This dataset provides a total of 1944 features obtained from the static and dynamic analysis reports of more than 19400 malware samples, more than 1000 of then belonging to malware samples obtained from APT attacks. The objective of this dataset is to provide researcher a tool to discern the differences between generic and APT-related malware samples.

Knowledge of the distribution of permeability and porosity in a reservoir is necessary for the prediction of future oil production, estimation of the location of bypassed oil, and optimization of reservoir management. The volume of data that can potentially provide information on reservoir architecture and fluid distributions has increased enormously in the past decade. The techniques developed in this research will make it easier to use all the available data in an integrated fashion. While it is relatively easy to generate plausible reservoir models that honor static data such as core, log, and seismic data, it is far more difficult to generate plausible reservoir models that honor dynamic data such as transient pressures, saturations, and flow rates. As a result, the uncertainty in reservoir properties is higher than it could be and reservoir management can not be optimized. In this project, we have developed computationally efficient automatic history matching techniques for generating geologically plausible reservoir models which honor both static and dynamic data. Specifically, we have developed methods for adjusting porosity and permeability fields to match both production and time-lapse seismic data and have also developed a procedure to adjust the locations of boundaries between facies to match production data. In all cases, the history matched rock property fields are consistent with a prior model based on static data and geologic information. Our work also indicates that it is possible to adjust relative permeability curves when history matching production data.

Since 2001, Vermont Department of Corrections (DOC) sex offender treatment providers and probation and parole officers have scored every adult male sex offender under community supervision on three measures of static (unchangeable) risk at intake (i.e., Static-99, RRASOR-Rapid Risk Assessment for Sex Offence Recidivism, and VASOR-Vermont Assessment of Sex Offender Risk) and one measure of dynamic (changeable) risk (i.e, SOTNPS) at intake and then every six months thereafter. This project conducted record reviews to verify the accuracy of the risk assessment scores and examined how scores on the Sex Offender Treatment Needs and Progress Scale (SOTNPS) and one or more of these static risk instruments can be combined into an overall model of risk assessment. An empirically derived decision-making model was created to assist correctional administrators, probation and parole officers, and treatment providers in allocating and delivering supervision and treatment services based on an individual's treatment needs and risk to sexually re-offend. Three hypotheses were tested. First, it was expected that one or more static risk measures (Static-99R, Static-2002R and VASOR) would predict sexual recidivism with moderate accuracy in the sample. Second, a dynamic risk measure, Sex Offender Treatment Needs and Progress Scale (SOTNPS), or a subset of risk factors contained in this measure would also predict sexual recidivism with moderate accuracy and be sensitive to the changes in dynamic risk over time. Third, a combined static and dynamic risk measure would predict sexual recidivism more accurate than either measure alone.

This dataset support the findings of the study "The stochastic capacitated lot-sizing problem under the static and static-dynamic uncertainty strategies" by Markus Mickein and Knut Haase.

This is the meta data as well as the raw experimental data for the paper: "Stretching the limits of dynamic and quasi-static flow testing on limestone powders" that published in Powder Technology.

These data contain 1) load string compliance mechanical data used to retrieve the absolute sample deformation (Compliance folder); 2) Acoustic emissions data (RawData_AEs folder); 3) Compaction data of the granular packs (RawData_Press folder). These data we used for the publication by Zorn et al., 2024 (https://doi.org/10.30909/vol.07.02.765783). A PDF document detailing further information of the contents of each folder entitled "Zorn_Compaction_Data_Overview" is provided. All data were collected and analysed at LMU Munich on samples from the Eifel Volcanic Field (Germany) and from the Krafla caldera (Iceland). The geographical location of the samples collected is of no relevance to this study, as the samples were selected for their physical attributes. All data were collected and analysed in 2023 and 2024. Loose fragments of volcanic rock from the Eifel Volcanic Field or Krafla caldera were placed in a metal cup and progressively loaded axially to a target load before either 1) removing the load (called "dynamic stressing tests"); or 2) holding the load for 6h or 5 days (called "dynamic followed by static tests"). All experiments were conducted using an Instron uniaxial press, and all displacement data are corrected for the deformation of the loading column (compliance). During each experiment, acoustic emissions sensors attached to the side of the cup to monitor cracking events These data were collected to understand the compaction behaviour of volcanic edifices that consist of interbedded layers of variably loose/coherent materials.

