Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Underactuated Spacecraft Control

Advances in Underactuated Spacecraft Control by Christopher Petersen Chair: Ilya V. Kolmanovksy This dissertation addresses the control of a spacecraft which either becomes underactuated as a consequence of onboard failures or is made underactuated by design. Successfully controlling an underactuated spacecraft can extend spacecraft operational life in orbit and improve the robustness of space missions. The novel contributions of the dissertation include the following. Firstly, switching feedback controllers are developed for the attitude control of an underactuated spacecraft equipped with two pairs of thrusters, or two reaction wheels (RWs), or two control moment gyros (CMGs). The problem is challenging; e.g., even in the zero total angular momentum case, no smooth or even continuous time-invariant feedback law for stabilizing a desired orientation exists. The overall method exploits the separation of the system states into inner-loop base variables and outer-loop fiber variables. The base variables track periodic reference trajectories, the amplitude of which is governed by parameters that are adjusted to induce an appropriate change in the fiber variables towards the desired pointing configuration. xiii Secondly, nonlinear Model Predictive Control (MPC) is applied to the attitude dynamics of an underactuated spacecraft with two RWs and zero angular momentum. Though such a system cannot be stabilized by any smooth or continuous time-invariant feedback law, MPC has the remarkable ability, which is exploited in this research, to generate control laws that are discontinuous in the state. By utilizing nonlinear MPC, the obstruction to stabilizability is overcome and attitude maneuvers can be performed while enforcing constraints. Thirdly, an unconventional pathway is discussed for recovering the linear controllability of an underactuated spacecraft with two RWs by accounting for the effects of solar radiation pressure (SRP) in the spacecraft attitude model. A comprehensive analysis of the addition of SRP torques into the attitude model is given, including necessary and sufficient conditions for recovering linear controllability. With linear controllability restored, conventional controllers can be designed for underactuated spacecraft. Lastly, a set of coupled translational and rotational equations of motion for a spacecraft in a central gravity field are derived. The spacecraft is assumed to have only internal attitude actuators and the equations of motion are such that they are relative with respect to an equilibrium orbit. These equations are then approximated, and for certain orbits, yield dynamics very similar to Hill-Clohessy-Wiltshire (HCW) dynamics. Under reasonable assumptions on the spacecraft configuration and equilibrium orbit, it is proven that the coupled dynamics are small-time locally controllable (STLC), which opens a path to utilizing conventional control techniques to move translationally in space by employing attitude control only.
Stress Reduction of Solar Cell with Nanostructures on Silicon Chip Surface

Silicon chip has been widely used in solar cell recently. The thinning of silicon chip, easily inducing surface defects, becomes necessary to produce solar cells more efficiently. The surface defects resulting in stress concentration on the silicon chip surface would be the source of chip failure. In this study, the finite element analysis was used to investigate the stress distribution near the surface crack of a solar cell on which the nanostructures were introduced to alleviate the induced stress. For the solar cell model, positive silver and negative aluminum electrodes were added on the top and bottom sides of silicon chip. The solar cell under four-point bending was simulated in analysis with and without nanostructures. The results show that the stresses reduce more than 50 % for the solar cell model with nanostructures. When the crack depth is deeper enough, the stress at crack tip is higher than that at junction near the electrode and the crack leads to the failure of solar cell. The effect of different section length of nanostructures on the stress distribution caused by the surface crack was also discussed.
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation

Finding optimal configurations in a geometric space is a key challenge in many technological disciplines. Current approaches either rely heavily on human domain expertise and are difficult to scale. In this paper we show it is possible to solve configuration optimization problems for whole-page recommendation using reinforcement learning. The proposed \textit{Tile Networks} is a neural architecture that optimizes 2D geometric configurations by arranging items on proper positions. Empirical results on real dataset demonstrate its superior performance compared to traditional learning to rank approaches and recent deep models.
Sample Complexity of Policy-Based Methods under Off-Policy Sampling and Linear Function Approximation

In this work, we study policy-based methods for solving the reinforcement learning problem, where off-policy sampling and linear function approximation are employed for policy evaluation, and various policy update rules (including natural policy gradient) are considered for policy improvement. To solve the policy evaluation sub-problem in the presence of the deadly triad, we propose a generic algorithm framework of multi-step TD-learning with generalized importance sampling ratios, which includes two specific algorithms: the λ -averaged Q -trace and the two-sided Q -trace. The generic algorithm is single time-scale, has provable finite-sample guarantees, and overcomes the high variance issue in off-policy learning. As for the policy improvement, we provide a univer-sal analysis that establishes geometric convergence of various policy update rules, which leads to an overall ˜ O ( ϵ − 2 ) sample complexity.
Predictive variational Bayesian inference as risk-seeking optimization

Since the Bayesian inference works poorly under model misspeciﬁcation, various solutions have been explored to counteract the shortcomings. Recently proposed predictive Bayes (PB) that directly optimizes the Kull-back Leibler divergence between the empirical distribution and the approximate predictive distribution shows excellent performances not only under model misspeciﬁcation but also for over-parametrized models. However, its behavior and superiority are still unclear, which limits the applications of PB. Speciﬁcally, the superiority of PB has been shown only in terms of the predictive test log-likelihood and the performance in the sense of parameter estimation has not been investigated yet. Also, it is not clear why PB is superior with misspeci-ﬁed and over-parameterized models. In this paper, we clarify these ambiguities by studying PB in the framework of risk-seeking optimization. To achieve this, ﬁrst, we provide a consistency theory for PB and then present intuition of robustness of PB to model mis-speciﬁcation using a response function theory. Thereafter, we theoretically and numerically show that PB has an implicit regularization eﬀect that leads to ﬂat local minima in over-parametrized models.
Multi‐mode resonator‐based ultra‐wideband power divider

A 3dB broadband power divider has been designed using multi‐mode resonator (MMR) technique. It resonates in four different modes of frequencies. Out of these, three of them are responsible for broadening the operating band, which may be controlled precisely by the impedance ratio of the resonator. It shows an equal magnitude, in‐phase power division at outputs from 3.1 to 10.7 GHz, which complies the FCC defined UWB. It has advantages like single layer design, via less, compact and operating band is tunable by the dimensions of the MMR. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2419–2422, 2016
An Unsupervised Hunt for Gravitational Lenses

Strong gravitational lenses allow us to peer into the farthest reaches of space by bending the light from a background object around a massive object in the foreground. Unfortunately, these lenses are extremely rare, and manually finding them in astronomy surveys is difficult and time-consuming. We are thus tasked with finding them in an automated fashion with few if any, known lenses to form positive samples. To assist us with training, we can simulate realistic lenses within our survey images to form positive samples. Naively training a ResNet model with these simulated lenses results in a poor precision for the desired high recall, because the simulations contain artifacts that are learned by the model. In this work, we develop a lens detection method that combines simulation, data augmentation, semi-supervised learning, and GANs to improve this performance by an order of magnitude. We perform ablation studies and examine how performance scales with the number of non-lenses and simulated lenses. These findings allow researchers to go into a survey mostly ``blind"and still classify strong gravitational lenses with high precision and recall.
Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning

This paper addresses the problem of active task selection which involves selecting the most informative tasks for meta-learning. We propose a novel active task selection criterion based on the mutual information between latent task vectors. Unfortunately, such a criterion scales poorly in the number of candidate tasks when optimized. To resolve this issue, we exploit the submodularity property of our new criterion for devising the ﬁrst active task selection algorithm for meta-learning with a near-optimal performance guarantee. To further improve our eﬃciency, we propose an online variant of the Stein variational gradient descent to perform fast belief updates of the meta-parameters via maintaining a set of forward (and backward) particles when learning (or unlearning) from each selected task. We empirically demonstrate the performance of our proposed algorithm on real-world datasets.
Mutant huntingtin exon-1 impaired GTPCH and DHFR expression in plants and mice

Pathophysiology associated with Huntington’s disease (HD) has been studied extensively in various cell and animal models since the 1993 discovery of the mutant huntingtin (mHtt) with abnormally expanded polyglutamine (polyQ) tracts as the causative factor. However, the sequence of early pathophysiological events leading to HD still remains elusive. To gain new insights into the polyQ-induced early pathogenic events, we expressed Htt exon1 (Httex1) with a normal (21), or an extended (42 or 63) number of polyQ in tobacco plants, which lack an Htt ortholog to avoid any associated effects from endogenous Htt. Here, we show that transgenic plants accumulated Httex1 proteins with corresponding polyQ tracts, and that mHttex1 induced protein aggregation and affected plant growth, especially root and root hair development, in a polyQ length-dependent manner. Quantitative proteomic analysis of young roots from severely affected Httex1Q63 and unaffected Httex1Q21 plants showed that the most impaired protein by polyQ63 is a GTP cyclohydrolase I (GTPCH) along with many its related one-carbon (C1) metabolic pathway enzymes. GTPCH is a key enzyme involved in folate biosynthesis in plants and tetrahydrobiopterin (BH4) biosynthesis in mammals. Validating studies in 4-week-old R6/2 HD mice expressing a mHttex1 showed reduced levels of GTPCH and dihydrofolate reductase (DHFR, a key folate utilization/alternate BH4 biosynthesis enzyme), and impaired C1 and BH4 metabolisms. Our findings from mHttex1 plants and mice reveal impaired expressions of GTPCH and DHFR and contribute to a better understanding of mHtt-altered C1 metabolism and C1 interconnected BH4 metabolism leading to the pathogenesis of HD.
Adaptive Classifiers for Nonstationary Environments

