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Abstract Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced.

Abstract This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects.

Abstract This paper demonstrates the capabilities of convolutional neural networks (CNNs) at classifying types of motion starting from time series, without any prior knowledge of the underlying dynamics. The paper applies different forms of deep learning to problems of increasing complexity with the goal of testing the ability of different deep learning architectures at predicting the character of the dynamics by simply observing a time-ordered set of data. We will demonstrate that a properly trained CNN can correctly classify the types of motion on a given data set. We also demonstrate effective generalisation capabilities by using a CNN trained on one dynamic model to predict the character of the motion governed by another dynamic model. The ability to predict types of motion from observations is then verified on a model problem known as the forced pendulum and on a relevant problem in Celestial Mechanics where observational data can be used to predict the long-term evolution of the system.

Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

The databases included on this article refers to variables and parameters belonging to the Space Traffic Management (STM), Evidence Theory and Machine Learning (ML) fields. They have been used for implementing ML for autonomously predict risk associated to a close encounter between two space (Sanchez and Vasile, On the Use of Machine Learning and Evidence Theory to Improve Collision Risk Management, Acta Astronautica, Special Issue for ICSSA2020, In Press [1]). The position of the objected is assumed to be affected by epistemic uncertainty, which has been modeled according to Dempster-Shafer Evidence theory (DSt) [2].Six datasets are presented. Two (DB1 and DB2, respectively) include samples of space object close encounters subject to epistemic uncertainty on the relative position. Other two databases (DB3 and DB4, respectively) include the values of the Cumulative Plausibility and Belief Curves (CPC and CBC, respectively) of each sample included in DB1. The remaining databases (DB5 and DB6), contain the value of the CPC and CBC of each sample included in DB2. All of them are synthetic databases created using computer simulation to obtain the results presented in [1]. DB1 database is constituted by 9,000 samples and 45 columns and a header, while DB2 is formed by 28,800 samples and 45 columns and a header. These databases come from a set of, respectively, 5 and 14 different families of encounter geometries defined by the range of values that can be assigned to the bounds of the intervals for the uncertain variables, assumed to be affected by epistemic uncertainty, considered to have been provided by two sources of information. The uncertain variables are: the miss distance, [µx, µy], on the impact plane (B plane), the standard deviation of the relative position projected on the B plane, [σx, σy], and the Hard Body Radius of the combined objects, HBR. The dataset is completed with STM related parameters: miss distance and covariance matrix of the uncertain ellipse projected on the B plane enclosing all samples defined by the uncertainty intervals, the Probability of Collision (PC) of this ellipse or the elapsed time to the Time of Closest Approach (TCA); with DSt related parameters: Belief and Plausibility of certain values of Pc; and the class of the event according to the classification detailed in [1]. DB3 and DB4 are constituted by 34 columns and 9000 rows containing the Plausibility and Belief for Pc values and the corresponding Probabilities of Collision necessary to build the CPC and CBC of the events in DB1, while DB5 and DB6 are constituted by 34 columns and 28,800 rows containing the Plausibility and Belief for Pc values and the corresponding Probabilities of Collision values necessary to build the CPC and CBC of the events in DB2.These databases have a potential usage by the ML community interested in STM as well as for the space community, especially, space operators interested in introduce epistemic uncertainty on collision risk assessment. These databases contribute to build a scarce field such as the databases of encounter events [3].

This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system.

As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

Inflammation has been recognized as a common trait in the pathogenesis of multifactorial diseases including obesity, where a low-grade inflammation has been established and may be responsible for the cardiovascular risk related to the disease. Obesity has also been associated with the increased incidence and a worse outcome of rheumatoid arthritis (RA) and osteoarthritis (OA). RA is characterized by systemic inflammation, which is thought to play a key role in accelerated atherosclerosis and in the increased incidence of cardiovascular disease, an important comorbidity in patients with RA. The inflammatory process underlying the cardiovascular risk both in obesity and RA may be mediated by adipocytokines, a heterogeneous group of soluble proteins mainly secreted by the adipocytes. Many adipocytokines are mainly produced by white adipose tissue. Adipocytokines may also be involved in the pathogenesis of OA since a positive association with obesity has been found for weight-bearing and nonweight-bearing joints, suggesting that, in addition to local overload, systemic factors may contribute to joint damage. In this review we summarize the current knowledge on experimental models and clinical studies in which adipocytokines were examined in obesity, RA, and OA and discuss the potential of adipocytokines as comorbidity biomarkers for cardiovascular risk.

This paper presents a novel approach for the preliminary design of Low-Thrust, many-revolution transfers. The main feature of the novel approach is a considerable reduction in the control parameters and a consequent gain in computational speed. Each spiral is built by using a predefined pattern for thrust direction and switching structure. The pattern is then optimised to minimise propellant consumption and transfer time. The variation of the orbital elements due to the thrust is computed analytically from a first-order solution of the perturbed Keplerian motion. The proposed approach allows for a realistic estimation of ΔV and time of flight required to transfer a spacecraft between two arbitrary orbits. Eccentricity and plane changes are both accounted for. The novel approach is applied here to the design of missions for the removal of space debris by means of an Ion Beam Shepherd Spacecraft. In particular, two slightly different variants of the proposed low-thrust control model are used for the different phases of the mission. Thanks to their low computational cost they can be included in a multiobjective optimisation problem in which the sequence and timing of the removal of five pieces of debris are optimised to minimise propellant consumption and mission duration.