esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GC-MS Analysis of Wild Strawberry Fatty Acids in Waste and SCO\({}_{2}\) Extract  
Prior to GC-MS analysis, the fatty acids in wild strawberry waste and WSWSCO\({}_{2}\) extract were converted to the corresponding methyl esters. In total, 3 g of crushed wild strawberry waste and 500 mg of WSWSCO\({}_{2}\) extract were added to 2 mL of dichloromethane, 15 mL of methanol, 2 drops of concentrated sulfuric acid, and 30 mg of methyl pentadecanoate (Sigma-Aldrich, Milan, Italy), which was used as internal standard for quantification purpose. Waste and extract samples were heated under reflux for 24 h and 1.5 h, respectively. The sample was then cooled on ice and extracted using 2 mL of diethyl ether. Phase separation between methanol and diethyl ether was achieved by adding 10 mL of water saturated with NaCl. Finally, the diethyl ether layer was recovered and analyzed using GC-MS.  
The GC system was an Agilent 7820A equipped with an autosampler and coupled to an Agilent 5977B MS (Agilent Technologies, Santa Clara, CA, USA). Chromatographic separation was achieved on an Agilent 88% -(cyanopropyl)aryl-polysiloxane (HP-88) column (100 m, 0.25 mm i.d., 0.2 \(\upmu\)m), setting the following oven temperature gradient: 0-5 min, 120 \({}^{\circ}\)C, then to 240 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min; isocratic for 10 min. Total run time: 55 min. Helium was used as carrier gas, and the column flow was 1 mL/min. Heater inlet was set at 300 \({}^{\circ}\)C, and injection volume was 1 \(\upmu\)L. MS parameters were the following: MS source temperature: 230 \({}^{\circ}\)C; quadrupole temperature: 150 \({}^{\circ}\)C. Data were acquired in the mass range 40-650 Da. Fatty acids identification was performed by comparison of the retention times and mass spectra of analytes with reference standards and comparing the data from the NIST database (ver. 2014).
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results  
The results of 14 days with scintillation used in the PPP improvement experiment are presented. Since a large number of graphs were generated from all 14-days data, graphs on 13 September 2017 at SAOOP station are shown as an example for visualization in Section 3.1 and the results on all the days were statistically tabulated and analyzed, as presented in Section 3.2.
## 3 Materials and Methods
### Analytical Instrumentation  
The system was equipped with simultaneous 1 DGC and GC\(\times\)GC in one instrument which can conduct both techniques at the same time without any change of columns. The system consisted of a gas chromatograph (7890B Agilent Technology, Santa Clara, CA, USA) coupled with a high-resolution quadrupole time-of-flight mass spectrometer (QTOFMS) (mass resolution 20,000 and a mass accuracy specification of 3 ppm) (7250, Agilent Technology). In the presented research, an HP-5 MS (5% phenyl-95% dimethylpolysiloxane, 30 m \(\times\) 250 \(\upmu\)m, 0.25 \(\upmu\)m film) was used as the \({}^{1}\)D column, and a DB-17 MS column (50% phenyl-50% dimethylpolysiloxane, 1.2 m \(\times\) 180 \(\upmu\)m, 0.18 \(\upmu\)m film) was used as the \({}^{2}\)D column. The samples were introduced by a split/splitless injector (SSL) system with an autosampler (PAL RSI 120, CTC Technologies). This study employed a technique to combine GC\(\times\)GC and 1 DGC components into a single system with the column outlet of each component connected at the same three-port splitter prior to the QTOFMS detection. This allowed direct comparison of the GC\(\times\)GC and 1 DGC results and avoided use of a second detector, which is simple and effective.  
