Protocol specifications, defined in Request for Comments (RFCs), play a critical role in ensuring the correctness of protocol software systems. To check consistency, specification–implementation pairs are essential for testing and verification. However, existing efforts in specification-to-code mapping remain largely manual and are typically limited to the file level, lacking the fine-grained granularity needed for function-level analysis, which is crucial for effective consistency checking. To address this gap, we present Spec2Code, the first LLM-driven framework that automates fine-grained mapping from protocol specifications to function implementations.Given a RFC document and a protocol codebase, Spec2Code first performs preprocessing to extract structured specification requirements (SRs) and function-level code representations, along with contextual and dependency information. To ensure scalability, Spec2Code employs a two-stage process comprising relevance filtering and clustering-based SR organization to reduce the candidate pairs. For accuracy, Spec2Code performs fine-grained constraint-level matching on each candidate SR–function pair using LLMs, leveraging enriched context to determine whether a function fully, partially, or does not relate to an SR.We evaluate Spec2Code on real-world implementations of HTTP, TLS and BFD protocols, including Apache Httpd, Nginx, OpenSSL, BoringSSL, FRRouting, and BIRD. Experimental results show that Spec2Code outperforms four state-of-the-art baselines, achieving up to 49%, 66%, and 66% improvement in precision, recall, and F1, respectively. Additionally, Spec2Code successfully recovers the mappings for 16 known inconsistency bugs and discovers 11 previously unreported inconsistencies using an integrated lightweight consistency verifier, 5 of which have been confirmed by project developers.

As one of bad smells in code, code clones may increase the cost of software maintenance and the risk of vulnerability propagation. In the past two decades, numerous clone detection technologies have been proposed. They can be divided into text-based, token-based, tree-based, and graph-based approaches according to their code representations. Different code representations abstract the code details from different perspectives. However, it is unclear which code representation is more effective in detecting code clones and how to combine different code representations to achieve ideal performance. In this paper, we present an empirical study to compare the clone detection ability of different code representations. Specifically, we reproduce 12 clone detection algorithms and divide them into different groups according to their code representations. After analyzing the empirical results, we find that token and tree representations can perform better than graph representation when detecting simple code clones. However, when the code complexity of a code pair increases, graph representation becomes more effective. To make our findings more practical, we perform manual analysis on open-source projects to seek a possible distribution of different clone types in the open-source community. Through the results, we observe that most clone pairs belong to simple code clones. Based on this observation, we discard heavyweight graph-based clone detection algorithms and conduct combination experiments to find out a suitable combination of token-based and tree-based approaches for achieving scalable and effective code clone detection. We develop the suitable combination into a tool called TACC and evaluate it with other state-of-the-art code clone detectors. Experimental results indicate that TACC performs better and has the ability to detect large-scale code clones.

To obtain digital elevation model (DEM) data through spaceborne interferometric synthetic aperture radar (InSAR) technology, the solution of nonlinear InSAR geolocation equations is one of the most important steps. On one hand, Newton iterative method is time-consuming to solve the equations. On the other hand, the conventional closed-form solution for low earth orbit (LEO) bistatic InSAR system is not suitable for the novel InSAR configuration of geostationary (GEO) transmitter with LEO receivers because of the significant geometry difference. To address these issues, this letter analyzes the bistatic GEO-LEO geometry in detail and exploits the bistatic InSAR equations to propose a GEO-LEO fast location method (GFLM), which obtains the effective closed-form solution for the novel system. Compared with the general Newton iterative method, the GFLM significantly improves the efficiency of geolocation for the bistatic GEO-LEO InSAR system with high precision. Moreover, the geolocation accuracy of the novel system under realistic parameters uncertainties is introduced. Finally, we carry out simulation experiments to verify the effectiveness and superiority of GFLM.

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.

The bistatic synthetic aperture radar (BiSAR) system consists of a geosynchronous Earth orbit (GEO) illuminator and a low Earth orbit (LEO) receiver. Compared with GEO SAR, it offers great advantages of higher signal-to-noise ratio (SNR) and finer spatial resolution with lower system complexity. The concept also raises significant technical challenges. The spacecraft formation has great effect on radar performance, such as spatial resolution, the angle of two-dimensional (2-D) resolution direction and noise-equivalent sigma zero (NESZ). This paper establishes the relationship between the imaging performance and the formation parameters. Then, a novel design method of spacecraft formation is presented, identifying the principal formation design choices and constraints. Finally, simulation results are provided for typical observational tasks, to verify the effectiveness of the proposed method.

Spacebome bistatic synthetic aperture radar (BiSAR) system with an inclined geosynchronous (GEO) illuminator and a low-earth-orbit (LEO) receiver is capable of providing higher signal-to-noise ratio (SNR), finer resolution and vast area of surveillance. However, spatial-variant characteristics of the echo signal caused by the complex bistatic formation raise technical challenges in data focusing. To address the issue, an improved chirp scaling (CS) algorithm based on time-domain perturbation is proposed. Firstly, the signal model of GEO-LEO bistatic SAR is derived based on the geometry. Then, two-dimension (2-D) perturbation functions are employed to correct the spatial-variant characteristics of the echo signal. Finally, the phase compensation functions are generated to compensate for the residual phase. The proposed algorithm is able to implement wide-swath and high-resolution imaging. Simulation results of point targets show the validity of the presented method.

High-precision DEM can be generated by SAR interferometry processing with GaoFen-3 repeat-pass data. This paper presents the first results from GaoFen-3 SAR Interferometry obtained with two interferometric SAR data pairs at the selected experiment sites. The temporal separations were 29 days. At the site the coherence requirements were met, resulting in high quality interferogram. The experimental results show that GaoFen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, high temporal correlation and spatial correlation, which has a good repeat-pass SAR interferometry application prospect.

Spaceborne bistatic synthetic aperture radar (SAR) system with an inclined geosynchronous (GEO) illuminator and a low-earth-orbit (LEO) receiver is capable of providing a vast area of surveillance and fine spatial resolution, which presents huge potentials for future earth observation. In this letter, inclined GEO-LEO azimuth multichannel SAR (MC-SAR) system for high-resolution wide-swath imaging is investigated. Starting from modeling geometry of inclined GEO-LEO bistatic MC-SAR, the signal model is analyzed in detail for the first time, and the equivalent positions of the received channels are obtained. Then, we found the spatial-variant residual phase error cannot be compensated accurately by conventional effective phase center (EPC) processing. Moreover, because of the complex bistatic configuration, the frequency-domain imaging algorithms become extremely difficult to achieve a well-focused and phase-preserved image. Meanwhile, the time-domain back-projection algorithm (BPA) faces with a technical challenge due to the nonuniform sampling in azimuth. To address these issues, a bistatic weighted BPA (BWBPA) for GEO-LEO MC-SAR is derived and presented. Without the procedure of EPC, the BWBPA can suppress azimuth ambiguities effectively and yield phase-preserved SAR images. Simulated data results show the validity of the presented method.