esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art and Theoretical Framework
### Design Considerations  
The design of the device considered a set of key factors, widely addressed in previous studies, ensuring stable and safe operation. These factors guarantee that the device operates efficiently under various operational conditions. Additionally, they focus on aspects that facilitate the achievement of reproducible and reliable results, meeting necessary technical requirements for the filtration process and ensuring the safe handling of the system [25].  
* Hydraulic factors: For the development of the device, critical hydraulic aspects were considered to ensure continuous flow and maintain constant pressure throughout the entire filtration process. To achieve this, a propulsion system powered by a compressed gas is controlled by a pressure regulator [26].
* Ergonomics and safety: The device was designed to be easy to operate. The height of the equipment was calculated to facilitate handling by the operator, reducing physical effort and fatigue during operation. Additionally, permanent connections were implemented in the system, eliminating the need for frequent reconnections of hoses and accessories, which reduces the risk of leaks and improves overall process safety [27].
* Durability and material compatibility: The materials used in the construction of the device were selected for their durability, ensuring resistance in use-intensive operating conditions. Additionally, the chemical compatibility of the components with the various fluids passing through the system was evaluated, ensuring that no degradation or unwanted reactions occur [28].
* Maintenance and accessibility: The modular design of the equipment facilitates access to its components, simplifying maintenance tasks and reducing downtime. This modular structure allows for quick and efficient replacements and repairs, ensuring continuous operation without extended interruptions.
* Control and monitoring: The device's control system is designed to be accessible, with a clear display of key parameters such as pressure and flow rate. This enables real-time monitoring, making it easier for the operator to supervise the process without requiring complex interventions.
###### Abstract  
The current study aims to investigate the effects of 3D virtual object complexity on visual working memory capacity in mobile augmented reality (MAR) interfaces. With the popularity of augmented reality technology in mobile applications, 3D virtual elements play a key role in interaction. However, prior studies ignored the correlation between virtual object presentation and visual working memory (VWM). Given that visual working memory capacity is closely related to overall cognitive ability, the current study explored the relationship between the complexity of 3D virtual objects and VWM capacity in AR interfaces. Sixty volunteers participated in a 5-point Likert scale rating to eliminate the interference factors of familiarity, concreteness and similarity in 3D virtual material objects. Then, we further conducted an MAR change detection paradigm experiment and successfully manipulated the complexity of 3D virtual objects and the set size. Thirty-one subjects completed the formal experiment. Three measurements (reaction time, proportion correct, and Cowan's K) were analyzed for nine experimental conditions (three object complexity levels and three object set sizes). The results confirmed that the visual working memory capacity in mobile AR interfaces is modulated by the complexity of the 3D virtual objects and set size, which decreases with the increase in complexity and set size of the displayed 3D virtual objects. As the complexity of the 3D virtual objects increases, the amount of resources allocated to each item decreases, resulting in a decrease in memory capacity and memory accuracy. This study highlights the effectiveness of VWM capacity in MAR interface design research and provides valuable insights into determining the best way to present 3D virtual objects.  
mobile augmented reality (MAR); visual working memory capacity; object complexity; 3D virtual elements +
Footnote †: journal: Applied sciences
# Research on VVC Intra-Frame Bit Allocation Scheme Based on Significance Detection
## 3 Model and Proposed Method
### Analysis of Rate Control Model
The overall flow of the program is shown in Figure 3.  
The scheme first constructs a lightweight dataset of video sequences then decides the set of improved CTUs in the frame based on the Canny edge detection-based saliency detection algorithm, color contrast-based saliency detection algorithm, and SATD-based complexity measure together, adjusts the improved CTUs with different multiplicity to expand the dataset, and finally fits the dataset data with a regression equation using the PLS model. The significance module and the regression equation are jointly embedded in the encoder to complete the rate control. Algorithm details will be presented in subsequent sections.
