Search

Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The problem of bias and fairness in ML algorithms is becoming an increasingly greater source of concern. In concurrence, existing literature has also indicated that mental health conditions can manifest differently across genders and cultures. We hypothesise that the representation of features (acoustic, textual, and visual) and their inter-modal relations would vary among subjects from different cultures and genders, thus impacting the performance and fairness of various ML models. We present the very first evaluation of multimodal gender fairness in depression manifestation by undertaking a study on two different datasets from the USA and China. We undertake thorough statistical and ML experimentation and repeat the experiments for several different algorithms to ensure that the results are not algorithm-dependent. Our findings indicate that though there are differences between both datasets, it is not conclusive whether this is due to the difference in depression manifestation as hypothesised or other external factors such as differences in data collection methodology. Our findings further motivate a call for a more consistent and culturally aware data collection process in order to address the problem of ML bias in depression detection and to promote the development of fairer agents and robots for wellbeing., Comment: 9 Pages, 7 Tables. To be published and indexed in the IEEE Xplore Digital Library under the ACII 2024 Workshop Proceedings

Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as interrupting people or having delayed responses, as well as their limited ability to understand human speech, i.e., failure in tasks like transcribing speech to text. These mistakes may disrupt interactions and negatively influence human perception of these robots. To address this problem, robots need to have the ability to detect human-robot interaction (HRI) failures. The ERR@HRI 2024 challenge tackles this by offering a benchmark multimodal dataset of robot failures during human-robot interactions (HRI), encouraging researchers to develop and benchmark multimodal machine learning models to detect these failures. We created a dataset featuring multimodal non-verbal interaction data, including facial, speech, and pose features from video clips of interactions with a robotic coach, annotated with labels indicating the presence or absence of robot mistakes, user awkwardness, and interaction ruptures, allowing for the training and evaluation of predictive models. Challenge participants have been invited to submit their multimodal ML models for detection of robot errors and to be evaluated against various performance metrics such as accuracy, precision, recall, F1 score, with and without a margin of error reflecting the time-sensitivity of these metrics. The results of this challenge will help the research field in better understanding the robot failures in human-robot interactions and designing autonomous robots that can mitigate their own errors after successfully detecting them.

Recent research in affective robots has recognized their potential in supporting human well-being. Due to rapidly developing affective and artificial intelligence technologies, this field of research has undergone explosive expansion and advancement in recent years. In order to develop a deeper understanding of recent advancements, we present a systematic review of the past 10 years of research in affective robotics for wellbeing. In this review, we identify the domains of well-being that have been studied, the methods used to investigate affective robots for well-being, and how these have evolved over time. We also examine the evolution of the multifaceted research topic from three lenses: technical, design, and ethical. Finally, we discuss future opportunities for research based on the gaps we have identified in our review -- proposing pathways to take affective robotics from the past and present to the future. The results of our review are of interest to human-robot interaction and affective computing researchers, as well as clinicians and well-being professionals who may wish to examine and incorporate affective robotics in their practices.

Recent studies show bias in many machine learning models for depression detection, but bias in LLMs for this task remains unexplored. This work presents the first attempt to investigate the degree of gender bias present in existing LLMs (ChatGPT, LLaMA 2, and Bard) using both quantitative and qualitative approaches. From our quantitative evaluation, we found that ChatGPT performs the best across various performance metrics and LLaMA 2 outperforms other LLMs in terms of group fairness metrics. As qualitative fairness evaluation remains an open research question we propose several strategies (e.g., word count, thematic analysis) to investigate whether and how a qualitative evaluation can provide valuable insights for bias analysis beyond what is possible with quantitative evaluation. We found that ChatGPT consistently provides a more comprehensive, well-reasoned explanation for its prediction compared to LLaMA 2. We have also identified several themes adopted by LLMs to qualitatively evaluate gender fairness. We hope our results can be used as a stepping stone towards future attempts at improving qualitative evaluation of fairness for LLMs especially for high-stakes tasks such as depression detection.

