esa-sceva/satcom-chunk-collection huggingface dataset:
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
###### Abstract  
This systematic review synthesizes 82 peer-reviewed studies published between 2014 and 2024 on the use of audio features in educational research. We define audio features as descriptors extracted from audio recordings of educational interactions, including low-level acoustic signals (e.g., pitch and MFCCs), speaker-based metrics (e.g., talk-time and participant ratios), and linguistic indicators derived from transcriptions. Our analysis contributes to the field in three key ways: (1) it offers targeted mapping of how audio features are extracted, processed, and functionally applied within educational contexts, covering a wide range of use cases from behavior analysis to instructional feedback; (2) it diagnoses recurrent limitations that restrict pedagogical impact, including the scarcity of actionable feedback, low model interpretability, fragmented datasets, and limited attention to privacy; (3) it proposes actionable directions for future research, including the release of standardized, anonymized feature-level datasets, the co-design of feedback systems involving pedagogical experts, and the integration of fine-tuned generative AI to translate complex analytics into accessible, contextualized recommendations for teachers and learners. While current research demonstrates significant technical progress, its educational potential is yet to be translated into real-world educational impact. We argue that unlocking this potential requires shifting from isolated technical achievements to ethically grounded pedagogical implementations.  
systematic literature review; audio features; artificial intelligence; educational context; explainability +
Footnote †: journal: Applied sciences
## 5 Conclusions  
The study introduced a novel three-dimensional fuzzy modeling technique using SPSA learning for DPS, which widely exists in the manufacturing industry. On the three-dimensional fuzzy modeling framework, AP clustering was used to learn the preceding components and adaptively determine the quantity of fuzzy rules. The three-dimensional fuzzy model used the Fourier space base functions as the resulting components, while the SPSA method was employed to dynamically acquire the coefficients of the Fourier space base functions. The suggested approach was implemented on a conventional distributed parameter system in the semiconductor manufacturing industry. The simulation experiment demonstrates the superiority of the three-dimensional fuzzy modeling method over the conventional space-time separation modeling method. Additionally, it shows that the Fourier space base function is more effective in revealing the spatial distribution characteristics of distributed parameter systems compared to the spatial function obtained through KL decomposition.  
The three-dimensional fuzzy modeling framework is an intelligent modeling framework for spatiotemporal coupled systems. Under this framework, the commonly used machine learning methods (such as clustering analysis, support vector machine, least square method, random gradient method, reinforcement learning, etc.) can be applied to learn the preceding components of the fuzzy model and the resulting components of the three-dimensional fuzzy model. In the future research work, we will try to apply reinforcement learning to three-dimensional fuzzy modeling. Through interactive learning with the environment, we can establish an online 3D fuzzy model for a nonlinear distributed parameter system.  
Conceptualization, T.W.; Data curation, S.W.; Formal analysis, C.C.; Funding acquisition, X.Z.; Investigation, C.C.; Methodology, X.Z.; Project administration, X.Z.; Resources, S.W.; Software, T.W.; Supervision, X.Z.; Validation, C.C.; Visualization, S.W.; Writing--original draft, T.W.; Writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the project from the National Natural Science Foundation of China under Grant 62073210.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company China Mobile (Suzhou) Software Technology Co., 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.
