Miracle-dz/UniFAR-FTUs huggingface dataset:
Remote sensing of thermal anomalies on active volcanoes

Thermal remote sensing is a valuable tool for monitoring active volcanoes. One can use it to detect smaller thermal anomalies, such as hot springs or fumaroles. It is successful also by detecting larger anomalies, such as lava flows or lava lakes. All these anomalies usually cover only a small area comparing to the ground sample distance of the sensors that are appropriate for their monitoring. This makes detecting and characterizing the thermal anomaly a difficult task that is solved by using more data from several bands covering the appropriate spectra. The most robust parameter to characterize a thermal anomaly is radiative heat flux. We need to analyse its time series in order to understand the volcano activity. Satellite data are influenced by the atmosphere, terrain, etc. Therefore, the time series always contains some noise. For a case study we applied Kalman filter to minimise the noise in the case study of Etna eruption in autumn 2002.
Monitoring the Ocean Surface Topography Virtual Constellation: Lessons Learned from the Contribution of SARAL/AltiKa

Altimetry missions are usually presented to the user communities with rather short lifetime durations of the order of five years. However in practice, most altimeters can remain in operations for twice as long, although often in a degraded state (coverage and/or accuracy) and with a higher risk of mission loss. Early in the design of the SARAL/AltiKa mission, this Ka-band technology demonstrator has been envisioned as a contributing mission to the so-called Ocean Surface Topography Virtual Constellation (OST-VC) of the Committee of Earth Observation Satellites (CEOS). This article uses the experience feedback from SARAL and its launch delay on the OST-VC. Using a simple yet quantified probability model, we illustrate the value of anticipating the periods where the constellation is fragile, and we identify the next periods where actions can be undertaken by CEOS Agencies to strengthen the OST-VC.
Persistent intermediate water warming during cold stadials in the southeastern Nordic seas during the past 65 k.y.

In the Nordic seas, conversion of inflowing warm Atlantic sur face water to deep cold water through convection is closely linked with climate. During the last glacial period, climate underwent rapid millennial-scale variability known as Dansgaard-Oeschger (DO) events, consisting of warm interstadials and cold stadials. Here we present the first benthic foraminiferal Mg/Ca- d 18 O record from the Nordic seas in order to reconstruct the ocean circulation on DO time scales. The record confirms that convection similar to modern took place in the Nordic seas during interstadials with cold bottom water temperatures (BWTs) close to modern temperatures. The results show gradual and pronounced BWT increases of 2–5 °C during stadials, indicating a stop or near stop in convection. The BWT peaks are followed by an abrupt drop in temperature at the onset of interstadials, indicating the abrupt start of convection and renewed generation of cold deep water. The rise in BWT during stadials confirms earlier interpretations of subsurface inflow of warm Atlantic water below a halocline reaching >1.2 km water depth. The results suggest that warm Atlantic water never ceased to flow into the Nordic seas during the glacial period; inflow at the surface during the Holocene and warm interstadials changed to subsurface and intermediate inflow during cold stadials. Our results suggest that it is the vertical shifts in the position of the warm Atlantic water that cause the abrupt surface warmings.
«Políticas de Conocimiento» Actuales en la Unión Europea: Diseño y Evaluación Críticos de la «Calidad» en una Europa del Conocimiento «Medible»

This paper investigates the content and the different «dimensions» of «quality» in the current «policies of knowledge» of the European Union as they are specified by the renewed Lisbon Strategy and in the frame of the construction of a «measurable Europe of Knowledge». The study analyses critically the policy discourses and policy practices of the European Union from 1994 to 2010 using both primary (e.g. official documents) and secon
Preferences for Redistribution in Latin America

This chapter examines the redistributive preferences of Latin Americans and investigates the factors that shape them. Using a detailed survey in eight Latin American countries, the study sheds new light on redistributive preferences and explores which aspects of redistribution are more popular and among which groups. The roles of selfinterest, perceptions of inequality, values, and the relationship between citizens and the public sphere in shaping attitudes to redistribution are discussed.
FORECAST FOR ELECTRONIC CIGARETTE USERS

Electronic cigarettes have rapidly gained popularity in the tobacco market, primarily among young adults. Marketers exploit the lack of evidence regarding their health effects and label them as a safe alternative to traditional cigarettes. Our study aims to expand the existing body of evidence on their impact on the human body. Objective: To identify the most significant characteristics of individuals using electronic cigarettes based on statistical analysis of clinical and laboratory examination data. Materials and Methods . The study included 102 participants (main group – 66 individuals using electronic cigarettes as an alternative form of smoking; control group – 36 individuals who did not use electronic or industrial cigarettes) aged 20 to 35 years. Each participant provided written informed consent to participate. Patient examinations included the following parameters: height, weight, body mass index (BMI) calculation; measurement of blood pressure, heart rate, pulse, and respiratory rate; throat swab collection. Results . According to our data, individuals in the main group had higher height, weight, and BMI values. A higher BMI may be associated with excessive stimulation of salivary glands and, consequently, increased gastric secretion and appetite stimulation. Electronic cigarette users had significantly higher blood pressure levels compared to the control group. Users of nicotine delivery systems in throat swabs had significantly higher levels of mucus, coccal flora, and neutrophil percentage, indicating the presence of bacterial agents in the oral cavity mucosa. This may be related to bacterial agents’ dissemination through nicotine delivery devices. We conducted multiple logistic regression analysis with potential patient characteristics. Conclusions . Electronic cigarette users are characterized by higher height, weight, BMI, blood pressure levels compared to non-smoking peers, as well as reduced adaptation potential and a prevalence of parasympathetic nervous system regulation. It was found that a typical electronic cigarette user is a male (OR = 15.9364) with reduced IFZ – adaptive potential of functional abilities (OR = 7.8128) and an abnormal amount of oral cavity flora (OR = 1.7748).
The eﬀect of PEG length on the size and guest uptake of PEG-capped MIL-88A particles †

The surface functionalization of MOF particles with poly(ethylene glycol) (PEG) is important for their use in biomedical applications. Here, the eﬀect of the molecular weight of a monovalent PEG-carboxylate capping ligand (MW PEG ) was investigated in a newly developed one-step, stoichiometric procedure that aims at functionalizing MIL-88A particles and achieving size control at the same time. The bulk of the MIL-88A particles is composed of iron( III ) oxide metal clusters connected by fumaric acid as the organic ligand. The surface is functionalized with monovalent PEG-carboxylate capping ligands of diﬀerent lengths. The size of the PEG-functionalized MIL-88A decreased with increasing MW PEG , and nanoMOFs were obtained for long ( Z 2 kDa) PEG chains. For lower MW PEG , higher concentrations of PEG were needed to obtain the maximum size eﬀect, but the resulting sizes were still larger than for long PEGs. BET surface area, elemental analysis, zeta potential, and infrared spectroscopy measurements showed that the PEG chains were attached to the surface of the MOF particles and not in their interior. Moreover, it was demonstrated that longer chains occupy a larger surface area, and the PEG chains adopt the low-density brush conformation. Uptake and release experiments with sulforhodamine B dye (as a model drug) showed a higher and faster uptake and release for MIL-88A functionalized with PEG (20 kDa) than for native MIL-88A, which is attributed to a larger surface-to-volume ratio for the PEG-covered particles, and to the well-hydrated and accessible nature of the PEG layer in an aqueous medium. Complete release of the dye was achieved in phosphate buﬀered saline, the majority by counter ion exchange, and a smaller fraction in the salt form.
2018). Singular knot bundle in light. Journal of the Optical Society of America A, 35(12), 1987-1999

: As the size of an optical vortex knot, imprinted in a coherent light beam, is decreased, nonparaxial eﬀects alter the structure of the knotted optical singularity. For knot structures approaching the scale of wavelength, longitudinal polarization eﬀects become non-negligible and the electric and magnetic ﬁelds diﬀer, leading to intertwined knotted nodal structures in the transverse and longitudinal polarization components which we call a knot bundle of polarization singularities. We analyze their structure using polynomial beam approximations, and numerical diﬀraction theory. The analysis reveals features of spin-orbit eﬀects and polarization topology in tightly-focused geometry, and we propose an experiment to measure this phenomenon.
Real Time System Handling Using Multi Fixed Weight Artificial Neural Network

Artificial Neural Network (ANN) algorithms are often used to predict the output according to the input given to it. Before being implemented, ANN requires a number of exercises (training) to obtain the appropriate weight, so that the difference between the output and the set target is as minimal as possible. The training process requires a lot of training data and the number of repeated trainings reaches hundreds, thousands or even millions of times. Therefore, this training requires a large memory and a very long time. Given these conditions, ANN cannot be directly implemented in a system that requires real-time data processing, with the speed of time and accuracy of the output. Especially when applied to vehicle systems that definitely demand speed and execution accuracy when encountering changes in sensor parameters embedded in it, so that unwanted conditions can be avoided. For this reason, training can be done first to obtain network weights, then these weights are injected into the system which requires real-time execution.
Differentiation of the microstructures of agarose hydrogels using terahertz time domain spectroscopy (THz-TDS)

Agarose hydrogels are widely used analytical separation media in chemistry and tissue scaffolds in biomedical engineering because of their highly tunable mechanical and rheological properties. The functional properties of these gels are attributed to their fibrillar mesh microstructure. However, a foil microstructure of the agarose hydrogel was recently observed and described. It is important to differentiate these two microstructure as they have different properties. In this work, the use of terahertz time domain spectroscopy (THz-TDS) to differentiate the two microstructure of agarose hydrogels was demonstrated. Using the global hydration number calculated from the THz permittivity, it was found that the two microstructures have different hydration environments, with the foil microstructure having a greater hydration number than the mesh microstructure. To validate the presence of the two distinct microstructures, cryo-scanning electron microcopy (cryo-SEM) was used to visualize the microstructures and rheometry was employed to demonstrate the difference in their chain flexibility.
HSP-TL: A Deep Metric Learning Model with Triplet Loss for Hit Song Prediction

The music industry is interested in the future success of a song and its presence in popular rankings such as the Billboard charts. However, a song's popularity might be impacted by variables such as music trends and social influences, which are indifferent to audio signals. In this paper, we present HSP-TL, a deep learning model, to identify likely hit songs. Our work combines temporal information and features derived from audio and lyrics to estimate the success of a recording. We adopt the concept of the triplet loss function to minimize the distance between objects with similar popularity. Also, we use convolutional neural networks on 2-D low-level audio features, contrary to the current approach. We use pre-trained language models for text-based feature extraction. Our method is evaluated on the Hit Song Prediction Dataset, which we enrich with the lyrics of each song. Our results show that the inclusion of lyrics improves song uniqueness and reflects musical trends. The proposed model outperforms the current approach by up to 8%.
Improving the Image Quality in High-Resolution 4D Radars with Sparse Arrays by Compressed Sensing

For autonomous driving, high-resolution radar sensors are required, as they operate even in poor light or weather conditions. To obtain detailed information about the vehicle’s surroundings, the resolution in all 4 measurement dimensions, i.e. range, velocity, azimuth, and elevation angle, has been improved in recent years. As large uniform rectangular arrays (URAs) would require an enormous number of channels, typically sparse antenna arrays are realized, although this results in a deteriorated image quality due to higher sidelobe levels in the angular domain. In this paper, the missing elements between the desired URA and the realized sparse antenna array are reconstructed by means of a compressed sensing algorithm. With a 1728 channel radar demonstrator, the effectiveness is shown in the direction-of-arrival (DoA) estimation of a point-target, where the main sidelobe is reduced by 60 dB. The improved perception is further analyzed with measurements of two and four reference targets in the same range-Doppler cell that differ in their radar cross section by 10dB and 24dB, respectively. Thereby, the dynamic range in the angular domain is enhanced from 16 dB to 25 dB and 20 dB, respectively. The improved image quality due to the reduced sidelobes is demonstrated with a scenario containing 13 scatterers. Finally, an intersection scenario with a pedestrian in the same range-Doppler cell as a car is shown. This represents a challenging scenario for state-of-the-art radar sensors, where compressed sensing enables the separation of both targets.
Two-Way Relaying for Single-Active Orbital Angular Momentum Index Modulation

In this paper, infrastructure-based two-way relaying with single-active orbital angular momentum index modulation (SA-OAM-IM), a special case of OAM-IM, is proposed for radio frequency line-of-sight communication. In the first time slot or transmission phase, two source/destination nodes simultaneously transmit information blocks to a relay node. After decoding, in the second time slot or transmission phase, the relay forms a single information block via network coding, which is then forwarded to the source/destination nodes. The information block is then decoded at the respective destination node via network coding. Hence, exchange of information blocks is achieved between the source/destination nodes. The scheme demonstrates a significantly improved bit error performance compared to its one-way relaying counterpart. The theoretical average bit error probability at the destination node is formulated for the proposed scheme and validated by Monte Carlo simulation results. A low-complexity near-maximum likelihood detector for SA-OAM-IM is further investigated.
Multi-Band Radio Frequency Energy Predictor for Advanced Energy Harvesting Cellular Bands Systems

Radio Frequency (RF) energy harvesting has been employed to power wireless devices. Nevertheless, RF energy harvesting encounters restrictions regarding the quantity of power it can harvest depending on signal accessibility. As a result, accurately predicting energy levels becomes crucial for enhancing the performance of energy harvesting circuits. Most research efforts have concentrated on enhancing power harvesting policies or theoretically estimating the energy obtained through RF energy harvesting. Moreover, the existing literature has primarily focused on single-band prediction approaches. This paper presents a multi-band RF energy prediction approach for RF energy harvesting systems. We collect real-time RF energy using software-defined radio technology. The proposed approach leverages Long Short-Term Memory (LSTM) neural networks to accurately predict the mean RF energy in different frequency bands for the next 100 samples, which corresponds to approximately one hour and a half. The research explores the research gap in modeling the radio frequency signal and the need for multi-band prediction techniques. The results demonstrate the effectiveness of the proposed approach in predicting RF energy across different frequency bands, with average accuracies above 98%.
Negotiation Strategies to Improve Distributed Power Allocation for Self-Organizing Heterogeneous Networks

As the traffic supported by wireless networks grows, new solutions to increase the throughput are emerging, such as heterogeneous networks, which face the challenge of self-organization in order to control the interference. A frequently used tool to model this problem is Game Theory, and we show that Communicate & Agree, a recent general-purpose negotiation-based algorithm for solving repeated games, can be applied to these network settings to provide Pareto-efficient results. As a particular application, we study a distributed power allocation problem, showing that Communicate & Agree provides better payoffs than a state-of-the-art baseline while being faster, thus making it an ideal candidate for self-organizing heterogeneous networks.
An Energy Efficient Smart Home Cooling System based on Fuzzy Control and IoT

One of the main and most practical applications of IOT technology is smart homes. Providing thermal comfort and temperature control play an important role in these homes. Therefore, smart Heating, Ventilation and Air Conditioning (HVAC) systems using the Internet of Things are growing. designing a smart heating, ventilation and air conditioning system needs a method for controlling the equipment. Fuzzy logic is an applicable method to achieve this goal. This paper attempts to use Internet of Things (IoT) in designing an automatic system of air conditioning applying fuzzy logic to increase users satisfaction, moreover decrease energy consumption. Therefore, a system is tested in three phases. In the first phase fuzzy logic and application layer protocol is studied and the MQTT protocol is selected. In the second phase system outline is designed, local cloud and an online temperature & humidity sensor is made and finally in the third phase some tests are done to evaluate the thermal comfort and energy consumption.
Generating Test Patterns for Chiplet Interconnects: Achieving Optimal Effectiveness and Efficiency

