Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage probabilities in the presence of correlated lognormal interferers

Several approaches that can be used to compute the distribution of a sum of correlated lognormal random variables (RVs) are investigated. Specifically, Wilkinson's approach (Schwartz and Yeh, 1982), an extension to Schwartz and Yeh's (1982) approach, and a cumulants matching approach (Schleher, 1977) are studied. The aim is to determine which method is best for computing the complementary distribution function (CDF) of a sum of correlated lognormal RVs considering both accuracy and computational effort. Then, using these techniques, the authors compute the outage probability of a desired lognormal shadowed signal in the presence of multiple correlated lognormal cochannel interferers. The outage results are presented as a function of the reuse factor. The reuse factor is defined as the distance between the centers of the two nearest cells using the same frequencies divided by the cell radius. It is a key parameter in the design of any frequency reuse system. Simulation results are used for verification and comparison. Overall, the results obtained show that among the three methods considered Wilkinson's approach may be the best method to compute the CDF of sums of correlated lognormal RVs (and hence the outage probability in correlated lognormal shadowed mobile radio environments). This is due to both its accuracy and computational simplicity over the range of parameters valid for practical applications. >
REMUS: a small, low cost AUV; system description, field trials and performance results

A new generation of autonomous underwater vehicles are being successfully utilized to support a number of scientific and military objectives. Despite their small size and low cost, these new vehicles are versatile, reliable and require only a two person support staff. These features make the vehicles affordable and available for use by a broad segment of the oceanographic community. The vehicle operators must be confident that they can cost effectively use these new tools with a minimum of support staff for missions such as coastal ocean surveys, and pollution identification and source tracking. An effort to design and fabricate a low cost, small, and accessible vehicle resulted in REMUS, Remote Environmental Measuring UnitS. The vehicle is 19 cm in diameter by 134 cm long and weighs 31 kg. It has an operating and control system based on the PC-104 form factor of the IBM-PC which can be connected to a laptop computer for system configuration. With 400 watt-hours of conventional lead acid batteries the vehicle has a useful range of 25 nautical miles at 3 knots, and a top speed of 5 knots. REMUS is capable of navigating itself using a variety of techniques including long and ultra-short baseline acoustic navigation, bottomlock Doppler navigation and GPS reception. The major system design features are described in this paper including the electrical power plant, the vehicle control system, the acoustic navigation system, and the user interface to the vehicle. The mechanical design and system performance are presented along with recent results from field tests of navigating the vehicle around a pair of transponders.
Oxidative Stress Induced By The Metallic Oxide The Copper Oxide (Cuo-Nps) On Terrestrial Snail Helix Aspersa

Our study focused on the evaluation of the toxicity of copper oxide nanoparticles (CuO-NPs) on a bioindicator; the land snail Helix aspersa. Their effects were studied by a targeted approach in the laboratory, by evaluating the oxidative stress biomarkers in hepatopancreas and kidney (GSH, GST, GPx, CAT, and LPO). The snails were exposed to increasing concentrations (50, 100, 150, and 200 mg/kg) of CuO-NPs mixed in wheat flour during a sub-chronic treatment period of 45 days. Our results show that: CuO-NPs can induce oxidative stress, by producing reactive oxygen species (ROS), which was confirmed by the decrease in glutathione (GSH) level and reduction of its metabolizing enzyme glutathione-s-transferase (GST) in both organs, as they trigger the detoxification system resulting in increased activity of the glutathione peroxidase (GPx) and catalase defense enzyme and lipide peroxidation indices within the hepatopancreas.
The Role of Inertia in Frequency Stability and The Implementation of Virtual Inertia in RES-based Power Plants

For economic and environmental reasons, the power system is changing. From having all the electric power production from large power plants based on fossil fuels to distributed generation based on renewable energy sources. This change results in a significant reduction in the system’s inertia, challenging the existing frequency control. This work presents the frequency response of a three-area power system with two power plants based on renewable energy sources and a thermal power plant in order to study the role of inertia for frequency responses. Namely, the implementation of virtual inertia devices in the RES-based power plants is simulated and analysed to conclude about its impact on the power system.
Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan

This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values and identified changes, time series have been developed. Sentinel satellite data and statistical indicators for the last few years for the agricultural territories of Kazakhstan are used as primary data. It is found that the upward trend in wheat quality, however, increases the size of fertilizers, variables based on the NDVI also significantly contribute to the forecasting model. It has been shown that the amount of applied fertilizer has stabilized in the past few years due to economic and environmental constraints, so NDVI-based models will become increasingly important for enhancing forecasting models. Four machine learning algorithms have been evaluated and compared, namely boosted regression trees (BRT) and support vector machine (SVM), to map and predict the field yield of the Experimental Oil Farm in East Kazakhstan using readily available additional data. Based on the results of the work, a forecast of crop yields and general statistical recommendations for increasing yields were obtained.
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial examples, e.g., word substitution attacks using only synonyms can easily fool a BERT-based sentiment analysis model. In this paper, we demonstrate that adversarial training, the prevalent defense technique, does not directly fit a conventional fine-tuning scenario, because it suffers severely from catastrophic forgetting: failing to retain the generic and robust linguistic features that have already been captured by the pre-trained model. In this light, we propose Robust Informative Fine-Tuning (RIFT), a novel adversarial fine-tuning method from an information-theoretical perspective. In particular, RIFT encourages an objective model to retain the features learned from the pre-trained model throughout the entire fine-tuning process, whereas a conventional one only uses the pre-trained weights for initialization. Experimental results show that RIFT consistently outperforms the state-of-the-arts on two popular NLP tasks: sentiment analysis and natural language inference, under different attacks across various pre-trained language models.
Investigation on Erosion of Continuous Bending Pipes in Different Directions

With the increasing demand for natural gas in the world, ensuring the safety and stability of pipeline transportation of natural gas has become an important subject. Compared with the studies focused on the erosion of single bend, T-pipe, and U-pipe, the studies on the continuous bend in different directions are scarce. In this paper, the model is characterized by three straight pipes with the same length and directions along the Z, Y, and X axes of the Cartesian coordinate system. The numerical simulation of gas-solid two-phase flow under different gas to solid mass ratios in the continuous bent pipeline is carried out by selecting the Oka erosion model. The results indicate that the velocity distribution of the second bend is different from that of the first bend. With the change of flow deflection angle and the accumulation of the centrifugal force, the shape of the velocity contours in the second bend turns from “basin” into a "comma" and the velocity experiences a process including rapid increase, slow decrease, gradual increase, and steep decrease. When the flow rate is 12.5 m/s, the maximum speed of the second bend is about 5 % greater than that of the first bend. Instead of a “straight-line” shape in the erosion area of the first bend, the second presents a "feather" shape. As the flow rate increases gradually, the shape characteristics of the "feather" erosion zone change from "short and dense" to "long and loose". Besides, the maximum erosion rate of the second bend is greater than that of the first even about 1.52 times. Finally, according to the simulation results, the protection suggestion of the continuous elbow is given.
FLUID: A Unified Evaluation Framework for Flexible Sequential Data

Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual, transfer, and representation learning have made substantial strides in learning under adverse conditions; each affording distinct advantages through methods and insights. These methods address different challenges such as data arriving sequentially or scarce training examples, however often the difficult conditions an ML system will face over its lifetime cannot be anticipated prior to deployment. Therefore, general ML systems which can handle the many challenges of learning in practical settings are needed. To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data). FLUID integrates the objectives of few-shot, continual, transfer, and representation learning while enabling comparison and integration of techniques across these subfields. In FLUID, a learner faces a stream of data and must make sequential predictions while choosing how to update itself, adapt quickly to novel classes, and deal with changing data distributions; while accounting for the total amount of compute. We conduct experiments on a broad set of methods which shed new insight on the advantages and limitations of current solutions and indicate new research problems to solve. As a starting point towards more general methods, we present two new baselines which outperform other evaluated methods on FLUID. Project page: https://raivn.cs. washington.edu/projects/FLUID/.
Optimisation of a Regenerator for Open Cycle Liquid Desiccant Air Conditioning Systems

Solar assisted air conditioning systems using liquid desiccants represent a promising option to decrease the high summer energy demand caused by electrically driven vapour compression machines. However, for these systems high efficient and reliable components need to be developed and the design has to be adjusted to each respective building design, location, and user behaviour. The paper presents the design of a prototype for one of the main components of liquid desiccant systems and reports on its experimental performance. A numerical model has been developed for the simulation environment TRNSYS and validated using the experimental data. Using this, a system model of a single family building for heating, cooling and domestic hot water for two Australian locations has been developed. Combining this with a Powel Algorithm, the control of the cooling and dehumidification system has been optimised.
ROAD DETECTION AND EXTRACTION USING HIGH RESOLUTION SATELLITE IMAGE

