esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Degradation of Silica Flakes  
Recently, the degradation of silica structures has been widely investigated. Especially, this is crucial for biological applications because it is important that the material after its functionality (e.g., drug delivery) can be removed from its location without adversely affecting the cells. Silica materials  
Figure 6: MTX releasing profiles in water (**A**) and phosphate-buffered saline (PBS) solution (**B**) at 37 \({}^{\circ}\)C.  
are degraded due to hydrolysis of siloxane bonds (-Si-O-Si-). The current state of the art reports that factors such as size [43], shape [44], or porosity [45] may be important in the degradation rate of silica structures.  
Basing on TEM images (Figures 7 and 8), it can be conclude that both kinds of silica flakes did not degrade in water, but PBS or medium had a huge influence on the materials' morphology. However, mSiO\({}_{2}\) after GO placed in PBS (Figure 7D-F) first lose their porosity, without significant changes to the overall shape of the flake. After 48 h in medium (Figure 7H), newly formed structures appeared, even though the porosity of the flakes is still visible. In the case of mSiO\({}_{2}\) after LDH Mg/Al, the overall shape of the flakes is maintained throughout the experiment, but the change in the porosity of the sample is also detected. The deformation appeared in the form of large holes in the nanoflakes structure.  
Figure 8: Transmission electron microscopy image of mSiO\({}_{2}\) after LDH immersed in water (**A–C**), the PBS solution (**D–F**) and medium (**G–I**) for 24 h (images **A,D,G**), 48 h (**B,E,H**), and 72 h (**C,F,I**).  
Figure 7: Transmission electron microscopy image of mSiO\({}_{2}\) after GO immersed in water (**A–C**), the PBS solution (**D–F**) and medium (**G–I**) for 24 h (images **A,D,G**), 48 h (**B,E,H**), and 72 h (**C,F,I**).  
Such differences in the process of flake degradation can be related to different effects. It can be e.g., due to the volume and size of the pores. Similar observations were described by [PERSON] and co-workers [46]. mSiO\({}_{2}\) after GO has a smaller specific surface area, and at the same time their pore size is slightly smaller, but the volume is almost three times larger than in the case of mSiO\({}_{2}\) after LDH. It is probable that the faster degradation of flakes after GO is caused by the solvent molecules, which are able to penetrate the structure faster and deeper. However, the fact that during the degradation of these flakes new and amorphous structures appear can be associated to the thickness of the flakes. The thin structure of the mSiO\({}_{2}\) after GO exhibits a short diffusion path of the dissolved SiO\({}_{2}\). The hydrolyzed silica during degradation did not settle on the flakes but was released into the solution. The newly formed silica particles such as spheres can be seen on Figure 7H,I. On the other hand, in the mSiO\({}_{2}\) after LDH, the blocking pores and degradation of silica flakes after a shorter period of time can be detected. No additional silica structures were observed.
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 3 Digital Simulation Prediction and Analysis Experiment of Assembly Deviation
### Modeling and Analysis of Assembly-Deviation Prediction Based on Actual Measurement Data
#### 3.3.3 Comparison of the Analysis Results of Two Assembly-Deviation-Simulation Models  
For the assembly of the core part of the micro gas turbine, the total radial clearance of the outermost clearance of the rotor turbine tip and turbine guide is 1.2 mm, as determined by the measurement without interference and collision; the radial runout of the rotor blade tip is 0.183 mm. When the rotor is pushed backwards by 0.47 mm, the pressurizer rotor collides with the pressurizer magazine. Two-thousand Monte Carlo assemblies are carried out on the fusion model of part design and measurement to obtain the digital assembly results. The simulation results obtained through the two assembly-simulation models are compared with the measurement results, the results of which are shown in Table 2.  
As can be seen from Table 2, the simulation results of the assembly deviation based on the three high points of the composition plane are comparable to the measurement results of the assembly, which is conducive to the prediction of assembly deviation.
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 3 Validating TL for Bridge Damage Detection Using Simulated Experiments
### Simulated Experiments  
A previous study [22] showed that, for local damage, POMs in the healthy structure, \(\varphi^{h}\), are related to POMs in the damaged structure, \(\varphi^{d}\), as:  
\[\Delta\varphi=\varphi^{d}-\ \varphi^{h} \tag{12}\]  
where \(\Delta\varphi\) is defined as a feature in the damage detection framework for any model with existing MU. They showed that \(\Delta\varphi\) for a target domain can be approximated using \(\Delta\varphi\) from a source domain and the estimated POMs using this method can be used for training the ANN. The neural network architecture consists of an input layer, a hidden layer with sigmoid activation function, and an output layer with identity matrix as an activation function. For more details regarding the ANN training algorithm, the readers are referred to [14].  
