Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions.

Dissolved organic carbon (DOC) is an indispensable component of the global carbon cycle. Previous research on runoff process and DOC loss mainly focused on surface flow, with few reports of the hydrological pathway of interflow or DOC loss via interflow. To address this deficiency, a series of rainfall simulations were conducted with three rainfall intensities of 60, 90, and 120 mm h ^-1 (R60, R90, and R120) and three slope gradients of 5, 15, and 25° (S5, S15, and S25) of purple soil. The initial time of surface flow was faster under high rainfall intensity and steep slope, and the initial time of interflow increased with increased rainfall intensity under gentle slope. In general, the surface flow rates increased first, and reached a steady state within 10–35 min. The interflow curves were single-peak curves for R60-S5 and R90-S5, but exhibited a continued rising trend for other treatments. The interflow volume occupied 69.2% of the total runoff volume under R60-S5, and the percentages of interflow decreased as the rainfall intensity and slope increased. These results indicated that interflow was an important hydrological pathway of purple soil. The DOC concentration of the surface flow decreased with rainfall duration, with opposite trend for DOC concentration of interflow. The DOC concentrations in the interflow were 1.35–2.34 times higher than those in the surface flow. However, the rainfall intensity and slope had little effect on DOC concentrations in both surface flow and interflow. Furthermore, the DOC loss fluxes via surface flow and interflow were 3.77–26.94 g and 0.41–13.73 g, respectively, and the ratios of interflow DOC loss fluxes to the total DOC loss fluxes gradually decreased with the increase of rainfall intensity and slope. Under R60, DOC loss via interflow was the major DOC loss pathway, accounting for 51.0%−78.4% of the total DOC loss, whereas for R90 and R120, DOC loss via surface accounted for >90%. Moreover, runoff volume was positively linearly correlated with the corresponding DOC loss fluxes in both the surface flow (R ² = 0.93, P < 0.01) and interflow (R ² = 0.99, P < 0.01). These results provide a scientific basis to estimate the fluxes of DOC loss and control carbon loss in the purple soil area of China.

In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran’s precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.

The objective of this study was to determine the distribution of cadmium (Cd) accumulation in tobacco and identify the relationship between soil properties and plant Cd concentration. Forty soil and tobacco samples were collected from the Sichuan Basin, China (Panzhihua, Luzhou, Guangyuan and Yibin). The results showed the highest Cd in soil (2.94 mg kg⁻¹) was in Yibin. Correspondingly, Cd concentrations in tobacco from Yibin were significantly higher than those from the other regions. The Cd concentrations in tobacco from Yibin, Luzhou, Guangyuan and Panzhihua were from 2.22 to 13.14, 0.59 to 2.12, 0.26 to 0.61, 0.10 to 0.37 mg kg⁻¹, respectively. The Cd concentrations in leaves were higher than those in stems and roots. The highest Cd concentrations were in lower leaves, between 0.36 and 13.14 mg kg⁻¹. In Yibin, the Cd in lower leaves nearly exceeded 10 mg kg⁻¹ and the ratio of the Cd tobacco and soil concentrations (the bioconcentration factor, BCF) was 9.41. Path analysis showed soil pH and cation exchange capacity (CEC) were the most significant properties exhibiting direct effects on Cd accumulation. Stepwise multiple linear regression showed that soil Cd, pH, CEC and organic carbon (OC) were the major factors controlling Cd bioavailability to tobacco leaves, explaining more than 94.0% of the variance. The results are useful in predicting the transfer of Cd from soil to tobacco.