Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

Traditionally, intra-spacecraft data distribution is performed using wired data-handling systems. However, this approach increases the complexity and dry mass of the satellite harness. Therefore, extensive research is done to replace, complement or extend the wired communications with wireless ones. In this context, this paper presents the UWB-based solution developed within the WiSAT project funded by ESA. Considering the determined set of requirements for intra-spacecraft wireless communications, the proposed VN360 UWB module implements a custom software stack of the ISA-100.11a protocol over the IEEE 802.15.4 UWB PHY layer. The solution is tested and validated in different laboratory environmental conditions (TRL 4), including the satellite mock-up built within WiSAT. The results prove the capacity of the VN360 UWB module to establish a reliable intra-spacecraft UWB connection.

UAV communications have seen a rapid rise in the last few years. The drone class of UAV has particularly become more widespread around the world, and illicit behavior using drones has become a problem. Therefore, localization, tracking, and even taking control of drones have also gained interest. Knowing the frequency of a target signal, its position can be determined (as the angle of arrival with respect to a fixed receiver point) using radio frequency-based localization techniques. One such technique is represented by the subspace-based algorithms that offer highly accurate results. This paper presents the implementation of the MUSIC algorithm on an SDR-based system using a uniform circular antenna array and its experimental evaluation in relevant outdoor environments for drone localization. The results show the capability of the system to indicate the AoA of the target signal. The results are compared with the actual direction computed from the log files of the drone application and validated with a professional direction-finding solution (i.e., Narda SignalShark equipped with the automatic direction-finding antenna).

The problem of determining the object’s position has recently become an active research area, due to the increased demand for accurate outdoor and indoor positioning. This paper presents a preliminary testing campaign of the Decawave MDEK1001 development kit which uses UWB transmissions to determine the position of a tag in a real-time location system. The performance is evaluated in terms of positioning accuracy, and measurement error variation of the tag coordinates. The results analysis highlights the potential of the system and provides guidelines on how to increase the positioning accuracy.

This paper presents a comprehensive study on monopulse secondary surveillance radar (MSSR) coverage. The design and radiation pattern of an improved MSSR antenna is presented herein, highlighting the horizontal and vertical factors of the SUM beam. Moreover, the impact of other determinant factors, such as signal reflection and atmospheric refraction, on the radar coverage were assessed in this work. Real positioning measurement data and coverage simulations were used to support and exemplify theoretical findings.