In this study, the performance of JDA-kernel for TL was investigated for a set of six simulated cases from a modeled bridge, as illustrated in Table 1[22]. Simulated experiments contain a set of modeled structures with identical geometry, materials, and topology and, therefore, have consistent feature and label spaces [23]. Stringer-to-floor beam connection end fixity ratio significantly affects the stress time histories results. In addition, due to the high variation of end fixity ratios, the difference between the actual and modeled ratios were high [37; 38]. Therefore, the end fixity ratio was selected as MU. Specific stringer-to-floor beam end fixity ratios were assigned to each model. Models in Table 1 had different levels of uncertainties represented by assigned spring coefficients for stringers and are denoted with a percentage from the base model spring coefficient. The uncalibrated base model from these experiments with no MU was identified as \(M\). Its calibrated version was denoted as \(M_{0}\), with other models having various MU levels compared to these base models. It should be noted that the same MU was assigned to both \(M_{0}\) and \(M_{1}\). The discrepancies between these two models is attributed to features generated for training the damage identification framework. \(M_{0}\) was trained using \(\varphi\) obtained from simulation, whereas \(M_{1}\) used estimated \(\varphi\) with \(\Delta\varphi\) for the same purpose.
## 3 Methodology
### Input Data  
For this paper, weather data from the year 2019 for the city of Eindhoven, the Netherlands is used as input for the simulations. This dataset contains hourly weather measurements and forecasts (up to 36 h ahead) of solar irradiance, outdoor temperature and wind speed. The summary statistics of the input data can be found in Table 1. From the summary statistics it can be observed that during the selected period in August, both the maximum and average irradiance are a bit lower than during the selected periods in June and July.
## 4 Future Research Directions  
Based on the above survey, we concluded that we can focus on various limitations and challenges that arise during the design and implementation of DSM schemes and integration of DERs. For an efficient, robust, and economical DSM scheme along with DERs, these limitations should be addressed. These limitations and challenges are categorized into two subgroups: DSM based open research direction and DERs based open research direction. These limitations include guaranteeing the reliability of the power system and communication networks used in DSM as well as the security and privacy of consumers, load profile modeling, consumers' participation in the DSM scheme, catering for consumers' preferences and priorities, maximization of DER usage, optimal placement of DERs and educating the consumers about energy consumption.
## 3 The Multi-Agent Framework
### Sensing Coverage  
Sensing coverage is also an important property of a sensor network, although this property was overlooked by many researchers. As described in [21], saving unnecessary power consumption can prolong the lifetime of sensor nodes, but does not necessarily imply that a better sensing coverage can be achieved. Hence, it is necessary to consider sensing coverage as a separate property.  
In many applications, sensor nodes are deployed randomly over the entire desired area. In this way, the sensing areas of different nodes may partially overlap. When a local area has a much higher node density than the average node density, a target location in that area may be covered by multiple sensors. On the other hand, if the node density of a local area is much lower than the average node density, a target location is more likely covered by only one sensor. The normalised effective sensing area, \(\eta\), of a sensor, \(v_{i}\), is calculated using Equation (5) [21].  
\[\eta=\eta_{0}+\sum_{m=1}^{\infty}\frac{\eta_{m}}{m+1} \tag{5}\]  
where \(\eta_{0}\) is the percentage of \(v_{i}\)'s sensing area that is covered by \(v_{i}\) only and \(\eta_{m}\) represents the percentage of \(v_{i}\)'s sensing area that is covered by \(v_{i}\) and any other \(m\) neighbouring nodes. The value of \(\eta\) is within \((0,1]\). If the sensing area of a node does not overlap with that of any other nodes, the value of \(\eta\) is one. On the other hand, if the sensing area of multiple nodes overlaps the sensing area of a node, the value of \(\eta\) may be much less than one.  
For the whole sensor network, the sensing coverage is the ratio between the total sensing area covered by all sensor nodes and the target area. For example, if the target is \(1000\) m\({}^{2}\) and all of the sensors totally cover 900 m\({}^{2}\) (due to some sensors failure), the sensing coverage of this sensor network now is \(0.9\).
## 3 Experimental Results
### Experimental Setup  
The measurement setup includes a BWO THz source, quasi-optical lenses to focus the radiated RF signal on the detector, a cryogenic cooling system with high-density polyethylene windows, and a Tektronix company Keithley Nano-voltmeter and current source (Figure 13). The Cryo Industries cryogenic cooling system enables testing the performance of the device at low temperatures, down to ~2 K. An important advantage of this cooling system is that the devices inside the chamber do not touch directly the liquid helium used for cooling, thus preventing contamination of the device. The BWO source is a miniature electrovacuum device placed in a metal packaging. When the BWO source is in a magnetic field and supplied with high voltage (up to 6.5 kV), it emits monochromatic electromagnetic radiation, with a power of up to ~10 mW, depending on the supplied voltage and magnetic field frequency and strength [41]. A photo and a discerption of the measurement setup is presented in the Appendix A.