In recent years, the affective computing (AC) and human-robot interaction (HRI) research communities have put fairness at the centre of their research agenda. However, none of the existing work has addressed the problem of machine learning (ML) bias in HRI settings. In addition, many of the current datasets for AC and HRI are "small", making ML bias and debias analysis challenging. This paper presents the first work to explore ML bias analysis and mitigation of three small multimodal datasets collected within both a human-human and robot-human wellbeing coaching settings. The contributions of this work includes: i) being the first to explore the problem of ML bias and fairness within HRI settings; and ii) providing a multimodal analysis evaluated via modelling performance and fairness metrics across both high and low-level features and proposing a simple and effective data augmentation strategy (MixFeat) to debias the small datasets presented within this paper; and iii) conducting extensive experimentation and analyses to reveal ML fairness insights unique to AC and HRI research in order to distill a set of recommendations to aid AC and HRI researchers to be more engaged with fairness-aware ML-based research., Comment: First two authors have equal contribution, alphabetic order

This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanced understanding of emotional cues in text generation. When prompted politely, all examined LLMs consistently generate disinformation at a high frequency. Conversely, when prompted impolitely, the frequency of disinformation production diminishes, as the models often refuse to generate disinformation and instead caution users that the tool is not intended for such purposes. This research contributes to the ongoing discourse surrounding responsible development and application of AI technologies, particularly in mitigating the spread of disinformation and promoting transparency in AI-generated content., Comment: 14 pages, 3 figures

Robotic coaches have been recently investigated to promote mental well-being in various contexts such as workplaces and homes. With the widespread use of Large Language Models (LLMs), HRI researchers are called to consider language appropriateness when using such generated language for robotic mental well-being coaches in the real world. Therefore, this paper presents the first work that investigated the language appropriateness of robot mental well-being coach in the workplace. To this end, we conducted an empirical study that involved 17 employees who interacted over 4 weeks with a robotic mental well-being coach equipped with LLM-based capabilities. After the study, we individually interviewed them and we conducted a focus group of 1.5 hours with 11 of them. The focus group consisted of: i) an ice-breaking activity, ii) evaluation of robotic coach language appropriateness in various scenarios, and iii) listing shoulds and shouldn'ts for designing appropriate robotic coach language for mental well-being. From our qualitative evaluation, we found that a language-appropriate robotic coach should (1) ask deep questions which explore feelings of the coachees, rather than superficial questions, (2) express and show emotional and empathic understanding of the context, and (3) not make any assumptions without clarifying with follow-up questions to avoid bias and stereotyping. These results can inform the design of language-appropriate robotic coach to promote mental well-being in real-world contexts.

In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour. Then, how to develop a machine learning (ML) model that can automatically generate multiple appropriate, diverse, realistic and synchronised human facial reactions from an previously unseen speaker behaviour is a challenging task. Following the successful organisation of the first REACT challenge (REACT 2023), this edition of the challenge (REACT 2024) employs a subset used by the previous challenge, which contains segmented 30-secs dyadic interaction clips originally recorded as part of the NOXI and RECOLA datasets, encouraging participants to develop and benchmark Machine Learning (ML) models that can generate multiple appropriate facial reactions (including facial image sequences and their attributes) given an input conversational partner's stimulus under various dyadic video conference scenarios. This paper presents: (i) the guidelines of the REACT 2024 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2024.

Robotic well-being coaches have been shown to successfully promote people's mental well-being. To provide successful coaching, a robotic coach should have the capability to repair the mistakes it makes. Past investigations of robot mistakes are limited to game or task-based, one-off and in-lab studies. This paper presents a 4-phase design process to design repair strategies for robotic longitudinal well-being coaching with the involvement of real-world stakeholders: 1) designing repair strategies with a professional well-being coach; 2) a longitudinal study with the involvement of experienced users (i.e., who had already interacted with a robotic coach) to investigate the repair strategies defined in (1); 3) a design workshop with users from the study in (2) to gather their perspectives on the robotic coach's repair strategies; 4) discussing the results obtained in (2) and (3) with the mental well-being professional to reflect on how to design repair strategies for robotic coaching. Our results show that users have different expectations for a robotic coach than a human coach, which influences how repair strategies should be designed. We show that different repair strategies (e.g., apologizing, explaining, or repairing empathically) are appropriate in different scenarios, and that preferences for repair strategies change during longitudinal interactions with the robotic coach.

