Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.
Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration

Maize, the most widely planted and highest yielding of the three major crops in the world, requires the development and breeding of new varieties to accommodate the shift towards mechanized harvesting. However, the moisture content of kernels during harvest poses a significant challenge to mechanized harvesting, leading to seed breakage and increased storage costs. Previous studies highlighted the importance of LEA (Late Embryogenesis Abundant) members in regulating kernel dehydration. In this study, we aimed to gain a better understanding of the relationship between the LEA family and grain dehydration in maize. Through expression pattern analysis of maize, we identified 52 LEA genes (ZmLEAs) distributed across 10 chromosomes, organized into seven subgroups based on phylogenetic analysis, gene structure, and conserved motifs. Evolutionary and selective pressure analysis revealed that the amplification of ZmLEA genes primarily resulted from whole-genome or fragment replication events, with strong purifying selection effects during evolution. Furthermore, the transcriptome data of kernels of two maize inbred lines with varying dehydration rates at different developmental stages showed that 14 ZmLEA genes were expressed differentially in the two inbreds. This suggested that the ZmLEA genes might participate in regulating the kernel dehydration rate (KDR) in maize. Overall, this study enhances our understanding of the ZmLEA family and provides a foundation for further research into its role in regulating genes associated with grain dehydration in maize.
A Novel Routing Solution for EV Fleets: A Real-World Case Study Leveraging Double DQNs and Graph-Structured Data to Solve the EVRPTW Problem

The transition from Internal Combustion Engine vehicles to Electric Vehicles presents challenges for fleet managers in terms of adapting operational processes and systems. One prominent challenge is the Electric Vehicle Routing Problem, which requires careful consideration of battery monitoring, efficient route planning, charging infrastructure availability, and vehicle performance management. The goal is to alleviate range anxiety and ensure effective fleet management. In this context, we propose a Reinforcement Learning approach in conjunction with graph-based modeling to solve Electric Vehicle Routing Problem with Time Window. This paper provides a novel approach addressing the need for efficient and sustainable electric vehicle fleet management. Our aim is to minimize the distance traveled while serving customers within their time windows using the combination of Structure2vect and Double Deep Q-Network. Real-world data from a public utility fleet company in Tunisia is utilized to evaluate the proposed model, and comparisons are made with conventional benchmarking strategies and other Reinforcement Learning approaches. The results highlight the effectiveness of the proposed model achieving a reduction up to 50% in traveled distance while demonstrating enhanced computational efficiency through reduced complexity and optimized runtime. Moreover, the obtained model can be directly applied to treat the large-scale adoption of electric vehicles in fleets.
Classifying land cover conversions in coastal wetlands in the Mediterranean: Pairwise comparisons of landsat images

This study classified land cover conversions by using quantitative change detection methods in the eastern Mediterranean coastal wetlands of Turkey. Three Landsat TM scenes acquired in 1984, 1998 and 2007 were analysed using image pairs of consecutive dates. The amounts of change were calculated for the periods from 1984 to 1998 and from 1998 to 2007, using binary change masks and supervised image classifications, in which eleven a priori defined land cover classes were used. Change areas accounted for 14.81 and 15.64 per cent of the total area, in the first and second periods, respectively. Conversions from/to sand dunes, mud flats and afforestation were by far the most extensive changes. Sand dunes and mud flats decreased, while afforestation and agriculture areas increased in the 23‐year period between 1984 and 2007. Five types of qualitative change were identified on the basis of land cover transformations within and between natural and anthropogenic systems. Type I changes, which indicated conversions from (semi)natural to anthropogenic systems, took place as agricultural expansion over coastal sand dunes, while Type II changes mostly occurred within agricultural systems. Type III changes included changes in human uses, leading to conversions from one anthropogenic system to another. Conversions between semi(natural) systems such as salt marshes and mud‐flats were typical Type IV changes. Type V changes were mainly due to conversions from agriculture to wetland categories or other semi(natural) cover types. Implications of these changes were discussed for management of coastal environmental resources. Copyright © 2011 John Wiley & Sons, Ltd.
Redesigning Educational Countermeasures to Increase Virginia Drivers’ Road-Sharing Safety Knowledge

The lack of alignment between drivers’ and cyclists’ road-sharing knowledge results in unsafe interactions. To address this issue, educational countermeasures must clearly present and apply evidence-based practices to increase the likelihood that drivers will learn how to share the road safely with cyclists. In this study, we redesigned an existing Virginia road-sharing safety educational handbook to support a series of experiments. The redesign was based on established principles of instructional, organizational, and visual design. Virginia drivers completed a comprehension test after reviewing road-sharing educational material online. Results showed that reviewing the redesigned brochure did not improve global comprehension, law-based knowledge, and procedural knowledge about sharing the road with cyclists. However, the improved design of the educational material enhanced drivers’ declarative knowledge of road-sharing laws and safety. Further research is needed to determine the effectiveness of transferring these design choices to other transportation domains, so policymakers and instructors can effectively prioritize approaches for improving road safety.
Inverse Rendering for Computer Graphics

Creating realistic images has been a major focus in the study of computer graphics for much of its history. This effort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geometry of objects, the reflectance of surfaces, and the lighting used to illuminate the scene. These images accurately describe the physical quantities that would be measured from a real scene. Because these algorithms can predict real images, they can also be used in inverse problems to work backward from photographs to attributes of the scene. Work on three such inverse rendering problems is described. The first, inverse lighting, assumes knowledge of geometry, reflectance, and the recorded photograph and solves for the lighting in the scene. A technique using a linear least-squares system is proposed and demonstrated. Also demonstrated is an application of inverse lighting, called re-lighting, which modifies lighting in photographs. The second two inverse rendering problems solve for unknown reflectance, given images with known geometry, lighting, and camera positions. Photographic texture measurement concentrates on capturing the spatial variation in an object's reflectance. The resulting system begins with scanned 3D models of real objects and uses photographs to construct accurate, high-resolution textures suitable for physically realistic rendering. The system is demonstrated on two complex natural objects with detailed surface textures. Image-based BRDF measurement takes the opposite approach to reflectance measurement, capturing the directional characteristics of a surface's reflectance by measuring the bidirectional reflectance distribution function, or BRDF. Using photographs of an object with spatially uniform reflectance, the BRDFs of paints and papers are measured with completeness and accuracy that rival that of measurements obtained using specialized devices. The image-based approach and novel light source positioning technique require only general-purpose equipment, so the cost of the apparatus is low compared to conventional approaches. In addition, very densely sampled data can be measured very quickly, when the wavelength spectrum of the BRDF does not need to be measured in detail.
The Contribution of Biotic Factors in Explaining the Global Distribution of Inorganic Carbon in Surface Soils

Soil inorganic carbon (SIC) plays a crucial role in regulating global carbon (C) cycling by linking the long‐term geological and short‐term biological C cycles. Soil inorganic carbon stocks are thought to be mainly driven by abiotic factors. However, despite the well‐known influence of vegetation and soil microbes on terrestrial C pools, the relative contribution of biotic and abiotic factors in explaining the global distribution of SIC remains virtually unknown. Here, we conducted a global field survey including information on SIC of 398 composite topsoil samples from 134 locations to investigate the contribution of biotic drivers in explaining the global distribution of SIC in surface soils compared with climate and abiotic factors. Overall, SIC content peaked in arid and temperate ecosystems with warmer and drier conditions, particularly shrublands. We further revealed that although soil properties (e.g., Ca and C/N ratio) explained the highest variance in SIC globally, biotic factors, associated with vegetation and soil microbes, explained a considerable proportion of the global variation in SIC. In particular, plant richness, plant cover, and fungal biomass were significantly and positively associated with SIC, suggesting that biotic control could play an important role in explaining the global distribution of topsoil SIC. We propose that changes in the biotic factors, such as alterations in vegetation and soil microbes resulting from global changes, may have important direct and indirect consequences for global SIC dynamics and terrestrial C‐climate feedback.
An iterative noisy annotation correction model for robust plant disease detection

Previous work on plant disease detection demonstrated that object detectors generally suffer from degraded training data, and annotations with noise may cause the training task to fail. Well-annotated datasets are therefore crucial to build a robust detector. However, a good label set generally requires much expert knowledge and meticulous work, which is expensive and time-consuming. This paper aims to learn robust feature representations with inaccurate bounding boxes, thereby reducing the model requirements for annotation quality. Specifically, we analyze the distribution of noisy annotations in the real world. A teacher-student learning paradigm is proposed to correct inaccurate bounding boxes. The teacher model is used to rectify the degraded bounding boxes, and the student model extracts more robust feature representations from the corrected bounding boxes. Furthermore, the method can be easily generalized to semi-supervised learning paradigms and auto-labeling techniques. Experimental results show that applying our method to the Faster-RCNN detector achieves a 26% performance improvement on the noisy dataset. Besides, our method achieves approximately 75% of the performance of a fully supervised object detector when 1% of the labels are available. Overall, this work provides a robust solution to real-world location noise. It alleviates the challenges posed by noisy data to precision agriculture, optimizes data labeling technology, and encourages practitioners to further investigate plant disease detection and intelligent agriculture at a lower cost. The code will be released at https://github.com/JiuqingDong/TS_OAMIL-for-Plant-disease-detection.
Resistivity Imaging for Mapping of Groundwater Contamination at the Municipal Landfill La Chureca, Managua, Nicaragua

SUMMARY The purpose of this study was to map and describe groundwater contaminations at the La Chureca landfill. The groundwater level in the landfill is higher than in the surrounding area creating a local deviation from the regional groundwater flow. Due to this the contaminated groundwater from the landfill spreads out in the nearby soil and groundwater. The resistivity imaging results clearly show a top layer of around 10 metres thickness with low resistivity, where the resistivity has an inverse correlation with distance to the landfill. The soil samples show that the upper 10 m of the soil consists of loose permeable volcanic ashes with high water content, which are followed by a compact impermeable layer of tuff. The correlation between low resistivity and contaminated groundwater is confirmed by chemical analyses of groundwater samples. The compact layer of tuff appears impermeable enough to stop the contamination from penetrating deeper than approximately 10 m, which is supported by the resistivity imaging that show no traces of low resistivity below the top layer. Based on the combined results the contamination plume seems to stretch around 400 m in the southern direction and not more than 1000 m in the western direction.
From brain to worksite: the role of fNIRS in cognitive studies and worker safety

Effective hazard recognition and decision-making are crucial factors in ensuring workplace safety in the construction industry. Workers’ cognition closely relates to that hazard-handling behavior. Functional near-infrared spectroscopy (fNIRS) is a neurotechique tool that can evaluate the concentration vibration of oxygenated hemoglobin [HbO2] and deoxygenated hemoglobin [HbR] to reflect the cognition process. It is essential to monitor workers’ brain activity by fNIRS to analyze their cognitive status and reveal the mechanism in hazard recognition and decision-making process, providing guidance for capability evaluation and management enhancement. This review offers a systematic assessment of fNIRS, encompassing the basic theory, experiment analysis, data analysis, and discussion. A literature search and content analysis are conducted to identify the application of fNIRS in construction safety research, the limitations of selected studies, and the prospects of fNIRS in future research. This article serves as a guide for researchers keen on harnessing fNIRS to bolster construction safety standards and forwards insightful recommendations for subsequent studies.
Associations Between the Suicidal Ideation and the Tolerance for Psychological Pain and Tolerance for Physical Pain in Patients Diagnosed with Major Depressive Disorder

Purpose Patients with major depressive disorder (MDD) may experience more frequent and severe psychological and physical pain complaints compared to those without depression. As the tolerance to psychological pain decreases in patients with MDD, the severity of suicidal ideation tends to increase. Furthermore, the tolerance for physical pain (TPP) has been related with suicidal behavior. We aimed to demonstrate the impact of TPP on suicidal ideation in patients with MDD in the presence of psychological pain. Patients and Methods We included 123 patients with MDD and 114 healthy volunteers who had no previous psychiatric diagnosis. Sociodemographic data form, Psychache Scale (PS) and Tolerance for Mental Pain Scale (TMPS) were used to assess psychological pain. Beck Scale for Suicide Ideation (BSIS) and Beck Depression Inventory (BDI) were administered to participants. To assess the TPP, we used a device based on the principle of electronic dynamometry. Results The mean BDI, BSIS, PS, and TPP scores in the MDD group were higher, and the mean TMPS score was lower than those in the control group (p < 0.001 for each, p = 0.03 for TPP). We found statistically significant correlations between BDI, TMPS, BSIS, PS, and TPP scores (p < 0.05 for each). TPP was a partial mediator in the relationship between TMPS and BSIS scores (β = −1.814; p < 0.001). Conclusion We found that tolerance of psychological pain was a strong predictor of suicidal ideation, and TPP was mediating this relationship. These findings suggest that considering both tolerance to psychological pain and TPP may be beneficial when assessing the risk of suicide in individuals with MDD.
Seafloor glacial features reveal the extent and decay of the last British Ice Sheet, east of Scotland

Three‐dimensional (3D) seismic datasets, 2D seismic reflection profiles and shallow cores provide insights into the geometry and composition of glacial features on the continental shelf, offshore eastern Scotland (58° N, 1–2° W). The relic features are related to the activity of the last British Ice Sheet (BIS) in the Outer Moray Firth. A landsystem assemblage consisting of four types of subglacial and ice marginal morphology is mapped at the seafloor. The assemblage comprises: (i) large seabed banks (interpreted as end moraines), coeval with the Bosies Bank moraine; (ii) morainic ridges (hummocky, push and end moraine) formed beneath, and at the margins of the ice sheet; (iii) an incised valley (a subglacial meltwater channel), recording meltwater drainage beneath former ice sheets; and (iv) elongate ridges and grooves (subglacial bedforms) overprinted by transverse ridges (grounding line moraines). The bedforms suggest that fast‐flowing grounded ice advanced eastward of the previously proposed terminus of the offshore Late Weichselian BIS, increasing the size and extent of the ice sheet beyond traditional limits. Complex moraine formation at the margins of less active ice characterised subsequent retreat, with periodic stillstands and readvances. Observations are consistent with interpretations of a dynamic and oscillating ice margin during BIS deglaciation, and with an extensive ice sheet in the North Sea basin at the Last Glacial Maximum. Final ice margin retreat was rapid, manifested in stagnant ice topography, which aided preservation of the landsystem record. Copyright © 2008 John Wiley & Sons, Ltd.
Settlement Chronology and Subsistence Patterns in Cabo Pulmo, Baja California Sur, Mexico

ABSTRACT Cabo Pulmo National Park (CPNP), within the East Cape of Baja California Sur, Mexico, is the location of a highly productive rocky reef ecosystem that was likely attractive to people throughout the Holocene. AMS radiocarbon dates from sites along the coast and faunal data from two excavated sites, D20 and D27, indicate people were present in the region by at least 7120–6755 cal b.p. (D34), but evidence of persistent occupation and possibly higher population densities postdates 2110–1945 cal b.p. (D27, Unit 1). The most prominent sites with intact stratified archaeological deposits appear to be focused adjacent to prominent rocky points, where assemblages include a variety of marine mollusks, fish, birds, terrestrial and marine mammals, and sea turtle remains. At both D20 and D27, there is evidence people targeted small fishes including sardines, likely with nets. This study highlights the strong archaeological potential in CPNP to address questions about human coastal adaptations and population history during the Holocene, emphasizing the importance of protecting cultural resources in an area at risk from growing tourism and residential development.
Introducing the “Franken‐Kiel” Carbonate Device: First Application to Δ47‐T Calibrations of Calcite and Dolomite

Recent interlaboratory efforts have enabled methodological refinements in carbonate clumped isotope geochemistry, including the adoption of a carbonate‐based reference frame, the InterCarb Carbon Dioxide Equilibration Scale (I‐CDES). This calcite‐based standardization scheme aims at simplifying sample preparatory routines and ensuring identical treatment for all standards and unknowns. While the I‐CDES is a major step forward for the production of coherent results by laboratories for calcite, two aspects of this reference frame may only approximate but not ensure the principle of identical treatment of standards and unknowns because (a) the 90°C‐acid digestion temperature favored by I‐CDES is not achievable by all analytical setups and (b) the clumped isotope systematics of other carbonate minerals, if reported within a calcite reference frame only, may introduce uncertainties. We present an upgraded Kiel IV carbonate device—the “Franken‐Kiel”—performing acid digestions up to a theoretical 135°C, an enhancement over the factory‐default temperature of 70°C. The optimized setup considerably reduces the reaction time needed for digesting samples and yields good precision on Δ47 (i.e., long‐term standard deviations of 0.027 and 0.005‰ for calcite and dolomite standards, respectively). We further re‐evaluated the Δ47‐T relationship for calcite and dolomite directly in the I‐CDES and showed consistency between the produced temperature calibration and previous calibrations for calcite. We propose a mineralogy correction for dolomite in the I‐CDES that allows to partially reconcile theory with experimentation. Overall, the Franken‐Kiel showed excellent performance and warrants further tests on more recalcitrant carbonates, such as siderite and magnesite at higher acidification temperatures.