Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Asteroid Characterization Through Nanosatellite Swarming

This article first defines a class of estimation problem called simultaneous navigation and characterization (SNAC), which is a superset of simultaneous localization and mapping (SLAM). A SNAC framework is then developed for the Autonomous Nanosatellite Swarming (ANS) mission concept to autonomously navigate about and characterize an asteroid including the asteroid gravity field, rotational motion, and 3-D shape. The ANS SNAC framework consists of three modules: 1) multiagent optical landmark tracking and 3-D point reconstruction using stereovision, 2) state estimation through a computationally efficient and robust unscented Kalman filter, and 3) reconstruction of an asteroid spherical harmonic shape model by leveraging a priori knowledge of the shape properties of celestial bodies. Despite significant interest in asteroids, there are several limitations to current asteroid rendezvous mission concepts. First, completed missions heavily rely on human oversight and Earth-based resources. Second, proposed solutions to increase autonomy make oversimplifying assumptions about state knowledge and information processing. Third, asteroid mission concepts often opt for high size, weight, power, and cost (SWaP-C) avionics for environmental measurements. Finally, such missions often utilize a single spacecraft, neglecting the benefits of distributed space systems. In contrast, ANS is composed of multiple autonomous nanosatellites equipped with low SWaP-C avionics. The ANS SNAC framework is validated through a numerical simulation of three spacecraft orbiting asteroid 433 Eros. The simulation results demonstrate that the proposed architecture provides autonomous and accurate SNAC in a safe manner without an a priori shape model and using only low SWaP-C avionics.
Rewind & Discard: Improving Software Resilience using Isolated Domains

Well-known defenses exist to detect and mitigate common faults and memory safety vulnerabilities in software. Yet, many of these mitigations do not address the challenge of software resilience and availability, i.e., whether a system can continue to carry out its function and remain responsive, while being under attack and subjected to malicious inputs. In this paper we propose secure rewind and discard of isolated domains as an efficient and secure method of improving the resilience of software that is targeted by run-time attacks. In difference to established approaches, we rely on compartmentalization instead of replication and checkpointing. We show the practicability of our methodology by realizing a software library for Secure Domain Rewind and Discard (SDRaD) and demonstrate how SDRaD can be applied to real-world software.
Interactive Indoor Logistics Robot Design by Interacting with Human

Indoor service robots have been designed to provide convenient services, but their communication with building control systems is a challenging and costly process. Our team proposes a new indoor service robot design concept that employs human-robot interaction skills instead of traditional building control systems. The robot uses advanced technologies such as AI human detection, chatbot with social skills, robot navigation, and communication with server systems. The main advantage of our design is the elimination of the costly building integration process, making it more affordable and accessible for a range of settings. Our team created a prototype of the robot, which we tested in a real-world environment, at the University of Auckland in New Zealand. A human study involving 49 participants evaluated the robot's feasibility and acceptability. Results show that the robot was well-received, and users found it user-friendly, socially engaging, and able to provide them with the necessary information and services without the need for complex building control systems. In conclusion, the use of indoor service robots with human-robot interaction skills offers a valuable solution to the challenges of building integration, making it a cost-effective and practical option for homes, hospitals, and other public spaces. Our study demonstrates the potential of this design concept and its ability to provide high-quality services to people.
Squeezing the Most Out of Congestion Window for Self-Clocked Rate Adaptation Algorithm in a 5G Environment

The congestion problem arises in current networking paradigms. Researchers seek different approaches to address this issue by proposing algorithms to control or avoid congestion. One of the primary approaches to handling efficient congestion is specifying the amount of data to be transmitted during a period, referred to as the congestion window (cwnd). This paper aims to optimize the congestion window modification by assessing and utilizing the Self-Clocked Rate Adaptation for Multimedia (SCReAM) algorithm's parameters. Our results indicate that we increased the average congestion window by 164.6% in the classic mode and 67.64% in the L4S/ECN mode.
The Study on the Energy Efficiency of the Bipedal-Walking Pattern of a Humanoid Robot Using Four-Bar-Linkage Toe Joints

