Miracle-dz/UniFAR-FTUs huggingface dataset:
A Perspective Survey on Industrial Knowledge Graphs: Recent Advances, Open Challenges, and Future Directions

As a result of the development of a new generation of artificial intelligence and carbon-neutral technologies, traditional industries are undergoing dramatic transformations. The exploration of industrial intelligence is still in its nascent stages, particularly lacking technical approaches to distill experiential knowledge from heterogeneous data sources originating from various origins. Knowledge Graphs (KG), as cutting-edge artificial intelligence technologies, can enable knowledge management and reuse while condensing valuable knowledge. As a result, fully utilizing KG's potential in the industrial field is critical to the realization of autonomous sensing, cognition, and the evolution of next-generation intelligent manufacturing systems. This paper starts with an overview of the current state of industrial knowledge graph development and shows how to construct an industrial knowledge graph (IKG). Following that, we provide a thorough and in-depth review of various industrial scenarios supported by knowledge graphs. Furthermore, this paper identifies the current challenges confronting industrial applications and proposes future research directions for IKG. It is hoped that this research will draw the attention of more researchers to the knowledge graph-based smart manufacturing paradigm and benefit their work.
A 0.89 to 2.1 GHz Tunable Power Amplifier for Multi-band Transmitter

This paper presents a multi-band power amplifier by using Benzocyclobutene inductor and tunable barium strontium titanate capacitor to achieve high quality (Q) factor to improve narrow bandwidth in tunable of power amplifier. The proposed power amplifier employed two stages with resistive shunt feedback at the input stage to provide a broadband gain and steady wideband input matching. Tunable frequency range from 0.89 GHz to 2.1 GHz is conducted by tunable BST capacitor from 1 pF to 20 pF. The tunable power amplifier achieves high power gain, good input and output matching, low power consumption and good power added efficiency over all frequency range of interest. It can be concluded that the proposed multi-band power amplifier is quite promising for future mobile terminals application.
Rare dynamic triggering of remote M ≥ 5.5 earthquakes from global catalog analysis

Probing the effects of a transient stress on the timing of an earthquake occurrence is necessary for understanding the remote interaction of large‐magnitude events. Global catalog data containing 35 years of M ≥ 5.5 earthquakes allow us to explore for periods of enhanced or suppressed seismic activity. We consider 113 M ≥ 7.5 main shocks between 1977 and 2012 and focus on seismic activity on time scales from seconds to days following these main shocks. We search for evidence of dynamic triggering of large‐magnitude events similar to the previously observed global increase during the first few days following the 2012 M8.6 Indian Ocean main shock. We restrict the analysis to regions of elevated strain during the passage of surface waves. Using a threshold of 0.1 microstrain (~3 kPa) and a temporal window of ±1 year, we stack daily seismicity rate curves using the exclusion‐zone declustered M ≥ 5.5 catalog events in order to resolve deviations from the background rate. Our results do not indicate a significant change in activity for at least 10 days when considering the collective set of 113 main shocks and subsets at M8.0 and M8.5 thresholds. The results also do not indicate immediate triggering of M ≥ 5.5 events. We do find two instances of increased seismicity in the elevated strain region within 10 days. These increases are subsequent to two main shocks, the 1977 M8.3 and 2012 M8.6, both located in the Indian Ocean. We conclude that a global change in M ≥ 5.5 earthquake rates following a transient stress from distant earthquakes is a rare occurrence.
Impact of Thermal Aging on Winding Insulation Loss-of-Life Fraction Using H∞ Algorithm for Integrated permanent Magnet In-Wheel Motor

