Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Detection Probabilities and Real-World Testing of a Human Immunity Inspired Intrusion Detection System

In the paper, a study of detection probabilities and experimental tests of an intrusion detection system (IDS) utilizing the artificial immune system (AIS) based on the negative selection approach are presented. The algorithm uses binary strings called receptors to detect intrusions in programs in the operating system. In the work, a statistical approach is used to calculate the probabilities of detecting the intrusions and the results are compared with the experimental tests. New experiments are conducted using a virtual machine as the environment to check the effectiveness of the proposed IDS. For this purpose, real-world malware samples are launched in the virtual machine. The research of the IDS is then presented, analyzed and concluded.
Use of Smart Metering Data for Distribution Network Operational Status Assessment

The initial scope of smart meter installations were to provide energy measurements for billing purposes. However, the exploitation of these measurements could create new paradigms for future distribution grid management. This paper presents a framework developed in the eeRIS (electric energy Residential Informational System) research project that could help distribution network operators incorporate smart metering data into their day to day business. These important data can be used to evaluate the quality of voltage supplied at each customer as well as to detect abnormal situations on the distribution grid. It is shown that smart metering data on the low voltage (LV) network can be used to mitigate several abnormal states occurring on the medium voltage (MV) network through distribution management system (DMS) functions and to detect tripping of non-telecontrolled protection devices. These functions were validated on test cases from the Greek distribution network.
A Simple Impedance Reshaping Method for Stability Enhancement of Grid-Following Inverter Under Weak Grid

The conventional vector current control (VCC) scheme has been widely used in the grid-connected inverter systems. However, it suffers from power limitation problems under weak grid conditions. This paper reveals that the dynamic effect of the phase-locked loop (PLL) is the main reason for destabilizing the small-signal stability of the grid-following inverter. The positive feedback introduced by the PLL can be observed from the small-signal control structure. Reshaping the converter output impedance can counteract the positive feedback and reduce the negative resistance in the impedance model. A novel impedance reshaping method is proposed in this paper, which can enhance the small-signal stability and extend the stability range of the system. Simulation results verify the effectiveness of the proposed method.
Optimal Reactive Power Control of Smart Inverters: Vestfold and Telemark Regional Network

The growing installation of distributed energy resources (DER) in the network allows an active interaction between the transmission and distribution system operators. This interaction is possible mainly by the power electronic converters (PECs) incorporated in the DER, used as an interface with the network. The PECs, also known as smart inverters, offers excellent controllability features and allow DER to provide procurement services to the distribution and transmission network. This paper focuses on evaluating the reactive power control features of the smart inverter considering eight control local modes following the IEEE 1547-2018 standard. Moreover, it is proposed an optimal reactive power controller able to minimise the active power losses in the network by optimally adapting the reactive power injection/absorption of the smart inverter. This optimal controller ensures all the busbar voltages are inside the permissible limits, and the reactive power limits of the smart inverter are enforced. The Vestfold and Telemark distribution network model was implemented using DIgSILENT PowerFactory to evaluate the optimal and the eight local reactive power control modes. Simulation results show the proposed optimal reactive power control is the best option to offer the service of active power loss minimisation using smart inverters for the daily steady-state performance of the DSO network.
To Digital Technologies of Patent Processing for Development of Critical Products

The current picture of digitalization of the world’s economy is quite accurately conveyed by the processes of anti-epidemic measures, distance learning and work, development and use of appropriate vaccines. While rich countries can successfully provide their residents with anti-epidemic remedies based on modern information or communication and biological technologies, poor countries usually need international assistance in the development and application of such technologies. The development of advanced technologies requires not only entrepreneurial activity, but also the social organization and institutional capacity inherent in rich countries. The work on empirical data highlights the current problem of the relationship between antitrust regulation and regulation of patent funds.
A Real-Time Gait Phase Recognition Method Based on Multi-Information Fusion

