Miracle-dz/UniFAR-FTUs huggingface dataset:
Hotspot Vegetation Structure and Terrain Monitoring of Dutch Coastal Dunes with LiDAR and Optical Camera's Mounted on Drones

Drones offer a flexible way to collect data for monitoring of natural areas. Changes in land cover can be seen in the RGB imagery and changes in the terrain are detectable from the LiDAR data. Differences in the Digital Terrain Models from 2017 and 2020 show the hotspots where blowouts and deposition occur and the Lidar profiles show the dynamics of the terrain in high detail. The results indicate significant morphological changes in the dunes that were not directly recognizable in the field. This clearly indicates to the terrain manager that the quality of the ecosystem is afresh and functions as a (near) natural
RVfpga: Using a RISC-V Core Targeted to an FPGA in Computer Architecture Education

RISC-V FPGA, also written RVfpga, is a set of two freely available courses developed by the authors and Imagination Technologies that enable users to understand and use the RISC-V instruction set architecture (ISA), a commercial RISC-V core and system, and the RISC-V ecosystem. The first course, RVfpga, includes comprehensive instructions, tools, and labs for targeting a commercial RISC-V processor to a field programmable gate array (FPGA) and then using and expanding it to learn about computer architecture, digital design, embedded systems, system-on-chip (SoC) design, and programming. The topics covered include targeting the RISC-V SoC to an FPGA, programming in C and RISC-V assembly, running programs in simulation or, optionally, in hardware, using peripherals and adding new ones to the SoC, and analyzing and modifying the RISC-V core and memory system, including adding new instructions to the core. The follow-on course, RVfpga-SoC, shows how to build a RISC-V SoC from building blocks and then run the Zephyr real-time operating system (RTOS) on it. At the completion of these courses, users will have a working RISC-V system and have hands-on experience exploring and using both the RISC-V SoC and the RISC-V toolchain, including compilers and simulators.
Arctic Sea ICE Mapping Using Sentinel-1 SAR Scenes with a Convolutional Neural Network

In this paper we focus on automated sea ice mapping based on satellite images with deep learning methods. We adjust the UNET++ convolutional neural network architecture for sea ice type semantic segmentation and create a per-pixel classifier for Sentinel-1 SAR scenes. The classification is done tile-wise, i.e. a Sentinel-1 scene is divided into tiles, classified, and then the results are joined back to form the classified scene. We address to the border effect appearing when a tiled classification is applied.
Multisource Data Fusion for the Detection of Settlements Without Electricity

The international charity SolarAid aims to provide access to lights in areas without electricity, and it is a challenge to accurately and efficiently transmit the lights to the areas in need. Multisource, multitemporal, and multimodal remote sensing images can provide rich information about the target area, so using multisource remote sensing images for accurate detection of human settlements without electricity is a feasible solution. In this paper two separate detection tasks are formulated: building two attention SENet for settlements detection and light detection using the Sentinel-2 dataset and the Suomi Visible Infrared Imaging Radiometer Suite (VIIRS) night time dataset, respectively. In addition, we study a new outlier removal method based on the pixel distribution characteristics of the VIIRS dataset for data pre-processing, and propose a post-processing method based on region continuity for further correction of the results. Experiments show that our method can maximize the use of multisource data information and rank first in the detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest.
Multi-Mission Remote Sensing Observations for Optimizing Hydrological Hazard Predictions

Satellite remote sensing observations offer unique possibilities to monitor, understand and predict hydrological hazards, particularly in ungauged catchments. In this study, we use state-of-the-art multi-mission remote sensing observations to update a hydrological model of the poorly instrumented Ogooué river in Gabon. We use a conceptual rainfall-runoff model forced with satellite-based climate observations; and calibrate the model parameters using an aggregated objective function including Sentinel-3 satellite altimetry and GRACE/GRACE-FO total water storage. The model is used to evaluate flood risk in combination with Sentinel-1 water surface extent and flood occurrence maps and to simulate the potential impact of climate change on the rainfall-runoff processes in the catchment.
Analysis of a Laser-induced Instructions Replay Fault Model in a 32-bit Microcontroller

In this paper, we present a method to obtain a new Laser Fault Injection (LFI)-induced fault model: replay of instructions on a 32-bit Microcontroller (MCU). This method allows a potential adversary to replay a block of two or four instructions with a fault rate up to 100%. These faults are induced by laser pulses and cause the instructions updating process of a Flash buffer to fail. As a result, the new instructions failing to be stored in the Flash buffer, the previous ones are replayed. We deeply studied the properties of this replay fault model by many experiments of laser fault injections. We have notably shown that the sensitivity window is proportional to the laser Pulse Width (PW), and that up to 20 instructions in a row were tested to be overwritten due to replaying five times the block of four instructions. The effects of the laser power and cache status (enabled or disabled) are also presented. Finally, we proposed and assessed a simple method to detect the LFI-induced replay faults using a hardware counter with different increments. Our results extend the ability of LFI on MCU, illustrating the accuracy and reproducibility of LFI.
To Pin or Not to Pin: Asserting the Scalability of QEMU Parallel Implementation

