esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
###### Abstract  
The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie-Ames-Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with \(R^{2}\) of 0.56, RMSE of 1.22 t ha\({}^{-1}\), and an average relative error of \(-6.01\%\). Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.  
remote sensing; winter wheat; CASA model; NPP; yield 2019 11, 1088; doi:10.3390/rs11091088
## 2 Coronary Artery Calcium Score
### Prognostic Value of CAC and Risk Assessment  
#### 2.3.1. Asymptomatic Subjects  
The most recent data suggest that, in addition to the established cardiac risk factors, the use of CAC is independently predictive of prognosis. Therefore, the rate of MACEs rises proportionally with increasing severity of coronary calcifications categorized by the Agatston calcium score [59; 60]. While individuals with CAC levels \(\geq 1000\) have a mortality rate comparable to high-risk secondary prevention patients, asymptomatic subjects with 0 coronary calcium show a persistently very low risk throughout multiple studies [61; 62].  
CAC has also been demonstrated to further stratify patients who were assessed as intermediate risk using the Framingham Risk Score alone [63]. CAC score was independently predictive of ischemic outcomes above and beyond historical risk variables in univariable and multivariable models estimating CAD occurrence at 4.3 years of follow-up [64]. Other research has demonstrated that the incremental risk prediction value of CAC extended to both younger and older individuals, diabetic patients, smokers, and the elderly [65].  
Furthermore, CAC may determine whether asymptomatic people might benefit from statin or aspirin as primary preventive therapy [66]. According to current recommendations for statin therapy, it is reasonable to assess CAC in patients with an intermediate risk of developing atherosclerotic cardiovascular disease [4]. However, none of the guidelines proposes management based on calcium score because of insufficient support from randomized clinical trials.
# Data Lake Governance: Towards a Systemic and Natural Ecosystem Analogy
## 2 Our Approach and Contribution  
Based on the definition of a \"system\", the ecosystem and supply chain are both considered intelligent systems that contain several components and are governed by specific rules and disciplines. A data lake is conceptually inspired by a natural lake. Consequently, all concepts that are frequently used in the data lake, originate from a natural lake ecosystem. From another perspective, supply chain management provides some appropriate concepts and processes that are also applicable to data lake management and data governance.  
Our study is based on the position that a data lake as a system, has many common and comparable elements with supply chains and natural ecosystems. Dealing with diverse and heterogeneous data in data lakes--like products in supply chains or species in ecosystems--requires hybrid solutions and methods for data management which can be accurately determined. In line with our focus on the data life cycle, we put the emphasis on designing practical methods to preserve valid data in the data lake and remove invalid or obsolete ones from the data lake. For instance, it is logical that some data will be separated from the data lake, like a defective product in supply chain. The data can also be brought back to the data lake or kept after its usage, like a reusable or recyclable product in close-loop or reverse supply chain [24] or like the information in backward flow across the chain. This addresses which data is concerned.  
In addition, a key challenge lies in the evaluation of data usage during their lifespan, because a data lake stores data that may be retrieved or queried in the future, rather than serving an immediate need [25].  
We would assume that data acts like products in the supply chain or water in a natural lake. Hence, they have a probabilistic lifespan and may be valid and useful (i.e., have high value for exploration and exploitation) or invalid and obsolete (i.e., have no value and increase the risk of data swamp). Therefore, to avoid storing invalid data and managing the data life cycle, we tackle the challenge of the data life expectancy by drawing analogies with processes used in the supply chain and ecosystem to govern data lakes.  
The questions are then:  
* Which aspects of the data lake are comparable with natural lake and supply chain?
* Which strategies should be derived from nature and supply chain for data governance?
* How should these strategies be generalized to a data lake?  
In this article, we contribute some first research positions, by:  
* Providing comparisons between a data lake, an ecosystem, and a supply chain (Element by Element);
* Relying on supply chain management strategies for data governance (Systematic Manner);
* Imitating nature principals to manage the data life cycle (Natural Manner).
