esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial Patterns of the Ecosystem Variables in Hubei Province  
To evaluate the mean state and changes in the ecosystem in Hubei Province, three ecosystem indicators are chosen in this study (Figure 3). The spatial patterns of the mean values of the three ecosystem variables are generally consistent. All mean annual LAI, NDVI, and GPP show high values in the western regions and low values in the central-eastern regions, indicating increases along the latitudes. High values of LAI, NDVI, and GPP in the west range between 2.5-4 m\({}^{2}\) m\({}^{-2}\), 0.5-0.7 and 16-18 gC m\({}^{-2}\) year\({}^{-1}\), respectively. There are two possible reasons for this spatial pattern. On the one  
Figure 2: Temporal trends in temperature and precipitation indices from 1961 to 2015 at the 52 meteorological stations distributed across Hubei Province. The colored dots represent the magnitude of the trends, and the black outlines indicate the significance at the \(p<0.05\) level.  
hand, the Shennongjia National Nature Reserve is located in the west, which has a high density of vegetation. On the other hand, the low ecosystem values may also result from human activities (e.g., cultivation and urbanization). For instance, the lowest values of LAI and NDVI are shown in Wuhan, which is the most urbanized city, and is also the capital city of Hubei Province.  
Increases in LAI, NDVI, and GPP are overall significant (\(p<0.05\)) for most pixels during 1982-2015. Slight and insignificant negative trends are only shown in some areas in the southern regions, central-eastern regions, and western regions of the study area for LAI, NDVI, and GPP, respectively. Since this study mainly focuses on the interannual variations in ecosystems, the standard deviations (SDs) of the three ecosystem variables are shown here, which are used to quantify the amount of variation in the temporal series data values. Generally, all three datasets exhibit a clear gradient from the west to the east, with high variabilities in the west and low variabilities in the east.
## 3 Proposed Methodology
### The Proposed SEResNet-BiGRU Model
#### 3.4.2 Structure of Gated Recurrent Unit  
To alleviate the vanishing gradient issue, the Gate Recurrent Unit (GRU) was presented as a unique RNN-based paradigm; nevertheless, the memory cells in the design result in a memory capacity demand. The GRU is a simplified variation of the LSTM since it does not contain a distinct memory cell in its design [48]. A GRU network has an update and reset gate that manages the update level of each hidden state. It determines which data needs to be transferred to the next stage and which does not, as seen in Figure 6a. GRU gathers hidden state \(h_{t}\) at time \(t\) from the output of the update gate \(z_{t}\), reset gate \(r_{t}\), current input \(x_{t}\), previous hidden state \(h_{t-1}\) is calculated as:  
\[z_{t}=\sigma(W_{z}x_{t}\oplus U_{z}h_{t-1}) \tag{4}\]  
\[r_{t}=\sigma(W_{r}x_{t}\oplus U_{r}h_{t-1}) \tag{5}\]  
\[g_{t}=tanh(W_{g}x_{t}\oplus U_{g}(r_{t}\otimes h_{t-1})) \tag{6}\]  
\[h_{t}=((1-z_{t})\otimes h_{t-1})\oplus(z_{t}\otimes g_{t}) \tag{7}\]  
where \(s\) is a sigmoid function and \(\oplus\) is an elementary addition operation, and \(\otimes\) is an elementary multiplication operation.  
To address the limitations of a conventional (unidirectional) RNN, [PERSON] and [PERSON] [49] created a bidirectional recurrent neural network (BiRNN) in 1997. Apart from the present input, the output incorporates past and future data at a given period. This is performed by concurrently training the network both forward and backward. To accomplish this, the neurons of a standard RNN are divided into two sections: one concerned with the forward direction and another for the backward direction. The positive neurons' results are not related to the negative neurons' results, and vice versa. This results in the overall structure depicted in Figure 6b. The following equations detail the computations required:  
\[\overrightarrow{h}_{t}=GRU(x_{t},\overrightarrow{h}_{t-1}) \tag{8}\]  
\[\overleftarrow{h}_{t}=GRU(x_{t},\overleftarrow{h}_{t+1}) \tag{9}\]  
\[h_{t}=[\overrightarrow{h}_{t},\overleftarrow{h}_{t}] \tag{10}\]
## 3 Results and Discussion
### Prediction of IENU
#### 3.3.2 Machine Learning

Table 7: The mean result of different ML algorithms across four datasets for IENU prediction. consistency across iterations. Regarding R\({}^{2}\) on the training data, both XGB and CB achieved high mean values of 0.82, suggesting their effectiveness in explaining the variance in the training data. On the testing dataset, XGB maintained its strong performance with a mean RMSE of 3.62 and a mean R\({}^{2}\) of 0.81, indicating its capability to generalize well to unseen data. CB also performed well on the testing dataset, with a mean RMSE of 3.83 and a mean R\({}^{2}\) of 0.78. These results suggest that both XGB and CB exhibited promising performance in capturing the underlying patterns in the training data and making accurate predictions on new, unseen data. RF and GB models also demonstrated competitive performance, with mean RMSE values of 3.76 and 5.10, respectively, and mean R\({}^{2}\) values of 0.80 and 0.62, respectively, on the training dataset. However, their performance slightly decreased on the testing dataset, indicating potential overfitting or a lack of generalization ability compared to XGB and CB. While no studies have targeted predicting IENU precisely, several researchers have focused on nitrogen use efficiency [71; 72; 73]. However, the metrics used in these studies are not directly comparable to those needed in this context. The results presented here build on the study by [13], which applied only the Monod model to predict plant dry matter yield (kg d.m./ha) at each harvest based on plant nitrogen (N) utilization data (kg N/ha). The model, fitted to all available data, showed a strong overall fit, suggesting that nitrogen effectively drives yield dynamics. However, since Monod was applied to the entire dataset without independent validation, there is a risk of overfitting. As a result, its predictive accuracy for new, unseen inputs remains uncertain and may lead to incorrect estimations under different conditions. Figure 3: Comparison results of train/test data using ML models for IENU prediction (data 2).
## 3 System Model
### Noise Model  
In order to study the quality of the communication link, it is necessary to calculate the signal-to-noise ratio, that is, the ratio of the useful signal power to the noise power in the signal. In order to accurately calculate the signal-to-noise ratio, it is necessary to obtain the power spectral density of the noise. Due to the unique nature of the terahertz frequency band, the terahertz wavelength is close to the size of some molecules, so many molecules in the atmosphere will resonate in the terahertz frequency band, resulting in molecular absorption, which in turn leads to frequency-selective attenuation, which also brings the channel. Additional noise, related channel noise research work has attracted extensive attention. [PERSON] & [PERSON] [16] analyzed the total noise in the terahertz band in the satellite-ground link, including molecular absorption noise and antenna noise. [PERSON] [17] studied the atmospheric noise of the communication link between the aircraft and the satellite and the thermal noise of the receiver. Studies have shown that additional atmospheric noise is introduced during molecular absorption due to the atmosphere acting as a blackbody radiator. However, this paper mainly studies the inter-satellite terahertz communication link. Therefore, considering that the noise in inter-satellite communication is mainly thermal noise at the receiver, the power spectral density of thermal noise varies with the receiver noise coefficient, and the noise power spectral density can be expressed as Equation (2).  
\[N(f)=k_{B}T\eta(f)10^{N_{f}/10} \tag{2}\]  
where, \(\eta(f)=\frac{hf}{k_{B}T[\exp(\hbar\
u/k_{B}T)-1]}\), \(T\) is the mean temperature, \(f\) is the frequency, \(N_{f}\) is the noise factor, \(h\) is [PERSON]'s constant, and \(k_{B}\) is [PERSON]'s constant.
## 3 Three-Dimensional Numerical Model
### Material Model and Parameters  
The stratum, grouting material, shield shell and lining were simulated using three-dimensional solid elements. For the grouting material, shield shell and lining, the elastic model was adopted. For soil, the Mohr-Coulomb model was adopted, and the material parameters used in this study are shown in Tables 1 and 2. The binding constraints were set at the interface of the soil-shield shell, soil-grout and grout-lining. As shown in Figure 7, the runway pavement structure consisted of a concrete layer and a pebble layer. In this model, the pavement structure was simplified as linear elastic materials with an elastic modulus of 10,000 MPa. The groundwater infiltration and soil consolidation creep were not considered in this model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Density (g/cm\({}^{3}\))** & **Elastic Module (MPa)** & **Poisson Ratio** \\ \hline Pavement & 2.5 & 10,000 & 0.25 \\ Shield shell & 2.5 & 30,000 & 0.25 \\ Initial grouting & 2.1 & 3 & 0.30 \\ Hardening grouting & 2.1 & 20 & 0.30 \\ Lining & 14.2 & 20,000 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters in the model.  
Figure 6: Three-dimensional numerical model of the shield tunnel.
