Search Results (9)

The continuous advancement of fire protection technologies has necessitated the development of comprehensive safety standards, leading to an increasingly diversified and specialized regulatory landscape. This has made it difficult for fire protection professionals to quickly and accurately locate the required fire safety standard information. In addition, the lack of effective integration and knowledge organization concerning fire safety standard entities has led to the severe fragmentation of fire safety standard information and the absence of a comprehensive “one map”. To address this challenge, we introduce FT-FLAT, an innovative CNN–Transformer fusion architecture designed specifically for fire safety standard entity extraction. Unlike traditional methods that rely on rules or single-modality deep learning, our approach integrates TextCNN for local feature extraction and combines it with the Flat-Lattice Transformer for global dependency modeling. The key innovations include the following. (1) Relative Position Embedding (RPE) dynamically encodes the positional relationships between spans in fire safety texts, addressing the limitations of absolute positional encoding in hierarchical structures. (2) The Multi-Branch Prediction Head (MBPH) aggregates the outputs of TextCNN and the Transformer using Einstein summation, enhancing the feature learning capabilities and improving the robustness for domain-specific terminology. (3) Experiments conducted on the newly annotated Fire Safety Standard Entity Recognition Dataset (FSSERD) demonstrate state-of-the-art performance (94.24% accuracy, 83.20% precision). This work provides a scalable solution for constructing fire safety knowledge graphs and supports intelligent information retrieval in emergency situations. Full article

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing. Full article

After finding a solution for the Hayward regular black hole (HRBH) in massive gravity, we embed the (3+1)-dimensional HRBHs both in massless and in massive gravities into (5+2)- and (6+3)-dimensional Minkowski spacetimes, respectively. Here, massive gravity denotes that a graviton acquires a mass holographically by broken momentum conservation in the HRBH. The original HRBH has no holographically added gravitons, which we call ‘massless’. Making use of newly found embedding coordinates, we obtain desired Unruh temperatures and compare them with the Hawking and local fiducial temperatures, showing that the Unruh effect for a uniformly accelerated observer in a higher-dimensional flat spacetime is equal to the Hawking effect for a fiducial observer in a black hole spacetime. We also obtain freely falling temperatures of the HRBHs in massless and massive gravities seen by freely falling observers, which remain finite even at the event horizons while becoming the Hawking temperatures in asymptotic infinity. Full article

With the development of the air traffic management system (ATM), the cyber threat for ATM is becoming more and more serious. The recognition of ATM cyber threat entities is an important task, which can help ATM security experts quickly and accurately recognize threat entities, providing data support for the later construction of knowledge graphs, and ensuring the security and stability of ATM. The entity recognition methods are mainly based on traditional machine learning in a period of time; however, the methods have problems such as low recall and low accuracy. Moreover, in recent years, the rise of deep learning technology has provided new ideas and methods for ATM cyber threat entity recognition. Alternatively, in the convolutional neural network (CNN), the convolution operation can efficiently extract the local features, while it is difficult to capture the global representation information. In Transformer, the attention mechanism can capture feature dependencies over long distances, while it usually ignores the details of local features. To solve these problems, a TextCNN-Flat-Lattice Transformer (TCFLTformer) with CNN-Transformer hybrid architecture is proposed for ATM cyber threat entity recognition, in which a relative positional embedding (RPE) is designed to encode position text content information, and a multibranch prediction head (MBPH) is utilized to enhance deep feature learning. TCFLTformer first uses CNN to carry out convolution and pooling operations on the text to extract local features and then uses a Flat-Lattice Transformer to learn temporal and relative positional characteristics of the text to obtain the final annotation results. Experimental results show that this method has achieved better results in the task of ATM cyber threat entity recognition, and it has high practical value and theoretical contribution. Besides, the proposed method expands the research field of ATM cyber threat entity recognition, and the research results can also provide references for other text classification and sequence annotation tasks. Full article

Measuring speed is a critical factor to reduce motion artifacts for dynamic scene capture. Phase-shifting methods have the advantage of providing high-accuracy and dense 3D point clouds, but the phase unwrapping process affects the measurement speed. This paper presents an absolute phase unwrapping method capable of using only three speckle-embedded phase-shifted patterns for high-speed three-dimensional (3D) shape measurement on a single-camera, single-projector structured light system. The proposed method obtains the wrapped phase of the object from the speckle-embedded three-step phase-shifted patterns. Next, it utilizes the Semi-Global Matching (SGM) algorithm to establish the coarse correspondence between the image of the object with the embedded speckle pattern and the pre-obtained image of a flat surface with the same embedded speckle pattern. Then, a computational framework uses the coarse correspondence information to determine the fringe order pixel by pixel. The experimental results demonstrated that the proposed method can achieve high-speed and high-quality 3D measurements of complex scenes. Full article

