esa-sceva/satcom-chunk-collection huggingface dataset:
# Stimulation Montage Achieves Balanced Focality and Intensity
## 3 Results
### Study with Multiple Targets
As shown in Figure 2C, the focality and target error are better than the case of SBFI in Figure 2A, but the intensities are lower. Compared to CLS, SBFI achieves the highest intensities at all three target regions, while at the same level of focality and target error. Mathematically, increasing the values of \(\lambda\) allows the second term to dominate, and the cost function is closer to the MI method. When \(\lambda\) approaches infinity, SBFI is equivalent to the MI algorithm. The first term leads the optimization if \(\lambda\) is decreased, as the cost function mainly minimizes the energies of the non-target area. Similar results are obtained for the EEG-guided multiple target study as a simple demonstration that our proposed method is applicable to solve clinical needs, as shown in Figure 3. CLS can preserve the distribution pattern, but always fails to achieve enough intensity. MI undoubtedly reaches desired intensity, but the high intensity is at the cost of stimulation focality and accuracy. This EEG-guided case shows that MI even impacts both hemispheres, which may be due to the fact that there are targets on both hemispheres. It is worth pointing out that CLS and MI produce two extremes: MI favors the intensity of the targets and CLS produces more focal stimulation. However, only SBFI can balance the trade-off between intensity and the focality of all target regions. By choosing the proper optimization parameters \(\lambda=diag(0.147,\,0.1049,\,0.1049)\), SBFI achieves desired intensity at all three target regions with reasonable focality and target error.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 4 Experimental Results Analysis
### Generalization Experiment  
Due to the difficulty in obtaining large-scale public dangerous objects datasets, this study evaluates the generalization performance of PSP-YOLO in hazardous-object detection tasks by constructing a dataset based on the same principles as the hazardous material dataset in Section 2.1. Specifically, six object categories (backpack, handbag, bottle, fork, knife, and scissors) were extracted from the MS COCO 2017 dataset [23]. This dataset includes 23,178 images from the MS COCO 2017 training set and 973 images from the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **Params/M** & **FLOPs/G** & **FPS/s** \\ \hline YOLO* & 91.5 & 68.6 & 2.9 & 13.9 & 89.6 \\ PConv-TestOne & 91.2 & 67.4 & 2.5 & 13.0 & 94.1 \\ PConv-TestTwo & 92.6 & 68.2 & 2.5 & 13.2 & 87.7 \\ PConv-TestThree & 92.6 & 68.3 & 2.5 & 13.3 & 104.6 \\ PSP-YOLO & 92.6 & 68.3 & 2.5 & 13.3 & 104.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PConv module position validity comparison experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **Params/M** & **FLOPs/G** & **FPS/s** \\ \hline YOLO* & 91.5 & 68.6 & 2.9 & 13.9 & 89.6 \\ PConv & 92.6 & 68.3 & 2.5 & 13.3 & 104.6 \\ G\_Ghost [20] & 91.1 & 67.4 & 2.9 & 13.3 & 65.2 \\ GhostNetV2 [21] & 90.7 & 66.1 & 2.9 & 13.3 & 83.2 \\ RepGhost [22] & 92.1 & 69.0 & 2.9 & 13.3 & 91.5 \\ PSP-YOLO & 92.6 & 68.3 & 2.5 & 13.3 & 104.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Module comparison experiment.  
test set. The dataset was randomly split into an 8:1:1 ratio: 80% of the data was used for training, 10% for hyperparameter tuning and validation, and the remaining 10% for final testing and evaluation.  
Experiments comparing YOLOv8n and PSP-YOLO were conducted on this dataset, as shown in Table 6. The models were trained from scratch with the following parameter settings: epochs = 300, batch size = 16, and other settings identical to Table 1. Compared to YOLOv8n, PSP-YOLO showed significant improvements in hazardous-object detection, with mAP@.5 and [EMAIL_ADDRESS]-0.95 increasing by 2.0% and 1.9%, respectively. Furthermore, PSP-YOLO demonstrated excellent floating-point operation performance while maintaining a low parameter count, highlighting its strong generalization capabilities.
