esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid-Recursive Feature Elimination for Efficient Feature Selection
###### Abstract  
As datasets continue to increase in size, it is important to select the optimal feature subset from the original dataset to obtain the best performance in machine learning tasks. Highly dimensional datasets that have an excessive number of features can cause low performance in such tasks. Overfitting is a typical problem. In addition, datasets that are of high dimensionality can create shortages in space and require high computing power, and models fitted to such datasets can produce low classification accuracies. Thus, it is necessary to select a representative subset of features by utilizing an efficient selection method. Many feature selection methods have been proposed, including recursive feature elimination. In this paper, a hybrid-recursive feature elimination method is presented which combines the feature-importance-based recursive feature elimination methods of the support vector machine, random forest, and generalized boosted regression algorithms. From the experiments, we confirm that the performance of the proposed method is superior to that of the three single recursive feature elimination methods.  
classification; feature selection; machine learning; recursive feature elimination +
Footnote †: journal: Applied Sciences
# Identification of Close Modes on Frequency in Rotating Systems
## 2 Close modes in Frequency
### Signals Coupling Using Imaginary Sensor
This complicates the modal parameter identification problem, unlike in rotating systems, where the damping ratios are higher, and the phase angles of each mode can be in any position from 0 to 360\({}^{\circ}\). However, during the present investigation, no documented cases were found of the balancing process being affected by the presence of close modes in frequency. This may be due primarily to the execution of offline balancing, which makes use of balancing benches that allow restricting the rotor's movement, thus ensuring that separate modes are obtained. A second possibility may be the small number of rotors that present this problem and that during the field balancing process the objective of reducing vibration levels to the allowed ranges is met by increasing the time and the number of runs. This second theory was presented by INEEL Turbomachinery staff dedicated to performing balancing services in Mexico. However, the high amount time consumed, as well as the need to increase rotor starts and stops to bring it to the desired vibration levels, generates dynamic efforts that reduce the life of the equipment. These particular cases in rotating systems in the field, in which the polar response diagrams show only one apparent vibration mode, occur when the stiffness values between horizontal and vertical modes tend to be the same. To reproduce this problem, it is assumed from Table 1 that \(\omega_{n\ x}\ =\ \omega_{m\ y}\ =\ 150.79\) rad/s, which generates the following polar response diagrams as is presented in Figure 5:  
Figure 5a,b represent the coupled mode with a red dotted line observed by a sensor at 0\({}^{\circ}\), while the black dotted line is observed by a sensor at 90\({}^{\circ}\), and the sensor placed at 45\({}^{\circ}\) and 135\({}^{\circ}\), represented in blue, appears to display a single vibration mode. Figure 4 graphically represents the close mode problem in rotors. A methodology for the identification of close modes in structures was presented recently in [14]. The study presented does not consider the effect of the phase angle to perform the identification. It should be noted that for both the horizontal and vertical mode of Figures 2, 4 and 5, neither does the maximum vibration amplitude in displacement at resonance occur exactly with a delay of 90\({}^{\circ}\) of the phase angle, nor is there a perfect circle for the vibration mode. This is an intrinsic behavior of a rotating system when the excitation force is due to imbalance. In [3], a numerical proposal is presented which ensures agreement between the maximum vibration amplitude and the 90\({}^{\circ}\) phase shift of the system response with respect to the excitation force. This technique is used in this article for the analysis presented below. Figure 5: Polar diagram with close modes at (**a**) 45\({}^{\circ}\) and (**b**) 135\({}^{\circ}\) (\(\upmu\)m).
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 2 Related Research
### Raft  
The RAFT (Recurrent All-Pairs Field Transforms) is an innovative approach designed to improve optical flow prediction accuracy significantly. By leveraging a recursive architecture combined with transformations for all pixel pairs, RAFT achieves impressive results in various challenging scenarios characterized by substantial image variations or intricate motion patterns. The core principle of RAFT lies in its recurrent architecture, which enables iterative refinement of predicted optical flows using contextual information from surrounding pixels at each location within input images. Moreover, by performing transformations on every possible pair of pixels, RAFT ensures comprehensive interactions throughout the entire image during training stages.  
To train the RAFT model effectively, a differentiable loss function is employed to minimize discrepancies between actual ground truth flows and predicted flows generated by RAFT estimations through backpropagation techniques. Overall, this approach presents promising advancements in accurate optical flow estimation while addressing complexities found in real-world environments. Figure 6 shows the structure of RAFT.  
Figure 5: The structure of LiteFlowNet.  
Figure 6: The structure of RAFT.
## 4 Conclusions  
In this paper, for the problem of insufficient positioning control accuracy due to the coupling error of a heavy-load longitudinal and transversal swing table (HLTST) under space-constrained conditions, an accurate error compensation method based on geometric parameter correction and process optimization is proposed, which reduces the positioning error of the table by correcting the kinematic model, optimizes the machining process based on the results of numerical simulation, and finally verifies it through error compensation experiments.  
The experimental results reveal that the inverse kinematics correction model established based on the contour error can significantly mitigate the direct impact of the circular surface machining error on the table's pose. By analyzing the error characteristics via numerical simulation, we can provide theoretical guidance for part design, error allocation, and equipment factory inspection, thereby ensuring the table's manufacturing accuracy. The error compensation model based on the assumption of rigid-body motion can mechanistically reflect the influence of geometric errors on the table's attitude and realize the decoupling compensation of the pitch and roll angles. In the range of \(-0.0278\pi\)\(\sim\) 0.0278\(\pi\), after the error compensation, the absolute average errors of the table's pitch and roll angles decrease by 96.94% and 65.63%, respectively. Under the maximum load condition, the error compensation model is still able to control the maximum positioning error of the HLTST within \(\pm\)5%. This demonstrates that the error compensation model significantly enhances positioning accuracy. Furthermore, the error compensation strategy, developed based on the sensitivity and coupling relationship of geometric errors, efficiently reduces the influence of highly sensitive error terms on the table's positioning accuracy. This prevents multiple compensations and accelerates the compensation process, greatly contributing to shorter precision assembly times and increased suspension loading efficiency.  
Conceptualization, M.L. and W.L.; methodology, R.B.; software, S.L. ([PERSON]); validation, M.L. and X.Y.; formal analysis, R.B.; investigation, L.J.; data curation, S.L. ([PERSON]); writing--original draft preparation, R.B. and M.L.; writing--review and editing, M.L. and W.L.; project administration, M.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 52475049.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available.  
**Acknowledgments:** Thanks to each of the authors for their contributions to the manuscript and for the support of the funding project.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Beijing Shiny Technology Co., Ltd., Beijing. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 4 Four Heterogeneous Signcryption Schemes for V2I Communication Scenarios
### Ibc\(\rightarrow\)PKI Many-to-One Signcryption (MOHSC-II)  
The **Setup** is the same as the above MOHSC-I algorithm. **PKI-KG** and **IBC-KG** algorithms have slight changes, i.e., the sender \(V_{i}\)'s key pair \((pk_{i}=H_{1}(ID_{i})=Q_{i}sk_{i}=sQ_{i})\) in an IBC, the recipient RSU's public/secret key \((pk_{r}=x_{r}P/sk_{r}=x_{r})\) in a PKI system.  
**Signcrypt**: Taking **params**, a random message \(m_{i}\), \(V_{i}\)'s secret key \(sk_{i}\), and RSU's public key \(pk_{r}\) as inputs, the algorithm executes the following procedures:  
1. Select a random \(r_{i}\in Z_{q}^{*}\) and compute \(R_{i}=r_{i}P_{pub}\);
2. Compute \(k_{i}=e(pk_{r},r_{i}sk_{i})\), \(c_{i}=m_{i}\oplus H_{2}(k_{i})\);
3. Calculate \(h_{i}=H_{3}(pk_{i},pk_{r},R_{i},c_{i})\), \(S_{i}=sk_{i}h_{i}\);
4. Transmit the signcryption ciphertext \(\sigma_{i}=(R_{i},c_{i},S_{i})\) to a nearby RSU.  
**Aggregate-Verify:** Upon receiving the ciphertexts \(\left\{\sigma_{i}\right\}_{i=1}^{n}\), the RSU computes \(S=\sum_{i=1}^{n}S_{i}\) to get the final aggregate signcryption ciphertext \(\sigma=(R_{1},\ldots,R_{n},c_{1},\ldots,c_{n},S)\). Then, the RSU executes the following procedures to verify \(\sigma\) by inputting the aggregate signcryption \(\sigma\), \(n\) vehicles' public keys \(\left\{pk_{i}\right\}_{i=1}^{n}\) and the receiver RSU's public key \(pk_{r}\):1. Compute \(h_{i}=H_{3}(pk_{i},pk_{r},R_{i},c_{i}),1\leq i\leq n\);
2. Verify \[e(S,P)=e(\sum\limits_{i=1}^{n}h_{i}pk_{i},P_{pub});\] (5)
3. If (5) is held, emits true, which means \(\sigma\) is valid. Otherwise, emits false and abort.  
**Unsigncrypt**: If the **Aggregate-Verify** algorithm outputs _true_, the receiver RSU takes the aggregate signcryption ciphertext \(\sigma\), its secret key \(sk_{r}\) and \(n\) vehicles' public keys \([pk_{i}]_{i=1}^{n}\) as inputs, then performs as follows:  
1. Compute \[k_{i}^{\prime}=e(pk_{i},sk_{r}R_{i});\] (6)
2. Compute \(m_{i}=c_{i}\oplus H_{2}(k_{i}^{\prime})\), and get the message \(m_{i}\).
# A Novel Mixed Reality Solution Based on Learning Environment for Sutures in Minor Surgery
## 2 Materials and Methods
### Procedures  
The experiment is composed of three phases. The first and second are experimental sessions of both learning methodologies: (i) Text-Reading and (ii) Mixed Reality practice. Text-Reading is a session to acquire knowledge about suture techniques by reading a descriptive text (a text adequately supervised by a health specialist). Mixed Reality practice is a session to acquire knowledge of a suture technique using our Mixed Reality application. Each participant reviews a specified part of theoretical content in each session. Then, as the third phase, the participants should complete a usability test of our tool, and a questionnaire about their subjective perceptions in relation to both learning forms.  
The experiment has been carried out under the same conditions for all users, avoiding the influence of invariant factors such as time, environment, device, etc. All participants were in the same physical space for the tests with the same lighting and both sessions lasted the same time (see Figure 2). Methodologically, the only difference between the sessions lies in the applied learning resource. This approach identifies as dependent variables the methodologies of each session (Mixed Reality Learning tool and Text-Reading) and as independent variable the results of the tests obtained after each session.  
Specifically, the subjects performed:  
1. Active Learning with MR Experience. The user initiated Suture MR, configured the experience, and carried out the interactive activity. This action was timed up on 10 min. Once it was finished, the participant took the retentive test.
2. Traditional Learning based on Text-Reading. The participant read the text for 10 min. After that he/she took the retentive test.
3. Evaluation. Participants complete questionnaires about their personal profile, their subjective assessment of the Suture MR, and the usability questionnaire using the SUS scale questionnaire.  
Each participant in the group was assigned one of the four suture procedures for each action and a specific needle configuration in phases I and II. Therefore, the permutation of four different procedures to be performed in two tests generates 16 different combinations for each group (of 16 participants). To sum up, under these conditions, each participant has studied different procedures between the Text-Reading and Mixed Reality sessions, and half of the participants have swapped the order of the sessions (i.e., first Mixed Reality, then Text-Reading).  
Regarding the evaluation system, all tests were composed of four multiple choice questions where hits add 1 point, errors subtract 0.5 points, and blank answers do not score. Thus, scores ranged from 0 to 4 points.