## 2 Study Area and Data Source
### Data Source  
There are three hydrological stations in the Chengbi River basin, namely Bashou, Pingtang, and Haokun. However, unlike the Bashou hydrological station, the other two stations were established relatively recently, so the recorded data from those stations covered a relatively short period of time. Therefore, the monthly runoff data from the Bashou station were used for analysis, providing a total of 492 months of data, from 1979 to 2019. The runoff data were provided by the Chengbi River Reservoir Authority by separating the run-off data of the designated section from the rainfall catchment area above the section. Before the runoff prediction, the amount of input data was determined in advance, and was used as the basis for prediction. Then, the following data were predicted and the optimal lag time was selected. When the amount of input data was small, the number of training times was low and the prediction effect was poor. When the amount of input data was too large, overfitting was easy and the prediction effect was also impacted. In this paper, by referring to the input lag time from the previous literature [27], 11 types of optimal sets (lag time of 1 to 11 months) were finally selected. After inputting the model for each scenario, the results under each scenario were obtained, and the final results were determined by comparing and selecting the preferences. After conducting several experiments, it was found that when the training set was divided from the validation set in a ratio of 8:2 [27], it was able to provide the training data needed to enable the machine learning algorithms to obtain lower error rates, as well as to achieve higher accuracy in the prediction of the validation dataset. In this case, the training set is divided from 1979/01 to 2011/10, and the validation set is divided from 2011/11 to 2019/12.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 3 Results and Discussion
### Infrastructure  
The Infrastructure category focuses on system architectures or software that helps support connecting VR and robots for interface development. The majority of papers in this review utilize ROS for the robot development and Unity for the VR development, and there is currently no standard way to interface between the two. Therefore, it is necessary to find a way to bridge the gap. Ref. [55] presents a system architecture to work with multi-robot systems using ROS and virtual reality interface developed in Unity. Several works have also provided open-source solutions to allow for WebSocket communication between ROS and Unity [56; 57]. Additionally, there is group at Siemens that is developing an open-source library called ROS# [58]. Ref. [59] provides an alternative to ROS# that decreases the message size and therefore in general allow for faster data transfer. However, their solution is currently not open-source. Ref. [60] presents a method capable of automatic calibration procedures to provide a spatial relationship between a robot cell and a VR system. Their work is provided as an open-source ROS package.
# Determination of the Optimum Amount of Superplasticizer Additive for Self-Compacting Concrete
## Materials and Methods
### Concrete Flowability  
In order to analyze the concrete flowability as a function of the SPA/C, standardized tests were carried out to characterize it. Specifically, the following tests were carried out: slump flow, L-box and V-funnel. Firstly, the aggregates were introduced into the concrete mixer from the largest to the smallest, after which the cement was added; at this point, the concrete mixer was started and approximately 90% of the adjusted amount of water was added. After 1 min of mixing, the remaining 10% of the adjusted amount of water and the SPA were added. The mixing procedure continued for 8 min before the workability tests were performed.  
Slump flow tests were carried out according to standard EN 12350-5 [35], which takes as a reference the test described in standard EN 12350-2 [36]. The concrete is filled into a conical metal mold supported by a metal plate, and after waiting for 30 s, it is lifted. Once it has been vertically removed, the following parameters are measured: the time that it takes for the concrete to reach 500 mm, marked on the metal plate (t\({}_{500}\)), and the slump flow diameters in two perpendicular directions once the concrete has stopped flowing (D\({}_{1}\) and D\({}_{2}\)). In addition to the acquisition of data defined by the standard, photos were taken of the final appearance of the slump flow given the information that it provides. An example of the process followed to measure each of the diameters can be seen in Figure 3.  
L-box tests are carried out in accordance with the EN 12350-10 [37] standard, consisting of filling the vertical part of the mold with fresh concrete continuously and without any compaction. After waiting 60 s, the gate is opened and, once concrete has finished flowing, the difference in the height of the fresh concrete between the inlet (H1) and outlet (H2) of the box is measured (Figure 4).  
Figure 3: Slump flow test diameter measurement example.  
V-funnel tests were carried out in accordance with the EN 12350-9 standard [38], consisting of evaluating the ability of the concrete to flow through restricted areas in a vertical direction, evaluating the blockage and observing the variation in flow speed. The V-funnel is shown Figure 5a,b. The test consists of filling the funnel continuously and without any compaction; after a waiting time of 10 s, the gate is opened, and at that moment, the time it takes for the total volume of concrete to flow through the opening is measured.
