Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancements in the utilization of screen-printed boron doping paste for high efficiency back-contact back-junction silicon solar cells

–- Screen printed boron doping paste can be used as a cost-effective and highly flexible dopant source in silicon photovoltaics. In combination with a co-diffusion approach, it can help mitigate some of the increased costs advanced solar cell concepts inherit. Aiming at the fabrication of high efficiency back-contact back-junction solar cells, we investigate the limitations of direct structured application regarding minimal feature sizes. We find minimal feature sizes of 75 and $120 \mu \mathrm{m}$ for n++ and $p ^{\mathbf{++}}$\textbf{-doped regions, respectively. We also demonstrate a fully compatible and homogeneous ($\Delta R_{\Box}/ R_{\Box} = {3}{\%}_{rel}$) co-diffusion process, creating all dopings at once.
Reconstruction of Complex Discrete-Valued Vector via Convex Optimization With Sparse Regularizers

In this paper, we propose a method for the reconstruction of a complex discrete-valued vector from its linear measurements. In particular, we mainly focus on the underdetermined cases, where the number of measurements is less than that of the unknown complex discrete variables, and propose a reconstruction approach of solving an optimization problem called sum of complex sparse regularizers (SCSR) optimization. The sum of sparse regularizers in the objective function can directly utilize the discrete nature of the unknown vector in the complex domain. We also propose an algorithm for the SCSR optimization problem on the basis of alternating direction method of multipliers. For the proposed convex regularizers, we analytically prove that the sequence obtained by the proposed algorithm converges to the optimal solution of the problem. To obtain better reconstruction performance, we further propose an iterative approach named iterative weighted SCSR (IW-SCSR), where we update the parameters in the objective function in each iteration by using the tentative estimate in the previous iteration. Simulation results show that IW-SCSR can reconstruct the complex discrete-valued vector from its underdetermined linear measurements and achieve good performance in the applications of overloaded multiple-input multiple-output signal detection and channel equalization.
Smartphone VPN App Usage and User Awareness Among Facebook Users

The information security threat landscape is evolving daily with attackers increasingly resorting to ‘social engineering’ tactics to obtain information about their victims. Such information could be even those published on platforms such as Facebook, which can provide deep insights into users’ activities, preferences, likes, dislikes and behavioural patterns. Information of such nature is of great value in today's technology driven society, which is why individuals and organisations are increasingly trying to gather and store them, with or without the consent of the end-user. A common attack vector used for such purposes are mobile apps, primarily due to the fact that smartphones are increasingly capturing data about their users. This research attempts to identify user awareness levels among Sri Lankan smartphone users, when the usage of VPN apps increased suddenly during the period in which social media were banned. Analysis of responses provided by 150 respondents show that 85% admitted to using VPN apps during this period, with only 21.74% of Non-VPN users being aware of the security risks associated with VPN apps.
Performance Prediction of Jupyter Notebook in JupyterHub using Machine Learning

In this paper, we employ machine learning to predict the performance of Jupyter notebook on JupyterHub. We show that the notebook's CPU profile, the notebook's RAM profile, number of users and average delay between cells are crucial features that impact the performance of the machine learning models to accurately predict the performance of Jupyter notebook in term of the response time. We characterize the performance of our model to predict the notebook's response time in terms of the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results show that the random forest model yields strongest performance to predict the performance of Jupyter notebook with MAPE equal to 9.849% and MAE equal to 13.768 seconds. with r-square equal to 0.93.
Towards Automated Book Identification and Cataloguing for an Online Library

We are in the era where people prefer the use of technology instead of traditional sources and are upgrading from real world approaches to virtual reality. An approach is made to create an online library, in which a model of the real world library is built, that provides features for the user to navigate into the library and not only select a book to read but also explore other books while navigating through it. The users can look around and browse books from the interactive library through a web browser, resulting in saving the time consumed for travelling. To detect a book of interest in the online library, our work focus on choosing an appropriate feature matching algorithm and further draw a boundary box on the book detected. The SURF algorithm provides better matching results compared to other feature detectors.
Incentivizing Data Quality in Blockchains for Inter-Organizational Networks " Learning from the Digital Car Dossier

Recent research reports the need for consistent incentives in blockchain-based systems. In this study, we investigate how incentives for a blockchain-based inter-organizational network should be designed to ensure a high quality of data, exchanged and stored within the network. For this, we use two complementary methodological approaches: an Action Design Research approach in combination with agent-based modelling, and demonstrate, through the example of a real-world blockchain project, how such an incentive system may be modelled. The proposed incentive system features a rating mechanism influenced by measures of data correction. We evaluate the incentive system in a simulation to show how effective the system is in terms of sustaining a high quality of data. Thus, the paper contributes to our understanding of incentives in inter- organizational settings and, more broadly, to our understanding of incentive mechanisms in blockchain economy.
Unified Small-Signal Model and Compensator Design of Flyback Converter With Peak-Current Control at Variable Frequency for USB Power Delivery

