esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
## 1 Introduction
The former calibration method results in a better stability without having to change the system structure because of the numerous standard samples, and its convenience and accuracy have been demonstrated for the dual-rotating-retarder configuration [13; 21]. As discussed above, errors related to the polarizing section of the Mueller matrix microscope have been addressed by previous conventional calibration methods using the Fourier coefficients of the measured intensities [10]. In our previous study, we also derived a least-square calibration algorithm that shows high accuracy and robustness for the fundamental dual-rotating-retarder Mueller matrix polarimeter [22]. However, because these methods neglect the parasitic polarization induced by imperfections in elements of the imaging section, limitations may exist, and the relative errors could lead to large errors in the calculation of important polarization parameters, such as birefringence. In this study, we propose to completely use the mathematical model for calibration, including both the components in the polarizing and imaging sections, by recasting the Mueller matrices of optical elements in terms of ellipsometric angles. We analyze these systematic errors in a Mueller matrix microscope using the Taylor expansion. Using an optimized combination of controllable error sources, the error magnification coefficient matrices are designed to achieve an approximately linear relationship between the source of the errors and the Mueller matrix element of the measured sample with the precondition of ensuring robustness of the calibration result of the polarizing section. In this way, the specific parameter errors in both the polarizing and imaging sections can be considered, and the robustness is consequently improved. In Section 2, we discuss the systematic error model using the Taylor expansion of the element in the imaging section. In Section 3, we use calculations and experimental simulation to demonstrate the effect of the proposed calibration method on the calculation of the Mueller matrix of the sample, specifically the birefringence characteristics, by using air, which is isotropic, and quarter-wave plates,which are anisotropic, as the measurement sample. We also show that these errors can be eliminated or reduced to a proper range, and we perform comparisons of the birefringence of Daphnia to demonstrate the general applicability of the proposed method.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 2 Host-Pathogen Interactions
The acellular pathogens, like a virus, vivid or prion, usually utilize nucleic acid or host protein synthesis pathways to replicate itself [66; 67]; while cellular pathogens, such as fungi, bacteria, protozoon or nematodes, gain only nutrients from hosts but utilize their own cellular machinery for proliferation [58; 68]. In this scenario, they need to secrete effectors into hosts, altering the host energy metabolism pathway or transferring nutrients to the infection site. On the whole, most of these processes can be mediated via protein-protein interactions, including cell surface protein interactions between hosts and pathogens, host cell surface protein and pathogen effector protein interactions, host-secreted protein and pathogen cell surface protein interactions. These proteins often undergo glycosylation during their maturation, playing a significant role in host-pathogen interactions. For example, epithelial human blood group antigens (HBGAs) on \(O\)-glycans function in adherence and invasion of pathogens [69]. Mucin MUC5 AC, which is glycosylated by HBGAs (Lewis b), can promote _Helicobacter pylori_ infection, but mucin MUC6 glycosylated by GlcNAc-capped glycans, can inhibit _H. pylori_ infection [16; 33]. In general, protein glycosylation in host strongly impacts pathogen binding and invasion [70; 71; 72].
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 7 FIT-IoT Performance Evaluation
### Discussion  
Figure 11 illustrates the full set of results for MQTT and CoAP. Even though MQTT attains in 4 out of 7 experiments a lower TTC, on the other 3 experiments MQTT shows a greater variability in results associated with both a higher frequency of messages and a higher number of receivers (MQTT-4, MQTT-6, MQTT-7).  
CoAP attains a higher TTC for scenarios when the frequency of messages is low (CoAP-1 and CoAP-2), but overall it shows a similar TTC across all scenarios. This seems to imply that CoAP is therefore more stable than MQTT to an increase in the number of senders/receivers, as well as to an increase in the frequency of messages to be exchanged.  
In both protocols messages are exchanged asynchronously. In CoAP, messages are stored per sender/server connection, while in MQTT they are only stored if receivers  
Figure 11: FIT-IoT results obtained for MQTT and CoAP.  
(client) subscribed them first. For a large number of receivers, there is the need to complete first subscription. MQTT therefore results in higher TTCs and packet loss for scenarios with a larger number of receivers (MQTT-4 and MQTT-7). While for the case where there is a large number of senders involved, but a low number of receivers (rf. to MQTT-5 or MQTT-6), there is an increase of TTC as expected, but no packet loss.  
The CoAP reliability mechanism provides more stability to an increase in the number of both senders and receivers, thus making this protocol more suitable for large-scale heterogeneous IoT environments.
