esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design and Experiment of Corn-Overlapped Strip Fertilizer Spreader
## 3 Simulation Test of Corn-Overlapped Strip Fertilizer Spreader
### Parameter Optimization Test
#### 3.3.3 Response Surface Analysis  
The single-factor test analysis showed that the transversal fertilizer uniformity coefficient \(Y_{1}\) and longitudinal fertilizer uniformity coefficient \(Y_{2}\) tended to decrease as the height \(X_{2}\) decreased and reached the lowest value at 400 mm, so the response surface analysis of the interaction term \(X_{1}X_{3}\) was conducted when the height \(X_{2}\) was selected as 400 mm,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Evaluation Indicators of the Longitudinal Fertilizer Uniformity Coefficient \(Y_{2}\)**} \\ \hline \multirow{2}{*}{**Source of Variance**} & **Square** & \multirow{2}{*}{**Degree of Freedom**} & **Mean Square** & **F-Value** & \multirow{2}{*}{**\(p\)-Value**} & **Significance** \\  & **Sum** & & & & \\ \hline Model & 0.050 & 9 & \(5.588\times 10^{-3}\) & 13.65 & \textless{}0.0001 & *** \\ \(X_{1}\) & \(2.469\times 10^{-3}\) & 1 & \(2.469\times 10^{-3}\) & 6.03 & 0.0289 & ** \\ \(X_{2}\) & \(4.528\times 10^{-4}\) & 1 & \(4.528\times 10^{-4}\) & 1.11 & 0.3120 & \\ \(X_{3}\) & \(3.237\times 10^{-3}\) & 1 & \(3.237\times 10^{-3}\) & 7.91 & 0.0147 & ** \\ \(X_{1}X_{2}\) & \(5.809\times 10^{-4}\) & 1 & \(5.809\times 10^{-4}\) & 1.42 & 0.2548 & \\ \(X_{1}X_{3}\) & \(7.163\times 10^{-4}\) & 1 & \(7.163\times 10^{-4}\) & 1.75 & 0.2086 & \\ \(X_{2}X_{3}\) & \(1.510\times 10^{-4}\) & 1 & \(1.510\times 10^{-4}\) & 0.37 & 0.5541 & \\ \(X_{1}{}^{2}\) & \(0.024\) & 1 & \(0.024\) & 59.63 & \textless{}0.0001 & *** \\ \(X_{2}{}^{2}\) & \(0.014\) & 1 & \(0.014\) & 34.78 & \textless{}0.0001 & *** \\ \(X_{3}{}^{2}\) & \(4.552\times 10^{-3}\) & 1 & \(4.552\times 10^{-3}\) & 11.12 & 0.0054 & *** \\ \hline Residual & \(5.32\times 10^{-3}\) & 13 & \(4.092\times 10^{-4}\) & & & \\ Misfit term & \(2.879\times 10^{-3}\) & 5 & \(5.758\times 10^{-4}\) & 1.89 & 0.2024 & \\ Pure error & \(2.441\times 10^{-3}\) & 8 & \(3.051\times 10^{-4}\) & & & \\ Total variation & \(0.056\) & 22 & & & & \\ \hline \hline \end{tabular} Note: ***\(p<0.01\) (highly significant); ** \(0.01<p<0.05\) (significant); \(p>0.1\) (non-effect).  
\end{table}
Table 6: Analysis of variance of average velocity of fertilizer particles.  
as shown in Figure 10. With the angle \(X_{1}\) increased, the transversal fertilizer uniformity coefficient \(Y_{1}\) showed a decreasing trend and subsequently became smaller, indicating that the spreading uniformity became better; as the width \(X_{3}\) increased, the decreasing trend of the transversal fertilizer uniformity coefficient \(Y_{1}\) slowed down compared with the angle \(X_{1}\), but also subsequently became smaller, indicating that the spreading uniformity became better and better with the increase in the width \(X_{3}\).
