Search Results (34)

This work examines the performance of a multi-antenna cooperative non-orthogonal multiple access (NOMA) network that employs short-packet communications and operates under the effect of hardware impairments. Specifically, a multi-antenna source transmits superposition-coded NOMA signals to a near user and a far user. Acting as a decode-and-forward (DF) relay, the near user adopts successive interference cancellation (SIC) to decode and subsequently forward the message intended for the far user. In addition, the transmission strategy at the source is the maximum ratio transmission (MRT) and the reception strategy at the far user is selection combining (SC). For Nakagami-m fading channels, closed-form expressions for the average block error rate (BLER) and effective throughput are derived. Then, the effective throughput is maximized through the optimization of the blocklength, accounting for constraints on transmission latency and reliability. The results obtained from simulations confirm the analytical findings and demonstrate that the proposed scheme, with a two-antenna source configuration, achieves a superior effective throughput, reaching up to 240% at a transmit signal-to-noise ratio (SNR) of 33 dB, compared to the existing NOMA scheme in the literature. Full article

Reliable transmission of high-quality video over wireless channels is challenged by fading and noise, which degrade visual quality and disrupt temporal continuity. To address these issues, this paper proposes a quantum communication framework that integrates quantum superposition with multi-input multi-output (MIMO) spatial diversity techniques to enhance robustness and efficiency in dynamic video transmission. The proposed method converts compressed videos into classical bitstreams, which are then channel-encoded and quantum-encoded into qubit superposition states. These states are transmitted over a $2\times 2$ MIMO system employing varied diversity schemes to mitigate the effects of multipath fading and noise. At the receiver, a quantum decoder reconstructs the classical information, followed by channel decoding to retrieve the video data, and the source decoder reconstructs the final video. Simulation results demonstrate that the quantum MIMO system significantly outperforms equivalent-bandwidth classical MIMO frameworks across diverse signal-to-noise ratio (SNR) conditions, achieving a peak signal-to-noise ratio (PSNR) up to 39.12 dB, structural similarity index (SSIM) up to 0.9471, and video multi-method assessment fusion (VMAF) up to 92.47, with improved error resilience across various group of picture (GOP) formats, highlighting the potential of quantum MIMO communication for enhancing the reliability and quality of video delivery in next-generation wireless networks. Full article

As the mining motor is used long-term in a complex multi-source noise environment composed of equipment group coordinated operations and high-frequency start–stop, its vibration signal has the features of significant strong noise interference, weak fault features, and the superposition of multiple working conditions coupling, which makes it arduous to efficiently extract and identify mechanical fault features. To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. According to the experimental results, the average accuracies of the proposed method were 97.7% and 93.38% for the noise-added CWRU bearing fault dataset and the actual operation dataset of the mine motor, respectively, which are significantly better than those of similar methods, showing that the approach in this study is superior in fault feature extraction and identification. Full article

Compared with traditional immunoassay methods, microfluidic immunoassay restricts the immune response in confined microchannels, significantly reducing sample consumption and improving reaction efficiency, making it worthy of widespread application. This paper proposes an exciting multi-frequency electrothermal flow (MET) technique by applying combined standing-wave and traveling-wave voltage signals with different oscillation frequencies to a three-period quadra-phase discrete electrode array, achieving rapid immunoreaction on functionalized electrode surfaces within straight microchannels, by virtue of horizontal pumping streamlines and transverse stirring vortices induced by nonlinear electrothermal convection. Under the approximation of a small temperature rise, a linear model describing the phenomenon of MET is derived. Although the time-averaged electrothermal volume force is a simple superposition of the electrostatic body force components at the two frequencies, the electro-thermal-flow field undergoes strong mutual coupling through the dual-component time-averaged Joule heat source term, further enhancing the intensity of Maxwell–Wagner smeared structural polarization and leading to mutual influence between the standing-wave electrothermal (SWET) and traveling-wave electrothermal (TWET) effects. Through thorough numerical simulation, the optimal working frequencies for SWET and TWET are determined, and the resulting synthetic MET flow field is directly utilized for microfluidic immunoassay. MET significantly promotes the binding kinetics on functionalized electrode surface by simultaneous global electrokinetic transport along channel length direction and local chaotic stirring of antigen samples near the reaction site, compared to the situation without flow activation. The MET investigated herein satisfies the requirements for early, rapid, and precise immunoassay of test samples on-site, showing great application prospects in remote areas with limited resources. Full article

