Abstract: Very Low Earth Orbit (VLEO) satellites, operating at altitudes below 450km, provide tremendous potential in the domain of remote sensing. Their proximity to Earth of-fers high resolution, low latency, and rapid revisit rates, allowing continuous moni-toring of dynamic systems and real-time delivery of vertically integrated earth ob-servation products. Nonetheless, the application of VLEO is not yet fully realized due to numerous complexities associated with VLEO satellite development, considering atmospheric drag, short satellite lifetimes, and social, political and legal regulatory fragmentation. This paper reviews the recent technological developments supporting sustainable VLEO operations with regards to aerodynamic satellite design, atomic oxygen barriers, and atmospheric-breathing electric propulsion (ABEP). Furthermore, the paper pro-vides an overview of the identification of regulatory and economic barriers that extort additional costs for VLEO ranging from frequency band allocation and space traffic management to life-cycle cost and uncertain commercial demand opportunities. Nevertheless, the commercial potential of VLEO operations is widely acknowledged, and estimated to lead to an economic turnover in the order of 1.5 B$ by 2030. Learning from the literature and prominent past experiences such as the DISCOVERER and the CORONA program, the study identifies key gaps and proposes a roadmap to sustainable VLEO development. The proposed framework emphasizes modular and serviceable satellite platforms, hy-brid propulsion systems, and globally harmonized governance in space. Ultimately, public-private partnerships and synergies across sectors will determine whether VLEO systems become part of the broader space infrastructure unlocking new capabilities for near-Earth services, environmental monitoring, and commercial innovation at the edge of space.

Abstract: Remote sensing image super-resolution (RSISR) aims to reconstruct high-resolution images from low-resolution observations of remote sensing data to enhance the visual quality and usability of remote sensors. Real world RSISR is challenging owing to the diverse degradations like blur, noise, compression, and atmospheric distortions. We propose hierarchical multi-task super- resolution framework including degradation-aware modeling, dual-decoder reconstruction, and static regularization-guided generation. Speciffcally, the degradation-wise module adaptively characterizes multiple types of degradation and provides effective conditional priors for reconstruction. The dual-doder platform incorporates both convolutional and Transformer branches to match local detail preservation as well as global structural consistency. Moreover, the static regularizing guided generation introduces prior constraints such as total variation and gradient consistency to improve robustness to varying degradation levels. Extensive experiments on two public remote sensing datasets show that our method achieves performance that is robust against varying degradation conditions.

Abstract: In this study, a detection framework is presented and evaluated that integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an effort to increase overall accuracy, robustness, as well as false-alarm reduction. To this end, sixteen (16) ML and deep learning (DL) models are employed on an internet of things (IoT) sensor dataset. Moreover, a range of YOLO models, such as older versions (YOLOv5n, YOLOv8n), as well as newer versions (YOLOv10n, YOLOv11n, YOLOv12n) are employed on an image-label based dataset. Model selection initially prioritizes lightweight architectures that are suitable for resource-constrained edge devices. Afterwards, the selected models are evaluated via well-known metrics, such as parameter count, F1-score/mean average precision (mAP) and real-time inference latency. In the same context, explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) for ML models and LIME (Local Interpretable Model-agnostic Explanations) for the YOLO detectors, are integrated to the platform as well. According to the presented results, the Explainable Sensor Fusion (ESF) achieves decent performance metrics on a resource-constrained hardware device, demonstrating a viable, explainable, and highly efficient solution for real-time smoke and fire emergency response in industrial environments.

Abstract: Ensuring the required microclimate parameters is the most critical task in hot climates. In pig farms, air cooling is provided by means of steam-compression chillers or water-evaporative cooling, which is the simplest way to cool the air. The implementation of water-evaporative cooling depends largely on the interaction of the media involved in this process. The paper considers the process of interaction of cooling water with the surface of a cellular polycarbonate heat exchanger. A mathematical model describing the process of wetting the sprayed surface of the heat exchanger is obtained. The dependence of theoretical water flow rate to provide air cooling in a given operation mode is determined. Production tests of recuperative heat recovery unit with heat exchanger made of cellular polycarbonate equipped with water evaporative cooling system were carried out. The efficiency of water-evaporative cooling of air has been determined, which was reflected in the temperature reduction by 6.3 °C. Characteristic operating modes of the unit, depending on the cooling water flow rate, providing effective air cooling are revealed.

Abstract: Many quality characteristics of products or services are commonly evaluated on ordinal scales with a finite number of categories. A systematic analysis of categorical variables collected over time may be very useful for a profitable management strategy. In order to measure customer satisfaction or quality improvement in a process, two or more quality characteristics are often conjointly measured and summarized by suitable indexes. A common practice suggests evaluating a synthetic index by mapping each outcome of a multivariate ordinal variable into numbers. This procedure is not always legitimate from the measurement theory point of view. Recently the author read the paper “Synthesis maps for multivariate ordinal variables in manufacturing” and found in it many ideas about Control Charts for Services. We analyse them from a “scientific point of view” to compare the authors findings with ours. The analysis shows the “same (=equivalent)” results for the “linguistic control charts” and different results for the “numeric Control Charts”: the cause is that they do not use correctly the Theory. The Control Limits in the Shewhart CCs are based on the Normal Distribution (Central Limit Theorem, CLT) and are not valid for non-normal distributed data: consequently, the decisions about the “In Control” (IC) and “Out Of Control” (OOC) states of the process are wrong. The Control Limits of the CCs are wrongly computed, due to unsound knowledge of the fundamental concept of Confidence Interval. Minitab and other software e (e.g. JMP, SAS) use the “T Charts”, claimed to be a good method for dealing with “rare events”, but their computed Control Limits of the CCs are wrong. The same happens for the Confidence Limits of the parameters of the distribution involved in the papers (Weibull, Inverse Weibull, Gamma, Binomial, Maxwell).

Abstract: The ground impedance (insulation resistance Risol) of photovoltaic (PV) modules is usually measured only in the dry state, even though arrays frequently operate under dew-wet or rain-wet conditions, when leakage paths can change. We measured dry insulation resistance Rdry and IEC 61215 MQT 15 wet leakage resistance Rwet for N = 37 field-aged crystalline-silicon modules from utility-scale plants and related the results to the IEC 40 MΩ·m² criterion (Rwet·A ≥ 40). The measurements used 1000 V DC and a 2 min dwell; Rwet was obtained in a salted bath with a solution resistivity < 3500 Ω·cm. The median Rdry was 42.4 GΩ, whereas the median Rwet was 462.5 MΩ, resulting in a median Rdry/Rwet ratio of ~110×. Three modules (8.1%) failed the 40 MΩ·m² limit already in the dry state, whereas eight modules (21.6%) failed under IEC-wet conditions; five were dry-pass/wet-fail cases that would have passed dry screening. For a representative area A = 1.8 m², a practical conservative dry triage threshold of approximately 55.5 GΩ identifies modules needing IEC-wet verification rather than serving as a stand-alone limit. Overall, combining dry and IEC-wet measurements improves safety and supports sustainability through resource-efficient repowering/revamping and end-of-life decisions in large PV fleets, particularly in hot climates.

Abstract: Geopolymer mortars were produced from recycled concrete powder (RCP) and recycled brick powder (RBP) at a 30/70 wt% ratio, activated with a hybrid alkaline solution (NaOH/Na₂SiO₃/KOH) and reinforced with sisal (Agave) fibers at 0–2 wt%. Mechanical performance (compression and flexural) and microstructure–phase evolution were as-sessed by XRD, FTIR, and SEM-EDS after low-temperature curing. Sisal addition de-livered a strength–toughness balance, with an intermediate dosage (~1–1.5 wt%) providing the best overall performance; higher dosages induced packing loss and fiber clustering. Microstructural evidence indicates the coexistence and co-crosslinking of N-A-S-H and C-(A)-S-H gels promoted by the RCP, which densifies the matrix and enhances fiber–matrix anchorage. Fractographic features support a crack-bridging/pull-out mechanism responsible for the improvement without penaliz-ing early-age strength. The study identifies a practical advantage of sisal reinforcement in RCP/RBP geopolymer mortars and links it to gel chemistry and interfacial phenom-ena, providing a reproducible pathway toward fiber-reinforced, low-embodied-carbon geopolymers derived from construction and demolition waste and suitable for durable construction applications.

Abstract: This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the time domain, enabling waveform reconstruction using only a ramp generator, an analog comparator, and a timer capture module. A theoretical framework is developed to formalize the voltage-to-time mapping, derive expressions for resolution and error, and identify conditions that ensure monotonicity and single-crossing behavior. Simulation results demonstrate high-fidelity reconstruction for both periodic and non-periodic signals, including real photoplethysmographic (PPG) waveforms, with errors approaching the theoretical quantization limit. A hardware implementation on a PSoC 5LP microcontroller confirms the practicality of the method under realistic operating conditions. Despite ramp nonlinearity, comparator delay, and sensor noise, the system achieves effective resolutions above 12 bits using only native mixed-signal peripherals and no conventional ADC. These results show that accurate waveform reconstruction can be obtained from purely temporal information, positioning time-encoded sensing as a viable alternative to traditional amplitude-based conversion. The minimal analog front end, low power consumption, and scalability of timer-based processing highlight the potential of the proposed approach for embedded instrumentation, distributed sensor nodes, and biomedical monitoring applications.

Abstract:

Multi-component polymer hydrogels present complex physiochemical interactions that make accurate compositional analysis challenging. This study evaluates three analytical techniques: Nuclear Magnetic Resonance (NMR), Advanced Polymer Chromatography (APC), and Thermogravimetric Analysis (TGA) to quantify polyvinyl alcohol (PVA) and polyethylene glycol (PEG) content in hybrid freeze-thaw derived PVA/PEG/PVP hydrogels. Hydrogels were synthesized using an adapted freeze–thaw method across a wide range of PVA:PEG ratios, with PVP included at 1 wt% to assess potential intermolecular effects. NMR and APC reliably quantified polymer content with low average errors of 2.77% and 2.01%, respectively, and were unaffected by phase separation or hydrogen bonding within the composite matrix. TGA enabled accurate quantification at PVA contents ≤62.5%, where PEG and PVA maintained distinct thermal decomposition behaviors. At higher PVA concentrations, increased hydrogen bonding and crystalline restructuring, confirmed by FTIR through shifts near 1140 cm⁻¹ and significant changes in the –OH region, altered thermal profiles and reduced TGA accuracy. Together, these findings establish APC as a high-throughput alternative to NMR for multi-component polymer analysis and outline critical thermal and structural thresholds that influence TGA-based quantification. This work provides a framework for characterizing complex polymer networks in biomedical hydrogel systems.

Abstract: In this paper, a wireless network architecture is considered that combines double Intelligent Reflecting Surfaces (IRSs), Energy Harvesting (EH), and Non-Orthogonal Multiple Access (NOMA) with cooperative relaying (C-NOMA), to leverage the performance of Non-Line-of-Sight (NLoS) communication and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behaviour and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in terms of spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication, compared to conventional systems, thereby facilitating green communication systems.

Abstract: The South Africa national programme aims to enhance energy in informal settlements by maximising access to energy, alleviating poverty, and promoting urban inclusion. Nevertheless, a high rate of unlawful connections and old distribution systems has been identified in municipalities already experiencing financial poverty. This article assesses the suitability of electrifying informal settlements, mainly by funding capital programmes with grants. These programmes have beneficial effects on municipal revenue, non-technical electricity losses (NTLs), network destruction, and sustainability in the long run. Based on recent studies, city reports, and case studies in large cities, this paper concludes that electrification without commensurate investment in revenue protection systems, reinforced infrastructure, and institutional changes drives losses in revenues and leads to additional maintenance burdens. This article ends with policy guidelines, which aim to unify the process of electrification with sustainable municipal revenue recovery.

Abstract:

The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents a flexible, portable, and expandable waste-to-energy concept that integrates gasification and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modeling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB – Technical University of Ostrava, Czech Republic: (i) a primary gasification facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modeled and simulated using in-house software developed in Python, employing stoichiometric balances, symbolic regression, and polynomial regression to represent chemical reactions and energy flows. The findings demonstrate that transparent models—such as stoichiometric modeling combined with interpretable machine learning—can accurately reproduce the operational behavior of waste-to-energy processes. Gasification is optimized for hydrogen generation and electricity production via fuel cells, whereas pyrolysis favors liquid fuel yield with syngas as a by-product. Molar mass relations are applied to ensure consistent conversion between mass and volume across gasification, pyrolysis, and combustion pathways, maintaining the conservation of mass. Overall, the integration of stoichiometric balance models with symbolic and polynomial regression provides a reliable and interpretable framework for simulating real waste-to-energy systems. The current results, based on bio-wood waste from the Czech Republic, validate the proposed methodology, which is made openly available to promote transparency, reproducibility, and further advancement of sustainable waste-to-energy technologies.

