esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this study, the mechanical behavior of subcellular organelles such as the CSK, NSK, membrane, and cytoplasm was assessed. A FE model, which included all these organelles was created to study the cell behavior under AFM indentation. The membrane, cytoplasm, and NE were modeled as the continuum parts, while CSK and NSK were modeled as the structural parts. Intermediate filaments were ignored since their contribution to the cell rigidity became significant at high cell deformation (over 20%) [4]. When there was a small deformation of the cell, there was only a slight increase in the reaction force when the intermediate filaments were added [35].  
After validating the model, the mechanical behavior of the cell components was considered separately. As expected, the cytoskeleton supports the largest portion of the reaction force compared to the others. The cytoplasm supports the second-largest part of the reaction force. The NSK and membrane created reaction forces in the same range but with a different slope. The F-\(\delta\) curve slope of the NSK was larger than that of the membrane, while the cytoplasm and membrane had approximately the same slope. Therefore, one may conclude that the NSK has a higher curve slope than the cytoplasm and, as a result, it is stiffer than the membrane and cytoplasm.  
Finally, the contribution of each one of the subcellular organelles was evaluated. Analysis of each component of the model resulted in the percentage of their participation in response to the external load applied to the whole model. The results showed that 90.4% of the cell's mechanical response is due to the CSK. A cytoplasm was responsible for 9.15%, and the membrane and NE, as two shell parts carry only 0.05% of the external load. The other 0.45% can be attributed to the NSK.  
In this research, several regular geometrical shapes were chosen to model the organelles because the exact shapes of these components are quite complex and they are often connected with irregular patterns in living cells. The approach and procedure described here was utilized for a round cell attached to the substrate. However, it can be used for other cell shapes, different mechanical stimuli, other cells in the suspension situation (like erythrocyte) and different physiological conditions (like osteocyte in the lacuna). This study will be continued in the future to investigate nonlinear material properties and with models that include more cellular components.  
Research plan; G.M.K. and H.N.O., finite element modeling; G.M.K., Writing; G.M.K. and H.N.O., Review and editing; A.V. All authors have read and agreed to the published version of the manuscript.  
This study was not funded by any organization.  
No applicable.  
The authors declare no conflict of interest.
# Supercapacitor-Based Energy Storage in Elevators to Improve Energy Efficiency of Buildings
## 4 Energy Storage System
### Supercapacitor Bank
For the elevator under study, the minimum required capacitance of each cell must be 612 F. In this capacitance range, commercially available cells have a nominal capacitance value of 400 F, 600 F, or 1100 F. Therefore, it is assumed that the supercapacitor bank should consist of 400 F cells connected in parallel to achieve an equivalent capacitance of 800 F. When 120 supercapacitors of 800 F are connected in series, the nominal capacitance of the supercapacitor bank is 6.7 F, which is 31 % more than the calculated minimum capacitance of the supercapacitor bank. For supercapacitor bank modeling, it is assumed that the supercapacitor bank consists of 240 cells of 400 F/2.7 V in parallel and series connection. The ESR of 400 F supercapacitor cells is about 3 m\(\Omega\), and it is assumed that, when two supercapacitor cells are connected in parallel, their ESR is 1.5 m\(\Omega\). When 120 supercapacitor pairs are connected in series, the equivalent ESR is 180 m\(\Omega\). The equivalent model used to model the SC bank is shown in Figure 8. All the supercapacitor bank parameters used for modeling are given in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Operating voltage range, \(U_{\text{over}}\) [V] & 150–300 \\ Usable stored energy, \(E_{\text{ass}}\) [k] & 226 \\ Total capacitance, \(C_{\text{tot}}\) [F] & 6.7 \\ ESR [m\(\Omega\)] & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SC bank parameters. Figure 8: Equivalent model of the SC bank. In elevator applications, there are cases where the SC bank needs to be charged/discharged quickly at higher currents (e.g., 20 A in this case). Higher currents increase the losses caused by the ESR of the supercapacitor. The ESR value depends on the frequency and temperature [34]. As stated in [34], the ESR can be represented by a fixed value for modeling losses in elevator applications. Each supercapacitor manufacturer usually specifies a constant RMS current value where the supercapacitor temperature rise is within tolerance and does not significantly affect the supercapacitor characteristics. For a 400 F supercapacitor cell, the recommended maximum RMS current value is about 20-30 A, depending on the manufacturer and the desired temperature rise. Assuming that two cells are connected in parallel, and the current is evenly distributed, the maximum continuous current capacity of the SC bank is about 40-50 A.
