Miracle-dz/UniFAR-FTUs huggingface dataset:
A LEGACY OF NEARLY 500 YEARS OF MINING IN POTOSÍ, BOLIVIA: ACID MINE DRAINAGE SOURCE IDENTIFICATION AND CHARACTERIZATION 1

Intensive mining and processing of silver, lead, tin and zinc ores have occurred in various locations within and around the city of Potosi, Bolivia since 1545. Surface and subsurface waters, stream sediments and soils are contaminated with various heavy metals. Acid mine drainage and processing plant effluent are primary contaminants in the headwaters of the economically vital, yet highly impacted, Rio Pilcomayo watershed. Previous studies have documented downstream heavy metal contamination. The acid mine drainage sources documented in this study help to link downstream pollution to primary origins. Selected acid mine drainage sources, from both operating and abandoned mines contributing to local streams, contained total metal concentrations of 0.284- 977 mg/L Al, 0.03-191 mg/L As, 0.025-50.68 mg/L Cd, 0.03-161 mg/L Cu, 0.15- 7,320 mg/L Fe, 0.3-438 mg/L Mn, 0.03-15.0 mg/L Pb and 1.46-11,760 mg/L Zn, with pH and specific conductance ranging from 2.46-6.39 and 893-19,070 μS/cm, respectively. Data were gathered during the dry season with flows ranging from nil to 4.59 L/s. Metals concentrations and pH values in all mine drainage sources sampled are several orders of magnitude above compliance with Bolivian environmental law.
Revolutionizing Log Parsing for Modern Software Systems

With the tremendous log volume generated by modern systems, automated log analysis becomes indispensable for discovering crucial insights into the behavior of running systems. The foremost step subsumed in an automated log analysis pipeline, called log parsing, significantly influences its entire performance. Despite its significance, log parsing lacks quality implementations and, in practice, suffers from fundamental limitations, thereby creating a bottleneck for discovering valuable insights at the log line level. As a consequence, in my PhD thesis, I endeavor to explore a novel paradigm, called entity parsing, which goes beyond previous work by not only addressing the current limitations of log parsing but by exploring a new avenue for log parsing that could lead to significant advances in how we perform log analysis. As preliminary results show, entity parsing, despite solving a more difficult problem than conventional log parsing, is viable and obtains a significantly better accuracy on comparable datasets. By applying a first-principles approach, entity parsing is based on three fundamental components: data, machine learning, and source code. These components, leveraged within the framework of entity parsing, process logs in a way that could significantly improve the overall dependability of systems, enhancing their availability, security, and reliability.
Average Block Error Rate Analysis of IRS-Aided NOMA Short-Packet Communication Systems

In this paper, we investigate the average block error rate (BLER) of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple-access (NOMA) in short-packet communications (SPC) over Nakagami-m fading channels. In the considered system, a base station wishes to communicate with two users by using NOMA and SPC techniques with the aid of an IRS. We derive not only the closed-form expressions for the average approximate BLER of users but also the average asymptotic BLER in a high transmit signal-to-noise ratio. Monte Carlo simulations are conducted to verify the derived analytical BLER expressions. The simulation results also unveil the impact of the number of IRS elements and blocklength on system BLER performance.
A sub-micrometric spintronic-based accelerometer

Accelerometers have widespread applications and are an essential component in many areas such as automotive, consumer electronics and industrial applications. Most commercial accelerometers are based on micro-electromechanical system (MEMS) that have limited scalability and power efficiency. Here we propose a novel spintronic-based accelerometer employing magnetic tunnel junctions (MTJs) as spintronic-diodes to develop compact and high-sensitivity accelerometers where the output voltage measured across the MTJs terminals is mapped directly into the external acceleration. The device exploits both mechanical and magnetic couplings and leverages the spin-diode effect for higher sensitivity at low-power input. The proposed technology represents a potentially competitive solution to current MEMS-based approaches.
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: 1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, 2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and 3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.
A Power Supply Rejection Based Approach for Robust Defect Detection in Operational Amplifiers

