esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Performance of a New-Type PSC I-girder for Railway Bridge Application
## 3 Performance Evaluation on Full-Scale PSC I-girder Specim
### Evaluation of Ultimate Load Bearing Capacity
#### 3.3.2 Test Results  
Figure 18 shows the crack map of the specimen at the maximum load of 6097 kN. Cracking initiated at the bottom in the center of the girder at 2530 kN. In view of the cracking pattern, the specimen exhibited typical flexural behavior until the maximum load.  
The structural performance of the specimen was evaluated by comparing the experimental values with the load and deflection at ultimate limit state caused by the live load specified in the design code. Figure 19a,b plot respectively the deflection at mid-span and the crack width according to the increase of the load. Table 6 arranges the flexural performance requirements at service limit state and ultimate limit state provided by the design code. Note that the flexural performance here relates only to the live load. Table 6 also summarizes the amplitude of the load corresponding to each limit state and the corresponding vertical deflection measured in the specimen. The amplitude of the load at service limit state and ultimate limit state was calculated based upon the flexural strength deduced from the design code and the distance from the support to the loading point (13.95 m). As a result, the vertical deflection measured at the maximum load (6097 kN) was 361 mm, which provided vertical deflection of 43 mm for the ultimate limit state (2150 kN) and 21.9 mm for the service limit state (1162 kN).  
Figure 19: Results of static test of specimen: (**a**) load-displacement curve; (**b**) load-crack width curve.  
Figure 17: Static loading test of PSC I-girder: (**a**) 4-point bending test set up; (**b**) photograph of actual test.  
Even if the static test could not be conducted until failure and was terminated at the maximum load of 6097 kN due to the limited capacity of the actuators, the specimen appears to secure sufficient safety factor considering the loads at service limit state and ultimate limit state. The corresponding vertical deflections also satisfied sufficiently the design limit (_L_/600 = 56.5 mm). Moreover, the crack width measured at the segment interfaces at each limit state fell below the allowable crack width of 0.2 mm specified in the design code. Consequently, the segmentally fabricated specimen showed monolithic behavior.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Crypto Components  
The TNODE5 microcontroller is equipped with hardware support for AES, ECC, and SHA-1 crypto operations. Each crypto accelerator has a 32-bit interface and can be separately enabled or disabled. The crypto cores build a single logic block that communicates to CPU over a 32-to-16-bit interface.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Methods
#### 2.3.2 Random Forest  
RF is a classic bagging model whose weak learner is the classification and regression tree (CART) decision tree model, which can be used for classification and regression [33]. RF training can be calculated in parallel, can estimate thousands of explanatory variables and can capture nonlinear modes between target variables and features. Compared with the traditional multiple linear regression model or parametric regression model and the neural network, RF does not need to set the function form in advance and can reduce the computational load [34]. Most importantly, the importance between variables can also be shown.  
The main flow of RF is as follows: In the original dataset, random sampling is put back to form different sample datasets, and then different decision tree models are built according to these datasets. For each sample, if there are M input variables, and then k feature subsets are selected from M features. Then, the optimal splitting feature among k features is used to split the node. For classification tree, CART selects the optimal node to segment the data according to the Gini index minimum principle; for regression tree, CART selects the optimal node to segment the data according to the square error minimization  
Figure 1: Technical flow of precipitation retrieval algorithm.  
criterion and stops the growth until the branch stop criterion is met. The final result is obtained based on the mode (for the classification model) or the average (for the regression model) of these decision tree models.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 3 New Requirement Space Development
### Brief Introduction on Existing Requirements  
Before elaborating on the new requirements, existing requirements are briefly introduced for completeness. The consistency models originate from distributed computing [58]. For virtual world  
Figure 6: Relation of emerging trends and design requirements.  
Figure 7: Taxonomy of design requirements and issues.  
systems, consistency can be studied in two domains [59]. In the discrete domain, it requires that two users can see the same set of events handled in causal order, called causal preservation. Yet, in the continuous domain, object state also changes over time, following some internal rules, e.g., physical laws. It, then, requires that two users can see the same state changes if they have received the same set of events, called perceptive consistency [60]. In practice, consistency error can be compensated by latency error to achieve the overall fairness, which is called playability [61]. Scalability is \"a desirable... ability of a system to accommodate an increasing number of elements or objects, to process growing volumes of work gracefully\" [62]. In virtual worlds, the scalability issues can be classified into world scalability and hotspot scalability. The challenge of world scalability comes from two sources [16]: large-scale real-time simulations and simultaneous visualization for many users, caused by user population increase. Hotspot scalability comes from another source: multiple heterogeneous actors with different operating characteristics. When many users gather at a small place, a hotspot is formed [63]. The scalability issues aim at minimizing system performance reduction with the increase of users and user interactions, either globally or regionally. Virtual world security issues can be classified as Internet security, social misconduct, and cheating. First, as virtual worlds belong to Internet applications, they inherit general Internet security concerns, including authentication and identity theft, payment and transaction integrity, malware and computer virus, etc. [64]. From a social perspective, misconduct may happen in virtual worlds, including vandalism, harassment and stalking, defamation and disparagement, etc. [64]. They can only be prevented by rules and moral codes. Finally, cheating is a technical issue specific to game worlds, which is ubiquitous in gaming communities [65], caused by the combination of malicious attempts and system faults. [66] has classified them into three types of security break and further divided them into 13 categories. They summarized that cheating in virtual worlds needs either be prevented or be detected for providing a fair playground.
## 3 Materials and Methods
### Systems Integration in Enabling Smart Farming and Efficient Decision-Making  
Data processing facilitate seamless data, processes, and systems integration. These integrations can yield efficient services such as farm registration, implementation of the notification system, and social media services. All this is enabled by various technologies, including IoT, remote sensing, AI, digital technology, and machine learning. In smart farming, various components enable actors to collaborate in information-sharing practices and service provision. Agricultural data are heterogeneous as they have different formats, types, intentions, methods of data collection, and protocol devices. Innovative farming systems require timely and accurate actions and decisions. Therefore, there is a need for efficient platforms to enable farmers to take appropriate action promptly to enhance farming practices and, ultimately, increase productivity and profitability.
## 5 Results and Discussion
### Engineering Validation  
Under complex geological conditions, it is often challenging to obtain a complete rock mass classification dataset. Therefore, handling missing data becomes a critical issue in model applications. This study collected an additional dataset with missing values to evaluate the performance of the ZOA-XGBoost model [28; 29; 30; 31; 32], as shown in Table 5. The incomplete dataset was input into the developed ZOA-XGBoost model, and its accuracy was calculated. For comparison, several commonly used machine learning models were also trained on the same dataset. These models included random forest (RF), CatBoost, decision tree (DT), SVM, ANN, k-nearest neighbors (KNN), and Naive Bayes (NB). Since not all models can handle missing values, four imputation methods (filling with 0, the mean, median, and mode of the training set) were used to process the missing data.  
Figure 12 presents the performance comparison of the different models on the incomplete dataset. As shown in Figure 12a, the ZOA-XGBoost model achieves an accuracy of 80.00% directly on the dataset with missing values, significantly outperforming other models after imputation. The accuracy of other models is relatively low when missing values are filled with 0. Figure 12b shows the performance when missing values were filled with the mean of the training set. Although the performance of other models improves, ZOA-XGBoost remains the best performer. Figure 12c illustrates the results when missing values were filled with the median. RF, CatBoost, and KNN demonstrate more stable performance compared to other models but still lag behind ZOA-XGBoost. Figure 12d shows  
Figure 11: ICE plots for four features and rock quality classes.  
the performance when missing values were filled with the mode. While other models demonstrate more balanced performance after mode imputation, they still do not match the performance of ZOA-XGBoost overall.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Project** & **UCS** & **RQD** & **Kv** & **W** & **Class** \\  & **(Mpa)** & **(\%)** & **(\%)** & **(L (min\(\cdot\)10 m)\({}^{-1}\))** & **Class** \\ \hline
1 & & 144.00 & 68.00 & 0.90 & - & II \\
2 & Jinzhou LPG cavern & 160.10 & 82.00 & 0.83 & - & II \\
3 & & 176.40 & 66.00 & 0.80 & - & II \\ \hline
4 & & 39.73 & 68.10 & - & 8.00 & III \\
5 & Jiaojia gold mine & 39.73 & 63.70 & - & 80.00 & IV \\
6 & & 50.20 & 76.30 & - & 15.00 & III \\ \hline  
gold mine & 38.00 & 23.00 & - & 16.30 & IV \\
27 & & 88.00 & 57.00 & - & 19.00 & III \\
28 & & 70.10 & 51.80 & - & 0.05 & II \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: Datasets with missing values.  
In summary, ZOA-XGBoost shows significant advantages and high robustness in handling missing values. Compared to other models, it adapts better to incomplete data and makes accurate predictions. This also validates the reliability and effectiveness of ZOA-XGBoost in practical engineering applications.
## 3 Results and Discussion
### Mixture Preparation and Combustion Analysis
Figure 6a,b show the effects of biogas compositions on the variations of \(\mathrm{W_{i}}\), CO, \(\mathrm{NO_{x}}\), and soot volume fraction with DME content in the fuel mixture. At any biogas compositions, when DME content increased, \(\mathrm{W_{i}}\) and \(\mathrm{NO_{x}}\) increased while CO and soot volume fraction decreased. At a given DME content, the increase in \(\mathrm{CH_{4}}\) concentration in biogas led to an increase in Wi and \(\mathrm{NO_{x}}\) emission. This was due to the increase in temperature resulted from the rise in the heat value of biogas. The effects of DME on \(\mathrm{W_{i}}\) and soot volume fraction were more significant with poor biogas than rich biogas. As DME content rose from 0% to 40%, \(\mathrm{W_{i}}\) increases by 20% and 10% with biogas M6C4 and biogas M8C2, respectively. It can be seen in Figure 6b, that as the engine was fueled with neat biogas, soot volume fraction rose by 30% as \(\mathrm{CH_{4}}\) concentration in biogas increased from 60% to 80%. When 40% DME was added to the biogas, the soot volume fraction was almost independent with biogas compositions. This was because the heat value ratio between DME and biogas was higher in the case of poor biogas than that in the case of rich biogas. By contrast, the effects of DME content on CO and \(\mathrm{NO_{x}}\) concentrations were stronger with the increase of \(\mathrm{CH_{4}}\) concentration in biogas. This was due to the increase in temperature, and complete combustion resulted from the rise in laminar flame speed at high concentrations of \(\mathrm{CH_{4}}\) and DME in the fuel mixture.
## 7 Summary  
Due to the tradeoff between swath and pixel size, most current satellites cannot provide data with both high spatial and temporal resolutions. However, this type of data is needed for many studies which monitor the land surface dynamics over complex areas and with rapid change. Spatiotemporal data fusion methods have been proposed to combine images from different satellites to solve the limitation of a single satellite. However, the development of this methodology is quite disorderly although more than 40 methods have been proposed in the past two decades. This study criticallyreviewed existing methods for spatiotemporal data fusion. The facts of the authors, journals, and citations were summarized. The main accomplishments of this review are as follows:  
1. Existing spatiotemporal data fusion methods were grouped into five categories based on the specific methodology for linking coarse and fine images. These five categories include unmixing-based, weight function-based, Bayesian-based, learning-based, and hybrid methods. Currently, there is no agreement reached about which method is superior. More inter-comparisons among those categories are needed to reveal the pros and cons of the different methods.
2. The main principles underlying existing spatiotemporal data fusion methods were explained. These principles include spectral mixing model, spatial dependence, and temporal dependence. Existing spatiotemporal data fusion methods used above principles and employed different mathematic tools to simplify these principles to make the developed methods implementable.
3. The major applications of spatiotemporal data fusion were reviewed. Spatiotemporal data fusion can be applied to any study which needs satellite images with high frequency and high spatial resolution. Most current applications are in the field of agriculture, ecology, and land surface process.
4. The issues and directions of further development of spatiotemporal data fusion methods were discussed. Spatiotemporal data fusion is one of the youngest research topics in the remote sensing field. There is still a lot of space for further improvements. Four issues were listed which are worth addressing in future studies: the high demand of data preprocessing, the difficulties in retrieving abrupt land cover change, the lack of an accuracy assessment method and data sets and low computing efficiency.  
This study was supported by the National Natural Science Foundation of China (Project No.41701378), the Research Grants Council of Hong Kong (Project No.25222717), and the research grant from The Hong Kong Polytechnic University (project no.1-ZE6Q). Lastly, special thanks to [PERSON] and [PERSON] for their contribution on the section of spatiotemporal data fusion applications.  
[PERSON] conceived, designed, and wrote the paper, with the help of the co-authors ([PERSON], [PERSON] and [PERSON]).  
The authors declare no conflict of interests. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 2 Materials and Methods
### Description of a Typical Problem of Medium-Term Forecasting of Demand for Flights
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Forecast Horizon** & **Methods Used** & **Modeling Results** \\ \hline SARIMA damp trend gray forecasting model for the airline industry [24] & & Improved DTGM model: & The MSE values for the DTGM model. For SARIMA with a dynamic seasonal damping factor (SDTTM) & The MSE values for the DTGM model. For the 8 routes analyzed, MAPE metrics are larger for DTGM than for SDTGM. The findings indicate that the proposed SDTGM model is more precise than the DTGM. \\ \hline Predictive model of air transportation management based on intelligent algorithms of wireless network communication [25] & & & For short-term forecasting: ARIMA has the best accuracy, while the gray forecasting method is the least efficient. It is not necessarily the case that the combined model is superior to the individual models. \\ Short-term forecast algorithm [25] & & & For medium-term forecasts (2000-2020): the linear combined model demonstrates the greatest accuracy, while the exponential smoothing method exhibits the least efficient performance. The impact of the combined model varies \\ \hline Forecasting air passenger numbers with a GVAR model [14] & Short-term forecast: one (h = 1) to four (h = 4) quarters ahead & Global vector autoregressive (GVAR) model & The accuracy of the models was assessed using MSE, MAE, and MAPE. The GVAR model demonstrates superior performance to the four benchmark models in the short term for h = 1, 2, 3. \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline ARIMA-LR exhibits higher accuracy, as evidenced by lower scores in comparison to the ARIMA model. Specifically, the MAE, MSE, and MAPE metrics demonstrate a reduction of 1.06, 29,02, and 0.03, respectively. In comparison to LR, the indices are reduced by 3.92 and 0.06, respectively  
\end{table} 
Table 1: Summary of passengers demand forecasting studies. The aforementioned analysis permits a comparison of existing models and methodologies for forecasting flight demand, with due consideration of the particulars of the data, available resources, and requisite forecast accuracy. This research proposes the utilization of the Prophet package for the purpose of modeling medium-term demand estimation for passenger air travel. Prophet offers a number of advantages, including the ability to account for seasonal variations in demand and the impact of holidays [28], flexibility in handling periodic and non-standard trends, and the capacity to address missing data and uncertainty [29]. The aforementioned advantages are especially pertinent in the context of developing a model for assessing the demand for passenger air travel in the medium term.
# Theoretical Framework for Characterizing Strain-Dependent Dynamic Soil Properties
## 3 Results and Analysis
### Correlation: Hysteretic Parameter and Confinement Stress  
The 38 data sets of resonant column test (RCT) compiled from the literature are compared with the proposed model for different confinement stress levels and soils. Figure 6 presents that (1) there is an inverse relation between hysteretic nonlinear parameter \(\alpha\) and confinement stress \(\sigma^{\prime}\); (2) sand leads to higher \(\alpha\) value than clay at the same confinement stress.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 5 Design of \(Less\) Protocol
### Scheduling Algorithms
#### 5.2.2 **K**-Spatial Sampling (K-S)  
K-Spatial sampling assumes information is distributed with spatial pattern. For example, in an urban monitoring WSN, nodes are densely deployed in some areas and sparsely deployed in other areas. Thus, we select smaller sampling rate in dense-deployed areas and vice versa. We define the sampling rate in these two ares as \(s_{1}\) and \(s_{2}\). With a predefined system sampling rate \(s\), \(s_{1}\), \(s_{2}\) and \(s\), should satisfy Equation (8).  
\[s*|N|=s_{1}*|N_{1}|+s_{2}*|N_{2}| \tag{8}\]  
where \(N_{1}\) and \(N_{2}\) are the sequences of nodes in dense-deployed areas and sparse-deployed areas, which is so called system knowledge, and \(|N_{1}|+|N_{2}|=|N|\). According to our analysis above, we also require \(s_{1}<s_{2}\). However, there are multiple solution sets \((s_{1},s_{2})\) for Equation (8). Here, we introduce \(\alpha=s_{2}/s_{1}\) as a controllable parameter which is adaptive for various WSN systems. The best \(\alpha\) differs among systems and is related to \(|N_{1}|/|N_{2}|\). In general, we set \(\alpha=1.5\). Together with \(s_{1}\) and \(s_{2}\), \(D\) can be derived as shown in Algorithm 2.  
```
1:Number of nodes \(|N|\), \(N_{1}\), \(N_{2}\), new sampling rate \(s_{1},s_{2}\)
2:The decision set \(D\) for the \(N\) nodes in time \(T\)
3:for\(i\in N\) and \(t\in T\)do
4:if\(i\in N_{1}\)then
5:\(D(i,t)=Rand()<s_{1}\) // the dense-deployed case.
6:elseif\(i\in N_{2}\)then
7:\(D(i,t)=Rand()<s_{2}\) // the sparse-deployed case.
8:endif
9:endfor
10:return\(D\)
```  
**Algorithm 2** The **K**-Spatial sampling (K-S) Algorithm.
## 4 Proposed Scheme
### Problem Formulation  
For the formation control of USVs in the leader-follower structure, the main problem is the question of how to form the predetermined formation shape with collision-free movement and maintain the global formation shape.  
An asynchronous formation control scheme based on reinforcement learning for multiple USVs is proposed to address the problems mentioned above, making the USV team generate a formation with a minimum total length of movement path and maintain the formation. The proposed scheme contains two parts: formation generation policy and decentralized formation maintenance policy. In formation generation, each USV observes the positions and velocities of all USVs by sensors and communication, and the positions of points in formation shape are given to all USVs, and then learn a formation generation policy \(\mu_{\theta_{i}}^{*}\) based on cost function \(J_{i}\) to make the team form the formation quickly with a series of optimal actions \(a^{*}\), as shown in Figure 2. However, only the leader collects the information about the team's goal; each follower obtains its immediate leader's position and velocity by communication and follows the formation maintenance policy \(\mu_{\theta_{i}}^{fm}\) to track the leader, as shown in Figure 3.
# Investigation of Gentamicin Release from
## 1 Introduction
Our findings also suggested that PDA exhibits a very capacious nature, providing benefits for a high loading efficiency of approx. 84% for gentamicin. Moreover, antimicrobial testing has also been reported for as-prepared particles against two microbial species Gram (+) _Staphylococcus aureus_ and Gram (-) _Pseudomonas aeruginosa_ to determine antimicrobial efficiencies. In a continuation of our previous work, here we present the quantification of gentamicin release over an extended period, using the dialysis method. An in vitro release test was performed for two batches with varied gentamicin-loading amounts (G-PDA NPs 1:1 and G-PDA NPs 0.6:1) at three pH conditions: 3; 5 and 7.4. The drug release quantification was carried out by liquid chromatography-mass spectrometry (LC-MS). Furthermore, the drug release profiles were thoroughly investigated by implementing mathematical models. Two kinetic models, namely the Ritzer-Peppas and Higuchi models, were found to be best suited, as revealed by the high correlation R\({}^{2}\) values. The curve fitting analysis further supported the pH-dependent drug release kinetics. Our findings suggested that pH-responsive drug release would prevent the premature release of gentamicin, hence retaining the drug's efficiency and safety.