# A Domain-Based, Adaptive, Multi-Scale, Inter-Subject Sleep Stage Classification Network
### Related Work  
With the development of deep learning, there have been many studies on sleep staging based on ECGs [13]. [PERSON] et al. (2018) extracted features of ECGs, which were input into a four-layer DNN model to distinguish the awake, REM, and NREM sleep stages. The results showed an accuracy of 77%, and [PERSON]'s Kappa coefficient was around 0.56 on the MIT-BIH Polysomnographic Database [14]. [PERSON] et al. (2018) combined convolutional neural networks (CNNs) and a support vector machine to improve the performance of sleep staging in terms of awake, light sleep, deep sleep, and REM based on cardiopulmonary coupling (CRC) spectrograms. The work was ten-fold cross-validated on the MIT-BIH Polysomnographic Database and achieved an accuracy of 75.4%, while [PERSON]'s Kappa coefficient was 0.54 [15]. [PERSON] et al. (2019) developed a CNN in combination with a long short-term memory (LSTM) recurrent neural network to stage sleep from an ECG and respiratory signals. Their networks were trained and evaluated on a large clinical dataset and achieved the best performance compared to other models for staging in terms of five sleep stages (Cohen's Kappa 0.585, 95% CI \(\pm\) 0.017) and discriminating awake vs. REM vs. NREM (Cohen's Kappa 0.760, 95% CI \(\pm\) 0.019) [16]. [PERSON] et al. (2019) used LSTM to classify the sleep stages into four classes (wake, REM, N1/N2, and N3) based on heart rate variability (HRV) derived from an ECG. The Cohen's Kappa for the model was 0.61 \(\pm\) 0.15, and the accuracy was 77.00 \(\pm\) 8.90% across the Siesta database [17]. [PERSON] et al. (2020) achieved an accuracy of 0.77 and an overall Kappa of 0.66 for four-sleep-stage classification using a deep neural network based on the instantaneous heart rate (IHR) time series, as extracted from a single-lead ECG [18].  
Generally, deep learning can automatically classify sleep based on manual features and ECG-derived signals into between two and four stages. Although the current methods have achieved good performance, there is still great room for improvement in inter-subject classification. An algorithm's generalization is restricted in inter-subject sleep staging [19] since ECG results are subject to individual differences, as charted in the field of identity recognition [20; 21]. Additionally, both imbalances between the awake, REM, light sleep, and deep sleep classes during training and the limited amount of information that can be obtained from single-lead ECGs negatively impact classification.
## 3 Results
### Maximum Von Mises Stress on Pedicle Screw and Rod  
The maximum von Mises stress on the screws in the computational models appeared at the region where the screws entered the UHMWPE blocks (Figure 4a). The von Mises stress on the pedicle screws was recorded as 677.23 MPa, 1070.91 MPa, 385.809 MPa, and 491.50 MPa in the FEM-FT-01, FEM-FT-02, FEM-PU-01, and FEM-PU-02 models, respectively, when placed under an axial force of 170 N. When the load was increased to 220 N, the FEM-FT-02 model showed the highest von Mises stress on the pedicle screws (Table 4). For the rod component in the FEM-FT-01, FEM-FT-02, FEM-PU-01, and FEM-PU-02 models, the maximum von Mises stress was 341.66 MPa, 369.67 MPa, 361.36 MPa, and 362.24 MPa, respectively, under a 170 N axial load, and the maximum value occurred at the interface between screw and rod. When the load was increased to 220 N, the FEM-FT-02 model showed the highest von Mises stress on the rods (Figure 4b and Table 5). The maximum von Mises stress on the pedicle screws in the FEM-PU-01 and FEM-PU-02 models was 43% and 54% less than that in the FEM-FT-01 and FEM-FT-02 models, respectively, while the maximum von Mises stress on the rod component decreased by 1.4% in both models. The stiffness of the FEM-PU-01 (45.09 N/mm) and FEM-PU-02 (30.09 N/mm) models increased by 4.4% and 14.2%, respectively, in comparison to the FEM-FT-01 (43.18 N/mm) and FEM-FT-02 (26.34 N/mm) models. The stiffness of all FEM models was found to be similar to the results from the mechanical fatigue test (EXP-FT-01: 42.48 \(\pm\) 0.42 N/mm; EXP-FT-02: 26.69 \(\pm\) 0.63 N/mm; EXP-PU-01: 44.96 \(\pm\) 0.71 N/mm; EXP-PU-02: 29.52 \(\pm\) 0.93 N/mm).
