Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationships. While supervised fine-tuning (SFT) on multimodal large language models achieves strong performance with massive labeled datasets, they struggle in data-scarce scenarios, leading to poor generalization. To address this limitation, we propose Geo-R1, a reasoning-centric reinforcement fine-tuning (RFT) paradigm for few-shot geospatial referring. Geo-R1 enforces the model to first generate explicit, interpretable reasoning chains that decompose referring expressions, and then leverage these rationales to localize target objects. This "reason first, then act" process enables the model to make more effective use of limited annotations, enhances generalization, and provides interpretability. We validate Geo-R1 on three carefully designed few-shot geospatial referring benchmarks, where our model consistently and substantially outperforms SFT baselines. It also demonstrates strong cross-dataset generalization, highlighting its robustness. Code and data will be released at: this https URL.

The Chinese Space Station Survey Telescope (CSST) is an upcoming Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxies and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.

Edge computing that enables satellites to process raw data locally is expected to bring further timeliness and flexibility to satellite-terrestrial networks (STNs). In this letter, we propose a three-layer edge computing protocol, where raw data collected by the satellites can be processed locally, or transmitted to other satellites or the ground station via multi-hop routing for further processing. The overall computing capacity of the proposed framework is maximized by determining the offloading strategy and routing formation, subject to channel capacity and hop constraints. Given that the problem scale grows exponentially with the number of satellites and maximum-allowed hops, the column generation approach is employed to obtain the global optimal solution by activating only a subset of variables. Numerical results reveal that the proposed three-layer computing protocol, when tolerating a 5-hop routing latency, achieves a 60% improvement in computation capacity compared to the single-layer local computing configuration.

This paper introduces an explicit reference governor-based control scheme tailored for addressing the velocity-free spacecraft attitude maneuver problem. This problem is subject to specific constraints, namely the pointing constraint, angular velocity constraint, and input saturation. The proposed control scheme operates in two layers, ensuring the asymptotic stability of the spacecraft's attitude while adhering to the aforementioned constraints. The inner layer employs output feedback control utilizing an angular velocity observer based on immersion and invariance technology. This observer facilitates attitude stabilization without the measurement of angular velocity. Through an analysis of the geometry associated with the pointing constraint, determination of the upper bound of angular velocity, and optimization of the control input solution, the reference layer establishes a safety boundary described by the invariant set. Additionally, we introduce the dynamic factor related to the angular velocity estimation error into the invariant set to prevent states from exceeding the constraint set due to unmeasurable angular velocity information. The shortest guidance path is then designed in the reference layer. Finally, we substantiate the efficacy of the proposed constrained attitude control algorithm through numerical simulations.

Global Navigation Satellite Systems (GNSS) employ inter-satellite links (ISLs) to reduce dependency on ground stations, enabling precise ranging and communication across satellites. Beyond their traditional role, ISLs can support extended applications, including providing navigation and communication services to external entities. However, designing effective contact plan design (CPD) schemes for these multifaceted ISLs, operating under a polling time-division duplex (PTDD) framework, remains a critical challenge. Existing CPD approaches focus solely on meeting GNSS satellites' internal ranging and communication demands, neglecting their extended applications. This paper introduces the first CPD scheme capable of supporting extended GNSS ISLs. By modeling GNSS requirements and designing a tailored service process, our approach ensures the allocation of essential resources for internal operations while accommodating external user demands. Based on the BeiDou constellation, simulation results demonstrate the proposed scheme's efficacy in maintaining core GNSS functionality while providing extended ISLs on a best-effort basis. Additionally, the results highlight the significant impact of GNSS ISLs in enhancing orbit determination and clock synchronization for the Earth-Moon libration point constellation, underscoring the importance of extended GNSS ISL applications.

This paper proposes an Adaptive Basis-inspired Deep Neural Network (ABI-DNN) for solving partial differential equations with localized phenomena such as sharp gradients and singularities. Like the adaptive finite element method, ABI-DNN incorporates an iteration of "solve, estimate, mark, enhancement", which automatically identifies challenging regions and adds new neurons to enhance its capability. A key challenge is to force new neurons to focus on identified regions with limited understanding of their roles in approximation. To address this, we draw inspiration from the finite element basis function and construct the novel Basis-inspired Block (BI-block), to help understand the contribution of each block. With the help of the BI-block and the famous Kolmogorov Superposition Theorem, we first develop a novel fixed network architecture named the Basis-inspired Deep Neural Network (BI-DNN), and then integrate it into the aforementioned adaptive framework to propose the ABI-DNN. Extensive numerical experiments demonstrate that both BI-DNN and ABI-DNN can effectively capture the challenging singularities in target functions. Compared to PINN, BI-DNN attains significantly lower relative errors with a similar number of trainable parameters. When a specified tolerance is set, ABI-DNN can adaptively learn an appropriate architecture that achieves an error comparable to that of BI-DNN with the same structure.

Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency. However, fast and reliable radio map prediction can be very challenging due to the complicated interaction between radio waves and the environment. In this paper, a novel Transformer based deep learning model termed as RadioNet is proposed for radio map prediction in urban scenarios. In addition, a novel Grid Embedding technique is proposed to substitute the original Position Embedding in Transformer to better anchor the relative position of the radiation source, destination and environment. The effectiveness of proposed method is verified on an urban radio wave propagation dataset. Compared with the SOTA model on RMP task, RadioNet reduces the validation loss by 27.3\%, improves the prediction reliability from 90.9\% to 98.9\%. The prediction speed is increased by 4 orders of magnitude, when compared with ray-tracing based method. We believe that the proposed method will be beneficial to high-efficiency wireless communication, real-time radio visualization, and even high-speed image rendering.

We present the results from a spectroscopic monitoring campaign to obtain reverberation-mapping measurements and investigate the broad-line region kinematics for active galactic nuclei (AGN) of Mrk~817 and NGC~7469. This campaign was undertaken with the Lijiang 2.4-meter telescope, the median spectroscopic sampling is 2.0 days for Mrk~817 and 1.0 days for NGC~7469. We detect time lags of the broad emission lines including H$\beta$, H$\gamma$, He~{\sc ii} and He~{\sc i} for both AGNs, and including Fe~{\sc ii} for Mrk~817 with respect to the varying AGN continuum at 5100~Å. Investigating the relationship between line widths and time lags of the broad emission lines, we find that the BLR dynamics of Mrk~817 and NGC~7469 are consistent with the virial prediction. We estimate the masses of central supermassive black hole (SMBH) and the accretion rates of both AGNs. Using the data of this campaign, we construct the velocity-resolved lag profiles of the broad H$\gamma$, H$\beta$, and He~{\sc i} lines for Mrk~817, which show almost the same kinematic signatures that the time lags in the red wing are slightly larger than the time lags in the blue wing. For NGC~7469, we only clearly construct the velocity-resolved lag profiles of the broad H$\gamma$ and H$\beta$, which show very similar kinematic signatures to the BLR of Mrk~817. These signatures indicate that the BLR of Keplerian motion in both AGNs seemingly has outflowing components during the monitoring period. We discuss the kinematics of the BLR and the measurements including SMBH mass and accretion rates.

Monocular pose estimation of non-cooperative spacecraft is significant for on-orbit service (OOS) tasks, such as satellite maintenance, space debris removal, and station assembly. Considering the high demands on pose estimation accuracy, mainstream monocular pose estimation methods typically consist of keypoint detectors and PnP solver. However, current keypoint detectors remain vulnerable to structural symmetry and partial occlusion of non-cooperative spacecraft. To this end, we propose a graph-based keypoints network for the monocular pose estimation of non-cooperative spacecraft, GKNet, which leverages the geometric constraint of keypoints graph. In order to better validate keypoint detectors, we present a moderate-scale dataset for the spacecraft keypoint detection, named SKD, which consists of 3 spacecraft targets, 90,000 simulated images, and corresponding high-precise keypoint annotations. Extensive experiments and an ablation study have demonstrated the high accuracy and effectiveness of our GKNet, compared to the state-of-the-art spacecraft keypoint detectors. The code for GKNet and the SKD dataset is available at this https URL.

Target tracking entails the estimation of the evolution of the target state over time, namely the target trajectory. Different from the classical state space model, our series of studies, including this paper, model the collection of the target state as a stochastic process (SP) that is further decomposed into a deterministic part which represents the trend of the trajectory and a residual SP representing the residual fitting error. Subsequently, the tracking problem is formulated as a learning problem regarding the trajectory SP for which a key part is to estimate a trajectory FoT (T-FoT) best fitting the measurements in time series. For this purpose, we consider the polynomial T-FoT and address the regularized polynomial T-FoT optimization employing two distinct regularization strategies seeking trade-off between the accuracy and simplicity. One limits the order of the polynomial and then the best choice is determined by grid searching in a narrow, bounded range while the other adopts $\ell_0$ norm regularization for which the hybrid Newton solver is employed. Simulation results obtained in both single and multiple maneuvering target scenarios demonstrate the effectiveness of our approaches.

This paper presents SNK, a novel simulation platform designed to evaluate the network performance of constellation systems for global Internet services. SNK offers realtime communication visualization and supports the simulation of routing between edge node of network. The platform enables the evaluation of routing and network performance metrics such as latency, stretch, network capacity, and throughput under different network structures and density. The effectiveness of SNK is demonstrated through various simulation cases, including the routing between fixed edge stations or mobile edge stations and analysis of space network structures.

Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which lead to severe mispredictions of symmetrical parts. Besides, point cloud noise disrupts consistent representations for point cloud and thus degrades the shape correspondence accuracy. To address the above issues, we propose a Self-Ensembling ORientation-aware Network termed SE-ORNet. The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts. Additionally, we design a selfensembling framework for unsupervised point cloud shape correspondence. In this framework, the disturbances of point cloud noise are overcome by perturbing the inputs of the student and teacher networks with different data augmentations and constraining the consistency of predictions. Extensive experiments on both human and animal datasets show that our SE-ORNet can surpass state-of-the-art unsupervised point cloud shape correspondence methods.

A novel photonics-based RF reception approach is proposed as a competitive solution to meet the current challenges of photonic-based approaches and to realize high performances at the same time. The proposed approach adopts the superheterodyne configuration by a combination manner of electronic techniques and photonic techniques, including the ultrawideband generation of optical LO, the two-stage photonic superheterodyne frequency conversion and the real-time IF compensation. An engineering prototype has been developed and its performance has been evaluated in the laboratory environment. The experiment results preliminarily verify the feasibility of the proposed approach and its engineering potential. The typical performances are as follows: 0.1 GHz~ 45GHz operation spectrum range (>40 GHz), 900 MHz instantaneous bandwidth, 101 dBHz2/3 SFDR and 130 dBHz LDR, image rejections of ~80 dB for 1st frequency conversion and >90 dB for 2nd frequency conversion.

Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained to learn the ability of handling classification tasks on extremely large or infinite episodes representing different classification task, each with a small labeled support set and its corresponding query set. In this work, we advance this few-shot classification paradigm by formulating it as a supervised classification learning problem. We further propose multi-episode and cross-way training techniques, which respectively correspond to the minibatch and pretraining in classification problems. Experimental results on a state-of-the-art few-shot classification method (prototypical networks) demonstrate that both the proposed training strategies can highly accelerate the training process without accuracy loss for varying few-shot classification problems on Omniglot and miniImageNet.

IP-based software design is a crucial research field that aims to improve efficiency and reliability by reusing complex software components known as intellectual property (IP) components. To ensure the reusability of these components, particularly in security-sensitive software systems, it is necessary to analyze the requirements and perform formal verification for each IP component. However, converting the requirements of IP components from natural language descriptions to temporal logic and subsequently conducting formal verification demands domain expertise and non-trivial manpower. This paper presents a case study on software IP components derived from aerospace embedded systems, with the objective of automating the requirement analysis and verification process. The study begins by employing Large Language Models to convert unstructured natural language into formal specifications. Subsequently, three distinct verification techniques are employed to ascertain whether the source code meets the extracted temporal logic properties. By doing so, five real-world IP components from the China Academy of Space Technology (CAST) have been successfully verified.

Cislunar space is emerging as a critical domain for human exploration, requiring robust infrastructure to support spatial users-spacecraft with navigation and communication demands. Deploying satellites at Earth-Moon three-body orbits offers an effective solution to construct cislunar space infrastructure (CLSI). However, scheduling satellite links to serve users necessitates an appropriate contact plan design (CPD) scheme. Existing CPD schemes focus solely on inter-satellite link scheduling, overlooking their role in providing services to users. This paper introduces a CPD scheme that considers two classes of satellite transponders: Reflector Links (RL) for high-volume data transfers and Phased Array Links (PL) for fast switching and navigation services. Our approach supports both satellites and spatial users in cislunar space. Simulations validate the scheme, demonstrating effective support for user while meeting satellite ranging and communication requirements. These findings provide essential insights for developing future Cislunar Space Infrastructures.

In this work, we investigate the rotational dynamics of the ginger-shaped near-Earth asteroid 4179 Toutatis, which was closely observed by Chang'e-2 at a distance of $770\pm120~$ meters from the asteroid's surface during the outbound flyby \citep{Huang2013} on 13 December 2012. A sequence of high-resolution images was acquired during the flyby mission. In combination with ground-based radar observations collected over the last two decades, we analyze these flyby images and determine the orientation of the asteroid at the flyby epoch. The 3-1-3 Euler angles of the conversion matrix from the J2000 ecliptic coordinate system to the body-fixed frame are evaluated to be $-20.1^\circ\pm1^\circ$, $27.6^\circ\pm1^\circ$ and $42.2^\circ\pm1^\circ$, respectively. The least-squares method is utilized to determine the rotational parameters and spin state of Toutatis. The characteristics of the spin-state parameters and angular momentum variations are extensively studied using numerical simulations, which confirm those reported by \citet{Takahashi2013}. The large amplitude of Toutatis' precession is assumed to be responsible for its tumbling attitude as observed from Earth. Toutatis' angular momentum orientation is determined to be described by $\lambda_{H}=180.2^{+0.2^\circ}_{-0.3^\circ}$ and $\beta_{H}=-54.75^{+0.15^\circ}_{-0.10^\circ}$, implying that it has remained nearly unchanged for two decades. Furthermore, using Fourier analysis to explore the change in the orientation of Toutatis' axes, we reveal that the two rotational periods are 5.38 and 7.40 days, respectively, consistent with the results of the former investigation. Hence, our investigation provides a clear understanding of the state of the rotational dynamics of Toutatis.