Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Managerial Overconfidence and Agency Costs on Cash Holdings within Blockchain Firms

We study the effects of managerial overconfidence and agency costs in cash holdings of blockchain firms, where the overconfidence is defined as a cognitive bias that a manager will underestimate volatility in an uncertain environment. We develop a real-options game model that incorporates both overconfidence and agency costs. The theoretical results show that future investment opportunities are necessary for holding cash when agency costs are within the firm. Expected returns from blockchain projects decrease with managerial overconfidence. The level of corporate cash holdings increases with overconfidence and decreases with agency costs Using the data of Chinese listed firms from 2010 to 2019, we find that the regression results are consistent with our theoretical findings. Moreover, we find that the blockchain firms’ cash holdings are higher than their peers. Our results shed some light on the impacts of behavioral characteristics on cash holdings.
Cloud Native Hardware Accelerated 5G virtualized Radio Access Network

Cloud Radio Access Network consists in deporting and pooling radio processing on shared servers for a better use of computing resources, more agile operations, and thus optimizing costs and energy performance. Some of these processes at the radio physical layer are highly demanding in terms of computing power, which implies high energy consumption. Our approach enables the automatic activation of hardware programmable acceleration resources that are better suited to these processes in terms of performance and energy efficiency. Low-Density Parity-Check Forward Error Correction functions standardized in 5th Generation of Mobile Networks are offloaded on these hardware acceleration resources. Kubernetes-based orchestration is made compatible with the use of a edge computing accelerated heterogeneous infrastructure (CPU, GPU, FPGA) that can be distributed at the edge of the operator’s network to deploy the RAN Distributed Unit. We assess the proof-of-concept implementation of our approach and evaluate cost in data offloading and energy consumption. This prefigures a full cloud orchestration for 5G access, such as recommended in the O-RAN consortium.
Timely Survey of Time-Sensitive Networking: Past and Future Directions

Time-sensitive networking (TSN) is a next generation local area network technology for the coexistence of information and operation technology, targeted to industrial automation, in-vehicle networks, and avionic networks in the industrial Internet of Things. This paper presents an up-to-date survey of the research efforts in TSN over the past few years, including the latest efforts in standardization by the IEEE. We review more than 170 TSN-related academic research papers published by major academic publishers and categorize these studies according to the topic, purpose, and methodology, analyzing research trends in TSN. The TSN core function deals with the functional and internal perspective to achieve ultra-low latency/jitter and ultra-reliability. Network management includes work with macroscopic and external views, such as configuration, management, and maintenance of TSN-enabled networks. In particular, we analyze the characteristics of existing studies that have evaluated the TSN network and propose future directions to advance the limits of TSN for real-world deployment. We aim this study to be a cornerstone for fellow and new researchers.
0.37-pJ/b/dB PAM-4 Transmitter and Adaptive Receiver with Fixed Data and Threshold Levels for 12-m Automotive Camera Link

This paper presents a transmitter and an adaptive receiver for the next-generation automotive camera link with four-level pulse amplitude modulation (PAM-4) signaling. The PAM-4 transmitter utilizes push-pull current drivers and a 3-tap feed-forward equalizer to improve impedance matching and eye margin, respectively. In order to cancel the sampler offset for a lower bit error rate (BER), instead of adjusting data and threshold levels, the gain of a programmable gain amplifier is adjusted adaptively under fixed data and threshold levels. The transmitter delivers 8-Gb/s PAM-4 signal to the adaptive receiver through a lossy 12-m cable (22-dB channel loss) with a BER of 1E-8, and the eye margin larger than 0.15 UI x 50 mV is measured for a BER of 1E-5. The prototype chips consume 65.2 mW (excluding PLL), exhibiting an FoM of 0.37 pJ/bit/dB.
Combination of EOG and EEG for emotion recognition over different window sizes

Considering the use of a multi-modal framework to enhance emotion recognition, we propose to combine electroencephalography (EEG) and electrooculogram (EOG) through decision level fusion(DLF) and feature level fusion(FLF) for emotion recognition. By using different temporal window sizes to segment the signal, we explore the duration of the emotion of the EOG signal and the EEG signal. Then, some temporal window sizes that are friendly to both EOG signal and EEG signal are selected for segmentation and emotion recognition. According to the different degree of dependence of subjects, the accuracy of the proposed algorithm on subject-dependent and subject-independent is verified on the DEAP dataset. For subject-dependent, using feature level fusion strategy with a window size of 6 seconds, the accuracy is 0.9562 in terms of arousal, and 0.9558 in terms of valence. For subject-independent, using feature level fusion strategy with a window size of 5 seconds, the accuracy is 0.8638 in terms of arousal, and 0.8542 in terms of valence. The experimental results show that the proposed algorithm can better enhance emotion recognition.
Modelling of an Underwater Optical Wireless Communication System With Misalignment Tolerance

