Spectral Mapping Research Articles

Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study in Moscow Region, Russia

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication.

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

From 100 MHz to 10 GHz: Unveiling the spectral evolution of the X-shaped radio galaxy in Abell 3670

Context. X-shaped radio galaxies (XRGs) are characterised by two pairs of misaligned lobes: active lobes hosting radio jets and the wings. None of the formation mechanisms proposed thus far are able to exhaustively reproduce the diverse features observed among XRGs. Emerging evidence has proposed the existence of sub-populations of XRGs forming via different processes. Aims. The brightest cluster galaxy (BCG) in Abell 3670 (A3670) is a dumbbell system hosting the XRG MRC 2011-298. The morphological and spectral properties of this interesting XRG were first characterised based on Karl G. Jansky Very Large Array (JVLA) data at 1–10 GHz. In the present work, we follow up on MRC 2011-298 with the upgraded Giant Metrewave Radio Telescope (uGMRT) at 120–800 MHz to further constrain its properties and origin. Methods. We carried out a detailed spectral analysis sampling different spatial scales. Integrated radio spectra, spectral index maps, radio colour-colour diagrams, and radiative age maps of both the active lobes and prominent wings were employed to test the origin of the source. Results. We confirm a progressive spectral steepening from the lobes to the wings. The maximum radiative age of the source is ~80 Myr, with the wings being older than the lobes by ≳30 Myr in their outermost regions. Conclusions. The observed properties are in line with an abrupt reorientation of the jets by ~90 deg from the direction of the wings to their present position. This formation mechanism is further supported by the comparison with numerical simulations in the literature, which additionally highlight the role of hydrodynamic processes in the evolution of large wings such as those of MRC 2011-298. It is plausible that the coalescence of supermassive black holes could have triggered the spin-flip of the jets. Moreover, we show that the S-shape of the radio jets is likely driven by precession with a period of P ~ 10 Myr.

Survey of non-thermal electrons around supermassive black holes through polarization flips

Abstract Optically thick non-thermal synchrotron sources notably produce linear polarization vectors that are parallel to projected magnetic field lines on the observer’s screen, although they are perpendicular in well-known optically thin cases. To elucidate the complex relationship between the vectors and fields, and to investigate the energy and spatial distribution of non-thermal electrons through the images, we perform polarization radiative transfer calculations at submillimeter wavelengths. Here the calculations are based on semi-analytic force-free jet models with non-thermal electrons with a power-law energy distribution. In the calculated images, we find a $90{^\circ }$-flip of linear polarization vectors at the base of counter-side (receding) jet near a black hole, which occurs because of large optical depths for the synchrotron self-absorption effect. The $90{^\circ }$-flip of LP vectors is also seen on the photon ring at a high frequency, since the optical depth along the rays is large there due to the light bending effect. In addition, we see the flip of the sign of circular polarization components on the counter-jet and photon ring. Furthermore, we show that these polarization flips are synthesized with large values in the spectral index map, and also give rise to outstanding features in the Faraday Rotation Measure map. Since the conditions of flipping depend on the magnetic field strength and configuration and the energy distribution of electrons, we can expect that the polarization flips will provide us with observational evidence for the presence of non-thermal electrons around the black hole, and a clue to the magnetic driving mechanism of plasma jets.

Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification.

Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time. Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification. We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at , two for at and , and one for water at . The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

Radiative signatures of circumplanetary disks and envelopes during the late stages of giant planet formation

During the late stages of giant planet formation, protoplanets are surrounded by a circumplanetary disk and an infalling envelope of gas and dust. For systems with sufficient cooling, material entering the sphere of influence of the planet falls inward and approaches ballistic conditions. Due to conservation of angular momentum, most of the incoming material falls onto the disk rather than directly onto the planet. This paper determines the spectral energy distributions of forming planets in this stage of evolution. Generalizing previous work, we consider a range of possible geometries for the boundary conditions of the infall and determine the two-dimensional structure of the envelope, as well as the surface density of the disk. After specifying the luminosity sources for the planet and disk, we calculate the corresponding radiative signatures for the system, including the emergent spectral energy distributions and emission maps. These results show how the observational appearance of forming planets depend on the input parameters, including the instantaneous mass, mass accretion rate, semimajor axis of the orbit, and the planetary magnetic field strength (which sets the inner boundary condition for the disk). We also consider different choices for the form of the opacity law and attenuation due to the background circumstellar disk. Although observing forming planets will be challenging, these results show how the observational signatures depend on the underlying properties of the planet/disk/envelope system.

