Dataset Card for test-data-generator

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/franciscoflorencio/test-data-generator/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/franciscoflorencio/test-data-generator.

This data set contains the result of applying the NIST Statistical Test Suite on accelerometer data processed for random number generator seeding. The NIST Statistical Test Suite can be downloaded from: http://csrc.nist.gov/groups/ST/toolkit/rng/documentation_software.html. The format of the output is explained in http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf.

Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

Sandbox Data Generator Market Outlook

According to our latest research, the global Sandbox Data Generator market size reached USD 1.41 billion in 2024 and is projected to grow at a robust CAGR of 11.2% from 2025 to 2033. By the end of the forecast period, the market is expected to attain a value of USD 3.71 billion by 2033. This remarkable growth is primarily driven by the increasing demand for secure, reliable, and scalable test data generation solutions across industries such as BFSI, healthcare, and IT and telecommunications, as organizations strive to enhance their data privacy and compliance capabilities in an era of heightened regulatory scrutiny and digital transformation.

A major growth factor propelling the Sandbox Data Generator market is the intensifying focus on data privacy and regulatory compliance across global enterprises. With stringent regulations such as GDPR, CCPA, and HIPAA becoming the norm, organizations are under immense pressure to ensure that non-production environments do not expose sensitive information. Sandbox data generators, which enable the creation of realistic yet anonymized or masked data sets for testing and development, are increasingly being adopted to address these compliance challenges. Furthermore, the rise of DevOps and agile methodologies has led to a surge in demand for efficient test data management, as businesses seek to accelerate software development cycles without compromising on data security. The integration of advanced data masking, subsetting, and anonymization features within sandbox data generation platforms is therefore a critical enabler for organizations aiming to achieve both rapid innovation and regulatory adherence.

Another significant driver for the Sandbox Data Generator market is the exponential growth of digital transformation initiatives across various industry verticals. As enterprises migrate to cloud-based infrastructures and adopt advanced technologies such as AI, machine learning, and big data analytics, the need for high-quality, production-like test data has never been more acute. Sandbox data generators play a pivotal role in supporting these digital initiatives by supplying synthetic yet realistic datasets that facilitate robust testing, model training, and system validation. This, in turn, helps organizations minimize the risks associated with deploying new applications or features, while reducing the time and costs associated with traditional data provisioning methods. The rise of microservices architecture and API-driven development further amplifies the necessity for dynamic, scalable, and automated test data generation solutions.

Additionally, the proliferation of data breaches and cyber threats has underscored the importance of robust data protection strategies, further fueling the adoption of sandbox data generators. Enterprises are increasingly recognizing that using real production data in test environments can expose them to significant security vulnerabilities and compliance risks. By leveraging sandbox data generators, organizations can create safe, de-identified datasets that maintain the statistical properties of real data, enabling comprehensive testing without jeopardizing sensitive information. This trend is particularly pronounced in sectors such as BFSI and healthcare, where data sensitivity and compliance requirements are paramount. As a result, vendors are investing heavily in enhancing the security, scalability, and automation capabilities of their sandbox data generation solutions to cater to the evolving needs of these high-stakes industries.

From a regional perspective, North America is anticipated to maintain its dominance in the global Sandbox Data Generator market, driven by the presence of leading technology providers, a mature regulatory landscape, and high digital adoption rates among enterprises. However, the Asia Pacific region is poised for the fastest growth, fueled by rapid digitalization, increasing investments in IT infrastructure, and growing awareness of data privacy and compliance issues. Europe also represents a significant market, supported by stringent data protection regulations and a strong focus on innovation across key industries. As organizations worldwide continue to prioritize data security and agile development, the demand for advanced sandbox data generation solutions is expected to witness sustained growth across all major regions.

Component Analysis

The Sandbox Data Genera

The Data Creation Tool market is booming, projected to reach $27.2 Billion by 2033, with a CAGR of 18.2%. Discover key trends, leading companies (Informatica, Delphix, Broadcom), and regional market insights in this comprehensive analysis. Explore how synthetic data generation is transforming software development, AI, and data analytics.

