Accuracy is an important factor in air traffic management which is why high requirements are necessary for each navigation system. The aim of this article is to describe the principles of the RelNav system and telemetry and their accuracy. We present the algorithms of the relative navigation system, which could be used for air traffic control in the case of the unavailability of satellite navigation system signals. This article sums up the different positioning methods, and deals with the accuracy of the relative navigation system (RelNav). Furthermore, the article considers the factors that influence the positioning accuracy. For this task, a computer simulation was created to evaluate the accuracy of the telemetric method. Next, we discuss the principles of telemetry and algorithms for calculating the position of the flying object (FO).

The article aims to support the Safety Management System’s implementation in the Visegrad Four (V4) countries by introducing supporting documentation at the national level or national legislation. The research subjects are the V4 countries (Poland, Hungary, the Czech Republic, Slovakia), where 39 air carriers operate. Safety management in today’s world is already necessary, yet introducing a Safety Management System (SMS) is voluntary in many areas, including some transport modes. In air transport, it is mandatory, and the general principles and guidelines of this system are set out in the International Civil Aviation Organization (ICAO), which has issued several regulations and recommendations. The lists of ICAO standards and essential translations can be considered a minimum, which is also approached by different authorities. The article points out the primary differences in SMS in individual V4 countries and presents which country provides the strongest support in implementing this system.

This article addresses anomaly detection in multichannel spatiotemporal data under strict low-false-alarm constraints (e.g., 1% False Positive Rate, FPR), a requirement essential for safety-critical applications such as signal interference monitoring in sensor networks. We introduce a lightweight, interpretable pipeline that deliberately avoids deep learning dependencies, implemented solely in NumPy and scikit-learn. The core innovation lies in fusing three complementary anomaly signals in an ensemble: (i) Principal Component Analysis (PCA) Reconstruction Error (MSE) to capture global structure deviations, (ii) Local Outlier Factor (LOF) on residual maps to detect local rarity, and (iii) Monte Carlo Variance as a measure of epistemic uncertainty in model predictions. These signals are combined via learned logistic regression (F*) and specialized Neyman–Pearson optimized fusion (F** and F***) to rigorously enforce bounded false alarms. Evaluated on synthetic benchmarks that simulate realistic anomalies and extensive SNR shifts (±12 dB), the fusion approach demonstrates exceptional robustness. While the best single baseline (MC-variance) achieves a True Positive Rate (TPR) of ≈0.60 at 1% FPR on the 0 dB hold-out, the fusion significantly raises this to ≈0.74 (F**), avoiding the performance collapse of baselines under degraded SNR (maintaining ≈ 0.62 TPR at −12 dB). This deployable solution provides a transparent, edge-ready anomaly detection capability that is highly effective at operating points critical for reliable monitoring in dynamic environments.

The COVID-19 pandemic has affected all areas of our society. The effects of the pandemic on the economy have not only affected mainly the development of transport and tourism but most especially air transport. Many airports in Europe and around the world have experienced a drop in passengers and cargo due to the COVID-19 pandemic. For many airports, this period, which began in 2019, meant huge losses due to a drop in passengers, leading to the collapse of several airlines. Despite the gradually improving situation, the impact of the pandemic is still being felt, and the number of passengers at airports is growing rather slowly. Thus, airports had to deal with the current situation and adapt their future operation to the current conditions. Even the leading airport in Central Europe - the Schwechat Airport in Vienna - was not spared from the effects of the current pandemic. This article aims to evaluate the effects of the COVID-19 pandemic on Schwechat airport. It focuses on the analysis of the development of the airport before and during the pandemic up to the present. Further, this article describes the problems that Schwechat airport dealt with throughout the period, with the loss of passengers, delays and the collapse of carriers. Subsequently, this article is part of a study aimed at identifying the negative effects of the pandemic on air transport and related industries worldwide. Predicting the development of the airport is a very important aspect, especially for the company's management and should be part of the risk management of every company.

We present the largest sample of gamma-ray transients observed by any CubeSat mission so far. Observations were acquired by a 1U CubeSat GRBAlpha, the smallest astrophysical space observatory, and a 3U CubeSat VZLUSAT-2. Both missions were technological pathfinders and carried a novel CsI scintillator-based detector read out by silicon photomultipliers. They operated on Sun-synchronous low Earth orbits below 550 km for about 4 yr; GRBAlpha between 2021 March and 2025 June and VZLUSAT-2 between 2022 January and 2025 November. Despite being technological experiments, they observed over 300 gamma-ray transients including gamma-ray bursts (GRBs), solar flares, soft gamma repeaters, and one outburst from an X-ray binary. Among these are the two brightest GRBs ever observed, GRB 221009A and GRB 230307A, without saturation and GRBs at redshifts up to z = 4.2. GRBAlpha also contributed to the InterPlanetary Network. Regular monitoring of transients was demonstrated by a detection rate of two transients or one GRB a week and the shortest time between two subsequent detections of only 42 minutes. We show that a constellation of nanosatellites around the Earth would observe at least 60% of Fermi/GBM GRBs with 5 σ significance and over 90% at the 3 σ level. GRBAlpha and VZLUSAT-2 prove that routine monitoring of the gamma-ray sky can also be done by low-cost and quickly developed nanosatellite missions.

Regional airports in post-EU accession Central Europe operate under demand conditions that differ substantially from those of major hubs, yet revenue management in these thin markets remains underexplored. This study examines whether temporal fare escalation observed on high-frequency routes also occurs for low-frequency regional services and whether carriers apply different pricing strategies on overlapping routes. Daily one-way fares were tracked on three routes from Košice International Airport, Slovakia, to the United Kingdom (Dublin, London Luton, and London Stansted) between 4 January and 2 February 2025, yielding 1738 observations across direct and connecting itineraries via Liverpool. Statistical analyses included paired t-tests, OLS regression with Newey–West standard errors and collection-session-clustered robustness checks, bootstrap inference for coefficient-of-variation ratios (10,000 resamples), Holm–Bonferroni correction, and log-transformation robustness checks. Four of six route–flight type combinations exhibited significant temporal fare escalation after multiple-testing correction. Wizz Air’s Luton service showed substantially greater fare variability than Ryanair’s Stansted service (coefficient of variation of 77.5% vs. 31.3%; bootstrap 95% CI for the ratio [1.29, 10.32]), despite similar mean fares. Connecting fares via Liverpool averaged 171% above direct Dublin fares, while daily prices on the London routes were strongly correlated (r = 0.816). These results reveal carrier-specific algorithmic pricing differences in thin regional markets with implications for consumer decision-making and for the affordability dimension of regional connectivity.