In this paper, a novel recognition method of multi-information fusion is proposed to improve the recognition accuracy of the gait phase. Firstly, a multi-information wireless multi-channel gait information acquisition system is built. Then, the system collects multiple gait information including plantar pressure information, joint angle information, and sEMG information, and uses a deep learning convolution neural network (CNN) for fusion to quickly and accurately identify the gait phase. Finally, three kinds of speed task experiments were carried out on three healthy subjects, and different models were compared to study the ability of multi-information used in gait phase recognition at different speeds. The experimental results verify the feasibility and superiority of the method.
Self-learning Control for Active Network Management

Active network management (ANM) using power electronic devices will become an essential tool for distribution network operators to deal with the variability of a large number of low-carbon technologies. To enable ANM, this paper proposes a control scheme based on deep reinforcement learning, as an alternative to traditional optimisation. The algorithm uses only a small number of network measurements and can learn approximations of optimal control actions, identified in offline simulations, via a neural network. Once trained, the control scheme chooses power converter set-points that can, for instance, even out loadings on different substations in real-time without the computational burden of high-level optimisation. The performance of the proposed control algorithm is validated against the optimal power flow (OPF) using data from real low-voltage networks. The results show that the solution and benefits are comparable to those obtained by the OPF.
Demonstration of Mechanisms for Coprecipitation and Encapsulation by Supercritical Antisolvent Process

Supercritical antisolvent process is a proven method for micronization and encapsulation of solute(s) dissolved in a solvent. When solution contains one solute, it is micronized to produce small particles; when solution contains two solutes, they either coprecipitate, or one of them encapsulates the other. Experiments with several systems for micronization and encapsulation have been described in the literature. In this work, a model has been developed for the process in which supercritical carbon dioxide flows countercurrently or cocurrently to an atomized solution. The model includes mass transport of the solute(s) and carbon dioxide, supersaturation, nucleation, and growth of particles of solute(s). Criteria for coprecipitation and encapsulation were identified, and on the basis of model predictions a new mechanism is proposed to explain the criteria. A solute encapsulates another solute if supersaturation of the former becomes high near the end of the process and it grows on the latter. If high supers.
Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal

While deforestation has traditionally been the focus for forest canopy disturbance detection, forest degradation must not be overlooked. Both deforestation and forest degradation influence carbon loss and greenhouse gas emissions and thus must be included in activity data reporting estimates, such as for the Reduced Emissions from Deforestation and Degradation (REDD+) program. Here, we report on efforts to develop forest degradation mapping capacity in Nepal based on a pilot project in the country’s Terai region, an ecologically complex physiographic area. To strengthen Nepal’s estimates of deforestation and forest degradation, we applied the Continuous Degradation Detection (CODED) algorithm, which uses a time series of the Normalized Degradation Fraction Index (NDFI) to monitor forest canopy disturbances. CODED can detect low-grade degradation events and provides an easy-to-use graphical user interface in Google Earth Engine (GEE). Using an iterative process, we were able to create a model that provided acceptable accuracy and area estimates of forest degradation and deforestation in Terai that can be applied to the whole country. We found that between 2010 and 2020, the area affected by disturbance was substantially larger than the deforested area, over 105,650 hectares compared to 2753 hectares, respectively. Iterating across multiple parameters using the CODED algorithm in the Terai region has provided a wealth of insights not only for detecting forest degradation and deforestation in Nepal in support of activity data estimation but also for the process of using tools like CODED in applied settings. We found that model performance, measured using producer’s and user’s accuracy, varied dramatically based on the model parameters specified. We determined which parameters most altered the results through an iterative process; those parameters are described here in depth. Once CODED is combined with the description of each parameter and how it affects disturbance monitoring in a complex environment, this degradation-sensitive detection process has the potential to be highly attractive to other developing countries in the REDD+ program seeking to accurately monitor their forests.
Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network

Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
An Improved Cloud Masking Method for GOCI Data over Turbid Coastal Waters

Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.
A Comprehensive Control Strategy for Multibus Nanogrids With Power Exchange Between Prosumers

Microgrids/nanogrids of prosumers must be able to adapt to new business opportunities related to energy trading. Therefore, highly accepted hierarchical control strategies should be modified to allow deliberate energy interchange among prosumers, even when the microgrid they are integrated in, operates islanded. This is the main contribution of this paper, as energy exchange between prosumers is rarely addressed when they are islanded from the main grid. This work proposes a comprehensive hierarchical control strategy for a multibus nanogrid of prosumers, each of them having photovoltaic generation, battery energy storage systems and loads, in such a way that different voltage and power setpoints for each prosumer allow such energy interchange. Power flow inside the nanogrid is controlled to guarantee the technical feasibility of the obtained setpoints. The proposed control strategy is defined and experimentally validated in both islanded and grid-connected modes, by means of a laboratory-scale prototype. In addition, a smooth transition between the two modes is demonstrated. An adequate capability of the power converters to follow setpoints is proved, with acceptable transient behavior.
A Bi-Directional LSTM Network for Estimating Continuous Upper Limb Movement From Surface Electromyography

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).
Short-Term Electrical Load Prediction Using ANN-Backpropagation

Electrical energy is a vital requirement in Indonesia. As the main electricity supplier, PLN (Indonesian Electric Power Company) is required to always provide the electricity needed. The instability of electricity consumption causes future capacity planning to be uncertain or nonlinear. Based on previous studies, the backpropagation type Artificial Neural Network (ANN) method has good performance in nonlinear electrical loads prediction for the short term. Yet, many variations of hidden layer in the ANN architecture causes the trial error process takes longer. So, this research is aimed to observe the hidden layer behavior in the learning of the ANN-Backpropagation method. This method is successfully applied as a simulation program for short-term electrical loads prediction. By varying the hidden layers, the best architecture is obtained by using 2 hidden layers, with 10 neurons each layer. The results of network test get an average error of 6.74% which is smaller than 8.24% for the average error of the coefficient method used by PLN.
Production and extraction of microbial lipids for enzymatic biodiesel synthesis = Produção e extração de lipídeos microbianos para síntese enzimática de biodiesel

The quality of biodiesel from lipids produced by the yeast Candida sp. LEB-M3 was predicted by the use of mathematical models for parameters that specify quality as a function of the fatty acid profile. The lipid production was studied according to the Experimental Design methodology, for different cultivation conditions for agitation and aeration. Lipidic compositions were affected by the cultivation conditions and the agitation presented a positive effect for the formation of monounsaturated fatty acids and negative effect for saturated fatty acids. Aeration had a positive effect on the formation of polyunsaturated fatty acids. According to the predictions by the mathematical models, the cetane number varied from 61-67, the oxidative stability from 11-17 hours, the iodine index from 55-75 gI2/100g, density from 852-868 Kg/m 3 . All cultivation conditions led to lipid compositions, whose predicted bioparameter values indicate that biodiesel from this lipid source should present current standard quality.
Retrieving Three-Dimensional Large Surface Displacements in Coal Mining Areas by Combining SAR Pixel Offset Measurements with an Improved Mining Subsidence Model

