esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental and Theoretical Validations
#### 2.5.2 Thermodynamic Modeling of the Solid-Liquid Equilibrium
The following equation details the model used in this work to compute the solid-liquid equilibrium of the peritectic compound:  
\[\sum_{i}\ln(x_{i}\gamma_{i}^{L})=\sum_{i}v_{i}\Bigg{(}\frac{\Delta_{m}h_{i}}{ R}\bigg{(}\frac{1}{T_{m}^{i}}-\frac{1}{T}\bigg{)}+\sum_{tr=1}^{n}\Bigg{(}\frac{ \Delta_{tr}h_{i}}{R}\Bigg{(}\frac{1}{T_{tr}^{i}}-\frac{1}{T}\bigg{)}\bigg{)} \Bigg{)}-\frac{\Delta_{r}G^{0}}{RT}, \tag{1}\]  
where \(i\) identifies the pure component, \(L\) characterizes the liquid phase, \(x_{i}\) is the liquid phase molar fraction of \(i\), \(\gamma_{i}\) is the liquid phase activity coefficient of \(i\), \(R\) is the universal gas constant, \(T_{m}\) and \(\Delta_{m}h\) are the melting temperature and enthalpy, respectively, \(T_{tr}\) and \(\Delta_{t}h\) are the polymorphic transition temperatures and enthalpies for the component \(i\), \(\
u_{i}\) is the stoichiometric coefficient associated to the reaction forming the peritectic compound, and \(\Delta_{r}G^{0}\) is Gibbs-free energy of the peritectic reaction. The model used for the depiction of the solid-liquid equilibrium of the PA-SA binary system part, not influenced by the peritectic reaction, is detailed in Equation (2):  
\[\ln(x_{i}\gamma_{i}^{L})=\frac{\Delta_{m}h_{i}}{R}\bigg{(}\frac{1}{T_{m}^{i}}- \frac{1}{T}\bigg{)}+\frac{\Delta_{tr}h_{i}}{R}\Bigg{(}\frac{1}{T_{tr}^{i}}- \frac{1}{T}\bigg{)}. \tag{2}\]  
It is a common form as described in References [22; 23]. The calculation of the liquid activity coefficient \(\gamma_{i}\) is performed using the UNIFAC-Dortmund method [24], and for each value of \(x_{i}\), the transition temperature satisfying the equation is calculated. It implies knowing the melting temperature and the enthalpy values as well as the polymorphic transition temperatures and enthalpies for each pure component. For our model, the considered melting properties values are presented in Table 1 and the polymorphic properties in Table 2. \begin{table}
\begin{tabular}{c c c} \hline  & **Palmitic Acid** & **Stearic Acid** \\ \hline \(T_{tr}\) (\({}^{\circ}\)C) & 51.6 & 51.3 \\  & & 52.8 \\  & & 58.5 \\ \hline \(\Delta_{tr}h\) (J/mol) & 2600 & 5400 \\  & & 2800 \\ \hline \(\Delta_{tr}H\) (J/g) & 10.14 & 18.98 \\  & & 20.04 \\  & & 9.84 \\ \hline \end{tabular}
\end{table} 
Table 2: Values of temperatures of transitions, molar enthalpies of transitions and mass molar enthalpies used in the model to take into account the polymorphic behavior of palmitic and stearic acids [20].
# Parametric Analysis of Rotational Effects in Seismic Design of Tall Structures
## 1 Introduction
The most important structural influences are the fundamental natural period and the effective height of the analyzed structure. So, the application of the familiar rule the square root of the sum of the squares (SRSS) to combine the horizontal and rocking effects may underestimate the overall seismic response. [PERSON] et al. [7] studied the determination of the sufficient number of modes considering the bending moment and shear force, the influence of a large number of degrees of freedoms and soil conditions. A general algorithm for approximate evaluation of soil-foundation-structure interaction effects is provided by [PERSON] [8]. New formulas that consider the earthquake rotational motion effects in the seismic loading of low- to medium-rise multistory buildings were presented by [PERSON] and [PERSON] [11]. The effects of soil-structure interaction and foundation type was studied by [PERSON] and [PERSON] [12]. In this paper, the focus is on practical and simple procedures that can be used for estimation of the measure of how the rotational components of ground motion on buildings is significant. Simple equations are developed that take into account a few parameters, e.g., estimation of the mass and stiffness of the building including soil characteristics and the type of the structural system and the height.
# Comparison of Topologies Generated by Evolutionary Neural Architecture Search
## 4 Discussion  
The fork-like structures found in the resulting topologies of the two experiments are interesting because they suggest a common underlying principle or pattern that may be present in the optimization process of neural network architectures. Despite the differences in the input data and tasks, the networks evolved to have a similar topology. Figure 5 shows the results of these experiments.  
Figure 4: Structure of MNIST Result.  
Figure 3: Evolution Process of MNIST.  
In the case of the MNIST experiment, the fork-like structure was found to be useful in classifying digits. In the Korean grammaticality judgement experiment, the network with the fork-like structure was found to perform better in terms of identifying grammatically correct sentences.  
In addition, in a previous paper, we experimented with the CIFAR-10 dataset using the same methodology [9]. CIFAR-10 is a dataset of 60,000 32 \(\times\) 32 pixel images divided into 10 classes; it is used for training and testing image classification algorithms [24]. The classes include airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Figure 6 shows the result structure of the CIFAR-10 dataset.  
The fact that the similar fork-like structures emerged in both experiments suggests that there may be a generalizable principle of neural network architecture optimization that is at play. It is possible that the fork-like structure provides an efficient way to capture and process information across different inputs and tasks. Further research could explore the possible benefits and limitations of the fork-like structure and whether it is applicable to other types of data and tasks.  
Figure 5: Structure of Experiments.  
Figure 6: Structure of CIFAR-10 dataset.  
The fork-like structure facilitates additional connections while preserving the original neural network structure. This design can be effectively integrated into pre-existing neural networks, and we plan to apply it to both image processing and natural language processing neural networks.  
One limitation of this experiment is the small dataset size, which may not be representative of the broader Korean language or its syntactic operations. Additionally, the experiment is limited to only four-word level sentences, which may not capture the complexity of longer sentences. Furthermore, while the resulting architecture is shown to be effective in classifying the grammaticality of the dataset, it is not clear whether it can be generalized to other linguistic tasks or datasets. Finally, the use of a neural architecture search algorithm may make it difficult to interpret the resulting architecture and understand the specific linguistic operations that it is encoding.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.5 Wind Energy  
A hybrid statistical-ML approach, as presented in [64], used DTs and RFs to diagnose turbine faults with over 92% accuracy. In [65], deep neural networks applied to supervisory control and data acquisition (SCADA) data predicted anomalies up to 72 h in advance. As outlined in [66], feature selection was prioritized over model complexity in forecasting subsystem failures. A CNN-LSTM hybrid model, introduced in [67], effectively distinguished soft and hard turbine failures using IoT data. Additionally, vibration monitoring using bagged trees achieved 87% accuracy under lab conditions [68].
## 1 Introduction
### Damage Models
#### 1.1.4 Gunawardana Model  
[PERSON] (2010) developed a damage model on the basis of Rice and Tracey model. Assuming spherical growth of voids and rigid-plastic behavior of materials, the fracture reference strain \(\varepsilon_{f}\) is calculated for different states of stress triaxiality as follows:  
\[\varepsilon_{f}=\varepsilon_{0}\exp\left(\frac{1}{2}-\frac{3}{2}\frac{\sigma_ {h}}{\sigma_{eq}}\right), \tag{14}\]In which \(\sigma_{h}\) is the hydrostatic stress component. For this model \(D\) is the damage parameter for which the growth rate is defined as follows:  
\[dD=\frac{d\varepsilon_{p}}{\varepsilon_{f}}, \tag{15}\]  
Integrating Equation (15) and using Equation (14) we obtain:  
\[D=\frac{1}{\varepsilon_{0}}\int\limits_{0}^{\varepsilon_{p}^{c}}\exp\left( \frac{3}{2}\frac{\sigma_{h}}{c_{eq}}-\frac{1}{2}\right)d\varepsilon_{p}, \tag{16}\]  
Note that \(\varepsilon_{p}^{c}\) is the plastic strain in each increment of loading in which \(D\) is calculated. Damage occurs when the \(D\) parameter reaches unity. The most important advantage of this model is that it requires only constant (\(\varepsilon_{0}\)) to be determined.