Recently, several works have explored if and how robotic coaches can promote and maintain mental well-being in different settings. However, findings from these studies revealed that these robotic coaches are not ready to be used and deployed in real-world settings due to several limitations that span from technological challenges to coaching success. To overcome these challenges, this paper presents VITA, a novel multi-modal LLM-based system that allows robotic coaches to autonomously adapt to the coachee's multi-modal behaviours (facial valence and speech duration) and deliver coaching exercises in order to promote mental well-being in adults. We identified five objectives that correspond to the challenges in the recent literature, and we show how the VITA system addresses these via experimental validations that include one in-lab pilot study (N=4) that enabled us to test different robotic coach configurations (pre-scripted, generic, and adaptive models) and inform its design for using it in the real world, and one real-world study (N=17) conducted in a workplace over 4 weeks. Our results show that: (i) coachees perceived the VITA adaptive and generic configurations more positively than the pre-scripted one, and they felt understood and heard by the adaptive robotic coach, (ii) the VITA adaptive robotic coach kept learning successfully by personalising to each coachee over time and did not detect any interaction ruptures during the coaching, (iii) coachees had significant mental well-being improvements via the VITA-based robotic coach practice. The code for the VITA system is openly available via: https://github.com/Cambridge-AFAR/VITA-system.

Research reproducibility - i.e., rerunning analyses on original data to replicate the results - is paramount for guaranteeing scientific validity. However, reproducibility is often very challenging, especially in research fields where multi-disciplinary teams are involved, such as child-robot interaction (CRI). This paper presents a systematic review of the last three years (2020-2022) of research in CRI under the lens of reproducibility, by analysing the field for transparency in reporting. Across a total of 325 studies, we found deficiencies in reporting demographics (e.g. age of participants), study design and implementation (e.g. length of interactions), and open data (e.g. maintaining an active code repository). From this analysis, we distill a set of guidelines and provide a checklist to systematically report CRI studies to help and guide research to improve reproducibility in CRI and beyond.

The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions. The goal of the challenge is to provide the first benchmark test set for multi-modal information processing and to foster collaboration among the audio, visual, and audio-visual affective computing communities, to compare the relative merits of the approaches to automatic appropriate facial reaction generation under different spontaneous dyadic interaction conditions. This paper presents: (i) novelties, contributions and guidelines of the REACT2023 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at \url{https://github.com/reactmultimodalchallenge/baseline_react2023}.

In dyadic interaction, predicting the listener's facial reactions is challenging as different reactions may be appropriate in response to the same speaker's behaviour. This paper presents a novel framework called ReactFace that learns an appropriate facial reaction distribution from a speaker's behaviour rather than replicating the real facial reaction of the listener. ReactFace generates multiple different but appropriate photo-realistic human facial reactions by (i) learning an appropriate facial reaction distribution representing multiple appropriate facial reactions; and (ii) synchronizing the generated facial reactions with the speaker's verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences. Experimental results demonstrate the effectiveness of our approach in generating multiple diverse, synchronized, and appropriate facial reactions from each speaker's behaviour, with the quality of the generated reactions being influenced by the speaker's speech and facial behaviours. Our code is made publicly available at \url{https://github.com/lingjivoo/ReactFace}., Comment: 10 pages, 6 figures

Generating facial reactions in a human-human dyadic interaction is complex and highly dependent on the context since more than one facial reactions can be appropriate for the speaker's behaviour. This has challenged existing machine learning (ML) methods, whose training strategies enforce models to reproduce a specific (not multiple) facial reaction from each input speaker behaviour. This paper proposes the first multiple appropriate facial reaction generation framework that re-formulates the one-to-many mapping facial reaction generation problem as a one-to-one mapping problem. This means that we approach this problem by considering the generation of a distribution of the listener's appropriate facial reactions instead of multiple different appropriate facial reactions, i.e., 'many' appropriate facial reaction labels are summarised as 'one' distribution label during training. Our model consists of a perceptual processor, a cognitive processor, and a motor processor. The motor processor is implemented with a novel Reversible Multi-dimensional Edge Graph Neural Network (REGNN). This allows us to obtain a distribution of appropriate real facial reactions during the training process, enabling the cognitive processor to be trained to predict the appropriate facial reaction distribution. At the inference stage, the REGNN decodes an appropriate facial reaction by using this distribution as input. Experimental results demonstrate that our approach outperforms existing models in generating more appropriate, realistic, and synchronized facial reactions. The improved performance is largely attributed to the proposed appropriate facial reaction distribution learning strategy and the use of a REGNN. The code is available at https://github.com/TongXu-05/REGNN-Multiple-Appropriate-Facial-Reaction-Generation.

