Miracle-dz/UniFAR-FTUs huggingface dataset:
Forest Carbon Sink Planning Model Based on Differential Evolution with Simulated Annealing

As the most severe environmental problem in the 21st century, global warming has become an inevitable and urgent issue to be solved. In this paper, a Forest Carbon Sink (FCS) planning model (Model I) is proposed to illustrate the forest stand composition and evaluate the overall capacity of FCS according to the forest carbon reserve, forest carbon sequestration, forest product carbon content, and carbon sink transfer restriction. Considering the felling cycle further, an ecological FCS model (Model II) is proposed, which pays more attention to the ability to balance the carbon sink benefit and ecological benefit. Based on the difference between Model I and Model II, the felling cycle plays an important role in total FCS. Considering the forest product value, a commercial FCS model (Model III) is proposed, which measures the forest value from two dimensions of carbon sink value and commercial value, and provided Pareto-optimal solutions. To solve the above models, Simulated Annealing-based Differential Evolution (SADE) and its multi-objective variant are developed to provide outstanding decisions for forest managers. Compared to natural forest, annual felling artificial forest achieve a 43.8% increasement on total FCS during 30 years, according to our solutions from SADE.
Forecasting of traffic variations from their preceding dynamics: Parametric vs non-parametric approaches

Internet traffic intensity variations contain significant information on the access pattern dynamics. On the one hand, variability in access patterns is a direct manifestation of the end users' and IoT devices behavior. On the other hand, a better understanding of the access pattern dynamics provides essential information for an early redistribution of traffic, leading to potentially more efficient dynamic routing algorithms. Traffic in large networks is typically governed by a complex interplay of auto-and cross-correlation patterns that largely determine its non-stationary nature. Here we have considered two approaches to the identification of the traffic variation model. The first approach is parametric and focuses on fitting the parameters of Seasonal Auto Regressive Integrated Moving Average with exogenous factors (SARIMAX). The second approach is based on training of a recurrent neural network (RNN). Both approaches have been validated explicitly using traffic data records over several days of monitoring at the uplink of a local campus network.
Improving the Hand Movement Classification Accuracy through EMG based on Histogram Feature Enhancement from Time Domain Representation

In order to control a prosthetic hand, electromyography (EMG) signals are essential sources of nervous information. A variety of factors have contributed to changes in the EMG signals' characteristics over time. These factors, such as stress, exhaustion, muscular dystrophy, shifting electrode locations, etc., when using high-density surface electromyography (HD-sEMG) electrodes, reduced the effectiveness of the myoelectric prosthesis and made it uncertain over time, between subjects, as well as during sessions. In this paper, in order to enhance myoelectric prostheses' functionality with regard to the non-stationary properties of EMG signals over time, HD-sEMG electrode technology is combined with histogram features. Ninapro database2 used for classifying the hand movements of five subjects through EMG signals generated from forearm muscle with linear discriminant analysis (LDA) classifier based on the proposed features. The classification performance based upon proposed histogram features achieved classification accuracies of 75.78 % for subject1, 73.53% for subject2, about 93.85% for subject3, 90.1% for subject4 and 86.71% for subject5 respectively. The findings establish that the time domain representation has a smaller impact on classification accuracy than do the histogram features.
Rate-Distortion in Non-Convex Families

Iterative constrained optimization often requires convexity conditions about the argument set in order to converge to the global optimum. One such instant is the parametric version of the Blahut algorithm for rate-distortion function computation. However, there are many interesting cases for which the parametric set is not convex, e.g a discrete reproduction alphabet at unknown (parametric) locations for a continuous source. In this paper we show examples of non-convex families for which the parametric Blahut algorithm does not converge to the global optimum, and suggest a combined parametric Blahut and random annealing (A) method to overcome this problem.
A Generative Adversarial Network with Attention Mechanism for Time Series Forecasting

Time series forecasting has widespread applications in everyday life, e.g. in the domains of weather forecasting, transportation planning, and electricity demand prediction. Due to the high dimensions, uncertainties, and changes of data in time series, accurately predicting future trends of time series remains a challenge. In this paper, we propose a new time series forecasting model, the Generative Adversarial Network Model with Attention Mechanism (GANAM), which uses a global residual network to enhance the attention mechanism module and acts as a generator to learn the deep temporal features and then uses the discriminator of the generative adversarial network for improving the forecasting capability. In addition, the classical time series model can lead to an accumulation of errors in the multi-step prediction process. The proposed model also can effectively reduce this problem by utilizing the generative adversarial network with an attention mechanism. The experiments on various real-world datasets have demonstrated the effectiveness of our model.
Code at the Receiver, Decode at the Sender: GRAND with Feedback

