esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Many CGMs are initially designed with a focus on crop growth at the level of individual plots or fields, and their parameter settings and structures tend to be relatively simplified, which makes it difficult to fully capture the complex uncertainties and interactions of meteorological conditions, soil characteristics, and agricultural management practices at the regional scale [46; 47]. Therefore, how to overcome these limitations while maintaining the model prediction accuracy, so that CGMs can be effectively applied in precision agriculture scale, is a key research topic in this field. Fortunately, DA strategies offer a promising solution to effectively integrate the strengths of RS and CGMs and address the aforementioned challenges. DA methods ingeniously combine multisource RS observational data with CGMs simulation outputs, aiming to harness the full potential of both to achieve a more precise and comprehensive understanding of crop growth processes [30; 48]. Compared with the methods using RS technology or crop model alone, the data assimilation method has greater flexibility, computational efficiency, and versatility. RS data have injected rich spatial information into CGMs, filling the spatial dimension gap that models usually lack. On the other hand, CGM can take advantage of the rich details of RS to perform more accurate simulations and predictions [49]. This complementary relationship significantly improves the accuracy of crop yield prediction and agricultural situation monitoring and provides a strong technical basis for the transformation of agricultural production to precision and high efficiency [4; 30; 50]. Therefore, in-depth study of the application of data assimilation in precision agriculture not only helps to overcome the limitations of existing technologies in agricultural monitoring and yield prediction but is also an important driving force to promote the modernization of agricultural production system. In view of this, we rely on Web of Science, Scopus, and Google Scholar tools to carry out in-depth retrieval around keywords such as \"Data assimilation\", \"Crop simulation\", and \"assimilation algorithm\". The results are shown in Figure 2. We find that the research situation of DA in the field of agriculture presents the following remarkable characteristics. From the time dimension, the research heat of DA in the field of agriculture shows a continuous upward trend, reaching an historical peak in 2022 (see Figure 2a). This shows that with the progress of science and technology and the growth of the demand for agricultural modernization, the application research of DA technology in agriculture is receiving more and more attention and investment. From the spatial dimension, China, the United States,
## 3 Results
### Dsc  
As explained in the characterization methods, unlike the flat RMS, the 3D-RMS could not be detached from the substrate. The mass of the 3D-RMS was estimated by measuring the surface area; nevertheless, the presence of the Si substrate during the DSC analysis led to an offset of \(>\)50% in total released enthalpy compared to the planar RMS. The additional inert mass generated by the presence of the silicon nanostructured substrate leads to a deviation of the detected heat flow from the real released heat flow by the sample. Consequently, the resulting value of integrated total enthalpy release during annealing will not be correct, hence a quantitative analysis and comparison were not possible. To allow for a semiquantitatively analysis of the results based on calorimetric investigations, however, the heat flow curve of the 3D-RMS was normalized on the depth of the first exothermic peak of the corresponding heat flow curve obtained from the flat RMS (Figure 8).  
A closer look at both curves reveals that for the Al/Ni 3D-RMS a higher amount of enthalpy is released during initial interdiffusion, \(\Delta\)H\({}_{\text{ID}}\), than for the planar RMS. Integration from the onset of interdiffusion up to 475 K, as shown exemplarily by the gray striped  
Figure 8: Comparison of the DSC heating scans obtained by heating with a constant rate of 0.333 K s\({}^{-1}\). The scans of the planar RMS and 3D-RMS are associated with the black and red curve, respectively. To compare both morphologies, the heat flow curve for the 3D-RMS was normalized on the peak depth of the first sharp exothermic event of the DSC scan with planar RMS.  
area in Figure 8, yields a value of \(-1639\) J/(mol of atoms) for the planar RMS. In comparison to this value, the 3D-RMS releases about 50% more enthalpy during interdiffusion. Furthermore, the peak minima of the exothermic events for the 3D-RMS are shifted to lower temperatures. This change is miniscule for the first two peaks with a difference of around 2 K. The difference for the peak temperature of the third exothermic event by 16 K is more apparent.
