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)
#### 3.2.2 Diarization-Based Features  
Diarization-based features aim to determine who is speaking and when enabling detailed turn-taking analyses within classroom interactions. By segmenting audio into speaker-specific tracks, these features capture metrics such as speaking time, the number of interventions, and the distribution of talk among participants [56]. Voice activity detection (VAD) often serves as a preliminary step to isolate speech segments from silence or background noise [57]. These segments are then grouped into speaker clusters using acoustic similarity, typically via embedding-based techniques (e.g., x-vectors) and clustering algorithms. This turn-level information supports the analysis of engagement patterns, such as who initiates discussions or how often students respond to peers [58].  
The main applications of diarization-based features fall within emotion and behavior analysis, particularly in the study of classroom participation and collaboration. Knowing who talks and how often provides insight into whether students engage equitably or whether discussions are dominated by specific individuals [59; 60]. These same metrics are also used in feedback-focused studies where the ratio of teacher-to-student speaking time or the distribution of teacher attention across the class is quantified [61]. When combined with emotional or linguistic indicators, diarization features can contribute to the detection of classroom climate patterns, such as identifying which speakers tend to offer encouragement or whether teacher-led sequences reflect particular instructional strategies.  
Diarization pipelines typically include a voice activity detection module, followed by segmentation and speaker clustering. These steps are commonly implemented using automated toolkits such as pyannote, Kaldi, or Whisper-based diarization, often complemented by pre-trained speaker embedding models. To improve accuracy, some studies incorporate manual corrections or use auxiliary inputs such as individual microphones or spatial data [9; 62].  
Notably, five studies combine diarization and acoustic features, as reported in Table 1. These combinations are often used to detect engagement patterns or to differentiate between teaching methodologies [9; 63]. By aligning speaker turns with acoustic variations, these studies offer a more comprehensive understanding of classroom dynamics than using either feature type alone. For instance, detecting frequent short student utterances alongside shifts in pitch and energy may signal active but uneven participation, while extended teacher monologues with low vocal variability might suggest a more transmissive teaching style. Such multimodal insights provide a nuanced lens through which researchers can infer not just who is speaking, but how that speaking behavior relates to pedagogical effectiveness.
## 4 Conclusions  
The results of the mechanical performance test performed for the development of a low-carbon, eco-friendly cement comprising a mixture of CSAe, slag, and SF are summarized as follows:  
1. Regardless of the substitution rate of CSAe, 1 d showed a low value of 7 MPa or less in all mixtures. However, the 28 d strength was at least 20 MPa or more. This suggests that CSAe is insufficient for promoting the early age hydration stage of slag; additional methods are needed. The 30% CSAe samples were lower than the 20% CSAe mixtures due to cracks and internal voids caused by excessive expansion. SF had a clear effect of improving the compressive strength. Therefore, the use of less than 30% CSAe and SF can effectively improve the cement's compressive strength.
2. The main hydration reactant was ettringite. In addition, as the content of CSAe increased, dehydrated ye'elimite also increased. Unlike in previous studies about OPC-based CSA cement, no other hydration products were observed in the slag-CSAe-SF cement in the present work. Ettringite, a representative hydration product, was confirmed through XRD, SEM, and TG/DTG analyses. In addition, ettringite reduced the pore structure and the drying shrinkage. This was clearly seen as the substitution rate of CSAe increased.
3. SF influences the improvement of compressive strength and controls the excessive expansion caused by CSAe. This is due to the very-fine-sized spherical particles with a high specific surface area of SF. The inhibition of the excessive expansion of SF was more evident in the 10% CSAe samples than in the 5% CSAe mixtures.  
The results of this study confirm that slag-CSAe-SF's low-carbon, ecofriendly cement can be used to manufacture cement that can produce structural concrete without using OPC. However, subsequent studies on early age strength improvement and durability are needed.  
Conceptualization, C.K. and T.K.; methodology, K.-Y.S. and T.K.; validation, C.K., K.-Y.S., and T.K.; formal analysis, T.K. and K.-Y.S.; investigation, C.K., T.-K.L. and K.-Y.S.; resources, C.K. and T.-K.L.; data curation, C.K. and T.K.; writing--original draft preparation, C.K. and T.K.; writing--review and editing, T.K.; visualization, T.K. and K.-Y.S.; supervision, T.K.; project administration, C.K. and T.K.; funding acquisition, C.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MOE) (NRF-2020R1I1A1A01056497).  
