Miracle-dz/UniFAR-FTUs huggingface dataset:
hemangioblastoma. Magnetic resonance imaging of spinal-cord

a small amount of metrizamide has flowed around edges of this large-volume lesion. B, 12 hr delayed axial CT image at C1-C2 level. Poorly defined high attenuation within spinal cord substance was not seen on 24 hr CT study, effectively proving this high attenuation as pathologic uptake of metrizamide. C and D, Axial intravenous contrast-enhanced CT scans. High attenuation lesion with serpiginous structures about it. Neither of these features was imaged before contrast enhancement. Image degradation by beam-hardening artifact.
MULTICAST PROTECTION WITH GROOMING BACKUP PATH IN ELASTIC OPTICAL NETWORK

The protection of connections in very high-speed networks such as elastic optical networks is an effective solution to limit the damage due to failures on the links in the network. We have therefore proposed a protection approach that uses backup path bundling to solve the problem of protection with connection bundling in EONs. The proposed algorithm makes it possible to build the primary path and the backup path of the connections by grooming the primary and backup paths which share links in common. Thus, this algorithm reduces the resources used such as spectral resources and transponders. The simulation results reveal that the proposed approach yields better performance in terms of blocking probability, transponder cost and spectrum utilization rate compared to the shared protection method without connection bundling
The distress of Marks & Spencer PLC in 2001: A multidimensional scaling analysis

La compania Marks & Spencer, tras un sorprendente periodo de dificultades, cerro sus tiendas no situadas en el Reino Unido. Que fue lo que le llevo a tomar tan drasticas medidas? En este trabajo se situa la crisis de la compania en el contexto de los demas grandes almacenes en el Reino Unido y se demuestra que una politica de no disminuir la distribucion de dividendos, condujo a un pago excesivo de impuestos, y una bajada en la rentabilidad. Todo ello se reflejo en una caida del precio de las acciones. La decision de cerrar sus tiendas en el extranjero fue un intento de retomar su imagen de empresa de calle mayor. Se usan tecnicas de estadistica multivariante, especialmente las que visualizan resultados estadisticos en forma de mapas que permiten anadir informacion cualitativa.
Adaptive Conditional GAN based Ka-Band PolSAR Image Simulation by Using X-Band PolSAR Image Transfer

Multi-band polarimetric synthetic aperture radar (PolSAR) has significant advantage in information extraction. However, the demanding acquisition requirement greatly prohibits its development. Typically, compared to low-frequency band PolSAR data, high-frequency band suffers more severe data insufficiency. In this paper, the authors proposed to resolve this issue by simulating Ka-band PolSAR images from X-band images. For this purpose, a conditional Generative Adversial Network (cGAN) based X-to-Ka band PolSAR image transfer network has been proposed. Adaptations in terms of preprocessing and loss function are made to the original cGAN so that it can be better adapted to PolSAR image processing. The proposed method is verified using the X- and Ka-band dataset acquired in Hainan, China by the Aerial Remote Sensing System of the Chinese Academy of Sciences. Experimental results demonstrate the feasibility of the proposed method.
Level 4 Autonomous Driving SoC, leveraging chiplet, advanced package and UCIe

With the unprecedented growth of High-Performance Compute (HPC) and Autonomous Driving (AD) seen in recent times, the traditional chip design strategy is falling short and encountering a fundamental manufacturing limit. Smaller silicon dies, or “chiplets,” combined in a single package, with aggregate silicon area much greater than a reticle, are becoming popular and showing great promise to effectively mitigate the yield and size challenges of the traditional approach.While chiplets solve some problems, they introduce new challenges of interoperability, higher interconnect power and latency, and the availability of a chiplet-to-chiplet IO that can meet bandwidth and reliability requirements.In this paper, we will demonstrate how heterogenous chiplets sourced from different vendors, and designed in diverse nodes, can leverage the UCIe interconnect standard, along with advanced packaging, to unleash unprecedented interconnect density, bandwidth, and automotive grade reliability with best-in-class power to pave the path for building a leading Level 4 AD product.
Implementation, Fabrication and Unit Testing of Phased Antenna Arrays for Wi-Fi Indoor Positioning

Recently, a new approach was developed for indoor positioning using two quadratic phase-swept antenna arrays. The first array sweeps the indoor hall in one direction, while the other sweeps the indoor hall in the perpendicular direction. With the synchronization of the sweeps of the antenna arrays and the SSID of each direction, the Wi-Fi receiver receives two SSIDs and hence will determine its location. The mechanism is controlled by a device. The location accuracy relies on the performance of the antenna arrays. This paper provides a superb antenna design to achieve this goal. This design is based on a new microstrip RF-circuit. Simulation results and implementation with unit testing are included.
Retrieving Soil Organic Matter and Soil Moisture Profiles of the Arctic Foothills Tundra Using P-band Polarimetric SAR Imagery