This manuscript presents a comprehensive all-digital design for testability (DfT) approach which achieves high coverage in detecting defects in operational amplifiers (op amps) along with their reference and bias circuits. The proposed method has the capability of been applied both at production test and for on-line health monitoring post-deployment to detect zero-time and latent defects. The built-in self-test (BIST) circuitry is simple to design, occupies negligible area and exerts minimal to no influence on the op amp's regular performance. The proposed method is based primarily on DC parametric sweep which significantly shortens the test time and consequently leads to a substantial reduction in production test costs. Additionally, due to the digital nature of the method, it can also easily be implemented with existing digital testing infrastructure like IJTAG on a system on chip (SOC). Furthermore, in this work we introduce the duo digital window comparator, a novel detector for testing defects in the Widlar reference and the bias circuitry. We validate our method using extensive transistor-level simulation of a two-stage miller compensated folded cascode amplifier along with widlar reference and current bias circuit. Simulation results show that proposed method achieves a high defect coverage of 99% in main amplifier and 100% in widlar reference and bias circuitry. All the designs in this work are implemented in TSMC 180nm technology.
Attributes of high-tech maintenance in the Manufacturing of exported horticultural produce in Kenya

The horticultural sector is currently the second largest foreign exchange earner in Kenya after tea. It employs more than a half a million people in the formal sector and over two million people in the informal sector. The major export destinations demand some minimum quality levels of the produce entering their market. There have been technological innovations with regard to seed priming, treatment, disease and insect resistance and overall quality improvement. However the increased profitability associated with such technological advancement has not been fully enjoyed in Kenya and other developing countries due to non-adoption, partial adoption or inappropriate adoption of the improved technologies. This necessitates the application of the full range of the available technology in the production process to maximize output and profits. This paper investigates the determinants of technology adoption in the production of horticultural export produce. Multistage and random sampling methods were used to select the study areas and horticultural export producers respectively. One hundred and twelve (112) respondents were interviewed using structured questionnaires. The study covered Nakuru, Kiambu, Laikipia, Thika and Machakos districts. The data was analyzed using the omnibus logistic function employing the SPSS software Version 10. Regression results indicated positive coefficients for levels of education, role of government, funds availability and membership to professional bodies by the firms alongside other variables. Results further revealed that failure to utilize technologies was due to financial constraints, inappropriate technologies, technical knowledge, nature of the businesses, poor infrastructure, access to information as well as the technologies themselves not being available. This paper concludes that education levels, government involvement, access to finance and membership to professional bodies positively influence technology adoption.
The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.
Drift Correction in Operando Electrochemical Impedance Spectroscopy for Batteries Research

Operando Electrochemical Impedance Spectroscopy on a cycling battery involves several challenges. As the battery state is continuously changing during discharge/charge cycling, the fundamental stability criterion is not fulfilled, and certain drift of the impedance data is emerged. In the present study, an algorithm for the drift correction was developed for different sets of synthetic data. Various computational steps were followed once created a given set of impedance data: single-frequency-point hysteresis correction, time-state drift correction, regularization of the Distribution of Relaxation Times using the Levenberg-Marquardt algorithm with a penalty contribution, and validation of the resulting data by two-pole Hilbert Transform. An evaluation of the influence of hysteresis noise proved a robust algorithm for the drift corrections of operando impedance.
The effect of discharge variability on the heat budget and tributary mixing dynamics of a proglacial river

A distinctive characteristic of proglacial streams is unsteady streamflow associated with diurnal ice melt. The role of discharge variability on downstream temperatures is not well understood. This study addressed the influence of diurnal discharge fluctuations on temperature by quantifying longitudinal heat advection and unsteady flow effects in a heat budget model for a proglacial stream in the Coast Mountains of British Columbia, Canada. Given advection has not been quantified in previous modeling studies, the dominant role of advection over surface heat fluxes found here was surprising. Advection was expected to have a considerable cooling effect due to the flow contributions from cold meltwater. This effect was confirmed while discharge generally increased; however, advection also exhibited a diurnal warming phase of similar magnitude and duration as the cooling phase, while flow generally decreased. The role of discharge variability on transverse mixing dynamics found in previous studies has been inconsistent. Here, transverse mixing lengths tended to be longer with greater tributary flow relative to the main channel. These findings need to be confirmed with further research.
The Case for Lazy Byzantine Fault Detection for Transactional Database Systems

