Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA\u27s Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

Overview of the MOSAiC expedition: Physical oceanography

Arctic Ocean properties and processes are highly relevant to the regional and global coupled climate system, yet still scarcely observed, especially in winter. Team OCEAN conducted a full year of physical oceanography observations as part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), a drift with the Arctic sea ice from October 2019 to September 2020. An international team designed and implemented the program to characterize the Arctic Ocean system in unprecedented detail, from the seafloor to the air-sea ice-ocean interface, from sub-mesoscales to pan-Arctic. The oceanographic measurements were coordinated with the other teams to explore the ocean physics and linkages to the climate and ecosystem. This paper introduces the major components of the physical oceanography program and complements the other team overviews of the MOSAiC observational program. Team OCEAN’s sampling strategy was designed around hydrographic ship-, ice- and autonomous platform-based measurements to improve the understanding of regional circulation and mixing processes. Measurements were carried out both routinely, with a regular schedule, and in response to storms or opening leads. Here we present alongdrift time series of hydrographic properties, allowing insights into the seasonal and regional evolution of the water column from winter in the Laptev Sea to early summer in Fram Strait: freshening of the surface, deepening of the mixed layer, increase in temperature and salinity of the Atlantic Water. We also highlight the presence of Canada Basin deep water intrusions and a surface meltwater layer in leads. MOSAiC most likely was the most comprehensive program ever conducted over the ice-covered Arctic Ocean. While data analysis and interpretation are ongoing, the acquired datasets will support a wide range of physical oceanography and multi-disciplinary research. They will provide a significant foundation for assessing and advancing modeling capabilities in the Arctic Ocean

Nano-micelle formation and aggregation in SBS-Modified asphalt induced by π-π interaction using molecular dynamics

Maintaining the storage stability of polymer-modified asphalt is crucial to its optimal performance. The phase separation in styrene–butadiene–styrene (SBS) -modified asphalt during high-temperature storage has not been solved due to the lack of fundamental understanding of intermolecular interaction. This study aims to reveal the formation of molecular nano-micelles and endogenous π-π interaction in the SBS-modified asphalt. Molecular simulation indicates that incorporating more than 7 % SBS into bitumen forms SBS self-aggregates that entangle with asphaltenes, causing a reorganization of the colloidal structure. The SBS aggregation is dominated by the π – π conjugate interactions and steric hindrance of confused aromatic. In low-content SBS-modified asphalt (7%), SBS interlocks parallel benzene rings and selectively adsorbs maltene from neat bitumen. High concentrations of SBS in the modified asphalt increase inter-SBS attraction but decrease asphaltene-SBS interaction, causing SBS self-aggregation and segregation from asphaltene-rich phase. Atomic force microscope and fluorescence microscope tests demonstrate the selective maltene adsorption and phase migration in high-content SBS-modified asphalt, and macro performance exhibits severe phase separation and degradation. This study provides a new perspective on improving the storage stability of high-content SBS-modified asphalt

Numerical Modeling of Shale Oil Considering the Influence of Micro- and Nanoscale Pore Structures

A shale reservoir is a complex system with lots of nanoscale pore throat structures and variable permeability. Even though shale reservoirs contain both organic and inorganic matter, the slip effect and phase behavior complicate the two-phase flow mechanism. As a result, understanding how microscale effects occur is critical to effectively developing shale reservoirs. In order to explain the experimental phenomena that are difficult to describe using classical two-phase flow theory, this paper proposes a new simulation method for two-phase shale oil reservoirs that takes into account the microscale effects, including the phase change properties of oil and gas in shale micro- and nanopores, as well as the processes of dissolved gas escape, nucleation, growth and aggregation. The presented numerical simulation framework, aimed at comprehending the dynamics of the two-phase flow within fractured horizontal wells situated in macroscale shale reservoirs, is subjected to validation against real-world field data. This endeavor serves the purpose of enhancing the theoretical foundation for predicting the production capacity of fractured horizontal wells within shale reservoirs. The impact of capillary forces on the fluid dynamics of shale oil within micro- and nanoscale pores is investigated in this study. The investigation reveals that capillary action within these micro- and nanoscale pores of shale formations results in a reduction in the actual bubble point pressure within the oil and gas system. Consequently, the reservoir fluid persists in a liquid monophasic state, implying a constrained mobility and diminished flow efficiency of shale oil within the reservoir. This constrained mobility is further characterized by a limited spatial extent of pressure perturbation and a decelerated pressure decline rate, which are concurrently associated with a relatively elevated oil saturation level

Effect of Different Anti-Stripping Agents on the Rheological Properties of Asphalt

Water damage is one of the main forms of early damage of highway asphalt pavement, which seriously affects the service performance of asphalt pavement. To address the problem of early water damage of asphalt pavement, the addition of anti-stripping agents in asphalt is an effective technical means. To further analyze the influence of anti-stripping agents on asphalt performance, Qingchuan rock asphalt, acylamine anti-stripping agent, fatty amine anti-stripping agent, rock asphalt + acylamine anti-stripping agent and rock asphalt + fatty amine anti-stripping agent were selected to modify the base asphalt. The rheological properties of modified asphalt were tested using a dynamic shear rheometer and bending beam rheometer. Combined with the mathematical-model-fitting test results of Sigmoidal, Burgers, viscoelastic continuum damage model (VECD), etc., the effects of different anti-stripping agents on the rheological properties of asphalt were evaluated through model parameters. The results showed that the Qingchuan rock asphalt could improve the high-temperature resistance to deformation of asphalt and improved the resistance to deformation of asphalt in a wide frequency domain but increased the risk of low-temperature cracking of asphalt. Acylamine anti-stripping agents and aliphatic amine anti-stripping agents reduced the elastic recovery ability and deformation resistance under a wide frequency domain of asphalt at high temperatures but enhanced the flexible deformation and ductility of asphalt at low temperatures. The compound anti-stripping agent not only improved the high-temperature deformation resistance of asphalt, but also improved the deficiency of rock asphalt’s low-temperature performance and effectively enhanced its performance

Crude Oil Source Identification of Asphalt via ATR-FTIR Approach Combined with Multivariate Statistical Analysis

The types of crude oil for producing asphalt have a decisive influence on various performance measures (including aging resistance and durability) of asphalt. To discriminate and predict the crude oil source of different asphalt samples, a discrimination model was established using 12 greatly different infrared (IR) characteristic absorption peaks (CAPs) as predictive variables. The model was established based on diverse fingerprint recognition technologies (such as principal component analysis (PCA) and multivariate logistic regression analysis) by using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). In this way, the crude oil source of different asphalt samples can be effectively discriminated. At first, by using PCA, the 12 CAPs in the IR spectra of asphalt samples were subjected to dimension reduction processing to control the variables of key factors. Moreover, the scores of various principal components in asphalt samples were calculated. Afterwards, the scores of principal components were analysed through modelling based on multivariate logistic regression analysis to discriminate and predict the crude oil source of different asphalt samples. The result showed that the logistic regression model shows a favourable goodness of fit, with the prediction accuracy reaching 93.9% for the crude oil source of asphalt samples. The method exhibits some outstanding advantages (including ease of operation and high accuracy), which is important when controlling the source and quality and improving the performance of asphalt