Oriented ice eddy detection network based on the Sentinel-1 dual-polarization data

The complex convergence of cold and warm ocean currents in the Nordic Seas provides suitable conditions for the formation and development of eddies. In the Marginal Ice Zones (MIZs), ice eddies contribute to the accelerated melting of surface sea ice by facilitating vertical heat transfer, which influences the evolution of the marginal ice zone and plays an indirect role in regulating global climate. In this paper, we employed high-resolution synthetic aperture radar (SAR) satellite imagery and proposed an oriented ice eddy detection network (OIEDNet) framework to conduct automated detection and spatiotemporal analysis of ice eddies in the Nordic Seas. Firstly, a high-quality RGB false-color imaging method was developed based on Sentinel-1 dual-polarization (HH+HV) Extra-Wide Swath (EW) mode products, effectively integrating denoising algorithms and image processing techniques. Secondly, an automatic ice eddy detection method based on oriented bounding boxes (OBB) was constructed to identify the ice eddy and output features such as horizontal scales, eddy centers and rotation angles. Finally, the characteristics of the detected ice eddies in the Nordic Seas during 2022-2023 were systematically analyzed. The results demonstrate that the proposed OIEDNet exhibits significant performance in ice eddy detection

Exploring Siamese network to estimate sea state bias of synthetic aperture radar altimeter

Sea state bias (SSB) is a crucial error of satellite radar altimetry over the ocean surface. For operational nonparametric SSB (NPSSB) models, such as two-dimensional (2D) or three-dimensional (3D) NPSSB, the solution process becomes increasingly complex and the construction of their regression functions pose challenges as the dimensionality of relevant variables increases. And most current SSB correction models for altimeters still follow those of traditional nadir radar altimeters, which limits their applicability to Synthetic Aperture Radar altimeters. Therefore, to improve this situation, this study has explored the influence of multi-dimensional SSB models on Synthetic Aperture Radar altimeters. This paper proposes a deep learning-based SSB estimation model called SNSSB, which employs a Siamese network framework, takes various multi-dimensional variables related to sea state as inputs, and uses the difference in sea surface height (SSH) at self-crossover points as the label. Experiments were conducted using Sentinel-6 self-crossover data from 2021 to 2023, and the model is evaluated using three main metrics: the variance of the SSH difference, the explained variance, and the SSH difference variance index (SVDI). The experimental results demonstrate that the proposed SNSSB model can further improve the accuracy of SSB estimation. On a global scale, compared to the traditional NPSSB, the multi-dimensional SNSSB not only decreases the variance of the SSH difference by over 11%, but also improves the explained variance by 5-10 cm2 in mid- and low-latitude regions. And the regional SNSSB also performs well, reducing the variance of the SSH difference by over 10% compared to the NPSSB. Additionally, the SNSSB model improves the computational efficiency by approximately 100 times. The favorable results highlight the potential of the multi-dimensional SNSSB in constructing SSB models, particularly the five-dimensional (5D) SNSSB, representing a breakthrough in overcoming the limitations of traditional NPSSB for constructing high-dimensional models. This study provides a novel approach to exploring the multiple influencing factors of SSB

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions

IPC02-27155 DEVELOPMENT OF LARGE DIAMETER X70 HIGH TOUGHNESS HSAW LINEPIPE FOR GAS TRANSMMISION

ABSTRACT X70 large diameter linepipe with helical seam SAW were developed, with1016mm OD and 14.6mm WT. Acicular ferrite type linepipe steel is adopted for the base material, which was found having high toughness and low yield strength loss after pipe forming. The very stringent requirements for toughness, i.e. 190J/140J for average/minimum for pipe body and 120J/90J for average/minimum for weld and HAZ were meet successfully. The yield strength loss due to Bauschinger effect was found lower than 20 MPa, which benefited

Concept Design of the “Guanlan” Science Mission: China’s Novel Contribution to Space Oceanography

