Search Results (141)

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual inspection, where airglow images were collected from the OMTI network at Shigaraki (34.85 E, 134.11 N) from October 1998 to October 2002. Nonetheless, a large dataset of airglow images are processed and classified for studying AGW seasonal variation in the middle atmosphere. In this article, a machine learning-based approach for image recognition of AGWs from ASAIs is suggested. Consequently, three convolutional neural networks (CNNs), namely AlexNet, GoogLeNet, and ResNet-50, are considered. Out of 13,201 deviated images, 1192 very weak/unclear AGW signatures were eliminated during the quality control process. All networks were trained and tested by 12,007 classified images which approximately cover the maximum solar cycle during the time-period mentioned above. In the testing phase, AlexNet achieved the highest accuracy of 98.41%. Consequently, estimation of AGW zonal and meridional phase velocities in the mesosphere region by a cascade forward neural network (CFNN) is presented. The CFNN was trained and tested based on AGW and neutral wind data. AGW data were extracted from the classified AGW images by event and spectral methods, where wind data were extracted from the Horizontal Wind Model (HWM) as well as the middle and upper atmosphere radar in Shigaraki. As a result, the estimated phase velocities were determined with correlation coefficient (R) above 0.89 in all training and testing phases. Finally, a comparison with the existing studies confirms the accuracy of our proposed approaches in addition to AGW velocity forecasting. Full article

Accurate estimation of path loss is essential for evaluating the impact of the propagation medium, determining transmission power requirements, and optimizing cell layouts for effective 5G millimetre wave coverage. At 28 GHz, rain attenuation is a critical factor, with its impact varying significantly based on environmental and regional characteristics. This study quantifies the degradation of 5G millimetre wave link budgets due to rainfall in South Africa and assesses the maximum coverage ranges for urban micro and urban macro deployments under varying rain intensities. The analysis focuses on Pretoria, a city characterized by diverse urban landscapes and seasonal thunderstorms. Urban micro cells are deployed on streetlights and building facades in dense zones such as Hatfield and Sunnyside to deliver high-capacity coverage. In contrast, urban macro cells target broader coverage from elevated structures, such as those in the Pretoria CBD. Using the Close-In path loss model for both line-of-sight and non-line-of-sight conditions, this study examines the relationships between link budget parameters, maximum path loss, and 5G millimetre wave link distances under rain-affected and clear-sky scenarios. The results highlight the significant influence of rainfall, particularly in non-line-of-sight conditions, and provide insights for designing efficient 5G networks tailored to South Africa’s unique climate. Full article

We present a curated, openly accessible dataset of 71 regional meteor events simultaneously recorded by optical and infrasound instrumentation between 2006 and 2011. These events were captured during an observational campaign using the all-sky cameras of the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. Each entry provides optical trajectory measurements, infrasound waveforms, and atmospheric specification profiles. The integration of optical and acoustic data enables robust linkage between observed acoustic signals and specific points along meteor trajectories, offering new opportunities to examine shock wave generation, propagation, and energy deposition processes. This release fills a critical observational gap by providing the first validated, openly accessible archive of simultaneous optical–infrasound meteor observations that supports trajectory reconstruction, acoustic propagation modeling, and energy deposition analyses. By making these data openly available in a structured format, this work establishes a durable reference resource that advances reproducibility, fosters cross-disciplinary research, and underpins future developments in meteor physics, atmospheric acoustics, and planetary defense. Full article

A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave information of both the ground wave and the sky wave in the detected signal. First, we improved the numerical model for accurately calculating the lightning sferics signals in the complex propagation environment of the Earth–ionosphere waveguide using the measured International Reference Ionosphere 2020. Subsequently, the sferics signal with multipath effect is transformed by time reversal and back propagated in the numerical model. Furthermore, a broadening factor reflecting the waveform dispersion in the back propagation is defined as the single-station focusing criterion to determine the optimal lightning propagation distance, considering the multipath effect and the focus of the PTR process. The experimental results demonstrate that the average root mean square error (RMSE) and the mean relative error (MRE) of the PTR method for the lightning distance estimation in the numerical simulation within the range of 100–1200 km are 5.517 km and 1.21%, respectively, and the average RMSE and the MRE for the natural lightning strikes to the Canton Tower from the measured data in the range of 181.643–1152.834 km are 9.251 km and 2.07%, respectively. Moreover, the correlation coefficients of the detection results are all as high as 0.999. These results indicate that the PTR method significantly outperforms the traditional ionospheric reflection method, demonstrating that it is able to perform a more accurate single-station lightning distance estimation by utilizing the compensation mechanism of the multipath effect on the sferics. The implementation of the proposed method has significant application value for improving the accuracy of single-station lightning location. Full article

In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article

Surface all-wave net radiation ($R_n$) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of $R_n$ from satellite data is gaining more and more attention. In order to obtain the daily $R_n$ ($R_{nd}$) from the instantaneous satellite observations, a parameter $C_d$, which is defined as the ratio between the $R_n$ at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for $C_d$ estimation, namely New Model, was proposed based on the comprehensive clear-sky $R_n$ measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate $C_d$ at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the $R_{nd}$ obtained by multiplying $C_d$ from New Model had the best accuracy, yielding an overall $R^2$ of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model $C_d$ estimate was applied with MODIS data to calculate $R_{nd}$. After validation, the $R_{nd}$ computed from $C_d$ was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with $R_{nd}$-validated $R^2$ of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new $C_d$ model only works for clear-sky, it has the strong potential to be used in estimating $R_{nd}$ from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

Electrically charged particles present in this layer of the Earth’s atmosphere can alter radio waves, such as those from GPS, Galileo, or BeiDou, resulting in non-estimated errors with respect to the available navigation models for the end user. For most positioning algorithms based in sequential filters, this effect is translated into a slow convergence towards a solution around the decimeter error level. If we consider that the ionosphere’s effect varies based on the user’s location and solar activity due to the atmosphere particle composition, it becomes clear that a global accurate model, valid across wide areas accounting for different seasons and timespans, is, at the very least, quite challenging. The focus of this paper is the demonstration of a global ionosphere model designed to improve the positioning accuracy of the end user through the estimation of ionospheric corrections to the broadcasted navigation message. Mathematically, this method is based on a spherical harmonic expansion model. This approach has the advantage of reducing the dependency from a highly densified station network where the ionosphere delay must be constantly estimated in dozens of locations, in favor of a simplified model that barely needs to be adjusted with a limited set of real-time data (around 40 stations). In this case, GMV’s global station network was used, which comprises geodetic-grade receivers tracking the signal in open-sky locations around the globe. The global ionospheric model is configured to process signals from GPS and Galileo constellations. To evaluate the performances of this model on the final user position estimation, several precise point positioning (PPP) solutions were computed at different locations. The results were compared with PPP solutions calculated without ionospheric corrections at the same stations. The goal of this paper is to show the significant performance improvement observed with the implementation of the global model. Full article

Coordinate registration (CR) is the key technology for improving the target positioning accuracy of sky-wave over-the-horizon radar (OTHR). The CR parameters are derived by matching the sea–land clutter classification (SLCC) results with prior geographic information. However, the SLCC results often contain mixed clutter, leading to discrepancies between land and island contours and prior geographic information, which makes it challenging to calculate accurate CR parameters for OTHR. To address these challenges, we transform the sea–land clutter data from Euclidean space into graph data in non-Euclidean space, and the CR parameters are obtained by calculating the similarity between graph pairs. And then, we propose a similarity calculation via a graph neural network (SC-GNN) method for calculating the similarity between graph pairs, which involves subgraph-level interactions and node-level comparisons. By partitioning the graph into subgraphs, SC-GNN effectively captures the local features within the SLCC results, enhancing the model’s flexibility and improving its performance. For validation, we construct three datasets: an original sea–land clutter dataset, a sea–land clutter cluster dataset, and a sea–land clutter registration dataset, with the samples drawn from various seasons, times, and detection areas. Compared with the existing graph matching methods, the proposed SC-GNN achieves a Spearman’s rank correlation coefficient of at least 0.800, a Kendall’s rank correlation coefficient of at least 0.639, a p@10 of at least 0.706, and a p@20 of at least 0.845. Full article

