Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-axis attitude determination from vector observations

Two computationally efficient algorithms are presented for determining three-axis attitude from two or more vector observations. The first of these, the TRIAD algorithm, provides a deterministic (i.e., nonoptimal) solution for the attitude based on two vector observations. The second, the QUEST algorithm, is an optimal algorithm which determines the attitude that achieves the best weighted overlap of an arbitrary number of reference and observation vectors. Analytical expressions are given for the covariance matrices for the two algorithms using a fairly realistic model for the measurement errors. The mathematical relationship of the two algorithms and their relative merits are discussed and numerical examples are given. The advantage of computing the covariance matrix in the body frame rather than in the inertial frame (e.g., in terms of Euler angles) is emphasized. These results are valuable when a single-frame attitude must be computed frequently. They will also be useful to the mission analyst or spacecraft engineer for the evaluation of launch-window constraints or of attitude accuracies for different attitude sensor configurations.
Stochastic Greedy Two-neighborhood Search Algorithm For Hybrid Disassembly Line Balancing Problem Considering Human Posture

With the continuous development of manufacturing industry, many waste products are produced as a side effect. Recycling these waste products can significantly improve the utilization of resources and promote the green development of the environment. Establishing a manual disassembly line is one way to recycle scrap, but the mixture of multiple disassembly lines can be more effective. This paper proposes a hybrid disassembly line balance problem that takes human posture into consideration. This problem combines the U -shaped and linear disassembly line features. We establish a mathematical model to obtain maximum profit, and propose to use the stochastic greedy two-neighborhood search algorithm can solve this problem. The algorithm searches different neighborhoods to obtain the near-optimal solution. The experimental results show that the algorithm can get a good solution.
Robust Adaptive Control of Servo Systems

This paper presents the evaluation of a novel Robust Adaptive Control algorithm on a servo system. The main feature of the proposed approach is the use of an updated law based on the e-modification, endowed with a saturation function that maintains the parameter estimates within known lower and upper bounds without resorting on parameter projection techniques. The performance and advantages of the proposed update law compared to the use of a standard updated law based on the e-modification are evaluated. The experimental results highlight the advantages of the proposed robust adaptive controller. In addition, the experiments also show that the use of the proposed update law produces a smooth control signal without large peaks.
Automation of Routine Information Processes in Supply Chain: Search Methodology and Tool, Evaluation of Potential Based on Lean Manufacturing

The paper aims to introduce the data-driven methods of the routine information processes analysis in supply chain. In terms of the supply chain digital transformation, the processes discovering and evaluating their potential of automation are considered essential. The paper shows quantitative methods of detection and evaluation and approach to measure their efficiency. We focus on lean manufacturing methods including customer value / business value stream mapping to find and evaluate the information processes that need to be automized. The results demonstrate that the methods described foster reducing waste of resources and improving efficiency in supply chain information processes.
A Study of Permission-based Malware Detection Using Machine Learning

Malware is becoming more prevalent, and several threat categories have risen dramatically in recent years. This paper provides a bird's-eye view of the world of malware analysis. It also presents a brief review of malware analysis approaches, common detection types, and some basic preventive strategies from various angles. The efficiency of five different machine learning methods (Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest, Decision Forest) combined with features picked from the retrieval of Android permissions to categorize applications as harmful or benign is investigated in this study. On a test set consisting of 1,168 samples (among these android applications, 602 are malware and 566 are benign applications) each consisting of 948 features (permissions), produce accuracy rates above 80% (Except Naive Bayes Algorithm with 65% accuracy). Of the considered algorithms TensorFlow Decision Forest performed the best with an accuracy of 90%.
Computer Vision Interface for Symbolic Programming of Cartesian Motion to introduce Visually Impaired Children into Robotic Sciences

Didactic material, aimed at sightless children interested in Robotics, should provide a suitable working environment for their specific sensory capacities and instill a spatial perception of motion and an ability to describe it in a logical and structured way. In this paper, to improve the user experience learning the basics of coding robotic motion in a non-visual environment, we introduce a symbolic programming interface of assembling tokens based on artificial vision recognition that preserves the working space free of obstacles and simplifies the programming process by sliding tokens together on a flat board instead of requiring complex electromechanical connections between them. The computer vision system detects symbols and numbers based on a Boundary Object approach that extracts the main features of the captured images and assigns them to the closest class contained in a reference library. Detected symbols and numbers are identified as part of an ordered array and interpreted as commands with an argument, providing auditory assistance in the case of errors, before being executed in a real mobile robot for instant learning reinforcement. Tokens were ergonomically redesigned for children allowing a simple manipulation for easy assembling and providing tactile and visual information to allow easy recognition by users, their instructors, and the artificial vision system. Throughout the paper, we discuss the design criteria before presenting the final implementation and its first results.
Design Exploration of RISC-V Soft-Cores through Speculative High-Level Synthesis