Due to its speed in cross-executing sequential code, dynamic binary translation is the unchallenged technology for full system-level simulation. Among the translators, QEMU has become the de facto solution. It introduced parallel host execution of the target cores a few years ago for the ARM instruction set architecture and this support is now also available, among others, for RISC-V. Given the popularity of these instruction sets in multi and many-core systems, assessing the scalability of their parallel implementation makes sense. In this paper, we use a subset of the PARSEC benchmark to measure the execution time of QEMU’s parallel implementation, to which we added the ability to pin a target processor to a host core or hardware thread. We report the results of a wealth of experiments we performed on a 16-core/32-thread x86-64 SMP machine. They show that the support of parallelism in QEMU scales well, and that, somewhat counter intuitively, pinning does not improve performance.
The Application of High Altitude Pseudo-Satellites for a Rapid Disaster Response

In case of emergency, a rapid first picture of the damaged area is required in order to have a better comprehension of the event and to deploy first aids. The continuous monitoring of the area affected by a disaster can also help to follow its evolution and to evaluate the consequences on population and man-made structures. The continuous monitoring is usually defined as persistence. In this paper, the deployment of HAPS (High Altitude Pseudo-Satellite) is proposed to monitor an emergency area right during the phase of disaster response, offering images in proximity and time persistence. Indeed, thanks to their proximity to the Earth surface (18–20 km of altitude) HAPS could provide images with greater spatial resolution than satellites and greater time resolution because of station-keeping or loiter capability on the desired area. Feasibility and effectiveness of this proposal is assessed by a simulation of a hypothetic HAPS usage in the realistic condition represented by the Central Italy earthquake in 2016.
Linear Principal Polarizations in Bistatic SAR Mission Companions

The paper investigates the polarimetry of bistatic Synthetic Aperture Radar (SAR) acquisitions over rough surfaces, with focus on the rotation of the scattered wave orientation at the companion antenna axes and on the optimal linear polarization in transmission. This latter is defined as the polarization achieving the maximum radar cross section and will be herewith recalled as principal polarization. The paper outlines a geometrical framework for the interpretation and the estimation of the principal polarizations. It is shown that the theoretical formulation provides a good agreement with the second-order analytical approach in [1]. The paper finally postulates that a bistatic illumination in the traditional $\mathrm{H}$ and V linear modes can be considered equivalent to a compact $\psi$ -pol, i.e. with the transmittion in a linear polarization rotated by $\psi$. For long baselines, such those as those envisioned by the ESA Harmony EE10 candidate, and for steep incidence angles, an equivalent $\pi/4$ -pol might be possible for rough surfaces.
Sub-Pulse Matching Correction for Radar Target Detection

A long time coherent accumulation method based on sub-pulse matching correction (SPMC) is proposed in this paper to solve the problems of radar moving target echo across range units and across Doppler. This method adopts the reverse search process from the echo signal to the target velocity, setting the sub-pulse matching factor to accomplish exponential sub-pulse search process. Under the premise of robust performance of parameter measurement and target detection, the calculation amount is greatly reduced compared with the original method. Then, through formula derivation and simulation calculation, the similarities and differences between the proposed method and Radon-Fourier transform (RFT) are analyzed and compared from the aspects of operation steps, blind velocity response and operation amount. Finally, several groups of simulation experiments are carried out to prove the universality and robustness of the proposed method.
Wormhole Computing in Networks-on-Chip

Nowadays, the increasing number of processing elements (PEs) in Multiprocessor Systems-on-Chip (MPSoCs) requires a scalable on-chip interconnect. Networks-on-Chip (NoCs) have emerged as the most promising communication technology for MPSoCs. Novel routers that reduce the communication overhead by integrating a processing layer into the communication layer of MPSoCs and the corresponding flow control of packets defined as Wormhole Computing are presented. The routers are constructed with processing units inside the input buffers providing application-specific operations that can be executed on transferred data. Hence, the communication time can be efficiently used by processing data that is sent by packets through the NoC. A KPN-based computation model has been modified to support not only the mapping of tasks to PEs but also the mapping to routers. In addition to analytical considerations, the novel routers have been evaluated with signal processing applications on a Xilinx Zynq SoC. The analytical considerations prove that a speedup can be achieved by moving a sequence of instructions from processors to routers. The results of both use cases show better exploitation of the communication time by up to 42.8% and a speedup of up to 5x for a single task.
The digital twin as an enabler of digital transformation: a sociotechnical perspective

