esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background of Applied Method
### Random Forest  
A random forest (RF) is an ensemble machine learning technique for classification and regression that constructs many decision trees at training. In regression tasks, the mean or average prediction of the individual trees is returned [76; 77; 52; 78; 72; 73; 74; 75; 76; 77; 78]. The first algorithm for random decision forests was proposed by [PERSON] [76]. Another algorithm extension was developed by [PERSON] [79]. Since 2006, the RF has been a registered trademark owned by Minitab, Inc. [80]. Unfortunately, in the literature, only low evidence of the RF application to the BOS process can be found (e.g., [34; 81]).  
The RF is trained through bagging or bootstrap aggregating. The bagging method generates the prediction, which uses different training data samples. The prediction is made based on the average exit from various trees. Predictive accuracy depends on the number of trees. The model is built on the training dataset with \(n\) training samples and \(N\) observations of the input set \(\mathbf{x}=\{\mathbf{x}_{1i},...,\mathbf{x}_{ni}\}_{i=1}^{N}\) with the response \(\mathbf{y}=\{y_{1},...,y_{n}\}\); bagging repeatedly (\(B\) times) selects a random sample with replacement of the training set and fits trees to these samples.  
For \(b=1,...,B\):  
* Sample, with replacement, \(n\) training examples from \(\mathbf{x}\), \(\mathbf{y}\); call these \(\mathbf{x_{b}}\), \(\mathbf{y_{b}}\).
* Train a classification or regression tree \(f_{b}\) on \(\mathbf{x_{b}}\), \(\mathbf{y_{b}}\).  
In BOS modeling, the training data be extracted from data tensor (1) or (2), depending on the chosen modeling approach. A trained forest makes predictions for unknown observations \(\mathbf{x}^{\prime}\) by averaging predictions from all trees on \(\mathbf{x}^{\prime}\) according to the following equation: \(\mathbf{x}^{\prime}\):  
\[y_{T/C}=\hat{f}(\mathbf{x}^{\prime})=\frac{1}{B}\sum_{b=1}^{B}f_{b}(\mathbf{x} ^{\prime}), \tag{20}\]  
The number of samples/trees, \(B\), is a free parameter. Typically, a few hundred to several thousand trees are used, depending on the size and nature of the training set [82]. In this paper, a generic RF function proposed by [PERSON] [83] was applied to the BOS process. In the working setup of RF modeling, the number of bags in the bootstrapping procedure was set to 5000.
## 1 Introduction
Due to the revisiting frequency and high resolution, Sentinel-2 data represent an important data source for crop mapping [13]. The volume of available SAR data has increased with the Sentinel-1A and B launches in April 2014 and April 2016, respectively. In contrast with optical satellite images, SAR signals can pass through clouds and obtain temporally continuous data regardless of the weather conditions [38]. Considering the imaging mechanism of SAR, backscatter is sensitive to the dielectric of the object and structure, including the size, shape, and orientation distribution of the scatterers [39; 40]. The feasibility of using crop classification with SAR data has been proven in many studies [41; 42; 43; 44]. With the emergence of cloud-based calculations, the computing capability has been greatly improved in recent years. Google, Amazon, Microsoft, and Alibaba have introduced cloud computing products, and Google and Amazon have archived a large catalogue of satellite imagery and provided geo-cloud computing for Earth science applications at the global scale in Google Earth Engine (GEE) and Amazon Web Service (AWS), respectively [10; 45]. GEE is an online programmable platform that includes preprocessed satellite data collection and assembles a conventional machine learning method and provides a visual user interface. The cloud-based platform has also been widely used for mapping forests [46], settlements and populations [47], African croplands [48], Indian urban boundaries [49], and land cover [50; 51]. [PERSON] et al. identified paddy rice cultivated areas in northeastern Asia in GEE using approximately 3290 Landsat 7 and 8 scenes with millions of servers around the world within one day, and attested to the power of this parallel processing, super-computing platform in crop-type mapping [10]. Our question is whether emerging cloud computing can be used to efficiently map corn on a large scale. The objective of this research is to develop a cloud computing-based method using optical and SAR images to identify the extent of corn cultivation at a 10-m resolution on the North China Plain. First, metric composites were calculated based on optical and SAR data collections, including Sentinel-2 and Sentinel-1 images. Then, these metrics were used as input for a random forest classifier in GEE to generate a pixel-based corn distribution. Finally, the pixel-based classification result was integrated with the object-based segmentation result. Specifically, the results of this research answer the following three questions: (1) Is the cloud computing-based method using optical and SAR images reliable and practical for mapping corn in a large area? (2) How well can the Sentinel-1 SAR data improve corn mapping? (3) Can GEE effectively support global scale crop thematic maps in the future?
## 1 Introduction
The resource allocation problem in SDM-EONs treating a fiber core as a new resource allocation variable is called the routing, core, and spectrum assignment (RCSA) problem. The traffic request suffers less transmission impairment when transmitted over short distance links, and can meet the transmission quality requirements of different modulation formats (MFs) at the receiving end. Therefore, using the same MF on links of different lengths inevitably leads to a waste of network spectrum resources and reduces the spectrum resource utilization. Therefore, flexible MFs can be considered when solving the network resource allocation problem. The RSA problem of EONs and the RCSA problem of SDM-EONs, considering selecting an appropriate MF for a signal to improve network resource utilization, are expanded into the routing, modulation format, and spectrum assignment (RMSA) problem and the routing, core, spectrum, and modulation format assignment (RCSMA) problem. According to these research methods, when solving the network resource allocation problem in SCNs, suitable network resource units need to be selected and flexible MFs can be considered to be utilized.
## 2 Tree Detection and Counting
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[82] & Satellites Sentinel-1 & & \\  & and Sentinel-2 & SAR and MS & Combining multi-sensor optical and SAR satellite \\ \hline
[11] & Aircraft - \({}^{*}\) & RGB & Morphological Recognition of Olive Grove Patterns \\ \hline
[22] & Satellite IKONOS & - & Tree detection and counting \\ \hline
[78] & Satellite Quickbird & MS & Evaluating four supervised \\  & & & classification algorithms, applied to pixel- and \\  & & & object-based classifications, for tree detection \\ \hline
[21] & Satellite Quickbird & MS & Tree detection and counting \\ \hline
[20] & Aircraft CESSA 421 & Airborne KODAK (MS) & Automatic assessment of quantitative agronomic and \\ \cline{2-3}  & Satellite Quickbird & MS & environmental indicators \\ \hline
[19] & Satellite Quickbird & PAN & Tree detection and counting \\ \hline \hline \end{tabular}
\end{table} 
Table 1: References dealing with olive trees detection and counting. The remote sensing (RS) platform used, the type of sensor and the main objectives of the researches are also indicated.
## 3 Results and Discussion
### Hardness and Elongation  
Figure 11 illustrates how the presence of Si\({}_{3}\)N\({}_{4}\) affects the hardness property of the composite material that was produced. The hardness of the AZ91 alloy was measured to be 34 BHN, but when 9 wt.% of Si\({}_{3}\)N\({}_{4}\) particles were added, the hardness increased to 76 BHN. Additionally, it was observed that increasing the amount of Si\({}_{3}\)N\({}_{4}\) reinforcement resulted in a corresponding increase in hardness. This is because Si\({}_{3}\)N\({}_{4}\) particles are inherently hard, and when they are incorporated into the softer matrix, they contribute to the overall hardness of the composite. The greatest increase in hardness was observed at a reinforcement content of 7 wt.%. Furthermore, it can be inferred that the greater the hardness and quantity of reinforcement, the higher the hardness of the composites, up to a certain extent. [PERSON] et al. (2020) reported, in their review, 72-84 and 44-65 hardness (HV) for AZ91/CNT and SiC/Mg composites, respectively. Further, [PERSON] et al. (2020) found the hardness result to be 80 HBN for Mg recycled waste composites, and similar results have been reported by other researchers ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020). It is evident that the hardness results in the present study reflect the previously reported results.  
Figure 11 also presents the changes in elongation percentage, which is shown on the secondary axis. According to the graph, the elongation is predicted to be 5.2% at 0 wt.% Si\({}_{3}\)N\({}_{4}\) and decreases to 2.1% for the AZ91/9%Si\({}_{3}\)N\({}_{4}\) composite. This indicates an increase in brittleness as the reinforcement particles are incorporated. The high brittleness of the Si\({}_{3}\)N\({}_{4}\) particles, when combined with the ductile AZ91 matrix, leads to the strengthening of the composites. In other words, the hardness and elongation are related to the strength of the composites. The percentage elongation in this study is consistent with previous publications ([PERSON] et al., 2020; [PERSON] et al., 2020).
## 4 Simulation Results and Discussion
### HR for the Resonance Frequency of 500 Hz
Besides the effect of shape changes, the effect of HR arrangement, serial and parallel along the duct as shown in Figure 6a,b, is studied. In each arrangement, the effect of the distance between necks in a serial arrangement and the offset distance between necks in a parallel arrangement is compared. The analysis results are depicted in Figure 6c,d. Note that, in the legend of the figures, GD and OD represent neck distance in parallel and serial arrangements, respectively, and the number means the additional distance to the baseline model. In the figure, it can be seen that transmission loss is increased with the increase in the offset distance in a parallel arrangement, while no distinct increment is observed in a parallel arrangement.
## 2 Delay Tolerant Networking Background
### DTN Overview  
In 2003, the DTN architecture was introduced in [8] to provide network connectivity to environments that the TCP/IP model failed to address, such as low-resource regions, outer space, or areas impacted by natural disasters that have intermittent and high latency links [3, 4]. DTN is an overlay network with the primary protocol being BP and the protocol unit of data being bundles. BP is analogous to a more robust version of Internet Protocol (IP), featuring endpoint identifiers, intermittent links, store-and-carry forwarding, security, and extendability. BP can run over any underlying network with a respective convergence layer adapter, including the TCP/IP suite of protocols [3]. Figure 1 provides an overview of what the network stack of a DTN network looks like when overlaid on a TCP/IP network. The bundle layer is able to utilize various transport layer protocols such as the User Datagram Protocol (UDP), the Transmission Control Protocol (TCP), and the Licklider Transmission Protocol (LTP) through the use of a protocol specific convergence layer adapter, e.g., the TCP Convergence Layer (TCPCL) [9].  
A formal document detailing the DTN architecture was introduced in 2007 [3], followed by the specification of experimental BP version 6 (BPv6) [10], the first documented version of a protocol for DTN. Throughout the years, BPv6 was implemented, deployed, and tested in an experimental manner, leading to the first officially standardized version of the protocol in January 2022, with the introduction of BP version 7 (BPv7) [11].  
The tests described in this paper use two DTN implementations developed by NASA. The first is the Interplanetary Overlay Network (ION) implementation developed by NASA's Jet Propulsion Laboratory [12]. ION has been deployed on the ISS [5] as a DTN gateway and has served as a reference implementation for many space-related DTN projects. To this reason, it was designed with resource-constrained flight-like hardware in mind. It features Bundle Protocol version 6 and 7, Licklider Transmission Protocol (LTP) [13], Contact Graph Routing (CGR) [14], and many space-related applications.  
The High-rate Delay Tolerant Networking (HDTN) project was developed by NASA Glenn Research Center [15]. HDTN is focused on developing a performance optimized, modular DTN architecture capable of supporting rates of 1 Gigabit per second and greater [16; 17; 18; 19]. Similar to ION, HDTN supports Bundle Protocol version 6 and 7, LTP, CGR, file transfer applications, and related tools. In addition, HDTN has been shown to be interoperable with ION and HDTN is able to detect what version of BP it received but can only send or respond with the BP version it have been configured to use.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 3 Methodology
### Framework of Our Method  
The framework of our method is shown in Figure 1. Our method mainly consists of feature extraction module, the AWNet module and a querying module.  
Figure 1: Framework of our proposed method.  
The feature extraction module can use any type of convolutional neural network model (e.g., ResNet, DenseNet, MobileNet, etc.) with the APL method to learn image features from training samples with noisy labels. The AWNet module aims to dynamically adjust the weighting hyperparameters of the APL method according to the predicted probability of the feature extraction module. In the training stage, two different metrics for the complexity and noise level of each sample image in a batch can be computed based on the predicted probability of the feature extraction module. The metric values of all images within the batch are used together as inputs to the AWNet module. With the AWNet, a different set of weighting hyperparameters, \(\alpha\) and \(\beta\), is obtained for each sampled image. To mitigate the negative effects of noisy labels in training samples, it is recommended to use more passive losses, which require a large \(\beta\). Conversely, for clean training samples with correct labels, it is advisable to use more active losses to facilitate quick convergence of the model, which requires a large \(\alpha\). The active passive loss can then be calculated for each image. The average APL of all images within a batch is used to update the classifier parameters of the image feature extraction module. In turn, the average value of the APL within the same batch is recalculated using the newly updated classifier and used to update the AWNet. Details of the two metrics and the adaptive weighted learning network are described in the following section.  