Not applicable.  
The authors thank the Core Research Facility of Pusan National University funded by the Korea Ministry of Education for the technical support on XRD, SEM, TG/DTG analysis.  
The authors declare no conflict of interest.
## 6 Exploration of Findings
### Case No. 2: AHC-Based Feature Selection
#### 6.3.2 Artificial Neural Networks (ANNs)  
Similarly, Figures 12 and 13 illustrate the performance of the ANN estimator using features selected by AHC and RF, respectively. Both instances of the ANN estimator exhibit high training scores (100% and 99%, respectively) and equivalent test scores of 99%, which indicates excellent performance on the training set and the ability to generalize well to unseen data. However, the test score for the AHC-based estimator exhibits fluctuations at the initial stage, before stabilizing and aligning with the training score after a certain number of features are added. This fluctuation can be attributed to the hyperparameter optimization process, which is specifically tuned to the features selected by the random forest model. Notably, the AHC-based ANN instance, Figure 12, achieves high accuracy with a comparatively smaller number of selected features, as indicated by the vertical black  
Figure 11: Performance analysis of the RFC with the RF-based feature selection method.  
Figure 10: Performance analysis of the RFC with the AHC-based feature selection method.  
line. Additionally, both algorithms show a gradual rise in the AUC score as more features are added.  
Nevertheless, the fitting time presents a contrasting narrative. The AHC-based ANN model shows significant variation, with an average fitting time of 22.88 s. In contrast, the RF-based ANN model, though slightly fluctuating, consistently achieves faster fitting times, averaging 12.33 s. This increase can be attributed to the iterative nature of the backpropagation algorithm, which demands extensive weight adjustments across multiple layers, leading to higher computational overhead.  
The AHC-selected features, while highly discriminative, contribute to this complexity by increasing the number of optimized parameters during training. However, in static operational environments, where model updates are infrequent, the ANN remains a highly reliable choice for fault diagnosis, offering superior classification accuracy despite the increased computational cost.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 5 In-pond WaterMon Sensor Design and Feasibility  
Sensors are devices deployed in the aquaponics system to collect real-time data on temperature, humidity, pH, dissolved oxygen, total dissolved solids, water level, etc. A detailed design of the sensors referred to as WaterMon has been carried out to achieve a step closer to the market.
# Communication Architecture in Mixed-Reality Simulations of Unmanned Systems
## 5 The Proposed Architecture Design
### Visibility Model  
The communication system in the MR simulation should provide the SWEs with a model of the wireless medium and simulate the neighbor communication visibility to provide a high fidelity simulation even on the message transport layer. Here, we propose employing a wireless network simulation in the communication subsystem of the MR simulation, as shown in Figure 5. In the proposed architecture, the simulation engine can be configured to intercept messages from SWEs and  
Figure 4: Routing principle in MR simulations.  
delay them when the model of the wireless medium signals is currently occupied. The messages are sent only to a subset of the receivers if they are not reachable at the moment; otherwise, the message is discarded completely, see Section 5.3.1 for further details. The evaluation of the delay and reachability of the message receivers is based on the presence of environmental obstacles, the positions of entities, their real or virtual communication equipment, the used MAC protocol, and a model of the actual utilization of wireless medium and RF signal propagation. An existing third-party wireless network simulator, e.g., OMNet++ [45] and GloMoSim [46], can be employed in the simulation for such a task, or a proprietary network simulation layer can be employed. A scheme of the proposed visibility modeling architecture within the simulation platform (GCS) of the MR simulation is depicted in Figure 5.  
A short artificial delay is added to the transmission because the messages, together with an evaluation of the transmission delay and visibility of the entities, are sent to the wireless network simulator. This delay can be lowered by the utilization of the simulation architecture with the SWEs located within the simulation platform (see Figure 2a) since the message exchange among the platform and SWEs is minimized in such a case.  
