Miracle-dz/UniFAR-FTUs huggingface dataset:
DIMA: Distributed IoT Modeling Architecture

In this work we present the Distributed IoT Modeling Architecture (DIMA) which is a novel, scalable, distributed framework for designing and deploying IoT systems based on a defined mission objective. DIMA assumes a centralized control source. However, while that controller is needed to interpret how the system should act given some set of environmental measurements, it is not involved in the moment-to-moment decisions of the network. Instead, the central controller defines the functions by which the system acts but lets the individual system elements deal with measuring the environment and reacting to events in real time. To do this, DIMA represents the IoT network as a set of actors and sensors. The sensor measurements are fed directly to the actors to make local decisions. The actors make decisions based on this local information and on objectives set by the central controller. This architecture allows DIMA to approach optimality without the delays and bottlenecks caused by centralized processing of all of the sensor information. We present an output of DIMA using intrusion detection and infrastructure modeling representations of IoT systems.
Signal Processing of Multi-Mode-Multi-Spatial (MOMS) in Line-of-Sight Channels

Orbital angular momentum (OAM) is a special branch of the traditional multi-input multi-output (MIMO) system, when the antennas sensitive to the linear momentum of the electromagnetic (EM) field are used to detect radio waves carrying OAM. In essence, the special pattern of the OAM wavefront phase distribution information is a special group of orthogonal bases of the EM field in the spatial dimension. The multi-mode multi-spatial (MOMS) scheme discussed in this paper is a generalized closed-loop multi-antenna communication system solution. To adapt more mobile application scenarios, and taking the divergence characteristic of OAM transmission into consideration, we no longer maintain the orthogonality between multiple OAM modes, and use successive interference cancellation (SIC) to obtain the spatial multiplexing gain of OAM. Compared with the traditional MIMO detection schemes, the MOMS scheme is more robust in the sparse multipath channel scenarios, and can obtain most spatial degrees of freedom (DoF) in terms of antenna aperture and channel geometry. Simulation results show that the performance of OAM-MIMO system using MOMS-SIC provides about 0.5 ~ 1.5dB for the system with different supported modes.
Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

The prognostic and health management (PHM) of rolling bearings has been a popular research area. Since bearing fault is inevitable during degradation, how to improve the PHM performance based on both degradation states and fault types is still an open problem. In this study, two multilabel learning algorithms are proposed for PHM of rolling bearings, named personalized binary relevance (PBR) and hierarchical multilabel K-nearest neighbor (HML-KNN), respectively. Degradation states and fault types are used as the labels of the bearing data so that each sample has a corresponding label sequence, that is to say, the PHM problem is converted to a multilabel learning problem. Both algorithms have a personalized search process, which can not only help samples build a personalized model to improve classification accuracy but also solve the problem of data imbalance between labels. At the same time, the two algorithms also have their own characteristics and focus on different application situations. The PBR algorithm has faster modeling speed, more flexible use, and replaceable subclassifiers. HML-KNN is a high-order algorithm with global information analysis capabilities through the hierarchical processing of data and the conversion of label information. Both methods have achieved good enough results in the XJTU-SY bearing dataset. In order to illustrate the practicality of the algorithm, the experimental part further increases the difficulty. Using only a single faulty sample as the training set to determine the type of compound fault, the algorithms also show high performance. In the era of industrial big data, the depth of data mining and the design of algorithm models will help us better manage equipment health.
Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks

Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.
Estimation of Liquid Fraction of Wet Snow by Using 2-D Video Disdrometer and S-Band Weather Radar

Wet snow may cause significant damage to humans and property, and thus, it is necessary to estimate the corresponding liquid fraction (FL). Consequently, the FL of wet snow was estimated using a novel technique; specifically, the particle shape irregularity (Ir) was estimated through the particle coordinate information obtained using 2-D video disdrometer (2DVD) measurements. Moreover, the possibility of quantitively estimating FL via Ir, based on the temperature (T), was examined. Eight snowfall cases from 2014 to 2016 were observed through a 2DVD installed in Jincheon, South Korea, to analyze the dominant properties of physical variables of snowflakes (i.e., the terminal velocity (VT), particle density (ρs), Ir, and FL) and the corresponding relationships according to the T ranges (−4.5 < T (°C) < 2.5) in which wet snow can occur. It was clarified that the volume-equivalent particle diameter (D)–FL and D–Ir relationships depended on T, and a relationship existed between Ir and FL. The analysis results were verified using the Yong-In Testbed (YIT) S-band weather radar and T-matrix scattering simulation. The D–FL relationship was implemented in the scattering simulation, and the results indicated that the simulated reflectivity (ZS) was highly correlated with the observed reflectivity (ZO) under all T classes. These features can provide a basis for radar analysis and quantitative snowfall estimation for wet snow with various FL values.
Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.
IDEAL: a Vector-Raster Hybrid Model for Efficient Spatial Queries over Complex Polygons

Geometric computation can be heavy duty for spatial queries, in particular for complex geometries such as polygons with many edges based on a vector-based representation. While many techniques have been provided for spatial partitioning and indexing, they are mainly built on minimal bounding boxes or other approximation methods, which will not mitigate the high cost of geometric computation. In this paper, we propose a novel vector-raster hybrid approach through rasterization, where pixel-centric rich information is preserved to help not only filtering out more candidates but also reducing geometry computation load. Based on the hybrid model, we develop an efficient rasterization based ray casting method for point-in-polygon queries and a circle buffering method for point-to-polygon distance calculation, which is a common operation for distance based queries. Our experiments demonstrate that the hybrid model can boost the performance of spatial queries on complex polygons by up to one order of magnitude.
Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference Around Mainland China via Attention-Augmented CNN from Daytime Satellite Imagery

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas

The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%.
Full-Wave Modeling and Inversion of UWB Radar Data for Wave Propagation in Cylindrical Objects

The nondestructive characterization of cylindrical objects is needed in many fields, such as medical diagnostics, tree trunk inspection, or concrete column testing. In this study, the radar equation of Lambot et al. is combined with cylindrical Green’s functions to fully model and invert ultra-wideband (UWB) ground-penetrating radar (GPR) data and retrieve the properties of cylindrical objects. Inversion is carried out using a lookup table (LUT) approach followed by local optimization to ensure retrieval of the global minimum of the objective function. Numerical experiments were conducted to analyze the capabilities of the developed inversion procedure to estimate the radius, permittivity, and conductivity of the cylinders. The full-wave model was validated in laboratory conditions on metallic and plastic pipes of different sizes. The adopted radar system consists of a lightweight vector network analyzer (VNA) connected to a single transmitting and receiving horn antenna. The numerical experiments highlighted the complexity of the inverse problem, mainly originating from the multiple propagation modes within cylindrical objects. The laboratory measurements demonstrated the accuracy of the forward modeling and reconstructions in far-field conditions.
Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.
Potentials of Airborne Hyperspectral AVIRIS-NG Data in the Exploration of Base Metal Deposit - A Study in the Parts of Bhilwara, Rajasthan

In this study, we have processed the spectral bands of airborne hyperspectral data of Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data for delineating the surface signatures associated with the base metal mineralization in the Pur-Banera area in the Bhilwara district, Rajasthan, India.The primaryhost rocks of the Cu, Pb, Zn mineralization in the area are Banded Magnetite Quartzite (BMQ), unclassified calcareous silicates, and quartzite. We used ratio images derived from the scale and root mean squares (RMS) error imagesusing the multi-range spectral feature fitting (MRSFF) methodto delineate host rocks from the AVIRIS-NG image. The False Color Composites (FCCs) of different relative band depth images, derived from AVIRIS-NG spectral bands, were also used for delineating few minerals. These minerals areeither associated with the surface alteration resulting from the ore-bearing fluid migration orassociated with the redox-controlled supergene enrichments of the ore deposit.The results show that the AVIRIS-NG image products derived in this study can delineate surface signatures of mineralization in 1:10000 to 1:15000 scales to narrow down the targets for detailed exploration.This study alsoidentified the possible structural control over the knownsurface distribution of alteration and lithocap minerals of base metal mineralizationusing the ground-based residual magnetic anomaly map. This observationstrengthens the importance of the identified surface proxiesas an indicator of mineralization. X-ray fluorescence analysis of samples collectedfromselected locations within the study area confirms the Cu-Pb-Zn enrichment. The sulfide minerals were also identified in the microphotographs of polished sections of rock samples collected from the places where surface proxies of mineralization were observed in the field. This study justified the investigation to utilize surface signatures of mineralization identified using AVIRIS-NG dataand validated using field observations, geophysical, geochemical, and petrographical data.
Blockchain-Based Collaborative Task Offloading in MEC: A Hyperledger Fabric Framework

