An unrestricted, global, and ubiquitous connectivity, available to anyone, everywhere, and anytime, is a key objective of the 6G vision. A true, seamless integration of terrestrial (T) and non-terrestrial (NT) networks is mandatory to reach such an ambitious target. From this perspective, the three-year ITA-NTN project (2023-2025), funded by the European Union within the framework of NextGenerationEU work program, aims to investigate advanced solutions for the seamless integration of Terrestrial and Non-Terrestrial networks. This paper considers the project aspects concerning the provision of seamless connectivity across the heterogeneous T and NT layers by proposing an integrated ecosystem, which enables transparent information exchange among the various communicating nodes belonging to each layer. The ecosystem relies on the software-based management of fully reconfigurable network architectures based on disruptive concepts like cell-free multiple-input multiple-output (MIMO), full software-defined radio (SDR)-based multi-waveforms physical layer design, use of aggressive capacity-oriented radio resource management strategies based on Non Orthogonal Multiple Access (NOMA), extended use of Software-Defined Networking (SDN), and Artificial Intelligence (AI) to monitor and manage the entire network architecture. The discussion of the achieved results validates the proposed ecosystemic approach both in terms of viability and quantitative performance improvement.

We study Unmanned Aerial Vehicle (UAV) assisted 5G uplink connectivity for disaster response, where a UAV acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink Non-Orthogonal Multiple Access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic Line of Sight/Non Line of Sight (LoS/NLoS), realistic receiver noise and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2−10.1 percentage points over strong baselines (OFDMA with heuristic placement, PSO-based placement/power, PPO without NOMA) while reducing median UE transmit power by 64.6%. Results are averaged over ≥5 random seeds with 95% confidence intervals; ablations isolate the gains from NOMA, adaptive SIC order, and bounded action parameterization. We discuss robustness to imperfect SIC and CSI errors, and release code/configurations to support reproducibility.

Semantic integration in Additive Manufacturing (AM) supervision demands unifying heterogeneous sensor streams and production context under strict latency and data-integrity constraints, yet conventional semantic stacks suffer from unpredictable query latencies, unguarded SPARQL (SPARQL Protocol and Resource Description Framework [RDF] Query Language) endpoints, and prohibitive reasoning overheads. This paper presents Semantic DREAM (Distributed Resilient and Emergent intelligence-based Additive Manufacturing), a security-aware architecture co-designed over a Web Ontology Language 2 Rule Language (OWL 2 RL) modular substrate with three mechanisms: (1) an abstract syntax tree (AST)-guarded read-only SPARQL engine that blocks destructive updates and caches template results; (2) a multi-protocol mashup layer aligning live Message Queuing Telemetry Transport (MQTT), Representational State Transfer (REST), Structured Query Language (SQL), and file feeds through Sensor, Observation, Sample, and Actuator (SOSA) observation minting and Quantities, Units, Dimensions, and Types (QUDT) unit normalization; and (3) an ingestion-time Shapes Constraint Language (SHACL) validation pipeline that gates observations before graph commit, decoupling structural checks from reasoning. Deductive materialization is performed offline at startup and verified with ROBOT as an intentional design choice to eliminate reasoning overhead from the streaming path, so online monitoring queries evaluate directly over the asserted graph. Single-host, lab-scale evaluations show that cached queries achieve sub-15 ms response times for recurring monitoring, while the worst-case event-driven decision cycle (incurring a post-invalidation cache miss) completes in 110 ms, well within the 200 ms supervisory deadline with 100% defect interception on injected anomalies. All measurements are single-host and lab-scale; factory-network transit and recorded telemetry are not evaluated.

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by $64.6\%$. The results are averaged over at least five random seeds, with $95\%$ confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility.

An unrestricted, global, and ubiquitous connectivity, available to anyone, everywhere, and anytime, is a key objective of the 6G vision. A true, seamless integration of terrestrial (T) and non-terrestrial (NT) networks is mandatory to reach such an ambitious target. From this perspective, the three-year ITANTN project (2023-2025), funded by the European Union within the framework of NextGenerationEU work program, aims to investigate advanced solutions for the seamless integration of Terrestrial and Non-Terrestrial networks. This paper considers the project aspects concerning the provision of seamless connectivity across the heterogeneous T and NT layers by proposing an integrated ecosystem, which enables transparent information exchange among the various communicating nodes belonging to each layer. The ecosystem relies on the software-based management of fully reconfigurable network architectures based on disruptive concepts like cell-free multiple-input multiple-output (MIMO), full software-defined radio-based multi-waveforms physical layer design, use of aggressive capacity-oriented radio resource management strategies based on Non Orthogonal Multiple Access (NOMA), extended use of SoftwareDefined Networking (SDN), and Artificial Intelligence (AI) to monitor and manage the entire network architecture. The discussion of the achieved results validates the proposed eco-systemic approach both in terms of viability and quantitative performance improvement.

Additive manufacturing (AM) monitoring is fundamentally constrained by the severe scarcity of annotated data for layer-wise segmentation. This paper addresses this bottleneck by introducing a scalable, high-fidelity synthetic data generation pipeline built on the Slice-100K dataset, capable of producing large volumes of layer-wise semantic segmentation masks. Through analysis of this large-scale synthetic data, we identify a systemic foreground–background class imbalance (1:24 ratio) inherent to AM monitoring, which causes standard Dice loss formulations to diverge catastrophically into a phenomenon we formalize as the “Dice Crash.” To effectively leverage large amounts of unlabeled data, we adapt the Meta Pseudo-Labeling (MPL) framework for industrial segmentation. We evaluate MPL’s true marginal utility by integrating it with both a standard U-Net and a robust state-of-the-art nnU-Net architecture. Experimental outputs show that while MPL yields substantial performance gains (+15.2%) on weak baselines, integrating it with an optimally configured strong baseline consistently improves segmentation accuracy and suppresses false foreground detections, thereby mitigating confirmation bias. These findings demonstrate that semi-supervised learning via continuous bilevel optimization offers a practical and robust enhancement to data-scarce additive manufacturing monitoring. Because any hidden defects in the topmost layer will be permanently buried by subsequent extrusion, this foundational layer-wise segmentation step is the most critical primitive of the monitoring pipeline.