This PDF file contains the front matter associated with SPIE Proceedings Volume 11755, including the Title Page, Copyright information, and Table of Contents.

Ultraviolet imaging and spectroscopy has been a fundamental underpinning of multiple discoveries in the solar system. Advanced in ultraviolet detector, coatings, and grating technologies in concert with advances in detector driving electronics can lead to new instrument capabilities in potentially more compact forms. We will discuss design and fabrication and testing of a modular delta doped CCD camera that can be integrated with a variety of ultraviolet instruments including a custom UV Offner spectrometer, a spatial heterodyne spectrometer as well as imagers and other spectrometer designs. In this context, we will briefly discuss delta doped silicon arrays, and the custom Offner spectrometer developed at JPL using the convex gratings fabricated for shorter wavelength.

Ultraviolet spectroscopy for astrophysics allows us to address key scientific goals ranging from exoplanets and their environments to stars of all types and ages to galaxies and their evolution. In order to achieve these goals, spectrographs must realize improvements in sensitivity and resolving power. Recent advancements in reflection gratings for X-ray astronomy have enabled concurrent increases in these performance requirements. The nanofabrication-based techniques are now being applied to UV gratings with electron beam lithography (EBL) lying at the heart of the processing steps. EBL allows for custom groove distributions on flat and curved substrates thus opening new parameter space for spectrograph optimization. Blazed profiles are also realizable using EBL itself or subsequent processing steps. We report here the early results of fabrication work and performance testing on UV gratings intended for use on a variety of platforms from upcoming suborbital rockets, to Explorer and Flagship concept missions.

As space missions are designed to push the boundaries of discovery, more stringent demands are placed on detectors. I will present the status and results from the characterization of our UV delta-doped Electron Multiplying CCDs (EMCCD) as an example of a detector with high potential to enable a greater science return. A custom coated, delta doped EMCCD provides a unique combination of architecture and delta-doping for single photon counting, high quantum efficiency, and UV sensitivity. This detector has been flown on a balloon-borne UV multi-object spectrograph, the Faint Intergalactic Redshifted Emission Balloon (FIREBall-2; FB-2) in September 2018, with another opportunity in the fall of 2020. FB-2 was the first instrument to fly this UV detector technology and has facilitated in proving the performance of delta-doped EMCCDs for future space missions and Technology Readiness Level (TRL) advancement. Part of this endeavor will be testing EMCCDs in a radiation environment.

Increasing demand of satellites usage, our community raises awareness of space debris, which could collide with our space inventions. To avoid an unnecessary cost, many engineers resolve this issue with a ground-based observation, which is one of the inexpensive ways of tracked celestial bodies in the Earth vicinity. To use a plain optical telescope without laser ranging to determine orbital parameters by using angles only through Gauss method, and with appropriate calibrating a telescope mounting on a ground station. In this paper, we propose three main parts by first, presenting a concept of calibrating technique on how to obtain observation angle pair when an object is not at the center of image sensor. Second, we optimize the Gauss method’s execution time. Third, we validate that our generated Two-Line Element can be used to track celestial bodies. In our experiment, an 0.7-meter optical telescope equips with image sensor which is located at National Astronomical Research Institute of Thailand, Chiang-Mai, Thailand to project stars on image sensor. The image is mapped to stars database to correct the magnification, shear, and rotation of the image sensor respective to the Cartesian coordinates as a function of astrometry engineering. The result of the method is a plate constant. It is used to correct positions of an interesting celestial body tracked. In this second main part, we investigate the execution time with the same accuracy to other solver of the Gauss method in the famous eight order polynomial. The proposed solver is Laguerre method to find a root finding with convergence rate of cubic. Finally, Our result is proved to be reliable to use as a Two-Line Element update in our telescope system.

