Integrated sensing and massive multiple-input-multiple-output (MIMO) communication (mMIMO-ISAC) at terahertz (THz) bands can provide vast spatial degrees of freedom and abundant bandwidth resources. However, the employment of a massive number of antennas will pose prominent challenges to both target sensing and channel training in THz-mMIMO-ISAC. In this article, our goal is to integrate the target sensing functionality into the channel estimation stage and develop a channel-training-aided target sensing framework to facilitate the efficient resource sharing of THz-mMIMO-ISAC. Specifically, by exploiting the sparse characteristics of THz mMIMO channels, we build up the intrinsic connection between the channel parameters and the target parameters in angular, delay, and Doppler dimensions. Then, we propose a shared channel training pattern accommodating the hybrid architecture constraints of THz transceiver. Both the channel estimation and the target sensing can be formulated as two structured tensor decomposition problems and then concurrently addressed at the UE and BS sides, respectively. Next, we propose a tensor-based parameter estimation algorithm to acquire the target and channel parameters, where the associated angles of arrival/departure, time delays, Doppler shifts, and coefficients can be extracted from the estimated factor matrices. In addition, we present the detailed derivation of the Cramér-Rao bound (CRB) for the considered parameter estimation problem in THz-mMIMO-ISAC. Numerical results demonstrate that the proposed algorithm can achieve the target parameters estimation performance close to their corresponding CRB, and recover the high-dimensional THz mMIMO channels with substantially reduced training overhead.

The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate change; with the accumulation of data from ocean remote sensing observations year by year, many scholars have started to use deep learning methods for SST prediction. In this paper, we use a dynamic region partitioning approach to process ocean big data and design a framework applied to a global SST short-term prediction system. On the architecture of a Long Short-Term Memory (LSTM) network, two deep learning multi-region SST prediction models are proposed, which extract temporal and spatial information of SST by encoding, using feature transformation and decoding to predict future multi-step states. The models are tested using OISST data and the model performance is evaluated by different metrics. The proposed MR-EDLSTM model and MR-EDConvLSTM model obtained the best results for short-term prediction, with RMSE ranging from 0.2712 °C to 0.6487 °C and prediction accuracies ranging from 97.60% to 98.81% for ten consecutive days of prediction. The results show that the proposed MR-EDLSTM model has better prediction performance in coastal areas, while the MR-EDConvLSTM model performs better in predicting the sea area near the equator. In addition, the proposed deep learning model has a smaller RMSE compared to the forecasting system based on the ocean model, indicating that the deep learning method has certain advantages in predicting global SST.

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes’ coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood.

Gravity geologic method is one of the important to derive seafloor topography by using altimetry-gravity, and its committed step is gridding of regional gravity anomaly. Hence, we proposed a topography constraint factor weight optimization (TCFWO) method based on ordinary kriging method. This method fully considers the influence of topography factors on the construction of regional gravity grid besides horizontal distance. The results of regional gravity anomaly models constructed in the Markus-Wake seamount area show that the TCFWO method is better than ordinary kriging method. Then, the above two regional gravity models were applied to invert the seafloor topography. The accuracy of derived topographic models was evaluated by using the shipborne depth data and existing seafloor topography models, including ETOPO1 and V19.1 model. The experimental results show that the accuracy of ST_TCFWO (seafloor topography model inverted by TCFWO method) is better than ST_KR (seafloor topography model inverted by kriging method) and ETOPO1 model. Compared with the ST_KR, the accuracy of the ST_TCFWO has improved about 26%. In addition, the accuracy of seafloor topography is affected by the variation of depth, the distribution of control points and the type of terrain. In different depth layers, the ST_TCFWO has better advantages than ST_KR. In the sparse shipborne measurements area, the accuracy of ST_TCFWO is better than that of V19.1, ETOPO1 and ST_KR. Moreover, compared to other models, ST_TCFWO performs better in flat submarine plain or rugged seamount area.

Exposure to malachite green (MG) may pose great health risks to humans; thus, it is of prime importance to develop fast and robust methods to quantitatively screen the presence of malachite green in water. Herein the application of extractive electrospray ionization mass spectrometry (EESI-MS) has been extended to the trace detection of MG within lake water and aquiculture water, due to the intensive use of MG as a biocide in fisheries. This method has the advantage of obviating offline liquid-liquid extraction or tedious matrix separation prior to the measurement of malachite green in native aqueous medium. The experimental results indicate that the extrapolated detection limit for MG was ~3.8 μg·L⁻¹ (S/N = 3) in lake water samples and ~0.5 μg·L⁻¹ in ultrapure water under optimized experimental conditions. The signal intensity of MG showed good linearity over the concentration range of 10–1000 μg·L⁻¹. Measurement of practical water samples fortified with MG at 0.01, 0.1 and 1.0 mg·L⁻¹ gave a good validation of the established calibration curve. The average recoveries and relative standard deviation (RSD) of malachite green in lake water and Carassius carassius fish farm effluent water were 115% (6.64% RSD), 85.4% (9.17% RSD) and 96.0% (7.44% RSD), respectively. Overall, the established EESI-MS/MS method has been demonstrated suitable for sensitive and rapid (<2 min per sample) quantitative detection of malachite green in various aqueous media, indicating its potential for online real-time monitoring of real life samples.

This article proposes a cognitive radar (CR) framework to improve the performance of the estimation and recognition for range-extended targets. To overcome the angular uncertainty and exploit the temporal correlation of target signatures, we introduce the static multimodel approach and the exponential correlation model to describe the dynamics of target signatures. The estimate of target signatures and the probability distribution of target hypotheses are updated recursively in response to the latest echo, ruled by the Bayesian theory. A waveform optimization module is integrated into the framework, which designs the waveform for the next emission according to the knowledge extracted from the echo history. A design criterion based on mutual information (MI) is derived, which implies the average estimation capability for all target hypotheses and the mean between-hypotheses divergence. The proposed method is examined using both synthetic data and electromagnetic (EM) simulation data of realistic targets.