The Data Masking Technology market is experiencing robust growth, driven by increasing concerns around data privacy regulations like GDPR and CCPA, and the rising need to protect sensitive data during development, testing, and other non-production environments. The market, currently estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by the expanding adoption of cloud-based solutions, the increasing demand for data masking tools across various industries (finance, healthcare, and government being particularly prominent), and the development of advanced masking techniques like tokenization and dynamic data masking. The market is segmented by deployment type (static and dynamic) and end-user (Small and Medium-Sized Enterprises (SMEs) and Large Enterprises). Large enterprises currently dominate the market due to their greater data volumes and stringent compliance requirements, but SMEs are showing increasing adoption rates as data security awareness improves and affordable solutions become more readily available. The key players in the market, including Informatica, Broadcom, Solix Technologies, Delphix, MENTIS, Micro Focus, Oracle, Compuware Corporation, ARCAD Software, and Ekobit d.o.o., are continuously innovating to offer advanced solutions that cater to the evolving needs of their clients. This includes integrating AI and machine learning capabilities for smarter data masking, enhancing usability and user experience, and providing broader support for diverse data types and formats. Despite the growth, challenges remain such as the complexities involved in implementing and managing data masking solutions, potential impact on data usability, and the ongoing need to adapt to continuously evolving regulatory landscapes. However, the overall market outlook remains positive, with substantial opportunities for growth and innovation in the years to come. The consistent market expansion is expected to continue, driven by the critical need for robust data protection strategies in an increasingly digital world.

The goal of this project was to develop computationally efficient automatic history matching techniques for generating geologically plausible reservoir models which honor both static and dynamic data. Solution of this problem is necessary for the quantification of uncertainty in future reservoir performance predictions and for the optimization of reservoir management.

Ground deformation at the natural resource extraction area results from the interplay of multiple anthropogenic and hydrogeological factors. Identifying the main factors and their relationship with deformation is crucial for mitigating disasters when extreme events happen. Here, we show how multimodal artificial intelligence models can disentangle the effects of dynamic and static features related to deformation in the Rhineland coalfield in Germany. The public dataset was applied in this study, as narrated above. Among them, InSAR and groundwater are time-series datasets; the rest are static datasets. These datasets are original and were used to generate the experimental dataset in this study. Detailed information can be obtained from the paper.

Static And Dynamic Design Solutions Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The data masking market, valued at $0.94 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.71% from 2025 to 2033. This surge is driven by escalating concerns around data privacy regulations like GDPR and CCPA, increasing cyber threats, and the rising adoption of cloud computing and big data analytics. Businesses across diverse sectors, including BFSI, healthcare, and IT & Telecom, are increasingly investing in data masking solutions to protect sensitive customer and business information while enabling data-driven decision-making. The dynamic nature of data masking solutions, offering real-time masking capabilities, and cloud deployment models offering scalability and cost-efficiency, are significant market drivers. While the on-premise deployment model still holds a notable market share, the cloud-based segment is projected to dominate the market due to its flexibility and ease of implementation. Growth is further fueled by the expanding adoption of data masking solutions in emerging markets like Asia Pacific and Latin America, where businesses are increasingly realizing the importance of data security and compliance. However, factors such as the high initial investment costs associated with implementing data masking solutions and the complexity in integrating them with existing systems pose challenges to market growth. The market segmentation reflects a diversified landscape, with static and dynamic data masking techniques catering to specific needs, and various end-user industries driving distinct demand patterns. The competitive landscape is characterized by a mix of established players like IBM and Oracle, alongside emerging innovative companies, resulting in a dynamic and evolving market environment. The increasing sophistication of data masking techniques, including tokenization, data sharding, and encryption, further contributes to the market's expansion. Recent developments include: August 2022 - IBM released a new update, IBM Cloud Pak Data V4.5.x, of Advanced data masking, extended the capability of data protection and location rules by protecting the data with advanced de-identification techniques. The techniques preserve the data's format and integrity. Because of the high data utility, data users such as data scientists, business analysts, and application developers may generate high-quality insights from protected data., April 2022 - Mage signed a technology partnership agreement with Imperva to provide a data masking alternative to Imperva's Data Security Fabric (DSF) built-in capabilities for de-identifying sensitive data.. Key drivers for this market are: Increase of Organizational Data Volumes. Potential restraints include: Technological Complexities Associated with Data Masking Challenge the Market Growth. Notable trends are: The BFSI Industry to Witness a Significant Growth.

