Miracle-dz/UniFAR-FTUs huggingface dataset:
A HTS-Excitation Modular Flux-Switching Linear Machine With Static Seals

This paper proposes and analyzes a novel high-temperature superconducting (HTS) modular flux-switching linear machine for long-stator applications, which adopts the stator with a low-cost salient-pole iron core and accommodates both the armature windings and HTS-excitation windings on the short mover, thus facilitating static seals of cooling Dewar for the HTS windings. The key of the proposed machine is to employ a modular mover structure composed of separate phase modules to achieve a complementary pattern, such that the cogging force can be significantly offset to effectively reduce the force ripple while offering the advantages of very symmetrical back electromotive force (EMF) and high fault tolerance. The operation principle and optimization analysis of the proposed machine with concern on the HTS-excitation characteristics are carried out. Also, by using the two-dimensional finite element method, the electromagnetic performances of the proposed machine are investigated to verify its validity. The results show that it can offer symmetrical and sinusoidal back-EMF, high fault tolerance, and low force ripple.
Cluster Restoration-Based Trace Signal Selection for Post-Silicon Debug

Trace signal selection is of great importance for post-silicon debug. Debuggers traditionally use state restoration to improve the observability of the trace data, and state restoration ratio (SRR) is computed after state restoration. In this paper, we exploit the combination of snapshot states and trace states to improve the observability. First, we propose a novel state restoration method, called cluster restoration. It uses both the snapshot states of flip-flop clusters at the beginning of tracing, and the tracing states of the clusters’ inputs during the tracing window to deterministically restore all states of these clusters during the tracing window. We also present a cluster restoration-based trace signal selection method to select clusters instead of trace signals directly, which includes two stages: 1) cluster generation and 2) cluster evaluation. For cluster generation, feedback loop-based cluster generation and backward tracing-based cluster generation techniques are proposed. For cluster evaluation, a new metric, called the global state restoration improvement is proposed to evaluate the candidate clusters. The experimental results show that in comparison to prior trace signal selection methods, our method can improve the SRR and reduce the runtime of trace signal selection as well.
iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0

The recent expansion of IoT-enabled (Internet of Things) devices in manufacturing contexts and their subsequent data-driven exploitation paved the way to the advent of the Industry 4.0, promoting a full integration of IT services, smart devices, and control systems with physical objects, their electronics and sensors. The real-time transmission and analysis of collected data from factories has the potential to create manufacturing intelligence, of which predictive maintenance is an expression. Hence the need to design new approaches able to manage not only the data volume, but also the variety and velocity, extracting actual value from the humongous amounts of collected data. To this aim, we present iSTEP, an integrated Self-Tuning Engine for Predictive maintenance, based on Big Data technologies and designed for Industry 4.0 applications. The proposed approach targets some of the most common needs of manufacturing enterprises: compatibility with both the on-premises and the in-the-cloud environments, exploitation of reliable and largely supported Big Data platforms, easy deployment through containerized software modules, virtually unlimited horizontal scalability, fault-tolerant self-reconfiguration, flexible yet friendly streaming-KPI computations, and above all, the integrated provisioning of self-tuning machine learning techniques for predictive maintenance. The current implementation of iSTEP exploits a distributed architecture based on Apache Kafka, Spark Streaming, MLlib, and Cassandra; iSTEP provides (i) a specific feature engineering block aimed at automatically extracting metrics from the production monitoring time series, which improves the predictive performance by 77% on average, and (ii) a self-tuning approach that dynamically selects the best prediction algorithm, which improves the predictive performance up to 60%. The iSTEP engine provides transparent predictive models, able to provide end users with insights into the knowledge learned, and it has been experimentally evaluated on a public unbalanced failure dataset, whose extensive results are discussed in the paper.
Investigation of the RCS for a finite bandpass frequency selective surface

Frequency selective surface (FSS) of finite size behaves differently with respect to an ideal infinite one due to the influence of fringe effect. The study on RCS of a finite FSS can quantitatively reveal the mechanism of the fringe effect, while there exist little study concerning this subject. This paper presents study of RCS for FSS with finite structures. The RCS for different sizes of FSS under plane wave illumination are studied. Quantitative relations between fringe effect and dimension of FSS are summarized. A new case is finally picked and tested to verify the validity of the relations.
A Search for the 835 Hz Superburst Oscillation Signal in the Regular Thermonuclear Bursts of 4U 1636-536

Burst oscillations are brightness asymmetries that develop in the burning ocean during thermonuclear bursts on accreting neutron stars. They have been observed during H/He-triggered (Type I) bursts and carbon-triggered superbursts. The mechanism responsible is not unknown, but the dominant burst oscillation frequency is typically within a few hertz of the spin frequency, where this is independently known. One of the best-studied burst oscillation sources, 4U 1636-536, has oscillations at 581 Hz in both its regular Type I bursts and in one superburst. Recently, however, Strohmayer & Mahmoodifar reported the discovery of an additional signal at a higher frequency, 835 Hz, during the superburst. This higher frequency is consistent with the predictions for several types of global ocean modes, one of the possible burst oscillation mechanisms. If this is the case then the same physical mechanism may operate in the normal Type I bursts of this source. In this paper we report a stacked search for periodic signals in the regular Type I bursts: we found no significant signal at the higher frequency, with upper limits for the single trial root-mean-square fractional amplitude of 0.57(6)%. Our analysis did, however, reveal that the dominant 581 Hz burst oscillation signal is present at a weak level even in the sample of bursts where it cannot be detected in individual bursts. This indicates that any cutoff in the burst oscillation mechanism occurs below the detection threshold of existing X-ray telescopes.
System-Level Power Delivery Network Analysis and Optimization for Monolithic 3-D ICs

As 2-D scaling reaches its limit, monolithic 3-D (M3D) IC is a leading contender for continuing equivalent scaling. Although M3D shows power and performance benefits over 2-D designs, designing a power delivery network (PDN) for M3D is challenging. In this paper, for the first time, we present a system-level PDN model of M3D designs focusing on both resistive (IR) and inductive ( $\text{L}di/dt$ ) components of power supply integrity. In addition, we present frequency- and time-domain analyses of M3D PDNs. We show that the additional resistance in M3D PDNs, while being worse for resistive drops, improves resiliency against ac current noise showing 35.9% peak impedance reduction compared to 2-D PDNs during worst case resonant oscillations. Then, we present methodologies to improve power supply integrity of M3D designs based on the observations. Our optimization methodologies offer up to 32.6% and 17.0% static and dynamic voltage drop reduction compared to the baseline M3D designs, respectively, showing 9.0% lower dynamic voltage drop compared to the 2-D counterparts.
Characterizing Input Methods for Human-to-Robot Demonstrations

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.
Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach

The paper presents an approach to detection of attacks against Internet-of-Things networks and devices which can be used in critical infrastructures. It is based on use of machine learning and big data processing. Feature of the offered approach is using the method of reduction of output data sets and application of various algorithms of machine learning based on distributed data processing. The paper compares the speed and accuracy of attack detection on the basis of machine learning algorithms in the local and distributed modes.
Improving Branch Prediction Accuracy on Multi-Core Architectures for Big Data

Branch prediction is an effective way to improve instruction supply and throughput of processor pipeline. How-ever, branch predictors usually rely on certain amount of branch history to achieve high prediction accuracy through warming up, which is time consuming. Besides, studies on improving performance of big data workloads show that significant commonality exists in instruction streams across different cores running the same workload. Based on the above observation, in this paper, we propose a technique for migrating branch history through thread migration to reduce the training process in the newly starting core. We conduct a variety of simulations to verify the effectiveness of our method. Results show that compared to not migrating, migrating branch history can improve the prediction accuracy in the new core by 24.26% and 34.08% on average for migrating history in T0 and migrating history in T0-T4 respectively. Besides, our method performs well across different cores.
On the Optimal Design of Hybrid-Electric Power Systems for Offshore Vessels

Evolution of technology is significantly increasing the presence of hybrid-electric power systems in offshore (support/operation) vessels. They require high dynamic response including dynamic positioning and high levels of reliability and safety. This paper presents a methodology for the optimal design of hybrid-electric power systems for all-electric offshore vessels focused on economic objectives. The methodology is based on the energy modeling of the most promising hybrid-electric power system architectures, as well as the use of an advanced multiobjective optimization technique to obtain the Pareto set of noninferior solutions, corresponding to the best sizing of the energy storage system and energy management strategy. Therefore, modern vessel power systems (ac or dc distribution systems designed around the use of power electronics converters and energy storage systems) can be optimized in terms of fuel consumption, emissions, and capital expenditure/operational expenditure according to the vessel operation modes and power profiles.
Efficient Real-Time Integrity Auditing With Privacy-Preserving Arbitration for Images in Cloud Storage System

