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Long-distance Real-Time Kinematic (RTK) positioning is crucial for applications in remote areas, such as maritime environments. Achieving 2–3 cm accuracy with RTK requires successful ambiguity resolution, which involves two main steps: identifying the best integer ambiguity candidate and confirming its validity. While previous research has largely concentrated on the first step, including the development of Cascading Ambiguity Resolution methods, and reducing tropospheric delay, studies on the validation of ambiguity for long-distance RTK are limited. This study conducts a thorough examination of ambiguity validation for long-distance RTK, focusing on two prevalent methods: the theoretical success rate and the R-ratio test. The results reveal several key insights. Firstly, the six commonly used bounds for the theoretical success rate are not an accurate reflection of the actual success rate, making them unsuitable for long-distance RTK applications. Secondly, the R-ratio test proves to be dependable when the threshold is set above 1.7, assuming there is a minimum observation period of one minute and at least ten satellites are visible. However, the probability of successfully resolving ambiguities with the R-ratio test does not surpass 50%. Additionally, if ambiguity resolution is not achieved within 20 min, simply prolonging the observation time is generally unproductive. To improve the performance of ambiguity resolution in practical situations that require extended observation times, this research proposes a novel ambiguity validation method. This new approach is based on the duration for which an integer ambiguity resolution candidate maintains the best status. This method aims to provide a reliable means of validating ambiguities in cases where the R-ratio test fails.

Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we retrieved the genomic sequences of nine B. brevis strains that had been assembled into complete genomes from the NCBI database. These genomic sequences were analyzed using phylogenetic analysis software, pan-genome analysis software, and secondary metabolite mining software. Results revealed that the genome size of B. brevis strains ranged from 6.16 to 6.73 Mb, with GC content ranging from 47.0% to 54.0%. Phylogenetic analysis classified the nine B. brevis strains into three branches. The analyses of ANI and dDDH showed that B. brevis NEB573 had the potential to become a new species of Brevibacillus and needed further research in the future. The pan-genome analysis identified 10032 gene families, including 3257 core gene families, 3112 accessory gene families, and 3663 unique gene families. In addition, 123 secondary metabolite biosynthetic gene clusters of 20 classes were identified in the genomes of nine B. brevis strains. The major types of biosynthetic gene clusters were non-ribosomal peptide synthase (NRPS) and transAT polyketide synthase (transAT-PKS). Furthermore, a large number of untapped secondary metabolites were identified in B. brevis. In summary, this study elucidated the pan-genome characteristics of the biocontrol bacterium B. brevis and identified its secondary metabolites, providing valuable insights for its further development and utilization.

Biodegradable mulch films (BMFs) are becoming increasingly popular in agricultural practices. However, research on the ecological impact of biodegradable mulch films on pepper–soil systems is still scarce. To compare the differential effects of BMFs and polyethylene (PE) mulch on soil chemical properties, soil bacterial community composition, and pepper cultivation, a study was conducted encompassing eight distinct treatments. These treatments included three varieties of polybutylene adipate terephthalate (PBAT) combined with polylactic acid (PLA) mulches: PP-JL, PP-SD, and PP-SH; a black polypropylene carbonate mulch (PPC-BK); a brown PPC mulch (PPC-BR); a polyethylene (PE) mulch; straw mulching (NCK); and an uncovered control (PCK). After applying mulches for 129 days, most PPC and PBAT + PLA films had reached the rupture phase, whereas the PE film was still in the induction phase. Pepper yield was obviously higher in all mulched treatments (4830 kg hm−1) than in the un-mulched control (3290 kg hm−1), especially the BMF PP-JL treatment, which showed the most notable improvements in yield. Although BMF treatments maintained a lower soil temperature than the PE film mulch, they were still higher than the un-mulched control. Furthermore, the soil bacterial community composition and ecological network were not markedly affected by different mulching conditions. However, the PP-SH treatment significantly increased the abundance of Pseudomonas, Nitrosomonas, and Streptomyces genera. Moreover, Lactobacillus and Gp16 were substantially more abundant in the PPC-black (BK) and PPC-brown (BR) treatments compared to the PE mulching treatment. This study could provide valuable insights into the ecological benefits of BMFs in pepper cultivation. However, as our experiments were conducted for only one season, it is imperative to undertake long-term experiments across consecutive seasons and years for a thorough understanding and comprehensive study.

Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable feature matching algorithm for spherical images via the combination of local geometric rectification and CNN (convolutional neural network) learned descriptor. First, image patches around keypoints are reprojected to their corresponding tangent planes based on a spherical camera imaging model, which uses scale and orientation data from the keypoints to achieve both rotation and scale invariance. Second, feature descriptors are then calculated from the rectified image patches by using a pre-trained separate detector and descriptor learning network, which improves the discriminability by exploiting the high representation learning ability of the CNN. Finally, after classical feature matching with the ratio test and cross check, refined matches are obtained based on an essential matrix-based epipolar geometry constraint for outlier removal. By using three real spherical images and an incremental structure from motion (SfM) engine, the proposed algorithm is verified and compared in terms of feature matching and image orientation. The experiment results demonstrate that the geometric distortions can be efficiently reduced from rectified image patches, and the increased ratio of the match numbers ranges from 26.8% to 73.9%. For SfM-based spherical image orientation, the proposed algorithm provides reliable feature matches to achieve complete reconstruction with comparative accuracy.

This study investigated the effects of adhesive resination and bamboo strand content on the physical and mechanical properties of thick Bamboo-Wood-oriented strand board (BWOSB), such as the air dry density (ADD), internal bond strength (IB), water absorption thickness swelling (TS), modulus of rupture (MOR), modulus of elasticity (MOE), and gluing properties. The raw materials used included large strands prepared from Chinese fir (Cunninghamia lanceolata), bamboo (Phyllostachys edulis), and modified isocyanate resin (PMDI). In this study, BWOSB specimens with different adhesive resination and bamboo strand content were fabricated, and their physical–mechanical properties were examined. It was found that the physical and mechanical properties of BWOSB with 8% PMDI resination were better than those with 5%, and their gluing damage was mostly in the form of the tearing of the raised vascular bundles of bamboo strands and the wood-breaking damage of wood strands. With the increase in the proportion of bamboo strands, the internal bonding strength and the short-span shear strength of BWOSB showed a tendency to decrease at first and then increase. The swelling rate of the water absorption thickness showed a tendency to decrease, and the other properties of BWOSB did not show a clear correlation with the change in the proportion of bamboo strands. The unique gluing interface between the bamboo and wood strands, which either used the “keyway” type of gluing effect or the “nail” type of gluing effect, determined the gluing performance of BWOSB, and the proportion of bamboo and wood strands influenced the gluing interface and gluing type of BWOSB, which ultimately affected the gluing performance of BWOSBs. The proportion of bamboo and wood strands also affected the gluing properties of BWOSBs by influencing the gluing interface and gluing type. This study provides a reference for the development of the production process of thick BWOSB and its application in the field of heavy load construction.

