Miracle-dz/UniFAR-FTUs huggingface dataset:
MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.
Network‐based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Pancreatic cancer is regarded as the most fatal and aggressive malignancy cancer due to its low 5‐year survival rate and poor prognosis. The approaches of early diagnosis and treatment are limited, which makes it urgent to identify the complex mechanism of pancreatic oncogenesis. In this study, we used RNA‐seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in paired tumor and normal pancreatic samples from ten patients. More than 1000 differentially expressed genes were identified, nearly half of which were also found to be differentially expressed in the majority of examined patients. Functional enrichment analysis revealed that these genes were significantly enriched in multicellular organismal and metabolic process, secretion, mineral transport, and intercellular communication. In addition, only 24 differentially expressed miRNAs were found, all of which have been reported to be associated with pancreatic cancer. Furthermore, an integrated miRNA‐mRNA interaction network was generated using multiple resources. Based on the calculation of disease correlation scores developed here, several genes present in the largest connected subnetwork, such as albumin, ATPase H+/K+ exchanging alpha polypeptide and carcinoembryonic antigen‐related cell adhesion molecule 1, were considered as novel genes that play important roles in the development of pancreatic cancer. Overall, our data provide new insights into further understanding of key molecular mechanisms underlying pancreatic tumorigenesis.
Geoinformation Models in Smart Grid Management

This paper investigates the problem of distribution of electricity capacity in the territory covered by a Smart Grid. The purpose of management is loss minimization caused by changes in the state of the outdoor environment. It is analyzed the issues of cartographical representation of knowledge about the behavior of the outdoor environment and use of this knowledge for planning of electrical supply and decision-making in abnormal situations.
A Novel Tri-Band Patch Antenna With Broadside Radiation and Its Application to Filtering Antenna

A tri-band patch antenna utilizing the TM10 mode together with modified TM20 and TM30 modes of a single patch is proposed in this communication. Through asymmetrically loading a stub on one radiating edge of the patch and cutting a rectangular slot offset from the other one, the current distributions of the TM20 and TM30 modes are perturbed, resulting in broadside radiation for all the first three modes of a single patch. Each operational frequency can be effectively controlled through varying the dimensions of the patch, slot, and loading stub. Good impedance matching is achieved simultaneously in three operational bands through aperture coupling. A prototype is designed, fabricated, and measured for validation. Based on this tri-band antenna, a new tri-band filtering antenna is further developed, showing good selectivity and broadside radiation characteristic.
Context-Awareness Mobile Devices for Traffic Incident Prevention

Several techniques have been developed in last years by automotive industry in order to protect drivers and car passengers. These methods, for instance the automatic brake systems and the cruise control, are able to intervene when there is a dangerous situation. With the aim to minimize these risks, in this paper we propose a method able to suggest to the driver the driving style to adopt in order to avoid dangerous situations. Our method is basically a two-level fuzzy systems: the first one is related to the driver under analysis, while the second one is a centralized server with the responsibility to send suggestions to drivers in order to prevent traffic incidents. We carried out a preliminary evaluation to demonstrate the effectiveness of the proposed method: we obtain of percentage variation ranging from 85.48% to 88.99% in the number of traffic incidents between the scenarios we considered using the proposed method and the scenario without the proposed method applied.
Towards Gaze-Based Mobile Device Interaction for the Disabled

Manual input is the dominant way to interact with mobile devices in everyday life. Innovations like multi-touch displays and virtual keyboards that allow user input by sliding a finger over the letters make this input more and more convenient. However, these systems assume that users have full control over their hand movements. This assumption excludes millions of people who suffer from loss of extremities, paralyses, or spasticities. In this paper, we motivate the urgent need for input mechanisms that make these technologies available for everybody. We focus on gaze-based interaction as eye movements can be tracked by built-in cameras of unmodified mobile devices without any further hardware requirements. As accurate point of gaze estimation on mobile devices is nontrivial, we propose a middleware that is not based on the absolute position of the pupils but rather takes eye movement patterns into account. This paper has four contributions. First, we motivate the need for alternative inputs methods besides manual input. Second, we discuss existing approaches and reveal their limitations. Third, we propose a middleware that allows gaze-based user interaction with mobile devices. Fourth, we provide a framework that allows developers to easily integrate gaze-based control into mobile applications.
Multi-stage Trajectory Optimization for Time-on-Target of Tactical Rockets Using Gauss Pseudospectral Method

