esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model and Inversion Method
### Coordinate Transformation
#### 2.3.2 Transformation of Geomagnetic Observation Component  
The transformation of geomagnetic observation component \(\left(N,E,U\right)_{S}\) is conducted through three steps:  
(1) In the spherical coordinate reference frame, Equation (11) is used to transform the geomagnetic observation component \(\left(N,E,U\right)_{S}\) in station center coordinate system to the geocentric space rectangular coordinate system \(\left(X,Y,Z\right)_{S-VAL}\);  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]_{S-VAL}=\left[\begin{array}{ccc}-\sin B_{0}\cos L_{0}&- \sin L_{0}&-\cos B_{0}\cos L_{0}\\ -\sin B_{0}\sin L_{0}&\cos L_{0}&-\cos B_{0}\sin L_{0}\\ \cos B_{0}&0&-\sin B_{0}\end{array}\right]\cdot\left[\begin{array}{c}N\\ E\\ U\end{array}\right]_{S} \tag{11}\]  
In Equation (11), \(\left(B_{0},L_{0}\right)\) represent the geocentric latitude and longitude in the spherical coordinate system, respectively.  
(2) Similarly, Equation (12) is used to transform \(\left(X,Y,Z\right)_{S-VAL}\) into the cone space rectangular coordinate system \(\left(X,Y,Z\right)_{C-VAL}\), namely:  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]_{C-VAL}=R_{y}(\varphi)\cdot R_{z}(\lambda)\cdot\left[ \begin{array}{c}X\\ Y\\ Z\end{array}\right]_{S-VAL}. \tag{12}\]  
(3) In the cone coordinate reference frame, Equation (13) is used to transform \(\left(X,Y,Z\right)_{C-VAL}\) to geomagnetic observation component \(\left(N,E,U\right)_{C}\) in station center coordinate system.  
\[\left[\begin{array}{c}N\\ E\\ U\end{array}\right]_{C}=\left[\begin{array}{ccc}-\sin B_{0}\cos L_{0}&-\sin B_{0} \sin L_{0}&\cos B_{0}\\ -\sin L_{0}&\cos L_{0}&0\\ -\cos B_{0}\cos L_{0}&-\cos B_{0}\sin L_{0}&-\sin B_{0}\end{array}\right].\left[ \begin{array}{c}X\\ Y\\ Z\end{array}\right]_{C-VAL} \tag{13}\]  
(Note: in Equation (13), \((N,E,U)_{C}\) correspond to the geomagnetic observation component \((X,Y,Z)\) in the spherical cap harmonic model in Equation (2)).
## 6 Conclusions  
This paper describes the implementation of a software receiver for GPS/WAAS L1 C/A code signals to demonstrate the feasibility of CRPA technology for civil applications. The developed approach performs pre-correlation beamforming without any prior calibration. The architecture of STAP is constructed in a software approach to achieve a CRPA. The optimum weights obtained by adaptive MVDR algorithm provide the interference rejection performance capable of rejecting multiple interferers.  
The components of software receiver are implemented and constructed efficiently using the SIMD assembly and C/C\({}^{++}\) multithreaded programming. In particular, the SIMD assembly code has been developed and optimized to achieve a fast correlation process of high resolution (16 bits) samples and is fully documented within the paper.  
The software receiver implementation is validated, demonstrating it can perform all-in-view pre-correlation beamforming up to twelve channels in real-time. The angle-frequency response calculation is also included in the software to illustrate the interference rejection performance of the receiver. The results of our experiments demonstrate interference rejection capability in the presence of a single higher power broadband interference with 40 dB J/S or six lower power broadband/narrowband interferers with 30 dB J/S.  