As China’s second operational Geostationary Interferometric Infrared Sounder, Fengyun-4B/GIIRS can provide temporally and spatially continuous atmospheric temperature profile (ATP) and atmospheric humidity profile (AHP) information, which can be used in cold wave monitoring and other meteorological applications. In this study, radiosonde observations and ERA5 reanalysis are used to assess the quality of operational Fengyun-4B/GIIRS ATP and AHP products released by the National Satellite Meteorological Centre (NSMC). The results are as follows: (1) Compared to Fengyun-4A/GIIRS, due to the improvement in the instruments, the usability of Fengyun-4B/GIIRS is enhanced, and the influence of clouds and land surfaces reduces its usability under clear-sky conditions and below 900 hPa. (2) The current operational quality-flagged algorithm can identify the Fengyun-4B/GIIRS ATP and AHP products with different accuracies well, providing beneficial information to users. Taking radiosonde observations as a reference, the RMSEs of the Fengyun-4B/GIIRS ATP and AHP products with the best quality (with the quality flag of “very good”) are around 1.5K and below 2 kg/kg, respectively, which is better than those of the Fengyun-4A/GIIRS ATP product. (3) Compared to the ERA5 reanalysis, due to the different coefficients in the retrieval algorithm, systematic overestimation and underestimation occur for the Fengyun-4B/GIIRS ATP product under clear-sky conditions and cloudy-sky conditions, respectively. (4) The biases and RMSEs of the Fengyun-4B/GIIRS ATP and AHP products have significant dependence on the satellite zenith angles when the angles are larger than 50°, but when the angles are smaller than 50°, the dependence is negligible. Full article

We present a Fisher information matrix study of the parameter estimation precision achievable by a class of future space-based, “mid-band”, gravitational wave interferometers observing monochromatic signals. The mid-band is the frequency region between that accessible by the Laser Interferometer Space Antenna (LISA) and ground-based interferometers. We analyze monochromatic signals observed by the TianQin mission, gLISA (a LISA-like interferometer in a geosynchronous orbit) and a descoped gLISA mission, gLISA_d, characterized by an acceleration noise level that is three orders of magnitude worse than that of gLISA. We find that all three missions achieve their best angular source reconstruction precision in the higher part of their accessible frequency band, with an error box better than ${10}^{-10}$ sr in the frequency band [${10}^{-1},10$] Hz when observing a monochromatic gravitational wave signal of amplitude $h_0={10}^{-21}$ that is incoming from a given direction. In terms of their reconstructed frequencies and amplitudes, TianQin achieves its best precision values in both quantities in the frequency band [${10}^{-2},4\times {10}^{-1}$] Hz, with a frequency precision ${\sigma }_{f_{gw}}=2\times {10}^{-11}$ Hz and an amplitude precision ${\sigma }_{h_0}=2\times {10}^{-24}$. gLISA matches these precisions in a frequency band slightly higher than that of TianQin, [$3\times {10}^{-2},1$] Hz, as a consequence of its smaller arm length. gLISA_d, on the other hand, matches the performance of gLISA only over the narrower frequency region, [$7\times {10}^{-1},1$] Hz, as a consequence of its higher acceleration noise at lower frequencies. The angular, frequency, and amplitude precisions as functions of the source sky location are then derived by assuming an average signal-to-noise ratio of 10 at a selected number of gravitational wave frequencies covering the operational bandwidth of TianQin and gLISA. Similar precision functions are then derived for gLISA_d by using the amplitudes resulting in the gLISA average SNR being equal to 10 at the selected frequencies. We find that, for any given source location, all three missions display a marked precision improvement in the three reconstructed parameters at higher gravitational wave frequencies. Full article

Unmanned Aerial Vehicle (UAV) high-resolution remote sensing imagery has been used for unprecedented coastal environment monitoring with ground sampling distance and time intervals of a few centimeters and seconds, respectively. However, high spatial-time resolutions of UAV remote sensing data consist of unexpected signals from water surface level changes induced by wind-driven currents and waves. This leads to non-linear and non-stationary forms of sun and sky glints in the UAV sea surface image. Consequently, these surface glints interfere with the detection of water body reflections and objects, reducing the accuracy and usability of the measurements. This study employed Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) to separate the spatial periodicity of time-continuous multispectral images of the sea surface from the original data and retain non-oscillatory signals called residual images. The residual images effectively represented the spatial-temporal radiance and flow variations in the water body by correcting the regions of surface glint. This study presents three key findings: First, homogeneous surface radiance data with surface glint removed from the raw image sequence was acquired using FA-MEMD. Second, the continuous surface glint removal effect is validated through water-leaving radiance (L_w-SBA) measurements obtained via the Skylight-Blocked Approach (SBA) method. Comparisons showed that R² values for the data obtained from clear water before and after surface glint removal were 0.02 and 0.56 with RMSE values of 8.37 × 10⁻⁵ and 5.51 × 10⁻⁵ W·m⁻²·sr⁻¹, respectively, indicating an improvement rate of 34.19%. Third, a comparative analysis with previous study methods demonstrated that our approach yielded spatially and temporally uniform homogeneous surface radiance data with less variability than traditional methods. The spatially and temporally synchronized residual images and the L_w-SBA data showed high similarity, confirming that the FA-MEMD technique effectively removed the surface glint from wave-induced roughness, enhancing the reliability of high-resolution UAV sea color observations. Full article

