastic material in which stress and strain are not linearly related, was simulated by the Mooney-Rivlin strain energy function. The material parameters used in the model were consistent with the full-scale test and as shown in Table 1. The Mooney-Rivlin parameters are shown in Table 2.  
Figure 8: (**a**) The 3D model and (**b**) cross-section of pipe corrosion.  
Figure 7: Pipe corrosion process.
## 2 Flood Mapping Needs in the HKH  
The HKH region extends over 3500 km, encompassing Myanmar in the east and reaching to Afghanistan in the west (dark blue region in Figure 1). The region spans over 3,441,000 km\({}^{2}\), covering about 2.9% of the global land mass and 18% of the global mountain regions [2]. With its high variability in topography, the region hosts 10 large rivers, including the Mekong, Yangtze, Yellow, Salween, Irrawady, Brahmaputra, Ganges, Tarim Indus, and Amu Darya rivers, making large areas of Afghanistan, Bangladesh, India, Nepal, and Pakistan susceptible to inundation by floodwaters during every rainy season [2]. Among these countries, Bangladesh has been experiencing record-high floods almost every year due to Bangladesh's unique geographical setting as the most downstream country in the HKH [25]. On average, about 26,000 km\({}^{2}\) of Bangladesh is inundated every year during the peak of monsoon season [1]. Floods can persist for several weeks in some regions of the country.  
Rapid change during the recent global warming period has altered meteorological and hydrological extreme events in the HKH, giving rise to a further increase in the frequency of flood hazards [2; 8]. Today, large areas of Bangladesh, Bhutan, India, Nepal, and Pakistan are subject to frequent extreme weather events, creating a hazard environment where floods can damage infrastructure and threaten livelihoods.  
Climate change-enhanced variability in precipitation patterns and associated increases in the occurrence of floods have also affected agricultural productivity and income across the HKH and led to increased vulnerability and uncertainty in food and nutrition security [26]. A survey of disaster impacts between 2000 and 2022 showed that floods have become the most common hazard in the HKH [3], accounting for 47% of people affected and 49% of the annual economic loss caused in the region (Figure 1).  
A key element of weather- and climate-related disaster risk reduction (DRR) is near real-time access to actionable information on the surface water extent, water depth, and event impacts on settlements and agriculture [27]. Unfortunately, the HKH lacks in situ observations for monitoring the impact of heavy rainfall, flooding, and inundation during severe weather events due to both the remoteness of the region as well as limited technical and economic capacities.
## 6 Conclusions  
We proposed and demonstrated an I-shaped REM and investigated the characteristics and changes of the quasi-static band gap. The ultra-low-frequency broadband mechanism was analyzed; the variation in the ultra-low-frequency broadband of the quasi-static I-shaped REMs was explored with the changes in the geometric parameters. Finally, the experimental sample based on the model was designed and prepared. The existence of an ultra-low-frequency broadband with an onset of close to 0 Hz in the structure was confirmed by using the stop-band and pass-band energy attenuation characteristics of the I-shaped REMs in different states.  
The investigations show that the band gap properties of the I-shaped REMs were affected by the inherent vibration properties of their structures. The constraints were found to influence the intrinsic properties of the structure, thereby affecting the band gap characteristics of the structure. The quasi-static I-shaped radial metamaterial structure exhibited ultra-low-frequency broadband characteristics. It was found that the generation mechanism of the ultra-low-frequency broadband was the formation of the ultra-low-frequency band gap by fixed constraints, and the resonance frequency of the oscillator was the key factor to realize the ultra-broadband. The band gap gradually increased with the increase in the parameter \(d/a\). The reason for this increase was that the metamaterial provided both equivalent stiffness and equivalent mass. The dynamic change in the equivalent stiffness \(K_{e}\) with \(d/a\) was the main reason for the change in the band gap. With the increase in the parameter \(d/a\), the torsional stiffness of the structure increased, the torsional vibration was weakened, the radial vibration was strengthened, and the width of the band gap was gradually widened. The experimental results showed that the quasi-static metamaterial had a wide frequency band of 0.1-1012 Hz, which confirmed that the quasi-static I-shaped REM can generate ultra-low-frequency broadband.  