## 2 KITSUNE Satellite
### Mission and System Requirements  
In the early stages of KITSUNE development, the idea is to capture artificial colorful patterns or characters within a 100 \(\times\) 100 m\({}^{2}\) space for entertainment and social purposes. The primary objective is to provide a 5-m-class resolution imaging service aligned with the mission statement. Wildfire detection is included as a secondary objective to increase the functionality of the imaging payload. Therefore, the following success criteria are generated to categorize the mission outcome: (1) minimal success with downlinking an out-of-focus image to the GS, (2) full success with capturing any letters or characters done  
Figure 1: KITSUNE 2U main bus system configuration (**left**) and the satellite overview with 3U imaging payload (**right**).  
by a group of people within a 100 \(\times\) 100 m\({}^{2}\) space, and (3) extra success of capturing images with 5-m-class resolution (<6 m/pixel) as well as correctly classifying wildfire images.  
The payload system requirements are generated according to the mission statement and objectives in addition to the following 6U CubeSat limitations:  
1. The ground resolution should be <6 m/pixel in addition to the ground swath of approximately 20 km;
2. The overall payload should be able to fit within a volume of 90.0 mm \(\times\) 90.0 mm \(\times\) 327.5 mm;
3. The camera sensor should have a pixel size larger than 3.0 \(\upmu\)m with a global shutter and a shutter speed of less than 1/3200 s. In addition, it should be able to capture six images in a row with approximately 1 frame per second;
4. The camera controller board (CCB) should capture images and transfer over C-band communication for downlink when it is requested by uplink commands. This could be either in real-time mode or downlink mode over C-band flash memory for stored images;
5. The camera sensor should capture RGB images with JPG compression (>90%) with correct colors;
6. The power consumption of the overall mission should be less than 10.0 Wh per orbit, and the in-rush current should be less than the overcurrent protection settings of the EPS;
7. The mission should be operated by the uplink commands both from UHF GS and C-band mobile GS. In addition, images and telemetry should be received by UHF GS and C-band main GS;
8. The satellite should point the camera and C-band Tx antenna with approximately 0.25\({}^{\circ}\) accuracy in target and nadir pointing modes by using the ADCS subsystem;
9. The electronics should survive a total ionization dose of approximately 200.0 Gy. In addition, they should be able to operate within the temperature range of \(-20.0\,^{\circ}\)C to +50.0 \({}^{\circ}\)C while the range of temperature difference should be between \(-5.0\,^{\circ}\)C to +5.0 \({}^{\circ}\)C for the lens components.
## 5 Quality Evaluation of Potatoes and Sweet Potatoes with NIR-Hyperspectral Imaging Techniques
### Physical Parameters and Texture Analysis Using Hyperspectral Imaging  
Physical parameters such as the color and textural attributes of roots and tubers have become an essential factor driving their final quality at a consumption stage. Consumers' preferences for product quality are influenced mainly by color, particularly when processing substantially impacts product quality [108]. [PERSON] et al. [109] reported that NIR-HSI was used to determine the color of potatoes. The textural attributes of cassava and yam products, such as boiled and pounded forms, are determined by a sensory evaluation, which may be a subjective and mechanical instrument measurement which requires considerable time [108]. Hyperspectral imaging has been used in evaluating the color and other physical characteristics of other tuber crops, such as potatoes and sweet potatoes [74; 106; 107; 109]. The color of potato slices was observed as they were being air dried using Vis/NIR hyperspectral imaging, and the R\({}^{2}\)P was as high as 0.91 when the PLSR was paired with feature wavelength selection techniques such as chosen interval partial least squares regression (iPLSR) [110]. PLSR was also used to determine the specific gravity and water absorption of sliced potatoes using hyperspectral imaging systems in the NIR spectra range of 900-1700 nm. With the linear weighted principal component regression algorithm, a coefficient of prediction (R\({}^{2}\)p) of 0.98 was obtained for specific gravity, and one of 0.97 was obtained for water absorption capacity [97]. The textural characteristics of potatoes and sweet potatoes were assessed during microwave baking using the MIR spectra (600-4000 cm\({}^{-1}\)); in this research, the LWPLSR performed better than PLSR in determining associated textural qualities such as chewiness, resilience, hardness, gumminess, cohesiveness, and springiness, with a maximum R\({}^{2}\)P value of 0.88 [111]. However, limited literature using it for the textural qualities of cassava and yam exists. Hyperspectral image spectroscopy can potentially support the genetic improvement target for cassava and yam breeding programs by exploring intrinsic quality traits such as color, texture, and selected biochemical parameters. These influence the specific characteristics of root and tuber crops during processing and consumption, with the advantage of nondestructive sampling. NIR-HSI could be adopted as a high throughput method for assessing the food quality of cassava and yam food products. The conventional techniques for texture measurement are destructive to the samples and are sometimes influenced by human factors in the case of the sensory test [112]. However, the hyperspectral imaging technique has found applications in evaluating the textural attributes of potatoes and sweet potatoes [34; 111].
