Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of the European Lightning Detection Network EUCLID in Case of Various Types of Current Pulses From Upward Lightning Measured at the Peissenberg Tower

In this paper, we present current pulses from upward lightning, which have been measured since 2011 at the new top structure of the Peissenberg Tower, Germany. The study comprises 38 negative and two positive flashes, which contained 199 current pulses. About 49 of them were return stroke current pulses, 133 of them were current pulses, which superimposed the initial continuous current (ICC-pulses), and 17 of them were M-component current pulses, which superimposed the continuing current of a preceding return stroke. The current pulses are used to evaluate the performance of the European lightning location system EUCLID. Fifty one (25.6%) out of the 199 current pulses were detected by EUCLID, 40 (81.6%) return stroke current pulses, and 11 (8.5%) ICC-pulses or M-component current pulses. The peak currents ranged from 0.1 to 40.8 kA. Two groups of current pulses could be identified. The first group is related to branches of nearby downward lightning which got in contact with the tower. Therefore, EUCLID reported much higher peak currents (more than 100%) compared to the peak currents measured at the Peissenberg Tower. The first group comprises the total of nine current pulses (six ICC-pulses, three return stroke current pulses). The second group of current pulses is related to upward lightning initiated from the top of the tower. The peak current inferred from EUCLID deviates much less from peak current measured at the Peissenberg Tower. The peak current was overestimated by about 20% by EUCLID. The second group comprises the total of 42 current pulses (4 ICC-pulses, 1 M-component current pulses, and 37 return stroke current pulses). The peak currents ranged from 3.1 to 40.8 kA, the geometric mean (GM) was 9.4 kA. About 30% of these events were misclassified as intra-cloud pulses by EUCLID. The GM of the location error was 161 m for all events, and 132 m considering only the return stroke current pulses.
Workplace learning in transient workplaces: the tourism and hospitality industry in the Arctic region

Purpose This paper aims to focus on conditions for workplace learning (WPL) in highly transient workplaces, exemplified by the tourism and hospitality sector in the Arctic region. The aim is to analyse and discuss how employees and employers view the conditions for employees’ WPL from their respective perspectives. Design/methodology/approach The study is based on a qualitative approach. Ten interviews with employers and ten interviews with employees were carried out. This opens for different perspectives, including identifying “learning gaps”. The analysis was thematic, with a focus on opportunities and challenges for WPL in these transient workplace contexts. Findings Overall, conditions for WPL seem unsatisfactory. On the one hand, both employees and employers see WPL as essential for staff retention. Employers also see WPL as a strategy for business development and, thus, profit. On the other hand, high staff turnover makes it challenging to strategically invest in and organize for WPL, especially formal learning. Hence, a Catch-22 situation emerges. Research limitations/implications As this study is qualitative in its scope, generalizations are analytical rather than statistical. Originality/value There is a shortage of studies on conditions for WPL, focusing particularly on transient workplaces. Moreover, by including employer and employee perspectives, the authors contribute to a gap in the literature. The empirical contribution of this paper thus lies in using a theoretical WPL framework on transient workplaces, exemplified by the tourism and hospitality industries in the Arctic region.
A Polarization-Reconfigurable High-Gain Microstrip Antenna

A novel polarization-reconfigurable cut ring microstrip antenna with high gain is proposed. This simple structure microstrip antenna consists of a ring radiation patch, two switches (p-i-n diodes), and six nonmetallic columns. By controlling the switches, the antenna can be operated on three polarized states: one state for linear polarization (LP), one state for left-hand circular polarization (LHCP), and one state for right-hand circular polarization (RHCP). In addition, the antenna peak gain can reach 10.0 dB for LP, LHCP, and RHCP over the operating bandwidth and stable unidirectional radiation patterns. The proposed antenna is fabricated and verified. By analyzing simulated and measured results, the measured common operating bandwidth of the proposed antenna on three states is from 3.86 to 3.98 GHz with the relative bandwidth of 3.1%. The cross-polarization of the antenna on the whole states is very low. Good polarization-reconfigurable characteristics of the proposed antenna have been obtained from 3.86 to 3.98 GHz. The proposed antenna is also a good candidate for advanced wireless communication systems.
A Statistical Study of High-Latitude Artificial Field-Aligned Irregularities

High power electromagnetic waves are able to modify the Earth’s ionosphere. A number of plasma waves and instabilities may be excited in this way, including patches of smallscale field-aligned electron density irregularities. The EISCAT heater, situated in northern Norway, has been used to transmit high power, high frequency radio waves into the ionosphere since its construction in the early 1980s. The CUTLASS coherent backscatter radars have been able to measure the consequent electron density perturbations in the ionosphere above the heater since their construction in the mid 1990s. This thesis contains a statistical study of CUTLASS Finland backscatter power measurements of patches of irregularities excited by the EISCAT heater during campaigns carried out between 1996 and 2002. A study of CUTLASS backscatter power measurements made during heater transmissions carried out at a fixed heater beam pointing direction, frequency and power has provided fundamental information on the effect of these heater beam parameters on the irregularities. A study of a selection of backscatter data from above the SPEAR heating facility has shown similar results. Backscatter data corresponding to experiments involving heater beam-sweeping and power-stepping have also been analysed. During beam-sweeping experiments the heater beam direction is steadily changed from northwardsto southwards-pointing. During powerstepping experiments the heater beam power is steadily increased and decreased. The results from these experiments give compelling evidence in support of the upper hybrid theory of irregularity excitation. Comparisons are made between statistical parameters of CUTLASS backscatter power distributions and modelled heater beam power distributions provided by the EZNEC4 software. In general a good agreement between the statistical parameters is observed, indicating the large influence of the heater beam on the irregularities. The influence of the Earth’s magnetic field is also evident and often leads to discrepancies between the statistical parameters of the two sets of distributions.
Atomic Force Microscopy of Phytosterol Based Edible Oleogels

This work reviews the use of atomic force microscopy (AFM) as a tool to investigate oleogels of edible triglyceride oils. Specific attention is given to those oleogels based on phytosterols and their esters, a class of material the authors have studied extensively. This work consists of a summary of the role of AFM in imaging edible oleogels, including the processing and preparation steps required to obtain high-quality AFM images of them. Finally, there is a comparison between AFM and other techniques that may be used to obtain structural information from oleogel samples. The aim of this review is to provide a useful introduction and summary of the technique for researchers in the fields of gels and food sciences looking to perform AFM measurements on edible oleogels.
The Prevalence of Atopic Dermatitis and Food Allergy in Children Living in an Urban Agglomeration—Is There a Current Relationship?

Atopic dermatitis (AD) prevalence in Poland is more frequent in individuals who live in a city. There are more studies demonstrating that long-term exposure to air pollutants is an independent risk factor for developing AD. The aim of the study was to assess the epidemiology of AD and food allergy (FA) in school children and adolescents living in Krakow, and to find a potential relationship between the incidence of atopic dermatitis with exposure to polluted air. In this paper, we presented the incidence of AD and FA between 2014 and 2018. We analyzed data collected from nearly 30,000 children aged 7–8 and adolescents aged 16–17 from the population of children and youth in Krakow. We correlated it with annual mean concentrations of PM10 and PM2.5, which indicated a gradual improvement in the air quality in Krakow. As our research results show that the prevalence of atopic dermatitis decreased with food allergy prevalence depending on the age group. We can suspect that this is the result of children growing out of a food allergy. It may be also influenced by more consequential eating habits in a group of adolescents and the elimination of allergenic foods from the diet. The decreasing incidence of atopic dermatitis appears to be also related to improvement in air quality.
The Patterns of Intraspecific Variations in Mass of Nectar Sugar along a Phylogeny Distinguish Native from Non-Native Plants in Urban Greenspaces in Southern England

To serve human needs, non-native species are selected based on an array of functional traits, which generally confer competitive advantages to these species in their recipient environments. Identifying non-obvious functional traits that indirectly inform human selection of non-natives to introduce into urban greenspaces is not yet part of common discussions in invasion biology. We tested whether functional traits integrated within a phylogenetic framework, may reveal those subtle criteria underlying the introduction of non-native plants into urban greenspaces. We found no differences in terms of functional traits between natives and non-natives. We also found no evidence that functional traits predict nectar production, irrespective of how nectar production was measured. Finally, we found that the mean sugar concentration of nectar per flower is evolutionarily shared both within closely related non-native plants as well as within close native plants. However, phylogenetically close species share similar intraspecific variation in mass of nectar sugar per flower, but this is true only for non-native plants, thus revealing a non-obvious selection criteria of non-native plants for urban greenspaces. Our results indicate that the phylogenetic patterns of intraspecific variation in mass of nectar sugar per flower is the major criterion distinguishing non-natives from native plants in urban greenspaces in Southern England.
Two Polarity-Sensitive Fluorescent Probes Based on Curcumin Analogs for Visualizing Polarity Changes in Lipid Droplets

As a class of highly dynamic organelles, lipid droplets (LDs) are involved in numerous physiological functions, and the changes in polarity of LDs are closely related to a variety of diseases. In this work, we developed two polarity-sensitive fluorescent probes (CC-CH and CC-Cl) based on curcumin analogs. CC-CH and CC-Cl with a donor–acceptor–donor (D–A–D) structure exhibited the property of intramolecular charge transfer (ICT); thus, their fluorescence emissions were significantly attenuated with increasing ambient polarity. Cell experiments indicated that CC-CH and CC-Cl showed excellent photostability, a low cytotoxicity, and a superior targeting ability regarding LDs. After treatment with oleic acid (OA) and methyl-β-cyclodextrin (M-β-CD), the polarity changes of LDs in living cells could be visualized by using CC-CH and CC-Cl. In addition, CC-CH and CC-Cl could monitor polarity changes of LDs in different pathological processes, including inflammatory responses, nutrient deprivation, and H2O2-induced oxidative stress. Therefore, CC-CH and CC-Cl are promising potential fluorescent probes for tracking intracellular LD polarity changes.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.
Hands on science : the case study of Pedra do Sal Interpretation Center

Pedra do Sal Interpretation Center offers an original experience, and the possibility to observe and touch all of Avencas beach fauna and flora, once it is equipped with a touch tank, representative of a rocky shore tide pool. In 2013 the Municipality of Cascais developed and installed the permanent exhibition “Cascais, from Land to Sea“ and adjusted all of its scholarly activities to this theme. Since the opening of the exhibition, the number of visitors of Pedra do Sal Interpretation Center has been increasing and is currently four times higher than in the past year (2012 vs
Comparison of volumetric changes at implant connector among three different types of abutment after cyclic loading

The aim�of this study was to compare the volumetric change of� dental�implant among three different types of abutments after cyclic loading.Thirty dental�implants (4.1x10 mm., Straumann? ) were used . All of them�were inserted in acrylic resin block with usual surgical protocol to gain primary stability and be able to retrieve for volumetric measurement later. Ten of each dental implant�connected with Titanium abutment (Group1: Variobase abutment Straumann? ), Gold abutment (Group2: UCLA abutment Straumann? ) and Zirconia abutment (Group 3: Care abutment Straumann? ) consecutively. Then all specimen was submitted to cyclic loading (1x106 cycles, axial load, 100N, 15 Hz) at 30oangulated. After loading, the volumetric of the dental�implants were�measured by true density analyzer (Accu Pyc II). The volumetric changes of dental implants among three abutment types were examined by one-way analysis of variance (ANOVA) followed by Bonferroni post-hoc analysis. A P value <0.05 was considered statistically significant. Results showed the dental implant for group 3( mean ? SD; 17.3302? 0.5560) was significantly different from Group 1 ( mean ? SD� 9.3982? 0.2128) and group 2 (� mean ? SD; 7.6164? 1.9165)�. In summary, dental�implants with zirconia abutments showed the greater of volumetric loss than the other groups.
Image Recognition and Reconstruction With Machine Learning: An Inverse Problem Approach

Image recognition and reconstruction are common problems in many image processing systems. These problems can be formulated as a solution to the linear inverse problem. This article presents a machine learning system model that can be used in the reconstruction and recognition of vectorized images. The analyzed inverse problem is given by the equations <inline-formula> <tex-math notation="LaTeX">$F\left ({\boldsymbol {x}_{ \boldsymbol {i}} }\right)= \boldsymbol {y}_{i}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}=F^{-1}\left ({\boldsymbol {y}_{i} }\right), i=1, \ldots, N$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$F\left ({\cdot }\right)$ </tex-math></inline-formula> is a linear mapping for <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}\in X\subset R^{n}, \boldsymbol {y}_{i}\in Y\subset R^{m}$ </tex-math></inline-formula>. Thus, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}_{i}$ </tex-math></inline-formula> can be seen as a projection of image <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}$ </tex-math></inline-formula> should be reconstructed as a solution to the inverse problem. We consider image reconstruction as an inverse problem using two different schemes. The first one, when <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}=F^{-1}\left ({\boldsymbol {y}_{i} }\right)$ </tex-math></inline-formula>, can be seen as an operation with associative memory, and the second one, when <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}=F^{-1}\left ({\boldsymbol {y}_{i} }\right)$ </tex-math></inline-formula>, can be implemented by creating random vectors for training sets. Moreover, we point out that the solution to the inverse problem can be generalized to complex-valued images <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}_{i}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}_{i}$ </tex-math></inline-formula>. In this paper, we propose a machine learning model based on a spectral processor as an alternative solution to deep learning based on optimization procedures.
LiDAR‐based object detection for agricultural robots

Light detection and ranging (LiDAR) sensors have proven to be a valuable tool to gather spatial information about the environment and are a crucial component in perception of autonomous systems. In the agricultural domain, state‐of‐the‐art algorithms for detection, classification, and tracking often utilize a combination of LiDAR and camera by fusing both semantic and spatial information. This is in part due to the availability of fast algorithms specifically optimized to make use of the convenient 2D data structure of RGB images. Still, there are limitations relying on cameras in agriculture because they highly depend on favorable lighting conditions and generally lack explicit geometric information, whereas LiDAR is especially advantageous in that regard as well as regarding range. This makes LiDAR particularly valuable for perception applications such as self‐localization, mapping, or object detection. The unstructured nature of 3D LiDAR point clouds, however, coupled with their possibly large size and density, presents a significant hurdle in terms of real‐time capability for these kinds of tasks. Object detection on 3D LiDAR sensor data is therefore challenging and solutions in agriculture usually are tied to very narrow use cases or rigorous down sampling to ensure real‐time applicability. Here, we present an algorithm featuring 2.5D map representations to avoid the computational drawbacks and ensure real‐time capability. We utilize established algorithms to project the 3D LiDAR data into two distinct maps and then apply object detection algorithms on each of those maps individually and to subsequently combine the information into a joint estimate. From the information stored in the 2.5D map, axis‐aligned bounding boxes for each object are computed containing information about position and dimensions. We present a proof of concept and assess the real‐time capability for a domain‐specific use case.
Effects of storm impacts in the Ebro delta coast

This document provides an overview of the effects of the impact of the main recent storms in the Ebro delta that have induced managerial problems. The reported storms have been identified as problematic events by the Generalitat de Catalunya (the local manager) because they have had some consequences from the ecological and/or economical standpoints. Affected coastal areas are described and each storm is described in detail in terms of hydrodynamic conditions (waves, astronomical and meteorological tides and wave runup). This reports serves to identify vulnerable areas to storm impact along the Ebro delta in terms of coastal erosion, overwash and hinterland inundation- as well as to characterise typical order of magnitude needed for inducing such processes.
ETUDE ET REALISATION D’UNE INTERFACE DE MESURE DE LA PRESSION ARTERIELLE.

The objective of our study is the development of a new electronic tensiometer. As an introduction, we presented the anatomy of the heart and its pressure to get a general idea on blood pressure in order to explain the machine function, it was necessary to do a literature review of its appearance before moving to the last chapter concerning the electronic circuits and programming. We dedicated the third to the description of the device components. This device has allowed us to expand our knowledge in this field of application and the interest of users. In fact, the facility of acquisition of data and their treatment as well as its size has been an advantage required to use. We beleive that we have we have provided in this report the necessary elements to clarify the usefulness and the development of our project.
A Review of Deep Learning CT Reconstruction From Incomplete Projection Data

Computed tomography (CT) is a widely used imaging technique in both medical and industrial applications. However, accurate CT reconstruction requires complete projection data, while incomplete data can result in significant artifacts in the reconstructed images, compromising their reliability for subsequent detection and diagnosis. As a result, accurate CT reconstruction from incomplete projection data remains a challenging research area in radiology. With the rapid development of deep learning (DL) techniques, many DL-based methods have been proposed for CT reconstruction from incomplete projection data. However, there are limited comprehensive surveys that summarize recent advances in this field. This article provides a comprehensive overview of the current state-of-the-art DL-based CT reconstruction from incomplete projection data, including acrlong SV reconstruction, acrlong LA reconstruction, acrlong MAR, acrlong IT, and ring artifact reduction. This survey covers various DL-based solutions to the five problems, potential limitations of existing methods, and future research directions.