esa-sceva/satcom-chunk-collection huggingface dataset:
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 10 Numerical Simulations and Results
### Numerical Results for \(T=0.5\) s
The results in Table 5 show that the RTAMS position and velocity errors of the IMM-UKF and IMM-CKF are nearly the same, and those of the  
Figure 8: ANEES in (**a**) 3\(g\), (**b**) 4\(g\), and (**c**) 5\(g\) cases for \(T=0.5\) s.  
Figure 9: (**a**) ANEES; (**b**) Trace (Cov)/Trace (MSEM) for 4\(g\), azimuth SD = 1 mrad, and \(T=0.5\) s.  
IMM-EKF are slightly higher. In the final NCV phase, the RTAMS errors of the three IMM filters nearly approximate their respective PCRLB values. Conversely, during the segment involving the NCT-, the discrepancies between the RTAMS errors recorded by the IMM filters and the corresponding PCRLB values are somewhat higher. From Figure 2, we observe that the trajectories of the aircraft are different in the three acceleration cases. Therefore, we cannot compare the accuracies of the filtering results among the three scenarios. We present the CPU times required for each execution of the Monte Carlo simulation in Table 6. The results in Table 6 indicate that the IMM-EKF completes the run in the least amount of CPU time. In comparison, the CPU time for both the IMM-UKF and IMM-CKF is approximately 1.6 times the CPU time of the IMM-EKF. Additionally, the IMM-CKF \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Acceleration** & **Segment** & **RTAMS Error** & **IMM-EKF** & **IMM-UKF** & **IMM-CKF** & **PCRLB** \\ \hline \multirow{4}{*}{\(3g\)} & \multirow{2}{*}{NCT-} & Position (m) & 8.663 & 8.616 & 8.615 & 6.871 \\ \cline{3-6}  & & Velocity (m/s) & 1.105 & 1.084 & 1.083 & 0.407 \\ \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 11.288 & 11.273 & 11.278 & 10.848 \\ \cline{3-6}  & & Velocity (m/s) & 1.193 & 1.192 & 1.192 & 1.175 \\ \hline \multirow{4}{*}{\(4g\)} & \multirow{2}{*}{NCT-} & Position (m) & 6.809 & 6.694 & 6.694 & 5.225 \\ \cline{2-6}  & & Velocity (m/s) & 0.925 & 0.859 & 0.859 & 0.349 \\ \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 9.076 & 8.986 & 8.986 & 8.712 \\ \cline{2-6}  & & Velocity (m/s) & 1.123 & 1.120 & 1.120 & 1.106 \\ \hline \multirow{4}{*}{\(5g\)} & \multirow{2}{*}{NCT-} & Position (m) & 4.689 & 4.470 & 4.470 & 3.330 \\ \cline{2-6}  & & Velocity (m/s) & 0.740 & 0.701 & 0.701 & 0.298 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Last NCV} & Position (m) & 5.985 & 5.845 & 5.845 & 5.655 \\ \cline{1-1} \cline{2-6}  & & Velocity (m/s) & 0.995 & 0.990 & 0.990 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RTAMS errors for \(T=0.5\) s.  
Figure 10: Mode probabilities for (a) \(3g\), (b) \(4g\), (c) \(5g\), and (d) \(6g\) cases for the IMM-CKF for \(T=0.5\) s.  
slightly outperforms the IMM-UKF in terms of CPU time, consuming a marginally smaller CPU time. This is due to the fact that the CKF uses \(2n\) cubature points, whereas the UKF uses \(2n+1\) sigma points.
## 6 Conclusions  
In this paper, the dynamics model of free-floating space robot with carrier position uncontrolled and attitude controlled is firstly established to solve the problem of on-orbit insertion and extraction of space robot. To avoid the introduction of acceleration signal, the filter operation is introduced based on the reconstruction. By using PPC method, the sliding mode controller is designed to ensure the transient and steady state performance of the system and compared with the sliding mode controller. The simulation results indicate that:  
1. The space robot successfully realized the operation of on-orbit insertion and extraction under the setting of simulation conditions. In addition, the vertical inserting and extracting and precise position tracking are ensured by the proposed algorithm. Therefore, the adverse effects brought out by the impact are also reduced.
