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Satellites engaged in observation missions often face severe pointing constraints, which can limit the ability of their solar panels optimize the generation of electrical power during payload operations. Some approaches to maximize power generation involve either using orientable solar panels, or performing orbital maneuvers to point fixed or deployable (non-orientable) solar panels towards the Sun, which can compromise the payload's ability to meet its pointing requirements. In this study, an alternative approach that enables high-consuming remote sensing payloads to operate for extended periods without requiring orientable solar panels is presented. The satellite under consideration is equipped with fixed or deployable (non-orientable) solar panels mounted on up to three different perpendicular faces. The on-board payload is required to remain within a fixed angle from a target direction, which can be either nadir or a specific geographical position. To derive an optimal tracking law, the projected solar array area at each instant is maximized, resulting in optimal electrical power generation. This methodology allows the satellite to maintain its pointing constraints and efficiently operate high-consuming payloads for longer periods. The proposed tracking law is validated using an actual mission scenario, comparing the power generation achieved by the proposed method against other techniques. The approach presented in this paper shows improved performance, enabling satellites equipped with non-orientable solar panels to fulfill their observation requirements while optimizing electrical power generation. This work offers significant benefits for satellite operators, reducing the need for costly orientable solar panels and enhancing the overall efficiency of satellite missions. • Guidance law for optimal power generation while complying with payload pointing constraints. • Simple and easy formulation, suitable for real-time systems. • Methodology validated and compared against other attitude strategies. • Integration and simulation of the guidance law into a control scheme. [ABSTRACT FROM AUTHOR]

Landslides are among the most dangerous and catastrophic natural hazards with countless concerns. In disaster rescue operations, fast and precise identification of landslides is necessary for timely and effective preventive actions. The landslide risk is anticipated to be reduced through their prediction, monitoring, and accurate detection using remote sensing technology. Moreover, deep learning algorithms have shown excellent improvement in various remote sensing applications. Recent scientific and intelligent technological innovations are needed to be applied to disaster management and assessment, particularly landslides. Therefore, this study aims to extract the landslide hazard information from multiple data sources, i.e., satellite and unmanned aerial vehicle (UAV) images, using a single staged object detection model, i.e., YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The data from distinct platforms are utilized to infer the synergies between them. The results of each database are evaluated quantitatively using standard methods, i.e., precision, recall, f-score, and mean average precision, whereas visual analysis of results is conducted for qualitative assessment. Based on the experimental results, the highest f-score is represented by YOLOv7 (0.995) and YOLOv5 (0.921) for satellite and UAV-based data, respectively. The quantitative results are further compared with previous research work to exhibit the novelty and competence of the proposed research. Our work demonstrates the application and feasibility of the YOLO model in landslide information extraction for quick hazard recovery operations. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite‐based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best‐performing regression models to estimate surface NO2 at 0.01° by 0.01° resolution, and we term this estimate as quasi‐NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross‐validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non‐Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications. Plain Language Summary: Nitrogen dioxide (NO2) is an air pollutant which causes cardiovascular and respiratory illnesses and reacts in the atmosphere to form other harmful pollutants. This necessitates accurate and reliable quantification of NO2 concentrations in the air. Ground monitors directly observe NO2 concentrations near the Earth's surface. However, monitors do not have sufficient spatial coverage to quantify NO2 at large scales. Satellite‐based instruments capture NO2 amounts across the Earth at increasingly high spatial resolution. However, satellite instruments cannot directly observe surface NO2 concentrations. In this study, we compare regression methods for estimating surface NO2 over the continental U.S. using satellite data and auxiliary land‐use variables. We find that NO2 estimated using multivariate regression models with transforms applied to inputs result in the highest agreement with surface NO2 among the regression methods we investigated. We then use the regression models to quantify surface NO2 concentration across the U.S. at 0.01° by 0.01° spatial resolution. Our work leverages the precision of ground observations and the high resolution of satellite data to accurately quantify surface NO2. The interpretable, generalizable, and easily applicable methods used in our study will facilitate the use of satellite data for air quality and human health assessments. Key Points: We compare regression methods to estimate surface nitrogen dioxide concentrations at 0.01° resolution using satellite and land use dataMultivariate linear regression with Anscombe‐transformed inputs has strongest agreement with surface nitrogen dioxide measurementsRegression methods provide accurate, low‐bias concentration estimates with minimal computational and data requirements [ABSTRACT FROM AUTHOR]

Societal Impact Statement Summary Unmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation. This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery. Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs. By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources. The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest. [ABSTRACT FROM AUTHOR]

• Satellite model based on attitude error in MRP and angular velocity error. • EMPC applied to pointing an underactuated satellite for large attitude maneuvers. • Satisfactory final pointing error of the algorithm, even in a critical actuator failure scenario. The space sector has become increasingly relevant, especially satellites, due to their ability to access different parts of the planet in a short space of time. The challenge of controlling the satellite's attitude is crucial to the success of a mission. However, the actuator failure condition makes this task even more challenging since the system becomes underactuated, and the control strategies that deal with this scenario and present a satisfactory computational cost are limited unless a simplification of the system dynamics is adopted, such as considering the total angular momentum equal to zero or the diagonal moment of inertia matrix. In this work, extended model predictive control (EMPC) was used to point a satellite in the nadir direction in the presence of actuator failure. This controller uses a model of the system to optimize the control variable over a finite prediction horizon, obtaining a near-optimal solution. The control strategy was tested through a Monte Carlo simulation in two failure scenarios, the first with a failure in the reaction wheel and then also with a failure in the magnetic-torque rod. The results showed that for the first failure scenario, the time required to perform pointing and the final error were lower than for the second failure scenario. The computational load was evaluated through a closed-loop simulation using an embedded processor based on the ARM platform. The simulation shows that the model requires less memory and is more efficient in terms of execution time. The robustness analysis shows that the controller is robust to external disturbance torques and errors of up to 50% in the inertia of the model, with no significant loss in performance. We suggest that the proposed technique is capable of performing pointing with satisfactory accuracy, even with two actuator failures. In addition, this approach can yield substantial cost savings, which is more pronounced for small satellite platforms such as CubeSats. [ABSTRACT FROM AUTHOR]

In this research, we explain comprehensive industrial and innovation results on using an artificial neural network (ANN) method to improve the performance of microstrip patch antennas for 5G, indoor-outdoor, and Ku band uses. To determine if an antenna is appropriate, this article discusses multiple methods, one of which is to do a simulation using validating software like high frequency structure simulator (HFSS) and Altair Feko. Based on the Rogers RT 5880 substrate, the antenna is constructed. There is a loss tangent of 0.0009 and its dimensions are 17.1053 mm in length and 16 mm in width. Its dielectric constant is 2.2. Despite its small size, it boasts an impressive maximum efficiency of almost 90% and a gain of approximately 8 dB. As an indicator of ANN model performance, we may look at the R-squared value (99%), the mean square error (MSE), which is approximately 0.0015, and the confidence interval (99%). The ANN models are the most accurate and have the lowest error rate when it comes to predicting efficiency and gain. The suggested antenna is a promising contender for the targeted Ku band, indoor/outdoor, and 5G uses, as verified by the clustering of computer simulation technology (CST), HFSS, and Altair Feko simulated results with the measured and predicted outcomes of ANN approach. [ABSTRACT FROM AUTHOR]

Wind plays a crucial role in shaping climatic and environmental conditions; however, its spatial and temporal variabilities over land remain poorly understood. This study utilizes combined datasets from multiple satellite platforms to examine the spatial patterns and temporal trends of wind speed over land during the past 30 years (1988-2017). The results demonstrate a significantly increasing trend in global wind speed, with variations on both annual and monthly scales. Spatially, wind speeds are lower in low-latitude regions, particularly in the tropics. High wind speeds primarily occur in mid- to high-latitude regions, with the highest speeds observed in the Arctic. Temporally, wind speed trends are characterized by an increase in low-speed winds and decrease in high-speed winds. Overall, wind speed shows a significant correlation with temperature in the Amazon, northern Europe, and central Asia. Negative wind-temperature correlation is confined to small regions in central North America and northern Africa. The findings of this study help clarify wind trends, providing valuable insights for future research on global climate and environmental change. [ABSTRACT FROM AUTHOR]

The Deep Space Climate Observatory (DSCOVR) spacecraft drifts about the Lagrangian point ≈ 1.4 − 1.6 × 106 km from Earth, where its Earth Polychromatic Imaging Camera (EPIC) observes the entire sunlit face of Earth every 1–2 h. In an attempt to detect “signals,” i.e., longer-term changes and semi-permanent features such as the ever-present ocean glitter, while suppressing geographic “noise,” in this study, we introduce temporally and conditionally averaged reflectance images, performed on a fixed grid of pixels and uniquely suited to the DSCOVR/EPIC observational circumstances. The resulting images (maps), averaged in time over months and conditioned on surface/cover type such as land, ocean, or clouds, show seasonal dependence literally at a glance, e.g., by an apparent extent of polar caps. Clear ocean-only aggregate maps feature central patches of ocean glitter, linking directly to surface roughness and, thereby, global winds. When combined with clouds, these blue planet “moving average” maps also serve as diagnostic tools for cloud retrieval algorithms. Land-only images convey the prominence of Earth’s deserts and the variable opacity of the atmosphere at different wavelengths. Insights into climate science and diagnostic and educational tools are likely to emerge from such average reflectance maps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration's (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA's operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA's benefit. STAR's satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications. [ABSTRACT FROM AUTHOR]

Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making. [ABSTRACT FROM AUTHOR]

Accurate predictions of streamflow and flood events are contingent upon the availability of reliable hydrometeorological data. In regions characterized by scarcity of ground‐based hydrometeorological observations, satellite and reanalysis data assume prominence as alternative predictors. Floods and droughts have emerged as a significant concern in Northern Ghana, yet the scarcity of ground‐based hydrometeorological data impedes effective prediction of these hydrological events. Consequently, the identification of suitable surrogate hydrometeorological data holds paramount importance in addressing these challenges. This study, therefore, assessed the accuracy of satellite and reanalysis data against ground‐based data in Northern Ghana. Rainfall and mean temperature spanning from 1998 to 2019 and soil moisture datasets from 2019 to 2022 were collected from GMet, ISMN (ground‐based), CHIRPS, PERSIANN‐CDR, ERA5, ARC2, MERRA‐2, TRMM and CFSR (satellite and reanalysis). Employing rigorous statistical measures, namely standard deviation, mean absolute error (MAE) and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN‐CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. In terms of mean temperature prediction, ERA5 surpassed MERRA‐2 and CFSR. Regarding soil moisture assessments, both ERA5 and CFSR offered satisfactory simulations. Hence, our findings advocate for the preference of CHIRPS (for rainfall data), ERA5 (for temperature data) and a combination of CFSR/ERA5 (for soil moisture data) as dependable primary data sources for streamflow modelling, drought analysis, flood prediction and water resource management in the context of Northern Ghana. [ABSTRACT FROM AUTHOR]

Hydrologists rely heavily on satellite sensors because they provide useful information for tracking, evaluating, managing water resources, aiding provision of safe drinking water, help preventing waterborne diseases, and address the challenges posed by climate change. Water conservation and the collection of hydrologic data have made remote sensing (RS) an invaluable tool. As a result, there are fewer hydrologic stations globally in terms of space because of various topography landforms, human limitations, and financial limits. A thorough examination of the RS satellite products’ hydrological applications is essential to finding a solution to this issue. By doing this, academicians, researchers, and conservationists in various professions can better understand the products and obtain the data needed for conservation. This paper primarily focuses on the following two objectives: 1). To synthesize the scientific information on satellite remote sensing application for hydrology, and 2). To explain the RS dataset sources for hydrologic parameters. Extensive literature search from reputable journal publishers. This review article synthesized vital sources of information for academicians, researchers, and government agencies involved in hydrology and water resources management. It is recommended that RS can be used as a data source for scarce, sparsely gauged, and inaccessible regions. [ABSTRACT FROM AUTHOR]

Satellite‐based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC‐Argo floats are now filling the information gap at depth. Here we use BGC‐Argo data to assess depth‐resolved information on chlorophyll‐a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data‐assimilating model replicates well the general seasonality and meridional gradients in surface and depth‐resolved chlorophyll‐a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float‐based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non‐iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m−3, which should allow the detection of seasonal changes in float‐based biomass (varying between 0.01 and >1 mg Chl m−3) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth‐resolved observations. Uncertainties in float bio‐optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color. Plain Language Summary: Changes in oceanic chlorophyll can indicate alterations in the biomass of photosynthetic algae, which sustain marine ecosystems and have the capacity to alter the climate system. Algae live not only in the surface of the ocean, where satellites can observe them, but also beneath the upper marine layer, where autonomous profiling systems (floats) are used to infer their vertical distribution. Here we use float profiling data to evaluate the representation of chlorophyll biomass with depth from a numerical ecosystem model informed by surface satellite information. Chlorophyll outputs from the model agree generally well with information from the floats in terms of timing and spatial representation of low and high chlorophyll areas. Overall, the model's chlorophyll concentrations are slightly higher than those indicated by the floats. However, float measurements are not perfect, as suggested by a localized comparison of both float and model chlorophyll profiles with field observations at various oceanic depths near the Hawaiian Islands. While neither float‐ nor model‐based data produce perfect estimations of oceanic chlorophyll across all three dimensions, their general agreement suggests that the model is able to mimic biological and physical dynamics both at and below the ocean surface. Key Points: Modeled vertical chlorophyll profiles obtained by assimilating satellite ocean color are compared against BGC‐Argo and in situ dataModel chlorophyll is higher but replicates well the general seasonality and meridional gradients of the BGC‐Argo array in the Southern OceanMean chlorophyll profiles from the model tend to be in better agreement with in situ data at station ALOHA (∼±50%) than float estimates [ABSTRACT FROM AUTHOR]

This article proposes the method for conducting thermal vibration analyses of the composite sandwich plate of a satellite in the space environment. Based on the continuous model, the method is derived using the Rayleigh-Ritz method and includes thermo-elastic coupling vibration by the space environment. In addition, the validity of the method is verified by the results of the finite element analysis and experimental analysis. Finally, parametric analysis is performed to investigate thermo-elastic coupling vibration characteristics of a satellite structure. This result allows a database to be design guidelines of composite sandwich structures for aerospace applications. We then concluded that the method is well-suited to thermally induced vibration analysis of a composite sandwich plate for aerospace applications due to their relative simplicity and computational efficiency. [ABSTRACT FROM AUTHOR]

Ultra-low Earth orbit (LEO) satellites are widely used in the military, remote sensing, scientific research, and other fields. The ultra-LEO satellite faces the harsh aerothermal environment, and the complex variable attitude task requires the radiator of the satellite to not only meet the heat dissipation requirements of the load but also to resist aerothermal flux. In this study, the aerothermal flux of 160–110 km was calculated, and the loop heat pipe (LHP) coupled with a thermo-electric cooler (TEC) and multi-layer insulation (MLI) were applied to ultra-LEO satellites to determine the variable switching and fast response of heat dissipation and heat protection. An aerothermal flux simulation test platform was built. After the assessment of the ultra-LEO aerothermal flux test, even when the head temperature was as high as 350 °C and the side radiator temperature was as high as 160 °C, the temperature of the internal heat source could be controlled within 22.5 °C through the efficient work of the thermal variable switch system. The study confirms the accuracy and feasibility of the system, which provides an important reference for the subsequent actual on-orbit mission. [ABSTRACT FROM AUTHOR]

The extent to which emission control technologies and policies have reduced anthropogenic NOx emissions from motor vehicles is large but uncertain. We evaluate a fuel-based emission inventory for southern California during the June 2021 period, coinciding with the Re-Evaluating the Chemistry of Air Pollutants in CAlifornia (RECAP-CA) field campaign. A modified version of the Fuel-based Inventory of Vehicle Emissions (FIVE) is presented, incorporating 1.3 km resolution gridding and a new light-/medium-duty diesel vehicle category. NOx concentrations and weekday-weekend differences were predicted using the WRF-Chem model and evaluated using satellite and aircraft observations. Model performance was similar on weekdays and weekends, indicating appropriate day-of-week scaling of NOx emissions and a reasonable distribution of emissions by sector. Large observed weekend decreases in NOx are mainly due to changes in on-road vehicle emissions. The inventory presented in this study suggests that on-road vehicles were responsible for 55-72% of the NOx emissions in the South Coast Air Basin, compared to the corresponding fraction (43%) in the planning inventory from the South Coast Air Quality Management District. This fuel-based inventory suggests on-road NOx emissions that are 1.5 ± 0.4, 2.8 ± 0.6, and 1.3 ± 0.7 times the reference EMFAC model estimates for on-road gasoline, light- and medium-duty diesel, and heavy-duty diesel, respectively.

Abstract Identification of Aedes aegypti breeding hotspots is essential for the implementation of targeted vector control strategies and thus the prevention of several mosquito-borne diseases worldwide. Training computer vision models on satellite and street view imagery in the municipality of Rio de Janeiro, we analyzed the correlation between the density of common breeding grounds and Aedes aegypti infestation measured by ovitraps on a monthly basis between 2019 and 2022. Our findings emphasized the significance (p ≤ 0.05) of micro-habitat proxies generated through object detection, allowing to explain high spatial variance in urban abundance of Aedes aegypti immatures. Water tanks, non-mounted car tires, plastic bags, potted plants, and storm drains positively correlated with Aedes aegypti egg and larva counts considering a 1000 m mosquito flight range buffer around 2700 ovitrap locations, while dumpsters, small trash bins, and large trash bins exhibited a negative association. This complementary application of satellite and street view imagery opens the pathway for high-resolution interpolation of entomological surveillance data and has the potential to optimize vector control strategies. Consequently it supports the mitigation of emerging infectious diseases transmitted by Aedes aegypti, such as dengue, chikungunya, and Zika, which cause thousands of deaths each year.

Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid negative consequences. In response to the increased need to report uncertainty, we bring attention to the promise of conformal prediction within the domain of EO. Unlike previous uncertainty quantification methods, conformal prediction offers statistically valid prediction regions while concurrently supporting any machine learning model and data distribution. To support the need for conformal prediction, we reviewed EO datasets and found that only 22.5% of the datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Current open implementations require moving large amounts of EO data to the algorithms. We introduced Google Earth Engine native modules that bring conformal prediction to the data and compute, facilitating the integration of uncertainty quantification into existing traditional and deep learning modelling workflows. To demonstrate the versatility and scalability of these tools we apply them to valued EO applications spanning local to global extents, regression, and classification tasks. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that accessible and easy-to-use tools, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of downstream uses such as operational monitoring and decision-making.

Centromeres are epigenetically specified by the histone H3 variant CENP-A. Although mammalian centromeres are typically associated with satellite DNA, we previously demonstrated that the centromere of horse chromosome 11 (ECA11) is completely devoid of satellite DNA. We also showed that the localization of its CENP-A binding domain is not fixed but slides within an about 500 kb region in different individuals, giving rise to positional alleles. These epialleles are inherited as Mendelian traits but their position can move in one generation. It is still unknown whether centromere sliding occurs during meiosis or during development. Here, we first improve the sequence of the ECA11 centromeric region in the EquCab3.0 assembly. Then, to test whether centromere sliding may occur during development, we map the CENP-A binding domains of ECA11 using ChIP-seq in five tissues of different embryonic origin from the four horses of the equine FAANG (Functional Annotation of ANimal Genomes) consortium. Our results demonstrate that the centromere is localized in the same region in all tissues, suggesting that the position of the centromeric domain is maintained during development.

Hydrazine's carcinogenic and toxic nature makes its use in space propulsion expensive and necessitates careful launch campaign planning, which may cause delays. One already established alternative is to use energetic salt-based ionic solution propellants. However, the costs remain high because these salts are costly and highly regulated energetic materials. Additionally, propellants based on these energetic salts produce much higher temperatures during decomposition and combustion, as is the case for hydrazine. Consequently, expensive refractory metal-based alloys and high-temperature oxidation-resistant catalytic reactors must be used, which further drives up the costs. Alternatively, hydrogen peroxide may be used, though only in applications with a low specific impulse requirement. This study explores an alternative approach: using nitromethane, a commonly available laboratory solvent. NMP-001 is a nitromethane-based monopropellant that contains additives that allow for low-pressure combustion and reduce sensitivity to mechanical stimuli. Multiple combustion chamber configurations with varying characteristic lengths were tested in rocket combustion experiments. With the 11.5 m L ∗ -configuration, a 15.1 bar low-pressure limit could be determined. The 7.3 and 5.0 m L ∗ -configurations yielded a 16.2 and 28.3 bar low-pressure combustion limits, respectively. These outcomes are significantly below the 30 bar limit reported in the literature. Despite using only a simple heat-sink combustion chamber with a non-sophisticated injector element and a non-optimized propellant formulation, combustion was 83 to 95 % efficient with a C* ranged between 1133 to 1302 m/s. These figures are higher than for hydrogen peroxide and are comparable to the theoretical performance of state-of-the-art green liquid monopropellants. • Existing alternative ionic propellants are costly and highly regulated due to their instability and energetic properties. • Nitromethane offers a cost-effective solution with comparable performance, leading to the development of NMP-001, a nitromethane-based monopropellant with reduced sensitivity to mechanical stimuli. • Tests showed a lower low-pressure combustion limit of 15 ba. This is significantly lower than the limits reported in the literature. • The measured C*-performance for NMP-001 is better than hydrazine and comparable to LMP-103S. [ABSTRACT FROM AUTHOR]

Abstract A combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5‐year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (> 95%) using object‐based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys.