esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Strains, Plasmids, Media, and Antibodies  
_Saccharomyces cerevisiae_ yeast display strain EBY100 (ATCC MYA-4941) was cultured at 30 \({}^{\circ}\)C with shaking in yeast extract peptone-dextrose (YPD) medium. The yeast cells harboring surface display vector were cultivated in synthetic dextrose casamino acids (SDCAA) medium (14.7 g/L sodium citrate dihydrate, 4.29 g/L citric acid monohydrate, 20 g/L D(+)-glucose, 6.7 g/L yeast nitrogen base without amino acids, 5 g/L Bacto(tm) (ThermoFisher Scientific, Waltham, MA, USA) casamino acids, 1% penicillin-streptomycin antibiotics, 100 \(\mu\)g/mL ampicillin, and 50 \(\mu\)g/mL kanamycin). The yeast cells were grown at 20 \({}^{\circ}\)C in synthetic galactose casamino acids (SG-CAA) medium (7.44 g/L sodium phosphate monobasic, 5.4 g/L sodium phosphate dibasic, 20 g/L D(+)-glucose, 6.7 g/L yeast nitrogen base without amino acids, 5 g/L Bacto(tm) casamino acids, 1% penicillin-streptomycin antibiotics, 100 \(\mu\)g/mL ampicillin, and 50 \(\mu\)g/mL kanamycin) during induction for surface display. The _Escherichia coli_ strain DH5\(\alpha\) was used for cloning and propagation of plasmid DNA. The pHEL5\(\alpha\)7-GFP plasmid was constructed based on the sequence of the pNACP plasmid [32]. There was a modified pNACP vector in which the acyl carrier protein (ACP) was replaced with the green fluorescent protein (GFP) [32; 33]. The HEL-5A7 [34] or other antigen-specific VNAR sequence was cloned into the N-terminus of Aga2p with _Hind_ III and _Bam_ HI sites in the modified pNACP cloning vector. The surface-displayed VNAR fusion protein was detected by cell-based enzyme-linked immunosorbent assay (ELISA), using Myc (human c-myc proto-oncogene) epitope tag mouse monoclonal antibody (Proteintech, Rosemont, IL, USA, catalog: 66004-1-Ig) and HRP-conjugated goat anti-mouse IgG (H + L) (Proteintech, catalog: SA00001-1). The detection of yeast clones binding to lysozyme (Sigma-Aldrich, St. Louis, MO, USA, catalog: L4919) was verified by cell-based ELISA, using rabbit polyclonal anti-lysozyme antibody (Abcam, Cambridge, UK, catalog: ab391) and HRP-conjugated goat anti-rabbit IgG (H + L) (Abcam, catalog: ab205718). When biotinylated lysozyme (Genetex, Irvine, CA, USA, catalog: GTX82960-pro) or antigen binds to yeast clones, phycoerythrin (PE)-conjugated streptavidin (Genetex, catalog: GTX85910) or peridinin-chlorophyll-protein (PerCP)-conjugated streptavidin (Biological, San Diego, CA, USA, catalog: 405213) is used to detect the binding complex in the fluorescence-related analysis. Allophycocyanin (APC)-conjugated streptavidin (Biological, catalog: 405207), biotinylated fFcRn (ACROBiosystems, Newark, DE, USA, catalog: FCN-F82W3), FIPV vaccine (Felocell(r) FIP, Zoetis, San Diego, CA, USA), and biotinylatedmouse anti-feline coronavirus nucleocapsid mAb (Bio-Rad, Hercules, CA, USA, catalog: MCA2594B) were also employed in the MACS and FACS analysis.
