620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
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The novel space-borne Global Navigation Satellite System Reflectometry (GNSS-R) technique has recently shown promise in monitoring the ocean state and surface wind speed with high spatial coverage and unprecedented sampling rate. The L-band signals of GNSS are structurally able to provide a higher quality of observations from areas covered by dense clouds and under intense precipitation, compared to those signals at higher frequencies from conventional ocean scatterometers. As a result, studying the inner core of cyclones and improvement of severe weather forecasting and cyclone tracking have turned into the main objectives of GNSS-R satellite missions such as Cyclone Global Navigation Satellite System (CYGNSS). Nevertheless, the rain attenuation impact on GNSS-R wind speed products is not yet well documented. Evaluating the rain attenuation effects on this technique is significant since a small change in the GNSS-R can potentially cause a considerable bias in the resultant wind products at intense wind speeds. Based on both empirical evidence and theory, wind speed is inversely proportional to derived bistatic radar cross section with a natural logarithmic relation, which introduces high condition numbers (similar to ill-posed conditions) at the inversions to high wind speeds. This paper presents an evaluation of the rain signal attenuation impact on the bistatic radar cross section and the derived wind speed. This study is conducted simulating GNSS-R delay-Doppler maps at different rain rates and reflection geometries, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions. Finally, the study demonstrates that at a wind speed of 30 m/s and incidence angle of 30 degrees, rain at rates of 10, 15, and 20 mm/h might cause overestimation as large as approximate to 0.65 m/s (2%), 1.00 m/s (3%), and 1.3 m/s (4%), respectively, which are still smaller than the CYGNSS required uncertainty threshold. The simulations are conducted in a pessimistic condition (severe continuous rainfall below the freezing height and over the entire glistening zone) and the bias is expected to be smaller in size in real environments.
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
Online hate is a topic that has received considerable interest lately, as online hate represents a risk to self-determination and peaceful coexistence in societies around the globe. However, not much is known about the explanations for adolescents posting or forwarding hateful online material or how adolescents cope with this newly emerging online risk. Thus, we sought to better understand the relationship between a bystander to and perpetrator of online hate, and the moderating effects of problem-focused coping strategies (e.g., assertive, technical coping) within this relationship. Self-report questionnaires on witnessing and committing online hate and assertive and technical coping were completed by 6829 adolescents between 12 and 18 years of age from eight countries. The results showed that increases in witnessing online hate were positively related to being a perpetrator of online hate. Assertive and technical coping strategies were negatively related with perpetrating online hate. Bystanders of online hate reported fewer instances of perpetrating online hate when they reported higher levels of assertive and technical coping strategies, and more frequent instances of perpetrating online hate when they reported lower levels of assertive and technical coping strategies. In conclusion, our findings suggest that, if effective, prevention and intervention programs that target online hate should consider educating young people about problem-focused coping strategies, self-assertiveness, and media skills. Implications for future research are discussed.
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.
Cyber victimization research reveals various personal and contextual correlations and negative consequences associated with this experience. Despite increasing attention on cyber victimization, few studies have examined such experiences among ethnic minority adolescents. The purpose of the present study was to examine the moderating effect of ethnicity in the longitudinal associations among cyber victimization, school-belongingness, and psychological consequences (i.e., depression, loneliness, anxiety). These associations were investigated among 416 Latinx and white adolescents (46% female; M age = 13.89, SD = 0.41) from one middle school in the United States. They answered questionnaires on cyber victimization, school belongingness, depression, loneliness, and anxiety in the 7th grade (Time 1). One year later, in the 8th grade (Time 2), they completed questionnaires on depression, loneliness, and anxiety. Low levels of school-belongingness strengthened the positive relationships between cyber victimization and Time 2 depression and anxiety, especially among Latinx adolescents. The positive association between cyber victimization and Time 2 loneliness was strengthened for low levels of school-belongingness for all adolescents. These findings may indicate that cyber victimization threatens adolescents’ school-belongingness, which has implications for their emotional adjustment. Such findings underscore the importance of considering diverse populations when examining cyber victimization.
The goal of this three-year longitudinal study was to examine the buffering effect of parental mediation of adolescents’ technology use (i.e., restrictive, co-viewing, and instructive) on the relationships among cyber aggression involvement and substance use (i.e., alcohol use, marijuana use, cigarette smoking, and non-marijuana illicit drug use). Overall, 867 (M age = 13.67, age range from 13–15 years, 51% female, 49% White) 8th grade adolescents from the Midwestern United States participated in this study during the 6th grade (Wave 1), 7th grade (Wave 2), and 8th grade (Wave 3). Results revealed that higher levels of Wave 2 instructive mediation weakened the association between Wave 1 cyber victimization and Wave 3 alcohol use and Wave 3 non-marijuana illicit drug use. The relationship was stronger between Wave 1 cyber victimization and Wave 3 alcohol use and Wave 3 non-marijuana illicit drug use when adolescents reported lower levels of Wave 2 instructive mediation. At lower levels of Wave 2 instructive mediation, the association between Wave 1 cyber aggression perpetration and Wave 3 non-marijuana illicit drug use was stronger. Implications of these findings are discussed in the context of parents recognizing their role in helping to mitigate the negative consequences associated with adolescents’ cyber aggression involvement.
A new micro/mesoporous hybrid clay nanocomposite prepared from kaolinite clay, Carica papaya seeds, and ZnCl2 via calcination in an inert atmosphere is presented. Regardless of the synthesis temperature, the specific surface area of the nanocomposite material is between ≈150 and 300 m2/g. The material contains both micro- and mesopores in roughly equal amounts. X-ray diffraction, infrared spectroscopy, and solid-state nuclear magnetic resonance spectroscopy suggest the formation of several new bonds in the materials upon reaction of the precursors, thus confirming the formation of a new hybrid material. Thermogravimetric analysis/differential thermal analysis and elemental analysis confirm the presence of carbonaceous matter. The new composite is stable up to 900 °C and is an efficient adsorbent for the removal of a water micropollutant, 4-nitrophenol, and a pathogen, E. coli, from an aqueous medium, suggesting applications in water remediation are feasible.