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274 results for “wakes”
FIGURE 19. Male left middle tibia and tarsal segment 1 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 19. Male left middle tibia and tarsal segment 1, dorsal aspect legs: (a) Conchopus ciliatus sp. nov.; (b) Conchopus crassinervis sp. nov.
FIGURE 9 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 9. Conchopus crassinervis sp. nov., male genitalia: (a) hypopygium; (b) ventral lobe; (c) cercus; (d) hypandrium in lateral view; (e) hypandrium in dorsal view.
FIGURE 17 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 17. Conchopus pacificus sp. nov., male genitalia: (a) ventral lobe; (b) cercus; (c) hypandrium in lateral view; (d) hypandrium in dorsal view.
FIGURE 8 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 8. Conchopus crassinervis sp. nov., male abdominal sterna: (a) 2nd (top) to 5th (bottom); (b) pedunculate process in lateral view; (c) sternum 6.
FIGURE 7 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 7. Conchopus crassinervis sp. nov., legs of male: (a) distal part of tibia, tarsal segments 1–2 of left foreleg in posterior view; (b) right foreleg in anterior view; (c) left middle femur in anterior view.
FIGURE 6 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 6. Conchopus ciliatus sp. nov., male genitalia: (a) hypopygium; (b) ventral lobe; (c) cercus; (d) hypandrium in lateral view; (e) hypandrium in dorsal view.
FIGURE 5 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 5. Conchopus ciliatus sp. nov., male abdominal sterna: (a) 1st (top) to 5th (bottom); (b) pedunculate process in lateral view; c, sternum 6.
FIGURE 3 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 3. Conchopus acrosticalis (Parent), male genitalia: (a) hypopygium; b, ventral lobe; c, cercus; d, hypandrium in lateral view; e, hypandrium in dorsal view.
FIGURE 2 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 2. Conchopus acrosticalis (Parent), male abdominal sterna: (a) 1st (top) to 5th (bottom); (b) pedunculate process in lateral view; c, sternum 6.
FIGURE 1 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 1. Conchopus acrosticalis (Parent), legs of male: (a) left fore leg in posterior view; (b) distal part of tibia, tarsal segments 1–2 of right fore leg in anterior view; (c) left middle femur in anterior view.
FIGURE 4 in A revision of the Hawaiian and Wake Island species of the genus Conchopus Takagi (Diptera, Dolichopodidae)
FIGURE 4. Conchopus ciliatus sp. nov., legs of male: (a) left fore leg in posterior view; (b) distal part of tibia, tarsal segments 1–2 of right foreleg in anterior view; c, left middle femur in anterior view.
Turek Laboratory Sleep-Wake Dataset and R Code for Analysis
<p>Sleep-wake dataset for MCI-Park mice and R code used for statistical analysis. </p>
Internet Appendix for: "Disentangling wake and projection effects in the aerodynamics of wind turbines with curved blades''
<p>This is the internet appendix of the paper: "Disentangling wake and projection effects in the aerodynamics of wind turbines with curved blades"</p>
Observations of island wakes at high Rossby numbers: Evolutions of submesoscale vortices and free shear layer
<p>The dataset contains shipboard and moored current and temperature measurements used to produce the figures in a manuscript entitled as "Observations of island wakes at high Rossby numbers: Evolutions of submesoscale vortices and free shear layer". All the data are in MATLAB file format.</p> <p>Fig3_Experiment2.mat: current velocity and temperature data shown in Figure 3 in ten surveys.</p> <p>Fig4_MOORING.mat: Moored zonal velocity data at W1 and W2 shown in Figure 4.</p> <p>Fig6_L6.mat: meridional current data, Ro and shear-squared shown in Figure 6.</p> <p>Fig7_Experiment1_T1.mat: temperature and current data in Figures 7a-c.</p> <p>Fig7_case05.mat: temperature and current data in Figures 7d and 7g.</p> <p>Fig7_case08.mat: temperature and current data in Figures 7e and 7h.</p> <p>Fig7_case11.mat: temperature and current data in Figure 7f and 7i.</p> <p>Fig8_(a)-(e): temperature and current data in Figure 8.</p> <p> </p> <p> </p>
FIGURE 2 in Caecum wakense: a new Caecidae (Gastropoda: Caenogastropoda) from Wake Atoll (Pacific Ocean)
FIGURE 2. Conceptual reconstruction of growth stages of Caecum wakense sp. nov.: A. Protoconch to teleoconch IV. B. Surface of teleoconchs I and II. C. Surface of teleoconchs III and IV. White dots indicate transition points.
NETTUNO Experiment 1 – Wake Development in Floating Wind Turbines
<p>This dataset, collected as part of the NETTUNO research project, includes measurements from wind tunnel tests on a 1:75 scale model wind turbine. The primary focus of the experiment was to analyze how platform motion in different directions affects the aerodynamics of the wind turbine rotor and the development of its wake. The dataset consists of two components:<br>• Measurements of the aerodynamic forces and moments experienced by the rotor under various platform motion conditions.<br>• Wind speed measurements collected at multiple downstream distances from the rotor, capturing the velocity profiles and turbulence characteristics within the turbine's wake.<br>The dataset is designed to serve as a comprehensive benchmark for researchers and engineers working on floating wind turbine aerodynamics, offering valuable insights for optimizing wind farm layouts and for developing simulation tools.</p>
Beneficial wake-capture effect for forward propulsion with a restrained wing-pitch motion of a butterfly
Unlike other insects, a butterfly uses a small amplitude of the wing-pitch motion for flight. From an analysis of the dynamics of real flying butterflies, we show that the restrained amplitude of the wing-pitch motion enhances the wake-capture effect so as to enhance forward propulsion. A numerical simulation refined with experimental data shows that, for a small amplitude of the wing-pitch motion, the shed vortex generated in the downstroke induces air in the wake region to flow towards the wings, which enables a butterfly to capture this induced flow and to acquire an additional forward propulsion. When the amplitude of the wing-pitch motion exceeds 45<sup>o</sup>, the flow induced by the shed vortex drifts away from the wings; it attenuates the wake-capture effect and causes the butterfly to lose a part of its forward propulsion. Our results provide a physical elucidation for a butterfly adopting a small amplitude of the wing-pitch motion to enhance the wake-capture effect and forward propulsion. This work clarifies the variation of the flow field correlated with the wing-pitch motion, which is useful in the design of a micro-aerial vehicle.
Supporting data for Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions
<p>Supporting data for "Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions" by Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri</p> <p>See README for data description.</p> <p>The code is available at: https://github.com/FPAL-Stanford-University/PadeOps</p>
Multivariate prediction on wake-affected wind turbines using graph neural networks (Eurodyn) database
<p>Database consisting of graphs generated using randomized layouts and PyWake simulations used in '<em>Multivariate prediction on wake-affected wind turbines using graph neural networks</em>', contribution to Eurodyn 2023. </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>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.