## 4 The Application of Artificial Intelligence in the Design of Basic Materials for Sports
### Piezoelectric Materials  
Piezoelectric materials are a class of functional materials capable of converting mechanical energy into electrical energy and vice versa. When subjected to external forces, piezoelectric materials generate electric charges. Conversely, when an electric field is applied, the materials undergo deformation. This unique property makes piezoelectric materials widely used in the detection of states in sports activities.  
In 2023, [PERSON] et al. [69] employed a machine learning (ML) strategy to systematically explore the performance of aluminum nitride-based piezoelectric materials with varying concentrations and compositions. The predicted piezoelectric strain coefficient (d33) closely matched experimental values for scandium (Sc), magnesium titanium (MgTi), and magnesium zirconium (MgZr) doped compounds. Notably, Sc\({}_{0.5}\)Al\({}_{0.5}\)N materials exhibited exceptional piezoelectric properties with values reaching 202 pC N\({}^{-1}\). These findings not only reveal the relationship between piezoelectric properties and material structure but also provide a theoretical basis for designing high-performance piezoelectric materials.  
Not only can the properties of piezoelectric materials be predicted by ML methods, but neural networks (such as Artificial Neural Networks (ANN)) can also perform this task efficiently. By simulating the learning mechanism of the human brain, NN can extract complex nonlinear relationships from many data, to accurately predict key performance indicators such as the piezoelectric coefficient and elastic modulus of materials. Compared with traditional machine learning methods, NNs show stronger generalization ability and prediction accuracy when dealing with high-dimensional data and complex structure-performance relationships.  
[PERSON] et al. [70] introduced an ANN method to evaluate the performance of 1-3 type piezoelectric composites, achieving prediction accuracy up to 99.999%. Compared to the traditional finite element method (FEM), the ANN model significantly reduces computational cost and time while maintaining accuracy, making it highly effective for designing hybrid piezoelectric composites.
# A New Approach to Fall Detection Based on the Human Torso Motion Model
## 3 Our Approach for Fall Detection
### The Key Concepts and Definitions in HTMM  
#### 3.1.1 Torso Line and Torso Vector  
There are in total 20 points of a person's skeleton that can be tracked by the Kinect sensor when the person is standing, while 10 points when sitting if used in front view. Additionally, the untracked joints can be estimated by the embedded program in Kinect. Especially when the part of the body that contains the joints can be detected, the positions of these untracked joints can be exactly estimated. Figure 1 shows the details of the joints in standing and sitting postures. A line connecting the shoulder center and the hip center is a key concept in our algorithm to calculate torso angle. This line is named as the torso line and marked in red as shown in Figure 1.  
In a red, green, blue (RGB) image, each pixel only contains 2D position information. However, the depth image provides each pixel's 3D position information. Figure 2 shows the joints in 3D depth image space. These joints of a person's skeleton can be considered as pixels in the depth image. With these 3D joint points of the skeleton, the computer can understand the meaning of human gestures when a set of complex activities is made by human beings in front of the Kinect. As the main purpose of our system is to alarm as soon as a fall happens, we only take the shoulder center and hip center joints into consideration. In other words, our method only pays attention to the human torso.  
The torso line can be represented as a vector and calculated by:  
\[\overrightarrow{HS}=(Xh-Xs,Yh-Ys,Zh-Zs), \tag{1}\]  
where \(\overrightarrow{HS}\) is the vector of the torso line. \(H(X_{h},Y_{h},Z_{h})\) is the position of the hip-center joint in 3D coordinates, and \(S(X_{s},Y_{s},Z_{s})\) indicates the position of the shoulder-center joint. We call the vector the torso vector in our approach.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 4 Classification of Nanofluids Based on Base Liquid
### Water-Based Nanofluid  
Water as a non-toxic natural absorber can physically bond with CO\({}_{2}\) and H\({}_{2}\)S. Some advantages of distilled water (DW) when being used as an absorber are its high surface tension and proper capacity for CO\({}_{2}\) absorption. Nevertheless, it has a weak performance in CO\({}_{2}\) recovery and has a low absorption rate [122]. Thus, many researchers suggested that dispersing nanoparticles in raw water can considerably increase gas absorption into the resultant nanofluid as depicted in Table 3.  