Abstract: Object detection has increasing attention within the field of remote sensing image analysis. Advance background, vertical views and variation in target kind and size in remote sensing images create object detection difficult task. Also, road is the important object for detecting and extracting using high resolution satellite image. As road plays important in geographic information system. In urban space road is essential infrastructure connecting different areas and also play important role in human civilization. Detection and extraction of road from aerial and satellite imaginary has gained and increasing attention. Road network has key role in development of transportation, road navigation, unnamed vehicle, geodata update, land use, land cover analysis and urban planning. Due to natural disaster like flood, earthquake it is necessary to check the quality of the road. It required large amount of time to check quality of road manually and it generate error. So, developing new method to road network from high resolution remote sensing images would be beneficial to geographical information system (GIS) and intelligent transportation system (ITS). With Development of Remote sensing technology, high resolution satellite image improve and obtain easily from which road can locate road accurately. Road recognition from remote sensing imaginary has two part: first is road detection and second is road extraction. This study aim to understand the deep learning and machine learning based method with the help of high resolution satellite image for detection and extraction of road. Index Terms – Remote sensing, machine learning, image processing.
Displacement and resonance behaviors of a piezoelectric diaphragm driven by a double-sided spiral electrode

This paper presents the design of a lead zirconate titanate (PZT) diaphragm actuated by double-sided patterned electrodes. Au/Cr electrodes were deposited on bulk PZT wafers by sputtering while patterned by a lift-off process. SU-8 thick film was used to form the structural layer. Double-spiral electrode induced in-plane poling and piezoelectric elongation are converted to an out-of-plane displacement due to the confined boundary condition. The influence of different drive configurations and electrode parameters on deflection has been calculated by finite element methods (FEM) using a uniform field model. Impedance and quasi-static displacement spectra of the diaphragm were measured after poling. A double-sided patterned electrode diaphragm can be actuated by more drive configurations than a single-sided one. Compared with a single-sided electrode drive, a double-sided out-of-phase drive configuration increases the coupling coefficient of the fundamental resonance from 7.6% to 11.8%. The displacement response of the diaphragm increases from 2.6 to 8.6 nm V−1. Configurations including the electric field component perpendicular to the poling direction can stimulate shear modes of the diaphragm.
A broadband, planar, monolithic resistive frequency doubler

This paper describes a resistive Schottky-diode frequency doubler having an untuned output frequency range of 16 to 40 GHz. The multiplier uses a unique planar structure to achieve 12 dB average conversion loss, 2 dBm output power, and 20 dBC fundamental-frequency rejection. <<ETX>>
Linear Electron Beam Assisted Roll-to-Roll in-Vacuum Flexographic Patterning for Flexible Thermoelectric Generators

In this work, we investigated the use of in-line linear electron beam irradiation (LEB) surface treatment integrated into a commercially compatible roll-to-roll (R2R) processing line, as a single fluorocarbon cleaning step, following flexography oil masking used to pattern layers for devices. Thermoelectric generators (TEGs) were selected as the flexible electronic device demonstrator; a green renewable energy harvester ideal for powering wearable technologies. BiTe/BiSbTe-based flexible TEGs (f-TEGs) were fabricated using in-line oil patterned aluminium electrodes, followed by a 600 W LEB cleaning step, in which the duration was optimised. A BiTe/BiSbTe f-TEG using an oil-patterned electrode and a 15 min LEB clean (to remove oil prior to BiTe/BiSbTe deposition) showed similar Seebeck and output power (S ~ 0.19 mV K−1 and p = 0.02 nW at ∆T = 20 K) compared to that of an oil-free reference f-TEG, demonstrating the success of using the LEB as a cleaning step to prevent any remaining oil interfering with the subsequent active material deposition. Device lifetimes were investigated, with electrode/thermoelectric interface degradation attributed to an aluminium/fluorine reaction, originating from the fluorine-rich masking oil. A BiTe/GeTe f-TEG using an oil-patterned/LEB clean, exceeded the lifetime of the comparable BiTe/BiSbTe f-TEG, highlighting the importance of deposited material reactivities with the additives from the masking oil, in this case fluorine. This work therefore demonstrates (i) full device architectures within a R2R system using vacuum flexography oil patterned electrodes; (ii) an enabling Electron beam cleansing step for removal of oil remnants; and (iii) that careful selection of masking oils is needed for the materials used when flexographic patterning during R2R.
Fundamental-mode dispersion-compensating fibers: design considerations and experiments

