Introduction to Drone Detection Radar with Emphasis on Automatic Target Recognition (ATR) technology

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need to consider kinetic features and signal signatures, such as micro-Doppler, in ATR techniques to efficiently recognize small drones. The authors also present a comprehensive drone detection radar system design that balances detection and tracking requirements, incorporating parameter adjustment based on scattering region theory. They offer an example of a performance improvement achieved using feedback and situational awareness mechanisms with the integrated ATR capabilities. Furthermore, the paper examines challenges related to one-way attack drones and explores the potential of cognitive radar as a solution. The integration of ATR capabilities transforms a 3D radar system into a 4D radar system, resulting in improved drone detection performance. These advancements are useful in military, civilian, and commercial applications, and ongoing research and development efforts are essential to keep radar systems effective and ready to detect, track, and respond to emerging threats.Comment: 17 pages, 14 figures, submitted to a journal and being under revie

An introduction to radar Automatic Target Recognition (ATR) technology in ground-based radar systems

This paper presents a brief examination of Automatic Target Recognition (ATR) technology within ground-based radar systems. It offers a lucid comprehension of the ATR concept, delves into its historical milestones, and categorizes ATR methods according to different scattering regions. By incorporating ATR solutions into radar systems, this study demonstrates the expansion of radar detection ranges and the enhancement of tracking capabilities, leading to superior situational awareness. Drawing insights from the Russo-Ukrainian War, the paper highlights three pressing radar applications that urgently necessitate ATR technology: detecting stealth aircraft, countering small drones, and implementing anti-jamming measures. Anticipating the next wave of radar ATR research, the study predicts a surge in cognitive radar and machine learning (ML)-driven algorithms. These emerging methodologies aspire to confront challenges associated with system adaptation, real-time recognition, and environmental adaptability. Ultimately, ATR stands poised to revolutionize conventional radar systems, ushering in an era of 4D sensing capabilities

NSs, the Silencing Suppressor of Tomato Spotted Wilt Orthotospovirus, Interferes with JA-Regulated Host Terpenoids Expression to Attract \u3cem\u3eFrankliniella occidentalis\u3c/em\u3e

Tomato spotted wilt orthotospovirus (TSWV) causes serious crop losses worldwide and is transmitted by Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae). NSs protein is the silencing suppressor of TSWV and plays an important role in virus infection, cycling, and transmission process. In this research, we investigated the influences of NSs protein on the interaction of TSWV, plants, and F. occidentalis with the transgenic Arabidopsis thaliana. Compared with the wild-type Col-0 plant, F. occidentalis showed an increased number and induced feeding behavior on transgenic Arabidopsis thaliana expressing exogenous NSs. Further analysis showed that NSs reduced the expression of terpenoids synthesis-related genes and the content of monoterpene volatiles in Arabidopsis. These monoterpene volatiles played a repellent role in respect to F. occidentalis. In addition, the expression level of plant immune-related genes and the content of the plant resistance hormone jasmonic acid (JA) in transgenic Arabidopsis were reduced. The silencing suppressor of TSWV NSs alters the emission of plant volatiles and reduces the JA-regulated plant defenses, resulting in enhanced attractiveness of plants to F. occidentalis and may increase the transmission probability of TSWV

The genome evolution and domestication of tropical fruit mango

Background: Mango is one of the world’s most important tropical fruits. It belongs to the family Anacardiaceae, which includes several other economically important species, notably cashew, sumac and pistachio from other genera. Many species in this family produce family-specific urushiols and related phenols, which can induce contact dermatitis. Results: We generate a chromosome-scale genome assembly of mango, providing a reference genome for the Anacardiaceae family. Our results indicate the occurrence of a recent whole-genome duplication (WGD) event in mango. Duplicated genes preferentially retained include photosynthetic, photorespiration, and lipid metabolic genes that may have provided adaptive advantages to sharp historical decreases in atmospheric carbon dioxide and global temperatures. A notable example of an extended gene family is the chalcone synthase (CHS) family of genes, and particular genes in this family show universally higher expression in peels than in flesh, likely for the biosynthesis of urushiols and related phenols. Genome resequencing reveals two distinct groups of mango varieties, with commercial varieties clustered with India germplasms and demonstrating allelic admixture, and indigenous varieties from Southeast Asia in the second group. Landraces indigenous in China formed distinct clades, and some showed admixture in genomes. Conclusions: Analysis of chromosome-scale mango genome sequences reveals photosynthesis and lipid metabolism are preferentially retained after a recent WGD event, and expansion of CHS genes is likely associated with urushiol biosynthesis in mango. Genome resequencing clarifies two groups of mango varieties, discovers allelic admixture in commercial varieties, and shows distinct genetic background of landraces

The genome evolution and domestication of tropical fruit mango

Background: Mango is one of the world’s most important tropical fruits. It belongs to the family Anacardiaceae, which includes several other economically important species, notably cashew, sumac and pistachio from other genera. Many species in this family produce family-specific urushiols and related phenols, which can induce contact dermatitis. Results: We generate a chromosome-scale genome assembly of mango, providing a reference genome for the Anacardiaceae family. Our results indicate the occurrence of a recent whole-genome duplication (WGD) event in mango. Duplicated genes preferentially retained include photosynthetic, photorespiration, and lipid metabolic genes that may have provided adaptive advantages to sharp historical decreases in atmospheric carbon dioxide and global temperatures. A notable example of an extended gene family is the chalcone synthase (CHS) family of genes, and particular genes in this family show universally higher expression in peels than in flesh, likely for the biosynthesis of urushiols and related phenols. Genome resequencing reveals two distinct groups of mango varieties, with commercial varieties clustered with India germplasms and demonstrating allelic admixture, and indigenous varieties from Southeast Asia in the second group. Landraces indigenous in China formed distinct clades, and some showed admixture in genomes. Conclusions: Analysis of chromosome-scale mango genome sequences reveals photosynthesis and lipid metabolism are preferentially retained after a recent WGD event, and expansion of CHS genes is likely associated with urushiol biosynthesis in mango. Genome resequencing clarifies two groups of mango varieties, discovers allelic admixture in commercial varieties, and shows distinct genetic background of landraces

