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The article reveals the importance of space for national security. The points of interaction between space activities and national security are identified, which reflect the growing importance of outer space for ensuring the security of States and emergency management, the growth of international cooperation in the field of space technologies. The newest possibilities of space to ensure national security (warning and military intelligence systems; communication systems and means of coordination; systems of space monitoring of the environment and resource management; systems of border and maritime surveillance; development of other technologies; development of global cooperation) are revealed. The problems of ensuring the priority of national security in the use of space resources have been formed, with the formation of a global strategy that geopolitical stability and prevention of the militarization of space should become key aspects for ensuring the national security of any State. [ABSTRACT FROM AUTHOR]

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Summary: Large low Earth orbit satellite constellations require machine learning methods for enabling autonomy in health keeping of the satellites. Autonomy in health keeping entail's fault detection, isolation and reconfiguration. However, prior to model building, it becomes imperative to conduct exploratory data analysis of the data to gain an intuition of data and to decide the best model. Univariate exploratory data analysis has been carried out on a BUS CURRENT sensor of electrical power system of a low Earth orbit satellite to gain an understanding of data. Various aspects of data like presence of outliers, sampling frequency, missing values, comparison of imputation methods to fill missing values seasonality and trend analysis, stationarity test on data, rolling mean and autocorrelation and partial auto correlation plots have been made, and a detailed statistical analysis of results has been conducted. [ABSTRACT FROM AUTHOR]

The spaceborne solar observation system is crucial for the study of space phenomena such as solar flares, which requires high tracking accuracy. This study presents a coupling model that integrates mechanical, electrical, and control models to investigate the structural flexibility effect on the micro-vibration response. We established a rigid–flexible model using mechanical parts. We considered the influence of flexible features while studying the dynamic responses in its operation. The state-space equations of the system showed that modal frequency, damping, and modal participation factors played significant roles. We derived transfer functions using the Laplace transform of the coupling models to better understand this mechanism, and Simulink models were thereby established. We simulated the acceleration responses of the rigid–flexible and rigid models under angle tracking modes, and the results showed significant differences. We also simulated the acceleration responses of the models under various control frequencies, and the optimal control frequency was thus obtained. Finally, we performed experiments, and the results indicated that the rigid–flexible model could better predict the motion and acceleration responses for the spaceborne solar observation system. This study provides valuable information for understanding the role of flexible features in space performance high-tracking accuracy instruments and for micro-vibration suppression research. [ABSTRACT FROM AUTHOR]

The use of global navigation satellite system reflectometry (GNSS-R) to detect and locate maritime targets is currently attracting considerable attention from researchers. However, some difficulties in detecting and locating maritime targets based on GNSS-R remain. First, targets are difficult to detect due to the influence of sea clutter; second, the low resolution of the delay-Doppler map (DDM) leads to low location accuracy. For maritime target detection, this study proposes a DDM symmetry-based target detection algorithm that can be used to supplement previous target detection methods. For location, this study proposes a maritime target location method using geometric semantic constraints based on spaceborne GNSS-R. Comparing the results of direct location and linear semantic constraint location of the South China Sea No. 4 (SCS-4) shows that after using geometric semantic constraints, the absolute accuracy increased by an average of 2.73 km and the relative accuracy increased by an average of 32.32%. This method can help improve the location accuracy of CYGNSS for maritime targets. [ABSTRACT FROM AUTHOR]

Blending of temporal high-frequency-low-spatial resolution MODIS with temporal low-frequency-highspatial resolution Landsat satellite imagery enhances the frequency and resolution of spatial data, thus enabling continuous monitoring of dynamic and rapidly changing environmental conditions. Blending can be particularly useful in areas with high cloud cover, such as during the monsoon season in the coastal zone of the Ganges Brahmaputra Delta (CZGBD). In this study, MODIS - Landsat blending of reflectance-derived remote sensing indices is trialled and evaluated in the CZGBD. The Sub-pixel class fraction change information Flexible Spatiotemporal DAta Fusion (SFSDAF) algorithm is used to obtain gap free 30 m and 16 - day frequency vegetation, salinity and water indices for the entire CZGBD. Pixels obtained through blending were compared to the observed pixels (i.e., not contaminated by clouds to evaluate the accuracy of SFSDAF). Results during the 'dry' months (October to March) had a combined mean coefficient of determination, R2 = 0.65 and mean root squared error, RMSE = 0.09 for vegetation indices, whereas the results during the 'wet' months (April to September) had a combined mean R² = 0.33 and mean RMSE = 0.12. The reduced accuracy of the blending during the monsoon months showcases the effects of cloudy conditions. Results for salinity and water indices showed similar behaviour as the vegetation indices, influenced by the cloudy monsoon season. The main cause of low accuracy during the 'wet' months is the paucity of data to perform the blending, even at the daily MODIS frequency. In addition, remote sensing indices with equations that normalised the range (generally between - 1 and 1) had better results when compared to remote sensing indices that had less constrained ranges. [ABSTRACT FROM AUTHOR]

We demonstrate geostationary satellite monitoring of large transient methane point sources with the US Geostationary Operational Environmental Satellites (GOES). GOES provides continuous 5- to 10-min coverage of the Americas at 1 to 2 km nadir pixel resolution in two shortwave infrared spectral bands from which large methane plumes can be retrieved. We track the full evolution of an extreme methane release from the El Encino--La Laguna natural gas pipeline in Durango, Mexico on 12 May 2019. The release lasted 3 h at a variable rate of 260 to 550 metric tons of methane per hour and totaled 1,130 to 1,380 metric tons. We report several other detections of transient point sources from oil/gas infrastructure, from which we infer a detection limit of 10 to 100 t h-1. Our results show that extreme releases of methane can last less than an hour, as from deliberate venting, and would thus be difficult to identify and quantify with low-Earth orbit satellites. [ABSTRACT FROM AUTHOR]

Over the last 15 years, new important developments occurred in the reference method and use of optical sensors dedicated to in-season potato crop nitrogen (N) status (CNS) monitoring. The reference method is based on N nutrition index (NNI) defined as the ratio of critical N concentration (%Nc) and actual plant N concentration (%N) during the growing season, %Nc being deduced from the critical N dilution curve (CNDC) in total crop biomass. New statistical insights on the 11 different existing potato CNDCs worldwide, considered as site- and time-specific because they are built on specific combinations of potato cultivars (G), environment (E) and crop management practices (M), led to the assumption that it could be possible to derive more generic CNDCs providing large sets of data (pairs of biomass and plant N% assessments) from different G×E×M situations. Practical application of the reference method for CNS assessment is labour intensive and time consuming due to plant sampling and laboratory analysis for N content. However, a more generic NNI reference method is valuable to define threshold or critical values of plant N-related biophysical or biochemical variables (BVs) that can be retrieved from optical sensor readings and used as quick indicators for CNS assessment. Optical sensors are classically used to measure leaf or canopy transmittance, reflectance and chlorophyll fluorescence of visible (VIS) and near-infrared (NIR) wavelengths of the electromagnetic spectrum and related to leaf chlorophyll content (LCC) demonstrated as strongly correlated to leaf N content (LNC). Optical sensors applied at the leaf level are mainly handheld devices based on transmittance and chlorophyll fluorescence and have specific advantages and weaknesses that are extensively described to derive BVs and to assess potato CNS. Their main application is dedicated for CNS assessment at small field scale showing low within-field variability. Reflectance-based, remotely sensed optical devices are applied at canopy level from different ground-based, airborne or spaceborne platforms. Ground-based and airborne approaches, combined with different reflectance data analysis and processing mathematical and statistical methods (vegetation indices, chemometrics, machine learning) or physically-based approach such as inversion of radiative transfer models, allowed reliable retrieve of N-related BVs for CNS assessment such as LCC, canopy chlorophyll content (CCC), leaf area index (LAI) or others BVs. Remote sensing from spaceborne platforms, with the recent and forthcoming launches of satellite missions equipped with multispectral and hyperspectral sensors and with enhanced spatial, temporal, spectral and radiometric resolutions in the VIS and NIR regions but also shortwave infrared (SWIR) region allowing to consider plant N related to leaf proteins, have opened new avenues for real-time and reliable CNS monitoring at field and within-field scale over large territories and based on the setup of generic N-related BVs across different growing conditions and multiple G×E×M scenarios. [ABSTRACT FROM AUTHOR]

Sustainable development is the current paradigm for development in the global south. Since 1976 nations of the global south have conducted space missions. This work investigates which impact on sustainable development in the global south spaceflight has. The majority of Sustainable Development Goals can be addressed with space activities, e.g. by setting up satellite infrastructures for communication, creating businesses or supporting protection of ecosystems. Short-term effects such as build-up of own industry for own satellites and infrastructure can be seen when analysing satellite numbers and mission types. The reduction in launch costs and miniaturization of satellites in the 2010s have increased satellite numbers, by improving accessibility to space technology. This led to a thriving space industry in global south nations e.g. Argentina. The continuity of the effects cannot yet be determined fully, due to the recency of most activities, even though for some nations, e.g. Brazil or Indonesia, activities have been continuous. Strategic integration of space activities into planned sustainable development could not be detected. Potential benefits were realized mostly via services and there is unused potential for technology application on Earth. [ABSTRACT FROM AUTHOR]