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Data Collection Environment and Procedures  
#### 2.1.1 Selection of Data Collection Environment  
For analyzing the correlation between driving behavior and traffic conflicts, this study examines a segment of the X412 County Road in Nanjing Province, China, extending 8.2 km from Lishu Village to Damao Village (as shown in Figure 2). This segment, typical of mountain roads in China, features a design speed of 40 km/h and incorporates a two-lane configuration with 3.5 m lane widths. The road geometry presents particularly challenging characteristics, with only four sections exhibiting horizontal curve radii exceeding 100 m, comprising less than 30% of the total length. The alignment includes 12 sharp turns, defined as curves with vertex angles greater than 90 degrees and radii less than 60 m, many of which qualify as hairpin bends. These sharp turns frequently coincide with steep gradients, characterized by longitudinal slopes averaging 5.3% and reaching maximums of 8%, which differs notably from mountain roads in European countries, where slopes can reach 12% with horizontal curves with 30 m radii. The section's significance is amplified by its role as a crucial transportation link, experiencing substantial traffic volumes with a diverse vehicle composition, including a considerable proportion of heavy vehicles. The combination of challenging geometric features, significant traffic demands, and complex vehicle interactions makes this segment particularly suitable for studying driving behavior and traffic conflicts. The road's characteristics, while representative of Chinese mountain roads, create unique safety challenges that warrant detailed investigation, especially considering the frequency of sharp turns and their correlation with accident risks.  
Figure 1: Research framework.  
The road segment, classified as a secondary road according to Chinese road design standards, presents significant geometric complexity with sharp turns and steep slopes. The roadway infrastructure follows standard design specifications with a designated speed of 40 km/h and comprises a two-lane configuration, each lane measuring 3.5 m in width, complemented by 1.5 m shoulders on both sides. The geometric design incorporates horizontal curves with minimum radii of 60 m, accommodating the mountainous terrain while maintaining safety standards. The vertical alignment exhibits considerable variation, with longitudinal grades reaching up to 8% and cross slopes maintained at 4% for adequate drainage. The clear width of 8.5 m and Road Grade-II loading capacity ensure sufficient space for vehicle movement and structural integrity. These geometric characteristics, while challenging from an operational perspective, are consistent with the regional topography and local transportation needs.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 2 Financial Time-Series Deep Clustering Network: TCN-Deep Clustering
### TCN-Deep Clustering Algorithm  
The TCN-Deep Clustering algorithm is shown in Algorithm 1. The model inputs include data set \(D\), which includes a large amount of historical stock data; the number of clusters \(K\); the maximum iteration \(N\); and the pre-trained iteration \(N_{p}\). The first row is initialized with k-means, which leads to the initial clustering center. The second and third rows pre-train the autoencoder network according to the reconstruction loss to obtain the initial network parameters \(\theta\) and the temporal feature \(z_{j}\). The fourth to ninth rows are trained iteratively according to the Adam [23], where the fifth row represents the encoder extracting the temporal features. The fifth and sixth rows calculate the distance between the features extracted by the encoder and the clustering centers and assign classes. The 9 th and 10 th rows calculate the target distribution and KL divergence. The 11 th row indicates that the iterative process stops when the cluster assignment is less than \(\delta\) for T consecutive iterative processes.  
```
Model: TCN-Deep Clustering Input: Data set \(D\); Number of clusters \(K\); Maximum iteration \(N\); Pre-trained iteration \(N_{p}\); Output: Clustering results \(S\);
1 K-means initialize the clustering center \(\mu=(\mu_{1},\mu_{2}\ldots\mu_{k})\)
2 for\(i=1\) to \(N_{p}\):
3\(L_{r}=\frac{1}{n}\sum_{i=1}^{n}\left\|x-x^{\prime}\right\|^{2}\), autoencoder preliminary feature extraction.
4 for\(i=1\) to \(N\):
5 Encoder extraction of input features: \(z_{j}=f\left(x_{j}\right)\)
6 Calculating the soft distribution \(p_{ij}\) of \(z_{j}\) with cluster class center \(\mu_{i}\)
7 Assign input data to clusters; Obtain \(S\)
8 Calculating the target distribution \(q_{ij}\)
9 Calculating \(L_{c}=KL(P\|Q)=\sum_{i}\sum_{j}p_{ij}\log\frac{p_{ij}}{q_{ij}}\)
10 Based on Adam, to update cluster centers \(u_{i}\) and encoder parameters \(\theta\)
11 end if \((i\)_Mod_\(T==0)\)&& Cluster assignment is less than the threshold \(\delta\)
```  
**Algorithm 1**TCN-Deep Clustering Algorithm.
# The Development of an OpenAI-Based Solution for Decision-Making
## 5 Discussion and Implications  
The development and implementation of an OpenAI-based decision-making platform using blockchain technology shows a significant advancement in creating secure, transparent, and efficient systems for organizational decision-making. This study shows the application of blockchain's decentralized validation and incorruptibility, alongside the predictive capabilities of AI, to solve complex decision-making problems in a transparent and scalable manner. One of the main findings is the seamless integration of OpenAI's API for real-time decision support. The app provided users with insights based on historical data by using natural language processing and predictive analytics, and this helped make informed decision-making while highlighting the importance of using artificial intelligence [3]. This AI-driven component proved important for supporting users in tasks such as voting, task management, and decision assignments. The platform's use of AI also minimized human biases by offering data-driven recommendations, which are crucial for objective decision-making. Furthermore, blockchain technology played a crucial role in making sure that there is security and transparency of the platform.  
