Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Cucurbita spp. (pumpkin) and development of value-added products emphasizing its nutritional and chemical composition

Pumpkin ( Cucurbita spp.) is a commonly grown vegetable across the world however, due to its underutilization as a food source a significant imbalance between pumpkin production and consumption has been identified. Many researchers have reported morphological diversity and differences in the nutritional and chemical composition among the pumpkin species which resulted in a selective distribution of Cucurbita species in different parts of the world. As evidenced from literature, pumpkin has been identified as a remarkable source of nutrients and reported with high protein, carbohydrate, vitamins, and especially with high amounts of antioxidants. Despite their nutritional value, post-harvest loss associated with pumpkins is significantly high which highlights the importance of utilization of pumpkins through value addition. The value addition of pumpkin has drawn a huge research interest over the past years which has emphasized the nutritional and chemical composition of pumpkin, especially in the edible (flesh, seeds) and non-edible (peel and pulp) fruits parts and pumpkin flowers in several value additions. This review furnishes an overview of Cucurbita spp. production and the post-harvest loss associated with pumpkin along with the updated information about the reported value additions based on existing literature.
6-DOF AUV Identification with MIMO-NARX

This paper applies the MIMO NARX neural model to identify AUV with six degrees of freedom. Strongly nonlinear dependencies are modeled using a MIMO NARX model trained using «input-output» experimental data. This results in a grey box model with a partially known internal structure that can be supervised. This is achieved by specifying a certain regressors type, which is feedback to the model input. Obtained results show that the neural model trained by the Levenberg–Marquardt numerical algorithm provides acceptable performance and accuracy. Validation of the model was performed using data from full-scale experiments, which allowed to identify the weaknesses and advantages of the resulting system more accurately.
Dynamic Environments and Robust SLAM: Optimizing Sensor Fusion and Semantics for Wheeled Robots

This paper proposes an approach to enhance the robustness and accuracy of visual simultaneous localization and mapping (SLAM) for ground wheeled mobile robots in dynamic environments. The proposed method incorporates encoder measurements to establish optimization constraints in bundle adjustment. To further improve robustness, a geometric technique utilizing KMeans clustering with epipolar constraints and the SegNet [1] for semantic segmentation is employed to filter out features detected on moving objects. These modifications are integrated into the state-of-the-art SLAM system ORB-SLAM3 [2] and demonstrate superior accuracy and real-time performance compared to the baseline approach. The effectiveness of the proposed method is demonstrated through multiple OpenLoris and IROS Lifelong SLAM competition scenarios.
GENERALIZED MINTY PREVARIATIONAL INEQUALITY, INVEX-INCREASE-ALONG-RAYS PROPERTY AND INVEX-STAR-SHAPED OPTIMIZATION PROBLEM

The purpose of this paper is to study some relations between generalized Minty pre- variational inequalities, invex-increase-along-rays properties, and invex-star-shaped optimiza- tion problems. We introduce the concepts of invex-star-shaped sets and invex-increase-along- rays functions, and establish the relations between invex-increase-along-rays properties and invex-star-shaped optimization problems. Further, under certain conditions, we investigate the relations between invex-increase-along-rays properties and generalized Minty prevariational in- equalities. As consequences, we obtain the equivalence of generalized Minty prevariational inequalities and invex-star-shaped optimization problems under suitable conditions. Finally, we prove the equivalence of generalized Minty prevariational inequalities and perturbed generalized Minty prevariational inequalities.
Design and Optimization of the Energy Harvesting Device for Wireless Sensors

With the swift advancement of Smart Grid and the great expansion of urban distribution networks, condition monitoring sensors have emerged as a potential solution to enhance the efficiency and effectiveness of power supply. Meanwhile sensor nodes utilized in condition monitoring systems are often powered by either batteries or line power. However battery-powered sensors could suffer from a limited energy supply and require replacement upon depletion, leading to raising the cost of maintenance. This paper introduces a novel approach to circumvent the aforementioned limitations by proposing the utilization of energy harvesting from the magnetic field surrounding electrical power lines. The energy harvesting apparatus consist of three primary components, namely energy-taking coils, rectifier and filter circuit, and DC load. Meanwhile this paper describes a proposed optimal design of the core for magnetic energy harvester and evaluates the essential dimensional parameters, including the outer diameter to inner radius distance, core length, and number of winding turns, that significantly influence the coil terminal’s induced voltage. Furthermore, the output power of the proposed energy harvester under the condition of 300A current applied to the cable and 600 coil turns is up to S.16 $\mu$W, which surpass the power consumption of most sensors. These findings demonstrate the efficacy of the proposed energy harvester in extracting energy from the surrounding magnetic fields of three-core cables, and confirm that its output power can meet the requirements of wireless sensors.
Effect of Delocalized Structure on Voltage Stabilizer to Improve Electrical Performance of Cross-linked Polyethylene