Discover the booming Static Data Acquisition System market! Our comprehensive analysis reveals key trends, growth drivers, and leading companies shaping this dynamic sector, projected to reach [estimated 2033 market size in billions] by 2033. Explore market segmentation, regional insights, and future growth potential.

Abstract

The popularity and wide adoption of JavaScript both at the client and server-side makes its code analysis more essential than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Luckily, there are quite a few tools to get this job done already. However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated.

In this paper, we systematically compare five static and two dynamic approaches for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and two static and two dynamic methods on 12 real-world Node.js modules. The tools under examination using static techniques were npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript. We performed dynamic analyzes relying on the nodejs-cg tool (a customized Node.js runtime) and the NodeProf instrumentation and profiling framework.

We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges. On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well. For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph. When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture. Nonetheless, a significant number of edges not detected by dynamic approaches are also reported. Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.

Index of static and dynamic parking information from Dutch municipalities and private parties

The enclosed dataset includes measurements of a thickness series of CoFeB thin film samples that were deposited at the NIST Magnetic Engineering Research Facility using magnetron sputtering. The thickness-dependent magnetic properties of CoFeB have significant technological relevance for predicting the read/write energy and speed of spin-transfer-torque magnetoresistive random access memory devices, which employ a nm-thick CoFeB film as the data storage layer. Ferromagnetic resonance observations were conducted using vector network analyzer ferromagnetic resonance at fixed frequencies over a broad frequency range. The ferromagnetic resonance field and resonance linewidth versus frequency were used to determine the spectroscopic g-factor, effective magnetization Meff, Gilbert damping and the inhomogeneous linewidth broadening, respectively. This is a preliminary release and additional data will be added as this project continues

The data profile that can be accessed here bundles data on locations and real-time occupancy data for parking spaces, P+R facilities and multi-storey car parks in Baden-Württemberg. The parking data profile is continuously being expanded, in particular with additional data on real-time occupancy. Corresponding extensions are made available directly via the interfaces listed here. Detailed information on the data profile can be found in the associated Factsheet data profile parking data. ###Providers currently included

The data is updated every 5 minutes. Click here for the Full screen view of the map. ### Available Data * * DB BahnPark GmbH, Parking API, Creative Commons Attribution 4.0 International CC BY 4.0, static data * * Verband Region Stuttgart – public body, data license Germany – attribution – Version 2.0, P+R facilities of the Stuttgart region for suburban and local transport, static data * * Integrated traffic control center (IVLZ) of the state capital of Stuttgart (dynamic real-time data from the parking guidance system of the LH Stuttgart) * * Parking data from the city of Neckarsulm from the MobiWert funding project, static data * * parking data from the city of Reutlingen, static data * * parking data from the city of Karlsruhe, static data * * parking data from the city of Heidelberg, dynamic data * * parking data of the city of Freiburg im Breisgau, dynamic data * * parking data from the city of Ulm, dynamic data * * parking data from the city of Konstanz, dynamic data * _* _ Parking data from the city of Mannheim, dynamic data * * Parking data from the city of Heilbronn, dynamic data * * Parking data from the city of Bietigheim-Bissingen, dynamic data, integration into DORA-API takes place promptly * * from the data set of the barrier-free travel chain: Parking options at public transport stops in BW and parking facilities at SPNV stops in BW, integration into DORA-API will take place promptly ### data available shortly * * integration of the static and dynamic parking data of the City of Herrenberg

