Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Drifting Control in 3D Car Racing Simulator

It is known that drifting is a driving technique, when a car is moving along some direction different from its steering trajectory, having a slip angle at the rear axle greater then at the forward axle. Even though it is often used in auto sport, it is still not applied to everyday life due to safety concerns. On the other hand, using all car control possibilities can provide safe driving even in extreme conditions without a crash. This article is devoted to the investigation and development of a drift controller and its application in electric car simulation with studying the problems of autonomous drifting and stabilization around an equilibrium state. To implement the drift controller, we used all-wheel drive Roborace Robocar in Speed Dreams car racing simulator, applying the mechanical bicycle and simplified nonlinear tire models to define tire parameters, and two state equilibrium model to compute the equilibrium points. Although the steering drift controller is rather simple and based on errors from equilibrium values, to find controller’s gains the model was linearized, and controller was represented as full-state feedback that guaranteed its stability.
Can Precursory Moment Release Scale With Earthquake Magnitude? A View From the Laboratory

Today, earthquake precursors remain debated. While precursory slow slip is an important feature of earthquake nucleation, foreshock sequences are not always observed, and their temporal evolution remains poorly constrained. We report on laboratory earthquakes conducted under upper‐crustal stress and fluid pressure conditions. The dynamics of precursors (slip, seismicity, and fault coupling) prior to the mainshock are dramatically affected by slight changes in fault conditions (fluid pressure and slip history). A relationship between precursory moment release and mainshock magnitude is systematically observed, independent of fault conditions. Based on nucleation theory, we derive a semiempirical scaling relationship which explains this trend for laboratory earthquakes. Several natural observations of earthquakes ranging from ~Mw 6.0–9.0, where precursory moment release could be estimated, seem to follow the extrapolation of the laboratory‐derived scaling law. Notwithstanding spatiotemporal complexity in natural seismicity, some moderate to large earthquake magnitudes might be estimated through integrated seismological and geodetic measurements of both seismic and aseismic slips during nucleation.
Voltage Drop Management and Step Up The Voltage of Sediment Microbial Fuel Cells

Sediment microbial fuel cell (SMFC) is a bio-electrochemical device which produces the pollution-free energy. SMFC generates low voltage with fluctuation between 0.982 V to 1.16 V of 60 days of the experiment. This voltage is not suitable to power the electronic device. So an ultra-low voltage DC-DC boost converter is proposed to regulated and step up the output voltage of SMFC. Performance of the boost converter is verified through simulation and experimental results. Single SMFC discharges in 30 sec by connecting the boost converter and gain maximum voltage in 210 sec after disconnecting the boost converter. Further, a scheme proposed for continuous energy harvesting from sediment microbial fuel cell in which eight parallel connected SMFCs provide an input voltage to boost converter one by one. It is noticed that the energy harvester DC-DC boost converter provides a continuous 2.56 V with the efficiency of 85.46%. The boost converter works well up to the voltage of 20 mV so this converter is appropriate for the SMFC energy harvesting. The proposed scheme is able to produce a suitable voltage for a small electronic device which needs a continuous power supply.
A strategy for high resolution elastic inversion for layer properties using a three-term AVO formulation

We develop a strategy for high resolution AVO inversion of reservoir elastic properties. The objective is to obtain a more accurate model of P-wave, S-wave velocity and density, which is well known to be an unstable problem. The formulation consists of solving, initially, the AVO inverse problem using a Bayesian formulation with a trivariate Cauchy a priori distribution, which stabilizes and incorporate sparsity information on the reflectivity and well-log information. Then, we solved a coupled constrained impedance inversion using mixed norm, L2 and total variation, to obtain the layer properties. We demonstrate the applicability of this strategy using the North Viking Graben open data set. The results show that all three elastic parameters are satisfactorily recovered, approximating our solution to a full waveform inversion solution on the same input data set.
Getting Children BARK Ready! : lesson plan for therapy dog handlers in schools

We have developed an educational program for children to provide information on the proper etiquette when around dogs and to prepare them for the responsibilities of dog ownership. The program would use therapy dogs to give children in small groups real interactions and hands-on experience with a dog. The class could be offered by certified therapy dog handlers at schools, community events or summer camps. Compensation for the hours served in providing the dog etiquette program would be facilitated through a community exchange or time bank, giving the handler and the school/organization an incentive to be involved in a positive way with their community.
Inner Loop Stability of Peak Current Controlled Cuk and SEPIC Converters

