esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Connected Vehicle Data to Evaluate National Trip Trends
## 4 Methodology  
For each CV journey, the chronological first and last waypoints are identified. These waypoints are treated as the origin and destination for the journey, respectively, and are referred to as such in the text that follows. The corresponding origin and destination geolocations are spatially joined to a publicly available dataset of census blockgroups. CV journeys whose origin has an associated ignition status of 'key on' and a destination that has a corresponding ignition status of 'key off' are selected to ensure only complete journeys are analyzed. Gaps in time between consecutive waypoints along a journey, beginning with the origin and ending at the destination, are recorded with their summation representing the trip duration and maximum value documented. The minimum time gap between waypoints was observed to be 1 s. Only journeys with a maximum time gap of 10 s between consecutive waypoints are considered. Similarly, great-circle distances between consecutive waypoints along a journey, beginning with the origin and ending at the destination, are recorded with the sum representing the estimated trip length and maximum value documented. Journeys with a maximum distance gap of 0.75 miles between consecutive waypoints are considered for this analysis. These spatial and temporal thresholds for filtering journeys may be modified as needed per the demands of the analysis and were selected to account for a reasonable tolerance of GPS errors as well as missed data points due to connectivity issues. Finally, only those CV trips that included at least two waypoints over a non-zero time period were selected. Insights into trip trends with very short or near-zero estimated lengths, enabled by these thresholds and waypoint level granularity, are valuable for policymakers in tracking emissions due to idling vehicles, a traditionally unattainable performance measure using survey methods, and especially important to longitudinally monitor the industry's shift towards alternative fuel vehicles. This methodology was utilized to reduce nearly 500 billion CV waypoint records into an aggregated set of nearly 1 billion CV trip records. The attributes derived for each trip and a short corresponding description for each are summarized in Table 1.
# An Improved Winkler Foundation Modulus for a Beam in a Full Space
## 4 Conclusions  
The elastic foundation beam model is widely used to obtain the responses of underground structures. The Winkler foundation modulus is key to determining the accuracy of the Winkler foundation modulus. In this paper, an improved Winkler foundation modulus using a full space is deduced, wherein the Winkler foundation modulus is obtained by fitting the bending moment from an elastic foundation beam model and a beam on a three-dimensional elastic foundation. Since the Winkler foundation modulus is only related to the normal interaction between the beam and the foundation, when solving the bending response based on the beam on the Winkler foundation and three-dimensional elastic foundation, only the normal action between the beam and the foundation should be considered, and the tangential action that affects the bending response of the beam should be ignored. By comparison with numerical solutions and previous studies, the proposed Winkler foundation modulus is verified. The proposed Winkler foundation modulus has a higher accuracy than the previous Winkler foundation modulus because the tangential interaction between the beam and the ground is ignored in the solution process. The proposed Winkler foundation modulus can be used to evaluate the responses of underground tunnels, especially tunnels with deep burial depths.  
Figure 10: The errors of the tunnel’s peak internal forces along the axis using different elastic moduli: (a) bending moment; (b) shear force [20].  
**Author Contributions:** Conceptualization, L.X. and X.D.; methodology, L.X., M.Z. and J.H.; software, L.X.; validation, L.X., J.H. and H.L.; data curation, J.H.; writing--original draft preparation, L.X. and J.H.; writing--review and editing, M.Z.; visualization, X.Z. and S.C.; supervision, X.D.; project administration, X.D. 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 (52378475), the National Key Research and Development Program of China (2022 YFC3004305, 2022 YFC3004304), the Postdoctoral Research Foundation of China (2023M730200), and the Beijing Postdoctoral Research Foundation (2023-zz-A135), which are gratefully acknowledged.  
Not applicable.  
The relevant data are all included in the paper.  
Author [PERSON] was employed by the company Guangzhou Yingli Technology Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction
### Experimental Results and Analysis
Attenuation coefficient (\(k\)) refers to the ratio of initial peak overpressure to peak overpressure after an abrupt cross-section increase of the pipe. Increase coefficient (\(\Omega\)) refers to the ratio of initial peak overpressure to peak overpressure after an abrupt cross-section decrease of the pipe. Reflection coefficient (\(\Psi\)) refers to the ratio of reflected wave peak overpressure to initial peak overpressure. Here,\(\Delta p_{3}\) represents initial peak overpressure before an abrupt cross-section increase, \(\Delta p_{4}\) represents peak overpressure after an abrupt cross-section increase, \(\Delta p_{6}\) represents initial peak overpressure before an abrupt cross-section decrease, \(\Delta p_{7}\) represents peak overpressure after an abrupt cross-section decrease, and \(\Delta p^{\prime}_{6}\) represents reflected wave peak overpressure after an abrupt cross-section decrease. Therefore, \(k\), \(\Omega\), and \(\Psi\) are calculated as follows:  
\[k=\frac{\Delta p_{3}}{\Delta p_{4}},\ \Omega=\frac{\Delta p_{6}}{\Delta p_{7}},\ \Psi=\frac{\Delta p^{\prime}_{6}}{\Delta p_{6}} \tag{1}\]  
In addition, \(S_{1}\) represents a cross-section area at either end of the pipe, and \(S_{2}\) represents a cross-section area of the middle segment of the pipe. Therefore, \(\frac{S_{2}}{S_{1}}\) represents the ratio of abruptly changing cross-section areas. Table 2 presents the experimental values of initial peak overpressure, reflected wave peak overpressure, and peak overpressure after abrupt cross-section area changes under different fuel volumes. The attenuation coefficient, increase coefficient, and reflection coefficient obtained using Equation (1) are also listed. Figure 5 shows the relationships between attenuation coefficient, increase coefficient, reflection coefficient, and the ratio of abrupt cross-section areas. The coefficients are affected by both the ratio of abrupt cross-section area change and initial peak pressure before abrupt cross-section area changes. When the ratio of abrupt cross-section area change is less than 2, the attenuation coefficient ranges from 1.069-1.211; when the ratio of abrupt cross-section area change is greater than 2, the attenuation coefficient ranges from 1.644-1.721. For the same ratio of abrupt cross-section area change, the attenuation coefficient increases with initial peak pressure before the abrupt cross-section area change increases, but the increase is limited. The increase coefficient decreases as the ratio of abrupt cross-section area change increases, but the variation is limited, whereas the reflection coefficient increases with the ratio of abrupt cross-section area change. For a fixed ratio of cross-section areas, the increase
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 5 ML Algorithms Application
### Hyperparameters Optimization and Models Validation  
Bayesian optimization offers several specific strengths more than traditional hyperparameter optimization techniques, such as Random Search and Grid Search, particularly in the context of ML models used for wind speed and power prediction [66]. One key advantage is its efficiency in finding optimal hyperparameters with fewer function evaluations. However, Random Search, which samples hyperparameters randomly, or Grid Search, which exhaustively tests all combinations within a predefined range, Bayesian optimization builds a probabilistic model (Gaussian processes) to predict the performance of different hyperparameter configurations [32]. This model enables it to intelligently explore the hyperparameter space by balancing exploration (testing new regions of the space) with exploitation (refining the search around promising configurations). This targeted approach reduces the number of experiments needed to find the best hyperparameters, which is particularly beneficial for complex models such as ensemble methods or deep learning architectures, where each evaluation can be computationally expensive [54].  
Another significant advantage of Bayesian optimization is its ability to handle noisy or expensive-to-evaluate objective functions. While techniques such as Grid and Random Search are agnostic to the complexity of the model and waste computational resources testing suboptimal configurations, Bayesian optimization incorporates the uncertainty of each function evaluation into its search process [67; 68]. By leveraging prior knowledge and updating its probabilistic model with each new evaluation, it can converge to optimal hyperparameters more quickly and with higher accuracy [69]. Additionally, this probabilistic method is more adaptable to high-dimensional hyperparameter spaces, where Random and Grid Searches may struggle due to the curse of dimensionality.  
