Miracle-dz/UniFAR-FTUs huggingface dataset:
NORTH-EAST ROMANIA AS A FUTURE SOURCE OF TREES FOR URBAN PAVED ENVIRONMENTS IN NORTH-WEST EUROPE

1 Abstract: Trees are an important feature of the urban environment. The problem today lies not in finding a wide range of well-adapted tree species for park environments, but in finding species suitable for urban paved sites. In terms of north-west Europe, it is unlikely that the limited native dendroflora will provide a large variety of tree species with high tolerance to the environmental stresses characterising urban paved sites in the region. However, other regions with a comparable climate but with a rich dendroflora can potentially provide new tree species and genera well-suited to the growing conditions at urban sites in north-west Europe. This paper examines the potential of a geographical area extending over north-east Romania and the Republic of Moldavia to supply suitable tree species for urban paved sites in Central and Northern Europe (CNE). The study involved comparing the temperature, precipitation, evapotranspiration and water runoff in the woodland area of Iasi, Romania, with those the current inner-city climate of Copenhagen, Denmark and those predicted for Copenhagen 2100. The latter included urban heat island effects and predicted global climate change. The results revealed similar pattern in summer water deficit and temperature between natural woodlands in Iasi and inner-city environment of Copenhagen today. On the other hand, there is a weak match between Iasi and the future Copenhagen. In order to match the future scenario of Copenhagen with the present situation in Iasi, a greater understanding in a early phase that the solution not only depends on suitable tree species, but also on technical solutions being developed in order to have trees in paved environments in the future. On the basis of precipitation and temperature data, natural woodlands in north-east Romania have the potential to be a source of suitable trees for urban paved environments in the CNE region, even for a future climate if other aspects in the planning of trees in paved sites are included.
Short-Term Load Forecasting Based on DBO-LSTM Model

Forecasting the load variation trend can assist the optimal dispatch and operation of power system effectively. However, the randomness of load variation and its coupling with other factors cause the predict work difficult to carry out. In order to solve these problems, a short-term power load combined forecasting model using Dung Beetle Optimizer (DBO) to optimize the Long Short Term Memory (LSTM) neural network is proposed. First, the data is integrated into the training set through data seasonal division and data normalization. Secondly, the LSTM parameters are optimally searched with the help of DBO. The original process of artificially setting parameters is changed into the automatic iterative search for optimal parameters by dung beetles in different behavior processes, and the LSTM model optimized by DBO is used to forecast the load value of the day to be predicted. Finally, compared with other model algorithms, the root mean square error, average absolute percentage error, average absolute error can be reduced by 19.09%, 27.09%, 25.13%.
Implications of Emotion Recognition Technologies: Balancing Privacy and Public Safety

As our world becomes increasingly interconnected, technology continues to evolve at an unprecedented pace. One of the most intriguing advancements in recent years is the development of emotion recognition technologies <xref ref-type="bibr" rid="ref1">[1]</xref>. These systems, powered by artificial intelligence algorithms for machine reasoning and learning, running on sophisticated hardware, can analyze human expressions, vocal tones, and body language to identify and interpret emotions <xref ref-type="bibr" rid="ref2">[2]</xref>. While the potential applications of this technology are vast, ranging from mental health support <xref ref-type="bibr" rid="ref3">[3]</xref> to enhancing customer experiences <xref ref-type="bibr" rid="ref4">[4]</xref>, it also raises significant concerns about personal privacy and public safety. This article delves into the complex implications of emotion recognition technologies, exploring the delicate balance between the promise of improved public safety and the protection of individual privacy.
Parameters Design of PLL to Enhance Inverter-grid System Stability

PLL bandwidth is a critical factor for the stability of the inverter grid-connected system in weak grids. However, there is currently a lack of a method that considers both system stability and PLL dynamic when designing PLL parameters. Therefore, this paper proposes a method for designing PLL parameters that take both factors into account. The method establishes a relationship between PLL bandwidth and phase margin, and designs the corresponding PLL parameters based on this relationship. The inverter-grid system using the designed PLL parameters can maintain stability at different inverter output power levels in weak grids. Finally, experimental results are presented to demonstrate the effectiveness of the method.
A Novel Model for Link Dynamics in Planar Snake Robots Using Internal Constraint Force Sensing

