Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Orbit Geometric Constraints Computation and Conformance for Astrosat Class of Spacecraft

Most of the current in-orbit astronomy observatories have waveband specific payloads, usually X-ray or Ultraviolet (UV). But many properties of celestial sources can be ascertained only if they are observed in multiple wavebands. One way to achieve these multi-wavelength studies is to get simultaneous coordinated observations using different satellites. This is, logistically speaking, a herculean task, if not impossible owing to the non-synchronicity of the existing space-based observatories. The solution to this problem is to have a satellite such as Astrosat which has several co-aligned instruments that span the desired spectral bands. This makes simultaneous observations in the desired wavebands possible. The increase in the number of payloads has also led to an increase in the complexity in space segment design, ground segment design, and mission management.In this paper, we explain the framework used to check various constraints for Astrosat. Also, we list the various constraints, both geometric and otherwise, corresponding to the different sensors and payloads of Astrosat. The algorithms used to compute the spacecraft attitude to conform with these constraints are also discussed. We also briefly describe the entire payload planning workflow, starting from receiving user requests to view celestial sources, all the way to the generation of commands that are finally uplinked to the satellite. We finally touch upon the ways in which improvements can be made in future satellites.
Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California

Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical dataset needed for improved fire behavior modeling and fire management. This study aims to investigate the geospatial interpolation techniques in mapping the daily fire progression and assess the accuracy of the derived maps from multisensor AF products. We focused on 42 large wildfires greater than 5000 acres in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for the comparison. The standard AF products from the moderate resolution imaging spectroradiometer (MODIS), the visible infrared imaging radiometer suite (VIIRS), and the combined products were used as inputs. We found that the estimated fire progression areas generated by the natural neighbor method with the combined MODIS and VIIRS AF input layers performed the best, with R2 of 0.7 ± 0.31 and RMSE of 1.25 ± 1.21 (103 acres) at a daily time scale; the accuracy was higher when assessed at a two-day rolling window, e.g., R2 of 0.83 ± 0.20 and RMSE of 0.74 ± 0.94 (103 acres). A relatively higher spatial accuracy was found using the 375 m VIIRS AF product as inputs, with a kappa score of 0.55 and an overall accuracy score of 0.59, when interpolated with the natural neighbor method. Furthermore, the locational pixel-based comparison showed 61% matched to a single day and an additional 25% explained within ±1 day of the estimation, revealing greater confidence in fire progression estimation at a two-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.
Fixed-Sign Binary Neural Network: An Efficient Design of Neural Network for Internet-of-Things Devices

High computational requirement and rigorous memory cost are the significant issues which limit Convolutional Neural Networks’ deployability in resource-constrained environments typically found in edge devices of Internet-of-Things (IoT). To address the problem, binary and ternary networks have been proposed to constrain the weights to reduce computational and memory costs. However, owing to the binary or ternary values, the backward propagations are not as efficient as normal during training, which makes it tough to train in edge devices. In this paper, we find a different way to resolve the problem and propose Fixed-Sign Binary Neural Network (FSB), which decomposes convolution kernel into sign and scaling factor as the prior researches but only trains the scaling factors instead of both. By doing so, our FSB avoids the sign involved in backward propagations and makes models easy to be deployed and trained in the IoT devices. Meanwhile, the convolution-acceleration architecture which we design for our FSB results in a reduced computing burden while achieving the same performance. Thanks to the efficiency of our FSB, even though we randomly initialize the sign and fix it to be untrainable, our FSB still has remarkable performances.
Monocular Visual Odometry using Learned Repeatability and Description

Robustness and accuracy for monocular visual odometry (VO) under challenging environments are widely concerned. In this paper, we present a monocular VO system leveraging learned repeatability and description. In a hybrid scheme, the camera pose is initially tracked on the predicted repeatability maps in a direct manner and then refined with the patch-wise 3D-2D association. The local feature parameterization and the adapted mapping module further boost different functionalities in the system. Extensive evaluations on challenging public datasets are performed. The competitive performance on camera pose estimation demonstrates the effectiveness of our method. Additional studies on the local reconstruction accuracy and running time exhibit that our system is capable of maintaining a robust and lightweight backend.
FPGA Implementation of LDPC Encoder Architecture for Wireless Communication Standards

In this paper a pipeline architecture is proposed for FPGA implementation of a quasi-cyclic LDPC (QC-LDPC) encoder. The results are provided for implementation on a Xilinx ZYNQ-7 ZC706 Evaluation Board for code with rate 5/6 and block lengths from 576 to 2304. The design is parameterized and can be easily rebuilt to support various code rates and code lengths. The base matrices H and code parameters are taken from the IEEE 802.16e standard. The number of logic elements (LUT), clock speed, and throughput of the encoder are presented for different code lengths. The throughput of up to 16 Gbps for IEEE 802.16e codes has been achieved.
Design of CMOS Chip to Waveguide Transition in eWLB Package for Full W-band Applications

