Fluid antenna systems (FAS) have recently emerged as a promising solution for sixth-generation (6G) ultra-dense connectivity. These systems utilize dynamic radiating and/or shaping techniques to mitigate interference and improve spectral efficiency without relying on channel state information (CSI). The reported improvements achieved by employing a single dynamically activated radiating position in fluid antenna multiple access (FAMA) are significant. To fully realize the potential of FAMA in multi-user multiplexing, we propose leveraging the unique fast-switching capabilities of a single radio-frequency (RF)-chain meta-fluid antenna structure to achieve multi-activation. This allows for a significantly larger set of independent radiating states without requiring additional signal processing. Simulations demonstrate that multi-activation FAMA enables robust multi-user multiplexing with a higher signal-to-interference ratio (SIR) under various Rayleigh-fading environments compared to other single RF-chain technologies. We further show that the SIR can be optimized within a 15~$μs$ timeframe under a multi-user Rayleigh-fading channel, making the proposed scheme highly suitable for fast-changing wireless environments. Verified through the theoretical Jakes' model, full three-dimensional (3D) electromagnetic (EM) simulations and experimental validation, multi-activation FAMA enables effective CSI-free, multi-user communication, offering a scalable solution for high-capacity wireless networks.

This paper proposes a robust composite control strategy for constrained continuous-time nonlinear systems by integrating sliding mode control (SMC) and model predictive control (MPC). SMC enhances disturbance rejection, while MPC handles constraints by solving an optimal control problem (OCP) based on the SMC input. The resulting control input ensures both robustness and constraint satisfaction. To improve computational efficiency, the OCP is solved only at sampling instants. Recursive feasibility and closed-loop stability are rigorously analyzed, and the method’s effectiveness is demonstrated on a cart-damper-spring system.

The noncoherent single-input-multiple-output (SIMO) system experiences severe cross-product mutual interference with a limited number of receive antennas, severely limiting the number of simultaneous users that can be served in the uplink. In Rician channel, a multiuser energy difference detection (EDD) scheme is introduced for dual-frequency complementary amplitude signals. It eliminates cross-product interference, significantly reducing the number of antennas required by the base station (BS), while extending the constellation space for energy detection (ED). Based on this extended constellation space and channel gain information, two optimized multiuser constellations are obtained by maximizing the minimum joint constellation distance and directly equalizing the receive constellation space. Additionally, the impacts of user mobility are analyzed, and a simple strategy is designed to maintain performance by updating the moving user’s path gain. Simulation results show that the proposed scheme can handle more simultaneous users with the same scale antennas, and it also exhibits strong robustness to user mobility at the same time.

Abstract In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

Soil moisture (SM) is an important variable for hydrological, meteorological, and agricultural studies and applications. The SM Operational Products System (SMOPS) was developed by the National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data, and Information Service (NESDIS) to operationally provide an integrated satellite SM data product. The Advanced Microwave Scanning Radiometer 2 (AMSR2) SM retrieval is an important component of the currently operational SMOPS. This study is proposed to refine the AMSR2 data product using an optimal machine-learning model, and this first paper of the two-part series is to intercompare the six commonly used machine-learning models including multiple linear regression (MLR), regression tree (RRT), random forest (RFT), gradient boosting (GBR), extreme gradient boosting (XGB), and artificial neural network (ANN). Results indicate that all of the six approaches can preserve the reference data information beyond the training time period, which ensures they predict past and future satellite retrievals without a new training procedure. Relative to other models, the XGB method is more successful to respect to the reference data SM Active Passive (SMAP) and the in situ observations from the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN). It has a good implication for the implementation of the XGB model to refine the AMSR2 SM retrievals in the second paper.

Advanced Microwave Scanning Radiometer-2 (AMSR2) is a successor of AMSR for Earth-Observation System (AMSR-E), while the third generation of AMSR (AMSR3) will be launched in the near future. The AMSR2 soil moisture product is also an important component of the Soil Moisture Operational Products System (SMOPS) datasets that are operationally produced by National Oceanic and Atmospheric Administration (NOAA). Refinement of the NOAA AMSR2 soil moisture data product can not only benefit the past AMSR-E and the upcoming AMSR3, but also improve the SMOPS data quality. In this second paper of the two-part series, the Extreme Gradient Boosting (XGB) model was trained using the AMSR2 6.925 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz brightness temperature (Tb) measurements in dual polarizations, ancillary maps and the vegetation index datasets, and in turn used to predict the daily global AMSR2 soil moisture retrievals from 2012 to 2021. Validation results show that the refined AMSR2 soil moisture retrievals (AMSRr) show an overwhelming advantage in data accuracy over the currently operational AMSR2 product (AMSRc). Comapred to the AMSRc, the developed AMSRr presents a significant improvement on data availability. Results also indicate that the refined AMSR2 datasets are comparable with the latest version Soil Moisture Active Passive soil moisture product. Based on this study, higher quality AMSRr soil moisture data product will be operationally produced in the NOAA, and will eventually benefit the NOAA SMOPS blended soil moisture product and its users.

Under the condition of limited number of antennas in current noncoherent single-input-multiple-output (SIMO), the key problems of serious mutual interference and small number of supported users are still difficult to overcome. In this paper, we proposed a multi-user massive SIMO based on energy difference detection (EDD) in Rician channels. The proposed method only needs to simply estimate the line-of-sight (LOS) amplitude gain of users. The cross-product interference caused by energy detection is eliminated by designing complementary amplitude constellations and performing energy difference detection at the base station (BS). It reduces the number of antennas required by the BS to distinguish data streams from different users. In addition, we also analyze the impact on the system performance when users move, and adopt the low-complexity approach of re-estimating the moving user’s coefficient to maintain a good performance. Simulation results show that the proposed method can use the LOS component of the Rician channels to support more concurrent users, and obtain a better performance than existing methods.

