Search Results (27)

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R² = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R² = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline. Full article

Background/Objectives: Spatial release from masking (SRM) refers to the improvement in speech understanding that occurs when a target talker is spatially separated from competing speech. Although normal-hearing (NH) listeners benefit substantially from spatially separating the maskers from the target, cochlear implant (CI) users experience markedly reduced advantages due to degraded spectral and binaural cue transmission. Electric–acoustic stimulation (EAS), which preserves low-frequency acoustic hearing in combination with electric stimulation, may partially restore these cues, but its benefits at small, conversationally relevant spatial separations remain poorly understood. Methods: This study measured speech identification thresholds using Coordinate Response Measure (CRM) sentences in NH listeners using natural, EAS, and simulated CI speech across five spatial configurations (0°, ±5°, ±10°, ±15°, ±30°). Speech identification thresholds were measured using a one-up/one-down adaptive procedure with Coordinate Response Measure (CRM) sentences. CI simulation used an eight-channel noise-band vocoder, whereas EAS simulation replaced the two lowest-frequency vocoder channels with low-pass speech (≤500 Hz). All stimuli were spatialized using head-related impulse responses generated from a validated virtual-acoustics model. Results: All stimulus types showed improved thresholds with increasing spatial separation; however, the magnitude of spatial release from masking (SRM) varied systematically. Natural speech produced the lowest thresholds and largest SRM, EAS speech yielded intermediate benefits, and simulated CI speech produced the smallest improvements. Notably, EAS and CI simulations were comparable at small separations, but EAS provided significantly greater SRM at ±15° and ±30°. Conclusions: These findings demonstrate that even partial low-frequency acoustic preservation enhances SRM at moderate spatial separations, highlighting the importance of EAS configurations for improving spatial hearing in CI-related listening environments. Full article

Ocular infections and inflammation represent a clear risk to eye health, but standard eye masks often lack the necessary therapeutic features. Moreover, most existing studies employ a blended electrospinning approach, which leads to an inhomogeneous spatial distribution of the therapeutic agents. However, using the coaxial technique can address these limitations. This study develops a coaxial electrospun nanofibrous eye mask with dual antibacterial and antioxidant functions, aiming to provide an innovative ocular treatment tool for eye care. Generally, a core-shell structured bilayer polycaprolactone-polylysine/polyvinyl alcohol-resveratrol (PCL-PLs/PVA-RSV) membrane is successfully prepared by coaxial electrospinning, where the core is resveratrol-loaded PVA and the shell is PLs-loaded PCL. Results show uniform fiber morphology, favorable hydrophilicity, and potential for sustained release due to core-shell design. The membrane significantly inhibits the growth of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli); at the same time, it exhibits excellent free radical scavenging ability and good component biocompatibility, achieving slow release of the two drugs and long-term antioxidant effect. This multifunctional platform offers a synergistic approach to combating microbial infection and oxidative stress, showing great potential for eye care. Full article

Large-scale mapping of forest canopy height (FCH) is crucial for accurately understanding ecosystem succession and forest carbon sinks. Recently, three high-resolution FCH products have been released, including global forest canopy height (GFCH), NNGI_FCH_China (NNGI_FCH), and ETH_GlobalCanopyHeight (ETH_GCH). This study provides a detailed assessment of these FCH products across China from forest area, spatial consistency, and overall accuracy, with additional analyses of forest classification errors and evaluation under a unified forest mask. The assessment is conducted using forest inventory data, the China land cover dataset, and field measurement data. The results show that NNGI_FCH had the smallest relative error of 13.4% and achieved better estimates of forest area in all regions but the north and northeast regions. GFCH had the highest spatial consistency of 70.8% nationwide, followed by NNGI_FCH (69.7%), which performed slightly better than GFCH in the east and northwest regions. ETH_GCH exhibited the lowest spatial consistency of 35.6% and remained below 50% across all regions except the northeast and south regions. ETH_GCH demonstrated the highest overall accuracy across the country, with an R² and RMSE of 0.56 and 4.14 m, followed by NNGI_FCH (R² = 0.49, RMSE = 3.38 m) and GFCH (R² = 0.48, RMSE = 3.38 m). Validation results of ETH_GCH were relatively stable in different regions of China, while those of NNGI_FCH varied more but still outperformed GFCH. This study offers valuable insights by evaluating large-scale FCH products, which will be a key basis for in-depth studies on the utilization and improvement of future FCH mapping. Full article

