esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System  
During the study of the Kalman filter algorithm, when severe interference exists in the navigation environment, regardless of whether the loosely coupled or tightly coupled approach is employed, it can lead to significant errors in the final fusion navigation positioning. Therefore, it is necessary to utilize neural network architectures to assist in correcting the signals before fusion.  
The algorithm of the Kalman filter assisted by neural networks [26] demonstrates outstanding performance in various application scenarios. The main advantages are as follows:  
1. Complementarity: Kalman filters are primarily suitable for linear systems and environments with Gaussian noise, while neural networks excel in handling nonlinear, non-Gaussian, or complex systems. Therefore, the combination of the two can fully utilize their respective strengths, complementing each other's shortcomings, and thereby more accurately describing and predicting the dynamic behavior of the system.
2. Improved prediction accuracy: Through the learning and modeling capabilities of neural networks, nonlinear characteristics of the system can be captured, thereby providing more accurate models and predictions for the Kalman filter. This helps reduce errors during the filtering process and improves prediction accuracy.
3. Strong adaptability: Due to the powerful learning and adaptation capabilities of neural networks, they can adapt to changes and uncertainties in system parameters.  
Therefore, even if the dynamic characteristics of the system change, neural-network-assisted Kalman filters can quickly adjust and adapt to the new environment.
* Enhanced robustness: When facing noise interference, missing data, or outliers, the combination of neural networks and Kalman filters can enhance the robustness of the system. Neural networks can learn and handle these exceptional situations, while Kalman filters can smooth and correct estimation results to some extent.
* Expanded application scope: By integrating neural networks and Kalman filters, the application scope of Kalman filters can be expanded to more complex systems and scenarios. For example, in fields such as autonomous driving, robot navigation, and financial forecasting, this fusion method can help achieve more accurate and reliable state estimation and prediction.  
Therefore, researching techniques that can improve the performance of navigation systems by constructing neural network architectures to correct signal errors has become one of the current focuses of research work.
###### Abstract  
Low Earth orbit (LEO) satellite networks are characterized by rapid topological changes, numerous network nodes and varying states of node resource constraints, which have resulted in traditional routing algorithms no longer being suitable for LEO satellite network routing. Therefore, this paper proposes an inductive learning architecture based on Graph Sample and Aggregate (GraphSAGE), which can significantly reduce the number of topology nodes to be trained, thereby reducing the computational complexity of the nodes. Then deep reinforcement learning (DRL) is employed for the continuous learning optimization of routing algorithms, and its generalization is improved by selecting GraphSAGE to construct the DRL agent. In the proposed graph neural-network-based routing optimization algorithm for LEO satellite networks, each Deep Q-Network (DQN) agent independently generates the hidden states of the nodes through the GraphSAGE model and uses them as inputs to the DRL model to make routing decisions. After a simulation and comparison, the proposed algorithm not only improves the overall network throughput, but also reduces the average end-to-end delay. The average throughput of the proposed algorithm increases by 29.47% and 18.42% compared to that of Dijkstra and the DQN, respectively. The average end-to-end delay is reduced by 39.76% and 15.29%, respectively, and can also adapt to changing topologies.  
LEO satellite; routing; graph neural network; DQN +
Footnote †: journal: Applied  
14 March 2024  
Revised: 20 April 2024  
Accepted: 27 April 2024  
Published: 30 April 2024
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 3 Results and Discussion
### Performance of Predictive Models
The predictive performance of ANN and GLM-AED at most stations with RMSE and MAE values in the range of 13.83-21.92 and 9.87-21.49 was better than that at station ER 58 (GLM-AED: RMSE = 60.85, MAE = 41.66; ANN: RMSE = 59.21, MAE = 37.21) and station ER 59 (GLM-AED: RMSE = 48.88, MAE = 32.64; ANN: RMSE = 47.19, MAE = 26.86), which is because stations ER 58 and ER 59 are along the southwest border in the western basin, resulting in the observations at these two stations during summer being larger than the simulations near the Maumee River plume [58], especially in the blooming year 2011 and 2015 [55; 59], since one-dimensional GLM-AED represents horizontal homogeneity and the nonlinear relationships in the ANN network prioritizes larger weights for the values with a higher frequency of input database to obtain the minimum statistical errors. Additionally, the computational time of the ANN model was less than two seconds, which was 30 times faster than GLM-AED.
## 4 Conclusions  
In this work we quantify the expected error propagation of missing values in RR-intervals timeseries to HRV features, as a function of preprocessing interpolation approach, and amount of missing data. The main findings of this study is that the interpolation of missing values in RR-intervals timeseries on time (i.e., the heartbeats timestamps) produces more reliable HRV features estimations compared to interpolation on duration.  
