esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The membrane-protein interactions of the ChR2 protein were quantified with the Continuum-Molecular Dynamics (CTMD) hybrid approach using a protocol described in detail previously [22; 23; 24; 25; 26; 27]. The all atom MD simulations were carried out following in detail the protocols that served in our simulations of membrane proteins (e.g., see References [22; 33; 34; 35]), employing NAMD software and the CHARMM36 force field for the protein and the membrane. ChR2 was modeled on the structure of the C1C2 chimera [13; 36; 37]. Alignment with the X-ray structure of ChR2 was performed using the MultiSeq package in VMD [38; 39]. The model was embedded in a patch of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer of dimensions \(\sim\)100 A by 100 A. The length of each simulation of the resulting system was at least 200 ns.  
Quantification of the residual hydrophobic mismatch was achieved as previously described [22; 27]. In brief, the surface area \(SA_{res,i}\) of the \(i\)th residue participating in unfavorable hydrophobic-polar interactions (i.e., when the residue's hydrophobicity does not match that of its environment) was calculated using modified residue-specific solvent accessible surface area (SASA). For hydrophobic residues, the RHM is SAmem,i, where SAmem,i = SASA calculated for the solute comprising the protein and the hydrophobic core of the lipid bilayer (C2-C2). For polar residues, the RHM is calculated as SA_res,i = SA_prot,i - SAmem,i, where polar residues are on the surface of the protein and within the lipid bilayer hydrophobic core and SA_prot,i = SASA with the solute comprising the protein only. An exception is made for tryptophan, which is favorably located at the interface [40].  
The energy penalty is directly proportional to SA_res,i and the energy penalty for the Nth TM (N = 1 to 7) is given by:  
\[\Delta\mathrm{G}_{res}\mathrm{=}\sum_{i=1}^{N_{TM}}\Delta\mathrm{G}_{res,i} \sim\sum_{i=1}^{N_{TM}}\sigma_{res}\mathrm{SA}_{res,i} \tag{1}\]  
where the constant of proportionality \(\sigma_{res}\) is taken as 0.028 kcal/(mol. A\({}^{2}\)) and NTM is the number of membrane-exposed residues in the TM [41].  
As described, neither arginine nor lysine are penalized when they are located close to the lipid headgroups [42]. In addition, serine and threonine exposed to the hydrophobic membrane interface can form hydrogen bonding with the helix backbone and are therefore not penalized [43].  
SASA are computed from all-atom molecular dynamics simulations (described below), with a probe radius of 1.4 A and using the \(g\)_ss_ utility of the GROMACS software [44].
## 4 Optimization Techniques
### Optimization Techniques Regarding Uncertainty
Figure 4: Relationship between uncertainty optimization and overall cost. In [48], the authors propose a stable distribution probability model and load prediction analysis to assess the uncertainties related to solar and wind generators in hybrid grids. It employs the CPLEX algorithm to solve the non-chance constrained model, converts the chance model to non-chance using stochastic simulation and neural network, and then applies chaos particle swarm optimization (CPSO) to solve it. It effectively deals with the uncertainty leading to an overall increase in the cost. In [49], the authors propose a risk-based uncertainty set optimization model, which is reduced using the MILP approach. The paper focuses on renewable power curtailment in a hybrid microgrid for an energy management system to achieve an optimal cost of operation. In [50], the authors present a novel energy management system to accurately determine the market rates due to weather conditions, policy and demand making, errors in forecasting techniques, cost of fuels, and the linked uncertainties by employing a robust optimization technique. The simulation results of using the Monte Carlo technique show a 53.31% reduction in fluctuations. In [51], the authors employ a robust optimization approach to satisfy the energy demand, maximizing the utilization of renewable resources and minimizing the fuel-based generators. The MILP technique is used with the MATLAB _linprog_ toolbox to simulate the system, showing an increase in system efficiency. In [52], the authors use an interval-partitioned un-certainty (IPU) based robust dispatch technique to close down the un-controllable renewable generators with the big M method. It employs column and constraint generation for the tri-level robust optimization and struggles to minimize the conservativeness of the results without any compromising in the system robustness. In [53], the authors employ a piecewise linearization strategy combined with the quadratic Newton-Gregory interpolating polynomial technique to tackle uncertainty in the developed risk-based optimization model of the hybrid microgrid. In [54], the authors propose a deterministic coordinated scheduling model and then apply a hybrid stochastic interval method to tackle the uncertainties in the hybrid microgrid. In [55], the authors propose a two-stage optimization process to cater to the uncertainties in generation system and consumption loads, leading to an enhanced robust power supply by system operators. Firstly, the stochastic optimization of the microgrid is carried out to reserve the regulation capacity in light of energy forecast uncertainties, followed by the rescheduling of initial planning according to signal request and economical offer from system operators. Based on the discussion on the uncertainty modeling in the references mentioned above, the authors present the classification of uncertainty modeling in Figure 5.  
