Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Short High-Order Non-Binary LDPC Decoder Architecture Using a Message-Adaptation EMS Algorithm

Short non-binary (NB) low-density parity-check (LDPC) codes provide excellent error rate performance compared to their binary counterparts. This paper presents an efficient layered decoder architecture for short high-order non-binary LDPC codes. A hardware-friendly message-adaptation extended Min-Sum (MA-EMS) algorithm is proposed, where a variety of truncation sizes and message compressions are used, such that the number of decoding cycles and the storage requirements can be reduced. A configurable design that supports a variety of truncation sizes is also proposed such that the hardware efficiency can be significantly increased. An early termination (ET) scheme is used so as to decrease the required number of decoding cycles. These techniques can greatly reduce the complexity of the decoder with almost no loss in performance. To demonstrate these techniques, a (64, 32) 256-ary LDPC decoder is implemented in a 90 nm process, which can provide a throughput of 322.9 Mbps and occupies an area of 6.74 mm2. The proposed MA-EMS decoder is able to achieve a similar error-rate performance and a much better area efficiency compared to the original EMS decoder.
Long‐term trends in extreme precipitation indices in Ireland

Knowledge of long‐term changes in climate extremes is vital to better understand climate variability and place present day extreme events in historical context. Analysis of trends in extreme precipitation in Ireland have largely been limited to the second half of the 20th century due to lack of data availability in digital format. Using recently digitized data, this study provides the first assessment of long‐term changes in extreme daily precipitation observed at 30 locations across Ireland. Quality control of rescued data is carried out before selected long‐term stations are tested for homogeneity using the RHtests software. Details of detected breakpoints and the application of adjustments to daily values are discussed. Eleven extreme precipitation indices are calculated on an annual basis and analysed to determine spatial and temporal trends in the frequency, intensity and magnitude of observed precipitation. The persistence of trends for varying record lengths and for two fixed periods (1910–2019 and 1940–2019) of analysis is assessed for all stations and indices. Results show increases in precipitation intensity, especially notable in the east and southeast of the island. Our findings also show that the contribution of heavy and extreme precipitation events to annual totals is increasing, while there was no persistent trends in annual totals or consecutive wet or dry days.
Actual Versus Estimated Utility Factor of a Large Set of Privately Owned Chevrolet Volts

In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles. Using data collected from Volts enrolled in The EV Project, this paper compares the real-world UF of two groups of Chevrolet Volts to estimated UF's based on J2841. The actual observed fleet utility factors (FUF) for the MY2011/2012 and MY2013 Volt groups studied were observed to be 72% and 74%, respectively. Usingmore » the EPA CD ranges, the method prescribed by J2841 estimates a FUF of 65% and 68% for the MY2011/2012 and MY2013 Volt groups, respectively. Volt drivers achieved higher percentages of distance traveled in EV mode for two reasons. First, they had fewer long-distance travel days than drivers in the national travel survey referenced by J2841. Second, they charged more frequently than the J2841 assumption of once per day - drivers of Volts in this study averaged over 1.4 charging events per day. Although actual CD range varied widely as driving conditions varied, the average CD ranges for the two Volt groups studied matched the EPA CD range estimates, so CD range variation did not affect FUF results. « less
Improvement of Hydrogen Production during Anaerobic Fermentation of Food Waste Leachate by Enriched Bacterial Culture Using Biochar as an Additive

It has become urgent to develop cost-effective and clean technologies for the rapid and efficient treatment of food waste leachate, caused by the rapid accumulation of food waste volume. Moreover, to face the energy crisis, and to avoid dependence on non-renewable energy sources, the investigation of new sustainable and renewable energy sources from organic waste to energy conversion is an attractive option. Green energy biohydrogen production from food waste leachate, using a microbial pathway, is one of the most efficient technologies, due to its eco-friendly nature and high energy yield. Therefore, the present study aimed to evaluate the ability of an enriched bacterial mixture, isolated from forest soil, to enhance hydrogen production from food waste leachate using biochar. A lab-scale analysis was conducted at 35 °C and at different pH values (4, no adjustment, 6, 6.5, 7, and 7.5) over a period of 15 days. The sample with the enriched bacterial mixture supplemented with an optimum of 10 g/L of biochar showed the highest performance, with a maximum hydrogen yield of 1620 mL/day on day three. The total solid and volatile solid removal rates were 78.5% and 75% after 15 days, respectively. Acetic and butyrate acids were the dominant volatile fatty acids produced during the process, as favorable metabolic pathways for accelerating hydrogen production.
Screening, Characterization and Identification of Sophorolipid-Producing Yeast Isolated from Palm Oil Effluent Polluted Soil

Aim: This study investigated the screening, characterization and identification of sophorolipid-producing yeast isolated from palm oil effluent polluted soil. Place and Duration: Two soil samples impacted with palm oil mill effluent were obtained from Elibrada and Rumuche in Emohua Local Government Area, Rivers State, Nigeria, between March 2020 and September 2021. Methodology: The soil samples were analyzed for physicochemical, geotechnical and microbiological qualities. The yeast isolates were identified using biochemical and 16S ITS molecular approaches. The isolates were screened for hydrocarbon degradation and sophorolipid production. The sophorolipid production was evaluated using emulsification index (E24), haemolytic activity, oil spread activity, drop collapse and blue agar plate assay (Cetyl trimethyl ammonium bromide CTAB Method). Results: The pH of the Palm oil mill effluent -impacted soil ranged between pH 6.18 to 6.34 the electrical conductivity value for Elibrada and Rumuche were 20.84 µS/cm and 80.19 µS/cm respectively while that of the unpolluted soil was 220.13 µS/cm. Permeability of the soil from Elibrada was 2.7 cm/s, Rumuche had 1.3 cm/s while the unpolluted soil had 5.6 cm/s. Total organic carbon for soil samples from Rumuche was 4.92%, Elibrada had 6.13% while the unpolluted soil had 8.74%. Oil and grease component for the Rumuche soil was 10500 mg/kg, Elibrada had 7200 mg/kg and the unpolluted soil had 28.0 mg/kg. Total fungal count for Rumuche soil samples was 3.8 × 104 CFU/g while Elibrada had 3.2 × 104 CFU/g. Eighty percent (80%) of the isolates were Candida spp. while 20% were Saccharomyces sp. There were four bands separated which were denoted as ITS 600bp and a Ladder L which was 500bp. The phylogenetic construct showed that the Y8 was 70.2% similar to Candida parapsilosis. The isolate Y3 was 95.8% similar to Candida haemulonis. Isolate Y9 had 100% similarity with Pichia kudriavzevii while Y13 had a 97.4% identity with Saccharomyces cerevisiae the GENBANK accession numbers were Candida haemulonis MW182014, Candida parapsilosis MW182015, Pichia kudriavzevii MW182016 and Saccharomyces cerevisiae MW182017. Conclusion: There is an increasing concern to develop a process for microbial isolation and characterization for effective biotechnological advancement using impacted environmental media as a bioresource.
Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.
An Experimental Investigation of Glass Breakage in Compartment Fires

An experimental investigation has been completed which examined the breaking of window glass by fire. The experiments were carried out in a compartment designed to achieve a two-layer fire environment characteristic of normal building fires. The experi mental data was collected from two test groups: the first for windows with their edges insulated from the fire (edge-protected) as occurs in normal window installations and the second for windows uniformly heated by the fire (edge-unprotected). The results of the edge-protected window tests indicated that the glass breakage was caused by a critical temperature difference between the central heated portion of the pane and the glass edge. The experimental work showed the critical value to be approximately 90°C. After the material properties of the glass were determined, the theoretical findings of Keski-Rahkonen were used to obtain a value of 70°C; the difference attributed to radia tive heating of the glass surface thermocouple. The test results also demonstr.
Measuring manufacturing competence : a fuzzy approach