## 2 Results and Discussion
### Experimental and Theoretical MS Spectra  
Mass spectra and the fragmentation scheme are the final proof that LSD is found in the samples at the crime scene. A possible fragmentation pattern under the electron impact conditions for LSD has been extensively investigated in the literature [8; 31; 32; 33]. The main fragments are positioned at 323, 221, 207, 181, and 72 \(m/z\) (Figure 4). These fragments were identified by the literature review of similar compounds [8; 29; 32; 33] and were optimized at the M05-2X/6-311++G(d,p) level of theory to prove the applicability of theoretical considerations for the determination of the mass pattern. The optimized structures of fragments are shown in Figure 3, except for the molecular ion, which has a structure identical to that presented in Figure 1.  
Figure 3: Electron density distribution of HOMO, LUMO, and LUMO+1 orbitals of the optimized structures of LSD (at M05-2X/6-311++G(d,p) level of theory, isovalue = 0.02).  
The fragment at 221 \(m/z\) represents a product of the diethylamide moiety removal upon ionization. In this fragment, the planarity of the six-membered ring is restored, along with its aromaticity, which makes this species completely planar and, therefore, stable. A methyl group is removed in the second step, and a fragment at 207 \(m/z\) is obtained. The extended \(\pi\)-electron delocalization is crucial for the stability of this fragment, although it is less stable than the previously described one due to the loss of the hyperconjugation effect of the methyl group. This is reflected in the lower intensity of the peak at 207 compared to the 221 \(m/z\)[8]. The peak at 181 \(m/z\) represents a fragment formed when the CH2=CH-C(O)-N(C\({}_{2}\)H\({}_{5}\))\({}_{2}\) fragment was removed. This step creates a stable species with a four-membered ring instead of a six-membered ring. The extended delocalization, together with restored planarity, leads to the increased stability of the fragment. Besides all of the mentioned effects, lone pairs of electrons on nitrogen additionally stabilize the structure through interactions such as LP(N)\(\rightarrow\)\(\pi\)\({}^{\prime}\)(C-C), as calculated in the second-order perturbation theory and NBO analysis [21; 34]. The last abundant fragment is common for all of the diethylamide derivatives of LSD. It is suggested that this fragment is formed when the C=O group is separated from the moiety mentioned in the first step. Complex fragmentation schemes of LSD and its analogs are shown elsewhere. Still, the theoretical methods are a valuable tool for predicting stability and intermolecular interactions governing the stability of the formed ions, radicals, and neutral species.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 7 Electro-Magnetic VVA (EMVVA) Systems  
### Opposed Solenoid EMVVA  
Most effort in camless VVA system development was devoted to EMVVA, actuated by a pair of opposed electromagnets and balanced by a pair of compression springs. It is capable of generating variable valve timing and duration, but with fixed lift operation.  
The developers of this technology include Valeo [19; 60; 61; 62], FEV [63; 64; 65; 30; 61], GM [29], Ford [66], Visteon [32], BMW [45], TRW [67], Siemens [68], MIT [69], Ibaraki University [70], LGD Technology [71], Instituto Motori of National Research Council of Italy [72], and Aura System [31].  
Valeo acquired the related technologies from FEV, Sagem, and Johnson Control and developed them to a more mature system, which was marketed as smart valve actuation (SVA) [73] and later as e-Valve [60; 62]. e-Valve claims to have reached the required maturity level for mass production [60].  
The key issues and challenges, some of which may remain unresolved at this point, for EMVVAs in general include the following:  
* Seating instability and the resulting noise and valve durability issues due to the highly nonlinear nature of the electro-magnetic latching force unique to the opposed solenoid design.  
[PERSON] et al. [69] incorporated a nonlinear spring or nonlinear mechanical transformer for better soft seating and/or low holding current.
* Need for an accurate, robust, and durable position sensor for each actuator [62].
* Limited or no capability to achieve a variable lift or low lift profile, necessary for some advanced combusions. [PERSON] [71] proposed incorporating a hydraulic mechanism for enhanced capability.
* High incremental cost, which is a challenge for camless VVAs. A four-cylinder engine with electronic actuation on only the intake valves is expected to cost about E300 more to build [62].
