esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Station Data Processing  
Firstly, we used the LoggerNet 4.0 software to format the eddy covariance, radiation, and gradient data obtained from the observation station, achieving the conversion from TOB3/5 to TOB1. Then, the EddyPro-7.0.6 software was used for outlier removal [50], time correction [51], coordinate transformation [52], and frequency adjustment [53]. Finally, the initial data with an observation frequency of 30 min were obtained.  
Due to objective factors such as human activities, environmental changes, and equipment maintenance, the initial data obtained in the preliminary steps might have some issues, such as abnormal, interrupted, or missing data. Therefore, comprehensive interpolation was required for the initial data. In this study, the Max Planck Institute filling tool was used: [[https://www.bgc-jena.mpg.de/](https://www.bgc-jena.mpg.de/)]([https://www.bgc-jena.mpg.de/](https://www.bgc-jena.mpg.de/)) (accessed on 25 July 2023). The coverage of eddy covariance data after preprocessing, quality control, and interpolation was 100%. This indicated that all stations had continuous, uninterrupted, high-quality eddy covariance observation data with a 30 min interval for the year 2020.  
The 30 min CO\({}_{2}\) data obtained through interpolation are collectively referred to as the net ecosystem exchange (NEE). Using the night/day comprehensive splitting method [3; 54], GPP and ecosystem respiration (Reco) [55] can be derived from NEE. The calculation formula is as follows:  
\[\text{GPP}=\text{Reco}\ -\ \text{NEE} \tag{1}\]  
The conversion from GPP with an observation interval of 30 min to daily GPP can be achieved through Equation (2). In addition, in order to achieve temporal resolution matching between GPP and SIF data, it was necessary to performed simple mathematical operations on daily GPP and ultimately convert it into station data with the same temporal resolution as satellite products (average GPP at 8-day intervals).  
\[\text{GPP}\ (\text{gcm}^{-2}\text{day}^{-1}\ )=\!\!\sum_{i=1}^{48}\text{GPP}( \text{CO}_{2}/\text{m}^{2}\text{s})\ \times 1800\times\frac{12}{44} \tag{2}\]
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 3 Method Validation  
We used 24 h averages on global scales of the values derived from real GPS precise ephemerides provided by GeoForschungsZentrum Potsdam (GFZ) and the precise ephemerides of the BDS-3+QZO simulation constellation, to validate the usefulness EGAPM and assess the method of satellite visibility and DOPs estimation. Constellation parameters of BDS-3+QZO and GPS are shown in Table 1.  
Estimations of the number of visible satellites and corresponding reference values at 5\({}^{\circ}\) CEA are shown in Figure 3. Using EGAPM, the root mean squares (RMS) between the estimated and reference number of visible satellites are 0.10 for 32 GPS satellites and 0.05 for 30 BDS-3+QZO satellites. Generally, the discrepancies between estimated and reference values at high latitudes are larger than other locations.  
The underestimating rates for the GPS and BDS-3+QZO constellation are calculated (Table 2), to assess the DOPs underestimation with a 5\({}^{\circ}\) CEA. The average underestimating rates are 7.95-11.07%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**BDS-3+QZO**} & **GPS** \\ \hline Orbit Type & MEO & QZO & GEO & MEO \\ Nominal Number & 24 & 3 & 3 & 32 \\ Inclination & 55\({}^{\circ}\) & 55\({}^{\circ}\) & 0\({}^{\circ}\) & 55\({}^{\circ}\) \\ Altitude (KM) & 21,528 & 38,950.6 (Apogee) & 35,786 & 20,200 \\ Period(s) & 46,404 & 86,170.5 & 86,170.5 & 43,080 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constellation parameters of third-generation BeiDou navigation satellite system (BDS-3)+ Quasi-Zenith orbit (QZO) and Global Positioning System (GPS) [28; 29; 30].