Many organizations find the emerging concept of the Digital Twin (DT) to be compelling as a potential means to reduce the time and cost to develop new products, support fielded products, and enable rapid innovation to respond to new market opportunities. However, realizing the full potential of a DT may require significant changes in the methods, processes, and tools for product development and support currently used by potential adopters in the organization. We introduce an approach for assessing the behaviors and capabilities of the DT and their relationships within the organization’s sociotechnical system. We believe this approach can enable DT implementation outcomes better aligned with the organization’s strategic goals and objectives, and help to clarify not only what the DT can do, but what it should do for the organization. The approach is explained using an illustrative case study example, which is based on interim progress of our ongoing research project.
Design for Restricted-Area and Fast Dilution using Programmable Microfluidic Device based Lab-on-a-Chip

Microfluidic lab-on-a-chip has emerged as a new technology for implementing biochemical protocols on small-sized portable devices targeting low-cost medical diagnostics. Among various efforts of fabrication of such chips, programmable microfluidic device (PMD) is a relatively new technology for implementation of flow-based lab-on-a-chips. A PMD chip is suitable for automation due to its symmetric nature. In order to implement a bioprotocol on such a reconfigurable device, it is crucial to automate sample preparation on a chip as well. Sample preparation, which is a front-end process to produce the desired target concentrations of the input reagent fluid, plays a pivotal role in every bioassay or bioprotocol. In this paper, first, a method referred as dilution algorithm in two steps (DATS) is proposed, which needs only two diluting operations for any target concentration to achieve. Then, we present another method called as dilution algorithm on a small dilution area (DASDA), which needs less area compared to that by DATS. Finally, we propose the heuristic for efficient dilution of biochemical fluids using a PMD chip referred as dilution algorithm in a restricted dilution area (DARDA) that produces more accurate (with less error) target concentration value on a restricted area of the PMD chip in a shorter mixing time. Simulation results reveal that DARDA outperforms a start-of-the-art dilution algorithm applicable for PMD chips in terms of three performance parameters namely mixing time, mixing area and error in target concentration.
Measuring and Understanding the Dynamics of Solar-Induced Fluorescence (SIF) and its Relation to Photochemical and Non-Photochemical Energy Dissipation - Scaling Leaf Level Regulation to Canopy and Ecosystem Remote Sensing

Solar-induced fluorescence (SIF) has become a promising remote sensing parameter to quantify actual photosynthesis beyond the ‘greenness' measurements. Despite the great advances in instrumentation to measure canopy SIF, we are still at the beginning of having concepts to quantitatively relate SIF to actual rates of photosynthesis. In this article, we discuss the three elements that are crucial to scale canopy SIF measurements to leaf function, namely (i) canopy structure and its bio-chemical composition determining light absorption, (ii) the functional status of photosynthetic light conversion and fluorescence emission under non-steady state conditions, and (iii) the re-absorption and scattering of the fluorescence signal within the canopy.
Moving Target Detection for Single-Channel Csar Based on Deep Neural Network

Moving target brings out the different position shifts and defocusing across the image sequences acquired by circular synthetic aperture radar (CSAR) due to the Doppler shift and range smear effects. In this paper, a novel moving target detection approach for single-channel CSAR is proposed based on deep neural network (DNN). A dual-channel densely connected convolutional network (DenseNet) in consideration of complex-valued information is exploited for distinguishing the ground clutter and moving target. In terms of limited CSAR measure data set available for training the DNN network, simulated moving target samples are generated and fused into the measured ones under the various motion parameters. Finally, experiments have demonstrated that the proposed DenseNet for single-channel CSAR system processes an accepted detection performance and effectively overcomes the insufficiency of the limited dataset applications.
Data-Driven Spatio-Temporal Interpolation of Sea Surface Sediment Concentration from Satellite-Derived Data: An OSSE Case-Study in the Bay of Biscay

Due to complex natural and anthropogenic forcings, the dynamics of suspended sediments within the ocean water column remains difficult to monitor. Nowadays however, more and more available information is coming from in situ and satellite measurements, as well as from simulation models. Data assimilation methods propose to combine all this information to produce the most precise results, allowing better analyzes of the processes in play. Here a comparison of data-driven methods is presented. Optimal Interpolation (OI), Empirical Orthogonal Function (EOF) based and Kalman Filter based methods are compared to a new one using neural networks. The latter is a Data Interpolation method based on convolutional AutoEncoders (DinAE). Present results show that DinAE better performs compared to other methods, having the lowest error budget and the highest learning of high frequency events.
Use Ensemble Learning to Estimate the Population and Assets Exposed to Tropical Cyclones

