Miracle-dz/UniFAR-FTUs huggingface dataset:
Slip boundaries in nanopores

The flow of liquid water confined within slit pores formed from amorphous surfaces of carbon and polydimethylsiloxane (PDMS) has been investigated using molecular dynamics techniques. For PDMS (including plasma treated forms) the slip length is found to be very small, and there are essentially stick boundary conditions. The slip lengths for amorphous carbon surfaces are significant; however, they are much smaller than those measured experimentally for nanopipes. We show, for the first time, that the slip length can be predicted using the Sokhan–Quirke equation for amorphous surfaces using only equilibrium properties.
Slope Handling for Quadruped Robots Using Deep Reinforcement Learning and Toe Trajectory Planning*

Quadrupedal locomotion skills are challenging to develop. In recent years, deep Reinforcement Learning promises to automate the development of locomotion controllers and map sensory observations to low-level actions. Moreover, the full robot dynamics model can be exploited, but no model-based simplifications are to be made. In this work, a method for developing controllers for the Laelaps II robot is presented and applied to motions on slopes up to 15°. Combining deep reinforcement learning with trajectory planning at the toe level, reduces complexity and training time. The proposed control scheme is extensively tested in a Gazebo environment similar to the treadmill-robot environment at the Control Systems Lab of NTUA. The learned policies produced promising results.
Behavioral and Physical Unclonable Functions (BPUFs): SRAM Example

Physical Unclonable Functions (PUFs) have gained a great interest for their capability to identify devices uniquely and to be a lightweight primitive in cryptographic protocols. However, several reported attacks have shown that virtual copies (mathematical clones) as well as physical clones of PUFs are possible, so that they cannot be considered as tamper-resistant or tamper-evident, as claimed. The solution presented in this article is to extend the PUFs reported until now, which are only physical, to make them Behavioral and Physical Unclonable Functions (BPUFs). Given a challenge, BPUFs provide not only a physical but also a behavioral distinctive response caused by manufacturing process variations. Hence, BPUFs are more difficult to attack than PUFs since physical and behavioral responses associated to challenges have to be predicted or cloned. Behavioral responses that are obtained from several measurements of the physical responses taken at several sample times are proposed. In this way, the behavioral responses can detect if the physical responses are manipulated. The analysis done for current PUFs is extended to allow for more versatility in the responses that can be considered in BPUFs. Particularly, Jaccard instead of Hamming distances are proposed to evaluate the similarity of behavioral responses. As example to validate the proposed solution, BPUFs based on Static Random-Access Memories (SRAM BPUFs), with one physical and one behavioral responses to given challenges, were analyzed experimentally using integrated circuits fabricated in a 90-nm CMOS technology. If an attacker succeeds in cloning the physical responses as reported, but does not attack the way to obtain the behavioral responses, the attacker fails on SRAM BPUFs. The highest probability to succeed in cloning the behavioral responses with a brute-force attack was estimated from experimental results as $1.5 \cdot 10^{-34}$ , considering the influence of changes in the operating conditions (power supply voltage, temperature, and aging).
Knowledge-Based Grasp Planning Using Dynamic Self-Organizing Network

Category-based methods for task-specified grasp planning have recently been proposed in the literature. Such methods, however, are normally time consuming in both training and grasp determination process and lack capabilities to improve grasping skills due to the fixed training data set. This paper presents an improved approach for knowledge-based grasp planning by developing a multi-layer network using self-organizing map. A number of grasp candidates are learned in the experiments and the information that is associated with these grasp candidates is clustered based on different criteria on each network layer. A codebook which is composed of a small number of generalized models and the corresponding task-oriented grasps is generated from the network. In addition, the proposed network is capable of automatically adjusting its size so that the codebook can be continuously updated from each interaction with the novel objects. In order to increase the accuracy and convergence rate of the clustering process, a new initialization method is also proposed. Simulation results present the advantages of the proposed initialization method and the auto-growing algorithm in terms of accuracy and efficiency over some conventional methods. Experimental results demonstrate that novel objects can be successfully grasped in accordance with desired tasks using the proposed approach.
Evaluating the Soundness of Security Metrics from Vulnerability Scoring Frameworks

Over the years, a number of vulnerability scoring frameworks have been proposed to characterize the severity of known vulnerabilities in software-dependent systems. These frameworks provide security metrics to support decision-making in system development and security evaluation and assurance activities. When used in this context, it is imperative that these security metrics be sound, meaning that they can be consistently measured in a reproducible, objective, and unbiased fashion while providing contextually relevant, actionable information for decision makers. In this paper, we evaluate the soundness of the security metrics obtained via several vulnerability scoring frameworks. The evaluation is based on the Method for Designing Sound Security Metrics (MDSSM). We also present several recommendations to improve vulnerability scoring frameworks to yield more sound security metrics to support the development of secure software-dependent systems.
Water Cooled High Current 490 kW 6-Pulse Thyristor Converter for Nuclear Power Plant Safety Analysis