Flyback converter with variable-frequency peak-current-mode (VFPCM) control is widely used in low-power adaptor and recently proposed universal serial bus power delivery application. The control scheme achieves high efficiency for the entire load range by reducing the switching frequency at light load. In the present study, an accurate unified small-signal model for VFPCM control in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is proposed. Based on the model, a compensation strategy is proposed to overcome the unstable problem in a wide range of operations and give engineers insight. Experimental and simulation results verify the proposed model and compensation strategy.
On a Correspondence between Probabilistic and Robust Invariant Sets for Linear Systems

Dynamical systems with stochastic uncertainties are ubiquitous in the field of control, with linear systems under additive Gaussian disturbances a most prominent example. The concept of probabilistic invariance was introduced to extend the widely applied concept of invariance to this class of problems. Computational methods for their synthesis, however, are limited. In this paper we present a relationship between probabilistic and robust invariant sets for linear systems, which enables the use of well-studied robust design methods. Conditions are shown, under which a robust invariant set, designed with a confidence region of the disturbance, results in a probabilistic invariant set. We furthermore show that this condition holds for common box and ellipsoidal confidence regions, generalizing and improving existing results for probabilistic invariant set computation. We finally exemplify the synthesis for an ellipsoidal probabilistic invariant set. Two numerical examples demonstrate the approach and the advantages to be gained from exploiting robust computations for probabilistic invariant sets.
Adaptive Guided Upsampling For Color Image Demosaicking

Color demosaicking is a significant step that enables digital cameras to recover full pixels from a raw image. In this paper, we propose an efficient color image demosaicking model based on guided upsampling. Guided upsampling using residual interpolation has been prominent in comparison to other methods. In the present study, we developed an adaptive guided upsampling method without RI. Our approach consists of adaptive guided upsampling and color-component difference interpolation. We utilize color difference interpolation and guided upsampling for its preprocessing in the G channel interpolation and interpolate the R and B channel with guided upsampling using recovered G channel. The results of the experiments prove that our approach is superior to RI-based methods on both objective and subjective performance for the Kodak and the IMAX datasets.
Hardware-in-the-Loop Platform for Assessing Battery State Estimators in Electric Vehicles

The development of new algorithms for the management and state estimation of lithium-ion batteries requires their verification and performance assessment using different approaches and tools. This paper aims at presenting an advanced hardware in the loop platform that uses an accurate model of the battery to test the functionalities of battery management systems (BMSs) in electric vehicles. The developed platform sends the simulated battery data directly to the BMS under test via a communication link, ensuring the safety of the tests. As a case study, the platform has been used to test two promising battery state estimators, the adaptive mix algorithm and the dual extended Kalman filter, implemented on a field-programmable gate array-based BMS. The results show the importance of the assessment of these algorithms under different load profiles and conditions of the battery, thus highlighting the capabilities of the proposed platform to simulate many different situations in which the estimators will work in the target application.
Uplink Rate Based on Massive MIMO Channel Estimation Approach

Transmitted signal is degraded and problem remains reconstruction of symbols. In massive MIMO systems, channel estimation has been widely discussed. This paper investigated on the inaccuracy of the channel estimation and its effect on the uplink rate. Best solution is to have the most efficient channel estimation method that is not adversely affected by noise, interference and pilot contamination; hence choice of MMSE method compared to others. After channel estimation, the uplink rate is calculated at the receivers with the ZF and MMSE detectors by using training or pilot symbols. We noted that the two detectors converge towards an asymptotic solution which allows the knowledge of the channel. It is shown that the estimation error increases proportionally when the L cells significantly increases. And the higher the pilot power contamination increases respectively, the decrease in dB of the uplink rate is related proportionally to the increase of the channel attenuation.
Wideband Low Axial Ratio and High-Gain Sequentially Rotated Antenna Array

A 2 × 2 sequentially rotated antenna array with high gain and wide axial ratio (AR) bandwidth is designed. The antenna element consists of a microstrip patch fed with a series power divider through four L-shaped probes. Low AR ≤ 1 dB is guaranteed by reiterating the employment of the series power divider at the array level, and optimizing the antenna element impedance matching bandwidth performance. A prototype of the antenna array is manufactured for the ultrahigh frequency radio frequency identification bandwidth, with overall size 1.32 × 1.32 × 0.065 λ<sup>3</sup>. Measurement shows a <italic>S</italic><sub>11</sub> ≤ −10 dB bandwidth of 36.5% (0.76–1.1 GHz), an AR ≤ 3 dB bandwidth of 28.8% (0.823–1.1 GHz) with AR ≤ 1 dB within 0.846–1.015 GHz (18.2%). Moreover, a peak gain of 13.2 dBic, and a 3 dB gain bandwidth of 26.1% are observed. The presented antenna exhibits outstanding AR performance and high gain despite its low design and implementation complexity.
Multi-modal Capsule Routing for Actor and Action Video Segmentation Conditioned on Natural Language Queries

