Nighttime Conditions Research Articles

The efficacy of massage as a nightly bedtime routine on infant sleep condition and mother sleep quality: A randomized controlled trial

Backgroundand Purpose: Infants' sleep disorders and parents' insufficient sleep are common problems in the infant care. The current study was conducted to assess the effectiveness of infant massage on infants' night-time sleep condition and mothers’ sleep quality. Methods140 number of 15–20 days old infants were randomly put into two different groups, one with bedtime messages and the other with normal infant routine care. For the intervention group, in addition to usual bedtime procedures such as changing diapers and breastfeeding, a 15-min massage was done before sleep for a period of two weeks. For the control group, only the usual above-mentioned bedtime procedures were followed. One week before the intervention, at the end of the first week, and two weeks after the intervention, the information was recorded for both groups. The Brief Infant Sleep Questionnaire, a personal information submission form, and Pittsburgh Sleep Quality Index for the mothers were the tools used to gather data in this study. ResultsInfants in two control and experimental groups showed meaningful differences in variables such as, sleep latency (P = 000, eta = 0.099), number of night waking (P = 0.03, eta = 0.027), and longest continuous sleep period (P = 0.03, eta = 0.026). As for other variables related to the infants' sleep, no meaningful differences were observed. In this study, there was no indication of a meaningful difference in the mother's overall night-time sleep quality between the two groups (P = 0.184, eta = 0.012) except for the duration of the mother's night-time sleep (P = 0.028, eta = 0.026) and the reduction of maternal sleep disorder (P = 0.020 eta = 0.029). As for other sub-factors of maternal sleep quality, no meaningful differences were seen. ConclusionThe findings of this study indicated that infants’ bedtime massages would improve some of the sleep markers of mothers and infants, and therefore, can be suggested as a practical, harmless, and cost-free method to improve sleep.

Spectral Analysis of the Martian Atmospheric Turbulence: InSight Observations

AbstractIn this study, we perform spectral analysis of Martian near‐surface turbulence using the in‐situ observations by NASA's InSight lander on Mars. A recent study on the daytime Martian boundary layer using the InSight observations showed that the conventional Kolmogorov energy cascade of inertial subrange fails to predict the spectral density of pressure (Banfield et al., 2020, https://doi.org/10.1038/s41561-020-0544-y). Here we extend this by investigating diurnal and seasonal variations in the spectral density of pressure, as the indicator of the Martian atmospheric turbulence. We show distinct spectral behaviors for the daytime and nighttime conditions. Moreover, we report the important effects of regional dust storms, gravity waves (GWs), bore, and solitary waves on the turbulent energy cascade. Our results show that the presence of a dust storm and GW activity can enhance the turbulence of the nighttime boundary layer of Mars despite the extreme stably stratified conditions compared to the terrestrial case.

Evaporating waterbody effects in a simplified urban neighbourhood: A RANS analysis

The incorporation of nature-based solutions comprising green and blue infrastructure is often touted as a way to cool cities and enhance pollutant removal. However, there is little agreement between different methodologies to measure the effect of any single intervention. Here, we present 3D steady RANS simulations to investigate the influence of waterbody on in-canyon flow structure, temperature (T∗) and water vapour (ω∗) distribution in a simplified urban neighbourhood. A novel solver that captures evaporation effects is developed and validated against wind tunnel experiments. Simulations are performed under neutral atmospheric conditions for forced- and mixed-convection cases and different air–water temperature differences, indicative of either daytime or night-time conditions. Results under forced convection show minimal impact on the flow structure, whilst T∗ and ω∗ effects are distributed primarily over and around the water surface. However, the mixed-convection case shows that a cooler waterbody weakens the principal vortex in the open square, whilst T∗ and ω∗ effects reach further upwind and are more widely distributed in the spanwise direction. A warmer waterbody is shown to disrupt the skimming flow structure, indicating a possible heat and pollutant removal mechanism from around the waterbody and also downwind canyons.