* Electrical power consumption. [PERSON] et al. [70] proposed a bias permanent magnet to reduce energy consumption and a seesaw architecture to improve performance and the fitness.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
## 2 Spatial Partitioning and Load Balancing
On the other hand, the traditional approach is to assign certain mesh cells to processors regardless of the motion the of mesh system. One of the merits of the traditional approach is that cell-to-cell connectivity does not need to be maintained since there is no disconnection/reconnection of moving cells. In addition, no time is spent for re-organization of mesh system spatially. However, the overlapping mesh blocks are still required to be brought together regardless of the approach followed. The traditional approach solves this problem by transferring overlapping mesh blocks and sending processed information back to the host processor. The downside of this approach is that the communication has to be repeated twice in every time step. In this study, time is saved by eliminating the need for sending processed information back to the host processor since overlapping mesh blocks reside in the same partitions.
# Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching
## 3 Results and Discussions
### KDE, NMEM, NAPS(KDE) and NAPS(NMEM) Comparisons
Figure 15 compares the computational times for KDE, NAPS(KDE), and NAPS(NMEM). As expected, KDE is much faster than NAPS. Nevertheless, NAPS(KDE) is slower than NAPS(NMEM) for sample sizes above \(2^{12}\) because block partitioning corroborates with properties that are favorable for NMEM. That is, NMEM ensures reasonable load balance by using a low number of Lagrange multipliers to model the PDF in each block. Since the computational time for KDE depends only on the number of sample points within a block, NAPS(KDE) is faster than NAPS(NMEM) for sample sizes less than \(2^{12}\) due to the initialization overhead cost of NMEM. However, since a large fraction of the total computational cost comes from partitioning the blocks, which is independent of the density estimation method, NAPS(KDE) and NAPS(NMEM) have similar computational speeds. Taking into account accuracy-speed tradeoffs, NAPS(NMEM) has the best performance. Furthermore, ongoing work on the C++ NAPS(NMEM) implementation suggests a significant gain in speed can be achieved.
## Materials and Methods
### FBG Array Fiber Fabrication, Calibration, and Characterization  
The fiber Bragg grating (FBG) array used for the thermometry was inscribed with the point-by-point technique using femtosecond laser radiation [21]. The method enables FBGs incription through a polyimide protective coating of a fiber resulting in durability and high mechanical strength of a fabricated sensor [22]. The high degree of flexibility makes the method most suitable for writing FBGs with different resonant wavelengths and different lengths [21], with specified refractive index profile [23], and an arbitrary position in the cross-section of fiber [24]. A Fibercor SM1500 (9/125) P fiber with a 20 \(\upmu\)m-thick polyimide protective coating was used for the fabrication of an FBG array enclosing 16 FBGs. Each FBG in the array has a length of 1 mm and a distance between adjacent FBGs of 5.000 mm, so the total (edge-to-edge) length of the FBG array was 76 mm. In order to separate FBGs in spectral-domain resonant wavelengths were distributed equidistantly in the range of 1520-1580 nm with \(\Delta\lambda\approx 4\) nm (Figure 3).  
The calibration of 16-element array FBG fibers was conducted before the experiments. The spectra for each temperature from 23 to 55 \({}^{\circ}\)C with 2 \({}^{\circ}\)C increments were recorded. The shifts of wavelengths corresponding to each FBG were observed for each temperature  
Figure 3: The reflection spectrum of a sensor array containing 16 FBGs.  
change, which is demonstrated in Figure 4. The resulting value of constant temperature coefficient k was approximately 8.8 pm/K.
## 2 VSWT-PMSG and LC Filter Model  
### Variable Speed Wind Turbine Model  
The mechanical power output of wind turbine captured from the wind power can be calculated as follows [10]:  
\[P_{w}=0.5\rho\pi R^{2}V_{w}^{3}C_{p}(\lambda,\beta) \tag{1}\]  
where \(P_{w}\) is the captured wind power (W), \(\rho\) is the air density (kg/m\({}^{3}\)), \(R\) is the radius of rotor blade (m), \(V_{w}\) is wind speed (m/s), and \(C_{p}\) is the power coefficient.  
The value of \(C_{p}\) is depended on tip speed ratio (\(\lambda\)) and blade pitch angle (\(\beta\)) based on the turbine characteristics as follows [11].  
\[C_{p}(\lambda,\beta)=c_{1}\left(\frac{c_{2}}{\lambda_{i}}-c_{3}\beta-c_{4} \right)e^{\frac{-c_{5}}{\lambda_{i}}}+c_{6}\lambda \tag{2}\]  
with  
\[\frac{1}{\lambda_{i}}=\frac{1}{\lambda-0.08\beta}-\frac{0.035}{\beta^{3}+1} \tag{3}\]  
where \(c_{l}\) to \(c_{6}\) denote characteristic coefficients of wind turbine (\(c_{l}=0.5176\), \(c_{2}=116\), \(c_{3}=0.4\), \(c_{4}=5\), \(c_{5}=21\) and \(c_{6}=0.0068\)) [11].  
The _Cp-\(\lambda\)_ characteristic for different values of \(\beta\) is shown in Figure 1. It is seen that the optimum value of \(C_{p}\) (\(C_{popt}=0.48\)) is achieved at \(\lambda=8.1\) with \(\beta=0^{\circ}\). This value of \(\lambda\) is set as the optimal value (\(\lambda_{opt}\)). Figure 2 depicts the characteristic between the turbine power output and the rotor speed for different wind speeds where the blade pitch angle is set at \(0^{\circ}\). The maximum power output (1 pu) of wind turbine is obtained at 12 m/s of wind speed and 1 pu of rotational speed.  
In variable speed wind turbine, the rotor speed of wind turbine (\(\omega_{r}\)) is measured in order to determine the Maximum Power Point Tracking (MPPT). The maximum power (\(P_{mppt}\)) can be calculated without measuring the wind speed as expressed in Equation (4) [12]. The reference power (\(P_{ref}\)) is limited within the rated power of generator.  
\[P_{mppt}=0.5\rho\pi R^{2}\left(\frac{\omega_{r}R}{\lambda_{opt}}\right)^{3}Cp_{ opt} \tag{4}\]  
Figure 1: \(Cp\)-\(\lambda\) characteristic for different pitch angle.  
Figure 2: Turbine power characteristic (\(\beta=0^{\circ}\)).
# An Overview on Atomization and Its Drug Delivery and Biomedical Applications
## 4 Drop Size and Distribution in Spray
The study was done by varying the mass flux of the gas and maintaining a sustained flux rate of the feed liquid, i.e., they derived a varying GLR ratio from 2.0% to 4.3%. They observed that the twin fluid nozzle delivered higher energy to the droplets when the GLR ratio is high and this gave an upgraded features of the atomization process, which was the opposite when the GLR ratio was smaller. In short the structural features of the nozzle/vent like the compact throat and departure diameter, average extent between liquid stream and atomizing nucleus, all play an important role in optimizing the droplet size and its distribution. They concluded that as the GLR value is increased, this can intensify both the primary and secondary atomization by providing an upgraded features of the atomization process and with lesser impact of the constructional variables of the twin fluid nozzle on the droplet dimension and its size dispersal [58]. Microencapsulated drug delivery systems are widely used in the field of pharmaceuticals and it can be generated by the ultrasonic atomization method. The size of these microdroplets play a major role in the efficiency of the drug delivery system, so it is necessary to optimise the size of these particles. [PERSON] et al. studied the relation of experimental variables and the properties of the substances used in the development of microencapsulated drug delivery systems with the size of the droplets. The size of the droplets were predicted by using three pragmatic associations that contemplate the properties of the bulk fluid and the experimental variables. Off the three relations, two were found definitive for calculating the droplet size [60]. Similarly, [PERSON] and his group studied the atomization in a two-fluid vent to develop investigational and numerical tools to calculate the size and dispersal of the atomized drops. They conducted the experimental conclusion using three different feed liquids, which are water, methanol and acetone, as the fluid properties are different for all the three fluids. They compared the experimental results with the mathematical correlations to understand how much the experimental data is deviating from the theoretical explanation. They explained the data using the Sauter Mean Diameter measurements and found that the two main factors that influence the size of the droplets are the two dimensionless numbers that is the Weber number and the GLR ratio [59]. [PERSON] et al. in 2017, varied the experimental and fluid specifications to characterize the droplets and found that the size of the atomized drops can be calculated from Weber number (obtained from relation between stagnation and surface tension of the liquid) and the profile of the nozzle used in the atomization process [48]. Thus, the major factors influencing the size and distribution of the droplets in an atomization process are the parameters governing the experimental apparatus and the bulk liquid.