As an ordered extension of evidence theory, Random permutation set (RPS) theory has received increasing attention due to its advantage in dealing with order-structured uncertain information. However, a significant research gap remains in the current literature concerning the construction of RPS. Building on the interpretation of RPS as a layer-2 belief structure, this paper proposes a data-driven hierarchical method for generating RPS, called DDHRPS. Specifically, DDHRPS first generates BPA from statistical features of data on the layer-1 belief structure, and then refines them with propensity information derived from distance analysis between samples to single classes, ultimately forming RPS on the layer-2 belief structure. Moreover, a DDHRPS-based classification algorithm (DDHRPSCA) is presented. Experimental comparisons involving two kinds of classifiers, namely, two uncertainty-based classifiers and seven machine learning classifiers validate the effectiveness and superiority of DDHRPSCA in handling uncertain information in classification tasks.

The structural information of offshore oil production equipment is the basis for the functional modification and upgrading of offshore oil drilling platforms. In order to solve the problems of low efficiency in the process of acquiring offshore oil production equipment structure information by traditional measurement methods, we propose a deep learning-based point cloud data processing scheme for offshore oil production equipment. First, a point cloud dataset of offshore oil production equipment for deep learning is constructed, and a deep learning network based on the two-step downsampling method and the local feature aggregation of each point after downsampling is implemented for the semantic segmentation of the dataset. Second, the combined point cloud filtering process based on radius filtering and statistical filtering is implemented on the segmented point cloud data. Third, an implicit surface reconstruction based on contextual prior information is implemented for offshore oil production equipment components. The dataset contains six types of point clouds, including pipelines, flanges, shelves, bends, valves, and oil recovery trees. Based on this dataset for semantic segmentation, the overall segmentation accuracy reaches 98.87% and the mIoU reaches 92.79%. Combined filtering is performed on the segmented offshore oil production equipment data, and the denoising rate can reach 94%. Finally, the denoised point cloud data is utilized for 3-D reconstruction, and the overall consistency accuracy can reach 1.85 mm, which can provide fast, efficient, and reliable data support for the upgrading of offshore oil rigs.

BACKGROUND: Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. RESULTS: Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema-a large multi-class schema for harmonizing various COVID-19 related resources. CONCLUSIONS: We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Recently, interferometric altimeters (IA), such as the Surface Water and Ocean Topography (SWOT) satellite, have been launched, and will improve the observation of ocean dynamics. The validation of altimeters is a complex and important process that can effectively improve their observation accuracy. For the IA validation of sea surface height in two-dimensional, it is normal to use the steric height to validate the sea surface height. However, the validation method is affected by many factors. In order to improve the validation accuracy, the South China Sea, which is a suitable area for validation of China’s offshore, was selected in this study to analyze the impact of wind on the validation procedures. Through the analysis of the spatial-temporal relationships between wind speed, steric height and sea surface height (SH-SSH), we found correlations between wind speed and validation results. The high wind speeds can lead to a certain deviation in the relationship between SH and SSH, and the special weather conditions make it unsuitable for validation. Meanwhile, in terms of time, May is the time when the wind speed is relatively low, making it more suitable for validation. These analyses are useful for the validation procedures in sea surface height.

SUMMARY: To meet the increased need of making biomedical resources more accessible and reusable, Web Application Programming Interfaces (APIs) or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info and MyChem.info for integrating annotations on genes, variants and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. AVAILABILITY AND IMPLEMENTATION: The BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Global Navigation Satellite System (GNSS) buoys were used to retrieve the significant wave heights (SWHs) and dominant periods of waves in the Qingdao coastal waters off China. The precise point positioning (PPP) and postprocessed kinematic (PPK) techniques were used to obtain the absolute motion of the GNSS buoys. Even though PPP has relatively low absolute positioning accuracy, this accuracy has a minimal influence on the spectrum in the wave frequency band. SWH values calculated from the PPK and PPP modes are nearly identical, with correlation coefficients and symmetric regression slopes both higher than 0.99. The SWH values calculated from PPP also show good agreement with a dedicated wave buoy, with a correlation coefficient of 0.935 and a difference of 0.82 cm ± 4.63 cm. A GaoFen-1 satellite image was used to assess the GNSS PPP wave spectrum via the dispersion relationship; both exhibit wave spectra with two peaks, consistent with the wave characteristics in the area.

