esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter
## 4 Results and Discussion
### Orthogonal Experiment Results and Analysis
#### 4.2.2 Analysis
A single-factor experiment was conducted to further validate the influence of moisture content. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Variance** & **Degree of** & \multicolumn{2}{c}{**\(F_{\text{max}}\) (N)**} & \multicolumn{2}{c}{**\(P_{\text{max}}\) (W)**} \\ \cline{3-6}
**Source** & **Freedom** & **\(F\)-Values** & **\(p\)-Values** & **\(F\)-Values** & **\(p\)-Values** \\ \hline Model & 9 & 120.28 & \textless{}0.0001 & 52.66 & \textless{}0.0001 \\ \(n\) & 1 & 700.69 & \textless{}0.0001 & 70.8 & \textless{}0.0001 \\ \(v\) & 1 & 273.14 & \textless{}0.0001 &  
A.H. samples with a diameter of 20 mm and moisture contents of approximately 0.0% (completely dry), 13.9%, 24.6%, 38.3%, and 46.3% were selected. Cutting experiments were performed on the test bench under a tool rotation speed of 1750 r/min and a feed rate of 60 mm/s, using peak cutting force and peak cutting power as evaluation metrics. The experimental results are shown in Figure 14.  
As moisture content increases, both peak cutting force and peak cutting power initially decrease and then increase, aligning with the trend depicted in the response curve graph. When cutting relatively dry shrub material, the tool and the cutting surface of the shrub exhibit a dry grinding phenomenon, resulting in higher friction and consequently increasing cutting force and cutting power. In addition to lubricants and greases, water also functions as a lubricant. As moisture content increases, it provides a lubricating effect at the contact point between the tool and the shrub surface, reducing the coefficient of friction and thereby decreasing cutting force and cutting power. However, as moisture content continues to rise, approaching or exceeding the fibre saturation point of the shrub, the increase in cell-bound water causes the material to become adhesive. This enhances the adhesive forces between shrub fibres, and in a moist environment, shrub chips become difficult to discharge, getting stuck in the teeth of the quasi-planetary cutter. Consequently, the cutting force and cutting power increase. Figure 11: Three-dimensional response surface diagram of peak cutting force model. ((**a**) \(F=\{\)(\(n\),\(\
u\),0); (**b**) \(F=\{\)(\(n\),0,\(\
u\)); (**c**) \(F=\) f(0,\(\
u\),\(w\))). Figure 12: Three-dimensional response surface diagram of peak cutting power model. ((**a**) \(P=\{\)(\(n\),\(\
u\),0); (**b**) \(P=\{\)(\(n\),0,\(w\)); (**c**) \(P=\{\)(0,\(\
u\),\(w\))}. In summary, conducting operations during early spring, when shrub moisture content is low, can effectively reduce peak cutting force and peak cutting power, improve the efficiency of shrub-cutting operations, and minimise wear on the quasi-planetary cutter. Figure 14: Results of moisture content test of A.H.  
Figure 13: Comparison of peak cutting force and peak cutting power between simulation and experimental results (A.H.; (**a**) \(F\) = f(**n**); (**b**) \(F\) = f(**v**); (**c**) \(P\) = f(**n**); (**d**) \(P\) = f(**v**)).
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 5 Experimentation and Results  
### Experimental Setup  
The proposed method for identity concealing in the dataset was executed on the CelebA [7] dataset. We chose part of the original dataset, forming a dataset containing 1000 female images, which were divided into six different categories based on the expression of the subject. The expression distribution of facial images in the dataset is shown in Figure 3. In the training process, the first 800 images were employed, leaving the remaining 200 for testing. The generated images of our proposed approach were then applied to three existing recognition algorithms, namely RCNN [9], DACL [10], and DAN [11], to evaluate the utility of generated dataset in the recognition of facial expressions. The validation results of these methods are presented in Section 5.4 of the paper.  
Figure 3: The expression distribution of the dataset.  
Figure 2: The workflow of the FPSP model.  
The FPSP model was implemented based on the TensorFlow and Keras frameworks and trained on an RTX 3080 GPU, significantly reducing the training time compared to traditional training. According to paper [67], the \(\beta_{m_{1}}\) and \(\beta_{m_{2}}\) parameters were set to 0.25, as when the values are in the range of 0.1 to 2.0, the results are not significantly different. By using the ADAM optimizer with a learning [72] rate of \(1\times 10^{-3}\), the model can effectively update the weights of the neural network during training, improving its accuracy. Other parameters included in the model are listed in Table 1.