To improve the reliability of in-wheel structures based on permanent magnet synchronous motors (PMSM), thermal management is a crucial requirement. However, online thermal analysis for in-wheel motors which drives electric vehicles (EVs) specially autonomous electric vehicles (A-EVs) is a highly challenging task due to the complex thermal distribution affected by geometrical and material parameters and intricate boundary conditions. In the pursuit of avoiding winding insulation to get overheated and the motor lifetime to be shortened, real-time temperature estimation is needed. In this contribution we focus on a model-based robust temperature estimation for a PMSM on the stator and rotor parts, specifically to evaluate gradual winding insulation deterioration. The proposed temperature estimation method is based on H∞ algorithm applied on a low-order lumped parameter thermal network (LPTN). Then, the insulation loss-of-life fraction is investigated when an over-current occurs during a short-time thermal overload. Finally, a comparison between numerical simulation results and experimental results confirms the robustness, feasibility and effectiveness of the presented model.
BGPEval: Automating Large-Scale Testbed Creation

BGP has been known to be vulnerable to hijacking and path manipulation attacks for many years. Several solutions have been proposed to secure either the origin, the path, or both. A known issue for new ideas is their evaluation. Simulation environments are easy to use but can only mimic real-world deployments to a certain extent. In this work, we propose BGPEval. A framework that is capable of creating large-scale testbeds based on the KVM hypervisor and Docker container technology that mimics the interconnection of ASes according to a provided AS graph. We use several layers of abstraction to spawn and interconnect as many as 55,000 containers. Our work takes significant effort away from an evaluator, who can now focus on the implementation rather than the creation of testbeds for the evaluation.
Advancing Urban Air Quality Monitoring: A Hybrid Mobile-Stationary Approach

This paper presents a modular and mobile environmental monitoring system that integrates an array of sensors to monitor various environmental parameters. The system offers flexibility through its modular design, allowing for the integration of sensors based on specific monitoring requirements. This waterproof system is equipped with sensors, a solar power source, and a cellular module for data transmission and Global Positioning System (GPS) tracking. Real-time data visualization and an alert system are provided through a user-friendly dashboard. Field evaluation demonstrates the system's resilience in harsh conditions and its ability to provide accurate and reliable environmental data. This work contributes to the advancement of environmental monitoring technologies and offers a cost-effective solution for assessing and managing environmental conditions.
CRISPR-Based Diagnostic for In-Field Detection of Plant Pathogens

Plant fungal pathogens are an increasingly common and costly threat to agricultural yields, resulting in wasted produce and the frequent use of fungicides. To enable more targeted use of fungicides, methods for detecting and identifying plant fungal infections in a rapid, low-cost, decentralized, and power-free manner are needed. CRISPR-based diagnostics have recently emerged as an effective tool for sensitive and specific pathogen detection in resource-limited settings. We present the preliminary designs of a CRISPR-based diagnostic for the soil-borne fungus Colletotrichum coccodes. Bioinformatics analysis led to the identification of four gRNA candidates targeting the ITS 1 and ITS 2 regions of C. coccodes for high specificity. The most promising gRNA was further screened and detection of DNA concentrations down to 500 pM without amplification are shown. This diagnostic for C. coccodes could be used in agricultural settings for rapid and cost-effective detection of fungal pathogens to enable high-yield sustainable agricultural practices.
HeartRhythm: ECG-Based Music Preference Classification in Popular Music

Electrocardiogram (ECG) is a promising signal for music psychology research, but it is rare to find a music study applying ECG to machine learning, especially in music preference tasks. This study conducts an ECG-based music preference classification (favored vs. non-favored). The model is evaluated on a MUSEC dataset providing ECG data from 20 humans listening to two types of music: music without (Melody) and with (Song) lyrics. We also investigate the efficiency of physiological and statistical features and compare the model efficiency of ECG with the electroencephalography (EEG) studies listening to the same songs. Our results reveal that the random forest algorithm with statistical features indicates the outstanding F1-scores in Melody and Song $(82.95 \pm 7.92\%$ and $84.13 \pm 5.14\%$, respectively). Moreover, the models can outperform the EEG studies. The result opens the opportunity of using ML with ECG to provide a better experience in appreciating music.
UAS-Based Antenna Radiation Pattern Measurement: A Practical Approach

