esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Growth and Wood Degradation Ability  
_C. laeye_ LE-BIN 1700 demonstrated the ability to grow and to destroy birch, alter and pine sawdust (Figures 1A and 7). The fungal mycelium completely colonized the Petri dishes in 10 days of growth on the birch and alter sawdust agar media, while the growth rate was about two times lower on the pine sawdust media (Figure 1A). _C. laeye_ demonstrated the ability to grow on agar media with different lignocellulosic components (i.e., lignin, CM cellulose, xylene, starch and pectin), and the highest growth rates were reported on media with starch and pectin (Figure 2). Under the same conditions, _T. hirsuta_ LE-BIN 072, a wood-destroying white rot fungus, showed approximately 2.5-3 times higher growth rates [30] than those of _C. laeye_. Fast-growing white rot fungi are known to cause more rapid wood decay than slow-growing fungi [42]. Therefore, the decrease in weight and the specific gravity of sawdust during the cultivation of the fast-growing _T. hirsuta_ and the slower-growing _C. laeye_ was evaluated in 60 and 120 days, respectively. At the same time, both fungi showed comparable weight loss and reduced specific gravity of sawdust at the end of cultivation (Figure 7). A distinctive feature of the _C. laeye_ was a significantly higher degradation and saccharification capacity on birch sawdust compared to alder and pine, which in turn correlated with a large increase in biomass during semi-solid cultivation of fungus on birch sawdust (Figure 3B). The _T. hirsuta_ was characterized by a more pronounced destruction of deciduous sawdust (i.e., birch and alder) as compared to coniferous one (pine sawdust) (Figure 7). Coniferous wood has, as usual, lower decay rates than deciduous wood [43]. Wood decay rates are significantly influenced by the chemical composition (content and composition of extractive compounds) and/or structural characteristics of the wood (composition and cellulose/hemicellulose/lignin ratio). A strong negative correlation between wood decay rate and extractive compound content was reported in [43]. Analysis of wood chemical composition showed that alder wood has the highest extractive compound content (6.6%) compared to birch and pine wood (2.9% and 5.2%, respectively) [44]. Each tree species is characterized by a unique class of extractive compounds. For example, alder extracts contain various diarlheptanoids and tannins, whereas pine extracts contain stilbenes, terpenoids and other compounds [45; 46]. According to [44], alder wood has a lower cellulose and hemicellulose content (44.1% and 33.1%, respectively) and a higher lignin content (22.0%) than birch wood (45.4%, 38.8% and 17.7%, respectively). It is noteworthy that it was the secretome of the alder sawdust medium (GP-B) that contained a significant amount of aryl alcohol oxidases (AA3_2 CAZy), which were absent in the other media (Figure 4, Supplementary Figure S1).
## 1 Introduction
Previous tests [1; 2; 3; 4; 7; 8] were mostly conducted on simply supported beams that are readily capable of achieving a sufficient anchorage length of GFRP bars. Thus, the bond characteristic and anchorage length of GFRP bars have been overlooked in evaluating the flexural strength and ductility of beams. On the other hand, it is frequently observed that the sufficient anchorage of FRP bars is hardly achieved in the exterior beam-column connections because of the limited column width. In addition, the exorbitant bending resistance feature of the hardened polymer blocks the hook fabrication for the anchorage of FRP bars. Overall, it is not an easy task to achieve the required anchorage length of FRP bars used for longitudinal reinforcement of beams in the exterior beam-column connections. However, there is still a lack of test data on the flexural performance of beams with GFRP bars in the beam-column connections, although the anchorage of longitudinal reinforcement significantly influences the flexural strength and ductility of such an element. Furthermore, it is extremely rare to examine the reliability of the code design equations for the effective stiffness and moment capacity of beams with insufficient anchorage length of GFRP bars. This study aims to examine the effect of poor anchorage length of GFRP bars used for a longitudinal reinforcement on the flexural and shear performances of high-strength concrete beams in the exterior beam-column connection. Three beam-column connection specimens with GFRP bars and three companion specimens with conventional steel reinforcing bars were tested under reversal cyclic loads applied at the free-end of the beam. For beam-column specimens governed by flexure, the effective inertia of the moment under service load, moment capacity, and ductility of the beam were evaluated and compared with the design equations of the code provision [5; 6], wherever feasible. In particular, the displacement ductility ratios of beams with GFRP bars were compared with those measured in the companion beams with steel bars with respect to the longitudinal reinforcement index. The initial inclined cracking strength and the ultimate strength of the beam governed by shear were compared with the predictions calculated using design equations [5,6].