A simple linear dataset with data generation code attached

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

A simple parabolic dataset with data generation code attached

The online test creator market is booming, projected to reach $6 billion by 2033! Learn about key drivers, trends, and top companies shaping this rapidly growing sector. Discover market insights, regional analysis, and future projections for online assessment tools.

The global database testing tool market is anticipated to experience substantial growth in the coming years, driven by factors such as the increasing adoption of cloud-based technologies, the rising demand for data quality and accuracy, and the growing complexity of database systems. The market is expected to reach a value of USD 1,542.4 million by 2033, expanding at a CAGR of 7.5% during the forecast period of 2023-2033. Key players in the market include Apache JMeter, DbFit, SQLMap, Mockup Data, SQL Test, NoSQLUnit, Orion, ApexSQL, QuerySurge, DBUnit, DataFactory, DTM Data Generator, Oracle, SeLite, SLOB, and others. The North American region is anticipated to hold a significant share of the database testing tool market, followed by Europe and Asia Pacific. The increasing adoption of cloud-based database testing services, the presence of key market players, and the growing demand for data testing and validation are driving the market growth in North America. Asia Pacific, on the other hand, is expected to experience the highest growth rate due to the rapidly increasing IT spending, the emergence of new technologies, and the growing number of businesses investing in data quality management solutions.

Static torque, no load, constant speed, and sinusoidal oscillation test data for a 10hp, 300rpm magnetically-geared generator prototype using either an adjustable load bank for a fixed resistance or an output power converter.

Static torque and no load test data for a 1hp, 300rpm axial-flux magnetically geared generator prototype developed by Texas A&M EMPE Lab.

The experiment tested the function of the drop generator for the DOLFIN research project.. Dataset provided by the ESDC. Please refer to the datasets landing page at http://esdcdoi.esac.esa.int/doi/html/data/hre/hreda/6bbc04e335f647e4b3fe7692a569ee24.html

This dataset was created using LeRobot Data Studio.

  Dataset Structure

meta/info.json: { "codebase_version": "v2.1", "robot_type": "koch_screwdriver_follower", "total_episodes": 5, "total_frames": 1220, "total_tasks": 1, "total_videos": 15, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5"}, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path":… See the full description on the dataset page: https://huggingface.co/datasets/jackvial/dataset-creator-refactor-test-0.

Generator Load Bank Test Service Market Outlook

According to our latest research, the global Generator Load Bank Test Service market size reached USD 890 million in 2024, reflecting robust demand across critical infrastructure sectors. The market is projected to grow at a CAGR of 6.2% during the forecast period, with the total value expected to hit USD 1.53 billion by 2033. This growth is primarily driven by stringent regulatory requirements for backup power reliability, the expanding footprint of data centers, and a rising awareness among end-users regarding the importance of preventive maintenance for power generation systems.

One of the primary growth factors for the Generator Load Bank Test Service market is the increasing reliance on uninterrupted power supply across various industries, particularly in sectors such as healthcare, data centers, and manufacturing. These industries cannot afford downtime, as it could result in substantial financial losses, compromised data integrity, or even threats to human life. As a result, there is a growing emphasis on regular testing and maintenance of standby generators to ensure they perform optimally during emergencies. Load bank testing services play a crucial role in simulating real-world loads, identifying latent faults, and validating the operational readiness of generator systems. This heightened focus on preventive maintenance is expected to drive sustained demand for generator load bank test services globally.

Another significant driver for market expansion is the surge in data center construction worldwide. The proliferation of cloud computing, IoT, and digital transformation initiatives has led to a dramatic increase in data center capacity requirements. Data centers are highly sensitive to power disruptions, necessitating rigorous testing of backup generators to guarantee seamless operation during grid failures. Load bank testing services are now an integral part of data center commissioning and ongoing maintenance strategies, ensuring compliance with industry standards and minimizing the risk of costly outages. Additionally, with the global shift toward renewable energy integration and distributed generation, the complexity of power systems is increasing, further accentuating the need for comprehensive load testing services.