Small satellites in low-Earth orbit (LEO) attract much attention from both industry and academia. The latest production and launch technologies constantly drive the development of LEO constellations. However, wideband signals, except text messages or voice in a few Kbps, cannot be transmitted directly from a LEO satellite to a standard mobile cellular phone due to the insufficient link budget. The current LEO constellation network has to use an extra ground device to receive the signal from the satellite first and then forward the signal to the user equipment (UE). To achieve direct network communications between LEO satellites and UE, in this article, we propose a novel distributed beamforming technology based on the superposition of electromagnetic (EM) waves radiated from multiple satellites that can significantly enhance the link budget. EM full-wave simulation and Monte Carlo simulation results are provided to verify the effectiveness of the proposed method. The simulation results show a nearly 6-dB enhancement using two radiation sources and an almost 12-dB enhancement using four sources. The received power enhancement could be doubled compared to the diversity gain in multiple-input, single-output (MISO). Furthermore, other practical application challenges, such as synchronization and the Doppler effect, are also presented. [ABSTRACT FROM AUTHOR]

The research sought to evaluate the normalized difference vegetation index (NDVI) of the study area located in the province of Tayacaja, which includes the districts of Acraquia, Ahuaycha, Pampas and Daniel Hernández, which is part of the central Andes of Peru. The data were collected in low water seasons with a longitudinal cut of 30 years with one sample per year, starting in 1993 until 2022; these samples corresponded to the month of August of each year. The images were extracted from maps from Landsat satellite databases, which were filtered for low cloud cover to avoid interference with the images. Maps from 1993 to 2012 were obtained from Landsat 5 satellite, while from 2013 to 2022 data were obtained from Landsat 8 satellite. The normalized difference vegetation index was determined using Quantum GIS based on the red and near infrared maps; being the minimum NDVI value obtained -0.18, which corresponds to the aquatic body of the Upamayu River that crosses from west to east the study area; while the highest NDVI obtained was 0.79 indicating a greater vegetation cover constituted by mainly eucalyptus plants. The mean NDVI of the 30 years is close to 0.21; this is an indicator that the vegetation is scarce and that it is decreasing mainly due to population growth. [ABSTRACT FROM AUTHOR]

At present, Global Navigation Satellite System (GNSS) satellites operate in medium Earth orbits (MEOs), geostationary orbits (GEOs), or inclined geosynchronous orbits (IGSOs). The QZSS consists of GEO and IGSO satellites. When we considering the QZSS skyplot in Japan and its surrounding areas, there are no satellites in the northern sky. This results in a geometric placement imbalance, and thereby, causes position errors. The fundamental method to solve this problem is to position a satellite in the vacant northern area. Therefore, in this research, the addition of navigation satellites with a highinclination, high-eccentricity orbit (a new type of orbit) is proposed. The vacant northern area can be filled effectively by adding a satellite using this orbit. Three satellites with this orbit were added to the QZSS in a simulation. Thereby, satellites were positioned effectively in the vacant northern area of the skyplot for Tokyo and Seoul. In addition, the improvement in performance was verified quantitatively through Horizontal Dilution of Precision (HDOP) and Vertical Dilution of Precision (VDOP). Accordingly, the addition of a satellite with a high-inclination, high-eccentricity orbit to the QZSS would enable more accurate positioning in Japan and its surrounding areas. [ABSTRACT FROM AUTHOR]

With the success of the Sentinel‐6 Michael Freilich satellite, the satellite radar altimetry record of sea level has now surpassed 30 years in length. This reference satellite mission has continued uninterrupted alongside a significant expansion of the satellite sea‐level observing network in the past decade. This expansion was punctuated by the launch of the Surface Water and Ocean Topography mission in December 2022, which will bring higher resolution measurements of sea level closer to the shore than ever before. Here, we take stock of where we are now in terms of using satellites to measure sea level, and what is still needed to address the growing need of those living along the coast for information to support planning and adaptation efforts. Key Points: With the launch of the Sentinel‐6 MF satellite, the satellite radar altimetry record of sea level has now surpassed 30 years in lengthOther satellites have been launched—including the Surface Water and Ocean Topography mission—that complement the altimeter missionFuture missions should be targeted to provide the information that coastal communities need to prepare for rising sea levels [ABSTRACT FROM AUTHOR]

NASA's ITS_LIVE project delivers information on global glacier dynamics and provides historical context for climate change with a record of how every glacier in the world has evolved over decades of satellite observation. To handle petabytes of data in the satellite archives and a constant influx of new observations, the project has adopted a cloud-native approach that is scalable, performant, user friendly, and embraces transparent and collaborative code development. As every discipline of Earth science is being transformed by a new era of remote sensing and cloud computing, ITS_LIVE offers a progressive approach to maximizing scientific advancements through open science. [ABSTRACT FROM AUTHOR]