This study proposes the inclusion of a novel voltage stabilizer to crosslinked polyethylene (XLPE) for improving the insulation performance of XLPE cables at higher voltage levels. The m-aminobenzoic acid and leucine were selected as voltage stabilizers to blend with XLPE base material, respectively. Thin film samples of about $\sim200\mu$m were prepared through a hot press method. Various electrical tests such as DC breakdown, DC conductivity, space charge distributions and surface potential decay were performed under $30^{\circ}\mathrm{C}, 50^{\circ}\mathrm{C}$ and $70\circ \mathrm{c}$. The trap distribution and the quantum chemical properties were calculated to study the mechanism. The results demonstrate that the addition of m-aminobenzoic acid can effectively improve the breakdown strength of XLPE, reduce the DC conductivity and space charge accumulation. However, the addition of leucine has no significant effect. The charge trap analysis indicates that the lower energy level traps are more advantageous to enhance the electrical properties of the material, while the quantum chemical calculation demonstrates that the delocalized structure dissipates the energy of the carriers. Thus, the delocalized structure of voltage stabilizer can effectively protect the XLPE molecular chain from the influence of high-energy charge carriers and hence improves the electrical insulation performance of XLPE.
Short-Circuit Power Transmission Line Analysis for a Nine-Phase Transmission Line Using Phase Frame Method

Short-circuit analysis has become crucial in power transmission line to prevent outage, and to transmit more power. Nowadays, several studies propose the use of multi-phase transmission lines to increase the amount of power that could be transmitted to consumers. Due to the complexity of these multi-phase systems, fault analysis should be done using a simplify technique. This paper proposed a Phase Frame method applied to analyze all type of short-circuit faults that may prevent a nine-phase transmission line to permanently supply an electrical load. Compared to Thevenin method or symmetrical components, the Phase Frame method is easy to be deployed and the equations obtained can be applied in any type of artificial intelligence technique to detect and classify as well as locate various power transmission line faults.
Research on the Intelligent Recognition Method of the Shipboard Equipment Status

In severe sea conditions, it is difficult for the crew of a survey ship to monitor the status of multiple equipment in multiple cabins. Based on this status, an intelligent recognition method of shipboard equipment status based on deep learning is studied. The manual valves not equipped with electronic monitoring means are selected as the research object, and the deep learning image recognition method is used to recognize the switching statue of the valves in real time. This method can be applied to the cabins equipped with fixed surveillance cameras. For cabins that are not equipped with cameras, surveillance cameras can be equipped on the crew. In this case, the location of the visual image capture is unknown. The peripheral fixed equipment can be loaded as a reference for visual posture recognition, and the posture information of the valve in the real environment can be solved to determine the true operating state of the valve. Experiments were carried out in the real cabin, and the accuracy rate of attitude recognition was 81.17%.
Numerical simulation of gas-induced orbital decay of binary systems in young clusters

A large fraction of stars (≈ 50% of the field population) are not single but part of a binary or multiple system. These binary systems form from the gas and dust in molecular clouds largely building clusters that are initially still embedded in the star-forming gas. Here the question arises whether the properties and frequency of binaries change during this gas-embedded phase. It is known that the gravitational interactions between stars in a cluster environment can destroy long-period binaries (> 105 days). However, not only can the interaction between the stars themselves change the binary properties but also those between binary systems and the surrounding gas. There, the binary potential torques the nearby gas, producing an outgoing acoustic wave. This wave transports angular momentum from the binary to the gas, resulting in a decay of the binary orbit. This effect is the central focus of the thesis presented here. First, an analytic approximation for the gas-induces orbital decay by Stahler (2010) was applied to a binary population and the results compared to observations. It was found that the process of orbital decay significantly changes the properties of short period binaries (< 105 days). The resulting period distribution resembles the one observed for solar-mass stars, but fails to do so for other mass ranges. The analytic approximation treats only the effect on binary systems with circular orbits and the wave generation is not calculated explicitly. Since, most binary systems have eccentric orbits, a 3D hydrodynamic simulation was developed to avoid these restrictions. It calculates the gravitational binary gas interaction, the wave generation, and the resulting orbital decay. An extensive parameter study was performed to investigate the dependency of the orbital decay on the binary and gas properties. It was found that the gas density, embedded time span and mass-ratio show a similar scaling as predicted by the analytic approximation. By contrast, all binary and gas properties which influence the wave generation show different dependencies. In particular, it is shown that eccentric orbits lead to a faster orbital decay than their circular counterparts. Eventually, all these effects were combined in a fit formula. Applying this fit-formula to a binary population, the resulting period distribution shows a better matching mass dependency, but still does not resemble the observed period distributions. The cluster model chosen here is only one example and it is still unknown which cluster types contribute to the field population. Furthermore, future observations of young binary systems and their environment could restrict the parameter space presented here. Having detailed knowledge of the binary’s environment, the method developed in this thesis can be used to deduce what impact the gas-induced orbital decay has on a binary population.
Optimizing Iterative Data-Flow Scientific Applications Using Directed Cyclic Graphs

