Abstract: The present disclosure provides an adhesive sheet. The adhesive sheet in the present disclosure comprises a substrate layer and an adhesive layer provided on at least one surface of the substrate layer. The surface roughness of the adhesive layer is as follows: Ra: 0.2 to 1.5 ?m, preferably 0.4 to 1.0 ?m; Rz: 2.0 to 15.0 ?m, preferably 3.0 to 10.0 ?m, and Ra/Rz is 3.0 to 30.0, preferably 5.0 to 20.0. The adhesive sheet in the present disclosure has excellent adhesion, flatness, and reusability (reworkability) and is easily peelable after use.

Abstract: One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

Abstract: One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

Abstract: Seismic exploration methods and data processing apparatuses employ a deep neural network to remove seismic interference (SI) noise. Training data is generated by combining an SI model extracted using a conventional model from a subset of the seismic data, with SI free shots and simulated random noise. The trained DNN is used to process the entire seismic data thereby generating an image of subsurface formation for detecting presence and/or location of sought-after natural resources.

Abstract: Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

Abstract: Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

Abstract: An optical detecting device capable of increasing signal-to-noise ratio (SNR) and economizing power consumption is installed on a wearable device. The optical detecting device includes a base, an optical detecting component and a light emitting module. The optical detecting component is disposed on the base and has a detecting surface normal vector. The light emitting module is disposed on the base and outputs a sampling signal to project onto an external object, and the optical detecting component can receive the sampling signal reflected from the external object. The light emitting module is slanted toward the optical detecting component, and an optical axis of spatial distribution of the sampling signal and the detecting surface normal vector are crossed to form a deviated angle.

Abstract: A method of reviewing SQL includes: obtaining slow log data; extracting a SQL sentence to be reviewed and basic information matched with the SQL sentence from the slow log data; identifying and parsing the SQL sentence to obtain a parsed result; reviewing the parsed result and/or the basic information in accordance with review items in a preset review template one by one to obtain a review result; and generating a review result set according to the review result.

Abstract: A method of reviewing SQL includes: obtaining slow log data; extracting a SQL sentence to be reviewed and basic information matched with the SQL sentence from the slow log data; identifying and parsing the SQL sentence to obtain a parsed result; reviewing the parsed result and/or the basic information in accordance with review items in a preset review template one by one to obtain a review result; and generating a review result set according to the review result.

Abstract: An optical detecting device capable of increasing signal-to-noise ratio (SNR) and economizing power consumption is installed on a wearable device. The optical detecting device includes a base, an optical detecting component and a light emitting module. The optical detecting component is disposed on the base and has a detecting surface normal vector. The light emitting module is disposed on the base and outputs a sampling signal to project onto an external object, and the optical detecting component can receive the sampling signal reflected from the external object. The light emitting module is slanted toward the optical detecting component, and an optical axis of spatial distribution of the sampling signal and the detecting surface normal vector are crossed to form a deviated angle.