## 1 Introduction
They divided the control process into two phases and used a simulated annealing algorithm to determine the target angles for both phases. [PERSON]. [38] studied the planar APAA, planar AAP, and planar AAAP systems, and achieved stable control of the systems by constructing motion trajectories for all active joints and optimizing the trajectory parameters using the differential evolution (DE) algorithm and designing continuous state feedback controllers for the active joints to track the motion trajectories. [PERSON]. [39] developed a controller based on the Fourier transform approach in the time corresponding to the fundamental wave frequency and employed a nonlinear observer to counteract perturbations before realizing stable control of the planar Pendubot. Regarding the underactuated manipulator whose end joint is a passive joint, referred to as a planar A\({}^{m}\)P (\(m\geq 2\)) manipulator, [PERSON] [37] proposed a trajectory planning and feedback control method to design a feedback stabilizing controller to move along the desired trajectory and realize the stabilizing control of the A\({}^{m}\)P system. The aforementioned analysis indicates that there is still a lack of a stability control strategy for underactuated manipulators with non-zero initial velocity and torque disturbance. Due to the aforementioned study, we offer a model degradation and iterative control approach for PMLUM with the last PL, considering several uncertainties. Firstly, according to the kinematic relationship of the underactuated manipulator, the DE algorithm is used to solve the inverse kinematics to obtain the target angle of each link when the position of the endpoint is known. After that, the control technique is divided into two stages based on the model deterioration. The first stage is to control the active link to reach the target angle, and the system is reduced to PVP. In the second stage, the iterative control method is used to achieve the control target of the last PL. Finally, the study selects the planar AAP and planar AAAP systems, and numerical simulations are conducted under three different scenarios that model the uncertainties. The experimental findings support the practicality of the recommended strategy. The main works and contributions of this paper are as follows:  
1. The dynamic model of the planar A\({}^{m}\)P system is established, and the target attitude angle corresponding to the position of the endpoint of the system is obtained by using DE algorithm. 2. Based on the nilpotent approximation method, the planar A\({}^{m}\)P system is reduced to a PVP, and the control process is divided into two stages. 3. In the first stage, the controller is designed according to the Lyapunov method to realize the control target of the active joint. In the second stage, the iterative control method is used to realize the stable control of the planar virtual Pendubot, that is, the control target of the passive joint is realized. 4. The method proposed in this paper can overcome the effects of non-zero initial velocity and initial torque disturbance; it is verified by simulation experiments.
## 5 Results and Discussion  
### Original Scheme of Forced Ventilation  
#### 5.1.1 Air Velocity Field  
During forced ventilation in a cavern, the jet flow and backflow exist at the same time, which plays a role in the dilution and discharge of harmful gases generated during construction; hence, the airflow distribution near the heading face is more complicated. In order to show the characteristics of the jet flow and backflow more clearly and intuitively, the velocity vector at profile \(y=18\) m and horizontal section \(z=5.5\) m were analyzed. It can be seen from Figure 8 that there are several obvious zones in the duct jet flow field, namely, the wall-attached jet zone, impinging jet zone, eddy zone, backflow zone. In the process of airflow from the air duct, the surrounding air is continuously entrained by the jet flow, and the jet distance and range are increased. However, due to the limited space of a blind heading cavern and the continuity of airflow, the backflow is formed at the working face, and the zone is larger than the jet area. The backflow interacts with the jet flow to form the eddy zone in the middle zone. The eddy zone has adverse effects on the discharge of harmful gas near the heading face, and some harmful gas circulates in the eddy zone and needs to be diluted and discharged for a long time, which also can be observed in [PERSON] et al.'s study [35].  
Figure 8: \(Cont\).  
Figure 7: The comparison between the simulated result and field test: (**a**) Air velocity in the main cavern and construction roadway; and (**b**) The change of CO concentration with time.  
Figure 9 shows the axial air velocity distribution at different locations in the cavern. There is a large fluctuation in air velocity near the heading face and the velocity is basically stable after more than 150 m from the heading face. In the stable airflow zone, the air velocity in the middle of the cavern section is high and the velocity in the area closer to the tunnel wall is smaller, which is completely consistent with the basic theory of fluid mechanics.
