Photonics simulation and modelling of skin for design of spectrocutometer

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Modelling Sustainable Industrial Symbiosis

Industrial symbiosis networks conventionally provide economic and environmental benefits to participating industries. However, most studies have failed to quantify waste management solutions and identify network connections in addition to methodological variation of assessments. This study provides a comprehensive model to conduct sustainable study of industrial symbiosis, which includes identification of network connections, life cycle assessment of materials, economic assessment, and environmental performance using standard guidelines from the literature. Additionally, a case study of industrial symbiosis network from Sodankylä region of Finland is implemented. Results projected an estimated life cycle cost of €115.20 million. The symbiotic environment would save €6.42 million in waste management cost to the business participants in addition to the projected environmental impact of 0.95 million tonne of CO2, 339.80 tonne of CH4, and 18.20 tonne of N2O. The potential of further cost saving with presented optimal assessment in the current architecture is forecast at €0.63 million every year.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A Review on Precise Orbit Determination of Various LEO Satellites

The need for precise orbit determination (POD) has grown significantly due to the increased amount of space-based activities appearing at an accelerating pace. POD has a positive contribution in achieving the requirements of Low-Earth Orbit (LEO) satellite mission which includes improved reliability and continuity. In this paper, we will review the POD approaches of various LEO satellites and discuss the accuracy levels obtained as well as the methods and algorithms used to achieve the POD of LEO satellites. With recent advancements in miniature space technology, a greater number of smaller low-cost satellites are launched into the LEO for various purposes. Furthermore, development in the Global Navigation Satellite Systems (GNSS) and chipsets played a vital role in revolutionizing the GNSS receiver technology. Lower-cost, smaller size but yet high performing GNSS receivers need to be implemented also in CubeSats in addition to the various terrestrial applications. POD using onboard GNSS receiver data will benefit the development of several upcoming space applications in the field of navigation systems, telecommunication, remote sensing, and earth observation. In the future, it is anticipated that LEO-based satellites enabled by POD can also offer positioning capabilities that will enhance GNSS and create vast opportunities for users with new features and possibilities to the navigation field.© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)fi=vertaisarvioitu|en=peerReviewed

An economic study of combined heat and power plants in district heat production

Combined heat and power plants are playing an essential role in Finland to reduce carbon dioxide emissions in district heat production. Some of the existing district heat producers need to adopt renewable energy sources to eliminate the use of fossil fuels. The northern countries face a challenge to tackle significant fluctuations of district heat consumption between summer and winter seasons concerning the production cost and the environmental impact of heat production. The municipality of Sodankylä is reorganising the district heat production to reduce emissions by constructing new wood-fuelled CHP plants. These plants will contribute to the existing district heating network. The profitability of CHP plants and the environmental impact in the region need evaluation. Results reveal that the profitable investment for the construction of CHP plants requires 16% subsidy with the predicted cost of heat production at 3.36 €/MWh. CO2 emissions from fossil fuels can potentially eliminated by adapting renewable fuel in the existing plant. CH4 and N2O emissions from district heat production reduced by 78% and 53%.© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Long-Term Results of Intralesional Triamcinolone Acetonide Injections in Keloid Treatment

Background: Intralesional triamcinolone acetonide (TAC) injections are often used as the first alternative for treating keloid scarring. The long-term outcome of this treatment is unclear. Also, undesirable local side effects have been recognized in clinical work and literature but they have been labelled as harmless and rare. Methods: We documented the long-term outcome of intralesional TAC injections in the treatment of keloid scars in Tampere University Hospital. The main objectives were to investigate the remission rate and the occurrence of local side effects. We assessed 105 patients (46 women, 59 men) with 138 TAC treated keloid scars at the outpatient clinic. The keloids were photographed and assessed with Patient and Observer Scar Assessment Scale (POSAS). Results: Of the 138 keloids, 90 (65%) were clinically in remission. Local side effects, including atrophy of the skin or the subdermal fat, telangiectasia and cortisone traces, occurred in 55% of the cases. The number of injections did not correlate with remission rate or the occurrence of local side effect. ROC curve analysis showed that surface area >620 mm2 was a prognostic factor for not responding to TAC treatment. Conclusion: According to this study, intralesional TAC injections seem to be effective in the treatment of small keloids but not in larger than 620 mm2. Local side effects were more frequent than previously reported and occurred even after just 1 injection. The side effects seem to be permanent in nature.fi=vertaisarvioitu|en=peerReviewed

