Miracle-dz/UniFAR-FTUs huggingface dataset:
Congestion Control based on Geographic Routing Algorithm for Wireless Sensor Networks

Resource control is a branch of congestion control in wireless sensor networks (WSNs). The congestion-oriented resource control schemes in WSNs often re-route traffic loads to avoid the congested areas. The re-routing overhead for congestion avoidance is particularly large in large networks. Thus, this kind of congestion control lacks of strong scalability. In this paper, a congestion control based on a geographic routing algorithm (CcbGr) for WSNs is proposed. It determines the congestion status of a node using thresholds of the queue length and the changing rate of the queue length. We adopt a geographic forwarding scheme in avoiding congestion. A new algorithm of distance update is also proposed to by-pass routing holes which are common for geographic routing. When a node is on the edge of a hole, it will increase the distance between itself and the sink according to specific rules. Simulation results show that CcbGr can effectively address network congestion with high scalability.
Live Trials with an Innovative Prototype Dedicated to Arrival Flow Management in Paris

In a previous paper, we presented the design of an innovative HMI adapted to air traffic flow management named IODA. Even though IODA was not initially foreseen to become an operational product, the very positive feedback by Flow Managers and ATM experts confirmed the potential to drive the product towards conducting live trials in an operational context. Today, Flow Managers, such as Tower/Approach Supervisors in Paris-CDG, Paris-Orly airports and Flow Management operators in Paris ACC, have to manage several interfaces to retrieve operational information, to monitor traffic flows and to manage arrival sequencing. This situation is making their tasks more difficult, the management of the different tools is complex and increases the risk of missing data and opportunities that would directly optimize the operational situation. Furthermore, current displays do not provide a full overview of the arrival traffic (i.e. geographical flows or metering points) nor do they propose combined tools or clear indicators to facilitate the monitoring. IODA enables the integration of multiple data sources coming from various origins and allows to present the information into a more integrated and innovative display, bringing the ability to access efficiently to the most relevant features to perform the tasks. As it had already been explained in a previous paper, user-centered and iterative design process, involving a multidisciplinary work team, enabled to design and to develop the final prototype. The prototype was then installed in Paris ACC (for the Flow Supervisor Position) and in Paris-Orly Control Tower (for the Tower Supervisor position) during planned runway works that required the closure of one to two runways. The associated decrease in arrival runway capacity gave a good opportunity to measure the benefits derived by the use of this new HMI during live Extended AMAN operations. In this paper, we are presenting the context of those 6-months live trials and the operational use that was foreseen when the prototype was designed. In a second part, we will address some considerations regarding the training and the installation of the prototype. Then, we describe the results of the experiments built thanks to questionnaires that were filled during the sessions, interviews, observations & debriefings. The live trials indeed demonstrated positive results, mainly in terms of improved collaboration, environment and flight efficiency. In a last part, we will address the foreseen evolutions of the prototype in the context of the arrivals for Paris-CDG airport. This context proposes new challenges and opportunities and the paper will highlight different scenarios where the IODA HMI could bring improvements to the current situation. In conclusion, we are highlighting the fact that even though there is room for improvements, the prototype definitively met the expectations and we propose an analysis of some of the reasons for its success.
PMSM Torque Ripple Minimization Using an Adaptive Iterative Learning Control*

Parasitic torque pulsations existing in permanent magnet synchronous motors (PMSMs) degrade their performance, particularly at low speeds. In this paper, an adaptive iterative learning control (ILC) strategy is proposed for torque ripple minimization in a PMSM drive system. An analysis is conducted to study the impact of ILC gains on the steady-state and transient performance of the system. Accordingly, a real-time tuning method based on multi-variable slidingmode extremum seeking (MSES) is proposed to adjust the ILC gains. The proposed controller is applied to a PMSM and its performance is verified by comparing the results with a similar controller using a non-adaptive ILC scheme. The results demonstrate the effectiveness of the proposed ILC scheme in achieving lower torque ripples and faster transient performance.
Improving Image Quality of Single Plane Wave Ultrasound via Deep Learning Based Channel Compounding