Calibration and validation (Cal/Val) of the sea surface height as measured by satellite radar altimeters is essential to understand altimeter biases, observation trends, and instrument aging. It also supports the long-term stability of the produced climate change records of sea level as determined by altimetry. In this article, we report the calibration of HY-2B and Jason-2/3 using the established research infrastructure and data sharing initiative introduced by the Altimetry Calibration Cooperation Plan (ACCP) of China. Currently, three ACCP calibration sites encompass the Wanshan Islands, and two national oceanic sites are located along the China coastline. For each Cal/Val site, the components of the facilities—the geodetic, sea level, and Global Navigation Satellite System (GNSS) infrastructure—and the followed monitoring procedures and calibration methods are described. The HY-2B performance was primarily evaluated using about two years data, which indicated a mean bias of −0.2 ± 4.2 cm. Confidence in the results is strong, because the HY-2B biases were cross compared and confirmed by all the three independent sites and the three satellite ground tracks. Compared with its predecessor HY-2A, HY-2B shows very stable observations with no linear drift at present. In addition, Jason-2 and Jason-3 were mainly assessed using Qianliyan site. Our results indicate that the Jason-3 sea-surface height bias is approximately 2–3 cm smaller than that of Jason-2 and that the long-term stability of Jason-2/3 shows no significant trend, which in good agreement with the international dedicated sites. The instrument noises of Jason-2/3 and HY-2B were estimated based on the ACCP sites. The results show that the instrument noise in the previous literature is underestimated. This was also consolidated by the result from wavenumber spectrum and global crossover point analysis. The code and Wanshan data used in these Cal/Val experiments are publicly available to facilitate further work in this domain (https://github.com/GenericAltimetryTools/CalAlti).

Abstract In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image.

Multispectral methods for remote sensing image have been widely applied to shallow water bathymetry by researchers. In nonideal conditions, even with the same spectral radiance, the points still have a very wide range of water depths. This means that spectral features alone are insufficient for water bathymetry. Hence, we need to extract other valuable features from a remote sensing image. This letter introduces a spatial feature for water bathymetry using remote sensing images. We propose a model that utilizes a multilayer perceptron (MLP) to integrate the spectral and spatial location features. Experimental results demonstrate that the proposed model yields a substantial performance improvement. The mean relative error is only 8.41%, and the root mean square error is reduced by 34%–68% when compared with three other models. Furthermore, the proposed model addresses well the problems caused by heterogeneous bottom types.

In this paper, the research progress and its application of the Chinese calibration sites for satellite altimeters are described. The geodetic surveying and the surface subsidence monitoring over the Qianliyan calibration site using a permanent GNSS station are described. The new calibration results for HY-2A, Jason-2&3, Saral, and Sentinel-3A are presented in detail. Then, the Wanshan sites which is still under construction in the southern Chinese coastal area are introduced. In addition, the wet delay of troposphere measured by Jason-2 AMR in 2010-2016 is evaluated through one site from the Chinese coastal GNSS network, which proves the feasibility of calibrating microwave radiometer wet delay through the Chinese coastal GNSS network.

In this paper the calibration methodology, data and models, and the absolute bias of HY-2A, Jason-2, and Saral/AltiKa based on the Qianliyan CAL/VAL site will be presented.

The absolute SSH (sea surface height) biases of three satellite altimeters Jason-2, Saral/AltiKa and HY-2 were determined using our GPS buoy at the Qian Li Yan Island of China, which are 9.3cm, 1.3cm and 66.8cm, respectively. In addition, the altimetry SWH (significant wave height) were assessed using GPS retrieved SWH, which shows good agreement between the GPS buoy and satellite altimeters. The detailed method and result are described in the paper.