esa-sceva/satcom-chunk-collection huggingface dataset:
# Balance Control of Brushless Direct Current Motor Driven Two-Rotor UAV
## 3 Mathematical Model and Control of the Two-Rotor UAV System
### Control of 2R-UAV Balance System Using Both Classical PID and 2-DOF PID Controllers
It is an optimization algorithm discovered by [PERSON] and [PERSON] (1995) and created by taking advantage of the swarm behaviour, as in many optimization techniques [25]. All particles in the swarm try to move their position closer to the best position within the swarm, this process continues until the best position is reached [26; 27]. All particles in the PSO algorithm are updated in each iteration with the best solution of the current iteration (\(pbest\)) and the best solution of all iterations (\(gbest\)) [26]. Expressions in which new particle velocities are determined are given in Equation (22), and new particle positions are given in Equation (23). Here, \(X_{n}\) and \(X_{n+1}\) are respectively the current and next positions of the particles, \(V_{n}\) and \(V_{n+1}\) are respectively the current and next speeds of the particles, \(c_{1}\) and \(c_{2}\) are the learning coefficients, and \(w\) is the inertia weight. \(rand_{1}\) and \(rand_{2}\) values are random numbers in the  
Figure 6: Classical and 2-DOF PID-controlled Matlab/Simulink model of the BLDCM driven 2R-UAV balance system. range \([0,1]\), which can optionally be sampled from a uniform distribution. These random values add randomness to the optimization algorithm. \[V_{n+1}=w\;V_{n}+c_{1}\;rand_{1}(\textit{pbest}_{n}-X_{n})+c_{2}\;rand_{2}( \textit{gbest}_{n}-X_{n}) \tag{22}\]  
\[X_{n+1}=X_{n}+V_{n+1} \tag{23}\]  
Figure 7 shows the flow chart of the PSO algorithm. PSO parameters used to determine classical and 2-DOF PID controller optimization process are given in Table 3.  
The cost and PSO algorithms developed in Matlab were used to run the Simulink model shown in Figure 6 in order to find the controller parameters. The time-weighted integral of absolute error (ITAE), whose mathematical formula is provided in Equation (24), was utilized as the cost function when calculating the controller parameters with PSO. \[J_{ITAE}=\int_{0}^{t_{simulation}}t\left|e(t)\right|dt \tag{24}\]  
Here, \(J_{ITAE}\) is the optimization cost or performance index, as stated in the literature, \(t\) refers to time and \(e(t)\) represents the error depending on time. Figure 8 illustrates the change in \(J_{ITAE}\) values during the optimization process used to determine the parameters of the controllers. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Classical PID** & **2-DOF PID** \\ \hline Number of individuals & 100 & 100 \\ Number of iterations & 100 & 100 \\ Number of parameters searched & 6 & 10 \\ Search space & \(0<(K_{p,i,d})<100\) & \(0<(K_{p,i,d})<100\) \\ \(w\) & 0.9 & 0.9 \\ \(c_{1}\) & 0.12 & 0.12 \\ \(c_{2}\) & 1.2 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSO parameters used during optimizations. Figure 7: Flow chart of the PSO algorithm. The optimization-derived PID controller parameters are listed in Table 4.  
The optimization-derived 2-DOF PID controller parameters determined by PSO.
## 3 Results and Discussion
### Evaluation of Fitted Spatial Models  
Recall from Section 2.2 that we consider bivariate spatial models for SIF and XCO\({}_{2}\) (with XCO\({}_{2}\) one month ahead of SIF) for four months in 2021 to represent the four seasons in North America. These models are constructed for SIF in February, April, July, and October 2021, and grouped with the secondary variable XCO\({}_{2}\) in March, May, August, and November 2021. The model is a mixture of 2000 random variables, with \(\alpha=0.  
ber 2021. Figure 5 shows the empirical and fitted (cross-) semivariogram models based on the standardized spatial residuals \(\mathbf{R}_{1}^{*}\) and \(\mathbf{R}_{2}^{*}\), for each of the four months; the fitted model parameters for all four months are available in the Supplementary Material.  
In Figure 5, the empirical semivariograms clearly indicate the presence of small-scale spatial dependence in SIF (and XCO\({}_{2}\)) not captured by the basis function trend surface. The fitted semivariogram models exhibit relatively small correlation lengths (on the order of 50 to 100 km in all cases). In July 2021, the empirical cross-semivariogram indicates pronounced spatial cross-dependence between the small-scale SIF and XCO\({}_{2}\) latent processes, and this is captured by the fitted cross-semivariogram model with cross-correlation coefficient, \(\rho_{12}\approx-0.13\), and cross-correlation length, \(\ell_{12}\approx 300\) km. The smaller cross-correlation coefficients and/or cross-correlation lengths in the other three months considered suggest that the cross-dependence between SIF and XCO\({}_{2}\) decreases when GPP, and hence SIF, is small. This suggests that when SIF activity is not prominent (e.g., when SIF values are small in Figure 1), it may be sufficient to de-noise and gap-fill SIF using the special case of kriging. Still, for many applications (e.g., agricultural planning and yield estimation), seasons of prominent SIF activity, such as the late spring and summer, are of primary interest. For months when SIF activity is prominent, we suggest operational decisions between cokriging and kriging be made based on the presence of dependence in the empirical cross-semivariogram for SIF and XCO\({}_{2}\). In what follows, we consider summer SIF activity through a focus on July 2021 where we have identified cross-dependence between SIF and XCO\({}_{2}\); results for the other three months shown in Figure 5 are provided in the Supplementary Material.
###### Abstract  
In deepwater oil and gas drilling, the high-molecular-weight encapsulator aggravates the thickening of the drilling fluid at low temperatures. Therefore, it is hard to manage the downhole pressure, and drilling fluid loss occurs. In this paper, a zwitterionic polymer P(AM-DMC-AMPS) which was the terpolymer of acrylamide, methacrylatoethyl trimethyl ammonium chloride, and 2-acrylamido-2-methylpropane sulfonic acid, was developed as a low-molecular-weight encapsulator. It was characterized by Fourier transform infrared spectrum analysis, nuclear magnetic resonance, and gel permeation chromatography. Moreover, the low-temperature rheology, shale inhibition and filtration properties of water-based drilling fluids (WBDFs) containing different encapsulators were experimentally investigated and compared. The results showed that the molecular weight of P(AM-DMC-AMPS) was about 260,000, much lower than that of the conventional encapsulators. In the deepwater drilling temperature range 4-75 \({}^{\circ}\)C, WBDF containing P(AM-DMC-AMPS) had lower and more stable rheological property because of its short molecular chains. The high shale recovery rate and low swelling rate indicated its strong shale inhibition performance, owing to its adsorption on the clay surface and the wrapping effect through both hydrogen bonding and electrostatic interaction. It also improved the filtration property of WBDF, and was compatible with other WBDF components. This product is expected to simultaneously realize the good encapsulation performance and low-temperature rheological property for deepwater drilling fluid.  
