Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Localization for Internet of Underground Things in Oil and Gas Reservoirs

Magnetic Induction (MI) is an efficient wireless communication method to deploy operational internet of underground things (IoUT) for oil and gas reservoirs. The IoUT consists of underground things which are capable of sensing the underground environment and communicating with the surface. The MI-based IoUT enable many applications, such as monitoring of the oil rigs, optimized fracturing, and optimized extraction. Most of these applications are dependent on the location of the underground things and therefore require accurate localization techniques. The existing localization techniques for MI-based underground sensing networks are two-dimensional and do not characterize the achievable accuracy of the developed methods, which are both crucial and challenging tasks. Therefore, this paper proposes a novel three-dimensional (3D) localization technique based on Isometric scaling (Isomap) for future IoUT. Moreover, this paper also presents the closed-form expression of the Cramer Rao lower bound (CRLB) for the proposed technique, which takes into account the channel parameters of the underground magnetic-induction. The derived CRLB provides the suggestions for an MI-based underground localization system by associating the system parameters with the error trend. Numerical results demonstrate that localization accuracy is affected by different channel and networks parameters such as the number of underground things, ranging error variance, size of the coils, and the transmitting power. The root mean square error performance of the proposed technique shows that increase in the number of turns of the coils, transmitting power, and the number of anchors improves the performance. Results also show that the proposed technique is robust to the ranging error variance in the range of 10 to 30 %; however, a further increase in the ranging error variance does not allow to achieve acceptable accuracy. Also, the results show that the proposed technique achieves an average of 30 % better localization accuracy compare to the traditional methods.
Improving context awareness reliability estimation for a wind turbine using an RBD model

All devices are fabricated from materials that degrade with time. Degradation will continue until some critical device parameter can no longer meet the required specification for proper device functionality. Reliability estimation can assess the current health of a system and predict its remaining life. This kind of analysis is critical to improve safety, optimize scheduled maintenance, reduce life-cycle cost and minimize down time. This work analyses the reliability of a 2MW wind turbine using the Reliability Block Diagram method. The paper compares the reliability estimated in standard environmental conditions and the reliability evaluated considering the real temperature and humidity values acquired using a SCADA system.
System Dynamics Modeling: A Prototype Technical-Economic Analyzation Tool for Supporting Sustainable Development in Operational Metal Mines

Technical-economic analyzation is critical to increasing a mine’s economic benefits and saving mineral resources for its sustainable development. However, since the mining system has many technical-economic indicators that are connected and that respond to each other, it is not easy to determine mining production and operation performance when an indicator changes or when multiple indicators change. Thus, the complicated system of operational metal mining cannot be easily solved in a general way. In this paper, system dynamics (SD), an alternative approach that can qualitatively and quantitatively assess mining production and operation from a system analysis perspective, is employed. Taking the Sanshandao gold mine in China as the industrial research context, we built an SD model based on an integrated stock and flow diagram, which is derived from the identification of technical-economic parameters and the system conceptualization of causal loop diagrams, including four subsystems of geology, mining production, mineral processing, and financial. After establishing the equations and testing the model based on historical data, the SD model simulated with the PLE 6.3 software can be used as a decision support tool to calculate the simulation results in many scenarios. Monte Carlo simulation is also introduced to consider uncertainties in the assessment. In the future, development of the prototype SD model will continue, and it will be verified by many more case studies to be a useful alternative tool for decision-making to improve the actual processes and to support the sustainable development of metal mine production and operation.
Low-Complexity Joint Time Synchronization and Channel Estimation for OFDM-Based PLC Systems

Orthogonal frequency division multiplexing (OFDM) plays an important role in power-line communications (PLC), due to its robustness to frequency-selective channels. Nevertheless, it also presents some persistent drawbacks over the years, some of them related to the synchronization and channel estimation and equalization, whose features are a key aspect in the global performance of the medium access technique. This work presents a novel timing synchronization and channel estimation method for OFDM under PLC conditions. Typical joint fine synchronization and channel estimation techniques for OFDM make use of the cross correlation from a transmitted pilot symbol with suitable correlation properties. However, these proposals often present the drawback of having a high computational load, sometimes reducing the feasibility of later practical implementations. Our proposal takes advantage of the properties of complementary pairs of sequences, as they provide good correlation properties, as well as efficient algorithms for generation and correlation. Furthermore, some practical thresholds for synchronization are derived, which adapt the system to the current signal-to-noise ratio and channel conditions. Finally, all the theoretical proposals have been validated by simulation, including an analysis of the performance achieved.
Flash Floods Prediction using Real Time data: An Implementation of ANN-PSO with less False Alarm