Under intensifying climate change impacts, accurate quantification of population exposure to urban flooding has become an imperative component of risk mitigation strategies, particularly when considering the dynamic nature of human mobility patterns. Previous assessments relying on neighborhood block-scale population estimates derived from conventional census data have been constrained by significant spatial aggregation errors. This study presents methodological advancements through the integration of social sensing data analytics, enabling unprecedented spatial resolution at the building scale while capturing real-time population dynamics. We developed an agent-based simulation framework that incorporates (1) building-based urban environment, (2) hydrodynamic flood modeling outputs, and (3) empirically grounded human mobility patterns derived from multi-source geospatial big data. The implemented model systematically evaluates transient population exposure through spatiotemporal superposition analysis of flood characteristics and human occupancy patterns across different urban functional zones in Lishui City, China. Firstly, multi-source points of interest (POIs) data are aggregated to acquire activated time of buildings, and an urban environment system at the building scale is constructed. Then, with population, buildings, and roads as the agents, and population behavior rules, activity time of buildings, and road accessibility as constraints, an agent-based model in an urban flood scenario is designed to dynamically simulate the distribution of population. Finally, the population dynamics of urban flood exposure under a flood scenario with a 50-year return is simulated. We found that the traditional exposure assessment method at the block scale significantly overestimated the exposure, which is four times of our results based on building scale. The proposed method enables a clearer portrayal of the disaster occurrence process at the urban local level. This work, for the first time, incorporates multi-source social sensing data and the triadic relationship between human activities, time, and space in the disaster process into flood exposure assessment. The outcomes of this study can contribute to estimate the susceptibility to urban flooding and formulate emergency response plans. Full article

Investigating spatio-temporal differentiation patterns of land-use conflicts in mountainous and flatland regions provides critical insights for optimizing spatial regulation strategies and advancing sustainable regional development. Using the Urban Agglomeration in Central Yunnan (UACY) as a case study, the production–living–ecological space (PLES) was classified through land-use functional dominance analysis based on 2010–2020 geospatial datasets. Spatio-temporal evolution patterns and mountain–dam differentiation were analyzed using spatial superposition, dynamic degree analysis, transfer matrices, and geospatial TuPu methods. A multi-scale conflict index incorporating landscape metrics was developed to assess PLES conflict intensities across spatial scales, with contribution indices identifying key conflict-prone spatial types. Analysis revealed distinct regional differentiation in PLES distribution and evolutionary trajectories during 2010–2020. Forest Ecological Space (FES) and Agricultural Production Space (APS) dominated both the entire study area and mountainous zones, with APS exhibiting particular dominance in dam regions. Grassland Ecological Space (GES) and Other Ecological Space (OES) experienced rapid conversion rates, contrasting with stable or gradual expansion trends in other space types. Change intensity was significantly greater in mountainous zones compared to flatland area (FA). PLES conflict exhibited marked spatial heterogeneity. FA demonstrated substantially higher conflict levels than mountainous zones, with evident scale-dependent variations. Maximum conflict intensity occurred at the 4000 m scale, with all spatial scales demonstrating consistent escalation trends during the study period. ULS, FES, and WES predominantly occurred in low-conflict zones characterized by stability, whereas APS, Industrial and Mining Production Space (IMPS), RLS, GES, and OES were primarily associated with high-conflict areas, constituting principal conflict sources. Full article