Consideration of severe accidents at a nuclear power plant is an essential component in nuclear safety analysis. The extent and nature of deformation is important to study from plant safety point of view. Under normal operating conditions, heat generated by fission reaction in fuel bundle is removed by pressurized heavy water coolant. During postulated loss of coolant accidents, decay heat is removed by emergency core cooling system. Even in case of complete failure of emergency core cooling system, moderator system prevents gross damage and maintain the integrity of fuel and fuel channels. Under postulated accidents, heat up of pressure tube along with internal pressure may lead to its two modes of deformation i.e. sagging and ballooning which depends on internal pressure in pressure tube. Hence, it is essential to validate safety and safety related systems designed for nuclear power plant to cope up severe accidents. Scale down experimental set-up in power has been designed and installed to simulate sagging and ballooning of pressure tube for 220 MWe Indian Pressurized Heavy Water Reactors. In experimental set-up, heat energy relevant to nuclear fission is simulated by traditional electrical heating. Considering direct heating of metallic pressure tube needing higher power to attain desired temperature of pressure tube to meet the situation of severe accident scenario, high current thyristor controlled converter system of 490 kW capacity has been chosen as a heat source. The converter system comprises forced de-mineralized water cooled rectifier transformer and thyristor controlled six pulse double star hexa-phase configuration with inter phase transformer operating either at constant current or constant power mode. The paper presents design, specifications, forced de-mineralized water cooling system, circuit simulation, hardware implementation along with performance demonstration of water cooled 7 kA at 70 V and 3.5 kA at 140 V, 490 kW thyristor controlled six pulse, hexa-phase converter system.
Design of High-Directivity Directional Couplers With 2-bit Fragment-type Structure

In this paper, a novel 2-bit scheme based on fragment-type structure on the microstrip line is presented. The 2-bit scheme is adding two new states in one cell on the basis of the traditional 1-bit scheme by introducing gap and thin line inside the cell. Without manual preset and optimization of parameters, suitable structure will be automatically searched combined with the multi-objective optimization algorithm MOEA/D-GO. For demonstration, two 2-bit wideband directional couplers at 2 GHz with the coupling of 10-dB and 20-dB are designed. They both provide excellent directivity, with maximum performance directivity of 65 dB and 49 dB, respectively. Compared with the traditional 1-bit fragmentation structure, the number of iterations achieving the same design objections is less without preset.
Ultra-Wideband Aided UAV Positioning Using Incremental Smoothing with Ranges and Multilateration

In this paper, we present a novel smoothing approach for ultra-wideband (UWB) aided unmanned aerial vehicle (UAV) positioning. Existing works based on smoothing or filtering estimate 3D position of UAV by updating a solution for each single 1D low-dimensional UWB range measurement. However, a low-dimensional single range measurement merely acts as a weak constraint in a solution space for UAV position estimation, and thus it can often lead to incorrect estimation in unfavorable conditions. Inspired by the idea that the multilateration outcome can be utilized as a measurement providing a strong constraint, we utilize two types of UWB-based measurements: (i) each single 1D range as a high-rate measurement with a weak constraint, and (ii) multilateration outcome as a low-rate measurement with a strong constraint. We propose an incremental smoothing-based method that seamlessly integrates these two types of UWB-based measurements and inertial measurement into a unified factor graph framework. Through experiments under a variety of scenarios, we demonstrate the effectiveness of the proposed method.
Learning-based Optimization Algorithms Combining Force Control Strategies for Peg-in-Hole Assembly

In this paper, an approach for automatic peg-in-hole assembly is proposed. The task is divided into two main steps: searching phase and inserting phase. First, a multilayer perceptron network is designed to address the hole search problem and a hybrid force position controller is introduced to ensure a safe and stable interaction with the external environment. Then, for the inserting phase, a variable impedance controller is adopted based on the fuzzy Q-learning algorithm to yield compliant behavior from the robot during the hole insertion process. This approach is a practical and general approach to solve complex peg-in-hole assembly problems by taking advantage of both learning-based algorithms and force control strategies, which can greatly improve the efficiency and safety of the industrial manufacturing process without identifying the unknown contact model and tuning tedious parameters. Finally, the peg-in-hole experimental results for an industrial robot verified the effectiveness and robustness of the proposed approach.
Model Predictive Position and Force Trajectory Tracking Control for Robot-Environment Interaction