## 3 Preliminaries
### MGF-Based Network Analysis  
In deterministic network calculus, the delay and PDV can be bounded in worst-case scenarios by combining traffic envelopes (upper bounds on arrivals) and service curves (lower bounds on services). The performance bounds obtained using deterministic calculus are tight for specific sample paths. Deterministic network calculus accumulates the bursts, leading to pessimistic bounds and over-utilization of resources. On the contrary, the probabilistic performance bounds obtained using stochastic network calculus provide a more realistic description of system performance than the worst-case analysis. In the probabilistic scenario where the arrival and the service process are stationary random processes, the delay bounds defined in (5) and (6) are reformulated as:  
\[\mathds{P}(W(t)>w^{\varepsilon^{-}})\leq\varepsilon^{\varepsilon}, \tag{7}\]  
where \(w^{\varepsilon^{-}}\) denotes the target probabilistic delay associated with the violation probability \(\varepsilon^{-}\). The performance bounds can be derived from the distributions of the processes, e.g., by using arrival and service processes' MGFs [14]. These approaches fall into the domain of stochastic network calculus and are most suitable for the analysis of stochastic multi-hop network models [15].  
MGFs of a random variable \(X\) are defined for any \(\theta\geq 0\) as  
\[M_{X}(\theta)=E\theta^{X}, \tag{8}\]  
where \(E\) is the expectation of its argument. Similarly, we can derive  
\[\overline{M}_{X}(\theta)=M_{X}(-\theta)=Ee^{-\theta X}. \tag{9}\]  
Using [PERSON]'s bound [16], the violation probability of an effective bound \(x\) for a random variable \(X\) is given below for any \(x\) and any \(\theta\geq 0\),  
\[\mathds{P}(X>x)\leq e^{-\theta x}E\theta^{X}. \tag{10}\]  
The \((\sigma(\theta),\rho(\theta))\)-traffic model shown in Figure 2 is applied to both through and cross traffic to derive closed-form solutions for end-to-end performance bounds. It is possible to describe a wide variety of traffic models using the \((\sigma(\theta),\rho(\theta))\)-traffic characterization [17], including periodic, fluid, on-off, and regulated sources. An arrival process \(A(s,t)\) with MGF \(M_{A}(\theta,s,t)\) is \((\sigma(\theta),\rho(\theta))\)- upper constrained for some \(\theta>0\) if, for all \(t\geq s\geq 0\), it holds that  
\[M_{A}(\theta,\delta)\leq e^{\theta(\sigma(\theta)+\rho(\theta)\delta)}, \tag{11}\]  
where \(\delta=t-s\geq 0\).  
The service available for the through traffic and not for the cross traffic is of prime interest, and it is called the leftover service. Therefore, we estimated the amount of service capacity offered to the through traffic in the presence of the cross traffic. The MGF of the leftover service of a latency rate (LR) server with latency \(T^{{}^{\prime}}\), capacity \(C\), and \((\sigma_{c}(\theta),\rho_{c}(\theta))\) cross traffic is bounded according to  
\[\overline{M}_{S^{{}^{\prime}}}(\theta,\delta)\leq e^{-\theta(C-\rho_{c}( \theta))(\delta-T^{{}^{\prime}}-T)^{+}}, \tag{12}\]  
where  
\[T=\frac{\sigma_{c}(\theta)}{C-\rho_{c}(\theta)}, \tag{13}\]  
is a latency induced by the cross traffic.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 4 Secure and Efficient Routing Protocol for Jellyfish Attack Prevention in V2X (SecE-V2X)
### SecE-V2X Routing Algorithm
```
// The steps taken while deciding to deliver a data packet selfPosition\(\leftarrow\)getPosition(); // Getting current node position destinationId\(\leftarrow\)getNodeId(destination); // Getting the ID of the destination node destinationPosition\(\leftarrow\)getPosition(destination); // Getting the position of the destination node myDistance\(\leftarrow\) (destinationPosition-selfPosition).length(); //Calculate the distance between the current node and the destination destination if destinationId in neighborsTable then //Check if the destination in neighborsTable sendPacketTo(destinationId); //Sending the packet to the destination else bestHonesty \(\leftarrow\) 0; for all i in neighborsTable do  neighborDistance \(\leftarrow\) (destinationPosition-neighborPosition).length(); // Calculate the distance between the neighbor node and the destination if (myDistance > neighborDistance) then if (neighborHonesty > bestHonesty) then // Choosing the neighbor with the best honesty bestHonesty \(\leftarrow\) neighborHonesty;  bestNeighbor \(\leftarrow\)getNodeId(neighbor); endif endif endfor if (bestHonesty > weightLevel) then //Check the weight level sendPacketTo(bestNeighbor); // Transferring the packet to the chosen next-hop node else chooseTwoBestNeighbors(bestNeighbor1, bestNeighbor2); // choosing the best-honesty pair of nodes sendPacketTo(bestNeighbor1); sendPacketTo(bestNeighbor2); endif endif
