Large-scale Deployment Research Articles

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.

Experimental verification of a data-driven algorithm for drive-by bridge condition monitoring

As the world’s transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost ‘drive-by’ monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either ‘damaged’ or ‘healthy’. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration.

Unlocking the Secondary Critical Raw Material Potential of Historical Mine Sites, Lousal Mine, Southern Portugal

A steady supply of mineral raw materials is vital for the transition to a low-carbon, circular economy. The number of active mines in Europe has severely declined over the last century and half, giving rise to many abandoned mining waste sites and corresponding geological heritage. Also, the rise in minerals demand for large-scale deployment of renewable energy requires the continued and steady availability of key minerals. The supply risk associated with unpredicted geopolitical events needs to be eliminated/mitigated. Historical mine waste sites are the answer but evaluating mine waste is a lengthy and costly exercise. The study, undertaken in the Lousal Mine, used small unmanned aerial systems (sUASs) to model and determine mine waste volumes by generating orthomosaic maps with quick, inexpensive, and reliable results. Calculated mine waste volumes between 308,478 m3 and 322,455 m3 were obtained. XRD and p-XRF techniques determined the mineralogy and chemistry of waste, which varied from mineralization and host rocks with hydrothermal alteration and numerous neogenic sulphates (mostly gypsum, rhomboclase, ferricopiapite, coquimbite, and jarosite) related with supergene processes and weathering. The study shows the viability of using these sUASs to successfully model historical mine waste sites in an initial phase and for future monitoring programs.

Power generation analysis of super-long gravity heat pipe geothermal systems

Highly-efficient and environment-benign heat extraction and utilization technologies are key to large-scale geothermal energy deployment. In recent developments, the innovative super-long gravity heat pipe (SLGHP) geothermal power plant has revolutionized deep geothermal power generation by obviating the need of flash devices or heat exchangers in vapor generation for turbine propulsion. This advancement promises substantial structural simplification, a marked reduction in exergy losses, and significant cost savings. Nevertheless, a comprehensive understanding of the performance of SLGHP power generation systems is still lacking in published research. For a single-well geothermal system, the geothermal gradient assumes critical importance as it serves as a key indicator of resource intrinsic conditions, and the depth of the well emerges as an essential controllable variable in manual drilling. The present investigation seeks to elucidate the impact of these two key parameters on the performance of SLGHP power generation systems. For this purpose, a semi-empirical formula is analytically derived to describe the heat extraction process within the SLGHP system. This formula is integrated with a thermo-economic model to evaluate the performance of the SLGHP power generation system. It is found that adjusting the two parameters can yield simultaneous increases in the heat extraction rate and the energy efficiency, thereby resulting in a significant boost in power generation of SLGHP. Specially, the SLGHP system of a 4000 m deep well and with a 0.045 K/m geothermal gradient can output 35.95 kW electricity, while that of a 5000 m deep well and with a 0.05 K/m geothermal gradient achieves 86.39 kW electricity output. In practical terms, it is found that the optimal strategy for an SLGHP power plant involves drilling multiple wells in a reservoir with a higher geothermal gradient. However, when repurposing abandoned oil/gas wells for power generation, the preference is for deep wells.

Ocean Bottom Seismometer Clock Correction using Ambient Seismic Noise

Ocean-bottom seismometers (OBSs) are equipped with seismic sensors that record acoustic and seismic events at the seafloor, which makes them suitable for investigating tectonic structures capable of generating earthquakes offshore. One critical parameter to obtain accurate earthquake locations is the absolute time of the incoming seismic signals recorded by the OBSs. It is, however, not possible to synchronize the internal clocks of the OBSs with a known reference time, given that GNSS signals are unable to reach the instrument at the sea bottom. To address this issue, here we introduce a new method to synchronize the clocks of large-scale OBS deployments. Our approach relies on the theoretical time-symmetry of time-lapse (averaged) crosscorrelations of ambient seismic noise. Deviations from symmetry are attributed to clock errors. This implies that the recovered clock errors will be obscured by lapse crosscorrelations' deviations from symmetry that are not due to clock errors. Non-uniform surface wave illumination patterns are arguably the most notable source which breaks the time symmetry. Using field data, we demonstrate that the adverse effects of non-uniform illumination patterns on the recovered clock errors can be mitigated by means of a weighted least-squares inversion that is based on station-station distances. In addition, our methodology permits the recovery of timing errors at the time of deployment of the OBSs. This error can be attributed to either: i) a wrong initial time synchronization of the OBS or ii) a timing error induced by changing temperature and pressure conditions while the OBS is sunk to the ocean floor. The methodology is implemented in an open-source Python package named OCloC, and we applied it to the OBS recordings acquired in the context of the IMAGE project in and around Reykjanes, Iceland. As expected, most OBSs suffered from clock drift. Surprisingly, we found incurred timing errors at the time of deployment for most of the OBSs.

Charging Stations for Large-Scale Deployment of Electric Vehicles

The large-scale adoption of electric vehicles will require a charging infrastructure that meets the new needs that will arise. Currently, the charging infrastructure for electric vehicles is still in the early stages of development, not least because of the low number of electric vehicles in use. However, there are still many questions to be answered when it comes to standardization in terms of connectors, DC or AC charging, and power, as well as both operational and economic issues. Although this topic has been the subject of numerous studies over the last ten years, there are still gaps to be filled, particularly with regard to the mix of different recharging strategies (normal, accelerated, fast, induction-track, etc.), as well as the economic and operational aspects. Moreover, the relationship between users and private cars is changing rapidly, and charging behaviors are not yet well established.

Co-assessment of costs and environmental impacts for off-grid direct air carbon capture and storage systems

Large-scale deployment of direct air carbon capture and storage (DACS) is required to offset CO2 emissions. To guide decision-making, a combined assessment of costs and environmental impacts for DACS systems is necessary. Here we present a cost model and life cycle assessment for several combinations of off-grid DACSs, powered by photovoltaic (PV) energy and heat pumps combined with battery storages to mitigate intermittency of the PV energy source. Utilization factors of DACSs are estimated for different locations, power of PV systems and battery capacities. We find that the cost optimal layout for a DACS in Nevada (USA) with a nominal CO2 removal capacity of 100,000tCO2 per year consists of 100 MW PV and 300MWh battery. Costs are $755 and $877 for gross and net removal of 1tCO2. The cost difference is explained by a carbon removal efficiency (CRE) of 88%. Of 16 evaluated environmental impact categories mineral resource use is most problematic. We conceive a dashboard which allows to track how changes to technical parameters, such as energy consumption or adsorbent degradation, impact costs, CRE and combined environmental impacts. In an optimized scenario and including tax credits, costs for net-removal of 1tCO2 will be $216 at a CRE of 93%.

China's energy-water-land system co-evolution under carbon neutrality goal and climate impacts

China's 2060 carbon neutrality goal has significant implications for energy, water, and land systems. However, the multi-sector dynamics among China's energy-water-land system have rarely been examined explicitly. This study adopts an integrated assessment framework to simulate China's energy-water-land system co-evolution under alternative carbon neutrality scenarios and climate impacts. Results show that although the net zero emission target provides the incentive for the energy system to move away from fossil fuels, total water withdrawal will increase due to the deployment of nuclear, bioenergy, and coal power plants with carbon capture and storage. Diversifying the negative emission technologies, by leveraging direct air capture technology, can alleviate the potential water stress and land use conflicts, which would otherwise be exacerbated by large-scale deployment of afforestation and bioenergy with carbon capture and storage. Northwest and northeast regions of China are the hotspots experiencing water withdrawal increases, while Bohai Rim and coastal regions are identified to experience fierce land competition. This study demonstrates the potential for general applicability to carry out resource planning and policy evaluation from the multi-sector coordination perspective.