We consider the problem of simplifying the typically complex task of deriving dynamical mechanical models of planar snake robots. More precisely we propose a modeling strategy that assumes the possibility of measuring the constraint forces acting between adjacent links in a snake robot, something that is now technologically possible thanks to currently available compact commercial sensors. We show that this information can be used to decouple the dynamics of each link in the snake robot to build a novel dynamic model that is simpler than the typical models in the available literature, but still powerful for predicting the movements of the robots. The proposed model may help to significantly reduce the computational complexity associated with model-based control and estimation schemes compared to other established models. Besides this, we show that the proposed model exhibits multiple properties that ease performing control, identification and state estimation tasks in general. More specifically, we show that parts of the dynamics of the model can be considered to be linear with a known non-linear exogenous disturbance which can be eliminated using feed-forward control. Finally, we show that linear Kalman Filters remain the best linear unbiased estimators for part of the state even when exogenous disturbances enter non-linearly in the system.
YOLOv5: Case Study of Image Resolution Influence on Fire Detection

Fire presents a dangerous occurrence in inhabited places or places occupied by humans. Apart from very high material damage, it can result in human fatalities. In the past, numerous sensor-based methods were developed in order to reduce the reaction time and improve fire detection accuracy. With the advent of modern convolutional neural networks (CNN), object detection models quickly emerged, together with novel fire detection methods utilizing said models. In this paper, we aim to demonstrate the performance of YOLOv5 on our custom dataset, which contains indoor fire occurrences. Furthermore, we are investigating the influence of the input image resolution on the models' performances. This is important due to the hardware limitations of the models that would be used in real-life applications. The research shows that different metrics (F1-Score, inference time, mAP50) can yield different models as the best-performing ones. However, since the models have to be relatively small, quick, and accurate, we have proposed the ranking-based evaluation of the models from the aspect of the input image resolution. The evaluation showed that the models obtained the best overall score when the input image resolution was set to $512 \times 512$ pixels.
A Novel Fast Mask Recurrent Convolutional Neural Network Developed to Detect the Accurate Location of PCG Murmur

Early detection of Valvular Heart Disorders (VHD) enables the best possible treatment & delays the onset of cardio illnesses. In this regard, the research made here examines the effectiveness of deep learning methods in Phonocardiography (PCG) data for the detection of VHD. An open-access collection of information containing 4 main VHDs such as first beat, systole, second cardiac murmur & diastolic pressure was used to generate PCG cardiac murmur. The cardiac murmur & sound collection of heart categorization cardiac murmur acoustic challenge by Pattern Classification, Statistic Modeling, & Computation Training have been the two cardiac murmur collections researchers analyzed. To understand the optical & time-dependent aspects of the murmur in PCG waveforms, develop a novel Fast Mask Recurrent Convolutional Neural Network (FMRCNN) based Bidirectional Long Short Term Memory (BiLSTM). The proposed system was given a set of sound features to distinguish between the regular & sound classes. The proposed approach was validated through 5-fold cross-validation on a sizable sample, yielding an F1 Score of 98% with sensitivity and precision of 96% & 100%, correspondingly.
A Federated Dictionary Learning Method for Process Monitoring With Industrial Applications

Edge computing is an indispensable technology in the Industry 4.0 era. The data are stored at the edge and transmission is prohibited due to concerns about data privacy. Thus, the traditional centralized data-driven modeling methods will face many difficulties and can hardly be used in practice. Federated learning aims to let local nodes learn a global model cooperatively while keeping their data localized. Motivated by the pioneering framework of federated learning, a federated dictionary learning method is proposed for process monitoring. In detail, local nodes train local data by the K-SVD method and learn local dictionaries. Then, local dictionaries, instead of local data, are transferred to the fusion center for calculation of the global dictionary. In order to guarantee an optimal global dictionary, a novel federated dictionary average strategy is introduced. Finally, the reconstruction errors of local data and the control limit are calculated based on the global dictionary for process monitoring. The performance of the proposed method is verified on a numerical simulation, the continuous stirred tank heater benchmark process, and the industrial aluminum electrolysis process. Process monitoring can help industrial enterprises monitor the operating status of the industrial process in real time based on the collected process data. However, industrial data are stored at the edge and enterprises pay more attention to the privacy of industrial data. The prohibition of external transmission of raw data leads to challenges for traditional centralized process monitoring methods. The federated dictionary learning method proposed in this article can have the good process monitoring performance under the premise of protecting data privacy, which is superior to some state-of-the-art methods. For example, in the aluminum electrolysis industrial process, the false alarm rate is only 1.91%, and the false discovery rate is 86.23%. The method has high data security and low model complexity, which can be quickly and conveniently promoted in other industrial processes, helping enterprises to ensure the safe and stable operation of industrial processes.
Cryptography based Identity Management for IoT Devices