The application of Gauss pseudospectral method to the multi-stage trajectory optimization of two-stage boosting tactical rocket is studied. First of all, atmospheric environment characteristics and the aerodynamic characteristics of the two-stage boosting tactical rockets are fitted with high precision. Secondly, according to the characteristics of the intermittent working of the two-stage boosting tactical rockets, the ballistic trajectory of each rocket is divided into four phases and the connection points are used to ensure the smooth connection between adjacent phases. Then, the Gauss pseudospectral method is used to convert the trajectory optimization problem into a nonlinear programming problem. In order to further improve the computational efficiency and reduce the difficulty of setting the initial value, an iterative strategy based on the initial value generator is designed to achieve the rapid optimization. Finally, full consideration is given to the various flight performance constraints of the rockets, and the model of multiple tactical rockets which are launched successively attacking on the ground target simultaneously is established. The simulation results show that the proposed algorithm has high precision and effectiveness.
Continuous Estimation of Motion State in GPS/INS Integration Based on NARX Neural Network

The accurate estimation of motion state has been the key node in the control of motion systems. The traditional GPS/INS (Global Position System/Inertial Navigation System) integration may be invalid in the outage of GPS signals. The continuous estimation framework and method were proposed to estimate the location in different environments. Firstly, a continuous framework was designed combining the traditional GPS/INS integration and the intelligent neural network. The methods were switched according to the condition of sensors to realize the continuous estimation. Secondly, NARX (Nonlinear Autoregressive with Exogenous Inputs) neural network was built to model the nonlinear mapping relation between INS and GPS. The time series data were analyzed in NARX neural network to excavate the data features in the time dimension. Lastly, the experiment was conducted to verify the method proposed. And the results showed that the solution is valid in the motion state estimation with INS when GPS is in the outage.
The Truthful Evolution and Incentive for Large-Scale Mobile Crowd Sensing Networks

With the rapid development in mobile devices, mobile crowdsourcing has become an important research focus. In large-scale mobile crowdsourcing, the effective evolution prediction and incentive mechanism are the key focuses to improve the efficiency of systems. The evolution model based on evolutionary game theory is researched to predict the evolution trends of mobile crowdsourcing systems effectively. Based on the evolution trends, the reputation updating mechanism (RUMG) is proposed to address free-riding and false-reporting problems. According to spatio-temporal privacy preserving, the incentive mechanism with spatio-temporal privacy preserving for mobile crowdsourcing is researched. In order to protect worker’s spatio-temporal privacy information effectively, a spatio-temporal privacy preserving based on $k$ -anonymity (LKAC) is proposed. In addition, the effectivenesses of the proposed RUMG and LKAC are verified through comparison experiments. This proposed mechanism also improves the security of system and resolves the free-riding and false-reporting problems of mobile crowdsourcing.
An Anti-swing Trajectory Approach for an Unmanned Aerial Vehicle with A Slung Payload

The quadrotor UAV with a slung load is a typical underactuated system because there is no direct control acting on the payload, which makes it challenging to achieve the quadrotor's precise positioning and the payload's swing elimination simultaneously. We solve the problem from the perspective of trajectory planning. An online anti-swing trajectory approach is proposed in this paper, which is convenient and efficient without iterative optimization. And we prove the convergence of the quadrotor's position and the payload's suppression by utilizing Lyapunov-based analysis. Finally, simulation results show the effectiveness of the proposed trajectory which has a good performance.
Deconstructing deployment of the innate immune lymphocyte army for barrier homeostasis and protection

The study of the immune system has shifted from a purely dichotomous separation between the innate and adaptive arms to one that is now highly complex and reshaping our ideas of how steady‐state health is assured. It is now clear that immune cells do not neatly fit into these two streams and immune homeostasis depends on continual dialogue between multiple lineages of the innate (including dendritic cells, innate lymphoid cells, and unconventional lymphocytes) and adaptive (T and B lymphocytes) arms together with a finely tuned synergy between the host and microbes which is essential to ensure immune homeostasis. Innate lymphoid cells are critical players in this new landscape. Here, we discuss recent studies that have elucidated in detail the development of ILCs from their earliest progenitors and examine factors that influence their identification and ability to drive immune homeostasis and long‐term immune protection.
Formation Control of UGVs Based on Artificial Potential Field