Over the last decade a large body of literature has pointed out the crucial role played by manufacturing in the achievement of the overall goals of a company. Some authors have attempted to summarise this role by introducing the concept of production competence: it measures a manufacturer's ability to support and implement a product-market specific business strategy and is calculated on the basis of current manufacturing performances. Such a measure of production competence can be associated with a short term perspective, as it disregards the future evolution of manufacturing performance. In fact, in many of today's industries, characterised by high turbulence, the current manufacturing performances could not be representative of the future ones and, consequently, of the manufacturing's overall ability to support the company's strategy in the long term. The purpose of this article is twofold: (1) to propose a new framework of production competence (which we will refer to as manufacturing competence), base.
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security concerns. Generating such adversarial variants by the defender is crucial to improving the resistance of DL-based malware detectors against them. This necessity has given rise to an emerging stream of machine learning research, Adversarial Malware example Generation (AMG), which aims to generate evasive adversarial malware variants that preserve the malicious functionality of a given malware. Within AMG research, black-box method has gained more attention than white-box methods. However, most black-box AMG methods require numerous interactions with the malware detectors to generate adversarial malware examples. Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth. In this study, we show that a novel DL-based causal language model enables single-shot evasion (i.e., with only one query to malware detector) by treating the content of the malware executable as a byte sequence and training a Generative Pre-Trained Transformer (GPT). Our proposed method, MalGPT, significantly outperformed the leading benchmark methods on a real-world malware dataset obtained from VirusTotal, achieving over 24.51% evasion rate. MalGPT enables cybersecurity researchers to develop advanced defense capabilities by emulating large-scale realistic AMG.
Panel data methods for microeconometrics using Stata

This presentation provides an overview of the subset of methods for panel data and the associated Stata xt commands most commonly used by microeconometricians. First, attention is focused on a short panel, meaning data on many individual units and few time periods. Examples include longitudinal surveys of many individuals and panel datasets on many firms. Then the data can be viewed as being clustered on the individual unit and panel methods used are also applicable to other forms of clustered data such as cross-section data from individual-level surveys conducted at many villages with clustering at the village level. Second, emphasis is placed on using the repeated measures aspect of panel data to estimate key marginal effects that can be interpreted as measuring causation rather than mere correlation. The leading methods assume time-invariant individual-specific effects (or “fixed effects”). Instrumental variables (IV) methods can also be used, with data from periods other than the current year potentially serving as instruments. Third, some analyses use dynamic models rather than static models. Particular interest lies in fitting models with both lagged dependent variables and fixed effects. The paper additionally surveys other panel methods used in econometrics, such as those for nonlinear models and those for dynamic panels with many periods of data.
Fibre gratings for hydrogen sensing

Liquid hydrogen has been intensively used in aerospace applications for the past 40 years and is of great interest for future automotive applications. Following major explosive risks due to the use of hydrogen in air, several studies were carried out in order to develop optical fibre sensors for the detection of hydrogen leakage. This paper aims at the presentation of new hydrogen sensors based on the use of fibre Bragg gratings (FBG) and long period gratings (LPG) coated by palladium nanolayers. The sensing principle based on the palladium–hydrogen interaction is presented, as well as experimental results. It is shown that both techniques could be used for hydrogen sensing but with a sensitivity enhanced by a factor up to 500 when using a LPG sensor. FBG sensors appear to be pure strain sensors and LPG sensors are mainly based on the coupling between the cladding modes and evanescent or surface plasmon waves. Preliminary results obtained with an in-fibre Mach–Zehnder interferometer configuration with in-series LPG sensors are also presented. They show potential interest to compensate for the thermal sensitivity of the fibre gratings.
Parameter extraction and correction for transmission lines and discontinuities using the finite-difference time-domain method

The finite-difference time-domain (FDTD) method is useful for performing broadband characterization of uniform transmission lines and discontinuities. Modeling a geometry often requires the implementation of an absorbing boundary condition (ABC). When this is the case, numerical reflections from the ABC's will add significant error to the calculated transmission line or scattering (S) parameters. This paper introduces a simple post-processing algorithm for extracting these parameters and correcting for numerical reflection error. Furthermore, this method is shown to have a unique relationship to Prony's method. Practical application and limitations of this technique are also discussed. Finally, the impedance and propagation constant of a microstrip line are calculated using this method.
Estimation tail parameter for Geometric Brownian motion

Right-tailed distributions are very important in many applications. There are many studies estimating the tail index. In this paper, we will estimate the tail parameter using the three (the Direct, Bootstrap and Double Bootstrap) methods. Our aim is to illustrate the best way to estimate the -stable with using simulation and real data for the daily Iraqi financial market dataset.
Proton Cross-Sections From Heavy-Ion Data: A Review of the Models

