esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Attention Mechanism (AM)  
AM can be explained by the perception mechanism in the human brain. The brain selects only necessary information from the outside world rather than perceiving all information at once. Similarly, in AM, attention weight is defined to prioritize important information and ignore irrelevant data. This process aims to give more attention to pertinent information. To map the first \(x_{t}\) to \(h_{t}\) in the AM process, use the following equation:  
\[h_{t}=f_{1}(h_{t-1},x_{t});\ h_{t}\in R^{s}, \tag{10}\]  
Here, \(f_{1}\) represents a non-linear activation function, \(h_{t}\) stands for the hidden state at time \(t\), and \(s\) denotes the size of hidden state. Additionally, the formulas mentioned below are employed to create an attention mechanism.  
\[a_{t}^{k}=V^{T}tanh\ (W_{1}[h_{t-1},C_{t-1}]+W_{2}x^{k}), \tag{11}\]  
\[\beta_{t}^{k}=softmax\left(\alpha_{t}^{k}\right)=\frac{exp\ \left(a_{t}^{k}\right)}{\sum_{i=1}^{n}exp\ \left(a_{t}^{k}\right)}, \tag{12}\]  
\[x^{k}=\left(x_{1}^{k},x_{2}^{k},x_{3}^{k},\ldots,x_{m}^{k}\right)^{T}\in R^{m}, \tag{13}\]  
where \(x^{k}\), represents a sequence of features, \(h_{t-1}\) denotes the previous hidden state in the LSTM unit, and \(C_{t-1}\) refers to the prior cell state in the LSTM unit. The vector \(V\), along with matrices \(W_{1}\) and \(W_{2}\), signify the model's learnable parameters. The length of \(\alpha_{k}\) vector is \(m\), and its \(ith\) item reveals the significance of the kth input feature sequence at time \(t\). \(\beta_{k}\) denotes an attention weight that contains an attention score for the kth feature sequence. The weighted input feature sequence \(z_{t}\) can be derived as follows.  
\[z_{t}=\left(\beta_{t}^{1}x_{t}^{1},\beta_{t}^{2}x_{t}^{2},\beta_{t}^{3}x_{t}^{ 3},\ldots,\beta_{t}^{n}x_{t}^{n}\right)^{T}, \tag{14}\]  
Afterwards, the initial input features of \(x_{t}\) are substituted with the \(z_{t}\) obtained from here, which leads to the update of the attention model. The end result of \(z_{t}\) becomes the novel input of the LSTM-DL model. Thanks to the attention weight, redundant feature sequences can be excluded, while important input feature sequences can be emphasized. The model's process flow is depicted in Figure 3 below [42].  
Figure 2: Bi-LSTM architecture [32].
## 5 Simulation and Mathematical Analysis
### Optimization of Communication Performance (The Case of DFO Compensation)  
Figure 12 shows the simulated DFO examination scenario for the LEO-SIN. This figure shows the trajectory of the satellite over 10 min and the three positions of the UE. Based on the different positions of the UE, the position and velocity can be calculated and the DFO can also be calculated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extrapolation Error** & **Proposed** & **SGP4** & **TBE** & **HPOP** \\ \hline MAE (km) & 0.19 & 0.3357 & 0.5589 & 0.07467 \\ Complexity & Simple & Simple & Simple & Complex \\ \hline \hline \end{tabular}
\end{table}
Table 6: The MAEs of the ephemeris optimization algorithm errors.  
Figure 11: Comparison of ephemeris optimization algorithm errors.  
Figure 12: The DFO scenario of the LEO-SIN.  
Figure 13 shows the DFO estimation performance of the proposed method. As Figure 13c show, within 35 h the DFO error estimate remains within 400 Hz. The sub-carrier spacing used in this simulation is 15 kHz. To meet the requirements, the DFO error should be less than 4% of this spacing, which is 600 Hz. Therefore, using the optimized ephemeris for DFO calculations allows for predictions up to 35 h.
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 3 Results and Discussion
### Fiber Performance Analysis
#### 3.1.2 Fiber Adsorption Properties  
The oil absorption rate test was conducted to assess the capacity of the three fibers to absorb oil, with the findings displayed in Table 3.  
The adsorption properties of the three fiber materials showed significant differences. Polypropylene fiber has the best adsorption performance, and its oil absorption rate is as high as 5.423. The oil absorption capacity of glass fiber is less than half that of polypropylene fiber, whereas basalt fiber's absorption rate is merely one-fifth of that of polypropylene, standing at just 0.879. This difference may be related to the density characteristics of fiber materials. The density of polypropylene fiber is only 0.9 g/cm\({}^{3}\)-0.92 g/cm\({}^{3}\), which is about one-third of the density of the other two fibers. In contrast to the other two types of fibers, polypropylene fiber boasts a greater surface area per unit weight, allowing it to absorb a larger quantity of oily substances [53]. While basalt and glass fibers share a comparable density of approximately 2.7 g/cm\({}^{3}\), their oil absorption rates differ markedly. This disparity arises from the inadequate dispersion of basalt fibers, which prevents the monofilament fibers from making complete contact with oily materials, thus restricting their absorption capacity [54]. Fibers that exhibit superior dispersion create a more intricate network within the bitumen. When the bitumen is subjected to load, the fiber can transfer and disperse stress in multiple directions. This process mitigates the negative impacts of vehicular loads, bolsters the bitumen's resistance to cracking, and ultimately enhances the longevity of the roadway [55; 56].
## 2 Related Work
### Hybrid Approaches and Post-Processing
The ML methods achieved better accuracy, particularly in challenging conditions (e.g., winter low flows) where traditional methods struggled. [PERSON] et al. (2023) introduced a \"bias learning\" component in data-driven streamflow models and reported that this approach significantly outperformed traditional statistical bias correction methods across 273 U.S. watersheds [38]. [PERSON] and [PERSON] [39] proposed a framework where Random Forest and LSTM were used as post-processors to improve streamflow predictions in 28 ungauged basins in Korea. By correcting errors from both a process-based model (PEHM) and a data-driven model (LSTM), their hybrid post-processing approach achieved notable improvements in prediction accuracy--particularly when combining both model outputs for post-processing. Similarly, [PERSON] et al. [40] systematically evaluated statistical post-processing methods across 300 Australian catchments and found that simple transformations like the Box-Cox (\(\lambda\) = 0.2) method significantly improved the reliability and sharpness of monthly and seasonal streamflow forecasts from a dynamic hydrological model. These examples highlight the versatility of hybrid modeling strategies--whether through input stacking or post-processing--in enhancing the accuracy, robustness, and operational usability of hydrological forecasts.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Correlative Methods
#### 2.5.5 Correlation to Spectral Indices
A maximum calibration R\({}^{2}\) = 0.84 (average of 2016 and 2017) was obtained using data from within the period mid to late July. However, in validation (using 2016 to predict 2017 and vice versa) the best result was obtained by combining the captures from late July and early August (R\({}^{2}\) = 0.35 and 0.43, PE = 14.8 and 13.3% for 2016 and 2017 seasons, respectively). The modified WOrId FOod STudies (WOFOST) model, which used the satellite-estimated leaf area index from the maximum vegetative period (per year) as an additional input, achieved an improved validation result (R\({}^{2}\) = 0.62 and 0.59, PE = 10.9 and 11.1% for 2016 and 2017, respectively). [PERSON] et al. [80] utilized measurements of canopy cover, canopy volume, and vegetation indices obtained from weekly RGB and bi-weekly multispectral UAS imagery in the prediction of tomato yield. Crop growth and growth rate curves were fitted to time series data, and a first derivative used to estimate maximum growth rate, day at a specific event, and duration of growth periods. Yield prediction models explained 65 percent of the variance in actual harvest yields, with information on phenotypic features derived from RGB images carrying more weight in the model than the multispectral data (NDVI and Excess Green (ExG), where ExG = 2g-r-b, where r, g and b are red, green and blue channels normalized to the sum of these three variables). In summary, the correlations of within-season attributes to yield is higher for annual than perennial fruit crops. The use of individual orchard, season-specific models has been recommended.