In the next generation of x86 CPUs, a new instruction set called Advanced Vector Extensions (AVX) will be incorporated which will have the capacity to further expedite the computation of the vector operations. The width of the SIMD register will be 256 bits, two times wider than that of the current registers. This would reduce the computational load by two times. Moreover, the AVX instruction set introduces a three-operand format where two source operands and one destination operand. This would decrease the number of instructions for moving data. Using the AVX instruction set, the software receiver would gain the ability to process wider bandwidth signals such as GPS/WAAS L5 signals in real time, which require a higher sampling rate. The structure of our software receiver is flexible to extend to GPS/WAAS L5 processing with minimal modifications. The Federal Aviation Administration (FAA) Alternative Position Navigation and Timing (APNT) study is interested in the use of CRPA with the WAAS L5 signal for robust time transfer under GPS interference [24].
## 3 Results
### Results of the Corrected BNU-1 Images  
The relative sensor normalization has been applied to improve the radiometric errors of BNU-1 level-0 images. As shown in Figure 3, obvious differences are observed between the original blue band and the panchromatic band. The DN values from the panchromatic band change relatively smoothly, and the column average curve (Figure 8 black) also appears to be slow, excessive, and relatively smooth. In the blue band where there is a problem, the column average curve (blue) of the original image has abnormal changes in the center field of view and the edge field of view, and the column average of the edge field of view is obviously low. After applying the improved histogram matching method, the column mean curve of the blue band is significantly corrected and appears its proportional relationship with the panchromatic band. The corrected images are shown in Figure 9. By comparing with the uncorrected images, the average value of the multi-spectral band in edge FOV column is effectively corrected, which leads to significant improvement in the RGB color composite image. The improved RGB image eliminates the unbalances at the edge FOV, enhances the image radiation accuracy, and provides the true color composite image with higher color contrast. Figure 10 shows the comparisons of the original and corrected BNU-1 images. It indicates that the uncorrected BNU-1 images have better visual performances than the original images.  
Figure 8: Comparisons of DN values before and after matching histogram of the CMOS1 and CMOS2 images in band B1. The red box highlights the DN values from the center columns of CMOS1 and CMOS2 images. The original images are the same as Figure 3. The black curve represents the panchromatic band. The blue and cyan curves represent the B1 band before and after applying the matching histogram method, respectively.  
Figure 9: Two true color composite scenes with orbit number 200214500 acquired on 8 October 2019. (**a**,**b**) represent the corrected CMOS1 image and CMOS2 image, respectively.
# CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
## 5 Case Study and Experimental Results
### Case Study: 1000 MW Thermal Power Plant  
One of our deployments of the continuous machine learning platform is the prediction system for 1000 MW coal-fired thermal power plant. This plant has 15,528 physical sensors around different equipment and generates data at about 3.8 G per day. The goal of prediction system is to forecast the future (30 and 60 s) values of major system metrics in a real-time manner. Eight major system metrics, after the discussion with plant operators, are selected for prediction, as shown in Table 1. According to different prediction targets, both experts' experience and data correlation analysis are used to select different features. Around 170 system metrics were selected as the input of models, some of which overlap among different models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modeling Targets** & **Features** & **MAE-30s** & **Algorithm** \\ \hline Power & 12 & 0.25 & Ridge \\ main steam temperature & 75 & 0.08 & Ridge \\ reheat steam temperature & 93 & 0.11 & Ridge \\ main steam pressure & 48 & 0.16 & Ridge \\ feedwater temperature & 49 & 0.06 & Ridge \\ active power & 10 & 0.20 & Ridge \\ Max superheater wall temp & 10 & 0.06 & LSTM \\ Max reheater wall temp & 10 & 0.05 & LSTM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information for eight different prediction models.  
Figure 3: The flow chart of the model seamless switching.  
The whole processing process starts from the real-time monitored data from the plant's DCS. The data flows this platform through an industrial gateway--HollySys HH800. After the data of the sensors pass through the industrial gateway, they enter Kafka, which is a message middleware. Spark Streaming, a stream processing technique, consumes the data from Kafka and forwards the data to both the real-time database (currently implemented via Redis) and the historical database (currently via MySQL). A _modelet_ gets its required features from the real-time database and _trainers_ acquire historical data via one custom-designed data conversion package designed to facilitate the data communication between _trainers_ and the historical database. An event mechanism is used to synchronize the execution of _modelets_, _trainers_ and the _orchestrator_. All those components are implemented as micro-services and deployed as Docker containers. In fact, about 20 different containers are deployed in the system.
