Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital SI Cancellation Based on LSTM for Co-frequency Co-time Full Duplex Communication Systems

To address the high self-interference (SI) in co-frequency co-time full duplex (CCFD) communication systems, a digital SI suppression method based on deep learning is proposed. A memory polynomial is firstly utilized to simulate the SI channel. Then a long short-term memory network (LSTM) is used to reconstruct the SI signal. The clean signal after interference suppression can be achieved by subtracting the SI signal at the receiver. To better reflect the changes of the SI signals after being transmitted through the multiple channels and reduce dependence on data volume, feature preprocessing and feature reconstruction are performed at the input of the LSTM. We use the multi-segment delayed transmitted signal as the multiple features and utilize the brainstorm optimization (BSO) technique to estimate the ideal time delay. We verify the proposed SI suppression scheme in the OFDM transmission system. The experimental results show that compared with the traditional least squares estimation (LS) and adaptive filter least mean square (LMS) algorithms, the proposed scheme has a significant performance boost. A SI signal suppression capability of 47.19 dB is achieved.
Factors influencing biological asset disclosures in agricultural companies in Indonesia

This study examines to what extent biological asset reporting is corresponding to the Indonesian Statement of Financial Accounting Standard 69 (PSAK 69) among Indonesia Stock Exchange-listed agricultural firms. In their annual report, such companies should report how they recognize and measure the fair value of the living plants or animals’ assets. This study also examines factors that might impact the extent of disclosure of biological assets. The examined factors are biological asset intensity, concentrated ownership, audit committee effectiveness, profitability, and size. Biological asset disclosures were measured by a checklist derived from PSAK 69 and International Accounting Standard (IAS) 41 on Agriculture. This study uses 53 firm-year observations of the listed agriculture companies from 2017 to 2020. Multiple regression analysis findings indicate that biological asset intensity and firm size affect favorably the amount of biological asset disclosures. The results indicate that the greater the living plants or animals owned by a company and the larger the firm, the greater the commitment of a company to disclose its biological assets in its annual report. The study sheds light on the capital market authority agency’s current policy to strengthen factors that may impact agricultural companies’ disclosure of their biological assets.
Research on surplus wind power consumption mechanism based on grid demand response

A method of using the demand response mechanism of the power grid to promote the consumption of surplus wind power is proposed for the consumption of new energy generation. In the proposed method, the replacement of the output of the gas turbine is used as an important way to consume the surplus power, and the consumption of the surplus wind power is realized by using the surplus wind power to replace part of the output of the gas turbine. With the objective function of the maximum consumable surplus wind power and the constraints of the maximum replaceable output of the gas turbine, the cost of the replacement output and the balance of supply and demand of the grid, a model of the maximum consumable surplus wind power considering the cost of the replacement output is constructed. In addition, a cost optimal allocation model considering the different quotations of surplus wind power resource owners is constructed to obtain the optimal surplus power resource consumption scheme.
Oblique surface wave propagation over a small undulation on the bottom of an ocean

The problem of oblique water wave diffraction by small undulation of the bottom of a laterally unbounded ocean is considered using linear water wave theory. A perturbation analysis is employed to obtain the velocity potential, the reflection and the transmission coefficients up to the first order in terms of integrals involving the shape functions c(x) representing the bottom undulation. Finite cosine transform is used to find the first order potential, and this potential is utilised in obtaining the first order reflection and transmission coefficients. Some particular forms of the shape function representing an exponentially damped undulation, a single hump and a patch of sinusoidal ripples are considered and the integrals for the reflection and transmission coefficients are evaluated. For the exponentially damped undulation, it is observed that the reflection ceases much before transmission while for the single hump, reflection and transmission go hand in hand up to a certain value of the wavenumber, after which they vanish. For the patch of sinusoidal ripples having the same wavenumber, the reflection coefficient up to the first order is found to be an oscillatory function in the quotient of twice the component of the wavenumber along x-axis and the ripple wavenumber. When this quotient becomes one, the theory predicts a resonant interaction between the bed and free surface, and the reflection coefficient becomes a multiple of the number of ripples. High reflection of the incident wave energy occurs if this number is large. Also, when a patch of ripples having different wavenumbers is considered the same result follows. Known results for the normal incidence are recovered as special cases for the patch of sinusoidal ripples. The theoretical observations are shown computationally.
Research on Health Diagnosis Technology of Key Equipment in Coal Mine Power Grid

Aiming at the problem that the traditional fault monitoring method of mine power supply equipment is single and can not effectively reflect the real-time working state of equipment and cables, this project studies a comprehensive intelligent fault diagnosis and monitoring early warning technology method. Through the research of two key technologies, power quality analysis technology based on harmonic method and insulation condition monitoring technology of electrical equipment, an intelligent diagnosis system for the health condition of mine power supply equipment is designed and formed. Through real-time monitoring of power grid harmonic parameters, power grid operation parameters, insulation parameters, and temperature parameters, the system can comprehensively analyze and judge the working status of various equipment and connecting cables in the underground power grid to comprehensively judge the faults of high explosive switches, transformers, and connecting cables, and achieve the purpose of fault location and early warning.
Open-circuit Fault-tolerant Control for Three-phase Permanent Magnet Synchronous Motor

In this paper, a single-phase open-circuit fault-tolerant control method for three-phase permanent magnet synchronous motor (PMSM) is proposed. Based on the 4-leg inverter and the redesigned vector transformation method, the switching of the motor control strategy after a single-phase open circuit can be realized, and the control characteristics of the motor are unchanged. This fault-tolerant control method can be easily combined with other control strategies and has a wide range of applications.
Simulation and modeling of manipulator for unmanned aerial vehicles

In order to improve the operation ability of unmanned aerial vehicles (UAV), this paper designs a lightweight manipulator for quadrotor UAV. First, according to the requirements of the task and the load of the UAV, an aerial manipulator with lightweight and small moment of inertia is designed, and the 3D model of the manipulator is established in the Creo software. The maximum deformation and stress under different load conditions were analyzed by Ansys Workbench software, and the dynamic analysis was carried out in ADAMS software to obtain the driving moment required by each joint of the manipulator under load conditions. Finally, the dynamic model of the aerial manipulator system is established according to the Newton-Euler method, and the maximum workspace of the manipulator in the hovering state is generated by the Monte Carlo method. The research results show that the deformation and stress level are small, and the workspace meets the operation requirements, which proves that the designed manipulator has certain engineering application value.
The Research and Analysis of Different Face Recognition Algorithms

With the background of fast developing artificial intelligence and Internet technology, face recognition, one of the product technologies under the theory of machine learning, has become widely applied on safety tests, identity confirmation, public security photos, and access control system. Based on the trend of its development speed, this technology will have well developed circumstances and the field of application will expand. One of the most essential reasons why it has become so popular is that the algorithms used for face recognition are advanced. In this paper, the author mainly researches five algorithms to realize face recognition based on relevant literature. They are separately CNN, PCA, SVM, MTCNN, and Facenet. After comparison and analysis, it can be found that CNN is one of the most suitable algorithms to recognize human faces, and PCA and SVM have problems of calculating speed. The use of complex neural networks can intensify the accuracy of face recognition just like MTCNN and Facenet. If combining different algorithms together and using their advantages in face recognition in different steps, the prediction of the algorithms will be better.
Rasterize the Lidar Point Cloud on The Ground Out Method Optimization Analysis

This paper is mainly aimed at the over-segmentation or under-segmentation phenomenon of the ground point cloud in the moving scene of the unmanned platform to identify the target. This paper proposes an optimized rasterization method to remove the ground point clouds appropriately. First, the partitioned data by quadrant is processed based on the asymmetric distribution of front-to-back and left-to-right point cloud data. Then, the ground estimation of the lowest point is carried out by drilling into the laser layer data, and the connection between the estimated points and the ground detection point cloud data is segmented using the road reflection intensity correction. Next, the ground point cloud detection filtering is optimized using the raster elevation difference. Finally, an obstacle continuum hypothesis model is used to improve the under-segmentation phenomenon that occurs inside the raster. The overall ground point cloud detection filtering effect, the algorithm has a certain degree of universality and ideal ground detection filtering effect within the road, the overall achieve the desired goal.
Photoluminescence of Si /spl delta/-doped InGaAs/GaAs heterostructures with double beam excitation

Photoluminescence (PL) of the undoped In/sub 0.2/Ga/sub 0.8/As/GaAs ans selectively Si /spl delta/-doped In/sub 0.2/Ga/sub 0.8/As/GaAs heterostructures with double excitation beams (He-Ne laser and white light) has been investigated. The increment of PL, which is the difference between the PL intensity with and without the white light, is obviously enhanced with an additional white light beam. PL increment of the undoped sample was the largest among all the samples. The increment for the samples with barrier doping is not as large as the undoped sample but more than the samples with doping in the quantum well. The PL increment of all the samples has shown the saturation behavior. The saturation is more pronounced for doped samples. An effective carrier density model is proposed to explain the experimental results.
Systems monitoring based on robust estimation of stochastic time series models

The problem of system monitoring under conditions of stochastic data heterogeneity based on time series models is considered. The stability of monitoring is proposed to be ensured through the use of convex-concave loss functions. An algorithm for estimating the variance of the main error distribution is proposed. This allows using robust procedures for estimating the parameters of stochastic time series models without a priori information about the variance value of the main error distribution. Using the Monte Carlo statistical test method, the estimates of the proposed robust methods are compared with the known methods of least squares, least modules, and Huber. It is shown that the introduced robust estimates of the parameters of stochastic models of time series win in accuracy and allow increasing the reliability of monitoring the state of systems.
SAR Autofocus Algorithm of iterative error compensation based on PGA

This paper proposes a motion compensation algorithm based on PGA. Our algorithm first uses the PGA algorithm to estimate roughly the phase error of the SAR data, which is then exploited through several iterations to compensate for the phase error and the range migration caused by the motion error to obtain a focused SAR image. A single iteration not only improves the image quality, but also improves the motion error compensation accuracy of the PGA algorithm in the next iteration. The simulation and measured data show that the algorithm can effectively compensate the motion error with fast convergence speed, good SAR image focusing effect and high error compensation accuracy.
Cathode and anode plasmas in short-pulse explosive field emission cathodes

Explosive field emission cathodes have been a subject of research for a number of years. These cathodes offer high current densities and total current without requiring a heater for the production of electrons. Generally these cathodes consist of some structure with a series of tips or metal–dielectric regions in which a large electric field enhancement can occur. A cathode plasma is then formed from these discharge points that then supplies the electrons necessary for space charge limited emission. This article reports on a series of optical measurements in which the cathode and anode plasmas of explosive field emission cathodes are observed. Three types of cathodes are investigated. These types are a polymer velvet cathode, a metal–dielectric cathode, and a tufted carbon fiber cathode in which the fibers have been coated with a cesium iodide salt. Cesium iodide coated carbon fiber cathodes have shown a great deal of promise for various field emitter applications. From these high speed photos, the evolut.
Feeding Japanese quail diets supplemented with probiotics and enzymes

This study assessed the effects of dietary supplementation with probiotics and enzymes on performance, blood antioxidant capacity, and cecal short-chain fatty acid concentration. Two hundred one-day-old quail chicks were randomly divided into four groups, each containing 50 chicks. All the chicks were fed with a basal diet for 35 days. The control group was fed the basal diet, and the experimental groups were fed the basal diet augmented with a commercial probiotic and enzyme supplement at 0.5 g/kg, 1 g/kg and 2 g/kg. At the end of experiment, initial live weight, final live weight, live weight gain and feed intake were affected significantly by the addition of the supplement. However, the supplement did not influence the feed conversion ratio. Increasing the levels of the probiotic and enzyme supplement Diazyme 256 in the diets caused linear and quadratic responses in malondialdehyde, glutathione, superoxide dismutase, and glutathione peroxidase level. Catalase showed a linear increase with the rise in level of the supplement. However, ceruloplasmin, albumin, total protein and globulin were not affected. Significant linear responses were observed in propionic acid, butyric acid and isocaproic acid with the increasing levels of the supplement. There were no significant differences in acetic acid, isobutyric acid, valeric acid, isovaleric acid, caproic acid, branched chain fatty acids, and short chain fatty acid concentrations for quails fed various levels of the supplement. In conclusion, the supplement Diazyme 256 containing probiotics and enzymes could be used in quail diets to improve growth performance, antioxidant capacity and intestinal health.
A Non-iterative Optimal Power Flow Calculation Method in Power System

The optimal power flow is very important in the study of power system, and it is of great significance for the economic operation of the power system to solve the active power output of the generator through the optimal power flow. In order to solve the problem that the traditional method is sensitive to the initial state and the large iteration times, this paper applies the holomorphic embedding method to solve the optimal power flow. By embedding holomorphic variables, the holomorphic embedding forms of constraints and KKT conditions are obtained. The power series coefficients of node voltage and generator active power output are calculated by non-iterative solution. Finally, the active power output of the generator is obtained by the method proposed in this paper, and then the lowest power generation cost of the system is calculated. Simulation results on IEEE study Case 3, Case 4, Case 30 and Case 39 verify the feasibility and accuracy of the proposed method.
Subspace: A Solution to the Farmer’s Dilemma

—In an effort to make blockchains more energy-efﬁcient, egalitarian, and decentralized, several new protocols employ consensus based on Proofs-of-Capacity (PoC), which replace compute-intensive mining with storage-intensive farming. We observe that PoC consensus introduces a unique mechanism design challenge, referred to as the farmer’s dilemma , which suggests that existing constructions are not actually incentive compatible. Simply put, farmers must decide whether to allocate scarce storage resources towards either maintaining the chain state and history or maximizing the amount of space they pledge towards consensus. Rational farmers will always choose the latter, at best becoming light clients, while at worst encouraging pooled farming under a few trusted operators. To resolve this dilemma, we introduce Subspace , a PoC blockchain in which farmers maintain neither the state nor the history, while retaining the security properties and decentralization beneﬁts of a full node. Consensus in Subspace is based on proofs of replicated storage of the history of the blockchain itself. Farmers store the history collectively, many times over, with each farmer storing as many replicas as their disk space allows. Consensus and computation are then decoupled, such that farmers only propose an ordering for transactions, while staked executor nodes maintain the state and compute transitions. This separation of concerns signiﬁcantly reduces the storage and compute overhead needed to operate a farmer, even in an Ethereum-style execution model, allowing for high levels of participation in consensus by ordinary users with commodity hardware.
Sensor Platform for Counting Users in Public Transport

There is a current trend towards the concentration of populations in large urban centres, thus over-loading public transport systems which, because they are not always able to handle high demand, end up having a negative impact on mobility and consequently on the environment. For transport operators to be able to improve the service provided, it is essential that they know the needs of users through passenger counting systems. To solve this problem, a platform was developed using sensor technologies, infrared and ultrasonic sensors, wireless networks, Wi-Fi, as well as location, to acquire data related to the entry and exit of people, the permanence through the identiﬁcation of mobile devices and the location of the vehicle through a GPS receiver. It was also implemented an estimation system on a central server with the purpose of calculating the afﬂuence of a vehicle using the data collected. Although the system tests were performed in a simulated environment, due to the limitations adjacent to the desiase COVID-19, it was veriﬁed that the ultrasounds correctly identiﬁed the passage of people, showing an F-Measure value above 0.97 for slow and normal speeds. Meanwhile, the identiﬁcation of mobile devices was not successful due to the fact that the system could not always detect the presence of them. With regard to the geographical location of the vehicle, the system showed a difference of less than 1 meter to the previously deﬁned point, with a standard deviation between 2 to 4 meters.
A Study on the Awareness Level and Attitude Level of the Green Consumer

Human activities through the years have degraded all the vital life supporting systems like air, water and land. An exposure to green products does not lead a consumer to buy the same. What really matters is the awareness in the consumer’s mind towards the purchase of green products. This paper tries to find out the awareness and attitude of consumers regarding the green products and the purchase of them and tries to look into the consumer beliefs and attitude on environment protection and their purchasing behavior of eco friendly products. Further, the paper tries to identify the efforts put by marketers in bringing green brand awareness in consumer mind. It is understood that consumers lack the exposure to green product marketing communication and more promotions are required to promote and sell products that are environmentally friendly and function effectively. The main objective of this study is to know the consumer awareness about the green products and its impact on green purchasing behavior. The research design used in this study is exploratory research design. It is believed that the Indian market could be made as a market for greener products with environmental values if proper marketing and promotion strategies are followed. The study in addition will try to investigate the behavior of the consumer and the impact of marketing communication to identify how they are persuaded again and again to opt for greener products on a regular basis. The data has been collected from the various sources and secondary data was collected from various published sources.
A review on autonomous electric vehicle communication networks-progress, methods and challenges

Electric vehicles have gained significance owing to its unavoidable supporting factors including environmental impacts and climate features. It has been noticed over last few decades that the increased number of manufacturers have focused on electric propulsion-based technology either pure electric or hybrid form with the support of electric vehicles in the automotive market. The adoption of these electric vehicle has obviously increased its competitive nature while compared to traditional internal combustion engine system. Moreover, the electric vehicles (EVs) possess substantial potential, not only in minimizing carbon emission but also in assisting required energy storage to contribute to the distributed renewable generation. There exist several increases in electric vehicle usage, but their level of massive adoption and existence by automotive consumers is connected with its delivered performance. One such important feature is the autonomous electric vehicle communication networks. This research provides a comprehensive review on overview of the electric vehicles and will discuss various existing works on autonomous driving vehicles. The paper compares existing communication networks and nuances associated in the context of an autonomous electric vehicle. Also, it critiques the existing technology and provides suggestive future work in the field to make communication networks resilient. An extensive review makes it possible to ascertain future research directions in the EV research field, which would result in massive future and instantaneous EV perception in the automotive market.