This paper presents a low loss and wideband CMOS chip to waveguide transition in embedded wafer embedded wafer level ball grid array (eWLB) package for full W-band application. The layout of chip, package, board, and waveguide is necessary considered due to much electromagnetic coupling among them. The design of chip to waveguide transition is divided into multiple steps. The transition of chip to package is firstly investigated, and then the one of chip-package-board and chip-package-board-waveguide. Low loss and wideband impedance matching are realized by carefully designing the redistribution layer in eWLB package and transmission line (TL) on PCB. An inline board-to-waveguide transition is utilized due to an intermediate step of a dielectric filled rectangular waveguide, which can simply the fabrication and obtain high performance. The results show that the proposed approach is an attractive candidate for W-band application.
TRASS: Time Reversal as Self-Supervision

A longstanding challenge in robot learning for manipulation tasks has been the ability to generalize to varying initial conditions, diverse objects, and changing objectives. Learning based approaches have shown promise in producing robust policies, but require heavy supervision and large number of environment interactions, especially from visual inputs. We propose a novel self-supervision technique that uses time-reversal to provide high level supervision to reach goals. In particular, we introduce the time-reversal model (TRM), a self-supervised model which explores outward from a set of goal states and learns to predict these trajectories in reverse. This provides a high level plan towards goals, allowing us to learn complex manipulation tasks with no demonstrations or exploration at test time. We test our method on the domain of assembly, specifically the mating of tetris-style block pairs. Using our method operating atop visual model predictive control, we are able to assemble tetris blocks on a KuKa IIWA-7 using only uncalibrated RGB camera input, and generalize to unseen block pairs. Project’s-page: https://sites.google. com/view/time-reversal
HomeShield: A Credential-Less Authentication Framework for Smart Home Systems

Smart home systems have become more and more prevailent in recent years. On the one hand, they make our everyday life more convenient; on the other hand, they suffer from the two notorious security problems, namely, the open-port problem and the overprivilege problem, making their security situations extremely worrying and uncheerful. In this article, we proposed HomeShield, a novel credential-less authentication framework to shield smart home systems by effectively defending against the attacks resulted from these two security problems without the need for sensitive credentials. We further detailed an implementation of HomeShield based on the side channels that are publicly available in Android smartphones serving as controllers of smart home systems and presented its workflow in protecting against various attacks caused by the open-port and overprivilege problems. Finally, we tested our HomeShield implementation on a real-world smart home system and considered four threat models that cover basically all practical attacks, including Mirai and its variants. We also considered the effectiveness of our HomeShield implementation on the SmartApps of the Samsung SmartThings platform, which also suffers from the open-port and overprivilege problems, even though its overprivilege issue has been extensively studied by the recently proposed works, such as ContexIoT and SmartAuth. The evaluation results indicate that our HomeShield realization can successfully defend against over 90% attack trials with an average latency of less than 1 s.
PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices

Power consumption is a major concern for cloud data-centers. In this context, cloud-native applications emerged in the last few years and fostered the adoption of the cloud computing model across many organizations. Cloud-native workloads are highly heterogeneous, co-located and latency-sensitive and are able to scale to a high number of machines. To properly manage their power consumption, within this paper we propose Power REgulator for Service Time Optimization (PRESTO), a latency-aware power-capping orchestrator. PRESTO defines an Observe Decide Act (ODA) loop to manage power consumption and average latency of microservice-based workloads by considering all the network interactions between microservices in the cluster. PRESTO reduces the power consumption by 37.13% on average with a control error that is below 12.5% and below 1.5ms on average w.r. t. an unconstrained execution.
A Biologically-inspired Soft Exosuit for Knee Extension Assistance during Stair Ascent

Stair ascending is laborious, which imposes very high load on human knee, leading to high risks of knee injuries. Therefore, in this paper, a biologically-inspired soft exosuit for knee extension assistance during stair ascent was designed and the assistant effect of proposed exosuit was evaluated. We placed Bowden cables in front of thigh and shank to mimic quadriceps muscle, generating knee extension moments by length contraction. A force-based position control was implemented to deliver a knee assistant moment profile, which was based on biological knee moment during normal stair ascent. Biomechanical parameters of three experimental conditions (without exosuit, with unpowered exosuit, with powered exosuit) were compared. Experimental results showed that with powered exosuit lower-limb joint kinematics exhibited minimal changes, while net (muscles plus cables) knee moment decreased by 10.92% and net knee power decreased by 30.1% as compared to without exosuit condition.
Compact Millimeter-Wave Dual-Polarization Antenna Based on Magneto-Electric Dipole