2. The output force of the manipulator end is tracked precisely and stably, which is good for these operations. The controlled system is robust which can adjust its output force according to the actual change of friction.
3. Compared with sliding mode controller, the PPC controller has a better convergence performance and a higher control accuracy. The calculation amount of PPC controller is reduced due to the acceleration signal is avoided being introduced and its control parameters are easy to be adjusted. This would benefit to the popularization and application of this method in practical engineering scenarios.  
Figure 17: Space robot end X&Y-direction trajectory tracking curve of on-orbit extraction operation.  
Figure 18: Output force of space robot end tracking curve of on-orbit extraction operation.  
**Author Contributions:** Conceptualization, D.L.; methodology, D.L. and H.A.; software, D.L. and L.C.; investigation, D.L. and H.A.; writing--original draft preparation, D.L.; writing--review and editing, D.L. and H.A.; supervision, H.A. and L.C.; funding acquisition, H.A. and L.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 51741502, 51775114, 11372073), Science and Technology Project of the Education Department of Jiangxi Province (Grant No. GJ)200864), Jiangxi University of Science and Technology PhD Research Initiation Fund (Grant No. 205200100514), the Fujian Provincial High-End Equipment Manufacturing Collaborative Innovation Center (Grant No. 2021-C-275).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 5 Results and Analysis  
### Analysis of Variance for the Model  
Analysis of variance for the established model and significance tests for the model and regression coefficients were performed, as shown in Table 4.  
From the analysis of variance in Table 3, it can be seen that the model significance test yielded \(p<0.0001\), indicating that the regression prediction model was significant. The F-value of 10.94 further confirmed the overall significance of the model. The residual sum of squares for the model was 873.71, with 25 degrees of freedom, and the mean squared error was 34.95. These metrics indicated a good fit of the model to the data and its ability to effectively capture the variability in the data. Therefore, this model can be reliably used to analyze the influence of various factors on the energy consumption of the ship's central cooling system.  
Figure 3: BBD of three factors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Independent Variable**} & \multirow{2}{*}{**Symbol**} & \multicolumn{4}{c}{**Level**} \\ \cline{3-5}  & & \(-\mathbf{1}\) & \(\mathbf{0}\) & \(\mathbf{1}\) \\ \hline Main Engine Power /kW & \(X_{1}\) & 4740 & 7110 & 9480 \\ Seawater Temperature/\({}^{\circ}\)C & \(X_{2}\) & 20 & 26 & 32 \\ Seawater Pump Speed/rpm & \(X_{3}\) & 470 & 985 & 1500 \\ Three-way Valve Opening & \(X_{4}\) & 20\% & 50\% & 80\% \\ Low-Temperature Fresh & \(X_{5}\) & 0.12 & 0.15 & 0.18 \\ Water Flow Rate/m\({}^{3}\)/s & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factors and levels of test.
###### Abstract  
Direct-drive electro-hydraulic servo valves are widely used in the aerospace industry, in the military, and in remote sensing control, but there is little research and discussion on their performance degradation and service life prediction. Based on previous research, erosion wear is the primary physical failure form of direct-drive electro-hydraulic servo valves, and parameters such as opening, oil contamination, and pressure difference are used as influencing factors of direct-drive electro-hydraulic servo valves. Pressure gain and leakage are used as performance degradation indicators of servo valves, and multiple types of sensors are used for data monitoring. Experimental benches are arranged and verified through experiments. Based on the data and laws obtained from the experiments, the exponential smoothing algorithm and the ARIMA model algorithm were used to establish a prediction model for the servo valve, and the dynamic prediction of the performance indexes was carried out. The error calculation and analysis of the prediction results and the experimental results were then carried out using the Copula function and other mathematical knowledge to verify the accuracy and applicability of this prediction model. This study provides theoretical support and practical guidance for applying and designing direct-drive electro-hydraulic servo valves in industrial applications such as aerospace, sensor experiments, and remote sensing control.  