In conclusion, a quasi-static I-shaped radial elastic metamaterial that exhibited ultra-low-frequency broadband was proposed and demonstrated. The research results have important practical significance for engineering fields such as seismic wave shielding and other ultra-low-frequency vibration reduction applications.  
Conceptualization, H.L.; Supervision, L.L.; Visualization, W.H.; Writing-original draft, Q.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (No. 51405368) and the Natural Science Foundation of China Shaanxi Province under grants (No. 2017J705024).  
Not applicable.  
The authors declare no conflict of interest.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 3 Methods
### Calculation of GPP Responses to Atmospheric CO\({}_{2}\) Concentrations  
The GPP datasets used in this study were preprocessed. All data were resampled to a resolution of \(1\times 1\) km, and all data were spatially referenced. To accentuate the differences, we quantified CFE using the approximate formula of \(\beta=\frac{\Delta GPP}{\Delta CO_{2}}\) proposed by [PERSON] in 1973 [14] Equation (3), represents the change in GPP for every 100 ppm increase in atmospheric CO\({}_{2}\) concentration. The response of GPP to changes in atmospheric CO\({}_{2}\) concentrations (\(\beta_{GPP}\)) was estimated using EC-LUE GPP and NIRv GPP combined with a random forest model and by setting two scenario modes to control CO\({}_{2}\) change. Scenario 1 (S1): All climate and atmospheric variables change realistically over time. Scenario 2 (S2): Atmospheric CO\({}_{2}\) concentrations were kept unchanged at 1982 levels, with other climate and atmospheric variables changing realistically over time. The difference between the GPP estimates in these two scenarios (\(\Delta GPP\)) is the extent of change in GPP caused by atmospheric CO\({}_{2}\) concentrations.  
\[\beta_{GPP}=\frac{\Delta GPP}{\Delta CO_{2}}\times 100 \tag{3}\]  
where \(\beta_{GPP}\) represents the change in GPP for every 100 ppm increase in atmospheric CO\({}_{2}\) concentration, \(\Delta GPP\) represents the amount of change in GPP caused by CO\({}_{2}\) changes, and \(\Delta CO_{2}\) represents the amount of change in CO\({}_{2}\).  
We trained the random forest model for scenario 1, and the GPP on each pixel in the study area under scenario 2 was reconstructed through the model. For example, the 1983 GPP was rebuilt, as shown in Equations (4) and (5).  
\[S1:GPP_{1983} \sim (CO_{2\,1983},Tmean_{1983},Pre_{1983},VPD_{1983},Ndep_{1983}) \tag{4}\]  
\[S2:GPP_{1983} \sim (CO_{2\,1982},Tmean_{1983},Pre_{1983},VPD_{1983},Ndep_{1983}) \tag{5}\]  
In the estimation process, we selected 28 years from 1982 to 2010 to train the model and the remaining 1 year for cross validation of the model (29 years in total). The model was run a total of 28 times to ensure that the simulated values were tested for each year from 1982 to 2010, and the final GPP values of the model simulation were the average of the 28 results. Finally, we calculated the \(\beta_{GPP}\) of each pixel on a per-year basis using the \(\Delta GPP\) of each pixel and its corresponding \(\Delta CO_{2}\) from 1982 to 2010 (Equation (6)).  
\[\beta_{GPP}^{t}=\frac{GPP_{S_{1}}^{t}-GPP_{S_{2}}^{t}}{CO_{2}^{t}-CO_{2}^{198 2}}\times 100 \tag{6}\]  
where \(t\) represents the year of calculation; \(\beta_{GPP}^{t}\) represents the response of \(GPP\) to atmospheric CO\({}_{2}\) concentrations in \(t\); \(GPP_{S_{1}}^{t}\) and \(GPP_{S_{2}}^{t}\) represent the predicted values of \(GPP\) under the S1 and S2 scenarios in \(t\), respectively; \(CO_{2}^{t}\) represents the global average CO\({}_{2}\) concentration in \(t\); and \(CO_{2}^{1982}\) represents the global average CO\({}_{2}\) concentration in 1982.