A compact millimeter-wave dual-polarization antenna based on magneto-electric (ME) dipole is proposed. The antenna consists of four horizontal metallic patches placed on the upper dielectric substrate acting as electric dipole, and four vertical metallic posts and a cross-shaped slot serving as magnetic dipole. The two pairs of ME-dipoles fed by the cross-shaped slot etched on the top layer of SIW cavity. Unlike the traditional magnetic dipole, four rectangular metallic patches are symmetrical about the center and connected to each other, which achieves wide bandwidth and impedance matching. The results of simulation and measurement show that the antenna realizes impedance bandwidth of 16.8% (from 24 to 28.4GHz) for S11<-10dBi, and the isolation between two ports within the whole operation band is larger than 10dB. By controlling two different input ports, the horizontal and vertical ME-dipole can be excited separately to achieve dual-polarization.
The Relative Confusion Matrix, a Tool to Assess Classifiablility in Large Scale Picking Applications

For bin picking robots in real logistics installations, the certainty of picking the correct product out of a mixed-product bin is essential. This paper proposes an approach for the robot to efficiently decide whether it can robustly distinguish the product to pick from the others in the bin. If not, the pick has to be routed not to the robot workstation but to a manual picking station. For this, we introduce a modified version of the confusion matrix, which we call the relative confusion matrix. We show how this matrix can be used to make the required decision, taking into account that all other products in the warehouse can be logically ruled out as they are not contained in the bin. Considering only this subset of products would require a re-computation of the standard confusion matrix. With the relative confusion matrix, no such re-computation is needed, which makes our approach more efficient. We show the usefulness of our approach in extensive experiments with a real bin picking robot, on simulated data, and on a publicly available image dataset.
Robot Telekinesis: Application of a Unimanual and Bimanual Object Manipulation Technique to Robot Control

Unlike large and dangerous industrial robots at production lines in factories that are strictly fenced off, collaborative robots are smaller and safer and can be installed adjacent to human workers and collaborate with them. However, controlling and teaching new moves to collaborative robots can be difficult and time-consuming when using existing methods, such as pressing buttons on a teaching pendant and physically grabbing and moving the robot via direct teaching. We present Robot Telekinesis, a novel robot interaction technique that lets the user remotely control the movement of the end effector of a robot arm with unimanual and bimanual hand gestures that closely resemble handling a physical object. Through formal evaluation, we show that using a teaching pendant is slow and confusing and that direct teaching is fast and intuitive but physically demanding. Robot Telekinesis is as fast and intuitive as direct teaching without the need for physical contact or physical effort.
Expression of defence-related genes in sunflower infected with broomrape

ABSTRACT Transcriptional activity of four defence-related genes (NPR1, PAL, defensin and PR5) in seven sunflower lines – resistant (MS-2161A), tolerant (MS-2039A) and susceptible (MS-2098A, MS-2091A, MS-2077A, MS-2067A and MS-1589A), infected with three broomrape populations (Tulcea, Romania; Soroca and Anenii Noi, Republic of Moldova), was studied in advanced stages of infection (90 days after sowing). Obtained results revealed that resistant genotypes are characterized through higher stability in transcriptional activity of the studied genes. Thus, resistance could be associated with ability rapidly to maintain and recover a normal level of metabolism under more intensive stress factors. Also, it was established that expression of PR5 and defensin genes was altered and revealed considerable deviations in this phase of adaptation, while NPR1 and PAL mostly showed values at the level of the control group, which allows assuming that these genes, in the moment of sample collection and analysis, had normalized transcriptional activity, probably being involved in early responses.
Product Discovery – Building the Right Things: Insights from a Grey Literature Review

Context: In recent years companies have faced challenges by high market dynamics, rapidly evolving technologies and shifting user expectations. Together with the adaption of lean and agile practices, it is increasingly difficult to predict upfront which products, features or services will satisfy the needs of the customers and the organization. Currently, many new products fail to produce a significant financial return. One reason is that companies are not doing enough product discovery activities. Product discovery aims at tackling the various risks before the implementation of a product starts. The academic literature only provides little guidance for conducting product discovery in practice. Objective: In order to gain a better understanding of product discovery activities in practice, this paper aims at identifying motivations, approaches, challenges, risks, and pitfalls of product discovery reported in the grey literature. Method: We performed a grey literature review (GLR) according to the guidelines to Garousi et al. Results: The study shows that the main motivation for conducting product discovery activities is to reduce the uncertainty to a level that makes it possible to start building a solution that provides value for the customers and the business. Several product discovery approaches are reported in the grey literature which include different phases such as alignment, problem exploration, ideation, and validation. Main challenges are, among others, the lack of clarity of the problem to be solved, the prescription of concrete solutions through management or experts, and the lack of cross-functional collaboration.
Position-based Impedance Control of a 2-DOF Compliant Manipulator for a Facade Cleaning Operation*

