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Uncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV-BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV-BS system that utilizes a high-capacity wireless backhaul operating in millimeter-wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.

The risk and damage of wildfires have been increasing due to various reasons including climate change, and the Republic of Korea is no exception to this situation. Burned area mapping is crucial not only to prevent further damage but also to manage burned areas. Burned area mapping using satellite data, however, has been limited by the spatial and temporal resolution of satellite data and classification accuracy. This article presents a new burned area mapping method, by which damaged areas can be mapped using semantic segmentation. For this research, PlanetScope imagery that has high-resolution images with very short revisit time was used, and the proposed method is based on U-Net which requires a unitemporal PlanetScope image. The network was trained using 17 satellite images for 12 forest fires and corresponding label images that were obtained semiautomatically by setting threshold values. Band combination tests were conducted to produce an optimal burned area mapping model. The results demonstrated that the optimal and most stable band combination is red, green, blue, and near infrared of PlanetScope. To improve classification accuracy, Normalized Difference Vegetation Index, dissimilarity extracted from Gray-Level Co-Occurrence Matrix, and Land Cover Maps were used as additional datasets. In addition, topographic normalization was conducted to improve model performance and classification accuracy by reducing shadow effects. The F1 scores and overall accuracies of the final image segmentation models are ranged from 0.883 to 0.939, and from 0.990 to 0.997, respectively. These results highlight the potential of detecting burned areas using the deep learning based approach.

In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios.

While emission and stability of metal–halide perovskites can be enhanced through heterostructural encapsulation, a controlled synthesis route to such structures is not trivial to realize. Here, the authors design a mechanochemistry-driven protocol for synthesizing highly luminescent CsPbBr3/Cs4PbBr6 heterostructures.

Enhancement of vessel detection performance in synthetic aperture radar (SAR) images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance, however, presentation of authentic position of vessels was essential. Accordingly, this article aimed to propose an algorithm, which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: automated identification system (AIS) and vessel-pass (VPASS) information. Two different analyses regarding the vessel detection output utilization were, therefore, presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via i) velocity estimator complementing the conventional fractional Fourier transform velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and ii) measuring the vessel orientation angle from Radon transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to convolutional neural network-based vessel detection algorithms demonstrating ideal detection performances.

Multidimensional electronic spectroscopy techniques still have many limitations. Here, the authors introduce a 2D electronic spectroscopy technique that uses broadband, synchronized mode-locked lasers to study dynamics on a wide range of timescales.

This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.

Satellite-based flood monitoring for providing visual information on the targeted areas is crucial in responding to and recovering from river floods. However, such monitoring for practical purposes has been constrained mainly by obtaining and analyzing satellite data, and linking and optimizing the required processes. For these purposes, we present a deep learning-based flood area extraction model for a fully automated flood monitoring system, which is designed to continuously operate on a cloud-based computing platform for regularly extracting flooded area from Sentinel-1 data, and providing visual information on flood situations with better image segmentation accuracy. To develop the new flood area extraction model using deep learning, initial model tests were performed more than 500 times to determine optimal hyperparameters, water ratio, and best band combination. The results of this research showed that at ‘waterbody ratio 30%’, which yielded higher segmentation accuracies and lower loss, precision, overall accuracy, IOU, recall, and F1 score of ‘VV, aspect, topographic wetness index, and buffer input bands’ were 0.976, 0.956, 0.894, 0.964, and 0.970, respectively, and averaged inference time was 744.3941 s, which demonstrate improved image segmentation accuracy and reduced processing time. The operation and robustness of the fully automated flood monitoring system were demonstrated by automatically segmenting 12 Sentinel-1 images for the two major flood events in Republic of Korea during 2020 and 2022 in accordance with the hyperparameters, waterbody ratio, and band combinations determined through the intensive tests. Visual inspection of the outputs showed that misclassification of constructed facilities and mountain shadows were extremely reduced. It is anticipated that the fully automated flood monitoring system and the deep leaning-based waterbody extraction model presented in this research could be a valuable reference and benchmark for other countries trying to build a cloud-based flood monitoring system for rapid flood monitoring using deep learning.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the ‘Sentinel-1 VV’ band are 95.77, 80.35, and 88.85, respectively, whereas those of ‘band combination VV, Slope, PC, and TRI’ are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of ‘band combination VV, Slope, PC, and TRI’ showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the ‘Sentinel-1 VV’ band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation.

We herein report a simultaneous frequency stabilization of two 780-nm external cavity diode lasers using a precision wavelength meter (WLM). The laser lock performance is characterized by the Allan deviation measurement in which we find σy=10−12 at an averaging time of 1000 s. We also obtain spectral profiles through a heterodyne spectroscopy, identifying the contribution of white and flicker noises to the laser linewidth. The frequency drift of the WLM is measured to be about 2.0(4) MHz over 36 h. Utilizing the two lasers as a cooling and repumping field, we demonstrate a magneto-optical trap of 87Rb atoms near a high-finesse optical cavity. Our laser stabilization technique operates at broad wavelength range without a radio frequency element.

Purpose To evaluate the clinicopathological and imaging features of mammography, ultrasonography, and magnetic resonance imaging (MRI) for breast cancer in Korean women under 40 years of age according to molecular subtypes. Materials and Methods We included 183 breast cancers in 176 consecutive women under 40 years old who had been diagnosed with breast cancer between January 2012 and November 2014. The patients' clinical and pathologic records were available as electronic medical records. A retrospective review of the pre-operative imaging studies was performed with 177 mammographies, 183 ultrasonographies, and 178 MRIs. Results Eighty-six percent (158/183) of lesions were symptomatic, with masses (147/183) as the most common presentation. Eighty percent (22/25) of the asymptomatic lesions were diagnosed via screening ultrasonography. The luminal A subtype was the most common (n = 79, 43%), human epidermal growth factor receptor 2-enriched subtype showed indistinct margins on mammography (p = 0.006), the triple negative subtype depicted a posterior enhancement on ultrasonography (p < 0.001) and rim enhancement on MRI (p < 0.001). Conclusion Breast cancers in Korean women under 40 years of age are commonly presented with a palpable mass, and luminal A is the most common molecular subtype. In our study, the imaging and pathologic characteristics of breast cancer in younger women were similar to those previously reported for older patients.

Purpose The purpose of this study was to analyze the detection rate of prostate cancers from targeted biopsy specimens of midline focal lesions and to investigate the ultrasonographic findings to reduce unnecessary additional targeted biopsies. Methods Ninety-eight men with midline focal lesions detected on transrectal ultrasonography were enrolled. Additional targeted biopsies for midline focal lesions were performed after 12-core random systematic biopsies. Correlations between the ultrasonographic characteristics of midline focal lesions and the pathologic results were analyzed. Results Twenty of 98 targeted biopsy cores (20.4%) were positive for malignancy. In a univariate analysis, midline focal lesions without bulging contours (P=0.023), with involved margins (P=0.001), without hypoechoic perilesional rims (P=0.005), and with longer diameters (P=0.005) were statistically significant for cancer detection. In a multivariate analysis, involved margin (P=0.027), having longer diameter (P=0.011) or absence of hypoechoic perilesional rim (P=0.025) made a statistically significant contribution to cancer detection. Conclusion Biopsy of midline focal lesions was not always non-significant in the detection of prostate cancer. Additional targeted biopsies should be considered in cases of midline focal lesions with involved margins but without hypoechoic perilesional rims.

ObjectivesTo evaluate the diagnostic performance and image quality of an iterative model-based reconstruction (IMR) using a 100-kVp protocol for the assessment of heavily calcified coronary vessels, compared to those of filtered back projection (FBP) and hybrid iterative technique (iDose4), and also compared to those of IMR with standard 120 kVp protocol.MethodsAmong patients with Agatston scores ≥ 400 who had undergone both coronary CT angiography (CCTA) and invasive coronary angiography (ICA), age- and sex-matched patients with body mass index < 30 were retrospectively enrolled from CCTA with low-kVp protocol (100 kVp, n = 30) and with standard-kVp protocol (120 kVp, n = 30). Image data were all reconstructed with FBP, iDose4, and IMR. In each dataset, the objective and subjective image quality, and diagnostic accuracy (> 50% in luminal reduction as compared with ICA) were assessed.ResultsIMR showed better objective and subjective image quality than FBP and iDose4 in both 100 kVp and 120 kVp groups (all p < 0.05). IMR showed a significantly improved all diagnostic performance compared with FBP (p < 0.05). Compared with iDose4, IMR significantly improved positive predictive value (85.0% vs. 80.5%; p < 0.05). There was no significant difference in image quality and diagnostic performance using IMR between the 100 kVp and 120 kVp groups.Conclusions100 kVp IMR may be useful for the assessment of heavily calcified coronary vessels, providing better diagnostic performance than FBP or iDose4 at the same dose, while maintaining similar diagnostic accuracy to 120 kVp IMR.

In this paper, theoretical derailment equations for cross-wind with frequency were derived to assess running safety. For a KTX (Korean high-speed train) unit, the wheel unloading ratios, which are the criteria for evaluating derailments in UIC (International union of railways) and TSI (Technical Specification for Interoperability) regulations, were calculated through the formula under the driving regulations according to cross-wind speeds, and the theoretical results were compared and evaluated through a multibody dynamics (MBD) simulation. In addition, the wheel unloading ratios were calculated for various frequencies of cross-winds. As a result of the formula and MBD, the wheel unloading ratios were shown to increase rapidly regardless of the dampers in suspension when the cross-wind frequency and the natural frequency of a vehicle were in agreement. Finally, we calculated the changes of wheel unloading ratio for different track gauges and found that these theoretical equations could calculate more accurate results than the existing Kunieda’s formula. The formula derived in this study has the advantage of considering various variables, such as fluctuant cross-winds, rail irregularities, and derailment behaviors, which were not considered in previous studies or Kunieda’s formula. It could be used for setting suspensions or railway vehicle specifications in the initial design stage.

Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.

This paper discusses a case of truck sharing as an application of the sharing economy. This case study examines a real mixed feed company with multiple factories. In this company’s operation, bulk trucks located in a factory had not previously been shared for delivery with other factories to their pre-assigned customers of stock farms. Therefore, this paper suggests a new delivery system that facilitates truck-sharing and analyzes its effects on the transport cost and trucks’ CO2 emissions. To this end, this paper develops vehicle routing models to represent the current delivery practice and the new truck-shared delivery (TSD). In addition, models are developed for a carbon control policy of an emission trading scheme (ETS) and the effects of the ETS on truck-sharing are investigated. Numerical analysis is conducted to identify the effects of the TSD and the carbon control policy and draw practical implications.

The magnitude of net carbon dioxide emissions resulting from global forest carbon change, and hence the contribution of forests to global climate change, is highly uncertain, owing to the lack of direct measurement by Earth observation and ground data collection. This paper uses a new method to evaluate this uncertainty with greater precision than before. Sources of uncertainty are divided into conceptualization and measurement categories and distributed between the spatial, vertical and temporal dimensions of Earth observation. The method is applied to Forest Reference Emission Level (FREL) reports and National Greenhouse Gas Inventories (NGGIs) submitted to the UN Framework Convention on Climate Change (UNFCCC) by 12 countries containing half of tropical forest area. The two sets of estimates are typical of those to be submitted to the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism of the UNFCCC and the 2023 Global Stocktake of its Paris Agreement, respectively. Assembling the Uncertainty Fingerprint of each estimate shows that Uncertainty Scores are between 10 and 14 for the NGGIs and 5 and 10 for the FREL reports, and so both exceed the threshold of 2 when it is advisable to evaluate uncertainty by standard statistical methods. Conceptualization uncertainties account for 60% of all uncertainties in the NGGIs and 47% in the FREL reports, e.g., there is incomplete coverage of forest carbon fluxes, and limited disaggregation of fluxes between different ecosystem types and forest carbon pools. Of the measurement uncertainties, all FREL reports base forest area estimates on at least medium resolution satellite data, compared with only 3 NGGIs; after REDD+ Readiness schemes, mean area mapping frequency has fallen to 2.3 years in Latin America and 3.0 years in Asia, but only 8.3 years in Africa; and carbon density estimates are based on national forest inventory data in all FREL reports but only 4 NGGIs. The effectiveness of the Global Stocktake and REDD+ monitoring will therefore be constrained by considerable uncertainties, and to reduce these requires a new phase of REDD+ Readiness to ensure more frequent national forest inventories and forest carbon mapping.

Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce into distinctive noncaseous granulomas within afflicted organs. Unraveling this disease necessitates the utilization of cellular or tissue-based imaging methods to both visualize and characterize the biochemistry of these sarcoid granulomas. Although hematoxylin and eosin stain, standard in routine use alongside cytological stains have found utility in diagnosis within clinical contexts, special stains such as Masson's trichrome, reticulin, methenamine silver, and Ziehl-Neelsen provide additional varied perspectives of sarcoid granuloma imaging. Immunohistochemistry aids in pinpointing specific proteins and gene expressions further characterizing these granulomas. Finally, recent advances in spatial transcriptomics promise to divulge profound insights into their spatial orientation and three-dimensional (3-D) molecular mapping. This review focuses on a range of preexisting imaging methods employed for visualizing sarcoid granulomas at the cellular level while also exploring the potential of the latest cutting-edge approaches like spatial transcriptomics and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), with the overarching goal of shedding light on the trajectory of sarcoidosis research. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a complex inflammatory fibrotic disease that affects multiple organ systems. It is characterized by the infiltration of lymphocytes and mononuclear phagocytes, which form non-caseating granulomas in affected organs. The lungs and intrathoracic lymph nodes are the most commonly affected organs. The underlying cause of sarcoidosis is unknown, but it is believed to occur in genetically predisposed individuals who are exposed to pathogenic organisms, environmental contaminants, or self and non-self-antigens. Recent research has suggested that the microbiome may play a role in the development of respiratory conditions, including sarcoidosis. Additionally, metabolomic studies have identified potential biomarkers for monitoring sarcoidosis progression. This review will focus on recent microbiome and metabolomic findings in sarcoidosis, with the goal of shedding light on the pathogenesis and possible diagnostic and therapeutic approaches. [ABSTRACT FROM AUTHOR]