In a setting where the forward and feedback channel are noisy BSCs, we show how capacity is nearly achievable in a scheme with only source coding on the forward channel. In representative settings with noisy feedback, GRAND makes the scheme not only possible but practical. The sender transmits uncoded messages, and the receiver provides a noise effect guess as in GRAND, which is channel-encoded and sent to the receiver. With noiseless, finite-length feedback our scheme provides the type of super exponential error behavior associated in previous work with infinite-capacity feedback channels. With noisy feed-back, which is the more common setting in most systems, our scheme permits forward throughput that is effectively the same as in a noiseless feedback case. Moreover, the feedback channel usage remains limited. We propose a target error rate as a useful design parameter.
The single degenerate channel for the progenitors of Type Ia supernovae

Abstract We have carried out a detailed study of the single-degenerate channel for the progenitors of type Ia supernovae (SNe Ia). In the model, a carbon-oxygen white dwarf (CO WD) accretes hydrogen-rich material from an unevolved or a slightly evolved non-degenerate companion to increase its mass to Chandrasekhar mass limit. Incorporating the prescription of Hachisu et al. (1999a) for the accretion efficiency into Eggleton's stellar evolution code and assuming that the prescription is valid for all metallicities, we performed binary stellar evolution calculations for more than 25,000 close WD binary systems with various metallicities. The initial parameter spaces for SNe Ia are presented in an orbital period-secondary mass (log Pi, M2i) plane for each Z. Adopting the results above, we studied the birth rate of SNe Ia for various Z via binary population synthesis. From the study, we see that for a high Z, SNe Ia occur systemically earlier and the peak value of the birth rate is larger if a single starburst is assumed. The Galactic birth rate from the channel is lower than (but comparable to) that inferred from observations. We also showed the distributions of the parameters of the binary systems at the moment of supernova explosion and the distributions of the properties of companions after supernova explosion. The former provides physics input to simulate the interaction between supernova ejecta and its companion, and the latter is helpful for searching the companions in supernova remnants.
Dual-Band Self-Decoupled MIMO Antenna for 5G Mobile Terminals

A dual-band multi-input and multi-output (MIMO) antenna with self-decoupled characteristic for 5G mobile terminals is presented in this paper. The proposed MIMO antenna possess a simple structure, which consists of two identical antenna elements placed very closely with an edge-to-edge spacing of only 0.012 λ0at 3.5 GHz. The simulated results show that the -10 dB impedance bandwidth of the proposed antenna are 180 MHz (3.42-3.60 GHz) in the low band and 290 MHz (4.75-5.04 GHz) in the high band, and isolation better than -18.4 dB at both two bands are achieved, which provides a promising solution to compact 5G MIMO mobile terminal antennas.
A New Convolutional Prognostics Network with Adaptive Kernels for Estimating Remaining Useful Life of Bearings Considering Variable Speed

Remaining useful life (RUL) prediction of rolling element bearings has become an indispensable technology in modern industries for safety guarantee and maintenance optimization of machines, and deep learning-based prognostics methods are the research hotspot in recent years. Existing deep prognostics networks, however, usually focus on RUL prediction of bearings under constant operating conditions, and the variation of rotational speed of bearings is not explicitly considered during network construction and representation learning. To address this limitation, a new convolutional prognostics network with adaptive kernels, named adaptive multi-scale convolutional network (AMSCN), is proposed in this paper. The proposed AMSCN uses vibration and speed signals as the network inputs, and builds two novel layers, i.e., an adaptive convolutional layer and an information merging layer, to enhance the representation learning ability, including automatic scale-varying degradation feature extraction and representation recalibrations based on time-series information. The proposed AMSCN is evaluated using life-cycle monitoring data from accelerated degradation tests of bearings under variable speed conditions, and is also compared with some existing prognostics methods. Experimental results validate the advantages of the proposed AMSCN in terms of accuracy and convergence for RUL prediction of bearings considering variable speed.
Leveraging Adaptive Redundancy in Multi-Core Processors for Realizing Adaptive Fault Tolerance in Mixed-Criticality Systems

Nowadays, embedded systems are ubiquitous and their functionality is becoming increasingly intertwined with various critical demands. Integrating functionality with different criticality demands has led to the emergence of mixed-criticality systems (MCS), which require adaptive fault tolerance to ensure the correctness of critical tasks. In this paper, we propose a hardware-based adaptive redundancy approach for multi-core systems, which aims to enhance the reliability and safety of MCS. Our approach involves the reconfiguration of two physical processor cores into a single logical core that executes the same program on demand. The logical core provides adaptive redundancy to detect and mask faults. However, this reconfiguration can potentially result in deadlocks. To address this issue, we identify the scenarios where deadlocks may occur and provide a countermeasure to prevent their emergence. By adopting this runtime adaptive and hardware-based adaptive redundancy method, we can improve the reliability and safety of mixed-criticality systems. At the same time we utilize the processor architecture to abstract the reconfiguration process.
Texture-based visualization of multi-field flow data