###### Abstract  
This article presents a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously proposed COA was designed for mobility applications in which single service points are sufficient for satisfying individual user demand. This framework is generalized here for applications in which the satisfaction of demand relies on the existence of two or more suitably located service stations, such as in the case of bike/car sharing systems. A new matrix factorization model is used as surrogate objective function for the optimization, allowing us to learn and exploit similar preferences among users w.r.t. service point locations. Based on this surrogate objective function, a mixed integer linear program is solved to generate an optimized solution to the problem w.r.t. the currently known user information. User interaction, refinement of the matrix factorization, and optimization are iterated. An experimental evaluation analyzes the performance of COA with special consideration of the number of user interactions required to find near optimal solutions. The algorithm is tested on artificial instances, as well as instances derived from real-world taxi data from Manhattan. Results show that the approach can effectively solve instances with hundreds of potential service point locations and thousands of users, while keeping the user interactions reasonably low. A bound on the number of user interactions required to obtain full knowledge of user preferences is derived, and results show that with 50% of performed user interactions the solutions generated by COA feature optimality gaps of only 1.45% on average.  
heuristic optimization; location planning; cooperative optimization; preference learning Article
# An Experimental Study on Ice Accretion under Bridge Cable in Different Conditions
## 2 Materials and Methods  
As shown in Figure 1, a piece of equipment called RHPW-60 CT was used to control the testing temperature and humidity, which had external dimensions of 10 m long, 5 m wide, and 2.8 m high. The equipment could produce a lowest temperature of \(-\)60 \({}^{\circ}\)C. As shown in Figure 2, the stay cable support platform was located on the floor of the laboratory and could support a stay cable with an inclination angle between 0\({}^{\circ}\) and 60\({}^{\circ}\), making its inclination angles 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\). The cable model was made of high-density polyethylene (HDPE) with a smooth, exposed surface, which is the same material used to cover the exterior of bridge indentured cables most commonly used in practice [18; 19]. The outer jacket of the cable was two meters long, and the diameters were D1 = 200 mm, D2 = 235 mm, and D3 = 315 mm, respectively. The stayed cable sheath used had a helix with an inclination angle of 45\({}^{\circ}\), a cross-section width of 2.5 mm, and a height of 2.5 mm.  
In order to simulate atmospheric icing, a spray device was installed in the cryogenic laboratory to simulate the sensation by injecting vertically falling precooled atomized droplets into the cold air. The spray nozzle was located near the ceiling of the cold store and was used to generate rainfall of specified intensity and range. As shown in Figure 3, the spray unit used a pneumatic nozzle with a nozzle diameter of 0.5 mm, a spray flow rate range of 0-3 L/min, and a chiller to enable the water temperature to be controlled over a range of 3 \(\pm\) 0.5 \({}^{\circ}\)C. The spray unit covered an area of approximately 3.0 m \(\times\) 6.0 m at the level of the laboratory floor. In order to achieve a more uniform spray intensity, a diagonal cable support device was placed in the middle of the spray area. The test was carried out under calm (wind-free) conditions to eliminate the effect of wind on the icing of cables. Before the icing test, the cable model was precooled to bring its surface temperature into thermal equilibrium with the set environment in the laboratory. The dynamic ice accumulation process during the tests lasted 60 min, and the following data were collected after each test: the number of icicle streaks formed, the maximum icicle streak length, and the ice rib width. The surface feature of the cable sides was recorded by taking photographs.
###### Abstract  
Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain-computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network's ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain-computer interface systems to help people with disability in the daily lives.  
Keywords:brain-computer interface (BCI); electroencephalogram (EEG); P300 +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### The Axial Velocity of the Double-Walled Confined Waterjet  
Figure 6 shows the variation of the mean centerline velocity in the axial direction for the free and double-wall confined waterjet cases. In this figure, the vertical axis represents the mean centerline velocity \(U_{c}\), which appears in the denominator, normalized by the jet exit velocity \(U_{c}\). The centerline velocity \(U_{c}\) was obtained by curve fitting the mean axial velocity profiles measured on the horizontal plane using the following equation  
\[U(y)=1.359U_{c}\ \exp\ (-(\frac{y-y_{c}}{r_{1/2}})^{2}) \tag{3}\]  
where \(U\) is the mean axial velocity, \(U_{c}\) is the mean centerline velocity, \(y\) is the vertical coordinate, \(y_{c}\) is the vertical position of the centerline, and \(r_{1/2}\) is the jet half-width measured at \(U=U_{c}/2\), \(y_{c}\) and \(r_{1/2}\) were calculated using the non-linear, least-squares Levengerg-Marquardt algorithm.  
It is clear that the centerline velocity measurements for all cases matched very well onto a straight line in the beginning. Then, the centerline velocity for the \(H/D=5\) started decaying at a lower rate at \(x/D=15\), compared to the free jet case. This is clearly affected by the vertical confinement. This may be explained by the conservation of mass which demands an increase in the jet velocity because the cross-sectional area of the jet becomes constrained by the boundaries compared to the free jet case.  
Figure 6: Distribution of centerline velocity in the axial direction.  
