esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### General Discussion and Future Prospects  
In general, during WOC, the loss of water resulted in a greater decrease in the weight and shrinkage of the 3D-printed square pillars. Furthermore, it induced the formation of cracks on the surface. In contrast, during SC, lower water loss resulted in less shrinkage and a smaller decrease in weight. In addition, the steam-cooked 3D-printed square pillars had a better appearance, because cracks were not formed on the surface. Similar results were found by [PERSON] et al. [24], where the formation of cracks and higher shrinkage were observed in microwaved square pillars made of rice starch compared to steamed ones. [PERSON] et al. [10] also reported shrinkage of 3D-printed foods after WOC. Because the temperature of gelatinization of starch shifts to higher temperatures under conditions of limited water availability [37], the temperature of gelatinization of the starch contained in the water oven-cooked square pillars might have increased, leading to lower gelatinization and weaker structure formation in comparison with the very well-gelatinized steam-cooked square pillars. This behavior might also have led to more homogenous steam-cooked square pillars than water oven-cooked ones. However, the steam-cooked multi-material square pillars were harder than the water oven-cooked multi-material square pillars. The steam-cooked 3D-printed square pillars made of rice starch were softer than the microwaved ones [24]. The presence of CP in the middle of the 3D-printed square pillars may have influenced this result. Overall, the SC process may be more convenient because the appearance, size, and weight of the 3D-printed square pillars were not significantly affected.  
In the future, analysis of the microstructure of the cooked 3D-printed square pillars through microscopic techniques, including scanning electron microscopy, may help elucidate the effects of cooking processes and food composition on the surface morphology of the 3D-printed foods. Furthermore, it is important to investigate the impact of cooking processes and food materials on the nutritional properties of 3D-printed foods. This can be achieved through proximate analysis, digestibility studies, and the identification of noteworthy nutrients, including proteins, to understand their interaction with consumers. Additionally, as this study only evaluated a single condition of time and temperature during the cooking process, a more comprehensive analysis employing different temperatures and cooking times could determine optimal conditions for WOC and SC. Moreover, sensory evaluation, with a particular emphasis on the texture and appearance of 3D-printed foods, can offer insights into consumer preferences and acceptability. Finally, the inclusion of other cooking processes, such as frying or baking could provide valuable insights into the changes undergone by 3D-printed foods during the cooking processes.
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 4 Results
### Variables and Machine Learning Model Selection
#### 4.1.2 Support Vector Machine (SVM)  
The performance of the Support Vector Machine (SVM) model provides insights into its ability to classify vitamin D levels. The results are illustrated in Figure 10, where panel  
Figure 9: (**a**) ROC curve result for predictions with the logistic regression model; (**b**) results of metric comparisons.  
The ROC curve (Figure 9(a)) demonstrated an AUC of 0.76, indicating a moderate level of discriminative power. This suggests that the model correctly classifies instances as either positive or negative with a 76% probability. The trade-off between sensitivity and specificity, as depicted in the curve, confirms that while the model performs reasonably well, further optimization could enhance its classification accuracy.  
A comparative analysis of key evaluation metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen's Kappa, and ROC-AUC, is provided in Figure 9(b). The overall accuracy of the SVM model was 66.05%, indicating that approximately two-thirds of the predictions were correct.  
Upon further analysis of class-specific performance, notable differences were observed. For Class 0 (insufficient vitamin D levels), the precision was 42.22%, the recall was 64.41%, and the F1-score was 51.01%, suggesting that the model struggled to correctly classify cases in this category. This limitation is likely attributable to an imbalance in the dataset, where Class 0 instances were underrepresented. In contrast, Class 1 (adequate vitamin D levels) exhibited significantly better classification performance, with a precision of 83.20%, a recall of 66.67%, and an F1-score of 74.02%, likely due to a higher representation of this class in the dataset.  
Additional evaluation metrics, such as the Matthews Correlation Coefficient (MCC) and Cohen's Kappa, provided further insights into classification robustness and agreement with true labels. The MCC value of 0.59 and Kappa value of 0.64 suggest that while the model achieved a moderate level of agreement with actual classifications, it did not effectively compensate for class imbalance.  
Overall, as illustrated in Figure 9(b), the SVM model demonstrated moderate classification effectiveness. While its performance was notably strong in classifying sufficient vitamin D levels, it struggled to accurately detect insufficient vitamin D levels. The precision score of 0.66, while reasonable, was lower than that of the Random Forest model (0.73), suggesting that the SVM model may benefit from additional hyperparameter tuning to improve its classification performance.  
Figure 9: **(a)** ROC curve result for predictions with the Support Vector Machine model; **(b)** results of metric comparisons.
# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 1 Introduction
Recently, super-resolution imaging through deep learning has increased research value in the computer vision and image processing fields. Zhang et al. [13] proposed a high-speed medical imaging super-resolution method based on a deep learning network, and Yongyang et al. [14] proposed road extraction of high-resolution remote sensing images using deep learning. Ugur et al. [15] proposed a comparative study of deep learning approaches for airplane detection in super-resolution satellites. In this study, we focus on an algorithm that extracts main features to improve image resolution, improves and learns the extracted features, and generates high-resolution images. By improving the resolution of alignment marks and patterns in photography in the semiconductor manufacturing industry, we propose an architecture with improved object detection performance. This paper's contributions are as follows:  
1. A Multi-Scale Residual Attention Block was constructed by applying an Attention Mechanism based on the Multi-Scale Residual Network. We proposed a High-Resolution (HR) model in which the resolution of Low-Resolution (LR) images is improved and the extracted features are improved by reconstructing the model structure of a multiscale network. 2. We proved that object detection is improved by increasing the image resolution of the proposed model. When detecting an object through a vision machine, the detection performance is improved by improving the resolution. 3. The data collected through the equipment is pre-processed and learned, and it is reliable in practical application through the analysis of the results, images, and detection obtained by conducting various experiments. The structure of this paper is as follows. In Section 2, alignment technology in photolithography, SISR and MSRN, and Attention Mechanisms are explained as related studies. Section 3 describes the proposed architecture and details. Section 4 describes the experimental progress, evaluation indicators, and experimental results. Section 5 describes the conclusion and future research.
# A Study of Emergency Aircraft Control During Landing
## 8 Research Summary  
This article addresses the challenge of controlling an aircraft during complete control surface failure. In such scenarios, maintaining flight and executing an emergency landing are tasks that primarily rely on engine thrust modulation. Manual thrust lever control is particularly difficult due to human perceptual limitations, including long response delays that compromise control precision--an issue that often hinders safe landings. A review of the existing technical literature reveals a limited number of automated solutions for aircraft emergency thrust control. Over the past 30 years, developed systems have demonstrated good performance in both simulation studies and real flight trials. However, due to a lack of interest from the aviation industry, this subject remains underexplored.  
Based on this background, the key challenges and constraints associated with designing an emergency thrust control system were identified. Consequently, the development of a control system based on linear-quadratic control with error integration (LQRI) was initiated. Utilising the MATLAB and Simulink environment and the X-Plane 11 flight simulator allowed for a faithful representation of flight dynamics. Through system configuration, aircraft model identification, control law design, and component integration, a functional thrust control system was realised.  
In the conducted software-in-the-loop (SIL) simulations, the system demonstrated correct performance. Despite the limitations of the research platform and issues related to cross-coupling between control channels, satisfactory tracking of the flight path angle and heading was achieved. Final testing involved automatic landing attempts under windless conditions and moderate crosswinds of up to 17 knots (8.75 m/s). In both cases, smooth touchdowns near the runway threshold were performed with a flight path angle exceeding \(-0.5^{\circ}\). These results indicate that the developed system could facilitate successful landings at approach speeds significantly higher than the aircraft's recommended approach speed. Additionally, crosswind landing tests provided insights into the system's robustness against external disturbances, which was deemed acceptable under the tested wind conditions.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 3 Magneto-Optical Transparent Nano-Ceramics
### Summary  
With the continuous improvement of techniques, the optical quality of transparent nano-ceramics has been greatly improved and is almost comparable to that of its crystals [61; 62]. Among the magneto-optical ceramic materials that have appeared so far, the research focus is mainly on TGG and TAG transparent nano-ceramics. As can be seen from the research results of the existing literature, most of the TGG and TAG transparent nano-ceramics have a light transmittance of 80% in their expected working band, and the Verdet constant meets the working requirements, which potentially gives them commercial value in large-scale applications. In addition, the magneto-optical properties of TAG transparent nano ceramics are changed by doping different rare earth elements. This can not only increase the Verdet constant but also could reduce the Verdet constant, which can regulate its magneto-optical characteristics by controlling the type and proportion of rare earth elements. This research also needs attention in the future study of TGG transparent nano-ceramics.
# Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence
## 2 Materials and Methods
### Verification of the Equipment Suitability  
The so-called tire identification number (TIN) was chosen in order to fulfill both goals of this part of the work. This code is a unique number that can be used to identify the type, dimensions, date of manufacture, manufacturer and other parameters of the selected tire. The recognized text is on the outside of the tire in its full state and on the inside mostly without the date of manufacture. The entire code represents a unique identifier for the model and dimensions of a particular sample. This text was chosen as an indicator of a satisfactory set up all components of designed camera system. After acquiring an image of it with different types of sensors, lenses and lightning configuration the best setup will be chosen for the application of visual control and defect detection of automobile tires. The quality of images will be tested with the OCR function in Matlab and followed with the pretrained R-CNN detector.  
An illustration and description of the individual parts of such a code can be seen in Figure 8. The TIN begins with the text \"DOT\" (1), which represent that this type of tire meets the safety standards of the Ministry of Transport [27]. Part (2) of the marking is already the identification code itself, where part (3) describes the code of the manufacturer and production plant. Part (4) represents the dimensions of the tire. Part (5), which consists of a maximum of four characters, represents a space for the manufacturer to indicate a more detailed specification of the type or selected characteristics of the tire. The last four digits (6) indicate the week and year of manufacture of a particular piece.  
After further tests on the text in English, it was found that the OCR function has a problem with some fonts, as well as with various reflections, double edges, etc. However, these elements occur often in the images of the examined object and their occurrence is present even after many attempts; it was not possible to suppress them using different configurations of lighting or other hardware. However, the images obtained in this way proved to be suitable for further use and network training for specific text font should solve all problems.  
Figure 9 shows one of the results from the application of the OCR function on data from the camera system. The TIN code of the tire was red successfully almost every time after creating a special language for this font. In the case displayed in Figure 9, OCR function assigned incorrectly only the part \"6 YOF\", where instead of number \"0\" it identified this symbol as the letter \"O\". However, this type of mistake when symbols are almost the same shape can be easily suppressed by defining specific conditions of the recognized text directly in the code. Results like this one prove that the approach to the visual control of tires with chosen hardware can be performed by training a neural network and object detector based on it.
# Location Selection Methods for Urban Terminal Co-Distribution Centers with Air-Land Collaboration
## 5 Analysis
### Operational Effectiveness of the Air-Ground Cooperative Co-Distribution Network  
To verify the validity and implementation of the model, the results were compared against those for a traditional distribution network with the same distribution demand. According to a field survey of the distribution network layout and operation in Wangsheren, about 70-80 distribution networks complete 300,000 daily deliveries. The cost mainly includes the costs of the construction or leasing of the network, labor, and vehicle purchase and use. The average distribution cost is CNY 0.5-0.6, and the average time for the network to complete the daily distribution is about 4 h. According to the calculation results of the proposed model in Section 5.3, the number of open-space cooperative terminal co-distribution centers is much lower than the number of traditional terminal points, which effectively improves the land intensification and urban spatial environment quality. In terms of cost and delivery time, the parameter settings in this paper mainly consider the current level of UAV equipment and market price. Restricted by existing problems, such  
Figure 7: Convergence speeds of the GWO and PSO algorithms.  
as limited endurance, load capacity, and battery loss, UAV co-distribution centers have a relatively high cost, and the single-delivery cost calculated by the model is 35-50% higher than that of traditional terminal distribution. However, the average time required for co-distribution centers to complete all deliveries is only 15-20% of that for traditional terminal outlets, indicating a significant improvement in the efficiency of terminal distribution and customer satisfaction. The main source of profit for distribution enterprises is higher-value-added services, such as upward receipt. Therefore, reduced delivery times greatly improve the service capability of this part of the business. Increasing the comprehensive income of distribution enterprises is of great significance. With improvements in the technical level of UAVs, the associated distribution costs will gradually decrease, and the labor cost of vehicle-sharing centers will be greatly increased.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results
### Analysis of Results
#### 5.3.3 Energy Consumption  
Figure 15 shows the total energy consumption for transmission and reception of all kinds of packets. The energy consumed during sleeping and CPU processing is ignored. As \(\lambda\) increases, the energy consumption increases since more _DATA_ packets attempt transmission. However, comparing to Seaweb and S\({}_{13}\) where the isolated nodes keep trying to send _DATA_ and end up exhausting battery, SOUNET can save considerable energy even at high traffic loads by letting the nodes recognize any possible isolation by themselves and sleep to save energy.
# An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments
## 6 Performance Evaluation
### Density of Objects  
Based on the test results summarized in Table 11, it is evident that accuracy is not influenced by the object density feature. The main reason behind this inference is the selection of the appropriate position of the crossing line. The proposed system detects objects in the entire frame, but it tracks the counted objects for the predefined region only. Though the video frame has a large number of objects, the algorithm selects only the objects within the search region. The number of the objects in the search region is at the most equal to the number of road lanes because the algorithm is continuously updating the tracked objects list by removing old objects. In rare cases, YOLO might visualize a single object as dual objects illustrated in the left image Figure 5. However, this is an object detection problem, and cannot be linked to a high object density environment. The percentage of accuracy remains the same in both high-density and low-density environments. In the dataset, video number 10 depicts the low-density environment, and video number 12 illustrates the high-density environment. The difference in the accuracy between video 10 and video 12 is negligible and the accuracy achieved by both is more than 90%.
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 2 Materials and Methods
### Complexity Analysis  
The number of multiplications and sums in each state of the trellis for different estimators is summarized in Table 1, requiring \(\mathcal{O}\left(LKM^{P}\right)\) multiplications. The capacity of MPFs for modeling a filtered stochastic process with the minimum number of basis functions after KLE [17; 18] makes this proposal the orthogonal equalizer with optimal performance and reduced complexity. This is the main achievement of this work.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Multiplications** & **Sums** \\ \hline MLSE & \(PM\) & \((P+1)M\) \\ Orthogonal MLSE & \(KM\) & \((K+1)M\) \\ Max-Log-MAP & \(PM\) & \((P+3)M\) \\ Orthogonal Max-Log-MAP & \(KM\) & \((K+3)M\) \\ Log-MAP & \(PM\) & \((P+5)M\) \\ Orthogonal Log-MAP & \(KM\) & \((K+5)M\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of multiplications and sums in each state of the trellis for different estimators. Maximum likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP).  
Table 2 compares the number of multiply-add complex operations, the number of memory locations, when required, and their update period in the described channel model for the classical and orthogonal equalizers mentioned above. The number of basis functions employed in orthogonal Fourier ([15]) and MPF equalizers are the values \(K_{1}\) and \(K_{2}\), respectively. From Table 1, given that each node has \(M^{P-1}\) states inside the trellis, it is possible to observe that the number of multiply-add operations required to compute \(\{\tilde{y}_{l}|\mathbf{b}_{l}\}_{l=0}^{L-1}\) in Equation (13) and Equation (16) is \(M^{P-1}K_{2}M\), \(L\) times. In [15], the number of multiply-add operations is \(LK_{1}M^{P}\). Following different strategies, the equation to obtain the memory locations used by the MPF equalizers, \(K_{2}M^{P}\), is similar to \(K_{1}M^{P}\) in [15]. The same happens with the number of multiplications to fill the memories that store the values \(g_{l}^{(k)}|\mathbf{b}_{l}\) in Equation (17), but the memories in the proposed equalizers do not need updates. Another marked difference respects the work in [15] is that MPFs do not have the observation period of \(L_{1}\geq L\) to compute the channel coefficients.  
Every time \(K_{2}<P\), the proposed methods lead to a simplified hardware implementation, which can result in less power consumption and lower latency. The trade-off in the hardware implementation is reflected in more memory locations, due to the need of storing all the \(M^{P}\) possible noiseless channel outputs \(g_{l}^{(k)}|\mathbf{b}_{l}\).
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes  
Computational Fluid Dynamics (CFD) models for simulating transient two-phase flows in pipelines allow us to predict the air-water interface when dealing with plug and stratified flow, air-water mixing, and air release-admission processes in pipelines. It is important to note that air in pipelines can be present in different forms, including the following: (i) dissolved air with a proportion of approximately 2%; (ii) air bubbles generated by discrete air pockets and turbulence processes; and (iii) air accumulated as a result of pipe filling and emptying processes [40].  
The most recent advances in the modelling of transient two-phase flows (filling and emptying processes) in water distribution systems have been analysed considering the following: (i) 2D or 3D modelling, (ii) algorithms of solution, and (iii) turbulence models.