Search Results (631)

The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water areas, while images of bottom objects were obtained using a UAV platform. The work consisted in preparing SSOs, thanks to which composite images were created. On such training data, 120 models based on the YOLO (You Only Look Once) network were obtained. The study confirmed the effectiveness of models created using YOLOv3, YOLOv5, YOLOv8, YOLOv9, and YOLOv10. A comparison was made between versions of YOLO. The influence of the amount of training data, SSO type, and augmentation parameters used in the training process was analyzed. The main parameter of model performance was the F1-score. The calculated statistics of individual models indicate that the most effective networks use partial augmentation, trained on sets consisting of 2000 SSOs. On the other hand, the increased transparency of SSOs resulted in increasing the diversity of training data and improving the performance of models. This research is developmental, and further research should improve the processes of obtaining detection models using deep networks. Full article

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

The strong nonlinearity of shallow-water waves significantly affects the dynamic response of floating offshore wind turbines (FOWTs), introducing additional complexity in motion behavior. This study presents a series of 1:80-scale experiments conducted on a 5 MW FOWT at a 50 m water depth, under regular, irregular, and focused wave conditions. The tests were conducted under regular, irregular, and focused wave conditions. The results show that, under both regular and irregular wave conditions, the platform’s motion and mooring tension increased as the wave period became longer, indicating a greater energy transfer and stronger coupling effects at lower wave frequencies. Specifically, in irregular seas, mooring tension increased by 16% between moderate and high sea states, with pronounced surge–pitch coupling near the natural frequency. Under focused wave conditions, the platform experienced significant surge displacement due to the impact of large wave crests, followed by free-decay behavior. Meanwhile, the pitch amplitude increased by up to 27%, and mooring line tension rose by 16% as the wave steepness intensified. These findings provide valuable insights for the design and optimization of FOWTs in complex marine environments, particularly under extreme wave conditions. Additionally, they contribute to the refinement of relevant numerical simulation methods. Full article

Funka Bay, located in southwest Hokkaido, is a vital fishing area with a shallow depth of less than 100 m. Seasonal flows of the Oyashio and Tsugaru Warm Current affect the marine environment, leading to significant changes in zooplankton communities, yet limited information is available on these variations. This study used ZooScan imaging to analyze seasonal and interannual changes in zooplankton abundance, biovolume, community structure, and size composition from 2019 to 2023. Water temperature was low in March–April and high in September–November, with chlorophyll a peaks occurring from February to April. Notable taxa such as Thaliacea, Noctiluca, and cladocerans were more common in the latter half of the year. Interannual variations included a decline in large cold-water copepods, Eucalanus bungii and Neocalanus spp., which were abundant in 2019 but decreased by 2023. Zooplankton abundance and biovolume showed synchronized seasonal changes, correlating with shifts in the Normalized Biovolume Size Spectra (NBSS) index, which measures size composition. Cluster analysis identified eight zooplankton communities, with Community A dominant from July to December across all years, while Community D was prevalent in early 2019 but was replaced in subsequent years. Community E emerged from March to April in 2021–2023. In 2019, large cold-water copepods were dominant, but from 2020 to 2023, appendicularians became the dominant group during the March–April period. The decline in large copepods is likely linked to marine heat waves, influencing yearly zooplankton community changes. Full article

The lacustrine to deltaic depositional systems of the Upper Jurassic Qigu Formation in the Yongjin area constitute a significant petroleum reservoir in the central Junggar Basin, China. Based on core observations, petrology analyses, paleoenvironment indicators and modern sedimentary analyses, sequence stratigraphy, lithofacies associations, sedimentary environment, evolution, and models were investigated. The Qigu Formation can be divided into a third-order sequence consisting of a lowstand systems tract (LST) and a transgressive systems tract (TST), which is further subdivided into six fourth-order sequences. Thirteen lithofacies and five lithofacies associations were identified, corresponding to shallow-water delta-front deposits. The paleoenvironment of the Qigu Formation is generally characterized by an arid freshwater environment, with a dysoxic to oxic environment. During the LST depositional period (SQ1–SQ3), the water depth was relatively shallow with abundant sediment supply, resulting in a widespread distribution of channel and mouth bar deposits. During the TST depositional period (SQ4–SQ6), the rapid rise in base level, combined with reduced sediment supply, resulted in swift delta retrogradation and widespread lacustrine sedimentation. Combined with modern sedimentary analysis, the shallow-water delta in the study area primarily comprises a composite system of single main channels and distributary channel-mouth bar complexes. The channel-bar complex eventually forms radially distributed bar assemblages with lateral incision and stacking. The distributary channel could incise a mouth bar deeply or shallowly, typically forming architectural patterns of going over or in the mouth bar. Reservoir test data suggest that the mouth bar sandstones are favorable targets for lithological reservoir exploration in shallow-water deltas. Full article

This study analyzes the lacustrine hydrocarbon source rocks of the Lower Cretaceous in the Erdengsumu sag of the Erlian Basin, evaluating their characteristics and identifying areas with oil resource potential, while also investigating the ancient lake environment, material source input, and controlling factors, ultimately developing a sedimentary model for lacustrine hydrocarbon source rocks. The findings suggest the following: (1) The lower Tengger Member (K₁bt₁) and the Aershan Formation (K₁ba) are the primary oil-producing strata, with an effective hydrocarbon source rock exhibiting a lower limit of total organic carbon (TOC) at 0.95%. The Ro value typically remains below 0.8%, indicating that high-maturity oil production has not yet been attained. (2) The oil generation threshold depths for the Dalestai and Sayinhutuge sub-sags are 1500 m and 1214 m, respectively. The thickness of the effective hydrocarbon source rock surpasses 200 m, covering areas of 42.48 km² and 88.71 km², respectively. The cumulative hydrocarbon generation intensity of wells Y1 and Y2 is 486 × 10⁴ t/km² and 26 × 10⁴ t/km², respectively, suggesting that the Dalestai sub-sag possesses considerable petroleum potential. The Aershan Formation in the Chagantala sub-sag has a maximum burial depth of merely 1800 m, insufficient to attain the oil generation threshold depth. (3) The research area’s productive hydrocarbon source rocks consist of organic matter types I and II₁. The Pr/Ph range is extensive (0.33–2.07), signifying a reducing to slightly oxidizing sedimentary environment. This aligns with the attributes of small fault lake basins, characterized by shallow water and robust hydrodynamics. (4) The low ratio of ∑nC₂₁₋/∑nC₂₂₊ (0.36–0.81), high CPI values (>1.49), and high C₂₉ sterane concentration suggest a substantial terrestrial contribution, with negligible input from aquatic algae–bacterial organic matter. Moreover, as sedimentation duration extends, the contribution from higher plants progressively increases. (5) The ratio of the width of the deep depression zone to the width of the depression in the Erdengsumu sag is less than 0.25. The boundary fault scale is small, its activity is low, and there is not much input from the ground. Most of the source rocks are in the reducing sedimentary environment of the near-lying gently sloping zone. Full article

This paper studies how intense internal waves (IIWs) affect the holographic reconstruction of the sound field generated by a moving source in a shallow-water environment. It is assumed that the IIWs propagate along the acoustic path between the source and the receiver. The presence of IIWs introduces inhomogeneities into the waveguide and causes significant mode coupling, which perturbs the received sound field. This paper proposes the use of holographic signal processing (HSP) to eliminate perturbations in the received signal caused by mode coupling due to IIWs. Within the HSP framework, we examine the interferogram (the received sound intensity distribution in the frequency–time domain) and the hologram (the two-dimensional Fourier transform of the interferogram) of a moving source in the presence of space–time inhomogeneities caused by IIWs. A key finding is that under the influence of IIWs, the hologram is divided into two regions that correspond to the unperturbed and perturbed components of the sound field. This hologram structure enables the extraction and reconstruction of the interferogram corresponding to the unperturbed field as it would appear in a shallow-water waveguide without IIWs. Numerical simulations of HSP application under the realistic conditions of the SWARM’95 experiment were carried out for stationary and moving sources. The results demonstrate the high efficiency of holographic reconstruction of the unperturbed sound field. Unlike matched field processing (MFP), HSP does not require prior knowledge of the propagation environment. These research results advance signal processing methods in underwater acoustics by introducing efficient HSP methods for environments with spatiotemporal inhomogeneities. Full article

In the freshwater snail L. stagnalis, two hours of shallow water crawling exercise are accompanied by the formation of memory, metabolic, neuronal, and behavioral changes, such as faster orientation in a novel environment. Interestingly, rest following exercise enhances serotonin and dopamine metabolism linked to the formation of memory and adaptation to novel conditions. However, the underlying transcriptional responses are not characterized. In this paper, we show that, while two hours of forced crawling exercise in L. stagnalis produce significant changes in nervous system gene expression, the subsequent rest induces a completely distinct transcriptional program. Chromatin-modifying, vesicle transport, and cell cycle genes were induced, whereas neurodevelopmental, behavioral, synaptic, and hormone response genes were preferentially repressed immediately after two hours of exercise. These changes were normalized after two hours of the subsequent rest. In turn, rest induced the expression of genes functioning in neuron differentiation and synapse structure/activity, while mitotic, translational, and protein degradation genes were repressed. Our findings are likely relevant to the physiology of exercise, rest, and learning in other species. For example, chronic voluntary exercise training in mice affects the expression of many homologous genes in the hippocampus. Moreover, in humans, homologous genes are pivotal for normal development and complex neurological functions, and their mutations are associated with behavioral, learning, and neurodevelopmental abnormalities. Full article

Investigating biodiversity in remote and harsh environments, particularly in the Southern Ocean, remains costly and challenging through traditional sampling methods such as trawling. Environmental DNA (eDNA) sampling, which refers to sampling genetic material shed by organisms from environmental samples (e.g., water), provides a more cost-effective and sustainable alternative to traditional sampling approaches. To study the biogeographic patterns of two typical mesopelagic fishes, Antarctic lanternfish (Electrona antarctica) and Antarctic deep-sea smelt (Bathylagus antarcticus), in the Cosmonaut Sea in the Indian Ocean sector of the Southern Ocean, we conducted both eDNA and trawling sampling at a total of 86 stations in the Cosmonaut Sea during two cruises in 2021–2022. Two sets of species-specific primers and probes were developed for a quantitative eDNA analysis of two fish species. Both the eDNA and trawl results indicated that the two fish species are widely distributed in the Cosmonaut Sea, with no significant difference in eDNA concentration, biomass, or abundance between stations. Spatially, E. antarctica tended to be distributed in shallow waters, while B. antarcticus tended to be distributed in deep waters. Vertically, E. antarctica was more abundant above 500 m, while B. antarcticus had a wider range of habitat depths. The distribution patterns of both species were affected by nutrients, with E. antarctica additionally affected by chlorophyll, indicating that their distribution is primarily influenced by food resources. Our study provides broader insight into the biogeographic patterns of the two mesopelagic fishes in the remote Cosmonaut Sea, demonstrates the potential of combining eDNA with traditional methods to study biodiversity and ecosystem dynamics in the Southern Ocean and even at high latitudes, and contributes to future ecosystem research and biodiversity conservation in the region. Full article

Based on drilling, core and seismic data, combined with the regional tectonic sedimentary evolution background, the sedimentary environment of the Triassic carbonate rocks in the Mangeshlak Basin was studied. A sedimentary facies model of this set of carbonate rocks was established. Research has shown that the Mangeshlak Basin underwent a complete large-scale marine transgression–regression sedimentary evolution process during the Triassic. During the early to middle Triassic, seawater gradually invaded the northwest region of the basin from northwest to southeast and gradually regressed in the late Middle Triassic. In the lower part of the Triassic carbonate rocks, the primary components are developed granular limestone or dolomite with oolitic structures, interspersed with a small amount of thin mudstone, which is a good reservoir; the upper part of the Triassic is mainly composed of sedimentary mudstone and mudstone, which can form good sealings. The hill-shaped reflections of the platform edge facies, along with the high-frequency, strong-amplitude, and moderately continuous reflections within the restricted platform interior, are clearly visible on the seismic profile. These features are consistent with the sedimentary environment and lithofacies characteristics revealed by drilling data along the profile. Drilling and seismic data revealed that the sedimentary environment of the early and middle Triassic in the basin is mainly composed of shallow water platform edges and restricted platforms, as well as carbonate rock slopes and open non-marine shelves in deep water areas. A sedimentary facies model of the Triassic carbonate rock segment in the basin was established, comprising restricted platforms, platform edges, carbonate rock slopes, and non-marine shelves. Unlike the modified Wilson marginal carbonate rock platform model, the carbonate rock platform edge in the Mangeshlak Basin does not develop reef facies. Instead, it is mainly composed of oolitic beach (dam) sediments, making it the most favorable sedimentary facies zone for the Triassic reservoir development in the basin. Full article

Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90. Full article

To provide safe drinking water in contaminated hydrogeological environments, it is essential to have precise geochemical information on contamination hotspots. In this study, Geographic Information System (GIS) and multivariate statistics were utilized to analyze the spatial patterns, occurrence, and major factors controlling uranium (U) concentrations in groundwater. The global and local Moran’s I indices were utilized to detect hotspots and cool spots of U distribution. The substantial positive global Moran’s I index (at a p-value of 0.05) revealed a geographical pattern in U occurrences. The spatial clusters displayed patterns of drinking water source with U concentrations below and above the WHO limit, categorized as “regional U cool spots” and “regional U hotspots”, respectively. Spatial autocorrelation plots revealed that the high–high potential spatial patterns for U were situated in the northeastern region of the study area. As the order of queen’s contiguity increased, prospective low–high spatial patterns transitioned from the Faridkot district to the Muktsar district for U. Further, the multivariate statistical analysis methods such as correlation and principal component analysis (PCA) plots revealed substantial positive associations (p-value < 0.05) between U and total dissolved solids (TDS), salinity (SL), bicarbonate (HCO₃⁻), and sodium (Na) in groundwater from both shallow and deeper depth, indicating that these water quality parameters can significantly influence the occurrence of U in the groundwater. The output of the random forest model shows that among the groundwater parameters, TDS is the most influential variable for enrichment of U in groundwater, followed by HCO₃⁻, Na, F⁻, SO₄²⁻, Mg, Cl⁻, pH, NO₃⁻, and K concentrations. Additionally, the results of health risk assessment indicate that 47.86% and 41.3% of samples pose risks to children and adults, respectively, due to F⁻−contamination. About 93.49% and 89.14% of samples pose a risk to children and adults, respectively, due to U contamination, whereas 51.08% and 39.13% of samples pose a risk to children and adults, respectively, from NO₃⁻ contamination. The current data indicates an urgent need to create cost-effective and efficient remediation techniques for groundwater contamination in this region. Full article

To explore the effects of different plant combinations in ecological floating beds on water quality purification and phytoplankton community structure in shallow eutrophic lakes, we conducted a survey of phytoplankton communities within ecological floating beds featuring distinct plant combinations in Meiliang Bay, Lake Taihu, during June and August 2021. The study focuses on two combinations: EA (Canna indica + Acorus calamus + Phragmites australis) and ES (Canna indica + Oenanthe javanica + Sagittaria sagittifolia). Results indicated that ecological floating beds significantly improved water quality, with the strongest restoration effects observed in the EA area. Specifically, turbidity was reduced by 47–89%, while chlorophyll a (Chl-a) concentration inhibition rates reached 82% in June and 54% in August. The comprehensive trophic state index (TLI) remained stable at levels indicating slight eutrophication (≤58.6). Phytoplankton community structure shifted from dominance by eutrophic functional groups (primarily FG M) toward greater diversity. In the EA area, the number of dominant functional groups increased from five (control) to six, and the abundance of the key cyanobacteria group (FG M) declined from 18.29% (control) to 7.86%. Redundancy analysis (RDA) revealed temporal changes in driving factors: nutrients were primary in June (explanation rate: 64.7%), while physical factors dominated in August (explanation rate: 51.2%). This study demonstrates that installing ecological floating beds with diverse plant combinations in shallow eutrophic lakes can effectively alter phytoplankton community structure and enhance in situ water restoration. Among the tested combinations, EA (Canna indica + Acorus calamus + Phragmites australis) exhibited the optimal restoration effect. These findings provide a scientific basis for water environment protection and aquatic biological resource restoration in shallow eutrophic lakes. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

In airborne LiDAR measurements of shallow water bathymetry, conventional data processing often overlooks the radiative losses associated with multiple scattering events, affecting detection accuracy. This study presents a Monte Carlo-based approach to construct a mathematical model that accurately characterizes the multiple returns in airborne laser bathymetric systems. The model enables rapid simulation of laser propagation through water, accounting for multiple scattering events. Based on the Beer–Lambert law and incorporating the parameters of typical Jerlov 1 clear coastal water, the proposed model achieves a seamless integration of the H-G phase function with a Monte Carlo random process, enabling accurate simulation and validation of pulse temporal broadening in waters with varying optical transparency. Unlike most existing studies, which primarily focus on modeling the laser emission process, this work introduces a novel perspective by analyzing the probability of light reception in LiDAR return signals, offering a more comprehensive understanding of signal attenuation and detection performance in underwater environments. The results demonstrate that, for detecting underwater targets at depths of 10 m, considering three or more scattering events improves the accuracy by ~7%. For detecting underwater targets at depths of 50 m, considering three or more scattering events improves the accuracy by 15~33%. These findings can help enhance the detection accuracy and efficiency of experimental systems. Full article