The recent technological advances in digitalization have revolutionized the industrial sector. Leveraging data analytics has now enabled the collection of deep insights into the performance and, as a result, the optimization of assets. Industrial drives, for example, already accumulate all the necessary information to control electric machines. These signals include but are not limited to currents, frequency, and temperature. Integrating machine learning (ML) models responsible for predicting the evolution of those directly collected or implicitly derived parameters enhances the smartness of industrial systems even further. In this article, data already residing in most modern electric drives has been used to develop a data-driven thermal model of a power module. A test bench has been designed and used specifically for training and validating the thermal digital twin undergoing various static and dynamic operating profiles. Different approaches, from traditional linear models to deep neural networks, have been implemented to emanate the best ML model for estimating the case temperature of a power module. Several evaluation metrics were then used to assess the investigated methods' performance and implementation in industrial embedded systems.

GEO communication satellites generate significant revenue but can only function reliably for approximately 10 years on orbit. One of the main drivers that limits the reliability of a GEO satellite is the electric power system, and in particular, anomalies related to batteries and degradation of the solar arrays. Given the high cost and relatively short lifespan of GEO satellites, there has been increased research activity towards developing on-orbit servicing systems. However, most of the existing servicing systems are expensive, highly customized, and focus on refueling tasks. On-orbit refueling can be very useful, however, it does not improve satellite reliability which is crucial for long-term missions. Therefore, we propose SSPARE (Space Solar Power Autonomously Reconfigurable Elements), a cost-effective, self-servicing power system. Aside from improving satellite reliability, SSPARE enables to generate up to 6 times more power per launch compared to a traditional GEO communication satellite. This study explores why GEO satellites fail and elaborates on the SSPARE concept. A comparison of SSPARE against a traditional on-orbit servicing mission highlights the benefits of the proposed concept. With humanity striving to become more and more Earth-independent, this work aims to build a foundation for future systems such as large solar power farms on-orbit.

The Vicor Corporation Powering Innovation podcast focuses on world-changing innovations, examining how electronics technologies can be applied to solve real-world challenges. The first episode in the series features DPI UAV Systems (DPI), a manufacturer of unmanned aerial vehicle (UAV) systems. This episode takes a deep dive into an Unmanned Multirotor Aerial Relay system and how DPI is extending communications range 3x with a new class of tethered UAVs. Joe Pawelczyk, Vice President of Operations at DPI, joins Robert Gendron, Vicor's Corporate Vice President, Product Development, to discuss the cutting-edge technology driving change that addresses real-world problems. The episode examines the communication networks needed for high-security communications, such as in military applications, and how DPI's technology is far exceeding today's standards.