The specific technical objectives of WP1000 -- State-of-the-art revision, gap analysis and finalized technical specs are:

Objective 1: Conduct a comprehensive review of the state-of-the-art in traffic forecasting. This includes classical time series methods, machine learning models, and recent advances in deep learning such as transformers and Kolmogorov-Arnold Networks (KANs). Their capabilities, requirements, and limitations are analysed in the context of satellite bandwidth demand prediction. 

Objective 2: Identify gaps and limitations in current forecasting solutions for GOVSATCOM scenarios. A critical assessment of the reviewed methods is performed to determine which approaches are best suited to the constraints and operational needs of GOVSATCOM services, including uncertainty estimation and forecasting resolution. 

Objective 3: Benchmark analysis. A benchmarking framework is defined to systematically compare selected models based on accuracy, explainability, resource consumption, and ability to generalize across multiple satellite beams and traffic profiles.

Objective 6: Assess data security and privacy risks. A high-level security risk analysis is performed, along with mitigation strategies including data anonymization, aggregation, and optional synthetic data generation using privacy-preserving techniques.

Objective 4: Define a finalised technical specification. All findings are consolidated into a technical specification that outlines requirements for model inputs and outputs, temporal resolution, prediction horizons, and integration with satellite network operations. 

Objective 5: Develop a high-level verification and validation plan. The project defines how models will be trained and tested using real satellite traffic data (provided by EUTELSAT) and/or synthetic datasets, ensuring reproducibility and performance transparency.