Search Results (206)

Weighted belief propagation (WBP) for the decoding of linear block codes is considered. In WBP, the Tanner graph of the code is unrolled with respect to the iterations of the belief propagation decoder. Then, weights are assigned to the edges of the resulting recurrent network and optimized offline using a training dataset. The main contribution of this paper is an adaptive WBP where the weights of the decoder are determined for each received word. Two variants of this decoder are investigated. In the parallel WBP decoders, the weights take values in a discrete set. A number of WBP decoders are run in parallel to search for the best sequence- of weights in real time. In the two-stage decoder, a small neural network is used to dynamically determine the weights of the WBP decoder for each received word. The proposed adaptive decoders demonstrate significant improvements over the static counterparts in two applications. In the first application, Bose–Chaudhuri–Hocquenghem, polar and quasi-cyclic low-density parity-check (QC-LDPC) codes are used over an additive white Gaussian noise channel. The results indicate that the adaptive WBP achieves bit error rates (BERs) up to an order of magnitude less than the BERs of the static WBP at about the same decoding complexity, depending on the code, its rate, and the signal-to-noise ratio. The second application is a concatenated code designed for a long-haul nonlinear optical fiber channel where the inner code is a QC-LDPC code and the outer code is a spatially coupled LDPC code. In this case, the inner code is decoded using an adaptive WBP, while the outer code is decoded using the sliding window decoder and static belief propagation. The results show that the adaptive WBP provides a coding gain of 0.8 dB compared to the neural normalized min-sum decoder, with about the same computational complexity and decoding latency. Full article

Green hydrogen will play a crucial role in the future of emission reduction in air traffic in the long-term, as it will completely eliminate CO₂ emissions and significantly reduce other pollutants such as contrails and nitrogen oxides. Hydrogen offers a promising alternative to kerosene for short- and medium-haul flights, particularly through direct combustion and hydrogen fuel cell technology in new aircraft concepts. Against the background of the immense capital-intensive infrastructure adjustments that are required at airports for this purpose and the simultaneously high future hydrogen demand for the shipping industry, this paper analyses the emission savings potential in Europe if airports near seaports would switch to hydrogen-powered flight connections. Full article

THz communication stands as a pivotal technology for 6G networks, designed to address the critical challenge of data demands surpassing current microwave and millimeter-wave (mmWave) capabilities. However, realizing Tbps and kilometer-range transmission confronts the “dual attenuation dilemma” comprising severe free-space path loss (FSPL) (>120 dB/km) and atmospheric absorption. This review comprehensively summarizes our group′s advancements in overcoming fundamental challenges of long-distance THz communication. Through systematic photonic–electronic co-optimization, we report key enabling technologies including photonically assisted THz signal generation, polarization-multiplexed multiple-input multiple-output (MIMO) systems with maximal ratio combining (MRC), high-gain antenna–lens configurations, and InP amplifier systems for complex weather resilience. Critical experimental milestones encompass record-breaking 1.0488 Tbps throughput using probabilistically shaped 64QAM (PS-64QAM) in the 330–500 GHz band; 30.2 km D-band transmission (18 Gbps with 543.6 Gbps·km capacity–distance product); a 3 km fog-penetrating link at 312 GHz; and high-sensitivity SIMO-validated 100 Gbps satellite-terrestrial communication beyond 36,000 km. These findings demonstrate THz communication′s viability for 6G networks requiring extreme-capacity backhaul and ultra-long-haul connectivity. Full article

In high-elevation mining areas, the roadbeds of certain surface ore haul roads are predominantly composed of sandstone. These sandstones are exposed to cold climatic conditions for long periods and are highly susceptible to erosion by the effects of freeze–thaw, which can degrade their support properties. This paper investigates the mechanism of strength deterioration of sandstone containing prefabricated cracks under cyclic loading and unloading after experiencing freeze–thaw. Sandstone specimens containing prefabricated cracks were prepared and subjected to 0, 20, 40, 60, and 80 freeze–thaw cycle tests. The strength changes were tested, and the crack extension process was analyzed using numerical simulation techniques. The study results show the following: 1. The wave propagation speed within the sandstone is more sensitive to changes in the number of freeze–thaw cycles. In contrast, mass damage shows significant changes only when more freeze–thaw cycles are experienced. 2. As the number of freeze–thaw cycles increases, the frequency of energy release from the numerical model accelerates. 3. The trend of the Cumulative Strain Difference (${\epsilon }_c$) reflects that the plastic strain difference between numerical simulation and actual measurement gradually decreases with increasing stress cycle level. 4. With the increase in freeze–thaw cycles, the damage morphology of the specimen undergoes a noticeable change, which is gradually transformed from monoclinic shear damage to X-shaped conjugate surface shear damage. 5. The number of tensile cracks dominated throughout the cyclic loading and unloading process, but with the increase in freeze–thaw cycles, the percentage of shear cracks increased. As the freeze–thaw cycles increase, sandstones are more inclined to undergo shear damage. These findings are important guidelines for road design and maintenance in alpine mining areas. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

