Sensors最新文献

Cloud detection constitutes a pivotal task in remote sensing preprocessing, yet detecting cloud boundaries and identifying thin clouds under complex scenarios remain formidable challenges. In response to this challenge, we designed a network model, named NFCNet. The network comprises three submodules: the Hybrid Convolutional Attention Module (HCAM), the Spatial Pyramid Fusion Attention (SPFA) module, and the Dual-Stream Convolutional Aggregation (DCA) module. The HCAM extracts multi-scale features to enhance global representation while matching channel importance weights to focus on features that are more critical to the detection task. The SPFA module employs a novel adaptive feature aggregation method that simultaneously compensates for detailed information lost in the downsampling process and reinforces critical information in upsampling to achieve more accurate discrimination between cloud and non-cloud pixels. The DCA module integrates high-level features with low-level features to ensure that the network maintains its sensitivity to detailed information. Experimental results using the HRC_WHU, CHLandsat8, and 95-Cloud datasets demonstrate that the proposed algorithm surpasses existing optimal methods, achieving finer segmentation of cloud boundaries and more precise localization of subtle thin clouds.

An S-band antenna has been designed, developed, measured, space-qualified, and integrated into the INTA ANSER satellite constellation and the future ANSER-AT mission. This antenna will be part of the space-to-ground communication link for the constellation, which consists of one Leader and two Followers. The novel antenna, mounted on the Leader, has been designed and manufactured with materials and processes specifically tested for space. It features dual circular polarization over a wide band without requiring a phase-shifting network, making it very compact and straightforward. Additionally, its gain patterns are highly stable within the desired band, improving its link capacity compared to the UHF monopole alternative used in the previous Leader. Currently, the antenna has been qualified and installed on INTA's Leader-S, set to launch in January 2025, as well as on the future ANSER-AT mission.

Movement smoothness is a critical metric for evaluating motor control and sensorimotor impairments, with increasing relevance in neurorehabilitation and everyday functional assessments. This study investigates the correlation between two smoothness metrics (Log Dimensionless Jerk): LDLJV, derived from body center of mass (BCoM) trajectories using a gold-standard stereophotogrammetric system, and LDLJA, calculated from acceleration data recorded via an inertial measurement unit (IMU) placed at the L1-L2 level. Ten healthy adults (six men and four women; height: 1.71 ± 0.08 m; body mass: 68.2 ± 10.2 kg; age: 34.5 ± 8.5 years) walked on a treadmill at seven different speeds, with stride-specific data analyzed to compute smoothness indices for three anatomical components (antero-posterior, medio-lateral, cranio-caudal). Concordance between the metrics was evaluated using Bland-Altman analysis, Spearman's correlation, and the mean absolute percentage error. The results revealed weak correlations and substantial biases across all components and speeds, reflecting inherent differences between IMU- and BCoM-derived data. Correcting biases improved alignment but did not eliminate discrepancies. The findings highlight that LDLJA captures only localized trunk accelerations, whereas BCoM-derived LDLJV approximates whole-body dynamics, making direct substitution infeasible. This study emphasizes the need for careful interpretation of IMU-based metrics and contributes to refining their application in real-world gait analyses.

Aiming to address the problem of limited transmission distance in applying reconfigurable intelligent surface (RIS) technology, this study leverages a tunnel simulation platform to investigate RIS-assisted wireless communication systems. Through theoretical derivation, we propose a path loss model formula specifically applicable to tunnel scenarios. Simulation results demonstrate that the proposed model accurately reflects the communication performance characteristics of RIS in tunnel scenarios, verifying the capability of RIS technology to enhance signal transmission distance within tunnels in rail transit engineering applications. This finding highlights the significant engineering potential and value of RIS technology.

Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater-sea surface-air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from -114 dB to -157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments.

It is almost impossible to overestimate the importance of the oceans for human society and the whole biosphere, either from the perspectives of climate change or sustainable development [...].

The development of an instrumented patellar prosthesis, able to measure the contact forces at the patellofemoral joint, can significantly aid in investigating the causes of total knee arthroplasty failures due to patellar complications. This study focuses on developing and validating an instrumented patellar prosthesis to measure contact forces in the patellofemoral joint. A piezoresistive force sensor was characterized and integrated into a conditioning circuit, with the aim of its implementation in the prosthesis. To measure medial and lateral forces independently, the sensors were trimmed in half. Compression tests (up to 2000 N) assessed sensor performance in terms of linearity (R² = 0.998 intact vs. 0.989 trimmed), repeatability (0.9% intact vs. 0.8% trimmed), and accuracy (1.7% intact vs. 2.3% trimmed) for forces up to 250 N. Higher force levels resulted in increased errors, but at a rate still comparable to that of existing sensors in the literature. Key considerations for the design of the instrumented prosthesis, such as minimizing point and shear loads, were identified. A prototype prosthesis capable of housing the sensor was proposed. The integrated system shows potential for improving the understanding of Total knee arthroplasty (TKA) failures through in vitro studies and could serve as an intraoperative tool for the evaluation of bone resections.

This study evaluates the combined use of mobile transects and fixed stations to analyze atmospheric urban heat islands (UHIs'a) in Temuco, Chile. Data were collected using 23 fixed stations and 3 mobile transects traversing predefined city routes, capturing temperature records at one-minute intervals. Results revealed moderate correlations between methodologies (coefficients: 0.55-0.62) and average temperature differences of 0.72 °C to 1.6 °C, confirming their compatibility for integrated use. UHI intensities ranged from weak (0.5 °C) to extremely strong (13 °C), with the highest urban temperature (33.1 °C) observed in Zone Z-3, contrasting with 25.4 °C at the rural Maquehue station. Simulations and isothermal maps identified four UHI zones, highlighting the influence of impervious surfaces, traffic density, and limited vegetation on temperature distribution. Fluctuation plots revealed rapid cooling in vegetated areas and high heat retention in dense urban zones. These findings validate the methodologies for spatial and temporal UHI analysis and provide actionable insights for urban planning. Targeted interventions, such as increasing vegetation in high-risk zones, are recommended to mitigate extreme heat and enhance thermal comfort in urban areas.

Magneto-electric dipole (ME-dipole) antennas offer several advantages, including wide impedance bandwidth, stable high gain, unidirectional radiation, and low back-lobe radiation patterns, making them suitable for modern wireless communication systems. However, the thickness of conventional ME-dipole antennas is typically about a quarter wavelength (0.25λo) at the center operating frequency, which may not be desirable for portable device applications. This work introduces a new feeding method that reduces the antenna profile and ground plane size while maintaining the same advantages. A suspended horizontal line is proposed to excite the cavity-less ME-dipole antenna through proximity coupling. The measured results demonstrate a wide impedance bandwidth of 45.3% (ranging from 2.05 GHz to 3.25 GHz) and an average in-band gain of 9 dBi with stable ±1 dBi in-band variation with a ground reflector of size about 0.89λo2. More importantly, the cavity-less design reduces the overall thickness of the antenna to 0.17λo at the center operating frequency.