## 1 Introduction
[15] designed three different fuel cell UAV power system schemes and compared their advantages and disadvantages through experiments. The results indicated that the single-fuel-cell power supply structure is the simplest but is limited by the fuel cell's output power, making it difficult to meet high-load demands. Compared to the passive control system, the fuel cell/battery active control system increases overall weight and complexity but enhances the UAV's adaptability to complex flight missions. [PERSON] et al. [16] aimed to minimize hydrogen consumption by establishing an optimization model with eight design parameters, including the maximum power of the fuel cell and battery capacity, and obtained an optimal configuration through model solving. The results demonstrated that the optimized power system configuration helps reduce the fuel cell's power level and hydrogen consumption during flight missions. [PERSON], [PERSON] et al. [17] analyzed the performance of UAVs powered by a fuel cell/battery hybrid system and a battery-only system, comparing them in terms of power system weight composition, cost, and environmental impact. [PERSON], [PERSON] et al. [18] studied the performance of fuel cell/battery hybrid UAVs under different mission scenarios, and the results indicated that selecting UAV mission profiles based on fuel cell performance helps reduce hydrogen consumption during flight. [PERSON], ZH et al. [19] proposed an evaluation and optimization method for the power system of a fuel cell distributed electric propulsion aircraft, derived a power flow optimization model for electric propulsion aircraft, and analyzed the optimization of battery life and efficiency for UAV power systems under different flight scenarios. However, existing two-stage optimization methods face challenges when applied to capacity configuration in energy systems, including uncertainty scenario generation, multi-time scale coupling, and balancing economic and environmental benefits. Therefore, this paper proposes a two-stage optimization configuration method based on the particle swarm algorithm for long-endurance hydrogen-powered hybrid unmanned aerial vehicle (UAV) capacity configuration. The structure of this paper is as follows: Section 2 describes the modeling of the hydrogen-powered hybrid UAV energy system and the uncertainty model of the energy system; Section 3 constructs a two-stage stochastic optimization problem for long-endurance hydrogen-powered hybrid UAV capacity configuration, considering uncertain factors, and establishes multi-time scale comprehensive evaluation indicators and corresponding objective functions; Section 4 presents parameter settings and configuration results; Section 5 concludes this paper.
# Isotropic IoT-Based Magnetic Flux Density Meter Implementation for ELF Field Measurements +
## 6 Magnetic Field Energy Harvesting (MF-EH) from the Operation of Commercial-Use Electrical Devices  
Several electric devices such as transformers, power supplies electric motors, hair dryers, etc., create powerful low-frequency magnetic fields capable of powering low-consumption applications (such as WSN sensors). In this section of our study, a Magnetic Field Energy Harvesting (MF-EH) circuit is also proposed for 50 Hz magnetic field utilization derived from these common-use electric devices in short distances. The proposed ELF energy harvester is based on an LC tuning circuit, with the same self-resonance frequency (\(f_{SRF}\)) as the operation of the public electricity distribution network (50 Hz). The correlation between the resonance frequency (\(f_{SRF}\)) of the circuit and the values of the tuning capacitor (\(C_{tun}\)) and the inductor coil (\(L_{tun}\)), respectively, is given by the Thomson Equations (30) and (31):  
\[C_{tun}=\frac{1}{4\pi^{2}\,{f_{SRF}}^{2}\,L_{tun}} \tag{30}\]  
and therefore  
\[L_{tun}=\frac{1}{4\pi^{2}\,{f_{SRF}}^{2}\,C_{tun}} \tag{31}\]  
The quality factor (\(Q\)) of the circuit is about 6.7, as calculated by Equation (32), as a series tuning circuit, where the resistance value of the coil (\(R_{coil}\)) is about 160 Ohms. To achieve resonance at extremely low frequencies (_ELF_), a relatively high value of either the capacitance or the coil inductance is required. The higher value of the inductance makes the proposed circuit more sensitive for energy harvesting from alternating magnetic fields of 50 Hz.  
\[Q=\frac{2\pi\,f_{SRF}L_{tun}}{R_{coil}}=\frac{1}{2\pi\,f_{SRF}R_{coil}C_{tun} }=\frac{1}{R_{coil}}\sqrt{\frac{L_{tun}}{C_{tun}}} \tag{32}\]  
For our implementation, a value of 3.4H has been chosen for the inductance achieved by hundreds of turns of 0.3 mm insulated copper wire, combined with the addition of an iron core in the resonant coil, as shown in Figure 9a. Thus, the capacitor value (\(C_{tun}\)) of the tuning circuit is required to be about 2.9 \(\mu\)F to achieve oscillation at the resonance frequency of 50 Hz. The parametric study of the components was carried out at the laboratory,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Freq. Response** & **Meas. Level** & **Connectivity** & **Type of Meas.** \\ \hline PMM EHP-50C [13] & 5 Hz–100 kHz & 1 nT–10 mT & Opt./USB & Magnetic/Electric Field \\ Nanda EHP-50F [38] & 1 Hz–400 kHz & 0.3 nT–10 mT & Opt./USB & Magnetic/Electric Field \\ Nanda EFA-300 [14] & 5 Hz–32 kHz & 1 nT–31.6 mT & Opt./Serial & Magnetic/Electric Field \\ Study in [9] & 300 kHz–100 MHz & 125 nT–20 mT & Not Mentioned & Magnetic Field \\ Study in [10] & \textless{}100 kHz & Not Mentioned & Magnetic/Electric Field \\ TM9120 [39] & 30 Hz–20 kHz & up to 200 \(\mu\)T & USB & Magnetic Field \\ This work & 40 Hz–10 kHz & 100 n–10 \(\mu\)T & WiFi, IoT (Blynk) & Magnetic Field \\ \hline \end{tabular}
\end{table}
Table 2: The comparison of the proposed meter with similar ELF measurement solutions.
## 3 Improved Stochastic-Batch Gradient Pile Descent
### The Improved Method Based on Stochastic-Batch Gradient Descent (S-BGD)  
The fundamental idea of the S-BGD method is to utilize the efficiency of stochastic gradient descent (SGD) in the early stages of iteration to quickly search for the region where the global optimum lies. Once the search point reaches the vicinity of the global optimum region, the method switches to batch gradient descent (BGD) with a larger step size to efficiently search for the global optimum within the parameter space, thereby improving fitting accuracy. Figure 2 illustrates the optimization process of the S-BGD method for a model with two-dimensional variables as an example.  
In Figure 2, the function \(L\) (\(x_{1}\), \(x_{2}\)) represents the optimization objective of the model, where \(x_{1}\) and \(x_{2}\) are the optimization variables. The initial point is located at point A, and the global optimum is at point C. The parameter training starts using the SGD method in the early stages of optimization to quickly search for the optimal parameter region at point B. Subsequently, the BGD method is employed within the optimal parameter region with larger steps to search for the global optimum at point C. As shown, the S-BGD method combines the advantages of the fast parameter search capability of SGD and the ability of BGD to find the global optimum, thereby improving the training speed while ensuring fitting accuracy.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### Simulation and Evaluation of DEC-KM  
The simulation for the modified DEC-KM protocol has been conducted for the same initial conditions and parameters described in Section 5.1 and gathered in Table 1.
## 4 Simulation Results  
The simulation tests were carried out in the Matlab/Simulink environment. The model of the control structure was made in Sim Power System toolbox. The Euler method was used with fixed step size equal to \(1\times 10^{-5}\) s.  
Figure 6 shows the detector responses obtained with a periodic signal interruption for different speed reference values. When the signal is interrupted, the stator current reaches several times the rated value. A greater impact on the operation of the drive system can be observed during failure in phase B. There are disturbances in the speed at the moments of signal interruption. It can also be seen in the transients of speed errors. In the event of a failure in phase B, the deviations from the set point are greater. The detector correctly recognizes the damaged phase at low, medium, and high speeds in both dynamic and steady states.  
Figure 5: Detector responses and transients of marker errors during lack of signal: (**a**) measurement white Gaussian noise (20 dB); (**b**) variable gain (\(1.2\ i_{a}\)); (**c**) in phase B in simulation tests.  
Figure 7 shows the detection of less significant types of failures. The results are shown for variable gain in phase A (1.2 \(i_{a}\)) and white Gaussian noise (20 dB) in phase B. The impact of this type of failure is imperceptible in speed, as seen in the speed error transients. Despite the insignificant impact on the control structure, the failure is correctly detected and located. Additionally, the detector correctly recognizes a failure, even in dynamic states.  
Figure 6: Detector responses obtained with a periodic signal interruption for different speed reference values for phase A (**a**) and phase B (**b**).