Miracle-dz/UniFAR-FTUs huggingface dataset:
Making an Array Database Language Server-Side Extensible

Server-side extensibility through dynamically linked external code is a common method in relational databases. In the field of Array Databases such User-Defined Functions (UDFs) sometimes even represent the architectural cornerstone for array functionality. On the downside, UDF implementation often suffers from high coding complexity.The rasdaman Array DBMS is a full-stack C++ implementation, so does not rely on some generic UDF mechanism. This allowed designing such an API from scratch, with particular emphasis on UDF coder convenience. The rasdaman UDFs rely on the general C++ client API classes. Based on a straightforward UDF interface definition adapter code is generated automatically. Experimental evaluation shows encouraging results, and the mechanism is going to be used in research and under operational conditions. We present the approach and motivate it through practical use cases.
Analysis and Implementation of Inductor Based Active Battery Cell Balancing Topology

Lithium ion batteries are most popular in present world. Battery cell balancing is an integral part of lithium ion battery packs for optimal use of battery capacity. The paper explains the methods of cell balancing in a battery pack. The advantages and disadvantages of both passive and active methods are highlighted. Further the analysis of active cell balancing topology using inductors is presented. The problem with conventional inductor based cell balancing is highlighted and further it is modified to achieve faster equalization time. The proposed topology is easily configured for even or odd cell battery packs. The method can be used to equalize lithium ion battery packs in Electric Vehicles as well as in spacecrafts. In this paper, a modified version of inductor based active cell balancing method, along with its control scheme is proposed and bread board results are presented.
Coordination Strategy of Large-Scale DFIG-Based Wind Farm for Voltage Support With High Converter Capacity Utilization

Due to the long-distance transmission, external grid cannot effectively support the access point voltage (APV) of the large-scale wind farm (WF), which thus entails DFIG-based WF to provide its ancillary services on reactive power generation. Considering factors of limited down-sized converter capacity, various operating modes of wind turbines (WTs), and the large pressure of large-scale WF communication on the central governor, this paper proposes a distributed active power and reactive power coordination scheme for APV support. A reactive power self-allocation scheme is developed and operated in each WT for reactive power dispatch. Based on the mechanism of distributed consensus control and aligning with the principle of preferential and proportional utilization of spare reactive power capacity (RPC), the proposed method collaborates the rotor side converters and the grid side converters of all WTs but without the necessity of knowing the total RPC. Further, in case that the total RPC is insufficient for the demand, a coordinating factor for RPC extension is generated to weaken the active power. Through a current-enforced PQ coordinating loop, it guarantees a high utilization of converter capacity as well as an exact compensation on the reactive power imbalance, which thereby reduces the wind power curtailment for the RPC release. Several case studies verify the effectiveness of the proposed method.
Video Question-Answering Techniques, Benchmark Datasets and Evaluation Metrics Leveraging Video Captioning: A Comprehensive Survey

While describing visual data is a trivial task for humans, it is an intricate task for a computer. This is even more challenging if the visual data is a video. Comprehending a video and describing it is called Video Captioning. This involves understanding the semantics of a video and then generating human-like descriptions of the video. It requires the collaboration of both research communities of computer vision and natural language processing. The captions generated by video captioning can be further utilized for video retrieval, summarization, question-answering, etc. Video Question-Answering (video-QA) involves querying the system to obtain an answer in response. This paper presents a brief survey of the video captioning techniques and a comprehensive review of existing techniques, datasets, and evaluation metrics for the task of video-QA. Video-QA techniques rely on the attention mechanism to generate relevant results. The presented survey shows that recent works on Memory Networks, Generative Adversarial Networks, and Reinforced Decoders, have the capability to handle the complexities and challenges of video-QA. Additionally, the graph-based methods, although less explored, give very promising results. In this article, we have discussed the emerging research directions and various application areas of video-QA.
Airport network traffic prediction in 5G scenarios: a deep learning approach