In this paper, we propose an end-to-end capsule network for pixel level localization of actors and actions present in a video. The localization is performed based on a natural language query through which an actor and action are specified. We propose to encode both the video as well as textual input in the form of capsules, which provide more effective representation in comparison with standard convolution based features. We introduce a novel capsule based attention mechanism for fusion of video and text capsules for text selected video segmentation. The attention mechanism is performed via joint EM routing over video and text capsules for text selected actor and action localization. The existing works on actor-action localization are mainly focused on localization in a single frame instead of the full video. Different from existing works, we propose to perform the localization on all frames of the video. To validate the potential of the proposed network for actor and action localization on all the frames of a video, we extend an existing actor-action dataset (A2D) with annotations for all the frames. The experimental evaluation demonstrates the effectiveness of the proposed capsule network for text selective actor and action localization in videos, and it also improves upon the performance of the existing state-of-the art works on single frame-based localization.
On Some New K-Samples Tests for Testing the Homogeneity of Distribution Laws

Properties of k-samples tests for testing the homogeneity of distribution laws have been investigated. Tests have been proposed in which the maximum of the two-samples statistics of the Smirnov, the Lehmann-Rosenblatt and the Anderson-Darling tests as applied by pairs to the k-samples was utilized. Models of limiting statistics distributions for the proposed tests as well as for the Anderson-Darling k-sample test have been made. Comparative analysis of the power of the tests has been done.
A Wideband, Low-Profile Log-Periodic Monopole Array With End-Fire Scanning Beams

In this letter, we propose a wideband, low-profile phased array with vertically polarized end-fire radiation patterns. The array consists of four identical log-periodic top-hat monopole antennas (LPTMAs) and could be completely embedded within a shallow cavity for sight invisible integrated installation on metallic platforms. Simulation results show that the proposed array operates over a wide frequency band of 1.0–3.0 GHz for active voltage standing wave ratio <2.0. It also possesses end-fire radiation patterns with ±45° beam scanning capability in the horizontal plane. Prototypes of the proposed LPTMA and a uniformly spaced linear array (1 × 4) are fabricated and validated in experiments. Measured results are in a good agreement with the simulation results. The measured results demonstrate that the proposed end-fire scanning array is attractive for applications that require wideband beam scanning, excellent aerodynamic, and stealth performances.
Study on hydrogen passivation behavior of SiNx film and its thermal annealing effect

Al$_{\mathbf {2}} \mathbf {O} _{\mathbf {3}}$/SiN$_{\mathbf {X}}$ passivation stacks have been widely adopted for high-efficiency silicon solar cells. We explored the effects of annealing step procedure and annealing temperature. It was shown that high iVoc were achieved when post annealing treatment performed than pre annealing. The reason for this result was analyzed by ATP images and SIMS profile. Post annealing treatment showed that hydrogen of SiNx migrates to between the silicon surface and Al$_{\mathbf {2}} \mathbf {O} _{\mathbf {3}}$
PVSITES: Supporting large-scale market uptake of Building-Integrated Photovoltaic technologies

The identified large potential for energy savings in buildings led the European Commission to adopt Directive 2010/31/EU, mandating that all new buildings in Europe must be Nearly Zero Energy Buildings (NZEB) by 2020. Renewable energy technologies and in particular the integration of photovoltaic systems in the built environmentoffer many possibilities to enable NZEB realizations. However, a continuous development of the building-integrated photovoltaics (BIPV) market in the next years necessarily demands the compliance with strict requirements in terms of design flexibility, aesthetics, durability, cost reduction, grid integration, testing standards and operation & maintenance. Within thiscontext, a European consortium of 15 partners contributes to the Horizon 2020 PVSITES project, developing BIPV technologies and systems for large-scale market deployment. The project demonstrates an ambitious set of BIPV solutions, specially tailored to provide a comprehensive and robustanswer to market demands. This paper focuses on some of the major results achieved at the time of writing. These include a detailed BIPV market and regulatory framework analysis and recommendations for the standardization of BIPV products. Novel developments of glazed crystalline silicon modules for BIPV integration are presented, with both opaque and see-thru variations. Also included are the selection of a battery technology best suited for inclusion in a custom-made building energy management system (BEMS), as well as a BIMcompatible software tool to assist the design of BIPV projects by architects and construction actors.
RF Energy Harvesting System with RFID-Enabled Charge Storage Monitoring