Near-infrared LED system to recognize road surface conditions for autonomous vehicles

Abstract. The driving safety of autonomous vehicles will strongly depend on their ability to recognize road surface conditions such as dry, wet, snowy and icy road. Currently, the existing investigations to detect road surface conditions still have limitations in daytime and nighttime conditions. The objective of this paper is to propose and develop a new system with three near-infrared (NIR) LED sources. This choice is based on the advantages of LED sources over laser diodes. They are less sensitive to temperature and have lower costs. Considering these advantages, the feasibility of the LED system to recognize road surface conditions is investigated. For this, the appropriate wavelengths of the LED tri-wavelength source are first computed from experimental data taking into account the specific LED spectral shape. In addition, the effect of the spectral bandwidth of the LED sources on the system performance is theoretically studied. Finally, the NIR LED system with the LED sources at 970, 1450 and 1550 nm is experimentally tested and validated with an incident angle from 78.7 to 86.2∘. According to the results of the experiments, the accuracy of the classification of snow, wet and water can reach 97 %, while the accuracy of the dry and wet road surface conditions is respectively 73 % and 68 %.

Investigating a Computational Explanation of the Black Hole Illusion

ABSTRACT Objective We investigated the role of Perrone’s algorithm in the Black Hole Illusion (BHI). After analyzing the algorithm and identifying two of its predictions, we empirically tested them with two on-line experiments. Background In 1983, Perrone proved that in daylight conditions it is possible to compute the descent angle using a ratio of retinal distances corresponding to the runway and surrounding context. Using the algorithm in nighttime conditions, with just the visible runway, pilots would overestimate the descent angle, leading to the BHI. Method Mathematical analysis indicates the algorithm predicts a large BHI; perhaps too large if there are no mitigating factors. As Perrone noted, the BHI illusion magnitude should be affected by runway width; we also found that some conditions predict a reverse BHI (pilots should underestimate their descent angle). In our experiments, participants observed a cockpit view of a runway during five seconds of steady approach. In a subsequent still image, participants indicated where they believed the plane would land if it continued its flight path. We measured the accuracy of the landing positions for various runway widths and various background contexts. Results The experiments did not show a BHI for any conditions; so the experiments do not validate the model predictions. Conclusion Based on our analyses, Perrone’s algorithm does not provide an adequate explanation of the Black Hole Illusion.

Constructed wetlands treating synthetic wastewater in response to day-night alterations: Performance and mechanisms

Constructed wetlands (CWs), as nature-based wastewater treatment systems, continuously maintain exchanges of material and energy with the natural environment. However, it is unclear how pollutant removal performance in CWs occurs in response to diurnal variation. Herein, we investigated the effects of day-night alterations on the rhizosphere microbial community in CWs. The results revealed that a dynamic alternate mechanism existed in the inner environment when the CWs were in steady operation, which was validated by rhythmic changes in the core microbial community, microbial metabolism activity, and pollutant removal shown in this study. The results showed that pollutant removal (e.g., total nitrogen) due to microbial processes was 1.31 times higher under daytime conditions than under nighttime conditions. Core microbial taxa of the rhizosphere that evolved with circadian rhythm (e.g., Chloroflexus and Beijerinckia) were mostly associated with carbon, nitrogen, phosphorus, and energy metabolism, with lower average relative abundance identified at night. Although higher activity of microbial metabolism was also observed in the daytime, nighttime conditions demonstrated higher gene levels that were related to carbon and nitrogen metabolic functions. Structural equation model (SEM) analysis further suggested that environmental factors exerted a significant influence in driving microbial processes for pollutant removal and that the microbial community played a greater role in promoting pollutant removal under nighttime conditions than under daytime conditions.

