The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea CO2flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in-situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in-situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in-situ measurements. To address this, we propose a novel Multi-Parametric Regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in-situ data from the Global Ocean Data Analysis Project (GLODAP), trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in-situ measurements. Additionally, seasonal and inter-annual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.

The increasing demand for memory in hyperscale applications has led to memory becoming a large portion of the overall datacenter spend. The emergence of coherent interfaces like CXL enables main memory expansion and offers an efficient solution to this problem. In such systems, the main memory can constitute different memory technologies with varied characteristics. In this paper, we characterize memory usage patterns of a wide range of datacenter applications across the server fleet of Meta. We, therefore, demonstrate the opportunities to offload colder pages to slower memory tiers for these applications. Without efficient memory management, however, such systems can significantly degrade performance.

There is an emerging recognition for Analysis Ready Datasets– an easy-to-use satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum user effort, and interoperability through time and other data sources. Analysis Ready Data (ARD) sets are Multi dimensional, Multi temporal, multi sensor data cubes in which each pixel is harmonized in-terms of geometry and radiometry. Decadal archives from Indian Resourcesat-2 series sensor data is a potential resource for long term terrestrial monitoring and decision making. The vision and promise of ISRO’s Resourcesat (RS) 2/2A ARD cubes is to lower the pre processing hurdles and expand the user community by making analysis ready data readily accessible. In coordination with CEOS, NRSC/ISRO has taken initiative to realize Optical Surface Reflectance (CARD4L-OSR), ARD products from RS2/2A sensors for Indian terrain. Automated Chain is established to generate surface reflectance (SR) products in near real time. Accuracy of RS-2/2A surface reflectance is better than 95% with L8-OLI surface reflectance. Better than 90% accuracy was achieved in radiometric harmonization of RS2 & RS2A sensors. Sample ARD data sets are also in good agreement with Harmonized-Landsat -Sentinel (HLS) datasets. Along with the methods and processes adopted to realize the ARD generation from RS2 sensors, this paper also highlights the suitability of these products to applications related to agriculture.

Radiometers aboard satellites provide an essential Sea Surface Temperature (SST) data archive, which is one of the key indicators of climate change. Since new radiometers are planned to fill the gap in the temporal data coverage, it is important to quantify the retrieval accuracy to utilize the data and seamlessly merge it into an existing SST data-archive. In this regard, we validate SST product from SLSTR/Sentinel-3A with in-situ measurements to create a reference frame for evaluating the new sensors products. We used NOAAiQuam SST dataset for validating satellite-retrievals, since it’s easily obtainable unlike skin-SST measurements. Several criteria have yielded a total of 30,353 high-quality cloud-free collocated data points between SLSTR andiQuam SST datasets for 2017. Detailed evaluation of biases globally under different wind speeds and precipitable water vapor conditions indicates the fundamental difference between the satellite and in-situ SST, and recommendations are made for how datasets should be handled. SLSTR-retrieved skin-SST agrees with theiQuam subsurface-SST, irrespective of region and time of measurement, within -0.17±0.04K for all-day, -0.11±0.06K for daytime and -0.21±0.05K for nighttime datasets, with respective RMSE values of 0.62±0.10K, 0.58±0.09K and 0.65±0.13K. These small differences are partly due to the difference between ocean skin-temperature and subsurface-temperature, measurement uncertainties and sampling mismatch. Comprehensive validation carried out for SLSTR-retrieved SST has direct implications for future validation studies and future blended infrared, microwave and in-situ SST products.

Video sharing (e.g., YouTube, Vimeo, Facebook, TikTok) accounts for the majority of internet traffic, and video processing is also foundational to several other key workloads (video conferencing, virtual/augmented reality, cloud gaming, video in Internet-of-Things devices, etc.). The importance of these workloads motivates larger video processing infrastructures and – with the slowing of Moore’s law – specialized hardware accelerators to deliver more computing at higher efficiencies. This paper describes the design and deployment, at scale, of a new accelerator targeted at warehouse-scale video transcoding. We present our hardware design including a new accelerator building block – the video coding unit (VCU) – and discuss key design trade-offs for balanced systems at data center scale and co-designing accelerators with large-scale distributed software systems. We evaluate these accelerators “in the wild" serving live data center jobs, demonstrating 20-33x improved efficiency over our prior well-tuned non-accelerated baseline. Our design also enables effective adaptation to changing bottlenecks and improved failure management, and new workload capabilities not otherwise possible with prior systems. To the best of our knowledge, this is the first work to discuss video acceleration at scale in large warehouse-scale environments.

Lunar floor fractured craters are a special class of craters modified by post impact mechanisms resulting from geological activities. Two theories of origin of FFC's have been hypothesized: magmatic intrusion and viscous relaxation. We attempt to validate the hypothesis drawn in favor of magmatic intrusion as formation mechanism. The approach was to extract and analyze those properties of FFC's which would be affected by crater formation mechanism so as to predict the mechanism they favor. The origin of FFCs is investigated by observing implications of morphology and morphometry on formation mechanism andby study of aerial distribution. This study focuses on analysis of morphological features namely diameter, depth, rim height, crater symmetry and presence and absence of interior features like moats. The key features/parameters were determined by in-house developed software and analyzed for 83 floor-fractured craters belonging to different classes. It was concluded that FFCs possess lower depth-to-diameter ratio as compared to predict through empirical relationship documented in literature. At the same time, FFCs possess intact rim heights, depicts moats in the floors and do not show any axis-symmetric deformation. All this provide strong evidence against probability of formation of crater by viscous relaxation hence supporting magmatic intrusion as origin of floor fractured craters.

