Miracle-dz/UniFAR-FTUs huggingface dataset:
Biotic homogenisation in bird communities leads to large-scale changes in species associations

The impact of global change on biodiversity is commonly assessed in terms of changes in species distributions, community richness and community composition. Whether and how much associations between species, i.e. the degree of correlation in their spatial co-occurrence, are also changing is much less documented and mostly limited to local studies of ecological networks. In this study, we quantify changes in large-scale patterns of species associations in bird communities in relation to changes in species composition.France. 2001-2017.Common breeding birds.We use network approaches to build three community-aggregated indices reflecting complementary aspects of species association networks. We characterise the spatio-temporal dynamics of these indices using a large-scale and high-resolution dataset of bird co-abundances of 109 species monitored for 17 years (2001-2017) from 1,969 sites across France. We finally test whether spatial and temporal changes in species association networks are related to species homogenisation estimated as the spatio-temporal dynamics of β-diversity and the proportion of habitat generalists. The consistency of these relationships is tested across three main habitats, namely woodland, grassland and human settlements.We document a directional change in association-based indices in response to modifications in β-diversity and in the proportion of generalists in space and time. Weaker associations and sparser networks were related to lower β-diversity and a higher proportion of generalists, suggesting an overlooked aspect of biotic homogenisation affecting species associations. We report that this overall pattern is not constant across habitats, with opposite relationships between biotic homogenisation and change in species association networks in urban versus forest communities suggesting distinct homogenisation processes.Although species association contain only partial signatures of species interactions, our study highlights that biotic homogenisation translates to finer changes in community structure by affecting the number, strength and type of species associations.
Circuit Breaker Control of Intelligent Relay Protection System

An intelligent relay protection system has been developed, which consists of several sensors for recording external factors. The sensitivity of the relay protection can be increased due to a more accurate calculation of the parameters of the relay protection, which will also help to avoid unnecessary trips and failures of the protection. The research results can be applied to create a multidimensional mathematical model that allows basing on initial data on weather conditions, time of year, soil resistance, current, voltage, etc. Automated selection of relay protection setpoint. A functional diagram of an intelligent relay protection system has been compiled. A block diagram for making a decision to shutdown of a damaged line by an intelligent relay protection system has been compiled. The main elements of digital relay protection are described, as well as the principles for implementing microprocessor-based circuit breaker control devices.
Self-Stabilizing Population Protocols With Global Knowledge

In the population protocol model, many problems cannot be solved in a self-stabilizing manner. However, global knowledge, such as the number of nodes in a network, sometimes enables the design of a self-stabilizing protocol for such problems. For example, it is known that we can solve the self-stabilizing leader election in complete graphs if and only if every node knows the exact number of nodes. In this article, we investigate the effect of global knowledge on the possibility of self-stabilizing population protocols in arbitrary graphs. Specifically, we clarify the solvability of the leader election problem, the ranking problem, the degree recognition problem, and the neighbor recognition problem by self-stabilizing population protocols with knowledge of the number of nodes and/or the number of edges in a network.
Compound Target Identification in Tissue-Specific Interaction Networks

Characterizing all possible side effects of compounds is not a trivial task due to unknown off-target proteins that might eventually lead lethal reactions. There is still a tremendous need of computational methods to identify protein targets of a new compound. We have performed a comprehensive analysis for identification of protein targets by integrating tissue-specific protein-protein interaction (PPI) networks and compound induced transcriptome data. Several network centrality metrics are computed to suggest the most probable off-targets of a given compound. The effects of interaction types between proteins and tissue-specific PPI networks are evaluated from multiple perspectives. Usage of network centrality metrics on a tissue-specific PPI network enhances the correct prediction rate of known targets of a given compound. The detailed analysis of successfully identified known targets indicated that degree and local radiality metrics are more practical for determining different types of target protein families, such as GPCR, chemokines, proteasome, and protein kinase families. Therefore, the proposed computational pipeline is applicable while investigating proteins from these families that can be especially targeted for treatment of complex diseases. To the best of our knowledge, this study for the first time presents how tissue-specific transcriptome changes alter the topological structure of PPI networks and the relative effects of tissue-specific networks in target identification. Overall, the proposed computational methods are practical tools for choosing more accurate protein targets for later computational analysis and wet-lab experiments.
Model for Analyzing Battery Parameters as a Function of Ambient Temperature

The paper considers a simulation model of electrochemical electrical energy storage based on the known electrical characteristics. A peculiar feature of the model is the use of parametric dependencies obtained by the regression analysis according to the available experimental data. The model implements a change in the voltage and internal resistance depending on the charge depth, as well as a change in the battery capacity depending on the ambient temperature. The article shows the implementation of the mathematical model in the MATLAB/Simulink environment and presents the results of the validity check of the proposed model under various external influences. It presents the results of model experiments to determine the change in the degree of charge, voltage, and the amount of stored energy over time at the specified values of the charge current. The model can be useful for modeling interconnected power systems with energy storage to simulate changes in the parameters of electric circuits due to the battery charge/discharge process, as well as under the influence of the ambient temperature.
Polarity Effect of Impulse Conditioning Characteristics under a Uniform Electric Field in Vacuum

We wish to determine if a polarity effect of impulse conditioning characteristics exist under a uniform electric field distribution in vacuum. To clarify the polarity effect, same breakdown test procedure is carried out for spherical electrode pairs before and after a polarity change. Field emission current is analyzed with mathematical compensation algorithm and to distinguish field emission induced breakdown (FEBD) and particle induced breakdown (PBD) during conditioning saturation. Electrode surface morphology are observed by SEM and surface roughness are measured. The following conclusions are drawn: Polarity effect of impulse conditioning characteristics exists under a uniform electric field in vacuum, and the average breakdown electric field after polarity change is higher than that before the polarity change. The cathode surface is covered with microparticles, whereas the anode surface is not after conditioning saturation. The occurrence percentage and average breakdown electric field of FEBD increase after the polarity change compared with those before the polarity change, while the percentage and breakdown electric field of PBD decrease. The physical evolution of the polarity effect is explained by taking the breakdown electric field, breakdown mechanisms, electrode surface morphology and roughness into consideration.
Analysis of Hybrid Model Error of Short-Term Forecast of Energy Consumption in Moscow

For short-term forecast of daily active energy consumption graphs in Moscow, there is proposed a hybrid model based on the Multichannel Singular Spectrum Analysis algorithm and a Neural Fuzzy Network taking into account for factual and forecast data of air temperature and daylight illumination. In the hybrid model in the multichannel singular spectrum analysis module, the time series of energy consumption and meteorological factors are decomposed into independent components - forming its additive, trend, harmonic and random components used in the neural fuzzy network module. The source data were archives of time series of energy consumption, air temperature, cloud amount and daylight illumination for the Moscow territory from 2012 to 2016. The software implemented in the C# language was used as a modeling tool. Short-term forecasts of daily active energy consumption graphs were carried out during the year (from September 2015 to August 2016). In most test examples, the results of the forecast for a month are obtained within the limits of permissible errors. The proposed version of short-term forecast of daily active energy consumption graphs allows obtaining an error in the range from 1 - 2.3 % during the year. In addition, the developed algorithm makes it possible to use seasonal patterns between daily active energy consumption graphs and meteorological factors for several years.
Intensive Care Unit False Alarm Identification Based on Convolution Neural Network

In the intensive care unit(ICU), excessive false alarms burden medical staff greatly, and cause a waste of medical resources as well. In order to alleviate false alarming in ICU, we constructed classification models based on convolutional neural network, which can directly deal with time series and avoid manually extracting features. Combining with grouping strategy, we tried two basic network structures, i.e. deep group convolutional neural network(DGCN) and embedded deep group convolutional network(EDGCN). After that, based on EDGCN, which was proved better, ensemble networks were constructed to elevate the performance further. Considering of the limited sample size, various data expansions were also tried. For comparison, we used a widely adopted index ‘Score’, which is defined as $100\times $ (TP+TN)/(TP+TN+FP $+ 5\times $ FN), as performance evaluation. Finally, we tested our model in the online sandbox, and got a score of 80.68. Although it is slightly lower than the best scores that have been reported, our models are end-to-end, which means the original time series can be automatically mapped into a binary output, without manual feature extraction. In addition, the innovative adoption of group convolution makes full use of information from multi-channel signals. Besides, this work provides ideas for relating network parameter selection with physiological or physical natures of signals. In the end, after further discussion, we also proposed potential elevation solutions.
Generative Adversarial Network-Based Transfer Reinforcement Learning for Routing With Prior Knowledge

With the incremental deployment of software defined networking, the routing algorithms have gained more power on observability and controllability. Deep reinforcement learning, as an experience-driven approach, shows considerable potential in routing problem with the help of the centralized controller. It is an adaptive, lightweight, and model-free approach to coping with dynamic runtime status, large-scale traffic, and heterogeneous objective of SDN routing. However, it is still not suitable for the variable and complex emerging networks, because the huge training cost prevents fast convergence in a varying or discrepant environment. In this paper, we propose a transfer reinforcement learning algorithm to improve the training efficiency, and handle the variation in network status and topology. Specifically, we leverage the generative adversarial network to learn domain-invariant features that is suitable for deep reinforcement learning-based routing in different network environments. This mechanism utilizes the previous model and accelerates the training process. We implement our routing algorithm in the production level software switches and controller, while evaluating it comprehensively with many topologies and network status distributions. The experimental results show that our work not only outperforms the state-of-the-art deep reinforcement learning-based routing frameworks, but also has more training efficiency than the naive transfer learning algorithm both on different topologies and network status distributions.
Controller Design for Autonomous Vehicle

Control system is a vital part of an autonomous vehicle. It is responsible for passenger's safety and better navigation on roads. In this paper, a control system design is discussed, along with lateral and longitudinal control for the autonomous vehicle. A speed profile generator is used for calculating the speed for better stability of the vehicle. Through the paper, implementation of a PID controller with Stanley control is discussed. Mathematical modeling is performed for the controller to analyze the effect of external factors on the vehicle and the proposed system is later coded in python language and later tested through simulation on CARLA simulator and results are discussed
Design and Analysis of 10T SRAM Cell with Stability Characterizations

Nowadays, the use of Static random-access memory (SRAM) is increasing in System on Chip and VLSI circuits with the arrival of portable devices. Our main focus of research is SRAM optimization because most parts of the chip are used by memories. In today's world, the main requirement of the industry is low power and high-performance memories. The scaling of CMOS devices gives rises to a various problem which can be overcome by FinFET device. In this paper a novel 10T SRAM cell design using FinFET is present at different technologies. A detailed comparison is carried out with a 10T CMOS cell for 45nm and 32nm technologies. For analysis purpose different parameters of SRAM such as power, static noise margin (SNM) and delay are taken into considerations. Improvement in the hold and read static noise margins (HSNM, RSNM) by 31% and 29% at 45nm and 41% and 50% at 32nm of FinFET based SRAM cell is obtained compared to CMOS based cell. Simulations have been carried out on the HSPICE tool at a supply of 0.5v and temperature of 25°C temperature.
Initial Design of Trusses using Topology Optimization in a Deep Learning Environment

Topology optimization is the tool of choice for obtaining the initial design of structural components. The resulting optimal design from topology optimization will be the input for subsequent structural optimizations with regard to shape, size, and layout. In reality, however, iterative solvers used in conventional SIMP (Simplified Isotropic Material with Penalization) based topology optimization schemes consume a very high computational power and therefore act as a bottleneck in the manufacturing process. In this work, an accelerated topology optimization technique based on deep learning is presented. Conditional Generative Adversarial Network (cGAN) architecture is used to predict the optimal topology of a given structure subject to a set of input parameters. This novel framework is showcased to generate initial truss designs for any combination of volume fractions and edge loading locations. Unlike prevalent topology optimization solvers, the proposed method obtains the accurate topologically optimised truss structure within milliseconds.
Machine Learning for NetFlow Anomaly Detection With Human-Readable Annotations

We propose a framework for anomaly detection in communication network logs along with automated extraction of human-readable annotations that explain the decision logic underlying each anomaly detection. For this purpose, we develop a machine learning methodology formulated in terms of a model comprised of an OR-combination of multiple Boolean logic based sentences. Each sentence is an empirically learned set of inequality conditions involving subsets of features. The feature set, which comprises the “alphabet” for human-readable annotations, is constructed using dynamic graph based spatio-temporal aggregation to extract human-understandable aggregates of network activity. These aggregates are constructed both in terms of computers (nodes in dynamic graph) and communications between computers (edges in dynamic graph). From the alphabet, the learned model identifies subsets of features that relate to each anomaly type and the combinations of conditions in terms of the feature subsets for detection of the specific anomaly type. Given a data point that the learned model detects as anomalous, the model identifies the specific features and their combinations related to the anomaly detection. These human-readable annotations provide a cyber-security analyst a transparent view into the decision logic underlying an anomaly detection.
Development and Research of the Current Measurement Scheme for Insulation Tests with High Voltage Industrial Frequency

When designing test voltage sources with AC power frequency for regular assessment of the insulation quality of high-voltage equipment, the task is to measure the current flowing through the test object. The authors have developed an algorithm and proposed a technical solution for measuring the current flowing through the test object when testing insulation with an alternating sinusoidal voltage of industrial frequency, which uses a precision resistor, an isolation amplifier and an ADC of a microcontroller. The developed algorithm for the study of the circuit for measuring the alternating current flowing through the test object when testing the insulation with an alternating sinusoidal voltage of the industrial frequency makes it possible to increase the speed of developing measuring devices. During the study of the stand of the circuit for measuring the alternating current flowing through the tested object when testing the insulation with an alternating sinusoidal voltage of industrial frequency, an experimental industrial sample was created that makes it possible to measure the rms current value within the range from 1 mA to 320 mA. The use of the developed circuit for measuring the current flowing through the test object when testing insulation with an alternating sinusoidal voltage of industrial frequency, allows you to obtain the rms current value in real time, and, therefore, to quickly monitor the current in high-voltage circuits for continuous monitoring. The introduction of this sample will make it possible, on its basis, to create a number of digital measuring milliammeters that allow measuring alternating current in high-voltage test installations for testing the insulation of objects from 15 nF to 200 nF, in the range from 1 mA to 1000 mA. The results obtained can be used in high voltage technology to study circuits for measuring the current flowing through the test object when testing insulation with an alternating sinusoidal voltage of industrial frequency.
An Autonomous Frequency Reconfigurable Antenna Using Slotline Open-Loop Resonators

This paper presents a slotline open-loop resonators based frequency reconfigurable antenna with autonomous switching of frequency bands. The dual-port slot antenna is designed to operate at 2.1 GHz when excited at port-1, and when port-2 is excited, the antenna can be reconfigured to operate at 2.85 GHz and 5.52 GHz, respectively. Port-1 is used to receive the control signal, which is converted to a DC signal using a rectifier. The rectified control signal is used to switch the operating frequency bands of the port-2. The proposed antenna configuration gives the flexibility of remote/wireless control of the operating frequency. The antenna prototype is fabricated and measured for observing the frequency switching at port-2. The measured results show that the antenna has been effectively switched from lower band frequency (2.8 GHz) to upper band frequency (5.41 GHz) when the PIN diode is ON. The proposed antenna in its final configuration can be potentially suitable for transmitter reconfigurable antenna without the need for external DC bias voltage.
Study of Impact of the Main Parameters of Synchronous Generators on the Dynamic Stability in the Syrian Power System

The importance of interconnection between electrical power systems has increased, largely due to the dependence of the whole world on electricity and ease of transfer between its countries vast, in addition to their economic and technical benefits. In this research, the dynamic impact of the interconnection on the rotor angle stability for Synchronous Generators, located on the Syrian network, were analyzed through the dynamic simulation of the power system. The none linear equations were built, then use the program PSS®E (Power system simulation), to identify the determinants of the system and determine critical fault clearing times of three-phase faults on the 400 and 230 kV. In addition, to identifying the angular revolving generators, thus to verify the stability whether it is on the independent network within the Syrian electrical system or as part of the five countries interconnection project.
Dust Figures as a Way for Mapping Surface Charge Distribution — A Review

Dust figures can be used to visualize surface charge distribution quickly and effectively. In this article, the development history and the measurement method of dust figure techniques are reviewed. The physical process and mechanism of charge recurrence is explained in the cases where different types of dust figures are used. Furthermore, recent research progress on dust figures is summarized and analyzed. Finally, future prospects for implementation of dust figure techniques to measure surface charge distribution are discussed.
2-D Simulation of the Electron Density Characteristics of a Special Plasma Device

The purpose of this article is to study the influence of different powers, pressures, frequencies, and gases on the electron density distribution in special structures. A simulation model is established, and the electronic density in a special capacitance coupled plasma (CCP) device is calculated over the power range of 0–10 kW, pressure range of 0–50 Pa, and frequency range of 13.56–300 MHz. The results show that the power is a logarithmic function of the electron density in helium and argon plasmas, and the peak value for the electron density occurs in the diffusion region. At a certain power, collisions between particles increase with increasing pressure, which leads to an increase in electron density. However, the high electron density region in the helium plasma is concentrated near the electrode, while the high density region in the argon plasma moves from the center of the device to the electrode with increasing gas pressure. At the same time, the increase in the power frequency increases the ionization rate, and energy is transferred to the electrons, which increases the electron density. However, the rate of increase in the electron density decreases with increasing power frequency at the same voltage and pressure. With increasing power frequency, the 2-D electron density distribution in the argon and helium plasmas is found to vary in a similar way to that found for increasing pressure.