Flash floods and hurricanes are caused by the release of energy inside the oceans. Hurricanes are very sudden and may lead to heavy infrastructural damage with loss revenues associated human and animal’s fatalities. Diversified techniques have been utilized to properly investigate the flash floods and hurricanes before the event. A hydro atmospheric and climatic change due to the hurricanes leads towards the high death toll. Approaches for the early prediction of flash floods and hurricanes may be categorized as (a) Modeling of the system (bathymetry), (b) Sensors and gauges-based measurement, (c) Radar-based images, (d) Satellite images and data, and (e) AI-based prediction. Comparative analysis of direct real-time data from the sensors and gauges, is more reliable compared to other techniques but it may contain some errors and missing information which leads towards the false alarms. Therefore, in this paper, a novel predictive hybrid algorithm (ANN PSO) has been applied to estimate the flash floods and hurricanes more precisely. A suitable combination of the sensors will give the benefit of better precision and improved accuracy when compare to the use of a single sensor. The combination of six process variables utilized in this paper for the measurement and investigation of the flash flood has been discussed. Real-time data of over forty eight (48) hours has been collected from PIR, Ultrasonic sensor, Temperature sensor, CO2 sensor, Rainfall module, Pressure, and temperature sensor. ANN feed-forward propagation is trained by using sample collected data from the multi-modal sensing device and applied for the classification of events while neurons are optimized by the particle swarm optimization (PSO), taking less processing time without requiring advanced complex computational resources. Results have proved that proposed AI based technique for the early identification of flash floods and hurricanes have worked more accurate and performance-wise better than the ongoing techniques. The results include flood probabilities and prediction analysis using proposed algorithm.
Renewable Energy Integration in Diesel-Based Microgrids at the Canadian Arctic

The effect of climate change is significant in the arctic regions of the world, with the carbon footprint from diesel-only based electricity generation in remote arctic communities adding to the environmental degradation through greenhouse gas (GHG) emission, oil spills, and black carbon. Moreover, the dependence on diesel and its associated costs are an economic problem for these communities, particularly in the Canadian Arctic, where governments subsidize this fuel. Thus, this article presents specific studies including new variable-speed generator (VSG) technologies that demonstrate the feasibility, impact, and benefits of introducing renewable energy (RE) together with VSGs in remote microgrids in the Canadian Arctic. More specifically, this article describes a two-step procedure to select remote communities for detailed feasibility studies of deployment of RE sources, including a generation expansion planning (GEP) framework and optimization model for RE and new VSG integration applied to the selected communities, to minimize diesel dependence of isolated microgrids and maximize the incorporation of environmentally friendly generation technologies. The proposed approach is applied to communities in Nunavut and the North West Territories in the Canadian Arctic, based on actual data, to study the technoeconomic feasibility of RE integration and develop business cases for diesel generation replacement with RE and VSG generation in these communities. The obtained optimal plans contain diesel-RE hybrid combinations that would yield substantial economic savings and reductions on GHG emissions, which are being used as the base for actual deployments in some of the studied communities.
Smart Wall: Passive Visible Light Positioning with Ambient Light Only

This paper reports the experimental results from a novel visible light positioning (VLP) system. The developed VLP is completely passive as it does not require the target to carry any active device or tag and, at the same time, it does not require any modification to the lighting infrastructure. The developed VLP localizes a target based on measuring the change it creates in the received signal strength (RSS) of the ambient light recorded at an array of photodiodes embedded in the wall. Experimental results from a prototype system show that a median error of 7.9 cm can be achieved.
Proximal Near-Infrared Spectral Reflectance Characterisation of Weeds Species in New Zealand Pasture