Temporal Stability Analysis of Lighting Conditions in Traffic Accidents

Different lighting conditions can result in accidents of different levels of severity. However, current studies lack the consideration of the heterogeneity and temporal stability of accident data under various lighting conditions. Therefore, three years’ worth of data were used to investigate the critical factors of accident severity. The random parameters logit model was employed to investigate the influence of different lighting conditions on temporal stability and heterogeneity. The critical factors affecting injury severity were also identified. The temporal stability and transferability of the models were investigated by a series of likelihood ratio tests. Based on different lighting conditions (daylight conditions, and night-time conditions with street lighting on), six models were established. Three kinds of accident injury severity levels were classified: property damage only (PDO), severe injury (SI), and fatal injury (FI). The estimation results showed contributing factors of accident severity were significantly different between the two kinds of lighting conditions. Additionally, accidents showed temporal instability. The proposed method can provide a guide for infrastructure construction, operation, and maintenance in traffic-safety management.

Gaze Preserving CycleGANs for Eyeglass Removal and Persistent Gaze Estimation

A driver’s gaze is critical for determining theirattention, state, situational awareness, and readiness to take over control from partially automated vehicles. Estimating the gaze direction is the most obvious way to gauge a driver’s state under ideal conditions when limited to using non-intrusive imaging sensors. Unfortunately, the vehicular environment introduces a variety of challenges that are usually unaccounted for - harsh illumination, nighttime conditions, and reflective eyeglasses. Relying on head pose alone under such conditions can prove to be unreliable and erroneous. In this study, we offer solutions to address these problems encountered in the real world. To solve issues with lighting, we demonstrate that using an infrared camera with suitable equalization and normalization suffices. To handle eyeglasses and their corresponding artifacts, we adopt image-to-image translation using generative adversarial networks to pre-process images prior to gaze estimation. Our proposed Gaze Preserving CycleGAN (GPCycleGAN) is trained to preserve the driver’s gaze while removing potential eyeglasses from face images. GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision. Our approach exhibits improved performance, interpretability, robustness and superior qualitative results on challenging real-world datasets.

A Comparison of Daytime and Nighttime Operating Speed on Rural Multi- Lane Highway Sections in Egypt

Traditionally, the 85th percentile of observed speeds is used to represent the operating speed associated with a specific site with certain geometric features. The presence of sunlight enhances the vision of drivers during daytime while visibility usually degrades during nighttime, especially on rural roads where no street lighting exists, and drivers would depend solely on their vehicle headlights. Visibility degradation is a safety concern which is magnified on horizontal curves. The objective of this study, therefore, is to compare operating speed in daytime and nighttime conditions on both tangent sections and horizontal curves. The analysis made use of spot speed data collected on horizontal curves of two major rural multilane highway in Egypt. Spot speed data were collected using speed guns during daytime and nighttime. The spot speed data were used to calculate the mean speed, variance of speed, and the 85th percentile speed. Analysis of Variance (ANOVA) was used to compare the speeds in daytime and nighttime. For most cases, it was shown that no significant difference existed in speed for daytime and nighttime. This was mainly attributed to the generous design features of the highway elements (e.g., large radii of horizontal curves, wide lanes, etc.). An attempt was made to develop operating speed models to see which variables impact speed during daytime. The results of operating speed models’ development were deemed inappropriate due to the lack of variability in speeds.

Ground-based Ku-band microwave observations of ozone in the polar middle atmosphere