## 3 Results
### Physical Stress
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & \multicolumn{4}{c}{**Mean (SD)**} & \multicolumn{4}{c}{**Statistics \(p\) (n\({}^{2}\))**} \\ \hline
**Instruction Methods** & \multicolumn{2}{c}{**Paper-Based Instruction**} & \multicolumn{2}{c}{**AR-Based Instruction**} & \multicolumn{2}{c}{**Instruction (n\({}^{2}\))**} & **Complexity (n\({}^{2}\))** & **Interaction** \\ \hline
**Task Complexity** & **High Difficulty** & **Low Difficulty** & **High Difficulty** & **Low Difficulty** & **Low Difficulty** & **Low Difficulty** & **Interaction** \\ \hline m-RR (ms) & 677.91 (0.55) & 774.22 (66.32) & 752.16 (102.48) & 767.42 (73.20) & 0.22 (0.06) & **0.00 (0.53)** & **0.002 (0.35)** \\ SDRR (ms) & 16.65 (2.29) & 18.38 (1.64) & 18.86 (2.30) & 18.96 (2.54) & 0.08 (0.99) & 0.08 (0.11) & 0.12 (0.9) \\ m-HR (ppm) & 89.49 (6.31) & 78.27 (6.51) & 80.46 (11.66) & 77.90 (8.16) & 0.13 (0.09) & **0.00 (0.60)** & **0.00 (0.37)** \\ SDRR (ppm) & 5.11 (0.97) & 3.59 (0.72) & 4.09 (1.34) & 3.85 (1.14) & 0.31 (0.04) & **0.00 (0.38)** & **0.00 (0.20)** \\ RMSSD (ms) & 12.31 (1.97) & 16.87 (2.77) & 18.85 (6.05) & 19.17 (5.07) & **0.01 (0.23)** & **0.00 (0.50)** & **0.00 (0.39)** \\ NN50 (ms) & 4.21 (4.15) & 4.14 (4.37) & 3.79 (3.31) & 2.21 (1.67) & 0.32 (0.04) & 0.23 (0.06) & 0.27 (0.05) \\ PNN50 (\(\uparrow\)s) & 0.21 (0.22) & 0.37 (0.34) & 0.28 (0.30) & 0.30 (0.34) & 0.98 (0) & 0.21 (0.06) & 0.35 (0.92) \\ HRVI & 5.19 (0.72) & 5.86 (0.712) & 5.75 (11.11) & 6.02 (1.18) & 0.25 (0.051) & **0.02 (0.19)** & 0.32 (0.04) \\ TINN (ms) & 202.14 (54.59) & 158.57 (31.40) & 162.14 (35.18) & 158.57 (41.34) & 0.15 (0.079) & **0.00 (0.20)** & **0.02 (0.19)** \\ VLF (\(\uparrow\)s) & 23.82 (7.75) & 20.11 (7.21) & 19.63 (8.41) & 17.61 (10.08) & 0.233 (0.054) & 0.087 (0.11) & 0.61 (0.01) \\ LF (\(\uparrow\)s) & 68.46 (7.54) & 56.26 (
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 2 Related Works
However, this setup requires complex cross-chain interactions for data storage and retrieval, which may impact efficiency. In 2021, [PERSON] et al. [29] proposed a system that utilizes blockchain and IPFS to store vehicle data securely and efficiently. In this system, IPFS is used for the off-chain storage of vehicle data, and its hash value is recorded on the blockchain to enhance query efficiency. This method eases blockchain storage limitations and provides a degree of security for off-chain data. However, security risks remain, as malicious actors might exploit the blockchain-stored hash value to access the IPFS data. Incorporating the aspect of data changes in the combined on-chain and off-chain data-sharing process leading to overheads, in 2024, [PERSON] et al. [30] proposed a blockchain-TEE medical data privacy protection and sharing scheme. This scheme enables reliable incremental updates and validations, thereby enhancing the security and efficiency of the blockchain-mediated data-sharing process. The aforementioned research highlights the integration of blockchain technology with existing data-sharing frameworks, with some studies focusing on cryptographic algorithm-based mechanisms to secure data on the blockchain. Given the growing volume of data, a hybrid approach is advocated, storing shared data on-chain and critical information off-chain. This security-centric strategy employs lightweight Elliptic Curve Cryptography (ECC) and Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) for encryption and decryption, complemented by IPFS for off-chain storage, thus mitigating blockchain storage constraints. The hash value of the IPFS-stored data is encrypted into ciphertext using lightweight cryptography. Additionally, this paper presents a scheme that enhances security and ensures data traceability through the incorporation of Non-Fungible Token (NFT) technology. The ciphertext of the hash value is embedded within an NFT and recorded on the blockchain, thereby capturing the transaction history of the shared data. This method prevents unauthorized decryption attempts by leveraging the immutability of blockchain hash values, offering both traceability and user authentication.
## 3 Results and Analysis
### GNSS-R Soil Moisture Retrieval Data Set  
The data set was simulated and trained to analyze the performance of the input variables of GNSS-R by using XGBoost. We considered the soil moisture content and the permittivity had a positive correlation, and some established soil permittivity models [54; 55] could be used to retrieve the soil moisture from permittivity, so the permittivity is the output variable that we obtained firstly using the bistatic radar equations for observation, under the assumption of a flat surface [53].  
The simulated training set mainly consists of the following input vectors:  
1. \(\theta\), Elevation data (from 35 degrees to 85 degrees);
2. \(G_{r}\), Receiver Gain (from 2.5 to 3.5 dB);
3. \(SNRh\), the signal to noise ratio from the reflected channel (from 2 to 26 dB);
4. \(P_{n}\), the total noise power of the receiver (from \(-130\) to \(-150\) dB).  
The range of the input vector was set with the idea of staying as close as possible to the experimental situation. Those variables (\(\theta\), \(G_{r}\), \(P_{n}\), and \(SNRh\)) were the observables for the variable importance analysis. Other input vectors, such as \(R_{r}\) (distance from the transmitter to a specular point), \(P_{t}\) (transmitted power from GPS satellite), \(G_{t}\) (transmitter antenna gain), and \(G_{pr}\) (signal processing gain) were constant numbers that depend on the GNSS-R system and were not shown here.  
With the GNSS-R retrieval procedure as shown in Figure 2, the simulated data were obtained and shown in order to understand the relationship between the SNR, elevation angle, and the permittivity in Figure 4. The dataset of Figure 4 contained 1000 samples. With the increment of the elevation angle and SNR, the permittivity was also increased as shown in Figure 4.  
Figure 5a shows the relationship between the SNR and the permittivity when the elevation angle was a constant number (e.g., 84\({}^{\circ}\)). Figure 5b considers the relationship between the elevation angle and  
Figure 4: Three-dimensional data set shown for the XGBoost algorithm.  
the permittivity when the SNR was a constant value (20 dB). It is noted that the obtained permittivity was computed from the formulas that comprise the variables SNR and elevation angle [53]. In Figure 5, it was possible to observe a significant result that the permittivity increased with the increment of the SNR when the elevation angle was a constant value. On the other hand, the permittivity was quite constant for elevation angle ranging between 50\({}^{\circ}\) to 85\({}^{\circ}\) when we fixed the value of the received SNR. The reason is that higher elevation angles lead to higher signal power received. In case of receiving the same SNR (Figure 5b), a surface with lower permittivity (lower soil moisture content) requires a signal from a higher elevation angle, compared with a higher permittivity surface.
