Miracle-dz/UniFAR-FTUs huggingface dataset:
Emissivity Based Indices for Drought and Forest Fire

The present paper aims to illustrate new indices of vegetation-soil dryness based on the surface emissivity complemented with atmospheric water vapor mixing ratio or related parameters, such as the dew point temperature. The indices are based on satellite measurements and they have been built using the hyperspectral infrared sensor IASI (Infrared Atmospheric Sounder Interfemoter) flying onboard the European Meteorological Platforms (MetOp). With the IASI instrument, we can retrieve simultaneously the surface emissivity and temperature, and thermo-dynamical parameters of air, such as temperature and water vapor mixing ratio profiles. Infrared surface emissivity (ε) is more closely related to surface type and coverage concerning the commonly used normalized differential vegetation index (or NDVI). By properly using surface emissivity in the infrared we defined a set of channels that are particularly sensitive to bare soil, green and senescent vegetation. IASI capability to sense the thermodynamic state of the atmosphere enables to retrieve both surface temperature (Ts) and dew point temperature (Td) close to the surface. The difference between these two quantities (Ts-Ts) is a direct measure of the hydric stress at the surface. Emissivity indices complemented with the last one, obtained from the same measurements, enable the individuating region to be subject to a risk of drought, hence and forest fire, and allow us to overcome the problem of lacking space and temporal consistency. We applied this methodology to the region of Balgarska Polyana in southern Bulgaria which was hit by intense fires in August 2016.
Retrieval and Validation of Long-Term Aerosol Optical Depth from AVHRR Over China Mainland

The global long-term aerosol optical depth (AOD) dataset has a great significance for the study of global climate change. Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) satellites can provide global observation from 1978 to present. In this paper, we have improved the algorithm for the retrieval of the AOD over land proposed by Xue et al in 2017 [1]. We obtain 0. 64μm band surface reflectance by utilizing a linear relationship between the surface reflectance at the wavelength of3. 75μm and 0. 64μm, which has been verified in the Moderate Resolution Imaging Spectroradiometer (MODIS). Considering difference of spectral response between AVHRR's bands and MODIS's bands, we calibrate this empirical relationship by fitting the simulated surface reflectance of the relative AVHRR and MODIS bands. A radiative transfer model for Lambertian surface and the look-up table (LUT) method are applied to NOAA-7, 9,11,14, 18 over China mainland (15° - 60° N, 70° - 140° E) from 1982 to 2011. Comparison of retrieval AVHRR AOD against AErosol RObotic NETwork (AERONET) data shows good consistency with more than 60% points within uncertainty of $\pm$ (0.05+0.25xAOD).
Wireless Vehicular Communication: C-ITS Field Measurements Using ITS-G5

The vehicular industry is going through an evolutionary phase of rapid scientific and technical development. It provides assistance for C-ITS (Cooperative Intelligent Transport Systems) that connects vehicles and roadside infrastructure i.e., Road Weather stations (RWS) and pedestrian. This is to offer an efficient and safe road transport system. Globally, the incorporation of ITS using DSRC (Dedicated Short-Range Communications) standard and ETSI (European Telecommunications Standards Institute) standard known as ITS-G5. ITS-G5 offer diverse feature of location-based services that were missing previously in cooperative vehicles. In this paper, we have presented the deployment of cooperative vehicle scenarios that aims to prevent road accidents and avoid casualties. It has been done by delivering time-critical road safety services to vehicles using ITS-G5. The deployment of cooperative vehicle scenarios uses different instruments for vehicles (i.e., Radars, LIDAR’s, friction sensors, weather sensors etc.): These different sensors and devices permits numerous advanced road services including snow/windstorms, heavy rain or road slippery identification. In this paper, we have identified the packet loss, vehicle locations, network latency and vehicles distance in dangerous situations i.e., emergency brake light, slow vehicle and emergency vehicle approaching. The C-ITS scenarios will support the wide-scale implementation and usage of advanced road weather safety and maintenance services with enhanced ITS-G5 networking capabilities.
Real Time Air and Water Quality Monitoring based on Distributed Sensor Network

Nowadays, air and water quality are a big problem in the cities because they affect human health deeply. To determine air and water quality, the monitoring system containing a wireless sensor node having several sensors has been developed with the internet of things (IoT) technology in this study. It regularly monitors various parameters such as temperature, humidity, bar pressure, wind direction, wind speed max, wind speed average, rain fall one hour, rain fall one day, $PM_{2.5}$ and PM10 for air quality monitoring and consumed water, waste water, incoming water, pH sensor, conductivity sensor, turbidity sensor, dissolved oxygen and temperature sensors for water quality monitoring. To improve smart city environments, air and water quality monitoring has to be common, present everywhere, and rapidly responsive. The monitoring system based on IoT is able to track air and water pollution in real time and transmit the information fast through a wide area network. This system can be integrated with innovative approaches like smart city. Therefore, the demand for a real time monitoring system will increase day by day.
A Framework for Fault Diagnosis using Continuous Bayesian Network and Causal Inference

Fault diagnosis in industrial facilities has traditionally been done using rule-based approaches, heuristics or expert-knowledge. Bayesian network provides a flexible and data-driven alternative that can reason under uncertainty. Most of the data being generated by sensors in industrial setups are continuous and the underlying data-generating models are essentially non-linear. This paper employs Bayesian network and proposes a framework that learns parameters of probability density functions of a continuous Bayesian network using neural network/s without requiring assumption of linear Gaussian model or discretization of continuous data. Moreover, an expression of probability query using learned parametric density functions and causal-inference based mathematical formulation of two tasks related to fault diagnosis –in the context of industrial plants– namely root-cause-analysis and identification of most-influential-path in Bayesian network have been provided.
Estimating Safety Factor Against Root Lodging Using Sentinel-1 Data

Lodging in wheat is one of the main constraints limiting yield and grain quality. Accurate information about crop lodging susceptibility during the growing season is critical for improving yield estimates and for targeting the expenditure on lodging control. In this context, this study aims to estimate safety factor against root lodging ($SF_{A}$) as a measure of lodging susceptibility by exploiting Sentinel-1 data using Extreme Gradient Boosting Regression. Through extensive field experiments during a crop season, several crop variables were collected from several plots in multiple visits, and the corresponding metrics were extracted from the Sentinel-1 images. Our results show that the field measured $SF_{A}$ correlated well with the field lodging and the cross-validated regression model could estimate $SF_{A}$ with an $R_{CV}^{2}=0.73$ and $RMSE_{CV}=0.59$. Thus, the $SF_{A}$ measure constitutes a state-of-the-art approach in the remote sensing community for the assessment of root lodging susceptibility.
Freemium Spectrum Sharing and Pricing

The pervasive virtualization of communication networks, including wireless networks, as well as the increasing proliferation of softwarized solutions in wireless communications pave the way for the highly flexible approach for spectrum sharing and pricing. In this paper, the popular idea of freemium licensing is applied to dynamic spectrum sharing and pricing. The proposed softwarized solution is evaluated by means of computer simulations, showing the benefits of such elastic solutions.
CSWAP: A Self-Tuning Compression Framework for Accelerating Tensor Swapping in GPUs

Graphic Processing Units (GPUs) have limited memory capacity. Training popular deep neural networks (DNNs) often requires a larger amount of memory than that a GPU may have. Consequently, training data needs to be swapped between CPUs and GPUs. Data swapping may become a bottleneck when its latency is longer than the latency of DNN computations. Tensor compression in GPUs can reduce the data swapping time. However, existing works on compressing tensors in the virtual memory of GPUs have two major issues: sub-optimal compression performance for varying tensor sparsity and sizes and lack of portability because its implementation requires additional (de)compression units in memory controllers. We propose a self-tuning tensor compression framework, named CSWAP, for improving the virtual memory management of GPUs. It has high portability and is minimally dependent on GPU architecture features. Furthermore, its runtime only applies compression on tensors that are deemed to be cost-effective considering their sparsity and size and the characteristics of compression algorithms. Finally, our framework is fully automated and can customize the compression policy for different neural network architectures and GPU architectures. Our experimental results using six representative memory-intensive DNN models show that CSWAP reduces tensor swapping latency by up to 50.9% and reduces the DNN training time by 20.7% on average with NVIDIA V100 GPUs compared to vDNN.
Global L-band Observatory for Water Cycle Studies (GLOWS)

L-band observations have proven useful for estimating soil moisture and ocean salinity variables to study the land surface and ocean. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was the first (2009-present) spaceborne L-band radiometer. This was followed by two L-band missions flown by the National Aeronautics and Space Administration (NASA) to measuresea surface salinity (Aquarius 2011–2015) and soil moisture (SMAP 2015-present). It is critical to continue the time series of L-band observations that these missions have begun. To address this need we propose a new low-cost instrument concept known as the Global L-band active/passive Observatory for Water cycle Studies (GLOWS) that will include an L- band radiometer and radar to provide data continuity. The new mission concept includes a deployable reflectarray lens antenna with a compact feed that can be flown on an Earth Venture class satellite in a EELV Secondary Payload Adapter (ESPA) Grande-class mission. GLOWS will continue the science observations of SMAP and SMOS at the same resolution and accuracy at substantially lower cost, size, and weight.
Downscaling of Satellite Soil Moisture Products and its Applications in Drought Monitoring