Affective robotics research aims to better understand human social and emotional signals to improve human-robot interaction (HRI), and has been widely used during the last decade in multiple application fields. Past works have demonstrated, indeed, the potential of using affective robots (i.e., that can recognize, or interpret, or process, or simulate human affects) for healthcare applications, especially wellbeing. This paper systematically review the last decade (January 2013 - May 2022) of HRI literature to identify the main features of affective robotics for wellbeing. Specifically, we focused on the types of wellbeing goals affective robots addressed, their platforms, their shapes, their affective capabilities, and their autonomy in the surveyed studies. Based on this analysis, we list a set of recommendations that emerged, and we also present a research agenda to provide future directions to researchers in the field of affective robotics for wellbeing.

According to the Stimulus Organism Response (SOR) theory, all human behavioral reactions are stimulated by context, where people will process the received stimulus and produce an appropriate reaction. This implies that in a specific context for a given input stimulus, a person can react differently according to their internal state and other contextual factors. Analogously, in dyadic interactions, humans communicate using verbal and nonverbal cues, where a broad spectrum of listeners' non-verbal reactions might be appropriate for responding to a specific speaker behaviour. There already exists a body of work that investigated the problem of automatically generating an appropriate reaction for a given input. However, none attempted to automatically generate multiple appropriate reactions in the context of dyadic interactions and evaluate the appropriateness of those reactions using objective measures. This paper starts by defining the facial Multiple Appropriate Reaction Generation (fMARG) task for the first time in the literature and proposes a new set of objective evaluation metrics to evaluate the appropriateness of the generated reactions. The paper subsequently introduces a framework to predict, generate, and evaluate multiple appropriate facial reactions.

Artificial intelligence is changing the way we create and evaluate information, and this is happening during an infodemic, which has been having dramatic effects on global health. In this paper we evaluate whether recruited individuals can distinguish disinformation from accurate information, structured in the form of tweets, and determine whether a tweet is organic or synthetic, i.e., whether it has been written by a Twitter user or by the AI model GPT-3. Our results show that GPT-3 is a double-edge sword, which, in comparison with humans, can produce accurate information that is easier to understand, but can also produce more compelling disinformation. We also show that humans cannot distinguish tweets generated by GPT-3 from tweets written by human users. Starting from our results, we reflect on the dangers of AI for disinformation, and on how we can improve information campaigns to benefit global health., Comment: dataset and software: https://osf.io/9ntgf 29 pages, 4 figures, 13 supplementary figures

The last decade has shown a growing interest in robots as well-being coaches. However, insightful guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative analysis drawing on a grounded theory approach, which was conducted with a three-step iterative design process which included user-centered design studies involving robotic well-being coaches, namely: (1) a user-centred design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a user-centred design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches., Comment: 57 pages, 9 figures

Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the developmental period characterized by deficits in the cognitive and social areas. Conversational agents have been increasingly explored to support therapeutic interventions for people with NDD. This survey provides a structured view of the crucial design features of these systems, the types of therapeutic goals they address, and the empirical methods adopted for their evaluation. From this analysis, we elaborate a set of recommendations and highlight the gaps left unsolved in the state of the art, upon which we ground a research agenda on conversational agents for NDD.

Virtual and robotic agents capable of perceiving human empathy have the potential to participate in engaging and meaningful human-machine interactions that support human well-being. Prior research in computational empathy has focused on designing empathic agents that use verbal and nonverbal behaviors to simulate empathy and attempt to elicit empathic responses from humans. The challenge of developing agents with the ability to automatically perceive elicited empathy in humans remains largely unexplored. Our paper presents the first approach to modeling user empathy elicited during interactions with a robotic agent. We collected a new dataset from the novel interaction context of participants listening to a robot storyteller (46 participants, 6.9 hours of video). After each storytelling interaction, participants answered a questionnaire that assessed their level of elicited empathy during the interaction with the robot. We conducted experiments with 8 classical machine learning models and 2 deep learning models (long short-term memory networks and temporal convolutional networks) to detect empathy by leveraging patterns in participants' visual behaviors while they were listening to the robot storyteller. Our highest-performing approach, based on XGBoost, achieved an accuracy of 69% and AUC of 72% when detecting empathy in videos. We contribute insights regarding modeling approaches and visual features for automated empathy detection. Our research informs and motivates future development of empathy perception models that can be leveraged by virtual and robotic agents during human-machine interactions., Comment: Accepted for publication and oral presentation at the International Conference on Affective Computing and Intelligent Interaction (ACII 2021)