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

A preliminary assessment of industrial symbiosis in Sodankylä

This study focuses on developing a possible architecture of planned industrial symbiosis in Sodankylä, Finland. The municipality of Sodankylä is considering the establishment of new businesses to boost the region's local economy. The preliminary assessment presented here evaluates some new markets, including combined heat and power plants, a biogas reactor, greenhouse farm, fish farm and several insect farms. These businesses should be able to fulfil the criteria of sustainability and circular economy. This study proposes an architecture where companies can quantify the value and the cost of material exchange. The combined life cycle cost and the net present value of symbiosis are estimated at €93 and €43 million respectively. The combined life cycle cost of waste management is calculated to be €6.40 million. The study's novelty is its projection of the quantified cost of bio-waste and recyclable waste of industries, highlighting the monetary value of industrial symbiosis where waste products can turn into industries' raw material. The value gained and cost reduced by such symbiosis is forecast at 14.65% and 6.8% respectively.© 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

An application of seasonal borehole thermal energy system in Finland

Borehole thermal energy system is an important component of the future low temperature heating networks. Applications of such systems are available around the world presenting various configurations. However, the mobility of the system from solar assisted to industrial heat has not yet evaluated. A 3D model of borehole thermal energy system created similar to Drake landing solar community project configuration. This model is validated with experimental measurements. The accuracy of the model estimated at 95%. Experimental measurements further utilized to create an artificial neural network model to predict modes of operation (charging/discharging). The accuracy of the model calculated at 97%. This study presents a possible application of storing excess heat from combined heat and power plants in Sodankylä, Finland. The municipality of Sodankylä is planning construction of new combined heat and power plants. These plants systematically shutdown during summer season leaving 1.53 ​MW of excess heat. The heat surplus can be stored in a heat storage. Simulations reveal that the model has storage capacity between 250 ​kW and 285 ​kW. In addition, there is a potential of five borehole thermal energy storage to store the entire excess heat. The novelty of the study is to test the mobility of borehole thermal energy system from solar assisted storage to industrial excess heat storage. The model used in a standardized manner considering the conventional combined heat and power plants supply temperature for working configuration of heat storage.©2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Temperature measurements on a solar and low enthalpy geothermal open-air asphalt surface platform in a cold climate region

Solar heat, already captured by vast asphalt fields in urban areas, is potentially a huge energy resource. The vertical soil temperature profile, i.e., low enthalpy geothermal energy, reveals how efficiently the irradiation is absorbed or radiated back to the atmosphere. Measured solar irradiation, heat flux on the asphalt surface and temperature distribution over a range of depths describe the thermal energy from an asphalt surface down to 10 m depth. In this study, those variables were studied by long-term measurements in an open-air platform in Finland. To compensate the nighttime heat loss, the accumulated heat on the surface should be harvested during the sunny daytime periods. A cumulative heat flux over one year from asphalt to the ground was 70% of the cumulative solar irradiance measured during the same period. However, due to the nighttime heat losses, the net heat flux during 5 day period was only 18% of the irradiance in spring, and was negative during autumn, when the soil was cooling. These preliminary results indicate that certain adaptive heat transfer and storage mechanisms are needed to minimize the loss and turn the asphalt layer into an efficient solar heat collector connected with a seasonal storage system.©2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Improving Precision GNSS Positioning and Navigation Accuracy on Smartphones using Machine Learning

In this work, we developed a precision positioning algorithm for multi-constellation dual-frequency global navigation satellite systems (GNSS) receivers that predicts the latitude and longitude from smartphone GNSS data. Estimation for all epochs that have at least four valid GNSS observations is generated. Receivers (especially low-cost receivers) often have limited channels and computational resources, therefore, the complexity of the algorithm used in them needs to be kept low. The datasets and results in this paper are based on the data provided by Google under the session "High Precision GNSS Positioning on Smartphones Challenge" in the Institute of Navigation (ION GNSS+ 2021) conference. We began by exploring and analysing the raw GNSS data which includes the training dataset and its ground truth and the test dataset without the ground truth. This analysis gave insight into the nature and correlation of the dataset and helped shape the algorithm that was proposed for the accuracy improvement problem. The design of the algorithm was done using data science techniques to compute the average of the predictions of several devices data in the same collection (training dataset baseline coordinates and their ground truth) and then the data was used to train a few selected machine learning algorithms namely, Linear Regression (LR), Bayesian Ridge (BR) and Neural Network (NN) to predict the offset of the test data baseline coordinates from the expected ground-truth (which was not provided). A simple weighted average (SWA) which combines all the previous three ML technique was also implemented. The results showed improvement in the position accuracy with the simple weighted average (SWA) method having the best accuracy followed by Bayesian Ridge (BR), Linear Regression (LR), and then Neural Network (NN) respectively.©2021 The Author. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed