Sas

## Overview

Sas is a dataset for object detection tasks - it contains Sasas annotations for 2,737 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

These SAS files are sample code used for the Monte Carlo studies in a manuscript on statistical properties of four effect size measures for the mediated effect.Citation:Miočević, M., O’Rourke, H. P., MacKinnon, D. P., & Brown, H. C. (2016). The bias and efficiency of five effect size measures for mediation models. Under review at Behavior Research Methods.

Charlie839242/SAS dataset hosted on Hugging Face and contributed by the HF Datasets community

(SAS7BDAT)

SAS script and input files for calculations of sensitivity and specificity based on different model settings and weather data in the weather data file supplied here.

One of three dataset to replicate numbers for tables and figures in the article "Using a Deliberative Poll on breast cancer screening to assess and improve the decision quality of laypeople" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

The raw data for each of the analyses are presented. Baseline severity difference (probands only) (Figure A in S1 Dataset), Repeated measures analysis of change in lesion severity (Figure B in S1 Dataset). Logistic regression of survivorship (Figure C in S1 Dataset). Time to cure (Figure D in S1 Dataset). Each data set is given as a SAS code for the data itself, and the equivalent analysis to that performed in JMP (and reported in the text). Data are presented in SAS format as this is a simple text format. The data and code were generated as direct exports from JMP, and additional SAS code added as needed (for instance, JMP does not export code for post-hoc tests). Note, however, that SAS rounds to less precision than JMP, and can give slightly different results, especially for REML methods. (DOCX)

The SAS2RAW database is a log of the 28 SAS-2 observation intervals and contains target names, sky coordinates start times and other information for all 13056 photons detected by SAS-2. The original data came from 2 sources. The photon information was obtained from the Event Encyclopedia, and the exposures were derived from the original "Orbit Attitude Live Time" (OALT) tapes stored at NASA/GSFC. These data sets were combined into FITS format images at HEASARC. The images were formed by making the center pixel of a 512 x 512 pixel image correspond to the RA and DEC given in the event file. Each photon's RA and DEC was converted to a relative pixel in the image. This was done by using Aitoff projections. All the raw data from the original SAS-2 binary data files are now stored in 28 FITS files. These images can be accessed and plotted using XIMAGE and other columns of the FITS file extensions can be plotted with the FTOOL FPLOT. This is a service provided by NASA HEASARC .

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Suramerica Comercial Sas Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

File List Code_and_Data_Supplement.zip (md5: dea8636b921f39c9d3fd269e44b6228c) Description The supplementary material provided includes all code and data files necessary to replicate the simulation models other demographic analyses presented in the paper. MATLAB code is provided for the simulations, and SAS code is provided to show how model parameters (vital rates) were estimated. The principal programs are Figure_3_4_5_Elasticity_Contours.m and Figure_6_Contours_Stochastic_Lambda.m which perform the elasticity analyses and run the stochastic simulation, respectively. The files are presented in a zipped folder called Code_and_Data_Supplement. When uncompressed, users may run the MATLAB programs by opening them from within this directory. Subdirectories contain the data files and supporting MATLAB functions necessary to complete execution. The programs are written to find the necessary supporting functions in the Code_and_Data_Supplement directory. If users copy these MATLAB files to a different directory, they must add the Code_and_Data_Supplement directory and its subdirectories to their search path to make the supporting files available. More details are provided in the README.txt file included in the supplement. The file and directory structure of entire zipped supplement is shown below. Folder PATH listing Code_and_Data_Supplement | Figure_3_4_5_Elasticity_Contours.m | Figure_6_Contours_Stochastic_Lambda.m | Figure_A1_RefitG2.m | Figure_A2_PlotFecundityRegression.m | README.txt | +---FinalDataFiles +---Make Tables | README.txt | Table_lamANNUAL.csv | Table_mgtProbPredicted.csv | +---ParameterEstimation | | Categorical Model output.xls | | | +---Fecundity | | Appendix_A3_Fecundity_Breakpoint.sas | | fec_Cat_Indiv.sas | | Mean_Fec_Previous_Study.m | | | +---G1 | | G1_Cat.sas | | | +---G2 | | G2_Cat.sas | | | +---Model Ranking | | Categorical Model Ranking.xls | | | +---Seedlings | | sdl_Cat.sas | | | +---SS | | SS_Cat.sas | | | +---SumSrv | | sum_Cat.sas | | | ---WinSrv | modavg.m | winCatModAvgfitted.m | winCatModAvgLinP.m | winCatModAvgMu.m | win_Cat.sas | +---ProcessedDatafiles | fecdat_gm_param_est_paper.mat | hierarchical_parameters.mat | refitG2_param_estimation.mat | ---Required_Functions | hline.m | hmstoc.m | Jeffs_Figure_Settings.m | Jeffs_startup.m | newbootci.m | sem.m | senstuff.m | vline.m | +---export_fig | change_value.m | eps2pdf.m | export_fig.m | fix_lines.m | ghostscript.m | license.txt | pdf2eps.m | pdftops.m | print2array.m | print2eps.m | +---lowess | license.txt | lowess.m | +---Multiprod_2009 | | Appendix A - Algorithm.pdf | | Appendix B - Testing speed and memory usage.pdf | | Appendix C - Syntaxes.pdf | | license.txt | | loc2loc.m | | MULTIPROD Toolbox Manual.pdf | | multiprod.m | | multitransp.m | | | ---Testing | | arraylab13.m | | arraylab131.m | | arraylab132.m | | arraylab133.m | | genop.m | | multiprod13.m | | readme.txt | | sysrequirements_for_testing.m | | testing_memory_usage.m | | testMULTIPROD.m | | timing_arraylab_engines.m | | timing_matlab_commands.m | | timing_MX.m | | | ---Data | Memory used by MATLAB statements.xls | Timing results.xlsx | timing_MX.txt | +---province | PROVINCE.DBF | province.prj | PROVINCE.SHP | PROVINCE.SHX | README.txt | +---SubAxis | parseArgs.m | subaxis.m | +---suplabel | license.txt | suplabel.m | suplabel_test.m | ---tight_subplot license.txt tight_subplot.m

View details of Fresh Yucca Import Data of Exportandina Sas Supplier to US with product description, price, date, quantity, major us ports, countries and more.

