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Remotely sensing the spatial distribution of exposed coal (EC) is significant for understanding the footprints of mining activities. However, widely applicable methods for the identification of EC surfaces remain inadequate because the choices of recent methods confront the diverse EC types and backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index (ACMI) which was empirically formulated by an iterative process of identifying parameters that maximize the separability of EC and non-EC surfaces. The performance of ACMI was tested in six study areas worldwide with different landscape types and coal types. Based on the visual inspection, ACMI was more effective in highlighting EC surfaces and suppressing non-EC surfaces than the existing methods. Compared with the sample points obtained through direct interpretation, ACMI obtained better EC mapping results than previous methods with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across all the selected Landsat images of the study areas, respectively. In addition, ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its default threshold. The default threshold makes EC mapping using ACMI an automated process. The new index has the potential to support a variety of mining-activity-related studies, such as the identification of mining disturbances and illegal mining detection at multi-spatial-temporal scales.

The spread of the Red Pal Weevil (RPW) has become an existential threat for palm trees around the world. In the Middle East, RPW is causing wide-spread damage to date palm Phoenix dactylifera L., having both agricultural impacts on the palm production and environmental impacts. Early detection of RPW is very challenging, especially at large scale. This research proposes a novel remote sensing approach to recognize and monitor red palm weevil in date palm trees, using a combination of vegetation indices, object detection and semantic segmentation techniques. The study area consists of date palm trees with three classes, including healthy palms, smallish palms and severely infected palms. This proposed method achieved a promising 0.947 F1 score on test data set. This work paves the way for deploying artificial intelligence approaches to monitor RPW in large-scale as well as provide guidance for practitioners.

Acoustic pyrometry is a non-contact measurement technology for monitoring furnace combustion reaction, diagnosing energy loss due to incomplete combustion and ensuring safe production. The accuracy of time of flight (TOF) estimation of an acoustic pyrometry directly affects the authenticity of furnace temperature measurement. In this paper presented is a novel TOF (i.e. time delay) estimation algorithm based on digital lock-in filtering (DLF) algorithm. In this research, the time-frequency relationship between the first harmonic of the acoustic signal and the moment of characteristic frequency applied is established through the digital lock-in and low-pass filtering techniques. The accurate estimation of TOF is obtained by extracting and comparing the temporal relationship of the characteristic frequency occurrence between received and source acoustic signals. The computational error analysis indicates that the accuracy of the proposed algorithm is better than that of the classical generalized cross-correlation (GCC) algorithm, and the computational effort is significantly reduced to half of that the GCC can offer. It can be confirmed that with this method, the temperature measurement in furnaces can be improved in terms of computational effort and accuracy, which are vital parameters in furnace combustion control. It provides a new idea of time delay estimation with the utilization of acoustic pyrometry for furnace., Comment: Under revision

The uncertainty of the energy measurement of the electron beam on circular electron positron collider (CEPC) must be smaller than 10$\mathrm{MeV}$ to make sure the accurate measurement of the mass of the Higgs boson. In order to simplify the energy measurement system, a new method is proposed by fitting the Compton edge of the energy distribution of the gamma ray from a microwave-electron Compton scattering. With our method, the uncertainty of the energy measurement is 6$\mathrm{MeV}$ for the electron energy of $120\mathrm{GeV}$ in the Higgs mode. In this system, the energy resolution of the gamma detection needs to reach $10^{-4}$. Therefore, only the high-purity germanium (HPGe) detector can meet the critical requirement. In a head-on collision mode, the initial photons should be microwave photons with the wavelength of 3.04 centimeters. A cylindrical resonant cavity with selected ${TM_{010}}$ mode is used to transmit microwaves. After the microwave-electron Compton backscattering, the scattered photons and the synchrotron-radiation background transmit a shielding structure and then are detected by a HPGe detector at the end of the beam line of the synchrotron-radiation applications. The hole radius in the side wall of the cavity is about $1.5\mathrm{mm}$ to allow the electron beam passing through. The results of the computer simulation technology (CST) software shows that the influence of the hole radius on the cavity field is negligible. The change of the resonance frequency can be easily corrected by fine-tuning the cavity size., Comment: 22 pages, 12 figures

We address the determination of the time resolution for the $\rm 100~\mu m$ 4H-SiC PIN detectors fabricated by Nanjing University (NJU). The time response to $\rm \beta$ particles from a $\rm ^{90}$Sr source is investigated for the detection of the minimum ionizing particles (MIPs). We study the influence of different reverse voltages, which correspond to different carrier velocities and device sizes, and how this correlates with the detector capacitance. We determine a time resolution $\rm (94\pm1)~ps$ for $\rm 100~\mu m$ 4H-SiC PIN detector. A fast simulation software, termed RASER (RAdiation SEmiconductoR), is developed, and validated by comparing the waveform obtained from simulated and measured data. The simulated time resolution is $\rm (73\pm 1)~ps$ after considering the intrinsic leading contributions of the detector to time resolution.