esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Design of the Goaly3 Multi-Mirror Telescope System with a Wide Field of View
## 1 Introduction  
The quest for finer angular resolution in astronomy will inevitably lead to larger apertures, which has pushed the limits of size and weight for monolithic primary mirrors. Large primary mirrors are increasingly difficult to fabricate and mount; moreover, they may even be impossible to launch for space-based systems. In response, technologies such as segmented and sparse aperture system are developed as the next generation of telescopes [1, 2, 3, 4]. One type of sparse aperture systems is the multiple-mirror telescope (MMT) [5], such as the proposed Giant Magellan Telescope (GMT), in which the primary mirror consists of seven 8.4 m segments and the secondary mirror is segmented to match the primary mirror with seven 1.1 m segments [6]. The other is the multi-telescope telescope (MTT) system [7, 8] in which all the sub-mirrors own their independent secondary mirrors. Compared with the MTT system, the MMT system does not need an expensive and complicated beam combiner to bring the light to a common focus [9]. However, the MMT system usually has a relatively small field of view (FOV) due to the co-phasing requirement of the sub-mirrors.  
The MIT Adaptive Reconnaissance Golay3 Optical Satellite (ARGOS) demonstrated the practicality of a modular architecture for space-based optical systems for the first time with a FOV of 3-10 arcmins. In 2008, [PERSON] et al. presented a MMT prototype [10] in which the spherical primary mirror consisted of eight annular sub-mirrors and one central mirror.  
The insertion of a four-element spherical corrector group before the image plane of the prototype achieves a FOV of \(0.152^{\circ}\). Compared with conventional spherical and aspheric surfaces (secondary surfaces), free-form surfaces offer higher design freedom, allowing for better optical performance and larger field of view with fewer optical surfaces. However, because free-form surfaces are rotationally asymmetric, the assembly, detection and control of optical systems with free-form surfaces are more complex [11].  
In this paper, we designed a Golay3 MMT with a wide field of view. The configuration of the Golay3 MMT is based on a Cassegrain system. The FOV of the Golay3 MMT is enlarged to \(1.5^{\circ}\) by adding a three-element aspheric corrector group in the converging light cone. In Section 2.1, we deduce the relationship between the fill factor \(F\) of the Golay3 MMT and the angular resolution. In Sections 2.2 and 2.3, an initial configuration and a corrector group of the Golay3 MMT are designed. In Section 3, we evaluate the performance of the Golay3 MMT. In Section 4, we conduct a tolerance analysis for the Golay3 MMT. Finally, we conclude the paper in Section 5.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 2 The Concepts and Ideas Behind Courcelle's Theorem
### Decomposing the Problems: Tree Decompositions  
Many difficult graph problems become very simple when we restrict attention to trees. The 3-colorability problem on trees does not even deserve to be called a \"problem\": for all input trees the answer is of course always \"sure, all trees are even 2-colorable, so this one is 3-colorable\". Even for the vertex cover problem on trees, our student might also have taken a completely different approach in order to find optimal vertex covers (based on kernelization, even though she probably would not know this): find a node whose children are all leaves, add it to the vertex cover and then remove it and its children; repeat until the tree has no more edges. No matter what logic we use to describe problems, algorithmic metatheorems will not be very useful when they can only be applied to trees.  
How can we relax the requirement that the inputs must be trees? The requirement enabled us to merge solutions for the subtrees easily because these subtrees only interface with the rest of the graph at a single vertex: their roots. All information concerning optimal solutions \"below\" the roots is irrelevant, it is only the root vertex that is connected to the rest. The idea behind tree decompositions is simply to replace the single vertex over which all information must flow by a small fixed number of vertices. Naturally, the difficulty now lies in defining how several vertices can \"block information from fleeing from a subgraph to the larger graph\" and how they do this in a \"tree-like\" fashion.  
For this, we use a game.
## 4 Results and Discussion
Furthermore, the construction and functionality of PLC equipment and ma-chines are enhanced. A supervised machine-learning mechanism was proposed. This is a general supervised learning process used to generate a global model that corresponds to the input objects and expected output, including the flux signal, color vision, and motor-moving trajectory path. Optimized PLC production line transfer processes have been previously solved, and new inference methods have been sought. These methods can provide a better path for the smart factory of a PLC production line. This is a process of the machine learning method used to solve current PLC production line problems. Figure 14 shows the face recognition results constructed by applying the neural theory. The Raspberry PI system is detected by fog computing, which outputs a recognition signal. We achieved synchronous uploading of the data to the cloud computing controller for the calculation and monitoring functions  
Figure 12: IoT PLC smart-factory-monitoring field platform: (**a**) physical layer sensing numerical platform, (**b**) detected signal upload to cloud platform. of factory population monitoring. In this study, the intelligent robot arm integrates the neural network theory and FOPID controller to evaluate the operating efficiency of the smart factory in terms of magnetic sensor signals, product image recognition, and motor signal control, thereby reducing the total cost by at least 50% and measuring the operation duration of each motor, as shown in Figure 15.  
In this study, the data were collected in the laboratory for more than half a year for numerical analysis and function adjustment. New research results in IoT and image transmission have been realized, which can smoothly achieve higher production efficiency and high quality in a shorter time. At the same time, the installation of the device has a multifunctional and responsive man-machine-coordinated production system. Figure 15 shows the results of these studies. The lead-time for manufacturing was reduced by 50%. In addition, the product can be visualized, and the elimination of defects helps to reduce defective products by 73% in the production of workpieces, which reduces the total cost of factory work, and enables a faster CNN method to improve the production delivery, respectively. These improvements have helped reduce the production area and inventory  
Figure 14: Robot motor control performance by using FOPID+NN algorithm: (**a**) motor operation duration, (**b**) operation efficiency. Figure 13: IoT PLC smart-factory-monitoring field in acoustic and vibration with Fog computing platform: (**a**) acoustic signal and (**b**) vibration signal. by at least 50%. This research has some relative benefits: for example, it can reduce the material storage space of upstream manufacturers. It can also use the excess space in the factory for service, business, and production line research and development.
###### Abstract
In this paper, a GS-based CD compensation scheme at the transceiver under nonlinear condition was studied and compared. The paper is organized as follows. The principle of the GS-based CD compensation scheme at the transceiver is detailed in Section 2. In Section 3, the simulation setup and the DSP flow are introduced. In Section 4, we demonstrate the simulation results, and the comparison of the joint LUT and GS-based CD compensation scheme between transmitter and receiver is discussed. Finally, we conclude this paper.
## Materials and Methods
### Quantitative Real-Time PCR  
The quantitative real-time PCR (qRT-PCR) was used to monitor the transcription of six key enzyme genes involved in solanesol biosynthesis. RNA was extracted from samples of tobacco leaves collected at five stages (0, 3, 6, 9, and 12 DAT) using TRIzol(r) reagent (Takara, Shiga, Japan). RNA was reverse-transcribed into complementary DNA (cDNA) using a PrimeScript(r) RT reagent Kit (Takara). Reactions were run in triplicate on an Applied Biosystems 7500 Real Time PCR system (Thermo Fisher Scientific), in a final volume of 25 \(\upmu\)L, containing 12.5 \(\upmu\)L SYBR(r) Premix Ex TaqMan II (2\(\times\)), 1.0 \(\upmu\)L forward and reverse primer (10 \(\upmu\)M), 2.0 \(\upmu\)L cDNA (50 ng/\(\upmu\)L), and 8.5 \(\upmu\)L ddH\({}_{2}\)O. Two-step PCR was performed according to the manufacturer's procedure, with an initial denaturation step at 95 \({}^{\circ}\)C for 30 s, followed by 40 cycles of 95 \({}^{\circ}\)C for 5 s and 60 \({}^{\circ}\)C for 30 s. The actin gene was used as the internal control. Primers used for qRT-PCR are listed in Table 1.
# Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances
## 4 Experiments and Analysis
### Experiment for Validating Analytic Continual Learning-Based Method in Novelty Detection  
In the novelty detection experiment with continual learning settings, appliance types in the streaming data are divided in the same way as in Section 4.4. At the beginning of the experiment, appliances in the pretraining dataset \(D_{0}\) are defined as the original appliances, and \(D_{1}\) to \(D_{16}\) are the new appliances. As continual learning progresses, \(D_{1}\) to \(D_{15}\) are sequentially transformed from new to original appliances. To ensure the presence of new appliances, the \(D_{16}\) dataset is retained. Given the absence of continual learning-based  
Figure 3: The average performance of continual learning-based methods in load identification: (**a**) ACC and (**b**) F1-macro.  
novelty detection methods in NILM, the continual learning approaches from Section 4.4 are applied to the proposed novelty detection branch. Unlike the load identification branch, the novelty detection branch maintains a constant model capacity as new appliances arrive, updating the parameters to map their features into the hypersphere. As a result, novelty detection faces greater challenges in continual learning. The results are shown in Figure 4. The proposed method performs more stably during continual learning and outperforms the existing methods, with its accuracy consistently above 0.7. The new appliance types include resistive appliances with operating modes similar to the original appliances (e.g., ovens, shoe warmers, kettles) and appliances with distinct waveform patterns (e.g., water pumps, desoldering machines, shredders). These appliances activate the novelty detection branch to map inputs into the hypersphere, causing a drop in the F1-macro in later rounds due to misdetecting fewer new appliances than the original ones. In addition, the baseline method without continual learning suffers from catastrophic forgetting, reducing its performance to near-random guessing. TSR still outperforms CIL in novelty detection due to typical waveform selection, but its accuracy drops to 0.6 at the end.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 5 Conclusions  
In this article, we provide a YOLOv7+GNN-based technique for automatic ship detection from the High-Resolution Satellite Image Dataset (HRSID), which is a high-resolution image of a synthetic aperture radar. This algorithm classifies ships with greater than 90% accuracy. The learning rate of the framework is as low as 0.001, and a batch size of 16 can produce the optimal results. For ship detection, the researchers used the Adam optimizer and YOLOv7 with GNN. The dataset's picture count can be enhanced to include a wider range of ship types in order to produce better results in the future.  
K.P.: data preprocessing, methodology investigation, writing--original draft, C.B.: writing--review and editing, methodology, project administration, P.L.M.: review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/chaozhong2010/HRSID](https://github.com/chaozhong2010/HRSID)]([https://github.com/chaozhong2010/HRSID](https://github.com/chaozhong2010/HRSID)) (accessed on 26 September 2022).  
We are thankful to [PERSON] for his insightful and helpful advice during the planning and writing of this research paper.  
The authors declare no conflict of interest.
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
## 2 Results and Discussion
### Differences in the Volatile Profiles among the Cultivars
#### 2.4.3 The Volatile Profile of Stem End (SE)  
The total number of volatile compounds in SE was lesser than the other tissues, and the aromatic proportion of SE from \"Tasti-Lee\", \"Tygress\", \"FL47\", and \"Cherokee Purple\" were lower than other tissues by 22.23%, 15.72%, 20.37%, and 23.27%, respectively, which suggested that SE provided a less powerful aroma than the other parts (Figure 2). Besides, higher levels of fatty series were shown in SE from \"Tygress\", \"Tasti-Lee\", and \"FL47\", with the existence of that which were hexanal, heptanal, and octanal. [PERSON] et al. [36] found that the fatty series in grapes were contributed mainly by the octanal one, which was consistent with our results.  
However, the concentrations of cis-3-hexenal and benzaldehyde were lesser in the SE than the other tissues. Additionally, geranyl acetone, 1-nitro-pentane, and 1-nitro-2-phenylethane, which conferred a \"sweet\", \"fruity\", \"flowery\", and \"spicy\" odor to tomato fruits, were not identified in SE. 1-nitro-2-phenylethane was a phenylalanine-derived volatile, and an important contributor to the tomato aroma as well. In tomatoes, the first and rate-limiting step was performed by aromatic amino acid decarboxylalases (AADCs), encoded by _LeAADCIA_, _LeAADC1B_, and _LeAADC2_[37]. These enzymes converted phenylalanine to phenethylamine, then phenethylamine was converted to phenylacetaldehyde by an as-yet-unidentified amine oxidase or to 1-nitro-2-phenethane by an uncharacterized series of reactions [1]. The first and rate-limiting step was regulated at the transcriptional level [38], which suggests that the reason why 1-nitro-2-phenylethane was not identified in SE might be due to lower transcriptional levels of _LeAADC1A_, _LeAADC1B_, and _LeAADC2_.
# Lactoferrin Production: A Systematic Review of the Latest Analytical Methods
###### Abstract  
Background: Lactoferrin is a major functional protein involved in maintaining human health, which possesses antioxidant, anti-inflammatory, antibacterial, and antiviral properties. Therefore, it can be used to support the treatment of viral and bacterial diseases, as well as in cancer prevention. Lactoferrin-manufacturing processes may compromise its protein structure and function, so it is necessary to establish reliable analytical methods for production efficiency and quality control purposes. This paper reviews the lactoferrin production processes, summarising the methods using various matrices (milk, milk powder, infant formula, whey, bovine lactoferrin lyophilised powder, yoghurt, colostrum, and human milk), the most popular purification methods, and sample preparation. Material and methods: The Medline and Embase databases were searched using the following phrases: \"lactoferrin\" and \"purification\" or \"isolation\" or \"extraction\" or \"separation\". The search was limited to recent studies from the last five years published in English up until 12 March 2025. Of the 573 articles identified, 17 were reviewed. Results: Lactoferrin purification and determination methods depend on the matrix used. The latest research focuses on improving parameters of lactoferrin determination, shortening time, improving efficiency or limiting costs, and even reducing toxicity by changing the reagents. The method of separating lactoferrin using magnetic beads or nanoparticles has been developed, as well as the determination parameters using high-performance liquid chromatography (HPLC). Conclusions: The current lactoferrin production techniques are characterised by increased efficiency and quality, but they require standardisation of the purification process depending on the matrix. The latest Lf determination methods are highly precise, and most of them produce high-quality Lf. This allows to introduce on the market a higher quality product, which can significantly improve standard approaches.  
lacetoferrin; instrumental analysis; determination of lactoferrin; purification; dairy products; infant formula; whey +
Footnote †: journal: Applied Sciences
## 5 Hypothesis Testing and Discussion
### Testing Hypothesis H2  
**Proof of Hypothesis 2 (H2).** Once a certain level of RES production is exceeded, price volatility increases and outliers become more frequent. \(\square\)  
A year-on-year analysis of the daily price spread (the difference between the maximum and minimum price) showed a significant break in 2022, when all the countries studied experienced a sharp increase in both prices and their daily fluctuations. While before 2022, price spreads were in the range of around 100-180 \(\upcstrom\)/MWh, during the crisis year 2022, they rose to 900-4000 \(\upcstrom\)/MWh. In Germany and Estonia, these extremes were most pronounced, while in countries with lower RES integration, the fluctuations remained more moderate.  
These results show that price volatility increases not only in the absolute sense but also in the frequency of occurrence of outliers, thus empirically confirming the validity of Hypothesis H2.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 3 Simulations and Experiments
### Simulation Verification
#### 3.1.3 Comparative Simulation Validation  
Under identical simulation conditions, a comparative analysis of the modified NSGA-II algorithm with PSO and ACO algorithms was conducted, focusing on convergence frequency, total path length, total turning angle, and runtime. The results of this comparison are presented in Figure 15.  
