Tecplot (ascii) and matlab files are posted here for the Static pressure coefficient data sets. To download all of the data in either tecplot format or matlab format, you can go to https://c3.nasa.gov/dashlink/resources/485/ Please consult the documentation found on this page under Support/Documentation for information regarding variable definition, data processing, etc.

Number of Github stars, forks, open issues, create and last modified dates for 30 open source static site generators (SSG), including Hugo, Jekyll and Gatsby.

IDEA Section 618 Data Products: Static Tables Part C Child Count and Settings Table 9 Number of and percent infants and toddlers ages three and older served under IDEA, Part C, by early intervention setting and state and race/ethnicity.

MAVEN Supra-Thermal And Thermal Ion Composition (STATIC) Particle Distributions. This collection contains files with time-ordered event rate data sorted into 12 rates x 64 energy bin arrays that are summed over multiple spins. Data are derived from APID d9. Description of the STATIC instrument can be found on the STATIC home page https://lasp.colorado.edu/maven/science/instrument-package/static/ and in the STATIC instrument publication https://doi.org/10.1007/s11214-015-0175-6.

AbstractContext: Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method: To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location and reports error chains. Each error chain presents the dependency between the fix location with at least one manifestation location. We used our tool for a case study of 471 GitHub repositories and conducted an expert interview to investigate usability implications of the change. Further, we benchmarked both analysis versions to compare the runtime impact. Result: We found that 50% of the projects with a report had at least one error chain. During our expert interview, all participants required fewer executions of the static analysis if they used our adapted version. Our performance benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4%. Conclusion: Our results indicate that error chains occur frequently in real-world projects and ignoring them can lead to imprecise evaluation results. The performance benchmark indicates that our tool is a feasible and efficient solution for detecting error-chains in real-world projects. Further, our results indicate that the usability of static analyses benefits from supporting error chains.DataThis artefact contains additional information for our evaluation.Folder code_study (RQ1)The folder JavaCryptographicAchitecture_BET contains the CrySL rules for the JCA that we used for the code study.The file SUBS.jar is the version of SUBS that we used for our code study.The file README.md describes how to use the Docker image for scanning the code with CogniCryptSUBS.The file CREDENTIALS.txt is a dummy file for the GitHub tokens required for the analysis.The file run_cc_subs.sh is a helper script to execute CogniCryptSUBS and used by the Docker container.The file Dockerfile is the Docker image used for the code study.Folder performance_analysis (RQ2)The folder 1_run_performance_analysis/JavaCryptographicArchitecture contains the CrySL rules for the JCA that we used for the benchmark that do not support Backward Error Tracking (BET).The folder 1_run_performance_analysis/JavaCryptographicArchitecture_BET contains the CrySL rules for the JCA that we used for the benchmark that support BET.The different 1_run_performance_analysis/*.jar files are the different evaluated versions of CogniCrypt and CogniCrypt_SUBS.The file 1_run_performance_analysis/Dockerfile is the Docker image used to execute the benchmark.The file 1_run_performance_analysis/run_performance_analysis.sh includes the commands to execute the different tools on our benchmark and the different target folders for the different configurations/groups of the benchmark.The folder 2_parse_results/data contains the results obtained for the five different configurations for the different tools.The file 2_parse_results/generate_graphics.py generates the graphics used in the paper.The folder results contains the graphics, such as Fig. 4, for the different configurations.Folder expert_interview (RQ3)The code examples for task 1 and 2 are in the folder expertinterview_examplecode1 and expert interview_examplecode2, respectively.The invitation and questions are in the file expert interview.md.An overview of the obtained results are in the file expert interview_results.csv.Further, we include the graphics for the runtime evaluation as pdf-files.ChangesVersion 2: Restructure the main folder to include one folder for each research question answered in the paper. Further, added data for the code study and more details for the performance benchmark.Version 1: Add details for the expert interview and pdf-files for the performance benchmark. All files were added to the main folder.

This ancillary SMAP product contains more than 50 data sets. These data sets contain the inputs necessary to create SMAP products from raw instrument counts, such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, soils information (primarily sand and clay fraction), vegetation parameters, and surface roughness parameters.

This bundle contains fully calibrated data in physical units, consisting of Coarse and Fine resolution 3d distributions and energy spectra and moments from onboard computations.

MAVEN Supra-Thermal And Thermal Ion Composition (STATIC) Particle Distributions. This collection contains files with time-ordered event rate data from a selected rate channel. Data are derived from APID da. Description of the STATIC instrument can be found on the STATIC home page https://lasp.colorado.edu/maven/science/instrument-package/static/ and in the STATIC instrument publication https://doi.org/10.1007/s11214-015-0175-6.

This dataset provides static information regarding the motorway network managed by Hellastron association. Included data resources provide information about customer service centers, electronic vehicles' charging infrastructure, interchanges, meteo stations, motorist service stations, parkings, toll plazas, SSTPA parking areas, and tunnels.

