esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 3 Design of Three Body Multi Closed Loop Control Law
### Position Follow-Up Control Law of Platform Subsystem  
If the position error of the center of mass of the platform subsystem is \(\mathbf{X}_{pfe}=\mathbf{X}_{pf}-\mathbf{X}_{pfd}\), then the translational dynamics of the platform subsystem are converted into error form:  
\[\ddot{\mathbf{X}}_{pfe}=-\ddot{\mathbf{X}}_{pfd}-M_{pf}^{-1}\mathbf{A}_{b}\mathbf{C}_{r}\mathbf{F}_{ mr} \tag{33}\]  
If we define \(\mathbf{y}_{pf}=\mathbf{X}_{pfe}+\dot{\mathbf{X}}_{pfe}\), then according to the equation, it can be obtained:  
\[\dot{\mathbf{y}}_{pf} =\dot{\mathbf{X}}_{pfe}+\ddot{\mathbf{X}}_{pfe} \tag{34}\] \[=\ddot{\mathbf{X}}_{pfe}-\ddot{\mathbf{X}}_{pfd}+M_{p}^{f-1}\mathbf{F}_{r}\] \[=M_{pf}^{-1}\mathbf{F}_{r}+\mathbf{N}_{pf}\]  
where \(\mathbf{F}_{r}\) is the projection of the reaction force of the electromagnetic force applied to the load subsystem by the Lorentz force magnetic levitation bearing in the inertial frame, i.e., \(\mathbf{F}_{r}=-A_{b}\mathbf{C}_{r}\mathbf{F}_{mr}\). \(N_{pf}\) is the interference term of the position equation, satisfying \(\mathbf{N}_{pf}=\dot{\mathbf{X}}_{pfe}-\ddot{\mathbf{X}}_{pfd}\).  
The designed linear sliding mode control law is as follows:  
\[\mathbf{F}_{r}(k) =M_{pf}T_{pf}^{-1}[\mathbf{y}_{d}(k+1)-\mathbf{y}(k)-\frac{1}{(1+T_{pf}) }\mathbf{e}(k)] \tag{35}\] \[+M_{pf}\mathbf{g}_{s}\mathbf{g}\mathbf{g}(\mathbf{s}(k))\]  
where \(\mathbf{g}_{s}\) is the parameter of the control law switching term, which has \(\mathbf{g}_{s}>|\mathbf{N}_{i}(k)|\) for any \(\mathbf{g}_{s}\). Based on the strategy that minimizes the impact on the attitude of the payload subsystem, the LFMB electromagnetic force distribution scheme for the above control law is formulated as follows:  
\[\mathbf{F}_{ur1}=-\mathbf{\
u}\mathbf{C}_{r}^{T}\mathbf{A}_{b}^{T}\mathbf{F}_{r} \tag{36}\] \[\mathbf{F}_{tr1}=-\mathbf{\
u}\mathbf{C}_{r}^{T}\mathbf{A}_{b}^{T}\mathbf{F}_{r}\] \[\mathbf{F}_{ar}=-\mathbf{\Delta}\mathbf{C}_{r}^{T}\mathbf{A}_{b}^{T}\mathbf{F}_{r}\]  
where \(\mathbf{\
u}=diag\{0.5\quad 0\quad 0.5\}\), \(\mathbf{\lambda}=diag\{0\quad 1\quad 0\}\), \(\mathbf{F}_{ur1},\mathbf{F}_{lr1},\mathbf{F}_{ar}\) are the electromagnetic forces for the upper and lower OLFMB and LLFMB, respectively.
## 3 Materials and Methods
### Study Design and Analytical Approach  
To validate the evaluation instrument, two experts independently analyzed each prompt, assigning a binary code of 1 if a particular technique was identified in the prompt and 0 if it was not. Cases where the two reviewers disagreed were flagged for further analysis. In such instances, a third expert acted as an arbitrator to resolve the discrepancies. To prevent bias, the arbitrator was blinded to the decisions of the initial reviewers, ensuring an impartial assessment of whether the prompt employed the technique in question. All reviewers were specialists in artificial intelligence applied to education and healthcare, providing the necessary expertise to accurately evaluate the use of the proposed techniques.  