One of four dataset to replicate numbers for tables and figures in the article "Mammography screening: eliciting the voices of informed citizens" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

Mailclub SAS Whois Database, discover comprehensive ownership details, registration dates, and more for Mailclub SAS with Whois Data Center.

In the period of ************* to ************, the SAS Group employed ***** men and ***** women. The largest group of employees, ***** people, was recorded in Denmark, although these numbers also include international employees outside Denmark, Sweden or Norway.

In a shared spectrum environment, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS), the secondary users with lower priority are managed by independent spectrum access systems (SASs) in order to protect the incumbents with higher priority from interference. The interference protection is guaranteed in terms of a percentile of the aggregate interference power. The current practice requires each SAS to obtain a global snapshot of interference and use a common algorithm to manage it. We present a simplified method to permit each SAS to independently manage its users while still meeting overall aggregate interference protection requirements. The data include statistical upper bound on aggregate interference for some known distributions, which is the core idea of the proposed method. The data also include numerical results of using the proposed interference protection criterion in terms of two metrics, the total number of users moved from the channel in order to protect the incumbent (i.e., the size of the move list) and the realized aggregate interference of all co-channel users at the incumbent. The data is associated with the letter, "Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment," M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.

This dataset contains the scrubbed chat logs from the Southeast Atmosphere Study (SAS) project, including NOMADSS (Nitrogen, Oxidants, Mercury and Aerosol Distributions, Sources and Sinks), from May 30 - July 17, 2013. The chat logs contain conversations between scientists and other field project participants regarding data collection within the SAS-NOMADSS project.

Scandinavian Airlines’ passenger numbers dropped by nearly ************** between 2019 and 2021 to around *** million passengers due to the coronavirus pandemic. The number of passengers on Scandinavian Airlines flights began rising again in the financial year 2022, totaling **** million scheduled passengers that year. The positive trend persisted in the subsequent year, 2024, with an approximately **** million passengers.

SAS Expander Market Outlook

According to our latest research, the global SAS Expander market size reached USD 1.35 billion in 2024, reflecting robust demand from data-intensive sectors. The market is projected to expand at a CAGR of 7.2% during the forecast period, reaching approximately USD 2.52 billion by 2033. This growth is primarily driven by the exponential rise in data generation, increasing adoption of high-performance storage solutions, and the expanding footprint of cloud computing and enterprise storage infrastructure worldwide.

One of the key growth factors propelling the SAS Expander market is the surging demand for scalable storage architectures in data centers and cloud service environments. As organizations continue to digitize operations and accumulate vast amounts of structured and unstructured data, there is a critical need for storage systems that offer high throughput, reliability, and flexibility. SAS Expanders, which enable multiple devices to connect to a single host, have become essential in building large-scale, cost-effective storage networks. The ongoing transformation of data centers from traditional on-premises models to hybrid and cloud-based architectures further amplifies the need for advanced SAS Expander solutions that can support dynamic workloads and rapid scaling.

Another significant factor fueling market expansion is the proliferation of enterprise applications and emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies generate unprecedented data volumes, necessitating robust backend storage frameworks. SAS Expanders play a pivotal role in facilitating seamless data transfer and storage management, ensuring optimal performance and minimal latency. Additionally, the increasing adoption of virtualization and containerization in enterprise IT environments is placing additional emphasis on storage scalability and flexibility, further accelerating the uptake of SAS Expander products across various industries.

Technological advancements and continuous innovation in SAS Expander design are also contributing to market growth. Vendors are focusing on enhancing product features such as higher port densities, improved data transfer rates, and advanced error correction mechanisms to meet the evolving needs of enterprise customers. The integration of SAS Expanders with NVMe and SSD technologies is creating new opportunities for performance optimization, particularly in mission-critical applications. Furthermore, the trend towards software-defined storage and hyper-converged infrastructure is driving the demand for SAS Expanders that can seamlessly integrate with next-generation storage platforms.

The role of SAS HBA (Host Bus Adapter) in the SAS Expander ecosystem is crucial as it serves as the interface between the server and the SAS Expander. SAS HBAs are designed to manage data flow between the connected storage devices and the host system, ensuring efficient data transfer and communication. With the increasing complexity and scale of data storage environments, the demand for high-performance SAS HBAs is growing. These adapters are essential for optimizing the performance of SAS Expanders by providing the necessary bandwidth and connectivity options to support multiple storage devices. As organizations continue to expand their storage infrastructure, the integration of advanced SAS HBAs becomes vital in maintaining system reliability and performance.

From a regional perspective, North America remains the dominant market for SAS Expander solutions, driven by the presence of leading technology firms, expansive data center infrastructure, and early adoption of advanced storage technologies. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, significant investments in data center construction, and the proliferation of cloud services in countries such as China, India, and Japan. Europe also presents substantial growth opportunities, supported by stringent data regulations and increasing adoption of enterprise storage solutions across various sectors. Collectively, these regional dynamics underscore the global relevance and growth trajectory of the SAS Expander market.

After the number of flights decreased by ** percent in 2020 due to the impact of the coronavirus pandemic and fell further in 2021, flight numbers began to recover in 2022. In 2022, SAS operated ******* scheduled flights. The positive trend persisted in the subsequent year, 2023, with a total of ******* flights.