Various techniques, applications, and tools for space situational awareness (SSA) have been developed for specific functions that can provide decision support tools. The generality of tools to enable a user-defined operating picture (UDOP) enables analysis across a wide variety of applications. This paper explores the Multisource AI Scorecard Table (MAST) for artificial intelligence/machine learning methods. Using the MAST categories, the Adaptive Markov Inference Game Optimization (AMIGO) SSA tool is presented as an example. The analysis reveals the importance of human interaction in the task, user, and technology operations. Recent advances in artificial intelligence (AI) have led to an explosion of multimedia applications (e.g., computer vision (CV) and natural language processing (NLP)) for different domains such as commercial, industrial, and intelligence. In particular, the use of AI applications is often problematic because the opaque nature of most systems leads to an inability for a human to understand how the results came about. A reliance on “black boxes” to generate predictions and inform decisions but requires explainability. This paper explores how MAST can support human-machine interactions to support the design and development of SSA tools. After describing the elements of MAST, the use case for AMIGO explains the general rating concept for the community to consider and modify for the interpretability of advanced data analytics that support various elements of data awareness.

Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter, in addition to real-time and hidden information constraints, greatly complicates the space awareness decision-making to control both ground-based and space-based surveillance assets. Space is considered as an important concern in modern frontiers because intelligence information from the space has become extremely vital for strategic decisions, which calls for persistent Space Domain Awareness (SDA). The presence of disagreeable actors in addition to real-time and hidden information constraints greatly complicates the decision-making process in satellite behavior detection as well as operational intent discovery. This paper designs and implements 3D-Convoltional Neural Networks (CNNs) for rapid discovery of evasive satellite behaviors from ground-based sensors, which measure the ranges, azimuth angles, and elevation angles in the Adaptive Markov Inference Game Optimization (AMIGO) tool. The novel 3D CNN extends the generic 2d CNN towards analysis from many perspectives. To generate the 3D CNN model, the training and validation data are simulated based on our game theoretic reasoning engine for elusive space behaviors detection, interactive adversary awareness, and intelligent probing. The performance of the 3D CNN is compared with the 2D CNN models from previous work which is shown for a 10% increase in accuracy.

Digital Beamforming has gained significant importance in radar applications in the past years. It helps improve radar performance while reducing mass and power. Improving these figures becomes even more important for space applications. The Space Exploration Synthetic Aperture Radar (SESAR) is a novel P-band (70 cm wavelength) radar instrument developed for planetary applications that will enable surface and near-subsurface measurements of Solar System planetary bodies. The radar will measure full polarimetry at meter-scale resolution, and perform beam steering through programmable digital beamforming architecture. The data obtained with SESAR will provide key information on buried ice and water signatures that can facilitate the design of future human and robotic exploration missions. In this paper we describe SESAR’s large antenna array, the sub-systems integration process, and the different environmental testing activities performed to the overall system in order to raise the Technology Readiness Level (TRL) for its future inclusion in a space-proven system.

Network topology inference is one of the most critical problems in the field of network awareness, whose goal is to determine the routing topology of a network from end-to-end measurements. Due to the large scale of the network and limited measurements, this problem is very difficult to solve. In the literature, possible solutions can be mainly summarized into two main categories, namely traceroute-based and tomography-based methods respectively. The traceroute-based method uses ICMP/UDP packets to collect the IP information of the routers along the route from the source to the destination. The tomography-based approach only relies on the collected end-to-end measurements and derives the network structure using statistical metrics of the measurements. In this work, we review the available methods in the literature and provide a summary of performing network topology inference.

Mobile edge computing is a new distributed computing paradigm which brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth in the dynamic mobile networking environment. Despite the improvements in network technology, data centers cannot always guarantee acceptable transfer rates and response times, which could be a critical requirement for many applications. The aim of mobile edge computing is to move the computation away from data centers towards the edge of the network, exploiting smart objects, mobile phones or network gateways to perform tasks and provide services on behalf of the cloud. In this paper, we design a task offloading scheme in the mobile edge network to handle the task distribution, offloading and management by applying deep reinforcement learning. Specifically, we formulate the task offloading problem as a multi-agent reinforcement learning problem. The decision-making process of each agent is modeled as a Markov decision process and deep Q-learning approach is applied to deal with the large scale of states and actions. To evaluate the performance of our proposed scheme, we develop a simulation environment for the mobile edge computing scenario. Our preliminary evaluation results with a simplified multi-armed bandit model indicate that our proposed solution can provide lower latency for the computational intensive tasks in mobile edge network, and outperforms than naïve task offloading method.

