Miracle-dz/UniFAR-FTUs huggingface dataset:
Power without a cause? Germany’s conflict avoidance and the integration of European defence procurement

ABSTRACT Germany’s state elites and general public have consistently preferred deepening integration of defence procurement at the European Union (EU) level. However, the German government has repeatedly acted against this well-established preference. First, it wanted the European Defence Agency to become a full-fledged armaments agency but tolerated its marginalization into a symbolic entity. Second, it preferred to ban protectionist defence procurement policies, such as offsets, but voted for a Code of Conduct that legitimized them. Despite Germany’s powerful position, conflict avoidance was its predominant strategy in the EU’s intergovernmental bargains. The government was unwilling to risk confronting other member states but passed the buck to the European Commission, satisficed on second-best outcomes, and thus acted as a power without a cause. By expanding the core state power (CSP) research program beyond its focus on domestic politics, we suggest re-considering CSP-integration as the art of the possible in iterative, mixed-motive situations.
Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

ABSTRACT In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance.
Improvement of Mangrove Soil Carbon Stocks Estimation in North Vietnam Using Sentinel-2 Data and Machine Learning Approach

ABSTRACT Quantifying total carbon (TC) stocks in soil across various mangrove ecosystems is key to understanding the global carbon cycle to reduce greenhouse gas emissions. Estimating mangrove TC at a large scale remains challenging due to the difficulty and high cost of soil carbon measurements when the number of samples is high. In the present study, we investigated the capability of Sentinel-2 multispectral data together with a state-of-the-art machine learning (ML) technique, which is a combination of CatBoost regression (CBR) and a genetic algorithm (GA) for feature selection and optimization (the CBR-GA model) to estimate the mangrove soil C stocks across the mangrove ecosystems in North Vietnam. We used the field survey data collected from 177 soil cores. We compared the performance of the proposed model with those of the four ML algorithms, i.e., the extreme gradient boosting regression (XGBR), the light gradient boosting machine regression (LGBMR), the support vector regression (SVR), and the random forest regression (RFR) models. Our proposed model estimated the TC level in the soil as 35.06–166.83 Mg ha−1 (average = 92.27 Mg ha−1) with satisfactory accuracy (R 2 = 0.665, RMSE = 18.41 Mg ha−1) and yielded the best prediction performance among all the ML techniques. We conclude that the Sentinel-2 data combined with the CBR-GA model can improve estimates of the mangrove TC at 10 m spatial resolution in tropical areas. The effectiveness of the proposed approach should be further evaluated for different mangrove soils of the other mangrove ecosystems in tropical and semi-tropical regions.
Political Background and Household Financial Asset Allocation in China

ABSTRACT Political background is an important factor in determining the household economic behavior. Using 2014–2018 households panel data from the China Family Panel Studies (CFPS), we investigate the effects of political background on China’s household asset allocation behavior. We find that political background has a significant positive impact on the financial market participation. Mediation analysis indicates that political background leads to higher household wealth, better social capital, and fewer credit constraints, thus promotes households investments. Further analysis shows that the marginal impact of political background on household investment behavior is more significant in Eastern and urban areas. Our results contribute to the existing literature on the relationship between the political background and the household investment behavior, also enhancing the understanding of the household portfolio heterogeneity.
Evolution of the optical response in a nanostructured fluorine doped zinc oxide thin film

We measured a strong two-photon absorption effect in a fluorine doped ZnO thin solid film prepared by a simple and low-cost chemical spray deposition method. We carry out our transmittance experiments with a near-resonant excitation with a single beam at 445 nm wavelength. By monitoring the change in temperature originated by the optical irradiation we were able to detect the participation of an important nonlinear optical absorption in the interaction. The effect of the aging time of the starting solution on the modification of photoluminescence (PL) and optical absorption exhibited by the sample was observed. We estimate that the formation of the nanostructured morphology of the thin solid films generates an enhancement in the near-resonant two-photon absorptive effect and in the PL of the zinc oxide.
Refining soil survey information for a Dutch soil series using land use history

Abstract. Differences in land‐use history within soil series, although not influencing soil classification, lead to variability of non‐diagnostic soil properties in soil databases. Regional studies that use soil databases are confronted with this considerable variability. This has, for example, been reported in regional studies focused on nitrate leaching from agricultural land. Such findings have a direct impact on regional assessments of nitrate leaching from dairy farms on sandy soils, a major environmental issue in the Netherlands. There is thus a need to deal with this variability in soil properties.
2-D and 3-D measurements of flame stretch and turbulence–flame interactions in turbulent premixed flames using DNS

Abstract Three-dimensional (3-D) measurements of flame stretch are experimentally challenging. In this paper, two-dimensional (2-D) and 3-D measurements of flame stretch and turbulence–flame interactions were examined using direct numerical simulation (DNS) data of turbulent premixed flames, and models to estimate 3-D statistics of flame stretch-related quantities by correcting 2-D measurements were developed. A variety of DNS cases were simulated, including three freely propagating planar flames without a mean shear and a slot-jet flame with a mean shear. The main findings are summarized as follows. First, the mean shear mainly influences the flame orientations. However, it does not change the flame stretch and turbulence–flame interactions qualitatively. The distributions of out-of-plane angle of all cases are nearly isotropic. Second, models were proposed to approximate the 3-D statistics of flame stretch-related quantities using 2-D measurements, the performance of which was verified by comparing modelled and actual 3-D surface averages and probability density functions of tangential strain rate, curvature and displacement velocity. Third, 2-D measurements of flame stretch capture properly the trends of the 3-D results, with flame surface area being produced in low curvature regions and destroyed in highly curved regions. However, the magnitude of flame stretch was under-estimated in 2-D measurements. Finally, 2-D and 3-D turbulence–flame interactions were examined. The flame normal vector is aligned with the most compressive strain rate in both 2-D and 3-D measurements. Meanwhile, the flame normal vector is misaligned (weakly aligned) with the most extensive strain rate in 3-D (2-D) measurements, highlighting the difference in 2-D and 3-D results of turbulence–flame interactions.
Effects of Intergroup Vicarious Ostracism on Individual Prejudicial Attributions and Aggressive Intentions

Abstract This study explores the effects of intergroup vicarious ostracism on individual prejudicial attributions and aggressive intentions. It takes Tibetan and Hui college students in northwestern China as participants. Study 1 and Study 2 explore the difference in observers' prejudicial attributions and aggressive intentions, respectively, when the group members who experienced ostracism (Tibetan college students) observed an in-group member being ostracized by out-group members versus an in-group member being ostracized by in-group members. Results show that those in-group participants, i.e., the Tibetan college students, who observed an in-group member being ostracized by out-group members, showed much higher prejudicial attributions, F(1, 106) = 19.65, p < . 001, ηp2 = . 156, and aggressive intentions, F(1, 108) = 10.51, p = . 002, ηp2 = . 089, toward ostracizers than those who observed an in-group member being ostracized by in-group members. In Study 3, Hui college students were recruited as participants to further test the results of Study 1 and Study 2. In addition, we also found that under the out-group conditions, prejudicial attribution mediates the effects of inclusionary status on aggressive intentions (95% bias-corrected confidence interval did not include zero; 95% CI [0.15, 0.69]). This study shows that ostracizers’ group membership could affect observers' prejudicial attributions and their aggressive intentions toward the ostracizers.
The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation

The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.
Spatio-Temporal Trends of Surface Energy Budget in Tibet from Satellite Remote Sensing Observations and Reanalysis Data

Being the highest and largest land mass of the earth, the Tibetan Plateau has a strong impact on the Asian climate especially on the Asian monsoon. With high downward solar radiation, the Tibetan Plateau is a climate sensitive region and the main water source for many rivers in South and East Asia. Although many studies have analyzed energy fluxes in the Tibetan Plateau, a long-term detailed spatio-temporal variability of all energy budget parameters is not clear for understanding the dynamics of the regional climate change. In this paper, satellite remote sensing and reanalysis data are used to quantify spatio-temporal trends of energy budget parameters, net radiation, latent heat flux, and sensible heat flux over the Tibetan Plateau from 2001 to 2019. The validity of both data sources is analyzed from in situ ground measurements of the FluxNet micrometeorological tower network, which verifies that both datasets are valid and reliable. It is found that the trend of net radiation shows a slight increase. The latent heat flux increases continuously, while the sensible heat flux decreases continuously throughout the study period over the Tibetan Plateau. Varying energy fluxes in the Tibetan plateau will affect the regional hydrological cycle. Satellite LE product observation is limited to certain land covers. Thus, for larger spatial areas, reanalysis data is a more appropriate choice. Normalized difference vegetation index proves a useful indicator to explain the latent heat flux trend. Despite the reduction of sensible heat, the atmospheric temperature increases continuously resulting in the warming of the Tibetan Plateau. The opposite trend of sensible heat flux and air temperature is an interesting and explainable phenomenon. It is also concluded that the surface evaporative cooling is not the indicator of atmospheric cooling/warming. In the future, more work shall be done to explain the mechanism which involves the complete heat cycle in the Tibetan Plateau.
A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula

A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion.
Proportional Trends of Continuous Rainfall in Indian Summer Monsoon

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.
Aerial Imagery Feature Engineering Using Bidirectional Generative Adversarial Networks: A Case Study of the Pilica River Region, Poland

Generative adversarial networks (GANs) are a type of neural network that are characterized by their unique construction and training process. Utilizing the concept of the latent space and exploiting the results of a duel between different GAN components opens up interesting opportunities for computer vision (CV) activities, such as image inpainting, style transfer, or even generative art. GANs have great potential to support aerial and satellite image interpretation activities. Carefully crafting a GAN and applying it to a high-quality dataset can result in nontrivial feature enrichment. In this study, we have designed and tested an unsupervised procedure capable of engineering new features by shifting real orthophotos into the GAN’s underlying latent space. Latent vectors are a low-dimensional representation of the orthophoto patches that hold information about the strength, occurrence, and interaction between spatial features discovered during the network training. Latent vectors were combined with geographical coordinates to bind them to their original location in the orthophoto. In consequence, it was possible to describe the whole research area as a set of latent vectors and perform further spatial analysis not on RGB images but on their lower-dimensional representation. To accomplish this goal, a modified version of the big bidirectional generative adversarial network (BigBiGAN) has been trained on a fine-tailored orthophoto imagery dataset covering the area of the Pilica River region in Poland. Trained models, precisely the generator and encoder, have been utilized during the processes of model quality assurance and feature engineering, respectively. Quality assurance was performed by measuring model reconstruction capabilities and by manually verifying artificial images produced by the generator. The feature engineering use case, on the other hand, has been presented in a real research scenario that involved splitting the orthophoto into a set of patches, encoding the patch set into the GAN latent space, grouping similar patches latent codes by utilizing hierarchical clustering, and producing a segmentation map of the orthophoto.
Characteristics of the Global Radio Frequency Interference in the Protected Portion of L-Band

The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active–Passive (SMAP) radiometer has been providing geolocated power moments measured within a 24 MHz band in the protected portion of L-band, i.e., 1400–1424 MHz, with 1.2 ms and 1.5 MHz time and frequency resolutions, as its Level 1A data. This paper presents important spectral and temporal properties of the radio frequency interference (RFI) in the protected portion of L-band using SMAP Level 1A data. Maximum and average bandwidth and duration of RFI signals, average RFI-free spectrum availability, and variations in such properties between ascending and descending satellite orbits have been reported across the world. The average bandwidth and duration of individual RFI sources have been found to be usually less than 4.5 MHz and 4.8 ms; and the average RFI-free spectrum is larger than 20 MHz in most regions with exceptions over the Middle East and Central and Eastern Asia. It has also been shown that, the bandwidth and duration of RFI signals can vary as much as 10 MHz and 10 ms, respectively, between ascending and descending orbits over certain locations. Furthermore, to identify frequencies susceptible to RFI contamination in the protected portion of L-band, observed RFI signals have been assigned to individual 1.5 MHz SMAP channels according to their frequencies. It has been demonstrated that, contrary to common perception, the center of the protected portion can be as RFI contaminated as its edges. Finally, there have been no significant correlations noted among different RFI properties such as amplitude, bandwidth, and duration within the 1400–1424 MHz band.