Search Results (119)

Understanding the distribution pattern and its drivers of species is crucial for developing effective and sustainable management strategies. Amphioctopus fangsiao is the octopus of significant commercial and ecological value along the coast of China, with multiple distinct populations. However, research on their ecological dynamics remains limited and requires further investigation. Here, ensemble models were constructed to examine the spatio-temporal distribution and inter-populational differentiation in environmental adaptability of A. fangsiao in the East China Sea (ECS) and the South Yellow Sea (SYS). Specifically, we generated the ensemble models by integrating Gradient Boosting Machine (GBM), Generalized Linear Models (GLMs), and Maximum Entropy Models (MaxEnt) for the different populations across four seasons, using fishery-independent data collected from 2015 to 2021. The results revealed two hotspots of A. fangsiao in the ECS and SYS: one is the area of SYS along the coastal waters, with latitudes 33° N–34° N and longitudes 120° E–122° E (northern population, NP); the other one is near the Kuroshio-adjacent area with latitudes 28.5° N–29° N and longitudes 123° E–124.5° E (southern population, SP). Both NP and SP exhibited distinct seasonal habitat preferences, with key environmental drivers showing seasonal variations. The NP tended to inhabit coastal waters with lower sea surface heights (SSHs), shallower water depth, and a narrower sea bottom salinity range (SBS). In contrast, SP preferred marine environments with a thicker mixed layer thickness (MLT) and higher concentrations of bottom chlorophyll-a (Chl_b). The environmental characterization of suitable habitats revealed distinct patterns in resource utilization and environmental adaptation strategies between the two populations. This study provides fundamental data for understanding A. fangsiao population dynamics and underscores the importance of considering population-specific habitat preferences within dynamic marine environments. Full article

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch. Full article

Sea surface height (SSH) serves as a fundamental geophysical parameter in oceanographic research. In 2023, China successfully launched the world’s first spaceborne interferometric GNSS-R (iGNSS-R) altimeter, which features dual-frequency multi-beam scanning, interferometric processing, and compatibility with three major satellite navigation systems: the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the Galileo Satellite Navigation System (GAL). This launch marked the first in-orbit validation of the iGNSS-R altimetry technology. This study provides a detailed overview of the iGNSS-R payload design and analyzes its dual-frequency delay mapping (DM) measurements. We developed a refined DM waveform-matching algorithm that precisely extracts the propagation delays between reflected and direct GNSS signals, enabling the retrieval of global sea surface height (SSH) through the interferometric altimetry model. For validation, we employed an inter-satellite crossover approach using Jason-3 and Sentinel-6 radar altimetry as references, achieving an unprecedented SSH accuracy of 17.2 cm at a 40 km resolution. This represents a breakthrough improvement over previous GNSS-R altimetry efforts. The successful demonstration of iGNSS-R technology opens up new possibilities for cost-effective, wide-swath sea level monitoring. It showcases the potential of GNSS-R technology to complement existing ocean observation systems and enhance our understanding of global sea surface dynamics. Full article

Traditional methods for the construction of intertidal digital elevation models (DEMs) require the integration of long-term multi-sensor datasets and struggle to capture the spatiotemporal variation caused by ocean dynamics. The SWOT (surface water and ocean topography) mission, with its wide-swath interferometric altimetry technology, provides instantaneous full-swath elevation data in a single pass, offering a revolutionary data source for high-precision intertidal topographic monitoring. This study presents a framework for SWOT-based intertidal DEM extraction that integrates data preprocessing, topographic slope map construction, and tidal channel masking. The radial sand ridge region along the Jiangsu coast is analyzed using SWOT L2 LR (Low Resolution) unsmoothed data from July 2023 to December 2024. Multisource validation data are used to comprehensively assess the accuracy of sea surface height (SSH) and land elevation derived from LR products. Results show that the root mean square error (RMSE) of SSH at Dafeng, Yanghe, and Gensha tide stations is 0.25 m, 0.19 m, and 0.32 m, respectively. Validation with LiDAR data indicates a land elevation accuracy of ~0.3 m. Additionally, the topographic features captured by LR products are consistent with the patterns observed in the remote sensing imagery. A 16-month time-series analysis reveals significant spatiotemporal variations in the Tiaozini area, particularly concentrated in the tidal channel areas. Furthermore, the Pearson correlation coefficient for the DEMs generated from SWOT data decreased from 0.94 over a one-month interval to 0.84 over sixteen months, reflecting the persistent impact of oceanic dynamic processes on intertidal topography. Full article