## 2 Materials and Methods
### Analytical and Chromatograph Methods  
The performance of the process was monitored by analytical characterizations of all samples and by the volume and quality of the biogas. Analytical measures of described parameters were performed at the beginning (IN) and at the end (OUT) of the assay experimental time. Total and volatile solids (TS, VS), chemical oxygen demand (COD), total nitrogen (Kjeldahl, TKN), ammonium (NH4\({}^{+}\)-N), and pH, were assayed according to standard methods [29]. Total content of phenols was determined by a colorimetric method [30], using caffe acid as standard for calibration curve.  
Volatile fatty acids (VFA) were evaluated by using a gas chromatograph (Hewlett Packard 5890), provided with a flame ionization detector and 2 m \(\times\) 2 mm Carbopack B-DA/4% Carbowax 20 M (80-120 mesh) column. Nitrogen was the carrier gas (30 mL/mn). Temperatures of the column, injector and detector were 170, 175 and 250 \({}^{\circ}\)C, respectively. Total VFA (acetate, propionate, butyrate, iso-butyrate, iso-valerate and valerate) concentrations were expressed as acetic acid.  
The methane content in biogas was measured by the injection of a 0.5-mL gas sample into a gas chromatograph (Varian CP 430-GC), equipped with a thermal conductivity detector and a Porapack S column of 1/8\({}^{\prime\prime}\)\(\times\) 3 m. Column, injector and detector temperatures were 50, 80 and 100 \({}^{\circ}\)C, respectively. Nitrogen was utilized as the carrier gas (20 mL/mn). Quantification of each produced gas was performed by comparing the obtained graphical peak areas with patterns of the injected gas mix at the beginning of each analysis.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 5 Conclusions
This sensitivity arises because GNSS-R measurements are sensitive to both the smaller (capillary) wave portion of the roughness spectrum, which is directly influenced by the instantaneous local wind stress, as well as to longer wavelength portions of the spectrum that are excited via a time-delayed energy cascade process from shorter to longer waves that has a time scale of 1-2 h. Such rapid changes in wind speed are found to happen infrequently (\(\sim\)3% of the time), so the impact on overall performance statistics is low. But this bias in the presence of large changes in wind speed is nonetheless a consistent property of GNSS-R wind products. The behavior of the bias with the NOAA product at high wind speeds is distinctly different. In this case, a large bias is always present because its retrieval algorithm has been tuned to expect the underdeveloped sea state conditions found in major organized storms whenever the wind speed is high. However, away from storms when the wind speed is high, the roughness spectrum tends to be quite different and a large retrieval bias results. Conceptualization, C.R. ; methodology, C.R., M.A.-K., S.A., R.B., D.M., D.P., A.R., D.T. and A.W. ; software, M.A.-K., S.A., R.B., D.M., D.P., A.R., D.T. and A.W. ; validation, C.R., M.A.-K., S.A., R.B., D.M., D.P. and A.W. ; formal analysis, C.R., M.A.-K., S.A., R.B., D.M., D.P. and A.W. ; investigation, C.R., M.A.-K., S.A., R.B., D.M., D.P. and A.W. ; resources, S.A., R.B., D.P., A.R. and D.T. ; data curation, S.A., R.B., D.P., A.R. and D.T. ; writing--original draft preparation, C.R., M.A.-K., S.A., R.B., D.M., D.P., D.T. and A.W. ; writing--review and editing, C.R., D.P. and A.W. ; visualization, M.A.-K., S.A., R.B., D.M., D.P., D.T. and A.W. ; supervision, C.R. ; project administration, C.R. ; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript. This research was supported in part by a NASA Science Mission Directorate contract 80 LARC21 DA003 with the University of Michigan. All CYGNSS data created or used during this study are openly available from the NASA Physical Oceanography Distributed Active Archive Center (PODAAC) at [[https://search.earthdata.nasa.gov/search?portal=podaac-cloud](https://search.earthdata.nasa.gov/search?portal=podaac-cloud)]([https://search.earthdata.nasa.gov/search?portal=podaac-cloud](https://search.earthdata.nasa.gov/search?portal=podaac-cloud)) (accessed on 31 January 2024). Authors [PERSON] was employed by the company Deimos Space UK Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.