However, the \(H/D=10,13,15\) cases seem to follow the free jet and are expected to decay at a slower rate at farther downstream locations. It can be concluded that the degree of confinement affects the location at which the centerline velocity deviates from the free jet case.  
When \(x/D=20\), there is a significant difference between the axial velocity in various confined widths, so we can use \(x/D=20\) as the reference position for the velocity analysis. In this position, there is a slight difference between the various speeds. Thus, it can reduce the speed measurement error and contribute to the analysis of the results in different directions.
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 7 Conclusions  
This paper presents the use of two-way relay cooperation along with one-way relay cooperation and direct transmission between WBAN sensors for improved reliability and energy efficiency. Considering Tier-1 communication and practical transmission parameters, the PER and EE are analyzed for all three modes in terms of source destination hop length and relay placement. The results show that an optimal packet size exists in each of the transmission mode that provides maximum EE. Using these results, a hybrid switching scheme including all three modes is devised that provided a gain of approximately 54% in efficient link length compared to direct link for in-body communication, 51.6% for on-body LOS and 50% for on-body NLOS. The system provides cooperative diversity, reduces the error rate, and improves energy efficiency along with a lesser number of implants or wearables. The results show that the hybrid scheme is also useful in quasi-static channel conditions. A JNC scheme is proposed for further improvement in link. It is shown that an extended hop length of 7.5-10% in one-way relay communication and 5-7.5% in two-way relay cooperation for in-body, 8.5-14% for one-way relay communication and 5.5-8.5% for two-way relay cooperation in on-body LOS scenario and 5-7% for one-way relay communication and 3.5-5% for two-way relay cooperation in case of on-body NLOS is achieved for cooperative communication in the simplest case i.e., convolutional code with code rate 1/2 and BCH codes with error correction capability of 2. The results show that selecting moderate code rates for convolution codes and error correcting capability for BCH codes would provide an efficient tradeoff between complexity, overhead and efficiency.
## 3 Test Results and Discussion
### Dynamic Stress-Strain Characteristics  
#### 3.2.1 Dynamic Splitting Test  
Under the impact load, the dynamic stress-strain curves of the specimens treated at different temperatures are shown in Figure 7. The dynamic splitting strength is the peak stress on the curve, which is one of the important indexes for reflecting the impact resistance of concrete. At the initial stage of loading, the curve increased linearly, and the specimen was in the elastic stage. When the impact load reached the splitting tensile strength of the specimen, the peak strain of the specimen was about 0.5%, many small micro-cracks appeared, the concrete in the loading area was crushed, and the specimen was damaged. With the increase in the load, the strength of the specimen decreased sharply, the cracks penetrated rapidly, many split concrete blocks were crushed, and the structure of the specimen was damaged. The dynamic splitting tensile strength curves of the concrete specimens at different temperatures are shown in Figure 8. At 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, and 400 \({}^{\circ}\)C, the dynamic splitting tensile strengths of the specimens were 80.34%, 74.93%, 65.81%, and 62.96% of the normal temperature, respectively. After the action of the high temperature, micro-cracks in the matrix structure appeared and the material deteriorated, resulting in the dynamic splitting tensile strength of the concrete gradually decreasing with the temperature. At the same time, the peak strain of the specimen also showed a decreasing trend. After the high-temperature treatment, the toughness of the concrete decreased significantly, so the specimen showed brittle failure in the failure stage.  
Figure 6: Failure modes of concrete specimens under compression at different temperatures: (**a**) 20 \({}^{\circ}\)C; (**b**) 100 \({}^{\circ}\)C; (**c**) 200 \({}^{\circ}\)C; (**d**) 300 \({}^{\circ}\)C; and (**e**) 400 \({}^{\circ}\)C.  
Figure 7: Dynamic stress–strain curve of specimens in dynamic splitting test.
## 2 Related Work
### A Brief Review of the Geodesic DGGS
#### 2.1.4 Transformation  
When choosing the partitioning method, we must select a method for creating a similar shape on the corresponding spherical surface. There are two types of approaches: _Direct Spherical Subdivision (DSS)_ and _Map Projection_[20]. DSS approaches involve the creation of a partition directly on the spherical surface that maps to the corresponding partition on the planar face(s). The simplest direct subdividing method is using great circle arcs that correspond to the cell edges on the planar face(s). It should be noted that the aperture 4 Class I DSS is actually the geodesic bisector subdivision. This partitioning method has been used extensively to construct spherical wavelets [13; 14; 15; 16]. Rather than using great circle arcs, [PERSON] et al. [9] proposed using small circle arcs that were optimized to achieve equal-area cell regions. Map projection approaches use an  
Figure 4: Four- and 9-fold triangular and hexagonal partitioning of a single triangular face.  
inverse map projection to transform a partition defined on the planar face(s) to the sphere. The most popular projections are the Gnomonic azimuthal projection, the Fuller Dymaxion projection [3; 7], the Quaternary Triangular Mesh (QTM) [2; 23], and the Snyder equal-area polyhedral projection.
