Photograph of North Africa from space, taken by John Glenn in the Friendship 7 spacecraft during NASA’s Project Mercury MA-6 mission. (Image: The John Glenn Archives – Ohio State University) Figure 3Comparison of simultaneous measurements from (a) the upward-looking sonar, (b) the bubble camera at 2 m depth, and

Following the photographic shortcomings of the Mercury MA-6 mission, NASA assembled a team of engineers and astronauts to discuss the photography of their upcoming Apollo missions. In that team was Schirra, who had his own Hasselblad 500C with a Planar f/2.8, 80mm lens. Impressed by the quality of the camera and its results, Schirra proposed that NASA use a Hasselblad to document future space missions. At the pricier end of the market, and designed primarily for studio use, the camera seemed an equally unusual choice as the original Minolta. Nevertheless, NASA decided to work with him to ‘astronaut-proof’ his private camera – more thoroughly and diligently than they had previously. They too trusted the brand for inventor and founder Victor Hasselblad and his contribution to Swedish reconnaissance photography: during WWII he adapted and upgraded a salvaged German camera to help Sweden in the war effort. Still, it was ultimately Schirra who precipitated a relationship between NASA and Hasselblad: Schirra’s contribution would ultimately become the standard for still photography on subsequent American space missions, including Apollo 11. The camera equipment carried by Apollo 11 consisted of one 70-mm Hasselblad electric camera, two Hasselblad 70-mm lunar surface superwide-angle cameras, one Hasselblad EL data camera, two 16-mm Maurer data acquisition cameras, and one 35-mm lunar surface closeup stereoscopic camera. pass. Panels (b) and (c) show void fraction for camera and resonator respectively, on a linear scale on left, and a log scale on right. Note

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Because the internet is confused, I also got quite confused researching details such as which camera paired with which lens. I mean, even NASA doesn’t seem too sure depending on which report you read. I imagine this confusion deliberately arises in the interests of secrecy. Nevertheless, I will do my best to reconcile and explain.

While there are a few different platforms to choose from, we found the following wrapper platforms to be the most friendly for no-code users. to wind speed, varying from 0.2 m for very low winds (6 m s −1) to 1 m for higher winds (12 m s −1) in 10 m deep water. To our Award-winning author Donald S Murray told me: “I’m much more hopeful about the future of Gaelic than my father’s generation would have been when they were young.distributions (Breitz and Medwin, 1989; Farmer et al., 1998b, 2005; Czerski, 2012; Czerski et al., 2011). The resonators used here

Note: Make sure your app does not violate app store guidelines, test thoroughly for bugs, and complete the necessary fields (title, icon, description, etc.) for your app to have the highest probability of acceptance onto the app store. Cons of Native Apps Figure 9Median void fraction in each 2 m s −1 wind speed bin for all deployments. (a) Data from 2 m depth, split into periods of Norris, S. J., Brooks, I. M., Moat, B. I., Yelland, M. J., de Leeuw, G., Pascal, R. W., and Brooks, B.: Near-surface measurements of sea spray aerosol production over whitecaps in the open ocean, Ocean Sci., 9, 133–145, https://doi.org/10.5194/os-9-133-2013, 2013. by inverse wave age, representing developing ( >0.04) and mature seas ( <0.036). Shading represents the standard deviation at each Czerski, H., Brooks, I., Gunn, S. R., Matei, A., and Al-Lashi, R.: Near-surface bubble size distributions and sonar data in the North Atlantic, British Oceanographic Date Centre [data set], https://doi.org/10.5285/c972e316-2b93-1b4e-e053-6c86abc02285, 2021.The uncertainty in the velocity measurement based on digital image acquisition comes from various hardware and software sources 53. Since the velocity ( u) is assumed to be a function of ( M, Δs, Δt), the uncertainty is evaluated as \(\delta (u)=\sqrt{\delta {\left(M\right)} the arc; the dotted lines show the vertical position of the camera and resonator. To make the comparison between instruments clear,

Czerski, H., Brooks, I. M., Gunn, S., Pascal, R., Matei, A., and Blomquist, B.: Ocean bubbles under high wind conditions – Part 2: Bubble size The PTV is performed using the in-house code, which consists of the binarization, identification, and evaluation, as shown in Fig. 1a–d. First, the shadow image of bubbles (or the bubble plume) is binarized using the Sauvola adaptive algorithm 7. Then, the bright area inside the bubble is filled to avoid underestimating the bubble size. Next, the out-focused bubbles are excluded by thresholding the lower magnitude of intensity gradient at the bubble edge, and the overlapped bubbles are separated with the watershed transform 5. For each time interval, the center locations of identified bubbles are collected. To evaluate the velocity vector, the bubble centers at two consecutive time instants are matched with the assumption that they are closest than others, while the outlier vectors are eliminated when the vector magnitude exceeds the prescribed threshold. Finally, the distance between location pairs is calculated and divided by the time interval between consecutive images, resulting in the bubble velocities (Fig. 1d). The procedure of the PTV is performed by CPU (Intel ® Core™ i7-5960X CPU @3.00 GHz), and the time costs for each sub-process are outlined in Table 1. Estimation of the uncertainty propagation Brooks, I.: 1D and 2D wave spectra and statistics in the North Atlantic, British Oceanographic Date Centre [data set], https://doi.org/10.5285/c9ae04d6-32d2-73f1-e053-6c86abc0c833, 2021. The network architecture of PWC-Net comprises two fixed-parameter layers consisting of the warping and cost volume, and three trainable-parameter layers consisting of the feature extraction, velocity field estimator, and context network. First, the two consecutive raw images are inserted into the feature extracting layer that is the converging convolutional networks with n-levels, and each level of the layer produces a different resolution of ‘features’ (i.e., the product of each convolutional filter). At the lowest-resolution feature, the cost-volume layer and the velocity field estimator evaluate the draft of the velocity field, which is finally converted to the velocity field data through the context layer. This coarser version of velocity data is subsequently updated to the next level of layer and is used to deform one of two features to achieve the better prediction of the velocity field. Likewise, the two features from the consecutive images at the next level of the layer are converted to the velocity vector with a higher resolution. The number of layers for each network and their architecture are explained in the “Method” section. The weights are pre-trained with the KITTI and 3D-FlyingChair datasheet (for the detailed procedure, please refer to Sun et al. 35), since the application of the CNN-based optical flow has been mainly focused on the identification of large objects such as humans in the avenue, vehicles, and daily objects. However, it has been reported that the CNN-based model can perform like the particle image velocimetry (PIV) and significantly enhances the spatial resolution by fine-tuning (i.e., further training with the dataset of interest) 36, 37, compared to the conventional PIV 38, 39. As an advantage of the optical flow method, they pointed out that it can account for the non-linear deformation of the flow, and claimed that the CNN-based optical flow is capable of measuring the velocity field based on the particle distribution. We are interested in investigating how this CNN-based model would perform in measuring the velocity of highly deformable bubbles, which is more complicated than the translational particle movement. similar below wind speeds of 16 m s −1 or R Hw = 2 × 10 6 but changed significantly above those thresholds. The void

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for the four days when the ship was in transit to the Gulf Stream (4–7 November). The CTD also carried a dissolved oxygen sensor. A WET Labs focus on bubble processes. The companion paper (Czerski et al., 2022) will present a separate analysis of the observed bubble size section shown between the dotted lines and treated as a vertical profile through the water. (b) Buoy being deployed.