The 1 DGC and GC\(\times\)GC conditions were the same and were as listed below: the GC injector was kept at 250 \({}^{\circ}\)C in splitless mode; helium (99.999%) was used as the carrier gas at a constant flow of 1.2 mL/min; oven temperature was initially set at 50 \({}^{\circ}\)C (held for 3 min), then increased to 250 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min (held for 7 min), for a total run time of 60 min. The GC\(\times\)GC system was coupled with an SSM1800 solid state modulator (J&X Technologies, China). The GC\(\times\)GC conditions were as follows: The cold zone temperature of the SSM was set at \(-\)50 \({}^{\circ}\)C. The temperatures of the entry hot zone and exit hot zone were +30 and +120 \({}^{\circ}\)C offset relative to oven temperatures, respectively, with a cap temperature of 320 \({}^{\circ}\)C for both hot zones. The modulation period was 4 s.  
The MS conditions were as follows: The electron ionization and the ion source and transfer line temperatures were set at 70 eV, 250 \({}^{\circ}\)C, and 280 \({}^{\circ}\)C, respectively. The MS scan rate was 50 Hz. The mass range was set to 50-500 \(m/z\) in full-scan acquisition mode.
# EEG-Based Attention Classification for Enhanced Learning Experience
## 2 Materials and Methods
### Implementing Real-Time Neurofeedback-Based Learning Using the EAC Model  
The EAC model was trained to classify attention states (HA vs. LA) based on correct and incorrect responses during the recall phase. The process involved preprocessing, wavelet feature extraction, constructing correlation matrices, applying thresholding to create binary networks, and classifying attention states. The system continuously monitored EEG signals in real-time to assess and adapt to the user's attention levels during the study.  
The system continuously monitored the user's EEG signals throughout the study sessions to assess attention levels. As shown in Figure 6, the system dynamically detected the user's attention states. Based on this real-time analysis, it adjusted the learning process by increasing the repetition frequency and reducing the flashcard difficulty when attention levels decreased in order to refocus the user and optimize learning engagement.  
Conversely, when high concentration levels were identified, the system raised the difficulty to maintain an optimal learning challenge. This adaptive approach aimed to keep students engaged at an optimal cognitive level, thereby improving learning efficiency and reducing stress.  
Mathematically, to adjust the difficulty of the quiz based on the concentration levels of the students, we define a binary variable \(C\), where \(C=1\) indicates high attention (HA) and \(C=0\) indicates low attention (LA). The performance of the model was evaluated on the basis of classification accuracy, enabling an adaptive learning system tailored to individual attention states.  
Let \(D\) represent the difficulty level of the quiz, ranging from 1 to 10. The adjustment function is defined as follows:  
\[D_{\text{new}}=D+\Delta D,\quad\Delta D=k(C-C_{\text{threshold}})\]  
where \(k\) is a scaling factor, and \(C_{\text{threshold}}=0.5\). When \(C>C_{\text{threshold}}\), the difficulty \(D\) increases, and when \(C<C_{\text{threshold}}\), it decreases. This formulation allows the system to iteratively adjust the quiz difficulty based on real-time attention levels, thereby optimizing the learning challenge dynamically.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.3.1 Lower amd Upper Failure Rates  
Figure 15 indicates the availability depending on the design states with different failure rates: lower, mean, and upper failure rates. As the failure rate was increased from lower to upper, the availability was decreased. In the case of lower and mean failure rates, the availability was slightly decreased with the design stages. In contrast, the availability was significantly reduced in the case of upper failure rate. When the design stage was changed from Stage I (PFD) to Stage II (P&ID), the availability was dramatically decreased in the case of upper failure rate. This indicated that the instrument devices gave a critical impact on the availability. The availabilities are 99.506% (lower) and 97.819% (upper) at Stage III. The upper means that the result is derived using upper failure rate in Table 2, and the lower is the reverse. This meant that the most optimistic availability is 99.506% and the most pessimistic availability is 97.819%.
