Very Compact Waveguide Orthomode Transducer in the K-Band for Broadband Communication Satellite Array Antennas.

A very compact waveguide orthomode transducer (OMT) is described in this paper. The design is characterized with a twofold rotationally symmetric cross-section in the probing area, adapted from a side-coupling OMT design, simultaneously enabling low port-to-port coupling and high cross-polarization discrimination (XPD) over a fractional bandwidth of about 15-20%. Compared to previously reported compact waveguide OMTs, the proposed design is simpler, thus facilitating its manufacture at millimeter-wave frequencies. The concept is demonstrated with a design in the K-band for a broadband communication satellite downlink over the frequency band of 17.3-20.2 GHz. For test purposes, transitions to standard waveguide WR42 are included, and the OMT is assembled with a conical horn antenna. The measured reflection and coupling coefficients are below -19.5 dB and -22.9 dB, respectively, over the nominal bandwidth, and they are in good agreement with the simulation's results. The on-axis XPD, measured in an anechoic chamber, is better than 30 dB over the nominal bandwidth, which is also in line with simulations. The proposed waveguide OMT may be designed to fit in a lattice below one wavelength at the highest operating frequency, which is desirable for dual-polarized closely spaced array antennas in low and medium Earth orbit communication satellite systems. The simple mechanical design of the proposed OMT makes it particularly appealing for additive manufacturing techniques, as demonstrated with a variant of the design having folded single-mode waveguides, which preserves the RF properties of the original design.

Ultra-High Capacity Optical Satellite Communication System Using PDM-256-QAM and Optical Angular Momentum Beams.

Twisted light beams such as optical angular momentum (OAM) with numerous possible orthogonal states have drawn the prodigious contemplation of researchers. OAM multiplexing is a futuristic multi-access technique that has not been scrutinized for optical satellite communication (OSC) systems thus far, and it opens up a new window for ultra-high-capacity systems. This paper presents the 4.8 Tbps (5 wavelengths × 3 OAM beams × 320 Gbps) ultra-high capacity OSC system by incorporating polarization division multiplexed (PDM) 256-Quadrature amplitude modulation (256-QAM) and OAM beams. To realize OAM multiplexing, Laguerre Gaussian (LG) transverse mode profiles such as LG00, LG140, and LG400 were used in the proposed study. The effects of the receiver's digital signal processing (DSP) module were also investigated, and performance improvement was observed using DSP for its potential to compensate for the effects of dispersion, phase errors, and nonlinear effects using the blind phase search (BPS), Viterbi phase estimation (VPE), and the constant modulus algorithm (CMA). The results revealed that the proposed OAM-OSC system successfully covered the 22,000 km OSC link distance and, out of three OAM beams, fundamental mode LG00 offered excellent performance. Further, a detailed comparison of the proposed system and reported state-of-the-art schemes was performed.

An Efficient Multi-Dimensional Resource Allocation Mechanism for Beam-Hopping in LEO Satellite Network.

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

Wideband Versatile Receiver for CubeSat Microwave Front-Ends.

One of the main features of CubeSats is represented by their extreme versatility, e.g., maintaining the same overall structure for different purposes. This requires high technological flexibility achievable in a cost-effective way while maintaining compact sizes. In this contribution, a microwave receiver specifically designed for CubeSat applications is proposed. Due to the wide input operating bandwidth, i.e., 2 GHz-18 GHz, it can be exploited for different purposes, e.g., satellite communication, radars, and electronic warfare systems. This is beneficial for CubeSat systems, whereby the possibility to share the same front-end circuit for different purposes is a key feature in reducing the overall size and weight. The downconverter was designed to minimize the spurious contributions at low frequency by taking advantage, at the same time, of commercial off-the-shelf components due to their cost-effectiveness. The idea behind this work is to add flexibility to the CubeSat communication systems in order to be reusable in different contexts. This feature enables new applications but also provides the largest bandwidth if required from the ground system. An accurate experimental characterization was performed to validate the downconverter performance with the aim of allowing easy system integration for the new frontier of CubeSat technologies. This paves the way for the most effective implementation of the Internet of Things (IoT), machine-to-machine (M2M) communications, and smart-everything services.