# Solid Phase-Based Microextraction Techniques in Therapeutic Drug Monitoring
## 3 Sample Pretreatment--Solid Phase-Based Microextraction Techniques
### Molecularly Imprinted Polymers and Thin Film Microextraction
MIPs and TFME have been applied for the extraction of drugs in several biological specimens such as plasma, serum and urine. [PERSON] et al. [91] developed a methodology for the determination of quetiapine and clozapine in 200 \(\mu\)L plasma and 500 \(\mu\)L urine, with extraction by TFME and analysis by high performance liquid chromatography coupled with ultraviolet detection. The authors obtained LOQ values of approximately 0.05 \(\mu\)g/mL for plasma samples and 0.01 \(\mu\)g/mL for urine samples, concluding that the method showed high potential as a powerful pretreatment technology for routine TDM in these biological samples. [PERSON] et al. [93] established a method for the determination of seven tricyclic antidepressants and metabolites in 700 \(\mu\)L of plasma. With extraction by joining thin film molecularly imprinted polymer techniques and analysis by UHPLC-MS/MS, LOQ values between 1 and 5 ng/mL were obtained, and the method was applied to plasma samples from patients who were prescribed these antidepressants. More recently, [PERSON] et al. [96] published a method for the determination of two \(\beta\)-Lactam antibiotics (imipenem and piperacillin) in plasma and bronchoalveolar lavage samples, using TFME for sample preparation. With LC-MS/MS analysis, and although without validation data presented for the bronchoalveolar lavage samples, they obtained an LOQ value of 0.01 mg/L for plasma samples. The authors concluded that the work demonstrates that only a small fraction of biologically active antibiotics reaches the site of infection, providing clinicians with a high-throughput tool for studies on personalized TDM when tailoring the dosing strategy to an individual patient. Another possible application of the MIP extraction procedure is the work of [PERSON] et al. [97] for monitoring hydrogen peroxide in plasma samples, fully validated and applied to unprocessed biological samples. [PERSON] et al. [98] have used MIPS for the identification of carcinoembryonic antigens in plasma samples, concluding that the developed extraction technique appears to be suitable for point-of-care applications in biomedical and clinical analysis.
## 2 Materials and Methods
### Base Classifiers  
Selecting base classifiers and meta-classifiers plays a vital role in establishing stacking ensemble classifiers. In order to select the appropriate base and meta-classifiers and compare them with stacking ensemble classifiers, we established seven commonly used classifiers, including support vector machine, k-nearest neighbors, decision tree, logistic regression, random forest, gradient boosting decision trees, and extreme gradient boosting decision trees. The following provides a brief introduction to each classifier.
## 7 Application Layer in WMSN  
The application layer in WMSN provides heterogeneous functionalities and supports many services, which include: 1) Multimedia processing and source coding techniques that depend on the application-specific requirements and capability of the hardware. 2) Effective communication with other application programs over the network to support collaborative in-network multimedia processing mechanisms. 3) Traffic Management and Admission Control.