direct-drive electro-hydraulic servo valves; performance degradation; erosion and wear; dynamic forecasting; machine learning +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Spectra Interpretation  
Changes in the UV-VIS spectra of the whisky samples at specific wavelengths were observed in either the raw (Figure 1) or the second derivative (Figure 2) spectra as a function of the increase in the temperate during scanning. The observed changes in the values of absorbance in the UV region can be associated with the presence of chromophores originated from compounds present during the maturation process in oak barrels, the presence of volatile phenolic compounds from peat burning during drying, as well as the presence of caramel added to adjust the colour of the whisky during the process, as reported by other authors [27; 2]. Absorption bands in the UV-VIS region between 210 nm and 282 nm (Figure 1, raw spectra) were reported to be associated with the presence of furfural and hydroxymethylfurfural (HMF) in aged alcoholic beverages stored in oak barrels [27; 28]. These authors also reported that, when furfural is extracted from the oak barrels, HMF might be the main component of caramel present in whisky [27; 28]. The latter is believed to be responsible for the high absorbance values around 282 nm in both the raw and the second derivative of the UV-VIS spectra of the whisky samples analysed (Figures 1 and 2) [27; 28]. During storage of whisky, HMF content tends to increase, as it is derived from the reaction of sugars (e.g., sucrose) with other compounds present in the oak barrels [2; 7; 28]. It has been also reported that when whisky is stored in oak wood, furanic compounds such as furfural, 5H-furanone, 2-furyl-1-propanona, 2-furoic acid, methyl furoate, and HMF might contribute to the typical colour and aroma of whisky [2; 28]. Therefore, these compounds might be responsible for the characteristic UV-VIS spectral properties in the whisky samples analysed [27; 28].  
Figure 1: UV-VIS spectra of whisky samples scanned at different temperatures (25, 35, 45, and 55 \({}^{\circ}\)C) in transmission.  
The second derivative of the UV-VIS spectra (Figure 2) of the whisky samples analysed showed absorptions around 210 nm, 222 nm, and 298 nm in addition to those observed in the raw spectra. A small trough around 300 nm was also observed. This wavelength has been reported to be specific to the presence of aromatic and unsaturated compounds in whisky, as reported by other authors [2, 27, 28]. This region is also associated with the presence of molecules having high antioxidant capacity, such as polyphenol compounds [2, 27, 28, 29]. As reported by other authors, the use of the second derivative provided a better interpretation of the UV-VIS spectra as an increase in the resolution of the absorbance at specific wavelengths [17]. The presence of the above wavelengths in the set of whisky samples analysed is of high importance, as other authors indicated that similar spectral regions can be used as markers to differentiate whisky samples according to region, brand, or blend [2, 27, 28].
## Nomenclature  
\begin{tabular}{l l} DER & Distribution energy resources \\ ESSs & Energy storage sources \\ LVDC & Low-voltage direct current \\ HVDC & High-voltage direct current \\ LVAC & Low-voltage alternating current \\ RES & Renewable energy sources \\ PSV & Platform supply vessel \\ SCIG & Squirrel-cage induction generator \\ IGBT & Insulated gate bipolar transistors \\ PV & Photovoltaic \\ MPPT & Maximum power point tracking \\ DC & Direct current \\ AC & Alternating current \\ DCCB & Direct current circuit breaker \\ ACCB & Alternating current circuit breaker \\ THD & Total harmonic distortion \\ \end{tabular}
## 6 Improved DPF with Frequency Regulation of DFIG Integrated through VSC-HVDC
### Optimization of \(H_{\rm DC}\) Based on Improved DPF  
When initializing \(H_{\rm DC}\), estimation of the frequency nadir considers frequency regulation by the SGs. The conservative value is adopted for a feasible solution. With the active power support from the DFIG, the frequency nadir is increased, yielding larger \(H_{\rm DC}\) and more inertia support from the VSC-HVDC.  
Therefore, to further increase the system inertia, the proposed DPF model may be applied to evaluate the maximum DC voltage deviation with existing \(H_{\rm DC}\), and its value is adjusted to provide the maximum inertia without violating the constraint of the DC voltage. The optimization of \(H_{\rm DC}\) is carried out in each time step for the optimal inertia during the process of frequency regulation, as shown in Figure 6.
## 2 Principle and Theoretical Analysis
### Generation of NLFM Signals
The time-frequency spectrum of LFM is shown in Figure 2a, and the compressed pulse is shown in Figure 2b. Using the method described earlier, NLFM pulses with a sweep range of 20 MHz and a pulse width of 4 \(\upmu\)s were generated through simulation. The compressed pulse's amplitude, after undergoing matched filtering, is shown in Figure 2b. In the figure, the main lobe width of the compressed pulse is measured at 10.2000 m, with a sidelobe suppression ratio reaching 12.8631 dB. In comparison to LFM, as shown in Figure 2b, where the main lobe width of LFM is 6.1850 and the sidelobe suppression ratio is 6.63492 dB, Figure 2c demonstrates the instantaneous frequency of NLFM, and Figure 2d shows the relative frequency spectra. It is evident that NLFM exhibits significant improvements in both main lobe width and sidelobe suppression ratio. \[u(t)=\frac{1}{\sqrt{\tau_{p}}}rect(\frac{t}{\tau_{p}})e^{j2\pi(f_{0}t+\frac{1}{2} \mu t^{2})} \tag{6}\]2 (**a**) Time-frequency spectra of LFM; (**b**) compressed pulses; (**c**) time-frequency spectra of NLFM; (**d**) relative frequency spectra of LFM and NLFM.
## 2 Materials and Procedures
### Characterization of the Additive Manufacturing Deposited Parts  
For macro- and microstructure examinations, each DP was cross-sectioned halfway parallel to the deposition direction, ground with silicon carbide abrasive papers up to 2400 grit, and then carefully polished to achieve a surface finish of 0.05 \(\upmu\)m alumina, followed by etching using [PERSON]'s solution (92 mL distilled water, 6 mL HNO\({}_{3}\), and 2 mL HF). The microstructure investigation was performed for the as-received AA7075-T6 rod, the AA2024-T4 substrate, and the AFSD products using an optical microscope (Type: Zeiss Axiovert 25 CA, Oberkochen, Germany) and a scanning electron microscope (SEM Quanta 3D 200i, FEI, USA). The average grain size and the grain size distribution were determined.  
To apply the X-ray diffraction technique, an XRD Panalytical/model X'Pert PRO device diffractometer using Cu K\(\alpha\)1 radiation was used to check the presence phases of the AA7075-T6 rod alloy BM, and the additive friction stir deposited materials, with a processing speed of 400 rpm and feeding rates of 1, 2, 3 and 4 mm/min. The test was fulfilled at 2 \(\uptheta\) from 10 to 100\({}^{\circ}\) and a step size of 0.02\({}^{\circ}\). The hardness of the AA7075-T6 BM and the sectioned DPs was measured using a Vickers hardness testing machine (Type: HWDV-75 TTS Unlimited, Osaka, Japan) at a 500 g load with a holding time of 15 s. To plot the hardness measurements of the deposited materials in contour maps, the selected  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**AA7075-T6**} \\ \hline Element & Si & Mn & Cu & Zn & Fe & Mg & Cr & Al \\ Wt.\% & 0.36 & 0.19 & 1.14 & 5.72 & 0.23 & 2.62 & 0.20 & Bal. \\ \hline \multicolumn{8}{c}{**AA2024-T4**} \\ \hline Element & Fe & Si & Mg & Ti & Zn & Mn & Cu & Al \\ Wt.\% & 0.13 & 0.32 & 1.15 & 0.14 & 0.12 & 0.44 & 4.48 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the AA7075-T6 and AA2024-T4 alloys.  
