esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Effects of Lubricant Thickness on the Interaction Force and HMS at the HDI  
The lubricant thickness not only influences the head-media spacing (_HMS_) through changing the _FH_, but, in addition, can itself affect the magnitude of _HMS_. Therefore, in the interpretation of effects of the lubricant thickness, Figure 11 shows variations of the adhesion force, contact force and _HMS_ as a function of the lubricant thickness, for \(z_{\mathrm{r}}\) = 11 nm, 10 nm and 7 nm, respectively. At \(z_{\mathrm{r}}\) = 11 nm, the slider is at the flying state over the entire range of the investigated lubricant thickness. A decrease in the _HMS_ and slight increase in the adhesion force with the decreasing lubricant thickness are found. When \(z_{\mathrm{r}}\) = 10 nm, the TP is in contact with the lubricant at 1 nm \(\leq\)\(t_{\mathrm{lub}}\)\(\leq\) 3 nm, at which the variation trends of the _HMS_ and adhesion force with the lubricant thickness are the same as for the case of the flying state. However, if \(t_{\mathrm{lub}}\)\(<\) 1 nm, the adhesion force increases remarkably because of the contact between the TP and the DLC layer. From the perspective of avoiding large adhesion force, the lubricant thickness should not be small. When \(z_{\mathrm{r}}\) = 7 nm, the slider operates at the TP-DLC contact state for the lubricant thickness ranging from 0.5 nm to 3 nm. It is found that the _HMS_ cannot be reduced further through decreasing the lubricant thickness.  
Figure 11: Force and head–media spacing versus the lubricant thickness under \(t_{\mathrm{DLC\text{-}disk}}\) = \(t_{\mathrm{DLC\text{-}slider}}\) = 2 nm: (**a**) Adhesion force \(F_{\mathrm{a}}\), Contact force \(F_{\mathrm{c}}\) and (**b**) Head-media spacing _HMS_.  
Figure 10: Pressure distribution in the section of \(y\) = 0 for different TP heights under \(t_{\mathrm{lub}}\) = \(t_{\mathrm{DLC\text{-}disk}}\) = \(t_{\mathrm{DLC\text{-}slider}}\) = \(2\) nm, \(z_{\mathrm{r}}\) = 10 nm.
## 2 Materials and Methods
### Inverse Kinematic Solution  
Presuming that \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) are the rotating angles of the frame \(G\)-_uvw_ concerning the frame \(O\)-_xyz_ in the sequence of \(x\)-, \(y\)-, and \(z\)-axes, the posture of \(G\) can be achieved through the following coordinate transformation:  
\[T_{G}^{O}\ =\ T_{G_{1}}^{O}T_{G}^{G_{1}}\ =\ \left[\begin{array}{cc}R_{G}^{O}&P_{G} ^{O}\\ 0&1\end{array}\right]\ =\ \left[\begin{array}{cc}R_{G_{1}}^{O}&P_{G_{1}}^{O}\\ 0&1\end{array}\right]\ \left[\begin{array}{cc}R_{G}^{G_{1}}&P_{G}^{G_{1}}\\ 0&1\end{array}\right] \tag{1}\]  
where  
\[\left\{\begin{array}{cc}R_{G_{1}}^{O}\ =\ R_{G^{\prime}}^{O},&P_{G_{1}}^{O}= \ \left[\begin{array}{cc}x_{G_{1}}&y_{G_{1}}&z_{G_{1}}\end{array}\right]^{ \mathrm{T}}\\ R_{G}^{G_{1}}\ =\ \left[\begin{array}{cc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right],&P_{G}^{G_{1}}\ =\ \left[\begin{array}{cc}0&0&h_{L}^{ \prime}\end{array}\right]^{\mathrm{T}}\end{array}\right.\]  
The \(R_{G}^{O}\) is the homogeneous transformation matrix; the \(h^{\prime}_{L}\) is the length of the distal bone segment \(GG_{1}\); \(P_{G_{1}}^{O}\) is the displacement vector of \(G_{1}\) concerning the frame \(O\)-_xyz_, and \(P_{G}^{G_{1}}\) is the displacement vector of \(G\) to the frame \(G_{1}\)-\(u_{1}v_{1}w_{1}\).  
