Sea surface salinity (SSS) is a fundamental parameter for understanding ocean phenomena and plays a vital role in studying global climate change and weather prediction models. Following the earlier launch of Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellites, the Chinese Ocean Salinity and Soil Moisture Mission (COSM) was successfully launched on November 14, 2024. The launched satellite is equipped with the 2-D L-band Aperture Synthesis Microwave Radiometer (LASMR) and the Microwave Imager Combined Active and Passive (MICAP) components to gather high-precision SSS information. This paper presents the Airborne LASMR (ALASMR), which features a Y-shaped two-dimensional synthetic aperture microwave radiometer and contains 11 antenna units with a unit spacing of 0.82λ. The ground test investigations of the ALASMR have been conducted to evaluate antenna patterns, test the sensitivity of receiving channels, and conduct ocean aviation experiments. To examine the flight observation, uniform salinity is assumed for the sea area obtained from the calibration platform of National Satellite Ocean Application Service (NSOAS) whereas the salinity gradient is taken for the Laizhou Bay area. The ALASMR exhibits enhanced imaging performance, with a spatial resolution of about 0.35 km and a width of about 1.24 km at the flight altitude of 1.2 km. The retrieval of SSS in the sea area of the ocean calibration platform is also demonstrated in this work. This indicates that ALASMR can reliably execute salinity observation of near-shore with high precision and provide a cross-calibration data source for COSM after its launch.

This article proposes a method for calculating the millimeter-wave (MMW) radiation of chaff corridor-target-sea scene. This method is used to analyze the attenuation of target brightness temperature (TB) by chaff corridor. First, a numerical method is proposed to obtain the facet model of chaff corridor, which is then combined with the facet models of sea and target obtained by the traditional method. Second, the ray tracing method is carried out to obtain the MMW radiation TB of the scene. In ray tracing, the emissivity, reflectivity, and transmittance of chaff corridor facets are calculated by the generalized equivalent conductor (GEC) method and the calculation method of reflection and transmission by a layered medium (RTLM). The emissivity and reflectivity of sea and target facets are calculated by their permittivity. Finally, to verify the correctness of the MMW radiation calculation method, a method for measuring the emissivity, reflectivity, and transmittance of chaff corridor is proposed. The TB of chaff corridor-metal plate and chaff corridor-ship-sea scenes is simulated and measured to analyze the TB attenuation caused by the chaff corridor. The results show that the calculation method is effective and the attenuation by the chaff corridor decreases with the increase of TB.

Increasing experimental evidence suggests that the human hippocampus, evolutionarily shaped by spatial navigation tasks, also plays an important role in language comprehension, indicating a shared computational mechanism for both functions. However, the specific relationship between the hippocampal formation's computational mechanism in spatial navigation and its role in language processing remains elusive. To investigate this question, we develop a prefrontal-hippocampal-entorhinal model (which called PHE-trinity) that features two key aspects: 1) the use of a modular continuous attractor neural network to represent syntactic structure, akin to the grid network in the entorhinal cortex; 2) the creation of two separate input streams, mirroring the factorized structure-content representation found in the hippocampal formation. We evaluate our model on language command parsing tasks, specifically using the SCAN dataset. Our findings include: 1) attractor dynamics can facilitate systematic generalization and efficient learning from limited data; 2) through visualization and reverse engineering, we unravel a potential dynamic mechanism for grid network representing syntactic structure. Our research takes an initial step in uncovering the dynamic mechanism shared by spatial navigation and language information processing.

Sea surface temperature (SST) and wind speed (SSWS) are two significant parameters in the coupled ocean-atmosphere system. Nowadays, space-borne microwave radiometers are the main approaches to measuring global SST and SSWS with high coverage and high accuracy. For conventional processing of passive microwave data, radiometer calibration is conducted to convert the radiometer raw output voltage data into the top of atmosphere (TOA) brightness temperatures (TBs). After calibration, sea surface parameters are retrieved from TBs using statistically or physically based retrieval methods. Considering the complex procedures of instrument calibration and retrieval, a novel estimation method based on deep learning is proposed to obtain SST and SSWS directly from radiometer voltages. A comprehensive matchup dataset, including the data measured by the scanning microwave radiometer (SMR) onboard the Chinese HY-2B satellite, the European reanalysis 5 (ERA5) products and the WindSat measurements, is used to develop and test the deep learning models. Validation against ERA5 and WindSat products indicates that the deep learning models perform well. To compare with traditional methods, we also retrieve SST and SSWS from brightness temperature data of SMR using a statistical regression retrieval algorithm. The comparison suggests that the deep learning models provide results close to and sometimes better than the regression algorithm. Furthermore, the feature importance of deep learning models and the dependence of their performances on sea state are analyzed. In this paper, it is demonstrated that the deep learning method is a reliable and feasible tool for SST and SSWS estimation from the radiometer voltages with high accuracy, which can simplify the data processing procedure and improve the processing efficiency.