Soil moisture is one of the most important land surface variables that controls not only water balance at land surface, but also land surface energy budget. As more and more satellite sensors are launched in recent years, a number of satellite soil moisture products are operationally produced with different spatial and temporal coverage and quality. However, the application of these products is usually limited by their spatial resolutions that is determined by their native sensor footprint sizes, which are usually tens of kilometers. To broaden the range of satellite soil moisture applications, products with finer resolution are in great need. This paper explores a soil moisture downscaling algorithm and its potential for drought monitoring at local scales. Downscaled soil moisture product is compared with soil moisture products produced from airborne measurements. The results show that the quality of the downscaled soil moisture product is comparable with the coarser resolution product but catches much more spatial details, which is very beneficial to applications such as drought monitoring.
A Hardware/Software Concept for Partial Logic Updates of Embedded Soft Processors at Runtime

Embedded systems are built from various hardware components and execute software on one or more microcontroller units (MCU). These MCUs usually contain a fixed integrated circuit, thus disallowing modifications to their logic at runtime. While this keeps the instruction set architecture (ISA) fixed as well, it leaves the software as the only flexible part in the system. But what if the MCU logic could be easily changed at runtime in order to fix bugs or if the ISA could be extended on-the-fly in order to introduce application-specific instructions and features on demand? This work demonstrates a concept for introducing more hardware flexibility through application-specific MCU modifications. Therefore, the MCU is implemented as a soft core on a field-programmable gate array (FPGA) and we reconfigure its logic with support of the operating system (OS) running on it. The reconfiguration happens on-the-fly, so no interruption of the application code or even a system restart is required. Therefore, (i) the MCU pipeline is specially designed for extensibility by new instructions, and (ii) the FPGA is selected to support partial self-reconfiguration of its logic cells at runtime. As long as an instruction is not yet part of the ISA, the OS supports its emulation to provide a consistent interface for applications. Apart, no special compiler support is required, but the application must provide either the emulation code or a hardware description for adding the required logic. For a proof of concept, we use a RISC-V based MCU on a Xilinx Artix-7 FPGA and for evaluating the general benefit of our approach we use an algorithm that is costly when executed with the original ISA but fast with application-specific instructions added at runtime. The experimental evaluation also shows that the on-the-fly hardware update does not disrupt or compromise the software execution flow.
Pre-Integrated Architectures for sustainable complex Cyber-Physical Systems

The paradigm of Cyber-Physical Systems is spreading widely across several industrial domains such as Automotive, Construction, Energy, Health, Manufacturing, Smart Cities. But the system architectures, processes and operations related to these Cyber-Physical Systems are reaching a high level of global complexity, which is difficult to sustain by the different stakeholders. In addition, new ambitious constraints are being added to the list of requirements that these Cyber-Physical Systems must comply with.The purpose of this paper is to propose the concept of pre-integrated architectures as solutions to improve the development and operational processes of these complex Cyber-Physical Systems. An outlook of the practical implementations and impacts in four industrial domains will be provided, in relationship with the developments performed in the CPS4EU project.
Accelerating GPU Message Communication for Autonomous Navigation Systems

Autonomous navigation systems consist of multiple software modules, such as sensing, object detection, and planning, to achieve traffic perception and fast decision making. Such a system generates a large amount of data and requires data processing and communication in real-time. Although accelerators, such as GPUs, have been exploited to speed up data processing, communicating GPU messages between modules is still lacking support, leading to high communication latency and resource contention. For such a latency-sensitive and resource-limited autonomous navigation system, high performance and lightweight message communication are crucial and demanding. To obtain both high performance and low resource usages, we first propose a novel pub-centric memory pool and an on-the-fly offset conversion algorithm to avoid unnecessary data movement. Secondly, we combine these two techniques and propose an efficient message communication on a single GPU. Finally, we extend this approach to multi-GPU and design a framework that natively supports GPU message communication for Inter-Process Communication. With comprehensive evaluation, results show our approach is able to reduce communication latency by 53.7% for PointCloud and Image messages compared to the state-of-the-art approach. Moreover, in the real autonomous navigation scenario, our approach reduces the end-to-end latency by 29.2% and decreases resource usage up to 58.9%.
Sensitivity to Soil Moisture Over an Agricultural Area by Exploiting a Model-Based Polarimetric Decomposition

