Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy transition changing land use – estimating large-scale PV and Power-to-X potential of present peat production areas

Due to EU Climate policies the use of peat as an energy source is winding down. In Finland alone there was approximately 900 km2 of active peat production area in 2021 and it has been proposed that these large and otherwise now unused areas, with often existing electricity distribution infrastructure, could be suitable for housing large scale solar photovoltaic (PV) installations. In this paper a geo-spatial analysis of the solar PV potential of the peat production areas in Finland with relation to existing power delivery infrastructure is studied by using openly available data. The Power-to-X potential of the PV generation is also estimated in relation to P2X related infrastructure. The main results of this study show that the peat production areas have significant potential for PV energy generation and they are largely located nearby power grid infrastructure. However if a large portion of the potential was utilized capacity problems in existing networks would likely occur. This could be alleviated with P2X. It was shown that peat production areas are also located close to P2X transport and end use infrastructure. P2X could be used for the needs of the transportation sector, heavy industries or transferred via existing gas pipelines.
Improving the Classification Effectiveness of Network Intrusion Detection Using Ensemble Machine Learning Techniques and Deep Neural Networks

Sophisticated cyber-attacks and ever-evolving threats have made securing networks highly complex due to the advent of Big data and Connected systems, and inaccuracy and incompetency of current Network Intrusion Detection Systems (NIDS). This poses a need for better network intrusion detection models to enhance network security and secure communication channels in the future. Over the years, machine learning and deep learning models have proven to be effective in detecting network intrusion and classification of attacks on networks. In this paper, we present our proposed NIDS based on machine learning and deep learning techniques to enhance the performance of current network intrusion detection systems. Decision tree, ensemble machine learning techniques like Random Forest and XGBoost, and Deep Neural Networks (DNN) have been used on the modern substitutes of the benchmark KDD CUP 99 dataset, the NSL KDD, and the UNSW NB-15. We apply unique feature selection methods and achieve competitive results. For Binary Classification, the results show that our models achieve high accuracies of more than 99.25% for the NSL KDD dataset and above 93% for UNSW NB15 dataset. For Multiclass Classification, our models achieve accuracies of more than 97.70% for NSL KDD and above S2.50% for the UNSW NB15 dataset.
Stratospheric trace gas measurements by the Airborne Submillimeter Radiometer ASUR during SCIA-VALUE 2002 (validation and utilisation of ENVISAT and SCIAMACHY data products)

Numerous measurements of the stratospheric trace gases ozone, HCl, HNO/sub 3/, and N/sub 2/O were performed by the Airborne Submillimeter Radiometer ASUR in September 2002. ASUR was deployed on board the German research aircraft FALCON during the SCIA-VALUE 2002 campaign. A latitudinal range between 80/spl deg/N and 5/spl deg/S was covered, enabling ASUR to perform extensive measurements in the tropics for the first time. First retrievals of stratospheric trace gas profiles are presented and their dependence on latitude is studied.
Implementation of National Samsat Online Application (SAMOLNAS): Applying The Unified Model of Electronic Government Adoption (UMEGA)

In order to move towards industry 4.0, the Indonesian government implements regional tax management using e-government. SAMOLNAS is an online technology e government for paying motor vehicle taxes. The aim of this research is to test UMEGA model on the successful of SAMOLNAS implementation. This research was conducted by distributing online questionnaires to users SAMOLNAS application in Jakarta. The sampling is used on this research is purposive sampling and total respondents are 106 vehicle taxpayers. Data was analyzed using Partial Least Square analysis. The research results indicates that the factors influencing SAMOLNAS users are effort expectancy, social influence, perceived risks has impact on user attitudes significant and can increase behavioral intention. Facility conditions has a significant impact on effort expectancy but cannot increase behavioral intentions. Performance expectancy has not affected on attitude.
An Embedded Estimator for Online Harmonic Monitoring in Power-Electronic Grids

A real-time harmonic estimator is proposed to provide a high-accuracy, computationally-efficient, and low-cache-cost solution that can be applied in embedded system for the online monitoring of numerous harmonics in wide frequency band in electronic power systems. Firstly, the fast-tuning filter bank, named FT-TFT FB, is presented, which can be fast generated by straightforwardly tuning the pseudo inverse of the basic single-frequency Taylor-Fourier basis matrix using the frequency-related diagonal weight matrix. Secondly, the robust FT-TFT estimator, named RFT-TFT, is proposed by estimating harmonics using the FT-TFT FB with a fast bias compensation process. It is verified using the numerical test that RFT-TFT estimator can achieve the same high accuracy as iterative TFT (I-TFT) estimator under steady and dynamic conditions but be much faster than I-TFT. The online application feasibility is further verified by implementing RFT-TFT estimator in C6748 DSP-based embedded prototype. Finally, this prototype is applied for the field signal analysis of $\mathbf {\pm }800$ kV Ultra HVDC transmission system and the real-time harmonic monitoring of one 0.3 MW distributed photovoltaic (PV) system, respectively.
Verification of the quantum-domain process using two nonorthogonal states