Dynamic functional connections analysis with spectral learning for brain disorder detection

Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders. However, most existing studies focus on the dFCs estimation to identify brain disorders by shallow temporal features and methods, which cannot capture the inherent temporal patterns of dFCs effectively. To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Concretely, dCSL includes two components, dFCs estimation module and dFCs analysis module. In the former, dFCs are estimated via the sliding window technique. In the latter, the spectral kernel mapping is first constructed by combining the Fourier transform with the non-stationary kernel. Subsequently, the spectral kernel mapping is stacked into a deep kernel network to explore higher-order temporal patterns of dFCs through spectral learning. The proposed dCSL, sharing the benefits of deep architecture and non-stationary kernel, can not only calculate the long-range relationship but also explore the higher-order temporal patterns of dFCs. To evaluate the proposed method, a set of brain disorder classification tasks are conducted on several public datasets. As a result, the proposed dCSL achieves 5% accuracy improvement compared with the widely used approaches for analyzing sequence data, 1.3% accuracy improvement compared with the state-of-the-art methods for dFCs. In addition, the discriminative brain regions are explored in the ASD detection task. The findings in this study are consistent with the clinical performance in ASD.

[formula omitted]-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates

The vibrational response of structural components carries valuable information about their underlying mechanical properties, health status and operational conditions. This underscores the need for the development of efficient physics-based inversion algorithms which, given a limited set of sensing data points and in the presence of measurement noise, can reconstruct the response at locations where measurement data is not available and/or identify the unknown mechanical properties. Addressing this challenge, Physics-Informed Neural Networks (PINNs) have emerged as a promising approach. PINNs seamlessly integrate governing equations into their architecture and have gained significant interest in solving inversion problems. In the context of learning and inversion of multimodal, multiscale vibrational responses, this paper introduces a novel spectral extension of PINNs, utilizing Fourier basis functions in the wavenumber domain, commonly known as k-space. The proposed method, referred to as k-space PINN (k-PINN), offers a robust framework for adjusting complexity and wavenumber composition of the response. Notably, the spectral formulation of k-PINN, coupled with the generally sparse representation of vibrations in k-space, facilitate efficient reconstruction and learning of broadband vibrations and alleviate the spectral bias associated with standard PINN. Additionally, the spectral solution space introduced by k-PINN substantially reduces the computational cost associated with computing physics-informed loss terms. We evaluate the effectiveness of the proposed methodology on reconstructing the bending vibrational mode shapes of a thin composite laminate and identifying its effective bending stiffness coefficients. Mode shapes are initially obtained from finite element simulation, and virtual test data with added noise are generated for evaluation purposes. It is shown that the proposed k-PINN methodology outperforms the standard PINN in terms of both learning and computational efficiency. The performance of k-PINN is further demonstrated by show casing its capability in learning different selections of symmetric, anti-symmetric and asymmetric mode shapes.

Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.

Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe the long-time dynamics of such systems, which is responsible for their most informative characteristics, we can identify a few slow collective variables (CVs) while treating the remaining fast variables as thermal noise. This enables us to simplify the dynamics and treat it as diffusion in a free-energy landscape spanned by slow CVs, effectively rendering the dynamics Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J. J. Phys. Chem. Lett. 2023, 14(22), 5216-5220], explores this strategy to learn slow CVs by maximizing a spectral gap of a transition matrix. In this work, we introduce several advancements into our framework, using a high-dimensional reversible folding process of a protein as an example. We implement an algorithm for coarse-graining Markov transition matrices to partition the reduced space of slow CVs kinetically and use it to define a transition state ensemble. We show that slow CVs learned by spectral map closely approach the Markovian limit for an overdamped diffusion. We demonstrate that coordinate-dependent diffusion coefficients only slightly affect the constructed free-energy landscapes. Finally, we present how spectral maps can be used to quantify the importance of features and compare slow CVs with structural descriptors commonly used in protein folding. Overall, we demonstrate that a single slow CV learned by spectral map can be used as a physical reaction coordinate to capture essential characteristics of protein folding.

