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346 results for “ships”
Data supplementing article "Estuarine circulation in a shallow but stratified estuary: Different responses to river discharge between deep ship channel and shoals"
<p>The dataset uploaded includes the measured salinity and velocity at two monitoring stations (one at the lower Mobile Bay and the other at the eastern edge of ship channel in middle Mobile Bay) and from multiple ship cruises crossing the lower, middle, and upper Mobile Bay. </p> <p>Detail information on the measurement frequency, date, and location can be found in the mat files. Records with bad quality are filled with NaN values. </p>
data of " Arctic sea fog observation along trans-Arctic shipping routes"
<p>2025-03-08 Erratum for the 【README.txt】 file : The ASOS station data in the 【ASOS Arctic sites.zip】 file covers the period from 1971 to 2023. However, in the article 'Observed Climatology and Formation Mechanisms of Sea Fog Along the Trans-Arctic Shipping Routes', the study timeframe was selected as 1979–2023 to account for data quality considerations and to facilitate cross-dataset comparisons.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Analysis and emissions data for "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?"
<p>Analysis scripts and SOx emissions data produced from STEAM emissions model (courtesy of Jukka-Pekka Jalkanen, FMI). Used in "What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds?" submitted to GRL.</p>
Characteristics of Mesoscale to Submesoscale Eddies in the Labrador Sea: Insights from Ship Observations
<p>Numerical simulation output from NATL60 model in the Labrador Sea: depth-averaged (15-100 m) horizontal velocity vector (u, v) for three snapshots (15 May 2013, 15 June 2013, 15 August 2013). The data was subject to an eddy tracking algorithm (Angular Momentum Eddy Detection and tracking Algorithm; AMEDA; https://doi.org/10.1175/JTECH-D-17-0010.1)</p>
Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations"
<p>Data and codes in support of "Accelerating Green Shipping by Spatially Optimized Offshore Charging Stations", including data, codes and figures.</p>
Results Maritime Transportation - Deep-Sea and Short-Sea Shipping Instances
<p>Each instance has a filename "scip_details_deep_n_i.txt"/"scip_details_short_n_i.txt", in which deep/short indicates that the trips are international/regional (deep sea shipping/short sea shipping), n is the number of ports (including the depot), and i is the index of the instance in the same group.</p> <p>The data in the file represent the console output of scip. It contains information on:</p> <ul> <li>computation time</li> <li>gap</li> <li>primal- and dualbound</li> <li>best found integer solution</li> </ul> <p> </p>
Data from: Is sex advantageous in adverse environments? A test of the abandon-ship hypothesis
Understanding the evolution and maintenance of sexual reproduction remains a long-standing challenge in evolutionary biology. Stress often induces sexual reproduction in facultatively sexual species (those species capable of both sexual and asexual reproduction). The abandon-ship hypothesis predicts higher allocation to sex under stress to allow low-fitness individuals to recombine their genotype, potentially increasing offspring fitness. However, effective tests of the abandon-ship hypothesis, particularly in multicellular organisms, are lacking. Here we test the abandon-ship hypothesis, using cyanogenic and acyanogenic defense phenotypes of the short-lived perennial herb Trifolium repens. Cyanogenesis provides an effective defense against herbivores and is under relatively simple genetic control (plants dominant for cyanogenesis at two alleles express the defended phenotype). Thus, maladapted individuals can acquire adaptive defense alleles for their offspring in a single episode of sexual reproduction. Plants were grown under high- and low-herbivory treatments (plants were exposed to herbivorous snails) and a control treatment (no herbivory). Herbivores reduced growth and fitness in all treated plants, but herbivory induced higher sexual allocation only in maladapted (acyanogenic) individuals. Overall, our results support the abandon-ship hypothesis.
Data from: Disentangling invasion processes in a dynamic shipping - boating network
The relative importance of multiple vectors to the initial establishment, spread, and population dynamics of invasive species remains poorly understood. This study used molecular methods to clarify the roles of commercial shipping and recreational boating in the invasion by the cosmopolitan tunicate, Botryllus schlosseri. We evaluated i) single vs. multiple introduction scenarios, ii) the relative importance of shipping and boating to primary introductions, iii) the interaction between these vectors for spread (i.e., the presence of a shipping-boating network), and iv) the role of boating in determining population similarity. Tunicates were sampled from 26 populations along the Nova Scotia, Canada, coast that were exposed to either shipping (i.e., ports), or boating (i.e., marinas) activities. A total of 874 individuals (~30 per population) from 5 ports and 21 marinas was collected and analyzed using both mitochondrial cytochrome c oxidase subunit I gene (COI) and 10 nuclear microsatellite markers. The geographical location of multiple hotspot populations indicates that multiple invasions have occurred in Nova Scotia. A loss of genetic diversity from port to marina populations suggests a stronger influence of ships than recreational boats on primary coastal introductions. Population similarity analysis reveals a clear dependence of marina populations on those that had been previously established in ports and connectivity due to a boating network better explains patterns in population similarities than does natural spread. We conclude that frequent primary introductions arise by ships and that secondary spread occurs gradually thereafter around individual ports, facilitated by recreational boating.
Narwhals react to ship noise and airgun pulses embedded in background noise
<p>Anthropogenic activities are increasing in the Arctic posing a threat to species with high seasonal site-fidelity, such as the narwhal Monodon monoceros. In this controlled sound exposure study, six narwhals were live-captured and instrumented with animal-borne tags providing movement and behavioural data, and exposed to concurrent ship noise and airgun pulses. All narwhals reacted to sound exposure by reduced buzzing rates, where the response was dependent on the magnitude of exposure defined as 1/distance to ship. Halving of buzzing rate, compared with undisturbed behaviour, and cessation of foraging occurred at 12 and ~7-8 km from the ship, respectively. The effect of exposure could be detected > 40 km from the ship. At distances > 5 km, the received high-frequency cetacean weighted sound exposure levels were below background noise indicating sensitivity of narwhals towards sound disturbance and demonstrating their ability to detect signals embedded in noise. Further studies are needed to evaluate the energetic costs of disrupted foraging due to sustained disturbance but the observed sensitivity should be considered in the management of anthropogenic activities in the Arctic. The results of this study emphasize the importance of controlled sound exposure studies in the wild to explore the auditory capabilities of odontocetes.</p>
Ship Wrecks dataset
<p>Dataset of shipwrecks around the world. Extracted from wikipedia</p>
Data for 'Adapting to a Foggy Future along Trans-Arctic Shipping Routes'
<p>1.【XXXX_bc_ff_and_seaice.mat】The data is simulated Arctic sea fog frequency during 1979-2018 by using 6-hourly ERA-Interim data and Polar WRF. The fog results are bias corrected by ICOADS observations.</p> <p> The dimension of the data is 5*95*95, which means five fog-diagnosed method (SW99, FSL, UPP, G2006 and G2009), 95 lontitude and 95 latitude. In the paper, we only use SW99 method, which is the first fog-diagnosed method.</p> <p>2.【ff_XXX_cmip5_best_model_XXXX_XXXX.mat】The data is projected Arctic sea fog frequency based on the "best" six models from CMIP5.</p> <p>3.【Code for deriving shipping routes.zip】The code for designing shipping routes. This version is the latest one (update in 20230331).</p> <p> </p> <p>More detail please sees the paper <strong>"Adapting to a Foggy Future along Trans-Arctic Shipping Routes</strong>".</p>
Keypoints Method for Recognition of Ship Wake Components in Sentinel-2 Images by Deep Learning
<p>The dataset used in the study consists of imagery capturing ship wake patterns. It is a manually curated dataset specifically created for the purpose of training and evaluating the wake component detection model. The dataset contains a collection of image chips, each focusing on a specific ship wake instance.</p> <p>The imagery in the dataset is acquired from satellite sensors, specifically on Sentinel-2 satellite imagery. Sentinel-2 provides multispectral data with high spatial resolution, allowing for detailed analysis of ship wake patterns. The dataset includes images captured on B8 spectral band, enabling the exploration of the wake detection model's performance under various spectral conditions. These images have been pre-processed (by scaling+CLAHE) to highlight ocean surface features.</p> <p>Each image chip in the dataset is annotated with keypoint locations representing specific wake components, such as the ship wake vertex, the ending of the turbulent wake, and the ending of Kelvin arms. These annotations serve as ground truth labels for training and evaluating the wake component detection model. </p> <p>Additionally, the dataset includes samples with variations in environmental conditions, such as different sea states, lighting conditions, and wake complexities. This variability allows for a comprehensive evaluation of the model's generalization capability and robustness across diverse scenarios.</p>
Replication package for: Market Power in Coal Shipping and Implications for U.S. Climate Policy
<p>This package contains all code necessary to reproduce the figures and tables in:</p> <p>Preonas, Louis (forthcoming). "Market Power in Coal Shipping and Implications for U.S. Climate Policy." Review of Economic Studies.</p> <p>The README document contains detailed instructions for conducting a full replication (from raw data), or for only replicating the analysis presented in the paper's figures and tables.</p>
xAI Ship Wakes in Sentinel-2 L2A images