The study of swell wave refraction phenomena using synthetic aperture radar (SAR) data has been found to be very useful in estimating the underwater topography (seabed structure). Near-shore water regions generate a wide range of surface signatures due to rapidly changing underwater depths, which cause waves to refract and finally align parallel to the shoreline. Another significant change, which is observed as gravity waves approach the shoreline, is that their wavelength decreases and, as the energy of the wave is constant in the absence of dissipating forces, amplitude increases. The strong correlation between the change in wavelength and the underlying topography makes it possible to estimate the bathymetry from the measured wavelength. Normally available global bathymetric maps (e.g., ETOPO-1 bathymetry toposheets) are out of date and provide bathymetry at a very coarse resolution. In this letter, swell wave refraction phenomena using Radar Imaging Satellite C-band SAR data over coastal regions of Mumbai have been studied. The wave-tracing technique has been used to derive the wavelength of swell waves in near-shore regions and analyze the wavelength change in order to retrieve underwater topography using dispersion relation with swell wave properties. This SAR-based technique can be used to derive high-resolution bathymetric maps for near-coastal regions. Also, with this technique, temporal variations in the seabed can be measured to infer geological processes.

An empirical chlorophyll algorithm has been developed using the coincidentinsituchlorophyll-a and remote sensing reflectance Rrsmeasurements from global ocean waters. The basic data set used for developing the algorithm was obtained by merging the bio-optical data from the global NASA bio-Optical Marine Algorithm Data (NOMAD) (~2438 spectra from ~3000 stations) and from the waters of the northern Arabian Sea (~159 spectra) collected by the Space Applications Centre, Ahmedabad, India. The chlorophyll-a concentration ranged from 0.01 to 50.0 mg ldr m-3for the data set used. Regression analysis between chlorophyll-a concentration and remote sensing reflectance in different bands and a combination of band ratios was performed. Algorithms using modified cubic polynomial (MCP) regression of Rrsratios with chlorophyll-a concentration showed good estimates of chlorophyll-a in full range of 0.01 to 50.0 mg ldr m-3of the merged data set. However, the best results were obtained by using MCP regression between maximum band ratio (MBR) of Rrs(443, 490, 510 nm)/Rrs555 nm with chlorophyll-a concentration having an r2of 0.96 and rms error of 0.12 for log-transformed data. The developed MBR-based algorithm named Ocean Colour Monitor (OCM)-2 chlorophyll algorithm was compared with the OC4v4 algorithm routinely used the for Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data processing. For the used data set, OC4v4 algorithm overestimated chlorophyll-a concentration for > 5.0 mg ldr m-3and yielded an r2of 0.90 with rms error of 0.23, when compared to the newly developed OCM-2 chlorophyll algorithm. It is proposed to use this OCM-2 chlorophyll algorithm with OCEANSAT-2 OCM data to be launched in the third quarter of the year 2008 by the Indian Space Research Organisation.

The Indian Remote Sensing P4 (IRS-P4) satellite has carried an Ocean Color Monitor (OCM) sensor for investigations on ocean color parameters in open and coastal seas. The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) sensor of the National Aeronautics and Space Administration is also an ocean color instrument, which is in concurrent operation with IRS-P4 OCM. Results of an intercomparison analysis are described for ocean color data from OCM and SeaWiFS, using a consistent methodology for atmospheric correction and bio-optical algorithms. Near synchronous data of OCM and SeaWiFS sensors was obtained on March 22, 2000 over parts of the Arabian Sea. The OCM and SeaWiFS image data were processed to estimate normalized water-leaving radiance (nL/sub w/) in 443-, 490-, 510-, and 555-nm spectral bands. A comparison was made for 10/spl times/10 colocated pixels of OCM and SeaWiFS images. Differences were observed in the estimated values of nL/sub w/ [443] and nL/sub w/ [490], respectively, obtained by OCM, when compared to SeaWiFS. However, OCM estimated nL/sub w/ [510] and nL/sub w/ [555] were comparable to that of SeaWiFS. An intercalibration approach for OCM was adopted using SeaWiFS calibration as "standard". An intercomparison of the top-of-the-atmosphere radiance measured by OCM and SeaWiFS sensor was performed for colocated pixels having similar viewing geometry. Individual OCM bands were vicariously recalibrated, and a gain coefficient for each OCM band was derived through this procedure. The OCM data for March 22, 2000 were reprocessed using derived gain coefficients, and new gain coefficients were further tested on an independent dataset of March 18, 2000. Results obtained for the recalibrated OCM data showed a better match up with the ocean color estimation obtained by SeaWiFS.