Chiplet-based multi-die packages (a.k.a. 2.5D- and 3D-ICs) implement large amounts of inter-die interconnects with micro-bumps. To achieve uniform heights, these micro-bumps are typically placed in large rectangular or hexagonal arrays. These interconnects are subject to manufacturing defects. Traditional interconnect automatic test pattern generation (I-ATPG) methods, such as the True/Complement Algorithm [1], focus on hard open and short defects with test pattern count 2 × ⌈log2(k)⌉ for k interconnects. However, the traditional I-ATPG methods indiscriminately cover short defects between all pairs of interconnects, including those for which shorts are unrealistic given their relative layout positions. In this paper we propose an effective and efficient interconnect test (E2ITEST). The proposed method improves the test effectiveness by covering both hard and weak variants of open and short defects. It improves the test efficiency by considering only shorts between adjacent interconnects. Because of this, it requires 8 × ⌈log2(4)⌉ = 16 test patterns, thereby decoupling it from the dependency on k, which is large already today and only expected to grow.
Self-Sufficient Clock Jitter Measurement Methodology Using Dithering-Based Calibration

For a Phase-Locked Loop (PLL), the variation of its output clock cycle time known as clock jitter is an important performance metric. A complete online clock jitter measurement is often performed in two stages – quantization of the clock cycle times into digital codes, and calibration of the digital codes into absolute jitter information in pico-seconds. The latter stage could be challenging as it requires accurate training clock signals as references. In this article, we propose a dithering-based scheme to resolve this issue with ease. For a cell-based PLL using a 90nm CMOS process, the post-layout transistor-level simulation supports that this is a simple yet effective method.
Integrating ADR and UAVs with Millimeter-Wave Transmission Under Rician Fading Channel

This study explores the Angle Diversity Receiver (ADR) in millimeter-wave settings with Rician fading. It aims to assess ADR’s effectiveness in overcoming fading and misalignment challenges at high frequencies. An unmanned aerial vehicle (UAV) acts as the transmitter, sending data from various nodes to the ADR system. The ADR system is intelligently designed with strategically placed antenna elements for full coverage, eliminating dead zones. This setup counters signal strength fluctuations due to Rician fading. By leveraging signal arrival angles, ADR aims for robust data reception. The study uses a Single-Input Multiple-Output (SIMO) system with Maximum Ratio Combining (MRC) and Selection Combining (SC). MRC optimally combines signals based on their signal-to-noise ratios, strengthening the overall signal. SC selects the best signal, combating fading-induced fluctuations and reducing their impact on received data. The combination of MRC and SC significantly enhances system performance, maintaining a consistent Bit Error Rate (BER) across all angles. Unlike traditional methods requiring path-following for moving transmitters, the proposed ADR-based approach offers a more efficient and reliable solution for mobile transmitter communication.
Torque Ripple Reduction of Yokeless and Segmented Armature (YASA) Motors by Novel Asymmetric Armature

This paper presents a novel torque ripple minimization technique for yokeless and segmented armature (YASA) axial flux machines by proposing asymmetric armature. Unlike conventional stator core segment in YASA machines, the proposed asymmetric armature utilizes a U-shaped stator core, and the stator windings are wound around two axial ends of the U-shaped stator core. The effectiveness of the proposed asymmetric armature is evaluated by a comparison study with the conventional armature YASA motor on a 76-kW application. Two motors are compared in terms of winding design, torque ripple, and cogging torque with the help of finite element analysis. The results showed that the proposed asymmetric armature can reduce torque ripple at rated operation and cogging torque by 87% and 45%, respectively, when compared with the conventional YASA motor armature.