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 3 System Design
### Safety Protection Device Design
#### 3.3.2 Mine Car Holding Rail Braking Device  
In order to further increase the safety of the inclined mine car in the transport of construction materials, when there is wire rope breakage and hoist equipment failure, the inclined mine car can be timely braking and does not occur in the inclined shaft jump rail overturning accident, resulting in property damage or personal safety accidents, there is an inclined mine car chassis installed holding rail brake device. The device structure diagram in Figure 6.  
Figure 6: Structure diagram of holding rail brake device for inclined mine car.  
In Figure 6, when the mine car is running normally, the opening spring is always in a compressed state under the tension of the wire rope, and the tie rod is connected to the wire rope through a series of connecting devices. When there is an abnormal situation leading to the occurrence of a sports car accident, the hoist wire rope slackens, then the spring opens under pressure because the external force disappears and restores to the original state. Its elongation drives the block fixed on the tie rod with backward movement. When the tie rod drives the block with backward movement, the upper part of the tie rod iron will also slide backward. In the process of sliding, the iron collision jaws make the jaws rotate, which then leads to the separation of the jaws and the support block. The brake springs because the squeeze pressure disappears and restores to the original state. This drives the guide rod with forward movement, driven by the guide rod, held down to hold the lower track, which completes the procedure to the rail to stop the mine car. Its brake after the photo is shown in Figure 7.
## 3 Methodology
### UFPA Propagation Model  
For the development of the research, the empirical propagation model called UFPA [25] was selected, which consists of several measurements carried out in the Amazonian region and is represented by (3).  
\[PL=K_{1}\log_{10}\left(d\right)+K_{2}\log_{10}\left(f\right)+K_{0}, \tag{3}\]  
where \(d\) is the distance between the transmitter and receiver in meters, \(f\) is the frequency in MHz, \(K_{1}\) and \(K_{2}\) are adjustable parameters according to the environment, and \(K_{0}\) is a correction factor expressed by (4):  
\[K_{0}=a-bX. \tag{4}\]  
Parameters \(a\) and \(b\) expressed in (4) are also obtained using the linear least squares technique, and factor \(X\) is calculated from (5).  
\[X = \frac{\left(Ht+\,Hr\right)\lambda}{\left(0.1\,*\,H_{OB}\right)} \tag{5}\]  
where \(Ht\) and \(Hr\) are the heights of the transmit and receive antennas, respectively; \(\lambda\) is the wavelength; \(H_{OB}\) is the average of the ambient obstructions.  
The model is used to estimate the signal coverage radius with the aid of (2) to establish reliable communication, thus replacing the received power with the sensitivity threshold in dBm. Therefore, it is necessary to adjust the parameters of the propagation model and define the threshold through a statistical analysis based on the data obtained during the measurement campaign.
## 2 Materials and Methods
Based on the aforementioned parametric study, the optimized antenna design parameters were arranged to achieve proper impedance matching, as tabulated in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value (mm)** & **Parameters** & **Value (mm)** \\ \hline \(L\) & 80 & \(e\) & 60 \\ \(W\) & 90 & \(f\) & 31 \\ \(a\) & 24.25 & \(g\) & 5 \\ \(b\) & 64 & \(h\) & 12 \\ \(c\) & 68 & \(i\) & 9.6 \\ \(d\) & 28 & \(j\) & 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antenna design parameters. Figure 4: Parametric study of the gap between patch and ground plane; the gap cannot be increased randomly because of the design constraint of the satellite structure. Figure 3: (**a**) Parametric study on the length of \(e\) and (**b**) parametric study on the length of the \(f\): the radiating element \(e\) has a strong influence on the resonant frequency rather than \(f\). The surface current distribution of the modified PIFA is depicted in Figure 5. The majority current was observed in the vicinity of the shorting wall and some strong current arose at the slot corner. This indicates the formation of a larger electrical patch to achieve lower frequency, even though more than 55% of the radiating element was truncated. Moreover, the current flow was distributed across the structure as well as the ground plane, which reduced interference with external objects that affected antenna performance.
## 4 Conclusions  
This study developed and validated a novel emulsification-separation method for RAP treatment, achieving effective separation while maintaining the aggregate's properties. The key findings are summarized as follows:  
1. Through orthogonal experiments, the following optimal separation conditions were established: 2 wt% SDBS surfactants, 1:2 emulsion-to-RAP ratio, 80 \({}^{\circ}\)C temperature, and 60 min processing at 1800 RPM. Under these conditions, the AC-13 RAP asphalt content was reduced to 2.24%, the PL decreased to 26.9, and the FMR reached 0.89. We processed RAP of different particle sizes separately and gradually mixed them to achieve the best results. The RAP of 9.5 mm achieved superior separation, with its asphalt content being 3.30%. Optimal results were achieved by mixing 13.2 and  
Figure 18: Mechanism of the aggregate size affecting the dispersion effect.  
9.5 mm RAPs in a 1:1 ratio, reducing the asphalt content to 1.67%, and the PL decreased to 18.06 with an FMR of 0.998.
2. The angularity of the emulsification of the RAP aggregate was slight decrease and its axial ratio was relatively small compared to that of the extracted aggregate, which is a reduction of only about 0.1. The roundness of the aggregates was slightly reduced, but no difference was observed between them.
3. The form of asphalt adhesion to the aggregate can also be observed from the microscopic morphology of the treated RAP aggregate surface. The asphalt on the surface of the better-treated coarse aggregate of 9.5 mm was basically washed off, and the morphology of the aggregate surface was exposed. The fine RAP aggregate of 2.36 mm, retained most of the asphalt mortar on its surface, further research will be conducted to address this.
4. Under optimal processing conditions, as the number of emulsion cycles increased, the asphalt content of the aggregate reached about 1.7% and then did not change much. As a result, the emulsion collected from the RAP emulsification-separation process could be reused in the system, achieving zero effluent discharge.  
P.C.: Conceptualization, Methodology, and Review and Editing; K.Y.: Data Curation and Writing--Original Draft Preparation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (51978070), the Key Research and Development Plan Project of Shaanxi Province (2023-YBSF-110), and the Fundamental Research Funds for the Central Universities, CHD (300102313206).  
Not applicable.  
Data Availability Statement: Data will be made available upon request.  
The authors declare no conflicts of interest.
## 4 Conclusions  
It was demonstrated in this paper that molten salt electrochemical technology can convert the greenhouse gas CO\({}_{2}\) into high value-added products such as hydrocarbons, hydrogen, oxygen, and carbon materials. In molten carbonates, carbon materials with unique morphologies can be prepared by adjusting reaction parameters or changing electrolyte compositions. CNTs can be synthesized at 750 \({}^{\circ}\)C with 10 mA/cm\({}^{2}\), initially, and 100 mA/cm\({}^{2}\), finally, in a pure Li\({}_{2}\)CO\({}_{3}\) electrolyte. Further, the SEM data show that the diameters of the CNTs are between 50-80 nm and their purity can reach more than 80%. It was also proven that the Cao and BaO formed during electrolysis are beneficial for synthesizing carbon sphere products with diameters of 200-300 nm at 750 \({}^{\circ}\)C with 200 mA/cm\({}^{2}\). Honeycomb carbon is obtained in Li-Na-K at 450 \({}^{\circ}\)C with a current density of 450 mA/cm\({}^{2}\). The addition of hydroxides changes the product from a solid carbon material to a combustible, carbon-based fuel gas. The CH\({}_{4}\) content of the product can reach 40.1% at 550 \({}^{\circ}\)C under 2.0 V. Meanwhile, by optimizing the electrolytic parameters, the CO content can rise by up to 33.6% at 550 \({}^{\circ}\)C under 3.5 V in the electrolyte of Li\({}_{1.43}\)Na\({}_{0.26}\)K\({}_{0.21}\)CO\({}_{3}\) + LiOH (90%: 10%, wt%)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Electrolysis Parameters**} & \multicolumn{3}{c}{**Gas Content**} \\ \cline{2-6}  & **Temperature** & **Voltage** & **CH\({}_{4}\)** & **CO** & **H\({}_{2}\)** \\  & **(\({}^{\circ}\)C)** & **(V)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 450 & 2.0 & 18.2 & \(\backslash\) & 81.7 \\
2 & 500 & 2.0 & 22.1 & \(\backslash\) & 77.6 \\
3 & 550 & 2.0 & 40.1 & \(\backslash\) & 59.5 \\
4 & 600 & 2.0 & 30.4 & 0.1 & 69.3 \\
5 & 650 & 2.0 & 27.3 & 2.4 & 69.9 \\
6 & 550 & 1.0 & \(\backslash\) & \(\backslash\) & 100 \\
7 & 550 & 1.5 & \(\backslash\) & \(\backslash\) & 100 \\
8 & 550 & 2.5 & 13.3 & 9.2 & 77.2 \\
9 & 550 & 3.0 & 5.4 & 20.2 & 74.0 \\
10 & 550 & 3.5 & 1.9 & 33.6 & 64.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the gaseous products in Li\({}_{1.43}\)Na\({}_{0.26}\)K\({}_{0.21}\)CO\({}_{3}\) + LiOH (90%: 10%, wt%) under different conditions.  
**Author Contributions:** Investigation, D.J., Q.J., C.Z. and W.D.; Project administration, H.W. and G.W.; Writing--original draft, D.J.; Writing--review & editing, D.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Natural Science Foundation of Heilongjiang Province (LH2021B005) and the Guiding Innovation Foundation of the Northeast Petroleum University (2021 YDL-09).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Shape Finding Method for the Main Cable of
## 4 Conclusions  
This paper develops a novel shape finding approach for main cable-only systems under specified loads. The governing differential equations for the main cable in Eulerian coordinate system are constructed. Two-layer numerical iterations are then conducted to solve the governing equations using the nonlinear finite element approximation. The proposed method is applied to three cases, i.e., 2D parallel and 3D spatial main cables of an earth-anchored suspension bridge as well as the 3D spatial main cables of a self-anchored suspension bridge to achieve the target configuration of the main cable. Comparisons with the SCM and other studies are also performed to validate the accuracy of the present method. Some conclusions can be summarized as follows:  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(x\) **(m)** & \(y_{di}\)**(m)** & \(z_{di}\)**(m)** \\ \hline
125.000 & 48.870 & 15.960 \\
137.500 & 48.994 & 15.960 \\
150.000 & 49.108 & 15.960 \\
162.500 & 49.210 & 15.960 \\
175.000 & 49.302 & 15.960 \\
187.500 & 49.383 & 15.960 \\
200.000 & 49.453 & 15.960 \\
212.500 & 49.512 & 15.960 \\
225.000 & 49.561 & 15.960 \\
237.500 & 49.599 & 15.960 \\
250.000 & 49.626 & 15.960 \\
262.500 & 49.642 & 15.960 \\
275.000 & 49.647 & 15.960 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinate values of joint points for suspenders and girder.  
Figure 16: Outer-layer and inner-layer iteration results for 3D spatial main cables in Case 3.  
1. The proposed shape finding technique enables an efficient and accurate estimation of the target configuration of the main cable for the suspension bridge.
2. The present NFEM performs the form-finding for the whole cable in the Eulerian coordinate system using a few iteration steps without any information of the cable material or cross area as compared with the commonly used SCM. The unstrained cable length can also be calculated by subtracting the elongation from the final length of the cable.
3. Both the SCM and the present NFEM methods have enough efficiency and accuracy for finding final cable shape of the suspension bridge. The proposed technique provides an alternative to be used by the bridge designers and engineers for the rapid estimation of both 2D parallel and 3D spatial cable curves in the preliminary design stage.  
Conceptualization, methodology, software, and writing, W.Z. and G.F.; Investigation and validation, W.Z., G.F. and Y.G.; Resources, supervision, and revision, J.C. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51978527 and 51778495; the Shanghai Pujiang Program, grant number 20 PJ1413600.  
Not applicable.  
The authors gratefully acknowledge the support of the National Natural Science Foundation of China (51978527, 51778495), the Shanghai Pujiang Program (20 PJ1413600). We thank the reviewers and the editor for the valuable comments and suggestions that helped us improve the manuscript.  
The authors declare no conflict of interest.