Interferometric synthetic aperture radar (InSAR) technology can obtain one-dimensional surface displacements in the radar line of sight (LOS). In the field of mining subsidence, large 3D movements often occur at the same time, and hence InSAR derived one-dimensional LOS displacements can hardly reflect the actual surface motion in mining areas. To realize the monitoring of three-dimensional large surface displacements in mining areas, a method for monitoring three-dimensional large surface displacements in mining areas that combines SAR pixel offset tracking (OT) and an improved mining subsidence model is proposed in this article. First, a new functional relationship between surface subsidence and horizontal movement combined with the characteristics of the overburden rock stress and the deformation characteristics of the fractured rock mass in coal mining areas is established. Then, a three-dimensional surface deformation model is established based on the proposed relationship between surface subsidence and horizontal movement and the radar projection equation, and finally, the optimal parameters of the deformation model are inverted iteratively using LOS deformation results obtained by OT method to retrieve the three-dimensional large displacements of the surface. The significant advantage of the method proposed in this article is that it can accurately acquire the 3D large surface displacements using only two SAR amplitude images with the same imaging geometry. To verify the accuracy and reliability of the proposed algorithm, two scenes of high-resolution spotlight TerraSAR-X images are used in this paper to conduct a three-dimensional surface displacement monitoring experiment on a working panel in the Daliuta mining area in Shaanxi Province, China, based on the proposed method. Experimental monitoring results show that the maximum surface subsidence is approximately 4.5 m, and the maximum horizontal movements in the strike and dip directions are approximately 1.4 m and 1.2 m, respectively. Using GPS measurements to verify the monitoring results, the root mean square error (RMSE) of vertical subsidence is 6.8 cm, and the RMSE of horizontal movement is 7.1 cm. Compared with those in the original mining subsidence model, the accuracies of vertical subsidence and horizontal movement in the proposed model are increased by 28.2% and 37.5%, respectively, which proves the reliability and accuracy of the proposed method.
CatChatter: Acoustic Perception for Mobile Robots

There are many examples in nature of animals using acoustics to understand and navigate the world around them. Inspired by this, we train an image-to-image translation network to learn the mapping from recorded chirps and echos from an environment to a 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> depth map of the environment. This work is focused on expanding on the capabilities of BatVision in a number of ways. We first propose various methods for data augmentation to help the model generalise on less data. We also propose changes to the model architecture to improve performance and training stability. Finally, we investigate the feasibility of 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> scene reconstruction by using more microphones and lidar based 3D SLAM data as ground truth for training the model.
Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions

Visible and infrared binocular all-sky-view cameras can provide continuous and complementary ground-based cloud observations. Accurate angular calibration for every pixel is an essential premise to further cloud analysis and georeferencing. However, most current calibration methods mainly rely on calibration plates, which still remains difficult for simultaneously calibrating visible and infrared binocular cameras, especially with different imaging resolutions. Thus, in this study, we present a simple and convenient angular calibration method for wide field-of-view visible and infrared binocular cameras. Without any extra instruments, the proposed method only utilizes the relation between the angular information of direct sun lights and the projected sun pixel coordinates to compute the geometric imaging parameters of the two cameras. According to the obtained parameters, the pixel-view-angle for the visible and infrared all-sky images is efficiently computed via back projection. Meanwhile, the projected pixel coordinates for the incident lights at any angle can also be computed via reprojection. Experimental results show the effectiveness and accuracy of the proposed angular calibration through the error estimation of reprojection and back projection. As a novel application, we successfully achieve visible and infrared binocular image registration at the pixel level after finishing angular calibration, which not only verifies the accuracy of calibration results, but also contributes to further cloud parameter analysis under these two different imaging features. The registration results, to our knowledge, also provide a reference for the current blank in visible and infrared binocular cloud image registration.
High-Surface-Area SBA-15 with Enhanced Mesopore Connectivity by the Addition of Poly(vinyl alcohol)

The influence of poly(vinyl alcohol) (PVA) in the synthesis of SBA-15 was investigated. It was found that, by the addition of PVA, the surface area and porosity of SBA-15 is increased, while the structure and size of the mesopores remain unchanged. Nitrogen sorption measurements indicate that PVA introduces additional pores with pore sizes of around 2 nm into the pore wall. Thus, a simple way of improving the porosity of mesoporous silica is presented that could enhance transport of substrates through the porous system, important for catalytic applications and also beneficial for replication and nanocasting purposes. We, furthermore, show that a heterogeneous catalyst, Pt/SBA-15, with high surface area can be prepared by the addition of PVA-stabilized platinum nanoparticle sols.