esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Generalization of the SVM-ML Model  
Using the RSP features during stimulation with cotton fabric as the baseline inputs, the SVM-ML model was established. Then, we first investigated the generalization performance of the model for classifying the upper-limb groups with the inputs of different fabrics, i.e., nylon, wool, and cotton. The measured RSP features in the respective stimulations were then input to the developed model, and the achieved accuracies are summarized in Table 4. The classification accuracies of the different fabric stimulations were not normally distributed (\(p<0.5\), Shapiro-Wilk test) in each frequency band. Significant intergroup differences in the accuracies (\(p<0.001\), Kruskal-Wallis test) with respect to fabric stimulation were observed in the delta, theta, alpha, beta, and gamma bands. The model achieved the highest classification accuracies of 75.4%, 83.5%, and 84.3% for the cotton, nylon, and wool stimulations, respectively, in the gamma band. Figure 4: Grid search results of \(\gamma\) and C in the SVM-ML model with RBF kernel using the 21-channel EEG RSP features of the delta, theta, alpha, beta, and gamma bands. Acc: accuracy. Peak Acc: highest classification accuracy of the SVM-ML model in the predefined range of (\(\gamma\), C); Peak Loc: location (\(\gamma\), C) corresponding to the highest classification accuracy of the SVM-ML model. The comparison of the overall accuracies of the SVM-ML model with respect to fabric stimulation in each band are shown in Figure 5. Significant differences in the accuracies were observed in the delta, theta, alpha, beta, and gamma bands for pairwise comparisons among the three different fabric stimulations (\(p<0.001\), Kruskal-Wallis with Bonferroni post-hoc test), except for the difference between nylon and wool in the gamma band (\(p>0.05\), Kruskal-Wallis with Bonferroni post-hoc test). The models with nylon and wool achieved significantly higher accuracies in the beta and gamma bands than those with cotton (\(p<0.001\), Kruskal-Wallis with [PERSON] post-hoc test). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fabric**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{2-7}  & **Delta** & **Theta** & **Theta** & **Delta** & **Beta** & **Gamma** \\ \hline Cotton & 53.2\% \(\pm\) 0.05 & 35.0\% \(\pm\) 0.06 & 22.3\% \(\pm\) 0.05 & 50.8\% \(\pm\) 0.05 & 75.4\% \(\pm\) 0.04 \\ \hline Nylon & 21.0\% \(\pm\) 0.04 & 40.6\% \(\pm\) 0.05 & 51.4\% \(\pm\) 0.04 & 63.2\% \(\pm\) 0.03 & 83.5\% \(\pm\) 0.02 \\ \hline Wool & 30.3\% \(\pm\) 0.04 & 25.6\% \(\pm\) 0.06 & 43.0\% \(\pm\) 0.05 & 69.2\% \(\pm\) 0.04 & 84.3\% \(\pm\) 0.03 \\ \hline Significance (\(p\)-value) & \textless{}0.001 *** & \textless{}0.001 *** & \textless{}0.001 *** & \textless{}0.001 *** & \textless{}0.001 *** \\ \hline \end{tabular} Note: Data are given as mean \(\pm\) SD. The significant differences are indicated by *** (\(p<0.001\), Kruskal–Wallis test).  
\end{table} 
Table 4: Overall accuracies of the SVM-ML model for classifying different fabric stimulations.
# Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
## Dynamic Response Analysis
### Ground Motion Input  
The engineering foundation site classification is Class II, and the seismic intensity scale is VIII. To better understand the structural vibration response, damage development process, and damage degree caused by strong earthquakes, the data from the strong earthquake database of the Pacific Earthquake Engineering Research (PEER) Centre was used as a reference to select waves with a magnitude of 6-8, with shear wave velocity of 500-750 m/s, and epicentral distance of 0-105 km. The natural wave Northridge-01 (Whittier-S. Alta Dr), which was close to the characteristic site period of 0.4 s, has been chosen, following the frequency band fixation method proposed by the Applied Technology Council (ATC-3) in 1978 [36]. The basic parameters of selected natural waves are shown in Table 3. The horizontal peak acceleration of severe earthquakes is 0.19 g, and the ratio to the vertical peak acceleration is 1:0.65. The seismic wave input interval is 0.01 s, and the total duration is 25 s. Two acceleration time-history curves are shown in Figure 3. The additional mass approach proposed by [PERSON] in 1933 during a seismic study on the dam body is also used when considering the force of the seismic load on the water body [37].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Elastic Modulus (GPa)** & **Density (Kg\(\cdot\)m\({}^{-3}\))** & **Poisson Ratio** & **Tensile Strength (MPa)** & **Compressive Strength (MPa)** \\ \hline Cushion caps(C30) & 30.0 & 2385 & 0.167 & 1.43 & 14.3 \\ Pier shafts/Pier & 32.5 & 2400 & 0.167 & 1.71 & 19.1 \\ caps(C40) & 34.5 & 2500 & 0.167 & 1.89 & 23.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Various material parameters of the aqueduct.  