In reported literature, generally, switch current is sensed to implement inner peak current programmed mode (PCPM) loop. However, two inductor and switch currents are the three possible inner loop variables in fourth order dc-dc converters like Cuk and SEPIC. This paper examines the stability of inner loop for these three different cases. Design of passives are purely done with the aim to restrict their ripple content. Inferences are made by studying the location of closed inner current loop transfer function poles. In course of analysis, it is found that mere selection of proper value of compensating ramp slope does not ensure the stability of PCPM. It is a necessary but not the sufficient condition. Ripple factors in inductor current selected for designing parameters play an important role in stability. Simulation and experimental results are presented to validate the derived findings.
A Novel Single-Phase T-Type PV Inverter with Improved DC Utilization

This manuscript presents and validates a novel transformerless single-phase 5-level inverter with a complete DC bus utilization for PV applications. The proposed inverter employs a switched capacitor-based network with a T-type structure. The use of T-type structure enables the anticipated multilevel inverter (MLI) to provide constant terminal voltages (i.e. voltage difference between source terminals and load neutral). This makes the inverter free from any kind of leakage current and EMI issues. Moreover, the switched capacitor network ensures complete dc-bus utilization by boosting the PV voltage two times. A carrier-based level shifted pulse width modulation technique (LSPWM) is used to operate the inverter. Simulation result and experimental result are presented to validate the operation and performance of the proposed MLI.
The Spill Over of Crime from Urban Centers: An Account of the Changing Spatial Distribution of Violent Crime in Guyana

As the rate of crime decelerates in the developed world, the opposite phenomenon is being observed in the developing world, including Latin America and the Caribbean. Crime in Latin America and the Caribbean has been concentrated in urban settings, but the expertise for studying crime and providing guidance on policing remain heavily rooted in the developed world. A hindrance to studying crime in the developing world is the difficulty in obtaining official data, allowing for generalizations on where crime is concentrated to persist. This paper tackles two challenges facing crime analysis in the developing world: the availability of data and an examination of whether crime is concentrated in urban settings. We utilized newspaper archival data to study the spatial distribution of crime in Guyana, South America, across the landscape, and in relation to rural indigenous villages. Three spatial analysis tools, hotspot analysis, mean center, and standard deviation ellipse were used to examine the changing distribution of crime across 20 years. Based on 3900 reports of violent crime, our analyses suggest that the center of the gravity of crime changed over the years, spilling over to indigenous peoples’ landscapes. An examination of murder, where firearms and bladed weapons were the weapons of choice, suggests that these weapons moved beyond the coastal zone. The movement of weapons away from the coast raises concerns for the security of indigenous peoples and their associated wildlife. Our analysis suggests that policing measures should seek to extend towards Amerindian landscapes, and this is perhaps indicative of Latin American states with demographics similar to Guyana’s.
Landscape-dynamic aspects of soil formation in the Lena River Delta

This article provides data on the study of pedodiversity and pecularities of soils and their spatial distribution in the territory of the Lena River Delta (Siberia, Russia). Special reference to geogenic factors of soil formation in the pedoenvironment are made. The main patterns of soil distribution in different geomorphic positions for Samoilovsky, Arga-Belir-Aryta, Kurungnakh and Hardang islands are discussed. The soil catenas of the 4 investigated islands are given as a reliable representation of the formation of soils and the development of soil-forming processes in various types of relief and geomorphological features. Soils are represented mainly by the following groups: Fluvisols, Umbrisols, Podzols, Cryosols, Histosols, Gleysols, which are closely related to the geomorphic terraces of the delta.
DC-Link Voltage Regulation of Full-Power Converter for WECS in Weak-Grid Using a Variable-flux Dual-Stator PMSG