Communication in both human-human and human-robot interac-tion (HRI) contexts consists of verbal (speech-based) and non-verbal(facial expressions, eye gaze, gesture, body pose, etc.) components.The verbal component contains semantic and affective information;accordingly, HRI work on the gesture component so far has focusedon rule-based (mapping words to gestures) and data-driven (deep-learning) approaches to generating speech-paired gestures basedon either semantics or the affective state. Consequently, most ges-ture systems are confined to producing either semantically-linkedor affect-based gesticures. This paper introduces an approach forenabling human-robot communication based on a theory-drivenapproach to generate speech-paired robot gestures using both se-mantic and affective information. Our model takes as input textand sentiment analysis, and generates robot gestures in terms oftheir shape, intensity, and speed.

Since the discovery of an ice particle plume erupting from the south polar terrain on Saturn's moon Enceladus, the geophysical mechanisms driving its activity have been the focus of substantial scientific research. The pattern and deposition rate of plume material on Enceladus' surface is of interest because it provides valuable information about the dynamics of the ice particle ejection as well as the surface erosion. Surface deposition maps derived from numerical plume simulations by Kempf et al. (2010) have been used by various researchers to interpret data obtained by various Cassini instruments. Here, an updated and detailed set of deposition maps is provided based on a deep-source plume model (Schmidt et al., 2008), for the eight ice-particle jets identified in Spitale and Porco (2007), the updated set of jets proposed in Porco et al. (2014), and a contrasting curtain-style plume proposed in Spitale et al. (2015). Methods for computing the surface deposition are detailed, and the structure of surface deposition patterns is shown to be consistent across changes in the production rate and size distribution of the plume. Maps are also provided of the surface deposition structure originating in each of the four Tiger Stripes. Finally, the differing approaches used in Porco et al. (2014) and Spitale et al. (2015) have given rise to a jets vs. curtains controversy regarding the emission structure of the Enceladus plume. Here we simulate each, leading to new insight that, over time, most emissions must be directed relatively orthogonal to the surface because jets "tilted" significantly away from orthogonal lead to surface deposition patterns inconsistent with surface images. Data for maps are available in HDF5 format for a variety of particle sizes at http://impact.colorado.edu/southworth_data.

A new model for the shape of the prominent eccentric ringlet in the gap exterior to Saturn's B-ring is developed based on Cassini imaging observations taken over about 8 years. Unlike previous treatments, the new model treats each edge of the ringlet separately. The Keplerian component of the model is consistent with results derived from Voyager observations, and $m=2$ modes forced by the nearby Mimas 2:1 Lindblad resonance are seen. Additionally, a free $m=2$ mode is seen on the outer edge of the ringlet. Significant irregular structure that cannot be described using normal-mode analysis is seen on the ringlet edges as well. Particularly on the inner edge, that structure remains coherent over multi-year intervals, moving at the local Keplerian rate. We interpret the irregular structure as the signature of embedded massive bodies. The long coherence time suggests the responsible bodies are concentrated near the edge of the ringlet. Long wake-like structures originate from two locations on the inner edge of the ringlet, revealing the locations of the two most massive embedded bodies in that region. As with the Voyager observations, the Cassini data sets showed no correlation between the width and the radius of the ringlet as would be expected for a self-gravitating configuration, except for a brief interval during late 2006, when the width-radius relation was similar to those seen in most other narrow eccentric ringlets in the Solar System., Comment: 36 pages, 13 figures

A new symplectic N-body integrator is introduced, one designed to calculate the global 360 degree evolution of a self-gravitating planetary ring that is in orbit about an oblate planet. This freely-available code is called epi_int, and it is distinct from other such codes in its use of streamlines to calculate the effects of ring self-gravity. The great advantage of this approach is that the perturbing forces arise from smooth wires of ring matter rather than discreet particles, so there is very little gravitational scattering and so only a modest number of particles are needed to simulate, say, the scalloped edge of a resonantly confined ring or the propagation of spiral density waves. The code is applied to the outer edge of Saturn's B ring, and a comparison of Cassini measurements of the ring's forced response to simulations of Mimas' resonant perturbations reveals that the B ring's surface density at its outer edge is 195+-60 gm/cm^2 which, if the same everywhere across the ring would mean that the B ring's mass is about 90% of Mimas' mass. Cassini observations show that the B ring-edge has several free normal modes, which are long-lived disturbances of the ring-edge that are not driven by any known satellite resonances. Although the mechanism that excites or sustains these normal modes is unknown, we can plant such a disturbance at a simulated ring's edge, and find that these modes persist without any damping for more than ~10^5 orbits or ~100 yrs despite the simulated ring's viscosity of 100 cm^2/sec. These simulations also indicate that impulsive disturbances at a ring can excite long-lived normal modes, which suggests that an impact in the recent past by perhaps a cloud of cometary debris might have excited these disturbances which are quite common to many of Saturn's sharp-edged rings., Comment: 55 pages, 13 figures, accepted for publication in the Astrophysical Journal

We report the discovery of several large "propeller" moons in the outer part of Saturn's A ring, objects large enough to be followed over the 5-year duration of the Cassini mission. These are the first objects ever discovered that can be tracked as individual moons, but do not orbit in empty space. We infer sizes up to 1--2 km for the unseen moonlets at the center of the propeller-shaped structures, though many structural and photometric properties of propeller structures remain unclear. Finally, we demonstrate that some propellers undergo sustained non-keplerian orbit motion. (Note: This arXiv version of the paper contains supplementary tables that were left out of the ApJL version due to lack of space)., Comment: 9 pages, 4 figures; Published in ApJL

Voyager images and Cassini occultation data have previously shown that the behavior of the outer edge of Saturn's massive B ring is determined only in part by a static response to the 2:1 inner Lindblad resonance with Mimas. In Cassini images of this region, we find, in addition to the expected wavenumber-2 forced distortion, evidence for unforced self-excited wavenumber-3, wavenumber-2, and wavenumber-1 normal modes. These are the first observations to suggest substantial wave amplification in Saturn's broad rings. Moreover, the presence of these free modes strongly implicates viscous overstability as their underlying cause and, by inference, the cause for most if not all of the unforced structures throughout the high-mass-density B ring and in other high-mass-density regions in Saturn's rings. Analysis of each of the inferred waves reveals a consistent lower bound on the average surface mass density of ~ 44 g/cm^2 for the outer 250 km of the ring, though the true surface density could be as high as 100 g/cm^2 or higher. Interference between the forced and free wavenumber-2 modes yields a total wavenumber-2 pattern that varies in amplitude and orientation with a characteristic period of ~ 5.5 years. We also find localized disturbances, including 3.5-km-tall vertical structures, that provide circumstantial evidence for embedded massive bodies in the Mimas resonance zone. The presence of such bodies is supported by the presence of a shadow-casting moonlet ~ 0.3 km wide near the ring's edge., Comment: 38 pages, 6 figures

(Abridged) The following describes a model of a broad planetary ring whose sharp edge is confined by a satellite's m^th Lindblad resonance (LR). This model uses a streamline formalism to calculate the ring's internal forces, namely, ring gravity, pressure, viscosity, as well as a hypothetical drag force. The model calculates the streamlines' forced orbit elements and surface density throughout the perturbed ring. The model is then applied to the outer edge of Saturn's B ring, which is maintained by an m=2 inner LR with the satellite Mimas. Ring models are used to illustrate how a ring's perturbed state depends on the ring's physical properties: surface density, viscosity, dispersion velocity, and the hypothetical drag force. A comparison of models to the observed outer B ring suggests that the ring's surface density there is between 10 and 280 gm/cm^2. The ring's edge also indicates where the viscous torque counterbalances the perturbing satellite's gravitational torque on the ring. But an examination of seemingly conventional viscous B ring models shows that they all fail to balance these torques at the ring's edge. This is due ring self-gravity and the fact that a viscous ring tends to be nearly peri-aligned with the satellite, which reduces the satellite's torque on the ring and makes the ring's edge more difficult to maintain. Nonetheless, the following shows that a torque balance can still be achieved in a viscous B ring, but only in an extreme case where the ratio of the ring's bulk/shear viscosities satisfy ~10^4. However, if the dissipation of the ring's forced motions is instead dominated by a weak drag force, then the satellite can exert a much stronger torque that can counterbalance the ring's viscous torque., Comment: Accepted for publication in the Astrophysical Journal on April 3, 2009