Revealing singular quantum phenomena in various non-Hermitian systems is a hot topic in condensed matter physics research, with the bulk-boundary correspondence being one of the core issues in non-Hermitian topological states. In addition, the spin-orbit coupling (SOC) applied to electrons moving in the electric field in the material can bring unique topological properties to the energy band of the material. We investigated the topological phase transition of a non-Hermitian Su–Schrieffer–Heeger (SSH) model with SOC in the generalized Brillouin zone (GBZ). We demonstrate that SOC can alter the position and number of phase transition points. Due to the non-Hermitian skin effect, the bulk-boundary correspondence is broken, and the local positions of zero mode and bulk eigenstates will also change. By unitary transformation, two subspaces were obtained, and the exact solution of topological phase transition was obtained in the GBZ. The exact solution of non-Hermitian systems with the Dresselhaus and Rashba types of SOC is consistent with the numerical solutions. This result can be applied to more complex non-Hermitian models, providing a strong reference for experimental researchers in topological materials. Full article

Compared to conventional pulse-limited altimeters (i.e., low-resolution mode, LRM), the synthetic aperture radar (SAR, i.e., high-resolution mode, HRM) altimeter offers superior precision and along-track resolution abilities. However, because the SAR altimeter relies on Doppler shifts caused by the relative movement between radar scattering points and the altimeter antenna, the geophysical parameters obtained by the SAR altimeter are sensitive to the direction of ocean wave movements driven by the wind and waves. Both practice and theory have shown that the wind and wave effects have a greater impact on HRM data than LRM. LRM values of range and significant wave height (SWH) from modern retracking are the best representations there are of these quantities, and this study aims to bring HRM data into line with them. In this study, wind and wave effects in SAR altimeter measurements were analyzed and corrected. The radar altimeter onboard the Sentinel-6 satellite is the first SAR altimeter to operate in an interleaved open burst mode. It has the capability of simultaneous generation of both LRM and HRM data. This study utilizes Sentinel-6 altimetry data and ERA5 re-analysis data to identify the influence of ocean waves. The analysis is based on the altimeter range and SWH differences between the HRM and LRM measurements with respect to different geophysical parameters derived from model data. Results show that both HRM range and SWH measurements are impacted by SWH and wind speed, and the HRM SWH measurements are also significantly impacted by vertical velocity. An upwave/downwave bias between HRM and LRM range is observed. To reduce wave impact on the SAR altimeter measurements, a back-propagation neural network (BPNN) method is proposed to correct the HRM range and SWH measurements. Based on Sentinel-6 measurements and ERA5 re-analysis data, our corrections significantly reduce biases between LRM and HRM range and SWH values. Finally, the accuracies of the sea surface height (SSH) and SWH measurements after correction are assessed using crossover analysis and compared against NDBC buoy data. The standard deviation (STD) of the HRM SSH differences at crossovers has no significant changes before (3.97 cm) and after (3.94 cm) correction. In comparison to the NDBC data, the root mean square error (RMSE) of the corrected HRM SWH data is 0.187 m, which is significantly better than that with no correction (0.265 m). Full article