Background/Objectives: Speech understanding in noise relies on both temporal fine structure (TFS) and temporal envelope (ENV) cues. While TFS primarily conveys interaural time differences (ITDs) at low frequencies, ENV cues can also support ITD processing, especially when TFS is unavailable or degraded. Expanding the ENV by increasing modulation depth has been proposed to improve speech perception, but its effects on spatial release from masking (SRM) and binaural temporal processing in normal-hearing listeners remain unclear. The goal of this study was to evaluate the effect of ENV enhancement on SRM in young adults with normal hearing and its influence on ITD sensitivity and interaural coherence (IC). Method: Thirty normal-hearing native Kannada speakers (19–34 years) participated. Speech stimuli consisted of Kannada sentences embedded in four-talker babble at −5, 0, and +5 dB signal to noise ratio (SNR). Target and masker were spatialized using head-related transfer functions at 0°, 15°, and 37.5° azimuths. Stimuli were presented with and without ENV enhancement (compression–expansion algorithm). Speech recognition scores were analyzed using generalized linear mixed models, and SRM was calculated as performance differences between co-located and spatially separated conditions. Cross-correlation analyses were performed to estimate ITDs and IC across SNRs. Result: ENV enhancement yielded significantly higher SRM values across all SNRs and spatial separations. Benefits were greatest at lower SNRs and wider target–masker separations. Cross-correlation analysis showed enhanced IC and more reliable ITD estimates under the expanded condition, particularly at moderate SNRs. Conclusions: Temporal ENV enhancement strengthens spatial unmasking and binaural timing cues in normal-hearing adults, especially under adverse listening conditions. These findings highlight its potential application in auditory rehabilitation and hearing technologies where ENV cues are critical. Full article

Large-scale terrestrial laser scanning (TLS) point clouds are increasingly used for applications such as digital twins and cultural heritage documentation; however, removing unwanted human points captured during acquisition remains a largely manual and time-consuming process. This study proposes a geometry-aware framework for automatically removing human noise from TLS point clouds by projecting 2D instance segmentation masks (obtained using You Only Look Once (YOLO) v8 with an instance segmentation head) into 3D space and validating candidates through multi-stage geometric filtering. To suppress false positives induced by reprojection misalignment and planar background structures (e.g., walls and ground), we introduce projection-followed geometric validation (or “geometric gating”) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA)-based planarity analysis, followed by cluster-level plausibility checks. Experiments were conducted on two real-world outdoor TLS datasets—(i) Osaka Metropolitan University Sugimoto Campus (OMU) (82 scenes) and (ii) Jinaimachi historic district in Tondabayashi (JM) (68 scenes). The results demonstrate that the proposed method achieves high noise removal accuracy, obtaining precision/recall/intersection over union (IoU) of 0.9502/0.9014/0.8607 on OMU and 0.8912/0.9028/0.8132 on JM. Additional experiments on mobile mapping system (MMS) data from the Waymo Open Dataset demonstrate stable performance without parameter recalibration. Furthermore, quantitative and qualitative comparisons with representative time-series geometric dynamic object removal methods, including DUFOMap and BeautyMap, show that the proposed approach maintains competitive recall under a human-only ground-truth definition while reducing over-removal of static structures in TLS scenes, particularly when humans are observed in only one or a few scans due to limited revisit frequency. The end-to-end processing time with YOLOv8 was 935.62 s for 82 scenes (11.4 s/scene) on OMU and 571.58 s for 68 scenes (8.4 s/scene) on JM, supporting practical efficiency on high-resolution TLS imagery. Ablation studies further clarify the role of each stage and indicate stable performance under the observed reprojection errors. The annotated human point cloud dataset used in this study has been publicly released to facilitate reproducibility and further research on human noise removal in large-scale TLS scenes. Full article