Life Cycle Greenhouse Gas Emissions of CO2-Enabled Sedimentary Basin Geothermal.

The expansion of renewable energy and the large-scale deployment of carbon dioxide (CO2) capture and storage (CCS) can decarbonize the power sector. The use of CO2 to extract geothermal heat from naturally porous and permeable sedimentary basins to generate electricity (CO2-plume geothermal (CPG) system) presents an opportunity to simultaneously generate renewable energy and geologically store CO2. In this study, we estimate the life cycle greenhouse gas (GHG) impacts of CPG systems through 12 scenarios in which CPG systems are combined with one of six CO2 sources (e.g., bioenergy with carbon capture and storage (BECCS) and iron and steel facilities) and operate in two geological settings. We find the life cycle GHG emissions of CPG systems ranging from -0.25 to -6.18 kg CO2eq/kWh. CPG systems can achieve the highest emissions reductions when utilizing the CO2 captured from BECCS. We evaluate uncertainty through a Monte Carlo simulation, demonstrating consistent net reductions in life cycle emissions and a local, one-parameter-at-a-time sensitivity analysis that identifies the CO2 capture capacity as the high-impact parameter of the results. Through the production of electricity, CPG systems can provide additional environmental benefits to the deployment of large-scale CCS.

An optimal siting and economically optimal connectivity strategy for urban green 5G BS based on distributed photovoltaic energy supply

The emergence of ultra-dense 5G networks and a large number of connected devices will bring with them significant increases in energy consumption, operating costs, and CO2 emissions. At the same time, the deployment of distributed photovoltaic (DPV) in megacities plays an important role in promoting the integration of “building-photovoltaic”, adjusting the city's clean energy supply system, and building a green, and low-carbon smart city. The development of a new “DPV-5G Base Station-Energy Storage (DPV-5G BS-ES)” coupled DC microgrid system and its pre-deployment investment costs are fundamental factors to be considered when the problem of large-scale DPV and BS deployment in cities has to be addressed. There are almost no published papers on this issue in the literature, which only consider the problem of maximizing the deployment of 5G BS service coverage or only perform optimization analyses for the performance of microgrid systems without considering the economic optimality and low-carbon green issues in the construction of urban utilities. In order to solve the problem of 5G BS deployment and economic optimization of topology in the “DPV-5G BS-ES” coupled DC microgrid system, a novel deployment strategy is proposed in this paper. The strategy is divided into: (a) The Geographic Information System (GIS) for extracting and analyzing 3D data from all buildings in the area. (b) A GIS-based formulation of a mixed integer quadratic constrained planning model (BSSCP) under discrete fixed radius coverage constraints is used to delineate building clusters within the area and determine the optimal deployment location of 5G BS under each sub-cluster. (c) An Improved Self-Organizing Mapping (ISOM) model combines GIS and microgrid performance parameters to address the economically optimal connection of grid lines for DPV in 3D space. (d) Simulation experiments were conducted for the power output and profitability of eight different PV panels and tracking systems to calculate the economically optimal type of DPV generation hardware. The experimental results show that the BSSCP model proposed in this study reduced the 5G BS deployment cost input by up to 35.8974 % compared to that of the control experimental model. Meanwhile, the ISOM model obtained an average reduction of 52.3741 % in the cost of connecting cables for the optimized 25 DPV planning and DC microgrid topology.

Smarter energy for a green and sustainable energy transition

Smarter energy for a green and sustainable energy transition Tietoevry, a leading technology company with a strong Nordic heritage and global capabilities, spearheads the implementation of innovative solutions for a sustainable energy transition. From the climate perspective, energy needs to be totally rethought. We need efficient means to manage generation and, most importantly, consumption. This is where new technologies enter the scene – minimising the environmental impact of energy usage means large-scale deployment of digital solutions and artificial intelligence. In recent years, Europe has been leading in driving a profound shift towards sustainability and a greener future. In the past two years, this “green transition” has seen significant initiatives emerge in managing energy through digitalisation.

Collaborative Approach for Achieving Ambitious Sustainability Goals: The Prosecco Sustainability Project

Despite attention to sustainability growing in the last decade in the wine sector, the rate of association with wine sustainability programs still appears to be limited. This is mainly related to the uncertainty about the economic benefits related to the implementation of sustainable best practices. In this regard, some studies highlighted that the presence of professionals capable of managing the requirements promoted by the standards generally leads to a positive perception of their impact on the economic performances of companies. Therefore, to encourage participation in sustainability programs, the adoption of a collaborative approach to share the skills and knowledge necessary to manage the transition from a conventional organization to a sustainable one seems to be a functional strategy. This paper presents the Prosecco Sustainability Project as a case study to investigate how a structured set of activities must be deployed through a participatory/collaborative approach to significantly improve the level of sustainability of a wide and heterogeneous community of grape and wine producers. The case study highlights that the collaboration between different actors and the guidance from a coordinating figure capable of fostering dialogue and cooperation among the various stakeholders represent key elements to the large-scale deployment of a sustainable management system.

Harmony in Integration: Unveiling Novel Paradigms in ERP Implementation and Trends

Purpose: Enterprise Resource Planning (ERP) systems, originating in the early 1970s, have undergone a transformative evolution, becoming pivotal in unifying various company functions such as marketing, finance, production, and human resources. These systems, like SAP's large-scale deployments since the 1990s, enable the swift collection and processing of real-time data for effective daily management control.
 Methodology: In the dynamic ERP landscape, Oracle stands out as a Leader in the 2023 Gartner® Magic Quadrant for Cloud ERP for Service-Centric Enterprises, particularly with its Oracle Fusion Cloud ERP. Recognized for financial management, order-to-cash, source-to-pay, and other ERP functionalities, Oracle excels in "Ability to Execute" and "Completeness of Vision."
 Findings: Despite their transformative impact, ERP system implementations could be more flawless. Challenges include non-user-oriented interfaces, partially migrated data, and insufficient adaptation to user needs. However, these hurdles can be surmounted through meticulous planning, customized training, and system fine-tuning to align with specific business requirements.
 Unique contributor to theory, policy and practice: This research article provides insight into ERP system implementation and current trends. It highlights challenges faced by businesses during implementation and proposing solutions. Emphasizing that ERP systems streamline processes and aid decision-making with real-time data, the study underscores that success requires a well-planned strategy and customization to meet individual business needs.

Long-distance continuous-variable quantum key distribution over 100-km fiber with local local oscillator.

Quantum key distribution (QKD) enables two remote parties to share encryption keys with security based on the laws of physics. Continuous-variable (CV) QKD with coherent states and coherent detection integrates well with existing telecommunication networks. Thus far, long-distance CV-QKD has only been demonstrated using a highly complex scheme where the local oscillator is transmitted, opening security loopholes for eavesdroppers and limiting potential applications. Here, we report a long-distance CV-QKD experiment with a locally generated local oscillator over a 100-kilometer fiber channel with a total loss of 15.4 decibels. This record-breaking distance is achieved by controlling the phase noise-induced excess noise through a machine learning framework for carrier recovery and optimizing the modulation variance. We implement the full CV-QKD protocol and demonstrate the generation of keys secure against collective attacks in the finite-size regime. Our results mark a substantial milestone for realizing CV quantum access networks with a high loss budget and pave the way for large-scale deployment of secure QKD.