Transmission anomalies in 2D photonic crystals from the checkerboard family: from broken to hidden symmetries and plasmon “spoofing”

In a field representation, the main symmetry of the electromagnetic response of complementary metal film structures is described by the Babinet principle, which is expected to be strictly obeyed by structures in vanishingly thin films of a perfect electric conductor. On the other hand, a softer version of this based on transmittance, the transmittance Babinet principle (TBP), is not so restrictive. The goal of this work is to study how severely this broken symmetry affects the optical response of such structures. We consider two geometrically distinct series of planar complementary structures from the checkerboard family: regular and bowtie. The self-complementary structure of these series is known to be very singular and breaks even the rigorous Babinet principle. Here, we study complete simulated transmittance spectral maps (T-Maps), which accumulate the whole spectral response of an entire series of structures in a single plot. The ab initio simulated T-Maps of these 2D photonic crystals were simulated for linearly polarized waves propagating perpendicular to these planar structures, made in a vanishingly thin film of a perfect electric conductor. While we confirm the expected long wavelength validity of the TBP, we show that in the frequency range where diffraction effects dominate, the standard derivation of the TBP no longer applies, and with the help of our T-Maps, we demonstrate a total collapse of the TBP in the structures considered. We show that this broken symmetry practically eliminates all but one transmission band on the hole side of the T-Maps and that the remaining strong band is a “spoof” plasmon, free of multiple frequency replicas, an important feature for optical filter applications. In addition, by symmetry arguments and simulations, we discovered that the T-Maps for bowtie and doubled-period regular structures are identical. We discuss how this hidden symmetry can benefit various applications by providing a convenient scaling, whereby simplified structures can deliver a tailored response.

Mid‐Infrared Single‐Photon Compressive Spectroscopy

AbstractSensitive mid‐infrared (MIR) spectroscopy plays an indispensable role in various photon‐starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi‐pixel arrays in parallel spectral acquisition. Here, an ultra‐sensitive MIR single‐pixel spectrometer is devised and implemented, which relies on high‐fidelity spectral upconversion and wavelength‐encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped‐pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single‐element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 under an illumination flux down to 0.01 photons nm–1 pulse–1. Moreover, faithful reconstructions at sub‐Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single‐pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single‐photon level.

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng

Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400–1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R2 value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R2 value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods.

CHANG-ES. XXXII. Spatially Resolved Thermal–Nonthermal Separation from Radio Data Alone—New Probes into NGC 3044 and NGC 5775

We have carried out spatially resolved thermal–nonthermal separation on two edge-on galaxies, NGC 3044 and NGC 5775, using only radio data. Narrowband imaging within a frequency band that is almost contiguous from 1.25 to 7.02 GHz (L band, S band, and C band) has allowed us to fit spectra and construct thermal, nonthermal, and nonthermal spectral index maps. This method does not require any ancillary Hα and IR data or rely on dust corrections that are challenging in edge-on galaxies. For NGC 3044, at 15″ resolution, we find a median thermal fraction of ∼13% with an estimated uncertainty in this fraction of ∼50% at 4.13 GHz. This compares well with the Hα mixture method results. We uncovered evidence for a vertical outflow feature reaching at least z ∼ 3.5 kpc in projection above the plane, reminiscent of M82's starburst wind. For the higher star formation rate galaxy, NGC 5775, at 12″ resolution, we find a median thermal fraction of 44% at 4.13 GHz with an estimated error on this fraction of 17%. Both galaxies show a change of slope (flattening) in L band. These results suggest that a radio-only method for separating thermal from nonthermal emission is not only feasible, but able to reveal new features that might otherwise be obscured in edge-on disks.

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature.

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.

CONCERTO at APEX On-sky performance in continuum

Context. CarbON CII line in post-rEionisation and ReionisaTiOn epoch (CONCERTO) instrument is a low-resolution mapping spectrometer based on lumped element kinetic inductance detector (LEKIDs) technology, operating at 130-310 GHz. It was installed on the 12-metre APEX telescope in Chile in April 2021 and was in operation until May 2023. CONCERTO’s main goals were the observation of [CII]-emission line fluctuations at high redshift and of the Sunyaev–Zel’dovich (SZ) signal from galaxy clusters. Aims. We present the data processing algorithms and the performance of CONCERTO in continuum by analysing the data from the commissioning and scientific observations. Methods. We developed a standard data processing pipeline to proceed from the raw data to continuum maps. Using a large dataset of calibrators (Uranus, Mars, and quasars) acquired in 2021 and 2022 at the APEX telescope across a wide range of atmospheric conditions, we measured the CONCERTO continuum performance and tested its stability against observing conditions. Further, using observations on the COSMOS field and observations targeting a distant sub-millimetre galaxy in the UDS field, we assessed the robustness of the CONCERTO performance on faint sources and compared our measurements with expectations. Results. The beam pattern is characterised by an effective full width at half maximum (FWHM) of 31.9 ± 0.6″ and 34.4 ± 1.0″ for high-frequency (HF) and low-frequency (LF) bands, respectively. The main beam is slightly elongated with a mean eccentricity of 0.46. Two error beams of ~65″ and ~130″ are characterised, allowing us to estimate a main beam efficiency of ~0.52. The field of view is accurately reconstructed and presents coherent distortions between the HF and LF arrays. LEKID parameters were robustly determined for 80% of the read tones. Cross-talks between LEKIDs are the first cause of flagging, followed by an excess of eccentricity for ~10% of the LEKIDs, all located in a given region of the field of view. Of the 44 scans of Uranus selected for the absolute photometric calibration, 72.5% and 78.2% of the LEKIDs were selected as valid detectors with a probability >70%. By comparing the Uranus measurements with a model, we obtain calibration factors of 19.5±0.6 Hz Jy−1 and 25.6±0.9 Hz Jy−1 for HF and LF, respectively. The point-source continuum measurement uncertainties are 3.0% and 3.4% for the HF and LF bands, ignoring the uncertainty in the model (which is <2%). This demonstrates the accuracy of the methods we deployed to process the data. Finally, the RMS of CONCERTO maps is verified to evolve as proportional to the inverse square root of the integration time. The measured noise-equivalent flux densities (NEFDs) for HF and LF are 115±2 mJy beam−1 s1/2 and 95±1 mJy beam−1 s1/2, respectively, obtained using CONCERTO data on the COSMOS field for a mean precipitable water vapour (pwv) and elevation of 0.81 mm and 55.7 deg. Conclusions. CONCERTO has unique capabilities in fast dual-band spectral mapping at ~30 arcsec resolution and with a ~18.5 arcmin instantaneous field of view. CONCERTO’s performance in continuum is perfectly in line with expectations.

An air quality and boundary layer dynamics analysis of the Los Angeles basin area during the Southwest Urban NOx and VOCs Experiment (SUNVEx)

Abstract. The NOAA Chemical Sciences Laboratory (CSL) conducted the Southwest Urban NOx and VOCs Experiment (SUNVEx) to study emissions and the role of boundary layer (BL) dynamics and sea-breeze (SB) transitions in the evolution of coastal air quality. The study presented utilizes remote sensing and in situ observations in Pasadena, California. Separate analyses are conducted on the synoptic conditions during ozone (O3) exceedance (>70 ppb) and non-exceedance (<70 ppb) days, as well as the fine-structure variability of in situ chemistry measurements during BL growth and SB transitions. Diurnal analyses spanning August 2021 revealed a markedly different wind direction during evenings preceding O3 exceedance (northerly) versus non-exceedance (easterly) days. Increased O3 occurred simultaneously with warmer and drier conditions, a reduction in winds, and an increase in volatile organic compounds (VOCs) and fine particulate matter (PM2.5). While the average BL height was lower and surface pressure was higher, the day-to-day variability of these quantities led to an overall weak statistical relationship. Investigations focused on the fine-structure variability of in situ chemistry measurements superimposed on background trends were conducted using a novel multivariate spectral coherence mapping (MSCM) technique that combined the spectral structure of two or more independent measurements through a wavelet analysis as reported by maximum-normalized scaleograms. A case study was chosen to illustrate the MSCM technique, where the dominant peaks in scaleograms were identified and compared to BL height during the growth phase. The temporal widths of peaks (τmax) derived from VOC and nitrogen oxide (NOx) scaleograms, as well as scaleograms combining VOCs, NOx, and variations in BL height, indicated a broadening with respect to time as the BL increased in depth. A separate section focused on comparisons between τmax and BL height during August 2021 revealed uncorrelated or weakly correlated scatter, except in the case of VOCs when really large τmax and relatively deep BL heights were ignored. Instances of large τmax and relatively deep BL heights occurred near sunrise and as onshore flow entered Pasadena, respectively. Wind transitions likely influenced both the dynamical evolution of the BL and tracer advection and thus offer additional challenges when separating factors contributing to the fine structure. Other insights gained from this work include observations of descending wind jets from the San Gabriel Mountains that were not resolved by the High-Resolution Rapid Refresh (HRRR) model and the derivation of intrinsic properties of oscillations observed in NOx and O3 during the interaction between an SB and enhanced winds above the BL that flowed in opposition to the SB.