While task offloading is one of the fundamental problems in Mobile Edge Computing (MEC) systems, most of the time it is only approached from the end-user point of view. In fact, traditional MEC frameworks do not consider the possibility of task offloading services among edge servers, which would be a means to increase the utilization and overall efficiency of the MEC architecture. However, such task offloading services could be vulnerable to many security and privacy threats. In this context, this paper proposes to use blockchain as an instrument to support the implementation of a task offloading scheme in a MEC framework from the standpoint of edge servers. The proposed mechanism runs on top of the Hyperledger Fabric blockchain that will provide a light-weight distributed interaction field that minimizes many security and privacy concerns. The main goal of this paper is to put forward a blockchain-based Service Oriented Architecture (SOA) that allows secure and private task offloading collaboration among edge servers in a MEC environment.
Attenuation of Seismic Multiples in Very Shallow Water: An Application in Archaeological Prospection Using Data Driven Approaches

Water-layer multiples pose a major problem in shallow water seismic investigations as they interfere with primaries reflected from layer boundaries or archaeology buried only a few meters below the water bottom. In the present study we evaluate two model-driven approaches (“Prediction and Subtraction” and “RTM-Deco”) to attenuate water-layer multiple reflections in very shallow water using synthetic and field data. The tests comprise both multi- and constant-offset data. We compare the multiple removal efficiency of the evaluated methods with two traditional methods (Predictive Deconvolution and SRME). Both model-driven approaches yield satisfactory results concerning the enhancement of primary energy and the attenuation of multiple energy. For the synthetic test cases, the multiple energy is reduced by at least 80% for the Prediction and Subtraction approach, and by more than 60% for the RTM-Deco approach. The application to two field data sets shows a significant amplification of primaries formerly hidden by the first water-layer multiple, with a reduction of multiple energy of up to 50%. The waveforms obtained from FD modeling match the true waveforms of the field data well and small deviations in time and amplitude can be removed by a time shift of the traces as well as an amplitude adaption to the field data. The field data examples should be emphasized, where the tested Prediction and Subtraction approach works significantly better than the traditional methods: the multiples are effectively predicted and attenuated while primary signals are highlighted. In conclusion, this shows that this method is particularly suitable in shallow water applications. Both evaluated multiple attenuation approaches could be successfully transferred to two other 3D systems used in shallow water near surface investigations. Especially the Prediction and Subtraction approach is able to enhance the primaries for both tested 3D systems with the multiple energy being reduced by more than 50%.
Automated Configuration of Router Port Queues via Model-Based Reinforcement Learning

The advent of 5G telecommunication systems has increased the need for stringent Quality of Service (QoS) requirements. A key resource in meeting these requirements are routers that perform efficient queue management, congestion control and flow prioritizations for effective network slicing. Configuring routers has traditionally been an expert driven process with static or rule-based configurations for individual flows. However, in dynamically varying traffic conditions, as are proposed in 5G use cases, these traditional approaches can generate sub-optimal configurations. In this paper, we propose a solution to this problem based on model-based Reinforcement Learning (RL) where the environment is modeled as a Partial Order Markov Decision Process (POMDP). The system is trained over different configurations for router ingress queue traffic policing, egress queue traffic shaping and traffic conditions to learn the state transition and observation probabilities of the POMDP model. The optimal policy of the learned model can then be deployed to generate optimal queue configurations automatically, adapting to current traffic conditions. These aspects are demonstrated over a real Ericsson use-case with configured routers for 5G slicing.