Deployment of sensor/actuator networks designed to address industrial and environmental applications makes possible the online acquisition of a large set of data from a monitored environment. Predictive models developed to work in non-stationary environments must then be able to detect changes, characterize and isolate faults and, if requested, adapt itself to track the change to maximize performance. This chapter expands the adaptive classifier study by presenting methods to learn the concept drift. It discusses the presentation of strategies for adapting the classifier to the new working conditions. The chapter summarizes the most common change detection tests present in the literature to address variation in the process X, while it describes the use of these tests to inspect the output of adaptive classifiers. The chapter presents an overview of a new breed of algorithms, commonly referred as concept drift algorithms, which are designed to work in such non-stationary environments.
Actin limits egg aneuploidies associated with female reproductive aging

Aging-related centromeric cohesion loss underlies premature separation of sister chromatids (PSSC) and egg aneuploidy in reproductively older females. Here we show that F-actin maintains chromatid association after cohesion deterioration in aged eggs. F-actin disruption in aged mouse eggs exacerbated PSSC, while its removal in young eggs induced extensive chromatid separation events generally only seen in advanced reproductive ages. In young eggs containing experimentally reduced cohesion, F-actin removal accelerated PSSC in a microtubule dynamics-dependent manner, suggesting that actin counteracts chromatid-pulling spindle forces. Consistently, F-actin stabilization restricted PSSC even when cohesion was acutely depleted by targeted protein degradation. We conclude that actin mitigates PSSCs arising from age-related cohesion depletion by limiting microtubule-driven chromatid separation. This is supported by a spindle-specific disruption of F-actin in aged mammalian eggs. One-Sentence Summary Actin counteracts microtubule-based pulling forces to reduce the effects of chromosome cohesion loss in aged mammalian eggs.
Charge Trapping in Organic Field-Effect Transistors and Applications for Photodetectors and Memory Devices (Ladingsvangst in organische veld-effect transistoren en toepassingen in fotodetectoren en geheugenelementen)

Jury: Prof. Dr. Ir. A. Haegemans, president Prof. Dr. Ir. R. Mertens, promotor Prof. Dr. Ir. P. Heremans, promotor Prof. Dr. Ir. R. Puers Prof. Dr. M. Van der Auweraer Prof. Dr. Ir. G. Groeseneken Prof. Dr. D. Knipp (Jacobs University Bremen) Prof. Dr. Ir. P. W. M. Blom (Rijksuniversiteit Groningen / Holst Centre) Dissertation presented in partial fulfillment of the requirements for the degree of Doctor in Engineering
Active variable geometry suspension for cars

This thesis investigates the characteristics and performance of a new type of active suspension for cars through modelling, simulation, control design and experimental testing. The Series Active Variable Geometry Suspension (SAVGS) [Evangelou et al., 2011] concept is first put in context by reviewing the history and current trends in automotive suspensions. Its potential is then critically evaluated and work is carried out to maximise its performance for various suspension functions. A multi-model multi-software modelling and simulation approach is followed throughout the thesis in order to cross-check and substantiate simulation results in the absence of experimental data. The simpler linear models are used to inform the selection of suitable parameter sets for the case studies, to synthesise control systems and to qualitatively validate the more complex, nonlinear multi-body models. The latter are developed as a platform to virtually test the system and its control algorithms. When possible, these tests are based on standard open-loop test manoeuvres and on standardised external disturbances. The SAVGS-retrofitted suspension displays a very nonlinear behaviour, which is at the same time a liability and an opportunity from the point of view of control. Nevertheless, different linear control techniques are effectively applied to improve various suspension functions: PIDs are applied to the lower frequency suspension functions such as mitigation of chassis attitude motions, and the H∞ framework is applied to the higher frequency suspension functions such as comfort and road holding enhancement. In all cases, a cascade control approach is employed, and mechanisms are implemented to ensure that physical and design actuator constraints are always respected. This thesis also covers the design and construction of a quarter-car experimental test rig facility. Step-by-step recommendations for its refinement as well as a testing plan are also outlined. To my mother. To my father.
Synchrophasor Technology: PMU Applications in Smart Grids

Synchrophasor technology was first introduced in 1980s. Since then, many electric utilities such as American Electric Power (AEP), Bonneville Power Administration (BPA), Southern California Edison (SCE), New York Power Authority (NYPA), as well as universities such as Virginia Polytechnic Institute (VPI), have been conducting R&D to explore and advance the application of synchrophasor technology. The R&D pace has accelerated recently, mainly due to active participation and leadership provided by US Department Of Energy (DOE) and North American Electric Reliability Corporation (NERC). These R&D efforts have shown that the synchrophasor technology has promise to greatly enhance the transmission planning, design & operations functions. This paper provides a brief description of synchrophasor technology and summarizes potential synchrophasor technology applications that can contribute towards the development of a smarter transmission grid.
SEER: Unsupervised and sample-efficient environment specialization of image descriptors