Wind Energy Conversion Systems (WECS) in weak-grid and stand-alone modes suffer from voltage and frequency fluctuations due to wind speed or/and load variations. However, it is needed to regulate output voltage before supplying to grid/load. Thus, generator output is rectified and then inverted to constant voltage and frequency before supplying to load. In standard configurations, rectifier and inverter both use controlled switches. Therefore, full control over the output voltage is achieved however, higher no. of electronic switches with their complex control circuitry and use of mechanical sensors jeopardizes reliability of the system. This paper proposes a WECS that uses half the no. of controlled switches with simpler control circuitry. It is to be noted that no mechanical sensors had been used in sensing the wind speed. The proposed system consists of a variable-flux dual-stator permanent magnet synchronous generator with an uncontrolled rectifier and an inverter working at constant modulation indexes. A proof-of-concept generator had been fabricated and tested under simulated wind conditions. The generator was tested for voltage regulation under two conditions. First under variable wind speed and constant load (VWCL) and second under constant wind speed and variable load (CWVL). It was observed from results that in proposed system the generator regulates its output voltage and power invariant to variable input wind speed and variable load.
Decentralized control for residential energy management of a smart users ʼ microgrid with renewable energy exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users ʼ controllable loads. We assume that each smart home can both buy / sell energy from / to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy / sell locally harvested renewable energy from / to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.
Improving Feature-based Visual SLAM by Semantics

Feature-based simultaneous localization and mapping (SLAM) algorithms with additional semantics can have better feature matching and tracking accuracies than the original SLAM algorithms. Therefore, this paper shows how to improve feature-based SLAM by only matching features from objects of the same semantic class. The basic idea is to use a deep neural network, YOLO (you only look once [1]), to classify objects and to associate features with the objects in whose bounding box they appear, thus giving features the semantic label of these objects. During feature matching of the SLAM algorithms, only features with the same semantic label are matched (e.g. books with books, bottles with bottles etc.), eliminating matches of similar features on different classes of objects. Experiments of classical ORB-SLAM2 with YOLO have been performed on an embedded PC. Additionally, ORB-SLAM2 with different versions of YOLO has also been tested on a powerful desktop GPU as well as on an Nvidia Jetson TX2 board. The experimental results show that using the semantic information given by object recognition methods reduces wrong feature matches in tracking and decreases the tracking lost cases.
GHS-UCDB R2019A - GHS Urban Centre Database 2015, multitemporal and multidimensional attributes

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. The Joint Research Centre (JRC) and the Directorate General for Regional Development (DG REGIO) of the European Commission support the GHSL activities. The GHSL contributes to the international partnership “GEO Human Planet Initiative”. The GHSL methods rely on automatic spatial data mining technologies allowing the extraction of analytics and knowledge from large amount of heterogeneous data including global, fine-scale satellite-image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources. The GHS Urban Centre Database (GHSUCDB) describes spatial entities called “urban centres” accordingly to a set of multi-temporal thematic attributes gathered from the GHSL sources integrated with other sources available in the open scientific domain. The Urban Centres are defined by specific cut-off values on resident population and built-up surface share in a 1x1 km global uniform grid. The input data it is generated by the GHSL, and the operating parameters are set in the frame of the “degree of urbanization” (DEGURBA) methodology. The DEGURBA is a methodology for delineation of urban and rural areas made for international statistical comparison purposes that is developed by the European Commission, the Organization for Economic Co-operation and Development (OECD), the Food and Agriculture Organization of the United Nations (FAO), UN-Habitat and the World Bank. The reference GHSL input data used to delineate the Urban Centres are included in the Community pre-Release of GHS Data Package (GHS CR2018) in support to the GEO Human Planet Initiative. The parameter set used to delineate the Urban Centres from the input data are included in the GHSL settlement classification model SMODv9s10E 2018. The reference epoch for the spatial delineation of the Urban Centres is 2015. The attributes of the GHS-UCDB have different time depth for a maximum of 40 years, depending on availability of the input sources.
Modeling and Voltage Regulation of Boost Inverter

A better performing, non-ideal high single stage inverter for generating a regulated, lower level THD ac output voltage which is greater than the unregulated dc input voltage is suggested in this literature. This work is developed in contrast to the traditional Voltage Source Inverter, which always generates lesser ac instantaneous output voltage with higher levels of THD. The suggested topology is supervised by a suitable instantaneous controlled input. A detailed mathematical modeling, a vast examination, and the simulated results are also included for verification of the non-ideal boost inverter topology presented. The obtained results effectively validate the suggested strategy.
Common Eland (Tragelaphus oryx) Population Trends in the Ukhahlamba-Drakensberg Park and Surrounds, South Africa, between 1942 and 2018

