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Abstract The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published.

Physical layer secret key generation is a novel research direction that has attracted many researchers in recent years due to its low computational capacity and ability to obtain information-theoretic security. In this paper, we propose two methods based on the modulus and the real and imaginary parts of the channel impulse response (CIR) matrix to generate a secret key between two legitimate devices in massive Multiple-Input Multiple-Output (MIMO) wireless communication systems. For these methods, the legitimate devices first estimate the channel by using a probing signal to find their CIRs. Next, the CIR matrices are transformed into single-row vectors. The first method uses a simple quantization algorithm to convert the modulus of the complex components of the vector into the final key bit sequence. The second method uses the same quantization algorithm to transform the highest points of the real and imaginary parts into two sequences. The XOR operation between these two sequences generates the final key sequence. The key samples with different lengths generated from the system can be assessed by the most crucial performance metric of randomness according to the National Institute of Standards and Technology (NIST) version 800-22REV1A. The numerical results show that the first method has a higher pass rate and more randomness than the second method. Moreover, our proposed methods are superior to existing methods.

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions.

Nowadays, laser cutting technology stands as one of the most cutting-edge technologies. Not only do laser cutting machines play a crucial role in the mechanical engineering industry, but also in numerous other industries including electronic circuit manufacturing, garment manufacturing, and particularly in the handicraft industry. This study presents a methodology for the design and production of laser cutting machines in Vietnam. A laser cutting machine has been successfully constructed, featuring a straightforward configuration, user-friendly operation, and low cost. The machine has the ability to perform laser cutting on wooden materials, achieving a cutting depth ranging from 1 to 2 mm. The proposed laser cutting machine prototype can cut at a speed of 1000 mm/min, thus meeting the specified requirements.

The present paper investigates the impact of pairing correlation on the nuclear level density (NLD) and radiative strength function (RSF) of an odd-odd and rare-earth 166Ho nucleus. By employing the exact thermal pairing (EP) solution in conjunction with both the temperature-dependent independent-particle model (EP+IPM) for the NLD and the phonon damping model (EP+PDM) for the RSF, we achieve good agreement with the experimental NLD and RSF data, which have been recently extracted by using the Oslo method. Notably, without adding any extra parameters, our calculations successfully reproduce the pygmy dipole resonances (PDR) and a portion of the strength around the region of scissor resonance (SR) in the RSF. This suggests that the SR in this nucleus may be less pronounced than that obtained in the previous analysis within the phenomenological model. These features are the consequences of the EP and the couplings of the PDM phonon to all the particle-hole (ph), particle-particle (pp), and hole-hole (hh) configurations within the EP+PDM, thus insisting on the importance of the underlying physics involved in this model. Utilizing the obtained NLD and RSF, we predict the 165Ho(n,γ)166Ho and Maxwellian-averaged cross sections, which exhibit remarkable agreement with all the available experimental data from the Experimental Nuclear Reaction Data and Karlsruhe Astrophysical Database of Nucleosynthesis in Stars. These results reflect the imprint of the pairing correlation on those quantities.

This paper shows the observation and estimation of convective wind gusts by different satellite sensors at the C-band (Sentinel-1 SAR) and L-band (ALOS-1 SAR and SMAP radiometer) over Lake Victoria, the Gulf of Guinea, and the Gulf of Mexico. These areas are significantly impacted by deep convection associated with strong surface winds and heavy rainfall. In particular, the collocation of Sentinel-1 and SMAP images enables the observation of the movement of surface wind gusts corresponding to that of deep convective clouds. The convective wind intensity estimated from Sentinel-1 and SMAP data varies from 10 to 25 m/s. Additionally, we present an agreement in the observation of deep convective clouds, dynamics, and strong surface winds by different satellite sensors, including Meteosat geostationary (GEO), Aeolus Lidar, and Sentinel-1 SAR, respectively. We also evaluate the estimated convective wind gusts by comparison with the in situ measurements of the weather stations installed in the Gulf of Mexico, where deep convection occurs regularly. The result shows an agreement between the two wind sources estimated and measured. Likewise, the peaks of the measured wind gusts correspond to the occurrence of deep convective clouds observed by the GOES-16 GEO satellite.

How do universities encourage academics to buy into a shared vision while often setting punitive targets in teaching and research? This article explores possible antecedents of a university's shared vision and its relationships with academics' research and teaching performance in the era of managerialism. This cross-country study of two large universities in the UK and Vietnam draws on data from multiple sources to uncover the key components of a university's shared vision. A survey strategy was adopted. Data were collected from different sources, using a stratified random sampling technique from academics of different schools at those universities. A total of 431 survey responses from academics at these universities were included for analysis, employing structure equation modelling. It provides fresh insights into whether having a shared vision can benefit academics' research and teaching performance. The findings of this study show that while achieving a high degree of shared vision may enhance research performance, it may do little to improve teaching performance. The study provides empirical evidence indicating that a shared vision emerges as strongly rooted within individual employees rather than managers, challenging the common belief that a shared vision emanates primarily from the top down. This article advances social exchange theory (SET) by showing the interdependence of workplace antecedents, personal attributes, interpersonal connections, and performance. It introduces a framework for the relationship between universities' shared vision with its possible antecedents and with academics' teaching performance and research performance. The article also discusses useful implications for higher education leaders, based on the findings of the study.

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions. [ABSTRACT FROM AUTHOR]

The selection of adsorbents is critical for developing an adsorption unit. In this study, a activated carbon fibers (ACF) and a granular‐activated carbon (GAC) were evaluated in dynamic toluene adsorption to determine their benefits and limitations. A variety of physicochemical approaches were used to characterize the samples. Adsorption under varied circumstances demonstrated that ACF has a larger adsorption capacity and a longer saturation time than GAC. The Langmuir isotherm suited equilibrium data well. Thermodynamic characteristics showed that adsorption was spontaneous and exothermic. The adsorption kinetics were found to be dominated by the pseudo‐first‐order model, with GAC having a greater sorption rate. Thermal regeneration appeared to be more favorable for ACF. [ABSTRACT FROM AUTHOR]

The research investigated changes in surface water quality (SWQ) in the canals and rivers of Can Tho province in the Mekong Delta in southern Vietnam. Surface water was sampled at 34 points through months of the end of the rainy and flood season (October 2023), the beginning of the dried season (December 2023), and the middle of the dried season (February 2024). The SWQ parameters were compared with limited values of the national technical standard on SWQ and the Vietnamese water quality index. We used principal component analysis (PCA) to find sources causing surface water pollution and cluster analysis (CA) to identify the similarities in SWQ parameters among sampling points. The research showed that Can Tho's average SWQ value ranged at a good level. The SWQ in the dry season is worse than in the rainy season. Pollution from suspended solids, organic matter, nutrients, and coliform was found in Can Tho's surface water. The PCA resulted in the three key sources of PCs 1, 2, and 3 being elucidated by 65.3% of SWQ changes. Twelve current monitoring parameters, including temperature, pH, dissolved oxygen, total suspended solids, total organic carbon, biological oxygen demand, chemical oxygen demand, ammonium, nitrite, nitrate, phosphate, and coliform, are influencing SWQ. It is suggested from the CA results that the 34 monitoring points could decrease to 30 points in the case of financial issues. The results are beneficial in reviewing the sampling points and monitoring parameters for SWQ in Can Tho. [ABSTRACT FROM AUTHOR]