RaVÆn: unsupervised change detection of extreme events using ML on-board satellites.

Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data analysis is performed on the ground after being transferred-downlinked-to a ground station. Constraints on the downlink capabilities, both in terms of data volume and timing, therefore heavily affect the response delay of any downstream application. In this paper, we introduce RaVÆn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs), with the specific purpose of on-board deployment. RaVÆn pre-processes the sampled data directly on the satellite and flags changed areas to prioritise for downlink, shortening the response time. We verified the efficacy of our system on a dataset-which we release alongside this publication-composed of time series containing a catastrophic event, demonstrating that RaVÆn outperforms pixel-wise baselines. Finally, we tested our approach on resource-limited hardware for assessing computational and memory limitations, simulating deployment on real hardware.

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network.

During the operation of navigation satellites, errors in the broadcast ephemeris orbits are caused by the influence of ingress factors and other factors. To address this phenomenon, this paper examines the use of the computational intelligence (CI) methods to implement track correction and to develop an optimized BP neural network model based on an improved particle swarm algorithm. The model improves the inertia weights and learning factor parameters of the particle swarm optimization (PSO) algorithm to improve the global optimization capability and accelerate the convergence speed. The improved PSO (IPSO) algorithm is used to perform a global optimization search for the hyperparameters of the BP neural network, and then the neural network model is trained by broadcast ephemeris Keplerian root number and regression parameters. The model is validated using the broadcast ephemeris data of the BDS-3 MEO and IGSO satellites, and the mean square error correction rate of multiple satellites with different correction models shows that the error correction rate of the IPSO-BPNN model can reach 70.2-84% and the error correction rate can be improved by 14.2-56.8% compared with the PSO-BPNN model. The proposed algorithm provides an important reference for BDS-3 and other global navigation satellite systems for improving the accuracy of satellite orbit determination.

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach.

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites' high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites' downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes.

The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications.

Resilient navigation in Global Navigation Satellite System (GNSS)-degraded and -denied environments is becoming more and more required for many applications. It can typically be based on multi-sensor data fusion that relies on alternative technologies to GNSS. In this work, we studied the potential of a low earth orbit (LEO) satellite communication system for a high-dynamic application, when it is integrated with an inertial measurement unit (IMU) and magnetometers. We derived the influence of the main error sources that affect the LEO space vehicle (SV) Doppler-based navigation on both positioning and attitude estimations. This allowed us to determine the best, intermediate and worst cases of navigation performances. We show that while the positioning error is large due to large orbit errors or high SV clock drifts, it becomes competitive with that of an inertial navigation system (INS) based on a better quality IMU if precise satellite orbits are available. On the other hand, the attitude estimation tolerates large orbit errors and high SV clock drifts. The obtained results suggest that LEO SV signals, used as signals of opportunity for navigation, are an attractive alternative in GNSS-denied environments for high dynamic vehicles.

Mapping silver eel migration routes in the North Sea.

Recent developments in tracking technology resulted in the mapping of various marine spawning migration routes of the European eel (Anguilla anguilla). However, migration routes in the North Sea have rarely been studied, despite many large European rivers and hence potential eel growing habitat discharge into the North Sea. In this study, we present the most comprehensive map to date with migration routes by silver European eels in the North Sea and document for the first time successful eel migration through the English Channel. Migration tracks were reconstructed for 42 eels tagged in Belgium and 12 in Germany. Additionally, some eels moved up north to exit the North Sea over the British Isles, confirming the existence of two different routes, even for eels exiting from a single river catchment. Furthermore, we observed a wide range in migration speeds (6.8-45.2 km day-1). We hypothesize that these are likely attributed to water currents, with eels migrating through the English Channel being significantly faster than eels migrating northward.

The annual cycle for whimbrel populations using the Western Atlantic Flyway.

Many long-distance migratory birds use habitats that are scattered across continents and confront hazards throughout the annual cycle that may be population-limiting. Identifying where and when populations spend their time is fundamental to effective management. We tracked 34 adult whimbrels (Numenius phaeopus) from two breeding populations (Mackenzie Delta and Hudson Bay) with satellite transmitters to document the structure of their annual cycles. The two populations differed in their use of migratory pathways and their seasonal schedules. Mackenzie Delta whimbrels made long (22,800 km) loop migrations with different autumn and spring routes. Hudson Bay whimbrels made shorter (17,500 km) and more direct migrations along the same route during autumn and spring. The two populations overlap on the winter grounds and within one spring staging area. Mackenzie Delta whimbrels left the breeding ground, arrived on winter grounds, left winter grounds and arrived on spring staging areas earlier compared to whimbrels from Hudson Bay. For both populations, migration speed was significantly higher during spring compared to autumn migration. Faster migration was achieved by having fewer and shorter stopovers en route. We identified five migratory staging areas including four that were used during autumn and two that were used during spring. Whimbrels tracked for multiple years had high (98%) fidelity to staging areas. We documented dozens of locations where birds stopped for short periods along nearly all migration routes. The consistent use of very few staging areas suggests that these areas are integral to the annual cycle of both populations and have high conservation value.

Induced electron radiation effect on the performance of inter-satellite optical wireless communication.

This paper provides the details of a study on the effects of electron radiation on the Performance of Inters-satellite Optical Wireless Communication (IsOWC). Academia and industry focus on solutions that can improve performance and reduce the cost of IsWOC systems. Spacecraft, space stations, satellites, and astronauts are exposed to an increased level of radiation when in space, so it is essential to evaluate the risks and performance effects associated with extended radiation exposures in missions and space travel in general. This investigation focuses on LEO, especially in the near-equatorial radiation environment. Radiation experiments supported with simulations have made it possible to obtain and evaluate the electron radiation impact on optoelectronics at the device level and system level performances. The electron radiation has induced a system degradation of 70%. This result demonstrates the importance of such an investigation to predict and take necessary and suitable reliable quality service for future space missions.

A cloud-based toolbox for the versatile environmental annotation of biodiversity data.

A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity-environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.

Characterizing bracken fern phenological cycle using time series data derived from Sentinel-2 satellite sensor.

Bracken fern is an invasive plant that has caused serious disturbances in many ecosystems due to its ability to encroach into new areas swiftly. Adequate knowledge of the phenological cycle of bracken fern is required to serve as an important tool in formulating management plans to control the spread of the fern. This study aimed to characterize the phenological cycle of bracken fern using NDVI and EVI2 time series data derived from Sentinel-2 sensor. The TIMESAT program was used for removing low quality data values, model fitting and for extracting bracken fern phenological metrics. The Sentinel-2 satellite-derived phenological metrics were compared with the corresponding bracken fern phenological events observed on the ground. Findings from our study revealed that bracken fern phenological metrics estimated from satellite data were in close agreement with ground observed phenological events with R2 values ranging from 0.53-0.85 (p < 0.05). Although they are comparable, our study shows that NDVI and EVI2 differ in their ability to track the phenological cycle of bracken fern. Overall, EVI2 performed better in estimating bracken fern phenological metrics as it related more to ground observed phenological events compared to NDVI. The key phenological metrics extracted in this study are critical for improving the precision in the controlling of the spread of bracken fern as well as in implementing active protection strategies against the invasion of highly susceptible rangelands.

Basking shark sub-surface behaviour revealed by animal-towed cameras.

While biologging tags have answered a wealth of ecological questions, the drivers and consequences of movement and activity often remain difficult to ascertain, particularly marine vertebrates which are difficult to observe directly. Basking sharks, the second largest shark species in the world, aggregate in the summer in key foraging sites but despite advances in biologging technologies, little is known about their breeding ecology and sub-surface behaviour. Advances in camera technologies holds potential for filling in these knowledge gaps by providing environmental context and validating behaviours recorded with conventional telemetry. Six basking sharks were tagged at their feeding site in the Sea of Hebrides, Scotland, with towed cameras combined with time-depth recorders and satellite telemetry. Cameras recorded a cumulative 123 hours of video data over an average 64-hour deployment and confirmed the position of the sharks within the water column. Feeding events only occurred within a metre depth and made up ¾ of the time spent swimming near the surface. Sharks maintained similar tail beat frequencies regardless of whether feeding, swimming near the surface or the seabed, where they spent surprisingly up to 88% of daylight hours. This study reported the first complete breaching event and the first sub-surface putative courtship display, with nose-to-tail chasing, parallel swimming as well as the first observation of grouping behaviour near the seabed. Social groups of sharks are thought to be very short term and sporadic, and may play a role in finding breeding partners, particularly in solitary sharks which may use aggregations as an opportunity to breed. In situ observation of basking sharks at their seasonal aggregation site through animal borne cameras revealed unprecedented insight into the social and environmental context of basking shark behaviour which were previously limited to surface observations.

An autonomous drone for search and rescue in forests using airborne optical sectioning.

Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning-based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

Optimized river diversion scenarios promote sustainability of urbanized deltas.

Socioeconomic viability of fluvial-deltaic systems is limited by natural processes of these dynamic landforms. An especially impactful occurrence is avulsion, whereby channels unpredictably shift course. We construct a numerical model to simulate artificial diversions, which are engineered to prevent channel avulsion, and direct sediment-laden water to the coastline, thus mitigating land loss. We provide a framework that identifies the optimal balance between river diversion cost and civil disruption by flooding. Diversions near the river outlet are not sustainable, because they neither reduce avulsion frequency nor effectively deliver sediment to the coast; alternatively, diversions located halfway to the delta apex maximize landscape stability while minimizing costs. We determine that delta urbanization generates a positive feedback: infrastructure development justifies sustainability and enhanced landform preservation vis-à-vis diversions.

Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kilauea eruption) using a low-cost sensor network.

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kilauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai'i Island in "vog" (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 µg m-3 and 1,200 parts per billion (ppb), respectively. The LCS network's high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island's population was exposed to elevated levels of fine PM than to SO2 Additionally, the spatially distributed network was able to resolve the volcanic plume's chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

A system of coordinated autonomous robots for Lagrangian studies of microbes in the oceanic deep chlorophyll maximum.

The deep chlorophyll maximum (DCM) layer is an ecologically important feature of the open ocean. The DCM cannot be observed using aerial or satellite remote sensing; thus, in situ observations are essential. Further, understanding the responses of microbes to the environmental processes driving their metabolism and interactions requires observing in a reference frame that moves with a plankton population drifting in ocean currents, i.e., Lagrangian. Here, we report the development and application of a system of coordinated robots for studying planktonic biological communities drifting within the ocean. The presented Lagrangian system uses three coordinated autonomous robotic platforms. The focal platform consists of an autonomous underwater vehicle (AUV) fitted with a robotic water sampler. This platform localizes and drifts within a DCM community, periodically acquiring samples while continuously monitoring the local environment. The second platform is an AUV equipped with environmental sensing and acoustic tracking capabilities. This platform characterizes environmental conditions by tracking the focal platform and vertically profiling in its vicinity. The third platform is an autonomous surface vehicle equipped with satellite communications and subsea acoustic tracking capabilities. While also acoustically tracking the focal platform, this vehicle serves as a communication relay that connects the subsea robot to human operators, thereby providing situational awareness and enabling intervention if needed. Deployed in the North Pacific Ocean within the core of a cyclonic eddy, this coordinated system autonomously captured fundamental characteristics of the in situ DCM microbial community in a manner not possible previously.

An efficient and provably secure key agreement scheme for satellite communication systems.

Satellite communication has played an important part in many different industries because of its advantages of wide coverage, strong disaster tolerance and high flexibility. The security of satellite communication systems has always been the concern of many scholars. Without authentication, user should not obtain his/her required services. Beyond that, the anonymity also needs to be protected during communications. In this study, we design an efficient and provably secure key agreement scheme for satellite communication systems. In each session, we replace user's true identity by a temporary identity, which will be updated for each session, to guarantee the anonymity. Because the only use of lightweight algorithms, our proposed scheme has high performance. Furthermore, the security of the proposed scheme is proved in the real-or-random model and the performance analysis shows that the proposed scheme is more efficient than some other schemes for satellite communication systems.