Abstract. Ground-based observations of 11.072 GHz atmospheric ozone (O3) emission have been made using the Ny-Ålesund Ozone in the Mesosphere Instrument (NAOMI) at the UK Arctic Research Station (latitude 78∘55′0′′ N, longitude 11∘55′59′′ E), Spitsbergen. Seasonally averaged O3 vertical profiles in the Arctic polar mesosphere–lower thermosphere region for night-time and twilight conditions in the period 15 August 2017 to 15 March 2020 have been retrieved over the altitude range 62–98 km. NAOMI measurements are compared with corresponding, overlapping observations by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument. The NAOMI and SABER version 2.0 data are binned according to the SABER instrument 60 d yaw cycles into nominal 3-month “winter” (15 December–15 March), “autumn” (15 August–15 November), and “summer” (15 April–15 July) periods. The NAOMI observations show the same year-to-year and seasonal variabilities as the SABER 9.6 µm O3 data. The winter night-time (solar zenith angle, SZA ≥ 110∘) and twilight (75∘ ≤ SZA ≤ 110∘) NAOMI and SABER 9.6 µm O3 volume mixing ratio (VMR) profiles agree to within the measurement uncertainties. However, for autumn twilight conditions the SABER 9.6 µm O3 secondary maximum VMR values are higher than NAOMI over altitudes 88–97 km by 47 % and 59 %, respectively in 2017 and 2018. Comparing the two SABER channels which measure O3 at different wavelengths and use different processing schemes, the 9.6 µm O3 autumn twilight VMR data for the three years 2017–2019 are higher than the corresponding 1.27 µm measurements with the largest difference (58 %) in the 65–95 km altitude range similar to the NAOMI observation. The SABER 9.6 µm O3 summer daytime (SZA < 75∘) mesospheric O3 VMR is also consistently higher than the 1.27 µm measurement, confirming previously reported differences between the SABER 9.6 µm channel and measurements of mesospheric O3 by other satellite instruments.

PSV-17 The Interaction Between Heat Stress and Zearalenone Toxicity in Porcine Skeletal Muscle

Abstract Heat stress (HS) and Zearalenone (Zen) exposure negatively impact growth, production efficiency, animal welfare, and both can be lethal. Zearalenone, a mycotoxin, can contaminate a variety of feedstuffs. As both HS and Zen independently can cause oxidative stress, we hypothesized that concomitant exposure to HS and Zen would cause oxidative stress in pig skeletal muscle more than either agent alone. Prepubertal gilts (n = 26) were fed placebo or Zen (40 ug/kg) twice daily and exposed to thermoneutral conditions (TN; 21.0 °C) or a 12 h diurnal HS challenge (simulated night - 32.2 °C; simulated day - 35.0 °C) for 7 d. Immediately following the environmental challenge, pigs were euthanized and the oxidative (STR) portion of the semitendinosus muscle was collected. Heat stress increased rectal temperature following the simulated nighttime (TN: 38.9 ± 0.04 °C; HS: 39.3 ± 0.04; P < 0.0001) and daytime (TN: 39.2 ± 0.09 °C; HS: 39.9 ± 0.08 °C; P < 0.0001) conditions. In STR, malondialdehyde-modified protein abundance (P = 0.1) and concentration (P < 0.1), markers of oxidative stress, tended to increase following Zen exposure, largely driven by the HS-Zen group. Heat stress increased protein expression of antioxidant enzymes catalase (p< 0.05) and superoxide dismutase 1 (P < 0.05), while Zen decreased glutathione peroxidase 1 (P < 0.05) transcript (P < 0.05) and protein (P < 0.05) abundance and enzymatic activity (P < 0.05). Although a mild HS paradigm and low Zen exposure were employed, oxidative stress markers were altered suggesting oxidative skeletal muscle is susceptible to HS- and Zen-mediated changes in redox balance. In total, these data raise the possibility that more severe HS exposures and greater Zen doses, similar to those sometimes encountered in production settings, may compromise muscle health.