###### Abstract  
Premature debonding between carbon fiber-reinforced polymer (CFRP) and concrete is a critical issue in structural reinforcement applications, often leading to a significant reduction in the load-carrying capacity of the system. This failure mode is typically initiated by inadequate adhesion at the interface, compromising the effectiveness of CFRP in enhancing the structural performance of concrete elements. To address these issues, this study explores the impact of silica sand on the mechanical and adhesion properties of epoxy resin composites. Initially, this paper investigates the physical and mechanical properties of epoxy resin composites by varying the ratios of silica sand from 0% to 15% by volume. Subsequently, it examines the effectiveness of these composites as sealing materials to enhance the bond strength between CFRP and concrete. Incorporating a 10% silica content improves the mechanical properties of the epoxy resin, with the tensile strength increasing from 29.47 MPa to 35.52 MPa and an elastic modulus from 4.38 GPa to 5.83 GPa. Furthermore, silica sand enhances the adhesion strength between CFRP and concrete, as confirmed by the increase in the pull-out force from 14.21 kN to 18.79 kN. Silica particles improve surface roughness and interlocking, contributing to a better load distribution and stress transfer at the interface. Therefore, silica-filled epoxy resin is an efficient material for CFRP-concrete bonding applications.  
epxosy resin; silica sand; CFRP-concrete bonding; pull-out force; structural reinforcement Article
## 2 Wavelength-Selective Phase-Shifting Digital Holography (WSPS-DH)  
A schematic of WSPS-DH is shown in Figure 1. WSPS-DH is enabled by the wavelength dependency of the intensity change induced by wavelength-dependent phase shifts of interference light. By introducing wavelength-dependent phase shifts to interference light, wavelength information is separated in the polar coordinate plane. A phase shifter such as a mirror with a piezo actuator, a liquid crystal, a birefringent material, a spatial light modulator, an acousto-optic modulator, or an electro-optic modulator is used to generate the phase shifts. When multiwavelength information is recorded, light at wavelengths are not absorbed by a filter, and wavelength-multiplexed phase-shifted holograms are sequentially obtained by changing the phases of interference fringes. Object waves at multiple wavelengths are separately obtained from the recorded wavelength-multiplexed images when phase-shifting interferometry selectively extracts wavelength information [9; 38; 39; 40; 41; 42; 43]. Diffraction integrals are applied to the extracted object waves, and a multiwavelength holographic 3D image is then reconstructed. Since no light at wavelengths are absorbed by a filter, WSPS-DH is expected to achieve high light-use efficiency. When the WSPS technique is compared to temporal frequency-division multiplexing, the number of recordings can be reduced, and measurement speed can be increased.  
Up to now, 2\(\pi\) ambiguity of phase [38; 39; 40; 41] or arbitrary symmetric phase shifts [9; 42; 43] are utilized to extract each object wave rigorously by solving systems of equations. Combining 2\(\pi\) phase ambiguity and arbitrary symmetric phase shifts enables a multiwavelength holographic 3D imaging with only 2\(N\) wavelength-multiplexed holograms and in total less than 1000 nm of movement of a mirror with a piezo actuator [50,51].
# Neutralization Treatment for Recycling Construction-Generated Soils
## 5 Verification Test  
### Neutralization Titration Test  
To evaluate the theoretical equation, experimental tests were conducted to determine the amount of acidic neutralizer required to neutralize the sample construction-generated soil. Two different samples were prepared based on the mixing method. One was prepared by mixing 200 g of the soil by hand, and the other was prepared by mixing 20 kg of the soil with a mixer. The physical and chemical properties of the soil used in the tests are shown in Table 2.  
In this study, three different neutralizers were used, namely, citric acid, succinic acid, and malic acid. They are all weak acids that can be purchased commercially and are easy to handle. Citric acid is an organic compound contained in citrus fruits. It is one of the hydroxy acids, being widely used as a food additive because of its refreshing sour taste. It is a colorless solid at room temperature and is available as hydrated and anhydrous crystals. Succinic acid is an organic compound used as a food additive. It is a clear, colorless solid at room temperature. Malic acid is one of the hydroxy acids; it was first discovered in unripe apples. It is an organic compound used as a food additive and is a colorless solid at room temperature. The chemical properties of the three neutralizers used in this study are shown in Table 3.  
The test procedure is as follows:  
1. Place the construction-generated sample soil in the container.
2. Add 0.5% neutralizer to the mass of the sample soil.
3. After the addition of the neutralizer, mix the sample for one minute. For the manually mixed sample, cover the container containing the soil sample and shake it upside down for one minute. For the mechanically mixed sample, mix the soil for 30 s in the clockwise direction and in the anti-clockwise direction for the next 30 s using a Pellegaia mixer, as shown in Figure 4.
4. After mixing, allow the mixture to settle for one minute, and then measure the pH and EC values. Measure the values at three different places and take the average of the three values. Figure 5 shows the condition of the construction-generated soil during the test.
5. Repeat Steps (2)-(4) until the average pH value is less than 7.
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 2 Monitoring Technology
### Computer Vision-Based Methods
The results from on-site data validation showed that the proposed method has excellent real-time capability, accuracy, and lighting robustness. Figure 4 illustrates the on-site hardware system for monitoring bridge traffic load, which mainly consists of the pavement-based weigh-in-motion (WIM) and the video surveillance system. Figure 4: On-site hardware layout of the traffic load monitoring (TLM) system [70]. [PERSON] and [PERSON] [71] developed a video-based technique for measuring stay-cable tension. They used a mobile handheld camera to record cable vibrations at a distance and incorporated a series of image-processing techniques to eliminate interference from the camera's motion. Then, the real-time frequency was estimated according to the time history of cable vibration, and finally, the real-time tension was determined. Based on this study, [PERSON] et al. [72] proposed a framework based on video measurement to reduce the estimation error of cable tension. The effectiveness and reliability of the proposed method were verified by comparing it with the actual tension of the Fred Hartman bridge in Texas. WSNs provide a convenient and reliable data monitoring tool for BHM systems, which can solve the layout and installation problems of traditional wired sensors. However, in order to avoid the loss of monitoring data during wireless transmission, research on wireless communication technology should continue. As a non-contact monitoring scheme, computer vision-based technology has been applied to the monitoring of structural vibration and displacement as well as traffic load, and has shown outstanding performance.
## 3 Proposed Framework
### BiLSTM-Biaffine Attention  
This method addresses the shortcomings of conventional feature extraction and relational understanding techniques, which often fail to effectively leverage long-distance contextual data and overlook complex interactions between aspect and opinion terms. By integrating the biaffine attention mechanism with BiLSTM, the model enhances aspect  
Figure 8: Four types of features for a sentence.  
level sentiment analysis. BiLSTM excels in capturing long-term textual dependencies and contextual nuances, whereas the biaffine attention mechanism further refines how attention weights are distributed across text elements, particularly between aspect and opinion terms.  
This fusion strategy not only enhances the ability of model to understand context, improving the precision of feature extraction and the capture of complex sentiment relationships, but also significantly boosts the generalization capabilities of model, enabling it to more accurately analyze and predict aspect-level emotions in diverse texts. By adopting this approach, we aim to overcome the limitations of traditional sentiment analysis methods and provide a more nuanced and efficient solution for complex textual sentiment analysis.  
The sequence of hidden textual representations \(H=\{h_{1},h_{2},\cdots,h_{n}\}\) obtained by BiLSTM is first reduced by MLP and then input into the biaffine attention mechanism, which is calculated through:  
\[h_{i}^{a}=\mathrm{MLP}_{a}(h_{i}) \tag{20}\]  
\[h_{j}^{o}=\mathrm{MLP}_{o}(h_{j}) \tag{21}\]  
Then, the adjacency matrix \(R\) is obtained by the biaffine attention mechanism as follows:  
\[g_{i,j}=h_{i}^{aT}U_{1}h_{j}^{o}+U_{2}(h_{i}^{a}\oplus h_{j}^{o})+b \tag{22}\]  
\[r_{i,j,k}=\frac{\exp(g_{i,j,k})}{\sum_{l=1}^{m}\exp(g_{i,j,l})} \tag{23}\]  
\[R=\mathrm{Biaffine}(\mathrm{MLP}_{a}(H),\mathrm{MLP}_{o}(H)) \tag{24}\]  
where \(U_{1}\), \(U_{2}\) and \(b\) are trainable weights and bias; \(\oplus\) denotes connection; \(g_{i,j}\) is the relationship between \(w_{i}\) and \(w_{j}\); \(m\) is the count of relationship types; and \(r_{i,j,k}\) represents the rating of the word for the \(K\)-th relation type of \((w_{i},w_{j})\); the adjacency matrix \(R\in R^{n\times n\times m}\) represents the relationship between words. Biaffine is the integration of the aforementioned steps.
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 1 Introduction
The findings will contribute to ongoing discussions on responsible AI adoption in academic settings, guiding educators, policymakers, and researchers toward effective and ethical AI integration. Therefore, the main contributions of this work are summarized as follows:  
1. Comprehensive Analysis of ChatGPT in Education: This study presents a systematic review of 40 peer-reviewed studies (2020-2024) using the PRISMA framework, offering a structured and evidence-based understanding of ChatGPT's applications in educational settings. 2. Identification of Opportunities for Educational Enhancement: The review highlights how ChatGPT can personalize learning, automate grading, and support educators, displaying its potential to enhance student engagement and optimize teaching efficiency. 3. Critical Examination of Challenges: It addresses major concerns such as academic integrity, AI bias, and the lack of inclusivity, contributing to a balanced understanding of both the benefits and risks of integrating ChatGPT into education. 4. Call for Ethical Governance and Future Research: The study emphasizes the need for ethical policy frameworks and future research on adaptive regulation and long-term pedagogical impact, guiding stakeholders in responsible AI adoption in education. Finally, this paper is organized as follows: Section 1 provides an overview of ChatGPT, Section 2 discusses its applications in education, Section 3 outlines the systematic review methodology, Section 4 explores the identified opportunities and future directions, and Section 5 presents the conclusion along with recommendations for future research.
## 3 Results
### Annual Sasa GPP and Community GPP at the Forest Scale for Each Forest Type  
The annual \(\text{GPP}_{\text{Sasa-community}}\) was similar for the evergreen coniferous forest, mixed forest, and canopy area in the deciduous broadleaved forest, but much greater in the gap area in the deciduous broadleaved forest (Table 4). The annual _Sasa_ GPP at the forest scale was also similar for the evergreen coniferous forest, mixed forest, and canopy area in the deciduous broadleaved forest, but much greater in the gap area in the deciduous broadleaved forest (Table 5). However, when considering the ratio of canopy area and gap area in the deciduous broadleaved forest, the annual _Sasa_ GPP was 2.1 t C ha\({}^{-1}\) yr\({}^{-1}\) in the deciduous broadleaved forest, which was similar to those of the other two forest types. The contribution of _Sasa_ GPP to the forest ecosystem GPP ranged from 11.2% for the deciduous  
Figure 7: Seasonal trends of the 10-day cumulative _Sasa_ community GPP (bars) and photosynthetic photon flux density (PPFD, lines) in the four sites. The 10-day cumulative _Sasa_ community GPP and PPFD indicate integrated values of 10-day _Sasa_ community GPP and PPFD.  
broadleaved forest to 19.1% for the mixed forest (Table 5). Since the annual _Sasa_ GPP was almost the same for each forest type, the difference in the contributions was caused by the difference in the GPP of trees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Estimate & \(p\) Value \\ \hline intercept & \(-\)0.49 & \(<\)0.001 \\ \hline PPFD & 1.15 & \(<\)0.001 \\ \hline SWC & 0.077 & 0.38 \\ \hline Leaf area & 0.56 & \(<\)0.001 \\ \hline T\({}_{\text{A}}\) & \(-\)0.11 & 0.090 \\ \hline RH & 0.048 & 0.55 \\ \hline Adjusted R\({}^{2}\) & 0.85 & \(p\) value & \(<\)0.001 \\ \hline \multicolumn{3}{c}{**(b) Mixed forest**} \\ \hline  & Estimate & \(p\) Value \\ \hline intercept & \(-\)0.75 & \(<\)0.001 \\ \hline PPFD & 1.31 & \(<\)0.001 \\ \hline Leaf area & 0.56 & \(<\)0.001 \\ \hline T\({}_{\text{A}}\) & 0.34 & \(<\)0.001 \\ \hline RH & \(-\)0.071 & 0.32 \\ \hline Adjusted R\({}^{2}\) & 0.75 & \(p\) value & \(<\)0.001 \\ \hline \multicolumn{3}{c}{**(c) Canopy area in Deciduous broadleaved forest**} \\ \hline  & Estimate & \(p\) Value \\ \hline intercept & 0.18 & \(<\)0.01 \\ \hline PPFD & 0.92 & \(<\)0.001 \\ \hline Leaf area & \(-\)0.056 & 0.097 \\ \hline T\({}_{\text{A}}\) & \(-\)0.081 & 0.14 \\ \hline RH & \(-\)0.47 & \(<\)0.01 \\ \hline Adjusted R\({}^{2}\) & 0.75 & \(p\) value & \(<\)0.001 \\ \hline \multicolumn{3}{c}{**(d) Gap area in Deciduous broadleaved forest**} \\ \hline  & Estimate & \(p\) Value \\ \hline intercept & 0.023 & \(<\)0.01 \\ \hline PPFD & 0.96 & \(<\)0.001 \\ \hline Leaf area & \(-\)0.048 & 0.11 \\ \hline T\({}_{\text{A}}\) & \(-\)0.21 & \(<\)0.001 \\ \hline RH & \(-\)0.058 & 0.095 \\ \hline Adjusted R\({}^{2}\) & 0.92 & \(p\) value & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of multiple regression analysis to elucidate factors that control the seasonal trends of the _Sasa_ community GPP in the four sites.