Urbanization and air quality as major drivers of altered spatiotemporal patterns of heavy rainfall in China

Context Land use/land cover change and other human activities contribute to the changing climate on regional and global scales, including the increasing occurrence of extreme precipitation events, but the relative importance of these anthropogenic factors, as compared to climatic factors, remains unclear. Objectives The main goal of this study was to determine the relative contributions of human-induced and climatic factors to the altered spatiotemporal patterns of heavy rainfall in China during the past several decades. Methods We used daily precipitation data from 659 meteorological stations in China from 1951 to 2010, climatic factors, and anthropogenic data to identify possible causes of the observed spatiotemporal patterns of heavy rainfall in China in the past several decades, and quantify the relative contributions between climatic and human-induced factors.This research was supported by the 973 Project ‘‘National Key Research and Development Program– Global Change and Mitigation Project: Global change risk of population and economic system: mechanisms and assessments’’ under Grant No. 201531480029, Ministry of Science and Technology of China, People’s Republic of China, the National Natural Science Foundation of Innovative Research Group Project ‘‘Earth Surface Process Model and Simulation’’ under Grant No. 41621061

Generation of ESTs for Flowering Gene Discovery and SSR Marker Development in Upland Cotton

BACKGROUND: Upland cotton, Gossypium hirsutum L., is one of the world's most important economic crops. In the absence of the entire genomic sequence, a large number of expressed sequence tag (EST) resources of upland cotton have been generated and used in several studies. However, information about the flower development of this species is rare. METHODOLOGY/PRINCIPAL FINDINGS: To clarify the molecular mechanism of flower development in upland cotton, 22,915 high-quality ESTs were generated and assembled into 14,373 unique sequences consisting of 4,563 contigs and 9,810 singletons from a normalized and full-length cDNA library constructed from pooled RNA isolated from shoot apexes, squares, and flowers. Comparative analysis indicated that 5,352 unique sequences had no high-degree matches to the cotton public database. Functional annotation showed that several upland cotton homologs with flowering-related genes were identified in our library. The majority of these genes were specifically expressed in flowering-related tissues. Three GhSEP (G. hirsutum L. SEPALLATA) genes determining floral organ development were cloned, and quantitative real-time PCR (qRT-PCR) revealed that these genes were expressed preferentially in squares or flowers. Furthermore, 670 new putative microsatellites with flanking sequences sufficient for primer design were identified from the 645 unigenes. Twenty-five EST-simple sequence repeats were randomly selected for validation and transferability testing in 17 Gossypium species. Of these, 23 were identified as true-to-type simple sequence repeat loci and were highly transferable among Gossypium species. CONCLUSIONS/SIGNIFICANCE: A high-quality, normalized, full-length cDNA library with a total of 14,373 unique ESTs was generated to provide sequence information for gene discovery and marker development related to upland cotton flower development. These EST resources form a valuable foundation for gene expression profiling analysis, functional analysis of newly discovered genes, genetic linkage, and quantitative trait loci analysis

Life Cycle Building Carbon Emissions Assessment and Driving Factors Decomposition Analysis Based on LMDI—A Case Study of Wuhan City in China

Carbon emissions calculation at the sub-provincial level has issues in limited data and non-unified measurements. This paper calculated the life cycle energy consumption and carbon emissions of the building industry in Wuhan, China. The findings showed that the proportion of carbon emissions in the construction operation phase was the largest, followed by the carbon emissions of the indirect energy consumption and the construction material preparation phase. With the purpose of analyzing the contributors of the construction carbon emissions, this paper conducted decomposition analysis using Logarithmic Mean Divisia Index (LMDI). The results indicated that the increasing buidling area was the major driver of energy consumption and carbon emissions increase, followed by the behavior factor. Population growth and urbanization, to some extent, increased the carbon emissions as well. On the contrary, energy efficiency was the main inhibitory factor for reducing the carbon emissions. Policy implications in terms of low-carbon construction development were highlighted

Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber

Traditional radar calibration methods often use one standard sphere as the standard calibration object, but researchers seldom discuss the size and the material content of the calibration sphere. We propose using a pair of different-sized spheres to verify the standard method. In a microwave anechoic chamber, the simulated and measured results show that the radar cross-section (RCS) values of two spheres with radii of 0.15 m and 0.05 m in the Mie region differ from their physical cross-sections in the optic region, and only in the optic region, their difference of RCS value (i.e., 9.5424 dB) can be approximately equal to the theoretical one (i.e., 9.6803 dB). Thus, radar calibration should be conducted in the same scattering region for both the calibration object and the calibrated target. The use of two different-sized spheres can aid in three applications: (1) verifying the scattering regions, (2) searching the pure area in the microwave anechoic chamber, and (3) locating the positions of the targets in the range profiles

Using Classify-While-Scan (CWS) Technology to Enhance Unmanned Air Traffic Management (UTM)

Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS recognizes the radar data of each radar cell in the radar beam using advanced automatic target recognition (ATR) algorithm and then integrates the recognized results into the tracking unit to obtain the real-time situational awareness results of the whole surveillance area. Real X-band radar data collected in a coastal environment demonstrate significant advancement in a powerful situational awareness scenario in which birds were chasing a ship to feed on fish. CWS technology turns a drone detection radar into a sense-and-alert planform that revolutionizes UTM systems by reducing the Detection Response Time (DRT) in the detection unit