The scope of this work is to indicate, test, and validate a practical approach that provides the radiation pattern of a directive antenna using UAS through in-field RF measurements. The proposed RF measurement method is useful for in-field RF measurements in remote areas. The UAS performs flights around the antenna to be characterized. During the measurements, the antenna is positioned in both horizontal and vertical polarizations. At the end of the measurement procedure, the data collected with the help of a spectrum analyzer is processed using a custom-built application, which provides the antenna radiation pattern in both the H-Field and E-Field. The half-power beam width and the main lobe directions are indicated. Consequently, the radiation pattern generated based on in-field measurements closely resembles the simulated angular width of the antenna provided by the manufacturer validating our practical approach.
Eavesdropping Vulnerabilities in Optical Fiber Networks: Investigating Macro-Bending-Based Attacks Using Clip-on Couplers

Fiber optic cables have long been considered a secure medium that prevents eavesdropping due to their complexity. However, there are attack methods that allow eavesdropping or denial of service. These include methods based on bending optical fibers. The danger resides in the possibility of being able to carry out an attack without having to interrupt the path. To facilitate such attacks, it is possible to use a clip-on coupler. This coupler is designed to allow eavesdropping on the link without interruption. In this study, we focus on evaluating the coupling efficiency of a simple but commercially available clip-on coupler. This work investigates the effectiveness of jamming into the optical link, i.e., replicating the jamming attack and measuring it by simulating real traffic conditions. To better understand the complexity of these attacks, we perform additional measurements to investigate the effect of clip-on coupling on changes in the polarization state. Through this research, we highlight the increasing vulnerability of optical fibers to eavesdropping attempts, especially those using macro-based methods. By evaluating the performance of commercially available clip-on coupling, we provide valuable insights into potential risks and mitigation strategies for securing modern optical communication networks.
Deploying Disaster-Resilient Service Function Chains Using Adaptive Multi-Path Routing

Network Function Virtualization (NFV) is a technology that deploys network services and functions as software components in data centers and cloud environments. One of its key applications is Service Function Chain (SFC), which chains Virtual Network Functions (VNFs) in a specific order to deliver a desired service. However, disaster resiliency is a critical challenge when deploying NFV and SFC, as natural disasters and hardware failures can disrupt network operations and lead to service interruption or degradation across an entire disaster zone (DZ). This paper presents a new method for protecting SFCs using multi-path routing, which enables to split an SFC on multiple DZ-disjoint working paths and leverage a shared backup path for protection. The proposed Multi-path Protection (MP) minimizes network resource consumption, including both the bandwidth for request routing and the computing resources for VNF execution. We propose a heuristic approach that offers a near-optimal SFC MP solution in a time-efficient way. Numerical results show that the proposed MP strategy outperforms traditional Dedicated Protection (DP) in terms of resource consumption, resulting in significant gain up to 20%.
Anatomy of a world-class source rock: Distribution and depositional model of Silurian organic-rich shales in Jordan and implications for hydrocarbon potential