## 3 Implementation
This ensures a seamless collaborative experience with reduced latency and no constraints related to user authority. When a command is activated, the server evaluates in real time the authority of the user who triggered the function, following two distinct logical paths (Figure 5):  
* If the user is the _Host_, the server automatically transmits the command to all participants, and the algorithm is executed in _multicast_. * If the user is a _Client_, the remote VR Pawn is temporarily promoted to _Host_, receives authorization to access the server, and can then transmit the algorithm in _Multicast_ to the participants. Figure 4: Teleportation ray casting (**left**); positioning Bookmarks (**right**) for quick access to key locations (e.g., off-board tender view). Figure 3: Material variant permutation example in real time: from striped marine fabric (**left**) to petrol-blue leather (**right**). Immersive elements such as spatial sound effects and dynamic environmental content (e.g., waves, boats in motion (Figure 6)) can be added to enhance emotional engagement and the overall quality of the experience. By means of volumetric sound occlusion, the pitch of waves and motor noises is reduced when an object is positioned between the sound source and the avatar, allowing users to experience a quieter and cozier environment once inside the loft. In addition, automatic exposure is enabled in the scene to ensure a smooth and realistic transition between the yacht's interior and exterior, where sunlight might otherwise appear overly bright or dazzling. Lastly, users can adjust the rendering quality by means of a graphic management tool designed and implemented by our team to access the **Game User Settings** (GUS) directly from the launch menu. This feature ensures that MAGIC is compatible with both current and future hardware with varying graphical capabilities.
# Analysis of Slope Stability in Ion-Adsorption Rare Earth Mine Under In Situ Leaching Condition
###### Abstract  
Ionic rare earth ores are primarily extracted through in situ shallow well leaching. The leaching solutions engage in a physical-chemical coupling effect with the rare earth ores, which diminishes the strength parameters of weathered ore layers. This reduction in strength leads to a decrease in the stability of the slope and an increased risk of landslides, significantly contributing to instability and damage within rare earth mines. This study employs Geo-Studio 2020 software to analyze the stability of the rare earth mine slope. A seepage field under natural conditions was simulated during dynamic leaching to evaluate the effects of continuous leaching on stress, displacement fields, and slope stability. The results indicate that the safety factor consistently exceeds the requirement of 1.1 throughout the leaching process, suggesting that the studied slope remains overall stable during the leaching and push-water operation stages. However, substantial deformation may occur at the bottom, middle, and upper sections of the slope, particularly in the rare earth ore and at the interfaces between each layer. Damage is predominantly concentrated in the toposoil and the fully weathered granite zone. Over time, the slope safety factor initially decreases before subsequently increasing. Consequently, targeted landslide prevention measures are essential during the mid to late stages of leaching. Based on the analytical results, measures to enhance slope stability have been proposed, providing theoretical guidance for slope management in in situ leaching mining sites for ionic rare earth ores.  
ion adsorption rare earth; in situ leaching; slope stability; numerical simulation Article
## 5 Conclusions  
In this numerical attempt, the two-phase (non-homogeneous) nanofluid pattern concerning convection heat transfer within a 3D wavy direct absorber solar collector is examined. The governing equations of the Navier-Stokes including the energy equations of the nanofluid are transformed into dimensionless form and then solved numerically by employing the Galerkin weighted residual finite-element technique. Impacts of various parameters such as the number of oscillations, wave amplitude, Rayleigh number, and nanoparticle volume fraction on the streamlines, isotherms, nanoparticle concentration, and heat transfer are obtained and discussed in this work. The following concluding remarks are drawn from the current analysis. 1. Increasing the number of oscillations leads to a higher temperature gradient region which is extended to the five concave portions of the top wall due to the extra energy of the upper wall. 2. It is found that an augmentation of the wave amplitude enhances the thermophoresis and Brownian influences, which clearly force the nanoparticles concentration to display a completely nonuniform trend in the region. 3. An augmentation in the local heat transfer is observed for both cases when increasing the number of waves as well as the wave amplitudes. However, this augmentation in the heat transfer is noticed to be higher by considering the role of the wave amplitude. Figure 16: (a) 3D-plot of the average Nusselt number with \(Ra\) and \(\phi\), (b) variation of the average Nusselt number with \(N\) for different \(\phi\), and (c) variation of the average Nusselt number with \(Ra\) for different \(\phi\) at \(N=4\) and \(A=0.1\). At lower Rayleigh number values, conductive heat transfer is more dominant than the convective heat transfer. Moreover, improving the Rayleigh number leads to higher heat transfer due to the strong convection current. * The average Nusselt number develops upon raising both the wave amplitude and the number of oscillations. For both cases, growing the length of the wavy surface results in more energy transfer from the hot surface. More importantly, the heat transfer enhancement is observed more significantly with the variation of the wave amplitude. * Augmentation of the nanoparticles concentration tends to boost the nanofluid thermal conductivity, and as a result, a gain in the average Nusselt number is obtained due to the higher thermal gradient and energy transport. Conceptualization, A.I.A., S.P., M.G., A.J.C., and I.H. ; Methodology, A.I.A., S.P., M.G., and A.J.C. ; Validation, A.I.A., S.P., M.G., A.J.C., and I.H. ; Investigation, A.I.A., S.P., M.G., A.J.C., and I.H. ; Writing--Original Draft Preparation, A.I.A., S.P., M.G., A.J.C., and I.H. ; Writing--Review and Editing, A.I.A., S.P., M.G., A.J.C., and I.H. ; Funding Acquisition, I.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Universiti Kebangsaan Malaysia (UKM) grant number GP-2019-K006388. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript.
