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New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

New for 2022, AeroTech® will deliver even more robust programming by teaming up with AeroMat to deliver learning opportunities dedicated to: Additive Manufacturing and Materials, Environment and Sustainable Aviation (Sustainability), Autonomy and AI, Safety and Human Factors, Modeling, Simulation and Testing, Cybersecurity / Cyber-Physical Security, Industry 4.0 Smart Manufacturing and Assembly, IDEAL Summit (inclusion, diversity, equity, accessibility and leadership), Advanced Air Mobility (AAM) and Multimodal Mobility (M3)

Day by day, airports adopt more IoT devices. However, airports are not exempt from possible failures due to malware’s proliferation that can abuse vulnerabilities. Computer criminals can access, corrupt, and extract information from individuals or companies. This paper explains the development of a propagation model, which started with a Delphi process. We discuss the preliminary implications for airports of the simulation model built from the Delphi recommendations.

SAE EDGE Research Reports provide examinations significant topics facing mobility industry today including Connected Automated Vehicle Technologies Electrification Advanced Manufacturing

SAE EDGE Research Reports provide examinations significant topics facing mobility industry today including Connected Automated Vehicle Technologies Electrification Advanced Manufacturing

SAE EDGE Research Reports provide examinations significant topics facing mobility industry today including Connected Automated Vehicle Technologies Electrification Advanced Manufacturing

This SAE Aerospace Recommended Practice (ARP) provides insights on how to perform a Cost Benefit Analysis (CBA) to determine the Return on Investment (ROI) that would result from implementing a blockchain solution to a new or an existing business process. The word “blockchain” refers to a method of documenting when data transactions occur using a distributed ledger with desired immutable qualities. The scope of the current document is on enterprise blockchain which gives the benefit of standardized cryptography, legal enforceability and regulatory compliance. The document analyzes the complexity involved with this technology, lists some of the different approaches that can be used for conducting a CBA, and differentiates its analysis depending on whether the application uses a public or a private distributed network.

Upon their arrival, Unmanned Autonomous Systems (UAS) brought with them many benefits for those involved in a military campaign. They can use such systems to reconnoiter dangerous areas, provide 24-hr aerial security surveillance for force protection purposes or even attack enemy targets all the while avoiding friendly human losses in the process. Unfortunately, these platforms also carry the inherent risk of being built on innately vulnerable cybernetic systems. From software which can be tampered with to either steal data, damage or even outright steal the aircraft, to the data networks used for communications which can be jammed or even eavesdropped on to gain access to sensible information. All this has the potential to turn the benefits of UAS into liabilities and although the last decade has seen great advances in the development of protection and countermeasures against the described threats and beyond the risk still endures.

In the past, aircraft network design did not demand for information security considerations. The aircraft systems were simple, obscure, proprietary and, most importantly for security, the systems have been either physically isolated or they have been connected by directed communication links. The union of the aircraft systems thus formed a federated network. These properties are in sharp contrast with today’s system designs, which rest upon platform-based solutions with shared resources being interconnected by a massively meshed and shared communication network. The resulting connectivity and the high number of interfaces require an in-depth security analysis as the systems also provide functions that are required for the safe operation of the aircraft. This network design evolution, however, resulted in an iterative and continuous adaption of existing network solutions as these have not been developed from scratch.

The basic requirements of AS9100A apply with the following clarifications. This document supplements the requirements of AS9100A for deliverable software. This supplement contains Quality System requirements for suppliers of products that contain deliverable embedded or loadable airborne, spaceborne or ground support software components that are part of an aircraft Type Design, weapon system, missile or spacecraft operational software and/or support software that is used in the development and maintenance of deliverable software. This includes the host operating system software including assemblers, compilers, linkers, loaders, editors, code generators, analyzers, ground simulators and trainers, flight test data reduction, etc., that directly support creation, test and maintenance of the deliverable software.

This aerospace standard outlines the minimum requirements for the quality assurance program of a distributor of new aircraft or aerospace parts and material. It is designed to aid in the surveillance and accreditation of a distributor who procures new parts and materials and resells these products to customers or other distributors in the aviation or aerospace industry, i.e., a PASS THROUGH distributor. This standard may be used to determine the adequacy and implementation of the distributor’s quality assurance program.

This document describes a process for use by ADHP integrators of EEE parts and sub-assemblies (items) that have been targeted for other applications. This document does not describe specific tests to be conducted, sample sizes to be used, nor results to be obtained; instead, it describes a process to define and accomplish application-specific qualification; that provides confidence to both the ADHP integrators, and the integrators’ customers, that the item will performs its function(s) reliably in the ADHP application.

This document outlines a standard practice for conducting system safety. In some cases, these principles may be captured in other standards that apply to specific commodities such as commercial aircraft and automobiles. For example, those manufacturers that produce commercial aircraft should use SAE ARP4754 or SAE ARP4761 (see Section 2 below) to meet FAA or other regulatory agency system safety-related requirements. The system safety practice as defined herein provides a consistent means of evaluating identified risks. Mishap risk should be identified, evaluated, and mitigated to a level as low as reasonably practicable. The mishap risk should be accepted by the appropriate authority and comply with federal (and state, where applicable) laws and regulations, executive orders, treaties, and agreements. Program trade studies associated with mitigating mishap risk should consider total life cycle cost in any decision.

Breathing Life into Artificial Intelligence and Next Generation Autonomous Aerospace Systems Robotic Rotational Molding Creates New Opportunities for Military and Aerospace Applications Rim-Driven Electric Aircraft Propulsion High-Speed Midwave Infrared Cameras Enable Military Test Range Tracking System What Today's Advances in Radar Technology Mean for Testing and Training Tackling Ruggedization Challenges for RF Communications in Software Defined Radios AUVSI XPONENTIAL 2023 The Blueprint for Autonomy Multi-Scale Structuring of the Polar Ionosphere Understanding a radically new sensing capability for polar ionospheric science introduced by observational evidence recently provided by the electronically steerable Resolute Bay Incoherent Scatter Radar (RISR). Stepped-Frequency Distributed Radar for Through-the-Wall Sensing A technical analysis of the effectiveness of distributed radar for through-the-wall sensing applications.

DoD to Deploy Thousands of Low Cost Autonomous Systems Under Replicator Program Top Productivity Improvement Tips for Manufacturing Turbine Discs FACE Technical Standard Offers MOSA Lessons for Safety-Critical Software in Any Sector Adamant: A Soon-to-be Open Source, Mission-Critical Flight Software Framework Written in Ada Benefits and Challenges of Direct-RF Sampling for Avionic Platforms More Airports Test RF as Counter Measure for UAS in Restricted Airspace Adapting U.S. Army Acquisition to Ensure the Reliability and Safety of Autonomous Vehicles This report presents several challenges that the U.S. Army will face in the transition to autonomous vehicles, challenges that are only magnified in the current acquisition environment with limited testing. Artificial intelligence algorithms introduce additional complexity, resulting in systems with a complex combination of human, machine, and autonomous controllers.