Understanding protein functions in biological processes is pivotal for disease elucidation and drug discovery. Despite notable progress, existing approaches primarily focus on function transfer under in-distribution (ID) settings, where training and test proteins exhibit high sequence similarity. As a result, their performance often degrades when applied to novel, diverse, and low-homology protein sequences, posing a major challenge for out-of-distribution (OOD) generalization encountered in practice. Towards this end, we develop ProteinScore, a graph transformer approach tailored to improve protein function prediction in OOD settings. ProteinScore integrates a label-invariant variational subgraph generator with self-supervised contrastive learning, thereby identifying meaning substructures within proteins. By highlighting informative features while filtering out redundant ones, ProteinScore improves generalization to diverse and low-homology sequences. Experiments on datasets with both experimentally resolved and AlphaFold2-predicted structures demonstrate that ProteinScore consistently outperforms strong baselines and provides biologically meaningful interpretability through accurately identifying binding sites. In addition, ProteinScore generalizes effectively to two additional downstream tasks, drug-target interaction classification and subcellular localization prediction, achieving superior predictive performance and reliable interpretability.

Depression is a kind of mental illness that is harmful to the development of society. Electroencephalography (EEG) is a promising tool in the area of auxiliary diagnosing diseases. In this paper, we develop a mutual information-based least absolute shrinkage and selection operator (MI-LASSO) model to learn representative features from the power spectral density (PSD) ratio extracted from data. Specifically, MI-LASSO adds an adaptive weight based on mutual information to LASSO, which can discriminate weights of different features. Following the feature selection accomplished by MI-LASSO, the feature set is input into the classifier. We design a stacking ensemble classifier composed of support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN). Compared to independent classifiers, stacking has a stronger ability to recognize depression. The proposed framework is validated on the open datasets: MODMA and the dataset from Hospital Universiti Sains Malaysia (HUSM). The best classification accuracy on MODMA achieved 99.025%. The best classification accuracy on the second dataset achieved 99.06%. The results indicate that our framework outperforms other EEG-based methods in the identification of depression. We conducted several experiments whose results demonstrate our framework can effectively assist in the diagnosis of depression based on EEG.

Nonlinear frequency modulation (NLFM) signal can construct the signal’s power spectral density to reduce sidelobes without loss of signal-to-noise ratio. LuTan-1 (LT-1) is an L-band spaceborne synthetic aperture radar mission which is launched in the beginning of 2022, and a high-precision NLFM signal generator is developed in LT-1. However, the existing model, i.e., the traditional frozen ionosphere model, can not accurately describe ionospheric dispersion effects faced by the NLFM signal due to the non-linear characteristic of the instantaneous frequency. Thus, an extended model is established in this paper to describe ionospheric dispersion effects of the NLFM signal. Then, the differences of ionospheric dispersion effects on the NLFM and linear frequency modulation signals are compared. Afterwards, a method that embedded into the focusing procedure is proposed, which aims to eliminate ionospheric dispersion effects for the NLFM signal. Finally, the hardware-in-the-loop simulations of point targets and distributed targets are performed to verify the proposed method. The method proposed in this paper is used in the ground processing system of LT-1.

One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems.

The ionosphere is a significant source of the phase dispersion on the low frequency radar signal for spaceborne synthesis aperture radar (SAR). LuTan-1 (LT-1) is an innovative spaceborne bistatic SAR (BiSAR) operated in L-band, which will be launch in 2021. Studying the impacts of ionospheric effects on spaceborne single-pass imaging and interferometry is a necessary work to support the ground processing system of LT-1. In this paper, The geometric distortions caused by the ionosphere, including the offsets of the SAR image and Digital Elevation Model, are analyzed firstly. Then the deterioration of the imaging performance caused by the quadratic phase error and the cubical phase error is analyzed, and simulation results of the point target are given to verify. Finally, a framework of ionospheric effects correction for BiSAR is proposed.

The bistatic synthetic aperture radar (BiSAR) plays an important role in remote sensing. However, the deviation between the two oscillators in BiSAR systems will cause a residual modulation of the echo signal. Therefore, the phase synchronization is an important issue that must be addressed in the BiSAR system. An advanced noninterrupted phase synchronization scheme is used for LuTan-1. The synchronization pulses are exchanged immediately after the ending time of the radar echo receiving window and before the starting time of the next pulse repetition interval, which will not interrupt the normal SAR operation. In order to evaluate the accuracy of the phase synchronization scheme, the model of phase synchronization is introduced at first. The hardware design and processing flow of LuTan-1 are introduced in detail. An innovative internal calibration strategy is also described. Then, the test data acquired by the ground validation system are analyzed to verify the effectiveness of the phase synchronization scheme. The signal-to-noise ratio (SNR) and the synchronization rate are the two most important factors to influence the accuracy in phase synchronization. The conclusions have guiding significance for the synchronization module design of LuTan-1 and the future BiSAR system.

Atomic Swap enables two parties to atomically exchange their own cryptocurrencies without trusted third parties. This paper provides the first quantitative analysis on the fairness of the Atomic Swap protocol, and proposes the first fair Atomic Swap protocol with implementations.