# Numerical Simulation of Fluid-Structure Coupling for a Multi-Blade Vertical-Axis Wind Turbine
## 4 Results and Analysis
### Deformation Analysis  
Figure 11 is the distribution diagram of blade deformation of different wind turbines. From the figure, we can find that the maximum deformation of the three blades is 1.9044 mm, and the minimum value is 0.71515 mm. The maximum deformation of the four blades is 0.84078 mm, with a minimum value of 0.20314 mm. The maximum deformation of the five blades is 2.4217 mm, with a minimum value of 0.12165 mm. The maximum deformation of the six blades is 11.658 mm, with a minimum value of 1.1918 mm. It can be seen from the figure that the maximum deformation of the blade basically appears in the position of the tip and four sides of the blade. At the same time, it can be seen that among the four wind turbine configurations with different blade numbers, the six-blade configuration has the highest maximum deformation, while the four-blade configuration has the lowest maximum deformation.  
Figure 11: _Cont._  
Figure 11: Vane deformation map of different wind turbines: (**a**) three blades; (**b**) four blades; (**c**) five blades; (**d**) six blades.
## 5 Conclusions
### Model Accuracy and Feasibility Analysis  
1. Discrete element simulation tests were conducted using the EDEM software, and a response surface test design was carried out on the established particle bonding model by combining the Box-Behnken design (BBD) with the response surface method (RSM). The degree of model fit was determined to be statistically significant through response surface analysis of variance (ANOVA), thereby establishing the accuracy and feasibility of the bonding model.
# Wearable Haptic Device for Stiffness Rendering of Virtual Objects in Augmented Reality
## 3 Experimental Study: Stiffness Rendering of Virtual Objects
### Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Stiffness** & **Mean** & **Standard** & **Std. Dev.** \\
**Rating** & **Value** & **Deviation** & **(Subjects)** \\ \hline \(KH_{r}\) & 4.92 & 0.81 & 0.57 \\ \(KH_{e}\) & 3.56 & 0.97 & 1.34 \\ \(VH\) & 3.16 & 1.20 & 1.39 \\ \(PH\) & 1.45 & 0.60 & 0.59 \\ No Feedback & 1.06 & 0.29 & 0.36 \\ \hline
**Realness** & **Mean** & **Standard** & **Std. Dev.** \\
**Rating** & **Value** & **Deviation** & **(Subjects)** \\ \hline \(KH_{r}\) & 4.82 & 0.90 & 0.77 \\ \(KH_{e}\) & 4.25 & 0.84 & 1.48 \\ \(VH\) & 3.37 & 0.91 & 1.34 \\ \(PH\) & 4.28 & 1.26 & 1.93 \\ No Feedback & 1.38 & 0.55 & 0.57 \\ \hline \end{tabular}
\end{table} 
Table 1: The experimental results of perceived stiffness and realness of each feedback condition; mean value, standard deviation, and standard deviation of mean values between subjects. Figure 6: (**A**) The mean values and standard deviations of the perceived stiffness for each feedback condition; (**B**) the mean values and standard deviations of the perceived realness for each feedback condition. condition 1.38, implying the importance of proper visual haptic feedback in finger-based manipulation, which is already verified in VR [24]. Moreover, compared to the other haptic feedbacks, the perceived realness is even fairly higher (\(VH\)) or similar (\(KH_{e}\)), which shows the efficacy of visual feedback particularly for rendering elastic/deformable objects. Despite the large standard deviation 1.26, which means that subjects perceive the visual deformation feedback diversely; this result presents some interesting facts that visually realistic rendering of virtual elastic objects would enhance the realness of interaction even without using additional haptic devices. For the no feedback condition, the perceived stiffness is the lowest value 1.06, which is even lower than the pseudo-haptic feedback rendering deformable objects. The perceived realness is also an extremely low value of 1.38 close to level one, which would imply that, when rendering a rigid virtual object without appropriate feedback, the subjects perceive the object not as a rigid/real one but a virtual/intangible one. This fact certifies again that the design of appropriate feedback conditions is crucial for finger-based manipulation in AR environments, the same as with the VR cases. In addition, these results verify that the proposed tendon-spring structure, which employs a soft spring (\(k_{s}\)) to prevent slack motion, negligibly affects free finger motion.