The emergence of data driven approaches such as Deep Learning has led to novel application of various aspects of science and engineering. It has recently entered the field of ultrasound image beamforming. In this work we investigate neural networks tailored to create images of the quality of multiple compounded plane wave excitations from the data of the central angle (0°) excitation only. The proposed network is used to produce pixel-wise weights to weigh a standard delay-and-sum image from all channel data available to a pixel. It is found to produce higher quality images than the classical reference reconstruction from the 0° angle data.
Simulation of Nonlinear Mechanical Effects in kHz and MHz Range Resonators with Finite Element Method and Harmonic Balance

The use of finite element method (FEM) with a harmonic balance (HB) solver is investigated for analyzing and optimizing nonlinear MEMS resonators vibrating in kHz and MHz range. The FEM+HB combination allows the inclusion of both elastic and inertial nonlinearities, which makes the method attractive for analyzing resonating structures. In particular, vibrating cantilevers have distributed inertial and elastic nonlinearities that have nearly equal but opposite magnitude; therefore, both effects must be included for an accurate analysis. The benefits of FEM+HB are demonstrated by optimizing nonlinearities in 32 kHz tuning fork resonators resulting in structures with nearly zero drive level dependency (DLD). The FEM+HB can also account for material nonlinearities. By comparing the FEM+HB results to experimental data, the magnitudes of third- and fourth-order elastic constants for heavily phosphorus doped silicon resonators are estimated.
Control of Single-Phase Solar PV-BES Microgrid

In this paper, a microgrid based on solar photovoltaic (PV) array and a battery energy storage (BES) is presented, which operates in an islanded mode and the grid connected mode. It changes the mode of operation seamlessly, while supplying uninterrupted power to the load. A cascaded SOGI (Second-Order-Generalized-Integrator) based control is used in GCM. The cascaded SOGI is used to extract the fundamental component of the nonlinear load current. A PV array power feed-forward component is used for improving its system dynamics. An optimal power from the solar PV array is acquired by using an I&C (Incremental Conductance) technique. In IM, a voltage control scheme is utilized, which controls the frequency and voltage at point of common coupling (PCC). The efficacy of cascaded SOGI based technique is demonstrated through test results at various conditions such as vaying load, changing solar irradiance and transition between the modes of operation.
Spatiotemporal variation of microbial community structure and physicochemical parameters in grass carp culture ponds

Abstract Microbes, as an important part of aquaculture ponds, can improve the recirculation of nutrients. This nearly 1-year-long study aimed to understand the factors and mechanisms that cause temporal changes in the concentration of microbiological indicators and nutrient concentration in the grass carp pond water of Chongqing Jiguan Aquaculture Limited. The study was comprised of five sampling campaigns, with samples spaced 2 months apart. Water temperature, dissolved oxygen and pH were measured in the field during sampling, and the concentration of nitrates, nitrites and phosphates were determined afterward in the laboratory. Subsequent analysis of 16s genome sequencing classified microorganisms into 32 phyla and 481 genera and revealed the core composition of the bacterial community for each season. Principal component analysis revealed that pathogens (Pseudomonas and Flavobacterium) are a significant negative correlation with water temperature and a positive correlation with ammonium concentration and dissolved oxygen. Bacillus and Streptomyces population size was negatively correlated with total phosphorus, while Lactobacillus and Bacteroides populations were positively correlated with water temperature and negatively correlated with ammonium salt. These findings reveal a significant relationship between microbial communities and the physicochemical parameters in aquaculture environments, providing a basis for further improving the aquaculture environment.
PinSout: Automatic 3D Indoor Space Construction from Point Clouds with Deep Learning

With the development of Light Detection and Ranging (LiDAR) technology, point cloud data is a valuable resource to build three-dimensional (3D) models of digital twins. The geospatial 3D model is the principal element to abstract a geographic feature with geometric and semantic properties. The 3D model data provides more efficiency to handle, retrieve, exchange, and visualize geographic features compared to point clouds. However, the construction of 3D models, especially indoor space where various objects exist, usually necessitates expensive time and manual labor resources to organize and extract the geometry information by authoring tools. This demonstration introduces Point-in Space-out (PinSout), a new framework to automatically generate 3D space models from raw 3D point cloud data by leveraging three open-source software: PointNet, Point Cloud Library (PCL), and 3D City Database (3DCityDB). The framework performs the semantic segmentation by PointNet, a deep learning algorithm for the point cloud, to assign a target label to each point from a point cloud, such as walls, floors, and ceilings. It then divides the point cloud into each label cluster and computes surface elements by PCL. Each surface is stored into a 3DCityDB database to export an OGC CityGML data. Finally, we evaluate the accuracy with two datasets: a synthetic point-cloud set of a 3D model and a real dataset taken from the exhibition halls.
Detection and Monitoring of Microwave Ablation by Ultrasound Imaging Based on Convolutional Neural Network