Byzantine fault detection (BFD) techniques are promising approaches for better scalability and practicality, even though they cannot mask Byzantine faults like Byzantine fault tolerance (BFT) techniques. However, the existing BFD protocol for database systems has suffered from long latency since it synchronously makes an agreement on the order of transactions between replicas when executing the transactions. In this paper, we explore an alternative BFD approach for database systems, which defers the expensive agreement and detects Byzantine faults lazily rather than detecting them in real-time. We discuss the challenges and design overview of our approach. We also present preliminary experimental results showing the benefit of our approach.
Do Mutations of Strongly Subsuming Second-Order Mutants Really Mask Each Other?

Mutation testing is a fault-based testing criterion that is used to measure the quality of the test suites of software systems. Due to its inherent high computational cost, many studies were published in the last decades aiming at reducing computational cost and human-effort for the mutation analysis. One of the most promising areas is searching for Strongly Subsuming Higher-Order Mutants (SSHOMs), which are rare and harder to kill than their constituent first-order mutants (FOMs). Therefore, they are valuable especially because they can replace their FOMs without loss of effectiveness in the mutation testing process. One of the explanations for the SSHOMs to be harder to kill than their constituent FOMs is that the single faults (mutations) can partially mask one another, so that the combination of them is harder to detect than any of the individual faults. However, we did not find in the literature an investigation of the masking phenomenon. Therefore, the goal of this paper is to start filling this gap. More specifically, for a mutation to mask the other one, it is necessary firstly that the execution of a test case reaches all involved mutations. Therefore, we designed two complementary studies to accomplish our goal. Study #1 focuses on searching for Strongly-Subsuming Second-Order Mutants (SS2OMs) and then analyzes reaching characteristics of their constituent FOMs. We found that almost half of the SS2OMs constituent FOMs are not even reaching the other FOM. In Study #2, we designed a search strategy that considers a second-order mutant killed only if both of their mutations are reached by a failing test case execution. This strategy found much more SS2OMs than in the first study.
The Influence of Cloudiness and Synoptic Situations on the Solar Radiation Balance in the Area of Kaffiøyra (Nw Spitsbergen) in the Summer Seasons 2010 and 2011

Abstract This article describes the influence of cloudiness and synoptic situations on individual components of the radiation balance such as: global solar radiation; surface-reflected radiation; longwave terrestrial and atmospheric radiation, and the long- and shortwave balance of two types of ground cover typical to Spitsbergen (the Kaffiøyra region), i.e. morainal and glacial. The research was carried out in the summer seasons of 2010 and 2011, using a Kipp & Zonen CNR4 net radiometer. A substantial influence of the presence of clouds on the individual components of the radiation balance was observed. The type of air masses related to specific synoptic situations was also found to affect the incoming and outgoing solar radiation, as well as its losses.
Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Nowadays, heterogeneous embedded platforms are extensively used in various low-latency applications, including the automotive industry, real-time IoT systems, and automated factories. These platforms utilize specific components, such as CPUs, GPUs, and neural network accelerators for efficient task processing and to solve specific problems with a lower power consumption compared to more traditional systems. However, since these accelerators share resources such as the global memory, it is crucial to understand how workloads behave under high computational loads to determine how parallel computational engines on modern platforms can interfere and adversely affect the system's predictability and performance. One area that remains unclear is the interference effect on shared memory resources between the CPU and GPU: more specifically, the latency degradation experienced by GPU kernels when memory-intensive CPU applications run concurrently. In this work, we first analyze the metrics that characterize the behavior of different kernels under various board conditions caused by CPU memory-intensive workloads on a Nvidia Jetson Xavier. Then, we exploit various machine learning methodologies aiming to estimate the latency degradation of kernels based on their metrics. As a result of this, we are able to identify the metrics that could potentially have the most significant impact when predicting the kernels completion latency degradation.
A Modified STAP Algorithm for Ground–Based Radar

The space–time adaptive algorithm provides signal processing through the application of 2D filtering techniques – in angle and Doppler frequency and by creating a weight vector, which is subsequently applied to the coherent samples received by the radar. The basic principle is to detect moving targets in cluttered environments. Several types of Space–Time–Adaptive Processing (STAP) algorithms used mainly for ground–based radars have been analyzed and are characterized by their parameters. Based on the criteria, a simulation of a ground radar model using the modified D-STAP algorithm has been performed, which is aimed at the detection of a single target. The simulation results indicate the correct implementation of the D-STAP algorithm to detect the target against the cluttered background and its future application into the radar system.
Tear Down The Wall: Unified and Efficient Intra-and Inter-Cluster Routing for FPGAs

Routing is one of the most time-consuming phases of the FPGA CAD flow, and its quality of result greatly impacts design speed and whether a successful implementation is achieved with the fixed wiring resources available. Routing is also strongly affected by the FPGA programmable interconnect architecture, making data-driven algorithms crucial to explore new fabrics. The VTR CAD flow has historically split the routing problem into two parts: within the logic cluster and between clusters. This allows a flexible specification of the programmable interconnect of each of these components and also simplifies the routing problem by splitting it into two sub-problems. However, for some architectures, this split can significantly reduce result quality, so in this work, we develop a single-stage router, run-flat, that can simultaneously optimize the intra-cluster and inter-cluster routing. We show that this new router increases the generality of architectures VTR can target and improves the quality of results at the cost of a run time increase. We detail router enhancements that reduce memory by removing non-essential nodes from the routing architecture, reduce run time by accurately ranking partial routing choices with an enhanced lookahead and efficiently resolve resource overuse at choke points with a new net-aware congestion model. Over the VTR benchmarks on an architecture with partial crossbars within the logic clusters, run-flat reduces the minimum channel width by 14% and critical path delay by 2% on average at the cost of 2.7 xthe router run time. On the Titan benchmark suite on a Stratix-IV-like architecture, run-flat reduces wirelength by 6% while taking 1.36 x the router run time.
A Novel Fundamental Frequency Modulation Strategy for Cascaded Multilevel STATCOM with Non-sinusoidal Load Currents

This paper proposes an improved method of fundamental frequency modulation for cascaded multi-level static compensators (STATCOMs). The non-sinusoidal nature of the current is one of the reasons for capacitor voltage imbalance in the direct current link of some of the compensators’ cells. As compared to conventional FFM techniques, the proposed method samples the capacitor voltage every quarter period and eliminates the voltage imbalance by using pulse patterns with specific DC capacitor voltage ripples. This method maintains the advantages of conventional FFM, such as low pulse frequency and high efficiency. In addition, it is applicable to STATCOMs with a relatively high number of cells per phase. The proposed method has been verified as effective in a model of a 35-MVA 12-chain STATCOM for an arc furnace whose actual currents were used as test signals.
Carrier Sensing of LoRa@FIIT Devices

With regards to the Internet of Things market, LoRa has emerged as the chief technology for devices with low-power requirements and long-range communications while maintaining low development costs. At present, pure ALOHA is used to access communication channels in LoRa networks. However, in high-density environments, this leads to frequent packet loss resulting in limited network scalability, as well as throughput. We are aiming to improve the scalability of LoRa networks without significantly affecting their energy consumption. The paper briefly discusses the LoRa@FIIT protocol, which has the potential to provide a more energy-efficient solution for specific applications compared to LoRaWAN. We implemented Lightweight Carrier Sensing within the LoRa@FIIT protocol as it provides less overhead than LoRaWAN, proposed network evaluation, to verify the feasibility of implementing LCS within the LoRa@FIIT protocol and released the code for implementation of the proposed solution. We also conducted various indoor and outdoor experiments to measure the impact of the implemented method on the packet loss rate and energy consumption. Based on our results, both packet loss rate and energy consumption have been significantly reduced.