Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Study of Ultra-Compact Microstrip UWB Bandpass Filter with Wide Stopband Using DGS Based Lowpass Filter

An ultra-compact ultra-wideband (UWB) bandpass filter (BPF) with good selectivity and wide stopband is proposed in this paper. Use of modified multimode resonator results in small filter size of 14 mm× 5.9 mm, whereas U-shaped defected ground structure (UDGS) based lowpass filter (LPF) provides wide stopband. The LPF structure is designed using four nonuniform configurations of DGS units along with a 50 Ω microstrip line. Each DGS unit is formed by cascading three isosceles UDGSs and analyzed in terms of an equivalent circuit model. The proposed filter has simulated passband (3-dB bandwidth) frequency range 3 – 11 GHz with wide stopband: attenuation better than 40 dB and 25 dB over the frequency ranges 11.7 – 20.5 GHz and 11.2 GHz - 30 GHz respectively.
Artifacts in natural products studies. An old and underestimated re-emerging problem

In this editorial we would like to focus the attention of readers on an old problem in studies of natural products which is still present today and – in many cases – seems to be underestimated by both Authors and Reviewers: the potential for introducing artifacts during the procedure of analyzing secondary metabolites. The study of a natural product includes a complex series of steps which begins with the collection of plant material and continues with several chemical treatments, chromatographic operations and instrumental analysis which eventually lead to the isolation and structure identifi cation of the phytochemicals present in the studied organism. Any one of these steps provides the possibility for undesired artifacts in the natural phytochemical pattern. This mostly occurs through unwanted chemical reactions (for example, the hydrolysis of esters and glycosides promoted by acidic conditions) but can also occur simply by the introduction of contaminants present in trace amounts in the organic solvents. For just one example, readers might remember the occurrence of phthalates in herbal ethanolic extract (Bianco et al. 2014 ). We won’t report an exhaustive list of cases here, since the scope of this Editorial is mainly to make sure that both Authors and Reviewers are consciously aware of these concerns when writing and refereeing. As the Editorial Team of Natural Product Research, we have occasionally received manuscripts reporting on the identifi cation of compounds which are suspected to be possible artifacts but recently the number of cases has increased. For this reason, we would like to ask Authors to take additional care when critiquing and evaluating the results of their work, not only checking for the correct identifi cation and elucidation of chemical structures of the isolated compounds but also, and in particular, for the possible presence of artifacts. In line with this request, we would also like to ask Reviewers, who already have a vital contribution to the high scientifi c level of the journal, to bear the possibility of artifacts in mind when refereeing. Identifying (or suspecting) the presence of an artifact is not a trivial operation. There is a myriad of possible causes for artifact formation and each is diff erent. Alongside the possible introduction of contaminants from the solvents used during the various procedures, there are many chemical reactions that may be promoted by particular conditions (pH, temperature, presence of reactive components, etc.). These ultimately could change the characteristics of the natural phytochemical pattern by interacting with the potentially sensitive components. If there is any suspicion at all that an artifact has been produced during research we would like to urge you to investigate and look for further evidence of confi rmation, avoiding any possible causes of the artifact, before it can be assured with certainty that a given compound is of natural origin.
AIDE: An Interactive Environment for Program Transformation and Parallelization

Program Transformation and Parallelization, with an intent of improving, the running time of the program, usually involves major restructuring, of the unoptimized or serial version of a given program. Evaluating the effectiveness of the transformations, and performing post-mortem checks, on the resulting program, is almost an impossible task, with out an environment, to correlate the source program, with the target program, and the various intermediate representations. For instance, our parallelizing compiler, uses several Intermediate Representations (IR), such as Three-Address instructions and several Graph based representations. To get a complete understanding, of the transformations carried out, it is necessary to visually and interactively tie, the various representations of the given program. Traditionally, Integrated Development Environments (IDEs), that combine the functionality of several toolkits, in a Graphical User Interface (GUI) framework, are provided in the Software Development Domain, to assist and improve programmer efficiency. We can adapt and apply these ideas, in the Program Transformation and Parallelization setting, to likewise aid the programmer. IDE in such an environment can help the user or programmer to Plan, Visualize, Execute, Analyze and Post-Process Program Transformation and Parallelization Tasks. However majority of the existing Program Transformation and Parallelization infrastructures, lack good visualization support or provide an incomplete environment. AIDE our solution to the visualization problem, is envisioned to fill this void by providing an IDE that is complete, convenient, efficient and productive.
Modeling Similarities Among Multi-Dimensional Financial Time Series