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
###### Abstract
The other is composed of the refractive index (\(n\)), the electrical conductivity of fiber-embedded epoxy matrix composite material, and the measurement of T-ray thickness for both glass fiber-reinforced plastics (GFRP) and thin Shim stock films, which are produced as a standard sample with an arbitrary thickness (ranging from tens of \(\upmu\)m to hundreds of \(\upmu\)m) [13]. Thus, the thicknesses for both GFRP and thin Shim stock films are measured using T-ray technology. Carbon fiber-reinforced plastics (CFRP) are conductive, but epoxy matrix is non-conductive [14; 15]. However, the carbon fiber of the CFRP-laminated plate has conductivity, enabling the T-ray characteristics evaluation of glass fiber and carbon fibers [16]. In this study, the results of the experiment on the T-ray were obtained based on the non-destructive evaluation methods using FRP composite materials. In addition, the correlation was performed between the fiber direction and the E-field of the GFRP composites and the CFRP composite-laminated plate according to the refractive index measurement technique, which shows the properties of various materials and the existence of conductivity. A new numerical method of measurement of refractive indexes in reflection and transmission modes was proposed. In addition, we performed a fundamental experimentation and brought a simple testing procedure for acquiring the thickness of samples as an existing NDE method. Here, the measured thickness and the reference thickness of the Shim stock films, which had a standard reference thickness, were compared. In addition, the thicknesses of the GFRP composites with non-conductivity were measured. Therefore, a difference in the time-of-flight (TOF) was utilized to measure the thickness of the GFRP composites using the T-ray. The effectiveness of a T-ray examination was successfully evaluated by comparing and reviewing the specimens using the resonance frequency.
# Filter Media-Packed Bed Reactor Fortification with Biochar to Enhance Wastewater Quality
## 5 Significance of Fortification with Biochar on DOC and P Removal by Sand  
Further analyses were performed to ascertain if the observed higher efficacy of the fortified Sa filter (SaC) did not occur by chance. Firstly, the [PERSON]'s test of equality of variance gave a p-value of \(>\)0.05 for DOC and P removal by the Sa and SaC filters, thus showing that the variance in DOC and P concentrations in their effluents could not be assumed to be equal. Therefore, the significance level corresponding to an assumption of equal variances was used instead. Under this statistic the p-value for \"t-test for equality of means\" was 0.034 for DOC and 0.022 for P removal. These showed that there was a statistically significant difference between the mean DOC and also between the mean P in the Sa and SaC effluents. Also, the linear regression (LR) coefficients (r2) between DOC and pH were 0.33, 0.66, and 0.70 for Sa, C, and SaC, respectively. The P removal efficiency-pH correlations (r) were 0.57, 0.82, and 0.85 for Sa, C, and SaC, respectively. This means that more than 65% of the change in DOC concentration of both C and SaC effluent could be explained by pH, whereas \(<\)50% did in the Sa effluent. Further, the LR model also revealed that 81% and 85% of variation in PO4\({}^{3-}\) sorption in the C and SaC effluent, respectively, was explained by the variation in their pH measure while only 53% did for Sa. Except for turbidity, P-removal in an earlier field-scale study [27] related to this current study was a bit lower. The warmer room conditions (17-25 \({}^{\circ}\)C), less-mixed influent wastewater, small loaded wastewater volumes (0.021 L) per dose, etc., and smaller particle sizes (2-4 mm) used in the current study are believed to have contributed largely to the higher efficacies of the media. Nevertheless, the same was observed in both studies, that the fortification of sand filters with biochar significantly improved its performance in removing P and DOC.
## 2 Theoretical Foundation  
The PD governing formulation [1] is an integral form, which is effective at the discontinuity without facing any difficulties. In the BBPD frame, a material point \(x_{i}\) interacts with other points \(x_{j}\) in the horizon \(H_{x_{i}}\) by bonds. The \(\delta\) denotes the nonlocal range of point \(x_{i}\), as depicted in Figure 2. The PD governing formulation of point \(x_{i}\) at time \(t\) is written as illustrated,  
\[\rho(x_{i})\ddot{\mathbf{u}}(x_{i},t)=\int_{H_{x_{i}}}\mathbf{f}(x_{i},x_{j},u (x_{i},t),\ u(x_{j},t),t)dV+\mathbf{b}(x_{i},t) \tag{1}\]  
where \(\ddot{\mathbf{u}}\) is the acceleration and \(\mathbf{f}\) designates the bond force constitutive function to describe the interaction on the point \(x_{i}\) exerted by point \(x_{j}\) in horizon \(H_{x_{i}}\) at time \(t\). \(u\) and \(dV\) represent the displacement and the volume of point \(x_{i}\), respectively. \(\mathbf{b}\) is the external loading density.  
The relative position between the point \(x_{i}\) and \(x_{j}\) in \(H_{x_{i}}\) is revealed as \(\xi=x_{j}-x_{i}\). \(\eta=u(x_{j},t)-u(x_{i},t)\) denotes the relative displacement at time \(t\).  
The bond force \(\mathbf{f}\) remains zero beyond \(H_{x_{i}}\), which is expressed as  
\[\mathbf{f}(\eta,\xi)=0\ \ \ \forall\eta,\ \ \ if\ \ \parallel\xi\parallel>\delta \tag{2}\]  
The bond force function \(\mathbf{f}\) has the following properties:  
The bond force function \(\mathbf{f}\) is derived from a potential \(\omega\) for the microelastic material [1]  
\[\mathbf{f}(\eta,\xi)=\frac{\partial\omega}{\partial\eta}(\eta,\xi)=\mathrm{f}( \eta,\xi)\frac{\eta+\xi}{\parallel\eta+\xi\parallel}\qquad\forall\eta,\xi \tag{3}\]  
in which \(\mathrm{f}(\eta,\xi)\) is a scalar-valued function and \(\frac{\eta+\xi}{\parallel\eta+\xi\parallel}\) denotes the direction vector of the bond after the movement of the points.  
It satisfies the balance of linear momentum, as  
\[-\mathbf{f}(\eta,\xi)=\mathbf{f}(-\eta,-\xi)\qquad\forall\eta,\xi \tag{4}\]  
It satisfies the balance of angular momentum, as  
\[(\eta+\xi)\times\mathbf{f}(\eta,\xi)=0 \tag{5}\]  
The PMB model describes the microelastic material [24] in which the bond force function is obtained from a microelastic potential, expressed as  
\[f(\eta,\xi)= \frac{\partial\omega}{\partial\eta}(\eta,\xi)= \frac{\partial(\frac{1}{2}\xi s^{2}\xi)}{\partial\eta}=cs\frac{\eta+\xi}{ \parallel\eta+\xi\parallel}\qquad\forall\eta,\xi \tag{6}\]  
Figure 2: Diagram of the BBPD.  
where \(c\) denotes the micromodulus function representing the bond stiffness and the numerical micromodulus solved in this paper will be used here. \(s=(|\eta+\xi|-|\xi|)/|\xi|\) reveals the elongation of a bond.  
The damage at point \(x_{i}\) is expressed in Equation (7)  
\[D(x_{i},t)=1-\frac{\int_{H_{x_{i}}}\mu(x_{i},t)dV}{\int_{H_{x_{i}}}dV} \tag{7}\]  
in which \(\mu(x_{i},t)\) represents the damage function of a bond,  
\[\mu(x_{i},t)=\left\{\begin{array}{ll}1&\qquad s<s_{0}\\ 0&\qquad else\end{array}\right. \tag{8}\]  
where \(s_{0}\) denotes the critical elongation, which will be obtained according to fracture energy [24] or fracture strength [35; 36].
## 8 Technical Challenges for 6G Development
### Implications of Expanding Carrier Bandwidths  
In the realm of 6G, which operates across bandwidths of several tens of GHz, designing a radio that utilizes a single carrier across the entire spectrum is nearly unfeasible. This issue is particularly pronounced when attempting to achieve uniform high performance and energy efficiency throughout the bandwidth, all while maintaining the linearity of the RF front-end circuits. In the case of 5G systems that operate in mmWave bands, the maximum bandwidth permitted for a carrier is capped at 400 MHz. For services operating at close range, even within THz bands, the bandwidth does not exceed 1 GHz [270]. This observation is significant because the shift toward mmWave and THz bands was initially driven by the prospect of accessing significantly more bandwidth than what traditional systems offer. Currently, mmWave frequency commercial devices combine four 100 MHz carriers, but the 3 GPP standards cap the maximum bandwidth for mmWave system carriersat 400 MHz. In practice, bandwidths greater than 100 MHz necessitate the use of carrier aggregation. For example, achieving a bandwidth of 10 GHz would require the aggregation of 100 such carriers. This introduces a substantial challenge, especially because calibrating radio hardware across these carriers is daunting at high frequencies where phase noise is a significant concern. With bandwidths this large, radio performance can vary greatly from the lower to the upper end of the band. The ultimate bandwidth and number of carriers that can be used are limited by the need for integrated antenna RF circuits and compliance with safety regulations concerning the effective isotropic radiated power. These constraints present a significant hurdle in design [270].
## 3 Materials and Methods
### Model Architectures
#### 3.3.1 Deep Q-Network
Once \(\pi_{\theta}(a|s)\) exceeds \((1+\epsilon)\pi_{\theta_{k}}(a|s)\), the minimum constraint activates, causing the term to reach a ceiling of \((1+\epsilon)\pi_{\theta_{k}}(a|s)\). Consequently, the new policy does not gain further benefit by diverging significantly from the previous policy. * Advantage is negative: Suppose the advantage for that state-action pair is negative; its contribution to the objective function diminishes to: \[L(s,a,\theta_{k},\ \theta)\ =\ \max\bigg{(}\frac{\pi_{\theta}(a|s)}{\pi_{ \theta_{k}}(a|s)},\ (1-\epsilon)\bigg{)}A^{\pi_{\theta_{k}}}(s,a).\] (8)Since the advantage is negative, the objective will increase if the action becomes less likely, i.e., if \(\pi_{\theta}(a|s)\) decreases. However, the max function in this expression imposes a cap on how much the objective can rise. Once \(\pi_{\theta}(a|s)\) falls below \((1-\epsilon)\pi_{\theta_{k}}(a|s)\), the maximum constraint activates, causing the term to reach a ceiling of \((1-\epsilon)\pi_{\theta_{k}}(a|s)\). Consequently, again, the new policy does not gain further benefit by diverging significantly from the previous policy. In this study, the PPO-clip algorithm was utilized to train a stochastic policy using an on-policy approach, where it explores by sampling actions based on the most recent iteration of its stochastic policy. The degree of randomness in action selection (the vulnerability type) is influenced by initial conditions and the training process. As training progresses, the policy tends to become less random since the update mechanism encourages the exploitation of rewards that have already been found. However, one drawback of PPO is that this process may cause the policy to get trapped in local optima. In the context of smart contract vulnerability detection, the clipping mechanism in PPO plays a vital role in maintaining policy stability during training. Due to the imbalanced nature of the dataset and the sparse reward structure (where correct classification often yields a binary reward), policy gradients can become highly volatile, especially when the agent overreacts to rare classes or noise in the data. The clip range in PPO ensures that updates to the policy remain conservative, preventing drastic shifts that could otherwise lead the model to prematurely overfit to dominant classes or fall into local optima. By bounding the policy change during optimization, the agent incrementally improves its decision making over time, maintaining robustness across classes and achieving more consistent learning across episodes. This is particularly valuable in environments like ours, where sequential decisions are driven by subtle graph structures rather than dense or uniform feedback. The full details can be found in Algorithm 2 below.
## 3 Results and Analysis  
### PCA and Combination Weights  
This study utilized IBM SPSS Statistics 25.0 to perform a principal component analysis on the indicator data. Table 2 presents the results of the principal component analysis for the years 2012 and 2020. It can be observed that the cumulative contribution rates of the first three principal components are all greater than 85%, indicating that these three components capture the majority of the characteristics of the five indicators. Specifically, PC1 has high loading values for FVC, LAI, and GPP, PC2 has a high loading value for Wet, and PC3 has a high loading value for LST. Therefore, we can summarize the extracted principal components as follows: PC1 represents the vegetation factor, represented by FVC, LAI, and GPP; PC2 represents the humidity factor, represented by Wet; and PC3 represents the thermal factor, represented by LST.  
Based on the principal component analysis results, the weights for each indicator can be calculated for each year from 2012 to 2020. These weights are then combined with the weights obtained from the analytic hierarchy process (AHP) to calculate the composite weight coefficients. Taking 2012 and 2020 as examples, the final weights are shown in Table 3.
## 2 Materials and Methods
### Large Mask Inpainting--LaMa
#### 2.2.3 Masks  
In LaMa, the masks play the role of clouds that result in the difficult information visualization in satellite images. Each \(x^{{}^{\prime}}\) training example is a real product of a training dataset overlaid by a synthetically generated mask.  
Based on [16], the used masks represent synthetic clouds and were generated in large sizes with uniform characteristics in polygonal chains and dilated by random widths, rectangles of arbitrary proportions and thin shapes, as shown in Figure 4. The set of masks belongs to three categories: random medium masks, random thick masks and random thin masks.
## 4 Experiment
### Results on Sensitivity Analysis of the EDA Method  
The experiment in the following Table 2 presents the result of sensitivity analysis for the hyperparameters \(\alpha\) in the EDA + SentiGAN model. There are three different ratios: 0.1, 0.2, and 0.3. If the changing ratio \(\alpha=0.3\) and \(\alpha=0.5\) for the negative class, the macro self-BLEU-2 is 0.9622 and 0.9504, respectively, which is higher than \(\alpha=0.1\). A high value of self-BLEU-2 indicates a high similarity of these generated sentences by SentiGAN. On the other hand, the Macro BLEU-2 must be higher because there is high semantic similarity between the generated sentences and the original training sentences. Therefore, the \(\alpha=0.1\) is the best hyperparameter which means that these generated sentences are more diverse and can provide the BERT classifier to learn more diverse training sentences.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Parameter** & \multicolumn{2}{c}{**Positive**} & \multicolumn{2}{c}{**Negative**} \\ \cline{2-6}  & \(\alpha\) & **Macro** & **Self-BLEU-2** & **Macro** & **Macro** & **Macro** \\  & & **BLEU-2** & **Self-BLEU-2** & **BLEU-2** & **Self-BLEU-2** \\ \hline EDA + & & & & & \\ SentiGAN & 0.1 & 0.7398 & 0.9532 & 0.6340 & 0.9420 \\ EDA + & & & & & \\ SentiGAN & 0.3 & 0.7310 & 0.9664 & 0.6728 & 0.9622 \\ EDA + & & & & & \\ SentiGAN & 0.5 & 0.6580 & 0.9554 & 0.6492 & 0.9504 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model tuning results on EDA hyperparameters of the GAN-based model.
## 5 Analysis of Experimental Results
### The Defect Is Located in the Unheated Area  
The second heating experiment aims to assess the algorithm's ability to detect pipeline defects in LTZ. First, a defect-free signal of the pipeline at room temperature is collected over a period. Then, an iron block measuring \(15\times 10\times 10\) mm is attached 2 m from one end of the pipeline as indicated by Defect 1 in Figure 8. The collected signal during this phase is shown in Figure 16.  
Figure 16 displays the reflected signal of the pipeline containing defects at room temperature. An echo is clearly visible at 2 m, corresponding to the position of the iron block. Afterward, one end of the pipeline is heated, completing the process after approximately 90 min, with the outer surface reaching 80 \({}^{\circ}\)C. DI curves for the LPM and BSS methods over time are illustrated in Figure 17.  
Figure 15: Variation curves of damage index for BSS method and LPM method with time.  
Figure 17 shows that the DI curves calculated by both methods remain low and stable when the pipeline is defect-free at room temperature. The defect is introduced at the 30 th minute, resulting in a noticeable step change in both curves, indicating that both algorithms effectively detect defects at room temperature. After 45 min, heating begins at one end of the pipeline. Following this, the DI of the BSS method increases continuously, eventually reflecting that the influence of the temperature change surpasses that of the defect. This could lead to missed detection in the pipeline with an NTF. In contrast, the DI for the LPM method remains stable despite the rising temperature, showing minimal sensitivity to temperature changes.  
This experiment demonstrates that the LPM method effectively mitigates the impact of NTF in pipelines and maintains strong detection capabilities for defects in LTZ.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## Appendix A  
In addition to the qualitative analysis, we also investigated the relationship between the LES of the delayed power waveform commonly used in satellite-based GNSS-R surveys and the snow depth. The LES-SD scatterplot drawn from November 2018 to February 2019 without limiting the satellite observation angle shows there is some relationship between snow properties and LES or SNR. However, the results are not as obvious as SR; therefore, for we employ SR for our analysis.