If a quantum channel or process cannot be described by any measure-and-prepare scheme, we may say the channel is in \textit{quantum domain} (QD) since it can transmit quantum correlations. The concept of QD clarifies the role of quantum channel in quantum information theory based on the local-operation-and-classical-communication (LOCC) paradigm: The quantum channel is only useful if it cannot be simulated by LOCC. We construct a simple scheme to verify that a given physical process or channel is in QD by using two non-orthogonal states. We also consider the application for the experiments such as the transmission or storage of quantum optical coherent states, single-photon polarization states, and squeezed vacuum states.
The Capability of Recurrent Neural Networks to Predict Turbulence Flow via Spatiotemporal Features

This study presents a deep learning (DL) neural network hybrid data-driven method that is able to predict turbulence flow velocity field. Recently many studies have reported the application of recurrent neural network (RNN) methods, particularly the Long short-term memory (LSTM) for sequential data. The airflow around the objects and wind speed are the most presented with different hybrid architecture. In some studies, the investigated data set in fluid dynamics were generated via known equations, and they have no random and chaotic behavior. Data series extracted from Computational Fluid Dynamics (CFD) have been used in many cases. This work aimed to determine a method with raw data that could be measured with devices in the airflow, wind tunnel, water flow in the river, wind speed and industry application to process in the DL model and predict the next time steps. This method suggests spatial-temporal data in time series, which matches the Lagrangian framework in fluid dynamics. Gated Recurrent Unit (GRU), the next generation of LSTM, has been employed to create a DL model and forecasting. Time series data source is from turbulence flow has been generated in a laboratory and extracted via 2D Lagrangian Particle Tracking (LPT). This data has been used for the training model and to validate the prediction in the suggested approach. The achievement via this method dictates a significant result and could be developed.
Predicting Atmospheric Variables in the MERRA-2 Database using Neural Networks

Meteorological research often requires modelling atmospheric variables. With an increase of interest in climate science due to the urgency of climate change, easier access to meteorological data could help accelerate climate mitigation efforts. Specifically, analysis in aviation fields would be impacted greatly with more accurate data. Equations from the International Standard Atmosphere (ISA) are conventionally used to calculate barometric variables but yield only ideal results because they are generalisations of our atmosphere. For applications in aviation and weather research, more accurate values are desired. NASA's MERRA-2 database provides detailed meteorological information about pressure, temperature, humidity, and wind in 3-hour intervals from 1980. However, the data is too large to be feasibly used and inaccessible. Making a mathematical model to summarise it is therefore incredibly valuable. Conventional statistical models fail to represent the complexity of such atmospheric data making machine learning a worthy competitor. This research has successfully explored the possibility of using Artificial Intelligence-based Neural Networks to create a model to predict atmospheric variables from 4-D inputs (latitude, longitude, altitude, and time) to greatly improve upon the standard barometric equations in the ISA.
Integrated Optical Phased Arrays on Silicon

Integrated optical phased arrays (OPA) have many applications from LiDAR and imaging to sensing and free-space optical communication. Despite recent advances, due to the large size of photonic devices compared to the wavelength of operation, realizing 2-D OP As with sub-wavelength element pitch is challenging and continues to be an open area of reseacch. In this paper, a review of various OPA implementations using silicon platforms is presented.
Modeling of Natural Language Requirements based on States and Modes

The relationship between states (status of a system) and modes (capabilities of a system) used to describe system requirements is often poorly defined. The unclear relationship could make systems of interest out of control because of the out of boundaries of the systems caused by the newly added modes. Formally modeling requirements can clarify the relationship between states and modes, making the system safe.To this end, the MoSt language (a Domain Specific Language implemented on the Xtext framework) is proposed to modeling requirements based on states and modes. In this article, the relationship between states and modes is firstly provided. The metamodel and grammar of the language are then proposed. Finally, a validator is implemented to realise static checks of the MoSt model. The grammar and the validator are integrated into a publicly available Eclipse-based tool. A case study on requirements for designing cars has been conducted to illustrate the feasibility of the MoSt language. In this case study, we injected 9 errors. The results show that all the errors were detected in the static analysis.
Neuropsychology Identification of Emotion and Attention Using Multiple Hybrid 2D Convolutional Neural Networks and Gated Recurrent Units