Regulatory mandates and industry standards are also catalyzing market growth. Governments and industry bodies across regions have established stringent guidelines for periodic generator testing, especially in critical infrastructure such as hospitals and utilities. These regulations are designed to enhance the reliability and safety of backup power systems, thereby safeguarding public health and essential services. As a result, organizations are increasingly outsourcing load bank testing to specialized service providers with the expertise and equipment to ensure full compliance. The growing trend toward outsourcing, combined with the rising adoption of digital monitoring and reporting tools, is expected to further propel the generator load bank test service market.

Regionally, North America continues to dominate the market, accounting for the largest share in 2024, driven by a mature industrial base, high data center density, and stringent regulatory frameworks. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid industrialization, urbanization, and significant investments in critical infrastructure. Emerging economies in Latin America and the Middle East & Africa are also experiencing increased adoption of load bank testing services, supported by expanding power generation capacities and modernization of healthcare and utility sectors. This evolving regional landscape is creating new opportunities for market participants and driving innovation in service delivery models.

Service Type Analysis

The generator load bank test service market is segmented by service type into resistive load bank testing, reactive load bank testing, and combined load bank testing. R

Dynamic testing of a Permanent Magnet Wet Gap Generator on a rotary test rig in a submerged condition

Generator Load Bank Test Scheduling Market Outlook

According to our latest research, the global generator load bank test scheduling market size reached USD 755 million in 2024, driven by the growing emphasis on preventive maintenance and operational reliability across critical infrastructure sectors. The market is projected to expand at a robust CAGR of 6.2% from 2025 to 2033, reaching an estimated USD 1.29 billion by 2033. This growth is fueled by stringent regulatory requirements, increasing investments in backup power systems, and the proliferation of data centers and critical facilities worldwide.

One of the primary growth factors for the generator load bank test scheduling market is the rising demand for uninterrupted power supply in essential sectors such as healthcare, data centers, and manufacturing. As these industries become increasingly dependent on continuous power for their operations, the necessity of regular generator testing using load banks has become a standard best practice. Generator load bank tests help verify the operational readiness of backup generators, ensuring they function optimally during power outages or emergencies. Moreover, the integration of advanced digital scheduling and monitoring solutions has streamlined the test scheduling process, making it more efficient and less prone to human error. This technological advancement not only enhances reliability but also reduces operational costs, further driving market growth.

Another significant driver is the evolving regulatory landscape, which mandates routine generator testing to comply with safety and performance standards. Regulatory bodies across regions, including the National Fire Protection Association (NFPA) in North America and similar organizations in Europe and Asia Pacific, have set stringent guidelines for periodic generator load testing in facilities like hospitals, data centers, and utilities. Non-compliance can lead to hefty fines, legal liabilities, and reputational damage. Consequently, organizations are increasingly investing in scheduled load bank testing services and solutions to ensure compliance while minimizing operational risks. This regulatory push is particularly strong in developed economies, where infrastructure reliability is paramount.

The ongoing expansion of mission-critical infrastructure, particularly data centers and healthcare facilities, is also contributing to the market's upward trajectory. The rapid digitalization of business processes, the surge in cloud computing, and the global boom in e-commerce have led to a significant increase in data center construction. These facilities require robust backup power systems and regular testing to prevent costly downtime. Similarly, the healthcare sector's reliance on uninterrupted power for life-saving equipment and operations has made generator load bank test scheduling an indispensable part of facility management. As these sectors continue to grow, the demand for reliable and efficient load bank test scheduling solutions is expected to rise in tandem.

Regionally, North America leads the generator load bank test scheduling market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to its mature infrastructure, strict regulatory standards, and high concentration of data centers and healthcare facilities. Europe’s market is propelled by modernization initiatives and increased investments in renewable energy, necessitating advanced backup power solutions. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid industrialization, urbanization, and the expansion of critical infrastructure in emerging economies such as China and India. Latin America and the Middle East & Africa are also showing promising growth potential due to increasing investments in power generation and oil & gas sectors.