The purpose of this study is to increase the energy efficiency of a biped-walking humanoid robot. A traditional biped-walking robot walks with its knees bent, which places an unnecessarily large load on the knees. Therefore, in this study, a biped-walking robot with toe joints is developed. This allows it to walk naturally by moving its center of gravity forward, without bending its knees. In addition, as the load acting on the knee joint is reduced, a four-bar linkage mechanism is applied to avoid an excessive load on the toe joint. The toe structure to which the four-bar linkage mechanism is applied distributes the load transmitted to each axis of the toe joint. A 52.2-cm biped-walking robot was developed with the above toes applied. As it walked, the zero-moment point (ZMP) was measured, through forward kinematics and inverse kinematics, and experiments were conducted by applying a walking pattern unlike that of a robot without toes. The maximum step length, maximum speed, ZMP graph, and maximum current were measured. Experiment compared these metrics with those of a flat-footed robot, to demonstrate the efficiency of the robot to which toes were applied. The maxwimum speed and maximum step length were measured to be faster and larger, respectively, than those of a robot without toes, and a smooth ZMP-change graph was confirmed. In addition, the current measurement showed a lower electric charge; thus, proving the energy efficiency of applying the proposed mechanism.
Second harmonic generation with giant angular and spectral acceptance

Frequency conversion and, more specifically, second harmonic generation, is a fundamental phenomenon in nonlinear optics, both for applications and fundamental research. The occurring of this last effect in crystals requires that the frequency-doubled propagating beam interferes constructively with the pump itself, a phase-matching (PM) condition which is due to momentum-conservation laws. These lead to wavelength-dependent constraints on the process geometry while the wavevector mismatch is accompanied by chromatic walk-off. Conversely, collinear PM in SHG can be obtained either through optical birefringence, which introduces wavelength and polarization constraints, and quasi-phase-matching, that requires periodic material microstructuring. We present the experimental demonstration of SHG with a spectral acceptance of more than 100 nm angular and acceptance up to ±40°, with no polarization selectivity or chromatic walk-off. This is achieved in a disordered potassium-based perovskite manifesting giant broadband refraction (GR), which determines a natural and unconditioned PM with no chromatic walk-off. Results open the way to highly efficient versatile and adaptable nonlinear optical devices.
Variable Admittance Control for Compliant Collaboration in Physical Human-Robot-Environment Interaction

This paper explores the development of intelligent and adaptive interaction control strategies for physical human-robot-environment interaction. Collaborative robots with variable admittance control have shown promising results in providing compliant behavior of the robot's end-effector in response to human guidance. However, instability can arise in the transition from free motion to contact motion due to fluctuations in force and imbalance. To address this issue, a decoupled interaction interface is proposed, which separates human and environmental forces for stable interaction. The proposed variable admittance control scheme uses an adaptive controller to update the unknown impact or poor regulation between the robot and its environment. A passivity framework is necessary to maintain the passivity of the overall system, which is derived using the Lyapunov approach. The proposed approach offers better stability and performance in physical human-robot-environment interaction.
Including Dynamic Security Constraints in Isolated Power Systems Unit Commitment/Economic Dispatch: a Machine Learning-based Approach

Isolated power systems with high shares of renewables can require additional inertia as a complementary resource to assure the system operation in a dynamic safe region. This paper presents a methodology for the day-ahead Unit Commitment/Economic Dispatch UC/ED for low-inertia power systems including dynamic security constraints for key frequency indicators computed by an Artificial Neural-Network (ANN)-supported Dynamic Security Assessment (DSA) tool. The ANN-supported DSA tool infers the system dynamic performance with respect to key frequency indicators following critical disturbances and computes the additional synchronous inertia that brings the system back to its dynamic security region, by dispatching Synchronous Condensers (SC) if required. The results demonstrate the effectiveness of the methodology proposed by enabling the system operation within safe frequency margins for a set of high relevance fault type contingencies while minimizing the additional costs associated with the SC operation.
AI Based Robot for Beach Cleaning

