esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 5 Spacecraft-Relative Navigation Model
### Relative Attitude Kinematics  
Since the attitude kinematics described by quaternions have a linear form, it is more convenient to calculate the spacecraft attitude with quaternions [38]. The quaternion is defined by the following:  
\[q\equiv\left[\begin{array}{c}\varrho\\ q_{4}\end{array}\right] \tag{47}\]  
with  
\[\varrho\equiv\left[q_{1},q_{2},q_{3}\right]^{T}=esin(\vartheta/2) \tag{48}\]  
\[q_{4}=\cos(\vartheta/2) \tag{49}\]  
where \(e\) and \(\vartheta\) denote the unit Euler axis and the rotation angle, respectively. Quaternions satisfy \(q^{T}q=\left\|q\right\|^{2}=1\). The relationship between quaternions and the attitude matrix is as follows:  
\[A(\mathbf{q})=\Xi^{T}(\mathbf{q})\psi(\mathbf{q}) \tag{50}\]  
with  
\[\Xi(\mathbf{q})\equiv\left[\begin{array}{c}q_{4}\mathbf{I}_{3\times 3}+\left[\varrho \times\right]\\ -\varrho^{T}\end{array}\right] \tag{51}\]  
\[\psi(\mathbf{q})\equiv\left[\begin{array}{c}q_{4}\mathbf{I}_{3\times 3}-\left[ \varrho\times\right]\\ -\varrho^{T}\end{array}\right] \tag{52}\]  
where \(\left[\varrho\times\right]\) is the cross-product matrix, provided by the following:  
\[\left[\varrho\times\right]\equiv\left[\begin{array}{ccc}0&-q_{3}&q_{2}\\ q_{3}&0&-q_{1}\\ -q_{2}&q_{1}&0\end{array}\right] \tag{53}\]  
Successive rotations could be converted into quaternion multiplication, as follows:  
\[A(\mathbf{q})A(\mathbf{q}^{\prime})=A(\mathbf{q}\otimes\mathbf{q}^{\prime}) \tag{54}\]  
with  
\[\mathbf{q}\otimes\mathbf{q}^{\prime}=\left[\psi(\mathbf{q})\quad\mathbf{q}\right]\mathbf{q}^{ \prime}=\left[\Xi(\mathbf{q}^{\prime})\quad\mathbf{q}^{\prime}\right]\mathbf{q} \tag{55}\]  
The quaternion kinematics equation could be written as the following:  
\[\dot{\mathbf{q}}=\frac{1}{2}\Xi(\mathbf{q})\mathbf{\omega}=\frac{1}{2}\Omega(\mathbf{\omega}) \mathbf{q} \tag{56}\]  
where \(\mathbf{\omega}=\left[\omega_{1},\omega_{2},\omega_{3}\right]^{T}\) indicates the angular rate vector and  
\[\Omega(\mathbf{\omega})\equiv\left[\begin{array}{cc}-\left[\mathbf{\omega}\times \right]&\mathbf{\omega}\\ -\mathbf{\omega}^{T}&0\end{array}\right] \tag{57}\]The error of quaternion is defined as \(\delta\mathbf{q}=\left[\delta\mathbf{q}^{T},\delta q_{4}\right]^{T}\). To ensure that the quaternion-based results follow the unit constraint, generalized Rodrigues parameters (GRPs) [10] are introduced to represent the pose error, which is defined as follows:  
\[\delta\mathbf{p}\equiv f\frac{\delta\mathbf{q}}{(a+\delta q_{4})} \tag{58}\]  
where \(\left\|\delta\mathbf{p}\right\|\) is equal to the error-angle for small errors, \(f\) is a scale factor that is commonly taken as \(f=2(a+1)\), and \(a\) is a parameter from 0 to 1. The inverse transformation from \(\delta\mathbf{p}\) to \(\delta\mathbf{q}\) is determined as follows:  
\[\delta q_{4}=\frac{-a\left\|\delta\mathbf{p}\right\|^{2}+f\sqrt{f^{2}+(1-a^{2}) \left\|\delta\mathbf{p}\right\|^{2}}}{f^{2}+\left\|\delta\mathbf{p}\right\|^{2}} \tag{59}\]  
\[\delta\mathbf{q}=f^{-1}(a+\delta q_{4})\delta\mathbf{p} \tag{60}\]
###### Abstract  
In this paper, an orthogonal type two-axis Lloyd's mirror interference lithography technique was employed to fabricate two-dimensional planar scale gratings for surface encoder application. The two-axis Lloyd's mirror interferometer is composed of a substrate and two reflective mirrors (X- and Y-mirrors), which are placed edge by edge perpendicularly. An expanded and collimated beam was divided into three beams by this interferometer, a direct beam and two reflected beams, projected onto the substrate, X- and Y-mirrors, respectively. The unexpected beam sections having twice reflected off the mirrors were blocked by a filter. The remaining two reflected beams interfered with the direct beam on the substrate, generating perpendicularly cross patterns thus forming two-dimensional scale gratings. However, the two reflected beams undesirably interfere with each other and generate a grating pattern along 45-degree direction against the two orthogonal direction, which influence the pattern uniformity. Though an undesired grating pattern can be eliminated by polarization modulation with introduction of waveplates, spatial configuration of waveplates inevitably downsized the eventual grating, which is a key parameter for grating interferometry application. For solving this problem, theoretical and experimental study was carefully carried out to evaluate the fabrication quality with and without polarization modulation. Two-dimensional scale gratings with a 1 \(\upmu\)m period in X- and Y-directions were achieved by using the constructed experiment system with a 442 nm He-Cd laser source. Atomic force microscopy (AFM) images and the result of diffraction performances demonstrated that the orthogonal type two-axis Lloyd's mirror interferometer can stand a small order undesired interference, that is, a degree of orthogonality between two reflected beams, denoted by \(\gamma\), no larger than a nominal value of 0.1.  