```  
**Algorithm 3** The next-hop selection steps in the SecE-V2X protocol.
## 4 Security Challenges and Solutions in 5G Network and LPWAN-IoT
### Overview of Security in 5G Network and LPWAN-IoT  
Wireless networks are susceptible to security breaches and vulnerabilities. The security breaches in 5G networks will be greater than the security and privacy issues in the previous wireless cellular generations. Also, because 5G will enable other new wireless technologies, there exist some potential security problems in these new network solutions as well. Hence, it is vital to highlight the security challenges that cut across the 5G ecosystem and the emerging technologies solution. LPWAN's solutions together with 5G networks pose varying security challenges. For instance, implementing security mechanisms to mitigate vulnerabilities in cellular LPWAN such as NB-IoT and LTE-M is simplified because NB-IoT and LTE-M are using the 5G native security protocol mechanisms to secure the IoT platform. On the other hand, the non-cellular LPWAN's such as LoRaWAN, Sigfox, etc., adjust or modify their security mechanisms to coexist and work with the security mechanism of 5G networks. However, the non-cellular LPWAN's solutions have intrinsic weak potential to function with standard security protocol mechanisms such as transport layer security (TLS) and other advanced authentication security. This makes it vulnerable for attackers to easily attack the edge, application server, and the core network. In the first place, LPWAN-based technologies are highly restricted considering the number of messages allowed for transmissions per day and their length. For that reason, considering typical authentication protocols employed in non-restricted systems is not a valid approach for these systems as they make use of several big-sized messages. Also, the LPWAN's devices have low computational complexity. This makes it difficult to withstand the complex security overhead required by the 5G operation.
# Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation
## 3 Problem Formulation  
In a recommendation scenario, the user set is represented by \(\mathcal{U}=\{u_{1},u_{2},\ldots,u_{M}\}\) and the item set is represented by \(\mathcal{V}=\{v_{1},v_{2},\ldots,v_{N}\}\). \(M\) is the number of users and \(N\) is the number of items. Graph theory is mainly used to study graphs. The graph is represented as \(\mathcal{G}=\{V,E\}\), \(V=\{v_{1},\ldots,v_{X}\}\) is the set of nodes and \(E=\{e_{1},\ldots,e_{Y}\}\) is the set of edges. The knowledge graph is a data structure based on a graph, which is composed of nodes and edges. The node is the entity and the edge is the relationship between two entities, and the knowledge graph connects different kinds of heterogeneous information and can be regarded as a heterogeneous graph.  
User-item Collaborative Graph (CG). It represents the user-item interaction, as well as the users' historical behavior data. \(\mathcal{G}_{1}=\{(u,y_{uv},v)|u\in\mathcal{U},v\in\mathcal{V}\}\) is denoted by the User-Item Collaborative Graph, where \(y_{uv}=1\) indicates that user \(u\) has interacted with item \(v\); otherwise \(y_{uv}=0\). Thus, the User-Item Collaborative Graph is also a bipartite graph.  