This papers proposes a multiple sensor-lens pair scheme to increase the misalignment tolerance and presents an accurate model of photon propagation based on the Monte Carlo simulation, that includes the photon refraction at the lenses interface and angular misalignment between emitter and receiver. The results show that the ideal divergence of the emitter’s beam is around 15º for a 1 metre Tx-Rx distance, increasing to 22º for a shorter distance of 0.5 metres, but being independent of the water turbidity, showing that the geometry of the link is the dominant factor in such short-range links. Additionally, it was concluded that a 7 lenses scheme is approximately 3 times more offset tolerable than a single lens. Rotating the emitter increases the optimal divergence while rotating the plane of sensors decreases it.
Encapsulation Techniques of Perovskite Solar Cells

Due to the rapid development of solar cells based on organic metal halide perovskite as light-absorber, the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has increased from 3.8% to 25.5%, which exceeds that of silicon ones. In the meantime, the stability of the device has also made breakthrough progress. However, the degradation of perovskite is the primary reason which largely limits large-scale application. PSCs quickly chemically react with the water/oxygen in the air and decompose when exposed to ambient condition. It is urgently necessary to develop appropriate encapsulation techniques to isolate perovskite from external stimuli including moisture, oxygen and UV light to effectively extend lifetime. In this paper, the influences and physical mechanisms of the stability of perovskite are summarized. Then, the main encapsulation methods and technical approaches to improve and enhance the stability of perovskite are also reviewed, including traditional rigid-substrate-based encapsulation, thin film encapsulation, and advanced encapsulation strategies. Finally, it overviews the challenges and further development strategies faced by PSCs encapsulation techniques in practical applications.
Artificial Neural Network Symbol Estimator With Enhanced Robustness to Nonlinear Phase Noise

This letter reports a novel approach for nonlinear phase noise mitigation, based on artificial neural networks (ANNs) tailored to classification applications and a pre-processing stage of feature engineering. Starting with a set of proof-of-concept simulations, we verify that the proposed system can achieve optimal performance for the additive white Gaussian noise (AWGN) channel. Then, considering a dispersion-less channel with strong nonlinear phase noise (NLPN) distortion, we demonstrate a Q-factor increase of 0.4dB, comparing with standard carrier-phase estimation (CPE) followed by minimum distance detection. Finally, simulating the propagation of 64Gbaud PM-16QAM over standard single mode fiber (SSMF), we verify that the ANN-based solution is effective on wavelength-division multiplexing (WDM) transmission conditions, enabling to increase the maximum signal reach by approximately 1 fiber span over the legacy CPE-enabled NLPN compensation.
Broadband Hybrid Precoding Scheme Based on Cyclic Delay in Terahertz Communications

The terahertz communication system with traditional hybrid precoding structure has the problem of beam diffusion. This problem leads to the serious loss of achievable rate and offsets the performance gain caused by bandwidth increase, so a broadband hybrid precoding scheme based on cyclic delay is proposed in this paper. Firstly, a broadband hybrid precoding framework based on cyclic delay is constructed. A delay element is placed at each antenna of the traditional full-connected hybrid precoding structure. Thus, a frequency-dependent phase shift is accomplished to compensate the beam diffusion. Then, the unit delay of delay element at each antenna is determined by maximizing the array gain of the sub-carriers, but there is a problem that the unit delay is not uniform. Finally, through the design of analog precoding, the unity of the unit delay can be realized, while maximizing the array gain of each sub-carrier. The simulation results show that the proposed scheme can achieve 100% array gain at any sub-carrier, and is suitable for solving the problem of beam diffusion under different bandwidth and different number of transmit antennas. Compared with the existing schemes, the achievable rate of the proposed scheme is very close to that of the full-digital precoding scheme, and has the advantage of high energy efficiency.
Ultra-Wideband Complex Permittivity Extraction of IC Packaging Materials beyond 100 GHz

In this work a simple but yet precise permittivity measurement concept is proposed that is especially suited for broadband characterization of IC packaging material beyond 100 GHz. The novel concept is based on the evaluation of the propagation coefficient of the TE10-mode of a rectangular waveguide with the use of an advanced analytic transmission line model. To eliminate parasitic conductor effects as the bulk conductivity and surface roughness of the waveguide walls, two measurements are carried out: One measurement with an air-filled waveguide and one measurement where the waveguide is filled with the material under test. With these two measurements and by applying the analytic transmission line model, the parasitic distortions can be eliminated. The approach enables ultra-wideband permittivity determination and is not limited to a specific frequency range. The complex permittivity of an investigated packaging material could almost continuously be extracted from 110 GHz up to 320 GHz. The method is experimentally verified on the basis of material samples with material parameters given in literature.
A Ka-Band Transformer-Based Switchless Bidirectional PA-LNA in 90-nm CMOS Process