<h2><strong>xS2Wakes: A dataset for xAI of Wakes in S-2 (L2A).</strong></h2><h3><strong>Summary</strong></h3><p>The dataset is derived from Sentinel-2 Level-2A (L2A) satellite images and focuses on the marine domain over Danish fjords. It provides a comprehensive collection of ship wakes and background clutter (referred to as "no_<i>wake</i>_crop") for remote sensing applications. The dataset has undergone post-processing through the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm with a clip limit value of 0.12 and a tile size of 16x16. The dataset comprises four spectral bands: B2, B3, B4, and B8.</p><h3><strong>Importance and Relevance to Remote Sensing Community</strong></h3><h4>Multifaceted Applications of Wake Detection</h4><p>Ship wake detection serves as a cornerstone in a multitude of domains that are critical to both human and environmental well-being:</p><p><strong>Navigational Safety</strong>: Understanding ship wakes can provide insights into water currents and traffic patterns. This is vital for ensuring the safe passage of marine vessels, particularly in narrow straits and busy ports.</p><p><strong>Environmental Monitoring</strong>: The study of ship wakes can reveal the influence of vessels on aquatic ecosystems. For instance, excessive wake turbulence can lead to coastal erosion and can disrupt marine habitats.</p><p><strong>Maritime Surveillance</strong>: Wake detection plays a crucial role in maintaining maritime security. Tracking the wakes of vessels can help in identifying illegal activities such as smuggling or unauthorized fishing.</p><h3><strong>Specifications</strong></h3><ul><li><strong>Data Source</strong>: Sentinel-2 L2A</li><li><strong>Region of Interest</strong>: Danish fjords</li><li><strong>Classes</strong>: Wake, No-Wake</li><li><strong>Number of Samples</strong>:<ul><li>Wake: 123</li><li>No-Wake: 150</li></ul></li><li><strong>Spectral Bands</strong>: B2 (Blue), B3 (Green), B4 (Red), B8 (NIR)</li><li><strong>Post-Processing</strong>: CLAHE (Clip Limit = 0.12, Tile Size = 16x16)</li><li><strong>Average Wake Chip Size</strong>: 390x351 pixels</li><li><strong>Average No-Wake Chip Size</strong>: 380x390 pixels</li></ul><h3><strong>Wake Detection and Analysis</strong></h3><h4>Traditional Methods and Their Limitations</h4><p>Traditionally, the process of ship wake detection has largely been a manual endeavor or employed simplistic statistical algorithms. Analysts would sift through satellite or aerial images to identify ship wakes, a process that is both time-consuming and prone to human error. Even automated statistical methods often lack the robustness needed to differentiate between true wakes and false positives, such as aquatic plants or natural water disturbances.</p><h4>Role of xAI (Explainable AI) in Wake Identification</h4><p>The introduction of explainable AI (xAI) techniques brings another layer of sophistication to wake analysis. While traditional machine learning models may offer high performance, they often act as "black boxes," making it difficult to understand how they arrive at a certain conclusion. In a critical domain like navigational safety or maritime surveillance, the ability to interpret and understand model decisions is indispensable. xAI methods can make these machine learning models more transparent, providing insights into their decision-making processes, which in turn can aid in fine-tuning or fully trusting the models.</p><h4>Spectral Bands Selected</h4><p>The inclusion of four key spectral bands—B2, B3, B4, and B8—offers the scope for multi-spectral analysis. Different bands can capture varying features of water and wake textures, thereby offering a richer feature set for machine learning models. We use these spectral bands as referred to in [Liu, Yingfei, Jun Zhao, and Yan Qin. "A novel technique for ship wake detection from optical images." <i>Remote Sensing of Environment</i> 258 (2021): 112375.] </p><h4>Understanding Optical vs. SAR Imaging Modalities</h4><p>It is important to note the fundamental differences between wakes captured in Synthetic Aperture Radar (SAR) images and those in optical imagery. In SAR images, narrow-V wakes often arise due to Bragg scattering, a phenomenon that does not exist at optical wavelengths. In optical images, bright lines close to turbulent wakes are actually foams generated by the interaction between the surface horizontal flow of turbulent wakes and the surrounding background waves (Ermakov et al., 2014; Milgram et al., 1993; Peltzer et al., 1992). This can make the detection of wakes in optical images more challenging as there are usually no bright lines near turbulent wakes, and Kelvin arms may also show dark contrast. Methods that solely rely on searching for a trough and peak pair, taking the trough as the turbulent wake, would miss many actual wakes and could also result in the identification of false wakes.</p><h4>Contrast Enhancement</h4><p>The application of the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm to this dataset allows for enhanced local contrast, enabling subtle features to become more pronounced. This significantly aids machine learning algorithms in feature extraction, thereby improving their ability to distinguish between complex patterns.</p><h4>Environment and Clutter Assessment</h4><p>In addition to wakes, the dataset contains samples labeled as "No-Wake," which include environmental clutter and clouds. These samples are crucial for training robust models that can differentiate wakes from similar-looking natural phenomena.</p>
HYDROcortisone Versus Placebo for Severe HospItal-acquired Pneumonia in Intensive Care Patients: the HYDRO-SHIP Study
ClinicalTrials.gov study NCT05354778. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Narwhals react to ship noise and airgun pulses embedded in background noise
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