Type Analysis

The generator load bank test scheduling market is segmented by type into resistive load banks, reactive load banks, resistive/reactive load banks,

Synthetic Data Generator for Telco AI Market Outlook

According to our latest research, the global Synthetic Data Generator for Telco AI market size reached USD 1.48 billion in 2024, reflecting the growing adoption of artificial intelligence and machine learning technologies across the telecommunications sector. The market is projected to expand at a robust CAGR of 33.2% from 2025 to 2033, reaching a forecasted value of USD 16.45 billion by 2033. This remarkable growth is primarily fueled by the increasing demand for high-quality, privacy-compliant training data to power AI-driven telco solutions, alongside the rapid digital transformation initiatives being undertaken by telecom operators worldwide.

One of the primary growth drivers for the Synthetic Data Generator for Telco AI market is the exponential rise in data privacy regulations and concerns surrounding the use of real customer data for AI model training. As telecom operators handle massive volumes of sensitive user information, compliance with regulations such as GDPR, CCPA, and other local data protection laws has become paramount. Synthetic data generators provide a viable solution by creating realistic, anonymized datasets that mimic real-world scenarios without exposing actual customer information. This enables telcos to accelerate AI development, enhance model accuracy, and reduce the risk of data breaches, thus fostering the widespread adoption of synthetic data generation tools across the industry.

Another significant factor propelling market growth is the increasing complexity of telco networks and the need for advanced analytics to optimize operations. With the deployment of 5G, IoT, and edge computing, telecommunications infrastructure has become more intricate, generating vast amounts of structured and unstructured data. Synthetic data generators empower telcos to simulate rare network events, test AI algorithms under diverse scenarios, and improve predictive maintenance, fraud detection, and customer analytics. This capability not only enhances operational efficiency but also reduces downtime and improves customer satisfaction, further driving the integration of synthetic data solutions in telco AI workflows.

Furthermore, the shift towards digital transformation and the adoption of cloud-native technologies by telecom operators are accelerating the demand for scalable, flexible synthetic data generation platforms. As telcos modernize their IT infrastructure and embrace cloud-based AI solutions, the need for on-demand, customizable synthetic datasets has surged. Synthetic data generators enable seamless integration with cloud platforms, support agile development cycles, and facilitate collaboration across distributed teams. This trend is expected to continue as telecom operators invest in next-generation AI applications to stay competitive, improve service delivery, and unlock new revenue streams.

Regionally, North America currently dominates the Synthetic Data Generator for Telco AI market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading telecom operators, advanced AI research capabilities, and a mature regulatory environment in these regions contribute to the rapid adoption of synthetic data solutions. Asia Pacific is poised for the fastest growth over the forecast period, driven by the expansion of 5G networks, increasing investments in AI, and the proliferation of connected devices. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as telcos in these regions accelerate their digital transformation journeys, albeit from a smaller base.

Component Analysis

The Synthetic Data Generator for Telco AI market is segmented by component into Software and Services. Software solutions form the backbone of this market, offering advanced tools for data synthesis, simulation, and integration with existing telco AI workflows. These platforms are designed to generate high-fid

Abstract A library containing highly portable implementations of most algorithms for (pseudo) random number generation has been developed, which might be used in any area of simulation which requires random number generators. Each generator is freely configurable by the user, so the RANEXP library is particularly well-suited for applications requiring different random number generators. The algorithms are implemented in C, but are callable from Fortran application program also.

Title of program: RANEXP Catalogue Id: ACTB_v1_0

Nature of problem Any Monte Carlo simulation or statistical test requiring uniform pseudorandom numbers.

Versions of this program held in the CPC repository in Mendeley Data ACTB_v1_0; RANEXP; 10.1016/0010-4655(94)90072-8

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

The high-speed digital signal generator market is booming, driven by 5G, data centers, and automotive advancements. Explore market size, CAGR, key players (Keysight, Rohde & Schwarz, Tektronix), and regional trends in this comprehensive analysis projecting robust growth to 2033.