esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Wind Tunnel Propeller Dynamometer for Group 2 Unmanned Aircraft
## 4 Discussion
### Uncertainty Analysis  
Instrument bias error in this study is summarized in Table 4. The thrust-torque load cell measured the thrust and torque produced by the propeller, the pressure transducer measured the dynamic pressure, and the Hall-effect sensor measured the rotational speed of the propeller. Bias error is sufficiently low in this study to support conclusions.  
In this study, an uncertainty analysis was performed on 18 \(\times\) 10 propeller data. The standard deviation of measurements from the torque and thrust sensors are computed and used to determine precision error for thrust coefficient and propeller efficiency calculations. Figure 15 includes a plot of error for an 18 \(\times\) 10 APC propeller. The plot of precision error shows how insignificant the error contribution is in the measurement data, as it is indistinguishable from the actual data measurement points. Therefore, the error in experimental data is sufficiently low.  
\begin{table}
\begin{tabular}{c c} \hline
**Instrument** & **Bias Error** \\ \hline Thrust–Torque load cell & 0.25\% RO \\ \hline Temperature probe & 0.05 \\ \hline Pressure transducer & 0.3\% \\ \hline Hall-effect sensor & 1.5 ms \\ \hline \end{tabular}
\end{table}
Table 4: Instrumentation error  
Figure 15: Plot of error on an 18 \(\times\) 10 APC propeller.  
Table 5 includes a breakdown of precision error contributions from the thrust and torque at a rotational speed of 3500 RPM and various wind tunnel speeds from 25 to 50 ft/s. Percent error for thrust and propeller efficiency is calculated using the standard deviation of thrust and propeller efficiency divided by average thrust and propeller efficiency values. The percent error is higher for thrust coefficient than propeller efficiency but does not exceed 7.7%.
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
## 2 Biodegradable Polymeric Architectures
### Micelles, Vesicles and Capsules
#### 2.4.2 Vesicles  
Polymeric vesicles or polymersomes are nano- or micrometer sized polymeric capsules with a bilayered membrane. Extensive applications can be envisioned in nanomedicine, in vivo diagnostics and drug delivery [6]. [PERSON] and [PERSON] reported the facile preparation of vesicles in pure water medium using diblock copolymer, poly(\(\varepsilon\)-caprolactone)-\(b\)-poly[2-(methacryloyloxy) ethyl phosphorylichline] (PCL-\(b\)-PMPC), which was synthesized using the combined methods of ROP, end-group modification and ATRP. These vesicles can be stabilized by sol-gel chemistry within the vesicle membrane [105]. In addition to the well-defined routine chemical polymerization methods, lipase-catalyzed condensation polymerization method was also used to synthesize biodegradable poly(10-hydroxydecanoic acid) (PHDA) and further modify it with ATRP initiator for grafting another hydrophobic polystyrene block for fabrication of polymeric nanoparticles in aqueous medium [106]. Vesicles can also be designed as pH sensitive for efficient DNA encapsulation and delivery, where the particles were prepared by poly(2-(methacryloyloxy)ethyl-phosphorylichline)-\(co\)-poly(2-(diisopropylamino)ethyl methacrylate) (PMPC-\(b\)-PDPA) diblock copolymers. The PMPC block is highly biocompatible and nonfouling, while the PDPA block is pH-sensitive (pKa -5.8-6.6, depending on the ionic strength) [107]. [PERSON] and coworkers reported a novel method for the preparation of biodegradable large compound vesicles with controlled size and narrow size distribution by using aqueous nanodroplets as templates. PEG-based large compound vesicles (LCVs) were prepared through a self-assembly process of the temperature-responsive 2-(2-methoxyethoxy) ethyl methacrylate-oligo(ethylene glycol) methacrylate-\(N\),\(N^{\prime}\)-cystamine bisacrylamide (MEO\({}_{2}\)MA-OEGMA-CBA) branched copolymer. The sizes of the LCVs can be easily tuned by the amount of surfactants and the cross-linked reaction in LCVs occurred during the fusion process of small vesicles without any additional cross-linking agent. The formed LCVs are uniform, low toxic and resistant to nonspecific protein adsorption. The biodegradable and biocompatible LCVs can act as a vector for proteins (Figure 10) [108].  
Figure 10: (**a**) Schematic outline of preparation of inverse emulsion nanodroplet templates containing the branched copolymer in the water phase and the possible mechanism for the formation of cross-linked large compound vesicles in nanodroplet templates; (**b**) TEM images of the self-assembled nanostructure of the branched copolymer in nanodroplet templates at different stages. Copyright 2014, Royal Society of Chemistry.
# SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
## 4 Results and Discussion
### Reversibility Analysis  
In the proposed schemes, the original image is recovered from the seed pixels after the extraction of data from the corresponding stego images. A difference between any pixel set of the two images (cover and stego), for both the schemes, results in a perfect black image, thus ensuring that the corresponding pixel difference between the two images is zero, as depicted in Figure 6. The subjective results, shown in Figure 6, show that both of the proposed schemes are perfectly reversible.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image No**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Technique-1** \\ **(without the Use of LDS)** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Technique-2** \\ **(with the Use of LDS)** \\ \end{tabular} } \\ \cline{2-6}  & **PSNR (dB)** & **SSIM** & **\%BER** & **PSNR(dB)** & **SSIM** & **\%BER** \\ \hline
1. & 40.089 & 0.979 & 0 & 43.73 & 0.989 & 0 \\
2. & 40.93 & 0.962 & 0 & 43.76 & 0.980 & 0 \\
3. & 40.982 & 0.969 & 0 & 43.74 & 0.984 & 0 \\
4. & 40.954 & 0.96 & 0 & 43.74 & 0.978 & 0 \\
5. & 40.958 & 0.97 & 0 & 43.75 & 0.983 & 0 \\
6. & 40.021 & 0.956 & 0 & 43.75 & 0.979 & 0 \\
7. & 40.923 & 0.942 & 0 & 43.75 & 0.968 & 0 \\
8. & 40.917 & 0.939 & 0 & 43.76 & 0.965 & 0 \\
9. & 40.953 & 0.966 & 0 & 43.75 & 0.981 & 0 \\
10. & 39.19 & 0.772 & 0 & 43.74 & 0.970 & 0 \\
11. & 38.947 & 0.751 & 0 & 43.76 & 0.967 & 0 \\
12. & 39.191 & 0.766 & 0 & 43.78 & 0.968 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective quality indices for Technique-1 and Technique-2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Scheme**} & **Average PSNR (dB)** & **Capacity (bpp)** \\ \hline [PERSON] et al. [25] & 33.24 & 0.96 \\ [PERSON] et al. [24] & 33.79 & 1.59 \\ [PERSON] et al. [30] & 46.36 & 0.75 \\ [PERSON] et al. [23] & 49.01 & 0.15 \\ (GA Scheme) & 49.00 & 0.15 \\ [PERSON] et al. [23] & 48.94 & 0.14 \\ [PERSON] and [PERSON] [11] & 47.61 & 1.5 \\ [PERSON] et al. [27] & 43.6 & 1.25 \\ Proposed & & \\ Technique-1 & 40.338 & 1.5 \\ Technique-2 & 43.75 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison to state-of-the-art techniques.
## 4 Results  
### Results of Automatic Segmentation of Heart Sounds  
The heart sound database can be downloaded from the Pascal Classifying Heart Sounds Challenge website [24]. Data have been gathered from two sources: (A) samples acquired through a smartphone using the iStethoscope app and (B) samples acquired in a hospital setting using a DigiScope electronic stethoscope. The database contains the following categories: normal, murmur, extra heart sound and artifact. Table 3 presents in detail the number of recordings and cardiac cycles for the two datasets. In [24], the results of manual segmentation carried out by experts are presented that serve to evaluate the performance of the segmentation algorithms. The objective of this challenge is to calculate the number of cardiac cycles and identify the S1 and S2 sounds of a recording. Tables 4 and 5 show the results obtained using dataset A and B respectively, where the evaluation metric is the error that exists between manual segmentation labels provided by the database and those obtained by the proposed method; that is, the difference between the samples corresponding to the S1 and S2 sounds with those detected by the segmentation algorithm. In [24], a spreadsheet is presented to evaluate the error of each sound. Table 6, the results of the total errors shown in [17; 18; 19; 20; 21] and the proposed method for datasets A and B are shown. Taking into account that the results presented in [19; 20; 21] were the best for the Pascal Challenge. Our method obtained a total error of 843,440.8 for dataset A and 17,074.1 for dataset B. These results are the best compared to the state-of-the-art approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**HS (File Name)** & **HB** & **Average Error** & **HS (File Name)** & **HB** & **Average Error** \\ \hline
201101070538 & 11.5 & 15,792.91 & 201103101140 & 9 & 58,920.83 \\
20110115127 & 10 & 177,625.15 & 201103140135 & 9.5 & 24,891.94 \\
201102081152 & 9.5 & 159,024.94 & 201103170121 & 10 & 343.15 \\
201102201230 & 11.5 & 17,384.91 & 201104122156 & 11.5 & 173,664.78 \\
201102270940 & 8.5 & 159,194.17 & 201106151236 & 9.5 & 56,598.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of segmentation for dataset A. (HB: heartbeat). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset A** & **Number of Recordings** & **Dataset B** & **Number of Recordings** \\ \hline Normal & 31 & Normal & 200 \\ Murmur & 34 & Normal noisy & 120 \\ Extra HS & 19 & Murmur & 95 \\ Artifact & 40 & Extra systole & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Database of heart sounds: Pascal Challenge. HS: heart sound.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Power Distribution
#### 2.2.3 Network Topologies  
The choice of network topology, such as radial grids, meshed types, and ring grids, plays a crucial role in power flow analysis for networked microgrids. Radial grids, characterized by a simple tree-like structure, make power flow analysis straightforward. Meshed grids, featuring multiple interconnected paths for enhanced fault tolerance, necessitate advanced algorithms for power flow analysis due to the presence of multiple routes. Ring grids, allowing bidirectional power flow for increased redundancy and reliability, introduce complexity in power flow analysis as injected power may cause voltage variations. In [87], existing methods addressing power flow problems considering different topologies are comprehensively reviewed.
# Development of a Simulation Model for HMT of a 50 kW Class Agricultural Tractor
###### Abstract  
This study was conducted to develop a simulation model of a 50 kW class hydro mechanical transmission (HMT) tractor and to verify the model by comparing the measured and simulated data, including the axle torque, rotational speed, and power transmission efficiency. The platform of the HMT was composed of the engine, hydrostatic unit (HSU), compound planetary gear, range shift, spiral level gear, and final reduction gear. The HMT had three gear stages and a maximum forward speed of 40 km/h. To evaluate the performance of the HTM, a test bench was installed based on the engine of the HMT platform, and a simulation model was developed using 3D simulation software. To compare the results of the simulation, a bench test using the platform was performed according to the gear stages. The similarities between the measured and simulated data were analyzed using the t-test. As a result, there were no significant differences for the axle torque, rotational speed, and power transmission efficiency. Finally, the power transmission efficiency between the measured and simulated results was compared and analyzed using linear regression analysis to validate the accuracy of the simulation model. The trend of the power transmission efficiency between the measured and simulated results appeared to be similar in all sections, and we obtained a simulation model with the accuracy of an R-squared value of more than 0.97. In conclusion, the measured and simulated results were similar to each other. Considering the results of this study, it will be useful to develop the HMT tractor and to improve the power transmission efficiency for the optimal design.  
a pacs: 1
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Case Representation
On the other hand, the CBR community is actively providing new representations of cases through deep learning (DL) technology and other forms of vectorization. For example, a pre-trained word2 vec model and a one-dimensional convolutional neural network were used to generate appropriate case representations [28], and a deep autoencoder and Siamese neural network (SNN) were used to generate text case representations [29]. This type of exploration provides corresponding processing methods for complex elements such as text and images in each case, but supervised learning based on DL has problems such as data labeling, hyperparameter optimization, network structure, and parameter design. Furthermore, how to express the rich structured cases of CBR in a network context is another key issue [30]. Finally, determining whether each new case generation and preservation need to update the network also requires relevant discussion and research.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 3 Heuristic Methods on a Simple Thermal Model Reduction and Correlation
### Model Parameters  
#### 3.7.1 Physical Constants  
* Stefan-Boltzmann constant: \(\sigma=5.67\times 10^{-8}\,\text{W}/\text{m}^{2}\cdot\text{K}^{4}\);
* Solar constant: \(S_{0}=1361\,\text{W}/\text{m}^{2}\);
* Solar absorptivity: \(\alpha=0.4\);
* Orbit period: \(T_{\text{day}}=\frac{24\times 3600}{16}\,\text{s}\).
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 4 Image Encryption and Decryption
### Scheme of Encryption
The image blocks are then converted to strings by c = 1 while \(p\leq K\) while \(q\leq L\) \(J_{4}(\)p.q:q + 31\()= B(c,\()\)\()\oplus J_{3}(\)p.q:q + 31\()\); B(c + 1,\()\) = uint8(sha256 hasher.ComputeHash \((J_{4}(p,q:q+31)))\); c = c + 1; end end
3. The encrypted image \(J_{4}\) to be sent to the recipient is obtained.
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 5 Simulation Results
### Results and Analysis
#### 5.3.4 Residual Energy Ratio  
Figure 10 depicts the variations in the residual energy ratio of nodes for the three protocols as the number of nodes increases. The residual energy ratio of a node signifies the ratio between the average remaining energy of all the nodes, following the completion of data packet forwarding, to the initial energy level of the nodes in the network. As evident from the graph, an augmentation in node count results in a progressive increase in the residual energy ratio for WLQRP, whereas VBF and HH-VBF exhibit a corresponding decline. This phenomenon arises primarily due to energy consumption being dominated by data packet forwarding and reception, with forwarding constituting a substantial portion. With an increase in node count, the frequency of packet forwarding diminishes in WLQRP, thereby leading to a gradual rise in the residual energy ratio.  
Figure 9: End-to-end delay.  
However, as inferred from preceding graphs, an elevation in node count for VBF corresponds to an increase in hop count, consequently resulting in a decrease in the residual energy ratio.
## 3 Results and Discussion
### Microstructure Analysis
Combined with XRD results, it can be concluded that the netlike eutectic structures are mainly composed of M\({}_{2}\)B and M\({}_{7}\)(C, B)\({}_{3}\). The content of Fe in the dentrite is the highest, indicating that the dentrite structures are mainly composed of martensite and ferrite. From Figure 4 it can also be seen that the amount of martensite decreases, while the amount of ferrite gradually increases with the increase of Mo content. This is because Mo can promote the formation of ferrite [39]. Dendrite 2 contains a certain amount of Cr, which can strengthen the matrix by solid solution strengthening [40]. According to the EPMA and XRD results, the content of C in the granular eutectic structure is higher, and the granular eutectic structure is therefore identified as M\({}_{23}\)(C, B)\({}_{6}\). Figure 5 presents the mapping scanning results of 4.0 wt. % Mo coating. It shows that the microstructures are inhomogeneous due to the severe elemental segregation that occurs during solidification. As can be seen from Figure 5b, e, f, the Ni, Fe, and Si elements are evenly distributed in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Point** & **Cr** & **C** & **B** & **Mo** & **Ni** & **Si** & **Fe** \\ \hline
1 & 19.978 & 4.877 & 4.789 & 4.674 & 1.910 & 0.428 & 63.344 \\
2 & 15.336 & 2.606 & - & 1.801 & 1.937 & 0.783 & 77.537 \\
3 & 14.224 & 11.034 & 0.139 & 1.780 & 1.697 & 0.779 & 70.347 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The electron probe microanalyzer (EPMA) results of the point scan (wt. %). Figure 4: Microstructure of coatings with different Mo content: (**a**) Mo-free; (**b**) 2.0 wt. % Mo; (**c**) 4.0 wt. % Mo; (**d**) 6.0 wt. % Mo. the matrix phase. The Mo, Cr, and C elements (showed in Figure 5c,d,g, respectively) are rich at the grain boundaries, which indicates that the microstructures of the grain boundaries are primarily carbides of Mo and Cr. EDS component analyses were done in this area, and the relative contents (wt. %) of the composition calculated are as follows: 72.7% Fe, 15.2% Cr, 4.6% Mo, 5.2% C, 0.7% Si, and 1.6% Ni. Figure 5: _Cont._
# Novel RNA Viruses Discovered in Weeds in Rice Fields
## 3 Results
### Characterization of Double-Stranded RNA Viruses  
The family _Partitiviriidae_ includes five genera, _Alphapartititivirus_, _Betapartitivirus_, _Gamma-partitivirus_, _Deltapartitivirus_ and _Cryspovirus_[55]. The members of the family _Partitiviriidae_ comprise non-enveloped viruses with bisegmented double-stranded (ds) RNA genomes that are 3.0 to 4.8 kb in size [55]. Here, we identified four dsRNA viruses in the weeds _Sagittaria trifolia_, _Digitaria sanguinalis_, _Paspulum distich_ and _Lindernia proumbens_; all the  
Figure 5: ML phylogenetic analysis of the order _Bunyavirilas_. Phylogenetic trees were constructed using the aligned sequences of RdRp. The names of new viruses discovered in this study are marked in red, the names of known viruses discovered in this study are marked in blue, the reference virus serial numbers and names are marked in black and the families and genera of viruses (and branches in minimized trees) in this study are marked in bold. The red box represents the clade of the virus identified in this study. The grey triangle represents the enlarged part of the red box.  
identified viruses clustered within the family _Partitiviridae_. Three novel viruses, Guiyang partiti-like virus 1 (GPIV1), Xiaogan partiti-like virus 1 (XPIV1) and Hangzhou partiti-like virus 1 (HPIV1), have genome lengths between 1689 and 1901 bp. These novel viruses displayed 59.2% to 77.9% aa identities with the closely related viruses. In addition, we identified one virus that displayed high sequence similarity (94.5%, aa identity) with the known virus Magnaporthe grisea partitivirus 1 (GenBankank no. MK279458); it was annotated as a variant of Magnaporthe grisea partitivirus 1. The genomes of the members of the family _Partitiviridae_ contain two segments: one encodes RdRp and the other encodes capsid protein [55]. The capsid-encoded segment was identified in Magnaporthe grisea partitivirus 1 and XPIV1, while it was not detected in GPIV1 and HPIV1 (Figure S13). Phylogenetic analysis revealed that XPIV1 clustered strongly within the genus _Alphapartitivirus_, suggesting that it was a new member of the genus _Alphapartitivirus_. In addition, GPIV1 formed a well-supported monophyletic group with maize-associated partiti-like virus; the new group appeared to represent a separate evolution distant from the members of the genus _Deltapartitivirus_ and the genus _Cryspovirus_. Hangzhou partiti-like virus 1 was closely related to the genus _Gammapartitivirus_, suggesting that it may be a new member of the genus _Gammapartitivirus_ (Figure 6).
## 3 Model Description
### Flexible Model of the System
#### 3.1.1 State-Space Representation  
It is convenient to use the state-space representation, so that the input of the system is the external forces and constraints, while the outputs are the displacements and velocities of the nodes. The state vector \(\mathbf{z}\), the input vector \(\mathbf{u}\) and the output vector \(\mathbf{y}\) are:  
\[\mathbf{z}\ \ =\ \{x;\,;x\};\ \mathbf{u}\ \ =\ \mathbf{f};\ \mathbf{y}\ \ =\ \{x;\,;x\}, \tag{2}\]  
while the state-space representation has the form:  
\[\left\{\begin{array}{l}\dot{z}\ =\ \mathbf{A}z+\mathbf{B}u\\ y\ =\ \mathbf{C}z+\mathbf{D}u\end{array}\right.; \tag{3}\]  
the values of the matrices \(\mathbf{A}\), \(\mathbf{B}\), \(\mathbf{C}\) and \(\mathbf{D}\) are obtained by rearranging Equation (1):  
\[[\mathbf{A}]\ =\ \left[\begin{array}{cc}\mathbf{0}&\mathbf{I}\\ -\mathbf{M}^{-1}\mathbf{K}&\mathbf{0}\end{array}\right];\ [\mathbf{B}]\ =\ \left[ \begin{array}{cc}\mathbf{0}\\ -\mathbf{M}^{-1}\end{array}\right];\ [\mathbf{C}]\ =\ \left[\begin{array}{cc} \mathbf{I}&\mathbf{0}\\ \mathbf{0}&\mathbf{I}\end{array}\right];\ [\mathbf{D}]\ =\ \left[\begin{array}{cc} \mathbf{0}\\ \mathbf{0}\end{array}\right]. \tag{4}\]
## 3 Case Study and Results Analysis
### Case Study 3  
In this case, the icing data from a 110 kV transmission line called \"Yangtongxian\" is selected to validate the robustness and stability of the proposed QFA-w-SVM icing forecasting model. Similarly, the five models are still used to make a comparison for prediction results.  
Figure 12: Forecasting errors of each model.  
Figure 13: Values of MAPE, MSE, AAE.  
Figure 14 shows the convergence plots of QFA-w-SVM, FA-w-SVM and PSO-SVM. As shown in the figure, QFA-w-SVM obtains the optimal value after 40 iterations while FA-w-SVM and PSO-SVM models converge to global solutions after 53 and 57 iterations, respectively. In other words, the QFA-w-SVM still has the fastest convergence speed and stronger global optimization ability compared with other models.  
The forecasting results and relative errors are shown in Figures 15 and 16 respectively. From the figures, it is obvious that the proposed QFA-w-SVM model still has better accuracy in icing forecasting, which demonstrates that the proposed model is superior for solving the icing forecasting  
Figure 17 shows the MAPE, MSE, and AAE values of the five models; it is clear that the values of MAPE, MSE, AAE of the proposed QFA-w-SVM model are the smallest among the five models, which again demonstrates that the proposed model performs with great robustness and stability.  
In summary, the proposed QFA-w-SVM model can greatly close the gap between forecasting values and original data, which means it outperforms the FA-w-SVM, PSO-SVM, SVM, and MLR models in icing forecasting with different voltage transmission lines. Moreover, the QFA-w-SVM which uses QFA to select the parameters of the SVM model, can effectively make a great improvement for icing forecasting accuracy. It might be a promising alternative for icing thickness forecasting.