Search Results (22)

The variations in the atmospheric refractivity in the lower atmosphere create a natural phenomenon known as atmospheric ducts. The atmospheric ducts allow radio signals to travel long distances. This can adversely affect telecommunication systems, as cells with similar frequencies can interfere with each other due to frequency reuse, which is intended to optimize resource allocation. Thus, the downlink signals of one base station will travel a long distance via the atmospheric duct and interfere with the uplink signals of another base station. This scenario is known as atmospheric duct interference (ADI). ADI could be mitigated using digital signal processing, machine learning, and hybrid approaches. To address this challenge, we explore machine learning and deep learning techniques for ADI prediction and mitigation in Time-Division Long-Term Evolution (TD-LTE) networks. Our results show that the Random Forest algorithm achieves the highest prediction accuracy, while a convolutional neural network demonstrates the best mitigation performance with accuracy. Additionally, we propose optimizing special subframe configurations in TD-LTE networks using machine learning-based methods to effectively reduce ADI. Full article

Atmospheric data forecasting traditionally relies on physical models, which simulate atmospheric motion and change by solving atmospheric dynamics, thermodynamics, and radiative transfer processes. However, numerical models often involve significant computational demands and time constraints. In this study, we analyze the performance of Gated Recurrent Units (GRU) and Long Short-Term Memory networks (LSTM) using over two decades of sounding data from the Xisha Island Observatory in the South China Sea. We propose a hybrid model that combines GRU and Random Forest (RF) in series, which predicts the presence of atmospheric ducts from limited data. The results demonstrate that GRU achieves prediction accuracy comparable to LSTM with 10% to 20% shorter running times. The prediction accuracy of the GRU-RF model reaches 0.92. This model effectively predicts the presence of atmospheric ducts in certain height regions, even with low data accuracy or missing data, highlighting its potential for improving efficiency in atmospheric forecasting. Full article

Air plate solar collectors provide a sustainable and efficient solution for building heating. The absorber plate collects solar radiation and converts it into heat. Atmospheric air is then circulated through the collector plate with perforated baffles by forced convection. The heated air is then directed through ducts into the building’s heating system. By significantly reducing reliance on fossil fuels for building heating, these collectors contribute to a lower life-cycle carbon footprint for buildings compared to conventional heating systems. While flat-plate solar collectors are widely used for renewable energy generation, their efficiency is frequently limited by the airflow path and the heat transfer efficiency within the collector. This study aims to quantify the impact of longitudinal, transverse, and perforated baffles with different hole diameters on the heat transfer characteristics and to identify the optimal design for maximizing thermal efficiency. This study also aims to integrate solar air collector in a conventional building and help reduce the overall energy demand of buildings and their associated carbon emissions. A three-dimensional numerical investigation was carried out on a flat-plate solar collector equipped with perforated transverse baffles with varying hole diameter and thickness. The results from the study predicted that perforated baffles with two holes with a diameter of 15 mm provided a maximum Nu of 79.56 and a pressure drop of 459 Pa for a Re of 8500. Full article

This study utilizes in situ measurements and numerical weather prediction forecasts curated during the Coupled Air–Sea Processes Electromagnetic Ducting Research (CASPER) east field campaign to assess how thermodynamic properties in the marine atmospheric surface layer influence evaporation duct shape independent of duct height. More specifically, we investigate evaporation duct shape through a duct shape parameter, a parameter known to affect the propagation of X-band radar signals and is directly related to the curvature of the duct. Relationships between this duct shape parameter and air sea temperature difference (ASTD) reveal that during unstable periods (ASTD < 0), the duct shape parameter is generally larger than in near-neutral or stable atmospheric conditions, indicating tighter curvature of the M-profile. Furthermore, for any specific duct height, a strong linear relationship between the near-surface-specific humidity gradient and the duct shape parameter is found, suggesting that it is primarily driven by near-surface humidity gradients. The results demonstrate that an a priori estimate of duct shape, for a given duct height, is possible if the near-surface humidity gradient is known. Full article

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921. Full article

The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model serves as the foundation for creating a forecast model to detect lower atmospheric ducts in this study. A set of prediction tests with different forecasting times focusing on the South China Sea domain was conducted to evaluate the short-term forecasting effectiveness of lower atmospheric ducts. The assessment of sounding observation data revealed that the prediction model performed well in predicting the characteristics of all types of ducts. The mean values of the forecasting errors were slightly lower than the reanalysis data but had lower levels of correlation coefficients. At an altitude of about 2000 m, the forecasted error of modified atmospheric refractivity reached peak values and then decreased gradually with increasing altitude. The accuracy of forecasted surface ducts was higher than that of elevated ducts. Noticeable land–sea differences were identified for the spatial distributions of duct characteristics, and the occurrence rates of both the surface and elevated ducts were high at sea. As for the differences among the forecasts of 24, 48, and 72 h ahead, the differences primarily occurred at altitude levels below 20 m and 500 m~1500 m, which are consistent with the differences in the duct height. Full article

This research investigates the use of phase change materials (PCMs) in thermal energy storage (TES) unit-based cooling systems to increase the efficiency of air conditioners (ACs) by reducing the air inlet temperature. This study aims to evaluate different configurations of PCM enclosures, and different PCMs (paraffin and salt hydrate), by changing the speed of inlet air to achieve heat reduction of inlet air. The study includes experimental and simulation investigations. Every configuration simulates the hot-season atmospheric conditions of the UAE. A duct containing enclosures of paraffin RT-31 and salt hydrate (calcium chloride hexahydrate) was used for the simulation study using ANSYS/Fluent. A conjugate heat transfer model employing an enthalpy-based formulation is developed to predict the optimized PCM number of series and optimum airflow rate. Four designs of the AC duct were modelled and evaluated that contained one to four series of PCM containers subjected to different levels of supplied air velocities ranging from 1 m/s–4 m/s. The simulation study revealed that employing four series (Design 4) of PCM enclosures at a low air velocity of 1 m/s enhanced the pre-cooling performance and reduced the outlet air temperature to 33 °C, yielding a temperature drop up to 13 °C. The performance of salt hydrate (calcium chloride hexahydrate) was observed to be better than paraffin (RT-31) in terms of the cooling effect. Characterization of paraffin wax (RT-31) and salt hydrate was performed to establish the thermophysical properties. The experimental setup based on a duct with integrated PCM enclosures was studied. The experiment was repeated for three days as the repeatability test incorporating RT-31 as the PCM and a 3 °C maximum temperature drop was observed. The drop in the outlet air temperature of the duct system quantifies the cooling effect. Net heat reduction was around 16%. Full article

Inversion of atmospheric ducts is of great importance in the field of performance evaluation for radar and communication systems. Since the model parameters in machine learning play a crucial role in prediction performance, this paper develops a random forest (RF) model integrated with Bayesian optimization (BO) called BO-RF for atmospheric duct prediction, and the BO is adopted to determine appropriate model parameters during the training process. In addition, the K-fold cross-validation (CV) method is also incorporated into the model to obtain the best model partition and overcome the overfitting problem. To test the performance of the proposed model, the results obtained by the BO-RF are compared with other commonly used methods, such as classical RF, extreme gradient boosting (XGBoost) with/without BO, and K-nearest neighbor (KNN) with/without BO. Comparisons demonstrate that BO-RF has the best accuracy and anti-noise ability for the estimation of duct parameters. Full article

This paper represents observations on detection of Very High Frequency (VHF) anomalous propagation over the area of the Adriatic Sea. During the research campaign, a Software Defined Radio (SDR) Automatic Identification System (AIS) receiver was employed for collection of AIS data packets at a fixed location in the Northern Adriatic. Data were collected during the 24-h period (25 February 2023 15:32 LT to 26 February 2023 15:32 LT), providing information from 115 AIS targets, or 159 965 AIS packets with 54.3% Packet Error Rate (PER), respectively. Subsequent analysis and post-processing of successfully demodulated signals and decoded packets was presented further. In certain instances, the SDR AIS receiver detected, received and decoded data packets from AIS targets distant several orders of magnitude larger than the VHF nominal ranges. To determine the magnitude of line-of-sight and over-the-horizon radio waves propagation, the great circle distances between the SDR AIS receiver antenna and AIS packets’ decoded positions were calculated, revealing hundreds of Nautical Miles (NM). Possible reasons for these occurrences, including tropospheric scattering, diffraction, ionospheric sporadic E layer and refraction were discussed and evaluated, in accordance, among others, with the previous research. By exclusion criteria and neglection of possible causes, it was concluded that the enhanced, over-the-horizon propagation of AIS signals occurred as a result of refraction effects, namely trapping/ducting, subrefraction and superrefraction. Data from nine World Meteorological Organization (WMO) radiosondes surrounding the greater reception area were collected for the same observation periods. Atmospheric profiles were created using Advanced Refractive Effects Prediction System (AREPS) program, and analysed for each individual station measurement. The results confirmed anomalous, over-the-horizon enhanced propagation and their probable origins, i.e., the occurrence of refractive conditions in the atmosphere over the Adriatic Sea area. These findings provide a solid foundation for further research in the area of propagation of VHF signals and their anomalous features caused by the atmospheric phenomenon effects. Full article

For maritime broadband communications, atmospheric ducts can enable beyond line-of-sight communications or cause severe interference. Due to the strong spatial–temporal variability of atmospheric conditions in near-shore areas, atmospheric ducts have inherent spatial heterogeneity and suddenness. This paper aims to evaluate the effect of horizontally inhomogeneous ducts on maritime radio propagation through theoretical analysis and measurement validation. To make better use of meteorological reanalysis data, we design a range-dependent atmospheric duct model. Then, a sliced parabolic equation algorithm is proposed to improve the prediction accuracy of path loss. We derive the corresponding numerical solution and analyze the feasibility of the proposed algorithm under the range-dependent duct conditions. A 3.5 GHz long-distance radio propagation measurement is utilized to verify the algorithm. The spatial distribution characteristics of atmospheric ducts in the measurements are analyzed. Based on actual duct conditions, the simulation results are consistent with the measured path loss. The proposed algorithm outperforms the existing method during the multiple duct periods. We further investigate the influence of different duct horizontal characteristics on the received signal strength. Full article