Pairs trading is one of the most successful strategies for stock investment. The performance of pairs trading heavily depends on modeling how similarity of two paired financial signals. Conventional methods measure similarity based on one-way or two-way signal, ignoring multiple information sources. In this paper, we propose a tensor-based framework to capture the intrinsic relations among multiple factors. Equities data is represented by tensors in firm-time-trading modes, on which tensor decomposition method is applied to seek a set of multilinear patterns for each mode. In this process, structural information is preserved which provides supplementary information for pairs trading. Experiments on stocks data of all constituent firms of $S \& P 500$ demonstrate the superior performance of the proposed framework when compared with some state-of-the-art methods.
Fuel Cost Modeling for Spinning Reserve Thermal Generating Units

In power system operations, the operating cost of thermal generating units is calculated based on polynomial functions of the units' power generation. Thus, they are applied only if these units are connected to the grid to serve the load. Spinning reserve units are kept running without committing them to the grid. Completely depending on the intercept of their polynomial equations does not help to determine the proper fuel consumption if the units are operated at different speeds. This paper treats the preceding phenomenon by considering different fuel-cost models specified for just these spinning reserve uncommitted/running thermal units. The results obtained from this study prove that the deficiency associated with the existing fuel-cost models can be resolved by replacing them with these novel fuel-cost models.
Efficient Query Induction for Content Searching in Unstructured Peer-to-Peer Network

When considering content sharing in unstructured Peer-to-Peer network, flooding is usually carried out for contents searching. However, due to the increase of the number of queries, the network is congested, and the improvement of this point has been discussed. In the former research, a method which records the routes of the past content delivery and makes the new queries for the same content follow the trail was proposed. In this research, the conventional method is improved and proposed as a new method which controls query inducing directions according to the number of hops to the destination peers. By using this improved method, network cost during contents' searching and transferring is expected to be reduced compared with the conventional method. The effectiveness of the proposed method is evaluated by computer simulations.
Impact of Shared LTE Network High Typical Traffic Loads on Smart Grid Demand Response Schemes

Smart grid (SG) demand response (DR) programs and their management attain higher importance as distributed energy generation becomes more popular in households due to reducing prices of small-scale renewable energy generation equipment. The use of public telecommunications infrastructure is a good candidate for enabling DR communications over SGs, but the LTE networks become excessively congested during peak hours and the SG DR traffic delivery can be degraded. The network simulations evaluate traffic volumes, delivery ratios, and delays of various traffic types, including SG DR communications, when an LTE macrocell network capacity is exceeded by an increased amount of typical traffic types (Skype video call, FTP, Yotube video stream, and HTTP). The results show that the SG DR traffic can be delivered, maintaining satisfactory communications performance also in a highly loaded network conditions. The QoS class of SG DR traffic transmitted in downlink direction can even be considered to be lowered below the QoS of typical traffic types.
Energy Saving in Industrial Wireless Power Recharge System: Simulation of a PI-Sliding Mode Control for a Non-Inverting Buck-Boost Converter

In this work, a PI-Sliding mode controller is developed in feedback loop from a non-inverting buck boost converter, in order to control the recharge of a Li-Ion battery of a Laser Guided Vehicle (LGV), which is the last stage of an industrial wireless recharge power station. In detail, the industrial case study regards the wireless recharge of a LGV employed to transport molds used for footwear soles production. The proposed solution has been modelled and developed in the MATLAB/Simulink® framework, and the performances of this controller are compared with the classical PID industrial controller. The obtained results depend only from the developed recharge system and can be applied in other industrial cases (e.g., car battery recharge). In particular, the numerical simulations show that the proposed solution has an important impact on the energy consumptions.
Geometry-Texture Decomposition/Reconstruction Using a Proximal Interior Point Algorithm

The geometry-texture decomposition of images produced by X-Ray Computed Tomography (CT) is a challenging inverse problem which is usually performed in two steps: reconstruction and decomposition. Decomposition can be used for instance to produce an approximate segmentation of the image, but this one can be compromised by artifacts and noise arising from the acquisition and reconstruction processes. We propose a geometry-texture decomposition based on a TV-Laplacian model, well-suited for segmentation and edge detection. The corresponding joint reconstruction and decomposition task from CT data is then formulated as a convex constrained minimization problem. We use our recently introduced proximal interior point method to solve this inverse problem in a reliable manner. Numerical experiments on realistic images of material samples illustrate the practical efficiency of the proposed approach. Our algorithm indeed compares favorably with a state-of-the-art method.
Parameter Estimation Using Quantized Cloud MIMO Radar Measurements

Joint location and velocity estimation is studied for cloud multiple-input multiple-output (MIMO) radar. To reduce communication burden, consider that local measurements received at each receiver contributed by each transmitter are quantized before sent to a fusion center. Unlike the existing literature on distributed and quantized sensor data estimation, the signal model for our problem turns out to be nonlinear and complex. We first analyze the quantization outputs, whose probability mass functions are utilized to calculate the maximum-likelihood (ML) estimates and the Cramer-Rao bounds (CRBs). Then, to facilitate computation, the quantization outputs are approximated as the inputs plus Gaussian quantization error. The corresponding ML estimates and CRBs are provided. Estimation performance under the direct and approximate analyses are compared and the effect of the quantization bits is presented.
Foundations and Challenges of Low-Inertia Systems (Invited Paper)