## 3 Method
Based on its current position, the most appropriate progression strategy is selected (as described in the next paragraph). This structured process enables organizations to use the matrix as more than a conceptual tool--it becomes a diagnostic and planning framework grounded in empirical data and repeatable assessment methods. By applying the tool, we could hypothesize two different behaviors in the path of an R&D activity:  
1. Vertical model, from top to bottom: this behavior represents a common scenario in the development of, e.g., electromechanical technologies, where the focus lies in bridging the gap between technological advancements and market introduction;
2. Horizontal model, from left to right: this behavior is frequently observed in the context of digital transformation, encompassing the shift from physical systems to digital realms, culminating in the use of simulation technologies such as, e.g., digital twin (DT). However, what we expect is not to have exclusively vertical or horizontal models. In fact, the desired outcome is to attain flexible and predictive behaviors, which are typically achieved by a combination of both vertical and horizontal approaches. The attractor for such outcomes, to which applications tend, lies in the lower right quadrant of the matrix. Effectively adopting the proposed roadmap tool necessitates a strategic approach, and several key tips can enhance its implementation. First and foremost, it is advisable to define clear Key Performance Indicators (KPIs) tailored to the specific objectives of the digital transformation initiative. These KPIs act as vital metrics, enabling organizations to monitor and assess progress at each step of the process. Additionally, careful consideration should be given to aligning the chosen digitalization path with the unique characteristics of the case at hand. By customizing the approach, companies can ensure a more seamless integration of new technologies into existing workflows. Furthermore, the tool is designed to allow movements only in vertical and horizontal directions on the roadmap. This intentional restriction discourages diagonal movements, which would represent a simultaneous horizontal and vertical shift, i.e., both in the Maturity Index and technology readiness level. The practice of moving one step at a time, either horizontally or vertically, emerges as a valuable strategy. This incremental approach promotes thorough and focused progression, minimizing the risk of overlooking critical aspects and increasing the likelihood of a successful digital transformation process.
## 4 Results and Discussion
### The Influence of Pile Length  
#### 4.1.1 The Analysis of the Pile Length's Effects on the Bearing Characteristics of the USCSP  
(1) Horizontal displacement of the USCSP  
The pile lengths of 8 m, 9 m, 10 m, 11 m, 12 m, and 13 m were chosen, and the length of the loaded section was guaranteed to be unchanged during the calculation. The changes rule of pile displacement with \(\alpha\) for different pile lengths during excavation is displayed in Figure 5. The figure shows that the horizontal displacement of the pile decreases gradually with the growth of \(\alpha\). With the pile length expanding, the horizontal displacement of the pile top decreases continuously. At 1 m excavation, the pile length increases from 8 m to 13 m, and the maximum displacement of the pile top reduces from 11.9 mm to 5.96 mm, decreasing by 49.9%; at 2 m excavation, the pile length increases from 8 m to 13 m, and the maximum displacement of the pile top reduces from 25.9 mm to 11.8 mm, decreasing by 54.4%.  
(2) Internal force of the USCSP  
Figure 6 shows the variation in pile section stresses for different pile lengths. With \(\alpha\) increasing, the pile stress shows a trend of improving first and then decreasing, and the pile stress gradually increases with the increase in the pile length, and when the pile  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Soil Layer** & **Designation** & **Thickness** & **Poisson’s** & **Young’s** & **Density** & **Friction** & **Cohesive** \\
**Number** & & **(m)** & **Ratio** & **Modulus (MPa)** & **(kg/m3)** & **Angle (\({}^{\circ}\))** & **Force (kPa)** \\ \hline
1 & Silt & 2 & 0.35 & 5.1 & 1630 & 13 & 8 \\
2 & Powdery clay & 8.3 & 0.33 & 12.6 & 1860 & 20 & 10 \\
3 & Powdered fine & 15 & 0.33 & 20 & 1890 & 20 & 18 \\
4 & Asphalt concrete & 0.15 & 0.3 & 1200 & 2400 & & \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the model’s soil layers.  
Figure 5: Pile displacement analysis for different pile lengths. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.  
length increases to 12 m, the increasing trend of the pile stress starts to be weakened. When the pile length increases from 8 m to 13 m at 1 m excavation, the maximum pile tensile stress increase from 242.3 kPa to 441.1 kPa, increasing by 82.2% and when the maximum pile compressive stress increased from 414.5 kPa to 635.6 kPa, increasing by 53.3%. When the pile length increases from 8 m to 13 m at 2 m excavation, the maximum tensile stress in the pile increased from 304.1 kPa to 852.1 kPa, increasing by 180.2% and the maximum compressive stress in the pile increased from 600.8 kPa to 1131.4 kPa, increasing by 88.3%. In addition, as the pile expanded, the relative position of the maximum stress point corresponding to the pile shifted upward; for example, the \(\alpha\) values corresponding to the maximum stress point of the pile were 0.68, 0.58, and 0.53 when the pile was at 8 m, 10 m, and 13 m, respectively.