## 1 Introduction  
Softball is widely played across the world. It is particularly prominent in the United States, Japan, Canada, Australia, and several European countries, such as the Netherlands and Italy [1]. According to the Statista Research Department [2], in 2021, the number of softball athletes in the U.S. reached 8.1 million. However, the physical demands of softball, involving intense movements, such as pitching, batting, and sprinting, lead to both acute injuries (i.e., sprains and fractures) and overuse injuries (i.e., shoulder or elbow strains) [3].  
In particular, upper-limb injuries related to throwing movements are the most common in softball [4, 5, 6, 7]. Continuous softball practice can lead to chronic overuse injuries, particularly in the shoulder and elbow joints, due to repetitive stresses from repeated throwing motions [8].  
The most common shoulder injuries among softball players are muscle-tendon strain, tendinitis, and subluxation [9; 10]. In severe cases, upper-body injuries may necessitate surgery, extending the rehabilitation period and return-to-play time. Moreover, after severe injuries or surgery, it is expected that players may alter their throw kinematics due to disuse or changes in muscular and tendon strength balance [11; 12].  
In general, athletes often exhibit compensatory movements following injuries, which can alter their kinematics in order to maintain their performance, despite physical limitations [13]. These adaptations can vary depending on the type of injury sustained and the specific demands of the sport. For example, wrist injuries in basketball players may alter shooting movements, decreasing their performance [14]. In addition, athletes have shown biomechanical deficits during non-specific motor tasks, such as the vertical jump test [15]. While these adaptations may benefit their short-term performance, they can lead to additional stresses on other body parts, potentially resulting in secondary injuries [3; 16].  
In light of this, to design and prescribe a sport-specific strength and conditioning program aimed at reducing injury risk, enhancing performance, and improving health and lifespan, it is crucial to understand how injuries can influence the kinematics of softball position players once they return to play [7; 8; 17; 18].  
While many studies on softball primarily concentrate on the windmill pitch executed by pitchers [6; 7; 19; 20; 21], it is important to note that pitchers are not the sole players susceptible to injuries. Indeed, injuries sustained during softball practice are more commonly linked to throwing activities not directly related to pitching, such as those carried out by position (non-pitchers) players [8; 9].  
The aim of this study was to assess the kinematic differences in the upper limb and trunk between healthy and shoulder-injured softball position players. It was hypothesized that residual kinematical differences and compensatory strategies would emerge in players that had suffered a shoulder injury compared to the healthy ones.
## 2 Mobility Models in FANETs
### Time-Dependent Mobility Model  
This type of mobility model tries to prevent sudden changes in speed and direction. Under this model, UAV movements are determined by various mathematical equations and several parameters, including the current time, prior directions, and speeds. For a smooth updating of motion, all of these parameters are considered.