An aircraft that has been carefully optimized for a single flight condition will tend to perform poorly at other flight conditions. For aircraft such as long-haul airliners, this is not necessarily a problem, since the cruise condition so heavily dominates a typical mission. However, other aircraft, such as Unmanned Aerial Vehicles (UAVs), may be expected to perform well at a wide range of flight conditions. Morphing systems may be a solution to this problem, as they allow the aircraft to adapt its shape to produce optimum performance at each flight condition. This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. By employing PCA, the geometric dimensionality of airfoil surfaces is reduced from 24 to 18 design variables while preserving 100% shape fidelity, thus establishing a compressed morphing parameterization space. A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. Additionally, Shapley Additive Explanation (SHAP) analysis reveals interpretable correlations between principal component modes and aerodynamic performances. Experimental results show that the optimized airfoil achieves a 54.66% increase in low-speed cruise lift-to-drag ratio and 10.90% higher climb lift compared to the baseline. Overall, the proposed framework not only enhances the adaptability of morphing airfoils across various low-speed flight conditions but also facilitates targeted surrogate refinement and efficient data acquisition in high-performance regions. Full article

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety. Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article

In light of the drawbacks of using fossil fuel, this paper investigates the competition between geographically spread charge point operators for future battery electric long-haul trucks along one of the busiest highways in Sweden. This is achieved using an agent-based model where trucks try to charge for a low price and still avoid queues in order to complete their transport mission. The charging need for a typical day at full electrification is derived from data from the Swedish Transport Administration. This typical day is simulated several times and in between these iterations the charge point operators adjust their prices and number of chargers, aiming to increase their profit. After a sufficiently long time of competition, a quasi-equilibrium is reached where, for example, prices and queueing times can be studied. The goal of the study is to estimate conditions for trucks and charge point operators in a future public fast-charging market. Assuming a price for electricity of 0.08 EUR/kWh, the results indicate that a system with low queuing problems is attainable with a mean price of 0.27 EUR/kWh or lower for public fast charging. It is also found that the behaviour of haulage companies, as a collective, can affect the future fast charging market to a great extent. If the hauliers are price-sensitive, they will be offered a low mean price, down to 0.11 EUR/kWh, but with queues, while if they are queue-sensitive, there will be almost no queues, but they will pay more to charge. Full article

Lake Maggiore is a deep subalpine lake that has been well studied since the last century thanks to a monitoring program funded by the International Commission for the Protection of Italian–Swiss Waters. The monitoring program comprises both abiotic and biotic parameters, including zooplankton pelagic organisms. In this study, we present a dataset of 15,563 records of population densities and standing stock biomass for zooplankton pelagic taxa recorded over 43 years (1981–2023). The long-term dataset is valuable for tracing changes in trophic conditions experienced by the lake during the last century (eutrophication and its reversal) and the impact of global warming. Zooplankton samples (Crustacea and Rotifera Monogononta) were collected within 0–50 m depth by vertical hauls with an 80 µm light plankton sampler. The sampling frequency was monthly, with the exception of the 2009–2012 period, which employed seasonal frequency. The estimation of zooplankton taxon abundance and of its standing stock biomass is crucial in order to quantify the flux of matter, energy, and pollutants up to the upper trophic levels of the food web. The dataset provided is also suitable for food web analysis because the zooplankton taxa have been classified according to their ecological roles (microphagous organisms; primary and secondary consumers). Full article

Background: With renewed interest in long-duration space missions, there is growing exploration into synthetic torpor as a countermeasure to mitigate physiological stressors. Sedative agents, particularly those used in clinical anesthesia, have been proposed to replicate aspects of natural torpor, including reduced metabolic rate, core temperature, and brain activity. Objectives: This systematic review aims to evaluate the potential of sedative agents to induce torpor-like states suitable for extended spaceflight. The review specifically investigates their pharmacokinetics, pharmacodynamics, and performance under space-related stressors such as microgravity and ionizing radiation. Methods: We conducted a comprehensive search across multiple databases (e.g., PubMed, Scopus, Web of Science) for studies published from 1952 to 2024. Eligible studies included experimental, preclinical, and clinical investigations examining sedative agents (especially inhalation anesthetics) in the context of metabolic suppression or space-relevant conditions. Screening, selection, and data extraction followed PRISMA guidelines. Results: Out of the screened records, 141 studies met the inclusion criteria. These were thematically grouped into seven categories, including torpor physiology, anesthetic uptake, metabolism, and inhalation anesthetics. Sedative agents showed variable success in inducing torpor-like states, with inhalation anesthetics demonstrating promising metabolic effects. However, concerns remain regarding delivery methods, safety, rewarming, and the unknown effects of prolonged use in space environments. Conclusions: Sedative agents, particularly volatile anesthetics, hold potential as tools for inducing synthetic torpor in space. Nevertheless, significant knowledge gaps and technical challenges persist. Further targeted research is required to optimize these agents for safe, controlled use in spaceflight settings. Full article

(1) Background: This narrative review examines in-vehicle tobacco smoke exposure among private, occupational, and commercial drivers, focusing on prevalence, nicotine biomarkers, and health consequences. (2) Methods: A comprehensive search on the PubMed, Scopus, and Embase databases was used to identify peer-reviewed, full-text, and English articles published between 2014 and 2024. Search terms were related to motor vehicles, tobacco smoke exposure, and drivers. Articles were selected for inclusion based on their relevance to in-vehicle smoking and second- or third-hand smoke exposure attributable to tobacco cigarettes through article title, abstract, and full-text screening. (3) Results: This review highlights the dangers of in-vehicle second- or third-hand smoke exposure, evidenced by the 17 articles included. Significant second-hand smoke exposure and biomarkers were revealed mostly among adolescents and children. However, a gap exists in addressing tobacco smoke exposure among occupational/commercial drivers, specifically, long-haul truck drivers (LHTDs), who have heightened exposure due to their work environment—the truck cabin—which may increase their lung cancer risk. (4) Conclusions: There is a significant literature gap regarding in-vehicle tobacco smoke exposure in occupational/commercial drivers. Future research should include nicotine biomarker usage to quantify nicotine exposures and smoking cessation intervention development tailored to LHTDs. Full article

The need to decarbonize the road transport sector is driving the evaluation of alternative solutions. From a long-term perspective, biomethane and e-methane are particularly attractive as green energy carriers and a part of the solutions for the sustainable freight on-road transport, as they offer significant CO₂-equivalent emissions savings in a net Well-to-Wheel assessment. However, to make methane-fuelled spark ignition (SI) heavy-duty (HD) engines competitive in the market, their efficiency must be comparable to the top-performing diesel applications that dominate the sector. To this end, dilution techniques such as exhaust gas recirculation (EGR) or lean air–fuel mixtures represent promising solutions. Within limits specific to the engine’s tolerance to the used strategy, charge dilution can improve thermal efficiency impact on the pumping and wall heat loss, and the heat capacity ratio (γ). However, their potential has never been explored in the case of methane SI HD engines characterized by a semi diesel-like combustion system architecture. This work presents an experimental study to characterize the energy and pollutant emission performance of a state-of-the-art SI HD gas single-cylinder engine (SCE) operating with EGR or with lean conditions. The engine type is representative of most HD powertrains used for long-haul purposes. The designed test plan is representative of the majority of on-road operating conditions providing an overview of the impact of the two dilution methods on the overall engine performance. The results highlight that both techniques are effective for achieving significant fuel savings, with lean combustion being more tolerable and yielding higher efficiency improvements (10% peak vs. 5% with EGR). Full article

The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system. Full article

Accurate and low-complexity phase estimation is crucial for optimal data recovery in coherent optical receivers, especially for applications in emerging scenarios such as low-margin optical networks, green networks, probabilistic-shaping modulation, the use of high-order modulation formats in hollow-core fibers, long-haul links operating at low signal-to-noise ratios, and free-space optical links in low-earth orbit satellites, among others. These new developments demand highly efficient and reliable data transmission methods, even under stringent conditions of minimal operational excess margin. This paper introduces an efficient pilot-assisted phase estimation strategy for coherent optical receivers, integrating robust interpolation techniques for quasi-optimal operation. Our approach significantly enhances phase estimation accuracy, addressing the unique challenges posed by these new scenarios. Through comprehensive simulations, we illustrate our method’s superiority over conventional methods, showcasing marked improvements in computational complexity and bit error rate. The results highlight the critical role of sophisticated interpolation in bolstering pilot-assisted phase estimation, offering a promising technique for optimizing performance in next-generation coherent optical receivers. Full article