The heat production rate data of lunar major basins were obtained from the Chang 'e-2 gamma-ray spectrometer data (http://moon.bao.ac.cn). A detailed description of the processing procedure for these data products is provided by Zhao et al. [2023]. The map bin size is 1.5° by 1.5°. The unit of heat production rate in these data is μW/m³.
Data Format: The format of the dataset is CSV format.
- Size of Dataset: The size of the dataset is 3.84 Go with 7,068,103 rows and 18 columns.
- The summary of the attributes:
1) Date: Date of the event, in the format MM/DD/YYYY.
2) Time: Time of day, expressed in 24-hour format HH:MM.
3) Timezone: Time zone specified during processing.
4) MACB: Associated with actions performed on a file in a file system: M for modification, A for access, C for change, B for creation.
5) Source: Short name of the source.
6) Sourcetype: More detailed description of the source.
Miami Shores Golf Club Round
The dataset utilized in this study corresponds to the year 2019 (all months) and covers an extensive region within the Mediterranean Basin. This particular year was selected to investigate seasonal variations in column-averaged dry-air mole fraction of methane (XCH₄), providing insights into methane dynamics over different climatic periods.
The series of file names starting with "01-CuIndex" are the raw data. "datasets122" refers to the processed dataset, and "test_datasets" is the test dataset.
The FIDAL RB-SUMMIT Dataset contain operational and telemetry data collected from an RB-SUMMIT robot used in the FIDAL project. In this project, the robot is teleoperated through an immersive control system, allowing remote operation in real time.
FIDAL project website: https://fidal-he.eu/
The rapid advancement of deep neural network (DNN) models has enabled their widespread application across various domains, including face recognition and natural language processing. However, data-driven DNN models are prone to erroneous behavior when inadequately trained, necessitating extensive predictive labeling of test data to identify and mitigate defects. Manual labeling, however, remains both labor-intensive and inefficient.
使用GSSI-3000设备，采集自校内
The CodyDroid Evaluation Dataset is curated to benchmark the Android SDK code generation capabilities of two lightweight language models: CodyDroid Model-1 (deepseek-coder-5.9M-kexer) and CodyDroid Model-2 (codegen-175K-mono-java). Each entry in the dataset consists of a natural language description of an Android development task, paired with code outputs from both models, a reference solution crafted by a developer, and a detailed qualitative analysis.
The scarcity of multimodal datasets in remote sensing, particularly those combining high-resolution imagery with descriptive textual annotations, limits advancements in context-aware analysis. To address this, we introduce a novel dataset comprising 12,473 aerial and satellite images sourced from established benchmarks (RSSCN7, DLRSD, iSAID, LoveDA, and WHU), enriched with automatically generated pseudo-captions and semantic tags.
This dataset contains synthetically generated data representing security profiles of mobile applications, designed for training and evaluating machine learning models for vulnerability detection. It was created as part of the research described in the paper "Machine Learning-Based Vulnerability Detection in Mobile Applications" by Yanguema and Yin.
This dataset has been curated to support the development and evaluation of lightweight object detection algorithms for precision pesticide spraying in orchard environments. It comprises annotated images captured in diverse natural lighting and occlusion conditions typical of real-world agricultural fields. The dataset includes high-resolution RGB images along with corresponding bounding box annotations in YOLO format, identifying key targets. All annotations are manually verified to ensure label accuracy.
There are two main battery voltage levels (400 V and 800 V) for electric vehicles (EVs). Conventional wireless power transfer (WPT) systems utilize different connection circuits to accommodate these voltages, which may cause additional power losses. This paper proposes a new LCC-LCC WPT system to accommodate both 400 V and 800 V battery voltages without compromising performance.
This dataset includes the development of models for extreme weather events in the expansion planning of the test system, as presented in the article: 'Power System Planning Considering Extreme Weather Events and Stability Criteria.
This dataset includes the development of models for extreme weather events in the expansion planning of the Chilean power system, as presented in the article: 'Power System Planning Considering Extreme Weather Events and Stability Criteria.
The development of automated techniques for speech analysis-based Parkinson's disease (PD) detection has attracted a lot of interest, especially because of its possible uses in health tele-monitoring. Due to the drawbacks of the ᾳ - Synuclein Seed Amplification Assay technique, scientists are looking more closely at speech signals as a potential substitute for PD detection. In order to identify PD, this proposal describes a thorough investigation that emphasizes using both voice and unvoiced source material.
This dataset provides experimental validation data supporting the proposed finite element method (FEM) for optimizing a directional eddy current testing (ECT) probe designed to detect in-plane waviness on the surface of carbon fiber prepreg. The study includes the design and evaluation of six ECT probe configurations, varying the angle and aspect ratio of the receiver coils. Sensitivity measurements were conducted to assess the probes' ability to detect fiber-related features.
This dataset, denoted as 𝑾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain API–Storage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.
The dataset includes eight urban scenes of different sizes and styles, as well as various lighting and weather conditions. Each scene contains 200 vehicles of different types, 100 pedestrians and 5,000 RGB images, semantic images, and point cloud files. The annotations include both the depth and 2D information of the objects.
Existing plain text-based video generation methods have limited expressiveness and struggle to provide detailed descriptions and precise control of attributes. To address this, we introduce rich text for video generation, which faces two main challenges: coherent control between frames and consistency among rich text attributes, plain text, and control regions.