Even though SWEs transmit their messages inside the SW network, thanks to the simulation platform, these messages are re-broadcast within the real wireless medium. This way, a controlled RF signal interference is \"injected\" into the wireless channel, so there is no need to model the medium for the HWEs. These modeling properties are experimentally verified in Section 7.
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Results
#### 5.2.1 The Comparison of Six Baseline Methods
While this design yields satisfactory results for the entity alignment task of the domain knowledge graph, it falls short in effectively filtering out noisy entities. As a consequence, SDEA+ slightly underperforms compared to DomainEA. Moreover, the results reveal a substantial enhancement in the performance of the baselines when we employed attribute triples to replace relationship triples for entity embedding in the entity alignment task of the domain knowledge graph. Specifically, FGWEA+, PEEA+, and SDEA+ achieved a 5.26-8.55% improvement in all metrics. In particular, FGWEA+, respectively, achieved improvements of 6.2%, 8.45%, 6.66%, and 7.16% in Hits@1, Hits@10, Hits@50, and MRR. PEEA+ achieved an average improvement of 6.8% across all metrics, with a particularly substantial improvement of 8.55% observed in Hits@50. SDEA+, respectively, achieved improvements of 7.8%, 8.19%, 6.5%, and 6.83% in Hits@1, Hits@10, Hits@50, and MRR. This improvement substantiates the indispensability of integrating attribute triples into the process of embedding.
## 2 Numerical Analysis
### The Standard k-\(\epsilon\) Model  
To simplify the complexity of numerical simulation, the incompressible, constant viscosity, and Newtonian fluid are assumed, in order to ignore the minor factors that slightly affected the simulation result. Also, turbulent flows are characterized by fluctuating velocity fields, and cause many eddies due to this fluctuation. With the implementation of turbulence treatment, the incompressible Navier-Stokes equations in adiabatic condition can be expressed as:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{i}}\Bigg{[}\mu\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}-\frac{2}{3}\gamma_{ij}\Bigg{)}\Bigg{]}+\frac{ \partial}{\partial x_{j}}\Big{(}-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}} \Big{)}, \tag{1}\]  
where Reynolds stress is modelled by employing the Boussinesq hypothesis [10]. Furthermore, the turbulent viscosity \(\mu_{i}\) is computed as a function of turbulence kinetic energy (\(k\)) and dissipation rate (\(\epsilon\)):  
\[-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}=\mu_{t}\Bigg{(}\frac{\partial u _{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\Bigg{)}-\frac{2} {3}\bigg{(}\rho k+\mu_{t}\frac{\partial u_{i}}{\partial x_{i}}\bigg{)}\gamma_ {ij}, \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho k\mu _{i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{ \sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G_{b}-\rho \epsilon-Y_{M}+S_{K}, \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho\epsilon)+\frac{\partial}{\partial x_{i}}( \rho\epsilon u_{i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{\epsilon}}\bigg{)}\frac{\partial\epsilon}{\partial x_{j}} \Bigg{]}+C_{1\epsilon}(G_{k}+C_{3\epsilon}+G_{b})-C_{2\epsilon}\rho\frac{ \epsilon}{\overline{k}}^{2}+S_{\epsilon}, \tag{4}\]  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon}, \tag{5}\]  
where \(G_{k}\) and \(G_{b}\) are the turbulent kinetic energies generated by the mean velocity gradients and buoyancy, respectively. \(S_{K}\) and \(S_{\epsilon}\) are the user-defined source terms, \(Y_{M}\) represents the contribution of the fluctuating dilatation in the compressible turbulence to the overall dissipation rate, and \(C_{\mu}\), \(C_{1\epsilon}\), \(C_{2\epsilon}\), \(C_{3\epsilon}\), \(\sigma_{k}\), and \(\sigma_{\epsilon}\) are the model constants [11].
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods
### Concept Drift Detection
#### 3.4.4 State Prediction Layer  
The purpose of the state prediction layer is to predict the possible subsequent states of the business process case, including the next activity to be executed, the remaining execution time, etc., based on the obtained business process case encoding by the HiP-Transformer model. Given a business process case \(\sigma=\langle e_{1},e_{2},\ldots,e_{n}\rangle\), suppose that \(\sigma=\langle\sigma_{1}^{\prime},\sigma_{2}^{\prime},\ldots,\sigma_{k}^{ \prime}\rangle\) is obtained after concept drift detection segmentation, where \(\sigma_{i}^{\prime}\)\((1\leq i\leq k)\) denotes the \(i\)-th event subsequence. As mentioned above, the encoding of each event, event subsequence, and the whole business process case can be obtained after the three-layer Transformer model. In general, the most recently executed information in the business process case is more important for the subsequent state prediction, so the following three types of information are selected as inputs in the state prediction layer.  
* The encoding vector of the last event \(e_{n}\) is selected in the first layer Transformer, noted as \(e_{n}\).
* The encoding matrix of the last event subsequence \(\sigma_{k}^{\prime}\) is selected in the second layer Transformer and is denoted as \(\boldsymbol{S}_{k}^{(sub)}\). Since this encoding matrix is related to the subsequence length, the last \(r_{1}\) column is selected as the input and is denoted as \(\boldsymbol{S}_{k,(end-r_{1}+1:end)}^{(sub)}\).
* The encoding matrix of the business process case \(\sigma\) is selected in the third layer Transformer and is denoted as \(\boldsymbol{\Gamma}\). Since this encoding matrix is related to the length of the sequence, the last \(r_{2}\) columns are selected as input and are denoted as \(\boldsymbol{\Gamma}_{(end-r_{2}+1:end)}\).  
The state prediction layer inputs the above three types of information into a Multi-layer Perceptron (MLP) network to obtain the prediction results, and the calculation process is as follows.  
\[y=MLP\Big{(}\boldsymbol{e}_{n}\oplus flatten\Big{(}\boldsymbol{S}_{k,(end-r_{ 1}+1:end)}^{(sub)}\Big{)}\oplus flatten\Big{(}\boldsymbol{\Gamma}_{(end-r_{2} +1:end)}^{(sub)}\Big{)}\Big{)} \tag{32}\]  
where \(\oplus\) denotes the vector join operation, and \(flatten()\) denotes flattening the matrix into vectors by rows. The MLP used in this paper consists of a three-layer neural network.  
Since different business process case states have different data types. For example, the activity prediction task is a multi-classification problem, and the remaining execution time prediction task is a regression problem. Therefore, the cross-entropy loss function and mean square error loss function are used for the next execution activity prediction and remaining execution time prediction, respectively, which enable back-propagation training of the whole HiP-Transformer model.
## 2 Materials and Methods
### SPME-GC-MS Conditions  
An Agilent 7890B GC system coupled to an HP 5973 MSD (Agilent--Santa Clara, CA, USA) equipped with the Gerstel Multipurpose Sampler (Gerstel Inc.--Linthicum, MD, USA) autosampler was used for evaluation experiments. Grape samples were prepared as juice (for linalool analysis) or homogenate (for IBMP analysis) as described in \"Evaluation of SPMESH sheet-DART-MS in comparison SPME-GC-MS.\" below, except that the 7 mL sample was combined with 10 \(\upmu\)L internal standard solution, 20 \(\upmu\)L sodium dodecylsulfate (SDS) to disrupt enzymatic activity and 3 mL saturated NaCl solution to increase volatility before loading into 20 mL amber autosampler vials. A 1 cm, 50/30 \(\upmu\)m PDMS/DVB/CAR SPME fiber (Supelco--Bellefonte, PA, USA) was pre-incubated at 70 \({}^{\circ}\)C for 60 s, incubated in the sample headspace at 70 \({}^{\circ}\)C for 1080 s and desorbed for 500 s starting at 62 \({}^{\circ}\)C, increasing to 225 \({}^{\circ}\)C at 90 \({}^{\circ}\)C/min in splitless mode for 2 min into a split/splitless injection port. A 30 m \(\times\) 0.25 mm \(\times\) 0.50 \(\upmu\)m DB-WAX UI column (Agilent--Santa Clara, CA, USA) with ultra-high purity helium carrier gas at a constant flow rate of 1.0 mL/min, initial pressure of 6.86 psi, purge flow of 50 mL/min (after 2 min of splitless) and total flow of 54 mL/min. The MS ion source temperature was 230 \({}^{\circ}\)C and interface temperature was 180 \({}^{\circ}\)C. The GC oven temperature ramp was as follows: start at 35 \({}^{\circ}\)C, increase to 42 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, increase to 75 \({}^{\circ}\)C at 11 \({}^{\circ}\)C/min, increase to 135 \({}^{\circ}\)C at 11 \({}^{\circ}\)C/min and increase to 240 \({}^{\circ}\)C at 120 \({}^{\circ}\)C/min for a final runtime of 24.4 min. The MS was operated in SIM mode, and the following ions were monitored: 121, 93, and 136 _m/z_ for linalool, 124 and 151 _m/z_ for IBMP, 124 and 74 _m/z_ for \(d_{3}\)-linalool and 154 and 127 _m/z_ for \(d_{3}\)-IBMP.