Knowledge Graph. The knowledge graph contains the side information in the recommendation, such as the attribute information of the item. Let \(\mathcal{G}_{2}=\{(h,r,t)\mid h\in\mathcal{E},r\in\mathcal{R},t\in\mathcal{E}\}\) denote the knowledge graph, where \(\mathcal{E}\) is the set of entities, \(\mathcal{R}\) is the set of relations, \((h,r,t)\) is a set of triples. Herein, \(h\) denotes the head entity, \(r\) denotes the relation and \(t\) denotes the tail entity. For example, the triple ([PERSON], Director of, Life of Pi) states the fact that [PERSON] is the director of the movie Life of Pi. The entities set consists of items and non-items. Thus, a set \(\mathcal{A}=\{(v,e)|v\in\mathcal{V},e\in\mathcal{E}\}\) is established to illustrate alignments between items in CG and entities in KG, where \((v,e)\) indicates the item \(v\) is able to aligned with the entity \(e\). The solid blue line illustrates the alignment process in Figure 1.  
Figure 1: Illustration of Collaborative Graph and Knowledge Graph. The left is collaborative graph, which represents interaction information between users and items. The right is knowledge graph, where solid blue lines indicate the alignment of items in the collaborative graph with entities in the knowledge graph. The orange circles represent side information in recommendation, such as the attributes of the item.  
Task Description. Given the user-item collaborative graph is \(\mathcal{G}_{1}\) and knowledge graph is \(\mathcal{G}_{2}\), the task is to predict the probability \(\hat{y}_{uv}\) that user \(u\) would be fond of the item \(v\).  
The list of symbols in the model is shown in Table 1.
###### Abstract
Adapting standard Kalman filtering within a control structure aims to push strategic boundaries by enabling accurate measurements, even in noisy conditions. Our goal goes beyond addressing challenges like drag coefficients and aerodynamic friction; we also aim to extend these principles to the dynamics by adding wind to the proposed Parrot Mambo, chosen for its compatibility with Matlab/Simulink. Using Parrot Mambo ensures a smooth transition from simulation to real-world scenarios, thus strengthening the practical relevance of the results. Drawing on recent advancements in the field, our work integrates insights from the backstepping control strategy, building on the analysis of Kalman filtering presented by [10]. The proposed control framework combines advanced filtering methods with a robust backstepping approach and PID regulation to achieve reliable trajectory tracking in dynamic indoor environments. The focus is on the development of a robust strategy of control for both attitude and altitude motion, which would be particularly resilient to external disturbances. The contributions of this work, in comparison to prominent existing works, are succinctly outlined below:  
Robust complex nonlinear structure with cascaded backstepping nonlinear controller: A closed-loop system is designed to deal with both altitude and attitude control. Taking into account external disturbances, sensor failure, and uncertainties, the Extended Kalman filtering (EKF) technique is integrated with the sensors to complete the control structure. The aim is to improve the accuracy of the sensed signals, which is crucial for better control system performance. Experimental Validation: A significant aspect of this research involves its practical application for validation. The proposed controllers, coupled with the extended Kalmanestimation strategy, were tested using indoor experiments taking into account the existence of the magnetic field. The wind disturbance from a fan was also taken into consideration to test the efficacy and robustness of the system in real-life dynamic scenarios. The rest of the paper is structured as follows: Section 2 introduces the Parrot Mambo unmanned aerial vehicle (UAV)'s hardware and software, along with a dynamic model of the quadcopter. Section 3 details the controller design, emphasizing signal processing within the nonlinear control framework. Section 4 describes the implementation of the control structure for practical testing and analyzes the results obtained from the proposed approach. Section 5 discusses the proposed approach in comparison with previously reported methods. Finally, Section 6 concludes the study and suggests directions for future research.