## 2 Related Work  
It is relevant to note that the use of events is transversal across several application areas, allowing for the detection of simple changes or even complex behaviors. This is the case in [7], where a graphical formalism for signal interpretation modeling is proposed, allowing for the identification of associated events and conditions, which can be used in the behavior model description of the system. Complex Event Processing (CEP) [8] has been used in a wide range of areas, including network management, databases, and discrete-event simulation. CEP handles the composition of events from an event cloud, leading to the implementation of Event-Driven Architectures and allowing for the extraction of information from distributed systems based on events and messages. In the context of controllers for embedded and cyber-physical systems, events, considered changes in the value of signals, are widely used by different modeling formalisms, technologies, and computational tools. Their use leads to an intrinsic reduction in the dimension of the model when graphical representations are considered. This is notably the case for some state-based computational models, such as reactive systems [9], state-charts [10], and Petri nets [11]. In [12], the approach followed in this paper was first proposed, relying on the definition of sub-models to encapsulate the detection of specific signal evolution and further execution concurrently with the main system's model. In [12], two types of signals were considered, namely, Boolean signals and multivalued signals. For Boolean signals, a set of associated events was proposed, namely, _Up_, _Down_, _UpOrDown_, _UpDown_, and _DownUp_. For multivalued signals, the set of events defined for Boolean signals was defined and extended with _UpDownHyst_ and _DownUpHyst_, introducing a hysteresis dependency on the analysis of multivalue signals. Building upon the work of [12], this paper refines their proposals on hysteresis dependencies and introduces a novel event type. This new event type incorporates an additional constraint when analyzing signal evolution, requiring a specific condition to be met for event generation. _IOPT Nets--Input-Output Place-Transition Nets--A Brief Overview_  
Carl Adam Petri introduced basic Petri net concepts in his 1962 Ph.D. thesis [13]. Since then, various classes of Petri nets have been proposed, making them a diverse family of languages [14]. Despite their diversity, all Petri nets can be represented through a graph with two types of nodes, named places and transitions, connected by directed arcs. Nodes of one type can only connect to nodes of the other type. Places are visually represented as circles or ellipses, while bars, squares, or rectangles denote transitions. These two types of nodes embody a dual perspective when modeling systems: places are associated with states or resources, while transitions are associated with changes in the state, actions, or resource production and consumption. A positive integer value, which can include zero, is associated with each place, is referred to as the \"place marking\", and is graphically represented by tokens in the form of small dots inside the place or by a number. The current system state is composed of a set of place markings. The system state undergoes changes when one or more transitions are fired. When a transition fires, it removes tokens from its input places and generates tokens into its output places according to the weights assigned to the arcs connecting the transition and the place. A transition in a Petri net model can only fire if it is _enabled_. For a transition to be enabled, each input place contains at least the number of tokens specified by the arc weight connecting that place to the transition. However, when the goal is to model automation systems and discrete-event controllers' behavior, it is of paramount importance to consider additional external conditions that must be met for a transition to fire.