Recovering the waste heat of a marine main engine (M/E) to generate electricity was an environmental way to minimize the carbon dioxide emissions for ships, especially with organic Rankine cycle (ORC) technology. The M/E had variable loads and operating times during voyage cycle, which directly affected the ORC thermodynamic potential. In this paper, a voyage cycle-based waste heat utilization from the M/E was introduced to provide reliable evaluation for proposing and designing ORC. The effect of various M/E loads and operating times on ORC performance among three dry-type substances was analyzed. The environmental impact was presented based on the data from one voyage cycle navigation of objective container ship. The results showed that Cyclohexane was capable of net power while Benzene was more suitable for thermal efficiency. The evaporator and condenser were the main irreversible components of the ORC system and required further optimization. Taking the operational profile into consideration, the evaporation pressures were 922–1248 kPa (Cyclohexane), 932–1235 kPa (Benzene) and 592–769 kPa (Toluene), respectively. During the voyage cycle, the carbon dioxide emissions were 99.68 tons (Cyclohexane), 96.32 tons (Benzene) and 60.99 tons (Toluene), respectively. This study provided certain reference for the design and investigation of ORC application to further improve the energy efficiency for container ship.

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

Based on the analysis of the waste heat distribution characteristics of a typical ship two-stroke low-speed main engine (model: MAN 8S65ME-C8.6HL, the specified maximum continuous rating SMCR: 21,840 kW) under different loads, two different types of organic Rankine cycle (ORC) systems, namely the basic system (BORC) and the preheated system(PORC), were constructed to recover the ship main engine’s exhaust gas waste heat and jacket cooling water waste heat. Using the thermodynamic simulation model of the system, the main performance indexes, including net output power of the two ORC systems were studied with the variation of seawater temperature and main engine load, and the annual ship fuel saving and annual carbon emission reduction generated by the two systems were compared and analyzed. It was found that the maximum net output power of the BORC system and PORC system were 445.3 kW and 491.3 kW, respectively, when the ship’s main engine load was 100%, and the outboard seawater temperature was 20 °C; the maximum thermal efficiency was 12.84% and 12.71%, respectively; under the annual operation, the fuel saving of BORC system and PORC system can be 456 tons and 510 tons, respectively, and the carbon emission reduction was 1416 tons and 1581 tons, respectively. The analysis found that the net output power of the PORC system is always greater than that of the BORC system. When the outboard seawater is lower, and the main engine load is more than 80%, the net output power difference between the PORC system and BORC system gradually expands, and the improvement of ORC system performance is more evident by adding a preheater. It can be concluded that when the ship was mainly operated in the sea area with low seawater temperature and the main engine was running under high load most of the time, selecting the PORC system to recover the waste heat of the main engine was more advantageous.

The subgrade construction in the permafrost forest region will aggravate the degradation of frozen soil, which will lead to the settlement of the subgrade. Based on the road project of National Highway 332 in the Great Khingan Mountains, by means of field observation, experimental testing, and numerical simulation, a thermo-hydro-mechanical coupling numerical model of the permafrost subgrade considering vegetation cover was established to analyze the influence of vegetation cover on the settlement behavior of the permafrost subgrade. The study indicates that vegetation cover mainly influences the seasonal active layer temperature of permafrost, and its cooling effect on permafrost in the warm season is more significant compared with the warming effect in the cold season. The volumetric water content of the subgrade with vegetation cover is greater than that without vegetation cover in the cold season. The situation is just the opposite in the warm season. The damage to the subgrade is mainly reflected in the settlement caused by the thawing of frozen soil. The maximum settlement of the subgrade with and without vegetation cover is 8.3 mm and 9.5 mm at the foot of the subgrade slope. After construction, the settlement behavior of the permafrost subgrade will undergo a degradation period of 3 years, a restoration period of 2 years, and finally, tend to be stable.

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.

In this work, the feasibility of solid-surface three-dimensional fluorescence (SSTF) in combination with chemometrics to rapidly and directly determine three PAHs in drill cuttings recycling products was studied for the first time. Due to the nondestructive characteristics of SSTF and the “mathematical separation” of chemometric three-way calibration, neither time-consuming sample pretreatments nor toxic organic reagents were involved in the determination. By using the smart “mathematical separation” function of the parallel factor analysis (PARAFAC) algorithm, clear spectral profiles together with reasonable quantitative results for the three target PAHs were successfully extracted from the total SSTF signals of drill cuttings recycling products without the need for chromatographic separation. The linearity of the calibration models was good (R2 > 0.96) and the average spiked recoveries of three target PAHs were between 88.1–102.7% with a relative standard deviation less than 20%. Nevertheless, given the green, fast, low-cost, and nondestructive advantages of the proposed strategy, it has the potential to be used as a fast screening approach and allow for a quick survey of PAHs in drill cuttings recycling products.

In this study, a main marine engine with a rating power of 21,840 kW for a ship sailing in an actual voyage was obtained as the research object. The engine’s exhaust gas and jacket cooling water were adopted as the heat source of the organic Rankine cycle (ORC) system developed for the main marine engine. The engine can consume high-sulfur or low-sulfur fuel oil, respectively, according to the different emission control requirements. The impact of the use of high-sulfur or low-sulfur fuel oil, and variations in engine load, amount of recoverable waste heat, outboard seawater temperature, and the ship’s steam demand were comprehensively considered, and the validated ORC system model was used for the analysis of the system’s performance and the ship’s energy saving for the whole voyage. The results demonstrated that when the ship adopted high-sulfur or low-sulfur fuel oil, the maximum total net power output of the ORC system was 449.3 kW and 753.1 kW, respectively. During the whole voyage of 1610.7 nautical miles, when high-sulfur fuel oil was used, the ORC system reduced carbon emission by 40.3 tons and 33.8 tons, respectively, in summer and in winter, and the fuel saving rates were 2.53% and 2.12%; when low-sulfur fuel oil was used, the ship’s carbon emissions were reduced by 62.1 tons and 61.8 tons, respectively, in summer and in winter, and the fuel saving rates were 3.91% and 3.89%.