Low-Complexity P Class to M Class PMU Model for Distribution Systems

The paper presents a new PMU (Phasor Measurement Unit) signal processing model compliant with the IEC/IEEE 60255-118-1 standard. The disturbances are rejected by a cascade digital prefilter made of sections, each with one nominal cycle long rectangular impulse response. The frequency is next estimated by the discrete-time frequency-gain transducer (DTFGT). A single section prefilter with the DTFGT is a 1.5 nominal cycle long P class compliant PMU. M class compliance requires 13 sections prefilter what, along with the DTFGT, gives a 13.4 cycle long PMU. By the elongation of the prefilter cascade, with one cycle increment, the transition from the 1.5 cycle P class to the 13.4 cycle M class PMU is obtained. Each cascade section filter is implemented recursively with only two additions and one multiplication independently of the assumed sampling frequency. The phasor is precisely estimated thanks to the proposed correction of amplitude attenuation and phase shift caused by the prefilter. The main advantages of the proposed PMU model are: full compliance with the standard, low computational complexity, superior rejection of harmonics, and possibility of phasor quality assessment based on different length PMU estimations. The proposed PMU model is primary meant for the distribution systems because of high immunity to disturbances and low computational complexity that makes it suitable for implementation in a low-cost hardware platform or as a software module in a virtual instrumentation and large-scale deployment.

EOS software evolution enabling LHC Run 3

EOS software evolution enabling LHC Run 3

A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments

A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments

The Entomological Efficacy of Piperonyl Butoxide (PBO) Combined with a Pyrethroid in Insecticide-Treated Nets for Malaria Prevention: A Village-Based Cohort Study Prior to Large-Scale Deployment of New Generation Mosquito Nets in Burkina Faso

The Entomological Efficacy of Piperonyl Butoxide (PBO) Combined with a Pyrethroid in Insecticide-Treated Nets for Malaria Prevention: A Village-Based Cohort Study Prior to Large-Scale Deployment of New Generation Mosquito Nets in Burkina Faso

Deployment Integration Strategy for Daytime Radiative Cooling Materials: A Comparative Numerical Analysis of Roof Cooling and Water-Cooling Panels

Daytime radiative cooling has emerged as a promising solution for continuous passive cooling. While significant progress has been made in material developments, questions persist regarding the potential large-scale deployment of this technology. Among the potential applications, two predominant uses of daytime radiative cooling materials arc under study: passive roof cooling and water-cooling panels. In the present study, a numerical assessment to compare the benefits of both integration schemes for passive daytime radiative cooling materials is conducted. These findings suggest adjusted approaches based on climatic considerations for the optimal deployment of daytime radiative cooling technologies worldwide.

Strategic design of VO2 encased in N-doped carbon as an efficient electrocatalyst for the nitrogen reduction reaction in neutral and acidic media.

Electrocatalytic nitrogen fixation to ammonia (NH3), a precursor for fertilizer production and a promising energy carrier, has garnered widespread interest as an environment-friendly and sustainable alternative to the energy-intensive fossil-feedstock-dependent Haber-Bosch process. The large-scale deployment of this process is contingent on the identification of inexpensive, Earth-abundant systems that can operate efficiently, irrespective of the electrolyte pH for the selective production of NH3. In this regard, we discuss the scalable synthesis of VO2 anchored on N-doped carbon (VO2@CN), and its applicability as a robust electrocatalyst for the nitrogen reduction reaction (NRR). Benefitting from the presence of exposed VO2, which presumably acts as the active site for nitrogen reduction, and its activity over a broad pH range (from acidic to neutral), VO2@CN exhibits a high NH3 yield of 0.31 and 0.52 μmol h-1 mgcat-1 and a maximum Faradaic efficiency (FE) of 67.9% and 61.9% at -0.1 V vs. RHE, under neutral and acidic conditions, respectively. The obscured reaction intermediates of the NRR were identified from in situ ATR-IR studies under both electrolyte conditions. Additionally, the high selectivity of the catalyst was ascertained from the absence of hydrazine production and the competing hydrogen evolution reaction (HER). However, ammonia production underwent a reduction over 12 h of continuous operation presumably owing to the leaching of catalyst under these electrolysis conditions, which was more pronounced in electrolytes with acidic pH. Overall, the present report unveils the performance of an earth-abundant vanadium oxide-based system as an efficient electrocatalyst for the NRR under acidic and neutral pH conditions.