Researchers have long been interested in developing a deeper understanding of the key physical mechanisms in fluid dynamics. Of particular interest are the complex relationships between the multiple scalar and vector quantities used to characterize scientific phenomena within the flow. Efforts to achieve a fundamental understanding of these key physical mechanisms remain limited mainly because of a lack of understanding of the nonlinear interactions that occur among the components of the flow. The goal through this work is to enable researchers to obtain a succinct, meaningful visual summary of the contents of a dataset that consists of multiple, coincident variables. This is accomplished through providing techniques that allow the creation of an image in which the important features of multiple scalar or vector fields can be understood both independently and in the context of the other fields. This research offers several new techniques for effectively using color and texture to simultaneously convey information about multiple co-located scalar and vector distributions. Specifically, we introduce: (1) color weaving, an alternative to traditional color compositing for simultaneously representing multiple distributions by allowing colors to be closely interwoven via the assignment of distinct separate hues to individual streamlines; (2) applying natural textures to streamlines to create a richly diverse set of possibilities for the visualization of multiple distributions within a flow; (3) embossing to encode the out-of-plane component of a 3D vector field defined over a 2D domain; (4) visualizing multiple 2D vector fields using strategies involving layers of disparate textures and overlapping streamlines. These methods ultimately enhance the ability to provide insight into the complicated interactions that occur within multi-field flow data.
Design and Analysis of HDMA-assisted Visible Light Communication System Based on Transmitting and Reflecting RIS

For this article, a hybrid domain multiple access (HDMA)-assisted visible light communication (VLC) system based on transmitting and reflecting reconfigurable intelligent surface (TR-RIS) is studied. HDMA is a new multiple access technology enjoying multi-domain signal information, which can effectively improve the system performance by carrying more users' information on the light emitting diodes (LEDs). Contrapose to the problem of multi-LED interference in the system, this paper introduces a transmitting RIS (T-RIS) angle diversity receiver (RADR) model, using the electronically controlled refractive index characteristics of the T-RIS and the angle diversity of the spatial optical signal, which can effectively alleviate interference problem between multiple LEDs. Furthermore, a system optimization model is proposed in this paper. Considering various constraints of the system, an optimization objective is established to maximize the communication capacity by optimizing the power distribution ratio and reflecting RIS (R-RIS) unit. The simulation results show that the proposed scheme can effectively improve the VLC system capacity compared to the existing ones.
Research on Active Obstacle Detection Algorithm of Rail Train Based on Multi-sensor

Aiming at the problems of false detection, missed detection and poor environmental adaptability when a single sensor detects obstacles in front of the rail train, an active obstacle detection algorithm for rail transit based on multi-sensor is proposed. Firstly, the track segmentation point set extracted by machine vision algorithm is used to fit the track by least square method to construct the driving envelope model. Secondly, the point cloud based on CFSFDP clustering is projected onto the image plane through the calibration of lidar and camera. The projected lidar clustering point cloud and camera image are combined with the driving envelope model to detect obstacles twice. Finally, based on the D-S evidence theory, the decision of the two detection results is made to overcome the detection limitations of a single sensor and improve the detection accuracy. The experimental results show that the algorithm has higher detection accuracy than the single visual or lidar sensor detection algorithm, and can effectively detect the sudden intrusion obstacles within the envelope boundary in front of the train.
Automatic Classification of Mathematical Formulas with Natural Language Processing

In e-learning, for predicting a learner's ability, it is necessary to not only analyze learning records but also consider the links between the types of study problems. For analyzing mathematical problems automatically, a formula classifier was developed with Natural Language Processing, which classifies formulas into mathematical operations including expansion, factorization, etc. It could classify 73% of formulas in test data into appropriate operations and visualize where this model was focused when classifying. From the visualization, this classifier can have the same judgement criteria as humans.
An Air Induced Tactile Display With Olfaction*

Scientists who are working in the field of haptic would like to overcome one of the profound challenges which is developing a multimodal tactile display. The user interfaces developed by them must include a higher number of modalities to create maximum realism while interacting with a user interface. However, advances in digital systems require a new method to create them and add additional factors for maximum satisfaction. In this paper, a tactile display that uses air to create a texture feeling is introduced. In addition, the convection method is employed for temperature feeling and the sense of smell is added in addition to the conventional modalities. The performance of the system was tested through psychophysical evaluation. The results showed that the developed surface has the potential to integrate in the current digital devices where human-machine interaction is a matter of interest.
UAV-based Networked Airborne Computing Simulator and Testbed Design and Implementation