# Edible Films Based on Ovine Second Cheese Whey with Oregon Essential Oil
## 3 Results and Discussion
### X-Ray Diffraction (XRD)  
As illustrated in Figure 7A, the diffractograms of the films WPI, SCW, and the mixtures of both (WS) reveal the occurrence of structural changes in two angular zones, depending on the sample. These changes may involve inter- or intra-molecular contacts of the polymeric chains or aggregates that become crystalline, as evidenced by the observation of peaks rather than bands. The low-angle zone, which corresponds to peaks and bands at 20 in the range of 3\({}^{\circ}\) to 15\({}^{\circ}\), shows the structural changes in the films. The diffractograms of the WPI films exhibit a poorly structured system, while the addition of SCW at varying proportions (all WS films) results in the formation of crystalline structures with peaks at 3.8, 5.8, and 9.8\({}^{\circ}\). In the high angle disturbance zone (15\({}^{\circ}\) to 30\({}^{\circ}\)), the WS 1:2 and SCW films exhibit variations indicative of a higher degree of structuring, suggesting enhanced homogeneity in the inter- or intra-molecular contacts. Denaturing agents, such as heating, produce conformational changes in whey proteins that can induce the formation of novel peaks in diffractograms, as has been reported by other authors [47]. The addition of SCW powder to film-forming solutions involves the incorporation of denatured whey proteins that may affect the crystalline structure of films.  
Figure 7: XRD patterns for the films with WPI and/or SCW (**A**) and the films with oregano essential oil (**B**) described in the materials and methods section. For arrows explanation see text.  
The diffractograms of the WS films to which oregano essential oil was added are shown in Figure 7B. In the low-angle zone, peaks showing a crystalline component are also observed in all of the samples, as has been mentioned above. In the WS 1:2 films with added EO, the relative intensities of peaks 3.8, 5.8 and 9.8\({}^{\circ}\) were found to be higher than in the WS 1:2 sample without any addition. Furthermore, in the high-angle zone, two small peaks (indicated by arrows in Figure 7B) appear in the WS 1:2 O1 and O2 films. It may be possible that the essential oil interacted with the denatured whey proteins, present in greater proportion in these samples, and increased the crystallinity of the films. [PERSON] et al. [48] have reported that the incorporation of orange peel essential oil improves the crystallinity of WPI-based films.
## 2 Exposure
### Dosimetry  
\"Dose\" in nanoparticle toxicology may be a more difficult concept to grasp than dose regarding, for example, highly soluble substances, as the dose does not always increase linearly with mass due to inherent tendency for NMs to agglomerate as a consequence of their high surface energy. Of course, the issue is also about which dose metric (metrology) do we need to apply in concentration-dose-response functions [5].  
Dose-response relationships need to be established for different dose metrics: mass, surface area, redox activity, _etc._ Then, we need research clarity on whether we are speaking only of the biologically available dose, and of what kind of NM. Availability is, for example, affected by aggregation and disaggregation, agglomeration and de-agglomeration. Considerations involve _in vitro_ assessment, validity and round robin testing, dosimetry and the relationship to _in vivo_ exposure and dosimetry. This should cover exposure dose, medium dose, delivered dose, intracellular dose, subcellular dose, as well as organism dose, tissue dose, _etc._ (p85, 89 _et passim_) [2].  
Concerning dose and inhalation, there are currently very few measurement techniques that simulate aspiration efficiency and the deposition in the trachea-bronchial and alveolar regions, which results in a mismatch between the concentration measured, the concentration inhaled, and the estimate of the deposited dose. To obtain health-related exposure information, modeling techniques have to be applied to the data. This lack of health-related exposure data, among other factors, makes the establishment of occupational exposure limits challenging. Clearly a number of specific recommendations for researchers and funders on the study of NMs and dose are contained in the above considerations.  
**Recommendation 10**: Researchers need to be placed in a position to confirm the particular dose they are working with, in realistic exposure conditions. This includes detailed characterization of NM dispersions _in situ_ under the assay conditions. Researchers need to be able to measure with determinable accuracy the internal dose in cells, as opposed to just reporting the mass of particles added to the media. The dosimetry must take account of issues such as NMs dispersion in the exposure media (which affects the available dose of NMs) and the potential for labels to leak out of labeled-NMs under biological conditions, confounding the interpretation of the uptake studies data.
# Field and Numerical Investigation of Taihu Resort Cut Slope Failure in Suzhou, China
###### Abstract  
This study aims to determine the cause of a landslide located at Taihu Resort in Suzhou, Jiangsu Province, China. Field monitoring has been conducted, including two-dimensional finite element method (FEM) numerical analyses to examine slope stability under different working conditions. The monitoring data indicated that the slope deformation was triggered in two phases: at a certain period when the slope was cut and moderate rainfall occurred in November; and afterward, when a continuous heavy rainfall intensity hit during the monsoon rainfall, which accelerated sliding of the slope. Numerical simulations show that stress and maximum displacement distributions formed and redistributed during the excavation process, where the shear strain occurred mainly in the front layer of soil. Additionally, the factor of safety of the slope decreases gradually with the increase in the excavation rate. During 15 days of continuous rainfall infiltration, the slope under heavy rainfall indicates a minimum factor of safety (FoS). Specifically, the slope excavation followed by heavy rainfall intensities contributed significantly to the landslide.  
Keywords:landslide; stability analysis; excavation; strength reduction; rainfall infiltration; factor of safety +
Footnote †: journal: Applied sciences
## 2 Anisotropic Damage Model
### Damage Evolution Equations
#### 2.3.1 Tension Damage  
An elastic-brittle strain-softening model was used for tension damage [43], as shown in the stress-strain relationship in Figure 2.  
Hence, the damage variable can be obtained as follows:  
\[D=\left\{\begin{array}{rl}0&\overline{\varepsilon}<\varepsilon_{t0}\\ 1-\frac{\lambda_{\mathrm{t}}\overline{\varepsilon}}{\varepsilon}&\varepsilon_ {t0}\leq\overline{\varepsilon}<\varepsilon_{tu}\\ 1&\overline{\varepsilon}\geq\varepsilon_{tu}\end{array}\right., \tag{17}\]  
where \(\lambda_{\mathrm{t}}=\sigma_{\mathrm{tr}}/\sigma_{t0}\) is the residual tensile strength coefficient; \(\sigma_{\mathrm{tr}}\) is the residual tensile strength; \(\varepsilon_{t0}=\sigma_{t0}/E_{0}\) is the elastic ultimate tensile strain; \(\varepsilon_{\mathrm{tu}}\) is the ultimate tensile strain before complete failure; \(\overline{\varepsilon}=\sqrt{\langle\varepsilon_{1}\rangle^{2}+\langle \varepsilon_{2}\rangle^{2}+\langle\varepsilon_{3}\rangle^{2}}\) is the equivalent tensile strain and \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(\varepsilon_{3}\) are the first, second, third principal strains, respectively.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
###### Abstract  
Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for fault diagnosis using periodic map spectrum feature maps as input features. In this study, the optimal decomposition parameters were first found adaptively using the VMD improved by the positive cosine optimisation algorithm; then, the transformer vibration signal was modally decomposed, and the periodic map spectrum features were plotted according to the differences in the energy distribution of the different modal components at different frequencies. Finally, the GoogLeNet classification model with the improved attention mechanism assigned different weights to the feature maps in both spatial and channel dimensions to improve the classification accuracy and achieve transformer fault diagnosis. The experimental results verified the validity of the adopted feature map and the proposed model, and the accuracy was significantly improved to 99.04% compared with the traditional GoogLeNet, which is valuable for engineering applications.  
fault diagnosis; sine-cosine optimisation; adaptive modal decomposition; periodic map spectrum; improving GoogLeNet +
Footnote †: journal: Applied sciences
## Materials and Methods
### Geophysical and Statistical Analysis  
The ecophysiological analysis focused on the assessment of the impact of the environmental variables on ecosystem fluxes GPP and ET. The effect of environmental parameters on GPP and ET was determined during the period from June to August, when xerothermic conditions are likely to occur, foliage development of black locust is completed and when understorey grass vegetation is wilted, fully decomposed and, thus, does not affect ecosystem fluxes.  
As the environmental parameters have simultaneous and added impacts on fluxes, the distinction of the partial impact of each environmental variable is quite difficult under field conditions. For the assessment of the partial impact of each variable on ecosystem fluxes on a daily basis, we applied Generalized Additive Models (GAMs) with the use of the mgcv package in R language [51]. GAMs allow the fitting of non-linear relationships between variables by using a sum of smooth functions of predictors, while maintaining the interpretability of general linear models. GAMs were applied to mean daily GPP and ET separately, using as independent variables the mean Rg, VPD, SWC and EVI, as a proxy of the vegetation phenological stage. Air temperature was not used as an independent factor in the analysis, as it is included in the VPD calculation, and it was also found to have a minimal and non-significant effect on ecosystem fluxes during the preliminary analysis. Each GAM was run as a single model that included all independent variables simultaneously. The partial dependence of GPP or ET on each independent variable was obtained by changing the selected independent variable for its whole range during the study period while keeping the other parameters constant at their mean values. In order to reduce biases, we used the Generalized Cross Validation (GCV) approach, which is used for smoothness selection and is the default fitting procedure in the mgcv package [51]. The \(\lambda\) value, which is a smoothing parameter in GAMs that does not presume the prior shape of the relationship between the variables, was set to 0.6 for each function to avoid overfitting, without further biases to the general shape of the curves. The number of observations for each variable was 215 (i.e., the number of the respective days during the study period) and the significance of the models was tested at least at \(p<0.01\). Data visualization was performed with the use of OriginPro version 2022b (OriginLab Corporation, Northampton, MA, USA).
## 4 Conclusions  
The analytical techniques presented in this study were very useful for the characterization and investigation of potential migrants from polyester-type coatings. Each technique provided complementary and valuable information. Firstly, coating materials were identified by ATR-FTIR spectrometer and confocal Raman microscopy. All samples presented an internal coating of polyester-type. To the best of our knowledge, this is the first time that confocal Raman microscopy has been used to characterize polymeric coatings, providing a characterization of the sample by imaging the different layers. Then, the use of GC-MS analytical methods made it possible, through a non-targeted screening, to identify potential volatile and semi-volatile migrant compounds present in polyester coatings. Several compounds were identified, including citrates, phthalates, adipates, alkanes, aldehydes, carboxylic acids, alcohols, diisocyanates, and fatty acids, among others. In addition, the signal series detected in the MALDI-TOF MS analysis, combining a homemade database with the common starting monomers used in the formulations of polyester coatings, allowed the elucidation of the starting substances used in the formulation of the samples analyzed, such as monomers and additives. Finally, the migration experiments carried out with ethanol 95% and subsequent analysis by LC-MS\({}^{\text{n}}\) revealed the presence of various polyester oligomers with their corresponding fragmentation pattern. The results of this study confirm the complex task of trying to identify the potential migrants from coatings. For these chemical migrants there are no toxicological data available nor migration limits established to date. Consequently, a risk assessment is necessary for these compounds, which should not be underestimated.  
Conceptualization, R.S., P.P.L. and A.R.B.d.Q.; methodology, R.S., P.P.L., J.B. and A.R.B.d.Q.; investigation, A.L.-C. and P.V.-L.; writing--original draft preparation, A.L.-C.; writing--review and editing, R.S., P.P.L., J.B., M.I.S. and A.R.B.d.Q.; supervision, R.S., J.B. and A.R.B.d.Q.; project administration, R.S. and A.R.B.d.Q.; funding acquisition, A.R.B.d.Q. All authors have read and agreed to the published version of the manuscript.  
The study was financially supported by the Ministerio de Ciencia, Innovacion y Universidades, by Fondo Europe de Desarrollo Regional (FEDER), and by Agencia Estatal de Investigacion ref. no. PGC2018-094518-B-I00 \"MIGRACOATING\" (MICIU/FEDER, UE).
## 2 Materials and Methods
### In Situ Measurements
#### 2.2.2 Canopy Reflectance  
The AMSPEC II spectra system [32] was installed at 31 m height of the flux tower to measure the reflectance of the canopy. The system consists of a UniSpec-DC dual-channel spectrometer (PP Systems, Amesbury, MA, USA), a PTU-D46 automatic tilt rotation device (FLIR Systems, Goleta, CA, USA), a computer and some accessories [30]. The spectrometer is equipped with both up-looking and down-looking fibers and covers a spectral range from around 300 to 1100 nm with 256 contiguous bands and 3.1-3.4 nm of FWHM. The up-looking fiber equipped with a cosine receptor is used to collect hemisphere incident irradiance, while the down-looking bare fiber simultaneously measures canopy radiance with an instantaneous field of view (IFOV) of 20\({}^{\circ}\). Then, the canopy reflectance was calculated to be the ratio of the canopy radiance and incident irradiance, see [30] for details. The canopy reflectance was sampled every 2-3 s and recorded every 15 min from sunrise to sunset [33].
# Traits and Transports: The Effects of Personality on the Choice of Urban Transport Modes
## 6 Discussion
### Discussion on the Findings
#### 6.1.2 Personality Traits and Use of Public Transportation  
Use of public transportation is positively related to Agreeableness and Environmental personality, and negatively related to Conscientiousness. The relation between use of public transportation and Agreeableness correspond to findings representative for the Swedish population [23]. This might be explained by a friendly attitude toward other travelers and that public transportation is suitable for those who cooperate with others. The relation between public transportation and Environmental personality is consistent with previous research, which have found that a high degree of Environmental personality increases the likelihood of choosing an environmentally friendly mode [18]. High degree of Agreeableness and Environmental personality are positively associated with altruistic behaviors [15; 17]. We suggest that future research investigate the relation between degree of altruism and use of public transportation. Regarding Conscientiousness, we argue in a similar way as for car driving. People with a high degree of Conscientiousness may perceive public transportation as less efficient and punctual, compared to their more easy-going and impulsive counterparts.  
Our findings on the Big Five personality traits and usage of public transport are inconsistent with some previous research [1]. In contrast to a previous study, which found that use of public transport was positively related to Extraversion and negatively related to Neuroticism, we did not find any significant effects of these traits. The inconsistency might be explained by cultural differences between Sweden and Iran, and/or methodological differences in terms of scales and statistical analyses. A national representative study of the Swedish population, using the same personality scale as in the present study, found that public transportation is negatively related to Extraversion. Again, this indicates the limitations of generalizing the results of the present study.
###### Abstract
The proposed algorithm can effectively reduce the localization error in 3D scenarios. The rest of the paper is organized as follows. Section 2 reviews the classical DV-Hop localization algorithm and analyzes the causes of errors at each stage. Section 3 introduces the principle and process of the proposed algorithm, including its extension to 3D scenarios. Algorithm performance evaluation and simulation results are shown in Section 4. Finally, Section 5 concludes this paper.
###### Abstract  
One of the aims of vibration signal analysis is the assessment of the condition of technical objects at each stage of operation based on a cause-and-effect relationship between the technical condition of the device and the features of the vibration signal available through the measurement experiment. The essential features of the signal (in the form of certain measures) are available through appropriate operations on the measurement signal. The problem arises when the measuring signal is not a fully deterministic one, it shows features common to deterministic and random signals. Consequently, some simple periodic signal measures such as spectrum  
mechanical system and their material parameters. The features of the random signal are related to the fluctuation of the input and the heterogeneity of the internal structure of the materials, and the technological tolerance of the geometrical dimensions of the individual elements of the system, in particular, the imperfect fit of the mating surfaces and their mutual clearances. Consequently, the frequency components of the measurement signal are characterized by some degree of uncertainty. Their amplitude and frequency cannot be precisely predicted and may differ from the frequency components of the reference signal. The evolution of the technical condition (the appearance of specific damages of increasing intensity) causes further changes in the measurement signal. New or previously existing signal frequency components appear or are modified [4]. All this means that the assessment of the technical condition on the basis of only selected frequency components of the spectrum of the measuring signal may not be reliable. These difficulties can be avoided by the frequency-frequency analysis of the diagnostic signal proposed by the author. The entire frequency spectrum is converted into an image (hereinafter referred to as the F-F image) in which the frequency components and their higher harmonics form characteristic patterns. Image patterns reflect the unique distribution of frequency components generated by particular vibrational processes. Instead of analyzing a chosen frequency component of the spectrum, entire unique patterns appearing in the F-F image are analyzed. It should be emphasized that several image patterns can be superimposed since several vibration processes coexist.