Recently, the highly increasing number of mobile devices, higher data rate, and big data are pushing forward the rapid development of 5G. The network traffic of airports will explosively increase in the upcoming 5G era, which will cause challenges in providing fast and stable Internet service with passengers in airports. In this paper, we propose a novel approach to predict airport network traffic with deep learning in 5G scenarios. Based on Haikou Meilan International Airport, we collected real-world datasets of 4G cell traffic and used the Recurrent Neural Network (RNN) algorithm to process them to make network traffic prediction in the airport. Simulation results reveal that our method has good performance, and takes obvious advantage over traditional ones.
Characterization of Dynamic Threshold Voltage in Schottky-Type p-GaN Gate HEMT Under High-Frequency Switching

The dynamics of the intrinsic threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {TH}}}{)}$ </tex-math></inline-formula> shift in 650-V Schottky-type p-GaN gate HEMT under high-frequency switching is investigated by a bootstrap voltage clamping circuit in which the GaN HEMT serves as the key bootstrapping device. This new testing setup covers a wide range of OFF-state drain bias (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {DSQ}}}{)}$ </tex-math></inline-formula> up to 400V, a short OFF-to-ON (or stress-to-sense) delay (<inline-formula> <tex-math notation="LaTeX">${T}_{{\text {delay}}}{)}$ </tex-math></inline-formula> of 60ns, and a high switching frequency (<inline-formula> <tex-math notation="LaTeX">${f}_{{\text {sw}}}{)}$ </tex-math></inline-formula> up to 2MHz. The dynamic <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {TH}}}$ </tex-math></inline-formula>’s dependences on <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {DSQ}}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${f}_{{\text {sw}}}$ </tex-math></inline-formula> are characterized by this testing setup. A higher V<sub>DSQ</sub> (up to 400V) results in a larger dynamic <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {TH}}}$ </tex-math></inline-formula> shift (<inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{{\text {TH}}}{)}$ </tex-math></inline-formula>. The <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{{\text {TH}}}$ </tex-math></inline-formula> exhibits a gradually increasing trend under the continuous high-frequency switching mode. In addition, after continuous switching operation for <inline-formula> <tex-math notation="LaTeX">$200\mu \text{s}$ </tex-math></inline-formula>, the maximum <inline-formula> <tex-math notation="LaTeX">$\Delta {V}_{{\text {TH}}}$ </tex-math></inline-formula> shows no <inline-formula> <tex-math notation="LaTeX">${f}_{{\text {sw}}}$ </tex-math></inline-formula> dependence with a wide range of frequency from 50kHz to 2MHz.
Rectifier effect in an atmospheric model with daily biospheric fluxes: impact on inversion calculation

Atmospheric CO2 measurements showstrong synoptic variability. To understand the contribution of the synoptic signals on atmospheric CO2 inversion, we simulate the cases of biospheric fluxes with and without synoptic variations (referred to as ‘Synoptic’ and ‘Reference’, respectively) using an atmospheric transport model, and then perform inversion analyses with these biospheric CO2 concentration fields. Results show the monthly and annually averaged CO2 concentration anomalies (Synoptic–Reference) are functions of the distance from the continental biospheric source regions. Remote sites (like Mauna Loa) show averaged monthly amplitude of ∼0.2 ppm, while continental sites show averaged monthly amplitudes of 1–2 ppm with maximum monthly amplitudes up to 7 ppm. Spatial scales of these monthly mean synoptic anomaly patterns may exceed 1000 km. These CO2 concentration patterns are the results of the interaction of the synoptic CO2 flux field and atmospheric transport, and may be referred to as the synoptic Rectifier Effect. Inversion CO2 fluxes for 1992–1995 are obtained using biospheric background fields with and without synoptic biospheric flux variations. The maximum magnitude differences in estimated monthly flux for land and ocean regions are ∼0.4 and ∼0.2 GtC month-1, respectively. The average land sink increases by 0.19 GtC yr-1 while the average ocean sink decreases by 0.30 GtC yr-1.
A Novel Lenticular Angle Gauge for High-Accuracy Fiducial Markers