“Beach cleaning robots” are used to pick up trash and debris from beaches. Beach cleaning robots are becoming more and more popular since they can work continuously and effectively without human labor, keeping beaches clean and safe for tourists. These robots can assist in reducing the quantity of plastic and other rubbish that ends up in the ocean and can also assist in protecting wildlife from harm caused by beach litter. The Raspberry Pi is a crucial component in our work to construct the AI-based robot. The Raspberry Pi Pico is used to control the movement of the object in the photo that is taken with the Pi- Camera. The waste is identified, using motors and a conveyor belt; it is collected and dumped separately to maintain cleanliness.
A Reinforcement Learning Method for Quadrotor Attitude Control Based on Expert Information

In this paper, a model-free reinforcement learning(RL) method of training a nonlinear attitude controller of a quadrotor is proposed. For the problem that the attitude controller is uncontrolled when trained by RL directly, the proposed method utilizes an expert to provide the prior information, i.e. the action’s judgement and suggestion, to guide the updating process. For the problem that the policy falls in local optima by the limitation of the expert, the proposed method maximize the entropy of the strategy to increase the exploratory behavior of the nonlinear attitude controller approximator. Furthermore, We employ the Proximal policy optimization algorithm (PPO) as the RL model and PID algorithm as the expert model to approach an exact attitude controller of a quadrotor based on the proposed method. Finally, the simulations experiments has been conducted to verify that our proposed method can train a true nonlinear attitude controller which has a better performance than the expert.
Robotic Attendance Scheme in the Classroom Using Artificial Intelligence and Internet of Things

Facial recognition is one example of the ways in which computer vision is being applied in modern, people-oriented utility apps. Facial recognition technology has improved in recent years, but it still lags behind other biometric authentication methods such as fingerprint and iris scanning or Radio Frequency Identification (RFID) cards in terms of accuracy. However, it is still frequently utilised due to the fact that the recognition procedure does not involve touching the device in any way. This research takes advantage of advances in facial recognition technology to offer an embedded device integrated AI and IoT-based automated attendance solution for use in smart classrooms. This study uses the ResNet architecture to explore a deep learning classification method for identifying student attendance. In this research, we used facial photos from students to train ResNet-18 and ResNet-50. Models educated to differentiate regular from irregular student attendance. Our photos were evaluated on 20%, 25%, and 40% of simulated and real-world datasets, respectively. Results from three types of testing data show that ResNet-50 outperforms ResNet-18 in rapports of accuracy, F1-measure, and mean accuracy value. This study makes use of the FER2013 dataset for labelling faces, as well as real-time student faces. This study uses a deep learning approach to show how dependable and repeatable student image analysis
Measured Anomalous Dispersion, Kerr Comb, and Lasing in Hybrid TeO2-Coated Si3N4 Waveguides

We report measured anomalous dispersion, Kerr comb generation, and lasing on hybrid waveguides based on a standard wafer scale 400-nm Si<inf>3</inf>N<inf>4</inf> coated with the TeO<inf>2</inf> film. The use of thin Si<inf>3</inf>N<inf>4</inf> ensures reduced stress and film crack and therefore fabrication of scalable and high yield waveguides through conventional CMOS process. On the other hand, the highly nonlinear TeO<inf>2</inf> film is added to enhance nonlinearity and engineer waveguide dispersion while also acting as a host of rare-earth dopants for amplification and lasing. Experimental results are presented showing that the normal dispersion of 400 nm-thick Si<inf>3</inf>N<inf>4</inf> waveguides can be engineered to anomalous by adding the TeO<inf>2</inf> film. For a 1.6-μm wide, 500 μm bend radius ring resonator with a 424-nm thick TeO<inf>2</inf> coating, dispersion values of ~25 and ~78 ps/nm•km was measured at 1552 nm for the TE and TM-modes, respectively. Also, by exploiting the rare-earth solubility of the TeO<inf>2</inf> film and depositing an Er-doped TeO<inf>2</inf> coating, a microdisk laser was observed at pump power of 12 mW. These results show a promising route to potentially monolithic integration of linear, nonlinear and active functionalities in a single photonic chip.
Navigation for a mobile robot to inspect aircraft