Mobile Edge Computing (MEC) is a key technology to support the emerging low-latency Internet of Things (IoT) applications. With computing servers deployed at the network edge, the computational tasks generated by mobile users can be offloaded to these MEC servers and executed there with low latency. Meanwhile, with the ever-increasing number of mobile users, the communication resource for offloading and the computational resource allocated to each user would become quite limited. As a result, it would be difficult for the MEC servers alone to process all the tasks in a timely manner. An effective approach to deal with this challenge is offloading a proportion of the tasks at MEC servers to the cloud servers, such that both types of servers are efficiently utilized to reduce latency. Given multiple MEC and cloud servers and the dynamics of communication latency, intelligent task assignment between different servers is required. In this paper, we propose a deep reinforcement learning (DRL) based task assignment scheme for MEC networks, aiming to minimize the average task processing latency. Two design parameters of task assignment are optimized, including cloud server selection and task partitioning. Such a problem is formulated as a Markov Decision Process (MDP) and solved with a DRL-based approach, which enables the edge servers to capture the system dynamics and make optimized task assignment strategies accordingly. Simulation results show that the proposed scheme can significantly lower the average task completion latency.

Deep neural networks (DNN) have been studied intensively in recent years, leading to many practical applications. However, there are also concerns about the security problems and vulnerabilities of DNN. Studies on adversarial network development have shown that relatively more minor perturbations can impact the DNN performance and manipulate its outcome. The impacts of adversarial perturbations have led to the development of advanced techniques for generating image-level perturbations. Once embedded in a clean image, these perturbations are not perceptible to human eyes and fool a well-trained deep learning (DL) convolutional neural network (CNN) classifier. This work introduces a new Critical-Pixel Iterative (CriPI) algorithm after a thorough study on critical pixels’ characteristics. The proposed CriPI algorithm can identify the critical pixels and generate one-pixel attack perturbations with a much higher efficiency. Compared to a one-pixel attack benchmark algorithm, the CriPI algorithm significantly reduces the time delay of the attack from seven minutes to one minute with similar success rates.

To replace current legacy inspection/maintenance methods with autonomous real-time health status tracking , the paper proposes a smart robotic system with integrated remaining useful life (RUL) prediction tailored for complex components, structures and systems (CSSs). Capabilities like artificial intelligence (AI)/machine learning (ML) utilizing sensing data along with other monitoring data assist in maintenance optimization. The designed system is based on the state-of-the-art reinforcement learning (RL) and deep learning (DL) framework, which consists of an input, modeling, and decision layer. To achieve better prediction accuracy with higher autonomy, a novel active robot-enabled inspection/maintenance system is deployed in the input layer to collect whole-field infrastructure sensing data and inspect critical CSSs. The deep RL approach is integrated with failure diagnostic and prognostic algorithms to train a risk-informed AI-based agent for controlling the robots. With the data collected from the input layer, the modeling layer first conducts data fusion and predicts RUL of components using an efficient Bayesian convolutional neural network (BCNN) algorithm. In the decision layer, a resilience-driven probabilistic decision-making framework will be developed to control the robot for automatically detecting local damage, e.g. defects, degradation, and recommend mitigation/recovery actions for the health management of infrastructure under uncertainty. The combined layers comprise a AI-risk-driven sensing system (AIRSS) which was tested on an Aero-Propulsion System turbofan engine.

A method is described for hardening GEO missile warning satellites against jamming by airborne lasers with wavelengths out-of-band to the satellite’s sensor. The lasers jam by heating the telescope optics to temperatures where the glow from the optics masks the signal from missile plumes. Cooling the relatively isolated scan mirror near the aperture of the telescope is a particular concern. Connecting cooling pipes to it could interfere with its motion. Spraying a cold gas on it could cause contamination. Here thermal radiation transport theory is used to show that cooling the walls of the fore-optics enclosure to ~ 130 K during an attack will adequately cool the scan mirror and protect the sensor from thermal jamming.

