The evaporation duct is a prevalent atmospheric structure in the marine lower troposphere with significant regional nonuniformity. This structure can trap radio waves inside its layer and enable them to propagate over the horizon with less loss. Therefore, accurately predicting the over-the-horizon propagation loss (OHPL) is important for optimizing the performance of radio-electronic systems. Considering the OHPL characteristics in nonuniform evaporation ducts, this article establishes an OHPL prediction model by incorporating prior information into the long short-term memory (LSTM)–transformer structure (IPILT–OHPL). The combination of the LSTM network and transformer is used to construct the LSTM–transformer, aimed at leveraging their respective strengths to extract important features of OHPL effectively. In addition, to improve the prediction accuracy, this article incorporates the evaporation duct height as an environmental prior information into the LSTM–transformer. Finally, this article comprehensively evaluates IPILT–OHPL model performance in different application scenarios. The evaluation results show that the established model not only has high prediction accuracy but also strong generalization ability, which provides a new method for efficiently predicting the OHPL in nonuniform evaporation ducts.

The ionosphere can be artificially modified by employing ground-based high-power high-frequency electromagnetic waves to irradiate the ionosphere. This modification is achieved through the nonlinear interaction between the electromagnetic waves and the ionospheric plasma, leading to changes in the physical properties and structure of the ionosphere. The degree of artificial modification of the ionosphere is closely related to the heating energy density of high-frequency pump waves. Due to the high density of neutral constituents in the lower ionosphere and the high frequency of electron-neutral collisions, the energy of heating pump waves will be absorbed and attenuated during the penetration of the low ionosphere, seriously affecting the heating effect. This paper proposes a method to reduce the absorption of ionospheric heating pump waves by releasing electron attachment chemicals into low ionosphere to form a large-scale electron density hole. A model for mitigating pump waves absorption based on SF₆ release is established, and the absorption at different frequencies is quantitatively calculated. The propagation characteristics of high-frequency signals in ionospheric holes are studied using a three-dimensional ray tracing method, and the results demonstrate that the chemical release method not only reduces the absorption attenuation of heating pump waves but also forms spherical electron density holes, which exhibit a focusing effect on the heating beam and enhance the heating effect. The results are of great significance for understanding the nonlinear interaction between electromagnetic wave and ionospheric plasma and improving the ionospheric heating efficiency.

The ionospheric E layer (90–150 km altitude) significantly influences ionospheric dynamics and plays a crucial role in radio wave propagation. The Tibetan Plateau, as the “Third Pole,” affects E-layer morphology due to its unique topographical factors. Given the limited systematic studies in this high-altitude region, this study analyzes E-layer spatiotemporal characteristics and their controlling mechanisms over the Tibetan Plateau and adjacent regions. We analyzed foE (critical frequency of E-layer) data from six ionospheric observation stations across the Tibetan Plateau and neighboring areas during 2013–2023, covering a complete solar cycle from solar minimum to maximum. Combined with sunspot numbers as solar activity indicators, we systematically examined diurnal, seasonal, and solar cycle variations to understand regional E-layer behavior patterns. Daytime foE values significantly exceed nighttime values, demonstrating strong solar control. Spatially, Kunming shows the strongest daytime E-layer intensity with peak values reaching 3.12 MHz, while Urumqi exhibits the weakest at 2.94 MHz. Daytime foE values decrease with increasing latitude, whereas nighttime values show opposite latitudinal trends, indicating pronounced diurnal distribution asymmetry. Kunming displays the largest day-night foE variation amplitude, while Urumqi shows the smallest changes. Notably, most stations exhibit E-layer intensity peaks in July rather than June when solar zenith angles are minimum, differing from typical mid-low latitude seasonal behavior. These patterns may be related to complex vertical atmospheric coupling influenced by the region’s unique topography, which could affect the spatiotemporal distribution of the E-layer over the Tibetan Plateau.

The ionospheric equivalent slab thickness is a key parameter for understanding the plasma distribution in the ionosphere, with direct relevance to satellite navigation, communication, and skywave over‐the‐horizon radar. However, traditional prediction methods often suffer from regional biases, limiting their global applicability. To address this, we develop a novel global prediction model for ionospheric slab thickness by integrating the Extreme Gradient Boosting (XGBoost) algorithm with Ensemble Learning (EL) techniques (XGBoost + EL). This hybrid approach leverages the strong nonlinear modeling capability of XGBoost and the robustness of EL to enhance prediction accuracy and generalization. The model was trained and tested on global occultation data from 2007 to 2020, incorporating key parameters such as vertical total electron content (vTEC), the F2‐layer critical frequency, and solar activity indices. Evaluation results demonstrate the model's high predictive accuracy, achieving a root mean square error (RMSE) of 62.3 km, a mean absolute error (MAE) of 41.5 km, a mean absolute percentage error (MAPE) of 13.1%, and a correlation coefficient ( R ) of 0.904. Notably, our model significantly outperforms the Neustrelitz equivalent Slab Thickness Model (NSTM), reducing RMSE, MAE, and MAPE by 54.8 km, 47.2 km, and 11.8%, respectively. It also shows superior performance over the International Reference Ionosphere (IRI) model, with corresponding reductions of 72.1 km, 57.1 km, and 14.7%. These findings underscore the considerable potential of machine learning for advancing global ionospheric modeling. Future work will focus on model optimization and assessing its applicability across diverse regions and temporal scales to further improve reliability.