The immutable nature of the blockchain ledger guaranteed that all decision-making processes, such as task assignments and voting, were recorded and verified in a tamper-proof manner at an organization called DADWORD IT. This feature also ensured accountability at every stage, a critical requirement in collaborative environments. The smart contracts embedded in the system further enhanced the decision-making process by automatically verifying conditions before executing decisions, offering a reliable and trustworthy system. The frontend and backend systems worked in tandem to provide a smooth and intuitive user experience. The frontend, built using Angular, provided real-time feedback, ensuring users could easily navigate the platform and perform actions such as registering, voting, and managing tasks. The backend successfully handled API calls, had secure data storage, and managed blockchain interactions effectively. It performed these functions by using Node.js and Python, and it ensured that all operations were processed securely and in real time. Such a detailed system has already proven to be helpful in a decision-making system for users.  
Although the current study has various achievements, there are some areas that can be improved upon. For example, the AI-driven recommendations provided valuable insights,and the AI model could increase the accuracy and relevance of the suggestions. Additionally, the blockchain integration ensured transparency and security but the consensus mechanism used (Proof of Authority) may need further evaluation to understand its long-term sustainability. Future iterations of the platform could explore alternative consensus models such as Proof of Stake, which may offer different levels of scalability and security.
# A Review on Bitumen Rejuvenation: Mechanisms, Materials, Methods and Perspectives
## 3 Mechanisms of Ageing and Rejuvenating  
### Ageing  
Due to the ageing process of bitumen and its corresponding increase in viscosity, the stiffness of asphalt pavement increases during its lifetime. The general, zero-order, description of aging is given in terms of an overall mechanism where some of the maltene medium is transformed into the asphaltene phase, resulting in higher asphaltene and lower maltene contents. This leads to a higher viscosity and lower ductility, due to the stronger polar-polar interactions between asphaltenes. In simpler words, \"when the asphaltene micelles are not sufficiently mobile to flow past one another under the applied stress, the resistance of asphalt binder to cracking or fracture is decreased\" as beautifully depicted by [PERSON] [4]. However, ageing is a more complex process involving different sub-mechanisms usually taking place in different time-scales. It can already take place during asphalt construction through volatilization of light components in the maltene. Then, long-term aging occurs in the field as a consequence of different processes:  
1. Oxidative, due to changes in composition through a reaction between bitumen constituents and atmospheric oxygen;
2. Evaporative, due to the evaporation of low-molecular weight components in the maltene. These compounds have higher vapor pressure and are somehow volatile so they can escape the maltene phase causing not only a change of its composition, but also an overall reduction of its amount in the bitumen;
3. Structural ageing, by a chemical reaction between molecular components causing polymerization with consequent formation of a structure within the bitumen (thixotropy) [33].  
The processes involved are schematically depicted in Figure 1.
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 4 Supporting Budgeting with Predictive Methods and Business Intelligence Tools
### Comparison of Forecasting Methods  
To compare the chosen methods, we selected three popular error measures based on [41], where \(Y_{i}\) is the observed value at time \(i\), \(\hat{Y}_{i}\) is the predicted value at time \(i\), and \(n\) is the number of observations (Table 1).  
Tables 2-7 present the results of the forecast error analyses, which are expressed by three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). These results were calculated for three different forecasting methods: linear regression, the XGBoost model, and the ARIMA/SARIMA models. The analyses were conducted for forecasts of various elements, including individual products, product groups, and a selected customer. For the built-in forecasting mechanism available in Power BI, it was not possible to determine the error values, because the tool generates forecasts in the form of a chart without providing specific numerical values, which makes it impossible to conduct a precise analysis of the quality of these forecasts.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Equation** \\ \hline MAE & \(\frac{1}{n}\sum_{i=1}^{n}|Y_{i}-\hat{Y}_{i}|\) \\ RMSE & \(\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-\hat{Y}_{i})^{2}}\) \\ MAPE & \(\frac{1}{n}\sum_{i=1}^{n}\left|\frac{Y_{i}-\hat{Y}_{i}}{\hat{Y}_{i}}\right| \cdot 100\%\) \\ \hline \end{tabular}
\end{table}
Table 1: Forecast error values for unit price for client B.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **MAE** & **RMSE** & **MAPE** \\ \hline Linear regression & 4493.22 & 6616.46 & 36.89\% \\ XGBoost & 5386.73 & 7493.90 & 42.92\% \\ ARIMA/SARIMA & 6560.43 & 8842.41 & 50.04\% \\ \hline \end{tabular}
\end{table}
Table 3: Forecast error values for number of pieces for client B.  