Imaging sonar is a crucial tool for underwater visual perception. Compared to 2D sonar images, 3D sonar images offer superior spatial positioning capabilities, although the data acquisition cost is higher and lacks open source references for data annotation, target detection, and semantic segmentation. This paper utilizes 3D imaging sonar to collect underwater data from three types of targets with 1534 effective frames, including a tire, mannequin, and table, in Liquan Lake, Shanxi Province, China. Based on these data, this study focuses on three innovative aspects as follows: rapid underwater data annotation, loss function optimization, and unsupervised moving target extraction in water. For rapid data annotation, a batch annotation method combining human expertise and multi-frame superposition is proposed. This method automatically generates single-frame target detection boxes based on multi-frame joint segmentation, offering advantages in speed, cost, and accuracy. For loss function optimization, a density-based loss function is introduced to address the issue of overfitting in dense regions due to the uneven distribution of point cloud data. By assigning different weights to data points in different density regions, the model pays more attention to accurate predictions in a sparse area, resulting in a 6.939 improvement in mIOU for semantic segmentation tasks, while lakebed mIOU achieved a high score of 99.28. For unsupervised moving target extraction, a multi-frame joint unsupervised moving target association extraction method called the Double DBSCAN, D-DBSCAN, is proposed. This method simulates human visual sensitivity to moving targets in water and uses a joint D-DBSCAN spatial clustering approach with single-frame and inter-frame superposition, achieving an improvement of 21.3 points in mAP. Finally, the paper summarizes the three proposed innovations and provides directions for further research. Full article

This paper primarily investigates the mechanism of bolt loosening under the Sine-on-Random (SOR) vibration excitation. Firstly, a theoretical model of bolt loosening response under the SOR synthesized excitation is established by a time–frequency conversion method, which converts the sine excitation into Power Spectrum Density (PSD) expression in the frequency domain and superimposes it with random vibration excitation to obtain the SOR synthesized excitation spectrum. Then, by means of a four-bolt fastened structure, the bolt loosening mechanisms under both the sine and random vibration excitation are deeply studied, respectively. Ultimately, based on the time–frequency conversion method of SOR synthesized excitation, the bolt loosening responses of the structure under SOR excitation with different tightening torques are analyzed. Furthermore, a three-stage criterion including the Steady Stage, Transition Stage, and Loosen Stage for bolt loosening under SOR excitation is revealed, and the relationship among the SOR synthesized vibration responses and the two forms of single vibration responses is explored based on a corrective energy superposition method by introducing the weight factors of the two single vibration responses under different tightening torques. Finally, test verifications for the four-bolt fastened structure are conducted and good consistencies with the results of the Finite Element Analysis (FEA) are shown. This study provides valuable insights into the detection and prevention of loosening in bolted connection structures under multi-source vibration environments and has important engineering reference significance. Full article

This research employs multi-source data including big data, remote sensing raster data, and statistical vector data. Through the superposition of tourism activity points of interest with remotely sensed inversion raster data like human carbon emissions, net primary productivity, and kilometer-grid GDP, the carbon emissions, carbon sinks, and economic output of tourism attractions are obtained. Data envelopment analysis and econometric models are utilized to assess the “carbon emissions–carbon sinks–economic output” coupling efficiency relationship and driving mechanism under the framework of the tourism carbon neutrality process. This research takes Gannan Tibetan Autonomous Prefecture in the Qinghai–Tibet Plateau region, which has had a severe response to global climate change and is particularly deficient in statistical and monitoring data, as an example. It is found that in Gannan Prefecture, which is at the primary stage of tourism development, with a high degree of dependence on the location and regional economic development level, the challenge of decoupling carbon emissions from the economy is significant. The carbon neutrality process in natural tourism attractions is marginally superior to that in cultural tourism attractions. However, even among natural tourism attractions, the number of spots achieving high carbon sink efficiency is extremely limited. There remains considerable scope for achieving carbon neutrality process through carbon sinks in the future. The location and vegetation conditions can exert a direct and positive influence on the improvement of carbon efficiency in tourist destinations. Establishing natural tourism attractions near cities is more conducive to facilitating carbon neutrality. This research highlights the advantages of remote sensing methods in specific sectors such as tourism where quality monitoring facilities and methods are lacking and provides a reference for evaluating the tourism carbon neutrality process and managing environmental sustainability on tourism attractions in similar regions and specific sectors worldwide. Full article

