esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Sun-oriented attitude control is one of the most important attitude control modes for most micro/nano satellites, which directly affects the on-orbit energy acquisition. Therefore, it is of great importance to use the simplest sensors and actuators and the most reliable algorithm to achieve sun-oriented attitude control. A sun-oriented spin-stabilized attitude control method for micro/nano satellites using only magnetic control is proposed in this paper, in which the control progress is divided into four phases: initial damping phase, sun-aligned phase, spin-up phase, and spin-stabilized phase. The influence of the shadow zone of the orbit, offset installation of sun sensor and solar panel, limitation of the field of view of the sun sensor, and environmental disturbance torques are all considered in the proposed method. The control performance is evaluated by numerical simulations, and the simulation results show that the proposed method is applicable to the satellite equipped with a sun sensor and a 3-axis magnetometer as attitude sensors and three magnetic torquers installed orthogonally as attitude actuators. The proposed method is applicable to most Earth-orbit satellites for which the geomagnetic field can provide sufficient attitude control torque.  
sun-oriented attitude; spin-stabilized; magnetic control; attitude determination and control system (ADCS) Article Research on Sun-Oriented Spin-Stabilized Attitude Control of Micro/Nano Satellite Using Only Magnetic Control.
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 3 Contributions  
In this paper, we are concerned with the problems of predicting the wind power and demand side management with the incorporation of wind power, demand and price. The uniqueness and originality of this work is given below. The contributions of this research work are listed below:  
1. A novel big data-driven wind power prediction model is proposed that combines the strengths of both the univariate and multivariate wind power forecasting techniques by using decomposed and exogenous inputs for forecasting; consequently, the forecasting accuracy is significantly enhanced.
2. The proposed model employs an existing method wavelet packet decomposition and an enhanced method Efficient DCNN (EDCNN) for feature extraction and forecasting, respectively.
3. A DSM algorithm is also proposed. The proposed DSM algorithm takes into account the day-ahead demand, day-ahead price and wind power.
4. The proposed DSM algorithm reduces the consumption cost and improves the load profile to almost a normal shape.
## 2 Methods for Determination of Lipophilicity
### Indirect Experimental Determination of Lipophilicity
#### 2.3.5 Immobilized Artificial Membrane Chromatography  
Immobilized artificial membrane (IAM) chromatography is the most widely used method for the determination of anisotropic lipophilicity [207]. Table 1 describes numerous examples of IAM chromatography applied to lipophilicity determination. The motivation behind IAM stationary phase is to better mimic specific interactions that solutes or drugs can establish with the membrane phospholipid bilayer. To fulfil this goal, IAM stationary phases are prepared by binding covalently phospholipids to a propylamino functionalized silica particles (Figure 4a) [27; 208]. The incorporated phospholipid can have a single or a double chain, but a double chain can better simulate biomembranes [27]. The remaining free propylamine residues on the surface of the silica backbone should be treated to suppress the undesired silanophilic interactions discussed above [27]. The most recent generation of IAM columns contains double-chain phosphatidylcholine, they are end-capped with decanoic, and propionic anhydride and are commercially available (IAM.PC.DD2 from Regis Technologies) [209]. A simple and economical alternative is the preparation of the stationary phase by coating a conventional RP column with phospholipids [34].  
Figure 4: (a) IAM HPLC column; (b) immobilized liposome chromatography (ILC) HPLC column; (c) schematic representation of micellar liquid chromatography (MLC).  
From a practical point of view, one of the main advantages of IAM columns is that they do not need the use of an organic modifier [27]. In fact, methanol as an organic modifier should be avoided as it disturbs the stability of the column by causing methanolysis of the phospholipids.  
