This paper presents the results, overall architecture, and setup configuration of over-the-air tests performed using a 5G NR-NTN (Non-Terrestrial Network) waveform, in Ku-Band, with satellites of the Eutelsat OneWeb LEO constellation. The tests included a full two-way chain with a 5G core, an NTN-enabled gNB, a 5G NR-NTN modem, and additional equipment described in this paper. The tests allowed to validate basic 5G-NTN features in a real environment, to measure performance, and to execute satellite beam handover according to 3GPP Release 19 specifications.
Non-Terrestrial Network communication (NTN) extends cellular systems to a larger coverage at a reduced deployment cost and is expected to play an important role in future 6G. This column presents NTN technology from the point of view of current infrastructure, 3GPP-based features, and future expandability towards 6G. A recent field demo based on 5G platforms illustrates actual spectrum efficiency, hardware/regulatory limitation, as well as tradeoffs on a real global Low Earth Orbit (LEO) constellation.
Selecting an appropriate evaluation metric for classifiers is crucial for model comparison and parameter optimization, yet there is not consensus on a universally accepted metric that serves as a definitive standard. Moreover, there is often a misconception about the perceived need to mitigate imbalance in datasets used to train classification models. Since the final goal in classifier optimization is typically maximizing the return of investment or, equivalently, minimizing the Total Classification Cost (TCC), we define Weighted Accuracy (WA), an evaluation metric for binary classifiers with a straightforward interpretation as a weighted version of the well-known accuracy metric, coherent with the need of minimizing TCC. We clarify the conceptual framework for handling class imbalance in cost-sensitive scenarios, providing an alternative to rebalancing techniques. This framework can be applied to any metric that, like WA, can be expressed as a linear combination of example-dependent quantities and allows for comparing the results obtained in different datasets and for addressing discrepancies between the development dataset, used to train and validate the model, and the target dataset, where the model will be deployed. It also specifies in which scenarios using UCCs-unaware class rebalancing techniques or rebalancing metrics aligns with TCC minimization and when it is instead counterproductive. Finally, we propose a procedure to estimate the WA weight parameter in the absence of fully specified UCCs and demonstrate the robustness of WA by analyzing its correlation with TCC in example-dependent scenarios.
Measurements of particle fluxes (protons and electrons) obtained with the Influence sur les Composants Avancées des Radiations de l’Espace for New Generation (ICARE_NG) monitor on the Eutelsat 7C orbit [Electric Orbit Raising to Geostationary Orbit (GEO)] are presented. Several comparisons are proposed with other instruments [magnetic electron ion spectrometer (MagEIS), relativistic proton spectrometer (RPS), and magnetospheric particle sensors high (MPSH)] and radiation models (AEP8, International Radiation Environment Near Earth (IRENE), and Global Radiation Earth ENvironment (GREEN)). Significant discrepancies have been found, especially with AEP8 model. The measurements when the satellite is in its operational orbit (GEO) have also been compared with the International Geostationary Electron (IGE) model and an excellent agreement is observed. For calculating fluxes from ICARE_NG outputs, two methods have been developed and their results are compared to consolidate our interpretations.