## Results
### Description of Fertilization Effects on Fluxes, Optical Data, and Vegetation Characteristics
Factors Controlling the Parameters of Light Use Efficiency Equation (LUE\({}_{p}\), LUE\({}_{f}\) and Fesc)  
The relative importance analysis with \"_Img_\" method shows that LUE\({}_{\text{P}}\) is the variable with the highest explained variance (R\({}^{2}\) = 0.67 \(\pm\) 0.054), followed by Fesc (R\({}^{2}\) = 0.62 \(\pm\) 0.06) and LUE\({}_{\text{f}}\) (R\({}^{2}\) = 0.46 \(+\) 0.06) (Figure 5). The variable that explains the most variance of LUE\({}_{\text{P}}\) is Ts (R\({}^{2}\) = 0.36 \(\pm\) 0.06), followed by LAI (R\({}^{2}\) = 0.13 \(\pm\) 0.05), Canopy N\(\%\) (R\({}^{2}\) = 0.06 \(\pm\) 0.04) and H (R\({}^{2}\) = 0.05 \(\pm\) 0.04). The main predictor of LUE\({}_{\text{f}}\) is %graminoids (R\({}^{2}\) = 0.15 \(\pm\) 0.07), followed by Ts (R\({}^{2}\) = 0.13 \(\pm\) 0.08), LAI (R\({}^{2}\) = 0.07 \(\pm\) 0.05), and Canopy N\(\%\) (R\({}^{2}\) = 0.05 \(\pm\) 0.03). The main predictor of Fesc is clearly %graminoids (R\({}^{2}\) = 0.52 \(\pm\) 0.03), followed by soil moisture (R\({}^{2}\) = 0.03 \(\pm\) 0.04) and Canopy N\(\%\) (R\({}^{2}\) = 0.02 \(\pm\) 0.02), the latter contributing only marginally. The results of the relative importance analysis for GPP, F\({}_{760}\), and F\({}_{760\text{leaf,fw}}\) show the importance of LAI that controls the seasonality of canopy structure and APAR (Figure S15). When substituting as predictor Ts with Ts - Ta, we found slightly better results than Ts alone when predicting GPP, F\({}_{760}\), and F\({}_{760\text{leaf,fw}}\) (Figure S8). However, including Ts - Ta does not improve the overall prediction, as the contribution to R\({}^{2}\) of LAI decreases, but the total R\({}^{2}\) remains similar. When predicting LUE\({}_{\text{P}}\), LUE\({}_{\text{f}}\), and Fesc, Ts - Ta is a worse predictor of LUE\({}_{\text{P}}\) than Ts (R\({}^{2}\) = 0.28 \(\pm\) 0.05). Figure 5: Relative importance analysis with \"Img\"(Lindeman, Merenda and Gold) method of Light use efficiency of photosynthesis (LUE\({}_{\text{P}}\)), Light use efficiency of fluorescence emission at 760 nm (LUE\({}_{\text{f}}\)) and escape probability of sun-induced fluorescence at 760 nm obtained from forward runs of SCOPE (Fesc\({}_{\text{fw}}\)). Predictors included in the analysis are: soil moisture, Shannon biodiversity index (H), canopy nitrogen content (N\(\%\)), surface temperature (Ts), relative abundance of legumes (%legumes), relative abundance of graminoids (%graminoids) and leaf area index (LAI). Error bars (1 SE) are calculated through bootstrapping (\(n=1000\)), but are not shown in the figure. They are however reported in the result section.
## Appendix B
### Multipath Propagation Factor  
In the process of calculating the multipath effect in the electromagnetic wave transmission process, it is first necessary to establish a vertical profile model of the actual ground on the transmission path from the radiation source to the reconnaissance receiver, and then analyze the possible arrival of the reconnaissance receiver antenna according to the electromagnetic wave reflection model. The radiation source reflects the signal. Finally, according to the electromagnetic characteristics of the reflecting surface and the transmission path, the reflection intensity and phase lag of the reflected signal are calculated, and then the composite signal of the direct wave and the reflected wave at the reconnaissance receiver is obtained. The calculation equation of multipath propagation factor is as follows:  
\[F=f_{d}|\sqrt{1+x_{2}+2 xcos\alpha}| \tag{13}\]  
where \(x=\frac{\Theta\varphi_{s}Df_{r}}{f_{d}}\) is the generalized reflection coefficient. \(\rho_{0}\) and \(\rho_{s}\) represent the electromagnetic reflection coefficient and roughness factor of the reflecting surface. \(D\) is the divergence factor of the reflecting surface. \(f_{d}\) and \(f_{r}\) represent the value of the amplitude of the pattern coefficient on the direct path and the reflected path, respectively. \(\alpha=\frac{2\pi\lambda}{\lambda}+\varphi+\beta_{r}-\beta_{d}\) is the total phase difference between direct wave and reflected wave. \(\sigma\) is the path length difference between the direct wave and the reflected wave. \(\varphi\) is the phase angle of reflection coefficient. \(\beta_{r}\) and \(\beta_{d}\) represent the phase of the reflected and direct directions of the pattern factor.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 2 Related Works
### Gaussian Predictive Processes (GPP) Model  
Gaussian predictive process (GPP) models are developed to address the significant computational cost associated with calculating the covariance matrix in Gaussian predictive process (GPP) models, particularly when modeling large spatiotemporal datasets [36]. GPP models work best on modeling spatial data points that are distributed over a large distance [49]. To date, relatively little research on GPP models has been conducted on spatiotemporal data since the current literature tends to focus on the theoretical features of GPP models, which makes them an interesting topic for research [50].
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 3 Results
### Water Quality and Fish Population  
The results of physico-chemical analyses of water in parts A and B of the reservoir before and after their connection showed that the water quality was appropriate for the fish population, especially cyprinids (carp family). The pH level measured in the pit lake ranged from 7.7 to 8.5 in part A and from 7.8 to 8.4 in part B. The concentration of the total phosphorus ranged from 0.03 to 0.28 and from 0.03 to 0.24 mg P/L (parts A and B, respectively), with a mean value after the connection equal to 0.082 mg P/L (Table 4). Biochemical oxygen demand ranged from 2.3 to 9.0 in part A, and from 2.1 to 5.2 in part B. Conductivity was changeable, and in part A, reached a maximum of 0.880 mS/cm in 2012, then declined to a maximum equal to 0.694 mS/cm in 2014. The maximum value of conductivity in part B was observed in 2013 and was equal to 0.726 mS/cm. The average concentration of total nitrogen varied among years and parts of the reservoir with the maximum in part A (2012) equal to 3.2 mg N/L and the minimum also observed in part A (2013) and equal to 1.3 mg N/L. In 2014, in both parts, the average concentration was 2.6 mg N/L.  
During the period 2017-2018, the following native fish species were observed: roach, perch, pike, bleak and non-native Gibel carp (_Carassius gibelio_).
## 2 Data and Methods
### Climate Indices
#### 2.2.1 El Nino-Southern Oscillation (ENSO)  
ENSO shifts irregularly back and forth between El Nino and La Nina every few years. Each phase triggers disruptions of temperature, precipitation, winds, and ocean circulation and thus has profound global impacts. El Nino conditions occur when abnormally warm waters accumulate in tropical latitudes of the central and eastern Pacific Ocean. Consequently, tropical rains that usually fall over the western Pacific shift eastward. During El Nino, regions in the western Pacific typically face severe drought conditions, while northwestern North America is more likely to experience warmer-than-average temperatures and more rain. La Nina conditions occur when cooler-than-average waters accumulate in the central and eastern tropical Pacific and tropical rains shift to the west. Seasonal precipitation impacts are generally opposite those of El Nino. The Nino 3.4 Index is an indicator used by NOAA for monitoring the ENSO state. It tracks the running three-month average of sea surface temperature (SST) in the east-central tropical Pacific between 120\({}^{\circ}\) and 170\({}^{\circ}\) W, called the Nino 3.4 region. This running three-month average SST is compared to a previous 30 year (1981-2010) mean SST to calculate SST anomaly. The El Nino condition is considered to be present when the temperature anomaly is +0.5 \({}^{\circ}\)C or higher, indicating  
Figure 1: Global long-term (2007–2019) monthly mean solar-induced fluorescence (unit: mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\)).  
the east-central tropical Pacific is significantly warmer than usual. La Nina conditions exist when the index is \(-0.5\)\({}^{\circ}\)C or lower, indicating the region is cooler than usual. The Nino 3.4 Index is essentially a time series of SST anomaly. Its monthly time series is processed by NOAA and available at: [[https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/](https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/)]([https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/](https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/)) (accessed on 15 June 2021).
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 4 Results and System Evaluation
### Decision-Making Scenarios  
The system's automated decision-making capability is illustrated through the following scenarios:  
**Scenario 1:** Critical Token Depletion. When a bus's token value drops below 150,000 (30% of maximum), the system automatically conducts the following:  
* Schedules maintenance within the next 48 h.
* Adjusts route assignments to minimize additional wear.
* Notifies maintenance crews with specific component diagnostics.  
**Scenario 2:** Adverse Weather Conditions. During extreme weather (below \(-20\)\({}^{\circ}\)C or above \(25\)\({}^{\circ}\)C), the following occurs:  
* The token depletion rate is dynamically adjusted.
* Maintenance thresholds are automatically lowered.
* Additional diagnostic checks are scheduled.  
**Scenario 3:** Operational Anomalies. When detecting unusual patterns (excessive stops and rapid token depletion), the following occurs:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter for Comparison** & **Traditional Service Management System** & **Service Management Using Blockchain** \\ \hline Process Holder & Chief mechanic or head of a transport company & The rules are written in the private blockchain \\ \hline Accounting of the order and time of service provision & Manually based on the manufacturer’s instructions & Automatically based on the actual operating conditions of the vehicle \\ \hline The number of errors in the process of organizing maintenance & Constantly, as it depends on the human factor & It is reduced as the blockchain is used, as the system is calibrated as it is filled with data \\ \hline Transportation safety & Depends on the decency of the staff & Depends on the calibration of the system, the level increases over time \\ \hline The speed of making a decision about the need for maintenance & Several days & Instantly \\ \hline Managing repair and maintenance history & Centralized, depends on specific people & Decentralized, can be transferred to a new management \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & Not controlled. Identified upon the fact of breakdowns or according to the maintenance regulations \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with traditional management system.  
Figure 3: Encoding and package preparation scheme (GDEPZ).  
* Immediate diagnostic procedures are initiated.
* Real-time alerts are sent to operations management.
* Preventive maintenance is scheduled based on pattern analysis.
## 4 Simulations and Discussion
### Preprocessing the Sample Data  
To estimate the relationship between the ideal star centroid position \(x_{0}\), the actual star centroid position \(\hat{x}_{g}\), and the systematic error \(\sigma_{\hat{x}_{g}}\) under the different Gaussian widths through the BA-ELM method, we devised a numerical simulation method to obtain the relationship function among them. In this paper, the sampling window was set to be \(3\times 3\) pixels, and two situations were simulated. The first case was when \(\sigma_{PSF}\) is smaller than the size of the sampling window, in which the systematic error is only caused by approximation error. The second case was when \(\sigma_{PSF}\) becomes larger than the size of the sampling window, and the systematic error includes both approximation error and truncation error. Thus, \(\sigma_{PSF}\) was set to be 0.3 and 0.671 for these cases, respectively. The same method can be employed to obtain the compensation template and eliminate the systematic error under different Gaussian widths.  
In our experiment, the star spot was projected onto the position \((5,7)\) of the image plane, and the star centroid position in the \(x\) direction ranged from 5 to 6. The pixel was subdivided evenly into 1000 parts, so the ideal star centroid position \(x_{0}\) was 5.0001, 5.0002, 5.0003, \(\cdots\), 6, that is, the simulation step was 0.001 pixels. In each simulation step, the actual star position \(\hat{x}_{g}\) and its systematic error \(\sigma_{\hat{x}_{g}}\) defined in Equation (15) were recorded. The 1000 samples were acquired to estimate the relationship function between \(\hat{x}_{g}\) and \(\sigma_{\hat{x}_{g}}\) when the Gaussian width was 0.3 or 0.671, as shown in Figure 7a,b, respectively, and they were used as input training samples for the BA-ELM model.  
In Figure 7, the maximum systematic error was 0.054 pixels when \(\sigma_{PSF}\) was 0.3, while under the condition \(\sigma_{PSF}\) = 0.671, the maximum systematic error increased to more than 0.1 p  
Figure 7: The relationship curve between \(\sigma_{\hat{x}_{g}}\) and \(\hat{x}_{g}\). (**a**) Gaussian width \(\sigma_{PSF}\) = 0.3. (**b**) Gaussian width \(\sigma_{PSF}\) = 0.671.  
enough to degrade the star sensor performance. We proposed the BA-ELM compensation method to decrease the systematic error. The 1000 samples were utilized to train the BA-ELM to estimate the relationship curves, and then the fitting accuracy and prediction accuracy were calculated in order to evaluate the BA-ELM compensation method.