Miracle-dz/UniFAR-FTUs huggingface dataset:
State and Development Prospects of ENERNET Networks

The article discusses the state and prospects of the scientific and technical direction of the Internet of Energy or ENERNET. ENERNET is a global energy network concept in which energy resources are monitored and managed using information and telecommunication technologies. The paper discusses a multi-level structure for building energy networks, similar to the multi-level structure used on the Internet. This structure allows for control and effective management of energy resources, including distributed sources and storage. Bringing energy sources closer to energy consumers reduces energy losses and improves energy efficiency. The routing of energy flows in the network is carried out using an energy router (ER), similar to a communication router, which forms a two-way transmission of energy in the form of packets similar to information packets. The heart of the energy router is a solid-state transformer (SST) and energy storage. The work was carried out with the financial support of SibSUTIS within the framework of the state assignment under the grant "Development and research of components of intelligent systems for electronics and energy".
Delineating Reliable Ground Control Points in SBAS-Insar Analysis with Phase Derivative Variance

In the SBAS-InSAR process, the conventional approach of determining ground control points (GCPs) can be error-prone. Thus, a new GCP selection approach using the phase derivative variance is proposed. Analyzing 45 descending-orbit Sentinel-1 datasets between October 2014 and June 2017 over the 2017 Xinmo landslide site, we separately identified ~10,600 and 100 GCP candidates with the conventional and proposed approaches. With four GCPs from the conventional method, the time-series displacement curves do not capture the accelerated deformation related to the landslide event. Corresponding to two sets of GCPs from the proposed approach, the displacement curves change gradually and capture the accelerated deformation. Thus, the approach can efficiently identify a small number of reliable and accurate GCP candidates for the SBAS-InSAR analysis.
Ensemble CNN with Enhanced Feature Subspaces for Imbalanced Hyperspectral Image Classification

Convolution neural network (CNN) has been successfully applied to hyperspectral image classification. However, multiclass imbalance is a major problem in the classification of hyper spectral images, and traditional CNN can hardly improve the accuracy of minority classes effectively. In this paper, a new ensemble CNN with enhanced feature subspaces (ECNN-EFSs) algorithm is proposed, which utilizes an imbalanced training set to train the model and achieves accurate classification. Experimental results on two common hyperspectral datasets show that the proposed algorithm outperforms the traditional CNN and ensemble CNN algorithms.
Apk2Img4AndMal: Android Malware Detection Framework Based on Convolutional Neural Network

In this study, the Apk2Img4AndMal framework, which provides information about the application without the need for static or dynamic attributes, is recommended. The proposed framework reads APK files in binary format and converts them to grayscale images. In the classification phase of the framework, the convolutional neural network (CNN) is used, which gives successful results in image classification. In this way, the required features are obtained through a CNN. Therefore, there is also no feature extraction phase as other dynamic or static analysis-based frameworks. This property is the most important advantage of the Apk2Img4AndMal framework. The proposed framework is tested with 24588 Android malware and 3000 benign applications. The highest performance achieved in the study is up to 94%, according to the accuracy metric.
Spatial-Spectral Total Variation Constrained Collaborative Tensor Regularization for Dual-Camera Compressive Hyperspectral Imaging

In this paper, we propose a novel tensor-based approach to improve the reconstruction performance for dual-camera compressive hyperspectral imaging. We formulate a coupled tensor decomposition model to maintain the consistency of the spatial structure of the HSI and the panchromatic image. We introduce a regularizer over the core tensor to collaboratively promote global spatial-spectral correlations in HSI. Besides, we incorporate an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure of the HSI. Then the alternating direction method of multipliers (ADMM) algorithm is applied to the optimization problem. Experimental results on a public dataset demonstrate the superiority of the proposed approach.
A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders’ concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.
Quantifying Tropical Forest Stand Structure Through Terrestrial and UAV Laser Scanning Fusion

Obtaining accurate and detailed structural forest information has been revolutionized with the emergence of laser scanning. The sampling limitations and potential of the different laser scanning platforms (e.g. TLS, UAV -LS) have, however, not been fully explored for dense tropical forests. We fused laser scanning data from the terrestrial (TLS) and drone (UA V -LS) platform for two dense tropical forest plots and calculated their vertical point density profiles to gain insight in their sampling abilities. Our results reveal the limitations of TLS to fully sample the top of the canopy of a dense tropical rainforest. We also demonstrate how multiple returns but also cheaper single returns UAV -LS systems can be applied to sample the forest structure.
Dual-Stream High Resolution Network for Multi-Source Remote Sensing Image Segmentation

Recently, the image segmentation has been a significant research direction in the field of optical remote sensing data processing. However, due to the limitation of the optical imaging mechanism, traditional image segmentation methods are not efficient for processing the optical remote sensing images, especially influencing by the complex weather conditions. In order to ensure the classification performance, synthetic aperture radar (SAR) data are employed as complementary to the data procedure for enhancing the capability of land cover interpretation. Then a dual-stream high-resolution network (HRNet) is proposed to combine two types of heterogeneous data (SAR and optical image), and a multi-modal squeeze-and-excitation (SE) module is exploited to make feature maps fused. Experiments show that the proposed method has excellent performance on the remote sensing data acquired by GF2 and GF3 satellites.
Laser Gyroscope Parameters Measuring System Development

Gyroscope with lasers implementation is an important field of study with great potential. An integrating rate gyroscope is called a laser gyroscope (LG). The interference occurs in the form of a lock-in zone (LZ) during the operation of the LG, which leads to insensitivity at low rotation speeds, e.g. a ship, a submarine, where the speed is less than the threshold. In this case, the LG is practically useless in the rotational speed determination. The impact of dithering on the LZ was investigated by modeling the LG operation equation and a measuring stand that reduces the effect of the LZ at low angular velocities was developed.
Navigation-Aided Automotive SAR Imaging in Urban Environments

Automated driving requires a huge number of on-board sensors to provide advanced functionalities, from parking assistance to emergency braking and environment mapping for target recognition/classification. While low-cost automotive-legacy radars are mostly used for target detection due to their limited angular resolution, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising imaging solution, provided that the motion is known with high accuracy. This paper assesses the benefits of a navigation-augmented SAR system exploiting multiple on-board sensors, e.g., Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. The results confirm the potential of the proposed multi-sensor-aided SAR system to obtain centimeter-level accurate images of the driving scenario.
Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal

Classification accuracy of remote sensing images with supervised learning depends on the quality and characteristics of training samples. Size is a key aspect of a sample and its impact on classification depends on several factors, including the classifier employed, dimension on the feature space and land cover characteristics. Random Forest classifier is considered to be of low sensitivity to variations in sample size. However, further investigation is required when feature spaces are large and training is performed with spectral subclasses of the land cover classes to be mapped. This paper proposes to assess the impact of sample size in the classification accuracy of Random Forest using multitemporal Sentinel-2 data and a detailed set of training subclasses to produce a map with general land cover classes. The results revealed similar classification accuracies after major reductions in sample size.
Monitoring Forest Above-Ground Biomass from Multifrequency Vegetation Optical Depth: A Preliminary Study

Vegetation Optical Depth (VOD) is highly sensitive to Above-ground Biomass (AGB), particularly at L-band, as this band is more sensitive to the canopy layer and has greater capacity to penetrate the vegetation. However, higher frequencies, such as C- and X-bands are more related to smaller vegetation structures. Then a combination of these three bands is expected to be relevant to accurate AGB retrievals. This study presents a comparison of three VOD products at three bands and evaluates the performance of a multi-frequency VOD combination by applying a Principal Component Regression (PCR) to Forest biomass. Results show that L-band VOD captures the 84% of the biomass and the PCR is built, mainly, for this band meaning that higher frequencies do not contribute substantially to the AGB retrieval in this study, still they do not affect the retrievals negatively.
Prediction of Telecommunication Network Outage Time Using Multilayer Perceptron Modelling Approach

As the demand in communication traffic grows; there should be the provision of reliable telecommunication network to users. Operators however, face a myriad of challenges in fulfilling their part of the contract such as network outage. The phenomenon of network outage has been a challenge that every network operator is consistently trying to avoid. In this study, a multilayer feedforward neural network also called multilayer perceptron (MLP) was adopted to model network outage time of network elements or systems. The MLP network was trained on a 150 samples of daily network outage time data obtained from the Network Operating Centre of an operator in Ghana. The data covered a period of 1st January to 30th May 2018 and was analysed with Matlab software. In developing the model, the input and output layers were kept constant, while the number of neurons were varied from 1 to 20 to obtain a good prediction. The performance of the models were measured by the Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and the Correlation Coefficient (R). After careful and extensive training, validation and testing, 20 models were developed. The MLP selected was 1-4-1 which produced MSE, RMSE, and an R-value of 0.0000024321, 0.00160 and 0.99993 respectively with a prediction accuracy of 97.5%. The study concludes that downtime prediction can be improved by feed forward neural network optimized using Levenberg-Marquardt with sigmoid function in the hidden and linear activation function in the output layer.
Subspace Optimal Transport for Spatial Bias Correction of Social Media Data: A Case Study of 2013 Boulder Flood Event

Social media data generated from individuals provides a unique opportunity to gain valuable insight on information flow, especially for emergency response. However, the inherent limitations associated to these data (particularly, the spatial bias) restrict its precise application. Existing research on spatial bias correction of social media data mainly face two issues: 1) the geographic extent in target domains may be underestimated, and 2) source elements may be transported within inappropriate distance. In this paper, we take 2013 Boulder, Colorado flood event as a case study, and present a new method called subspace optimal transport (SOT). Our proposed SOT aims at transporting biased tweets from dry to real flooded areas with a relatively close distance. Specifically, a comparison between our newly developed SOT and the traditional optimal transport (OT) and geographic optimal transport (GOT) is performed. Experimental results demonstrate that our new SOT method is able to correct the spatially biased geo-referenced tweets, with high precision and excellent computing performance.
Development of a Multi-City Deprived Area Mapping Ecosystem

The number of people living in deprived urban areas within low and middle income countries (LMICs) is large and predicted to continue to grow. Mapping these areas over time and space in a consistent manner is important for monitoring the Sustainable Development Goals (SDGs) and degree of deprivation between cities. This work describes the development of a mapping ecosystem to do this. The first steps are to define urban extents and collect the data needed for developing deprived area models. The goal of the mapping ecosystem is to produce maps of ranges in deprived areas that are comparable between cities and can be used by local governments and researchers to map within city variations in deprivation. By developing and describing this ecosystem, we hope to replicate this in other cities and countries so that this type of work can be expanded.
Weakly Supervised Semantic Change Detection via Label Refinement Framework

Semantic change detection is a meaningful but challenging task in the remote sensing community. The currently dominant approaches are mainly based on deep learning. However, the lack of high-resolution annotations is the main bottleneck for semantic change detection at scale when using these state-of-the-art deep learning models. In this paper, the label refinement framework is proposed for weakly-supervised semantic change detection, which allows the deep network to learn from low-resolution labels and produce high-resolution semantic change maps, thus alleviating the data-hungry problem. This framework contains four parts: coarse label training’ pseudo-label refinement, multitask change detection and post-process. The experimental results on 2021 IEEE GRSS Data Fusion Contest Track MSD dataset confirmed the effectiveness of the proposed method. Additionally, our method wins 4th place in the 2021 IEEE GRSS Data Fusion Contest Track MSD (DFC21-MSD).
XANDAR: X-by-Construction Design framework for Engineering Autonomous & Distributed Real-time Embedded Software Systems

The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, development of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems, which relies on Machine Learning (ML) and AI applications when combined with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are Advanced Driver-Assistance Systems (ADAS) that are prone to various safety/security vulnerabilities. The XANDAR project aims at developing a mature SW toolchain (from requirements analysis to the actual code integration on target including V&V) fulfilling the needs of industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel (real-time, safety-, and security)-by-Construction paradigm.
Near Real-Time Wildfire Detection in Southwestern China Using Himawari-8 Data