This paper presents a physics-based radar modeling framework that enables joint retrievals of the Arctic tundra permafrost active layer soil organic matter content and soil moisture profile using P-band polarimetric SAR. Initially, an extensive set of field observations are used to model the subsurface soil moisture and organic matter profiles with independent model parameters. Then a new organic soil dielectric model is used to translate the soil profile properties into equivalent dielectric properties that bridge the soil field properties to their manifestation in radar measurements. Finally, a multi-layered dielectric structure is adopted by an electromagnetic scattering computational model that predicts equivalent backscattering coefficients resulting from the soil profile state parameters. These pieces together construct the forward model, which is at the heart of a physics-based radar retrieval algorithm. Finally, we integrate the developed model into a retrieval scheme, in which the soil moisture and organic profile model parameters are estimated using radar backscattering coefficients measured by AirMOSS P-band radar. We show retrieved soil moisture and soil organic matter profiles, derived pixel-wise by the algorithm providing the first airborne-driven soil organic carbon map.
Uav-Borne Bistatic Sar and Insar Experiments in Support of STV and SDC Target Observables

The ongoing Distributed Aperture Radar Tomographic Sensors (DARTS) project at NASA Jet Propulsion Laboratory aims to mature and demonstrate multi-static SAR measurements for fine-scale 3D imaging of surface topography, vegetation, and surface deformation and change. The project explores the use of drones as SAR platforms and integrates software-defined radar on RF system-on-chip for compact and flexible radar instruments. This paper highlights the progress in DARTS hardware development, experiments, and data processing. The recent experiments have successfully demonstrated monostatic interferometry as well as acquisition and processing of bi-static SAR imagery. By leveraging the advantages of multi-static SAR and drone-based platforms, the project aims to build a testbed for future missions design and enhanced SAR imaging capabilities for scientific applications.
Validating Soil Moisture with Farmers in Mind: A New Approach for Remote Sensing and Modeling in the US Corn Belt

Evaluation of soil moisture information should consider when key crop development stages occur and, ultimately, when farmers must make decisions based on soil moisture status. Therefore, we assessed the performance of three microwave satellites (SMAP, SMOS, and METOP/ASCAT) and three reanalysis models (MERRA-2, NARR, and WEPP) in the U.S. Corn Belt in the context of agricultural management. Thermal time and crop progress reports from the USDA-NASS defined critical transition periods of crop growth and management decisions for the validation process. Contrary to calendar timelines like annual segments, these key events separate the year into five irregular segments. Measurements at the South Fork Core Validation Site from 2016 to 2020 show that the two passive microwave satellites are dry compared to in-situ observations, but the active satellite and reanalysis model products were almost always wetter. Overall, most products perform the best during the pre-planting and post-harvest segments when no crops are present and worst during the active management phase when crops are starting to grow.
Buried Target Detection with a UAV Based GPSAR-System using a Circle-Hough-Transformation

The use of unmanned aerial vehicles as carriers for remote sensing became more and more popular over the past few years. As a result, UAV-based ground penetrating synthetic aperture radars were presented. These sensor systems can be used in many different applications, such as archaeology or landmine detection. Especially due to the increasing number of newly buried landmines it is important to perform this task efficiently. The goal of this paper is to present a new detection algorithm to detect shallowly buried objects with a UVA-based GPSAR-system. Therefore the phase image of a complex GPSAR-image is used to apply a Circle-Hough-Transformation. Due to the extent of the target signatures over several depths, the detections from multiple focusing depths could be combined and increase the receiver operating characteristic (ROC) of the system. Results show that with the Circle-Hough-Transformation the number of false alarms is less and the true detections, with the presence of circular signatures in the phase image, are at the same level as with an constant false alarm rate detector.
Volatile organic compound distributions during the NACHTT campaign at the Boulder Atmospheric Observatory: Influence of urban and natural gas sources