By simulating the phospholipid bilayer of biological membranes, the retention factor obtained with IAM chromatography correlates better with biological partition as the retention mechanism considers not only hydrophobic but also polar/ionic interactions [27; 208]. For neutral solutes, the correlation between log P and the IAM partition coefficient (log kW-IAM) is strong. For ionized compounds, the correlation is poor due to the contribution of electrostatic interactions, which are not recognized in the octanol-water system [189]. When compared with partitioning with liposomes, log kIAM are well correlated for hydrophobic analytes [190]. Due to their resemblance to biological barriers, IAM chromatography has been successfully applied to calculate complex pharmacokinetic properties, such as cell permeation [210], intestinal absorption [183], unbound volume of distribution [184], and drug-induced phospholipids [186].
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: Max-\(\kappa\)-ColorableSubgraph  
Problem.Given a graph \(G=(V,E)\) with \(n\) vertices and \(m\) edges, and \(\kappa\) colors, maximize the size (number of edges) of a properly vertex-\(\kappa\)-colorable subgraph.  
The domain \(F\) is the set of colorings \(\mathbf{x}\) of \(G\), an assignment of a color to each vertex. (Note that here and throughout, the term \"colorings\" includes _improper_ colorings.) The domain \(F\) can be represented as the set of length \(n\) strings over an alphabet of \(\kappa\) characters, \(\mathbf{x}=x_{1}x_{2}\ldots x_{n}\), where \(x_{i}\in[\kappa]\). The objective function \(f:[\kappa]^{n}\rightarrow\mathbb{N}\) counts the number of properly colored edges in a coloring:  
\[f(\mathbf{x})=\sum_{\{u,v\}\in E}\text{{NEQ}}(x_{u},x_{v}). \tag{5}\]  
We then built up machinery to define mixing operators for a QAOA approach to this problem. Since some mixing operators are more naturally expressed in one encoding rather than another, we found it useful to describe different mixing operators in different encodings, though we emphasize that doing so is merely for convenience; all mixing operators are encoding-independent, so the descriptions may be translated from one encoding to another. The domain \(F\) is naturally expressed as strings of \(d\)-dits, a \(d\)-valued generalization of bits. For the present problem, \(d=\kappa\). In addition to discussing colorings as strings of dits, we used a \"one-hot\" encoding into \(n\kappa\) bits, with \(x_{i,c}\) indicating whether or not vertex \(i\) is assigned color \(c\).  
Figure 2 below shows a mapping to qubits in the one-hot encoding for Max-\(\kappa\)-ColorableSubgraph. We explain this and other possible mappings generally over the remainder of the section.  
Figure 2: Example: Quantum alternating operator ansatz mapping for Max-\(\kappa\)-ColorableSubgraph with \(\kappa=3\) in the one-hot encoding. The 4-node graph on the left is mapped to 12 qubits on the right, one vertical layer for each color. The solid lines show pairs of qubits acted on by the phase operator, which checks if adjacent vertices have the same color. The dashed lines show the qubits acted on by the mixing operator, which mixes the possible colors of each vertex independently.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 2 Data and Methodology
### Methodology
#### 2.2.4 Performance Evaluation  
Some evaluation metrics are used to evaluate the enhancements made to the quality of images [59; 60; 61]. One of these metrics is the Peak Signal-to-Noise Ratio (PSNR) value, which is calculated using the Mean Square Error (MSE) as shown in Equation (1), where \(I(i,j)\) denotes the pixel value of the original image at the relevant coordinates, and \(K(i,j)\) represents the pixel value of the enhanced image at those exact coordinates. The PSNR value, expressed in decibels (dB) units (2), provides a measure of the quality of the enhanced image compared to the original, with higher values indicating better quality. The \(M\) value signifies the maximum possible pixel value, and the \(mxn\) denotes the image size. Another important metric is the Structural Similarity Index (SSIM), which evaluates the quality of a reconstructed image by examining the similarity in luminance, contrast, and structural parameters between two images [62]. The SSIM is calculated using Equation (3), where \(l(x,y)\) indicates the luminance between the images, \(c(x,y)\) represents the contrast, and \(s(x,y)\) reflects the structure. A high SSIM value indicates substantial similarity between the images.  