Microwave ablation (MWA) is widely used in clinical treatment, but conventional ultrasound B-mode monitoring imaging method cannot provide real-time changes in the ablation area accurately during thermal ablation. With the further maturity of classification technology of convolutional neural network (CNN), the CNN will certainly play an important role in the processing and analysis of medical images. In this study, we proposed and evaluated an US image based on CNN architecture for the detection and monitoring of thermal lesions induced by MWA in porcine liver. The values of the ablation area under the receiver operating characteristic curve for US image based on CNN and B-mode image were 0.8728 and 0.6904, respectively. The results show that it is feasible to use convolutional neural network to monitor the changes of ablation area during MWA.
A Comprehensive Analysis of Grid Synchronization Systems for Power Converters

This paper presents our investigation of the second-order and third-order systems in the grid synchronization applications. The generalized second-order and third-order synchronization systems are previously proposed based on the transfer function approach. However, this approach is based on the necessary conditions of the two signals that are always 90◦ difference in phase and are equal in magnitude only at the grid frequency. A grid synchronization system, in the correct form of the generalized system, cannot work if its poles are on the right half-plane. Thus, in this paper, the constraints for the generalized systems are derived from the system stability aspects. Under these constraints, the real parts of system poles are negative and guarantee the proper operations of the proposed systems. The simulation results and experimental results have verified the theoretical analysis. The proposed generalized systems are able to generate two perfectly orthogonal outputs to identify the grid information, i.e. voltage magnitude and phase angle. In addition, the generalized third-order system shows its excellent performance under distorted grid conditions.
Role of Asynchronous Motor Modelling in Driven Railway Wheelset Dynamical Simulation Model

In this paper, we proposed an improvement in existing Asynchronous Motor (ASM) Simulink model, considering the possibility of replacing the existing ASM mathematical model based on dq transformation, with another proposed one based on ABC natural coordinate frame. This idea leads us to consider a replacement of standardly implemented PI controllers in control of the torque delivered from the traction motor, with a non-linear model predictive control (MPC). We expect not only better control results, but also a better selection and use of directly measurable quantities in the detection of an unsuitable operating condition such as ripple in torque, loss of adhesion linked with loss of power decreased to select the best voltage vector to control the torque of the rail wear, etc.
Deep Learning Approach for LOS and NLOS Identification in the Indoor Environment

Due to confined spaces and various obstacles e.g. walls, furniture, indoor environment may be considered as a harsh and disturbing in terms of the indoor radiocommunication services operation. The given paper presents FNN (Feedforward Neural Network) method for LOS (Line-Of-Sight) and NLOS (Non-Line-Of-Sight) identification which may support mitigation of such a negative influence. Described FNN architecture was evaluated based on a real indoor measurements collected with the use of the UWB (Ultra Wideband) radio modules.
Parameters Design and Stability Analysis of Weak-Grid Interfaced Inverter with Droop Control

Droop-controlled inverter (DCI) is known for appealing characteristics in load sharing and stability enhancing. By simulating the external droop feature of synchronous generator, the DCI can also naturally offer necessary frequency/voltage regulation or provide active/passive power support when interfaced to the utility grid. However, with the growing penetration of distributed power generation system (DPGS), the large impedance in weak grid may arouse unexpected oscillation problem. In order to evaluate the stability performance of the DCI when it is interfaced to weak grid, the sequence- impedance model of DCI is established for the first time in this paper. The controller parameters of DCI are designed step by step under the theoretical guide. On this basis, the interactive oscillation risk between DCI and weak grid is analyzed in detail through Nyquist criteria. The results show that DCI exhibits excellent adaptability in weak grid integration. All these theoretical conclusions are tested and verified via simulations and experiments eventually.
Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.
Orthogonal Signal Generation based PLL using Arbitrary Order Exact Differentiator with Inherent Disturbance Rejection for Single Phase Systems