This paper presents a switchless bidirectional power amplifier-low noise amplifier (PA-LNA) in 90-nm CMOS process for millimeter-wave (mm-wave) phased-array front-end chip. This PA-LNA uses current-type transformer (TF) as a bidirectional matching network for PA and LNA inputs/outputs without using lossy T/R switches. As a side benefit, avoiding the use of the T/R switches not only saves the chip area, but also prevents performance degradation in PA and LNA modes. The proposed PA-LNA achieves peak small-signal gain of 18.3 dB and 17.6 dB in PA and LNA mode, respectively. In the PA mode, it achieves the measured peak saturated output power (Psat) of 15.1 dBm with 29 % peak power-added efficiency (PAEMAX) and 13.3 dBm peak 1-dB output power (OP1dB) at 33 GHz, while LNA mode achieves minimum noise figure (NF) of 4.7 dB at 36 GHz. The reverse isolation in both modes is better than 43 dB. The core size without pads is 0.21 mm2.
Synchronous Reference Frame current control of Aalborg-type PV inverters

Transformerless photovoltaic (PV) inverters with voltage step-up capability are currently the preferred interface for integrating PV generation to a single phase of the electric grid. Among the different topologies employed for such inverters, dual-mode time-sharing topologies offer high conversion efficiencies, but present control challenges that can result in significant grid current distortion. Existing control approaches operate in the stationary reference frame and use different controllers for the Buck and Boost modes of operation. This paper presents a new current control approach for Aalborg-type Buck in Buck, Boost in Boost PV inverters, which operates in the Synchronous Reference Frame and uses a common controller for Buck and Boost operation. The proposed method achieves high output current quality even at unfavourable conditions of low PV voltage and/or power levels. Simulation results in MATLAB-Simulink are presented to illustrate that it can reduce the Total Harmonic Distortion (THD) of the grid current by two to three times as compared to the existing alternative.
Risky Zone Avoidance Strategies for Drones

We consider the problem of a drone having to traverse a terrain. Traversal of the terrain exposes the drone to certain risks, e.g., concentration of chemicals, severe thunderstorm wind gusts or any disturbing weather phenomenon. The goal of the drone is to navigate the terrain while minimizing the amount of risk. We develop a framework for quantifying the exposure to risk factors in a circular zone model. We propose risky zone avoidance navigation strategies for rectilinear or curvilinear drone trajectories. We validate the work using numeric simulations.
Behavioral Based Insider Threat Detection Using Deep Learning

The most detrimental cyber attacks are usually not originated by malicious outsiders or malware but from trusted insiders. The main advantage insider attackers have over external elements is their ability to bypass security checks and remain undiscovered, this may cause serious damage to the organizational assets.This paper focuses on insider threat detection through behavioral analysis of users. User behavior is categorized as normal or malicious based on user activity. A series of events and activities are analyzed for feature selection to efficiently detect adversarial behavior. Selected feature vectors are used for model training during the implementation phase. A deep learning based approach is proposed that detects insiders with greater accuracy and low false positive rate. A rich event / user role based feature set containing Logon/Logoff events, User_role, Functional_unit etc are used for detection. The dataset used is the CMU CERT synthetic insider threat dataset r4.2. Performance of our proposed algorithm has been compared to other well-known techniques i.e. long short term Memory- convolutional neural network, random forest, long short term memory- recurrent neural network, one class support vector machine , Markov chain model,multi state long short term memory & convolutional neural network, gated recurrent unit & skipgram. The comparison proved that our novel approach produces relatively good accuracy( 90.60%), precision(97%) and F1 Score (94%).
Frequency locked loop with frozen mechanism

With the increasing penetration of renewable energy systems into the power grid, stability and safe operation during grid faults have become a great challenge. The phase and frequency of the grid voltage are the important information for the acquisition of the reference value of the grid-connected converter control signal and ensure the smooth, reliable and stable operation of the grid-connected converter system. In this paper, a new concept the frequency locked loop with frozen mechanism is proposed, which is used for solving the grid synchronization problem of the power grid under unbalanced and distorted operating conditions. The frequency locked loop with frozen mechanism consists of the frequency-locked loop (FLL) based on the Second-order generalized integrator (SOGI) which tuned at grid frequency and the frequency frozen mechanism which guarantee the stability during the severe faults.
Positioning Technology Trends and Solutions Toward 6G