Figure 2: Aqueduct structure model. (**a**) Numerical model of the aqueduct structure; (**b**) Aqueduct structure details. a, b, c, and d are feature points of damage.
# Comparison of Analytical Methods for Determining Methylesterification and Acetylation of Pectin
## 1 Introduction  
Pectin is a complex polysaccharide with diverse structures in the primary cell wall and middle lamellae of plants [1]. Pectin plays a crucial role in regulating the mechanical properties of the cell wall and has several industrial applications related to its gelling properties [2, 3]. Pectin can be esterified with methanol or acetic acid. Generally, methylesterification is widespread in pectin, whereas acetyl-esterification is less abundant [4]. The degree of methylesterification (DM) is defined as the molar percentage of carboxyl groups esterified with the methyl group. The degree of acetylation (DAc) is defined as the molar percentage of galacturonosyl residues esterified with the acetyl group [5]. DM and DAc have significant impacts on the properties and physiological activities of pectin and play an important role in the strength and flexibility of plant cell walls, such as fruit softening during storage which is caused by the de-esterification of pectin [6, 7]. DM and DAc also influence the gelling, stabilization, and thickening of pectin. DM dictates the conditions and mechanisms of gel formation [8, 9]. The pharmacological activities of pectin are also correlated to the degree of DM and DAc formation. It has been reported that low DM pectin can significantly reduce cholesterol concentration in hamster plasma and liver compared to high DM pectin [10]. Characterization of pectin includes not only analysis of sugar composition and molecular weight, but also of the DM and DAc.  
Until now, various methods have been established for the determination of DM and DAc. The earliest approach used for determining the degree of DM was titration, including acid-base titration, colloid titration, and isoelectric spot determination [11, 12, 13], often used in industrial production. In recent years, FT-IR has often been used to determine the DMof pectin, because this approach requires no pretreatment of the sample [7; 14; 15]. The kit manufactured by Megazyme (i.e., Acetic Acid Assay Kit) can be used to detect the content of acetic acid quantitatively following the saponification of pectin [16]. HPLC and GC methods have also been reported for simultaneously determining the DM and DAc of pectin. With these methods, pectin is saponified to produce methanol and acetic acid, and then detected by HPLC or GC [17; 18; 19; 20]. Additionally, \({}^{1}\)H-NMR has been used in determining the DM and DAc of pectin, but this method is seldom used because of its higher cost [21; 22]. There were some reports concentrated on the determination of DM by different methods and discussed advantages and limitations of the applied methods [17; 23; 24; 25].  
In this paper, we compared three frequently used approaches, including titration, FT-IR, and HPLC for determining the DM and DAc of citrus pectin, and listed the advantages and limitations of these methods. Furthermore, we improved the sample pro-treatment and neutralization process of HPLC analysis, which can simplify the operation.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 6 Conclusions  
This paper primarily addresses the issue of trajectory oscillations that may arise in Euclidean space filters, particularly when faced with increased noise and changing environmental characteristics. By introducing the concept of manifolds into the Unscented Kalman Filter (UKF) and extending the dimension of the groups, we present a three-dimensional manifold localization framework (LIF-M) tailored for unstructured environments. Furthermore, the system's overall performance is enhanced through auxiliary relocalization and point cloud counter-distortion techniques.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Path** & **Max Error/m** \\ \hline
1 & \([-95.2,\,-31.1]\)\(->\)\([43.2,\,-101.2]\) & 0.0243 \\
2 & \([-115.1,\,-28.5]\)\(->\)\([53.2,\,-105.4]\) & 0.01687 \\
3 & \([84.1,\,-52.3]\)\(->\)\([67.3,\,101.3]\) & 0.00532 \\
4 & \([2.35,\,1.92]\)\(->\)\([-27.3,\,-56.2]\) & 0.0482 \\
5 & \([-59.3,\,52.3]\)\(->\)\([79.79,\,-38.2]\) & 0.0651 \\
6 & \([-78.7,\,50.24]\)\(->\)\([-114.5,\,-25.1]\) & 0.0091 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of dynamic performance testing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Point Position** & **Max Error/m** \\ \hline
1 & \([-95.4,\,19.2]\) & 0.042 \\
2 & \([-71.2,\,39.4]\) & 0.051 \\
3 & \([0.0,\,-5.39]\) & 0.055 \\
4 & \([-10.2,\,-28.7]\) & 0.031 \\
5 & \([83.2,\,-39.3]\) & 0.033 \\
6 & \([59.7,\,-59.5]\) & 0.024 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of static performance testing.  
