Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Image Quality of Moving Ships for GF-3NG Based on Simultaneous AIS Information

The synthetic aperture radar (SAR) is an important means of ship surveillance, but the motion of the ship leads to azimuth position offset, false targets, and azimuth defocusing for the spaceborne high-resolution and wide-swath (HRWS) SAR system, causing the degradation of imaging quality. The automatic identification system (AIS) can provide real-time information of the ships, which is an important auxiliary method for ship surveillance. Up to now, the traditional fusion of SAR and AIS mainly has focused on location matching and auxiliary recognition, and the next generation of GaoFen-3 (GF-3NG) satellite is equipped with both a SAR sensor and an AIS sensor to obtain the SAR images and simultaneous AIS information of ships. Consequently, this paper proposes a novel scheme to improve the imaging quality of moving ships for GF-3NG using AIS information. In this paper, through introducing the virtual stationary target, the slant range derivation (SRD) algorithm is proposed to estimate the radial velocity (RV) and the radial acceleration (RA) between the ship and the SAR platform relative to the stationary scene. According to the calculated RV, the azimuth position offset can be estimated and the ship can be repositioned on the image. After that, the traditional method is conducted to suppress the false targets. Finally, the method of using the RA to refocus ship slices is proposed. Additionally, the experiment results based on real data illustrate the effectiveness of the proposed methods.
A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.
Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency

Three-dimensional (3D) building models are closely related to human activities in urban environments. Due to the variations in building styles and complexity in roof structures, automatically reconstructing 3D buildings with semantics and topology information still faces big challenges. In this paper, we present an automated modeling approach that can semantically decompose and reconstruct the complex building light detection and ranging (LiDAR) point clouds into simple parametric structures, and each generated structure is an unambiguous roof semantic unit without overlapping planar primitive. The proposed method starts by extracting roof planes using a multi-label energy minimization solution, followed by constructing a roof connection graph associated with proximity, similarity, and consistency attributes. Furthermore, a progressive decomposition and reconstruction algorithm is introduced to generate explicit semantic subparts and hierarchical representation of an isolated building. The proposed approach is performed on two various datasets and compared with the state-of-the-art reconstruction techniques. The experimental modeling results, including the assessment using the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark LiDAR datasets, demonstrate that the proposed modeling method can efficiently decompose complex building models into interpretable semantic structures.
Peak Age of Information Minimization for UAV-Aided Wireless Sensing and Communications

The freshness of information characterized by the age-of-information (AoI) is a key performance indicator (KPI) in Internet of Things (IoT) networks. In this paper, we consider a wireless sensor network (WSN) with an unmanned aerial vehicle (UAV) as a relay. A sensing-then-forwarding scheme is proposed, where the UAV first collects data packets from all sensor nodes and then forwards to a destination node. The average peak AoI is minimized by jointly optimizing the UAV trajectory, communication power, and service time. The feasibility of the formulated problem can be checked by solving a second order cone programming (SOCP), and a successive convex approximation (SCA) based algorithm with provable convergence is developed to obtain an at least suboptimal solution. Through extensive simulations, the proposed algorithm is shown to provide better convergence and much smaller peak AoI than the alternating optimization (AO) based algorithm.
An Optimal Sensor Deployment Technique for Spectrum Monitoring Applications

Spectrum monitoring is a key element of spectrum management process, in which spectrum sensors are considered the most crucial component. In this paper, we discuss the problem of optimal sensor deployment for spectrum monitoring applications. Optimal sensor deployment can significantly reduce the cost of sensor deployment and improve spectrum monitoring performance and efficiency. An optimal sensor deployment technique is developed, which provides the minimized number of sensors required, as well as their locations of deployment, for monitoring transmissions by desired stations in an area of interest. The problem of optimal sensor deployment is formulated using binary linear integer programming that is solved using efficient numerical optimization algorithms. The technique is scalable in the sense that it considers the effects of pre-installed or existing spectrum sensors in the area. Finally, we apply the technique to an Ottawa area for deploying sensor to monitor all down-link transmissions in the 800 MHz Land Mobile Radio (LMR) band to demonstrate its effectiveness and performance.
Elevation Spatial Variation Analysis and Compensation in GEO SAR Imaging

Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation measurements. Three-dimensional (3D) scene imaging mode is crucial for long-term observation using GEO SAR. However, this mode will bring a new kind of space-variant error in elevation. In this paper, we focus on the analysis of the elevation space-variant error. First, the decorrelation problems caused by the spatial variation are presented. Second, by combining with the SAR imaging geometry, the elevation spatial variation is decomposed into two-dimensional (2D) space variation of range and azimuth. Third, an imaging algorithm is proposed to solve the 3D space variation and improve the focusing depth. Finally, simulations with dot-matrix targets and distributed targets are performed to validate the imaging method.
A Novel Hybrid Model for Task Dependent Scheduling in Container-based Edge Computing

In traditional edge computing, the task from the Internet of Things (IoT) is usually offloaded to edge server. It will be uploaded to the remote cloud if the edge server cannot process it. A task can be processed on the server, only if the server has configured the corresponding function program. However, each edge server can only configure a small number of functions due to the limited computing, storage, and bandwidth resources. Moreover, modern tasks from IoT devices become more and more diverse, which are also accompanied by complex dependencies. It increases the processing time overhead to the task processed in remote cloud due to huge transmission delay. In this paper, we design a container-based edge computing system, where a task can be executed on a server only if the server has configured the corresponding container, if not the server can fetch it from other edge servers or remote cloud. Based on the system, we propose a novel hybrid model, called CBASGA, with the aim to minimize the job complete time, which combines Chaos-based Beetle Antennae Search and Genetic Algorithm. Our experimental results show that the designed system reduces the average job completion time by 4.2% compared with the comparison system, and CBASGA reduces the average job completion time by at least 21.7% compared with baselines.
Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.
Sorption of Uranium Ions from Their Aqueous Solution by Resins Containing Nanomagnetite Particles