This study adopts a quantitative approach, structured into descriptive and correlational phases, to address its objectives comprehensively. In the first phase, the study focuses on Objective 1, which seeks to identify emerging dimensions of the prompt strategies employed by higher education students. To achieve this, an Exploratory Factor Analysis (EFA) was conducted, grouping the techniques used into distinct dimensions based on their patterns of co-occurrence.  
The second phase addresses Objectives 2 and 3. In relation to Objective 2, the study explores the relationship between the types of techniques students used to formulate prompts and the quality of the responses generated by the AI. This phase includes a descriptive analysis, followed by correlation and regression analyses aimed at identifying patterns and potential causal links. For Objective 3, the focus is on characterizing the simple and complex strategies observed in students' prompt-writing processes. A Rasch model is applied to assess the difficulty of the responses generated by the AI, offering insights into how specific strategies may influence the overall complexity of the interaction.
## 3 All-Optical Ultrasound Transducers and Imaging
### Imaging Applications  
As the promising development of AOUSTs, advanced applications by AOUSI are of particular interest. Several biomedical imaging applications are demonstrated. (i) The high frequency and  
Figure 8: (**A**) Schematic of ultrasound imaging where the 532-nm laser is absorbed by the dichroic filter. (**B**) Schematic of photoacoustic imaging where the 750-nm laser passes through the filter for photoacoustic excitation. Reproduced with permission from [102], Elsevier GmbH, 2014.  
broad bandwidth associated with the all-optical approach can potentially be used for virtual biopsy of biological tissue [94]. (ii) Vascular tissue imaging is studied by an AOUST probe [77]. The ultrasound images of vascular tissues from swine aorta show good correspondence with histological images. (iii) A miniature needle probe for AOUSI is developed for the applications in guiding interventional pain management procedures [103]. The probe is able to provide real-time M-mode images. (iv) An ultra-high resolution AOUSI system is built to investigate cell mechanics [104]. The information of mechanical properties with a sub-cell resolution is significantly useful to study fundamental biological processes and disease conditions. The system operates in the 10 to 100 GHz range and thus provides a sub-cell resolution of 10 nm. Figure 9 shows AOUSI cell images of the nuclear region and lamellipodium and their white-light images for comparison. With such ultra-high resolution, details of cells can be resolved. (v) A miniature side-looking AOUST probe is developed for IVUS imaging [105]. The axial resolution of 70 \(\upmu\)m and the imaging depth in tissue greater than 1 cm are achieved. Ex vivo imaging of tissues including swine tissue and diseased human tissue is demonstrated, showing the potential of this probe for IVUS applications.
## 3 The Application of Conventional ML Algorithms
### Support Vector Machine (SVM)
The researchers chose partial least squares discriminant analysis (PLSDA), linear SVM, and nonlinear SVM as classifiers and nonlinear SVM achieved the best performance across all these extraction methods. Except for the overall performance, this work calculated the correlation between the confidence produced by each feature method and discussed the fusion of confidences from the classifier and prescreening. Another work in 2019 discussed the performance of two kinds of edge histogram descriptors (EHD), Log Gabor (an improvement of Gabor filter), gprHOG (an HOG-based methods for GPR), and spatial edge descriptors (SED) in BTD [16]. The other feature extraction methods used SVM classifier, while gprHOG was implemented with two random forest classifiers. The curves of receiver operating characteristics (ROC curve, a curve jointly specifies the rate of detection and level of false alarm) when these algorithms were utilized in the detection of threats buried deep and not deep were presented. Log Gabor had the best performance on deeply buried threats substantially, and SED outperformed the other algorithms over most of the curves on threats buried not so deep. In the analysis part, the researcher discussed the similarity of the algorithm design practices and the difference in the conception of these methods.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 2 Water Supply System Architecture
### Water Treatment Plant  
This station's primary objective is to safegu end consumer's health. In this station, the water source is altered through some process to obtain a quality that meets some defined objectives. As such, the raw water is treated and purified in this station. At the treatment plant, conventional methods, such as clarification (filtration, coagulation), disinfection (chlorination), and desalination, take place [27]. Clarification is used to separate suspended water impurities, while the disinfection destroys pathogenic bacteria to avoid waterborne diseases [27]. Usually, desalination procedures are used to deliver to areas where supplies of freshwater are not easily accessible. Freshwater is separated from saltwater by this method. Water quality is efficiently tracked at this station. In some cases, the treated water may be transferred to the distribution system via underground pipes or pumped to a service reservoir for storage.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.3 Reinforcement Learning in Power Control Games  
A power control algorithm called reinforcement learning (RL) in PCGs is reported to improve the performance of WBASN by a learning algorithm [19]. Although the control power in this algorithm is similar to PAPU [17] and PCG [18], it is distinctive due to its reinforcement learning algorithm. RL algorithm based on game theory considers \(M\) users in WBASNs as \(M\) RL-agents. Each RL-agent transmits at the maximum data rate, and RL's payoff function is considered as a reward function to choose the best-responding player. The reward function is updated at each state of network changes.