interference lithography; two-axis planar scale grating; Lloyd's mirror; surface encoder 1
# Analysis of Lightweight Structure Mesh Topology of Geodesic Domes
## 7 Discussion  
Creating the mesh of a geodesic dome structure is a process based on various methods of generating its mesh topology, considering appropriate connections of the resulting nodes using strut elements. The designed lightweight covering structure is efficient and economical and allows for covering large areas without using internal supports. To further emphasize the advantages of this type of structure, it is worth paying attention to its optimization process, aimed at obtaining the lowest possible weight of the designed geodesic dome. Dome optimization has been the subject of many scientific considerations for years. The various optimization algorithms developed are constantly analyzed, improved, and used to reduce the weight of the domes and thus enhance the value of lightweight structures. Taking into account various design variables during the optimization process reduces the weight of the structure to varying degrees. For example, treating the number of rings as well as the number of nodes as design variables determines the number of structure elements ([PERSON] and [PERSON] [53]). In turn, the height of the dome changes the length of its elements ([PERSON] [28]). However, adapting the cross-section of strut elements directly affects the weight of the covering, which is presented in this paper and in the paper by [PERSON] [47].  
The results presented in the paper will be used for further analysis, considering other aspects apart from geometry, mesh topology, statics, and the aspect of sustainable development. We are talking here primarily about an extended analysis of the behavior of geodesic domes shaped based on a regular octahedron under a given dynamic load, considering various methods of creating their topology. Initial research on this topic has already been carried out in the papers of [PERSON] and [PERSON] [49], [PERSON] et al. [50], as well as [PERSON] and [PERSON] [51]. It should be added that this research used numerical software (Robot Structural Analysis 2023 and Dlubal RFEM 6) based on finite element methods, and it is planned to continue using this type of software in the future
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 1 Introduction
When dealing with large-scale visualization data in the field of computational fluid dynamics, the limitations of a single machine in terms of memory capacity and rendering efficiency make it unable to meet the demands of real-time interactive control for parallel visualization of large-scale flow fields [13]. Therefore, the computation of visualization features and graphics rendering processes must rely on supercomputers [14]. From the perspective of supercomputer architecture, the visualization terminal is connected to the rendering nodes via Ethernet (with a bandwidth of hundreds of megabits or gigabits per second), while the interconnection between rendering nodes utilizes high-speed interconnects such as Intel InfiniBand (with a bandwidth of tens of gigabits per second), resulting in a difference in bandwidth of several hundred to thousands of times. Consequently, the multi-level asymmetric communication between the visualization terminal and the rendering nodes becomes one of the bottlenecks in achieving real-time interactive control for large-scale flow-field visualization. The key to driving visualization computations on a supercomputer from a visual terminal while ensuring real-time visual interaction control lies in coordinating and managing multi-level asymmetric communication. In summary, our research in this series makes the following contributions. Firstly, we propose a data-partitioning strategy based on grid segmentation by constructing spatial model metadata and reconstructing grid topology through the grid and coordinate-mapping models. This strategy effectively divides the spatial model into sub-datasets and assigns them to different computational nodes, enabling efficient access to large-scale flow field data. Secondly, we design a real-time interactive method for large-scale flow fields that adequately coordinates