Search

This paper addresses the problem of removing 3D effects as one of the most challenging problems related to 2D electrical resistivity tomography (ERT) monitoring of embankment structures. When processing 2D ERT monitoring data measured along linear profiles, it is fundamental to estimate and correct the distortions introduced by the non-uniform 3D geometry of the embankment. Here, I adopt an iterative 3D correction plus 2D inversion procedure to correct the 3D effects and I test the validity of the proposed algorithm using both synthetic and real data. The modelled embankment is inspired by a critical section of the Parma River levee in Colorno (PR), Italy, where a permanent ERT monitoring system has been in operation since November 2018. For each model of the embankment, reference synthetic data were produced in Res2dmod and Res3dmod for the corresponding 2D and 3D models. Using the reference synthetic data, reference 3D effects were calculated to be compared with 3D effects estimated by the proposed algorithm at each iteration. The results of the synthetic tests showed that even in the absence of a priori information, the proposed algorithm for correcting 3D effects converges rapidly to ideal corrections. Having validated the proposed algorithm through synthetic tests, the method was applied to the ERT monitoring data in the study site to remove 3D effects. Two real datasets from the study site, taken after dry and rainy periods, are discussed here. The results showed that 3D effects cause about ±50% changes in the inverted resistivity images for both periods. This is a critical artifact considering that the final objective of ERT monitoring data for such studies is to produce water content maps to be integrated in alarm systems for hydrogeological risk mitigation. The proposed algorithm to remove 3D effects is thus a rapid and validated solution to satisfy near-real-time data processing and to produce reliable results. [ABSTRACT FROM AUTHOR]

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project. [ABSTRACT FROM AUTHOR]

The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform. [ABSTRACT FROM AUTHOR]

Low-frequency aperture arrays represent sensitive instruments to detect signals from radio astronomic sources situated in the universe. In Italy, the Sardinia Aperture Array Demonstrator (SAAD) consists of an ongoing project of the Italian National Institute for Astrophysics (INAF) aimed to install an aperture array constituted of 128 dual-polarized Vivaldi antennas at the Sardinia Radio Telescope (SRT) site. The originally envisaged 128 elements of SAAD were re-scoped to the 16 elements of its precursor named SADino, with the aim to quickly test the system with a digital beam-former based on the Italian Tile Processing Module (iTPM) digital back-end. A preliminary measurements campaign of radio frequency interference (RFI) was performed to survey the less contaminated spectral region. The results of these measurements permitted the establishment of the technical requirements for receiving a chain for the SADino telescope. In this paper, the design, implementation, and characterization of this signal acquisition chain are proposed. The operative frequency window of SAAD and its precursor, SADino, sweeps from 260 MHz to 420 MHz, which appears very attractive for radio astronomy applications and radar observation in space and surveillance awareness (SSA) activities. [ABSTRACT FROM AUTHOR]

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution. [ABSTRACT FROM AUTHOR]

This paper aims to describe the outcomes of the Main10ance project, which focused on developing an integrated HBIM-GIS platform to support the maintenance and conservation plans for the historic built heritage. The pilot case is the UNESCO complex of the Sacri Monti, located in northern Italy and Switzerland, which consists of groups of chapels and architectural artifacts holding significant historical and cultural value. Given their importance, specific maintenance plans involving multiple stakeholders and specialists are required. This study focuses on creating a unified system that semantically and spatially describes the architectural elements of the case study and the surrounding context and indoor assets. After a 3D integrated metric survey and the related data processing, parametric 3D models were created in a BIM environment, and a spatial database was developed to incorporate both geometric and alphanumeric entities. The database enables interoperability among different actors and domains, gathering heritage-related information crucial for restoration and conservation purposes. Additionally, the custom 4MAIN10ANCE web platform was developed with different levels of details (LODs), enabling the retrieval of both 2D and 3D data and establishing connections between the BIM models of the chapels and associated information. [ABSTRACT FROM AUTHOR]