While the Internet of Things (IoT) and connected devices offer numerous benefits for enterprises, they have also increased the need for strong security measures. Due to the quantity of sensitive data it sends and retains, this interconnected ecosystem is vulnerable to attacks and breaches. To ease these fears, researchers have developed a novel mutual authentication method that uses dynamic keys for encryption and decryption. The IoT environment is protected from regular attacks thanks to the complete security architecture offered by this authentication technique. The impact of a key compromise is reduced by employing dynamic keys rather than static ones. Organizations adopting IoT and wearable technologies can benefit from implementing this authentication technique in a number of ways. Currently, the suggested systems use complicated and resource-intensive cryptographic techniques, increasing computation time and making it impossible to establish communications. By creating a secure communication path and limiting access to and exchange of data between authorized devices, it lowers the possibility of data interception or manipulation. By using dynamic keys that are produced specifically for each session to encrypt and decode data, the system increases security by making the data more resistant to cryptographic assaults. This dynamic key management method lessens the effects of a compromised key and raises the IoT ecosystem’s general security.
Economic Dispatch Problem based on Risk-Seeking IGDT approach

Integration of renewable generation is a present operational challenge for power systems. Essential grid operations, such as economic power dispatch (ED), are affected by the uncertainty of renewable sources. Therefore, this study proposes an economic dispatch model considering renewable generation uncertainty. The model considers the Information Gap Decision Theory (IGDT) framework to incorporate uncertainty into the ED formulation. For this purpose, a multiobjective risk-seeking IGDT model is developed, allowing the operator to assess uncertainty-associated risks. The IGDT model is subsequently validated using a modified IEEE 24-bus test system. It is observed that the IGDT proposal achieves operational savings and integrates renewable generation adequately. Finally, the numerical results indicate that the proposed IGDT framework can assist operators in making decisions based on achieving a target cost for a given level of uncertainty.
Agriculture Monitoring System with Efficient Resource Management using IoT

IoT-based agriculture monitoring systems have recently been developed for effective resource management to maximize agricultural output. To collect real-time data on numerous factors, including soil moisture, temperature, humidity, light intensity, soil nutrient level, and animal health status, the proposed system uses wireless sensors placed in fields and livestock farms. Predictive analytics are generated for effective resource management utilizing cloud-based tools that process and evaluate the received data. The system also has a smartphone that lets farmers remotely monitor and manage different agricultural operations. Numerous agricultural procedures may be automated with the use of renewable energy can be captured and stored using energy harvesting and storage technologies to power the system. The system offers farmers real-time data, insights, and predictive analytics to support sustainable agriculture. This enables them to take smart actions and use available resources most.
Polynomial Chaos Expansion for Uncertainty Quantification in Closed-Loop Reservoir Management

Closed-loop reservoir management (CLRM) is a model-based optimal control procedure that aims at optimizing oil and gas production strategies under both physical and operational constraints and large model uncertainties. Using stochastic simulation in this decision-making process is imperative due to the large uncertainties that impact the model predictions. However, this often involves performing a large number of model evaluations repeatedly to integrate an ensemble of realizations that represent the input uncertainty. In CLRM, this requires excessive computational effort due to the complexity of the nonlinear and high-dimensional reservoir simulation models. In this study, a surrogate modeling technique, namely, polynomial chaos expansion (PCE), is leveraged for efficient and accurate implementation of the stochastic reservoir simulation in CLRM. A PCE for the reservoir dynamics is computed and employed to propagate the uncertainty without the need for additional expensive model evaluations. This can reduce the computational burden in both the forward and inverse problems of the CLRM. Results show that the PCE surrogate model can accurately quantify the uncertainty and evaluate a large number of model realizations at the cost of evaluating a polynomial, compared to the full model evaluations using Monte Carlo simulations.
Exergetic evaluation of hybrid combined cycle (HCC) of direct waste incineration (MSWI) with electricity generation

The social and economic growth of industrialized and developing countries face challenges related to energy security and the proper management of municipal solid waste (MSW) from their cities, supporting social, environmental and administrative issues that require engineering solutions to convert the problem of MSW management as part of the solution in energy security. Using the Waste to Energy technology (WtE), this study evaluates through an analytical approach the plant thermodynamic performances in a hybrid combined cycle (HCC), identifying the synergistic benefits of the thermal connection between the exhaust gases of the gas turbine (GT) and the saturated steam produced from the MSW boiler by means of an exergetic analysis. The results of this research present an energy feasibility for repowering MSWI thermal power plants with electric generation or other plants using low quality fuels in the steam cycle.
PRIDES: A Power Rising Descending Signature for Improving IoT Security

This work presents a novel digital signature to characterize the rising and descending patterns of IoT device power consumption, which can be used to detect abnormal operations. The digital signature, referred to as PRIDES, can be generated using simple digital-like circuits and hence incurs very low hardware overhead. A novel PRIDES generation circuit is implemented with a 90 nm CMOS technology and its operation is validated via transistor-level simulation. A PRIDES-based methodology to detect the insertion of potentially malicious codes is presented and its effectiveness is demonstrated with using power consumption data measured from hardware experiments.
Neural Network Architecture Optimization Method for Defect Recognition

This paper presents an approach for defect recognition, where a neural network architecture search technique is employed. The technique utilizes reinforcement learning algorithms to autonomously search for the most optimal neural network structure. The technique aims to address the issues encountered by deep learning models in defect recognition tasks, such as imbalanced data labels, high computational complexity, and the need for many hyperparameter adjustments. The main approach of the technique is to use reinforcement learning algorithms to train agents to generate new network structures within a given search space and evaluate and update these structures based on a predefined reward function. By continuously generating new candidate structures and evaluating and updating them based on the reward function, the agent can adaptively explore and optimize network structures within the search space to improve model performance and accuracy. Compared to traditional manual model design, the neural network architecture search technique based on reinforcement learning offers higher efficiency and accuracy, enabling researchers to automate the design and optimization of deep learning models, reduce design and tuning time and cost, and improve model performance and accuracy to better meet practical application requirements. Ultimately, the agent will return the optimal network structure for defect recognition tasks.
Optic Sensor-based VOCs Prediction using Machine Learning Techniques

The detection of volatile organic compounds (VOCs) using fiber optic sensors has become an active area of research in recent years due to its potential for real-time monitoring in a variety of applications, including environmental monitoring, industrial process control, and healthcare. The application of machine learning (ML) techniques for the prediction of VOCs will intelligently automate the process. In the proposed approach, the use of a subset of ML i.e., a deep learning (DL) technique namely, one-dimensional Convolutional Neural Network (1D CNN) for the classification of VOCs namely, ethanol, isopropyl alcohol (IPA) and methanol at two different regions of wavelengths of spectral data have been explored. The spectroscopic data represent the spectra of the different concentrations of VOCs obtained through the real-time experiment. The model is subjected to the real-time spectroscopic data of VOCs generated with the help of the developed clad modified fiber optic sensor (CMFOS). The performance of the model in classifying various VOCs is validated using performance metrics and confusion matrices.
Learning to Build Statistical Indicators from Open Data Sources

One of the biggest challenges facing official statistics today is the use of the massive amount of data generated on the web or by sensors and other electronic devices for the production of statistical figures. This paper presents the building of several statistical indicators from different Open Data sources. All the indicators have been built using a common methodological approach to estimate changes across time. The purpose of the paper is to show the different problems that must be addressed when using these data sources and to learn about the different ways to cope with them. The first Open Data source is traffic sensors data, where the data about the geographical location of the sensors permits to compute traffic intensity indicators at detailed geographical level. Apart from being proxies or lead indicators for economic activity, the figures can be used to measure the impact of different traffic arrangements in specific areas. Before constructing the indicators for the following source, call records from a multichannel citizen attention service, the data have been analyzed using Natural Language Processing tools to identify several categories of topics for the requests received. Other Open Data sources, Twitter messages and scraped data from a digital newspapers’ library website, are studied using similar tools in both situations. A rough idea about the evolution for the general sentiments in Spain is obtained from Twitter messages. From scraped data, the evolution of the average opinions and sentiments in the country’s newspapers is similarly computed. Usually, it is accepted that the ideas expressed in the newspapers are relevant to conform public opinion. On the other hand, an interesting result obtained in our research is that individuals react stronger and more quickly than newspapers to some social, political or economic events.
Android Malware Analysis using Coefficient of Multiple Correlation

Android being the most famous Operating System (OS) for smart hand-held devices also serves as a prime attraction for cyber-criminals and black hat hackers. Hacking into these devices through malware applications gives them access to the data which can be used for personal gains. These applications have been a prime source of cyber crimes, information leaks, financial frauds, and much more. In order to get control over malware applications, it is of utmost importance to detect these applications before they are installed on any system. This can prevent huge losses for mankind. Malware detection has become a challenge due to constant technological advancements. Most of the data about an app including permissions, activities, services, etc can be extracted using its manifest file.The concept of Multiple Correlation Coefficient has been used to rank these permissions and then build a machine learning and deep learning model using various classifiers in a k-fold setup. The results suggest that the presented model gives a detection accuracy of 97.54% with Random Forest Classifier. This accuracy is observed with the top 210 permissions as ranked by Multiple Correlation Coefficient.