The manufacturing geometrical variability in axial compressors is a stochastic source of uncertainty, implying that the real geometry differs from the nominal design. This causes the real geometry to lose the ideal axial symmetry. Considering the aerofoils of a stator vane, the geometrical variability affects the flow traversing it. This impacts the downstream rotor, especially when considering the aeroelastic excitation forces. Optical surface scans coupled with a parametrisation method allow for acquiring the information relative to the real aerofoils geometries. The measured data are included in a multi-passage and multi-stage CFD setup to represent the mistuned flow. In particular, low excitation harmonics on the rotor vane are introduced due to the geometrical deviations of the upstream stator. The introduced low engine orders, as well as their amplitude, depend on the stator geometries and their order. A method is proposed to represent the phenomena in a reduced CFD domain, limiting the size and number of solutions required to probabilistically describe the rotor excitation forces. The resulting rotor excitation forces are reconstructed as a superposition of disturbances due to individual stator aerofoils geometries. This indicates that the problem is linear in the combination of disturbances from single passages. Full article

With the integration of clean energy and new power electronic devices into the power grid, the superposition of harmonic sources has become increasingly apparent and common. There is an urgent need to effectively identify composite harmonic sources in the new energy grid. This article proposes a multi-label composite harmonic source classification method that integrates knowledge representation with the transformer model. First, triplets from harmonic monitoring data are extracted and TransR models are used to train time-frequency feature representation vectors. Then, the transformer model is trained to learn the data features of different harmonic sources. Finally, based on the multi-label classification method, composite harmonic sources are identified. This article integrates the semantic information of time-frequency features into the data samples, increasing the interpretability of the model while expanding the inter-class features, which is conducive to the classification and recognition of the model. Compared with other deep learning recognition methods, verification based on simulation data and measured data shows that this method has low training complexity and higher recognition accuracy. Full article

CO₂ geological storage combined with deep saline water recovery technology (CO₂-EWR) is one of the most effective ways to reduce carbon emissions. Due to the complex structural features, it is difficult to use CO₂-EWR technology in Huaiyin Sag, Subei basin, East China. In this study, the multi-source information superposition evaluation technology of GIS was utilized for the selection of CO₂ storage sites and water displacement potential target areas in this area, which mainly focused on the sandstone reservoirs of Cretaceous Pukou Formation. Based on the results, a three-dimensional injection–extraction model was established. Various scenarios with different production/injection well ratios (PIR) were simulated. Research has shown that the suitability of the surrounding site of Huaiyin Power Plant can be divided into two levels: relatively suitable and generally suitable; the area in the generally suitable level accounts for more than 80%. At a PIR of 1, CO₂ is distributed asymmetrically, whereas at PIRs of 2 or 4, CO₂ is distributed symmetrically. When the number of production wells is constant, a higher injection rate results in a faster expansion rate of the CO₂ plume. This means that the time taken for the CO₂ plume to reach the production wells is shorter. Reservoir pressure increases rapidly after more than 60 years of CO₂ injection at lower PIR values, while at higher PIRs, reservoir pressure eventually stabilizes. Higher PIR values correspond to higher gas saturation, indicating a greater capacity for CO₂ sequestration with more producing wells. When PIR = 4, the total CO₂ injection increased by 55.73% compared to PIR = 1. However, the extraction of saline decreases with an increase in the number of producing wells, resulting in a decrease in replacement efficiency. This study provides a theoretical basis and technical support for the implementation of large-scale CO₂-EWR engineering and technology demonstration in this region. Full article