In terms of localization performance, our approach bears similarities to the iterative error-based approach to Localization in Non-Structural environments (LINS), while exhibiting improved robustness against disturbances compared to the standard UKF. Our results underscore the crucial assistance that state updates in manifold space provide in attitude estimation.  
Finally, it is apparent that the sensitivity of the algorithm on the z-axis plays a role in promoting the global localization of humanoid robots.  
Software, S.Z.; Validation, S.Z.; Formal analysis, S.Z.; Resources, Y.L.; Data curation, Y.L.; Writing--original draft, S.Z.; Writing--review & editing, Q.L.; Supervision, Q.L.; Project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data in figures and tables used to support the findings of this study are included herein.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Synthesis of L-Glutamine Derivative **4** and **5**
#### 2.3.9. (S)-22-Amino-2,19-dioxo-9,12,15-trioxa-3-thia-18-azatricosan-23-oic acid **26**  
Compound **25** (50 mg, 0.144 mmol) was dissolved in MeOH (1.5 mL, 0.1 M). Thioacetic acid (0.19 mmol) and DPAP (0.03 mmol) were added to this solution and the reaction was stirred at r.t. under UV light exposure at 365 nm. After 2 h, TLC (CH\({}_{2}\)Cl\({}_{2}\)/MeOH/NH\({}_{3}\) (aq) 7:3:0.2) indicated the complete conversion of the starting compound; the reaction was concentrated and the residue was purified by flash chromatography (silica gel, eluentCH\({}_{2}\)Cl\({}_{2}\)/MeOH/NH\({}_{3}\) (aq) 7:3:0.2). A total of 32 mg (0.075 mmol) of thioester **26** was obtained (53% yield).  
\({}^{1}\)H NMR (500 MHz, CD\({}_{3}\)OD) \(\delta\) 3.66-3.60 (m, 7H, -CH\({}_{2}\)CH\({}_{2}\)O-, H2), 3.60-3.57 (m, 2H, -CH\({}_{2}\)CH\({}_{2}\)O-), 3.55 (t, J = 5.4 Hz, 2H, H7), 3.48 (t, J = 6.5 Hz, 2H, H12), 3.37 (t, J = 5.5 Hz, 2H, H6), 2.87 (t, J = 7.3 Hz, 2H, H16a,b), 2.45 (t, J = 7.1 Hz, 2H, H4a,b), 2.30 (s, 3H, CH\({}_{3
# Targeted Molecular Imaging of Pancreatic Cancer with a Miniature Endoscope
## 2 Materials and Methods  
### Animal Model and Multifunctional Nanoparticles  
In this study, a mouse pancreatic cancer cell line panc02-derived tumor model was used. The pancreatic tumor model was established by injecting pancreatic cancer MIA PaCa-2 cells into the pancreas of 8-week-old female nude mice using a surgical procedure approved by the Institute of Animal Use Committee (IACUC) of Emory University. Strict animal care procedures approved by the Emory University IACUC and based on guidelines from the National Institutes of Health (NIH) guide for the Care and Use of Laboratory Animals were followed. Orthotopically xenografted pancreatic tumors reached several millimeters in diameter and were ready for experiments in about 3 to 4 weeks. Six mice separated into two groups (three in each group) were used in this study to prove the concept of our miniaturized endoscopic imaging system for pancreatic cancer. For systemic delivery, 100 picomoles (pmol) with or without targeting IONPs, with or without polyethylene glycol (PEG) coating IONPs, were injected into the tail vein of the mice once per week for a total of two to four injections. In each group, each mouse received the injection of one of the three types of multifunctional nanoparticles: (1) near-infrared (NIR) 830-maleimide fluorescence dye conjugated to the 10-nm core size IONPs without targeting ligands, NIR830-IONP; (2) NIR830-IONP plus conjugation of amino-terminal fragment (ATF) targeting, NIR830-ATF-IONP; (3) NIR830-ATF-IONP plus polyethylene glycol (PEG) coating, NIR830-ATF-PEG-IONP. Thus, for each type of nanoparticles, two mice were used for the experiments. PEG was used to stabilize the nanoparticles and to modify surface properties to reduce the non-specific uptake of nanoparticles by macrophages in the reticuloendothelial system to improve the targeted delivery of the nanoparticles [34].