## 3 Results and Discussions
### SEE Characteristics of MgO/Si Double-Layer Structures  
The SEY curves of the sample measured in the experiment are shown in Figure 4. Here, parameter \(d_{1}\) is used to represent the thickness of MgO coatings. Figure 5 shows the relationship between the SEY peak value and the coating thickness, as well as the relationship between primary electron energy corresponding to the SEY peak value and the thickness of MgO coating. Table 1 lists the typical parameters of the SEY curves shown in Figure 4. From Figure 4, it can be observed that SEY of the 1 nm MgO coating sample increases significantly compared with that of the Si substrate, which indicates that coating an ultrathin MgO film on Si will improve the surface SEY greatly. Meanwhile, it also indicates that the most surface of the material has a significant influence on its SEY. In addition, the results in Figure 5 show that the SEY of MgO/Si double-layer structures rises as the thickness of MgO coating increases, and merely, the SEY increment gradually decreases. Additionally, SEY tends to be stable when the coating thickness exceeds 30 nm. For the MgO/Si double-layer structure of 30 nm thickness, its SEY is relatively close to the SEY of pure MgO, especially in the region of \(E_{\text{p}}\) less than 1200 eV. When \(E_{\text{p}}\) increases from 1200 eV, difference of SEY between 30 nm sample and pure MgO reveals gradually; this  
Figure 3: (a) Section image of 30 nm MgO coating observed by SEM; (b,c) the surface roughness images of 20 nm and 30 nm MgO coatings characterized by AFM.  
phenomenon indicates that the Si substrate layer starts to affect SEY of the double-layer structure at this time, and also indicates that the electron penetration depth in MgO is close to 30 nm when \(E_{\text{p}}\) equals to 1200 eV. Meanwhile, the escape depth of inner secondary electrons also plays an important role on SEY, and it should be comprehensively considered when analyzing the SEY distinctions among different MgO coatings.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & **MgO** & **Si** & **1 nm** & **3 nm** & **7 nm** & **12 nm** & **20 nm** & **30 nm** \\ \hline \(\delta_{\text{m}}\) & 6.15 & 1.61 & 2.63 & 4.27 & 5.14 & 5.72 & 5.93 & 6.11 \\ \(E_{\text{p}}\)m/eV & 600 & 200 & 250 & 350 & 500 & 550 & 550 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical SEY parameters of MgO/Si double-layer samples.  
Figure 4: Measured SEY of MgO/Si double-layer samples.  
Figure 5: The influence of coating thickness on SEY peak value \(\delta_{\text{m}}\).
# Structural Features and Properties' Characterization of
## 3 Results and Discussion
### Thermogravimetric Analysis  
We used TGA to evaluate the ESO introduction's effect on the analytes' thermal stability. The TGA data are given in Figures 2 and 3, while the characteristic points of mass change are shown in Table 2.  
Figure 2: The samples’ TGA curves.  
The temperature shifts at 5 and 10% mass loss points, i.e., at the initial stage of thermodestruction, toward lower temperature ranges with higher ESO content are most likely due to the volatility of this component and desorption.  
The TGA curves of all of the PLA-NR mixes are clearly step-like in their mass-loss appearance, demonstrating an evident sharp change at about 360 \({}^{\circ}\)C. Such situations are typical for similar compounds, and this was confirmed by earlier studies of PLA mixed with either low-molecular NR [1] or epoxidized NR [14]. PLA's thermal stability improved greatly when mixed with NR. This is proven by the shift of the 50% sample decomposition temperature by 10-14 \({}^{\circ}\)C. This can be explained by the higher molecular weight of NR in comparison with PLA, as well as by PLA's very low thermal stability [29]. The introduction of ESO also had a positive effect on thermal stability: the increasing oil content caused the 50% mass loss temperature to shift by 4-6 \({}^{\circ}\)C toward higher values. The most significant increase in thermal stability was caused by the introduction of five mass parts of ESO; after this, the parameter did not increase so noticeably (the M5 differs from M20 by only about 2 \({}^{\circ}\)C). This may be due to two factors: firstly, ESO is traditionally known as a stabilizer for polymer compounds (chlorine-containing and others) with a predictable behavior; secondly, the structure of ESO molecules combining polar and nonpolar fragments, which  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{1}{c}{**5\% Mass Loss**} & \multicolumn{1}{c}{**10\% Mass Loss**} & \multicolumn{1}{c}{**50\% Mass Loss**} & \multicolumn{1}{c}{**90\% Mass Loss**} \\  & **Temperatures, \({}^{\circ}\)C** & **Temperatures, \({}^{\circ}\)C** & **Temperatures, \({}^{\circ}\)C** & **Temperatures, \({}^{\circ}\)C** \\ \hline Initial PLA & 293 & 301 & 328 & 344 \\ M0 & 295 & 314 & 338 & 435 \\ M5 & 282 & 311 & 342 & 435 \\ M10 & 251 & 293 & 343 & 433 \\ M15 & 250 & 288 & 343 & 444 \\ M20 & 244 & 274 & 344 & 448 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The TGA data.  
Figure 3: The samples’ DTG curves.  
are considerably different in nature, can act as a kind of PLA-NR compatibilizer. Thus, the introduction of ESO causes closer interfacial interaction of the studied polymers.
###### Abstract  
This paper presents a damage identification method for offshore jacket platforms using partially measured modal results and based on artificial intelligence neural networks. Damage identification indices are first proposed combining information of six modal results and natural frequencies. Then, finite element models are established, and damages in structural members are assumed by reducing the structural elastic modulus. From the finite element analysis for a training sample, both the damage identification indices and the damages are obtained, and neural networks are trained. These trained networks are further tested and used for damage prediction of structural members. The calculation results show that the proposed method is quite accurate. As the considered measurement points of the jacket platform are near the waterline, the prediction errors keep below 8% when the damaged members are close to the waterline, but may rise to 16.5% when the damaged members are located in deeper waters.  
Keywords:natural frequencies; modal shapes; damage identification index; artificial neural networks; jacket platform; finite element model +
Footnote †: journal: Applied Mechanics and Physics Letters
## 5 Data Preparation and Filtering
### Filtering of Raw Operational Data Techniques
#### 5.2.2 Haar Wavelet Transformation  
The Haar wavelet transformation (HWT) is a discrete wavelet transform system used to denoise and detects outliers. [PERSON] [60] suggested the use of HWT to decompose signals into several levels of signal components that yield a better result in training the ANN for accurate classification and diagnosis [60]. The HWT follows a straightforward technique in breaking down the signal into coefficients by sliding a fixed duration size of a window along the signal represented as  
\[\psi(t)=\left\{\begin{array}{ccc}1&\text{for}&0\leq t<\frac{t}{2}\\ -1&\text{for}&\frac{1}{2}\leq t<\frac{t}{2}\\ 0&\text{for}&\text{otherwise}\end{array}\right. \tag{7}\]  
where \(\psi(t)\) is the Haar wavelet and \(t\) the time.  
By sliding the window through the signal, it decomposes the signal into multiple levels to produce details and approximation coefficients equation, as shown in Figure 12. The decomposition levels depend on the degree of signal refinement to be accomplished by incrementing the level accordingly. The higher the level introduced, the more sensitive the fluctuation of data points will be filtered, contributing to the likelihood of relevant data points being filtered, resulting in an overall smoother signal. Therefore, the selection of the  
Figure 11: Outlier detection using 3\(\sigma\) rule [59].  
Figure 10: Outlier detected around sampling point for main engine power and fuel consumption [59].  
level is an important factor while modelling the HWT algorithm. Figures 12 and 13 show the decomposition of the signal data points in many subsets of increments of the levels. The newly form collection of wavelet coefficients functions as a threshold in evaluating the signal data points' acceptance or rejection. [PERSON] et al. [61] applied the HWT wavelet decomposition to the fuel consumption data collected from an HCV. An example of the filtering data is given in Figure 14.  
Figure 12: Multi-level decomposition by HWT showing the wavelet coefficients \(A_{ij}\) and \(B_{ij}\). \(N\) is the total level of decomposition considered in HWT.  
Figure 13: Visualization of different decomposition levels by HWT.
## 3 Results and Discussion
### Technical Parameters
#### 3.6.3 Thickness  
The data of thickness of the unmodified PETE fabrics and PETE-Cu\({}^{(0)}\) composites are listed in Table 6. These data displayed negligible differences with the average values of measured thickness at 1 kPa in the range of 0.66-0.67 mm.