Formation and radiolytic alteration of uraniferous solid bitumen related to hydrothermal base-metal mineralization in the Bytíz deposit, Příbram district, Czech Republic

The Bytíz deposit is a part of the Příbram uranium and base-metal ore district. It is an example of a vein-type deposit with polyphase hydrothermal mineralization. Samples of uraniferous solid bitumen from Bytíz with U content up to 38 wt% are characterized petrologically, geochemically, and mineralogically using EPMA, Raman and infrared microspectroscopy. The bitumen-bearing samples consist of base-metal sulfides: galena, sphalerite, pyrite, chalcopyrite, and also minor amounts of tetrahedrite, stibnite, and acanthite, associated with Mn-bearing calcite, quartz and silicates (chlorite, muscovite). Solid bitumens were found in the form of small veins and droplets, and roundish to irregular accumulations, in association with uraninite and carbonate veins.U-bearing minerals in the studied samples are represented by uraninite and more rarely by coffinite. Three generations of uraninite in association with solid bitumen were distinguished: 1. spherulites and large grains, filled with organic phase in the cracks; 2. as a part of complex textures inside areas with organic matter; in this case, the uraninite was assumed to have been remobilized; and 3. small inclusions in the latest calcite veins.More than 80 vol% of the solid bitumen from the vein fillings appeared to be radiolytically altered. Radiolytic alteration results in changes in optical properties and in composition, and in the formation of various textures around uraninite grains: halos, and irregular textures from simple massive to flow, dendritic, and fractured to a very complex morphology. The random reflectance values of unaltered mineral-free bitumen range from 0.45% to 0.99%, while in the radiolytically altered bitumen the average reflectance values are higher, from approximately 1.72% to 3.44%.The degree of graphitization of the organic matter was assessed by infrared micro-spectroscopy. Spectral maps show significant destruction changes of the aliphatic CH bonds and an increase in the content of oxygen functional groups in the vicinity of U minerals.On the element distribution maps, obtained by EPMA, the distribution of S, U, Pb and other elements across solid bitumen in the vicinity of uraninite and coffinite has a very heterogeneous character. An elevated content of sulfur in bitumen was also found, as well as a clear interdependence between S and C. It is suggested that the presence of sulfur in solid bitumen may result in ‘clouding’ of the solid bitumen with tiny stibnite grains. The dark rims of the halos observed under the optical microscope may be due to an elevated U content at the rims around the uraninite.Based on analysis of complex textural relationships of the solid bitumens with coexisting minerals, the formation of solid bitumen in association with uraninite is therefore assumed to relate to several stages of the influx of hydrothermal fluid. The temperature of the fluid, associated with bitumen formation was estimated to be up to 270 °C, according to chlorite thermometry.

Weyl's Theorem for Algebraically (\(\wp, \wr, \varrho\))-Paranormal and Algebraically (\(\wp, \wr, \varrho\))-*-Paranormal Operators

Present L be an algebraically (\(\wp, \wr, \varrho\))-Paranormal and algebraically (\(\wp, \wr, \varrho\))-*-Paranormal operators on \(L^2\) space. We examine Weyl's theorem, a-Browder's theorem and spectral mapping theorem holds for weyl's spectrum of L and essential approximate point spectrum of L.

DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar co-ordinate transformation method was employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) was utilized to classify these feature maps. The model was validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieved excellent classification performance. Experimental results on three public HSI datasets demonstrated that the proposed method accurately recognized different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking

Compared to hyperspectral trackers that adopt the “pre-training then fine-tuning” training paradigm, those using the “pre-training then prompt-tuning” training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt template design, thus failing to bridge the domain gap between hyperspectral data and pre-trained models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability and require re-training for the different spectral bands of hyperspectral data, leading to the inefficient use of computational resources. In order to address the aforementioned problems, we propose a spectral similarity prompt learning approach for hyperspectral object tracking (SPTrack). First, we introduce a spectral matching map based on spectral similarity, which converts 3D hyperspectral data with different spectral bands into single-channel hotmaps, thus enabling cross-spectral domain generalization. Then, we design a channel and position attention-based feature complementary prompter to learn blended prompts from spectral matching maps and three-channel images. Extensive experiments are conducted on the HOT2023 and IMEC25 data sets, and SPTrack is found to achieve state-of-the-art performance with minimal computational effort. Additionally, we verify the cross-spectral domain generalization ability of SPTrack on the HOT2023 data set, which includes data from three spectral bands.