Wildfire, one of the most serious disasters in the world, making a huge threat to local economic development and residents' life and property safety. Therefore, it is crucial to realize near real time wildfire detection in specific high wildfire risk area. Himawari-8 is a state of art Geostationary Orbit Satellites (GOS), which can realize near-real time wildfire detection and dynamic monitoring of wildfire changes. In this study, three features strategies: spectral, spectral calculated and spectral with spatial were used to extract features from Himawari-8 and Topography data. Next, Random Forest models were trained by these features. And then, we evaluated models on Himawari-8 data generated in March 29, 2020 6:10 UTC, March 30, 2020 5:50 UTC, March 31, 2020 5:30 UTC and April 1,2020 5:50 UTC to detect specific wildfire events in Southwestern China. The results showed an overall precision of 64.24%, 64.08% and 68.07%, and an overall F1-score of 0.737, 0.677, 0.724 based on three different features strategies. This study proved that our models have the ability to detect wildfire point accurately, and model with strategy.3 performed the best when considering precision, omission and F1-score.
Toward Dataset Construction for Remote Sensing Image Interpretation

With the rapid advancement of remote sensing (RS) technology, RS image interpretation has made great progress and been widely used in broad applications, in which the constructed benchmark datasets for developing and testing intelligent interpretation algorithms have been playing an increasingly critical role. Motivated by the essential prerequisites of dataset in the development of RS image interpretation algorithms, this manuscript provides a discussion on dataset construction for RS image interpretation. Specifically, we first analyze the current challenges of developing algorithms for RS image interpretation and a review on the widespread RS image datasets is conducted through the bibliometric analysis. We then propose some principles and discuss the methodology on constructing benchmark datasets. An implementation on creating the RS scene classification dataset demonstrates the practicability of our proposed framework and the experimental results show that our constructed dataset can serve as a promising benchmark for RS image scene interpretation.
Structured Building Extraction from High-Resolution Satellite Images with a Hybrid Convolutional Neural Network

Detecting buildings with structure information (e.g., rooflines) from satellite images is a significant yet challenging task. Existing methods usually suffer from the spatial and spectral diversity and complexity of the architectures. In this paper, a deep learning-based approach is proposed to extract structured building rooflines. We use convolutional neural networks to detect corner and line segment primitives. Meanwhile, a collaborative branch of semantic annotation information is combined to obtain the building segmentation map, which ensures the spatial and topological relations of the extracted primitives. Experiments on the SpaceNet dataset show that our proposed approach improves the accuracy of building extraction. Furthermore, the planar graph representation promotes three-dimensional (3D) reconstruction and other subsequent applications.
Fully Automated Sar Based Oil Spill Detection Using Yolov4

The Eastern Mediterranean Sea is known as oil pollution hotspot because of high marine traffic and a growing number of oil and gas industrial activities inside, which makes efficient monitoring oil spills important in this area. Spaceborne Synthetic Aperture Radar (SAR) plays an important role for oil spill detection with its advantage of wide coverage and all-weather observations. However, discriminating whether the dark formations in the SAR imagery are from actual oil spills or look-alikes has been a challenging part. This study applied You Only Look Once version 4 (YOLOv4) object detection algorithm as an one-class (i.e. oil spill) object detector for learning oil spill features inside the Region of Interests (ROIs) and the background information from the rest of the image. The preliminary results pointed out that the pixel threshold for removing some tiny oil spills is suggested as they appeared regularly in the study area but are hardly visible. The average precisions (AP) of the trained model on validation and test sets are 67.80% and 65.37%, showing that the model is not overfitting on our training and validation sets. In addition, this study recommended some data augmentation strategies which might help improve the results.
Evaluation of SPEC CPU and SPEC OMP on the A64FX

We evaluated the A64FX processor used in the supercomputer Fugaku using the SPEC CPU and SPEC OMP benchmark suites. As a result, we found the performance of the A64FX processor, which had 48 cores, was lower than that of the Xeon with dual sockets of 24 cores each in SPEC CPU int and fp. In SPEC OMP, due to the effect of the Xeon’s Hyperthread, the A64FX performance was lower than the performance of the Xeon with single socket of 28 cores. But in several benchmarks in SPEC CPU fp and SPEC OMP, the A64FX performance was higher due to its high memory bandwidth. In addition, by comparing the performance and power using the power control mechanism of the A64FX, it was confirmed that power can be reduced without affecting the performance when not using all cores.