Data

16 pages, 3695 KiB

Open AccessArticle

Parallel Simplex, an Alternative to Classical Experimentation: A Case Study

by Francisco Zorrilla Briones, Inocente Yuliana Meléndez Pastrana, Manuel Alonso Rodríguez Morachis and José Luís Anaya Carrasco

Data 2024, 9(12), 147; https://doi.org/10.3390/data9120147 - 10 Dec 2024

Abstract

Experimentation is a strong methodology that improves and optimizes processes. Nevertheless, in many cases, real-life dynamics of production demands and other restrictions inhibit the use of these methodologies because their use implies stopping production, generating scrap, jeopardizing demand accomplishments, and other problems. Proposed [...] Read more.

Experimentation is a strong methodology that improves and optimizes processes. Nevertheless, in many cases, real-life dynamics of production demands and other restrictions inhibit the use of these methodologies because their use implies stopping production, generating scrap, jeopardizing demand accomplishments, and other problems. Proposed here is an alternative methodology to search for the best process variable levels and optimize the response of the process without the need to stop production. This algorithm is based on the principles of the Variable Simplex developed by Nelder and Mead and the continuous iterative process of EVOPS developed by Box, which is then modified as a simplex by Spendley. It is named parallel simplex because it searches for the best response with three independent Simplexes searching for the same response at the same time. The algorithm was designed for three simplexes of two input variables each. The case study documented shows that it is efficient and effective. Full article

(This article belongs to the Special Issue Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply Chain Optimization)

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23 pages, 7192 KiB

Open AccessArticle

Data Decomposition Modeling Based on Improved Dung Beetle Optimization Algorithm for Wind Power Prediction

by Jiajian Ke and Tian Chen

Data 2024, 9(12), 146; https://doi.org/10.3390/data9120146 - 9 Dec 2024

Abstract

Accurate wind power forecasting is essential for maintaining the stability of a power system and enhancing scheduling efficiency in the power sector. To enhance prediction accuracy, this paper presents a hybrid wind power prediction model that integrates the improved complementary ensemble empirical mode [...] Read more.

Accurate wind power forecasting is essential for maintaining the stability of a power system and enhancing scheduling efficiency in the power sector. To enhance prediction accuracy, this paper presents a hybrid wind power prediction model that integrates the improved complementary ensemble empirical mode decomposition (ICEEMDAN), the RIME optimization algorithm (RIME), sample entropy (SE), the improved dung beetle optimization (IDBO) algorithm, the bidirectional long short-term memory (BiLSTM) network, and multi-head attention (MHA). In this model, RIME is utilized to improve the parameters of ICEEMDAN, reducing data decomposition complexity and effectively capturing the original data information. The IDBO algorithm is then utilized to improve the hyperparameters of the MHA-BiLSTM model. The proposed RIME-ICEEMDAN-IDBO-MHA-BiLSTM model is contrasted with ten others in ablation experiments to validate its performance. The experimental findings prove that the proposed model achieves MAPE values of 5.2%, 6.3%, 8.3%, and 5.8% across four datasets, confirming its superior predictive performance and higher accuracy. Full article

(This article belongs to the Topic Decision-Making and Data Mining for Sustainable Computing)

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17 pages, 6026 KiB

Open AccessArticle

Formalization for Subsequent Computer Processing of Kara Sea Coastline Data

by Daria Bogatova and Stanislav Ogorodov

Data 2024, 9(12), 145; https://doi.org/10.3390/data9120145 - 9 Dec 2024

Abstract

This study aimed to develop a methodological framework for predicting shoreline dynamics using machine learning techniques, focusing on analyzing generalized data without distinguishing areas with higher or lower retreat rates. Three sites along the southwestern Kara Sea coast were selected for this investigation. [...] Read more.