For instance, the CNT-water was used by [PERSON] et al., as a binary nanofluid for the absorption of ammonia. The experiment results indicated that a solution mixture of 0.23 wt.% CNTs in water yields a 16.2% improvement in the absorption of the fluid [123]. [PERSON] et al., have dispersed copper and graphene nanoparticles in a solution mixture of water and ethylene glycol and reported a considerable improvement on the base fluid thermophysical properties [124]. A numerical investigation was carried out by [PERSON] et al. on the carbon dioxide adsorption by hollow fiber membranes using two different solutions of SiO\({}_{2}\)/water and CNT/water. The CNT/water fluid had better performance compared to SiO\({}_{2}\) nanoparticles and its rate of CO\({}_{2}\) capture was 16% higher than the other solution. The reason is the lower adsorption capacity of SiO\({}_{2}\) nanoparticles compared to CNT nanoparticles [125]. The suspension of SiO\({}_{2}\) in water was also assessed from mass and heat transfer aspects and a significant enhancement was observed in mass and heat transfer rates that were 18% and 47%, respectively [126]. [PERSON] et al., used magnetic nanoparticles to enhance the CO\({}_{2}\) absorption rate of the base fluid [99]. [PERSON] et al., used Fe\({}_{3}\)O\({}_{4}\) and NiO suspension in water for the absorption of CO\({}_{2}\) in a packed bed. The results indicated an improvement in mass transfer performance
## 3 Results
### Cell Proliferation/Viability and Metabolic Activity  
The effect of different materials on the proliferation of hG-MSCs was investigated after 3, 7, and 14 days (Figure 2). The cells seeded on TCP possessed the highest proliferation level at all time points of measurement (\(p<0.05\)), and the proliferation of hG-MSCs seeded on GPP was significantly higher compared with that on VSCM after 3 and 7 days of incubation (\(p<0.01\)). On day 14, formazan dye also tended to be higher in the hG-MSCs seeded on GPP than in the cells grown on VSCM (\(p=0.07\)). Cell growth on GPP and TCP was promoted over time, whereas hG-MSCs seeded on VSCM maintained a relatively slow proliferation rate during the early incubation time.  
hG-MSCs were metabolically active on different material surfaces (Figure 3). Consistent with the proliferation, the resorufin production level was the highest in cells seeded on TCP (\(p<0.05\)). hG-MSCs grown on GPP had significantly higher metabolic activity than those grown on VSCM after 3 and 14 days of incubation (\(p<0.01\)). Nonetheless, a similar tendency was observed after 7 days; the differences were not statistically significant (\(p=0.07\)). The metabolic activities of cells growing on all the different substrates continuously increased within 2 weeks.  
Figure 1: Morphology of hG-MSCs on different surfaces. Representative fluorescence microscopy images of hG-MSCs on GPP (**a**,**d**,**g**), VSCM (**b**,**e**,**h**), and TCP (**c**,**f**,**i**). Actin filaments (red) and nuclei (blue) of cells were stained after 3, 7, and 14 days. Images show one representative donor and were taken at 100-fold magnification. Scale bars correspond to 50 μm. It can be observed that the morphology of cells adhered to the three different materials varied.
# Experimental and Numerical Studies on the Structural Performance of a Double Composite Wall
## 3 Numerical Approach
### Finite Element Model  
The shear test results showed that although the bolts used in the assembly of PC panels contributed to the improvement in the shear performance of the DCW, they failed to exhibit the yield strain until the maximum load was reached. It is estimated that this occurred because the bolt was not fully embedded in the PC panel concrete, but instead was composite with the PC panel by the grout injected into the bolt hole during the assembly process, as shown in Figure 4e. Thus, the bolt and the PC panel failed to achieve a fully composite behavior. Therefore, in this study, a finite element analysis using ABAQUS/Standard [21], a general-purpose analysis software, was performed to investigate the shear behavior of the DCW specimens according to the bond properties of the bolts and PC panels.  
Figure 10 shows the finite element analysis model for the DCW specimens. PC panels, CIP concrete, and bolts were modeled as solid elements; reinforcing bars were modeled as truss elements; and the PC panel and reinforcing bar were modeled to achieve a perfect bond. In addition, since the end slip that occurred between the PC panel and the CIP concrete in the experiment was negligibly small, the PC panel and CIP concrete were modeled to be fully composite, and the material models corresponding to each element were applied in the same manner as that presented in the non-linear flexural analysis model. The loading and support plates were also modeled to realistically simulate the experimental conditions as they were, and displacement control method was used to apply the loads to the specimens.  
For the DCW-SB specimen, to consider the bond behavior of the PC panel and bolts in the shear analysis, the constitutive laws specified in the _fib_ model code 2010 [22] were applied to the bond-slip relationship between PC panel and bolt elements, as shown in Figure 10b, and assuming \"all other bond condition\", the maximum bond stress (\(\tau_{\text{max}}\)) was calculated as \(1.25\sqrt{f_{cm}}\), where \(f_{cm}\) is the mean compressive strength. Note that the bond-slip relationship was modified and applied to ABAQUS in a bi-linear form, as shown in the lower part of Figure 10b. In addition, the concrete damaged plasticity (CDP) model [23] was used as the yield criterion of concrete, and an angle of 37\({}^{\circ}\)[24] was applied as the dilation angle based on the existing literature.