Miracle-dz/UniFAR-FTUs huggingface dataset:
Year-Around C- and L- Band Observation Around the Mosaic Ice Floe with High Spatial and Temporal Resolution

In September 2019, the German research icebreaker Polarstern started the largest multidisciplinary Arctic expedition, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to ice floes at high Arctic for a whole year, thus including the winter season, the main goal of the expedition is to better understand and quantify relevant processes within the atmosphere-ice-ocean system that impact the sea ice, ultimately leading to improved climate models. Satellite remote sensing, specially multi-frequency synthetic aperture radar (SAR) plays a major role to achieve this goal. Two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage, and on the other hand to obtain a radar response that carries as much information as possible. A comprehensive set of C- and L- band SAR images were acquired during the course of MOSAiC. In this initial study we evaluate the effects of seasonal changes on C- and L-band backscatter in respect to three different sea ice types, i.e., Young Ice, Smooth Ice and Rough/Deformed Ice along with the performance of sea ice type retrieval of a established algorithm. Areas of deformed, smooth and young sea ice were observed in the vicinity of R/V Polarstern and these areas are included whenever possible in the yearlong time series. For both frequencies a change in all backscatter channels values can be observed during the early melt season. This is first noticeable in the C-band images and later followed by a change in the L-band images, probably caused by their different penetration depth and volume scattering sensitivities.
Hed-Unet: A Multi-Scale Framework for Simultaneous Segmentation and Edge Detection

Segmentation models for remote sensing imagery are usually trained on the segmentation task alone. However, for many applications, the class boundaries carry semantic value. To account for this, we propose a new approach that unites both tasks within a single deep learning model. The proposed network architecture follows the successful encoder-decoder approach, and is improved by employing deep supervision at multiple resolution levels, as well as merging these resolution levels into a final prediction using a hierarchical attention mechanism. This framework is trained to detect the coastline in Sentinel-1 images of the Antarctic coastline. Its performance is then compared to conventional single-task approaches, and shown to outperform these methods. The code is available at https://github.com/khdlr/HED-UNet.
Satellite Image Future Landscape Prediction using Conditional Adversarial Networks

Satellite images are important to get information related to the Earth's resources and environment. Landsat, a series of Earth-observing satellite missions, tracks use of land and records changes in it due to natural and human activities. Automated temporal analysis and prediction of landscape has various benefits in its planning and development. In this work, we use Conditional Adversarial Networks for the Landsat satellite image prediction. We not only learned a mapping from input images to output images, i.e., mapping from satellite images from 1999–2000 to satellite images from 2009–2010, but we also learned a loss function to train this mapping. The results are satisfactory in the sense that they capture the general trends over time.
Urban Flood Mapping of the July 2020 Kyushu, Japan Heavy Rain Based on Interferometric Coherence of Sentinel-1 Images

This study examined a methodology for urban flood mapping based on discrimination analysis of pre- and co-event interferometric coherences obtained from multitemporal Sentinel-1 SAR images. The method was applied to the areas in Kumamoto prefecture, Kyushu Island, Japan affected by the heavy rain in July 2020. Two pre-event images, one post-event image, and existing land cover map were analyzed with the help of inundation inventory data estimated from aerial photos. We revealed that the co-event coherences in the inundation urban areas were significantly decreased from the pre-event coherences. The result of the discriminant analysis of the pre- and co-event coherences shows the inundation areas were well extracted, and the inundation areas not identified in the inventory were also extracted in the coastal areas.
Exploitations of Multiple Rows Hammering and Retention Time Interactions in DRAM Using X-Ray Radiation