The response of the thermospheric daytime longitudinally averaged zonal and meridional winds and neutral temperature to the 2020/2021 major sudden stratospheric warming (SSW) is studied at low-to middle latitudes (0◦ - 40◦N) using observations by NASA’s ICON and GOLD satellites. The major SSW commenced on 1 January 2021 and lasted for several days. Results are compared with the non-SSW winter of 2019/2020 and pre-SSW period of December 2020. Major changes in winds and temperature are observed during the SSW. The northward and westward winds are enhanced in the thermosphere especially above ∼140 km during the warming event, while temperature around 150 km drops up to 50 K compared to the pre-SSW phase. Changes in the zonal and meridional winds are likely caused by the SSW-induced changes in the propagation and dissipation conditions of internal atmospheric waves. Changes in the horizontal circulation during the SSW can generate upwelling at low-latitudes, which can contribute to the adiabatic cooling of the low-latitude thermosphere. The observed changes during the major SSW are a manifestation of long-range vertical coupling in the atmosphere.

The research sought to evaluate the normalized difference vegetation index (NDVI) of the study area located in the province of Tayacaja, which includes the districts of Acraquia, Ahuaycha, Pampas and Daniel Hernández, which is part of the central Andes of Peru. The data were collected in low water seasons with a longitudinal cut of 30 years with one sample per year, starting in 1993 until 2022; these samples corresponded to the month of August of each year. The images were extracted from maps from Landsat satellite databases, which were filtered for low cloud cover to avoid interference with the images. Maps from 1993 to 2012 were obtained from Landsat 5 satellite, while from 2013 to 2022 data were obtained from Landsat 8 satellite. The normalized difference vegetation index was determined using Quantum GIS based on the red and near infrared maps; being the minimum NDVI value obtained -0.18, which corresponds to the aquatic body of the Upamayu River that crosses from west to east the study area; while the highest NDVI obtained was 0.79 indicating a greater vegetation cover constituted by mainly eucalyptus plants. The mean NDVI of the 30 years is close to 0.21; this is an indicator that the vegetation is scarce and that it is decreasing mainly due to population growth.

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals.

Numerous application fields of automation the industrial processes have demonstrated favorable outcomes with modern control algorithms that rely on artificial intelligence. Therefore, some researchers have endeavored to implement these algorithms in the attitude control of satellites by evaluating their performance in simulated scenarios. However, due to the associated cost and risks, there is a scarcity of experimental data available for testing new attitude control algorithms on a real satellite. To address this issue, simulation of satellite positioning based on the dynamic model of the satellite with reaction wheels was carried out to perform a comparative analysis of a hybrid proportional integral and derivative (PID) fuzzy control algorithm and a classical PID control algorithm, regarding the analysis of the obtained performances. The obtained results indicate that using a Hybrid Fuzzy PID control algorithm produces better performances, than the PID control algorithm.

A peak effect minimization problem in the free motion of linear systems is considered in the paper. The paper proposes an iterative procedure for the peak effect minimization using a combination of the recently proposed gramian-based approach and the theory of using the condition number of an eigenvectors matrix for the upper bound estimations of the system state processes. Minimization of control costs is based on the analysis of the singular value decomposition of a gramian of control costs, followed by the formation of major and minor estimations of the gramian. Minimization of peak effect in the trajectories of free movement of systems is carried out by minimizing the condition number of the eigenvectors matrix of the matrix of a stable closed-loop system, while the state matrix with the desired eigenvalues and eigenvectors is designed with the generalized modal control. The development of an iterative algorithm for the peak effect minimization in the trajectories of linear systems under any non-zero initial conditions with restricted control is based on an aggregated index. The index takes into account both the estimate of the gramian of control costs and the condition number of the eigenvectors matrix of the stable closed-loop system. Minimization of the aggregated index makes it possible to ensure minimal deviations in the trajectories of free movement of systems of the considered class. The procedure is applied to a system of two satellites with restricted control, where peak effects in satellites relative trajectories are minimized. Two cases of peak affect minimization are considered. In the first case, the peak effect minimization in the trajectories of free movement of satellites is carried out only by minimizing the gramian of control costs. In the second case, the peak effect minimization is realized using the developed algorithm. The results illustrate the efficiency of the procedure and indicate the decrease of the peak effect for the satellites relative trajectories.

