esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Contrast Enhancement and Local Light Adjustment  
Most research defines contrast enhancement as adjusting the dynamic range of pixel intensity distribution for good contrast enhancement in images facing low contrast concerns. Contrast deterioration is mainly due to some external factors such as background light and environment.  
Low-contrast images may reduce the visibility for digital users [61]. Most techniques for contrast enhancement are histogram-based or Retinex-based schemes [62]. In the histogram-based method, the main idea is that contrast-related pixel values globally or locally should have a Gaussian distribution. Therefore, these histogram-based techniques try to correct the pixel values' distribution globally or locally according to a related window size definition [63]. Due to their simple functionality, they are easy to implement.  
On the other hand, this histogram-based scheme in special situations hardly produces outstanding image quality, especially due to the complexity of image scenes and information loss in either low-contrast or high-contrast images. Further, they also eventually change the brightness of the processed image [64; 65; 66]. Therefore, several new related works have introduced new boundary conditions in the form of different regularization strategies.  
For example, one can name the so-called Retinex-based method introduced by [PERSON] [67]. It is based on human color perception. The main concept of Retinex is based on composing illumination and reflections. The Single-Scale Retinex (SSR) is the implementation of center or surround Retinex. It provides reflectance as a result of the algorithm-like difference-of-Gaussian function. The Multi-Scale Retinex is similar to the previous one, but it contains a weighted sum of different SSR. Due to mapping color in an extra dimension, typically,after executing this algorithm, a color correction is required [62]. Both methods have also been implemented via convolutional neural networks, but the Retinex-based scheme has resulted in very low-contrast images [62].  
Overall, a review of all related works shows a lack of good models that can significantly enhance document images containing certain distortion types (e.g., shadows, spotlights, etc.), and further, document images containing several distortion types simultaneously. Therefore, such a robust model that can face harshly distorted document images is very needed.
# Instability Study of Magnetic Journal Bearing under S-CO\({}_{2}\) Condition
###### Abstract  
A supercritical CO\({}_{2}\) (S-CO\({}_{2}\))-cooled Brayton cycle is under development for distributed power applications for remote regions. In order to successfully develop it, issues of controlling shaft levitation with bearings have to be solved. From several studies, magnetic bearings have been suggested for reliable levitation performance with reduced cost and complexity. However, several studies on magnetic bearing show that instability issues under high-pressure fluid and high-speed operating conditions may exist. The purpose of this research is to provide background for understanding the instability of magnetic bearings under S-CO\({}_{2}\) conditions and propose functional requirements of the magnetic bearing. Thus, the rotating shaft with magnetic bearings operating under high pressure fluid was first analyzed. To test the theory, a magnetic bearing test rig was constructed. By comparing experimental data to the analysis results, the analysis results were verified. Therefore, the analysis results can be used for predicting instability in the future and can contribute to the development of better magnetic bearing controllers.  
supercritical carbon dioxide Brayton cycle (S-CO\({}_{2}\) Brayton cycle); magnetic bearing; instability +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Selection of Polymerization Monomers  
Functional monomer is the key parameter in preparing MIP. In a previous study, we reported the NIP library approach for the screening of the best monomer [30]. The NIP library consisted of 18 cross-linked co-polymers synthesized from monomers commonly used in molecular imprinting. The NIP library, in the form of SPE cartridges, was investigated and successfully applied for water-compatible selective MIP preparation. In this study, a small NIP library was made in the form of polymer-coated chips. The NIP library consisted of five extensively applied monomers, namely MAA, 4-VP, VI, AA, and DMEA, for the preparation of five different MIP- and NIP-coated chips. The procedure described in 2.5 adsorption test was used for the monomer selection. As shown in Figure 2, IBA presented stronger adsorption on the all MIPs than corresponding NIPs. The order of relative strength of IBA adsorption on these MIPs was positively correlated to that of corresponding NIPs. This result is consistent with that reported by [PERSON] and with the results of our previous studies [30; 31]. [PERSON] et al. reported that there is a clear and positive correlation between the apparent affinity constants measured for the NIP and MIP libraries. Among the polymer coatings studied here, the MAA-based coating was found to exhibit the highest change in relative fluorescence intensity due to IBA adsorption, which indicates that the polymer shared the strongest binding interaction with the target molecule. This was probably because hydrogen bonding is the major type of interaction between IBA and the functional monomers in the polymer, and MAA has the strongest hydrogen bonding interaction with IBA in the monomers tested in this study. As shown in Figure 2, the MAA-based NIP coating exhibited strong binding to IBA. In the MIP coating, IBA and MAA could form a complex in the polymer mixture, resulting in a three-dimensional cross-linked structure via copolymerization. After elution of the template, a predetermined complementary binding site remained in the polymer coating.  
