and communication technologies, as well as powerful cloud-based analytics, IIoT intends to enable cyber-physical systems (CPSs). Applying IIoT and CPSs to the industrial domain brings closer the physical machines and the digital world and facilitates the autonomous prediction of failures, the triggering of the maintenance processes, and the provision of self-organized logistics to respond to the changes in the production. IIoT also enables the realization of the Industry 4.0 concept, where 4.0 represents the fourth industrial revolution relying on advanced intelligent networking technologies [5]. The industrial applications cover a broad spectrum of use cases and each one has its own set of challenges. On the one hand, a wide variety of licensed or unlicensed industrial, scientific, and medical (ISM) standard microwave or millimeter-wave (mm-wave) frequencies should be used on a global, regional, or national basis, in order to establish wireless connections in the era of IIoT depending on the requirements and the limitations of each application scenario [6]. At these frequencies, advanced antennas should be designed to address the sensing and detection/tracking of objects, as well as the onward transmission over the wireless IIoT networks. In this respect, antenna attributes, such as compactness, simplicity, low-cost, stability, directivity, wider beam-scanning, radiation efficiency, and mutual-coupling between the radiating elements, are of great importance. Therefore, metamaterial-based leaky-wave antennas (LWA) [7], planar broadband antennas based on meandered line loops [8], wideband printed monopole antennas [9], and densely packed array antennas with embedded metamaterial electromagnetic bandgap (EMBG) structures [10] represent candidate antenna solutions. On the other hand, the endmost goal of shaping an IIoT ecosystem is the creation of a network of remoted factories that can effectively and autonomously adapt to production requirements and share their resources. Besides the above, some special IIoT applications would require coverage in rugged and remote locations, e.g., desert, valley, ocean, and forest, beyond the indoor and outdoor radio coverage of conventional cellular networks. In such situations, there exist commercial and engineering difficulties of exclusively constructing and/or exploiting terrestrial networks and obtaining connectivity with reasonable capital expenditure (CAPEX) and operation and maintenance expenditure (OPEX). To achieve ubiquitous connectivity and materialize the vision of IIoT, supplementing and extending the terrestrial communication networks is indispensable [11]. In this context, non-terrestrial networks (NTNs) constitute the driving infrastructure to obtain global IIoT and extend radio coverage by exploiting the available degrees of freedom from both the space- and air-based nodes [12, 13, 14]. Nevertheless, integrating NTNs in ultra-dense heterogeneous networks (UDHNs) [15, 16] with a density of possibly thousands of nodes per square kilometer and applying innovative physical layer (PHY) techniques cannot be directly feasible. The reasons are the special features of the spaceborne and airborne platforms, the particularity of the propagation environment, as well as the problems imposed by the underlying radio propagation channels. As far as complex, dynamic, and mission-critical communication scenarios with requirement for ultra-reliable and low-latency communications (URLLC) are concerned, artificial intelligence (AI) [17, 18, 19] can revolutionize the decision-making processes with respect to the maintenance of the radio connections as well as the processing and distribution of data. Specifically, AI includes a number of sophisticated algorithms, techniques, and methods and achieves the emulation of the human brain reasoning process, enabling the ability to extract specific knowledge and repeating motifs from a series of observations.