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Natural killer (NK) cells are a promising allogeneic immunotherapy option due to their natural ability to kill tumor cells, and due to their apparent safety. This study describes the development of a GMP-compliant manufacturing protocol for the local production of functionally potent NK cells tailored for high-risk acute myeloid leukemia (AML) and neuroblastoma (NBL) patients. Moreover, the quality control strategy and considerations for product batch specifications in early clinical development are described. The protocol is based on the CliniMACS Prodigy platform and Natural Killer Cell Transduction (NKCT) (Miltenyi Biotec). NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with IL-2 and IL-15 cytokines. Three CliniMACS Prodigy processes demonstrated the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product. Characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, the NK cells exhibited increased killing efficacy against patient-derived CD33 + AML blasts and NBL cells in vitro, suggesting a potential therapeutic benefit in AML and NBL. [ABSTRACT FROM AUTHOR]

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot's workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology. [ABSTRACT FROM AUTHOR]

In this study, a combination of Plackett–Burman and Box–Behnken designs is applied to discover the relationships between the components of rubber compounds and technical specifications. Optimization of rubber compound formulation is realized by support vector regression integrated genetic algorithm to minimize compound cost. Twelve components potentially affecting the technical specifications of rubber compound, which are natural rubber, carbon black, white filler, stearic acid, zinc oxide, antiozonant, antioxidant, process oil, curing retarder, curing agent, and accelerator, are screened through Plackett–Burman design to decide the significant variables. Afterwards, four significant parameters, including carbon black, process oil, curing agent, and accelerator are analyzed using Box–Behnken design to minimize the number of experiments while obtaining the correlation between formulation and specifications. Lastly, a support vector regression integrated genetic algorithm is implemented to predict optimum compound formulation at minimum cost. Highlights: Optimization of rubber compound to reduce the mixture and curing cost.Combination of Plackett–Burman and Box–Behnken designs.Integration of support vector regression to genetic algorithm.Correlations between the amounts of components and technical specifications. [ABSTRACT FROM AUTHOR]

The continual progress pace of the development of the Wi-Fi generations may not fulfill the expected tremendous data growth of future smart-home network devices and applications. Throughout this article, we show a comprehensive design of the futuristic smart-home network that distributes massive data for indoor devices while fully supporting intelligent functions. We start by drawing a vision for various application scenarios of information and communication technologies in smart-home networks beyond the 2030s. Then, we appraise the technical specifications of the candidate intelligent home network. To achieve these technological speculations, we propose a novel holistic networking solution that is efficient and intelligent from service and operation perspectives. Specifically, we detail the proposed network architecture, elements, and enabling technologies. To accelerate the research progress and implementation, we further point out the future research directions. Finally, we discuss the non-technological factors that could affect the future deployment of the proposed network solution. [ABSTRACT FROM AUTHOR]

The continual progress pace of the development of the Wi-Fi generations may not fulfill the expected tremendous data growth of future smart-home network devices and applications. Throughout this article, we show a comprehensive design of the futuristic smart-home network that distributes massive data for indoor devices while fully supporting intelligent functions. We start by drawing a vision for various application scenarios of information and communication technologies in smart-home networks beyond the 2030s. Then, we appraise the technical specifications of the candidate intelligent home network. To achieve these technological speculations, we propose a novel holistic networking solution that is efficient and intelligent from service and operation perspectives. Specifically,we detail the proposed network architecture, elements, and enabling technologies. To accelerate the research progress and implementation, we further point out the future research directions. Finally, we discuss the non-technological factors that could affect the future deployment of the proposed network solution. [ABSTRACT FROM AUTHOR]

This article studies the practical road performance of recycled materials from construction waste, relying on the paving test section of the supporting project for the Qingdao Cross-Sea Bridge. The research focuses on the construction technology and road performance of using recycled construction waste materials in urban road sub-base construction. Through indoor tests such as sieving and unconfined compressive strength tests, relevant technical indicators were obtained and analyzed. Additionally, periodic core sampling, compaction tests, and rebound deflection tests were conducted on-site according to relevant standards to thoroughly investigate the specific effects of using construction waste in practice and to analyze and evaluate the actual feasibility of the materials for road use. The results indicate that the particle gradation of the construction mix in the test section aligns well with the target gradation, and the dosage of the mixing agent meets the design requirements. The 7-day unconfined compressive strength already satisfied the technical requirements for heavy and extremely heavy traffic on highways as specified in the "Technical Specifications for Construction of Highway Pavement Subbase" (JTG/T F20-2015), with the 14-day strength generally reaching 7 MPa. Core sampling revealed good aggregate gradation, smooth and straight profiles, and the thickness and strength of all parts meet the specifications. The compaction levels met the testing requirements, the surface deflection values showed a decreasing trend, and the deformation resistance was good, consistent with the general development patterns of semi-rigid sub-bases. [ABSTRACT FROM AUTHOR]

Cobalt (Co) is a hazardous element of significant environmental concern, primarily due to its potential leaching toxicity. However, the current assessments of leached Co residues have focused solely on the total cobalt concentration, often overlooking the distinct Co species that contribute to its hazardous nature. This study attempts to determine the impact of cobalt speciation on the toxicity of cobalt and the related hazardous characteristics. The objective of this study is to enhance the understanding of how different Co species influence the environmental toxicity of leached residues. Cobalt speciation is studied by a multivariate analysis including ignitability, reactivity, corrosiveness, acute toxicity, leaching toxicity, and toxic substance concentration. The tested concentrations are compared with the identification standards and technical specifications in China. Co species, particularly cobalt oxide, are identified as the main contributors to the toxicity of the leached Co residue. It is also noted that Co occurrence significantly affects the calculation results of cumulative toxicity, thus impacting the hazard characteristics of leached cobalt residue. The findings benefit risk evaluators and decision makers by offering a new approach for managing leached Co residues and providing a scientific foundation for the development of relevant laws and regulations in China. [ABSTRACT FROM AUTHOR]

Featured Application: This paper addresses the practical application of unmanned aerial vehicles (UAVs) in modern transportation systems, mainly focusing on how UAVs can be effectively integrated into intelligent transportation systems (ITSs) to improve efficiency, safety, and decision-making in smart cities. It offers a framework for understanding current advancements and guides future research and development in this evolving field. Integrating unmanned aerial vehicles (UAVs) into intelligent transportation systems (ITSs) will be pivotal in shaping next-generation smart cities. This paper proposes a novel three-layered framework for integrating UAVs into intelligent transportation systems (ITSs) and reviews the current developments, challenges, and future directions in this emerging field. This framework provides a comprehensive overview of the key components of UAV-integrated ITSs, encompassing UAV specifications and deployment strategies, communication networks, and data utilization for traffic management. The first layer explores UAVs' technical specifications, deployment strategies, and trajectory optimization, essential for maximizing UAV performance in transportation contexts. The second layer addresses the communication networks between UAVs and vehicles, along with the use of UAVs for responsive traffic monitoring. This includes the development of robust communication protocols and real-time traffic analysis to enhance system efficiency. The third layer focuses on advanced data collection processing techniques and complexities, reviewing the methods for analyzing the traffic data collected by UAVs for decision-making in transportation management. Moreover, the paper presents the current UAV-enabled ITS implementation, highlighting key challenges and future research directions. By providing a comprehensive overview of UAV-enabled ITSs, this study presents a significant portrayal of the current landscape of UAV integration in ITSs and serves as a foundation for future advancements in smart city infrastructure. [ABSTRACT FROM AUTHOR]

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains—such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy—demonstrating the fNIRS-EEG system's potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine. [ABSTRACT FROM AUTHOR]

Background/Objectives: Biological products are emerging as therapeutic management options for intervertebral disc (IVD) degenerative affections and lower back pain. Autologous and allogeneic cell therapy protocols have been clinically implemented for IVD repair. Therein, several manufacturing process design considerations were shown to significantly influence clinical outcomes. The primary objective of this study was to preclinically qualify (chondrogenic potential, safety, resistance to hypoxic and inflammatory stimuli) cryopreserved primary progenitor cells (clinical grade FE002-Disc cells) as a potential cell source in IVD repair/regeneration. The secondary objective of this study was to assess the cell source's delivery potential as cell spheroids (optimization of culture conditions, potential storage solutions). Methods/Results: Safety (soft agar transformation, β-galactosidase, telomerase activity) and functionality-related assays (hypoxic and inflammatory challenge) confirmed that the investigated cellular active substance was highly sustainable in defined cell banking workflows, despite possessing a finite in vitro lifespan. Functionality-related assays confirmed that the retained manufacturing process yielded strong collagen II and glycosaminoglycan (GAG) synthesis in the spheroids in 3-week chondrogenic induction. Then, the impacts of various process parameters (induction medium composition, hypoxic incubation, terminal spheroid lyophilization) were studied to gain insights on their criticality. Finally, an optimal set of technical specifications (use of 10 nM dexamethasone for chondrogenic induction, 2% O2 incubation of spheroids) was set forth, based on specific fine tuning of finished product critical functional attributes. Conclusions: Generally, this study qualified the considered FE002-Disc progenitor cell source for further preclinical investigation based on safety, quality, and functionality datasets. The novelty and significance of this study resided in the establishment of defined processes for preparing fresh, off-the-freezer, or off-the-shelf IVD spheroids using a preclinically qualified allogeneic human cell source. Overall, this study underscored the importance of using robust product components and optimal manufacturing process variants for maximization of finished cell-based formulation quality attributes. [ABSTRACT FROM AUTHOR]

Background: Expansion of the aesthetic treatment armamentarium with novel injectable products has greatly improved our ability to reshape and enhance the facial profile. However, full‐face "profiloplasty" with injectables remain a nascent concept and is insufficiently considered in daily practice. While techniques have been widely published for treating the individual facial areas that constitute an attractive profile—including the forehead, nose, lips, jawline, and chin—the literature remains fragmented on more universal methods for profile improvement. Aims: To collate disparate knowledge on profile optimization with injectables, and define a holistic, practical, and user‐friendly approach to profiloplasty and facial contouring. Methods: Consideration is given to key anatomical aspects and the changes that occur with age; patient assessment parameters for planning profiloplasty; and the technical specifications, product selection, and essential safety considerations for minimizing the risk of complications and optimizing the effectiveness of treatment. Results: Relevant case studies are presented. Conclusions: Holistic approaches of this type are essential if we are to maximize the potential of full‐face treatment with injectables—both now and in the future. [ABSTRACT FROM AUTHOR]

Coordinate measuring machines are widely used in the industrial field due to their ease of automation. However, estimating the measurement uncertainty is a delicate task, especially when controlling for deviation, given the large number of factors that influence the measurement. A precise estimate of the uncertainty is crucial to avoid incorrect conformity assessments. The purpose of this study is to control geometrical-form tolerance specifications, taking into consideration their associated uncertainty. A surface fitting model based on the least squares criterion is proposed, allowing one to obtain the variance–covariance matrix by iterative calculation according to the Levenberg–Marquard optimization method. The form deviation is then evaluated following the Geometrical Product Specifications (GPS) Standard, and its associated uncertainty is estimated using the guide to the expression of uncertainty in measurement (GUM) propagation of the uncertainty law. Finally, the conformity assessment is performed based on the measured deviation and its associated uncertainty. Different results for the measurement of straightness, flatness, circularity, roundness, and cylindricity are presented and detailed. This model is thereafter validated by a Monte Carlo simulation, and interlaboratory comparisons of the obtained results were performed, which showed satisfactory outcome. This contribution is of great use to manufacturing companies and metrology laboratories, allowing them to meet the normative guidelines, which stipulates that each measurement result must be accompanied by its associated uncertainty. [ABSTRACT FROM AUTHOR]

In late 2023, the United Nations conference on climate change (COP28), which was held in Dubai, encouraged a quick move from fossil fuels to renewable energy. Solar energy is one of the most promising forms of energy that is both sustainable and renewable. Generally, photovoltaic systems transform solar irradiance into electricity. Unfortunately, instability and intermittency in solar radiation can lead to interruptions in electricity production. The accurate forecasting of solar irradiance guarantees sustainable power production even when solar irradiance is not present. Batteries can store solar energy to be used during periods of solar absence. Additionally, deterministic models take into account the specification of technical PV systems and may be not accurate for low solar irradiance. This paper presents a comparative study for the most common Deep Learning (DL) and Machine Learning (ML) algorithms employed for short-term solar irradiance forecasting. The dataset was gathered in Islamabad during a five-year period, from 2015 to 2019, at hourly intervals with accurate meteorological sensors. Furthermore, the Grid Search Cross Validation (GSCV) with five folds is introduced to ML and DL models for optimizing the hyperparameters of these models. Several performance metrics are used to assess the algorithms, such as the Adjusted R2 score, Normalized Root Mean Square Error (NRMSE), Mean Absolute Deviation (MAD), Mean Absolute Error (MAE) and Mean Square Error (MSE). The statistical analysis shows that CNN-LSTM outperforms its counterparts of nine well-known DL models with Adjusted R2 score value of 0.984. For ML algorithms, gradient boosting regression is an effective forecasting method with Adjusted R2 score value of 0.962, beating its rivals of six ML models. Furthermore, SHAP and LIME are examples of explainable Artificial Intelligence (XAI) utilized for understanding the reasons behind the obtained results. [ABSTRACT FROM AUTHOR]

Since 2017, the Space Research Centre of the Polish Academy of Sciences in Warsaw, Poland, has measured and gathered over 35,000 hours of observations with the use of the LOw Frequency ARray (LOFAR) radio telescope. This paper outlines the Standard Data Product acquired from the LOFAR PL610 station located in Borówiec, Poland. Within this context, we detail the data products that are accessible, provide metadata descriptions for them, and include an example of the data under both quiet and disturbed ionospheric conditions. [ABSTRACT FROM AUTHOR]

Gender equality is one of the Sustainable Development Goals (SDGs) of the 2030 Agenda. However, in the advertising industry, several studies have shown the low presence of women working in the field of creativity, occupying management positions and being part of festival juries. The aim of the study is to analyse the presence of women in the field of advertising festivals. The methodology is based on a quantitative analysis of the 2000 to 2023 editions of the Club de Creativos National Creativity Awards and the Cannes Lions International Festival of Creativity. The composition of the juries of both festivals, the special awards for professionals in the sector (cdec de Honor and Lion of St. Mark, respectively), the technical specifications of the grand prizes (National Grand Prize for Creativity and Grand Prix won by Spain, respectively) and the management teams of the organising entities are analysed in the indicated period. The results demonstrate the underrepresentation of women in advertising festivals, especially in the composition of the juries, in the special recognitions and in the technical sheets of the most outstanding campaigns, corroborating the findings of previous research. Despite the emerging initiatives that defend parity in the advertising profession, this equality is still not real, nor does it materialize in the events that make the industry visible and recognize its evolution: advertising festivals. [ABSTRACT FROM AUTHOR]

