A transportable optical clock refer to the 4s 2 S 1/2 -3d 2 D 5/2 electric quadrupole transition at 729 nm of single 40 Ca + trapped in mini Paul trap has been developed. The physical system of 40 Ca + optical clock is re-engineered from a bulky and complex setup to an integration of two subsystems: a compact single ion unit including ion trapping and detection modules, and a compact laser unit including laser sources, beam distributor and frequency reference modules. Apart from the electronics, the whole equipment has been constructed within a volume of 0.54 m 3 . The systematic fractional uncertainty has been evaluated to be 7.7×10 -17 , and the Allan deviation fits to be 14 2.3 10    by clock self-comparison with a probe pulse time 20 ms.

Estuaries have been sites of intensive human activities during the past century. Tracing the evolution of subaqueous topography in estuaries on a decadal timescale enables us to understand the effects of human activities on estuaries. Bathymetric data from 1955 to 2010 show that land reclamation decreased the subaqueous area of Lingding Bay, in the Pearl River estuary, by ~170 km2 and decreased its water volume by 615 × 106 m3, representing a net decrease of 11.2 × 106 m3 per year and indicating the deposition of approximately 14.5 Mt/yr of sediment in Lingding Bay during that period. Whereas Lingding Bay was mainly governed by natural processes with slight net deposition before 1980, subsequent dredging and large port engineering projects changed the subaqueous topography of the bay by shallowing its shoals and deepening its troughs. Between 2012 and 2013, continuous dredging and a surge of sand excavation resulted in local changes in water depth of ± 5 m/yr, far exceeding the magnitude of natural topographic evolution in Lingding Bay. Reclamation, dredging, and navigation-channel projects removed 8.4 Mt/yr of sediment from Lingding Bay, representing 29% of the sediment input to the bay, and these activities have increased recently.

Seabed acoustic images are image data mosaics derived from seafloor acoustic backscattering intensity data, which is related to the type of sediment covering the seabed. Therefore, submarine sediment classification can be realized using seabed acoustic images, and has been studied extensively. Recently, deep learning has also rapidly advanced; in particular, deep convolutional neural networks (CNNs) are now being used to achieve remarkable results in the field of image processing-showing that they are well-suited for image classification tasks. Previous studies have used GoogleNet to classify large-scale side-scan sonar data, with some sediments being well-classified. However, deep learning is data-driven and, theoretically, the greater the depth, the stronger is the learning ability of the feature. It is worth noting that the dataset used for sediment classification can sometimes be small. Hitherto, no related research has analyzed the feasibility and applicability of a CNN classifier for a small-sized seabed acoustic image dataset, so we adopted two different CNN classifier models to conduct the classification experiment in this study. As the results show, the CNN classifier can be applied to the classification of sediments based on a small-sized seabed acoustic image dataset, and the classification performance of shallow CNN was found to be better than that of the deep CNN on existing side-scan sonar data. In particular, the accuracy obtained from the results of several sediment classification experiments using a shallow CNN classifier ranged between 93.4% (Sand Wave) and 87.54% (Reef).INDEX TERMS Deep convolutional neural network, seabed acoustic image, seabed sediment classification.