Figure 1: (**a**) Consumable rod chamfer, (**b**) setup of the AFSD process of the AA7075 rod on the AA2024 plate, (**c**) onset of deposition process, (**d**) schematic drawing of thermocouple layout, and (**e**) final deposited product.  
area of the tested specimen was divided into five horizontal and fifteen vertical lines with a 2 mm step to adjust the indenter locations, as depicted in Figure 2.  
To determine the maximum compressive strength, a universal testing machine (WDW-300D, made in China) was used to perform compression tests. The ram speed was kept constant at 1 mm/min. The compression test specimen was prepared according to ASTM E9.
## 3 Problem Formulation
### Upper Level: Aggregator's Viewpoint  
The understudy aggregator participates in the DA and balancing market to maximize its expected profit by supplying EVs' demand. This profit includes the revenue obtained from selling energy to the EV owners and from reducing load based on negative imbalance prices. The upper level problem is presented below:  
\[\underset{\begin{subarray}{c}E^{DA}_{t,\omega},E^{+B}_{t,\omega},E^{-B}_{t, \omega},\\ \lambda^{ch}_{t,\omega},E^{ch}_{t,\omega},\end{subarray}}{\underset{\begin{subarray}{c} \omega\in\Omega\end{subarray}}{\sum}p(\omega)\underset{t\in T}{\sum}\Big{[}E^{ ch}_{t,\omega},\lambda^{ch}_{t,\omega}-E^{D,A}_{t,\omega}\lambda^{DA}_{t,\omega}-E^{+B}_{t, \omega}\lambda^{+B}_{t,\omega}+E^{-B}_{t,\omega}\lambda^{-B}_{t,\omega}\Big{]} +\beta\Bigg{[}\zeta-\frac{1}{1-\alpha}\underset{\omega\in\Omega}{\sum}p(\omega )\iota(\omega)\Bigg{]} \tag{2}\]  
\[E^{ch}_{t,\omega}=E^{DA}_{t,\omega}+E^{+B}_{t,\omega}-E^{-B}_{t,\omega}\qquad \forall t\in T,\quad\forall\omega\in\Omega \tag{3}\]  
\[-\underset{t\in T}{\sum}\Big{[}E^{ch}_{t,\omega}\lambda^{ch}_{t,\omega}-E^{DA }_{t,\omega}\lambda^{DA}_{t,\omega}-E^{+B}_{t,\omega}\lambda^{+B}_{t,\omega}+E^ {-B}_{t,\omega}\lambda^{-B}_{t,\omega}\Big{]}+\zeta-\iota(\omega)\leq 0,\qquad \forall\omega\in\Omega \tag{4}\]  
\[\iota(\omega)\geq 0,\qquad\forall\omega\in\Omega \tag{5}\]  
\[E^{ch}_{t,\omega}=E^{D}_{t,\omega}\underset{\begin{subarray}{c}\xi\in\Xi \end{subarray}}{\sum}v(\xi)X_{50}(\xi)\qquad\forall t\in T,\quad\forall\omega\in\Omega \tag{6}\]  
\[E^{DA}_{t,\omega}=E^{DA}_{t,\omega^{\prime}}\ \forall t\in T,\quad\forall\omega\in\Omega \tag{7}\]  
\[E^{+,B}_{t,\omega}\leq P^{\max}\qquad\forall t\in T,\quad\forall\omega\in\Omega \tag{8}\]  
\[E^{-,B}_{t,\omega}\leq E^{ch}_{t,\omega}\ \forall t\in T,\quad\forall\omega\in\Omega \tag{9}\]  
where, \(\alpha\) and \(\iota(\omega)\) are the confidence level and auxiliary variable to calculate risk, respectively. Equation (2) explains the objective function from the aggregator's perspective in which \(\beta\) is the risk coefficient that keeps the tradeoff between the expected profit and CVaR. Constraint (3) expresses the energy balance in each time period and scenario. Constraints (4) and (5) are related to the CVaR term. The EVs' demand that is supplied by the aggregator is considered by Equation (6). The non-anticipativity is expressed in (7). The limitation for the energy traded in the positive and negative balancing market is represented in (8) and (9), respectively.  