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 5 Results
### User Traffic Prediction Module Results
#### 5.1.1 Long Short-Term Memory Results  
The architecture described in Section 4.1.1 resulted from trials conducted with different configurations of LSTM-based architectures. The models were trained to a maximum of 100 epochs with a mechanism for early stopping (i.e., the 100 epochs were usually not reached due to early stopping).  
In Table 2, the tests carried out to select the number of LSTM layers, which are the core part of any LSTM model, are recorded. The results show that a two-layer LSTM is better, and this seems to align with fundamental principles of DL regarding the complexity  
Figure 3: A general overview of our CNN-based user allocation module.  
and bias-variance trade-off of models [35]. An observation from the results is that the one-layer LSTM slightly underfits and more than 2 layers slightly overfit the dataset with the current hardware.  
In Figure 4, the predicted next 60 time steps of user traffic of an instance of the dataset are depicted. As shown in Figure 4, the first few time steps have very high accuracy, and the further the predictions move from these time steps, the greater the chance of inaccurate predictions, as can be seen in the time step 50 and thereafter. Though the general trend of the traffic seems to be predicted correctly, the actual value of the users is miscalculated. Another interesting detail is that some spikes of 1 time step in traffic are usually not predicted as they are not part of some trend and seem to be accidents in the general trend of the traffic caused by some external factor.  
The results are satisfactory and show a very promising inclination to improve with just simple hardware upgrades as the dataset is large enough to support stronger and larger training trials. More specifically, the error in predictions is significantly lower than 1%, and the absolute error being about 1000 users is an excellent result. The latter might suggest the ability for further prediction in time with insignificant inaccuracies.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
###### Abstract  
Weed detection technology is of paramount significance in achieving automation and intelligence in weed control. Nevertheless, it grapples with several formidable challenges, including imprecise small target detection, high computational demands, inadequate real-time performance, and susceptibility to environmental background interference. In response to these practical issues, we introduce CCCS-YOLO, a lightweight weed detection algorithm, built upon enhancements to the Yolov5s framework. In this study, the Faster_Block is integrated into the C3 module of the YOLOv5s neck network, creating the C3_Faster module. This modification not only streamlines the network but also significantly amplifies its detection capabilities. Subsequently, the context aggregation module is enhanced in the head by improving the convolution blocks, strengthening the network's ability to distinguish between background and targets. Furthermore, the lightweight Content-Aware ReAssembly of Feature (CARAFE) module is employed to replace the upsampling module in the neck network, enhancing the performance of small target detection and promoting the fusion of contextual information. Finally, Soft-NMS-EIoU is utilized to replace the NMS and CIoU modules in YOLOv5s, enhancing the accuracy of target detection under dense conditions. Through detection on a publicly available sugar beet weed dataset and sesame weed datasets, the improved algorithm exhibits significant improvement in detection performance compared to YOLOv5s and demonstrates certain advancements over classical networks such as YOLOv7 and YOLOv8.  
Weeds detection; Yolov5s; C3_Faster; CARAFE; contest aggregation; Soft-NMS-EIoU +
Footnote †: journal: Applied sciences
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 3 Proposed Method
### Parameter Refinement  
We also propose a very fast refinement algorithm inspired by [PERSON] et al. [10]. It is very simplified to reduce the number of iterations. Instead of going over every 4 possible values for the affine parameter, the proposed method encoder only checks the neighbors with a step size of \(2\Delta\), refines to \(\Delta\) and then refines the quarter pixel translation parameter only once. In case the best value for the affine parameter is zero after the initial neighbor check, the refinement is aborted. In this case, only four affine prediction estimations had to be performed, much fewer than in [PERSON] et al. even in the cases of an early abort.
# Brief Review of Recent Developments in Fiber Lasers
## 6 Fiber Laser Applications
### Material Processing  
The application of fiber lasers in the field of material processing mainly includes laser cutting, laser welding, and laser marking. High-speed and high-precision material processing can be achieved using high-power and high-beam-quality fiber lasers.  
The use of fiber lasers has allowed, for the first time, for the generation of striation-free cuts in the case of oxygen-assisted laser cutting of 1 mm and 2 mm thick mild steel sheets [103]. Nowadays, fiber laser flexibility enables the cutting of a wide range of materials from steel and aluminum to nonmetal materials such as plastics and glass of different thicknesses. The fiber laser cutting process results in a high-quality cutting edge. High-power CW fiber lasers are commonly used for industrial applications. As the power output increases, the cutting speed and the ability to process thicker materials improve, making them ideal for high-volume production.  
The excellent mode quality and high focusability of low-power single-mode fiber lasers are the most important features for achieving small spot sizes in the range of some microns. As a result, the welding of microparts with very high processing speeds, the lowest energy input, reduced heat-affected zones, and minimized distortion has been achieved [104]. At present, fiber laser welding creates strong welds and good-quality joints, even with dissimilar materials. With the benefit of consistent accuracy, laser welding is well suited to manufacturing processes with repetitive operations. Fiber lasers are also very useful in microprecision welding and laser spot welds.  
Fiber lasers are perfect for laser marking. Laser marking is a process in which a laser beam is used to mark or engrave a surface at a high speed and low-operating cost.
## 5 Performance and CPU Usage Analysis
### Random Read/Write  
Figure 7 depicts the performance for random read and random write operations. First, for random read, runc shows the highest performance (2458 MB/s on average), while the Kata containers have the lowest performance (422 MB/s on average) on average. Also, the performance of all runtimes increases as the block size increases. Similar to sequential read, Firecracker achieves a higher performance than gVisor and Kata containers. For the 1 MB blocks, Firecracker shows the highest performance (4749 MB/s on average), which is 4.3 times higher than the Kata containers (1113 MB/s on average) and 26.1% higher than gVisor (3766 MB/s on average). Regarding the performance for random write, Firecracker achieves the highest performance (3842 MB/s on average), while the Kata containers show the lowest performance (302 MB/s on average) independent of the block size. For the 1 MB blocks, Firecracker reaches 4671 MB/s, which is 5.7 times higher than the Kata containers (815 MB/s on average).  
Also, we measure the CPU usage of the runtimes for the random read and write as in Figure 8. It shows that there is the significant lowait for random read with small blocks such as 4 KB and 16 KB, which explains the low performance of Figure 6(a) for the small blocks. Also, comparing with the sequential read in Figure 5(a), the random read in Figure 7(a) has a larger Iowait for all runtimes: For the sequential and random read, this is 7.4% and  
Figure 6: CPU usage in processing sequential file I/O operations under runc (R), Kata containers (K), gVisor (G), and Firecracker (F). (**a**) Sequential read, and (**b**) sequential write.  
54.0% (runc), 4.6% and 18.0% (Kata containers), 7.1% and 32.6% (gVisor), and 2.0% and 19.9% (Firecracker) on average. This indicates that there is a performance bottleneck in processing random read requests. Figure 8 also shows that the random read has a higher Iowait than random write: For read and write, this is 54.0% and 3.0% (runc), 18.0% and 14.6% (Kata containers), 32.6% and 15.5% (gVisor), and 19.9% and 1.4% (Firecracker) on average. This is because the read and write operations have different characteristics: read operations are performed synchronously, while write operations are asynchronous. Such differences render random write to have less Iowait CPU usage than random read.
## 4 Results and Discussion
### Performance Comparison Between DA-OA-ICOS and WM-OA-ICOS  
The performance comparison between DA-OA-ICOS and WM-OA-ICOS is summarized in Table 1. For the DA-OA-ICOS, an SNR of 16.2 and an MDL was 2.2 ppm at 117 s integration time were achieved. In contrast, the WM-OA-ICOS exhibited a higher SNR of 113.9 and an improved MDL of 1.2 ppm at 106 s integration time. Compared to the DA-OA-ICOS under the same experimental conditions, the WM-OA-ICOS improved the SNR by 7.0 times and the MDL by 1.8 times. The key for the performance improvement was the application of the WMS. The advantage of WMS was that the absorption peak amplitude of the acquired 2f signal was proportional to the gas concentration. In addition, through the selection of the optimal modulation depth, WMS was able to effectively suppress the 1f laser noise and the baseline slope, thereby significantly reducing the excess noise [34]. As a result, the WM-OA-ICOS sensor could realize better SNR and MDL.