For 1-D aperture synthesis (1-D AS) radiometers, truncated sampling occurs in the frequency domain due to the system baseline limitation. Therefore, there is an obvious Gibbs oscillation in the reconstructed image. To solve this problem, an imaging method based on a 1-D convolutional neural network (1-D CNN) is proposed in this article. Compared with deep learning methods based on 2-D convolutions, the 1-D convolution not only reduces the amount of computation but also produces further performance improvements. The input data of the network are the 1-D visibility function samples, and the output data are the 1-D brightness temperature (BT) samples. The network learns the mapping relationship from the training of the 1-D visibility function samples and 1-D BT samples to complete 1-D AS imaging without any prior knowledge. To verify the performance of this imaging method, simulations and experiments based on the airborne C-band 1-D microwave interferometric radiometer (ACMIR) system are implemented. The simulation and experimental results demonstrate that the proposed AS-CNN method achieves higher performance than the inverse fast Fourier transform (IFFT) method in terms of image quality and Gibbs phenomenon suppression. In the case of an antenna failure and missing baseline, the AS-CNN method proposed in this article can still obtain a BT image with high imaging quality, which shows that the robustness of the network is better than that of the IFFT method.

The medium- and large- scale global sea surface temperature (SSTs), which is an important ocean parameter, are mainly measured by the space-borne microwave radiometry. However, the resolution and sensitivity are greatly limited by antenna size, orbit altitude, and flight speed. In this paper, an airborne C-band one-dimensional (1-D) microwave interferometric radiometer (ACMIR) is developed to obtain the SSTs with a high resolution and sensitivity. The spatial resolution of the ACMIR ranges from 40.7m to 612m, corresponding to the flight height of 500m to 8000m, while the sensitivity varies from 0.25K to 0.36K, associating with the boresight to the edge of the field of view. In order to evaluate the performance of the ACMIR, an ocean aviation experiment was conducted in the coastal area of the Yellow Sea in September 2020. The land observation results indicate that typical ground objects can be clearly distinguished. Furthermore, thesea surface brightness temperatures acquired by the ACMIR are compared with the estimates based on the radiative transfer model, with SST retrieval error of 0.77°C. The ACMIR can offer a high-resolution and accuracy of SST especially in coastal areas, and can meet several application requirements related to SSTs.

Recently, a number of articles have proposed query tree algorithms based on bit tracking to solve the multitag collision problem in radio frequency identification systems. However, these algorithms still have problems such as idle slots and redundant prefixes. In this paper, a time slot detection‐based M‐ary tree (Time Slot Detection based M‐ary tree, TSDM) tag anticollision algorithm has been proposed. When a collision occurs, the reader sends a predetection command to detect the distribution of the m‐bit ID in the 2m subslots; then, the time slot after predetection is processed according to the format of the frame‐like. The idle time slots have been eliminate through the detection. Using a frame‐like mode, only the frame start command carries parameters, and the other time slot start commands do not carry any parameters, thereby reducing the communication of each interaction. Firstly, the research status of the anticollision algorithm is summarized, and then the TSDM algorithm is explained in detail. Finally, through theoretical analysis and simulation, it is proved that the time cost of the TSDM algorithm proposed in this paper is reduced by 12.57%, the energy cost is reduced by 12.65%, and the key performance outperforms the other anticollision algorithms.

In the aperture synthetic radiometer system, good correction of systematic errors is the precondition of high quality image. So far the calibration of the receiving channel errors has been down relatively well, however, the calibration of the antenna errors remains to be improved. The paper proposed an inorbit external calibration method for aperture synthetic radiometer using external points on the ground, and the method aimed at the antenna error. Simulation proved the accuracy and feasibility of the method, then the implementary scheme was discussed. The method is of significant referential value to the image quality improvement and the practical engineering realization.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-Daperture synthesis radiometer working at L and X-band with full polarization measurement for obtaining high precise measurement of sea salinity and soil moisture. In order to better verify the performance of the system and the validity of the calibration methods, the demonstrator prototype of FPASMR was used in the airborne calibration experiment. Because of the limit of the observation window, the aircraft and load balancing, the demonstrator of 19 X-band antennas Y-shaped array was chosen. This paper is going to show the results of experiment.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer working at L and X-band with full polarization measurement for obtaining high precise measurement of sea surface salinity. FPASMR consists of dual Y-shaped arrays containing 23 L-band antennas per arm plus one in the center in L-band array and the same numbers of X-band antennas in X-band array. The demonstrator prototype of FPASMR has been developed and the validation has also been fulfilled.

This paper briefly discusses the working principle of synthetic aperture microwave radiometer system. Through the system sensitivity and repeatability test, The sensitivity and repeatability of the system are obtained. Based on the test, a correction algorithm for calibration error of calibration network sensitivity and receiver repeatability is proposed, which can be used to calibrate the data in different environments on the satellite and provide the basis for the future onboard calibration.

Either soil moisture or ocean salinity plays important role in globe water cycle. They are key parameters for monitoring the globe climate change [1]. Aperture Synthesis Microwave Imaging Radiometer will measure global sea surface salinity with 50-km spatial resolution, and the average monthly salinity accuracy aims at 0.1 psu (parts per thousand). This requires the radiometer has long-term calibration stability of <0.3K over 30 days. A special research was developed to achieve this objective. The research has addressed several areas including noise diode sensitivity versus temperature, L-band low-noise receiver performance under different test conditions and some components characterization which used in the Radiometer. Many of the research results and the calibration methods have been used to help to improve the performance of the radiometer.