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
###### Abstract  
The use of carbon fiber reinforced plastic (CFRP) is increasing in engineering applications such as aerospace, automobiles, defense, and construction. Excellent strength-to-weight ratio, high impact toughness, and corrosion resistance make CFRP highly suitable for aerospace applications. Curing temperature, curing time, and autoclave pressure are among the most important curing parameters affecting the properties of CFRP. Tensile strength, impact toughness, and hardness of CFRP were selected as desirable properties for optimization. A 23 full factorial design of experiment (DOE) was employed by varying curing temperature (120 and 140 \({}^{\circ}\)C), curing time (90 and 120 min), and autoclave pressure (3 and 7 bar) while keeping the number of experiments to a minimum level. The cured samples were subjected to tensile strength, impact toughness, and hardness tests at room temperature as per relevant ASTM standards. Analysis of variance (ANOVA) was used, and it was found that tensile strength, impact toughness, and hardness were influenced most significantly by temperature and time. The maximum tensile strength and hardness were achieved for curing cycle parameters of 140 \({}^{\circ}\)C, 120 min, and 7 bar, and impact toughness was maximized for 140 \({}^{\circ}\)C, 120 min, and 3 bar. A concept of composite desirability function was used to achieve simultaneous optimization of conflicting tensile strength and impact toughness properties for the specific application of aircraft skin.  
autocave; carbon fiber reinforced plastic; curing process; desirability function; simultaneous optimization
## 3 Results and Discussion: Environmental and Human Health Impact Assessment of Cooked Ham Production
### Environmental Life Cycle Assessment of Cooked Ham Production
Our results were consistent with those of other studies: most of the impact of meat products comes from the agricultural production stage of the raw material. With regard to the depletion of abiotic resources, comparison with other studies on meat products was not possible because the depletion of nonrenewable mineral natural resources was not taken into account. However, the overall trend showed a decrease in the concentration of mineral ore [75]. According to the study by [PERSON] et al. [72], slaughtered pork represents 57.1 g SO\({}_{2}\) eq.kg\({}^{-1}\). Slaughter yield and cooked ham production could be responsible for the difference in these emissions. The important contribution of raw material production to the eutrophication impact category was due to environmental emissions (nitrates in water and ammonia in the air) from intensive pig farming [76]. The emissions from a cooked ham were at least twice as high as those of a poultry product (19.9 to 29.9 g of PO\({}_{4}\)\({}^{3-}\).kg\({}^{-1}\) product, [77]) and as those of slaughtered pork (23.3 g PO\({}_{4}\)\({}^{3-}\).kg\({}^{-1}\) product, [72]).
# Applications of Cold Atmospheric Pressure Plasma in Density
## 2 CAPP Biological Activities
At the cellular level, ROS regulate growth, apoptosis and other signalling processes, while at the system level, they contribute to complex functions, including regulation of immune response [58]. In addition, ROS are emerging as the most important agents in the bacterial response to lethal stress. Currently, the effects of superoxide, hydrogen peroxide and hydroxyl radical have been studied. Superoxide and hydrogen peroxide arise when molecular oxygen oxidizes redox enzymes that transfer electrons to other substrates. Hydrogen peroxide that can be produced from the dismutation of superoxide serves as a substrate for the formation of hydroxyl radicals. If this oxidative process is not controlled, an accumulation of hydroxyl radicals can occur. The hydroxyl radical breaks down nucleic acids, carbonylated proteins and peroxidised lipids which can lead to cell death [59]. Reactive nitrogen species (RNS) can be both harmful or beneficial to living systems. At low concentrations, RNS can play an important role as a regulatory mediator in signalling. On the other hand, at moderate or high concentrations, RNS are harmful to living organisms and can inactivate important cellular molecules [60]. Nitric oxide is an important regulator of physiological processes [61] and can mediate the harmful cellular toxicity of metabolic enzymes, generating nitrite peroxide as a final product of reaction with superoxide [62]. The exact mechanism of CAPP and microbial cell interaction is not fully understood yet, but presently it is widely accepted that its antimicrobial activity is associated with synergetic action of two major CAPP components, UV radiation and RONS. They can break covalent bonds of stable compounds, such as the peptidoglycan from the bacterial cell walls and peroxidation of lipids in the cell membrane [17; 44; 56; 63; 64; 65]. Also, CAPP interaction with prokaryotic cells can cause cellular rupture by electro erosion with formation of ionic pores and subsequent loss of cellular content [11, 63]. CAPP can also break covalent bonds in the polymeric matrix of microbial biofilms, favouring their disruption [28, 45, 46, 56, 66].