Miracle-dz/UniFAR-FTUs huggingface dataset:
Irradiation Experiments on High Efficiency Nanowire Solar Cells Including Tilted Incidence Angle

Nanowire based solar cells have promising potential to become the next generation of space solar cells. In this study we corroborate their high radiation tolerance by irradiating high efficiency GaAs (efficiency > 15 %) nanowire solar cells with 1 MeV electrons at a fluence of 1016 e−/cm2. Additionally, we theoretically show that the damage in nanowire solar cells is expected to be slightly dependent on the incidence angle and the geometry of the nanowire array. However, experimentally, two sets of GaAs nanowire solar cells have been irradiated with 1 MeV electrons at normal incidence and with a 25° tilted angle exhibiting a similar degradation.
Subcell Development for Wafer-Bonded III-V//Si Tandem Solar Cells

This work focuses on the material properties of two III-V semiconductors, AlGaAs and GaInAsP, and their usage as middle cell absorber materials in a wafer-bonded III-V//Si triple-junction solar cell. To this end single-junction solar cells were grown epitaxially lattice matched on GaAs wafers using metalorganic chemical vapor phase epitaxy (MOVPE). By optimizing the growth temperature and the V/III ratio we could increase the open-circuit voltage at a target absorber bandgap of 1.50 eV by up to 100 mV. In the future these results will be implemented into two-terminal III-V//Si triple-junction solar cells to increase the conversion efficiency beyond the current record of 34.1 % under the AM1.5g solar spectrum.
Combining Vision Sensing with Knowledge Database for Environmental Modeling in Dual-arm Robot Assembly Task

In this paper, focusing on the vision-based dual-arm collaborative assembly, a knowledge-based representation model is proposed. Different from the traditional assembly behavior in which the assembly parameters are obtained by fixing the assembly object at a specified position in a specified posture, the proposed model uses the prior knowledge bound to the model to generate assembly parameters through point cloud matching and calculation. To a certain extent, the dependence and restriction on the fixing device during the assembly process are eliminated. And the proposed is verified on ABB YuMi. The simulations prove that the proposed approach can achieve a good performance for assembly tasks.
Quality control system of automobile bearing production based on edge cloud collaboration

In view of the strict quality control and traceability and high equipment management requirements in the current production process of automobile bearing products, a quality control system for shaft products based on the collaboration of edge cloud is proposed. This system realizes the data collection and analysis of various production factors in the bearing processing and assembly process through the workshop Internet of things, the storage, processing and analysis of multi-source heterogeneous data through the cloud platform, and the quality control and optimization of bearing products, production equipment management and optimization, product quality management and traceability functions through the micro service architecture. This paper proposes an edge cloud collaborative system architecture based on multi-source data acquisition and fusion technology, equipment anomaly warning technology and edge computing technology. The experimental results show that the system realizes the intelligent control of the whole process of bearing production, greatly improves the product quality and production efficiency, and provides a new mode for digital and intelligent bearing production process.
DNA Sequence Error Corrections based on TensorFlow

The study aims to use artificial intelligent model to accelerate multi-disciplinary sciences such as biology and physics in scientific discoveries to predict protein structure based on its genetic sequences. This paper presents an intelligent model to correct error sequences of the DNA. Therefore, dataset in genome structure will be used to predict error corrections in DNA sequences of proteins. Accordingly, Nucleus library and TensorFlow are integrated and used for these corrections. To correct sequence errors of DNA, three types of errors: insert spurious base, delete of base, and substitute one base by another. The paper will implement a computational deep neural network based on CNN with TensorFlow to correct such DNA sequence errors.
The hydration of paper studied with solid-state magnetisation-exchange 1H NMR spectroscopy

Abstract The wide-line 1H nuclear magnetic resonance (NMR) spectrum of paper in equilibrium with ambient humidity consists of super-imposed relatively broad and narrow lines. The narrower line is of the order of 2 kHz wide at half the maximum height, while the broader line is of the order of 40 kHz in width at half height. On the basis of these line widths, the narrow line is assigned to water sorbed to the paper, and the broad line to the polymeric constituents of the paper. It was not possible to distinguish between the various polymeric components of paper contributing to the 1H NMR spectra. A modified Goldman-Shen pulse sequence was used to generate a spatial magnetisation gradient between the polymer and water phases. The exchange of magnetisation between protons associated with water and those associated with the macromolecules in paper was observed. The exchange of magnetisation is discussed within a heat transfer model for homonuclear dipolar coupling, with exchange being characterised by a spin-diffusion coefficient. Consideration of the magnitude of the initial rate of the exchange process and estimates of the spin-spin relaxation times based on 1H line widths indicate that some water must exist in a sufficiently immobile state as to allow homonuclear dipolar interactions between adjacent polymer and water protons. Thus, water sorbed onto paper must exist in at least two states in mass exchange with each other. This observation allows certain conclusions to be drawn about the ratio of free/bound water as a function of moisture content and the dispersal of water within the polymer matrix.
A Comparative Analysis of Machine Learning Techniques for Classification and Detection of Malware