\[MSE=\frac{1}{mxn}\sum_{i=1}^{N}(I(i,j)-K(i,j))^{2} \tag{1}\]  
\[PSNR=10\log_{10}\frac{M^{2}}{(MSE)} \tag{2}\]  
\[SSIM=l(x,y)c(x,y)s(x,y) \tag{3}\]  
Different evaluation metrics are utilized to assess the quality of object detection in images. Commonly preferred metrics include Precision, Recall, and Mean Average Precision (mAP), and Intersection Over Union (IoU). The metrics are derived from values obtained from the confusion matrix, which includes True Positive (\(TP\)), False Positive (\(FP\)), True Negative (\(TN\)), and False Negative (\(FN\)). Precision is calculated with Equation (4), which quantifies the ratio of \(TP\) to the total number of \(TP\) and \(FP\). Recall, calculated using Equation (5), reflects the ratio of \(TP\) to the total number of \(TP\) and \(FN\). The mAP, calculated with (6), computes the area under the Precision-Recall curve, yielding a single value that summarizes the model's performance in terms of both Precision and Recall. \(AP_{i}\) denotes the AP value for a class, while \(n\) indicates the total number of classes. The IoU is calculated with (7) and quantifies the overlap between a predicted bounding box and a ground truth bounding box. \(A\) parameter represents the predicted bounding box, and \(B\) represents the ground truth bounding box. The mAP value is also calculated using various threshold values of the IoU, including mAP50 and mAP50-95. Precision, Recall, mAP50, and mAP50-95 values were utilized for this study.  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{6}\]  
\[IoU=\frac{|A\cap B|}{|A\cup B|} \tag{7}\]
## 5 Design of Front-End Application Layer Function Pages  
### Registration and Login Page  
The user account information for the monitoring system is stored in the \"user\" table. On the registration page, a Form is created through HTML language. To prevent repeated form submissions, a random captcha is generated through the MyCaptcha class.  
After the user submits registration information, the front-end application layer sends the registration information to the back-end service layer interface to verify the captcha. If captcha verification fails, the user is redirected to the registration page. If captcha verification is successful, the back-end service layer checks the user table in the database for the existence of the user's registration ID. If the user ID already exists, a prompt message \"Current user ID already exists, please register again\" is returned. If the user ID does not exist, the user information is written to the user table and a prompt message \"Registration succeeded\" is returned, followed by redirection to the homepage of the monitoring system.  
Upon entering the login page, the user is prompted to enter their username (user_name) and password (user_pass). If the input is empty or does not meet the predefined conditions, the user is prompted to re-enter the information according to the requirements. The front-end application layer performs basic logic validation on the username and password to exclude invalid user access in advance, reducing the load on the database and preventing malicious attacks. When the basic validation is passed, the login request is sent to the back-end service layer, which then checks whether the username and password exist in the user table and whether they are correct. Finally, the result is returned.
## 3 Results and Discussion
### Structural Analysis
#### 3.1.2 Raman Spectroscopy  
Raman spectroscopy is an important scientific technique for probing defects and/or impurities and therefore a powerful tool for revealing the space symmetry of materials. Changes in the characteristic vibrational modes of CuO were studied via Raman scattering experiments, as presented in Figure 4, for pure and Al doped CuO films, at various concentrations of Al. The three Raman active modes predicted by group theory have been identified and their room temperature frequencies determined and subsequently compared to the literature.  
The fine shift and broadening of the Raman spectra of CuO crystals are mainly due to doping and size effects. A shift in the Raman spectrum describes phenomena that could be linked to structural disorder and defects (lack of oxygen and/or surface impurities) as well as to potential confinement of phonons. However, a change in polarizability during molecular and/or structural vibration is an essential requirement of obtaining the Raman spectra of samples. Consequently, Raman spectra are made of three main modes, i.e., the \(Ag\) and the \(2 Bg\) located at 278.94, 330.40 and 616.91 cm\({}^{-1}\) respectively. The Raman spectra of CuO samples show that as grain size decreases, the Raman peaks shift to lower wavenumber and become broader owing to size effects. Furthermore, as the segregation of Al dopants takes place in grain boundaries, it is believed to cause an aggravation in crystallinity. Such peaks are widely reported [17; 18], thus confirming the presence of a single CuO phase with a monoclinic structure, which reasonably corroborates XRD analysis.  
