Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximum Hosting Capacity Evaluation Method of Solar Energy in Integrated Energy Distribution System based on enhanced-SOCP

As solar energy has been widely used in the world, it is significant to reasonably evaluate the maximum hosting capacity of solar energy in realizing efficient utilization of solar energy and guaranteeing system running safely. Firstly, a maximum hosting capacity model of solar energy in integrated energy distribution system based on the electrical-thermal comprehensive utilization of solar energy is proposed, and the distribution network, heating network, natural gas network constraints are fully considered. Then, an enhanced SOCP-based method is adopted in this paper to solve this problem. By using second-order cone programming (SOCP) relaxation and linearization, the original non-convex nonlinear programming model is converted into a mixed integer second-order cone programming model. To ensure the exactness of SOCP relaxation and improve the computation efficiency, increasingly tight linear cuts of distribution system and natural gas system are added to the SOCP relaxation. Finally, after the analysis and comparison of case studies, the effectiveness of the proposed model and method are verified.
Selection of the 3-D Shearlet Cubes for Improving Hyperspectral Image Joint Sparse Classification

The emergence of developed hyperspectral sensors has improved the spectral and spatial resolutions of the images, leading to a high-level understanding of the remotely sensed scenes. Several methods have been proposed to exploit those resolutions and to extract representative features serving for diverse applications, like classification. For instance, contextual classifiers provide better accuracy using features that represent the spatial context, and not restricting themselves to the raw spectral observations that the image offers. In a previous work, we proposed a method that uses the 3D-Shearlet Transform features along with the spatial and spectral information to better classify a given HSI. We got interesting accuracies that outperformed state-of-the-art methods. In this paper, we propose not to use all the 3D-Shearlet Transform coefficients, but only the most relevant of them. To do so, we resort to use a feature selection method based on the Fisher Discrimination Criterion (FDC). Experimental results showed that the FDC helped select the most representative cubes and outperformed our previously obtained results.
A Three-Stage Cascade Rotating Regression Network for SAR Target Rotation Detection

With the development of deep learning, SAR target rotating detection has become a research hotspot. However, the regression process of the rotated boxes is challenging, especially for the rotating detection in target gathering areas such as docks and airports. This is because the regions of interest corresponding to adjacent targets have a relatively large overlap, which may cause data misalignment during the regression and classification process. To solve this problem, we propose a three-stage cascaded rotating regression network (3SCR2Net). Specifically, a horizontal anchor will be rotated and corrected three times continuously. 3SCR2Net overcomes the original problem of information misalignment caused by rotation detection in a complex background. In addition, two decoders are designed in this paper to decode the regression parameters. Experimental results on the SSDD+ dataset demonstrate that the proposed network outperforms five state-of-the-art methods in mean average accuracy (mAP).
A LiDAR-Based 3D Indoor Mapping Framework with Mismatch Detection and Optimization

In this paper, we propose a novel LiDAR-based mapping framework for geometry-featureless scenarios, combining learning-based mismatch detection and intensity-assisted registration optimization. The mismatch detection method hybridizes point cloud features and temporal features of pose to detect the mismatch position, and the optimization method uses the intensity of captured road markers by LiDAR to optimize mismatch pose. Experiments on underground garages data demonstrate that the proposed method performs better localization and mapping accuracy than the LOAM.
Multisource Cross-Scene Classification Using Fractional Fusion and Spatial-Spectral Domain Adaptation

To solve the limitation of labeled samples in hyperspectral image (HSI) classification, cross-scene learning methods are developed recently. However, the disparity caused by environmental variation between HSI scenes is still a challenge. As a supplement, light detection and ranging (LiDAR) data provides elevation and spatial information regardless the variations. In this paper, we propose a multisource cross-scene classification method using fractional fusion and spatial-spectral domain adaptation to reduce disparity between scenes. The spatial information of HSI is preserved by fractional differential masks (FrDM) firstly. Then the LiDAR data is utilized for spectral alignment of HSI. The utilization of LiDAR data reduces the pixel-level disparity between scenes. At last, a spatial-spectral domain adaptation network is proposed for feature extraction and classification. Experimental results on HSI and LiDAR scenes show 5% improvements in overall accuracy compared with state-of-the-art methods.
How and When Leader Job Insecurity Influences Engineer In-Role and Extra-Role Behaviors

This article investigates how and when leader job insecurity influences engineer job performance and organizational citizenship behavior (OCB). By integrating social information processing perspective and dual-path model, this article hypothesizes that leader job insecurity influences engineer job performance and OCB negatively via engineer job frustration and occupational self-efficacy. Moreover, drawing from social support resource theory, we hypothesize that coworker support moderates the impacts of leader job insecurity on engineer job frustration and occupational self-efficacy. We test these hypotheses using data from a sample of 266 leader-engineer dyads. Consistent with our theoretical framework, we find that engineer job frustration and occupational self-efficacy mediates the negative and indirect effect of leader job insecurity on engineer job performance and OCB. In addition, coworker support moderates the relationship between leader job insecurity and mediators (i.e., job frustration and occupational self-efficacy) as well as the indirect impacts of leader job insecurity on engineer job performance and OCB.
Four-Port Orthogonal Circularly Polarized Dual-Band MIMO Antenna With Polarization and Spatial Diversity Using A Dual-Band Linear-to-Circular Polarization Converter

A dual-band circularly polarized (CP) four-port multiple-input multiple-output (MIMO) antenna for K/Ka-bands fixed-satellite service is proposed in this article. The single element of MIMO is composed of a cascade of two dipoles of different lengths resonating at around 20 and 30 GHz. An array of 7×7 elements of the unit cell of dual-band linear-to-circular polarization converter is placed at a distance of λo/4 (λo is calculated with respect to 19.51 GHz) in the end-fire direction of MIMO antenna to obtain an orthogonal CP wave at the two frequency bands. The proposed CP antenna shows good MIMO performance with correlation coefficient (CC) less than 0.19 and diversity gain (DG) better than 0.99 at both the frequency bands. The proposed MIMO also agrees with polarization and spatial diversity, and the CP nature at lower and upper frequency bands can be interchanged by exciting the orthogonal ports. A prototype of the MIMO antenna is fabricated, and the measured CP bandwidths of 11.51% and 3.69% is obtained in the lower (19.65–22.05 GHz) and upper (29.25–30.35 GHz) frequency bands, respectively. The proposed antenna can be used for Ka-band: 29.5–30.0 and 19.7–20.2 GHz fixed satellite service as uplink and downlink frequencies, respectively.
Learning Transformations between Heterogeneous SAR and Optical Images for Change Detection

Change detection based on heterogeneous images is challenging because of the distribution variance caused by imaging properties of different types of sensor. Most methods deal with this problem by transforming features into a common space. However, the lack of available labeled data limits the training of complex models and representation of heterogeneous distributions. In this paper, we propose to train a network via abundant unlabeled data by adopting cyclic adversarial pre-training in order to learn the relationship between heterogeneous distributions. After pre-training, for change detection, we introduce a constraint to maintain consistency of image content, to avoid the participation of changed pixels in training. Experiments on heterogeneous optical and SAR images prove the effectiveness of our proposed method.
Quantifying the Uncertainty of Short-Term Vegetation Anomalies Detection Using Eo-Based Coarse-Resolution Vegetation Products

Satellite-based Earth Observation systems archived a variety of vegetation products during the last 50 years, which can reveal regional to global ecosystem dynamics across diverse spatiotemporal scales. The anomaly metrics such as Vegetation Condition Index (VCI) defined by comparing the current vegetation growth condition to historical average status based on long-term EO-based vegetation products were widely used to delineate abnormal vegetation variation exerted by either climatic or anthropogenic factors (e.g., droughts, wildfires). However, currently available long-term vegetation products may differ from each other in terms of sensors (observational platform or spectral bands), bio-physical definitions (e.g., NDVI, EVI, LAI, and VOD), spatiotemporal resolution, as well as the time-spans, which results in inconsistency across these vegetation products. Taking the VCI as an example, this study evaluated the uncertainty of vegetation anomalies detected based on different vegetation products over the middle reach of the Yangtze River by explicitly considering the effect of sensors, biophysical definitions, and time-spans. The preliminary results showed that VCI derived from NDVI products from different sensors (AVHRR vs. MODIS) induced significant inconsistent anomalies over most landscapes. The differences resulting from products with different biophysical definitions (NDVI vs. EVI, LAI, and VOD) are much lower than those from different sensors but still significant over specific areas. As for the time-spans, the 20-year NDVI based VCI presented a considerable reduction in variance over the study area on average compared to VCI calculated based on 5-year NDVI. In summary, caution should be taken when applying EO-based vegetation products for vegetation anomalies mapping, especially for quantitative assessment.
Spatial Downscaling of Vegetation Optical Depth Using the Modis and Srtm Observations