## 4 Numerical Modeling  
Numerical modeling for the R block using Plaxis 3D software to determine the horizontal and vertical deformations of the raft was performed. Modeling was designed for the 3.8 m thick raft within axes 12 to 19 in the Y-direction and axes C to K in X-direction. Figure 14 shows the loading system on a reinforced concrete square-section raft podium with a side length of 72.9 m. The load transmitted from the columns at floor level B4 was represented by a point load (indicated in the figure with a blue circle), and from the walls of the core of the building as a linearly distributed load along the axis of the walls of the core (indicated as a blue line).  
Figure 14: Numerical modeling in Plaxis 3D: (**a**) loading scheme; (**b**) volumetric strains.  
Figure 13: Zones subjected to tension strains.  
The columns had a different loading area and, accordingly, a different load, and therefore in Figure 14b they have a different size of the arrow designation, depending on the magnitude of the applied load. The mesh was designed by software, and total dead and live load were applied at the final stage. The weather conditions were not considered in this simulation. The design soil parameters were taken from Table 1.  
Settlement in vertical direction at the raft surface level indicated the maximum displacement at the center of the core and was equal to 38 mm (Figure 15). The smallest settlement of the column in the circle surrounding the building core was 14 mm at a distance of 26 m from the center of the core. The differential settlement was 24 mm in the horizontal plane.  
Horizontal displacement at the surface level of the raft in block R ranges from 1 mm to \(-\)1 mm in the X-direction (Figure 16). The sign of displacement alternates from tension to compression and corresponds to 1 mm deformation per 36 m, which is half of the building width. Thus, according to the results of Plaxis 3D modeling, the largest difference in the vertical displacement was 24 mm, and in the horizontal plane 1 mm within a distance from the center of the core up to 30 m. When determining the relative deformation in the horizontal plane, such a displacement is negligibly small and is less than 1 \(\mu\epsilon\). The distribution of deformations in the X- and Y-direction is presented gradually with smooth transitions and does not display point loads from columns.  
Figure 15: Settlement in vertical Z-direction: (**a**) view of the surface of the raft; (**b**) volumetric model.
## 2 Cooperative SCARA System  
In this study, a cooperative robotic system composed of two identical SCARA robots was analyzed, and its specifications are listed in Table 1. Due to their identical configuration, both robots can work together on coordinated tasks, leveraging their high precision and speed in three-dimensional movements. By synchronizing their movements and sharing information, they optimize efficiency, increase load capacity by simultaneously handling objects, expand their work area, and improve precision in complex tasks. This collaboration facilitates the handling of large objects, enables the completion of intricate assemblies, and enhances the task distribution, thereby maximizing productivity. Figure 1 shows the SCARA system prototype, which was designed using SolidWorks (SolidWorks: [[https://www.solidworks.com/](https://www.solidworks.com/)]([https://www.solidworks.com/](https://www.solidworks.com/))) 2022 software.  
To determine the values of the mass, center of mass, and moment of inertia of each robot link, data obtained directly from SolidWorks 2022 were used, as presented in Table 1.  
Figure 1: SCARA cooperative system prototype.  
To implement correct trajectory planning and precise and efficient control in SCARA robots during cooperative tasks without collisions, it is important to perform kinematic and dynamic analyses of the robots, as this allows for the following:  
* Optimizing the movement of the robots: Through direct and inverse kinematic modeling, precise trajectories can be planned and executed, ensuring that the robots achieve their objectives accurately. In addition, a detailed model is essential for predicting and avoiding collisions in real time when multiple robots work together.
* Implementing advanced controllers: Dynamic system modeling facilitates the implementation of sophisticated controllers such as Nonlinear Model Predictive Controller (NLMPC) and sliding mode control (SMC), which are used to improve the system's stability and response to various internal and external disturbances.
* Performing precise simulations: Detailed models allow simulations to be carried out in different software, such as MatLab, Simulink, and others, to evaluate the system's performance in various scenarios. This helps to identify and correct potential problems, ensuring that the simulations closely resemble reality.