Triclosan uptake and transformation by the green algae Euglena gracilis strain Z

Triclosan is an antimicrobial chemical present in consumer products that is frequently detected in aquatic environments. In this research, we investigated the role of a common freshwater microalgae species, Euglena gracilis for triclosan uptake and transformation in open–water treatment wetlands. Lab–scale wetland bioreactors were created under various conditions of light (i.e., continuous (white) light, red light, and in the dark), media (i.e., wetland, autoclaved wetland, Milli–Q, and growth media water), and presence or absence of algae. Triclosan and its potential transformation products were identified in the water and algae phases. Triclosan transformation occurred most rapidly with reactors that received continuous (white) light, with pseudo first–order rate constants, k, ranging from 0.035 to 0.292 day−1. This indicates that phototransformation played a major role in triclosan transformation during the day, despite light screening by algae. Algae contributed to the uptake and transformation of triclosan in all reactors, and algae and bacteria both contributed to triclosan biotransformation under dark conditions, representative of nighttime conditions. Some transformation products were formed and further transformed, e.g., triclosan–O–sulfate, methoxy and diglucosyl conjugate of hydroxylated triclosan, and dimethoxy and glucosyl conjugate of 2,4–dichlorophenol, suggesting their minimal accumulation over the 25 days of the experiments. This study shows that the combined action of light, microbes, and algae allows the safe transfer and transformation of triclosan in open–water treatment wetlands.

Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles

Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%.

Diurnal Differences in Tropical Maritime Anvil Cloud Evolution

AbstractSatellite observations of tropical maritime convection indicate an afternoon maximum in anvil cloud fraction that cannot be explained by the diurnal cycle of deep convection peaking at night. We use idealized cloud-resolving model simulations of single anvil cloud evolution pathways, initialized at different times of the day, to show that tropical anvil clouds formed during the day are more widespread and longer lasting than those formed at night. This diurnal difference is caused by shortwave radiative heating, which lofts and spreads anvil clouds via a mesoscale circulation that is largely absent at night, when a different, longwave-driven circulation dominates. The nighttime circulation entrains dry environmental air that erodes cloud top and shortens anvil lifetime. Increased ice nucleation in more turbulent nighttime conditions supported by the longwave cloud-top cooling and cloud-base heating dipole cannot compensate for the effect of diurnal shortwave radiative heating. Radiative–convective equilibrium simulations with a realistic diurnal cycle of insolation confirm the crucial role of shortwave heating in lofting and sustaining anvil clouds. The shortwave-driven mesoscale ascent leads to daytime anvils with larger ice crystal size, number concentration, and water content at cloud top than their nighttime counterparts.Significance StatementDeep convective activity and rainfall peak at night over the tropical oceans. However, anvil clouds that originate from the tops of deep convective clouds reach their largest extent in the afternoon hours. We study the underlying physical mechanisms that lead to this discrepancy by simulating the evolution of anvil clouds with a high-resolution model. We find that the absorption of sunlight by ice crystals lofts and spreads the daytime anvil clouds over a larger area, increasing their lifetime, changing their properties, and thus influencing their impact on climate. Our findings show that it is important not only to simulate the correct onset of deep convection but also to correctly represent anvil cloud evolution for skillful simulations of the tropical energy balance.

Secondary organic aerosol and organic nitrogen yields from the nitrate radical (NO<sub>3</sub>) oxidation of alpha-pinene from various RO<sub>2</sub> fates

Abstract. The reaction of α-pinene with NO3 is an important sink of both α-pinene and NO3 at night in regions with mixed biogenic and anthropogenic emissions; however, there is debate on its importance for secondary organic aerosol (SOA) and reactive nitrogen budgets in the atmosphere. Previous experimental studies have generally observed low or zero SOA formation, often due to excessive [NO3] conditions. Here, we characterize the SOA and organic nitrogen formation from α-pinene + NO3 as a function of nitrooxy peroxy (nRO2) radical fates with HO2, NO, NO3, and RO2 in an atmospheric chamber. We show that SOA yields are not small when the nRO2 fate distribution in the chamber mimics that in the atmosphere, and the formation of pinene nitrooxy hydroperoxide (PNP) and related organonitrates in the ambient atmosphere can be reproduced. Nearly all SOA from α-pinene + NO3 chemistry derives from the nRO2+ RO2 pathway, which alone has an SOA mass yield of 56 (±7) %. Molecular composition analysis shows that particulate nitrates are a large (60 %–70 %) portion of the SOA and that dimer formation is the primary mechanism of SOA production from α-pinene + NO3 under simulated nighttime conditions. Synergistic dimerization between nRO2 and RO2 derived from ozonolysis and OH oxidation also contribute to SOA formation and should be considered in models. We report a 58 (±20) % molar yield of PNP from the nRO2+ HO2 pathway. Applying these laboratory constraints to model simulations of summertime conditions observed in the southeast United States (where 80 % of α-pinene is lost via NO3 oxidation, leading to 20 % nRO2+ RO2 and 45 % nRO2+ HO2), we estimate yields of 11 % SOA and 7 % particulate nitrate by mass and 26 % PNP by mole from α-pinene + NO3 in the ambient atmosphere. These results suggest that α-pinene + NO3 significantly contributes to the SOA budget and likely constitutes a major removal pathway of reactive nitrogen from the nighttime boundary layer in mixed biogenic–anthropogenic areas.