Phobos, the innermost satellite of Mars, displays an extensive system of grooves that are mostly symmetric about its sub-Mars point. Phobos is steadily spiraling inward due to the tides it raises on Mars lagging behind Phobos' orbital position and will suffer tidal disruption before colliding with Mars in a few tens of millions of years. We calculate the surface stress field of the deorbiting satellite and show that the first signs of tidal disruption are already present on its surface. Most of Phobos' prominent grooves have an excellent correlation with computed stress orientations. The model requires a weak interior that has very low rigidity on the tidal evolution time scale, overlain by an approximately 10-100 m exterior shell that has elastic properties similar to lunar regolith as described by Horvath et al. (1980).

Observations of Enceladus have revealed active jets of material erupting from cracks on its south polar surface. It has previously been proposed that diurnal tidal stress, driven by Enceladus' orbital eccentricity, may actively produce surface movement along these cracks daily and thus may regulate when eruptions occur. Our analysis of the stress on jet source regions identified in Cassini ISS images reveals tidal stress as a plausible controlling mechanism of jet activity. However, the evidence available in the published and preliminary observations of jet activity between 2005 and 2007 may not be able to solidify the link between tidal stress and eruptions from fissures. Ongoing, far more comprehensive analyses based on recent, much higher resolution jetting observations have the potential to prove otherwise.

Observations by Cassini's Imaging Science Subsystem (ISS) of Enceladus' south polar region at high phase angles has revealed jets of material venting into space. Observations by Cassini's Composite Infrared Spectrometer (CIRS) have also shown that the south polar region is anomalously warm with hotspots associated with geological features called the Tiger Stripes. The Tiger Stripes are large rifts near the south pole of Enceladus, which are typically about 130 km in length, 2 km wide, with a trough 500 m deep, and are l1anked on each side by 100m tall ridges. Preliminary triangulation of jets as viewed at different times and with different viewing geometries in Cassini ISS images taken between 2005 and 2007 have constrained the locations of eight major eruptions of material and found all of them associated with the south polar fractures unofficially the 'Tiger Stripes', and found four of them coincident with the hotspots reported in 2006 by CIRS. While published ISS observations of jet activity suggest that individual eruption sites stay active on the timescale of years, any shorter temporal variability (on timescales of an orbital period, or 1.3 Earth days, for example) is more difficult to establish because of the spotty temporal coverage and the difficulty of visually isolating one jet from the forest of many seen in a typical image. Consequently, it is not known whether individual jets are continuously active, randomly active, or if they erupt on a predictable, periodic schedule. One mechanism that may control the timing of eruptions is diurnal tidal stress, which oscillates between compression/tension as well as right and left lateral shear at any given location throughout Enceladus' orbit and may allow the cracks to open and close regularly. We examine the stresses on the Tiger Stripe regions to see how well diurnal tidal stress caused by Enceladus' orbital eccentricity may possibly correlate with and thus control the observed eruptions. We then identify possible mechanisms by which tidal stress can provide access to the surface for volatile material and implications for observed jet activity.

We have updated the orbits of the small inner Saturnian satellites using additional Cassini imaging observations through 2007 March. Statistically significant changes from previously published values appear in the eccentricities and inclinations of Pan and Daphnis, but only small changes have been found in the estimated orbits of the other satellites. We have also improved our knowledge of the masses of Janus and Epimetheus as a result of their close encounter observed in early 2006.

Cassini has identified a geologically active province a the south pole of Saturn's moon Enceladus. The shape of Enceladus suggests a possible intense heating epoch in the past by capture into a 1:4 secondary spin/orbit resonance.

