Assessment of Ground-Based Microwave Radiometry for Calibration of Atmospheric Variability in Spacecraft Tracking

In a suggested radio propagation experiment using a deep space antenna, accurate calibration of the propagation delay through the Earth’s atmosphere is essential. One or two microwave radiometers can be used for this purpose. Differences in precise locations of the radiometer(s) and antenna to be calibrated leave a residual wet path delay value. We computed the Allan Standard Deviation (ASD) of this residual, as well as the one resulting from different pointing positions in the plane of the sky, by simulations. Pointing offsets, e.g., to avoid solar radiation into the radiometer beam, lead in general to an increased ASD. However, for many observation geometries a deliberate pointing offset can compensate for the location differences. In the case studied we found a reduction of the ASD with up to 45% compared to the ASD obtained for a zero pointing offset. The size of the calculated ASD depends strongly on the model parameters used, e.g., the turbulence strength parameter C_n^2, which has a significant natural variation over a year

Why non-technical skills matter in surgery. New paradigms for surgical leaders

The surgical literature is paying more and more attention to the topic of soft or non-technical skills (NTS), defined as those cognitive and social skills that characterize high-performing individuals and teams. NTS are essential in supporting surgeons in dealing with unexpected situations. During the COVID-19 pandemic, NTS have been considered crucial in defining situation awareness, enhancing decision making, communicating among groups and teams, and fostering leadership. With a “looking back and planning forward” approach, the current perspective aims at deepening the contribution of NTS for surgeons to deal with the unexpected challenges posed by the COVID crisis, surgical emergencies, the introduction of new technologies in clinical practice, to understand how such skills may help shape the surgical leaders of the future

Performance Characterization of ESA's Tropospheric Delay Calibration System for Advanced Radio Science Experiments

Media propagation noises are amongst the main error sources of radiometric observables for deep space missions, with fluctuations of the tropospheric excess path length representing a relevant contributor to the Doppler noise budget. Microwave radiometers currently represent the most accurate instruments for the estimation of the tropospheric delay and delay rate along a slant direction. A prototype of a tropospheric delay calibration system (TDCS), using a 14 channel Ka/V band microwave radiometer, has been developed under a European Space Agency contract and installed at the deep space ground station in Malargüe, Argentina, in February 2019. After its commissioning, the TDCS has been involved in an extensive testbed campaign by recording a total of 44 tracking passes of the Gaia spacecraft, which were used to perform an orbit determination analysis. This work presents the first statistical characterization of the end-to-end performance of the TDCS prototype in an operational scenario. The results show that using TDCS-based calibrations instead of the standard GNSS-based calibrations leads to a significant reduction of the residual Doppler noise and instability

Numerical fluid dynamics simulation for drones’ chemical detection

The risk associated with chemical, biological, radiological, nuclear, and explosive (CBRNe) threats in the last two decades has grown as a result of easier access to hazardous materials and agents, potentially increasing the chance for dangerous events. Consequently, early detection of a threat following a CBRNe event is a mandatory requirement for the safety and security of human operators involved in the management of the emergency. Drones are nowadays one of the most advanced and versatile tools available, and they have proven to be successfully used in many different application fields. The use of drones equipped with inexpensive and selective detectors could be both a solution to improve the early detection of threats and, at the same time, a solution for human operators to prevent dangerous situations. To maximize the drone’s capability of detecting dangerous volatile substances, fluid dynamics numerical simulations may be used to understand the optimal configuration of the detectors positioned on the drone. This study serves as a first step to investigate how the fluid dynamics of the drone propeller flow and the different sensors position on-board could affect the conditioning and acquisition of data. The first consequence of this approach may lead to optimizing the position of the detectors on the drone based not only on the specific technology of the sensor, but also on the type of chemical agent dispersed in the environment, eventually allowing to define a technological solution to enhance the detection process and ensure the safety and security of first responders

Theory of decoherence in a matter wave Talbot-Lau interferometer

We present a theoretical framework to describe the effects of decoherence on matter waves in Talbot-Lau interferometry. Using a Wigner description of the stationary beam the loss of interference contrast can be calculated in closed form. The formulation includes both the decohering coupling to the environment and the coherent interaction with the grating walls. It facilitates the quantitative distinction of genuine quantum interference from the expectations of classical mechanics. We provide realistic microscopic descriptions of the experimentally relevant interactions in terms of the bulk properties of the particles and show that the treatment is equivalent to solving the corresponding master equation in paraxial approximation.Comment: 20 pages, 4 figures (minor corrections; now in two-column format

Enhancing Radiation Detection by Drones through Numerical Fluid Dynamics Simulations

This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions

Enhancing radiation detection by drones through numerical fluid dynamics simulations

This study addresses the optimization of the location of a radioactive-particle sensor on a drone. Based on the analysis of the physical process and of the boundary conditions introduced in the model, computational fluid dynamics simulations were performed to analyze how the turbulence caused by drone propellers may influence the response of the sensors. Our initial focus was the detection of a small amount of radioactivity, such as that associated with a release of medical waste. Drones equipped with selective low-cost sensors could be quickly sent to dangerous areas that first responders might not have access to and be able to assess the level of danger in a few seconds, providing details about the source terms to Radiological-Nuclear (RN) advisors and decision-makers. Our ultimate application is the simulation of complex scenarios where fluid-dynamic instabilities are combined with elevated levels of radioactivity, as was the case during the Chernobyl and Fukushima nuclear power plant accidents. In similar circumstances, accurate mapping of the radioactive plume would provide invaluable input-data for the mathematical models that can predict the dispersion of radioactivity in time and space. This information could be used as input for predictive models and decision support systems (DSS) to get a full situational awareness. In particular, these models may be used either to guide the safe intervention of first responders or the later need to evacuate affected regions