# Dual Thermal-/Electrical-Responsive Luminescent 'Smart' Window
###### Abstract  
As buildings are a large energy user, it is important to not only reduce their consumption, but also have them generate their own electricity. Here, we describe a smart window that could reduce electricity consumption, normally used for air conditioning and lighting, by absorbing excess solar radiation with dichroic fluorescent dye molecules aligned in a switchable liquid crystal host and guiding the re-emitted light energy to the edges of the device, where it can be used to generate electricity via attached photovoltaic cells. The liquid crystals are responsive both to temperature changes and applied electrical fields. At higher temperatures, transmission decreases due to increased disorder in the liquid crystals, while the application of an electrical field increases transmission by effectively realigning the dyes for minimal absorption. Using alternative configurations, a window with a transparent rest state was also produced, in which transmission can be decreased by applying an electrical field; the thermal response remains identical.  
1 29 January 2020; 17 February 20  
Recent work by [PERSON] et al. [5] has shown that the transmission of a smart window can be thermally controlled using a dichroic dye in a liquid crystal (LC). Three different LC phases (i.e., smectic, nematic and isotropic) were used to create high-, mid- and low-transmitting states. Inspired by this system and our previous work on switchable LSCs [22; 23], we show a dual electrically and thermally responsive LSC based on a fluorescent dye doped LC mixture. This system decreases its transmission with increasing temperature with a manual override that can be used to either increase or decrease the transmission by application of an electric potential, depending on the design. The edge-emitted light can then be used for the generation of electricity by attaching photovoltaic cells, or redirected for crop growth or daylighting.
# Observing APOD with the AuScope VLBI Array
## 4 Correlation and Post-Processing
### Near-Field Delays  
Contrary to extragalactic radio sources, which are usually observed by the geodetic VLBI systems, Earth-orbiting satellites are situated in the Earth's near field. As a result, standard VLBI delay models (e.g., IERS Conventions, Chapter 11 [36]) are not suitable for satellites, mainly because the assumption that incoming wave fronts are plane is not valid. In our study, we need modeled near-field delays for APOD at two stages: (1) for the correlation a priori delays--the so-called correlator input model--and (2) for the data analysis, which is discussed in Section 5.  
For the delay computation, we used VieVS 3.0 [27], which is able to model near-field delays by iteratively solving the light-time equation. The implemented formalism is described in [37]. Near-field delays are calculated in the geocentric celestial reference system (GCRS), taking into account gravitational effects of bodies within our solar system. The initial ITRF2014 station positions were corrected for various tidal and non-tidal effects, as is common in VLBI processing, to obtain the most accurate values for the actual observation epochs. Earth orientation parameters (EOP), which are required for the transition from the GCRS to the TRS, were taken from the C04 time series provided by the International Earth Rotation and Reference Systems Service (IERS). Satellite positions were taken from the final APOD orbit solution provided by the satellite operator (BACC). As described earlier (Section 2.2), accuracies of the final orbit solutions were on the level of 10-20 m only. The orbit data were provided as position and velocity time series in 1 s intervals in the WGS84 system, which were then interpolated in VieVS by 9 th-order Lagrange polynomials.  
For the correlator input model, delays between stations and the geocenter--so-called geocentric delays--are needed rather than delays between two stations on the Earth's surface (baseline delays). Fifth-order polynomials valid over 30 s were fitted to the time series of geocentric delays calculated in VieVS. The coefficients of the polynomials were then written to the DiFX input model files (IM-files). For more details on the practical implementation in VieVS, we refer to [22].