The role of a cross-linker in an imprinted polymeric network is to secure the functional groups of the functional monomers in specific locations and directions around the template molecules, thereby preserving the structure of the binding sites (cavities). The recognition ability of molecularly imprinted polymers and their physical and chemical properties strongly depend on the degree of cross-linking and on the nature of the cross-linker [32]. In this study, the most commonly used cross-linking agents (DVB, EGDMA, and TRIM) were selected for the preparation of three different MIP- and NIP-coated chips using MAA as the functional monomer and chloroform as the polymerization solvent. Because relatively strong background fluorescence was observed on the DVB-based polymer, only the other two cross-linkers were investigated further.  
After IBA binding, the relative fluorescence intensity (141.04) of EGDMA based MIP coating was higher than that (92.33) of TRIM-based MIP coating, thus EGDMA was selected and used for the rest of the study.
## 2 Materials and Methods
### PSSM Feature Extraction
#### 2.3.3 k-Separated Bigram-PSSM (KSB-PSSM)  
This method extends the DPC-PSSM concept by considering amino acid pairs separated by k residues (k-spaced pairs or k-separated bigrams) within the PSSM profile [22]. It calculates the transfer probability of k-spaced amino acid pairs across all positions, resulting in a 400-dimensional feature vector.  
The KSB-PSSM feature vector is a 400-dimensional vector, where each element is denoted as \(K_{i,j}(k)\) and corresponds to the transfer probability of a pair of amino acids, \(i\) and \(j\), separated by \(k\) residues. This is represented as follows:  
\[\text{KSB-PSSM}=(K_{1,1}(k),\dots,K_{1,20}(k),K_{2,1}(k),\dots,K_{2,20}(k), \dots,K_{20,1}(k),\dots,K_{20,20}(k))^{\text{T}}\]  
The calculation of a single element \(K_{i,j}(k)\) is as follows:  
\[K_{i,j}(k)=\sum_{t=1}^{L-k}P_{t,i}^{\text{norm}}\times P_{t+k,j}^{\text{norm}} \quad\text{for }1\leq i,j\leq 20\]  
where \(L\) represents the protein sequence length and \(P_{t,i}^{\text{norm}}\) and \(P_{t+k,j}^{\text{norm}}\) represent the normalized PSSM values at positions \(t\) and \(t+k\) for amino acid types \(i\) and \(j\), respectively.  
By employing these three PSSM-based feature extraction methods, we aimed to capture a diverse range of evolutionary information embedded in the PSSM profiles, including overall amino acid composition, short-range dipeptide interactions, and long-range evolutionary dependencies, providing a comprehensive representation of protein sequences for FRP prediction.
## 3 Network Handover Algorithm Based on Location Prediction
### Trajectory Clustering  
DBSCAN algorithm is a density-based spatial clustering algorithm. It uses a region with sufficient density as the distance center and connects samples of the same category. These connected samples continuously expand the region, this process known as clustering. In dealing with spatiotemporal trajectory data, DBSCAN can effectively deal with data with uneven density. Mobile trajectories are usually not uniformly distributed; for example, an important machine next to the work console and other locations may be very dense. DBSCAN can automatically adapt to the density distribution of the data through the density threshold and fast clustering. In the data collection, there will be noise points caused by measurement errors. DBSCAN can categorize low-density points as \"noise\" without forcing them to belong to a specific clustering, which makes DBSCAN highly robust when dealing with mobile trajectory data.  
The residence time of the user in a particular state and the density of trajectories passing through a grid cell can highlight the importance of that location, such as near a key machine or working console. These important locations are extracted from the processed trajectory data. Then, the DBSCAN algorithm is used to identify high-density regions and group them into clusters. The definition of important location points is provided in Equation (12). A location is regarded as important if any one of the three conditions is satisfied.  
\[\left\{\begin{aligned} & stoptime>\delta_{t}\\ & num(angle)>\delta_{u},(\frac{\pi}{4}\leq angle\leq\frac{7\pi}{4 })\\ & N>\delta_{n}\end{aligned}\right. \tag{12}\]  
where _stoptime_ is the dwell time, and \(\delta_{u}\) is the threshold for the number of deflection angles. In this paper, we assume that user behavior changes when \(\frac{\pi}{4}\leq angle\leq\frac{7\pi}{4}\), and \(\delta_{n}\) is the threshold for the number of grid trajectory points.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 4 Results
### Visualization of Pricing Dynamics  
The 3D surface plot above illustrates the predicted prices of European put options as a function of the spot price (S) and time to maturity (T), as generated by the trained deep neural network. The surface demonstrates the model's ability to capture the expected pricing dynamics:  
Figure 4 show a three-dimensional surface plot was constructed to visualize the predicted prices of European put options as a function of spot price and time to maturity. The resulting surface effectively captures the expected pricing dynamics, demonstrating the model's ability to learn the complex relationships governing option valuation.  