Abstract With the success of the Sentinel‐6 Michael Freilich satellite, the satellite radar altimetry record of sea level has now surpassed 30 years in length. This reference satellite mission has continued uninterrupted alongside a significant expansion of the satellite sea‐level observing network in the past decade. This expansion was punctuated by the launch of the Surface Water and Ocean Topography mission in December 2022, which will bring higher resolution measurements of sea level closer to the shore than ever before. Here, we take stock of where we are now in terms of using satellites to measure sea level, and what is still needed to address the growing need of those living along the coast for information to support planning and adaptation efforts.

Abstract Volcanic volatile emissions provide information about volcanic unrest but are difficult to detect with satellites. Volcanic degassing affects plants by elevating local CO2 and H2O concentrations, which may increase photosynthesis. Satellites can detect plant health, or a reaction to photosynthesis, through a Normalized Difference Vegetation Index (NDVI). This can act as a potential proxy for detecting changes in volcanic volatile emissions from space. We tested this method by analyzing 185 Landsat 5 and 8 images of the Tern Lake thermal area (TLTA) in northeast Yellowstone caldera from 1984 to 2022. We compared the NDVI values of the thermal area with those of similar nearby forests that were unaffected by hydrothermal activity to determine how hydrothermal activity impacted the vegetation. From 1984 to 2000, plant health in the TLTA steadily increased relative to the background forests, suggesting that vegetation in the TLTA was fertilized by volcanic CO2 and/or magmatic water. Hydrothermal activity began to stress plants in 2002, and by 2006, large swathes of trees were dying in the hydrothermal area. Throughout most of the 1990s, the least healthy plants were located in the area which became the epicenter of hydrothermal activity in 2000. These findings suggest that plant‐focused measurements are sensitive to minor levels of volcanic unrest which may not be detected by other remote sensing methods, such as infrared temperature measurements. This method could be a safe and effective new tool for detecting changes in volatile emissions in volcanic environments which are dangerous or difficult to access.

Transposable elements (TEs) and satellite DNAs, two major categories of repetitive sequences, are expected to accumulate in non-recombining genome regions, including sex-linked regions, and contribute to sex chromosome evolution. The dioecious plant, Humulus scandens, can be used for studying the evolution of the XX/XY1Y2 sex chromosomes. In this study, we thoroughly examined the repetitive components of male and female H. scandens using next-generation sequencing data followed by bioinformatics analysis and florescence in situ hybridization (FISH). The H. scandens genome has a high overall repetitive sequence composition, 68.30% in the female and 66.78% in the male genome, with abundant long terminal repeat (LTR) retrotransposons (RTs), including more Ty3/Gypsy than Ty1/Copia elements, particularly two Ty3/Gypsy lineages, Tekay and Retand. Most LTR-RT lineages were found dispersed across the chromosomes, though CRM and Athila elements were predominately found within the centromeres and the pericentromeric regions. The Athila elements also showed clearly higher FISH signal intensities in the Y1 and Y2 chromosomes than in the X or autosomes. Three novel satellite DNAs were specifically distributed in the centromeric and/or telomeric regions, with markedly different distributions on the X, Y1, and Y2 chromosomes. Combined with FISH using satellite DNAs to stain chromosomes during meiotic diakinesis, we determined the synapsis pattern and distinguish pseudoautosomal regions (PARs). The results indicate that the XY1Y2 sex chromosomes of H. scandens might have originated from a centric fission event. This study improves our understanding of the repetitive sequence organization of H. scandens genome and provides a basis for further analysis of their chromosome evolution process. [ABSTRACT FROM AUTHOR]

Orbital space enables many essential services, such as weather forecasting, global communication, navigation, Earth observation for environmental and agricultural management, and national security applications. Orbit use is increasingly defined by firms launching coordinated fleets—“constellations”—of satellites into low-Earth orbit. These firms operate in markets with few or no competitors, such as the market for broadband internet provision to rural areas. How will oligopolistic competition shape the allocation of orbital space? We analyze orbital-use patterns and economic welfare when two profit-maximizing firms operate satellite constellations with sophisticated collision avoidance systems. We compare this duopoly equilibrium to public utility constellations designed and regulated to maximize economic welfare from orbit use. We show that imperfect competition reduces economic welfare from orbit use by up to 12%—$1.1 billion USD—per year and distorts the allocation of orbital space. The nature of the distortion depends on the magnitude of constellation-related environmental damages. When damages are low, economic welfare is maximized by larger-than-equilibrium constellations. When damages are high, economic welfare is maximized by smaller-than-equilibrium constellations. Between the growing commercial and national interests in outer space and the importance of low-Earth orbit to space exploration, orbit-use management is likely to be a fruitful and policy-relevant area for economic research. We conclude with a discussion of future research directions in orbit-use management relevant to policymakers around the world. [ABSTRACT FROM AUTHOR]

In 2022, the Hunga volcano eruption in Tonga generated atmospheric pressure waves that propagated globally and produced tsunamis in all the world's oceans. The largest pressure wave, with an amplitude of several hundred pascals, is the Lamb wave. Standard Lamb wave models, incorporating the sound‐speed as a function of temperature, satisfactorily explain observations in the near‐field but not in the far‐field. We show that an augmented Lamb wave model that includes the effects of wind and topography accurately reproduces the wavefronts observed by satellites and barometers, including those close to the antipode. Winds, first suggested to explain the travel times of Lamb waves from Krakatau in 1883, are now shown to also play a major role in shaping their waveforms; temperature and topography play smaller, but still detectable, roles. Our augmented model provides a significant advance for the development of early warning and hazard assessments for the meteotsunamis these waves produce. Plain Language Summary: The January 2022 explosive eruption of the Hunga volcano in Tonga produced a pressure wave (of a type known as a Lamb wave) in the atmosphere, which was detected worldwide. This wave circled the Earth more than once, and generated tsunami in unexpected times and places. We have derived a mathematical description that allows us to quickly and accurately model the observations of this atmospheric wave. The description includes the effects of winds, temperature, and topography. The wave modeled using this description reproduces satellite and ground observations much better than simpler models, notably the complex pattern of the wave near the antipode of the eruption. Our model clearly identifies global winds as the crucial influence on global‐scale Lamb‐wave propagation, and provides modeling tools for possible future occurrences of such waves and the global tsunamis created by them. Key Points: A augmented model is proposed to simulate the propagation of planetary scale Lamb waves, incorporating wind, temperature and topographyWinds play a primary role shaping Lamb waves in the far field, especially near the antipode of the sourceThe augmented Lamb wave model will help better assess far‐field volcanic tsunami hazards [ABSTRACT FROM AUTHOR]

Landslide time‐of‐failure prediction is crucial in natural hazards, often requiring precise measurements from in situ instruments. This instrumentation is not always possible, and remote‐sensing techniques have been questioned for detecting precursors and predicting landslides. Here, based on high frequency acquisitions of the PlanetScope satellite constellation, we study the kinematics of a large landslide located in Peru that failed in June 2020. We show that the landslide underwent a progressive acceleration in the 3 months before its failure, reaching at most 8 m of total displacement. The high frequency of satellite revisit allows us to apply the popular Fukuzono method for landslide time‐of‐failure prediction, with sufficient confidence for faster moving areas of the landslide. These results open new opportunities for landslide precursors detection from space, but also show the probable seldom applicability of the optical satellites for landslide time‐of‐failure prediction. Plain Language Summary: Many catastrophic landslides are preceded by specific movements, for example, a constant increase in velocity over time. These accelerations form the basis for predicting the landslide time‐of‐failure. The measurement of these movements is usually based on instruments installed on the landslide itself. However, this instrumentation is not always possible and, in recent years, the acquisition of satellite images at increasingly higher frequencies (e.g., PlanetScope satellites with a revisit frequency of less than 1 day) has raised interest in landslide monitoring and prediction. In order to assess the capability of PlanetScope satellites for this prediction, we analyze here the pre‐failure movements of a large landslide in southern Peru, which collapsed in June 2020 and dammed a river. We show that the landslide underwent a progressive acceleration in the 3 months prior to its failure, reaching a total displacement of 8 m. Based on the displacement time‐series of the fastest areas of the landslide, we predict the landslide time‐of‐failure 3 weeks in advance. Despite these encouraging results for landslide precursor detection from space, this study also shows the seldom use of optical satellites for landslide time‐of‐failure prediction. Key Points: High frequency optical satellites can detect the acceleration precursory to the Achoma landslide failureThe acceleration follows a Voight law, classical of the tertiary creep of materialsThe time‐series of ground displacement may be used to predict the time‐of‐failure for pixels of total displacement larger than 5 m [ABSTRACT FROM AUTHOR]

Coastal regions (including estuaries and deltas) are very complex environments with diverse hydrodynamic and bio-geomorphological contexts and with important socio-economic and ecological problems. These systems are among the most affected by human impact through urbanization and port activities, industrial and tourism activities. They are directly affected by the impact of climate change on sea level, storm surges frequency and strength, as well as recurrence of coastal river floods. A sustainable future for coastal zones depends on our capacity to implement systematic monitoring with focus on: (1) forcings affecting coastal zones at different spatio-temporal scales (sea level rise, winds and waves, offshore and coastal currents, tides, storm surges, river runoff in estuaries and deltas, sediment supply and transport, vertical land motions and land use); (2) morphological response (e.g., shoreline migration, topographical changes). Over the last decades, remote sensing observations have contributed to major advances in our understanding of coastal dynamics. This paper provides an overview of these major advances to measure the main physical parameters for monitoring the coastal, estuarine and delta environments and their evolution, such as the water level and hydrodynamics near the shoreline, water/sediment contact (i.e., shoreline), shoreline position, topography, bathymetry, vertical land motion, bio-physical characteristics of sediments, water content, suspended sediment, vegetation, and land use and land cover. [ABSTRACT FROM AUTHOR]