## 1 Introduction
Second, the effects of meteorological variations need to be minimized, in order to emphasize the impacts of LUCC on changes in vegetation primary production. Several studies evaluated the combined effects of LUCC and climate change on vegetation primary production in urban areas [6; 10]. The effect of meteorological variations on vegetation primary production may offset the changes in primary production that are associated with LUCC [6; 27]. Quantifying the exclusive contributions of LUCC has been insufficiently investigated in previous studies. Third, previous analyses of the impacts of LUCC on vegetation primary production were usually based on data collected every three or five years or over a longer period, ignoring the LUCC process during the study period [7; 8]. However, as land covers change rapidly in the urban areas, a certain amount of pixels experienced more than one change during the study period. Exploring the impacts of yearly LUCC on GPP may provide insights into the interactions between urbanization and the carbon cycle. In this study, we are particularly interested in the impacts of LUCC on vegetation GPP in the urban area. GPP, the photosynthetic uptake of carbon by plants, is a key variable in the global carbon cycle, as well as an indicator of vegetation health and dynamics. The objective of this study is to quantify the impacts of the year-to-year LUCC on the dynamics of vegetation GPP in a typical densely urbanized Chinese city, Wuhan. As the coarse spatial resolution of MODIS data may miss the detailed LUCC, GPP was estimated at 30-m spatial resolution using land cover maps derived from Landsat images, eight-day synthetic normalized difference vegetation index (NDVI) data generated by STARFM, and ground-based meteorological data. In order to minimize the effects of meteorological variations and emphasize the impacts of LUCC, GPP was estimated with the meteorological data of a baseline year for all of the studied years. The impacts of LUCC on vegetation GPP in Wuhan were quantified during 2000-2013, because Wuhan has experienced rapid urban expansion since 2000, and also, both Landsat and MODIS images were available during the studied period.
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 3 Limitation of Lutein Sequestration in Algal Oil Droplets
### Lutein Degradation without Proper Sequestration  
Without proper sequestration, lutein is trapped on chloroplast membranes and increases the possibility of degradation under the stress of UV-light, heat, ROS and by the catalyzation of carotenoid cleavage dioxygenases (CCDs). The two hydroxyl-groups on the chemical structure of lutein trap it on the surface, or inside, the chloroplast's inner membrane. A similar phenomenon has been investigated in vitro by exploring the effect of carotenoid incorporation into liposomes [71, 72]. Lutein can be incorporated tightly with phospholipid membranes as its polar hydroxyl-groups intercalates into the membrane structure and interact with the polar sides and the membranes' aqueous environment. The rigidity of the phospholipid membrane is positively correlated with the amount of lutein incorporated.  
Therefore, without the assembly of lutein into photosynthetic proteins or the trafficking of lutein into the lipid droplet, lutein could be concentrated in the inner envelope membrane with the thylakoid membrane. This leads to feedback limitation on lutein biosynthesis and the degradation of lutein via photo-oxidation and carotenoid cleavage dioxygenase (CCDs) catalyzation [45, 47].  
Carotenoid cleavage dioxygenases (CCDs) constitute a widespread enzyme family consisting of an activation center on top of the structure and a Fe\({}^{2+}\) ion, coordinated with four histidine residues [73, 74]. The Fe\({}^{2+}\) ion activates oxygen, which is involved with the reaction center. The activated oxygen cleaves the carbon-carbon double bond on the polyene chain of the carotenoid's structure. The CCDs protein is located at the envelope and plastoglobuli membrane of arabidopsis [75, 76, 77]. It is also located on the chromoplast membrane of watermelon, tomato, carrot, caulflower, papaya, pepper and citrus [78, 79].  
