Operational Monitoring Research Articles

Challenges to Accident Prevention for High-Speed Vessels Used in the Brazilian Amazon

The Brazilian Amazon has one of the largest river networks in the world, connecting riverine communities, many of which have no roadways. The use of high-speed vessels, including those that are locally made, is necessary for the transportation of people to perform their day-to-day activities. However, regional news and social media data have shown that these vessels are involved in accidents. This work describes some of the key challenges to improving the sustainability and safety of regional high-speed vessels. Field research was performed in the state of Amazonas to document regional vessels and provide graphic evidence of the possible risk factors that could cause accidents as the path to prevent them. Some risk mitigation alternatives were discussed. Cameras and a drone were used to capture information either from the shore or from small boats. The main challenges are related to the adequate management of seasonal changes in river depths; wave and wind behavior; wave wash effects; the interaction of vessels with rocks, sand banks, mud, and vegetation; embarkation and disembarkation activities; the monitoring and regulation of safety requirements and dangerous operations; and the slowness in implementing technological advances that would improve vessels’ safety. It is hoped that the work in this paper will contribute to improving the sustainability of marine activities in the Amazon and similar regions worldwide.

Study and diagnostic of electrical faults in asynchronous machines

Electric drives increasingly use asynchronous motors because of their robustness, their specific power and their cost. Their maintenance and diagnosis therefore become an economic issue.Industrial requirements in terms of maintenance direct research towards a diagnosis using electromagnetic quantities such as flux, current, voltage, and more particularly the stator current which only requires a current sensor which gives an image on the stator phase current, on the other hand this technique makes it possible to improve the reliability of the motor and to increase its service life. It is important to detect early the faults that can appear in these machines and therefore to develop operation monitoring methods has made it possible to analyze the impact of faults on the behavior of the motor. It has made it possible to obtain specific signatures for electrical failures and to predict their evolution. we studied the short-circuit fault in the three-phase asynchronous motor which is the spectral analysis of the stator currents by making a modeling by the finite element method using the FLUX 2D software.

Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V

Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available.

Optimized operation of AC–DC microgrid cluster with modified PLL and socket protocol

AbstractMicrogrids integrating inverter based resources face challenges like stability issues during voltage fluctuations and reliance on diesel generators in islanded mode. This paper presents a novel control strategy for photovoltaic and wind‐integrated microgrids to enhance reliability and efficiency. The strategy utilizes a modified phase‐locked loop with droop control for seamless synchronization in grid‐connected and islanded modes. Central to this approach is the localized autonomous controller with a socket protocol interfaced communication layer, dynamically managing operations based on real‐time conditions such as generation, load, and battery state of charge. By integrating battery storage systems, the strategy reduces diesel generator dependency during grid outages. Comprehensive simulations and controller hardware‐in‐the‐loop testing validate its effectiveness, demonstrating improved stability and decreased reliance on diesel generators. The socket protocol enhances real‐time communication, monitoring, and optimization of microgrid operations, ensuring optimal battery storage system utilization and minimal diesel usage. The proposed strategy addresses critical microgrid challenges, offering significant advancements in enhancing system resilience.

Plant Translocations in France: Identifying Gaps between Knowledge, Practice and Perception by Conservation Actors.

In the current context of global changes, threatened flora is declining and homogenising at the expense of rare and protected species. Among conservation biology and ecological restoration techniques, plant translocation is one of the recommendations increasingly used. However, translocation remains risky and is recommended as a last resort to conserve protected flora in land use planning. Furthermore, it raises ethical questions partly linked to genetic processes. In this context, we studied how plant translocations are perceived by conservation actors in France, including their genetic aspects. The analysis of translocation perception complements that of feedback on concrete translocation operations and allows us to provide a qualitative assessment of current practices. We have adopted an interdisciplinary approach to survey different types of actors in nature conservation, involved or not in at least one stage of translocation operations (preparation, implementation and monitoring). Three main types of translocation perception divide our sample quite strongly (50% favourable/ 45% unfavourable/ 5% undecided). Surprisingly, their professional activity or the level of involvement in such an operation have no influence on their opinion on translocation, nor on the proposals of alternative measures. Only 15% of the actors involved in translocations used genetic data at least once. To conclude, it would be necessary to promote the sharing of feedback from past experiences. This would allow an up-to-date list of species unacceptable for translocation. Comparing the results of different protocols and implementation conditions for the same species or group should improve overall translocation success rates.

Improvement of operational monitoring of the psychological state of a person in prolonged isolation

Introduction. The article describes a methodological scheme for operational psychological monitoring of the crew in isolation, simulating the effect of space flight factors on humans. Examples of intermediate experimental results in the form of qualitative operational data (psychological profiles of participants) are given. The methodological approach can be applied in the practice of operational monitoring of work teams (crews) whose professional activities take place in conditions of social isolation and/or the action of extreme environmental factors. The aim of the study is to test a comprehensive, minimally invasive method of psychological monitoring of participants in an isolation experiment to obtain operational data. Materials and methods. The SIRIUS-23 experiment is the fourth stage of the 365-day SIRIUS international project. The participants in the simulated isolation were 6 people, 2 men (±34.5) and 4 women (±30.5). The basic principles of building a diagnostic scheme for psychological monitoring are complexity and an individual approach. The scheme includes three structural blocks (score scale, semi-structured confidential conversation, computerized techniques), each of which is aimed at solving a specific task. Results. The authors presented the depersonalized psychological data of two anonymized participants of the experiment as intermediate results of the study. Psychological profiles contain generalized operational data, divided by time for ease of analysis into three equal parts — trimesters. The use of three diagnostic blocks allows us to describe the dynamics of changes in the psycho-emotional state of specialists working in isolation: the appearance of adverse signs of mental asthenization. Limitations. The study is limited to a small sample of subjects. Conclusion. As a result of the application of the methodological scheme, each of its structural blocks complements each other. For example, certain signs of asthenization in daily recordings of negotiations, which are obtained using a score scale (negatively colored negative reactions, parasitic words, etc.), are confirmed by data from computer techniques and correspondence. The use of a three-part methodological scheme is possible in areas of professional activity where people are under the influence of extreme factors. Thus, there is an interpretative context (an understanding of the reasons is being formed, the basis for making recommendations is being laid). Ethics. The experimental program was approved by the section of the Academic Council and approved by the Biomedical Ethics Commission at the Russian Scientific Center — Institute of Biomedical Problems of the Russian Academy of Sciences (protocol No. 643 dated 07.07.2023).

Mapping forest cover and change as continuous variables is essential to advance consistency across forest monitoring products

ABSTRACT The rapid expansion of freely available Earth observation data, combined with advancements in forest monitoring capabilities, has led to the production of a variety of datasets on forest cover and change at the national and global scales. These datasets are essential for informing science and policy. Yet, this surge in available data products comes with the challenge of reconciling differences in the mapping of forest, non-forest, and forest loss across products. Differences in forest cover mapping between products result in divergent estimates of forest area change, potentially constraining the utility of these products to inform science and policy, consistently, across scales. We use the case of Colombia to demonstrate that these inconsistencies can be largely resolved by advancing operational forest monitoring capabilities toward the representation of forest cover and change as continuous rather than discrete variables. The analysis consisted of a comparison between the global forest change product (GFC) and the official national datasets on forest cover change for Colombia (IDEAM) in terms of overall and per-class classification agreement and estimates of forest-area loss. The comparison was performed after harmonizing the GFC dataset (HGFC) to maximize its overall classification agreement with the IDEAM map, based on the adoption of optimum percent tree cover thresholds, applied to GFC for subnational units. Based on these results, we evaluate whether classification agreement and disagreement between mapped forest and non-forest classes by HGFC and IDEAM can be explained by differences in continuous physical forest attributes, represented by forest Canopy Height (CH) and Above-Ground Biomass Density (AGBD). We produced the analysis for the entire country and also for mountainous and non-mountainous areas separately. We show that the optimal threshold in tree cover that maximizes map agreement, varies largely between subnational units, with values ranging between 20% and 100%. Results show a high overall agreement and also high agreement for stable forest and non-forest classes. Agreement between pixels classified as forest loss was much lower, regardless of the map used as reference. Classification agreement was lower for mountainous areas than for non-mountainous areas, particularly for forest loss and stable forest classes. Pixels representing classification disagreement were characterized by intermediate values of CH and AGBD that are significantly different from pixels classified as non-forest and forest for both maps, with significantly lower and higher CH and AGBD values, respectively. Differences were more notable in non-mountainous areas than in mountainous ones. We conclude that solving these large existing discrepancies in forest classification and forest loss estimates between datasets requires advancing operational capabilities toward the representation of forest cover and change as continuous rather than as categorical variables, with particular emphasis on mountainous areas. We illustrate how moving in such a direction can (1) promote consistency between forest cover definitions and map representation, (2) facilitate comparability among products and estimates of forest area loss at different scales of analysis, and (3) promote accountability in forest area loss assessments.

Аналіз стану поверхневих вод населених пунктів Закарпатської області протягом 2022-2023 років

Objective. To study the pollution of surface waters of settlements of the Transcarpathian region during 2022-2023. Materials and methods. The objects of the study were the surface waters of settlements of the Transcarpathian region. The statistical data of the Transcarpathian Regional Center for Hydrometeorology on operational and diagnostic monitoring of surface waters of 34 dams of Transcarpathia was analyzed in accordance with the Decree of the Cabinet of Ministers of 19.09.1918, No. 758 "On Approval of the Procedure for State Monitoring of Waters." The assessment of physicochemical indicators of the quality of rivers of settlements of the Transcarpathian region was carried out in accordance with the Order of the Ministry of Ecology and Natural Resources of Ukraine of 14.01.2018, No. 5 "On the approval of the Methodology for classifying the body of surface water, as well as the classification of an artificial or significantly altered body of surface water to one of the classes of ecological potential of an artificial or significantly altered body of surface water". The obtained materials were processed statistically using the Microsoft Excel computer program. Results. In total, during 2022-2023, 3,876 measurements of physico-chemical indicators of surface water pollution in populated areas of the Transcarpathian Regionwere carried out, of which 3,306 measurements of these indicators in 174 samples were measured by operational monitoring, and 570 measurements were made by diagnostic ones in 30 samples. Pollution of the surface waters of settlements in the Transcarpathian region occurred mainly due to the ingress of insufficiently treated sewage, but a small amount of atmospheric precipitation that fell during the studied years also played a role. The research was conducted monthly, and the average values of the content of pollutants were determined quarterly for the years under study. Thus, according to the results of observations in all quarters during 2022-2023, the main pollutants of surface water in settlements of the Transcarpathian region are ammonium compounds, namely: ammonia Nitrogen, nitrite Nitrogen and nitrate Nitrogen. Conclusions. 1. Significant contamination of surface waters of populated areas of the Transcarpathian region by organic and chemical compounds is observed during the 2022-2023 research years. 2. During 2022-2023, water was found to be the most polluted in the Tova river, the village of Baranyntsi, the Verke river in the town of Beregovo and the Kosino-Bovtradsky canal, the village of Koson, in which the concentrations of physical and chemical indicators, such as mineralization, ammonium Nitrogen, nitrite Nitrogen, nitrate Nitrogen, and chloride ion significantly exceeded the MPC. 3. A number of measures have been developed to prevent surface water pollution of the rivers of the Transcarpathian region, namely, sufficient funding for the construction and reconstruction of most sewage and water supply facilities can improve the quality of surface water. 4. In order to prevent pollution of surface water, it is necessary to put into practice modern technologies of wastewater treatment of settlements and industrial enterprises, modernization of existing and construction of new water treatment facilities, as well as to systematically carry out a set of measures for sanitary and hygienic monitoring of the quality of surface water of rivers in the Transcarpathian region. Keywords. Water samples, surface water, water pollution, preventive measures.

Impact of Situation Awareness Variations on Multimodal Physiological Responses in High-Speed Train Driving.

In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety.

Predicting Operational Events in Mechanized Weed Control Operations by Offline Multi-Modal Data and Machine Learning Provides Highly Accurate Classification in Time Domain

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in this study based on multi-modal data to classify relevant operational events in mechanized weed control operations. The architecture of a neural network was tuned in terms of the number of hidden layers and neurons, and the regularization term was set at various values to obtain optimally tuned models for three data modalities: triaxial acceleration data coupled with speed extracted from GNSS signals (AS), triaxial acceleration (A), and speed alone (S). In the training and validation phase, the models based on AS and A achieved a very high classification accuracy, accounting for 92 to 93% when considering four relevant events. In the testing phase, which was run on unseen data, the classification accuracy reached figures of 91 to 92%, indicating a good generalization ability of the models. The results point out that multimodal data are able to provide the features for distinguishing events and add spatial context to the monitored operations, standing as a suitable solution for offline, partly automated monitoring. Future studies are required to see how the capabilities of online, real-time technologies such as deep learning coupled with computer vision can add more context and improve classification performance.

The development of physical state and exhaustion factors monitoring system with capability of collecting, estimating and analysing data for military personnel

The purpose of the research analysis of known invasive and non-invasive examination methods, determination of the necessary criteria for assessing the degree of fatigue of submariners, a survey of Navy personnel involved in service on submarine ships was conducted, on the basis of which a justification of research directions was carried out for the subsequent development of a system for collecting, evaluating and analyzing the physiological state and factors affecting the fatigue of military personnel.Methods: the use of an experimental non-invasive research method to determine the degree of fatigue and physiological condition of submariners based on three criteria during the month, the analysis of the results obtained, the establishment of a comparative analysis method during the development, a structural analysis of the system based on previously known devices and sensors.Results. The dependence of the studied indicators on the time of watch keeping and their connection with the increase in fatigue were found. It has been established that in order to develop a system for monitoring the fatigue of submariners, a systematic analysis and objective assessment should be carried out, including: determination of methods for monitoring the condition of military personnel; application of knowledge from medicine, physiology, biophysics, biomechanics, bioengineering, electronics, circuitry and programming, to develop effective methods for monitoring physical condition and fatigue. Based on the analysis of domestic and foreign techniques and devices, a proprietary method for implementing a system for collecting assessments, analyzing the physiological state and factors affecting the fatigue of military personnel is proposed.Conclusion. It is concluded that it is necessary to conduct non-invasive studies of the degree of fatigue, before proposing the development and proposal of its own device and methodology for assessing the degree of fatigue, capable of implementing individual features of the system for operational monitoring in order to improve combat readiness, situational awareness, health, safety, mobility of submariners.The development of such monitoring systems and devices is necessary in the modern world to prevent or prevent disasters without reducing the efficiency of submariners.

Monitoring System Design Using GPS Tracker and Operational Vehicle Maintenance Case Study of PT. Pertamina (Persero)

The Utilization of GPS and GSM technology in operational vehicle monitoring offers convenience and efficiency in management. This research designs a GPS-based vehicle monitoring system that is integrated with vehicle maintenance management to assist companies in making decisions related to fuel efficiency, predictive maintenance scheduling, and safety aspects. By monitoring vehicle position and condition in real-time, companies can optimize travel routes, reduce travel time and reduce operational costs. The system also includes tire pressure and fuel percentage monitoring, which serve as important indicators in an accurate and sustainable vehicle maintenance strategy. This research uses quantitative methods to analyze various aspects in designing a GPS-based monitoring system that is integrated with operational vehicle maintenance management. With the resulting data, managers are able to identify trends that support predictive maintenance as well as preventive steps to reduce the risk of sudden damage. The research results show that the integration of GPS and GSM provides significant benefits in improving performance and reliability for vehicles, especially in the distribution and logistics sectors. The implementation of this system is expected to support the company in maintaining optimal vehicle performance and contribute to operational sustainability.

TECHNOLOGICAL COMPLICATIONS (FORMATION DAMAGE) DURING CO2 INJECTION FOR ENHANCED OIL RECOVERY. PART 3. PREVENTING AND REPAIRING FORMATION DAMAGE CAUSED BY CO2 INJECTION

The article considers technological complications arising during CO2 injection for enhanced oil recovery, with a focus on the formation of asphaltenes in the pore space and their negative impact on production efficiency. Formation of asphaltene solids in the reservoir leads to an increase in the skin factor, their removal to the wells, accumulation in the wellbore and on the surface of the equipment, which reduces oil flow to the well and requires expensive remedial work. Methods of identification and monitoring of asphaltene deposits are considered. Results of laboratory studies confirming the difficulty of quantitative assessment of asphaltene deposition are given, as well as practical examples of application of various technologies for their prevention and removal. Methods including the use of inhibitors and dispersants and the use of aromatic solvents to restore reservoir properties are reviewed. It also emphasises the need for continuous monitoring of field operations and the development of effective asphaltene management methods, which is critical to improving the economic feasibility of CO2 injection projects.

Exploring the relationships between ground observations and remotely sensed hazelnut spring phenology.

Crop phenology is very important in regular crop monitoring. Generally, phenology is monitored through field observation surveys or satellite data. The relationships between ground observations and remotely sensed derived phenological data can enable near-real-time monitoring over large areas, which has never been attempted on hazelnuts. In this study, we extracted phenological metrics derived from MODIS Enhanced Vegetation Index (EVI) in hazelnut production regions and compared them with the spring ground phenological data (BBCH scale) from orchards located in the same area of Turkey over the period from 2019 to 2022. We observed a specific temporal dynamic between remote sensing phenometrics and ground observations. The metrics Greenup, Upturning Date, and Threshold 20% metrics corresponded to the early of EVI growth and were synchronous with the female flowering of hazelnut and ending before bud break. The metrics Threshold 50% and Start of season were associated with the steepest portion of the EVI curve, i.e., canopy greening and thickening, and occurred between ovaries enlargement and leaves unfolding. The metrics Peak of Season, Stabilization Date, and Maturity corresponded to the end of spring vegetative growth. The main outcomes are that (i) female flowering occurred before 20% of vegetation development (BBCH 64P occurred about one month before Threshold 20%), (ii) phenometrics from satellite remote sensing (i.e., Upturning Date and Threshold 20%) well-reflected leaf emergence (rs = 0.30 and rs = 0.32, respectively; p < 0.05) and unfolding (rs = 0.35 and rs = 0.39, respectively; p < 0.05), and (iii) cluster appearance temporally aligned with the peak of the EVI curve (Stabilization Date and BBCH 71P differed by around 4 days). Our method is transferable to operational phenology monitoring, and future applications will consider the senescence season and the effect of environmental variability on the comprehension of vegetation dynamics.

Underestimation of meteorological drought intensity due to lengthening of the drought season with climate change

Abstract Meteorological drought may lead to water shortages, which has negative impacts on water-dependent sectors. Whilst there is a wealth of studies on changing drought intensity or frequency due to climate change, much less is known regarding potential shifts in the timing of drought. The purpose of this study is to analyze the timing of the drought season in the Netherlands and climatic changes therein, with a special focus on the onset of the drought season. Based on an analysis of meteorological observations in the Netherlands over the period 1965–2023, we conclude that the Dutch meteorological drought season has extended forward in time. On average, the drought season starts 16 d earlier in the period 1994–2023 compared to 1965–1993. This is mostly the result of an increase in potential evapotranspiration, while the amount of precipitation does not show a clear change at the start of the growing season. Using three climate model ensembles, we show that a forced climate change signal exists, but that natural variability also plays a role. Following this assessment of trends in meteorological variables, we analyze the consequences for the operational monitoring of meteorological drought. In the Netherlands, this is done by means of the ‘precipitation deficit’-indicator, based on a fixed-in-time starting point (1 April) of the drought season. The combination of this fixed starting point and the observed earlier onset of the drought season, means that in some years the indicator underestimates drought intensity, and that climatic trends are underestimated. We therefore advocate for an update of the operational drought indicator, such that meteorological drought occurring before 1 April will not be missed.

Efektivitas Penyelenggaraan Tempat Pelelangan Ikan (TPI) Desa Sungaibuntu Kecamatan Pedes Kabupaten Karawang Jawa Barat

The fish auction serves as a marketing center for the distribution of fish. The implementation of the fish auction is expected to enhance the selling prices of the fishermen's catch. The purpose of this research is to analyze the level of effectiveness of the fish auction implementation. Effectiveness is measured in two ways: first, effectiveness is measured using the Mahmudi formula. Variables measured include the number of boats landing fish at the fish auction, fish production, fish prices, revenue, auction fees, fish auction operational activities, and the reporting system at Sungaibuntu’s fish auction. Second, effectiveness is measured according to the fishermen's perceptions, with variables including fish auction as a facility, auction activities, and services. This research also analyzes the comparison of fish prices and the income of fishermen selling fish at the fish auction and outside the fish auction. This research uses descriptive quantitative methods. Primary data collection involves field observations, questionnaires, and interviews, while secondary data is obtained from relevant institutions, theses, and journals. Simple random sampling is used to obtain 37 fishermen respondents. The research results show that the overall effectiveness ratio values measured by the Mahmudi’s formula for the period 2020-2022, for the effectiveness of boat activities, auction fees, fish production, and auction revenue, are categorized as "Less Effective." Meanwhile, the effectiveness of TPI operational activities and monitoring reporting systems achieves a level of "Very Effective," and the effectiveness of fish prices at Sungaibuntu TPI reaches the level of "Effective." For effectiveness measured according to the fishermen's perceptions, the variables of fish auction as a service and facility are categorized as "Less Effective." Meanwhile, fish auction as a facility is categorized as "Effective." There is a difference in the income of fishermen selling fish at the fish auction compared to fishermen selling fish outside the fish auction.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

Control of simulated ocean ecosystem indicators by biogeochemical observations

To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.

NOAAGlobalTemp Version 6: An AI-Based Global Surface Temperature Dataset

Abstract The National Oceanic and Atmospheric Administration Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than 4 times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring. NGTv6 was released in February 2024.

Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment.

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.