Preflight inspection is an important part of aircraft maintenance, as it does not affect only safety but also expenditure. In order to reduce the risks from human errors and the cost of operation, a mobile robot is introduced as an effective solution. This research examines navigation for aircraft inspection using a mobile robot. The two path planning methods, Dijkstra with DWA and A* with TEB, are compared by navigation performance in the simulation and three environments which are office, atrium, and hangar, to ensure that the robot is able to handle a variety of situations. In addition, an ultrasonic sensor was installed to perform obstacle avoidance and support LIDAR when it failed. The result is that both algorithms have their advantages depending on the purpose; if an accurate position is required, A* with TEB should be selected, while Dijkstra with DWA offers a smooth path and less time spent in small and complex environments. The notable parameters in navigation for the mobile robot, TurtleBot3, are presented, and limitations are detailed. The use of the autonomous mobile robot could support the operation with high precision and repeatability.
Modeling of Ground Source Heat Pump Systems and Their Operation Under Uncertainties

This paper presents three mathematical formulations to represent uncertainties for the dispatch of aggregated Ground Source Heat Pumps (GSHPs). Model Predictive Control (MPC) and Monte Carlo Simulations (MCS) approaches are first discussed, based on an existing deterministic model that represents the operation and participation of the aggregator in electricity markets, to account for uncertainties in the electricity prices and ambient temperatures on the dispatch of aggregated GSHP systems. A novel Robust Optimization (RO) approach is then proposed and developed, and all the methods are validated, analyzed, and compared using a realistic test system. It is shown that the proposed RO approach is computationally efficient with respect to the MPC and MCS approaches, and properly represents the considered uncertainties, demonstrating the advantage of the presented RO technique for GSHP dispatch by aggregators.
The Cluster-based Cognitive Radio Sensor Networks that are Wireless Aware of PUEA and SSDF Attacks

This paper presents a secure Cognitive RadioWireless Sensor Network (CR-WSN) architecture. CR-WSN is a cluster based network in which the sensors are hierarchically structured. Each cluster is chosen with single cluster head that operates on ISM band frequencies. Sensor data from cluster members are aggregated by cluster head. Cluster heads forwards the collected data to sink based on selection of a shortest path using Dijkstra algorithm. Due to the problem of spectrum scarcity, Cognitive Radios are subjected to two main attacks for utilizing the unlicensed spectrum. We resolve Spectrum Sensing Data Falsification attack and Primary User Emulation attack. Dynamic Spectrum Sensing Technique is involved for performing spectrum sensing. We propose a regression based spectrum sensing data falsification attack detection technique and mathematical SHA with Digital Signature is applied for primary user emulation attack detection. Extensive implementation is handled in Network Simulator 3 and comparative results are illustrated for betterment of throughput and end-to-end delay efficiencies.
Research on Arc Restraint in Dynamic Response Process of A New Self-Driving Fault Current Limiter

In view of the limitation of vacuum circuit breakers (VCBs) in breaking high-rise rate short-circuit current, this paper proposes a new type of self-driving fault current limiter (SD-FCL). The SD-FCL can be self-triggered to limit the capacity and the peak value of short-circuit current. Therefore, the SD-FCL can reduce the power burden and improve the reliability of VCBs. This paper proposes a topology of SD-FCL and based on its working principle to establish a MHD arc model of the dynamic response process. Then this paper investigates the effect of driving-rail materials on the arc characteristics and analyses the arc temperature and shape changes under four driving-rail materials. The simulation results show that the lower rail materials’ conductivity and thermal conductivity, the lower the arc energy. In terms of current limiting effect, the SD-FCL can act as quickly as 4. 2ms to limit a peak short-circuit current of 16. 5kA to 13. 35kA and the maximum efficiency of current limited is 55%. The SD-FCL can be combined with VCBs to break higher short-circuit current.
Statistical Penalized-likelihood CT Image Reconstruction with Plug-and-play Priors

