esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SDS-PAGE Electrophoresis  
The results obtained after performing the electrophoresis assays revealed a higher abundance of proteins in the extract obtained under the optimal UAE conditions (30 min) compared to lowest UAE (0.5 min), except for skin side streams, which presented a higher abundance in the lowest treatment (Figure 7).  
Moreover, different protein profiles were observed between the different side streams (head, skin, backbone, and viscera). The higher protein concentrations were detected in backbone and viscera extracts. In general, for all side streams the main part of proteins extracted had a low molecular weight, ranging from <15 to 50 kDa, with most presenting in the molecular weight band of 25-50 kDa. However, the size of the main part of proteins was <15 kDa in viscera. This fact can be attributed to a higher protein hydrolysis in this specific side stream. It should be highlighted that in head and backbone extracts, proteins of high molecular weight (100-250 kDa) were also identified.  
Similar results were also reported by [PERSON] et al. [11]. These authors analyzed the protein size from mackerel side streams extracts obtained after ultrasound-alkaline-assisted extraction. They observed a low content of large proteins (100-500 kDa) and a high content of proteins ranging from 10 to 40 kDa. This fact could suggest that some hydrolytic process of large proteins is taking place during UAE. In this line, [PERSON] et al. [38] also reported changes in the collagen fiber structure and its breakdown after ultrasound treatment. As it is known, proteins or hydrolysates of low molecular weight are more digestible [39].
## 2 System Model
### Channel Model
#### 2.3.1 Array Gain  
Using the AoDs from the simulated paths in NYUSIM we have determined, for each path, the array gain \(g\) towards each specific MT. The distributions of the array gain \(G\) using the 3 GPP active antenna array model, presented in Figure 3, has been fit into an exponential distribution with probability density function (PDF) \(f_{G}\) defined as  
\[f_{G}(g)=ae^{-qg}. \tag{10}\]
## 2 Materials and Methods
### Analytical Methods
#### 2.9.4 Antioxidant Capacity  
Antioxidant capacity was measured using two radical scavenging methods: ABTS (2018) and DPPH (2018) assays. For both methods, the wall material (GA) was previously precipitated with ethanol for ABTS assay or methanol for DPPH assay in the reconstituted microcapsules. The precipitation was necessary since the wall material is insoluble in these solvents, which are used for the preparation of the radicals (2018, 2019). After the precipitation,the solution was centrifugated at 1796\(\times\)\(g\) (3400 rpm) for 10 min and filtered through a glass microfiber filter paper with 0.6 \(\upmu\)m pore size (ADVANTEC, Tokio, Japan). Aliquots of the filtrate were used for the analysis.  
For the ABTS assay, the ABTS radical was produced by reacting 7 mM ABTS solution with 140 mM potassium persulfate (2.45 mM potassium persulfate final concentration) and leaving them to stand for 12 h at room temperature in the dark. Subsequently, the ABTS radical solution was diluted with ethanol to obtain an absorbance of 0.70 (\(\pm\)0.04) at 734 nm. For the analysis, 3 mL of the ABTS radical solution was added to 0.3 mL of the sample and the solution was left to stand for 6 min in the dark. Finally, the absorbance was measured at 734 nm.  
For the DPPH assay, 3.9 mL of 0.06 mM DPPH solution (previously diluted with methanol to obtain an absorbance of approximately 1.0) was added to 0.1 mL of the sample and left to stand for 60 min in the dark at room temperature. Finally, the absorbance was measured at 515 nm.  
The antioxidant capacity was determined for both methods using Trolox as standard. In both methods, a control with the addition of the solvent or distiller water (instead of extract or reconstituted microcapsules) was used. Results were expressed as \(\upmu\)moles of Trolox equivalents per g dry weight (\(\upmu\)mol TE/g\({}_{\text{dw}}\)). The analyses were performed in triplicate.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Operation Related Problems in WSNs
#### 3.2.4 Network Lifetime  
For the prolonged operation of WSNs, efficient utilization of energy is one of the critical issues. In [91], a strategy is proposed for the maximization of the lifetime of the network by using a multi-objective clustering algorithm. To maximize the lifetime, the proposed algorithm controls the energy depletion of cluster heads in a way to balance their load which results in prevention of faster death of highly loaded cluster heads. A multi-objective optimization formulation has been proposed in [92] which selects the cluster head to maximize the lifetime of the network. Transmission range of the sensor node can affect the battery depletion and hence the lifetime of the network. In [93], an optimal transmission range has been searched to maximize the lifetime of the network by using an ant based heuristic algorithm.
# Noise Reduction Using Synthetic Microjet Excitation in Supersonic Rectangular Jets
## 3 Results
### Far-Field Acoustics
While all of the mentioned methods contribute to noise reduction in rectangular jets, the current work distinguishes itself by achieving broadband noise mitigation through tailored unsteady excitation. In contrast to passive methods [26], which are limited to geometrical shielding, and frequency-specific active methods [27, 28], the excitation in this study directly targets the jet plume shear layer, offering a broader spectrum of noise reduction and enhanced practical applicability.
## 3 Seismic Damage Model
### Modified Usami Model  
Our experimental results indicate the impact of energy dissipated by the hollow RC pier structure on the damage level at different displacement loads should be considered. At a relatively small displacement load, this impact of pier energy consumption would be lower than that at a large displacement load, given an equal cumulative hysteretic energy dissipation value; this indicates that the impact of the hysteretic energy on the damage varies with the amplitude of displacement load. In this paper, a new seismic damage model for RC bridge piers based on the modified version of the Usami damage model, herein referred to as the M-Usami Model(Modified Usami Model), is proposed:  
\[D=(1-\gamma)\sum_{j=1}^{N_{1}}\left(\frac{\delta_{i,\max}-\delta_{y}}{\delta_{ u}-\delta_{y}}\right)^{c}+\gamma\sum_{i=1}^{N_{h}}\left[\frac{E_{i}\left( \delta_{\max,i}^{\pm}/\delta_{u}\right)^{2}}{F_{y}\big{(}\delta_{u}-\delta_{y} \big{)}}\right]^{c} \tag{4}\]  
where \(\delta_{max,i}^{\pm}\) is the maximum positive or negative displacement load in \(i\)th half loading cycle. The different impacts of energy dissipation at different displacement loads are expressed the square of the ratio of \(\delta_{max,i}^{\pm}\) to \(\delta_{u}\).  
For a simplified calculation of Equation (4) in practical applications, the integral calculation of the hysteretic energy dissipation can be simplified through correlation analysis. A simplified formula between the accumulated hysteresis dissipated energy (\(E_{i}\)) and the ductility coefficient \(\mu_{i}\) can be established and shown as  
\[X-\text{direction}:\ E_{cum,i}\ =\ 12.54\mu_{i}^{1.661}E_{y} \tag{5}\]  
\[Y-\text{direction}:\ E_{cum,i}\ =\ 16.95\mu_{i}^{1.601}E_{y} \tag{6}\]  
where \(E_{cum,i}\) is the total accumulative hysteresis energy to \(i\)th half cycle, \(E_{y}\ =\ P_{y}\Delta y/2\), and \(\mu_{i}\) is the ductility coefficient in the \(i\)th cycle. The displacement ductility factor is defined as the displacement corresponding to 85% of the maximum lateral load divided by the yield displacement, and the yield displacement is calculated by the double straight-line energy equivalent area method [30]. This is shown in Figure 11, where S1 + S3 = S2, and point B is the yield point. The accumulative hysteresis energy of the \(i\)th half cycle is obtained according to:  
\[Y-\text{direction}:\ E_{i}\approx(E_{cum,i}-E_{cum,i-1})/2 \tag{7}\]  
The accumulative hysteresis energy of \(i\)th half cycle in \(X\) and \(Y\) direction can be simplified to be expressed as:  
\[X-\text{direction}:\ E_{i}\approx 6.27\left(\mu_{i}^{1.661}-\mu_{i-1}^{1.661} \right)E_{y} \tag{8}\]  
\[Y-\text{direction}:\ E_{i}\approx 8.475\left(\mu_{i}^{1.601}-\mu_{i-1}^{1.601} \right)E_{y} \tag{9}\]
# Enabling Role-Based Orchestration for Cloud Applications
## 3 TOSCA Model-Based Approach to Cloud Application Orchestration
### Architectural Design of a Topology Model-Based Orchestration System  
As illustrated in Figure 6, the architecture of our orchestration system consists of three parts: TOSCA modeling layer at the top, extended OAM runtime at the bottom, and model conversion engine that bridges the two domains:  
1. First, TOSCA Service Templates in YAML files are loaded and processed by the parser in TOSCA module. Besides built-in TOSCA normative types and the meta-model, the parser also consumes a set of types tailored to specifying entities introduced by our approach. Outputs from the TOSCA parser are stored in the memory to be passed on to the conversion engine.
2. The conversion engine performs translation from TOSCA to OAM. Each of the normative TOSCA types as well as customized ones has a corresponding OAM typed entity stored in the TOSCA-to-OAM type mapping repository. The conversion engine is responsible for indexing the mappings and converting the components of TOSCA Topology Templates into OAM Application Configurations. Additionally, if any TOSCA workflow definition is contained in the Service Template, the engine converts it into workflow directives executed by the workflow engine.