Observing the dependency of option prices on the spot price reveals a clear inverse relationship where option values decrease as the underlying asset price increases. This behavior aligns with financial theory, as put options gain value when the asset price falls below the strike price. Similarly, the impact of time to maturity on option prices follows expected patterns, with values declining sharply as expiration approaches. The diminishing time value of options near expiration is a well-established principle in financial derivatives, and the model successfully replicates this phenomenon.  
Figure 3: Comparison of actual option prices (from the Black–Scholes formula dashed line) and predicted option prices (from the deep neural network circle).  
The smoothness of the generated surface, along with its strong alignment with theoretical expectations, confirms that the neural network has effectively learned the underlying dynamics of the Black-Scholes model. The model generalizes well across different values of spot price and time to maturity, reinforcing its robustness and predictive accuracy. The ability to generate smooth and consistent pricing surfaces further supports the claim that deep learning models can serve as powerful tools for financial modeling, particularly in derivative pricing.  
The results presented in this section collectively demonstrate the high accuracy and adaptability of the proposed DNN for option pricing. The model successfully approximates theoretical prices with minimal error, maintaining strong predictive performance across various conditions. However, as previously discussed, the reliance on synthetic datasets necessitates further validation using real-world financial data. The next section provides an in-depth discussion of these findings, their implications, and potential areas for improvement.
## 3 Method
### Evaluation Metrics  
The model was evaluated using accuracy, precision, recall, \(F1\)-score, and average precision (\(AP\)). Accuracy measures the ratio of correctly classified instances to the total instances. Precision evaluates the accuracy of the positive predictions. It evaluates the ratio of true positive predictions to the total positive predictions. Recall measures the model's ability to identify true positive cases. It is the ratio of true positive predictions to the actual positive instances. The \(F1\)-score combines precision and recall into a single metric by calculating their harmonic mean. The \(AP\) is the average of precision values at various levels of recall. These metrics are formulated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{5}\]  
\[AP=\sum_{n}P(R_{n})\Delta R_{n} \tag{6}\]  
where \(TP\) is true positive and represents chickens correctly classified, \(TN\) is true negative and refers to non-chickens correctly classified, \(FP\) is false positive which denotes non-chickens incorrectly classified as chickens, \(FN\) is false negative and represents chickens incorrectly classified as non-chickens, \(P\) is the precision, \(R\) is the recall, \(P(R_{n})\) is the precision at the recall level \(R_{n}\), and \(\Delta R_{n}\) is the change in recall from the previous recall level to \(R_{n}\).
## 3 Results and Discussion
### Effect of Adsorbent Dose  
The study of the effect of the adsorbent dose is essential in the adsorption process in order to determine the most cost-effective amount to be used and to effectively remove the pollutants without wasting the adsorbent. The results obtained from experiments with different doses of ACSCGs are shown in Figure 9. The percentage removal increased with an increasing dose of ACSCGs from 0.01 to 0.1 g/100 mL for CV and from 0.01 to 0.15 g/100 mL for Cr (VI), which then remained constant beyond these ranges. This initial rapid increase could be attributed to the increase in the number of adsorption sites by increasing the dose of the adsorbent. Insufficient or excessive dosing of the adsorbent would result in low removal efficiency or high cost; therefore, 0.1 g/100 mL and 0.15 g/100 mL of ACSCGs were chosen as the optimal doses for the retention of CV and Cr (VI), respectively. In this context, [PERSON] et al. [81] studied the removal of Cr (VI) by adsorption on a microporous nano-activated carbon derived from orange peel. They showed that 3 g/L is the optimal dose of the adsorbent for a solution of C0 = 300 mg/L. In another study, [PERSON] et al. [82] tested the efficiency of activated cocoa leaves' biochar in the adsorption of CV. The experimental results showed that the optimal dose is 0.4 g/L and that above this dose CV dye-uptake and percentage CV dye removed decreased.
# Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology
## Nomenclatures  
\begin{tabular}{p{142.3 pt} p{284.5 pt}} PNRA & National Plan of Research in Antarctica \\ GHI & Global Horizontal Irradiance, I [W-m\({}^{-2}\)] \\ DNI & Direct Normal Irradiance on horizontal plane, Ib [W-m\({}^{-2}\)] \\ Diff.HI & Diffuse Irradiance on horizontal plane, Id [W-m\({}^{-2}\)] \\ AWS & Automatic Weather Station \\ MSD & mean standard deviation \\ RMSE & Root Mean Squared Error \\ MZS & Mario Zucchelli permanent station \\ DC & Dome C, Concordia permanent station \\ CSP & Concentrated Solar Power \\ ENI & Extraterrestrial Irradiance on normal plane, Ion [W-m\({}^{-2}\)] \\ EHI & Extraterrestrial Irradiance on horizontal plane, Io [W-m\({}^{-2}\)] \\ MBE & Mean Bias Error \\ MAE & Mean Absolute Error \\ RMSE & Root Mean Squared Error \\ ITASE & International Trans Antarctic Scientific Expedition \\ MGT & Micro Gas Turbine \\ ENEA & Italian National Agency for New Technologies, Energy and Sustainable Economic Development \\ DTE-STSN & ENEA-Department Energetic Technology, Division Solar Thermic, Thermodynamic, and Smart Network \\ SSPT-PROTER & ENEA-Divisione Protezione e valorizzazione del territorio e del capitale natural \\ \(\theta_{\mathrm{z}}\) & Solar zenith angle [rad], [\({}^{\circ}\)] \\ \(l_{\mathrm{bn}}\) & Direct normal irradiance on normal plane, [W-m\({}^{-2}\)] \\ \(K_{\mathrm{T}}\) & Global atmospheric transmission coefficient at specific location \\ \(K_{\mathrm{bn}}\) & Transmission coefficient for normal direct radiation \\  & The fraction of the diffused radiation with respect to global radiation (refer to the horizontal plane) \\ k & horizontal plane \\ \(\alpha\), \(\beta\) & Boland-Ridley parameters \\ \(E_{\mathrm{el}}\) & parabolic dish electricity production \\ \(E_{\mathrm{sol}}\) & Available solar energy input \\ \(Q_{\mathrm{sol}}\) & Solar energy actually harvested by the system \\ \end{tabular}
## 2 Modeling the Orbital Dynamics
### Earth Non-Spherical Gravity Perturbation Solution  
The bulge at equator exerts a pull on the satellite toward the equatorial plane. Whether the satellite is above or below the equatorial plane, the orbital plane will move toward the equator and make a precession motion [27]. The main perturbation solutions of \(J_{2}\) are listed below.  
The first-order secular terms of \(J_{2}\) are  
\[\begin{cases}a_{\mathrm{sec}}^{(1)}=0,\quad e_{\mathrm{sec}}^{(1)}=0,\quad i _{\mathrm{sec}}^{(1)}=0,\\ \Omega_{\mathrm{sec}}^{(1)}=-\frac{3J_{2}}{2p^{2}}\cos i\,n,\\ \omega_{\mathrm{sec}}^{(1)}=\frac{3J_{2}}{2p^{2}}\Big{(}2-\frac{ 5}{2}\sin^{2}i\Big{)}n,\\ M_{\mathrm{sec}}^{(1)}=\frac{3J_{2}}{2p^{2}}\Big{(}1-\frac{ 3}{2}\sin^{2}i\Big{)}\sqrt{1-e^{2}}\,n.\end{cases} \tag{4}\]  
The second-order secular terms of \(J_{2}\) are \[\left\{\begin{array}{l}a_{\text{sec}}^{(2)}=0,\,e_{\text{sec}}^{(2)}=0,\,i_{ \text{sec}}^{(2)}=0,\\ \Omega_{\text{sec}}^{(2)}=-\left(\frac{3\lambda_{2}}{2p^{2}}\right)^{2}\cos i \left[\left(\frac{3}{2}+\frac{1}{6}e^{2}+\sqrt{1-e^{2}}\right)-\sin^{2}i\left( \frac{5}{3}-\frac{5}{24}e^{2}+\frac{3}{2}\sqrt{1-e^{2}}\right)\right]n_{r}\\ \omega_{\text{sec}}^{(2)}=\left(\frac{3\lambda_{2}}{2p^{2}}\right)^{2}\left[ \left(4+\frac{7}{12}e^{2}+2\sqrt{1-e^{2}}\right)-\sin^{2}i\left(\frac{103}{12 }+\frac{3}{8}e^{2}+\frac{11}{2}\sqrt{1-e^{2}}\right)+\sin^{4}i\left(\frac{215 }{48}-\frac{15}{32}e^{2}+\frac{15}{4}\sqrt{1-e^{2}}\right)\right]n_{r}\\ M_{\text{sec}}^{(2)}=\left(\frac{3\lambda_{2}}{2p^{2}}\right)^{2}\sqrt{1-e^{2} }\left[\frac{1}{2}\left(1-\frac{3}{2}\sin^{2}i\right)^{2}\sqrt{1-e^{2}}+\left( \frac{5}{2}+\frac{10}{3}e^{2}\right)-\sin^{2}i\left(\frac{19}{3}+\frac{26}{3} e^{2}\right)\\ \quad\quad+\sin^{4}i\left(\frac{233}{48}+\frac{103}{12}e^{2}\right)+\frac{e^{4 }}{1-e^{2}}\left(\frac{35}{12}-\frac{35}{4}\sin^{2}i+\frac{315}{32}\sin^{4}i \right)\right]n.\end{array}\right. \tag{5}\]  
The first-order short-period terms of \(\lambda_{2}\) are  
\[\left\{\begin{array}{l}a_{\text{sp}}^{(1)}=\frac{3\lambda_{2}}{2\,a}\left\{ \frac{2}{3}(1-\frac{3}{2}\sin^{2}i)\left[\frac{a^{3}}{r^{3}}-(1-e^{2})^{-3/2} \right]+\sin^{2}i\frac{a^{3}}{r^{3}}\cos\left(2f+2\omega\right)\right\},\\ \epsilon_{\text{sp}}^{(1)}=\left(\frac{3\lambda_{2}}{2p^{2}}\right)\left\{ \frac{1}{3}(1-\frac{3}{2}\sin^{2}i)\left[e\left(\frac{1}{1+\sqrt{1-e^{2}}}+ \sqrt{1-e^{2}}\right)+\cos f\left(3(1+e\cos f)+(\cos f)^{2}\right)\right]\\ \quad+\frac{1}{2}\sin^{2}i\left[\left(e+\cos f\left(3(1+e\cos f)+(e\cos f)^{ 2}\right)\right)\cos 2(f+\omega)-\left(1-e^{2}\right)\left(\cos(f+2\omega)+\frac{1}{3} \cos(3f+2\omega)\right)\right]\right\},\\ i_{\text{sp}}^{(1)}(t)=\left(\frac{3\lambda_{2}}{2p^{2}}\right)\sin 2i \left\{\frac{e}{4}\cos(f+2\omega)+\frac{1}{4}\cos(2f+2\omega)+\frac{e}{12} \cos(3f+2\omega)\right\},\\ \Omega_{\text{sp}}^{(1)}(t)=-\left(\frac{3\lambda_{2}}{2p^{2}}\right)\cos i \left((f-M+e\sin f)-\frac{1}{  
By substituting short-period terms (Equation (6)) and long-period terms (Equation (7)) into the first equation in Equation (3), the mean elements and osculating elements conversion can be realized.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Application of Fine-Tuned Model to Architectural Space: 360\({}^{\circ}\) VR Experience
#### 4.2.2 Application of Fine-Tuned Model Version 8  
The fine-tuned AI model (Version 8) was used to classify participants' emotional states in the 360\({}^{\circ}\) VR environment into three categories: 'happy', 'neutral', and'sad'. Rather than conducting a detailed statistical analysis, this study focused on identifying EEG patterns and emotional classification results across the two environments.  
During data collection, each participant experienced both DDP and Long Island City for three minutes each, resulting in a total session duration of six minutes per participant. EEG signals were recorded continuously throughout the sessions, and specific time segments were extracted after the experience. In the preprocessing stage, artefacts were removed, EEG signals were scaled per channel, and the data were converted into JSONL format before being input into Version 8 of the fine-tuned model. Based on this processed data, the model predicted the most probable emotional state among 'happy', 'neutral', and'sad'.  
The results were analysed by visualising the most activated EEG regions (e.g., frontal, parietal, or temporal lobes) for each participant and comparing the predicted emotional distribution (%). This approach provided an intuitive interpretation of how VR environmental characteristics and individual differences influenced EEG-based emotional responses (Figure 7).