According to the problem of UGVs' formation control in the process of motion, a formation control method of UGVs based on artificial potential field is proposed, established on the collision avoidance, aggregation and speed matching rules of UGVs. First establish the UGVs' kinetic model in accordance to the motion rules, then design the formation control algorithm based on artificial potential field function, which is used to control the formation during the movement of UGVs. Finally, the results of simulation and physical experiment verify the effectiveness of formation control method, which is also suitable for the formation control of large-scale UGVs.
Semi-Supervised Learning with Flexible Discriminator Objective in Generative Adversarial Networks Framework

Semi-supervised learning aims to learn a discriminative classifier with partially labeled data. This could leverage the large amount of unlabeled data which is relatively unlimited to enhance the model's generalization performance. The capability of prevalent deep models could be further improved with good semi-supervised mechanisms. Generative adversarial networks (GAN) has been adopted in this field and obtained strong empirical results, which mainly results from the adversarially generated samples that could help to build the decision boundaries in more reasonable places. In this paper, a flexible discriminator objective in GAN framework is proposed, which involves a supervised loss and a conditional entropy loss both added to the discriminator objective conditionally. A competitive semi-supervised classification accuracy could be obtained with our method.
A Unified Flicker Noise Model for FDSOI MOSFETs Including Back-bias Effect

A physics-based unified flicker noise model for FDSOI transistor is proposed. Flicker noise power spectral density (PSD) at the front and back interfaces are calculated using oxide-trap-induced carrier number (CNF) and correlated surface mobility fluctuation (CMF) mechanisms. The model predicts correct flicker noise behavior from weak inversion region to strong inversion region for a wide range of the front and backgate voltages. The proposed model is computationally efficient and implementable in any SPICE model for circuit simulations.
Demonstration Abstract: A Toolkit for Specifying Service Level Agreements for IoT applications

Today we see the use of the Internet of Things (IoT) in various application domains such as healthcare, smart homes, smart cars, and smart-x applications in smart cities. The number of applications based on IoT and cloud computing is projected to increase rapidly over the next few years. IoT-based services must meet the guaranteed levels of quality of service (QoS) to match users' expectations. Ensuring QoS through specifying the QoS constraints using Service Level Agreements (SLAs) is crucial. Therefore, as a first step toward SLA management, it is essential to provide an SLA specification in a machine-readable format. In this paper, we demonstrate a toolkit for creating SLA specifications for IoT applications. The toolkit is used to simplify the process of capturing the requirements of IoT applications. We present a demonstration of the toolkit using a Remote Health Monitoring Service (RHMS) usecase. The toolkit supports the following: (1) specifying the Service-Level Objectives (SLO) of an IoT application at the application level; (2) specifying the workflow activities of the IoT application; (3) mapping each activity to the required software and hardware resources and specifying the constraints of SLOs and other configuration- related metrics of the required hardware and software; and (4) creating the composed SLA in JSON format.
Corner Mismatch Model for Fast Non-Monte Carlo Best and Worst Cases Simulation

In this paper, we propose a non-Monte Carlo corner mismatch model, that enables circuit designers to simulate the best-case and the worst-case mismatch variation of their circuit performance fast. Standard mismatch model needed to be run in statistical Monte Carlo simulation which is time-consuming. Corner mismatch model can be run fast in non-Monte Carlo simulation. The concept of corner mismatch model is similar to the process variation corner models such as Fast-Fast (FF) or Slow-Slow (SS) model. We have demonstrated that corner mismatch model is useful in designing mismatch circuit such as current mirror. Besides the fast non-Monte Carlo simulation, corner mismatch model also allows designers to determine how many sigma of mismatch variation to be applied to the transistors in the matching pair. This enables the designers to analyze the amount of mismatch variation that can cause a critical impact to the entire circuit performance.
Indexes Prediction of Blast Furnace Gas Flow Using the T-S Fuzzy Model of Weighting Rule Firing Level Based on Two Kinds of Cluster Prototypes