The methodological approach of hammering multiple rows is newly proposed to evaluate today’s SDRAMs, employed with in-DRAM mitigation circuits. The multiple rows are selected based on the one-row hammering test (single row hammering without refresh commands) and are exploited to defeat the employed mitigation algorithm. We irradiated the target sample using an X-ray to observe the reactions of the mitigation circuit when various combinations of multiple rows are hammered. The results showed a four times reduction in the number of hammering thresholds under the one-row hammering test. The same radiated sample showed no errors when one or a few rows were hammered due to the built-in mitigation circuit. However, multiple rows hammering (MRH) demonstrated its effectiveness by generating errors despite an active mitigation circuit. In this paper, we explore the X-ray damage results in the aging of the DRAM sample and induces vulnerabilities from the row hammering error perspective. Also, we use the error bits detected by MRH to investigate the coverage pitfalls of the mitigation circuit employed in the sample DRAM. Finally, we newly evaluate the remaining retention time under row hammering stress to explain the coverage loss in the mitigation strategy based solely on hammering counts.
Inductive Wireless Power Transfer Charging for Electric Vehicles–A Review

Considering a future scenario in which a driverless Electric Vehicle (EV) needs an automatic charging system without human intervention. In this regard, there is a requirement for a fully automatable, fast, safe, cost-effective, and reliable charging infrastructure that provides a profitable business model and fast adoption in the electrified transportation systems. These qualities can be comprehended through wireless charging systems. Wireless Power Transfer (WPT) is a futuristic technology with the advantage of flexibility, convenience, safety, and the capability of becoming fully automated. In WPT methods resonant inductive wireless charging has to gain more attention compared to other wireless power transfer methods due to high efficiency and easy maintenance. This literature presents a review of the status of Resonant Inductive Wireless Power Transfer Charging technology also highlighting the present status and its future of the wireless EV market. First, the paper delivers a brief history throw lights on wireless charging methods, highlighting the pros and cons. Then, the paper aids a comparative review of different type’s inductive pads, rails, and compensations technologies done so far. The static and dynamic charging techniques and their characteristics are also illustrated. The role and importance of power electronics and converter types used in various applications are discussed. The batteries and their management systems as well as various problems involved in WPT are also addressed. Different trades like cyber security economic effects, health and safety, foreign object detection, and the effect and impact on the distribution grid are explored. Prospects and challenges involved in wireless charging systems are also highlighting in this work. We believe that this work could help further the research and development of WPT systems.
The Impact of Barometric Variations on the Sea Level in Coastal Areas Using GNSS Reflectometry

The Local Inverse Barometer (LIB) effect is a complex phenomenon which consists in the response in sea level surface variations to barometric changes. This contribution aims to study the evolution of the impact of this phenomenon on sea level in different sites in coastal areas. Four GNSS stations were used all over the world (Port-Tudy on Groix island (France), Tarifa in Spain, Fort -de- France on Martinique island (overseas France) and Lyttelton in New-Zealand) in order to retrieve coastal sea level height through SNR - based GNSS reflectometry. This sea level height estimates are then extensively compared to oceanic tides and surge effects due to pressure variations. The results show that the impact of the LIB effect on sea level is strongly dependent on the location but also on the spatial extension and timescales.
Augmenting Data Systems with Prediction based Embeddings

One of the challenges of improving the search and use of complex Earth science data is designing and incorporating semantic components in existing Earth science data systems. Many projects have addressed this by using a knowledge engineering approach. However, using ontologies has inherent limitations as a practical and scalable approach. Data-driven strategies based on natural language processing, coupled with Machine Learning, provide an alternative approach. Data-driven approaches utilize existing corpus available as unstructured text. This paper describes a hybrid strategy that uses a data-driven approach to build an embedding from a large corpus of Earth science journal publications while leveraging existing ontologies to develop validation tests to evaluate the embedding's robustness and correctness. The paper also describes the use of this embedding in two different applications. The first application provides a semantic mapping service to bridge the gap between a science application need and the appropriate instruments or datasets required to address that need. The second application is keyword recommender to make the data set tagging process efficient for the data operators and ensure keyword consistency within a data catalog.
Cropland Topsoil Properties Mapping by Applying a Machine Learning Algorithm to Open Access Copernicus Data

Novel computational algorithms along with cloud computing services present a great potential to revolutionize the processing of Earth Observation (EO) data for topsoil mapping. This work presents the first insights of the WORLDSOILS project, where open-access Copernicus Sentinel-2 data and auxiliary terrain attributes were synergistically utilized to derive regression models. Building on key results from well-studied data mining approaches, the current study compares various approaches of temporal mosaicking to generate a multi-year median composite dataset of exposed bare soil pixels, over cropland areas in European Union. Finally, we utilized a Random Forest model based on soil samples (calibration = 80% and validation 20%) of the recently released LUCAS 2015 database to predict soil clay content and organic carbon. Following a masking approach to generate a composite of multi-date bare soil pixels, a promising prediction performance (R2 = 0.52, n = 7605) was achieved for clay content, while the predictive performance for soil organic carbon was significantly lower (R2 = 0.30).
Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Data center network (DCN) is the backbone of many emerging applications from smart connected homes to smart traffic control and is continuously evolving to meet the diverse and ever-increasing computing requirements of these applications. The data centers often have tens of thousands of components such as servers and switches/routers that work together to achieve a common objective and serve these applications. Managing such large data centers is a tedious process and demands automation, intelligent control and decision making within the data center. Recently both the industry and academia have focused on bringing intelligence to the control, automation, and management of DCNs. Despite the variety of works that surveyed ML for networking, to the best of our knowledge, none has focused on DCN, which makes this survey original. Readers in the academic and industrial communities will all benefit from a comprehensive discussion of the ML solutions applied in DCN to address critical essential problems, including workload forecasting, traffic flow control, traffic classification and scheduling, topology management, network state prediction, root cause analysis, and network security. Furthermore, this article outlines the challenges and concludes with the future research venues in adopting ML for automatic, intelligent and autonomous DCNs.
Time-and-Frequency Hybrid Multiplexing for Flexible Ambiguity Controls of DFT-coded MIMO OFDM Radar

A time-and-frequency hybrid multiplexing technique for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) radar is proposed. Discrete Fourier transform (DFT)-coded OFDM waveforms are introduced, which allow it to be free from range-dependent angle errors. These are readily used to provide both time-domain multiplexing and frequency-domain multiplexing in the MIMO OFDM radar. The DFT- coded frequency-domain multiplexing shortens the maximum unambiguous range, while the DFT- coded time-domain multiplexing lowers the maximum Doppler ambiguity. A hybrid of both domain multiplexing techniques can mitigate the respective limitations by adaptively selecting the proper DFT matrix size in each multiplexing domain. This allows to solve the intrinsic range and Doppler ambiguity problems of MIMO radars by controlling the hybrid ratio of the two kinds of DFT- code based multiplexing methods. Range-Doppler and range-angle maps of four examples with different hybrid multiplexing ratios are simulated with a MIMO OFDM radar numerical platform.
Learning Without Forgetting: A New Framework for Network Cyber Security Threat Detection

Progressive learning addresses the problem of incrementally learning new tasks without compromising the prediction accuracy of previously learned tasks. In the context of artificial neural networks, several algorithms exist for achieving the progressive learning goal of learning without forgetting. However, these algorithms have traditionally been tested on the well-known and widely available datasets from the domain of image understanding and computer vision. Very little has been done on exploring the suitability of progressive learning algorithms in the important area of network threat detection. On a more fundamental level, progressive learning algorithms are still faced with the challenge of predicting the ultimate ability of a given neural network architecture to add more tasks to its repertoire without undergoing catastrophic forgetting. The goal of this paper is to address such a challenge in the context of cyber security threat detection. It does so by providing a unified conceptual and computational framework where progressive learning algorithms can be analyzed, compared, and contrasted in terms of their learning capacity and prediction accuracy for specific datasets from the cloud cyber security domain. In particular, this paper provides rigorous metrics for predicting the onset of catastrophic forgetting in the cyber security domain and contrasts them with their usage in the imaging domain. Our extensive numerical results show that progressive learning, along with the proposed criteria for catastrophic forgetting, provides a very structured framework for automating network threat detection as new threats emerge throughout network operation.
Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.
Detecting Clearcut Deforestation Employing Deep Learning Methods and SAR Time Series