The inverse kinematics of the fixator can be resolved through the closed-loop method. The closed vector chain is shown in Figure 1c. The vector \(I_{i}\) (\(i\) = 1, 2,..., 6) of rod length can be achieved by Equation (2).  
\[I_{i}\ =\ B_{i}-A_{i}\ =\ \left[\begin{array}{ccc}l_{ix}&l_{iy}&l_{iz}\end{array} \right]^{T} \tag{2}\]  
Figure 1: (**a**) The neutral position of the bone-fixator system. (**b**) Schematic diagram of the frame. The points \(A_{i}\) (\(i\) = 1, 2,..., 6) and \(B_{i}\) (\(i\) = 1, 2,..., 6) are the centers of the Hooke and spherical joints. (**c**) The closed vector chain used to solve the vector \(I_{i}\).  
where \(A_{i}\), \(B_{i}\) (\(i=1,2,\ldots,6\)) are position vectors concerning origin \(O\) in the frame \(O\)-\(xyz\). Then, the rod length \(I_{i}\) (\(i=1,2,\ldots,6\)) is achieved through Equation (3).  
\[|I_{i}|\ =\ \sqrt{l_{ix}^{2}+l_{iy}^{2}+l_{iz}^{2}} \tag{3}\]  
Thus, the correction prescription, the length of the rods \(I_{i}\) (\(i=1,\,2,\ldots,6\)) at each step, can be achieved through the inverse kinematic solution.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Deep Autoencoder** & \multicolumn{2}{c}{**Selected Features**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \hline
**Architecture** & **DT** & **DA** & **DT** & **DA** \\ \hline
12-24-10-256-6-150-256 & d6, d3, d1, d5, d2, d4 & d6, d3, d1, d2 & 88.5 & 89.3 \\
16-32-14-256-4-150-256 & d3, d1, d2, d4 & d3, d1, d1, d2 & 83.2 & 79.8 \\
16-32-14-256-6-150-256 & d2, d1, d4, d5 & d2, d1, d4, d5, d6, d3 & 91.3 & 91.3 \\
3-6-11-96-4-7-256 & d3, d2, d4, d1 & d3, d2, d4, d5, d6, d3 & 84.8 & 80.3 \\
3-6-12-128-4-75-256 & d4, d1, d2, d3 & d4, d1, d2, d3, d6, d3 & 81.7 & 81.6 \\
6-10-10-96-4-75-256 & d4, d1, d3, d2 & d1, d4, d3, d3, d6, d3 & 84.4 & 82.1 \\
6-12-12-128-4-150-512 & d4, d1, d2, d3 & d4, d1, d2, d3, d6, d3 & 89.6 & 89.7 \\
8-16-10-96-4-75-256 & d1, d3, d4, d2 & d1, d4, d3, d3, d6, d3 & 82.7 & 80.3 \\
8-16-12-128-4-150-512 & d1, d2, d3, d4 & d1, d2, d3, d4, d6, d3 & 86.4 & 87.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Selected deep features in order of importance and corresponding classification accuracy for DT and DA classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline
**Deep Autoencoder** & \multicolumn{2}{c}{**Selected Features**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \hline
**Architecture** & **DT** & **DA** & **DT** & **DA** \\ \hline
12-24-10-256-6-150-256 & d6, d3, d1, d5, d2, d4 & d6, d3, d1, d2 & 88.5 & 89.3 \\
16-32-14-256-4-150-256 & d3, d1, d2, d4 & d3, d1, d1, d2 & 83.2 & 79.8 \\
16-32-14-256-6-150-256 & d2, d1, d4, d5 & d2, d1, d4, d5, d6, d3 & 91.3 & 91.3 \\
3-6-11-96-4-7-256 & d3, d2, d4, d1 & d3, d2, d4, d5, d6, d3 & 84.8 & 80.3 \\
3-6-12-128-4-75-256 & d4, d1, d2, d3 & d4, d1, d2, d3, d6, d3 & 81.7 & 81.6 \\
6-10-10-96-4-7-256 & d4, d1, d3, d2 & d1, d4, d3, d3, d6, d3 & 84.4 & 82.1 \\
6-12-12-128-4-150-512 & d4, d1, d2, d3 & d4, d1, d2, d3, d6, d3 & 89.6 & 89.7 \\
8-16-10-96-4-7-256 & d1, d3, d4, d2 & d1, d4, d3, d3, d6, d3 & 82.7 & 80.3 \\
8-16-12-128-4-150-512 & d1, d2, d3, d4 & d1, d2, d3, d4, d6, d3 & 86.4 & 87.2 \\ \hline \end{tabular}
\end{table} 
Table 3: Selected combined standard and deep features in order of importance and corresponding classification accuracy for DT and DA classifiers. Figure 5 presents feature importance estimates as provided by the DT classifier in the case of combined features (Table 3, the third autoencoder architecture: 16-32-14-256-6-150-256). It can be observed that the first three features (the most important ones) belong to the deep features set. Figure 6 illustrates the representation of CFG and GFC samples in the space of the two most relevant features, namely d1 and d2. This result is only illustrative, as the classification in this study was performed in the space of more features (6 features, or 4 features in the case of restricted autoencoders with only 4 middle layer units). Figure 4: Classification accuracies of DT and DA classifiers for different feature sets. Figure 5: Importance estimates for combined features, as provided by the DT classifier.
