## 1 Introduction  
An innovative, digital, modular, and fully integrated air data system (ADS) is designed and manufactured for the small aircraft transportation (SAT) community that belongs to the CS-23 category [1], suitable for fly-by-wire (FBW) applications. The proposed certifiable ADS is partially based on synthetic sensors [2] and can be easily extended to other aircraft categories, such as unmanned aerial vehicles (UAV) or urban air mobility (UAM) vehicles. The present work is conducted following guidelines from ARP4754 [3] that define recommended practices for development and safety assessment processes for avionic systems. The practices prescribed by these documents are recognized by the airworthiness authorities (e.g., European Aviation Safety Agency (EASA)) as acceptable means for showing compliance with airworthiness regulations [1, 4]. Typically, a simplex air data system (ADS) is made up of external (i.e., protruding from the aircraft fuselage) parts, such as probes and vanes. The air data functions (ADFs) shall calculate all air parameters as required by the standard AS8002 [5] from direct measures of the following:  
* local static pressure, \(P_{s}\);
* local dynamic pressure, \(q_{c}\);
* local air temperature (static, \(OAT\), or total, \(TAT\));
* local angle of attack, \(\alpha\) or AoA;
* local angle of sideslip, \(\beta\) or AoS.  
Synthetic sensors are researched extensively in the academic field of air data, but there are no examples of synthetic sensors certified for civil operations. Air data synthetic sensors are mainly used as (1) Pitot-free aircraft speed estimators [6], (2) vane/sensor-free flow angle estimators [7], and (3) Pitot and vane/sensor-free for both airspeed and flow angle estimators [8].  
As the avionic background is mature to welcome such innovations on board, synthetic solutions can be replaced or added to physical (or mechanical) sensors in order to analytically increase the system redundancy [9; 10; 11; 12]. Another chance is to use synthetic sensors to monitor physical sensors and to accommodate possible failures [13; 14; 15]. In fact, the concurrent use of dissimilar sources of the same air data (physical and synthetic ones) can be beneficial to solve some issues related to common failure modes or incorrect failure diagnosis of a modern air data system [16; 17].  
As far as the flow angle estimation is concerned, the first example can be found in [18; 19] and recently in [11; 20; 21; 22; 23; 24; 25]. Model-based (e.g., Kalman filter), data-driven (e.g., neural networks), and model-free [26; 27] are the approaches commonly used. The proposed ADS employs flow angle synthetic sensor based on pretrained neural networks [28] because (1) their determinism (e.g., with respect to Kalman filters) would ease the certification process and (2) low computational effort is required (e.g., with respect to fuzzy approaches [29]).  
The present paper presents a safety analysis of the ADS with a nonredundant (or simplex) architecture. In fact, the objective of the present work is to assess if the simplex ADS can meet the required specifications and, on the other hand, identify the most critical air data system parts and sub-parts to identify possible technological gaps to fill in order to achieve the airworthiness safety objectives with a nonredundant architecture.  
This paper begins with a description of the standard procedures followed to perform the safety analysis in Section 2. The ADS is introduced to describe all parts and features in Section 3. The ADS parts' failure rates (FRs) are evaluated in Section 4. The safety analysis is presented in Section 2, where the results related to the simplex ADS are evaluated and then compared to the safety objectives in order to establish whether a simplex configuration could satisfy the safety requirements. The main outcomes of the present work are collected in Section 6.