Recent extensive study was performed on Jacobaea vulgaris that characterized the plant leaves using hyperspectral reflectance against the successional stage of vegetation. It reported a high similarity of the leaf spectral reflectance measurement over the plant’s different stages of growth. This paper extends the earlier study by characterizing the proximal spectral reflectance measurements of three species of common New Zealand pasture weeds: (a) Jacobaea Vulgaris; (b) Rubus; and (c) Ulex growing in three different common soil pastures, specifically: (i) Typic Orthic Gley; (ii) Typic Orthic Granular; (iii) Typic Orthic Brown. The research goes on to determine the inter- and intra-species proximal spectral reflectance variation of the studied common weeds. Finally, it examines the suitability and extent of accuracy of different statistical analysis methods when applied on proximal spectral reflectance measurement to identify the three common species of weeds growing on New Zealand pastures.
Reducing the Time Consumption of Vibration Correction Methods for Absolute Gravimeters

Free-fall absolute gravimeters are widely employed to measure the gravitational acceleration, commonly known as g. It applies an interferometer to measure the trajectory of a free-falling object. The measurement precision of the absolute gravimeters is limited by the ground vibration. A vibration correction method is often applied in a noisy environment including a moving platform, sometimes it is required to get the g value quickly. It takes much time about 6 min to calculate the g value for one set measurement (no less than 25 drops). In order to quickly and accurately obtain the g value in hostile environments, there are two modifying methods including applying a lower sampling rate than original sampling rate of the vibration signal, and applying a division on trajectory data method. An equally spaced in distance division method is used. The results show, when applying the sampling rate of the vibration signal 1 MHz and the division factor 14, the total consumption time is reduced to one quarter of the time, from about 6 min to 1.5 min. Meanwhile, the error between the corrected results with and without the modifying methods is below 10 μGal, and the standard deviation of the g value has no significant change. In the future, promisingly it will be used to quick and precise dynamic absolute gravity measurement in hostile environments.
MuSLoc: Circular Array Based Indoor Localization with COTS APs

Recent researches on indoor localization have achieved the success of localizing a device within tens of centimeters accuracy. Most of the cases it requires special infrastructures, external hardware support or a lot of finger-printings via huge training or crowdsourcing about the surroundings in order to achieve this accuracy. This paper presents MuSLoc, a low cost indoor localization system which is able to achieve high accuracy in localizing regular commercial mobile devices without help of fingerprinting, infrastructure or external hardware support. MuSLoc emulates regular antenna arrays of mobile devices with specially configured MUSIC algorithm over synthetic aperture radar (SAR) in order to find the direction of arrival (DoA). Finally, it is integrated with a novel heuristic location search algorithm, named HeLE which outperforms traditional methods in indoor localization.
Experimental characterization of off-the-shelf LEDs as photodetectors for waking up microcontrollers

The use of LEDs as photodetectors has been subject of study for a long time. However, LEDs manufacturers do not provide information about the response of LEDs to the light. It is essential for the designers of electronic devices to know what the behavior of LEDs is so as to develop new applications in this area. In this work, we propose the use of commercially off-the-shelf LEDs, illuminated by the flashlight of a smartphone, as a contactless wake-up system for microcontrollers that are in a sleep mode. In particular, the generated LED voltage is proposed as the wake-up signal. So, in order to choose the most suitable LED and depending of the main interfering light, the currentvoltage (I-V) characterization of different kind of LEDs is performed for two scenarios: outdoors and indoors. It is demonstrated that placing a resistor in parallel with the LEDs allows a better discrimination between the LED voltage coming from the flashlight and that from the interfering lights. Results show that red and blue LEDs are more appropriate for outdoor and indoor applications, respectively.
Correlating foot posture with foot mobility using a high-accuracy foot measurement system