In this work, the sensitivity to soil moisture of different scattering mechanisms observed by an airborne polarimetric radar operating at L-band has been investigated. The main objective was assessing the capability of a fully polarimetric radar system to disentangle the change in soil moisture under different vegetation covers. We used polarimetric data collected by the NASA UA VSAR airborne radar flying over the Yucatan Lake site in Louisiana. Six overflights were analysed, and six regions of interest characterized by different vegetation covers were selected. The temporal trends of the magnitude of different scattering mechanisms, according to the Freeman-Durden and the Nonnegative Eigenvalue decompositions, as well as of the NDVI and a nearby precipitation gauge are analysed and discussed with reference to the theoretical expectations.
Experimental Comparison of Registration Methods for Multisensor Sar-Optical Data

Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.
3D Point Cloud Generation Using Adversarial Training for Large-Scale Outdoor Scene

Three-dimensional (3D) point clouds are becoming an important part of the geospatial domain. During research on 3D point clouds, deep-learning models have been widely used for the classification and segmentation of 3D point clouds observed by airborne LiDAR. However, most previous studies used discriminative models, whereas few studies used generative models. Specifically, one unsolved problem is the synthesis of large-scale 3D point clouds, such as those observed in outdoor scenes, because of the 3D point clouds' complex geometric structure. In this paper, we propose a generative model for generating large-scale 3D point clouds observed from airborne LiDAR. Generally, because the training process of the famous generative model called generative adversarial network (GAN) is unstable, we combine a variational autoen-coder and GAN to generate a suitable 3D point cloud. We experimentally demonstrate that our framework can generate high-density 3D point clouds by using data from the 2018 IEEE GRSS Data Fusion Contest.
Retrieval and Validation of Vertical Forest Lai Profile from Airborne Lidar Data

Leaf area index (LAI) is an important vegetation parameter. The vertical profile of forest LAI is critical for understanding the forest carbon, nitrogen, and water cycles and remote sensing radiative transfer processes. This study developed a method to estimate the LAI profile from airborne laser scanning (ALS) data during the leaf-on season. The LAI profile was validated using field measurement data from the digital hemispherical photography (DHP). The results show that both DHP and ALS data can get the vertical distribution of LAI, and similar trends can be obtained. The cumulative LAI derived from ALS data shows a good relationship with that from the DHP (R2=0.84). The LAI profile derived from ALS can be applied to land surface models and remote sensing radiative transfer models.
Spatial and Temporal Interpolation of CYGNSS Soil Moisture Estimations

High Spatio-temporal soil moisture is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising opportunity for high-resolution soil moisture retrievals. NASA's Cyclone Global Navigation Satellite System is a preeminent GNSS-R application that offers high spatial and temporal resolution observations from Earth's surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily soil moisture predictions. This work investigates multidimensional spatial and temporal interpolation of the CYGNSS soil moisture estimates using methods such as linear, nearest, and natural interpolation. The results indicate that the interpolation error (RMSE) was 0.032 $m^{3}/m^{3}$, 0.038 $m^{3}/m^{3}$, and 0.030 $m^{3}/m^{3}$ for linear, nearest, and natural interpolation, respectively. The results also show that interpolated and observed CYGNSS SM values have the similar performance metrics when validated with the SMAP 9-km gridded SM product.
Efficient Design Space Exploration of OpenCL Kernels for FPGA Targets Using Black Box Optimization

Nowadays, many industries are in favor of using intelligent design space exploration as opposed to brute-force analysis. In many applications, the design space is defined by multiple variables and their interactions. Although brute-force analysis is very simple, it is rarely scalable when the number of variables in the system increases. With the rising complexity of hardware designs, more intelligent approaches are needed to explore the design options. This paper proposes using smart meta-heuristic search algorithms such as Grey Wolf Optimization (GWO) in conjunction with Bayesian Optimization (BO) to solve this problem. We show that we can further reduce the design effort using a surrogate model that is created based on a novel hybrid GWO-BO method. The surrogate model is a useful abstraction to detect functional and physical inter-dependencies in the system in order to accurately predict its performance (e.g. throughput or latency). We evaluate our methodology and show that it can produce competitive results in order to find the best design variables that maximize the performance of the system. Finally, we compare our results with previous statistical and heuristic methods proposed in the literature and find that the proposed GWO-BO method always performs better than the other considered methods.
Spatial Resolution Improvement via Radar Parameter Adjustment for Extremely-High-Squint Spotlight SAR

The extremely-high-squint spotlight synthetic aperture radar (SAR) has been widely used in military and commercial fields. Due to the extremely-high-squint angle, the point spread function (PSF) used for SAR resolution evaluation suffers from severe distortion and thus results in non-uniform spatial resolution. To solve this problems, this paper proposes a novel radar parameter adjustment (RPA) method to correct the PSF distortion during the data acquisition. The mechanism is to achieve an orthogonal PSF by correcting the wavenumber spectrum distortion. In this case, the radar parameters, i.e., the center frequency and the chirp rate, vary at every azimuth sampling position. The proposed approach is validated via the computer simulations.