The flat slab-flanged wall (FSFW) coupled system has gained popularity in recent years; however, its seismic performance remains an issue, as beams and columns in it are commonly eliminated. To tackle this problem, embedding concrete-filled steel tubes (CFSTs) in wall piers has been proposed to strengthen the system; the viability of this approach has been verified at the member level. Along this line, this study embarks on a shaking table testing of a 1/8-scale five-story FSFW structure equipped with CFSTs in walls, with an aim to understand the overall seismic behavior of such an enhanced system. As with the practice in many countries, the plan layout of the test structure consisted of four rows of wall piers, thus presenting a ‘fish-bone’ floor configuration that relied only upon the walls to resist gravity and lateral loads. The structure was subjected to a suite of input ground motions along with white-noise excitations. By so doing, its damage progression, pattern and dynamic characteristics were clearly identified. Furthermore, a non-linear time history analysis was conducted using PERFORM-3D, and the goodness-of-fit of the computed responses to the experimental records was examined. Findings indicated that the application of CFSTs was instrumental in resisting the simulated earthquake loads acting on the FSFW system, hence the global response limits required by codes of practice were met, even in the case of extremely strong earthquakes. Nevertheless, the junction between the shear walls and floor slabs was found to be the weakest links in the whole system. Designers are thus cautioned to implement proper detailing in those regions to prevent local distress, though it did not appear to acutely impair the system’s collapse-resisting capacity. Full article

Making use of the higher dimensional global embedding Minkowski spacetime (GEMS), we embed (3 + 1)-dimensional Schwarzschild and Reissner-Nordström (RN) black holes written by the Painlevé-Gullstrand (PG) spacetimes, which have off-diagonal components in metrics, into (5 + 1)- and (5 + 2)-dimensional flat ones, respectively. As a result, we have shown the equivalence of the GEMS embeddings of the spacetimes with the diagonal and off-diagonal terms in metrics. Moreover, with the aid of their geodesic equations satisfying various boundary conditions in the flat embedded spacetimes, we directly obtain freely falling temperatures. We also show that freely falling temperatures in the PG spacetimes are well-defined beyond the event horizons, while they are equivalent to the Hawking temperatures, which are obtained in the original curved ones in the ranges between the horizon and the infinity. These will be helpful to study GEMS embeddings of more realistic Kerr, or rotating BTZ black holes. Full article

A dual-frequency all-in-one Global Navigation Satellite System (GNSS) receiver with a multi-core 32-bit RISC (reduced instruction set computing) application processor was integrated and manufactured as a System-on-Chip (SoC) in a 110 nm CMOS (complementary metal-oxide semiconductor) process. The GNSS RF (radio frequency) front-end with baseband navigation engine is able to receive, simultaneously, Galileo (European Global Satellite Navigation System) E1/E5ab, GPS (US Global Positioning System) L1/L1C/L5, BeiDou (Chinese Navigation Satellite System) B1/B2, GLONASS (GLObal NAvigation Satellite System of Russian Government) L1/L3/L5, QZSS (Quasi-Zenith Satellite System development by the Japanese government) L1/L5 and IRNSS (Indian Regional Navigation Satellite System) L5, as well as all SBAS (Satellite Based Augmentation System) signals. The ability of the GNSS to detect such a broad range of signals allows for high-accuracy positioning. The whole SoC (system-on-chip), which is connected to a small passive antenna, provides precise position, velocity and time or raw GNSS data for hybridization with the IMU (inertial measurement unit) without the need for an external application processor. Additionally, user application can be executed directly in the SoC. It works in the −40 to +105 °C temperature range with a 1.5 V supply. The assembled test-chip takes 100 pins in a QFN (quad-flat no-leads) package and needs only a quartz crystal for the on-chip reference clock driver and optional SAW (surface acoustic wave) filters. The radio performance for both wideband (52 MHz) channels centered at L1/E1 and L5/E5 is NF = 2.3 dB, G = 131 dB, with 121 dBc/Hz of phase noise @ 1 MHz offset from the carrier, consumes 35 mW and occupies a 4.5 mm² silicon area. The SoC reported in the paper is the first ever dual-frequency single-chip GNSS receiver equipped with a multi-core application microcontroller integrated with embedded flash memory for the user application program. Full article

We study the problem of construction of global isometric embedding for spherically symmetric black holes with negative cosmological constant in various dimensions. Firstly, we show that there is no such embedding for 4D RN-AdS black hole in 6D flat ambient space, completing the classification which we started earlier. Then we construct an explicit embedding of non-spinning BTZ black hole in 6D flat ambient space. Using this embedding as an anzats, we then construct a global explicit embedding of d-dimensional Schwarzschild-AdS black hole in a flat $(d+3)$ -dimensional ambient space. Full article