There has been a recent surge in the development of dispersion-compensating fibers (DCFs) for upgrading 1.31-μm systems to high-bit-rate systems operating at 1.55 μm; these DCFs have relied on refractive-index-profile designs1–3 or on the use of higher-order modes close to cutoff4 for compressing broadened pulses back to their original pulse widths. In this paper we present experimental results of three fiber designs that use the fundamental mode, and we discuss the parameters that affect their performance. Dispersion and loss values for the fibers are given, and their use in both digital and analog systems is described.
Quantum supremacy and hardness of estimating output probabilities of quantum circuits

Motivated by the recent experimental demonstrations of quantum supremacy, proving the hardness of the output of random quantum circuits is an imperative near term goal. We prove under the complexity theoretical assumption of the non-collapse of the polynomial hierarchy that approximating the output probabilities of random quantum circuits to within $\exp(-\Omega(m\log m))$ additive error is hard for any classical computer, where $m$ is the number of gates in the quantum computation. More precisely, we show that the above problem is #P-hard under BPPNP reduction. In the recent experiments, the quantum circuit has n-qubits and the architecture is a two-dimensional grid of size $\sqrt{n}\times\sqrt{n}$ [1]. Indeed for constant depth circuits approximating the output probabilities to within $2^{-\Omega(n\log n)}$ is hard. For circuits of depth $\log n$ or $\sqrt{n}$ for which the anti-concentration property holds, approximating the output probabilities to within $2^{-\Omega(n\log^{2}n)}$ and $2^{-\Omega(n^{3/2}\log n)}$ is hard respectively. We then show that the hardness results extend to any open neighborhood of an arbitrary (fixed) circuit including the trivial circuit with identity gates. We made an effort to find the best proofs and proved these results from first principles, which do not use the standard techniques such as the Berlekamp–Welch algorithm, the usual Paturi's lemma, and Rakhmanov's result.
Studies on the Occurrence of Extended-Spectrum Beta-Lactamase (ESBL) Producing E. coli in Chicken Environments Lebechukwu Ogechukwu Rapuruchukwu, Obodo Nathanael C. and Onah

Samples were collected from 70 poultries and 20 slaughters in Enugu north local government area (L.G.A), Udi L.G. A and Enugu south L.G. A areas of Enugu State, Nigeria. A total number of 125 samples were analyzed in this research with the aim of screening for ESBL producing Escherichia coli with objective of isolating ESBL producing Escherichia coli in chicken feed, chicken water, chicken excretes, chicken slaughters and chicken environment in the poultries located in the above mentioned areas. Twenty samples were analyzed each for the chicken feeds, waters, excretes, slaughters and environment and a total of 100 samples of E. coli were tested for ESBL production. Seventy five samples (72%) of E.coli were confirmed positive for ESBL production resulting from 16% ESBL positive samples from chicken feeds , 12% ESBL positive samples from chicken water, 17% ESBL positive samples from chicken excretes , 15% ESBL positive samples from chicken slaughters and 12% ESBL positive samples from chicken environment. To confirm ESBL production due to plasmid mediated origin or genetic mediated origin, a total of 72 samples of E.coli were cured or treated of plasmid using Acridine orange dye and retested for ESBL production. Out of the 72 samples of E.coli cured, 62 samples (86.1%) were cured and do not show ESBL production after retest and are therefore referred to as plasmid mediated while 10 samples (13.8%) were cured of plasmid but still show resistant to penicillin and cephalosporin after retest due to other reasons such as lack of penicillin binding protein (PBP) and not plasmid and thus are called Genetic mediated ESBL.
Performance evaluation of offloading LDPC decoding to an FPGA in 5G baseband processing

—Ofﬂoading of computationally expensive physical layer processing such as the forward error correction, is one of the key enablers for a fully virtualized open radio access network (RAN). In this paper we show such an ofﬂoading architecture and will demonstrate it using the OpenAirInterface 5G New Radio open source software and the Xilinx T1 telco accelerator card. We will show the feasibility and the potential savings of such an architecture.
Implementation of Cascade Multilevel Inverter-based STATCOM

Abstract This paper deals with the real time implementation of 11-level cascade multilevel inverter-based STATCOM using personal computer (PC) and ADD-ON cards for power system voltage control at the point of common coupling. Complete hardware and software development procedures have been explained in depth for direct as well as indirect control schemes. Experimental results are presented and it is shown that voltage control is achieved using cascade multilevel inverter based STATCOM, thus verifying the very basic purpose of STATCOM. Real time implementation using PC-based control technique has certain merits over digital signal processor based implementation, and some of these merits are high processing speed and low cost of implementation; therefore, PC-based implementation can be used in industry as well as for utility applications.