## 3 Results and Discussion
This is because NYCC represents the driving conditions in urban centers with frequent starts and stops. UDDS is also used to evaluate the performance of light vehicles in urban conditions. On the other hand, JC08, China Common and FTP-75 cycle are standards for crowded urban driving conditions in Japan, China and the US, respectively. It can be seen from Figure 7a that, under these driving conditions, the vehicles maintain a low speed most of the time with frequent and significant acceleration and deceleration, which prohibits the engine from operating at its high efficiency regime (Figure 6a). Therefore, the CO\({}_{2}\) emission of CV is a lot higher than that of EV under these crowded urban conditions. The carbon emission of NEDC (Figure 9h) is similar to that under FTP-75 (Figure 9g). Additionally, it is found that the difference between EV with and without ebraking is larger in these urban driving conditions. This is because the frequent and substantial deceleration in the urban area recovers more electricity than other driving conditions. Therefore, it is suggested to activate ebraking for urban driving conditions in order to reduce carbon emission. Although it was found that EVs generally have less well-to-wheel CO\({}_{2}\) emissions across difference driving cycles as well as countries/regions than CVs, it should be noted that EV growth can lead to potential reshaping of the electricity load curve as well as increasing total electricity demand, which can place new strains grid in near to midterm, and in the long run, respectively [34]. While the impact of power-demand of the EVs on the grid as well as its corresponding emission characteristics is important and warrants additional research, the topic itself is complex and is beyond the scope of this study.
## 2 Related Work
### Graph Neural Networks-Based Learning  
In the real world, many important datasets are stored in the form of graphs, such as social network information, knowledge graphs, protein networks, molecular structures, etc. These graph networks are not structured information images but unstructured information. A graph usually contains two parts of information: one part is the adjacency matrix, which is the information about the connection relationship between nodes, and the other part is the feature matrix, which is the information about the state of the nodes. Graph Neural Networks (GNN) [22; 23] is an extension of neural networks for processing graph-structured data. GNNs have become a widely used graph analysis method due to their good performance and interpretability. GNN obtains dependencies in the graph by propagating information among nodes in the network, and the node updates its own state based on the state of its neighbors.  
The hypergraph is different from the simple graph. For simple graphs, the degree of each edge is limited to two, i.e., each edge is linked with two nodes, while the hypergraphs allow edges with degrees of more than two, that is, each edge can be linked with multiple nodes. Therefore, the hypergraph neural network [7; 8; 24] is more convenient for modeling the correlation of higher-order data. At present, the hypergraph neural network has been widely used in image classification [25; 26], recommendation systems [27; 28; 29], disease diagnosis [30], trajectory prediction [31] and other fields.  
The relationships between odor molecules and odor descriptors are higher-order and go beyond pairwise relationships. However, there is no work on modeling this relation as a hypergraph structure currently. In this paper, we use hypergraph neural networks to explore its higher-order semantic interactions and make olfactory perception predictions.
## 1 Introduction
Taken together, these findings imply that because of the reduction in drying air humidity, other drying parameters should be carefully adjusted to achieve the proper drying conditions. It is fundamental to note that the presented literature data do not report on the effect of air humidity tested in a wide range of parameters and on SD process performance. Thus, this paper presents the results with new basic knowledge about the role of drying air humidity in the shaping of SD conditions. This research was conductedbased on model feed materials and the knowledge drawn from this study can be further implemented in different food matrixes, as well in different industrial sectors that would benefit from modifying spray drying process parameters in order to enhance process yield or powders stability and their physical properties. The aim of this research was to study the influence of drying air humidity on the performance of model feed materials (distilled water and MD solutions), spray drying based on the analysis of the inlet and outlet air parameters (temperature, relative and absolute humidity), calculation of specific energy and drying air consumption, powder recovery, and powder properties (moisture content, water activity, particles morphology, loose bulk and tapped densities, flowability, and particles size). The research hypothesis presumed that (1) the drying air humidity influences the spray drying performance, and in consequence its reduced humidity would improve the process conditions, possibly reducing the drying temperature; (2) the reduced drying temperature deteriorates drying conditions. The process performance is strongly dependent on the amount of water to be evaporated, thus the reduction in water content in feed solution (increase in solids content) and reduced feed rate would consequently improve spray drying performance. The research was divided into three stages: Stage I: spray drying/evaporation of distilled water at variable feed rate, drying air humidity and air temperature; Stage II: spray drying of 30% (solids, _w/w_) MD solutions at the same levels of independent variables as in stage I; Stage III: optimization of MD solutions spray drying process variables (inlet air temperature, drying air humidity, feed solution concentration) for maximum powder recovery and powders of low moisture content and activity.