The integration of onboard computing capabilities with unmanned aerial vehicles (UAV) has gained significant attention in recent years as part of mobile computing paradigms such as mobile edge computing (MEC), fog computing, and mobile cloud computing. To enhance the performance of airborne computing, networked airborne computing (NAC) aims to interconnect UAVs through direct flight-to-flight links, with UAVs sharing resources with each other. However, despite the growing interest in NAC and UAV-based computing, existing studies rely heavily on numerical simulations for performance evaluation and lack realistic simulators and hardware testbeds. To fill this gap, this paper presents the development of two NAC platforms: a realistic simulator based on ROS and Gazebo, and a hardware testbed with multiple UAVs communicating and sharing computing resources. Through simulation and real flight tests with two computation applications, we evaluate the platforms and examine the impact of mobility on NAC performance. Our findings offer valuable insights into NAC and provide guidance for future advancements.
Measurement of Electromagnetic Property of Dielectric Material Based on Transmission/Reflection Method

An improved transmission/reflection method for mm wave band is proposed in this paper. The derivation of the traditional transmission/reflection method is shown firstly. Then, the thickness resonance problem of the traditional transmission/reflection method in mm wave band is solved. The last, the improved method was used to measure the Ka band electromagnetic parameters of several materials. The experimental results were achieved, which verified the improved method in mm wave band. In the next, this method will be extended to W band.
Network Topology and Sensitivity Analysis

Network topology of electrical circuits depends on the selection of one or more independent node pair voltages or branch currents as variables. Any network can be represented as an oriented graph and the concepts of fundamental loops and cut sets can be used to find the minimum number of independent variables to find the other variables in the network. In this paper we, analyse the relation between branch currents and node pair voltages with respect to different tie set and cut set schedule. Also, we investigate the deviation in the solution of a system with perturbations using an amplification factor called condition number.
Re-thinking computation offload for efficient inference on IoT devices with duty-cycled radios

While a number of recent efforts have explored the use of "cloud offload" to enable deep learning on IoT devices, these have not assumed the use of duty-cycled radios like BLE. We argue that radio duty-cycling significantly diminishes the performance of existing cloud-offload methods. We tackle this problem by leveraging a previously unexplored opportunity to use early-exit offload enhanced with prioritized communication, dynamic pooling, and dynamic fusion of features. We show that our system, FLEET, achieves significant benefits in accuracy, latency, and compute budget compared to state-of-art local early exit, remote processing, and model partitioning schemes across a range of DNN models, datasets, and IoT platforms.
Doppler Positioning of LEO Satellites Based on Orbit Error Compensation and Weighting

Doppler positioning using the signals of opportunity (SOP) from low Earth orbit (LEO) satellites is a supplement and enhancement to the global navigation satellite system (GNSS). However, due to its non-navigation design, it could not obtain high-precision spatial and temporal information of satellite orbit, which significantly affects the accuracy of positioning results. To address this issue, the orbit error equivalent Doppler measurement error (OEEDE) of LEO satellites under the accuracy of auxiliary orbit information is modeled for the first time in this article. And according to this model analysis, a two-step improved positioning method based on orbit error compensation and weighting (OECW) is proposed to weaken the effect. Experiments using multiple groups of real acquired Iridium-Next and Orbcomm satellite signals have shown that the OECW method achieves higher positioning accuracy than existing methods, especially for the Iridium-Next satellite signals, where the horizontal positioning accuracy is improved by more than 70%. The OECW positioning method proposed in this article is of great significance for the performance improvement of LEO positioning systems.
Tunable X-rays from free electrons interacting with van der Waals materials

We theoretically predict and experimentally demonstrate highly tunable X-ray sources from free electrons - of kinetic energy ranging from a few kilovolts to a few hundred kilovolts - interacting with van der Waals (vdW) materials to generate Smith-Purcell radiation. The photon energy of the output X-rays is readily tuned via the energy of the free electrons, the composition of the vdW material target and the angle between the free electron beam and the vdW material target. As an application of this compact, tunable X-ray platform, we experimentally demonstrate the phenomenon of quantum recoil in free electron spontaneous emission.
you do it for me?”: Understanding Use-by-Proxy in Interactive Systems

“I can’t reach”, “my hands are full”, “I’m driving”—can you do it for me? If using a smartphone is challenging for a user because of either physical or cognitive encumbrances, they often ask another person to perform the desired task on their behalf. In this situation, the user with the motivation or goal to perform the task is not directly using the device but is instead working through an intermediary, a use-by-proxy, where the proxy-user has limited initiative. Through a qualitative study, we probe this use-by-proxy phenomenon. We explore triggers, frequencies, and breakdowns that confound useby-proxy interaction. We identify the challenges both for the enduser and the proxy-user (e.g., that proxy-user input is a deficient form of interaction for both the main user and the proxy user) and discuss consequences and implications for the design of this uneven collaborative interaction.