###### Abstract  
Microextrusion-based bioprinting within a support bath material is an emerging additive manufacturing paradigm for complex three-dimensional (3D) tissue construct fabrication. Although a support bath medium enables arbitrary in-process geometries to be printed, a significant challenge lies in preserving the shape fidelity upon the extraction of the support bath material. Based on the bioprinting in a support bath paradigm, this paper advances quantitative analyses to systematically determine the printability of cell-laden liquid hydrogel precursors towards filament-based tissue constructs. First, a yield stress nanoclay material is judiciously selected as the support bath medium owing to its insensitivity to temperature and ionic variations that are considered in the context of the current gelatin-alginate bio-ink material formulation. Furthermore, phenomenological observations for the rheology-mediated print outcomes enable the compositions for the bio-ink material (10% gelatin, 3% alginate), in tandem with the support bath medium (4% nanoclay, 0.5% CaCl\({}_{2}\)), to be identified. To systematically evaluate the performance outcomes for bioprinting within a support bath, this paper advances an experimental parametric study to optimize the 3D structural shape fidelity by varying parameters such as the layer height, extrusion flowrate, printing temperature, and printhead speed, towards fabricating complex 3D structures with the stabilization of the desired shape outcome. Specifically, it is found that the layer height and printhead speed are determinant parameters for the extent of successive layer fusion. Moreover, maintenance of an optimal bath temperature is identified as a key parameter for establishing the printability for the hydrogel bio-ink. Studying this effect is enabled by the custom design of a PID temperature control system with integration with the bioprinter for real-time precision control of the support bath temperature. In order to qualify the printed construct, a surface irregularity metric, defined as the average height difference between consecutive local maximum and minimum points of the binary image contour for the printed structure, is advanced to evaluate the quality of the printed constructs. Complex one-to-four bifurcating tubular structure prints demonstrate the applicability of the optimized bioprinting parameter space to create exemplar 3D human vessel-like structures. Finally, a cell viability assay and perfusion test for a printed cell-laden tubular element demonstrates high cell survival rates and leakage-free flow, respectively.  
3D bioprinting; support bath; gelatin-alginate; hydrogel; tubular structure; printability 1
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Analysis of Experimental Results  
#### 5.5.1 SCDF Experiments  
This set of experiments focuses on a single source domain scenario, where the operating conditions are identical for both the source and target domains. However, the fault types in the target domain do not intersect with those in the source domain. This scenario corresponds to real-world situations where target domains may have domain-specific faults due to particular reasons, and leveraging common fault data from other factories with similar operating conditions can aid in diagnosis. The specific task settings in this set of experiments are presented in Table 5.  
The experimental results are presented in Table 6, and the average accuracy of all tasks is shown in Figure 5. The results demonstrate that FedCDAE-MN outperforms other methods significantly under three distinct operating conditions: Load 0, Load 1, and Load 2. It exhibits superior accuracy across most tasks, indicating its excellent generalization capabilities for different fault types, particularly under low-load conditions. This ability to perform well across a wide range of tasks demonstrates the robustness and adaptability of FedCDAE-MN in accommodating diverse fault scenarios. However, it is worth noting that under the more challenging Load 3 condition, while FedCDAE-MN's accuracy shows a slight decrease, it remains above average for most tasks, except for the ball (B) task. This observation suggests that while FedCDAE-MN maintains its effectiveness in diagnosing faults across a range of conditions, there may be room for fine-tuning or specialized adaptation to further enhance its performance in specific scenarios. Notably, FedCDAE-MN achieves the highest accuracy in the OuterRace (O) task across all operating conditions, showcasing its exceptional generalization performance for outer ring faults. The average accuracy of the final FedCDAE-MN method reaches 93%, whereas other methods fall below 90%, representing a substantial 3% improvement. These findings demonstrate the efficacy of FedCDAE-MN in addressing the challenges of few-shot fault diagnosis within single-source cross-domain scenarios. In summary, the experimental outcomes not only  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Source Domain** & **Target Domain** \\ \hline WIOB & Normal, IOB (7 mm), IOB (14 mm) & Normal, WorstIOB (21 mm) \\ B & Normal, Inner Race, Outer Race & Normal, Ball \\ I & Normal, Ball, Outer Race & Normal, Inner Race \\ O & Normal, Ball, Inner Race & Normal, Outer Race \\ \hline \hline \end{tabular}
\end{table}
Table 5: Task settings.  
validate the superiority of FedCDAE-MN in diverse operating conditions but also highlight its potential for specialization in specific fault detection tasks.
