# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 2 JDL Classification  
To observe how fusion systems are structured and applied, it is necessary to consider the JDL model, which has been used as a fundamental paradigm for many subsequent models and practical implementations designed over the years [29]. The concept of data fusion was first formalised by the scientific community through the development of the JDL data fusion model (Figure 1), which is still a standard reference in the industry today. The model was assigned several primary objectives: (i) to provide a common framework for discussion on information fusion; (ii) to enable the recognition of problems that can be solved by applying fusion techniques; (iii) to standardise the characteristics of fusion-type problems; (iv) to provide a framework that can be used to guide the design of computational solutions; (v) to enable the classification of various fusion processes.  
The JDL model has several basic components: data sources, a database management system (DBMS), a human-computer interface (HCI), and a five-level fusion process. The sources provide raw data and can be sensors, prior knowledge (e.g., geographical or reference data), databases, or manual user input. The DBMS enables the system to store, retrieve, and manage data effectively, supporting the entire fusion process. The HCI comprises all the mechanisms and tools through which the user interacts with the system or receives output, such as visual displays, alarms, graphical symbols, and acoustic alerts. The five main levels of the JDL model are described below: Level 0 describes the estimation and prediction of the state of an object from raw signals or pixel-level data through association and characterisation. Level 1 involves the integration of sensor data to estimate and predict the state of the entities under observation, i.e., their position, speed, attributes, and identity. Level 2 considers the relationships between multiple entities and events in order to establish their state within a broader contextual scenario. Level 3 includes the analysis and prediction of the consequences of planned actions in various operational scenarios. Level 4 enables goal-oriented data collection and processing through adaptive management to improve overall performance and decision-making. Subsequently, Level 5, called cognitive or user refinement [29], was included to provide human interpretation, decision support, and learning processes. While the original JDL model is primarily concerned with the fusion of data of the same type, our system, Hybrid Information Fusion, is concerned with heterogeneous data sources. This makes one-to-one mapping to the JDL architecture more complicated. However, our system fits better with Level 1 as it deals with the fusion of inputs from different sources to determine the state and identity of cryptographic factors obtained from quantum-inspired and prime number-based fused inputs.  
As we will see below, the Joint Distribution Layering (JDL) model is a conceptual reference for integrating and justifying the overlapping of different domains in key generation (inspired by quantum mechanics, sieving, fusion). It has no direct impact on the mathematical robustness of the key but guides the modular design of the system and ensures epistemological consistency in the fusion of methods. In short, it is a determining factor in design rather than a standalone security component.  
Figure 1: Overview of the JDL architecture: The framework integrates multilevel processing and JDL fusion model. Each level represents a different stage of data transformation.