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The Pannonian-Carpathian-Alpine Seismic Experiment (PACASE) is a collaborative project based on a large, passive seismic network comprising 214 temporary stations. Among the primary aims are the imaging of the Earth's crustal, lithospheric and upper mantle structure, including joint inversions; monitoring and mapping of seismic activity; and interpretation of the data from seismotectonic and geodynamic perspectives. The base of the cooperation is a high-quality, broadband seismic network covering the very centre of Europe: the Eastern Alpine and Western Carpathian Mountain ranges, the Bohemian Massif, and the sedimentary Molasse and Pannonian Basins. In this overview, we focus on the implementation and achieved field goals of PACASE, such as seismic station configuration, general network organization, data availability and access to the dataset. With selected seismological examples, we demonstrate the good usability of the records of earthquake detection, and a first publication attests to the structural imaging capability of the PACASE data. We assess the background noise level at various stations and its variations in time and space. Our aim is to collect all practical information relevant to serve as a long-term reference for the PACASE. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This research aims to critically assess the suitability of current ISO life cycle assessment standards and practices for the challenges of decarbonisation through the use of carbon capture and the circular economy. Currently, the handling of wastes, including carbon dioxide, in life cycle assessment varies from sector to sector. We propose several methodological innovations to improve transparency and comparability of life cycle assessments to aid in the decarbonisation transition. Methods: Three approaches have been used to analyse the shortcomings of life cycle assessment for carbon capture and circular practices: Recent standards and practices have been assessed to determine their suitability for decarbonisation; Life cycle assessment and industry experts were consulted through a workshop held at the University of Bath in September of 2022; Case studies of industrial decarbonisation projects funded by IDRIC have been conducted to apply solutions to the shortcomings identified through the former methods. The issues identified have been categorised into three key areas: (i) guidelines and standards; (i) temporal and regional issues and (iii) data quality analysis. Results and discussion: The methods of handling carbon capture, waste valorisation and circular practices were found to vary significantly from sector to sector. Temporal aspects are frequently aggregated in a specific point of time, leading to low resolution by neglecting emissions over the duration of the process that creates them. Regionalisation was found to be hampered by regional characterisation factors being representative of larger regions but unsuitable at county or state scale. Uncertainty and sensitivity analyses, which are key to assessing the validity of the impacts of new and emerging technologies, were found to be neglected or only partially conducted. Conclusions and recommendations: The ISO life cycle assessment standards require updating to provide consistency in methodologies to make them suitable for use with carbon capture and circular systems and to avoid ambiguity. We recommend that the life cycle assessment community focuses on developing more consistent standards and practices between sectors to address carbon capture and circularity; improving the implementation of temporal aspects of impacts; increasing the number of studies including uncertainty and sensitivity analyses and moving towards global uncertainty in favour of local sensitivity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This manuscript presents a data quality analysis and holistic ‘machine learning-readiness’ evaluation of a representative set of large-scale, real-world phasor measurement unit (PMU) datasets provided under the United States Department of Energy-funded FOA 1861 research program. A major focus of this study is to understand the present-day suitability of large-scale, real-world synchrophasor datasets for application of commercially-available, off-the-shelf big data and supervised or semi-supervised machine learning (ML) tools and catalogue any major obstacles to their application. To this end, dataset quality is methodically examined through an interconnect-wide quantifications of basic bad data occurrences, a summary of several harder-to-detect data quality issues that can jeopardize successful application of machine learning, and an evaluation of the adequacy of event log labeling for supervised training of models used for online event classification. A global ‘six-point’ statistical analyses of several key dataset variables is demonstrated as a means by which to identify additional hard-to-detect data quality issues, also providing an example successful application of big data technology to extract insights regarding reasonable operational bounds of the US power system. Obstacles for application of commercial ML technologies are summarized, with a particular focus on supervised and semi-supervised ML. Lessons-learned are provided regarding challenges associated with present-day event labeling practices, large spatial scope of the dataset, and dataset anonymization. Finally, insight into efficacy of employed mitigation strategies are discussed, and recommendations for future work are made.

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Providing high-quality measurement data in analytical system of air pollution monitoring and their key importance for smart cities residents. The article presents selected, main elements of an air pollution automatic monitoring system with analytical subsystems concept in smart cities based on examples from Poland, implemented system in Pomerania, the concept of new system in Warsaw city and pilot research in Nowy Sącz city. All systems are the result of teamwork, ranging from design, development of new methodology and software to implementation in real-time air pollutants smart cities monitoring systems. Focused on the most neuralgic elements: data quality subsystems, new ideas of smart mobility measurement stations and their ability to use in future research and models. Special attention was paid to stochastic models and statistic methodology proposed and used in data diagnostics as analytical system engineering. [ABSTRACT FROM AUTHOR]

Copyright of Hydrographic Surveying & Charting / Haiyang Cehui is the property of Hydrographic Surveying & Charting Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)