Stakeholders developing water quality improvement plans for lakes and reservoirs are challenged by the sparsity of in‐situ data and the uncertainty ingrained in management decisions. This study explores how satellite images can fill gaps in water quality databases and provide more holistic assessments of impairments. The study site is an impaired water body that is serving as a pilot for improving state‐wide nutrient management planning processes. An existing in‐situ database was used to calibrate semi‐analytical models that relate satellite reflectance values to turbidity and total suspended solids (TSS). Landsat‐7 images from 1999 to 2020 that overpass High Rock Lake, North Carolina were downloaded and processed, providing 42 turbidity and 39 TSS satellite and in‐situ match‐ups for model calibration and validation. Model r‐squared values for the fitted turbidity and TSS models are 0.72 and 0.74, and the mean absolute errors are 14.6 NTU and 3.2 mg/L. The satellite estimates were compared to the in‐situ data and simulated TSS values produced by a calibrated hydrologic‐hydrodynamic model. The process‐based model is considered less accurate than the satellite model based on statistical performance metrics. Comparisons between data sources are illustrated with time series plots, frequency curves, and aggregate decision metrics to highlight the dependence of lake impairment assessments on the spatial and temporal frequency of available data and model accuracy. [ABSTRACT FROM AUTHOR]

The Lucy mission will encounter five Jupiter Trojans during its mission with three of the five already known to be multiple systems. These include a near-equal-mass binary, a small and widely separated satellite, and one intermediate-size satellite system. This chapter reviews the current state of knowledge of Trojan asteroid satellites in the context of similar satellite systems in other small body populations. The prospects for the detection of additional satellites as well as other near-body phenomena are considered. The scientific utility of satellites makes their observation with Lucy an important scientific priority for the mission. [ABSTRACT FROM AUTHOR]

This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author's presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution. [ABSTRACT FROM AUTHOR]

In the modern world, dams and the artificial reservoirs behind them serve the increasing demand for water across diverse needs such as agriculture, energy production, and drinking water. As dams continue to proliferate, monitoring water availability influenced by reservoir operations is now of paramount importance. The Reservoir Assessment Tool (RAT) is a data-driven software that integrates satellite remote sensing with hydrological models, enabling the estimation of key reservoir parameters such as inflow, outflow, surface area, evaporation and storage changes. The earliest rendition of RAT (version 1.0) was set up for 1598 reservoirs around the world with limitations in functional robustness, updating frequency and scalability. Some of these limitations on updating frequency and functional robustness were addressed in version 2.0 that was later made operational for the inter-governmental agency of the Mekong River Commission. Recognizing the need for scalability to mobilize the global water management community to benefit from satellite remote sensing, we hereby introduce RAT version 3.0. This version is optimized for accelerating open collaboration among users for continuous improvement and customization of RAT to enable reservoir management breakthroughs. RAT 3.0 represents a wholesale overhaul from the previous versions to empower the global community of users and developers in the spirit of the open-source movement. RAT 3.0 allows reservoir monitoring advancements and new functional developments that can be freely exchanged and seamlessly integrated for continuous evolution of the software. A centralized web application has also been established to facilitate the storage and dissemination of global reservoir monitoring information along with comprehensive training resources. RAT 3.0 aspires to usher the traditional water management community into the era of satellite remote sensing. The global impact of the software can be expected to increase as uptake spreads, enabling a more sustainable and equitable utilization of our planet's water resources. [ABSTRACT FROM AUTHOR]

China is expanding its Arctic presence by developing infrastructure in the global commons that intersect with the region. Operations in outer space, the deep sea and cyberspace minimise the need for terrestrial footholds and generate data, a virtual resource. To analyse the epistemic and geopolitical consequences of developing the Arctic global commons as a vertically and digitally integrated volume, we examine a critical form of Chinese 'remote infrastructure': optical, synthetic aperture radar, and navigation satellites. We argue that first, by generating data about the Arctic, these instruments turn China into a regional knowledge producer. Second, as remote observations outnumber field observations, Chinese polar science may shift the regional balance of knowledge towards spaceborne and marine observations. Third, China's emergence as an Arctic knowledge producer may motivate the state to contribute to regional governance as remote sensing and large‐scale, computationally intensive techniques become privileged decision‐making tools. To transcend the terrestrial and maritime fixes that predominate research on China and the Arctic, we call for greater attention to the influence of epistemic capacities on geopolitics. China is expanding its Arctic presence by developing infrastructure in the global commons intersecting with the region. Operations in outer space, the deep sea, and cyberspace minimise the need for terrestrial footholds and generate data, a virtual resource. To analyse the epistemic and geopolitical consequences of developing the Arctic commons as a vertically and digitally integrated volume, we examine a critical form of Chinese 'remote infrastructure': optical, synthetic aperture radar, and navigation satellites. [ABSTRACT FROM AUTHOR]