Fluorinated Fe3O4 microspheres with 7.1 ± 1.4 wt% of fluoride (F-Fe3O4-1) were prepared via glycothermal synthesis. Fluorination significantly enhanced the activity of F-Fe3O4-1 in catalytic wet peroxide oxidation of anionic dyes (including orange G (OG) and congo red) at pH ~7. However, the promotional effect of fluorination became less obvious for amphoteric rhodamine B and was not observed for cationic methylene blue. After reacting with H2O2 (40 mM) for 2 h at pH 6.5 and 40 °C, the decolorization rates of OG (0.1 mM) and the pseudo-first-order rate constant were 96.8% and 0.0284 min−1 over F-Fe3O4-1 versus 17.6% and 0.0011 min−1 over unmodified Fe3O4. The effects of reaction parameters (initial H2O2 concentration and pH value and reaction temperature) on OG decolorization with H2O2 over F-Fe3O4-1 were investigated. The reusability of F-Fe3O4-1 was demonstrated by OG decolorization in eight consecutive runs. Fluorination increased the isoelectric point of F-Fe3O4-1 to 8.7 and facilitated the adsorption and degradation of anionic dyes on the surface of F-Fe3O4-1 at pH ~7. Scavenging tests and EPR spectra supported that hydroxyl radicals were the main reactive species for the OG decolorization over F-Fe3O4-1.

As carbon dioxide emissions arising from fossil energy consumption and fossil fuels are gradually increased, it is important for the low-carbon operation of ships to recover diesel engine waste heat. A newly developed dual-loop organic Rankine cycle (ORC) system to recover waste heat from a marine main engine (M/E) was designed in this paper. The exhaust gas (EG) heat was recovered by the high-temperature (HT) loop. The jacket cooling water (JCW) heat and the condensation heat of the HT loop were recovered by the low-temperature (LT) loop. Toluene, cyclohexane, benzene, R1233zd (E), R245fa, and R227ea were selected as the working fluids. The influence of the condenser thermal parameters on the LT loop was analyzed using the pinch point method. The performance of the dual-loop ORC was investigated under various working fluid combinations. The maximum net power of the HT loop can reach 253.4 kW when using cyclohexane as the working fluid, and the maximum thermal efficiency of the HT loop can reach 18.5% with benzene as the working fluid. Meanwhile, higher condensation temperatures and levels of condensation heat of the HT loop have a positive effect on the performance of the LT loop. However, in most conditions, the HT loop condensation heat could not provide enough heat for the LT loop’s working fluid to start the boiling process. The total net power of the dual-loop ORC system was 410.6 kW with Cyclohexane in the HT loop and R1233zd (E) in the LT loop, resulting in a 10.9% improvement in the marine main engine thermal efficiency.

In order to solve the problem of premature grape ripening due to global warming, inter-row peanut growing in viticulture was applied. In this two-year (2018–2019) study, the peanut (Arachis hypogaea L.) was used to cover the ground between rows in the vineyards located in the semi-arid Northwest China, Xinjiang. The results showed that reflected solar radiation and temperature around the fruit zone with the peanuts growing were decreased. Compared with clean tillage, the grapes with covering peanuts had lower total soluble solids (TSS) and higher titratable acidity (TA) in the berries. Lower alcohol content and higher total acid (TA) was also found in their corresponding wines. Inter-row peanut growing treatment significantly decreased the contents of flavonols in the grapes and their wines in the two consecutive years, but no significant effect on flavanols was observed in the resulting wines. Norisoprenoids and esters in the grapes and the wines were increased with the peanut growing treatment, respectively. Additionally, compared to clean tillage, the peanut covering significantly improved the sensory value of the wines, especially the aroma complexity of the wines. This study helps us to better understand the feasibility of applying inter-row peanut growing in the viticulture of ground management in the semi-arid climate of Northwest China.

Ubiquitous and seamless indoor-outdoor (I/O) localization is the primary objective for gaining more user satisfaction and sustaining the prosperity of the location-based services (LBS) market. Regular users, on the other hand, may be unaware of the impact of activating multiple localization sources on localization performance and energy consumption, or may lack experience deciding when to enable or disable localization sources in different environments. Consequently, an automatic handover mechanism that can handle these decisions on a user’s behalf can appreciably improve user satisfaction. This study introduces an enhanced I/O environmental awareness service that provides an automated handover mechanism for seamless navigation based on multi-sensory navigation integration schemes. Moreover, the proposed service utilizes low-power consumption sensor (LPCS) indicators to execute continuous detection tasks and invoke GNSS in confusion scenarios, and transition intervals to make the most firm decision on the credibility of the LPCS-triggered transition and compensate for indicator thresholds. In this manner, GNSS are used for short intervals that help reduce detection latency and power consumption. Consequently, the proposed service guarantees accurate and reliable I/O detection while preserving low power consumption. Leveraging the proposed service as an automated handover helped realize seamless indoor-outdoor localization with less switching latency, using an integrated solution based on extended Kalman filter. Furthermore, the proposed energy-efficient service was utilized to confine crowdsourced data collection to the required areas (indoors and semi-indoors) and prevent excess data collection outdoors, thereby reducing power drainage. Accordingly, the negative impact of data collection on the user’s device can be mitigated, participation can be encouraged, and crowdsourcing systems can be widely adopted.

Ambiguity resolution at Continuously Operating Reference Station (CORS) network sites is the key step in the whole processing chain of Network Real Time Kinematic (NRTK). An appropriate ambiguity-resolution speed is important, and single-epoch ambiguity resolution has not been realized yet, especially for large-scale CORS. We attempt to realize single-epoch ambiguity resolution for a large-scale CORS network by neglecting tropospheric delay through forming difference between satellites with close mapping functions whether they belong to the same or different GNSSs. As only two frequency bands are shared among GPS, Galileo and BeiDou, the biggest challenge is how to get this single-epoch ambiguity solution for wide-lane combinations of L1 and L5 when the difference is formed between satellites of different GNSSs. The proposed method includes five steps for ambiguity resolution for different combinations: extra wide-lane, wide-lane, inter-GNSS wide-lane, subset narrow-lane and narrow-lane. The single-epoch ambiguity-resolution performance is assessed based on GNSS observations from two long-distance baselines formed with IGS stations, BRUX-REDU and BAUT-LEIJ, separated by distances of approximately 104 km and 151 km, respectively. The numerical results show that the fixing rate of the single-epoch ambiguity resolution can reach more than 90%, so for a large-scale CORS network, single-epoch ambiguity resolution is feasible and can be realized in the future.