Figure 3: Variation of the (002)-oriented crystallite size and the micro strain of pure and Al–doped CuO films grown at 350 \({}^{\circ}\)C via spray pyrolysis as a function of the Al concentration.
# Cardiac Radiofrequency Ablation Simulation Using a 3D-Printed Bi-Atrial Thermochromic Model
## 1 Introduction
Current applications include, but are not limited to, implant and prosthetic design, biomedical device testing, and, notably, pre-operative/procedural planning and surgical/interventional simulation training [7]. Furthermore, advances in the field of additive manufacturing have facilitated the production of objects with complex geometries. Notably, the incorporation of patient scans as the basis for model design enables the fabrication of patient-specific simulation aids bearing greater anatomical accuracy. This is particularly relevant in the rehearsal of complex surgical/interventional procedures, also allowing for anatomical variation between patients. In our previous work, we evaluated the use of 3D-printed thermoplastics for creating patient-specific whole heart models that were multimodal-imaging-compatible [8]. We investigated a low-cost, soft-tissue-mimicking copolymer filament, known as Layfomm-40 from the Poro-Lay series (CC-Products, Koln, Germany) [9; 10]. Layfomm-40 filament is rigid and consists of polyvinyl alcohol (PVA) and a thermoplastic elastomer (TPE) composite. This allows the material to be 3D printed using a fusion deposition modelling (FDM) printer at low cost. Once the 3D print is soaked in water, the PVA dissolves and leaves a spongey, microporous TPE composite which mimics soft tissue. In our work, we found Layfomm-40 to be an excellent material for creating cardiac models in terms of soft material properties and imaging properties [8]. The aim of this work was to evaluate the use of Layfomm-40 for creating a physical cardiac RFA simulator that could satisfy as many as possible of the requirements mentioned previously. We investigated the electrical and thermal conductivities of Layfomm-40 to ensure compatibility with RFA and EAMSs, investigated the use of thermochromic pigments for RFA-sensitivity, and investigated techniques for optimum visibility of Layfomm-40 under X-ray fluoroscopy. We integrated our findings to produce a bi-atrial, patient-specific model that was housed in a custom enclosure, and we tested this simulator in the cardiac catheterization laboratory environment to prove the overall concept.
###### Abstract
[25] estimated rice ET under water-saving irrigation by the PM model after calibrating the canopy resistance model parameters, in which LAI is used to scale up the stomatal conductance for water vapor from the leaf scale to the canopy scale. To correctly assess the water use efficiency of crops, the rates of photosynthesis and transpiration of vegetation are usually estimated in a coupled way [26; 27] because both carbon dioxide and water vapor go through leaves via the stomata. In addition, the stomatal conductance is sensitive to the photosynthesis rate [28]. Recently, [PERSON] et al. [29] and [PERSON] et al. [30] proposed a routinely applicable water-carbon coupling model (PML-V2), which combines the carbon and vapor fluxes through the response of the canopy conductance to the photosynthesis rate. The PML-V2 model estimates GPP and ET using the light-response curve method and the PM model, respectively. Although the PML-V2 model has been tested at 95 EC sites globally [29], the usage of the model is seldomly evaluated for rice paddy fields. In this paper, based on eddy covariance (EC) data from a double-cropping rice paddy field in the lower reaches of the Poyang Lake basin, China, we intend to (1) test the usage of the PML-V2 model for a double-cropping rice paddy ecosystem in this area, (2) study the differences in key parameters of the model (light-responses, canopy conductances, and water-carbon coupling characteristics) between early rice and late rice, and (3) upscale the in situ GPP, ET, and water use efficiency (WUE) to the regional scale in the lower reaches in the Poyang Lake plain using the PML-V2 model.