Geodetic-grade Global Navigation Satellite System (GNSS) receivers designed to implement permanent stations represent the most complex and costly technology in the field of GNSS instrumentation. On the other hand, a large number of innovative applications, highly demanding in terms of positioning precision and accuracy, is pushing the implementation of networks of permanent stations with a higher and higher spatial density. In this scenario, the development of brand new GNSS reference stations, which combine the most advanced technologies in the field of data availability and integrity together with reduced costs (of instrumentation, installation and management) is becoming of paramount importance. For this reason, in 2019 the EU Agency for the Space Programme (EUSPA) has financed a research project, called "next Generation gnss REference stATion—GREAT", aimed at developing and demonstrating the potentiality of a brand new GNSS receiver suitable to implement permanent stations. This paper describes the solution developed by the project consortium, composed of four Small or Medium Enterprises (SMEs) based in Italy, France and Belgium, and the preliminary results achieved in the field tests. [ABSTRACT FROM AUTHOR]

GNSS positioning in urban scenarios suffers for the scarce visibility of satellites. Integration with 5G services for positioning could improve this situation. In this paper, the digital surface models (DSMs) relevant to different urban scenarios, namely residential streets and urban canyons, are simulated around one observer in northern Italy (Milano) for one day of the year chosen as an example. The time series of the number of in-view GNSS satellites, their geometry and the derived quality indexes (position dilution of precision (PDOP)) are computed and analyzed. As expected, in urban canyons, a significant number of epochs does not provide four satellites within view, and many more epochs present really mediocre PDOPs. In residential streets, the situation is always quite fair. Different geometric configurations of 5G base stations are simulated around the observer. The availability of 5G times of arrival (ToAs) and their differences (TDoAs) is hypothesized, and the integration of these observations with GNSS pseudoranges is analyzed, again in terms of the PDOPs. In residential streets, 5G availability improves the positioning. In urban canyons, the optimal configuration of 5G base stations (five base stations around the observer) completely solves the positioning problem for all the epochs of the day. Less favorable configurations (four and three base stations) improve epochs with poor PDOPs in a GNSS-only configuration. They allow the positioning of epochs with few satellites but cannot completely replace the GNSS. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park in Italy during the period of the COVID-19 pandemic. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provided input to the risk management team with which they could respond immediately mitigating any apparent risk and reducing the perceived risk. A two-stage approach was adopted to prune a massively large dataset from Twitter. In the first phase, a social network analysis and visualisation tool NodeXL was used to determine the most recurrent words, which was achieved using polarity. This resulted in a suitable subset. In the second phase, the subset was subjected to sentiment and emotion mapping by survey participants. This led to a hybrid approach of using automation for pruning datasets from social media and using a human approach to sentiment and emotion analysis. Whilst suffering from COVID-19, equally, people suffered due to loneliness from isolation dictated by the World Health Organisation. The work revealed that despite such conditions, people's sentiments demonstrated a positive effect from the online discussions on the Herculaneum site. [ABSTRACT FROM AUTHOR]

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields. [ABSTRACT FROM AUTHOR]

Groundwater resource assessment and forecasting in mountain areas requires the monitoring of two conditions, local meteorological conditions, and springs' groundwater parameters. The reliability of the monitoring data and conditions are linked to the technical instrumentation, multiparametric probes, and sensors. This paper presents a set of attractive tools and sensors for springs' groundwater resource monitoring and assessment in mountain basins. Data from the combination of weather station sensors with spring flow-rate instruments, installed in the alpine Mascognaz basin, can guarantee an entire understanding of how one set of parameters can affect other results, defining consequential cause-and-effect relationships. Since a large part of the Alpine groundwater bodies are exploited for drinking purposes, understanding the evolution of their rechange processes requires making the right economic and instrumental investments aimed at using them according to forecast predictions and sustainable development goals. [ABSTRACT FROM AUTHOR]

Ground vehicles equipped with vision-based perception systems can provide a rich source of information for precision agriculture tasks in orchards, including fruit detection and counting, phenotyping, plant growth and health monitoring. This paper presents a semi-supervised deep learning framework for automatic pomegranate detection using a farmer robot equipped with a consumer-grade camera. In contrast to standard deep-learning methods that require time-consuming and labor-intensive image labeling, the proposed system relies on a novel multi-stage transfer learning approach, whereby a pre-trained network is fine-tuned for the target task using images of fruits in controlled conditions, and then it is progressively extended to more complex scenarios towards accurate and efficient segmentation of field images. Results of experimental tests, performed in a commercial pomegranate orchard in southern Italy, are presented using the DeepLabv3+ (Resnet18) architecture, and they are compared with those that were obtained based on conventional manual image annotation. The proposed framework allows for accurate segmentation results, achieving an F1-score of 86.42% and IoU of 97.94%, while relieving the burden of manual labeling. [ABSTRACT FROM AUTHOR]