The electric power system is currently undergoing a period of unprecedented changes. Environmental and sustainability concerns lead to replacement of a significant share of conventional fossil fuel-based power plants with renewable energy resources. This transition involves the major challenge of substituting synchronous machines and their well-known dynamics and controllers with power electronics-interfaced generation whose regulation and interaction with the rest of the system is yet to be fully understood. In this article, we review the challenges of such low-inertia power systems, and survey the solutions that have been put forward thus far. We strive to concisely summarize the laid-out scientific foundations as well as the practical experiences of industrial and academic demonstration projects. We touch upon the topics of power system stability, modeling, and control, and we particularly focus on the role of frequency, inertia, as well as control of power converters and from the demand-side.
A Theoretical Cooperative Work-Flow Net-Based Framework for Odometric and Probabilistic SLAM

In robotic mapping and localization, simultaneous localization and mapping (SLAM) is defined as the computational problem of constructing or updating a map while simultaneously keeping track of the agent's location. In this paper, We present a formal framework for Work-flow net using SLAM and Odometric-based probabilistic approach. We also propose a new extension for work-flow nets, which themselves are an extension of Petri-Nets, to be learned by the robot to model the environment. The learned Workflow net is then used to facilitate navigation through this environment. We propose a theory of soundness for the extended work-flow net.
Design of Energy Management System for a Small Solar-powered Unmanned Aerial Vehicle

An energy management system (EMS) is necessary to provide the UAV propulsion system with the energy from multiple power sources. This paper presents a new control strategy of EMS for a small solar-powered unmanned aerial vehicle (UAV). The EMS based on the value of DC bus voltage can efficiently manage the power from PV modules and the storage system, which support the UAV operation steadily even under potential rapidly varying atmospheric condition. In this EMS, PV modules can operate at multi-mode by self-adaption control strategy and storage system can be flexibly controlled to coordinate system energy power flow. The results of simulation and experiments are included to verify the effectiveness of the proposed EMS.
A Missing Data Recovery Method of Sparse Graph Signal in GFT Domain

In this paper, a missing data recovery method of sparse graph signal in graph Fourier transform (GFT) domain is presented. First, $\pmb{K}$ -sparse graph signal which only has $\pmb{K}$ non-zero elements in GFT domain is defined. The conventional $\pmb{K}$ - bandlimited constraint is only a special case of $\pmb{K}$ -sparse constraint. Then, missing data recovery problem of $\pmb{K}$ -sparse graph signal is formulated as an optimization problem such that it can be solved by using basis pursuit, orthogonal matching pursuit or iterative hard thresholding method. Finally, real temperature data are used to demonstrate the effectiveness of the proposed recovery method.
Limitations of Constrained CRB and an Alternative Bound

The constrained Cramér-Rao bound (CCRB) is a mean-squared-error (MSE) lower bound for non-Bayesian constrained parameter estimation under some unbiasedness conditions. In this paper, we demonstrate limitations of this bound in the case of nonlinear parametric constraints. We consider the problem of constant modulus signal estimation. It is shown that in this problem the CCRB unbiasedness conditions are too restrictive and that the commonly-used constrained maximum likelihood (CML) estimator does not satisfy them and has lower MSE than the CCRB. An alternative lower bound, which is based on the Lehmann-unbiasedness conditions, is used as an alternative benchmark for constrained parameter estimation. As opposed to the CCRB, it is shown that this alternative bound is valid for the CML estimator in the considered problem.
Efficient Fuzzy Techniques for Medical Data Clustering

Researchers in data mining field aims to design efficient and scalable algorithms when clustering high dimensional data. Nowadays, data have become more massive and complex resulting in big data and high dimension datasets that require efficient methods to analyze them. For that reason, as the data become larger and more diverse, maintaining cluster quality and speed is necessary when adapting an existing algorithms. Various clustering methods can deal with low dimension datasets yet as the dimensionality increases these methods tend to fail. There are number of fuzzy clustering techniques that are available for clustering high dimension data efficiently. Fuzzy C-mean, FCM, and its extension Gustafson Kessel, GK, are two examples for clustering data of high dimensions. In this paper, FCM and GK were used for medical data clustering in particular, Wisconsin Breast Cancer, WBC, dataset obtained from UCI repository were used in the experiments. The two algorithms were compared to other work. The results of the experiments showed a high performance in accuracy for both FCM and GK.
A New Robust Control Strategy for Multistage PV Battery Chargers