Intrusion detection is a critical component of cybersecurity, enabling timely identification and mitigation of network threats. This study proposes a novel two-stage intrusion detection framework using the CSE-CIC-IDS2018 dataset, a comprehensive and realistic benchmark for network traffic analysis. The research explores two distinct approaches: the stacked autoencoder (SAE) approach and the Apache Spark-based (ASpark) approach. Each of these approaches employs a unique feature representation technique. The SAE approach leverages an autoencoder to learn non-linear, data-driven feature representations. In contrast, the ASpark approach uses principal component analysis (PCA) to reduce dimensionality and retain 95% of the data variance. In both approaches, a binary classifier first identifies benign and attack traffic, generating probability scores that are subsequently used as features alongside the reduced feature set to train a multi-class classifier for predicting specific attack types. The results demonstrate that the SAE approach achieves superior accuracy and robustness, particularly for complex attack types such as DoS attacks, including SlowHTTPTest, FTP-BruteForce, and Infilteration. The SAE approach consistently outperforms ASpark in terms of precision, recall, and F1-scores, highlighting its ability to handle overlapping feature spaces effectively. However, the ASpark approach excels in computational efficiency, completing classification tasks significantly faster than SAE, making it suitable for real-time or large-scale applications. Both methods show strong performance for distinct and well-separated attack types, such as DDOS attack-HOIC and SSH-Bruteforce. This research contributes to the field by introducing a balanced and effective two-stage framework, leveraging modern machine learning models and addressing class imbalance through a hybrid resampling strategy. The findings emphasize the complementary nature of the two approaches, suggesting that a combined model could achieve a balance between accuracy and computational efficiency. This work provides valuable insights for designing scalable, high-performance intrusion detection systems in modern network environments. Full article

The impact of global warming on fish distribution is a key factor in fishery management and sustainable development. However, limited knowledge exists regarding the influence of environmental factors on the distribution of Evynnis cardinalis under climate change. This study addresses this gap by predicting the species distribution under current conditions and three future climate scenarios (SSP126, SSP370, and SSP585) using five individual models and four ensemble models. The results demonstrate that the ensemble models outperform the single models, with majority voting (EMca) achieving the highest accuracy (ROC = 0.97, TSS = 0.85). Bathymetry (BM) and the sea surface height (SSH) are the primary factors influencing the distribution. The predictions indicate that the currently suitable habitats of E. cardinalis are primarily located in the Beibu Gulf region of the northern South China Sea. Under future climate scenarios, suitable habitat areas are expected to expand to higher latitudes and deeper waters, though highly suitable habitats in the western Guangdong coastal waters, western Beibu Gulf, and southwestern offshore waters of Hainan Island will significantly decrease. Full article

This study examines the potential distribution of Mackerel scad (Decapterus macarellus) in the South China Sea under future climate scenarios (SSP 1.26, SSP 2.45, SSP 5.85) using an ensemble species distribution model (SDM). Key environmental variables included sea surface salinity (SSS), sea surface height (SSH), sea surface temperature (SST), mixed-layer depth (MLD), chlorophyll-a concentration (CHL), and sea-bottom temperature (SBT). Results show that SST and MLD are the primary drivers of habitat suitability, with current suitable habitats concentrated in the northern offshore areas. Projections for the 2050s and 2090s indicate a reduction in suitable habitats, particularly under high-emission scenarios, with more gradual reductions under low-emission scenarios. Habitat loss is most pronounced in the northern South China Sea, while the central region is projected to see an expansion of suitable habitats. These findings highlight the climate impact on D. macarellus distribution and inform sustainable management strategies for the species in the region. Full article

With the accumulation of synthetic aperture radar (SAR) altimetry data and advancements in retracking algorithms, the improved along-track spatial resolution and signal-to-noise ratio have significantly enhanced the availability and precision of sea surface height (SSH) measurements, particularly in challenging environments such as coastal areas, ocean currents, and polar regions. These improvements have refined the accuracy and reliability of mean sea surface (MSS) models, which in turn have enhanced the precision of mean dynamic topography (MDT) and geostrophic current models. However, in-depth research is required to quantify the specific contributions of SAR altimetry to these critical regions and their impacts on the MSS, MDT, and geostrophic currents. Given that DTU21MSS (Technical University of Denmark MSS 2021) incorporates a substantial amount of SAR altimetry data, this study utilized independent Sentinel-3A altimetric observations to evaluate the signal improvements of DTU21MSS compared with DTU15MSS, with a focus on its performance in polar, coastal, and current regions. In addition, a least-squares-based approach was employed to assess the impact of the improved MSS model on the deduced MDT and geostrophic current signals. The numerical results revealed that DTU21MSS achieved an accuracy improvement of ~8% within 20 km offshore compared with DTU15MSS. In the polar regions within 100 km offshore, DTU21MSS exhibited a maximum signal enhancement of ~0.1 m, with overall improvements of 10–20%. The DTU21MSS-derived MDT solution demonstrates better consistency with validation data, reducing the standard deviation of misfits from 0.058 m to 0.054 m. Signal enhancements of maximumly 0.1 m were observed in the polar regions and the Mediterranean/Red Sea. Furthermore, improvements in the MSS and its error information could directly enhance the deduced MDT models, highlighting its foundational role in precise oceanographic modeling. Full article