This study aimed to develop a methodological framework for predicting shoreline dynamics using machine learning techniques, focusing on analyzing generalized data without distinguishing areas with higher or lower retreat rates. Three sites along the southwestern Kara Sea coast were selected for this investigation. The study analyzed key coastal features, including lithology, permafrost, and geomorphology, using a combination of field studies and remote sensing data. Essential datasets were compiled and formatted for computer-based analysis. These datasets included information on permafrost and the geomorphological characteristics of the coastal zone, climatic factors influencing the shoreline, and measurements of bluff top positions and retreat rates over defined time periods. The positions of the bluff tops were determined through a combination of imagery with varying resolutions and field measurements. A novel aspect of the study involved employing geostatistical methods to analyze erosion rates, providing new insights into the shoreline dynamics. The data analysis allowed us to identify coastal areas experiencing the most significant changes. By continually refining neural network models with these datasets, we can improve our understanding of the complex interactions between natural factors and shoreline evolution, ultimately aiding in developing effective coastal management strategies. Full article

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18 pages, 11734 KiB

Open AccessData Descriptor

Multi-Modal Dataset of Human Activities of Daily Living with Ambient Audio, Vibration, and Environmental Data

by Thomas Pfitzinger, Marcel Koch, Fabian Schlenke and Hendrik Wöhrle

Data 2024, 9(12), 144; https://doi.org/10.3390/data9120144 - 9 Dec 2024

Abstract

The detection of human activities is an important step in automated systems to understand the context of given situations. It can be useful for applications like healthcare monitoring, smart homes, and energy management systems for buildings. To achieve this, a sufficient data basis [...] Read more.

The detection of human activities is an important step in automated systems to understand the context of given situations. It can be useful for applications like healthcare monitoring, smart homes, and energy management systems for buildings. To achieve this, a sufficient data basis is required. The presented dataset contains labeled recordings of 25 different activities of daily living performed individually by 14 participants. The data were captured by five multisensors in supervised sessions in which a participant repeated each activity several times. Flawed recordings were removed, and the different data types were synchronized to provide multi-modal data for each activity instance. Apart from this, the data are presented in raw form, and no further filtering was performed. The dataset comprises ambient audio and vibration, as well as infrared array data, light color and environmental measurements. Overall, 8615 activity instances are included, each captured by the five multisensor devices. These multi-modal and multi-channel data allow various machine learning approaches to the recognition of human activities, for example, federated learning and sensor fusion. Full article

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21 pages, 1477 KiB

Open AccessArticle

A Data Storage, Analysis, and Project Administration Engine (TMFdw) for Small- to Medium-Size Interdisciplinary Ecological Research Programs with Full Raster Data Capabilities

by Paulina Grigusova, Christian Beilschmidt, Maik Dobbermann, Johannes Drönner, Michael Mattig, Pablo Sanchez, Nina Farwig and Jörg Bendix

Data 2024, 9(12), 143; https://doi.org/10.3390/data9120143 - 6 Dec 2024

Abstract

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now [...] Read more.

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs. Full article

20 pages, 12402 KiB

Open AccessArticle

Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network

by Yiya Diao, Changhe Li, Sanyou Zeng and Shengxiang Yang

Data 2024, 9(12), 142; https://doi.org/10.3390/data9120142 - 6 Dec 2024

Abstract

Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem’s landscape features, which has limited their effectiveness in practice. [...] Read more.

Contaminant Source Identification in Water Distribution Network (CSWIDN) is critical for ensuring public health, and optimization algorithms are commonly used to solve this complex problem. However, these algorithms are highly sensitive to the problem’s landscape features, which has limited their effectiveness in practice. Despite this, there has been little experimental analysis of the fitness landscape for CSWIDN, particularly given its mixed-encoding nature. This study addresses this gap by conducting a comprehensive fitness landscape analysis of CSWIDN using the Nearest-Better Network (NBN), the only applicable method for mixed-encoding problems. Our analysis reveals for the first time that CSWIDN exhibits the landscape features, including neutrality, ruggedness, modality, dynamic change, and separability. These findings not only deepen our understanding of the problem’s inherent landscape features but also provide quantitative insights into how these features influence algorithm performance. Additionally, based on these insights, we propose specific algorithm design recommendations that are better suited to the unique challenges of the CSWIDN problem. This work advances the knowledge of CSWIDN optimization by both qualitatively characterizing its landscape and quantitatively linking these features to algorithms’ behaviors. Full article