Among the various challenges that spaceborne radar observations of the ocean face, the following two issues are probably of a higher priority: inadequate dynamic resolution, and ineffective vertical penetration. It is therefore the vision of the National Laboratory for Marine Science and Technology of China that two highly anticipated breakthroughs in the coming decade are likely to be associated with radar interferometry and ocean lidar (OL) technology, which are expected to make a substantial contribution to a submesoscale-resolving and depth-resolving observation of the ocean. As an expanded follow-up of SWOT and an oceanic counterpart of CALIPSO, the planned “Guanlan” science mission comprises a dual-frequency (Ku and Ka) interferometric altimetry (IA), and a near-nadir pointing OL. Such an unprecedented combination of sensor systems has at least three prominent advantages. (i) The dual-frequency IA ensures a wider swath and a shorter repeat cycle which leads to a significantly improved temporal and spatial resolution up to days and kilometers. (ii) The first spaceborne active OL ensures a deeper penetration depth and an all-time detection which leads to a layered characterization of the optical properties of the subsurface ocean, while also serving as a near-nadir altimeter measuring vertical velocities associated with the divergence, and convergence of geostrophic eddy motions in the mixed layer. (iii) The simultaneous functioning of the IA/OL system allows for an enhanced correction of the contamination effects of the atmosphere and the air-sea interface, which in turn considerably reduces the error budgets of the two sensors. As a result, the integrated IA/OL payload is expected to resolve the ocean variability at submeso and sub-week scales with a centimeter-level accuracy, while also partially revealing marine life systems and ecosystems with a 10-m vertical interval in the euphotic layer, moving a significant step forward toward a “transparent ocean” down to the vicinity of the thermocline, both dynamically and bio-optically

Ultra-small topological spin textures with size of 1.3nm at above room temperature in Fe78Si9B13 amorphous alloy

Topologically protected spin textures, such as skyrmions1,2 and vortices3,4, are robust against perturbations, serving as the building blocks for a range of topological devices5-9. In order to implement these topological devices, it is necessary to find ultra-small topological spin textures at room temperature, because small size implies the higher topological charge density, stronger signal of topological transport10,11 and the higher memory density or integration for topological quantum devices5-9. However, finding ultra-small topological spin textures at high temperatures is still a great challenge up to now. Here we find ultra-small topological spin textures in Fe78Si9B13 amorphous alloy. We measured a large topological Hall effect (THE) up to above room temperature, indicating the existence of highly densed and ultra-small topological spin textures in the samples. Further measurements by small-angle neutron scattering (SANS) reveal that the average size of ultra-small magnetic texture is around 1.3nm. Our Monte Carlo simulations show that such ultra-small spin texture is topologically equivalent to skyrmions, which originate from competing frustration and Dzyaloshinskii-Moriya interaction12,13 coming from amorphous structure14-17. Taking a single topological spin texture as one bit and ignoring the distance between them, we evaluated the ideal memory density of Fe78Si9B13, which reaches up to 4.44*104 gigabits (43.4 TB) per in2 and is 2 times of the value of GdRu2Si218 at 5K. More important, such high memory density can be obtained at above room temperature, which is 4 orders of magnitude larger than the value of other materials at the same temperature. These findings provide a unique candidate for magnetic memory devices with ultra-high density.Comment: 26 pages, 4 figure

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade

Skeleton-Based Dynamic Hand Gesture Recognition Using an Enhanced Network with One-Shot Learning

Dynamic hand gesture recognition based on one-shot learning requires full assimilation of the motion features from a few annotated data. However, how to effectively extract the spatio-temporal features of the hand gestures remains a challenging issue. This paper proposes a skeleton-based dynamic hand gesture recognition using an enhanced network (GREN) based on one-shot learning by improving the memory-augmented neural network, which can rapidly assimilate the motion features of dynamic hand gestures. Besides, the network effectively combines and stores the shared features between dissimilar classes, which lowers the prediction error caused by the unnecessary hyper-parameters updating, and improves the recognition accuracy with the increase of categories. In this paper, the public dynamic hand gesture database (DHGD) is used for the experimental comparison of the state-of-the-art performance of the GREN network, and although only 30% of the dataset was used for training, the accuracy of skeleton-based dynamic hand gesture recognition reached 82.29% based on one-shot learning. Experiments with the Microsoft Research Asia (MSRA) hand gesture dataset verified the robustness of the GREN network. The experimental results demonstrate that the GREN network is feasible for skeleton-based dynamic hand gesture recognition based on one-shot learning