Data-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task directed acyclic graph (DAG) once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the taskiter, a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple immediate successor locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the taskiter and the immediate successor heuristic in 8 iterative benchmarks. Using small task granularities, we obtain a geometric mean speedup of 2.56x over the reference OmpSs-2 implementation, and a 3.77x and 5.2x speedup over the LLVM and GCC OpenMP runtimes, respectively.
Study on the Degradation of FRP Rod of Composite Insulator under Electrical Stress and Salt Fog

Decay-like fracture of the composite insulators has become a major threat to the operation of high voltage transmission lines. Nevertheless, the mechanism of decay-like degradation is still not clear enough. In this paper, electrical stress as well as salt fog is applied to a FRP core rod partly covered by silicone rubber sheath to simulate the decay-like degradation process. At the end of the experiment, the FRP core rod is found to be eroded seriously by surface discharge, and carbonization appears in local position. According to SEM observation, the epoxy resin is melt and decomposed. Besides, air bubbles and holes can be witnessed on the melted epoxy resin matrix. The result of FTIR exhibits that the content of methyl group, aromatic group and alicyclic group gets lower compared to the intact position of FRP core rod, while the content of hydroxyl group increases. TGA result further indicates that the content of epoxy resin reduces, which is consistent with the FTIR measurement. This work provides a possible method for the study of the mechanism of decay-like degradation.
Design and Implementation of Multi-Retinal Disease Classification Using Deep Neural Network

Abstract-Automated approaches are needed to monitor retinal disease. A strategy for detecting retinal disease using DNN is included. DNN can also be added to the data by extracting applications according to search patterns. A method based on fundus color images has been proposed, and the authors use this training to recognize retinal disease accordingly. Without prompt diagnosis and treatment, retinal problems can lead to a permanent loss of imagination and hope. Symptoms should be diagnosed early under discipline to find appropriate treatment and cure. The Deep Diction model remains out-of-date with guidance and confirms statistics under Kilter for Marshall-specific retinal diseases, a prerequisite for which myriad common retinal diseases are still labeled and normalized. You can also Thisknowledge seems to indicate which retinal photographs may have aged, suggesting problems with group communication when using DNNs. More than 6 different retinal diseases are included in this dataset because a DNNmodel. A DNN implemented using the ResNetmodel is designed for retinal images with varying risk features and depths. The recommended method has been observed to be more than 80% true.
Microwave Frequency Synthesizers: A Tutorial

This tutorial provides a brief introduction to the field of frequency synthesis. It starts with general definitions and requirements followed by a review of the main synthesizer architectures. Direct analog, direct digital, and indirect techniques are compared in terms of performance, circuit complexity, and cost impact. The design tradeoffs are analyzed and complemented with a review of fractional-N, direct digital synthesizers (DDSs), frequency offset, multiloop, and other schemes. The current state and development trends of microwave frequency synthesizers will be reviewed.
Brain Tumour Detection from MRI Images Using Deep CNN

The brain is one of the most important organs in humans. Abnormal growth in the brain occurs, which may be a tumor. In this world, humansin any age group suffer from brain tumors. Stress is also a cause of brain tumors. Early detection can cure completely, which became the main objective of the work. In this research work, brain tumor detection from its images is investigated. Experiments are done using deep learning, wherein a convolutional neural networkis used. The brain is one of the activeparts of the central nervous system. Detectinga tumor in the brain is very difficult as the size, shape, and location of the tumor differ for each individual. If the brain tumor is identified and detected primarily as early, the chance of patients’treatment is very high. Normally, diagnosis is madeusing magnetic resonanceimaging. Our methodologies predictive models obtainedvery promising visual and quantitative results at real-time speed.
Study on the Working Mode and Impedance-Based Stability Criterion of Generalized Electric Spring

When the electric spring (ES) is used in the face of critical load clustering, the repetitive ES parallel structure introduces significant redundancy into the system. To address this issue, this paper further investigates the concept and topology of generalized electric spring(G-ES), analyzes the working model of G-ES, and discusses in depth the working mode of G-ES under the two cases of unidirectional and bidirectional power supply. The control strategy of the G-ES is designed to achieve current feedforward compensation through the load factor. The stability of G-ES is studied based on impedance analysis, and the stability criterion of G-ES under weak grid conditions is given. The results show that there are 100 working modes of the G-ES. The simulations and experiments are conducted to verify the correctness of the control method and stability impedance criterion proposed in this paper.
Apprentissage supervisé de règles de décision multiclasses avec contraintes de performances évolutives

This paper proposes a method using labeled data to learn a decision rule for multiclass problems with class-selective rejection and evolutionary performance constraints. The class-conditional densities are selected from a parameterized family of estimators by maximizing the likelihood criterion. The optimal decision rule, minimizing the loss function and satisfying each of the time evolutionary constraints, is determined using the time-independent densities. A two dimensional toy problem with three evolutionary constraints is carried out to study the efficiency of the proposed method.
Electrical Stress Simulation of DC Cable in ±100 kV VSC-HVDC System

With fast development of voltage-source-converters (VSC) HVDC transmission technology, the technology of DC cable with extruded insulation system has also been developed rapidly. The application voltage levels of DC cable based on thermoset insulation are 200kV, 320kV and 525kV. Compared with thermoset insulation, thermoplastic insulated cables have advantages in environmental friendliness, current carrying capacity, production speed, etc. However, there is currently no high-voltage thermoplastic insulated DC cable system in China. In this paper, the electrical stress simulation research of ±100kV VSC-HVDC transmission system has been carried out, and the electrical stress of the 100kV VSC HVDC transmission project on the thermoplastic insulated cable system has been determined. It provides a reference for formulation development of the insulation material and design of the cable system structure for 100kV thermoplastic insulated DC cables.
The Algorithm Based on Supervised Time Series Segmentation of Researching Dynamic Gesture Recognition

The Algorithm use the changes of pixels in the time domain in image sequences and the correlation between adjacent frames, find the corresponding relationship between the previous frame and the current frame to collect samples, ensure that the whole gesture can be collected without too many redundant frames. Use two-way propagation search mechanism, shortest length constraint lmin and maximum length constraint lmax are introduced to reduce the search space. By calculating an estimate ei of a point, determinate of segmentation points for sequence segmentation quantitatively, then calculate the joint probability of each characteristic distribution under each state, category matching based on maximum probability. The experiment uses a large number of samples to train the model and compares it with other algorithms, it shows that the algorithm has achieved good results in dynamic gesture recognition.
Economic Optimization of Green Methanol production by Coupling Wind Power Generation with Biomass Power Generation

Reducing carbon emissions in the chemical industry is an important condition for achieving "carbon peaking and carbon neutralization". As an important basic chemical industry, methanol production process is still dominated by coal chemical industry. It is urgent to find a new path and explore a green methanol production process. Due to the volatility and intermittency of wind power or photovoltaic power generation, it is difficult to meet the demand for methanol production only by using new energy for power generation, and there is also a lack of carbon sources. Therefore, it can be considered to use biomass power generation and wind power or photovoltaic power generation for green methanol manufacturing. A green methanol production system coupled with wind power and biomass power generation and its economic optimal allocation method are established in this paper. Under the premise of multi energy flow stability constraints, the full life cycle cost of the coupled system is minimized. Based on a green methanol scene in Inner Mongolia, a simulation analysis model is established. The simulation analysis results show that the economic benefits can be improved and the system operation effect can be optimized through multi energy flow coupling.
Power Loss and Thermal Temperature Calculation of IGBT Module by Mathematical Equations

Today, inverters are used in many industrial applications such as battery systems, fuel cells, solar cells, wind turbines, motor drives and uninterruptible power supplies. There are semiconductor switches in the structure of the inverters. Most of the inverter losses occur on semiconductor switches. If the losses in the semiconductor switches are calculated correctly, the heatsink can be selected appropriately. In this study, the conduction losses, switching losses and thermal temperature values of the two-level neutral point inverter with a power of 20kVA were calculated using mathematical equations and Fuji IGBT simulator program. During the calculations, manufacturer catalog data of IGBT module and heatsink were used. The calculations were made at ambient temperature of 25°C and the inverter was operated under 40% and 100% load. The results were given using figures and tables.