Tropical cyclone (TC) is one of the major meteorological disasters in the world, which seriously threatens the safety of human life and property. In this study, we adopt the tropical cyclone best track archive and the global tropical cyclone exposure data sets, using the ensemble machine learning approach for exposed to the TCs of the population and asset to estimate. To the best of our knowledge, this research is the first hazards assessment study using machine learning methods to estimate the exposure of tropical cyclones to population and property end-to-end. In particular, we validated our model using data from the country most affected by tropical cyclones. The results depicted a 10% improvement over traditional methods. The correlation coefficient r between the predicted value and the exposure data is 0.813, demonstrating a strong correlation between the predicted results and the great potential of machine learning to solve the problem of hazards assessment. However, there is still uncertainty in assessing the impact of tropical cyclones, especially the impact caused by high wind speeds.
Estimating Biomass From Sentinel-3 Altimetry Data: A Sensitivity Analysis

ALtimetry for BIOMass (ALBIOM) is a research project funded by the European Space Agency to study the possibility of estimating above ground biomass by means of low- and high-resolution Sentinel-3 altimetry data. We present preliminary results of a sensitivity analysis, developed to assess in what extent waveforms and altimetry observables are affected by the presence of forests. Ku- and C-band altimetry data have been collected and processed to properly select well-tracked waveforms over land, which are then related to collocated biomass data. A statistical analysis has been performed, highlighting a sensitivity of the estimated normalized radar cross section with respect to forest biomass, even though it is strongly disturbed by the soil topography within the radar footprint. Additionally, the inaccurate positioning of the time-tracking window limits the number of useful data.
Unconstrained Aerial Scene Recognition with Deep Neural Networks and a New Dataset

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task-multi-scene recognition in single images. To this end, we create a large-scale dataset, called Mul-tiScene dataset, composed of 100,000 unconstrained images each with multiple labels from 36 different scenes. Among these images, 14,000 of them are manually interpreted and assigned ground-truth labels, while the remaining images are provided with crowdsourced labels, which are generated from low-cost but noisy OpenStreetMap (OSM) data. By doing so, our dataset allows two branches of studies: 1) developing novel CNNs for multi-scene recognition and 2) learning with noisy labels. We experiment with extensive baseline models on our dataset to offer a benchmark for multi-scene recognition in single images. Aiming to expedite further researches, we will make our dataset and pre-trained models available11https://github.com/Hua-YS/Multi-Scene-Recognition.
Radio Frequency Interference Detection for SAR Data Using Spectrogram-Based Semantic Network

Radio frequency interference (RFI) has been a pervasive and critical issue for space-borne synthetic aperture radar (SAR). The presence of RFI could lead to incorrect image interpretation and biased parameter retrieval, making the RFI detection a necessity to preserve overall data quality. In this paper, we propose an approach for detecting RFI signals in SAR raw data using time-frequency semantic analysis. Employing the U-Net convolutional neural network enables identification of target echoes and RFI signatures in 2D time-frequency representation with high probability. The detection process is realized without setting predefined thresholds, and could achieve superior performance without requiring large number of training samples.
Calculation of Bistatic Reflection with River Currents

This work is devoted to the presentation of an original approach for calculating the characteristics of microwave radiation scattered in forward direction from wind waves formed in the presence of a constant current. Within the framework of this approach, the concepts of effective wind speed and direction, depending on the velocity and direction of the current, are used to define the wavenumber spectrum of surface waves in the presence of a constant current. To set the frequency spectrum of the waves, the harmonic frequencies are additionally recalculated depending on the velocity and direction of the current. The pa per presents the dependence of the wave spectrum on the current velocity and on the angle between the wind and current directions. Next, the wave parameters are calculated, which are necessary to calculate the Doppler spectrum (DS) of the reflected radiation. To calculate the DS, an approach is used that considers the speeds of the receiver and emitter, the antenna patterns of the receiving and emitting antennas, and depends on 6 second-order statistical moments describing the reflecting surface. In this work, the dependences of the bistatic radar scattering cross-section, width, and shift of the DS of reflected radiation on the azimuthal angle of the sensing plane and the wind direction are constructed.
MRTA: Multi-Resolution Training Algorithm for Multitemporal Semantic Change Detection

The multitemporal semantic change detection challenge track (Track MSD) in the 2021 Data Fusion Contest is to extract the land cover changes of the US state of Maryland from 2013 to 2017, but only low-resolution label data is provided. We present a multi-resolution training algorithm (MRTA) to alleviate the overfitting of the model on the coarse labels. First, using low-resolution coarse labels to train FCN, the average IoU can reach 0.5253. Generating pseudo-labels using this network, they are combined with coarse labels to form a multi-resolution label combination, and perform iterative fine-tuning and retrain. After that, by analyzing the loss and gain indicators of specific categories, it was found that the discrimination effect of the water area was poor, so a strong classifier was trained for the water area. We also implemented strategies such as model voting and weighted training to improve model performance. Finally, our method achieves 0.6445 mIoU on the test set, ranking 3rd in Track 2 of the IEEE Data Fusion Contest of 2021.