Neuropsychology studies the relationship between the structure and function of the brain with human psychological behavior. One of its uses in neuromarketing is to evaluate the response of marketing products based on an analysis of brain activity through an Electroencephalogram (EEG). Neuropsychology captures EEG variables such as emotion, attention, and enjoyment. It is one or a combination. Using multi variables is an advantage in representing more comprehensive neuromarketing assessments. Each variable has its characteristics, and using multi variables separately needs multi networks or separate networks. This paper proposed multiple networks. Each variable EEG signal is filtered using Wavelet according to the frequency band that characterizes it. Then, EEG signals from multiple channels were extracted using two-dimensional Convolutional Neural Networks (2D-CNN) as spatial directions, then identified using Recurrent Neural Networks (RNN) as temporal. Gated Recurrent Unit (GRU) is one technique to arrange memory in RNN applications. We call the proposed method multiple hybrid 2D CNN-GRU. Experiments prove that the use of multiple networks outperformed single networks. In addition, it is seen that 2D-CNN performs better than 1D-CNN in spatial extraction. The choice of learning weight correction technique also affects performance, so the proposed method provided an accuracy of 91.7% and a Loss value of 0.2807.
A Low-Loss 77 GHz Sub-Sampling Passive Mixer Integrated in a 28-nm CMOS Radar Receiver

This paper demonstrates that using sub-sampling mixer topology can be the right choice to implement a millimeter-wave (mmW) receiver. In a 77 GHz radar receiver, it allows to reduce the burden of a 77 GHz LO distribution chain, in term of area and consumption, while preserving RF performances. Demonstration is done from two topologies of x3 sub-sampling mixers implemented in 28-nm FD-SOI CMOS technology. The best proposed passive mixer exhibits a -2.1 dB conversion gain ($G$cv), a IdB input-referred compression power (ICPldB) of +3.5 dBm, and a 16.8 dB Single Side Band Noise Figure (NFSSB). The mixer LO signal shaping consumption is 32 mW on a 1.2 V supply while the passive mixer core does not require any DC power.
Subjective and Objective Testing in Support of the JPEG Pleno Point Cloud Compression Activity

Point clouds have many applications in today’s society ranging from entertainment to autonomous driving. With these new applications comes the need to compress the growing volume of point cloud data in a manner that is both suitable for human visualization and machine processing applications. The JPEG Pleno Point Cloud activity has been working toward a learning-based coding standard for point clouds, offering a single-stream, compact compressed domain representation, supporting advanced flexible data access functionalities targeting both in-teractive human visualization, and effective performance for 3D processing and machine-related computer vision tasks. As part of this activity, the JPEG Committee has been performing a number of exploration studies to evaluate existing coding standards as well set up baseline anchors and examine objective metrics against which new learning-based solutions may be compared. This article provides an overview of the JPEG Pleno Point Cloud activity and discusses challenges and solutions to the problem of evaluating and comparing cloud coding solutions. Experimental results will be presented demonstrating methodologies used by the JPEG Committee for point cloud compression assessment as well as outlining the performance of current state of the art compression standards on point clouds as well as the sensitivity of the objective metrics used for this activity to various adjustable parameters.
Malware analysis and multi-label category detection issues: Ensemble-based approaches

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.
GENeSYS-MOD Turkey: Quantitative Scenarios for Low Carbon Futures of the Turkish Energy System

This paper examines the quantitative scenarios for low-carbon futures of the Turkish energy system at aggregated (country level) and regionally disaggregated (NUTS-1 level) levels. We have employed four different storylines for the future European energy system. They are quantified and implemented for the European energy system (30 regions, mostly single countries, including Turkey) using the open-source global energy system model, GENeSYS-MOD v3.0. We have compared the results of all scenarios at aggregated and disaggregated levels and found that there are significant differences among them. Specifically, the hydrogen production (and its use) has increased considerably in the disaggregated model when compared to the aggregated level results. The major reason for these differences is found to be the better estimation of regional renewable capacity factors (wind and solar) in the disaggregated level compared to aggregated level.
Shear viscosity in neutron star cores

We calculate the shear viscosity $\ensuremath{\eta}\ensuremath{\approx}{\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}+{\ensuremath{\eta}}_{\mathrm{n}}$ in a neutron star core composed of nucleons, electrons, and muons (${\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}$ being the electron-muon viscosity, mediated by collisions of electrons and muons with charged particles, and ${\ensuremath{\eta}}_{\mathrm{n}}$ the neutron viscosity, mediated by neutron-neutron and neutron-proton collisions). Deriving ${\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}$, we take into account the Landau damping in collisions of electrons and muons with charged particles via the exchange of transverse plasmons. It lowers ${\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}$ and leads to the nonstandard temperature behavior ${\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}\ensuremath{\propto}{T}^{\ensuremath{-}5/3}$. The viscosity ${\ensuremath{\eta}}_{\mathrm{n}}$ is calculated taking into account that in-medium effects modify nucleon effective masses in dense matter. Both viscosities, ${\ensuremath{\eta}}_{\mathrm{e}\ensuremath{\mu}}$ and ${\ensuremath{\eta}}_{\mathrm{n}}$, can be important, and both are calculated including the effects of proton superfluidity. They are presented in the form valid for any equation of state of nucleon dense matter. We analyze the density and temperature dependence of $\ensuremath{\eta}$ for different equations of state in neutron star cores, and compare $\ensuremath{\eta}$ with the bulk viscosity in the core and with the shear viscosity in the crust.
Video Streaming Evaluation Using Priority Queuing in P4 Programmable Networks