Since the initial finding of exoplanets, planet detection has gained prominence as a field of research in astronomy. Using a variety of techniques, including gravitational microlensing, direct imaging, astrometry, and the radial velocity approach, more than 5,000 exoplanets have been found. The majority of confirmed exoplanet discoveries, have been made using the transit method. The machine learning (ML) models used in earlier approaches for prediction of exoplanets involve usage of large datasets and complex architectures, which takes a lot of time for training and hence detection of exoplanets. This work has presented a novel exoplanet detection approach based on the Object Detection algorithm- You only look once (YOLO). The data was collected for 221 confirmed exoplanets detected by Telescope and Transiting Exoplanet Survey Satellite (TESS) and light curves were downloaded for the same. The annotations were generated around the brightness dips in the light curves, followed by YOLO-object detection. The presented work not only predicts the dips in brightness in the light curve accurately but also takes less time to train the proposed model. The work has achieved a mAP 0.5 of 0.82, a precision of 0.85, and a recall of 0.81. Additionally, a comparison of YOLO, MobileNet-SSDv2, and EfficientDet Lite is presented. The EfficientDet Lite D2 scores mAP0.5 0.66 and Average Recall 0.56, which is very satisfactory considering it is running on low computing power devices such as mobile phones, Raspberry Pi and other microcomputers. [ABSTRACT FROM AUTHOR]

The high-altitude platform station (HAPS) concept has recently received notable attention from both industry and academia to support future wireless networks. A HAPS can be equipped with fifth-generation (5G) and beyond technologies such as massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS). Hence, it is expected that HAPS will support numerous applications in both rural and urban areas. However, this comes at the expense of high energy consumption and thus shorter loitering time. To tackle this issue, we envision the use of a multimode HAPS that can adaptively switch among different modes so as to reduce energy consumption and extend the HAPS loitering time. These modes comprise a HAPS super macro base station (HAPS-SMBS) mode for enhanced computing, caching, and communication services; a HAPS relay station (HAPS-RS) mode for active communication; and a HAPS-RIS mode for passive communication. This multimode HAPS ensures that operations rely mostly on the passive communication payload, while switching to an energy-greedy active mode only when necessary. In this article, we begin with a brief review of HAPS features compared with other nonterrestrial systems, followed by an exposition of the different HAPS modes proposed. Subsequently, we illustrate the envisioned multimode HAPS and discuss its benefits and challenges. Finally, we validate the multimode efficiency through a case study. [ABSTRACT FROM AUTHOR]

Recently, megaconstellations with a massive number of low Earth orbit (LEO) satellites are being considered as a possible solution for providing global coverage due to relatively low latency and high throughput compared to geosynchronous orbit (GEO) satellites. However, as the number of satellites and operators participating in the LEO constellation increases, inter-satellite interference will become more severe, which may yield marginal improvement or even decrement in network throughput. In this article, we introduce the concept of satellite clusters that can enhance network performance through satellites’ cooperative transmissions. The characteristics, formation types, and transmission schemes for the satellite clusters are highlighted. Simulation results evaluate the impact of clustering from coverage and capacity perspectives, showing that when the number of satellites is large, the performance of clustered networks outperforms the unclustered ones. The viable network architectures of the satellite cluster are proposed based on the 3rd Generation Partnership Project (3GPP) standard. Finally, the future applications of clustered satellite networks are discussed. [ABSTRACT FROM AUTHOR]

Current methods for detecting atmospheric plumes and inferring point source rates from high-resolution satellite imagery are labor intensive and not scalable to the growing satellite dataset available for methane point sources. Here we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a machine learning U-Net architecture for image segmentation. The second step quantifies point source rate from the masked plume using wind speed information and either a convolution neural network (CNN) or a physics-based Integrated Mass Enhancement (IME) method. The algorithm can process 62 128×128 images per second on a single core. We train the algorithm with large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point source observability Ops = Q/(UW?B) as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise κB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h-1 over surfaces with low background noise and succeeds for larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates while the CNN method is biased toward the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2-4 m s-1 is optimal for detection and quantification of point sources from satellite data. [ABSTRACT FROM AUTHOR]