Influence of geological factors on surface deformation due to hydrocarbon exploitation using time-series InSAR: A case study of Karamay Oilfield, China

Surface deformation due to hydrocarbon extraction from buried reservoirs may gradually evolve to geological hazards, which can undermine the safety of infrastructure facilities. Monitoring the surface deformation and studying on the influencing factors of surface deformation have great significance to ensure the stability of oilfield development, and prevent geological hazards. In this study, Sentinel-1 interferometric synthetic aperture radar (InSAR) data of Karamay Oilfield acquired between January 2018 to December 2020 was used to map how the land surface has deformed in response to hydrocarbon exploitation. Based on the monitoring results of time series InSAR, geological data, and oilfield data, the correlations between the different factors and the surface deformation were analyzed. The results show that the reservoir buried depth, porosity and permeability have an impact on the surface deformation, and the influence on surface uplift is obviously greater than that on surface subsidence. Surface uplift decreases with the increasing buried depth and the decreasing porosity and permeability, and the correlation between porosity and surface uplift is the best. However, the impact is limited in the area with shallow reservoir depth, high porosity, and high permeability, such as the heavy oil blocks in the study area

Dark Count of 20-inch PMTs Generated by Natural Radioactivity

The primary objective of the JUNO experiment is to determine the ordering of neutrino masses using a 20-kton liquid-scintillator detector. The 20-inch photomultiplier tube (PMT) plays a crucial role in achieving excellent energy resolution of at least 3% at 1 MeV. Understanding the characteristics and features of the PMT is vital for comprehending the detector's performance, particularly regarding the occurrence of large pulses in PMT dark counts. This research paper aims to further investigate the origin of these large pulses in the 20-inch PMT dark count rate through measurements and simulations. The findings confirm that the main sources of the large pulses are natural radioactivity and muons striking the PMT glass. By analyzing the PMT dark count rate spectrum, it becomes possible to roughly estimate the radioactivity levels in the surrounding environment.Comment: 10 pages, 8 figures, and 5 table

In Situ Electrochemical Oxidation of Cu2S into CuO Nanowires as a Durable and Efficient Electrocatalyst for Oxygen Evolution Reaction

Development of cost-effective oxygen evolution catalysts is of capital importance for the deployment of large-scale energy-storage systems based on metal-air batteries and reversible fuel cells. In this direction, a wide range of materials have been explored, especially under more favorable alkaline conditions, and several metal chalcogenides have particularly demonstrated excellent performances. However, chalcogenides are thermodynamically less stable than the corresponding oxides and hydroxides under oxidizing potentials in alkaline media. Although this instability in some cases has prevented the application of chalcogenides as oxygen evolution catalysts and it has been disregarded in some others, we propose to use it in our favor to produce high-performance oxygen evolution catalysts. We characterize here the in situ chemical, structural, and morphological transformation during the oxygen evolution reaction (OER) in alkaline media of CuS into CuO nanowires, mediating the intermediate formation of Cu(OH). We also test their OER activity and stability under OER operation in alkaline media and compare them with the OER performance of Cu(OH) and CuO nanostructures directly grown on the surface of a copper mesh. We demonstrate here that CuO produced from in situ electrochemical oxidation of CuS displays an extraordinary electrocatalytic performance toward OER, well above that of CuO and Cu(OH) synthesized without this transformation

Experimental Determination of Effective Minority Carrier Lifetime in HgCdTe Photovoltaic Detectors Using Optical and Electrical Methods

This paper presents experiment measurements of minority carrier lifetime using three different methods including modified open-circuit voltage decay (PIOCVD) method, small parallel resistance (SPR) method, and pulse recovery technique (PRT) on pn junction photodiode of the HgCdTe photodetector array. The measurements are done at the temperature of operation near 77 K. A saturation constant background light and a small resistance paralleled with the photodiode are used to minimize the influence of the effect of junction capacitance and resistance on the minority carrier lifetime extraction in the PIOCVD and SPR measurements, respectively. The minority carrier lifetime obtained using the two methods is distributed from 18 to 407 ns and from 0.7 to 110 ns for the different Cd compositions. The minority carrier lifetime extracted from the traditional PRT measurement is found in the range of 4 to 20 ns for x=0.231–0.4186. From the results, it can be concluded that the minority carrier lifetime becomes longer with the increase of Cd composition and the pixels dimensional area

GPI-Based Secrecy Rate Maximization Beamforming Scheme for Wireless Transmission With AN-Aided Directional Modulation

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The ?rst one is the useful precoding vector of transmitting con?dential messages to the desired user while the second one is the arti?cial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for con?dential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R2 was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment