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This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas. [ABSTRACT FROM AUTHOR]

The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth's electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data. [ABSTRACT FROM AUTHOR]

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy. [ABSTRACT FROM AUTHOR]

The selection of the optimal methodology for the 3D geometric documentation of cultural heritage is a subject of high concern in contemporary scientific research. As a matter of fact, it requires a multi-source data acquisition process and the fusion of datasets from different sensors. This paper aims to demonstrate the workflow for the proper implementation and integration of geodetic, photogrammetric and laser scanning techniques so that high-quality photorealistic 3D models and other documentation products can be generated for a complicated, large-dimensional architectural monument and its surroundings. As a case study, we present the monitoring of the Mehmet Bey Mosque, which is a landmark in the city of Serres and a significant remaining sample of the Ottoman architecture in Greece. The surveying campaign was conducted in the context of the 2022–2023 annual workshop of the Interdepartmental Program of Postgraduate Studies "Protection Conservation and Restoration of Cultural Monuments" of the Aristotle University of Thessaloniki, and it served as a geometric background for interdisciplinary cooperation and decision-making on the monument restoration process. The results of our study encourage the fusion of terrestrial laser scanning and photogrammetric datasets for the 3D modeling of the mosque, as they supplement each other as regards geometry and texture. [ABSTRACT FROM AUTHOR]

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems. [ABSTRACT FROM AUTHOR]

This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (NRMSE) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.10 ha of processing tomato in central Greece, to assess the performance of Vis at different temporal scales. In addition, VIs were connected with the crop phenology to establish the annual dynamics of the crop. The highest Pearson coefficient (r) values occurred during a period of 80 to 90 days, indicating the strong relationship between the VIs and the yield. Specifically, RVI presented the highest correlation values of the growing season at 80 (r = 0.72) and 90 days (r = 0.75), while NDVI performed better at 85 days (r = 0.72). This output was confirmed by the AutoML technique, which also indicated the highest performance of the VIs during the same period, with the values of the adjusted R2 ranging from 0.60 to 0.72. The most precise results were obtained with the combination of ARD regression and SVR, which was the most successful combination for building an ensemble (adj. R2 = 0.67 ± 0.02). [ABSTRACT FROM AUTHOR]

The monitoring of the water stage in streams and rivers is essential for the sustainable management of water resources, particularly for the estimation of river discharges, the protection against floods and the design of hydraulic works. The Institute of Marine Biological Resources and Inland Waters of the Hellenic Centre for Marine Research (HCMR) has developed and operates automatic stations in rivers of Greece, which, apart from their monitoring role, offer opportunities for testing new monitoring equipment. This paper compares the performance of a new ultrasonic sensor, a non-contact water stage monitoring instrument, against a pressure transducer, both installed at the same location in an urban stream of the metropolitan area of Athens. The statistical and graph analysis of the almost one-year concurrent measurements from the two sensors revealed that stage differences never exceeded 7%, while the ultrasonic measurements were most of the time higher than the respective pressure transducer ones during the low flow conditions of the dry period and lower during the wet period of the year, when high flow events occurred. It is also remarkable that diurnal air temperature variations under stable hydrologic conditions had an impact on the measured stage from the ultrasonic sensor, which varied its stage measurements within a small but non-negligible range, while the pressure transducer did not practically fluctuate. Despite a slightly increased sensitivity of the ultrasonic sensor to meteorological conditions, the paper concludes that non-contact sensors for the monitoring of the water stage in rivers can be useful, especially where danger for possible damage of submersible instruments is increased. [ABSTRACT FROM AUTHOR]

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content ( ∘ Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) ( ∘ Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R 2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices. [ABSTRACT FROM AUTHOR]

This paper presents the results of an operational nationwide burnt area mapping service realized over Greece for the years 2007-2011, through the implementation of the so-called BSM_NOA dedicated method developed at the National Observatory of Athens for post-fire recovery management. The method exploits multispectral satellite imagery, such as Landsat-TM, SPOT, FORMOSAT-2, WorldView and IKONOS. The analysis of fire size distribution reveals that a high number of fire events evolve to large and extremely large wildfires under favorable wildfire conditions, confirming the reported trend of an increasing fire-severity in recent years. Furthermore, under such conditions wildfires affect to a higher degree areas at high altitudes, threatening the existence of ecologically significant ecosystems. Finally, recent socioeconomic changes and land abandonment has resulted in the encroachment of former agricultural areas of limited productivity by shrubs and trees, resulting both in increased fuel availability and continuity, and subsequently increased burnability. [ABSTRACT FROM AUTHOR]

The Permanent Scatterers Interferometric SAR technique (PSInSAR) is a method that accurately estimates the near vertical terrain deformation rates, of the order of ∼1 mm year-1, overcoming the physical and technical restrictions of classic InSAR. In this paper the method is strengthened by creating a robust processing chain, incorporating PSInSAR analysis together with algorithmic adaptations for Permanent Scatterer Candidates (PSCs) and Permanent Scatterers (PSs) selection. The processing chain, called PerSePHONE, was applied and validated in the geophysically active area of the Gulf of Corinth. The analysis indicated a clear subsidence trend in the north-eastern part of the gulf, with the maximum deformation of ∼2.5 mm year-1 occurring in the region north of the Gulf of Alkyonides. The validity of the results was assessed against geophysical/geological and geodetic studies conducted in the area, which include continuous seismic profiling data and GPS height measurements. All these observations converge to the same deformation pattern as the one derived by the PSInSAR technique. [ABSTRACT FROM AUTHOR]

Expected global climate change is allegedly becoming more intense, and the impacts on water resources are being tracked in various hydroclimatic regimes. The present research investigates a hydrologically important area of Greece, where four natural lakes are concentrated. It aims to quantify any potential long-term trends in lake water area, precipitation, and temperature timeseries. Water area timeseries spanning four decades are estimated by the mNDWI from Landsat satellite imagery and used as an index of each lake's water storage. Precipitation and temperature measurements are obtained from the open access datasets Hydroscope and ERA5-Land, respectively. All of the timeseries were tested seasonally and annually with the Pettitt and Mann–Kendal tests for statistically significant breakpoints and trends detection. No timeseries analysis resulted in a statistically significant (at 0.05 or 0.1 levels) annual or seasonal trend. The hydroclimatic regime over the past forty years in western Greece is found to have been relatively stable. Land use was also assessed to have been relatively unchanging, converging to the overall stability of the local water regime. However, the findings of this research should not be interpreted as a reassurance against climate change, but as a call to further research for the detailed regional and local assessment of climate change and hydroclimatic variability with acknowledged statistical approaches. [ABSTRACT FROM AUTHOR]

Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination ( R 2 ), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm ( R 2 > 0.8 , RPIQ ≥ 4 ), while a good fit was attained for the Chardonnay variety from SVR ( R 2 = 0.63 , RMSE = 2.10 , RPIQ = 2.24 ), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way. [ABSTRACT FROM AUTHOR]

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented. [ABSTRACT FROM AUTHOR]

Earth observation sensors continually provide datasets with different spectral and spatial characteristics, while a series of pre- and postprocessing techniques are needed for calibration purposes. Nowadays, a variety of satellite images have become accessible to researchers, while big data cloud platforms allow them to deal with an extensive number of datasets. However, there is still difficulty related to these sensors meeting specific needs and challenges such as those of cultural heritage and supporting archaeological research world-wide. The harmonization and synergistic use of different sensors can be used in order to maximize the impact of earth observation sensors and enhance their benefit to the scientific community. In this direction, the Committee on Earth Observation Satellites (CEOS) has proposed the concept of virtual constellations, which is defined as "a coordinated set of space and/or ground segment capabilities from different partners that focuses on observing a particular parameter or set of parameters of the Earth system". This paper provides an overview of existing and future earth observation sensors, the various levels of interoperability as proposed by Wulder et al., and presents some preliminary results from the Thessalian plain in Greece using integrated optical and radar Sentinel images. The potential for archaeolandscape studies using virtual constellations is discussed here. [ABSTRACT FROM AUTHOR]