3. Once OAM Application Configurations are prepared, our OAM-extended Kubernetes runtime can fulfill the deployment and orchestration of the application in a Kubernetes cluster.  
Figure 6: TOSCA-based orchestration system architecture.  
Further details regarding the composition and operation of each of the three parts are discussed below.
# Energy Sustainability in Wireless Sensor Networks: An Analytical Survey
## 4 Algorithm-Based Energy Sustainability in WSNs
### Protocol Stack of Sensor Nodes and BSs  
The operation of both every sensor node and the base station is managed by the so called protocol stack that, as illustrated in Figure 6, consists of five layers, i.e.: the application layer, the transport layer, the network layer, the data link layer, and the physical layer [1; 3; 44].  
Each one of these layers is responsible for definite tasks [1]. Specifically:  
* Application layer establishes the interface between the end user and the application. According to the type of the application and its specific characteristics, this layer is able to modify its content using the most suited algorithm;
* Transport layer ensures the preservation of the data flow;
* Network layer is responsible for the routing of the transferred data from the transport layer to their destination;
* Data Link layer is responsible for multiplexing of data streams, error control, medium access control (MAC) and detection of data frames. In this particular layer, point-to-point, as well as point-to-multipoint connections within a network, become dependable;
* Physical layer is responsible for the selection of the communication frequency, the generation of the carrier frequency, the signal detection, the signal modulation, and the data encryption.  
In addition, the appropriate operation of WSNs depends on five management planes that namely are: power, mobility, task, QoS (Quality of Service), and security plane [44]. Specifically:  
* The power plane preserves energy by managing the way power is consumed;
* The mobility plane ensures the retainment of data routes by monitoring and recording the nodes' movement;
* Sensing tasks in a specific area of the network are scheduled and assigned by the task management plane to only some of the nodes, enabling the rest of them to perform tasks, such as routing and data aggregation;  
Figure 6: Protocol stack of a wireless node.  
* Fault tolerance, error control and operation's optimization are handled by the QoS management plane, in accordance with specific QoS metrics;
* The monitoring, management and the control of network's security is regulated by the security plane.
## 4 Optimal \(m\)-Value Selection  
This section provides a numerical analysis to compute the optimal values (X*) that minimizes the cost function C(\(m\)). Applying the Newton-Raphson formula [31] on the derivative of Equation (8), the recursive equation is:  
\[\texttt{X}_{\texttt{k}+1}=\texttt{X}_{\texttt{k}}-\frac{2(\alpha+\beta)+ \gamma^{\texttt{X}_{\texttt{k}}}[(\ln\gamma)[5\alpha+2(\alpha+\beta)\texttt{ X}_{\texttt{k}}]-2(\alpha+\beta)]}{\gamma^{\texttt{X}_{\texttt{k}}}(\ln\gamma)^{2}[5 \alpha+2(\alpha+\beta)\texttt{X}_{\texttt{k}}]} \tag{17}\]  
Figure 9: The \(C(m)\) values when the residence time is exponentially distributed with mean \(\mu^{-1}\). (**a**) The \(C(m)\) values when \(\alpha=10,\beta\leq\alpha\). (**b**) The \(C(m)\) values when \(\alpha=5,\beta\geq\alpha\). (**c**) The \(C(m)\) values when \(\alpha=5,10,\beta\leq\alpha\).  
where \(X_{0}=1\) and \(k=0,1,2,\ldots\).  
In Table 1, the optimal values \(X^{\star}\) are given for different \(\alpha\), \(\beta\), and \(\gamma\), where \(\lambda=z\mu\), and \(z=1\), \(2\), \(3\), \(4\), \(5\), \(10\), \(20\), and determined according to different combinations of \(\alpha\) and \(\beta\) values. We assumed that the values of (\(\beta\)) were (1, 2, 3, 4, 5, 10, 15, 20, 75, 100) and the values of \(\alpha\) were (1, 5, 10, 20, 100). Clearly, the value of \(X^{\star}\) increased when the ratio (\(\gamma\)) increased (i.e., \(\lambda\) increased), and \(X^{\star}\) increased slightly with the large increase in \(\alpha\) values for any specific fixed value of the request ratio (\(\gamma\)). On the other hand, \(X^{\star}\) decreased when (\(\beta\)) increased. However, the results of Table 1 confirmed the consistency of the relation between the optimal value \(C(m)\), \(\alpha\), \(\beta\) and \(\gamma\) that were previously deduced. In this context, the main factors that increased the authentication requests were the medical status and the number of the patient's connected sensors.
## 5 Experimental Results and Discussion
### Evaluation Metrics
#### 5.2.2 Decoding-Complexity, Energy Reduction Performance, and Video Quality Impact  
Next, the impact on video quality, decoding-complexity, and the respective energy reduction achieved at a particular decoding-complexity by the proposed algorithm (e.g., complexity level L2 is considered in this case), are compared against the state-of-the-art algorithms while keeping HM 16.0 as the reference. The impact on video quality for a given bit rate is assessed using the impact on PSNR given by  
\[\Delta\text{PSNR}=\text{PSNR}_{\kappa}-\text{PSNR}_{HM}, \tag{35}\]  
where \(\text{PSNR}_{HM}\) and \(\text{PSNR}_{\kappa}\) are the resultant average PSNRs for the reconstructed video sequences when using HM 16.0 and proposed and other state-of-the-art algorithms, respectively. Similarly, the reduction in decoding-complexity and the corresponding energy reduction are assessed using  
\[\Delta\Gamma=100\times\frac{(C_{\kappa}-C_{HM})}{C_{HM}}, \tag{36}\]  
and  
\[\Delta E=100\times\frac{(E_{\kappa}-E_{HM})}{E_{HM}}, \tag{37}\]  
respectively. Finally, for normalized comparison purposes, the proposed and state-of-the-art algorithms are assessed on the decoding-complexity and energy reduction achieved for a 1 dB PSNR loss in the video quality. In this case, \(\Delta\Gamma(\%)\) per \(\Delta\text{PSNR}(\text{dB})\) is given by  
\[\widetilde{\Delta\Gamma}(\%/\text{dB})=\frac{\Delta\Gamma}{\Delta\text{PSNR }}. \tag{38}\]  
Similarly, \(\Delta E(\%)\) per \(\text{PSNR}(\text{dB})\) is defined as  
\[\widetilde{\Delta E}(\%/\text{dB})=\frac{\Delta E}{\Delta\text{PSNR}}, \tag{39}\]  
where \(\Delta\Gamma\) and \(\Delta E\) are calculated as per (36) and (37), respectively.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 4 Conclusions  
In this paper, an endmember extraction method based on quadratic clustering is proposed to better handle spectral variability. CSVM first adopts SLIC-based spatial clustering to segment HSI into homogeneous partitions, and the average of pure pixels obtained by spectral purity analysis in each partition is taken as representative endmember, which reduces the effect of local-scope spectral variability. Then CVSM performs spectral clustering to merge spectrally related representative endmembers to further alleviate large-scope spectral variability, and the final endmembers are determined by simplex with maximum volume. CSVM is implemented from spatial and spectral dimension, which takes full of spatial and spectral information and effectively eliminates the effect of spectral variability.  
Experiments are conducted to evaluate the performance of CSVM in terms of endmember extraction, abundance estimation, noise robustness and processing time. Through parameter analysis, the optimal parameters are first set for our experiments and future use. From the experimental results, the following can be concluded: (1) CSVM greatly improves the extraction accuracy by reducing the effect of spectral variability and performs better on datasets with high spectral contrast between endmembers; (2) CSVM provides better performance on abundance estimation with FCLS abundance estimation algorithm; (3) because of the average operations after clustering, CSVM also shows strong noise robustness; and (4) clustering makes CSVM consume lots of time, and it is one of our future research topics to further accelerate our method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Samson** & **Jasper Ridge** & **Cuprite** \\ \hline VCA & 0.0386 & 0.0655 & 0.2258 \\ SGA & 0.4009 & 0.7169 & 17.1127 \\ AVMAX & 0.0641 & 0.0897 & 0.5941 \\ MVSA & 0.3790 & 0.6237 & 8.6719 \\ AMEE & 0.4283 & 0.1243 & 0.5960 \\ ECSO & 4.6723 & 12.4110 & 73.6082 \\ KMEE & 0.0497 & 0.1481 & 1.0280 \\ FSPM & 0.0975 & 0.1256 & 2.3582 \\ SEMMAV & 0.7669 & 1.1362 & 28.0885 \\ SPEW & 0.1374 & 0.2175 & 1.9181 \\ CSVM & 0.7855 & 1.0408 & 7.6302 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CPU processing time of all test algorithms on Samson, Jasper Ridge and Cuprite datasets (in seconds).  
