esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Plume Tracing Algorithm
### Results of the Comparative Analysis  
To evaluate the performance of the above algorithms, 100 simulation experiments were carried out under two conditions of the environment. First, an environment with low and constant wind was tested as shown in the example in Figure 3. Under these conditions, the algorithms reached the source most of the times. An environment with highly-variant wind effects was also tested, as shown in the example in Figure 4. In this configuration, the GS maintained its average utility, the TS increased its utility in about 12%, the SA increased its average utility in about 15% and the ISA average utility was reduced in about 20%.  
According to the box-plot of the algorithm's performance analysis, shown in Figures 3 and 4, the ISA approach maintains a superior performance in the highly variant environments.  
When introducing the component of velocity towards the positions with high levels of the pollutant concentration, the basic simulated annealing methodology improved significantly. This fact will be exploited in the next section for designing the final algorithm to localize the source of the pollutant.
## 4 Modeling Errors of KANs and Discussion
### Modeling Errors of KAN-B  
The results of KAN-B appear in Table 6. KAN-B exhibited minimal discrepancies between training and test errors, indicating no overfitting/underfitting. The sMAPE values demonstrated tight control over relative errors. The MaxAE values (0.4950 training,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Category** & **Error** & **Value** \\ \hline \multirow{8}{*}{KAN-A} & & MSE & 0.0019 \\  & & MAE & 0.0108 \\  & Training & sMAPE & 5.4526 \\  & & MaxAE & 0.7555 \\  & & R\({}^{2}\) & 0.9949 \\  & & Adjusted R\({}^{2}\) & 0.9949 \\ \cline{2-4}  & & MSE & 0.0007 \\  & & MAE & 0.0099 \\  & Test & sMAPE & 4.6164 \\  & & MaxAE & 0.3350 \\  & & R\({}^{2}\) & 0.9983 \\  & & Adjusted R\({}^{2}\) & 0.9983 \\ \cline{2-4}  & & Total runtime & 614.5063 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modeling errors of KAN-A.  
0.4736 test) showed robust fitting of challenging samples. With a runtime of \(\sim\)300 s (half of KAN-A), its simple structure delivered computational efficiency.  
R\({}^{2}\) and Adjusted R\({}^{2}\) remained near 1, confirming that KAN-B fully explained target variance and statistically input contributions. This implies that KAN-B learned universal underlying patterns in the data. The derived mathematical expressions are valid and trustworthy.
###### Abstract  
Currently, GNSS reflectometry based on the signal-to-noise ratio (SNR) has become an established tool in ocean remote sensing. Here, the distance between an antenna and the water surface is measured by analyzing the oscillation of the SNR observation. Due to the antenna gain pattern, this oscillation is more pronounced for satellite signals coming from low elevation angles. Additionally, the sea surface roughness is related to the attenuation of the SNR oscillation. Hence, the significant wave height (SWH) can be estimated by analyzing the SNR signal. In this work, a method is presented with which the SWH can be calculated from the attenuation's damping coefficient of the SNR observations measured with surface-based receivers. The method's usability is demonstrated using data from a static antenna operated in the German Bight and with data from a ship-based antenna. The estimated SWH values were validated against numerical wave model data. For both experiments, a high correlation was found.  
GNSS reflectometry; GNSS SNR; interference pattern technique; damping coefficient; significant wave height; ship measurement +
Footnote †: journal: Remote Sensing, 2019, 11, 409
# Emergence of White Organic Light-Emitting Diodes Based on Thermally Activated Delayed Fluorescence
## 3 Approaches for WOLEDs Based on TADF
### Hybrid WOLEDs Based on Blue TADF and Phosphorescence
#### 3.4.2 Hybrid WOLEDs Possessing Blue TADF Exciplex and Complementary Phosphorescence Materials
To further improve the performance of hybrid WOLEDs possessing blue TADF exciplex and complementary phosphorescence materials, precise exciton allocation is significant, aside from the use of efficient blue TADF exciplex [163; 164; 165]. Particularly, the notorious efficiency roll-off problem can be solved by dint of precise exciton allocation, given that high triplet exciton density at high luminance deteriorates the efficiency [166; 167; 168; 169; 170]. [PERSON] et al. recently demonstrated a series of high-performance WOLEDs, by managing the exciton allocation [171]. For example, they fabricated the all-fluorescence WOLEDs using blue TADF exciplex host and TBRb as the guest (CDBP:PO-T2T:TBRb), achieving the forward-viewing EQE of 20.8% and PE of 75.4 lm W\({}^{-1}\), the highest values for all-fluorescence WOLEDs. Then, they developed hybrid WOLEDs with low efficiency roll-off, obtaining two-color hybrid WOLEDs with maximum EQE of 28.3% and PE of 102.9 lm W\({}^{-1}\), remaining 25.8% and 63.5 lm W\({}^{-1}\) at 1000 cd m\({}^{-2}\). Furthermore, a three-color WOLED yields a CRI of 86, an EQE of 29.4% and PE of 75.5 lm W\({}^{-1}\). The structure of fluorescent WOLED is ITO/MoO\({}_{3}\) (10 nm)/TAPC: MoO\({}_{3}\) (20.0%, 50 nm)/CDBP (10 nm)/CDBP:PO-T2T:TBRb (1:1, 0.3%, 20 nm)/PO-T2T (10 nm)/PO-T2T: Li\({}_{2}\)CO\({}_{3}\) (3%, 45 nm) /Li\({}_{2}\)CO\({}_{3}\) (1 nm)/Al (100 nm). The tri-EML hybrid WOLED with the EML of CDBP:PO-T2T: PO-01 (4:1, 5%, 3 nm)/CDBP:PO-T2T: PO-01 (1:1, 0.3%, 8 nm)/CDBP:PO-T2T: PO-01 (1:4, 5%, 3 nm), while other layers are the same as those of fluorescent WOLED. In their devices, nearly all electrically produced singlet and triplet excitons can be efficiently utilized for white emission, yielding the unity EQE. Besides, with the help of exciplex-sandwich emissive architecture, the performance of hybrid WOLEDs are among the best WOLEDs. As shown in Figure 14, because the blue exciplex possesses high T\({}_{1}\), the generated triplet excitons on the blue-EML can be transferred to the adjacent green-EML and then the red-EML for radiative decay, producing the white emission. For this sandwich-EML, it not only confines singlets and triplets into the emissive zone but also utilize all singlets and triplets because the diffused excitons from the middle emissive zone can undergo radiative transition in the bilateral emissive zone. Therefore, the unity exciton utilization ratio is achieved by this unique working mechanism.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 2 Materials and Methods
### ROT and ROTI  
Rate of change of the TEC Index (ROTI) is defined as the standard deviation of the rate of TEC (ROT). It is used in the detection and investigation of occurrences of ionospheric  
Figure 1: 868 GNSS receivers (red dots) used in present work. The Sun at 14.50 UT (black dot) on 12 May 2021, and the magnetic equator (black line).  
irregularities. ROTI was estimated by dual-frequency GNSS data with the time interval of 5 min by using Equation (3) [68; 69; 70; 71; 72]:  
\[ROTI=\sqrt{(ROT^{2})-\langle ROT\rangle^{2}} \tag{3}\]  
where \(\langle\cdot\rangle\) represents the temporal average. The ROT and ROTI values are typically expressed in units of TECu/min. ROT is defined as the TEC variation rate of two successive epochs as stated in Equation (4) [68; 69; 70; 71]:  
\[ROT=\frac{STEC_{t}^{i}-STEC_{t-1}^{i}}{k_{t}-k_{t-1}} \tag{4}\]  
where \(k\), \(t\), and \(i\) represent the GPS time, the epoch, and the number of observation satellites, respectively.  
According to [PERSON] et al. [73], the ROTI value can be divided into three groups: weak, if \(0.25\leqslant\text{ROTI}<0.5\); moderate, if \(0.5\leqslant\text{ROTI}<1\); and strong, if ROTI \(\geqslant 1\).
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
## 2 Principle and Simulation Analysis
### The Working Principle of the Runout Detection System
Using this information, the following is obtained:  
Figure 2.1  
In order to ensure that a clear image is captured of the point of light at each point on the photodetector, the optical path design of the imaging system, the object plane, the image plane, and the main plane of the optical system should satisfy the \"Scheimpflug\" condition, i.e., the laser beam of the laser transmitter, the main optical axis of the imaging optical system, and the extension of the main plane of the position detector should intersect at a point P, as shown in Figure 2.2
# Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks
## 2 Cyberattacks in Satellite Networks
* Drive-by attack: Such attacks are a common way to install and download malicious software without any explicit permission from the user. This usually happens while visiting a compromised website that will automatically download and install to infect the device, steal information, or corrupt stored data. This is also known as a drive-by download as this does not require any action on the part of victim. A drive-by download attack can take advantage of the security vulnerability of an app, operating system, or web browser [46]. * Password attack: Passwords are required to obtain access to an information system, storage devices, and emails. Password attacks use special techniques and software to guess the passwords of users to gain access to their system. Such access to a user's password can be obtained by smart guessing by combining date of birth, place of birth, pet's name etc., using social engineering and tricks and gaining access to a password database [47]. * SQL injection attack: Structured Query Language (SQL) attacks are mostly common with database-driven websites. The attacker inserts SQL commands that can lead to access, modify, and delete databases or completely shut them down [48]. * Cross-site scripting (XSS) attack: XSS attacks inject malicious code into a trusted website. The users visiting the website will be vulnerable to give away personal information or download software onto their computers [49]. * Eavesdropping attack: Eavesdropping attacks occur by intercepting, modifying, or deleting the data that are being exchanged between two parties. It takes advantage of an unsecured communication network to snoop on the data transfer in transit [50]. * Malware attack : Such attacks uses malicious or unwanted software designed to harm the information system. It can lead to stealing, deleting, or encrypting the data, gaining access and control of the device, locking the device, and injecting some spy software. Some examples include virus, worm, ransomware, trojan horse, bots, spyware, adware, etc. [51]. For launching a DDoS attack on the LEO satellite network, the attacker will use several compromised bots/users located on the ground by combining various kinds of cyberattack techniques to gain access or control of the end users on the ground spread across a vast geographical area. After gaining complete control of these users, it can flood and overwhelm the data traffic of the downlink/uplink or inter-satellite link of the satellite communication link to deny services to the genuine users.
## 5 Evaluation of Scale Effects in the Validation of GOCI-Derived SSC  
The high temporal resolution of the geostationary ocean color sensor (GOCI) enables a detailed study of the spatiotemporal variability of SSC in Hangzhou Bay, which is critical for understanding sediment transport and deposition processes. Prior to investigating these processes, however, it is necessary to assess the accuracy of the GOCI SSC retrievals. In the validation of GOCI-derived SSC using in situ measurements, scale effects introduce uncertainties that must be quantitatively evaluated. Quantifying the impact of these scale effects on the validation results is a key issue addressed in this study.  
Hangzhou Bay exhibits pronounced spatiotemporal variability in SSC, particularly in transition zones between high- and low-sediment waters. Field measurements conducted during high, medium, and low tides indicate that SSC can fluctuate dramatically within an hour [6].  
Traditionally, the validation of GOCI SSC retrievals is performed by comparing the in situ measurements with the mean value computed over a 3 \(\times\) 3 window, where each GOCI pixel represents a 500 m resolution area covering 1500 m \(\times\) 1500 m. However, in such highly variable environments, a single in situ point may not adequately represent the average conditions within this window, thereby introducing considerable uncertainty in the validation results.
## 2 Materials and Methods
### Machine Learning Pipeline
#### 2.9.4. Classification of Pain Intensity Levels  
We assessed the impact of three different input datasets on intensity level classification according to the PFI ranking. The first dataset included only valence and arousal (VAL_ARO), the second consisted of the top-ranked features from the \"PHY_12\" feature set (PHY_Sel), and the third used the top-ranked features from the \"PHY_12 + VAL_ARO\" feature set (Combined_Sel). The same RF classifier, using a repeated nested CV design, stratified splitting, SMOTE-based oversampling, and hyperparameter settings consistent with the PFI analysis for intensity-level classification, was used for all classifications. Macro F1 was the optimization metric for hyperparameter tuning, and the best parameter set identified during the inner CV was retrained on the entire outer training fold. The resulting model was evaluated on the outer test fold, with the macro F1 (for the training and test sets) and balanced accuracy, recall, and specificity recorded (Table S1). These outer-foldmetrics were averaged over all folds and repetitions, and all performance measures are reported as means and standard deviations. We computed the normalized confusion matrix following the same procedure as used in the baseline-versus-pain classification, presenting the performance metrics and normalized confusion matrices for all three input datasets.  
To benchmark the RF model, we repeated the intensity-level 6 vs. 7 classifications with two alternative algorithms: a linear-kernel SVM and k-nearest neighbors (k-NN). For the SVM, the regularization parameter C was tuned at three levels--0.01, 0.1, and 1--whereas the k-NN model was evaluated with neighborhood sizes of 13, 15, and 17. All remaining preprocessing was identical to that used in the RF pipeline.  
To assess the impact of individual random forest hyperparameters, we conducted a one-factor-at-a-time (OFAT) sensitivity analysis. The grid search evaluated three parameters, the number of decision trees (15 or 25), the minimum samples needed to split a node (15 or 20), and the minimum samples required at a leaf node (6 or 12), resulting in eight unique combinations. These values were chosen a priori to mitigate overfitting risk. Each combination was passed through the identical preprocessing pipeline and evaluated with a repeated outer-loop CV (20 \(\times\) 5-fold); all hyperparameter settings were tested directly in the outer folds, with no inner tuning loop. Test F1 scores were recorded for all eight configurations, with performance differences attributed to the varying parameter in each OFAT comparison.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 1 Introduction
Specifically, an iterative optimization algorithm automatically adjusts two additional coefficients (one for each upper or lower surface) to keep the skin length of the new airfoil equal to that of initial airfoil. Based on the observation of existing morphing airfoils, some authors suggested varying the chord length during morphing. [PERSON] et al. [17] proposed a modified CST method in order to maintain structural consistency, based on the morphing structural assumptions of compliant ribs, rigid spar, constant leading edge radius, and passive surface length. The chord length is adopted as the parameter, and the constant length of the passive surface assumption is satisfied by solving a fixed-point iteration problem. [PERSON] et al. [18] developed a knowledge-based skin structural (KBSS) feedback method considering the wing-box, airfoil area, skin length, and curvature constraints, and performed morphing wing shape optimization using GAs. Such parameterization, which considers the general characteristics of the morphing airfoil without considering specific morphing mechanisms, is beneficial for the morphing aerodynamic shape optimization. However, current optimizations based on feasible morphing shape parameterization are solved by GAs, which requires an enormous evaluation of the aerodynamic responses that are time-consuming when a high fidelity CFD solver is involved. With the rapid development of a CFD adjoint solver, gradient optimization has the potential to be an affordable tool for resolving the problem mentioned above. In each optimization iteration, the computation requires solving one flow and several adjoint problems depending on the number of functions to be evaluated. Consequently, the computational time of evaluating the sensitivities is independent of the number of the design variables. In this paper, we focus on the aerodynamic shape optimization with a parameterization dedicated to the morphing, particularly for two-dimensional morphing airfoil design. A gradient-based morphing shape optimization framework is presented that uses a Reynolds-averaged Navier-Stokes (RANS) CFD solver, an advanced adjoint implementation, a robust mesh deformation algorithm, and an innovate shape parameterization method. In this method, the shape of the morphing system is implicitly represented by the parameterization that not only keeps the shape invariant in the wing-box region but also provides skin length control. Using this framework, an optimization example that minimized the drag by morphing the leading and trailing edges was carried out, and the potential of drag reduction was investigated. The paper is organized as follows: the feasible morphing airfoil geometry parameterization and the optimization framework are detailed described in Section 2; then, in Section 3, the presented method is applied on a transonic airfoil to increase the performance by minimizing the drag coefficient at cruise; finally, the conclusions are summarized in Section 4.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 6 Smart Contract Specification
### Data Storage And Indexing  
A critical aspect of using a smart contract is identifying which data need to be stored and the most appropriate type of storage. All the data required for use in different functions of the smart contract are stored in the data structure defined in Listing 1 and are indexed by \(id\). For this purpose, we use the EVM's storage component (see Section 2). These data are provided by the document recipient, who calls the _accept_ function to confirm receipt of the message, and are verified by the smart contract before the message delivery status is set to \"accepted\".  
Three possible states of delivery are defined (see \(State\) in Listing 1):  
* \(undefined\), which is the default value when the structure is created;
* _accepted_, which indicates that the recipient has successfully executed the _accept_ function; and
* _delivered_, which indicates that the sender has published the key for retrieving the delivered document.  
```
1 enumState{undefined,accepted,delivered}
2 structdata{
3 uint256 td;//deadline
4 addressaddrA;//senderaddress
5 addressaddrB;//recipientaddress
6 Statesatus;//deliverystatus
7}
8 mapping(bytes32=>data)private.docDelivery;
```  
As shown in Listing 1, the \(data\) structure is defined to store the following information:  
* \(td\), which is the timestamp indicating the deadline by which the recipient can accept document delivery;
* \(addrA\) and \(addrB\), which store the blockchain addresses of the sender and recipient, respectively;
* \(status\), which indicates the current state of document delivery.  
The smart contract allows information to be stored for different instances of document delivery, and the delivery identifier \(id\) is used to index the data structure associated with each delivery, which is defined as _docDelivery_. For this purpose, we use a mapping, which is a type of variable that enables us to represent a table that stores data as type-value pairs.
## 3 Results and Discussion
### Effects of the Quantity of Hidden Neurons  
Quantity of neurons in the hidden layer had great impact on the performance of the RBF ANN. Generally, the precision of the RBF ANN could be improved as the quantity of the hidden layer increased. However, too many neurons in the hidden layer will prolong the convergence rate of the RBF ANN, and even lead to failure to train the model. In order to determine the optimum number of hidden neurons, multiple experiments were executed. Twelve RBF ANN models with various hidden layer were constructed firstly. The quantity of hidden neurons were 60, 65, 70, 71, 72, 73, 75, 80, 85, 90, 95, and 100, respectively. The parameter \(\sigma\) and \(\alpha\) were still set to 1.0 and 0.9. Afterwards, the twelve models were trained by the same dataset with 180 samples. Finally, they were tested with the dataset in Table 5.  
The MSED of the models with various hidden layers are shown in Table 6. When the quantity of the hidden neurons increased from 60 to 72, the MSED declined gradually, while rising gradually as the quantity of the hidden neurons increased from 72 to 90. From 90 to 100, the MSED descended again. Overall, the MSED of the model with 72 hidden neurons has the minimum value relatively. Therefore, it could be considered that the precision of the RBF ANN model reached its maximum when the quantity of neurons in the hidden layer was 72.