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.6 Overall Operation of ATMALS-GSK
Looking at complex composite functions like 21-28, ATMALS-GSK either performs on par with the best (e.g., functions 26 and 28, both achieving perfect or near-perfect solutions like eGSK) or slightly underperforms in terms of variance but retains competitive means. Notably, in function 24, despite not having the best mean, ATMALS-GSK is very close to eGSK and performs better than all other variants, suggesting good generalization. The results in Tables 9-12 show that across most benchmark problems, the STDs of ATMALS-GSK are relatively controlled, signaling dependable convergence behavior. Its hybridization strategy, by embedding adaptive tuning and local search layers, appears to provide resilience against premature convergence--a known weakness in some simpler modifications. The results show that ATMALS-GSK proves itself to be a strong contender among the GSK family. While it does not dominate in every single function, it consistently offers balanced, reliable, and often superior performance, especially for complex or multimodal problems. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Fun. ** & **GSKK**[16] & **AGSK**[46] & **APGSKK**[45] & **FDBACKSK**[36] & **eGSKK**[49] & **ATMALS-GSK** \\ \hline
1 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 2 Materials and Methods
### Mathematical Models
#### 2.2.3 Heat Transfer Analysis  
The Pennes biological heat transfer equation is shown below [8]:  
\[\rho c\frac{\partial T}{\partial t}=\
abla\cdot(k\
abla T)+\omega_{b}C_{b}(T_ {b}-T)+Q_{met}+Q_{ext} \tag{8}\]  
where \(T\), \(\rho\), \(C\) and \(k\) are the temperature (\({}^{\circ}\)C), density (kg/m\({}^{3}\cdot\)s), specific heat (J/kg\(\cdot\)K) and thermal conductivity (W/m\(\cdot\)K) of the tissue, respectively, \(w_{b}\), \(c_{b}\), \(T_{b}\) are the blood perfusion rate (kg/m\({}^{3}\cdot\)s), specific heat of blood (J/kg\(\cdot\)K) and blood temperature (\({}^{\circ}\)C), respectively, and \(Q_{met}\) (W/m\({}^{3}\)) is the heat generated by tissue metabolism. \(Q_{ext}\) is the external heat source, i.e., the energy generated by microwaves (W/m\({}^{3}\)). The metabolic heat production (\(Q_{met}\)) is usually neglected; the external heat source (\(Q_{ext}\)) is shown below:  
\[Q_{ext}=\frac{\sigma\left|\stackrel{{\rightarrow}}{{E}}\right|}{2\rho} \tag{9}\]  
The Pennes biological heat transfer equation was modified to obtain a heat transfer model based on porous media. The local thermal equilibrium assumption is also used, and the temperature field of the lung tissue can be obtained by calculation, considering that the blood and the lung tissue have the same temperature [23]. The equation is shown as follows:  
\[\begin{array}{l}(\rho c)_{eff}\frac{\partial T}{\partial t}+(\rho c)_{b}(u \frac{\partial T}{\partial r}+w\frac{\partial T}{\partial z})\\ =k_{eff}(\frac{\partial^{2}T}{\partial r^{2}}+\frac{\partial^{2}T}{\partial z ^{2}})+Q_{met}+Q_{ext}\end{array} \tag{10}\]  
\[\begin{array}{l}(\rho c)_{eff}=(1-\phi)(\rho c)_{s}+\phi(\rho c)_{b}\\ k_{eff}=(1-\phi)k_{s}+\phi k_{b}\end{array} \tag{12}\]  
where \(\varphi\) is the porosity of porous media, subscripts _eff_, \(s\), and \(b\) represent the effective value, solid phase (i.e., lung tissue), and liquid phase (i.e., blood).  