We present in this paper a structural health monitoring study of the Egyptian lighthouse of Rethymnon in Crete, Greece. Using structural vibration data collected on a limited number of sensors during a 3-month period, we illustrate the potential of the stretching method for monitoring variations in the natural frequencies of the structure. The stretching method compares two signals, the current that refers to the actual state of the structure, with the reference one that characterizes the structure at a reference healthy condition. For the structure under study, an 8-day time interval is used for the reference quantity while the current quantity is computed using a time window of 24 h. Our results indicate that frequency shifts of 1% can be detected with high accuracy allowing for early damage assessment. We also provide a simple numerical model that is calibrated to match the natural frequencies estimated using the stretching method. The model is used to produce possible damage scenarios that correspond to 1% shift in the first natural frequencies. Although simple in nature, this model seems to deliver a realistic response of the structure. This is shown by comparing the response at the top of the structure to the actual measurement during a small earthquake. This is a preliminary study indicating the potential of the stretching method for structural health monitoring of historical monuments. The results are very promising. Further analysis is necessary requiring the deployment of the instrumentation (possibly with additional instruments) for a longer period of time. [ABSTRACT FROM AUTHOR]

Radar Interferometry is a widely used method for estimating ground deformation, as it provides precision to a few millimeters to centimeters, and at the same time, a wide spatial coverage of the study area. On 9 July 1956, one of the strongest earthquakes of the 20th century in the area of the South Aegean, occurred in Amorgos, with a magnitude of Mw = 7.7. The objective of this research is to map ground deformation in Amorgos island, using InSAR techniques. We conducted a multi-temporal analysis of all available data from 2003 to 2019 by exploiting historical ENVISAT SAR imagery, as well as the dense archive of Sentinel-1 SLC imagery. Persistent Scatterer Interferometry (PS) and Small Baseline Subset (SBAS) methods were implemented. Results of both data-sets indicate a small-scale deformation on the island. A multi-track analysis was implemented on Sentinel-1 data to decompose the line of sight velocities to vertical and horizontal. The central south coast is experiencing horizontal movement, while uplift of a maximum value of 5 mm/y is observed in the southeastern coast. The combination of the good spatial coverage achievable via InSAR, with GPS measurements, is suggested an important tool for the seamless monitoring of Amorgos island towards tectonic hazard estimation. [ABSTRACT FROM AUTHOR]

The SITHON system, a fully wireless optical imaging system, integrating a network of in-situ optical cameras linking to a multi-layer GIS database operated by Control Operating Centres, has been developed in response to the need for early detection, notification and monitoring of forest fires. This article presents in detail the architecture and the components of SITHON, and demonstrates the first encouraging results of an experimental test with small controlled fires over Sithonia Peninsula in Northern Greece. The system has already been scheduled to be installed in some fire prone areas of Greece. [ABSTRACT FROM AUTHOR]

In response to the urging need of fire managers for timely information on fire location and extent, the SITHON system was developed. SITHON is a fully digital thermal imaging system, integrating INS/GPS and a digital camera, designed to provide timely positioned and projected thermal images and video data streams rapidly integrated in the GIS operated by Crisis Control Centres. This article presents in detail the hardware and software components of SITHON, and demonstrates the first encouraging results of test flights over the Sithonia Peninsula in Northern Greece. It is envisaged that the SITHON system will be soon operated onboard various airborne platforms including fire brigade airplanes and helicopters as well as on UAV platforms owned and operated by the Greek Air Forces. [ABSTRACT FROM AUTHOR]

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 - 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution. [ABSTRACT FROM AUTHOR]