Hyperparameter tuning is conducted using Bayesian optimization on the validation set (15%), employing 5-fold cross-validation to identify optimal parameters based on Equations (1)-(4):  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{\hat{y}_{i}}\times 100 \tag{1}\]\[MSE=\frac{1}{n}\sum_{i=1}^{n}[y_{i}-\hat{y}_{i}]^{2} \tag{2}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\ \ \left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{n}\ \ \left(y_{i}-\overline{y}_{i}\right)^{2}},\ \ 0\ \leq\ R^{2}\ \leq\ 1 \tag{3}\]  
\[R^{*2}=R^{2}-\frac{\left(1-R^{2}\right)K}{n-\left(K+1\right)} \tag{4}\]  
where K is the number of included variables and \(R^{*2}\) represents the adjusted number of features included in the algorithm, whereas \(R^{*2}\) is lower than the \(R^{2}\) value.
# Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty
## 2 Relevant Studies on PC Production Scheduling Under Uncertain Environments
### Reactive Approach  
The reactive approach has been extensively explored in the context of PC production scheduling. [PERSON] and [PERSON] [9] proposed a GA-based multi-heuristic schedule repair model for schedule disturbance resolution, such as completed element rejection and design change. [PERSON] and [PERSON] [10] proposed schedule adjustment principles to respond to demand variability, such as the magnitude and frequency of due date changes. [PERSON] and [PERSON] [11] developed a two-level rescheduling model for precast production with multiple production lines to minimize the rescheduling costs when due dates change. [PERSON] et al. [12] proposed an approach for optimizing the shop floor rescheduling of multiple production lines for PC production in production emergencies such as machinery breakdown and rush orders. [PERSON] et al. [2] proposed a dynamic PC scheduling model including a PC production simulation module and a new heuristic rule responding to due date changes. [PERSON] et al. [13] proposed the multi-objective genetic-algorithm-based dynamic flow shop scheduling model for prefabricated component production, which incorporates demand fluctuations such as the advancement of the due date, insertion of urgent components, and order cancellation. [PERSON] et al. [14] proposed a hybrid rescheduling optimization model for precast production to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. [PERSON] et al. [3] proposed a PC production scheduling model that incorporates a deep reinforcement learning approach to minimize the total tardiness of PCPs by coping with dynamic store conditions in real time. [PERSON] et al. [15] proposed a three-layer scheduling disruption management model for scenarios in which order advancement and emergency order insertion occur concurrently in PC production workshops to minimize the maximum completion time, the earliness and tardiness penalties, and carbon emissions. [PERSON] and [PERSON] [16] proposed a Gene Expression Programming (GEP)-based optimized dynamic dispatching rule to respond to due date changes.  
In summary, these studies highlight the diversity and adaptability of reactive approaches in addressing disruptions in PC production scheduling. They emphasize techniques such as genetic algorithms, simulation models, and heuristic methods to adjust schedules dynamically. Most of these approaches focus on minimizing tardiness or other performance penalties while responding to various disruptions like demand fluctuations, machine breakdowns, and urgent orders. However, while reactive strategies are effective in mitigating disruptions, their reliance on frequent schedule adjustments can lead to operational inefficiencies and increased complexity, posing challenges for practical implementation [6].