## 1 Introduction
The S-O performance measures such as the level crossing rate (LCR) and average fade duration (AFD) are of considerable importance for channel coding, the interleaver design, burst error (BE) rate and throughput analysis in FSO communications. Moreover, the BE rate can be used for the cross-layer design of error-control protocols with a rate adaptation in the FSO systems [38]. This paper provides a performance evaluation of an RIS-assisted FSO link over F-S \(\mathcal{F}\) distribution evaluated for various TI fading conditions. Notice that the experimental concept of RIS is validated in [4] for distinct free-space path-loss models and the Fisher-Snedecor \(\mathcal{F}\) distribution is considered in [13] for modelling the turbulence effects in FSO communications validating its use through Monte Carlo simulations. In this context, we provide novel, closed-form (C-F) PDF and CDF expressions in terms of the Gaussian hyper-geometric function. The C-F PDF and CDF expressions are further employed for the derivation of the moment generating function (\(\mathcal{M}_{MGF}\)), OP, ergodic capacity (\(C_{\gamma}\)) and average BER for various binary modulation techniques. Moreover, the S-O statistical expressions are additionally provided and examined. The F-O and the S-O statistical results of the considered FSO RIS-ACs link exposed to various TI fading severity conditions are numerically evaluated and analysed. To the best of the author's knowledge, there are no reported results on the unified F-O and S-O performance analysis of the FSO RIS-ACs over F-S \(\mathcal{F}\) TI fading channels.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers
### Hysteresis Controller Design  
The hysteresis band controller was designed with a hysteresis band of 2% of the reference current. The hysteresis controller uses the same inductance values as the PI and PR controllers. No capacitance was connected before connecting the inverter to the grid.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 3 Methodology  
In the paper, the impact of the multipath effect was determined for both code and carrier-phase observations as well as for positioning results. The multipath error for observation was determined in the statistical domain, taking into account the impact of measurement noise and its time correlation in the form of STD values of the multipath impact on observations. The research was also conducted using the wavelet transform, with which both observations and positioning results were analyzed.
## 2 PHA in Biotechnology
### Other Applications in Medicine  
Polyhydroxyalkanoates have other biomedical applications beyond the production of scaffolds and drug carriers. [PERSON], [PERSON], and [PERSON] [78] demonstrated a process of incorporation of silver nanoparticles into a PHB and chitosan copolymer. Silver nanoparticles' antimicrobial activity led to positive results regarding the Gram-negative bacteria _Escherichia coli_ and _Salmonella typhi_, Gram-positive _Streptococcus pneumoniae_, and the fungus _Aspergillus fumigatus_. In addition, chitosan is essential for this outcome, which opens possibilities for the use of this compound in various sectors of the biomedical area. Poly-based nanoparticles (3 HV-co-3 HB), produced by _Halomonas pacifica_ ASL10 and _Halomonas salifodiane_ ASL11 using substrates derived from algae hydrolysate, presented antibacterial characteristics that make them suitable for use in the pharmaceutical industry and food packaging [79].  
Another application for PHAs is the treatment of wounds and injuries. Some works report the efficiency of the PHAs, especially blended with other materials. Poly(3-hydroxyoctanoate) showed positive results when incorporated with Ag nanoparticles for antimicrobial wound dressing on the treatment of skin and soft tissue infections, being effective against methicillin-resistant _Staphylococcus aureus_, in addition to nontoxic biodegradation, reducing environmental impact and being suitable for long-term applications [80].  
An additional function of PHAs is the treatment is the blend of the SCL-PHA P(3 HB) and the MCL-PHA P(3 HB-co-3 HD), combining the properties of both polymers, making it suitable for the confection of wound dressing. These nanofibers were incorporated with AgNPs via a dip-coating method, contributing to cellular metabolism in the wound-healing process [81].  
[PERSON] et al. [82] describe the production of MCL-PHA from crude glycerol obtained from biodiesel production. The resulting polymer exhibits reduced hydrophobicity and crystallinity, which makes it suitable for the production of elastic and flexible films (Figure 4). Consequently, this material finds potential applications in the biomedical field, particularly in the development of injury treatment materials. This aligns with the findings of [PERSON] et al. [83], who also investigated MCL-PHA properties. Furthermore, this material demonstrated use in the biomedical sector and the manufacturing of adhesives (Figure 4). Additionally, its elastic, adhesive, and soft properties at room temperature made it suitable for implementation in the field of agriculture.  
An application similar to drug delivery was reported by [PERSON] et al. [84], where a herbicide delivery system composed of P(3 HB) and P(3 HB/3 HV) was tested. Metribuzin, a herbicide derived from 1,2_A_-triazine, demonstrated the best release results with an index of 44-48%, and in the tests carried out during the growth of the plant, _Elsholtza ciliata_ microparticles loaded with metribuzin and tribenuron-methyl demonstrated the best result.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Network Protocol
#### 4.1.2 Enhanced mmWave Scalable TCP Protocol
With improved network stability and throughput, we have therefore obtained good results with the mmS-Transport Control Protocol. To proceed, we process this network protocol using channel estimation.