communication resources between different cluster systems and between cluster systems and visualization terminals. This method enables real-time visual interaction control for parallel visualization of large-scale flow fields. Lastly, we validate the proposed data-partitioning strategy using typical flow-field data, demonstrating a sevenfold increase in data access speed. The effectiveness of this data-partitioning strategy is further supported by vivid visualizations. Through interactive control experiments with datasets containing 500 million and 800 million grids, we verify that our proposed communication management method achieves real-time visual interaction control for large-scale flow-field data.
# Artificial Intelligence Applications to Smart City and Smart Enterprise
## 2 Contribution
### Vehicular Traffic Prediction  
Within this topic, [PERSON] et al. [1] used Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure used to model the recent, daily-periodic, and weekly-periodic spatial-temporalcorrelations of the traffic data, respectively. Experimental results demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.  
A novel generative deep learning architecture, called TrafficWave, is proposed by [PERSON] et al. [2] and applied to vehicular traffic prediction. The technique is compared with the best performing state-of-the-art approaches: stacked auto encoders, long short-term memory, and gated recurrent unit. Results show that the proposed system performs a valuable Mean Absolute Percentage Error ( MAPE) reduction when compared with other state of art techniques.  
In reference [3], the authors propose a Grassmann-manifold-based neural network model to analyze traffic surveillance videos. The approach is able to consider the inner relation among adjacent cameras. The accuracy of the traffic congestion evaluation is improved, compared with several traditional methods. Experimental results also report the effects of different factors on performance.  
While the previous works are centered on generic vehicular traffic flow analysis, authors of [4] explore bus traffic flow and its specific scenario patterns. A spatio-temporal residual network is used for prediction aims. Fully connected neural networks capture the bus scenario patterns, and improved residual networks capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate the viability of the proposed solution also being able to outperform four baseline methods.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 13 By-Products and Degradation in Photocatalysis
Another risk stems from surface modifications of nanomaterials due to photocorrosion or fouling. During photocatalysis, photoinduced reactions can alter the surface chemistry of catalysts, leading to the loss of active sites or the accumulation of contaminants. This not only diminishes the photocatalytic performance but may also promote the formation of unintended by-products, as fouled surfaces may catalyze unselective reactions. Furthermore, the altered surfaces of degraded nanomaterials could interact with biological membranes or macromolecules in unforeseen ways, introducing additional risks to ecosystems and human health.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 5 Results and Discussion  
### Experimental Parameters and Evaluation Metrics  
In this study, the CLMFD model was trained using data from the Xi'an to Shanyang route and tested on data from the Xi'an to Hanzhong and Xi'an to Baomao routes. To ensure a fair comparison across different models, several parameters were standardized, including the Adam optimizer, a learning rate of 0.001, and the use of Mean Squared Error (MSE) as the loss function, with training conducted over 100 epochs and a batch size of 32. Additionally, other parameters were adjusted based on the specific characteristics of each model. The detailed parameter settings for each model are as follows: the BP neural network uses a 7 \(\times\) 128, 128 \(\times\) 1 architecture; the random forest (RF) model is configured with 100 trees, a maximum depth of 10, square root of the total number of features for max features, minimum samples for splitting set to 2, and minimum samples for leaf nodes set to 1; the RNN uses a 7 \(\times\) 128, 128 \(\times\) 1 architecture; the Transformer is configured with 2 encoder layers, 4 attention heads, and a hidden layer size of 128; PatchTST uses a time window size of 48, a patch length of 16, 3 encoder layers, a hidden layer size of 128, and 4 attention heads; in the CLMFD model, both the decoder layer of the teacher model and the network structure of the student model consist of 4 layers.  