Although the effect of CCD expression on carotenoid accumulation was not evident in microalgae species, proteomics data of _C. reinhardtii_ indicated that a protein located in eyespot was homologous to the CCDs of _Synechocystis_ spp. [80]. Thus, CCDs may still degrade different kinds of carotenoids, including lutein, in microalgal species. Since lutein is vulnerable under ROS stress and easily degraded by CCDs without LHC storage, it is critical to better preserve lutein in this condition. The esterification of lutein could address this problem. However, lutein cannot be esterified in most algal strains, as discussed below.
## 4 EAT Segmentation Methods via Pericardium Delineation
### Deep Learning-Based Methods  
Deep learning removes the need to select appropriate features for training, and with CNN-based architectures, the network can utilize very complex high- and low-level features to segment the pericardium. One of the first applications of CNNs to EAT segmentation is proposed in [PERSON] et al. (2016). A summary of their method is shown in Figure 10. The authors developed a multi-task framework consisting of two convolutional neural networks. The first CNN performs three separate tasks. The first task is classifying whether or not the given slice contains the heart, which enables stopping the process early and saves performance costs. The second task segments the union of epicardial and paracardial structures, while the third task segments epicardial and paracardial areas separately. The epicardial mask from the first network is used to re-sample the input CT scan, which is then fed into the second CNN, which delineates the pericardium line given a CT scan in radial coordinates. Total EAT volume is obtained by thresholding the segmented epicardial area to the range of adipose tissue. The authors report a Dice coefficient of 0.81 for EAT segmentation, and a time of under 20 s to process one CT scan. Their method is validated on a cohort of 250 patients. The authors also report an inter-observer Dice coefficient of 0.89 on 30 randomly selected patients. This indicates that their EAT segmentation does not perform within the limits of inter-observer variability, however, it is an order of magnitude faster.  
More recent approaches rely on comparatively simpler architectures. Instead of using multi-task learning, the methods most commonly rely on training a U-Net-based model to segment the pericardium from a slightly processed input image. For instance, [PERSON] et al. (2017) uses a U-Net-based structure by connecting two cascading U-Nets. A summary of their method is presented in Figure 11. Their first network detects the pericardium from an input CT slice. The output pericardium segmentation is coarse and noisy, so the authors process the output in a morphological processing layer using morphological reconstruction, opening, and closing. This segmented shape is then used as a mask for the inputs to the second U-Net network that performs EAT segmentation. The authors report a mean Dice coefficient of 0.91 on 10 patients taken from [PERSON] et al. (2016).  
Even simpler similar approaches exist where the final EAT segmentation is obtained by thresholding the pericardium region and not using a deep learning model, such as [PERSON] et al. [44]. The input to the network has two channels, the original CT slice and another channel where each pixel has the same value of the slice depth of that slice. This gives the convolutional neural network access to some depth information of each slice, thus improving performance without requiring a fully 3D neural network. The authors report a mean Dice coefficient of 0.86 using 2-fold cross-validation on 20 patients from [PERSON] et al. [20].
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 3 Proposed Solution
### Proposed Encoded Component Hash Trie (ECHT) Data Structure
#### 3.1.2 ECHT Operations
By using BCM, we avoid returning a wrong reply due to an incorrect partial component matching. Also, iterating over components while using hashing is faster than iterating over characters, as shown in Section 5. In BCM, two variables (bcm_key, bcm_value) are initialized to nil, and then the entry_key is divided into components, and the trie is traversed based on these components. While walking the path, if the traversed node has a valid non-empty value, this value will be stored as bcm_value, and the node key will be appended to the bcm_key. This means that the BCM pair will be updated as needed while traversing the path towards the last component. Both the BCM key and value will be returned as a reply for the BCM lookup. Hence, BCM works the same way as exact matching, with two differences: (1) it stores the last best match while traversing the tree, and (2) it returns two parts of a reply instead of one. Therefore, there may be multiple cases for the BCM reply to the caller: (1) when no component is matched, a (nil, nil) is returned; (2) when all components are matched, a (bcm_key, bcm_value) is returned, which is equivalent to (entry_key, entry_value); and (3) when few components are matched, a (bcm_key, bcm_value) is returned. The ECHT lookup operation process is shown in Figure 3.
# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 5 Discussions
The experimental optimization results showed that the ideal process parameters were a pressure-holding switch position of 20.99 mm, a melt endpoint of 96.19 mm, an injection endpoint of 21.75 mm, a holding pressure of 39.8 bar, a nozzle temperature of 224.7 \({}^{\circ}\)C, and a melt temperature of 234.8 \({}^{\circ}\)C. When applied in production trials, these settings yielded products whose weight and thickness consistently fell within the target specification ranges of 66-70 g and 3-5 mm, respectively. The experimental data demonstrated minimal variation in process conditions such as production time and temperature, providing a stable operating environment that was essential for the accuracy of the predictive model. These findings validate the robustness of the BPNN-PSO framework in guiding practical process parameter selection. Performance evaluation based on RMSE and MAE metrics affirmed the model's reliability. Thickness prediction achieved an RMSE of 0.8229 and an MAE of 0.6523, while weight prediction yielded an RMSE of 1.5123 and an MAE of 1.2070. Cross-validation results were closely aligned with test set results, indicating that the model generalized well across unseen data and did not suffer from overfitting. Although the prediction performance for thickness was slightly better than for weight, both predictions remained within acceptable error margins, ensuring that product quality objectives were met. The relatively small difference between RMSE and MAE across both targets indicated a uniform distribution of prediction errors, further supporting the model's stability. Overall, this research demonstrates that integrating BPNN and PSO provides an effective solution for optimizing injection molding processes, particularly when experimental resources are limited. Combining systematic experimental design, stable production data, and advanced machine learning optimization offers a practical and scalable approach to enhancing manufacturing quality and efficiency. Moreover, the proposed system shows strong potential for broader application across various plastic molding products, especially when rapid setup and minimal trial production are critical. Future studies could enhance this framework by incorporating multi-objective optimization, exploring nonlinear models, or implementing real-time adaptive learning to advance innovative manufacturing initiatives further.
# Retrofitting a Building's Envelope: Sustainability Performance of ETICS with ICB or EPS
## 5 Results
### E Performance of External Thermal Insulation Composite System (ETICS) with ICB
#### 5.1.3 Economic Costs and Benefits of ETICS with ICB  
The results achieved for the economic balance regarding the use of ETICS with ICB show that the NPV of the C2C cost of the external wall alternatives (Figure 5) is proportional to the thickness of ICB applied at stages A1-A3, A4, and A5 varying from 31% to 33% and at the end-of-life with 1%. The NPV of the maintenance, repair and replacement operations (stages B2-B4) is similar for all alternatives and represents between 52% and 55% for the reference wall and 41% for the remaining solutions. The remaining contribution of the NPV is about 46% for W1 and 43% for W8 and, according to their heating and cooling needs, and represents 24% to 26% for the remaining solutions.  
With the results shown in Figure 5, one can conclude that there are no wall alternatives with ETICS with ICB that can provide any \"economic savings\" in comparison to the reference solutions. However, the results shown were obtained considering a consumption of energy during the B6 sub-stage to satisfy only 10% of the needs for heating and cooling (a realistic value for Portugal in the present). These \"economic savings\" only arise, as shown in Figure 6, if higher values are considered to simulate future expectable scenarios for dwellings or multi-familiar residential buildings. In this figure, it is possible to conclude that, considering a consumption of energy during the B6 substage to fulfil 35% or more of the heating and cooling needs, the alternatives with 9 cm of ICB in ETICS (W7 in the single-leaf wall group) have a better performance than the reference walls. In the cavity wall group, this value has to be increased to 50% to allow another alternative with 9 cm of ICB in ETICS (W14) to be cheaper than the reference walls.  
Figure 4: C2C consumption of nonrenewable primary energy (PE-NRe, in MJ, without energy for heating and cooling)/m\({}^{2}\) of the external wall alternatives with ICB.