Figure 2: The probablity density function corresponding to the forecasted errors of rival aggregators’ prices.
## 3 Challenges
### Topology Changes  
Topology directly affects the network lifetime in WSNs. Depending on the application, CR wireless sensors may be deployed statically or dynamically. In any type of WSN, hardware failure is common due to hardware malfunctioning and energy depletion. The topologies for CR-WSNs may be the same as conventional WSNs, but they are prone to change more frequently than _ad hoc_ CRNs. [PERSON] _et al._[7] reported that CR-WSNs have the following topologies: (i) _Ad Hoc_ CR-WSNs; (ii) Clustered CR-WSNs; (iii) Heterogeneous and Hierarchical CR-WSNs; and (iv) Mobile CR-WSNs.  
Basically, the minimum output power required to transmit a signal over a distance \(\delta\) is proportional to \(\delta^{n}\), where \(2\leq n<4\). The exponent \(n\) is closer to four for low-lying antennae and near-ground channels, as is typical in wireless sensor network communication. Therefore, routes that have more hops with shorter hop distances can be more power efficient than those with fewer hops but longer hop distances [53].  
Nevertheless, it is not always possible to find such a route in static sensor networks topology. Therefore, an adaptive self-configuration topology mechanism is important for CR-WSNs for obtaining scalability, reducing energy consumption and achieving better network performance. An adaptive self-configuration topology mechanism performs better than static topology, even though it is a challenging issue to design and implement. This area has not received much research attention.  
Figure 4: Hardware structure of CR wireless sensor.
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Application to Floor Classification and Landmark Detection  
#### 3.3.1 Floor Classification  
To apply the proposed semisupervised discriminant analysis algorithm in Section 3.2.4 to the floor classification, the label of the training data should be defined as the floor level. As a result, the transformation matrix plays a role as dividing the classes with respect to each floor level. In the test phase, when a test RSSI measurements set is arrived, it is first processed by the transformation matrix in Equation (13). Second, the floor level is estimated by \(k\)-NN method, which selects the averagely nearest class point in the training data to the test data.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 4 Experiment and Analysis
### Results and Analysis  
#### 4.2.1 Image Enhancement  
To verify the effectiveness of the adaptive image enhancement algorithm proposed in this study, a comparison test is conducted on certain images containing vehicle logos under different lighting conditions [39].  
As shown in Figure 3, we select three representative image enhancement results, images under normal lighting, dark lighting, and strong lighting conditions. As shown in Figure 3a, the comparison reveals that the images in the original CTSD data are not uniformly illuminated but are locally unclear and, therefore, not easily detected and recognized. As shown in Figure 3b, after the adaptive image enhancement, the overall RGB histogram distribution becomes more uniform [40], logos appear prominent, the images' contrast and brightness are significantly improved, the images' details are outstanding, and the original image quality is preserved, thus conducive to logo detection and recognition and thus meeting the requirements of vehicle logo detection in this study [41].  
The contrast analysis of images with the above three different lighting conditions demonstrates that the adaptive enhancement algorithm proposed in this study can effectively boost the image quality under complex lighting conditions while providing good samples for subsequent detection, thus conducive to the improvement in detection performance.