## 4 Results and Discussion  
### Decorrelation Coefficient and Defect Sensitive Window  
According to Equation (3), the correlation and decorrelation coefficients can be determined from the waveforms with and without a defect. The stretching method demonstrates great stability to external noise and is applied here [16]. The decorrelation coefficients can be calculated according to Equation (8):  
\[DC(\varepsilon)=1-CC(\varepsilon)=1-\frac{\int_{\Pi}^{\prime 2}h_{k}[t(1- \varepsilon)]h_{0}[t]dt}{\sqrt{\int_{\Pi}^{\prime 2}h_{k}^{2}[t(1-\varepsilon)]dt \int_{\Pi}^{\prime 2}h_{0}^{2}[t]dt}}, \tag{8}\]  
in which the window length \(T=t_{2}-t_{1}\) is set to 10 \(\upmu\)s and the beginning of the time window \(t_{1}\) is shifted sequentially with a time step of 5 \(\upmu\)s each time. The stretching factor \(\varepsilon\) is chosen to vary from \(-0.015\) to \(0.015\), from which the decorrelation coefficient is decided when reaching the minimum (Figure 4). The total results for all decorrelation coefficients varied with time are shown in Figure 5. They all increase with time as a growing interaction between the diffuse wave field and the defect takes place. The maximum coefficients are found when \(t_{1}=45\)\(\upmu\)s, which is chosen for the defect localization study. S-R1 and S-R2 are more sensitive to the defect compared to that of S-R3 and S-R4 due to their different positions relative to the defect. After \(t_{1}=45\)\(\upmu\)s, the decorrelation coefficients decrease because of the reflection part interference and signal attenuation.  
Figure 4: Decorrelation coefficient calculation example for the waveforms presented in Figure 2 when \(t_{1}=45\)\(\upmu\)s (decorrelation coefficient \(=0.0679\) when \(\varepsilon=0.0026\)).
## 5 Adaptive Termination Rule of the Measurement-Formation
Assume that, \(\hat{x}^{M}=\hat{x}^{M-1}=\ldots=\hat{x}^{M-\Delta\ M}\) and \(\hat{x}^{M}\) is not the correct reconstruction result of \(x\), then \(x\
eq\hat{x}^{M-\Delta M}=\ldots=\hat{x}^{M}\) is valid. Because we have obtained the solution result \(\hat{x}^{M-\Delta M}\) at the Step \(M-\Delta M\), equation \(\left(\varphi^{M-\Delta M}\right)^{T}\hat{x}^{M-\Delta M}=y^{M-\Delta M}= \left(\varphi^{M-\Delta M}\right)^{T}x\) comes into existence. Thus, according to the assumption that \(\hat{x}^{M-\Delta M}=\hat{x}^{M}\), it is easy to obtain \(\left(\varphi^{M-\Delta M}\right)^{T}(x-\hat{x}^{M})=0\). By following this idea, for \(\Delta M\) measurement coefficient vectors generated from Step \(M-\Delta M+1\) to Step \(M\), we can also get equations\(\left(\varphi^{i}\right)^{T}(x-\hat{x}^{M})=0,i=M-\Delta M+1,\ldots,M\). Let \(V\) be a vector space which is spanned from the non-zero vector \(\hat{x}-\hat{x}^{M}\), and if this equation set is correct, entire \(\Delta M\) measurement coefficient vectors \(\varphi^{i}\) must exactly belong to the orthogonal complement of \(V\) (denoted as \(W^{\perp}\) where \(W^{\perp}=\left\{x\middle|x\in\mathbb{R}^{N}s.t.\ (x,y)=0\ for\ all\ y\in V\right\}\)). For a 1-dimensional nonzero linear transformation \((x-\hat{x}^{M})^{\prime}\), its orthogonal complement is an \((n-1)\)-dimensional subspace of \(\mathbb{R}^{N}\). Hence, from Lemma 1, it can be seen that the probability of an imprecise solution \(\hat{x}^{M}\) being calculated and kept is greater than \(3^{-\Delta M}\), as desired. Proposition 1 provides us a termination rule of sparse Bernoulli measurement formation by comparing several construction results. In our experiments, signals are generated with the same signal length and sparse degree as shown in Figure 3. We test the signal reconstruction under the termination rule proposed in Proposition 1 with different \(\Delta M\) values, and the experiment results are shown in Table 1. We can see that Proposition 1 presents a very efficient terminating condition in the reconstruction of sparse Bernoulli measurement method. The larger the quantity (\(\Delta M\)) is adopted, the lower error reconstruction generates (_i.e._, 128 errors happen under \(\Delta M=1\), while only 2 under \(\Delta M=2\)). Thus, our data gathering process can adapt to the various requirements of network applications by adjusting the value of \(\Delta M\).