This study starts from the physical perspective of electromagnetic wave propagation in ionosphere media, and the skywave OTH (over-the-horizon) ionosphere channel model is established for hypersonic vehicles based on the ray-tracing method, and this study identifies the key parameters influencing channel characteristics. Secondly, using the re-entry trajectory of the RAM C-II flight experiment as an example, dynamic multipath channel characteristic parameters—such as loss, delay, and Doppler shift—are analyzed in multiple seasons during the noon and midnight periods at a communication frequency of 14 MHz. The results indicate that the settling effect of the ionosphere at midnight makes the changes in the channel more complex, with the irregular sudden appearance and disappearance of multipath numbers. In addition, channel loss is greater in low-elevation propagation mode than in high-elevation propagation mode, indicating that the channel multipath exhibits high loss and low delay characteristics. The skywave communication channel model for hypersonic vehicles, and the dynamic multipath channel characteristic parameters presented in this study offer valuable support for the design, development, and evaluation of long-distance TT&C (Tracking, Telemetering, and Command) communication systems. Full article

The short-wave infrared (SWIR) grating imaging spectrometer based on indium gallium arsenide (InGaAs) material inverts the atmospheric methane concentration by measuring the scattered light signals in the sky. This study proposes spectral and radiometric calibration methods for the characteristics of the spectrometer, such as the small-area array, high signal-to-noise ratio, and high spectral resolution. Four spectral response function models, namely, the Gauss, Lorentz, Voigt and super-Gaussian models, were compared during spectral calibration. With a fitting residual of 0.032, the Gauss model was found to be the most suitable spectral response function for the spectrometer. Based on the spectral response function, the spectral range and spectral resolution of the spectrometer were determined to be 1592.4–1677.2 and 0.1867 nm, respectively. In addition, radiometric calibration of the spectrometer was achieved by combining an integrating sphere and linear measuring instrument. Moreover, absolute and relative radiometric calibrations of the spectrometer were performed. The low signal response problem caused by the quantum efficiency of the detector at long wavelength was corrected, and the uncertainty and non-stability uncertainty of absolute radiometric calibration were calculated to be less than 0.2%. Finally, the calibrated spectrometer was used to accurately measure the solar scattering spectrum in the SWIR band, and the solar spectrum was simulated by the radiative transfer model for verification; the measurement error was found to be 5%. Concurrently, a methane sample gas experiment was performed using the integrating-sphere light source, and the measurement error was less than 4%. This fully proves the effectiveness of the spectral and radiometric calibrations of the SWIR spectrometer and strongly guarantees a subsequent, rapid and accurate inversion of atmospheric methane concentration. Full article

Urban heat islands (UHIs) increase urban warming and reduce outdoor thermal comfort due to changing surface characteristics and climate change. This study investigates the role of green walls (GWs) in mitigating UHI, improving outdoor thermal comfort, and reducing carbon emissions under current and future (2050) scenarios. Focusing on Via della Consolata, Turin, Italy, the study combines remote sensing for UHI detection and numerical simulations for thermal analysis during seasonal extremes. The results show that GWs slightly reduce air temperatures, with a maximum decrease of 1.6 °C in winter (2050), and have cooling effects on mean radiant temperature (up to 2.27 °C) during peak summer solar radiation. GWs also improve outdoor comfort, reducing the Universal Thermal Climate Index by 0.55 °C in the summer of 2050. The energy analysis shows that summer carbon emission intensity is reduced by 31%, despite winter heating demand increasing emissions by 45%. The study highlights the potential of GWs in urban climate adaptation, particularly in dense urban environments with low sky view factors. Seasonal optimization is crucial to balance cooling and heating energy demand. As cities face rising temperatures and heat waves, the integration of GWs offers a sustainable strategy to improve microclimate, reduce carbon emissions, and mitigate the effects of UHI. Full article