Model-based iterative reconstruction (MBIR) methods in CT imaging can provide better image quality and potentially lower radiation dose when compared with analytical methods. Recently, attempts have been made to regularization strategies in MBIR to reconstruct the details while suppressing the noise in the reconstructed images. There are also attempts to denoise CT images with advanced image denoisers though it might introduce artefacts which are difficult to interpret. To combine the strengths of both categories, a recently introduced framework termed Plug-and-play (PnP) is to incorporate advanced image denoisers into the MBIR. Within the PnP framework, in this paper, rather than using least square fidelity term to model the physical aspect of the imaging process, we propose to use statistical penalized-likelihood method where the physical-based Poisson noise model was used. The experimental results confirm the ability of the proposed method in reconstructing the details while retaining the result in an interpretable way.
Backstepping Controller Design for Maximum Power Harvesting from Solar-Wind Hybrid Energy Conversion System

Renewable energy sources are essential in the battle against escalating dangers associated with climate change and global warming. Among these resources, wind and solar energy are readily available and frequently used. An effective approach to harnessing these sources involves integrating a wind energy conversion system (WECS) and a solar energy conversion system (SECS) and utilizing the combined output to supply the grid. This paper presents a backstepping controller to maximize the power transfer from this hybrid system to the grid. The proposed controller enables the fast realization of the maximum power point (MPP), resulting in grid-side total harmonic distortion (THD) below 5%. The results are simulated in MATLAB/Simulink to demonstrate the system's effectiveness.
Search for CP violation using triple product asymmetries in Lb->pK-pi+pi-, Lb->pK-K+K- and Xib0->pK-K-pi+ decays

A search for CP and P violation using triple-product asymmetries is performed with Λb→ pK−π+π−, Λb→ pK−K+K− and Ξ0 b → pK−K−π+ decays. The data sample corresponds to integrated luminosities of 1.0 fb−1 and 2.0 fb−1, recorded with the LHCb detector at centre-of-mass energies of 7 TeV and 8 TeV, respectively. The CP and P -violating asymmetries are measured both integrating over all phase space and in specific phase-space regions. No significant deviation from CP or P symmetry is found. The first observation of Λb → pKχc0(1P )(→ π+π−,K+K−) decay is also reported. Published in JHEP 08 (2018) 039 c © 2018 CERN for the benefit of the LHCb collaboration. CC-BY-4.0 licence. †Authors are listed at the end of this paper. ar X iv :1 80 5. 03 94 1v 3 [ he pex ] 2 0 A ug 2 01 8
System Identification Based Grid Agnostic Adaptive Droop Control Strategy

This paper investigates the implementation possibilities of the recently proposed system identification-based adaptive droop control scheme under varying grid conditions. The paper also provides a detailed analysis of the impact of reactive droop parameter on the power decoupling. At the distribution level, the feeder impedance comprises of both, inductive and resistive components, leading to the problem of power coupling. To counter the problem of power coupling an adaptive droop control strategy was proposed that tunes the reactive droop coefficient while respecting the trade-off between reactive power sharing accuracy, small signal stability and the voltage drop. In this paper, two main contributions are presented. Firstly, a mathematical relationship is deduced that shows the impact of reactive droop coefficient on the coupling between active and reactive power. This relationship offers a comprehensive understanding for selecting suitable tuning parameters for adaptive droop control. Secondly, the performance of the proposed controller is evaluated over a wide range of grid impedance compositions through simulation. The results demonstrate that (i) increasing reactive power droop coefficient has a marginally diminishing effect on the power decoupling, which aligns with the mathematical deduction and (ii) the system-identification based droop controller can be implemented in any grid, irrespective of its impedance composition, making the controller grid-agnostic.