(This article belongs to the Topic Water and Energy Monitoring and Their Nexus)

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6 pages, 1360 KiB

Open AccessData Descriptor

A Dataset of Plant Species Richness in Chinese National Nature Reserves

by Chunjing Wang, Wuxian Yan and Jizhong Wan

Data 2024, 9(12), 141; https://doi.org/10.3390/data9120141 - 30 Nov 2024

Abstract

This comprehensive dataset on the number of plant species, genera, and families in 383 national nature reserves in China has been compiled based on the available literature. Heilongjiang Province and the Guangxi Zhuang Autonomous Region have the highest number of nature reserves. Species [...] Read more.

This comprehensive dataset on the number of plant species, genera, and families in 383 national nature reserves in China has been compiled based on the available literature. Heilongjiang Province and the Guangxi Zhuang Autonomous Region have the highest number of nature reserves. Species richness is relatively high in the Jinfoshan, Dabashan, Wenshan, Hupingshan, and Shennongjia Nature Reserves. This dataset provides important baseline information on plant species richness coupling with genus and family numbers in Chinese national nature reserves and should help researchers and environmentalists understand the dynamic species changes in various nature reserves. This detailed and reliable information may serve as the foundation for future plant research in Chinese nature reserves and play a positive role in promoting more effective natural protection, biological distribution, and biodiversity conservation in these areas. Full article

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21 pages, 6066 KiB

Open AccessArticle

Algorithm for Trajectory Simplification Based on Multi-Point Construction in Preselected Area and Noise Smoothing Processing

by Simin Huang and Zhiying Yang

Data 2024, 9(12), 140; https://doi.org/10.3390/data9120140 - 29 Nov 2024

Abstract

Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points [...] Read more.

Simplifying trajectory data can improve the efficiency of trajectory data analysis and query and reduce the communication cost and computational overhead of trajectory data. In this paper, a real-time trajectory simplification algorithm (SSFI) based on the spatio-temporal feature information of implicit trajectory points is proposed. The algorithm constructs the preselected area through the error measurement method based on the feature information of implicit trajectory points (IEDs) proposed in this paper, predicts the falling point of trajectory points, and realizes the one-way error-bounded simplified trajectory algorithm. Experiments show that the simplified algorithm has obvious progress in three aspects: running speed, compression accuracy, and simplification rate. When the trajectory data scale is large, the performance of the algorithm is much better than that of other line segment simplification algorithms. The GPS error cannot be avoided. The Kalman filter smoothing trajectory can effectively eliminate the influence of noise and significantly improve the performance of the simplified algorithm. According to the characteristics of the trajectory data, this paper accurately constructs a mathematical model to describe the motion state of objects, so that the performance of the Kalman filter is better than other filters when smoothing trajectory data. In this paper, the trajectory data smoothing experiment is carried out by adding random Gaussian noise to the trajectory data. The experiment shows that the Kalman filter’s performance under the mathematical model is better than other filters. Full article

(This article belongs to the Special Issue IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues)

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32 pages, 969 KiB

Open AccessArticle

Detective Gadget: Generic Iterative Entity Resolution over Dirty Data

by Marcello Buoncristiano, Giansalvatore Mecca, Donatello Santoro and Enzo Veltri

Data 2024, 9(12), 139; https://doi.org/10.3390/data9120139 - 25 Nov 2024

Abstract

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want [...] Read more.