low-molecular-weight encapsulator; deepwater drilling fluid; low-temperature rheological property; shale inhibition; filtration property; zwitterionic polymer +
Footnote †: journal: Applied Physics Letters
# Norovirus Detection in Ready-To-Eat Salads by Propidium Monoazide Real Time RT-PCR Assay
## Materials and Methods
### Detection of NoV GI and GII with PMAxx Real Time RT-PCR
#### 2.3.3 Mengovirus Real Time RT-PCR  
In accordance with ISO/TS 15216-2 (2017), Mengo virus real time RT-PCR was conducted in order to assess extraction efficiency. Briefly, the assay was performed using an RNA UltraSense(tm) One-Step Quantitative RT-PCR System (Thermo Fisher Scientific), 250 nM probe (labeled with FAM), 500 nM forward primer, and 900 nM reverse primer. The used primers amplify a region between 110 and 209 nucleotides corresponding to the deleatant mengo virus strain MC\({}_{0}\), giving a product of 100 bp; forward primer Mengo110 (5'-GCGGCGTCCTGCCGAAAAGT-3'), reverse primer Mengo209 (5'-GAAGTAACATATAGACAGACGCACAC-3'), and probe Mengo147 (5'FAM-ATCACATTACTGGCCGAACG-BNFQ3') were employed. Mengo virus RNA was extracted by heating the virus at 95 \({}^{\circ}\)C for 5 \(\pm\) 0.5 min, chilling, and centrifuging at \(\geq\) 3.000 g for 1 min. Then, the supernatant containing virus RNA was used as a template. The standard curve was performed by adding 5 uL of undiluted, 10\({}^{-1}\), 10\({}^{-2}\), and 10\({}^{-3}\) mengo virus RNA to 20 uL of RNA UltraSense(tm) One-Step Quantitative RT-PCR System (Thermo Fisher Scientific). The thermocycling conditions included an RT step at 55 \({}^{\circ}\)C for 1 h, followed by 5 min at 95 \({}^{\circ}\)C and 45 cycles of 15 s at 95 \({}^{\circ}\)C, 1 min at 60 \({}^{\circ}\)C, and 1 min at 65 \({}^{\circ}\)C. Curves with at least three points close to the line of the best fit and Pearson's coefficients of determination (R2) values \(>\) 0.98 were used for calculations. The percentage extraction efficiency for each sample was determined by comparing the Ct values for the sample RNA extract with those for the mengo virus dilution series. Any sample with an extraction efficiency of \(<\)1% was re-analyzed, first by re-extracting the viral RNA from the stored homogenate, then by testing the stored RTE salads. Results for any sample giving three extraction efficiency results of \(<\)1% were considered invalid.
# Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale
## 1 Introduction
As low-Earth orbiting satellites such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory have been designed to sample the diurnal variations of precipitation, their integration with polar-orbiting satellites needs a robust inter-calibration due to significant differences in sensor characteristics [19; 20; 21; 22]. This study utilizes a constellation of PMW instruments onboard both sun-synchronous and non-sun-synchronous satellites such as the GPM Microwave Imager (GMI) onboardthe GPM Core Observatory, the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission (GCOM-W1) satellite, and the Special Sensor Microwave Imager/Sounder (SSMIS) sensors onboard the US Air Force Defense Meteorological Satellite Program (DMSP) satellites. Retrievals from all these sensors will fill temporal data coverage gaps and potentially enhance the accuracy of predicting the estimated time of FT transition states for each land cover type and region. The proposed data fusion is performed for 2016 where a half-hourly diurnal cycle of the PMW TBs is rendered. It is expected that the diurnal cycle patterns of TBs vary locally with season and land cover type, and are different in magnitudes for different frequencies and polarizations [6; 7]. Hence, the objective of this study is twofold: (1) to generate half-hourly TB diurnal cycle from combination of the intercalibrated GMI, AMSR2, and SSMIS measurements for common channels, and (2) to depict spatially consistent maps of diurnal brightness temperature range (DTR) and assess its variability for different vegetation types at the global scale. This is the first time that we are able to construct TB diurnal cycle with the existence of time varying GMI TB observations along with sun-synchronous radiometers at global scale. Section 2 describes the datasets used in this study, and the method for the computation of TB diurnal cycle is described in detail in Section 3. Results are presented and discussed in Section 4, and conclusions are outlined in Section 5.
## Abstract  
This study aimed to compare the anti-inflammatory activity of 10-year-old _Dendropanax morbifera_ Levy (DM) leaf extracts. The leaves were collected during different seasons (May, August, and November), and the extracts were prepared using different methods (hot water, 30% ethanol, or 60% ethanol). Lipopolysaccharide-stimulated RAW264.7 cells were treated with these extracts for 12 h. The anti-inflammatory effects were evaluated by measuring the production of nitrite; prostaglandin E\({}_{2}\) (PGE\({}_{2}\)); and inflammatory cytokines such as interleukin (IL)-6, IL-1\(\beta\), and tumor necrosis factor-alpha, in addition to the mRNA expression levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 and activation of the nuclear factor xB (NF-kB)/mitogen-activated protein kinase (MAPK) pathways. The amyrin and polyphenol compositions of the extracts were analyzed using a triple time-of-flight mass spectrometer and high-performance liquid chromatography. The 30% ethanol extract harvested in May and 60% ethanol extracts collected in August and November displayed the highest inhibitions of nitrite, PGE\({}_{2}\), and inflammatory cytokines. The 60% ethanol extract harvested in August suppressed activation of the NF-kB and MAPK signaling pathways. The contents of amyrin and polyphenol compounds were highly dependent on the ethanol concentration used during each season. These results suggest that ethanol extracts of DM leaves may have the potential to regulate inflammatory responses.  
Keywords:anti-inflammatory activity; _Dendropanax morbifera_; amyrin; RAW264.7 macrophage; NF-kB/MAPK pathways +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results
### Comparison with State-of-the-Art Techniques  
In this section, we compare the ReTrackVLM performance against state-of-the-art MOT methods across benchmark datasets, including MOT15, MOT16/17, MOT20, DanceTrack, and WildTrack. We evaluate the effectiveness of the cross-modal embeddings module, zero-shot ReID integration, and the performance enhancements from fine-tuning YOLOv8 as the detector. Key metrics such as MOTA, MOTP, and IDSw are assessed to gauge our approach against recent advancements. Distinct fine-tuned weights were employed for the YOLOv8 detector and CLIP models, optimized for the specific datasets to ensure peak performance in detection and ReID tasks. We selected various methods for comparison--DeepSort, ByteTrack, Deep-OC-SORT, StrongSORT, BoostTrack, SFSORT, Hybrid-SORT, UCMCTrack, TrackFormer, MOTR, and TransTrack--due to their strong performance across the datasets and their compatibility with our evaluation. These methods are recognized for robust detection and track management, with advanced features like occlusion handling and hybrid strategies, aligning with our goal of a comprehensive comparison. We present a comprehensive comparison of MOT performance across benchmark datasets in Table 5, evaluating several state-of-the-art trackers alongside our proposed ReTrackVLM model. For MOT15, ReTrackVLM achieves competitive results, scoring highest in PT (286) and solidly in MOTA (84.0) and MOTP (88.6), though it has a higher IDSw (1147) than top methods like Hybrid-SORT and SFSORT. In MOT16/17, ReTrackVLM leads in MOTA (79.7) and PT (428) but records a higher IDSw (1889) than some competitors. For MOT20, it excels in MOTA (78.0) and shows balanced MT (727) and low ML (234), demonstrating robustness in challenging environments. While other methods like Deep-OC-SORT and BoostTrack score higher in DanceTrack, ReTrackVLM maintains strong performance across metrics, indicating its versatility. Figure 7: Qualitative trajectory tracking results of ReTrackVLM on DanceTrack, MOT15, MOT16, MOT17, and MOT20 datasets. For each dataset, trajectories are visualized at t = 1, t = 100, t = 200, and t = N, with N representing the total frame count (except for MOT20, where t = N is excluded due to excessive overlap). Below the visualizations, the number of tracks per frame is plotted, reflecting the session’s complexity and total duration. One can state that ReTrackVLM delivers consistently competitive results across multiple datasets, particularly excelling in MOTA and PT metrics, showcasing its strength in track consistency and accuracy. However, it shows potential for improvement in identity matching, especially in crowded scenes like MOT20, and could benefit from further optimization in complex motion tasks, as evidenced by its performance on DanceTrack. These findings suggest that ReTrackVLM strikes a balanced trade-off between accuracy and robustness but requires fine-tuning to address IDSw challenges.
## 5 Environmental Applications
### Air Samples

Table 4: _Cont._  
Typical fields of applications are the analysis of VOC, odorous compounds, or micro-pollutants in indoor air, atmospheric air, or gaseous effluents from industrial or agricultural operations. For example, [PERSON] and [PERSON] [82] developed a simple and fast method based on on-fiber derivatization using BSTFA for the analysis of formaldehyde and other carbonyl compounds in indoor air. For a 5 min fiber exposure time, the limits of detection were below 0.5 \(\upmu\)g/m\({}^{3}\), the average reproducibility was 15%, and the linearity of the calibration curves was satisfactory. Of the micro-pollutants, formaldehyde is of particular concern as it was recognized as \"carcinogenic to humans\" (class 1) by the International Agency for Research on Cancer. It is found in nearly every indoor atmosphere because of its numerous sources, such as furniture, human activity (tobacco), glues, and varnishes [82]. Several authors used SPME as a screening method for the characterization of odorous compounds in gaseous effluents, e.g., livestock operations [320] or production of poultry feather and viscera meal [325] in order to develop suitable abatement strategies. SPME can also be used for analysis of airborne particulate matter after collection on filters [330,333].
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 4 Open Research Directions
### Integration of Blockchain and IoV  
Blockchains can provide a large number of innovative solutions in most IoV application scenarios. In most IoV scenarios, many data are generated and exchanged. There are many classical technologies that cannot be effectively applied to IoV scenarios. In addition, in this scenario, increasing communication connections may cause security holes. Another aspect is that the introduction of blockchain in the IoV not only improves trust, security, and privacy but also improves system performance and automation. Therefore, in this scenario, the powerful technology of blockchain should be used to improve the flexibility of the system and the ability to process a large amount of data. However, as blockchain research is still in its infancy, not only is it complex to analyze, but there are still many issues to be solved, relating to regulatory and legal concerns, performance, limited storage, security and privacy, optimized consensus, and incentive mechanisms [68].
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 3 Theoretical Strain Transfer Analysis
### Strain Transfer Rate Distribution of the \(n\)-Segment of the Optical Fiber  
The average strain transfer rate is the average strain in the affixed range of the optical fiber divided by the matrix strain. Average strain transfer rate of the \(n\)-segment is:  
\[\overline{\alpha}=\frac{\overline{\epsilon_{g}}}{\epsilon_{i}}=\frac{\int_{0 }^{\mathrm{L}_{1}}\epsilon_{g}(x)\mathrm{d}x}{\epsilon_{i}\cdot\mathrm{L}_{1} }=\frac{1}{a}\bigg{\{}1-\frac{\sinh(\sqrt{a}\cdot k\cdot\mathrm{L}_{1})}{ \sqrt{a}\cdot k\cdot\mathrm{L}_{1}\cdot\cosh(\sqrt{a}\cdot k\cdot\mathrm{L}_{ 1})}\bigg{\}} \tag{24}\]  
According to the equivalent section method, equilibrium equations of B+ and B\(-\) sections are presented as follows:  
\[\mathrm{E}_{g}\cdot\epsilon_{g2}(x)\cdot\pi\cdot r_{g}^{2}=\mathrm{E}_{g} \cdot\overline{\epsilon_{g}}(x)\cdot\pi\cdot r_{g}^{2} \tag{25}\]  
\[\epsilon_{g2}(x)=\overline{\epsilon_{g}}(x) \tag{26}\]The strain transfer rate of a clamped FBG sensor is equal to the average strain transfer rate in the range of afixed optical fiber:  
\[\alpha=\overline{\alpha}=\frac{1}{a}\bigg{\{}1-\frac{\sinh(\sqrt{a}\cdot k\cdot \mathrm{L}_{1})}{\sqrt{a}\cdot k\cdot\mathrm{L}_{1}\cdot\cosh(\sqrt{a}\cdot k \cdot\mathrm{L}_{1})}\bigg{\}} \tag{27}\]  
The \(m\)-segment of the optical fiber is constrained by the interlayer with uniformly distributed surface shear stress, generating the non-uniform axial strain as observed in Equation (23). Because there are no constraints, the \(n\)-segment of the optical fiber has a constant section stress with the uniform axial strain as shown in Equation (27). For clamped FBG sensors, the strain transfer rate is equal to the average strain transfer rate in the affixed range of optical fiber. In contrast, the strain transfer rate of an embedded FBG sensor is equal to maximum strain transfer rate along the optical fiber.
## 3 Results
### Achieved Accuracy of the Artificial Neural Network  
In this section, the result of ANN approximation with its best configuration found was validated with the results of traditional calculations, i.e., without the use of ANN, but with the use of [PERSON]'s algorithm [10]. As can be concluded by analyzing the graphs in Figures 5 and 6, the best results, i.e., those with the lowest MSE, were obtained by a two-layer network with 1024 and 8192 neurons. The model of this network was used to compare the results from ANN with the results from the homogenization algorithm. Six examples were selected to show the actual performance of the best ANN. The parameters of six examples of RVE considered are presented in Table 3. They were generated randomly within \begin{table}
\begin{tabular}{c|c c c} \hline \hline \multirow{2}{*}{**Effective Stiffness**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Homogenization**} & \multirow{2}{*}{**Error**} \\  & \multicolumn{2}{c}{**Homogenization**} & [10] & \\ \hline \(A_{11}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 109.0 & 109.1 & 0.09\% \\ \(A_{22}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 130.1 & 132.0 & 1.44\% \\ \(A_{12}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 25.53 & 25.52 & 0.04\% \\ \(A_{33}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 44.68 & 44.61 & 0.16\% \\ \(D_{11}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.2419 & 0.2441 & 0.9\% \\ \(D_{22}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.2613 & 0.2697 & 3.11\% \\ \(D_{12}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.0543 & 0.0545 & 0.37\% \\ \(D_{33}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.0949 & 0.0952 & 0.32\% \\ \(R_{44}\) & \(\mathrm{\left|Pa\cdot m\right|}\) & 36.15 & 36.2 & 0.14\% \\ \(R_{55}\) & \(\mathrm{\left|Pa\cdot m\right|}\) & 41.08 & 40.86 & 0.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the effective stiffnesses for the first example. \begin{table}
\begin{tabular}{c|c c c} \hline \hline \multirow{2}{*}{**Effective Stiffness**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Homogenization**} & \multirow{2}{*}{**Error**} \\  & \multicolumn{2}{c}{**Homogenization**} & [10] & \\ \hline \(A_{11}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 90.64 & 90.33 & 0.34\% \\ \(A_{22}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 125.8 & 127.6 & 1.41\% \\ \(A_{12}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 21.23 & 21.1 & 0.62\% \\ \(A_{33}\) & \(\mathrm{\left|kPa\cdot m\right|}\) & 39.05 & 39.1 & 0.13\% \\ \(D_{11}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.2946 & 0.2967 & 0.71\% \\ \(D_{22}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.3296 & 0.3396 & 2.94\% \\ \(D_{12}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.0667 & 0.067 & 0.45\% \\ \(D_{33}\) & \(\mathrm{\left|Pa\cdot m^{3}\right|}\) & 0.1164 & 0.1173 & 0.77\% \\ \(R_{44}\) & \(\mathrm{\left|Pa\cdot m\right|}\) & 31.09 & 30.88 & 0.68\% \\ \(R_{55}\) & \(\mathrm{\left|Pa\cdot m\right|}\) & 40.50 & 40.64 & 0.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the effective stiffnesses for the third example.