## 3 Results
### Boundary Conditions and Initial Values  
Accurate boundary conditions are required for a mathematical model that can successfully analyze the transport characteristics of each species. Initial value setting is also one of the key steps to achieve the convergent simulation results in iterative solvers. In the study, the normal fluxes are too tiny to account for the membrane boundaries; hence, all species, as well as electrons, disappear at the membrane boundaries. The membrane is also assumed as impermeable for all gases. The dissolved water and protons mainly transport through the ionomer phase supporting the assumption that these two species have zero fluxes at both external surfaces of CLs [39]. Based on the Dirichlet boundary conditions, the values of electrostatic potentials of \(\varphi_{e}\) at the end of the surface boundary of MEA are 0 and U, respectively. The hydrogen in anode side boundary condition can be given as follows:  
\[\chi_{H_{2}}^{a}=\alpha_{H_{2}}\left(1-\chi_{H_{2}O}^{a}\right) \tag{20}\]  
where \(\chi_{H_{2}O}^{a}\) is the boundary condition of vapor in the anode, which is expressed by  
\[\chi_{H_{2}O}^{a}=\frac{RH_{a}P_{sat}(T_{a})}{P_{a}} \tag{21}\]  
In a similar fashion, the gases in the cathode side are listed as follows:  
\[\chi_{O_{2}}^{c}=\alpha_{O_{2}}\left(1-\chi_{H_{2}O}^{c}\right) \tag{22}\]  
\[\chi_{H_{2}O}^{c}=\frac{RH_{c}P_{sat}(T_{c})}{P_{c}} \tag{23}\]  
As temperature exists in all domains in this study, the values at the anode and the cathode sides are given as \(T_{a}\) and \(T_{c}\), respectively. The parameters involved in this simulation model are referred from the presented literature and some public data of the commercial products. These specified characteristics are substantial to furnish the established model. Many parameters are not fixed values, but often, assumptions must be made and some parameters must be set to a constant within a reasonable range of variation to simplify the model and reduce the calculation cost. The saturated pressure of water vapor is a remarkable parameter related to the phase change of liquid vapor in macro homogeneous MEA modeling. The temperature within the water boiling point is estimated using the Antoine equation as follows [38]:  
\[\text{ln}(P_{sat})=23.2-\frac{3816.44}{T-46.13} \tag{24}\]Condensation/evaporation is another important water activity, and the related coefficients are as follows:  
\[\gamma_{i}=\left\{ \begin{array}{ll}k_{e}a_{\text{lg}}S_{red}&if\text{ { condensation}}\\ k_{c}a_{\text{lg}}(1-S_{red})&if\text{ {evaporation}}\end{array} \right. \tag{25}\]  
The accurate expression of current density plays a crucial role in modeling the distribution of temperature, gas concentration, gas consumption. By measurement of equivalent weight ionomer, the Bulter-Volmer equation is expressed by the following [21]:  
\[i_{0}=0.27\ \times\exp\left[\frac{16\text{kJ/mol}}{R}\left(\frac{1}{T_{ref}}- \frac{1}{T}\right)\right](\text{A/cm}^{2}) \tag{26}\]  
Bruggeman correction \(\varepsilon_{i}^{1.5}\) s used in the calculations of ionic conductivity and water diffusivity.