## 4 PDC Fuzzy Control
### Comparison of Implementing a PDC vs. PD Controller  
In order to highlight the advantages of using the proposed strategies, we simulated a classic PD controller and compared its performance with the one obtained with the PDC strategy. We can see from Figure 6 that the PD controller is not able to reach the desired voltages at different step inputs due to the fact that its gains can only be tuned for a specific region of operation. When the desired voltage is defined at a different region of operation, the proposed PDC controller uses the obtained firing value \(\mu_{ik}\) from the fuzzy strategy to modify the corresponding controller's gains. It is worth mentioning that this type of electromechanical systems has different regions of operation due to its intrinsic characteristics. These characteristics are given by the non-linear nature and complexity of the system due to its different components, such as dead zones stemming from the actuator utilized in the internal combustion engine's accelerator, voltage fluctuations, and non-linearities within the electric load and its associated electronic speed controller.
## 3 Results and Discussion
The volume content of the fibers measured by each method was \(\sim\)62%, and the weight of the added HNT was calculated by assuming that the HNT was dispersed in the epoxy resin. Figure 7 shows the graph comparing the theoretical strength obtained by calculation with the carbon fiber reinforced composite material and interlaminar fracture toughness energy added with HNT. The theoretically calculated values were \(\sim\)20 MPa higher than the test specimens without HNT. Figure 6: Positive resultant forces and moment. The difference in the strengthening effect due to the additive content was insufficient in the theoretical calculation. Theoretically, the calculation of the strength of a composite material does not seem to have any effect on the theoretically calculated value because it is dominated by fiber, which is a reinforcing material, rather than a known resin. However, the measured value of HNT was the highest at 5 wt % HNT, because the binding force at the interface increased with increasing amount of added HNT. Moreover, it is considered that the addition of HNT which is not calculated theoretically is due to the interfacial adhesion strengthening effect. The cohesion of the HNT in the carbon fiber reinforced composite material was investigated by SEM. Figure 8 shows the photograph of specimens taken at 5 wt % HNT having the highest tensile strength value, and at 8 wt %, the tensile strength decreases with increasing content. The photograph of the sample with 8 wt % HNT shows a considerable amount of agglomerated HNTs.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
###### Abstract
Although the overlay methods complement traditional network protocols, their performance is still unable to break through the bottleneck of the protocol stack due to the deployment at the application layer, and they cannot be directly applied to ICN. Moreover, there have been some attempts to implement multipath forwarding based on multiple addresses of terminal devices identified by globally unique identifiers in the ICN paradigm of stand-alone name resolution [18; 19]. However, these methods can only be applied in the scenario of multi-homed terminals. In this context, we propose a novel multipath transmission scheme applied in an IP-compatible ICN architecture. Our goal is to make full use of the characteristics and functions of ICN and the diversity of IP paths to improve the data transmission efficiency and robustness. The main contents of this paper are as follows:  
* We introduce the overall layout of our ICN protocol stack and propose a novel multipath transmission scheme in an IP-compatible ICN architecture. We regard the multipath transmission process as a kind of service and the multipath transmission service ID (MPSID) is assigned. Based on the MPSID, multiple parallel paths can be constructed between data sources and receivers by utilizing ICN routers as relay nodes. * We propose a path management mechanism to make full use of multipath resources. To reduce the overhead and avoid a poor selection, the initial path can be determined according to the historical transmission information. Besides, by measuring the congestion degree of the selected path during transmission, path switching can be performed to avoid bad paths. * We conduct a series of experiments to verify the performance of our multipath transmission scheme. The experimental results show that our proposed method has a significant improvement in terms of average throughput, average flow completion time, and average chunk completion time. The rest of this paper is organized as follows. We review the related research on multipath transmission mechanisms of IP networks and ICN networks, respectively, in Section 2. In Section 3, we describe the overall layout of our ICN protocol stack and the principle and process of ID-based multipath transmission scheme. In Section 4, the path management mechanism is described in detail. Then, we conduct simulation experiments of the multipath transmission scheme and discuss the experimental results in Section 5. Finally, we conclude our research and look forward to future work in Section 6.