## 2 Literature Review and Theoretical Framework  
### Foundations of Immersive Learning and Virtual Classrooms  
The emergence of immersive learning environments, including VR/AR, has significantly impacted educational practices, improving engagement, interactivity, and accessibility [6]. Immersive learning, characterized by simulations and 3D environments, allows students to engage deeply with the content, enabling experiences previously inaccessible in traditional classrooms [11]. The potential of immersive technologies to increase motivation and active participation by providing experiential learning opportunities that appeal to various learning styles is emphasized [12]. Furthermore, recent studies suggest that immersive environments offer enhanced visualization for complex subjects, such as STEM (science, technology, engineering, and mathematics), and allow students to experiment safely in virtual laboratories [13; 14]. Virtual classrooms within Metaverse expand on these principles by facilitating real-time interaction in a fully immersive shared space where students can participate in collaborative activities, simulations, and interactive discussions [15]. Despite these advancements, challenges remain regarding the scalability and integration of immersive technologies within existing educational systems [16]. Scholars noted that while immersive learning can be beneficial, factors such as device accessibility, network stability, and the need for specialized pedagogical methods often limit the successful adoption of VR/AR in schools [17; 18]. Furthermore, immersive learning's potential to support diverse student populations has highlighted the need for customization and inclusion within virtual environments, which requires advanced design frameworks compatible with educational standards [19]. Thus, while immersive learning has laid a strong foundation, the literature reveals a pressing need for comprehensive and adaptable models like the Metaverse classroom that integrate technological, social, and educational dimensions [20].
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 4 Results
### Subjective Experiment  
To demonstrate the effectiveness of the algorithm model, this paper selected 100 images from the low-light dataset as the test set, ensuring that these images did not appear in the training set. The enhanced results were compared with the enhancement effects of algorithms such as CLAHE [22], LR3M [23], DeepUPE [24], Zero-DCE [25], LIME [26], RetinexNet, MSRCR, EnlightenGAN, and the algorithm proposed by [PERSON] et al. [27]. The subjective experimental results are shown in Figure 7 for comparison:  
Figure 7: _Cont._  
The experimental results indicate that the adaptive histogram equalization algorithm, CLAHE, which enhances images by limiting contrast, has limited effectiveness for low-light image enhancement. The LR3M algorithm, which enhances images based on illumination estimation, also fails to effectively improve the brightness and contrast of the images. DeepUPE, a deep learning-based algorithm for illumination estimation, exhibits poor overall enhancement in brightness and contrast, making it difficult to discern content in certain areas, which hinders overall image interpretation. The Zero-DCE algorithm controls image exposure using a loss function, which improves the overall brightness and contrast of the images but still results in numerous dark areas. The LIME algorithm, which achieves enhancement through illumination estimation, reduces the extent of dark areas but fails to address the significant noise issues present in the images. The enhanced results of the RetinexNet algorithm appear blurred, making it difficult to extract detailed information. Although the algorithm proposed by [PERSON] et al. further improves the brightness of the images, it still suffers from excessive noise, and the enhanced images exhibit color distortion. The MSRCR algorithm, which includes a color restoration module, mitigates color distortion to some extent but introduces severe noise problems and significant loss of detail information. The EnlightenGAN algorithm based on generative adversarial networks overcomes the dependency on paired low-light/normal-light image datasets and achieves overall impressive enhancement results. However, it exhibits less noticeable enhancement in extremely dark areas, resulting in less clarity in the details of such regions. In comparison to the aforementioned algorithms, the proposed low-light image enhancement algorithm in this paper effectively accomplishes the enhancement task. It enhances image brightness and contrast while suppressing color distortion and noise. The enhanced images have moderate brightness and clear details, resulting in favorable subjective visual effects.
## 2 Methods and Process Procedures
### Cloud and Cloud Shadow Identification  
#### 2.3.1 General Formula  
Following the generation of synthetic images using PSRFM, the next challenging was to identify cloudy and cloud shadow pixels of the cloud contaminated images. If we compared a synthetic image at \(t_{k}\) to the corresponding cloud contaminated image acquired at the same date, based on previous studies [24; 26], the reflectance represented by digital numbers (DN) of the two images should be close to each other for most cloud-free pixels as the root mean square error and absolute mean error were small for all individual bands [24; 26]. Consequently, a large difference of a pixel value for the two images was most likely caused by clouds or cloud shadows and other issues such as the haze on the observational image:  
\[dDN(i,j,b)=DN_{o}(i,j,b)-DN_{s}(i,j,b) \tag{1}\]  
\[dDN(i,j,b)=\left\{\begin{array}{ll}\geq T&\mbox{$a$ cloud contaminated pixel}\\ <T&\mbox{$a$ cloud free pixel}\end{array}\right. \tag{2}\]  
where \((i,j)\) is a pixel location and \(DN_{o}\) and \(DN_{s}\) are the digital number of the observation and synthetic images of band b, respectively. \(dDN\) is the pixel value difference of the two images. \(T\) is the threshold to separate cloud contaminated pixels from cloud-free ones.
## 2 Theoretical Analysis of Moon-Based Earth Observational Angles
### Definition and Calculation of Observational Angles  
In the following subsections, the coordinates of the solar and lunar nadirs are deduced based on the orbital elements. Subsequently, three observational angles are defined and calculated according to the solar and lunar nadirs. Finally, an ACNI is proposed to determine the characteristics of the angular combination.