Solar energetic particle events (SEPEs) can have a significant effect on the design and operation of earth orbiting and interplanetary spacecraft. In relation to this, the calculation of proton fluences and fluxes are considered, describing the current state of the art in statistical modeling. A statistical model that can be used for the estimation of integrated proton fluences for different mission durations of greater than one year is reviewed. The gaps in the modeling capabilities of the SEPE environment, such as a proton flux model, alpha particle and heavy ion models and solar cycle variations are described together with the prospects for the prediction of events using neural networks.

Solar heavy ions from the JPL Heavy Ion Model have been transported into low Earth orbit using the Schultz cutoff criterion for L-shell access by ions of a specific charge to mass ratio.

During the last year the International Standards Organization has initialed meetings among representatives of the spacefaring nations to develop standardized models for space environments. This work is intended to facilitate the design of joint missions and the development of international markets for spacecraft components. NASA is heavily involved in this effort, and other government agencies are coming on board. This paper describes progress and plans for this activity.

Making certain that spacecraft command sequences do not violate any constraints is often tedious and expensive in terms of both personnel and software development. To reduce this cost, we have pursued the development of a flexible system for specifying models of spacecraft behavior in response to commands as well as constraints on that behavior. The potential need for modeling complex spacecraft behavior required that the system be designed to be usable both on a conventional workstation and a parallel supercomputer. Finally, it needed to be intuitive enough for the the intended mission operations users to easily design sets of rules and models to automate tedious, resource-consuming constraint checking of commands. We have defined a Specification And Verification Environment (SAVE) for spacecraft flight rules.

Robotic Spacecraft are controlled by onboard sets of commands called.

We describe an updated predictive engineering model for the interplanetary fluence of protons with energies respectively greater than 1, 4, 10, 30, and 60 MeV. This has been the first opportunity to derive a model from a data set that has been collected in space over a long enough period of time to produce a valid sample of solar proton events. The model provides a quantitative basis for estimating the exposures to solar protons of spacecraft during missions of varying length and of surfaces and atmospheres of solar system objects. The data sets contain several major proton events comparable to the 1972 event. For the cases of the over 10 and over 30 MeV particles, the fluences are somewhat lower than in our earlier model No over 1, over 4, and over 60 MeV proton fluence models have been published in the literature previously. We present our results in a convenient graphical form which may be used to calculate the 1 AU fluence expected at a given confidence level as a function of the length of the exposure. A method of extending this estimate to other heliocentric distances is described.

This study was carried out to increase the acuracy and energy range of predictive models of interplanetary proton fluences. Such an estimate is often needed when spacecraft spend a signigicant amount of time in the interplanetary environmnet.

Robotic spacecraft are controlled by sets of commands called 'sequences.' These sequences must be checked against mission constraints. Making our existing constraint checking program faster would enable new capabilities in our uplink process. Therefore, we are rewriting this program to run on a parallel computer. To do so, we had to determine how to run constraint-checking algorithms in parallel and create a new method of specifying spacecraft models and constraints. This new specification gives us a means of representing flight systems and their predicted response to commands which could be used in a variety of applications throughout the command process, particularly during anomaly or high-activity operations. This commonality could reduce operations cost and risk for future complex missions. Lessons learned in applying some parts of this system to the TOPEX/Poseidon mission will be described.

Spacecraft surface charging, which is primarily a current balance phenomenon, is in general a function of the dominant currents to and from the vehicle's surface. Within the near-earth magnetosphere the dominant currents to the surface are the ambient space plasma fluxes between approximately 0 and 100 keV. A major effort to understand the near-earth environment was initiated when spacecraft charging became a major issue. The present paper has the objective to summarize the basic features of the models which have resulted from this effort. A description is given of four categories of models, based primarily on the degree of empirical and theoretical input. Types of quantitative models are discussed, taking into account definitions, statistical models, analytic models, static models, and time-dependent models. Engineering models are also considered, giving attention to baseline models and 'worst-case' models.

Environments surrounding the major extraterrestrial bodies in the solar system and their interactions with spacecraft power systems are summarized. The environments associated with neutrals/dust, low energy plasma, and where applicable, magnetospheres are discussed for a wide variety of cases. The impact of these environments on power systems - in particular, radiation effects, spacecraft charging, plasma interactions, surface sputtering/erosion, and induced currents - are presented. As power systems must be designed to survive in these hostile environments, it is important that they be taken into account in planning future power systems.