Silurian organic-rich (hot) shales were the source of large amounts of hydrocarbons in northern Gondwana, with supergiant and giant fields in the Middle East and North Africa. A study of these black shales has been conducted in Jordan, where they represent the source for the Risha gas field. Two organically enriched horizons occur in the Silurian in Jordan, termed the lower hot shale and the upper hot shale. Deposition of the transgressive lower hot shale occurred during the early Llandoverian and was restricted to the earliest Silurian paleodepressions. Three lower hot shale depocenters have been identified in Jordan; they are located in the western Risha, eastern Wadi Sirhan, and Jafr areas. The eastern Risha area was part of a larger scale paleohigh covering northeast Jordan, most of Syria and Iraq, and north-central Saudi Arabia (Qusaiba area). At least in Jordan, the high coincides with the depocenter of the latest Ordovician glaciation. Sedimentation of the upper hot shale occurred around peak sea level during the late Llandoverian and/or early Wenlockian and was limited to the distal parts of the shelf (Risha area), in front of the prograding silty-sandy deltaic front.Thermal maturity increases from immature in the Southern Desert outcrops to late or postmature in northern Jordan. Organic richness and pyrolysis data deteriorate significantly with increasing thermal maturity because of hydrocarbon generation. Prior to maturation, maximum organic richness is interpreted to have considerably exceeded 10% with good S2 (remaining generative potential) yields, as reflected in the values of the immature lower hot shale in the shallow borehole BG-14 in the Southern Desert outcrop area and exploration well Hunt JF-1 in the Jafr area.Identification and isopach mapping of the lower hot shale is based on its characteristic uranium enrichment. The gamma-ray/total organic carbon correlation coefficient increases with increasing maturity, suggesting that uranium is less vulnerable to maturity effects than the organic matter itself. The gamma-ray log, therefore, can be used as a proxy for the prematuration organic richness, representing a suitable basis for cross-basin comparisons of original organic richness trends.A two-phase generation of hydrocarbons is interpreted for both the Risha and Wadi Sirhan areas, with the first as a pre-Hercynian phase in the Carboniferous and the second as a less intense generation phase during the Mesozoic to early Tertiary. In contrast, gas generation of the Silurian-sourced giant fields in the central part of the Arabian Peninsula and Iran was restricted to the post-Hercynian. In Jordan, both structural and stratigraphic play concepts are envisaged, including fault, paleohigh, and channel plays. Key elements that need attention in future exploration of the Silurian plays in Jordan are Ordovician reservoir quality and timing of maturation. Poor permeabilities have been reported from wells in both the Risha and Wadi Sirhan areas.
Depositional and halokinetic-sequence stratigraphy of the Neoproterozoic Wonoka Formation adjacent to Patawarta allochthonous salt sheet, Central Flinders Ranges, South Australia

Abstract Parts of two third-order Neoproterozoic (Marinoan) depositional sequences are documented in the Wilpena Group (Wonoka Formation and Bonney Sandstone) at Patawarta diapir, located in the central Flinders Ranges, South Australia. These sequences represent an overall regressive succession transitioning upwards from outer to middle wave-dominated shelf deposits to a tidally dominated barrier bar to coastal plain. The lower, middle, upper limestone and green mudstone informal members of the Wonoka Formation comprise the Highstand Systems Tract of the lower sequence. The Sequence Boundary is at the top of the Wonoka green mudstone member and is overlain by the Lowstand Systems Tract of the upper sequence, which includes the lower dolomite, sandstone and upper dolomite beds of the Patsy Hill Member of the Bonney Sandstone. The upper sequence Transgressive Systems Tract comprises the Bonney Sandstone. These units comprise one complete tapered composite halokinetic sequence (CHS). The lower halokinetic-sequence boundary is associated with the Maximum Flooding Surface of the lower depositional sequence and the upper halokinetic-sequence boundary is interpreted as the Transgressive Surface of the overlying depositional sequence where an angular truncation of up to 90° is documented.
The cold surface layer on Storglaciären

The thickness of the cold, water free surface layer was mapped using a high frequency radar. In the ablation area of Storglaciaren the layer was approximately 30 m thick. It was thicker along the edges and thinner in the central part of the tongue. The thickness pattern shows similarities with the pattern of the vertical velocity on Storglaciaren. Downgoing or slightly upgoing movement is associated with large depths of the cold surface layer and conversely, high rates of upgoing movement give shallow depts. The mapping was performed using a stepfrequency radar (K-radar) which through Fourier analysis transformed changes in phase and amplitude of continuous wave (CW) signals into the time domain. The equipment was drawn on a sledge by a snow-mobile. The system is well prepared for field use and gives a good opportunity for data processing. Introduction Storglaciaren is situated in the Kebnekaise massif in northern Sweden (67? 55'N, 18? 35'E). It has been described by Schytt (1959), 0strem and ot ers (1973), Hooke and others (1983a, 1983b) and Holmlund (1987, 1988a). The glacier can be classified as a temperate valley glacier with a divided accumulation area (Fig. 1) and with a perenial cold (<0? C) surface layer in its lower parts (Schytt 1968, 0strem and others 1973, and Hooke and others 1983a). The cold surface layer of glaciers of such kind is best described from a thermal point of view but can, however, also be described hydrologically, as
Anomalous dispersion of sea ice in the Fram Strait region