The RISC- V ecosystem is quickly growing and has gained a lot of traction in the FPGA community, as it permits free customization of both ISA and micro- architectural features. However, the design of the cor- responding micro-architecture is costly and error-prone. We address this issue by providing a flow capable of automatically synthesizing pipelined micro-architectures directly from an Instruction Set Simulator in C/C++. Our flow is based on HLS technology and bridges part of the gap between Instruction Set Processor design flows and High- Level Synthesis tools by taking advantage of speculative loop pipelining. Our results show that our flow is general enough to support a variety of ISA and micro-architectural extensions, and is capable of producing circuits that are competitive with manually designed cores.
Maximum Power Point Tracking for direct drive Wind Turbines: An Active Disturbance Rejection Control approach

In this article, an Active Disturbance Rejection Control (ADRC) is proposed for the Maximum Power Point Tracking (MPPT) task on a Direct Drive (DD) wind turbine. The used model takes into account the flexibility of the shaft coupling the blades’ rotor with the permanent magnet synchronous generator (PMSG). The proposed linear control scheme offers a high degree of robustness and good tracking performance, despite the presence of significant disturbances and the fact that a large part of the dynamics is ignored and treated as an unknown disturbance. The ADRC method also avoids measuring the entire set of phase variables of the system. Realistic digital computer simulations are provided that test the validity of the approach.
Characteristics of Controlled Mach 2 Elliptic Jet

Mixing characteristics of a Mach 2 elliptic jet, with rectangular tabs placed along the major and minor axes are studied, at different levels of nozzle expansion. At all the pressure ratios, the jet with tabs along the minor axis experiences better mixing than the jet with tabs along the major axis. Also, at all the nozzle pressure ratios, the tabs along the major axis retard the mixing promotion, leading to core longer than the uncontrolled jet. For the range of nozzle pressure ratios studied, the mixing promotion caused by the tabs along the minor axis is the most effective for the highest studied overexpansion level corresponding to nozzle pressure ratio 4, leading to a core length reduction of 65%. The least mixing promoting efficiency for this orientation of tabs is found at the marginally underexpanded level of nozzle pressure ratio 8, at which the core length reduction caused is just 5%. The mixing promoting capability of the tabs along the minor axis is found to decrease with decrease in the adver.
Electricity Consumption Forecast of Clusters of Buildings Based on Recurrent Neural Networks

During the last decade, the relevance of improving the energy efficiency and thus reduce the energy consumption of buildings has gained momentum for many reasons. In addition to economic and sustainability reasons, an important factor is to ensure and maintain comfortable and healthy conditions inside buildings. Indeed, the study of the behavior of users inside buildings is essential to ensure comfortable and healthy conditions in living environments and cannot be avoided when defining energy saving measures. To achieve this goal, this paper presents a simulation framework for the prediction of the electricity consumption of cluster of buildings based on users' behaviors. The framework is based on model-based approaches simulating the energy consumption of buildings and statistical models representing the behavior of users. Simulated energy consumption profiles are then used to train recurrent neural networks that, based on real energy consumption data, can be used to tune the statistical and deterministic parameters of the simulation models. University campuses comprising different type of buildings are used as reference use case in this paper, as representative example of energy cluster of buildings.
The Impact of Hardware Folding on Dependability in Spaceborne FPGA-based Neural Networks

Commercial SRAM-based field-programmable gate arrays (FPGAs) are becoming popular computing platforms for building efficient Neural Network (NN) accelerators for space missions. FPGAs can implement custom NN architectures that are tailored to the requirements of the mission to improve the performance-to-watt ratio of the design. However, SRAM FPGAs are vulnerable to radiation-induced Single Event Upsets (SEUs), imposing significant design-for-reliability challenges. In this work, we study the impact of hardware folding on the dependability of Binarised NN (BNN) FPGA accelerators. Hard-ware folding configures the level of resource sharing in the design. We implemented three design versions of a BNN that performs image classification. The BNNs were generated with FINN, an open-source framework for developing quantised NNs on AMD-Xilinx FPGAs. The BNNs were implemented on a Zynq-7020 system-on-chip FPGA and tested with configuration memory fault injection experiments to estimate their SEU vulnerability. The three BNN design versions have a maximum (Max), medium (Med), and minimum (Min) folding factor, respectively. Assuming a Low Earth Orbit (LEO), our results show that the Med BNN has the highest Mean Time Between Failure (MTBF) and the Min has the lowest MTBF. However, Min has the highest Mean Executions Between Failure (MEBF) due to its high computational performance.
Sentiment Analysis of Board Secretaries’ Q&R Data

