Search Results (412)

PSR B1259-63/LS 2883 is a well-studied gamma-ray binary hosting a pulsar in a 3.4-year eccentric orbit around a Be-type star. Its non-thermal emission spans from radio to TeV energies, exhibiting a significant increase near the periastron passage. This paper is dedicated to the analysis of INTEGRAL observations of the system following its last periastron passage in June 2024. We aim to study the spectral evolution of this gamma-ray binary in the soft (0.3–10 keV) and hard (30–300 keV) X-ray energy bands. We performed a joint analysis of the data taken by INTEGRAL/ISGRI in July–August 2024 and quasi-simultaneous Swift/XRT observations. The spectrum of the system in the 0.3–300 keV band is well described by an absorbed power law with a photon index of $\Gamma =1.42\pm 0.03$. We place constraints on potential spectral curvature, limiting the break energy $E_{\mathrm{b}}>30$ keV for $\Delta \Gamma >0.3$ and cutoff energy $E_{\mathrm{cutoff}}>150$ keV at a 95% confidence level. For one-zone leptonic emission models, these values correspond to electron distribution spectral parameters of $E_{\mathrm{b},\mathrm{e}}>0.8$ TeV and $E_{\mathrm{cutoff},\mathrm{e}}>1.7$ TeV, consistent with previous constraints derived by H.E.S.S. Full article

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

The Earth Radiation Budget (ERB), a measure of the difference between incoming solar irradiance and outgoing reflected and emitted radiant energy, is a fundamental property of Earth’s climate system. The Libera satellite mission will measure the ERB’s outgoing components to continue the long-term climate data record established by NASA’s Clouds and the Earth’s Radiant Energy System (CERES) mission. In addition to ensuring data continuity, Libera will introduce a novel split-shortwave spectral channel to quantify the partitioning of the outgoing reflected solar component into visible and near-infrared sub-components. However, converting these split-shortwave radiances into the ERB-relevant irradiances requires the development of split-shortwave Angular Distribution Models (ADMs), which demand extensive angular sampling. Here, we show how Rotating Azimuthal Plane Scan (RAPS) parameters—specifically operational cadence and azimuthal scan rate—affect the observational coverage of a defined scene and angular space. Our results show that for a fixed number of azimuthal rotations, a relatively slow azimuthal scan rate of 0.5° per second, combined with more time spent in the RAPS observational mode, provides a more comprehensive sampling of the desired scene and angular space. We also show that operating the Libera instrument in RAPS mode at a cadence between every fifth day and every other day for the first year of space-based operations will provide sufficient scene and angular sampling for the observations to achieve radiance convergence for the scenes that comprise more than half of the expected Libera observations. Obtaining radiance convergence is necessary for accurate ADMs. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

A maximum entropy (ME) methodology was used to infer the Hubble constant from the temperature anisotropies in cosmic microwave background (CMB) measurements, as measured by the Planck satellite. A simple cosmological model provided physical insight and afforded robust statistical sampling of a parameter space. The parameter space included the spectral tilt and amplitude of adiabatic density fluctuations of the early universe and the present-day ratios of dark energy, matter, and baryonic matter density. A statistical temperature was estimated by applying the equipartition theorem, which uniquely specifies a posterior probability distribution. The ME analysis inferred the mean value of the Hubble constant to be about 67 km/sec/Mpc with a conservative standard deviation of approximately 4.4 km/sec/Mpc. Unlike standard Bayesian analyses that incorporate specific noise models, the ME approach treats the model error generically, thereby producing broader, but less assumption-dependent, uncertainty bounds. The inferred ME value lies within 1σ of both early-universe estimates (Planck, Dark Energy Signal Instrument (DESI)) and late-universe measurements (e.g., the Chicago Carnegie Hubble Program (CCHP)) using redshift data collected from the James Webb Space Telescope (JWST). Thus, the ME analysis does not appear to support the existence of the Hubble tension. Full article

This study investigates the complementary noise control capabilities of two passive jet noise mitigation strategies: a traditional ejector nozzle and a novel application of 3M™ Nextel™ 312 ceramic fabric as a thermal–acoustic liner on the central cone of a micro turbojet nozzle. Three nozzle configurations, baseline, ejector, and Nextel-treated, were evaluated under realistic operating conditions using traditional and advanced acoustic diagnostics applied to data from a five-microphone circular array. The results show that while the ejector provides superior directional suppression and low-frequency redistribution, making it ideal for far-field noise control, it maintains high total energy levels and requires structural modifications. In contrast, the Nextel lining achieves comparable reductions in overall noise, especially in high-frequency ranges, while minimizing structural impact and promoting spatial energy dissipation. Analyses in both the time-frequency and spatial–spectral domains demonstrate that the Nextel configuration not only lowers acoustic energy but also disrupts coherent noise patterns, making it particularly effective for near-field protection in compact propulsion systems. A POD analysis further shows that NEXTEL more evenly distributes energy across mid-order modes, indicating its role in smoothing spatial variations and dampening localized acoustic concentrations. According to these results, ceramic fabric linings offer a lightweight, cost-effective solution for reducing the high noise levels typically associated with drones and UAVs powered by small turbojets. When combined with ejectors, they could enhance acoustic suppression in compact propulsion systems where space and weight are critical. Full article

The transient operation of pump turbines generates significant flow-induced instabilities, prompting a comprehensive numerical investigation using the SST $k-\omega$ turbulence model to examine these instability effects throughout the complete operating range in turbine mode. This study specifically analyzes the evolutionary mechanisms of unsteady flow dynamics under ten characteristic off-design conditions while simultaneously characterizing the pressure fluctuation behavior within the vaneless space (VS). The results demonstrate that under both low-speed conditions and near-zero-discharge conditions, the VS and its adjacent flow domains exhibit pronounced flow instabilities with highly turbulent flow structures, while the pressure fluctuation amplitudes remain relatively small due to insufficient rotational speed or flow rate. Across the entire turbine operating range, the blade passing frequency (BPF) dominates the VS pressure fluctuation spectrum. Significant variations are observed in both low-frequency components (LFCs) and high-frequency, low-amplitude components (HF-LACs) with changing operating conditions. The HF-LACs exhibit relatively stable amplitudes but demonstrate significant variation in the frequency spectrum distribution across different operating conditions, with notably broader frequency dispersion under runaway conditions and adjacent operating points. The LFCs demonstrate significantly higher spectral density and amplitude magnitudes under high-speed, low-discharge operating conditions while exhibiting markedly reduced occurrence and diminished amplitudes in the low-speed, high-flow regime. This systematic investigation provides fundamental insights into the flow physics governing pump-turbine performance under off-design conditions while offering practical implications for optimizing transient operational control methodologies in hydroelectric energy storage systems. Full article

Optimizing artificial lighting in controlled-environment agriculture is crucial for enhancing crop productivity and resource efficiency. This study presents a spectral–spatial co-optimization strategy for LED lighting tailored to the physiological needs of Italian lettuce (Lactuca sativa L. var. italica). A miniature plant factory system was developed with dimensions of 400 mm × 400 mm × 500 mm (L × W × H). Seven customized spectral treatments were created using 2835-packaged LEDs, incorporating various combinations of blue and violet LED chips with precisely controlled concentrations of red phosphor. The spectral configurations were aligned with the measured absorption peaks of Italian lettuce (450–470 nm and 640–670 nm), achieving a spectral mixing uniformity exceeding 99%, while the spatial light intensity uniformity surpassed 90%. To address spatial light heterogeneity, a particle swarm optimization (PSO) algorithm was employed to determine the optimal LED arrangement, which increased the photosynthetic photon flux density (PPFD) uniformity from 83% to 93%. The system operates with a fixture-level power consumption of only 75 W. Experimental evaluations across seven treatment groups demonstrated that the E-spectrum group—comprising two violet chips, one blue chip, and 0.21 g of red phosphor—achieved the highest agronomic performance. Compared to the A-spectrum group (three blue chips and 0.19 g of red phosphor), the E-spectrum group resulted in a 25% increase in fresh weight (90.0 g vs. 72.0 g), a 30% reduction in SPAD value (indicative of improved light-use efficiency), and compared with Group A, Group E exhibited significant improvements in plant morphological parameters, including a 7.05% increase in plant height (15.63 cm vs. 14.60 cm), a 25.64% increase in leaf width (6.37 cm vs. 5.07 cm), and a 6.35% increase in leaf length (10.22 cm vs. 9.61 cm). Furthermore, energy consumption was reduced from 9.2 kWh (Group A) to 7.3 kWh (Group E). These results demonstrate that integrating spectral customization with algorithmically optimized spatial distribution is an effective and scalable approach for enhancing both crop yield and energy efficiency in vertical farming systems. Full article

This study presents a photonic integrated optical sensor based on a dual-polarization microring resonator with angular gratings on a silicon-on-insulator (SOI) waveguide, enabling simultaneous and precise refractive index (RI) and temperature measurements. Due to the distinct energy distributions for transverse electric ($\mathit{TE}$) and transverse magnetic ($\mathit{TM}$) modes in SOI waveguides, these modes show distinct sensitivity responses to the variation in ambient RI and temperature. Simultaneous measurements of both temperature and RI are enabled by exciting both these transverse modes in the microring resonator structure. Furthermore, incorporating angular gratings into the microring resonator’s inner sidewall extends the temperature measurement range by mitigating free spectral range limitations. This work presents a novel approach to dual-polarization microring resonators with angular gratings, offering an enhanced temperature measurement range and detection limit in optical sensing applications requiring an extended temperature range. The proposed structure is able to yield a simulated temperature measurement range of approximately 35 nm with a detection limit as low as $2.99\times {10}^{-5}$. The achieved temperature sensitivity is 334 pm/°C and RI sensitivity is 13.33 nm/RIU for the ${\mathit{TE}}_0$ mode, while the ${\mathit{TM}}_0$ mode exhibits a temperature sensitivity of 260 pm/°C and an RI sensitivity of 76.66 nm/RIU. Full article

Spectrally selective emitters (SSEs) have attracted considerable attention, because of radiative cooling, which could dissipate the heat from earth to outer space through the atmospheric window without any energy input. Intrinsically inorganic SSEs have significant advantages to other SSEs, such as the low fabrication cost due to the extremely simple structures and long life span under solar exposure. However, few inorganic materials can act as intrinsic SSEs due to the limited emissions in the atmospheric window. Here, we propose a strategy to design intrinsic SSEs by complementing the IR-active phonons in atmospheric window with anion groups. Accordingly, we demonstrate borates containing both [BO₃]³⁻ and [BO₄]⁵⁻ units can exhibit high emissivity within the whole atmospheric window, because the IR-active phonons of [BO₃]³⁻ units usually locate around 8 and 13 μm, while those of [BO₄]⁵⁻ units distribute in 9~11 μm. Furthermore, K₃B₆O₁₀Cl and BaAlBO₄ are selected as two examples to display their near-unity emissivity (>95%) within the whole atmospheric window experimentally. These results not only offer a new strategy for the design of intrinsic SSEs, but also endow wide band-gap borates containing both [BO₃]³⁻ and [BO₄]⁵⁻ units with great potential applications for radiative cooling. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

In this work, we present updated models of the spectral energy distributions (SEDs) for two high-frequency-peaked BL Lac objects (HBLs), that is, 1ES 0414+009 and 1ES 1959+650. The hard gamma-ray spectra observed during their flaring states suggest the presence of an additional emission component beyond the standard synchrotron self-Compton (SSC) scenario. We explore the possibility that this hard gamma-ray emission arises from inverse Compton (IC) scattering by Bethe–Heitler pairs produced along the line of sight, pointing to a more complex high-energy emission mechanism in these sources. Full article

This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at $R{e}_H=7.7\times {10}^5$. DMD enabled the identification of coherent structures in the near and far wake by isolating their individual oscillation frequencies and spatial energy distributions. These structures were classified into three regimes based on their underlying mechanisms: symmetry breaking, bubble pumping, and large-scale vortex shedding in range of $St\le 0.2$. The energy contributions of these flow regimes were quantified across different regions of the flow field and compared to the aerodynamic forces on the body. Additionally, the linear correlation between pressure and velocity components was examined using Pearson correlation coefficients of DMD spectral amplitudes. The locations of maximum and minimum correlation values, as well as their relationship to energy contributions, were identified and analyzed in detail. Full article

Leaf mass per area (LMA) represents the allocation of carbon resources per unit leaf area, which is closely related to the photosynthetic capacity of tree leaves. Clarifying the distribution features of LMA is very useful in understanding nutrient and energy transmission and photosynthetic capacity in the canopy. To this aim, the leaf samples of varied forest types were collected, and LMA and related spectral data were measured. The Partial Least Squares (PLS), Linear Mixed Models (LMM), Support Vector Machine Regression (SVM), and Random Forest (RF) methods were used to establish a new model of three-dimension LMA prediction by using vegetation index, DBH, and the vertical and horizontal position of leaves in this study. The results found that the LMA varies significantly with the change in the spatial position of the leaves and horizontal distance to the tree trunk. Statistically speaking, changes in LMA were not significantly related to the direction where the leaves were located. The best model of 3D LMA estimation was RF with a 10-fold R² value of 0.939. Compared to the RF model, the maximum and minimum of R² of 10-fold testing of other models increased by 23.75% and 55.87%. The results indicated that RF has a strong generalization ability and can predict the LMA distribution in 3D with a high accuracy. This study showed a reference for LMA 3D feature distribution and is helpful in clarifying the photosynthetic capacity of the canopy. Full article