This manuscript presents a new overall control strategy for a standalone photovoltaic (PV) battery charging system. The battery can be regarded as an energy storage element in the system for reliable operation, or it can be considered as the load itself while being charged from null to full. The charger consists of a boost converter followed by a buck converter that charges the battery. The two converters are interfaced using a dc link capacitor that can be a super capacitor for increased energy buffer capability and to meet power spikes needed by the load. Traditionally, the boost converter is controlled to achieve maximum power point tracking (MPPT), while the output voltage of the boost converter is regulated by the buck converter, which connects to the battery. In this paper, a coordinated control strategy for the two converters is proposed and implemented to achieve multi-objective performance. When the optimal charging power is less than the offered maximum PV power, a power-matching control mode is initiated to keep the charging process smooth. In this mode of operation, the dc link voltage will be kept constant. When the PV maximum input power is less than the battery optimal charging power, the system shifts into MPPT control. In this paper, the dc link voltage information is used to guide the entire supervisory control system. By offering a range of voltage variation across the de link capacitor, the system can ride through fast shading or partial shading dynamics, which stabilizes the charging process in such normally occurring environmental conditions. Simulation results show the performance of the proposed control strategy.
Global Deletome Profile of Saccharomyces cerevisiae Exposed to the Technology-Critical Element Yttrium

The emergence of the technology-critical-element yttrium as a contaminant in the environment raises concern regarding its toxicological impact on living organisms. The molecular mechanisms underlying yttrium toxicity must be delineated. We considered the genomic phenotyping of a mutant collection of Saccharomyces cerevisiae to be of particular interest to decipher key cellular pathways involved either in yttrium toxicity or detoxification mechanisms. Among the 4733 mutants exposed to yttrium, 333 exhibited modified growth, of which 56 were sensitive and 277 were resistant. Several functions involved in yttrium toxicity mitigation emerged, primarily vacuolar acidification and retrograde transport. Conversely, functional categories overrepresented in the yttrium toxicity response included cytoskeleton organization and endocytosis, protein transport and vesicle trafficking, lipid metabolism, as well as signaling pathways. Comparison with similar studies carried out using other metals and stressors showed a response pattern similar to nickel stress. One third of the identified mutants highlighted peculiar cellular effects triggered by yttrium, specifically those affecting the pheromone-dependent signaling pathway or sphingolipid metabolic processes. Taken together, these data emphasize the role of the plasma membrane as a hotspot for yttrium toxicity. The up-to-now lack of data concerning yttrium toxicity at the cellular and molecular levels makes this pioneer study using the model S. cerevisiae an excellent first basis for the assessment of yttrium toxicity toward eukaryotes.
People and Vehicles in Danger - A Fire and Flood Detection System in Social Media

This paper presents a novel warning system framework for detecting people and vehicles in danger. The system was tested in several images compiled from Flickr and other social media sources and is highly suggested to get integrated in future warning surveillance and safety systems for preventing or solving crisis events. The proposed framework recruits State-of-the-Art deep learning technologies so as to solve a series of image processing and machine learning challenges and provides a near real-time localization solution for detecting and scoring severity safety levels of people and vehicles in flood and fire images.
Delay in Chaum's Anonymizing Mix Networks

Various anonymizing services and networks rapidly have grown both in numbers and variance. This paper focuses on an evaluation of delay strategies in Chaum's anonymizing mixes. The metrics of anonymity were obtained from packet delay entropies, and additional simulations established a clear dependency between these metrics and the ability of packet mixing to mitigate attacks on confidentiality based on pattern recognition of packet arrival instances.
False Data Injection Attacks Against State Estimation in Smart Grids: Challenges and Opportunities

Static-State Estimation has been a key function of electric power grids for almost 50 years. During that time, power system engineers have been depending on it to monitor and control power networks, optimize power flow, and perform contingency analysis. State Estimation has always been vulnerable to cyberattacks targeting the availability and integrity of the grid. Nowadays, with the rapid expansion of ICT integration into power systems towards a Smart Grid, this cybersecurity threat is even more pronounced. In order to prepare for the new cybersecurity challenges brought upon by Smart Grids, this paper serves as a concentrated summary of the research on stealth false data injection attacks against Static-State Estimation.
Circular ARC-Based Optimal Path Planning for Skid-Steer Rovers

During a path, skid-steer rovers' power consumption is highly dependent to their turning radius. For example, a point turn requires a lot of energy compared to the straight line motion. Accordingly, in the path planning for these kind of rovers, turning radius is the main factor that should be considered explicitly. In this paper, a new approach of path planning for the skid-steer rovers is proposed. The idea is to use paths that are constructed from circular arcs. Accordingly, the radii of the circular arcs can be determined to obtain an energy efficient path. In other words, the conditions in which a simple circular arc-based path is energy efficient are analytically investigated. Therefore, the proposed method in this paper, is the stepping stone to find globally optimal paths (energy efficient paths) for skid-steer rovers while using the general arcs.