# Static and Dynamic Response of FG-CNT-Reinforced Rhombic Laminates
## 1 Introduction
The effect of agglomerated CNT on the linear static analysis of nanocomposite plate and shell was studied by [PERSON] et al. [26]. Size phenomena in elastic nanobeams can be conveniently carried out by stress-driven nonlocal continuum mechanics proposed by [PERSON] and [PERSON] [27] to overcome all difficulties of the strain-driven nonlocal theory of elasticity. A general treatment on nonlocal integral formulations of elasticity for nanomaterials is presented in [28] and successfully applied by [29; 30; 31; 32] to size-dependent static and dynamic structural problems of current technical interest. [PERSON] et al. [33] provided a numerical solution for the vibration analysis of FG-CNTRC elliptical plates and the extended rule of the mixture was used for calculating the effective material properties. The free vibration analysis of nanocomposite plate and shell using FSDT was explored by [PERSON] et al. [34]. [PERSON] et al. [35] discussed the effect of orientation of CNT on the static and vibration behavior of FG-CNTRC skew plates. The literature review indicates that few works for the static and dynamic analysis of FG-CNT-reinforced rhombic laminates are available. Therefore, in this paper, an effort was made to conduct a behavioral study of the FG-CNT rhombic laminates using HSDT, which removes the need for a shear correction factor. The finite element coding was developed by authors of the present mathematical model using a \(C^{0}\) nine-noded finite element. Since results for FG-CNT-reinforced rhombic laminates subjected to trigonometrical loading are not available in the literature, the present analysis results may serve as a benchmark for the researchers working in this field.
## 2 Objective  
The improved BECI replacement decision system established in this article is aimed at achieving the objectives of two aspects, namely, realizing the standardization of BECI on-site inspection and the automations of both the technical condition assessment and the replacement plan selection. Accordingly, the proposed decision system can be divided into two stages, as illustrated in Figure 1.  
Stage 1: Technical condition assessment. The on-site inspection is conducted on the target BECI, which will be hierarchically classified according to various installation types. The inspection index evaluation is performed to provide the basis of progressive assessment calculation, and \"whether or not to replace\" is determined.  
Stage 2: Selection of replacement plan based on HCWOA. Twelve kinds of design, construction, and management requirements are introduced as the decision criteria, where the hierarchy model for selecting the performance-based optimal replacement plan is constructed via AHP. The HCWOA is designed and utilized to modify and handle the proposed mathematical model, where the optimal plan is selected precisely.
## 6 Performance Evaluation
### Comparative Analysis
#### 6.3.3 Resource Utilization  
Resource utilization is increased through task clustering using the DSC algorithm. Deadline and makespan are considered to classify each upcoming task via prioritization. Figure 7 presents the comparison of resource utilization.  
Figure 5: Comparison of response time.  
Figure 6: Comparison of makespan.  
The resource utilization of each task depends on its requirement. When upcoming tasks utilize the required resources, the resource utilization of our proposed method increases. Resources comprise CPU, I/O, and memory. Each VM represents resource parameters as \(D_{c}\), \(S_{c}\), and \(D_{m}\), which refer to CPU idle rate, CPU speed, and memory idle rate, respectively. The DSC algorithm is used in our proposed work to cluster upcoming tasks on the basis of their priorities obtained by computing their deadline and makespan. Each prioritized task is then ranked using MHEFT, and the highest priority task is selected. Our proposed method increases resource utilization metrics via clustering and highest priority task selection by up to 98% compared with that of the existing method.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction
### Dataset Description  
The raw form of the dataset contained information about six twin cargo ships provided by a maritime enterprise. Ten time-series per ship were recorded with a sampling period of 1 min, including multiple voyages of the same ships, as well as ship- and weather-related data. The recordings had a duration of 1 year (from 1 July 2021 to 30 June 2022), resulting in 514,525 and 525,005 total samples per time-series for ships 1 and 2, respectively.  
In specific, the following variables are available for each cargo ship:  
1. **Speed over ground (SOG)**: It is the speed of the vessel relative to the surface of the Earth, measured in knots.
2. **Speed through water (STW)**: It is the speed of the vessel relative to the water currents, measured in knots.
3. **Heading**: It is the direction in which the ship is pointing and it is expressed as the angular distance relative to north (0\({}^{\circ}\)), clockwise through 359\({}^{\circ}\).
4. **Continuous wind speed (CWS)**: It is the wind velocity amplitude expressed in units of m/s.
5. **Discretized wind speed (DWS)**: It is the quantized version of CWS, expressed in Beaufort scale (bft), and takes the categorical values of 0-12, mapping with the 13 wind types (from calm to hurricane-force wind categories).