The Foot Posture Index (FPI) has in recent years become one of the most useful foot posture assessment tools due to its proven high reliability and validity. The main advantage of the FPI over other foot posture assessment techniques is the ability to provide a multi-segment analysis of the foot from six different foot components. The FPI is yet to be assessed for reliability against dynamic foot mobility and posture measures based on the dorsum of the foot as a point of reference. Therefore, this study utilized a developed dynamic 3D measurement system to determine the relationship between the FPI scores with the dynamic Foot Mobility Magnitude (FMM) measures. From the study, it was found that there was no significant correlation between the FPI scores of foot posture and the dynamic behavior of the foot, indicating that the only way to assess foot mobility is through dynamic image-based measurements.
Distributed Sampling of Multiple Sinusoids with Finite Rate of Innovation

In this paper, we propose a new distributed sampling scheme for multiple sinusoids signals, which can further reduce the number of samples for each sampling channel. Based on the frequency sparse common support (SCS) model, we recover the signals using samples from several channels jointly. We propose a distributed time-staggered sampling (DTSS) system, which requires only $\lceil K+K / P\rceil$ samples per non-delay channel and K samples for the delay channel when recovering K frequency components by P channels. In addition, the staggered time is required no longer than the Nyquist sampling interval in the method. Simulation results verify that the proposed methods can successfully recover the multiple sinusoids signals at a lower sampling rate.
A Monitoring and Management System for Energy Storage Integration in Smart Grids

The integration of distributed storage systems (DSSs) at users and prosumers level can significantly contribute to energy efficiency and increase profits from renewable energy exploitation, thanks to suitable scheduling of charging and discharging periods. On the other hand, to preserve network stability and secure operation, Distribution Systems Operators (DSOs) are interested in DSSs control and cooperation with the grid. In this framework this work proposes a simple monitoring and management system for DSSs scheduling and its integration in a distributed measurement and control system architecture for smart grids. In the paper the proposed algorithm is tested on a simple case study at home level; the obtained results show its feasibility in addressing both users/prosumers economic interests and DSOs needs for grid secure operation.
Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.
“Paper”Based Sensor for Deformation Measurements

A key issue for the success of IoT, industry 4.0 and precision agriculture is the development of sensors based on new materials and technologies. In particular, sensors which are environmentally friendly and low cost are of interest. Cellulose is one of the most abundant material on the earth. Eventually, it is green, cheap and presents several intriguing mechanical and electrical properties. In this paper the sensing properties of an all-polymeric composite, realized by using bacterial cellulose, organic conductors and ionic liquids are investigated. More specifically, it is shown that this novel composite is capable of sensing flexural deformations and producing a corresponding electrical signal. The obtained results pave the road to the possibility of realizing a new class of deformation sensors that fits the requirements of more sustainable sensor technologies. The sensor has been conceived, realized and an experimental campaign with the characterization has been performed obtaining very intriguing results.
Gradient of the Mutual Information in Stochastic Systems: A Functional Approach

In this letter, using a general functional model for the stochastic systems we show that, the gradient of the mutual information with respect to any arbitrary system parameters can be computed in general. Relying on this viewpoint, the validity condition of one of the main available results on the gradient of the mutual information is investigated. Also, the result on the general additive noise system model in the literature is recovered as a special case.
Two Tell-tale Perspectives of PTSD

Global refugee crisis around the world has displaced millions of people from their homes. Although some of them adjust well, many suffer from significant psychological distress, such as post-traumatic stress disorder (PTSD), owing to exposure to traumatic events and hardships. Here, diagnosis and access to psychological health care present particular challenges for various human-centered design issues. Therefore, analyzing the case of Rohingya refugees in Bangladesh, we propose a two-way diagnosis of PTSD using (i) short inexpensive questionnaire to determine its prevalence, and (ii) low-cost portable EEG headset to identify potential neurobiological markers of PTSD. To the best of our knowledge, this study is the first to use consumer-grade EEG devices in the scarce-resource settings of refugees. Moreover, we explored the underlying structure of PTSD and its symptoms via developing various hybrid models based on Bayesian inference by combining aspects from both reflective and formative models of PTSD, which is also the first of its kind. Our findings revealed several key components of PTSD and its neurobiological abnormality. Moreover, challenges faced during our study would inform design processes of screening tools and treatments of PTSD to incorporate refugee experience in a more meaningful way during contemporary and future humanitarian crisis.