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want to avoid integrating different entities or missing matches. However, existing approaches struggle with the challenges posed by rapidly changing data and the presence of dirtiness, which requires an iterative refinement during the time. We present Detective Gadget, a novel system for iterative ER that seamlessly integrates data-cleaning into the ER workflow. Detective Gadgetemploys an alias-based hashing mechanism for fast and scalable matching, check functions to detect and correct mismatches, and a human-in-the-loop framework to refine results through expert feedback. The system iteratively improves data quality and matching accuracy by leveraging evidence from both automated and manual decisions. Extensive experiments across diverse real-world scenarios demonstrate its effectiveness, achieving high accuracy and efficiency while adapting to evolving datasets. Full article

(This article belongs to the Section Information Systems and Data Management)

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16 pages, 362 KiB

Open AccessArticle

CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data

by Christian Gück, Cyriana M. A. Roelofs and Stefan Faulstich

Data 2024, 9(12), 138; https://doi.org/10.3390/data9120138 - 23 Nov 2024

Abstract

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data [...] Read more.

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data, or one of the few publicly available datasets that lack detailed information about the faults. Moreover, many publications highlight a couple of case studies where fault detection was successful. With this paper, we publish a high quality dataset that contains data from 36 wind turbines across 3 different wind farms as well as the most detailed fault information of any public wind turbine dataset as far as we know. The new dataset contains 89 years worth of real-world operating data of wind turbines, distributed across 44 labeled time frames for anomalies that led up to faults, as well as 51 time series representing normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point. Furthermore, we propose a new scoring method, called CARE (Coverage, Accuracy, Reliability and Earliness), which takes advantage of the information depth that is present in the dataset to identify good early fault detection models for wind turbines. This score considers the anomaly detection performance, the ability to recognize normal behavior properly, and the capability to raise as few false alarms as possible while simultaneously detecting anomalies early. Full article

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11 pages, 2069 KiB

Open AccessData Descriptor

Dual Transcriptome of Post-Germinating Mutant Lines of Arabidopsis thaliana Infected by Alternaria brassicicola

by Mailen Ortega-Cuadros, Laurine Chir, Sophie Aligon, Nubia Velasquez, Tatiana Arias, Jerome Verdier and Philippe Grappin

Data 2024, 9(11), 137; https://doi.org/10.3390/data9110137 - 18 Nov 2024

Abstract

Alternaria brassicicola is a seed-borne pathogen that causes black spot disease in Brassica crops, yet the seed defense mechanisms against this fungus remain poorly understood. Building upon recent reports that highlighted the involvement of indole pathways in seeds infected by Alternaria, this [...] Read more.

Alternaria brassicicola is a seed-borne pathogen that causes black spot disease in Brassica crops, yet the seed defense mechanisms against this fungus remain poorly understood. Building upon recent reports that highlighted the involvement of indole pathways in seeds infected by Alternaria, this study provides transcriptomic resources to further elucidate the role of these metabolic pathways during the interaction between seeds and fungal pathogens. Using RNA sequencing, we examined the gene expression of glucosinolate-deficient mutant lines (cyp79B2/cyp79B3 and qko) and a camalexin-deficient line (pad3), generating a dataset from 14 samples. These samples were inoculated with Alternaria or water, and collected at 3, 6, and 10 days after sowing to extract total RNA. Sequencing was performed using DNBseq™ technology, followed by bioinformatics analyses with tools such as FastQC (version 0.11.9), multiQC (version 1.13), Venny (version 2.0), Salmon software (version 0.14.1), and R packages DESeq2 (version 1.36.0), ClusterProfiler (version 4.12.6) and ggplot2 (version 3.4.0). By providing this valuable dataset, we aim to contribute to a deeper understanding of seed defense mechanisms against Alternaria, leveraging RNA-seq for various analyses, including differential gene expression and co-expression correlation. This work serves as a foundation for a more comprehensive grasp of the interactions during seed infection and highlights potential _targets for enhancing crop protection and management. Full article

(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)

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19 pages, 9663 KiB

Open AccessData Descriptor

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

by Ludovica De Gregorio, Giovanni Cuozzo, Riccardo Barella, Francisco Corvalán, Felix Greifeneder, Peter Grosse, Abraham Mejia-Aguilar, Georg Niedrist, Valentina Premier, Paul Schattan, Alessandro Zandonai and Claudia Notarnicola

Data 2024, 9(11), 136; https://doi.org/10.3390/data9110136 - 16 Nov 2024

Abstract

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of [...] Read more.

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m³·m⁻³ were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information. Full article

(This article belongs to the Topic Techniques and Science Exploitations for Earth Observation and Planetary Exploration)

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8 pages, 489 KiB

Open AccessData Descriptor

Dataset to Quantify Spillover Effects Among Concurrent Green Initiatives

by Rong Zhang, Qi Zhang, Conghe Song and Li An

Data 2024, 9(11), 135; https://doi.org/10.3390/data9110135 - 13 Nov 2024

Abstract

Green initiatives are popular mechanisms globally to enhance environmental and human wellbeing. However, multiple green initiatives, when overlapping geographically and _targeting the same participants, may interact with each other, giving rise to what is termed “spillover effects”, where one initiative and its outcomes [...] Read more.

Green initiatives are popular mechanisms globally to enhance environmental and human wellbeing. However, multiple green initiatives, when overlapping geographically and _targeting the same participants, may interact with each other, giving rise to what is termed “spillover effects”, where one initiative and its outcomes influence another. This study examines the spillover effects among four major concurrent initiatives in the United States (U.S.) and China using a comprehensive dataset. In the U.S., we analysed county-level data in 2018 for the Conservation Reserve Program (CRP) and the Environmental Quality Incentives Program (EQIP), both operational for over 25 years. In China, data from Fanjingshan and Tianma National Nature Reserves (2014–2015) were used to evaluate the Grain-to-Green Program (GTGP) and the Forest Ecological Benefit Compensation (FEBC) program. The dataset comprises 3106 records for the U.S. and 711 plots for China, including several socio-economic variables. The results of multivariate linear regression indicate that there exist significant spillover effects between CRP & EQIP and GTGP & FEBC, with one initiative potentially enhancing or offsetting another’s impacts by 22% to 100%. This dataset provides valuable insights for researchers and policymakers to optimize the effectiveness and resilience of concurrent green initiatives. Full article

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11 pages, 456 KiB

Open AccessData Descriptor

The Design of a Script Identification Algorithm and Its Application in Constructing a Text Language Identification Dataset

by Mamtimin Qasim, Wushour Silamu and Minghui Qiu

Data 2024, 9(11), 134; https://doi.org/10.3390/data9110134 - 11 Nov 2024

Abstract

Script identification is easier to implement than language identification, and its identification rate is very high. The fewer languages are identified when using a language identification algorithm, the higher the identification rate is. However, no systematic study on SI involving multiple languages and [...] Read more.

Script identification is easier to implement than language identification, and its identification rate is very high. The fewer languages are identified when using a language identification algorithm, the higher the identification rate is. However, no systematic study on SI involving multiple languages and determining how to construct relevant language identification datasets has been conducted. Therefore, in this paper, we discuss and design a script identification algorithm and the construction of a language identification dataset based on script groups. The data sources in this paper comprise 261 different languages’ text corpora from the Leipzig Corpora Collection, which are grouped into 23 different script groups. In the Unicode encoding scheme, different scripts are arranged into different code regions. Based on this feature, we propose a written script identification algorithm based on regular expression matching, the micro F-score of which reaches 0.9929 in sentence-level script identification experiments. To reduce noise when constructing the language identification dataset for each script, a script identification algorithm is used to filter out other-script content in each text. Full article

(This article belongs to the Section Information Systems and Data Management)

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13 pages, 3724 KiB

Open AccessData Descriptor

Additions to Space Physics Data Facility and pysatNASA: Increasing Mars Global Surveyor and Mars Atmosphere and Volatile EvolutioN Dataset Utility

by Teresa M. Esman, Alexa J. Halford, Jeff Klenzing and Angeline G. Burrell

Data 2024, 9(11), 133; https://doi.org/10.3390/data9110133 - 8 Nov 2024

Abstract

The Space Physics Data Facility (SPDF) is a digital archive of space physics data and is useful for the storage, analysis, and dissemination of data. We discuss the process used to create an amended dataset and store it on the SPDF. The operational [...] Read more.