Cloud storage provides an inexpensive and effective means for the storage and management of images, which in turn occupy a huge proportion and are usually stored in an archived mode. Considering the security and efficiency requirements of cloud images, an efficient real-time integrity audit scheme is in urgent need. However, existing solutions cannot be directly applied since they do not take the characteristics of cloud images into account and thus take enormous computations, communications, and storage to generate, transfer, and store authentication data. Moreover, the result of auditing cannot be used as evidence to prove the guilt of cloud service provider since the verifier whom is specified by the client may hide its misbehavior. Reversible watermarking is a potential way to achieve lightweight real-time audit for cloud images without introducing permanent distortion. Nevertheless, existing algorithms cannot provide stable capacity for authentication data of fixed length. In addition, it entails security problems once it is used to solve the fairness problem. This paper proposes an efficient real-time integrity audit scheme specific to cloud images with fair arbitration support. The scheme is based on the presented adaptive reversible watermarking algorithm which provides a fixed embedding capacity for images to embed authentication data. To address fairness problem under the proposed mechanism, we adopt Diffie–Hellman key exchange scheme to design a new challenge-response protocol under the established simplified consensus mechanism, so that replay attack resistance and privacy-preserving fair arbitration are achieved. The security analysis and performance evaluation show that the proposed scheme has provable security and greatly enhanced efficiency comparing with the state of the art.
Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.
Competition between mobile genetic elements drives optimization of a phage-encoded CRISPR-Cas system: insights from a natural arms race

CRISPR-Cas systems function as adaptive immune systems by acquiring nucleotide sequences called spacers that mediate sequence-specific defense against competitors. Uniquely, the phage ICP1 encodes a Type I-F CRISPR-Cas system that is deployed to target and overcome PLE, a mobile genetic element with anti-phage activity in Vibrio cholerae. Here, we exploit the arms race between ICP1 and PLE to examine spacer acquisition and interference under laboratory conditions to reconcile findings from wild populations. Natural ICP1 isolates encode multiple spacers directed against PLE, but we find that single spacers do not equally interfere with PLE mobilization. High-throughput sequencing to assay spacer acquisition reveals that ICP1 can also acquire spacers that target the V. cholerae chromosome. We find that targeting the V. cholerae chromosome proximal to PLE is sufficient to block PLE and propose a model in which indirect chromosomal spacers are able to circumvent PLE by Cas2-3-mediated processive degradation of the V. cholerae chromosome before PLE mobilization. Generally, laboratory acquired spacers are much more diverse than the subset of spacers maintained by ICP1 in nature, showing how evolutionary pressures can constrain CRISPR-Cas targeting in ways that are often not appreciated through in vitro analyses.
Phenotype and Variant Spectrum in the LAMB3 Form of Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a heterogeneous group of inherited disorders characterized by abnormal formation of dental enamel, either in isolation or as part of a syndrome. Heterozygous variants in laminin subunit beta 3 (LAMB3) cause AI with dominant inheritance in the absence of other cosegregating clinical features. In contrast, biallelic loss-of-function variants in LAMB3 cause recessive junctional epidermolysis bullosa, characterized by life-threatening skin fragility. We identified 2 families segregating autosomal dominant AI with variable degrees of a distinctive hypoplastic phenotype due to pathogenic variants in LAMB3. Whole exome sequencing revealed a nonsense variant (c.3340G>T, p.E1114*) within the final exon in family 1, while Sanger sequencing in family 2 revealed a variant (c.3383-1G>A) in the canonical splice acceptor site of the final exon. Analysis of cDNA from family 2 revealed retention of the final intron leading to a premature termination codon. Two unerupted third molar teeth from individual IV:5 in family 2 were subject to computerized tomography and scanning electron microscopy. LAMB3 molar teeth have a multitude of cusps versus matched controls. LAMB3 enamel was well mineralized but pitted. The architecture of the initially secreted enamel was abnormal, with cervical enamel appearing much less severely affected than coronal enamel. This study further defines the variations in phenotype-genotype correlation for AI due to variants in LAMB3, underlines the clustering of nonsense and frameshift variants causing AI in the absence of junctional epidermolysis bullosa, and highlights the shared AI phenotype arising from variants in genes coding for hemidesmosome proteins.
An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability.
Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.
RESIDUAL STRENGTH ANALYSIS OF STIFFENED PANELS USING R-CURVES OF SKIN MATERIAL

This paper presents a method of the residual strength analysis of stiffened structures with a two-bay crack in the skin under a broken stiffening element. This method takes into account the stable crack growth in the skin by means of R–curve of the skin material. The analyses of the residual strength of riveted and integrally stiffened wing and fuselage panels were carried out using this method developed. Results on the residual strength analyses of these panels were compared with test data. The conclusion was drawn about the accuracy and efficiency of the offered method of analysis.