A comprehensive suite of volatile organic compounds (VOCs) was measured at the semirural Boulder Atmospheric Observatory (BAO) in northeast Colorado during the Nitrogen, Aerosol Composition, and Halogens on a Tall Tower (NACHTT) campaign during the winter of 2011. A signature of elevated nonmethane hydrocarbon (NMHC) mixing ratios was observed throughout the campaign. The C2‐C5 alkane mixing ratios were an order of magnitude greater than the regional background. Light alkane mixing ratios were similar to those at urban sites impacted by petrochemical industry emissions with ethane and propane reaching maximums of over 100 ppbv. The mean (± standard deviation) calculated total OH reactivity (7.0 ± 5.0 s−1) was also similar to urban sites. Analysis of VOC wind direction dependence, emission ratios with tracer compounds, and vertical profiles up to 250 m implicated regional natural gas production activities as the source of the elevated VOCs to the northeast of BAO and urban combustion emissions as the major VOC source to the south of BAO. Elevated acetonitrile and dimethyl sulfide mixing ratios were also associated with natural gas emissions. Fluxes of natural gas associated NMHCs were determined to estimate regional emission rates which ranged from 40 ± 14 Gg yr−1 for propane to 0.03 ± 0.01 Gg yr−1 for n‐nonane. These emissions have the potential to impact downwind air quality as natural gas associated NMHCs comprised ≈24% of the calculated OH reactivity. The measurements described here provide a baseline for determining the efficacy of future policies designed to control emissions from natural gas production activities.
HSI-SSTRANS: Hyperspectral Image Classification with Spectral and Space Transformer

Hyperspectral image (HSI) classification is an important technique in the field of remote sensing. In the HSI classification task, there is the phenomenon of different spectral information of the same substance and different substances of the same spectral information. In order to solve this problem, a method for processing spectral information and spatial information is proposed. At the same time, the method based on convolutional neural network (CNN) only considers local information and limits its representation ability. In order to obtain more features, the Transformer structure is used to extract global information in spectrum and space. Spectral and Space Transformer is built to join feature of spatial-spectral to obtain the HSI classification structure.We evaluate the classification performance of the proposed method on IndianPines and Houston by conducting experiments, showing the superiority over other transformer networks and achieving a improvement in comparison with other backbone networks.
Lightweight Multiscale Attention-Aware Method for Semantic Segmentation of Urban Structural Buildings in Drone Aerial Imagery

Every year, the lives of millions of people all over the world are affected by natural disasters such as floods, earthquakes and hurricanes. Urban and under-development communities are often hit the most during disasters. Disaster resilience requires proper planning and timely response. Recently, there has been growing interest in the potentials of computer vision and artificial intelligence to tackle challenges related to disaster resilience and emergency preparedness. Compared to cities, detecting structural buildings in urban environments is a more challenging task. Within this context, this paper presents a novel lightweight multiscale attention-aware method for semantic segmentation of urban structural buildings in drone aerial imagery. Detailed experiments were conducted to evaluate our proposed method using drone aerial images from Zanzibar city, Tanzania. Despite being lightweight, significant performance gains were achieved compared to relevant state-of-the-art methods in the literature.
A Case Study in RFI at L-band Detected by SMAP

Even when radio frequency interference (RFI) is detected, very little is known about the sources of the interference. More information about the sources would facilitate the design of systems to deal with the interference. Reporting of interference by the RFI teams for SMAP and SMOS through international channels has resulted in a decrease in RFI and identification of several sources. Two such cases that have been identified in the USA and are reported here.
HyperHeight Lidar Compressive Sampling and Machine Learning Reconstruction of Forested Landscapes

Low-altitude airborne lidars deliver high spatial resolution swath mapping using dense laser footprint sampling but only in limited areas, while satellite lidars offer global sampling but are hampered by low resolution due to sparse footprints. This work presents a novel approach to satellite lidar remote sensing designed to address the low spatial resolution by leveraging the principles of compressive sensing and machine learning applied to a highly efficient, adaptive lidar capable of dense footprint sampling. Compressive sensing enables the distribution of footprints across a swath with a density appropriate to recover the features of interest, without unnecessarily oversampling the terrain. Machine learning techniques are employed to reconstruct the compressive lidar measurements, leading to high-resolution, dense coverage, and a broad field-of-view per swath pass. HyperHeight Data Cubes are introduced, which offer a wealth of information about the 3D structure of a scene, including digital surface models, canopy height models and the internal organization of canopies. Training data was obtained from NASA's G-LiHT airborne lidar, and simulations of satellite observations performed on various forest types across the US demonstrate the efficacy of the new lidar imaging approach.
Knowledge Transfer and Model Compression for Misaligned Building Labels

Convolutional neural networks (CNNs) have achieved high precision in the extraction of low-rise buildings from very high-resolution (VHR) remote sensing images. However, there are two major challenges with high-rise buildings: i) off-nadir aerial images causing the misalignment in the high-risers and their labels, and (ii) finding the CNN of the right size fit for the available data. To address these challenges, we develop a workflow for two knowledge transfer techniques namely, knowledge distillation and supervised domain adaptation with three new multi-resolution urban building datasets: (i) teacher data from off-nadir images, (ii) student data from orthorectified images, and (iii) evaluation data with off-nadir images. The student and evaluation data include complex high-rise and skyscrapers buildings. The results show that knowledge distillation and supervised domain adaptation can minimise the effects of misaligned labels and possible domain shifts while also compressing network parameters by at least 74.99% and 99.5%.
Pansharpening by Efficient and Fast Unsupervised Target-Adaptive CNN