The development of modern sensitive lightweight robots allows the use of robot arms in numerous new scenarios. Especially in applications where interaction between the robot and an object is desired, e.g. in assembly, conventional purely position-controlled robots fail. Former research has focused, among others, on control methods that center on robot-environment interaction. However, these methods often consider only separate scenarios, as for example a pure force control scenario. The present paper aims to address this drawback and proposes a control framework for robot-environment interaction that allows a wide range of possible interaction types. At the same time, the approach can be used for setpoint generation of position-controlled robot arms, where no interaction takes place. Thus, switching between different controller types for specific interaction kinds is not necessary. This versatility is achieved by a model predictive control-based framework which allows trajectory following control of joint or end-effector position as well as of forces for compliant or rigid robot-environment interactions. For this purpose, the robot motion is predicted by an approximated dynamic model and the force behavior by an interaction model. The characteristics of the approach are discussed on the basis of two scenarios on a lightweight robot.
Using Deep Learning for Object Distance Prediction in Digital Holography

Deep Learning (DL) has marked the beginning of a new era in computer science, particularly in Machine Learning (ML). Nowadays, there are many fields where DL is applied such as speech recognition, automatic navigation systems, image processing, etc [1]. In this paper, a Convolutional Neural Network (CNN), more precisely a CNN built on top of DenseNet169, is proven to be helpful in predicting object distance in computer-generated holographic images. The problem is addressed as a classification problem where 101 classes of images were generated, each class corresponding to a different distance value from the object at a micrometer scale. Experiments show that the proposed network is efficient in this context, being able to classify with a 100% accuracy level if trained properly.
Autochthonous starter effect on the microbiological, physicochemical and sensorial characteristics of Ibores goat's milk cheeses

The effect of adding four Lactococcus lactis ssp. lactis autochthonous strains as single starters (A, B, C and D) on the microbiological, physicochemical and sensory characteristics of Ibores raw goat's milk cheese was investigated. Batches with a commercial starter (E) and with raw goat's milk without starter (F) were also made as controls. At the first day of ripening, the pH values of cheeses manufactured with any type of starter were significantly lower than those made without starter. There were significant differences (P < 0.05) in pH between batches, although no clear relationship with the starter type appeared. No significant differences (P < 0.05) in values of total solids (TS) or NaCl content were found between any of the batches after 7 d of ripening. The levels of the nitrogen fractions SN/TN and TCASN/TN were significantly (P < 0.05) higher in cheeses without starter after 7 and 30 d of ripening, but no significant differences were found between batches made with different starters. The evolution of lactic acid bacteria counts during ripening were very similar for all batches. While the enterococci counts in all types of cheeses throughout ripening remained practically constant, in cheeses manufactured with the commercial starter they were significantly (P < 0.05) lower at any given stage of ripening than those of the remaining types of cheeses. In cheeses made with autochthonous starters, the levels of coliforms and coagulase- positive staphylococci were reduced earlier in ripening than in the E and F cheeses. The best scores in the overall sensorial characteristics were awarded cheeses manufactured with two of the autochthonous starters, although the differences were not significant (P < 0.05) relative to cheeses made without starter. Goat milk cheese / autochthonous starter / ripening / sensorial quality
Accurate Prediction of COVID-19 (+) Using AI Deep VGG16 Model

Current research aims at the efficient prediction of COVID-19 (+) by employing advanced machine intelligence techniques by means of lung X-rays. In this paper, we have presented the promising VGG16 transfer learning model for the accurate and faster diagnosis of COVID-19 (+). The system provides a binary classification of the lung X-ray image into COVID-19 (+) and Normal. The effectiveness of the system being proposed is appraised by means of the performance metrics such as accuracy, precision, recall, and f1 score. Experiments were performed with 2000 X-ray specimens. For the two-class classification of the reported sample size, the proposed VGG16 model provides an outstanding recognition accuracy of 99.5%, which is loftier to all the contemporary methods provided in the literature. The suggested approach is extremely efficient and precise, for that reason, it can be used to aid and support radiologists and healthcare professionals to identify COVID-19 (+) utilizing the lung X-rays.
ARPDR: An Accurate and Robust Pedestrian Dead Reckoning System for Indoor Localization on Handheld Smartphones

The proliferation of mobile computing has prompted Pedestrian Dead Reckoning (PDR) to be one of the most attractive and promising indoor localization techniques for ubiquitous applications. The existing PDR approaches either suffer position drifts caused by accumulative errors or are sensitive to various users. This paper presents ARPDR, an accurate and robust PDR approach to improve the accuracy and robustness of indoor localization methods. Particularly, we propose a novel step counting algorithm based on motion models by deeply exploiting inertial sensor data. We then combine step counting with adaptive thresholding to personalize the PDR system for different users. Furthermore, we propose a novel stride-heading model with a deep neural network to predict stride lengths and walking orientations, thus the displacement errors are significantly reduced. Extensive experiments on public datasets demonstrate that ARPDR outperforms the state-of-the-art PDR methods.
Navigation-Assistant Path Planning within a MAV team

In micro aerial vehicle (MAV) operations, the success of a mission is highly dependent on navigation performance, which has raised recent interests on navigation-aware path planning. One of the challenges lies in that optimal motions for successful navigation and the designated mission are often different in unknown, unstructured environments, and only sub-optimality may be obtained in each aspect. We aim to organize a two-MAV team that can effectively execute the mission and simultaneously guarantee navigation quality, which consists of a main-agent responsible for mission and a sub-agent for navigation of the team. Especially, this paper focuses on path planning of the sub-agent to provide navigational assistance to the main-agent using a monocular camera. We adopt a graph-based receding horizon planner to find a dynamically feasible path in order for the sub-agent to help the main-agent’s navigation. In this process, we present a metric for evaluating the localization performance utilizing the distribution of the features projected to the image plane. We also design a map management strategy and pose-estimation support mechanism in a monocular camera setup, and validate their effectiveness in two scenarios.
Synchronous Head Movement as a Crowd-Behavior-Based Security System

Individuals react in response to internal or external stimuli, whether visual, auditory, gustatory, olfactory, cutaneous, kinesthetic, or vestibular. This behavior is not fully utilized to infer possible security incidents taking place or about to take place in a defined geographical area outside of the range or field-of-view of security systems. Sensors are in place in the form of human senses. If these natural sensors are utilized together with advances in deep learning technology, researchers will be equipped to build advanced security solutions. In this paper, we propose a security system based on the crowd behavior of synchronous head movement (SHMOV). The system provides an alert of a possible security incident if synchronous head movement occurs among a crowd in a specific area by analyzing the video stream from a camera. We assessed the proposed SHMOV system using an experiment on 20 participants in auditory, visual, and olfactory settings. This experiment demonstrated the technology’s potential, with 100%, 100%, and 80% incident detection accuracy and alerts issued 9, 24, and 47 seconds after the start of each incident, respectively.
Integrating Model Predictive Control and Dynamic Waypoints Generation for Motion Planning in Surgical Scenario

In this paper we present a novel strategy for motion planning of autonomous robotic arms in Robotic Minimally Invasive Surgery (R-MIS). We consider a scenario where several laparoscopic tools must move and coordinate in a shared environment. The motion planner is based on a Model Predictive Controller (MPC) that predicts the future behavior of the robots and allows to move them avoiding collisions between the tools and satisfying the velocity limitations. In order to avoid the local minima that could affect the MPC, we propose a strategy for driving it through a sequence of waypoints. The proposed control strategy is validated on a realistic surgical scenario.
Aviation Data Lake: Using Side Information to Enhance Future Air-Ground Vehicle Networks

Future denser air-ground vehicle networks (AGVNs) face challenges such as resource allocation, mobility management, secure transmission, and so on. At the same time, surveillance is a must for modern air traffic management. This motivates us to find opportunities in the aerial vertical by forming a conceptual surveillance plane for aerial vehicles. In this article, we propose an enhanced software-defined network architecture where the surveillance plane can provide local and global surveillance information to macro stations, acting as a side system for the communication links. We review air- ground communications and, by summarizing challenges and opportunities, propose the enhanced architecture of side-information-assisted networks in detail. We then present how we obtain, organize, manage, and utilize the local and global side information by a so-called aviation data lake (ADL). The data lake can be easily connected with advanced machine learning schemes and, thus, provide timely, context-aware metrics and predictions.
Electric Waterjet Thruster Vessel Development – Concept, Charger, and Battery Monitoring

This paper exhibits an overall design of an electric waterjet vessel. Marine craft electrification especially the propulsion system transfer from the diesel engine to electric switched reluctance motor realizes the environmental-friendly utilization. A corresponding waterproof inductive wireless charger which could operate under water-immersed condition is introduced to prompt higher marine craft electrical penetration. This work presents the first development of a novel design of a marine charging system on a jet boat environment for lower power application. The use of near field magnetic transfer lifts the power conversion efficiency and at the same time, improves the safety and reliability of the charging operation. The system has been built and tested successfully to validate the concept boat.