Previous studies used the temperature-related variables from model outputs (e.g., 2-m temperature) for precipitation phase determination (i.e., rain-snow separation). This study presents a new idea for precipitation phase determination using brightness temperatures (TBs) from Advanced Technology Microwave Sounder. It is found that TB-based phase discrimination shows comparable determination performance to that from model outputs over land. In contrast, TB-based phase discrimination over ocean performs noticeably worse than that from model outputs. Further analyses reveal that the phase determination performance over land from TBs slightly depends on the satellite local zenith angle. However, the determination performance over ocean strongly depends on the satellite local zenith angle, with the skill score decreasing sharply from 0.74 near nadir to 0.55 near the edge. These results imply that over land TBs may be used directly for phase determination, which can be extended to other operational and future microwave sounders with similar channels available.

A NOAA satellite snowfall rate product is being produced operationally at near real-time. The product is retrieved using measurements from passive microwave radiometers aboard ten polar-orbiting satellites. The algorithm consists of a statistical snowfall detection model and a 1DVAR-based snowfall rate estimation component. The product has benefited from continuous development. Recent advances in bias correction technique have significantly improved the product performance. A validation study was conducted against a radar and gauge combined precipitation dataset recently. The results show that the snowfall rate product agrees well with the validation `truth', e.g. correlation coefficients for all satellites are around 0.6 with low bias and RMS. Some case studies also demonstrate the ability of the algorithm at capturing snowfall pattern and intensity. Currently, this product is being applied in weather forecasting and hydrology.

Resource scheduling is one of the main problems for multi-satellite Tracking, Telemetry and Command (TT&C) networks. Traditional multi-resource joint scheduling algorithms are with long solution time, low efficiency, high computational cost, and simple description on the system. Deep Neural Network (DNN) provides a possible new way to solve those problems, but it is difficult to handle correlations among the input data. This motivates our work to solve the strong correlation problem based on the accumulated historical data, and thus enables DNN for TT&C resource scheduling. By discretizing the data, multiple constraints and related attributes are transformed into different flags, and some binary bits of the data are used to reflect the constraint relationship. Then, we can use DNN model and construct an intelligent TT&C resource scheduling system to handle multiple constraints and data attributes (such as priorities among tasks and others). This improves the efficiency of TT&C resources utilization and automation. Effectiveness of the proposed model is verified by simulations.

The proposed work aims to enhance the capabilities of the passive microwave measurements on board NOAA and MetOp satellites for snowfall identification. Two independent methods based on the same sensors (AMSU/MHS) were applied and qualitatively inter-compared during snowstorms over Europe by using the NIMROD radar network as ground truth. The first method developed at NOAA is a statistical algorithm that computes the probability of snowfall using a logistic regression and the principal components of the high frequency brightness temperature measurements at MHS or ATMS channel frequencies 89 GHz and above. The second approach for snowfall detection (183-WSLSF) developed by CNR-ISAC is a prototype based on the preexistent 183-WSL retrieval method. By considering as limit of snowfall formation the rainy clouds located from a few hundred meters to 5–6 km, the 183-WSLSF combines channel sensitivities from 90 and 190 GHz to identify snowfall areas in the precipitating cores.

Microwave (MW) satellite data are widely used as input in numerical weather prediction models and also in other applications such as climate monitoring and re-analysis. MW satellite data are prone to different problems, including geolocation errors. These data do not have a fine spatial resolution like visible and infrared data; therefore, the accuracy of their geolocation cannot be easily determined using the normal methods such as superimposing coastlines on the satellite images. Currently, no geolocation correction is performed on data from MW instruments aboard the satellites in the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite program. However, geolocation error can be a significant source of bias in the satellite measurements. In this paper, we investigated and corrected the geolocation errors of the observations from the Advanced Microwave Sounding Unit (AMSU)-A aboard NOAA-15 to NOAA-19, AMSU-B aboard NOAA-15 to NOAA-17, and Microwave Humidity Sounder (MHS) aboard NOAA-18 and NOAA-19. We used the difference between ascending and descending observations along the coastlines to quantify the geolocation errors in terms of the satellite attitudes (Euler angles), i.e., pitch, roll, and yaw. Then, new geographical coordinates and scan/local zenith angles were calculated using new attitudes. The results show that NOAA-15 AMSU-A2 instrument has a mounting error of about 1.2$^{\circ}$cross-track, and$-0.5^{\circ}$along-track, NOAA-16 AMSU-A1 and -A2 instruments have a mounting error of about$-0.5^{\circ}$along-track, and NOAA-18 AMSU-A2 instrument has a mounting error of more than$-1^{\circ}$along-track.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provide global information on atmospheric temperature profile, water vapor, cloud, precipitation, etc. However, a pronounced asymmetric cross-scan bias of the AMSU-A window channels was discovered, and it severely impacted water cycle product generation. Several approaches, including vicarious cold and hot reference calibration techniques, are applied to characterize the cross-scan bias. The bias pattern appears to be stable through several years of data examined from the same satellite but is quite different among those onboard the different NOAA (NOAA-15, NOAA-16, NOAA-17, NOAA-18, and NOAA-19) and EUMETSAT (MetOp-A) satellites. The scan bias may be caused by sensor polarization misalignment or cross-polarization, even after the radiance/brightness temperature data have been geocorrected with regard to geolocation and view angles. Based upon the characterization information, two-point and three-point correction approaches are proposed; both approaches provide promising results for AMSU-A window channels at brightness temperature level and product level and outperform the current operational correction approach, which is essentially a one-point correction. This serves as the first step toward a more stable fundamental and thematic climate data record to be used in hydrological and meteorological applications.

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.