6. **Wind direction (WD)**: It is the direction of the true wind relative to the ship's on-board heading. It varies from 0\({}^{\circ}\) to 360\({}^{\circ}\) (wind on the bow at 0\({}^{\circ}\), wind on the beam at 90\({}^{\circ}\), wind on the stern at 180\({}^{\circ}\)).  
7. **Draft forward (DF)**: The draft forward (bow) is measured (depth in meters) at the perpendicular of the bow according to predefined depth levels.
8. **Draft aft (DA)**: The draft aft (stern) is measured (depth in meters) at the perpendicular of the stern according to predefined depth levels.
9. **Trim**: The trim of a ship is the difference between the DF and DA (depth in meters) relative to the designed waterline located at the middle of the ship. It determines the minimum depth of water a ship can safely navigate.
10. **Main Engine Power (MEP)**: It is the total power supplied by the prime mover(s) installed on a ship to provide propulsion and is expressed in kW. Obviously, it is positively correlated with the electricity requirements of the ship's diesel engine, thus being directly proportional to the fuel oil consumption.
# Evaluating the Feasibility of Euler Angles for Bed-Based Patient Movement Monitoring
## 3 Results
For example, when rollover 3 is compared to other rollover types, the maximum dissimilarity value is 5.9 \(\times\) 10\({}^{-4}\). However, the dissimilarity value  
Figure 8: An instance of a particular bodily motion executed in bed (rollover 3 as reference data set) and the corresponding dissimilarity values obtained from comparing it to other similar rollovers versus contrasting it with diverse body movements. Figure 9: To identify movements in bed, dissimilarity values, yielded by a Procrustes analysis, are compared when calculated using the Euler angles associated with different rollovers (Figure 7a) versus using the Euler angles associated with other bodily movements. Such relatively significant differences suggest that these dissimilarity values have potential as markers for detecting specific forms of movement within a bed environment. significantly increases to at least 0.0145 when it is contrasted with less similar movements or diverse body movements. The difference in these dissimilarity values helps in identifying and grouping similar and dissimilar types of movements. Figure 9 expands on this approach by comparing the movements in bed and the dissimilarity values obtained from Euler angles associated with different rollovers versus other bodily movements. The dissimilarity value reaches a maximum of 0.0025 when comparing all rollovers to each other. When all body movements are compared with every measured rollover, the minimum value is 0.01. This four-fold contrast in values suggests that these dissimilarity values can effectively serve as an identifying factor for movement classification. Thus, Figures 8 and 9 both demonstrate how these relatively significant differences in dissimilarity values can serve as potential markers for detecting specific forms of movement within a bed environment.
## 2 Experiment Details
### Corrosion Protection Performance of GE-EP@OIMs  
#### 2.5.1 Electrochemical Experiment  
Electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization scanning (PDP), Wuhan CorrTest Instruments Co., Ltd., Wuhan, Hubei Province, China. were used to investigate the corrosion inhibition performance and mechanism of solid corrosion inhibitors in 3.5 wt% NaCl solutions at 20 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C and different release time. Three-electrode system was employed, with L80 steel as the working electrode, a Pt electrode as the auxiliary electrode, and a saturated calomel electrode as the reference electrode. An open circuit potential (OCP) test lasting 2 h was first conducted in a CS350, CorrTest Instruments Co., Ltd., Wuhan, Hubei Province, China electrochemical workstation to ensure the stability of the test system. Following this, an EIS test was performed with a 10 mV sinusoidal perturbation potential, with a measurement frequency range from 100,000 Hz to 0.01 Hz, and at a test temperature of 80 \({}^{\circ}\)C. The software ZSimp Win (version  
Figure 2: (**a**): UV-vis spectra for different OIM solutions of known concentration; (**b**): the fitted standard curve of OIMs.  
1.0) was used to fit the impedance data, and the corrosion inhibition efficiency of the inhibitor was calculated using Formula (2):  
\[n_{EIS}(\%)=\left(\frac{R_{total}-R_{total}^{0}}{R_{total}}\right)\times 100 \tag{2}\]  
where \(R_{total}^{0}\) represents the total resistance of L80 in the corrosion solution, and \(R_{total}\) represents the total resistance of L80 in the corrosion solution with solid corrosion inhibitor (\(R_{total}=\text{R}_{\text{s}}+\text{R}_{\text{film}}+\text{R}_{\text{ct}}\)).  