—Image descriptor based place recognition is an important means for loop-closure detection in SLAM. The currently best performing image descriptors for this task are trained on large training datasets with the goal to be applicable in many different environments. In particular, they are not optimized for a speciﬁc environment, e.g. the city of Oxford. However, we argue that for place recognition, there is always a speciﬁc environment – not necessarily geographically deﬁned, but speciﬁed by the particular set of descriptors in the database. In this paper, we propose SEER, a simple and efﬁcient algorithm that can learn to create better descriptors for a speciﬁc environment from such a potentially very small set of database descriptors. The new descriptors are better in the sense that they will be more suited for image retrieval on these database descriptors. SEER stands for Sparse Exemplar Ensemble Representations. Both sparsity and ensemble representations are necessary components of the proposed approach. This is evaluated on a large variety of standard place recognition datasets where SEER considerably outperforms existing methods. It does not require any label information and is applicable in online place recognition scenarios. Open source code is available. 1
On the Insecurity of Vehicles Against Protocol-Level Bluetooth Threats

Cars are some of the most security-critical consumer devices. On the one hand, owners expect rich infotainment features, including audio, hands-free calls, contact management, or navigation through their connected mobile phone. On the other hand, the infotainment unit exposes exploitable wireless attack surfaces. This work evaluates protocol-level Bluetooth threats on vehicles, a critical but unexplored wireless attack surface. These threats are crucial because they are portable across vehicles, and they can achieve impactful goals, such as accessing sensitive data or even taking remote control of the vehicle. Their evaluation is novel as prior work focused on other wireless attack surfaces, notably Bluetooth implementation bugs. Among relevant protocol-level threats, we pick the KNOB and BIAS attacks because they provide the most effective strategy to impersonate arbitrary Bluetooth devices and are not yet evaluated against vehicles.Testing vehicles is challenging for several reasons, and we had to design a cost-effective methodology based on hybrid lab/on the road experiments. We evaluated 5 popular infotainment units (e.g., KIA and Toyota units) in the lab and 3 recent cars (e.g., Suzuki and Skoda cars) in a controlled on-the-road environment. We describe our methodology in detail to allow other researchers to reproduce and extend our results. Our Bluetooth protocol-level security evaluation uncovers worrisome facts about the state of vehicular security. For example, all tested devices are vulnerable to BIAS and KNOB, despite the patches in the Bluetooth standard. For example, the standard mandates keys with 7 bytes of entropy, but the tested devices accept keys with 1 byte of entropy. Moreover, all tested devices employ weak and outdated Bluetooth security parameters (e.g., weak authentication protocols and ciphers).
Master Computer Science Effects of the power distance index on multicultural software engineering teams

With the advent of global software engineering, companies have started hiring highly skilled software developers from around the globe. Also, due to globalization, people are migrating to different countries around the globe to work in multicultural teams. With this mix of team members belonging to different cultural backgrounds, there have arisen various impediments in the functioning of software development teams leading to the need to study and understand the effects of culture in these multicultural software engineering teams. There are various factors in terms of cultural differences that need to be understood in the formation of teams. Also, companies (and employees) should be considerate towards the cultural influences which can greatly impact mental and social well-being of employees and hence improve overall team efficiency by keeping these factors in mind. According to Hofstede, such multicultural teams can be studied on various cultural dimensions such as power distance, individualism vs. collectivism, uncertainty avoidance, masculinity vs. femininity and long-term vs. short-term orientation. The goal of this research is to understand the effects of power distance index on multicultural software engineering teams. For this purpose, interviews were conducted with ten participants from eight different software engineering/development companies. The participants discussed about their background details, various cultural aspects in their teams and their relationships with colleagues, managers or higher management and juniors in their team. Thematic analysis was applied on the interview data which revealed several effects of power distance and other cultural factors which affect day-to-day working of team members within multicultural software development teams. The effects of power distance that were revealed with thematic analysis relate to inclusiveness, employee empowerment, team dynamics, team structure, power distribution, communication with hierarchy, work distribution within team members and preference for organization structure. The analysis also uncovered several other cultural factors that affect multicultural software development team’s performance and productivity that relate to virtual communication, multicultural work environment, time-zone differences, cultural interpretation of language, colleagues of same culture and the level of adaptation with new culture. By understanding the above-mentioned factors, we gain an understanding of how to improve overall team efficiency and productivity within multicultural teams by overcoming challenges that come up in communication and collaboration due to the differences stemming from power distance.