Lenticular angle gauge (LEAG) is a planar pattern that visualizes relative orientation by the position of the black line, which moves according to the viewing angle. LEAG has the potential to turn arbitrary planar patterns into high-accuracy markers. The author has developed a new LEAG in which the direction of movement of the black line is 90◦ different from that of the previous LEAG. With this, it is no longer necessary to place two LEAGs orthogonally to create a high-accuracy fiducial marker. This paper describes the principle and behavior of the new LEAG, and the results of its functional test. A new design of high-accuracy marker under development is also presented.
Oil Spray Cooling with Hairpin Windings in High-Performance Electric Vehicle Motors

This paper presents a survey about implementing hairpin winding with an oil spray cooling system on the new generation of electrical vehicle (EV) motors as an option to produce high power density, high efficiency, cost-effectiveness, lightweight, reliable EV motors. It provides the advantages and drawbacks of this rectangular winding compared to random winding and considers the hairpin winding production aspects. According to the high AC losses of this configuration, the paper considers two conventional approaches for calculating these losses with their advantages and drawback. Then it proposes the novel hybrid FEA method for calculating the AC copper losses to overcome the weaknesses of analytical and numerical methods. Finally, it provides a holistic view related to the oil spray cooling of the electrical machine and points the future work associated with this cooling method
A Wideband Non-Uniform Metasurface-Based Circularly Polarized Reconfigurable Antenna

This paper presents a low-profile circularly polarized (CP) antenna with wideband and polarization-reconfigurable characteristics. The proposed antenna consists of a truncated-corner patch working as a CP source and a non-uniform metasurface (MS) acting as parasitic elements for performance enhancement. The non-uniform MS is utilized to produce additional operating bands in the higher frequency range, which are then combined with the lower band produced by the driven patch to significantly broaden the overall operating bandwidth. The polarization switching mechanism is based on the ON/OFF states of two p-i-n diodes. The fabricated prototype shows the measured overlapped bandwidth between −10 dB impedance and 3-dB axial ratio is 25.6% and peak broadside gain is 7.3 dBic.
Analysis of Solar PV fed Cascaded H-bridge Converter during Low Voltage Ride Operation

As the penetration of medium and high voltage grid connected solar PV converter increases, the next generation solar PV system must require staying connected to the grid during grid sags and supplying reactive power to support the grid voltages. This paper presents the operation of solar PV fed cascaded H-bridge converter considering balanced and unbalanced power generation among the phases as well as Low Voltage Ride Through (LVRT) operation. An expression of zero-sequence voltage is derived to control the active power flow among the phases for maintaining dc link voltage under unbalanced power generation and unsymmetrical voltage sags. In this paper, the positive sequence current is supplied to the grid to meet the LVRT requirements. It is also censured that the grid currents magnitude would not exceed the overcurrent rating of the converter. The strategy of continued operation of the converter during grid voltage sags under balanced and unbalanced power generation is validated through MATLAB/Simulink.
Reliability Analysis of the Integrated Navigation System Based on Real Trajectory and Calculation of Safety Margin Between Trains

A safety margin must always be maintained between trains. To this end, position data of all trains must be provided to the railway system in real time. Basically, this data has been provided based on the infrastructure installed on the railway. However, enormous costs are required to install the infrastructure, and continuous maintenance is required. In addition, cases of accidents due to un-detection or false-detection of trains are being reported as news. Due to these problems, researches on infra-less navigation system for trains have been conducted. Taking into account the blocking phenomenon of Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) signals in tunnels, the navigation system for trains should constitute an integrated navigation system. However, the accuracy and reliability of navigation solution provided by the integrated navigation system are not always satisfied. And this can lead to instability of the railway system. Therefore, the navigation solution for trains must have high accuracy and also the reliability information of the corresponding navigation solution must be provided. In this article, we construct an Inertial Navigation System (INS)/GPS/Non-Holonomic Constraints (NHC) integrated navigation system for trains with NHC motion. A simulation trajectory is generated considering the real trajectory, and the accuracy and reliability of the navigation information according to the trajectory and measurements are analyzed together. And based on this information, we propose a method to calculate the safety margin between trains. The performance of the proposed technology is verified through a real trajectory-based Monte Carlo simulation.
Quantitative Evaluation of Both Histological and Mechanical Recovery in Injured Tendons Using Fourier-Transform Second-Harmonic-Generation Microscopy