Mixed-model assembly is a particular set of production lines assembling a family of product models with similar specifications. Designing paced assembly lines faces two primary problems: balancing and sequencing. The balancing quality is closely associated with the described production sequence. Although these two are problems of one assembly method, they do not occur simultaneously; balancing poses a problem during the line designing, whereas sequencing becomes problematic at the fluctuating demand of markets. The present research presents a balancing and sequencing problem and the proper times to set up the machines between tasks. Unlike a majority of published studies, this paper contains two successive tasks' setup times in dynamic periods, in which periods also impact the flowing period. A mathematical is described with a number of objective functions, reducing the inappropriate assembly line sequence, reducing setup cost, and reducing the inappropriate product balance and the impact of this situation on incomplete tasks. Thus, the literature has presented several metaheuristic algorithms to solve the problems nearly optimally. This study uses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, a suitable approach, to create models and solutions. Various problems are designed and compared in different sizes, and the decision variable sensitivity is investigated to prepare managerial intuitions. The findings propose that the presented algorithm can solve the research problems more efficiently. [ABSTRACT FROM AUTHOR]

Automatic monitoring is an important means to ensure the safety of high-speed railway operation during the construction period of adjacent high-speed railway projects. However, the installation of monitoring equipment on the trackside may have a certain impact on the safety of high-speed railway operation. This paper used the aerodynamic calculation method in the Code for Design of High-speed Railway and the calculation method of internal force of anchorage connection and the limit state of bearing capacity in the Technical Specification for Post-Installed Fastenings in Concrete Structures to calculate and analyze the impact of aerodynamic effect on the total station, protection box and monitoring prisms 3.5 m away from the line centerline on the roadbed under the condition of 350 km/ h train speed. The calculation results show that under the influence of high-speed railway aerodynamic effect, the expansion bolts of the total station, protection box and monitoring prisms bear less tensile and shear forces than the design values of the bolt steel failure tensile and shear bearing capacity. The tensile forces borne by the anchor foundation concrete are all less than the design values of the concrete cone failure tensile bearing capacity, meeting the requirements of the specifications. [ABSTRACT FROM AUTHOR]

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas. [ABSTRACT FROM AUTHOR]

The reduction of sodium chloride (NaCl) content, commonly known as salt, in processed meat products is one of the objectives of health organizations and government authorities to achieve healthier products. This reformulation of traditional meat products with protected designations poses more constraints, as they have a more consolidated quality image and less margin for change, since consumers appreciate the products for their unique sensory characteristics. The aim of this work is to present some of the strategies that have been explored to obtain meat products with low sodium content. Information related to the characteristics of traditional meat products with quality marks and geographical indications in different studies is discussed in opposition to the information recorded in their product specifications. It was found that the product specifications of meat products with Portuguese Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI) show a wide variation in the NaCl content, much higher than the recommended values. Thus, one of the requirements to be implemented will be the parameterization of NaCl levels and their monitorization by control and certification organizations as a way to ensure product quality. It is also urgent to examine whether healthy innovation strategies may affect the quality of traditional PDO or PGI meat products and whether they can be included in the respective product specifications. [ABSTRACT FROM AUTHOR]

The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand's EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability. [ABSTRACT FROM AUTHOR]

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Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas. [ABSTRACT FROM AUTHOR]

Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution. [ABSTRACT FROM AUTHOR]

This document discusses the intersection of sustainability and digitalization in manufacturing companies, highlighting the challenges they face in addressing environmental and social impacts. The concept of Maintenance 4.0 is introduced as a way to enhance and advance maintenance processes using technologies such as the Industrial Internet of Things (IIoT), Augmented Reality (AR), Virtual Reality (VR), Big Data Analytics (BDA), and AI. The document also provides an overview of 13 articles included in a special issue on Maintenance 4.0 and sustainability, covering topics such as predictive maintenance, augmented reality, digital twins, and environmental considerations in maintenance activities. The authors emphasize the importance of Industry 4.0 technologies in transforming maintenance processes and supporting sustainable development goals. [Extracted from the article]

Graphene growth on widely used dielectrics/insulators via chemical vapor deposition (CVD) is a strategy toward transfer-free applications of CVD graphene for the realization of advanced composite materials. Here, we develop graphene-skinned alumina fibers/fabrics (GAFs/GAFFs) through graphene CVD growth on commercial alumina fibers/fabrics (AFs/AFFs). We reveal a vapor-surface-solid growth model on a non-metallic substrate, which is distinct from the well-established vapor-solid model on conventional non-catalytic non-metallic substrates, but bears a closer resemblance to that observed on catalytic metallic substrates. The metalloid-catalytic growth of graphene on AFs/AFFs resulted in reduced growth temperature (~200 °C lower) and accelerated growth rate (~3.4 times faster) compared to that obtained on a representative non-metallic counterpart, quartz fiber. The fabricated GAFF features a wide-range tunable electrical conductivity (1-15000 Ω sq−1), high tensile strength (>1.5 GPa), lightweight, flexibility, and a hierarchical macrostructure. These attributes are inherited from both graphene and AFF, making GAFF promising for various applications including electrical heating and electromagnetic interference shielding. Beyond laboratory level preparation, the stable mass production of large-scale GAFF has been achieved through a home-made roll-to-roll system with capacity of 468-93600 m2/year depending on product specifications, providing foundations for the subsequent industrialization of this material, enabling its widespread adoption in various industries. Chemical vapor deposition (CVD) of graphene on widely used insulators can promote its application for functional composite materials. Here, the authors report the large-scale production of graphene alumina fibers/fabrics via a metalloid-catalytic CVD growth process on commercial alumina, showing their application for electrical heating and electromagnetic shielding. [ABSTRACT FROM AUTHOR]

Pharmacopeia monographs are not intended to establish biosimilarity. However, the US Food and Drug Administration (FDA) has stopped the US Pharmacopeia (USP) from creating monographs for biological drugs due to the need for side-by-side comparisons with the reference products. The USP can create Biological Product Specifications (BPS), not to be labeled as monographs, based on the analytical testing of reference products and validated test methods that will remove the need for side-by-side analytical testing of biosimilars with reference products. Scientific arguments confirm that this plan is logical and capable of creating global quality standards for biosimilars to allow their interchangeability with other biosimilars. While the regulatory agencies have waived many high-cost biosimilar tests, analytical assessment is the most sensitive test; reducing its cost will further enhance the entry of biosimilars with no clinically meaningful difference. [ABSTRACT FROM AUTHOR]

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are "better" for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel. [ABSTRACT FROM AUTHOR]

Fuel cell vehicles are considered as the direct alternative to fuel vehicles due to their similar driving range and refueling time. The United Nations World Forum for Harmonization of Vehicle Regulations (UN/WP29) released the Global Technical Regulation on Hydrogen and Fuel Cell Vehicles (GTR13) in July 2013, which was the first international regulation in the field of fuel cell vehicles. There exist some differences between GTR13 and the existing safety technical specifications and standards in China. This paper studied the safety requirements of the GTR13 compressed hydrogen storage system, analyzed the current hydrogen storage safety standards for fuel cell vehicles in China, and integrated the advantages of GTR13 to propose relevant suggestions for future revision of hydrogen storage standards for fuel cell vehicle in China. [ABSTRACT FROM AUTHOR]

In the process of industrial production, products with different specifications (i.e., the difference in geometry, process conditions, and machine conditions, etc.) have different quality data distributions, which lead to a decrease in the accuracy of traditional data-driven quality prediction models that require the same quality data distribution. At the same time, due to economic cost factors, obtaining a large amount of accumulated data for different specifications is difficult, and the re-modeling data accumulation of multiple cross-specifications is insufficient. In order to solve the quality prediction problem of production with different data distributions and poor data accumulation, we use the deep transfer learning (DTL) method with unsupervised dynamic domain adaptation (DDA) to transfer the domain invariant features learned from labeled specification products (source domain) to other unlabeled new specification products (target domain). In order to improve the success rate of cross-domain quality prediction, the Wasserstein distance adapter is designed to match appropriate source domain samples and target domain samples to build multiple transfer tasks that are suitable for transfer. At the same time, the dynamic distribution adaptation and dynamic adversarial adaptation are combined to extract the domain invariant features to improve the adaptability of the prediction model for products with new specifications (e.g., size difference) and unlabeled and limited quality data. Finally, a comprehensive experiment is carried out using the actual production data of products with different specifications. The experimental results show that compared with the traditional non-transfer deep learning methods, the MAE, RMSE, and R2 of the proposed DTL method are improved by 18.26%, 16.66%, and 22.48% respectively. Compared with other transfer methods, the MAE, RMSE, and R2 of the DTL proposed in this paper are improved by 10.45%, 10.96%, and 9.72% respectively. [ABSTRACT FROM AUTHOR]

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns. [ABSTRACT FROM AUTHOR]

Background: 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is commonly used for diagnosis of dementia because brain glucose metabolism reflects neuronal activity. However, as [18F]FDG is an analogue of glucose, accumulation of tracer in the brain is affected by plasma glucose levels. In contrast, cerebral blood flow (CBF) tracers are theoretically unaffected by plasma glucose levels and are therefore expected to be useful alternatives for the diagnosis of dementia in patients with diabetes. The techniques currently used for CBF imaging using single photon emission computed tomography (SPECT) and [15O]H2O positron emission tomography (PET), but these are limited by their insufficient resolution and sensitivity for regional brain imaging, especially in patients with brain atrophy. N-isopropyl-4-[11C]methylamphetamine ([11C]MMP) is a possible CBF tracer with high resolution and sensitivity that exhibits comparable performance to that of [15O]H2O in conscious monkey brains. We performed process validation of the radiosynthesis and preclinical development of [11C]MMP prior to clinical translation. Results: The decay-corrected yields of [11C]MMP at the end of synthesis were 41.4 ± 6.5%, with 99.7 ± 0.3% radiochemical purity, and 192.3 ± 22.5 MBq/nmol molar activity. All process validation batches complied with the product specifications. The acute toxicity of MMP was evaluated at a dose of 3.55 mg/kg body weight, which is 10,000 times the potential maximum clinical dose of [11C]MMP. The acute toxicity of [11C]MMP injection at 150 or 200 times, to administer a postulated dose of 740 MBq of [11C]MMP, was also evaluated after the decay-out of 11C. No acute toxicity of MMP and [11C]MMP injection was found. No mutagenic activity was observed for MMP. The effective dose calculated according to the Medical Internal Radiation Dose (MIRD) method was 5.4 µSv/MBq, and the maximum absorbed dose to the bladder wall was 57.6 µGy/MBq. MMP, a derivative of phenylalkylamine, showed binding to the sigma receptor, but had approximately 1/100 of the affinity of existing sigma receptor imaging agents. The affinity for other brain neuroreceptors was low. Conclusions: [11C]MMP shows acceptable pharmacological safety at the dose required for adequate PET imaging. The potential risk associated with [11C]MMP PET imaging is well within the acceptable dose limit. [ABSTRACT FROM AUTHOR]

Background: Integrating diverse sensor technologies into smart insoles offers significant potential for monitoring biomechanical metrics; enhancing sports performance; and managing therapeutic interventions, diseases, disorders, and other health-related issues. The variation in sensor types and applications requires a systematic review to synthesize existing evidence and guide future innovations. Objectives: This review aims to identify, categorize, and critically evaluate the various sensors used in smart insoles, focusing on their technical specifications, application scopes, and validity. Methods: Following the PRISMA guidelines, a search was conducted in three major electronic databases, namely, PubMed, Scopus, and Web of Science, for relevant literature published from 2014 to 2024. Other works not located in the mentioned databases were added manually by parallel searches on related themes and suggestions from the website of the databases. To be eligible, studies were required to describe sensor implementation in insoles, specify the sensor types, and report on either validation experiments or practical outcomes. Results: The search identified 33 qualifying studies. Proper analysis revealed a dominance of pressure sensors, with accelerometers and gyroscopes also being widely used. Critical applications included gait analysis, posture correction, and real-time athletic and rehabilitation feedback. The review also examined the relative effectiveness of different sensor configurations. Conclusions: This systematic review comprehensively classifies sensor technologies within smart insoles and highlights their broad application potential across various fields. Future research should aim to standardize measurement protocols, enhance sensor integration, and advance data processing techniques to boost functionality and clinical applicability. [ABSTRACT FROM AUTHOR]

Over the past two decades, e-participation has become increasingly relevant as a result of digitization and the evolution of information and communication technologies. Yet the perspectives of providers and users with regard to the requirements on an e-participation system are not sufficiently considered jointly. This paper investigates the requirements and challenges of users and providers. The study is based on a mixed methods approach with an online survey of those responsible for e-participation processes in various administrative and planning areas as well as semi-structured expert interviews. In addition, we conducted proband tests to investigate the usability of digital citizen participation tools. The results show that accessibility, retrievability, effectiveness, interaction in the digital arena, security, technical specifications, resources, media literacy and the use of participation as a basis for profound decision-making are key requirements for e-participation systems. These demands requirements are interrelated and influence the quality and effectiveness of participation processes. To meet these requirements, we propose an e-participation ecosystem that integrates the different dimensions of digital participation and takes into account the interaction between actors, demands and contextual conditions. [ABSTRACT FROM AUTHOR]

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human–computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity. These features have drawn considerable attention in the health domain, as NET provides accessible solutions for long-term and continuous health monitoring and a comfortable and interactive user interface. Herein, this work offers an inaugural concise review of NET for health, encompassing approximately 70 related articles published over the past two decades and supplemented by an in-depth examination of 30 literatures from the preceding five years. This paper provides a concise analysis of health-related NET technologies from aspects of technical specifications, data processing workflows, and the practical advantages and limitations. In addition, the specific applications of NET are introduced and compared, revealing that NET is fairly influencing our lives and providing significant convenience in daily routines. Lastly, we summarize the current outcomes of NET and highlight the limitations. [ABSTRACT FROM AUTHOR]

Many aviation education and training programs are expanding their learning materials to include virtual learning environments (VLEs) that utilize virtual reality (VR) technology. While developing training materials and the VLE, an understanding of technical specification requirements is needed to inform stakeholders who need to choose a headset for a given training scenario. As part of a larger initiative to compare the usability of multiple VR headsets for implementation into procedural training, a review of the extant literature was conducted to identify technical specifications of VR head-mounted displays, followed by a pilot test of a headmounted display (HMD) comparison methodology that considers VR HMD technical specifications ("tech specs") and usability aspects. This report identifies key technical specifications for VR HMDs which can be used as a guideline as technology changes. Further research and collaboration are anticipated to elaborate on the technical specifications to recommend for aviation training initiatives. [ABSTRACT FROM AUTHOR]

This study aims to propose a thermal loop model-based simulation and analysis method for predicting the hotspot temperature of converter transformers in Ultra-High Voltage Direct Current (UHVDC) transmission systems, aiming to address the technical deficiencies in hotspot temperature monitoring of converter transformers in UHVDC transmission engineering. With the advancement of UHVDC transmission technology, converter transformers have emerged as critical electrical components. Operating at elevated voltage levels and accommodating large capacities, these converter transformers experience substantial losses within their windings. Consequently, the issue of winding overheating has become increasingly prominent. The method involves utilizing MATLAB/Simulink to simulate the temperature rise model of converter transformers. The hotspot temperature model adopted in this paper is simple and easy to understand, enabling effective simulation analysis of the top-oil temperature and hotspot temperature of the converter transformer. Configuring simulation parameters derived from existing technical specifications and monitoring data, and subsequently computing the hotspot temperature to satisfy engineering criteria. The simulation analysis of the converter transformer with a rated capacity of 412 300 kVA is conducted using Infolytica MagNet. By comparing the measured data of the top oil temperature and hotspot temperature of the converter transformer with the calculation results of the standard model and the simulation results, the rationality and accuracy of the heat circuit model calculation are verified. This hotspot temperature simulation analysis method provides a new solution for the hotspot temperature detection of the converter transformer. [ABSTRACT FROM AUTHOR]

Background: Acute otitis media (AOM) causes inflammation and hearing loss. Ventilation tubes are key in treatment. 3D printing improves prostheses in otorhinolaryngology, offering precision and greater adaptability. Materials and methods: An experimental study was conducted with Wistar rats from July to December 2020. 3D tympanostomy tube models were designed, with technical specifications and tests performed on inexpensive 3D printers. The tympanostomy tube was inserted endoscopically. Results: Procedures were performed on five rats with implants in both ears. Pre-intervention pathologies, such as atical retraction and glue ear, were found. The PLA-printed tympanostomy tube showed improvement after adjustments. Histopathological results revealed significant middle and inner ear damage. Conclusion: In our study, the design and 3D printing of implants fulfilled the desired functions when modified, with a height of 5 mm. Complications included PLA degradation and ear damage. There were no adverse events during observation, highlighting the need for further research on 3D-printed implants. [ABSTRACT FROM AUTHOR]

The foundation industries in the UK were responsible for emitting 42 Mt CO2eq in 2020, which is approximately 10% of the yearly territorial greenhouse gas emissions. The UK government decarbonisation roadmap issued in 2015 predicted that high-tech strategies such as carbon capture and utilisation, hydrogen and biofuels, as well as electrification of processes are key for achieving the climate mitigating targets by 2050. In this study, a critical assessment was performed on the limitations to achieve these high-tech strategies such as biomass availability, capital investment, and technology readiness. The study is the first to use the UK carbon budget values as the resource limit for the high-tech decarbonisation strategies. The findings show that the significant uncertainty associated with the high-tech scenarios limits their decarbonisation potential by 2050. More importantly, to stay within the mid-century carbon budget for the foundation industries, 20–40% reduction in production, through circular economic strategies such as material efficiency and/or changes in product specifications, is required in order to achieve the decarbonisation targets. [ABSTRACT FROM AUTHOR]

This study aimed to analyze and optimize the thermal induction hardening process applied to toothed transmission gears, focusing on thermal aspects, structural deformation, and topology optimization, while exploring the feasibility of various materials and operating conditions. The research simulated thermal and deformation behavior using a computer model, comparing results with experimental data through the Ansys® platform 2022 R1. The methodology encompassed thermal and deformation analyses, topology optimization to identify removable regions without compromising part integrity, and a sensitivity study to evaluate the different materials and operating conditions. This study validates the precision of computational models in predicting thermal and deformation behavior in toothed gears under thermal induction hardening, introducing topology optimizations and alternative materials, and providing novel perspectives for the more efficient and cost-effective manufacturing of these components. Comparative thermal analysis revealed a maximum relative error of less than 6% between temperatures from the computer model and experimental results, while deformation comparisons exhibited a maximum relative error of less than 7%, affirming the simulation model's accuracy in predicting and managing deformations within acceptable thresholds. Topology optimization successfully pinpointed removable regions without compromising structural integrity, enabling the production of lighter and more economical devices. Future endeavors should concentrate on additional tests to verify the feasibility of reducing power and cooling temperature without compromising product specifications. Furthermore, it is advisable to explore alternative materials and apply the developed methodology in diverse industrial settings to generalize the findings and amplify the impact of the proposed optimizations. [ABSTRACT FROM AUTHOR]

Ammonia as a fuel has been known for several years, but only relatively recently has it attracted interest for study and use as a marine fuel. A forthcoming generation of sustainable, economically viable, and safe technologies for the large-scale onboard storage of ammonia as a marine fuel is being developed. This article presents a structured approach to carrying out Quality Function Deployment (QFD) for evaluating a project solution at the strategic level. The "House of Quality" (HoQ) method is utilised since it provides the means for inter-functional planning and communications among people with different problems and responsibilities. The project requirements are defined and lead to functional technical characteristics to satisfy these needs. The approach allows for the application of a weighting scheme to rank the perceived relative importance of the requirements, the difficulty involved in implementing the technologies, and the relationship between requirements and functional technical characteristics. QFD analysis prioritises safety compliance, technical specifications, emission control, and operational efficiency. The added value of this work is beyond the life of a project since it may provide assistance to the stakeholders in assessing the applicability of ammonia fuel solutions given the resources available to them. This feature will enable the justification of further investment into specific technologies based on their potential impact. [ABSTRACT FROM AUTHOR]

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The grouting parameters for cyclic grouting are often set based on pressure grouting or experience, which makes it difficult to achieve optimal compactness. To better comprehend the distribution of the cement slurry and improve grouting compactness, the flow mechanism was analyzed to determine the key grouting parameters. Subsequently, a cyclic grouting experiment was conducted, and the grouting compactness was detected using the impact‐echo method. For further study, a 2D numerical model was established, and the influence of grouting parameters on compactness was investigated. The results revealed that grouting compactness was influenced by the duct length, slurry viscosity, and grouting pressure. However, based on the technical specifications, the impact of slurry viscosity on compactness was found to be minimal. Longer ducts posed a greater challenge to achieving dense grouting and required higher grouting pressures. Nevertheless, when the duct length exceeded 330 m, attaining dense grouting became increasingly difficult. Finally, grouting pressures were recommended for ducts of varying lengths by considering both grouting compactness and cost. The results of this study offer guidance for the practical applications of cyclic grouting in engineering. [ABSTRACT FROM AUTHOR]

Thanks to the recent development of innovative instruments and software with high accuracy and resolution, 3D modelling provides useful insights in several sectors (from industrial metrology to cultural heritage). Moreover, the 3D reconstruction of objects of artistic interest is becoming mandatory, not only because of the risks to which works of art are increasingly exposed (e.g., wars and climatic disasters) but also because of the leading role that the virtual fruition of art is taking. In this work, we compared the performance of four 3D instruments based on different working principles and techniques (laser micro-profilometry, structured-light topography and the phase-shifting method) by measuring four samples of different sizes, dimensions and surface characteristics. We aimed to assess the capabilities and limitations of these instruments to verify their accuracy and the technical specifications given in the suppliers' data sheets. To this end, we calculated the point densities and extracted several profiles from the models to evaluate both their lateral (XY) and axial (Z) resolution. A comparison between the nominal resolution values and those calculated on samples representative of cultural artefacts was used to predict the performance of the instruments in real case studies. Overall, the purpose of this comparison is to provide a quantitative assessment of the performance of the instruments that allows for their correct application to works of art according to their specific characteristics. [ABSTRACT FROM AUTHOR]

The Avalanche Terrain Exposure Scale (ATES) is an avalanche terrain classification system used to assess and communicate the exposure of backcountry terrain to the threat from avalanches, independent of daily hazard conditions. Commonly known as terrain ratings, these classifications are determined by an analysis of individual terrain parameters, which are then systematically combined to produce a single rating. The ATES model includes technical specifications for assessing terrain as well as corresponding communication scales for effectively sharing ratings with different kinds of backcountry users. ATES ratings are found in guidebooks and route descriptions or displayed spatially on maps. The system was originally introduced in Canada in 2004 as a risk management tool in conventional avalanche safety practices for public recreation and workplace avalanche safety. This paper introduces ATES v.2, an update to the system that expands the original scale from three levels to five by including Class 0 – Non-Avalanche Terrain, and Class 4 – Extreme Terrain. The original ATES v.1/04 and the ATES Zoning Model are merged into a single, five-level, updated version of ATES. ATES ratings can be applied as Areas, Zones, Corridors, or Routes and then communicated using models for backcountry travel and waterfall ice climbing. [ABSTRACT FROM AUTHOR]

In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users' experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm. [ABSTRACT FROM AUTHOR]

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems. [ABSTRACT FROM AUTHOR]

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements. [ABSTRACT FROM AUTHOR]

The regional coverage satellite constellations are in demand for fulfilling earth observation needs of specific user regions of interest as opposed to global coverage thus enhancing the revisit period for the area. In this paper, we present a few key mission aspects of the regional coverage constellation designed and operated by the Indian Space Research Organization (ISRO) in low earth orbit. ISRO developed three X-Band Synthetic Aperture Radar (SAR) Earth Observation satellites and placed them in three inclined orbit planes to enhance the Indian region's coverage. This is India's first inclined orbit constellation designed for remote sensing applications in low earth orbit. The satellites were built around a standard bus carrying a Synthetic Aperture Radar payload with a deployable Radial Rib Antenna (RRA). They were designed to achieve a high agility capability. As SAR sensors are capable of all-weather, day, and night imaging; thus, most suitable in inclined orbits where the illumination conditions vary continuously for imaging. Though the inclined orbits provide more revisit opportunities to the region of interest, their characteristics pose challenges in mission design and operations. Though the mission design and planning involved a host of elements, this paper brings out the details of a few key mission analysis elements, planning aspects and operations that were critical in the course of various mission phases. The mission analysis included mission-specific requirements like launch windows selection for the constellation and reference attitude generation specific to inclined orbit geometry. Mission planning had to take care of Radial Rib Antenna (RRA) deployment requirements which were very critical for the mission, apart from taking care of individual subsystem requirements. The SAR payload data product quality and targeting accuracy are very sensitive to the satellite image point range (vis-a-vis SAR operation data window). Extensive post-launch calibration activity was carried out to meet all these mission requirements and data product specifications. The challenge was in fixing the payload beam alignment and fixing the uncertainties in onboard and ground computations. The paper also describes the methods of fixing all possible error sources affecting the high-resolution SAR payload performance. • The regional coverage satellite constellations are in demand for area targets coverage. • Inclined orbit constellations are more suitable for regional coverage using SAR sensors. • Mission operations plans need to consider all the specific requirements of spacecraft systems. • Pre-launch and post -launch calibrations of sensors is mandatory for achieving system performance. [ABSTRACT FROM AUTHOR]