Automating the systematic monitoring of deforestation in the Brazilian biomes has become imperative. In this sense, a promising research field lies upon the exploitation of orbital imaging based on Synthetic Aperture Radar (SAR) sensors, since this technology is less affected by cloud cover, allowing systematic data acquisitions. In addition, the growing availability of with no charge SAR data products enables investigations on the use of time series extracted from this category of instruments, paving the way for more sophisticated temporal analyzes. This work presents the results of a SAR time series classification model designed to identify clearcut deforestation patterns in time, through an Artificial Intelligence approach known as Recurrent Neural Networks. The classification was performed using 5216 samples of Sentinel-1 time series within the Amazon basin, reaching an overall accuracy of 96.74%.
Development of the Terrestrial Snow Mass Mission

For northern countries like Canada, seasonal snow cover is a key component of the water cycle and a commodity of high importance to public safety, economic sustainability, and ecosystem function. Despite this importance, snow water equivalent (SWE - the amount of water stored by snow) information from existing surface observing networks and satellite data does not adequately address most user needs. To address this gap, a new synthetic aperture radar (SAR) mission capable of providing information on terrestrial SWE at previously unrealized spatial resolution is currently under development. The Terrestrial Snow Mass Mission (‘TSMM’) will provide moderate resolution (500m) dual frequency (13.5/17.25 GHz) Ku-band radar measurements across all northern hemisphere snow covered areas every 7 days. Data from this mission will be used at Environment and Climate Change Canada to (1) provide a new level of information on the temporal/spatial variability in SWE in support of climate services, and (2) feed into environmental prediction and analysis systems to improve weather and hydrological forecasts.
EWMA control charts for multivariate autocorrelated processesT1

In this paper, a semiparametric control scheme for multivariate Markov processes of order one is introduced. We utilize copula-based semiparametric stationary Markov models to transform original multivariate autocorrelated processes to the ones in which the marginal information of the monitored characteristics are separate out from their dependence structures. Meanwhile, the autocorrelations within them are also characterized by copulas. As such, one could focus on monitoring changes in the location of characteristics marginally under a more generalized assumption that observations could be stochastically dependent. The proposed chart can reduce to the traditional multivariate EWMA charts if the underlying process is multivariate Gaussian with stochastically independent observations. In addition, the margin of each time series is fitted by a newly developed semiparametric approach using the transformed Bernstein polynomial prior. Specifically, it allows an initial parametric guess (such as normal) on a monitored characteristic; then by adding more details via data, any departure from this initial guess will be captured and used for adjusting the initial to obtain robust estimation. Gaussian copulas are then used for modeling both autocorrelations within each time series and correlations among them.
Resistance of Austenitic Stainless Steels to Ductility-Dip Cracking: Mechanisms

In Ni-based alloys, precipitates that form along grain boundaries (GBs) during terminal solidification have been shown to pin GBs and resist GB sliding, which can cause ductility-dip cracking (DDC). As a result, it is often suggested that the stainless steel skeletal/lacy  in a  matrix resists DDC because it pins GBs. In the present study, austenitic stainless steels 304, 316, 310, and 321 were quenched with liquid Wood’s metal (75˚C) during welding. Quenching captured the elevated-temperature micro-structure and simultaneously induced cracking, thus revealing the mechanisms of the resistance to DDC. In addition, DDC was much higher in 310 than 304, 316, and 321, which is consistent with results of conventional tests. Both 304 and 316 solidified as columnar  grains, with continuous  formed along GBs soon after solidification to resist DDC along the GBs. 321 solidified as equiaxed grains of  instead of columnar, and the tortuous GBs associated with equiaxed grains resisted DDC. 310, however, solidified as coarse, straight  grains with little  along the GBs, and solidification GBs migrated to become locally straight. The resulting GBs were long, straight, and naked, which is ideal for DDC. In 304, 316, or 321, skeletal/lacy  in a  matrix did not exist in the fusion zone near the mushy zone, where DDC occurs. This proved skeletal/lacy  cannot resist DDC as often suggested. Instead, the present study identified two new mechanisms of resistance to DDC: 1) formation of continuous or nearly continuous  along boundaries of columnar  grains and 2) solidification as equiaxed  grains.