Quantitative assessment of healing after tendon injury is important for orthopedic practice. Although histological and mechanical recovery are assessed by histological staining and tensile testing respectively, both tests are destructive and invasive, making it difficult to be applied to single sample. Here, as a non-destructive and less invasive approach for quantitative assessment of tendon healing, we applied Fourier-transform second-harmonic-generation (FT-SHG) microscopy to an animal model of tendon healing. FT-SHG microscopy enables orientation analysis of collagen fibers independently of the SHG image brightness. The extracted FT-SHG parameter showed a significant difference between the healing sample and the control sample, indicating the potential for quantitative parameter of histological recovery. More importantly, the FT-SHG parameter indicated good correlation (coefficient of determination, R2 = 0.62) with the Young's modulus obtained by tensile testing of the same sample. These results indicate the potential of the proposed method for quantitative evaluation of both histological and mechanical recovery after tendon injury.
Enhanced Spectrum Access for QoS Provisioning in Multi-Class Cognitive D2D Communication System

Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.
Investigations on an SYNRM coupled to a constant power load connected to a weak grid

Constant power loads like electric traction load, lathe machines, grinding machines and coiler drivers are important assets to the industry. Considering that these constant power loads are coupled to motors which are connected to weak grids where voltage dips are frequent, this paper introduces a novel analysis of the effect on speed and torque of a synchronous reluctance motor connected to a constant power load on the occurrence of a voltage dip greater than the tolerable voltage dip margin for a particular operating point. The focus of this paper is to estimate the amount of reduction in speed as well as torque and changes in the other operating quantities from the set reference values if the dc bus voltage reduces below a pre specified permissible limit. Also the method of restoration of initial operating speed on the occurrence of a voltage dip is also discussed in this paper. The proposed analysis predicting the degree of changes in all the operating quantities is validated by simulations carried out in Simulink/MATLAB environment.
Exploring Predictive Capabilities of GOES and SDO/EVE Data for Flare Forecasting

We explore the predictive capabilities of full disk integrated solar X-ray and extreme ultraviolet emission data from the sun, collected by the Geostationary Operational Environmental Satellites and the EVE/ESP instrument on the Solar Dynamics Observatory (SDO) in view of forecasting solar flares. A purely data driven approach is studied which uses suitably preprocessed time sequence data of fixed length. Models of different complexity are tested - ranging from a model that just uses the per-sequence average up to models that use several layers of Long-Short Term Memory neural networks which are designed to capture details about the sequential nature of the data. A True Skill Statistics of roughly 60% is obtained even by a very simple model based on the per-sequence average of GOES data. This performance can not be significantly outperformed by more complex models or by incorporating EVE/ESP data from SDO. Further work would be needed to gain more evidence whether the considered full disk data contains sufficient predictive capacity for improving the performance for flare forecasting.
Predicting Stock Price Movements with Text Data using Labeling based on Financial Theory

We apply Natural Language Processing and super-vised Machine Learning to predict stock price movements, based on approximately 30,000 ad-hoc disclosures issued by publicly traded companies on Nasdaq OMX Stockholm. Three different labeling methods, based on financial theory, are defined and assessed. The best results, using Logistic Regression and TF-IDF with character-grams, achieve an increase of 6,3 percentage points above a majority class baseline. These results show that corporate ad-hoc disclosures, which are regulated to represent novel and value-relevant information, are particularly well-suited for this task. Furthermore, the most sophisticated labeling technique used, Jensen’s Alpha in the context of the Capital Asset Pricing Model, helps the model achieve its highest accuracy. The results therefore show that financial theory can help isolate the effect of an informational event on stock prices, improving the supervised Machine Learning approach. Finally, an algorithmic trading strategy is simulated with the best model, yielding positive abnormal returns.
Memory Efficient Graph Convolutional Network based Distributed Link Prediction

Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph 1. Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).