Malicious software, commonly known as malware, is one of the most harmful threats developed by cyber attackers to intentionally cause damage or gaining access to computer systems. Malware has evolved over the years and comes in all shapes with different types and functions depending on the goals of the developer. Virus, Spyware, Bots, and Ransomware are just some examples of malware. While those described above found themselves causing issues by accident, however, they all share one thing in common, harming the system. As a response, many infection treatments and detecting methods have been proposed. The signature-based methods are currently utilized to delete malware; however, these methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. Contrarily, the use of machine learning-based detection has been recognized as one of the most modern and notable methods. Specifically, these methods can be categorized based on their analysis technique into static, dynamic, or hybrid. The purpose of this work was to provide a survey that determines the best features extraction and classification methods that result in the best accuracy in detecting malware. Moreover, a review of representable research papers in this topic is represented with a detailed tabular comparison between them based on their accuracy in detecting malware. Among these methods, the J48 algorithm and Hybrid analysis outperformed the others with the accuracy of 100% in detecting malware in the Windows system. On the other hand, the same accuracy has been achieved in the Android system when employing the Decision Tree algorithm through Dynamic analysis. We believe that this study performs a base for further research in the field of malware analysis with machine learning methods.
Anomaly Detection in Distribution Power System based on a Condition Monitoring Vector and Ultra- Short Demand Forecasting

This paper presents a proactive intrusion detection system (IDS) for smart distribution power systems. The considered attack scenario is manipulation of the advanced measuring infrastructures (AMIs) readings and/or smart inverters data. These manipulated data from the grid edge devices mislead the grid operator for making proper operational planning decisions. In a stealthy attack model, where the attacker compromises significant number of these smart devices, serious demand-supply unbalance can occur that may result in major blackouts. The proposed IDS is based on a condition monitoring vector (CMV) equipped with a learned ultra-short-term demand forecasting (USTDF) mechanism. This cybersecurity approach is able to verify smart devices readings. In the proposed method, the instantaneous difference of collected AMIs and other smart devices data with the ultra-short term forecasted demand is defined as the CMV. This vector probes a pre-defined error band for identifying the compromised smart devices. The learned USTDF mechanism is based on the distribution grid historical load profile and the temperature data for the goal area. An accurate multi-dimensional regression model is developed and learned for forecasting the load behavior in this area. Finally, the suspicious areas are flagged or become separated from the main grid by the network operator based on the proposed CMV outcomes and the output of decision-making module. The proposed IDS aims to enhance the cybersecurity of the smart devices at the grid-edge that plays major role in ensuring the resiliency of the grid. The theoretical analyses are verified by several case studies.
FLEXNET: Flexible Networks for IoT based services

Internet of Things is becoming one of the main triggers in designing and deploying new services aiming at fulfilling the wide demand imposed by end-users. Usually, concrete solutions addressing the optimization of the wireless segment are found in the literature. However, it is much less frequent to find end-to-end solutions to be easily adopted by the corresponding stakeholders. It is in this context that FLEXNET brings an integrated solution, relying on cutting-edge technologies, dealing with a wide set of technical requirements imposed by the different applications and services.
Impact of Soiling Deposition on CdTe and Si PV Modules in Different Climate Zones in Brazil

This preliminary case-study report summarizes 5-years of research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. This paper presents a methodology to quantify the soiling ratio (SRatio) and soiling rate (SRate) in PV modules of thin-film cadmium telluride (CdTe) and polycrystalline silicon (p-Si) through measurements from monitoring soiling stations installed in different climatic zones in Brazil. In these studies, the meteorological data and the electrical and thermal parameters of the photovoltaic modules have been collected and analyzed, and a mathematical model was developed to determine the soiling ratio (SRatio) and soiling rate (SRate) for each site (in compliance with the IEC 61724–1 monitoring standard and IEC 62670–3). The SRatio for short-circuit current (SRatioIsc) and maximum power (SRatioPmax) data from the PV modules were evaluated and compared with objective of characterizing the uniformity of the soiling deposition process and to monitor any associated issues with non-uniform thermal effects. Non-uniform soiling is dicussed associated with framed modules and with effects of precipitation. Finally, other parameters are discussed for these climate conditions.
AiCE: automating horizon scanning for the detection of emerging technologies