Figure 5: Classification of uncertainty modeling.
## 3 Results
### Evaluation
#### 3.4.1 Object Detection Computation Time  
The total computation time for the 51-m-long reference track, encompassing a total volume of 14.37 million voxels, amounted to 22.07 s. Of this, roughly 90% (19.98 s) was dedicated to data pre-processing, leaving 1.14 s for the actual inference process.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Performance and Resources Metrics
#### 5.7.2 Energy Consumption
Overall, the division of labor in the ant model allowed for a more even distribution of energy, while the baseline model's lack of task coordination and communication resulted in the highest energy consumption. The honeybee model had the lowest overall energy consumption due to its dynamic but extremely efficient control system, while the firefly model's bioluminescent communication system helped to reduce energy consumption after the initial peak. These changes highlight the importance of efficient task management, communication protocols, and control schemes to minimize energy utilization in swarm robotic systems.
## 1 Introduction  
The use of gamification in therapeutic settings has gained significant attention as an innovative approach to increase patient compliance, particularly among children. Gamification refers to the incorporation of game-like elements--such as rewards, challenges, and progress systems--into non-game environments [1]. When applied to therapy, these elements transform routine or challenging medical tasks into engaging and enjoyable experiences, making it easier for young patients to participate actively in their treatment.  
The roots of integrating play into therapy can be traced back to the development of play therapy in psychological treatments, where play was used as a medium for children to express emotions and work through psychological challenges [2]. Over time, this approach has been adopted and expanded within healthcare settings, where the goal is to use play and game elements to encourage compliance with medical protocols [3, 4]. As children are more receptive to interactive and entertaining formats, gamification presents a unique opportunity to improve compliance with medical treatments.  
Key to the success of these methods is the application of core gamification principles, such as positive reinforcement, progress tracking, and immediate feedback. These elements help foster a sense of accomplishment and control, creating a more positive therapeutic experience. When children are rewarded for completing tasks or reaching milestones, they are more likely to stay engaged with their therapy, reducing resistance and increasing their willingness to adhere to treatment protocols.  
Recent technological innovations have further propelled the use of gamification in therapeutic settings. Wearable technology, virtual reality (VR)-based therapy, and mobile health games are increasingly being developed to support children's healthcare routines [5]. By incorporating these technologies, healthcare providers have found new ways to increase motivation, reduce anxiety, and improve compliance, especially in long-term or complex treatments where maintaining patient engagement is crucial.  
This evolving field holds great promise, offering a bridge between the therapeutic needs of children and their natural affinity for play. As healthcare providers continue to explore the potential of gamification, the focus remains on making therapy not only effective but also enjoyable, transforming compliance from a challenge into a playful experience.
## 3 Results and Discussion
### Analysis of Interfacial Interaction Using the Lorenz-Park Method  
Using the equation proposed by [PERSON] and [PERSON], the interfacial interaction between the filler and the matrix was evaluated. Figure 1a presents a graphic of \(Q_{f}/Q_{g}\) as function of e\({}^{-z}\) for the VNR-based nanocomposite specimens reinforced with MWCNTs as well as for a neat VNR sample as a reference. The values of parameters \(a\) and \(b\) are the constants of the equation, with numerical values of 0.96 and 0.03, respectively, giving a correlation coefficient (\(R\)) of 0.92.  