# A New Route for Low Pressure and Temperature
###### Abstract  
PtRu/MoS2 nanoparticles (NPs) (PtRu alloy partially coated by one-layer MoS2 nanosheets) were prepared through a 'wet chemistry' approach. The obtained NPs were directly embedded, at 5 parts per hundred resin/rubber (phr) loading, in a poly (divinylbenzene-co-vinyl benzyl chloride) hyper-crosslinked (HCL) resin, synthesized via bulk polymerization of the resin precursors, followed by conventional FeCl3 post-crosslinking. The obtained HCL nanocomposites were characterized to evaluate the effect of the NPs. It shows a high degree of crosslinking, a good dispersion of NPs and a surface area up to \(1870\pm 20\) m\({}^{2}\)/g. The catalytic activity of the HCL nanocomposite on phenol wet air oxidation was tested at low air pressure (P\({}_{\text{air}}\) = 0.3 MPa) and temperature (T = 95 \({}^{\circ}\)C), and at different phenol concentrations. At the lower phenol concentration, the nanocomposite gives a total organic carbon (TOC) conversion of 97.1%, with a mineralization degree of 96.8%. At higher phenol concentrations, a phenol removal of 99.9%, after 420 min, was achieved, indicating a quasi-complete depletion of phenol, with a TOC conversion of 86.5%, corresponding to a mineralization degree of 84.2%. Catalyst fouling was evaluated, showing good reusability of the obtained nanocomposite.  
hyper-crosslinked resin; nanocatalyst; phenol removal; catalytic wet air oxidation +
Footnote †: journal: International Journal of Heat and Mass Transfer
## 3 Results
### Validation Results
#### 3.2.2 Evaluating the Practicability of Data Augmentation
Table S8 shows the results of the feature importance scores in each datum by the machine learning algorithms. The feature importance scores of the support vector machine were not calculated in JASP 0.18.0 [40]; therefore, the scores of these models are not described in Table S8. Table 4 presents the correlation analysis results between the original and other data. Figure S1A,B and Table S9A,B display the detailed results. Averaged correlation coefficients are the average values of each correlation coefficient across machine learning algorithms in each dataset. A value close to 1 indicates that the orders or numerical values of the variable/feature importance in any dataset resemble those in the original dataset. Most of the datasets yielded averaged correlation coefficient values from 0.500 to approximately 0.700, excluding two \"Augmented\" group datasets (\"Real (original) + Synthesized (CTGAN)\" and \"Real (original) + Synthesized (TVAE)\"). The averaged correlation coefficients in the synthesized datasets (\"Nonaugmented\" group) were almost the same as those in the \"Augmented\" group, excluding datasets related to the original dataset. The dataset synthesized by TVAE yielded the worst performance in the concordance analysis, whereas that synthesized by CopulaGAN yielded the highest averaged correlation coefficient among the synthesized datasets. In particular, its Spearman's rho values were higher than those of the other synthesized and seed datasets. For the \"Augmented\" group, only the CopulaGAN-related dataset yielded a higher value than the averaged correlation coefficient between the original and seed datasets. This suggests that the concordance with the feature importance between the original and small-sample-sized datasets might improve by 3.42% when augmenting a small-sample-sized dataset with synthesized data generated by CopulaGAN (\"Real (original) vs. Real (seed)\" = 0.701/\"Real (original) vs. Augmented [Real (seed) + Syn (CopulaGAN)]\" = 0.725). The actual predictive performance vs. stability map (Figure 9A) was created based on the columns \"Averaged value of evaluation metrics\" and \"SD of AVEM\" in Table 3. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset**} & \multicolumn{6}{c}{**Machine Learning Algorithm**} \\ \cline{2-7}  & & \multicolumn{2}{c}{**Boosting**} & \multicolumn{2}{c}{**Random Forest**} & \multirow{2}{*}{ \begin{tabular}{c} **Averaged** \\ **Correlation** \\ **Coefficients** \\ \end{tabular}