## 3 Results and Discussion
### Process Development for Microalgal Biorefinery  
The microfluidic collision treatment presented in this study offers a unique advantage by simultaneously enhancing both biomass and astaxanthin productivity without the need for extreme environmental conditions. By mechanically simulating cyst germination and promoting cell proliferation under ambient conditions, this method avoids the trade-offs typically associated with nutrient starvation [24], high salinity [25], and intensity of light [23]. Moreover, this mechanostimulation strategy is particularly well-suited for integration into biorefinery processes aimed at producing multiple bioactive compounds from _H. lacustris_. Unlike harsh stress methods that compromise cell viability or require additional chemical inputs [27; 30], this approach enhances overall cell growth and biomass accumulation, providing a stronger foundation for extracting other valuable metabolites, such as polyunsaturated fatty acids and carotenoids [7; 58]. These attributes make microfluidic collision treatment a promising and environmentally friendly technique for sustainable microalgal biorefinement.  
While this study identified optimal flow rates and loop numbers for promoting cell growth, there remains significant potential for further refinement of microfluidic parameters, including cell load, cell alignment efficacy, T-junction design, and channel geometry [40]. Fine-tuning these aspects, along with optimizing photosynthetic culture conditions, such as light intensity, CO\({}_{2}\) concentration, and nutrient availability, could further improve astaxanthin productivity [59]. Scaling up the microfluidic collision process for industrial applications will require the development of high-throughput systems capable of efficiently processing large volumes of microalgal cultures [33; 60]. Furthermore, exploring the broader application of microfluidic collision treatment beyond _H. lacustris_ could open new opportunities. Given the versatility of this technique, it may be applicable to other microalgal species or even different types of organisms with similar cyst or spore-forming life cycles [61; 62; 63]. Expanding the scope of this research could establish microfluidic collision as a universal tool for enhancing the productivity of a wide range of bioactive compounds.
# Intelligent Space Object Detection Driven by Data from Space Objects
## 1 Introduction
To address this problem, [PERSON] et al. introduced a regression-based object detection algorithm called YOLO (You Only Look Once) [18]. It significantly improved the speed of detection while maintaining a certain level of accuracy. [PERSON] et al. integrated the regression of YOLO with the anchor box mechanism of Faster R-CNN, through which the SSD (Single Shot MultiBox Detector) algorithm was developed [19]. This algorithm maintained the high performance of YOLO in time efficiency while ensuring the satisfactory outcome of precise bounding box localization. [PERSON] et al. continued improving the YOLO algorithm, which promoted the development of YOLO9000 [20], YOLOv3, and subsequently YOLOv7 frameworks [20; 21; 22; 23]. Due to the high cost and difficulty in collecting space data, most space target datasets are generated through model simulation [24]. Also, the complexity of real imaging in space is ignored. In space, the targets are set against a cluttered background, with variations in altitude. Meanwhile, they are subject to the impact of various cosmic rays, solar storms, and thermal noise from electronic devices, among others [25]. In light of this, it is proposed in this study to simulate real space scenes through multi-image fusion [26]. To be specific, 49 real satellite 3D models, involving 500 real satellite images, were applied to create an original dataset of 17,942 images. Through semi-automatic labeling, five components of the satellite were annotated to obtain a total of 39,000 labels, making it one of the few large-scale datasets for space target component recognition. Furthermore, RepPoint was used to optimize the ELAN module of YOLOv7, which led to the ELAN-R module. Also, the YOLOv7-R model was proposed. This algorithm model, as verified on the dataset, outperforms other existing algorithm models in accuracy by at least 1.9%. The main contributions of this paper are summarized as follows:  
1. A space target component detection dataset of 17,942 images was created;
2. An image fusion method was adopted to simulate the real scenarios in space to the maximum extent;
3. Semi-automatic labeling was performed for dataset annotation, which reduced the cost of manual annotation significantly;
4. The YOLOv7-R model algorithm was proposed, whose better performance was verified.