The Space Physics Data Facility (SPDF) is a digital archive of space physics data and is useful for the storage, analysis, and dissemination of data. We discuss the process used to create an amended dataset and store it on the SPDF. The operational software to generate the archival data software uses the open-source Python package pysat, and an end-user module has been added to the pysatNASA module. The result is the addition of data products to the Mars Global Surveyor (MGS) magnetometer (MAG) dataset, its archival location on SPDF, and pysat compatibility. The primary and metadata format increases the convenience and efficiency for users of the MGS MAG data. The storage of planetary and heliophysics data in one location supports the use of data throughout the solar system for comparison, while pysat compatibility enables loading data in an identical format for ease of processing. We encourage the use of the outlined process for past, present, and future space science missions of all sizes and funding levels. This includes balloons to Flagship-class missions. Full article

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15 pages, 996 KiB

Open AccessData Descriptor

The VNF Cybersecurity Dataset for Research (VNFCYBERDATA)

by Believe Ayodele and Victor Buttigieg

Data 2024, 9(11), 132; https://doi.org/10.3390/data9110132 - 8 Nov 2024

Abstract

Virtualisation has received widespread adoption and deployment across a wide range of enterprises and industries throughout the years. Network Function Virtualisation (NFV) is a technical concept that presents a method for dynamically delivering virtualised network functions as virtualised or software components. Virtualised Network [...] Read more.

Virtualisation has received widespread adoption and deployment across a wide range of enterprises and industries throughout the years. Network Function Virtualisation (NFV) is a technical concept that presents a method for dynamically delivering virtualised network functions as virtualised or software components. Virtualised Network Function (VNF) has distinct advantages, but it also faces serious security challenges. Cyberattacks such as Denial of Service (DoS), malware/rootkit injection, port scanning, and so on can _target VNF appliances just like any other network infrastructure. To create exceptional training exercises for machine or deep learning (ML/DL) models to combat cyberattacks in VNF, a suitable dataset (VNFCYBERDATA) exhibiting an actual reflection, or one that is reasonably close to an actual reflection, of the problem that the ML/DL model could address is required. This article describes a real VNF dataset that contains over seven million data points and twenty-five cyberattacks generated from five VNF appliances. To facilitate a realistic examination of VNF traffic, the dataset includes both benign and malicious traffic. Full article

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7 pages, 1284 KiB

Open AccessData Descriptor

Influence of Temperature Variability on the Efficacy of Negative Ions in Removing Particulate Matter and Pollutants: An Experimental Database

by Paola M. Ortiz-Grisales, Leidy Gutiérrez-León and Carlos D. Zuluaga-Ríos

Data 2024, 9(11), 131; https://doi.org/10.3390/data9110131 - 8 Nov 2024

Abstract

Cities globally must make urgent decisions to ensure a sustainable future as rising pollution, particularly PM2.5, poses severe health risks like respiratory and heart diseases. PM2.5’s harmful composition also impacts vegetation and the environment. Immediate government intervention is necessary to mitigate these effects. [...] Read more.

Cities globally must make urgent decisions to ensure a sustainable future as rising pollution, particularly PM2.5, poses severe health risks like respiratory and heart diseases. PM2.5’s harmful composition also impacts vegetation and the environment. Immediate government intervention is necessary to mitigate these effects. This study tackles the urgent problem of reducing PM2.5 levels in Medellín’s urban and indoor environments, where pollution presents serious health risks. To explore effective solutions, this research provides new data on the interaction between particulate matter from various pollutants and negative ions under different temperature conditions, offering valuable insights into air quality improvement strategies. Using a high-voltage system, ions bind to pollutants, accelerating their removal. Experiments measured temperature, humidity, formaldehyde, volatile organic compounds, negative ions, and PM2.5 in a 40 cm³ chamber across various conditions. Pollutants tested included cigarette smoke, incense, charcoal, and gasoline at two voltage levels and three temperature ranges. The data, available in CSV format, were based on 36,000 samples and repeated tests for reliability. This resource is designed to support studies investigating particulate matter control in urban and indoor environments, as well as to improve our understanding of negative ion-based air purification processes. The data are publicly available and structured in formats compatible with leading data analysis platforms. Full article

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9 pages, 3118 KiB

Open AccessData Descriptor

Non-Destructive Wood Analysis Dataset: Comparing X-Ray and Terahertz Imaging Techniques

by Caroline Marc, Bertrand Marcon, Louis Denaud and Stéphane Girardon

Data 2024, 9(11), 130; https://doi.org/10.3390/data9110130 - 5 Nov 2024

Abstract

Wood density measurement plays a crucial role in assessing wood quality and predicting its mechanical performance. This dataset was collected to compare the accuracy and reliability of two non-destructive techniques, X-rays and terahertz waves, for measuring wood density. While X-rays have been commonly [...] Read more.

Wood density measurement plays a crucial role in assessing wood quality and predicting its mechanical performance. This dataset was collected to compare the accuracy and reliability of two non-destructive techniques, X-rays and terahertz waves, for measuring wood density. While X-rays have been commonly used in the industry due to their effectiveness, they pose health risks due to ionizing radiation. Terahertz waves, on the other hand, are non-ionizing and offer high spatial resolution. This article presents a database of wood samples measurements obtained using both techniques, on the same 110 samples with a fine location of the measuring points, on a wide range of wood species (tropical and temperate ones) and densities, from 111 kg·m⁻³ to 1086 kg·m⁻³. The database includes X-ray and terahertz scans, sample dimensions, moisture content, and color photographs. Full article

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18 pages, 4493 KiB

Open AccessArticle

Data Hub for Life Cycle Assessment of Climate Change Solutions—Hydrogen Case Study

by Shiva Zargar, Miyuru Kannangara, Giovanna Gonzales-Calienes, Jianjun Yang, Jalil Shadbahr, Cyrille Decès-Petit and Farid Bensebaa

Data 2024, 9(11), 129; https://doi.org/10.3390/data9110129 - 5 Nov 2024

Abstract

Life cycle assessment, which evaluates the complete life cycle of a product, is considered the standard methodological framework to evaluate the environmental performance of climate change solutions. However, significant challenges exist related to datasets used to quantify these environmental indicators. Although extensive research [...] Read more.

Life cycle assessment, which evaluates the complete life cycle of a product, is considered the standard methodological framework to evaluate the environmental performance of climate change solutions. However, significant challenges exist related to datasets used to quantify these environmental indicators. Although extensive research and commercial data on climate change technologies, pathways, and facilities exist, they are not readily available to practitioners of life cycle assessment in the right format and structure using an open platform. In this study, we propose a new open data hub platform for life cycle assessment, considering a hierarchical data flow starting with raw data collected on climate change technologies at laboratory, pilot, demonstration, or commercial scales to provide the information required for policy and decision-making. This platform makes data accessible at multiple levels for practitioners of life cycle assessment, while making data interoperable across platforms. The proposed data hub platform and workflow are explained through the polymer electrolyte membrane electrolysis hydrogen production as a case study. The climate change environment impact of 1.17 ± 0.03 kg CO₂ eq./kg H₂ was calculated for the case study. The current data hub platform is limited to evaluating environmental impacts; however, future additions of economic and social aspects are envisaged. Full article

(This article belongs to the Section Information Systems and Data Management)

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