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

Misophonia is a condition characterized by intense, involuntary distress or anger in response to specific sounds, often leading to irritation or aggression. While the condition is recognized for its emotional and behavioral impacts, little is known about its physiological and perceptual effects. The current study aimed to explore the physiological correlates and perceptual consequences of misophonia through a combination of electrophysiological, perceptual, and cognitive assessments. Seventeen individuals with misophonia and sixteen control participants without the condition were compared. Participants completed a comprehensive battery of tests, including (a) cortical event-related potentials (ERPs) to assess neural responses to standard and deviant auditory stimuli, (b) the spatial release from the speech-on-speech masking (SRM) paradigm to evaluate speech segregation in background noise, and (c) the flanker task to measure selective attention and cognitive control. The results revealed that individuals with misophonia exhibited significantly smaller mean peak amplitudes of the N1 and N2 components in response to oddball tones compared to controls. This suggests a potential underlying neurobiological deficit in misophonia patients, as these components are associated with early auditory processing. However, no significant differences between each group were observed in the P1 and P2 components regarding oddball tones or in any ERP components in response to standard tones. Despite these altered neural responses, the misophonia group did not show differences in hearing thresholds, speech perception abilities, or cognitive function compared to the controls. These findings suggest that while misophonia may involve distinct neurophysiological changes, particularly in early auditory processing, it does not necessarily lead to perceptual deficits in speech perception or cognitive function. Full article

Sulfur dioxide (SO₂) is sourced by degassing magma in the shallow crust; hence its monitoring provides information on the rates of magma ascent in the feeding conduit and the style and intensity of eruption, ultimately contributing to volcano monitoring and hazard assessment. Here, we present a new algorithm to extract SO₂ data from the TROPOMI imaging spectrometer aboard the Sentinel-5 Precursor satellite, which delivers atmospheric column measurements of sulfur dioxide and other gases with an unprecedented spatial resolution and daily revisit time. Specifically, we automatically extract the volcanic clouds by introducing a two-step approach. Firstly, we used the Simple Non-Iterative Clustering segmentation method, which is an object-based image analysis approach; secondly, the K-means unsupervised machine learning technique is applied to the segmented images, allowing a further and better clustering to distinguish the SO₂. We implemented this algorithm in the open-source Google Earth Engine computing platform, which provides TROPOMI imagery collection adjusted in terms of quality parameters. As case studies, we chose three volcanoes: Mount Etna (Italy), Taal (Philippines) and Sangay (Ecuador); we calculated sulfur dioxide mass values from 2018 to date, focusing on a few paroxysmal events. Our results are compared with data available in the literature and with Level 2 TROPOMI imagery, where a mask is provided to identify SO₂, finding an optimal agreement. This work paves the way to the release of SO₂ flux time series with reduced delay and improved calculation time, hence contributing to a rapid response to volcanic unrest/eruption at volcanoes worldwide. Full article

Background: Spatial release from masking (SRM) is the improvement in speech intelligibility when the masking signals are spatially separated from the target signal. Young, normal- hearing listeners have a robust auditory sys-tem that is capable of using the binaural cues even with a very small spatial separation between the target and the maskers. Prior studies exploring SRM through simulated cochlear implant (CI) speech have been completed using substantial spatial separations, exceeding 45° between the target signal and masking signals. Nevertheless, in re-al-world conversational scenarios, the spatial separation between the target and the maskers may be considerably less than what has been previously investigated. This study presents SRM data utilizing simulated CI speech with young, normal-hearing listeners, focusing on smaller but realistic spatial separations between the target and the maskers. Methods: Twenty-five young, normal-hearing listeners participated in this study. Speech identification thresholds, the target-to-masker ratio required to accurately identify 50% of the target words, were measured for both natural speech and simulated CI speech. Results: The results revealed that young, normal-hearing listeners had significantly higher speech identification thresholds when presented with simulated CI speech in comparison to natural speech. Furthermore, the amount of SRM was found to be greater for natural speech than for the simulated CI speech. Conclusions: The data suggests that young normal-hearing individuals are capable of utilizing the interaural level difference cues in the simulated cochlear implant signal to achieve masking release at reduced spatial separations between the target and the maskers, highlighting the auditory system’s capability to extract these interaural cues even in the presence of degraded speech signals. Full article

(1) Background: The aim of the present study was to assess the impact of reverberation on speech perception in noise and spatial release from masking (SRM) in bimodal or bilateral cochlear implant (CI) users and CI subjects with low-frequency residual hearing using combined electric–acoustic stimulation (EAS). (2) Methods: In total, 10 bimodal, 14 bilateral CI users and 14 EAS users, and 17 normal hearing (NH) controls, took part in the study. Speech reception thresholds (SRTs) in unmodulated noise were assessed in co-located masker condition (S0N0) with a spatial separation of speech and noise (S0N60) in both free-field and loudspeaker-based room simulation for two different reverberation times. (3) Results: There was a significant detrimental effect of reverberation on SRTs and SRM in all subject groups. A significant difference between the NH group and all the CI/EAS groups was found. There was no significant difference in SRTs between any CI and EAS group. Only NH subjects achieved spatial release from masking in reverberation, whereas no beneficial effect of spatial separation of speech and noise was found in any CI/EAS group. (4) Conclusions: The subject group with electric–acoustic stimulation did not yield a superior outcome in terms of speech perception in noise under reverberation when the noise was presented towards the better hearing ear. Full article

Forest fires pose a multifaceted threat, encompassing human lives and property loss, forest resource destruction, and toxic gas release. This crucial disaster’s global occurrence and impact have risen in recent years, primarily driven by climate change. Hence, the scope and frequency of forest fires must be collected to establish disaster prevention policies and conduct relevant research projects. However, some countries do not share details, including the location of forest fires, which can make research problematic when it is necessary to know the exact location or shape of a forest fire. This non-disclosure warrants remote surveys of forest fire sites using satellites, which sidestep national information disclosure policies. Meanwhile, original data from satellites have a great advantage in terms of data acquisition in that they are independent of national information disclosure policies, making them the most effective method that can be used for environmental monitoring and disaster monitoring. The Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite has worldwide coverage at a daily temporal resolution and spatial resolution of 375 m. It is widely used for detecting hotspots worldwide, enabling the recognition of forest fires and affected areas. However, information collection on affected regions and durations based on raw data necessitates identifying and filtering hotspots caused by industrial activities. Therefore, this study used VIIRS hotspot data collected over long periods and the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to develop ST-MASK, which masks said hotspots. By targeting the concentrated and fixed nature of these hotspots, ST-MASK is developed and used to distinguish forest fires from other hotspots, even in mountainous areas, and through an outlier detection algorithm, it generates identified forest fire areas, which will ultimately allow for the creation of a global forest fire watch system. Full article

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article

Mesocarnivores are major components of carnivore assemblages, and they play important roles in structuring communities and regulating community dynamics. In Southwest China, many apex predators have been extirpated, and this has potentially resulted in the ecological release of mesocarnivores. Estimating the sizes of mesocarnivore populations is challenging. We used DNA derived from hairs and spatial capture–mark–recapture techniques to estimate the population size of masked palm civets (Paguma larvata) in Laohegou Nature Reserve in the Minshan Mountains of Sichuan Province, China. In the fall of 2014 and the spring of 2015, we collected 144 and 230 hair samples, respectively, at 30 baited stations. We used 16S rRNA fragments, microsatellite genotyping, and sexing to determine that 191 samples were derived from 44 masked palm civet individuals (24 males and 20 females). Using spatially explicit capture–recapture analysis, we estimated that there were 82 ± 13 masked palm civets in the study area, with a density of 1.7 individuals/km². This is the first study to estimate the population size of masked palm civets in the wild. Our data provide important new information on the density of masked palm civets. Full article

Providing students with an adequate acoustic environment is crucial for ensuring speech intelligibility in primary school classrooms. Two main approaches to control acoustics in educational facilities consist of reducing background noise and late reverberation. Prediction models for speech intelligibility have been developed and implemented to evaluate the effects of these approaches. In this study, two versions of the Binaural Speech Intelligibility Model (BSIM) were used to predict speech intelligibility in realistic spatial configurations of speakers and listeners, considering binaural aspects. Both versions shared the same binaural processing and speech intelligibility backend processes but differed in the pre-processing of the speech signal. An Italian primary school classroom was characterized in terms of acoustics before (reverberation, T20 = 1.6 ± 0.1 s) and after (T20 = 0.6 ± 0.1 s) an acoustical treatment to compare BSIM predictions to well-established room acoustic measures. With shorter reverberation time, speech clarity and definition improved, as well as speech recognition thresholds (SRTs) (by up to ~6 dB), particularly when the noise source was close to the receiver and an energetic masker was present. Conversely, longer reverberation times resulted (i) in poorer SRTs (by ~11 dB on average) and (ii) in an almost non-existent spatial release from masking at an angle (SRM). Full article