ONGOING FOLLOW-UP NEA OBSERVATIONS IN UKRAINE AND CHINA USING RDS CCD TECHNIQUE

The current state of near-Earth asteroids (NEAs) observations shows an annual increase in the number of newly discovered objects However, the frequency distribution of NEAs by size shows a sharp decrease in the number of objects with size less than 300 m, which contradicts the results of theoretical modeling of the NEA population. Considering definition of potentially hazardous asteroids (PHA), only objects with diameters more than 140 m could pose catastrophic consequences to the Earth and mankind in general. But in the same time, impacts of smaller size objects could lead to significant consequences on local level and their large predicted number increases this probability. Due to their small size which results in faint apparent magnitude, such NEAs are discovered in a short interval of their close approach (CA) to the Earth, when their apparent magnitude are tending to be as bright as possible for a given size. This is not only facilitates the detection of such new objects but also increases their observability by small ground-based telescopes. However, apparent rate of motion during this time might exceed 10 deg d −1 making the observations challenging. The used Rotating-drift-scan CCD (RDS CCD) technique allows to get images of fast-moving objects as a point, that in turn to determine the coordinates of their image centers with sufficient astrometric precision. Obtained in current research project positions show errors in the range ± (0.2″ − 0.3″) in both coordinates with comparison both to JPL's HORIZONS 1 system and NEODyS-2 2 service. The part of observations was obtained around time moment of minimal distance to the Earth during current CA for newly discovered NEAs. Such observations are important to extend observed orbital arc for reliable improvement of their orbit determinations and reducing orbital uncertainty, so it will be possible to recover them in next apparitions.

A Novel Approach to Asteroid Impact Monitoring

Orbit-determination programs find the orbit solution that best fits a set of observations by minimizing the root mean square of the residuals of the fit. For near-Earth asteroids, the uncertainty of the orbit solution may be compatible with trajectories that impact Earth. This paper shows how incorporating the impact condition as an observation in the orbit-determination process results in a robust technique for finding the so-called virtual impactors, i.e., the regions in parameter space leading to impacts. The impact pseudo-observation residuals are the b-plane coordinates at the time of close approach and the uncertainty is set to a fraction of the Earth radius. The extended orbit-determination filter converges naturally to an impacting solution if allowed by the observations. The uncertainty of the resulting orbit provides an excellent geometric representation of the virtual impactor. As a result, the impact probability can be efficiently estimated by exploring this region in parameter space using importance sampling. The proposed technique can systematically handle a large number of estimated parameters, account for nongravitational forces, deal with nonlinearities, and correct for non-Gaussian initial uncertainty distributions. The algorithm has been implemented into a new impact-monitoring system at JPL called Sentry-II, after undergoing extensive testing. The main advantages of Sentry-II over the previous Sentry system are that Sentry-II can systematically process orbits perturbed by nongravitational forces and that it is generally more robust when dealing with pathological cases. The run times and completeness of both systems are comparable, with the impact probability of Sentry-II for 99% completeness being 3 × 10−7.

Influence of non-gravitational effects on the Centaur upper stage of the Surveyor 2 spacecraft

A small near-Earth asteroid, discovered by the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) on September 17, 2020, turned out to be a part of the Centaur upper stage of the Surveyor 2 spacecraft launched by NASA on September 20, 1966 and subsequently crashed. This object had moved in a heliocentric orbit until it was under the influence of Earth’s gravitational field. As a result, a close approach to the Earth took place at a distance of about 50000 km on December 1, 2020. Despite the fact that the Centaur escaped back into a new orbit around the Sun in March 2021, it is of special interest for research, in particular, to consider the impact of non-gravitational effects on its orbital characteristics. Thus, it was calculated that the maximum displacement of the object trajectory due to the influence of solar radiation pressure over 15 years (the next close approach will take place in 2036) can be about 10.3-13.5 km, depending on the albedo. Estimations of the Yarkovsky effect showed that the magnitude of the expected change in the semi-major axis of Centaur’s orbit is from -8.1 • 10−13 to 1.6 10−13, depending on the angle of its rotation.