According to the observations of [PERSON] and [PERSON], \(a\) constant value above 0.7 suggests a strong interaction between the MWCNT filler and the VNR matrix. A similar result was obtained by [37] for the constants \(a\) and \(b\) in compounds derived from natural rubber containing leather residues [37]. The decrease in \(Q_{f}/Q_{g}\) values as the MWCNTs are inserted is evident in Figure 1b, confirming the effective interaction between the matrix and the filler.  
In ref. [60], graphene and graphene oxide (GO) were evaluated in terms of their interaction with natural rubber. Graphene is considered a promising reinforcement material for nanocomposites; however, the authors detected \(Q_{f}/Q_{g}\) values of 0.82 and 0.85 for graphene and GO, respectively, indicating a lower interaction than that of MWCNTs in a VNR matrix. Based on the Lorenz-Park method, the performance of paper sludge in NR composites was evaluated using maleic anhydride as a coupling agent, as described in ref. [61]. With increasing amounts of filler, it was reported that \(Q_{f}/Q_{g}\) values increased. As the amount of filler increases, the interaction between the filler and the matrix decreases. Paper sludge and rubber are hydrophobic, and this is attributed to their hydrophilic natures.  
In conclusion, all VNR/MWCNT nanocomposite specimens are made of the same elastomer and have the same crosslinking system. As shown in Figure 1b, an increase in filler leads to a decrease in \(Q_{f}/Q_{g}\), which can be attributed to improved mechanical properties due to a good interfacial interaction between VNR chains and MWCNTs [37]. Alternatively, higher \(Q_{f}/Q_{g}\) values indicate lower interaction between MWCNTs and the VNR matrix.
## 1 Introduction
Researchers [6; 10; 15; 16; 17] have reported that the ionospheric perturbations during the recovery phase of geomagnetic storms at the mid-latitude and low latitude are mainly caused by (1) the transport of energy from high latitudes in the form of traveling ionospheric disturbances (TIDs), (2) changes in global wind pattern due to Joule heating, and (3) equatorward propagating thermospheric composition with a reduced ratio of [O/N\({}_{2}\)] gas. In this paper, we analyzed the effects of six intense geomagnetic storms on the _f_/6F2 at a low-latitude station, Darwin (Geomagnetic coordinates, 21.96\({}^{\circ}\) S, 202.84\({}^{\circ}\) E), a low-mid-latitude station, Townsville (28.95\({}^{\circ}\) S, 220.72\({}^{\circ}\) E), and a mid-latitude station, Canberra (45.65\({}^{\circ}\) S, 226.30\({}^{\circ}\) E) during 2012. These six intense storm events considered for the analysis occurred on 8-9 March (\(Dst_{min}=-145\) nT), 23-24 April (\(Dst_{min}=-120\) nT), 14-15 July (\(Dst_{min}=-139\) nT), 30 September-3 October (\(Dst_{min}=-122\) nT), 7-10 October (\(Dst_{min}=-109\) nT), and 13-14 November (\(Dst_{min}=-108\) nT) during 2012. An intense geomagnetic storm is defined when the minimum value of the \(Dst\) index falls under the criterion, \(-200\) nT \(\leq Dst\leq-100\) nT [18; 19]. The peak auroral electrojet index (\(AE\)) and southward IMF \(B_{Z}\) values associated with these storms during their main phase are given in Table 1. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Storm** & **Min. Dsf (nT)** & **Max. AE (nT)** & **IMF B\({}_{Z}\) (nT \& \& h)** & \multicolumn{2}{c}{**Darwin**} & \multicolumn{2}{c}{**Townsville**} & \multicolumn{2}{c}{**Canberra**} \\ \cline{2-10}  & & & & _foF2_ & **\(\Delta\)/foF (\%)** & _foF2_ & \(\Delta\)/foF2 (\%)** & _foF2_ & **\(\Delta\)/foF2 (\%)** \\ \hline
8-9 March & \(-\)145 & 1785 & \(-\)18.48 (-10) & No change & — & No change & — & Increase & 30.0\% \\
23-24 April & \(-\)120 & 1383 & \(-\)158.08 (-13) & No change & — & No change & — & No change & — \\
14-15 July & \(-\)139 & 1368 & \(-\)19.92 (-31) & Increase & \(+\)23.8 & Increase & \(+\)25.0\% & Increase & \(+\)27.4 \\
30 September-3 October & \(-\)122 & 987 & \(-\)20.40 (-16) & Increase & \(+\)35.7 & Increase & \(+\)33.0 & Increase & \(+\)45.3 \\