The spatial distribution of droplet size and velocity affects the deposition and distribution on the target. In order to investigate the influence of different adjuvant and pressures on the spatial distribution of droplet size and velocity in atomization area of different nozzles, air induction flat fan nozzle IDK120-03, multi-range flat fan nozzle LU120-03 and anti-drift flat fan nozzle AD120-03 were selected. Phase Doppler Interferometer (PDI) was used to analyze and compare the distribution of droplet size and velocity in the atomization area of three nozzles when four typical adjuvant Maisi, Maidao, Adsee AB-600 and Surun sprayed at different pressures. The results show that the volume median diameter of droplet size has no obvious change along the vertical direction of the nozzle center and increases with distance in the horizontal direction, the droplet size decreases with increasing pressure at the same position, the adjuvant all increases the droplet size (about 12%, 12%, 10% and 9% for Maisi, Maidao, Surun and Adsee AB-600, respectively), IDK120-03 nozzle droplet size is the largest and LU120-03 nozzle is the smallest in the same position. For droplet velocity distribution, droplet velocity decrease in distance along the vertical and horizontal direction, respectively, the droplet velocity increases with increasing pressure at the same position, compared with water, the droplet velocity increased by about 13%, 9%, 8%, and 4% for Maisi, Maidao, Surun, and Adsee AB-600, respectively, the velocity of AD nozzle is the largest and IDK nozzle is the smallest at the same position. The experiment can provide a basis for the selection of adjuvants and nozzles in pesticide application, and provide a data base for studying the distribution of droplets on the target.

Space weather events adversely impact the operations of Global Navigation Satellite Systems (GNSS). Understanding space weather mechanisms, interactions in the atmosphere, and the extent of their impact are useful in developing prediction and mitigation models. In this study, the hourly lightning occurrence and its impact on ionospheric disturbances, quantified using the Rate of Total electron content Index (ROTI), were assessed. The linear correlation between diurnal lightning activity and ROTI in the coastal region of southern China where lightning predominates in the daytime was initially negative contrary to a positive correlation in southern Africa where lighting predominates in the evening. After appreciating and applying the physical processes of gravity waves, electromagnetic waves and the Trimpi effect arising from lightning activity, and the time delay impact they have on the ionosphere, the negative correlation was overturned to a positive one using cross-correlation. GNSS has demonstrated its capability of revealing the impact lightning has on the ionosphere at various times of the day.

Compared with the medium voltage ac (MVAC) collection system, the medium voltage dc (MVDC) one for renewable energy sources has many advantages. High-power dc/dc converters are one of the key stages of the MVDC collection system to boost the voltage generated by photovoltaic or wind turbine. A novel hybrid three-level dc/dc converter utilizing a blocking capacitor to realize zero-voltage zero-current-switching (ZVZCS) is proposed. A higher overall efficiency can be achieved by reducing conduction losses. Detailed experimental results on a scaled-down hardware prototype rated at 150 V/750 V/1 kW are demonstrated to verify the proposed converter performance.

Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment.

Covering crops was a commonly used viticultural technique to adjust the vineyard microclimate, thus affecting the grape and wine quality. In this two-year study, the purslane (Portulaca oleracea L.) was used to cover the lands between rows in the vineyards located in the semi-arid Northwest China, Xinjiang. Results showed that the photosynthetically active radiation around the fruit zone and the temperature with the purslane covering treatment decreased. Compared with the clean tillage, covering purslane had lower TSS and higher TA in the grape berries, while lower alcohol content and higher TA was also found in their corresponding wines. Covering purslane treatment significantly increased the contents of anthocyanin and flavonol in the grapes and wines in the year 2018, but no significant effect on flavanols was observed in the wines. Norisoprenoids, esters, and C6 alcohols in the grapes and wines were increased in the purslane covering treatment, respectively. Additionally, compared to the clean tillage, the purslane covering treatment significantly improved the sensory value of the wines, especially the floral aroma and the complexity of the wines. This study helped us to better understand the feasibility of applying covering purslane in viticulture in the semi-arid climate of Northwest China.

The network real-time kinematic (RTK) technique uses continuously operating reference stations (CORS) within a geographic area to model the distance dependent errors, allowing users in the area to solve ambiguities. A key step in network RTK is to fix ambiguities between multiple reference stations. When a new satellite rises or when maintenance happens, many unknown parameters are involved in the mathematical model, and traditional methods take some time to estimate the integer ambiguities reliably. The purpose of this study is the single-epoch ambiguity resolution on small-scale CORS network with inter-station distance of around 50 km. A new differencing scheme is developed to explore the full potential of multi-frequency Global Navigation Satellite System (GNSS). In this scheme, a differencing operation is formed between satellites with the closest mapping functions. With the new differencing scheme, tropospheric error can be mostly neglected after the correction, as well as the double-differencing operation. Numerical tests based on two baselines of 49 km and 35 km show that the success rate of ambiguity resolution can reach more than 90%. The single-epoch ambiguity resolution for reference stations brings many benefits to the network RTK service, for example, the instantaneous recovery after maintenance or when a new satellite rises.

Traditional coseismic displacement retrieval generally uses real-time kinematic (RTK) and precise point positioning (PPP) services. However, both RTK and real-time PPP need a network link to transmit the corrected data. Although the network link may be interrupted when an earthquake happens, the PPP-B2b service broadcasted by geostationary orbit (GEO) satellites will not be affected. Its service range mainly covers China and the surrounding areas. In this research, the PPP method with PPP-B2b service based on constrained coordinates is proposed and overcomes the limitation of the network link and long convergence time. First, the accuracy of orbits and clock offsets for the PPP-B2b service is evaluated and compared with real-time service (RTS). Then, the simulated experiments are carried out using the PPP method with PPP-B2b service based on constrained coordinates, which tests the accuracy by calculating the coordinate displacement of the measurement station. The results show that the accuracy of PPP-B2b orbits in the radial direction is within 0.1 m. Moreover, regarding the accuracy of clock offsets, the PPP-B2b service is no more than 3.5 cm. This validates the feasibility of replacing RTS products with PPP-B2b. In the 15 min simulated experiments, the root mean square (RMS) of horizontal and vertical directions is maintained within 3 cm.

Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) is used to ascertain the impact of space weather events on navigation and communication systems. TEC is detrended by several methods to show this impact. Information from the detrended TEC may or may not necessarily represent a geophysical parameter. In this study, two commonly used detrending methods, Savitzky–Golay filter and polynomial fitting, are evaluated during thunderstorm events in Hong Kong. A two-step approach of detection and distinguishing is introduced alongside linear correlation in order to determine the best detrending model. Savitzky–Golay filter on order six and with a time window length of 120 min performed the best in detecting lightning events, and had the highest moderate positive correlation of 0.4. That the best time frame was 120 min suggests that the observed disturbances could be travelling ionospheric disturbance (TID), with lightning as the potential source.

In this study, we present a Rate of Total Electron Content Index (ROTI)-aided equatorial plasma bubbles (EPBs) detection method based on a Global Navigation Satellite System (GNSS) ionospheric Total Electron Content (TEC). This technique seeks the EPBs occurrence time according to the ROTI values and then extracts the detrended ionospheric TEC series, which include EPBs signals using a low-order, partial polynomial fitting strategy. The EPBs over the Hong Kong area during the year of 2014 were detected using this technique. The results show that the temporal distribution and occurrence of EPBs over the Hong Kong area are consistent with that of previous reports, and most of the TEC depletion error is smaller than 1.5 TECU (average is 0.63 TECU), suggesting that the detection method is feasible and highly accurate. Furthermore, this technique can extract the TEC depletion series more effectively, especially for those with a long duration, compared to previous method.

This paper applies the emerging hybrid active third-harmonic current injection converter (H3C) to the battery energy storage system (BESS), forming a novel H3C-BESS structure. Compared with the commonly used two-stage VSC-BESS, the proposed H3C-BESS has the capability to reduce the passive components and switching losses. The operation principle of the H3C-BESS is analyzed and the mathematical model is derived. The closed-loop control strategy and controller design are proposed for different operation modes of the system, which include the battery current/voltage control and the injected harmonic current control. In particular, active damping control is realized through the grid current control, which could suppress the LC-filter resonance without the need of passive damping resistors. Simulation results show that the proposed topology and its control strategy have fast dynamic response, with a setup time of less than 4 ms. In addition, the total harmonic distortions of battery current and grid currents are only 2.54% and 3.15%, respectively. The amplitude of the injected harmonic current is only half of the grid current, indicating that the current injection circuit generates low losses. Experimental results are also provided to verify the validity of the proposed solution.

The differential global navigation satellite system (DGNSS) is an enhancement system that is widely used to improve the accuracy of single-frequency receivers. However, distance-dependent errors are not considered in conventional DGNSS, and DGNSS accuracy decreases when baseline length increases. In network real-time kinematic (RTK) positioning, distance-dependent errors are accurately modelled to enable ambiguity resolution on the user side, and standard Radio Technical Commission for Maritime Services (RTCM) formats have also been developed to describe the spatial characteristics of distance-dependent errors. However, the network RTK service was mainly developed for carrier-phase measurements on professional user receivers. The purpose of this study was to modify the local-area DGNSS through the use of network RTK corrections. Distance-dependent errors can be reduced, and accuracy for a longer baseline length can be improved. The results in the low-latitude areas showed that the accuracy of the modified DGNSS could be improved by more than 50% for a 17.9 km baseline during solar active years. The method in this paper extends the use of available network RTK corrections with high accuracy to normal local-area DGNSS applications.

We investigated the effects of the austenitizing temperature on the microstructure, hardness, and tensile properties of case-carburized steel after vacuum carburization at 930 °C and then re-austenitization at 820–900 °C followed by oil quenching and tempering. The results show that fractures occurred early with the increase in the austenitizing temperature, although all the carburized specimens showed a similar case hardness of 800 HV0.2 and case depth of 1.2 mm. The highest fracture stress of 1919 MPa was obtained for the experimental steel when the austenitizing temperature was 840 °C due to its fine microstructure and relatively high percentage of retained austenite transformed into martensite during the tensile tests. We also found that the stress–strain behavior of case-carburized specimens could be described by the area-weighted curves of the carburized case and the core in combination. The strain hardening exponent was about 0.4 and did not vary with the increase in the austenitizing temperature. We concluded that the optimum austenitizing temperature was around 840 °C for the experimental steel.

FRP (Fiber Reinforced Polymer) Bar reinforced coral concrete beam is a new type of structural member that has been used more and more widely in marine engineering in recent years. In order to study and predict the flexural performance of CFRP reinforced coral concrete beams, the flexural rigidity, crack morphology and failure mode of concrete were studied in detail. The results show that under the condition of similar reinforcement ratio, the flexural rigidity of CFRP reinforced coral concrete beam is significantly lower than that of ordinary reinforced concrete beam. Increasing the cross-section reinforcement ratio within a certain range can increase the bending stiffness of the test beam or reduce the deflection, but the strength utilization rate of CFRP reinforcement is greatly reduced. The short-term bending stiffness of the CFRP reinforced coral concrete beam calculated by the existing standard formula is obviously higher. This paper proposes a modified formula for introducing the strain inhomogeneity coefficient (ψ) of CFRP bars and considers the relative slip between CFRP bars and coral concrete to predict the short-term flexural stiffness of coral concrete beams reinforced by CFRP bars. The formula was verified with the test results, and it was proved that the formula has a good consistency with the test results.

Global navigation satellite systems (GNSS) can attain centimeter level positioning accuracy, which is conventionally provided by real-time precise point positioning (PPP) and real-time kinematic (RTK) techniques. Corrections from the data center or the reference stations are required in these techniques to reduce various GNSS errors. The time-relative positioning approach differs from the traditional PPP and RTK in the sense that it does not require external real-time corrections. It computes the differences in positions of a single receiver at different epochs using phase observations. As the code observations are not used in this approach, its performance is not affected by the noise and multipath of code observations. High reliability is another advantage of time-relative precise positioning because the ambiguity resolution is not needed in this approach. Since the data link is not required in the method, this approach has been widely used in remote areas where wireless data link is not available. The main limitation of time-relative positioning is that its accuracy degrades over time between epochs because of the temporal variation of various errors. The application of the approach is usually limited to be within a time interval of less than 20 min. The purpose of this study was to increase the time interval of time-relative positioning and to extend the use of this method to applications with a longer time requirement, especially in remote areas without wireless communication. In this paper, the main error sources of the time-relative method are first analyzed in detail, and then the approach to improve the accumulated time relative positioning method is proposed. The performance of the proposed method is assessed using both static and dynamic observations with a duration as long as several hours. The experiments presented in this paper show that, among the four scenarios tested (i.e., GPS, GPS/Galileo, GPS/Galileo/BeiDou, and GPS/Galileo/BeiDou/GLONASS), GPS/Galileo/BeiDou performed best and GPS/Galileo/BeiDou/GLONASS performed worst. The maximum positioning errors were mostly within 0.5 m in the horizontal direction, even after three hours with GPS/Galileo/BeiDou. It is expected that the method could be used for positioning and navigation for as long as several hours with decimeter level horizontal accuracy in remote areas without wireless communication.

Real-time precise point positioning (RTPPP) is a popular positioning method that uses a real-time service (RTS) product to mitigate various Global Navigation Satellite Systems (GNSS) errors. However, communication links are not available in the ocean. The use of a communication satellite for data transmission is so expensive that normal users could not afford it. The BeiDou short-message service provides an efficient option for data transmission at sea, with an annual fee of approximately 160 USD. To perform RTPPP using BeiDou short messages, the following two challenges should be appropriately addressed: the maximum size of each BeiDou message is 78 bytes, and the communication frequency is limited to once a minute. We simplify the content of RTS data to minimize the required bandwidth. Moreover, the orbit and clock corrections are predicted based on minute-interval RTS orbital and clock corrections. An experiment was conducted to test the performance of the proposed method. The numerical results show that the three-dimensional positioning precision can reach approximately 0.4 m with combined GPS + GLONASS and approximately 0.2 m with combined GPS + GLONASS + Galileo + BeiDou.

The high precision positioning can be easily achieved by using real-time kinematic (RTK) and precise point positioning (PPP) or their augmented techniques, such as network RTK (NRTK) and PPP-RTK, even if they also have their own shortfalls. A reference station and datalink are required for RTK or NRTK. Though the PPP technique can provide high accuracy position data, it needs an initialisation time of 10–30 min. The time-relative positioning method estimates the difference between positions at two epochs by means of a single receiver, which can overcome these issues within short period to some degree. The positioning error significantly increases for long-period precise positioning as consequence of the variation of various errors in GNSS (Global Navigation Satellite System) measurements over time. Furthermore, the accuracy of traditional time-relative positioning is very sensitive to the initial positioning error. In order to overcome these issues, an improved time-relative positioning algorithm is proposed in this paper. The improved time-relative positioning method employs PPP model to estimate the parameters of current epoch including position vector, float ionosphere-free (IF) ambiguities, so that these estimated float IF ambiguities are used as a constraint of the base epoch. Thus, the position of the base epoch can be estimated by means of a robust Kalman filter, so that the position of the current epoch with reference to the base epoch can be obtained by differencing the position vectors between the base epoch and the current one. The numerical results obtained during static and dynamic tests show that the proposed positioning algorithm can achieve a positioning accuracy of a few centimetres in one hour. As expected, the positioning accuracy is highly improved by combining GPS, BeiDou and Galileo as a consequence of a higher amount of used satellites and a more uniform geometrical distribution of the satellites themselves. Furthermore, the positioning accuracy achieved by using the positioning algorithm here described is not affected by the initial positioning error, because there is no approximation similar to that of the traditional time-relative positioning. The improved time-relative positioning method can be used to provide long-period high precision positioning by using a single dual-frequency (L1/L2) satellite receiver.

To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper proposes a significance detection algorithm based on the fusion of multi-visual features after detecting the sea-sky-lines. The gradient edges of the sea-surface images are enhanced using a Gaussian low-pass filter to eliminate the effect of the image gradients pertaining to the clouds, wave points, and illumination. The potential region and points of the sea-sky-line are identified. The sea-sky-line is fitted through polynomial iterations to obtain a sea-surface image containing the target object. The saliency subgraphs of the high and low frequency, gradient texture, luminance, and color antagonism features are fused to obtain an integrated saliency map of the sea-surface image. The saliency target area of the sea surface is segmented. The effectiveness of the proposed method was verified. The average detection rate and time for the sea-sky-line detection were 96.3% and 1.05 fps, respectively. The proposed method outperformed the existing saliency models on the marine obstacle detection dataset and Singapore maritime dataset, with mean absolute errors of 0.075 and 0.051, respectively.

Current studies of global navigation satellite systems (GNSS)-based bistatic synthetic aperture radar (GNSS-SAR) is focused on static objects on land. However, moving target imaging is also very significant for modern SAR systems. Imaging a moving target has two main problems. One is the unknown range cell migration; the other is the motion parameter estimation, such as the target’s velocity. This paper proposes a moving target imaging formation algorithm for GNSS-SAR. First, an approximate bistatic range history is derived to describe the phase variation of the target signal along the azimuth time. Then, a keystone transform is employed to correct the range cell migration. To address the motion parameter estimation, a chirp rate estimation method based on short-time Fourier transform and random sample consensus is proposed with high processing efficiency and robust estimation errors in low signal-to-noise ratio scenes. The estimated chirp rate can calculate the target’s velocity. Finally, azimuth compression derivation is performed to accomplish GNSS-SAR imaging. A maritime experimental campaign is conducted to validate the effectiveness of the proposed algorithm. The two cargo ships in the SAR images have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range and the ships’ length along the cross-range.

Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point features, RGB-D simultaneous localization and mapping (SLAM) is easy to fail in low textured scenes. To overcome the problem, this paper proposes a robust RGB-D SLAM system fusing both points and lines, because lines can provide robust geometry constraints when points are insufficient. To comprehensively fuse line constraints, we combine 2D and 3D line reprojection error with point reprojection error in a novel cost function. To solve the cost function and filter out wrong feature matches, we build a robust pose solver using the Gauss–Newton method and Chi-Square test. To correct the drift of camera poses, we maintain a sliding-window framework to update the keyframe poses and related features. We evaluate the proposed system on both public datasets and real-world experiments. It is demonstrated that it is comparable to or better than state-of-the-art methods in consideration with both accuracy and robustness.

In this study, the characteristics and causes of the seasonal variations in plasma bubble occurrence over the Hong Kong area were investigated using the local Global Navigation Satellite System (GNSS) network. Generally, the occurrences of plasma bubbles were larger in the two equinoxes than in the two solstices. Furthermore, two seasonal asymmetries in plasma bubble occurrence were observed: plasma bubble activity was more frequent in the spring equinox than in the autumn equinox (equinoctial asymmetry), and more frequent in the summer solstice than in the winter solstice (solstitial asymmetry). The equinoctial asymmetry could be explained using the Rayleigh–Taylor (R–T) instability mechanism, due to larger R–T growth rates in the spring equinox than in the autumn equinox. However, the R–T growth rate was smaller in the summer solstice than in the winter solstice, suggesting the R–T instability mechanism was inapplicable to the solstitial asymmetry. Our results showed there were more zonally propagating atmospheric gravity waves (GWs) induced by thunderstorm events over the Hong Kong area in the summer solstice than the winter solstice. So, the solstitial asymmetry could be attributed to the seeding mechanism of thunderstorm-driven atmospheric GWs.

Several pedestrian navigation solutions have been proposed to date, and most of them are based on smartphones. Real-time recognition of pedestrian mode and smartphone posture is a key issue in navigation. Traditional ML (Machine Learning) classification methods have drawbacks, such as insufficient recognition accuracy and poor timing. This paper presents a real-time recognition scheme for comprehensive human activities, and this scheme combines deep learning algorithms and MEMS (Micro-Electro-Mechanical System) sensors’ measurements. In this study, we performed four main experiments, namely pedestrian motion mode recognition, smartphone posture recognition, real-time comprehensive pedestrian activity recognition, and pedestrian navigation. In the procedure of recognition, we designed and trained deep learning models using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) networks based on Tensorflow framework. The accuracy of traditional ML classification methods was also used for comparison. Test results show that the accuracy of motion mode recognition was improved from 89.9 % , which was the highest accuracy and obtained by SVM (Support Vector Machine), to 90.74 % (LSTM) and 91.92 % (CNN); the accuracy of smartphone posture recognition was improved from 81.60 % , which is the highest accuracy and obtained by NN (Neural Network), to 93.69 % (LSTM) and 95.55 % (CNN). We give a model transformation procedure based on the trained CNN network model, and then obtain the converted . t f l i t e model, which can be run in Android devices for real-time recognition. Real-time recognition experiments were performed in multiple scenes, a recognition model trained by the CNN network was deployed in a Huawei Mate20 smartphone, and the five most used pedestrian activities were designed and verified. The overall accuracy was up to 89.39 % . Overall, the improvement of recognition capability based on deep learning algorithms was significant. Therefore, the solution was helpful to recognize comprehensive pedestrian activities during navigation. On the basis of the trained model, a navigation test was performed; mean bias was reduced by more than 1.1 m. Accordingly, the positioning accuracy was improved obviously, which is meaningful to apply DL in the area of pedestrian navigation to make improvements.

Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

Consumer-grade RGBD sensors that provide both colour and depth information have many potential applications, such as robotics control, localization, and mapping, due to their low cost and simple operation. However, the depth measurement provided by consumer-grade RGBD sensors is still inadequate for many high-precision applications, such as rich 3D reconstruction, accurate object recognition and precise localization, due to the fact that the systematic errors of RGB sensors increase exponentially with the ranging distance. Most existing calibration models for depth measurement must be carried out with different distances. In this paper, we reveal the mechanism of how an infrared (IR) camera and IR projector contribute to the overall non-centrosymmetric distortion of a structured light pattern-based RGBD sensor. Then, a new two-step calibration method for RGBD sensors based on the disparity measurement is proposed, which is range-independent and has full frame coverage. Three independent calibration models are used for the calibration for the three main components of the RGBD sensor errors: the infrared camera distortion, the infrared projection distortion, and the infrared cone-caused bias. Experiments show the proposed calibration method can provide precise calibration results in full-range and full-frame coverage of depth measurement. The offset in the edge area of long-range depth (8 m) is reduced from 86 cm to 30 cm, and the relative error is reduced from 11% to 3% of the range distance. Overall, at far range the proposed calibration method can improve the depth accuracy by 70% in the central region of depth frame and 65% in the edge region.

To generate indoor as-built building information models (AB BIMs) automatically and economically is a great technological challenge. Many approaches have been developed to address this problem in recent years, but it is far from being settled, particularly for the point cloud segmentation and the extraction of the relationship among different elements due to the complicated indoor environment. This is even more difficult for the low-quality point cloud generated by low-cost scanning equipment. This paper proposes an automatic as-built BIMs generation framework that transforms the noisy 3D point cloud produced by a low-cost RGB-D sensor (about 708 USD for data collection equipment, 379 USD for the Structure sensor and 329 USD for iPad) to the as-built BIMs, without any manual intervention. The experiment results show that the proposed method has competitive robustness and accuracy, compared to the high-quality Terrestrial Lidar System (TLS), with the element extraction accuracy of 100%, mean dimension reconstruction accuracy of 98.6% and mean area reconstruction accuracy of 93.6%. Also, the proposed framework makes the BIM generation workflows more efficient in both data collection and data processing. In the experiments, the time consumption of data collection for a typical room, with an area of 45−67 m 2 , is reduced to 4−6 min with an RGB-D sensor from 50−60 min with TLS. The processing time to generate BIM models is about half minutes automatically, from around 10 min with a conventional semi-manual method.

One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

The pseudolite system is a good alternative for indoor positioning systems due to its large coverage area and accurate positioning solution. However, for common Global Positioning System (GPS) receivers, the pseudolite system requires some modifications of the user terminals. To solve the problem, this paper proposes a new pseudolite-based indoor positioning system architecture. The main idea is to receive real-world GPS signals, repeat each satellite signal and transmit those using indoor transmitting antennas. The transmitted GPS-like signal can be processed (signal acquisition and tracking, navigation data decoding) by the general receiver and thus no hardware-level modification on the receiver is required. In addition, all Tx can be synchronized with each other since one single clock is used in Rx/Tx. The proposed system is simulated using a software GPS receiver. The simulation results show the indoor positioning system is able to provide high accurate horizontal positioning in both static and dynamic situations.

Mobile Laser Scanning (MLS) systems are widely applied for spatial data collection and support applications in many aspects. In recent years, MLS technology had been introduced to railway applications and greatly enhanced the spatial detail and efficiency when compared to traditional approaches. However, the advance of MLS technology is not completely applied to railway environment. Typical MLS systems rely on integrated navigation through the use of Inertial Navigation Systems (INS) and Global Navigation Satellite Systems (GNSS) for geo-referencing, while operation under long-term GNSS outages or even GNSS-free environments, such as underground railway or long tunnels, remains a challenging issue due to the degraded operation of standalone inertial navigation. Commercial MLS systems usually employ high performance inertial measurement units (IMU) and various strategies to manage GNSS outages, but GNSS components are still necessary prior to and after experiencing the loss of GNSS signals. To tackle the problem of permanent GNSS outages, alternative methods are introduced to replace the GNSS and so allow the use of MLS systems in GNSS-free underground railway environments. Such approaches encourage the MLS systems to be developed into the Underground Railway Laser Scanning (URLS) systems, which may provide several alternative operational functions for the management of underground railway operation.

Gravity waves (GWs) generated in the lower atmosphere can propagate upwards to ionospheric height. In this study, we investigated the correlation between ionospheric GWs detected by Global Navigation Satellite System (GNSS)-derived total electron content data and thunderstorm events recorded by a local lightning-detection network in the low-latitude region of Southern China during a four-year period, from 2014 to 2017. Ionospheric GWs were detected on both thunderstorm and non-thunderstorm days. Daytime ionospheric GW activity on high-thunderstorm days showed a similar convex-function-like diurnal variation to thunderstorm activity, which is different to the concave-function-like pattern on non-thunderstorm days. Daytime ionospheric GW activity on low-thunderstorm days showed an approximately linear rising trend and was of a larger magnitude than that of high-thunderstorm days, suggesting it may be mixed by non-thunderstorm origins. Night-time enhancement of ionospheric GW activity was observed on thunderstorm days but not on non-thunderstorm days. Furthermore, ionospheric GW activity on thunderstorm days showed a positive correlation to solar activity. These findings can effectively distinguish thunderstorm-related ionospheric GWs from those of non-thunderstorm origins and provide more comprehensive knowledge of thunderstorm−ionosphere coupling in low-latitude areas.

In this study, a single firing was used to convert stabilized polyacrylonitrile (PAN) fibers and ceramic forming materials (kaolin, feldspar, and quartz) into carbon fiber/ceramic composites. For the first time, PAN carbonization and ceramic sintering were achieved simultaneously in one thermal cycle and the microscopic morphologies and physical features of the obtained carbon fiber/ceramic composites were characterized in detail. The obtained carbon fiber/ceramic composite showed comparable flexural strength as commercial ceramic tiles. Meanwhile, the composite showed exceptional electro-thermal performance based on the electro-thermal performance of the carbonized PAN fibers, which could reach 108 ℃ after 15 s, 204 ℃ after 90 s, and 292 ℃ after 450 s at 5 V (2.6 A), thereby making the ceramic composite a good candidate as an indoor climate control heater, defogger device, kettle, and other heating element.

Eggshell membrane (ESM), which consists of unique interwoven shell membrane fibers, provides a unique supporting platform for functional nanoparticles in catalysis and sensing. This work reports a novel strategy for fabricating sunlight-driven photothermal conversion composite membranes by loading graphene oxide (GO) and gold nanoparticles (AuNPs) on the three-dimension (3D) network structured eggshell membrane. Surface morphologies and chemical elements were characterized by scanning electron microscopy and X-ray photoelectron spectroscopy. High photothermal conversion under simulated sunlight irradiation, which may be caused by the synergistic effect of GO and AuNPs, was achieved by coating both GO and AuNPs onto ESM. The temperature of ESM modified with AuNPs, and then GO increased from 26.0 °C to 49.0 °C after 10 min of light irradiation. Furthermore, the nanoscaled GO and AuNPs could add benefit to the heating localization of the obtained composite membrane. It is expected this biocompatible ESM modified with GO and AuNPs would have great potential in drug release and photothermal therapy applications.

Pangolins are endangered animals in urgent need of protection. Identifying and cataloguing the viruses carried by pangolins is a logical approach to evaluate the range of potential pathogens and help with conservation. This study provides insight into viral communities of Malayan Pangolins (Manis javanica) as well as the molecular epidemiology of dominant pathogenic viruses between Malayan Pangolin and other hosts. A total of 62,508 de novo assembled contigs were constructed, and a BLAST search revealed 3600 ones (≥300 nt) were related to viral sequences, of which 68 contigs had a high level of sequence similarity to known viruses, while dominant viruses were the Sendai virus and Coronavirus. This is the first report on the viral diversity of pangolins, expanding our understanding of the virome in endangered species, and providing insight into the overall diversity of viruses that may be capable of directly or indirectly crossing over into other mammals.

This study presents a comprehensive urban canyon pedestrian navigation scheme. This scheme combines smart phone internal MEMS sensors, GNSS and beacon observations together. Heading estimation is generally a key issue of the PDR algorithm. We design an orientation fusion algorithm to improve smart phone heading using MEMS measurements. Static and kinematic tests are performed, superiority of the improved heading algorithm is verified. We also present different heading processing solutions for comparison and analysis. Heading bias increases with time due to error accumulation and model inaccuracy. Thus, we develop a related heading calibration method based on beacons. This method can help correct smart phone headings continuously to decrease cumulative error. In addition to PDR, we also use GNSS and beacon measurements to integrate a fusion location. In the fusion procedure, we design related algorithms to adjust or limit the use of these different type observations to constrain large jumps in our Kalman filter model, thereby making the solution stable. Navigation experiments are performed in the streets of Mong Kok and Wanchai, which are typically the most crowded areas of Hong Kong, with narrow streets and many pedestrians, vehicles and tall buildings. The first experiment uses the strategy PDR + GNSS + beacon, in east–west orientation street, in which 10 m positioning error is improved from 30 % (smart phone internal GNSS) to 80 % and in south–north orientation street, in which 15 m positioning error is improved from 20 % (smart phone internal GNSS) to 80 % . The second experiment performs two long-distance tests without any beacons, in which the fusion scheme also has significant improvement, that is, 10 m positioning error is improved from 38 % to 60 % .

The boundary truncation of the kernel function affects the numerical accuracy and calculation stability of the smooth particle hydrodynamics (SPH) method and has been one of the key research fields for this method. In this paper, an efficient and stable boundary processing method for the SPH method was introduced by adopting an improved boundary interpolation method (i.e., the improved Shepard method) which needs only the sum of direct accumulation for fixed-boundary particles to improve the numerical stability and computational efficiency of the fixed ghost particle method. The improvement effect of the method was demonstrated by comparing it with different interpolation methods using the cases of still water, a wave generated by dam-breaking, and a solitary wave attacking problem with fixed walls and a moveable wall. The results showed that the new boundary processing method for SPH can help remarkably improve the efficiency of calculation and reduce the oscillations of pressure when simulating various flows.