## 3 Results and Discussion
### Determination of the Significant Factors Affecting the Extraction Process
This technology is known for its simplicity and lower costs compared with other conventional extraction techniques [38]. The high-frequency waves promote cavitation, resulting in cell wall breakage and solvent diffusion [39]. Ultrasound-assisted extraction has advantages over other techniques, including the shorter residence time of sample particles in the solvent, the lower amounts of solvents needed, and higher yields [40]. The solvent type was found to be statistically non-significant considering ABTS and TPC levels in the SCG extracts. Methanol contains a smaller aliphatic fragment than ethanol, which should allow an easier solubilization of natural products with intermediate polarity. However, methanol is a highly toxic alcohol and must be completely removed from the extract. On the other hand, high molecular phenolics are preferably solubilized in ethanol, which is non-toxic and considered as GRAS (generally recognized as safe) [41]. For these reasons, ethanol and its hydroalcoholic mixtures were chosen as solvents in the second stage of the extraction optimization. The adequacy and predictive efficiency of the model have to be verified with the adequate statistical expressions such as the coefficient of determination (R\({}^{2}\)), adjusted R\({}^{2}\) and predicted R\({}^{2}\)[26]. It is apparent from Table 4, that both the ABTS and TPC models present a very good fit to the data obtained, which was confirmed by the high values of R\({}^{2}\) (>0.8). In addition, the adjusted R\({}^{2}\) levels were in very close proximity to R\({}^{2}\), which indicates that non-significant variables were not included in the final model. The predicted R\({}^{2}\) for both the ABTS and TPC models were within 20% of the adjusted R\({}^{2}\), indicating that the models are not overfitted. Figure 1: Pareto charts for the effects of organic solvent concentration (A), SMR (C), ultrasound assistance (D) on the antioxidant activity (**a**) and extraction of total phenolic compounds (**b**) after non-significant terms elimination. Based on the acquired models, multiple response prediction for antioxidant activity (ABTS) and total phenolics content (TPC) was carried out. According to the analysis, the optimal conditions for maximizing the ABTS and TPC in SCG extracts were as follows: organic solvent concentration--60%, SMR--40 mL/g SCG d.m., and application of ultrasound assistance.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 3 Results and Analysis  
### Standalone Models of FPG  
We first evaluate standalone models recovered by different FPG configurations listed in Table 1. The results in January (2006) are demonstrated in Figure 2, while other months show similar results. In Figure 2, the black curve represents the true average HIS signal in January (an integrated signal of terrestrial hydrological water, continental ice-sheets, and the solid earth [24]), while other curves are the degree geoid height errors (DGHE) of different FPG standalone models in this month. Observing signal (in black) and error (in colored) curves, we find that the recovered models are gradually dominated by noises after degree \(n=26\), which is indicated by the intersection point of signal curve and error curves around degree 26. Among four FPG standalone models, the FPG-91 in general produces smaller errors especially for degrees beyond 60. When looking to low-degree SHC near the intersection point, we find that the retrograde FPGs (FPG-91 and FPG-93) outperform the prograde ones (FPG-89 and FPG-87). The expected degradations of FPG-87 and FPG-93 due to their larger polar gaps compared to FPG-89 and FPG-91, however, is not apparent in Figure 2. This can be explained by denser ground tracks gained by FPG-87 and FPG-93 in the mid and low latitudes, which finally compensate the negative effects caused by large missing polar data, as discussed in [51] (pp. 204-214).  
Further comparisons are carried out in terms of absolute SHC differences \((|\Delta C_{lm}|,|\Delta S_{lm}|)\) of each degree \(l\) and order \(m\), as drawn in Figure 3. Subfigures (a), (b), (c), and (d) stand for results of FPG-87, FPG-89, FPG-93, and FPG-91, respectively. Now we can clearly observe the deficiencies of FPG-87 and FPG-93 in zonal SHC, i.e., \(C_{l0}\), estimation, which are demonstrated by larger \(|\Delta C_{l0}|\) values in (a) and (c) compared to that in (b) and (d). Recall the approximate relationship between the polar gap size and the maximum recoverable gravity signal in Section 2.2, the weaknesses of FPG-87 and FPG-93, with 3-degree polar gaps, are expected to become much more pronounced in terms of high-degree, e.g., 180 d/o [11], stati  
Figure 2: True HIS signal and degree geoid height errors (DGHE) of different FPG standalone models in January 2006.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 3 Methodology  
### Proposed Model Design  
The proposed forecasting model combined EEMD method and LSTM neural network technique to form the hybrid EEMD-based LSTM forecasting model. The main implementation process of EEMD method and LSTM neural network technique is described in the next section.
## 1 Introduction  
### Background  
Rainfall is a natural phenomenon that shows spatiotemporally varying behavior. These characteristics with chaotic dynamic patterns make it difficult to predict its future behavior. Moreover, a common shortcoming of rainfall data is the inherent inclusion of missing values. This is because the data are collected from different gauging stations located in a widespread geographical area. The malfunctioning of equipment, relocation of gauging stations, network interruptions, natural hazards, and emergencies like pandemics may disturb the continuous measurement of this natural phenomenon [1].  
Ignoring the stations with missing data leads to an information loss due to the strong spatial correlation between meteorological stations. On the other hand, modeling rainfall data with missing values negatively impacts the accuracy of rainfall models due to the discontinuity of the time sequence [2]. Hence, identification of a suitable mechanism to address this issue is a crucial step in an effective rainfall forecasting.  
When the missing values occur at random, modeling them to estimate their values is not required. However, if the missing values are not random, then the missingness cannot be ignored and must be modeled and predicted [3]. This is known as missing value imputation and it is carried out as a data preprocessing step. Much research has been conducted using different types of missing value imputation methods. However, studies on imputing the missing values for spatiotemporal data (including meteorological records) are rare [4; 5; 6; 7]. This study presents a novel and more efficient spatiotemporal methodology to impute missing values in rainfall data for a few rain gauging stations. The proposed methodology was tested using rainfall data collected from six neighbouring rain gauging stations (which are considered reference stations) of Ratnapura, which was identified as the target rain gauging station in our main study of rainfall forecasting. The reason for our choice is that Ratnapura is a flood-prone area in Sri Lanka due to its frequent exposure to heavy rainfall throughout the year [8; 9]. In 2003, flash flood events in Ratnapura accounted for financial damage of LKR 50.6 million [9].
## 4 Results
### Capacity Fade Forecasting Results
The results presented in Table 3 show a trend such that the prediction accuracy improves with the amount of data considered (number of cycles in the training set). The mean values of the RMSE for the ARIMA forecasting are 0.064 and 0.046 for \(\Delta V_{RMS}\) and \(\Delta C\), respectively. In the case of LF, the mean values are 0.112 and 0.069 for \(\Delta V_{RMS}\) and \(\Delta C\), respectively. Since the mean RMSE values of \(\Delta V_{RMS}\) and \(\Delta C\) are similar, it can be concluded that \(\Delta V_{RMS}\) can be used as an estimator of the capacity fade \(\Delta C\), while requiring less data (see Figure 1). It is also shown that ARIMA generally provides better forecasting results than the LF method. Figure 8 shows the experimental values of the cell terminal voltage vs. the SOC at the reference cycle (blue line, using the Nernst fitting from experimental data) and at different cycles (black dashed lines), and the predictions of future discharge cycles (solid magenta line) using the Nernst model described by (11) and \(\Delta V_{RMS,n}\) and \(\Delta C_{n}\). The SOC is calculated by applying (13) once the estimated value of \(\Delta C_{n}\) is known, while the cell terminal voltage in Figure 8 is the voltage obtained from (11) minus \(\Delta V_{RMS,n}\). The magenta line (predicted future discharge cycles) should be very close to the black dashed line corresponding to the objective data. The results presented in Figure 8 show that the discharge behavior depends on the predicted values of \(\Delta V_{RMS,n}\) and \(\Delta C_{n}\) at cycle \(n\). However, it accurately approximates the discharging behavior when the same discharge current is applied. Table 4 shows the RMSE values between the measured data (dashed black lines) and predictions made by the proposed degradation model and the Nernst equation (solid magenta lines). From the results presented in Figure 8 and Table 4, it can be concluded that at a given cycle \(n\), if some known discharge conditions are expected, the discharge behavior of the system can be approximated using the proposed \(\Delta V_{RMS}\) health indicator.