There is an increasing popularity of domain-specific languages such as P4 and of the capability to write code for improving the device performance. Therefore, the time-critical traffic becomes decisive to avoid network and service disruption due to a poor implementation. To handle real-time traffic and latency-sensitive applications (e.g., video streaming) in a Software-Defined Network (SDN) environment, the trustworthy and secure mechanisms are needed to guarantee a minimum end-to-end delay and to manage response time. This paper presents a P4-based switch solution to improve the data-control performance and to provide efficient queuing in SDN. Additionally, we studied how priority queuing mechanisms can help in achieving a solid video quality. In contradiction, a poor network implementation can lead to a longer network delay, which can make the video unplayable. The network was developed using the Behavioral Model v2 of the P4 language.
Parametrization and Identification of Dynamic Systems

Parametrization of dynamic systems is an important part in analysing and control of various systems arising either in health sciences or in the engineering word. This talk will consider basic system theoretic concepts like Toeplitz operators and Hankel operators associated to linear and certain class of nonlinear systems. Model structures will be derived and the parameter estimation problems related to the specific parametrizations will be discussed, too. Concerning nonlinear systems, special attention will be paid to the representations in bilinear forms. These structures will be derived from the Hankel operator of the specific nonlinear system. Finally a recently studied problem of embedding nonlinear system to linear models and the related identification problem will be investigated with interpretations of available results.
The puzzle of high temperature superconductivity in layered iron pnictides and chalcogenides

The response of the worldwide scientific community to the discovery in 2008 of superconductivity at T c = 26 K in the Fe-based compound LaFeAsO1−x F x has been very enthusiastic. In short order, other Fe-based superconductors with the same or related crystal structures were discovered with T c up to 56 K. Many experiments were carried out and theories formulated to try to understand the basic properties of these new materials and the mechanism for T c. In this selective critical review of the experimental literature, we distill some of this extensive body of work, and discuss relationships between different types of experiments on these materials with reference to theoretical concepts and models. The experimental normal-state properties are emphasized, and within these the electronic and magnetic properties because of the likelihood of an electronic/magnetic mechanism for superconductivity in these materials.
Autonomous Peg Transfer—a Gateway to Surgery 4.0

The appearance and global spread of Robot-Assisted Minimally Invasive Surgery (RAMIS) induced a revolution in healthcare during the last few decades. Many believe that the next step in the advancement of surgery will be subtask-level automation. Currently, a number of research groups are working on the automation of surgical subtasks, and in the last few years, the automation of simple surgical training exercises received more and more attention. At present, the most intensively researched of those training exercises is the peg transfer, probably due to its simplicity, enabling to elaborate the basic principles and best algorithms for automation. This paper presents an overview of the current state of the research in this particular area, emphasizing benchmarks and different variations of the peg transfer training exercise. An example implementation of autonomous peg transfer on the research-enhanced da Vinci Surgical System, the da Vinci Research Kit (DVRK), utilizing RGB-D camera stream, is also shown. Finally, advanced variations on the peg transfer training exercise, suitable as future research directions are proposed, gradually increasing challenge in the process of automation.
Optical investigations of the normal and superconducting states reveal two electronic subsystems in iron pnictides

Infrared reflectivity measurements on several 122 iron pnictides reveal the existence of two electronic subsystems. The one gapped due to the spin-density-wave transition in the parent materials, such as ${\text{EuFe}}_{2}{\text{As}}_{2}$, is responsible for superconductivity in the doped compounds, such as $\text{Ba}{({\text{Fe}}_{0.92}{\text{Co}}_{0.08})}_{2}{\text{As}}_{2}$ and $\text{Ba}{({\text{Fe}}_{0.95}{\text{Ni}}_{0.05})}_{2}{\text{As}}_{2}$. Analyzing the dc resistivity and scattering rate of this contribution, a hidden ${T}^{2}$ dependence is found in the normal state. The second subsystem gives rise to incoherent background, present in all 122 compounds, which is basically temperature independent but affected by the superconducting transition.