The distribution state of the blast furnace (BF) gas flow is an important reference for the analysis of BF operation states. Bosh gas volume and wind pressure are two main indexes which reflect the distribution state of the BF gas flow. Therefor, predictions of these two indexes are important guidance for the field operation of BF. In recent years, the T-S fuzzy model plays an important role in nonlinear systems modeling and forecasting. As a method of information extraction, fuzzy clustering is crucial to the modeling quality of the T-S fuzzy model. However, the information extraction of datasets is incomplete based on one cluster prototype. Furthermore, hyper-shape of high-dimensional datasets is unknown. It is also unsuitable to cluster high-dimensional datasets based on one cluster prototype in previous researches. To improve the modeling accuracy, we consider the hyper-spherical-shaped cluster prototype and the hyper-plane-shaped cluster prototype in this paper. Then the T-S fuzzy model of weighting rule firing level is proposed to predict indexes of the BF gas flow. Simulation results verify that our model process superiority of accuracy, which makes the method serve better for practical production.
Fixation probability and fixation time in structured populations

The rate of biological evolution depends on the fixation probability and on the fixation time of new mutants. Intensive research has focused on identifying population structures that augment the fixation probability of advantageous mutants. But these `amplifiers of natural selection' typically increase fixation time. Here we study population structures that achieve a trade-off between high fixation probability and short fixation time. First, we show that no amplifiers can have asymptotically lower absorption time than the well-mixed population. Then we design population structures that substantially augment the fixation probability with just a minor increase in fixation time. Finally, we show that those structures enable higher effective rate of evolution than the well-mixed population provided that the rate of generating advantageous mutants is relatively low. Our work sheds light on how population structure affects the rate of evolution. Moreover, our structures could be useful for lab-based, medical or industrial applications of evolutionary optimization.
Novel Pattern-Diversity-Based Decoupling Method and Its Application to Multielement MIMO Antenna

In this paper, a novel decoupling method without adding any decoupling structure is investigated, and it is able to apply to multiple-input and multiple-output (MIMO) antenna designs with dual and multiple elements. The decoupling mechanism applied in here is to change the electrical length of the radiators to manipulate the radiation pattern for obtaining pattern diversity and good isolation simultaneously. The design mechanism and procedures for realizing pattern diversity are presented. To validate the proposed method, a two-element monopole antenna array with inverted-L elements is initially studied with the isolation better than 20 dB. Next, a four-element MIMO antenna utilizing two different types of monopole elements is proposed to further verify the feasibility of the proposed decoupling method. Finally, an eight-element MIMO antenna operating at 3.5 GHz is implemented with a compact total size of $40\times 100$ mm2, and the measured isolations between two elements are better than 15 dB. Envelope correlation coefficient and channel capacity are calculated to evaluate the MIMO performance, and good results are obtained. The target application of the proposed design is for laptop, small cell base station, or WiFi box with multiple elements.
Stability of Neutral Type Descriptor Markov Jump Systems with Time-Varying Delays

This paper deals with the problem of stochastic stability of neutral type descriptor Markov jump systems with time-varying delays. Using Lyapunov-Krasovskii functional, model transformation and free-weighting matrix method, a delay-dependent stability criterion is obtained in the form of linear matrix inequalities. Finally, a numerical example is presented to illustrate the effectiveness of the method.
Conceptual Design and Kinematic Analysis of a Novel Open Field 3DOF Multi-Gripper Pot Seedlings Transplanting Robot

Pot seedlings transplanting is an activity in horticulture and agricultural production industry. It involves the transfer of pot seedlings from seedlings try into growing media in a greenhouse or an open agricultural field for further growth. This activity is a labor intensive, time consuming and requires high accuracy and precision, thus there is a need for mechanized operation to meet the challenges. In the current state, the automated and robotic seedlings transplanters have low production capability and most of them are met to work in greenhouse. This paper focuses on the conceptual design, kinematic analysis and optimal dimensional synthesis of a novel 3DOF multi-gripper pot seedlings transplanting robot to be used in agricultural open field. The proposed robot is composed of two identical 2DOF translational parallel robots, 1DOF multi-gripper end-effector, conveyors and drives. During conceptual design phase, the speed, work efficiency and transplanting agronomical practices are taken into consideration. Some constraints are also taken into consideration during the dimensional synthesis of this parallel robot to obtain the robot dimensions. Furthermore, workspace analysis and trajectory planning are carried out. Simulation results indicate that the desired workspace lie within the reachable workspace of the manipulator and 6 seedlings per single transplanting cycle can be achieved in 1.8 sec.