esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GPP Estimation and Validation  
#### 2.4.1 Carnegie-Ames-Stanford Approach (CASA) Model  
The CASA model was selected to estimate the GPP of the study area at a 16-day resolution. This model calculates GPP as the product of absorbed photosynthetically active radiation (APAR, unit: M)\(\cdot\)m\({}^{-2}\)) and LUE (\(\epsilon\), unit: gC-M)\({}^{-1}\)) [41; 42].  
\[GPP(x,t)=APAR(x,t)\times\epsilon(x,t) \tag{3}\]  
where GPP\((x,t)\) is the total fixed GPP of pixel \(x\) in period \(t\), APAR\((x,t)\) is the total amount of absorbed photosynthetically active radiation over the period, and \(\epsilon(x,t)\) is the actual LUE.  
\[APAR(x,t)=Rs(x,t)\times 0.5\times FPAR(x,t) \tag{4}\]  
\[\epsilon(x,t)=\epsilon^{*}(x,t)\times T(x,t)\times W(x,t) \tag{5}\]  
where \(R_{s}(x,t)\) is the total solar radiation, \(FPAR(x,t)\) is the fraction of photosynthetically active radiation absorbed by the vegetation, \(\epsilon^{*}(x,t)\) is the maximum LUE, \(T(x,t)\) is the temperature stress factor, and \(W(x,t)\) is the water stress factor.  
The CASA model was primarily used to calculate net primary productivity (NPP). In this study, referencing previous research, improvements were made to the LUE calculations, as shown in Table 2[43].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vegetation Type** & \(\epsilon_{max}\) **(gC-M)\({}^{-1}\)** \\ \hline GRA & 0.645 \\ FOR & 0.917 \\ WET & 1.137 \\ SHR & 0.552 \\ CRO & 0.645 \\ OTH & 0 \\ \hline \hline \end{tabular} Note: GRA = grassland; FOR = forest; WET = wetland; SHR = shrub; CRO = cropland; OTH = others.  
\end{table}
Table 2: Maximum light use efficiency (\(\epsilon_{max}\)) for different vegetation types.
## 3 Results  
### Synthesis and Characterization of Vancomycin-Functionalized Gold Nanoparticles  
The synthesized vancomycin-functionalized and non-functionalized AuNPs (PEI-AuNP@Van and PEI-AuNPs) were evaluated using a UV-Vis spectrophotometer, and a strong SPR signal was observed at 515 nm (Figure 1a), which is consistent with the characteristic SPR value of AuNPs. Notably, PEI-AuNP@Van demonstrated a high absorbance and a sharp spectrum compared to PEI-AuNPs. TEM characterization confirmed that PEI-AuNP@Van had an actual size of \(\sim\)7 nm (Figure 1b,d). Figure 1c revealed the presence of a surface coating of vancomycin and PEI molecules, confirming the presence of surface functionalization. The crystallinity and diffraction angles of the synthesized PEI-AuNPs and PEI-AuNP@Van were investigated by XRD at angles between 10\({}^{\circ}\)and 80\({}^{\circ}\). Figure 1e shows the assigned peaks for PEI-AuNPs and PEI-AuNP@Van; the peaks at 38.4\({}^{\circ}\), 44.6\({}^{\circ}\), 64.6\({}^{\circ}\), and 77.7\({}^{\circ}\) correspond to the (111), (200), (220), and (311) lattice planes, respectively. The peak located at 38.4\({}^{\circ}\) was of a higher intensity than the other peaks; these results confirmed the face-centered unit cell (FCC) structure of the material. The hydrodynamic radii of PEI-AuNP@Van were recorded, which showed a value of -58 nm; the value for PEI-AuNPs was \(\sim\)23 nm (as indicated in Figure 1f). The increase observed in the hydrodynamic radii indicates the functionalization of vancomycin. As indicated in Figure 1B, the zeta potential of PEI-AuNPs was \(\sim\)43.2 mV; however, when PEI-AuNP@Van was functionalized with vancomycin (PEI-AuNP@Van), the surface charge increased to -40 mV. This increase in zeta potential is in agreement with the high stability of the nanoparticles.  