The data masking technology market is experiencing robust growth, driven by increasing regulatory compliance needs (like GDPR and CCPA) and the rising adoption of cloud computing and big data analytics. Businesses are increasingly recognizing the critical need to protect sensitive data during development, testing, and other non-production environments. This necessitates robust data masking solutions that ensure compliance while maintaining data usability for various purposes. The market is segmented by application (small and medium-sized enterprises (SMEs) and large enterprises) and by type (static and dynamic masking). While large enterprises currently dominate the market due to their greater resources and higher data volumes, the SME segment shows strong growth potential as awareness of data security and compliance increases. Dynamic masking, offering real-time data protection, is gaining traction over static masking due to its adaptability and enhanced security features. The North American market currently holds a significant share, but regions like Asia-Pacific are witnessing rapid growth, fueled by the expanding digital economy and increasing data security concerns. Competitive landscape analysis reveals key players such as Informatica, Broadcom, and Solix Technologies, each vying for market dominance through innovation, strategic partnerships, and acquisitions. The forecast period (2025-2033) projects continued expansion, driven by technological advancements in AI-powered masking and the evolving needs of diverse industries. The restraints on market growth include the high initial investment cost of implementing data masking solutions, especially for SMEs, and the complexity of integrating these solutions into existing IT infrastructures. However, the increasing availability of cloud-based and SaaS solutions is mitigating this challenge. Furthermore, the ongoing evolution of data privacy regulations and the emergence of new cyber threats continue to reinforce the demand for robust and adaptable data masking technologies. The market's future trajectory is positive, with continued growth projected across all segments and regions. This growth will be significantly influenced by advancements in AI and machine learning, enabling more sophisticated and efficient data masking techniques, and by the ongoing development and adoption of cloud-native data masking platforms. The market shows immense potential for further expansion due to the constantly evolving data security landscape and the growing necessity for protecting sensitive data across diverse industries.

The Data Masking Technologies Software market is experiencing robust growth, driven by increasing concerns over data privacy regulations like GDPR and CCPA, coupled with the rising adoption of cloud computing and big data analytics. The market's expansion is fueled by the need to protect sensitive data during development, testing, and other non-production environments, while maintaining data usability for various purposes. Major players such as Microsoft, IBM, Oracle, and Informatica are actively contributing to this growth through continuous innovation and strategic partnerships. The market is segmented by deployment (on-premise and cloud), masking technique (dynamic, static, tokenization, pseudonymization), and organization size (SMEs and large enterprises). We estimate the 2025 market size to be around $2.5 billion, with a Compound Annual Growth Rate (CAGR) of approximately 15% projected from 2025 to 2033. This growth is expected to be consistent across various regions, with North America and Europe maintaining significant market share due to early adoption and stringent data privacy regulations. However, emerging economies in Asia-Pacific and Latin America are also showing promising growth potential as digital transformation initiatives gather pace. While the market presents lucrative opportunities, challenges remain. The complexity of implementing data masking solutions, particularly in large and diverse data environments, can hinder adoption. Furthermore, the need for skilled professionals to manage and maintain these systems presents a barrier for some organizations. Nonetheless, the rising cyber security threats and increasing penalties for data breaches are compelling organizations to invest heavily in robust data masking solutions. The future of the market will see increased demand for advanced masking techniques, including AI-powered solutions that can automatically identify and mask sensitive data with minimal manual intervention. Integration with other security tools and platforms is also becoming increasingly important to provide a holistic data security approach.