In this paper a tool is presented for the automation of horizon scanning. Horizon scanning is the systematic process of examining the scientific literature to identify opportunities, risks, threats and emerging issues. This tool has been named the Autonomous information Comprehension Engine (AiCE). AiCE has the ability to ingest large quantities of unstructured text based data from a variety of sources, extract a wide variety of features and analyse trends across the corpus of collected source material. AiCE is used to analyse science and technology literature and identify developments which may be of strategic relevance to a specific domain. In this paper AiCE is introduced and its components are outlined. The benefit of the proposed system is demonstrated through a time expenditure study comparing this new system to the traditional manual process of horizon scanning. The proposed system is also demonstrated by analysing literature from the computer vision community to extract relevant research to the natural language processing community.
Deep Learning based Segmentation for Multi MR Imaging Protocols using Transfer Learning for PET Attenuation Correction

Magnetic resonance (MR) image segmentation is a robust technique used for PET attenuation correction. However, the segmentation of the brain into different tissue classes is a challenging task because of the similarity of pixel intensity values. The objective of this work is to propose a deep learning network to segment T1-weighted MR images of a dataset consists of 50 patients. Additionally, transfer learning is applied to segment another MR image protocol which is T2-weighted. The pretrained network with T1-weighted images is finetuned then tested with a dataset of 14 patients only. The Dice coefficients of air, soft tissue, and bone classes for T1-weighted MR images are 0.98, 0.92, and 0.79 respectively. The results of transfer learning show the feasibility of finetuning a deep network trained with T1-weighted images to segment T2-weighted images.
Contrast Variation Small-Angle Neutron Scattering Study of the Structure of Block Copolymer Micelles in a Slightly Selective Solvent at Semidilute Concentrations

The structure of micelles of an asymmetric polystyrene−polyisoprene (PS−PI) diblock copolymer has been studied in di-n-butyl phthalate (DBP), which is a slightly selective solvent for PS. The polymer has a molecular weight of 11 500 for the fully deuterated d-PS block and 48 500 for the PI block. Contrast variation SANS using mixtures of protonated and deuterated solvent and a polymer volume fraction of 18% at 10 °C were performed to get detailed information on the structure of the micelles. An additional contrast was obtained by performing a small-angle X-ray scattering measurement on a sample in protonated DBP. It was found that micelles are formed with an anisometric PI-rich core surrounded by dissolved d-PS chains. The data were analyzed using model form factors of micelles with, respectively, cylindrical and ellipsoidal cores. It was necessary to include a term to describe the concentration fluctuations within the swollen PI cores. The particle interference effects originating from the high volume fr.
Formal Multi-Objective Synthesis of Continuous-State MDPs

This letter studies formal synthesis of control policies for continuous-state MDPs. In the quest to satisfy complex combinations of probabilistic temporal logic specifications, we derive a robust linear program for policy synthesis that is solved on a finite-state approximation of the system and is then refined back to a policy for the original system. This linear programming approach leverages occupation measures and enables the multi-objective optimizations needed to handle more complex probabilistic specifications.
A 28-GHz Full-Duplex Phased Array Front-End Using Two Cross-Polarized Arrays and a Canceller

This article proposes a 28-GHz full-duplex (FD) phased arrays, which consists of 64-element transmitting and receiving arrays, and a 28-GHz radio frequency (RF) canceller. The phased arrays are designed with $2\times 2$ beamformer chips, and the transmitter (TX) and receiver (RX) arrays are oriented to have cross polarized configurations for high isolation. The 28-GHz RF canceller is based on an identical $2\times 2$ beamformer chip with two external different delay taps. Since the canceller is located at the input and output ports of arrays instead of antennas, it cannot eliminate the nonlinearities and noises in the phased arrays. Therefore, these nonlinearities and noise in the TX and RX arrays are experimentally analyzed to demonstrate that the proposed cross-polarized arrays have enough isolation to achieve suppression to the level of the RX noise floor. The FD phased-array system at 28.5–29.5 GHz has 57-dB isolation between the TX input and RX output ports, where the cross-polarization improves 10-dB isolation comparing to co-polarization and the RF canceller provide additional 10-dB self-interference (SI) suppression. Also, the nonlinearities and noise components of the received SI signals are lower than the noise floor at the output of the RX array. The system requires only 15.5-dB of additional digital cancellation to reduce the SI to the RX noise floor of 1-GHz bandwidth while maintaining the transmitting EIRP at 41 dBm.
Proportional-Fairness Resource Allocation for a Downlink Multicarrier NOMA System