Management of protected areas requires an understanding of the long-term population trends of large mammal species that may influence ecosystem functioning. Common eland (Tragelaphus oryx) are the most abundant large mammal species in the uKhahlamba-Drakensberg Park. Aerial and ground surveys have been conducted over the years to monitor the common eland population. This study addressed long-term trends in population size, structure, distribution and mortality of the common eland between 1942 and 2018, examining changes in relation to climate, natural impacts, management, and efficacy of surveys. The common eland population in the Drakensberg remained relatively stable from 1942 to 1982, then increased between 1983 and 1990. However, a continuous decline was observed between 2003 and 2018 concomitant with a 37% reduction in distribution range within the Park. Adults constituted 59.4% of the population over the entire period, while subadults and calves accounted for 19.1% and 18.8%, respectively, with the remaining 2.7% of unknown ages. The ratio of adults: subadults: calves was 1: 0.32: 0.32 and males: females was 1: 3.63, indicating a skewed sex ratio and a low number of juveniles in the population. Human activities had a substantial impact on the population, with 30.3% of mortalities attributed to poaching. However, permitted offtakes of an unknown number may be the most important cause of mortality. Increased movement of eland onto farmlands outside of the protected area appears to have contributed to a decline in their distribution within the Park in recent years; however, this does not affect population trend since those on farmland are still included in counts. There is no cause for concern regarding the population trend at present; however, if the observed population decline continues well beyond the population numbers recorded in 1942, then there is cause for a management response by Ezemvelo KwaZulu-Natal Wildlife for the common eland population in the Drakensberg.
Detecting and Tracking Coastal Precipitation in the Tropics: Methods and Insights into Multiscale Variability of Tropical Precipitation

We propose an algorithm to detect and track coastal precipitation systems and we apply it to 18 years of the high-resolution (8 km and 30 min) Climate Prediction Center CMORPH precipitation estimates in the tropics. Coastal precipitation in the Maritime Continent and Central America contributes to up to 80% of the total rainfall. It also contributes strongly to the diurnal cycle over land with the largest contribution from systems lasting between 6 and 12 h and contributions from longer-lived systems peaking later in the day. While the diurnal cycle of coastal precipitation is more intense over land in the summer hemisphere, its timing is independent of seasons over both land and ocean because the relative contributions from systems of different lifespans are insensitive to the seasonal cycle. We investigate the hypothesis that coastal precipitation is enhanced prior to the arrival of the Madden–Julian oscillation (MJO) envelope over the Maritime Continent. Our results support this hypothesis and show that, when considering only coastal precipitation, the diurnal cycle appears reinforced even earlier over islands than previously reported. We discuss the respective roles of coastal and large-scale precipitation in the propagation of the MJO over the Maritime Continent. We also document a shift in diurnal cycle with the phases of the MJO, which results from changes in the relative contributions of short-lived versus long-lived coastal systems.
Mimicking Spike-Timing-Dependent Plasticity with Emulated Memristors

Memristors have found application in neuromorphic circuits and it has been shown that, under certain conditions, they may mimic the behavior of neuronal synapses. Experimenting with memristor-based synapses has several problems. Indeed, memristor samples are difficult to obtain and tweaking their parameters to adapt their behavior requires a long fabrication and testing process. For this reason, simulation and emulation become attractive alternatives for the study of memristive systems. We postulate that emulation has the advantage of working with real-world circuits and not stylized simulation models. In this paper, we propose a basic memristor emulation architecture and show that it can be used to mimic certain characteristics of synapses.
Breast Cancer Diagnosis using Quality Control Charts and Logistic Regression

In this work, we propose data mining techniques for the diagnostic of breast cancer. The contribution of this work is data pre-processing which includes outliers removal and dimensionality reduction to visualize the data. After data visualization, the most appropriate machine learning algorithm can be proposed to perform this diagnosis. For the evaluation of our proposed approach, we have used UCI machine learning breast cancer Wisconsin (original) dataset (WBCD). We applied principal component analysis for dimensionality reduction in the pre-processing and different control charts and box plots for data visualization. After data visualization, WBCD is considered to be linearly separable and the logistic regression model is proposed for classification in the diagnosis. The evaluation of our model is performed using 10 fold cross validation with accuracy, recall, F1 score, confusion matrix and receiver operating characteristic (ROC) curves. In our proposed work, we have achieved 99.48% as accuracy, which is the highest result found on this dataset for 70-30% training-test partition.