Abstract: This study introduces an all-fiber optic sensing network based on fiber Bragg grating (FBG) technology for structural health monitoring (SHM) of launch vehicle payload fairings un-der extreme thermo-mechanical conditions. A wavelength–space dual-multiplexing ar-chitecture enables full-field strain and temperature monitoring with minimal sensor de-ployment. Structural deformations are reconstructed from local measurements using the inverse finite element method (iFEM), achieving sub-millimeter accuracy. High-temperature experiments verified that FBG sensors maintain a strain accuracy of 0.8 με at 500 °C, significantly outperforming conventional sensors. Under 15 MPa mechanical loading and 420 °C thermal shock, the fairing structure exhibited no damage propagation. The sensing system captured real-time strain distributions and deformation profiles, con-firming its suitability for aerospace SHM. The combined use of iFEM and FBG enables high-fidelity, large-scale deformation reconstruction, offering a reliable solution for reusa-ble aerospace structures operating in harsh environments.

Abstract: New nuclear power plant (NPP) designs, particularly advanced reactors and small modular reactors (SMRs) are expected to be highly automated, changing job demands and shifting the roles and responsibilities of operators. The expanded capabilities of machines and their more prominent role in plant operation means that operators need new information to support effective human-automation teaming and the maintenance of situation awareness. To understand the impact of new automation and artificial intelligence (AI) technology in NPP control rooms, a systematic literature review (SLR) on function allocation (FA) methods was conducted. This SLR focused on four areas. (1) Identifying the prevalence of quantitative, qualitative, and mixed methodologies. (2) Developments in levels of automation frameworks. (3) Revisions to Fitts List. (4) Ena-bling factors for improved access to data-driven approaches. The review was limited to work occurring after 1983, when the U.S. Nuclear Regulatory Commission published research on FA [1]. The results of the review demonstrate that many of the post-1983 methods are qualitative and descriptive. The review also identified several themes for managing human out of the loop issues. The discussion closes with proposed future work leveraging large language models and simulator-based approaches to enhance existing FA methods.

Abstract: The objective of the article is to present investigates the feasibility of driver-state assessment in a real automotive environment using a mobile long-wave infrared (LWIR) thermal camera. Unlike visible-spectrum systems, thermal imaging provides illumination-invariant and temperature-dependent information that is particularly advantageous inside a vehicle, where lighting conditions vary substantially. A handheld microbolometer (UTi260M) was used to record thermal video of a driver during prolonged, monotonous driving with a stabilized cabin temperature. Pixel-wise temperature reconstruction, spatial noise estimation, uniformity analysis, and NETD approximation were applied to evaluate thermal image quality and to quantify thermophysiological changes associated with drowsiness. The thermal recordings revealed characteristic pre-sleep markers, including head droop, reduced neuromuscular correction, elevated and spatially uniform facial temperature, and diminished thermal variability. These patterns correspond to known physiological responses to fatigue, reduced sympathetic activation, and warm cabin exposure. The analysis demonstrates that mobile thermal imaging can reliably capture early indicators of declining vigilance and can support the development of non-contact driver-monitoring systems. The findings further suggest that integrating temperature-driven alerting thresholds into mobile applications may provide an additional preventive mechanism against drowsiness-related accidents.

Abstract: Aiming at continuum robots with high flexibility but poor stiffness, which limits its application in certain high-precision and high-load occasions, and the traditional method of changing stiffness has the problems of complicated structure, small range and slow response, etc., and this paper proposes a stiffness adjustment method based on Twisted Multi-String Actuators (hereinafter referred to as TSA) for bionic spine-like continuum robots. Firstly, a bionic spine-like configuration design is proposed to accommodate the variable stiffness method of the force-locking. Secondly, the proposed TSA variable stiffness method is theoretically analysed in terms of geometrical relationship and stiffness to provide a basis for constructing other mathematical models such as its string-twisted. Finally, an experimental prototype was constructed for flexibility testing, and then the experiments of the TSA variable stiffness method under the conditions of two/three/four-strand string were carried out to investigate the retraction and stiffness characteristics under different numbers of torsion turns and different loads, respectively. The results demonstrate that the stiffness of the robot increases with the TSA method, and the increase in the number of string strands improves the failure point of the robot, and the characteristic curves show that the design and model in this paper are more effective than the traditional force-locking design with single string. The design is simple, responsive and has a large adjustment range, which provides a reference value for the study of the stiffness of continuum robots.

Abstract: To address the challenges of insufficient frequency regulation resources and diverse response capabilities in the Yunnan power grid caused by large-scale integration of renewable energy, this paper proposes a cooperative frequency regulation strategy for a hybrid energy storage system incorporating electrolytic aluminum load. First, the frequency regulation model is established for the integrated system comprising electrolytic aluminum load, abandoned mine pumped storage power station, and electrochemical energy storage. A frequency regulation method for electrochemical energy storage is designed, considering control mode weighting factors and state-of-charge (SOC) recovery characteristics. Subsequently, an improved filter with variable filtering time constants is developed based on the area control error (ACE). The high-frequency and low-frequency signals output by the filter are compensated by electrochemical energy storage and abandoned mine pumped storage, respectively. Furthermore, a frequency regulation strategy accounting for frequency regulation zone division is designed. Finally, simulation results under typical scenarios demonstrate that the proposed strategy effectively improves the SOC characteristics of electrochemical energy storage and enhances the frequency regulation performance of the hybrid energy storage system (HESS), while preventing overcharging and over discharging to extend the lifespan of energy storage devices.

Abstract: This study evaluated the life-cycle cost and financial sustainability of a fecal sludge management system using planted drying beds and constructed wetlands in Vientiane Capital, Lao PDR, with a focus on resource recovery from sludge-based fertilizers. Primary data were obtained from semi-structured interviews with key stakeholders and multi-year operational and financial records, complemented by regional benchmarking against com-parable systems in Southeast Asia (SEA). A 15-year life-cycle cost analysis, applying dis-count rates of 3–7% and inflation rates of 3–5%, encompassed capital, opera-ton-and-maintenance, replacement, and end-of-life costs, alongside a sensitivity analysis of key cost and revenue drivers. The system’s annualized cost of USD 237,072 contrasts with revenues of USD 100,822 per year from treatment fees and fertilizer sales, leaving a persistent financing gap of USD 136,250 per year (57.5% of total costs). Although unit treatment costs fall within the regional mid-range, the cost recovery rate of 42.5% is 1.2–1.8 times lower than that in comparable Southeast Asian cities, where cross-subsidies via water bills, stronger regulatory enforcement, and more mature fertilizer markets improve financial performance. The findings indicate that resource recovery alone cannot ensure financial self-sufficiency and highlight the need for integrated financing strategies, regulatory reforms to increase regular fecal sludge emptying, and expanded markets for re-source recovery products. Keywords: Fecal sludge management; life cycle cost analysis; resource recovery; planted drying beds with constructed wetlands; urban sanitation services; financial gaps.

Abstract:

The functional integration of the upper-limb prosthesis is critical for long-term user satisfaction, yet high rates of device abandonment persist. Primary factors contributing to this trend are high cognitive load and difficulties associated with learning muscle control. To address these challenges, a proposal for the development and preliminary evaluation of an Extended Reality (XR) training scenario is presented. The prototype uses an adaptation of a PPG sensor to measure residual limb muscle activity, mapping these signals to control a virtual prosthetic hand. The XR environment represents a controlled platform for trainees to practice gripping in a variety of virtual objects. The approach allows real-time biofeedback enhancing control for the user, aiming to establish a more effective training to improve the adoption and functional outcomes of upper-limb prostheses.

Abstract: Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the Urban Heat Island effect and enhancing stormwater man-agement, alongside benefits for public health and biodiversity. Effective GI imple-mentation remains challenging, particularly in dense, rapidly urbanized Mid Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project. The project aims to bridge the theo-ry-practice gap, enabling pilot implementations in multiple Italian Mid Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, traf-ficked urban area projected to face "extremely strong heat stress" by 2050. Design in-terventions included spatial optimization, strategic species replacement, creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area, a measurable reduction in the UTCI index, a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces.