Spectral Loss Research Articles

Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.

Multiscale Fusion of Panchromatic and Multispectral Images Based on Adaptive Iterative Filtering

This paper proposes an efficient and high-fidelity image fusion method based on adaptive smoothing filtering for panchromatic (PAN) and multispectral (MS) image fusion. The scale ratio reflects the ratio of spatial resolution between the panchromatic image and the multispectral image. When facing a multiscale fusion task, traditional methods are unable to simultaneously handle the problems of spectral resolution loss resulting from high scale ratios and the issue of reduced spatial resolution due to low scale ratios. To adapt to the fusion of panchromatic and multispectral satellite images of different scales, this paper improves the problem of the insufficient filtering of high-frequency information of remote sensing images of different scales by the classic filter-based intensity modulation (SFIM) model. It uses Gaussian convolution kernels instead of traditional mean convolution kernels and builds a Gaussian pyramid to adaptively construct convolution kernels of different scales to filter out high-frequency information of high-resolution images. It can adaptively process panchromatic multispectral images of different scales, iteratively filter the spatial information in panchromatic images, and ensure that the scale transformation is consistent with the definition of multispectral images. Using 15 common fusion methods, this paper compares the experimental results of ZY-3 with scale ratio 2.7 and SV-1 with scale ratio 4 data. The results show that the method proposed in this paper retains good spatial information for image fusion at different scales and has good spectral preservation.

SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.

Sound absorption using sonic crystals with coupled Helmholtz resonators

Sonic crystals are made up of a periodic arrangement of sound scatterers and absorbers that exhibit band-gap behaviour, allowing them to block out certain band of frequencies of sound (Bragg's law). This allows artificially engineered structures, through proper design of geometry and placements of scatterers, to achieve specific acoustic properties for sound absorption and noise mitigation. Embedding Helmholtz resonators into sonic crystals have been shown to improve the low-frequency attenuation performance as a combined system. In this study, we investigated the interaction of coupled Helmholtz resonators within a sonic crystal. Parametric studies were conducted using the finite element method on the spectral insertion loss of these sonic crystals with various arrangements of the Helmholtz resonators. The results show that sharp attenuation at specific frequencies may be achieved by proper tuning of these resonators, improving the low-frequency performance of the sonic crystals as a broadband sound absorber.

ZeRGAN: Zero-Reference GAN for Fusion of Multispectral and Panchromatic Images.

In this article, we present a new pansharpening method, a zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high spatial resolution panchromatic (PAN) images. In the proposed method, zero-reference indicates that it does not require paired reduced-scale images or unpaired full-scale images for training. To obtain accurate fusion results, we establish an adversarial game between a set of multiscale generators and their corresponding discriminators. Through multiscale generators, the fused high spatial resolution MS (HR MS) images are progressively produced from LR MS and PAN images, while the discriminators aim to distinguish the differences of spatial information between the HR MS images and the PAN images. In other words, the HR MS images are generated from LR MS and PAN images after the optimization of ZeRGAN. Furthermore, we construct a nonreference loss function, including an adversarial loss, spatial and spectral reconstruction losses, a spatial enhancement loss, and an average constancy loss. Through the minimization of the total loss, the spatial details in the HR MS images can be enhanced efficiently. Extensive experiments are implemented on datasets acquired by different satellites. The results demonstrate that the effectiveness of the proposed method compared with the state-of-the-art methods. The source code is publicly available at https://github.com/RSMagneto/ZeRGAN.

Stable PbS colloidal quantum dot inks enable blade-coating infrared solar cells

Infrared solar cells are more effective than normal bandgap solar cells at reducing the spectral loss in the near-infrared region, thus also at broadening the absorption spectra and improving power conversion efficiency. PbS colloidal quantum dots (QDs) with tunable bandgap are ideal infrared photovoltaic materials. However, QD solar cell production suffers from small-area-based spin-coating fabrication methods and unstable QD ink. Herein, the QD ink stability mechanism was fully investigated according to Lewis acid–base theory and colloid stability theory. We further studied a mixed solvent system using dimethylformamide and butylamine, compatible with the scalable manufacture of method-blade coating. Based on the ink system, 100 cm2 of uniform and dense near-infrared PbS QDs (~ 0.96 eV) film was successfully prepared by blade coating. The average efficiencies of above absorber-based devices reached 11.14% under AM1.5G illumination, and the 800 nm-filtered efficiency achieved 4.28%. Both were the top values among blade coating method based devices. The newly developed ink showed excellent stability, and the device performance based on the ink stored for 7 h was similar to that of fresh ink. The matched solvent system for stable PbS QD ink represents a crucial step toward large area blade coating photoelectric devices.Graphical

DL-PC algorithm for partially-connected hybrid precoding in massive MIMO systems

In hybrid precoding, a partially-connected (PC) architecture has aroused significant attention for its low hardware complexity. Unfortunately, the hybrid precoding with the PC architecture may suffer from a severe spectral efficiency loss. To overcome the loss, a deep learning-based hybrid precoding algorithm for the PC architecture is proposed in this paper. Firstly, the optimization problem of hybrid precoding is transformed into a neural network prediction problem. Furthermore, a convolutional neural network(CNN) is designed to predict the hybrid precoder, where the channel matrix is used as input. Especially, to meet the constraints of the hybrid precoder, two lambda layers are introduced into the CNN architecture. Specifically, a lambda1 layer is constructed to address the constant modulus constraint of the analog precoding matrix and the block diagonal constraint. Then, a lambda2 layer is constructed to optimize the digital precoding matrix and address the transmit power constraint. Finally, the vectorized hybrid precoding matrix is output by the well-trained network. Simulation results show that the proposed algorithm can enhance spectral efficiency and energy efficiency with lower computation time.

MGDIN: Detail Injection Network for HSI and MSI Fusion Based on Multiscale and Global Contextual Features

ABSTRACT Hyperspectral remote sensing images (HSI) are characterized as rich spectral information with low spatial resolution, and a cost-effective way for the spatial information supplement is the fusion to multispectral remote sensing images (MSI). This study proposed a detail injection network for HSI and MSI fusion based on multiscale and global contextual features (MGDIN), which extracts features of different scales using residual multi-scale convolution, and captures the contextual information and long-range dependencies via global contextual block. MGDIN improves the spatial and spectral qualities of fused images by minimizing a new loss function that considers content, spectral and edge losses. Experiments on five publicly available datasets (including Botswana, Pavia Centre, Pavia University, Washington DC Mall and Houston) show that MGDIN outperforms the popular algorithms in terms of fusion quality and learning ability. The new loss function is also better than the popular loss functions in the experiments on the Botswana data set.

Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Automatic modulation recognition (AMR) serves as a crucial component in domains such as cognitive radio and electromagnetic countermeasures, acting as a significant prerequisite for the efficient signal processing of receivers. Deep neural networks (DNNs), despite their effectiveness, are known to be vulnerable to adversarial attacks. This vulnerability has inspired the introduction of subtle interference to wireless communication signals—interference so minuscule that it is difficult for the human eye to discern. Such interference can mislead eavesdroppers into erroneous modulation pattern recognition when using DNNs, thereby camouflaging communication signal modulation patterns. Nonetheless, the majority of current camouflage methods used for electromagnetic signal modulation recognition rely on a global perturbation of the signal. They fail to consider the local agility of signal disturbance and the concealment requirements for bait signals that are intercepted by the interceptor. This paper presents a generator framework designed to produce perturbations with sparse properties. Furthermore, we introduce a method to reduce spectral loss, which minimizes the spectral difference between adversarial perturbation and the original signal. This method makes perturbation more challenging to monitor, thereby deceiving enemy electromagnetic signal modulation recognition systems. The experimental results validated that the proposed method significantly outperformed existing methods in terms of generation time. Moreover, it can generate adversarial signals characterized by high deceivability and transferability even under extremely sparse conditions.

Hyperspectral and multispectral remote sensing image fusion using SwinGAN with joint adaptive spatial-spectral gradient loss function

ABSTRACT Hyperspectral remote sensing image (HSI) fusion with multispectral remote sensing images (MSI) improves data resolution. However, current fusion algorithms focus on local information and overlook long-range dependencies. The parameter of network tuning prioritizes global optimization, neglecting spatial and spectral constraints, and limiting spatial and spectral reconstruction capabilities. This study introduces SwinGAN, a fusion network combining Swin Transformer, CNN, and GAN architectures. SwinGAN's generator employs a detail injection framework to separately extract HSI and MSI features, fusing them to generate spatial residuals. These residuals are injected into the supersampled HSI to produce the final image, while a pure CNN architecture acts as the discriminator, enhancing the fusion quality. Additionally, we introduce a new adaptive loss function that improves image fusion accuracy. The loss function uses L1 loss as the content loss, and spatial and spectral gradient loss functions are introduced to improve the spatial representation and spectral fidelity of the fused images. Our experimental results on several datasets demonstrate that SwinGAN outperforms current popular algorithms in both spatial and spectral reconstruction capabilities. The ablation experiments also demonstrate the rationality of the various components of the proposed loss function.

Variations in the intermediate wind region of the blue supergiant 55 Cygni

Context. The quantitative near-infrared (NIR) spectroscopic synthesis is an important technique for determining wind properties of massive stars. The Brα line is an excellent mass-loss tracer and provides valuable information on the physical conditions of intermediate-wind regions. The knowledge of the wind properties gained by studying the NIR lines could provide extra ingredients to the theory of line-driven winds, mainly because the standard theory does not predict observed properties of blue supergiants, such as high values for the β parameter (β> 2), low terminal velocities, and mass-loss variability. Aims. We seek to enhance our understanding of the wind properties of B supergiants. To this end, we propose analysing their NIR spectra over different epochs to study wind variability and its connection with phenomena arising from regions close to the photosphere. Methods. We present the first sets of multi-epoch high-resolution K- and L-band spectra of 55 Cyg acquired with the Gemini Near-InfraRed Spectrograph (GNIRS). We measured line equivalent widths and modelled the Brα line to derive (unclumped) mass-loss rates. Synthetic line profiles were computed for a homogeneous spherical wind by solving the radiative transfer equations in the co-moving frame for a multi-level atom in non-local thermodynamic equilibrium (NLTE). Results. We observe variations in the spectral lines originating in the upper photosphere and the wind. The perturbations, on average, have periods of ~13 and ~23 days; the latter is similar to that found previously from optical data (22.5 days). The NIR lines observed in 2013 are described with the same wind structure used to model a quasi-simultaneous observation in Hα. By contrast, from observations taken in 2015, we derived a higher mean mass-loss rate. Variations in the mass-loss rate are also detected within a few weeks. Interestingly, we find that the profile shape of the Hu14 line sets constraints on the mass loss. Moreover, we find the Mg II doublet in emission, which suggests a tenuous circumstellar gas ring or shell. Conclusions. The variability detected in the NIR H emission lines of 55 Cyg is related to changes in the mass-loss rate, which doubled its value between 2013 and 2015. Furthermore, the short-term variability (within three weeks) in the spectral lines and mass loss supports the hypothesis of strange-mode oscillations. This pilot project demonstrates the importance of comprehensive monitoring of blue supergiants’ variability to deeply understand the physical properties of their stellar winds and the role of pulsations in recurrently enhancing mass loss.

Solar Panel Study in the Desert Climate of Abu Dhabi, United Arab Emirates

The negative effects of climate change and global warming are no longer a futuristic issue to deal with. This is the result of the continued and extensive use of fossil fuels and subsequent greenhouse gasses emissions such as CO2. Earth has been warming at an alarmingly fast pace the last 40 years due to greenhouse gas emission and the trapping of heat in the atmosphere. To slow down and eliminate this unnatural warming rate a reduction in greenhouse gas emissions is essential. This is accomplished by an increase in the deployment and use of clean renewable energy. Solar energy continues to play a significant role in the clean energy shift due to the abundance and unlimited supply of energy provided by the sun. The use of solar energy is on an exponential rise thanks to recent government policies to reduce emissions. For example, solar energy production in the United Arab Emirates (UAE) from 2009-2019 shows an exponential rise starting in 2016 reaching almost 3500 GWh of production in 2019 [1]. The UAE is also home to the Mohammed Bin Rashid Al Maktoum solar park. The largest single site solar park the world. This project will help reduce more than 6.5 million tons of emissions and will have capacity of 5000 megawatts [2].The United Arab Emirates is an ideal region for solar due the amount of sunshine and with very few cloudy/rainy days. However, desert climates have their own unique challenges, such as extreme heat and soling. Temperatures can reach above 40 in summer months in the UAE with high humidity and effect of soiling from dust is not very well understood [3]. Also, what is important is finding the optimal angle and direction of roof top solar panels to maximize the power and limit these negative effects.In this work, we studied a real solar panel on the rooftop in Abu Dhabi. The solar panel was bought from a local vendor in the UAE. The solar panel is polysilicon with specs: 10W, Vmp 18V, Imp 0.56A, Voc 24.4V and ISC 0.69A under AM1.5G at 25°C. We placed the panel on a roof top in Abu Dhabi at Khalifa University. To find the optimal angle and location we tested the panels at 12noon to maximum sun power. We first fixed the angle around 22° based on literature, and then modified the direction from north, south, west and east to find which direction gave the peak. We found that south gave the highest power from the rest (10.32W). Then we changed from angle from this data from 0°-90° in steps of 10°. The data showed the maximum power is when panel is facing south with angle between 25°-30°. The optimum placement of solar panels is key in helping to mitigate the negative desert effects such at heat and soiling. In addition, to this study, we also studied the panel for a continuous period to see effects of soling and longer-term heat exposure. More on this data will be added to in the poster to be presented.In summary, the use for solar panels is key to help prevent the negative effect of climate change. In desert like conditions, while there is plenty of sunshine, the negative effects of soling and temperature is not fully studied yet. Finding the optimum, angle and direction is key it get the peak performance of the panel and helping to mitigate some of these negative effects. Also studying the long-term effects of the temperature and soiling is important. Finally, knowing the negative effects can help researchers find new materials and solutions for solar cells for desert like environments.[1] E. Agency, "Renewables Information," July 2021. [Online].[2] T. National, "Road to Net Zero," 09 May 2019. [Online][3] O. Albadwawi et al., "Quantification of spectral losses of Natural soiling and detailed microstructural analysis of Dust collected from Different locations in Dubai, UAE," 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC), 2019, pp. 3115-3118, doi: 10.1109/PVSC40753.2019.8980937. Figure 1

Embedding Fiber Bragg Grating Sensors in Carbon Composite Structures for Accurate Strain Measurement

Fiber Bragg Grating (FBG) sensors written by femtosecond laser pulses in polyamide-coated low bending loss optical fibers are succesfully embedded in carbon composite structures, following laminating and light resin moulding processes which optimize the size of each ply to address aesthetic, drapability and structural requirements of the final components. The sensors are interrogated by a tunable laser operating at around 1.55 μm and their response to temperature and strain variations is characterized in a thermally controlled chamber and by bending tests using suspended calibrated loads and a laser scanning system. Experimental results are in good agreement with simulations, confirming that the embedding process effectively overcomes potential issues related to FBG spectral distortion, birefringence and losses. In particular the effects of the composite material non homogeneity and FBG birefringence are investigated to evaluate their impact on the monitoring capabilities. A bi-material mechanical beam model is proposed to characterize the orthotropic laminates, pointing out better accuracy in estimating the applied load with respect to the classical homogeneous beam model. A comparative analysis, performed on different instrumented carbon composite samples and supported by theory, points out the repeatability of the FBG sensors embedding process and the effectiveness of the technology for real time accurate strain measurement. Based on such measurements damages and/or changes in local stiffness can be effectively detected, allowing for structural health monitoring of composite structures for applications in specific industrial fields like automotive and aerospace.

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes.

DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping.

In spite of achieving promising results in hyperspectral image (HSI) restoration, deep-learning-based methodologies still face the problem of spectral or spatial information loss due to neglecting the inner correlation of HSI. To address this issue, we propose an innovative deep recurrent convolution neural network (DnRCNN) model for HSI destriping. To the best of our knowledge, this is the first study on HSI destriping from the perspective of inner band and interband correlation explorations with the recurrent convolution neural network. In the novel DnRCNN, a selective recurrent memory unit (SRMU) is designed to respectively extract the correlative features involved in spectral and spatial domains. Moreover, an innovative recurrent fusion (RF) strategy incorporated with group concatenation is further proposed to remove strip noise and preserve scene details using the complementary features from SRMU. Experimental results on extensive HSI datasets validated that the proposed method achieves a new state-of-the-art (SOTA) HSI destriping performance.

Post-processed posteriors for sparse covariances

We consider Bayesian inference of sparse covariance matrices and propose a post-processed posterior. This method consists of two steps. In the first step, posterior samples are obtained from the conjugate inverse-Wishart posterior without considering the sparse structural assumption. The posterior samples are transformed in the second step to satisfy the sparse structural assumption through a generalized thresholding function. This non-traditional Bayesian procedure is justified by showing that the post-processed posterior attains the optimal minimax rates under the spectral norm loss in high-dimensional settings. We also propose the post-processed posterior for contaminated data and apply it to the estimation of the sparse idiosyncratic covariance of the approximate factor model. The advantages of our method are demonstrated via a simulation study and a real data analysis with S&P 500 data.

Development of a widely-targeted metabolomics method based on gas chromatography-mass spectrometry

Gas chromatography-mass spectrometry (GC-MS) detectors are widely used detection instruments owing to their distinct advantages over other analytical techniques, including lower sample consumption, higher sensitivity, faster analysis speed, and simultaneous separation and analysis. Metabolomics is an important component of system physiology that concerns systematic studies of the metabolite spectrum in one or more biological systems, such as cells, tissues, organs, body fluids, and organisms. Unfortunately, conventional GC-MS detectors also feature low scan rates, high ion loss rates, and a narrow concentration detection range, which limit their applications in the field of metabolomics. Therefore, establishing a GC-MS-based metabolomic analysis method with wide coverage is of great importance. In this research, a widely-targeted metabolomics method based on GC-MS is proposed. This method combines the universality of untargeted metabolomics with the accuracy of targeted metabolomics to realize the qualitative and semi-quantitative detection of numerous metabolites. It does not require a self-built database and exhibits high sensitivity, good repeatability, and strong support for a wide range of metabolic substances. The proposed method was used to establish the relationship between the retention time of straight-chain fatty acid methyl esters (FAMEs) and their retention index (RI) in the FiehnLib database based on the metabolite information stored in this database. We obtained a linear relationship that could be described by the equation y=40878x-47530, r2=0.9999. We then calculated the retention times of metabolites in the FiehnLib database under the experimental conditions based on their RI. In this way, the effects of significant variations in peak retention times owing to differences in the chromatographic column, temperature, carrier gas flow rate, and so on can be avoided. The retention time of a substance fluctuates within a certain threshold because of variations in instrument performance, matrix interference, and other factors. As such, the retention time threshold of the substance must be determined. In this paper, the retention time threshold was set to 0.15 min to avoid instrument fluctuations. The optimal scan interval was optimized to 0.20 s (possible values=0.10, 0.15, 0.20, 0.25, and 0.30 s) because longer sampling periods can lead to spectral data loss and reductions in the resolution of adjacent chromatographic peaks, whereas shorter sampling periods can result in deterioration of the signal-to-noise ratio of the collected signals. The metabolite quantification ions were optimized to avoid the interference of quantification ion peak accumulation in the case of similar peak times, and a selected ion monitoring (SIM) method table was constructed for 611 metabolites, covering 65% of the metabolic pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes). The developed method covered 39 pathways, including glycolysis, the tricarboxylic acid cycle, purine metabolism, pyrimidine metabolism, amino acid metabolism, and biosynthesis. Compared with the full-scan untargeted GC-MS method, the widely-targeted GC-MS method demonstrated a 20%-30% increase in the number of metabolites detected, as well as a 15%-20% increase in signal-to-noise ratio. The results of stability tests showed that 84% of the intraday relative standard deviations (RSDs) of metabolite retention times were less than 2% and 91% of that were less than 3%; moreover, 54% of the interday RSDs of metabolite retention times were less than 2% and 76% of that were less than 3%. The detection and analysis results of common biological samples confirmed that the proposed method greatly improved the quantity and signal-to-noise ratio of the detected metabolites and is applicable to substances that are thermally stable, volatile, or volatile after derivation and have relative molecular masses lower than 600. Thus, the widely-targeted GC-MS method can expand the application scope of GC-MS in metabolomics.

Soiling model for spectral reflectance of solar mirrors

In concentrating solar power plants, mirrors are used to redirect solar radiation onto a receiver. The optical yield of a plant is related to the mean value of the near-specular reflectance in the range of interest of incidence and acceptance angles for that plant. To avoid the need for an exhaustive measurement of the solar mirror at various incidence and acceptance angles, a predictive method based on the Equivalent Model Algorithm for Solar Mirrors was recently proposed. Whilst developed for clean surfaces, unfortunately that model is inaccurate for soiled solar mirrors. In this paper, an additional model to describe the spectral reflectance loss caused by soiling of solar mirrors is presented and successfully tested on a set of artificially and naturally-soiled specimens. The model is based on absorption, scattering and diffraction. Its parameters are obtained by best-fitting a set of experimental hemispherical and near-specular reflectance spectra, both in soiled and cleaned state. The best-fit software developed is now published as open source software, providing access also to non-specialized users. The model also allows to correlate simple handheld reflectometer readings used in the solar field (with typically limited spectral and acceptance angle information) to the most meaningful parameter determining the plant yield: the solar-weighted near-specular spectral reflectance of the soiled reflector. Such correlations are specific for the plant, the kind of local dust and the chosen portable reflectometer.

Dynamic-Subarray With Fixed Phase Shifters for Energy-Efficient Terahertz Hybrid Beamforming Under Partial CSI

Terahertz (THz) communications are regarded as a pillar technology for the 6G systems, by offering multi-ten-GHz bandwidth. To overcome the huge propagation loss, THz ultra-massive MIMO systems with hybrid beamforming are proposed to offer high array gain. Notably, the adjustable phase shifters considered in most existing hybrid beamforming studies are power-hungry and difficult to realize in the THz band. Moreover, due to the ultra-massive antennas, full channel-state-information (CSI) is challenging to obtain. To address these practical concerns, in this paper, an energy-efficient dynamic-subarray with fixed phase shifters (DS-FPS) architecture is proposed for THz hybrid beamforming. To compensate for the spectral efficiency loss caused by the fixed phase of FPS, a switch network is inserted to enable dynamic connections. In addition, by considering the partial CSI, we propose a row-successive-decomposition (RSD) algorithm to design the hybrid beamforming matrices for DS-FPS. A row-by-row (RBR) algorithm is further proposed to reduce the computational complexity. Extensive simulation results show that, the proposed DS-FPS architecture with the RSD and RBR algorithms achieves much higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of the DS-FPS architecture with the proposed algorithms is robust to the CSI error.

Effect of hearing protection attenuation on impulse insertion loss and kurtosis

Kurtosis (the fourth standardized moment of the sound pressure) has been used to assess the additional risk of hearing loss for complex or impulsive noise exposures. Murphy et al. [2012] reported the impulse peak insertion loss of five hearing protection devices (HPDs) for firearm impulse noise. Fackler et al. [2017] reported a spectral insertion loss for several HPDs assessed with firearm and shock tube impulse noise sources. Murphy [2019] previously reported the effect of five hearing protection devices on the kurtosis and insertion loss assessed with jackhammer noise. Unprotected Jackhammer noise exhibited kurtosis values between about 15 to 17, whereas protected exposures exhibited kurtosis between about 3 to 12. Anderson and Argo [2022] reported that insertion loss was unaffected by kurtosis level for seventeen HPDs measured on an acoustic test fixture under headphones. This paper will apply the complex transfer functions from Murphy et al. [2012] to the jackhammer noises measured by Murphy [2019]. The levels of the unoccluded noises transformed to the ear canal of the fixture will be compared to occluded jackhammer noises levels measured in the fixture.