New observations of ocean surface topography obtained by wide-swath satellite interferometry require new capabilities to process spatially correlated errors in order to assimilate these data into numerical models. The sea surface height (SSH) variations have to be weighted against other types of assimilated data using information on their precision, as represented by the inverse of the SSH error covariance matrix $\mathit{R}$. The latter can be well approximated by a block-circulant (BC) structure and, therefore, allows numerically efficient implementation in operational data assimilation (DA) systems. In this note, we extend the technique of approximating $\mathit{R}$ for wide-swath altimeters by including the uncertainties associated with the state of the atmosphere. It is shown that such an extension keeps the BC approximation error within acceptable (±10%) bounds in a wide range of environmental conditions and could be beneficial for improving the accuracy of SSH retrievals from wide-swath altimeter observations. Full article

Traditional spherical sector microphone arrays using omnidirectional microphones face limitations in modal strength and spatial resolution, especially within spherical sector configurations. This study aims to enhance array performance by developing a spherical sector array employing first-order cardioid microphones. A model based on spherical sector harmonic (SSH) functions is introduced to extend the benefits of spherical harmonics to sector arrays. Modal strength analysis demonstrates that cardioid microphones in open spherical sectors enhance nonzero-order strengths and eliminate the nulls associated with spherical Bessel functions. We find that the spatial resolution of spherical cap arrays depends on the array’s maximum order and the limiting polar angle, but is independent of the microphone gain pattern. We assess direction-of-arrival (DOA) estimation performance for coherent wideband sources using the array manifold interpolation method, and compare cardioid and omnidirectional arrays through simulations in both open and rigid hemispherical configurations. The results indicate that cardioid arrays outperform omnidirectional ones on DOA estimation tasks, with performance improving alongside increased microphone directivity in the open hemispherical configuration. Specifically, hypercardioid microphones yielded the best results in the open configuration, while subcardioid microphones (without nulls) were optimal in rigid configurations. These findings demonstrate that spherical sector arrays of first-order cardioid microphones offer improved modal strength and DOA estimation capabilities over traditional omnidirectional arrays, providing significantly enhancing performance in spherical sector array processing. Full article

Deep learning-based autoencoders represent a promising technology for use in network-based attack detection systems. They offer significant benefits in managing unknown network traces or novel attack signatures. Specifically, in the context of critical infrastructures, such as power supply systems, AI-based intrusion detection systems must meet stringent requirements concerning model accuracy and trustworthiness. For the intrusion response, the activation of suitable countermeasures can greatly benefit from additional transparency information (e.g., attack causes). Transformers represent the state of the art for learning from sequential data and provide important model insights through the widespread use of attention mechanisms. This paper introduces a two-stage transformer-based autoencoder for learning meaningful information from network traffic at the packet and sequence level. Based on this, we present a sequential attention weight perturbation method to explain benign and malicious network packets. We evaluate our method against benchmark models and expert-based explanations using the CIC-IDS-2017 benchmark dataset. The results show promising results in terms of detecting and explaining FTP and SSH brute-force attacks, highly outperforming the results of the benchmark model. Full article

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing $N_t$ sub-channels of $\mathcal{K}$-sparse to 2$N_t$ irrespective of CIR residual compensation. Full article