This article is a survey of models used for the calculation of proton single-event upset (SEU) cross-sections from heavy-ion data. Models are evaluated using data in the published literature. The best predictions were given by the analytic models with no free parameters. However, in many cases, the models underpredict the measured cross-section, and a lack of precision may prevent a good prediction on a given device. Additionally, the data used to derive the models has not changed significantly over the last 30 years.
Image Processing Technique for Plant Canopy Cover Evaluation

ABSTRACT Amicrocomputer-based image processing procedure was developed for evaluating the percent canopy cover of crops from overhead slide photographs. The procedure was designed to be non-destructive and use the entire image for calculating canopy cover. Statistical comparisons are provided between a visual inspection method, the computer procedure, and leaf area measurements with a leaf area meter. In comparison to leaf area measurements, the computer procedure produced acceptable canopy cover estimates, while the visual inspection procedure estimates were larger than the leaf area measurement results. Under actual field situations, the computer-based procedure also worked well, however, the computer method could process only 43 of the 50 slides in the sample. Although human error was reduced by the computer procedure, time requirements for processing were increased. The cost of the image processing system is a limiting factor for wide use of the technique unless the computer and camera system have additional applications.
Image Analysis for Quantifying Marquisette Damage in Home Laundering

The fabric damage in home laundering is often measured to evaluate the performance of a washing machine. Currently, the standard methods of measuring the fabric damage are manual based. We developed an image-analysis method in an effort to create an automatic and objective way of evaluating on the fabric damage caused by a washing machine. The images of washed marquisette swatches were processed with Gabor filters to extract distorted yarns from the regular weave and unwanted objects in the output image were removed by judging their shape factors. The parameters of Gabor filters were estimated based on an unwashed marquisette sample, and are applicable to the samples with the same weave structure. A preliminary experiment on evaluating the marquisette damage incurred in washing shows that the image analysis evaluations have a strong correlation with the subjective assessment based on the standard of the Association of Home Appliance Manufacturers (AHAM).
Notes on a Psychodynamic Theory of Terrorist Behavior

Abstract Because of the diversity of terrorist groups and causes, there is no one terrorist “mindset. ” Within this diversity it is useful to distinguish two major categories: the “anarchic‐ideologues,” such as the Red Army Faction, committed to destroying the world of their fathers; and the “nationalist‐secessionists,” such as ETA of the Basques, who carry on the mission of their fathers. There is a tendency for marginal, isolated, and inadequate individuals from troubled family backgrounds to be attracted to the path of terrorism, so that for many, belonging to the terrorist group is the first time they truly belonged, and the group comes to represent family. This creates powerful pressures to conform within the group, for to disagree is to be seen as disloyal, and to risk losing the group. Organized against society, the group is seen as all good, and the outside society as all bad, this being the rationale for committing violent anti‐society acts.
Thermal behavior in different roof coverings on classrooms

2 . Three classrooms were covered with asphaltic bitumen of three different colors (red tile, matte white and ebony black) the last one was covered with typical clay of the region. Two temperature sensors were installed in each classroom. The first one was fixed outside of the calssrooom at 0.40 m above the roof surface and the second one was installed 0.15 m below the ceiling. The temperature data were stored over ten minutes with a set of remote terminal units which form a wireless sensor network. The results showed significant differences in temperature between the four classrooms with a grade of significance of 0.05, and it showed that the color of the roof deck significantly influences the thermal behavior inside the classrooms. The influence of external temperature over the internal temperature was quantified with the r-Person coefficient, which was found at a mean value of 0.98.
Evaluating Data Redistribution in PaRSEC

Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. The classical redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Besides distribution, data size is also a performance-critical parameter as it affects the reshuffling algorithm in terms of cache and communication efficiency, as well as potential parallelism. In addition, task-based runtime systems have gained popularity recently as a potential candidate to address the programming complexity on the way to exascale. In this scenario, it becomes paramount to develop a flexible redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions and take data size into account. In this paper, we detail a flexible redistribution algorithm and implement an efficient approach in a task-based runtime system, PaRSEC. Performance results show great capability compared to the theoretical bound and ScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution, data size, and data format.