Radio frequency (RF) energy scavenging is a compelling approach to energize the low-power wireless devices. We present an energy harvesting system consists of a low-power RF switch circuitry and a passive UHF RFID tag. When the voltage at the storage capacitor terminals exceeds 0.58 V, RF switch connects the UHF RFID microchip to a dipole-type tag antenna. This way, an RFID reader can detect the charge storage level wirelessly with minimal power consumption at the harvester. In this paper, we detail the development of the system and present results from both simulations and measurement. Overall, we were able to achieve 0.58 V at the storage capacitor and detect the storage level indicator tag at the distance of 5.1 m in an experiment where regular 8.7 dBi patch antennas were connected to the harvester input and output of an RFID reader emitting 2.5 W EIRP.
Automated Detection of Solar Cell Defects with Deep Learning

Nowadays, renewable energies play an important role to cover the increasing power demand in accordance with environment protection. Solar energy, produced by large solar farms, is a fast growing technology offering environmental friendly power supply. However, its efficiency suffers from solar cell defects occurring during the operation life or caused by environmental incidents. These defects can be made visible using electroluminescence (EL) imaging. A manual classification of these EL images is very time and cost demanding and prone to subjective inter-examiner variations. For a fully automated defect detection, we introduce a deep learning based classification pipeline operating on the EL images. This includes image preprocessing for distortion correction, segmentation and perspective correction as well as a deep convolutional neural network for solar defect classification with special emphasis on dealing with highly imbalanced dataset. The impact of minority oversampling and data augmentation on the system accuracy is investigated. The performance of our proposed classification pipeline is demonstrated by applying it to a real world dataset.
How Much Will Tiny IoT Nodes Profit from Massive Base Station Arrays?

In this paper we study the benefits that Internet-of-Things (IoT) devices will have from connecting to a massive multiple-input-multiple-output (MIMO) base station. In particular, we study how many users that could be simultaneously spatially multiplexed and how much the range can be increased by deploying massive base station arrays. We also investigate how the devices can scale down their uplink power as the number of antennas grows with retained rates. We consider the uplink and utilize upper and lower bounds on known achievable rate expressions to study the effects of the massive arrays. We conduct a case study where we use simulations in the settings of existing IoT systems to draw realistic conclusions. We find that the gains which ultra narrowband systems get from utilizing massive MIMO are limited by the bandwidth and therefore those systems will not be able to spatially multiplex any significant number of users. We also conclude that the power scaling is highly dependent on the nominal signal-to-noise ratio (SNR) in the single-antenna case.
High-Precision Anti-Disturbance Gimbal Servo Control for Control Moment Gyroscopes via an Extended Harmonic Disturbance Observer

In control moment gyroscopes (CMGs), the gimbal is expected to rotate at a low speed with high precision so that high-precision gyroscopic torque can be generated to realize high-precision spacecraft attitude control. However, there are complex and multiple disturbances in the gimbal servo systems, which may deteriorate the gimbal control performance to a great extent. In this paper, an extended harmonic disturbance observer (EHDO)-based composite controller is proposed to reject the effects of multiple disturbances on the control performance of gimbal servo systems. Firstly, an EHDO is developed for an mth-order model describing disturbance dynamics in which the rotor dynamic imbalance torque along gimbal axis is modeled as a harmonic, and the others are approximated as a polynomial. Compared with conventional extended disturbance observers, EHDO can estimate multiple disturbances with high precision even with a lower bandwidth. Secondly, a backstepping-based composite controller is designed to achieve high-performance gimbal control for CMGs. Finally, simulation and experimental results are presented to demonstrate the effectiveness of the proposed method.
On negative imaginary synthesis via solutions to Riccati equations

This paper tackles the problem of finding conditions for the existence of an output feedback controller such that the closed-loop system becomes strictly negative imaginary (SNI) and internally stable. In order to achieve this objective, we derive a set of necessary and sufficient conditions to check the SNI property of systems with a non necessarily minimal state-space realization. Then, we propose a set of sufficient conditions for the controller synthesis problem and we also provide a formula for such controller.
Secant Conditions in Power Network Stability

The problem of decentralized frequency control in power networks has received an increasing attention in recent years due to its significance in modern power systems and smart grids. Nevertheless, generation dynamics often involve turbine/governor dynamics, which, in conjunction with non-linearities associated with generation and power flow, increase significantly the complexity in the analysis, and are not adequately addressed in the literature. In this paper we show how incremental secant gain conditions can be used in this context to reduce the conservatism in the analysis. The stability conditions derived are decentralized and are demonstrated throughout the paper with various examples.