After the EIS test, a dynamic potential scan was immediately carried out with a scan range of \(-\)400-400 mV and a scan rate of 0.5 mVs\({}^{-1}\). The measured polarization curve was fitted by the Tafel extrapolation method to obtain dynamic potential parameters such as self-corrosion current density (\(\text{i}_{corr}\)), and the corrosion inhibition efficiency was calculated by Formula (3):  
\[n_{Tafel}(\%)=\left(\frac{\dot{i}_{cor}^{0}-i_{cor}}{\dot{i}_{cor}^{0}}\right) \times 100 \tag{3}\]  
where \(i_{cor}^{0}\) represents the current density of L80 in the corrosion solution; 0.1 \(\mu\)A\(\cdot\)mm\({}^{-2}\); \(i_{cor}\) represents the current density of L80 in the corrosion solution with added solid corrosion inhibitor, 0.1 \(\mu\)A\(\cdot\)mm\({}^{-2}\).
## 2 Materials and Methods
### Mean Monthly Temperature  
Mean monthly temperature data were collected from the NASA GHRSST (Group for High-Resolution Sea Surface Temperature, Level 4) project platform. This platform collects daily ocean temperature data up to 1 m depth across the globe. It uses AVHRR (Advanced Very High Resolution Radiometer), AMSR-E (Advanced Microwave Scanning Radiometer), GOES (Geostationary Operational Environmental Satellite), and MODIS (Moderate Resolution Imaging Spectroradiometer) systems (Naval Oceanographic Office, GHTSST Level 4, 2008). This platform allows the extraction of oceanographic temperatures every 625 km\({}^{2}\) (25 \(\times\) 25 km). The daily temperature values were used to collect and calculate the monthly mean temperature values in 2018 (Figure 2a) and 2019 (Figure 2b) within the boundaries of each area studied.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**YearAreas** & **A** & **B** & **C** & **D** & **E** & **Total** \\ \hline
2018 & 55 & 65 & 12 & 23 & 110 & 265 \\ \hline
2019 & 45 & 59 & 18 & 22 & 105 & 249 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of stations for water collection per area in the 2018 and 2019 field surveys.
## 3 Performance Evaluation
### Compatibility  
The mutual interference and compatibility between different signals are important performance indicators for navigation signals, and the generally used indicator is the spectral separation coefficient (SSC). The SSC measures the amount of interference caused by other signals in the same band. The compatibility of a signal becomes better with decreasing SSC value; its expression is  
\[\chi=\int_{-B_{r}/2}^{B_{r}/2}G_{l}(f)G_{n}(f)\mathrm{d}f \tag{30}\]  
where \(G_{l}(f)\) is the normalized PSD of interference signals.  
Table 3 shows SSC values for different signals. It can be seen that Pulse-OTFS(10,1023) and OTFS(10,1023) provide the same SSCs and also keep a low level of mutual interference with other signals. The results suggest that Pulse-OTFS has two advantages when utilized as a navigation signal: it minimizes interference with existing GNSS signals and Pulse-OTFS signals from different satellites.  
The performance of the pulse OTFS signal has been extensively evaluated in this section. In summary, the proposed signal mitigates the high acquisition complexity of the original OTFS and traditional navigation signals to enable fast acquisition of the future massive LEO satellite signals and has good anti-multipath performance with advantages in terms of code-tracking accuracy and compatibility, while the Locata signal also reduces acquisition complexity but its delay measurement precision is not as good as that of the proposed signal.  
Figure 11: Comparison of anti-multipath performance for different signals. (**a**) Multipath error envelope and (**b**) average multipath error envelope. \(a=-6\) dB, \(d=1\) chip.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SSC (dB)** & **Pulse-OTFS(10,1023)** & **OTFS(10,1023)** & **Pulse-BPSK(5)** & **BPSK(5)** \\ \hline Pulse-OTFS(10,1023) & \(-70.11\) & \(-70.11\) & \(-70.32\) & \(-70.32\) \\ OTFS(10,1023) & \(-70.11\) & \(-70.11\) & \(-70.32\) & \(-70.32\) \\ Pulse-BPSK(5) & \(-70.32\) & \(-70.32\) & \(-68.41\) & \(-68.41\) \\ BPSK(5) & \(-70.32\) & \(-70.32\) & \(-68.41\) & \(-68.41\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: SSC values for different signals with a 20 MHz pre-filtering bandwidth.