The results indicate that of the methods used, the smallest errors are obtained with the XGBoost and linear regression methods. ARIMA/SARIMA models yield the largest errors due to limitations of scalability to multiple time series, i.e., the inability to select parameters individually for each time series considered. In addition, the data stationary requirement for real sales data is rarely present. Based on these observations, forecasting can be better organized by data category. This can also allow results from different methods to be combined more effectively, leading to the averaging of forecasts to improve their ultimate quality.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 2 Related Work
### Deep-Learning-Based Methods  
Since the 2020s, deep learning technologies have matured further, particularly with the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing the accuracy and efficiency of traffic classification. [PERSON] et al. [20] first proposed an end-to-end encrypted traffic classification method based on one-dimensional CNNs in 2017, which integrates feature extraction, feature selection, and classification into a unified framework. [PERSON] [21] used a method combining Stacked Autoencoders (SAEs) and CNN, merging the feature extraction and classification stages into a single system. [PERSON] subsequently introduced HAST-IDS, an intrusion detection system based on hierarchical spatiotemporal features [22], which employs CNNs to learn low-level spatial features of network traffic and Long Short-Term Memory networks (LSTMs) to capture high-level temporal features. In 2023, [PERSON] et al. [23] proposed a network traffic classification method that introduces an attention mechanism into the CNN-LSTM framework, embedding SENet to weight and redistribute high-order spatial features, thereby extracting key spatial features from the network flow. Additionally, [PERSON] et al. [24] introduced a model named TFE-GNN (temporal fusion encoder using graph neural networks) in 2023, aimed at achieving the fine-grained classification of encrypted traffic through graph neural networks.
# Numerical Fit Modeling for Temperature Mitigation in
## 1 Introduction
### Modeling Options
#### 1.1.3 One-Dimensional Energy Budget Equations  
Numerical models can be created in one dimension from energy budget equations. One advantage is the simplicity of observing changes in the flow of energy. However, a disadvantage is that it might simplify the scientific phenomena too much.  
Most of the simple models do not consider interaction with clouds. This is a significant issue in humid regions, but is less significant in arid regions on cloudless days.  
Changes in horizontal advection would not be modeled, just like other one-dimensional models. The heat mitigation is planned to have a buffer zone around its perimeter. Horizontal advection would have the most effect around the edges of a target zone, but the effect is less further inward. Advection will be discussed further in later sections of the paper.  
One of the goals of this research is to determine whether a one-dimensional fit model can produce the same results as with an SCM. If it can, then it allows for greater use.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 5 Conclusions and Future Work
### Outlook
#### 5.2.3 Transfer and Meta-Learning  
For engineers, an important question is, if we have trained a PUNN using data collected from city \(A\), can we directly generalize the prediction out of the NN using data from city \(B\)? Traffic datasets from two cities could differ drastically in the underlying traffic dynamics (arising from heterogeneity in driver behavior such as that in San Francisco and Mumbai), traffic environments, road conditions, initial and boundary conditions, and traffic demands. To address this challenge, we have to modify the input of the PUNN without using \((x,t)\) but other attributes that vary across datasets, including, but not limited to, road type, geometry, lane width, lane number, speed limit, travel demands, traffic composition, and so on. One option is to employ Fourier neural operator (FNO) and its variants, e.g., the physics-informed FNO, to encode both the initial and boundary conditions as part of the inputs and to learn the nonlinear traffic-related operator directly, in other words, mapping \((x,t)\) to the solutions associated with the given initial and boundary conditions [117]. Moreover, selection of training and validation datasets that can mitigate training bias would help with transfer learning, and one approach is to use an adversarial game [118] to select these two datasets and train a robust PIDL model.  
Another direction to meet the transfer needs is to ask, how can we train a family of related physics-based PDEs (such as LWR and ARZ) and generalize the tuned hyperparameters to other physics members? Meta-learning the parameters involved in the PIDL pipeline could be a potential solution [119].