In the query stage, we first use the optimal model obtained in the training stage to extract the features of the query image. Then, we calculate the similarity between the feature vector of the query image and the feature vector of each image in the database. In this paper, the Euclidean distance is selected as the similarity metric. Finally, based on the descending order of image feature similarity, the top-ranked images are selected as the final retrieval results.
# Bionic Intelligent Algorithms Used in Helicopter Individual Blade Control Optimization
## 2 Methods
### The Establishment of Rotor Aeroelasticity Model
#### 2.1.3 Variation of Virtual Work \(\delta\)W\({}_{\text{i}}\)  
To obtain the accurate rotor aerodynamic force, it was not only necessary to obtain an accurate induced velocity distribution on the rotor-disc, but also to establish an accurate airfoil aerodynamic model [9]. To calculate the induced velocity, the Pitt-Peters dynamic inflow model was used in this article. The induced velocity could be described along the azimuth of the paddle \(\overline{\psi}\) and the blade span \(\overline{r}\):  
\[\overline{v}_{i}(\overline{r},\overline{\varphi})=\lambda_{0}+\lambda_{1c} \frac{r}{R}cos\psi+\lambda_{1s}\frac{r}{R}sin\psi \tag{11}\]  
The Leishman-Beddoes unsteady dynamic stall aerodynamic model was used to calculate the aerodynamic force. This model described the relationship between the lift and the angle of attack.  
The Figure 4 showed the force and moment acting on the profile. The lift moment \(L\), drag moment \(C\), and aerodynamic moment \(M\) on the profile could be expressed combined with the forward flight speed \(V\):  
\[\left\{\begin{array}{l}L=\frac{1}{2}\rho V^{2}cC_{N}\\ C=\frac{1}{2}\rho V^{2}cC_{C}\\ M=\frac{1}{2}\rho V^{2}cC_{C}\end{array}\right. \tag{12}\]  
This model described the relationship between the lift and the angle of attack. The normal force coefficient \(C_{N}\), chord force coefficient \(C_{C}\), and pitch moment coefficient \(C_{M}\) needed to be calculated.  
* **Normal force coefficient \(C_{N}\)**; When the airfoil angle of attack changes \(\Delta\alpha\), the circulation normal force response could be expressed as: \[\left(C_{N}\right)_{n}=\left(C_{N}^{C}\right)_{n}+\left(C_{N}^{I}\right)_{n}+ \left(C_{N}^{q}\right)_{n}\] (13) \[\left\{\begin{array}{l}\left(C_{N}^{C}\right)_{n}=C_{L\alpha}(M)(\alpha_{n }-X_{n}-Y_{n})\\ \left(C_{N}^{I}\right)n=\frac{4k_{1}T_{1}}{M}\left(\frac{\Delta\alpha_{n}}{ \Delta t}-D_{n}\right)\\ \left(C_{N}^{I}\right)n=\frac{-k_{1}T_{1}}{M}\left(\frac{\Delta\alpha_{n}}{ \Delta t}-D_{n}^{q}\right)\end{array}\right.\] (14)
* **Chord force coefficient \(C_{C}\)**; \[\left(C_{C}\right)_{n}=\left(C_{N}\right)_{n}tan\alpha_{e}\] (15)
* **Pitching moment coefficient \(C_{M}\)**; \[\left(C_{M}\right)_{n}=\left(C_{M}^{C}\right)_{n}+\left(C_{M}^{I}\right)_{n}+ \left(C_{M}^{q}\right)_{n}\] (16)  
Figure 4: Actual angle of attack (AOA).  
\[\left\{\begin{array}{l}\left(C_{M}^{C}\right)_{n}=\left(\frac{1}{4}-x_{ac} \right)\left(C_{N}^{C}\right)_{n}\\ \left(C_{M}^{I}\right)n=-\frac{1}{4}\left(C_{N}^{I}\right)_{n}\\ \left(C_{M}^{I}\right)n=-\frac{1}{4}\left(C_{N}^{I}\right)_{n}-\frac{k_{1}^{2 }T_{I}}{3M}\left(\frac{\Delta d_{n}}{\Delta t}-D_{n}^{qq^{\prime}}\right)\end{array}\right. \tag{17}\]  
According to the above equations, the corresponding external force virtual work coefficient (blade aerodynamic force) could be calculated.  
