One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as $7.3$ dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 $\mathrm{M}$$\mathrm{Hz}$, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc.

This study presents a single-case multi-sensor experimental study of thermal loading and emission variation in a high-mileage gasoline engine operating at idle under deliberately impaired cooling until mechanical failure. A production vehicle equipped with a naturally aspirated gasoline engine with a displacement of 1600 cc was operated under relatively steady-state conditions at idle, while gaseous emissions (CO, CO₂, HC, NO_x, and O₂), air–fuel ratio λ, particle number (PN), oil temperature, infrared thermal indicators, and acoustic performance variation were continuously monitored. The results are interpreted primarily in terms of their dependence on the engine oil temperature. They show that despite stable conditions of the air–fuel ratio and an almost constant amount of residual oxygen in the exhaust gases, progressive thermal loading leads to pronounced changes in the behavior of the emissions emitted by the engine during its operation. Hydrocarbon emissions show increased variability and escalation at elevated engine oil temperatures, while nitrogen oxides show a strong temperature-dependent increase, consistent with thermally driven formation mechanisms. The most significant response is observed in the particle number (PN) emissions, which go from low and stable levels to a rapid, multi-step increase in a narrow temperature range preceding mechanical failure. Under the tested cooling impairment scenario, emission behavior was dominated by cumulative thermal stress rather than mixture composition effects. In the investigated case, particle number emissions emerged as a sensitive indicator of system-level thermal instability. The findings provide experimentally documented insight into the system-level progression toward thermal runaway under impaired cooling conditions and its measurable impact on emission behavior in the tested engine.

The interaction between solar activity and the Earth’s ionosphere is an important area of study in space weather research. Solar flares and associated radio bursts can disrupt the D-layer of the ionosphere, causing variations in radio wave propagation and affecting radio communication systems. Ground-based observations of solar activity and ionospheric dynamics provide valuable information that complements satellite observations and enhances their spatial resolution. Very low frequency (VLF) measurements are used to detect changes in the D-region of the ionosphere caused by solar flares. At the same time, VHF radio spectrometry allows direct observation of solar radio bursts. The Antarctic region offers favorable conditions for such observations due to the low level of anthropogenic electromagnetic interference. This makes it suitable for highly sensitive radio measurements and for studying Sun-Terrestrial interactions under specific polar conditions. The purpose of the article is to present an overview of the first Bulgarian polar radio observatory, installed at the Bulgarian Antarctic Base “St. Kliment Ohridski” during the 33rd Bulgarian Antarctic Expedition. The article describes the logistics, system architecture, experimental methods, and initial results related to solar activity, ionospheric disturbances, and geomagnetic field variations.

In this paper we present a hybrid convolutional neural network–hidden Markov model framework for detecting solar flare events of intensity greater than or equal to M1.0 from very low frequency signals via their induced sudden ionospheric disturbances. The convolutional neural network processes fixed-length windows of raw very low frequency signals and their temporal derivatives to produce probabilistic flare estimates, which serve as emission probabilities for a two-state hidden Markov model. Viterbi decoding enforces temporal consistency, suppressing spurious fluctuations and yielding physically plausible event sequences. The approach is specifically designed to detect the onset-to-peak interval of flare events and, with further development, could operate in real time for early flare warning. The model was trained and evaluated on very low frequency data from the DHO38 transmitter in Germany to a receiver near Birr, Ireland. Sample-level evaluation achieved a balanced accuracy of 0.819 and a Matthews correlation coefficient of 0.529, while event-level detection reached a peak F1-score of 0.558 for moderate-to-strong flares of intensity greater than or equal to C6.0. These results demonstrate automated, physically consistent detection of solar flares based on sudden ionospheric disturbances, indicating the potential of the proposed approach, when combined across multiple receivers, to act as a low-cost complement to satellite-based monitoring.