The optical satellite observations are affected by cloud cover. Vegetation Optical Depth (VOD) has the potential to provide insights into plant water and vegetation structure. The main objective of this study is to spatially downscale VOD using a Moderate Resolution Imaging Spectrometer (MODIS) and Shuttle Radar Topographic Mission (SRTM) observations. The study considered India geography and post-monsoon cropping season (locally called Rabi season). Data for three different Rabi crop seasons, i.e., 2017–18, 2018–19, and 2019–20, was used in the analysis. The VOD estimates at 25 km scale derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were used for spatial down-scaling. Three day VOD composites were created to cover the study area. MODIS products such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), albedo (black and white sky), and Land Surface Temperature (LST) were similarly acquired at a 3-day interval by constructing a 3-day composite. In addition, SRTM digital elevation model with a spatial resolution of 90 m was used in this study. We carried out regression modeling where VOD was used as a dependent variable, with NDVI, NDWI, Albedo (black and white sky), LST, and elevation as independent variables. We compared three regression algorithms, viz., Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR), using the R-square (R2) as the assessment metric. A comparison between the various regression techniques showed that the SVR outperformed monthly and seasonal models. Further more, a comparison of monthly and seasonal models revealed that the model generated with January data performed best, with an R2 of 0.85, followed by R2 of 0.82, 0.80, and 0.78 for March, December, and February, respectively. The R2 for the seasonal model was 0.83. Finally, for the wheat crop time series of down-scaled VOD and Sentinel 2 based NDVI was compared to gain insights on seasonal variations in VOD. We found that down-scaled VOD and NDVI have a significant agreement.
Research on Ransac Filtering Optimization Method of Steel Structure Buildings Based on Morphological Features

The laser point cloud data of complex steel structure buildings and structures includes a large number of noise points, and because the shielding is serious, it is easy to lead to the extremely uneven point density, leading to serious impacts on accurate noise elimination. Traditional filtering methods commonly use a single mathematical model, which is difficult to achieve effective filtering. In this paper, a filtering method combining RANSAC algorithm with point cloud principal component analysis and connectivity analysis is proposed. Firstly, according to the cylinder structural characteristics of the steel structure, RANSAC algorithm is used to detect the cylinder point cloud, eliminating some scattered noise points. Then the principal component analysis is carried out to obtain the noise points appeared as a small number of clusters. Finally, according to the connectivity characteristics of the structure, the point cloud connectivity is used to distinguish the noise point cluster from the steel structure point cloud, so as to obtain a more accurate and complete steel structure point cloud. In this paper, the laser point cloud of a sports venue is taken as the experimental data, processed by the proposed filtering method, and then compared with the traditional filtering method. The experimental results show that the filtering method can eliminate the noise more accurate and obtain a more complete steel structure point cloud.
Tracking Coastal Change in American Samoa by Mapping Local Vertical Land Motion with PS-InSAR

Characterizing diverse contributions to coastal land change is a key step to mitigating the effects of rising sea levels that threaten coastal communities. This task is particularly critical for small island communities in tectonically active regions, which are highly vulnerable to the effects of sea level rise. We highlight here a case study to extract regional estimates of vertical land motion (VLM) over American Samoa, which in recent years has observed increased nuisance flooding. We used persistent scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) to derive high-resolution estimates of crustal deformation in populated regions over Tutuila Island from 2016 to 2021. While the area is small and highly vegetated and poses challenges for InSAR, we were able to construct a regional map of the estimated deformation rate in these areas and validate the time-series with a local GPS station. Our preliminary results suggest that PS-InSAR has the ability to capture local and regional deformation patterns. Further work to refine our VLM estimate will include models to account for more complex atmospheric effects and estimates of error margins, as well as integration of our results into models of sealevel change that can be used by local stakeholders.
A U-Net Based Deep Learning Approach for Seismic Random Noise Suppression

Seismic data are often contaminated by random noise or even more complex types of noise, resulting in poor quality of seismic data with low signal-to-noise. Seismic random noise suppression is a crucial procedure in seismic data processing. Deep learning methods have been successfully applied to suppress seismic random noise. In this study, we propose a U-net based deep learning method to suppress seismic random noise. We add several dropout layers to the U-net to effectively avoid overfitting and set the output of the network as the residual units to enhance the training efficiency of our network. Moreover, the cosine similarity index is incorporated into the loss function to reserve the lateral continuity of geological structures. The denoising results of synthetic seismic data and field VSP data demonstrate that the proposed network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.
Short-term forecast of the total and spectral solar irradiance

Among several heliophysical and geophysical quantities, the accurate evolution of the solar irradiance is fundamental to forecast the evolution of the neutral and ionized components of the Earth's atmosphere.We developed an artificial neural network model to compute the evolution of the solar irradiance in near-real time. The model is based on the assumption that that great part of the solar irradiance variability is due to the evolution of the structure of the solar magnetic field. We employ a Layer-Recurrent Network (LRN) to model the complex relationships between the evolution of the bipolar magnetic structures (input) and the solar irradiance (output). The evolution of the bipolar magnetic structures is obtained from near-real time solar disk magnetograms and intensity images. The magnetic structures are identify and classified according to the area of the solar disk covered. We constrained the model by comparing the output of the model and observations of the solar irradiance made by instruments onboard of SORCE spacecraft. Here we focus on two regions of the spectra that are covered by SORCE instruments. The generalization of the network is tested by dividing the data sets on two groups: the training set; and, the validation set. We have found that the model error is wavelength dependent. While the model error for 24-hour forecast in the band from 115 to 180 nm is lower than 5%, the model error can reach 20% in the band from 180 to 310 nm. The performance of the network reduces progressively with the increase of the forecast period, which limits significantly the maximum forecast period that we can achieve with the discussed architecture. The model proposed allows us to predict the total and spectral solar irradiance up to three days in advance.
Retrieving of Data Similarity using Metadata on a Data Analysis Competition Platform

In recent years, instead of closing data and analysis skills in-house, there has been much interest in widely releasing data analysis knowledge on the web. A data exchange platform is a type of digital platform that exchanges data between stakeholders, e.g., data owners, users, and analysts. However, the datasets handled on such platforms are independently acquired and stored by the data providers for their own purposes. These datasets are not based on the premise of coordination and combination, and there is currently little information available to discuss the systematic organization and combination of these datasets. In this study, we focus on a metadata, summary information of data, and examine the similarity of data on a data exchange platform using natural language processing. In our experiments, we use the metadata from the data exchange platform Kaggle. To compare the similarity of the data, our method employs word2vec and BERT as vectorize methods and converts data descriptions to vectors. Then, our method measures the distances of each vector by calculating cosine similarities between each vector. From experimental results, we found that Kaggle has the same character as other data exchange platforms. Additionally, the results indicated the usability of the natural language processing-based method for extracting similar data pairs.
Decentralized Storage for Scientific Data

Emergence of new sensor technologies and increased parallelism has increased the size of scientific datasets dramatically in recent years. At the same time, research communities are demanding support for reproducible research, in which datasets and results are made available to other researchers. Such datasets do not change frequently, but future experiments can be derived from them.The volume of these datasets requires storage systems that scale across many machines, and also support highly selective queries to minimize the costs associated with unwanted data access.Decentralized Content Addressable Storage (CAS) systems such as the Inter-Planetary File System (IPFS) are promising as a way of disseminating datasets resulting from scientific research since they are scalable, the stored data is immutable, and versioning is straightforward. DS2, our prototype system for decentralized storage of spatial scientific data, bridges the gap between a scientist’s spatial view of a dataset and IPFS, the underlying decentralized filesystem. DS2 is intended to allow selective retrieval of data from very large datasets spanning many hosts.In this paper we investigate the feasibility of DS2 running on top of IPFS to store and retrieve large spatial scientific datasets. We also describe the decentralized metadata structures developed to support storage and retrieval of spatial datasets.
Data-Driven Hybrid Beamforming for Uplink Multi-user MIMO in Mobile Millimeter-Wave Systems

To enable user diversity while balancing tradeoffs between cost and flexibility, we exploit hybrid analog-digital beamforming for multi-user mobile systems. By combining array and spatial signal-processing techniques, highly directional beams can be formed with high beamforming gain to achieve sufficient link budget. In this way, fine selection of codewords for analog beamforming is essential to ensure the uplink rate, which may increase the latency of establishing a reliable communication link, especially for mobile millimeter-wave communication systems. In order to guarantee the reliability of communication, adaptive data-driven beam tracking is proposed to find the suitable beamformer/combiner pair to achieve the given signal-to-interference-plus-noise ratios constraint. Unlike the model-based approach, the proposed approach is dependent only on the real-time measurement data based on a dynamic linearization representation of a time-varying pseudo-gradient parameter estimation procedure. Numerical analyses show that the proposed beam tracking algorithm can achieve good tracking performance with lower training overhead compared with traditional schemes.
Quantum Kalb-Ramond Field in D-dimensional de Sitter Spacetimes

In this work we investigate the quantum theory of the Kalb-Ramond fields propagating in $D-$dimensional de Sitter spacetimes using the dynamic invariant method developed by Lewis and Riesenfeld [J. Math. Phys. 10, 1458 (1969)] to obtain the solution of the time-dependent Schr\"odinger equation. The wave function is written in terms of a $c-$number quantity satisfying of the Milne-Pinney equation, whose solution can be expressed in terms of two independent solutions of the respective equation of motion. We obtain the exact solution for the quantum Kalb-Ramond field in the de Sitter background and discuss its relation with the Cremmer-Scherk-Kalb-Ramond model.