The generalized expression for the final aerodynamic virtual work (blade aerodynamic force) could be written as:  
\[\delta W_{i}=\delta q_{i}^{T}\cdot Q_{i}^{A} \tag{18}\]
## 2 Methodology  
### Overall Model Framework  
Figure 1 shows the model framework described in this paper. As illustrated in Figure 1, the overall operation process of this prediction model follows these 4 steps:  
(1) Data Preprocessing: initially, the raw data undergo zero-padding operations and normalization to yield a cleaned dataset, which serves as the original input sequence.  
(2) Variational Mode Decomposition (VMD): The cleaned dataset is then subjected to VMD to extract the corresponding Intrinsic Mode Functions (IMFs), obtaining a set of \(n\) IMFs. The VMD process involves iterative parameter initialization, optimization, and the update and check mechanism to produce the final IMFs.  
(3) IMF Prediction: the obtained IMFs are standardized using the following formula:  
\[I{MF^{\prime}}_{i}=\frac{IM{F^{\prime}}_{i}-IM{F_{i}}_{i}min}{IM{F_{i}}_{i}max -IM{F_{i}}_{i}min} \tag{1}\]  
Each of the resulting IMFs is then fed into a seasonality detection module. This module utilizes the autocorrelation function (ACF) and partial autocorrelation function (PACF) to identify IMFs with significant seasonal characteristics [33; 34]. The IMFs exhibiting strong seasonality are input into the SARIMA model, while those without strong seasonality, which exhibit stronger nonlinear and non-periodic patterns, are directed to an improved TimesNet model.  
(4) Reconstruction: Finally, after each IMF has been predicted through either SARIMA or TimesNet, all the predicted components are reconstructed to generate the final prediction. This reconstruction step is essential because the VMD decomposes the original sequence into several IMFs, and reconstructing these predicted IMFs allows for the restoration of the overall predicted sequence [35]. This comprehensive framework captures both strong seasonal components and nonlinear features in the data, thus effectively utilizing all the available information for accurate prediction outcomes.  
In this paper, a dataset constructed from real passenger flow data of the Hangzhou subway is used, and the sliding window technique is applied for forecasting. The data are segmented into five-minute intervals, with a total of 232 time steps in a day (covering the data within the statistics time), which serves as the length of the forecasting window  
Figure 1: The overall model framework.  
(the sliding window length). Additionally, the sliding window can be adjusted based on experimental requirements. For each day forecasted, the window slides by 232 time steps, and the actual values from the previous day are used to update the dataset.  
As shown in Figure 2, we utilized sliding windows of 232 time steps (a full day) to capture passenger flow patterns over different time horizons.  
Similarly, the prediction of each component obtained after Variational Mode Decomposition (VMD) also employs the sliding window concept. A detailed description of the VMD module and improved TimesNet model as well as the SARIMA model will be provided in the subsequent sections.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.6 Frechet Distribution (F2)  
For estimation with MOM, parameter \(\alpha\) has the following approximate forms depending on the coefficient of variation \(\mathrm{C}_{\mathrm{v}}\):  
\[\alpha=\exp\left(\begin{array}{c}0.928192087-0.358563901\cdot\ln(\mathrm{C} _{\mathrm{v}})+0.206039026\cdot\ln(\mathrm{C}_{\mathrm{v}})^{2}-\\ 0.023717668\cdot\ln(\mathrm{C}_{\mathrm{v}})^{3}-0.029286344\cdot\ln(\mathrm{C} _{\mathrm{v}})^{4}+0.003741887\cdot\ln(\mathrm{C}_{\mathrm{v}})^{5}+\\ 0.008353766\cdot\ln(\mathrm{C}_{\mathrm{v}})^{6}+0.002758305\cdot\ln(\mathrm{C} _{\mathrm{v}})^{7}+0.000306859\cdot\ln(\mathrm{C}_{\mathrm{v}})^{8}\end{array}\right) \tag{84}\]  
\[\beta=\frac{\mu}{\Gamma\left(1-\frac{1}{\alpha}\right)} \tag{85}\]  
For estimation with the L-moments, parameters have the following expressions [15]:  
\[\alpha=\frac{\ln(2)}{\ln\left(\frac{\mathrm{L}_{2}+\mathrm{L}_{1}}{\mathrm{L} _{1}}\right)} \tag{86}\]  
\[\beta=\frac{\mathrm{L}_{1}}{\Gamma\left(1-\frac{1}{\alpha}\right)} \tag{87}\]