Confirmation of Vancomycin Functionalization on Gold Nanoparticles by Using Fluorescence Spectroscopy  
Vancomycin is a fluorescent antibiotic with an intense fluorescence emission at 335 nm when excited at 280 nm. The fluorescence characteristics of PEI-AuNPs and PEI-AuNP@Van were examined to confirm the functionalization of AuNPs with vancomycin [(33)]. The emission wavelengths (\(\uplambda\)em) of PEI-AuNPs, vancomycin, and PEI-AuNP@Van at an excitation wavelength of 280 nm are shown in Figure 2. For excitation at 280 nm, vancomycin exhibited intense emission at 335 nm, and PEI-AuNPs and PEI-AuNP@Van were observed at 380 and 420 nm, respectively. As shown in Figure 2, the emission wavelength of PEI-AuNP@Van shifted substantially, with a low-intensity peak at 300 and 335 nm and a high-intensity peak at 420 nm, indicating vancomycin functionalization on the gold nanoparticle surface, corroborating the TEM results shown in Figure 1c.
## 6 White Rot Fungi: An Overview
### Mechanism of Pollution Degradation by WRF
#### 6.5.3 Enzyme-Mediated Oxidation  
Laccase is a multicopper oxidase enzyme, which can degrade a range of phenolic and non-phenolic compounds. Laccase has less redox potential than MnP and LiP, but its activity can be enhanced with the help of small molecules mediators, which can expand the range of substrates/pollutants that can be oxidized by the enzyme [62]. In fact, these mediators can transfer electrons between the enzyme and pollutants, thus facilitating the breakdown and oxidation of the pollutant [63].  
The mediators generally applied in WRF-based bioremediation are the low-molecular-weight redox mediators, such as synthetic dyes and phenolic compounds [64]. These mediators act as electron shuttles, assisting the transfer of electrons between the enzymes and recalcitrant pollutants. They can accept electrons from the ligninolytic enzymes generated by WRF and transport them to the target pollutants, efficiently starting the degradation process.  
The availability of mediators in the reaction medium has numerous valuable effects on enzymatic reactions. Firstly, mediators can directly work together with the enzymes to augment their stability and activity. By acting as electron carriers, mediators can reproduce the active form of enzymes and inhibit their inactivation due to oxidative damage [65]. This process permits the enzymes to effectively take part in the degradation of pollutants over prolonged periods. Secondly, mediators can broaden the range of pollutants that can be treated by WRF. A few recalcitrant pollutants, like polycyclic aromatic hydrocarbons (PAHs) and chlorinated compounds, are intrinsically resistant to direct enzymatic attack by WRF's ligninolytic enzymes. However, the addition of mediators can overcome this limitation by  
Figure 3: Generic pollutants degradation mechanism of WRF.  
allowing indirect degradation pathways. The mediators support the production of reactive radicals, like hydroxyl radicals, which can react with and treat recalcitrant pollutants [66].  
The selection of appropriate mediators is essential for optimizing the bioremediation process. The selection of a mediator relies on the specific pollutants targeted for treatment and the enzymatic systems of the WRF species being employed. Various mediators may exhibit varying efficiencies and selectivity in increasing the degradation of specific pollutants.
###### Abstract  
Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products.  
The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.  
Keywords:sun-induced chlorophyll fluorescence; crop yield; soybean; corn; deep neural network
# Research on Optimization Design of Geometric Parameters of a Novel Frame-Embedded Track (NFET)
## 3 Establishment of a Finite Element Model of NFET
### Load Values  
#### 3.2.1 Train Vertical Load  
For the wheelset load, the most commonly used calculation method is called the quasi-static calculation method. The so-called quasi-static calculation method is to apply the basic principle of static calculation to the static calculation of the track structure, and then, according to the dynamic characteristics of the wheel-rail system, it is simplified as the dynamic increment problem of the wheel load [23; 24]. Many countries in the world use the speed coefficient method to determine the vertical load of the train. It is believed that, when the train is running on the track, the dynamic effect between the wheel and the rail leads to the increase of the wheel load of the rail [25]. However, the value of the speed coefficient varies from country to country. This paper adopts the calculation formula of vertical dynamic wheel load commonly used in China, as shown in Equation (1) [26].  
\[P_{\text{d}}=P\big{(}1+\alpha+\beta_{\text{P}}\big{)} \tag{1}\]  
where \(P_{\text{d}}\) is vertical dynamic wheel load; \(P\) is the static wheel load, the train axle weight is 12.5 t, and the single-strand rail static wheel load is 6.25 t; \(\alpha\) is the speed coefficient, which can be calculated by the formula \(\alpha=0.6\
u/100\), and the running speed is taken as 120 km/h, considering the later promotion and development of the trans; \(\beta_{\text{P}}\) is called the eccentric load coefficient of the curve segment, which is taken as 0, because the paper only considers the straight line interval. In summary, the train vertical load used for the finite element model calculation is 107.5 kN.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 3 Digital Twin Architecture Proposal for Interurban Intelligent Transportation Systems  
The objective of building the digital twin proposed in this paper is to mitigate incidents on the highway by making recommendations to the drivers. The digital twin follows the basic framework for DTs, consisting of a real world containing cameras, sensors, and other devices to detect vehicles, and a digital world that replicates the real world. This digital world receives input data from connected vehicles and from the roadside infrastructure on the highway. The data is processed and used to predict whether there is a congestion or pollution incident on the highway. When an incident is detected, a trigger is set off, which will turn on simulation work, including real-time vehicle data on the highway and the detected incident. According to the output of this simulation analysis, a strategy will be recommended to mitigate the effect created by this incident.  
Figure 2 shows the proposed digital twin architecture. The Aggregation module, as its name suggests, receives and aggregates data from different sources. The Pre-Processing module pre-processes the data and transforms it into the needed format. From the traffic patterns, this module can also detect the incident, or a manual input for the incident detection can be employed. When an incident is detected, a trigger is sent to the Traffic Simulator module to activate it. Aimsun Next [28] has been chosen as the simulation tool. The simulator receives the pre-processed data and the incident, and runs simulations to test the different strategies. The outputs of these simulations are sent to the Decision Engine, which will decide the best strategy to reduce congestion or pollution levels, based on the cause of the incident. This strategy is sent to the real world through informative panels on the highway or to connected vehicles.  
Figure 2: Digital twin architecture.  
We have considered two use cases to address the two types of incidents assessed by our digital twin: the congestion use case and the pollution use case.
## 4 Implementation
### Objectness Clarification Process  
#### 4.4.1 Method to Enhance Objectness  
Based on the experimental results presented above, we could remain the areas of vehicles by properly removing the background without an overreaction to camera shake when the DSSIM threshold was set to 0.3. However, the performance of our model decreased because of damage to objectness caused by strong filtering with high DSSIM threshold values.  
To deal with this problem, we applied two additional handlings for the input images of our model. First, we expanded the ROI areas in the DSSIM-MAP provided to the ROI filter. Because ROI areas are marked with black color in our study, their expansions can be done by image transformation with 'dilate' operations. We used a 3 \(\times\) 3 size kernel for dilation and repeated this transformation five times. With this process, we can restore each object's objectness damaged by ROI filtering. Figure 10 presents two images before and after performing the ROI expansions.  
When comparing Figure 10a,b, the area in the foreground is expanded by dilation, reducing the amount of area of the object that is destroyed.  
Second, we changed the color of non-ROI areas with a more saturated color so that they did not receive attention of a regional proposal network (RPN). This task was performed in the non-ROI color switcher. Since black, white, and red colors are generally used for the vehicle's surface, headlights, and turn signals, an area with these colors can be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DSSIM Threshold** & **Class** & **Precision** & **Recall** & **F1** & **mAP** \\ \hline \multirow{3}{*}{Original} & Bus & 0.54 & 0.86 & 0.67 & \multirow{3}{*}{76.32} \\  & Car & 0.54 & 0.91 & 0.68 & \\  & Truck & 0.45 & 0.95 & 0.61 & \\ \hline \multirow{3}{*}{DSSIM \(>\)0.1} & Bus & 0.49 & 0.80 & 0.61 & \multirow{3}{*}{66.09} \\  & Car & 0.51 & 0.82 & 0.63 & \\  & Truck & 0.47 & 0.87 & 0.61 & \\ \hline \multirow{3}{*}{DSSIM \(>\)0.2} & Bus & 0.52 & 0.73 & 0.61 & \multirow{3}{*}{56.92} \\  & Car & 0.52 & 0.70 & 0.60 & \\ \cline{1-1}  & Truck & 0.52 & 0.74 & 0.62 & \\ \hline \multirow{3}{*}{DSSIM \(>\)0.3} & Bus & 0.59 & 0.64 & 0.62 & \multirow{3}{*}{40.14} \\  & Car & 0.52 & 0.50 & 0.51 & \\ \cline{1-1}  & Truck & 0.50 & 0.48 & 0.49 & \\ \hline \multirow{3}{*}{DSSIM \(>\)0.4} & Bus & 0.67 & 0.23 & 0.35 & \multirow{3}{*}{15.56} \\  & Car & 0.56 & 0.19 & 0.28 & \\ \cline{1-1}  & Truck & 0.41 & 0.25 & 0.31 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inference Performance with different DSSIM threshold levels.  
misunderstood as a vehicle. Thus, we used the more saturated green color (RGB values: 0, 255, 0) for the non-ROI areas.
## 2 Materials and Methods
### Evaluation  
The 70/30 random separation was used to separate training and testing data. Eighty-one knees were subdivided randomly into training and testing sets, i.e., the training set contained 57 knees while the testing set contained 24 knees. Note that the separation was done manually at the knee level, not at the image level, to guarantee that the images from the same knee will not exist in both training and testing.The reason we used 70/30 random separation instead of running the cross-validation is that our dataset is large enough (which contains 4941 slices) to test if the changing pattern of cartilage is predicable.  
We adopted multiple metrics to evaluate the performance of our prediction models. These metrics include Mathew's correlation coefficient (MCC), the F-measure, recall (also known as sensitivity), precision (also regarded as the positive predictive value (PPV)), and area under the receiver operating characteristic (ROC) curve (AUC). The MCC provides a powerful evaluation of accuracy within the machine learning approaches especially in the case where the number of negative and positive samples is not balanced. The F-measure depicts the overall accuracy of the classification as a weighted average for recall and precision for a specific confidence threshold. The area under ROC curve (AUC) provides a comprehensive evaluation of the classifier as the trade-off between true positive rate and false positive rate is adjusted. Below are the formulas for the evaluation metrics.  
\[MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{1}\]  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
Figure 2: CDI locations and the 3 corresponding manual segmentation slices (X1, X2, and X3) on medial tibia.  
\[Precision = \frac{TP}{TP+FP} \tag{3}\] \[F-Measure = 2\times\left(\frac{Precision\times Recall}{Precision+Recall}\right) \tag{4}\]  
In this study, the \"positive\" case is defined as the change (actually loss) of the cartilage since a gradual loss of cartilage is an important sign of knee osteoarthritis. Correspondingly, no change of cartilage is a \"negative\" sign. To label if there is a real change, i.e., ground truth, we need to measure the thickness of each cartilage position in the baseline and follow-up years. Here we consider manual segmentation and CDI as two different ways to measure the thickness of cartilage and aim to compare which way is more reliable and applicable to track the change of cartilage.  
Based on the above definitions of positive and negative, TN (true negative) is defined as a position where thickness does not change within the 12 months, and the ANN model predicts so. TP (true positive) is defined as a position where the thickness value changes within the 12 months and the ANN model predicts so. Correspondingly, FP (false positive) is defined as there is no change in the thickness within the 12 months but the ANN predicts that there is a change, and FN (false negative) is defined as there is a change in the thickness within the 12 months but the ANN predicts that there is no change for that position.
# Enhanced Nondestructive Testing Using Pulsed Eddy Current and Electromagnetic Ultrasonic Techniques
## 3 Choice of Compound Detection Scheme  
There are two principal modes for integrating electromagnetic ultrasound and pulsed eddy current technologies: the system layer and the mechanism layer. Because both techniques are based on the principle of electromagnetic induction, they can gather defect information by analyzing the voltage signal from the detection coil. Using the same excitation source for the integrated probe yields a detection signal that encapsulates both pulsed eddy current and electromagnetic ultrasonic data. Separating the eddy current and ultrasonic signals within the composite signal enables the extraction of detailed defect information and facilitates a comprehensive assessment of material flaws. In the mechanism layer approach, a high-frequency excitation signal is required to generate ultrasonic waves. However, under such high-frequency conditions, the pulsed eddy current method may face challenges in detecting deeper material defects.  
Therefore, this study adopts the system layer composite approach. This strategy involves applying both high- and low-frequency excitation signals to the same detector from a single excitation source, thereby obtaining independent signals for pulsed eddy current and electromagnetic ultrasonic detection. The pulsed eddy current signal is utilized to detect  
Figure 2: Schematic of ultrasonic S-wave detection.  
defects on the upper surface and near-surface of the material, while the electromagnetic ultrasonic signal is employed to identify material corrosion thinning and deeper defects, as depicted in Figure 3.  
This combined mode maximizes the benefits of both pulsed eddy current testing and electromagnetic ultrasonic testing, enabling effective detection of surface defects, deep internal flaws, and corrosion thinning in materials. A schematic illustrating the detection capabilities is presented in Figure 4.
## 3 Proposed TCN Model Architecture
### Model Description  
In this section, we describe the proposed model using TCN. In the proposed system, we map sequence to label for predicting communication data. The proposed TCN-based prediction model is shown in Figure 4. Each of the sequence data has eight samples \(X=\{x_{1},x_{2},\ldots,x_{8}\}\) for QPSK and each sequence has four samples \(X=\{x_{1},x_{2},x_{3},x_{4}\}\) for BPSK. The TCN-based model is placed on the receiver side for generating demodulated data label \(y_{k}\). In the beginning, the input layer receives the sequence data which are complex, including real and imaginary parts via a wireless channel. Thus, the input features of the convolutional layer are 8 and 4. TCN blocks are employed for processing, connected in feedforward propagation. As depicted in Figure 4, four dilation factors were used in the convolution dilation layers 1, 2, 4, and 8. Next, the output of the first TCN block \(Q^{(1)}\) is connected to the input of the second block. The second block produces output \(Q^{(2)}\) which is then connected to the input of the third block. Again, the output of the third block \(Q^{(3)}\) is then connected to the fourth block of TCN. The output of the fourth block \(Q^{(4)}\) is connected to a fully connected layer. Each TCN block is connected in a serial way and the last output is connected to a fully connected layer and is mathematically expressed as follows:  
\[F^{(1)}=Q^{(4)}*W+b, \tag{14}\]  
where \(W\) is the weight matrix and \(b\) is the bias vector. The fully connected layer \(F^{(1)}\) is connected to Softmax layer as follows:  
\[S^{(1)}=Z^{(1)}(F^{(1)}), \tag{15}\]  
\[Z^{(1)}(\vec{a})=\frac{exp^{\mu_{j}}}{\sum_{k}exp^{\mu_{k}}}, \tag{16}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Modulation & BPSK, QPSK \\ FFT size & 128 \\ Transmitter antenna & 2 \\ Receiver antenna & 1 \\ Number of users & 2 \\ Number of RIS elements & 512 (\(32\times 16\)) \\ Number of paths from BS to RIS & 2 \\ Number of paths from RIS to UE & 2 \\ Noise & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset generation parameters.  
where \(Z^{(1)}\) is the Softmax function. Finally, the output of the classification layer is as follows:  
\[y^{(i)}=f(S^{(1)}), \tag{17}\]  
where \(y^{(i)}\) is the ith predicted label of the input feature.