Under identical simulation conditions, the performance of the improved NSGA-II algorithm was compared with the PSO and ACO algorithms in terms of convergence times, total path length, total turning angle, and runtime. The results are presented in Figure 15.  
Figure 14: Ablation experiment results.  
Figure 15: Comparative analysis of optimization results: improved NSGA-II versus PSO and ACO algorithms.  
To validate the comparative results, we conducted statistical significance testing using ANOVA (\(p<0.001\)), Tukey HSD, and Kruskal-Wallis tests where appropriate. The analysis showed that the improved NSGA-II algorithm significantly outperforms the PSO and ACO algorithms across all evaluated metrics. The improved NSGA-II algorithm reduced convergence times by 127 iterations compared to PSO, and 148 iterations compared to ACO (Kruskal-Wallis test, \(p<0.001\)). The improved NSGA-II algorithm achieved a 1105.16 mm reduction in total path length compared to PSO, and a 438.07 mm reduction compared to ACO (ANOVA, \(p<0.001\)). The improved NSGA-II algorithm decreased the total turning angle by 420.65\({}^{\circ}\) compared to PSO, and 187.52\({}^{\circ}\) compared to ACO (ANOVA, \(p<0.001\)). The runtime of the improved NSGA-II algorithm was reduced by 49.64% compared to PSO, and by 61.54% compared to ACO (Tukey HSD, \(p<0.001\)).  
Therefore, in terms of convergence times, running time, and trajectory results, the improved NSGA-II algorithm demonstrates superior performance, providing a basis for further application in boom-type roadheader cutting trajectory planning.
# Imprint of Mesoscale Eddies on Air-Sea Interaction in the Tropical Atlantic Ocean
On average, TAO eddies persist for several months and can cross the Atlantic basin from east to west [1; 7]. As shown by ARGO data, most of these eddies are located subsurface, with their maximum temperature and salinity anomalies located below the pycnocline [8; 13]. A unique feature of these subsurface eddies is their inverse SST signature compared to surface intensified eddies, with negative SST anomalies for subsurface AEs and positive for CEs [13; 14; 31]. Referred to as \"Inverse Eddy SST Anomalies\" or \"Abnormal Mesoscale Eddies\", they make up approximately one third of the global oceanic mesoscale eddy population [32]. The abnormal SST anomalies in mesoscale eddies have been linked to factors such as vertical mixing, particularly during ocean re-stratification in spring and early summer [13; 33], or the surrounding large-scale SST distribution [13; 34; 35]. Indeed, the SST signature/anomaly of an eddy may change as it moves through different water masses and during its decay, through the absorption of surrounding water masses [35]. Thus, mesoscale eddies with inverse SST anomalies may impact ocean-atmosphere interactions differently from eddies with the same polarity but a normal SST signature, particularly at the seasonal scale. The aim of this study is to investigate the impact of these cold-core or warm-core eddies, regardless of their cyclonic or anticyclonic polarity, on latent heat fluxes (LHF), sensible heat fluxes (SHF), ocean infrared fluxes (IRF) at the ocean-atmosphere interface and their relationship with PR anomalies in the TAO region. The paper first provides a detailed presentation of the dataset and methodology used. Then, it analyzes the SST anomalies across mesoscale eddies in the TAO to determine the presence of warm-core or cold-core eddies. This preliminary analysis serves as the foundation for the subsequent examination of the eddies' influence on heat fluxes and PR anomalies.
## 4 Review: Slant Links Rain Fade Models
### Limitations  
The model underestimates the computed values down to outage times of 0.001% of a mean year. At a low elevation angle, the incoming signal beam intercepts more than one rain cell [31]. The model is suitable only within the frequency range of 4-6 GHz, and which is validated by the authors; more analysis is required for higher frequencies. However, the model does not consider rainfall distributed over the cell in a non-uniform pattern, as in a real case, non-uniformly distributed rainfall does not normally exist.