The harsh space environment at geosynchronous orbit (GEO) induces differential charging of spacecraft surfaces due to fluxes of high energy electrons onto and through them. Thus, satellite surfaces can charge thousands of volts with respect to each other whereas entire satellites can charge tens of thousands of volts negative of their surrounding space plasma. The ensuing electric fields can cause local discharges (arcs), endangering the normal operation of the satellite. Solar cell coverglass contamination induced by the high rate of arcing is sufficient to produce the ~1.5 percent/ year power loss in excess of radiation damage on the global positioning system (GPS) satellites. This work focuses on evaluation of a GEO space weather effect, caused by 90 keV high-energy electron radiation, on material properties of different types of commonly used in space solar cell coverglasses (CMX, fused silica, and 0214). Charge analysis performed with a GPS Block IIF NASCAP model demonstrated that the use of CMX, a high-conductivity coverglass, may help to mitigate differential charging and prevent arc-induced contamination. Finally, radiofrequency observations by the Arecibo 305 m telescope of GEO satellites with different configurations have registered abundant arcing of satellites utilizing less conductive coverglasses and no arcing on two with CMX coverglasses. It is the object of the current study to see how space weathering of different coverglass types may alter these results.

Spent Nuclear Fuel (SNF) management is one of the major challenges in the nuclear power field. Several disposals, reprocessing and recycling techniques and concepts are proposed and implemented, however, the associated challenges have not been completely resolved yet. Therefore, in this work another useful application of SNF in space applications is explored. The overarching goal of this work is to explore the possibility of using nuclear spent fuel in the so-called ion-thrusters. The proposed design consists of a jet engine that utilizes the extraordinary radioactivity from SNF to ionize a propellant that is used as the thrust. A preliminary basic design is proposed and then evaluated based on simulation predictions. MCNP is used to model a simplified design of the proposed Spent Nuclear Fuel Ion Propulsion Engine (SNIP) and estimate the ionization reaction rate and therefore the thrust exit velocity and specific impulse of the thruster.

As the number of image sensor output increase the circuit cards and cable designs have become complex and the power unmanageable in space payloads. This paper reports recent ADC integrated circuit (IC) developed for space environment to alleviate the most pressing concerns of the space payload systems. These ADCs are integrated into multi-channel ASICs (4 to 40 per chip) that greatly reducing the system size, weight, power and cost. The ADCs range from 8-bit to 16-bit digitizers with noise approaching the quantization limit while also maintaining ultra-low power dissipation. 14-bit 20 MSPS digitization at 50 mW and 16-bit 250 KSPS for just 5mW is acheived. Novel on the fly programmable architecture, linearity, power dissipation, SNR performance, radiation tolerance and other critical performance parameters are reported. Utility of these ICs is discussed in a wide variety of instruments suitable for LEO, GEO or other orbits.

The sliding innovation filter is a new type of predictor-corrector estimation method. The strategy is used to estimate relevant states of interests and has been found to be robust to modeling uncertainties and disturbances. In this paper, a second-order formulation of the sliding innovation filter is presented to improve its estimation performance in terms of accuracy while maintaining robustness. The strategy is applied to an aerospace system that has been designed for experimentation. The results are compared with the well-known Kalman filter, and future work is considered.

This paper contains a comparison of several sigma-point Kalman filters, including the unscented Kalman filter (UKF), the cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). The comparison is based on a simulated electro-hydrostatic actuator, which is commonly used for flight surface actuation in aerospace systems. This brief study compares the response, convergence rate, root mean square error, the maximum absolute error, and the stability of these sigma-point Kalman filters.

With the rapid advancement of imaging technology, space-based remote sensing instruments are becoming more sophisticated and are producing substantially more amounts of data for downloading. Data alteration is very likely to occur during the transmission over the long distances from probes to carrier spacecraft and subsequently back to Earth,. Cyclic Redundancy Check (CRC) is the most well-known data package error check technique which has been used in many applications. Unfortunately, due to its serial computation process, it could be a bottleneck for critical applications that require rapid processing. To overcome such issue, we present here a parallel CRC computational method based on an FPGA with simulation and testing to validate the methodology.

Remote sensing of the Earth allows receiving medium, high spatial resolution, and hyperspectral measurements from spacecraft. This study presents a remote sensing application of using time-series satellite images for monitoring solid waste disposal facilities (WDF). We proposed a method for satellite image processing using the percolation for physicochemical analysis of soil cover of industrial waste facilities. This work aims to study different methods for assessing percolation parameters from space images. The article discusses ways of fractal-percolation, chemical, and regression analysis. The proposed algorithm results are shown on the example of the solid household and the industrial waste landfill. The received results can serve as the basis for developing a methodology for assessing the effectiveness of measures to neutralize the underlying surface of the WDF against the filtrate and seep it into the soil using remote sensing technologies of Earth.