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The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning. [ABSTRACT FROM AUTHOR]

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model's robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset. [ABSTRACT FROM AUTHOR]

BackgroundStable isotopes play a crucial role in a variety of fields such as energy, military, semiconductor, agriculture, medicine, pharmacology, biology, food industry, and chemistry. With the rapid growth of nuclear science and technology applications in China, there has been an increasing demand for isotopes that cannot be met by current production capacities. Thus, the development of electromagnetic isotope separators capable of producing high yields and high isotopic purity has become necessary.PurposeThis study aims to develop an electromagnetic isotope separator based on a 2.45 GHz microwave ion source and isotopic magnet for studying a number of important heavy isotopes, such as xenon and molybdenum isotopes.MethodsFirstly, adjustable axial magnetic field in the source was designed by a double-solenoids to obtain high density plasma, and a high coupling efficiency matching waveguide was optimized by CST microwave computing module. Then. a crucible built in the discharge chamber was used to melt metal oxide for generating heavy metal ion beams. Finally, the discharge chamber, microwave coupling waveguides and heating oven of the ion source were simulated and designed for the generation of heavy ions.ResultsSimulation result shows that the temperature around the crucible is 917 ℃ when the current of heating wire is set to 70 A, and 100 mA hydrogen beam is generated during commissioning. The designed crucible in the discharge chamber can generate metal vapor efficiently for ionization, and achieve producing 20 emA Xe+ and 5 emA Mo+ respectively at the energy of 40 keV.ConclusionsThe feasible scheme of the magnetic field and microwave coupling design of this study are verified. The design of the 2.45 GHz electron cyclotron resonance (ECR) ion source provides a feasible and effective solution for the high yields isotope ions.

Since light propagation in a multimode fiber (MMF) exhibits visually random and complex scattering patterns due to external interference, this study numerically models temperature and curvature through the finite element method in order to understand the complex interactions between the inputs and outputs of an optical fiber under conditions of temperature and curvature interference. The systematic analysis of the fiber's refractive index and bending loss characteristics determined its critical bending radius to be 15 mm. The temperature speckle atlas is plotted to reflect varying bending radii. An optimal end-to-end residual neural network model capable of automatically extracting highly similar scattering features is proposed and validated for the purpose of identifying temperature and curvature scattering maps of MMFs. The viability of the proposed scheme is tested through numerical simulations and experiments, the results of which demonstrate the effectiveness and robustness of the optimized network model. [ABSTRACT FROM AUTHOR]

The fatigue behavior of cotton warps was studied by a newly weaving load simulator (WLS) developed in our laboratory. Reborn hairiness, strength retention, and elongation retention of sized warps were adopted to evaluate the physical and mechanical properties of sized warps under stretch-abrasion cyclic loading. The influences of different fatigue cycles on the above three indicators were also discussed. The results indicated that the fatigue behavior of the cotton warps accompanied by abrasion yields a three-parameter Weibull distribution. All the fitting plots show acceptable linearity. Moreover, there is a strong relationship of quadratic polynomial between the tensile properties and the fatigue cycle of the sized warps according to the scatter fitting (R2 > 91.08%). Similarly, there is also a good relationship of quadratic polynomial between the reborn hairiness index and the fatigue cycle of the sized warps (R2 > 94.51%). Finally, regardless of the strength retention, elongation retention, and reborn hairiness, the physical and mechanical properties of the cotton warps still change with the continuous increase of the fatigue cycle after 40% of the fatigue cycle, but it is not significant. The research was helpful to estimate the capacity of the warps to sustain failure.

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy (MH) as a primary physics goal. The excellent energy resolution and the large fiducial volume anticipated for the JUNO detector offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. In this document, we present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. Following an introduction summarizing the current status and open issues in neutrino physics, we discuss how the detection of antineutrinos generated by a cluster of nuclear power plants allows the determination of the neutrino MH at a 3-4σ significance with six years of running of JUNO. The measurement of antineutrino spectrum with excellent energy resolution will also lead to the precise determination of the neutrino oscillation parameters sin2θ12, Δm212, and | Δmee2 to an accuracy of better than 1%, which will play a crucial role in the future unitarity test of the MNSP matrix. The JUNO detector is capable of observing not only antineutrinos from the power plants, but also neutrinos/antineutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, and solar neutrinos. As a result of JUNO's large size, excellent energy resolution, and vertex reconstruction capability, interesting new data on these topics can be collected. For example, a neutrino burst from a typical core-collapse supernova at a distance of 10 kpc would lead to ∼5000 inverse-beta-decay events and ∼2000 all-flavor neutrino-proton ES events in JUNO, which are of crucial importance for understanding the mechanism of supernova explosion and for exploring novel phenomena such as collective neutrino oscillations. Detection of neutrinos from all past core-collapse supernova explosions in the visible universe with JUNO would further provide valuable information on the cosmic star-formation rate and the average core-collapse neutrino energy spectrum. Antineutrinos originating from the radioactive decay of uranium and thorium in the Earth can be detected in JUNO with a rate of ∼400 events per year, significantly improving the statistics of existing geoneutrino event samples. Atmospheric neutrino events collected in JUNO can provide independent inputs for determining the MH and the octant of the θ23 mixing angle. Detection of the 7Be and 8B solar neutrino events at JUNO would shed new light on the solar metallicity problem and examine the transition region between the vacuum and matter dominated neutrino oscillations. Regarding light sterile neutrino topics, sterile neutrinos with 10-5 eV2 < Δm412 < 10-2 eV2 and a sufficiently large mixing angle θ14 could be identified through a precise measurement of the reactor antineutrino energy spectrum. Meanwhile, JUNO can also provide us excellent opportunities to test the eV-scale sterile neutrino hypothesis, using either the radioactive neutrino sources or a cyclotron-produced neutrino beam. The JUNO detector is also sensitive to several other beyondthe-standard-model physics. Examples include the search for proton decay via the p → K+ + ν decay channel, search for neutrinos resulting from dark-matter annihilation in the Sun, search for violation of Lorentz invariance via the sidereal modulation of the reactor neutrino event rate, and search for the effects of non-standard interactions. The proposed construction of the JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics in a timely and cost-effective fashion. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.