A comparison of daytime and nighttime pedestrian road-crossing behavior using an immersive virtual environment

Objective Reduced visibility for both drivers and pedestrians is a key factor underlying the higher risk of vehicle-pedestrian collisions in dark conditions. This study investigated the extent to which pedestrians adjust for the higher risk of road crossing at night by comparing daytime and nighttime pedestrian road crossing using an immersive virtual environment. Method Participants physically crossed a single lane of continuous traffic in an immersive pedestrian simulator. Participants were randomly assigned to either the daytime or the nighttime lighting condition. The primary measures were the size of the gap selected for crossing and the timing of crossing motions relative to the gap. Results The results showed that there were no significant differences in gap selection or movement timing in daytime vs. nighttime lighting conditions. However, there was a marginal increase in the time to spare after crossing the road when crossing in the dark, likely due to an accumulation of small differences in gap choices and movement timing. Conclusion This study suggests that pedestrians do not adjust their road crossing to account for greater risk at night. As such, this study adds to our understanding of the potential risk factors for pedestrian injuries and fatalities in nighttime conditions.

Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches

Characterizing the spatiotemporal variability of the Urban Heat Island (UHI) and its drivers is a key step in leveraging thermal comfort to create not only healthier cities, but also to enhance urban resilience to climate change. In this study, we developed specific daytime and nighttime multiple linear regression (MLR) and random forest (RF) models to analyze and predict the spatiotemporal evolution of the Urban Heat Island intensity (UHII), using the air temperature (Tair) as the response variable. We profited from the wealth of in situ Tair data and a comprehensive pool of predictors variables — including land cover, population, traffic, urban geometry, weather data and atmospheric vertical indices. Cluster analysis divided the study period into three main groups, each dominated by a combination of weather systems that, in turn, influenced the onset and strength of the UHII. Anticyclonic circulations favored the emergence of the largest UHII (hourly mean of 5.06 °C), while cyclonic circulations dampened its development. The MLR models were only able to explain a modest percentage of variance (64 and 34% for daytime and nighttime, respectively), which we interpret as part of their inability to capture key factors controlling Tair. The RF models, on the other hand, performed considerably better, with explanatory power over 96% of the variance for daytime and nighttime conditions, capturing and mapping the fine-scale Tair spatiotemporal variability in both periods and under each cluster condition. The feature importance analysis showed that the meteorological variables and the land cover were the main predictors of the Tair. Urban planners could benefit from these results, using the high-performing RF models as a robust framework for forecasting and mitigating the effects of the UHI.