# A Meshless Radial Point Interpolation Method for Solving Fractional Navier-Stokes Equations
## 5 Conclusions  
This paper thoroughly evaluated the Radial Point Interpolation (RPI) method for time-fractional Navier-Stokes equations, demonstrating its cost-effectiveness and mesh-free approach for two-dimensional problems. Numerical trials, particularly focusing on the Taylor-Green vortex benchmark, highlighted the method's performance and accuracy, identifying strengths and areas for improvement. The analysis revealed the intricate relationship between spatial and temporal discretization, polynomial order, and fractional order in determining the accuracy of a solution. While the RPI method showed promising results, it faces several challenges, particularly in selecting optimal spatial form parameters, handling irregular geometries, and managing high-dimensional problems. The method suffers from scalability issues, computational inefficiency, and numerical instability, with no theoretical convergence guarantee. Proper parameter tuning is crucial yet time-consuming, significantly impacting accuracy and robustness. These factors interact in complex ways, significantly influencing the overall quality of the numerical results. This highlighted the need for more specialized techniques or parameter selection adjustments to improve the method's accuracy and stability.  
Our future research will focus on refining the RPI algorithm to address the current limitations, particularly in pressure field calculations, aiming to further optimize its performance. This will likely involve developing more advanced error reduction techniques and enhancing the algorithm's ability to handle complex, real-world fluid dynamics problems. The goal is to extend the RPI method's applicability to a broader range of challenging scenarios, ultimately making it a more robust tool in computational fluid dynamics.  
[PERSON] led the project, developed the method, identified technical errors, and ensured a thorough review and editing process to verify the manuscript's clarity and cohesion; [PERSON] carried out the investigation, providing expertise in synthesizing and analyzing the literature; [PERSON] contributed to conceptualization and programming; [PERSON] supervised the project, offering guidance on content selection and contributing to the visualization of key findings. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflicts of interest.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
###### Abstract  
In this paper, the issue of roll channel for attitude control for small air-to-surface missiles suffering from multiple disturbances is investigated. The dynamic model of roll channel is established and the roll controller of roll channel based on the active disturbance rejection control (ADRC) is designed. Based on the extended state observer, the disturbance is observed and compensated to track the roll rate accurately. Then, simulations and verification are carried out for rudder efficiency, barycenter location deviation, steering gear stuck, wind disturbance, drift error of inertial measurement unit (IMU) and other disturbances. The control effect is also compared with proportional integral derivative (PID) control and other ADRC algorithms. The results demonstrate that the algorithm has a good ability to suppress multiple disturbances. It can meet the control performance requirements of small air-to-surface missiles.  
extended state observer; attitude control; disturbance suppression Article  
A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 3 Results and Discussion
### Eight-Cluster Model
The large urban/suburban areas of Chicago, Detroit, Minneapolis, Boston, New York City, etc. are in Cluster 7, which is most strongly affected by developed land cover. Additionally, Cluster 1 dominates coastlines both to the ocean and along the Great Lakes, as well as lakes. Figure 4 (left) shows a zoomed-in cluster map of Utah. The west/southwest region of the United States (including Utah) is primarily represented by Clusters 8 and 2, which are most strongly correlated with shrubland and evergreen land cover, respectively. Indeed, we see many mountainous forested regions of Utah (e.g., High Unita Wilderness) grouped into Cluster 2 and flatter desert-like areas (e.g., the Sevi  
Figure 3: Cluster assignments in the Great Lakes region and Northeast as a result of clustering 51 geospatial features with a 270-m spatial resolution. 8. Interestingly, the Great Salt Lake Desert in northwestern Utah is assigned to Cluster 2, despite not containing evergreen trees or having significant slope. However, the Great Salt Lake Desert is a unique region and likely has the most similarities with the climate typical of Cluster 2. In Utah, both the Great Salt Lake and Utah Lake are assigned to Cluster 1, while the most populated cities (e.g., Salt Lake City, Ogden, and Provo) are assigned to Cluster 7. All eight clusters are present in Utah, possibly indicating a wider variety of environments than in many other states. Figure 4 (right) shows a zoomed-in cluster map of the eastern/southeastern coastal plains, much of which are represented by Cluster 3, which is positively correlated with average winter dew point temperatures and wetlands. Deciduous forests are evident in Cluster 5 throughout much of the Piedmont and Appalachian Mountains. The coastline, bays, rivers, and lakes are grouped into Cluster 1, while urban/suburban areas are well-mapped in Cluster 7. In particular, the developed northern Virginia-Washington DC-Baltimore-Philadelphia corridor is striking. As apparent with Clusters 6 and 7, anthropogenic features (e.g., cultivation and urban land development) play a role in landscape-level clusters. However, the way in which anthropogenic features influence clustering varies across spatial scales. For example, urban and suburban areas (Cluster 7) demonstrate a concentrated effect on the clusters, whereas cultivated areas and railroads (Cluster 6) have a much broader effect on clusters that occur over large expanses (e.g., much of the Midwest). Humans have an influential role on ecosystems, and it is not surprising that anthropogenic features may impose additive effects on the clusters across landscapes. Figure 4: Cluster assignments in Utah (**left**) and along the eastern/southeastern coastal plains (**right**) as a result of clustering 51 geospatial features with a 270-m spatial resolution.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology
### Missing Data Imputation Methods  
#### 2.2.1 Li  
LI is a method that replaces median missing data by using data at both ends of the data series. For example, let us consider the sequence of data samples shown in Figure 4, \(\mathrm{D_{n}(T)}=\mathrm{D(nT)}\), with uniform sampling of function \(\mathrm{D(T)}\) with step T.  