In order to deepen the understanding of the occurrence mechanism and water vapor transport characteristics of the heavy rain process in the extreme arid region of Xinjiang, a rare heavy rain process in the Tarim Basin during the period of 18–22 July 2021 was comprehensively analyzed by using multi-source data. The results show that the upper tropospheric South Asian high was distributed in a “west-high-east-low” pattern during the rainstorm process, and the rainstorm area was located on the right side of the upper jet stream entrance area, while the middle-level Iranian high pressure, Baikal high pressure and Central Asian low pressure formed a “two-highs and one-low” circulation situation. The coupling of the high and low air jets and the strong vertical upward motion provided favorable dynamic conditions. Rainstorm water vapor mainly comes from the Mediterranean Sea, Central Asia and the Indian Ocean, and it enters the basin in four paths: west, east to west, west to east, and southwest and south. The water vapor mainly flows into the middle layer of the western boundary and the southern boundary and the lower layer of the eastern boundary, and it flows out from the middle and upper layer of the eastern boundary. The negative moist potential vorticity region at a low level has a strong indicator significance for the occurrence and development of heavy rain, and the superposition of positive and negative moist potential vorticity regions at vertical height is conducive to the occurrence and development of heavy rain. Full article

Monopole communication towers play an irreplaceable role in modern communication systems. Because of its high flexibility, lateral loads control the deformation of a monopole communication tower. Dynamic displacement can be used to describe structural vibration characteristics and real-time deformation situations. Therefore, this article proposes a monopole communication tower deformation monitoring method based on multi-source data fusion using dynamic displacement as the evaluation indicator. This method calculates the strain displacement using the strain-mode superposition methodology. Then, the strain displacement and acceleration obtained are processed using Kalman filtering technology to reconstruct the real-time displacements of the monopole communication tower. The effectiveness of this method was verified using numerical simulations and model experiments, respectively. In addition, parametric analysis shows that this method is suitable for processing multi-rate data, has good noise resistance in strong noise environments, and can effectively reconstruct the displacement near the tower bottom. The results indicate that the method has favorable robustness and can accurately reconstruct low-frequency and high-frequency displacements of the monopole communication tower. Full article

The source of oil and gas and the stages of oil and gas accumulation in the “double-Paleo” field of the HZ-A structure in the Pearl River Mouth Basin are analyzed, and the spatiotemporal coupling relationship of the key conditions of oil and gas accumulation are discussed to reconstruct the process of oil and gas accumulation. Based on previous research results, which are based the characteristics of biomarker compounds, the oil and gas in the HZ-A structure double Paleogene field came from the Paleogene Wenchang Formation hydrocarbon source rocks in the HZ26 sub-sag. By means of the casting thin section identification and inclusion homogenization temperature measurement, this paper reveals the three major hydrocarbon accumulation periods and corresponding fluid charging types in the “double-Paleo” field of the HZ-A structure in the Pearl River Mouth Basin. The results show that 13.8–10 Ma is the charging period of low mature crude oil, 10–5.3 Ma is the charging period of mature crude oil, and from 5.3 Ma is the natural gas charging period. Based on actual geological, drilling, logging, and seismic data, the key conditions for hydrocarbon accumulation in the HZ-A structure “double-Paleo” field are sorted out; that is, the source conditions are characterized by high-quality lacustrine source rocks generating early oil and late gas and a near-source continuous hydrocarbon supply. The reservoir conditions are characterized by weathering and superposition of a fracture zone that transforms into a reservoir, and a large-scale sandstone rock mass that transforms into a reservoir. The caprock conditions are characterized by the stacking of several thin mudstones that form a seal and the combination of multiple lithologies that block hydrocarbon migration. The trap conditions are characterized by multistage uplift structure traps and fracture-lithology combination control traps. The transport conditions are characterized by multi-stage cross-bed transport of source-connected faults and lateral differential transport of shallow sand in deep fractures. Finally, oil and gas accumulation models of the HZ-A structure double Paleogene field were established, and the accumulation process was reconstructed. The overall process involved three stages, with the first stage being the localized oil-displacing-water mode, the second being the large-scale oil-displacing-water mode, and the third being the late progressive gas-displacing-oil mode. Full article