In the 4G era, cellular positioning was used for emergency services and services associated to lawful interception. In 5G, commercial use cases have gained momentum and use cases like factory automation, transportation, and logistics are included in 5G besides the regulatory use cases. Toward 6G, it is anticipated that positioning and location services will be fundamental part of the system demanded by most commercial applications, such as AR/VR/XR, gaming, sensing, low-cost tracking and new industrial applications with extremely high accuracy. As a result, positioning accuracy and latency requirements are anticipated to tighten further from 5G. Thus, positioning and location services needs to be designed as an integral part of 5G evolution to address these requirements in a scalable and efficient manner both for devices and networks. This paper discusses the technical trends and enablers to realize this vision towards 6G, such as latency and accuracy enhancements, and low-cost positioning.
A Real-Time Output 50-GS/s 8-bit TI-ADC with Dedicated Calibration Techniques and Deterministic Latency

This paper presents a real-time output 50-GS/s 8-bit time-interleaved analog-to-digital converter (ADC) with dedicated calibration techniques and deterministic latency, where the full-speed converted data are output by 16-lane transmitters. Digital gain correction, digital-detection-analog-correction offset calibration, and coarse-fine two-step time-skew calibration are combined to optimize the ADC's performance. An edge detector and phase selector associated with a pair of common near-end data-transmission position and far-end data-collection instant are designed to avoid reset competition and implement deterministic latency. Fabricated in a 40-nm CMOS process, the prototype ADC achieves an outstanding SNDR of 36.80 dB at 50 GS/s with a 19.123-GHz input, where 10.25 dB and 7.05 dB is optimized by offset-gain calibration and time-skew calibration, respectively. The ADC core occupies an area of 1.232 mm2 and consumes 894 mW.
EEG-Based Familiar and Unfamiliar Face Classification Using Differential Entropy Feature

This study presents a novel approach for familiar and unfamiliar face classification based on electroencephalography (EEG). Firstly, the raw EEG epoch is temporally split into three overlapped segments, and each segment is decomposed into multiple sub-bands by band-pass filters. Then, differential entropy is employed to extract discriminative EEG features. Finally, the obtained features are concatenated and classified with the support vector machine (SVM). The results yielded on our database indicate that the proposed method can achieve a mean accuracy of 76.2% over five participants. This work primarily demonstrates that differential entropy is an effective feature for EEG-based familiar and unfamiliar face classification, and has the potential to be applied to other EEG-based visual task analyses.
A method of research for the reliability of solder joint shape

In this paper a method of research for the reliability of solder joint shapes is proposed. By using Surface Evolver software, finite element analysis, minimum energy principle and gradient descent method to calculate the minimum state of energy, the final shape of the solder joint is predicted. By studying the shape of SOP solder joints and using Surface Evolver software, the error between model parameters and slice parameters is finally less than ±10%, which reduces the error between device models and slices. The reliability and life of solder joints are more accurately predicted.
Compact Laser Driver for Ultrasonic Arbitrary Position and Width Pulse Sequences Generation

Laser ultrasound offers several benefits in comparison to immersion or air-coupled ultrasound: it directly generates stress on the sample surface; it has a wide bandwidth and a small acoustic source can be produced. Conventionally, high-power pulsed lasers are required therefore equipment is bulky and expensive. Here we propose to use mid-power laser diodes, which are small and low cost. To solve the low signal amplitude problem with lower power lasers, the use of spread spectrum signal, arbitrary position, and width binary pulse sets is proposed. To date, a laser driver for this task has not been available. This article describes the development of such a compact driver, where the essential electronics occupy an area of $10\,\,\times20$ mm. The whole system with a fixture for kinematic mount is comparable in size to a conventional piezoelectric ultrasonic transducer. The driver can supply pulse sets of up to 40 A. Individual pulse duration can vary between 20 and 1000 ns (0.5–25-MHz ultrasound range) and the total pulse train duration is limited by the laser type used. Experiments show that up to 10- $\mu \text{s}$ long pulse sets can be used at 10-A current, without laser degradation. Current waveforms, beam profile, and optical response signals were measured for three rectified topologies. It was concluded that the GaN-based constant current switch topology has the best performance, but a power MOSFET in a source current feedback topology can also be used to generate pulses down to 20 ns. Photoacoustic response signals from chirp and phase shift keying modulation have been demonstrated.
Land cover semantic segmentation of Port Area with High Resolution SAR Images Based on SegNet

The SAR image port region semantic segmentation algorithm combined with the deep learning mechanism is of great significance to the port intelligence and information construction. However, there are few researches on the pixel-level semantic segmentation of port area with SAR images. The port spatial pattern monitoring based on remote sensing images still has problems such as complicated monitoring procedures, low robustness, and insufficient datasets. To take advantage of SAR images and explore the potential of convolutional neural networks in the SAR port area images, a dataset containing eight land types of port was established, and a pixel-level segmentation framework for port areas based on SegNet was proposed in this paper. The pixel-level classification of high-resolution SAR port area images based on small datasets was realized. The comparison experiments with SVM and random forest algorithm proved that the algorithm proposed in this paper can achieved an overall accuracy of 91.6% on a SAR image of port area, and better maintain the edge characteristics of different land types.