NASA’s Black Marble Multiangle Nighttime Lights Temporal Composites

In addition to the daily Black Marble products (VNP46A1/A2), the recently released temporal composite products (VNP46A3/A4) are generated from daily high-quality, atmospheric, and lunar-Bidirectional Reflectance Distribution Function (BRDF) corrected, Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime radiances (VNP46A2). This letter describes the algorithm of NASA’s Black Marble monthly and annual (VNP46A3/A4) Nighttime Light (NTL) composites that are operationally produced for multiple view angle categories and snow conditions, along with ancillary metrics and mandatory quality assurance flags. Boxplot metrics are calculated to systematically remove outliers and minimize reliance on empirical or regional thresholds. VNP46A3/A4 products represent all nighttime conditions (i.e., moon-lit and moon-free periods), leading to an increased number of high-quality observations. To fully make use of the VIIRS DNB’s wide-swath (3040 km), both VNP46A3/A4 are generated for three view zenith angle categories (near-nadir, off-nadir, and all-angles) and two different snow conditions (snow-free and snow-covered). The introduction of global multidirectional data should be of interest to the scientific and applications communities. In particular, they can enable standardized intercomparisons across NTL products and allow a greater capability to track additional dimensions of NTL change, including the intrinsic characteristics of human settlements and their infrastructure. VNP46A3/A4 products are currently made available retrospectively via NASA’s Level-1 and Atmosphere Archive and Distribution System (LAADS) from the start of the Suomi-National Polar-orbiting Partnership (S-NPP) mission period (c. January 2012).

Effect of 21 June 2020 solar eclipse on the ionosphere using VLF and GPS observations and modeling

A solar eclipse event provides a great opportunity to examine the behavioral concept of the ionospheric electron density (N e ) variability in the low latitude region. The current work presents our outcomes from the simultaneous assessment of Tweeks (radio atmospherics) and radio signals (fixed frequency of the transmitter's signal) from multifarious VLF transmitters observed at Varanasi (Geog. Lat. 25.27 0 N, Geog. Long. 82.98 0 E, Geomag. Lat. 14 0 55′N). To find the presence of disturbances in the ionosphere Global Positioning System (GPS) data at Hyderabad (geog. lat. 17 0 20 / N, long. 78 0 30 / E) and Bangalore (geog. lat. 12 0 58 / N, long. 77 0 33 / E) is also analyzed during the period of the solar eclipse on 21 June 2020. As the Sun was eclipsed, the nighttime phenomenon of ‘Tweeks' was also observed in the daytime through the annular solar eclipse due to nighttime conditions as the solar disc was dusked. Tweek analysis shows the variation in the ionospheric reflection heights (∼8–11 km) and electron density (∼3–2 cm −3 ) in the D-region during the eclipse. The reflection height of the D-region ionosphere increases from ∼84 km and goes to ∼95 km and then decreases to ∼87 km. Electron concentration (electron density) decreased throughout the eclipse from 24 cm −3 to 21 cm −3 and then increases to 23 cm −3 . Eclipse-imposed modifications in VLF transmitter’s (HWU and NWC) signals displays an average change (decrease) of 2.8 dB and 0.8 dB in the signal strength of 18.3 kHz (HWU) and 19.8 kHz (NWC) transmitters respectively and a rise in virtual reference height (H′) and sharpness factor (β), as compared with normal days. The de-trended value of total electron content (DTEC) variations at both stations clearly shows the presence of travelling ionospheric disturbances (TIDs) having wave-like features. The Fast Fourier Transform (FFT) analysis shows that periodicity at both the station lies in two regimes one belongs to a period between 20 and 50 min and the other belongs to 50–90 min indicating such oscillation observed in the ionosphere are induced by atmospheric gravity waves (AGWs) generated during the period of the solar eclipse.

Ship classification based on convolutional neural networks

ABSTRACT The main bottleneck limiting the use of traditional ship classification methods is the manual extraction of ship images before classification. To solve this problem, a ship classification method based on a convolutional neural network (CNN) is proposed in this paper. A CNN model can autonomously extract image features, avoiding complex feature selection and extraction processes. In view of the problem of an insufficient number of ship samples, transfer learning was applied to train the model using the ImageNet dataset, effectively alleviating the over-fitting phenomenon in the training process. Experiments showed that the CNN model had an accuracy of 98% in ship classification using the SHIP-3 dataset. The CNN was robust to external environmental challenges – such as illumination – the accuracy of ship classification in foggy and night-time conditions reaching 75%, greatly exceeding the performance of traditional machine learning algorithms.