In the Internet era, due to the rapid development of investors communication with public companies, people have diversified ways to express their opinions, thus generating a large amount of data, which contains valuable information. In this paper, we use a combination of the financial sentiment dictionary and Bert to analyze the sentiment of investors’ questions based on the Q&R data of board secretaries on the platform "Easy Interactive" (http://irm.cninfo.com.cn/) launched by Shenzhen Stock Exchange, and the final accuracy rate is 92%, which is 16% higher than the traditional sentiment analysis methods. Compared with offline research, financial news, stock forums, social software, and other data, the Q&R data selected in this paper has less noise and is more intuitive. Moreover, this paper considers knowledge in the financial domain in sentiment analysis and has domain friendliness and model generalization in the financial domain by combining the financial domain sentiment lexicon with the Bert model with adversarial training.
SDNaaS: Software-Defined Networking as an IXP Service

Network operators need to manage the full lifecycle of controller instance clusters to ensure a high-performance and reliable distributed control plane service for software-defined networks (SDN). However, setting up and configuring multiple controller instances is a resource-intensive and complex process. Outsourcing to a third-party provider may alleviate the challenges associated with managing and orchestrating hardware and software infrastructure required for operating a distributed SDN control plane. We present SDN-as-a-Service (SDNaaS), an Internet exchange point-based SDN control plane service provider. We analyzed public IXP datasets and show that these Internet infrastructures possess desirable characteristics for hosting mission-critical network services. By leveraging IXP strengths, and implementing techniques for coordinating disparate resources (aggregating capacity), compartmentalizing controller services (decoupling controller functions into distinct yet composable services), and client-side resource discovery (delegating controller selection to switches), SDNaaS can provide the capability for network operators to scale controller processing capacity by as much as 2.6x as compared to single site while concurrently enhancing system availability.
Federated Learning approach for Auto-scaling of Virtual Network Function resource allocation in 5G-and-Beyond Networks

This paper deals with Network Slicing-based 5G Networks to support the varying demands of customers and for efficient resource utilization. A network slice can be defined as a set of network and virtual network function (VNF) resources deployed across multiple administrative domains. Here, multi-domain refers to multiple infrastructure providers spread across different geographic regions. Slice demands and QoS requirements may vary dynamically, which can be satisfied by scaling the allotted VNF resources. The VNF scaling problem can be posed as a time series forecasting problem that predicts future VNF resources based on the slice traffic demand. 5G deployments with multiple domains pose a serious challenge in terms of data privacy since one domain may need access to the data of another domain for efficient resource allocation using the conventional forecasting approaches that requires data aggregation. In this paper, we use the federated learning approach in which the training data remains within the respective domains but learns a shared model by aggregating locally-computed updates. We evaluate the applicability of federated settings in VNF scaling using two state-of-the-art deep learning models, Long Short-Term Memory (LSTM) and Gated recurrent units (GRU). We present a comparison of the performance of the proposed federated system against the centralized system. Additionally, synthetic data in each domain has been generated using Generative Adversarial Networks (GAN) to improve the forecasting results.
Efficient Online Learning Based Cross-Tier Uplink Scheduling in HetNets

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called OLIUS, that can deal with the exponentially growing state and action space. Furthermore, OLIUS is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of OLIUS and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.
Terabytes and Terabits/s Packet Caching in ICN Routers using Programmable Switches

This paper designs a per-packet caching mechanism of information-centric networking (ICN) routers on a combination of a programmable switch and computers, where DRAM devices of the computers are used to cache data packets. The forwarding bottleneck for the routers is the switch ports’ bandwidth consumed by sending data packets between the switch and the computers. In order to resolve the bottleneck, the paper proposes two algorithms to reduce traffic between them: cache admission and lazy responses. The prototype implementation demonstrates the feasibility of Tbps- and terabyte-class caching.
Resource Allocation and Retransmission Scheme for URLLC in Industrial Wireless Networks with Mixed Traffic

Industrial Wireless Networks (IWNs) are expected to guarantee the Ultra-Reliable Low-Latency Communication (URLLC) in future manufacturing systems. However, due to limited resources and unstable radio environment, it is an inherent challenge to improve transmission reliability while maintaining low latency. This paper considers the uplink URLLC of mixed traffic (deterministic traffic and sporadic traffic) in IWNs with multiple channels. For deterministic traffic, an Automatic On-demand Retransmission scheme based on NACK REpetitions (AOR-NRE) is proposed. For sporadic traffic, a Flexible Repetition Coding-based Contention scheme (FRCC) is proposed. Then, the adaptive decision for mixed traffic on a time slicing based scheme and a frequency slicing based scheme is explored based on reliability analysis of AOR-NRE and FRCC. Finally, the advantages of the proposed retransmission schemes over existing works and the significance of adaptive decision are demonstrated via numerical results.
Modeling and Control for a Class of Non-linear Systems With Non-affine Polynomial Control Input

In this work we deal with the control of a non-affine nonlinear system in the control input. Said system is a thermoelectric module (TEM) powered by a buck-type DC/DC converter. The mathematical model of the module is polynomial in the control input. The proposed control technique is the LQR control plus an integral action. Said controller is designed on the basis of an approximate linearization of the combined reducer-TEM dynamic model. The effectiveness of the proposed technique is verified through experimental results obtained with an experimental platform designed for this purpose.
Identification of Influencers to Analyze User Loyalty in the Implementation of Megaprojects

The article studies the concept of influencers and methods of searching for them within graph networks. The search of influencers in YouTube social network for the purpose of creating a user loyalty program during the implementation of mega projects is considered as an example. This work was supported by RFBR grant No. 20-010-00708\22.
Memory-efficient RMT Matching Optimization Based on MBitTree

Reconfigurable match tables (RMT) is a pro-grammable pipeline architecture for packet processing. The ar-chitecture searches for action instructions by matching keywords in the packet header vector to modify the packet header. Among them, exact matching uses hash matching, while mask matching is currently more widely implemented using the Ternary Content Addressable Memory (TCAM). TCAM has high classification performance, but its high cost and power consumption make it difficult to scale to large-scale rule sets. MBitTree, a decision tree based on multi-bit cutting implemented on FPGA, is considered to be one of the most scalable packet classification algorithms due to its fast classification speed and low memory footprint. Therefore, MBitTree is applied in the matching action stage of RMT to improve the mask matching and reduce the memory overhead of RMT. According to the characteristics of RMT pipeline, MBitTree is mapped and optimized to improve pipeline efficiency and make full use of hardware resources. In addition, for the first time, we propose to move the key extractor in each stage of RMT to the action engine of the previous stage to save the memory overhead and processing time caused by the key extractor in each stage. We implement a prototype RMT based on MBitTree matching on FPGA, and the implementation results show that our method can achieve a throughput of over 200 Gbps for 10K rule sets and greatly reduce the memory overhead.
Model Predictive Control for Single-Phase Cascaded H-Bridge Five-Level Inverter

The model predictive control (MPC) for single-phase cascaded H-bridge (CHB) five-level inverter is presented. The discrete-time model of the CHB five-level inverter as well as the MPC algorithm proposed for this system are explained in detail. These are programmed and simulated digitally using the Matlab/Simulink software to observe the performance of the inverter in terms of the total harmonic distortion (THD) of the current and the tracking of its reference, especially when the current reference presents sudden changes. The developed MPC, which is discrete in nature, is compared to a level shifted pulse width modulation (PWM) control scheme, as it is one of the most widely used modulation strategies in this type of multilevel converters, to perform a comparative analysis. The simulation results show that the MPC algorithm presents an excellent performance, especially when sudden load changes occur and in the THD level that the current presents.
Helpfulness Prediction for VR Application Reviews: Exploring Topic Signals for Causal Inference

Recently, with the development of stereo display and 3D graph-ics technology, virtual environments and applications are growing rapidly, e.g., video conferencing, and virtual reality (VR) applications. These are several E-commerce platforms that are designed for the transactions of VR applications. Consumers on these platforms can comment on a VR application after purchase. In this paper, we attempt to predict the helpfulness of these VR application reviews. To this end, we propose a topic-causal regression model that explores the influence of topic features in VR application reviews and nu-merical information on helpfulness. Specifically, we first apply the most classic topic model, Latent Dirichlet Allocation, to extract the topic signals in VR application reviews. Then, we construct a topic regression for causal inference. We perform extensive experiments on a real-world dataset collected from Oculus. The experimental results demonstrate that our model can estimate regression weights for topic factors and analyze their influence on the helpfulness of VR application reviews.