To comprehensively evaluate the performance of the fuel consumption prediction model, this study employed four commonly used evaluation metrics: MSE, RMSE, MAE, and R\({}^{2}\). In the experiments, the Python programming language was used, along with popular libraries such as pandas, numpy, XGBoost, scikit-learn, PyTorch, and TensorFlow for data processing, feature selection, and model construction.  
Figure 5: Feature correlation heatmap.
## 4 Results and Discussion
### Study B: Reverse Stroke Analysis through Segmentation Technique
At the full stroke level analysis, all the evaluated features have a percentage error below 10% when compared to the baseline state. Furthermore, when the signal segmentation technique is applied, the segments for the RMS and standard deviation features all stabilize and are found to be within the 10% error limit. Moreover, the suspect segment, shown in the faulty dataset analysis in Figure 15, showed a magnitude decrease and fell below the 10% limit at the same segment level analysis after the repair, as Figure 17 illustrates. However, when applying the segmentation technique to evaluate the kurtosis feature, it can be observed that at the half stroke level, as shown in Figure 18, the second half segment exhibits a kurtosis value with a 14% error when compared to the baseline state, exceeding the imposed 10% limit. When the signal is further segmented to the quarter stroke level, the last quarter segment exceeds the 20% limit, prompting the system to label that segment as faulty. This demonstrates the power of the segmentation technique, since it indicates other anomalous segments that should be also checked. Lastly, it can also be observed that the error of the feature, especially in the newly identified suspect segments, is much higher compared to the baseline state at the same segments. Figure 16: Reverse stroke faulty signal segmentation analysis. (**a**) Peak-to-Peak, (**b**) Kurtosis. Figure 17: Reverse stroke repair signal segmentation analysis. (**a**) RMS, (**b**) Standard Deviation. Figure 18: Reverse stroke repair signal segmentation analysis. (**a**) Peak-to-Peak, (**b**) Kurtosis.
# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 5 Simulations and Analysis
A Rogers 5880 dielectric substrate with 20 mm thickness and 3.48 dielectric constant was used for the antenna design because it has low signal loss, low dielectric loss, low outgassing (which is good for space applications), and cheap circuit fabrication. The total number of elements in the array was 100. The length and width of a single patch were estimated to be 7.4 mm and 9.5 mm, respectively. Elements were uniformly spaced by 8.947 mm and 6.847 mm along \(x\) and \(y\), respectively. All elements were fed with the same excitation value which equal to one to emulate the array without failure (reference array). Then \(K\) failures (in this case, the estimated percentage of failure rate) were also initiated intentionally by making the excitation equal to zero in order to model the AUT effectively. At first, we considered the reference array, i.e., the array without failures. The excitation coefficients are depicted in Figure 10, and the reconstructed excitation coefficients are presented in Figure 11. Also, for quantitative knowledge of the error estimated, the computed excitation error in dB is shown in Figure 12. The result indicates an exact reconstruction in the case of the reference array, and shows a low probability of a false alarm. Also, considering an AUT with \(K=5\) element failures (\(\Phi=5\%\)) (elements with zero excitation) because failed elements are usually of small number in practice, the resulted excitation coefficients are presented in Figure 13, and the estimated excitations by 30 random noisy measurement points are presented in Figure 14, while the dB excitation error is depicted in Figure 3:0 The result is an indication of good estimation of RA excitations and the locations of the faulty elements. Figure 3:1 Designed microstrip patch antenna array in Ansys HFSS for diagnosis. Figure 3:2 The antenna array for diagnostic purposes: (**a**) S-parameter; (**b**) RA radiation pattern; (**c**) AUT radiation pattern. Figure 3:3 Reconstructed excitation field of reference array employing 30 random noisy measurement points. Figure 3:4 Reference array reconstructed excitation error in dB by 30 random noisy measurement points. Figure 3:5 Excitation field of reference array. Figure 3:6 AUT with \(K=5\) failures \(\Phi=5\%\): reconstructed excitation error field by 30 random noisy measurement points. Figure 3:7 Reference excitation field of AUT with \(\Phi=5\%\). Figure 3:8 AUT with \(K=5\) failures \(\Phi=5\%\): reconstructed excitation error in dB by 30 random noisy measurement points.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 1 Introduction
Until now, SPME Arrow has been employed for the determination of VOCs in a wide variety of environmental and food stuffs including waste water and atmospheric air samples [12], brown rice vinegar [13], Korean salt-fermented fish sauce [14] and soy sauce [2]. The aim of this research was the development and optimization of a reliable analytical SPME Arrow method for the extraction and preconcentration of volatile components of milk samples prior to their determination by gas chromatography-mass spectrometry (GC-MS). Moreover, comparison of the SPME Arrow with the conventional SPME approach for VOC analysis in milk samples took place to evaluate the effectiveness of the novel extraction technique.