7-9 October & \(-\)99 and \(-\)109 & 1000 and 963 & \(-\)15.75 and \(-\)15.13 (-20 and 16) & No change & — & Increase & \(+\)37.6 & Increase & \(+\)28.2 \\
13–14 November & \(-\)108 & 1009 & \(-\)17.61 (-19) & No change & — & Increase & \(+\)35.1 & Increase & \(+\)40.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the minimum value of _Dst_ index and maximum value of auroral electrolyte (_AE_) index and southward _Z_-component of interplanetary magnetic field (IMF B\({}_{Z}\)) and its duration during the main phase of the storms. The ionospheric response during the main phase for six intense geomagnetic storms at Darwin, Townsville and Canberra stations during 2012 is also given. The detailed variation of IMF B\({}_{Z}\) can be seen in the Supplementary Figure S1.
## 4 Discussion
### Estimation of the Bioactive Compound and Antioxidant Activity  
Correlating the Results from an ANOVA Test Completed on the Studied Biochemical Profile  
A one-way ANOVA test on the content of polyphenols, flavonoids, and antioxidant activity of sprouts illuminated with sunlight (control), white, blue, and red LEDs demonstrated that there is no statistical significance (\(p>0.05\)) in the sample analyses, despite the averages for polyphenol, flavonoid, and antioxidant activity being different. The \(p\)-value varying between 0.247432 and 0.527117 supported the null hypothesis H0, and the value of F was not in the rejection region; it was smaller than F crit.  
Correlating the Results for Morphometric and Gravimetric Measurements with the Results for the Estimation of the Bioactive Compound and Antioxidant Activity  
Our previous studies regarding the effect of illumination with blue LED revealed an enhancement of the metabolic pathways for the biosynthesis of proteins and flavonoids, polyphenols of _Cannabis sativa_ L. sprouts [12]. Similarly, [11] reported that the polyphenol content of sprouts buckwheat (sprouting from seeds with tests) illuminated with blue LED was higher in comparison with treatment with white and red LED light. Other results have shown that red light increased total phenolic compounds, flavonoid contents in green perilla (_Perilla frutescens_ L. [PERSON]), and Chinese foxglove (Rehmannia glutinosa Libos.) plants in contrast to white light; moreover, blue light was much more efficient at synthesizing bioactive ingredients than red light [23,24].  
All the aspects analyzed will open new future directions for research studies regarding the detection of new compounds with antioxidant effects induced by LED treatments, using different types of substrates (other than biochar) and monitoring the accumulation of these metabolites without interfering in the biochemical assay.  
The LSD test applied after ANOVA for comparisons between groups revealed that the statistical differences between variants concerning the treatment with LED are recorded in the following cases: the weight of the sprout for variants V1/V3 and V3/V7 (Figure 9); the sprout lengths (Figure 7) for all variants, less so V1/V3; and sprouts radicles length  
Figure 10: The statistical significance determined by LSD (Least Significant Difference) test values of flavonoid concentration in _Lepidium sativum_ L. sprouts cultivated on gauze (V1 control, illuminated with sunlight; V3, illuminated with white LED; V5, illuminated with blue LED; and V7, illuminated with red LED experimental variant), t crit value = 2.306004.  
Figure 9: The statistical significance determined by LSD (Least Significant Difference) test values of _Lepidium sativum_ L. sprouts weight on gauze (V1 control, illuminated with sunlight; V3, illuminated with white LED; V5, illuminated with blue LED; and V7, illuminated with red LED experimental variant) or biochar (V2 control, illuminated with sunlight; V4, illuminated with white LED; V6, illuminated with blue LED; and V8, illuminated with red LED experimental variant), t crit value = 1.980626002.