Data created by the authors of "Unveiling the predictive power of static structure in glassy systems" published in "Nature Physics".

Bapst, V., Keck, T., Grabska-Barwińska, A. et al. Unveiling the predictive power of static structure in glassy systems. Nat. Phys. 16, 448–454 (2020). https://doi.org/10.1038/s41567-020-0842-8

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Road width for static road network data. Lastkajen

IDEA Section 618 Data Products: Static Tables

Part C Child Count and Settings

1. Number of infants and toddlers ages birth through 2 and 3 and older, and percentage of population, receiving early intervention services under IDEA, Part C, by age and state.
2. Number and percent of infants and toddlers receiving early intervention services under IDEA, Part C, by age group, gender and state.
3. Number of at-risk infants and toddlers ages birth through 2 receiving early intervention services under IDEA, Part C, by age and state.
4. Number and percent of infants and toddlers receiving early intervention services under IDEA, Part C, by age and state.
5. Number and percent of infants and toddlers receiving early intervention services under IDEA, Part C, by age and state and race/ethnicity.
6. Number and percent of at-risk infants and toddlers birth through 2 receiving early intervention services under IDEA, Part C, by race/ethnicity and state.
7. Number of infants and toddlers birth through 2 served under IDEA, Part C, by early intervention setting and state.
8. Number and percent of infants and toddlers ages birth through 2 served under IDEA, Part C, by early intervention setting and state and race/ethnicity.
9. Number of and percent infants and toddlers ages three and older served under IDEA, Part C, by early intervention setting and state and race/ethnicity.
10. Cumulative count of infants and toddlers birth through age 2 receiving early intervention services under IDEA, Part C, by gender and state
11. Cumulative count of infants and toddlers birth through age 2 receiving early intervention services under IDEA, Part C, by race/ethnicity and state

Part C Dispute Resolution

1. Number and percent of written, signed complaints through dispute resolution procedures for infants and toddlers ages birth through 2 years, served under IDEA, Part C, by case status and state.
2. Number and percent of mediations held through dispute resolution procedures for infants and toddlers ages birth through 2 years, served under IDEA, Part C, by case status and state.
3. Number and percent of hearings (fully adjudicated) through dispute resolution procedures for infants and toddlers ages birth through 2 years, served under IDEA, Part C, by case status and state.

Part C Exiting

1. Number of infants and toddlers ages birth through 2 served under IDEA, Part C, who exited Part C programs, by exit reason and state.
2. Number of infants and toddlers through age 2 with disabilities served under IDEA, Part C, who exited Part C, by exit reason and state by race/ethnicity.
3. Number and percent of male/female infants and toddlers ages birth through 2 with disabilities served under IDEA, Part C, who exited Part C, by exit reason and state.

714 Global import shipment records of Static Mixer with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Static Data Desensitization System market is experiencing robust growth, driven by increasing regulatory compliance needs (like GDPR and CCPA), the rising volume of sensitive data, and a growing awareness of data breach risks across diverse sectors. The market, currently estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. Key drivers include the expanding adoption of cloud computing and big data analytics, which necessitates robust data protection mechanisms. The Finance and Government sectors are major adopters, owing to their stringent data privacy regulations and the sensitive nature of the data they handle. However, the complexity of implementation and the high initial investment costs associated with implementing these systems pose challenges to market expansion, particularly among smaller organizations. Technological advancements, such as the development of more sophisticated encryption algorithms (including Format Retained Encryption and Strong Encryption Algorithms) and AI-powered data masking solutions, are shaping market trends. Furthermore, the market is segmented by application (Government, Finance, Operator, Medical System, Other) and by type of encryption algorithm used, influencing pricing and adoption strategies. Competition is fierce, with established players like Microsoft, IBM, Oracle, and SAP alongside specialized security vendors like Informatica, Imperva, and Palo Alto Networks vying for market share. The North American market currently holds the largest share, followed by Europe and Asia-Pacific, with growth expected across all regions fueled by increasing digital transformation initiatives globally. The forecast period (2025-2033) promises significant opportunities for vendors who can provide scalable, cost-effective, and user-friendly solutions. Successful players will need to focus on providing comprehensive solutions that address the diverse needs of different industries, integrate seamlessly with existing IT infrastructures, and offer proactive threat detection and response capabilities. Furthermore, a focus on compliance certifications and partnerships with data security consultancies will be crucial for building trust and expanding market reach. The continuous evolution of data privacy regulations and cyber threats will necessitate ongoing innovation in data desensitization techniques, creating a dynamic and evolving market landscape.

The deployment position coordinates of the traffic control system vehicle detector (VD), the starting and ending points of the road section, and other data.

Road classification for static road network data. Lastkajen

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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