Boundary condition
* Application of axisymmetric boundary for heat transfer analysis [24]. \[\hat{n}\cdot(K_{eff}\
abla T-\left(\rho c_{P}\right)_{b}\overset{\rightarrow}{u}T)=0\] (13)
* The initial temperature of the porous lung was set to 37 \({}^{\circ}\)C [24]. \[T(t_{0})=37^{\circ}\text{C}\] (14)
* Consider only the heat transfer from the lung tissue without considering the microwave antenna. Consider the outer boundary of the lung tissue as insulation [24]. \[\hat{n}\cdot(K_{eff}\
abla T)=0\] (15)
## 3 Results and Discussion
### Behavior of Volatile Compounds during Storage
Hexanal increased progressively in all storage conditions by between 4 and 44%, reaching its highest levels after 5 months of storage during condition C5, reaching a value of 9.17 mg/kg. This volatile compound has two formation pathways, one through the lipoxygenase pathway during oil processing and the other through an autoxidation mechanism [31]. Both pathways result in the formation of 13-hydroperoxylinoleic acid, which by enzymatic reaction (lipoxygenase pathway) or by decomposition of the hydroperoxide through homolytic cleavage reactions and \(\beta\)-scission of the C12-C13 linkage, would form hexanal. Therefore, the hexanal content at baseline (Fresh EVOO) originates from the milling of the olive and the malaxation process of the olive paste [31], while the increase in hexanal during storage is due to lipid autoxidation [8]. Hexanal contributes to the perception of a sweet-green sensory note in EVOO when its concentration surpasses 0.075 mg/kg [46], but it also contributes to the rancid perception when there is an even higher concentration [47]. [PERSON] and [PERSON] [48] used the concentration of hexanal as a basic indicator of the quality of olive oil stored in various packaging materials and storage conditions. [PERSON] et al. [49] reported a decrease in EVOO volatile compounds related to flavor of close to 90% after 12 months of storage at room temperature in Verdeal Transmontana olive oils. Figure 2: Evolution of secoriidoid compounds, decarboxymethylated phenols, and oxidized phenols during 12 months of storage for the conditions (**a**) C2 (23 \(\pm\) 2 \({}^{\circ}\)C, with light) and (**b**) C5 (40 \(\pm\) 2 \({}^{\circ}\)C, in darkness).
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 2 Background
### Mathematics for Newly Proposed Performance Measures
#### 2.2.2 Spread of Non-Violating Decisions  
The spread of non-violating decisions is one of the four measures proposed in this article. This measure estimates how well spread out the preferred decisions are within the bounding box located in the objective space. The greater the value of this measure, the better the performance of an algorithm. The calculation of this measure is presented in Algorithm 1.  
```
1:procedureSpreadEstimator(outcomes)
2:\(\triangleright\) outcomes: objective vectors preferred by DM
3: Get count of outcomes, N \(\leftarrow\) count(outcomes)
4:ifN \(\leq\) 1 then\(\triangleright\) 1 or zero outcomes, spread is zero
5:ifN\(==\) 2 then\(\triangleright\) 2 outcomes
6: return norm\({}_{2}\)(outcomes\((2)-\) outcomes\((1)\))\(\triangleright\) return spread between 2 values
7:dtot \(\leftarrow\) 0\(\triangleright\) more than 2 outcomes, calculation required - initialize total spread, \(dot\)
8:firstNode \(\leftarrow\) outcomes(1)\(\triangleright\) get a node
9: currentNode \(\leftarrow\) firstNode \(\triangleright\) set current node
10:whileunProcessedNodes(outcomes) \(>\) 1 do\(\triangleright\) process each outcome
11: MarkNodeAsProcessed(currentNode) \(\triangleright\) mark outcome as processed
12:nearestNode=getNearestNode(currentNode,outcomes) \(\triangleright\) find nearest node to outcome being processed
13:dist = norm\({}_{2}\)(currentNode \(-\) nearestNode) \(\triangleright\) calcuated distance between these two solutions
14:dtot \(\leftarrow\) dot + dist \(\triangleright\) add their distance to total distance
15:currentNode \(\leftarrow\) nearestNode
16:dist = norm\({}_{2}\)(currentNode \(-\) firstNode) \(\triangleright\) finally calculate distance to first solution
17:dtot \(\leftarrow\) dtot + dist \(\triangleright\) add last distance to total distance
18:return dot \(\triangleright\) return total distance
```  
**Algorithm 1** Spread of Non-violating decisions