Magnetic amine resins composed of nanomagnetite (Fe3O4) core and glycidyl methacrylate (GMA)/N,N′-methylenebisacrylamide (MBA) shell were prepared by suspension polymerization of glycidyl methacrylate with N,N′-methylenebisacrylamide in the presence of nanomagnetite particles and immobilized with different amine ligands. These resins showed good magnetic properties and could be easily retrieved from their suspensions using an external magnetic field. Adsorption behaviors of uranium ions on the prepared resins were studied. Maximum sorption capacities of uranium ions on R-1 and R-2 were found to be 92 and 158 mg/g. Uranium was extracted successfully from three granite samples collected from Gabal Gattar pluton, North Eastern Desert, Egypt. The studied resins showed good durability and regeneration using HNO3.
Intercalibration of MERIS, MODIS, and OLCI Satellite Imagers for Construction of Past, Present, and Future Cyanobacterial Biomass Time Series

Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a “spectral shape” (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002–2012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012–2015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.
The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.
Correction Factors for Outdoor Concentrations of PM2.5Measured with Portable Real-time Monitors Compared with Gravimetric Methods: Results from South Korea

This study investigated the association between concentrations obtained with portable real-time monitors and those obtained with gravimetric methods in national urban air-quality monitoring sites in Seoul, South Korea. We used the SidePak AM510 Personal Aerosol Monitor (TSI Inc., 500 Cardigan Road Shoreview, MN) and DustTrak DRX 8533 (TSI Inc., 500 Cardigan Road Shoreview, MN) as portable real-time monitors for measuring concentrations and compared these values with those measured with the PMS-103 or SEQ 47/50 models operated by Federal Reference Method (FRM) or the European Committee for Standardization(ECS), respectively, in national urban air-quality monitoring sites in Seoul. Measurements were conducted every other day in the winter and spring seasons of 2014. The estimated daily mean concentrations of ranged between 13.4 and using AM 510 and between 22.0 and using DustTrak. The Spearman correlation coefficient for concentrations between AM 510 and gravimetric results was 0.99, and the correlation between DustTrak and gravimetric results was 0.87. The correction factor suggested was 0.42 and 0.29 for AM 510 and DustTrak, respectively. We found that concentrations measured with real-time monitors could overestimate true concentrations and therefore the application of a correction factor (0.43) is strongly suggested for quantification when Real-time monitors were operated of levels at urban atmospheric environment of South Korea.
Joint User Grouping and Power Allocation for NOMA-Based UAV Relaying Networks

As flying relays, UAVs can quickly set up relay communication links for different missions, to enhance the receiving signal power, increase the system capacity, and expand the communication coverage. In this paper, we investigate a non-orthogonal multiple access (NOMA) based UAV relaying network that helps data transmission from a base station (BS) to remote multiple users. To improve spectral efficiency of the investigated UAV relaying network, we provide a generalized user grouping protocol that partitions users into multiple groups and transmit data via NOMA within the same group. Then, we further formulate a joint optimization problem of the NOMA user grouping, the UAV position, and the UAV transmit power to maximize the system throughput. The formulated problem is non-convex which makes it difficult to solve directly, hence, we propose an iterative algorithm to obtain an approximate optimal solution. Simulation results demonstrate that our proposed NOMA-based UAV relaying scheme achieves significant throughput gains compared with other benchmark schemes.
WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

A reliable obstacle detection system is crucial for autonomous surface vehicles (ASVs) to realize fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks, such as poor detection for small objects, low estimation accuracy caused by water surface reflection, and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoder–decoder structured deep semantic segmentation network, which is a water obstacle detection network based on image segmentation (WODIS), to solve the abovementioned problems. The first design feature of WODIS utilizes the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea–sky line areas, an attention refine module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a feature fusion module (FFM) is introduced to help concatenate the multidimensional high-level features in the decoder network. The WODIS is tested and cross-validated using four different types of maritime datasets with the results demonstrating that the mean intersection over union (mIoU) of WODIS can achieve superior segmentation effects for sea-level obstacles to values as high as 91.3%.
RegARD: Symmetry-Based Coarse Registration of Smartphone's Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.
B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core

The increase in mobile traffic which is accompanied by a random workload, variations necessitate an upgrade of mobile network infrastructure to maintain acceptable network performance. Scaling the mobile core network (Evolved Packet Core (EPC)) has attracted the attention of the research community and many scaling solutions that utilized either horizontal or vertical scaling have been proposed. Most of these solutions tend to scale the EPC entities on virtual machines (which usually takes time to instantiate) using a dedicated scaling module at the expense of an increase in overhead. In this paper, we propose a predictive horizontal auto-scaling mechanism for a container-based EPC that utilizes the embedded functionalities offered by Amazon Web Services (AWS) to scale the containerized EPC entities according to their CPU utilization. We further, formulate an optimal load balancer to distribute traffic to multiple instances to achieve fairness and maximize their throughput. The proposed auto-scaling model is implemented on the AWS cloud platform and evaluated against the number of successful attach processes, RAM, and CPU utilization. The results reveal RAM utilization does not saturate as the number of User Equipment (UE) increases for all entities and the MME CPU utilization is more affected as the number of UE’s request increases.