## 4 Results and Discussion
### Statistical Analysis of Groundwater Hydrochemistry
As illustrated in Figure 3, the hydrochemical composition of groundwater in the Beichuan River basin is predominantly characterized by the HCO3-Ca type, accounting for 54.17% of the groundwater samples. This is followed by the Cl-Na type, representing 33.33%, and the Mixed Cl-Mg-Ca type, which constitutes 12.5%. With increasing Total Dissolved Solids (TDS), there is a progressive shift in water chemistry from the HCO3-Ca type to the Cl-Na type. This trend suggests that the hydrochemical evolution of groundwater in the Beichuan River basin transitions from the HCO3-Ca type to the mixed Cl-Mg-Ca type and finally to the Cl-Na type as one moves from upstream to downstream. Trace elements and toxic metals, including nitrogen (N), fluoride (F), iodine (I), arsenic (As), iron (Fe), manganese (Mn), and zinc (Zn), were analyzed for their potential impact on groundwater quality and safety in the Beichuan River basin. The analysis reveals that only the concentrations of NO3- and NO2- and F- occasionally surpass the thresholds set by the World Health Organization [51] and Chinese guidelines [52], posing a risk to groundwater quality. Specifically, fluoride concentrations vary from 0.12 mg/L to 3.12 mg/L, with a mean of 0.78 mg/L. Approximately 33.33% of the groundwater samples exceed the Chinese guideline of 1 mg/L for potable water, with these enriched waters predominantly located  
Figure 3: Piper diagram of groundwater regarding different TDS in the Beichuan River watershed. in the downstream areas (Figure 2). NO3\({}^{-}\) concentrations range from 0.06 to 69.33 mg/L, averaging 26.94 mg/L, with 20.83% of the samples exceeding the WHO's recommended standard, primarily in the valley regions (Figure 2). This suggests that human activities have impacted groundwater quality to some extent [8]. Additionally, the maximum NO3\({}^{-}\) concentration is 0.04 mg/L, with 12.50% of samples exceeding the drinking water safety limit of 0.02 mg/L. Collectively, these findings underscore the need for vigilance regarding localized nitrogen and fluoride contamination, which could compromise the suitability of groundwater for drinking in the Beichuan River basin (Table 2).
###### Abstract  
The objective of this study was to determine the combined usage possibilities of ultraviolet (UV) and ultrasonic (US) processes in the pasteurization of pomegranate juice. For this purpose, UV, US, and combined UV+US pasteurization of pomegranate juice were optimized using experimental designs, such as the Central Composite Design (CCD) and Factorial Design (FD), and compared with the conventional pasteurization process. Total phenolic content (TPC), color a*, water-soluble dry matter (*Brix), turbidity, anthocyanin, DPPH, HPLC TPC, and yeast and mold count were used as quality parameters during all of the processes. The results showed that the application of 50 *C, 3.5 L/min flow rate and 5.1 mW/cm2 UV dose, and 10 min US (200 Watt) together reduced the microbial population below the detection limits. The integration of UV+US processes into the pasteurization process could limit microbial activity at lower temperatures and times than the conventional pasteurization process, thus preserving the existing bioactive compounds.  
pasteurization; pomegranate juice; ultrasound; ultraviolet; optimization; experimental design Article