**Author Contributions:** Conceptualization, X.Z.; methodology, X.Z.; software, X.Z.; validation, X.Z.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, T.X.; visualization, T.X.; supervision, Y.W.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
## 2 Computational Models
### Chemical Mechanisms
Similar to Equation (12), the molar concentration of the SO\({}_{2}\) gas, \(C_{g}^{\text{SO}2}\), can be obtained as the molecular weight of the SO\({}_{2}\)\(W_{\text{SO}2}\) = 64:  
\[C_{g}^{\text{SO}_{2}}=\frac{\rho_{g}\alpha_{g}Y_{\text{SO}_{2}}}{W_{\text{SO} _{2}}} \tag{13}\]  
The chemical source term \(S\) in Equation (5) is modelled as follows [5; 9; 10]:  
\[S=W_{\text{SO}_{2}}A_{int}(\frac{1}{k_{S}^{\text{SO}_{2}}}+\frac{H_{\text{SO} _{2}}}{k_{l}^{\text{SO}_{2}}E_{\text{SO}_{2}}})^{-1}(C_{g}^{\text{SO}_{2}}-H _{\text{SO}_{2}}C_{l}^{\text{SO}_{2}}) \tag{14}\]  
In Equation (14), \(A_{\text{int}}\) is the interfacial contact area per unit volume, which is defined as \(6\alpha_{l}/d_{l}\). The [PERSON]'s law constant \(H_{\text{SO}2}\) is 82. The mass transfer coefficient \(k_{g}^{\text{SO}2}\) for SO\({}_{2}\) in the gas is 4 \(\times\)\(10^{-5}\) m/s while that in liquid phase (\(k_{l}^{\text{SO}2}\)) is 3.4 \(\times\)\(10^{-4}\) m/s. The enhancement factor \(E_{\text{SO}2}\) is 1.3 in this study. All the chemical relevant constants above are taken based on the values of 58 \({}^{\circ}\)C [5]. In this study, the flow solver we use is ANSYS Fluent [22]. The computational procedures that couple the flow solver and chemical reactions are described as follows:  
* Solve the Eulerian-Eulerian two-phase flow models from Equations (1) to (5) at the current time step, while the chemical source term \(S\) in Equation (14) is obtained from the previous time step. * Obtain \(C_{l}^{Slurry}\) from Equation (12) and \(C_{g}^{\text{SO}2}\) from Equation (13) according to the flow variables of the current time step. * Input \(C_{l}^{Slurry}\) from Equation (12) into a prepared chemical database by UDF. This database is written in Matlab to solve the sets from Equations (8) to (10). With \(C_{l}^{Slurry}\) given from flow solver, this chemical database can solve the remaining unknowns and then output the value of \(C_{l}^{\text{SO}2}\) to the flow solver. * Insert \(C_{g}^{\text{SO}2}\) from step 2 and \(C_{l}^{\text{SO}2}\) from step 3 into Equation (14) to calculate a new chemical source term \(S\) for the next time step. * Update the time step and repeat step 1 to 4 if necessary. The couple between flow solver and chemical database can avoid too many additional transport equations for species introduced in Figure 2, which can save computational time and reduce the ensuing numerical difficulties [5].
## 2 Results and Discussion
### Application Testing of Controlled Morphology Hyperbranched Polymers  
#### 2.2.1 Viscosity  
The kinematic viscosities of the HB polymers detailed in Table 2 dissolved in base oil at concentrations of 0.0, 0.25, 0.5, 1.0 and 5.0 wt% were recorded at both 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. This data is shown in Figure 3, and from an inspection of the data, it was apparent that the viscosity for each solution increased as the concentration of each polymer increased.  
Figure 3: Plot of the change in viscosity of both linear and HB polymer base oil solutions with polymer content measured at 40 \({}^{\circ}\)C (**left**) and 100 \({}^{\circ}\)C (**right**), where the EP-copolymer is the current commercial linear polymer used in the industrial formulations.  
However, the viscosity of the standard linear polymer increased at a far greater rate than that of the HB polymers. This was attributed to the branched/globular structure of the HB polymer, which means it is more tightly compact. Additionally, it contains relatively short interbranch and branch-to-terminal group-chain lengths. Therefore, it was hypothesised that this resulted in a lower hydrodynamic volume for the HB, when compared to the linear polymer. Thus, it is less easily penetrated by other chains, reducing intercalation/reptation and so producing a lower increase in viscosity as the concentration of the polymer increases.  
It was also clear from Figure 3 that the trend in viscosity increased when comparing the HB variations reflected by the molecular weight of the polymers. The highest molecular weight semi-block-like structure exhibited a higher viscosity compared to the lower molecular weight block-like species at the same concentration. However, even the highest molecular weight HB, when included in the formulation at a 5 wt%, produced a significantly lower viscosity increase than the linear benchmark polymer at one fifth of this concentration.