The recent paradigm shift from model-based to data-driven approaches has involved a growing number of data-fusion tasks. Specifically for pansharpening, unsupervised deep learning methods have been recently explored with the goal of overcoming the generalization limits shown by early pansharpening convolutional neural networks based on supervised training schemes. Furthermore, some of these exploit the target-adaptive modality to face the scarcity of training data. On the downside, combining usupervised training and target adaptivity causes a non-negligible increase of the computational cost. This work presents a new target adaptive scheme that allows to keep limited the computational cost at inference time while preserving accuracy.
Hybrid Differential Morphological Profile Enabled Faster R-Cnn For Object Detection In High-Resolution Remote Sensing Imagery

Deep neural network (DNN) algorithms have increased dramatically across a wide variety of remote sensing applications. However, as computer vision (CV) researchers have utilized the power of DNN, additional shortcomings have become more clear, including the reliance of DNNs on texture information rather than shape information to perform feature extraction for CV tasks. This lack of shape can negatively impact many remote sensing applications, where shape information can provide the key discriminatory features in object detection. To improve the utilization of shape information in DNNs, a novel model architecture was developed that incorporated shape information extracted using the Differential Morphological Profile (DMP), which was directly embedded in the DNN. While this network showed the ability to outperform traditional DNNs in CV tasks, there existed several instances in which the tradeoff of spectral and shape information proved ineffective. In this study, we present a Hybrid DMP-enabled network that utilizes both the spectral information of traditional DNNs and the shape information extracted by DMP. Our results show that this network is capable of outperforming competing methods in overhead remote sensing object detection.
A General Target Tracking and Sample Generation Framework for Satellite Videos Leveraging on Geometry-Aware Low-Rank Representation

Video satellites observe the Earth with high spatial-temporal resolution, empowering remote sensing with better capability for emergency needs. The tracking of moving targets naturally has received a great deal of attention. Many methods are proposed and achieve satisfying results after years of research, where the low-rank representation (LRR) shows great potential due to its weak assumptions on data. However, the original LRR lacks the consideration of frame-wise geometric inconsistency led by imperfect video stabilization, significant view change, and elevation difference, resulting in pseudo-motion and declined performance on some video sequences. Therefore, we propose a geometry-aware LRR (Geo-LRR) that simultaneously considers the alignment and the target tracking in videos. Moreover, to relieve the dilemma of high-quality samples, we generate pixel-wise annotations with straightforward post-processing on sparse images. Extensive experiments demonstrate the effectiveness of our method with 2.051 pixel-wise RMSE compared with groundtruth.
Machine Learning for Spatiotemporal Mapping and Monitoring of Mangroves and Shoreline Changes Along a Coastal Arid Region

Mangroves are coastal ecosystems with enormous ecological benefits. These coastal protectors provide a living environment to many marine organisms, and it is considered a unique contributor against climate change in their carbon storage and sequestration process. Mangroves experience severe losses due to natural factors and intensive anthropogenic activities. Therefore, mapping, monitoring, and obtaining consistent recent information about these valuable resources is essential for conservation and protection. The United Arab Emirates (UAE) is the home of sixty million mangroves covering an area of more than 180 km2 and storing 43,000 tons of carbon dioxide yearly [1]. The mangrove area located in the coastal region of UAE provides various benefits to the region and is considered a protective shield against the risk of erosion and sea intrusion. Therefore, UAE promised in the Conference of the Parties 2026 (COP26) to plant 100 million mangroves by the year 2030 [1]. Remote sensing and digital image processing techniques had proven to understand the mangrove ecosystem dynamics. Therefore, this study aims to investigate the changes in the Mangrove area and the effect of these changes on coastal erosion hazards over the last 20 years. The first step is to use a pixel-based machine learning (ML) classifiers along with multi-temporal, medium-resolution Landsat satellite images within Google Earth Engine (GEE) cloud computing platform, to create multi-temporal mangrove distribution maps of the UAE coastal area during the last 20 years. Second, qualitative and quantitative evaluations are conducted using ground truth data to validate the robustness of the proposed methodology and the accuracy of the results. Finally, coastline analysis is carried out using open-source tools, such as Digital Shoreline Analysis System (DSAS) [2] to estimate coastline changes and analyze coastal erosion risk. This method allows the identification of the mangrove gains and losses, as well as measurement of the change of coastline (accretion and erosion) over the past 20 years. The generated maps can lead to improvements in the ecosystems’ management and protection procedures. This study presents an effective workflow for mangrove detection and temporal mapping, using open-source medium-resolution satellite images, big data processing platforms, such as GEE, and open-source tools, such as DSAS.