In Figure 5, it is assumed that there is a linear relationship between data \(\mathrm{D_{m}(T)}\) and data \(\mathrm{D_{n}(T)}\).  
Thus, \(\mathrm{D_{n}(T)}\) can be calculated by use of Equation (1) as below:  
\[\mathrm{D_{n}(T)}=\ \frac{(\mathrm{m-n})\mathrm{D_{1}(T)}+(\mathrm{n-1}) \mathrm{D_{m}(T)}}{\mathrm{m-1}} \tag{1}\]  
Likewise, other missing data can be filled by LI. This method is stable when missing data are sparse and discrete. Figure 5 depicts the structure of a hypothetical dataset with missing values. If the magnitude of \(\mathrm{n}\) in a dataset is small, LI is easy, fast, affords accuracy, and provides smooth results. These criteria imply that there is a small amount of missing data. This also suggests that the LI is comparatively sound, since data are not likely to fluctuate rapidly in a short time frame. However, when the missing data interval lengths increase, the imputation accuracy degrades severely [22].
## 6 Discussions
### Near-Miss Recognition (Eye-Tracking) Under Stressor Conditions
Based on Kendall tau correlation analysis, extraversion (\(p\)-value = 0.047 < 0.05) significantly correlated with EDR for all stressor conditions. Individuals with high extraversion personalities who are outgoing, bold, and adventurous reacted actively to stressor conditions, which increased their skin conductance and EDR value. Neuroticism personality trait is depicted as correlated with heart rate (HR) measures across all stressor conditions. A significant correlation (\(p\)-value = 0.009 < 0.01) with a negative relationship between neuroticism and heart rate exists during near-miss recognition activity. Individuals who score low on neuroticism traits tend to show calm and relaxed attributes, while individuals under stressor conditions show a significant increase in heart rate measurement due to the activation of the SNS response, triggering their flight or fight response. Conscientiousness and openness showed a correlation between personality traits versus visual attention and adaptation levels of stressor conditions during near-miss recognition. The positive correlation between conscientiousness and the time to first fixation (\(p\)-value = 0.03 < 0.05) indicates that more conscientious individuals are likely to fixate on near-miss incidents late compared to other individuals who are less conscientious. This observation is contrary to previous studies' findings that people with low conscientious traits are usually prone to a higher risk of accidents [102]. This variation in conclusions can result from stressor conditions induced on participants in this study. Openness is the least studied trait concerning safety behavior and near-miss identification. Moreover, openness showed a contradictory correlation between visual attention and adaptation levels for near-miss recognition performance. There was a significant correlation (\(p\)-value = 0.02 < 0.05) and a negative relationship between openness and  
Figure 19: Stressor condition vs. time to first fixation. visual attention (fixation duration). Individuals with high openness scores tend to be curious, intellectual, imaginative, and fixated on the lower duration of near-miss AOIs, whereas a significant correlation (_p_-value = 0.041 < 0.05) and positive relationship between openness and adaptation level (time to first fixation) during near-miss recognition activity was observed.
## 2 Data  
### COSMIC-2 Data and UCAR COSMIC-2 WetPrf and WetPrf2 Data Products  
With a six-satellite constellation at a 24-degree inclination, COSMIC-2 mainly covers 45\({}^{\circ}\)N-45\({}^{\circ}\)S and provides about 5000 occultation profiles daily. Researchers [14; 41] have summarized the applications of the COSMIC-1 mission on NWP, climate, and space weather since 2006. [PERSON] et al. [14] have also summarized the potential challenges of using RO data in the moisture tropical tropospheric regions. Researchers [15; 29; 30; 31] specified that the RO retrieval uncertainty might vary with RO bending angle inversion methodologies, especially in the lower troposphere. COSMIC-2 has three to seven occultations per 5 \(\times\) 5 grid over the 15\({}^{\circ}\)N-15\({}^{\circ}\)S and has one to two occultations from 30\({}^{\circ}\)N-45\({}^{\circ}\) N and 30\({}^{\circ}\)S to 45\({}^{\circ}\)S per 5 \(\times\) 5 grid (Figure 2). Researchers [15] indicated the uniform temporal coverage of the COSMIC-2 occultations occurred mainly between 30\({}^{\circ}\)N-30\({}^{\circ}\)S. COSMIC-2 measurements are of increased signal noise ratio (SNR) compared to other RO missions [15]. Several studies discussed how high-SNR COSMIC-2 data may affect the retrieval results in the lower troposphere [15; 28]. With the higher SNR, researchers [15] showed that more than 85% of COSMIC-2 data penetrate to 2 km altitudes, while other RO missions have about 80% occultations penetrating to the same height. Researchers [35] showed that the mean COSMIC-2 penetration depth (for 80% occultations) within 30\({}^{\circ}\)N-30\({}^{\circ}\)S is 2.0 km altitude, which is 100 m lower than those from GeoOptics receivers.  
The implementation procedures for converting the COSMIC-2 raw measurements to bending angle profiles are detailed at [[https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf](https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf)]([https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf](https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf)) (accessed on 26 September 2022) (NewRAOM hereafter). The UCAR inversion procedures to convert RO doppler to bending angle are described in [29; 30; 31; 42]. We used the COSMIC-2 RO refractivity downloaded from UCAR COSMIC Data Analysis and Archive Center (UCAR/CDAAAC) at [[https://cdaac-www.cosmic.ucar.edu/cdaac/index.html](https://cdaac-www.cosmic.ucar.edu/cdaac/index.html)]([https://cdaac-www.cosmic.ucar.edu/cdaac/index.html](https://cdaac-www.cosmic.ucar.edu/cdaac/index.html)) (accessed on 26 September 2022) in this study.  
Figure 2: Global distribution of COSMIC-2 RO observation number over a 5 \(\times\) 5 latitude and longitude grid for a typical day.
## 3 Material and Methods
### Computational Costs and Scalability  
Given the size of the EPIVIGILA dataset, containing six million rows, computational efficiency is a critical factor when selecting machine learning methods for real-time or large-scale healthcare applications. To evaluate scalability, we recorded the training and test times for each method across the different sampling techniques. The analysis was conducted on an AMD Ryzen 7 5800U processor with 16 GB of RAM, running Windows 10 Enterprise (64-bit).  
Table 10 summarizes the computational costs for training and testing each model. Methods like XGBoost and MLP demonstrated faster training times compared to ensemble methods like bagging and AdaBoost, which required additional computational overhead due to their iterative nature. Sampling techniques such as SMOTE and ADASYN introduced moderate increases in preprocessing time due to synthetic data generation, whereas ROS and RUS were computationally lighter. Despite higher training costs, ensemble, and cost-sensitive methods consistently showed robust performance across evaluation metrics, making them suitable for batch-processing scenarios. For real-time applications, algorithms like SVM and MLP, paired with simpler sampling techniques, offered a balance between efficiency and performance.  
The computational cost table reflects realistic benchmarks, considering algorithm complexity, dataset size, scalability, and test times. Simple models like Naive Bayes and decision trees are faster due to straightforward computations, while ensemble methods like AdaBoost and bagging incur higher costs from iterative training. With six million rows, scalability ratings show Naive Bayes as highly efficient, while XGBoost and SVM perform well due to advanced optimizations. Test times highlight Naive Bayes as faster for single predictions compared to ensemble methods. The values provide a practical basis for comparison, though specific results may vary with hardware and implementation.  
\begin{table}
\begin{tabular}{l l} \hline
**Model** & **Parameters and Values** \\ \hline SVM & kernel = ’rbf’; C = 1.0; gamma = ’scale’ \\ \hline MLP & hidden\_layer\_sizes = (100,); activation = ’relu’; solver = ’adam’; max\_iter = 200 \\ \hline NB & var\_smoothing = 1\(\times\) 10\({}^{-9}\) \\ \hline DT & criterion = ’gini’; max\_depth = None; min\_samples\_split = 2; min\_samples\_leaf = 1 \\ \hline XGBoost & booster = ’gbtree’; learning\_rate = 0.1; max\_depth = 6; n\_estimators = 100; objective = ’binary:logistic’ \\ \hline AdaBoost & base\_estimator = DecisionTreeClassifier(max\_depth = 1); n\_estimators = 50; learning\_rate = 1.0 \\ \hline Bagging & base\_estimator = DecisionTreeClassifier(); n\_estimators = 10; max\_samples = 1.0; bootstrap = True \\ \hline CS RF & n\_estimators = 100; max\_depth = None; sampling\_strategy = ’auto’ \\ \hline CS bagging & base\_estimator = DecisionTreeClassifier(); n\_estimators = 10; sampling\_strategy = ’auto’; replacement = True \\ \hline \end{tabular}
\end{table}
Table 9: Summary of parameters and values for machine learning models.