A new resource allocation scheme is proposed for a downlink multicarrier non-orthogonal multiple access (NOMA) system with K users and N subcarriers. The scheme is developed from consideration of an optimization problem of user selection, subcarrier assignment, and power allocation to maximize the sum capacity under a general proportional user fairness constraint. A user selection method is first described based on the achieved capacity-to-fairness-factor ratio of each user at present, where two users with the smallest ratios are paired for NOMA transmission on an available subcarrier. Then, a joint subcarrier assignment and power allocation algorithm is presented for the pair of users for NOMA sum capacity maximization. The above procedure is repeated until all the subcarriers have been assigned to users for transmission. The proposed scheme involves low time complexity of O(N2 + K2) to provide a suboptimal solution to the resource allocation problem. Computer simulation results also show that it achieves better system capacity performance than the conventional orthogonal frequency division multiple access scheme and a related multicarrier NOMA work with every two users having identical fairness factors.
An improved fuzzing approach based on adaptive random testing

Fuzzing is a highly automated testing technique. It has been widely used in software vulnerability mining. American fuzzy lop (AFL) is one of the most effective fuzzing tools, with low resource consumption and a variety of efficient fuzzy test strategies. However, because it uses a random testing (RT) algorithm when generating test cases, there is a problem of low quality and low test efficiency. In this paper, we propose an improved fuzzing testing approach based on adaptive random testing (ART) to enhance the effectiveness of AFL test case generation. We also introduce AFL-ART, a new fuzzing tool based on ART. According to the experimental results, AFLART can enhance AFL test case generation, and improve fuzzing testing efficiency.
Application of Deep Learning in Automated Meal Recognition

Deep learning is a widely used data analysis tool and has its proven value in solving problems and challenges in data science. In the nutrition domain, automated recognition of meals is an essential task within the food quality control and diet management. Adequate food supply and precise distribution of nutrients are extremely important. The availability of deep learning to facilitate these tasks would improve a critical step of the meal service process. Therefore, the aim of this research is to study deep learning applications as automated meal recognition for patients at the Canberra Hospital, specifically using convolutional neural networks (CNN). The application of applying deep learning to food quality control are important in reducing human mistakes that may result to providing wrong foods to patients in the current food service at Canberra Hospital.
Segmentation of Deposition Periods: An Opportunity to Improve PV Soiling Extraction

Soiling profiles are commonly assumed to have sawtooth shapes, made of alternating cleaning events and soiling deposition periods. The rates at which soiling deposit on the PV modules are considered to be constant for each period. In reality, events such as changes in climatic conditions can lead to a sudden variation in soiling deposition rate. These changes cannot be reproduced if cleanings are the only events modelled to extract soiling profiles directly from PV performance data. For this reason, in this work, the use of change points and segmented regression is proposed to improve the extraction of soiling profiles through the model of up to two deposition rates per period in between cleanings. The results show that the quality of soiling extraction can be enhanced compared to a cleanings-only identification approach if both cleanings and change points are considered.
Modeling nonlinear photovoltaic degradation rates

It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate ($R_{D}$) may exhibit nonlinear behavior, which neglecting it may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear $R_{D}$. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the “real” $R_{D}$ value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.
Discrete-Time Quasi Sliding Mode Control of Single-phase T-type Inverters for Residential PV Applications

This research focuses on improving the performance of single-phase grid-connected DC/AC converters, which is intensively used in PV systems. First, the single-phase T-type topology which offers more voltage level than the conventional H-bridge is used to lower the total harmonic distortion (THD) of the output current. Second, the quasi-sliding mode control in combination with a lump disturbance compensation is used for the current control loop. This approach guarantees that the output current quickly tracks its reference in a few sampling cycles despite the existence of the modeling error. Finally, a DC bus voltage controller with a discrete-time notch filter that exactly extracts the average DC bus voltage from the second harmonic ripple is designed. The proper design of the notch filter plays a key role not only in the improvement of the DC bus voltage control-loop but also in the reduction of the overall THD of the line current. The validity of the proposed solution is verified by numerical simulation using Matlab/Simulink.