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

Use of a evolutionary algorithm to optimize interplanetary transfers

In this paper, it was studied the optimization of the cost of interplanetary missions with emphasis on reducing fuel consumption. To achieve this goal, a genetic algorithm was implemented to optimize the total impulse of orbital transfer. It was implemented a case of sending a space vehicle from Earth to a another planet using a gravity assist maneuver (swing by), in this paper it was chose sending a spacecraft from Earth to Mars with a close approach to the Venus. The method employed can be used for impulsive interplanetary missions in general, and so the solution found can become an initial solution for numerical methods of optimization of low thrust maneuvers

Anxiety Levels of Undergraduate and Clerkship Medical Students during the COVID-19 Pandemic

The COVID-19 pandemic has brought a devastating impact on the world. Medical students who belong to psychologically vulnerable groups also share more burdens due to the medical education academic demands, curriculum transition to virtually-delivered format, and the risk of being infected by the disease during clinical settings. This study aims to identify the anxiety level of undergraduate and clerkship medical students to create proper and effective strategies to build good mental status among medical students during the COVID-19 pandemic. It is a cross-sectional study. The survey was conducted using an online questionnaire to assess respondents’ identity, demographic data, family history, perceptions about online/offline learning, and the researchers used the Taylor Manifest Anxiety Scale (TMAS) test to measure the anxiety level of the subjects. The subjects of this study were 164 medical students, divided into two groups, 94 final year undergraduate students and 70 final year clerkship students who were still doing their clinical rotations at the hospital. The findings of this study informed that the average anxiety level was 18.3 for undergraduate students and 19.6 for clerkship students. The TMAS score was higher among clerkship students than undergraduate students. However, the statistical analysis showed no difference (p=0.306) in TMAS scores between clerkship and undergraduate students. A close approach and continuous observation are needed because the higher the TMAS score indicates the higher the anxiety level.

Neurological Soft Signs (NSS) in Census-Based, Decade-Adjusted Healthy Adults, 20 to >70 Years of Age

Neurological soft signs (NSS) represent minor neurological features and have been widely studied in psychiatric disease. The assessment is easily performed. Quantity and quality may provide useful information concerning the disease course. Mostly, NSS scores differ significantly between patients and controls. However, literature does not give reference values. In this pilot study, we recruited 120 healthy women and men to build a cross-sectional, census-based sample of healthy individuals, aged 20 to >70 years, subdivided in 10-year blocks for a close approach to the human lifeline. Testing for NSS and neurocognitive functioning was performed following the exclusion of mental and severe physical illness. NSS scores increased significantly between ages 50+ and 60+, which was primarily accountable to motor signs. Gender and cognitive functioning were not related to changes of scores. Although the number of individuals is small, study results may lay a foundation for further validation of NSS in healthy individuals.

Serial-robot Wrist-singularity Mitigation Along Alternative Optimally Adjusted Paths

Adjusting the displacement path of a serial robot encountering the wrist singularity to pass either through the singularity or around it mitigates its adverse effects. Both such path adjustments are commonly called singularity avoidance and are applied here to either a spherical or an offset wrist. These adjustments avoid high joint rates that can occur at singularity encounter. A recent through-the singularity method limits joint rates and accelerations in the robot with either a spherical or offset wrist when conducting a constant rate of traversal of the tool manipulated by the robot. A kinematic model adding multiple virtual joints allows a modified high-order path-following algorithm to maintain accurate tool position while achieving an optimal level of tool deviation in orientation. Whereas a path reversal resulting from a turning-point type singularity had been revealed for an offset wrist over a finite range of close-approach, these conditions are met when connecting the isolated path segments. Procedures are developed here with this capability for an around-the-singularity path. Choosing between the through and around-singularity alternatives offers the overall optimum.

The Manifold Of Variations: impact location of short-term impactors

The interest in the problem of small asteroids observed shortly before a deep close approach or an impact with the Earth has grown a lot in recent years. Since the observational dataset of such objects is very limited, they deserve dedicated orbit determination and hazard assessment methods. The currently available systems are based on the systematic ranging, a technique providing a two-dimensional manifold of orbits compatible with the observations, the so-called Manifold Of Variations. In this paper we first review the Manifold Of Variations method, to then show how this set of virtual asteroids can be used to predict the impact location of short-term impactors, and compare the results with those of already existent methods.

Spacecraft collision avoidance challenge: Design and results of a machine learning competition

Spacecraft collision avoidance procedures have become an essential part of satellite operations. Complex and constantly updated estimates of the collision risk between orbiting objects inform various operators who can then plan risk mitigation measures. Such measures can be aided by the development of suitable machine learning (ML) models that predict, for example, the evolution of the collision risk over time. In October 2019, in an attempt to study this opportunity, the European Space Agency released a large curated dataset containing information about close approach events in the form of conjunction data messages (CDMs), which was collected from 2015 to 2019. This dataset was used in the Spacecraft Collision Avoidance Challenge, which was an ML competition where participants had to build models to predict the final collision risk between orbiting objects. This paper describes the design and results of the competition and discusses the challenges and lessons learned when applying ML methods to this problem domain.