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods. Full article

The evaporation duct is a particular type of atmospheric structure that always appears on the open ocean. Predicting the evaporation duct height (EDH) accurately and in a timely manner is of great significance for the practical application of marine wireless communication equipment. Understanding the characteristics of EDH time series is an essential prerequisite for establishing an appropriate prediction model. Moreover, the sampling timescales of EDH data may influence the dynamic characteristics of the EDH time series as well. In this study, EDH time series datasets at three timescales, hourly, daily, and monthly, were constructed as the case study. Statistical methods, namely the augmented Dickey–Fuller test and Ljung–Box test, were adopted to verify the stationary and white noise characteristics of the EDH time series. Then, rescaled range analysis was applied to calculate the Hurst exponent to study the fractal characteristics of the EDH time series. An extensive analysis and discussion of the chaotic dynamics of the EDH time series are provided. From the perspective of nonlinear dynamics, the phase space was constructed from the time delay $\tau$ and embedding dimension m, which were calculated from the mutual information method and the Grassberger–Procaccia algorithm, respectively. The maximum Lyapunov exponent was also calculated by the small data volume method to explore the existence of chaos in the EDH time series. According to our analysis, the EDH time series are stationary and have a non-white noise characteristic. The Hurst exponents for all three timescales were greater than 0.5, indicating the predictability of the EDH time series. The phase space diagrams exhibited strange attractors in a well-defined region for all the timescales, suggesting that the evolution of the EDH time series can possibly be explained by deterministic chaos. All of the maximum Lyapunov exponents were positive, confirming the chaos in the EDH time series. Further, stronger chaotic characteristics were found for the finer-resolution time series than the coarser-resolution time series. This study provides a new perspective for scholars to understand the fluctuation principles of the evaporation duct at different timescales. The findings from this study also lay a theoretical and scientific foundation for the future application of chaotic prediction methods in the research on the evaporation duct. Full article

Since the evaporation duct height (EDH) only covers the antenna height of most shipborne microwave radars, mastering the EDH in advance has great significance in achieving long-range target detection. In this paper, a set of hydrological and meteorological sensors based on the gradient meteorological instrument (GMI) were built to monitor the evaporation duct of the South China Sea (SCS). However, the monitoring needed to be interrupted during the battery replacement of the sensor, which could result in the loss of some important data collection. On the basis of the inductively coupled power transfer (ICPT) technology, the resonance principle was used to compensate the inductive reactance on the closed steel ring (CSR), and the energy stored in the super capacitor was introduced for data collection and return. A novel measuring system for the detection of an evaporation duct was proposed. To avoid iterative calculation by setting the initial value of the current evaporation duct models in large-scale and multi time evaporation duct prediction and diagnosis, on the basis of the non-iterative air–sea flux (NAF) model, the EDH was obtained by introducing the K theoretical flux observation method into the atmospheric refractive index equation. Finally, preliminary experimental results are presented for the detection of evaporation duct to demonstrate the feasibility and effectiveness of the proposed system. The communication accuracy rate of the proposed system was 99.7%. The system transmission power reached 22.8 W. The research results of the NAF model adaptability showed that the mean value of the EDH was 8.7 m, which was lower than the mean EDH of the SCS. The EDH calculated by the NAF model in the unstable air–sea stratification state was slightly lower than that calculated by the NPS model. The diagnosis of the EDH by the NAF model was similar to that of the NPS model, but the calculation stability of the NAF model was better. Full article

In a regenerative air sweeper, airflow and dust particles entering the system are filtered and recirculated within the system. The uncirculated portion of the exhaust air in the system spreads to the ambient air, and PM_2.5 dust in the air can poison the environment and adversely affect human health. The development of an airflow control system to reduce road dust emissions and improve air quality was the main contribution of this study. A regenerative air sweeper airflow control system is designed to direct the air from the centrifugal fan back into the pickup head to fully absorb the dust particles and balance the positive and negative air pressures inside the pickup head. The modeling and analysis of the dust control system were performed using an experimental test rig system. A mathematical model of the fundamental parameters of the regenerative air sweeper and dust control system was established. Computational fluid dynamics (CFD) ANSYS was used for the analysis to determine the direction of airflow via the suction and inlet ducts. The discrete particle model (DPM) accurately predicted particle trajectories and measured the suction efficiency of particles of different shapes and types. By controlling the circulating harmful air flow in the system, the amount of PM_2.5 released into the atmosphere was reduced by 90%. The suction efficiency of the 200 $\mathsf{\mu }\mathrm{m}$ sized sand particles was higher than 95%. The results provide theoretical and methodological assistance for the development of improved road sweeper dust control systems. Full article

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R² which is over 0.92, demonstrating the superiority of this method. Full article