Figure 6: Poincaré plot of a user’s RR-intervals timeseries recorded in 5 min with different percentage of missing values (i.e., 0%, 30%, 50% and 70%) handled with different interpolation methods (i.e., nearest neighbor, linear, quadratic and cubic spline) on both time and duration.  
By using this preprocessing approach, the quantification of the expected error on HRV features caused by a huge amount of missing values (e.g., motion artifacts on a wrist-worn wearable device) can support better estimations of users' well-being, by assessing their HRV features. This enables continuous passive monitoring of users' cardiovascular activity in a non-obtrusive way, collecting data during their daily activities that could enable further research on preventative health.  
A limitation of this study is the fact that we limited our focus on healthy subjects with normal heart activity, limiting the analysis to large amounts of missing values induced by motion artifacts, ignoring physiological phenomena such as ectopic beats.  
Future studies will be useful for researcher and companies, which give insight into heart rate variability recorded by wrist worn IoT wearable devices, in order to better understand the potentiality of the data extracted from these devices to make inference about people health status. Future work is needed to assess the influence of missing values simulated in accordance with motion and mechanical artifacts induced by external stimuli during data acquisition by using wrist worn IoT wearable devices. Finally, future works will also include the investigating the influence of missing values on HRV features on short timeseries (e.g., 2 min, 1 min and 30 s) and the identification of the shortest time required to obtain accurate estimation of users' HRV features.  
Conceptualization, D.M., A.R. and M.C.; methodology, D.M., A.R. and M.C.; validation, D.M., A.R. and M.C.; formal analysis, D.M., A.R. and M.C.; investigation, D.M., A.R. and M.C.; writing--original draft preparation, D.M., A.R., M.C. and D.A.C.; writing--review and editing, D.M., A.R., M.C. and D.A.C.; visualization, A.R. and M.C.; supervision, D.M. and D.A.C.; project administration, D.M. and D.A.C.; funding acquisition, D.M. and D.A.C.  
This work is partially supported by the European Community's H2020 Program under the funding scheme INFRAIA-1-2014-2015: Research Infrastructures grant agreement 654024, www.sobigdata.eu, SoBigData. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.
## 3 Results and Discussion
Different letters indicate significant differences among cultivars and within each planting system; n.s., non-significant. tested were higher compared to those from trials with 'Arbequina' in SHD systems under the same environmental conditions [39], highlighting the power of biodiversity. Besides, the relatively large size of the 'Calatina' fruit (about 5 g) allows for a high harvesting efficiency not only with straddle machines, which can harvest also small fruit (1 g) such as that of 'Arbequina', but also with trunk shakers. This feature gives 'Calatina' and other cultivars with similar traits great adaptability to different planting systems that can be harvested with different methods and machines, contributing to a greater flexibility of olive growing systems. The biennial results on the quality of the virgin olive oils did not show any significant effect of the planting system, so data from different planting systems were pooled together for each cultivar. On the other hand, oils obtained from 'Calatina' and 'Abunara' olives compared with 'Nocellara del Belice', one of the main Sicilian olive cultivars, showed some interesting aspects. In particular, there were no significant differences regarding the standard quality parameters; in fact, all the oils in the two-year period showed values well within the legal limits fixed for the extra virgin olive oil (Table 2). Figure 7: Cumulated oil yield per hectare across the seven years of trial for ‘Abunara’, ‘Calatina’, and ‘Nocellara del Belice’ olive trees grown as central leaders at \(2\times 5\) m (CLx2, (**A**)), free palmette at \(3\times 5\) m (FFx3, (**B**)), globe vase at \(4\times 5\) m (GVx4, (**C**)), and polyconic vase at \(5\times 5\) m (PVx5, (**D**)). Error bars represent standard errors of the means. Mean separation by Tukey’s honestly significant difference (HSD, \(p<0.05\)). Different letters indicate significant differences among cultivars and within each planting system; n.s., non-significant. As for the fatty acid composition, despite a considerable variability between cultivars, 'Calatina' produced virgin olive oils with a high content of oleic acid, well above 70%, significantly higher than the levels found in the oils from 'Nocellara' (Table 3). The level of palmitic acid was then low, while 'Calatina' showed adequate amounts for linolenic acid. The virgin olive oil produced from 'Abinara' divives showed also relatively high oleic acid, around 70%, but in any case, comparable with the levels observed in 'Nocellara' oils and lower than 'Calatina' oils (Table 3). The results obtained from 'Calatina' are particularly interesting because the olive trees were grown in dry, warm climatic conditions where it is more difficult to produce olive oils with a high content of oleic acid than in the cooler areas of the temperate zones. It should be remembered that a high level of oleic acid, in addition to having positive health effects, increases the oil oxidative stability. Hence, the use of 'Calatina' olive trees in dry and warm areas could favor the production of better-quality olive oils with longer shelf life.