Use of Derivative based PLL is one of the simplest and fastest solution for phase-locked loops in single phase systems. The dynamics of Derivative-PLL are comparatively better than other methods, but it exhibits high frequency noise amplification and also under performs in non-ideal grid conditions. In this paper, a robust arbitrary order exact differentiator is proposed as a solution to generate noise free, fast and exact orthogonal signal by utilizing second order generalized differentiator method which is used further to formulate a PLL that is immune to high frequency noise and performs well under various disturbances in voltage, frequency, phase, harmonics, noise and DC-offset in grid voltage. The proposed PLL is modeled using MATLAB/SIMULINK and is tested for standard grid conditions. Typical results are reported and are compared with existing PLLs. It is found that the proposed method is not only simple and immune to noise and disturbances but also superior compared to other PLLs.
Generalized ZPUC Topology for Modular Multilevel Converter Applications

Z packed U-cell (ZPUC) is a novel generalized topology of multi-level converter which is appropriate to generate numerous voltage levels through single DC source. This topology is located as a submodule in modular multilevel converters (MMCs) aimed to increase the voltage levels and voltage stress reduction in switches simultaneously. Voltage balancing integrated with phase shift pulsed width modulation (PS-PWM) strategy without using external control system is employed in this paper to balance the voltage of flying capacitors (FCs). Matlab- Simulink is employed to simulate the ZPUC converter in three- phase system in stand-alone and grid connected mode. In addition, experimental tests are implemented on a 3 kVA prototype of ZPUC topology to validate the performance of the converter.
Construction and Optimization of Digital Twin Model for Hardware Production Line

A digital twin system about hardware production line is designed in this paper. Based on 3D modeling and visualization technology, a model database is established to meet the needs of production line. Genetic Algorithm is used to improve the process layout, which raises the equipment utilization. In order to achieve better obstacle avoidance, the robot path planning is carried out to avoid interference. By combining the above results, the digital model is built in Demo3D software to realize the simulate and virtual debugging. The result shows that this system of hardware production line can complete the debugging of real physical equipment in virtual environment. The feasibility and practicability of this method are verified.
Interpolation-Based Switching and Conduction Losses Model of GaN Transistors

Gallium nitride (GaN) transistors have demonstrated their potential as a high switching frequency, high power density, and high-efficiency power device. There has been substantial research undertaken on the switching and conduction losses calculation, where most studies have calculated them using an analytical approach. This paper presents an interpolation-based method to calculate switching and conduction losses. This interpolation method employs a combination of the GaN transistor and driving parameters such as parasitic capacitances, parasitic inductances, gate resistors, drain-to-source voltage, instantaneous current, and temperature. A value for switching loss is considered as a base value and the different combinations of circuits will be reached by using interpolated equations. The second aim of this study is to investigate the temperature-dependency of the ON state resistance: two values at different temperatures are considered and ON resistance will be obtained by linear interpolation. Finally, the accuracy of the proposed model is validated by using some scenarios on LTSPICE simulations.
Design and Fabrication of Focused Ultrasonic Transducers Using P(VDF-TrFE)/BT Single-crystal Micro-platelets Composite Film

This work developed a high performance P(VDF-TrFE)/BaTiO3 single-crystal micro-platelets composite film for ultrasonic transducers. The (001)-aligned BaTiO3 (BT) single-crystal micro-platelets were prepared by a two-step molten salt synthesis technique and then dispersed into P(VDF-TrFE) matrix by the layer-by-layer self-assembly technology. The prepared composite film exhibited improved piezoelectric and dielectric properties, with piezoelectric constant about 52 pC/N, and relative permittivity about 55 at 100 Hz, which would definitely contribute to better transducer performance as compared with traditional PVDF copolymer.
A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems

The distribution of measurement noise is commonly considered as an assumed Gaussian model in power systems, but this assumption is not always true in reality. This article introduces a distributed maximum-likelihood-based state estimation approach for multiarea power systems using the student’s <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-distribution measurement noise model. The <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-distribution has the property of “thick tail” to better model the occurrence of outliers and is fairly flexible to model different noise statistics. The finite-time average consensus algorithm is utilized in conjunction with an influence function to realize the proposed distributed approach within a totally distributed framework. Based on the local measurement residuals and the limited information exchanged with neighboring areas, each local area can obtain the global optimum system-wide robust state estimates, while the existing distributed state estimation methods can only get local estimates. Moreover, the communication scheme is more flexible and can be totally different from the transmission lines between local areas. Simulations tested on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed distributed approach.