This paper presents the design of a compliant manipulator using a series elastic actuator (SEA) and a mechanism for precisely measuring the force acting on the contact part of the manipulator without using a force sensor. It is important to maintain a constant contact force between the compliant manipulator and the wall in order to guarantee cleaning performance, and the ball screw mechanism is used to adapt to changes in the distance and the angle. Position-based impedance control is used to maintain a constant contact force when the manipulator interacts with the wall of the building, and the results confirm that the system stability is guaranteed when using SEA, regardless of the variation in the actual stiffness of the manipulator. The results of extensive experimentation using the test bench demonstrate the force tracking performance against various types of wall changes using the stiff wet-type cleaning manipulator. The results indicate that the stiffness of SEA affects the force tracking performance and system stability under the condition of the manipulator and environment interaction, and that the system stability and control performance can be improved by applying a robust force measurement mechanism to noise.
Secure Electronic Transactions via Cryptography Techniques

In recent years, many proposals for electronic payments via open network have been emerged. One of them is Secured Electronic Transaction (SET) protocol advertised through VISA and MASTERCARD that is presently spread all-inclusive. Although SET has numerous advantages over other proposals for straightforwardness and sociability, there seems to be an agreement on relative inefficiency of the protocol. In this paper, we simply give a brief overview of the secure electronic transaction with the help of the cryptography. This paper divide into different two sections in the first section give the brief introduction of the secure electronic transaction, the second section give the overview of the cryptography, third section for legal risk in electronic transaction and finally an overview of the secure transaction with the help of cryptography
Learning Affordance Space in Physical World for Vision-based Robotic Object Manipulation

What is a proper representation for objects in manipulation? What would human try to perceive when manipulating a new object in a new environment? In fact, instead of focusing on the texture and illumination, human can infer the "affordance" [36] of the objects from vision. Here "affordance" describes the object's intrinsic property that affords a particular type of manipulation. In this work, we investigate whether such affordance can be learned by a deep neural network. In particular, we propose an Affordance Space Perception Network (ASPN) that takes an image as input and outputs an affordance map. Different from existing works that infer the pixel-wise probability affordance map in image space, our affordance is defined in the real world space, thus eliminates the need of hand-eye calibration. In addition, we extend the representation ability of affordance by defining it in a 3D affordance space and propose a novel training strategy to improve the performance. Trained purely with simulation data, ASPN can achieve significant performance in the real world. It is a task-agnostic framework and can handle different objects, scenes and viewpoints. Extensive real-world experiments demonstrate the accuracy and robustness of our approach. We achieve the success rates of 94.2% for singular-object pushing and 92.4% for multiple-object pushing. We also achieve the success rates of 97.2% for singular-object grasping and 95.4% for multiple-object grasping, which outperform current state-of-the-art methods.
A Boost Converter-Based High-Voltage Pulsed-Power Supply

This article proposes a new and a generalized topology of high voltage pulse generators for different load conditions. It is modular, reliable, and is capable of generating both unipolar and bipolar pulsed output together with adjustable pulse amplitude, frequency, and width. First, the principle of operation of bipolar high voltage pulsed power source (BHVPPS) is introduced, and thereafter an extensive mathematical analysis is established. Detailed design procedure for the selection of passive components is presented. To verify the established mathematical analysis, a two-stage BHVPPS prototype is designed, and its effectiveness is validated both in powersim (PSIM) simulations and experimentation.
Epileptic seizure detection using Multivariate Empirical Mode Decomposition and Support Vector Machines

Automatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis of long-term electroencephalographic signals is the most reliable technique, automatic seizures detection can help the physicians in comparing seizures and extracting common patterns. In this paper, a new approach to classify background activity and pre-ictal stereoelectroencephalographic signals is proposed. Linear and nonlinear features are extracted directly from the derived intrinsic mode functions of multivariate empirical mode decomposition technique and the classification is performed using support vector machines. The effectiveness of the proposed approach is evaluated using real datasets. Our results show good performance of the proposed approach since an accuracy of 100% is achieved using the first intrinsic mode function and a window size of 1024 samples.