## 5 Conclusions  
With the aim of solving the problem of the cross-media motion trajectory tracking of a coaxial HAUV under modeling uncertainties and external disturbances, an improved cascade SMC-PD control strategy is proposed to realize cross-domain motion. In the position loop, the composite robust controller was designed by combining the observer technique and the adaptive technique to meet the strong robustness requirements of the cross-domain process. On the other hand, the attitude error dynamics model of the HAUV was established by using the unit quaternion, which solves the singularity problem of the HAUV under large inclination maneuvers. In the cascaded control system, the inner loop controller needs a faster response speed, so a feedforward PD controller with a simple structure was adopted. The disturbance observer was added to the PD controller, which effectively enhanced its robustness. In addition, adaptive variable parameter technology based on the learning rate was introduced to the controller design process, which further improved the response speed of the controller. The Lyapunov theory was used to provide a rigorous proof of the stability of the closed-loop system. Finally, two typical cross-domain maneuverability strategies were simulated. By comparing the simulation results, the effectiveness and superiority of the proposed method for coaxial HAUV cross-media motion control under oceanic disturbances were verified.  
In our future work, we plan to apply this proposed composite control to a real cross-domain platform to evaluate the algorithm's performance through physical experiments. At the same time, new intelligence algorithms will continue to be developed.  
Conceptualization, M.L., S.W. and F.L.; methodology, M.L. and F.L.; software, M.L. and S.W.; validation, F.L. and W.Y.; formal analysis, F.L. and S.W.; writing--review and editing, M.L. and F.L.; writing--review and editing, F.L. and W.W.; visualization, M.L. and W.Y.; supervision, Y.S. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Feng Lei Youth Innovation Fund of CARDC, grant/award numbers (FL018070012 and ZZZH2000907098); the National Defense Key Laboratory Fund, grant/award number (614222003061709); and Key Laboratory of Cross-Domain Flight Interdisciplinary Technology Fund, grant/award number (2024-ZY0301).  
Not applicable.  
Figure 23: Evolution of control input signal in working case 2.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup
### Three-Dimensional Model and Mesh
#### 2.5.2 Mesh and Boundary Conditions  
In order to simulate the operation of the pump-jet propeller in a marine environment, the pump-jet propeller was placed in a large cylindrical area, i.e., the external flow field, with the same axis as the pump-jet propeller shown in Figure 4. The external flow field inlet was set as the speed inlet, and outlet was set as the static pressure outlet. The cylindrical wall was set as the free slip wall, while other walls were set as the non-slip wall. The maximum diameter of rotor of the pump-jet propeller is D. The inlet and outlet boundary of computational domain of the external flow field are 4D and 6D away from the rotor, respectively. The diameter of the external flow field is 5D.  
ANSYS ICEM software was used to generate the structured mesh and O-grid block was used around the rotor blade. In order to capture the vortex motion near the rotor blade and the inner wall of the duct, the boundary layer mesh of this part was further refined, where the tip clearance boundary layer is 20 layers and the blade wall boundary layer is 10 layers. The mesh around the stator blades was also optimized with 20 boundary layer nodes. Since the flow separation is more likely at the inlet and outlet of the duct, the number of boundary layer nodes near the duct, the diversion cap, and the wake hood was also increased. The final mesh distribution of the pump jet propeller is shown in Figure 5, where Figure 5 shows the mesh of the entire system of the pump-jet propeller, while Figure 5,c show the boundary layers around the rotor blades and in the tip clearance area, respectively. In order to ensure a good transition of flow field information between different computational domains, the number of nodes on the interface between external flow field and rotor, rotor and stator, stator and external flow field is adjusted to be \(10^{5}\) order of magnitude.  
Figure 4: Computational domain.  
Figure 3: Pump-jet propeller model.  
The grid resolution of SST \(k\)-\(\omega\) model is fine and the Y+ values should be less than 30. Y+ values around the pump-jet propeller are listed in Table 1:
# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection  
In standard feature selection approaches the pipeline of tasks (Figure 2a) include features representation prior to selection, whereas in the ontology-based feature selection pipeline there is need to first extract the related features from the input data (after prepossessing) according to a utilized ontology (mapping) and then select those features that are more suitable for the classification task (Figure 2b).  
Figure 1: Example ontology.  
The main research domain where ontologies have been employed in terms of selecting specific features is document classification, where predefined categories are associated with free-text unstructured documents based on their content. The continuous increase of volumes of text documents on the Web makes text classification an important tool for searching information. Due to their enormous scale in terms of the number of classes, training examples, features, and feature dependencies, text classification applications present considerable research challenges.  
In the following paragraphs we present related works organized according to selected and representative application domains. For each domain, we provide a summarized description of the related work and a table that organizes their main features according to specific criteria.
## 8 Current Challenges and Future Prospects
### Smartphone and the IoT  
Information communication technologies such as smartphones and the IoT can change agriculture in developing countries by facilitating access to markets and providing problem-solving information. These technologies can also bridge the rural-urban gap, including the next generation of growers. Most agricultural-related smartphone services in developing countries have provided simple functions due to the limitations of available delivery technologies [79; 80; 81]. Fast-moving smartphones and the IoT may soon change by giving many opportunities for developing complex services. Today, users can use a wide variety of devices to spread more complex information. Simultaneously, smartphones and the IoT have become increasingly popular and interconnected through expanded smartphone networks, allowing for the collection and processing of large amounts of data to help agricultural farming and the establishment of social networks for information exchange and learning.  
This article discusses relevant knowledge and implications, but further study is required to assess how the latest tools and technologies can be practically used to supportagriculture in developing nations. For example, what challenges exist and what is needed to eliminate them? The smartphone and IoT technology developers also need to ensure that their services continue to meet a wide range of user needs, rather than concentrating too much on new machinery that may not be available to limited resource underserved growers.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 3 Analysis of the Forecasting Results
### Prediction Intervals Evaluation
#### 3.6.1 Evaluation of Combined Prediction Intervals  
Just as point forecasts can be combined and estimate the evaluation of its accuracy, prediction intervals can also be combined and estimate the accuracy of the resultant combined PI. In this study, we made use of two prediction interval combination methods, the simple average and the median method, to produce a combined PI. Simple average gets the arithmetic mean of the PIs from individual models using the lower and upper bound estimate. A row-wise summation of the lower and upper limits is carried out and averaged by taking each model at the three quantiles considering the total number of models used. The mathematical formulation is given in Equation (27). The median method also follows the simple average method. We take the median values of all the models considered at a particular quantile. The mathematical expression is given in Equation (28). Table 8 presents the combined prediction interval evaluation using the same metrics as the individual PI model evaluation.  
In Table 8, the simple average prediction interval combination method gives better PI than the median method at the 90% and 95% confidence values using PICP. However, it gives an invalid PI at the 99% confidence level recording a value smaller than the confidence level value, while the median method gave a satisfactory coverage value for its PICP. The median method provided reliable and better quality PI than the simple average method since it records lower values for its PINAW and PICAW at the 90% and 95% confidence levels. The 99% confidence value presents the median method as better than the average method using PINAW, while PICAW shows the opposite. The PINAD value for the 90% confidence level records a tie for the two combination methods, while the median method recorded the best value for its degree of deviation using PINAD at the 95% and 99% confidence levels. Lastly, an increment was seen in the number of PI, not within the lower and upper limit range for the two methods at the 90% and 95% confidence values. The 99% confidence value shows more number of actuals below the lower limit. These present the median method as the best PI combination method over the simple average method.
# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results
### Detailed Comparison of Algorithms
Continuous reconnaissance is carried out for this process, and the total execution time of each algorithm is calculated (target one from (900, 900, and 100) m to (660, 720, 100) m, and target two from (900, 100, and 100) m to (900, 400, and 100) m). It is assumed that only when the algorithm completes the FANET's construction of time step \(t\) will the monitored target will arrive from the position at \(t\) to the position at \(\tau\). Figure 14 shows the execution time of different algorithms to complete the entire continuous reconnaissance process. There is no iterative process in RWP, and the selection process at each moment is entirely random, so its average execution time is about 12.46-13.11% of that of PSO and TARF. TARF has a similar complexity as PSO, but it optimizes the maintenance of network connectivity and the avoidance of high-threat areas. Based on the original TARF, TA&CRFO is designed to selectively realize the self-adaptive regulation of UAVs, which effectively reduces the iterative operation of the algorithm. When the parameter threshold \(\lambda\) is set to 5.23, the TA&CRFO's execution time is about 19.86-20.31% of that of TARF, even though its effect is slightly inferior to that of the TARF algorithm in other aspects (Figures 9-13). Figure 14: The average execution time of different algorithms. Figure 13: The shortest UAV distance of FANET constructed by different algorithms.
## 4 Results and Discussion
### Comparison with Other P300 Detectors  
In this subsection, we further compare the proposed kernel FDA-SVM P300 detector with other commonly used detectors, including LDA (also called Fisher LDA, FLDA) [26], Bayesian LDA (BLDA) [28; 29; 37], and the gradient boosting with ordinary least squares regression (OLS) algorithm [27]. In the following, we briefly review these methods and then provide the comparison results.
# Cold-Adapted Fungi: Goldmine of Biomolecules Applicable in Industry
## 1 Introduction  
The Earth is often described as a 'cold' planet. It is estimated that as much as 85% of the Earth's biosphere is permanently exposed to low temperatures of less than 5 \({}^{\circ}\)C [1, 2, 3]. Cold ecosystems are distributed across a vast range of environments, from the Arctic and Antarctic to high mountain ranges and the depths of the oceans [2]. Low temperatures are observed in a number of different environments, including the deep sea, snow caps, permafrost, sea ice and glaciers, the mesosphere and stratosphere, cold lakes, deserts, and caves [2].  
Cold ecological niches have been colonized by a wide range of organisms from all three domains of life, including filamentous fungi and yeasts. Cold-adapted microorganisms are undoubtedly the most biodiverse and widely distributed group among extremophiles [4, 5].  
Fungi that inhabit cold ecosystems are referred to as psychrophiles or psychotrophs. The term was first introduced by [PERSON]. It is derived from the Greek words _'psychors'_, meaning cold, and _'flos'_, meaning friend [6]. The classification of psychrophiles is based on their ability to grow at temperatures below 0 \({}^{\circ}\)C, with a maximum growth rate at temperatures up to 15 \({}^{\circ}\)C. They are unable to grow above 20 \({}^{\circ}\)C. Facultative psychrophiles, also known as psychotrophs, are able to grow in environments with less temperature stability, with an upper critical growth temperature above 20 \({}^{\circ}\)C [7, 8]. The ability of cold-lovingfungi to endure harsh cold environments suggests that these microorganisms may have evolved a multitude of additional surviving strategies and biochemical pathways, enabling them to biosynthesize a range of intriguing novel biomolecules. This review will discuss the biodiversity of cold-adapted fungi, their survival strategies, which are largely contingent on their origin, and their capacity to produce distinctive psychrozymes and bioactive molecules with potential applications in biotechnology, especially in food technology.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 4 Implementation  
The performance analysis of post-quantum cryptography algorithms is based on implemented CLI application and _libqs_ library [19]. _Libqs_ provides cryptography-related elements, and Click is responsible for creating the command line interface. All the elements combine to form a development environment. These solutions not only provide a functional interface but also facilitate efficient programming.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 3 Dedicated Short-Range Communication (DSRC)
### IEEE 802.11p DSRC  
The Institute of Electrical and Electronics Engineers (IEEE) introduced a protocol of 1609 family of standards and called it wireless access in vehicular environments (WAVE) to support DSRC-based operations [51]. Higher layers of the protocol stack are based on the IEEE 1609 series standard, while the physical layer is based on IEEE 802.11p [52]. This is further detailed in Figure 3, showing the two planes constituting the DSRC protocol: the management plane and the data plane.  
DSRC is considered as a viable technology for vehicular communication, addressing both V2I and V2V communications. The Federal Communications Commission (FCC) dedicated 75 MHz of spectrum at the 5.9 GHz band for implementing V2V and V2I communications using DSRC [24; 53]. The authors in [54] explain that DSRC is a preferred technology for wireless communication in vehicular networks due to its low latency, high reliability, security, and possible allocation of high priority messages for safety applications. This is also illustrated in Figure 3, showing that the data plane supports two protocols, WAVE short message protocols (WSMP) for high priority and safety applications with low latency, and IPv6 for less critical applications [55].  
Despite its advantages in message prioritization and low latency, the short operation range of DSRC technologies based on IEEE 802.11p introduces difficulties in non-line-of-sight (NLOS) operation, which weakens the communication link between the EVs and the RSUs during traffic congestion. In fact, the performance of DSRC rapidly deteriorates in high density traffic networks due to massive packet collisions during simultaneous transmissions [56]. This, accordingly, introduces problems with quasi-dynamic charging coordination, particularly where EVs benefit from high traffic densities to extend their effective charging duration and increase the amount of received energy.