Bootstrap-Based Receiver Synchronization and System Discovery in B2X: An Extension of ATSC 3.0

Addressing the increasing and diversified demands of multicast and broadcast services require highly efficient multicast and broadcast technologies. Broadcast networks, such as Advanced Television Systems Committee 3.0 (ATSC 3.0), are inherently designed to support these services and continue to evolve to meet growing performance and scalability requirements. At the same time, smartphones are increasingly used for video streaming and other high-volume services, placing growing pressure on mobile network capacity. Interworking between broadcast and mobile networks is therefore an important enabler for efficient and seamless service delivery. In this context, Broadcast-to-Everything (B2X) extends ATSC 3.0 to support enhanced interoperability with Third Generation Partnership Project (3GPP) mobile systems while maintaining low cross-correlation with ATSC 3.0 bootstrap signals, supporting reliable system identification in scenarios where multiple waveforms may be present. Bootstrap signaling, which enables initial signal detection and synchronization, is a key feature of ATSC-based waveform discovery and synchronization, and B2X further extends this capability through a scalable bootstrap framework supporting a range of bandwidth configurations. This paper investigates system discovery through bootstrap signal detection at the B2X receiver and presents key design-related findings, including parameter selection and cross-testing with ATSC 3.0. We present extensive simulations of the receiver performance under diverse propagation and mobility conditions, ranging from stationary to high-speed scenarios. The results demonstrate the robustness of the B2X bootstrap signaling design across a broad range of channel conditions relevant to multicast and broadcast operation.

SimGen: Simulator-conditioned Driving Scene Generation

Controllable synthetic data generation can substantially lower the annotation cost of training data in autonomous driving research and development. Prior works use diffusion models to generate driving images conditioned on the 3D object layout. However, those models are trained on small-scale datasets like nuScenes, which lack appearance and layout diversity. Moreover, the trained models can only generate images based on the real-world layout data from the validation set of the same dataset, where overfitting might happen. In this work, we introduce a simulator-conditioned scene generation framework called SimGen that can learn to generate diverse driving scenes by mixing data from the simulator and the real world. It uses a novel cascade diffusion pipeline to address challenging sim-to-real gaps and multi-condition conflicts. A driving video dataset DIVA is collected to enhance the generative diversity of SimGen, which contains over 147.5 hours of real-world driving videos from 73 locations worldwide and simulated driving data from the MetaDrive simulator. SimGen achieves superior generation quality and diversity while preserving controllability based on the text prompt and the layout pulled from a simulator. We further demonstrate the improvements brought by SimGen for synthetic data augmentation on the BEV detection and segmentation task and showcase its capability in safety-critical data generation. Code, data, and models will be made available.

Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.