# Effect of the Silanization of Aerosil OX50 in the Properties of Light-Cured Dental Composites
The latter is of great importance since these materials will be placed in a humid environment where unreacted monomers may dissolve or the filler particles can be leached into the oral environment, affecting its clinical behavior and its biocompatibility [14]. Obtaining good properties in composite resins depends largely on the bond between the matrix and the filler. With a good bond, the detachment of inorganic particles is avoided, as well as the penetration of water in the interface, and then the deterioration of the restoration. To ensure the formation of a proper interphase, the filler is treated with coupling agents, generally silanes. The most widely used silane is \(\gamma\)-methacryloxymethyltrimethoxysilane (MPS), capable of chemically interacting with both the matrix and the surface of the filler [15,16]. In the 1970s, \"two-step\" silanization methods were developed, either by dry contact or in solution, which involve a pretreatment of the filler with the silane before its incorporation into the monomeric mixture for the formulation of composite resins. Other methods that have been used include the \"one-step\" treatment, which consists of incorporating the silane directly into the organic matrix [17]. Several types of aerosil have been used as previously reported and MPS silanized aerosil with smaller particle size rendered better adhesion with a Bis-GMA matrix but the amount of sorbed water increased. However, the use of larger particle sizes (40 nm) like aerosil OX50 did not severely affect their mechanical performance as their bending strength is slightly lowered without sacrificing flexural modulus [17]. Recently, changes have been introduced in the composition of dental resins with the aim of improving their properties and in order to reduce the use of bisphenol A, where the main concern is the possible release of monomers [5]. In addition to these alternative monomers, the use of monomers with antibacterial activity has also been pursued but the most significant enhancements and changes have been in fillers such as size reduction and shape enhancement (plate-like, rod-like, nanoparticles) [18]. In this research, MPS is introduced into organic matrices not only as a partial diluent of Bis-GMA (Bis-GMA/TEEGDMA/MPS) but also as a coupling agent. Therefore, the aim of this study was to evaluate the influence of the surface treatment of aerosil OX50, used as an inorganic filler, on various properties of dental composites including degree of conversion, depth of cure, water sorption, and solubility.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 5 Discussion  
### Synergistic Enhancement and Attentional Optimization  
The most striking outcome is the demonstrable synergy in the multimodal condition. The performance leap in Group D cannot be fully attributed to the independent contributions of sound or pictures alone, as evidenced by its significant margin over both Group B (text + image) and Group C (text + sound). This super-additive effect underscores the dynamic integration of auditory and visual information streams. Eye movement data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Group Segmentation** & **Accuracy** & **F** & \(\boldsymbol{p}\) \\ \hline \multirow{2}{*}{B/D} & B & 82.07\% & \multirow{2}{*}{29.967} & \multirow{2}{*}{<0.001} \\ \cline{2-2} \cline{5-5}  & D & 92.00\% & & \\ \hline \multirow{2}{*}{C/D} & C & 76.00\% & \multirow{2}{*}{47.874} & \multirow{2}{*}{<0.001} \\ \cline{2-2} \cline{5-5}  & D & 92.00\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Experimental data on the combined effect of images and sound.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **ASC (Unimodal)** & **ASC (Multimodal)** & \
\begin{tabular}{c} **Readiness** \\ **Gains** \\ \end{tabular} &
\begin{tabular}{c} **SD-FD** \\ **(Multimodal)** \\ \end{tabular} & **MCSI** \\ \hline B/D & 265.04 & 236.52 & 9.93 & 3.91 & 0.54 \\ C/D & 259.40 & 236.52 & 16.00 & 3.91 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Calculated data related to MCSI.  
elucidate the cognitive foundation of this synergy: a strategic reallocation of attentional resources. Participants receiving synchronized audiovisual input (Group D) exhibited significantly reduced average saccade counts (ASCs) compared to both unimodal groups and the visual-only group receiving pictures (Group B). This decrease in ASC indicates more focused attention and less exploratory visual searching. Concurrently, dwell time analysis revealed a critical shift: attention was drawn away from the primary text definition area (T1)--with significantly shorter fixation durations there compared to groups without pictures (Group C) or without sound (Group B)--and towards the semantically relevant picture area (P), where fixation durations were sustained or increased. This pattern suggests that the synchronized auditory input (pronunciation) acted as a guide, enabling learners to rapidly disambiguate the target word and efficiently anchor its meaning to the visual representation, thereby optimizing encoding efficiency and reducing extraneous cognitive load associated with decoding or searching within the text areas.