## 2 Materials and Methods  
### The High-Dorsiflexion Assistive System  
Figure 1 shows the high-dorsiflexion assistive system, and Figure 1 depicts the configuration of the system [25]. Insoles included force sensors (Flexiforce A301 Sensor, Tekscan Inc., South Boston, MA, USA) to detect grounding status of the first metatarsal bone, which was reported as the position with the largest ground reaction force at push-off. Through a wireless module (nRF24L01, Nordic Semiconductor, Trondheim, Norway), the force sensor data were transmitted to the microcontroller, also with an embedded wireless module (nRF52832, Nordic Semiconductor, Norway). The microcontroller transmits signals through a wired connection to solenoid valves (BV214A, MAC Valves Inc., Wixom, MI, USA) that control air supply and release to the artificial muscle tube (DMSP-10, Festo Inc., Esslingen am Neckar, Baden-Wurttemberg, Germany). A portable cylinder with 74 g compressed carbon dioxide was used as the air source for the system. For easy execution, we used a stationary air compressor with a 30 L tank (SW-231, Fujiwara Sangyo Co., Ltd., Hyogo, Japan). A tension spring (Tokai Spring Industries Inc., Osaka, Japan) was placed in series with the artificial muscle between the knee and forefoot. Parameters of the system including assistance activation delay time were manually set with a self-developed graphical interface, and the manipulation signal was transmitted wirelessly to the microcontroller. The lower limb is only 0.35 kg while providing up to 75 Nm dorsiflexion torque assistance. Previous research suggests that an external torque of 13.5 Nm is required to assist ankle dorsiflexion from foot drop to a normal posture [28]. Therefore, our system could fully assist swing-phase dorsiflexion even with no dorsiflexion ability.  
Figure 2 depicts the control diagram, and Figure 2 depicts the system's assistance in one gait cycle. Upon toe-off, no ground reaction force is sensed by the force sensor, and the artificial muscle compresses to assist dorsiflexion during the whole swing phase. From heel contact to middance of gait, the artificial muscle keeps contracting while the tension spring extends and provides a resistive dorsiflexion torque, which assists the heel rocker function during the loading response phase [29]. After the middance phase, the ground reaction force at the forefoot increases and the artificial muscle extends until the next toe-off. In this research, we focus on the swing-phase dorsiflexion assistance provided by the artificial  
Figure 1: The high-dorsiflexion assistive system: (**a**) Photograph of the system; (**b**) system configuration.  
muscle. Using the graphical interface, the activation of dorsiflexion assistance was timed based on the specified value in milliseconds after toe-off.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis
### Kinematic Analysis  
Due to the large size of the MF satellite envelope, there may be a risk of interference between the satellite components and the door cover in the process of satellite separation. It is critical to conduct a kinematic simulation of the satellite separation process to determine whether any components interfere and to verify the mechanism's rational design. First, the model of the entire satellite was roughened, leaving only its outline, and the satellite's mass was given an appropriate density of 20 kg. The satellite's center of mass and moment of inertia corresponds to the three-dimensional model of the MF satellite. The separation spring's elastic coefficient was chosen to be 252 N/m by taking into account the satellite's separation velocity and ignoring factors such as friction. Figure 12 illustrates the separation process.  
As illustrated in Figure 12, there is no interference between any of the components and the door cover throughout the satellite ejection process. The collision between the protruding object on the satellite surface and the separation mechanism frame, as well as the sailboard and the hatch door, should be given special consideration during the simulation process.
## 2 Field Testing  
### Test Materials  
Double-walled corrugated pipe with a diameter of 800 mm was used in field testing. The specific parameters are shown in Table 1. The instruments used for data measurements and analysis include strain gauges, dynamic earth pressure boxes, a vibration roller, and an acquisition device. The mass of the vibratory roller was 22 t, the vibration frequency was 28 Hz/33 Hz, and the vibration amplitude was 1.86 mm/0.93 mm. The specific parameters are shown in Table 2.