An equivalence result for VC classes of sets

Let R and θ be infinite sets and let A # R × θ. We show that the class of projections of A onto R is a Vapnik–Chervonenkis (VC) class of sets if and only if the class of projections of A onto θ is a VC class. We illustrate the result in the context of semiparametric estimation of a transformation model. In this application, the VC property is hard to establish for the projection class of interest but easy to establish for the other projection class

Insights into UAS Accidents and Incidents

The proliferation and extension of unmanned aircraft systems from military to civil and public use applications has rapidly outpaced the safety analysis that is normally associated with the introduction of a new and novel aircraft. Insights into the types of anomalous events associated with accidents and incidents involving civil and public use unmanned aircraft systems operating in the National Airspace System were derived from an information synthesis of archival publically available reports from the FAA Preliminary Reports of Unmanned Aircraft System Accidents and Incidents database, as recorded in the Aviation Safety Information and Analysis Sharing system. The vast majority of anomalous events were related to equipment failures, primarily lost link, distantly followed by a variety of non-equipment related events involving pilot/operator errors, such as altitude deviations, airspace violations, and procedural deviations. The identification of types of events associated with accidents and incidents provided insights for consideration when developing and implementing mitigation strategies for the design, training, and operation of unmanned aircraft systems

Validation of New Technology Using Legacy Metrics: Examination of SURF-IA Alerting for Runway Incursion Incidents

This study demonstrated an innovative method of utilizing expert raters and actual high-risk incidents to identify shortcomings of using legacy metrics to measure the effectiveness of new technology designed to mitigate hazardous incidents. Expert raters were used to validate the Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF-IA) model for providing alerts to pilots to reduce the occurrence of pilot deviation type runway incursion incidents categorized as serious (Category A or B) by the legacy FAA/ICAO Runway Incursion Severity Classification (RISC) model. The study concluded that the SURF-IA model did not yield an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model. The different outcomes between the RISC and SURF-IA models may result in misleading information when using the reduction in serious runway incursion incidents as a metric for the benefit of SURF-IA technology

Anthropometry Considerations in the Design and Evaluation of Flight Deck Displays and Controls: Literature Review

The regulatory requirements and guidance for showing compliance with the Federal Aviation Administration anthropometric considerations in aircraft design are widely dispersed in an array of source documents. Consequently, they are difficult for aircraft manufacturers/applicants to identify, interpret, and address thereby extending the time to certify new/modified aircraft and aircraft systems. This literature review is intended to provide a compendium of the anthropometry considerations in the design certification of displays and controls in aircraft flight decks. This document is not a compliance document and the references herein are frequently updated, hence should be verified by the reader

Synthesis of Unmanned Aircraft Systems Safety Reports

The proliferation and extension of unmanned aircraft systems from military to civil and public use applications has rapidly outpaced the safety analysis that is normally associated with the introduction of a new and novel aircraft. Insights into the types of anomalous events associated with accidents and incidents involving civil and public use unmanned aircraft systems operating in the National Airspace System were derived from an information synthesis of archival, publically available reports from the Aviation Safety Information and Analysis Sharing system. The vast majority of anomalous events were related to equipment failures, primarily lost link, distantly followed by a variety of non-equipment-related events involving pilot/operator errors, such as near-mid-air collisions, altitude deviations, airspace violations, and procedural deviations. The identification of types of anomalous events associated with safety reports provided insights for consideration when developing and implementing mitigation strategies for the design, training, and operation of unmanned aircraft systems

Validation of New Technology using Legacy Metrics: Examination of Surf-IA Alerting for Runway Incursion Incidents

New flight deck technology designed to mitigate runway incursions may not be effective in triggering a flight deck alert to avoid high speed surface collisions for runway incursions classified as serious by legacy metrics. This study demonstrated an innovative method of utilizing expert raters and actual high-risk incidents to identify shortcomings of using legacy metrics to measure the effectiveness of new technology designed to mitigate hazardous incidents. Expert raters were used to validate the Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF-IA) model for providing alerts to pilots to reduce the occurrence of pilot deviation type runway incursion incidents categorized as serious (Category A or B) by the FAA/ICAO Runway Incursion Severity Classification (RISC) model. This study used archival data from Aviation Safety Information Analysis and Sharing (ASIAS) incident reports and video reenactments developed by the FAA Office of Runway Safety. Two expert raters reviewed nine pilot deviation type serious runway incursion incidents. The raters applied the baseline minimally compliant implementation of the RTCA/DO 323 SURF-IA model to determine which incidents would have an alerting SURF-IA outcome. Inter-rater reliability was determined by percentage agreement and Cohen’s kappa and indicated perfect agreement between the raters who assessed six of the incidents with a SURF-IA alerting outcome and three as non-alerting. Specific aircraft states were identified in the baseline SURF-IA model that precluded an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model: (a) wrong runway departures, (b) no alert if traffic entered runway after ownship lift-off from same runway, and (c) helicopter operations. The study concluded that the SURF-IA model did not yield an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model. Even if the SURF-IA model had performed to design, the best it could have achieved would have been a 70% alerting outcome for incidents classified as serious by the legacy RISC model metric. In the qualitative analysis both raters indicated that neither the legacy RISC definition of on-runway nor the SURF-IA definition was appropriate. Hence, the raters’ recommendation was not to adopt either model’s definition, but rather develop an entirely new definition through further study. The raters were explicit about the criticality of appropriate and harmonized definitions used in the models. The different outcomes between the RISC and SURF-IA models may result in misleading information when using the reduction in serious runway incursion incidents as a metric for the benefit of SURF-IA technology. It is recommended that prior to using the ASIAS runway incursion data as a metric for the benefit of SURF-IA, the FAA develop a process for identifying and tracking ASIAS reported PD type serious runway incursion incidents which will not trigger a SURF-IA alert. Consideration should be made to improving the SURF-IA model technical capabilities to accommodate all possible aircraft states that the RISC model would classify as serious runway incursion incidents

Detectability of Clothing Color by Small Unmanned Aircraft Systems for Search and Rescue Operations

Search-and-rescue operations are adopting small unmanned aircraft system technology to aid traditional human search parties operating on foot or by vehicle, which can be hindered by challenging terrain and obstacles. This study utilized three camera equipped small unmanned aircraft to collect aerial images of a simulated human torso in a rural/remote desert summer daytime environment. The study compared detection rates for 10 different garment colors using image analysis software. Results indicated that garment color was statistically significant for detection rate. However, the garment colors with the highest detection rates did not correspond with the garment colors recommended by retail outlets, government agencies, or hunting clubs. As the use of small unmanned aircraft for search and rescue operations continues to increase, it is imperative that individuals conducting activities that have a higher risk for getting lost or injured are aware of the limitations of emerging technology, and that they wear the most visible color of clothing to ensure the best chance of rescue, should the need arise

Standardization Roadmap for Unmanned Aircraft Systems, Version 2.0

This Standardization Roadmap for Unmanned Aircraft Systems, Version 2.0 (“roadmap”) is an update to version 1.0 of this document published in December 2018. It identifies existing standards and standards in development, assesses gaps, and makes recommendations for priority areas where there is a perceived need for additional standardization and/or pre-standardization R&D. The roadmap has examined 78 issue areas, identified a total of 71 open gaps and corresponding recommendations across the topical areas of airworthiness; flight operations (both general concerns and application-specific ones including critical infrastructure inspections, commercial services, and public safety operations); and personnel training, qualifications, and certification. Of that total, 47 gaps/recommendations have been identified as high priority, 21 as medium priority, and 3 as low priority. A “gap” means no published standard or specification exists that covers the particular issue in question. In 53 cases, additional R&D is needed. As with the earlier version of this document, the hope is that the roadmap will be broadly adopted by the standards community and that it will facilitate a more coherent and coordinated approach to the future development of standards for UAS. To that end, it is envisioned that the roadmap will continue to be promoted in the coming year. It is also envisioned that a mechanism may be established to assess progress on its implementation

Public Acceptance of Guidance and Regulations for Space Flight Participation

Space flight participants are not professional astronauts and not subject to the rules and guidance covering space flight crewmembers. Ordinal logistic regression of survey data was utilized to explore public acceptance of current medical screening recommendations and regulations for safety risk and implied liability for civil space flight participation. Independent variables constituted participant demographic representations while dependent variables represented current Federal Aviation Administration guidance and regulations. Odds ratios were derived based on the demographic categories to interpret likelihood of acceptance for the criteria. Significant likely acceptance of guidance and regulations was found for five of twelve demographic variables influencing public acceptance of one or more areas of guidance and regulations: age, household size, marital status, employment status, and employment class. Increases in age and household size, never married, employed full-time, and self-employed exhibited significance in increased likelihood of acceptance of one or more areas of the guidance and regulations for space flight participation. Findings are intended to inform government regulators and commercial space industries on what guidance and regulations the different demographics of the public are willing to accept

AN EQUIVALENCE RESULT FOR VC CLASSES OF SETS

Let R and Q be infinite sets and let A ʕ R ϫ Q+ We show that the class of projections of A onto R is a Vapnik-Chervonenkis~VC! class of sets if and only if the class of projections of A onto Q is a VC class+ We illustrate the result in the context of semiparametric estimation of a transformation model+ In this application, the VC property is hard to establish for the projection class of interest but easy to establish for the other projection class