## 5 Conclusions
To ensure a realistic analysis, real-world data from \"Starlink-on-the-Road-Dataset\", were used to assess the robustness and sensitivity of these algorithms during LEO satellite handovers. The simulation results showed that the SD-TOPSIS algorithm consistently outperforms the other algorithms under study in terms of robustness, as it exhibits the lowest sensitivity and standard deviation of scores across the four scenarios in response to small variations in the criteria values. Moreover, the SD-TOPSIS algorithm reduces sensitivity compared to both Entropy-TOPSIS and Importance-TOPSIS for all the four scenarios, making it particularly well-suited for handover in satellite communication systems where criteria such as upload speed, download speed, ping, and packet loss vary rapidly, due to the mobility of LEO satellites. Future work will explore hybrid algorithms that combine SD-TOPSIS with advanced methods such as machine learning. This integration could enhance adaptability and enable real-time decision-making, addressing the dynamic nature of LEO satellite networks while improving their handover performance and resilience. Conceptualization, P.B.N. and M.M.T. ; Methodology, P.B.N. and A.K.L. ; Software, P.B.N. ; Validation, A.K.L., M.M.T. and P.S.N. ; Formal analysis, P.B.N. ; Resources, A.K.L. and V.P. ; Data curation, P.B.N. ; Writing--original draft preparation, P.B.N. ; Writing--review and editing, M.M.T., M.-L.L.B., P.S.N. and V.P. ; Visualization, P.B.N. ; Supervision, A.K.L. and M.M.T. ; Project administration, A.K.L. and M.M.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research received no external funding.
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
## 5 Discussion
### Performance of DiagElect  
The results presented in Table 4 confirm the reliability of DiagElect as an electrical measurement network for PQ analysis. Nodes 1 and 2, previously identified as non-distorting, and nodes 3 and 4, identified as distorting, as shown in Table 2, were confirmed by the results in Table 4. This identification was reinforced by the current spectra for nodes 3 and 4 depicted in Figure 10.  
Focusing on the computed parameters detailed in Table 4 for each microgrid node, the HGI value for node 1 is nearly zero, indicating that node 1 is a linear load, as expected. Figure 11a shows the waveforms obtained for node 1, where different amplitudes of phase currents due to the load imbalance can be observed.  
Similarly, for node 2, the HGI value is near zero, confirming it is a linear load. Figure 11b illustrates the current lagging the voltage, characteristic of the inductive nature of the load.  
In contrast, node 3 exhibits an HGI value not near zero, confirming it as a harmonic source. The waveforms corresponding to this node are shown in Figure 11c, where non-sinusoidal current waveforms are evident.  
Likewise, node 4 shows a high HGI value, far from zero, confirming it as another harmonic source. The voltage and current waveforms for node 4 are presented in Figure 11d, also displaying non-sinusoidal current waveforms.  
DiagElect effectively measures voltage and current simultaneously from each node in a microgrid, regardless of the load type connected. Its adaptable and scalable design enhances this capability, allowing it to be employed across various microgrid configurations.  
The system meets the requirements for spectrum calculation established in current standards. In the proposed experimental microgrid, four loads are connected to the same point, the PCC, as shown in Figure 1, with one SDMD placed between the PCC and each load. DiagElect can also be used to evaluate more complex microgrids with multiple PCCs by placing an SDMD at each PCC, thereby providing a more comprehensive and valuable PQ analysis.  
DiagElect is also suitable for industrial environments, benefiting from robust voltage and current sensors and a communication network that supports synchronous measurements across multiple nodes.  
Moreover, DiagElect can perform the necessary calculations for PQ analysis, including the identification of distortion sources, which is one of the most challenging aspects of PQ assessment. The results shown in Table 4 demonstrate DiagElect's capability for conducting PQ analysis within a microgrid.
## 5 Computational Results
### Small VRF Instances
#### 5.1.2 Comparison of Heuristic Algorithms with Exact Solutions  
In order to compare performances of heuristic algorithms with CP exact method, we run all algorithms for five independent replications with different seed numbers. Relative percent deviation values from upper bounds for ten different instances of each job-machine combinations are calculated as follows:  
\[RPD=\frac{(M-M_{\mathit{LIB}})\times 100}{M_{\mathit{UB}}} \tag{19}\]  
where \(M\) is the makespan value generated by any heuristic; and \(M_{\mathit{UB}}\) is the upper bound provided in [9]. Note that, for each instance, we record the average RFD of five replications for statistical analysis purposes, especially, for interval graphs. The solutions of the CP model are limited to 3600 s and its average CPU times are given in Table 4. \(\mathrm{IG_{ALL}}\), \(\mathrm{IG_{RS}}\), and VBIH algorithms are run for five replications with three different time limits 15, 30, and \(45\times n\times m\). As expected, the performance of these algorithms is much better than those by CP exact model, and they improve the upper bounds provided in [9], which means that the proposed algorithm and other IG algorithms can find good (optimal in some cases) solutions in a very short time. As the solution time increases, the solution quality of VBIH algorithm increases and according to the RPD, it gives the best solutions amongst all other algorithms. It should be noted that the VBIH algorithm further improves 64 out 240 upper bounds for small VRF instances within a very short time.
## 5 Results and Discussion
(2017) where the Kagome type of lattice structure performance in compression is equal, in magnitude, to the solid specimen. The performance of specimens with 70% infill density is highly regarded, including all the observations greater than the strength to mass ratio of solid specimens. A continuously increasing trend is observed for both frequencies. Results of the second test series are more recognized, as the performance of each specimen of this series is much better than the solid specimen with 70% infill density, as shown in Tables 8 and 9. From the graphical representations in Figures 9 and 10, it can be seen that, for minor increases in values of specimen mass, the range of improvement in strength to mass ratio in comparison to solid specimen, is from 23.6% (min.) to 68.4% (max.). It is also observed that mass of the specimens range from 58.4 g to 62.72 g, and their performances are in the range of 27.8 MPa to 40.2 MPa i.e., for an improvement of 7.4% in material, their performance is 44.6% better than the specimen with the least values. For the first series, these values are with a 7.7% increase in material and a 17.5% increase in strength is achieved. Performance in the second series is further investigated and evaluated according to material distribution within the sample. Figures 8 and 9 show that the amount of  
Figure 9: Graphical representation of the relationship between mass versus vertical shift (**a**) with 100% infill density (**b**) with 70% infill density. material utilized for the construction of specimens is, approximately, the same, but their strength to mass ratios are different and greater than the minor difference in their masses, if any. Comparing the values of strength to mass ratio show that higher values relate to frequency 21, and the material distribution in higher frequency specimens is more widened in the case of 70% in contrast to 100% specimens (refer to Tables 3 and 4). The thickness of solid bars is lesser for frequency 21 and compared to frequency 19 and, for such situations with the same masses of specimen, the much expanded material distribution of specimens with frequency 21 is justified. The solid bars serve as pillars internally and provide supports to the whole geometry. For these reasons, a greater level of compressive strength is achieved when comparison is made between 70% infill solid part and 70% infill cellular lattice structure specimens. The above research findings are supported through previous published research works by [PERSON] et al. (2017); [PERSON] et al. (2017). Using Direct Metal Laser Sintering process (DMLS), similar observations are made when the unit cell sizes of the cellular lattice structure are reduced to 3 mm, 5 mm, and 7 mm, respectively, within the same volume fraction. With the reduction in the cell size, sizes of struts also decrease, and during manufacturing, the thinner struts cool down much rapidly, leading to finer microstructure and stronger struts. Therefore, as the dimensions of the struts reduce, improvement is observed in the strength and mechanical properties of the cellular lattice structure.
# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
###### Abstract
By introducing the angle of attack as an additional input [17], they were able to maintain high accuracy for arbitrary angle of attack. They further developed a geometry- and boundary-condition-adaptive CNN-based ROM for fast flow field prediction [18]. In the latter work, the average prediction error dropped to 6% and the prediction speed was two orders of magnitude faster than the numerical simulation. Few studies related to the field reconstruction in channels are available. [PERSON] et al. [19] proposed a CNN network to predict the flow field in a 2D micro channel, reporting good accuracy and fast computational speed of the model. In the medical industry, CNN has also reported several very striking research results. [PERSON] et al. [20] was the first to propose the use of CNN for drug-drug interaction (DDI) extraction and demonstrated superiority over other DDI extraction methods. Subsequent scholars [21; 22] conducted more in-depth exploration and improved the accuracy of drug-drug interaction extraction. [PERSON] [23] established a gray relational analysis (GRA) and related drug evaluation system for mental illness based on deep CNN, which combined with the medical knowledge base, analyzed the disease results and provided data theory to guide the follow-up treatment of depression. [PERSON] et al. [24] used chemical images with CNNs and molecular fingerprints with random forests and chemical-structure-derived molecular representations to predict specific medical subheading (MeSH) \"therapeutic uses\" classes that do not require empirical measurements of effects of chemicals, hence broadly apply to predict drug classes after training. In this study, we apply CNN-based ROM for real-time prediction of the drug trajectory and concentration field with various injection positions. According to our literature review, the CNN-based ROM has not been applied to the real-time spatial drug distribution prediction in vascular interventional treatment. Furthermore, we propose a new network encoding architecture, establishing two convolutional paths avoid interference between two respective inputs: injection site and blood vessel geometry, thereby enhancing its learning efficiency and improving model performance. Next, we present the proposed ROM, including the data preparation and the architecture of the deep neural network. We then introduce the spatial-temporal drug trajectory and concentration prediction results of three types of blood vessels with various injection sites. Finally, we state our conclusions in the last section.