The single-particle dispersion of sea ice in the Fram Strait region is investigated using ice drift buoys deployed from 2002 to 2009 within the Fram Strait Cyclones and the Arctic Climate System Study campaigns. A new method to estimate the direction of the mean flow, based on a satellite drift product, is introduced. As a result, the bias in the dispersion introduced by the mean flow is eliminated considering only the displacements of the buoys in the cross-stream direction. Results show an absolute dispersion growing quadratically in time for the first 3 days and an anomalous dispersion regime exhibiting a strongly self-similar scaling following a 5/4 power law for time scales larger than 6 days persisting over the whole time series of length 32 days. The non-Gaussian distribution of the velocity fluctuations with a skewness of −0.15 and a kurtosis of 7.33 as well as the slope of the Lagrangian frequency spectrum between −2 and −1 are in agreement with the anomalous diffusion regime. Comparison with data from the International Arctic Buoy Program yields similar results with an anomalous dispersion starting after 10 days and persisting over the whole time series of length 50 days. The results suggest the presence of deformation and shear acting on the sea ice dispersion. The high correlation between the cross-stream displacements and the cross-stream wind velocities shows the important role of the wind as a source for the anomalous dispersion.
Combating Sensor Drift with an LSTM Neural Network Enhanced by Autoencoder Preprocessing

This paper presents a novel approach to compensate for sensor long-term drift by combining an autoencoder with a long short-term neural network (LSTM). Specifically, an autoencoder is utilized to model the sensor's long-term drift response, and the learned latent space representation is then fed into the input of the subsequent LSTM networks for length estimation. The proposed algorithm is experimentally validated using a single kirigami flexible sensor stretched by a moving platform for length change estimation. The results demonstrate that, compared to a standard LSTM, the proposed algorithm achieves a 76% reduction in root mean square error for length estimation, with a corresponding improvement in the coefficient of determination $R^{2}$ from -3.09 to 0.77.
Predicting the Performance of DNNs to Support Efficient Resource Allocation

Numerous organizations are adopting sophisticated Machine Learning (ML) algorithms for their operations. To ensure the optimal performance of ML systems, organizations require insights into the response time of such systems under realistic user workloads. However, despite the widespread adoption of ML models, research on predicting the response time of a system serving an ML model under varying resources and user workloads is limited. In this paper, we address this gap by proposing a modeling approach to predict response times of multiple well-known Deep Neural Networks (DNNs) under simultaneously varying resource settings and user workloads. We join a classifier and a regressor to identify the optimal resource setting for meeting a DNN's response time target, and to predict the response time under the allocated resource setting. Our technique enables performance modeling without the need to collect extensive data during system operation, thus empowering pre-deployment predictions. The results demonstrate that our approach can generalize to unseen resource and workload scenarios, guaranteeing accurate predictions of compliance with response time targets 98.05% of the time and offering response time predictions with a mean prediction error of 9.10%.
Human-Inspired Stretch and Joint-Bend Sensing System Based on Flexible Sensors

Bio-mimicking of human tactile sensing system using flexible and reliable sensors and electrical circuits for developing future robots and prosthetics with better sensory capabilities has gained importance in recent times. In addition, development of artificial proprioception systems capable of locating, monitoring and controlling different parts and joints in robots is also vital. In this work, using a highly flexible, reliable, and easy to fabricate ecoflex-graphite-graphene carbon paste (GCP) based piezoresistive strain sensor, a Wheatstone bridge and a simulated leaky-integrate-and-fire (LIF) neuron, an artificial tactile system was developed which also includes the proprioception functionalities. The developed system generated neural spike trains when the sensor was strained, and the firing rates of the presented system varied between 7–26 spikes per min while increasing the strain levels from 5% to 50%. The spiking behaviour resembles an afferent neuron connected to a slowly-adapting mechanoreceptor. The relationships between firing rate with different stretch and bending angle in the sensor were also calculated to develop an artificial proprioception system. The presented work could pave the way for making next generation smart robots, prosthetics, gloves, and tactile sensors.